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Danial, N. N. & Korsmeyer, S. J (2004) Cell death: critical control points. Cell 116, 205219.ABSTRACT Autophagy is the natural catabolic process characterized by controlled digestion of damaged cellular components. Being a major pathway that deals with various types of cellular stress, autophagy exhibits a large regulatory network which directly interlinks with mechanisms involved in apoptosis, a process of programmed cell death. My primary goal was to design a suitable MatLab based algorithm that would later be utilized to determine the degree of communication between the two pathways. Caspase inhibitors (particularly caspase 6 inhibitors) were used to stall the degradation process in human cells which were subsequently exposed to apoptosis inducing agents (e.g. Trail). Every treatment underwent antibody labeling (e.g. anti LC3) followed by imaging through immunofluorescence. Resulting data was then converted to numeric format upon which the mathematical algorithms were constructed. Developed software is a subject to fine adjustment and harvests multiple types of output data including LC3 puncta intensity, number and size. Additional scripts also enabled it to generate an arbitrary [A] constant which represents the magnitude of autophagic response for a given treatment. It was shown that autophagy is significantly upregulated in the cells undergoing apoptosis. Trail time killing assay illustrated an exponential increase in [A] constant, which reached its peak value 130mins post exposure. INTRODUCTION Subdivision: · Background on autophagy and apoptosis (e.g. shared components is reg. network) ü General description ü Apoptosis - caspase dep.\indep. routes ü Autophagy ATG gene family ü FADD Atg5 interactions ü Bcl-2 Beclin1 (Atg6) interactions · Project aim: is autophagy activated during apoptosis? · Main pathways of studying autophagy (e.g. autophagy markers) · Main complications imposed by apoptotic stages in cells · Existing software and methods · Mathematical and computational background · Data that can be extracted from images and how to do it · Why mathematical approach? · Outcomes summarized Both autophagy and apoptosis are genetically regulated, self destructive processes that effectively determine the cells fate [1]. Apoptosis is the best-described form of programmed cell death and involves the activation of catabolic enzymes in particular proteases in signaling cascades, which leads to the rapid demolition of cellular structures and organelles [2, 3, 4]. (Also nutrition) Autophagy provides a similar outcome however the degraded material is first sequestered into autophagic vesicles and only then hydrolyzed following lysosomal fusion. Although the exact role played by autophagy in the cells undergoing apoptosis is unclear (could be protective or destructive), the crosstalk between regulatory mechanisms governing the two aforementioned pathways is clearly present. Possible points of contact include FADD Atg5 and Bcl-2 Atg6 interactions [1]. The former one being of a particular interest as it is located directly downstream the TRAIL receptor (stimulated in lab to induce apoptosis) (include the pathway image). One of the project aims was to test the hypothesized link between caspase dependent apoptosis and autophagy, while also developing a set of computational tools fit to carry out the required analysis. On the biochemical side of things, LC3 II (further referred to as LC3) labeling has proven to be a reliable method for monitoring autophagy and autophagy-related processes, including autophagic cell death [5]. Recruitment of the latter protein to autophagosomal membranes occurs along with the vesicle formation, thus allowing to measure the variations in their number as a function of an increase in punctate LC3 [6] (include a figure). Another important point of consideration was being able to distinguish between apoptotic and non apoptotic cells in order to produce distinct uncompromised counts for every specific treatment. The latter was achieved through GRASP65 labelling. GRASP65 (Golgi reassembly and stacking protein of 65 KDa) is a cis-Golgi protein with roles in Golgi structure, membrane trafficking and cell signaling. It is cleaved by caspase-3 early in apoptosis, promoting Golgi fragmentation [7]. As a result apoptotic cells can be detected through indirect immunofluorescence microscopy by tracking the dispersion of a label and thus signal loss. The same detection method applies to LC3 puncta. Many of the complications encountered over the course of the study are imposed by the dramatic changes in cell morphology, ultimately causing cells to fragment into `apoptotic bodies' (defined here as plasma membrane-enclosed remnants that are completely released from the body of the cell) upon receiving the death signal [8]. In adherent cells the latter is also accompanied by loss of contact with neighbours and/or substratum, probably via caspase cleavage of cell-cell contact factors, closely followed by surface blebbing (zeiosis) [8]. Condensation and rounding up of intracellular material eventually leads to overlapping of labelled components making them unresolvable. This problem was partially tackled by zVEID promoted caspase 6 inhibition. Biochemical approaches are very efficient when it comes to experimental design; however a large part of any research is centered on data collection and analysis. Computational intervention at the latter stages does not only speed up the process, but also reduces the human error factor, especially when operating with extensive datasets. The study of autophagy/apoptosis interface generated around 160 image files, each containing more than one cell, harboring a varying number of LC3 puncta. Assuming each snapshot averaged at 4 cells per field and that it would take about 1 minute to analyze every captured cell (considering all the time it would take to adjust the threshold, remove background noise, etc.), were left with 10.6 hours. Does not seem like much, until one realizes that this is only one run-through that does not provide any actual statistical output and is limited to simply counting dots on the screen. Yet another benefit of computational methodology originates from the fact that large portions of information contained within the acquired images cannot be accessed through purely visional analysis. In the case of LC3 puncta, additional datasets containing puncta size and intensity can be produced, adding up to a much broader look into the nature of the studied pathways. The latter was achieved by converting images into a two dimensional numeric array and then constructing a MatLab script able to manipulate with it in a predefined manner. This study illustrates how the aforementioned script is constructed and applied to establish the link between apoptosis and autophagy. Output data generated gave strong evidence of autophagy upregulation in apoptotic cells. This was particularly well demonstrated by Trail time killing assay, which yielded an estimate of the response magnitudes across 7 time points. When plotted, the obtained values formed a sigmoid curve suggesting a burst in autophagosome formation 60mins post treatment.
REFERENCES 1. Thorburn A (2008) Apoptosis and autophagy: regulatory connections between twosupposedly different processes. Apoptosis 13:19. 2. M. Chiara Maiuri, Einat Zalckvar, Adi Kimchi & Guido Kroemer (2007) Self-eating and self-killing: crosstalk between autophagy and apoptosis. Nature Reviews Molecular Cell Biology 8:741-752. Danial, N. N. & Korsmeyer, S. J (2004) Cell death: critical control points. Cell 116, 205219.
8. Jon D. Lane, Victoria J. Allan, Philip G. Woodman (2005) Active relocation of chromatin and endoplasmic reticulum into blebs in late apoptotic cells. Journal of Cell Science 118:4059-4071.
Date: 2016-04-22; view: 699
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