Shaohua Gu

The paper combines experimental and bioinformatic methods to assess amino acid auxotrophy in 315 Pseudomonas isolates, finding most are prototrophic with few auxotrophs and multiple biosynthesis pathways.

eBiota designs microbial communities from large strain pools to generate target products using optimization and deep learning, showing accurate predictions and experimental validation.

The paper develops a framework to trace siderophore biosynthesis to primary metabolism and applies it to identify metabolic differences between beneficial and pathogenic bacteria.

The paper develops a functional-space framework to analyze siderophore biosynthesis across bacteria, revealing ecological drivers over phylogenetic constraints.

The paper develops a co-evolution algorithm to predict bacterial iron interaction networks based on siderophore synthetase-receptor gene pairs across Pseudomonas genomes, revealing habitat- and pathogen-specific interactions.

The paper develops a bioinformatic pipeline to predict bacterial siderophore structures and receptors from genome sequences, revealing significant diversity in pyoverdines and their receptors.

SIDERITE identifies new iron-binding molecules by analyzing chemical structures and confirming their iron-binding ability through experimental validation.

The paper identifies plant traits as the main driver of core microbiomes in different root compartments and shows their distinct impacts on plant metal accumulation and growth.

The paper shows that moderate cheating in microbial communities supports biodiversity by creating self-sustaining exploitation cycles, while excessive cheating leads to system collapse.

The paper introduces a standardized NRPS architecture using conserved motifs, leading to new subtype classifications, novel motifs, and insights into reengineering challenges.