Ye Gao

This study uses adaptive laboratory evolution to identify five transcriptional mechanisms enabling Escherichia coli to grow at high temperatures.

This study identifies CyuR as a dual regulator in E. coli that contributes to antimicrobial resistance through Cys-dependent mechanisms and modulates antibiotic transporter expression.

The paper identifies the functions of seven LysR-type transcription factors in E. coli using multi-omics methods, revealing their roles in nitrogen source utilization and amino acid metabolism.

This study identifies and characterizes 34 new DNA-binding transcription factors in E. coli, predicts their functions, and expands the known total number of TFs.

The paper constructs a transcriptional regulatory network for Escherichia coli using machine learning on a large RNA-seq compendium, identifying regulatory modules and novel regulons.

The paper identifies regulons of five two-component systems in E. coli using RNA-seq and ChIP-exo data, revealing novel regulatory relationships and non-cognate inducers.

The paper reconstructs the Fur pan-regulon in E. coli strains, revealing core, accessory, and unique regulons and their functional diversity.

The paper decomposes the Escherichia coli transcriptome into 70 independent regulatory signals, showing that 50 correspond to known transcriptional regulators and revealing a principle of signal summation in prokaryotic gene regulation.

The paper develops a quantitative framework to predict ROS stress responses in E. coli across molecular, network, and genetic levels.

The paper integrates ChIP-exo and RNA-seq data to identify Cra's binding sites and regulon genes in E. coli, revealing its role in optimizing growth on poor carbon sources through metabolic regulation.