Biology subjects

Acharya, K. K.

Publications and source records attributed to Acharya, K. K..

9 recordsLinked to original sources

bioRxiv2026

Integrative Multi-Tissue Analysis Identifies Synaptic Gene Networks Specific to Major Depressive Disorder in Women

Major Depressive Disorder (MDD) shows marked gender differences in prevalence and molecular signatures. Transcriptomic studies of post-mortem human brain tissue have reported alterations in the expression of synapse-related genes in MDD, including gender-specific patterns. But it remains unclear whether transcriptional changes observed in the brains of women with MDD are detectable in peripheral blood and conserved in experimental stress models. Whole-blood RNA sequencing was performed in women with MDD (n = 6) and matched healthy controls (n = 4). Differentially expressed genes (DEGs) were compared with previously reported female-specific blood and post-mortem brain transcriptomic datasets where selected overlapping synapse-associated genes were evaluated in the hippocampus and prefrontal cortex of female mice exposed to Chronic Variable Mild Stress (CVMS). Peripheral blood analysis identified DEGs enriched for synaptic organization, neuronal structure, and ion transport pathways. A substantial proportion of DEGs overlapped with previously reported datasets from peripheral blood, female MDD brain transcriptomic studies, and genes showing exclusive/enriched expression in the normal human brain. Network-based prioritization identified seven synapse-associated genes (SHANK2, SHANK3, CACNG8, GPHN, PICK1, NRXN2 and DNM2) for further analysis. In the female CVMS model, several of these genes showed altered expression in the hippocampus and/or prefrontal cortex, alongside behavioural changes and reduced dendritic spine density. These findings highlight shared transcriptional signals across human blood and human brain datasets, as well as in the mouse brain. However, larger studies are required to confirm and validate these observations.

neuroscience↗

bioRxiv2025

Systematic Comparative Analysis of Pathway Databases from a User-Perspective

Pathway Databases are essential particularly in the post-genomic era as they provide organized information about biological pathways and processes. However, there are too many pathway databases with varying features. These variations, stemming from multiple factors, create challenges for users in making objective selections among the databases. Hence, there is a need to systematically compare these databases from a users perspective. Indeed, there is a need to compile all such databases first. We first compiled pathway databases through a thorough literature search and shortlisted those suitable for human pathway studies. We designed a query set consisting of genes, pathways, and conditions and performed a quantitative comparison of the output from these shortlisted databases. Furthermore, to assess update frequency among the pathway databases, we curated potential novel genes involved in the MAPK pathway and compared them with the information available in pathway databases. The quantitative comparison of pathway databases showed that PathDIP yielded the highest number of pathways when queried with gene and condition names, followed by other databases. However, when queried for pathway names, Reactome yielded the highest number of pathways, followed by PathDIP, KEGG, and others. The results also support the notion that most scientists may have been making very arbitrary choice of pathway databases.

bioinformatics↗

bioRxiv2024

Comparative Analysis of DNA Structural Parameters and the Corresponding Computational Tools to Differentiate Regulatory DNA Motifs and Promoters

Analyzing and distinguishing functionally distinct DNA regions is crucial for various applications, including predicting DNA motifs and promoters, and exploring the mechanisms of gene expression regulation in disease conditions. Our understanding of mammalian promoters, particularly those associated with differentially expressed genes (DEGs), particularly remains limited. However, existing methods for such analysis require refinement. Despite the value of DNA Structural Parameters (DSPs), users often struggle to objectively select parameters and tools, especially given the limited options available. This study addresses this challenge by thoroughly investigating DSP-tool combinations - particularly the local structural parameters that can be analyzed via web-interfaces, with a goal to discern human DNA motifs and promoters. What sets this study apart are the following aspects: a) examination of disease-associated promoters; b) attention to regulatory specific DNA motifs; c) compilation and comparison of all publicly available online tools and parameters for analyzing DNA structures, and test all available DSP-tool combinations. Through the execution of over half a million queries, the study identified DSP-tool combinations that consistently outperformed others in differentiating DNA sequences across various types of analyses. Notably, the propeller twist emerged as a standout DSP, while DNAshape, complemented by DNAshapeR scripts, demonstrated exceptional performance among the tools across four distinct types of analyses: testing motifs, sequences post motif insertion, comparing promoters with control sequences, and analyzing promoters of genes either up- or down-regulated under disease conditions. Significant alterations were observed in the values of multiple DSPs for 100-nucleotide-long promoter and control sequences following the insertion of single motifs such as triplex target sites (TTS), quadruplex-forming structural motifs (QSFM), and transcription factor binding sites (TFBS). However, no single combination proved universally successful. For instance, when analyzing promoters associated with differential gene expression, different combinations proved effective across various human diseases. These findings provide valuable insights for making informed selections among available options for sequence-based queries.

bioinformatics↗

bioRxiv2021

Integrated transcriptomic meta-analysis provides novel insights into breast cancer molecular subtypes

Breast cancer is the most common cancer in women worldwide. There are four major breast cancer subtypes (luminal A, luminal B, HER2-enriched and triple-negative/TNBC). TNBC is the most aggressive form with the worst prognosis. However, the differences among the subtypes have not been completely established at the molecular level, thereby limiting therapeutic and diagnostic options for TNBC. We performed a meta-analysis of microarray and RNA-sequencing data to identify candidate genes with an expression-based association in each molecular subtype of breast cancer. The protein interaction network of the candidate genes was analyzed to discover functionally significant gene clusters and hub genes. Potential therapeutic candidates for TNBC were explored through gene-miRNA interactions. We identified 316, 347, 382, and 442 candidate genes in luminal A, luminal B, HER2 and TNBC subtypes, respectively. A total of 135 (26 up- and 109 down-regulated) candidate genes were shared by all four subtypes. Functional analysis of the candidate genes indicated up-regulation of cell cycle and p53 signaling pathways and down-regulation of multiple signaling pathways. COL10A1 was found to be highly up-regulated in all subtypes. It may be a good target for research towards multiple types of applications, including therapeutics. KIF4A, a commonly up-regulated X-chromosome gene was significantly associated with the survival of breast cancer patients. Protein interaction network and centrality analysis revealed that low-moderately differentially regulated genes play an important role in functional cascades across proteins in breast cancer subtypes and may be potential candidates for therapeutics. Targeting FN1 (fibronectin 1), the key up-regulated hub gene by miR-1271 5p may be an important molecular event to be targeted for potential therapeutic application in TNBC.

cancer biology↗

bioRxiv2020

Developing bio-technical reliability among future researchers and industrial work-force in developing countries: a success story

A common problem in the biotech sector of developing countries is that a large portion of students attain a poor conceptual understanding of the basic theory and/or lack proficiency in basic laboratory skills even as they complete higher studies such as a masters degree. A small scale solution was developed in the form of a unique post-graduate diploma program that imparted reliable skills in students with good theoretical knowledge. The course of 6 to 8 months duration, with an optional 6 months internship, was successfully conducted for 8 batches. Most students of this course inculcated the right laboratory practices. This was evident by safe operations, and precise as well as accurate results of quantitative experiments conducted by them. They learned to work independently as well as in teams. They could create and follow standard operating procedures, and contribute to general laboratory maintenance. Every student also designed, prepared and conducted routine molecular biology experiments. The following aspects are suggested for successfully dealing with the patchy and varied capacities of life science students during higher (post bachelors) studies: a) careful selection of students, b) training with prioritized objectives, c) attention to basic lab-practices, d) opportunities to self-learn, e) structured group discussions, f) teacher(s) with genuine interest and passion, g) reasonable infrastructure, and h) maintaining a good student-instructor ratio. The course objectives, structure, teaching methods, and experiences are presented here along with some of the relevant data, including statistics about improvement in the precision and accuracy in experimental results by students. Limitations in this course and a general critical perspective of the routine higher education have also been discussed briefly.

scientific communication and education↗

bioRxiv2019

Listing candidate diagnostic markers and exploring the molecular basis of human spermatogenesis and a type of male infertility (Non-Obstructive Azoospermia), with transcriptomics via the next generation sequencing methods

Studying the molecular basis of Non-Obstructive Azoospermia (NOA), a type of male infertility with failed spermatogenesis at various stages, can also help in exploring molecular basis of human spermatogenesis and possibly pave way to identify new targets for male contraceptive development. Hence, we initiated a functional genomics study by applying RNA-seq. Testicular biopsies collected from donors with Non-Obstructive Azoospermia (NOA), Obstructive Azoospermia (OA), Congenital Bilateral Absence of the Vas Deferens (CBAVD), and Varicocele (VA) conditions. Strong association of 100+ genes with human spermatogenesis and NOA has been detected via NGS-based transcriptomic analysis. In addition, 20 RNA molecules have been short-listed for potential diagnostic applications (non-obstructive azoospermia vs. obstructive azoospermia, varicocele or normal). A hierarchical list of several genes and alternatively spliced mRNAs, transcribed differentially in NOA, is reported - based on a strength of association. Such association with NOA, spermatogenesis or both is a new finding for many genes as revealed by a comparison with a newly prepared comprehensive list of genes having such association with human spermatogenesis/NOA. Many top-ranking genes involved in viral gene expression were up-regulated in testes from NOA-patients, while those associated with an antiviral mechanism were down-regulated. A tangential finding: while most well-established control mRNAs did not qualify, two new ones worked best in RT-qPCR experiments. Needle-aspiration of testicular biopsies, followed by the use of short-listed promising candidate biomarkers (i.e., 16 mRNA & 4 chimeric transcripts) and control mRNAs in RT-qPCR-based diagnostic assays, may help to avoid open surgeries in future.

developmental biology↗

bioRxiv2019

Therapeutically actionable PAK4 is amplified, overexpressed and involved in bladder cancer progression

Muscle-invasive bladder carcinomas (MIBCs) are aggressive genitourinary malignancies. Disease incidence and survival rates vary based on aggressiveness and treatment options. Metastatic urothelial carcinoma of the bladder is generally incurable by current chemotherapy and leads to early mortality. For a minority ([~]20%) of patients, T-cell checkpoint inhibitors provide durable benefits following prior platinum therapy. Recent studies have identified molecular subtypes of MIBCs with different sensitivities to frontline therapy, suggesting heterogeneity in these tumors and pointing to the importance of molecular characterization of MIBCs to provide effective treatment. We have performed multi-omic profiling of the kinome to identify therapeutic targets that are overexpressed in a subset of BLCAs. Our analyses revealed amplification and overexpression of P21 (RAC1) activated kinase 4 (PAK4) in a subset of BLCAs. For these tumors, multiplex kinase assay profiling identified corresponding PAK4 target substrates. By performing experiments using cultured bladder cancer cells, we confirmed the role of PAK4 in BLCA cell proliferation and invasion. Furthermore, our studies showed that a PAK4 inhibitor was effective in curtailing growth of BLCA cells. Transcriptomic analyses identified elevated expression of another kinase, Protein Tyrosine Kinase 6 (PTK6), upon treatment with a PAK4 inhibitor. Similarly, RNA interference of PAK4 led to elevated expression of PTK6. Treatment with a combination of kinase inhibitors (vandetanib and dasatinib) showed enhanced sensitivity compared to either drug alone. Thus, PAK4 may be therapeutically actionable for a subset of MIBC patients with amplified and/or overexpressed PAK4 in their tumors. Our results also indicate that combined inhibition of PAK4 and PTK6 may overcome resistance to PAK4. These observations warrant clinical investigations with selected BLCA patients.

cancer biology↗

bioRxiv2019

How helpful are the protein-protein interaction databases and which ones?

Protein-protein interactions (PPIs) are critical, and so are the databases and tools (resources) concerning PPIs. But in absence of systematic comparisons, biologists/bioinformaticians may be forced to make a subjective selection among such protein interaction databases and tools. In fact, a comprehensive list of such bioinformatics resources has not been reported so far. For the first time, we compiled 375 PPI resources, short-listed and performed preliminary comparison of 125 important ones (both lists available publicly at startbioinfo.com), and then systematically compared human PPIs from 16 carefully-selected databases. General features have been first compared in detail. The coverage of experimentally verified vs. all PPIs, as well as those significant in case of disease-associated and other types of genes among the chosen databases has been compared quantitatively. This has been done in two ways: outputs manually obtained using web-interfaces, and all interactions downloaded from the databases. For the first approach, PPIs obtained in response to gene queries using the web interfaces were compared. As a query set, 108 genes associated with different tissues (specific to kidney, testis, and uterus, and ubiquitous) or diseases (breast cancer, lung cancer, Alzheimers, cystic fibrosis, diabetes, and cardiomyopathy) were chosen. PPI-coverage for well-studied genes was also compared with that of less-studied ones. For the second approach, the back-end-data from the databases was downloaded and compared. Based on the results, we recommend the use of STRING and UniHI for retrieving the majority of experimentally verified protein interactions, and hPRINT and STRING for obtaining maximum number of total (experimentally verified as well as predicted) PPIs. The analysis of experimentally verified PPIs found exclusively in each database revealed that STRING contributed about 71% of exclusive hits. Overall, hPRINT, STRING and IID together retrieved ~94% of total protein interactions available in the databases. The coverage of certain databases was skewed for some gene-types. The results also indicate that the database usage frequency may not correlate with their advantages, thereby justifying the need for more frequent studies of this nature.

bioinformatics↗

bioRxiv2018

SYSTEMATIC ANALYSES OF AUTOSOMAL RECOMBINATION RATES FROM THE 1000 GENOMES PROJECT UNCOVERS THE GLOBAL RECOMBINATION LANDSCAPE IN HUMANS

BackgroundMeiotic recombination plays an important role in evolution by shuffling different alleles along the chromosomes, thus generating the genetic diversity across generations that is vital for adaptation. The plasticity of recombination rates and presence of hotspots of recombination along the genome has attracted much attention over two decades due to their contribution to the evolution of the genome. Yet, the variation in genome-wide recombination landscape and the differences in the location and strength of hotspots across worldwide human populations remains little explored.\n\nResultsWe make use of the untapped linkage disequilibrium (LD) based genetic maps from the 1000 Genomes Project (1KGP) to perform in-depth analyses of finescale variation in the autosomal recombination rates across 20 human populations to uncover the global recombination landscape. We have generated a detailed map of human recombination landscape comprising of a comprehensive set of 88,841 putative hotspots and 80,129 coldspots with their respective strengths across populations, about 2/3rd of which were previously unknown. We have validated and assessed the number of historical putative hotspots derived from the patterns of LD that are currently active in the contemporary populations using a recently published high-resolution pedigree-based genetic map, constructed and refined using 3.38 million crossovers from various populations. For the first time, we provide statistics regarding the conserved, shared, and unique hotspots across all the populations studied.\n\nConclusionsOur analysis yields clusters of continental groups, reflecting their shared ancestry and genetic similarities in the recombination rates that are linked to the migratory and evolutionary histories of the populations. We provide the genomic locations and strengths of hotspots and coldspots across all the populations studied which are a valuable set of resources arising out our analyses of 1KGP data. The findings are of great importance for further research on human hotspots as we approach the dusk of retiring HapMap-based resources.

evolutionary biology↗