Exposure to Molybdate Results in Metabolic Disorder: An Integrated Study of the Urine Elementome and Serum Metabolome in Mice

The increasing use of molybdate has raised concerns about its potential toxicity in humans. However, the potential toxicity of molybdate under the current level of human exposure remains largely unknown. Endogenous metabolic alterations that are caused in humans by environmental exposure to pollutants are associated with the occurrence and progression of many diseases. This study exposed eight-week-old male C57 mice to sodium molybdate at doses relevant to humans (0.01 and 1 mg/kg/day) for eight weeks. Inductively coupled plasma mass spectrometry (ICP-MS) and ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS) were utilized to assess changes in urine element levels and serum metabolites in mice, respectively. A total of 838 subjects from the NHANES 2017-2018 population database were also included in our study to verify the associations between molybdenum and cadmium found in mice. Analysis of the metabolome in mice revealed that four metabolites in blood serum exhibited significant changes, including 5-aminolevulinic acid, glycolic acid, l-acetylcarnitine, and 2,3-dihydroxypropyl octanoate. Analysis of the elementome revealed a significant increase in urine levels of cadmium after molybdate exposure in mice. Notably, molybdenum also showed a positive correlation with cadmium in humans from the NHANES database. Further analysis identified a positive correlation between cadmium and 2,3-dihydroxypropyl octanoate in mice. In conclusion, these findings suggest that molybdate exposure disrupted amino acid and lipid metabolism, which may be partially mediated by molybdate-altered cadmium levels. The integration of elementome and metabolome data provides sensitive information on molybdate-induced metabolic disorders and associated toxicities at levels relevant to human exposure.

The Role of Gut Microbiota and Microbiota-Related Serum Metabolites in the Progression of Diabetic Kidney Disease

Objective: Diabetic kidney disease (DKD) has become the major cause of end-stage renal disease (ESRD) associated with the progression of renal fibrosis. As gut microbiota dysbiosis is closely related to renal damage and fibrosis, we investigated the role of gut microbiota and microbiota-related serum metabolites in DKD progression in this study.

Methods: Fecal and serum samples obtained from predialysis DKD patients from January 2017 to December 2019 were detected using 16S rRNA gene sequencing and liquid chromatography-mass spectrometry, respectively. Forty-one predialysis patients were divided into two groups according to their estimated glomerular filtration rate (eGFR): the DKD non-ESRD group (eGFR ≥ 15 ml/min/1.73 m²) (n = 22), and the DKD ESRD group (eGFR < 15 ml/min/1.73 m²) (n = 19). The metabolic pathways related to differential serum metabolites were obtained by the KEGG pathway analysis. Differences between the two groups relative to gut microbiota profiles and serum metabolites were investigated, and associations between gut microbiota and metabolite concentrations were assessed. Correlations between clinical indicators and both microbiota-related metabolites and gut microbiota were calculated by Spearman rank correlation coefficient and visualized by heatmap.

Results: Eleven different intestinal floras and 239 different serum metabolites were identified between the two groups. Of 239 serum metabolites, 192 related to the 11 different intestinal flora were mainly enriched in six metabolic pathways, among which, phenylalanine and tryptophan metabolic pathways were most associated with DKD progression. Four microbiota-related metabolites in the phenylalanine metabolic pathway [hippuric acid (HA), L-(−)-3-phenylactic acid, trans-3-hydroxy-cinnamate, and dihydro-3-coumaric acid] and indole-3 acetic acid (IAA) in the tryptophan metabolic pathway positively correlated with DKD progression, whereas L-tryptophan in the tryptophan metabolic pathway had a negative correlation. Intestinal flora g_Abiotrophia and g_norank_f_Peptococcaceae were positively correlated with the increase in renal function indicators and serum metabolite HA. G_Lachnospiraceae_NC2004_Group was negatively correlated with the increase in renal function indicators and serum metabolites [L-(−)-3-phenyllactic acid and IAA].

Conclusions: This study highlights the interaction among gut microbiota, serum metabolites, and clinical indicators in predialysis DKD patients, and provides new insights into the role of gut microbiota and microbiota-related serum metabolites that were enriched in the phenylalanine and tryptophan metabolic pathways, which correlated with the progression of DKD.

Omics: The way forward to enhance abiotic stress tolerance in Brassica napus L

Plant abiotic stresses negative affects growth and development, causing a massive reduction in global agricultural production. Rapeseed (Brassica napus L.) is a major oilseed crop because of its economic value and oilseed production. However, its productivity has been reduced by many environmental adversities. Therefore, it is a prime need to grow rapeseed cultivars, which can withstand numerous abiotic stresses. To understand the various molecular and cellular mechanisms underlying the abiotic stress tolerance and improvement in rapeseed, omics approaches have been extensively employed in recent years. This review summarized the recent advancement in genomics, transcriptomics, proteomics, metabolomics, and their imploration in abiotic stress regulation in rapeseed. Some persisting bottlenecks have been highlighted, demanding proper attention to fully explore the omics tools. Further, the potential prospects of the CRISPR/Cas9 system for genome editing to assist molecular breeding in developing abiotic stress-tolerant rapeseed genotypes have also been explained. In short, the combination of integrated omics, genome editing, and speed breeding can alter rapeseed production worldwide.

Urinary metabolite quantitative trait loci in children and their interaction with dietary factors

Human metabolism is influenced by genetic and environmental factors. Previous studies have identified over 23 loci associated with more than 26 urine metabolites levels in adults, which are known as urinary metabolite quantitative trait loci (metabQTLs). The aim of the present study is the identification for the first time of urinary metabQTLs in children and their interaction with dietary patterns. Association between genome-wide genotyping data and 44 urine metabolite levels measured by proton nuclear magnetic resonance spectroscopy was tested in 996 children from the Human Early Life Exposome project. Twelve statistically significant urine metabQTLs were identified, involving 11 unique loci and 10 different metabolites. Comparison with previous findings in adults revealed that six metabQTLs were already known, and one had been described in serum and three were involved the same locus as other reported metabQTLs but had different urinary metabolites. The remaining two metabQTLs represent novel urine metabolite-locus associations, which are reported for the first time in this study [single nucleotide polymorphism (SNP) rs12575496 for taurine, and the missense SNP rs2274870 for 3-hydroxyisobutyrate]. Moreover, it was found that urinary taurine levels were affected by the combined action of genetic variation and dietary patterns of meat intake as well as by the interaction of this SNP with beverage intake dietary patterns. Overall, we identified 12 urinary metabQTLs in children, including two novel associations. While a substantial part of the identified loci affected urinary metabolite levels both in children and in adults, the metabQTL for taurine seemed to be specific to children and interacted with dietary patterns.

High-Performance Liquid Chromatography-Mass Spectrometry-Based Lipid Metabolite Profiling of Acromegaly

CONTEXT

Metabolic disorders, especially dysregulated lipid metabolism, increase the risk of cardiovascular mortality in acromegaly. Previous studies measuring plasma macromolecular lipids have yielded conflicting results.

PURPOSE

To explore the plasma lipid metabolite profiles by metabolomics analysis and identify potential metabolites associated with cardiac function in acromegaly.

METHODS

Plasma was obtained from 80 newly diagnosed, untreated patients with acromegaly and 80 healthy controls. Echocardiography was performed. Based on the results of an oral glucose tolerance test (OGTT), patients were categorized into 2 groups: normal glucose tolerance (NGT, n = 28) and impaired glucose tolerance or diabetes mellitus (IGT/DM, n = 52). High-performance liquid chromatography-mass spectrometry (HPLC-MS)-based metabolomics analysis was conducted. Data were processed by principal components analysis (PCA), orthogonal partial least square-discriminant analysis (OPLS-DA), and MetaboAnalyst 4.0. Associations between metabolic substances and cardiovascular parameters were also explored.

RESULTS

Metabolomics uncovered a distinct metabolic pattern between acromegaly and healthy controls, and perturbed pathways mainly include glycerophospholipid metabolism, sphingolipid metabolism, as well as linoleic acid metabolism. Collective analysis showed that phosphatidylethanolamine (PE) (22:6/16:0) was positively correlated with LV mass, while lysophosphatidylcholine (LysoPC) (16:0) was positively correlated with fractional shortening (FS) and left ventricle ejection fraction (LVEF).

CONCLUSION

Patients with acromegaly have distinct lipid metabolite profiling, while PE (22:6/16:0) and LysoPC (16:0) are correlated with cardiac structure and function, which may contribute to the risk of cardiovascular complications.

Metabolomic Analysis of Toxoplasma gondii Tachyzoites

This protocol describes the use of ¹³C-stable isotope labeling, combined with metabolite profiling, to investigate the metabolism of the tachyzoite stage of the protozoan parasite Toxoplasma gondii. T. gondii tachyzoites can infect any nucleated cell in their vertebrate (including human) hosts, and utilize a range of carbon sources that freely permeate across the limiting membrane of the specialized vacuole within which they proliferate. Methods for cultivating tachyzoites in human foreskin fibroblasts and metabolically labeling intracellular and naturally egressed tachyzoites with a range of ¹³C-labeled carbon sources are described. Parasites are harvested and purified from host metabolites, with rapid metabolic quenching and ¹³C-enrichment in intracellular polar metabolites quantified by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). The mass isotopomer distribution of key metabolites is determined using DExSI software. This method can be used to measure perturbations in parasite metabolism induced by drug inhibition or genetic manipulation of enzyme levels and is broadly applicable to other cultured or intracellular parasite stages.

Untargeted metabolomics reveals alterations in metabolites of lipid metabolism and immune pathways in the serum of rats after long-term oral administration of Amalaki rasayana

Amalaki rasayana, a traditional preparation, is widely used by Ayurvedic physicians for the treatment of inflammatory conditions, cardiovascular diseases, and cancer. Metabolic alterations induced by Amalaki rasayana intervention are unknown. We investigated the modulations in serum metabolomic profiles in Wistar rats following long-term oral administration of Amalaki rasayana. Global metabolic profiling was performed of the serum of rats administered with either Amalaki rasayana (AR) or ghee + honey (GH) for 18 months and control animals which were left untreated. Amalaki rasayana components were confirmed from AR extract using HR-LCMS analysis. Significant reductions in prostaglandin J2, 11-dehydrothromboxane B2, and higher levels of reduced glutathione and glycitein metabolites were observed in the serum of AR administered rats compared to the control groups. Eleven different metabolites classified as phospholipids, glycerophospholipids, glucoside derivatives, organic acids, and glycosphingolipid were exclusively observed in the AR administered rats. Pathway analysis suggests that altered metabolites in AR administered rats are those associated with different biochemical pathways of arachidonic acid metabolism, fatty acid metabolism, leukotriene metabolism, G-protein mediated events, phospholipid metabolism, and the immune system. Targeted metabolomics confirmed the presence of gallic acid, ellagic acid, and arachidonic acid components in the AR extract. The known activities of these components can be correlated with the altered metabolic profile following long-term AR administration. AR also activates IGF1R-Akt-Foxo3 signaling axis in heart tissues of rats administered with AR. Our study identifies AR components that induce alterations in lipid metabolism and immune pathways in animals which consume AR for an extended period.

[Application of metabolomics in neonatal clinical practice]

Metabolomics is an emerging and popular subject in the post-genome era, and a large number of studies have been noted on the application of metabolomics in health evaluation, growth and development evaluation, disease diagnosis, and therapeutic efficacy evaluation. As a special period of life, the neonatal period is characterized by rapid cell renewing, consumption of a lot of energy and materials, and changes in metabolic pathways, all of which affect the level of metabolites. However, there is still no reference standard for metabolic level and profile in neonates. This article reviews the current status of metabolic research on neonatal growth and development and common diseases and related clinical application of metabolomics, so as to provide new ideas for nutrition guidance and evaluation, selection of therapeutic regimens, and new drug research in neonates.

Understanding mixed environmental exposures using metabolomics via a hierarchical community network model in a cohort of California women in 1960's

Even though the majority of population studies in environmental health focus on a single factor, environmental exposure in the real world is a mixture of many chemicals. The concept of "exposome" leads to an intellectual framework of measuring many exposures in humans, and the emerging metabolomics technology offers a means to read out both the biological activity and environmental impact in the same dataset. How to integrate exposome and metabolome in data analysis is still challenging. Here, we employ a hierarchical community network to investigate the global associations between the metabolome and mixed exposures including DDTs, PFASs and PCBs, in a women cohort with sera collected in California in the 1960s. Strikingly, this analysis revealed that the metabolite communities associated with the exposures were non-specific and shared among exposures. This suggests that a small number of metabolic phenotypes may account for the response to a large class of environmental chemicals.

Biochemical, Metabolomic, and Genetic Analyses of Dephospho Coenzyme A Kinase Involved in Coenzyme A Biosynthesis in the Human Enteric Parasite Entamoeba histolytica

Coenzyme A (CoA) is an essential cofactor for numerous cellular reactions in all living organisms. In the protozoan parasite Entamoeba histolytica, CoA is synthesized in a pathway consisting of four enzymes with dephospho-CoA kinase (DPCK) catalyzing the last step. However, the metabolic and physiological roles of E. histolytica DPCK remain elusive. In this study, we took biochemical, reverse genetic, and metabolomic approaches to elucidate role of DPCK in E. histolytica. The E. histolytica genome encodes two DPCK isotypes (EhDPCK1 and EhDPCK2). Epigenetic gene silencing of Ehdpck1 and Ehdpck2 caused significant reduction of DPCK activity, intracellular CoA concentrations, and also led to growth retardation in vitro, suggesting importance of DPCK for CoA synthesis and proliferation. Furthermore, metabolomic analysis showed that suppression of Ehdpck gene expression also caused decrease in the level of acetyl-CoA, and metabolites involved in amino acid, glycogen, hexosamine, nucleic acid metabolisms, chitin, and polyamine biosynthesis. The kinetic properties of E. histolytica and human DPCK showed remarkable differences, e.g., the Km values of E. histolytica and human DPCK were 58-114 and 5.2 μM toward dephospho-CoA and 15-20 and 192 μM for ATP, respectively. Phylogenetic analysis also supported the uniqueness of the amebic enzyme compared to the human counterpart. These biochemical, evolutionary features, and physiological importance of EhDPCKs indicate that EhDPCK represents the rational target for the development of anti-amebic agents.

A targeted metabolomics approach for clinical diagnosis of inborn errors of metabolism

Metabolome, the ultimate functional product of the genome, can be studied through identification and quantification of small molecules. The global metabolome influences the individual phenotype through clinical and environmental interventions. Metabolomics has become an integral part of clinical research and allowed for another dimension of better understanding of disease pathophysiology and mechanism. More than 95% of the clinical biochemistry laboratory routine workload is based on small molecular identification, which can potentially be analyzed through metabolomics. However, multiple challenges in clinical metabolomics impact the entire workflow and data quality, thus the biological interpretation needs to be standardized for a reproducible outcome. Herein, we introduce the establishment of a comprehensive targeted metabolomics method for a panel of 220 clinically relevant metabolites using Liquid chromatography-tandem mass spectrometry (LC-MS/MS) standardized for clinical research. The sensitivity, reproducibility and molecular stability of each targeted metabolite (amino acids, organic acids, acylcarnitines, sugars, bile acids, neurotransmitters, polyamines, and hormones) were assessed under multiple experimental conditions. The metabolic tissue distribution was determined in various rat organs. Furthermore, the method was validated in dry blood spot (DBS) samples collected from patients known to have various inborn errors of metabolism (IEMs). Using this approach, our panel appears to be sensitive and robust as it demonstrated differential and unique metabolic profiles in various rat tissues. Also, as a prospective screening method, this panel of diverse metabolites has the ability to identify patients with a wide range of IEMs who otherwise may need multiple, time-consuming and expensive biochemical assays causing a delay in clinical management.

MetaboSignal: a network-based approach for topological analysis of metabotype regulation via metabolic and signaling pathways

Summary

MetaboSignal is an R package that allows merging metabolic and signaling pathways reported in the Kyoto Encyclopaedia of Genes and Genomes (KEGG). It is a network-based approach designed to navigate through topological relationships between genes (signaling- or metabolic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape of metabolic phenotypes.

Availability and Implementation

MetaboSignal is available from Bioconductor: https://bioconductor.org/packages/MetaboSignal/

Supplementary information

Supplementary data are available at Bioinformatics online.

Advances in Computational Analysis of Metabolomic NMR Data

In this chapter we discuss some of the more recent developments in preprocessing and statistical analysis of NMR spectra in metabolomics. Bayesian methods for analyzing NMR spectra are summarized and we describe one particular approach, BATMAN, in more detail. We consider techniques based on statistical associations, such as correlation spectroscopy (e.g. STOCSY and recent variants), as well as approaches that model the associations as a network and how these change under different biological conditions. The link between metabolism and genotype is explored by looking at metabolic GWAS and related techniques. Finally, we describe the relevance and current status of data standards for NMR metabolomics.

Topological analysis of metabolic networks integrating co-segregating transcriptomes and metabolomes in type 2 diabetic rat congenic series

Background

The genetic regulation of metabolic phenotypes (i.e., metabotypes) in type 2 diabetes mellitus occurs through complex organ-specific cellular mechanisms and networks contributing to impaired insulin secretion and insulin resistance. Genome-wide gene expression profiling systems can dissect the genetic contributions to metabolome and transcriptome regulations. The integrative analysis of multiple gene expression traits and metabolic phenotypes (i.e., metabotypes) together with their underlying genetic regulation remains a challenge. Here, we introduce a systems genetics approach based on the topological analysis of a combined molecular network made of genes and metabolites identified through expression and metabotype quantitative trait locus mapping (i.e., eQTL and mQTL) to prioritise biological characterisation of candidate genes and traits.

Methods

We used systematic metabotyping by ¹H NMR spectroscopy and genome-wide gene expression in white adipose tissue to map molecular phenotypes to genomic blocks associated with obesity and insulin secretion in a series of rat congenic strains derived from spontaneously diabetic Goto-Kakizaki (GK) and normoglycemic Brown-Norway (BN) rats. We implemented a network biology strategy approach to visualize the shortest paths between metabolites and genes significantly associated with each genomic block.

Results

Despite strong genomic similarities (95–99 %) among congenics, each strain exhibited specific patterns of gene expression and metabotypes, reflecting the metabolic consequences of series of linked genetic polymorphisms in the congenic intervals. We subsequently used the congenic panel to map quantitative trait loci underlying specific mQTLs and genome-wide eQTLs. Variation in key metabolites like glucose, succinate, lactate, or 3-hydroxybutyrate and second messenger precursors like inositol was associated with several independent genomic intervals, indicating functional redundancy in these regions. To navigate through the complexity of these association networks we mapped candidate genes and metabolites onto metabolic pathways and implemented a shortest path strategy to highlight potential mechanistic links between metabolites and transcripts at colocalized mQTLs and eQTLs. Minimizing the shortest path length drove prioritization of biological validations by gene silencing.

Conclusions

These results underline the importance of network-based integration of multilevel systems genetics datasets to improve understanding of the genetic architecture of metabotype and transcriptomic regulation and to characterize novel functional roles for genes determining tissue-specific metabolism.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-016-0352-6) contains supplementary material, which is available to authorized users.

Identification of Altered Metabolomic Profiles Following a Panchakarma-based Ayurvedic Intervention in Healthy Subjects: The Self-Directed Biological Transformation Initiative (SBTI)

The effects of integrative medicine practices such as meditation and Ayurveda on human physiology are not fully understood. The aim of this study was to identify altered metabolomic profiles following an Ayurveda-based intervention. In the experimental group, 65 healthy male and female subjects participated in a 6-day Panchakarma-based Ayurvedic intervention which included herbs, vegetarian diet, meditation, yoga, and massage. A set of 12 plasma phosphatidylcholines decreased (adjusted p < 0.01) post-intervention in the experimental (n = 65) compared to control group (n = 54) after Bonferroni correction for multiple testing; within these compounds, the phosphatidylcholine with the greatest decrease in abundance was PC ae C36:4 (delta = −0.34). Application of a 10% FDR revealed an additional 57 metabolites that were differentially abundant between groups. Pathway analysis suggests that the intervention results in changes in metabolites across many pathways such as phospholipid biosynthesis, choline metabolism, and lipoprotein metabolism. The observed plasma metabolomic alterations may reflect a Panchakarma-induced modulation of metabotypes. Panchakarma promoted statistically significant changes in plasma levels of phosphatidylcholines, sphingomyelins and others in just 6 days. Forthcoming studies that integrate metabolomics with genomic, microbiome and physiological parameters may facilitate a broader systems-level understanding and mechanistic insights into these integrative practices that are employed to promote health and well-being.

Epigenetics and nutrition-related epidemics of metabolic diseases: Current perspectives and challenges

We live in a world fascinated by the relationship between disease and nutritional disequilibrium. The subtle and slow effects of chronic nutrient toxicity are a major public health concern. Since food is potentially important for the development of "metabolic memory", there is a need for more information on the type of nutrients causing adverse or toxic effects. We now know that metabolic alterations produced by excessive intake of some nutrients, drugs and chemicals directly impact epigenetic regulation. We envision that understanding how metabolic pathways are coordinated by environmental and genetic factors will provide novel insights for the treatment of metabolic diseases. New methods will enable the assembly and analysis of large sets of complex molecular and clinical data for understanding how inflammation and mitochondria affect bioenergetics, epigenetics and health. Collectively, the observations we highlight indicate that energy utilization and disease are intimately connected by epigenetics. The challenge is to incorporate metabolo-epigenetic data in better interpretations of disease, to expedite therapeutic targeting of key pathways linking nutritional toxicity and metabolism. An additional concern is that changes in the parental phenotype are detectable in the methylome of subsequent offspring. The effect might create a menace to future generations and preconceptional considerations.

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 in Diabetic Kidney Disease: Unraveling the Biochemistry of a Silent Killer

The development of new therapies for chronic diseases, such as diabetic kidney disease (DKD), will continue to be hampered by lack of sufficient biomarkers that will provide insights and will be responsive to treatment interventions. The recent application of metabolomic technologies, such as nuclear magnetic resonance and mass spectroscopy, has allowed large-scale analysis of small molecules to be interrogated in a targeted or untargeted manner. Recent advances from both human and animal studies that have arisen from metabolomic analysis have recognized that mitochondrial function and fatty acid oxidation play key roles in the development and progression of DKD. Although many challenges in the technology for clinical chronic kidney disease (CKD) are yet to be validated, there will very likely be ongoing major contributions of metabolomics to develop new biochemical understanding for diabetic and CKD. The clinical application of metabolomics and accompanying bioinformatic tools will likely be a cornerstone of personalized medicine triumphs for CKD.

Multi-omic data integration and analysis using systems genomics approaches: methods and applications in animal production, health and welfare

In the past years, there has been a remarkable development of high-throughput omics (HTO) technologies such as genomics, epigenomics, transcriptomics, proteomics and metabolomics across all facets of biology. This has spearheaded the progress of the systems biology era, including applications on animal production and health traits. However, notwithstanding these new HTO technologies, there remains an emerging challenge in data analysis. On the one hand, different HTO technologies judged on their own merit are appropriate for the identification of disease-causing genes, biomarkers for prevention and drug targets for the treatment of diseases and for individualized genomic predictions of performance or disease risks. On the other hand, integration of multi-omic data and joint modelling and analyses are very powerful and accurate to understand the systems biology of healthy and sustainable production of animals. We present an overview of current and emerging HTO technologies each with a focus on their applications in animal and veterinary sciences before introducing an integrative systems genomics framework for analysing and integrating multi-omic data towards improved animal production, health and welfare. We conclude that there are big challenges in multi-omic data integration, modelling and systems-level analyses, particularly with the fast emerging HTO technologies. We highlight existing and emerging systems genomics approaches and discuss how they contribute to our understanding of the biology of complex traits or diseases and holistic improvement of production performance, disease resistance and welfare.

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.

Real-world data in the molecular era-finding the reality in the real world

Real-world data (RWD) promises to provide a pivotal element to the understanding of personalized medicine. However, without true representation (or the reality) of the patient-disease biosystem and its molecular contributors, RWD may hamper rather than help this advancement. In this review article, we discuss RWD vs. clinical reality and the disconnects that exist currently (emphasizing molecular medicine), and methods of closing the gaps between RWD and reality.

Multi-Target Analysis and Design of Mitochondrial Metabolism

Analyzing and optimizing biological models is often identified as a research priority in biomedical engineering. An important feature of a model should be the ability to find the best condition in which an organism has to be grown in order to reach specific optimal output values chosen by the researcher. In this work, we take into account a mitochondrial model analyzed with flux-balance analysis. The optimal design and assessment of these models is achieved through single- and/or multi-objective optimization techniques driven by epsilon-dominance and identifiability analysis. Our optimization algorithm searches for the values of the flux rates that optimize multiple cellular functions simultaneously. The optimization of the fluxes of the metabolic network includes not only input fluxes, but also internal fluxes. A faster convergence process with robust candidate solutions is permitted by a relaxed Pareto dominance, regulating the granularity of the approximation of the desired Pareto front. We find that the maximum ATP production is linked to a total consumption of NADH, and reaching the maximum amount of NADH leads to an increasing request of NADH from the external environment. Furthermore, the identifiability analysis characterizes the type and the stage of three monogenic diseases. Finally, we propose a new methodology to extend any constraint-based model using protein abundances.

Functional metabolomics: from biomarker discovery to metabolome reprogramming

Metabolomics is emerging as a powerful tool for studying metabolic processes, identifying crucial biomarkers responsible for metabolic characteristics and revealing metabolic mechanisms, which construct the content of discovery metabolomics. The crucial biomarkers can be used to reprogram a metabolome, leading to an aimed metabolic strategy to cope with alteration of internal and external environments, naming reprogramming metabolomics here. The striking feature on the similarity of the basic metabolic pathways and components among vastly different species makes the reprogramming metabolomics possible when the engineered metabolites play biological roles in cellular activity as a substrate of enzymes and a regulator to other molecules including proteins. The reprogramming metabolomics approach can be used to clarify metabolic mechanisms of responding to changed internal and external environmental factors and to establish a framework to develop targeted tools for dealing with the changes such as controlling and/or preventing infection with pathogens and enhancing host immunity against pathogens. This review introduces the current state and trends of discovery metabolomics and reprogramming metabolomics and highlights the importance of reprogramming metabolomics.

Identifying biochemical phenotypic differences between cryptic species

Molecular genetic methods can distinguish divergent evolutionary lineages in what previously appeared to be single species, but it is not always clear what functional differences exist between such cryptic species. We used a metabolomic approach to profile biochemical phenotype (metabotype) differences between two putative cryptic species of the earthworm Lumbricus rubellus. There were no straightforward metabolite biomarkers of lineage, i.e. no metabolites that were always at higher concentration in one lineage. Multivariate methods, however, identified a small number of metabolites that together helped distinguish the lineages, including uncommon metabolites such as Nε-trimethyllysine, which is not usually found at high concentrations. This approach could be useful for characterizing functional trait differences, especially as it is applicable to essentially any species group, irrespective of its genome sequencing status.

Metabolic Profiling and Phenotyping of Central Nervous System Diseases: Metabolites Bring Insights into Brain Dysfunctions

Metabolic phenotyping corresponds to the large-scale quantitative and qualitative analysis of the metabolome i.e., the low-molecular weight <1 KDa fraction in biological samples, and provides a key opportunity to advance neurosciences. Proton nuclear magnetic resonance and mass spectrometry are the main analytical platforms used for metabolic profiling, enabling detection and quantitation of a wide range of compounds of particular neuro-pharmacological and physiological relevance, including neurotransmitters, secondary messengers, structural lipids, as well as their precursors, intermediates and degradation products. Metabolic profiling is therefore particularly indicated for the study of central nervous system by probing metabolic and neurochemical profiles of the healthy or diseased brain, in preclinical models or in human samples. In this review, we introduce the analytical and statistical requirements for metabolic profiling. Then, we focus on key studies in the field of metabolic profiling applied to the characterization of animal models and human samples of central nervous system disorders. We highlight the potential of metabolic profiling for pharmacological and physiological evaluation, diagnosis and drug therapy monitoring of patients affected by brain disorders. Finally, we discuss the current challenges in the field, including the development of systems biology and pharmacology strategies improving our understanding of metabolic signatures and mechanisms of central nervous system diseases.

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

Background

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

Results

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

Conclusions

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

Electronic supplementary material

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

A design automation framework for computational bioenergetics in biological networks

The bioenergetic activity of mitochondria can be thoroughly investigated by using computational methods. In particular, in our work we focus on ATP and NADH, namely the metabolites representing the production of energy in the cell. We develop a computational framework to perform an exhaustive investigation at the level of species, reactions, genes and metabolic pathways. The framework integrates several methods implementing the state-of-the-art algorithms for many-objective optimization, sensitivity, and identifiability analysis applied to biological systems. We use this computational framework to analyze three case studies related to the human mitochondria and the algal metabolism of Chlamydomonas reinhardtii, formally described with algebraic differential equations or flux balance analysis. Integrating the results of our framework applied to interacting organelles would provide a general-purpose method for assessing the production of energy in a biological network.

Metabolic phenotyping and systems biology approaches to understanding metabolic syndrome and fatty liver disease

Metabolic syndrome, a cluster of risk factors for type 2 diabetes mellitus and cardiovascular disease, is becoming an increasing global health concern. Insulin resistance is often associated with metabolic syndrome and also typical hepatic manifestations such as nonalcoholic fatty liver disease. Profiling of metabolic products (metabolic phenotyping or metabotyping) has provided new insights into metabolic syndrome and nonalcoholic fatty liver disease. Data from nuclear magnetic resonance spectroscopy and mass spectrometry combined with statistical modeling and top-down systems biology have allowed us to analyze and interpret metabolic signatures in terms of metabolic pathways and protein interaction networks and to identify the genomic and metagenomic determinants of metabolism. For example, metabolic phenotyping has shown that relationships between host cells and the microbiome affect development of the metabolic syndrome and fatty liver disease. We review recent developments in metabolic phenotyping and systems biology technologies and how these methodologies have provided insights into the mechanisms of metabolic syndrome and nonalcoholic fatty liver disease. We discuss emerging areas of research in this field and outline our vision for how metabolic phenotyping could be used to study metabolic syndrome and fatty liver disease.

Classical MALDI-MS versus CE-based ESI-MS proteomic profiling in urine for clinical applications

Human urine is an attractive and informative biofluid for medical diagnosis, which has been shown to reflect the (patho)-physiology of not only the urogenital system, but also others such as the cardiovascular system. For this reason, many studies have concentrated on the study of the urine proteome, aiming to find relevant biomarkers that could be applied in a clinical setting. However, this goal can only be achieved after reliable quantitative and qualitative analysis of the urinary proteome. In the last two decades, MS-based platforms have evolved to become indispensable tools for biomarker research. In this review, we will present and compare two of the most clinically relevant analytical platforms that have been used for the study of the urinary proteome, namely CE-based ESI-MS and classical MALDI-MS. These platforms, although not directly comparable, have been extensively used in proteomic profiling and therefore their comparison is fundamentally relevant to this field.

Staphylococcus aureus metabolic response to changing environmental conditions - a metabolomics perspective

Microorganisms preserve their metabolic function against a wide range of external perturbations including biotic or abiotic factors by utilizing cellular adaptations to maintain cell homeostasis. Functional genomics aims to detect such adaptive alterations on the level of transcriptome, proteome and metabolome to understand system wide changes and to identify interactions between the different levels of biochemical organization. Microbial metabolomics measures metabolites, the direct biochemical response to the environment, and is pivotal to the understanding of the variability and dynamics of bacterial cell metabolism. Metabolomics can measure many different types of compounds including primary metabolites, secondary metabolites, second messengers, quorum sensing compounds and others, which all contribute to the complex bacterial response to an environmental change. Recent data confirmed that many metabolic processes in pathogenic bacteria are linked to virulence and invasive capabilities. Deciphering bacterial metabolism in response to specific environmental conditions and in specific genetic backgrounds will help map the complex network between the metabolome and the other "-omes". Here, we will review a selection of case studies for the pathogenic Gram-positive bacterium Staphylococcus aureus and summarize the current state of metabolomics literature covering staphylococci metabolism under different physiological states.

The impact of network medicine in gastroenterology and hepatology

In the footsteps of groundbreaking achievements made by biomedical research, another scientific revolution is unfolding. Systems biology draws from the chaos and complexity theory and applies computational models to predict emerging behavior of the interactions between genes, gene products, and environmental factors. Adaptation of systems biology to translational and clinical sciences has been termed network medicine, and is likely to change the way we think about preventing, predicting, diagnosing, and treating complex human diseases. Network medicine finds gene-disease associations by analyzing the unparalleled digital information discovered and created by high-throughput technologies (dubbed as "omics" science) and links genetic variance to clinical disease phenotypes through intermediate organizational levels of life such as the epigenome, transcriptome, proteome, and metabolome. Supported by large reference databases, unprecedented data storage capacity, and innovative computational analysis, network medicine is poised to find links between conditions that were thought to be distinct, uncover shared disease mechanisms and key drivers of the pathogenesis, predict individual disease outcomes and trajectories, identify novel therapeutic applications, and help avoid off-target and undesirable drug effects. Recent advances indicate that these perspectives are increasingly within our reach for understanding and managing complex diseases of the digestive system.

Serum metabolic signatures induced by a three-day intensified exercise period persist after 14 h of recovery in runners

This study investigated changes in the human serum metabolome elicited by a 3-day period of intensified training. Runners (N = 15, mean ± SD age, 35.2 ± 8.7 years) ran for 2.5 h/day on treadmills at ∼70% VO2max for 3 days in a row, with blood samples collected pre-exercise, and immediately and 14 h post-exercise. Samples were analyzed using gas and liquid chromatography/mass spectrometry (GC-MS, LC-MS), with compounds identified based on comparison to more than 2800 purified standards. Repeated measures ANOVA was used to identify metabolites that differed significantly across time, with multiple testing corrected by the false discovery rate (FDR) (q-value). Immediately following the 3-day exercise period, significant 2-fold or higher increases in 75 metabolites were measured, with all but 22 of these metabolites related to lipid/carnitine metabolism, 13 to amino acid/peptide metabolism, 4 to hemoglobin/porphyrin metabolism, and 3 to Krebs cycle intermediates (q-values < 0.001). After a 14 h overnight recovery period, 50 of the 75 metabolites remained elevated, with 8 decreased (primarily amino acid-related metabolites) (q-values < 0.05). Among the top 20 metabolites, the mean fold changes were 12.4 ± 5.3 and 2.9 ± 1.3 immediately and 14-h post-exercise, respectively. Significant decreases (40-70%, q < 0.01) in 22 metabolites (primarily related to lysolipid and bile acid metabolism) were measured post-exercise, with all but 4 of these still decreased after 14 h rest recovery (q < 0.025). Runners experienced a profound systemic shift in blood metabolites related to energy production especially from the lipid super pathway following 3 days of heavy exertion that was not fully restored to pre-exercise levels after 14 h recovery.

Systems biology and "omics" tools: a cooperation for next-generation mycorrhizal studies

Omics tools constitute a powerful means of describing the complexity of plants and soil-borne microorganisms. Next generation sequencing technologies, coupled with emerging systems biology approaches, seem promising to represent a new strategy in the study of plant-microbe interactions. Arbuscular mycorrhizal fungi (AMF) are ubiquitous symbionts of plant roots, that provide their host with many benefits. However, as obligate biotrophs, AMF show a genetic, cellular and physiological complexity that makes the study of their biology as well as their effective agronomical exploitation rather difficult. Here, we speculate that the increasing availability of omics data on mycorrhiza and of computational tools that allow systems biology approaches represents a step forward in the understanding of arbuscular mycorrhizal symbiosis. Furthermore, the application of this study-perspective to agriculturally relevant model plants, such as tomato and rice, will lead to a better in-field exploitation of this beneficial symbiosis in the frame of low-input agriculture.

Metabolomics as a tool to investigate abiotic stress tolerance in plants

Metabolites reflect the integration of gene expression, protein interaction and other different regulatory processes and are therefore closer to the phenotype than mRNA transcripts or proteins alone. Amongst all –omics technologies, metabolomics is the most transversal and can be applied to different organisms with little or no modifications. It has been successfully applied to the study of molecular phenotypes of plants in response to abiotic stress in order to find particular patterns associated to stress tolerance. These studies have highlighted the essential involvement of primary metabolites: sugars, amino acids and Krebs cycle intermediates as direct markers of photosynthetic dysfunction as well as effectors of osmotic readjustment. On the contrary, secondary metabolites are more specific of genera and species and respond to particular stress conditions as antioxidants, Reactive Oxygen Species (ROS) scavengers, coenzymes, UV and excess radiation screen and also as regulatory molecules. In addition, the induction of secondary metabolites by several abiotic stress conditions could also be an effective mechanism of cross-protection against biotic threats, providing a link between abiotic and biotic stress responses. Moreover, the presence/absence and relative accumulation of certain metabolites along with gene expression data provides accurate markers (mQTL or MWAS) for tolerant crop selection in breeding programs.