Biology subjects

Nair, M. B.

Publications and source records attributed to Nair, M. B..

3 recordsLinked to original sources

bioRxiv2024

EVOLVE: A Web Platform for Evolutionary Phase Analysis and New Variant Exploration from Multi-Sequence Data

Recurrent updates from the World Health Organization (WHO) on refining definitions for Variants of Concern (VOCs), Variants of Interest (VOIs), and Variants Under Monitoring (VUMs) underscore the need for systematic investigations to develop quantifiable metrics that differentiate critical phases of variant evolution. This study illustrates how protein data can be systematically analyzed by applying phase transition principles from statistical mechanics, where quantifying mutational response function (MRF) helps identify concerning variants along evolutionary paths. To support the exploration of metrics like mutational entropy, MRF, and other relevant indicators for novel or uncharacterized viruses and pathogenic bacteria, we introduce EVOLVE--a web platform that empowers researchers across diverse fields to access sequence-based analyses. EVOLVE streamlines data upload and analysis with a user-friendly interface and comprehensive tutorials. Overcoming the challenges with large sequence-space analysis its AI-driven data-learning module integrates evolutionary dynamics with microscopic mutational information, enabling the prediction of future site-specific mutations in prospective missense variants. Access EVOLVE for free at https://www.evolve-0fm3.onrender.com.

biophysics↗

bioRxiv2023

Cell-state transitions and frequency-dependent interactions among subpopulations together explain the dynamics of spontaneous epithelial-mesenchymal heterogeneity in breast cancer

Individual cells in a tumour can be distributed among Epithelial (E) and Mesenchymal (M) cell-states, as characterised by the levels of canonical E and M markers. Even after E and M (E-M) subpopulations are isolated and then cultured independently, E-M heterogeneity can re-equilibrate in each population over time, sometimes regaining the initial distribution of the parental cell population. However, it remains unclear which population-level processes give rise to the dynamical changes in E-M heterogeneity observed experimentally, including 1) differential growth, 2) cell-state switching, and 3) frequency-dependent growth or state-transition rates. Here, we analyse the necessity of these three processes in explaining the dynamics of E-M population distributions as observed in PMC42-LA and HCC38 breast cancer cells. We find that growth differences among E and M subpopulations, with and without any frequency-dependent interactions (cooperation or suppression) among E-M sub-populations, are insufficient to explain the observed population dynamics. This insufficiency is ameliorated by including cell-state transitions, albeit at slow rates, in explaining both PMC42-LA and HCC38 cells data. Further, our models predict that treatment of HCC38 cells with TGF{beta} signalling and JAK2/3 inhibitors could significantly enhance the transition rates from M state to E state, but does not prevent transitions from E to M. Finally, we devise a selection criterion to identify the next most informative time points for which future experimental data can optimally improve the identifiability of our estimated best fit model parameters. Overall, our study identifies the necessary population-level processes shaping the dynamics of E-M heterogeneity in breast cancer cells.

systems biology↗

bioRxiv2020

The pangenome analysis of the soil-borne fungal phytopathogen Rhizoctonia solaniand development of a comprehensive web resource: RsolaniDB

Rhizoctonia solani is a collective group of genetically and pathologically diverse basidiomycetous fungus that damages economically important crops. Its isolates are classified into 13 Anastomosis Groups (AGs) and subgroups having distinctive morphology and host range. The genetic factors driving the unique features of R. solani pathology are not well characterized due to the limited availability of its annotated genomes. Therefore, we performed genome sequencing, assembly, annotation and functional analysis of 12 R. solani isolates covering 7 AGs and selected subgroups (AG1-IA, AG1-IB, AG1-IC, AG2-2IIIB, AG3-PT (isolates Rhs 1AP and the hypovirulent Rhs1A1), AG3-TB, AG4-HG-I (isolates Rs23 and R118-11), AG5, AG6, and AG8), in which six genomes are reported for the first time, wherein we discovered unique and shared secretomes, CAZymes, and effectors across the AGs. Using a pangenome comparative analysis of 12 R. solani isolates and 15 other basidiomycetes, we also elucidated the molecular factors potentially involved in determining the AG-specific host preference, and the attributes distinguishing them from other Basidiomycetes. Finally, we present the largest repertoire of R. solani genomes and their annotated components as a comprehensive database, viz. RsolaniDB, with tools for large-scale data mining, functional enrichment and sequence analysis not available with other state-of-the-art platforms, to assist mycologists in formulating new hypotheses.

genomics↗