I. A SYSTEMS LEVEL UNDERSTANDING OF DIFFERENTIAL CELL-FATE OUTCOME IN RESPONSE TO THE SAME STIMULUS. In an effort to find answers that could explain differential cell-fate outcome in response to the same uniform stimulus, we explored the link between regulatory network architecture and the genome-scale dynamics of the underlying entities (genes, mRNAs, and proteins). By classifying DNA-binding TFs in the yeast regulatory network into three hierarchical groups/layers (top, core, and bottom) and integrating diverse genome-scale datasets, we found that at the protein level, the top-layer TFs (which trigger/initiate regulatory cascades) are relatively abundant, long-lived, and showed more cell-to-cell variability compared to the downstream (core- and bottom-layer) TFs. This and other results led us to conclude that the variability in expression of top-layer TFs might confer a selective advantage, as this may permit at least some members in a clonal cell population to initiate an effective response to fluctuating environments, whereas the tight regulation of the core- and bottom-layer TFs may minimize noise propagation and ensure fidelity in regulation. We proposed that the interplay between network architecture and TF dynamics could permit differential utilization of the same underlying gene network by distinct members of a clonal cell population thereby allowing at least some members in the population to effectively adapt to (or survive in) changing environments. This result is critical to understanding phenotypic variability in fluctuating environments, where individuals of the same population exhibit different phenotypes in response to the same stimulus, e.g., fractional survival or cell-death in clonal cell populations upon drug treatment in diseases such as cancer. Previous studies have shown that (i) the dynamics of the regulatory proteins, which either dictate cell death or survival, varied widely between individual cancer cells, and (ii) naturally occurring differences in the levels/states of proteins regulating apoptosis are the primary causes of cell-to-cell variability in the timing and probability of death in individual members of the population upon induction of apoptosis. We suggested that this dynamic variability in expression level of key regulatory proteins permits differential sampling (i.e., the survival network or the apoptotic network) of the same underlying regulatory network (governing all cells) by different members in a clonal population, which might result in divergent cell-fate outcomes among different individuals in an otherwise identical cell population. Our findings have implications in developmental processes and cellular differentiation that involve populations of cells, and in synthetic biology experiments aimed at engineering gene regulatory circuits. In particular, the dynamics of TFs in terms of their abundance, half-life, and noise cannot be ignored as modulating these attributes could affect the outcome of a regulatory cascade. This study takes us one step closer towards a better understanding of how cells adapt to changing environments, how different phenotypic outcomes are mediated in clonal cell populations, and how mutations that disrupt the dynamics of key regulatory proteins may influence disease conditions. II. EPIGENETIC MODIFICATIONS POISE GENES FOR FUTURE EXPRESSION Chromatin modifications have been implicated in the regulation of gene expression. While association of certain modifications with expressed or silent genes has been established, it remains unclear how changes in chromatin environment relate to changes in gene expression. In this article, we used ChIP-seq (chromatin immunoprecipitation with massively parallel sequencing) to analyze the genome-wide changes in chromatin modifications during activation of total human CD4(+) T cells by T-cell receptor (TCR) signaling. Surprisingly, we found that the chromatin modification patterns at many induced and silenced genes are relatively stable during the short-term activation of resting T cells. Active chromatin modifications were already in place for a majority of inducible protein-coding genes, even while the genes were silent in resting cells. Similarly, genes that were silenced upon T-cell activation retained positive chromatin modifications even after being silenced. To investigate if these observations are also valid for miRNA-coding genes, we systematically identified promoters for known miRNA genes using epigenetic marks and profiled their expression patterns using deep sequencing. We found that chromatin modifications can poise miRNA-coding genes as well. Our data suggest that miRNA- and protein-coding genes share similar mechanisms of regulation by chromatin modifications, which poise inducible genes for activation in response to environmental stimuli. III. INFERRING PROTEIN DOMAIN INTERACTIONS FROM INCOMPLETE PROTEIN-PROTEIN INTERACTION NETWORKS Protein-protein interactions, though extremely valuable towards a better understanding of protein functions and cellular processes, do not provide any direct information about the regions/domains within the proteins that mediate the interaction. Most often, it is only a fraction of a protein that directly interacts with its biological partners. We have cleverly combined the use of genetic and functional data to infer as precisely as possible the interactions between functional domains of proteins in, and on the surface of, living cells. The significance of his work can hardly be overstated, as it lies at the heart of making further advances in our identification and understanding of metabolic and signaling pathways that respond to environmental factors. We presented a new graph-theory based approach guided by Gene Ontology information to infer protein domain interactions from cross-species protein-protein interaction networks. Overall, this and our previous body of work in this area is pivotal towards uncovering previously unrecognized protein domain interactions, which is a critical step towards (i) molecular level understanding of protein-protein interactions (ii) precise identification of binding sites, (iii) acquisition of insights into the causes of deleterious mutations at interaction sites, and most importantly (iv) development of drugs to inhibit pathological protein interactions. In addition to pursuing our own research and tools development in this area, we also mainten a protein domain interaction database, which is a critically important resource for experimental biologists who seek to test for new protein and domain interactions.
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