Biology subjects

Ray, U.

Publications and source records attributed to Ray, U..

6 recordsLinked to original sources

bioRxiv2025

Gene regulatory network transitions reveal the central transcription factors in lung adenocarcinoma progression

Transcription factors play a central role in cancer growth, progression, and metastasis, and contribute to intratumor phenotypic plasticity which enable drug tolerance and cancer relapse. Changes in the regulatory activities of transcription factors in cancer may not always be detected by investigating mutational signatures or differential expression of the transcription factor, as done in traditional analysis. In addition, past studies have focused on the activities of transcription factors in tumors as a whole and thus, have not fully captured the heterogeneity in gene regulation among different cell types within the tumor microenvironment. In this work, through an analysis of the transitions in regulatory network architecture and gene regulation dynamics, we identify the central transcription factors associated with lung adenocarcinoma progression. The gene NR2F1, associated with neurodevelopment and cancer dormancy, emerge as a key transcription factor in the progression of lung adenocarcinoma. We further identify transcription factors that are only active in cancer samples and uncover how changes in gene regulation dynamics influence intratumor heterogeneity. Taken together, our work elucidates the transitions in gene regulatory network during cancer progression, identifies central transcription factors in this process, and reveals the complex regulatory changes cooccurring in different cell types within the tumor microenvironment.

systems biology↗

bioRxiv2025

Role of drug induced nuclear CTSL (nCTSL) in DNA damage response in cancer- therapeutic implications

ABSTRACT Background: Despite treatment advances, ovarian cancer (OC) remains the deadliest gynecological cancer, with a ~70% mortality rate. OCs initially respond to platinum chemotherapy but often develop resistance. While PARP inhibitors improve relapse-free survival in responsive cases, they do not extend overall survival. This underscores the need for effective combination therapies beyond BRCA status. The lysosomal protein cathepsin L (CTSL) can traffic to the nucleus, where it can regulate DNA repair and serve as a chromatin modifier. Here, we tested the hypothesis that nucleoside analog clofarabine (CLF) induced nuclear CTSL (nCTSL) serves as a biomarker of sensitivity to DNA-damaging agents in ovarian cancer. Methods: Role of nuclear CTSL in DNA damage, synergy with PARPis and CRM1 inhibitor, tumor regression was analyzed by 3D-spheroid culture, colony forming assay, subcellular fractions, comet assay, and IFC/ western-blotting using PARP inhibitor-sensitive and -resistant OC cell lines, ex vivo cultures of patient-derived OC ascites cells (OVA), primary OCs, and patient-derived xenografts (PDXs). Results: In OC cell lines, treatment with CLF induced nCTSL in a subset of models-designated CLF-responsive (CLF-r)-and sensitized them to the PARP inhibitors olaparib and rucaparib. In CLF-non-responsive (CLF-nr) models, nCTSL trafficking and drug synergy were achieved with the CLF+olaparib combination. Synergy was observed in 47% of CLF-r and 24% of CLF-nr OVA samples. Mechanistically, CLF monotherapy induced nuclear import of CTSL via KPNB1 in CLF-r cells, while combination treatment with CLF+olaparib was required in CLF-nr cells. CLF also downregulated the nuclear export protein CRM1 in both CLF-r and CLF-nr models. CTSL knockdown conferred resistance to CLF+olaparib in both cohorts, supporting a functional role for nCTSL in therapeutic response. Notably, in a subset of OVAs (29%) classified as CLF-resistant (CLF-Res), CLF failed to downregulate CRM1; however, pharmacological inhibition of CRM1 with KPT8602 restored sensitivity to CLF+PARP inhibitor treatment. In vivo, CLF+olaparib treatment in CLF-r and CLF-nr PDX models led to enhanced DNA damage, reduced tumor burden, and prolonged survival. Conclusion: Collectively, these findings identify nCTSL as a predictive marker of response to DNA-damaging therapies and support the CLF+olaparib combination as a promising strategy for overcoming drug resistance in OC via CTSL-mediated DNA damage.

cancer biology↗

bioRxiv2023

Ribosome demand links transcriptional bursts to protein expression noise

Stochastic variation in protein expression generates phenotypic heterogeneity in a cell population and has an important role in antibiotic persistence, mutation penetrance, tumor growth and therapy resistance. Studies investigating molecular origins of noise have predominantly focused on the transcription process. However, the noise generated in the transcription process is further modulated by translation. This influences the expression noise at the protein level which eventually determines the extent of phenotypic heterogeneity in a cell population. Studies across different organisms have revealed a positive association between translational efficiency and protein noise. However, the molecular basis of this association has remained unknown. In this work, through stochastic modeling of translation in single mRNA molecules and empirical measurements of protein noise, we show that ribosome demand associated with high translational efficiency in a gene drives the correlation between translational efficiency and protein noise. We also show that this correlation is present only in genes with bursty transcription. Thus, our work reveals the molecular basis of how coding sequence of genes, along with their promoters, can regulate noise. These findings have important implications for investigating protein noise and phenotypic heterogeneity across biological systems.

systems biology↗

bioRxiv2022

Comparative multi-OMICS single-cell atlas of five COVID-19 (rAdVV and mRNA) vaccines describe unique and distinct mechanisms of action

COVID-19 vaccines based on a range of expression platforms have shown considerable protective efficacy, generating antibody and T cell immune responses. However, molecular pathways underpinning COVID-19 vaccine priming of immunity against the SARS-CoV-2 virus have not yet been explored extensively. This analysis is critical to optimization of future vaccination strategies, schedules, and combinations. Thus, we investigated a cohort of individuals pre- and post-vaccination to understand the humoral and cellular immune response against different COVID-19 vaccines, including recombinant adenoviral vector (rAdVV) and mRNA-based vaccines. Single-cell RNA sequencing allowed characterization of monocytes, T, NK and B cell activation at the transcriptomics/proteomic level, in response to different COVID-19 vaccines. Our data revealed that different COVID-19 vaccines elicit a unique and distinct mechanism of action. Specifically, we revealed that rAdVV vaccines negatively regulate CD4+ T cell activation, leukocytes chemotaxis, IL-18 signalling and antigen presentation by monocytes whilst mRNA vaccines positively regulate NKT cell activation, platelets activation and chemokine signalling pathways. An antigen-specific T cell response was already observed following the 1st vaccine dose and was not further augmented after the subsequent 2nd dose of the same vaccine and it was dependent on the type of vaccination used. Our integrated three layered-analyses highlights that COVID-19 vaccines evoke a strong but divergent immune response at the RNA, protein, and cellular levels. Our approach is able to pinpoint efficacy and mechanisms controlling immunity to vaccination and open the door for better vaccination which could induce innate and adaptive immunity equally in the long term. Key findingsO_LIDecrease in major three cell types classical and non-classical monocytes and NK type III cells after COVID-19 vaccination C_LIO_LIIndividual vaccination (AZ, JJ, MD, PB) has differential effect on various immune cell subsets and regulates unique cell populations, whilst no change was observed for CV vaccination C_LIO_LIrAdVV and mRNA vaccines have different mechanism of action for activation of lymphocytes and monocytes, respectively C_LIO_LIrAdVV vaccines negatively regulates CD4+ T cell activation, leukocytes chemotaxis, IL-18 signalling and antigen presentation whilst mRNA vaccines positively regulate NKT cell activation, platelets activation and chemokine signalling pathways. C_LIO_LIAn antigen-specific T cell response was prompted after the 1st vaccine dose and not augmented after the subsequent 2nd dose of the same vaccine. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/507666v1_ufig1.gif" ALT="Figure 1"> View larger version (59K): org.highwire.dtl.DTLVardef@11fd7eborg.highwire.dtl.DTLVardef@198a9c7org.highwire.dtl.DTLVardef@1b28735org.highwire.dtl.DTLVardef@1cadbb5_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2021

Barcoded Competitive Clone-Initiating Cell (BC-CIC) Analysis Reveals Differences in Ovarian Cancer Cell Genotype and Niche Specific Clonal Fitness During Growth and Metastasis In Vivo

During oncogenesis, pathogenic clones develop which contain cells capable of spreading throughout the body, ultimately compromising vital organ functions and physiology. Understanding how metastatic clones develop and spread is critical for improving cancer treatments. However, our understanding of these processes has been hampered by a paucity of quantitative methodologies to comprehensively map, track and characterize such clones. To address this shortcoming, we have developed a DNA barcoding and next-generation sequencing based system-wide clonal tracking technology integrated with a computational data analysis pipeline called Clone-Initiating Cell (CIC) Calculator. The CIC Calculator interfaces with the CIC Morbus Mandala (CIC-MM) plot, a novel tool to visually comprehend and detect four distinct categories that explains their complex relationships with various tissues/organ sites. Further, we describe machine learning approaches to study CIC number, frequency, and estimate clone size and distribution demonstrating distinct growth patterns, and their inter-relationships and their routes of metastatic spread at clonal resolution. We demonstrate these methodologies, using our novel multifunctional lentiviral barcode libraries, and specifically barcoded tubal-ovarian metastatic OVCAR5 cell lines (engineered to express varying levels of metastasis promoting LRRC15 gene) and co-injected cells in a competitive CIC assay into tubal or ovarian sites in highly immunodeficient NSG mice. DNA was isolated from primary tumors, omental/bowel metastasis and system-wide anatomical site/organs. Amplicon sequencing libraries were constructed with spike-in-control barcodes (serving as internal calibration controls) to estimate absolute clone sizes. The computational pipeline CIC Calculator was then used to deconvolute and filter the data, set stringent thresholds, and generate high-quality information on CIC numbers and frequencies, clone sizes, linkages across sites and classify clones based on their extent of metastatic activity. Using of CIC-MM plot, statistical models and machine learning approaches, we generated high-resolution clonal maps of metastasis for each animal. The information generated included clone types and system-wide metastasis, similar and dissimilar clonal patterns of dominance at heterotopic sites and their routes of metastases. The data revealed previously unknown influences of cellular genotype and their implanted sites on selecting certain clones with specific system-wide clonal patterns, and identified rare LRRC15 expressor clones (classified as CIC.Toti) predisposed to exploit all sites, albeit at varying degrees of dominance. The genomic technology and computational methodology described here are tissue-agnostic. They enable rapid adoption for an investigation into various stages of system-wide metastasis and growth of transplantable malignant cells at the highest clonal resolution.

cancer biology↗

bioRxiv2020

A virus that has gone viral: Amino acid mutation in S protein of Indian isolate of Coronavirus COVID-19 might impact receptor binding and thus infectivity

Since 2002, beta coronaviruses (CoV) have caused three zoonotic outbreaks, SARS-CoV in 2002, MERS-CoV in 2012, and the recent outbreak of SARS-CoV-2 late in 2019 (also named as COVID-19 or novel coronavirus 2019 or nCoV2019. Spike(S) protein, one of the structural proteins of this virus plays key role in receptor (ACE2) binding and thus virus entry. Thus, this protein has attracted scientists for detailed study and therapeutic targeting. As the 2019 novel coronavirus takes its course throughout the world, more and more sequence analyses are been done and genome sequences getting deposited in various databases. From India two clinical isolates have been sequenced and the full genome deposited in GenBank. We have performed sequence analyses of the spike protein of the Indian isolates and compared with that of the Wuhan, China (where the outbreak was first reported). While all the sequences of Wuhan isolates are identical, we found point mutations in the Indian isolates. Out of the two isolates one was found to harbour a mutation in its Receptor binding domain (RBD) at position 407. At this site arginine (a positively charged amino acid) was replaced by isoleucine (a hydrophobic amino acid that is also a C-beta branched amino acid). This mutation has been seen to change the secondary structure of the protein at that region and this can potentially alter receptor ding of the virus. Although this finding needs further validation and more sequencing, the information might be useful in rational drug designing and vaccine engineering.

microbiology↗