Biology subjects

Joshi, T.

Publications and source records attributed to Joshi, T..

4 recordsLinked to original sources

bioRxiv2021

An mRNA processing pathway suppresses metastasis by governing translational control from the nucleus

Cancer cells often co-opt post-transcriptional regulatory mechanisms to achieve pathologic expression of gene networks that drive metastasis. Translational control is a major regulatory hub in oncogenesis, however its effects on cancer progression remain poorly understood. To address this, we used ribosome profiling to compare genome-wide translation efficiencies of poorly and highly metastatic breast cancer cells and patient-derived xenografts. We developed novel regression-based methods to analyze ribosome profiling and alternative polyadenylation data, and identified HNRNPC as a translational controller of a specific mRNA regulon. Mechanistically, HNRNPC, in concert with PABPC4, binds near to poly(A) signals, thereby governing the alternative polyadenylation of a set of mRNAs. We found that HNRNPC and PABPC4 are downregulated in highly metastatic cells, which causes HNRNPC-bound mRNAs to undergo 3 UTR lengthening and subsequently, translational repression. We showed that modulating HNRNPC expression impacts the metastatic capacity of breast cancer cells in xenograft mouse models. We also found that a small molecule, previously shown to induce a distal-to-proximal poly(A) site switching, counteracts the HNRNPC-PABPC4 driven deregulation of alternative polyadenylation and decreases the metastatic lung colonization by breast cancer cells in vivo.

cancer biology↗

bioRxiv2021

Allosteric control of Ubp6 and the proteasome via a bidirectional switch

The proteasome is the principal cellular protease, and recognizes target proteins that have been covalently marked by ubiquitin chains. The ubiquitin signal is subject to rapid editing at the proteasome, allowing it to reject substrates based on topological features of their attached ubiquitin chains. Editing is mediated by a key regulator of the proteasome, deubiquitinating enzyme Ubp6. The proteasome activates Ubp6, whereas Ubp6 inhibits the proteasome-both by deubiquitinating proteasome-bound ubiquitin conjugates, and through a noncatalytic effect that does not involve deubiquitination. We report mutants in both Ubp6 and proteasome subunit Rpt1 that abrogate Ubp6 activation. The Ubp6 mutations fall within its ILR element, defined here, which is conserved from yeast to mammals. The ILR is a component of the BL1 blocking loop, other parts of which obstruct ubiquitin access to the catalytic groove in free Ubp6. Rpt1 docking at the ILR opens the catalytic groove by rearranging not only BL1 but also a novel network of three directly interconnected active-site-blocking loops. Ubp6 activation and noncatalytic proteasome inhibition by Ubp6 are linked in that they were eliminated by the same Ubp6 and Rpt1 mutations. Ubp6 and ubiquitin together drive the proteasome into a unique conformational state associated with proteasome inhibition. Our results identify a multicomponent allosteric switch that exerts simultaneous control over the activity of both Ubp6 and the proteasome, and suggest that their active states are in general mutually exclusive. The findings lead to a new paradigm for allosteric control of deubiquitinating enzymes.

biochemistry↗

bioRxiv2020

Inductive Inference of Gene Regulatory Network Using Supervised and Semi-supervised Graph Neural Networks

Discovering gene regulatory relationships and reconstructing gene regulatory networks (GRN) based on gene expression data is a classical, long-standing computational challenge in bioinformatics. Computationally inferring a possible regulatory relationship between two genes can be formulated as a link prediction problem between two nodes in a graph. Graph neural network (GNN) provides an opportunity to construct GRN by integrating topological neighbor propagation through the whole gene network. We propose an end-to-end gene regulatory graph neural network (GRGNN) approach to reconstruct GRNs from scratch utilizing the gene expression data, in both a supervised and a semi-supervised framework. To get better inductive generalization capability, GRN inference is formulated as a graph classification problem, to distinguish whether a subgraph centered at two nodes contains the link between the two nodes. A linked pair between a transcription factor (TF) and a target gene, and their neighbors are labeled as a positive subgraph, while an unlinked TF and target gene pair and their neighbors are labeled as a negative subgraph. A GNN model is constructed with node features from both explicit gene expression and graph embedding. We demonstrate a noisy starting graph structure built from partial information, such as Pearsons correlation coefficient and mutual information can help guide the GRN inference through an appropriate ensemble technique. Furthermore, a semi-supervised scheme is implemented to increase the quality of the classifier. When compared with established methods, GRGNN achieved state-of-the-art performance on the DREAM5 GRN inference benchmarks. GRGNN is publicly available at https://github.com/juexinwang/GRGNN. HighlightsO_LIWe present a novel formulation of graph classification in inferring gene regulatory relationships from gene expression and graph embedding. C_LIO_LIOur method leverages a powerful framework, gene regulatory graph neural network (GRGNN), which is flexible and powerful to ensemble statistical powers from a number of heuristic skeletons. C_LIO_LIOur results show GRGRNN outperforms previous supervised and unsupervised methods inductively on benchmarks. C_LIO_LIGRGNN can be interpreted and explained following the biological network motif hypothesis in gene regulatory networks. C_LI

bioinformatics↗

bioRxiv2020

Traceback of Core Transcription Factors for Soybean Root Growth Maintenance under Water Deficit

Some crops inhibit shoot growth but maintain root growth under water-deficit conditions. Unraveling the molecular mechanisms of root plasticity under water deficit conditions in plants remains a major challenge. We developed an efficient platform for identifying core transcription factors (TFs) that collectively regulate each other and/or themselves in response to water stress, and exploring their interconnected regulatory circuitry involved in root growth maintenance under water deficit in soybean. We performed multi-species phylogenetic footprinting combined with spatial-temporal transcriptome analysis of soybean (Glycine max) roots under water deficit to identify conserved motifs that function in the water-stress response. Using these functional conserved cis-motifs, we applied a new approach to trace back motifs-associated core TFs ingroup as signal mediators, which mediate signaling between abiotic and endogenous stimuli. We integrated a co-functional TF-TF network and conserved motif-centered TF-DNA networks to construct a core TF network defined by mutual cross-regulation among core TFs. We found that core TF ARG (Abscisic acid response element binding factor-like Root Growth regulator) represses BRG (Brassinosteroid enhanced expression-like Root Growth regulator) expression through binding to its promoter at a conserved binding site. ARG and BRG antagonistically regulate Phytochrome-interacting factor-like Root Growth regulator (PRG) and combinatorially regulate some other core TFs. These core TFs form complex regulatory circuits to integrate light and multiple hormone signaling pathways and maintain root growth in response to varying degrees of water stress. Our study provides valuable information to unravel the complicated mechanisms of molecular networks involved in the regulation of root growth under water deficit.

plant biology↗