Engineering topics

Fiehn, Oliver

Publications and source records attributed to Fiehn, Oliver.

9 records

DOE OSTI2025

Author Correction: A universal language for finding mass spectrometry data patterns

Correction to: Nature Methodshttps://doi.org/10.1038/s41592-025-02660-z, published online 12 May 2025. This article was originally published under standard Springer Nature license (© The Author(s), under exclusive licence to Springer Nature America, Inc.). It is now available as an open-access paper under a Creative Commons Attribution 4.0 International license, © The Author(s). The error has been corrected in the HTML and PDF versions of the article.

Damiani, Tito↗

DOE OSTI2025

A substrate-multiplexed platform for profiling enzymatic potential of plant family 1 glycosyltransferases

Plants have expanded various biosynthetic enzyme families to produce a wide diversity of natural products; however, most enzymes encoded in plant genomes remain uncharacterized, highlighting the need for new functional genomic approaches. Here, we report a platform enabling the rapid functional characterization of plant family 1 glycosyltransferases, which serve important roles in plant development, defense, and communication. Using substrate-multiplexed reactions, mass spectrometry, and automated analysis, we screen 85 enzymes against a diverse library of 453 natural products, for a total of nearly 40,000 possible reactions. The resulting dataset reveals a widespread promiscuity and a strong preference for planar, hydroxylated aromatic substrates among family 1 glycosyltransferases. We also characterize glycosyltransferases with an unusually wide substrate scope and with a non-canonical Cys-Asp catalytic dyad. This work establishes a widely-applicable enzymatic screening pipeline, reflects the immense glycosylation capability of plants, and has implications in biocatalysis, metabolic engineering, and gene discovery.

Sirirungruang, Sasilada↗

DOE OSTI2024

metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics

Liquid chromatography–high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified matrix amenable to further analysis. Variability in the analytical factors that influence liquid chromatography separations complicates data alignment. This is prominent when aligning data acquired in different laboratories, generated using non-identical instruments, or between batches from large-scale studies. Previously, we developed metabCombiner for aligning disparately acquired LC-MS metabolomics datasets. Here, we report significant upgrades to metabCombiner that enable the stepwise alignment of multiple untargeted LC-MS metabolomics datasets, facilitating inter-laboratory reproducibility studies. To accomplish this, a “primary” feature list is used as a template for matching compounds in “target” feature lists. We demonstrate this workflow by aligning four lipidomics datasets from core laboratories generated using each institution’s in-house LC-MS instrumentation and methods. We also introduce batchCombine, an application of the metabCombiner framework for aligning experiments composed of multiple batches. metabCombiner is available as an R package on Github and Bioconductor, along with a new online version implemented as an R Shiny App.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Predicting chronic postsurgical pain: current evidence and a novel program to develop predictive biomarker signatures

Chronic pain affects more than 50 million Americans. Treatments remain inadequate, in large part, because the pathophysiological mechanisms underlying the development of chronic pain remain poorly understood. Pain biomarkers could potentially identify and measure biological pathways and phenotypical expressions that are altered by pain, provide insight into biological treatment targets, and help identify at-risk patients who might benefit from early intervention. Biomarkers are used to diagnose, track, and treat other diseases, but no validated clinical biomarkers exist yet for chronic pain. To address this problem, the National Institutes of Health Common Fund launched the Acute to Chronic Pain Signatures (A2CPS) program to evaluate candidate biomarkers, develop them into biosignatures, and discover novel biomarkers for chronification of pain after surgery. This article discusses candidate biomarkers identified by A2CPS for evaluation, including genomic, proteomic, metabolomic, lipidomic, neuroimaging, psychophysical, psychological, and behavioral measures. Acute to Chronic Pain Signatures will provide the most comprehensive investigation of biomarkers for the transition to chronic postsurgical pain undertaken to date. Data and analytic resources generatedby A2CPS will be shared with the scientific community in hopes that other investigators will extract valuable insights beyond A2CPS’s initial findings. This article will review the identified biomarkers and rationale for including them, the current state of the science on biomarkers of the transition from acute to chronic pain, gaps in the literature, and how A2CPS will address these gaps.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Metabolite Damage and Damage Control in a Minimal Genome

Analysis of the genes retained in the minimized Mycoplasma JCVI-Syn3A genome established that systems that repair or preempt metabolite damage are essential to life. Several genes known to have such functions were identified and experimentally validated, including 5-formyltetrahydrofolate cycloligase, coenzyme A (CoA) disulfide reductase, and certain hydrolases. Furthermore, we discovered that an enigmatic YqeK hydrolase domain fused to NadD has a novel proofreading function in NAD synthesis and could double as a MutT-like sanitizing enzyme for the nucleotide pool. Finally, we combined metabolomics and cheminformatics approaches to extend the core metabolic map of JCVI-Syn3A to include promiscuous enzymatic reactions and spontaneous side reactions. This extension revealed that several key metabolite damage control systems remain to be identified in JCVI-Syn3A, such as that for methylglyoxal.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Multi-Site Observational Study to Assess Biomarkers for Susceptibility or Resilience to Chronic Pain: The Acute to Chronic Pain Signatures (A2CPS) Study Protocol

Chronic pain has become a global health problem contributing to years lived with disability and reduced quality of life. Advances in the clinical management of chronic pain have been limited due to incomplete understanding of the multiple risk factors and molecular mechanisms that contribute to the development of chronic pain. The Acute to Chronic Pain Signatures (A2CPS) Program aims to characterize the predictive nature of biomarkers (brain imaging, high-throughput molecular screening techniques, or “omics,” quantitative sensory testing, patient-reported outcome assessments and functional assessments) to identify individuals who will develop chronic pain following surgical intervention. The A2CPS is a multisite observational study investigating biomarkers and collective biosignatures (a combination of several individual biomarkers) that predict susceptibility or resilience to the development of chronic pain following knee arthroplasty and thoracic surgery. This manuscript provides an overview of data collection methods and procedures designed to standardize data collection across multiple clinical sites and institutions. Pain-related biomarkers are evaluated before surgery and up to 3 months after surgery for use as predictors of patient reported outcomes 6 months after surgery. The dataset from this prospective observational study will be available for researchers internal and external to the A2CPS Consortium to advance understanding of the transition from acute to chronic postsurgical pain.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Chemical-damage MINE: A database of curated and predicted spontaneous metabolic reactions

Spontaneous reactions between metabolites are often neglected in favor of emphasizing enzyme-catalyzed chemistry because spontaneous reaction rates are assumed to be insignificant under physiological conditions. However, synthetic biology and engineering efforts can raise natural metabolites' levels or introduce unnatural ones, so that previously innocuous or nonexistent spontaneous reactions become an issue. Problems arise when spontaneous reaction rates exceed the capacity of a platform organism to dispose of toxic or chemically active reaction products. While various reliable sources list competing or toxic enzymatic pathways' side-reactions, no corresponding compilation of spontaneous side-reactions exists, nor is it possible to predict their occurrence. We addressed this deficiency by creating the Chemical Damage (CD)-MINE resource. First, we used literature data to construct a comprehensive database of metabolite reactions that occur spontaneously in physiological conditions. We then leveraged this data to construct 148 reaction rules describing the known spontaneous chemistry in a substrate-generic way. We applied these rules to all compounds in the ModelSEED database, predicting 180,891 spontaneous reactions. The resulting (CD)-MINE is available at https://minedatabase.mcs.anl. gov/cdmine/#/home and through developer tools. We also demonstrate how damage-prone intermediates and end products are widely distributed among metabolic pathways, and how predicting spontaneous chemical damage helps rationalize toxicity and carbon loss using examples from published pathways to commercial products. We explain how analyzing damage-prone areas in metabolism helps design effective engineering strategies. Finally, we use the CD-MINE toolset to predict the formation of the novel damage product N-carbamoyl proline, and present mass spectrometric evidence for its presence in Escherichia coli.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Quantum Chemistry Calculations for Metabolomics

A primary goal of metabolomics and exposomics studies is to fully characterize the small molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversi-ty present within the metabolome and exposome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials (“standards”), which are not available for most molecules. Computational quantum chemis-try methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alter-native route for building reference libraries, i.e., in silico libraries for “standards-free” identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solu-tion. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectros-copy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics and exposomics studies. We expect this review will inspire researchers in the field of small molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.

Metabolism↗

DOE OSTI2020

The foxtail millet ( Setaria italica ) terpene synthase gene family

Terpenoid metabolism plays vital roles in stress defense and the environmental adaptation of monocot crops. Here, we describe the identification of the terpene synthase (TPS) gene family of the panicoid food and bioenergy model crop foxtail millet (Setaria italica). The diploid S. italica genome contains 32 TPS genes, 17 of which were biochemically characterized in this study. Unlike other thus far investigated grasses, S. italica contains TPSs producing all three ent-, (+)- and syn-copalyl pyrophosphate stereoisomers that naturally occur as central building blocks in the biosynthesis of distinct monocot diterpenoids. Conversion of these intermediates by the promiscuous TPS SiTPS8 yielded different diterpenoid scaffolds. Additionally, a cytochrome P450 monooxygenase (CYP99A17), which genomically clustered with SiTPS8, catalyzes the C19 hydroxylation of SiTPS8 products to generate the corresponding diterpene alcohols. The presence of syntenic orthologs to about 19% of the S. italica TPSs in related grasses supports a common ancestry of selected pathway branches. Among the identified enzyme products, abietadien-19-ol, syn-pimara-7,15-dien-19-ol and germacrene-D-4-ol were detectable in planta, and gene expression analysis of the biosynthetic TPSs showed distinct and, albeit moderately, inducible expression patterns in response to biotic and abiotic stress. In vitro growth-inhibiting activity of abietadien-19-ol and syn-pimara-7,15-dien-19-ol against Fusarium verticillioides and Fusarium subglutinans may indicate pathogen defensive functions, whereas the low antifungal efficacy of tested sesquiterpenoids supports other bioactivities. Together, these findings expand the known chemical space of monocot terpenoid metabolism to enable further investigations of terpenoid-mediated stress resilience in these agriculturally important species.

59 BASIC BIOLOGICAL SCIENCES↗