Abstract
Self-renewal, the ability of a stem cell to divide repeatedly while maintaining an undifferentiated state, is a defining characteristic of all stem cells. Here, we clarify the molecular foundations of mouse embryonic stem cell (mESC) self-renewal by applying a proven Bayesian network machine learning approach to integrate high-throughput data for protein function discovery. By focusing on a single stem-cell system, at a specific developmental stage, within the context of well-defined biological processes known to be active in that cell type, we produce a consensus predictive network that reflects biological reality more closely than those made by prior efforts using more generalized, context-independent methods. In addition, we show how machine learning efforts may be misled if the tissue specific role of mammalian proteins is not defined in the training set and circumscribed in the evidential data. For this study, we assembled an extensive compendium of mESC data: ∼2.2 million data points, collected from 60 different studies, under 992 conditions. We then integrated these data into a consensus mESC functional relationship network focused on biological processes associated with embryonic stem cell self-renewal and cell fate determination. Computational evaluations, literature validation, and analyses of predicted functional linkages show that our results are highly accurate and biologically relevant. Our mESC network predicts many novel players involved in self-renewal and serves as the foundation for future pluripotent stem cell studies. This network can be used by stem cell researchers (at http://StemSight.org) to explore hypotheses about gene function in the context of self-renewal and to prioritize genes of interest for experimental validation.
Highlights
Stem cells, uniquely characterized by their ability to self-renew and differentiate, are a promising tool for biomedical research and cell-based therapy
The Mouse Genome Informatics (MGI) database referenced 68 Gene Ontology (GO) term annotations for Stat3, a protein expressed in more than 174 mouse tissues [28]. (Appendix S1 lists symbols, names, and synonyms for all genes mentioned and includes references for abbreviations used in this article.) In mouse embryonic stem cell (mESC), Stat3 is a regulator of self-renewal through the LIF-induced JAK/STAT signaling pathway; in hepatocytes, it is involved in many physiological processes, from liver regeneration to apoptosis to metabolism [28,29,30]
We manually curated a positive reference of 2056 pair-wise gene relationships among 354 genes associated with mESC self-renewal or annotated to signaling pathways involved in early embryonic development (Table S1), based on information extracted from 98 recent journal articles (Table S2)
Summary
Uniquely characterized by their ability to self-renew and differentiate, are a promising tool for biomedical research and cell-based therapy. These special cells play pivotal roles in many stages of normal organism development as well as tissue homeostasis and repair [1]. A comprehensive, systems-level view of pluripotent cell self-renewal processes will advance our knowledge of stem cell biology, and facilitate the development of safer biomedical applications. Differentiated cells have more stable gene expression profiles that reflect their distinct roles within tissues and organs; the molecular composition of these mature cells differs dramatically depending on cellular function [5]. Most bioinformatics and systems biology approaches have not yet addressed cell- and tissue-specific concerns
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