Vivek K. Mutalik

This study identifies host determinants for phage infection in Mycobacterium smegmatis using CRISPR interference, revealing receptor proteins and lipid components involved in phage entry.

This study identifies host factors critical for ssRNA and ssDNA phage infection in E. coli, revealing conserved dependencies and lineage-specific requirements.

The paper investigates how different chemical stressors induce prophage replication in Acinetobacter baumannii strains, revealing differential induction patterns across host backgrounds.

The study identifies surface glycan layers as primary or optional recognition substrates for phage infection in E. coli O157:H7, revealing their role in receptor access and infection outcomes.

PhageTAILor uses LightGBM on 74 detector features to flag prophages and bacterial phage tail-like elements (tailocins, T6SS, eCIS), recovering 12 of 13 validated tailocins under leave-one-genus-out.

This paper constructs a phage-bacteria interaction atlas for Klebsiella pneumoniae and develops a genome-guided machine learning model to predict phage-host interactions, outperforming traditional methods.

This study identifies 42 bacterial genes involved in Klebsiella phage infection using RB-TnSeq, revealing cross-family resistance and genus-specific gene dependencies.

This paper introduces a method for efficient, single-step genome editing of bacteriophages using targeted DNA nicking, achieving high recombination rates without reducing phage titers.

The paper identifies and characterizes prophages in A. baumannii isolates using computational analysis and experimental induction, showing their potential as therapeutic agents against antibiotic-resistant strains.

The paper develops a genome-based framework to predict bacteriophage infectivity against diverse Pseudomonas aeruginosa strains using machine learning and experimental data.

The paper predicts phage receptor specificity from genome data using large-scale genetic screens and machine learning, achieving high accuracy for E. coli phages.

This paper develops a machine learning framework to predict strain-level phage-host interactions from genomes, achieving high accuracy without phylogenetic constraints.

The paper introduces Droplet RB-TnSeq to study phage-host interactions by isolating single mutant cells with defined phage titers, identifying host factors beyond receptor-level interactions.

The paper identifies genes critical for bacterial cold shock adaptation using transposon sequencing in Escherichia coli and Bacillus subtilis, revealing conserved and species-specific mechanisms.

This study demonstrates that retron-mediated recombineering can be applied across multiple bacterial species, achieving high editing efficiencies in several phyla.

This study investigates how plant-bacteria-phage interactions affect nitrogen acquisition and exometabolite composition by comparing phage-lysed and uninfected bacterial necromass.