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The UC Davis West Coast Metabolomics Center Collection: From Compound Identification to Discoveries in Metabolism

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A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Advances in Metabolomics".

Deadline for manuscript submissions: closed (17 August 2023) | Viewed by 17027

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Special Issue Editor

Prof. Dr. Oliver Fiehn

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Guest Editor

West Coast Metabolomics Center, University of California, 451 Health Sciences Drive, Davis, CA 95616, USA
Interests: metabolomics technologies; cheminformatics and analytical chemistry

Special Issue Information

Dear Colleagues,

The West Coast Metabolomics Center (WCMC) conducts more than 300 studies per year on many different projects. Collaborating laboratories focus on biology (Taha, Hammock), human nutrition and method development (Newman), and cheminformatics (Fiehn). Formally instituted in 2012, the WCMC has now matured. We here invite papers on research products that have been performed in collaboration with the WCMC faculty members or using the WCMC service core for data acquisition. We will highlight the many facets of our research, covering method development, informatics and basic biology, or biomedical applications. Topic areas range from fundamentals in compound identification to data normalization, statistics, to various applications in biology and biomedicine. Applications include studies on human glioblastoma, sexual dimorphisms of mouse knockouts, gut microbiome studies and others. Submission of manuscripts focusing on the function and regulation of lipids are especially welcome.

For all the papers submitted to this Special Issue, an Editorial Board member from Metabolites who do not have conflict of interest with WCMC will be invited to make decisions to avoid any conflict of interest.

Prof. Dr. Oliver Fiehn
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

cancer metabolism
mass spectrometry
nutrition
gut microbiome
cheminformatics
mouse models
complex lipids
endocannabinoids
inflammation

Published Papers (11 papers)

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15 pages, 2307 KiB

Open AccessArticle

Changes in Uterine Metabolome Associated with Metritis Development and Cure in Lactating Holstein Cows

by Eduardo B. de Oliveira, Hugo F. Monteiro, Jessica M. V. Pereira, Deniece R. Williams, Richard V. Pereira, Noelia Silva Del Rio, Paulo R. Menta, Vinicius S. Machado and Fabio S. Lima

Metabolites 2023, 13(11), 1156; https://doi.org/10.3390/metabo13111156 - 16 Nov 2023

Viewed by 1419

Abstract

The objective of this study was to identify alterations in the vaginal discharge (VD) metabolome and potential biomarkers to predict metritis development and a cure in dairy cows. This prospective cohort study was conducted on two dairies located in CA and TX. Vaginal discharge was evaluated and collected using the Metricheck^® device. Cows were examined for metritis at 4, 7, and 9 days in milk (DIM). Cows with a fetid, watery, and reddish-brown uterine discharge were classified as having metritis and randomized to receive ceftiofur (n = 10) or remain untreated (n = 7). A cure was defined as the absence of a fetid, watery, reddish-brown uterine discharge at 14 d after enrollment. Vaginal discharge samples were collected from 86 cows within 6 h after parturition, at 4 and 7 DIM, at metritis diagnosis, and at 4 and 7 days after metritis diagnosis. Cows with metritis (MET; n = 17) were paired with counterparts without metritis (HTH) of a similar DIM and parity (n = 34). The uterine metabolome was evaluated using untargeted gas chromatography time-of-flight mass spectrometry (GC-TOF-MS). Metabolomic data were analyzed using the MetaboAnalyst 5.0. Data were log-transformed and auto-scaled for normalization. Univariate analyses, including the fold-change, were performed to identify the metabolites linked to metritis development and its cure and principal component analysis and partial least squares discriminant analysis were performed to explain metabolite variance between animals developing or not developing metritis and being cured or not being cured of metritis. Comparing HTH with MET cows at calving, 12 metabolites were upregulated, and one was downregulated. At four and seven DIM, 51 and 74 metabolites, respectively, were altered between MET and HTH cows. After metritis development, three and five metabolites were upregulated in cows that were cured and in cows that received treatment and were cured, respectively. In all scenarios, the metabolites lignoceric, malic, and maleic acids, ornithine, and hypotaurine, which are associated with arginine/aminoacyl-tRNA biosynthesis and taurine/purine metabolism, were upregulated in HTH cows. Metritis was associated with changes in the uterine metabolome. Cows not being cured of metritis had changes in the uterus metabolome independent of receiving ceftiofur or remaining untreated. Metabolome analysis may be an important tool to understand the vaginal discharge changes during postpartum and the dynamics of metritis development and cures and help to identify biomarkers to predict metritis being cured. Full article

(This article belongs to the Special Issue The UC Davis West Coast Metabolomics Center Collection: From Compound Identification to Discoveries in Metabolism)

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14 pages, 5670 KiB

Open AccessArticle

Serum-Based Lipid Panels for Diagnosis of Idiopathic Parkinson’s Disease

by Lina A. Dahabiyeh, Refat M. Nimer, Maha Rashed, Jeremiah D. Wells and Oliver Fiehn

Metabolites 2023, 13(9), 990; https://doi.org/10.3390/metabo13090990 - 2 Sep 2023

Cited by 3 | Viewed by 1429

Abstract

Parkinson’s disease (PD) is a highly prevalent neurodegenerative movement disorder with an unclear etiology and a lack of definite diagnostic tests and effective treatments. About 95% of PD cases are idiopathic, in which none of the well-known genes underlying familial parkinsonism are mutated. We used untargeted liquid chromatography–mass spectrometry (LC-MS/MS) to profile the serum lipidome of 50 patients with different stages of idiopathic PD (early, mid, or advanced) and 45 age-matched controls. When comparing the PD patients to the control subjects, 169 lipids were significantly altered in both a univariate analysis and a multivariate partial least-squares discriminant analysis (PLS-DA). Compared to the controls, the patients with PD had higher levels of unsaturated triacylglycerides (e.g., TG O-56:9 and TG 52:3), saturated lysophosphatidylcholines (LPC 17:0, 16:0, and 15:0), and hydroxyeicosatetraenoic acid (12-HETE), while lower levels of phosphatidylserines (e.g., PS 40:4 and PS 16:0_22:4), sphingomyelins (SM 42:1), and ceramides (e.g., Cer 40:0 and 42:0) were found between the PD patients and the controls. A panel of 10 significantly altered lipids (PS 40:0, Cer 40:0, Cer 42:0, LPC 17:0, LPC 15:0, PC 37:7, PE O-40:8, PC O-42:4, FA 23:0, and SM 42:1) resulted in a strong receiver operating characteristic curve with an AUC = 0.974. This panel may, therefore, be useful for diagnosing PD. In addition, lipid panels may prove useful for distinguishing among the progression stages of PD. Using one-way ANOVA, 155 lipid species were significantly altered among the PD stages. Parkinson’s disease progressed from the early to advanced stages with decreasing levels of PC 31:1, PC 38:4, and LPE 22:5. Conversely, LPC-O 20:0, PC O-42:3, FA 19:0, and FA 22:2 showed an increase in their levels with disease progression. Overall, this study shows an intriguing number of robust changes in specific serum lipids that may become useful for diagnosing PD and its progression, once panels have been validated in larger clinical trials and prospective studies. Full article

(This article belongs to the Special Issue The UC Davis West Coast Metabolomics Center Collection: From Compound Identification to Discoveries in Metabolism)

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14 pages, 3246 KiB

Open AccessArticle

Bayesian Statistics Improves Biological Interpretability of Metabolomics Data from Human Cohorts

by Christopher Brydges, Xiaoyu Che, Walter Ian Lipkin and Oliver Fiehn

Metabolites 2023, 13(9), 984; https://doi.org/10.3390/metabo13090984 - 31 Aug 2023

Viewed by 1937

Abstract

Univariate analyses of metabolomics data currently follow a frequentist approach, using p-values to reject a null hypothesis. We here propose the use of Bayesian statistics to quantify evidence supporting different hypotheses and discriminate between the null hypothesis versus the lack of statistical power. We used metabolomics data from three independent human cohorts that studied the plasma signatures of subjects with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The data are publicly available, covering 84–197 subjects in each study with 562–888 identified metabolites of which 777 were common between the two studies and 93 were compounds reported in all three studies. We show how Bayesian statistics incorporates results from one study as “prior information” into the next study, thereby improving the overall assessment of the likelihood of finding specific differences between plasma metabolite levels. Using classic statistics and Benjamini–Hochberg FDR-corrections, Study 1 detected 18 metabolic differences and Study 2 detected no differences. Using Bayesian statistics on the same data, we found a high likelihood that 97 compounds were altered in concentration in Study 2, after using the results of Study 1 as the prior distributions. These findings included lower levels of peroxisome-produced ether-lipids, higher levels of long-chain unsaturated triacylglycerides, and the presence of exposome compounds that are explained by the difference in diet and medication between healthy subjects and ME/CFS patients. Although Study 3 reported only 92 compounds in common with the other two studies, these major differences were confirmed. We also found that prostaglandin F2alpha, a lipid mediator of physiological relevance, was reduced in ME/CFS patients across all three studies. The use of Bayesian statistics led to biological conclusions from metabolomic data that were not found through frequentist approaches. We propose that Bayesian statistics is highly useful for studies with similar research designs if similar metabolomic assays are used. Full article

(This article belongs to the Special Issue The UC Davis West Coast Metabolomics Center Collection: From Compound Identification to Discoveries in Metabolism)

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14 pages, 2357 KiB

Open AccessArticle

Exploring the Impact of Organic Solvent Quality and Unusual Adduct Formation during LC-MS-Based Lipidomic Profiling

by Tomas Cajka, Jiri Hricko, Lucie Rudl Kulhava, Michaela Paucova, Michaela Novakova, Oliver Fiehn and Ondrej Kuda

Metabolites 2023, 13(9), 966; https://doi.org/10.3390/metabo13090966 - 22 Aug 2023

Cited by 1 | Viewed by 2027

Abstract

Liquid chromatography–mass spectrometry (LC-MS) is the key technique for analyzing complex lipids in biological samples. Various LC-MS modes are used for lipid separation, including different stationary phases, mobile-phase solvents, and modifiers. Quality control in lipidomics analysis is crucial to ensuring the generated data’s reliability, reproducibility, and accuracy. While several quality control measures are commonly discussed, the impact of organic solvent quality during LC-MS analysis is often overlooked. Additionally, the annotation of complex lipids remains prone to biases, leading to potential misidentifications and incomplete characterization of lipid species. In this study, we investigate how LC-MS-grade isopropanol from different vendors may influence the quality of the mobile phase used in LC-MS-based untargeted lipidomic profiling of biological samples. Furthermore, we report the occurrence of an unusual, yet highly abundant, ethylamine adduct [M+46.0651]⁺ that may form for specific lipid subclasses during LC-MS analysis in positive electrospray ionization mode when acetonitrile is part of the mobile phase, potentially leading to lipid misidentification. These findings emphasize the importance of considering solvent quality in LC-MS analysis and highlight challenges in lipid annotation. Full article

(This article belongs to the Special Issue The UC Davis West Coast Metabolomics Center Collection: From Compound Identification to Discoveries in Metabolism)

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25 pages, 4840 KiB

Open AccessFeature PaperArticle

Sexual Dimorphism of the Mouse Plasma Metabolome Is Associated with Phenotypes of 30 Gene Knockout Lines

by Ying Zhang, Dinesh K. Barupal, Sili Fan, Bei Gao, Chao Zhu, Ann M. Flenniken, Colin McKerlie, Lauryl M. J. Nutter, Kevin C. Kent Lloyd and Oliver Fiehn

Metabolites 2023, 13(8), 947; https://doi.org/10.3390/metabo13080947 - 15 Aug 2023

Viewed by 1389

Abstract

Although metabolic alterations are observed in many monogenic and complex genetic disorders, the impact of most mammalian genes on cellular metabolism remains unknown. Understanding the effect of mouse gene dysfunction on metabolism can inform the functions of their human orthologues. We investigated the effect of loss-of-function mutations in 30 unique gene knockout (KO) lines on plasma metabolites, including genes coding for structural proteins (11 of 30), metabolic pathway enzymes (12 of 30) and protein kinases (7 of 30). Steroids, bile acids, oxylipins, primary metabolites, biogenic amines and complex lipids were analyzed with dedicated mass spectrometry platforms, yielding 827 identified metabolites in male and female KO mice and wildtype (WT) controls. Twenty-two percent of 23,698 KO versus WT comparison tests showed significant genotype effects on plasma metabolites. Fifty-six percent of identified metabolites were significantly different between the sexes in WT mice. Many of these metabolites were also found to have sexually dimorphic changes in KO lines. We used plasma metabolites to complement phenotype information exemplified for Dhfr, Idh1, Mfap4, Nek2, Npc2, Phyh and Sra1. The association of plasma metabolites with IMPC phenotypes showed dramatic sexual dimorphism in wildtype mice. We demonstrate how to link metabolomics to genotypes and (disease) phenotypes. Sex must be considered as critical factor in the biological interpretation of gene functions. Full article

(This article belongs to the Special Issue The UC Davis West Coast Metabolomics Center Collection: From Compound Identification to Discoveries in Metabolism)

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17 pages, 3371 KiB

Open AccessArticle

Denoising Autoencoder Normalization for Large-Scale Untargeted Metabolomics by Gas Chromatography–Mass Spectrometry

by Ying Zhang, Sili Fan, Gert Wohlgemuth and Oliver Fiehn

Metabolites 2023, 13(8), 944; https://doi.org/10.3390/metabo13080944 - 13 Aug 2023

Cited by 2 | Viewed by 1349

Abstract

Large-scale metabolomics assays are widely used in epidemiology for biomarker discovery and risk assessments. However, systematic errors introduced by instrumental signal drifting pose a big challenge in large-scale assays, especially for derivatization-based gas chromatography–mass spectrometry (GC–MS). Here, we compare the results of different normalization methods for a study with more than 4000 human plasma samples involved in a type 2 diabetes cohort study, in addition to 413 pooled quality control (QC) samples, 413 commercial pooled plasma samples, and a set of 25 stable isotope-labeled internal standards used for every sample. Data acquisition was conducted across 1.2 years, including seven column changes. In total, 413 pooled QC (training) and 413 BioIVT samples (validation) were used for normalization comparisons. Surprisingly, neither internal standards nor sum-based normalizations yielded median precision of less than 30% across all 563 metabolite annotations. While the machine-learning-based SERRF algorithm gave 19% median precision based on the pooled quality control samples, external cross-validation with BioIVT plasma pools yielded a median 34% relative standard deviation (RSD). We developed a new method: systematic error reduction by denoising autoencoder (SERDA). SERDA lowered the median standard deviations of the training QC samples down to 16% RSD, yielding an overall error of 19% RSD when applied to the independent BioIVT validation QC samples. This is the largest study on GC–MS metabolomics ever reported, demonstrating that technical errors can be normalized and handled effectively for this assay. SERDA was further validated on two additional large-scale GC–MS-based human plasma metabolomics studies, confirming the superior performance of SERDA over SERRF or sum normalizations. Full article

(This article belongs to the Special Issue The UC Davis West Coast Metabolomics Center Collection: From Compound Identification to Discoveries in Metabolism)

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12 pages, 1805 KiB

Open AccessArticle

SMetaS: A Sample Metadata Standardizer for Metabolomics

by Parker Ladd Bremer and Oliver Fiehn

Metabolites 2023, 13(8), 941; https://doi.org/10.3390/metabo13080941 - 12 Aug 2023

Viewed by 1219

Abstract

Metabolomics has advanced to an extent where it is desired to standardize and compare data across individual studies. While past work in standardization has focused on data acquisition, data processing, and data storage aspects, metabolomics databases are useless without ontology-based descriptions of biological samples and study designs. We introduce here a user-centric tool to automatically standardize sample metadata. Using such a tool in frontends for metabolomic databases will dramatically increase the FAIRness (Findability, Accessibility, Interoperability, and Reusability) of data, specifically for data reuse and for finding datasets that share comparable sets of metadata, e.g., study meta-analyses, cross-species analyses or large scale metabolomic atlases. SMetaS (Sample Metadata Standardizer) combines a classic database with an API and frontend and is provided in a containerized environment. The tool has two user-centric components. In the first component, the user designs a sample metadata matrix and fills the cells using natural language terminology. In the second component, the tool transforms the completed matrix by replacing freetext terms with terms from fixed vocabularies. This transformation process is designed to maximize simplicity and is guided by, among other strategies, synonym matching and typographical fixing in an n-grams/nearest neighbors model approach. The tool enables downstream analysis of submitted studies and samples via string equality for FAIR retrospective use. Full article

(This article belongs to the Special Issue The UC Davis West Coast Metabolomics Center Collection: From Compound Identification to Discoveries in Metabolism)

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10 pages, 2097 KiB

Open AccessArticle

Differences in the Stool Metabolome between Vegans and Omnivores: Analyzing the NIST Stool Reference Material

by Raquel Cumeras, Tong Shen, Luis Valdiviez, Zakery Tippins, Bennett D. Haffner and Oliver Fiehn

Metabolites 2023, 13(8), 921; https://doi.org/10.3390/metabo13080921 - 7 Aug 2023

Viewed by 1763

Abstract

To gain confidence in results of omic-data acquisitions, methods must be benchmarked using validated quality control materials. We report data combining both untargeted and targeted metabolomics assays for the analysis of four new human fecal reference materials developed by the U.S. National Institute of Standards and Technologies (NIST) for metagenomics and metabolomics measurements. These reference grade test materials (RGTM) were established by NIST based on two different diets and two different samples treatments, as follows: firstly, homogenized fecal matter from subjects eating vegan diets, stored and submitted in either lyophilized (RGTM 10162) or aqueous form (RGTM 10171); secondly, homogenized fecal matter from subjects eating omnivore diets, stored and submitted in either lyophilized (RGTM 10172) or aqueous form (RGTM 10173). We used four untargeted metabolomics assays (lipidomics, primary metabolites, biogenic amines and polyphenols) and one targeted assay on bile acids. A total of 3563 compounds were annotated by mass spectrometry, including 353 compounds that were annotated in more than one assay. Almost half of all compounds were annotated using hydrophilic interaction chromatography/accurate mass spectrometry, followed by the lipidomics and the polyphenol assays. In total, 910 metabolites were found in at least 4-fold different levels in fecal matter from vegans versus omnivores, specifically for peptides, amino acids and lipids. In comparison, only 251 compounds showed 4-fold differences between lyophilized and aqueous fecal samples, including DG O-34:0 and methionine sulfoxide. A range of diet-specific metabolites were identified to be significantly different between vegans and omnivores, exemplified by citrinin and C17:0-acylcarnitine for omnivores, and curcumin and lenticin for vegans. Bioactive molecules like acyl alpha-hydroxy-fatty acids (AAHFA) were differentially regulated in vegan versus omnivore fecal materials, highlighting the importance of diet-specific reference materials for dietary biomarker studies. Full article

(This article belongs to the Special Issue The UC Davis West Coast Metabolomics Center Collection: From Compound Identification to Discoveries in Metabolism)

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19 pages, 2601 KiB

Open AccessArticle

The Impacts of Slc19a3 Deletion and Intestinal SLC19A3 Insertion on Thiamine Distribution and Brain Metabolism in the Mouse

by Anita Wen, Ying Zhu, Sook Wah Yee, Brian I. Park, Kathleen M. Giacomini, Andrew S. Greenberg and John W. Newman

Metabolites 2023, 13(8), 885; https://doi.org/10.3390/metabo13080885 - 26 Jul 2023

Viewed by 1028

Abstract

The Thiamine Transporter 2 (THTR2) encoded by SLC19A3 plays an ill-defined role in the maintenance of tissue thiamine, thiamine monophosphate, and thiamine diphosphate (TDP) levels. To evaluate the impact of THTR2 on tissue thiamine status and metabolism, we expressed the human SLC19A3 transgene in the intestine of total body Slc19a3 knockout (KO) mice. Male and female wildtype (WT) and transgenic (TG) mice were fed either 17 mg/kg (1×) or 85 mg/kg (5×) thiamine hydrochloride diet, while KOs were only fed the 5× diet. Thiamine vitamers in plasma, red blood cells, duodenum, brain, liver, kidney, heart, and adipose tissue were measured. Untargeted metabolomics were performed on the brain tissues of groups with equivalent plasma thiamine. KO mice had ~two- and ~three-fold lower plasma and brain thiamine levels than WT on the 5× diet. Circulating vitamers were sensitive to diet and equivalent in TG and WT mice. However, TG had 60% lower thiamine but normal brain TDP levels regardless of diet, with subtle differences in the heart and liver. The loss of THTR2 reduced levels of nucleic acid and amino acid derivatives in the brain. Therefore, mutation or inhibition of THTR2 may alter the brain metabolome and reduce the thiamine reservoir for TDP biosynthesis. Full article

(This article belongs to the Special Issue The UC Davis West Coast Metabolomics Center Collection: From Compound Identification to Discoveries in Metabolism)

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14 pages, 2726 KiB

Open AccessStudy Protocol

Short-Chain Fatty Acid Levels after Fecal Microbiota Transplantation in a Pediatric Cohort with Recurrent Clostridioides difficile Infection

by Alison T. Jess, George Hany Eskander, My H. Vu and Sonia Michail

Metabolites 2023, 13(10), 1039; https://doi.org/10.3390/metabo13101039 - 27 Sep 2023

Cited by 1 | Viewed by 978

Abstract

Though antibiotics are the mainstay treatment for Clostridioides difficile, a large population of individuals infected will experience recurrence. In turn, fecal microbiota transplantation (FMT) has emerged as a promising treatment for recurrent C. difficile infection (rCDI). Mechanistically, by providing a healthy, diverse flora to the infected individual, FMT “resets” the underlying gut microbiome dysbiosis associated with rCDI. A proposed mechanism through which this occurs is via microbiome metabolites such as short-chain fatty acids (SCFAs); however, this has not been previously studied in pediatric patients. Using mass spectroscopy, we quantified pre- and post-transplant levels of acetate, isovalerate, butyrate, formate, and propionate in pediatric patients diagnosed with rCDI (n = 9). We compared pre- and post-transplant levels within the rCDI cohort at 1, 3, 6, and 12 months post-transplant and correlated these levels with healthy controls (n = 19). We witnessed a significant difference in the combined SCFA levels and the individual levels of acetate, butyrate, isovalerate, and propionate in the pre-treatment rCDI cohort compared to the healthy controls. In addition, there was a significant increase in combined SCFA levels at 12 months post-transplant within the rCDI group compared to that of their pre-transplant levels, and, more specifically, acetate, propionate, and isovalerate increased from pre-transplant to 12 months post-transplant. The longitudinal aspect of this study allowed us to identify mechanisms that contribute to the durability of responses to FMT, as well as characterize the unique patterns of short-chain fatty acid level recovery in rCDI pediatric patients. Full article

(This article belongs to the Special Issue The UC Davis West Coast Metabolomics Center Collection: From Compound Identification to Discoveries in Metabolism)

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9 pages, 1187 KiB

Open AccessTechnical Note

Automatic Assignment of Molecular Ion Species to Elemental Formulas in Gas Chromatography/Methane Chemical Ionization Accurate Mass Spectrometry

by Shunyang Wang, Luis Valdiviez, Honglian Ye and Oliver Fiehn

Metabolites 2023, 13(8), 962; https://doi.org/10.3390/metabo13080962 - 19 Aug 2023

Cited by 1 | Viewed by 1083

Abstract

Gas chromatography–mass spectrometry (GC-MS) usually employs hard electron ionization, leading to extensive fragmentations that are suitable to identify compounds based on library matches. However, such spectra are less useful to structurally characterize unknown compounds that are absent from libraries, due to the lack of readily recognizable molecular ion species. We tested methane chemical ionization on 369 trimethylsilylated (TMS) derivatized metabolites using a quadrupole time-of-flight detector (QTOF). We developed an algorithm to automatically detect molecular ion species and tested SIRIUS software on how accurate the determination of molecular formulas was. The automatic workflow correctly recognized 289 (84%) of all 345 detected derivatized standards. Specifically, strong [M − CH₃]⁺ fragments were observed in 290 of 345 derivatized chemicals, which enabled the automatic recognition of molecular adduct patterns. Using Sirius software, correct elemental formulas were retrieved in 87% of cases within the top three hits. When investigating the cases for which the automatic pattern analysis failed, we found that several metabolites showed a previously unknown [M + TMS]⁺ adduct formed by rearrangement. Methane chemical ionization with GC-QTOF mass spectrometry is a suitable avenue to identify molecular formulas for abundant unknown peaks. Full article

(This article belongs to the Special Issue The UC Davis West Coast Metabolomics Center Collection: From Compound Identification to Discoveries in Metabolism)

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