Biology subjects

Arora, I.

Publications and source records attributed to Arora, I..

3 recordsLinked to original sources

bioRxiv2024

Echidna: A Bayesian framework for quantifying gene dosage effect impacting phenotypic plasticity

Phenotypic plasticity, the ability of cells to adapt their behavior in response to genetic or environmental changes, is a fundamental biological process that drives cellular diversity in both normal and pathological contexts, including in tumor evolution. While chromosomal instability and somatic copy number alterations (CNAs) are known to influence cellular states, it remains difficult to separate genetic from cell non-autonomous mechanisms that govern transcriptional variability. Here, we present Echidna, a Bayesian hierarchical model that integrates single-cell RNA sequencing (scRNA-seq) and bulk whole-genome sequencing (WGS) data to quantify the impact of CNAs on gene expression dynamics. By jointly inferring clone-specific CNA profiles and uncovering clonal dependencies, Echidna bridges genomic and transcriptomic landscapes within and across multiple time points, enabling the decoupling of gene dosage effects from cell-extrinsic factors on phenotypic plasticity. Applying Echidna to patient tumor specimens, we demonstrate its superior performance in clonal reconstruction and derive insights into resistance mechanisms.

bioinformatics↗

bioRxiv2023

mtx-COBRA: Subcellular localization prediction for bacterial proteins

BackgroundBacteria can have beneficial effects on our health and environment; however, many are responsible for serious infectious diseases, warranting the need for vaccines against such pathogens. Bioinformatic and experimental technologies are crucial for the development of vaccines. The vaccine design pipeline requires identification of bacteria-specific antigens that can be recognized and induce a response by the immune system upon infection. Immune system recognition is influenced by the location of a protein. Methods have been developed to determine the subcellular localization (SCL) of proteins in prokaryotes and eukaryotes. Bioinformatic tools such as PSORTb can be employed to determine SCL of proteins, which would be tedious to perform experimentally. Unfortunately, PSORTb often predicts many proteins as having an "Unknown" SCL, reducing the number of antigens to evaluate as potential vaccine targets. MethodWe present a new pipeline called subCellular lOcalization prediction for BacteRiAl Proteins (mtx-COBRA). mtx-COBRA uses Metas protein language model, Evolutionary Scale Modeling, combined with an Extreme Gradient Boosting machine learning model to identify SCL of bacterial proteins based on amino acid sequence. This pipeline is trained on a curated dataset that combines data from UniProt and the publicly available ePSORTdb dataset. ResultsUsing benchmarking analyses, nested 5-fold cross-validation, and leave-one-pathogen-out methods, followed by testing on the held-out dataset, we show that our pipeline predicts the SCL of bacterial proteins more accurately than PSORTb. Conclusionsmtx-COBRA provides an accessible pipeline with greater efficiency to classify bacterial proteins with currently "Unknown" SCLs than existing bioinformatic and experimental methods.

bioinformatics↗

bioRxiv2020

Nutritional combinatorial impact on the gut microbiota and plasma short-chain fatty acids levels in the prevention of mammary cancer in Her2/neu estrogen receptor-negative transgenic mice

Breast cancer is the second leading cause of cancer-related mortality in women. Various nutritional compounds possess anti-carcinogenic properties which may be mediated through their effects on the gut microbiota and its production of short-chain fatty acids (SCFAs) for the prevention of breast cancer. We evaluated the impact of broccoli sprouts (BSp), green tea polyphenols (GTPs) and their combination on the gut microbiota and SCFAs metabolism from the microbiota in Her2/neu transgenic mice that spontaneously develop estrogen receptor-negative [ER (-)] mammary tumors. The mice were grouped based on the dietary treatment: control, BSp, GTPs or their combination from beginning in early life (BE) or life-long from conception (LC). We found that the combination group showed the strongest inhibiting effect on tumor growth volume and a significant increase in tumor latency. BSp treatment was integrally more efficacious than the GTPs group when compared to the control group. There was similar clustering of microbiota of BSp-fed mice with combination-fed mice, and GTPs-fed mice with control-fed mice at pre-tumor and post-tumor in both BE and LC groups. The mice on all dietary treatment groups incurred a significant increase of Adlercreutzia genus and S24-7 family in the both BE and LC groups. We found no change in SCFAs levels in the plasma of BSp-fed, GTPs-fed and combination-fed mice of the BE group. Marked changes were observed in the mice of the LC group consisting of significant increases in propionate and isobutyrate in GTPs-fed and combination-fed mice. These studies indicate that nutrients such as BSp and GTPs differentially affect the gut microbial composition in both the BE and LC groups and the key metabolites (SCFAs) levels in the LC group. The findings also suggest that temporal factors related to different time windows of consumption during the life-span can have a promising influence on the gut microbial composition, SCFAs profiles and ER (-) breast cancer prevention.

microbiology↗