Metabolic phenotyping in clinical and surgical environments

Metabolomics and Heart Diseases: From Basic to Clinical Approach

BACKGROUND

The field of metabolomics has been steadily increasing in size for the last 15 years. Advances in analytical and statistical methods have allowed metabolomics to flourish in various areas of medicine. Cardiovascular diseases are some of the main research targets in metabolomics, due to their social and medical relevance, and also to the important role metabolic alterations play in their pathogenesis and evolution. Metabolomics has been applied to the full spectrum of cardiovascular diseases: from patient risk stratification to myocardial infarction and heart failure. However - despite the many proof-ofconcept studies describing the applicability of metabolomics in the diagnosis, prognosis and treatment evaluation in cardiovascular diseases - it is not yet used in routine clinical practice. Recently, large phenome centers have been established in clinical environments, and it is expected that they will provide definitive proof of the applicability of metabolomics in clinical practice. But there is also room for small and medium size centers to work on uncommon pathologies or to resolve specific but relevant clinical questions.

OBJECTIVES

In this review, we will introduce metabolomics, cover the metabolomic work done so far in the area of cardiovascular diseases.

CONCLUSION

The cardiovascular field has been at the forefront of metabolomics application and it should lead the transfer to the clinic in the not so distant future.

Reduced plasma levels of small HDL particles transporting fibrinolytic proteins in pulmonary arterial hypertension

BACKGROUND

Aberrant lipoprotein metabolism has been implicated in experimental pulmonary hypertension, but the relevance to patients with pulmonary arterial hypertension (PAH) is inconclusive.

OBJECTIVE

To investigate the relationship between circulating lipoprotein subclasses and survival in patients with PAH.

METHODS

Using nuclear magnetic resonance spectroscopy, 105 discrete lipoproteins were measured in plasma samples from two cohorts of patients with idiopathic or heritable PAH. Data from 1124 plasma proteins were used to identify proteins linked to lipoprotein subclasses. The physical presence of proteins was confirmed in plasma lipoprotein subfractions separated by ultracentrifugation.

RESULTS

Plasma levels of three lipoproteins from the small high-density lipoprotein (HDL) subclass, termed HDL-4, were inversely related to survival in both the discovery (n=127) and validation (n=77) cohorts, independent of exercise capacity, comorbidities, treatment, N-terminal probrain natriuretic peptide, C reactive protein and the principal lipoprotein classes. The small HDL subclass rich in apolipoprotein A-2 content (HDL-4-Apo A-2) exhibited the most significant association with survival. None of the other lipoprotein classes, including principal lipoprotein classes HDL and low-density lipoprotein cholesterol, were prognostic. Three out of nine proteins identified to associate with HDL-4-Apo A-2 are involved in the regulation of fibrinolysis, namely, the plasmin regulator, alpha-2-antiplasmin, and two major components of the kallikrein-kinin pathway (coagulation factor XI and prekallikrein), and their physical presence in the HDL-4 subfraction was confirmed.

CONCLUSION

Reduced plasma levels of small HDL particles transporting fibrinolytic proteins are associated with poor outcomes in patients with idiopathic and heritable PAH.

A Comprehensive LC-QTOF-MS Metabolic Phenotyping Strategy: Application to Alkaptonuria

BACKGROUND

Identification of unknown chemical entities is a major challenge in metabolomics. To address this challenge, we developed a comprehensive targeted profiling strategy, combining 3 complementary liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) techniques and in-house accurate mass retention time (AMRT) databases established from commercial standards. This strategy was used to evaluate the effect of nitisinone on the urinary metabolome of patients and mice with alkaptonuria (AKU). Because hypertyrosinemia is a known consequence of nitisinone therapy, we investigated the wider metabolic consequences beyond hypertyrosinemia.

METHODS

A total of 619 standards (molecular weight, 45–1354 Da) covering a range of primary metabolic pathways were analyzed using 3 liquid chromatography methods—2 reversed phase and 1 normal phase—coupled to QTOF-MS. Separate AMRT databases were generated for the 3 methods, comprising chemical name, formula, theoretical accurate mass, and measured retention time. Databases were used to identify chemical entities acquired from nontargeted analysis of AKU urine: match window theoretical accurate mass ±10 ppm and retention time ±0.3 min.

RESULTS

Application of the AMRT databases to data acquired from analysis of urine from 25 patients with AKU (pretreatment and after 3, 12, and 24 months on nitisinone) and 18 HGD−/− mice (pretreatment and after 1 week on nitisinone) revealed 31 previously unreported statistically significant changes in metabolite patterns and abundance, indicating alterations to tyrosine, tryptophan, and purine metabolism after nitisinone administration.

CONCLUSIONS

The comprehensive targeted profiling strategy described here has the potential of enabling discovery of novel pathways associated with pathogenesis and management of AKU.

Urine Nuclear Magnetic Resonance (NMR) Metabolomics in Age-Related Macular Degeneration

Biofluid biomarkers of age-related macular degeneration (AMD) are still lacking, and their identification is challenging. Metabolomics is well-suited to address this need, and urine is a valuable accessible biofluid. This study aimed to characterize the urinary metabolomic signatures of patients with different stages of AMD and a control group (>50 years). It was a prospective, cross-sectional study, where subjects from two cohorts were included: 305 from Coimbra, Portugal (AMD patients n = 252; controls n = 53) and 194 from Boston, United States (AMD patients n = 147; controls n = 47). For all participants, we obtained color fundus photographs (for AMD staging) and fasting urine samples, which were analyzed using 1H nuclear magnetic resonance (NMR) spectroscopy. Our results revealed that in both cohorts, urinary metabolomic profiles differed mostly between controls and late AMD patients, but important differences were also found between controls and subjects with early AMD. Analysis of the metabolites responsible for these separations revealed that, even though distinct features were observed for each cohort, AMD was in general associated with depletion of excreted citrate and selected amino acids at some stage of the disease, suggesting enhanced energy requirements. In conclusion, NMR metabolomics enabled the identification of urinary signals of AMD and its severity stages, which might represent potential metabolomic biomarkers of the disease.

Real-Time Pure Shift HSQC NMR for Untargeted Metabolomics

Sensitivity and resolution are key considerations for NMR applications in general and for metabolomics in particular, where complex mixtures containing hundreds of metabolites over a large range of concentrations are commonly encountered. There is a strong demand for advanced methods that can provide maximal information in the shortest possible time frame. Here, we present the optimization and application of the recently introduced 2D real-time BIRD 1H-13C HSQC experiment for NMR-based metabolomics of aqueous samples at 13C natural abundance. For mouse urine samples, it is demonstrated how this real-time pure shift sensitivity-improved heteronuclear single quantum correlation method provides broadband homonuclear decoupling along the proton detection dimension and thereby significantly improves spectral resolution in regions that are affected by spectral overlap. Moreover, the collapse of the scalar multiplet structure of cross-peaks leads to a sensitivity gain of about 40-50% over a traditional 2D HSQC-SI experiment. The experiment works well over a range of magnetic field strengths and is particularly useful when resonance overlap in crowded regions of the HSQC spectra hampers accurate metabolite identification and quantitation.

Novel strategies for clinical investigation and biomarker discovery: a guide to applied metabolomics

Metabolomics is an emerging technology that is increasing both in basic science and in human applications, providing a physiological snapshot. It has been highlighted as one of the most wide ranging and reliable tools for the investigation of physiological status, the discovery of new biomarkers and the analysis of metabolic pathways. Metabolomics uses innovative mass spectrometry (MS) allied to chromatography or nuclear magnetic resonance (NMR). The recent advances in bioinformatics, databases and statistics, have provided a unique perception of metabolites interaction and the dynamics of metabolic pathways at a system level. In this context, several studies have applied metabolomics in physiology- and disease-related works. The application of metabolomics includes, physiological and metabolic evaluation/monitoring, individual response to different exercise, nutritional interventions, pathological processes, responses to pharmacological interventions, biomarker discovery and monitoring for distinct aspects, such as: physiological capacity, fatigue/recovery and aging among other applications. For metabolomic analyses, despite huge improvements in the field, several complex methodological steps must be taken into consideration. In this regard, the present article aims to summarize the novel aspects of metabolomics and provide a guide for metabolomics for professionals related to physiologist and medical applications.

Visceral Adipose Tissue Modulates Radiosensitivity in Oesophageal Adenocarcinoma

Oesophageal adenocarcinoma (OAC) is an exemplar model of obesity-associated cancer. Response to neoadjuvant chemoradiotherapy (NA CRT) is a clinical challenge. We examined if visceral adipose tissue and obesity status alter radiosensitivity in OAC. The radioresistant (OE33R) and radioresponsive (OE33P) OAC isogenic model was cultured with adipose tissue conditioned media from three patient cohorts: non-cancer patients, surgery only OAC patients and NA CRT OAC patients. Cell survival was characterised by clonogenic assay, metabolomic profiling by nuclear magnetic resonance spectroscopy and adipokine receptor gene expression by qPCR. A retrospective in vivo study compared tumour response to NA CRT in normal weight (n=53) versus overweight/obese patients (n=148). Adipose conditioned media (ACM) from all patient cohorts significantly increased radiosensitivity in radioresistant OE33R cells. ACM from the NA CRT OAC cohort increased radiosensitivity in OE33P cells. Metabolomic profiling demonstrated separation of the non-cancer and surgery only OAC cohorts and between the non-cancer and NA CRT OAC cohorts. Gene expression profiling of OE33P versus OE33R cells demonstrated differential expression of the adiponectin receptor-1 (AR1), adiponectin receptor-2 (AR2), leptin receptor (LepR) and neuropilin receptor-1 (NRP1) genes. In vivo overweight/obese OAC patients achieved an enhanced tumour response following NA CRT compared to normal weight patients. This study demonstrates that visceral adipose tissue modulates the cellular response to radiation in OAC.

Solid phase microextraction coupled to mass spectrometry via a microfluidic open interface for rapid therapeutic drug monitoring

Tranexamic acid (TXA) is an antifibrinolytic used during cardiac surgery that presents high inter-patient variability.

Metabolomics in the study of retinal health and disease

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

Aging-related compensated hypogonadism: Role of metabolomic analysis in physiopathological and therapeutic evaluation

Aging is a complex process that increases the risk of chronic disease development. Hormonal and metabolic alterations occur with aging, such as androgen activity decrease. Studies aim to understand the role of testosterone replacement therapy (TRT) in males, however biomarkers and the metabolic responses to TRT are not well characterized. Therefore, the present study investigated TRT effect in young adult and aged rats by metabolomics. Male Wistar rats were divided into four groups: adult and adult + testo (6months), old and old + testo (25-27months). TRT animals received daily testosterone propionate (1 mg/kg/subcutaneous). TRT changed the testicular weight index decrease induced by aging but did not change the body weight and liver weight index. Sera were analyzed by liquid chromatograph high resolution mass spectrometry (LCMS/MS). Testosterone was quantified by target LCMS/MS. A total of 126 metabolites were detected with known identification altered by TRT by non-target metabolomics analysis. Multivariate statistics shows that all groups segregated individually after principal component analysis. The treatment with testosterone induced several metabolic alterations in adult and old rats that were summarized by variable importance on projection score, metabolite interaction and pathway analysis. Aging-related hypogonadism induces a pattern of systemic metabolic alterations that can be partially reversed by TRT, however, this treatment in aged rats induces novel alterations in some metabolites that are possible new targets for monitoring in patients submitted to TRT.

Metabolic profiling and targeted lipidomics reveals a disturbed lipid profile in mothers and fetuses with intrauterine growth restriction

Fetal growth may be impaired by poor placental function or maternal conditions, each of which can influence the transfer of nutrients and oxygen from the mother to the developing fetus. Large-scale studies of metabolites (metabolomics) are key to understand cellular metabolism and pathophysiology of human conditions. Herein, maternal and cord blood plasma samples were used for NMR-based metabolic fingerprinting and profiling, including analysis of the enrichment of circulating lipid classes and subclasses, as well as the number of sub-fraction particles and their size. Changes in phosphatidylcholines and glycoproteins were prominent in growth-restricted fetuses indicating significant alterations in their abundance and biophysical properties. Lipoprotein profiles showed significantly lower plasma concentrations of cholesterol-intermediate density lipoprotein (IDL), triglycerides-IDL and high-density lipoprotein (HDL) in mothers of growth-restricted fetuses compared to controls (p < 0.05). In contrast, growth-restricted fetuses had significantly higher plasma concentrations of cholesterol and triglycerides transporting lipoproteins [LDL, IDL, and VLDL, (p < 0.005; all)], as well as increased VLDL particle types (large, medium and small). Significant changes in plasma concentrations of formate, histidine, isoleucine and citrate in growth-restricted fetuses were also observed. Comprehensive metabolic profiling reveals that both, mother and fetuses of pregnancies complicated with fetal growth restriction have a substantial disruption in lipid metabolism.

An integrated portable system for single chip simultaneous measurement of multiple disease associated metabolites

Metabolites, the small molecules that underpin life, can act as indicators of the physiological state of the body when their abundance varies, offering routes to diagnosis of many diseases. The ability to assay for multiple metabolites simultaneously will underpin a new generation of precision diagnostic tools. Here, we report the development of a handheld device based on complementary metal oxide semiconductor (CMOS) technology with multiple isolated micro-well reaction zones and integrated optical sensing allowing simultaneous enzyme-based assays of multiple metabolites (choline, xanthine, sarcosine and cholesterol) associated with multiple diseases. These metabolites were measured in clinically relevant concentration range with minimum concentrations measured: 25 μM for choline, 100 μM for xanthine, 1.25 μM for sarcosine and 50 μM for cholesterol. Linking the device to an Android-based user interface allows for quantification of metabolites in serum and urine within 2 min of applying samples to the device. The quantitative performance of the device was validated by comparison to accredited tests for cholesterol and glucose.

Metabolomics: a challenge for detecting and monitoring inborn errors of metabolism

Timely newborn screening and genetic profiling are crucial in early recognition and treatment of inborn errors of metabolism (IEMs). A proposed nosology of IEMs has inserted 1,015 well-characterized IEMs causing alterations in specific metabolic pathways. With the increasing expansion of metabolomics in clinical biochemistry and laboratory medicine communities, several research groups have focused their interest on the analysis of metabolites and their interconnections in IEMs. Metabolomics has the potential to extend metabolic information, thus allowing to achieve an accurate diagnosis for the individual patient and to discover novel IEMs. Structural and functional information on 247 metabolites associated with 147 IEMs and 202 metabolic pathways involved in various IEMs have been reported in the human metabolome data base (HMDB). For each metabolic gene, a new computational approach can be developed for predicting a set of metabolites, whose concentration is predicted to change after gene knockout in urine, blood and other biological fluids. Both targeted and untargeted mass spectrometry (MS)-based metabolomic approaches have been used to expand the range of disease-associate metabolites. The quantitative targeted approach, in conjunction with chemometrics, can be considered a basic tool for validating known diagnostic biomarkers in various metabolic disorders. The untargeted approach broadens the identification of new biomarkers in known IEMs and allows pathways analysis. Urine is an ideal biological fluid for metabolomics in neonatology; however, the lack of standardization of preanalytical phase may generate potential interferences in metabolomic studies. The integration of genomic and metabolomic data represents the current challenge for improving diagnosis and prognostication of IEMs. The goals consist in identifying both metabolically active loci and genes relevant to a disease phenotype, which means deriving disease-specific biological insights.

Identification of Comprehensive Metabotypes Associated with Cardiometabolic Diseases in the Population-Based KORA Study

SCOPE

"Metabotyping" describes the grouping of metabolically similar individuals. We aimed to identify valid metabotypes in a large cohort for targeted dietary intervention, for example, for disease prevention.

METHODS AND RESULTS

We grouped 1729 adults aged 32-77 years of the German population-based KORA F4 study (2006-2008) using k-means cluster analysis based on 34 biochemical and anthropometric parameters. We identified three metabolically distinct clusters showing significantly different biochemical parameter concentrations. Cardiometabolic disease status was determined at baseline in the F4 study and at the 7 year follow-up termed FF4 (2013/2014) to compare disease prevalence and incidence between clusters. Cluster 3 showed the most unfavorable marker profile with the highest prevalence of cardiometabolic diseases. Also, disease incidence was higher in cluster 3 compared to clusters 2 and 1, respectively, for hypertension (41.2%/25.3%/18.2%), type 2 diabetes (28.3%/5.1%/2.0%), hyperuricemia/gout (10.8%/2.3%/0.7%), dyslipidemia (19.2%/18.3%/5.6%), all metabolic (54.5%/36.8%/19.7%), and all cardiovascular (6.3%/5.5%/2.3%) diseases together.

CONCLUSION

Cluster analysis based on an extensive set of biochemical and anthropometric parameters allows the identification of comprehensive metabotypes that were distinctly different in cardiometabolic disease occurrence. As a next step, targeted dietary strategies should be developed with the goal of preventing diseases, especially in cluster 3.

Novel biomarkers for patient stratification in colorectal cancer: A review of definitions, emerging concepts, and data

Colorectal cancer (CRC) treatment has become more personalised, incorporating a combination of the individual patient risk assessment, gene testing, and chemotherapy with surgery for optimal care. The improvement of staging with high-resolution imaging has allowed more selective treatments, optimising survival outcomes. The next step is to identify biomarkers that can inform clinicians of expected prognosis and offer the most beneficial treatment, while reducing unnecessary morbidity for the patient. The search for biomarkers in CRC has been of significant interest, with questions remaining on their impact and applicability. The study of biomarkers can be broadly divided into metabolic, molecular, microRNA, epithelial-to-mesenchymal-transition (EMT), and imaging classes. Although numerous molecules have claimed to impact prognosis and treatment, their clinical application has been limited. Furthermore, routine testing of prognostic markers with no demonstrable influence on response to treatment is a questionable practice, as it increases cost and can adversely affect expectations of treatment. In this review we focus on recent developments and emerging biomarkers with potential utility for clinical translation in CRC. We examine and critically appraise novel imaging and molecular-based approaches; evaluate the promising array of microRNAs, analyze metabolic profiles, and highlight key findings for biomarker potential in the EMT pathway.

An Overview of Metabolic Phenotyping in Blood Pressure Research

PURPOSE OF THE REVIEW

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

RECENT FINDINGS

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

Expression and Metabolomic Profiling in Axial Spondyloarthritis

PURPOSE OF REVIEW

The purpose of this review is to highlight recent evidence with respect to expression and metabolomic profiling in axial spondyloarthritis (axSpA) that included ankylosing spondylitis (AS).

RECENT FINDINGS

AxSpA is not only characterized by the strongest genetic contribution for any complex rheumatic disease but is also influenced by environmental and immunological factors. Large-scale association-based studies have identified over 100 genetic variants contributing to 30% of the genetic risk of ankylosing spondylitis. Recent studies in global expression and metabolomic profiling appear to highlight common themes despite differences in tissues, populations, techniques, and relative paucity of patients in many of these studies. Expression studies support a role for immunomodulation and bone remodeling in the pathogenesis and progression of axSpA/AS, while metabolomic studies implicate the importance of the intestinal microbial metabolism as well as fat and choline metabolic pathways in AS.

Omics research project on prospective cohort studies from the Tohoku Medical Megabank Project

Population-based prospective cohort studies are indispensable for modern medical research as they provide important knowledge on the influences of many kinds of genetic and environmental factors on the cause of disease. Although traditional cohort studies are mainly conducted using questionnaires and physical examinations, modern cohort studies incorporate omics and genomic approaches to obtain comprehensive physical information, including genetic information. Here, we report the design and midterm results of multi-omics analysis on population-based prospective cohort studies from the Tohoku Medical Megabank (TMM) Project. We have incorporated genomic and metabolomic studies in the TMM cohort study as both metabolome and genome analyses are suitable for high-throughput analysis of large-scale cohort samples. Moreover, an association study between the metabolome and genome show that metabolites are an important intermediate phenotype connecting genetic and lifestyle factors to physical and pathologic phenotypes. We apply our metabolome and genome analyses to large-scale cohort samples in the following studies.

Metabotypes of response to bariatric surgery independent of the magnitude of weight loss

Objective

Bariatric surgery is considered the most efficient treatment for morbid obesity and its related diseases. However, its role as a metabolic modifier is not well understood. We aimed to determine biosignatures of response to bariatric surgery and elucidate short-term metabolic adaptations.

Methods

We used a LC- and FIA-ESI-MS/MS approach to quantify acylcarnitines, (lyso)phosphatidylcholines, sphingomyelins, amino acids, biogenic amines and hexoses in serum samples of subjects with morbid obesity (n = 39) before and 1, 3 and 6 months after bariatric surgery. K-means cluster analysis allowed to distinguish metabotypes of response to bariatric surgery.

Results

For the first time, global metabolic changes following bariatric surgery independent of the baseline health status of the subjects have been revealed. We identify two metabolic phenotypes (metabotypes) at the interval 6 months-baseline after surgery, which presented differences in the levels of compounds of urea metabolism, gluconeogenic precursors and (lyso)phospholipid particles. Clinically, metabotypes were different in terms of the degree of improvement in insulin resistance, cholesterol, low-density lipoproteins and uric acid independent of the magnitude of weight loss.

Conclusions

This study opens new perspectives and new hypotheses on the metabolic benefits of bariatric surgery and understanding of the biology of obesity and its associated diseases.

Utility of metabolic profiling of serum in the diagnosis of pregnancy complications

INTRODUCTION

Currently there are no clinical screening tests available to identify pregnancies at risk of developing preeclampsia (PET) and/or intrauterine growth restriction (IUGR), both of which are associated with abnormal placentation. Metabolic profiling is now a stable analytical platform used in many laboratories and has successfully been used to identify biomarkers associated with various pathological states.

METHODS

We used nuclear magnetic resonance spectroscopy (NMR) to metabolically profile serum samples collected from 143 pregnant women at 26-41 weeks gestation with pregnancy outcomes of PET, IUGR, PET IUGR or small for gestational age (SGA) that were age-matched to normal pre/term pregnancies.

RESULTS

Spectral analysis found no difference in the measured metabolites from normal term, pre-term and SGA samples, and of 25 identified metabolites, only glutamate was marginally different between groups. Of the identified metabolites, 3-methylhistidine, creatinine, acetyl groups and acetate, were determined to be independent predictors of PET and produced area under the curves (AUC) = 0.938 and 0.936 for the discovery and validation sets. Only 3-hydroxybutyrate was determined to be an independent predictor of IUGR, however the model had low predictive power (AUC = 0.623 and 0.581 for the discovery and validation sets).

CONCLUSIONS

A sub-panel of metabolites had strong predictive power for identifying PET samples in a validation dataset, however prediction of IUGR was more difficult using the identified metabolites. NMR based metabolomics can identify metabolites strongly associated with disease and has the potential to be useful in developing early clinical screening tests for at risk pregnancies.

Wisdom of artificial crowds feature selection in untargeted metabolomics: An application to the development of a blood-based diagnostic test for thrombotic myocardial infarction

INTRODUCTION

Heart disease remains a leading cause of global mortality. While acute myocardial infarction (colloquially: heart attack), has multiple proximate causes, proximate etiology cannot be determined by a blood-based diagnostic test. We enrolled a suitable patient cohort and conducted a non-targeted quantification of plasma metabolites by mass spectrometry for developing a test that can differentiate between thrombotic MI, non-thrombotic MI, and stable disease. A significant challenge in developing such a diagnostic test is solving the NP-hard problem of feature selection for constructing an optimal statistical classifier.

OBJECTIVE

We employed a Wisdom of Artificial Crowds (WoAC) strategy for solving the feature selection problem and evaluated the accuracy and parsimony of downstream classifiers in comparison with traditional feature selection techniques including the Lasso and selection using Random Forest variable importance criteria.

MATERIALS AND METHODS

Artificial Crowd Wisdom was generated via aggregation of the best solutions from independent and diverse genetic algorithm populations that were initialized with bootstrapping and a random subspaces constraint.

RESULTS/CONCLUSIONS

Strong evidence was observed that a statistical classifier utilizing WoAC feature selection can discriminate between human subjects presenting with thrombotic MI, non-thrombotic MI, and stable Coronary Artery Disease given abundances of selected plasma metabolites. Utilizing the abundances of twenty selected metabolites, a leave-one-out cross-validation estimated misclassification rate of 2.6% was observed. However, the WoAC feature selection strategy did not perform better than the Lasso over the current study.

SWATHtoMRM: Development of High-Coverage Targeted Metabolomics Method Using SWATH Technology for Biomarker Discovery

The complexity of metabolome presents a great analytical challenge for quantitative metabolite profiling, and restricts the application of metabolomics in biomarker discovery. Targeted metabolomics using multiple-reaction monitoring (MRM) technique has excellent capability for quantitative analysis, but suffers from the limited metabolite coverage. To address this challenge, we developed a new strategy, namely, SWATHtoMRM, which utilizes the broad coverage of SWATH-MS technology to develop high-coverage targeted metabolomics method. Specifically, SWATH-MS technique was first utilized to untargeted profile one pooled biological sample and to acquire the MS² spectra for all metabolites. Then, SWATHtoMRM was used to extract the large-scale MRM transitions for targeted analysis with coverage as high as 1000-2000 metabolites. Then, we demonstrated the advantages of SWATHtoMRM method in quantitative analysis such as coverage, reproducibility, sensitivity, and dynamic range. Finally, we applied our SWATHtoMRM approach to discover potential metabolite biomarkers for colorectal cancer (CRC) diagnosis. A high-coverage targeted metabolomics method with 1303 metabolites in one injection was developed to profile colorectal cancer tissues from CRC patients. A total of 20 potential metabolite biomarkers were discovered and validated for CRC diagnosis. In plasma samples from CRC patients, 17 out of 20 potential biomarkers were further validated to be associated with tumor resection, which may have a great potential in assessing the prognosis of CRC patients after tumor resection. Together, the SWATHtoMRM strategy provides a new way to develop high-coverage targeted metabolomics method, and facilitates the application of targeted metabolomics in disease biomarker discovery. The SWATHtoMRM program is freely available on the Internet ( http://www.zhulab.cn/software.php ).

Non-targeted metabolomic biomarkers and metabotypes of type 2 diabetes: A cross-sectional study of PREDIMED trial participants

AIM

To characterize the urinary metabolomic fingerprint and multi-metabolite signature associated with type 2 diabetes (T2D), and to classify the population into metabotypes related to T2D.

METHODS

A metabolomics analysis using the ¹H-NMR-based, non-targeted metabolomic approach was conducted to determine the urinary metabolomic fingerprint of T2D compared with non-T2D participants in the PREDIMED trial. The discriminant metabolite fingerprint was subjected to logistic regression analysis and ROC analyses to establish and to assess the multi-metabolite signature of T2D prevalence, respectively. Metabotypes associated with T2D were identified using the k-means algorithm.

RESULTS

A total of 33 metabolites were significantly different (P<0.05) between T2D and non-T2D participants. The multi-metabolite signature of T2D comprised high levels of methylsuccinate, alanine, dimethylglycine and guanidoacetate, and reduced levels of glutamine, methylguanidine, 3-hydroxymandelate and hippurate, and had a 96.4% AUC, which was higher than the metabolites on their own and glucose. Amino-acid and carbohydrate metabolism were the main metabolic alterations in T2D, and various metabotypes were identified in the studied population. Among T2D participants, those with a metabotype of higher levels of phenylalanine, phenylacetylglutamine, p-cresol and acetoacetate had significantly higher levels of plasma glucose.

CONCLUSION

The multi-metabolite signature of T2D highlights the altered metabolic fingerprint associated mainly with amino-acid, carbohydrate and microbiota metabolism. Metabotypes identified in this patient population could be related to higher risk of long-term cardiovascular events and therefore require further studies. Metabolomics is a useful tool for elucidating the metabolic complexity and interindividual variation in T2D towards the development of stratified precision nutrition and medicine. Trial registration at www.controlled-trials.com: ISRCTN35739639.

Bi-clustering of metabolic data using matrix factorization tools

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We propose a positive matrix factorization bi-clustering strategy for metabolic data.

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The approach automatically determines the number and composition of bi-clusters.

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We demonstrate its superior performance compared to other techniques.

Metabolic phenotyping technologies based on Nuclear Magnetic Spectroscopy (NMR) and Mass Spectrometry (MS) generate vast amounts of unrefined data from biological samples. Clustering strategies are frequently employed to provide insight into patterns of relationships between samples and metabolites. Here, we propose the use of a non-negative matrix factorization driven bi-clustering strategy for metabolic phenotyping data in order to discover subsets of interrelated metabolites that exhibit similar behaviour across subsets of samples. The proposed strategy incorporates bi-cross validation and statistical segmentation techniques to automatically determine the number and structure of bi-clusters. This alternative approach is in contrast to the widely used conventional clustering approaches that incorporate all molecular peaks for clustering in metabolic studies and require a priori specification of the number of clusters. We perform the comparative analysis of the proposed strategy with other bi-clustering approaches, which were developed in the context of genomics and transcriptomics research. We demonstrate the superior performance of the proposed bi-clustering strategy on both simulated (NMR) and real (MS) bacterial metabolic data.

An integrated method for direct interrogation of sphingolipid homeostasis in the heart and brain tissues of mice through postnatal development up to reproductive senescence

Development of rapid metabolomic methods poised for pathway discovery is expected to facilitate the identification of therapeutic candidates in the metabolomic approach to translational medicine. Using sphingolipid homeostasis as a prototype, we present herein an integrated method to facilitate a fast interrogation of altered sphingolipid (and phospholipid) metabolism associated with perturbed endolysosomal functions in mammalian systems. Constructed upon high performance liquid chromatography coupled to mass spectrometry, this method allows semi-quantitative measurements of more than 300 individual species within 20 min. The method was applied to investigate cardiac- and neural-specific developmental changes in sphingolipid regulation from the postnatal stage to reproductive senescence in mice, revealing that endogenous lysobisphosphatidic acids and specific complex glycosphingolipids are tightly co-regulated to foster concerted reductions in sphingolipid levels at distinct stages of postnatal development. Our lipidomic data suggest that such changing regulatory patterns in sphingolipid homeostasis is attributed to differential endolysosomal degradation of complex sphingolipids, which may be critical in ensuring efficient sphingolipid catabolism and organismal health at each stage of postnatal development.

Human Plasma Metabolomics Study across All Stages of Age-Related Macular Degeneration Identifies Potential Lipid Biomarkers

PURPOSE

To characterize the plasma metabolomic profile of patients with age-related macular degeneration (AMD) using mass spectrometry (MS).

DESIGN

Cross-sectional observational study.

PARTICIPANTS

We prospectively recruited participants with a diagnosis of AMD and a control group (>50 years of age) without any vitreoretinal disease.

METHODS

All participants underwent color fundus photography, used for AMD diagnosis and staging, according to the Age-Related Eye Disease Study classification scheme. Fasting blood samples were collected and plasma was analyzed by Metabolon, Inc. (Durham, NC), using ultrahigh-performance liquid chromatography (UPLC) and high-resolution MS. Metabolon's hardware and software were used to identify peaks and control quality. Principal component analysis and multivariate regression were performed to assess differences in the metabolomic profiles of AMD patients versus controls, while controlling for potential confounders. For biological interpretation, pathway enrichment analysis of significant metabolites was performed using MetaboAnalyst.

MAIN OUTCOME MEASURES

The primary outcome measures were levels of plasma metabolites in participants with AMD compared with controls and among different AMD severity stages.

RESULTS

We included 90 participants with AMD (30 with early AMD, 30 with intermediate AMD, and 30 with late AMD) and 30 controls. Using UPLC and MS, 878 biochemicals were identified. Multivariate logistic regression identified 87 metabolites with levels that differed significantly between AMD patients and controls. Most of these metabolites (82.8%; n = 72), including the most significant metabolites, belonged to the lipid pathways. Analysis of variance revealed that of the 87 metabolites, 48 (55.2%) also were significantly different across the different stages of AMD. A significant enrichment of the glycerophospholipids pathway was identified (P = 4.7 × 10-9) among these metabolites.

CONCLUSIONS

Participants with AMD have altered plasma metabolomic profiles compared with controls. Our data suggest that the most significant metabolites map to the glycerophospholipid pathway. These findings have the potential to improve our understanding of AMD pathogenesis, to support the development of plasma-based metabolomics biomarkers of AMD, and to identify novel targets for treatment of this blinding disease.

Environmental metabolomics with data science for investigating ecosystem homeostasis

A natural ecosystem can be viewed as the interconnections between complex metabolic reactions and environments. Humans, a part of these ecosystems, and their activities strongly affect the environments. To account for human effects within ecosystems, understanding what benefits humans receive by facilitating the maintenance of environmental homeostasis is important. This review describes recent applications of several NMR approaches to the evaluation of environmental homeostasis by metabolic profiling and data science. The basic NMR strategy used to evaluate homeostasis using big data collection is similar to that used in human health studies. Sophisticated metabolomic approaches (metabolic profiling) are widely reported in the literature. Further challenges include the analysis of complex macromolecular structures, and of the compositions and interactions of plant biomass, soil humic substances, and aqueous particulate organic matter. To support the study of these topics, we also discuss sample preparation techniques and solid-state NMR approaches. Because NMR approaches can produce a number of data with high reproducibility and inter-institution compatibility, further analysis of such data using machine learning approaches is often worthwhile. We also describe methods for data pretreatment in solid-state NMR and for environmental feature extraction from heterogeneously-measured spectroscopic data by machine learning approaches.

High-resolution Magic-angle Spinning (HR-MAS) NMR Spectroscopy

Since the beginning of high-resolution magic-angle spinning (HR-MAS) NMR spectroscopy in 1990s, we have witnessed tremendous instrumentation and methodological advancements in the HR-MAS NMR technique for semisolids. With HR-MAS, it is now possible to acquire reliable high-quality spectra in a routine and high-throughput fashion, and it has become a well-integrated metabolic screening tool for ex vivo biospecimens such as tissue biopsies, cells and organisms for NMR-based metabolomics research. This chapter provides the basic principles of HR-MAS and describes a few recent noteworthy developments that could strengthen the role of HR-MAS as a frontline NMR technique for metabolomics.

Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction

We introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS²), and NMR into a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter out the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture, and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana. The NMR/MS² approach is well suited to the discovery of new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases.

The Expressed Genome in Cardiovascular Diseases and Stroke: Refinement, Diagnosis, and Prediction: A Scientific Statement From the American Heart Association

There have been major advances in our knowledge of the contribution of DNA sequence variations to cardiovascular disease and stroke. However, the inner workings of the body reflect the complex interplay of factors beyond the DNA sequence, including epigenetic modifications, RNA transcripts, proteins, and metabolites, which together can be considered the "expressed genome." The emergence of high-throughput technologies, including epigenomics, transcriptomics, proteomics, and metabolomics, is now making it possible to address the contributions of the expressed genome to cardiovascular disorders. This statement describes how the expressed genome can currently and, in the future, potentially be used to diagnose diseases and to predict who will develop diseases such as coronary artery disease, stroke, heart failure, and arrhythmias.

Metabolomics: Challenges and Opportunities in Systems Biology Studies

Metabolomics has the capability of providing predisposition, diagnostic, prognostic, and therapeutic biomarker profiles of individual patients, since a large number of metabolites can be measured in an unbiased manner from biological samples. In this setting, ¹H-Nuclear Magnetic Resonance (NMR) spectroscopy of biofluids such as plasma, urine, and fecal water offers the opportunity to identify patterns of biomarker changes that reflects the physiological or pathological status of an individual patient.In this chapter, we show as a metabolomics study can be used to diagnose a disease, classifying patients as healthy or as pathological taking into account individual variability.

Urinary metabolic profiling of asymptomatic acute intermittent porphyria using a rule-mining-based algorithm

INTRODUCTION

Metabolomic profiling combines Nuclear Magnetic Resonance spectroscopy with supervised statistical analysis that might allow to better understanding the mechanisms of a disease.

OBJECTIVES

In this study, the urinary metabolic profiling of individuals with porphyrias was performed to predict different types of disease, and to propose new pathophysiological hypotheses.

METHODS

Urine ¹H-NMR spectra of 73 patients with asymptomatic acute intermittent porphyria (aAIP) and familial or sporadic porphyria cutanea tarda (f/sPCT) were compared using a supervised rule-mining algorithm. NMR spectrum buckets bins, corresponding to rules, were extracted and a logistic regression was trained.

RESULTS

Our rule-mining algorithm generated results were consistent with those obtained using partial least square discriminant analysis (PLS-DA) and the predictive performance of the model was significant. Buckets that were identified by the algorithm corresponded to metabolites involved in glycolysis and energy-conversion pathways, notably acetate, citrate, and pyruvate, which were found in higher concentrations in the urines of aAIP compared with PCT patients. Metabolic profiling did not discriminate sPCT from fPCT patients.

CONCLUSION

These results suggest that metabolic reprogramming occurs in aAIP individuals, even in the absence of overt symptoms, and supports the relationship that occur between heme synthesis and mitochondrial energetic metabolism.

Serum Metabolic Profile Alteration Reveals Response to Platinum-Based Combination Chemotherapy for Lung Cancer: Sensitive Patients Distinguished from Insensitive ones

Most lung cancers are diagnosed at fairly advanced stages due to limited clinical symptoms. Platinum-based chemotherapy, either as single regimen or in combination with radiation, is one of the major recommendations for the patients. Earlier evaluation of the effectiveness of the chemotherapies is critical for developing better treatment plan given the toxicity of the chemotherapeutic reagents. Drug efficacy could be reflected in the systemic metabolism characteristics though knowledge about which remains scarce. In this study, serum metabolism influence of three types of commonly used platinum-based combination chemotherapy regimens, namely cisplatin with gemcitabine, vinorelbine or docetaxel, were studied using pattern recognition coupled with nuclear magnetic resonance techniques. The treated patients were divided into sensitive or insensitive subgroups according to their response to the treatments. We found that insensitive subjects can be identified from the sensitive ones with up-regulation of glucose and taurine but reduced alanine and lactate concentrations in serum. The combination chemotherapy of lung cancer is accompanied by disturbances of multiple metabolic pathways such as energy metabolism, phosphatidylcholine biosynthesis, so that the treated patients were marginally discriminated from the untreated. Serum metabolic profile of patients shows potential as an indicator of their response to platinum-based combination chemotherapy.

Metabolomics based predictive biomarker model of ARDS: A systemic measure of clinical hypoxemia

Despite advancements in ventilator technologies, lung supportive and rescue therapies, the outcome and prognostication in acute respiratory distress syndrome (ARDS) remains incremental and ambiguous. Metabolomics is a potential insightful measure to the diagnostic approaches practiced in critical disease settings. In our study patients diagnosed with mild and moderate/severe ARDS clinically governed by hypoxemic P/F ratio between 100-300 but with indistinct molecular phenotype were discriminated employing nuclear magnetic resonance (NMR) based metabolomics of mini bronchoalveolar lavage fluid (mBALF). Resulting biomarker prototype comprising six metabolites was substantiated highlighting ARDS susceptibility/recovery. Both the groups (mild and moderate/severe ARDS) showed distinct biochemical profile based on 83.3% classification by discriminant function analysis and cross validated accuracy of 91% using partial least squares discriminant analysis as major classifier. The predictive performance of narrowed down six metabolites were found analogous with chemometrics. The proposed biomarker model consisting of six metabolites proline, lysine/arginine, taurine, threonine and glutamate were found characteristic of ARDS sub-stages with aberrant metabolism observed mainly in arginine, proline metabolism, lysine synthesis and so forth correlating to diseased metabotype. Thus NMR based metabolomics has provided new insight into ARDS sub-stages and conclusively a precise biomarker model proposed, reflecting underlying metabolic dysfunction aiding prior clinical decision making.

Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Large-Scale Epidemiology: A Primer on -Omic Technologies

Detailed metabolic profiling in large-scale epidemiologic studies has uncovered novel biomarkers for cardiometabolic diseases and clarified the molecular associations of established risk factors. A quantitative metabolomics platform based on nuclear magnetic resonance spectroscopy has found widespread use, already profiling over 400,000 blood samples. Over 200 metabolic measures are quantified per sample; in addition to many biomarkers routinely used in epidemiology, the method simultaneously provides fine-grained lipoprotein subclass profiling and quantification of circulating fatty acids, amino acids, gluconeogenesis-related metabolites, and many other molecules from multiple metabolic pathways. Here we focus on applications of magnetic resonance metabolomics for quantifying circulating biomarkers in large-scale epidemiology. We highlight the molecular characterization of risk factors, use of Mendelian randomization, and the key issues of study design and analyses of metabolic profiling for epidemiology. We also detail how integration of metabolic profiling data with genetics can enhance drug development. We discuss why quantitative metabolic profiling is becoming widespread in epidemiology and biobanking. Although large-scale applications of metabolic profiling are still novel, it seems likely that comprehensive biomarker data will contribute to etiologic understanding of various diseases and abilities to predict disease risks, with the potential to translate into multiple clinical settings.

A Phytochemical-Sensing Strategy Based on Mass Spectrometry Imaging and Metabolic Profiling for Understanding the Functionality of the Medicinal Herb Green Tea

Low-molecular-weight phytochemicals have health benefits and reduce the risk of diseases, but the mechanisms underlying their activities have remained elusive because of the lack of a methodology that can easily visualize the exact behavior of such small molecules. Recently, we developed an in situ label-free imaging technique, called mass spectrometry imaging, for visualizing spatially-resolved biotransformations based on simultaneous mapping of the major bioactive green tea polyphenol and its phase II metabolites. In addition, we established a mass spectrometry-based metabolic profiling technique capable of evaluating the bioactivities of diverse green tea extracts, which contain multiple phytochemicals, by focusing on their compositional balances. This methodology allowed us to simultaneously evaluate the relative contributions of the multiple compounds present in a multicomponent system to its bioactivity. This review highlights small molecule-sensing techniques for visualizing the complex behaviors of herbal components and linking such information to an enhanced understanding of the functionalities of multicomponent medicinal herbs.

Metabolomics Applications in Precision Medicine: An Oncological Perspective

Nowadays, cancer therapy remains limited by the conventional one-size-fits-all approach. In this context, treatment decisions are based on the clinical stage of disease but fail to ascertain the individual´s underlying biology and its role in driving malignancy. The identification of better therapies for cancer treatment is thus limited by the lack of sufficient data regarding the characterization of specific biochemical signatures associated with each particular cancer patient or group of patients. Metabolomics approaches promise a better understanding of cancer, a disease characterized by significant alterations in bioenergetic metabolism, by identifying changes in the pattern of metabolite expression in addition to changes in the concentration of individual metabolites as well as alterations in biochemical pathways. These approaches hold the potential of identifying novel biomarkers with different clinical applications, including the development of more specific diagnostic methods based on the characterization of metabolic subtypes, the monitoring of currently used cancer therapeutics to evaluate the response and the prognostic outcome with a given therapy, and the evaluation of the mechanisms involved in disease relapse and drug resistance. This review discusses metabolomics applications in different oncological processes underlining the potential of this omics approach to further advance the implementation of precision medicine in the oncology area.

Metabolomic and transcriptomic profiling of hepatocellular carcinomas in Hras12V transgenic mice

Activation of the Ras/MAPK pathway is prevalently involved in the occurrence and development of hepatocellular carcinoma (HCC). However, its effects on the deregulated cellular metabolic processes involved in HCC in vivo remain unknown. In this study, a mouse model of HCC induced by hepatocyte-specific expression of the Hras12V oncogene was investigated using an integrative analysis of metabolomics and transcriptomics data. Consistent with the phenotype of abundant lipid droplets in HCC, the lipid biosynthesis in HCC was significantly enhanced by (1) a sufficient supply of acetyl-CoA from enhanced glycolysis and citrate shuttle activity; (2) a sufficient supply of NADPH from enhanced pentose phosphate pathway (PPP) activity; (3) upregulation of key enzymes associated with lipid biosynthesis; and (4) downregulation of key enzymes associated with bile acid biosynthesis. In addition, glutathione (GSH) was significantly elevated, which may result from a sufficient supply of 5-oxoproline and L-glutamate as well as an enhanced reduction in the process of GSSG being turned into GSH by NADPH. The high level of GSH along with elevated Bcl2 and Ucp2 expression may contribute to a normal level of reactive oxygen species (ROS) in HCC. In conclusion, our results suggest that the lipid metabolism, glycolysis, PPP, tricarboxylic acid (TCA) cycle, citrate shuttle activity, bile acid synthesis, and redox homeostasis in the HCC induced by ras oncogene are significantly perturbed, and these altered metabolic processes may play crucial roles in the carcinogenesis, development, and pathological characteristics of HCC.

Improving Visualization and Interpretation of Metabolome-Wide Association Studies: An Application in a Population-Based Cohort Using Untargeted 1H NMR Metabolic Profiling

¹H NMR spectroscopy of biofluids generates reproducible data allowing detection and quantification of small molecules in large population cohorts. Statistical models to analyze such data are now well-established, and the use of univariate metabolome wide association studies (MWAS) investigating the spectral features separately has emerged as a computationally efficient and interpretable alternative to multivariate models. The MWAS rely on the accurate estimation of a metabolome wide significance level (MWSL) to be applied to control the family wise error rate. Subsequent interpretation requires efficient visualization and formal feature annotation, which, in-turn, call for efficient prioritization of spectral variables of interest. Using human serum ¹H NMR spectroscopic profiles from 3948 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we have performed a series of MWAS for serum levels of glucose. We first propose an extension of the conventional MWSL that yields stable estimates of the MWSL across the different model parameterizations and distributional features of the outcome. We propose both efficient visualization methods and a strategy based on subsampling and internal validation to prioritize the associations. Our work proposes and illustrates practical and scalable solutions to facilitate the implementation of the MWAS approach and improve interpretation in large cohort studies.

Metabolomics As a Tool for the Characterization of Drug-Resistant Epilepsy

Purpose

Drug resistance is a critical issue in the treatment of epilepsy, contributing to clinical emergencies and increasing both serious social and economic burdens on the health system. The wide variety of potential drug combinations followed by often failed consecutive attempts to match drugs to an individual patient may mean that this treatment stage may last for years with suboptimal benefit to the patient. Given these challenges, it is valuable to explore the availability of new methodologies able to shorten the period of determining a rationale pharmacologic treatment. Metabolomics could provide such a tool to investigate possible markers of drug resistance in subjects with epilepsy.

Methods

Blood samples were collected from (1) controls (C) (n = 35), (2) patients with epilepsy “responder” (R) (n = 18), and (3) patients with epilepsy “non-responder” (NR) (n = 17) to the drug therapy. The samples were analyzed using nuclear magnetic resonance spectroscopy, followed by multivariate statistical analysis.

Key findings

A different metabolic profile based on metabolomics analysis of the serum was observed between C and patients with epilepsy and also between R and NR patients. It was possible to identify the discriminant metabolites for the three classes under investigation. Serum from patients with epilepsy were characterized by increased levels of 3-OH-butyrate, 2-OH-valerate, 2-OH-butyrate, acetoacetate, acetone, acetate, choline, alanine, glutamate, scyllo-inositol (C < R < NR), and decreased concentration of glucose, lactate, and citrate compared to C (C > R > NR).

Significance

In conclusion, metabolomics may represent an important tool for discovery of differences between subjects affected by epilepsy responding or resistant to therapies and for the study of its pathophysiology, optimizing the therapeutic resources and the quality of life of patients.

Long-term effects of early antibiotic intervention on blood parameters, apparent nutrient digestibility, and fecal microbial fermentation profile in pigs with different dietary protein levels

Backgroud

This study aimed to determine the effects of early antibiotic intervention (EAI) on subsequent blood parameters, apparent nutrient digestibility, and fecal fermentation profile in pigs with different dietary crude protein (CP) levels. Eighteen litters of piglets (total 212) were randomly allocated to 2 groups and were fed a creep feed diet with or without in-feed antibiotics (olaquindox, oxytetracycline calcium and kitasamycin) from postnatal d 7 to d 42. On d 42, the piglets within the control or antibiotic group were mixed, respectively, and then further randomly assigned to a normal- (20%, 18%, and 14% CP from d 42 to d 77, d 77 to d 120, and d 120 to d 185, respectively) or a low-CP diet (16%, 14%, and 10% CP from d 42 to d 77, d 77 to d 120, and d 120 to d 185, respectively), generating 4 groups. On d 77 (short-term) and d 185 (long-term), serum and fecal samples were obtained for blood parameters, microbial composition and microbial metabolism analysis.

Results

EAI increased (P < 0.05) albumin and glucose concentrations in low-CP diet on d 77, and increased (P < 0.05) urea concentration in normal-CP diet. On d 185, EAI increased (P < 0.05) globulin concentration in normal-CP diets, but decreased glucose concentration. For nutrient digestibility, EAI increased (P < 0.05) digestibility of CP on d 77. For fecal microbiota, the EAI as well as low-CP diet decreased (P < 0.05) E. coli count on d 77. For fecal metabolites, on d 77, EAI decreased (P < 0.05) total amines concentration but increased skatole concentration in low-CP diet. On d 185, the EAI increased (P < 0.05) putrescine and total amines concentrations in low-CP diets but reduced (P < 0.05) in the normal-CP diets. The low-CP diet decreased the concentrations of these compounds.

Conclusions

Collectively, these results indicate that EAI has short-term effects on the blood parameters and fecal microbial fermentation profile. The effects of EAI varied between CP levels, which was characterized by the significant alteration of glucose and putrescine concentration.

Electronic supplementary material

The online version of this article (doi:10.1186/s40104-017-0192-2) contains supplementary material, which is available to authorized users.

Metabotyping and its application in targeted nutrition: an overview

Metabolic diversity leads to differences in nutrient requirements and responses to diet and medication between individuals. Using the concept of metabotyping – that is, grouping metabolically similar individuals – tailored and more efficient recommendations may be achieved. The aim of this study was to review the current literature on metabotyping and to explore its potential for better targeted dietary intervention in subjects with and without metabolic diseases. A comprehensive literature search was performed in PubMed, Google and Google Scholar to find relevant articles on metabotyping in humans including healthy individuals, population-based samples and patients with chronic metabolic diseases. A total of thirty-four research articles on human studies were identified, which established more homogeneous subgroups of individuals using statistical methods for analysing metabolic data. Differences between studies were found with respect to the samples/populations studied, the clustering variables used, the statistical methods applied and the metabotypes defined. According to the number and type of the selected clustering variables, the definitions of metabotypes differed substantially; they ranged between general fasting metabotypes, more specific fasting parameter subgroups like plasma lipoprotein or fatty acid clusters and response groups to defined meal challenges or dietary interventions. This demonstrates that the term ‘metabotype’ has a subjective usage, calling for a formalised definition. In conclusion, this literature review shows that metabotyping can help identify subgroups of individuals responding differently to defined nutritional interventions. Targeted recommendations may be given at such metabotype group levels. Future studies should develop and validate definitions of generally valid metabotypes by exploiting the increasingly available metabolomics data sets.

Gut microbiota modulation of chemotherapy efficacy and toxicity

Evidence is growing that the gut microbiota modulates the host response to chemotherapeutic drugs, with three main clinical outcomes: facilitation of drug efficacy; abrogation and compromise of anticancer effects; and mediation of toxicity. The implication is that gut microbiota are critical to the development of personalized cancer treatment strategies and, therefore, a greater insight into prokaryotic co-metabolism of chemotherapeutic drugs is now required. This thinking is based on evidence from human, animal and in vitro studies that gut bacteria are intimately linked to the pharmacological effects of chemotherapies (5-fluorouracil, cyclophosphamide, irinotecan, oxaliplatin, gemcitabine, methotrexate) and novel targeted immunotherapies such as anti-PD-L1 and anti-CLTA-4 therapies. The gut microbiota modulate these agents through key mechanisms, structured as the 'TIMER' mechanistic framework: Translocation, Immunomodulation, Metabolism, Enzymatic degradation, and Reduced diversity and ecological variation. The gut microbiota can now, therefore, be targeted to improve efficacy and reduce the toxicity of current chemotherapy agents. In this Review, we outline the implications of pharmacomicrobiomics in cancer therapeutics and define how the microbiota might be modified in clinical practice to improve efficacy and reduce the toxic burden of these compounds.

Human plasma metabolomics in age-related macular degeneration (AMD) using nuclear magnetic resonance spectroscopy

PURPOSE

To differentiate the plasma metabolomic profile of patients with age related macular degeneration (AMD) from that of controls, by Nuclear Magnetic Resonance (NMR) spectroscopy.

METHODS

Two cohorts (total of 396 subjects) representative of central Portugal and Boston, USA phenotypes were studied. For each cohort, subjects were grouped according to AMD stage (early, intermediate and late). Multivariate analysis of plasma NMR spectra was performed, followed by signal integration and univariate analysis.

RESULTS

Small changes were detected in the levels of some amino acids, organic acids, dimethyl sulfone and specific lipid moieties, thus providing some biochemical information on the disease. The possible confounding effects of gender, smoking history and age were assessed in each cohort and found to be minimal when compared to that of the disease. A similar observation was noted in relation to age-related comorbidities. Furthermore, partially distinct putative AMD metabolite fingerprints were noted for the two cohorts studied, reflecting the importance of nutritional and other lifestyle habits in determining AMD metabolic response and potential biomarker fingerprints. Notably, some of the metabolite changes detected were noted as potentially differentiating controls from patients diagnosed with early AMD.

CONCLUSION

For the first time, this study showed metabolite changes in the plasma of patients with AMD as compared to controls, using NMR. Geographical origins were seen to affect AMD patients´ metabolic profile and some metabolites were found to be valuable in potentially differentiating controls from early stage AMD patients. Metabolomics has the potential of identifying biomarkers for AMD, and further work in this area is warranted.

Simultaneous Quantification of Amino Metabolites in Multiple Metabolic Pathways Using Ultra-High Performance Liquid Chromatography with Tandem-mass Spectrometry

Metabolites containing amino groups cover multiple pathways and play important roles in redox homeostasis and biosyntheses of proteins, nucleotides and neurotransmitters. Here, we report a new method for simultaneous quantification of 124 such metabolites. This is achieved by derivatization-assisted sensitivity enhancement with 5-aminoisoquinolyl-N-hydroxysuccinimidyl carbamate (5-AIQC) followed with comprehensive analysis using ultra-high performance liquid chromatography and electrospray ionization tandem mass spectrometry (UHPLC-MS/MS). In an one-pot manner, this quantification method enables simultaneous coverage of 20 important metabolic pathways including protein biosynthesis/degradation, biosyntheses of catecholamines, arginine and glutathione, metabolisms of homocysteine, taurine-hypotaurine etc. Compared with the reported ones, this method is capable of simultaneously quantifying thiols, disulfides and other oxidation-prone analytes in a single run and suitable for quantifying aromatic amino metabolites. This method is also much more sensitive for all tested metabolites with LODs well below 50 fmol (at sub-fmol for most tested analytes) and shows good precision for retention time and quantitation with inter-day and intra-day relative standard deviations (RSDs) below 15% and good recovery from renal cancer tissue, rat urine and plasma. The method was further applied to quantify the amino metabolites in silkworm hemolymph from multiple developmental stages showing its applicability in metabolomics and perhaps some clinical chemistry studies.

Potential Impact and Study Considerations of Metabolomics in Cardiovascular Health and Disease: A Scientific Statement From the American Heart Association

Through the measure of thousands of small-molecule metabolites in diverse biological systems, metabolomics now offers the potential for new insights into the factors that contribute to complex human diseases such as cardiovascular disease. Targeted metabolomics methods have already identified new molecular markers and metabolomic signatures of cardiovascular disease risk (including branched-chain amino acids, select unsaturated lipid species, and trimethylamine-N-oxide), thus in effect linking diverse exposures such as those from dietary intake and the microbiota with cardiometabolic traits. As technologies for metabolomics continue to evolve, the depth and breadth of small-molecule metabolite profiling in complex systems continue to advance rapidly, along with prospects for ongoing discovery. Current challenges facing the field of metabolomics include scaling throughput and technical capacity for metabolomics approaches, bioinformatic and chemoinformatic tools for handling large-scale metabolomics data, methods for elucidating the biochemical structure and function of novel metabolites, and strategies for determining the true clinical relevance of metabolites observed in association with cardiovascular disease outcomes. Progress made in addressing these challenges will allow metabolomics the potential to substantially affect diagnostics and therapeutics in cardiovascular medicine.