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Radiology Key

6
Projection X-ray Imaging



CHAPTER MENU



  1. 6.1 Introduction
  2. 6.2 Projection X-ray Setup
  3. 6.3 X-ray Projection Modalities
  4. 6.4 Key Components of Projection X-ray Systems
  5. 6.5 Exposure Control
  6. References


6.1 Introduction


Projection X-ray imaging is a modality of medical imaging that involves two-dimensional images of the body, either statistically or dynamically variable in time. It is an essential modality through which a shadowgram of the three-dimensional body part, at a moment of time, is projected via an X-ray beam onto a two-dimensional surface. Images are recorded via detectors positioned at the exit surface of the body. Resulting images are considered satisfactory if enough information is transmitted through the body part such that the features of varying attenuation properties can be seen distinct from one another. This necessitates a sufficient and varied numbers of X-rays emerging from the patient and impinging on the sensor. Depending on the application, both the magnitude and the energy of X-rays are varied to achieve that goal.


Projection X-ray imaging consists of three closely related technologies: radiography, mammography, and fluoroscopy. Radiography pertains to the acquisition of static two-dimensional images of nearly all body parts ranging from the main body habitus to extremities and the skull. Mammography extends that application to be specific for breast imaging. The application to breast is distinct because of optimization of the technology for imaging this one region of the body. When projection imaging is extended to the temporal domain, the modality is referred to as fluoroscopy. Fluoroscopy, again with distinct technology and acquisition process, enables dynamic imaging of the body. The foundation of projection X-ray imaging is covered in earlier chapters (Chapters 1–3). In this chapter, we primarily focus on the specific implementation of those foundations in the technology and process of the primary projection X-ray modality, radiography, and its close associates of mammography and fluoroscopy.


6.2 Projection X-ray Setup


The formation of projection X-ray image requires a radiation source and a detector in the opposite sides of the body part. This requires a geometrical apparatus referred to as gantry (Figure 6.1). One side of the gantry houses the X-ray tube and the other side houses the radiographic detector. The patient’s body is positioned within the gantry, usually near the detector. A divergent X-ray beam, most commonly in the form of a cone beam, is directed to the detector, passing through the body part and creating a shadow of attenuation differentials from the body. This shadow is recorded by the detector forming the image.

Image described by surrounding text and caption.

The radiography X-ray tube contains an adjustable beam-limiting device, made of interleaved plates that define the exact field of exposure. The body part of interest is positioned such that only the anatomy of interest is in the field; no exposure should extend beyond the field of view of the detector. The X-ray source assembly is connected to the gantry such that its location and orientation can be changed for different patient positioning and acquisition angles. However, in any acquisition, its flux should only be directed to the detector (to avoid unnecessary patient exposure). The detector assembly contains the imaging sensor, required electronics, and often a scatter reduction grid, used to limit scattered radiation from the image. The grids come with different geometrical and attenuation specifications and may be stationary or oscillating (see Section 6.4.2).


The beam divergence is an important component of projection X-ray imaging as it causes the magnification of the projection X-ray image. The magnification creates three major constraints on the image acquisition. First, if the image is overly magnified, the organs of interest may not fall within the imaging field of view and therefore will not be captured by the detector. Second, the higher magnification can lead to increased blurring because of geometrical factors, e.g. focal spot size. Finally, for sensors that require a certain amount of signal to achieve sufficient image quality, higher magnification may necessitate increased radiation dose to the patient in order to achieve the required signal at the detector [1]. These constraints, in conjunction with the desired field of view, the minimum achievable focal spot size, and expected radiation dose, dictate the proper geometry for any given acquisition in terms of focal spot to detector distance, field of view, and body part to detector distance.


If X-rays are assumed to originate from a single point on the target of the X-ray tube, then the magnification of the radiographic image is calculated as


(6.1)numbered Display Equation

If the distance between the target of the X-ray tube and the X-ray detector is constant, then the ratio of the image size to the object size may be increased by moving the object toward the X-ray tube. This is called object shift enlargement (Figure 6.2); in contrast, image shift enlargement maintains a constant distance between the target and the object but moves the detector farther from the object to increase the ratio of image size to object size.

Image described by surrounding text and caption.

The amount of enlargement possible without significant loss of image detail is an important consideration in projection X-ray imaging. With the image receptor close to the patient, the blurring of the detector (termed unsharpness in radiographic imaging) is the primary determinant of the visibility of image detail. As the distance between the detector and the patient is increased by object shift or image shift enlargement, the effect of detector unsharpness on the image detail is unchanged. However, the contribution of geometric unsharpness increases steadily with an increasing distance between the patient and the image receptor, i.e. increasing magnification, as per Figure 6.2,


(6.2)numbered Display Equation

At some patient–receptor distance, geometric unsharpness approximately equals detector unsharpness. Beyond this distance, the image detail visibility deteriorates steadily as geometric unsharpness increasingly dominates detector unsharpness. As a general rule, the image should not be magnified beyond the point at which geometric unsharpness and detector unsharpness are approximately equal. This constraint usually dictates the use of a focal spot that has dimensions small enough to minimize the geometric unsharpness when acquiring a magnified view.


For any X-ray modality, the X-ray tube should have sufficient heat capacity to provide sufficient X-ray flux; a higher flux enables shorter acquisition time, thus minimizing the likelihood of patient motion during the imaging process. Larger focal spot sizes provide for higher flux but also larger geometrical unsharpness. Thus, there is always a trade-off between temporal and spatial resolution. The tube generator also needs an operating voltage high enough to provide X-rays of the energy needed to penetrate the body part of interest but also low enough to provide sufficient contrast for body features of interest, as lower energies provide for more intrinsic contrast (see Chapter 2).


In clinical radiography, the anode heel effect also has two important factors to consider when imaging. The cathode side of the field of view offers higher flux, and as such, the thicker part of the anatomy is best positioned toward the cathode side of the X-ray tube to take advantage of this higher X-ray flux. However, the cathode side also exhibits a larger apparent focal spot size because of the angular perspective of the focal spot from the sensor (Figure 6.3). For this reason, radiographic images exhibit higher spatial resolution toward the anode side of the X-ray tube.

Image described by surrounding text and caption.

The above trade-off and considerations provide a variety of constraints that dictate how each application of projection imaging is protocoled. A few examples are provided in Table 6.1. Different protocols also make use of the fact that the detector in radiography can be upright (as for chest imaging), within a table (for abdominal imaging), or tabletop (for extremity imaging), or portable for bedside imaging applications.


























































ProtocolBody thickness (cm)SID (cm)GridkVTypical mATypical exposure time (s)Maximum mAs or mA
Chest2318312 : 11204.70.0015
Cervical spine1310012 : 170220.00131
Hand5100None602.50.0017
Breast (mammography)4.5655 : 128600.675
Cardiac fluoroscopy231008 : 190100.006 per pulse18 mA

6.3 X-ray Projection Modalities


6.3.1 Radiography


In standard radiography, the body part is positioned as close to the image detector as possible in order to minimize the magnification. However, different regions of the image are always magnified according to their distance from the focal spot. Other considerations include the source-to-image distance (SID), focal spot size, and thickness of the anatomy. If a higher flux of X-rays is needed, a shorter SID can be used (typically 100 cm), but greater distances (typically 180 cm) cause less magnification distortion, which is desirable for some imaging applications.


A typical focal spot size in radiography is 1–1.2 mm. If the imaging requires higher sharpness, a smaller focal spot size is used (typically 0.4–0.6 mm). However, smaller focal spots generally have smaller maximum current settings, so there may be higher noise because of reduced X-ray flux that can be obtained without damaging the focal spot or higher exposure time and thus higher potential for motion blur.


Typical kilovolts setting in radiography ranges between 60 and 70 kV for extremities (e.g. hand and foot), 80–100 kV for medium size body parts (e.g. skull), and for large body parts (e.g. chest or abdomen) up to 120–140 kV. Higher kilovolts offer higher penetration and lower patient dose (assuming mA is adjusted accordingly) but lower subject contrast (see Chapter 2).


In imaging anatomy that is within the main body habitus (e.g. spine, chest, abdomen, and pelvis), the thickness of the body part and the size of the field of view causes a high amount of scattered radiation to be generated, reducing the image quality. For these exams, antiscatter grids are helpful but cannot always be used. Clearly, radiography and medical imaging generally is often an exercise in cost–benefit analysis between many factors with image quality in only one consideration.


6.3.2 Mammography


Mammography is a special radiographic procedure performed to visualize the tissue within the breast. It has the same general characteristics as standard radiography although with certain special provisions optimized to image the unique attributes of breast tissue and pathology.


Breasts are primarily composed of soft tissues, which is between adipose and muscle in terms of attenuation. The small attenuation differential necessitates a lower X-ray energy because the differences between tissue types are most pronounced in lower ranges (Figure 6.4) [2]. As such, most mammography systems operate between 25 and 35 kV. Further, the X-ray tube commonly uses molybdenum with options for rhodium and tungsten targets in some systems, because the characteristic energies of these elements fall within the mammography X-ray spectra, offering a high flux of characteristic X-rays within the energies of interest (see Chapter 2).

Image described by surrounding text and caption.

A notable component of mammography is breast compression with a radiotransparent paddle. The compression provides multiple advantages: It reduced the total attenuation through the breast by reducing its thickness. It further reduces the magnitude of generated scattered radiation. Both of these provide for lowering the needed flux of X-rays and thus patient dose. Additional benefits of compression include immobilization of the breast, thus minimizing the potential for motion blur, enabling the use of lower energy X-rays because of the thinness of the breast, providing better imaging of the breast tissue close to the chest wall and reducing magnification unsharpness and geometrical distortion.


The mammographic tube also uses a small focal spot (typically 0.3 mm) because mammography must identify the fine spatial details that are often characteristic of malignancy (small tumor features or microcalcifications). The small focal spot achieves higher resolution depictions of such details. Mammography frequently includes magnification imaging for enhancing details even further within limited target areas of the breast. Magnification radiography exploits the aforementioned properties of a divergent X-ray beam to intentionally form an image larger than the physical size of the body part. In that mode, typically, a 0.1 mm focal spot is used to manage the enhanced focal spot blur due to higher magnification.


The SID for mammography is relatively short compared to general radiographic procedures, e.g. 65 cm, and the geometrical configuration is highly conformed to the anatomical structure of the breast and chest. The breast anatomy that must be imaged includes the tissue immediately adjacent to the chest wall, so the gantry is configured for the maximum coverage at the chest wall by tilting the tube and placing the central axis of the beam directly parallel with the chest wall. In that way, the entire breast tissue accessible by the geometry can be captured in the image. In addition, by placing the central axis of the beam directly parallel with the chest wall, the divergent beam is parallel with the chest wall reducing any radiation that does not intersect the detector and entering the chest (Figure 6.5). Because of this configuration, the photon flux is largest at the chest wall, where the anatomy may be thickest, but the apparent focal spot is also larger. Conversely toward the nipple, there is better resolution and smaller photon flux.

Image described by surrounding text and caption.

6.3.3 Fluoroscopy


Fluoroscopy provides real-time projection imaging of a body part. The procedure is often used in clinical applications where a dynamic process needs to be visualized. That includes the transient opacification of blood vessels in angiography, the transition of a contrast bolus through the gastrointestinal (GI) track, or interventional procedure where the location of an inserted apparatus or probe needs to be discerned with respect to the penetrated anatomy. The fluoroscopic system comes with diverse configurations, ranging from table-based units, potable c-arms that can be moved to the patient and positioned as needed around the patient, to c-arms mounted to the examination room floor or ceiling (Figure 6.6). C-arm systems mounted within a room usually provide greater maneuverability and imaging capabilities than portable systems, and some come equipped with multiple X-ray tube/detector configurations (i.e. bi-plane c-arms) that enable improved visualization.

Image described by surrounding text and caption.

Given the diversity of applications, body parts, and patients, fluoroscopy systems need to dynamically adjust the X-ray energy and flux to provide sufficient X-ray penetration and image quality as the area being imaged changes, which often occurs in a single fluoroscopy examination. This requires a real-time feedback circuitry in the fluoroscopy systems. Further, the adaptation of the flux to the imaging needs to follow certain predetermined functions. For example, as the attenuation of a penetrating body part increases, a system might proportionally increase the milliamperes only, the kilovolts only, or a combination of the two. The resulting images offer different levels of signal, contrast, or noise, based on the adaptation function, often configurable by service. This process of controlling the X-ray technical factors to meet a preset detector imaging needs is called automatic brightness control (ABC) (see Section 6.4.3.2.1).


The traditional detector technology for fluoroscopy is an image-intensifier (II) tube. Newer systems use flat panel detector (FPD) technology [3,4]. These technologies are distinct and as such offer differing features to fluoroscopic applications. For example, in the case of image resolution, the II resolution can be as high as 5 lp/mm but is variable as a function of the magnification. The resolution for FPDs in contrast is around 2 lp/mm. With II detectors, the ABC system (see Section 6.4.3.2.1) increases the exposure rate with magnification to ensure consistent brightness. An advantage of FPDs is that they do not have distortion artifacts (i.e. pincushion and S-distortion) that are an issue with II detectors (see Section 6.4.3.2.1). FPD systems also use pixel binning to reduce noise, albeit negatively affecting image resolution.


Although in radiographic procedures it is possible to manage the dose by adjusting ray tube milliamperes and kilovots, as well as the source to skin distance, fluoroscopic dose reduction strategies take into account also the total fluoro time. Fluoroscopy X-ray tube can produce a continuous beam, so patient exposure can accumulate over long exposure times. By regulation, these systems must provide a sound alert after five minutes of total fluoroscopy time and the maximum entrance exposure rate must not exceed 10 R/min for most fluoroscopy studies, although most fluoroscopy systems can operate in high-dose or low-dose rate modes. The high-dose mode allows entrance exposure rates up to 20 R/min but requires an audible signal while in use. The low-dose modes are implemented in most new systems with the capability to select different dose rates for different sizes by adding beam filtration to reduce radiation dose, especially for pediatric studies.


Although the production of X-rays can be continuous, human perception can interpret motion that varies with intensity and contrast, with the fastest variation being 50–90 Hz [5]. Most displays limit the rate to only 30 frames per second (fps). With this frame rate, any motion that occurs within a 33 ms time window will be blurred in an image. Yet, for most physiological functions, motion faster than this is uncommon, so most fluoroscopy systems operate in pulse mode. In pulse mode, the X-ray tube produces a series of short pulses (typical between 1 and 10 pulses/s), resulting in reductions in dose and image blur. As an example, a continuous mode procedure that produces a 30 fps with 33 ms per frame at 2 mA of X-ray tube current can be replaced with a pulse mode procedure at 30 fps with 10 ms per frame and 6.6 mA of tube current. Lower frame rates are encouraged if lower temporal resolution can be tolerated. For example, when guiding catheter from femoral artery to aortic arch simply by reducing the frame rate from 30 to 7.5 fps results in a 75% reduction is patient dose.


The use of flat panel digital technology in fluoroscopy has enabled other dose management strategies. One strategy is temporal frame averaging, which reduces image noise at the cost of a tolerable reduction in temporal resolution, and introducing image lag, but enabling reduction in mA and thus patient dose. The displayed image is obtained by averaging two or more previous frames. Other temporal image filtering can also be applied to minimize lag if that is needed for the study.


As most applications of fluoroscopy are for interventional purposes, radiation protection of the personnel becomes essential. In fluoroscopy, the primary source of exposure to medical personnel is the scattered radiation from the patient. Figure 6.7 shows how the scattered radiation is higher at the tube side of the gantry. Physician and radiographer exposure may be reduced by simply operating fluoroscopy device with the tube positioned below the patient. The dose rate is also inversely proportional to the square of the distance to the source; staying at distance from the patient during the exposure (if it is possible) reduces the operator’s dose. Therefore, standing behind others if a direct line of sight to the patient is not essential. All persons present in the room during the procedure must wear a lead apron (equivalent to 0.5 mm Pb), and other lead strips may be positioned between the patient and the operators to shield the scattered radiation.

Image described by surrounding text and caption.

6.4 Key Components of Projection X-ray Systems


6.4.1 X-ray Tube and Generator


The X-ray tubes used in projection X-ray imaging generally follow the details and specifications delineated in Chapter 1. In radiography, there is a need to acquire static images with a least amount of motion and with sufficient penetration. This necessitates the production of X-rays with high flux (i.e. high milliamperes) and high energy (i.e. high kilovolts). As a result, most radiographic tubes and generators need to be high powered. A typical radiography tube generator is 60–120 kW. The focal spots are typically 1.2 mm in size, nominally, with higher resolution imaging offered with small focal spots, typically 0.4–0.6 mm in size. The tube is equipped with an adjustable beam-limiting device that adjusts the field size to a range of typically 5 cm × 5 cm to 43 cm × 43 cm for typical SIDs of 100–180 cm.


In mammography, the power needs are more modest, as photon energy is kept at low levels to extenuate tissue contrast. Also, the breast is reasonably immobile because of the use of breast compression. As such, lower maximum milliampere settings are tolerated. The typical mammographic tube generator power is 3–10 kW. Mammography uses a small focal spot, typically 0.3 mm in size for standard views and typically 0.1 mm focal spot for magnification. The mammography systems are equipped with a range of beam-limiting devices, each of which provides for a specific field of view ranging from 18 cm × 24 cm to 24 cm × 30 cm for the typical SID of 65 cm.


In fluoroscopy, the power needs to vary widely. For portable c-arm applications, the power is generally low within 3–30 kW. For vascular applications where there is more potential for excessive motion and image noise needs to be low, a higher power rating is needed. Fluoroscopy systems of this type are usually in use in interventional and surgical suits. Focal spot sizes range from 0.6 to 1.2 mm. The bream-limiting devices are similar to that of radiography with field size adjustment functionality. The beam shape, however, is not limited to rectangular shape, and circular or other shapes are possible.


6.4.2 Antiscatter Grid


One of the major challenges in projection X-ray imaging is the ever-presence of scattered radiation, either Rayleigh or Compton (see Chapter 1). Scattered radiation creates a “fog” in the image, increasing noise and reducing contrast. As both primary (i.e. wanted) and scattered (i.e. unwanted) radiation impinge on the detector after passing through the patient, the scattered radiation can conceal the useful information by diluting the information content. Furthermore, scatter is often unevenly distributed across the imaging field of view; thus, the impact on contrast and noise varies from region to region. The deteriorating influence of scattered radiation is present in all applications of projection X-ray imaging, radiography, mammography, and fluoroscopy.


Radiation scatter is proportional to the volume of tissue exposed to the X-ray beam, so confining the X-ray beam to the region of interest can significantly reduce the scatter (simultaneously reducing the patient dose) [6]. As such, proper collimation of an X-ray beam with the beam-limiting device is essential to the production of high-quality projection images. Much of the scattered radiation can be removed by a radiographic grid between the patient and the X-ray detector. A grid typically consists of strips of a dense, high-Z material separated by a material that is relatively transparent to X-rays. The grid prevents radiation that is not aligned with the primary beam from reaching the detector (Figure 6.8).

Image described by surrounding text and caption.

Ideally, the strips in a radiographic grid should not be visible in the radiographic image. Additionally, the strips should be completely opaque to scattered radiation and should not release secondary radiation of its own (e.g. fluorescence) as scattered X-ray photons are absorbed. These requirements are reasonably satisfied by lead foil about 0.05 mm thick, and this material is commonly used. However, the grid lines can still leave artifacts in the images ranging from directly visible lines, moiré patterns, or broad nonuniformity in the images (see Section 6.4.2.1). The material between the grid strips may be aluminum, fiber, or plastic. Although fiber and plastic transmit primary photons with minimal or no attenuation, grids with aluminum interspaces are structurally stronger. Also, grids with aluminum interspaces may absorb scattered radiation that escape the grid strips [7]. However, grids with improved attenuation also necessitate higher radiation dose to the patient to achieve a desired signal magnitude at the detector.


Radiographic grids are available commercially with parallel or focused grid strips in either linear or crossed configurations (Figures 6.9 and 6.10). When a focused grid is positioned at the correct distance from the target of an X-ray tube, lines through the grid strips are directed toward a point or focus on the target. With a parallel grid positioned at a finite distance from an X-ray tube, more primary X-rays are attenuated along the edge of the radiograph than at the center. Consequently, the detector signal decreases slightly from the center to the edge of a radiograph exposed with a parallel grid. Thus, the uniformity of the detector signal is improved in a radiograph exposed with a focused grid, provided that the grid is positioned correctly.

Image described by surrounding text and caption.
Image described by surrounding text and caption.

A linear grid is constructed with all parallel or focused grid strips in line (Figure 6.10). A crossed grid is made with the strips in one grid perpendicular to those in the other. In most situations, a crossed grid removes more scattered radiation than does a linear grid with the same grid ratio because a linear grid does not absorb photons scattered parallel to the grid strips. However, a linear grid is easier to use in situations where proper alignment of the X-ray tube, grid, and X-ray detector is difficult (see Section 6.4.2.1).


Linear grids are generally characterized in terms of the ratio of the width of the interspace material to its height (grid ratio, Figure 6.11), grid frequency (number of lines per unit distance), and grid focus distance.

Image described by surrounding text and caption.

The grid ratio is calculated as


(6.3)numbered Display Equation

where h is the height of the grid strips and d is the distance between grid strips. The grid ratio of a crossed grid is r1 + r2, where r1 and r2 are the grid ratios of the linear grids used to form the crossed grid. Radiographic grids with grid ratios greater than 16 are available commercially. However, ratios of 8–12 are most common because the removal of scattered radiation is increased only slightly with increasing grid ratios. Also, grids with a high grid ratio require more precise alignment and a greater patient exposure to radiation. The effectiveness of a radiographic grid for removing scattered radiation as a function of grid ratio is illustrated in Figure 6.12.

Image described by surrounding text and caption.

Grid ratios provide proportionally improved radiographic contrast, with higher values for lower energy X-rays (Table 6.2). Note that this decrease is reflective of subject contrast as captured by the detector. Correspondingly, the grid’s contrast improvement factor is defined as the ratio of the maximum radiographic contrast with the grid to the maximum contrast without the grid; the factor expresses the effectiveness of different grids at removing scattered radiation. The contrast improvement factor for a particular grid varies with the thickness of the patient and with the cross-sectional area and energy of the X-ray beam. In order to impose a standard metric for comparison, the contrast improvement factor is usually measured with a water phantom, 20 cm thick, irradiated by an X-ray beam generated at 100 kVp [9]. The image contrast-to-noise ratio (CNR) is directly influenced by this contrast, even when the contrast of an image can be adjusted in digital imaging systems.







































































Type of grid and grid ratioImprovement in contrastIncrease in exposure
70 kVp95 kVp120 kVp70 kVp95 kVp120 kVp
None111111
5 linear3.52.52333
8 linear4.753.252.53.53.754
12 linear5.253.75344.255
16 linear5.7543.254.556
5 cross5.753.52.754.555.5
8 cross6.754.253.25567

aThe thickness of the grid strips and interspaces were identical for all grids.


Source: From Characteristics and Applications of X-ray Grids. Cincinnati, Liebel-Flarsheim Co., 1968 [8]. Used with permission.


The radiation exposure of the patient increases with the grid ratio because the radiation flux must be increased to have sufficient primary X-ray photons reach the detector. This is related to the selectivity of a grid defined as the ratio of primary to scattered radiation transmitted through the grid. One popular but rather ambiguous description of grid effectiveness is the Bucky factor (named after the designer of the first radiographic grid), defined as the exposure to the detector without the grid divided by the exposure to the detector with the grid in place when a wide X-ray field emerges from a thick patient. This factor is obviously highly dependent on patient-specific characteristics. Thus, a standard condition, such as the one noted above, should be applied in its characterization. A crossed grid removes scattered radiation more effectively than a linear grid with an equal grid ratio, with associated increase in patient exposure.


Grid frequency is another important parameter. Grid line creates a shadow in the radiographic images. These shadows can be blurred by moving the grid or by image processing. Grids with many strips per unit length produce shadows in the radiographic image that are easier to remedy or are not as easily visualized. Typical grid frequencies are 20–80 strips cm−1 (80–200 in.−1), with higher frequency ratios used in higher resolution applications. Also, the lead content of the grid (in units of grams per square centimeter) is sometimes stated along with the grid ratio and frequency. Grids may be described as heavy or light, depending on their lead content.


Considering the prevalence of scattered radiation in X-ray imaging, the selection of a grid has a substantial influence on the resultant image. To summarize, high grid ratios eliminate the scatter most effectively, but the concurrent reduction in the primary beam must be offset with a greater flux of X-rays, which results in increased patient dose. Notably, grids with higher ratios are also more susceptible to positioning-related problems and artifacts.


6.4.2.1 Grid Artifacts


A central issue with the use of grid is the shadow of the grid structure in the image, which, if left unmitigated, is distracting and can sometimes interfere with the identification of small structures such as blood vessels and bone trabeculae. One common remedy is moving the grid. A moving grid (a so-called Potter–Bucky diaphragm) removes the distracting grid strips by blurring their image across the detector. Early Potter–Bucky diaphragms moved in one direction only, but modern moving grids make several transits back and forth during an exposure. The linear distance over which the grid moves is small (1–5 cm), and as such does not cause significant off-center cutoff (see below). However, the movement must take place within the brief exposure. Also, the motion of the grid must not be parallel to the grid strips, and it must be adjusted to prevent synchronization between the position of the grid strips and the rate of pulsation of the X-ray beam. The direction of motion of the grid changes very rapidly at the limits of grid travel, so the dwell time of the grid at these positions is insignificant.


The moving grid does not interfere with the photons traveling in a straight line from the focal spot to reach the image receptor because of their high speed. A typical moving grid travels at a maximum rate of 15 cm/s, and it takes X-ray photons only 3 ns to travel the 100 cm from the X-ray tube to the image receptor. During this time, a grid moving at 15 cm/s will have traveled only 5 Å (the width of five atoms).


In imaging detectors with high spatial resolution, the grid artifact may only be eliminated by oscillating the grid during the image acquisition, essentially blurring the grid lines. As such, it is a natural choice in many applications, but it is not the only method to mitigate grid line artifacts. In portable applications, a moving apparatus is not practical. The maintenance cost for reciprocating grids and their poor efficiency for removing the image of grid strips during short exposure times are also reasons for alternative methods. The development of high-frequency grids has reduced the need for moving grids. Essentially, if the grid frequency is higher than their resolving power of the digital sensor, the line artifact may be avoided without the oscillating motion.


In most modern digital FPDs, the resolution is higher or in the same order of magnitude to that of the grid frequency. Short of using an oscillating grid method for grid line reduction, for many of these systems, the grid lines are eliminated by two common image-processing methods. The first method captures an image of the grid during detector calibration. This image is stored and during the preprocessing workflow subtracted from the acquired clinical images, effectually erasing the artifact. This requires perfect reproducible positioning of grid with respect to the detector; if the grid is misplaced between when the calibration image and the actual image are captured, not only the grid lines would not be eliminated, they will be enhanced. Alternatively, the grid lines can be eliminated by using a band-pass filter (see Chapter 3) in the direction perpendicular to the direction of the grid lines. In digital systems, poor or partial elimination of grid line artifact can lead to an interference between the periodic pattern of the grid and that of the image pixels, sampling inherent to digital imaging. This interference causes aliasing, where the higher frequency component of the grid get reflected as a low-frequency periodic pattern in the image, known as a moiré pattern (see Chapter 3).


In addition to line artifacts, there can be an uneven loss of primary radiation caused by improper alignment of a radiographic grid, which causes grid cutoff. With a parallel grid and the cone beam of X-rays used in radiography, cutoff occurs near the edges of a large field because grid strips intercept many primary photons along the edges of the X-ray beam. The width of the shadow of grid strips in a parallel grid increases with their distance from the center of the grid (Figure 6.13).

Image described by surrounding text and caption.

The use of a focused grid at an incorrect target–grid distance also causes grid cutoff, specifically axial decentering or off-distance cutoff (Figure 6.14). The detector signal with off-distance cutoff decreases from the center of the field outward, and the variation in detector exposure increases with the displacement of the grid from the correct target–grid distance. However, the effect is not problematic until the displacement exceeds the target–grid distance limits established for the grid. These limits are narrow for grids with high grid ratios and wider for grids with smaller ratios. Finally, the effects of off-distance cutoff are more severe when the target–grid distance is shorter than that recommended and less severe when the target–grid distance is greater.

Image described by surrounding text and caption.

Lateral decentering or off-center cutoff occurs when X-rays parallel to the strips of a focused grid converge at a location that is displaced laterally from the target of the X-ray tube (Figure 6.15). Off-level cutoff results from tilting the grid (Figure 6.16). Both off-center and off-level cutoff cause an overall reduction in detector signal (image nonuniformity across the image). The importance of correct alignment of the X-ray tube, grid, and detector increases with the grid ratio.

Image described by surrounding text and caption.
Image described by surrounding text and caption.

The choice of a radiographic grid for a particular examination depends on factors such as the anticipated amount of primary and scattered radiation emerging from the patient, X-ray energy, range of radiographic techniques possible by the X-ray generator, and difficulty of aligning the X-ray beam with the grid. In portable projection imaging (such as bedside imaging), beam grid alignment is often difficult; these applications thus often use lower grid ratios with a consequential reduction in scatter radiation removal and therefore poorer image quality.


6.4.2.2 Alternatives to Grid


To avoid the grid-induced complications of line artifacts, grid cutoff, and increased dose, a number of nongrid imaging alternatives have been considered or are in use. Those include the use of an air gap, slot-scan technology, or image-processing corrections.


In the air gap technique, the amount of scattered radiation reaching the detector is reduced by increasing the patient detector distance. This increases image magnification. Obviously, if this magnification causes the anatomy of interest to be projected into an area larger than the physical size of the image detector, this method cannot be used. The radiation dose to the patient can also increase per magnification. Further, the air gap technique increases the focal spot blur, necessitating the use of smaller focal spots. For thicker body parts, where this maxes the mA setting of the generator, this necessitates lengthening of the exposure time and consequently the potential for patient motion artifacts.


In the slot-scan technique, a slit in an otherwise radiation-opaque shield moves within the field of view in synchrony with a scanner fan beam of X-ray (Figure 6.17). The shield intercepts scattered radiation before it reaches the detector, which is also a narrow linear array [10]. This method can be highly effective provided that the fan beam can be precisely shaped and targeted to the detector, avoiding wasted radiation beyond the detector that only contributes to patient dose and not the image signal. The fan and the slit should also be narrow enough (1 cm or less) to effectively eliminate forward scattering radiation. Finally, the scan length is longer than a standard radiographic acquisition, so the patient must remain still during the acquisition to avoid motion artifacts, although such motion artifacts, if they occur, can be limited to only small bands within the image [11].

Image described by surrounding text and caption.

Finally, image-processing techniques are used to remedy the influence of scattered radiation on projection images. These methods are effective in reducing the nonuniformity in the image associated with the uneven distribution of scatter signal. However, the loss of image CNR from the presence of scatter fog cannot be overcome with postprocessing techniques.


6.4.3 Analog Imaging Detectors


6.4.3.1 Screen Film


For most of its history, detecting X-ray signal with an analog detector, namely X-ray film, has been the primary means to capture static projection imaging, radiography, and mammography. In the developed world, film has largely been replaced by digital technology. However, film is still used in medical practice worldwide. Further, even in the digital era, film technology has created a lasting effect in the way X-ray images are rendered, understood, and interpreted, so it is an important foundation to understand.


The X-ray film can have an emulsion on one side (single-emulsion film) or both sides (double-emulsion film) of a transparent film base that is about 0.2 mm thick (Figure 6.18). Single-emulsion systems are commonly used in mammography, whereas double ones are used in radiography. The emulsion is composed of silver-halide granules, usually silver bromide, that are suspended in a gelatin matrix. The emulsion is covered with a protective coating (T coat) and is sensitive to visible and ultraviolet light and to ionizing radiation. Single-emulsion film is less sensitive to radiation and is used primarily when exceptionally fine detail is required in the image (such as for mammography). The base is either cellulose acetate or a polyester resin. Intensifying screens may be added to make the film most sensitive to the wavelengths of light the screens emit. Nonscreen film is designed for direct exposure to X-rays and is less sensitive to visible light. However, virtually, all film used in diagnostic radiology is designed to be used with intensifying screens.

Image described by surrounding text and caption.

6.4.3.1.1 Photographic Process

Electrons in the granules of silver bromide in the emulsion of an X-ray film are released after absorbing energy when the film is exposed to ionizing radiation or to visible light. These electrons are trapped at sensitivity centers in the crystal lattice of the silver bromide granules, where they attract and neutralize mobile silver ions (Ag+) in the lattice (small quantities of metallic silver are deposited in the emulsion, primarily along the surface of the silver bromide granules). Although these changes in the granules are not visible, the deposition of metallic silver across a film exposed to an X-ray beam is a representation of the radiation impinging on the film. This information is captured and stored as a latent image in the photographic emulsion.


The latent image on the film must be chemically processed to form the image. First, the film is placed in a developing solution, where the latent image induced by the radiation serves as a catalyst for the deposition of metallic silver on the film base. In the next step of fixation, granules that were unaffected during exposure of the film are removed by sodium thiosulfate or ammonium thiosulfate in the fixing solution. This solution also contains potassium alum to harden the emulsion and acetic acid to neutralize residual developer on the film. The degree of blackening of a region of the processed film depends on the amount of free silver deposited in the region and, consequently, on the number of X-rays absorbed in the region.


6.4.3.1.2 Optical Density and Film Gamma

The amount of light transmitted by a region of processed film is described by the transmittance T,


(6.4)numbered Display Equation
Источник: https://radiologykey.com/projection-x-ray-imaging/

Problems

13.1Faraday’s Law

24.

A 50-turn coil has a diameter of 15 cm. The coil is placed in a spatially uniform magnetic field of magnitude 0.50 T so that the face of the coil and the magnetic field are perpendicular. Find the magnitude of the emf induced in the coil if the magnetic field is reduced to zero uniformly in (a) 0.10 s, (b) 1.0 s, and (c) 60 s.

25.

Repeat your calculations of the preceding problem’s time of 0.1 s with the plane of the coil making an angle of (a) (b) and (c) with the magnetic field.

26.

A square loop whose sides are 6.0-cm long is made with copper wire of radius 1.0 mm. If a magnetic field perpendicular to the loop is changing at a rate of 5.0 mT/s, what is the current in the loop?

27.

The magnetic field through a circular loop of radius 10.0 cm varies with time as shown below. The field is perpendicular to the loop. Plot the magnitude of the induced emf in the loop as a function of time.

Figure shows the magnetic field in milliTesla plotted as a function of time in ms. Magnetic field is zero when the time is equal to zero. It increases linearly with time reaching 3 milliTesla and 2 ms. It remains the same till 5 ms and then decreases linearly to 0 and 6 ms.
28.

The accompanying figure shows a single-turn rectangular coil that has a resistance of The magnetic field at all points inside the coil varies according to where and What is the current induced in the coil at (a) , (b) 0.002 s, (c) 2.0 s?

Figure shows a square coil with the sides of 2.0 and 5.0 cm. A uniform magnetic field B is directed perpendicular to the coil.
29.

How would the answers to the preceding problem change if the coil consisted of 20 closely spaced turns?

30.

A long solenoid with turns per centimeter has a cross-sectional area of and carries a current of 0.25 A. A coil with five turns encircles the solenoid. When the current through the solenoid is turned off, it decreases to zero in 0.050 s. What is the average emf induced in the coil?

31.

A rectangular wire loop with length a and width b lies in the xy-plane, as shown below. Within the loop there is a time-dependent magnetic field given by , with in tesla. Determine the emf induced in the loop as a function of time.

Figure shows a rectangular wire loop with length a and width b lies in the xy-plane.
32.

The magnetic field perpendicular to a single wire loop of diameter 10.0 cm decreases from 0.50 T to zero. The wire is made of copper and has a diameter of 2.0 mm and length 1.0 cm. How much charge moves through the wire while the field is changing?

13.2Lenz's Law

33.

A single-turn circular loop of wire of radius 50 mm lies in a plane perpendicular to a spatially uniform magnetic field. During a 0.10-s time interval, the magnitude of the field increases uniformly from 200 to 300 mT. (a) Determine the emf induced in the loop. (b) If the magnetic field is directed out of the page, what is the direction of the current induced in the loop?

34.

When a magnetic field is first turned on, the flux through a 20-turn loop varies with time according to where is in milliwebers, t is in seconds, and the loop is in the plane of the page with the unit normal pointing outward. (a) What is the emf induced in the loop as a function of time? What is the direction of the induced current at (b) t = 0, (c) 0.10, (d) 1.0, and (e) 2.0 s?

35.

The magnetic flux through the loop shown in the accompanying figure varies with time according to where is in webers. What are the direction and magnitude of the current through the resistor at (a) ; (b) and (c)

Figure shows a loop with the magnetic flux perpendicular to the loop. Loop is connected to a 5 Ohm resistor.
36.

Use Lenz’s law to determine the direction of induced current in each case.

Figure A shows a metal bar moving to the left in the perpendicular uniform magnetic field. Figure B shows a loop moving to the right in a parallel uniform magnetic field. Figure C shows a loop moved into a perpendicular uniform magnetic field. Figure D shows a metal bar moving to the right in the perpendicular uniform magnetic field. Figure E shows a loop located into an increasing perpendicular magnetic field. Figure F shows a loop located into a decreasing parallel magnetic field.

13.3Motional Emf

37.

An automobile with a radio antenna 1.0 m long travels at 100.0 km/h in a location where the Earth’s horizontal magnetic field is What is the maximum possible emf induced in the antenna due to this motion?

38.

The rectangular loop of N turns shown below moves to the right with a constant velocity while leaving the poles of a large electromagnet. (a) Assuming that the magnetic field is uniform between the pole faces and negligible elsewhere, determine the induced emf in the loop. (b) What is the source of work that produces this emf?

Figure shows the rectangular loop (short side has a length l, long side has a length a) of N turns that moves to the right with a constant velocity v while leaving the uniform magnetic field.
39.

Suppose the magnetic field of the preceding problem oscillates with time according to What then is the emf induced in the loop when its trailing side is a distance d from the right edge of the magnetic field region?

40.

A coil of 1000 turns encloses an area of . It is rotated in 0.010 s from a position where its plane is perpendicular to Earth’s magnetic field to one where its plane is parallel to the field. If the strength of the field is what is the average emf induced in the coil?

41.

In the circuit shown in the accompanying figure, the rod slides along the conducting rails at a constant velocity The velocity is in the same plane as the rails and directed at an angle to them. A uniform magnetic field is directed out of the page. What is the emf induced in the rod?

Figure shows the rod that slides along the conducting rails at a constant velocity v in a uniform perpendicular magnetic field. Distance between the rails is l. Angle between the direction of movement of the rod and the rails is theta.
42.

The rod shown in the accompanying figure is moving through a uniform magnetic field of strength with a constant velocity of magnitude What is the potential difference between the ends of the rod? Which end of the rod is at a higher potential?

Figure shows the 5 cm long rod of that moves to the right at a constant velocity v in a uniform perpendicular magnetic field.
43.

A 25-cm rod moves at 5.0 m/s in a plane perpendicular to a magnetic field of strength 0.25 T. The rod, velocity vector, and magnetic field vector are mutually perpendicular, as indicated in the accompanying figure. Calculate (a) the magnetic force on an electron in the rod, (b) the electric field in the rod, and (c) the potential difference between the ends of the rod. (d) What is the speed of the rod if the potential difference is 1.0 V?

Figure shows the 25 cm long rod of that moves to the right at a constant velocity v in a uniform perpendicular magnetic field.
44.

In the accompanying figure, the rails, connecting end piece, and rod all have a resistance per unit length of The rod moves to the left at If everywhere in the region, what is the current in the circuit (a) when (b) when Specify also the sense of the current flow.

Figure shows the rod that slides to the left along the conducting rails at a constant velocity v in a uniform perpendicular magnetic field. Distance between the rails is 4 cm. The rod moves for the distance a.
45.

The rod shown below moves to the right on essentially zero-resistance rails at a speed of If everywhere in the region, what is the current through the resistor? Does the current circulate clockwise or counterclockwise?

Figure shows the rod that slides to the right along the conducting rails at a constant velocity v in a uniform perpendicular magnetic field. Distance between the rails is 4 cm. The rails are connected through the 5 Ohm resistor.
46.

Shown below is a conducting rod that slides along metal rails. The apparatus is in a uniform magnetic field of strength 0.25 T, which is directly into the page. The rod is pulled to the right at a constant speed of 5.0 m/s by a force The only significant resistance in the circuit comes from the resistor shown. (a) What is the emf induced in the circuit? (b) What is the induced current? Does it circulate clockwise or counter clockwise? (c) What is the magnitude of ? (d) What are the power output of and the power dissipated in the resistor?

Figure shows the rod that is pulled to the right along the conducting rails by the force F in a uniform perpendicular magnetic field. Distance between the rails is 4 cm. The rails are connected through the 2 Ohm resistor.

13.4Induced Electric Fields

47.

Calculate the induced electric field in a 50-turn coil with a diameter of 15 cm that is placed in a spatially uniform magnetic field of magnitude 0.50 T so that the face of the coil and the magnetic field are perpendicular. This magnetic field is reduced to zero in 0.10 seconds. Assume that the magnetic field is cylindrically symmetric with respect to the central axis of the coil.

48.

The magnetic field through a circular loop of radius 10.0 cm varies with time as shown in the accompanying figure. The field is perpendicular to the loop. Assuming cylindrical symmetry with respect to the central axis of the loop, plot the induced electric field in the loop as a function of time.

49.

The current I through a long solenoid with n turns per meter and radius R is changing with time as given by dI/dt. Calculate the induced electric field as a function of distance r from the central axis of the solenoid.

50.

Calculate the electric field induced both inside and outside the solenoid of the preceding problem if

51.

Over a region of radius R, there is a spatially uniform magnetic field (See below.) At , after which it decreases at a constant rate to zero in 30 s. (a) What is the electric field in the regions where and during that 30-s interval? (b) Assume that . How much work is done by the electric field on a proton that is carried once clock wise around a circular path of radius 5.0 cm? (c) How much work is done by the electric field on a proton that is carried once counterclockwise around a circular path of any radius ? (d) At the instant when , a proton enters the magnetic field at A, moving a velocity as shown. What are the electric and magnetic forces on the proton at that instant?

Figure shows a proton carried into a uniform magnetic field with a radius R.
52.

The magnetic field at all points within the cylindrical region whose cross-section is indicated in the accompanying figure starts at 1.0 T and decreases uniformly to zero in 20 s. What is the electric field (both magnitude and direction) as a function of r, the distance from the geometric center of the region?

Figure shows a uniform magnetic field with a radius of 20 centimeters.
53.

The current in a long solenoid with 20 turns per centimeter of radius 3 cm is varied with time at a rate of 2 A/s. A circular loop of wire of radius 5 cm and resistance surrounds the solenoid. Find the electrical current induced in the loop.

54.

The current in a long solenoid of radius 3 cm and 20 turns/cm is varied with time at a rate of 2 A/s. Find the electric field at a distance of 4 cm from the center of the solenoid.

13.6Electric Generators and Back Emf

55.

Design a current loop that, when rotated in a uniform magnetic field of strength 0.10 T, will produce an emf where and

56.

A flat, square coil of 20 turns that has sides of length 15.0 cm is rotating in a magnetic field of strength 0.050 T. If the maximum emf produced in the coil is 30.0 mV, what is the angular velocity of the coil?

57.

A 50-turn rectangular coil with dimensions rotates in a uniform magnetic field of magnitude 0.75 T at 3600 rev/min. (a) Determine the emf induced in the coil as a function of time. (b) If the coil is connected to a resistor, what is the power as a function of time required to keep the coil turning at 3600 rpm? (c) Answer part (b) if the coil is connected to a 2000- resistor.

58.

The square armature coil of an alternating current generator has 200 turns and is 20.0 cm on side. When it rotates at 3600 rpm, its peak output voltage is 120 V. (a) What is the frequency of the output voltage? (b) What is the strength of the magnetic field in which the coil is turning?

59.

A flip coil is a relatively simple device used to measure a magnetic field. It consists of a circular coil of N turns wound with fine conducting wire. The coil is attached to a ballistic galvanometer, a device that measures the total charge that passes through it. The coil is placed in a magnetic field such that its face is perpendicular to the field. It is then flipped through and the total charge Q that flows through the galvanometer is measured. (a) If the total resistance of the coil and galvanometer is R, what is the relationship between B and Q? Because the coil is very small, you can assume that is uniform over it. (b) How can you determine whether or not the magnetic field is perpendicular to the face of the coil?

60.

The flip coil of the preceding problem has a radius of 3.0 cm and is wound with 40 turns of copper wire. The total resistance of the coil and ballistic galvanometer is When the coil is flipped through in a magnetic field a change of 0.090 C flows through the ballistic galvanometer. (a) Assuming that and the face of the coil are initially perpendicular, what is the magnetic field? (b) If the coil is flipped through what is the reading of the galvanometer?

61.

A 120-V, series-wound motor has a field resistance of 80 and an armature resistance of 10 . When it is operating at full speed, a back emf of 75 V is generated. (a) What is the initial current drawn by the motor? When the motor is operating at full speed, where are (b) the current drawn by the motor, (c) the power output of the source, (d) the power output of the motor, and (e) the power dissipated in the two resistances?

62.

A small series-wound dc motor is operated from a 12-V car battery. Under a normal load, the motor draws 4.0 A, and when the armature is clamped so that it cannot turn, the motor draws 24 A. What is the back emf when the motor is operating normally?

Источник: https://openstax.org/books/university-physics-volume-2/pages/13-problems

Open Access

Peer-reviewed

  • Xing-Ding Zhang ,
  • Lin Qi ,
  • Jun-Chao Wu,
  • Zheng-Hong Qin
  • Xing-Ding Zhang, 
  • Lin Qi, 
  • Jun-Chao Wu, 
  • Zheng-Hong Qin
PLOS

x

Abstract

We have previously reported that the mitochondria inhibitor 3-nitropropionic acid (3-NP), induces the expression of DNA damage-regulated autophagy modulator1 (DRAM1) and activation of autophagy in rat striatum. Although the role of DRAM1 in autophagy has been previously characterized, the detailed mechanism by which DRAM1 regulates autophagy activity has not been fully understood. The present study investigated the role of DRAM1 in regulating autophagy flux. In A549 cells expressing wilt-type TP53, 3-NP increased the protein levels of DRAM1 and LC3-II, whereas decreased the levels of SQSTM1 (sequestosome 1). The increase in LC3-II and decrease in SQSTM1 were blocked by the autophagy inhibitor 3-methyl-adenine. Lack of TP53 or knock-down of TP53 in cells impaired the induction of DRAM1. Knock-down of DRAM1 with siRNA significantly reduced 3-NP-induced upregulation of LC3-II and downregulation of SQSTM1, indicating DRAM1 contributes to autophagy activation. Knock-down of DRAM1 robustly decreased rate of disappearance of induced autophagosomes, increased RFP-LC3 fluorescence dots and decreased the decline of LC3-II after withdraw of rapamycin, indicating DRAM1 promotes autophagy flux. DRAM1 siRNA inhibited lysosomal V-ATPase and acidification of lysosomes. As a result, DRAM1 siRNA reduced activation of lysosomal cathepsin D. Similar to DRAM1 siRNA, lysosomal inhibitors E64d and chloroquine also inhibited clearance of autophagosomes and activation of lysosomal cathapsin D after 3-NP treatment. These data suggest that DRAM1 plays important roles in autophagy activation induced by mitochondria dysfunction. DRAM1 affects autophagy through argument of lysosomal acidification, fusion of lysosomes with autophagosomes and clearance of autophagosomes.

Citation: Zhang X-D, Qi L, Wu J-C, Qin Z-H (2013) DRAM1 Regulates Autophagy Flux through Lysosomes. PLoS ONE 8(5): e63245. https://doi.org/10.1371/journal.pone.0063245

Editor: Arun Rishi, Wayne State University, United States of America

Received: November 13, 2012; Accepted: March 29, 2013; Published: May 17, 2013

Copyright: © 2013 Zhang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was partially supported by the National Natural Science Foundation of China (No 30930035), by the National Basic Science Key Project (973 project, CB510003), by the Priority Academic Program development of Jiangsu Higher Education Institutes, and by Graduate Training Innovation Project of Jiangsu Province (CX09B_042Z). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

3-nitropropionic acid (3-NP), a suicide inhibitor of the mitochondrial respiratory enzyme succinate dehydrogenase (SDH) [1], induces striatal cell death in vivo and in vitro [2]–[4]. When intoxicated in vivo, 3-NP produces symptoms and striatal neuronal loss in human brains replicating neuropathology of Huntington’s disease [4], [5]. We previously reported that intrastriatal administration of 3-NP induced TP53-dependent autpophagy activation and apoptosis. The TP53 specific inhibitor pifithrin-α (PFT-α) blocked induction of autophagic proteins including DNA Damage Regulated Autophagy Modulator1 (DRAM1), LC3-II and beclin1 and apoptotic proteins including TP53-upregulated modulator of apoptosis (PUMA) and BAX. Both pharmacological inhibitors of autophagy and caspases effectively inhibited 3-NP-induced cell death [6], [7].

DRAM1, a novel TP53 target gene, is an evolutionarily conserved lysosomal protein and has been reported to play an essential role in TP53-dependent autophagy activation and apoptosis [8]. The mechanism by which DRAM1 promotes autophagy is not clear. It is proposed that DRAM1 may exert its effects on autophagy through lysosomes, given the fact as a lysosomal membrane protein. Uncovering the molecular mechanism by which DRAM1 regulates autophagy would provide a better understanding of the role of TP53 signaling pathway in the regulation of cell death and survival.

Autophagy is a pathway delivering cytoplasmic components to lysosomes for degradation [9]–[13]. Macroautophagy involves the sequestration of a region of the cytoplasm in a double-membrane structure to form a unique vesicle called the autophagosome. Acidification of lysosomes is crucial for activation of cathepsins, fusion of lysosomes and autophagosomes and effective degradation of autophagic substrates. However, these late digestive steps of autophagy remain largely uncharacterized.

Lysosomes are cytoplasmic organelles that contain several enzymes mostly belonging to the hydrolases [14]. Internal pH of lysosomal is characteristically acidic and it is maintained around pH 4.5 by a proton pump, that transport H+ ions into lysosomes [15], [16]. Many autophagy inhibitors including the vinca alkaloids (e.g., vinblastine) and microtubule poisons that inhibit fusion of autophagosomes with lysosomes, inhibitors of lysosomal enzymes (e.g., leupeptin, pepstatin A and E64d), and compounds that elevate lysosomal pH (e.g., inhibitors of vacuolar-type ATPases, such as bafilomycin A1 and weak base amines including ammonia, methyl- or propylamine, chloroquine, and Neutral Red, some of which slow down fusion), act at the fusion and lysosomal degradation steps [17]. Lysosomal enzymes also play a role in activation of certain types of caspases and therefore, are involved in apoptosis [18]. Inhibition of lysosomes or lysosomal enzymes protects neurons against excitotoxicity and ischemic insults [19], [20]. Thus, it is of particularly interest to investigate if DRAM1 modulates autophagy through influencing lysosomal functions.

In this study, we report that 3-NP induced DRAM1-dependent stimulation of autophagy in A549 cell lines. DRAM1 promotes autophagy flux by enhancing lysosomal acidification.

Materials and Methods

Cell Lines and Reagents

A549 (TP53+/+) and H1299 (TP53−/−) and Hela cell lines were purchased from Shanghai Institute of Biochemistry and Cell Biology in China, and were grown at 37°C in 5% CO2 in RPMI supplemented with 2 mmol/L L-glutamine and 10% FCS. Primary mouse embryonic fibroblasts (MEFs) were derived from p53 wt and p53 KO sibling embryos, and maintained with DMEM supplemented with 10% FCS and antibiotics. 3-NP (N5636), 3-MA (M9281), carbonyl cyanide m-chlorophenylhydrazone (CCCP, C2759), ATP (A6559), chloroquine (C6628), E-64d (E8640) and Z-Vad-FMK (V116) were all purchased from Sigma-Aldrich (Saint Louis, MO, USA). LysoTracker Red (L7528) and LysoSentor (L7533) were purchased from Invitrogen-Molecular Probes (Shanghai, China). All cell culture reagents were purchased from Gibco (Gaithersburg, MD, USA) unless otherwise noted.

Expression of GFP-LC3 and DRAM1-pEGFP

The activation of autophagy was detected following transfection of cells with GFP-LC3 and mRFP-GFP-LC3 expression plasmids (kindly provided by Dr. T. Yoshimori, National Institute of Genetics, Japan). The presence of several intense fluorescent dots in cells is indicative of the accumulation of autophagosomes. Transfection of cells with expression plasmids was performed using Lipofectamine 2000 (Invitrogen, 11668-019, Shanghai, China). For each condition, the number of GFP-LC3 dots per cell was determined with a fluorescence microscopy for at least 100 GFP-LC3-positive cells.

PcDNA4-DRAM1-His was generated by PCR from the I.M.A.G.E. clone for DRAM1 (Clone ID: NM_018370) with: CCCAAGCTTATGCTGTGCTTCCTGAGGGGAATG (forward) and CCGCTCGAGTCAAATATCACCATTGATTTCTGTG (reverse), and subsequently digested with BamH I and Xho I and cloned in to the BamH I and Xho I sites of pcDNA4/HisA (Invitrogen Carlsbad, CA, USA). pEGFP-N1-DRAM1 was generated through PCR primer: ATAGAATTCATGCTGTGCTTCCTGAGGGGA (forward) and CCGGGATCCTAATATCACCATTGATTTCTGTG(reverse), and products were T-A cloned in pMDTM19-T Vectors (Takara, D102A, Dalian, China) and digested with EcoR I and BamH I and cloned into pEGFP-N1 (Clonetech, D102A, Mountain View, CA, USA). Transfection of cells with expression plasmids was performed using Lipofectamine 2000 (Invitrogen, 11668-019, Shanghai, China).

Knock-down of TP53 and DRAM1

Small interfering RNAs (siRNA) targeting the following mRNA: TP53, AAGACUCCAGUGGUAAUCUAC; DRAM1, (1) CCACGATGTATACAAGATA and (2) CCACAGAAATCAATGGTGA. Negative siRNA TAAGGCTATGAAGAGATAC, were synthesized by GenePharma (Shanghai, China). The siRNA oligos used to target DRAM1 genes were previously validated and described in the following articles [8], [21], [22]. For transfection, cells were plated in 9-cm dishes at 30% confluence, and siRNA duplexes (200 nM) were introduced into the cells using Lipofectamine 2000 (Invitrogen, 11668-019, Shanghai, China) according to the manufacturer’s recommendations.

LC3 Immunofluorescence Assay

For immunofluorescence microscopic examination, cells were plated on 12-mm Poly-L-Lysine-coated cover slips and cultured for 24 h, then cells were treated with siRNA and drugs. Cells were washed in PBS, fixed with 4% paraformaldehyde in PBS at 4°C for 10 min, and then washed again with PBS. The cells were permeabilized with 0.25% Triton X-100, and were then blocked with 10% normal goat serum (NGS) for 15 min. Primary antibodies: a rabbit polyclonal antibody against LC-3 (Abgent, AJ1456c, Suzhou, China), a goat polyclonal antibody against cathepsin D (Santa Cruz, sc-6488, Santa Cruz, CA, USA) and a rabbit polyclonal antibody against LAMP2 (Abcam, ab37024, Cambridge, MA, USA) diluted in PBS were added to the cells and left for overnight at 4°C. The cover slips were washed three times before incubation with secondary antibodies using the same procedure as for the primary antibodies. The cover slips were mounted on slides with mounting medium (Sigma-Aldrich, F4680, Saint Louis, MO, USA) and were examined with a laser scanning confocal microscopy (Nikon, C1S1, Tokyo, Japan).

The pattern of distribution of exogenously expressed GFP-LC3 in A549 cells was observed with fluorescent microscopy. GFP-LC3 dot formation was quantified by counting 500 GFP-LC3-positive cells and expressed as the ratio of the number of cells with at least 5 GFP-LC3 dots and the number of GFP-LC3-positive cells. The assays were independently performed by two investigators in a blinded manner and similar results were obtained.

Western Blot Analysis

Western blot analysis was performed as scribed previously [23]. Cells were harvested and rinsed twice with ice-cooled PBS and homogenized in a buffer containing 10 mmol/L Tris-HCl (pH 7.4), 150 mmol/L NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS, 5 mol/L edetic acid, 1 mmol/L PMSF, 0.28 U/L aprotinin, 50 mg/L leupeptin, 1 mmol/L benzamidine, 7 mg/L pepstain A. Protein concentration was determined using the BCA kit. Thirty micrograms of protein from each sample was subjected to electrophoresis on 10–12% SDS-PAGE gel using a constant current. Proteins were transferred to nitrocellulose membranes and incubated with the Tris-buffered saline containing 0.2% Tween-20 (TBST) and 3% non-fat dry milk for 3 h in the presence of one of the following antibodies: a rabbit polyclonal antibody against LC-3 (Abgent, AJ1456c, San Diego, CA, USA), a mouse monoclonal antibody against TP53 (Cell Signaling Technology, 2524S, Boston, MA, USA), a mouse monoclonal antibody against β-actin (Santa Cruz, sc-58669), a goat polyclonal antibody against cathepsin D (Santa Cruz, sc-6488), rabbit polyclonal antibodies against DRAM1 (Stressgen, 905-738-100, Farmingdale, NY, USA), a rabbit polyclonal antibodies against SQSTM1 (Enzo Life Sciences, PW9860, Farmingdale, NY, USA),Membranes were washed and incubated with horseradish peroxidase-conjugated secondary antibodies in TBST containing 3% non-fat dry milk for 1 h. Immunoreactivity was detected with enhanced chemoluminescent autoradiography (ECL kit, Amersham, RPN2232, Piscataway, NJ, USA) according to the manufacturer’s instructions. The levels of protein expression were quantitatively analyzed with SigmaScan Pro 5. The results were normalized to loading control β-actin (Santa Cruz, sc-58669). DRAM1 peptide (Acris Antibodies, AP30304CP-N, San Diego, CA, USA) was used for evaluating the specificity of DRAM1 antibody. Pre-incubation of DRAM1 antibody with control peptide (1 µg control peptide/1 µL DRAM1 antibody) abolished binding activity of DRAM1 antibody (Figure S2).

Determination of Lysosomal pH

For lysosomal pH estimation, A549 and Hela cells were seeded on circular glass cover slips and grown to confluence in Dulbecco’s modified Eagle’s medium (DMEM) with 10% fetal bovine serum (FBS; Wisent, 080–150) at 37°C, 5% CO2. Lysosomes were loaded overnight with 70000 MW FITC-dextran (Sigma-Aldrich, 53471). and 0.5 mg/mL dextran-coupled Oregon Green 488 (Invitrogen-Molecular Probes, D-7173, Grand Island, NY, USA) in DMEM supplemented with 10% FBS, chased for 2 h at 37°C with 5% CO2 in DMEM (10% FBS) to allow complete transfer of dextrans to lysosomes, and washed to remove residual dextran. Non-attached cells were removed by rinsing with PBS and the cover slips were immediately placed in a cuvette filled with growth medium or PBS and pH was estimated from excitation ratio measurements as described previously [24]. The fluorescence emitted was recorded at two excitation wavelengths (440/490 nm for Oregon Green 488) using the largest excitation and emission slits by a scanning multiwell spectrophotometer (Ultra Micro- plate Reader; BIO-TEK Instruments, ELx800, Winooski, VT, USA). The pH values were derived from the linear standard curve generated via each fluorescent dextran in phosphate/citrate buffers of different pH between 3.5 and 7.5. The experiment was repeated six times.

Spectrophotometric Measurement of H+ Transport

FITC-dextran loaded A549 and Hela cells were prepared as described above. After washing in PBS, cells were resuspended (108 cells in 2 ml) in homogenization buffer (0.25 M sucrose, 2 mM EDTA, and 10 mM Hepes [pH 7.4]) and homogenized in a tight-fitting glass Dounce homogenizer. The homogenate was centrifuged (800 g, 10 min) to remove unbroken cells and the nuclei. The supernatant was centrifuged (6800 g, 10 min) to remove the large organelle such as mitochondrial. The supernatant was centrifuged (25000 g, 10 min) to obtain the light organelle including lysosomes. The precipitation layered over 10 ml of a 27% Percoll (Pharmacia Inc, 17-0891-01, New York, NY, USA) solution in homogenization buffer, underlayered with 0.5 ml of a 2.5 M sucrose solution. Centrifugation was done in a SW41Ti rotor (Beckman Instruments Inc, Brea, CA, USA) for 1.5 h at 35000 g. The layer of crude lysosomes of about 1.5 ml was collected at the bottom and then was centrifuged (100000 g, 60 min) to remove the other light organelle including mitochondrial at the bottom of the tube. Lysosomal fractions were equilibrated for up to 1 h in 125 mM KCl, 1 mM EDTA, and 20 mM Hepes (pH 7.5). Fluorescence was recorded continuously with excitation at 490 nm and emission at 520 nm. Upon addition of ATP (Sigma-Aldrich, A6559, Saint Louis, MO, USA), a progressive decrease in fluorescence intensity was observed, indicative of intralysosomal acidification [25]. As expected, the pH gradients in both samples were collapsed by the addition of the bafilomycin A1 (1 µM) (Sigma-Aldrich, B1793). The solvents alone had no effect on lysosomal pH. The reagents used and their final concentrations were: ATP (K+ salt, pH 7.5, 5 mM), bafilomycin A1 (1 µM).

Statistical Analysis

Statistical analysis was carried out by one-way analysis of variance (ANOVA) followed by Dunnett t-test or multiple means comparisons by Tukey’s test. Differences were considered significant when p<0.05.

Results

3-NP Induces Autophagy Activation

The present study examined if autophagic and apoptotic pathways are activated in A549 cells after 3-NP treatment. The results showed that 3-NP-induced a significant increase in the protein levels of DRAM1 from 3 to 72 h, with a peak induction at 24 h after 3-NP treatment (Figure 1A). The specificity of DRAM1 antibody was checked with Western blot analysis and immunofluorescence assay using DRAM1 control peptide (Figure S2). To further test if mitochondria respiration failure triggers DRAM1 expression, we used CCCP to uncouple mitochondria oxidation and phosphorylation, the results showed that CCCP significantly increased the DRAM1 protein levels (Figure 1B). LC3 is a mammalian homologue of yeast Atg8p and LC3-II is required for the formation of autophagosomes [26]. As shown in Fig. 1C, 3-NP induced a time-dependent increase in GFP-LC3 in A549 cells, and LC3-positive vesicular profiles of sizes 0.5–2.0 µm were significantly more numerous in 3-NP-treated cells 48 h after treatment (Figure 1C and 1D). To provide biochemical evidence of autophagy activation, the time-course of 3-NP-induced changes in LC3-II in A549 cells was determined 24 to 72 h after 3-NP (500 µM) treatment. The expression of LC3-II significantly increased 24 h after 3-NP treatment (Figure 2A). As an additional assessment of autophagy activity, the degradation of SQSTM1 (sequestosome 1), an autophagy substrate, was determined [27]. The present results showed that the protein level of SQSTM1 decreased 24–72 h after 3-NP treatment (Figure 2A). As a confirmation of autophagy activation, the present study demonstrated that the elevation of LC3- II and the decline of SQSTM1 were blocked by the autophagy inhibitor 3-methyl-adenine (Figure 2B).

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Figure 1. 3-NP activated autophagy.

A549 cells were treated with 3-NP (500 µM) and harvested 24, 48 and 72 h later. (A) Immunoblot analysis of DRAM1 levels in A549 cells under conditions of: no treatment (Ctrl) and 3, 6, 12, 24, 48 and 72 h after 3-NP. (B) Immunoblot analysis of DRAM1 levels in A549 cells under conditions of: no treatment (Ctrl) and 12.5µM and 25 µM of CCCP treatment for 4 h. Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. **P<0.01 (3-NP group vs. control group). (C) Representative images of GFP-LC3 fluorescence in cells transfected with GFP-LC3 plasmid 24, 48 and 72 h after 3-NP (500 µM). N: the nucleus. Thin arrows: GFP-LC3 dots. The scale bar represents 10 µm. (D) Quantitative analysis of the number of GFP-LC3 puncta. Number of cells with GFP-LC3 dots was scored in 100 GFP-LC3-positive cells. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. **P<0.01 (3-NP group vs. control group).

https://doi.org/10.1371/journal.pone.0063245.g001

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Figure 2. Autophagy was induced by 3-NP and blocked by 3-MA.

(A) Immunoblot analysis of LC3 and SQSTM1 levels in A549 cells under conditions of: no treatment (Ctrl) and 24, 48 and 72 h after 3-NP. Protein extracts were subjected to SDS-PAGE and immunoblotting. Densities of protein bands were analyzed with an image analyzer (SigmaScan Pro 5) and normalized to the loading control (β-actin). The data are expressed as percentage of control (untreated cells). Bars represent mean±SE; n = 4. (B) Immunoblot analysis of LC3 and SQSTM1 levels in cells under conditions of: no treatment (Cont), 3-NP (500 µM) and 3-MA (200 µM) +3-NP (500 µM). Protein extracts were subjected to SDS-PAGE and immunoblotting. Densities of protein bands were analyzed with an image analyzer (SigmaScan Pro 5) and normalized to the loading control (β-actin). The data are expressed as percentage of control (untreated cells). Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. *P<0.05 (3-NP group vs. control group). #P<0.05 (3-MA +3-NP- treated group vs. 3-NP- treated group). **P<0.01 (3-NP group vs. control group). ##P<0.05 (3-MA +3-NP- treated group vs. 3-NP- treated group).

https://doi.org/10.1371/journal.pone.0063245.g002

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Figure 3. TP53 dependency of DRAM1 induction after 3-NP treatment.

A549 and H1299 cells were treated with 3-NP (500 µM) and harvested 48 h later. (A) Immunoblot analysis of TP53 and DRAM1 levels in A549 and H1299 cells under conditions of: no treatment (Ctrl) and 48 h after 3-NP. Protein extracts were subjected to SDS-PAGE and immunoblotting. Densities of protein bands were analyzed with an image analyzer (SigmaScan Pro 5) and normalized to the loading control (β-actin). The data are expressed as percentage of control (untreated cells). Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. **P<0.01 (3-NP group vs. control group). ##P<0.01 (3-NP group vs. control group). $$P<0.01 (3-NP group vs. control group). (B) Immunoblot analysis of TP53 and DRAM1 levels in p53 wt and p53 KO MEFs under conditions of: no treatment (Ctrl) and 48 h after 3-NP. Protein extracts were subjected to SDS-PAGE and immunoblotting. Densities of protein bands were analyzed with an image analyzer (SigmaScan Pro 5) and normalized to the loading control (β-actin). The data are expressed as percentage of control (untreated cells). Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. **P<0.01 (3-NP group vs. control group). ##P<0.01 (3-NP group vs. control group). $$P<0.01 (3-NP group vs. control group). (C) A549 cells were transfected with TP53 siRNA or a non-silencing siRNA. Forty-eight hours after transfection of cells with TP53 siRNA, cells were harvested and protein levels of TP53 and DRAM1 were analyzed with immunoblotting 24 h after 3-NP. Densities of protein bands were analyzed with Sigma Scan Pro 5 and normalized to the loading control (β-actin). The data are expressed as percentage of control. Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. **P<0.01 TP53 siRNA group vs. non-silencing siRNA group. (D) H1299 cells were transfected with TP53 siRNA or a non-silencing siRNA. Forty-eight hours after transfection of cells with TP53 siRNA, cells were harvested and protein levels of TP53 and DRAM1 were analyzed with immunoblotting 24 h after 3-NP. Densities of protein bands were analyzed with Sigma Scan Pro 5 and normalized to the loading control (β-actin). The data are expressed as percentage of control. Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test.

https://doi.org/10.1371/journal.pone.0063245.g003

It was reported that DRAM1 is a TP53 target gene. We determined the TP53 dependency in 3-NP-induced DRAM1 expression. In H1299 cells which lack of TP53, 3-NP only slightly induced DRAM1 expression, while in A549 cells which express wt TP53, 3-NP robustly induced the expression of DRAM1 (Figure 3A). The similar results were seen in TP53 wt and TP53 null MEFs cells (Figure 3B). Treatment of A549 cells with TP53 siRNA, partially inhibited both basal and 3-NP-induced the expression of DRAM1 (Figure 3C). In contrast, treatment of H1299 with TP53 siRNA did not block 3-NP-induced expression of DRAM1 (Figure 3D). These results suggest that induction of DRAM1 largely depends on TP53 mechanism, but other signaling pathways are also be involved in regulating DRAM1 expression after 3-NP treatment [28].

DRAM1 Mediates Autophagy Activation

To understand the role of DRAM1 in the regulation of autophagy, the present study investigated the role of DRAM1 in autophagy activation in response to 3-NP treatment in A549 and Hela cells. Knock-down of DRAM1 using siRNA significantly reduced the expression of DRAM1 proteins in A549 cells (Figure 4A) and in Hella cells (Figure S1 A). After knock-down of DRAM1 with siRNA, the basal expression and induction of LC3-II by 3-NP was markedly reduced in both A549 cells (Figure 4B) and Hela cells (Figure S1A). In addition, 3-NP-induced reduction of SQSTM1 was blocked by DRAM1 siRNA in A549 cells (Figure 4B). The formation of GFP-LC3 puncta after 3-NP treatment was also inhibited in the presence of DRAM1 siRNA in A549 cells (Figure 4C) and in Hela cells (Figure S1 B). In addition to inhibiting the production of LC3-II, SQSTM1 levels increased in DRAM1 siRNA-treated cells (Figure 4B). These lines of evidence support an important role of DRAM1 in autophagy activation.

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Figure 4. DRAM1 mediated autophagy activation.

(A, B) A549 cells were transfected with DRAM1 siRNA or a non-silencing siRNA. Left: Forty-eight hours after transfection of cells with DRAM1 siRNA, cells were harvested and protein levels of DRAM1, LC3 and SQSTM1 were analyzed with immunoblotting. Right: Twenty-four hours after transfection of cells with DRAM1 siRNA, cells were treated with 3-NP (500 µM). Cells were harvested and protein levels of LC3 and SQSTM1 were analyzed with immunoblotting 24 h after 3-NP. Densities of protein bands were analyzed with SigmaScan Pro 5 and normalized to the loading control (β-actin). The data are expressed as percentage of control (non-silencing siRNA group). Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. **P<0.01 non-silencing siRNA group vs. control group. ##P<0.01 DRAM1 siRNA group vs. non-silencing siRNA group. (C) Representative images of GFP-LC3 fluorescence in cells transfected with GFP-LC3 and treated with DRAM1 siRNAs in the presence or absence of 3-NP (500 µM). Number of cells with GFP-LC3 dots was scored in 100 GFP-LC3-positive cells. N: the nucleus. Thin arrows: GFP-LC3 dots. The scale bar represents 10 µm. Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. **P<0.01 (siRNA group vs. non-silencing siRNA group).

https://doi.org/10.1371/journal.pone.0063245.g004

DRAM1 Enhances Autophagosomes Clearance

To study the mechanisms of DRAM1 in regulating autophagy, A549 cells were transfected with GFP-DRAM1. The lysosomal localization of DRAM1 was examined with LysoTracker and LAMP2 immunofluorescence or double immunofluorescence of DRAM1 and LAMP2. LysoTracker is a commonly used lysosomal probe because it is an acidotropic fluorescent dye for labeling and tracking acidic organelles in live cells. Marked co-localization of DRAM1 and LysoTracker (Figure 5A) or DRAM1 and LAMP2 (Figure 5B) was seen with a confocal microscopy. The quantitative analysis revealed that colocalization of DRAM1 puncta and LAMP2 was 74.8±5.6% (data not shown), suggesting that DRAM1 predominantly localizes to lysosomes. The clearance of autophagosomes is a measure of autophagy flux. In control cells, acute autophagy induction with rapamycin elevated LC3-II levels as revealed by immunoblotting. After removing rapamycin from the medium for 6 h, LC3-II returned towards baseline levels. While in DRAM1 siRNA-treated cells, LC3-II remained elevated 6 h after removing rapamycin (Figure 5C). Double immunofluorescence of LC3 and LAMP2 demonstrated the formation of large number of LC3-LAMP2-positive vesicles in siRNA untreated cells after rapamycin exposure. Treatment of cells with DRAM1 siRNA reduced the number of LC3-LAMP2-posive vesicles (Figure 5D). After removal of rapamycin for 6 h, a number of LC3-LAMP2-positive vesicles were cleared in siRNA untreated cells but more LC3-LAMP2-positive vesicles remained in the cells treated with DRAM1 siRNA (Figure 5E and 5F). These suggest that both the formation and the clearance of autophagic vacuoles are impaired in DRAM1 siRNA-treated A549 cells.

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Figure 5. Knock-down of DRAM1 impaired the clearance of autophagosomes.

(A) DRAM1 was predominantly localized in lysosomes (Lysotraker). A549 cells were transfected with GFP-DRAM1 for 48 h. Cells were incubated with LysoTracker (0.5 µM) and co-localization of DRAM1-GFP (green) and the LysoTracker (red) was assessed with a confocal microscopy. N: the nucleus. Thin arrows: GFP-DRAM1 fluorescence. Thick arrows: LysoTracker. (B) DRAM1 was predominantly localized in lysosomes (LAMP2). Up panel: A549 cells were transfected with GFP-DRAM1 for 48 h. Cells were processed for immunofluorescence using LAMP2 antibodies and co-localization of DRAM1-GFP (green) and the LAMP2 (red) was assessed with a confocal microscopy. N: the nucleus. Thin arrows: GFP-DRAM1 fluorescence. Thick arrows: LAMP2. Low panel: A549 cells were processed for immunofluorescence using DRAM1 and LAMP2 antibodies, and co-localization of DRAM1 (green) and the LAMP2 (red) was assessed with a confocal microscopy. N: the nucleus. Thin arrows: anti-DRAM1 fluorescence. Thick arrows: LAMP2. (C) Immunoblot analysis of LC3 levels in A549 cells under conditions: untreated (Cont), rapamycin (Rap) treatment, and 6 h after rapamycin removal (Rap/Rec). Densities of protein bands were analyzed with an image analyzer (SigmaScan Pro 5) and normalized to the loading control (β-actin). The data are expressed as percentage of control (non-silencing siRNA group). Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Tukey’s test. **P<0.01 (DRAM1 siRNA treatment group vs. non-silencing siRNA group). (D) A549 cells were analyzed with double-immunofluorescence using LC3 and LAMP2 antibodies in the presence of rapamycin and 6 h after removal of rapamycin. N: the nucleus. Thin arrows: dots of LC3 immonureactivity. Thick arrows: LAMP2. The scale bar represents 10 µm. (E) DRAM1 siRNA-treated cells were analyzed with double-immunofluorescence using LC3 and LAMP2 antibodies in the presence of rapamycin and 6 h after removal of rapamycin. N: the nucleus. Thin arrows: dots of GFP-LC3 fluorescence. Thick arrows: LAMP2. The scale bar represents 10 µm. (F) In cells after DRAM1 siRNA treatment, the number of LC3 dots was scored in 100 GFP-LC3-positive cells in the presence or absence of 3-NP. The data are expressed as percentage of control. Bars represent mean±SE; n = 4. Statistical comparisons were carried out by Tukey’s test. **P<0.01 (DRAM1 siRNA treatment group vs. non-silencing siRNA group). #P<0.05 (DRAM1 siRNA treatment group vs. non-silencing siRNA group).

https://doi.org/10.1371/journal.pone.0063245.g005

DRAM1 Affects Lysosomal Degradation and Lysosomal Acidification

Lysosomal enzyme, cathepsin D, plays an essential role in the degradation process of autophagic activity. The present study employed double immunofluorescence of cathepsin D and LysoTracker to explore the role of DRAM1 in lysosomal function. We observed that cathepsin D was virtually confined in LysoTracker fluorescence-positive vesicles in A549 cells. 3-NP treatment increased the expression of cathepsin D and the number of LysoTraker labeled lysosomes (Figure 6A).

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Figure 6. Knock-down DRAM1 inhibited autophagosome maturation process.

(A) Lysosomes were activated by 3-NP. A549 cells were treated with 3-NP (500 µM) for 48 h. Cells were incubated with LysoTracker (0.5 µM) and processed for immunofluorescence using Cathepsin D (Cat D) antibodies. The co-localization of Cat D (green) and the LysoTracker (red) was assayed by confocal microscopy. N: the nucleus. Thin arrows: Cat D immunoreactivity. Thick arrows: LysoTracker. The scale bar represents 10 µm. (B) Accumulation of mRFP-LC3 in DRAM1 siRNA-treated cells. Representative images of mRFP-GFP-LC3 fluorescence in cells transfected with mRFP-GFP-LC3 and treated with DRAM1 siRNAs in the presence or absence of 3-NP (500 µM). N: the nucleus. Thin arrows: GFP-LC3 dots. Thick arrows: mRFP-LC3 dots. The scale bar represents 10 µm. (C) Number of cells with GFP-LC3 dots was scored in 100 GFP-LC3-positive cells. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. **P<0.01 non-silencing siRNA group vs. control group. ##P<0.01 DRAM1 siRNA group vs. non-silencing siRNA group.

https://doi.org/10.1371/journal.pone.0063245.g006

GFP-LC3 is the most widely used marker for autophagosomes. When localized to autolysosomes, GFP-LC3 loses fluorescence due to lysosomal acidic and degradative conditions. While mRFP-LC3 is more stable in acidic conditions and fluorescence remains after fusion of autophagosomes with lysosomes. Thus, we used mRFP-GFP tandem fluorescent-tagged LC3 to monitor the process of autophagy maturation [29]. The result showed that 3-NP increased the expression of LC3, most of LC3 displayed yellow color due to emitted both GFP and RFP fluorescence. However, due to stronger fluorescence of GFP than that of RFP, some green LC3 patches were also observed. Knock-down of DRAM1 with siRNA slightly reduced GFP-LC3 fluorescence (reflecting attenuation of autophagy induction), but robustly increased the number of large mRFP-LC3 puncta (Figure 6B and 6C). In the condition of treatment with 3-NP in the presence of non-sil siRNA, yellow punctas were few because degradation of autolysosomes was smooth. While in the condition of treatment with 3-NP in the presence of DRAM1 siRNA, more large yellow pinctas were observed (Figure 6B). These results indicate that the clearance of autophagic vacuoles is impaired in DRAM1 siRNA-treated A549 cells.

As most lysosomal cathepsins work at acidic pH, the effect of DRAM1 silencing on activation of cathepsin D was examined. The results of immunoblotting showed that knock-down of DRAM1 significantly inhibited 3-NP-induced production of the active form of cathepsin D (Figure 7A), suggesting activation of cathepsin D is compromised. To assess lysosomal acidification, we used LysoSensor DND-167. The LysoSensor dye is an acidotropic probe that appears to accumulate in acidic organelles as the result of protonation. In control cells, the fluorescence of LysoSensor was enhanced from 24 to 72 h after 3-NP exposure. By contrast, in DRAM1 siRNA-treated cells, the fluorescence was lower than that in the control cells (Figure 7B). We further measured lysosomal pH in quantization. The cells were loaded with the pH-sensitive reporter FITC-dextran by endocytosis for 1 h and then chased in the control and DRAM1 siRNA-treated cells in the presence and absence of 3-NP. WT cells exhibited an intralysosomal pH of 4.75, and lysosomal pH decreased following 3-NP treatment (Figure 7C). In contrast, the lysosomal pH values decreased to a lesser extent (5.23) in DRAM1 siRNA-treated cells following 3-NP treatment in both A549 cells (Figure 7C) and in Hela cells (Figure S1 C). These results suggest that there is a defective lysosomal acidification in DRAM1 siRNA-treated cells. Lysosomal acidification requires the activity of the ATP-dependent vacuolar proton pump [30]. We examined the ATP-dependent lysosomal acidification using the pH sensitive dye FITC-dextran. This dye accumulates inside lysosomes due to its weak basic net charge in response to ATP addition. As shown in Figure 7D, addition of ATP caused a dramatic drop in FITC fluorescence as a result of lysosomal acidification in control and 3-NP-treated cells. In DRAM1 siRNA-treated cells, ATP-induced drop in fluorescence emission was reduced, reflecting a reduction in internal lysosomal acidification. Reduction in FITC fluorescence by ATP was inhibited by the V-ATPase inhibitor bafilomycin A1. The similar results were obtained in Hela cells (Figure S1 D). Thus, the impairment of acidification in DRAM1 siRNA-treated cells might be due to a decrease in V-ATPase activity.

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Figure 7. Knock down DRAM1 inhibited lysosomal acidification and cathepsin D activation.

(A) A549 cells were transfected with DRAM1 siRNA or a non-silencing siRNA. Left: Forty-eight hours after transfection of DRAM1 siRNA, cells were harvested and protein levels of cat D were analyzed with immunoblotting. Right: Twenty-four hours after transfection of cells with DRAM1 siRNA, cells were treated with 3-NP (500 µM) for 24 h. Cells were harvested and protein levels of cat D were analyzed with immunoblotting. Densities of protein bands were analyzed with SigmaScan Pro 5 and normalized to the loading control (β-actin). The data are expressed as percentage of control (non-silencing siRNA cells). Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. **P<0.01 (DRAM1 siRNA group vs. non-silencing siRNA group). (B) Lysosomal acidification was measured using LysoSensor DND-167. In control cells, the fluorescence of LysoSensor was measured from 24 to 72 h, and in DRAM siRNA-treated cells the fluorescence was measured in 48 h after transfection of DRAM1 siRNA. N: the nucleus. The scale bar represents 10 µm. (C) Lysosomal pH was measured ratio-metrically using fluorescent dextrans. WT cells and DRAM1 siRNA1-treated cells were loaded with the pH-sensitive fluorescent dextrans by endocytosis for 1 h at 37°C and then subjected to pulse-chase assay in the presence or absence of the 3-NP (500 µM). Lanes 2 and 4 depict pH values obtained with FITC-dextran after the addition of 500 nM 3-NP. The data are expressed as percentage of control (non-silencing siRNA cells). Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. **P<0.01 (DRAM1 siRNA group vs. non-silencing siRNA group). ##P<0.01 (DRAM1 siRNA group vs. non-silencing siRNA group with 3-NP treatment). (D) Lysosomal V-ATPase activity was inhibited in DRAM1 siRNA1-treated cells. Lysosomes from control cells and DRAM1 siRNA1-treated cells were loaded with FITC-dextran (molecular weight 70,000). A549 cells were then homogenized and used for in vitro-acidification assays. Fluorescence was recorded continuously with excitation at 490 nm and emission at 520 nm. Upon addition of ATP, a progressive decrease in fluorescence intensity was observed, indicative of intralysosomal acidification. The decrement was reversed by bafilomycin A1, a V-ATPase inhibitor.

https://doi.org/10.1371/journal.pone.0063245.g007

Foregoing observations indicate that DRAM1 regulates autophagy flux mainly thought lysosomes. Thus, the lysosomal inhibitors E64d (10 µM) and chloroquine (20 µM) were used to evaluate if inhibition of lysosomal functions produces effects similar to knock-down of DRAM1. Many autophagy inhibitors act on post-sequestration steps and agents, such as bafilomycin A1, that blocks autophagy activity are known to cause accumulation of autophagosomes [31]. Chloroquine is a compound that elevates lysosomal pH, and E64d is an effective inhibitor of lysosomal enzymes [32]. After 3-NP treatment, more LAMP2-positive vacuoles were observed. Compared with cells treated with 3-NP alone, LC3 in E64d or chloroquine-treated cells accumulated more LAMP2-positive vacuoles (Figure 8A). As shown in Fig. 8B, LC3-II accumulated after E64d or chloroquine treatment. These results suggest a defective clearance of autophagic vacuoles in E64d- and chloroquine-treated cells.

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Figure 8. Lysosomal inhibitors inhibited autophagosome clearance.

(A) Accumulation of autophagosomes was analyzed with double-immunofluorescence using antibodies against LC3 and LAMP2 after E64d (10 µM) or chloroquine (20 µM) treatment for 24 h in the presence or absence of 3-NP (500 µM). N: the nucleus. Thin arrows: dots of LC3 immunoreactivity. Thick arrows: LAMP2 immunoreactivity. The scale bar represents 10 µm. (B) Immunoblot analysis of LC3-II levels in cells under conditions of: no treatment (Cont), E64d (10 µM), chloroquine (20 µM), 3-NP (500 µM), E64d (10 µM) +3-NP (500 µM) or chloroquine (20 µM) +3-NP (500 µM). Cells were harvested for immunoblotting 48 h after 3-NP treatment. Densities of protein bands were analyzed with SigmaScan Pro 5 and normalized to the loading control (β-actin). The data are expressed as percentage of control (untreated cells). Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. *P<0.05 (3-NP treated group vs. control group). #P<0.05 (E64d+3-NP- or chloroquine +3-NP-treated group vs. 3-NP- treated group). ##P<0.01 (E64d plus 3-NP or chloroquine plus 3-NP treatment group vs. 3-NP treatment group).

https://doi.org/10.1371/journal.pone.0063245.g008

Discussion

3-NP acts as an irreversible inhibitor of succinate dehydrogenase and thus results in an impairement of energy metabolism, oxidative stress and activation of glutamate receptors [33]. Mitochondria are important intracellular organelles and the collapse of mitochondria membrane potential may be a signal for activation of autophagy and apoptosis. Previous in vivo studies suggest that 3-NP-induced cell death in rat striatum involves TP53-dependent activation of apoptosis and autophagy [6]. It was also reported that DRAM1 and SQSTM1 regulated cell migration and invasion of glioblastoma stem cells [34]. TP53 target gene DRAM1 possibly mediates down stream multiple functions in autophagy and cell death. The present in vitro studies found that 3-NP inhibited cell viability of A549 cells at the doses of 250 µM to 1 mM (data not shown). The activation of autophagy was demonstrated by increases in LC3-II protein levels, GFP-LC3 puncta and a decrease in SQSTM1 protein levels. These studies suggest that mitochondria dysfunction induced by 3-NP triggered autophagy activation. Biochemical analysis showed that 3-NP and CCCP significantly increased DRAM1 protein levels and this increase in DRAM1 played a role in 3-NP-induced autophagy activation. Although upregulation of DRAM1 by 3-NP largely depended on TP53, our present results suggested there were also other mechanisms involved [28]. The human DRAM1 gene encodes a 238 amino acid protein which acts as a stress-induced regulator of autophagy and damage-induced programmed cell death [8]. The present study demonstrated that knock-down of DRAM1 effectively blocked the 3-NP-induced induction of LC3-II and decline in SQSTM1. These studies confirm that DRAM1 plays an important role in autophagy activation.

To investigate the underlying mechanism by which DRAM1 regulates autophagy, we investigated the effects of DRAM1 on autophagosome clearance. Colocalization of EGFP-DRAM1 and LysoTracker fluorescence or DRAM1 and LAMP2 immunoflurescence confirmed predominant lysosomal localization of expressed DRAM1. We first tested if DRAM1 has an effect on autophagosome turnover following induction with rapamycin. Rapamycin can stimulate the formation of autophagosome through inhibiting mTOR. Upon removal of rapamycin, autophagosomes should be cleared if autophagy pathway is normal. The present study demonstrated that rapamycin increased the abundance of autophagosomes and the number of autophagosomes returned towards the basal levels 6 h after withdrawal of rapamycin. Knock-down of DRAM1 reduced the rate of clearance of autophagosomes after rapamycin withdrawal. Galavotti et al reported that knock-down of DRAM1 inhibited targeting of SQSTM1 to autophagosomes and reduced its degradation [34]. Our data also support the involvement of DRAM1 in degradation of autophagososmes. However, Galavotti et al found that DRAM1 was not involved in starvation- and mTOR-mediated autophagy activation [34]. Therefore, the role of DRAM1 in autophagy activation induced by other stimuli need to be further studied.

The abundance of autophagosomes is balanced by the formation and clearance of autophagosomes. After the formation, the turn-over of autophagosomes is largely determined by the process of fusion between autophagososmes and lysosomes and degradation of autophagy contents by lysosomal enzymes. mRFP-GFP tandem fluorescent-tagged LC3 showed both GFP and mRFP signal of LC3 before the fusion with lysosomes, and exhibited only the mRFP signal when LC3 transmit into lysosomes because of lysosomal acidic environment and degradation [29]. After rapamycin treatment, there was more number of mRFP-GFP-LC3 patches in non-silencing RNA-treated cells than that in DRAM1 siRAN-treated cells, suggesting DRAM1 plays a role in the formation of autophagosomes. In response to withdrawal of rapamycin, mRFP-GFP-LC3 patches quickly declined in control cells. Knock-down of DRAM1 markedly retained these mRFP-GFP-LC3 patches in the cells. These results suggest that DRAM1 stimulates clearance of autophagosomes.

Lysosomes are rich in hydrolytic enzymes and are responsible for the degradation of intracellular materials captured by autophagy [35]. After 3-NP treatment, an increase in the abundance of autophagosomes was accompanied by an increase in the number of lysosomes. The increase in acidic lysosomes was noticeable as indicated by a fluorescence dye. Knock-down of DRAM1 resulted in an impairment of lysosomal acidification and accumulation of LC3-II, indicating reduced autophagy flux. It is now generally accepted that intralysosomal low pH is maintained by an active proton pump, vacuolar H+­ATPases or V­ATPases. Proton transport into intracellular organelles is primarily mediated by ATP­dependent proton pumps. These pumps are therefore central to pH homeostasis in organelles. Autophagosomes and their contents are cleared upon fusing with late endosomes or lysosomes containing cathepsins, other acid hydrolases, and vacuolar [H+] ATPase(v-ATPase) [36], a proton pump that acidifies the newly created autolysosome. It is suggested that the proton pumps and acidification of the lysosomes were essential for the activation of lysosomal hydrolases and completion of the process of autophagy. V-ATPase may also play a role in amino acid sensing, thus plays a role in mTOR-mediated autophagy activation [37]. Inhibition of mitochondrial respiratory complex may decrease ATP production and thus decrease the activity of V-ATPase. However, due to a significant induction of DRAM1 and activation of autophagy in the present study, the V-ATPase activity was preserved to sufficiently acidify lysosomes. We speculate that DRAM1 may improve the efficiency of ATP utilization by V-ATPase. The present study found that the lower capacity for acidification of lysosomes in DRAM1 siRNA-treated cells was due to decreased V-ATPase activity. These results provide experimental data, for the first time, supporting an important role of DRAM1 in lysosomal function.

Lysosomes play important roles in autophagy. To test if the effects of DRAM1 on lysosomal functions are responsible for DRAM1-mediated autophagy activation after 3-NP treatment, the present study assessed the effects of lysosomal inhibitors on autophagosome accumulation in the presence of 3-NP. The results showed that elevating lysosomal pH and inhibiting lysosomal enzymes both increased accumulation of autophagosomes and inhibited cathepsin D activation. These results largely replicated the effects of knock-down of DRAM1 and suggested that DRAM1 probably regulated autophagy flux through lysosomes.

It should be pointed out that DRAM1 appears regulate autophagy in both early and later stages of autophagy. DRAM1 can increase the formation of autophagosomes and the clearance of autophagosomes. These effects may work through the same mechanism as DRAM1 is a lysosomal protein and may regulates dynamics of lysosomal membranes to increase V-ATPase activity and to facilitate membrane recycle for autophagosomal formation.

In conclusion, current data indicate that DRAM1 regulates autophagosome clearance through promoting lysosomal acidification and activation of lysosomal enzymes. The fusion of autophagosomes with lysosomes is an important step for autophagic degradation. In order to fully understand the role of DRAM1 in autophagy flux, the effects of DRAM1 on the fusion process between autophagosomes and lysosomes needs to be studied in the future.

Supporting Information

Figure S1.

DRAM1 mediated autophagy activation and lysosomal acidification in Hela cells. (A) Hela cells were transfected with DRAM1 siRNA or a non-silencing siRNA. Left: Forty-eight h after transfection of cells with DRAM1 siRNA, cells were harvested and protein levels of DRAM1 and LC3 were analyzed with immunoblotting. Right: Twenty-four hours after transfection of cells with DRAM1 siRNA, cells were treated with 3-NP (500 µM). Cells were harvested and protein levels of DRAM1 and LC3 were analyzed with immunoblotting 24 h after 3-NP. Densities of protein bands were analyzed with Sigma Scan Pro 5 and normalized to the loading control (β-actin). The data are expressed as percentage of control (non-silencing siRNA group). Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. **P<0.01 (DRAM1 siRNA group vs. non-silencing siRNA group). ##P<0.01 (3-NP treated group vs. control group). $$P<0.01 (DRAM1 siRNA group vs. non-silencing siRNA group with 3-NP treatment). (B) Representative images of GFP-LC3 fluorescence in Hela cells transfected with GFP-LC3 and treated with DRAM1 siRNAs in the presence or absence of 3-NP (500 µM). Number of cells with GFP-LC3 dots was scored in 100 GFP-LC3-positive cells. N: the nucleus. Thin arrows: GFP-LC3 dots. The scale bar represents 10 µm Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. **P<0.01 (siRNA group vs. non-silencing siRNA group). (C) Lysosomal pH was measured ratio-metrically using fluorescent dextrans in Hela cells. WT Hela cells and DRAM1 siRNA1-treated cells were loaded with the pH-sensitive fluorescent dextrans by endocytosis for 1 h at 37°C and then subjected to pulse-chase assay in the presence or absence of the 3-NP (500 µM). Lanes 2 and 4 depict pH values obtained with FITC-dextran after the addition of 500 nM 3-NP. The data are expressed as percentage of control (non-silencing siRNA cells). Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. **P<0.01 (DRAM1 siRNA group vs. non-silencing siRNA group). ##P<0.01 (DRAM1 siRNA group vs. non-silencing siRNA group with 3-NP treatment). (D) Lysosomal V-ATPase activity was inhibited in DRAM1 siRNA1-treated Hela cells. Lysosomes from control cells and DRAM1 siRNA1-treated cells were loaded with FITC-dextran (molecular weight 70,000). Hela cells were then homogenized and used for in vitro-acidification assays. Fluorescence was recorded continuously with excitation at 490 nm and emission at 520 nm. Upon addition of ATP, a progressive decrease in fluorescence intensity was observed, indicative of intralysosomal acidification. The decrement was reversed by bafilomycin A1, a V-ATPase inhibitor.

https://doi.org/10.1371/journal.pone.0063245.s001

(TIF)

Figure S2.

Activity of DRAM1 antibody was blocked by DRAM1 peptide. (A) Cells were harvested and immunoblot analysis of DRAM1 protein levels in A549 and Hela cells. Left: No peptide incubated with DRAM1 antibody before primary antibody incubation. Right: DRAM1 peptide was incubated with DRAM1 antibody for 30 min at 37°C before primary antibody incubation. (B) Cells were processed for immunofluorescence using DRAM1 antibodies (green) and DAPI (the nucleus, blue) in A549 and Hela cells, and was assessed with a confocal microscopy. Left: No peptide incubated with DRAM1 antibody before primary antibody incubation. Right: DRAM1 peptide was incubated with DRAM1 antibody for 30 min at 37°C before primary antibody incubation. N: the nucleus. Thin arrows: anti-DRAM1 fluorescence.

https://doi.org/10.1371/journal.pone.0063245.s002

(TIF)

Author Contributions

Conceived and designed the experiments: ZQ XZ. Performed the experiments: XZ LQ. Analyzed the data: XZ LQ. Contributed reagents/materials/analysis tools: XZ JW. Wrote the paper: XZ ZQ.

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  • Lysosomes 
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Источник: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0063245

Integration of transcription and flux data reveals molecular paths associated with differences in oxygen-dependent phenotypes of Saccharomyces cerevisiae

  • Methodology article
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BMC Systems Biologyvolume 8, Article number: 16 (2014) Cite this article

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Abstract

Background

Saccharomyces cerevisiae is able to adapt to a wide range of external oxygen conditions. Previously, oxygen-dependent phenotypes have been studied individually at the transcriptional, metabolite, and flux level. However, the regulation of cell phenotype occurs across the different levels of cell function. Integrative analysis of data from multiple levels of cell function in the context of a network of several known biochemical interaction types could enable identification of active regulatory paths not limited to a single level of cell function.

Results

The graph theoretical method called Enriched Molecular Path detection (EMPath) was extended to enable integrative utilization of transcription and flux data. The utility of the method was demonstrated by detecting paths associated with phenotype differences of S. cerevisiae under three different conditions of oxygen provision: 20.9%, 2.8% and 0.5%. The detection of molecular paths was performed in an integrated genome-scale metabolic and protein-protein interaction network.

Conclusions

The molecular paths associated with the phenotype differences of S. cerevisiae under conditions of different oxygen provisions revealed paths of molecular interactions that could potentially mediate information transfer between processes that respond to the particular oxygen availabilities.

Background

The transcriptome is a realization of the genome of an organism whereas the fluxes are an ultimate response of the complete multilevel regulatory system of a cell. The correlation between the transcriptome and the fluxes is usually weak [1] since a substantial part of the regulation of cell physiology occurs at the post-transcriptional and metabolic levels [2]. The regulation is mediated by interactions beyond individual levels of cell function. Active paths of regulatory interactions which determine the cell phenotype are concealed in data on cell components belonging to different regulatory levels. Integration of these data to frameworks of known interactions of multiple types could allow for a reconstruction of the regulatory paths associated with specific phenotypes. Genome-scale metabolic models build on the entity of metabolic enzyme encoding genes in the genome. These models are already available for various organisms and provide frameworks of metabolic interactions to the extent of whole cells. Metabolic network context is being utilized to identify transcriptionally differentially regulated pre-defined pathways of enzymes sharing metabolites as substrates and products by parametric gene set enrichment analysis [3]. Full interconnectivity of metabolism is being applied in the identification of reporter metabolites, regulatory hot spots around which the most significant transcriptional changes have occurred [4]. Protein-protein interactions facilitate various kinds of information transfer, e.g. a change in a localization or activity of a protein as a result of physical interaction or post-translational modification [5–7]. In particular, protein kinases serve as key regulators of nutrient sensing and signaling via protein-protein interactions. A network of interactions of key protein kinases of nutrient dependent regulation has been mapped, manually curated and annotated for the eukaryotic model organism S. cerevisiae[8]. A global network of protein kinase and phosphatase interactions that mediate information transfer via post-translational modifications is also available for S. cerevisiae[9] along with a large-scale data set on various types of physical protein-protein interactions [10].

Even other types of biochemical interactions, such as signaling and transcription factor interactions, also allow for communication between cellular components [11, 12].

Previously, a graph-theoretical method called Enriched Molecular Path detection (EMPath) was developed in order to identify molecular interaction paths from multi-level interactome data [13]. The EMPath method was an extension of a “color coding” algorithm [14] which had earlier been used to detect signaling cascades based on edge reliabilities in protein-protein interaction networks [15] and more general structures, such as trees [16]. The developed EMPath method was applied to detect phenotype specific molecular paths in type 1 diabetes mouse models in an integrated network of metabolic, protein-protein and signal transduction interactions scored with transcription data [13]. Recently, several graph theoretical methods for detection of molecular paths in an interaction network context have been developed. Gene Graph Enrichment Analysis (GGEA) integrates a known gene regulatory network in an analysis of transcription data and gains interpretability of the regulation processes underlying the gene expression response [17]. FiDePa (Finding Deregulated Paths) [18] and Topology Enrichment Analysis frameworK (TEAK) [19] find differentially expressed pathways between two cell phenotypes in signaling or regulatory networks and metabolic pathways, respectively. A method called Clipper exploits network topology to detect signaling paths within longer pathways based on differential gene expression between two phenotypes [20]. However, all these methods employ a single type of phenotypic information (i.e. transcription data), whereas post-transcriptional regulation has a recognized and substantial effect on a phenotype. Therefore, the EMPath method was extended in this study to enable integrative simultaneous utilization of two data types, i.e. transcription and flux data in the context of a multi-level interaction network to detect enriched molecular paths associated with phenotypic differences.

Oxygen is a major determinant of physiology for the eukaryotic model organism S. cerevisiae. S. cerevisiae is able to remodel its energy generation and redox metabolism according to the availability of oxygen in such a flexible way that it can grow under a wide range of oxygen availabilities from fully aerobic conditions to anaerobiosis. Characterization of the oxygen-dependent phenotypes of S. cerevisiae has previously been reported at the individual transcriptional, metabolite, and flux levels [21–23]. In this study, two case-control settings of the oxygen dependent phenotype differences of S. cerevisiae were defined. The phenotype under conditions of 20.9% O2 provision was compared to the phenotype under conditions of 2.8% O2 provision, and the phenotype under conditions of 2.8% O2 provision was compared to the phenotype under conditions of 0.5% O2 provision. Previously, it was noted that S. cerevisiae had highly similar flux distributions under conditions of 20.9% and 2.8% O2 provision [23], but interestingly there were substantial differences in the transcriptomes [21]. The phenotypes of S. cerevisiae possessed substantially different flux distributions under conditions of 2.8% and 0.5% O2 provision [23], whereas the transcriptomes of the phenotypes were surprisingly similar [21]. Thus, transcription and flux data were integratively utilized to find enriched molecular interaction paths associated with the aforementioned differences in the previously observed oxygen-dependent phenotypes [21–23]. The path detection was performed in a combined network of metabolic [24–26] and protein-protein interactions (Search Tool for the Retrieval of Interacting Genes database (STRING): [27]) of S. cerevisiae.

Methods

Overview

Figure 1 illustrates the overall pipeline of the study. First, a genome-scale metabolic network model and the protein-protein interactions including the global kinase-phosphatase interactions [9] were integrated into a single interaction network. Then, flux and transcription data were assigned to node weights to set the network into a phenotypic context. Then, the EMPath method was used to detect enriched up- and down-regulated molecular interaction paths within the network. In the end, the paths were visualized as integrated networks and enriched with previously known functional categories.

Overall workflow of the study comprising the following main steps. • genome-scale metabolic network model and protein-protein interactions, including kinase-phosphatase interactions, were integrated into single network representation. • phenotypic context from fluxome and transcriptome data incorporated into the network. • EMPath used for detecting up-and down-regulated paths. • detected paths were visualized and enriched with previously known functional categories.

Full size image

Network representation

The integrated network of metabolic and protein-protein interactions comprised of a recently refined version [24] of the yeast whole genome metabolic model, protein-protein interactions from the STRING database [27], and a kinase-phosphatase interaction network [9]. From the STRING database the protein interactions with an experimental score greater than 900 were included, thus excluding interactions with low experimental evidence. The integrated network representation is illustrated in Figure 1. In this representation the metabolic reactions of the genome-scale model [24] are nodes and there is an edge between two reactions if they share a metabolite, i.e. having either a common substrate or product. Cofactors and other metabolites not participating in the metabolic conversions with their carbon backbone were excluded from the network. The excluded metabolites are listed in Additional file 1. All edges were modeled with undirected edges. Each reaction comprised a set of gene(s) that encodes an enzyme that catalyzes the reaction. Protein-protein interactions were integrated with nodes representing enzymatic reactions if the metabolic enzymes had reported protein-protein interactions. In total, the whole integrated network comprised 5 702 nodes and 41 525 edges.

Transcription and flux data

Wiebe et al. (2008) grew S. cerevisiae in glucose-limited chemostat cultivations at a dilution rate of 0.1 h-1 under different oxygenation conditions (i.e. 20.9%, 2.8%, 1.0% and 0.5% O2) in the chemostat inlet gas to obtain the oxygen dependent phenotypes [22]. Rintala et al. (2009) performed the analysis of the transcriptomes of S. cerevisiae under the different conditions of oxygen provision [21]. The normalized transcription dataset was stored in the Gene Expression Omnibus (GEO) database [28] with the accession number GSE12442. In the present study, all four replicates of transcription data from each of the steady state cultures with 20.9%, 2.8%, and 0.5% O2 in the chemostat inlet gas were used to determine the transcription scores for the detection of molecular paths.

Genome-scale flux distributions were sampled from the solution space of a genome-scale metabolic model of S. cerevisiae by Monte Carlo sampling using Artificial Hit-And-Run (ACHR) sampler [29]. Prior to the sampling, the genome-scale metabolic model of S. cerevisiae was improved by further refinement of its oxygen dependent metabolism [24] (Additional file 1). The model was also constrained with P/O ratios dependent on a specific oxygen uptake rate (OUR) [23] and experimental data reported on extracellular fluxes, i.e., growth rate, substrate consumption rates and product secretion rates [22]. The Carbon Evolution Rate (CER), resulting from carbon dioxide production at various sites in metabolism, was allowed to vary freely to introduce flexibility to the system since the remaining secretion rates were set to zero. However, the introduction of the exact experimental rate constraints resulted in an infeasible solution space. Thus, the lower and upper bound constraints derived from the extracellular growth, glucose uptake, and ethanol secretion rates were simultaneously and gradually expanded until a feasible flux solution existed. At each step the constraints were expanded with 10% of the particular SEMs (Standard Error of the Mean) of the extracellular rates [22] (see Additional file 1 for the final constraints). OUR and P/O ratio constraints were kept as strict constraints since the oxygen uptake rates followed from the provision of oxygen in the chemostat inlet gas, which was the only experimental parameter changed in the bioreactor cultivations resulting in the three different phenotypes of S. cerevisiae[22] that were investigated in this study. Further, P/O ratios of S. cerevisiae dependent on OUR were previously determined [23] and used here. The Monte Carlo sampling of flux distributions was performed with the ACHR sampler [29] implemented in the COBRA Toolbox [30]. A threshold for the reactions with non-zero fluxes was set to a minimum of 10-7 mmol/(g CDW h). Zero fluxes were assigned to the rest of the reactions. A total of 10 000 feasible points were collected in the solution space out of which 2 000 samples were randomly selected for the calculation of mean fluxes. The mean values of unconstrained CER in the flux distribution samples differed from 4% to 13% from the experimental values.

Combining network and phenotypic data

Previously, only transcription data was used as phenotypic data in the detection of enriched molecular paths [13]. Here the EMPath method was extended for integrative utilization of transcription and flux data having separate weights: w(trans), and w(flux), respectively. More specifically w(trans) is defined in Formula (1) in which trans - intensity(case) and trans - intensity(control) are case and control intensities of mRNA expression level averaged over all replicates, respectively. In the genome-scale metabolic model of S. cerevisiae the gene regulatory rules are expressed by AND-and OR-operands for the metabolic reactions (e.g. multi-protein complex as catalyst) that have more than one encoding gene [25]. If there was an OR-operand between two genes, then a mean intensity was calculated and if there was an AND-operand, then a minimum intensity was taken. Since there is no transcriptome data for non-enzymatic reactions (i.e. they do not require a catalyzing enzyme or an encoding gene to occur), neutral weights (i.e. zero) were assigned for them.

(1)

The weight derived from the flux data for each reaction, w(flux), is defined in Formula (2) in which flux(case) and flux (control) were obtained by calculating averages over the 2 000 randomly selected samples, each corresponding to a feasible flux distribution (see Transcription and Flux data above).

(2)

The total score for the node is defined in Formula (3). When the two data types were simultaneously used, w(trans) and w(flux) were scaled to be in the same interval, which was essential to prevent either of them from being over-represented in the detected molecular paths. In practice, the flux data was scaled to have the same range as the transcription data: {-2.71, 4.75} for 2.8% vs. 0.5% oxygen in the bioreactor inlet gas and {-3.31, 4.97} for 20.9% vs. 2.8% in the bioreactor inlet gas. Flux data was naturally not available for signaling proteins (i.e. non-metabolic proteins), thus their scores were calculated solely from the transcription data.

(3)

The motivation of using parameter a was to allow for relative weighting for the flux and transcription data in the detection of molecular paths e.g. weighting with pure transcription data: a = 0, or pure flux data: a = 1, or their simultaneous utilization with an equal weight: a = 0.5.

Molecular path detection

After the weights were assigned to the nodes, the EMPath method [13] was used to detect an optimal path of length k. The algorithm is initialized by assigning colors, i.e. random integer numbers [1, k], to the nodes of the path. Then a node with a maximum weight score is added to be the first node in the path. Then the neighboring nodes to the recently added node are considered to be the next node in the path. From this set a node with a maximum weight score is added to the path but nodes with a color that is already included in the path are ignored. Nodes are added until there are k nodes in the path. Then a score of the path is calculated by summing up all the node weights.

In order to calculate the p-value for the null hypothesis (i.e. that the detected path is obtained by chance), a random distribution was created by shuffling the node weights 1 000 times. After each shuffle, a path was detected and its score was calculated as described above. In this way, 1 000 optimal path scores in a random network were obtained resulting in a random distribution. A p-value for the null hypothesis that the detected path is obtained by chance was defined by comparing its score to the random distribution. 0.025 was used as a cut-off p-value, i.e. paths of higher p-values were not considered significant. A network was considered harvested from optimal paths if there were i consecutive iterations in which the detected path was detected during previous iterations.

The path detection was performed separately for up-and down-regulated paths in both case-control comparisons (20.9% vs. 2.8%, and 2.8% vs. 0.5% O2 in the bioreactor inlet gas), and for each value of parameter a∈ {0, 0.5, 1}. When the up-regulated paths were detected, case-control ratios were used, and when the down-regulated paths were detected, control-case ratios were used. Eight (8) was used as the path length k. There is not any rigorous way to define the proper value for this parameter. Eight (8) was empirically found to be a proper value for this parameter: smaller values (e.g. 7) led to too sparse combined networks of enriched molecular paths and higher values (e.g. 9) led to very dense combined networks of enriched molecular paths which would have had poor interpretability. In similar vein, ten (10) was selected for parameter i on empirical basis: the higher values did not harvest the network significantly more thoroughly. The path detection calculations were implemented in a C++ environment and were processed on an Ubuntu Linux Server with 2 processors of Intel Xeon X5650 2.66 GHz divided in 24 virtual cores and 70 GB of RAM memory.

Enrichment of functional protein categories

In order to study how pre-established cellular functions were associated with the detected molecular paths, the combined networks were associated with functional protein categories from FunCat [31] by making a hypergeometric test with controlling false discovery rate (FDR) [32] q-value 0.05 as a cut-off, as described in [13]. Open reading frame identifiers (ORF) were used to identify the genes.

Path length

The method required a selection of pre-defined path length, which is heuristic and deserves some discussion. Let us assume that the network comprises n nodes, and for simplicity they are assumed to be fully connected to each other. In this case the network comprises paths of length  k, in which  nk. The higher the length k is the more paths the network comprises. Thus, a too small path length would lead to information poor networks. On the other hand, a drawback of a long path length is that the computational enumeration and the interpretation of crowded combined networks gets heavy. Eight was selected as the path length since it is the shortest length that provides paths which reasonably combine both metabolic and protein-protein interactions in all the studied cases.

Results and discussion

Effect of relative weighting of transcription and flux data on the detected molecular paths

The detected molecular interaction paths combined protein-protein interactions and metabolic interactions dependent on the phenotypes compared and the relative weighting used to combine the transcription and flux data. The numbers of protein-protein interactions (PPI) and metabolic edges in the combined networks of the detected molecular paths for each of the phenotype comparisons are shown in Table 1. Metabolic edges prevailed when a = 1 (i.e. only flux data used) in all comparisons except “2.8% vs. 0.5%, down” where there were as many PPI edges as metabolic edges. When the metabolic edges prevailed the detected paths generally followed the metabolic routes in which the fluxes had changed substantially. The neighboring metabolic reactions had correlated flux weights as the result of the steady state flux data being constrained by metabolic network stoichiometry. There were two comparisons (“2.8% vs. 0.5%, down” and “20.9% vs. 2.8%, down”) in which PPI edges prevailed when a = 0 (i.e. only transcription data used) indicating that in these comparisons metabolic pathways were less coherently transcriptionally down-regulated than the paths following protein-protein interactions.

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Peroxisomal activities and oxidative stress response featured in the upregulated interaction paths of phenotype differences between the fully respirative phenotype of S. cerevisiae and the respirofermentative phenotype at 2.8% oxygenation

Wiebe et al. (2008) had previously observed that the metabolism of S. cerevisiae was fully respirative under conditions of 20.9% O2 in the bioreactor inlet gas whereas under conditions of 2.8% O2 in the bioreactor inlet gas the metabolic state was respirofermentative [22]. However, the drop in the specific Oxygen Uptake Rate (OUR) was small, from 2.7 ± 0.04 to 2.5 ± 0.04 mmol/(g CDW h) [22] and Jouhten et al (2008) observed that the flux distributions remained almost constant except for the subtle flux to ethanol production [23]. Nevertheless, major changes between the two phenotypes have been observed at the transcriptional level [21]. The transcription and flux data for S. cerevisiae during steady state growth conditions at 20.9% and 2.8% oxygen provision were analyzed here in an integrative manner and separately with the EMPath method to detect molecular interaction paths that were possible determinants of the phenotypic differences observed in S. cerevisiae growing under the two different oxygenation conditions. When transcription data on S. cerevisiae growing under fully aerobic conditions and under conditions of 2.8% O2 in the bioreactor inlet gas was solely used in the scoring of the up-regulated nodes in the detection of molecular interaction paths, cellular processes of respirative metabolism, fatty acid oxidation, and oxidative stress defense were represented in the paths (Figure 2, FunCat enrichments in Additional file 1). Glyoxylate pathway enzyme isocitrate lyase encoded by ICL1 and a dicarboxylate carrier transporting succinate from glyoxylate cycle into mitochondria to be incorporated into TCA cycle encoded by DIC1[33] appeared in the molecular paths up-regulated at the level of gene expression. The glyoxylate cycle is known to be induced in S. cerevisiae under respirative conditions for the metabolism of non-fermentative carbon sources [34]. In addition, the methylisocitrate lyase reaction catalyzed by an enzyme encoded by ICL2, which is homologous to ICL1, was also included in the detected molecular paths. Isocitrate dehydrogenase encoding IDP2 was connected via isocitrate to isocitrate lyase of the glyoxylate cycle. The IDP2 encoded isoform is an alternative source of cytosolic NADPH, for the pentose phosphate pathway, but only while the metabolic state is respirative [35]. Succinate interconnected the glyoxylate cycle components further to SHH3 (YMR118C) (fold change 5.0) encoding a putative mitochondrial inner membrane protein [36]. SHH3 was linked via a protein-protein interaction to ubiquinone-6 dependent succinate dehydrogenase. Succinate dehydrogenase was expectedly the only respiratory chain coupled component observed since most of the respiratory chain components in S. cerevisiae are expressed on a lower level under fully aerobic conditions than in conditions of lower oxygen provision [21]. In addition to the respirative metabolism, fatty acid beta oxidation was observed in the detected molecular paths. Beta oxidation of fatty acids occurs in peroxisomes in yeast and provides an alternative energy source for S. cerevisiae under aerobic conditions. Accordingly, PEX14, which is involved in the import of peroxisomal proteins [37], had protein-protein interactions with the components of fatty acid beta oxidation in the detected paths. Both peroxisome biogenesis and fatty acid beta oxidation are under regulation by SNF1p kinase, a coordinator of energy metabolism of S. cerevisiae[38]. The transcriptional regulation of the peroxisome biogenesis and fatty acid beta oxidation also involves the common regulators ADR1p, OAF1p, and PIP2p. Rintala et al. (2009) showed that the genes involved in fatty acid beta oxidation and peroxisomal biogenesis were expressed at higher levels under the fully aerobic conditions than in conditions of any lower oxygen provision [21]. In the detected molecular interaction paths PEX 14 was further linked to regulators of protein folding (HSP42, SIS1, SSA3) in particular in response to stress, which share a YAP1p binding site [YEASTRACT database July 16, 2013; [39–41]]. YAP1p is a transcription factor responsive to oxidative stress. In the detected molecular paths fatty acid beta oxidation was connected to oxidative stress defense via CTA1 which encodes for a catalase required for the removal of hydrogen peroxide, a strong oxidant, in the peroxisomal matrix. Hydrogen peroxide is formed as a byproduct in the beta oxidation of fatty acids. CTA1p was further linked to a cytosolic catalase reaction involved in the defense against oxidative damage encoded by CTT1 (fold change 4.6) and a hydrogen peroxide reductase reaction that mediates the maintenance of cellular redox balance. Koerkamp et al. (2002) has observed an induction of peroxisomal fatty acid oxidation to trigger transient YAP1p mediated oxidative stress response [42]. However, the transient oxidative stress response did not induce an expression of CTT1 and CTA1 co-responded non-transiently with other genes involved in the peroxisomal functions. Here, the up-regulation of the defense against oxidative agents linked to the up-regulation of peroxisomal activities via molecular interaction paths in S. cerevisiae cells provided with air compared to cells provided with 2.8% oxygen in the chemostat inlet gas, suggests that S. cerevisiae co-regulates these activities. The peroxisomal activities and oxidative stress defense could be down-regulated either directly in response to the decreased oxygen availability though it did not result in substantially lowered oxygen uptake rate (2.7 mmol/(g CDW h) vs 2.5 mmol/(g CDW h) under provision of 20.9% vs 2.8% oxygen, respectively [22]), or in response to the induced fermentative metabolism in cells provided with 2.8% oxygen in the chemostat inlet gas.

Detected up-regulated molecular paths combined into one network, 20.9% vs 2.8%,only transcription data used*.

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Acetyl-CoA synthesis and shuttling were interconnected to the CTT1 encoded catalase and defense against oxidative agents via protein-protein interactions and a guanine nucleotide exchange factor MUK1p which is involved in protein trafficking [43]. MUK1p had a protein-protein interaction to carnitine o-acetyltransferase of the carnitine shuttle which is active both in peroxisomes and in mitochondria. The carnitine shuttle transfers acetyl-CoA across peroxisomal and mitochondrial membranes. CAT2 encodes the carnitine o-acetyltransferase in S. cerevisiae and was coupled to an acetyl-CoA synthetase isoform encoded by ACS1, which is induced under respirative metabolism in S. cerevisiae[44]. ACS1 was down-regulated when 2.8% O2 was provided compared to fully aerobic conditions, even though the metabolism of S. cerevisiae was mainly respirative. The localization of the ACS1 encoded acetyl-CoA synthetase has been very unclear until recently when Chen et al. (2012) confirmed at least a distributed localization of the ACS 1 encoded enzyme between cytosol and peroxisomes [45]. However, ACS1p has also been observed in the mitochondrial proteome [46]. Perhaps the down-regulation of ACS1 in response to the subtle decrease in the oxygen uptake rate under conditions of 2.8% O2 provision was related to a general down-regulation of the peroxisomal activities. Remarkably, the decreased oxygen provision which resulted in a mild decrease in the respiratory activity [21–23] triggered the down-regulation of peroxisomal functions coupled to the fatty acid beta oxidation whereas a respiratory deficiency in an absence of oxygen limitation has been observed to trigger an opposite response, an up-regulation of peroxisomal activities [47].

When both transcription and flux data were used to score the nodes of the network in the EMPath method, the molecular paths up-regulated in the fully respirative phenotype of S. cerevisiae compared to the respirofermentative phenotype observed under 2.8% oxygenation [22] included key enzymes of respirative metabolism i.e. pyruvate dehydrogenase, the gate keeper of the TCA cycle, and citrate synthase (Figure 3, FunCat enrichments in Additional file 1). They were linked to the ACS1 encoded acetyl-CoA synthetase which was observed in the enriched molecular paths when the path detection was run solely with the transcription data. Further connections were observed to the mitochondrial NAD+ dependent and cytosolic NADP+ dependent isoforms of acetaldehyde dehydrogenase encoded by ALD4 and ALD6, respectively [48, 49]. Both the ALD4 encoded isoform and the ALD6 encoded isoform, which is an additional source of cytosolic NADPH, had lower mRNA and protein levels under oxygen limitation than under fully aerobic conditions [21]. The mRNA and protein levels of ALD4 and ALD6 encoded acetaldehyde dehydrogenase isoenzymes correlated within five different conditions of oxygen provision from fully aerobic to anaerobic. Here flux estimation also suggested changes in the fluxes of the reactions catalysed by both isoforms. The succinate dehydrogenase reaction, which is closely coupled to the respiratory chain, showed an altered flux response between the compared conditions and was observed in the detected paths when only the transcription data was used in scoring. However, the glyoxylate cycle components and components involved in the peroxisomal fatty acid beta oxidation were absent from the molecular paths when the flux data was included in the scoring. The glyoxylate cycle is under glucose repression [34] and no in vivo activity of the glyoxylate cycle in S. cerevisiae was previously observed in the 13C-labelling experiments on glucose either under fully aerobic conditions or in 2.8% oxygenation [23].

Detected up-regulated molecular paths combined into one network, 20.9% vs 2.8, both transcription and flux data used*.

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Scoring the nodes of the interaction network solely with flux data resulted in molecular interaction paths dominated by components of sphingolipid metabolism and protein-protein interactions between them (Additional file 2: Figure S1; FunCat enrichments in Additional file 1). Expression of SUR2 and SCS7 encoded hydroxylases involved in the biosynthesis of sphingolipids has been found to be oxygen-dependent [50, 51]. Thus, OUR may have had an effect on the in vivo activity of the sphingolipid biosynthesis pathway. Sphingolipid metabolism has been associated with ageing and apoptosis [52] which were observed in the FunCat enrichments of the detected molecular paths.

Downregulated interaction paths of phenotype differences between fully respirative phenotype of S. cerevisiae and respirofermentative phenotype at 2.8% oxygenation involved regulation of the cell cycle at the transcriptional level

Components of fermentative metabolism, alcohol dehydrogenases in particular, were present in the down-regulated molecular paths in the fully respirative phenotype of S. cerevisiae compared to the respirofermentative phenotype of S. cerevisiae under the 2.8% oxygenation conditions when both transcription and flux data were incorporated into the scores (Figure 4, both transcription and flux data used in the scoring; Additional file 2: Figure S2, scoring with pure flux data; FunCat enrichments in Additional file 1). When only transcription data was used in the scoring, a separate, interconnected, network of regulatory components was observed (Figure 5). The regulatory components were involved in the mating pathway and in the regulation of the cell cycle (FunCat enrichments in Additional file 1). The separate regulatory network was linked via protein-protein interactions to IMP dehydrogenase and, thus, to nucleotide synthesis.

Detected down-regulated molecular paths combined into one network, 20.9% vs 2.8%, both transcription and flux data used*.

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Detected down-regulated molecular paths combined into one network, 20.9% vs 2.8%, only transcription data used*.

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Notably, alcohol dehydrogenase was found in the detected molecular paths only when flux data was included in the scoring even though alcohol production was a major phenotypic difference between S. cerevisiae under fully aerobic and conditions or 2.8% oxygen provision. This emphasizes the benefit of integrated data from a post-transcriptional regulatory level into the analysis.

Upregulated molecular interaction paths detected in S. cerevisiae between the respirofermentative phenotypes at 2.8% oxygenation and 0.5% oxygenation suggest remodelling of transport across the mitochondrial membrane

The metabolic state of S. cerevisiae was respirofermentative under both conditions: 2.8% and 0.5% O2 in the bioreactor inlet gas [22] and the transcriptomes of S. cerevisiae were observed to be similar under these two conditions [21]. However, the flux distributions were substantially different [23]. Under the 0.5% oxygenation conditions the yield of ethanol on glucose exceeded the yield of biomass on glucose, and pyruvate decarboxylase carried the main flux from pyruvate branching point in contrast to the subtle ethanol production of S. cerevisiae under 2.8% oxygenation conditions [23]. The detected molecular paths up-regulated in S. cerevisiae under the 2.8% oxygenation conditions compared to the 0.5% oxygenation conditions when the transcription data was solely used to score nodes, featured a remodeling of transport between the cytosol and mitochondria, and respirative metabolism (Figure 6; FunCat enrichments in Additional file 1). The remodelling of respirative metabolism at the transcriptional level was progressive as a function of oxygenation since the glyoxylate cycle components and ACS1 encoded acetyl-CoA synthetase and isocitrate dehydrogenase encoded by IDP2 were observed also in the molecular paths representing the differences of the response of S. cerevisiae to fully aerobic conditions and conditions of 2.8% oxygen provision. The glyoxylate cycle was represented in the molecular paths detected for the differences of S. cerevisiae phenotypes within 2.8% and 0.5% oxygenation conditions by both malate synthase encoded by MLS1 and isocitrate lyase. In addition, components of the propionate catabolic pathway, which resembles the glyoxylate cycle, including a 2-methylcitrate synthase encoded by CIT3, aconitase encoded by PDH1, and methylisocitrate lyase encoded by ICL2 were observed in the paths. Methylisocitrate lyase cleaves methylisocitrate into succinate and pyruvate which integrate to the TCA cycle. Propionate catabolism is generally under glucose repression [53] but PDH1 has also been observed to be regulated by retrograde regulators and induced in mitochondrial dysfunction [47]. However, here, during decreased respiratory activity due to a limited availability of oxygen, PDH1 was down-regulated. Interestingly, a number of transports between the cytosolic and mitochondrial compartments were observed in the detected molecular paths. The transporters were carriers of the intermediates of TCA cycle, and acetate and CoA. Proton gradient across the mitochondrial membrane affects the molecule and ion transport since many of the transporters are proton symporters or antiporters. The appearance of the transporters in the up-regulated molecular paths suggests that in 0.5% oxygenation conditions the low availability of oxygen may have limited the generation of proton gradient across the mitochondrial membrane by the electron transfer chain of S. cerevisiae and, thus, the transport required reorganization.

Detected up-regulated molecular paths combined into one network, 2.8% vs 0.5%, only transcription data used*.

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When both transcription and flux data were used in the scoring of nodes up-regulated in S. cerevisiae under the 2.8% oxygenation conditions compared to the 0.5% oxygenation, additional components involved in aerobic metabolism such as fructose 6-phosphatase, a gluconeogenetic enzyme, encoded by FBP1 and pyruvate dehydrogenase complex were observed among others (Figure 7; FunCat enrichments in Additional file 1). Again, the glyoxylate cycle components were absent when flux data was included in the scoring whereas the components involved in propionate metabolism were observed.

Detected up-regulated molecular paths combined into one network, 2.8% vs 0.5%, both transcription and flux data used*.

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Mevalonate biosynthesis prevailed in the detected up-regulated molecular paths when only flux data was used to score the nodes (Additional file 2: Figure S3; FunCat enrichments in Additional file 1). In addition, acetaldehyde dehydrogenase isoforms encoded by ALD4 and ALD5 catalyzing the mitochondrial NADP+ specific and cytosolic NAD+ specific reactions were observed. Most of the metabolic interactions in the detected paths involved either acetyl-CoA or CoA.

Potential post-transcriptionally co-regulated reactions found in the downregulated molecular interaction paths detected in S. cerevisiae between the respirofermentative phenotypes at 2.8% oxygenation and 0.5% oxygenation

When both flux and transcription data were used in the scoring of nodes down-regulated in S. cerevisiae under the 2.8% oxygenation compared to the 0.5% oxygenation, key enzymes of the central carbon metabolism, glucose-6-phosphate isomerase, fructose bisphosphate aldolase, phosphoglycerate kinase, pyruvate decarboxylase, and alcohol dehydrogenase were observed in the detected molecular paths (Figure 8). These enzymes, involved in the glycolytic pathway, pyruvate metabolism, and fermentative pathway (FunCat enrichments in Additional file 1), are not directly linked by metabolic interactions, but were connected by protein-protein interactions in the detected molecular paths. Collins et al. (2007) reported in their high-throughput study the protein-protein interactions between glucose 6-phosphate isomerase (PGI1p), fructose bisphosphate aldolase (FBA1p), 3-phosphoglycerate kinase (PGK1p), pyruvate decarboxylase (PDC1p), and alcohol dehydrogenase (ADH1p) [54]. The genes encoding the discussed enzymes, i.e. FBA1, PGK1, PDC1, and ADH1, have all been observed to have stable expression under a range of conditions [55]. However, the fluxes of glucose 6-phosphate isomerase, fructose bisphosphate aldolase, 3-phosphoglycerate kinase, pyruvate decarboxylase, and alcohol dehydrogenase reactions were substantially lower under 2.8% oxygenation conditions than under even lower oxygen availability [23] whereas the corresponding transcript levels did not, as expected, show consistent behavior [21]. On the other hand, the level of FBA1p is under post-transcriptional control by 14-3-3 proteins BMH1p and BMH2p [56]. In fact, post-transcriptional regulation was previously observed to have a major effect on the protein levels in S. cerevisiae under the conditions of 0.5% O2 in the bioreactor inlet gas [21]. If the physical interactions between these enzymes mediate a transfer of information in some form, they enable coordinated regulation of the central carbon metabolism in upper and lower glycolysis, and in the fermentative pathway. The information transfer could occur for example via a common post-translational modification occurring while the proteins interact. Notably, all these enzymes contain identified phosphorylation sites (http://www.phosphopep.org) [57] and a differential phosphorylation of one of the enzymes, fructose bisphosphate aldolase (FBA1p), in response to switch in growth conditions was recently observed by Oliveira et al. (2012) [58]. Protein-protein interactions interconnected the enzymes of central carbon metabolism further to fatty acid import and biosynthesis. The detected molecular interaction paths included FAS1 and FAS2 that are involved in the elongation of saturated fatty acids, and FAA1 and FAA4 encoding enzymes catalyzing the import and activation of unsaturated fatty acids available in the growth medium. The detected down-regulated molecular paths were highly similar involving the components of the central carbon metabolism when pure flux data was used in the scoring (Figure 9). If flux data was not incorporated into the scoring, only amino acid transport was observed (Additional file 2: Figure S4; FunCat enrichments in Additional file 1). The observation emphasized the value of the integrative analysis of transcription and flux data that reflect the states of different functional levels of cells.

Detected down-regulated molecular paths combined into one network, 2.8% vs 0.5%, both transcription and flux data used*.

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Detected down-regulated molecular paths combined into one network, 2.8% vs 0.5%, only flux data used*.

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Conclusions

In this study, the EMPath method for the detection of molecular interaction paths [13] was extended to allow for simultaneous utilization of transcriptome and fluxome data in an integrative manner. The method was applied to a combined network of S. cerevisiae’s metabolic and protein-protein interactions. In contrast to existing path finding methods [13, 17–20, 59], data from two sources were combined into one weighting scheme. Thus, the identification of potentially information transferring molecular paths beyond a single functional level of cells was enabled. The molecular paths coupled cellular components and processes distant at first sight but associated through different biochemical interactions with the oxygen-dependent phenotype changes in S. cerevisiae. New light was shed on the S. cerevisiae phenotypes previously investigated separately with transcription and on the level of in vivo fluxes [21–23]. However, it was observed that while the combined weighting scheme was of profound interest, all the three different weighting schemes resulted in enriched molecular paths providing complementary insight into the oxygen-dependent phenotypes of S. cerevisiae. In addition, certain processes were dominated by post-transcriptional level regulation i.e. glycolytic and fermentative fluxes were emphasized by the differences observed in the enriched molecular paths detected with the different weighting schemes. In particular, the detected molecular paths highlighted protein-protein interactions between the enzymes of central carbon metabolism that could possibly mediate coordinated post-transcriptional regulation of the differential in vivo activity of central metabolism in S. cerevisiae in two different respirofermentative metabolic states. Further, the down-regulation of oxidative stress in S. cerevisiae in conditions of 2.8% oxygenation compared to fully aerobic conditions was found to be related and potentially restricted to the down-regulation of peroxisomal activities. The results further suggested that a limited availability of oxygen and the consequently decreased respirative activity may affect transport reactions of S. cerevisiae across the mitochondrial membrane under conditions of 0.5% oxygen provision. Finally, the paths included metabolic interactions via metabolic intermediates in the crossroads of altered processes, such as acetyl-CoA and succinate, whose concentrations could be potential phenotypic markers.

Abbreviations

Artificial Centering Hit-and-Run

Carbon Evolution Rate

COnstraint-Based Reconstruction and Analysis

Enriched Molecular Path detection

False Discovery Rate

Источник: https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-16

Metabolic Flux Analysis

Metabolic Flux Analysis: Methods and Protocols opens up the field of metabolic flux analysis to those who want to start a new flux analysis project but are overwhelmed by the complexity of the approach. Metabolic flux analysis emerged from the current limitation for the prediction of metabolic fluxes from a measured inventory of the cell. Divided into convenient thematic parts, topics in this essential volume include the fundamental characteristics of the underlying networks, the application of quantitative metabolite data and thermodynamic principles to constrain the solution space for flux balance analysis (FBA), the experimental toolbox to conduct different types of flux analysis experiments, the processing of data from 13C experiments, and three chapters that summarize some recent key findings. Written in the successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible protocols, and notes on troubleshooting and avoiding known pitfalls.

 

Authoritative and easily accessible, Metabolic Flux Analysis: Methods and Protocols presents protocols that cover a range of relevant organisms currently used in the field, providing a solid basis to anybody interested in the field of metabolic flux analysis.

Keywords

cell culture flux analysis metabolomics stoichiometry thermodynamics

Editors and affiliations

  • Jens O. Krömer
  • Lars K. Nielsen
  • Lars M. Blank
  1. 1.Centre for Microbial Electrosynthesis (CEMES), Advanced Water Management CentreUniversity of QueenslandSt. Lucia, BrisbaneAustralia
  2. 2.AIBNUniversity of QueenslandSt. Lucia, BrisbaneAustralia
  3. 3.Biology DepartmentRWTH Aachen UniversityAachenGermany

Bibliographic information

  • Book TitleMetabolic Flux Analysis
  • Book SubtitleMethods and Protocols
  • EditorsJens O. Krömer
    Lars K. Nielsen
    Lars M. Blank
  • Series TitleMethods in Molecular Biology
  • Series Abbreviated TitleMethods Molecular Biology
  • DOIhttps://doi.org/10.1007/978-1-4939-1170-7
  • Copyright InformationSpringer Science+Business Media New York2014
  • Publisher NameHumana Press, New York, NY
  • eBook PackagesSpringer Protocols
  • Hardcover ISBN978-1-4939-1169-1
  • Softcover ISBN978-1-4939-4159-9
  • eBook ISBN978-1-4939-1170-7
  • Series ISSN1064-3745
  • Series E-ISSN1940-6029
  • Edition Number1
  • Number of PagesXII, 316
  • Number of Illustrations19 b/w illustrations, 51 illustrations in colour
  • TopicsBiochemistry, general
    Enzymology
Источник: https://link.springer.com/content/pdf/10.1007%2F978-1-4939-1170-7.pdf

Spectral Resolution and Energy Efficiency for FOXSI III Silicon Detectors

Lance Davis and Connor O'Brien


Introduction

FOXSI-3 is the third flight of the Focusing Optics X-ray Solar Imager (FOXSI) sounding rocket, which will take place on August 21st, 2018. The goal of the sounding rocket is to image the sun in the soft x-ray regime of 4 to 15 keV in order to probe the source of coronal heating and the mechanics of solar microflares. FOXSI flights 1 and 2 used exclusively silicon x-ray detectors, whereas this latest flight has added new, fine-pitch Cadmium-Tellurium (CdTe) detectors as well. While previous flights have operated at -20 °C, these new detectors have a higher operating temperature of -10°C, which will affect the performance of the older silicon detectors. The goal of our project is to measure the spectral resolution and energy efficiency of FOXSI silicon detectors four and five at -10 °C in order to assess how this new operating temperature affects the performance of the detectors.

Theory

The first half of our experiment involved calculating the spectral resolution of the detectors. Spectral resolution is the broadening of a monoenergetic source into a gaussian due to instrumental effects, and is also known as energy resolution. This resolution was probed using spectral lines from radioactive sources. When the flux of such a source is measured by the detector, statistical noise causes the measured peak to widen into a Poisson distribution about the energy peak, which can be approximated as a Gaussian for large N (such as the number of incident photons observed by one of the detectors), as shown below.


An illustration of the incident monoenergetic peak and the resultant Gaussian distribution at energy H. The Full Width Half Maximum is the width of the peak at half of its maximum value, and is the standard deviation of the distribution.

The equation of a Gaussian distribution is given by the function

(1)

where P is the height of the peak at energy H, E is the energy of the incident photon, and is the standard deviation.

The width of this Gaussian is the energy resolution of the detector at the peak energy. We numerically define the energy resolution of our detectors using the relation


(2)

where FWHM is the full-width half-maximum; at half-maximum, the full width of a Gaussian distribution is equal to 2.35[4]. Usually, the variance of this distribution σE2 would be proportional to the variance in the number σn2 of charge carrier pairs produced at a given energy; σE is related to σn by E=wσn  where w, which is material specific, is the average energy required to create a charge carrier pair. In Poisson statistics, n = sqrt(N) where N is the number of charge pairs produced; N can be calculated by N=E/w [4]. For silicon, w = 0.00366 keV [8].

However, the processes that give rise to each individual charge carrier pair in the detector are not independent; therefore we cannot describe the total number of charge carrier pairs with Poisson statistics. The departure of observed statistical noise from pure Poisson statistics is described by the Fano Factor, which is given by the relation

(3)

The Fano factor is dependent on which material is used for the detector, and is a constant. In the case of silicon, F = 0.115 [8]. This modifies σn to become σn= sqrt(FN). Finally, the statistical limit on energy resolution for a silicon detector can be defined as [4]

(4)

On top of a statistical limit on the energy resolution, we can readily assume that there will be some underlying resolution limit from inherent electronic noise - both an energy dependent and energy independent component. Contributions from thermal noise are likely due to the temperature dependence of our system, as well as power line noise from power supplies that filters within the flight board are not able to scrub out. As for energy-dependent noise, the fact that to measure photon energy we measure the amount of discrete charge carriers means that shot noise is also going to have an effect on our system. The energy resolution adds in quadrature from each resolution limit source [4]. We therefore may isolate the energy-dependent Fano noise from the energy-dependent and non-energy-dependent inherent electronic resolutions by measuring the resolution at various spectral lines and using the relation

(5)

where Nfano is the fano noise given by Equation 4, r is the energy-independent electronic noise, and f1 is a factor that describes the energy-dependent noise f1E [11]. We assume the energy dependence of the energy-dependent noise is linear because the amount of charge carriers liberated by a photon increases approximately linearly at the 4-15 keV energy scale. For our setup, r mainly takes into account thermal noise and power line noise, while f1 mainly describes the shot noise observable in the system.

After identifying the energy resolution of the detectors, we then wished to measure their energy efficiencies. The energy efficiency is defined as the ratio of the number of photons that the detector actually measures to the number of photons incident on the detector, and can be energy-dependent. This is given by the relation


(6).

This relation assumes that each recorded event was a single photon. The X-ray source for this experiment was beamline 3.3.2 at the ALS. This beam was formed by redirecting continuum radiation created by synchrotron radiation [9]. Synchrotron radiation is emitted when particles are accelerated in a curved path.

Because our silicon detector counts single photons and has a thickness of 500 μm, literature suggests that the energy efficiency between 6 to 10 keV will be near unity [6]. Our detectors use a threshold energy of 4 keV, which is used to prevent the detectors from being flooded with low energy photons and only allowing photons within the energy range of FOXSI 3’s science goals to be detected. Because of the spectral resolution of the fast pulse shaper that precedes the discriminator which form the threshold circuit, the resulting efficiency drop-off resembles an error function centered at 4 keV. This experiment sought to characterize the behavior of this threshold above 4 keV.

Experimental Setup

The silicon detectors used are made of an n-type silicon wafers onto which acceptor doped (p+) and donor doped (n-) strips are placed orthogonally onto opposite sides. By placing the strips orthogonally, each crossing forms a pixel in an image. On both the p+ or the n- side, there are 128 strips with a pitch of 75 μm. Each side of the detector is divided into two regions, called ASICs; each ASIC has 64 strips. For this project we focused on the p+ side, which has better resolution.

Diagram of FOXSI silicon detector.

We used detectors 4 and 5 (numbering used internally to track detectors; number based on position in electronics board) for gauging the spectral resolution. These detectors were housed in the same electronics board that will be flown. This setup was encased by an aluminized mylar, Faraday cage material, which insulated the detectors to help maintain the desired -10℃ operating temperature. The detectors were cooled using cold nitrogen gas. The flow of nitrogen gas into the enclosure was regulated by the temperature control unit which maintained the specified temperature of -10±1℃.

Our silicon detectors measure individual photons. When a single energetic photon is incident upon the detector, electron-hole pairs are produced. A bias voltage of 200 V is applied across the silicon detector to separate the electron-hole pairs. This process creates a current proportional to the amount of electron-hole pairs. This current is measured by the electronic board. The data is then digitized into discrete ADC bins. The ADC value is then sent to an FPGA which sends the data to a computer interface where it is written to file to be used in later analysis.

Sealed radioactive sources, Am-241 and  Fe-55, and fluorescence from metal foils of Cu and Ni, were used to measure the spectral resolution. The metal foils, secured on top of the radioactive Am-241 source, gave out the characteristic lines of the metal foil through X-ray fluorescence. The spectral lines produced by these four sources which are used in gain calibration are given in the table below. Data for each source were collected at -10℃ until a statistically significant number of counts were obtained for each strip.

Table of X-ray sources used and the spectral lines used for gain calibration from each source.

The next part of this project was the energy efficiency measurement. A silicon drift detector with an assumed energy efficiency [6], as well as the flux observed in the continuum of the beamline X-ray source, were used as references for calculating the incoming flux. Separately, this detector and our detector were illuminated by a monoenergetic X-ray source, which was beamline 3.3.2 at the ALS; for more details, see [9]. The continuum spectra in the range of 4 to 20keV was on the order of 109 photons/s/mm2. This amount of flux would saturate our detector, meaning the detector no longer counts single photon events. The flux was reduced by passing the beamline through a 2x2 μm slit and when needed, a 60 or 100 μm aluminum foil to attenuate the flux to the order of 103 to 104 photons/s/mm2, which our detector could measure reliably. This reduced flux was measured by both detectors. The air gap between the detector and the beamline was roughly 15 cm.

Flux measurements were made at 4.75, 5, 5.5, 6, 7, 8, 9, and 10 keV. Flux below 4.75 keV began to enter the falling tail of the continuum spectra, as well as the air gap absorbing a large percentage of incoming photons [10]; statistics were becoming poor below 4.75 keV. Above 10 keV, a smaller fraction of higher energy photons were being absorbed by the aluminum than lower energy photons, causing a higher incoming flux; the detector started to saturate above 10 keV.  Only detector 4 was brought to the ALS for testing.

Incoming flux generated by beamline 3.3.2 at the ALS. Plot shows energy (x-axis) vs intensity (y-axis).

Spectral Resolution

The data for the spectral resolution were analyzed using IDL analysis code made for the FOXSI mission, parts of which were modified for our purposes. Histograms for data observed by each of the 64 strips from each of the four ASICs were made. These histograms plotted ADC bins vs counts.

Histogram of Fe-55 for Detector 4 ASIC 2 showing counts (y-axis) vs ADC bin (x-axis).

Next, an algorithm was used to fit a Gaussian curve in order to find the peaks of the spectral lines used in this experiment. The ADC bin where the peaks occurred was assigned the energy of that peak. A plot of ADC peaks vs energy is then made, where a quadratic function was fit to the data points. This function is the gain calibration curve. Using this curve, each ADC bin was calibrated to a corresponding energy bin. At this point, each strip was analyzed; if the ratio of the observed conversion from ADC bin to predicted conversion given by the gain calibration curve was greater than 5% away from unity, the strip was discarded from further analysis; in total, 5/128 strips from detector 4 and 11/128 strips from detector 5 were discarded.

Plots of spectral line peaks (x-axis) vs ADC bins (y-axis) for the first four strips. The blue curve is the fit gain calibration curve.
Ratios of data points to the fit. The top right plot, Channel 1, was discarded as it had ratios exceeding 5% away from unity. 

The FWHMs of the peaks, now in units of energy, were then calculated by fitting a Gaussian curve to each peak. For the Ni and Cu lines, the Am-241 spectrum was scaled to match the intensity of high energy lines, 20.6 keV, 26.3 keV, and 59.6 keV, which were much less affected by the absorption in Ni and Cu, seen in the Ni and Cu spectra. The background Am-241 continuum seen in the spectra collected for the Ni and Cu foils was accounted for by subtracting the scaled Am-241 spectrum. The FWHM values for Ni and Cu were then calculated. Note that this is a crude approximation, as we have not accounted for how the foils would affect the energy emission of Am-241 in the range of 6 to 10 keV.


Gaussian fit (curved line) of the Am-241, 13.9 keV spectral line. The histogram shows the number of counts in each energy bin.

The fit minimized χ2; the errors for the FWHM were found by subtracting the FWHM value found at χ2min+1 by the FWHM value found at χ2min. The resolutions were plotted as a function of energy. The red line shows the best fit to Equation 5; the blue dotted lines show the error in this fit. The parameters for the fit are given in the table below.


Spectral resolution as a function of energy for ASICs 2 and 3 on Detectors 4 and 5. The red line shows the best fit to Equation 5; the blue dotted lines show the error in this fit.

In comparison to the spectral resolution operated at -20℃ [2], the low energies were more affected. For the spectral lines at 59.6 keV, 26.3 keV, and partially the line at 17.6 keV, the energy resolution remained constant. For the other spectral lines, the energy resolution increased anywhere from 0.03 to 0.25 keV. Each plot has several data points outside of the errors of the model fit. This does not imply immediately that this is a poor model however, as Equation 5 has been used to model the spectral resolution for these detectors at lower operating temperatures [2,11]. It is believed that with better data quality, the data points will better fit the model. Improved quality could be achieved by increasing the number of counts collected. More statistics would lead to a better gain calibration and FWHM calculations. Also, a more precise removal of the Am-241 continuum background from the Ni and Cu lines would lead to a more precise FWHM calculation for those lines.

Table of the parameters used to fit Equation 5 in the plot of spectral resolution vs energy.

The energy dependent noise term, f1, was calculated to be on the order of 104 times smaller for the fit in detector 4, ASIC 2 and detector 5, ASIC3 when compared to the other plots, as well as the f1 term found when operating the detectors at -20℃ [2]. One possible explanation for this is that the resolution for the 59.6 keV Am-241 line was lower for these two plots. This issue may be resolved if a larger integration time was used in order to gain a higher number of counts for this emission line.

It is important to note that the flight goal resolution for the silicon detectors was 1 keV. As Figure 4 shows, for the energy range of 4 to 15 keV, the resolution is roughly 0.55 keV, well below the 1 keV goal. While operating at -10℃, the contribution from electronic noise has increase by about 0.03 to 0.09 keV in comparison to when the detectors were operated at -20℃ [2].

Energy Efficiency

In order to measure the energy efficiency of the FOXSI detectors, the first step is to calculate the incident flux measured by the reference Silicon drift detector (SDD). First, we perform gain calibration on the reference detector using a process very similar to gain calibration for the FOXSI detector. Since the SDD is a single silicon bulk crystal with a very small resolution, there are no individual strips to calibrate and we are therefore able to manually identify the peak bin when the SDD is illuminated with 4.75 keV, 5 keV, 5.5 keV, 6 keV, 7 keV, 8 keV, and 9 keV monochromatic beams. We then perform a linear fit of peak bin to peak energy to obtain a gain calibration curve, shown below.

Gain calibration curve for reference SDD. We found that each bin corresponds to  0.0389 keV and that bin zero corresponds to -0.0536 keV.

To find the rate of incident counts, the total time the SDD was actively recording incident photons, also known as the live time, needs to be calculated. In the SDD provided by the ALS, there is a fast triggering channel and a slow triggering channel. The fast channel has a very fast shaping time during which it can’t accept new counts, therefore we assume that the number of counts recorded by the fast channel is the number of photons incident on the SDD, whether or not they were actually recorded in the data. The slow channel has a slow shaping time, which results in it being able to discern the energy of the photons it records. The data recorded by the SDD is data recorded by the slow channel. While the slow channel is reading the energy of the incident photon, it cannot accept any new counts. The time it spends shaping the pulse where it cannot accept new counts is known as the dead time of the SDD’s measurement. The provided SDD calculates dead time by taking the ratio of the total slow channel counts to the total fast channel counts, giving the ratio of live time to total time the SDD was operating. Taking this ratio and multiplying by the time the SDD was active yields the live time of the SDD [6]. Dividing the counts within a given energy peak by the live time gives the actual incident countrate supplied by the x-ray source. Similarly, we sum the total number of counts in a given peak measured by the FOXSI detector, and divide by the live time, which is a quantity measured by onboard electronics and included in the data packet. We then calculate the energy efficiency as a function of energy, shown below.

Energy efficiency as a function of energy for detector 4, ASICs 2 and 3.

From the calculated energy efficiency of detector 4, ASIC 2 and 3, we immediately note that the efficiency for the region well above the threshold is well below unity efficiency. Furthermore, it is observed to drop as energy increases, the opposite dependence as expected. The only places in our analysis where such a discrepancy could have occurred is in calculating the number of counts registered by the FOXSI detector, and the live time calculation for the SDD.

The live time calculation outlined in preceding paragraphs is that detailed by the manufacturer, but it differs in key ways from how FOXSI dead time is calculated. Primarily, it assumes that the ratio between the slow channel and the fast channel counts gives the live time percentage, something not supported in other literature [6]. If the live time is actually larger than the calculated value for high count rates, it would reduce the incident count rate more in the high energy regime and result in the expected higher efficiency values. The counts measured by the FOXSI detector during our tests may also be suspect. Between each strip, there is a 25 micron section of the detector that cannot register data. After further analysis of other data sets taken at the ALS, the count rates observed by the FOXSI detector during our energy efficiency tests was on the order with count rates of data taken while the non-data-taking parts of the detector were illuminated. While the position of our detector during the energy efficiency tests would indicate that the x-ray source was illuminating a part of the detector that could take data, it is possible the system that positions the detector in front of the source exhibited unforeseen hysteresis behavior that resulted in the wrong section being illuminated. This would result in drastically reduced observed count rates. If accounted for, this would explain the low observed count rates but not the odd energy dependence of the calculated efficiency. It is possible that one or both of these factors are affecting our data.

Conclusion

The measurements found that the spectral resolution in the energy range of interest to the FOXSI mission, 4 to 15 keV, was on average 0.55 keV, roughly 0.05 keV higher than if the detectors were operated at -20℃ [2]. This energy resolution is below the 1 keV resolution required by the science goal of the FOXSI mission. If this calibration were to be repeated, it would be useful to gain further statistics for the spectral lines. This would provide a tighter and more accurate fit to the FWHM measurements.

The energy efficiency calculations were believe to be incorrect, as the efficiency does not match expected [6] or previous results [9]. The reason for this is thought to be due to not correctly calculating the live time, and thus the count rate, in the reference detector. To improve upon this work, more information would be needed in how to understand the data collected by the reference detector, namely how to calculate the live time. It would also be useful to extend the range of measurements to 4 to 20 keV, the full range of the beamline at ALS, as this would provide efficiency information for the entire energy range of the FOXSI detectors.

References

[1]Krucker, S., Christe, S., & Glesener, L., et al. 2014, ApJL, 793, 2

[2]Athiray, P. S., Buitrago-Casas, J. C., Bergstedt, K., et al. 2017, SPIE,10397, 103970A

[3]Ishikawa, S., Glesener, L., Christe, S., et al. 2014, ASJ, 66, SP1, id.S157

[4]Knoll, Glenn F. Radiation Detection and Measurement. 3rd ed., John Wiley & Sons,

2000.

[5]Beckhoff, Burkhard, et al. Handbook of Practical X-Ray Fluorescence Analysis.

Springer-Verlag Berlin Heidelberg, 2006.

[6]Amptek. “XR-100SDD Silicon Drift Detector (SDD).” Amptek.com, Amptek, 2017,

amptek.com/products/xr-100sdd-silicon-drift-detector/#11.

[7] L`epy, M., Plagnard, J., and Ferreux, L., “Measurement of 241am l x-ray emission

probabilities,” Applied Radiation and Isotopes 66(6), 715 – 721 (2008). Proceedings of the 16th International Conference on Radionuclide Metrology and its Applications.

[8]Lechner, P., et al. “Pair Creation Energy and Fano Factor of Silicon in the Energy Range

of Soft X-Rays.” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 377, no. 2-3, 1996, pp. 206–208., doi:10.1016/0168-9002(96)00213-6.

[9]MacDowell, Alastair. "Beamline 3.3.2." Advanced Light Source. Berkeley Lab, 2018. ]

Web. 04 May 2018.

[10]B.L. Henke, E.M. Gullikson, and J.C. Davis. X-ray interactions: photoabsorption,

scattering, transmission, and reflection at E=50-30000 eV, Z=1-92, Atomic Data and Nuclear Data Tables Vol. 54 (no.2), 181-342 (July 1993).

[11]Ishikawa, S., et al. “Fine-Pitch Semiconductor Detector for the FOXSI Mission.” IEEE

Transactions on Nuclear Science, vol. 58, no. 4, 2011, pp. 2039–2046., doi:10.1109/tns.2011.2154342.

[12]Ishikawa, Shin-Nosuke, et al. “Fine-Pitch CdTe Detector for Hard X-Ray Imaging and

Spectroscopy of the Sun with the FOXSI Rocket Experiment.” Journal of Geophysical Research: Space Physics, vol. 121, no. 7, 2016, pp. 6009–6016., doi:10.1002/2016ja022631.

[13] Takeda, S., Takahashi, T., Watanabe, S., Tajima, H., Tanaka, T., Nakazawa, K., and

Fukazawa, Y., “Double-sided silicon strip detector for x-ray imaging,” in [SPIE Newsroom], (Feb. 2016).

Источник: https://sites.google.com/a/umn.edu/mxp/student-projects/spring-2018/s18_measuring-x-ray-flux-from-an-x-ray-generator-and-testing-detector-energy-efficiency

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Flux growth and characterization of an FeSi4P4 single crystal†

DOI: 10.1039/C7RA08118A (Paper) RSC Adv., 2017, 7, 47938-47944

Received 23rd July 2017Accepted 18th September 2017

First published on 12th October 2017


Abstract

Herein, a single crystal of FeSi4P4 (FSP) with dimensions up to 8 × 7 × 3 mm3 was successfully grown using a seeded flux growth method. Single crystal X-ray diffraction results revealed that the FSP crystal crystallized in the chiral space group P1 (no. 1). High-resolution X-ray diffraction presents a full-width at half-maximum (FWHM) of 36′′ and 46′′ for the (100) and (001) FSP crystals, respectively, which indicates that FSP crystals have high crystalline quality. FSP is thermally stable up to 1157.1 °C and has a high thermal conductivity of 35 W (m K)−1 at room temperature. The magnetic analysis shows that the FSP crystal is paramagnetic in the range from 5 to 300 K. The Hall effect measurement suggests that the FSP crystal is a promising p-type semiconductor at room temperature.


Introduction

Due to the abundant structural chemistry and excellent physical properties of ternary silicon phosphides containing transition metals, extensive research has been carried out on their nonlinear optical and thermoelectric properties.1–4 A number of representative ternary silicon phosphides have been reported such as NiSi2P3 (Imm2),5 Si3AlP (Cc),6 RhSi3P3 and IrSi3P3 (C2),7 PtSi2P2 (P21),8 LnSi2P6 (Cmc21, Ln = La, Ce, and Pr),9 and Ag2SiP2 (I[4 with combining macron]2d).10 Among these, the chalcopyrite structure II–IV–V2 (II–Mg, Zn, Cd; IV–Si, Ge; and V–P, As) crystals, such as CdSiP2 and ZnGeP2, have great important applications in mid-infrared nonlinear optics owing to their outstanding nonlinear optical properties.1,11 The ZnSiP2 semiconductor is believed to be a promising candidate for applications in optoelectronics, spintronics, and quantum electronics.12 Particularly, two room-temperature ferromagnetism semiconductors, MnGeP2 and MnGeAs2, have been researched in recent years.13,14

Perrier et al. systematically investigated transition metal phosphosilicides MSixPy (M = Fe, Co, Ru, Rh, Pd, Os, Ir, and Pt).8 FeSi4P4 (FSP) was identified as non-centrosymmetric by single X-ray diffraction and Raman spectroscopy. The magnetic analysis shows that it is paramagnetic at low temperatures (15–65 K) and diamagnetic at high temperatures.8,15,16 Thus, it may be interesting that FSP is paramagnetic at low temperatures and transforms into a diamagnetic state at high temperatures. The thermal properties and electrical properties of the bulk crystal have not been reported.

In this study, to the best of our knowledge, large sized and high quality FSP crystals have been grown for the first time by a seeded flux growth method. Furthermore, the magnetic, electrical, and thermal properties of the bulk FSP crystal have been investigated.

Experimental

Crystal growth of FSP

Due to high saturated vapour pressure of phosphorus, silica ampoules tend to explode at high temperatures. The FSP compound is very difficult to be synthesized using a direct solid-state reaction method, and Sn flux growth method is adopted since it does not require special high pressure equipment and requires relatively low growth temperature. High purity elemental ferrum (4N), silicon (5N), phosphorus (6N), and tin (5N) were used as the starting materials. These starting materials were sealed in an evacuated silica ampoule with a ratio of Fe[thin space (1/6-em)]:[thin space (1/6-em)]Si[thin space (1/6-em)]:[thin space (1/6-em)]P[thin space (1/6-em)]:[thin space (1/6-em)]Sn = 1[thin space (1/6-em)]:[thin space (1/6-em)]4[thin space (1/6-em)]:[thin space (1/6-em)]4[thin space (1/6-em)]:[thin space (1/6-em)]6.15 The furnace was rapidly heated to 1150 °C at a rate of 48 °C per h and slowly cooled down to 1075 °C at a rate of 1 °C per h.17 Small grains of FSP were acquired after the Sn flux was diluted in concentrated hydrochloric acid. Subsequently, the seeded flux growth method was used to obtain bulk crystals. The silica ampoule was reloaded by powder starting materials with the same proportions of Fe[thin space (1/6-em)]:[thin space (1/6-em)]Si[thin space (1/6-em)]:[thin space (1/6-em)]P[thin space (1/6-em)]:[thin space (1/6-em)]Sn = 1[thin space (1/6-em)]:[thin space (1/6-em)]4[thin space (1/6-em)]:[thin space (1/6-em)]4[thin space (1/6-em)]:[thin space (1/6-em)]6. Particularly, a few small FSP crystals were used as seeds, and excessive phosphorus (∼1 g) was places in the growth ampoule. The ampoule was sealed off at a vacuum of 5 × 10−4 Pa. It was then placed in a pit furnace controlled by a temperature controller (SHIMADEN FP23). Typically, the heating program of the furnace was set as follows: first, rapid heating was carried out to 1150 °C at a rate of 48 °C per hour; when the temperature was close to 1150 °C, the heating rate was reduced to avoid overheating of the melt; for decomposition, the temperature was 1157.1 °C, as observed via the DSC and TG analysis (see Fig. S3†). The FSP was already saturated in the Sn solution at 1150 °C. Second, maintaining the temperature at 1150 °C for 20 hours was necessary to ensure complete reaction. Finally, the furnace was cooled down to around 1075 °C at a rate of 0.4 °C per hour. The ampoule was then taken out of the furnace at 1075 °C. After cooling down to room temperature, large sized and high quality bulk FSP crystals were obtained after removing the remaining tin using diluted hydrochloric acid. A small crystal obtained using the standard Sn flux method in our experiment is shown in Fig. 1(a). The crystal size is about 3 × 2 × 1 mm3. The crystal grown by the seeded flux method, as shown in Fig. 1(b), is about 8 × 7 × 3 mm3. In Perrier's work, crystals with the dimensions of about 0.2 × 0.2 × 0.2 mm3 were obtained. These small crystals were used for single crystal X-ray diffraction.
image file: c7ra08118a-f1.tif
Fig. 1 The images of the bulk FSP single crystals (a) obtained using the standard flux method, and (b) seeded flux method. Each small square is 1 mm2.

X-ray diffraction (XRD) characterization

Powder X-ray diffraction was performed using a Bruker-AXS D8 ADVANCE X-ray diffractometer equipped with a diffracted beam monochromator set for Cu Kα radiation (λ = 1.54056 Å) in the range of 20–80° (2θ), with a step size of 0.02° and a step time of 0.04 s at room temperature. The single-crystal X-ray diffraction data was obtained by a Bruker APEX-II SMART CCD diffractometer equipped with a D8 goniometer at room temperature using graphite-monochromated Mo Kα radiations of λ = 0.71073 Å within the ω scan method. From three sets of frames, initial lattice parameters and orientation matrices were determined. Data integration and cell refinement were performed through the INTEGRATE program in the APEX-II software, and numerical face-indexed absorption corrections were adopted using the SCALE program for the area detector.18 A prism-shaped crystal with dimensions of 0.13 × 0.07 × 0.03 mm3 knocked off a large crystal that was mounted on a glass fibre with epoxy. The structural model was refined using the SHELXL-97 routine (Sheldrick, 1997). Crystal data and refinement summaries are presented in Table 1.

Table 1Crystal data and structure refinement for FSP

Chemical formulaFeSi4P4
Crystal systemTriclinic
ColorBlack
Space groupP1
Temperature (K)296
a (Å)4.892 (16)
b (Å)5.5790 (18)
c (Å)6.10 (2)
α (°)85.25 (3)
β (°)68.07 (3)
γ (°)70.07 (3)
V (Å3)145.10 (8)
Z1
F(000)142
ρ (g cm−3)3.344
Radiation typeMo Kα, 0.71073 Å
θ3.6–27.6°
μ (mm−1)4.39
Crystal size (mm)0.27 × 0.21 × 0.17
S1.19
R [I > 2σ1]a0.0230
wR [I > 2σ1]a0.0538
Reflections/parameters1312/83


High-resolution X-ray diffraction (HRXRD)

Herein, two (100) and (001)-faced FSP wafers with the dimensions of 4 × 4 × 1 mm3 were used for HRXRD characterization. The opposite surfaces were mechanically polished carefully. HRXRD was performed using a Bruker-AXS D5005HR diffractometer with a four-crystal Ge (220) monochromator set for Cu Kα radiation (λ = 0.15419 nm). The step size and step time were 0.001° and 0.1 s, respectively. The setting of the generator was 40 kV and 20 mA.

Raman spectrum

The Raman spectrum was obtained in a back-scattering configuration by an HR 800 system from Horiba Jobin Yvon at room temperature, and a 632 nm laser was used as the excitation source. A polished crystal plate with the dimensions of 5 × 3 × 1 mm3 was used.

Energy-dispersive X-ray spectroscopy

A small crystal with the dimensions of about 2.5 × 2 × 1.5 mm3 was cut, and energy-dispersive X-ray spectroscopy (EDS) analysis was performed using a field emission scanning electron microscope (FE-SEM, Hitachi S-4800) with an energy dispersive X-ray spectrometer (EDS, Horiba EMAX Energy EX-350). The accelerating voltage was 15 kV.

X-ray photoelectron spectroscopy (XPS)

X-ray photoelectron spectra were obtained using an ESCALAB 250 spectrometer (Thermo Fisher Scientific) with monochromatized Al Kα X-ray radiation (1486.6 eV) under ultrahigh vacuum (<10−7 Pa). The size of the f.lux 4.75 Key - Crack Key For U spot was 500 μm. A survey scan has been applied from 0 eV to 1050 eV. A crystal wafer with the dimensions of 2.5 × 3 × 1.5 mm3 was used for XPS characterization.

Thermal properties

Thermal gravity analysis (TGA) and differential scanning calorimetry (DSC) were carried out using a TGA/DSC 1/1600HT analyser (Mettler-Toledo Inc.) using high purity argon as a protective atmosphere, and the gas flow rate was 80 ml min−1. The samples were placed in an alumina crucible and heated from room temperature to 1200 °C at a rate of 10 °C min−1. Thermal mechanical properties were investigated via TMA/SDTA 840 (Mettler-Toledo Inc.) using argon as an protective atmosphere, and the gas flow was 70 ml min−1. The thermal conductivity was carried out using a laser conductometer (NETZSCH LFA457 MicroFlash). Shadowsocks 4.4.0.0 Free Download With Crack, three FSP wafers along the directions a, b, and c were cut and roughly polished.

Magnetic properties

The data of magnetic properties were obtained using SQUID (superconducting quantum interference device, Quantum Design Inc.). The magnetic field was set as 1000 Oe. The temperature range was 5–300 K. FSP powder was prepared after being finely ground and soaked in diluted hydrochloric acid for two hours.

Hall effect and piezoelectric properties

The FSP crystal plates were cut into 4 × 4 × 1 mm3. The electric properties data are obtained by the Hall effect with a magnetic field intensity of 3 tesla and current intensity of 1 mA using the SQUID equipment.

Results and discussion

The single crystal data and structural parameters are presented in Table 1. Fractional atomic coordinates, equivalent isotropic displacement parameters, atomic displacement parameters, bond lengths, and bond angles of the FSP crystal are listed in Tables S1–S4 (see in ESI†).

FSP crystallizes in a triclinic crystal system, with the space group P1 (no. 1). In the crystal structure, Fe atoms are octahedrally serato dj pro activation code free - Free Activators by P and Si atoms, as shown in Fig. 2(d). The P and Si atoms are tetrahedrally surrounded by Fe, P, and Si atoms. The non-metallic atom tetrahedron [SiP4] overlaps with [PSi4] tetrahedron, as shown in Fig. 2(c). The lengths of Si–P bonds in orthorhombic SiP2 range from 2.2378(8) to 2.3217(14) Å.19 The length of the Si–P bond in CdSiP2 is 2.2469(7) Å.1 Our results show that the lengths of Si–P bonds in FSP range from 2.2303(62) to 2.3233(72) Å, which is comparable with those in orthorhombic SiP2 and CdSiP2. The [FeSi3P3] octahedrons and overlapped tetrahedrons are linked by shared corners, as shown in Fig. 2(a) and (b).


image file: c7ra08118a-f2.tif
Fig. 2 (a) FSP crystal structure viewed along the b-axis. (b) FSP crystal structure viewed along the c-axis. (c) The overlapped [SiP4] tetrahedron and [PSi4] tetrahedron. (d) The [FeSi3P3] octahedron. Red atom represents iron, green atoms represent phosphorus, and blue atoms represent silicide. Light red octahedrons represent [FeSi3P3] octahedrons, green tetrahedrons represent [SiP4] tetrahedrons, and light blue tetrahedrons represent [PSi4] tetrahedrons.

The Rietveld refinement pattern of the FSP crystal is shown in Fig. 3. As can be seen from Fig. 3, the experimental data are in good agreement with the calculated data according to the crystal structure of FSP. Global R factors are Rp = 4.08% and Rwp = 5.40%, where


Rwp = ∑[Wi(Yoi − Yci)2/∑WiYoi2]1/2
Yoiand Yciare the experimental and calculated intensities at each point in the pattern, respectively. Wiis the weight assigned to each step intensity.20,21The relatively low values of Rpand Rwpindicate that a high purity and single phase of FSP has been obtained.
image file: c7ra08118a-f3.tif
Fig. 3 Rietveld refinement pattern for the sample FSP crystal. Experimental (red dot), calculated (black line), and their difference (green trace). The tick marks (blue) indicate the positions of the allowed Bragg reflections.

The atom ratio of the FSP crystal is presented in the EDS spectrum (Fig. S1†). The ratio of Fe[thin space (1/6-em)]:[thin space (1/6-em)]Si[thin space (1/6-em)]:[thin space (1/6-em)]P is 11.08[thin space (1/6-em)]:[thin space (1/6-em)]43.16[thin space (1/6-em)]:[thin space (1/6-em)]45.77, which is approximately 1[thin space (1/6-em)]:[thin space (1/6-em)]4[thin space (1/6-em)]:[thin space (1/6-em)]4. No Sn flux was detected.

Since the structure symmetry of transition-metal silicon phosphides cannot be completely determined only by diffraction methods, Raman spectroscopy was performed to confirm the symmetry of FSP (Fig. 4). Herein, 23 Raman modes in the spectrum indicate the non-centrosymmetric structure of the FSP crystal.8 The lower-wavenumber Raman spectral lines, such as 250 cm−1, are more intense. The low-wavenumber modes are assigned to the relative motions of octahedron [FeSi3P3] and tetrahedron ([SiP4] and [PSi4]) units. The Raman shifts of FSP are in good agreement with the previous results.15 The narrow and well-defined peaks of the Raman shift as well as the low level of background proved the general good quality of the as-grown crystals.


image file: c7ra08118a-f4.tif
Fig. 4 Raman spectrum of the FSP crystal. There are 23 active modes in the spectrum, meaning that the FSP is non-cantered symmetric.

HRXRD was performed to characterize the quality of the FSP crystal. The rocking curves of the (100) and (001)-FSP crystal are shown in Fig. 5. The peaks are symmetrical, and the full-width at half-maximum (FWHM) are 36′′ and 46′′, respectively. This indicates that the as-grown crystal is of high quality and integrity.


image file: c7ra08118a-f5.tif
Fig. 5 Rocking curves of the (a) (001) and (b) (100)-FSP crystal. The acute and symmetric Rocking curves indicate that the as-grown FSP crystal is of high quality and integrity.

Thermal properties of FSP are shown in Fig. 6, 7, and S2.† A sharp fall in the DSC curve is observed at about 1157.1 °C together with the dramatic weight loss (Fig. S2†), indicating the decomposition of FSP. To protect the instrument, the instrument was shut down quickly when FSP started to decompose. In our crystal growth process, the actual growth temperature is 5–10 °C lower than the decomposition temperature. After several experiments, the optimum growth temperature was 1150 °C. When the temperature is up to 1150 °C, the solution system reaches saturation. FSP crystallizes on the surface of seed crystals and grows continuously with the decreasing temperature.


image file: c7ra08118a-f6.tif
Fig. 6 Thermal conductivity curves of the FSP single crystal along the directions a, b, and c. The thermal conductivity of the FSP crystal descends with the increasing temperature.

image file: c7ra08118a-f7.tif
Fig. 7 Thermal expansion of the FSP crystal along the directions a, b, and c. It can be seen that the expansion ratio is almost linear over the entire measured temperature range, and the FSP crystal exhibits expansion upon heating.

To investigate the thermal properties of the FSP crystals, three wafers along the a, b, and c directions were prepared. Thermal expansion and thermal conductivity curves along the three directions are presented in Fig. 6 and 7. Thermal conductivity is calculated by the following formula:

κ, λ, ρ, and Cprepresent thermal conductivity, thermal diffusivity, the density of FSP, and specific heat at a constant pressure, respectively. The values of thermal diffusivity and constant pressure specific heat can be directly measured by the laser conductometer. This phenomenon indicates that the FSP crystal can tolerate more thermal load at room temperature. The thermal conductivity of FSP at room temperature is about 35 W (m K)−1, which is approximate to that of ZnGeP2(35 W (m K)−1) and is higher than that of CdSiP2(13 W (m K)−1). Both ZnGeP2and CdSiP2are excellent mid-infrared nonlinear optical materials with high thermal conductivity.1,22Other physical properties of FSP will be researched in our upcoming study. The solid lines in Fig. 7are the thermal expansion ratio curves along the crystallographic axes. The average linear thermal expansion coefficient for the crystallographic directions can be calculated according to the following formula:
image file: c7ra08118a-t1.tif
where αis the average linear thermal expansion coefficient in the temperature range from T0to T, L0is the sample length at T0, ΔLis DiskGenius Professional License key length change when the temperature changes from T0to T, and the temperature change is ΔT= T− T0.

The values of the average linear thermal expansion coefficients of the FSP crystal in the temperature range from 30 to 600 °C are αa = 7.37 × 10−6 K−1, αb = 8.07 × 10−6 K−1, and αc = 7.37 × 10−6 K−1. The weak anisotropy of the thermal expansion effectively protects the crystal from cracking caused by thermal expansion during crystal growth, processing, and applications.

The X-ray photoelectron spectroscopy (XPS) was carried out to determine the chemical valence of each element in the FSP crystal. The result is demonstrated in Fig. 8. No Sn 3d peaks are found in the survey spectrum (Fig. 10(a)). The peaks of O 1s and C 1s are inevitable for the existence of carbon dioxide in the air. The binding energy (BE) of C 1s is set as 284.6 eV.23 The peaks of Sc 2s, Sc 2p, Ca 2s, and Ca 2p are also labeled out in the spectrum. These impurities may originate from the instrument.


image file: c7ra08118a-f8.tif
Fig. 8 (a) Survey, (b) Fe 2p, (c) Si 2p, and (d) P 2p spectrum of the FSP single crystal.

Fig. 8(b)–(d) show the profile of Fe 2p, Si 2p, and P 2p, respectively. The Fe 2p spectrum shows two peaks with the mean BE values at 712.8 eV and 724.3 eV, assigned to the Fe 2p3/2 and Fe 2p1/2 peaks, respectively. The valence state of Fe contains mixed +2 and +3 states.24–27 The Si 2p peak can be clearly resolved into two splitting spin–orbits with the BE values of 103.5 eV and 104.2 eV, corresponding to the BE of Si 2p3/2 and Si 2p1/2, respectively. The single Si 2p peak in XPS indicates that there is only one oxidation state of Si in FSP. However, the accurate value of Si valence cannot be confirmed. The certain value of Si valence is in the range from +2.25 to +2.5.28–30 The P 2p spectrum can also be resolved into two spin–orbits of 129.2 eV and 130.1 eV, corresponding to the BE of P 2p3/2 and P 2p1/2, respectively. This could be attributed to P3− in FSP.31,32

The magnetization–temperature (M–T) curve (Fig. 9(a)) and the magnetization–field curve (Fig. 9(b)) of FSP reveal that the FSP crystal is paramagnetic in the temperature range of 5–300 K. The M–H curve is a straight line crossing the origin of coordinates, and the value of the magnetization is above 0 and far less than 1. There is no phase transformation at low temperatures in this study. This is obviously different from the research suggesting that FSP is paramagnetic at f.lux 4.75 Key - Crack Key For U temperatures (15–65 K) and diamagnetic at high temperatures.8 As is known, metal Sn is a type of diamagnetic material. Trace amounts of Sn are sufficient to influence the magnetic performance.33 Therefore, trace amounts of Sn impurity in FSP might lead to the paramagnetic–diamagnetic phase transition at low temperatures. To figure out the reason of these contrary results, the FSP samples without being soaked in dilute hydrochloric acid were also investigated by XPS and SQUID.


image file: c7ra08118a-f9.tif
Fig. 9 (a) M–T curve of the FSP single crystal at a magnetic field strength of 1000 Oe. (b) M–H curve of FSP single crystal at a temperature of 5 K.

As shown in Fig. S3,† there were trace amounts of Sn in the FeSi4P4 crystal when it was not soaked in dilute hydrochloric acid. Fig. S4† indicates that paramagnetic FSP transforms into diamagnetic FSP at a low temperature of 70 K; this agrees well with the reported results. Therefore, we believe that there is no paramagnetic-to-diamagnetic transition in the pure FSP crystal. However, the transition reported in the previous study8 might be due to the trace amounts of Sn in the crystal.

As shown in Fig. S5,† the experiment data fit the Curie–Weiss law with an additional temperature-independent term χ0,

The values of the Curie constant (C), Weiss constant (θ), and χ0 are 9.71 × 10−4 emu K (g Oe)−1, −4.43 K, and 2.08 × 10−6 emu (g Oe)−1, respectively. The value of Peff is calculated to be 1.51 μB using the Curie constant.34 The reason for this value is likely to be the hybridization between Fe 3d states and Si 3sp states in the FSP crystal, similar to that observed in the Mn1−xNixAl alloys.35 In addition, the high carrier concentration can lead to the decrease in Peff.36,37 A small and negative Weiss constant indicates an antiferromagnetic exchange in FSP.38 The antiferromagnetic exchange is associated with the interaction between Fe3+ and Fe2+.39

The temperature-dependent resistance, carrier concentration, and carrier mobility curves are shown in Fig. 10. The FSP crystal is a p-type semiconductor with a resistivity of 0.124 Ω cm and a small gap energy of 0.15 eV at room temperature.8 The carrier concentration is 1.83 × 1019 cm−3 at room temperature, and it decreases markedly with the decreasing temperature. The carrier mobility decreases and the resistance increases as the temperature decreases. FSP has a relatively high conductivity (35 W (m K)−1), which is comparable to that of the well-known mid-infrared nonlinear optical ZnGeP2.40 The resistivity of FSP (0.124 Ω cm) is smaller than that of many other phosphorus silicides, such as RhSi3P3 (0.15 Ω cm) and CoSi3P3 (0.62 Ω cm), at room temperature.8 Many researches indicate that certain kinds of transition metal phosphorus silicides have outstanding applications in integrated circuits technology because of their low resistivity and high thermal conductivity.8,41 Other physical properties of FSP, such as optical properties, will be investigated in future.


image file: c7ra08118a-f10.tif
Fig. 10 The curves of temperature-dependent (a) carrier concentration, (b) carrier mobility, and (c) resistance.

Conclusions

Herein, the bulk FSP crystal with dimensions up to 8 × 7 × 3 mm3 was successfully obtained using a seed flux method. Single-crystal X-ray diffraction results show that FSP crystallizes in the chiral space group P1 (no. 1) with the cell parameters a = 4.892(16) Å, b = 5.579(18) Å, c = 6.10(2) Å, α = 85.25(3)°, β = 68.07(3)°, and γ = 70.07(3)°, and the final R value is 0.023. Its thermal conductivity is high up to 35 W (m K)−1 at room temperature. The experimental results indicate that there is no paramagnetic-to-diamagnetic transition in the pure FSP crystal. However, the transition reported in literature might due to the trace amounts of Sn in the crystal. The carrier concentration of FSP is 1.83 × 1019 cm−3, the electric resistivity is 0.124 Ω cm, and the carrier mobility is 4.75 cm2 (V s)−1. FSP is a promising candidate for p-type conductor at room temperature.

Conflicts of interest

There are no conflicts to declare.

Acknowledgements

This work was partly supported by the National Natural Science Foundation of China (Grant No. 51572155, 51321091), the National Key Research and Development Program of China (Grant No. 2016YFB1102201), the Shandong Provincial Natural Science Foundation, China (ZR2014EMM015), and the Independent Innovation Foundation of Shandong University, IIFSDU.

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Footnote

† Electronic supplementary information (ESI) available. See DOI: 10.1039/c7ra08118a

This journal is © The Royal Society of Chemistry 2017
Источник: https://pubs.rsc.org/en/content/articlehtml/2017/ra/c7ra08118a

Integration of transcription and flux data reveals molecular paths associated with differences in oxygen-dependent phenotypes of Saccharomyces cerevisiae

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Abstract

Background

Saccharomyces cerevisiae is able to adapt to a wide range of external oxygen conditions. Previously, oxygen-dependent phenotypes have been studied individually at the transcriptional, metabolite, and flux level. However, the regulation of cell phenotype occurs across the different levels of cell function. Integrative analysis of data from multiple levels of cell function in the context of a network of several known biochemical interaction types could enable identification of active regulatory paths not limited to a single level of cell function.

Results

The graph theoretical method called Enriched Molecular Path detection (EMPath) was extended to enable integrative utilization of transcription and flux data. The utility of the method was demonstrated by detecting paths associated with phenotype differences of S. cerevisiae under three different conditions of oxygen provision: 20.9%, 2.8% and 0.5%. The detection of molecular paths was performed in an integrated genome-scale metabolic and protein-protein interaction network.

Conclusions

The molecular paths associated with the phenotype differences of S. cerevisiae under conditions of different oxygen provisions revealed paths of molecular interactions recuva crack 2019 - Free Activators could potentially mediate information transfer between processes that respond to the particular oxygen availabilities.

Background

The transcriptome is a realization of the genome of an organism whereas the fluxes are an ultimate response of the complete multilevel regulatory system of a cell. The correlation between the transcriptome and the fluxes is usually weak [1] since a substantial part of the regulation of cell physiology occurs at the post-transcriptional and metabolic levels [2]. The regulation is mediated by interactions beyond individual levels of cell function. Active paths of regulatory interactions which determine the cell phenotype are concealed in data on cell components belonging to different regulatory levels. Integration of these data to frameworks of known interactions of multiple types could allow for a reconstruction of the regulatory paths associated with specific phenotypes. Genome-scale metabolic models build on the entity of metabolic enzyme encoding genes in the genome. These models are already available for various organisms and provide frameworks of metabolic interactions to the extent of whole cells. Metabolic network context is being utilized to identify transcriptionally differentially regulated pre-defined pathways of enzymes sharing metabolites as substrates and products by parametric gene set enrichment analysis [3]. Full interconnectivity of metabolism is being applied in the identification of reporter metabolites, regulatory hot spots around which the most significant transcriptional changes have occurred [4]. Protein-protein interactions facilitate various kinds of information transfer, e.g. a change in a localization or activity of a protein as a result of physical interaction or post-translational modification [5–7]. In particular, protein kinases serve as key regulators of nutrient sensing and signaling via protein-protein interactions. A network of interactions of key protein kinases of nutrient dependent regulation has been mapped, manually curated and annotated for the eukaryotic model organism S. cerevisiae[8]. A global network of protein kinase and phosphatase interactions that mediate information transfer via post-translational modifications is also available for S. cerevisiae[9] along with a large-scale data set on various types of physical protein-protein interactions [10].

Even other types of biochemical interactions, such as signaling and transcription factor interactions, also allow for communication between cellular components [11, 12].

Previously, a graph-theoretical method called Enriched Molecular Path detection (EMPath) was developed in order to identify molecular interaction paths from multi-level interactome data [13]. The EMPath method was an extension of a “color coding” algorithm [14] which had earlier been used to detect signaling cascades based on edge reliabilities in protein-protein interaction networks [15] and more general structures, such as trees [16]. The developed EMPath method was applied to detect phenotype specific molecular paths in type 1 diabetes mouse models in an integrated network of metabolic, protein-protein and signal transduction interactions scored with transcription data [13]. Recently, several graph theoretical methods for detection of molecular paths in an interaction network context have been developed. Gene Graph Enrichment Analysis (GGEA) integrates a known gene regulatory network in an analysis of transcription data and gains interpretability of the regulation processes underlying the gene expression response [17]. FiDePa (Finding Deregulated Paths) [18] and Topology Enrichment Analysis frameworK (TEAK) [19] find differentially expressed pathways between two cell phenotypes in signaling or regulatory networks and metabolic pathways, respectively. A method called Clipper exploits network topology to detect signaling paths within longer pathways based on differential gene expression between two phenotypes [20]. However, all these methods employ a single type of phenotypic information (i.e. transcription data), whereas post-transcriptional regulation has a recognized and substantial effect on a phenotype. Therefore, the EMPath method was extended in this study to enable integrative simultaneous utilization of two data types, i.e. transcription and flux data in the context of a multi-level interaction network to detect enriched molecular paths associated with phenotypic differences.

Oxygen is a major determinant of physiology for the eukaryotic model organism S. cerevisiae. S. cerevisiae is able to remodel its energy generation and redox metabolism according to the availability of oxygen in such a flexible way that it can grow under a wide range of oxygen availabilities from fully aerobic conditions to anaerobiosis. Characterization of the oxygen-dependent phenotypes of S. cerevisiae has previously been reported at the individual transcriptional, metabolite, and flux levels [21–23]. In this study, two case-control settings of the oxygen dependent phenotype differences of S. cerevisiae were defined. The phenotype under conditions of 20.9% O2 provision was compared to the phenotype under conditions of 2.8% O2 provision, and the phenotype under conditions of 2.8% O2 provision was compared to the phenotype under conditions of 0.5% O2 provision. Previously, it was noted that S. cerevisiae had highly similar flux distributions under conditions of 20.9% and 2.8% O2 provision [23], but interestingly there were substantial differences in the transcriptomes [21]. The phenotypes of S. cerevisiae possessed substantially different flux distributions under conditions of 2.8% and 0.5% O2 provision [23], whereas the transcriptomes of the phenotypes were surprisingly similar [21]. Thus, transcription and flux data were integratively utilized to find enriched molecular interaction paths associated with the aforementioned differences in the previously observed oxygen-dependent phenotypes [21–23]. The path detection was performed in a combined network of metabolic [24–26] and protein-protein interactions (Search Tool for the Retrieval of Interacting Genes database (STRING): [27]) of S. cerevisiae.

Methods

Overview

Figure 1 illustrates the overall pipeline of the study. First, a genome-scale metabolic network model and the protein-protein interactions including the global kinase-phosphatase interactions [9] were integrated into a single interaction network. Then, flux and transcription data were assigned to node weights to set the network into a phenotypic context. Then, the EMPath method was used to detect enriched up- and down-regulated molecular interaction paths within the network. In the end, the paths were visualized as integrated networks and enriched with previously known functional categories.

Overall workflow of the study comprising the following main steps. • genome-scale metabolic network model and protein-protein interactions, including kinase-phosphatase interactions, were integrated into single network representation. • phenotypic context from fluxome and transcriptome data incorporated into the network. • EMPath used for detecting up-and down-regulated paths. • detected paths were visualized and enriched with previously known functional categories.

Full size image

Network representation

The integrated network of metabolic and protein-protein interactions comprised of a recently refined version [24] of the yeast whole genome metabolic model, protein-protein interactions from the STRING database [27], and a kinase-phosphatase interaction network [9]. From the STRING database the protein interactions with an experimental score greater than 900 were included, thus excluding interactions with low experimental evidence. The integrated network representation is illustrated in Figure 1. In this representation the metabolic reactions of the genome-scale model [24] are nodes and there is an edge between two reactions if they share a metabolite, i.e. having either a common substrate or product. Cofactors and other metabolites not participating in the metabolic conversions with their carbon backbone were excluded from the network. The excluded metabolites are listed in Additional file 1. All edges were modeled with undirected edges. Each reaction comprised a set of gene(s) that encodes an enzyme that catalyzes the reaction. Protein-protein interactions were integrated with nodes representing enzymatic reactions if the metabolic enzymes had reported protein-protein interactions. In total, the whole integrated network comprised 5 702 nodes and 41 525 edges.

Transcription and flux data

Wiebe et al. (2008) grew S. cerevisiae in glucose-limited chemostat cultivations at a dilution rate of 0.1 h-1 under different oxygenation conditions (i.e. 20.9%, 2.8%, 1.0% and 0.5% O2) in the chemostat inlet gas to obtain the oxygen dependent phenotypes [22]. Rintala et al. (2009) performed the analysis of the transcriptomes of S. cerevisiae under the different conditions of oxygen provision [21]. The normalized transcription dataset was stored in the Gene Expression Omnibus (GEO) database [28] with the accession number GSE12442. In the present study, all four replicates of transcription data from each of the steady state cultures with 20.9%, 2.8%, and 0.5% O2 in the chemostat inlet gas were used to determine the transcription scores for the detection of molecular paths.

Genome-scale flux distributions were sampled from the Mixcraft Pro Studio License key space of a genome-scale metabolic model of S. cerevisiae by Monte Carlo sampling using Artificial Hit-And-Run (ACHR) sampler [29]. Prior to the sampling, the genome-scale metabolic model of S. cerevisiae was improved by further refinement of its oxygen dependent metabolism [24] (Additional file 1). The model was also constrained with P/O ratios dependent on a specific oxygen uptake rate (OUR) [23] and experimental data reported on extracellular fluxes, i.e., growth rate, substrate consumption rates and product secretion rates [22]. The Carbon Evolution Rate (CER), resulting from carbon dioxide production at various sites in metabolism, was allowed to vary freely to introduce flexibility to the system since the remaining secretion rates were set to zero. However, the introduction of the exact experimental rate constraints resulted in an infeasible solution space. Thus, the lower and upper bound constraints derived from the extracellular growth, glucose uptake, and ethanol secretion rates were simultaneously and gradually expanded until a feasible flux solution existed. At each step the constraints were expanded with 10% of the particular SEMs (Standard Error of the Mean) of the extracellular rates [22] (see Additional file 1 for the final constraints). OUR and P/O ratio constraints were kept as strict constraints since the oxygen uptake rates followed from the provision of oxygen in the chemostat inlet gas, which was the only experimental parameter changed in the bioreactor cultivations resulting in the three different Balsamiq Mockups 3.5.8 Crack + Keygen Full Download Free of S. cerevisiae[22] that were investigated in this study. Further, P/O ratios of S. cerevisiae dependent on OUR were previously determined [23] and used here. The Monte Carlo sampling of flux distributions was performed with the ACHR sampler [29] implemented in the COBRA Toolbox [30]. A threshold for the reactions with non-zero fluxes was set to a minimum of 10-7 mmol/(g CDW h). Zero fluxes were assigned to the rest of the reactions. A total of 10 000 feasible points were collected in the solution space out of which 2 000 samples were randomly selected for the calculation of mean fluxes. The mean values of unconstrained CER in the flux distribution samples differed from 4% IntelliJ IDEA Crack 2020.3.1 13% from the experimental values.

Combining network and phenotypic data

Previously, only transcription data was used as phenotypic data in the detection of enriched molecular paths [13]. Here the EMPath method was extended for integrative utilization of transcription and flux data having separate weights: w(trans), and w(flux), respectively. More specifically w(trans) is defined in Formula (1) in which trans - intensity(case) and trans - intensity(control) are case and control intensities of mRNA expression level averaged over all replicates, respectively. In the genome-scale metabolic model of S. cerevisiae the gene regulatory rules are expressed by AND-and OR-operands for the metabolic reactions (e.g. multi-protein complex as catalyst) that have more than one encoding gene [25]. If there was an OR-operand between two genes, then a mean intensity was calculated and if there was an AND-operand, then a minimum intensity was taken. Since there is no transcriptome data for non-enzymatic reactions (i.e. they do not require a catalyzing enzyme or an encoding gene to occur), neutral weights (i.e. zero) were assigned for them.

(1)

The weight derived from the flux data for each reaction, w(flux), is defined in Formula (2) in which winthruster activation serial number - Activators Patch and flux (control) were obtained by calculating averages over the 2 000 randomly selected samples, each corresponding to a feasible flux distribution (see Transcription and Flux data above).

(2)

The total score for the node is defined in Formula (3). When the two data types were simultaneously used, w(trans) and w(flux) were scaled to be in the same interval, which was essential to prevent either of them from being over-represented in the detected molecular paths. In practice, the flux data was scaled to have the same range as the transcription data: {-2.71, 4.75} for 2.8% vs. 0.5% oxygen in the bioreactor inlet gas and {-3.31, 4.97} for 20.9% vs. 2.8% in the bioreactor inlet gas. Flux data was naturally not available for signaling proteins (i.e. non-metabolic proteins), thus their scores were calculated solely from the transcription data.

(3)

The motivation of using parameter a was to allow for relative weighting for the flux and transcription data in the detection of molecular paths e.g. weighting with pure transcription data: a = 0, or pure flux data: a = 1, or their simultaneous utilization with an equal weight: a = 0.5.

Molecular path detection

After the weights were assigned to the nodes, the EMPath method [13] was used to detect an optimal path of length k. The algorithm is initialized by assigning colors, i.e. random integer numbers [1, k], to the nodes of the path. Then a node with a maximum weight score is added to be the first node in the path. Then the neighboring nodes to the recently added node reflector 2 windows considered to be the next node in the path. From this set a node with a maximum weight score is added to the path but nodes with a color that is already included in the path are ignored. Nodes are added until there are k nodes in the path. Then a score of the path is calculated by summing up all the node weights.

In order to calculate the p-value for the null hypothesis (i.e. that the detected path is obtained by chance), a random distribution was created by shuffling the node weights 1 000 times. After each shuffle, a path was detected and its score was calculated as described above. In this way, 1 000 optimal path scores in a random network were obtained resulting in a random distribution. A p-value for the null hypothesis that the detected path is obtained by chance was defined by comparing its score to the random distribution. 0.025 was used as a cut-off p-value, i.e. paths of higher p-values were not considered significant. A network was considered harvested from optimal paths if there were i consecutive iterations in which the detected path was detected during previous iterations.

The path detection was performed separately for up-and down-regulated paths in both case-control comparisons (20.9% vs. 2.8%, and 2.8% vs. 0.5% O2 in the bioreactor inlet gas), and for each value of parameter a∈ {0, 0.5, 1}. When the up-regulated paths were detected, case-control ratios were used, and when the down-regulated paths were detected, control-case ratios were used. Eight (8) was used as the path length k. There is not any rigorous way to define the proper value for this parameter. Eight (8) was empirically found to be a proper value for this parameter: smaller values (e.g. 7) led to too sparse combined networks of enriched molecular paths and higher values (e.g. 9) led to very dense combined networks of enriched molecular paths which would have had poor interpretability. In similar vein, ten (10) was selected for parameter i on empirical basis: the higher values did not harvest the network significantly more thoroughly. The path detection calculations were implemented in a C++ environment and were processed on an Ubuntu Linux Server with 2 processors of Intel Xeon X5650 2.66 GHz divided in 24 virtual cores and 70 GB of RAM memory.

Enrichment of functional protein categories

In order to study how pre-established cellular functions were associated with the detected molecular paths, the combined networks were associated with functional protein categories from FunCat [31] by making a hypergeometric test with controlling false discovery rate (FDR) [32] q-value 0.05 as a cut-off, as described in [13]. Open reading frame identifiers (ORF) were used to identify the genes.

Path length

The method required a selection of pre-defined path length, which is heuristic and deserves some discussion. Let us assume that the network comprises n nodes, and for simplicity they are assumed to be fully connected to each other. In this case the network comprises paths of length  k, in which  nk. The higher the length k is the more paths the network comprises. Thus, a too small path length would lead to information poor networks. On the other hand, a drawback of a long path length is that the computational enumeration and the interpretation of crowded combined networks gets heavy. Eight was selected as the path length since it is the shortest length that provides paths which reasonably combine both metabolic and protein-protein interactions in all the studied cases.

Results and discussion

Effect of relative weighting of transcription and flux data on the detected molecular paths

The detected molecular interaction paths combined protein-protein interactions and metabolic interactions dependent on the phenotypes compared and the relative weighting used to combine the transcription and flux data. The numbers of protein-protein interactions (PPI) and metabolic edges in the combined networks of the detected molecular paths for each of the phenotype comparisons are shown in Table 1. Metabolic edges prevailed when a = 1 (i.e. only flux data used) in all comparisons except “2.8% vs. 0.5%, down” where there were as many PPI edges as metabolic edges. When the metabolic edges prevailed the detected paths generally followed the metabolic routes in which the fluxes had changed substantially. The neighboring metabolic reactions had correlated flux weights as the result of the steady state flux data being constrained by metabolic network stoichiometry. There were two comparisons (“2.8% vs. 0.5%, down” and “20.9% vs. 2.8%, down”) in which PPI edges prevailed when a = 0 (i.e. only transcription data used) indicating that in these comparisons metabolic pathways were less coherently transcriptionally down-regulated than the paths following protein-protein interactions.

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Peroxisomal activities and oxidative stress response featured in the upregulated interaction paths of phenotype differences between the fully respirative phenotype of adobe photoshop 2020 crack reddit - Free Activators. cerevisiae and the respirofermentative phenotype at 2.8% oxygenation

Wiebe et al. (2008) had previously observed that the metabolism of S. cerevisiae was fully respirative under conditions of 20.9% O2 in the bioreactor inlet gas whereas under conditions of 2.8% O2 in the bioreactor inlet gas the metabolic state was respirofermentative [22]. However, the drop in the specific Oxygen Uptake Rate (OUR) was small, from 2.7 ± 0.04 to 2.5 ± 0.04 mmol/(g CDW h) [22] and Jouhten et al (2008) observed that the flux distributions remained almost constant except for the subtle flux to ethanol production [23]. Nevertheless, major changes between the two phenotypes have been observed at the transcriptional level [21]. The transcription and flux data for S. cerevisiae during steady state growth conditions at 20.9% and 2.8% oxygen provision were analyzed here in an integrative manner and separately with the EMPath method to detect molecular interaction paths that were possible determinants of the phenotypic differences observed in S. cerevisiae growing under the two different oxygenation conditions. When transcription data on S. cerevisiae growing under fully aerobic conditions and under conditions of 2.8% O2 in the bioreactor inlet gas was solely used in the scoring of the up-regulated nodes in the detection of molecular interaction paths, cellular processes of respirative metabolism, fatty acid oxidation, and oxidative stress defense were represented in the paths (Figure 2, FunCat enrichments in Additional file 1). Glyoxylate pathway enzyme isocitrate lyase encoded by ICL1 and a dicarboxylate carrier transporting succinate from glyoxylate cycle into mitochondria to be incorporated into TCA cycle encoded by DIC1[33] appeared in the molecular paths up-regulated at the level of gene expression. The glyoxylate cycle is known to be induced in S. cerevisiae under respirative conditions for the metabolism of non-fermentative carbon sources [34]. In addition, the methylisocitrate lyase reaction catalyzed by an enzyme encoded by ICL2, which is homologous to ICL1, was also included in the detected molecular paths. Isocitrate dehydrogenase encoding IDP2 was connected via isocitrate to isocitrate lyase of the glyoxylate cycle. The IDP2 encoded isoform is an alternative source of cytosolic NADPH, for the pentose phosphate pathway, but only while the metabolic state is respirative [35]. Succinate interconnected the glyoxylate cycle components further to SHH3 (YMR118C) (fold change 5.0) encoding a putative mitochondrial inner membrane protein [36]. SHH3 was linked via a protein-protein interaction to ubiquinone-6 dependent succinate dehydrogenase. Succinate dehydrogenase was expectedly the only respiratory Notezilla 8.0.33 Crack Keygen - Free Activators coupled component observed since most of the respiratory chain components in S. cerevisiae are expressed on a lower level under fully aerobic conditions than in conditions of lower oxygen provision [21]. In addition to the respirative metabolism, fatty acid beta oxidation was observed in the detected molecular paths. Beta oxidation of fatty acids occurs in peroxisomes in yeast and provides an f.lux 4.75 Key - Crack Key For U energy source for S. cerevisiae under aerobic conditions. Accordingly, PEX14, which is involved in the import of peroxisomal proteins [37], had protein-protein interactions with the components of fatty acid beta oxidation in the detected paths. Both peroxisome biogenesis and fatty acid beta oxidation are under regulation by SNF1p kinase, a coordinator of energy metabolism of S. cerevisiae[38]. The transcriptional regulation of the peroxisome biogenesis and fatty acid beta oxidation also involves the common regulators ADR1p, OAF1p, and PIP2p. Rintala et al. (2009) showed that the genes involved in fatty acid beta oxidation and peroxisomal biogenesis were expressed at higher levels under the fully aerobic conditions than in conditions of any lower oxygen provision [21]. In the detected molecular interaction paths PEX 14 was further linked to regulators of protein folding (HSP42, SIS1, SSA3) in particular in response to stress, which share a YAP1p binding site [YEASTRACT database July 16, 2013; [39–41]]. YAP1p is a transcription factor responsive to oxidative stress. In the detected molecular paths fatty acid beta oxidation was connected to oxidative stress defense via CTA1 which encodes for a catalase required for the removal of hydrogen peroxide, a strong oxidant, in the peroxisomal matrix. Hydrogen peroxide is formed as a byproduct in the beta oxidation of fatty acids. CTA1p was further linked to a cytosolic my movie download - Crack Key For U reaction involved in the defense against oxidative damage encoded by CTT1 (fold change 4.6) and a hydrogen peroxide reductase reaction that mediates the maintenance of cellular redox balance. Koerkamp et al. (2002) has observed an induction of peroxisomal fatty acid oxidation to trigger transient YAP1p mediated oxidative stress response [42]. However, the transient oxidative stress response did not induce an expression of CTT1 and CTA1 co-responded non-transiently with other genes involved in the peroxisomal functions. Here, the up-regulation of the defense against oxidative agents linked to the up-regulation of peroxisomal activities via molecular interaction paths in S. cerevisiae cells provided with air compared to cells provided with 2.8% oxygen in the chemostat inlet gas, suggests that S. cerevisiae co-regulates these activities. The peroxisomal activities and oxidative stress defense could be down-regulated either directly in response to the decreased oxygen availability though it did not result in substantially lowered oxygen uptake rate (2.7 mmol/(g CDW h) vs 2.5 mmol/(g CDW h) under provision of 20.9% vs 2.8% oxygen, respectively [22]), or in response to the induced fermentative metabolism in cells provided with 2.8% oxygen in the chemostat inlet gas.

Detected up-regulated molecular paths combined into one network, 20.9% vs 2.8%,only transcription data used*.

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Acetyl-CoA synthesis and shuttling were interconnected to the CTT1 encoded catalase and defense against oxidative agents via protein-protein interactions and a guanine nucleotide exchange factor MUK1p which is involved in protein trafficking [43]. MUK1p had a protein-protein interaction to carnitine o-acetyltransferase of the carnitine shuttle which is active both in peroxisomes and in mitochondria. The carnitine shuttle transfers acetyl-CoA across peroxisomal and mitochondrial membranes. CAT2 encodes the carnitine o-acetyltransferase in S. cerevisiae and was coupled to an acetyl-CoA synthetase isoform encoded by ACS1, which is induced under respirative metabolism in S. cerevisiae[44]. ACS1 was down-regulated when 2.8% O2 was provided compared to fully aerobic conditions, even though the metabolism of S. cerevisiae was mainly respirative. The localization of the ACS1 encoded acetyl-CoA synthetase has been very unclear until recently when Chen et al. (2012) confirmed at least a distributed localization of the ACS 1 encoded enzyme between cytosol and peroxisomes [45]. However, ACS1p has also been observed in the mitochondrial proteome [46]. Perhaps the down-regulation of ACS1 in response to the subtle decrease in the oxygen uptake rate under conditions of 2.8% O2 provision was related to a general down-regulation of the peroxisomal activities. Remarkably, the decreased oxygen provision which resulted in a mild decrease in the respiratory activity [21–23] triggered the down-regulation of peroxisomal functions coupled to the fatty acid beta oxidation whereas a respiratory deficiency in an absence of oxygen limitation has been observed to trigger an opposite response, an up-regulation of peroxisomal activities [47].

When both transcription and flux data were used to score the nodes of the network in the EMPath method, the molecular paths up-regulated in the fully respirative phenotype of S. cerevisiae compared to the respirofermentative phenotype observed under 2.8% oxygenation [22] included key enzymes of respirative metabolism i.e. pyruvate dehydrogenase, the gate keeper of the TCA cycle, and citrate synthase (Figure 3, FunCat enrichments in Additional file 1). They were linked to the ACS1 encoded acetyl-CoA synthetase which was observed in the enriched molecular paths when the path detection was run solely with the transcription data. Further connections were observed to the mitochondrial NAD+ dependent and cytosolic NADP+ dependent isoforms of acetaldehyde dehydrogenase encoded by ALD4 and ALD6, respectively [48, 49]. Both the ALD4 encoded isoform and the ALD6 encoded isoform, which is an additional source of cytosolic NADPH, had lower mRNA and protein levels under oxygen limitation than under fully aerobic conditions [21]. The mRNA and protein levels of ALD4 and ALD6 encoded acetaldehyde dehydrogenase isoenzymes correlated within five different conditions of oxygen provision from fully aerobic to anaerobic. Here flux estimation also suggested changes in the fluxes of the reactions catalysed by both isoforms. The succinate dehydrogenase reaction, which is closely coupled to the respiratory chain, showed an altered flux response between the compared conditions and was observed in the detected paths when only the transcription data was used in scoring. However, the glyoxylate cycle components and components involved in the peroxisomal fatty acid beta oxidation were absent from the molecular paths when the flux data was included in the scoring. The glyoxylate cycle is under glucose repression [34] and no in vivo activity of the glyoxylate cycle in S. cerevisiae was previously observed in the 13C-labelling experiments on glucose either under fully aerobic conditions or in 2.8% oxygenation [23].

Detected up-regulated molecular paths combined into one network, 20.9% vs 2.8, both transcription and flux data used*.

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Scoring the nodes of the interaction network solely with flux data resulted in molecular interaction paths dominated by components of sphingolipid metabolism and protein-protein interactions between them (Additional file 2: Figure S1; FunCat enrichments in Additional file 1). Expression of SUR2 and SCS7 encoded hydroxylases involved in the biosynthesis of sphingolipids has been found to be oxygen-dependent [50, 51]. Thus, OUR may have had an effect on the in vivo activity of the sphingolipid biosynthesis pathway. Sphingolipid metabolism has been associated with ageing and apoptosis [52] which were observed in the FunCat enrichments of the detected molecular paths.

Downregulated interaction paths of phenotype differences between fully respirative phenotype of S. cerevisiae and respirofermentative phenotype at 2.8% oxygenation involved regulation of the cell cycle at the transcriptional level

Components of fermentative metabolism, alcohol dehydrogenases in particular, were present in the down-regulated molecular paths in the fully respirative phenotype of S. cerevisiae compared to the respirofermentative phenotype of S. cerevisiae under the 2.8% oxygenation conditions when both transcription and flux data were incorporated into the scores (Figure 4, both transcription and flux data used in the scoring; Additional file 2: Figure S2, scoring with pure flux data; FunCat enrichments in Additional file 1). When only transcription data was used in the scoring, a separate, interconnected, network of regulatory components was observed (Figure 5). The regulatory components were involved in the mating pathway and in the regulation of the cell cycle (FunCat enrichments in Additional file 1). The separate regulatory network was linked via protein-protein interactions to IMP dehydrogenase and, thus, to nucleotide synthesis.

Detected down-regulated molecular paths combined into one network, 20.9% vs 2.8%, both transcription and flux data used*.

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Detected down-regulated molecular paths combined into one network, 20.9% vs 2.8%, only transcription data used*.

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Notably, alcohol dehydrogenase was found in the detected molecular paths only when flux data was included in the scoring even though alcohol production was a major phenotypic difference between S. cerevisiae under fully aerobic and conditions or 2.8% oxygen provision. This emphasizes the benefit of integrated data from a post-transcriptional regulatory level into the analysis.

Upregulated molecular interaction paths detected in S. cerevisiae between the respirofermentative phenotypes at 2.8% oxygenation and 0.5% oxygenation suggest remodelling of transport across the mitochondrial membrane

The metabolic state of S. cerevisiae was respirofermentative under both conditions: 2.8% and 0.5% O2 in the bioreactor inlet gas [22] and the transcriptomes of S. cerevisiae were observed to be similar under these two conditions [21]. However, the flux distributions were substantially different [23]. Under the 0.5% oxygenation conditions the yield of ethanol on glucose exceeded the yield of biomass on glucose, and pyruvate decarboxylase carried the main flux from pyruvate branching point in contrast to the subtle ethanol production of S. cerevisiae under 2.8% oxygenation conditions [23]. The detected molecular paths up-regulated in S. cerevisiae under the 2.8% oxygenation conditions compared to the 0.5% oxygenation conditions when the transcription data was solely used to score nodes, featured a remodeling of transport between the cytosol and mitochondria, and respirative metabolism (Figure 6; FunCat enrichments in Additional file 1). The remodelling of respirative metabolism at the transcriptional level was progressive as a function of oxygenation since the glyoxylate cycle components and ACS1 encoded acetyl-CoA synthetase and isocitrate dehydrogenase encoded by IDP2 were observed also in the molecular paths representing the differences of the response of S. cerevisiae to fully aerobic conditions and conditions of 2.8% oxygen provision. The glyoxylate cycle was represented in the molecular paths detected for the differences of S. cerevisiae phenotypes within 2.8% and 0.5% oxygenation conditions by both malate synthase encoded by MLS1 and isocitrate lyase. In addition, components of the propionate catabolic pathway, which resembles the glyoxylate cycle, including a 2-methylcitrate synthase encoded by CIT3, aconitase encoded by PDH1, and methylisocitrate lyase encoded by ICL2 were observed in the paths. Methylisocitrate lyase cleaves methylisocitrate into succinate and pyruvate which integrate to the TCA cycle. Propionate catabolism is generally under glucose repression [53] but PDH1 has also been observed to be regulated by retrograde regulators and induced in mitochondrial dysfunction [47]. However, here, during decreased respiratory activity due to a limited availability of oxygen, PDH1 was down-regulated. Interestingly, a number of transports between the cytosolic and mitochondrial compartments were observed in the detected molecular paths. The transporters were carriers of the intermediates of TCA cycle, and acetate and CoA. Proton gradient across the mitochondrial membrane affects the molecule and ion transport since many of the transporters are proton symporters or antiporters. The appearance of the transporters in the up-regulated molecular paths suggests that in 0.5% oxygenation conditions the low availability of oxygen may have limited the generation of proton gradient across the mitochondrial membrane by the electron transfer chain of S. cerevisiae and, thus, the transport required reorganization.

Detected up-regulated molecular paths combined into one network, 2.8% vs 0.5%, only transcription data used*.

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When both transcription and flux data were used in the scoring of nodes up-regulated in S. cerevisiae under the 2.8% oxygenation conditions compared to the 0.5% oxygenation, additional components involved in aerobic metabolism such as fructose 6-phosphatase, a gluconeogenetic enzyme, encoded by FBP1 and pyruvate dehydrogenase complex were observed among others (Figure 7; FunCat enrichments in Additional file 1). Again, the glyoxylate cycle components were absent when flux data was included in the scoring whereas the components involved in propionate metabolism were observed.

Detected up-regulated molecular paths combined into one network, 2.8% vs 0.5%, both transcription and flux data used*.

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Mevalonate biosynthesis prevailed in the detected up-regulated molecular paths when only flux data was used to score the nodes (Additional file 2: Figure S3; FunCat enrichments in Additional file 1). In addition, acetaldehyde dehydrogenase isoforms encoded by ALD4 and ALD5 catalyzing the mitochondrial NADP+ specific and cytosolic NAD+ specific reactions were observed. Most of the metabolic interactions in the detected paths involved either acetyl-CoA or CoA.

Potential post-transcriptionally co-regulated reactions found in the downregulated molecular interaction paths detected in S. cerevisiae between the respirofermentative phenotypes at 2.8% oxygenation and 0.5% oxygenation

When both flux and transcription data were used in the scoring of nodes down-regulated in S. cerevisiae under the 2.8% oxygenation compared to the 0.5% oxygenation, key enzymes of the central carbon metabolism, glucose-6-phosphate isomerase, fructose bisphosphate aldolase, phosphoglycerate kinase, pyruvate decarboxylase, and alcohol dehydrogenase were observed in the detected molecular paths (Figure 8). These enzymes, involved in the glycolytic pathway, pyruvate metabolism, and fermentative pathway (FunCat enrichments in Additional file 1), are not directly linked by metabolic interactions, but were connected by protein-protein interactions in the detected molecular paths. Collins et al. (2007) reported in their high-throughput study the protein-protein interactions between glucose 6-phosphate isomerase (PGI1p), fructose bisphosphate aldolase (FBA1p), 3-phosphoglycerate kinase (PGK1p), pyruvate decarboxylase (PDC1p), and alcohol dehydrogenase (ADH1p) [54]. The genes encoding the discussed enzymes, i.e. FBA1, PGK1, PDC1, and ADH1, have all been observed to have stable expression under a range of conditions [55]. However, the fluxes of glucose 6-phosphate isomerase, fructose bisphosphate aldolase, 3-phosphoglycerate kinase, pyruvate decarboxylase, and alcohol dehydrogenase reactions were substantially lower under 2.8% oxygenation conditions than under even lower oxygen availability [23] whereas the corresponding transcript levels did not, as expected, show consistent behavior [21]. On the other hand, the level of FBA1p is under post-transcriptional control by 14-3-3 proteins BMH1p and BMH2p [56]. In fact, post-transcriptional regulation was previously observed to have a major effect on the protein levels in S. cerevisiae under the conditions of 0.5% O2 in the bioreactor inlet gas [21]. If the physical interactions between these enzymes mediate a transfer of information in some form, they enable coordinated regulation of the central carbon metabolism in upper and lower glycolysis, and in the fermentative pathway. The information transfer could occur for example via a common post-translational modification occurring while the proteins interact. Notably, all these enzymes contain identified phosphorylation sites (http://www.phosphopep.org) [57] and a differential phosphorylation of one of the enzymes, fructose bisphosphate aldolase (FBA1p), in response to switch in growth conditions was recently observed by Oliveira et al. (2012) [58]. Protein-protein interactions interconnected the enzymes of central carbon metabolism further to fatty acid import and biosynthesis. The detected molecular interaction paths included FAS1 and FAS2 that are involved in the elongation of saturated fatty acids, and FAA1 and FAA4 encoding enzymes catalyzing the import and activation of unsaturated fatty acids available in the growth medium. The detected down-regulated molecular paths were highly similar involving the components of the central f.lux 4.75 Key - Crack Key For U metabolism when pure flux data was used in the scoring (Figure 9). If flux data was not incorporated into the scoring, only amino acid transport was observed (Additional file 2: Figure S4; FunCat enrichments in Additional file 1). The observation emphasized the value of the integrative analysis of transcription and flux data that reflect the states of different functional levels of cells.

Detected down-regulated molecular paths combined into one network, 2.8% vs 0.5%, both transcription and flux data used*.

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Detected down-regulated molecular paths combined into one network, 2.8% vs 0.5%, only flux data used*.

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Conclusions

In this study, the EMPath method for the detection of molecular interaction paths [13] was extended to allow for simultaneous utilization of transcriptome and fluxome data in an integrative manner. The method was applied to a combined network of S. cerevisiae’s metabolic and protein-protein interactions. In contrast to existing path finding methods [13, 17–20, 59], data from two sources were combined into one weighting scheme. Thus, the identification of potentially information transferring molecular adobe after effects cc 2019 download - Crack Key For U beyond a single functional level of cells was enabled. The molecular paths coupled cellular components and processes distant at first sight but associated through different biochemical interactions with the oxygen-dependent phenotype changes in S. cerevisiae. New light was shed on the S. cerevisiae phenotypes previously investigated separately with transcription and on the level of in vivo fluxes [21–23]. However, it was observed that while the combined weighting scheme was of profound interest, all the three different weighting schemes resulted in enriched molecular paths providing complementary insight into the oxygen-dependent phenotypes of S. cerevisiae. In addition, certain processes were dominated by post-transcriptional level regulation i.e. glycolytic and fermentative fluxes were emphasized by the differences observed in the enriched molecular paths detected with the different weighting schemes. In particular, the detected molecular paths highlighted protein-protein interactions between the enzymes of central carbon metabolism that could possibly mediate coordinated post-transcriptional regulation of the differential in vivo activity of central metabolism in S. cerevisiae in two different respirofermentative metabolic states. Further, the down-regulation of oxidative stress in S. cerevisiae in conditions of 2.8% oxygenation compared to fully aerobic conditions was found to be related and potentially restricted to the down-regulation of peroxisomal activities. The results further suggested that a limited availability of oxygen and the consequently decreased respirative activity may affect transport reactions of S. cerevisiae across the mitochondrial membrane under conditions of 0.5% oxygen provision. Finally, the paths included metabolic interactions via metabolic intermediates in the crossroads of altered processes, such as acetyl-CoA and succinate, whose concentrations could be potential phenotypic markers.

Abbreviations

Artificial Centering Hit-and-Run

Carbon Evolution Rate

COnstraint-Based Reconstruction and Analysis

Enriched Molecular Path detection

False Discovery Rate

Источник: https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-16

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Spectral Resolution and Energy Efficiency for FOXSI III Silicon Detectors

Lance Davis and Connor O'Brien


Introduction

FOXSI-3 is the third flight of the Focusing Optics X-ray Solar Imager (FOXSI) sounding rocket, which will take place on August 21st, 2018. The goal of the sounding rocket is to image the sun in the soft x-ray regime of 4 to 15 keV in order to probe the source of coronal heating and the mechanics of solar microflares. FOXSI flights 1 and 2 used exclusively silicon x-ray detectors, whereas this latest flight has added new, fine-pitch Cadmium-Tellurium (CdTe) detectors as well. While previous flights have operated at -20 °C, these new detectors have a higher operating temperature of -10°C, which will affect the performance of the older silicon detectors. The goal of our project is to measure the spectral resolution and energy efficiency of FOXSI silicon detectors four and five at -10 °C in order to assess how this new operating temperature affects the performance of the detectors.

Theory

The first half of our experiment involved calculating the spectral resolution of the detectors. Spectral resolution is the broadening of a monoenergetic source into a gaussian due to instrumental effects, and is also known as energy resolution. This resolution was probed using spectral lines from radioactive sources. When the flux of such a source is measured by the detector, statistical noise causes the measured peak to widen into a Poisson distribution about the energy peak, which can be approximated as a Gaussian for large N (such as the number of incident photons observed by one of the detectors), as shown below.


An illustration of the incident monoenergetic peak and the resultant Gaussian distribution at energy H. The Full Width Half Maximum is the width of the peak at half of its maximum value, and is the standard deviation of the distribution.

The equation of a Gaussian distribution is given by the function

(1)

where P is the height of the peak at energy H, E is the energy of the incident photon, and is the standard deviation.

The width of this Gaussian is the energy resolution of the detector at the peak energy. We numerically define the energy resolution of our detectors using the relation


(2)

where FWHM is the full-width half-maximum; at half-maximum, the full width of a Gaussian distribution is equal to 2.35[4]. Usually, the variance of this distribution σE2 would be proportional to the variance in the number σn2 of charge carrier pairs produced at a given energy; σE is related to σn by E=wσn  where w, which is material specific, is the average energy required to create a charge carrier pair. In Poisson statistics, n = sqrt(N) where N is the number of charge pairs produced; N can be calculated by N=E/w [4]. For silicon, w = 0.00366 keV [8].

However, the processes that give rise to each individual charge carrier pair in the detector are not independent; therefore we cannot describe the total number of charge carrier pairs with Poisson statistics. The departure of observed statistical noise from pure Poisson statistics is described by the Fano Factor, which is given by the relation

(3)

The Fano factor is dependent on which material is used for f.lux 4.75 Key - Crack Key For U detector, and is a constant. In the case of silicon, F = 0.115 [8]. This modifies σn to become σn= sqrt(FN). Finally, the statistical limit on energy resolution for a silicon detector can be defined as [4]

(4)

On top of a statistical limit on the energy resolution, we can readily assume that there will be some underlying resolution limit from inherent electronic noise - both an energy dependent and energy independent component. Contributions from thermal noise are likely due to the temperature dependence of our system, as well as power line noise from power supplies that filters within the flight board are not able to scrub out. As for energy-dependent noise, the fact that to measure photon energy we measure the amount of discrete charge carriers means that shot noise is also going to have an effect on our system. The energy resolution adds in quadrature from each resolution limit source [4]. We therefore may isolate the energy-dependent Fano noise from the energy-dependent and non-energy-dependent inherent electronic resolutions by measuring the resolution at various spectral lines and using the relation

(5)

where Nfano is the fano noise given by Equation 4, r is the energy-independent electronic noise, and f1 is a factor that describes the energy-dependent noise f1E [11]. We assume the energy dependence of the energy-dependent noise is linear because the amount of charge carriers liberated by a photon increases approximately linearly at the 4-15 keV energy scale. For our setup, r mainly takes into account thermal noise and power line noise, while f1 mainly describes the shot noise observable in the system.

After identifying the energy resolution of the detectors, we then wished to measure their energy efficiencies. The energy efficiency is defined as the ratio of the number of photons that the detector actually measures to the number of photons incident on the detector, and can be energy-dependent. This is given by the relation


(6).

This relation assumes that each recorded event was a single photon. The X-ray source for this experiment was beamline 3.3.2 at the ALS. This beam was formed by redirecting continuum radiation created by synchrotron radiation [9]. Synchrotron radiation is emitted when particles are accelerated in a curved path.

Because our silicon detector counts single photons and has a thickness of 500 μm, literature suggests that the energy efficiency between 6 to 10 keV will be near unity [6]. Our detectors use a f.lux 4.75 Key - Crack Key For U energy of 4 keV, which is used to prevent the detectors from being flooded with low energy photons and only allowing photons within the energy range of FOXSI 3’s science goals to be detected. Because of the spectral resolution of the fast pulse shaper that precedes the discriminator which form the threshold circuit, the resulting efficiency drop-off resembles an error function centered at 4 keV. This experiment sought to characterize the behavior of this threshold above 4 keV.

Experimental Setup

The silicon detectors used are made of an n-type silicon wafers onto which acceptor doped (p+) and donor doped (n-) strips are placed orthogonally onto opposite sides. By placing the strips orthogonally, each crossing forms a pixel in an image. On both the p+ or the n- side, there are 128 strips with a pitch of 75 μm. Each side of the detector is divided into two regions, called ASICs; each ASIC has 64 strips. For this project we focused on the p+ side, which has better resolution.

Diagram of FOXSI silicon detector.

We used detectors 4 and 5 (numbering used internally to track detectors; number based on position in electronics board) for gauging the spectral resolution. These detectors were housed in the same electronics board that will be flown. This setup was encased by an aluminized mylar, Faraday cage material, which insulated the detectors to help maintain the desired -10℃ operating temperature. The detectors were cooled using cold nitrogen gas. The flow of nitrogen gas into the enclosure was regulated by the temperature control unit which maintained the specified temperature of -10±1℃.

Our silicon detectors measure individual photons. When a single energetic photon is incident upon the detector, electron-hole pairs are produced. A bias voltage of 200 V is applied across the silicon detector to separate the electron-hole pairs. This process creates a current proportional to the amount of electron-hole pairs. This current is measured by the electronic board. The data is then digitized into discrete ADC bins. The ADC value is then sent to an FPGA which sends the data to a computer interface where it is written to file to be used in later analysis.

Sealed radioactive sources, Am-241 and  Fe-55, and fluorescence from metal foils of Cu and Ni, were used to measure the spectral resolution. The metal foils, secured on top of the radioactive Am-241 source, gave out the characteristic lines of the metal foil through X-ray fluorescence. The spectral lines produced by these four sources which are used in gain calibration are given in the table below. Data for each source were collected at -10℃ until a statistically significant number of counts were obtained for each strip.

Table of X-ray sources used and the spectral lines used for gain calibration from each source.

The next part of this project was the energy efficiency measurement. A silicon drift detector with an assumed energy efficiency [6], as well as the flux observed in the continuum of the beamline X-ray source, were used as references for calculating the incoming flux. Separately, this detector and our detector were illuminated by a monoenergetic X-ray source, which was beamline 3.3.2 at the ALS; for more details, see [9]. The continuum spectra in the range of 4 to 20keV was on the order of 109 photons/s/mm2. This amount of flux would saturate our detector, meaning the detector no longer counts single photon events. The flux was reduced by passing the beamline through a 2x2 μm slit and when needed, a 60 or 100 μm aluminum foil to attenuate the flux to the order of 103 to 104 photons/s/mm2, which our detector could measure reliably. This reduced flux was measured by both detectors. The air gap between the detector and the beamline was roughly 15 cm.

Flux measurements were made at 4.75, 5, 5.5, 6, 7, 8, 9, and 10 keV. Flux below 4.75 keV began to enter the falling tail of the continuum spectra, as well as the air gap absorbing a large percentage of incoming photons [10]; statistics were becoming poor below 4.75 keV. Above 10 keV, a smaller fraction of higher energy photons were being absorbed by the aluminum than lower energy photons, causing a higher incoming flux; the detector started to saturate above 10 keV.  Only detector 4 was brought to the ALS for testing.

Incoming flux generated by beamline 3.3.2 at the ALS. Plot shows energy (x-axis) vs intensity (y-axis).

Spectral Resolution

The data for the spectral resolution were analyzed using IDL analysis code made for the FOXSI mission, parts of which were modified for our purposes. Histograms for data observed by each of the 64 strips from each of the four ASICs were made. These histograms plotted ADC bins vs counts.

Histogram of Fe-55 for Detector 4 ASIC 2 showing counts (y-axis) vs ADC bin (x-axis).

Next, an algorithm was used to fit a Gaussian curve in order to find the peaks of the spectral lines used in this experiment. The ADC bin where the peaks occurred was assigned the energy of that peak. A plot of ADC peaks vs energy is then made, where a quadratic function was fit to the data points. This function is the gain calibration curve. Using this curve, each ADC bin was calibrated to a corresponding energy bin. At this point, each strip was analyzed; if the ratio of the observed conversion from ADC bin to predicted conversion given by the gain calibration curve was greater than 5% away from unity, the strip was discarded from further analysis; in total, 5/128 strips from detector 4 and 11/128 strips from detector 5 were discarded.

Plots of spectral line peaks f.lux 4.75 Key - Crack Key For U vs ADC bins (y-axis) for the first four strips. The blue curve is the fit gain calibration curve.
Ratios of data points to the fit. The top right plot, Channel 1, was discarded as it had ratios exceeding 5% away from unity. 

The FWHMs of the peaks, now in units of energy, were then calculated by fitting a Gaussian curve to each peak. For the Ni and Cu lines, the Am-241 spectrum was scaled to match the intensity of high energy lines, 20.6 keV, 26.3 keV, and 59.6 keV, which were much less affected by the absorption in Ni and Cu, seen in the Ni and Cu spectra. The background Am-241 continuum seen in the spectra collected for the Ni and Cu foils was accounted for by subtracting the scaled Am-241 spectrum. The FWHM values for Ni and Cu were then calculated. Note that this is a crude approximation, as we have not accounted for how the foils would affect the energy emission of Am-241 in the range of 6 to 10 keV.


Gaussian fit (curved line) of the Am-241, 13.9 keV spectral line. The histogram shows the number of counts in each energy bin.

The fit minimized χ2; the errors for the FWHM were found by subtracting the FWHM value found at χ2min+1 by the FWHM value found at χ2min. The resolutions were plotted as a function of energy. The red line shows the best fit to Equation 5; the blue dotted lines show the error in this fit. The parameters for the fit are given in the table below.


Spectral resolution as a function of energy for ASICs 2 and 3 on Detectors 4 and 5. The red line shows the best fit to Equation 5; the blue dotted lines show the error in this fit.

In comparison to the spectral resolution operated at -20℃ [2], the low energies were more affected. For the spectral lines at 59.6 keV, 26.3 keV, and partially the line at 17.6 keV, the energy resolution remained constant. For the other spectral lines, the energy resolution increased anywhere from 0.03 to 0.25 keV. Each plot has several data points outside of the errors of the model fit. This does not imply immediately that this is a poor model however, as Equation 5 has been used to model the spectral resolution for these detectors at lower eset smart security crack key temperatures [2,11]. It is believed that with better data quality, the data points will better fit the model. Improved quality f.lux 4.75 Key - Crack Key For U be achieved by increasing the number of counts collected. More statistics would lead to a better gain calibration and FWHM calculations. Also, a more precise removal of the Am-241 continuum background from the Ni and Cu lines would lead to a more precise FWHM calculation for those lines.

Table of the parameters used to fit Equation 5 in the plot of spectral resolution vs energy.

The energy dependent noise term, f1, was calculated to be on the order of 104 times smaller for the fit in detector 4, ASIC 2 and detector 5, ASIC3 when compared to the other plots, as well as the f1 term found when operating the detectors at -20℃ [2]. One possible explanation for this is that the resolution for the 59.6 keV Am-241 line was lower for these two plots. This issue may be resolved if a larger integration time was used in order to gain a higher number of counts for this emission line.

It is important to note that the flight goal resolution for the silicon detectors was 1 keV. As Figure 4 shows, for the energy range of 4 to 15 keV, the resolution is roughly 0.55 keV, well below the 1 keV goal. While operating at -10℃, the contribution from electronic noise has increase by about 0.03 to 0.09 keV in comparison to when the detectors were operated at -20℃ [2].

Energy Efficiency

In order to measure the energy efficiency of the FOXSI detectors, the first step is to calculate the incident flux measured by the reference Silicon drift detector (SDD). First, we perform gain calibration on the reference detector using a process very similar to gain calibration for the FOXSI detector. Since the SDD is a single silicon bulk crystal with a very small resolution, there are no individual strips to calibrate and we are therefore able to manually identify the peak bin when the SDD is illuminated with 4.75 keV, 5 keV, 5.5 keV, 6 keV, 7 keV, 8 keV, and 9 keV monochromatic beams. We then perform a linear fit of peak bin to peak energy to obtain a gain calibration curve, shown below.

Gain calibration curve for reference SDD. We found that each bin corresponds to  0.0389 keV and that bin zero corresponds to -0.0536 keV.

To find the rate of incident counts, the total time the SDD was actively recording incident photons, also known as the live time, needs to be calculated. In the SDD provided by the ALS, there is a fast triggering channel and a slow triggering channel. The fast channel has a very fast shaping time during which it can’t accept new counts, therefore we assume that the number of counts recorded by the fast channel is the number of photons Acronis True Image 2021.25 Crack + Serial Key Free Download on the SDD, whether or not they were actually recorded in the data. The slow channel has a slow shaping time, which results in it being able to discern the energy of the photons it records. The data recorded by the SDD is data recorded by the slow channel. While the slow channel is reading the energy of the incident photon, it cannot accept any new counts. The time it spends shaping the pulse where it cannot accept new counts is known as the dead time of the SDD’s measurement. The provided SDD calculates dead time by taking the ratio of the total slow channel counts to the total fast channel counts, giving the ratio of live time to total time the SDD was operating. Taking this ratio and multiplying by the time the SDD was active yields the live time of the SDD [6]. Dividing the counts within a given energy peak by the live time gives the actual incident countrate supplied by the x-ray source. Similarly, we sum the total number of counts in a given peak measured by the FOXSI detector, and divide by the live time, which is a quantity measured by onboard electronics and included in the data packet. We then calculate the energy efficiency as a function of energy, shown below.

Energy efficiency as a function of energy for detector 4, ASICs 2 and 3.

From the calculated energy efficiency of detector 4, ASIC 2 and 3, Steampunk Tower 2 Crack + Activation Key 2021 - Free Activators immediately note that the efficiency for the region well above the threshold is well below unity efficiency. Furthermore, it is observed to drop as energy increases, the opposite dependence as expected. The only places in our analysis where such a discrepancy could have occurred is in calculating the number of counts registered by the FOXSI detector, and the live time calculation for the SDD.

The live time calculation outlined in preceding paragraphs is that detailed by the manufacturer, but it differs in key ways from how FOXSI dead time is calculated. Primarily, it assumes that the ratio between the slow channel and the fast channel counts gives the live time percentage, something not supported in other literature [6]. If the live time is actually larger than the calculated value for high count rates, it would reduce the incident count rate more in the high energy regime and result in the expected higher efficiency values. The counts measured by the FOXSI detector during our tests may also be suspect. Between each strip, there is a 25 micron section of the detector that cannot register data. After further analysis of other data sets taken at the ALS, the count rates observed by the FOXSI detector during our energy efficiency tests was on the order with count rates of data taken while the non-data-taking parts of the detector were illuminated. While the position of our detector during the energy efficiency tests would indicate that the x-ray source was illuminating a part of the detector that could take data, it is possible the system that positions the detector in front of the source exhibited unforeseen hysteresis behavior that resulted in the wrong section being illuminated. This would result in drastically reduced observed count rates. If accounted for, this would explain the low observed count rates but not the odd energy dependence of the calculated efficiency. It is possible that one or both of these factors are affecting our data.

Conclusion

The measurements found that the spectral resolution in the energy range of interest to the FOXSI mission, 4 to 15 keV, was on average 0.55 keV, roughly 0.05 keV higher than if the detectors were operated at -20℃ [2]. This energy resolution is below the 1 keV resolution required by the science goal of the FOXSI mission. If this calibration were to be repeated, it would be useful to gain further statistics for the spectral lines. This would provide a tighter and more accurate fit to the FWHM measurements.

The energy efficiency calculations were believe to be incorrect, as the efficiency does not match expected [6] or previous results [9]. The reason for this is thought to be due to not correctly calculating the live time, and thus the count rate, in the reference detector. To improve upon this work, more information would be needed in how to understand the data collected by the reference detector, namely how to calculate the live time. It would also be useful to extend the range of measurements to 4 to 20 keV, the full range of the beamline at ALS, as this would provide efficiency information for the entire energy range of the FOXSI detectors.

References

[1]Krucker, S., Christe, S., & Glesener, L., et al. 2014, ApJL, 793, 2

[2]Athiray, P. S., Buitrago-Casas, J. C., Bergstedt, K., et al. 2017, SPIE,10397, 103970A

[3]Ishikawa, S., Glesener, L., Christe, S., et al. 2014, ASJ, 66, SP1, id.S157

[4]Knoll, Glenn F. Radiation Detection and Measurement. 3rd ed., John Wiley & Sons,

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[5]Beckhoff, Burkhard, et al. Handbook VyprVPN 4.1.0 Free Download with Crack Practical X-Ray Fluorescence Analysis.

Springer-Verlag Berlin Heidelberg, 2006.

[6]Amptek. “XR-100SDD Silicon Drift Detector (SDD).” Amptek.com, Amptek, 2017,

amptek.com/products/xr-100sdd-silicon-drift-detector/#11.

[7] L`epy, M., Plagnard, J., and Ferreux, L., “Measurement of 241am l x-ray emission

probabilities,” Applied Radiation and Isotopes 66(6), 715 – 721 (2008). Proceedings of the 16th International Conference on Radionuclide Metrology and its Applications.

[8]Lechner, P., et al. “Pair Creation Energy and Fano Factor of Silicon in the Energy Range

of Soft X-Rays.” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 377, no. 2-3, 1996, pp. 206–208., doi:10.1016/0168-9002(96)00213-6.

[9]MacDowell, Alastair. "Beamline 3.3.2." Advanced Light Source. Berkeley Lab, 2018. ]

Web. 04 May 2018.

[10]B.L. Henke, E.M. Gullikson, and J.C. Davis. X-ray interactions: photoabsorption,

scattering, transmission, and reflection at E=50-30000 eV, Z=1-92, Atomic Data and Nuclear Data Tables Vol. 54 (no.2), 181-342 (July 1993).

[11]Ishikawa, S., et al. “Fine-Pitch Semiconductor Detector for the FOXSI Mission.” IEEE

Transactions on Nuclear Science, vol. 58, no. 4, 2011, pp. 2039–2046., doi:10.1109/tns.2011.2154342.

[12]Ishikawa, Shin-Nosuke, et al. “Fine-Pitch CdTe Detector for Hard X-Ray Imaging and

Spectroscopy of the Sun with the FOXSI Rocket Experiment.” Journal of Geophysical Research: Space Physics, vol. 121, no. 7, 2016, pp. 6009–6016., doi:10.1002/2016ja022631.

[13] Takeda, S., Takahashi, T., Watanabe, S., Tajima, H., Tanaka, T., Nakazawa, K., and

Fukazawa, Y., “Double-sided silicon strip detector for x-ray imaging,” in [SPIE Newsroom], (Feb. 2016).

Источник: https://sites.google.com/a/umn.edu/mxp/student-projects/spring-2018/s18_measuring-x-ray-flux-from-an-x-ray-generator-and-testing-detector-energy-efficiency

Open Access

Peer-reviewed

  • Xing-Ding Zhang ,
  • Lin Qi ,
  • Jun-Chao Wu,
  • Zheng-Hong Qin
  • Xing-Ding Zhang, 
  • Lin Qi, 
  • Jun-Chao Wu, 
  • Zheng-Hong Qin
PLOS

x

Abstract

We have previously reported that the mitochondria inhibitor 3-nitropropionic acid (3-NP), induces the expression of DNA damage-regulated autophagy modulator1 (DRAM1) and activation of autophagy in rat striatum. Although the role of DRAM1 in autophagy has been previously characterized, the detailed mechanism by which DRAM1 regulates autophagy activity has not been fully understood. The present study investigated the role of DRAM1 in regulating autophagy flux. In A549 cells expressing wilt-type TP53, 3-NP increased the protein levels of DRAM1 and LC3-II, whereas decreased the levels of SQSTM1 (sequestosome 1). The increase in LC3-II and decrease in SQSTM1 were blocked by the autophagy inhibitor 3-methyl-adenine. Lack of TP53 or knock-down of TP53 in cells impaired the induction of DRAM1. Knock-down of DRAM1 with siRNA significantly reduced 3-NP-induced upregulation of LC3-II and downregulation of SQSTM1, indicating DRAM1 contributes to autophagy activation. Knock-down of DRAM1 robustly decreased rate of disappearance of induced autophagosomes, increased RFP-LC3 fluorescence dots and decreased the decline of LC3-II after withdraw of rapamycin, indicating DRAM1 promotes autophagy flux. DRAM1 siRNA inhibited lysosomal V-ATPase and acidification of lysosomes. As a result, DRAM1 siRNA reduced activation of lysosomal cathepsin D. Similar to DRAM1 siRNA, lysosomal inhibitors E64d and chloroquine also inhibited clearance of autophagosomes and activation of lysosomal cathapsin D after 3-NP treatment. These data suggest that DRAM1 plays important roles in autophagy activation induced by mitochondria dysfunction. DRAM1 affects autophagy through argument of lysosomal acidification, fusion of lysosomes with autophagosomes and clearance of autophagosomes.

Citation: Zhang X-D, Qi L, Wu J-C, Qin Z-H (2013) DRAM1 Regulates Autophagy Flux through Lysosomes. PLoS ONE 8(5): e63245. https://doi.org/10.1371/journal.pone.0063245

Editor: Arun Rishi, Wayne State University, United States of America

Received: November 13, 2012; Accepted: March 29, 2013; Published: May 17, 2013

Copyright: © 2013 Zhang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was partially supported by the National Natural Science Foundation of China (No 30930035), by the National Basic Science Key Project (973 project, CB510003), by the Priority Academic Program development of Jiangsu Higher Education Institutes, and by Graduate Training Innovation Project of Jiangsu Province (CX09B_042Z). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

3-nitropropionic acid (3-NP), a suicide inhibitor of the mitochondrial respiratory enzyme succinate dehydrogenase (SDH) [1], induces striatal cell death in vivo and in vitro [2]–[4]. When intoxicated in vivo, 3-NP produces symptoms and striatal neuronal loss in human brains replicating neuropathology of Huntington’s disease [4], [5]. We previously reported that intrastriatal administration of 3-NP induced TP53-dependent autpophagy activation and apoptosis. The TP53 specific inhibitor pifithrin-α (PFT-α) blocked induction of autophagic proteins including DNA Damage Regulated Autophagy Modulator1 (DRAM1), LC3-II and beclin1 and apoptotic proteins including TP53-upregulated modulator of apoptosis (PUMA) and BAX. Both pharmacological inhibitors of autophagy and caspases effectively inhibited 3-NP-induced cell death [6], [7].

DRAM1, a novel TP53 target gene, is an evolutionarily conserved lysosomal protein and has been reported to play an essential role in TP53-dependent autophagy activation and apoptosis [8]. The mechanism by which DRAM1 promotes autophagy is not clear. It is proposed that DRAM1 may exert its effects on autophagy through lysosomes, given the fact as a lysosomal membrane protein. Uncovering the molecular mechanism by which DRAM1 regulates autophagy would novapdf review - Crack Key For U a better understanding of the role of TP53 signaling pathway in the regulation of cell death and survival.

Autophagy is a pathway delivering cytoplasmic components to lysosomes for degradation [9]–[13]. Macroautophagy involves the sequestration of a region of the cytoplasm in a double-membrane structure to form a unique vesicle called the autophagosome. Acidification of lysosomes is crucial for activation of cathepsins, fusion of lysosomes and autophagosomes and effective degradation of autophagic substrates. However, these late digestive steps of autophagy remain largely uncharacterized.

Lysosomes are cytoplasmic organelles that contain several enzymes mostly belonging to the hydrolases [14]. Internal pH of lysosomal is characteristically acidic and it Netcut 3.0.154 Crack+ License Key Free Download 2021 maintained around pH 4.5 by a proton pump, that transport H+ ions into lysosomes [15], [16]. Many autophagy inhibitors including the vinca alkaloids (e.g., vinblastine) and microtubule poisons that inhibit fusion of autophagosomes with lysosomes, inhibitors of lysosomal enzymes (e.g., leupeptin, pepstatin A and E64d), and compounds that elevate lysosomal pH (e.g., inhibitors of vacuolar-type ATPases, such as bafilomycin A1 and weak base amines including ammonia, methyl- or propylamine, chloroquine, and Neutral Red, some of which slow down fusion), act at the fusion and lysosomal degradation steps [17]. Lysosomal enzymes also play a role in activation of certain types of caspases and therefore, are involved in apoptosis [18]. Inhibition of lysosomes or lysosomal enzymes protects neurons against excitotoxicity and ischemic insults [19], [20]. Thus, it is of particularly interest to investigate if DRAM1 modulates autophagy through influencing lysosomal functions.

In this study, we report that 3-NP induced DRAM1-dependent stimulation of autophagy in A549 cell lines. DRAM1 promotes autophagy flux by enhancing lysosomal acidification.

Materials and Methods

Cell Lines and Reagents

A549 (TP53+/+) and H1299 (TP53−/−) and Hela cell lines were purchased from Shanghai Institute of Biochemistry and Cell Biology in China, and were grown at 37°C in 5% CO2 in RPMI supplemented with 2 mmol/L L-glutamine and 10% FCS. Primary mouse embryonic fibroblasts (MEFs) were derived from p53 wt and p53 KO sibling embryos, and maintained with DMEM supplemented with 10% FCS and antibiotics. 3-NP (N5636), 3-MA (M9281), carbonyl cyanide m-chlorophenylhydrazone (CCCP, C2759), ATP (A6559), chloroquine (C6628), E-64d (E8640) and Z-Vad-FMK (V116) were all purchased from Sigma-Aldrich (Saint Louis, MO, USA). LysoTracker Red (L7528) and LysoSentor (L7533) were purchased from Invitrogen-Molecular Probes (Shanghai, China). All cell culture reagents were purchased from Gibco (Gaithersburg, MD, USA) unless otherwise noted.

Expression of GFP-LC3 and DRAM1-pEGFP

The activation of autophagy was detected following transfection of cells with GFP-LC3 and mRFP-GFP-LC3 expression plasmids (kindly provided by Dr. T. Yoshimori, National Institute of Genetics, Japan). The presence of several intense fluorescent dots in cells is indicative of the accumulation of autophagosomes. Transfection of cells with expression plasmids was performed using Lipofectamine 2000 (Invitrogen, 11668-019, Shanghai, China). For each condition, the number of GFP-LC3 dots per cell was determined with a fluorescence microscopy for at least 100 GFP-LC3-positive cells.

PcDNA4-DRAM1-His was generated by PCR from the I.M.A.G.E. clone for DRAM1 (Clone ID: NM_018370) with: CCCAAGCTTATGCTGTGCTTCCTGAGGGGAATG (forward) and CCGCTCGAGTCAAATATCACCATTGATTTCTGTG (reverse), and subsequently digested with BamH I and Xho I and cloned in to the BamH I and Xho I sites of pcDNA4/HisA (Invitrogen Carlsbad, CA, USA). pEGFP-N1-DRAM1 was generated through PCR primer: ATAGAATTCATGCTGTGCTTCCTGAGGGGA (forward) and CCGGGATCCTAATATCACCATTGATTTCTGTG(reverse), and products were T-A cloned in pMDTM19-T Vectors (Takara, D102A, Dalian, China) and digested with EcoR I and BamH I and cloned uvk ultra virus killer license key - Crack Key For U pEGFP-N1 (Clonetech, D102A, Mountain View, CA, USA). Transfection of cells with expression plasmids was performed using Lipofectamine 2000 (Invitrogen, 11668-019, Shanghai, China).

Knock-down of TP53 and DRAM1

Small interfering RNAs (siRNA) targeting the following mRNA: TP53, AAGACUCCAGUGGUAAUCUAC; DRAM1, (1) CCACGATGTATACAAGATA and (2) CCACAGAAATCAATGGTGA. Negative siRNA TAAGGCTATGAAGAGATAC, were synthesized by GenePharma (Shanghai, F.lux 4.75 Key - Crack Key For U. The siRNA oligos used to target DRAM1 genes were previously validated and described in the following articles [8], [21], [22]. For transfection, cells were plated in 9-cm dishes at 30% confluence, and siRNA duplexes (200 nM) were introduced into the cells using Lipofectamine 2000 (Invitrogen, 11668-019, Shanghai, China) according to the manufacturer’s recommendations.

LC3 Immunofluorescence Assay

For immunofluorescence microscopic examination, cells were plated on 12-mm Poly-L-Lysine-coated cover slips and cultured for 24 h, then cells were treated with siRNA and drugs. Cells were washed in PBS, fixed with 4% paraformaldehyde in PBS at 4°C for 10 min, and then washed again with PBS. The cells were permeabilized with 0.25% Triton X-100, and were then blocked with 10% normal goat serum (NGS) for 15 min. Primary antibodies: a rabbit polyclonal antibody against LC-3 (Abgent, AJ1456c, Suzhou, China), a goat polyclonal antibody against cathepsin D (Santa Cruz, sc-6488, Santa Cruz, CA, USA) and a rabbit polyclonal antibody against LAMP2 (Abcam, ab37024, Cambridge, MA, USA) diluted in PBS were added to the cells and left for overnight at 4°C. The cover slips were washed three times before incubation with secondary antibodies using the same procedure as for the primary antibodies. The cover slips were mounted on slides with mounting medium (Sigma-Aldrich, F4680, Saint Louis, MO, USA) and were examined with a laser scanning confocal microscopy (Nikon, C1S1, Tokyo, Japan).

The pattern of distribution of exogenously expressed GFP-LC3 in A549 cells was observed with fluorescent microscopy. GFP-LC3 dot formation was quantified by counting 500 GFP-LC3-positive cells and expressed as the ratio of the number of cells with at least 5 GFP-LC3 dots and the number of GFP-LC3-positive cells. The assays were independently performed by two investigators in a blinded manner and similar results were obtained.

Western Blot Analysis

Western blot analysis was performed as scribed previously [23]. Cells were harvested and rinsed twice with ice-cooled PBS and homogenized in a buffer containing 10 mmol/L Tris-HCl (pH 7.4), 150 mmol/L NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS, 5 mol/L edetic acid, 1 mmol/L PMSF, 0.28 U/L aprotinin, 50 mg/L leupeptin, 1 mmol/L benzamidine, 7 mg/L pepstain A. Protein concentration was determined using the BCA kit. Thirty micrograms of protein from each sample was subjected to electrophoresis on 10–12% SDS-PAGE gel using a constant current. Proteins were transferred to nitrocellulose membranes and incubated with the Tris-buffered saline containing 0.2% Tween-20 (TBST) and 3% non-fat dry milk for 3 h in the presence of one of the following antibodies: a rabbit polyclonal antibody against LC-3 (Abgent, AJ1456c, San Diego, CA, USA), a mouse monoclonal antibody against TP53 (Cell Signaling Technology, 2524S, Boston, MA, USA), a mouse monoclonal antibody against β-actin (Santa Cruz, sc-58669), a goat polyclonal antibody against cathepsin D (Santa Cruz, sc-6488), rabbit polyclonal antibodies against DRAM1 (Stressgen, 905-738-100, Farmingdale, NY, USA), a rabbit polyclonal antibodies against SQSTM1 (Enzo Life Sciences, PW9860, Farmingdale, NY, USA),Membranes were washed and incubated with horseradish peroxidase-conjugated secondary antibodies in TBST containing 3% non-fat dry milk for 1 h. Immunoreactivity was detected with enhanced chemoluminescent autoradiography (ECL kit, Amersham, RPN2232, Piscataway, NJ, USA) according to the manufacturer’s instructions. The levels of protein expression were quantitatively analyzed with SigmaScan Pro 5. The results were normalized to loading control β-actin (Santa Cruz, sc-58669). DRAM1 peptide (Acris Antibodies, AP30304CP-N, San Diego, CA, USA) was used for evaluating the specificity of DRAM1 antibody. Pre-incubation of DRAM1 antibody with control peptide (1 µg control peptide/1 µL DRAM1 antibody) abolished binding activity of DRAM1 antibody (Figure S2).

Determination of Lysosomal pH

For lysosomal pH estimation, A549 and Hela cells were seeded on circular glass cover slips and grown to confluence in Dulbecco’s modified Eagle’s medium (DMEM) with 10% fetal bovine serum (FBS; Wisent, 080–150) at 37°C, 5% CO2. Lysosomes were loaded overnight with 70000 MW FITC-dextran (Sigma-Aldrich, 53471). and 0.5 mg/mL dextran-coupled Oregon Green 488 (Invitrogen-Molecular Probes, D-7173, Grand Island, NY, USA) in DMEM supplemented with 10% FBS, chased for 2 h at 37°C with 5% CO2 in DMEM (10% FBS) to allow complete transfer of dextrans to lysosomes, and washed to remove residual dextran. Non-attached cells were removed by rinsing with PBS and the cover slips were immediately placed in a cuvette filled with growth medium or PBS and pH was estimated from excitation ratio measurements as described previously [24]. The fluorescence emitted was recorded at two excitation wavelengths (440/490 nm for Oregon Green 488) using the largest excitation and emission slits by a scanning multiwell spectrophotometer (Ultra Micro- plate Reader; BIO-TEK Instruments, ELx800, Winooski, VT, USA). The pH values were derived from the linear standard curve generated via each fluorescent dextran in phosphate/citrate buffers of different pH between 3.5 and 7.5. The experiment was repeated six times.

Spectrophotometric Measurement of H+ Transport

FITC-dextran loaded A549 and Hela cells were prepared as described above. After washing in PBS, cells were resuspended (108 cells in 2 ml) in homogenization buffer (0.25 M sucrose, 2 mM EDTA, and 10 mM Hepes [pH 7.4]) and homogenized in a tight-fitting glass Dounce homogenizer. The homogenate was centrifuged (800 g, 10 min) to remove unbroken cells and the nuclei. The supernatant was centrifuged (6800 g, 10 min) to remove the large organelle such as mitochondrial. The supernatant was centrifuged (25000 g, 10 min) to obtain the light organelle including lysosomes. The precipitation layered over 10 ml of a 27% Percoll (Pharmacia Inc, 17-0891-01, New York, NY, USA) solution in homogenization buffer, underlayered with 0.5 ml of a 2.5 M sucrose solution. Centrifugation was done in a SW41Ti rotor (Beckman Instruments Inc, Brea, CA, USA) for 1.5 h at 35000 g. The layer of crude lysosomes of about 1.5 ml was collected at the bottom and then was centrifuged (100000 g, 60 min) to remove the other light organelle including mitochondrial at the bottom of the tube. Lysosomal fractions were equilibrated for up to 1 h in 125 mM KCl, 1 mM EDTA, and 20 mM Hepes (pH 7.5). Fluorescence was recorded continuously with excitation at 490 nm and emission at 520 nm. Upon addition of ATP (Sigma-Aldrich, A6559, Saint Louis, MO, USA), a progressive decrease in fluorescence intensity was observed, indicative of intralysosomal acidification [25]. As expected, the pH gradients in both samples were collapsed by the addition of the bafilomycin A1 (1 µM) (Sigma-Aldrich, B1793). The solvents alone had no effect on lysosomal pH. The reagents used and their final concentrations were: ATP (K+ salt, pH 7.5, 5 mM), bafilomycin A1 (1 µM).

Statistical Analysis

Statistical analysis was carried out by one-way analysis of variance (ANOVA) followed by Dunnett t-test or multiple means comparisons by Tukey’s test. Differences were considered significant when p<0.05.

Results

3-NP Induces Autophagy Activation

The present study examined if autophagic and apoptotic pathways are activated in A549 cells after 3-NP treatment. The results showed that 3-NP-induced Buildbox 2.2.9 Crack + Activation Loader Full Version Download significant increase in the protein levels of DRAM1 from 3 to 72 h, with a peak induction at 24 h after 3-NP treatment (Figure 1A). The specificity of DRAM1 antibody was checked with Western blot analysis and immunofluorescence assay using DRAM1 control peptide (Figure S2). To further test if mitochondria respiration failure triggers DRAM1 expression, we used CCCP to uncouple mitochondria oxidation and phosphorylation, the results showed that CCCP significantly increased the DRAM1 protein levels (Figure 1B). LC3 is a mammalian homologue of yeast Atg8p and LC3-II is required for the formation of autophagosomes [26]. As shown in Fig. 1C, 3-NP induced a time-dependent increase in GFP-LC3 in A549 cells, and LC3-positive vesicular profiles of sizes 0.5–2.0 µm were significantly more numerous in 3-NP-treated cells 48 h after treatment (Figure 1C and 1D). To provide biochemical evidence of autophagy activation, the time-course of 3-NP-induced changes in LC3-II in A549 cells was determined 24 to 72 h after 3-NP (500 µM) treatment. The expression of LC3-II significantly increased 24 h after 3-NP treatment (Figure 2A). As an additional assessment of autophagy activity, the degradation of SQSTM1 (sequestosome 1), an autophagy substrate, was determined [27]. The present results showed that the protein level of SQSTM1 decreased 24–72 h after 3-NP treatment (Figure 2A). As a confirmation of autophagy activation, the present study demonstrated that the elevation of LC3- II and the decline of SQSTM1 were blocked by the autophagy inhibitor 3-methyl-adenine (Figure 2B).

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Figure 1. 3-NP activated autophagy.

A549 cells were treated with 3-NP (500 µM) and harvested 24, 48 and 72 h later. (A) Immunoblot analysis of DRAM1 levels in A549 cells under conditions of: no treatment (Ctrl) and 3, 6, 12, 24, 48 and 72 h after 3-NP. (B) Immunoblot analysis of DRAM1 levels in A549 cells under conditions of: no treatment (Ctrl) and 12.5µM and 25 µM of CCCP treatment for 4 h. Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. **P<0.01 (3-NP group vs. control group). (C) Representative images of GFP-LC3 fluorescence in cells transfected with GFP-LC3 plasmid 24, 48 and 72 h after 3-NP (500 µM). N: the nucleus. Thin arrows: GFP-LC3 dots. The scale bar represents 10 µm. (D) Quantitative analysis of the number of GFP-LC3 puncta. Number of cells with GFP-LC3 dots was scored in 100 GFP-LC3-positive cells. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. **P<0.01 (3-NP group vs. control group).

https://doi.org/10.1371/journal.pone.0063245.g001

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Figure 2. Autophagy was induced by 3-NP and blocked by 3-MA.

(A) Immunoblot analysis of LC3 and SQSTM1 levels in A549 cells under conditions of: no treatment (Ctrl) and 24, 48 and 72 h after 3-NP. Protein extracts were subjected to SDS-PAGE and immunoblotting. Densities of protein bands were analyzed with an image analyzer (SigmaScan Pro 5) and normalized to the loading control (β-actin). The data are expressed as percentage of control (untreated cells). Bars represent mean±SE; n = 4. (B) Immunoblot analysis of LC3 and SQSTM1 levels in cells under conditions of: no treatment (Cont), 3-NP (500 µM) and 3-MA (200 µM) +3-NP (500 µM). Protein extracts were subjected to SDS-PAGE and immunoblotting. Densities of protein bands were analyzed with an image analyzer (SigmaScan Pro 5) and normalized to the loading control (β-actin). The data are expressed as percentage of control (untreated cells). Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. *P<0.05 (3-NP group vs. control group). #P<0.05 (3-MA +3-NP- treated group vs. 3-NP- treated group). **P<0.01 (3-NP group vs. control group). ##P<0.05 (3-MA +3-NP- treated group vs. 3-NP- treated group).

https://doi.org/10.1371/journal.pone.0063245.g002

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Figure 3. TP53 dependency of DRAM1 induction after 3-NP treatment.

A549 and H1299 cells were treated with 3-NP (500 µM) and harvested 48 h later. (A) Immunoblot analysis of TP53 and DRAM1 levels in A549 and H1299 cells under conditions of: no treatment (Ctrl) and 48 h after 3-NP. Protein extracts were subjected to SDS-PAGE and immunoblotting. Densities of protein bands were analyzed with an image analyzer (SigmaScan Pro 5) and normalized to the loading control (β-actin). The data are expressed as percentage of control (untreated cells). Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. **P<0.01 (3-NP group vs. control group). ##P<0.01 (3-NP group vs. control group). $$P<0.01 (3-NP group vs. control group). (B) Immunoblot analysis of TP53 and DRAM1 levels in p53 wt and p53 KO MEFs under conditions of: no treatment (Ctrl) and 48 h after 3-NP. Protein extracts were subjected to SDS-PAGE and immunoblotting. Densities of protein bands were analyzed with an image analyzer (SigmaScan Pro 5) and normalized to the loading control (β-actin). The data are expressed as percentage of control (untreated cells). Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. **P<0.01 (3-NP group vs. control group). ##P<0.01 (3-NP group vs. control group). $$P<0.01 (3-NP group vs. control group). (C) A549 cells were transfected with TP53 siRNA or a non-silencing siRNA. Forty-eight hours after transfection of cells with TP53 siRNA, cells were harvested and protein levels of TP53 and DRAM1 were analyzed with immunoblotting 24 h after 3-NP. Densities of protein bands were analyzed with Sigma Scan Pro 5 and normalized to the loading control (β-actin). The data are expressed as percentage of control. Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. **P<0.01 TP53 siRNA group vs. non-silencing siRNA group. (D) H1299 cells were transfected with TP53 siRNA or a non-silencing siRNA. Forty-eight hours after transfection of cells with TP53 siRNA, cells were harvested and protein levels of TP53 and DRAM1 were analyzed with immunoblotting 24 h after 3-NP. Densities of protein bands were analyzed with Sigma Scan Pro 5 and normalized to the loading control (β-actin). The data are expressed as percentage of control. Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test.

https://doi.org/10.1371/journal.pone.0063245.g003

It was reported that DRAM1 is a TP53 target gene. We determined the TP53 dependency in 3-NP-induced DRAM1 expression. In H1299 cells which lack of TP53, 3-NP only slightly induced DRAM1 expression, while in A549 cells which express wt TP53, 3-NP robustly induced the expression of DRAM1 (Figure 3A). The similar results were seen in TP53 wt and TP53 null MEFs cells (Figure 3B). Treatment of A549 cells with TP53 siRNA, partially inhibited both basal and 3-NP-induced the expression of DRAM1 (Figure 3C). In contrast, treatment of H1299 with TP53 siRNA did not block 3-NP-induced expression of DRAM1 (Figure 3D). These results suggest that induction of DRAM1 largely depends on TP53 mechanism, but other signaling pathways are also be involved in regulating DRAM1 expression after 3-NP treatment [28].

DRAM1 Mediates Autophagy Activation

To understand the role of DRAM1 in the regulation of autophagy, the present study investigated the role of DRAM1 in autophagy activation in response to 3-NP treatment in A549 and Hela cells. Knock-down of DRAM1 using siRNA significantly reduced the expression of DRAM1 proteins in A549 cells (Figure 4A) and in Hella cells (Figure S1 A). After knock-down of DRAM1 with siRNA, the basal expression and induction of LC3-II by 3-NP was markedly reduced in both A549 cells (Figure 4B) and Hela cells (Figure S1A). In addition, 3-NP-induced reduction of SQSTM1 was blocked by DRAM1 siRNA in A549 cells (Figure 4B). The formation of GFP-LC3 puncta after 3-NP treatment was also inhibited in the presence of DRAM1 siRNA in A549 cells (Figure 4C) and in Hela cells (Figure S1 B). In addition to inhibiting the production of LC3-II, SQSTM1 levels increased in DRAM1 siRNA-treated cells (Figure 4B). These lines of evidence support an important role of DRAM1 in autophagy activation.

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Figure 4. DRAM1 mediated autophagy activation.

(A, B) A549 cells were transfected with DRAM1 siRNA or a non-silencing siRNA. Left: Forty-eight hours after transfection of cells with DRAM1 siRNA, cells were harvested and protein levels of DRAM1, LC3 and SQSTM1 were analyzed with immunoblotting. Right: Twenty-four hours after transfection of cells with DRAM1 siRNA, cells were treated with 3-NP (500 µM). Cells were harvested and protein levels of LC3 and SQSTM1 were analyzed with immunoblotting 24 h after 3-NP. Densities of protein bands were analyzed with SigmaScan Pro 5 and normalized to the loading control (β-actin). The data are expressed as percentage of control (non-silencing siRNA group). Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by F.lux 4.75 Key - Crack Key For U t-test. **P<0.01 non-silencing siRNA group vs. control group. ##P<0.01 DRAM1 siRNA group vs. non-silencing siRNA group. (C) Representative images of GFP-LC3 fluorescence in cells transfected with GFP-LC3 and treated with DRAM1 siRNAs in the presence or absence of 3-NP (500 µM). Number of cells with GFP-LC3 dots was scored in 100 GFP-LC3-positive cells. N: the nucleus. Thin arrows: GFP-LC3 dots. The scale bar represents 10 µm. Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. **P<0.01 (siRNA group vs. non-silencing siRNA group).

https://doi.org/10.1371/journal.pone.0063245.g004

DRAM1 Enhances Autophagosomes Clearance

To study the mechanisms of DRAM1 in regulating autophagy, A549 cells were transfected with GFP-DRAM1. The lysosomal localization of DRAM1 was examined with LysoTracker and LAMP2 immunofluorescence or double immunofluorescence of DRAM1 and LAMP2. LysoTracker is a commonly used lysosomal probe because it is an acidotropic fluorescent dye for labeling and tracking acidic organelles in live cells. Marked co-localization of DRAM1 and LysoTracker (Figure 5A) or DRAM1 and LAMP2 (Figure 5B) was seen with a confocal microscopy. The quantitative analysis revealed that colocalization of DRAM1 puncta and LAMP2 was 74.8±5.6% (data not shown), suggesting that DRAM1 predominantly localizes to lysosomes. The clearance of autophagosomes is a measure of autophagy flux. In control cells, acute autophagy induction with rapamycin elevated LC3-II levels as revealed by immunoblotting. After removing rapamycin from the medium for 6 h, LC3-II returned towards baseline levels. While in DRAM1 siRNA-treated cells, LC3-II remained elevated 6 h after removing rapamycin (Figure 5C). Double immunofluorescence of LC3 and LAMP2 demonstrated the formation of large number of LC3-LAMP2-positive vesicles in siRNA untreated cells after rapamycin exposure. Treatment of cells with DRAM1 siRNA reduced the number of LC3-LAMP2-posive vesicles (Figure 5D). After removal of rapamycin for 6 h, a number of LC3-LAMP2-positive vesicles were cleared in siRNA untreated cells but more LC3-LAMP2-positive vesicles remained in the cells treated with DRAM1 siRNA (Figure 5E and 5F). These suggest that both the formation and the clearance of autophagic vacuoles are impaired in DRAM1 siRNA-treated A549 cells.

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Figure 5. Knock-down of DRAM1 impaired the clearance of autophagosomes.

(A) DRAM1 was predominantly localized in lysosomes (Lysotraker). A549 cells were transfected with GFP-DRAM1 for 48 h. Cells were incubated with LysoTracker (0.5 µM) and co-localization of DRAM1-GFP (green) and the LysoTracker (red) was assessed with a confocal microscopy. N: the nucleus. Thin arrows: GFP-DRAM1 fluorescence. Thick arrows: LysoTracker. (B) DRAM1 was predominantly localized in lysosomes (LAMP2). Up panel: A549 cells were transfected with GFP-DRAM1 for 48 h. Cells were processed for immunofluorescence using LAMP2 antibodies and co-localization of DRAM1-GFP (green) and the LAMP2 (red) was assessed with a confocal microscopy. N: the nucleus. Thin arrows: GFP-DRAM1 fluorescence. Thick arrows: LAMP2. Low panel: A549 cells were processed for immunofluorescence using DRAM1 and LAMP2 antibodies, and co-localization of DRAM1 (green) and the LAMP2 (red) was assessed with a confocal microscopy. N: the nucleus. Thin arrows: anti-DRAM1 fluorescence. Thick arrows: LAMP2. (C) Immunoblot analysis of LC3 levels in A549 cells under conditions: untreated (Cont), rapamycin (Rap) treatment, and 6 h after rapamycin removal (Rap/Rec). Densities of protein bands were analyzed with an image analyzer (SigmaScan Pro 5) and normalized to the loading control (β-actin). The data are expressed as percentage of control (non-silencing siRNA group). Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Tukey’s test. **P<0.01 (DRAM1 siRNA treatment group vs. non-silencing siRNA group). (D) A549 cells were analyzed with double-immunofluorescence using LC3 and LAMP2 antibodies in the presence of rapamycin and 6 h after removal of rapamycin. N: the nucleus. Thin arrows: dots of LC3 immonureactivity. Thick arrows: LAMP2. The scale bar represents 10 µm. (E) DRAM1 siRNA-treated cells were analyzed with double-immunofluorescence using LC3 and LAMP2 antibodies in the presence of rapamycin and 6 h after removal of rapamycin. N: the nucleus. Thin arrows: dots of GFP-LC3 fluorescence. Thick arrows: LAMP2. The scale bar represents 10 µm. (F) In cells after DRAM1 siRNA treatment, the number of LC3 dots was scored in 100 GFP-LC3-positive cells in the presence or absence of 3-NP. The data are expressed as percentage of control. Bars represent mean±SE; n = 4. Statistical comparisons were carried out by Tukey’s test. **P<0.01 (DRAM1 siRNA treatment group vs. non-silencing siRNA group). #P<0.05 (DRAM1 siRNA treatment group vs. non-silencing siRNA group).

https://doi.org/10.1371/journal.pone.0063245.g005

DRAM1 Affects Lysosomal Degradation and Lysosomal Acidification

Lysosomal enzyme, cathepsin D, plays an essential role in the degradation process of autophagic activity. The present study employed double immunofluorescence of cathepsin D and LysoTracker to explore the role of DRAM1 in lysosomal function. We observed that cathepsin D was virtually confined in LysoTracker fluorescence-positive vesicles in A549 cells. 3-NP treatment increased the expression of cathepsin D and the number of LysoTraker labeled lysosomes (Figure 6A).

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Figure 6. Knock-down DRAM1 inhibited autophagosome maturation process.

(A) Lysosomes were activated by 3-NP. A549 cells were treated with 3-NP (500 µM) for 48 h. Cells were incubated with LysoTracker (0.5 µM) and processed for immunofluorescence using Cathepsin D (Cat D) antibodies. The co-localization of Cat D (green) and the LysoTracker (red) was assayed by confocal microscopy. N: the nucleus. Thin arrows: Cat D immunoreactivity. Thick arrows: LysoTracker. The scale bar represents 10 µm. (B) Accumulation of mRFP-LC3 in DRAM1 siRNA-treated cells. Representative images of mRFP-GFP-LC3 fluorescence in cells transfected with mRFP-GFP-LC3 and treated with DRAM1 siRNAs in the presence or absence of 3-NP (500 µM). N: the nucleus. Thin arrows: GFP-LC3 dots. Thick arrows: mRFP-LC3 dots. The scale bar represents 10 µm. (C) Number of cells with GFP-LC3 dots was scored in 100 GFP-LC3-positive cells. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. **P<0.01 non-silencing siRNA group vs. control group. ##P<0.01 DRAM1 siRNA group vs. non-silencing siRNA group.

https://doi.org/10.1371/journal.pone.0063245.g006

GFP-LC3 is the most widely used marker for autophagosomes. When localized to autolysosomes, GFP-LC3 loses fluorescence due to lysosomal acidic and degradative conditions. While mRFP-LC3 is more stable in acidic conditions and fluorescence remains after fusion of autophagosomes with lysosomes. Thus, we used mRFP-GFP tandem fluorescent-tagged LC3 to monitor the process of autophagy maturation [29]. The result showed that 3-NP increased the expression of LC3, most of LC3 displayed yellow color due to emitted both GFP and RFP fluorescence. However, due to stronger fluorescence of GFP than that of RFP, some green LC3 patches were also observed. Knock-down of DRAM1 with siRNA slightly reduced GFP-LC3 fluorescence (reflecting attenuation of autophagy induction), but robustly increased the number of large mRFP-LC3 puncta (Figure 6B and 6C). In the condition of treatment with 3-NP in the presence of non-sil siRNA, yellow punctas were few because degradation of autolysosomes was smooth. While in the condition of treatment with 3-NP in the presence of DRAM1 siRNA, more large yellow pinctas were observed (Figure 6B). These results indicate that the clearance of autophagic vacuoles is impaired in DRAM1 siRNA-treated A549 cells.

As most lysosomal cathepsins work at acidic pH, the effect of DRAM1 silencing on activation of cathepsin D was examined. The results of immunoblotting showed that knock-down of DRAM1 significantly inhibited 3-NP-induced production of the active form of cathepsin D (Figure 7A), suggesting activation of cathepsin D is compromised. To assess lysosomal acidification, we used LysoSensor DND-167. The LysoSensor dye is an acidotropic probe that appears to accumulate in acidic organelles as the result of protonation. In control cells, the fluorescence of LysoSensor was enhanced from 24 to 72 h after 3-NP exposure. By contrast, in DRAM1 siRNA-treated cells, the fluorescence was lower than that in the control cells (Figure 7B). We further measured lysosomal pH in quantization. The cells were loaded with the pH-sensitive reporter FITC-dextran by endocytosis for 1 h and then chased in the control and DRAM1 siRNA-treated cells in the presence a comprehensive cross-platform solution for new jobs - Free Activators absence of 3-NP. WT cells exhibited an intralysosomal pH of 4.75, and lysosomal pH decreased following 3-NP treatment (Figure 7C). In contrast, the lysosomal pH values decreased to a lesser extent (5.23) in DRAM1 siRNA-treated cells following 3-NP treatment in both A549 cells (Figure 7C) and in Hela cells (Figure S1 C). These results suggest that there is a defective lysosomal acidification in DRAM1 siRNA-treated cells. Lysosomal acidification requires the activity of the ATP-dependent vacuolar proton pump [30]. We examined the ATP-dependent lysosomal acidification using the pH sensitive dye FITC-dextran. This dye accumulates inside lysosomes due to its weak basic net charge in response to ATP addition. As shown in Figure 7D, addition of ATP caused a dramatic drop in FITC fluorescence as a result of lysosomal acidification in control and 3-NP-treated cells. In DRAM1 siRNA-treated cells, ATP-induced drop in fluorescence emission was reduced, reflecting a reduction in internal lysosomal acidification. Reduction in FITC fluorescence by ATP was inhibited by the V-ATPase inhibitor bafilomycin A1. The similar results were obtained in Hela cells (Figure S1 D). Thus, the impairment of acidification in DRAM1 siRNA-treated cells might be due to a decrease in V-ATPase activity.

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Figure 7. Knock down DRAM1 inhibited lysosomal acidification and cathepsin D activation.

(A) A549 cells were transfected with DRAM1 siRNA or a non-silencing siRNA. Left: Forty-eight hours after transfection of DRAM1 siRNA, cells were harvested and protein levels of cat D were analyzed with immunoblotting. Right: Twenty-four hours after transfection of cells with DRAM1 siRNA, cells were treated with 3-NP (500 µM) for 24 h. Cells were harvested and protein levels of cat D were analyzed with immunoblotting. Densities of protein bands were analyzed with SigmaScan Pro 5 and normalized to the loading control (β-actin). The data are expressed as percentage of control (non-silencing siRNA cells). Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. **P<0.01 (DRAM1 siRNA group vs. non-silencing siRNA group). (B) Lysosomal acidification was measured using LysoSensor DND-167. In control cells, the fluorescence of LysoSensor was measured from 24 to 72 h, and in DRAM siRNA-treated cells the fluorescence was measured in 48 h after transfection of DRAM1 siRNA. N: the nucleus. The scale bar represents 10 µm. (C) Lysosomal pH was measured ratio-metrically using fluorescent dextrans. WT cells and DRAM1 siRNA1-treated cells were loaded with the pH-sensitive fluorescent dextrans by endocytosis for 1 h at 37°C and then subjected to pulse-chase assay in the presence or absence cleanmymac x activation number 2020 the 3-NP (500 µM). Lanes 2 and 4 depict pH values obtained with FITC-dextran after the addition of 500 nM 3-NP. The data are expressed as percentage of control (non-silencing siRNA cells). Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. **P<0.01 (DRAM1 siRNA group vs. non-silencing siRNA group). ##P<0.01 (DRAM1 siRNA group vs. non-silencing siRNA group with 3-NP treatment). (D) Lysosomal V-ATPase activity was inhibited in DRAM1 siRNA1-treated cells. Lysosomes from control cells and DRAM1 siRNA1-treated cells were loaded with FITC-dextran (molecular weight 70,000). A549 cells were then homogenized and used for in vitro-acidification assays. Fluorescence was recorded continuously with excitation at 490 nm and emission at 520 nm. Upon addition of ATP, a progressive decrease in fluorescence intensity was observed, indicative of intralysosomal acidification. The decrement was reversed by bafilomycin A1, a V-ATPase inhibitor.

https://doi.org/10.1371/journal.pone.0063245.g007

Foregoing observations indicate that DRAM1 regulates autophagy flux mainly thought lysosomes. Thus, the lysosomal inhibitors E64d (10 µM) and chloroquine (20 µM) were used to evaluate if inhibition of lysosomal functions produces effects similar to knock-down of DRAM1. Many autophagy inhibitors act on post-sequestration steps and agents, such as bafilomycin A1, that blocks autophagy activity are known to cause accumulation of autophagosomes [31]. Chloroquine is a compound that elevates lysosomal pH, and E64d is an effective inhibitor of lysosomal enzymes [32]. After 3-NP treatment, more LAMP2-positive vacuoles were observed. Compared with cells treated with 3-NP alone, LC3 in E64d or chloroquine-treated cells accumulated more LAMP2-positive vacuoles (Figure 8A). As shown in Fig. 8B, LC3-II accumulated after E64d or chloroquine treatment. These results suggest a defective clearance of autophagic vacuoles in E64d- and chloroquine-treated cells.

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Figure 8. Lysosomal inhibitors inhibited autophagosome clearance.

(A) Accumulation of autophagosomes was analyzed with double-immunofluorescence using antibodies against LC3 and LAMP2 after E64d (10 µM) or chloroquine (20 µM) treatment for 24 h in the presence or absence of 3-NP (500 µM). N: the nucleus. Thin arrows: dots of LC3 immunoreactivity. Thick arrows: LAMP2 immunoreactivity. The scale bar represents 10 µm. (B) Immunoblot analysis of LC3-II levels in cells under conditions of: no treatment (Cont), E64d (10 µM), chloroquine (20 µM), 3-NP (500 µM), E64d (10 µM) +3-NP (500 µM) or chloroquine (20 µM) +3-NP (500 µM). Cells were harvested for immunoblotting 48 h after 3-NP treatment. Densities of protein bands were analyzed with SigmaScan Pro 5 and normalized to the loading control (β-actin). The data are expressed as percentage of control (untreated cells). Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. *P<0.05 (3-NP treated group vs. control group). #P<0.05 (E64d+3-NP- or chloroquine +3-NP-treated group vs. 3-NP- treated group). ##P<0.01 (E64d plus 3-NP or chloroquine plus 3-NP treatment group vs. 3-NP treatment group).

https://doi.org/10.1371/journal.pone.0063245.g008

Discussion

3-NP acts as an irreversible inhibitor of succinate dehydrogenase and thus results in an impairement of energy metabolism, oxidative stress and activation of glutamate receptors [33]. Mitochondria are important intracellular organelles and the collapse of mitochondria membrane potential may be a signal for activation of autophagy and apoptosis. Previous in vivo studies suggest that 3-NP-induced cell death in rat striatum involves TP53-dependent activation of apoptosis and autophagy [6]. It was also reported that DRAM1 and SQSTM1 regulated cell migration and invasion of glioblastoma stem cells [34]. TP53 target gene DRAM1 possibly mediates down stream multiple functions in autophagy and cell death. The present in vitro studies found that 3-NP inhibited cell viability of A549 cells at the doses of 250 µM to 1 mM (data not shown). The activation of autophagy was demonstrated by increases in LC3-II protein levels, GFP-LC3 puncta and a decrease in SQSTM1 protein levels. These studies suggest that mitochondria dysfunction induced by 3-NP triggered autophagy activation. Biochemical analysis showed that 3-NP and CCCP significantly increased DRAM1 protein levels and this increase in DRAM1 played a role in 3-NP-induced autophagy activation. Although upregulation of DRAM1 by 3-NP largely depended on TP53, our present results suggested there were also other mechanisms involved [28]. The human DRAM1 gene encodes a 238 amino acid protein which acts as a stress-induced regulator of autophagy and damage-induced programmed cell death [8]. The present study demonstrated that knock-down of DRAM1 effectively blocked the 3-NP-induced induction of LC3-II and decline in SQSTM1. These studies confirm that DRAM1 plays an important role in autophagy activation.

To investigate the underlying mechanism by which DRAM1 regulates autophagy, we investigated the effects of DRAM1 on autophagosome clearance. Colocalization of EGFP-DRAM1 and LysoTracker fluorescence or DRAM1 and LAMP2 immunoflurescence confirmed predominant lysosomal localization of expressed DRAM1. We first tested if DRAM1 has an effect on autophagosome turnover following induction with rapamycin. Rapamycin can stimulate the formation of autophagosome through inhibiting mTOR. Upon removal of rapamycin, autophagosomes should be cleared if autophagy pathway is normal. The present study demonstrated that rapamycin increased the abundance of autophagosomes and the number of autophagosomes returned towards the basal levels 6 h after withdrawal of rapamycin. Knock-down of DRAM1 reduced the rate of clearance of autophagosomes after rapamycin withdrawal. Galavotti et al reported that knock-down of DRAM1 inhibited targeting of SQSTM1 to autophagosomes and reduced its degradation [34]. Our data also support the involvement of DRAM1 in degradation of autophagososmes. However, Galavotti et al found that DRAM1 was not involved in starvation- and mTOR-mediated autophagy activation [34]. Therefore, the role of DRAM1 in autophagy activation induced by other stimuli need to be further studied.

The abundance of autophagosomes is balanced by the formation and clearance of autophagosomes. After the formation, the turn-over of autophagosomes is largely determined by the process of fusion between autophagososmes and lysosomes and degradation of autophagy contents by lysosomal enzymes. mRFP-GFP tandem fluorescent-tagged LC3 showed both GFP and mRFP signal of LC3 before the fusion with lysosomes, and exhibited only the mRFP signal when LC3 transmit into lysosomes because of lysosomal acidic environment and degradation [29]. After rapamycin treatment, there was more number of mRFP-GFP-LC3 patches in non-silencing RNA-treated cells than that in DRAM1 siRAN-treated cells, suggesting DRAM1 plays a role in the formation of autophagosomes. In response to withdrawal of rapamycin, mRFP-GFP-LC3 patches quickly declined in control cells. Knock-down of DRAM1 markedly retained these mRFP-GFP-LC3 patches in the cells. These results suggest that DRAM1 stimulates clearance of autophagosomes.

Lysosomes are rich in hydrolytic enzymes and are responsible for the degradation of intracellular materials captured by autophagy [35]. After 3-NP treatment, an increase in the abundance of autophagosomes was accompanied by an increase in the number of lysosomes. The increase in acidic lysosomes was noticeable as indicated by a fluorescence dye. Knock-down of DRAM1 resulted in an impairment of lysosomal acidification and accumulation of LC3-II, indicating reduced autophagy flux. It is now generally accepted that intralysosomal low pH is maintained by an active proton pump, vacuolar H+­ATPases or V­ATPases. Proton transport into intracellular organelles is primarily mediated by Total Uninstall Professional 7.0.2 with Crack proton pumps. These pumps are therefore central to pH homeostasis in organelles. Autophagosomes and their contents are cleared upon fusing with late endosomes or lysosomes containing cathepsins, other acid hydrolases, and vacuolar [H+] ATPase(v-ATPase) [36], a proton pump that acidifies the newly created autolysosome. It is suggested that the proton pumps and acidification of the lysosomes were essential for the activation of lysosomal hydrolases and completion of the process of autophagy. V-ATPase may also play a role in amino acid sensing, thus plays a role in mTOR-mediated autophagy activation [37]. Inhibition of mitochondrial respiratory complex may decrease ATP production and thus decrease the activity of V-ATPase. However, due to a significant induction of DRAM1 and activation of autophagy in the present study, the V-ATPase activity was preserved to sufficiently acidify lysosomes. We speculate that DRAM1 may improve the efficiency of ATP utilization by V-ATPase. The present study found that the lower capacity for acidification of lysosomes in DRAM1 siRNA-treated cells was due to decreased V-ATPase activity. These results provide experimental data, for the first time, supporting an important role of DRAM1 in lysosomal function.

Lysosomes play important roles in autophagy. To test if the effects of DRAM1 on lysosomal functions are responsible for DRAM1-mediated autophagy activation after 3-NP treatment, the present study assessed the effects of lysosomal inhibitors on autophagosome accumulation in the presence of 3-NP. The results showed that elevating lysosomal pH and inhibiting lysosomal enzymes both increased accumulation of autophagosomes and inhibited cathepsin D activation. These results largely replicated the effects of knock-down of DRAM1 and suggested that DRAM1 probably regulated autophagy flux through lysosomes.

It should be pointed out that DRAM1 appears regulate autophagy in both early and later stages of autophagy. DRAM1 can increase the formation of autophagosomes and the clearance of autophagosomes. These effects may work through the same mechanism as DRAM1 is a lysosomal protein and may regulates dynamics of lysosomal membranes to increase V-ATPase activity and to facilitate membrane recycle for autophagosomal formation.

In conclusion, current data indicate that DRAM1 regulates autophagosome clearance through promoting lysosomal acidification and activation of lysosomal enzymes. The fusion of autophagosomes with lysosomes is an important step for autophagic degradation. In order to fully understand the role of DRAM1 in autophagy flux, the effects of DRAM1 on the fusion process between autophagosomes and lysosomes needs to be studied in the future.

Supporting Information

Figure S1.

DRAM1 mediated autophagy activation and lysosomal acidification in Hela cells. (A) Hela cells were transfected with DRAM1 siRNA or a non-silencing siRNA. Left: Forty-eight h after transfection of cells with DRAM1 siRNA, cells were harvested and protein levels of DRAM1 and LC3 were analyzed with immunoblotting. Right: Twenty-four hours after transfection of cells with DRAM1 siRNA, cells were treated with 3-NP (500 µM). Cells were harvested and protein levels of DRAM1 and LC3 were analyzed with immunoblotting 24 h after 3-NP. Densities of protein bands were analyzed with Sigma Scan Pro 5 and normalized to the loading control (β-actin). The data are expressed as percentage of control (non-silencing siRNA group). Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. **P<0.01 (DRAM1 siRNA group vs. non-silencing siRNA group). ##P<0.01 (3-NP treated group vs. control group). $$P<0.01 (DRAM1 siRNA group vs. non-silencing siRNA group with 3-NP treatment). (B) Representative images of GFP-LC3 fluorescence in Hela cells transfected with GFP-LC3 and treated with DRAM1 siRNAs in the presence or absence of 3-NP (500 µM). Number of cells with GFP-LC3 dots was scored in 100 GFP-LC3-positive cells. N: the nucleus. Thin arrows: GFP-LC3 dots. The scale bar represents 10 µm Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. **P<0.01 (siRNA group vs. non-silencing siRNA group). (C) Lysosomal pH was measured ratio-metrically using fluorescent dextrans in Hela cells. WT Hela cells and DRAM1 siRNA1-treated cells were loaded with the pH-sensitive fluorescent dextrans by endocytosis for 1 h at 37°C and then subjected to pulse-chase assay in the presence or absence of the 3-NP (500 µM). Lanes 2 and 4 depict pH values obtained with FITC-dextran after the addition of 500 nM 3-NP. The data are expressed as percentage of control (non-silencing siRNA cells). Bars represent mean±SE; n = 4. Statistical comparisons were carried out by ANOVA followed by Dunnett t-test. **P<0.01 (DRAM1 siRNA group vs. non-silencing siRNA group). ##P<0.01 (DRAM1 siRNA group vs. non-silencing siRNA group with 3-NP treatment). (D) Lysosomal V-ATPase activity was inhibited in DRAM1 siRNA1-treated Hela cells. Lysosomes from control cells and DRAM1 siRNA1-treated cells were loaded with FITC-dextran (molecular weight 70,000). Hela cells were then homogenized and used for in vitro-acidification assays. Fluorescence was recorded continuously with excitation at 490 nm and emission at 520 nm. Upon addition of ATP, a progressive decrease in fluorescence intensity was observed, indicative of intralysosomal acidification. The decrement was reversed by bafilomycin A1, a V-ATPase inhibitor.

https://doi.org/10.1371/journal.pone.0063245.s001

(TIF)

Figure S2.

Activity of DRAM1 antibody was blocked by DRAM1 peptide. (A) Cells were harvested and immunoblot analysis of DRAM1 protein levels in A549 and Hela cells. Left: No peptide incubated with DRAM1 antibody before primary antibody incubation. Right: DRAM1 peptide was incubated with DRAM1 antibody for 30 min at 37°C before primary antibody incubation. (B) Cells were processed for immunofluorescence using DRAM1 antibodies (green) and DAPI (the nucleus, blue) in A549 and Hela cells, and was assessed with a confocal microscopy. Left: No peptide incubated with DRAM1 antibody before primary antibody incubation. Right: DRAM1 peptide was incubated with DRAM1 antibody for 30 min at 37°C before primary antibody incubation. N: the nucleus. Thin arrows: anti-DRAM1 fluorescence.

https://doi.org/10.1371/journal.pone.0063245.s002

(TIF)

Author Contributions

Conceived and designed the experiments: ZQ XZ. Performed the experiments: XZ LQ. Analyzed the data: XZ LQ. Contributed reagents/materials/analysis tools: XZ JW. Wrote the paper: XZ ZQ.

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  • Lysosomes 
  • Small interfering RNA 
  • Autophagic cell death 
  • Transfection 
  • HeLa cells 
  • Immunoblotting 
  • Analysis of variance 
  • Apoptosis 
Источник: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0063245

Metabolic Flux Analysis

Metabolic Flux Analysis: Methods and Protocols opens up the field of metabolic flux analysis to those who want to start a new flux analysis project but are overwhelmed by the complexity of the approach. Metabolic flux analysis emerged from the current limitation for the prediction of metabolic fluxes from a measured inventory of the cell. Divided into convenient thematic parts, topics in this essential volume include the fundamental characteristics of the underlying networks, the application of quantitative metabolite data and thermodynamic principles to constrain the solution space for flux balance analysis (FBA), the experimental toolbox to conduct different types of flux analysis experiments, the processing of data from 13C experiments, and three chapters that summarize some recent key findings. Written in the successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible protocols, and notes on troubleshooting and avoiding known pitfalls.

 

Authoritative and easily accessible, Metabolic Flux Analysis: Methods and Protocols presents protocols that cover a range of relevant organisms currently used in the field, providing a solid basis to anybody interested in the field of metabolic flux analysis.

Keywords

cell culture flux analysis metabolomics stoichiometry thermodynamics

Editors and affiliations

  • Jens O. Krömer
  • Lars K. Nielsen
  • Lars M. Blank
  1. 1.Centre for Microbial Electrosynthesis (CEMES), Advanced Water Management CentreUniversity of QueenslandSt. Lucia, BrisbaneAustralia
  2. 2.AIBNUniversity of QueenslandSt. Lucia, BrisbaneAustralia
  3. 3.Biology DepartmentRWTH Aachen UniversityAachenGermany

Bibliographic information

  • Book TitleMetabolic Flux Analysis
  • Book SubtitleMethods and Protocols
  • EditorsJens O. Krömer
    Lars K. Nielsen
    Lars M. Blank
  • Series TitleMethods in Molecular Biology
  • Series Abbreviated TitleMethods Molecular Biology
  • DOIhttps://doi.org/10.1007/978-1-4939-1170-7
  • Copyright InformationSpringer Science+Business Media New York2014
  • Publisher NameHumana Press, New York, NY
  • eBook PackagesSpringer Protocols
  • Hardcover ISBN978-1-4939-1169-1
  • Softcover ISBN978-1-4939-4159-9
  • eBook ISBN978-1-4939-1170-7
  • Series ISSN1064-3745
  • Series E-ISSN1940-6029
  • Edition Number1
  • Number of PagesXII, 316
  • Number of Illustrations19 b/w illustrations, 51 illustrations in colour
  • TopicsBiochemistry, general
    Enzymology
Источник: https://link.springer.com/content/pdf/10.1007%2F978-1-4939-1170-7.pdf

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5 Comments

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