02495nas a2200205 4500008004100000022001400041245006600055210006400121260000800185300000800193490000700201520190500208653001002113653001402123653001502137653001502152100001402167700002302181856008502204 2019 eng d a1139-670900aRe-purposing is needed for beneficial bugs, not for the drugs0 aRepurposing is needed for beneficial bugs not for the drugs cMAR a1-60 v223 a

Between 150 and 200 species of plants, insects, birds or mammals go extinct every day. We do not have any idea what the global extinction rate for microorganisms is. What is clear is that we have already lost a maximum number of the microbes that used to live in and on our skins. Many of our microbial partners are facing extinction as we apply selection pressures that are unprecedented in our long-standing relationships. Recent estimates are that we have lost at least one third of the diversity of our skin microbiome. Every day, most of us bath or shower in water that contains chlorine or fluorine; these additives do a great job of killing pathogenic microbes, but they are probably not helping our skin microbiome. Most of the people apply cosmetic products every day, as these products contain preservatives that prevent microbial growth on the shelf. These same chemicals may well kill microbes on the skin. The daily use of high-pH soaps probably will not help microbial life that is adapted to living on the skin's natural pH of 5. The rise in the rate of C-section births from around 5% in 1970 to more than 30% today is likely to be a contributing factor. Vaginal microbes seed our skins at birth and C-sections disrupt this process. The overuse of broad-spectrum antibiotics has contributed to the loss of our microbial partners in all body sites and the skin is no exception. It is now clear that skin is an ecosystem that is dependent on commensal microbes for optimal health. In general, a diverse ecosystem is a healthy ecosystem that is robust in the face of change. Low-diversity ecosystems are more fragile and susceptible to dysbiosis. Eczema and acne rates have increased rapidly over the last 50 years. These diseases are almost unknown in hunter-gatherer communities. Now, we face two exciting challenges: finding out which species matter and how to get them back.

10aDrugs10aDysbiosis10aMicrobiome10aProbiotics1 aRam, Hari1 aDastager, Syed, G. uhttps://library.ncl.res.in/content/re-purposing-needed-beneficial-bugs-not-drugs01854nas a2200265 4500008004100000022001400041245010800055210006900163260000900232300000800241490000700249520099900256653001901255653002501274653001501299653001301314653002501327653001601352653001801368653001501386100002301401700001901424700002401443856012101467 2019 eng d a1664-302X00aWeb-gLV: a web based platform for lotka-volterra based modeling and simulation of microbial populations0 aWebgLV a web based platform for lotkavolterra based modeling and cFEB  a2880 v103 a

The affordability of high throughput DNA sequencing has allowed us to explore the dynamics of microbial populations in various ecosystems. Mathematical modeling and simulation of such microbiome time series data can help in getting better understanding of bacterial communities. In this paper; we present Web-gLV- a GUI based interactive platform for generalized Lotka-Volterra (gLV) based modeling and simulation of microbial populations. The tool can be used to generate the mathematical models with automatic estimation of parameters and use them to predict future trajectories using numerical simulations. We also demonstrate the utility of our tool on few publicly available datasets. The case studies demonstrate the ease with which the current tool can be used by biologists to model bacterial populations and simulate their dynamics to get biological insights. We expect Web-gLV to be a valuable contribution in the field of ecological modeling and metagenomic systems biology.

10alotka-volterra10amicrobial population10aMicrobiome10aModeling10anumerical-simulation10atime-series10avisualization10aweb-server1 aKuntal, Bhusan, K.1 aGadgil, Chetan1 aMande, Sharmila, S. uhttps://library.ncl.res.in/content/web-glv-web-based-platform-lotka-volterra-based-modeling-and-simulation-microbial00772nas a2200217 4500008004100000245010000041210006900141260000900210300001100219490000700230653001800237653002200255653002600277653001500303653001500318100002600333700002400359700002300383700002400406856012400430 2020 eng d00aEviMass: a literature evidence-based miner for human microbial associations (vol 10, 849, 2019)0 aEviMass a literature evidencebased miner for human microbial ass cNOV  a6140510 v1110aHuman Disease10aliterature mining10amicrobial association10aMicrobiome10aWeb server1 aSrivastava, Divyanshu1 aBaksi, Krishanu, D.1 aKuntal, Bhusan, K.1 aMande, Sharmila, S. uhttps://library.ncl.res.in/content/evimass-literature-evidence-based-miner-human-microbial-associations-vol-10-849-201900850nas a2200241 4500008004100000022001400041245012700055210006900182260000800251300001100259490000700270653001500277653002000292653003200312653001500344653001600359653001800375653001500393100002400408700002300432700002400455856012900479 2020 eng d a1664-302X00aTime: a web application for obtaining insights into microbial ecology using longitudinal microbiome data (vol 9, 36, 2018)0 aTime a web application for obtaining insights into microbial eco cNOV a6052950 v1110aclustering10acommunity state10aGranger causality algorithm10aMicrobiome10atime series10avisualization10aWeb server1 aBaksi, Krishanu, D.1 aKuntal, Bhusan, K.1 aMande, Sharmila, S. uhttps://library.ncl.res.in/content/time-web-application-obtaining-insights-microbial-ecology-using-longitudinal-microbiome-000868nas a2200253 4500008004100000022001400041245012800055210006900183260000900252300001100261490000700272653001900279653002500298653001500323653001300338653002500351653001600376653001800392653001500410100002300425700001900448700002400467856012300491 2021 eng d a1664-302X00aWeb-gLV: a web based platform for lotka-volterra based modeling and simulation of microbial populations (vol 10, 288, 2019)0 aWebgLV a web based platform for lotkavolterra based modeling and cJAN  a6053080 v1110alotka-volterra10amicrobial population10aMicrobiome10aModeling10anumerical-simulation10atime-series10avisualization10aweb-server1 aKuntal, Bhusan, K.1 aGadgil, Chetan1 aMande, Sharmila, S. uhttps://library.ncl.res.in/content/web-glv-web-based-platform-lotka-volterra-based-modeling-and-simulation-microbial-002726nas a2200277 4500008004100000022001400041245011000055210006900165260000800234300001100242490000800253520182900261653001502090653002402105653001502129653001702144653001602161653000902177100002102186700002502207700001702232700002302249700002302272700002702295856012602322 2022 eng d a0944-501300aExploring the core microbiota in scented rice (Oryza sativa L.) rhizosphere through metagenomics approach0 aExploring the core microbiota in scented rice Oryza sativa L rhi cOCT a1271570 v2633 a

Rice is a major food crop cultivated around the globe. Specially scented rice varieties are of commercial importance but they are low-yielding. The rhizospheric microflora plays a significant role in improving yield and aroma. However, the core microbiome of the scented rice rhizosphere is comparatively less explored. Here, we analyzed the core microbiome associated with the rhizosphere of the scented (Ambemohar-157 and Dehradun basmati) in comparison with non-scented rice (Kolam and Arize 6444 Gold) cultivated at two different geoclimatic zones of India (Maharashtra and Uttarakhand) using the metagenomics approach. The alpha and beta diversity analysis showed that the microbial communities associated with scented and non-scented varieties significantly changes with respect to richness, diversity, and evenness. The taxonomic profiling revealed the variation in composition, diversity, and abundance of the microbiome in terms of phyla and genera associated with scented rice varieties over non-scented. The cluster analysis distinguishes the microbial communities based on their geographical positions. The core microbiome analysis revealed that scented rice rhizosphere shelters distinct and unique microbiota. 28.6 % of genera were exclusively present only in the scented rice rhizosphere. The putative functional gene annotation revealed the high abundance of genes related to the biosynthesis of 2-acetyl-1-pyrroline (2AP) precursors in scented rice. The precursor feeding analysis revealed proline as a preferred substrate by 2AP synthesizing bacteria. The 2AP precursor proline and proline metabolism genes showed a positive correlation. The scented rice-specific rhizobacteria pointed out in this study can be used as bioinoculants for enhancing aroma, yield, and sustainable rice cultivation.

10aMetagenome10aMicrobial community10aMicrobiome10aOryza sativa10aRhizosphere10arice1 aDhondge, Harshal1 aBarvkar, Vitthal, T.1 aPaul, Dhiraj1 aDastager, Syed, G.1 aPable, Anupama, A.1 aNadaf, Altafhusain, B. uhttps://library.ncl.res.in/content/exploring-core-microbiota-scented-rice-oryza-sativa-l-rhizosphere-through-metagenomics02808nas a2200289 4500008004100000022001400041245014300055210006900198260000800267300001100275490000800286520184800294653002302142653001902165653002402184653001702208653001502225653001702240100001902257700001802276700002302294700001602317700001902333700002202352700001902374856012502393 2023 eng d a0048-969700aSpatio-temporal variation of the microbiome and resistome repertoire along an anthropogenically dynamic segment of the Ganges River, India0 aSpatiotemporal variation of the microbiome and resistome reperto cMAY a1621250 v8723 a

Aquatic ecosystems are regarded as a hub of antibiotic and metal resistance genes. River Ganges is a unique riverine system in India with socio-cultural and economic significance. However, it remains underexplored for its microbiome and associated resistomes along its anthropogenically impacted course. The present study utilized a nanopore sequenc-ing approach to depict the microbial community structure in the sediments of the river Ganges harboring antibiotic and metal resistance genes (A/MRGs) in lower stretches known for anthropogenic impact. Comprehensive microbiome analyses revealed resistance genes against 23 different types of metals and 28 classes of antibiotics. The most dominant ARG category was multidrug resistance, while the most prevalent MRGs conferred resistance against copper and zinc. Seasonal differences dismally affected the microbiota of the Ganges. However, resistance genes for fosmidomycin and tetracycline varied with season ANOVA, p < 0.05. Interestingly, 333 and 334 ARG subtypes were observed at all the locations in pre-monsoon and post-monsoon, respectively. The taxa associated with the dominant ARGs and MRGs were Pseudomonas and Burkholderia, which are important nosocomial pathogens. A substantial phage diversity for pathogenic and putrefying bacteria at all locations attracts attention for its use to tackle the dissemination of antibiotic and metal-resistant bacteria. This study suggests the accumulation of antibiotics and metals as the driving force for the emergence of resistance genes and the affiliated bacteria trafficking them. The present metagenomic as-sessment highlights the need for comprehensive, long-term biological and physicochemical monitoring and mitigation strategies toward the contaminants associated with ARGs and MRGs in this nationally important river.

10aAntibiotics (ARGs)10aBacteriophages10aHeavy metals (MRGs)10aMetagenomics10aMicrobiome10aRiver Ganges1 aSamson, Rachel1 aRajput, Vinay1 aYadav, Rakeshkumar1 aShah, Manan1 aDastager, Syed1 aKhairnar, Krishna1 aDharne, Mahesh uhttps://library.ncl.res.in/content/spatio-temporal-variation-microbiome-and-resistome-repertoire-along-anthropogenically02874nas a2200229 4500008004100000245011900041210006900160260000800229490000700237520206500244653001902309653002302328653001502351653002602366653002302392100001902415700001902434700001902453700002202472700001902494856013102513 2025 eng d00aDeciphering the comprehensive microbiome of glacier-fed Ganges and functional aspects: implications for one health0 aDeciphering the comprehensive microbiome of glacierfed Ganges an cAUG0 v133 a

Glacier-fed rivers are significant ecological components of the river catchments, yet their microbial diversity and the associated antimicrobial potential remain underexplored. The Ganges is a glacier-fed river of immense cultural, religious, and ecological significance that supports over 400 million people downstream, providing essential water for agriculture, industry, and daily use. Despite its importance, the microbial community composition and antimicrobial potential, across its relatively pristine origin, remain largely underexplored. One possible explanation for this could be the lower microbial load in the upstream glacier-fed region, which likely results in a reduced DNA yield, insufficient for whole-metagenome sequencing, in contrast to the more biologically diverse and nutrient-rich lower reaches. In this study, we developed an efficient DNA extraction and amplification method using low-input DNA to sequence the microbiome from sediments of the glacier-fed Ganges River in pre-monsoon and post-monsoon over 2 years. Taxonomic and functional diversity of bacterial and viral (phage) communities were analyzed, together with the seasonal variations in their composition. Significant differences in microbial communities were observed in response to seasonal shifts (P < 0.05). During the dry season, Proteobacteria and Actinobacteria were predominant, while Bacteroidetes and Firmicutes were abundant post-monsoon (P < 0.05). The microbiome harbors potential for the biosynthesis of streptomycin, phenylpropanoid, penicillin, and cephalosporins. Bacteriophages from Podoviridae, Myoviridae, and Siphoviridae showed lytic potential against putrefying and pathogenic bacteria. This first comprehensive study on the glacier-fed Ganges River highlights significant seasonal shifts in microbial diversity. The initial insights into the functional profile of the bacterial and phage diversity offer opportunities to explore various natural compounds and enzymes to tackle antimicrobial resistance under the one-health canopy.

10aBacteriophages10aglacier-fed-Ganges10aMicrobiome10aSecondary metabolites10aspecial properties1 aSamson, Rachel1 aKumar, Shubham1 aDastager, Syed1 aKhairnar, Krishna1 aDharne, Mahesh uhttps://library.ncl.res.in/content/deciphering-comprehensive-microbiome-glacier-fed-ganges-and-functional-aspects-implications02096nas a2200241 4500008004100000022001400041245008200055210006900137260000800206300000800214490000800222520129400230653003001524653001901554653002001573653003901593653001501632100002901647700002501676700002101701700002301722856010901745 2025 eng d a0302-893300aMicrobiome knowledge graph as a tool to understand bacteria-host associations0 aMicrobiome knowledge graph as a tool to understand bacteriahost  cAUG a2220 v2073 a

Gut bacteria are well known to significantly influence human health and physiology. Knowledge Graph (KG) can effectively integrate the heterogenous factors modulating gut bacteria-host associations. Limited studies describe the construction and application of KGs capturing these associations for domain experts. This work outlines a methodology for constructing microbiome-centric KG and demonstrates how it enhances conventional microbiome data analysis workflows. Towards construction and deployment of this domain centric KG, methodologies involved in collection of data, selecting relevant entities and relationships, and preprocessing them are discussed. Key relevant entities include bacteria, host genetic and immune factors, chemicals and diseases. The KG construction in both RDF (Resource Description Framework) and LPG (Labeled Property Graph) models are demonstrated. Comparison of the querying techniques in both these models and applications of the KG using biologically relevant case studies are also presented. Overall, the work is intended to provide domain experts with a complete protocol for construction of a microbiome-centric KG starting from entity selection and schema design to utilizing the KG for microbiome data analysis and hypothesis generation.

10aBacteria-host association10aBioinformatics10aKnowledge graph10aKnowledge graph question answering10aMicrobiome1 aPudavar, Anand, Eruvessi1 aBaksi, Krishanu, Das1 aPokhrel, Vatsala1 aKuntal, Bhusan, K. uhttps://library.ncl.res.in/content/microbiome-knowledge-graph-tool-understand-bacteria-host-associations