Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an analytical workflow for non-targeted metabolic profiling approaches, utilizing liquid chromatography–mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data and, finally, to identify and interpret the compounds that have emerged as interesting.

The mechanisms by which exercise benefits patients with non-alcoholic fatty liver disease (NAFLD), the most common liver disease worldwide, remain poorly understood. A non-targeted liquid chromatography-mass spectrometry (LC–MS)-based metabolomics analysis was used to identify metabolic changes associated with NAFLD in humans upon exercise intervention (without diet change) across four different sample types—adipose tissue (AT), plasma, urine, and stool. Altogether, 46 subjects with NAFLD participated in this randomized controlled intervention study. The intervention group (n = 21) performed high-intensity interval training (HIIT) for 12 weeks while the control group (n = 25) kept their sedentary lifestyle. The participants' clinical parameters and metabolic profiles were compared between baseline and endpoint. HIIT significantly decreased fasting plasma glucose concentration (p = 0.027) and waist circumference (p = 0.028); and increased maximum oxygen consumption rate and maximum achieved workload (p < 0.001). HIIT resulted in sample-type-specific metabolite changes, including accumulation of amino acids and their derivatives in AT and plasma, while decreasing in urine and stool. Moreover, many of the metabolite level changes especially in the AT were correlated with the clinical parameters monitored during the intervention. In addition, certain lipids increased in plasma and decreased in the stool. Glyco-conjugated bile acids decreased in AT and urine. The 12-week HIIT exercise intervention has beneficial ameliorating effects in NAFLD subjects on a whole-body level, even without dietary changes and weight loss. The metabolomics analysis applied to the four different sample matrices provided an overall view on several metabolic pathways that had tissue-type specific changes after HIIT intervention in subjects with NAFLD. The results highlight especially the role of AT in responding to the HIIT challenge, and suggest that altered amino acid metabolism in AT might play a critical role in e.g. improving fasting plasma glucose concentration.Trial registration ClinicalTrials.gov (NCT03995056).

Background & Aims Non‐alcoholic fatty liver disease (NAFLD) has been associated with multiple metabolic abnormalities. By applying a non‐targeted metabolomics approach, we aimed at investigating whether serum metabolite profile that associates with NAFLD would differ in its association with NAFLD‐related metabolic risk factors. Methods & Results A total of 233 subjects (mean ± SD: 48.3 ± 9.3 years old; BMI: 43.1 ± 5.4 kg/m2; 64 male) undergoing bariatric surgery were studied. Of these participants, 164 with liver histology could be classified as normal liver (n = 79), simple steatosis (SS, n = 40) or non‐alcoholic steatohepatitis (NASH, n = 45). Among the identified fasting serum metabolites with higher levels in those with NASH when compared to those with normal phenotype were the aromatic amino acids (AAAs: tryptophan, tyrosine and phenylalanine), the branched‐chain amino acids (BCAAs: leucine and isoleucine), a phosphatidylcholine (PC(16:0/16:1)) and uridine (all FDRp < 0.05). Only tryptophan was significantly higher in those with NASH compared to those with SS (FDRp < 0.05). Only the AAAs tryptophan and tyrosine correlated positively with serum total and LDL cholesterol (FDRp < 0.1), and accordingly, with liver LDLR at mRNA expression level. In addition, tryptophan was the single AA associated with liver DNA methylation of CpG sites known to be differentially methylated in those with NASH. Conclusions We found that serum levels of the NASH‐related AAAs and BCAAs demonstrate divergent associations with serum lipids. The specific correlation of tryptophan with LDL‐c may result from the molecular events affecting LDLR mRNA expression and NASH‐associated methylation of genes in the liver.

Introduction Maternal metabolism changes substantially during pregnancy. However, few studies have used metabolomics technologies to characterize changes across gestation. Objectives and methods We applied liquid chromatography–mass spectrometry (LC–MS) based non-targeted metabolomics to determine whether the metabolic profile of serum differs throughout the pregnancy between pre-eclamptic and healthy women in the FINNPEC (Finnish Genetics of Preeclampsia Consortium) Study. Serum samples were available from early and late pregnancy. Results Progression of pregnancy had large-scale effects to the serum metabolite profile. Altogether 50 identified metabolites increased and 49 metabolites decreased when samples of early pregnancy were compared to samples of late pregnancy. The metabolic signatures of pregnancy were largely shared in pre-eclamptic and healthy women, only urea, monoacylglyceride 18:1 and glycerophosphocholine were identified to be increased in the pre-eclamptic women when compared to healthy controls. Conclusions Our study highlights the need of large-scale longitudinal metabolomic studies in non-complicated pregnancies before more detailed understanding of metabolism in adverse outcomes could be provided. Our findings are one of the first steps for a broader metabolic understanding of the physiological changes caused by pregnancy per se.

Psychological stress is a suggested risk factor of metabolic disorders, but molecular mediators are not well understood. We investigated the association between the metabolic profiles of fasting plasma and the improvement of psychological well-being using non-targeted liquid chromatography-mass spectrometry (LC-MS) platform. The metabolic profiles of volunteers participating in the face-to-face intervention group (n = 60) in a randomised lifestyle intervention were compared to ones of controls (n = 64) between baseline and 36-week follow-up. Despite modest differences in metabolic profile between groups, we found associations between phosphatidylcholines (PCs) and several parameters indicating stress, adiposity, relaxation, and recovery. The relief of heart-rate-variability-based stress had positive, while improved indices of recovery and relaxation in the intervention group had an inverse association with the reduction of e.g. lysophosphatidylcholines (LPC). Interleukin-1 receptor antagonist and adiposity correlated positively with the suppressed PCs and negatively with the elevated plasmalogens PC(P-18:0/22:6) and PC(P-18:0/20:4). Also, we found changes in an unknown class of lipids over time regardless of the intervention groups, which also correlated with physiological and psychological markers of stress. The associations between lipid changes with some markers of psychological wellbeing and body composition may suggest the involvement of these lipids in the shared mechanisms between psychological and metabolic health.www.nature.com/scientificreports www.nature.com/scientificreports/ Study visits. The participants came to the study centres after an overnight fast at baseline (week 0), after the intervention (week 10), and follow-up (week 36), for venous blood sampling and measurement of body composition (body weight, height, body fat percentage, and waist circumference) 19 . Inflammation markers hsCRP, IL-1Ra, and high molecular weight adiponectin (HMW-adiponectin) were measured in the serum samples 19 . Metabolic profiling was performed on plasma samples obtained at the baseline (week 0) and follow-up (week 36) measurement (Fig. 1), comprising 248 samples for metabolomics analysis altogether. Plasma samples were stored at −80 °C until the laboratory procedure. LC-MS analysis.We used the in-house developed web application, Wranglr (https://github.com/antonvsdata/ wranglr) to automate samples recoding, randomisation, and worklists generation for LC-MS experiments. As the input, Wranglr takes a spreadsheet containing necessary information about the samples such as sample identifier and study group. The output is worklists for LC-MS software containing quality control (QC) and autoMS/MS samples along with a processed spreadsheet containing sample information, injection order, plate positions, data file paths, and other relevant information. Wranglr is implemented in R 3.4.0 32 using the Shiny package version 1.0.3 33 .100 µl sample that has been properly thawed on ice was mixed with 400 µl of LC-MS-grade acetoni...

Background: Heavy alcohol use has been associated with altered circulating metabolome. We investigated whether changes in the circulating metabolome precede incident diagnoses of alcohol-related diseases. Methods: This is a prospective population-based cohort study where the participants were 42-to 60-year-old males at baseline (years 1984 to 1989). Subjects who received a diagnosis for an alcohol-related disease during the follow-up were defined as cases (n = 92, mean follow-up of 13.6 years before diagnosis). Diagnoses were obtained through linkage with national health registries. We used 2 control groups: controls who self-reported similar levels of alcohol use as compared to cases at baseline (alcohol-controls, n = 92), and controls who self-reported only light drinking at baseline (control-controls, n = 90). A nontargeted metabolomics analysis of baseline serum samples was performed. Results: There were significant differences between the study groups in the baseline serum levels of 64 metabolites: in amino acids (e.g., glutamine [FDR-corrected q-value = 0.0012]), glycerophospholipids (e.g., lysophosphatidylcholine 16:1 [q = 0.0008]), steroids (e.g., cortisone [q = 0.00001]), and fatty acids (e.g., palmitoleic acid [q = 0.0031]). The main finding was that after controlling for baseline levels of self-reported alcohol use and the biomarker of alcohol use, gamma-glutamyl transferase, and when compared to both alcohol-control and control-control group, the alcohol-case group had lower serum levels of asparagine (Cohen's d = À0.48 [95% CI À0.78 to À0.19] and d = À0.49 [À0.78 to À0.19], respectively) and serotonin (d = À0.45 [À0.74 to À0.15], and d = À0.46 [À0.75 to À0.16], respectively), with no difference between the two control groups (asparagine d = 0.00 [À0.29 to 0.29] and serotonin d = À0.01 [À0.30 to 0.29]). Conclusions: Changes in the circulating metabolome, especially lower serum levels of asparagine and serotonin, are associated with later diagnoses of alcohol-related diseases, even after adjustment for the baseline level of alcohol use.

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The essential role of gut microbiota in health and disease is well recognized, but the biochemical details that underlie the beneficial impact remain largely undefined. To maintain its stability, microbiota participates in an interactive host-microbiota metabolic signaling, impacting metabolic phenotypes of the host. Dysbiosis of microbiota results in alteration of certain microbial and host metabolites. Identifying these markers could enhance early detection of certain diseases. We report LC–MS based non-targeted metabolic profiling that demonstrates a large effect of gut microbiota on mammalian tissue metabolites. It was hypothesized that gut microbiota influences the overall biochemistry of host metabolome and this effect is tissue-specific. Thirteen different tissues from germ-free (GF) and conventionally-raised (MPF) C57BL/6NTac mice were selected and their metabolic differences were analyzed. Our study demonstrated a large effect of microbiota on mammalian biochemistry at different tissues and resulted in statistically-significant modulation of metabolites from multiple metabolic pathways (p ≤ 0.05). Hundreds of molecular features were detected exclusively in one mouse group, with the majority of these being unique to specific tissue. A vast metabolic response of host to metabolites generated by the microbiota was observed, suggesting gut microbiota has a direct impact on host metabolism.