A batch correction method for liquid chromatography-mass spectrometry data that does not depend on quality control samples

The Impact of Milk on Gut Permeability, Fecal 16S rRNA Gene Microbiota Profiling, and Fecal Metabolomics in Children with Moderate Malnutrition in Sierra Leone: A Double-Blind, Randomized Controlled Trial

BACKGROUND

Bovine milk is a beneficial ingredient in teh treatment of malnutrition.

OBJECTIVES

Our objectives were to determine the effect of dietary milk protein and milk carbohydrate on the intestinal permeability, fecal 16S rRNA gene configuration, and fecal metabolomics of children with moderate malnutrition.

METHODS

This was a randomized, double-blind, controlled trial among 413 children with wasting in rural Sierra Leone who received 1 of the following 4 supplementary foods, which differed in sources of protein and carbohydrate: milk protein and milk carbohydrate (MPMC), milk protein and vegetable carbohydrate (MPVC), vegetable protein and milk carbohydrate (VPMC), or a control group consuming entirely vegetable-based food (VPVC). After 4 wk, urine and stool were collected from participants enrolled with mid-upper arm circumference of <12.1 cm. Urine was analyzed for lactulose excretion (%L). Stool samples were subjected to both 16S rRNA gene analysis to assess β-diversity and untargeted metabolomic abundance.

RESULTS

Among the 386 children who completed permeability testing, the mean difference (95% CI) in %L excretion as compared with VPVC was 0.01 (-0.05, 0.07) for MPMC, 0.05 (-0.01, 0.11) for MPVC, and 0.01 (-0.05, 0.07) for VPMC. Of the 374 children who provided a stool sample that was analyzed, the β-diversity among bacterial taxa was similar between dietary groups (P > 0.05 for all comparisons). No significant differences between dietary groups were seen among the 20 most abundant bacterial taxa. Among the 5769 unique metabolomic features identified, greater flavonoid levels in VPVC were seen.

CONCLUSIONS

Abnormal intestinal permeability do not improve with 4 wk of supplementary feeding. Fecal rRNA do not differ with consumption of different diets. This trial was registered at clinicaltrials.gov as NCT04216043.

A homoeostatic switch causing glycerol-3-phosphate and phosphoethanolamine accumulation triggers senescence by rewiring lipid metabolism

Cellular senescence affects many physiological and pathological processes and is characterized by durable cell cycle arrest, an inflammatory secretory phenotype and metabolic reprogramming. Here, by using dynamic transcriptome and metabolome profiling in human fibroblasts with different subtypes of senescence, we show that a homoeostatic switch that results in glycerol-3-phosphate (G3P) and phosphoethanolamine (pEtN) accumulation links lipid metabolism to the senescence gene expression programme. Mechanistically, p53-dependent glycerol kinase activation and post-translational inactivation of phosphate cytidylyltransferase 2, ethanolamine regulate this metabolic switch, which promotes triglyceride accumulation in lipid droplets and induces the senescence gene expression programme. Conversely, G3P phosphatase and ethanolamine-phosphate phospho-lyase-based scavenging of G3P and pEtN acts in a senomorphic way by reducing G3P and pEtN accumulation. Collectively, our study ties G3P and pEtN accumulation to controlling lipid droplet biogenesis and phospholipid flux in senescent cells, providing a potential therapeutic avenue for targeting senescence and related pathophysiology.

Small molecule biomarker discovery: Proposed workflow for LC-MS-based clinical research projects

Mass spectrometry focusing on small endogenous molecules has become an integral part of biomarker discovery in the pursuit of an in-depth understanding of the pathophysiology of various diseases, ultimately enabling the application of personalized medicine. While LC-MS methods allow researchers to gather vast amounts of data from hundreds or thousands of samples, the successful execution of a study as part of clinical research also requires knowledge transfer with clinicians, involvement of data scientists, and interactions with various stakeholders. The initial planning phase of a clinical research project involves specifying the scope and design, and engaging relevant experts from different fields. Enrolling subjects and designing trials rely largely on the overall objective of the study and epidemiological considerations, while proper pre-analytical sample handling has immediate implications on the quality of analytical data. Subsequent LC-MS measurements may be conducted in a targeted, semi-targeted, or non-targeted manner, resulting in datasets of varying size and accuracy. Data processing further enhances the quality of data and is a prerequisite for in-silico analysis. Nowadays, the evaluation of such complex datasets relies on a mix of classical statistics and machine learning applications, in combination with other tools, such as pathway analysis and gene set enrichment. Finally, results must be validated before biomarkers can be used as prognostic or diagnostic decision-making tools. Throughout the study, quality control measures should be employed to enhance the reliability of data and increase confidence in the results. The aim of this graphical review is to provide an overview of the steps to be taken when conducting an LC-MS-based clinical research project to search for small molecule biomarkers.

Habituation to Livestock Trailer and Its Influence on Stress Responses during Transportation in Goats

This experiment was conducted to determine the effects of habituation to livestock trailers on stress responses in goats transported for long periods. Intact male Spanish goats (12-month old; BW = 31.6 ± 0.34 kg; N = 168) were separated into two treatment (TRT) groups and maintained on two different paddocks. Concentrate supplement was fed to one group inside two livestock trailers (5.0 × 2.3 m each; habituated group, H), while the other group received the concentrate supplement, but not inside the trailers (non-habituated, NH). After 4 weeks of habituation period, goats were subjected to a 10-h transportation stress in four replicates (n = 21 goats/replicate/TRT). Blood samples were collected by a trained individual by jugular venipuncture into vacutainer tubes before loading (Preload), 20 min after loading (0 h), and at 2-h intervals thereafter (Time) for analysis of stress responses. There was a tendency for a TRT effect (p < 0.1) on tyramine and metanephrine concentrations. Phenylethylamine and 5-methoxytryptamine concentrations were significantly greater (p < 0.05) in the H group compared to the NH group. Both dopamine and 5-methoxytryptamine concentrations decreased (p < 0.05) with transportation time; however, TRT × Time interaction effects were not significant. Habituation to trailers may be beneficial in mood and energy stabilization in goats during long-distance transportation.

Extensive identification of serum metabolites related to microbes in different gut locations and evaluating their associations with porcine fatness

Gut microbiota plays important roles in host metabolism. Whether and how much the gut microbiota in different gut locations contributes to the variations of host serum metabolites are largely unknown, because it is difficult to obtain microbial samples from different gut locations on a large population scale. Here, we quantified the gut microbial compositions using 16S rRNA gene sequencing for 1070 samples collected from the ileum, cecum and faeces of 544 F6 pigs from a mosaic pig population. Untargeted metabolome measurements determined serum metabolome profiles. We found 1671, 12,985 and 103,250 significant correlations between circulating serum metabolites and bacterial ASVs in the ileum, cecum, and faeces samples. We detected nine serum metabolites showing significant correlations with gut bacteria in more than one gut location. However, most metabolite-microbiota pairwise associations were gut location-specific. Targeted metabolome analysis revealed that CDCA, taurine, L-leucine and N-acetyl-L-alanine can be used as biomarkers to predict porcine fatness. Enriched taxa in fat pigs, for example Prevotella and Lawsonia intracellularis were positively associated with L-leucine, while enriched taxa in lean pigs, such as Clostridium butyricum, were negatively associated with L-leucine and CDCA, but positively associated with taurine and N-acetyl-L-alanine. These results suggested that the contributions of gut microbiota in each gut location to the variations of serum metabolites showed spatial heterogeneity.

Regularized adversarial learning for normalization of multi-batch untargeted metabolomics data

MOTIVATION

Untargeted metabolomics by mass spectrometry is the method of choice for unbiased analysis of molecules in complex samples of biological, clinical or environmental relevance. The exceptional versatility and sensitivity of modern high-resolution instruments allows profiling of thousands of known and unknown molecules in parallel. Inter-batch differences constitute a common and unresolved problem in untargeted metabolomics, and hinder the analysis of multi-batch studies or the intercomparison of experiments.

RESULTS

We present a new method, Regularized Adversarial Learning Preserving Similarity (RALPS), for the normalization of multi-batch untargeted metabolomics data. RALPS builds on deep adversarial learning with a three-term loss function that mitigates batch effects while preserving biological identity, spectral properties and coefficients of variation. Using two large metabolomics datasets, we showcase the superior performance of RALPS as compared with six state-of-the-art methods for batch correction. Further, we demonstrate that RALPS scales well, is robust, deals with missing values and can handle different experimental designs.

AVAILABILITY AND IMPLEMENTATION

https://github.com/zamboni-lab/RALPS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Maternal plasma diacylglycerols and triacylglycerols in the prediction of gestational diabetes mellitus

OBJECTIVE

To define the lipidomic profile in plasma across pregnancy, and identify lipid biomarkers for gestational diabetes mellitus (GDM) prediction in early pregnancy.

DESIGN

Case-control study.

SETTING

Tertiary referral maternity unit.

POPULATION OR SAMPLE

Plasma samples from 100 GDM and 100 normal glucose tolerance (NGT) women, divided into a training set (GDM first trimester = 50, GDM second trimester = 40, NGT first trimester = 50, NGT second trimester = 50) and a validation set (GDM first trimester = 45, GDM second trimester = 34, NGT first trimester = 44, NGT second trimester = 40).

METHODS

Plasma samples were collected in the first (11⁺⁰ to 13⁺⁶ weeks), second (19⁺⁰ to 24⁺⁶ weeks), and third trimesters (30⁺⁰ to 34⁺⁶ weeks), and tested by ultra-high-performance liquid chromatography coupled with electrospray ionisation-quadrupole-time of flight-mass spectrometry; The GDM prediction model was established by the machine-learning method of random forest.

MAIN OUTCOME MEASURES

Gestational diabetes mellitus.

RESULTS

In both the GDM and NGT group, lyso-glycerophospholipids were down-regulated, whereas ceramides, sphingomyelins, cholesteryl ester, diacylglycerols (DGs) and triacylglycerols (TGs) and glucosylceramide were up-regulated across the three trimesters of pregnancy. In the training dataset, seven TGs and five DGs demonstrated good performance in the prediction of GDM in the first and second trimesters (area under the curve [AUC] = 0.96 with 95% confidence interval [CI] of 0.93-1 and AUC = 0.97 with 95% CI of 0.95-1, respectively), independent of maternal body mass index (BMI) and ethnicity. In the validation dataset, the predictive model achieved an AUC of 0.88 and 0.94 at the first and second trimesters, respectively.

CONCLUSIONS

Our results have proposed new lipid biomarkers for the first trimester prediction of GDM, independent of ethnicity and BMI.

Combination bezafibrate and nivolumab treatment of patients with advanced non-small cell lung cancer

Despite the success of cancer immunotherapies such as programmed cell death-1 (PD-1) and PD-1 ligand 1 (PD-L1) inhibitors, patients often develop resistance. New combination therapies with PD-1/PD-L1 inhibitors are needed to overcome this issue. Bezafibrate, a ligand of peroxisome proliferator-activated receptor-γ coactivator 1α/peroxisome proliferator-activated receptor complexes, has shown a synergistic antitumor effect with PD-1 blockade in mice that is mediated by activation of mitochondria in T cells. We have therefore now performed a phase 1 trial (UMIN000017854) of bezafibrate with nivolumab in previously treated patients with advanced non-small cell lung cancer. The primary end point was the percentage of patients who experience dose-limiting toxicity, and this combination regimen was found to be well tolerated. Preplanned comprehensive analysis of plasma metabolites and gene expression in peripheral cytotoxic T cells indicated that bezafibrate promoted T cell function through up-regulation of mitochondrial metabolism including fatty acid oxidation and may thereby have prolonged the duration of response. This combination strategy targeting T cell metabolism thus has the potential to maintain antitumor activity of immune checkpoint inhibitors and warrants further validation.

Metabolomic exploration of the effects of habituation to livestock trailer and extended transportation in goats

Goats raised for meat production are often transported long distances. Twelve-month-old male Spanish goats were used to determine the effects of habituation to trailers on plasma metabolomic profiles when transported for extended periods. In a split-plot design, 168 goats were separated into two treatment (TRT; whole plot) groups and maintained on two different paddocks. Concentrate supplement was fed to one group inside two livestock trailers (habituated group, H), while the other group received the same quantity of concentrate, but not inside the trailers (non-habituated, NH). Goats were subjected to a 10-h transportation stress in 4 replicates (n = 21 goats/replicate/TRT) after 4 weeks of habituation period. Blood samples were collected prior to loading, 20 min after loading (0 h), and at 2, 4, 6, 8, and 10 h of transportation (Time; subplot). A targeted quantitative metabolomics approach was employed to analyze the samples. The data were analyzed using R software and MIXED procedures in SAS. Several amino acids (alanine, serine, glycine, histidine, glutamate, trans-hydroxyproline, asparagine, threonine, methylhistidine, ornithine, proline, leucine, tryptophan) were higher (p < 0.05) in the H group compared to the NH group. Six long-chain acylcarnitines were higher (p < 0.05), while free (C0) and short-chain (C3, C5) carnitines were lower (p < 0.05) in the NH goats compared to the H goats. In general, amino acid concentrations decreased and long-chain acylcarnitine (>C10) levels increased with transportation time (p < 0.05). Butyric acid, α-ketoglutaric acid, and α-aminoadipic acid concentrations were lower (p < 0.05) and β-hydroxybutyric acid concentrations were higher in the NH goats compared to the H goats. Plasma glucose, non-esterified fatty acid (NEFA) and urea nitrogen concentrations were significantly influenced by Time (p < 0.01). Plasma NEFA concentrations were significantly lower (p < 0.01) in the H group than the NH group. Habituation to trailers can be beneficial in enhancing stress coping abilities in goats due to higher concentrations of metabolites such as butyrate and certain amino acids that support antioxidant activities and immune function. Plasma long-chain acylcarnitines may be good indicators of stress during long-distance transportation in goats.

Following Roux-en-Y gastric bypass surgery, serum ceramides demarcate patients that will fail to achieve normoglycemia and diabetes remission

BACKGROUND

Obesity is a prevalent health threat and risk factor for type 2 diabetes. In this study, we evaluate the relationship between ceramides, which inhibit insulin secretion and sensitivity, and markers of glucose homeostasis and diabetes remission or recursion in patients who have undergone a Roux-en-Y gastric bypass (RYGB).

METHODS

The Utah Obesity Study is a prospective cohort study, with targeted ceramide and dihydroceramide measurements performed on banked serum samples. The Utah Obesity Study consists of 1,156 participants in three groups: a RYGB surgery group, a non-surgery group denied insurance coverage, and severely obese population controls. Clinical examinations and ceramide assessments were performed at baseline and 2 and 12 years after RYGB surgery.

FINDINGS

Surgery patients (84% female, 42.2 ± 10.6 years of age at baseline) displayed lower levels of several serum dihydroceramides and ceramides at 2 and 12 years after RYGB. By contrast, neither the control group (77% female, 48.7± 6.4 years of age at baseline) nor the non-surgery group (95% female, 43.0± 11.4 years of age at baseline) experienced significant decreases in any species. Using a linear mixed effect model, we found that multiple dihydroceramides and ceramides positively associated with the glycemic control measures HOMA-IR and HbA1c. In surgery group participants with prevalent diabetes, ceramides inversely predict diabetes remission, independent of changes in weight.

CONCLUSIONS

Ceramide decreases may explain the insulin sensitization and diabetes resolution observed in most RYGB surgery patients.

FUNDING

Funded by the National Institutes of health (NIH), The Juvenile Diabetes Research Foundation, and the American Heart Association.

Normalizing and Correcting Variable and Complex LC-MS Metabolomic Data with the R Package pseudoDrift

In biological research domains, liquid chromatography-mass spectroscopy (LC-MS) has prevailed as the preferred technique for generating high quality metabolomic data. However, even with advanced instrumentation and established data acquisition protocols, technical errors are still routinely encountered and can pose a significant challenge to unveiling biologically relevant information. In large-scale studies, signal drift and batch effects are how technical errors are most commonly manifested. We developed pseudoDrift, an R package with capabilities for data simulation and outlier detection, and a new training and testing approach that is implemented to capture and to optionally correct for technical errors in LC-MS metabolomic data. Using data simulation, we demonstrate here that our approach performs equally as well as existing methods and offers increased flexibility to the researcher. As part of our study, we generated a targeted LC-MS dataset that profiled 33 phenolic compounds from seedling stem tissue in 602 genetically diverse non-transgenic maize inbred lines. This dataset provides a unique opportunity to investigate the dynamics of specialized metabolism in plants.

Chemical Characterization and Non-targeted Analysis of Medical Device Extracts: A Review of Current Approaches, Gaps, and Emerging Practices

The developers of medical devices evaluate the biocompatibility of their device prior to FDA's review and subsequent introduction to the market. Chemical characterization, described in ISO 10993-18:2020, can generate information for toxicological risk assessment and is an alternative approach for addressing some biocompatibility end points (e.g., systemic toxicity, genotoxicity, carcinogenicity, reproductive/developmental toxicity) that can reduce the time and cost of testing and the need for animal testing. Additionally, chemical characterization can be used to determine whether modifications to the materials and manufacturing processes alter the chemistry of a patient-contacting device to an extent that could impact device safety. Extractables testing is one approach to chemical characterization that employs combinations of non-targeted analysis, non-targeted screening, and/or targeted analysis to establish the identities and quantities of the various chemical constituents that can be released from a device. Due to the difficulty in obtaining a priori information on all the constituents in finished devices, information generation strategies in the form of analytical chemistry testing are often used. Identified and quantified extractables are then assessed using toxicological risk assessment approaches to determine if reported quantities are sufficiently low to overcome the need for further chemical analysis, biological evaluation of select end points, or risk control. For extractables studies to be useful as a screening tool, comprehensive and reliable non-targeted methods are needed. Although non-targeted methods have been adopted by many laboratories, they are laboratory-specific and require expensive analytical instruments and advanced technical expertise to perform. In this Perspective, we describe the elements of extractables studies and provide an overview of the current practices, identified gaps, and emerging practices that may be adopted on a wider scale in the future. This Perspective is outlined according to the steps of an extractables study: information gathering, extraction, extract sample processing, system selection, qualification, quantification, and identification.

Inferring early-life host and microbiome functions by mass spectrometry-based metaproteomics and metabolomics

Humans have a long-standing coexistence with microorganisms. In particular, the microbial community that populates the human gastrointestinal tract has emerged as a critical player in governing human health and disease. DNA and RNA sequencing techniques that map taxonomical composition and genomic potential of the gut community have become invaluable for microbiome research. However, deriving a biochemical understanding of how activities of the gut microbiome shape host development and physiology requires an expanded experimental design that goes beyond these approaches. In this review, we explore advances in high-throughput techniques based on liquid chromatography-mass spectrometry. These omics methods for the identification of proteins and metabolites have enabled direct characterisation of gut microbiome functions and the crosstalk with the host. We discuss current metaproteomics and metabolomics workflows for producing functional profiles, the existing methodological challenges and limitations, and recent studies utilising these techniques with a special focus on early life gut microbiome.

DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies

As a powerful phenotyping technology, metabolomics provides new opportunities in biomarker discovery through metabolome-wide association studies (MWAS) and the identification of metabolites having a regulatory effect in various biological processes. While mass spectrometry-based (MS) metabolomics assays are endowed with high throughput and sensitivity, MWAS are doomed to long-term data acquisition generating an overtime-analytical signal drift that can hinder the uncovering of real biologically relevant changes. We developed “dbnorm”, a package in the R environment, which allows for an easy comparison of the model performance of advanced statistical tools commonly used in metabolomics to remove batch effects from large metabolomics datasets. “dbnorm” integrates advanced statistical tools to inspect the dataset structure not only at the macroscopic (sample batches) scale, but also at the microscopic (metabolic features) level. To compare the model performance on data correction, “dbnorm” assigns a score that help users identify the best fitting model for each dataset. In this study, we applied “dbnorm” to two large-scale metabolomics datasets as a proof of concept. We demonstrate that “dbnorm” allows for the accurate selection of the most appropriate statistical tool to efficiently remove the overtime signal drift and to focus on the relevant biological components of complex datasets.

Identification of the relationship between the gut microbiome and feed efficiency in a commercial pig cohort

Feed efficiency (FE) is an economically important trait in pig production. Gut microbiota plays an important role in energy harvest, nutrient metabolism, and fermentation of dietary indigestible components. Whether and which gut microbes affect FE in pigs are largely unknown. Here, a total of 208 healthy Duroc pigs were used as experimental materials. Feces and serum samples were collected at the age of 140 d. We first performed 16S rRNA gene and metagenomic sequencing analysis to investigate the relationship between the gut microbiome and porcine residual feed intake (RFI). 16S rRNA gene sequencing analysis detected 21 operational taxonomic units showing the tendency to correlation with the RFI (P < 0.01). Metagenomic sequencing further identified that the members of Clostridiales, e.g., Ruminococcus flavefaoiens, Lachnospiraceae bacterium 28-4, and Lachnospiraceae phytofermentans, were enriched in pigs with low RFI (high-FE), while 11 bacterial species including 5 Prevotella spp., especially, the Prevotella copri, had higher abundance in pigs with high RFI. Functional capacity analysis suggested that the gut microbiome of low RFI pigs had a high abundance of the pathways related to amino acid metabolism and biosynthesis, but a low abundance of the pathways associated with monosaccharide metabolism and lipopolysaccharide biosynthesis. Serum metabolome and fecal short-chain fatty acids were determined by UPLC-QTOF/MS and gas chromatography, respectively. Propionic acid in feces and the serum metabolites related to amino acid metabolism were negatively correlated with the RFI. The results from this study may provide potential gut microbial biomarkers that could be used for improving FE in pig production industry.

Targeted Metabolomic Assessment of the Sub-Lethal Toxicity of Halogenated Acetic Acids (HAAs) to Daphnia magna

Halogenated acetic acids (HAAs) are amongst the most frequently detected disinfection by-products in aquatic environments. Despite this, little is known about their toxicity, especially at the molecular level. The model organism Daphnia magna, which is an indicator species for freshwater ecosystems, was exposed to sub-lethal concentrations of dichloroacetic acid (DCAA), trichloroacetic acid (TCAA) and dibromoacetic acid (DBAA) for 48 h. Polar metabolites extracted from Daphnia were analyzed using liquid chromatography hyphened to a triple quadrupole mass spectrometer (LC-MS/MS). Multivariate analyses identified shifts in the metabolic profile with exposure and pathway analysis was used to identify which metabolites and associated pathways were disrupted. Exposure to all three HAAs led to significant downregulation in the nucleosides: adenosine, guanosine and inosine. Pathway analyses identified perturbations in the citric acid cycle and the purine metabolism pathways. Interestingly, chlorinated and brominated acetic acids demonstrated similar modes of action after sub-lethal acute exposure, suggesting that HAAs cause a contaminant class-based response which is independent of the type or number of halogens. As such, the identified metabolites that responded to acute HAA exposure may serve as suitable bioindicators for freshwater monitoring programs.

Effect of host breeds on gut microbiome and serum metabolome in meat rabbits

BACKGROUND

Gut microbial compositional and functional variation can affect health and production performance of farm animals. Analysing metabolites in biological samples provides information on the basic mechanisms that affect the well-being and production traits in farm animals. However, the extent to which host breeds affect the gut microbiome and serum metabolome in meat rabbits is still unknown. In this study, the differences in phylogenetic composition and functional capacities of gut microbiota in two commercial rabbit breeds Elco and Ira were determined by 16S rRNA gene and metagenomic sequencing. The alternations in serum metabolome in the two rabbit breeds were detected using ultra-performance liquid chromatography system coupled with quadrupole time of flight mass spectrometry (UPLC-QTOFMS).

RESULTS

Sequencing results revealed that there were significant differences in the gut microbiota of the two breeds studied, suggesting that host breeds affect structure and diversity of gut microbiota. Numerous breed-associated microorganisms were identified at different taxonomic levels and most microbial taxa belonged to the families Lachnospiraceae and Ruminococcaceae. In particular, several short-chain fatty acids (SCFAs) producing species including Coprococcus comes, Ruminococcus faecis, Ruminococcus callidus, and Lachnospiraceae bacterium NK4A136 could be considered as biomarkers for improving the health and production performance in meat rabbits. Additionally, gut microbial functional capacities related to bacterial chemotaxis, ABC transporters, and metabolism of different carbohydrates, amino acids, and lipids varied greatly between rabbit breeds. Several fatty acids, amino acids, and organic acids in the serum were identified as breed-associated, where certain metabolites could be regarded as biomarkers correlated with the well-being and production traits of meat rabbits. Correlation analysis between breed-associated microbial species and serum metabolites revealed significant co-variations, indicating the existence of cross-talk among host-gut microbiome-serum metabolome.

CONCLUSIONS

Our study provides insight into how gut microbiome and serum metabolome of meat rabbits are affected by host breeds and uncovers potential biomarkers important for breed improvement of meat rabbits.

MEK inhibition reprograms CD8+ T lymphocytes into memory stem cells with potent antitumor effects

Regenerative stem cell-like memory (TSCM) CD8+ T cells persist longer and produce stronger effector functions. We found that MEK1/2 inhibition (MEKi) induces TSCM that have naive phenotype with self-renewability, enhanced multipotency and proliferative capacity. This is achieved by delaying cell division and enhancing mitochondrial biogenesis and fatty acid oxidation, without affecting T cell receptor-mediated activation. DNA methylation profiling revealed that MEKi-induced TSCM cells exhibited plasticity and loci-specific profiles similar to bona fide TSCM isolated from healthy donors, with intermediate characteristics compared to naive and central memory T cells. Ex vivo, antigenic rechallenge of MEKi-treated CD8+ T cells showed stronger recall responses. This strategy generated T cells with higher efficacy for adoptive cell therapy. Moreover, MEKi treatment of tumor-bearing mice also showed strong immune-mediated antitumor effects. In conclusion, we show that MEKi leads to CD8+ T cell reprogramming into TSCM that acts as a reservoir for effector T cells with potent therapeutic characteristics.

Mass-Spectrometry-Based Food Metabolomics in Routine Applications: A Basic Standardization Approach Using Housekeeping Metabolites for the Authentication of Asparagus

The low reproducibility of non-targeted liquid chromatography-mass spectrometry-based metabolomics approaches represents a major challenge for their implementation in routine analyses, because it is impossible to compare individual measurements directly with each other, if they were not analyzed in the same batch. This study describes a normalization process based on housekeeping metabolites in plant-based raw materials, which are present in comparatively constant concentrations and are subject to no or only minor deviations as a result of exogenous influences. As a model, an authenticity study was selected to determine the origin of white asparagus (Asparagus officinalis). Using three model data sets and one test data set, we were able to show that samples that have been measured independently of one another can be correctly assigned in terms of origin after the normalization with housekeeping metabolites. The procedure does not require internal standards or the measurements of further reference samples and can also be applied to other matrices and scientific issues.

MRMkit: Automated Data Processing for Large-Scale Targeted Metabolomics Analysis

MRMkit is an open-source software package designed for automated processing of large-scale targeted mass spectrometry-based metabolomics data. With improvements in the automation of sample preparation for LC-MS analysis, a challenging next step is to fully automate the workflow to process raw data and ensure the quality of measurements in large-scale analysis settings. MRMkit capitalizes on the richness of large-sample data in capturing peak shapes and interference patterns of transitions across many samples and delivers fully automated, reproducible peak integration results in a scalable and time-efficient manner. In addition to fast and accurate peak integration, the tool also provides reliable data normalization functions and quality metrics along with visualizations for fast data quality evaluation. In addition, MRMkit learns retention time offset patterns by user-specified compound classes and makes recommendations for peak picking in multimodal ion chromatograms. In summary, MRMkit offers highly consistent and scalable data processing capacity for targeted metabolomics, substantially curtailing the time required to produce the final quantification results after LC-MS analysis.

FT-NIR spectroscopy and RP-HPLC combined with multivariate analysis reveals differences in plant cell suspension cultures of Thevetia peruviana treated with salicylic acid and methyl jasmonate

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Near infrared spectroscopy was used for the detection of phenolic content in plant cell cultures.

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Multivariate analysis applied to HPLC data was satisfactory to determine changes in the phenolic profile.

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Dihydroquercetin increased significantly in T. peruviana cultures treated with SA/MeJA.

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Chlorogenate and dihydroquercetin are possible biomarkers of the MeJA effects in T. peruviana.

Plant cell suspension culture of T. peruviana is a feasible biotechnological platform for the production of secondary metabolites with anti-proliferative/cytotoxic activity, as phenolic compounds (PC); however, different in in vitro growth conditions may affect the production, demanding strategies to increase the metabolite biosynthesis, as well as the development of sensitive and rapid analytical methods for metabolite monitoring. The Fourier transform near-infrared (FT-NIR) spectroscopy and Reversed-phase high-performance liquid chromatography (RP-HPLC) combined with Multivariate analysis (MVA) were used to detect significant differences in the PC production in cultures treated with two elicitors. The results suggest that the FT-NIR-MVA is useful for discriminating samples according to the treatment, showed significant influence of the PC signal. RP-HPLC-MVA showed that the elicitor effect occurs at 72 h post-elicitation. Detection of dihydroquercetin (maximum concentration = 12.59 mg/L), a flavonoid with anti-cancer properties, is highlighted. Future studies will be aimed at scaling this culture to increase the productivity of dihydroquercetin.

Assessment of substrate biodegradability improvement in anaerobic Co-digestion using a chemometrics-based metabolomic approach

Anaerobic co-digestion (AcoD) can increase methane production of anaerobic digesters in plants treating wastewater sludge by improving the nutrient balance needed for the microorganisms to grow in the digesters, resulting in a faster process stabilization. Substrate mixture proportions are usually optimized in terms of biogas production, while the metabolic biodegradability of the whole mixture is neglected in this optimisation. In this aim, we developed a strategy to assess AcoD using metabolomics data. This strategy was explored in two different systems. Specifically, we investigated the co-digestion of wastewater sludge with different proportions of either grass or fish waste using untargeted High Performance Liquid Chromatography coupled to Mass Spectrometry (HPLC-MS) metabolomics and chemometrics methods. The analysis of these data revealed that adding grass waste did not improve the metabolic biodegradability of wastewater sludge. Conversely, a synergistic effect in the metabolic biodegradability was observed when fish waste was used, this effect being the highest for 25% of fish waste. In conclusion, metabolomics can be regarded as a promising tool both for characterizing the biochemical processes occurring during anaerobic digestion, and for providing a better understanding of the anaerobic digestion processes.

Food Phenotyping: Recording and Processing of Non-Targeted Liquid Chromatography Mass Spectrometry Data for Verifying Food Authenticity

Experiments based on metabolomics represent powerful approaches to the experimental verification of the integrity of food. In particular, high-resolution non-targeted analyses, which are carried out by means of liquid chromatography-mass spectrometry systems (LC-MS), offer a variety of options. However, an enormous amount of data is recorded, which must be processed in a correspondingly complex manner. The evaluation of LC-MS based non-targeted data is not entirely trivial and a wide variety of strategies have been developed that can be used in this regard. In this paper, an overview of the mandatory steps regarding data acquisition is given first, followed by a presentation of the required preprocessing steps for data evaluation. Then some multivariate analysis methods are discussed, which have proven to be particularly suitable in this context in recent years. The publication closes with information on the identification of marker compounds.

An approach for feature selection with data modelling in LC-MS metabolomics

The data processing workflow for LC-MS based metabolomics study is suggested with signal drift correction, univariate analysis, supervised learning, feature selection and unsupervised modelling. The proposed approach requires only an annotation-free peak table and produces an extremely reduced set of the most relevant features together with validation via Receiver Operating Characteristic analysis for selected predictors, cross-validation and unsupervised projection. The presented study was initially optimised by its own experimental set and then was successfully tested by using 36 datasets from 21 publicly available metabolomics projects. The suggested workflow can be used for classification purposes in high dimensional metabolomics studies and as a first step in exploratory analysis, data projection, biomarker selection, data integration and fusion.

Combination of host immune metabolic biomarkers for the PD-1 blockade cancer immunotherapy

BACKGROUNDCurrent clinical biomarkers for the programmed cell death 1 (PD-1) blockade therapy are insufficient because they rely only on the tumor properties, such as programmed cell death ligand 1 expression frequency and tumor mutation burden. Identifying reliable, responsive biomarkers based on the host immunity is necessary to improve the predictive values.METHODSWe investigated levels of plasma metabolites and T cell properties, including energy metabolism markers, in the blood of patients with non-small cell lung cancer before and after treatment with nivolumab (n = 55). Predictive values of combination markers statistically selected were evaluated by cross-validation and linear discriminant analysis on discovery and validation cohorts, respectively. Correlation between plasma metabolites and T cell markers was investigated.RESULTSThe 4 metabolites derived from the microbiome (hippuric acid), fatty acid oxidation (butyrylcarnitine), and redox (cystine and glutathione disulfide) provided high response probability (AUC = 0.91). Similarly, a combination of 4 T cell markers, those related to mitochondrial activation (PPARγ coactivator 1 expression and ROS), and the frequencies of CD8+PD-1hi and CD4+ T cells demonstrated even higher prediction value (AUC = 0.96). Among the pool of selected markers, the 4 T cell markers were exclusively selected as the highest predictive combination, probably because of their linkage to the abovementioned metabolite markers. In a prospective validation set (n = 24), these 4 cellular markers showed a high accuracy rate for clinical responses of patients (AUC = 0.92).CONCLUSIONCombination of biomarkers reflecting host immune activity is quite valuable for responder prediction.FUNDINGAMED under grant numbers 18cm0106302h0003, 18gm0710012h0105, and 18lk1403006h0002; the Tang Prize Foundation; and JSPS KAKENHI grant numbers JP16H06149, 17K19593, and 19K17673.

From waste to food: Optimising the breakdown of oil palm waste to provide substrate for insects farmed as animal feed

Waste biomass from the palm oil industry is currently burned as a means of disposal and solutions are required to reduce the environmental impact. Whilst some waste biomass can be recycled to provide green energy such as biogas, this investigation aimed to optimise experimental conditions for recycling palm waste into substrate for insects, farmed as a sustainable high-protein animal feed. NMR spectroscopy and LC-HRMS were used to analyse the composition of palm empty fruit bunches (EFB) under experimental conditions optimised to produce nutritious substrate rather than biogas. Statistical pattern recognition techniques were used to investigate differences in composition for various combinations of pre-processing and anaerobic digestion (AD) methods. Pre-processing methods included steaming, pressure cooking, composting, microwaving, and breaking down the EFB using ionic liquids. AD conditions which were modified in combination with pre-processing methods were ratios of EFB:digestate and pH. Results show that the selection of pre-processing method affects the breakdown of the palm waste and subsequently the substrate composition and biogas production. Although large-scale insect feeding trials will be required to determine nutritional content, we found that conditions can be optimised to recycle palm waste for the production of substrate for insect rearing. Pre-processing EFB using ionic liquid before AD at pH6 with a 2:1 digestate:EFB ratio were found to be the best combination of experimental conditions.

Statistical Workflow for Feature Selection in Human Metabolomics Data

High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations.

Host Gender and Androgen Levels Regulate Gut Bacterial Taxa in Pigs Leading to Sex-Biased Serum Metabolite Profiles

Gut microbiota regulates host metabolism and immunity. The phylogenetic composition of gut microbiota is influenced by diverse factors that include host gender. In this study, the effects of gender on gut microbial composition and its subsequent influence on serum metabolites in pigs were evaluated. The bacterial composition of feces samples was determined by 16S rRNA gene sequencing in 293 pure-bred Duroc pigs (108 gilts and 185 entire boars) and 64 validated pigs from an eight-breed mosaic F₆ population. Twenty-eight F₆ boars were castrated at 80 days of age to evaluate the effects of androgen on gut microbial composition. Untargeted serum metabolite features were determined in 45 boars and 26 gilts by an ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS). The study observed an obvious influence of host gender on the gut microbial composition and identified numerous sex-biased bacterial taxa. These included Veillonellaceae, Roseburia, Bulleidia, and Escherichia which showed the higher abundance in boars, and Treponema and Bacteroides which were over-represented in gilts. Castration significantly shifted the fecal microbiota composition of the boars toward that of gilts. The predicted functional pathways of the gut microbiome related to obesity and energy harvest were enriched in gilts, and positively associated with gilt-enriched bacteria. Functional pathways related to peptidases and carbohydrate metabolism were enriched in boars and positively associated with boar-enriched bacteria. Serum metabolites related to androgen and cresol metabolism were identified as sex-biased metabolites. Correlation analysis between serum metabolites and sex-biased bacteria identified that the serum concentration of androgen-related metabolites was positively correlated with Bulleidia and Escherichia, but negatively associated with Treponema, suggesting a significant interaction between gut microbiota and host sex hormone metabolism. These results offer basic knowledge of how host gender and gut microbiota influence host metabolism.

Distinctive phytohormonal and metabolic profiles of Arabidopsis thaliana and Eutrema salsugineum under similar soil drying

Arabidopsis and Eutrema show similar stomatal sensitivity to drying soil. In Arabidopsis, larger metabolic adjustments than in Eutrema occurred, with considerable differences in the phytohormonal responses of the two species. Although plants respond to soil drying via a series of concurrent physiological and molecular events, drought tolerance differs greatly within the plant kingdom. While Eutrema salsugineum (formerly Thellungiella salsuginea) is regarded as more stress tolerant than its close relative Arabidopsis thaliana, their responses to soil water deficit have not previously been directly compared. To ensure a similar rate of soil drying for the two species, daily soil water depletion was controlled to 5-10% of the soil water content. While partial stomatal closure occurred earlier in Arabidopsis (Day 4) than Eutrema (from Day 6 onwards), thereafter both species showed similar stomatal sensitivity to drying soil. However, both targeted and untargeted metabolite analysis revealed greater response to drought in Arabidopsis than Eutrema. Early peaks in foliar phytohormone concentrations and different sugar profiles between species were accompanied by opposing patterns in the bioactive cytokinin profiles. Untargeted analysis showed greater metabolic adjustment in Arabidopsis with more statistically significant changes in both early and severe drought stress. The distinct metabolic responses of each species during early drought, which occurred prior to leaf water status declining, seemed independent of later stomatal closure in response to drought. The two species also showed distinct water usage, with earlier reduction in water consumption in Eutrema (Day 3) than Arabidopsis (Day 6), likely reflecting temporal differences in growth responses. We propose Arabidopsis as a promising model to evaluate the mechanisms responsible for stress-induced growth inhibition under the mild/moderate soil drying that crop plants are typically exposed to.

Standardization procedures for real-time breath analysis by secondary electrospray ionization high-resolution mass spectrometry

Despite the attractiveness of breath analysis as a non-invasive means to retrieve relevant metabolic information, its introduction into routine clinical practice remains a challenge. Among all the different analytical techniques available to interrogate exhaled breath, secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS) offers a number of advantages (e.g., real-time, yet wide, metabolome coverage) that makes it ideal for untargeted and targeted studies. However, so far, SESI-HRMS has relied mostly on lab-built prototypes, making it difficult to standardize breath sampling and subsequent analysis, hence preventing further developments such as multi-center clinical studies. To address this issue, we present here a number of new developments. In particular, we have characterized a new SESI interface featuring real-time readout of critical exhalation parameters such as CO₂, exhalation flow rate, and exhaled volume. Four healthy subjects provided breath specimens over a period of 1 month to characterize the stability of the SESI-HRMS system. A first assessment of the repeatability of the system using a gas standard revealed a coefficient of variation (CV) of 2.9%. Three classes of aldehydes, namely 4-hydroxy-2-alkenals, 2-alkenals and 4-hydroxy-2,6-alkedienals―hypothesized to be markers of oxidative stress―were chosen as representative metabolites of interest to evaluate the repeatability and reproducibility of this breath analysis analytical platform. Median and interquartile ranges (IQRs) of CVs for CO₂, exhalation flow rate, and exhaled volume were 3.2% (1.5%), 3.1% (1.9%), and 5.0% (4.6%), respectively. Despite the high repeatability observed for these parameters, we observed a systematic decay in the signal during repeated measurements for the shorter fatty aldehydes, which eventually reached a steady state after three/four repeated exhalations. In contrast, longer fatty aldehydes showed a steady behavior, independent of the number of repeated exhalation maneuvers. We hypothesize that this highly molecule-specific and individual-independent behavior may be explained by the fact that shorter aldehydes (with higher estimated blood-to-air partition coefficients; approaching 100) mainly get exchanged in the airways of the respiratory system, whereas the longer aldehydes (with smaller estimated blood-to-air partition coefficients; approaching 10) are thought to exchange mostly in the alveoli. Exclusion of the first three exhalations from the analysis led to a median CV (IQR) of 6.7 % (5.5 %) for the said classes of aldehydes. We found that such intra-subject variability is in general much lower than inter-subject variability (median relative differences between subjects 48.2%), suggesting that the system is suitable to capture such differences. No batch effect due to sampling date was observed, overall suggesting that the intra-subject variability measured for these series of aldehydes was biological rather than technical. High correlations found among the series of aldehydes support this notion. Finally, recommendations for breath sampling and analysis for SESI-HRMS users are provided with the aim of harmonizing procedures and improving future inter-laboratory comparisons.

Metabolomics in the study of retinal health and disease

Metabolomics is the qualitative and quantitative assessment of the metabolites (small molecules < 1.5 kDa) in body fluids. The metabolites are the downstream of the genetic transcription and translation processes and also downstream of the interactions with environmental exposures; thus, they are thought to closely relate to the phenotype, especially for multifactorial diseases. In the last decade, metabolomics has been increasingly used to identify biomarkers in disease, and it is currently recognized as a very powerful tool with great potential for clinical translation. The metabolome and the associated pathways also help improve our understanding of the pathophysiology and mechanisms of disease. While there has been increasing interest and research in metabolomics of the eye, the application of metabolomics to retinal diseases has been limited, even though these are leading causes of blindness. In this manuscript, we perform a comprehensive summary of the tools and knowledge required to perform a metabolomics study, and we highlight essential statistical methods for rigorous study design and data analysis. We review available protocols, summarize the best approaches, and address the current unmet need for information on collection and processing of tissues and biofluids that can be used for metabolomics of retinal diseases. Additionally, we critically analyze recent work in this field, both in animal models and in human clinical disease, including diabetic retinopathy and age-related macular degeneration. Finally, we identify opportunities for future research applying metabolomics to improve our current assessment and understanding of mechanisms of vitreoretinal diseases, and to hence improve patient assessment and care.

MetaboClust: Using interactive time-series cluster analysis to relate metabolomic data with perturbed pathways

MOTIVATION

Modern analytical techniques such as LC-MS, GC-MS and NMR are increasingly being used to study the underlying dynamics of biological systems by tracking changes in metabolite levels over time. Such techniques are capable of providing information on large numbers of metabolites simultaneously, a feature that is exploited in non-targeted studies. However, since the dynamics of specific metabolites are unlikely to be known a priori this presents an initial subjective challenge as to where the focus of the investigation should be. Whilst a number of feed-forward software tools are available for manipulation of metabolomic data, no tool centralizes on clustering and focus is typically directed by a workflow that is chosen in advance.

RESULTS

We present an interactive approach to time-course analyses and a complementary implementation in a software package, MetaboClust. This is presented through the analysis of two LC-MS time-course case studies on plants (Medicago truncatula and Alopecurus myosuroides). We demonstrate a dynamic, user-centric workflow to clustering with intrinsic visual feedback at all stages of analysis. The software is used to apply data correction, generate the time-profiles, perform exploratory statistical analysis and assign tentative metabolite identifications. Clustering is used to group metabolites in an unbiased manner, allowing pathway analysis to score metabolic pathways, based on their overlap with clusters showing interesting trends.

Integrating transcriptomic techniques and k-means clustering in metabolomics to identify markers of abiotic and biotic stress in Medicago truncatula

INTRODUCTION

Nitrogen-fixing legumes are invaluable crops, but are sensitive to physical and biological stresses. Whilst drought and infection from the soil-borne pathogen Fusarium oxysporum have been studied individually, their combined effects have not been widely investigated.

OBJECTIVES

We aimed to determine the effect of combined stress using methods usually associated with transcriptomics to detect metabolic differences between treatment groups that could not be identified by more traditional means, such as principal component analysis and partial least squares discriminant analysis.

METHODS

Liquid chromatography-high resolution mass spectrometry data from the root and leaves of model legume Medicago truncatula were analysed using Gaussian Process 2-Sample Test, k-means cluster analysis and temporal clustering by affinity propagation.

RESULTS

Metabolic differences were detected: we identified known stress markers, including changes in concentration for sucrose and citric acid, and showed that combined stress can exacerbate the effect of drought. Changes in roots were found to be smaller than those in leaves, but differences due to Fusarium infection were identified. The transfer of sucrose from leaves to roots can be seen in the time series using transcriptomic techniques with the metabolomics time series. Other metabolite concentrations that change as a result of treatment include phosphoric acid, malic acid and tetrahydroxychalcone.

CONCLUSIONS

Probing metabolomic data with transcriptomic tools provides new insights and could help to identify resilient plant varieties, thereby increasing future crop yield and improving food security.

Simultaneous Extraction of RNA and Metabolites from Single Kidney Tissue Specimens for Combined Transcriptomic and Metabolomic Profiling

Tissue analysis represents a powerful tool for the investigation of disease pathophysiology. However, the heterogeneous nature of tissue samples, in particular of neoplastic, may affect the outcome of such analysis and hence obscure interpretation of results. Thus, comprehensive isolation and extraction of transcripts and metabolites from an identical tissue specimen would minimize variations and enable the economic use of biopsy material which is usually available in limited amounts. Here we demonstrate a fast and simple protocol for combined transcriptomics and metabolomics analysis in homogenates prepared from one single tissue sample. Metabolites were recovered by protein precipitation from lysates originally prepared for RNA isolation and were analyzed by LC-QTOF-MS after HILIC and RPLC separation, respectively. Strikingly, although ion suppression was observed, over 80% of the 2885 detected metabolic features could be extracted and analyzed with high reproducibility (CV ≤ 20%). Moreover fold changes of different tumor and nontumor kidney tissues were correlated to an established metabolomics protocol and revealed a strong correlation ( r_p ≥ 0.75). In order to demonstrate the feasibility of the combined analysis of RNA and metabolites, the protocol was applied to kidney tissue of metformin treated mice to investigate drug induced alterations.

An Overview of Metabolic Phenotyping in Blood Pressure Research

PURPOSE OF THE REVIEW

This review presents the analytical techniques, processing and analytical steps used in metabolomics phenotyping studies, as well as the main results from epidemiological studies on the associations between metabolites and high blood pressure.

RECENT FINDINGS

A variety of metabolomic approaches have been applied to a range of epidemiological studies to uncover the pathophysiology of high blood pressure. Several pathways have been suggested in relation to blood pressure including the possible role of the gut microflora, inflammatory, oxidative stress, and lipid pathways. Metabolic changes have also been identified associated with blood pressure lowering effects of diets high in fruits and vegetables and low in meat intake. However, the current body of literature on metabolic profiling and blood pressure is still in its infancy, not fully consistent and requires careful interpretation. Metabolic phenotyping is a promising approach to uncover metabolic pathways associated with high blood pressure and throw light into the complex pathophysiology of hypertension.

Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies

BACKGROUND

Quality assurance (QA) and quality control (QC) are two quality management processes that are integral to the success of metabolomics including their application for the acquisition of high quality data in any high-throughput analytical chemistry laboratory. QA defines all the planned and systematic activities implemented before samples are collected, to provide confidence that a subsequent analytical process will fulfil predetermined requirements for quality. QC can be defined as the operational techniques and activities used to measure and report these quality requirements after data acquisition.

AIM OF REVIEW

This tutorial review will guide the reader through the use of system suitability and QC samples, why these samples should be applied and how the quality of data can be reported.

KEY SCIENTIFIC CONCEPTS OF REVIEW

System suitability samples are applied to assess the operation and lack of contamination of the analytical platform prior to sample analysis. Isotopically-labelled internal standards are applied to assess system stability for each sample analysed. Pooled QC samples are applied to condition the analytical platform, perform intra-study reproducibility measurements (QC) and to correct mathematically for systematic errors. Standard reference materials and long-term reference QC samples are applied for inter-study and inter-laboratory assessment of data.

Evaluation of intensity drift correction strategies using MetaboDrift, a normalization tool for multi-batch metabolomics data

In recent years, mass spectrometry-based metabolomics has increasingly been applied to large-scale epidemiological studies of human subjects. However, the successful use of metabolomics in this context is subject to the challenge of detecting biologically significant effects despite substantial intensity drift that often occurs when data are acquired over a long period or in multiple batches. Numerous computational strategies and software tools have been developed to aid in correcting for intensity drift in metabolomics data, but most of these techniques are implemented using command-line driven software and custom scripts which are not accessible to all end users of metabolomics data. Further, it has not yet become routine practice to assess the quantitative accuracy of drift correction against techniques which enable true absolute quantitation such as isotope dilution mass spectrometry. We developed an Excel-based tool, MetaboDrift, to visually evaluate and correct for intensity drift in a multi-batch liquid chromatography - mass spectrometry (LC-MS) metabolomics dataset. The tool enables drift correction based on either quality control (QC) samples analyzed throughout the batches or using QC-sample independent methods. We applied MetaboDrift to an original set of clinical metabolomics data from a mixed-meal tolerance test (MMTT). The performance of the method was evaluated for multiple classes of metabolites by comparison with normalization using isotope-labeled internal standards. QC sample-based intensity drift correction significantly improved correlation with IS-normalized data, and resulted in detection of additional metabolites with significant physiological response to the MMTT. The relative merits of different QC-sample curve fitting strategies are discussed in the context of batch size and drift pattern complexity. Our drift correction tool offers a practical, simplified approach to drift correction and batch combination in large metabolomics studies.