Predicting network activity from high throughput metabolomics

A metabolomic study on high-risk stroke patients determines low levels of serum lysine metabolites: a retrospective cohort study

Metabolic alteration at early neurological deterioration during cerebral ischemia.

Computational Strategies for Biological Interpretation of Metabolomics Data

Biological interpretation of metabolomics data relies on two basic steps: metabolite identification and functional analysis. These two steps need to be applied in a coordinated manner to enable effective data understanding. The focus of this chapter is to introduce the main computational concepts and workflows during this process. After a general overview of the field, three sections will be presented: the first section will introduce the main computational methods and bioinformatics tools for metabolite identification using spectra from common analytical platforms; the second section will focus on introducing major bioinformatics approaches for functional enrichment analysis of metabolomics data; and the last section will discuss the three main workflows in current metabolomics studies, including the chemometrics approach, the metabolic profiling approach and the more recent chemo-enrichment analysis approach. The chapter ends with summary and future perspectives on computational metabolomics.

Chronic psychological stress and high-fat high-fructose diet disrupt metabolic and inflammatory gene networks in the brain, liver, and gut and promote behavioral deficits in mice

The mechanisms underlying the association between chronic psychological stress, development of metabolic syndrome (MetS), and behavioral impairment in obesity are poorly understood. The aim of the present study was to assess the effects of mild chronic psychological stress on metabolic, inflammatory, and behavioral profiles in a mouse model of diet-induced obesity. We hypothesized that (1) high-fat high-fructose diet (HFHF) and psychological stress would synergize to mediate the impact of inflammation on the central nervous system in the presence of behavioral dysfunction, and that (2) HFHF and stress interactions would impact insulin and lipid metabolism. C57Bl/6 male mice underwent a combination of HFHF and two weeks of chronic psychological stress. MetS-related conditions were assessed using untargeted plasma metabolomics, and structural and immune changes in the gut and liver were evaluated. Inflammation was measured in plasma, liver, gut, and brain. Our results show a complex interplay of diet and stress on gut alterations, energetic homeostasis, lipid metabolism, and plasma insulin levels. Psychological stress and HFHF diet promoted changes in intestinal tight junctions proteins and increases in insulin resistance and plasma cholesterol, and impacted the RNA expression of inflammatory factors in the hippocampus. Stress promoted an adaptive anti-inflammatory profile in the hippocampus that was abolished by diet treatment. HFHF increased hippocampal and hepatic Lcn2 mRNA expression as well as LCN2 plasma levels. Behavioral changes were associated with HFHF and stress. Collectively, these results suggest that diet and stress as pervasive factors exacerbate MetS-related conditions through an inflammatory mechanism that ultimately can impact behavior. This rodent model may prove useful for identification of possible biomarkers and therapeutic targets to treat metabolic syndrome and mood disorders.

Temporal bacterial and metabolic development of the preterm gut reveals specific signatures in health and disease

BACKGROUND

The preterm microbiome is crucial to gut health and may contribute to necrotising enterocolitis (NEC), which represents the most significant pathology affecting preterm infants. From a cohort of 318 infants, <32 weeks gestation, we selected 7 infants who developed NEC (defined rigorously) and 28 matched controls. We performed detailed temporal bacterial (n = 641) and metabolomic (n = 75) profiling of the gut microbiome throughout the disease.

RESULTS

A core community of Klebsiella, Escherichia, Staphyloccocus, and Enterococcus was present in all samples. Gut microbiota profiles grouped into six distinct clusters, termed preterm gut community types (PGCTs). Each PGCT reflected dominance by the core operational taxonomic units (OTUs), except of PGCT 6, which had high diversity and was dominant in bifidobacteria. While PGCTs 1-5 were present in infants prior to NEC diagnosis, PGCT 6 was comprised exclusively of healthy samples. NEC infants had significantly more PGCT transitions prior to diagnosis. Metabolomic profiling identified significant pathways associated with NEC onset, with metabolites involved in linoleate metabolism significantly associated with NEC diagnosis. Notably, metabolites associated with NEC were the lowest in PGCT 6.

CONCLUSIONS

This is the first study to integrate sequence and metabolomic stool analysis in preterm neonates, demonstrating that NEC does not have a uniform microbial signature. However, a diverse gut microbiome with a high abundance of bifidobacteria may protect preterm infants from disease. These results may inform biomarker development and improve understanding of gut-mediated mechanisms of NEC.

Untargeted Metabolomics Strategies-Challenges and Emerging Directions

Metabolites are building blocks of cellular function. These species are involved in enzyme-catalyzed chemical reactions and are essential for cellular function. Upstream biological disruptions result in a series of metabolomic changes and, as such, the metabolome holds a wealth of information that is thought to be most predictive of phenotype. Uncovering this knowledge is a work in progress. The field of metabolomics is still maturing; the community has leveraged proteomics experience when applicable and developed a range of sample preparation and instrument methodology along with myriad data processing and analysis approaches. Research focuses have now shifted toward a fundamental understanding of the biology responsible for metabolomic changes. There are several types of metabolomics experiments including both targeted and untargeted analyses. While untargeted, hypothesis generating workflows exhibit many valuable attributes, challenges inherent to the approach remain. This Critical Insight comments on these challenges, focusing on the identification process of LC-MS-based untargeted metabolomics studies-specifically in mammalian systems. Biological interpretation of metabolomics data hinges on the ability to accurately identify metabolites. The range of confidence associated with identifications that is often overlooked is reviewed, and opportunities for advancing the metabolomics field are described. Graphical Abstract ᅟ.

Fecal metabolomics in pediatric spondyloarthritis implicate decreased metabolic diversity and altered tryptophan metabolism as pathogenic factors

We have previously shown alterations in the composition of the gut microbiota in children with enthesitis-related arthritis (ERA). To explore the mechanisms by which an altered microbiota might predispose to arthritis, we performed metabolomic profiling of fecal samples of children with ERA. Fecal samples were collected from two cohorts of children with ERA and healthy control subjects. Nano-liquid chromatography-mass spectroscopy (LC-MS) was performed on the fecal water homogenates with identification based upon mass: charge ratios. Sequencing of the 16S ribosomal DNA (rDNA) on the same stool specimens was performed. In both sets of subjects, patients demonstrated lower diversity of ions and under-representation of multiple metabolic pathways, including the tryptophan metabolism pathway. For example, in the first cohort, out of 1500 negatively charged ions, 154 were lower in ERA patients, compared with only one that was higher. Imputed functional annotation of the 16S ribosomal DNA sequence data demonstrated significantly fewer microbial genes associated with metabolic processes in the patients compared with the controls (77 million versus 58 million, P=0.050). Diminished metabolic diversity and alterations in the tryptophan metabolism pathway may be a feature of ERA.

Computational Metabolomics: A Framework for the Million Metabolome

"Sola dosis facit venenum." These words of Paracelsus, "the dose makes the poison", can lead to a cavalier attitude concerning potential toxicities of the vast array of low abundance environmental chemicals to which humans are exposed. Exposome research teaches that 80-85% of human disease is linked to environmental exposures. The human exposome is estimated to include >400,000 environmental chemicals, most of which are uncharacterized with regard to human health. In fact, mass spectrometry measures >200,000 m/z features (ions) in microliter volumes derived from human samples; most are unidentified. This crystallizes a grand challenge for chemical research in toxicology: to develop reliable and affordable analytical methods to understand health impacts of the extensive human chemical experience. To this end, there appears to be no choice but to abandon the limitations of measuring one chemical at a time. The present review looks at progress in computational metabolomics to provide probability-based annotation linking ions to known chemicals and serve as a foundation for unambiguous designation of unidentified ions for toxicologic study. We review methods to characterize ions in terms of accurate mass m/z, chromatographic retention time, correlation of adduct, isotopic and fragment forms, association with metabolic pathways and measurement of collision-induced dissociation products, collision cross section, and chirality. Such information can support a largely unambiguous system for documenting unidentified ions in environmental surveillance and human biomonitoring. Assembly of this data would provide a resource to characterize and understand health risks of the array of low-abundance chemicals to which humans are exposed.

High-resolution metabolomics of occupational exposure to trichloroethylene

Background: Occupational exposure to trichloroethylene (TCE) has been linked to adverse health outcomes including non-Hodgkin’s lymphoma and kidney and liver cancer; however, TCE’s mode of action for development of these diseases in humans is not well understood.

Methods: Non-targeted metabolomics analysis of plasma obtained from 80 TCE-exposed workers [full shift exposure range of 0.4 to 230 parts-per-million of air (ppm_a)] and 95 matched controls were completed by ultra-high resolution mass spectrometry. Biological response to TCE exposure was determined using a metabolome-wide association study (MWAS) framework, with metabolic changes and plasma TCE metabolites evaluated by dose-response and pathway enrichment. Biological perturbations were then linked to immunological, renal and exposure molecular markers measured in the same population.

Results: Metabolic features associated with TCE exposure included known TCE metabolites, unidentifiable chlorinated compounds and endogenous metabolites. Exposure resulted in a systemic response in endogenous metabolism, including disruption in purine catabolism and decreases in sulphur amino acid and bile acid biosynthesis pathways. Metabolite associations with TCE exposure included uric acid (β = 0.13, P-value = 3.6 × 10⁻⁵), glutamine (β = 0.08, P-value = 0.0013), cystine (β = 0.75, P-value = 0.0022), methylthioadenosine (β = −1.6, P-value = 0.0043), taurine (β = −2.4, P-value = 0.0011) and chenodeoxycholic acid (β = −1.3, P-value = 0.0039), which are consistent with known toxic effects of TCE, including immunosuppression, hepatotoxicity and nephrotoxicity. Correlation with additional exposure markers and physiological endpoints supported known disease associations.

Conclusions: High-resolution metabolomics correlates measured occupational exposure to internal dose and metabolic response, providing insight into molecular mechanisms of exposure-related disease aetiology.

High-resolution metabolomics to identify urine biomarkers in corticosteroid-resistant asthmatic children

BACKGROUND

Corticosteroid (CS) treatment has been established as the first anti-inflammatory treatment for adults and children with asthma. However, a subset of patients fails to respond to combined systemic and inhaled CS treatment.

OBJECTIVE

This study was aimed at further understanding CS resistance among children with severe asthma.

METHODS

High-resolution metabolomics was performed on urine samples from CS-respondent (n = 15) and CS-nonrespondent (n = 15) children to determine possible urine biomarkers related to CS resistance. The metabolic phenotypes of CS responders and CS nonresponders were analyzed using bioinformatics including Manhattan plot with false- discovery rate, hierarchical cluster analysis, Kyoto Encyclopedia Genes and Genomes, and Mummichog pathway analysis.

RESULTS

The 2-way hierarchical cluster analysis study determined 30 metabolites showing significantly different levels between CS responders and CS nonresponders. The important metabolites annotated were 3,6-dihydronicotinic acid (126.05 m/z, RT: 106, [M+H]⁺), 3-methoxy-4-hydroxyphenyl(ethylene)glycol (185.05 m/z, RT: 155, [M+H]⁺), 3,4-dihydroxy-phenylalanine (198.07 m/z, RT: 446, [M+H]⁺), γ-glutamylcysteine (236.06 m/z, RT: 528, [M+S(34)+H]⁺), Cys-Gly, (253.06 m/z, RT: 528, [M-NH₃+H]⁺), and reduced Flavin mononucleotide (517.0794 m/z, RT: 533, [M+NaCl]⁺). Tyrosine metabolism, degradation of aromatic compounds, and glutathione metabolism are suggested to be significant pathways relating to CS resistance.

CONCLUSIONS

High-resolution metabolomics is a promising approach in asthma research. Five candidate markers were identified to be related to CS-resistant children with severe asthma. These compounds, upon validation, may contribute further in the understanding of CS resistance among children with severe asthma through the use of urine.

Metabolic pathways of lung inflammation revealed by high-resolution metabolomics (HRM) of H1N1 influenza virus infection in mice

Influenza is a significant health concern worldwide. Viral infection induces local and systemic activation of the immune system causing attendant changes in metabolism. High-resolution metabolomics (HRM) uses advanced mass spectrometry and computational methods to measure thousands of metabolites inclusive of most metabolic pathways. We used HRM to identify metabolic pathways and clusters of association related to inflammatory cytokines in lungs of mice with H1N1 influenza virus infection. Infected mice showed progressive weight loss, decreased lung function, and severe lung inflammation with elevated cytokines [interleukin (IL)-1β, IL-6, IL-10, tumor necrosis factor (TNF)-α, and interferon (IFN)-γ] and increased oxidative stress via cysteine oxidation. HRM showed prominent effects of influenza virus infection on tryptophan and other amino acids, and widespread effects on pathways including purines, pyrimidines, fatty acids, and glycerophospholipids. A metabolome-wide association study (MWAS) of the aforementioned inflammatory cytokines was used to determine the relationship of metabolic responses to inflammation during infection. This cytokine-MWAS (cMWAS) showed that metabolic associations consisted of distinct and shared clusters of 396 metabolites highly correlated with inflammatory cytokines. Strong negative associations of selected glycosphingolipid, linoleate, and tryptophan metabolites with IFN-γ contrasted strong positive associations of glycosphingolipid and bile acid metabolites with IL-1β, TNF-α, and IL-10. Anti-inflammatory cytokine IL-10 had strong positive associations with vitamin D, purine, and vitamin E metabolism. The detailed metabolic interactions with cytokines indicate that targeted metabolic interventions may be useful during life-threatening crises related to severe acute infection and inflammation.

Serum metabolomic signatures discriminate early liver inflammation and fibrosis stages in patients with chronic hepatitis B

Chronic HBV (CHB) infected patients with intermediate necroinflammation and fibrosis are recommended to receive antiviral treatment. However, other than liver biopsy, there is a lack of sensitive and specific objective method to determine the necroinflammation and fibrosis stages in CHB patients. This study aims to identify unique serum metabolomic profile associated with histological progression in CHB patients and to develop novel metabolite biomarker panels for early CHB detection and stratification. A comprehensive metabolomic profiling method was established to compare serum samples collected from health donor (n = 67), patients with mild (G < 2 and S < 2, CHB1, n = 52) or intermediate (G ≥ 2 or S ≥ 2, CHB2, n = 36) necroinflammation and fibrosis. Multivariate models were developed to differentiate CHB1 and CHB2 from controls. A set of CHB-associated biomarkers was identified, including lysophosphatidylcholines, phosphatidylcholines, phosphatidylinositol, phosphatidylserine, and bile acid metabolism products. Stratification of CHB1 and CHB2 patients by a simple logistic index, the PIPSindex, based on phosphatidylinositol (PI) and phosphatidylserine (PS), was achieved with an AUC of 0.961, which outperformed all currently available markers. A panel of serum metabolites that differentiate health control, CHB1 and CHB2 patients has been identified. The proposed metabolomic biosignature has the potential to be used as indicator for antiviral treatment for CHB management.

Deployment-Associated Exposure Surveillance With High-Resolution Metabolomics

OBJECTIVE

The aim of this study was to assess the suitability of high-resolution metabolomics (HRM) for measure of internal exposure and effect biomarkers from deployment-related environmental hazards.

METHODS

HRM provides extensive coverage of metabolism and data relevant to a broad spectrum of environmental exposures. This review briefly describes the analytic platform, workflow, and recent applications of HRM as a prototype environmental exposure surveillance system.

RESULTS

Building upon techniques available for contemporary occupational medicine and exposure sciences, HRM methods are able to integrate external exposures, internal body burden of environmental agents, and relevant biological responses with health outcomes.

CONCLUSIONS

Systematic analysis of existing Department of Defense Serum Repository samples will provide a high-quality, cross-sectional reference dataset for deployment-associated exposures while at the same time establishing a foundation for precision medicine.

Revealing disease-associated pathways by network integration of untargeted metabolomics

Uncovering the molecular context of dysregulated metabolites is crucial to understand pathogenic pathways. However, their system-level analysis has been limited owing to challenges in global metabolite identification. Most metabolite features detected by untargeted metabolomics carried out by liquid-chromatography-mass spectrometry cannot be uniquely identified without additional, time-consuming experiments. We report a network-based approach, prize-collecting Steiner forest algorithm for integrative analysis of untargeted metabolomics (PIUMet), that infers molecular pathways and components via integrative analysis of metabolite features, without requiring their identification. We demonstrated PIUMet by analyzing changes in metabolism of sphingolipids, fatty acids and steroids in a Huntington's disease model. Additionally, PIUMet enabled us to elucidate putative identities of altered metabolite features in diseased cells, and infer experimentally undetected, disease-associated metabolites and dysregulated proteins. Finally, we established PIUMet's ability for integrative analysis of untargeted metabolomics data with proteomics data, demonstrating that this approach elicits disease-associated metabolites and proteins that cannot be inferred by individual analysis of these data.

Training in metabolomics research. II. Processing and statistical analysis of metabolomics data, metabolite identification, pathway analysis, applications of metabolomics and its future

Metabolomics, a systems biology discipline representing analysis of known and unknown pathways of metabolism, has grown tremendously over the past 20 years. Because of its comprehensive nature, metabolomics requires careful consideration of the question(s) being asked, the scale needed to answer the question(s), collection and storage of the sample specimens, methods for extraction of the metabolites from biological matrices, the analytical method(s) to be employed and the quality control of the analyses, how collected data are correlated, the statistical methods to determine metabolites undergoing significant change, putative identification of metabolites and the use of stable isotopes to aid in verifying metabolite identity and establishing pathway connections and fluxes. This second part of a comprehensive description of the methods of metabolomics focuses on data analysis, emerging methods in metabolomics and the future of this discipline. Copyright © 2016 John Wiley & Sons, Ltd.

Clinical Metabolomics: The New Metabolic Window for Inborn Errors of Metabolism Investigations in the Post-Genomic Era

Inborn errors of metabolism (IEM) represent a group of about 500 rare genetic diseases with an overall estimated incidence of 1/2500. The diversity of metabolic pathways involved explains the difficulties in establishing their diagnosis. However, early diagnosis is usually mandatory for successful treatment. Given the considerable clinical overlap between some inborn errors, biochemical and molecular tests are crucial in making a diagnosis. Conventional biological diagnosis procedures are based on a time-consuming series of sequential and segmented biochemical tests. The rise of “omic” technologies offers holistic views of the basic molecules that build a biological system at different levels. Metabolomics is the most recent “omic” technology based on biochemical characterization of metabolites and their changes related to genetic and environmental factors. This review addresses the principles underlying metabolomics technologies that allow them to comprehensively assess an individual biochemical profile and their reported applications for IEM investigations in the precision medicine era.

Amino Acid Metabolism is Altered in Adolescents with Nonalcoholic Fatty Liver Disease-An Untargeted, High Resolution Metabolomics Study

OBJECTIVE

To conduct an untargeted, high resolution exploration of metabolic pathways that was altered in association with hepatic steatosis in adolescents.

STUDY DESIGN

This prospective, case-control study included 39 Hispanic-American, obese adolescents aged 11-17 years evaluated for hepatic steatosis using magnetic resonance spectroscopy. Of these 39 individuals, 30 had hepatic steatosis ≥5% and 9 were matched controls with hepatic steatosis <5%. Fasting plasma samples were analyzed in triplicate using ultra-high resolution metabolomics on a Thermo Fisher Q Exactive mass spectrometry system, coupled with C18 reverse phase liquid chromatography. Differences in plasma metabolites between adolescents with and without nonalcoholic fatty liver disease (NAFLD) were determined by independent t tests and visualized using Manhattan plots. Untargeted pathway analyses using Mummichog were performed among the significant metabolites to identify pathways that were most dysregulated in NAFLD.

RESULTS

The metabolomics analysis yielded 9583 metabolites, and 7711 with 80% presence across all samples remained for statistical testing. Of these, 478 metabolites were associated with the presence of NAFLD compared with the matched controls. Pathway analysis revealed that along with lipid metabolism, several major amino acid pathways were dysregulated in NAFLD, with tyrosine metabolism being the most affected.

CONCLUSIONS

Metabolic pathways of several amino acids are significantly disturbed in adolescents with elevated hepatic steatosis. This is a novel finding and suggests that these pathways may be integral in the mechanisms of NAFLD.

Targeting the untargeted in molecular phenomics with structurally-selective ion mobility-mass spectrometry

Systems-wide molecular phenomics is rapidly expanding through technological advances in instrumentation and bioinformatics. Strategies such as structural mass spectrometry, which utilizes size and shape measurements with molecular weight, serve to characterize the sum of molecular expression in biological contexts, where broad-scale measurements are made that are interpreted through big data statistical techniques to reveal underlying patterns corresponding to phenotype. The data density, data dimensionality, data projection, and data interrogation are all critical aspects of these approaches to turn data into salient information. Untargeted molecular phenomics is already having a dramatic impact in discovery science from drug discovery to synthetic biology. It is evident that these emerging techniques will integrate closely in broad efforts aimed at precision medicine.

Longitudinal Metabolite Profiling of Cerebrospinal Fluid in Normal Pressure Hydrocephalus Links Brain Metabolism with Exercise-Induced VEGF Production and Clinical Outcome

Idiopathic normal pressure hydrocephalus is a neurological disease caused by abnormal cerebrospinal fluid flow and presents with symptoms such as dementia. Current therapy involves the removal of excess cerebrospinal fluid by shunting. Not all patients respond to this therapy and biomarkers are needed that could facilitate the characterization of patients likely to benefit from this treatment. Here, we measure brain metabolism in normal pressure hydrocephalus patients by performing a novel longitudinal metabolomic profiling study of cerebrospinal fluid. We find that the levels of brain metabolites correlate with clinical parameters, the amount of vascular endothelial growth factor in the cerebrospinal fluid, and environmental stimuli such as exercise. Metabolomic analysis of normal pressure hydrocephalus patients provides insight into changes in brain metabolism that accompany cerebrospinal fluid disorders and may facilitate the development of new biomarkers for this condition.

Metabolomics: beyond biomarkers and towards mechanisms

Metabolomics, which is the profiling of metabolites in biofluids, cells and tissues, is routinely applied as a tool for biomarker discovery. Owing to innovative developments in informatics and analytical technologies, and the integration of orthogonal biological approaches, it is now possible to expand metabolomic analyses to understand the systems-level effects of metabolites. Moreover, because of the inherent sensitivity of metabolomics, subtle alterations in biological pathways can be detected to provide insight into the mechanisms that underlie various physiological conditions and aberrant processes, including diseases.

Plasma Metabolomics of Common Marmosets (Callithrix jacchus) to Evaluate Diet and Feeding Husbandry

Common marmosets (Callithrix jacchus) are an important NHP model for the study of human aging and age-related diseases. However, the full potential of marmosets as a research model has not been realized due to a lack of evidence-based, standardized procedures for their captive management, especially regarding diet and feeding husbandry. In the present study, we conducted a high-resolution metabolomics analysis of plasma from marmosets from a 3-mo dietary crossover study to determine whether significant metabolic differences occur with a semisynthetic chemically defined (purified) diet as needed for controlled nutrition research. Marmosets were fed a standard, diverse-ingredient diet, followed by a semisynthetic purified diet, and then were switched back to the standard diet. The standard diet used in this analysis was specific to the animal facility, but it is similar in content to the diets currently used for other marmoset colonies. High-resolution metabolomics of plasma with liquid chromatography-mass spectrometry and bioinformatics was used to measure metabolic differences. The concentration of the essential amino acids methionine, leucine/isoleucine, lysine, and threonine were higher when marmosets were fed the purified diet. In contrast, phenylalanine concentrations were higher during exposure to the standard diet. In addition, metabolic pathway enrichment and analysis revealed differences among metabolites associated with dopamine metabolism and the carnitine shuttle. These results show that diet-associated differences in metabolism occur in marmosets and suggest that additional nutritional studies with detailed physiologic characterization are needed to optimize standard and purified diets for common marmosets.

Chemometric methods in data processing of mass spectrometry-based metabolomics: A review

This review focuses on recent and potential advances in chemometric methods in relation to data processing in metabolomics, especially for data generated from mass spectrometric techniques. Metabolomics is gradually being regarded a valuable and promising biotechnology rather than an ambitious advancement. Herein, we outline significant developments in metabolomics, especially in the combination with modern chemical analysis techniques, and dedicated statistical, and chemometric data analytical strategies. Advanced skills in the preprocessing of raw data, identification of metabolites, variable selection, and modeling are illustrated. We believe that insights from these developments will help narrow the gap between the original dataset and current biological knowledge. We also discuss the limitations and perspectives of extracting information from high-throughput datasets.

Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases

Complex and dynamic networks of molecules are involved in human diseases. High-throughput technologies enable omics studies interrogating thousands to millions of makers with similar biochemical properties (eg, transcriptomics for RNA transcripts). However, a single layer of "omics" can only provide limited insights into the biological mechanisms of a disease. In the case of genome-wide association studies, although thousands of single nucleotide polymorphisms have been identified for complex diseases and traits, the functional implications and mechanisms of the associated loci are largely unknown. Additionally, the genomic variants alone are not able to explain the changing disease risk across the life span. DNA, RNA, protein, and metabolite often have complementary roles to jointly perform a certain biological function. Such complementary effects and synergistic interactions between omic layers in the life course can only be captured by integrative study of multiple molecular layers. Building upon the success in single-omics discovery research, population studies started adopting the multi-omics approach to better understanding the molecular function and disease etiology. Multi-omics approaches integrate data obtained from different omic levels to understand their interrelation and combined influence on the disease processes. Here, we summarize major omics approaches available in population research, and review integrative approaches and methodologies interrogating multiple omic layers, which enhance the gene discovery and functional analysis of human diseases. We seek to provide analytical recommendations for different types of multi-omics data and study designs to guide the emerging multi-omic research, and to suggest improvement of the existing analytical methods.

A longitudinal analysis of the effects of age on the blood plasma metabolome in the common marmoset, Callithrix jacchus

Primates tend to be long-lived for their size with humans being the longest lived of all primates. There are compelling reasons to understand the underlying age-related processes that shape human lifespan. But the very fact of our long lifespan that makes it so compelling, also makes it especially difficult to study. Thus, in studies of aging, researchers have turned to non-human primate models, including chimpanzees, baboons, and rhesus macaques. More recently, the common marmoset, Callithrix jacchus, has been recognized as a particularly valuable model in studies of aging, given its small size, ease of housing in captivity, and relatively short lifespan. However, little is known about the physiological changes that occur as marmosets age. To begin to fill in this gap, we utilized high sensitivity metabolomics to define the longitudinal biochemical changes associated with age in the common marmoset. We measured 2104 metabolites from blood plasma at three separate time points over a 17-month period, and we completed both a cross-sectional and longitudinal analysis of the metabolome. We discovered hundreds of metabolites associated with age and body weight in both male and female animals. Our longitudinal analysis identified age-associated metabolic pathways that were not found in our cross-sectional analysis. Pathways enriched for age-associated metabolites included tryptophan, nucleotide, and xenobiotic metabolism, suggesting these biochemical pathways might play an important role in the basic mechanisms of aging in primates. Moreover, we found that many metabolic pathways associated with age were sex specific. Our work illustrates the power of longitudinal approaches, even in a short time frame, to discover novel biochemical changes that occur with age.

Sequencing the exposome: A call to action

The exposome is a complement to the genome that includes non-genetic causes of disease. Multiple definitions are available, with salient points being global inclusion of exposures and behaviors, and cumulative integration of associated biologic responses. As such, the concept is both refreshingly simple and dauntingly complex. This article reviews high-resolution metabolomics (HRM) as an affordable approach to routinely analyze samples for a broad spectrum of environmental chemicals and biologic responses. HRM has been successfully used in multiple exposome research paradigms and is suitable to implement in a prototype universal exposure surveillance system. Development of such a structure for systematic monitoring of environmental exposures is an important step toward sequencing the exposome because it builds upon successes of exposure science, naturally connects external exposure to body burden and partitions the exposome into workable components. Practical results would be repositories of quantitative data on chemicals according to geography and biology. This would support new opportunities for environmental health analysis and predictive modeling. Complementary approaches to hasten development of exposome theory and associated biologic response networks could include experimental studies with model systems, analysis of archival samples from longitudinal studies with outcome data and study of relatively short-lived animals, such as household pets (dogs and cats) and non-human primates (common marmoset). International investment and cooperation to sequence the human exposome will advance scientific knowledge and also provide an important foundation to control adverse environmental exposures to sustain healthy living spaces and improve prediction and management of disease.

High-Resolution Metabolomics: Review of the Field and Implications for Nursing Science and the Study of Preterm Birth

Most complex health conditions do not have a single etiology but rather develop from exposure to multiple risk factors that interact to influence individual susceptibility. In this review, we discuss the emerging field of metabolomics as a means by which metabolic pathways underlying a disease etiology can be exposed and specific metabolites can be identified and linked, ultimately providing biomarkers for early detection of disease onset and new strategies for intervention. We present the theoretical foundation of metabolomics research, the current methods employed in its conduct, and the overlap of metabolomics research with other "omic" approaches. As an exemplar, we discuss the potential of metabolomics research in the context of deciphering the complex interactions of the maternal-fetal exposures that underlie the risk of preterm birth, a condition that accounts for substantial portions of infant morbidity and mortality and whose etiology and pathophysiology remain incompletely defined. We conclude by providing strategies for including metabolomics research in future nursing studies for the advancement of nursing science.

New insights into the complex regulation of the glycolytic pathway in Lactococcus lactis. I. Construction and diagnosis of a comprehensive dynamic model

This article and the companion paper use computational systems modeling to decipher the complex coordination of regulatory signals controlling the glycolytic pathway in the dairy bacterium Lactococcus lactis.

Reference Standardization for Mass Spectrometry and High-resolution Metabolomics Applications to Exposome Research

The exposome is the cumulative measure of environmental influences and associated biological responses throughout the lifespan, including exposures from the environment, diet, behavior, and endogenous processes. A major challenge for exposome research lies in the development of robust and affordable analytic procedures to measure the broad range of exposures and associated biologic impacts occurring over a lifetime. Biomonitoring is an established approach to evaluate internal body burden of environmental exposures, but use of biomonitoring for exposome research is often limited by the high costs associated with quantification of individual chemicals. High-resolution metabolomics (HRM) uses ultra-high resolution mass spectrometry with minimal sample preparation to support high-throughput relative quantification of thousands of environmental, dietary, and microbial chemicals. HRM also measures metabolites in most endogenous metabolic pathways, thereby providing simultaneous measurement of biologic responses to environmental exposures. The present research examined quantification strategies to enhance the usefulness of HRM data for cumulative exposome research. The results provide a simple reference standardization protocol in which individual chemical concentrations in unknown samples are estimated by comparison to a concurrently analyzed, pooled reference sample with known chemical concentrations. The approach was tested using blinded analyses of amino acids in human samples and was found to be comparable to independent laboratory results based on surrogate standardization or internal standardization. Quantification was reproducible over a 13-month period and extrapolated to thousands of chemicals. The results show that reference standardization protocol provides an effective strategy that will enhance data collection for cumulative exposome research. In principle, the approach can be extended to other types of mass spectrometry and other analytical methods.

Local false discovery rate estimation using feature reliability in LC/MS metabolomics data

False discovery rate (FDR) control is an important tool of statistical inference in feature selection. In mass spectrometry-based metabolomics data, features can be measured at different levels of reliability and false features are often detected in untargeted metabolite profiling as chemical and/or bioinformatics noise. The traditional false discovery rate methods treat all features equally, which can cause substantial loss of statistical power to detect differentially expressed features. We propose a reliability index for mass spectrometry-based metabolomics data with repeated measurements, which is quantified using a composite measure. We then present a new method to estimate the local false discovery rate (lfdr) that incorporates feature reliability. In simulations, our proposed method achieved better balance between sensitivity and controlling false discovery, as compared to traditional lfdr estimation. We applied our method to a real metabolomics dataset and were able to detect more differentially expressed metabolites that were biologically meaningful.

Blood transcriptomics and metabolomics for personalized medicine

Molecular analysis of blood samples is pivotal to clinical diagnosis and has been intensively investigated since the rise of systems biology. Recent developments have opened new opportunities to utilize transcriptomics and metabolomics for personalized and precision medicine. Efforts from human immunology have infused into this area exquisite characterizations of subpopulations of blood cells. It is now possible to infer from blood transcriptomics, with fine accuracy, the contribution of immune activation and of cell subpopulations. In parallel, high-resolution mass spectrometry has brought revolutionary analytical capability, detecting > 10,000 metabolites, together with environmental exposure, dietary intake, microbial activity, and pharmaceutical drugs. Thus, the re-examination of blood chemicals by metabolomics is in order. Transcriptomics and metabolomics can be integrated to provide a more comprehensive understanding of the human biological states. We will review these new data and methods and discuss how they can contribute to personalized medicine.

The effects of age and dietary restriction on the tissue-specific metabolome of Drosophila

Dietary restriction (DR) is a robust intervention that extends lifespan and slows the onset of age-related diseases in diverse organisms. While significant progress has been made in attempts to uncover the genetic mechanisms of DR, there are few studies on the effects of DR on the metabolome. In recent years, metabolomic profiling has emerged as a powerful technology to understand the molecular causes and consequences of natural aging and disease-associated phenotypes. Here, we use high-resolution mass spectroscopy and novel computational approaches to examine changes in the metabolome from the head, thorax, abdomen, and whole body at multiple ages in Drosophila fed either a nutrient-rich ad libitum (AL) or nutrient-restricted (DR) diet. Multivariate analysis clearly separates the metabolome by diet in different tissues and different ages. DR significantly altered the metabolome and, in particular, slowed age-related changes in the metabolome. Interestingly, we observed interacting metabolites whose correlation coefficients, but not mean levels, differed significantly between AL and DR. The number and magnitude of positively correlated metabolites was greater under a DR diet. Furthermore, there was a decrease in positive metabolite correlations as flies aged on an AL diet. Conversely, DR enhanced these correlations with age. Metabolic set enrichment analysis identified several known (e.g., amino acid and NAD metabolism) and novel metabolic pathways that may affect how DR effects aging. Our results suggest that network structure of metabolites is altered upon DR and may play an important role in preventing the decline of homeostasis with age.

Metabolic Consequences of Chronic Alcohol Abuse in Non-Smokers: A Pilot Study

An alcohol use disorder (AUD) is associated with an increased susceptibility to respiratory infection and injury and, upon hospitalization, higher mortality rates. Studies in model systems show effects of alcohol on mitochondrial function, lipid metabolism and antioxidant systems. The present study applied high-resolution metabolomics to test for these changes in bronchoalveolar lavage fluid (BALF) of subjects with an AUD. Smokers were excluded to avoid confounding effects and compliance was verified by cotinine measurements. Statistically significant metabolic features, differentially expressed by control and AUD subjects, were identified by statistical and bioinformatic methods. The results show that fatty acid and acylcarnitine concentrations were increased in AUD subjects, consistent with perturbed mitochondrial and lipid metabolism. Decreased concentrations of methyl-donor compounds suggest altered one-carbon metabolism and oxidative stress. An accumulation of peptides suggests proteolytic activity, which could reflect altered epithelial barrier function. Two metabolites of possible microbial origin suggest subclinical bacterial infection. Furthermore, increased diacetylspermine suggests additional metabolic perturbations, which could contribute to dysregulated alveolar macrophage function and vulnerability to infection. Together, the results show an extended metabolic consequence of AUD in the bronchoalveolar space.

MetabNet: An R Package for Metabolic Association Analysis of High-Resolution Metabolomics Data

Liquid-chromatography high-resolution mass spectrometry provides capability to measure >40,000 ions derived from metabolites in biologic samples. This presents challenges to confirm identities of known chemicals and delineate potential metabolic pathway associations of unidentified chemicals. We provide an R package for metabolic network analysis, MetabNet, to perform targeted metabolome-wide association study of specific metabolites to facilitate detection of their related metabolic pathways and network structures.

Application of gas chromatography/flame ionization detector-based metabolite fingerprinting for authentication of Asian palm civet coffee (Kopi Luwak)

Development of authenticity screening for Asian palm civet coffee, the world-renowned priciest coffee, was previously reported using metabolite profiling through gas chromatography/mass spectrometry (GC/MS). However, a major drawback of this approach is the high cost of the instrument and maintenance. Therefore, an alternative method is needed for quality and authenticity evaluation of civet coffee. A rapid, reliable and cost-effective analysis employing a universal detector, GC coupled with flame ionization detector (FID), and metabolite fingerprinting has been established for discrimination analysis of 37 commercial and non-commercial coffee beans extracts. gas chromatography/flame ionization detector (GC/FID) provided higher sensitivity over a similar range of detected compounds than GC/MS. In combination with multivariate analysis, GC/FID could successfully reproduce quality prediction from GC/MS for differentiation of commercial civet coffee, regular coffee and coffee blend with 50 wt % civet coffee content without prior metabolite details. Our study demonstrated that GC/FID-based metabolite fingerprinting can be effectively actualized as an alternative method for coffee authenticity screening in industries.

Metabolomics and diabetes: analytical and computational approaches

Diabetes is characterized by altered metabolism of key molecules and regulatory pathways. The phenotypic expression of diabetes and associated complications encompasses complex interactions between genetic, environmental, and tissue-specific factors that require an integrated understanding of perturbations in the network of genes, proteins, and metabolites. Metabolomics attempts to systematically identify and quantitate small molecule metabolites from biological systems. The recent rapid development of a variety of analytical platforms based on mass spectrometry and nuclear magnetic resonance have enabled identification of complex metabolic phenotypes. Continued development of bioinformatics and analytical strategies has facilitated the discovery of causal links in understanding the pathophysiology of diabetes and its complications. Here, we summarize the metabolomics workflow, including analytical, statistical, and computational tools, highlight recent applications of metabolomics in diabetes research, and discuss the challenges in the field.

Prediction of intracellular metabolic states from extracellular metabolomic data

Metabolic models can provide a mechanistic framework to analyze information-rich omics data sets, and are increasingly being used to investigate metabolic alternations in human diseases. An expression of the altered metabolic pathway utilization is the selection of metabolites consumed and released by cells. However, methods for the inference of intracellular metabolic states from extracellular measurements in the context of metabolic models remain underdeveloped compared to methods for other omics data. Herein, we describe a workflow for such an integrative analysis emphasizing on extracellular metabolomics data. We demonstrate, using the lymphoblastic leukemia cell lines Molt-4 and CCRF-CEM, how our methods can reveal differences in cell metabolism. Our models explain metabolite uptake and secretion by predicting a more glycolytic phenotype for the CCRF-CEM model and a more oxidative phenotype for the Molt-4 model, which was supported by our experimental data. Gene expression analysis revealed altered expression of gene products at key regulatory steps in those central metabolic pathways, and literature query emphasized the role of these genes in cancer metabolism. Moreover, in silico gene knock-outs identified unique control points for each cell line model, e.g., phosphoglycerate dehydrogenase for the Molt-4 model. Thus, our workflow is well-suited to the characterization of cellular metabolic traits based on extracellular metabolomic data, and it allows the integration of multiple omics data sets into a cohesive picture based on a defined model context.

Invariance and plasticity in the Drosophila melanogaster metabolomic network in response to temperature

Background

Metabolomic responses to extreme thermal stress have recently been investigated in Drosophila melanogaster. However, a network level understanding of metabolomic responses to longer and less drastic temperature changes, which more closely reflect variation in natural ambient temperatures experienced during development and adulthood, is currently lacking. Here we use high-resolution, non-targeted metabolomics to dissect metabolomic changes in D. melanogaster elicited by moderately cool (18°C) or warm (27°C) developmental and adult temperature exposures.

Results

We find that temperature at which larvae are reared has a dramatic effect on metabolomic network structure measured in adults. Using network analysis, we are able to identify modules that are highly differentially expressed in response to changing developmental temperature, as well as modules whose correlation structure is strongly preserved across temperature.

Conclusions

Our results suggest that the effect of temperature on the metabolome provides an easily studied and powerful model for understanding the forces that influence invariance and plasticity in biological networks.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-014-0139-6) contains supplementary material, which is available to authorized users.

Autophagy is essential for effector CD8(+) T cell survival and memory formation

The importance of autophagy in the generation of memory CD8(+) T cells in vivo is not well defined. We report here that autophagy was dynamically regulated in virus-specific CD8(+) T cells during acute infection of mice with lymphocytic choriomeningitis virus. In contrast to the current paradigm, autophagy decreased in activated proliferating effector CD8(+) T cells and was then upregulated when the cells stopped dividing just before the contraction phase. Consistent with those findings, deletion of the gene encoding either of the autophagy-related molecules Atg5 or Atg7 had little to no effect on the proliferation and function of effector cells, but these autophagy-deficient effector cells had survival defects that resulted in compromised formation of memory T cells. Our studies define when autophagy is needed during effector and memory differentiation and warrant reexamination of the relationship between T cell activation and autophagy.

WITHDRAWN: Recent advances in chemometric methods for plant metabolomics: A review

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Comparative transcriptomics and metabolomics in a rhesus macaque drug administration study

We describe a multi-omic approach to understanding the effects that the anti-malarial drug pyrimethamine has on immune physiology in rhesus macaques (Macaca mulatta). Whole blood and bone marrow (BM) RNA-Seq and plasma metabolome profiles (each with over 15,000 features) have been generated for five naïve individuals at up to seven timepoints before, during and after three rounds of drug administration. Linear modeling and Bayesian network analyses are both considered, alongside investigations of the impact of statistical modeling strategies on biological inference. Individual macaques were found to be a major source of variance for both omic data types, and factoring individuals into subsequent modeling increases power to detect temporal effects. A major component of the whole blood transcriptome follows the BM with a time-delay, while other components of variation are unique to each compartment. We demonstrate that pyrimethamine administration does impact both compartments throughout the experiment, but very limited perturbation of transcript or metabolite abundance was observed following each round of drug exposure. New insights into the mode of action of the drug are presented in the context of pyrimethamine's predicted effect on suppression of cell division and metabolism in the immune system.

Effects of age, sex, and genotype on high-sensitivity metabolomic profiles in the fruit fly, Drosophila melanogaster

Researchers have used whole-genome sequencing and gene expression profiling to identify genes associated with age, in the hope of understanding the underlying mechanisms of senescence. But there is a substantial gap from variation in gene sequences and expression levels to variation in age or life expectancy. In an attempt to bridge this gap, here we describe the effects of age, sex, genotype, and their interactions on high-sensitivity metabolomic profiles in the fruit fly, Drosophila melanogaster. Among the 6800 features analyzed, we found that over one-quarter of all metabolites were significantly associated with age, sex, genotype, or their interactions, and multivariate analysis shows that individual metabolomic profiles are highly predictive of these traits. Using a metabolomic equivalent of gene set enrichment analysis, we identified numerous metabolic pathways that were enriched among metabolites associated with age, sex, and genotype, including pathways involving sugar and glycerophospholipid metabolism, neurotransmitters, amino acids, and the carnitine shuttle. Our results suggest that high-sensitivity metabolomic studies have excellent potential not only to reveal mechanisms that lead to senescence, but also to help us understand differences in patterns of aging among genotypes and between males and females.

After the feature presentation: technologies bridging untargeted metabolomics and biology

Liquid chromatography/mass spectrometry-based untargeted metabolomics is now an established experimental approach that is being broadly applied by many laboratories worldwide. Interpreting untargeted metabolomic data, however, remains a challenge and limits the translation of results into biologically relevant conclusions. Here we review emerging technologies that can be applied after untargeted profiling to extend biological interpretation of metabolomic data. These technologies include advances in bioinformatic software that enable identification of isotopes and adducts, comprehensive pathway mapping, deconvolution of MS(2) data, and tracking of isotopically labeled compounds. There are also opportunities to gain additional biological insight by complementing the metabolomic analysis of homogenized samples with recently developed technologies for metabolite imaging of intact tissues. To maximize the value of these emerging technologies, a unified workflow is discussed that builds on the traditional untargeted metabolomic pipeline. Particularly when integrated together, the combination of the advances highlighted in this review helps transform lists of masses and fold changes characteristic of untargeted profiling results into structures, absolute concentrations, pathway fluxes, and localization patterns that are typically needed to understand biology.

Systems biological approaches to measure and understand vaccine immunity in humans

Recent studies have demonstrated the utility of using systems approaches to identify molecular signatures that can be used to predict vaccine immunity in humans. Such approaches are now being used extensively in vaccinology, and are beginning to yield novel insights about the molecular networks driving vaccine immunity. In this review, we present a broad review of the methodologies involved in these studies, and discuss the promise and challenges involved in this emerging field of "systems vaccinology."