Biology subjects

Panchal, V.

Publications and source records attributed to Panchal, V..

4 recordsLinked to original sources

bioRxiv2026

Induced microfold cells are required for the function of pulmonary tertiary lymphoid organs that promote mucosal immunity

Tertiary lymphoid organs (TLOs) arise in response to infection and inflammation. We show that influenza-induced lung TLOs are comprised of inducible M cell (iM cell)-inducible bronchus-associated lymphoid tissue (iBALT) functional composites. iM cells are necessary for the appropriate architecture of adjacent immune follicles, which contain clonally expanded and sequence-diversified B cells. iM cell ablation results in fewer antigen-specific germinal center B cells, T follicular helper cells, and T helper memory cells, and a decrease in secretory IgA and IgG production accompanied by a loss of viral control. We find that the iM cells arise from epithelial progenitors that respond to a RANKL signal from infiltrating T cells, consistent with a model in which the patchy inflammation typical of pneumonia results in the unpredictable and varying distribution of coupled iM cells and adjacent iBALT. We propose that iM cells could be harnessed to promote infection control and enhance mucosal vaccination.

immunology↗

bioRxiv2020

STIL a novel link in Shh and Wnt signaling, endowing oncogenic and stem like attributes to colorectal cancer

Discovery of potent gene regulating tumorigenesis and drug resistance is of high clinical importance. STIL is an oncogene, however its molecular insights and role in colorectal oncogenesis are unknown. In this study we have explored role of STIL in tumorigenesis and studied its molecular targets in colorectal cancer (CRC). STIL silencing reduced proliferation and tumor growth in CRC. Further, STIL was found to regulate stemness markers CD133 & CD44 and drug resistant markers Thymidylate synthase, ABCB1 & ABCG2 both in in-vitro and in-vivo CRC models. In addition, over expression of STIL mRNA was found to be associated with reduced disease free survival in CRC cases. To our surprise we observed an Shh independent regulation of stemness and drug resistant genes mediated by STIL. Interestingly, we found an Shh independent regulation of {beta}-catenin mediated by STIL via p-AKT, which partially answers Shh independent regulatory mechanism of CSC markers by STIL. Our study suggest an instrumental role of STIL in molecular manifestation of CRC and progression.

cancer biology↗

bioRxiv2019

High-dimensional Bayesian phenotype classification and model selection using genomic predictors

MotivationIn this paper we describe a Bayesian hierarchical model termed PMMLogit for classification and model selection in high-dimensional settings with binary phenotypes as outcomes. Posterior computation in the logistic model is known to be computationally demanding due to its non-conjugacy with common priors. We combine a Polya-Gamma based data augmentation strategy and use recent results on Markov chain Monte-Carlo (MCMC) techniques to develop an efficient and exact sampling strategy for the posterior computation. We use the resulting MCMC chain for model selection and choose the best combination(s) of genomic variables via posterior model probabilities. Further, a Bayesian model averaging (BMA) approach using the posterior mean, which averages across visited models, is shown to give superior prediction of phenotypes given genomic measurements.\n\nResultsUsing simulation studies, we compared the performance of the proposed method with other popular methods. Simulation results show that the proposed method is quite effective in selecting the true model and has better estimation and prediction accuracy than other methods. These observations are consistent with theoretical results that have been developed in the statistics literature on optimality for this class of priors. Application to two well-known datasets on colon cancer and leukemia identified genes that have been previously reported in the clinical literature to be related to the disease outcomes.\n\nAvailabilitySource code is publicly available on GitHub at https://github.com/v-panchal/PMML.\n\nContactdlinder@augusta.edu\n\nSupplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2019

REVERSE ENGINEERING GENE NETWORKS USING GLOBAL-LOCAL SHRINKAGE RULES

Inferring gene regulatory networks from high-throughput omics data has proven to be a computationally demanding task of critical importance. Frequently the classical methods breakdown due to the curse of dimensionality, and popular strategies to overcome this are typically based on regularized versions of the classical methods. However, these approaches rely on loss functions that may not be robust and usually do not allow for the incorporation of prior information in a straightforward way. Fully Bayesian methods are equipped to handle both of these shortcomings quite naturally, and they offer potential for improvements in network structure learning. We propose a Bayesian hierarchical model to reconstruct gene regulatory networks from time series gene expression data, such as those common in perturbation experiments of biological systems. The proposed methodology utilizes global-local shrinkage priors for posterior selection of regulatory edges and relaxes the common normal likelihood assumption in order to allow for heavy-tailed data, which was shown in several of the cited references to severely impact network inference. We provide a sufficient condition for posterior propriety and derive an efficient MCMC via Gibbs sampling in the Appendix. We describe a novel way to detect multiple scales based on the corresponding posterior quantities. Finally, we demonstrate the performance of our approach in a simulation study and compare it with existing methods on real data from a T-cell activation study.

systems biology↗