Pieter C. Dorrestein

The paper uses a colon simulator to study microbial lipid shifts in the gut and validates findings using human samples.

The paper examines how environmental and maternal factors influence infant gut metabolism through microbiome and metabolome profiling in Bangladeshi mother-infant pairs.

This study examines how maternal ampicillin exposure during pregnancy and postpartum affects fecal microbiome and metabolic profiles, linking these changes to increased offspring weight gain.

The paper develops a combined untargeted and semi-targeted metabolomics approach to profile small intestinal and fecal metabolomes, revealing distinct metabolomes and potential new bile acids.

The paper presents a multi-organ metabolomics atlas in AD mouse models, identifying dysregulated metabolites and microbiome associations linked to oxidative stress and cognitive impairment.

This study identifies distinct microbial and metabolic signatures in VEO-IBD compared to healthy controls, linking microbiome changes to metabolic dysregulation.

This paper introduces a mass spectrometry filtering tree to distinguish bile acid isomers in LC-MS/MS data and identifies a novel deoxycholyl-2-aminophenol amidate linked to whole grain consumption.

This study shows that gut microbiota and their metabolites contribute to OSA-induced atherosclerosis, particularly in the aorta, through specific bacterial imbalances and bile acid dysregulation.

The paper compares ethanol and methanol extraction methods for fecal metabolomics, showing ethanol is safer for off-site collection and preserves metabolic profiles.

This study evaluates imputation methods for untargeted metabolomics data, finding that kNN and RF imputation perform poorly with missing data, especially MNAR, and that missing data proportion significantly affects accuracy.

The paper assesses how common storage conditions and additives affect urinary metabolite profiles, finding minimal impact from storage but significant changes from a DNA-stabilizer.

The paper creates a spectral library of N-acyl lipids from metabolomics data, identifies novel compounds, and links them to health conditions like HIV and cognitive impairment.

The paper introduces ms1-id, a tool for structural annotation of MS1 data in LC-MS and MS imaging, enabling level 2/3 annotation and uncovering previously overlooked metabolites.

The paper develops a drug exposure resource from untargeted metabolomics data to identify medications without clinical records.

The paper genotypes Y and mitochondrial chromosomes in heterogeneous stock rats and finds no significant associations with complex traits, except for gene expression on these chromosomes.

This study uses spatial metabolomics to identify bactericidal compounds in citrus plants infected with Candidatus Liberibacter asiaticus, suggesting potential alternatives to antibiotics for HLB treatment.

This study identifies genetic factors enabling Bacteroides fragilis to transition from commensal to pathogenic by analyzing 813 strains and their genomic diversity.

The paper curates LC-MS/MS data from plant extracts to create a taxonomically focused database for exploring plant-derived molecules.

This study examines how ampicillin exposure via different routes affects bile acid metabolism in mice, finding significant changes in maternal bile acids and increased infant weight.

The paper creates a suspect spectral library for untargeted metabolomics by propagating annotations from public MS/MS spectra to improve interpretation of unannotated spectra.

This study combines high-resolution physical and multi-omic sampling to identify DA biosynthetic gene expression in frontal zones of Monterey Bay during declining upwelling.

The paper develops a functional metabolomics strategy to annotate metabolites in the human scalp and identifies the metabolic niche of Staphylococcus epidermidis.

The paper introduces ChemWalker, a method that improves annotation propagation on molecular networks using random walks, enhancing scalability and efficiency compared to previous approaches.

The paper updates the spectrum_utils Python package to integrate mass spectrometry standards, enhance data processing, and unify visualization across proteomics and metabolomics applications.

NPOmix connects mass spectrometry fragmentation data to biosynthetic gene clusters using a k-nearest neighbor classifier, improving metabolite discovery and structure prediction.

The paper shows that sample collection time significantly affects microbiome study conclusions, more than experimental interventions or dietary changes.

MS2Prop predicts drug-likeness of natural products from unannotated MS/MS spectra without requiring chemical structure annotation.

The paper identifies and annotates heterogeneous multimeric metabolite ions in LC-MS data using labeled and unlabeled Pichia pastoris extracts and demonstrates their presence in public and additional datasets.

The paper compares cosine, modified cosine, and neutral loss methods for MS/MS spectrum alignment to find structurally related molecules, finding modified cosine performs best.

This study uses native metabolomics to identify a family of protease inhibitors from cyanobacteria, revealing their structure and potent biological activity.