Biology subjects

Verma, B. K.

Publications and source records attributed to Verma, B. K..

4 recordsLinked to original sources

bioRxiv2024

NSD2 is a requisite subunit of the AR/FOXA1 neo-enhanceosome in promoting prostate tumorigenesis

The androgen receptor (AR) is a ligand-responsive transcription factor that binds at enhancers to drive terminal differentiation of the prostatic luminal epithelia. By contrast, in tumors originating from these cells, AR chromatin occupancy is extensively reprogrammed to drive hyper-proliferative, metastatic, or therapy-resistant phenotypes, the molecular mechanisms of which remain poorly understood. Here, we show that the tumor-specific enhancer circuitry of AR is critically reliant on the activity of Nuclear Receptor Binding SET Domain Protein 2 (NSD2), a histone 3 lysine 36 di-methyltransferase. NSD2 expression is abnormally gained in prostate cancer cells and its functional inhibition impairs AR trans-activation potential through partial off-loading from over 40,000 genomic sites, which is greater than 65% of the AR tumor cistrome. The NSD2-dependent AR sites distinctly harbor a chimeric AR-half motif juxtaposed to a FOXA1 element. Similar chimeric motifs of AR are absent at the NSD2-independent AR enhancers and instead contain the canonical palindromic motifs. Meta-analyses of AR cistromes from patient tumors uncovered chimeric AR motifs to exclusively participate in tumor-specific enhancer circuitries, with a minimal role in the physiological activity of AR. Accordingly, NSD2 inactivation attenuated hallmark cancer phenotypes that were fully reinstated upon exogenous NSD2 re-expression. Inactivation of NSD2 also engendered increased dependency on its paralog NSD1, which independently maintained AR and MYC hyper-transcriptional programs in cancer cells. Concordantly, a dual NSD1/2 PROTAC degrader, called LLC0150, was preferentially cytotoxic in AR-dependent prostate cancer as well as NSD2-altered hematologic malignancies. Altogether, we identify NSD2 as a novel subunit of the AR neo-enhanceosome that wires prostate cancer gene expression programs, positioning NSD1/2 as viable paralog co-targets in advanced prostate cancer.

cancer biology↗

bioRxiv2023

Informing virtual clinical trials of hepatocellular carcinoma with spatial multi-omics analysis of a human neoadjuvant immunotherapy clinical trial

Human clinical trials are important tools to advance novel systemic therapies improve treatment outcomes for cancer patients. The few durable treatment options have led to a critical need to advance new therapeutics in hepatocellular carcinoma (HCC). Recent human clinical trials have shown that new combination immunotherapeutic regimens provide unprecedented clinical response in a subset of patients. Computational methods that can simulate tumors from mathematical equations describing cellular and molecular interactions are emerging as promising tools to simulate the impact of therapy entirely in silico. To facilitate designing dosing regimen and identifying potential biomarkers, we developed a new computational model to track tumor progression at organ scale while reflecting the spatial heterogeneity in the tumor at tissue scale in HCC. This computational model is called a spatial quantitative systems pharmacology (spQSP) platform and it is also designed to simulate the effects of combination immunotherapy. We then validate the results from the spQSP system by leveraging real-world spatial multi-omics data from a neoadjuvant HCC clinical trial combining anti-PD-1 immunotherapy and a multitargeted tyrosine kinase inhibitor (TKI) cabozantinib. The model output is compared with spatial data from Imaging Mass Cytometry (IMC). Both IMC data and simulation results suggest closer proximity between CD8 T cell and macrophages among non-responders while the reverse trend was observed for responders. The analyses also imply wider dispersion of immune cells and less scattered cancer cells in responders samples. We also compared the model output with Visium spatial transcriptomics analyses of samples from post-treatment tumor resections in the original clinical trial. Both spatial transcriptomic data and simulation results identify the role of spatial patterns of tumor vasculature and TGF{beta} in tumor and immune cell interactions. To our knowledge, this is the first spatial tumor model for virtual clinical trials at a molecular scale that is grounded in high-throughput spatial multi-omics data from a human clinical trial.

bioengineering↗

bioRxiv2021

Substrate stiffness modulates integrin α5 expression and ECM-associated gene expression in fibroblasts

Biomaterials, like polydimethylsiloxane (PDMS), are soft, biocompatible, and tuneable, which makes them useful to delineate specific substrate factors that regulate the complex landscape of cell-substrate interactions. We used a commercial formulation of PDMS to fabricate substrates with moduli 40 kPa, 300 kPa, and 1.5 MPa, and cultured HMF3S fibroblasts on them. Gene expression analysis was performed by RT-PCR and Western blotting. Cellular and nuclear morphologies were analyzed using confocal imaging, and MMP-2 and MMP-9 activities were determined with gelatin zymography. Results, comparing mechanotransduction on PDMS substrates with control petridishes, show that substrate stiffness modulates cell morphologies and proliferations. Cell nuclei were rounded on compliant substrates and correlated with increased tubulin expression. Proliferations were higher on stiffer substrates with cell cycle arrest on softer substrates. Integrin 5 expression decreased on lower stiffness substrates, and correlated with inefficient TGF-{beta} activation. Changes to the activated state of the fibroblast on higher stiffness substrates were linked to altered TGF-{beta} secretion. Collagen I, collagen III, and MMP-2 expression levels were lower on compliant PDMS substrates as compared to stiffer ones; there was little MMP-9 activity on substrates. These results demonstrate critical feedback mechanisms between substrate stiffness and ECM regulation by fibroblasts which is highly relevant in diseases like tissue fibrosis.

bioengineering↗

bioRxiv2021

Flux vs burden: biosensor optimization for dynamic pathway engineering

Recent progress in synthetic biology allows the construction of dynamic control circuits for metabolic engineering. This technology promises to overcome many challenges encountered in traditional pathway engineering, thanks to their ability to self-regulate gene expression in response to bioreactor perturbations. The central components in these control circuits are metabolite biosensors that read out pathway signals and actuate enzyme expression. However, the construction of metabolite biosensors is a major bottleneck for strain design, and a key challenge is to understand the relation between biosensor dose-response curves and pathway performance. Here we employ multiobjective optimization to quantify performance trade-offs that arise in the design and calibration of metabolite biosensors. Our approach reveals strategies for tuning dose-response curves along an optimal trade-off between production flux and the cost of an increased expression burden on the host. We explore properties of control architectures built in the literature, and identify their advantages and caveats in terms of performance and robustness to growth conditions and leaky promoters. We demonstrate the optimality of a control circuit for glucaric acid production in Escherichia coli, which has been shown to increase titer by 2.5-fold as compared to static designs. Our results lay the groundwork for the automated design of control circuits for pathway engineering, with applications in the food, energy and pharmaceutical sectors.

synthetic biology↗