Biology subjects

Krishna, S.

Publications and source records attributed to Krishna, S..

57 records · Page 4Linked to original sources

bioRxiv2017

Dynamic expression of tRNA-derived small RNAs define cellular states

Transfer RNA (tRNA)-derived small RNAs (tsRNAs) have recently emerged as important regulators of protein translation and shown to have diverse biological functions. However, the underlying cellular and molecular mechanisms of tsRNA function in the context of dynamic cell-state transitions remain unclear. Here we report the identification of a set of tsRNAs upregulated in differentiating mouse embryonic stem cells (mESCs). Mechanistic analyses revealed primary functions of tsRNAs in regulating polysome assembly and translation. Notably, interactome studies with differentially-enriched tsRNAs revealed a switch in associations with effector RNPs and target mRNAs in different cell-states. We also demonstrate that a specific pool of tsRNAs can interact with Igf2bp1, an RNA-binding protein, to influence the expression of the pluripotency-promoting factor-c-Myc, thereby providing evidence for tsRNAs in modulating stem cell-states in mESCs. Finally, tsRNA expression analyses in distinct, heterologous cell and tissue models of stem/transformed versus differentiated/normal states reveal that tsRNA-mediated regulation of protein translation may represent a global biological phenomenon associated with cell-state transitions.\n\nOne Sentence SummaryIdentification and functional characterization of tRNA-derived small RNAs (tsRNAs) in cell state switches.

molecular biology↗

bioRxiv2017

Daam2 Driven Degradation Of VHL Promotes Gliomagenesis

Von Hippel-Landau (VHL) protein is a potent tumor suppressor regulating numerous pathways that drive cancer, but mutations in VHL are restricted to limited subsets of malignancies. Here we identified a novel mechanism for VHL suppression in tumors that do not have inactivating mutations. Using developmental processes to uncover new pathways contributing to tumorigenesis, we found that Daam2 promotes glioma formation. Protein expression screening identified an inverse correlation between Daam2 and VHL expression across a host of cancers, including glioma. These in silico insights guided corroborating functional studies, which revealed that Daam2 promotes tumorigenesis by suppressing VHL expression. Furthermore, biochemical analyses demonstrate that Daam2 associates with VHL and facilitates its ubiquitination and degradation. Together, these studies are the first to define an upstream mechanism regulating VHL suppression in cancer and describe the role of Daam2 in tumorigenesis.\n\nStatement of SignificanceWe found that the glial developmental factor Daam2 promotes glioma tumorigenesis by suppressing VHL expression. Our studies show, for the first time, a regulatory mechanism that operates upstream of VHL in cancer and provides an explanation for how VHL expression is extinguished in tumors that do not have inactivating mutations.

cancer biology↗

bioRxiv2017

Detection Of Cooperatively Bound Transcription Factor Pairs Using ChIP-seq Peak Intensities And Expectation Maximization

Transcription factors (TFs) often work cooperatively, where the binding of one TF to DNA enhances the binding affinity of a second TF to a nearby location. Such cooperative binding is important for activating gene expression from promoters and enhancers in both prokaryotic and eukaryotic cells. Existing methods to detect cooperative binding of a TF pair rely on analyzing the sequence that is bound. We propose a method that uses, instead, only ChIP-seq peak intensities and an expectation maximization (CPI-EM) algorithm. We validate our method using ChIP-seq data from cells where one of a pair of TFs under consideration has been genetically knocked out. Our algorithm relies on our observation that cooperative TF-TF binding is correlated with weak binding of one of the TFs, which we demonstrate in a variety of cell types, including E. coli, S. cerevisiae and M. musculus cells. We show that this method performs significantly better than a predictor based only on the ChIP-seq peak distance of the TFs under consideration. This suggests that peak intensities contain information that can help detect the cooperative binding of a TF pair. CPI-EM also outperforms an existing sequence-based algorithm in detecting cooperative binding. The CPI-EM algorithm is available at https://github.com/vishakad/cpi-em.

genomics↗