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Niranjan Nagarajan

Publications and source records attributed to Niranjan Nagarajan.

2 recordsLinked to original sources

Microbiome features associated with persistent intestinal carriages of Escherichia coli ST131 in a Southeast Asian cohort study.

Escherichia coli sequence-type 131 (ST131) is the dominant global extraintestinal pathogen capable of asymptomatic intestinal carriage and sustained household transmission, challenging infection control. Despite its clinical significance, the ecological determinants of gut persistence remain poorly understood. We performed shotgun metagenomics on fecal samples to investigate gut microbiome features associated with ST131-positive samples, distinct host carrier statuses (persistent, intermittent and non-carriers) and household risks in a study of a Southeast Asian cohort. Here, we show that ST131 carriage was associated with compositional shifts without reducing species alpha-diversity. Regression analyses identified depletion of commensal taxa and the 1,5-anhydrofructose degradation pathway in ST131-positive samples. Persistent carriers exhibited highly perturbed microbiome enriched with pathobionts, aerobactin- and lipopolysaccharide (LPS)-biosynthesis pathways. Comparing household risk groups to control, revealed that biotin biosynthesis and 1,5-anhydrofructose degradation may influence ST131 co-colonization through both direct and indirect mechanisms. Machine learning analyses identified metabolic pathways as stronger discriminators of persistent carriage than taxonomic features. Genomic-resolved analysis of clinical ST131 isolates revealed conserved genes for iron-acquisition, LPS and antibiotic resistance determinants. Overall, while commensals and metabolism may influence initial ST131 colonization, persistent carriage is associated with specific microbial and metabolic adaptations, providing potential targets to limit intestinal ST131 persistence.

Humans

Challenges and Opportunities in Analyzing Cancer-Associated Microbiomes.

The study of cancer-associated microbiomes has gained significant attention in recent years, spurred by advances in high-throughput sequencing and metagenomic analysis. Microbiome research holds promise for identifying noninvasive biomarkers and possibly new paradigms for cancer treatment. In this review, we explore the key computational challenges and opportunities in analyzing cancer-associated microbiomes (in tumor/normal tissues and other body sites, e.g., gut, oral, and skin), focusing on sequencing-driven strategies and associated considerations for taxonomic and functional characterization. The discussion covers the strengths and limitations of current analysis tools for identifying contamination, determining compositional bias, and resolving species and strains, as well as the statistical, metabolic, and network inferences that are essential to uncover host-microbiome interactions. Several key considerations are required to guide the choice of databases used for metagenomic analysis in such studies. Recent advances in spatial and single-cell technologies have provided insights into cancer-associated microbiomes, and Artificial Intelligence-driven protein function prediction might enable rapid advances in this field. Finally, we provide a perspective on how the field can evolve to manage the ever-growing size of datasets and generate robust and testable hypotheses. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .

Humans