Biology subjects

Bakshi, U.

Publications and source records attributed to Bakshi, U..

4 recordsLinked to original sources

bioRxiv2026

Root exudates enhance the ecological fitness of a non-native PGPR and reshape rhizosphere microbiome assembly

Root exudates play critical roles in rhizosphere microbiome assembly. Nevertheless, the molecular mechanisms by which plant growth-promoting rhizobacteria (PGPR) perceive these signals, adapt to the rhizosphere, and influence native microbial communities remain poorly understood. Here, using maize (Zea mays cv. Early Golden Bantam) and the rhizobacterium Priestia aryabhattai AB211, we took a multi-omics approach combining metabolomics, bacterial transcriptomics and proteomics, and rhizosphere metagenomics to elucidate the mechanism underlying this process. Maize root exudates were enriched in diverse carbon and nitrogen metabolites that promoted bacterial growth and induced extensive molecular reprogramming of pathways involved in nutrient acquisition, chemotaxis, motility, stress adaptation, and plant growth promotion, thereby enhancing rhizosphere competence. In field-conditioned soil, AB211 persisted as a core member of the maize rhizosphere microbiome, promoted early seedling growth, and reshaped microbial community composition and function. Root exudates-primed (RE-primed) AB211 further enhanced the enrichment of microbial functions associated with nutrient cycling, quorum sensing, plant growth promotion, and rhizosphere fitness, highlighting the role of root exudates in modulating the rhizosphere-adaptative potential of beneficial bacteria. These findings establish a mechanistic framework linking root exudates composition with PGPR adaptation and microbiome assembly, providing a foundation for engineering ecologically adapted microbial inoculants to improve crop productivity and sustainable agriculture.

microbiology↗

bioRxiv2026

Cytoplasmic capping enzyme targeted, hypoxia-responsive RNAs, RORA and KCTD16 modulate the aggressiveness of CoCl2-induced hypoxic osteosarcoma cells

Hypoxia is a defining feature of the solid tumour microenvironment and a major determinant of therapeutic response. Hypoxia-inducible factors (HIFs) are central regulators of transcriptional reprogramming under hypoxic stress. Hypoxia can paradoxically elicit both tumour-promoting and tumour-suppressive outcomes, suggesting regulatory mechanisms beyond canonical HIF-dependent pathways. Emerging evidence indicates that hypoxia-responsive RNAs (HRRs) may also be regulated independently of HIFs, with posttranscriptional stabilization playing a critical determinant of hypoxic adaptation. Cytoplasmic mRNA recapping mediated by the cytoplasmic capping enzyme (cCE) has recently emerged as an important post-transcriptional regulatory process, yet its role in hypoxia-driven RNA regulation remains poorly understood. Here, we aimed to identify novel HRRs that modulate cellular adaptability to hypoxia and to determine whether these transcripts are regulated by cCE. Using CoCl2-induced hypoxia, we observed a significant reduction in osteosarcoma cell aggressiveness, characterized by decreased proliferation, clonogenic survival, and migratory capacity. Transcriptomic profiling of hypoxic osteosarcoma cells identified RORA and KCTD16 as significantly upregulated and function as suppressors of tumour cell aggressiveness. Integrative in-silico CAGE tag analysis followed by cap-specific biochemical assays confirmed that both transcripts are post-transcriptionally stabilized by cCE. Mechanistically, hypoxia-induced stabilization of HIF1 transcriptionally elevated RORA and KCTD16 expression, while cCE further reinforced their stability post-transcriptionally. Stabilization of these cCE-targeted HRRs resulted in suppression of the oncogenic proliferation driver c-Myc, thereby attenuating the aggressive phenotype of hypoxic osteosarcoma cells. Collectively, our findings identify cCE as a previously unrecognized post-transcriptional regulator in hypoxia biology and reveal a RNA-centric mechanism by which hypoxia can restrain tumour aggressiveness.

molecular biology↗

bioRxiv2021

TaxiBGC: a Taxonomy-guided Approach for the Identification of Experimentally Verified Microbial Biosynthetic Gene Clusters in Shotgun Metagenomic Data

Biosynthetic gene clusters (BGCs) in microbial genomes encode for bioactive secondary metabolites (SMs), which can play important roles in microbe-microbe and host-microbe interactions. Given the biological significance of SMs and the current profound interest in the metabolic functions of microbiomes, the unbiased identification of BGCs from high-throughput metagenomic data could offer novel insights into the complex chemical ecology of microbial communities. Currently available tools for predicting BGCs from shotgun metagenomes have several limitations, including the need for computationally demanding read-assembly; predicting a narrow breadth of BGC classes; and not providing the SM product. To overcome these limitations, we developed TaxiBGC (Taxonomy-guided Identification of Biosynthetic Gene Clusters), a command-line tool for predicting experimentally characterized BGCs (and inferring their known SMs) in metagenomes by first pinpointing the microbial species likely to harbor them. We benchmarked TaxiBGC on various simulated metagenomes, showing that our taxonomy-guided approach could predict BGCs with much-improved performance (mean F1 score: 0.56, mean PPV score: 0.80) compared to directly identifying BGCs by mapping sequencing reads onto the BGC genes (mean F1 score: 0.49, mean PPV score: 0.41). Next, by applying TaxiBGC on 2,650 metagenomes from the Human Microbiome Project and various case-control gut microbiome studies, we were able to associate BGCs (and their SMs) with different human body sites and with multiple gastrointestinal diseases, including Crohns disease and liver cirrhosis. In all, TaxiBGC provides an in silico platform to predict experimentally characterized BGCs and their SM production potential in metagenomic data while demonstrating important advantages over existing techniques. IMPORTANCECurrently available bioinformatics tools to identify BGCs from metagenomic sequencing data are limited in their predictive capability or ease of use to even computationally-oriented researchers. We present an automated computational pipeline called TaxiBGC, which predicts experimentally characterized BGCs (and infers their known SMs) in shotgun metagenomes by first considering the microbial species source. Through rigorous benchmarking techniques on simulated metagenomes, we show that TaxiBGC provides a significant advantage over existing methods. When demonstrating TaxiBGC on thousands of human microbiome samples, we associate BGCs encoding for bacteriocins with different human body sites and diseases, thereby elucidating a possible novel role of this antibiotic class in maintaining the stability of microbial ecosystems throughout the human body. Furthermore, we report for the first time gut microbial BGC associations shared amongst multiple pathologies. Ultimately, we expect our tool to facilitate future investigations into the chemical ecology of microbial communities across diverse niches and pathologies.

bioinformatics↗

bioRxiv2020

A Novel Index for Predicting Health Status Using Species-level Gut Microbiome Profiling

The development of a biologically-interpretable and robust metric that provides clear insight into the general health status (i.e. healthy or non-healthy) of ones gut microbiome remains an important target in human microbiome research. We introduce the Gut Microbiome Health Index (GMHI), a mathematical formula that determines the degree to which a gut microbiome profile reflects good or adverse health. GMHI was formulated based on microbial species specific to healthy gut ecosystems. These species were identified through a multi-study, integrative analysis on 4,347 human stool metagenomes from 34 published studies across healthy and 12 different disease or abnormal bodyweight conditions. When demonstrated on our population-scale meta-dataset, GMHI is the most robust and consistent predictor of general health compared to -diversity indices commonly considered as markers for gut health. Validation of GMHI on 679 samples from 9 additional studies resulted in remarkable reproducibility in distinguishing healthy and non-healthy groups. Our findings suggest that gut taxonomic signatures can indeed serve as robust predictors of general health, and highlight the importance of how data sharing efforts can provide broadly-applicable novel discoveries.

bioinformatics↗