Preprocessing and Pretreatment of Metabolomics Data for Statistical Analysis

Estimating Gestational Age and Prediction of Preterm Birth Using Metabolomics Biomarkers

Preterm birth (PTB) is a leading cause of morbidity and mortality in children aged under 5 years globally, especially in low-resource settings. It remains a challenge in many low-income and middle-income countries to accurately measure the true burden of PTB due to limited availability of accurate measures of gestational age (GA), first trimester ultrasound dating being the gold standard. Metabolomics biomarkers are a promising area of research that could provide tools for both early identification of high-risk pregnancies and for the estimation of GA and preterm status of newborns postnatally.

Urinary metabolomic profiling of a cohort of Colombian patients with systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is an autoimmune and multisystem disease with a high public health impact. Lupus nephritis (LN), commonly known as renal involvement in SLE, is associated with a poorer prognosis and increased rates of morbidity and mortality in patients with SLE. Identifying new urinary biomarkers that can be used for LN prognosis or diagnosis is essential and is part of current active research. In this study, we applied an untargeted metabolomics approach involving liquid and gas chromatography coupled with mass spectrometry to urine samples collected from 17 individuals with SLE and no kidney damage, 23 individuals with LN, and 10 clinically healthy controls (HCs) to identify differential metabolic profiles for SLE and LN. The data analysis revealed a differentially abundant metabolite expression profile for each study group, and those metabolites may act as potential differential biomarkers of SLE and LN. The differential metabolic pathways found between the LN and SLE patients with no kidney involvement included primary bile acid biosynthesis, branched-chain amino acid synthesis and degradation, pantothenate and coenzyme A biosynthesis, lysine degradation, and tryptophan metabolism. Receiver operating characteristic curve analysis revealed that monopalmitin, glycolic acid, and glutamic acid allowed for the differentiation of individuals with SLE and no kidney involvement and individuals with LN considering high confidence levels. While the results offer promise, it is important to recognize the significant influence of medications and other external factors on metabolomics studies. This impact has the potential to obscure differences in metabolic profiles, presenting a considerable challenge in the identification of disease biomarkers. Therefore, experimental validation should be conducted with a larger sample size to explore the diagnostic potential of the metabolites found as well as to examine how treatment and disease activity influence the identified chemical compounds. This will be crucial for refining the accuracy and effectiveness of using urine metabolomics for diagnosing and monitoring lupus and lupus nephritis.

Total cucurbitacins from Herpetospermum pedunculosum pericarp do better than Hu-lu-su-pian (HLSP) in its safety and hepatoprotective efficacy

The pericarp of Herpetospermum pedunculosum (HPP) has traditionally been used for treating jaundice and hepatitis. However, the specific hepatoprotective components and their safety/efficacy profiles remain unclear. This study aimed to characterize the total cucurbitacins (TCs) extracted from HPP and evaluate their hepatoprotective potential. As a reference, Hu-lu-su-pian (HLSP), a known hepatoprotective drug containing cucurbitacins, was used for comparison of chemical composition, effects, and safety. Molecular networking based on UHPLC-MS/MS identified cucurbitacin B, isocucurbitacin B, and cucurbitacin E as the major components in TCs, comprising 70.3%, 26.1%, and 3.6% as determined by RP-HPLC, respectively. TCs treatment significantly reversed CCl4-induced metabolic changes associated with liver damage in a dose-dependent manner, impacting pathways including energy metabolism, oxidative stress and phenylalanine metabolism, and showed superior efficacy to HLSP. Safety evaluation also showed that TCs were safe, with higher LD50 and no observable adverse effect level (NOAEL) values than HLSP. The median lethal dose (LD50) and NOAEL values of TCs were 36.21 and 15 mg/kg body weight (BW), respectively, while the LD50 of HLSP was 14 mg/kg BW. In summary, TCs extracted from HPP demonstrated promising potential as a natural hepatoprotective agent, warranting further investigation into synergistic effects of individual cucurbitacin components.

Nuclear Magnetic Resonance Spectroscopy Metabolomics in Idiopathic Intracranial Hypertension to Identify Markers of Disease and Headache

BACKGROUND AND OBJECTIVE

We evaluated the metabolomic profile in the CSF, serum, and urine of participants with idiopathic intracranial hypertension (IIH) compared with that in controls and measured changes in metabolism associated with clinical markers of disease activity and treatment.

METHODS

A case-control study compared women aged 18-55 years with active IIH (Friedman diagnostic criteria) with a sex-matched, age-matched, and body mass index-matched control group. IIH participants were identified from neurology and ophthalmology clinics from National Health Service hospitals and underwent a prospective intervention to induce disease remission through weight loss with reevaluation at 12 months. Clinical assessments included lumbar puncture, headache, papilledema, and visual measurements. Spectra of the CSF, serum, and urine metabolites were acquired using proton nuclear magnetic resonance spectroscopy.

RESULTS

Urea was lower in IIH participants (CSF, controls median ± IQR 0.196 ± 0.008, IIH 0.058 ± 0.059, p < 0.001; urine, controls 5971.370 ± 3021.831, IIH 4691.363 ± 1955.774, p = 0.009), correlated with ICP (urine p = 0.019) and headache severity (CSF p = 0.031), and increased by 12 months (CSF 12 months; 0.175 ± 0.043, p = 0.004, urine; 5210.874 ± 1825.302, p = 0.043). The lactate:pyruvate ratio was increased in IIH participants compared with that in controls (CSF, controls 49.739 ± 19.523, IIH 113.114 ± 117.298, p = 0.023; serum, controls 38.187 ± 13.392, IIH 54.547 ± 18.471, p = 0.004) and decreased at 12 months (CSF, 113.114 ± 117.298, p < 0.001). Baseline acetate was higher in IIH participants (CSF, controls 0.128 ± 0.041, IIH 0.192 ± 0.151, p = 0.008), correlated with headache severity (p = 0.030) and headache disability (p = 0.003), and was reduced at 12 months (0.160 ± 0.060, p = 0.007). Ketones, 3-hydroxybutyrate and acetoacetate, were altered in the CSF at baseline in IIH participants (3-hydroxybutyrate, controls 0.074 ± 0.063, IIH 0.049 ± 0.055, p = 0.019; acetoacetate, controls 0.013 ± 0.007, IIH 0.017 ± 0.010, p = 0.013) and normalized at 12 months (0.112 ± 0.114, p = 0.019, 0.029 ± 0.017, p = 0.015, respectively).

DISCUSSION

We observed metabolic disturbances that are evident in the CSF, serum, and urine of IIH participants, suggesting global metabolic dysregulation. Altered ketone body metabolites normalized after therapeutic weight loss. CSF:serum urea ratio was altered, which may influence ICP dynamics and headache. Elevated CSF acetate, known to stimulate trigeminal sensitization, was associated with headache morbidity. These alterations of metabolic pathways specific to IIH provide biological insight and warrant mechanistic evaluation.

TRIAL REGISTRATION INFORMATION

Registered on ClinicalTrials.gov, NCT02124486 (submitted April 22, 2014) and NCT02017444 (submitted December 16, 2013).

Graph Properties of Mass-Difference Networks for Profiling and Discrimination in Untargeted Metabolomics

Untargeted metabolomics seeks to identify and quantify most metabolites in a biological system. In general, metabolomics results are represented by numerical matrices containing data that represent the intensities of the detected variables. These matrices are subsequently analyzed by methods that seek to extract significant biological information from the data. In mass spectrometry-based metabolomics, if mass is detected with sufficient accuracy, below 1 ppm, it is possible to derive mass-difference networks, which have spectral features as nodes and chemical changes as edges. These networks have previously been used as means to assist formula annotation and to rank the importance of chemical transformations. In this work, we propose a novel role for such networks in untargeted metabolomics data analysis: we demonstrate that their properties as graphs can also be used as signatures for metabolic profiling and class discrimination. For several benchmark examples, we computed six graph properties and we found that the degree profile was consistently the property that allowed for the best performance of several clustering and classification methods, reaching levels that are competitive with the performance using intensity data matrices and traditional pretreatment procedures. Furthermore, we propose two new metrics for the ranking of chemical transformations derived from network properties, which can be applied to sample comparison or clustering. These metrics illustrate how the graph properties of mass-difference networks can highlight the aspects of the information contained in data that are complementary to the information extracted from intensity-based data analysis.

metaboprep: an R package for preanalysis data description and processing

MOTIVATION

Metabolomics is an increasingly common part of health research and there is need for preanalytical data processing. Researchers typically need to characterize the data and to exclude errors within the context of the intended analysis. Whilst some preprocessing steps are common, there is currently a lack of standardization and reporting transparency for these procedures.

RESULTS

Here, we introduce metaboprep, a standardized data processing workflow to extract and characterize high quality metabolomics datasets. The package extracts data from preformed worksheets, provides summary statistics and enables the user to select samples and metabolites for their analysis based on a set of quality metrics. A report summarizing quality metrics and the influence of available batch variables on the data are generated for the purpose of open disclosure. Where possible, we provide users flexibility in defining their own selection thresholds.

AVAILABILITY AND IMPLEMENTATION

metaboprep is an open-source R package available at https://github.com/MRCIEU/metaboprep.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Pharmacometabolomics Applied to Personalized Medicine in Urological Cancers

Prostate cancer (PCa), bladder cancer (BCa), and renal cell carcinoma (RCC) are the most common urological cancers, and their incidence has been rising over time. Surgery is the standard treatment for these cancers, but this procedure is only effective when the disease is localized. For metastatic disease, PCa is typically treated with androgen deprivation therapy, while BCa is treated with chemotherapy, and RCC is managed primarily with targeted therapies. However, response rates to these therapeutic options remain unsatisfactory due to the development of resistance and treatment-related toxicity. Thus, the discovery of biomarkers with prognostic and predictive value is needed to stratify patients into different risk groups, minimizing overtreatment and the risk of drug resistance development. Pharmacometabolomics, a branch of metabolomics, is an attractive tool to predict drug response in an individual based on its own metabolic signature, which can be collected before, during, and after drug exposure. Hence, this review focuses on the application of pharmacometabolomic approaches to identify the metabolic responses to hormone therapy, targeted therapy, immunotherapy, and chemotherapy for the most prevalent urological cancers.

Harnessing Metabolomics to Advance Epilepsy Research

Metabolomics is the laboratory analysis and scientific study of the metabolome—that is, the entire collection of small molecule chemicals in an organism. The metabolome represents the functional state of an organism and provides a multifaceted readout of the aggregate activity of endogenous (cellular) and exogenous (environmental) processes. In this review, we discuss how the integrative and dynamic properties of the metabolome create unique opportunities to study complex pathologies that evolve and oscillate over time, like epilepsy. We explain how the scientific progress and clinical applications of metabolomics remain hampered by biological and technical challenges, and we propose best practices to overcome these challenges so that metabolomics can be used in a rigorous and effective manner to further epilepsy research.

Multivariate analysis of NMR-based metabolomic data

Nuclear magnetic resonance (NMR) spectroscopy allows for simultaneous detection of a wide range of metabolites and lipids. As metabolites act together in complex metabolic networks, they are often highly correlated, and optimal biological insight is achieved when using methods that take the correlation into account. For this reason, latent-variable-based methods, such as principal component analysis and partial least-squares discriminant analysis, are widely used in metabolomic studies. However, with increasing availability of larger population cohorts, and a shift from analysis of spectral data to using quantified metabolite levels, both more traditional statistical approaches and alternative machine learning methods have become more widely used. This review aims at providing an overview of the current state-of-the-art multivariate methods for the analysis of NMR-based metabolomic data as well as alternative methods, highlighting their strengths and limitations.

Metabolomic profiling of stool of two-year old children from the INSIGHT study reveals links between butyrate and child weight outcomes

BACKGROUND

Metabolomic analysis is commonly used to understand the biological underpinning of diseases such as obesity. However, our knowledge of gut metabolites related to weight outcomes in young children is currently limited.

OBJECTIVES

To (1) explore the relationships between metabolites and child weight outcomes, (2) determine the potential effect of covariates (e.g., child's diet, maternal health/habits during pregnancy, etc.) in the relationship between metabolites and child weight outcomes, and (3) explore the relationship between selected gut metabolites and gut microbiota abundance.

METHODS

Using ¹ H-NMR, we quantified 30 metabolites from stool samples of 170 two-year-old children. To identify metabolites and covariates associated with children's weight outcomes (BMI [weight/height² ], BMI z-score [BMI adjusted for age and sex], and growth index [weight/height]), we analysed the ¹ H-NMR data, along with 20 covariates recorded on children and mothers, using LASSO and best subset selection regression techniques. Previously characterized microbiota community information from the same stool samples was used to determine associations between selected gut metabolites and gut microbiota.

RESULTS

At age 2 years, stool butyrate concentration had a significant positive association with child BMI (p-value = 3.58 × 10^-4 ), BMI z-score (p-value = 3.47 × 10^-4 ), and growth index (p-value = 7.73 × 10^-4 ). Covariates such as maternal smoking during pregnancy are important to consider. Butyrate concentration was positively associated with the abundance of the bacterial genus Faecalibacterium (p-value = 9.61 × 10^-3 ).

CONCLUSIONS

Stool butyrate concentration is positively associated with increased child weight outcomes and should be investigated further as a factor affecting childhood obesity.

Comparison of HPLC and NMR for quantification of the main volatile fatty acids in rumen digesta

Accurate quantification of volatile fatty acid (VFA) concentrations in rumen fluid are essential for research on rumen metabolism. The study comprehensively investigated the pros and cons of High-performance liquid chromatography (HPLC) and 1H Nuclear magnetic resonance (1H-NMR) analysis methods for rumen VFAs quantification. We also investigated the performance of several commonly used data pre-treatments for the two sets of data using correlation analysis, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The molar proportion and reliability analysis demonstrated that the two approaches produce highly consistent VFA concentrations. In the pre-processing of NMR spectra, line broadening and shim correction may reduce estimated concentrations of metabolites. We observed differences in results using multiplet of different protons from one compound and identified "handle signals" that provided the most consistent concentrations. Different data pre-treatment strategies tested with both HPLC and NMR significantly affected the results of downstream data analysis. "Normalized by sum" pre-treatment can eliminate a large number of positive correlations between NMR-based VFA. A "Combine" strategy should be the first choice when calculating the correlation between metabolites or between samples. The PCA and PLS-DA suggest that except for "Normalize by sum", pre-treatments should be used with caution.

Binary Simplification as an Effective Tool in Metabolomics Data Analysis

Metabolomics aims to perform a comprehensive identification and quantification of the small molecules present in a biological system. Due to metabolite diversity in concentration, structure, and chemical characteristics, the use of high-resolution methodologies, such as mass spectrometry (MS) or nuclear magnetic resonance (NMR), is required. In metabolomics data analysis, suitable data pre-processing, and pre-treatment procedures are fundamental, with subsequent steps aiming at highlighting the significant biological variation between samples over background noise. Traditional data analysis focuses primarily on the comparison of the features' intensity values. However, intensity data are highly variable between experimental batches, instruments, and pre-processing methods or parameters. The aim of this work was to develop a new pre-treatment method for MS-based metabolomics data, in the context of sample profiling and discrimination, considering only the occurrence of spectral features, encoding feature presence as 1 and absence as 0. This "Binary Simplification" encoding (BinSim) was used to transform several benchmark datasets before the application of clustering and classification methods. The performance of these methods after the BinSim pre-treatment was consistently as good as and often better than after different combinations of traditional, intensity-based, pre-treatments. Binary Simplification is, therefore, a viable pre-treatment procedure that effectively simplifies metabolomics data-analysis pipelines.

Metabolomics to understand placental biology: Where are we now?

Metabolomics, the application of analytical chemistry methodologies to survey the chemical composition of a biological system, is used to globally profile and compare metabolites in one or more groups of samples. Given that metabolites are the terminal end-products of cellular metabolic processes, or 'phenotype' of a cell, tissue, or organism, metabolomics is valuable to the study of the maternal-fetal interface as it has the potential to reveal nuanced complexities of a biological system as well as differences over time or between individuals. The placenta acts as the primary site of maternal-fetal exchange, the success of which is paramount to growth and development of offspring during pregnancy and beyond. Although the study of metabolomics has proven moderately useful for the screening, diagnosis, and understanding of the pathophysiology of pregnancy complications, the placental metabolome in the context of a healthy pregnancy remains poorly characterized and understood. Herein, we discuss the technical aspects of metabolomics and review the current literature describing the placental metabolome in human and animal models, in the context of health and disease. Finally, we highlight areas for future opportunities in the emerging field of placental metabolomics.

Metabolomics Data Treatment: Basic Directions of the Full Process

The present chapter describes basic aspects of the main steps for data processing on mass spectrometry-based metabolomics platforms, focusing on the main objectives and important considerations of each step. Initially, an overview of metabolomics and the pivotal techniques applied in the field are presented. Important features of data acquisition and preprocessing such as data compression, noise filtering, and baseline correction are revised focusing on practical aspects. Peak detection, deconvolution, and alignment as well as missing values are also discussed. Special attention is given to chemical and mathematical normalization approaches and the role of the quality control (QC) samples. Methods for uni- and multivariate statistical analysis and data pretreatment that could impact them are reviewed, emphasizing the most widely used multivariate methods, i.e., principal components analysis (PCA), partial least squares-discriminant analysis (PLS-DA), orthogonal partial least square-discriminant analysis (OPLS-DA), and hierarchical cluster analysis (HCA). Criteria for model validation and softwares used in data processing were also approached. The chapter ends with some concerns about the minimal requirements to report metadata in metabolomics.

Metabolomic Studies for the Evaluation of Toxicity Induced by Environmental Toxicants on Model Organisms

Environmental pollution causes significant toxicity to ecosystems. Thus, acquiring a deeper understanding of the concentration of environmental pollutants in ecosystems and, clarifying their potential toxicities is of great significance. Environmental metabolomics is a powerful technique in investigating the effects of pollutants on living organisms in the environment. In this review, we cover the different aspects of the environmental metabolomics approach, which allows the acquisition of reliable data. A step-by-step procedure from sample preparation to data interpretation is also discussed. Additionally, other factors, including model organisms and various types of emerging environmental toxicants are discussed. Moreover, we cover the considerations for successful environmental metabolomics as well as the identification of toxic effects based on data interpretation in combination with phenotype assays. Finally, the effects induced by various types of environmental toxicants in model organisms based on the application of environmental metabolomics are also discussed.

Metabolomics Studies in Psoriatic Disease: A Review

Metabolomics investigates a broad range of small molecules, allowing researchers to understand disease-related changes downstream of the genome and proteome in response to external environmental stimuli. It is an emerging technology that holds promise in identifying biomarkers and informing the practice of precision medicine. In this review, we summarize the studies that have examined endogenous metabolites in patients with psoriasis and/or psoriatic arthritis using nuclear magnetic resonance (NMR) or mass spectrometry (MS) and were published through 26 January 2021. A standardized protocol was used for extracting data from full-text articles identified by searching OVID Medline ALL, OVID Embase, OVID Cochrane Central Register of Controlled Trials and BIOSIS Citation Index in Web of Science. Thirty-two studies were identified, investigating various sample matrices and employing a wide variety of methods for each step of the metabolomics workflow. The vast majority of studies identified metabolites, mostly amino acids and lipids that may be associated with psoriasis diagnosis and activity. Further exploration is needed to identify and validate metabolomic biomarkers that can accurately and reliably predict which psoriasis patients will develop psoriatic arthritis, differentiate psoriatic arthritis patients from patients with other inflammatory arthritides and measure psoriatic arthritis activity.

Pharmacometabonomics: data processing and statistical analysis

Individual variations in drug efficacy, side effects and adverse drug reactions are still challenging that cannot be ignored in drug research and development. The aim of pharmacometabonomics is to better understand the pharmacokinetic properties of drugs and monitor the drug effects on specific metabolic pathways. Here, we systematically reviewed the recent technological advances in pharmacometabonomics for better understanding the pathophysiological mechanisms of diseases as well as the metabolic effects of drugs on bodies. First, the advantages and disadvantages of all mainstream analytical techniques were compared. Second, many data processing strategies including filtering, missing value imputation, quality control-based correction, transformation, normalization together with the methods implemented in each step were discussed. Third, various feature selection and feature extraction algorithms commonly applied in pharmacometabonomics were described. Finally, the databases that facilitate current pharmacometabonomics were collected and discussed. All in all, this review provided guidance for researchers engaged in pharmacometabonomics and metabolomics, and it would promote the wide application of metabolomics in drug research and personalized medicine.

Metabolic phenotyping and cardiovascular disease: an overview of evidence from epidemiological settings

Metabolomics, the comprehensive measurement of low-molecular-weight molecules in biological fluids used for metabolic phenotyping, has emerged as a promising tool to better understand pathways underlying cardiovascular disease (CVD) and to improve cardiovascular risk stratification. Here, we present the main methodologies for metabolic phenotyping, the methodological steps to analyse these data in epidemiological settings and the associated challenges. We discuss evidence from epidemiological studies linking metabolites to coronary heart disease and stroke. These studies indicate the systemic nature of CVD and identify associated metabolic pathways such as gut microbial cometabolism, branched-chain amino acids, glycerophospholipid and cholesterol metabolism, as well as activation of inflammatory processes. Integration of metabolomic with genomic data can provide new evidence for involved biochemical pathways and potential for causality using Mendelian randomisation. The clinical utility of metabolic biomarkers for cardiovascular risk stratification in healthy individuals has not yet been established. As sample sizes with high-dimensional molecular data increase in epidemiological settings, integration of metabolomic data across studies and platforms with other molecular data will lead to new understanding of the metabolic processes underlying CVD and contribute to identification of potentially novel preventive and pharmacological targets. Metabolic phenotyping offers a powerful tool in the characterisation of the molecular signatures of CVD, paving the way to new mechanistic understanding and therapies, as well as improving risk prediction of CVD patients. However, there are still challenges to face in order to contribute to clinically important improvements in CVD.

The ABRF Metabolomics Research Group 2016 Exploratory Study: Investigation of Data Analysis Methods for Untargeted Metabolomics

Lack of standardized applications of bioinformatics and statistical approaches for pre- and postprocessing of global metabolomic profiling data sets collected using high-resolution mass spectrometry platforms remains an inadequately addressed issue in the field. Several publications now recognize that data analysis outcome variability is caused by different data treatment approaches. Yet, there is a lack of interlaboratory reproducibility studies that have looked at the contribution of data analysis techniques toward variability/overlap of results. The goal of our study was to identify the contribution of data pre- and postprocessing methods on metabolomics analysis results. We performed urinary metabolomics from samples obtained from mice exposed to 5 Gray of external beam gamma rays and those exposed to sham irradiation (control group). The data files were made available to study participants for comparative analysis using commonly used bioinformatics and/or biostatistics approaches in their laboratories. The participants were asked to report back the top 50 metabolites/features contributing significantly to the group differences. Herein we describe the outcome of this study which suggests that data preprocessing is critical in defining the outcome of untargeted metabolomic studies.

Strategy for Intercorrelation Identification between Metabolome and Microbiome

Accumulating evidence points to the strong and complicated associations between the metabolome and the microbiome, which play diverse roles in physiology and pathology. Various correlation analysis approaches were applied to identify microbe-metabolite associations. Given the strengths and weaknesses of the existing methods and considering the characteristics of different types of omics data, we designed a special strategy, called Generalized coRrelation analysis for Metabolome and Microbiome (GRaMM), for the intercorrelation discovery between the metabolome and microbiome. GRaMM can properly deal with two types of omics data, the effect of confounders, and both linear and nonlinear correlations by integrating several complementary methods such as the classical linear regression, the emerging maximum information coefficient (MIC), the metabolic confounding effect elimination (MCEE), and the centered log-ratio transformation (CLR). GRaMM contains four sequential computational steps: (1) metabolic and microbial data preprocessing, (2) linear/nonlinear type identification, (3) data correction and correlation detection, and (4) p value correction. The performances of GRaMM, including the accuracy, sensitivity, specificity, false positive rate, applicability, and effects of preprocessing and confounder adjustment steps, were evaluated and compared with three other methods in multiple simulated and real-world datasets. To our knowledge, GRaMM is the first strategy designed for the intercorrelation analysis between metabolites and microbes. The Matlab function and an R package were developed and are freely available for academic use (comply with GNU GPL.V3 license).

Genetic variance of metabolomic features and their relationship with body weight and body weight gain in Holstein cattle1

In recent years, metabolomics has been used to clarify the biology underlying biological samples. In the field of animal breeding, investigating the magnitude of genetic control on the metabolomic profiles of animals and their relationships with quantitative traits adds valuable information to animal improvement schemes. In this study, we analyzed metabolomic features (MFs) extracted from the metabolomic profiles of 843 male Holstein calves. The metabolomic profiles were obtained using nuclear magnetic resonance (NMR) spectroscopy. We investigated 2 alternative methods to control for peak shifts in the NMR spectra, binning and aligning, to determine which approach was the most efficient for assessing genetic variance. Series of univariate analyses were implemented to elucidate the heritability of each MF. Furthermore, records on BW and ADG from 154 to 294 d of age (ADG154-294), 294 to 336 d of age (ADG294-336), and 154 to 336 d of age (ADG154-336) were used in a series of bivariate analyses to establish the genetic and phenotypic correlations with MFs. Bivariate analyses were only performed for MFs that had a heritability significantly different from zero. The heritabilities obtained in the univariate analyses for the MFs in the binned data set were low (<0.2). In contrast, in the aligned data set, we obtained moderate heritability (0.2 to 0.5) for 3.5% of MFs and high heritability (more than 0.5) for 1% of MFs. The bivariate analyses showed that ~12%, ~3%, ~9%, and ~9% of MFs had significant additive genetic correlations with BW, ADG154-294, ADG294-336, and ADG154-336, respectively. In all of the bivariate analyses, the percentage of significant additive genetic correlations was higher than the percentage of significant phenotypic correlations of the corresponding trait. Our results provided insights into the influence of the underlying genetic mechanisms on MFs. Further investigations in this field are needed for better understanding of the genetic relationship among the MFs and quantitative traits.

Metabolomics: a promising diagnostic and therapeutic implement for breast cancer

Breast cancer (BC) is the most commonly diagnosed cancer among women and the leading cause of cancer death. Despite the advent of numerous diagnosis and treatment methods in recent years, this heterogeneous disease still presents great challenges in early diagnosis, curative treatments and prognosis monitoring. Thus, finding promising early diagnostic biomarkers and therapeutic targets and approaches is meaningful. Metabolomics, which focuses on the analysis of metabolites that change during metabolism, can reveal even a subtle abnormal change in an individual. In recent decades, the exploration of cancer-related metabolomics has increased. Metabolites can be promising biomarkers for the screening, response evaluation and prognosis of BC. In this review, we summarized the workflow of metabolomics, described metabolite signatures based on molecular subtype as well as reclassification and then discussed the application of metabolomics in the early diagnosis, monitoring and prognosis of BC to offer new insights for clinicians in breast cancer diagnosis and treatment.

The Effect of Exercise Training on Myocardial and Skeletal Muscle Metabolism by MR Spectroscopy in Rats with Heart Failure

The metabolism and performance of myocardial and skeletal muscle are impaired in heart failure (HF) patients. Exercise training improves the performance and benefits the quality of life in HF patients. The purpose of the present study was to determine the metabolic profiles in myocardial and skeletal muscle in HF and exercise training using MRS, and thus to identify targets for clinical MRS in vivo. After surgically establishing HF in rats, we randomized the rats to exercise training programs of different intensities. After the final training session, rats were sacrificed and tissues from the myocardial and skeletal muscle were extracted. Magnetic resonance spectra were acquired from these extracts, and principal component and metabolic enrichment analysis were used to assess the differences in metabolic profiles. The results indicated that HF affected myocardial metabolism by changing multiple metabolites, whereas it had a limited effect on skeletal muscle metabolism. Moreover, exercise training mainly altered the metabolite distribution in skeletal muscle, indicating regulation of metabolic pathways of taurine and hypotaurine metabolism and carnitine synthesis.

Metabolic profiling identifies phospholipids as potential serum biomarkers for schizophrenia

Schizophrenia (SCZ) is a multifactorial psychiatric disorder. However, the molecular pathogenesis of SCZ remains largely unknown, and no reliable diagnostic test is currently available. Phospholipid metabolism is known to be disturbed during disease processes of SCZ. In this study, we used an untargeted liquid chromatography-mass spectrometry (LC-MS)-based metabolic profiling approach to measure lipid metabolites in serum samples from 119 SCZ patients and 109 healthy controls, to identify potential lipid biomarkers for the discrimination between SCZ patients and healthy controls. 51 lipid metabolites were identified to be significant for discriminating SCZ patients from healthy controls, including phosphatidylcholines (PCs), lysophosphatidylcholines (LPCs), phosphatidylethanolamines (PEs), lysophosphatidylethanolamines (LPEs) and sphingomyelins (SMs). Compared to healthy controls, most PCs and LPCs, as well as all PEs in patients were decreased, while most LPEs and all SMs were increased. A panel of six lipid metabolites could effectively discriminate SCZ patients from healthy controls with an area under the receiver-operating characteristic curve of 0.991 in the training samples and 0.980 in the test samples. These findings suggest that extensive disturbances of phospholipids may be involved in the development of SCZ. This LC-MS-based metabolic profiling approach shows potential for the identification of putative serum biomarkers for the diagnosis of SCZ.

Skeletal muscle metabolism in rats with low and high intrinsic aerobic capacity: Effect of aging and exercise training

Purpose

Exercise training increases aerobic capacity and is beneficial for health, whereas low aerobic exercise capacity is a strong independent predictor of cardiovascular disease and premature death. The purpose of the present study was to determine the metabolic profiles in a rat model of inborn low versus high capacity runners (LCR/HCR) and to determine the effect of inborn aerobic capacity, aging, and exercise training on skeletal muscle metabolic profile.

Methods

LCR/HCR rats were randomized to high intensity low volume interval treadmill training twice a week or sedentary control for 3 or 11 months before they were sacrificed, at 9 and 18 months of age, respectively. Magnetic resonance spectra were acquired from soleus muscle extracts, and partial least square discriminative analysis was used to determine the differences in metabolic profile.

Results

Sedentary HCR rats had 54% and 30% higher VO_2max compared to sedentary LCR rats at 9 months and 18 months, respectively. In HCR, exercise increased running speed significantly, and VO_2max was higher at age of 9 months, compared to sedentary counterparts. In LCR, changes were small and did not reach the level of significance. The metabolic profile was significantly different in the LCR sedentary group compared to the HCR sedentary group at the age of 9 and 18 months, with higher glutamine and glutamate levels (9 months) and lower lactate level (18 months) in HCR. Irrespective of fitness level, aging was associated with increased soleus muscle concentrations of glycerophosphocholine and glucose. Interval training did not influence metabolic profiles in LCR or HCR rats at any age.

Conclusion

Differences in inborn aerobic capacity gave the most marked contrasts in metabolic profile, there were also some changes with ageing. Low volume high intensity interval training twice a week had no detectable effect on metabolic profile.

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.

No wisdom in the crowd: genome annotation in the era of big data - current status and future prospects

Science and engineering rely on the accumulation and dissemination of knowledge to make discoveries and create new designs. Discovery-driven genome research rests on knowledge passed on via gene annotations. In response to the deluge of sequencing big data, standard annotation practice employs automated procedures that rely on majority rules. We argue this hinders progress through the generation and propagation of errors, leading investigators into blind alleys. More subtly, this inductive process discourages the discovery of novelty, which remains essential in biological research and reflects the nature of biology itself. Annotation systems, rather than being repositories of facts, should be tools that support multiple modes of inference. By combining deduction, induction and abduction, investigators can generate hypotheses when accurate knowledge is extracted from model databases. A key stance is to depart from 'the sequence tells the structure tells the function' fallacy, placing function first. We illustrate our approach with examples of critical or unexpected pathways, using MicroScope to demonstrate how tools can be implemented following the principles we advocate. We end with a challenge to the reader.

The future perspective: metabolomics in laboratory medicine for inborn errors of metabolism

Metabolomics can be described as a simultaneous and comprehensive analysis of small molecules in a biological sample. Recent technological and bioinformatics advances have facilitated large-scale metabolomic studies in many areas, including inborn errors of metabolism (IEMs). Despite significant improvements in the diagnosis and treatment of some IEMs, it is still challenging to understand how genetic variation affects disease progression and susceptibility. In addition, a search for new more personalized therapies and a growing demand for tools to monitor the long-term metabolic effects of existing therapies set the stage for metabolomics integration in preclinical and clinical studies. While targeted metabolomics approach is a common practice in biochemical genetics laboratories for biochemical diagnosis and monitoring of IEMs, applications of untargeted metabolomics in the clinical laboratories are still in infancy, facing some challenges. It is however, expected in the future to dramatically change the scope and utility of the clinical laboratory playing a significant role in patient management. This review provides an overview of targeted and global, large-scale metabolomic studies applied to investigate various IEMs. We discuss an existing and prospective clinical applications of metabolomics in IEMs for better diagnosis and deep understanding of complex metabolic perturbations associated with the etiology of inherited metabolic disorders.