Metabolomics Platform Research Articles

The association between HIV-1 Tat and Vif amino acid sequence variation, inflammation and Trp-Kyn metabolism: an exploratory investigation

BackgroundHIV-1 has well-established mechanisms to disrupt essential pathways in people with HIV, such as inflammation and metabolism. Moreover, diversity of the amino acid sequences in fundamental HIV-1 proteins including Tat and Vif, have been linked to dysregulating these pathways, and subsequently influencing clinical outcomes in people with HIV. However, the relationship between Tat and Vif amino acid sequence variation and specific immune markers and metabolites of the tryptophan-kynurenine (Trp-Kyn) pathway remains unclear. Therefore, this study aimed to investigate the relationship between Tat/Vif amino acid sequence diversity and Trp-Kyn metabolites (quinolinic acid (QUIN), Trp, kynurenic acid (KA), Kyn and Trp/Kyn ratio), as well as specific immune markers (sCD163, suPAR, IL-6, NGAL and hsCRP) in n = 67 South African cART-naïve people with HIV.MethodsSanger sequencing was used to determine blood-derived Tat/Vif amino acid sequence diversity. To measure Trp-Kyn metabolites, a LC–MS/MS metabolomics platform was employed using a targeted approach. To measure immune markers, Enzyme-linked immunosorbent assays and the Particle-enhanced turbidimetric assay was used.ResultsAfter adjusting for covariates, sCD163 (p = 0.042) and KA (p = 0.031) were higher in participants with Tat signatures N24 and R57, respectively, and amino acid variation at position 24 (adj R2 = 0.048, β = -0.416, p = 0.042) and 57 (adj R2 = 0.166, β = 0.535, p = 0.031) of Tat were associated with sCD163 and KA, respectively.ConclusionsThese preliminary findings suggest that amino acid variation in Tat may have an influence on underlying pathogenic HIV-1 mechanisms and therefore, this line of work merits further investigation.

Systems Approach to Investigate the Role of Fruit and Vegetable Types on Vascular Function in Pre-Hypertensive Participants: Protocol and Baseline Characteristics of a Randomised Crossover Dietary Intervention.

The evidence on the impact of fruits and vegetable types on cardiovascular risk factors remains limited. Specifically, the utilisation of biomarkers to objectively measure dietary compliance and metabolic responses is emerging. This protocol and baseline characteristics of a pilot randomised controlled, crossover, dietary intervention study aimed to examine the effects of citrus fruits, cruciferous vegetables, or common fruits and vegetables on cardiovascular risk factors. A total of 39 volunteers with untreated prehypertension was recruited and consumed a standardised, provided diet with eight daily portions of citrus fruits and cruciferous vegetables, common fruits and vegetables, or a low fruit and vegetable diet (two portions/d, control diet) in a random order for 2 weeks each, separated by a wash-out week. A targeted cohort-based recruitment strategy was utilised and resulted in 74% of participants recruited by re-contacting preselected individuals from two cohort studies with a 15% average enrolment rate. Participants had an average age of 54.4 years (±6.1 years), BMI of 27.9 kg/m2, and BP of 135/81 mmHg and were mainly male (67%). The primary outcome was office blood pressure; secondary outcomes included arterial stiffness, lipid profiles, inflammation, cognitive function, and subjective mood. Biofluids, i.e., 24 h urine, stool, and blood samples, were collected for biomarker measurements with multiple metabolomic platforms and the gut microbial composition, together with traditional dietary biomarkers.

Metabolomic biomarkers and altered phenylalanine metabolic pathway in preschool children with atopic dermatitis - A pilot study.

Background Atopic dermatitis (AD) has high prevalence in children. Current AD diagnosis and management focuses only on clinical phenotypes, but do not explore the endophenotypes, which are more important because they are a series of biomarkers linking clinical phenotype and genotype Aims Metabolomics can qualitatively and quantitatively capture real-time dynamic changes in a wide range of small molecule metabolites. This pilot study evaluated metabolomics biomarkers and altered metabolic pathways in preschool children with AD, aiming to explore the underlying molecular mechanisms and signalling pathways of the disease. Methods Blood samples of 23 preschool children with AD and 23 healthy children without AD or any other skin disease were collected. The untargeted metabolomic measurements were performed on a SCIEX-AD ultraperformance liquid chromatography system coupled with an AB SCIEX X500B QTOF system. Characteristics of small molecules in AD children were assessed and their associations with AD clinical index were evaluated. Altered metabolic pathways in AD children were also analysed using a comprehensive metabolomics platform. Results A total of 1,969 metabolites were identified, of which AD children exhibited 377 significantly altered metabolites. Multivariate statistical analysis demonstrated that the AD group and the control group could be clearly separated. Volcano plot analysis illustrated that 144 metabolites were up-regulated and 233 metabolites were down-regulated in AD children. The Severity Scoring of Atopic Dermatitis (SCORAD index) showed a moderate-to-strong association with estrogens, carotenes, leukotrienes, flavonols and keto acids in AD children (|r|=0.440-0.557). Several pathways, including the phenylalanine metabolism, were identified as altered in AD children. Limitations A small group of children was included in the study; the results need to be validated in larger sample sizes. Conclusion Results of this study illustrate potential alterations in metabolites and the phenylalanine metabolic pathway in preschool children with AD. Although this is a pilot study with a limited sample size, it may provide a new perspective for exploring the pathogenesis of AD, and for personalised treatment modalities.

Geniposide exerts the antidepressant effect by affecting inflammation and glucose metabolism in a mouse model of depression

Depression is a severe mental illness affecting patient's physical and mental health. However, long-term effects of existing therapeutic modalities for depression are not satisfactory. Geniposide is an iridoid compound highly expressed in gardenia jasminoides for removing annoyance. The activity of geniposide against depression has been widely studied while most studies concentrated on the expression levels of gene and protein. Herein, the aim of the present study was to employ non-target metabolomic platform of serum to investigate metabolic changes of depression mice and further verify in hippocampus for analyzing the antidepressant mechanism of geniposide. Then we discovered that 9 metabolites of serum were significantly increased in depressive group (prostaglandin E2, leukotriene C4, arachidonic acid, phosphatidylcholine (PC, 16:0/16:0), LysoPC (18:1 (9Z)/0:0), phosphatidylethanolamine (14:0/16:0), creatine, oleamide and aminomalonic acid) and 6 metabolites were decreased (indoxylsulfuric acid, testosterone, lactic acid, glucose 6-phosphate, leucine and valine). The levels of arachidonic acid, LysoPC, lactic acid and glucose 6-phosphate in hippocampus were consistent change with serum in depression mice. Most of them showed significant tendencies to be normal by geniposide treatment. Metabolic pathway analysis indicated that arachidonic acid metabolism and glucose metabolism were the main pathogenesis for the antidepressant effect of geniposide. In addition, the levels of serum tumor necrosis factor-α and interleukin-1 were increased in depressive mice and reversed after geniposide treatment. This study revealed that abnormal metabolism of inflammatory response and glucose metabolism of the serum and hippocampus involved in the occurrence of depressive disorder and antidepressant effect of geniposide.

Comprehensive metabolome characterization of leaves, internodes, and aerial roots of Vanilla planifolia by untargeted LC-MS and GC × GC-MS.

Untargeted metabolomics is a powerful tool that provides strategies for gaining a systematic understanding of quantitative changes in the levels of metabolites, especially when combining different metabolomic platforms. Vanilla is one of the world's most popular flavors originating from cured pods of the orchid Vanilla planifolia. However, only a few studies have investigated the metabolome of V. planifolia, and no LC-MS or GC-MS metabolomics studies with respect to leaves have been performed. The aim of the study was to comprehensively characterize the metabolome of different organs (leaves, internodes, and aerial roots) of V. planifolia. Characterization of the metabolome was achieved using two complementary platforms (GC × GC-MS, LC-QToF-MS), and metabolite identification was based on a comparison with in-house databases or curated external spectral libraries. In total, 127 metabolites could be identified with high certainty (confidence level 1 or 2) including sugars, amino acids, fatty acids, organic acids, and amines/amides but also secondary metabolites such as vanillin-related metabolites, flavonoids, and terpenoids. Ninty-eight metabolites showed significantly different intensities between the plant organs. Most strikingly, aglycons of flavonoids and vanillin-related metabolites were elevated in aerial roots, whereas its O-glycoside forms tended to be higher in leaves and/or internodes. This suggests that the more bioactive aglycones may accumulate where preferably needed, e.g. for defense against pathogens. The results derived from the study substantially expand the knowledge regarding the vanilla metabolome forming a valuable basis for more targeted investigations in future studies, e.g. towards an optimization of vanilla plant cultivation.

Discovery and Identification of Three Homocysteine Metabolites by Chemical Derivatization and Mass Spectrometry Fragmentation.

Discovery and identification of a new endogenous metabolite are typically hindered by requirements of large sample volumes and multistage purifications to guide synthesis of the standard. Presented here is a metabolomics platform that uses chemical tagging and tandem mass spectrometry to determine structure, direct synthesis, and confirm identity. Three new homocysteine metabolites are reported: N-succinyl homocysteine, 2-methyl-1,3-thiazinane-4-carboxylic acid (MTCA), and homolanthinone.

Implications of Ocular Confounding Factors for Aqueous Humor Proteomic and Metabolomic Analyses in Retinal Diseases.

To assess the impact of ocular confounding factors on aqueous humor (AH) proteomic and metabolomic analyses for retinal disease characterization. This study recruited 138 subjects (eyes): 102 with neovascular age-related macular degeneration (nAMD), 18 with diabetic macular edema (DME), and 18 with cataract (control group). AH samples underwent analysis using Olink Target 96 proteomics and Metabolon's metabolomics platform Data analysis included correlation, differential abundance, and gene-set analysis. In total, 756 proteins and 408 metabolites were quantified in AH. Total AH protein concentration was notably higher in nAMD (3.2-fold) and DME (4.1-fold) compared to controls. Pseudophakic eyes showed higher total AH protein concentrations than phakic eyes (e.g., 1.6-fold in nAMD) and a specific protein signature indicative of matrix remodeling. Unexpectedly, pupil-dilating drugs containing phenylephrine/tropicamide increased several AH proteins, notably interleukin-6 (5.4-fold in nAMD). Correcting for these factors revealed functionally relevant protein correlation clusters and disease-relevant, differentially abundant proteins across the groups. Metabolomics analysis, for which the relevance of confounder adjustment was less apparent, suggested insufficiently controlled diabetes and chronic hyperglycemia in the DME group. AH protein concentration, pseudophakia, and pupil dilation with phenylephrine/tropicamide are important confounding factors for AH protein analyses. When these factors are considered, AH analyses can more clearly reveal disease-relevant factors. Considering AH protein concentration, lens status, and phenylephrine/tropicamide administration as confounders is crucial for accurate interpretation of AH protein data.

Daphnetin Ameliorates Neuropathic Pain via Regulation of Microglial Responses and Glycerophospholipid Metabolism in the Spinal Cord.

Neuropathic pain (NP) is a common type of chronic pain caused by a lesion or disease of the somatosensory nervous system. This condition imposes a considerable economic burden on society and patients. Daphnetin (DAP) is a natural product isolated from a Chinese medicinal herb with various pharmacological activities, such as anti-inflammatory and analgesic properties. However, the underlying mechanisms of these effects are not fully understood. In the present study, we aimed to investigate DAP's anti-inflammatory and analgesic effects and explore the underlying mechanisms of action. The NP model was established as chronic constrictive injury (CCI) of the sciatic nerve, and pain sensitivity was evaluated by measuring the mechanical withdrawal threshold (MWT) and thermal withdrawal threshold (TWT). The activation of microglia in the spinal dorsal horn was measured via immunofluorescence staining. Protein levels were measured using a western blot assay. Using a mass-spectrometry proteomics platform and an LC-MS/MS-based metabolomics platform, proteins and metabolites in spinal cord tissues were extracted and analyzed. DAP treatment ameliorated the MWT and TWT in CCI rats. The expression of IL-1β, IL-6, and TNF-α was inhibited by DAP treatment in the spinal cords of CCI rats. Moreover, the activation of microglia was suppressed after DAP treatment. The elevation in the levels of P2X4, IRF8, IRF5, BDNF, and p-P38/P38 in the spinal cord caused by CCI was inhibited by DAP. Proteomics and metabolomics results indicated that DAP ameliorated the imbalance of glycerophospholipid metabolism in the spinal cords of CCI rats. DAP can potentially ameliorate NP by regulating microglial responses and glycerophospholipid metabolism in the CCI model. This study provides a pharmacological justification for using DAP in the management of NP.

Therapeutic effects of Lingguizhugan decoction in a rat model of high-fat diet-induced insulin resistance

BACKGROUND Lingguizhugan (LGZG) decoction is a widely used classic Chinese medicine formula that was recently shown to improve high-fat diet (HFD)-induced insulin resistance (IR) in animal studies. AIM To assess the therapeutic effect of LGZG decoction on HFD-induced IR and explore the potential underlying mechanism. METHODS To establish an IR rat model, a 12-wk HFD was administered, followed by a 4-wk treatment with LGZG. The determination of IR status was achieved through the use of biochemical tests and oral glucose tolerance tests. Using a targeted meta-bolomics platform to analyze changes in serum metabolites, quantitative real-time PCR (qRT-PCR) was used to assess the gene expression of the ribosomal protein S6 kinase beta 1 (S6K1). RESULTS In IR rats, LGZG decreased body weight and indices of hepatic steatosis. It effectively controlled blood glucose and food intake while protecting islet cells. Metabolite analysis revealed significant differences between the HFD and HFD-LGZG groups. LGZG intervention reduced branched-chain amino acid levels. Levels of IR-related metabolites such as tryptophan, alanine, taurine, and asparagine decreased significantly. IR may be linked to amino acids due to the contemporaneous increase in S6K1 expression, as shown by qRT-PCR. CONCLUSIONS Our study strongly suggests that LGZG decoction reduces HFD-induced IR. LGZG may activate S6K1 via metabolic pathways. These findings lay the groundwork for the potential of LGZG as an IR treatment.

The metabolome-wide signature of major depressive disorder.

Major Depressive Disorder (MDD) is a common, frequently chronic condition characterized by substantial molecular alterations and pathway dysregulations. Single metabolite and targeted metabolomics platforms have revealed several metabolic alterations in depression, including energy metabolism, neurotransmission, and lipid metabolism. More comprehensive coverage of the metabolome is needed to further specify metabolic dysregulations in depression and reveal previously untargeted mechanisms. Here, we measured 820 metabolites using the metabolome-wide Metabolon platform in 2770 subjects from a large Dutch clinical cohort with extensive clinical phenotyping (1101 current MDD, 868 remitted MDD, 801 healthy controls) at baseline, which were repeated in 1805 subjects at 6-year follow up (327 current MDD, 1045 remitted MDD, 433 healthy controls). MDD diagnosis was based on DSM-IV psychiatric interviews. Depression severity was measured with the Inventory of Depressive Symptomatology Self-report. Associations between metabolites and MDD status and depression severity were assessed at baseline and at 6-year follow-up. At baseline, 139 and 126 metabolites were associated with current MDD status and depression severity, respectively, with 79 overlapping metabolites. Adding body mass index and lipid-lowering medication to the models changed results only marginally. Among the overlapping metabolites, 34 were confirmed in internal replication analyses using 6-year follow-up data. Downregulated metabolites were enriched with long-chain monounsaturated (P = 6.7e-07) and saturated (P = 3.2e-05) fatty acids; upregulated metabolites were enriched with lysophospholipids (P = 3.4e-4). Mendelian randomization analyses using genetic instruments for metabolites (N = 14,000) and MDD (N = 800,000) showed that genetically predicted higher levels of the lysophospholipid 1-linoleoyl-GPE (18:2) were associated with greater risk of depression. The identified metabolome-wide profile of depression indicated altered lipid metabolism with downregulation of long-chain fatty acids and upregulation of lysophospholipids, for which causal involvement was suggested using genetic tools. This metabolomics signature offers a window on depression pathophysiology and a potential access point for the development of novel therapeutic approaches.

In-depth organic mass cytometry reveals differential contents of 3-hydroxybutanoic acid at the single-cell level

Comprehensive single-cell metabolic profiling is critical for revealing phenotypic heterogeneity and elucidating the molecular mechanisms underlying biological processes. However, single-cell metabolomics remains challenging because of the limited metabolite coverage and inability to discriminate isomers. Herein, we establish a single-cell metabolomics platform for in-depth organic mass cytometry. Extended single-cell analysis time guarantees sufficient MS/MS acquisition for metabolite identification and the isomers discrimination while online sampling ensures the high-throughput of the method. The largest number of identified metabolites (approximately 600) are achieved in single cells and fine subtyping of MCF-7 cells is first demonstrated by an investigation on the differential levels of 3-hydroxybutanoic acid among clusters. Single-cell transcriptome analysis reveals differences in the expression of 3-hydroxybutanoic acid downstream antioxidative stress genes, such as metallothionein 2 (MT2A), while a fluorescence-activated cell sorting assay confirms the positive relationship between 3-hydroxybutanoic acid and target proteins; these results suggest that the heterogeneity of 3-hydroxybutanoic acid provides cancer cells with different ability to resist surrounding oxidative stress. Our method paves the way for deep single-cell metabolome profiling and investigations on the physiological and pathological processes that occur during cancer.

#2756 Metabolomic prediction of end stage renal disease in large national biobanks

Abstract Background and Aims Identifying at-risk individuals likely to progress to end-stage renal disease as early as possible is important to effectively concentrate preventative interventions. Existing predictive tools, like the kidney failure risk score, are effective for individuals who already have moderate to severe kidney disease. Here we examine whether comprehensive metabolomics can improve performance in individuals with mildly impaired kidney function. Method Using an NMR metabolomics platform we measured >200 biomarkers including creatinine, albumin, lipids, fatty acids, amino acids, glycolysis metabolites and inflammation markers in blood samples from all 500,000 participants of the UK Biobank, who have >10 years of linked prospective follow-up data. We used machine learning to develop a score to predict kidney disease progression based on biomarkers, age and sex. We compared its performance to eGFR in a held out set of individuals in UK Biobank, and replicated in 200,000 participants of the Estonian Biobank. Results Among individuals with prevalent diabetes, hypertension or eGFR < 90 the biomarker score performs as well as or better than eGFR for all individuals. In particular it outperforms among individuals with only moderate (eGFR 60-75) or mild (eGFR 75-90) impairment of kidney function with an AUC increase from 0.63 to 0.72 and 0.48 to 0.70, respectively (Figure). This improvement is driven by contributions from numerous biomarkers, including lipids and inflammation markers. Furthermore, because these biomarkers capture diverse metabolic risk, the biomarker score also strongly predicts major adverse cardiovascular outcomes. Conclusion This large dataset allows detailed investigation of the value of metabolomics in identifying future risk of severe kidney disease early. In particular these biomarkers can highlight individuals who do not already have very low eGFR.

Comparative analysis of circulating metabolomic profiles identifies shared metabolic alterations across distinct multistressor military training exercises.

Military training provides insight into metabolic responses under unique physiological demands that can be comprehensively characterized by global metabolomic profiling to identify potential strategies for improving performance. This study identified shared changes in metabolomic profiles across three distinct military training exercises, varying in magnitude and type of stress. Blood samples collected before and after three real or simulated military training exercises were analyzed using the same untargeted metabolomic profiling platform. Exercises included a 2-wk survival training course (ST, n = 36), a 4-day cross-country ski march arctic training (AT, n = 24), and a 28-day controlled diet- and exercise-induced energy deficit (CED, n = 26). Log2-fold changes of greater than ±1 in 191, 121, and 64 metabolites were identified in the ST, AT, and CED datasets, respectively. Most metabolite changes were within the lipid (57-63%) and amino acid metabolism (18-19%) pathways and changes in 87 were shared across studies. The largest and most consistent increases in shared metabolites were found in the acylcarnitine, fatty acid, ketone, and glutathione metabolism pathways, whereas the largest decreases were in the diacylglycerol and urea cycle metabolism pathways. Multiple shared metabolites were consistently correlated with biomarkers of inflammation, tissue damage, and anabolic hormones across studies. These three studies of real and simulated military training revealed overlapping alterations in metabolomic profiles despite differences in environment and the stressors involved. Consistent changes in metabolites related to lipid metabolism, ketogenesis, and oxidative stress suggest a potential common metabolomic signature associated with inflammation, tissue damage, and suppression of anabolic signaling that may characterize the unique physiological demands of military training.NEW & NOTEWORTHY The extent to which metabolomic responses are shared across diverse military training environments is unknown. Global metabolomic profiling across three distinct military training exercises identified shared metabolic responses with the largest changes observed for metabolites related to fatty acids, acylcarnitines, ketone metabolism, and oxidative stress. These changes also correlated with alterations in markers of tissue damage, inflammation, and anabolic signaling and comprise a potential common metabolomic signature underlying the unique physiological demands of military training.

Intake Biomarkers for Nutrition and Health: Review and Discussion of Methodology Issues.

Metabolomics profiles from blood, urine, or other body fluids have the potential to assess intakes of foods and nutrients objectively, thereby strengthening nutritional epidemiology research. Metabolomics platforms may include targeted components that estimate the relative concentrations for individual metabolites in a predetermined set, or global components, typically involving mass spectrometry, that estimate relative concentrations more broadly. While a specific metabolite concentration usually correlates with the intake of a single food or food group, multiple metabolites may be correlated with the intake of certain foods or with specific nutrient intakes, each of which may be expressed in absolute terms or relative to total energy intake. Here, I briefly review the progress over the past 20 years on the development and application intake biomarkers for foods/food groups, nutrients, and dietary patterns, primarily by drawing from several recent reviews. In doing so, I emphasize the criteria and study designs for candidate biomarker identification, biomarker validation, and intake biomarker application. The use of intake biomarkers for diet and chronic disease association studies is still infrequent in nutritional epidemiology research. My comments here will derive primarily from our research group's recent contributions to the Women's Health Initiative cohorts. I will complete the contribution by describing some opportunities to build on the collective 20 years of effort, including opportunities related to the metabolomics profiling of blood and urine specimens from human feeding studies that approximate habitual diets.

The Metabolic and Lipidomic Fingerprint of Torin1 Exposure in Mouse Embryonic Fibroblasts Using Untargeted Metabolomics.

Torin1, a selective kinase inhibitor targeting the mammalian target of rapamycin (mTOR), remains widely used in autophagy research due to its potent autophagy-inducing abilities, regardless of its unspecific properties. Recognizing the impact of mTOR inhibition on metabolism, our objective was to develop a reliable and thorough untargeted metabolomics workflow to study torin1-induced metabolic changes in mouse embryonic fibroblast (MEF) cells. Crucially, our quality assurance and quality control (QA/QC) protocols were designed to increase confidence in the reported findings by reducing the likelihood of false positives, including a validation experiment replicating all experimental steps from sample preparation to data analysis. This study investigated the metabolic fingerprint of torin1 exposure by using liquid chromatography-high resolution mass spectrometry (LC-HRMS)-based untargeted metabolomics platforms. Our workflow identified 67 altered metabolites after torin1 exposure, combining univariate and multivariate statistics and the implementation of a validation experiment. In particular, intracellular ceramides, diglycerides, phosphatidylcholines, phosphatidylethanolamines, glutathione, and 5'-methylthioadenosine were downregulated. Lyso-phosphatidylcholines, lyso-phosphatidylethanolamines, glycerophosphocholine, triglycerides, inosine, and hypoxanthine were upregulated. Further biochemical pathway analyses provided deeper insights into the reported changes. Ultimately, our study provides a valuable workflow that can be implemented for future investigations into the effects of other compounds, including more specific autophagy modulators.

Consistency of metabolite associations with measured glomerular filtration rate in children and adults.

There is interest in identifying novel filtration markers that lead to more accurate GFR estimates than current markers (creatinine and cystatin C) and are more consistent across demographic groups. We hypothesize that large-scale metabolomics can identify serum metabolites that are strongly influenced by glomerular filtration rate (GFR) and are more consistent across demographic variables than creatinine, which would be promising filtration markers for future investigation. We evaluated the consistency of associations between measured GFR (mGFR) and 887 common, known metabolites quantified by an untargeted chromatography- and spectroscopy-based metabolomics platform (Metabolon) performed on frozen blood samples from 580 participants in Chronic Kidney Disease in Children (CKiD), 674 participants in Modification of Diet in Renal Disease (MDRD) Study and 962 participants in African American Study of Kidney Disease and Hypertension (AASK). We evaluated metabolite-mGFR correlation association with metabolite class, molecular weight, assay platform and measurement coefficient of variation (CV). Among metabolites with strong negative correlations with mGFR (r < -0.5), we assessed additional variation by age (height in children), sex, race and body mass index (BMI). A total of 561 metabolites (63%) were negatively correlated with mGFR. Correlations with mGFR were highly consistent across study, sex, race and BMI categories (correlation of metabolite-mGFR correlations between 0.88 and 0.95). Amino acids, carbohydrates and nucleotides were more often negatively correlated with mGFR compared with lipids, but there was no association with metabolite molecular weight, liquid chromatography/mass spectrometry platform and measurement CV. Among 114 metabolites with strong negative associations with mGFR (r < -0.5), 27 were consistently not associated with age (height in children), sex or race. The majority of metabolite-mGFR correlations were negative and consistent across sex, race, BMI and study. Metabolites with consistent strong negative correlations with mGFR and non-association with demographic variables may represent candidate markers to improve estimation of GFR.

Comparative characterization of the infant gut microbiome and their maternal lineage by a multi-omics approach

The human gut microbiome establishes and matures during infancy, and dysregulation at this stage may lead to pathologies later in life. We conducted a multi-omics study comprising three generations of family members to investigate the early development of the gut microbiota. Fecal samples from 200 individuals, including infants (0-12 months old; 55% females, 45% males) and their respective mothers and grandmothers, were analyzed using two independent metabolomics platforms and metagenomics. For metabolomics, gas chromatography and capillary electrophoresis coupled to mass spectrometry were applied. For metagenomics, both 16S rRNA gene and shotgun sequencing were performed. Here we show that infants greatly vary from their elders in fecal microbiota populations, function, and metabolome. Infants have a less diverse microbiota than adults and present differences in several metabolite classes, such as short- and branched-chain fatty acids, which are associated with shifts in bacterial populations. These findings provide innovative biochemical insights into the shaping of the gut microbiome within the same generational line that could be beneficial in improving childhood health outcomes.

Using machine learning to identify proteomic and metabolomic signatures of stroke in atrial fibrillation

Atrial fibrillation (AF) is a common cardiac arrhythmia, with stroke being its most detrimental comorbidity. The exact mechanism of AF related stroke (AFS) still needs to be explored. In this study, we integrated proteomics and metabolomics platform to explore disordered plasma proteins and metabolites between AF patients and AFS patients. There were 22 up-regulated and 31 down-regulated differentially expressed proteins (DEPs) in AFS plasma samples. Moreover, 63 up-regulated and 51 down-regulated differentially expressed metabolites (DEMs) were discovered in AFS plasma samples. We integrated proteomics and metabolomics based on the topological interactions of DEPs and DEMs, which yielded revealed several related pathways such as arachidonic acid metabolism, serotonergic synapse, purine metabolism, tyrosine metabolism and steroid hormone biosynthesis. We then performed a machine learning model to identify potential biomarkers of stroke in AF. Finally, we selected 6 proteins and 6 metabolites as candidate biomarkers for predicting stroke in AF by random forest, the area under the curve being 0.976. In conclusion, this study provides new perspectives for understanding the progressive mechanisms of AF related stroke and discovering innovative biomarkers for determining the prognosis of stroke in AF.

Genome-wide characterization of circulating metabolic biomarkers

Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1–7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8–11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.

Development of a point-of-care colorimetric metabolomic sensor platform

Metabolomics is the large-scale study of small molecule metabolites within a biological system. It has applications in measuring dietary intake, predicting heart disease risk, and diagnosing cancer. Metabolites are often measured using high-end analytical tools such as mass spectrometers or large spectrophotometers. However, due to their size, cost, and need for skilled operators, using such equipment at the bedside is not practical. To address this issue, we have developed a low-cost, portable, optical color sensor platform for metabolite detection. This platform includes LEDs, sensors, microcontrollers, a power source, and a Bluetooth chip enclosed within a 3D-printed light-tight case. We evaluated the color sensor's performance using both a range of dyed water samples as well as well-established colorimetric reactions for specific metabolite detection. The sensor accurately measured creatinine, L-carnitine, ascorbate, and succinate well within normal human urine levels with accuracy and sensitivity equal to or better than a standard laboratory spectrophotometer. Our color sensor offers a cost-effective, portable alternative for measuring metabolites via colorimetric assays, thereby enabling low-cost, point-of-care metabolite testing.