http://223.31.159.10:8080/jspui/handle/123456789/11 Collections of Institutional Publications of NIPGR Scientists Mon, 15 Jun 2026 11:14:03 GMT 2026-06-15T11:14:03Z http://223.31.159.10:8080/jspui/handle/123456789/1828 Title: Polyphenols-rich Indian barberry berries extract alleviates inorganic arsenic exposure-induced cognitive impairments and associated gut microflora alterations Authors: Vandana; Gupta, Shweta; Sharma, Rajni; Pandey, Ashutosh; Bishnoi, Mahendra; Rawal, Rakesh; Das, Santasabuj; Singh, Dhirendra Pratap Abstract: Arsenic, a globally prevalent environmental toxin that can lead to neuro-behavioural changes. Oxidative stress and activation of inflammatory cascades are prominent mechanisms underlying these effects. The present study investigated the effects of polyphenol-rich extracts from Berberis aristata (Indian barberry) against inorganic arsenic-induced cognitive impairments in a murine model and presented mechanistic insights into its functional food properties. Response Surface Methodology (RSM)-guided hydro-alcoholic extracts were prepared and chemically characterized for their antioxidant activity, total phenolic contents (TPC) and free radical scavenging activities (RSA). UHPLC and LC-MS-based profiling of polyphenols, anthocyanins, and proanthocyanidins was performed. In-vitro toxicity studies in hepatic and colonic cancer cell lines, followed by in-vivo evaluation of these extracts in inorganic arsenic-exposed mice for spatial navigation tasks and passive avoidance-based learning were performed. Further assessments included neurotransmitter levels, histopathological investigations, qRT-PCR-based gene expression analysis, inflammatory cytokines and oxido-nitrosative stress markers in the brain and gastrointestinal tract, Evan's blue dye-based ileum permeability, and short chain fatty acids (SCFAs) estimation, along with Oxford Nanopore-based 16S rRNA metagenomics in cecal contents and PICRUSt2-based functional prediction of metagenomic data. RSM-optimized methods for polyphenol extraction yielded extracts with high TPC and RSA, with flavanols, phenolic acids, and proanthocyanidins identified as major polyphenols, and no in-vitro toxicity was observed. The extracts significantly prevented arsenic exposure-induced cognitive impairment, altered neurotransmitter turnover, neuroinflammation and gastrointestinal tract inflammation, oxidative stress-induced damage, increased ileum permeability, SCFA alteration, and gut microbial dysbiosis. These findings underscore the therapeutic/preventive potential of this polyphenol-rich extract against environmental toxicant-induced neurotoxicity, potentially involving gut microbiota-associated pathways. Description: Accepted date: 26 May 2026 Thu, 01 Jan 2026 00:00:00 GMT http://223.31.159.10:8080/jspui/handle/123456789/1828 2026-01-01T00:00:00Z http://223.31.159.10:8080/jspui/handle/123456789/1827 Title: Chlorpyrifos degradation by Zhihengliuella sp. ISTPL4: An esterase-driven actinobacterial platform for organophosphorus bioremediation Authors: Aggarwal, Himanshi; Chaudhary, Divya; Kumari, Taruna; Pradhan, Nischal; Mishra, Vaibhav; Kumar, Antresh; Singh, Anamika; Pandey, Ashutosh; Chaturvedi, Navaneet; Dufossé, Laurent; Mishra, Arti; Joshi, Naveen Chandra Abstract: Organophosphorus pesticides (OPs) are widely used agrochemicals that pose serious risks to the environmental and human health due to their persistence and toxicity. This study reports, for the first time, chlorpyrifos (CPF) degradation by actinobacterium Zhihengliuella sp. ISTPL4. Strain ISTPL4 utilized various OPs, including dimethoate, monocrotophos, CPF, and malathion, with the highest growth observed in the presence of CPF as the sole carbon and energy source. Optimal growth and degradation occurred at 28 °C, pH 5, and 3% inoculum in minimal salt medium (MSM). Under optimized conditions, strain ISTPL4 degraded 76.95% of 600 mg L-1 CPF within 7 days. GC-MS analysis identified benzene, 1,3-bis(1,1-dimethylethyl) and phenol, 2,4-bis(1,1-dimethylethyl) as intermediates without the formation of toxic metabolite 3,5,6-trichloro-2-pyridinol (TCP). Whole genome analysis revealed five putative esterase genes potentially associated with CPF degradation. Molecular docking identified carboxylesterase B as the most favorable CPF-binding enzyme, while molecular dynamics simulations supported the stability of the enzyme-substrate complex. A putative metabolic pathway for CPF degradation by strain ISTPL4 was proposed. These findings highlight the potential of Zhihengliuella sp. ISTPL4 as a promising candidate for sustainable bioremediation of OP-contaminated environments. Description: Accepted date: 17 May 2026 Thu, 01 Jan 2026 00:00:00 GMT http://223.31.159.10:8080/jspui/handle/123456789/1827 2026-01-01T00:00:00Z http://223.31.159.10:8080/jspui/handle/123456789/1826 Title: Uncovering the biosynthetic potential of Amycolatopsis: New insights into glycopeptide antibiotic and polyketide gene clusters Authors: Bisht, Niyati; Mayilraj, Shanmugam; Kaur, Navjot; Kumar, Shailesh Abstract: Background: : Amycolatopsis species are renowned producers of a vast array of biologically active molecules, including Glycopeptide antibiotics (GPAs), polyketides, siderophores, and terpenes. Despite their clinical significance, the full biosynthetic genetic capacity and evolutionary diversification of Amycolatopsis remain unexplored. Methods and Results: We analyzed 16 Amycolatopsis strains, including six newly sequenced in this work, six from our previously published datasets, and four retrieved from NCBI. Phylogenetic, pangenome, and antiSMASH-based genome-mining analyses were performed to identify secondary metabolite gene clusters, with a focus on NRPS, PKS, terpenes, and siderophores. Conserved glycopeptide gene clusters found across Cluster A strains, encoding core NRPSs, P450 oxygenases, and tailoring enzymes with variations consistent with the structural GPA types. Analysis showed conserved but distinct GPA BGC organization corresponding to the type I, II, and III subclasses, as well as their genetic, structural, and functional diversifications. A. azurea DSM 43854T produced A35512B rather than azureomycins, while A. alba DSM 44262T produced vancomycin. Six previously unreported Cluster A strains were found to encode putative GPA gene clusters, and LC–MS profiling predicted GPA production of nogabecin from A. keratiniphila subsp. keratiniphila DSM 44409T and A33512B from A. thailandensis JCM 16380T. GPA biosynthetic capacity was largely restricted to Cluster A, but in Cluster C, in the case of A. balhimycina DSM 44591T. Type II PKS, siderophore, and terpene gene clusters were also explored for these strains. Conclusions: This study provides a comparative genomic overview of Amycolatopsis Cluster A, highlighting GPA diversity and revealing broader potential for secondary metabolites. Description: Accepted date: 08 June 2026 Thu, 01 Jan 2026 00:00:00 GMT http://223.31.159.10:8080/jspui/handle/123456789/1826 2026-01-01T00:00:00Z http://223.31.159.10:8080/jspui/handle/123456789/1825 Title: PFGPred: A stack ensemble classifier for the identification of fusion genes in plants Authors: Hamid, Fiza; Mukherjee, Kanka; Chaudhary, Sakshi; Kaushik, Love; Kumar, Shailesh Abstract: Fusion genes play crucial roles in plant biological processes but remain far less explored than their human counterparts, largely due to limited validated datasets and the absence of plant-specific prediction tools. Existing approaches often produce high false-positive rates, restricting reliable discovery. To address this gap, we developed Plant Fusion Gene Predictor (PFGPred), an ensemble machine learning framework that integrates Random Forest, XGBoost, and long short-term memory (LSTM) models into a meta-classifier for accurate identification of true and false fusion genes from RNA-Seq data. PFGPred was trained on a high-confidence dataset of fusion genes validated by both RNA-Seq and whole-genome sequencing from Arabidopsis thaliana, Oryza sativa, Triticum aestivum, and Zea mays, to predict and rank candidate fusion genes for future functional validation. It outperformed individual baseline models, achieving accuracies of 0.97 on training data and 0.77 on independent test data. When evaluated on human datasets, it achieved 0.71 accuracy with lower sensitivity, reflecting biological differences between plant and human fusion events. Comparative analyses confirmed that PFGPred reliably identifies validated fusions, demonstrating its utility as a cost-effective, plant-specific prediction tool for high-throughput fusion gene screening and functional genomics research. It is freely available as a web server at http://www.nipgr.ac.in/PFGPred. Description: Accepted date: 26 May 2026 Thu, 01 Jan 2026 00:00:00 GMT http://223.31.159.10:8080/jspui/handle/123456789/1825 2026-01-01T00:00:00Z