Unknown Metabolites Research Articles

Causal Effects of Blood Metabolites and Obstructive Sleep Apnea: A Mendelian Randomization Study.

Obstructive sleep apnea (OSA) is one of the most common forms of sleep-disordered breathing. Studies have shown that certain changes in metabolism play an important role in the pathophysiology of OSA. However, the causal relationship between these metabolites and OSA remains unclear. We use a mendelian randomization (MR) approach to investigate the causal associations between the genetic liability to metabolites and OSA. We performed a 2-sample inverse-variance weighted mendelian randomization analysis to evaluate the causal effects of genetically determined 486 metabolites on OSA. Multiple sensitivity analyses were performed to assess pleiotropy. We used multivariate mendelian randomization analyses to assess confounding factors and mendelian randomization Bayesian model averaging to rank the significant biomarkers by their genetic evidence. We also conducted a metabolic pathway analysis to identify potential metabolic pathways. We identified 14 known serum metabolites (8 risk factors and 6 protective factors) and 12 unknown serum metabolites associated with OSA. These 14 known metabolites included 8 lipids( 1-arachidonoylglycerophosphoethanolamine, Tetradecanedioate, Epiandrosteronesulfate, Acetylca Glycerol3-phosphate, 3-dehydrocarnitine, Margarate17:0, Docosapentaenoaten3;22:5n3), 3 Aminoacids (Isovalerylcarnitine,3-methyl-2-oxobutyrate,Methionine), 2 Cofactors and vitamins [Bilirubin(E,ZorZ,E),X-11593--O-methylascorbate], 1Carbohydrate(1,6-anhydroglucose). We also identified several metabolic pathways that involved in the pathogenesis of OSA. MR (mendelian randomization) approach was performed to identify 6 protective factors and 12 risk factors for OSA in the present study. 3-Dehydrocarnitine was the most significant risk factors for OSA. Our study also confirmed several significant metabolic pathways that were involved in the pathogenesis of OSA. Valine, leucine and isoleucine biosynthesis metabolic pathways were the most significant metabolic pathways that were involved in the pathogenesis of OSA.

Simultaneous quantification of endogenous biomarkers and detection of testosterone esters in human serum using gas chromatography-high-resolution mass spectrometry.

High-resolution mass spectrometry (HRMS) has been demonstrated to be an alternative platform for quantitative analyses, identifying unknown compounds and gathering information for the elucidation of chemical structures. This work describes a method to detect 13 esters of testosterone (T) and 5 biomarkers in 0.1mL of human serum using gas chromatography (GC) coupled to HRMS. Analytes were extracted from serum after deproteinization and liquid-liquid extraction. The trimethylsilyl derivatives were analyzed using a gas chromatograph coupled to HRMS at low electron energy to minimize molecule fragmentation. The acquisition in profiling full-scan mode was applied with a resolving power of 30 000 at m/z 400. Linearity, lower limit of quantitation, and measurement uncertainty were assessed. Precision and accuracy were assessed at 0.5 and 2ng/mL, respectively. Mass accuracy (MA) and mass extraction window (MEW) were also evaluated. T esters showed a linear response between 0.25 and 10ng/mL (except for undecanoate, enanthate, and propionate that showed lineal responses between 0.5 and 10ng/mL and isocaproate between 2 and 10ng/mL); detection limits remained between 0.1 and 0.5ng/mL and accuracy between 81% and 119%. The MA (MEW = 10ppm) was maintained between -2.4 and 4.8ppm. The biomarkers (T, androstenedione, dehydroepiandrosterone [DHEA], estradiol, and 17-OH-progesterone) showed a linear response within the evaluated range; quantification limits remained between 0.1 and 0.5ng/mL (except for DHEA), the accuracy between 88% and 99%, and precision between 3.5% and 10.8%. Measurement uncertainties were found between 5.6% and 17.2%. MA (MEW = 3ppm) was maintained between -0.47 and 0.12 ppm. The method to detect T esters and five endogenous biomarkers in serum using GC coupled to HRMS showed linear responses up to 10ng/mL with adequate precision, accuracy, and uncertainties. It was possible to distinguish cholesterol from T-isocaproate based on the MEW of 10ppm, preventing false positives. In addition, this method allows searching for other biomarkers and/or unknown metabolites and other ester forms not included here but at a later stage if necessary.

Causal relationship between human blood metabolites and risk of ischemic stroke: a Mendelian randomization study.

Background: Ischemic stroke (IS) is a major cause of death and disability worldwide. Previous studies have reported associations between metabolic disorders and IS. However, evidence regarding the causal relationship between blood metabolites and IS lacking. Methods: A two-sample Mendelian randomization analysis (MR) was used to assess the causal relationship between 1,400 serum metabolites and IS. The inverse variance-weighted (IVW) method was employed to estimate the causal effect between exposure and outcome. Additionally, MR-Egger regression, weighted median, simple mode, and weighted mode approaches were employed as supplementary comprehensive evaluations of the causal effects between blood metabolites and IS. Tests for pleiotropy and heterogeneity were conducted. Results: After rigorous selection, 23 known and 5 unknown metabolites were identified to be associated with IS. Among the 23 known metabolites, 13 showed significant causal effects with IS based on 2 MR methods, including 5-acetylamino-6-formylamino-3-methyluracil, 1-ribosyl-imidazoleacetate, Behenoylcarnitine (C22), N-acetyltyrosine, and N-acetylputrescine to (N (1) + N (8))-acetate,these five metabolites were positively associated with increased IS risk. Xanthurenate, Glycosyl-N-tricosanoyl-sphingadienine, Orotate, Bilirubin (E,E), Bilirubin degradation product, C17H18N2O, Bilirubin (Z,Z) to androsterone glucuronide, Bilirubin (Z,Z) to etiocholanolone glucuronide, Biliverdin, and Uridine to pseudouridine ratio were associated with decreased IS risk. Conclusion: Among 1,400 blood metabolites, this study identified 23 known metabolites that are significantly associated with IS risk, with 13 being more prominent. The integration of genomics and metabolomics provides important insights for the screening and prevention of IS.

Predicting Retention Time in Unified-Hydrophilic-Interaction/Anion-Exchange Liquid Chromatography High-Resolution Tandem Mass Spectrometry (Unified-HILIC/AEX/HRMS/MS) for Comprehensive Structural Annotation of Polar Metabolome.

The accuracy of the structural annotation of unidentified peaks obtained in metabolomic analysis using liquid chromatography/tandem mass spectrometry (LC/MS/MS) can be enhanced using retention time (RT) information as well as precursor and product ions. Unified-hydrophilic-interaction/anion-exchange liquid chromatography high-resolution tandem mass spectrometry (unified-HILIC/AEX/HRMS/MS) has been recently developed as an innovative method ideal for nontargeted polar metabolomics. However, the RT prediction for unified-HILIC/AEX has not been developed because of the complex separation mechanism characterized by the continuous transition of the separation modes from HILIC to AEX. In this study, we propose an RT prediction model of unified-HILIC/AEX/HRMS/MS, which enables the comprehensive structural annotation of polar metabolites. With training data for 203 polar metabolites, we ranked the feature importance using a random forest among 12,420 molecular descriptors (MDs) and constructed an RT prediction model with 26 selected MDs. The accuracy of the RT model was evaluated using test data for 51 polar metabolites, and 86.3% of the ΔRTs (difference between measured and predicted RTs) were within ±1.50 min, with a mean absolute error of 0.80 min, indicating high RT prediction accuracy. Nontargeted metabolomic data from the NIST SRM 1950-Metabolites in frozen human plasma were analyzed using the developed RT model and in silico MS/MS prediction, resulting in a successful structural estimation of 216 polar metabolites, in addition to the 62 identified based on standards. The proposed model can help accelerate the structural annotation of unknown hydrophilic metabolites, which is a key issue in metabolomic research.

Enhanced metabolic detoxification is associated with fluroxypyr resistance in Bassia scoparia.

Auxin-mimic herbicides chemically mimic the phytohormone indole-3-acetic-acid (IAA). Within the auxin-mimic herbicide class, the herbicide fluroxypyr has been extensively used to control kochia (Bassia scoparia). A 2014 field survey for herbicide resistance in kochia populations across Colorado identified a putative fluroxypyr-resistant (Flur-R) population that was assessed for response to fluroxypyr and dicamba (auxin-mimics), atrazine (photosystem II inhibitor), glyphosate (EPSPS inhibitor), and chlorsulfuron (acetolactate synthase inhibitor). This population was resistant to fluroxypyr and chlorsulfuron but sensitive to glyphosate, atrazine, and dicamba. Subsequent dose-response studies determined that Flur-R was 40 times more resistant to fluroxypyr than a susceptible population (J01-S) collected from the same field survey (LD50 720 and 20 g ae ha-1, respectively). Auxin-responsive gene expression increased following fluroxypyr treatment in Flur-R, J01-S, and in a dicamba-resistant, fluroxypyr-susceptible line 9,425 in an RNA-sequencing experiment. In Flur-R, several transcripts with molecular functions for conjugation and transport were constitutively higher expressed, such as glutathione S-transferases (GSTs), UDP-glucosyl transferase (GT), and ATP binding cassette transporters (ABC transporters). After analyzing metabolic profiles over time, both Flur-R and J01-S rapidly converted [14C]-fluroxypyr ester, the herbicide formulation applied to plants, to [14C]-fluroxypyr acid, the biologically active form of the herbicide, and three unknown metabolites. The formation and flux of these metabolites were faster in Flur-R than J01-S, reducing the concentration of phytotoxic fluroxypyr acid. One unique metabolite was present in Flur-R that was not present in the J01-S metabolic profile. Gene sequence variant analysis specifically for auxin receptor and signaling proteins revealed the absence of non-synonymous mutations affecting auxin signaling and binding in candidate auxin target site genes, further supporting our hypothesis that non-target site metabolic degradation is contributing to fluroxypyr resistance in Flur-R.

Discovery of a Novel Lidocaine Metabolite by Human Liver Microsome and Identification of Microbial Species Which Produces the Same Metabolite.

Preparation of drug metabolites at the milligram scale is essential for determining the structure and toxicity of drug metabolites. However, their preparation using recombinant proteins and human liver microsomes (HLM) is often difficult because of technical and ethical issues. Reproducing human drug metabolism in food-derived microorganisms may be useful for overcoming these challenges. In this study, we identified an unknown metabolite of the anaesthetic drug lidocaine, which is metabolised by HLM. By screening for lidocaine metabolic activity in five types of foods (blue cheese, shiitake mushroom, natto, yoghurt, and dry yeast), we found that bacteria isolated from natto reproduced the lidocaine metabolic reaction that occurs in HLM. A fraction containing the unknown lidocaine metabolite was prepared through mass cultivation of a Bacillus subtilis standard strain, ethyl acetate extraction, open column chromatography, and HPLC purification. We identified the unknown metabolite as 3-(2,6-dimethylphenyl)-1-ethyl-2-methyl-4-imidazolidinone using NMR. Our results showed that food-derived microorganisms can produce large amounts of human drug metabolites via large-scale cultivation. Additionally, food microorganisms that can reproduce drug metabolism in humans can be used to examine drug metabolites at a low cost and without ethical issues.

RNA-seq analysis reveals modulation of inflammatory pathways by an enriched-triterpene natural extract in mouse and human macrophage cell lines

Chronic inflammation is crucial in developing insulin resistance and type 2 diabetes. Previous studies have shown that a leaf extract of Eucalyptus tereticornis, with ursolic acid (UA), oleanolic acid (OA), and ursolic acid lactone (UAL) as the main molecules (78 %) mixed with unknown minor metabolites (22 %), provided superior anti-inflammatory, hypoglycemic, and hypolipidemic effects than reconstituted triterpenoid mixtures in macrophage cell lines and a pre-diabetic mouse model. Further identification of the molecular mechanisms of action of this mixture of triterpenes is required. This study aims to analyse the RNA expression profiles of mouse and human macrophage cell lines treated with the natural extract and its components. Activated macrophage cell lines were treated with the natural extract, UA, OA, UAL or a triterpene mixture (M1). RNA was extracted and sequenced using the DNBseq platform and the EnrichR software to perform gene enrichment analysis using the Gene Ontology database, Kyoto Encyclopedia of Genes and Genomes, and Reactome. To conduct clustering analysis, we standardised the normalised counts of each gene and applied k-means clustering. The combination of molecules in the natural extract has an additive or synergic effect that affects the expression of up-regulated genes by macrophage activation. Triterpenes (M1) regulated 76 % of human and 68 % of mouse genes, while uncharacterised minority molecules could regulate 24 % of human and 32 % of mouse genes. The extract inhibited the expression of many cytokines (IL6, IL1, OSM), chemokines (CXCL3), inflammatory mediators (MMP8 and MMP13) and the JAK-STAT signalling pathway in both models. The natural extract has a more powerful immunomodulatory effect than the triterpene mixture, increasing the number of genes regulated in mouse and human models. Our study shows that Eucalyptus tereticornis extract is a promising option for breaking the link between inflammation and insulin resistance.

Prioritization before dereplication, an effective strategy to target new metabolites in whole extracts: ghosalin from Murraya paniculata root.

Re-discovery of known metabolites is a common challenge in natural product-based drug discovery, and to avoid re-discovery, dereplication has been proposed for identifying known metabolites at the early stage of isolation. A majority of methods use LCMS to profile the extract and ignore the known mass. LC-HRMS profiling may generate a long mass list of metabolites. The identification of a new metabolite is difficult within the mass list. To overcome this, it was hypothesized that identifying a 'new metabolite' in the whole metabolome is more difficult than identifying it within the class of metabolites. A prioritization strategy was proposed to focus on the elimination of unknown and uncommon metabolites first using the designed bias filters and to prioritize the known secondary metabolites. The study employed Murraya paniculata root for the identification of new metabolites. The LC-HRMS-generated mass list of 509 metabolites was subjected to various filters, which resulted in 93 metabolites. Subsequently, it was subjected to regular dereplication, resulting in 10 coumarins, among which 3 were identified as new. Further, chromatographic efforts led to the isolation of a new coumarin, named ghosalin (1). The structure of the new compound was established through 2D NMR and X-ray crystallography. Cytotoxicity studies revealed that ghosalin has significant cytotoxicity against cancer cell lines. The proposed prioritization strategy demonstrates an alternative way for the rapid annotation of a particular set of metabolites to isolate a new metabolite from the whole metabolome of a plant extract.

The relationship between metabolites and gout: a Mendelian randomization study.

Gout is closely tied to metabolism, yet there is limited evidence on how metabolites may cause or prevent the condition. This study utilized a two-sample Mendelian randomization (MR) analysis to evaluate the causal relationship between 1,400 serum metabolites and gout. We primarily employed the inverse variance-weighted (IVW) method to estimate causal effects, supplemented by MR-Egger regression, weighted median, simple mode, and weighted mode for comprehensive evaluations. Additionally, we conducted tests for pleiotropy and heterogeneity. After a rigorous selection process, we identified eight known metabolites and four unknown metabolites associated with gout. Among the eight known metabolites, Glucuronide of piperine metabolite C17H21NO3 and the Phosphate to mannose ratio were positively associated with an increased risk of gout. Conversely, levels of 5 alpha-androstan-3 beta, 17 alpha-diol disulfate, Pantoate, N-carbamoylalanine, Sphingomyelin (d18:0/20:0, d16:0/22:0), Hydroxypalmitoyl sphingomyelin (d18:1/16:0(OH)), and Mannose were linked to a decreased risk of gout. This study identified eight metabolites from 1,400 blood samples significantly linked to gout risk. Integrating genomics and metabolomics offers valuable insights for gout screening and prevention, indicating that specific blood metabolites can help identify individuals at higher risk.

COLMARppm: A Web Server Tool for the Accurate and Rapid Prediction of 1H and 13C NMR Chemical Shifts of Organic Molecules and Metabolites.

Despite rapid progress in metabolomics research, a major bottleneck is the large number of metabolites whose chemical structures are unknown or whose spectra have not been deposited in metabolomics databases. Nuclear magnetic resonance (NMR) spectroscopy has a long history of elucidating chemical structures from experimentally measured 1H and 13C chemical shifts. One approach to characterizing the chemical structures of an unknown metabolite is to predict the 1H and 13C chemical shifts of candidate compounds (e.g., metabolites from the Human Metabolome Database (HMDB)) and compare them with chemical shifts of the unknown. However, accurate prediction of NMR chemical shifts in aqueous solution is challenging due to limitations of experimental chemical shift libraries and the high computational cost of quantum chemical methods. To improve NMR prediction accuracy and applicability, an empirical prediction strategy is introduced here to provide an accurately predicted chemical shift for organic molecules and metabolites within seconds. Unique features of COLMARppm include (i) the training library exclusively consisting of high quality NMR spectra measured under standard conditions in aqueous solution, (ii) utilization of NMR motif information, and (iii) leveraging of the improved prediction accuracy for the automated assignment of experimental chemical shifts for candidate structures. COLMARppm is demonstrated in terms of accuracy and speed for a set of 20 compounds taken from the HMDB for chemical shift prediction and resonance assignment. COLMARppm is applicable to a wide range of small molecules and can be directly incorporated into metabolomics workflows.

Cerebrospinal fluid metabolome‐wide association study of Alzheimer’s disease provides insight into molecular pathways

AbstractBackgroundMany genetic risk variants have been identified for Alzheimer’s disease (AD), but their functional consequence on molecular pathways are unclear. Quantitative trait loci (QTL) studies help elucidate the functional effect of genetic variance on (neuro)biological processes. The understanding of AD mechanisms has been limited due to the relative inaccessibility of in vivo brain tissue and its surroundings. In this study, we integrated genetic and metabolomics cerebrospinal fluid (CSF) data, to understand how AD risk variants influence neurobiological processes.MethodWe performed a genome‐wide mQTL study on 5,543 CSF metabolite levels using data from 977 subjects (age 52.7±16.6 years, 63.8%female) from the Amsterdam Dementia Cohort (n = 487; 220 controls, 87 mild cognitive impairment, 180 AD‐type dementia) and the Utrecht cohort (n = 490 controls). We measured CSF metabolites with three platforms: GC‐TOF MS (primary metabolism, 393 metabolites), CSH‐QTOF MS/MS (complex lipids, 3,532 metabolites), and HILIC‐QTOF MS/MS (biogenic amines, 1,618 metabolites). Association signals between genetic variants and CSF metabolite levels were tested using linear regression, adjusted for principal components, age, sex and site (Amsterdam/Utrecht) using a Bonferroni‐corrected genome‐wide significance threshold (5.0e−8 /754 independent CSF signals = 6.0e−11). To identify metabolites with predicted CSF levels associated with AD, we integrated metabolite summary statistics with AD summary statistics (Kunkle et al., 2019; Bellenguez et al., 2022), using a metabolome‐wide association study (MWAS) approach (FUSION software).ResultWe identified 89 genome‐wide CSF mQTLs, including 89 independent loci for 62 CSF metabolite levels. Most mQTLs were biogenic amines (81), followed by mQTLs for the primary (5) metabolism and complex lipids (3) (Figure 1). MWAS results included 24 metabolite‐AD associations with P<0.05. The strongest associations included variants proximal to the CANX gene with unannotated CSF metabolite 1.09_397.32 (PBellenguez = 2.4e−6; PKunkle = 3.4e−3), and CCDC124 variants with 8.76_225.06 (PBellenguez = 2.9e−3; PKunkle = 2.8e−5) (Figure 2).ConclusionWe identified novel metabolite‐AD risk associations, including CANX and CCDC124 with unknown CSF metabolites. In future work we aim to identify these CSF metabolites using raw spectra and intensity data. This study shows that the multi‐modal integration of genetics with CSF metabolomics helps in the functional interpretation of genetic variance, and could therefore serve as useful approach for other brain‐related disorders.

Causal effects of genetically determined blood metabolites on multiple myeloma: a Mendelian randomization study.

Previous studies have shown that metabolites play an important role in phenotypic regulation. However, the causal relationship between metabolites and multiple myeloma has not been adequately investigated. Here, we attempt to explore the causal effects of genetically determined blood metabolites on multiple myeloma. The large-scale public blood metabolites and multiple myeloma datasets from independently published genome-wide association studies (GWAS) were used to explore the causal relationship between each genetically determined blood metabolite and multiple myeloma through inverse variance weighted (IVW), weighted median, MR-Egger and mode-based estimation methods. Sensitivity tests were performed to evaluate the stability and reliability of the results by MR-Egger regression and leave-one-out methods. Metabolic pathway analysis was further explored using filtered data. Statistical analyses were all performed in R. Among 452 metabolites, ten known metabolites and three unknown metabolites had significant causal relationship with multiple myeloma (P < 0.05). Four known metabolites, 3-methyl-2-oxovalenate, oxidized bilirubin, isovalerylcarnitine and glutamine carnitine, reached statistical significance in IVW models. Metabolic pathways analysis identified four significant pathways. The occurrence of multiple myeloma may have a causal relationship with these four metabolites, and there are four metabolic pathways that are also related to the occurrence of multiple myeloma. This can provide new ideas for exploring early screening and treatment of multiple myeloma.

A bacterial spermidine biosynthetic pathway via carboxyaminopropylagmatine.

Spermidine, a ubiquitous polyamine, is known to be required for critical physiological functions in bacteria. Two principal pathways are known for spermidine biosynthesis, both of which involve aminopropylation of putrescine. Here, we identified a spermidine biosynthetic pathway via a previously unknown metabolite, carboxyaminopropylagmatine (CAPA), in a model cyanobacterium Synechocystis sp. PCC 6803 through an approach combining 13C and 15N tracers, metabolomics, and genetic and biochemical characterization. The CAPA pathway starts with reductive condensation of agmatine and l-aspartate-β-semialdehyde into CAPA by a previously unknown CAPA dehydrogenase, followed by decarboxylation of CAPA to form aminopropylagmatine, and ends with conversion of aminopropylagmatine to spermidine by an aminopropylagmatine ureohydrolase. Thus, the pathway does not involve putrescine and depends on l-aspartate-β-semialdehyde as the aminopropyl group donor. Genomic, biochemical, and metagenomic analyses showed that the CAPA-pathway genes are widespread in 15 different phyla of bacteria distributed in marine, freshwater, and other ecosystems.

Profile of PKS and NRPS Gene Clusters in the Genome of Streptomyces cellostaticus NBRC 12849T

Polyketides and nonribosomal peptides are major secondary metabolites in members of the genus Streptomyces. Streptomyces cellostaticus is a validly recognized species and the type strain produces cellostatin. However, little is known about whether it has the potential to produce diverse polyketides and nonribosomal peptides. Here, we sequenced the whole genome of S. cellostaticus NBRC 12849T and surveyed polyketide synthase (PKS) and nonribosomal peptide synthetase (NRPS) gene clusters in the genome. The genome encoded 12 PKS, one NRPS and eight hybrid PKS/NRPS gene clusters. Among the 21 gene clusters, products of 10 gene clusters were annotated to be an annimycin congener, fuelimycins, lankamycin, streptovaricin, spore pigment, flaviolin, foxicin, blasticidin, lankacidin and an incarnatapeptine congener via our bioinformatic analysis. Although the other clusters were orphan and their products were unknown, five of them were predicted to be compounds derived from two independent diketides, a tridecaketide, a triketide and a tetraketide with a cysteine residue, respectively. These results suggest that S. cellostaticus is a source of diverse polyketides and hybrid polyketide/nonribosomal peptides, including unknown and new secondary metabolites.

In silico and in vitro metabolism studies of the new synthetic opiate AP-237 (bucinnazine) using bioinformatics tools.

The recent emergence of new synthetic opioids (NSOs) compounds in the illicit market is increasingly related to fatal cases. Identification and medical care of NSO intoxication cases are challenging, particularly due to high frequency of new products and extensive metabolism. As the study of NSO metabolism is crucial for the identification of these drugs in cases of intoxication, we aimed to investigate the metabolism of the piperazine NSO AP-237 (= bucinnazine). Two complementary approaches (in silico and in vitro) were used to identify putative AP-237 metabolites which could be used as consumption markers. In silico metabolism studies were realized by combining four open access softwares (MetaTrans, SyGMa, Glory X, Biotransformer 3.0). In vitro experiments were performed by incubating AP-237 (20µM) in differentiated HepaRG cells during 0h, 8h, 24h or 48h. Cell supernatant were extracted and analyzed by liquid chromatography coupled to high-resolution mass spectrometry and data were reprocessed using three strategies (MetGem, GNPS or Compound Discoverer®). A total of 28 phase I and six phase II metabolites was predicted in silico. Molecular networking identified seven putative phase I metabolites (m/z 203.154, m/z 247.180, m/z 271.180, two m/z 289.191 isomers, m/z 305.186, m/z 329.222), including four previously unknown metabolites. Overall, this cross-disciplinary approach with molecular networking on data acquired in vitro and in silico prediction enabled to propose relevant candidate as AP-237 consumption markers that could be added to mass spectrometry libraries to help diagnose intoxication.

Causal relationship between blood metabolites and risk of five infections: a Mendelian randomization study

ObjectiveInfectious diseases continue to pose a significant threat in the field of global public health, and our understanding of their metabolic pathogenesis remains limited. However, the advent of genome-wide association studies (GWAS) offers an unprecedented opportunity to unravel the relationship between metabolites and infections.MethodsUnivariable and multivariable Mendelian randomization (MR) was commandeered to elucidate the causal relationship between blood metabolism and five high-frequency infection phenotypes: sepsis, pneumonia, upper respiratory tract infections (URTI), urinary tract infections (UTI), and skin and subcutaneous tissue infection (SSTI). GWAS data for infections were derived from UK Biobank and the FinnGen consortium. The primary analysis was conducted using the inverse variance weighted method on the UK Biobank data, along with a series of sensitivity analyses. Subsequently, replication and meta-analysis were performed on the FinnGen consortium data.ResultsAfter primary analysis and a series of sensitivity analyses, 17 metabolites were identified from UK Biobank that have a causal relationship with five infections. Upon joint analysis with the FinGen cohort, 7 of these metabolites demonstrated consistent associations. Subsequently, we conducted a multivariable Mendelian randomization analysis to confirm the independent effects of these metabolites. Among known metabolites, genetically predicted 1-stearoylglycerol (1-SG) (odds ratio [OR] = 0.561, 95% confidence interval [CI]: 0.403–0.780, P < 0.001) and 3-carboxy-4-methyl-5-propyl-2-furanpropanoate (CMPF) (OR = 0.780, 95%CI: 0.689–0.883, P < 0.001) was causatively associated with a lower risk of sepsis, and genetically predicted phenylacetate (PA) (OR = 1.426, 95%CI: 1.152–1.765, P = 0.001) and cysteine (OR = 1.522, 95%CI: 1.170–1.980, P = 0.002) were associated with an increased risk of UTI. Ursodeoxycholate (UDCA) (OR = 0.906, 95%CI: 0.829–0.990, P = 0.029) is a protective factor against pneumonia. Two unknown metabolites, X-12407 (OR = 1.294, 95%CI: 1.131–1.481, P < 0.001), and X-12847 (OR = 1.344, 95%CI: 1.152–1.568, P < 0.001), were also identified as independent risk factors for sepsis.ConclusionsIn this MR study, we demonstrated a causal relationship between blood metabolites and the risk of developing sepsis, pneumonia, and UTI. However, there was no evidence of a causal connection between blood metabolites and the risk of URTI or SSTI, indicating a need for larger-scale studies to further investigate susceptibility to certain infection phenotypes.

Labile Metabolite Profiling in Human Blood Using Phosphorus NMR Spectroscopy.

Phosphorus metabolites occupy a unique place in cellular function as critical intermediates and products of cellular metabolism. Human blood is the most widely used biospecimen in the clinic and in the metabolomics field, and hence an ability to profile phosphorus metabolites in blood, quantitatively, would benefit a wide variety of investigations of cellular functions in health and diseases. Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy are the two premier analytical platforms used in the metabolomics field. However, detection and quantitation of phosphorus metabolites by MS can be challenging due to their lability, high polarity, structural isomerism, and interaction with chromatographic columns. The conventionally used 1H NMR, on the other hand, suffers from poor resolution of these compounds. As a remedy, 31P NMR promises an important alternative to both MS and 1H NMR. However, numerous challenges including the instability of phosphorus metabolites, their chemical shift sensitivity to solvent composition, pH, salt, and temperature, and the lack of identified metabolites have so far restricted the scope of 31P NMR. In the current study, we describe a method to analyze nearly 25 phosphorus metabolites in blood using a simple one-dimensional (1D) NMR spectrum. Establishment of the identity of unknown metabolites involved a combination of (a) comprehensively analyzing an array of 1D and two-dimensional (2D) 1H/31P homonuclear and heteronuclear NMR spectra of blood; (b) mapping the central carbon metabolic pathway; (c) developing and using 1H and 31P spectral and chemical shift databases; and finally (d) confirming the putative metabolite peaks with spiking using authentic compounds. The resulting simple 1D 31P NMR-based method offers an ability to visualize and quantify the levels of intermediates and products of multiple metabolic pathways, including central carbon metabolism, in one step. Overall, the findings represent a new dimension for blood metabolite analysis and are anticipated to greatly impact the blood metabolomics field.

MIST-CF: Chemical Formula Inference from Tandem Mass Spectra.

Chemical formula annotation for tandem mass spectrometry (MS/MS) data is the first step toward structurally elucidating unknown metabolites. While great strides have been made toward solving this problem, the current state-of-the-art method depends on time-intensive, proprietary, and expert-parametrized fragmentation tree construction and scoring. In this work, we extend our previous spectrum Transformer methodology into an energy-based modeling framework, MIST-CF: Metabolite Inference with Spectrum Transformers for Chemical Formula prediction, for learning to rank chemical formula and adduct assignments given an unannotated MS/MS spectrum. Importantly, MIST-CF learns in a data-dependent fashion using a Formula Transformer neural network architecture and circumvents the need for fragmentation tree construction. We train and evaluate our model on a large open-access database, showing an absolute improvement of 10% top 1 accuracy over other neural network architectures. We further validate our approach on the CASMI2022 challenge data set, achieving nearly equivalent performance to the winning entry within the positive mode category without any manual curation or postprocessing of our results. These results demonstrate an exciting strategy to more powerfully leverage MS2 fragment peaks for predicting MS1 precursor chemical formulas with data-driven learning.

Tracking a broad inventory of cyanotoxins and related secondary metabolites using UHPLC-HRMS

Rising temperatures caused by global warming and elevated nutrient influx promote the growth of harmful cyanobacteria in freshwater ecosystems affecting human and environmental health. In this work, UHPLC-HRMS was used to simultaneously determine 7 common microcystins and 18 additional cyanobacterial metabolites in natural freshwater samples. Therefore, the particulate material of the water from Lake Stechlin in northern Brandenburg (Germany) was collected, extracted, and analyzed. The chromatographic method for the separation of cyanotoxins was optimized to enable the quantification of common toxins. By investigating additional cyanotoxins for which standards are unavailable, two more microcystins, three cyanopeptolins, five anabaenopeptins, one aeruginosin, and three additional secondary metabolites could be identified. In addition to these molecules, five unknown metabolites were detected and could be linked to Planktothrix rubescens, a native species in Lake Stechlin. Applying the proposed method yielded insights on cyanotoxin prevalence in northern German lowland Lake Stechlin and will enable further studies to elucidate additional coherences. Furthermore, by correlating additional biological, hydrodynamic, and ecological data with information on the distribution and productivity of cyanobacteria, the proposed method with the expanded toxin inventory can provide important information in risk assessment about the quality and toxicity of the ecosystem in and around the water.

Causal Link between Human Blood Metabolites and Asthma: An Investigation Using Mendelian Randomization

Background: Asthma, a chronic inflammatory respiratory ailment, is characterized by variable airflow obstruction and heightened bronchial reactivity. Despite therapeutic advancements, a comprehensive comprehension of its underlying metabolic mechanisms remains elusive. Metabolomics has emerged as a powerful approach to investigating the complex connections between serum metabolites and disease pathogenesis. However, exploring the causal relationship between serum metabolites and asthma susceptibility demands meticulous examination to unveil potential therapeutic targets. Methods: Mendelian randomization (MR) approach was explored to investigate the potential causal associations between serum metabolites and asthma risk. The main analysis employed the inverse variance weighted method, supported by supplementary approaches such as MR-Egger, weighted median, weighted mode, and sample mode. To enhance the strength and credibility of our results, we conducted sensitivity analyses encompassing heterogeneity testing, assessment of horizontal pleiotropy, and leave-one-out analysis. Additionally, pathway enrichment analysis was performed to further elucidate the results. Results: We identified 18 known and 12 unknown metabolites with potential associations with asthma risk. Among known metabolites, seven exhibited protective effects (e.g., 4-acetamidobutanoate, allantoin, kynurenine, oxidized bilirubin*), while eleven were considered risk factors (e.g., ornithine, N-acetylornithine, alanine). Through the integration of four additional MR models and sensitivity analyses, we revealed a connection between 4-acetamidobutanoate and approximately 6% lower asthma risk (OR = 0.94, 95% CI: 0.90–0.98). Conclusions: Our MR analysis uncovered protective and risk-associated metabolites, alongside 12 unknown metabolites linked to asthma. Notably, 4-acetamidobutanoate demonstrated a nominal 6% reduction in asthma risk, highlighting its potential significance.