Dean P. Jones

This study shows Lactococcus lactis subsp. cremoris reprograms the gut-liver axis and protects against liver injury through metabolic and antioxidant mechanisms.

This study shows antipsychotics alter gut microbiota and metabolome, increasing susceptibility to infection in mice.

MSMICA improves metabolite identification in untargeted metabolomics by integrating mass spectrometry data, retention time, and biological evidence, achieving high accuracy across mammalian datasets.

The paper detects enzymology-defined precursor-product correlations in human plasma LC-MS data, showing consistency across longitudinal and cross-sectional studies.

The paper develops and tests the Dietary Metabolite Inflammatory Index (DMII) to assess diet-related chronic inflammation using metabolomics data, showing correlations with inflammation biomarkers.

This study identifies metabolic pathways associated with stress hyperglycemia during cardiac surgery using longitudinal metabolomics and machine learning.

The paper proposes identification probability as an automated and transferable method to assess metabolite identification confidence in metabolomics.

This study shows SURF1 mutations impair human neurogenesis by generating patient-derived iPS cells and demonstrating defects in neuronal differentiation and bioenergetics.

This study investigates the effects of reduced VMAT activity in C. elegans, showing increased sensitivity to MPP+ and metabolic changes, suggesting conserved monoamine function and potential for metabolomics in disease research.

This study conducts a metabolomics analysis of cervical dystonia, identifying 289 significant metabolic differences and 9 associated biological pathways.

This study shows that the 3q29 deletion causes sex-specific metabolic changes in mice, which are not linked to behavioral issues, and that a high-fat diet can improve weight in females without worsening behavior.

The paper identifies metabolites in plasma associated with memory decline in middle-aged individuals using metabolomics.

This study uses metabolomics to identify metabolic differences between Parkinson’s disease and control patients, highlighting the impact of levodopa on observed variations.

xMWAS integrates multiple omics datasets, identifies sub-networks, and performs differential network analysis across conditions.