In the last decade omics technologies enabled detailed and system-wide analysis of complex biological samples. Genomics, transcriptomics and metabolomics all benefited tremendously from technological advances in their respective fields. Proteomics was revolutionised by mass spectrometry, which allowed simultaneous identification of thousands of proteins in cells, tissues and organisms. And this mainly qualitative revolution, quickly turned quantitative. This work had two main objectives. Firstly, to apply the state of the art instrumentation, data analysis and bioinformatics methods to better our understanding of basic cell biology in a model organism Saccharomyces cerevisiae. Specifically, to quantitatively describe the effects of perturbations, such as adverse environmental conditions or chaperone gene deletions, on protein abundances in the cell. Additionally, it was aimed to demonstrate and evaluate the ability of a new timeof-flight mass spectrometer to perform large-scale absolute quantification. First, it was found that yeast cells are remarkably robust to deletions of major chaperone hub proteins (Ssa1p or Ssb1p deletions). This ability was attributed to network structure and redistribution of folding workload among other related chaperones rather than simple functional redundancy. Second, to build on the first set of results, a detailed time resolved description of yeast proteome dynamics in response to heat stress was provided for the wild type and Ssb1p chaperone mutant strains. In this study, for the first time in the literature, temporal expression patterns of many hallmark heat shock proteins were elucidated. Globally, a slow and sustained proteome remodelling or 'buffering' was revealed in both strains. However, it was also shown that the cells knocked out for the Ssb1p chaperone respond to heat in a distinctly different manner to the wild type strain. Finally, consistent and reproducible absolute quantification of multiple yeast proteomes was demonstrated using a new commercial time-of-flight mass spectrometer with ion mobility separation capabilities. The data obtained revealed global differences in cellular protein content between various chaperone prefoldin mutants as well as differential expression of a set of proteins promising to be interesting targets for further investigations.

Energy balance includes energy intake and energy expenditure. Either excessive food intake or insufficient physical activity will increase the body mass and cause obesity, a worldwide health problem. In the US, more than two-thirds of people are obesity or overweight. Conversely, it is well accepted that reducing energy intake can increase the life span and the resistance to age-related diseases. MicroRNAs are highly conserved non-coding RNA molecules with a length of 21-23 nucleotides. Recent studies show that numerous microRNAs are associated with the regulation of oxidative stress, inflammation, insulin signaling, apoptosis, and angiogenesis that relate to obesity. However, the role of microRNAs in the regulation of energy balance in central nervous system remains unknown, especially within the hypothalamus, a primary site of energy balance control. In this project, microRNA, and mRNA were profiled using microarray technology. Furthermore, quantitative proteomics were used to identify differential protein levels during fasting, and in a genetically obese mouse model, Mice were given either a 24-hour fast, or ad libitum access to food. Hypothalamic RNA and microRNA samples were analyzed by microarray, using both the Affymetrix and Toray 3D mRNA and microRNA platforms. No microRNAs were found to be differentially expressed between two treatments, whereas numerous mRNAs were significantly regulated by fasting, including 7 cell cycle related genes. Hypothalamic protein samples from WT and N2KO mice treated either to ad lib feeding or 24-hour fasting were analyzed by MSE quantitative proteomics. Over 650 proteins were identified with some proteins showing significantly different abundances between or among the four groups. Between ad lib fed WT and N2KO mice, 53 proteins were differentially expressed, with some of these linked to neurodegeneration, NAD synthesis, and the citrate acid cycle (TCA). Overall, the results of this study suggest that while microRNA-mediated mechanisms are not significant modulators of hypothalamic gene expression upon a 24 hour fast, cell cycle gene expression changes represent a major contributor to the fasting response. Moreover, Nlhl2 might play an important role in the neurodegeneration and mitochondrial metabolism. / Ph. D.

The marine bacterium Sphingopyxis alaskensis was isolated as one of the most numerically abundant bacteria from cold (4–10??C) nutrient depleted waters in the North Pacific Ocean. The objective of this study was to examine cold adaptation of S. alaskensis by using proteomics to examine changes in global protein levels caused by growth at low (10??C) and high (30??C) temperatures. Stable isotope labelling-based quantitative proteomics was used, and a rigorous post-experimental data processing workflow adapted from microarray-based methods was developed. The approach included metabolic labelling with 14N/15N and normalisation and statistical testing of quantitative proteomics data. Approximately 400,000 tandem mass spectra were generated resulting in the confident identification of 2,135 proteins (66% genome coverage) and the quantitation of 1,172 proteins (37% genome coverage). Normalisation approaches were evaluated using cultures grown at 30??C and labelled with 14N and 15N. For 10??C vs. 30??C experiments, protein quantities were normalised within each experiment using a multivariate lowess approach. Statistical significance was assessed by combining data from all experiments and applying a moderated t-test using the empirical Bayes method with the limma package in R. Proteins were ranked after calculating the B-statistic and the Storey-Tibshirani false discovery rate. 217 proteins (6% genome coverage) were determined to have significant quantitative differences. In achieving these outcomes a range of factors that impact on quantitative proteomics data quality were broadly assessed, resulting in the development of a robust approach that is generally applicable to quantitative proteomics of biological system. The significantly differentially abundant proteins from the proteomics data provided insight into molecular mechanisms of cold adaptation in S. alaskensis. Important aspects of cold adaptation included cell membrane restructuring, exopolysaccharide biosynthesis, lipid degradation, carbohydrate and amino acid metabolism, and increased capacity of transcriptional and translational processes. A number of cold adaptive responses in S. alaskensis were novel, including a specific cold-active protein folding pathway, a possible thermally-controlled stringent response, and biosynthesis of intracellular polyhydroxyalkanoate reserve material. The overall study provided important new insight into the evolution of growth strategies necessary for the effective competition of S. alaskensis in cold, oligotrophic environments.

The marine bacterium Sphingopyxis alaskensis was isolated as one of the most numerically abundant bacteria from cold (4–10??C) nutrient depleted waters in the North Pacific Ocean. The objective of this study was to examine cold adaptation of S. alaskensis by using proteomics to examine changes in global protein levels caused by growth at low (10??C) and high (30??C) temperatures. Stable isotope labelling-based quantitative proteomics was used, and a rigorous post-experimental data processing workflow adapted from microarray-based methods was developed. The approach included metabolic labelling with 14N/15N and normalisation and statistical testing of quantitative proteomics data. Approximately 400,000 tandem mass spectra were generated resulting in the confident identification of 2,135 proteins (66% genome coverage) and the quantitation of 1,172 proteins (37% genome coverage). Normalisation approaches were evaluated using cultures grown at 30??C and labelled with 14N and 15N. For 10??C vs. 30??C experiments, protein quantities were normalised within each experiment using a multivariate lowess approach. Statistical significance was assessed by combining data from all experiments and applying a moderated t-test using the empirical Bayes method with the limma package in R. Proteins were ranked after calculating the B-statistic and the Storey-Tibshirani false discovery rate. 217 proteins (6% genome coverage) were determined to have significant quantitative differences. In achieving these outcomes a range of factors that impact on quantitative proteomics data quality were broadly assessed, resulting in the development of a robust approach that is generally applicable to quantitative proteomics of biological system. The significantly differentially abundant proteins from the proteomics data provided insight into molecular mechanisms of cold adaptation in S. alaskensis. Important aspects of cold adaptation included cell membrane restructuring, exopolysaccharide biosynthesis, lipid degradation, carbohydrate and amino acid metabolism, and increased capacity of transcriptional and translational processes. A number of cold adaptive responses in S. alaskensis were novel, including a specific cold-active protein folding pathway, a possible thermally-controlled stringent response, and biosynthesis of intracellular polyhydroxyalkanoate reserve material. The overall study provided important new insight into the evolution of growth strategies necessary for the effective competition of S. alaskensis in cold, oligotrophic environments.

No description available.

Background: The unicellular green alga Chlamydomonas reinhardtii is an important eukaryotic model organism for the study of photosynthesis and plant growth. In the era of modern highthroughput technologies there is an imperative need to integrate large-scale data sets from highthroughput experimental techniques using computational methods and database resources to provide comprehensive information about the molecular and cellular organization of a single organism. Results: In the framework of the German Systems Biology initiative GoFORSYS, a pathway database and web-portal for Chlamydomonas (ChlamyCyc) was established, which currently features about 250 metabolic pathways with associated genes, enzymes, and compound information. ChlamyCyc was assembled using an integrative approach combining the recently published genome sequence, bioinformatics methods, and experimental data from metabolomics and proteomics experiments. We analyzed and integrated a combination of primary and secondary database resources, such as existing genome annotations from JGI, EST collections, orthology information, and MapMan classification. Conclusion: ChlamyCyc provides a curated and integrated systems biology repository that will enable and assist in systematic studies of fundamental cellular processes in Chlamydomonas. The ChlamyCyc database and web-portal is freely available under http://chlamycyc.mpimp-golm.mpg.de.

The main approach to studying the proteome is a technique called data dependent acquisition (DDA). In DDA, peptides are analyzed by mass spectrometry to determine the protein composition of a biological isolate. However, DDA is limited in its ability to analyze the proteome, in that it only selects the most abundant ions for analysis, and different protein identifications can result even if the same sample is analyzed multiple times in succession. Data independent acquisition (DIA) is a newly developed method that should be able to solve these limitations and improve our ability to analyze the proteome. We used an implementation of DIA (SWATH) to perform relative protein quantitation in the model bacterial system, Clostridium stercorarium, using two different carbohydrate sources, and found that it was able to provide precise quantitation of proteins and was overall more consistent in its ability to identify components of the proteome than DDA. Relative quantitation of proteins is an important method that can determine which proteins are important to a biochemical process of interest. How we determine which proteins are differentially regulated between different conditions is an important question in proteomic analysis. We developed a new approach to analyzing differential protein expression using variation between biological replicates to determine which proteins are being differentially regulated between two conditions. This analysis showed that a large proportion of proteins identified by quantitative proteomic analysis can be differentially regulated and that these proteins are in fact related to biological processes. Analyzing changes in protein expression is a useful tool that can pinpoint many key processes in biological systems. However, these techniques fail to take into account that enzyme activity is regulated by other factors than controlling their level of expression. Activity based protein profiling (ABPP) is a method that can determine the activity state of an enzyme in whole cell proteomes. We found that enzyme activity can change in response to a number of different conditions and that these changes do not always correspond with compositional changes. Mass spectrometry techniques were also used to identify serine hydrolases and characterize their expression in this organism. / February 2016

Microglia act as the frontline immune defense in the brain. Microglial responses can be either neurotoxic, through the release of reactive oxygen and nitrogen species and inflammatory cytokines, or neurotrophic. Microglial activation due to chronic ethanol exposure has been implicated in neuroinflammation. We use mass spectrometric metabolic labeling techniques to explore and quantify the microglial proteome in immortalized cell lines and in vivo enriched microglia. Our proteomic profiling and subsequent validation suggests that microglia do activate in response to ethanol exposure, but the activation falls short of the classical, or M1 state of inflammatory activation, as no downstream markers for reactive species nor inflammatory cytokines can be found. Additionally, proteomic profiling suggests a partial activation marked by increased cell engulfment and cell movement in addition to increased release of inf-gamma and tgf-beta.

In this thesis, the detection of global proteomics alteration and changes in neuropeptide distribution caused by neuropathic pain in rat spinal cord tissue was the main focus. Neuropathic pain (NP) is a major clinical syndrome caused by disease or dysfunction of the nervous system and often mediated by neuronal networks in the spinal cord. The estimated prevalence of NP is 6-8% in general population. Only in the United States, the indirect cost associated with chronic pain has been estimated to 100 billion dollars each year and NP substantially contributes to this cost. So far, the underlying mechanisms of NP are not well understood. Proteomics techniques are commonly used in biology system studies, due to its high throughput, capability of unbiased analysis and sensitivity. It builds up a bridge to link genes, peptides, proteins, and the disease. Two proteomic/peptidomic approaches were developed, evaluated and discussed in this thesis. Both of them were further applied in the studies of neuropathic pain. First approach is a quantitative proteomic approach using liquid chromatography combined with Fourier transform mass spectrometry (LC-FTMS), which is developed for quantitative analysis of proteins originated from small central nervous system (CNS) samples. This approach was successfully applied in the study of the rat spinal cord tissue proteome in a neuropathic pain model. Another approach is using matrix assisted laser desorption ionization mass spectrometry (MALDI-MS) for the visualization of the distribution of neuropeptides in rat spinal cord, which in the future will be applied in investigating the ongoing signal transmission under neuropathic pain conditions. Results provided by these two methods are of high importance for the general understanding of the underlying pathophysiological mechanisms and potential identification of new targets for novel treatment of neuropathic pain.

Alzheimer’s Disease is the most common form of dementia, affecting 48 million people worldwide. Despite this fact, only 45% of the patients have received the diagnose. The reason behind this is the fact that the cause of the disease is still unclear. Several hypotheses have been suggested, with main focus in the imbalance between the production and the clearance of Αβ in the brain (formation of plaques) or hyperphosphorylation of the tau protein (formation of tangles). In order to have a better understanding of what is actually happening in the brain, more biomarkers need to be developed. Keeping this in mind, we tried to develop a method to monitor the protein levels of SAP in the brain. SAP is a glycoprotein, normally produced by the liver in acute phase immune responses. SAP has been correlated with AD in the 1980s and quite recently it has been shown that SAP is elevated in AD patients, but not in individuals with plaques and no dementia. For this reason, we developed a mass spectrometry based targeted quantification method for monitoring SAP in the brain, as well as C9, a blood contamination reference protein. Our method is robust enough to be further used in large studies, in order to investigate the role of SAP in AD.