Biology subjects

Bhandary, P.

Publications and source records attributed to Bhandary, P..

6 recordsLinked to original sources

bioRxiv2026

CCT2 expression marks a translation-activated state in sporadic ALS motor neurons and is associated with functional decline

Sporadic disease accounts for about 90% of amyotrophic lateral sclerosis (ALS) cases, and its biological heterogeneity is a major contributor to the failure of trials that enroll patients under a single diagnosis. Because sporadic ALS lacks a shared genetic anchor, identifying biological signals is critical for effective patient stratification. We analyzed induced pluripotent stem cell derived motor neurons from 619 AnswerALS participants using transcriptomics, proteomics, and whole-genome sequencing and identified CCT2 as the first single-gene marker of a molecular state within sporadic ALS, providing a measurable basis for trial enrollment according to biology. We first anchored the analysis in known ALS genotypes. C9orf72 and SOD1 motor neurons did not converge on a common signature but retained genotype-specific differences that bulk postmortem tissue compresses. Scoring 391 sporadic patients along three transcriptomic axes (bioenergetic stress, cell cycle, and inflammation) resolved three states. One state was marked by CCT2, a TRiC chaperonin subunit, which distinguished it from the quiescent state with an area under the curve of 0.892. CCT2 was also elevated at the protein level and associated with the rate of ALSFRS-R decline, although this association weakened after adjustment for culture composition. Upstream regulator analysis placed this state downstream of MYC and mTORC1 activation. No germline test across 360 whole genomes explained these states. Validation using Target ALS data showed that the relationship between CCT2 and the cell cycle program was also present in postmortem cortex enriched for layer 5b projection neurons.

bioinformatics↗

bioRxiv2024

An Integrative Study to Investigate Sex-Specific Biomarkers in Bladder Cancer Patients

Bladder cancer shows distinct sex-related patterns, with male patients experiencing significantly higher incidence and female patients facing worse survival outcomes. In this paper, we aimed to address the lack of understanding of the biological mechanisms responsible for this sex-based divergence through an integrative analysis using bladder cancer data from TCGA and GTEx. Our study analyzed various bladder cancer data types, including genomic mutation data, gene expression data, and clinical data. We conducted an in-depth study of protein-protein interactions, pathway analysis, survival analysis, and immune cell correlations. Notably, we discovered that the androgen receptor (AR) related pathways were unique to male hub genes, while the Wnt signaling pathway was unique to female hub genes. Additionally, we identified 14 hub genes with significant sex-biased survival rates, including known DLGAP5, SOX2, LAMA2, and COL5A2, as well as new discoveries of male-specific markers ERCC5, NID1, and ANK2, and female-specific RAD51C, COL22A1 and COL5A2. Furthermore, we identified four male hub genes--DAXX, IKBKB, PDGFRA, and PPARG--that overlapped with immune-related genes. The expression of these genes exhibited differential interactions with immune cells between males and females. These insights could pave the way for more personalized and effective therapeutic interventions tailored to male and female patients.

bioinformatics↗

bioRxiv2024

Using GPT-4 to write a scientific review article: a pilot evaluation study

GPT-4, as the most advanced version of OpenAIs large language models, has attracted widespread attention, rapidly becoming an indispensable AI tool across various areas. This includes its exploration by scientists for diverse applications. Our study focused on assessing GPT-4s capabilities in generating text, tables, and diagrams for biomedical review papers. We also assessed the consistency in text generation by GPT-4, along with potential plagiarism issues when employing this model for the composition of scientific review papers. Based on the results, we suggest the development of enhanced functionalities in ChatGPT, aiming to meet the needs of the scientific community more effectively. This includes enhancements in uploaded document processing for reference materials, a deeper grasp of intricate biomedical concepts, more precise and efficient information distillation for table generation, and a further refined model specifically tailored for scientific diagram creation.

bioinformatics↗

bioRxiv2023

Computational Framework for Statistical Epistasis Supports XOR Penetrance Function in a Living System

Statistical epistasis has been studied extensively because of its potential to provide evidence for genetic interactions for phenotypes, but there have been methodological limitations to its exhaustive, widespread application. We present new algorithms for the interaction coefficients for standard regression models for epistasis that permit many varied encodings for the interaction terms for loci and efficient memory usage. The algorithms are given for two-way and three-way epistasis and may be generalized to higher order epistasis. Statistical tests for the interaction coefficients are also provided. We also present an efficient matrix based algorithm for permutation testing for two-way epistasis. We offer a proof and experimental evidence that methods that look for epistasis only at loci that have main effects may not be justified. Given the computational efficiency of the algorithm, we applied the method to a rat data set and mouse data set, with at least 10000 loci and 1000 samples each, using the standard Cartesian encoding and the XOR penetrance function for the interactions, to test for evidence of statistical epistasis for the phenotype of body mass index. This study revealed that the XOR penetrance function found greater evidence for statistical epistasis in many more pairs of loci in both data sets and in the rat data set, those pairs of loci found using the XOR penetrance function are enriched for biologically relevant pathways. Author summaryEpistasis, the interaction between two or more genes, is likely integral to the study of genetics and present throughout nature. Yet, it is seldom fully explored as most approaches primarily focus on single-locus effects (such as GWAS), partly because analyzing all pairwise and higher-order interactions requires significant computational resources. Many current methods for epistasis detection only consider a Cartesian encoding for interaction terms. This is likely limiting as epistatic interactions can evolve to produce varied relationships between genes, some non-linear. In this work we describe computationally efficient algorithms for the detection of statistical epistasis that allow for varied interaction encodings for modeling epistasis. Our methodology efficiently detects pairwise and three-way epistatic interactions in two closely related species (rat and mouse) under both Cartesian and XOR interaction encodings. Our results in both species show that many biologically relevant epistatic relationships would have been undetected if only one interaction encoding was applied providing evidence that more varied models for interaction may need to be applied to describe epistasis that occurs in living systems.

genetics↗

bioRxiv2021

FINDER: An automated software package to annotate eukaryotic genes from RNA-Seq data and associated protein sequences

BackgroundGene annotation in eukaryotes is a non-trivial task that requires meticulous analysis of accumulated transcript data. Challenges include transcriptionally active regions of the genome that contain overlapping genes, genes that produce numerous transcripts, transposable elements and numerous diverse sequence repeats. Currently available gene annotation software applications depend on pre-constructed full-length gene sequence assemblies which are not guaranteed to be error-free. The origins of these sequences are often uncertain, making it difficult to identify and rectify errors in them. This hinders the creation of an accurate and holistic representation of the transcriptomic landscape across multiple tissue types and experimental conditions. Therefore, to gauge the extent of diversity in gene structures, a comprehensive analysis of genome-wide expression data is imperative. ResultsWe present FINDER, a fully automated computational tool that optimizes the entire process of annotating genes and transcript structures. Unlike current state-of-the-art pipelines, FINDER automates the RNA-Seq pre-processing step by working directly with raw sequence reads and optimizes gene prediction from BRAKER2 by supplementing these reads with associated proteins. The FINDER pipeline (1) reports transcripts and recognizes genes that are expressed under specific conditions, (2) generates all possible alternatively spliced transcripts from expressed RNA-Seq data, (3) analyzes read coverage patterns to modify existing transcript models and create new ones, and (4) scores genes as high- or low-confidence based on the available evidence across multiple datasets. We demonstrate the ability of FINDER to automatically annotate a diverse pool of genomes from eight species. ConclusionsFINDER takes a completely automated approach to annotate genes directly from raw expression data. It is capable of processing eukaryotic genomes of all sizes and requires no manual supervision - ideal for bench researchers with limited experience in handling computational tools.

bioinformatics↗

bioRxiv2019

Maximizing prediction of orphan genes in assembled genomes

Proteins encoded by newly-emerged genes ("orphan genes") share no sequence similarity with proteins in any other species. They provide organisms with a reservoir of genetic elements to quickly respond to changing selection pressures. Here, we systematically assess the ability of five gene annotation pipelines to accurately predict genes in genomes according to phylostratal origin. BRAKER and MAKER are existing, popular ab initio tools that infer gene structures by machine learning. Direct Inference is an evidence-based pipeline we developed to predict gene structures from alignments of RNA-Seq data. The BIND pipeline integrates ab initio predictions of BRAKER and Direct inference; MIND combines Direct Inference and MAKER predictions. We use highly-curated Arabidopsis and yeast annotations as gold-standard benchmarks, and cross-validate in rice. Each pipeline under-predicts orphan genes (as few as 11 percent, under one prediction scenario). Increasing RNA-Seq diversity greatly improves prediction efficacy. The combined methods (BIND and MIND) yield best predictions overall, BIND identifying 68% of annotated orphan genes and 99% of ancient genes in Arabidopsis. We provide a light weight, flexible, reproducible solution to improve gene prediction.

bioinformatics↗