Biology subjects

Parmar, C.

Publications and source records attributed to Parmar, C..

5 recordsLinked to original sources

bioRxiv2025

Constitutively active RAS prolongs Cdc42 signalling while attenuating MAPK signalling during fission yeast mating

The small GTPase RAS is a signalling hub activating multiple pathways, which may respond differently to a constitutively active RAS mutation. We explored this issue using fission yeast, where RAS-mediated pheromone signalling (PS) activates the MAPKSpk1 and Cdc42 pathways. We observed that in cells with the yeast RAS mutant, ras1.G17V, the MAPKSpk1 activation was attenuated similarly to that in wildtype cells, whereas Cdc42 activation was prolonged. We built a mathematical model implementing PS negative-feedback circuits and competition between the two Ras1 effectors, MAPKKKByr2 and Cdc42-GEFScd1. The model robustly predicted the MAPKSpk1 activation dynamics of an additional 20 PS mutants. In support of the model, a recombinant Cdc42-GEFScd1 competed with MAPKKKByr2 for Ras1 binding and overexpression of the Ras binding domain of either Cdc42-GEFScd1 or MAPKKKByr2 inhibited both downstream pathways. Our study has established that the constitutively active RAS signalling propagates differently to downstream pathways where the system prevents MAPK overactivation.

cell biology↗

bioRxiv2024

Spatial mapping of immunosuppressive cancer-associated fibroblast gene signatures in H&E-stained images using additive multiple instance learning

The relative abundance of cancer-associated fibroblast (CAF) subtypes influences a tumors response to treatment, especially immunotherapy. However, the extent to which the underlying tumor composition associates with CAF subtype-specific gene expression is unclear. Here, we describe an interpretable machine learning (ML) approach, additive multiple instance learning (aMIL), to predict bulk gene expression signatures from H&E-stained whole slide images (WSI), focusing on an immunosuppressive LRRC15+ CAF-enriched TGF{beta}-CAF signature. aMIL models accurately predicted TGF{beta}-CAF across various cancer types. Tissue regions contributing most highly to slide-level predictions of TGF{beta}-CAF were evaluated by ML models characterizing spatial distributions of diverse cell and tissue types, stromal subtypes, and nuclear morphology. In breast cancer, regions contributing most to TGF{beta}-CAF-high predictions ("excitatory") were localized to cancer stroma with high fibroblast density and mature collagen fibers. Regions contributing most to TGF{beta}-CAF-low predictions ("inhibitory") were localized to cancer epithelium and densely inflamed stroma. Fibroblast and lymphocyte nuclear morphology also differed between excitatory and inhibitory regions. Thus, aMIL enables a data-driven link between tissue phenotype and transcription, offering biological interpretability beyond typical black-box models.

cancer biology↗

bioRxiv2022

Biologically relevant integration of transcriptomics profiles from cancer cell lines, patient-derived xenografts and clinical tumors using deep neural networks

Cell lines and patient-derived xenografts are essential to cancer research, however, the results derived from such models often lack clinical translatability, as these models do not fully recapitulate the complex cancer biology. It is critically important to better understand the systematic differences between cell lines, xenografts and clinical tumors, and to be able to identify pre-clinical models that sufficiently resemble the biological characteristics of clinical tumors across different cancers. On another side, direct comparison of transcriptional profiles from pre-clinical models and clinical tumors is infeasible due to the mixture of technical artifacts and inherent biological signals. To address these challenges, we developed MOBER, Multi-Origin Batch Effect Remover method, to simultaneously extract biologically meaningful embeddings and remove batch effects from transcriptomic datasets of different origin. MOBER consists of two neural networks: conditional variational autoencoder and source discriminator neural network that is trained in adversarial fashion. We applied MOBER on transcriptional profiles from 932 cancer cell lines, 434 patient-derived tumor xenografts and 11159 clinical tumors and identified pre-clinical models with greatest transcriptional fidelity to clinical tumors, and models that are transcriptionally unrepresentative of their respective clinical tumors. MOBER can conserve the biological signals from the original datasets, while generating embeddings that do not encode confounder information. In addition, it allows for transformation of transcriptional profiles of pre-clinical models to resemble the ones of clinical tumors, and therefore can be used to improve the clinical translation of insights gained from pre-clinical models. As a batch effect removal method, MOBER can be applied widely to transcriptomics datasets of different origin, allowing for integration of multiple datasets simultaneously.

bioinformatics↗

bioRxiv2022

Targeted Sequencing of Alzheimers Disease Genes in Gujarat Cohort

Alzheimers Disease (AD) is the most common type of dementia affecting cognitive and behavioural functions. Its a complex disease that results from the modest effect of gene interaction and environmental factors, as a result of which its exact pathogenesis is still unknown. In the present study, we have performed an association study of exonic regions of 94 genes with AD using the Haloplex target enrichment method and genotyping of 4 genes NOTCH4(rs367398), NEP(rs61760379), APOE (rs121918398), and ATP7B(rs1801243) using PCR RFLP technique. In this study, we screened 32 AD cases and 11 control samples. We have identified 19 variants assigned to 16 genes that were significantly associated with AD with a p-value < 0.05. However, no variants have passed multiple test corrections except NOTCH4/rs367398 genotyped using the PCR-RFLP approach. The functional enrichment analysis of the significant genes for the disease indicates that the genes are significantly involved in AD. Also, functional enrichment analysis for biological processes using ClueGo and network analysis using the STRING database indicated that the genes are directly and indirectly playing a role in AD pathogenesis. The eQTL analysis of the variants with GTEx dataset indicate that out of 19 variants ACHE/rs1799806 and NOTCH4/rs367398 variants is showing significant eQTL for the neighbouring genes for different brain regions and whole blood. Regardless, additional studies with a large cohort are required to establish the role and prevalence of these variants in the local population of Gujarat.

genomics↗

bioRxiv2018

Constitutively active RAS in S. pombe causes persistent Cdc42 signalling but only transient MAPK activation

The small GTPase RAS is a signalling hub for many pathways and oncogenic human RAS mutations are assumed to over-activate all of its downstream pathways. We tested this assumption in fission yeast, where, RAS-mediated pheromone signalling (PS) activates the MAPKSpk1 and Cdc42 pathways. Unexpectedly, we found that constitutively active Ras1.G17V induced immediate but only transient MAPKSpk1 activation, whilst Cdc42 activation persisted. Immediate but transient MAPKSpk1 activation was also seen in the deletion mutant of Cdc42-GEFScd1, a Cdc42 activator. We built a mathematical model using PS negative-feedback circuits and competition between the two Ras1 effectors, MAPKKKByr2 and Cdc42-GEFScd1. The model robustly predicted the MAPKSpk1 activation dynamics of an additional 21 PS mutants. Supporting the model, we showed that a recombinant Cdc42-GEFScd1 fragment competes with MAPKKKByr2 for Ras1 binding. Our study has established a concept that the constitutively active RAS propagates differently to downstream pathways where the system prevents MAPK overactivation. HighlightsO_LIConstitutively active Ras1.GV prolongs Cdc42 activation in S. pombe pheromone signalling C_LIO_LIRas1.GV results in an immediate but only transient MAPKSpk1 activation C_LIO_LIThe RAS effector pathways MAPKSpk1 and Cdc42 compete with each other for active Ras1 C_LIO_LIPredictive modelling explains MAPKSpk1 activation dynamics in 24 signaling-mutants C_LI eTOC BlurbS. pombe Ras1 activates the MAPKSpk1 and Cdc42 pathways. Kelsall et al. report that the constitutively active Ras1.G17V mutation, which causes morphological anomalies, induces prolonged Cdc42 activation but only a transient MAPKSpk1 activation followed by attenuation. Mathematical modelling and biochemical data suggest a competition between the MAPKSpk1 and Cdc42 pathways for active Ras1.

molecular biology↗