Biology subjects

Schymanski, E. L.

Publications and source records attributed to Schymanski, E. L..

10 recordsLinked to original sources

bioRxiv2026

MassBank Human: The annotated human metabolome

Mass spectrometry-based metabolomics is essential for deciphering human metabolic functions and metabolite dark matter. However, the dark matter of the human metabolome is intricately hidden. Therefore, we developed MassBank Human, a curated library that distills 36,512,540 MS2 spectra from 76 public human datasets into 203,886 high-quality spectra via spectral averaging, recalibration, and filtering to achieve high mass accuracy. We further used three metabolite annotation tools and developed a Zipf-Bayesian rank aggregation framework to aggregate their rankings with false discovery rate control. This framework assigned molecular formulas, chemical classes, and planar structures to 55,564, 22,619, and 14,198 spectra, respectively. Approximately half of the annotated structures potentially originated from food, pollutants, or gut microbiota. In a metabolome analysis of colorectal cancer cells, MassBank Human doubled structural annotation coverage (10.6% to 21.2%), identifying acetylated polyamines as markers of metabolic rewiring. This community resource provides an annotated foundation for exploring human metabolomes.

bioinformatics↗

bioRxiv2026

Unlocking the Bile Acid Universe: Advanced Workflows and a Multidimensional Library of 280 Unique Species

Microbes and bile acids are tightly intertwined, especially in the gut. While the liver produces primary bile acids from cholesterol, gut bacteria transform these into diverse secondary forms which act as powerful signaling molecules, influencing host metabolism and immune function. Since bile acid changes are increasingly linked to health and disease, their accurate measurement in the gut and circulation is essential. Analytical evaluations, however, remain challenging as many bile acids co-elute in liquid chromatography (LC), share identical precursor masses in mass spectrometry (MS), and produce similar tandem mass spectrometry (MS/MS) spectra. As a result, conventional LC-MS/MS workflows struggle to differentiate bile acids, motivating the addition of orthogonal separations such as ion mobility spectrometry (IMS). Here, we assess optimal bile acid extraction parameters for stool, serum, and plasma; compare LC conditions; and assess electrospray ionization performance across polarities. Additionally, we created a multidimensional reference library containing LC retention times, IMS collision cross section values, and accurate precursor masses for 280 unique bile acids (264 endogenous and 16 deuterium-labeled species) including unconjugated, host-conjugated, and microbially conjugated bile acids. This multidimensional library empowers bile acid identification in complex samples and enables a more comprehensive exploration of their biological roles and disease associations.

microbiology↗

bioRxiv2025

BioTransformer4.0 a comprehensive computational tool for small molecule metabolism prediction

BioTransformer 4.0, the successor to BioTransformer 3.0, is a freely available in silico metabolism prediction tool. It integrates both knowledge-based and machine learning approaches to predict metabolites for small molecules using one of seven modules: abiotic, environmental, CYP450, phase II, enzyme commission-based, human gut microbial, and all human metabolism. It also provides a customizable sequence prediction module that allows users to simulate multi-step metabolic transformations by chaining among the first six different modules. BioTransformer 4.0 can make predictions more efficiently and accurately than the previous version, as it includes more than 130 new reaction rules, and also an optional validation module to improve the efficiency by restricting the number of predicted metabolites, due to their similarity among real human metabolites. We evaluated its performance by running the six-step all-human metabolism prediction on the DrugBank dataset of 2,457 known biotransformations, and the PhytoHub dataset of 633 known biotransformations - achieving recall values of 87.2% (resp., 91.6%) for the DrugBank (resp., PhytoHub) datasets.

bioinformatics↗

bioRxiv2025

Reference Library for Suspect Non-targeted Screening of Environmental Toxicants Using Ion Mobility Spectrometry-Mass Spectrometry

As our health is affected by the xenobiotic chemicals we are exposed to, it is important to rapidly assess these molecules both in the environment and our bodies. Targeted analytical methods coupling either gas or liquid chromatography with mass spectrometry (GC-MS or LC-MS) are commonly utilized in current exposure assessments. While these methods are accepted as the gold standard for exposure analyses, they often require multiple sample preparation steps and more than 30 minutes per sample. This throughput limitation is a critical gap for exposure assessments and has resulted in an evolving interest in using ion mobility spectrometry and MS (IMS-MS) for non-targeted studies. IMS-MS is a unique technique due to its rapid analytical capabilities (millisecond scanning) and detection of a wide range of chemicals based on unique collision cross section (CCS) and mass-to-charge (m/z) values. To increase the availability of IMS-MS information for exposure studies, here we utilized drift tube IMS-MS to evaluate 4,685 xenobiotic chemical standards from the Environmental Protection Agency Toxicity Forecaster (ToxCast) program including pesticides, industrial chemicals, pharmaceuticals, consumer products, and per- and polyfluoroalkyl substances (PFAS). In the analyses, 3,993 [M+H]+, [M+Na]+, [M-H]- and [M+]+ ion types were observed with high confidence and reproducibility ([≤]1% error intra-laboratory and [≤]2% inter-laboratory) from 2,140 unique chemicals. These values were then assembled into an openly available multidimensional database and uploaded to PubChem to enable rapid IMS-MS suspect screening for a wide range of environmental contaminants, faster response time in environmental exposure assessments, and assessments of xenobiotic-disease connections.

ecology↗

bioRxiv2025

Updated Guidance for Communicating PFAS Identification Confidence with Ion Mobility Spectrometry

Over the last decade, global contamination from per- and polyfluoroalkyl substances (PFAS) has become apparent due to their detection in countless matrices worldwide, from consumer products to human blood to drinking water. As researchers implement non-targeted analyses (NTA) to more fully understand the PFAS present in the environment and human bodies, clear guidance is needed for consistent and objective reporting of the identified molecules. Confidence levels for small molecules analyzed and identified with high-resolution mass spectrometry (HRMS) have existed since 2014; however, unification of currently-used levels and improved guidance for their application is needed due to inconsistencies in reporting and continuing innovations in analytical methods. Here, we (i) investigate current practices for confidence level reporting of PFAS identified with liquid chromatography (LC), gas chromatography (GC), and/or ion mobility spectrometry (IMS) coupled with HRMS and (ii) propose a simple, unified confidence level guidance that incorporates both PFAS-specific attributes and IMS collision cross section (CCS) values. SynopsisUnified and simplified requirements to guide confidence level assignment in non-targeted PFAS identification efforts with ion mobility spectrometry. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/634925v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1b9808dorg.highwire.dtl.DTLVardef@1a6e663org.highwire.dtl.DTLVardef@5f8813org.highwire.dtl.DTLVardef@1dea0d1_HPS_FORMAT_FIGEXP M_FIG For Table of Contents only C_FIG

scientific communication and education↗

bioRxiv2024

fluxTrAM: Integration of tracer-based metabolomics data into atomically resolved genome-scale metabolic networks for metabolic flux analysis

Quantitative inference of intracellular reaction rates is essential for characterising metabolic phenotypes. The classical experimental method for measuring metabolic fluxes makes use of stable-isotope tracing of metabolites through the metabolic network, followed by mass spectrometry analysis. The most common 13C-based metabolic flux analysis requires multidisciplinary knowledge in analytical chemistry, cell biology, and mathematical modelling, as well as the use of multiple independent tools for handling mass spectrometry data. Besides, flux analysis is usually carried out within a small network to validate a specific biological hypothesis. To overcome interdisciplinary barriers and extend flux interpretation towards a genome-scale level, we developed fluxTrAM, a semi-automated pipeline for processing tracer- based metabolomics data and integrating it with atomically resolved genome-scale metabolic networks to enable flux predictions at genome-scale. fluxTrAM integrates different software packages inside and outside of the COBRA Toolbox v3.4 for the generation of metabolite structure and reaction databases for a genome-scale model, labelled mass spectrometry data processing into standardised mass isotopologue distribution data (MID), and metabolic flux analysis. To demonstrate the utility of this pipeline, we generated 13C-labeled metabolomics data on an in vitro human induced pluripotent stem cell (iPSC)-derived dopaminergic neuronal culture and processed 13C-labeled MID datasets. In parallel, we generated a cheminformatic database of standardised and context-specific metabolite structures, and atom-mapped reactions for a genome-scale dopaminergic neuronal metabolic model. MID data could be exported into established flux inference software for conventional flux inference on a core model scale. It could also be integrated into the atomically resolved metabolic model for flux inference at genome-scale using moiety fluxomics method. The core model flux solution and moiety flux solution were then compared to two additional flux solutions predicted via flux balance analysis and entropic flux balance analysis. The extensive computational flux analysis and comparison helped to better evaluate the obtained flux feasibility of the neuron-specific genome-scale model and suggested new tracer-based metabolomics experiments with novel labeling configurations, such as labelling a moiety within the thymidine metabolite. Overall, fluxTrAM enables the automation of labelled liquid chromatography (LC)-mass spectrometry (MS) data processing into MID datasets and atom mapping for any given genome-scale metabolic model. It contributes to the standardisation and high throughput of metabolic flux analysis at genome- scale.

systems biology↗

bioRxiv2023

Dopamine-iron homeostasis interaction rescues mitochondrial fitness in Parkinson's disease

Imbalances of iron and dopamine metabolism along with mitochondrial dysfunction have been linked to the pathogenesis of Parkinsons disease (PD). We have previously suggested a direct link between iron homeostasis and dopamine metabolism, as dopamine can increase cellular uptake of iron into macrophages thereby promoting oxidative stress responses. In this study, we investigated the interplay between iron, dopamine, and mitochondrial activity in neuroblastoma SH-SY5Y cells and human induced pluripotent stem cell (hiPSC)-derived dopaminergic neurons differentiated from a healthy control and a PD patient with a mutation in the -synuclein (SNCA) gene. In SH-SY5Y cells, dopamine treatment affected the expression of transmembrane iron transporters and cellular iron accumulation. Furthermore, dopamine supplementation led to decreased mitochondrial respiration and reduced mitochondrial fitness, including reduced mtDNA copy number and citrate synthase activity, increased oxidative stress and impaired aconitase activity. In dopaminergic neurons derived from a healthy control individual, dopamine showed comparable effects as observed in SH-SY5Y cells. The hiPSC-derived PD neurons harboring an endogenous SNCA mutation demonstrated altered mitochondrial iron homeostasis, reduced mitochondrial capacity along with increased oxidative stress and alterations of tricarboxylic acid cycle linked metabolic pathways compared with control neurons. Importantly, dopamine treatment of these PD neurons promoted a rescue effect by increasing mitochondrial respiration, activating antioxidant stress response, and normalizing altered metabolite levels linked to mitochondrial function. These observations provide evidence that dopamine affects iron homeostasis, intracellular stress responses and mitochondrial function in healthy cells, while dopamine supplementation can restore this disturbed regulatory network in PD cells.

neuroscience↗

bioRxiv2023

CLN3 deficiency leads to neurological and metabolic perturbations during early development

Juvenile Neuronal Ceroid Lipofuscinosis (or Batten disease) is an autosomal recessive, rare neurodegenerative disorder that affects mainly children above the age of 5 years and is most commonly caused by mutations in the highly conserved CLN3 gene. Here, we generated cln3 morphants and stable mutant lines in zebrafish. Although neither morphant nor mutant cln3 larvae showed any obvious developmental or morphological defects, behavioral phenotyping of the mutant larvae revealed higher basal activity, hyposensitivity to abrupt light changes and hypersensitivity to pro-convulsive drugs. Importantly, in-depth metabolomics and lipidomics analyses revealed significant accumulation of several glycerophosphodiesters (GPDs) and a global decrease of bis(monoacylglycero)phosphate (BMP) species, two classes of molecules previously proposed as potential biomarkers for CLN3 disease based on independent studies in other organisms. We could also demonstrate GPD accumulation in human-induced pluripotent stem cell-derived cerebral organoids carrying a pathogenic variant for CLN3. Our models revealed that GPDs accumulate at very early stages of life in the absence of functional CLN3 and highlight glycerophosphoinositol and BMP as promising biomarker candidates for pre-symptomatic CLN3 disease.

molecular biology↗

bioRxiv2022

Open Access Repository-Scale Propagated Nearest Neighbor Suspect Spectral Library for Untargeted Metabolomics

Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of public MS/MS spectra. Annotations were propagated based on structural relationships to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimers brain phenotype. The nearest neighbor suspect spectral library is openly available through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.

bioinformatics↗

bioRxiv2020

Interactive MS/MS Visualization with the Metabolomics Spectrum Resolver Web Service

The growth of online mass spectrometry metabolomics resources, including data repositories, spectral library databases, and online analysis platforms has created an environment of online/web accessibility. Here, we introduce the Metabolomics Spectrum Resolver (https://metabolomics-usi.ucsd.edu/), a tool that builds upon these exciting developments to allow for consistent data export (in human and machine-readable forms) and publication-ready visualisations of tandem mass spectrometry spectra. This tool supports the Human Proteome Organization - Proteomics Standards Initiatives Universal Spectrum Identifier (USI) specification, which has been extended to deal with the metabolomics use cases. To date, this resource already supports data formats from GNPS, MassBank, MS2LDA, MassIVE, MetaboLights, and Metabolomics Workbench and is integrated into several of these resources.

bioinformatics↗