Oliver Fiehn

This study identifies shared metabolic changes across five longevity interventions in mice, highlighting ergothioneine and lipid remodeling as key features.

The paper identifies coordinated variation between immune systems and microbiomes in healthy humans, linking it to tonic interferon states.

The paper uses machine learning to identify plasma metabolomic and proteomic features that distinguish slow-aging mice from controls, finding triglycerides with varying fatty acid chain lengths as key predictors.

This study shows that SARS-CoV-2 deISGylation by PLpro suppresses innate immunity and promotes viral replication by modulating cellular metabolism.

This study shows heat-induced phosphatidylserine (PS) increases drive HSPA1A's plasma membrane localization, with PS synthesis inhibition reducing this localization.

The paper uses machine learning to analyze spatial lipidomic changes in mouse lungs exposed to allergens and ozone, revealing sex-specific lipid distribution differences.

This study investigates lipidomic and transcriptomic changes in HeLa cells under heat shock and recovery, revealing extensive lipid remodeling and transcriptional regulation.

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

The study identifies saliva metabolite changes associated with SARS-CoV-2 infection and disease severity, highlighting potential non-invasive biomarkers for COVID-19.

The paper identifies APOE genotype-related metabolite associations with biological age and bioenergetics in humans, showing counterintuitive patterns in {varepsilon}2 and {varepsilon}4 carriers.

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.

This study analyzes the lipidome of PTSD patients and trauma-exposed non-PTSD individuals, finding sex-specific lipid subclass alterations linked to PTSD severity and health outcomes.

The paper introduces IDSL.GOA, a tool for Gene Ontology analysis of metabolomic datasets, highlighting its ability to identify overrepresented GO terms not covered by existing pathway databases.

This study shows that P2-HNF4 regulates ketogenesis in the liver and is involved in circadian metabolism, with implications for gender-specific ketone body levels.

This study identifies allele-specific metabolic differences in HCM mouse models, linking lipid metabolism to early-stage cardiac dysfunction.

The paper applies Bayesian statistics to metabolomics data from human ME/CFS cohorts, revealing more consistent and biologically meaningful metabolic differences compared to frequentist methods.

The paper improves compound identification and inter-laboratory reproducibility in untargeted lipidomics using harmonized methods and spectral analysis.

MassQL, an open-source query language, lets users search raw mass spectrometry data from any vendor for isotope, fragmentation and neutral-loss patterns, from one file to entire public repositories.

This study analyzes NADK2 deficiency in mouse models to identify metabolic and gene expression changes, revealing similarities and differences with another neurological disease model.

This study identifies associations between bile acid profiles and symptoms of depression and anxiety in MDD patients, finding higher anxiety correlates and treatment failure predictors.

The paper links indoxyl sulfate, a gut microbiome-derived metabolite, to anxiety symptoms and specific brain connectivity patterns in patients with depression.

The paper uses targeted metabolomics to analyze changes in acylcarnitines, amines, and lipids in response to citalopram/escitalopram treatment in MDD patients, identifying metabolic correlates of treatment response.

MS-DIAL 4 provides a comprehensive lipidome atlas with retention time, collision cross section, and MS/MS data to standardize and accelerate lipidomics research.

The paper identifies under-reported metabolic genes as potential drug targets in ERneg breast tumors using multi-omics data and text mining.

The paper provides a metabolomics dataset from 30 mouse gene knockouts and wildtypes, identifying 832 metabolites across multiple analytical methods.

The paper investigates the association between triglyceride principal component scores and Alzheimer's disease biomarkers in the ADNI cohort, finding significant links with MCI, AD, and specific brain structures.

The paper identifies lipid sets in blood associated with Alzheimer's disease biomarkers and clinical symptoms using lipidomic profiling in the ADNI1 cohort.