Biology subjects

Kandpal, M.

Publications and source records attributed to Kandpal, M..

4 recordsLinked to original sources

bioRxiv2025

Parallel multiOMIC analysis reveals glutamine deprivation enhances directed differentiation of renal organoids

Metabolic pathways play a critical role in driving differentiation but remain poorly understood in the development of kidney organoids. In this study, parallel metabolite and transcriptome profiling of differentiating human pluripotent stem cells (hPSCs) to multicellular renal organoids revealed key metabolic drivers of the differentiation process. In the early stage, transitioning from hPSCs to nephron progenitor cells (NPCs), both the glutamine and the alanine-aspartate-glutamate pathways changed significantly, as detected by enrichment and pathway impact analyses. Intriguingly, hPSCs maintained their ability to generate NPCs, even when deprived of both glutamine and glutamate. Surprisingly, single cell RNA-Seq analysis detected enhanced maturation and enrichment for podocytes under glutamine-deprived conditions. Together, these findings illustrate a novel role of glutamine metabolism in regulating podocyte development.

bioengineering↗

bioRxiv2024

An in-silico comparative analysis of lncRNA expression and their role in the pathogenesis of representative fungal, bacterial and viral infections in rice

Long non-coding RNAs (lncRNAs) perform prominent role in the regulation of gene expression during plant development and stress response by directly interacting with DNA, RNA, proteins, and/or triggering production of small regulatory RNA molecules. The objective of our study is to understand the systems-level response of the same plant species to highly diverse pathogens across different kingdoms and evaluate the patterns of similarity vs differences, specifically in the context of lncRNAs. Towards this objective, we performed a comparative in silico analysis of lncRNAs of Rice that are differentially expressed in response to infection by bacteria (Xanthomonas oryzae), fungus (Magnaporthe oryzae) and virus (Rice black dwarf virus). Using a tailored lncRNA analysis pipeline, we successfully identified 1125, 719 and 240 lncRNAs in Xanthomonas oryzae infection susceptible cultivar CT9737-6-1-3P-M, Magnaporthe oryzae susceptible LTH accession, and Rice black streaked dwarf virus susceptible Wuyujing No. 7 rice cultivars respectively. The in-silico predicted Cis- and Trans-target genes of lncRNAs were subsequently used to identify the pathways modulated by these lncRNA and how they cluster into unique categories of plant responses to pathogen infections. To further substantiate the role of predicted lncRNAs in plant defence and immune response our analysis finds that many of the lncRNAs co-localize with the QTLs associated with Blast and Bacterial blight resistance in rice. Our in silico analysis provides a list of common and unique pathogen specific lncRNAs that can provide vital insights into the generic vs tailored mechanisms adopted by rice in different infection scenarios.

plant biology↗

bioRxiv2024

Automated Navigation of the lncRNA Transcriptome: A comprehensive SnakeMake based computational Pipeline for robust Identification of lncRNAs and their putative targets

BackgroundLong non-coding RNAs (lncRNAs) have emerged as potent regulatory elements in cellular processes. The substantial increase in transcriptomic data resulting from high-throughput RNA sequencing necessitates effective approaches for the identification and functional annotation of lncRNAs. MethodTo address this need, we have developed a SnakeMake-based pipeline. Our pipeline automates and integrates several key steps: 1) RNA-seq analysis using Hisat2 and stringTie, (2) lncRNA identification using inhouse python scripts and tools CPC2 and BLASTX, (3) prediction of cis- and trans- gene targets of lncRNAs, and (4) KEGG pathway enrichment to obtain biological insights. Importantly, the pipeline allows users to customize parameters for each step through a user-friendly configuration file (config.yaml), enhancing flexibility and ease of use. One of the distinctive features of our approach is its single command execution, facilitating multiple runs without the need for extensive user intervention. This not only enhances user convenience but also ensures reproducibility of analyses across different studies. ResultWe applied our pipeline on rice, sorghum, and human RNA-seq data, to identify (1) List of all differentially expressed transcripts., (2) List of differentially expressed lncRNAs, (3) lncRNA target genes, (4) Enriched pathways to which target genes belong and (5) Obtain a visualization output in the form of a bubble plot that depicts the enriched pathways. Our approach can help researchers obtain valuable biological insights into how lncRNAs contribute to various biological functions. ConclusionThe distinctive features of our SnakeMake-based automation pipeline position it as a versatile asset for researchers seeking a user-friendly, adaptable, robust, and reproducible solution for pan species lncRNA analysis. By efficiently uncovering the regulatory roles of lncRNAs in cellular processes, this pipeline has the potential to shed light on various biological phenomena, such as developmental biology, disease progression, and cellular response to external stimuli. GRAPHICAL ABSTRACTThis study presents a SnakeMake-based pipeline for identifying and annotating long non-coding RNAs (IncRNAs) from RNA sequencing data. It integrates RNA-seq analysis, IncRNA identification, gene target prediction, and pathway enrichment, with customizable parameters through a user-friendly configuration file. The pipelines single command execution enhances convenience and reproducibility. (The bubble chart in the figure is a representative chart and provided as an example.) O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/608522v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@9e2930org.highwire.dtl.DTLVardef@1a219e9org.highwire.dtl.DTLVardef@153462dorg.highwire.dtl.DTLVardef@243d3d_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

SWEET11 and SWEET12 transporters function in tandem to modulate sugar flux in Arabidopsis: An account of the underlying unique structure-function relationship

Sugar will eventually be exported transporters (SWEETs) have been identified as a unique class of sugar efflux transporters in all biological kingdoms. AtSWEET11 and AtSWEET12 in Arabidopsis act synergistically to perform distinct physiological roles, particularly in apoplasmic phloem loading, seed filling, and sugar level alteration at the site of pathogen infection. Plasma membrane-localized AtSWEET11 and AtSWEET12 transporters exclusively facilitate sucrose transport along the concentration gradient. This article examines the sucrose binding pocket of AtSWEET11 and AtSWEET12 using docking studies, and how they act synergistically in various functions throughout plant development and during abiotic and biotic stresses. Further, we highlight the phylogenetic and the in-silico analyses of AtSWEET11 and AtSWEET12 orthologs from 39 economically important plant species that could provide new platforms for future studies on sugar allocation mechanisms across the different plant families. In-depth understanding of these transporters and their molecular regulatory mechanisms could be harnessed for crop improvement and crop protection.

plant biology↗