Biology subjects

Varma, B.

Publications and source records attributed to Varma, B..

5 recordsLinked to original sources

bioRxiv2026

A Cherry-Flavoured E-Cigarette Adduct, BPGA, Reprograms Alveolar Epithelial Cell Fate Through Epithelial-to-Mesenchymal Transition and Evasion of Apoptosis

E-cigarettes have attracted significant attention as a safer substitute for conventional tobacco smoking. However, they have introduced new inhalable toxicants, including benzaldehyde-propylene glycol acetal (BPGA)--a chemical adduct produced by cherry-flavoured e-cigarettes. The health risks associated with such flavour-derived acetals remain insufficiently elucidated at the cellular level. This study investigated the role of BPGA in the progression of epithelial-to-mesenchymal transition (EMT)-like changes in alveolar epithelial cells (A549 cells). A549 cells exposed to various concentrations of BPGA were analysed for cell viability, morphology, mitochondrial function, lysosomal health, and cytoskeletal integrity using viability assays and fluorescence imaging. Intracellular reactive oxygen species (ROS) production was quantified using the 2,7-dichlorodihydrofluorescein diacetate (DCFH-DA) assay. Antioxidant enzyme expression, inflammatory responses, and EMT-associated phenotypic alterations were evaluated using quantitative reverse transcription polymerase chain reaction (qRT-PCR) and immunofluorescence (IF) assays. Exposure of alveolar epithelial cells to BPGA caused a concentration-dependent decrease in cell viability. BPGA exposure resulted in mitochondrial membrane depolarisation, lysosomal damage, cytoskeletal changes, and stress fibre formation, which altered cell morphology. It significantly increased intracellular ROS production. As a result, antioxidant enzyme levels were upregulated as a protective response. However, during severe oxidative stress, this response was overwhelmed. Excess ROS disrupted cellular homeostasis and initiated apoptosis, though not completely. ROS also acted as a signalling molecule, promoting the upregulation of inflammatory mediators. These changes were associated with altered EMT marker expression, suggesting that BPGA might drive EMT-like remodelling. In conclusion, BPGA, a chemical adduct from e-cigarette vapour, induces alveolar injury by promoting oxidative stress, inflammation, and EMT-related changes, which may explain a mechanism by which e-cigarette exposure could lead to lung injury and pulmonary fibrosis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=169 SRC="FIGDIR/small/724520v1_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@2e9797org.highwire.dtl.DTLVardef@e2543eorg.highwire.dtl.DTLVardef@108bb1aorg.highwire.dtl.DTLVardef@33e710_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG

pharmacology and toxicology↗

bioRxiv2026

Re-analysis of Transcriptomic and Proteomic Data Using Multi-Omics Approaches Identifies Biomarkers of Diabetes-Associated Complications in an INS Mutant Pig Model

Mutant Insulin Induced Diabetes of Youth (MIDY) is an established porcine model caused by the INSC94Y mutation, which results in misfolded insulin, leading to severe {beta}-cell loss and hyperglycemia. Understanding disease pathophysiology is critical for identifying biomarkers and therapeutic targets, and animal models play a key role in this process. In this study, we re-analyzed published transcriptomic and proteomic data from the MIDY model using advanced multi-omics approaches and our in-house SurfacOmics tool. This integrative analysis identified ADAMTS17 as a novel biomarker, suggesting a potential association in diabetes-associated immune dysfunction and delayed wound healing through ECM-immune interplay.

bioinformatics↗

bioRxiv2025

SurfacOmics: an R shiny application integrating variable Feature Selection for gene biomarker discovery using Elastic-Net Regularization

Affordable sequencing technologies have resulted in a rapid rise in genomic and proteomic data. As a result, a massive amount of data is being analyzed, and the outcomes must be summarized as relevant clinical biomarkers. One of the major challenges is enabling wet-lab researchers to make meaningful inferences from the data, even in the absence of expertise in pipeline development and statistical training. We present a user-friendly R shiny application, SurfacOmics, which allows the user to perform biomarker identification via penalized regression algorithms. It also enables researchers to choose a crucial binary variable, such as treatment or group, from the study design metadata to anticipate potential biomarkers. We have introduced a novel concept of scoring scheme to characterize potential biomarkers for prediction, prioritization, and ranking. The tool integrates Gene Ontology information for sub-cellular localization together with a manually curated knowledgebase to provide valuable insights on biological processes, molecular functions and antibody resources, offering a comprehensive view in a single interface.

bioinformatics↗

bioRxiv2023

A Robust Deep Learning Approach for Joint Nuclei Detection and Cell Classification in Pan-Cancer Histology Images

Advanced image processing methods have shown promise in computational pathology, including the extraction of crucial microscopic features from histology images. Accurate detection and classification of cell nuclei from whole-slide images (WSI) play a crucial role in capturing the molecular and morphological landscape of the tissue sample. They enable widespread downstream applications, including cancer diagnosis, prognosis, and discovery of novel markers. Robust nuclei detection and classification are challenging due to the high intra-class variability and inter-class similarity of the microscopic morphological features. This is further compounded by the domain shift arising due to the variability in tissue types, staining protocols, and image acquisition. Motivated by the ability of the recent deep learning techniques to learn complex patterns in a biasfree manner, we develop a novel and robust deep learning model TransNuc, based on vision transformers, for simultaneous detection and classification of cell nuclei from H&E stained WSI. We benchmarked TransNuc on the comprehensive Open Pan-cancer Histology Dataset (PanNuke), sampled from over 20,000 WSI, comprising 19 different tissue types and five clinically important cell classes, namely, Neoplastic, Epithelial, Inflammatory, Connective, and Dead cells. TransNuc exhibited superior performance compared to the state-of-theart, including Hover-Net and Micro-Net. TransNuc was able to learn robust feature representations and thereby perform consistently better for the abundant classes such as neoplastic, and the under-represented classes such as dead cells. Similar performance gains were also obtained for epithelial and connective classes that have a significant inter-class morphological similarity.

pathology↗

bioRxiv2023

Composition of nasopharyngeal microbiota in individuals with SARS-COV-2 infection across three COVID-19 waves in India

Multiple variants of the SARS-CoV-2 virus have been plaguing the world through successive waves of infection over the past three years. Studies by independent research groups across geographies have shown that the microbiome composition in COVID-19 patients (CP) differ from that of healthy individuals (CN). However, such observations were based on limited-sized sample-sets collected primarily from the early days of the pandemic. Here, we study the nasopharyngeal microbiota in COVID-19 patients, wherein the samples have been collected across the three COVID-19 waves witnessed in India, which were driven by different variants of concern. We also present the variations in microbiota of symptomatic vs asymptomatic COVID-19 patients. The nasopharyngeal swabs were collected from 589 subjects providing samples for diagnostics purposes at Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India. CP showed a marked shift in the microbial diversity and composition compared to CN, in a wave-dependent manner. Rickettsiaceae was the only family that was noted to be consistently depleted in CP samples across the waves. The genera Staphylococcus, Anhydrobacter, Thermus, and Aerococcus were observed to be highly abundant in the symptomatic CP patients when compared to the asymptomatic group. In general, we observed a decrease in the burden of opportunistic pathogens in the host microbiota during the later waves of infection. To our knowledge, this is the first longitudinal study which was designed to understand the relation between the evolving nature of the virus and the changes in the human nasopharyngeal microbiota. Such studies not only pave way for better understanding of the disease pathophysiology but also help gather preliminary evidence on whether interventions to the host microbiota can help in better protection or faster recovery.

microbiology↗