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The leading hypothesis of why organisms age is the “Free Radical Theory of Aging”, which states that the accumulation of reactive oxygen species (ROS), such as superoxide (O2•-) and hydrogen peroxide (H2O2), causes protein, lipid and DNA damage and leads to the observed age-related decline of cells and tissues. A major obstacle in analyzing the role of oxidative stress in aging organisms is the inability to precisely localize and quantify the oxidants, to identify proteins and pathways that might be affected, and ultimately, to correlate changes in oxidant levels with the lifespan of the organism. To directly monitor the onset and extent of oxidative stress during the lifespan of Caenorhabditis elegans, we utilized the fluorescent H2O2 sensor protein HyPer, which enabled us to quantify endogenous peroxide levels in different tissues of living animals in real time. We made the surprising observation that wildtype C. elegans is exposed to very high peroxide levels during development. Peroxide levels drop rapidly as the animals mature, and low peroxide levels then prevail throughout the reproductive age, after which an age-accompanying increase of peroxide level is observed. These results were in excellent agreement with findings obtained by using the highly quantitative redox proteomic technique OxICAT, which monitors the oxidation status of redox-sensitive proteins as read-out for onset, localization, and protein targets of oxidative stress. By using OxICAT, we detected increased protein thiol oxidation during the development of C. elegans and in aging animals. Many processes in C. elegans might potentially contribute to the elevated peroxide levels observed during development, including cuticle formation, apoptosis, proliferation, gametogenesis, or ROS signaling. The finding that all investigated C. elegans mutants regardless of their lifespan are exposed to high developmental peroxide levels argues for ROS accumulation to be a universal and necessary event. Yet, recovery from the early oxidative boost might determine the subsequent adult lifespan, as we found that long-lived daf-2 mutants transition faster to reducing conditions than short-lived daf-16 mutants, which retain higher peroxide levels throughout their mature life. These results suggest that changes in the cellular oxidant homeostasis, encountered at a very early stage in life, might determine subsequent redox levels and potentially the lifespan of organisms. Manipulation of developmental oxidant levels using glucose restriction or a short bolus of superoxide caused a disruption in developmental growth, a delay in reproduction, and a shortened lifespan. These results suggest that developmental oxidant levels are fine-tuned and optimized. Future experiments are aimed to investigate the sources of developmental hydrogen peroxide, and to elucidate whether active down-regulation of antioxidant enzymes during the larval period might foster peroxide accumulation. Preliminary results indicate that this might indeed be the case for peroxiredoxin 2, whose expression was significantly lower during development than at later stages in life. Finally, we investigated whether the observed variances in the developmental peroxide levels of individual worms within a synchronized wildtype population might be responsible for the observed significant variances in lifespan, and hence could serve as a predictor for adult lifespan. Preliminary results revealed that neither too low nor too high peroxide levels during development are beneficial for the lifespan of wildtype worms, suggesting that ROS level during development might be optimized for maximized lifespan. Future experiments aim to reveal the processes that are affected by ROS and which might influence the individual’s lifespan early in life.
Approaches to the Analysis of Proteomics and Transcriptomics Data based on Statistical Methodology
(2014)
Recent developments in genomics and molecular biology led to the generation of an enormous amount of complex data of different origin. This is demonstrated by a number of published results from microarray experiments in Gene Expression Omnibus. The number was growing in exponential pace over the last decade. The challenge of interpreting these vast amounts of data from different technologies led to the development of new methods in the fields of computational biology and bioinformatics. Researchers often want to represent biological phenomena in the most detailed and comprehensive way. However, due to the technological limitations and other factors like limited resources this is not always possible. On one hand, more detailed and comprehensive research generates data of high complexity that is very often difficult to approach analytically, however, giving bioinformatics a chance to draw more precise and deeper conclusions. On the other hand, for low-complexity tasks the data distribution is known and we can fit a mathematical model. Then, to infer from this mathematical model, researchers can use well-known and standard methodologies. In return for using standard methodologies, the biological questions we are answering might not be unveiling the whole complexity of the biological meaning. Nowadays it is a standard that a biological study involves generation of large amounts of data that needs to be analyzed with a statistical inference. Sometimes data challenge researchers with low complexity task that can be performed with standard and popular methodologies as in Proteomic analysis of mouse oocytes reveals 28 candidate factors of the "reprogrammome". There, we established a protocol for proteomics data that involves preprocessing of the raw data and conducting Gene Ontology overrepresentation analysis utilizing hypergeometric distribution. In cases, where the data complexity is high and there are no published frameworks a researcher could follow, randomization can be an approach to exploit. In two studies by The mouse oocyte proteome escapes maternal aging and CellFateScout - a bioinformatics tool for elucidating small molecule signaling pathways that drive cells in a specific direction we showed how randomization can be performed for distinct complex tasks. In The mouse oocyte proteome escapes maternal aging we constructed a random sample of semantic similarity score between oocyte transcriptome and random transcriptome subset of oocyte proteome size. Therefore, we could calculate whether the proteome is representative of the trancriptome. Further, we established a novel framework for Gene Ontology overrepresentation that involves randomization testing. Every Gene Ontology term is tested whether randomly reassigning all gene labels of belonging to or not belonging to this term will decrease the overall expression level in this term. In CellFateScout - a bioinformatics tool for elucidating small molecule signaling pathways that drive cells in a specific direction we validated CellFateScout against other well-known bioinformatics tools. We stated the question whether our plugin is able to predict small molecule effects better in terms of expression signatures. For this, we constructed a protocol that uses randomization testing. We assess here if the small molecule effect described as a (set of) active signaling pathways, as detected by our plugin or other bioinformatics tools, is significantly closer to known small molecule targets than a random path.