Plasma metabolomic profiles enhance precision medicine for volunteers of normal health. Proc Natl Acad Sci U S A. 2015;112:E4901-E4910. [PMID: 26283345 DOI: 10.1073/pnas.1508425112]

A Review of Pulmonary Toxicity of Electronic Cigarettes in the Context of Smoking: A Focus on Inflammation

The use of electronic cigarettes (e-cigs) is increasing rapidly, but their effects on lung toxicity are largely unknown. Smoking is a well-established cause of lung cancer and respiratory disease, in part through inflammation. It is plausible that e-cig use might affect similar inflammatory pathways. E-cigs are used by some smokers as an aid for quitting or smoking reduction, and by never smokers (e.g., adolescents and young adults). The relative effects for impacting disease risk may differ for these groups. Cell culture and experimental animal data indicate that e-cigs have the potential for inducing inflammation, albeit much less than smoking. Human studies show that e-cig use in smokers is associated with substantial reductions in blood or urinary biomarkers of tobacco toxicants when completely switching and somewhat for dual use. However, the extent to which these biomarkers are surrogates for potential lung toxicity remains unclear. The FDA now has regulatory authority over e-cigs and can regulate product and e-liquid design features, such as nicotine content and delivery, voltage, e-liquid formulations, and flavors. All of these factors may impact pulmonary toxicity. This review summarizes current data on pulmonary inflammation related to both smoking and e-cig use, with a focus on human lung biomarkers. Cancer Epidemiol Biomarkers Prev; 26(8); 1175-91. ©2017 AACR.

Chemical Analysis of Astragali Complanati Semen and Its Hypocholesterolemic Effect Using Serum Metabolomics Based on Gas Chromatography-Mass Spectrometry

The hypocholesterolemic protective effect of the dried seed of Astragalus complanatus (ACS) was investigated in rats fed with normal diet, high cholesterol diet (HCD), and HCD plus 70% ethanol extract of ACS (600 mg/kg/day) by oral gavage for four weeks. ACS extract was tested to be rich in antioxidants, which may be contributed to its high content of phenolic compounds. Consumption of ACS remarkably suppressed the elevated total cholesterol (p < 0.01) and LDL-C (p < 0.001) induced by HCD. Chemical constituents of ACS extract were analyzed by ultra-performance liquid chromatography coupled with electrospray ionization orbitrap mass spectrometry and the results showed that the ACS extract mainly consisted of phenolic compounds including flavonoids and flavonoid glycosides. In addition, based on the serum fatty acid profiles, elucidated using gas chromatography-mass spectrometry, free and esterified fatty acids including docosapentaenoic acid, adrenic acid, dihomo-γ-linolenic acid and arachidonic acid were regulated in ACS treatment group. Western blot results further indicated the protein expression of peroxisome proliferator-activated receptor alpha (PPARα) (p < 0.05) in liver was upregulated in ACS treatment group. To conclude, our results clearly demonstrated that ACS provides beneficial effect on lowering HCD associated detrimental change.

A wellness study of 108 individuals using personal, dense, dynamic data clouds

Personal data for 108 individuals were collected during a 9-month period, including whole genome sequences; clinical tests, metabolomes, proteomes, and microbiomes at three time points; and daily activity tracking. Using all of these data, we generated a correlation network that revealed communities of related analytes associated with physiology and disease. Connectivity within analyte communities enabled the identification of known and candidate biomarkers (e.g., gamma-glutamyltyrosine was densely interconnected with clinical analytes for cardiometabolic disease). We calculated polygenic scores from genome-wide association studies (GWAS) for 127 traits and diseases, and used these to discover molecular correlates of polygenic risk (e.g., genetic risk for inflammatory bowel disease was negatively correlated with plasma cystine). Finally, behavioral coaching informed by personal data helped participants to improve clinical biomarkers. Our results show that measurement of personal data clouds over time can improve our understanding of health and disease, including early transitions to disease states.

Temporal Signal Pattern Recognition in Mass Spectrometry: A Method for Rapid Identification and Accurate Quantification of Biomarkers for Inborn Errors of Metabolism with Quality Assurance

Mass spectrometry (MS)-based metabolomic initiatives that use conventional separation techniques are limited by low sample throughput and complicated data processing that contribute to false discoveries. Herein, we introduce a new strategy for unambiguous identification and accurate quantification of biomarkers for inborn errors of metabolism (IEM) from dried blood spots (DBS) with quality assurance. A multiplexed separation platform based on multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS) was developed to provide comparable sample throughput to flow injection analysis-tandem MS (FIA-MS/MS) but with greater selectivity as required for confirmatory testing and discovery-based metabolite profiling of volume-restricted biospecimens. Mass spectral information is encoded temporally within a separation by serial injection of three or more sample pairs, each having a unique dilution pattern, alongside a quality control (QC) that serves as a reference in every run to facilitate between-sample comparisons and/or batch correction due to system drift. Optimization of whole blood extraction conditions on DBS filter paper cut-outs was first achieved to maximize recovery of a wide range of polar metabolites from DBS extracts. An interlaboratory comparison study was also conducted using a proficiency test and retrospective neonatal DBS that demonstrated good agreement between MSI-CE-MS and validated FIA-MS/MS methods within an accredited facility. Our work demonstrated accurate identification of various IEM based on reliable measurement of a panel of primary or secondary biomarkers above an upper cutoff concentration limit for presumptive screen-positive cases without stable isotope-labeled reagents. Additionally, nontargeted metabolite profiling by MSI-CE-MS with temporal signal pattern recognition revealed new biomarkers for early detection of galactosemia, such as N-galactated amino acids, that are a novel class of pathognomonic marker due to galactose stress in affected neonates.

Metabolomics and Cardiology: Toward the Path of Perinatal Programming and Personalized Medicine

Heart diseases are one of the leading causes of death in Western Countries and tend to become chronic, lowering the quality of life of the patients and ending up in a massive cost for the Health Systems and the society. Thus, there is a growing interest in finding new technologies that would allow the physician to effectively treat and prevent cardiac illnesses. Metabolomics is one of the new "omics" sciences enabling creation of a photograph of the metabolic state of an individual exposed to different environmental factors and pathologies. This review analyzed the most recent literature about this technology and its application in cardiology in order to understand the metabolic shifts that occur even before the manifestation of these pathologies to find possible early predictive biomarkers. In this way, it could be possible to find better treatments, ameliorate the patient's quality of life, and lower the death rate. This technology seems to be so promising that several industries are trying to set up kits to immediately assess the metabolites variations in order to provide a faster diagnosis and the best treatment specific for that patient, offering a further step toward the path of the development of a tailored medicine.

Gut Microbiome-Based Metagenomic Signature for Non-invasive Detection of Advanced Fibrosis in Human Nonalcoholic Fatty Liver Disease

The presence of advanced fibrosis in nonalcoholic fatty liver disease (NAFLD) is the most important predictor of liver mortality. There are limited data on the diagnostic accuracy of gut microbiota-derived signature for predicting the presence of advanced fibrosis. In this prospective study, we characterized the gut microbiome compositions using whole-genome shotgun sequencing of DNA extracted from stool samples. This study included 86 uniquely well-characterized patients with biopsy-proven NAFLD, of which 72 had mild/moderate (stage 0-2 fibrosis) NAFLD, and 14 had advanced fibrosis (stage 3 or 4 fibrosis). We identified a set of 40 features (p < 0.006), which included 37 bacterial species that were used to construct a Random Forest classifier model to distinguish mild/moderate NAFLD from advanced fibrosis. The model had a robust diagnostic accuracy (AUC 0.936) for detecting advanced fibrosis. This study provides preliminary evidence for a fecal-microbiome-derived metagenomic signature to detect advanced fibrosis in NAFLD.

Serum Metabolomic Profiling of Piglets Infected with Virulent Classical Swine Fever Virus

Classical swine fever (CSF) is a highly contagious swine infectious disease and causes significant economic losses for the pig industry worldwide. The objective of this study was to determine whether small molecule metabolites contribute to the pathogenesis of CSF. Birefly, serum metabolomics of CSFV Shimen strain-infected piglets were analyzed by ultraperformance liquid chromatography/electrospray ionization time-of-flight mass spectrometry (UPLC/ESI-Q-TOF/MS) in combination with multivariate statistical analysis. In CSFV-infected piglets at days 3 and 7 post-infection changes were found in metabolites associated with several key metabolic pathways, including tryptophan catabolism and the kynurenine pathway, phenylalanine metabolism, fatty acid and lipid metabolism, the tricarboxylic acid and urea cycles, branched-chain amino acid metabolism, and nucleotide metabolism. Several pathways involved in energy metabolism including fatty acid biosynthesis and β-oxidation, branched-chain amino acid metabolism, and the tricarboxylic acid cycle were significantly inhibited. Changes were also observed in several metabolites exclusively associated with gut microbiota. The metabolomic profiles indicate that CSFV-host gut microbiome interactions play a role in the development of CSF.

metabolic profiling of Parkinson's disease and mild cognitive impairment

BACKGROUND

Early diagnosis of Parkinson's disease and mild cognitive impairment is important to enable prompt treatment and improve patient welfare, yet no standard diagnostic test is available. Metabolomics is a powerful tool used to elucidate disease mechanisms and identify potential biomarkers.

OBJECTIVES

The objective of this study was to use metabolic profiling to understand the pathoetiology of Parkinson's disease and to identify potential disease biomarkers.

METHODS

This study compared the serological metabolomic profiles of early-stage Parkinson's patients (diagnosed < 12 months) to asymptomatic matched controls using an established array based detection system (DiscoveryHD4™, Metabolon, UK), correlating metabolite levels to clinical measurements of cognitive impairment.

RESULTS

A total of 1434 serological metabolites were assessed in early-stage Parkinson's disease cases (n = 41) and asymptomatic matched controls (n = 40). Post-quality control, statistical analysis identified n = 20 metabolites, predominantly metabolites of the fatty acid oxidation pathway, associated with Parkinson's disease and mild cognitive impairment. Receiver operator curve assessment confirmed that the nine fatty acid oxidation metabolites had good predictive accuracy (area under curve = 0.857) for early-stage Parkinson's disease and mild cognitive impairment (area under curve = 0.759).

CONCLUSIONS

Our study indicates that fatty acid oxidation may be an important component in the pathophysiology of Parkinson's disease and may have potential as a diagnostic biomarker for disease onset and mild cognitive impairment. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Whole-genome sequencing identifies common-to-rare variants associated with human blood metabolites

Genetic factors modifying the blood metabolome have been investigated through genome-wide association studies (GWAS) of common genetic variants and through exome sequencing. We conducted a whole-genome sequencing study of common, low-frequency and rare variants to associate genetic variations with blood metabolite levels using comprehensive metabolite profiling in 1,960 adults. We focused the analysis on 644 metabolites with consistent levels across three longitudinal data collections. Genetic sequence variations at 101 loci were associated with the levels of 246 (38%) metabolites (P ≤ 1.9 × 10^-11). We identified 113 (10.7%) among 1,054 unrelated individuals in the cohort who carried heterozygous rare variants likely influencing the function of 17 genes. Thirteen of the 17 genes are associated with inborn errors of metabolism or other pediatric genetic conditions. This study extends the map of loci influencing the metabolome and highlights the importance of heterozygous rare variants in determining abnormal blood metabolic phenotypes in adults.

Metabolomics for clinical use and research in chronic kidney disease

Chronic kidney disease (CKD) has a high prevalence in the general population and is associated with high mortality; a need therefore exists for better biomarkers for diagnosis, monitoring of disease progression and therapy stratification. Moreover, very sensitive biomarkers are needed in drug development and clinical research to increase understanding of the efficacy and safety of potential and existing therapies. Metabolomics analyses can identify and quantify all metabolites present in a given sample, covering hundreds to thousands of metabolites. Sample preparation for metabolomics requires a very fast arrest of biochemical processes. Present key technologies for metabolomics are mass spectrometry and proton nuclear magnetic resonance spectroscopy, which require sophisticated biostatistic and bioinformatic data analyses. The use of metabolomics has been instrumental in identifying new biomarkers of CKD such as acylcarnitines, glycerolipids, dimethylarginines and metabolites of tryptophan, the citric acid cycle and the urea cycle. Biomarkers such as c-mannosyl tryptophan and pseudouridine have better performance in CKD stratification than does creatinine. Future challenges in metabolomics analyses are prospective studies and deconvolution of CKD biomarkers from those of other diseases such as metabolic syndrome, diabetes mellitus, inflammatory conditions, stress and cancer.

Metabolomics for the masses: The future of metabolomics in a personalized world

Current clinical practices focus on a small number of biochemical directly related to the pathophysiology with patients and thus only describe a very limited metabolome of a patient and fail to consider the interations of these small molecules. This lack of extended information may prevent clinicians from making the best possible therapeutic interventions in sufficient time to improve patient care. Various post-genomics '('omic)' approaches have been used for therapeutic interventions previously. Metabolomics now a well-established'omics approach, has been widely adopted as a novel approach for biomarker discovery and in tandem with genomics (especially SNPs and GWAS) has the potential for providing systemic understanding of the underlying causes of pathology. In this review, we discuss the relevance of metabolomics approaches in clinical sciences and its potential for biomarker discovery which may help guide clinical interventions. Although a powerful and potentially high throughput approach for biomarker discovery at the molecular level, true translation of metabolomics into clinics is an extremely slow process. Quicker adaptation of biomarkers discovered using metabolomics can be possible with novel portable and wearable technologies aided by clever data mining, as well as deep learning and artificial intelligence; we shall also discuss this with an eye to the future of precision medicine where metabolomics can be delivered to the masses.

Metabolomic biomarkers as strong correlates of Parkinson disease progression

OBJECTIVE

To determine whether a Parkinson disease (PD)-specific biochemical signature might be found in the total body metabolic milieu or in the CSF compartment, especially since this disorder has systemic manifestations beyond the progressive loss of dopaminergic nigrostriatal neurons.

METHODS

Our goal was to discover biomarkers of PD progression. Using ultra-high-performance liquid chromatography linked to gas chromatography and tandem mass spectrometry, we measured concentrations of small-molecule (≤1.5 kDa) constituents of plasma and CSF from 49 unmedicated, mildly affected patients with PD (mean age 61.4 years; mean duration of PD 11.4 months). Specimens were collected twice (baseline and final) at intervals up to 24 months. During this time, mean Unified Parkinson's Disease Rating Scale (UPDRS) parts 2 + 3 scores increased 47% (from 28.8 to 42.2). Measured compounds underwent unbiased univariate and multivariate analyses, including fitting data into multiple linear regression with variable selection using least absolute shrinkage and selection operator (LASSO).

RESULTS

Of 575 identified plasma and 383 CSF biochemicals, LASSO led to selection of 15 baseline plasma constituents with high positive correlation (0.87, p = 2.2e^-16) to baseline-to-final change in UPDRS parts 2 + 3 scores. Three of the compounds had xanthine structures, and 4 were either medium- or long-chain fatty acids. For the 15 LASSO-selected biomarkers, pathway enrichment software found no overrepresentation among metabolic pathways. CSF concentrations of the dopamine metabolite homovanillate showed little change between baseline and final collections and minimal correlation with worsening UPDRS parts 2 + 3 scores (0.29, p = 0.041).

CONCLUSIONS

Metabolomic profiling of plasma yielded strong prediction of PD progression and offered biomarkers that may provide new insights into PD pathogenesis.

The metabolomics of psoriatic disease

Metabolomics is an emerging new "omics" field involving the systematic analysis of the metabolites in a biologic system. These metabolites provide a molecular snapshot of cellular activity and are thus important for understanding the functional changes in metabolic pathways that drive disease. Recently, metabolomics has been used to study the local and systemic metabolic changes in psoriasis and its cardiometabolic comorbidities. Such studies have revealed novel insights into disease pathogenesis and suggest new biochemical signatures that may be used as a marker of psoriatic disease. This review will discuss common strategies in metabolomics analysis, current findings in the metabolomics of psoriasis, and emerging trends in psoriatic metabolomics.

Frontiers of high-throughput metabolomics

Large scale metabolomics studies are increasingly used to investigate genetically different individuals and time-dependent responses to environmental stimuli. New mass spectrometric approaches with at least an order of magnitude more rapid analysis of small molecules within the cell's metabolome are now paving the way towards true high-throughput metabolomics, opening new opportunities in systems biology, functional genomics, drug discovery, and personalized medicine. Here we discuss the impact and advantages of the progress made in profiling large cohorts and dynamic systems with high temporal resolution and automated sampling. In both areas, high-throughput metabolomics is gaining traction because it can generate hypotheses on molecular mechanisms and metabolic regulation. We conclude with the current status of the less mature single cell analyses where high-throughput analytics will be indispensable to resolve metabolic heterogeneity in populations and compartmentalization of metabolites.

Integration of pharmacometabolomics with pharmacokinetics and pharmacodynamics: towards personalized drug therapy

Personalized medicine, in modern drug therapy, aims at a tailored drug treatment accounting for inter-individual variations in drug pharmacology to treat individuals effectively and safely. The inter-individual variability in drug response upon drug administration is caused by the interplay between drug pharmacology and the patients' (patho)physiological status. Individual variations in (patho)physiological status may result from genetic polymorphisms, environmental factors (including current/past treatments), demographic characteristics, and disease related factors. Identification and quantification of predictors of inter-individual variability in drug pharmacology is necessary to achieve personalized medicine. Here, we highlight the potential of pharmacometabolomics in prospectively informing on the inter-individual differences in drug pharmacology, including both pharmacokinetic (PK) and pharmacodynamic (PD) processes, and thereby guiding drug selection and drug dosing. This review focusses on the pharmacometabolomics studies that have additional value on top of the conventional covariates in predicting drug PK. Additionally, employing pharmacometabolomics to predict drug PD is highlighted, and we suggest not only considering the endogenous metabolites as static variables but to include also drug dose and temporal changes in drug concentration in these studies. Although there are many endogenous metabolite biomarkers identified to predict PK and more often to predict PD, validation of these biomarkers in terms of specificity, sensitivity, reproducibility and clinical relevance is highly important. Furthermore, the application of these identified biomarkers in routine clinical practice deserves notable attention to truly personalize drug treatment in the near future.

Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine

It has been experimentally shown that host-microbial interaction plays a major role in shaping the wellness or disease of the human body. Microorganisms coexisting in human tissues provide a variety of benefits that contribute to proper functional activity in the host through the modulation of fundamental processes such as signal transduction, immunity and metabolism. The unbalance of this microbial profile, or dysbiosis, has been correlated with the genesis and evolution of complex diseases such as cancer. Although this latter disease has been thoroughly studied using different high-throughput (HT) technologies, its heterogeneous nature makes its understanding and proper treatment in patients a remaining challenge in clinical settings. Notably, given the outstanding role of host-microbiome interactions, the ecological interactions with microorganisms have become a new significant aspect in the systems that can contribute to the diagnosis and potential treatment of solid cancers. As a part of expanding precision medicine in the area of cancer research, efforts aimed at effective treatments for various kinds of cancer based on the knowledge of genetics, biology of the disease and host-microbiome interactions might improve the prediction of disease risk and implement potential microbiota-directed therapeutics. In this review, we present the state of the art of sequencing and metabolome technologies, computational methods and schemes in systems biology that have addressed recent breakthroughs of uncovering relationships or associations between microorganisms and cancer. Together, microbiome studies extend the horizon of new personalized treatments against cancer from the perspective of precision medicine through a synergistic strategy integrating clinical knowledge, HT data, bioinformatics, and systems biology.

Living on the edge: substrate competition explains loss of robustness in mitochondrial fatty-acid oxidation disorders

Background

Defects in genes involved in mitochondrial fatty-acid oxidation (mFAO) reduce the ability of patients to cope with metabolic challenges. mFAO enzymes accept multiple substrates of different chain length, leading to molecular competition among the substrates. Here, we combined computational modeling with quantitative mouse and patient data to investigate whether substrate competition affects pathway robustness in mFAO disorders.

Results

First, we used comprehensive biochemical analyses of wild-type mice and mice deficient for medium-chain acyl-CoA dehydrogenase (MCAD) to parameterize a detailed computational model of mFAO. Model simulations predicted that MCAD deficiency would have no effect on the pathway flux at low concentrations of the mFAO substrate palmitoyl-CoA. However, high concentrations of palmitoyl-CoA would induce a decline in flux and an accumulation of intermediate metabolites. We proved computationally that the predicted overload behavior was due to substrate competition in the pathway. Second, to study the clinical relevance of this mechanism, we used patients’ metabolite profiles and generated a humanized version of the computational model. While molecular competition did not affect the plasma metabolite profiles during MCAD deficiency, it was a key factor in explaining the characteristic acylcarnitine profiles of multiple acyl-CoA dehydrogenase deficient patients. The patient-specific computational models allowed us to predict the severity of the disease phenotype, providing a proof of principle for the systems medicine approach.

Conclusion

We conclude that substrate competition is at the basis of the physiology seen in patients with mFAO disorders, a finding that may explain why these patients run a risk of a life-threatening metabolic catastrophe.

Electronic supplementary material

The online version of this article (doi:10.1186/s12915-016-0327-5) contains supplementary material, which is available to authorized users.

Monogenic diabetes: Implementation of translational genomic research towards precision medicine

Various forms of early onset non-autoimmune diabetes are recognized as monogenic diseases, each subtype being caused by a single highly penetrant gene defect at the individual level. Monogenic diabetes (MD) is clinically and genetically heterogeneous, including maturity onset diabetes of the young and infancy-onset and neonatal diabetes mellitus, which are characterized by functional defects of insulin-producing pancreatic β-cells and hyperglycemia early in life. Depending on the genetic cause, MD differs in the age at diabetes onset, the severity of hyperglycemia, long-term diabetic complications, and extrapancreatic manifestations. In this review we discuss the many challenges of molecular genetic diagnosis of MD in the face of a substantial genetic heterogeneity, as well as the clinical benefit and cost-effectiveness of an early genetic diagnosis, as demonstrated by simulation models based on lifetime complications and treatment costs. We also discuss striking examples of proof-of-concept of genomic medicine, which have enabled marked improvement in patient care and long-term clinical management. Recent advances in genome editing and pluripotent stem cell reprogramming technologies provide new opportunities for in vitro diabetes modeling and the discovery of novel drug targets and cell-based diabetes therapies. A review of these future directions makes the case for exciting translational research to further our understanding of the pathophysiology of early onset diabetes.

Metabolic features of chronic fatigue syndrome

More than 2 million people in the United States have myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). We performed targeted, broad-spectrum metabolomics to gain insights into the biology of CFS. We studied a total of 84 subjects using these methods. Forty-five subjects (n = 22 men and 23 women) met diagnostic criteria for ME/CFS by Institute of Medicine, Canadian, and Fukuda criteria. Thirty-nine subjects (n = 18 men and 21 women) were age- and sex-matched normal controls. Males with CFS were 53 (±2.8) y old (mean ± SEM; range, 21-67 y). Females were 52 (±2.5) y old (range, 20-67 y). The Karnofsky performance scores were 62 (±3.2) for males and 54 (±3.3) for females. We targeted 612 metabolites in plasma from 63 biochemical pathways by hydrophilic interaction liquid chromatography, electrospray ionization, and tandem mass spectrometry in a single-injection method. Patients with CFS showed abnormalities in 20 metabolic pathways. Eighty percent of the diagnostic metabolites were decreased, consistent with a hypometabolic syndrome. Pathway abnormalities included sphingolipid, phospholipid, purine, cholesterol, microbiome, pyrroline-5-carboxylate, riboflavin, branch chain amino acid, peroxisomal, and mitochondrial metabolism. Area under the receiver operator characteristic curve analysis showed diagnostic accuracies of 94% [95% confidence interval (CI), 84-100%] in males using eight metabolites and 96% (95% CI, 86-100%) in females using 13 metabolites. Our data show that despite the heterogeneity of factors leading to CFS, the cellular metabolic response in patients was homogeneous, statistically robust, and chemically similar to the evolutionarily conserved persistence response to environmental stress known as dauer.

Enabling Efficient and Confident Annotation of LC-MS Metabolomics Data through MS1 Spectrum and Time Prediction

Liquid chromatography coupled to electrospray ionization-mass spectrometry (LC-ESI-MS) is a versatile and robust platform for metabolomic analysis. However, while ESI is a soft ionization technique, in-source phenomena including multimerization, nonproton cation adduction, and in-source fragmentation complicate interpretation of MS data. Here, we report chromatographic and mass spectrometric behavior of 904 authentic standards collected under conditions identical to a typical nontargeted profiling experiment. The data illustrate that the often high level of complexity in MS spectra is likely to result in misinterpretation during the annotation phase of the experiment and a large overestimation of the number of compounds detected. However, our analysis of this MS spectral library data indicates that in-source phenomena are not random but depend at least in part on chemical structure. These nonrandom patterns enabled predictions to be made as to which in-source signals are likely to be observed for a given compound. Using the authentic standard spectra as a training set, we modeled the in-source phenomena for all compounds in the Human Metabolome Database to generate a theoretical in-source spectrum and retention time library. A novel spectral similarity matching platform was developed to facilitate efficient spectral searching for nontargeted profiling applications. Taken together, this collection of experimental spectral data, predictive modeling, and informatic tools enables more efficient, reliable, and transparent metabolite annotation.

Animal-Free Chemical Safety Assessment

The exponential growth of the Internet of Things and the global popularity and remarkable decline in cost of the mobile phone is driving the digital transformation of medical practice. The rapidly maturing digital, non-medical world of mobile (wireless) devices, cloud computing and social networking is coalescing with the emerging digital medical world of omics data, biosensors and advanced imaging which offers the increasingly realistic prospect of personalized medicine. Described as a potential “seismic” shift from the current “healthcare” model to a “wellness” paradigm that is predictive, preventative, personalized and participatory, this change is based on the development of increasingly sophisticated biosensors which can track and measure key biochemical variables in people. Additional key drivers in this shift are metabolomic and proteomic signatures, which are increasingly being reported as pre-symptomatic, diagnostic and prognostic of toxicity and disease. These advancements also have profound implications for toxicological evaluation and safety assessment of pharmaceuticals and environmental chemicals. An approach based primarily on human in vivo and high-throughput in vitro human cell-line data is a distinct possibility. This would transform current chemical safety assessment practice which operates in a human “data poor” to a human “data rich” environment. This could also lead to a seismic shift from the current animal-based to an animal-free chemical safety assessment paradigm.

Metabolomics: Bridging the Gap between Pharmaceutical Development and Population Health

Metabolomics has emerged as an essential tool for studying metabolic processes, stratification of patients, as well as illuminating the fundamental metabolic alterations in disease onset, progression, or response to therapeutic intervention. Metabolomics materialized within the pharmaceutical industry as a standalone assay in toxicology and disease pathology and eventually evolved towards aiding in drug discovery and pre-clinical studies via supporting pharmacokinetic and pharmacodynamic characterization of a drug or a candidate. Recent progress in the field is illustrated by coining of the new term—Pharmacometabolomics. Integration of data from metabolomics with large-scale omics along with clinical, molecular, environmental and behavioral analysis has demonstrated the enhanced utility of deconstructing the complexity of health, disease, and pharmaceutical intervention(s), which further highlight it as an essential component of systems medicine. This review presents the current state and trend of metabolomics applications in pharmaceutical development, and highlights the importance and potential of clinical metabolomics as an essential part of multi-omics protocols that are directed towards shaping precision medicine and population health.

Metabolomics and metabolic pathway networks from human colorectal cancers, adjacent mucosa, and stool

Background

Colorectal cancers (CRC) are associated with perturbations in cellular amino acids, nucleotides, pentose-phosphate pathway carbohydrates, and glycolytic, gluconeogenic, and tricarboxylic acid intermediates. A non-targeted global metabolome approach was utilized for exploring human CRC, adjacent mucosa, and stool. In this pilot study, we identified metabolite profile differences between CRC and adjacent mucosa from patients undergoing colonic resection. Metabolic pathway analyses further revealed relationships between complex networks of metabolites.

Methods

Seventeen CRC patients participated in this pilot study and provided CRC, adjacent mucosa ~10 cm proximal to the tumor, and stool. Metabolomes were analyzed by gas chromatography-mass spectrometry (GC/MS) and ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS). All of the library standard identifications were confirmed and further analyzed via MetaboLync^TM for metabolic network interactions.

Results

There were a total of 728 distinct metabolites identified from colonic tissue and stool matrices. Nineteen metabolites significantly distinguished CRC from adjacent mucosa in our patient-matched cohort. Glucose-6-phosphate and fructose-6-phosphate demonstrated 0.64-fold and 0.75-fold lower expression in CRC compared to mucosa, respectively, whereas isobar: betaine aldehyde, N-methyldiethanolamine, and adenylosuccinate had 2.68-fold and 1.88-fold higher relative abundance in CRC. Eleven of the 19 metabolites had not previously been reported for CRC relevance. Metabolic pathway analysis revealed significant perturbations of short-chain fatty acid metabolism, fructose, mannose, and galactose metabolism, and glycolytic, gluconeogenic, and pyruvate metabolism. In comparison to the 500 stool metabolites identified from human CRC patients, only 215 of those stool metabolites were also detected in tissue. This CRC and stool metabolome investigation identified novel metabolites that may serve as key small molecules in CRC pathogenesis, confirmed the results from previously reported CRC metabolome studies, and showed networks for metabolic pathway aberrations. In addition, we found differences between the CRC and stool metabolomes.

Conclusions

Stool metabolite profiles were limited for direct associations with CRC and adjacent mucosa, yet metabolic pathways were conserved across both matrices. Larger patient-matched CRC, adjacent non-cancerous colonic mucosa, and stool cohort studies for metabolite profiling are needed to validate these small molecule differences and metabolic pathway aberrations for clinical application to CRC control, treatment, and prevention.

Electronic supplementary material

The online version of this article (doi:10.1186/s40170-016-0151-y) contains supplementary material, which is available to authorized users.

Informing 21st-Century Risk Assessments with 21st-Century Science

Understanding and preventing adverse impacts from chemicals in the environment is fundamental to protecting public health, and chemical risk assessments are used to inform public health decisions in the United States and around the world. Traditional chemical risk assessments focus on health effects of environmental contaminants on a chemical-by-chemical basis, largely based on data from animal models using exposures that are typically higher than those experienced by humans. Results from environmental epidemiology studies sometimes show effects that are not observed in animal studies at human exposure levels that are lower than those used in animal studies. In addition, new approaches such as Toxicology in the 21st Century (Tox21) and exposure forecasting (ExpoCast) are generating mechanistic data that provide broad coverage of chemical space, chemical mixtures, and potential associated health outcomes, along with improved exposure estimates. It is becoming clear that risk assessments in the future will need to use the full range of available mechanistic, animal, and human data to integrate multiple types of data and to consider nontraditional health outcomes and end points. This perspective was developed at the "Strengthening the Scientific Basis of Chemical Safety Assessments" workshop, which was cosponsored by the U.S. Environmental Protection Agency and the National Institute of Environmental Health Sciences, where gaps between the emerging science and traditional chemical risk assessments were explored, and approaches for bridging the gaps were considered.

Sharpening Precision Medicine by a Thorough Interrogation of Metabolic Individuality

Precision medicine is an active component of medical practice today, but aspirations are to both broaden its reach to a greater diversity of individuals and improve its “precision” by enhancing the ability to define even more disease states in combination with associated treatments. Given complexity of human phenotypes, much work is required. In this review, we deconstruct this challenge at a high level to define what is needed to move closer toward these aspirations. In the context of the variables that influence the diverse array of phenotypes across human health and disease – genetics, epigenetics, environmental influences, and the microbiome – we detail the factors behind why an individual's biochemical (metabolite) composition is increasingly regarded as a key element to precisely defining phenotypes. Although an individual's biochemical (metabolite) composition is generally regarded, and frequently shown, to be a surrogate to the phenotypic state, we review how metabolites (and therefore an individual's metabolic profile) are also functionally related to the myriad of phenotypic influencers like genetics and the microbiota. We describe how using the technology to comprehensively measure an individual's biochemical profile – metabolomics – is integrative to defining individual phenotypes and how it is currently being deployed in efforts to continue to elaborate on human health and disease in large population studies. Finally, we summarize instances where metabolomics is being used to assess individual health in instances where signatures (i.e. biomarkers) have been defined.

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Untargeted biochemical profiling has the potential to phenotype individuals where genetic associations do not seem to show penetrance

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Metabolomics can be leveraged with other ‘omics data to discern phenotype information that is driven by environmental, microbiota, or epigenetic factors.

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Tracking the biochemical profile of individuals may help discern effectiveness or response to treatment or disease progression.

UPLC-QTOF/MS based metabolomics reveals metabolic alterations associated with severe sepsis

Severe sepsis (SS) remains among the leading causes of death in both developed and developing countries.

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.