No abstract

The oocyte is the only cell of the body that can reprogram transplanted somatic nuclei and sets the gold standard for all reprogramming methods. Therefore, an in-depth characterization of its proteome holds promise to advance our understanding of reprogramming and germ cell biology. To date, limitations on oocyte numbers and proteomic technology have impeded this task, and the search for reprogramming factors has been conducted in embryonic stem (ES) cells instead. Here, we present the proteome of metaphase II mouse oocytes to a depth of 3699 proteins, which substantially extends the number of proteins identified until now in mouse oocytes and is comparable by size to the proteome of undifferentiated mouse ES cells. Twenty-eight oocyte proteins, also detected in ES cells, match the criteria of our multilevel approach to screen for reprogramming factors, namely nuclear localization, chromatin modification, and catalytic activity. Our oocyte proteome catalog thus advances the definition of the "reprogrammome", the set of molecules--proteins, RNAs, lipids, and small molecules--that enable reprogramming.

The long-standing view of 'immortal germline vs mortal soma' poses a fundamental question in biology concerning how oocytes age in molecular terms. A mainstream hypothesis is that maternal ageing of oocytes has its roots in gene transcription. Investigating the proteins resulting from mRNA translation would reveal how far the levels of functionally available proteins correlate with mRNAs and would offer novel insights into the changes oocytes undergo during maternal ageing. Gene ontology (GO) semantic analysis revealed a high similarity of the detected proteome (2324 proteins) to the transcriptome (22 334 mRNAs), although not all proteins had a cognate mRNA. Concerning their dynamics, fourfold changes of abundance were more frequent in the proteome (3%) than the transcriptome (0.05%), with no correlation. Whereas proteins associated with the nucleus (e.g. structural maintenance of chromosomes and spindle-assembly checkpoints) were largely represented among those that change in oocytes during maternal ageing; proteins associated with oxidative stress/damage (e.g. superoxide dismutase) were infrequent. These quantitative alterations are either impoverishing or enriching. Using GO analysis, these alterations do not relate in any simple way to the classic signature of ageing known from somatic tissues. Given the lack of correlation, we conclude that proteome analysis of mouse oocytes may not be surrogated with transcriptome analysis. Furthermore, we conclude that the classic features of ageing may not be transposed from somatic tissues to oocytes in a one-to-one fashion. Overall, there is more to the maternal ageing of oocytes than mere cellular deterioration exemplified by the notorious increase of meiotic aneuploidy.

BackgroundExperimentalists are overwhelmed by high-throughput data and there is an urgent need to condense information into simple hypotheses. For example, large amounts of microarray and deep sequencing data are becoming available, describing a variety of experimental conditions such as gene knockout and knockdown, the effect of interventions, and the differences between tissues and cell lines.ResultsTo address this challenge, we developed a method, implemented as a Cytoscape plugin called ExprEssence. As input we take a network of interaction, stimulation and/or inhibition links between genes/proteins, and differential data, such as gene expression data, tracking an intervention or development in time. We condense the network, highlighting those links across which the largest changes can be observed. Highlighting is based on a simple formula inspired by the law of mass action. We can interactively modify the threshold for highlighting and instantaneously visualize results. We applied ExprEssence to three scenarios describing kidney podocyte biology, pluripotency and ageing: 1) We identify putative processes involved in podocyte (de-)differentiation and validate one prediction experimentally. 2) We predict and validate the expression level of a transcription factor involved in pluripotency. 3) Finally, we generate plausible hypotheses on the role of apoptosis, cell cycle deregulation and DNA repair in ageing data obtained from the hippocampus.ConclusionReducing the size of gene/protein networks to the few links affected by large changes allows to screen for putative mechanistic relationships among the genes/proteins that are involved in adaptation to different experimental conditions, yielding important hypotheses, insights and suggestions for new experiments. We note that we do not focus on the identification of 'active subnetworks'. Instead we focus on the identification of single links (which may or may not form subnetworks), and these single links are much easier to validate experimentally than submodules. ExprEssence is available at http://sourceforge.net/projects/expressence/.