Biology subjects

Mahadik, A.

Publications and source records attributed to Mahadik, A..

3 recordsLinked to original sources

bioRxiv2026

Early Partitioning of Structural Paralog Diversity Shapes Immune Evolution Across Habitat Transitions in Gobiiform Fishes

Habitat transitions expose species lineages to novel pathogen regimes and are often hypothesized to drive adaptive diversification of immune gene families. However, the temporal association between gene family diversification and ecological change remains unresolved. To gain deeper insight into the relationship between the diversification of species and the evolution of their immune system, we investigated the evolutionary history of Toll-like receptors (TLRs) across Gobiiformes, a clade characterized by repeated transitions across aquatic and amphibious environments. TLRs are well-studied membrane-bound pattern recognition receptors that play crucial roles in detecting pathogens and immune activation. Phylogenomic, structural, and sequence analyses reveal that a major expansion of TLR22 predates many ecological transitions, with early paralog diversification partitioning receptor architectures into distinct structural regimes that persist across lineages. Subsequent evolution is concentrated in the extracellular ligand-binding, leucine-rich repeat (LRR) domains, where localized sequence and structural variation enables likely functional tuning without major architectural innovation. These results indicate that ecological transitions do not require repeated evolution of new immune receptor forms, but can instead be facilitated by reconfiguration of pre-existing immunogenetic diversity. These findings raise the possibility that expansions of immune gene families occurring early in a clades evolutionary history may commonly persist and subsequently act as substrates for evolutionary responses to environmental change.

evolutionary biology↗

bioRxiv2023

Computational workflow for predicting transcriptional modulation to achieve therapeutically desired cellular conversion for regenerative medicine

IntroductionRegenerative medicine promises a cure for currently incurable diseases and pathological conditions. Its central idea is to leverage healthy cells to regenerate diseased cells, tissues or organs through the process of cellular reprogramming. The most common method to achieve this is by modulating the activity of specific transcription factors. However, the large number of protein-coding genes and transcription factors in humans and their complex interactions poses a challenge in identifying the most suitable ones for modulation. Here, we propose a computational workflow that facilitates the prediction of such transcription factors for achieving desired cellular reprogramming, along with highlighting their mechanistic basis in terms of the gene regulatory network of the target cell type. MethodsIn this paper, we propose a synergistic workflow that leverages existing computational tools: TransSynW, PAGA and SIGNET, a software - Cytoscape and two databases - TRRUST and UniProt. It uses single-cell transcriptome data of the starting and target cell types as inputs. We demonstrate this workflow by predicting suitable transcriptional modulations for reprogramming of human foreskin fibroblasts to oculomotor neurons. ResultsUsing the workflow, we hypothesized the core drivers for specific cellular reprogramming along with their functional understanding for experimental applications. The workflow predicted the transcription factors for modulation and provided insight into their differential expression dynamics and influence on the predicted gene regulatory network of the target cells. ConclusionOur computational workflow helps extract meaningful predictive and mechanistic insights from high-dimensional biological data, which otherwise is difficult to accomplish from individual tools alone. We believe this workflow can help researchers generate mechanistically founded hypotheses for achieving desired cellular reprogramming as a step towards regenerative medicine. HighlightsO_LICombine computational tools as workflows to gain predictive and mechanistic insights C_LIO_LIThe workflow predicts suitable transcription factors for targeted cellular reprogramming C_LIO_LIGain insight into the influence of transcriptional modulation on gene regulatory network C_LIO_LIThe workflow generates mechanistically founded hypotheses for transcriptional modulation C_LIO_LIRationalized experimental design for targeted cellular reprogramming for regenerative therapies C_LI

bioinformatics↗

bioRxiv2022

Proteochemometric method for pIC50 prediction of Flaviviridae

Viruses remain an area of concern despite constant development of antiviral drugs and therapies. One of the contributors among others is the flaviviridae family of viruses. Like other spaces, antiviral peptides (AVP) are gaining importance for studying flaviviridae family. Along with antiviral properties of peptides, information about bioactivity takes it even closer to accurate predictions of peptide capabilities. Experimental identification of bioactivity of each potential peptide is an expensive and time consuming task. Computational methods like Proteochemometric modelling (PCM) are promising for prediction of bioactivity based on peptide and target sequence. The additional edge PCM methods bring in is the aspect of considering both peptide and target properties instead of only looking at peptide properties. In this study, we propose prediction of pIC50 for AVP against flaviviridae family target proteins. The target proteins were manually curated from literature. Here we utilize the PCM descriptors as peptide descriptors, target descriptors and cross term descriptors. We observe taking peptide and target information improves the results qualitatively and gives better pIC50 predictions. The R2 and MAPE values are 0.85 and 8.44 % respectively

bioinformatics↗