Concurrent class analysis identifies discriminatory variables from metabolomics data on isovaleric acidemia

Metabolomics data are typically complex and high dimensional. Multivariate dimension-reducing techniques have thus been developed for analysing metabolomics data to disclose underlying relationships, with principal component analysis (PCA) as the technique mostly applied. Despite its widespread use in metabolomics, PCA has shortcomings that limit its applicability. Several approaches have been made to overcome these limitations and we describe an advanced disjoint PCA (DPCA) model, termed concurrent class analysis and abbreviated as CONCA. CONCA is a new model, and is unique in linking DPCA models to a traditional PCA model. This is accomplished by restructuring the input data matrix, applying DPCA group models to the restructured data, and combining the DPCA models in order to replicate a traditional PCA. We applied the CONCA model to a metabolomics data set on isovaleric acidaemia (IVA), a rare inherited metabolic disorder. The outcome showed that three of the variables with high discrimination value identified through the CONCA analysis are prominent organic acid biomarkers for IVA. Moreover, three further minor metabolites associated with the disease, and two as a consequence of treatment, were likewise identified as important discriminatory variables. The benefit of the CONCA model thus is its ability to disclose information concerning each individual group and to identify the variables important in discrimination (VIDs) which are also responsible for group separation.

     

Concurrent class analysis identifies discriminatory variables from metabolomics data on isovaleric acidemia

Metabolomics data are typically complex and high dimensional. Multivariate dimension-reducing techniques have thus been developed for analysing metabolomics data to disclose underlying relationships, with principal component analysis (PCA) as the technique mostly applied. Despite its widespread use in metabolomics, PCA has shortcomings that limit its applicability. Several approaches have been made to overcome these limitations and we describe an advanced disjoint PCA (DPCA) model, termed concurrent class analysis and abbreviated as CONCA. CONCA is a new model, and is unique in linking DPCA models to a traditional PCA model. This is accomplished by restructuring the input data matrix, applying DPCA group models to the restructured data, and combining the DPCA models in order to replicate a traditional PCA. We applied the CONCA model to a metabolomics data set on isovaleric acidaemia (IVA), a rare inherited metabolic disorder. The outcome showed that three of the variables with high discrimination value identified through the CONCA analysis are prominent organic acid biomarkers for IVA. Moreover, three further minor metabolites associated with the disease, and two as a consequence of treatment, were likewise identified as important discriminatory variables. The benefit of the CONCA model thus is its ability to disclose information concerning each individual group and to identify the variables important in discrimination (VIDs) which are also responsible for group separation.     

Global metabolomics characterization of bacteria: pre-analytical treatments and profiling

Pre-analytical treatments of bacteria are crucial steps in bacterial metabolomics studies. In order to achieve reliable samples that can best represent the global metabolic profile in vivo both qualitatively and quantitatively, many sample treatment procedures have been developed. The use of different methods makes it difficult to compare the results among different groups. In this work, E. coli samples were tested by using NMR spectroscopy. Both liquid N₂ and cold methanol quenching procedures reduce the cell membrane integrity and cause metabolites leakage. However, liquid N₂ quenching affected the cell viability and the NMR metabolites’ profile less than cold methanol procedure. Samples obtained by metabolite extraction were significantly superior over cell suspensions and cell lysates, with a higher number of detectable metabolites. Methanol/chloroform extraction proved most efficient at extraction of intracellular metabolites from both qualitative and quantitative points of view. Finally, standard operating procedures of bacterial sample treatments for NMR metabolomics study are presented.     

Metabolite profiling of 5′-AMP induced hypometabolism

We have previously demonstrated that 5′-adenosine monophosphate (5′-AMP) can be used to induce deep hypometabolism in mice and other non-hibernating mammals. This reversible 5′-AMP induced hypometabolism (AIHM) allows mice to maintain a body temperature about 1 °C above the ambient temperature for several hours before spontaneous reversal to euthermia. Our biochemical and gene expression studies suggested that the molecular processes involved in AIHM behavior most likely occur at the metabolic interconversion level, rather than the gene or protein expression level. To understand the metabolic processes involved in AIHM behavior, we conducted a non-targeted comparative metabolomics investigation at multiple stages of AIHM in the plasma, liver and brain of animals that underwent AIHM. Dozens of metabolites representing many important metabolic pathways were detected and measured using a metabolite profiling platform combining both liquid-chromatography–mass spectrometry and gas-chromatography–mass spectrometry. Our findings indicate that there is a widespread suppression of energy generating metabolic pathways but lipid metabolism appears to be minimally altered. Regulation of carbohydrate metabolites appears to be the major way the animal utilizes energy in AIHM and during the following recovery process. The 5′-AMP administered has largely been catabolized by the time the animals have entered AIHM. During AIHM, the urea cycle appears to be functional, helping to avoid ammonia toxicity. Of all tissues studied, brain’s metabolite flux is the least affected by AIHM.     

Genetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms

Metabolomic profiling is a powerful approach to characterize human metabolism and help understand common disease risk. Although multiple high-throughput technologies have been developed to assay the human metabolome, no technique is capable of capturing the entire human metabolism. Large-scale metabolomics data are being generated in multiple cohorts, but the datasets are typically profiled using different metabolomics platforms. Here, we compared analyses across two of the most frequently used metabolomic platforms, Biocrates and Metabolon, with the aim of assessing how complimentary metabolite profiles are across platforms. We profiled serum samples from 1,001 twins using both targeted (Biocrates, n = 160 metabolites) and non-targeted (Metabolon, n = 488 metabolites) mass spectrometry platforms. We compared metabolite distributions and performed genome-wide association analyses to identify shared genetic influences on metabolites across platforms. Comparison of 43 metabolites named for the same compound on both platforms indicated strong positive correlations, with few exceptions. Genome-wide association scans with high-throughput metabolic profiles were performed for each dataset and identified genetic variants at 7 loci associated with 16 unique metabolites on both platforms. The 16 metabolites showed consistent genetic associations and appear to be robustly measured across platforms. These included both metabolites named for the same compound across platforms as well as unique metabolites, of which 2 (nonanoylcarnitine (C9) [Biocrates]/Unknown metabolite X-13431 [Metabolon] and PC aa C28:1 [Biocrates]/1-stearoylglycerol [Metabolon]) are likely to represent the same or related biochemical entities. The results demonstrate the complementary nature of both platforms, and can be informative for future studies of comparative and integrative metabolomics analyses in samples profiled on different platforms.     

Comparative analysis of plasma metabolomics response to metabolic challenge tests in healthy subjects and influence of the FTO obesity risk allele

The measurement of metabolites during intravenous or nutritional challenges may improve the identification of novel metabolic signatures which are not detectable in the fasting state. Here, we comprehensively characterized the plasma metabolomics response to five defined challenge tests and explored their use to identify interactions with the FTO rs9939609 obesity risk genotype. Fifty-six non-diabetic male participants of the KORA S4/F4 cohort, including 25 homozygous carriers of the FTO risk allele (AA genotype) and 31 carriers of the TT genotype were recruited. Challenges comprised an oral glucose tolerance test, a standardized high-fat high-carbohydrate meal and a lipid tolerance test, as well as an intravenous glucose tolerance test and a euglycemic hyperinsulinemic clamp. Blood was sampled for biochemical and metabolomics measurement before and during the challenges. Plasma samples were analyzed using a mass spectrometry-based metabolomics approach targeting 163 metabolites. Linear mixed-effects models and cluster analysis were performed. In both genotype groups, we observed significant challenge-induced changes for all major metabolite classes (amino acids, hexose, acylcarnitines, phosphatidylcholines, lysophosphatidylcholines and sphingomyelins, with corrected p-values ranging from 0.05 to 6.7E?37), which clustered in five distinct metabolic response profiles. Our data contribute to the understanding of plasma metabolomics response to diverse metabolic challenges, including previously unreported metabolite changes in response to intravenous challenges. The FTO genotype had only minor effects on the metabolite fluxes after standardized metabolic challenges.     

Mass spectrometry methods in metabolomics

The review deals with metabolomics, a new and rapidly growing area directed to the comprehensive analysis of metabolites of biological objects. Metabolites are characterized by various physical and chemical properties, traditionally studied by methods of analytical chemistry focused on certain groups of chemical substances. However, current progress in mass spectrometry has led to formation of rather unified methods, such as metabolic fingerprinting and metabolomic profiling, which allow defining thousands of metabolites in one biological sample and therefore draw “a modern portrait of metabolomics.” This review describes basic characteristics of these methods, ways of metabolite separation, and analysis of metabolites by mass spectrometry. The examples shown in this review, allow to estimate these methods and to compare their advantages and disadvantages. Besides that, we consider the methods, which are of the most frequent use in metabolomics; these include the methods for data processing and the required resources, such as software for mass spectra processing and metabolite search database. In the conclusion, general suggestions for successful metabolomic experiments are given.     

Human metabolic correlates of body mass index

A high body mass index (BMI) is a major risk factor for several chronic diseases, but the biology underlying these associations is not well-understood. Dyslipidemia, inflammation, and elevated levels of growth factors and sex steroid hormones explain some of the increased disease risk, but other metabolic factors not yet identified may also play a role. In order to discover novel metabolic biomarkers of BMI, we used non-targeted metabolomics to assay 317 metabolites in blood samples from 947 participants and examined the cross-sectional associations between metabolite levels and BMI. Participants were from three studies in the United States and China. Height, weight, and potential confounders were ascertained by questionnaire (US studies) or direct measurement (Chinese study). Metabolite levels were measured using liquid-phase chromatography and gas chromatography coupled with mass spectrometry. We evaluated study-specific associations using linear regression, adjusted for age, gender, and smoking, and we estimated combined associations using random effects meta-analysis. The meta-analysis revealed 37 metabolites significantly associated with BMI, including 19 lipids, 12 amino acids, and 6 others, at the Bonferroni significance threshold (P < 0.00016). Eighteen of these associations had not been previously reported, including histidine, an amino acid neurotransmitter, and butyrylcarnitine, a lipid marker of whole-body fatty acid oxidation. Heterogeneity by study was minimal (all P _{heterogeneity} > 0.05). In total, 110 metabolites were associated with BMI at the P < 0.05 level. These findings establish a baseline for the BMI metabolome, and suggest new targets for researchers attempting to clarify mechanistic links between BMI and disease risk.     

Metabolomics of urinary organic acids in respiratory chain deficiencies in children

Metabolomic analysis of the urinary organic acids from 39 selected children with defined respiratory chain deficiencies (RCDs) was performed using untargeted gas chromatography–mass spectrometry, revealing the presence of 255 endogenous and 46 exogenous substances. Variable reduction identified 92 variables from the endogenous substances, which could be analysed by univariate and multivariate statistical methods. Using these methods, no characteristic organic acid biomarker profile could be defined of practical value for diagnostic purposes for complex I (CI), complex III (CIII) and multiple complex (CM) deficiencies. The statistical procedures used did, however, disclose 24 metabolites that were practical highly (d > 0.75) and statistically (P < 0.05) significant for the combined and clinically closely related group of RCDs. Several of these metabolites occur in single enzyme inherited metabolic diseases, but most were not previously reported to be linked to the metabolic perturbations that are due to RCDs. Ultimately, we constructed a global metabolic profile of carbohydrate, amino acid and fatty acid catabolism, illuminating the diverse and complex biochemical consequences of these disorders. This metabolomics investigation disclosed a metabolite profile that has the potential to define an extended and characteristic biosignature for RCDs and the development of a non-invasive screening procedure for these disorders.     

Metabolomics reveals stage-specific metabolic pathways of microbial communities in two-stage anaerobic fermentation of corn-stalk

Analysis of intracellular metabolites is essential to delineate metabolic pathways of microbial communities for evaluation and optimization of anaerobic fermentation processes. The metabolomics are reported for a microbial community during two stages of anaerobic fermentation of corn stalk in a biogas digester using GC–MS. Acetonitrile/methanol/water (2:2:1, by vol) was the best extraction solvent for microbial community analysis because it yielded the largest number of peaks (>200), the highest mean summed value of identified metabolites (23) and the best reproducibility with a coefficient of variation of 30 % among four different extraction methods. Inter-stage comparison of metabolite profiles showed increased levels of sugars and sugar alcohols during methanogenesis and fatty acids during acidogenesis. Identification of stage-specific metabolic pathways using metabolomics can therefore assist in monitoring and optimization of the microbial community for increased biogas production during anaerobic fermentation.     

Mining the Unknown: A Systems Approach to Metabolite Identification Combining Genetic and Metabolic Information

Recent genome-wide association studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified. The identification of these “unknown metabolites” is still a demanding and intricate task, limiting their usability as functional markers of metabolic processes. As a consequence, previous GWAS largely ignored unknown metabolites as metabolic traits for the analysis. Here we present a systems-level approach that combines genome-wide association analysis and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. We apply our method to original data of 517 metabolic traits, of which 225 are unknowns, and genotyping information on 655,658 genetic variants, measured in 1,768 human blood samples. We report previously undescribed genotype–metabotype associations for six distinct gene loci (SLC22A2, COMT, CYP3A5, CYP2C18, GBA3, UGT3A1) and one locus not related to any known gene (rs12413935). Overlaying the inferred genetic associations, metabolic networks, and knowledge-based pathway information, we derive testable hypotheses on the biochemical identities of 106 unknown metabolites. As a proof of principle, we experimentally confirm nine concrete predictions. We demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification, hypertension, and insulin resistance. Our approach is generic in nature and can be directly transferred to metabolomics data from different experimental platforms.     

The biological interpretation of metabolomic data can be misled by the extraction method used

The field of metabolomics is getting more and more popular and a wide range of different sample preparation procedures are in use by different laboratories. Chemical extraction methods using one or more organic solvents as the extraction agent are the most commonly used approach to extract intracellular metabolites and generate metabolite profiles. Metabolite profiles are the scaffold supporting the biological interpretation in metabolomics. Therefore, we aimed to address the following fundamental question: can we obtain similar metabolomic results and, consequently, reach the same biological interpretation by using different protocols for extraction of intracellular metabolites? We have used four different methods for extraction of intracellular metabolites using four different microbial cell types (Gram negative bacterium, Gram positive bacterium, yeast, and a filamentous fungus). All the quenched samples were pooled together before extraction, and, therefore, they were identical. After extraction and GC?CMS analysis of metabolites, we did not only detect different numbers of compounds depending on the extraction method used and regardless of the cell type tested, but we also obtained distinct metabolite levels for the compounds commonly detected by all methods (P-value?<?0.001). These differences between methods resulted in contradictory biological interpretation regarding the activity of different metabolic pathways. Therefore, our results show that different solvent-based extraction methods can yield significantly different metabolite profiles, which impact substantially in the biological interpretation of metabolomics data. Thus, development of alternative extraction protocols and, most importantly, standardization of sample preparation methods for metabolomics should be seriously pursued by the scientific community.     

基于气相色谱-质谱联用技术的代谢组学方法在转植酸酶基因玉米代谢物指纹图谱分析中的应用

目的:建立气相色谱-质谱联用技术(GC-MS)的代谢组学方法,初步研究转基因株系与对照株系之间代谢物指纹图谱的差异性,为转基因作物安全的评价提供参考。方法:优化提取条件,考察色谱条件,并采用主成分分析(PCA)数据处理方法对转基因株系及对照进行模式识别。结果:优化了提取条件及色谱条件,建立了GC-MS的代谢组学方法,获得了小分子的代谢产物的表达谱,发现转基因与其对照之间呈现出显著性差异。结论:优化的GC-MS的代谢组学方法可以从代谢水平检测转基因作物,找出差异性,为转基因作物的检测与评价提供技术支持。     

Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism

With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.     

Untargeted metabolomics reveals a lack of synergy between nifurtimox and eflornithine against Trypanosoma brucei

A non-targeted metabolomics-based approach is presented that enables the study of pathways in response to drug action with the aim of defining the mode of action of trypanocides. Eflornithine, a polyamine pathway inhibitor, and nifurtimox, whose mode of action involves its metabolic activation, are currently used in combination as first line treatment against stage 2, CNS-involved, human African trypanosomiasis (HAT). Drug action was assessed using an LC-MS based non-targeted metabolomics approach. Eflornithine revealed the expected changes to the polyamine pathway as well as several unexpected changes that point to pathways and metabolites not previously described in bloodstream form trypanosomes, including a lack of arginase activity and N-acetylated ornithine and putrescine. Nifurtimox was shown to be converted to a trinitrile metabolite indicative of metabolic activation, as well as inducing changes in levels of metabolites involved in carbohydrate and nucleotide metabolism. However, eflornithine and nifurtimox failed to synergise anti-trypanosomal activity in vitro, and the metabolomic changes associated with the combination are the sum of those found in each monotherapy with no indication of additional effects. The study reveals how untargeted metabolomics can yield rapid information on drug targets that could be adapted to any pharmacological situation.     

Sauvignon blanc metabolomics: grape juice metabolites affecting the development of varietal thiols and other aroma compounds in wines

The pathway for the biogenesis of varietal thiols, such as 3-mercaptohexanol (3MH), 3-mercaptohexyl acetate (3MHA) and 4-mercapto-4-methylpentan-2-one (4MMP) in Sauvignon blanc (SB) wines is still an open question. Varietal thiol development requires yeast activity, but poor correlation has been found between thiols and their putative respective precursors. This research is the first application of metabolomics to unravel metabolites in the grape juice that affect the production of varietal thiols in wines. Comprehensive metabolite profiling of 63 commercially harvested SB juices were performed by combining gas chromatography–mass spectrometry and nuclear magnetic resonance spectroscopy. These juices were fermented under controlled laboratory conditions using a commercial yeast strain (EC1118) at 15 °C. Correlation of thiol concentration in the wines with initial metabolite profiles identified 24 metabolites that showed positive correlation (R > 0.3) with both 3MH and 3MHA, while only glutamine had positive correlation with 4MMP. Subsequently, we carried out juice manipulation experiments by adding subsets of these 24 metabolites in a 2011 SB grape juice in order to validate the hypotheses generated by metabolomics. The juice manipulation results confirmed metabolomics hypotheses and revealed grape juice metabolites that significantly impact on the development of three major varietal thiols and other aroma compounds of SB wines.     

Comprehensive kinetic and substrate specificity analysis of an arylsulfatase from Helix pomatia using mass spectrometry

Sulfatases hydrolyze sulfated metabolites to their corresponding alcohols and are present in all domains of life. These enzymes have found major application in metabolic investigation of drugs, doping control analysis and recently in metabolomics. Interest in sulfatases has increased due to a link between metabolic processes involving sulfated metabolites and pathophysiological conditions in humans. Herein, we present the first comprehensive substrate specificity and kinetic analysis of the most commonly used arylsulfatase extracted from the snail Helix pomatia. In the past, this enzyme has been used in the form of a crude mixture of enzymes, however, recently we have purified this sulfatase for a new application in metabolomics-driven discovery of sulfated metabolites. To evaluate the substrate specificity of this promiscuous sulfatase, we have synthesized a series of new sulfated metabolites of diverse structure and employed a mass spectrometric assay for kinetic substrate hydrolysis evaluation. Our analysis of the purified enzyme revealed that the sulfatase has a strong preference for metabolites with a bi- or tricyclic aromatic scaffold and to a lesser extent for monocyclic aromatic phenols. This metabolite library and mass spectrometric method can be applied for the characterization of other sulfatases from humans and gut microbiota to investigate their involvement in disease development.     

A metabolomics investigation of a hyper- and hypo-virulent phenotype of Beijing lineage M. tuberculosis

Despite a number investigations using rapid sequencing and comparative genomic techniques, attempting to characterise the phenomenon of varying degrees of virulence within the Mycobacterium tuberculosis species, the underlying causes for this still remain largely unexplained. The Beijing lineage of M. tuberculosis has received much attention due to a reported increased pathogenicity and global dissemination. In order to better understand these varying states of virulence, a GCxGC-TOFMS metabolomics research approach was used to compare the varying metabolomes of a hyper- and hypo-virulent Beijing strain of M. tuberculosis, and subsequently identify those metabolite markers differing between these strains. Multi- and univariate statistical analysis of the analysed metabolome data was used to identify those metabolites contributing most to the differences seen between the two sample groups. A general decrease in various carbohydrates, amino acids and lipids associated with cell wall structure and function, were detected in the hyper-virulent Beijing strain, comparatively. Additionally, components of mycothiol metabolism, virulence protein formation and energy production in mycobacteria, were also seen to differ when comparing the two groups. This metabolomics investigation is the first to identify the metabolite markers associated with an increased state of virulence, indicating increased metabolic activity, increased growth/replication rates, increased cell wall synthesis and an altered antioxidant mechanism, all of which would contribute to this organisms increased pathogenicity and survival ability.     

Metabolomic profiling reveals bacterial metabolic adaptation strategies and new metabolites

How has metabolomics helped our understanding of infectious diseases? With the threat of antimicrobial resistance to human health around the world, metabolomics has emerged as a powerful tool to comprehensively characterize metabolic pathways to identify new drug targets. However, its output is constrained to known metabolites and their metabolic pathways. Recent advances in instrumentation, methodologies, and computational mass spectrometry have accelerated the use of metabolomics to understand pathogen–host metabolic interactions. This short review discusses a selection of recent publications using metabolomics in infectious/bacterial diseases. These studies unravel the links between metabolic adaptations to environments and host metabolic responses. Moreover, they highlight the importance of enzyme function and metabolite characterization in identifying new drug targets and biomarkers, as well as precision medicine in monitoring therapeutics and diagnosing diseases.