Sneha Couvillion

This study maps how endophytes alter root metabolism under drought using chemical imaging and machine learning, identifying metabolites as indicators of drought and endophyte presence.

This study characterizes the chemodiversity of root exudates in tall wheatgrass, revealing a diverse lipid composition and higher carbon content in hydrophobic exudates.

The paper investigates how soil moisture and structure affect microbial phenotypes in a simplified soil analog system, revealing key roles of chitin-degrading bacteria and stress responses in carbon use efficiency.

This study shows that maternal obesity alters lipid metabolism, metabolomics, and gene expression in bone marrow myeloid cells of offspring, potentially contributing to adult metabolic and cardiovascular disease risk.

This study investigates changes in human milk composition after SARS-CoV-2 infection or vaccination using multi-omics approaches, finding greater compositional changes post-infection than post-vaccination.

The paper develops a machine learning workflow for auto-deconvolution of GC-MS data within GNPS, enabling molecular networking and applications in breathomics and volatilome mapping.

MetFish enables LC-MS-based exo-metabolomics analysis in hypersaline samples, quantifying metabolites with high sensitivity and reproducibility in various environments.