Browsing by Author "Bisht, Niyati"

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Identification of NRPS and type II PKS biosynthetic gene cluster (s) encoding decaplanin and kigamicin from Amycolatopsis regifaucium DSM 45072T
(Oxford University Press, 2025) Bisht, Niyati; Mayilraj, Shanmugam; Kumar, Shailesh; Kaur, Navjot
Amycolatopsis regifaucium, a Gram-positive actinomycete, is a prolific source of biologically active compounds, including polyphenol antibiotics like kigamicins. This study presents the draft genome of Amycolatopsis regifaucium DSM 45072T (= GY080T), which spans 8.28 Mbp and is assembled into 62 contigs, with annotation revealing 312 subsystems, 7,966 coding genes, and 52 RNAs, with a GC content of 68.5 mol%. We found a significant genomic diversity within the genus, revealing variations in core and accessory genomic elements across species. Multiple biosynthetic gene clusters (BGCs) have been identified, including a previously unidentified glycopeptide antibiotic (GPA) gene cluster and a type II polyketide synthase (PKS) gene cluster, highlighting the organism's metabolic versatility and potential for the biosynthesis of novel natural products. Our analysis confirmed the production of decaplanin, an antibiotic previously attributed to Amycolatopsis decaplanina DSM 44594T. Correspondingly, the gene cluster responsible for decaplanin biosynthesis is identified in A. regifaucium DSM 45072T and A. decaplanina DSM 44594T. Additionally, a putative type II PKS gene cluster is predicted within the glycopeptide antibiotic-producing clade (Cluster A) of the genus Amycolatopsis. Genomics insights from Amycolatopsis regifaucium DSM 45072T represent it as a promising genetic resource with significant implications for biotechnological and pharmaceutical innovation, particularly in discovering and developing novel antimicrobial agents.
The intersection of AI and genomics in health and disease: Advancements and applications
(Elsevier B.V., 2026) Kaushik, Love; Vivek, A T; Arora, Simran; Hamid, Fiza; Mukherjee, Kanka; Bisht, Niyati; Chaudhary, Sakshi; Shukla, Jagriti; Nawani, Sakshi; Kumar, Shailesh
AI and genomics are revolutionizing precision medicine by using machine learning (ML) to analyze large-scale next-generation sequencing (NGS) data, identifying genetic mutations and biomarkers for personalized therapies. In practice, this accelerates drug discovery and enhances variant detection, while in cancer genomics, AI enables early detection via liquid biopsies and refines treatment by integrating multi-omics data to improve therapeutic precision. However, challenges such as data biases in underrepresented populations, limited model interpretability, and ethical concerns regarding privacy and algorithmic inequity hinder clinical adoption and demand robust governance. Efforts to diversify datasets also face standardization hurdles, although explainable AI and federated learning provide promising solutions for improving transparency and privacy. In this chapter, we discuss the role of AI in advancing genomics from diagnostics to novel therapies and emphasize the need for equitable frameworks to ensure responsible implementation, thereby paving the way for breakthroughs in personalized medicine.
Uncovering the biosynthetic potential of Amycolatopsis: new insights into glycopeptide antibiotic and polyketide gene clusters
(Oxford University Press, 2026) Bisht, Niyati; Mayilraj, Shanmugam; Kaur, Navjot; Kumar, Shailesh
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.