Daniel Figeys

This study uses metaproteomics to analyze how culture medium and donor-specific factors influence the establishment of in vitro fecal microbial communities over time.

MetaUmbra infers genome-level presence from metaproteomic peptides using statistical methods, accurately recovering expected genomes in benchmark tests.

This study identifies VDAC1 as a regulator of DJ-1's mitochondrial matrix localization, showing its role in maintaining mitochondrial homeostasis and neuronal survival under stress.

MetaPilot improves metaproteomics analysis by using genome-aware search-space refinement, enhancing peptide identification in DDA and DIA datasets.

The paper investigates how oligosaccharide structure and donor age influence gut microbiome function through ex vivo fermentation, metaproteomics, and metabolomics.

MetaTree provides an interactive web platform for visualizing and comparing hierarchical quantitative data across groups while preserving topology.

The paper uses metaproteomics to show that gut microbiomes have personalized functional responses to different oligosaccharides, highlighting the need for individualized prebiotic strategies.

This study maps metaproteomic responses of human gut microbiota to 312 drugs, identifying 47 compounds that significantly alter microbial protein profiles and reveal functional state shifts.

This study shows that early-life gut microbiome and stress-axis disruptions impair immune and neurodevelopmental health by altering microbial diversity, SCFA production, and neuroimmune responses.

This study uses metaproteomics to identify age-specific changes in the gut microbiome of SARS-CoV-2-infected hamsters, revealing functional alterations and host-microbiome interactions.

MetaX reanalyzes metaproteomic data using a peptide-centric approach and Operational Taxa-Functions (OTF) to better understand microbial functional contributions.

The paper introduces PhyloFunc, a new functional beta-diversity metric for metaproteomic data that incorporates phylogenetic relationships to better capture functional dissimilarities between microbial communities.

MetaLab enables comprehensive DDA and DIA metaproteomics analysis by supporting both modes without requiring DDA results, improving peptide identification in human gut microbiome studies.

This study evaluates how different experimental workflows affect fecal metaproteomic results, showing that DIA-PASEF improves protein identification and small protein recovery compared to DDA.

MetaDIA reduces the search space for DIA gut metaproteomics by prioritizing peptides based on species and functional abundance, achieving results consistent with traditional DDA-based methods.

This study uses multi-omic profiling to analyze Caco-2 cell differentiation, showing similar protein and pathway changes in spontaneous and medium-induced differentiation, with butyrate accelerating differentiation and altering protein expression related to host-microbiome interactions.

This study introduces a streamlined TMT labeling method using pre-aliquoted dry reagents, improving efficiency and reducing variability in proteomic and metaproteomic analyses.

The paper uses single-cell RNA-Seq to study neural stem cell (NSC) defects in 3xTG-AD mice, finding reduced NSC activation and impaired neurogenesis linked to AD pathology.

This study identifies elevated paucimannosidic and truncated N-glycans in the microbiota of pediatric ulcerative colitis patients compared to controls, suggesting a potential role for glycan metabolism in disease pathogenesis.

This study quantifies protein-level functional redundancy in the human gut microbiome using ultra-deep metaproteomics and network analysis, revealing high redundancy and environmental influences on functional organization.

RTStaR standardizes and registers lipidomic datasets to match and identify unique lipid species across different matrices and organisms.

MetaProClust-MS1 rapidly clusters metaproteomes using short MS1 profiles, showing robust microbiome shifts and correlated cluster topologies compared to traditional tandem mass spectrometry.

The paper characterizes lysine acetylation in the human gut microbiome and identifies differential acetylation patterns in Crohn’s disease patients.

MealTime-MS improves protein identification in mass spectrometry by using real-time supervised learning to reduce redundant data collection and enhance detection of low-abundance proteins.

MetaLab 2.0 improves PTM identification and quantification in metaproteomics by providing an open search workflow with higher accuracy and sensitivity than traditional methods.

pepFunk improves functional analysis of metaproteomic gut microbiome data by focusing on peptides rather than proteins, identifying more enriched KEGG pathways.

The paper introduces a chemoenzymatic method to quantitatively analyze N-glycan types on glycoproteins, demonstrating its application in characterizing glycan heterogeneity in IgG1 and MCF7 cells.