Metabolomics: from pattern recognition to biological interpretation

Identification and enrichment of potential pathways in the buffy coat of patients with DRE using non-targeted metabolomics integrated with GEO Datasets

BACKGROUND

This study aims to identify potential biomarkers in the buffy coat of drug-resistant epilepsy (DRE) patients with mesial temporal lobe epilepsy and to elucidate associated pathways.

METHODS

A comprehensive non-targeted metabolomic and Gene Expression Omnibus (GEO) datasets analysis was first performed on buffy coat from DRE patients and non-epilepsy (CON) patients. Potential enriched biomarkers and pathways were integrated with gene expression profiles from GEO datasets to identify robust biomarkers.

RESULTS

In the DRE group, there were 15 patients (10 males and 5 females), with an average age of (37.67 ± 15.53) years. In the CON group, there were 10 patients (7 males and 3 females), with an average age of (51.60 ± 18.20) years. A total of 27 potential biomarkers were identified, including 7 down-regulated and 8 up-regulated. Additionally, 9 potential pathways related to DRE were identified. Notably, purine metabolism, tryptophan metabolism and aminoacyl-tRNA metabolism were closely related to DRE. Purine metabolism was up-regulated, while aminoacyl-tRNA and tryptophan metabolism were down-regulated.

CONCLUSIONS

The integration of metabolomic data with GEO datasets analysis offers a new strategy to identify robust biomarkers and pathways. The findings obtained from the buffy coat analysis offer potential insights for the diagnosis and treatment of DRE.

Network structure and fluctuation data improve inference of metabolic interaction strengths with the inverse Jacobian

Based on high-throughput metabolomics data, the recently introduced inverse differential Jacobian algorithm can infer regulatory factors and molecular causality within metabolic networks close to steady-state. However, these studies assumed perturbations acting independently on each metabolite, corresponding to metabolic system fluctuations. In contrast, emerging evidence puts forward internal network fluctuations, particularly from gene expression fluctuations, leading to correlated perturbations on metabolites. Here, we propose a novel approach that exploits these correlations to quantify relevant metabolic interactions. By integrating enzyme-related fluctuations in the construction of an appropriate fluctuation matrix, we are able to exploit the underlying reaction network structure for the inverse Jacobian algorithm. We applied this approach to a model-based artificial dataset for validation, and to an experimental breast cancer dataset with two different cell lines. By highlighting metabolic interactions with significantly changed interaction strengths, the inverse Jacobian approach identified critical dynamic regulation points which are confirming previous breast cancer studies.

Description of the fasted serum metabolomic signature of lean and obese cats at maintenance and of obese cats under energy restriction

This study aimed to investigate the serum metabolomic profile of obese and lean cats as well as obese cats before and after energy restriction for weight loss. Thirty cats, 16 obese (body condition score 8 to 9/9) and 14 lean (body condition score 4 to 5/9), were fed a veterinary weight loss food during a 4-week period of weight maintenance (L-MAINT and O-MAINT). The 16 obese cats were then energy restricted by a 60% energy intake reduction with the same food for a 10-week period (O-RESTRICT). Fasted serum metabolites were measured using nuclear magnetic resonance and direct infusion mass spectrometry after the maintenance period for L-MAINT and O-MAINT cats and after the energy restriction period for O-RESTRICT and compared between groups using a two-sided t-test. Obese cats lost 672 g ± 303 g over the 10-week restriction period, representing a weight loss rate of 0.94 ± 0.28% per week. Glycine, l-alanine, l-histidine, l-glutamine, 2-hydroxybutyrate, isobutryric acid, citric acid, creatine, and methanol were greater in O-RESTRICT compared to O-MAINT. There was a greater concentration of long-chain acylcarnitines in O-RESTRICT compared to both O-MAINT and L-MAINT, and greater total amino acids compared to O-MAINT. Glycerol and 3-hydroxybutyric acid were greater in O-MAINT compared to L-MAINT, as were several lysophosphatidylcholines. Thus, energy restriction resulted in increased dispensable amino acids in feline serum which could indicate alterations in amino acid partitioning. An increase in lipolysis was not evident, though greater circulating acylcarnitines were observed, suggesting that fatty acid oxidation rates may have been greater under calorie restriction. More research is needed to elucidate energy metabolism and substrate utilization, specifically fatty acid oxidation and methyl status, during energy restriction in strict carnivorous cats to optimize weight loss.

Emerging trends and applications of metabolomics in food science and nutrition

The study of all chemical processes involving metabolites is known as metabolomics. It has been developed into an essential tool in several disciplines, such as the study of plant physiology, drug development, human diseases, and nutrition. The field of food science, diagnostic biomarker research, etiological analysis in the field of medical therapy, and raw material quality, processing, and safety have all benefited from the use of metabolomics recently. Food metabolomics includes the use of metabolomics in food production, processing, and human diets. As a result of changing consumer habits and the rising of food industries all over the world, there is a remarkable increase in interest in food quality and safety. It requires the employment of various technologies for the food supply chain, processing of food, and even plant breeding. This can be achieved by understanding the metabolome of food, including its biochemistry and composition. Additionally, Food metabolomics can be used to determine the similarities and differences across crop kinds, as an indicator for tracking the process of ripening to increase crops' shelf life and attractiveness, and identifying metabolites linked to pathways responsible for postharvest disorders. Moreover, nutritional metabolomics is used to investigate the connection between diet and human health through detection of certain biomarkers. This review assessed and compiled literature on food metabolomics research with an emphasis on metabolite extraction, detection, and data processing as well as its applications to the study of food nutrition, food-based illness, and phytochemical analysis. Several studies have been published on the applications of metabolomics in food but further research concerning the use of standard reproducible procedures must be done. The results published showed promising uses in the food industry in many areas such as food production, processing, and human diets. Finally, metabolome-wide association studies (MWASs) could also be a useful predictor to detect the connection between certain diseases and low molecular weight biomarkers.

Correlations for untargeted GC × GC-HRTOF-MS metabolomics of colorectal cancer

INTRODUCTION

Modern comprehensive instrumentations provide an unprecedented coverage of complex matrices in the form of high-dimensional, information rich data sets.

OBJECTIVES

In addition to the usual biomarker research that focuses on the detection of the studied condition, we aimed to define a proper strategy to conduct a correlation analysis on an untargeted colorectal cancer case study with a data set of 102 variables corresponding to metabolites obtained from serum samples analyzed with comprehensive two-dimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry (GC × GC-HRTOF-MS). Indeed, the strength of association existing between the metabolites contains potentially valuable information about the molecular mechanisms involved and the underlying metabolic network associated to a global perturbation, at no additional analytical effort.

METHODS

Following Anscombe's quartet, we took particular attention to four main aspects. First, the presence of non-linear relationships through the comparison of parametric and non-parametric correlation coefficients: Pearson's r, Spearman's rho, Kendall's tau and Goodman-Kruskal's gamma. Second, the visual control of the detected associations through scatterplots and their associated regressions and angles. Third, the effect and handling of atypical samples and values. Fourth, the role of the precision of the data on the attribution of the ranks through the presence of ties.

RESULTS

Kendall's tau was found the method of choice for the data set at hand. Its application highlighted 17 correlations significantly altered in the active state of colorectal cancer (CRC) in comparison to matched healthy controls (HC), from which 10 were specific to this state in comparison to the remission one (R-CRC) investigated on distinct patients. 15 metabolites involved in the correlations of interest, on the 25 unique ones obtained, were annotated (Metabolomics Standards Initiative level 2).

CONCLUSIONS

The metabolites highlighted could be used to better understand the pathology. The systematic investigation of the methodological aspects that we expose allows to implement correlation analysis to various fields and many specific cases.

Integrative analysis of metabolomics and proteomics unravels purine metabolism disorder in the SOD1G93A mouse model of amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with progressive paralysis of limbs and bulb in patients, the cause of which remains unclear. Accumulating studies suggest that motor neuron degeneration is associated with systemic metabolic impairment in ALS. However, the metabolic reprogramming and underlying mechanism in the longitudinal progression of the disease remain poorly understood. In this study, we aimed to investigate the molecular changes at both metabolic and proteomic levels during disease progression to identify the most critical metabolic pathways and underlying mechanisms involved in ALS pathophysiological changes. Utilizing liquid chromatography-mass spectrometry-based metabolomics, we analyzed the metabolites' levels of plasma, lumbar spinal cord, and motor cortex from SOD1^G93A mice and wildtype (WT) littermates at different stages. To elucidate the regulatory network underlying metabolic changes, we further analyzed the proteomics profile in the spinal cords of SOD1^G93A and WT mice. A group of metabolites implicated in purine metabolism, methionine cycle, and glycolysis were found differentially expressed in ALS mice, and abnormal expressions of enzymes involved in these metabolic pathways were also confirmed. Notably, we first demonstrated that dysregulation of purine metabolism might contribute to the pathogenesis and disease progression of ALS. Furthermore, we discovered that fatty acid metabolism, TCA cycle, arginine and proline metabolism, and folate-mediated one‑carbon metabolism were also significantly altered in this disease. The identified differential metabolites and proteins in our study could complement existing data on metabolic reprogramming in ALS, which might provide new insight into the pathological mechanisms and novel therapeutic targets of ALS.

Model-driven data curation pipeline for LC-MS-based untargeted metabolomics

INTRODUCTION

There is still no community consensus regarding strategies for data quality review in liquid chromatography mass spectrometry (LC-MS)-based untargeted metabolomics. Assessing the analytical robustness of data, which is relevant for inter-laboratory comparisons and reproducibility, remains a challenge despite the wide variety of tools available for data processing.

OBJECTIVES

The aim of this study was to provide a model to describe the sources of variation in LC-MS-based untargeted metabolomics measurements, to use it to build a comprehensive curation pipeline, and to provide quality assessment tools for data quality review.

METHODS

Human serum samples (n=392) were analyzed by ultraperformance liquid chromatography coupled to high-resolution mass spectrometry (UPLC-HRMS) using an untargeted metabolomics approach. The pipeline and tools used to process this dataset were implemented as part of the open source, publicly available TidyMS Python-based package.

RESULTS

The model was applied to understand data curation practices used by the metabolomics community. Sources of variation, which are often overlooked in untargeted metabolomic studies, were identified in the analysis. New tools were used to characterize certain types of variations.

CONCLUSION

The developed pipeline allowed confirming data robustness by comparing the experimental results with expected values predicted by the model. New quality control practices were introduced to assess the analytical quality of data.

Creep Feeding and Weaning Influence the Postnatal Evolution of the Plasma Metabolome in Neonatal Piglets

Data on the evolution of blood metabolites and metabolic markers in neonatal piglets are scarce, although this information is vital to detect physiological aberrations from normal development. We aimed to characterize age- and nutrition-related changes in the plasma metabolome and serum biochemistry of suckling and newly weaned piglets and assess metabolite patterns as physiological markers for the two phases. In two replicate batches (n = 10 litters/group), piglets either received sow milk alone or were additionally offered creep feed from day 10 until weaning (day 28). Blood was collected from one piglet/litter on days 7, 14, 21, 28, 31 and 35 of life, totaling five females and five males/group/day. Signature feature ranking identified plasma triglycerides (TG) as discriminative for age and nutrition during the suckling phase. Influential TG 20:4_36:5, TG 17:0_34:2 and TG 18:2_38:6 were higher in creep-fed piglets on days 14, 21 and 28 of life, respectively, compared to only sow milk-fed piglets. Metabolites belonging to pathways within histidine, D-glutamine and D-glutamate metabolism as well as hippuric acid were distinctive for the postweaning compared to the suckling period. In conclusion, plasma lipid profiles especially corresponded to the type of nutrition in the suckling phase and showed a strong weaning effect.

Biomarkers: Promising and valuable tools towards diagnosis, prognosis and treatment of Covid-19 and other diseases

The use of biomarkers as early warning systems in the evaluation of disease risk has increased markedly in the last decade. Biomarkers are indicators of typical biological processes, pathogenic processes, or pharmacological reactions to therapy. The application and identification of biomarkers in the medical and clinical fields have an enormous impact on society. In this review, we discuss the history, various definitions, classifications, characteristics, and discovery of biomarkers. Furthermore, the potential application of biomarkers in the diagnosis, prognosis, and treatment of various diseases over the last decade are reviewed. The present review aims to inspire readers to explore new avenues in biomarker research and development.

Lamiophlomis rotata attenuates rheumatoid arthritis by regulating sphingolipid and steroid hormone metabolism

Rheumatoid arthritis (RA) is a chronic and progressive autoimmune disease. Lamiophlomis rotata (L. rotata) (Benth.) Kudo, an essential medicinal plant in traditional Tibetan medicine, is useful in treating RA. The purpose of this study was to evaluate L. rotata's anti-RA effect and to analyze its serum metabolites and lipids to predict the possible action pathways. Female and male rats were immunized with CFA to induce arthritis. Paw volumes were measured, and arthritis index analysis and histological analysis were performed to check the effects of L. rotata. ELISA was used to measure the levels of inflammatory cytokines (IL-1β, TNF-α, IL-6, and IL-10) and oxidative stress (MDA, SOD, GSH, and CAT). UPLC/Q-Orbitrap-MS was used to identify untargeted metabolites and lipids in serum. Metabolite validation was performed using UPLC/QQQ-MS. L. rotata application significantly reduced arthritis indices and paw swelling in AIA rats, and diminished inflammation and bone fractures in joint tissues. Sphingolipid (SP) and steroid hormone biosynthesis was found to be closely related to L. rotata's intervention in RA. In addition, our experiments also confirmed that females were more likely than males to develop RA. These findings provide clues and a scientific basis for the mechanism of L. rotata in treating RA.

Obesity, inflammation, and cancer in dogs: Review and perspectives

Obesity is the most common nutritional disease in dogs, and its prevalence has increased in recent decades. Several countries have demonstrated a prevalence of obesity in dogs similar to that observed in humans. Chronic low-grade inflammation is a prominent basis used to explain how obesity results in numerous negative health consequences. This is well known and understood, and recent studies have pointed to the association between obesity and predisposition to specific types of cancers and their complications. Such elucidations are important because, like obesity, the prevalence of cancer in dogs has increased in recent decades, establishing cancer as a significant cause of death for these animals. In the same way, intensive advances in technology in the field of human and veterinary medicine (which even proposes the use of animal models) have optimized existing therapeutic methods, led to the development of innovative treatments, and shortened the time to diagnosis of cancer. Despite the great challenges, this review aims to highlight the evidence obtained to date on the association between obesity, inflammation, and cancer in dogs, and the possible pathophysiological mechanisms that link obesity and carcinogenesis. The potential to control cancer in animals using existing knowledge is also presented.

Uncovering PFAS and Other Xenobiotics in the Dark Metabolome Using Ion Mobility Spectrometry, Mass Defect Analysis, and Machine Learning

The identification of xenobiotics in nontargeted metabolomic analyses is a vital step in understanding human exposure. Xenobiotic metabolism, transformation, excretion, and coexistence with other endogenous molecules, however, greatly complicate the interpretation of features detected in nontargeted studies. While mass spectrometry (MS)-based platforms are commonly used in metabolomic measurements, deconvoluting endogenous metabolites from xenobiotics is also often challenged by the lack of xenobiotic parent and metabolite standards as well as the numerous isomers possible for each small molecule m/z feature. Here, we evaluate a xenobiotic structural annotation workflow using ion mobility spectrometry coupled with MS (IMS-MS), mass defect filtering, and machine learning to uncover potential xenobiotic classes and species in large metabolomic feature lists. Xenobiotic classes examined included those of known high toxicities, including per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and pesticides. Specifically, when the workflow was applied to identify PFAS in the NIST SRM 1957 and 909c human serum samples, it greatly reduced the hundreds of detected liquid chromatography (LC)-IMS-MS features by utilizing both mass defect filtering and m/z versus IMS collision cross sections relationships. These potential PFAS features were then compared to the EPA CompTox entries, and while some matched within specific m/z tolerances, there were still many unknowns illustrating the importance of nontargeted studies for detecting new molecules with known chemical characteristics. Additionally, this workflow can also be utilized to evaluate other xenobiotics and enable more confident annotations from nontargeted studies.

In-depth characterisation of the urine metabolome in cats with and without urinary tract diseases

INTRODUCTION

Our understanding of the urine metabolome and its association with urinary tract disease is limited in cats.

OBJECTIVES

We conducted a case-control study to characterise the feline urine metabolome, investigate its association with chronic kidney disease (CKD) and feline idiopathic cystitis (FIC), and assess its compositional relationship with the urine microbiome.

METHODS

The urine metabolome of 45 owned cats, including 23 controls, 16 CKD, and 6 FIC cases, was characterised by an untargeted metabolomics approach using high-performance chemical isotope labelling liquid chromatography-mass spectrometry.

RESULTS

We detected 9411 unique compounds in the urine of controls and cases and identified 1037 metabolites with high confidence. Amino acids, peptides, and analogues dominated these metabolites (32.2%), followed by carbonyl compounds (7.1%) and carbohydrates (6.5%). Seven controls from one household showed a significant level of metabolome clustering, with a distinct separation from controls from other households (p value < 0.001). Owner surveys revealed that this cluster of cats was fed dry food only, whereas all but one other control had wet food in their diet. Accordingly, the diet type was significantly associated with the urine metabolome composition in our multivariate model (p value = 0.001). Metabolites significantly altered in this cluster included taurine, an essential amino acid in cats. Urine metabolome profiles were not significantly different in CKD and FIC cases compared with controls, and no significant compositional relationship was detected between the urine metabolome and microbiome.

CONCLUSION

Our study reveals in-depth diversity of the feline urine metabolome composition, and suggests that it can vary considerably depending on environmental factors.

Metabolomics Analysis of Amniotic Fluid in Euploid Foetuses with Thickened Nuchal Translucency by Gas Chromatography-Mass Spectrometry

Persistence of a fetal thickened nuchal translucency (NT), one of the most sensitive and specific individual markers of fetal disorders, is strongly correlated with the possibility of a genetic syndrome, congenital infections, or other malformations. Thickened NT can also be found in normal pregnancies. Several of its pathophysiological aspects still remain unexplained. Metabolomics could offer a fresh opportunity to explore maternal-foetal metabolism in an effort to explain its physiological and pathological mechanisms. For this prospective case-control pilot study, thirty-nine samples of amniotic fluids were collected, divisible into 12 euploid foetuses with an enlarged nuchal translucency (>NT) and 27 controls (C). Samples were analyzed using gas chromatography mass spectrometry. Multivariate and univariate statistical analyses were performed to find a specific metabolic pattern of >NT class. The correlation between the metabolic profile and clinical parameters was evaluated (NT showed an R2 = 0.75, foetal crown-rump length showed R2 = 0.65, pregnancy associated plasma protein-A showed R2 = 0.60). Nine metabolites significantly differing between >NT foetuses and C were detected: 2-hydroxybutyric acid, 3-hydroxybutyric, 1,5 Anydro-Sorbitol, cholesterol, erythronic acid, fructose, malic acid, threitol, and threonine, which were linked to altered pathways involved in altered energetic pathways. Through the metabolomics approach, it was possible to identify a specific metabolic fingerprint of the fetuses with >NT.

Tryptophan and Its Metabolites in Lung Cancer: Basic Functions and Clinical Significance

Lung cancer is the most lethal malignancy worldwide. Recently, it has been recognized that metabolic reprogramming is a complex and multifaceted factor, contributing to the process of lung cancer. Tryptophan (Try) is an essential amino acid, and Try and its metabolites can regulate the progression of lung cancer. Here, we review the pleiotropic functions of the Try metabolic pathway, its metabolites, and key enzymes in the pathogenic process of lung cancer, including modulating the tumor environment, promoting immune suppression, and drug resistance. We summarize the recent advance in therapeutic drugs targeting the Try metabolism and kynurenine pathway and their clinical trials.

Characterizing Multidevice Capillary Vibrating Sharp-Edge Spray Ionization for In-Droplet Hydrogen/Deuterium Exchange to Enhance Compound Identification

Multidevice capillary vibrating sharp-edge spray ionization (cVSSI) source parameters have been examined to determine their effects on conducting in-droplet hydrogen/deuterium exchange (HDX) experiments. Control experiments using select compounds indicate that the observed differences in mass spectral isotopic distributions obtained upon initiation of HDX result primarily from solution-phase reactions as opposed to gas-phase exchange. Preliminary studies have determined that robust HDX can only be achieved with the application of same-polarity voltage to both the analyte and the deuterium oxide reagent (D₂O) cVSSI devices. Additionally, a similar HDX reactivity dependence on the voltage applied to the D₂O device for various analytes is observed. Analyte and reagent flow experiments show that, for the multidevice cVSSI setup employed, there is a nonlinear dependence on the D₂O reagent flow rate; increasing the D₂O reagent flow by 100% results in only an ∼10-20% increase in deuterium incorporation for this setup. Instantaneous (subsecond) response times have been demonstrated in the initiation or termination of HDX, which is achieved by turning on or off the reagent cVSSI device piezoelectric transducer. The ability to distinguish isomeric species by in-droplet HDX is presented. Finally, a demonstration of a three-component cVSSI device setup to perform multiple (successive or in combination) in-droplet chemistries to enhance compound ionization and identification is presented and a hypothetical metabolomics workflow consisting of successive multidevice activation is briefly discussed.

Separation of Chromatographic Co-Eluted Compounds by Clustering and by Functional Data Analysis

Peak overlapping is a common problem in chromatography, mainly in the case of complex biological mixtures, i.e., metabolites. Due to the existence of the phenomenon of co-elution of different compounds with similar chromatographic properties, peak separation becomes challenging. In this paper, two computational methods of separating peaks, applied, for the first time, to large chromatographic datasets, are described, compared, and experimentally validated. The methods lead from raw observations to data that can form inputs for statistical analysis. First, in both methods, data are normalized by the mass of sample, the baseline is removed, retention time alignment is conducted, and detection of peaks is performed. Then, in the first method, clustering is used to separate overlapping peaks, whereas in the second method, functional principal component analysis (FPCA) is applied for the same purpose. Simulated data and experimental results are used as examples to present both methods and to compare them. Real data were obtained in a study of metabolomic changes in barley (Hordeum vulgare) leaves under drought stress. The results suggest that both methods are suitable for separation of overlapping peaks, but the additional advantage of the FPCA is the possibility to assess the variability of individual compounds present within the same peaks of different chromatograms.

The Quest for System-Theoretical Medicine in the COVID-19 Era

Precision medicine and molecular systems medicine (MSM) are highly utilized and successful approaches to improve understanding, diagnosis, and treatment of many diseases from bench-to-bedside. Especially in the COVID-19 pandemic, molecular techniques and biotechnological innovation have proven to be of utmost importance for rapid developments in disease diagnostics and treatment, including DNA and RNA sequencing technology, treatment with drugs and natural products and vaccine development. The COVID-19 crisis, however, has also demonstrated the need for systemic thinking and transdisciplinarity and the limits of MSM: the neglect of the bio-psycho-social systemic nature of humans and their context as the object of individual therapeutic and population-oriented interventions. COVID-19 illustrates how a medical problem requires a transdisciplinary approach in epidemiology, pathology, internal medicine, public health, environmental medicine, and socio-economic modeling. Regarding the need for conceptual integration of these different kinds of knowledge we suggest the application of general system theory (GST). This approach endorses an organism-centered view on health and disease, which according to Ludwig von Bertalanffy who was the founder of GST, we call Organismal Systems Medicine (OSM). We argue that systems science offers wider applications in the field of pathology and can contribute to an integrative systems medicine by (i) integration of evidence across functional and structural differentially scaled subsystems, (ii) conceptualization of complex multilevel systems, and (iii) suggesting mechanisms and non-linear relationships underlying the observed phenomena. We underline these points with a proposal on multi-level systems pathology including neurophysiology, endocrinology, immune system, genetics, and general metabolism. An integration of these areas is necessary to understand excess mortality rates and polypharmacological treatments. In the pandemic era this multi-level systems pathology is most important to assess potential vaccines, their effectiveness, short-, and long-time adverse effects. We further argue that these conceptual frameworks are not only valid in the COVID-19 era but also important to be integrated in a medicinal curriculum.

The effects of exercise training on lipid profile in patients with sarcoidosis

This study aimed to determine the use of lipid profiling to assess the effects of moderate intensity exercise training (ET) on patients with sarcoidosis. Fourteen patients with sarcoidosis (mean age, 46.0 ± 9.6 years) were examined before and after 3-week of ET programme in hospital settings. Symptoms (fatigue: FAS, dyspnoea: MRC), lung function tests and physical function tests (6 MWT, muscle force) were measured before and after ET. Proton nuclear magnetic resonance (NMR) spectroscopy combined with orthogonal partial least squares-discriminant analysis (OPLS-DA) was used to determine lipid profile before and after ET. Twenty-five NMR signals from lipid compounds were selected for further analysis as well as serum lipid and inflammatory markers. Three weeks of ET results in improvement of symptoms (FAS: 27.5 vs. 21.0; p < 0.001, MRC: 0.86 vs. 0.14; p = 0.002) and physical function (6MWT: 508.43 vs. 547.29; p = 0.039). OPLS-DA analysis of the lipid profiles of patients with sarcoidosis revealed differences among the samples before and after ET, including decreases in fatty acids (p < 0.017), triglycerides (p < 0.022) and total cholesterol (p < 0.020). Other changes included shifts in fatty acids oxidation products and triacylglycerol esters. A short-time, in-hospital exercise training benefits patients with sarcoidosis by enhancing their physical function. Additionally, positive effect on lipid profile was observed also in this study. It is suggested that lipid profiling could become a new prognostic method to assess effects of pulmonary rehabilitation in patients with sarcoidosis.

Untargeted Metabolomic Analysis Combined With Multivariate Statistics Reveal Distinct Metabolic Changes in GPR40 Agonist-Treated Animals Related to Bile Acid Metabolism

Metabolomics has been increasingly applied to biomarker discovery, as untargeted metabolic profiling represents a powerful exploratory tool for identifying causal links between biomarkers and disease phenotypes. In the present work, we used untargeted metabolomics to investigate plasma specimens of rats, dogs, and mice treated with small-molecule drugs designed for improved glycemic control of type 2 diabetes mellitus patients via activation of GPR40. The in vivo pharmacology of GPR40 is not yet fully understood. Compounds targeting this receptor have been found to induce drug-induced liver injury (DILI). Metabolomic analysis facilitating an integrated UPLC-TWIMS-HRMS platform was used to detect metabolic differences between treated and non-treated animals within two 4-week toxicity studies in rat and dog, and one 2-week toxicity study in mouse. Multivariate statistics of untargeted metabolomics data subsequently revealed the presence of several significantly upregulated endogenous compounds in the treated animals whose plasma level is known to be affected during DILI. A specific bile acid metabolite useful as endogenous probe for drug-drug interaction studies was identified (chenodeoxycholic acid-24 glucuronide), as well as a metabolic precursor indicative of acidic bile acid biosynthesis (7α-hydroxy-3-oxo-4-cholestenoic acid). These results correlate with typical liver toxicity parameters on the individual level.

Physiological and Proteomic Signatures Reveal Mechanisms of Superior Drought Resilience in Pearl Millet Compared to Wheat

Presently, pearl millet and wheat are belonging to highly important cereal crops. Pearl millet, however, is an under-utilized crop, despite its superior resilience to drought and heat stress in contrast to wheat. To investigate this in more detail, we performed comparative physiological screening and large scale proteomics of drought stress responses in drought-tolerant and susceptible genotypes of pearl millet and wheat. These chosen genotypes are widely used in breeding and farming practices. The physiological responses demonstrated large differences in the regulation of root morphology and photosynthetic machinery, revealing a stay-green phenotype in pearl millet. Subsequent tissue-specific proteome analysis of leaves, roots and seeds led to the identification of 12,558 proteins in pearl millet and wheat under well-watered and stress conditions. To allow for this comparative proteome analysis and to provide a platform for future functional proteomics studies we performed a systematic phylogenetic analysis of all orthologues in pearl millet, wheat, foxtail millet, sorghum, barley, brachypodium, rice, maize, Arabidopsis, and soybean. In summary, we define (i) a stay-green proteome signature in the drought-tolerant pearl millet phenotype and (ii) differential senescence proteome signatures in contrasting wheat phenotypes not capable of coping with similar drought stress. These different responses have a significant effect on yield and grain filling processes reflected by the harvest index. Proteome signatures related to root morphology and seed yield demonstrated the unexpected intra- and interspecies-specific biochemical plasticity for stress adaptation for both pearl millet and wheat genotypes. These quantitative reference data provide tissue- and phenotype-specific marker proteins of stress defense mechanisms which are not predictable from the genome sequence itself and have potential value for marker-assisted breeding beyond genome assisted breeding.

Applications of Multi-Omics Technologies for Crop Improvement

Multiple "omics" approaches have emerged as successful technologies for plant systems over the last few decades. Advances in next-generation sequencing (NGS) have paved a way for a new generation of different omics, such as genomics, transcriptomics, and proteomics. However, metabolomics, ionomics, and phenomics have also been well-documented in crop science. Multi-omics approaches with high throughput techniques have played an important role in elucidating growth, senescence, yield, and the responses to biotic and abiotic stress in numerous crops. These omics approaches have been implemented in some important crops including wheat (Triticum aestivum L.), soybean (Glycine max), tomato (Solanum lycopersicum), barley (Hordeum vulgare L.), maize (Zea mays L.), millet (Setaria italica L.), cotton (Gossypium hirsutum L.), Medicago truncatula, and rice (Oryza sativa L.). The integration of functional genomics with other omics highlights the relationships between crop genomes and phenotypes under specific physiological and environmental conditions. The purpose of this review is to dissect the role and integration of multi-omics technologies for crop breeding science. We highlight the applications of various omics approaches, such as genomics, transcriptomics, proteomics, metabolomics, phenomics, and ionomics, and the implementation of robust methods to improve crop genetics and breeding science. Potential challenges that confront the integration of multi-omics with regard to the functional analysis of genes and their networks as well as the development of potential traits for crop improvement are discussed. The panomics platform allows for the integration of complex omics to construct models that can be used to predict complex traits. Systems biology integration with multi-omics datasets can enhance our understanding of molecular regulator networks for crop improvement. In this context, we suggest the integration of entire omics by employing the "phenotype to genotype" and "genotype to phenotype" concept. Hence, top-down (phenotype to genotype) and bottom-up (genotype to phenotype) model through integration of multi-omics with systems biology may be beneficial for crop breeding improvement under conditions of environmental stresses.

Metabolomics in the development and progression of rheumatoid arthritis: A systematic review

OBJECTIVE

A systematic review and analysis of data from several rheumatoid arthritis metabolomics studies attempts to determine which metabolites can be used as potential biomarkers for the diagnosis of rheumatoid arthritis and to explore the pathogenesis of rheumatoid arthritis.

METHODS

We searched all the subject-related documents published by EMBASE, PubMed, Web of Science, and Cochrane Library from the database to the September 2019 publication. Two researchers independently screened the literature and extracted the data. QUADOMICS tool was used to assess the quality of studies included in this systematic review.

RESULTS

A total of 10 studies met the inclusion criteria of systematic review, including 502 patients with rheumatoid arthritis and 373 healthy people. Among them, the biological samples utilised for metabolomic analysis include: serum (n=8), urine (n=1) and synovial fluid (n=1). Some metabolites play an important role in rheumatoid arthritis: glucose, lactic acid, citric acid, leucine, methionine, isoleucine, valine, phenylalanine, threonine, serine, proline, glutamate, histidine, alanine, cholesterol, glycerol, and ribose.

CONCLUSIONS

Metabolomics provides important new opportunities for further research in rheumatoid arthritis and is expected to elucidate the pathogenesis of rheumatoid arthritis that has not been fully understood before.

Metabolomics analysis reveals differences in evolution between maize and rice

Metabolites are the intermediate and final products of metabolism, which play essential roles in plant growth, evolution and adaptation to changing climates. However, it is unclear how evolution contributes to metabolic variation in plants. Here, we investigated the metabolomics data from leaf and seed tissues in maize and rice. Using principal components analysis based on leaf metabolites but not seed metabolites, metabolomics data could be clearly separated for rice Indica and Japonica accessions, while two maize subgroups, temperate and tropical, showed more visible admixture. Rice and maize seed exhibited significant interspecific differences in metabolic variation, while within rice, leaf and seed displayed similar metabolic variations. Among 10 metabolic categories, flavonoids had higher variation in maize than rice, indicating flavonoids are a key constituent of interspecific metabolic divergence. Interestingly, metabolic regulation was also found to be reshaped dramatically from positive to negative correlations, indicative of the differential evolutionary processes in maize and rice. Moreover, perhaps due to this divergence significantly more metabolic interactions were identified in rice than maize. Furthermore, in rice, the leaf was found to harbor much more intense metabolic interactions than the seed. Our result suggests that metabolomes are valuable for tracking evolutionary history, thereby complementing and extending genomic insights concerning which features are responsible for interspecific differentiation in maize and rice.

PANOMICS meets germplasm

Genotyping-by-sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. More and more resource studies are emerging providing 1000 and more genotypes and millions of SNPs for one species covering a hitherto inaccessible intraspecific genetic variation. The larger the databases are growing, the better statistical approaches for genomic selection will be available. However, there are clear limitations on the statistical but also on the biological part. Intraspecific genetic variation is able to explain a high proportion of the phenotypes, but a large part of phenotypic plasticity also stems from environmentally driven transcriptional, post-transcriptional, translational, post-translational, epigenetic and metabolic regulation. Moreover, regulation of the same gene can have different phenotypic outputs in different environments. Consequently, to explain and understand environment-dependent phenotypic plasticity based on the available genotype variation we have to integrate the analysis of further molecular levels reflecting the complete information flow from the gene to metabolism to phenotype. Interestingly, metabolomics platforms are already more cost-effective than NGS platforms and are decisive for the prediction of nutritional value or stress resistance. Here, we propose three fundamental pillars for future breeding strategies in the framework of Green Systems Biology: (i) combining genome selection with environment-dependent PANOMICS analysis and deep learning to improve prediction accuracy for marker-dependent trait performance; (ii) PANOMICS resolution at subtissue, cellular and subcellular level provides information about fundamental functions of selected markers; (iii) combining PANOMICS with genome editing and speed breeding tools to accelerate and enhance large-scale functional validation of trait-specific precision breeding.

Molecular Mechanisms of Microbial Survivability in Outer Space: A Systems Biology Approach

Since the dawn of space exploration, the survivability of terrestrial life in outer space conditions has attracted enormous attention. Space technology has enabled the development of advanced space exposure facilities to investigate in situ responses of microbial life to the stress conditions of space during interplanetary transfer. Significant progress has been made toward the understanding of the effects of space environmental factors, e.g., microgravity, vacuum and radiation, on microorganisms exposed to real and simulated space conditions. Of extreme importance is not only knowledge of survival potential of space-exposed microorganisms, but also the determination of mechanisms of survival and adaptation of predominant species to the extreme space environment, i.e., revealing the molecular machinery, which elicit microbial survivability and adaptation. Advanced technologies in -omics research have permitted genome-scale studies of molecular alterations of space-exposed microorganisms. A variety of reports show that microorganisms grown in the space environment exhibited global alterations in metabolic functions and gene expression at the transcriptional and translational levels. Proteomic, metabolomic and especially metabolic modeling approaches as essential instruments of space microbiology, synthetic biology and metabolic engineering are rather underrepresented. Here we summarized the molecular space-induced alterations of exposed microorganisms in terms of understanding the molecular mechanisms of microbial survival and adaptation to drastic outer space environment.

An Integrated Multi-Disciplinary Perspectivefor Addressing Challenges of the Human Gut Microbiome

Our understanding of the human gut microbiome has grown exponentially. Advances in genome sequencing technologies and metagenomics analysis have enabled researchers to study microbial communities and their potential function within the context of a range of human gut related diseases and disorders. However, up until recently, much of this research has focused on characterizing the gut microbiological community structure and understanding its potential through system wide (meta) genomic and transcriptomic-based studies. Thus far, the functional output of these microbiomes, in terms of protein and metabolite expression, and within the broader context of host-gut microbiome interactions, has been limited. Furthermore, these studies highlight our need to address the issues of individual variation, and of samples as proxies. Here we provide a perspective review of the recent literature that focuses on the challenges of exploring the human gut microbiome, with a strong focus on an integrated perspective applied to these themes. In doing so, we contextualize the experimental and technical challenges of undertaking such studies and provide a framework for capitalizing on the breadth of insight such approaches afford. An integrated perspective of the human gut microbiome and the linkages to human health will pave the way forward for delivering against the objectives of precision medicine, which is targeted to specific individuals and addresses the issues and mechanisms in situ.

Physiological and Proteomic Signatures Reveal Mechanisms of Superior Drought Resilience in Pearl Millet Compared to Wheat

Presently, pearl millet and wheat are belonging to highly important cereal crops. Pearl millet, however, is an under-utilized crop, despite its superior resilience to drought and heat stress in contrast to wheat. To investigate this in more detail, we performed comparative physiological screening and large scale proteomics of drought stress responses in drought-tolerant and susceptible genotypes of pearl millet and wheat. These chosen genotypes are widely used in breeding and farming practices. The physiological responses demonstrated large differences in the regulation of root morphology and photosynthetic machinery, revealing a stay-green phenotype in pearl millet. Subsequent tissue-specific proteome analysis of leaves, roots and seeds led to the identification of 12,558 proteins in pearl millet and wheat under well-watered and stress conditions. To allow for this comparative proteome analysis and to provide a platform for future functional proteomics studies we performed a systematic phylogenetic analysis of all orthologues in pearl millet, wheat, foxtail millet, sorghum, barley, brachypodium, rice, maize, Arabidopsis, and soybean. In summary, we define (i) a stay-green proteome signature in the drought-tolerant pearl millet phenotype and (ii) differential senescence proteome signatures in contrasting wheat phenotypes not capable of coping with similar drought stress. These different responses have a significant effect on yield and grain filling processes reflected by the harvest index. Proteome signatures related to root morphology and seed yield demonstrated the unexpected intra- and interspecies-specific biochemical plasticity for stress adaptation for both pearl millet and wheat genotypes. These quantitative reference data provide tissue- and phenotype-specific marker proteins of stress defense mechanisms which are not predictable from the genome sequence itself and have potential value for marker-assisted breeding beyond genome assisted breeding.

Metabolomics: A Way Forward for Crop Improvement

Metabolomics is an emerging branch of "omics" and it involves identification and quantification of metabolites and chemical footprints of cellular regulatory processes in different biological species. The metabolome is the total metabolite pool in an organism, which can be measured to characterize genetic or environmental variations. Metabolomics plays a significant role in exploring environment-gene interactions, mutant characterization, phenotyping, identification of biomarkers, and drug discovery. Metabolomics is a promising approach to decipher various metabolic networks that are linked with biotic and abiotic stress tolerance in plants. In this context, metabolomics-assisted breeding enables efficient screening for yield and stress tolerance of crops at the metabolic level. Advanced metabolomics analytical tools, like non-destructive nuclear magnetic resonance spectroscopy (NMR), liquid chromatography mass-spectroscopy (LC-MS), gas chromatography-mass spectrometry (GC-MS), high performance liquid chromatography (HPLC), and direct flow injection (DFI) mass spectrometry, have sped up metabolic profiling. Presently, integrating metabolomics with post-genomics tools has enabled efficient dissection of genetic and phenotypic association in crop plants. This review provides insight into the state-of-the-art plant metabolomics tools for crop improvement. Here, we describe the workflow of plant metabolomics research focusing on the elucidation of biotic and abiotic stress tolerance mechanisms in plants. Furthermore, the potential of metabolomics-assisted breeding for crop improvement and its future applications in speed breeding are also discussed. Mention has also been made of possible bottlenecks and future prospects of plant metabolomics.

Closing the 'phenotype gap' in precision medicine: improving what we measure to understand complex disease mechanisms

The central concept underlying precision medicine is a mechanistic understanding of each disease and its response to therapy sufficient to direct a specific intervention. To execute on this vision requires parsing incompletely defined disease syndromes into discrete mechanistic subsets and developing interventions to precisely address each of these etiologically distinct entities. This will require substantial adjustment of traditional paradigms which have tended to aggregate high-level phenotypes with very different etiologies. In the current environment, where diagnoses are not mechanistic, drug development has become so expensive that it is now impractical to imagine the cost-effective creation of new interventions for many prevalent chronic conditions. The vision of precision medicine also argues for a much more seamless integration of research and development with clinical care, where shared taxonomies will enable every clinical interaction to inform our collective understanding of disease mechanisms and drug responses. Ideally, this would be executed in ways that drive real-time and real-world discovery, innovation, translation, and implementation. Only in oncology, where at least some of the biology is accessible through surgical excision of the diseased tissue or liquid biopsy, has "co-clinical" modeling proven feasible. In most common germline disorders, while genetics often reveal the causal mutations, there still remain substantial barriers to efficient disease modeling. Aggregation of similar disorders under single diagnostic labels has directly contributed to the paucity of etiologic and mechanistic understanding by directly reducing the resolution of any subsequent studies. Existing clinical phenotypes are typically anatomic, physiologic, or histologic, and result in a substantial mismatch in information content between the phenomes in humans or in animal 'models' and the variation in the genome. This lack of one-to-one mapping of discrete mechanisms between disease and animal models causes a failure of translation and is one form of 'phenotype gap.' In this review, we will focus on the origins of the phenotyping deficit and approaches that may be considered to bridge the gap, creating shared taxonomies between human diseases and relevant models, using cardiovascular examples.

Using metabolomics to assess the sub-lethal effects of zinc and boscalid on an estuarine polychaete worm over time

INTRODUCTION

Zinc is a heavy metal commonly detected in urban estuaries around Australia. Boscalid is a fungicide found in estuaries, both in water and sediment, it enters the system predominantly through agricultural run-off. Zinc is persistent while boscalid breaks down, with a half-life of 108 days. Both contaminants are widely distributed and their effects on ecosystems are not well understood.

OBJECTIVES

The aim of this study was to determine the metabolite changes in Simplisetia aequisetis (an estuarine polychaete) following laboratory exposure to a sub-lethal concentration of zinc or boscalid over a 2-week period.

METHODS

Individuals were collected at six time points over a 2-week period. Whole polychaete metabolites were extracted and quantified using a multi-platform approach. Polar metabolites were detected using a semi-targeted GC-MS analysis and amine containing compounds were analysed using a targeted LC-MS analysis. Total lipid energy content was also analysed for Simplisetia aequisetis.

RESULTS

The pathways that responded to zinc and boscalid exposure were alanine, aspartate and glutamate metabolism (AAG); glycine, serine and threonine metabolism (GST) and metabolites associated with the tricarboxylic acid cycle (TCA). Results showed that changes in total abundance of some metabolites could be detected as early as 24-h exposure. Changes were detected in the metabolites before commonly used total lipid energy assays identified effects.

CONCLUSION

A multi-platform approach provided a holistic overview of the metabolomic response to contaminants in polychaetes. This approach shows promise to be used in biomonitoring programs to provide early diagnostic indicators of contamination and exposure.

Challenges in Identifying the Dark Molecules of Life

Metabolomics is the study of the metabolome, the collection of small molecules in living organisms, cells, tissues, and biofluids. Technological advances in mass spectrometry, liquid- and gas-phase separations, nuclear magnetic resonance spectroscopy, and big data analytics have now made it possible to study metabolism at an omics or systems level. The significance of this burgeoning scientific field cannot be overstated: It impacts disciplines ranging from biomedicine to plant science. Despite these advances, the central bottleneck in metabolomics remains the identification of key metabolites that play a class-discriminant role. Because metabolites do not follow a molecular alphabet as proteins and nucleic acids do, their identification is much more time consuming, with a high failure rate. In this review, we critically discuss the state-of-the-art in metabolite identification with specific applications in metabolomics and how technologies such as mass spectrometry, ion mobility, chromatography, and nuclear magnetic resonance currently contribute to this challenging task.

Metabolically unhealthy overweight individuals have high lysophosphatide levels, phospholipase activity, and oxidative stress

BACKGROUND & AIMS

Metabolically unhealthy overweight (MUO) individuals and metabolically healthy overweight (MHO) individuals differ in biomarkers of atherogenesis. Metabolomic approaches enable studies of the metabolic variables underlying these differences.

METHODS

We determined the metabolomes in plasma samples from 34 MUO and 34 MHO individuals matched for sex, age, and body mass index (BMI) to identify potential metabolic markers or pathways associated with atherogenic traits.

RESULTS

This analysis revealed that the MUO group had significantly higher levels of glycolic acid, 6 lysophosphatidylethanolamines (lysoPEs), and 12 lysophosphatidylcholines (lysoPCs). Although the two groups had similar total body fat percentages and lean body masses, MUO individuals had larger visceral fat areas (VFAs). They also had greater circulating lipoprotein-associated phospholipase A₂ (Lp-PLA₂) activity and higher levels of oxidized low-density lipoprotein (ox-LDL) and urinary 8-epi-prostaglandin F_2α (8-epi-PGF_2α), reflecting higher risks for oxidative and lipid-related tissue damage. The following measures were positively correlated: VFA and ox-LDL; ox-LDL and Lp-PLA₂ activity; and lysoPC, lysoPE, and 8-epi-PGF_2α levels. Chronic plasma lysoPC increases were associated with atherogenic traits, higher levels of mean ox-LDL, 8-epi-PGF_2α, Lp-PLA₂, and visceral fat accumulation in MUO compared to MHO individuals.

CONCLUSIONS

This panel of plasma metabolites distinguishes MUO from MHO individuals and will enable future research on fat dysregulation and obesity.

Metabolomics in Plant Stress Physiology

Metabolomics is an essential technology for functional genomics and systems biology. It plays a key role in functional annotation of genes and understanding towards cellular and molecular, biotic and abiotic stress responses. Different analytical techniques are used to extend the coverage of a full metabolome. The commonly used techniques are NMR, CE-MS, LC-MS, and GC-MS. The choice of a suitable technique depends on the speed, sensitivity, and accuracy. This chapter provides insight into plant metabolomic techniques, databases used in the analysis, data mining and processing, compound identification, and limitations in metabolomics. It also describes the workflow of measuring metabolites in plants. Metabolomic studies in plant responses to stress are a key research topic in many laboratories worldwide. We summarize different approaches and provide a generic overview of stress responsive metabolite markers and processes compiled from a broad range of different studies. Graphical Abstract.

Preoperative Metabolic Signatures of Prostate Cancer Recurrence Following Radical Prostatectomy

Technological advances in mass spectrometry (MS), liquid chromatography (LC) separations, nuclear magnetic resonance (NMR) spectroscopy, and big data analytics have made possible studying metabolism at an "omics" or systems level. Here, we applied a multiplatform (NMR + LC-MS) metabolomics approach to the study of preoperative metabolic alterations associated with prostate cancer recurrence. Thus far, predicting which patients will recur even after radical prostatectomy has not been possible. Correlation analysis on metabolite abundances detected on serum samples collected prior to surgery from prostate cancer patients ( n = 40 remission vs n = 40 recurrence) showed significant alterations in a number of pathways, including amino acid metabolism, purine and pyrimidine synthesis, tricarboxylic acid cycle, tryptophan catabolism, glucose, and lactate. Lipidomics experiments indicated higher lipid abundances on recurrent patients for a number of classes that included triglycerides, lysophosphatidylcholines, phosphatidylethanolamines, phosphatidylinositols, diglycerides, acyl carnitines, and ceramides. Machine learning approaches led to the selection of a 20-metabolite panel from a single preoperative blood sample that enabled prediction of recurrence with 92.6% accuracy, 94.4% sensitivity, and 91.9% specificity under cross-validation conditions.

Effect of the storage atmosphere on metabolomics of harvested tomatoes (Solanum lycopersicum L.)

Harvested tomatoes were stored under atmospheres that were normoxic, anoxic, or modified (altered O2 and CO2 concentrations). Each atmosphere was created by storing the tomatoes at 25°C for up to 8 days in different kinds of pouches. During storage, metabolites of the tomatoes were measured using metabolomics. We obtained score plots of the metabolites on eighth day of storage by principal component analysis. There was a tendency for groups to be divided on the basis of score plot according to the composition of each gas. PC1 and PC2 seemed to correspond to the influence of O2 and CO2 concentrations, respectively, and the total contribution rate of the two axes was 72%, so that we concluded that the metabolites were affected mainly by O2 and CO2 concentrations. The results indicate that metabolomics may be an effective tool to reveal the relationship between metabolic state of harvested fruits and the atmosphere.

Influence of Metabolite Extraction Methods on 1H-NMR-Based Metabolomic Profiling of Enteropathogenic Yersinia

Metabolite extraction is one of the critical steps in microbial metabolome analysis. It affects both the observed metabolite content and biological interpretation of the data. Several methods exist for metabolite extraction of microbes, but the literature is not consistent regarding the sample model, adequacy, and performance of each method. In this study, an optimal extraction protocol for Yersinia intracellular metabolites was investigated. The effect of five extraction protocols consisting of different extraction solvent systems (60% methanol, 100% methanol, acetonitrile/methanol/water (2:2:1), chloroform/methanol/water (2:1:1), and 60% ethanol) on Yersinia metabolic profiles were compared. The number of detected peaks, sample-to-sample variation, and metabolite yield were used as criteria. Extracted metabolites were analyzed by ¹H-NMR and principal component analysis (PCA), as well as partial least squares discriminant analysis (PLS-DA) multivariate statistics. The extraction protocol using 100% methanol as the extraction solvent provided the highest number of detected peaks for both Yersinia species analyzed, yielding more spectral information. Together with the reproducibility and spectrum quality, 100% methanol extraction was suitable for intracellular metabolite extraction from both species. However, depending on the metabolites of interest, other solvents might be more suitable for future studies, as distinct profiles were observed amongst the extraction methods.

Metabolomics identifies increases in the acylcarnitine profiles in the plasma of overweight subjects in response to mild weight loss: a randomized, controlled design study

Background

Using metabolomics technique to analyze the response to a dietary intervention generates valuable information concerning the effects of the prescribed diet on metabolic regulation. To determine whether low calorie diet (LCD)-induced weight reduction causes changes in plasma metabolites and metabolic characteristics.

Methods

Overweight subjects consumed a LCD (n = 47) or a weight maintenance diet (control, n = 50) in a randomized, controlled design study with a 12-week clinical intervention period. Plasma samples were analyzed using an UPLC-LTQ-Orbitrap MS.

Results

The 12-week LCD intervention resulted in significant mild weight loss, with an 8.3% and 10.6% reduction observed in the visceral fat area (VFA) at the level of the lumbar vertebrae L1 and L4, respectively. The LCD group showed a significant increase in the mean change of serum free fatty acids compared to the control group. In the LCD group, we observed a significant increase in the acylcarnitine (AC) levels, including hexanoylcarnitine, L-octanoylcarnitine, 9-decenoylcarnitine, trans-2-dodecenoylcanitine, dodecanoylcarnitine, 3,5-tetradecadiencarnitine, cis-5-tetradecenoylcarnitine, 9,12-hexadecadienoylcarnitine, and 9-hexadecenoylcarnitne at the 12-week follow-up assessment. When the plasma metabolite changes from baseline were compared between the control and LCD groups, the LCD group showed significant increases in hexanoylcarnitine, L-octanoylcarnitine, trans-2-dodecenoylcanitine, and 3,5-tetradecadiencarnitine than the control group. Additionally, the changes in these ACs in the LCD group strongly negatively correlated with the changes in the VFA at L1 and/or L4.

Conclusion

Mild weight loss from 12-week calorie restriction increased the plasma levels of medium- and long-chain ACs. These changes were coupled with a decrease in VFA and an increase in free fatty acids.

Trial registration

NCT03135132; April 26, 2017.

Electronic supplementary material

The online version of this article (10.1186/s12944-018-0887-1) contains supplementary material, which is available to authorized users.

Serum lipidomics in diagnostics of sarcoidosis

The aim of this study was to determine the use of the lipid profile of patients with sarcoidosis and compare it with healthy subjects. We assume that lipid profile of serum in sarcoidosis differs from the lipid profile of control subjects. Serum was collected from 14 patients with II stage of sarcoidosis and 14 control subjects (healthy volunteers). Proton NMR spectroscopy combined with discriminant analyses, OPLS-DA (orthogonal partial least squares projections to latent structures discriminant analysis), was used. Thirty four NMR signals of lipid compounds were selected. OPLS-DA model consisted of three components and very good explain the data and also predict the data. Discriminant analysis correctly classified patients according to their groups for 92.9% of sarcoidose and 100% of control. From multivariate discriminant analysis we obtain a list of potentialbiomarkers which are statistically significant and which separate one class from another. These biomarkers are statistically significant, but not necessarily biochemically significant. They may have biochemical significance and they may be the biomarkers we are interested in, however, this must be established through extensive testing. Presented method allows distinguishing between healthy subject and sarcoidosis patients. (Sarcoidosis Vasc Diffuse Lung Dis 2018; 35: 150-153).

Does the microbiome and virome contribute to myalgic encephalomyelitis/chronic fatigue syndrome?

Myalgic encephalomyelitis (ME)/chronic fatigue syndrome (CFS) (ME/CFS) is a disabling and debilitating disease of unknown aetiology. It is a heterogeneous disease characterized by various inflammatory, immune, viral, neurological and endocrine symptoms. Several microbiome studies have described alterations in the bacterial component of the microbiome (dysbiosis) consistent with a possible role in disease development. However, in focusing on the bacterial components of the microbiome, these studies have neglected the viral constituent known as the virome. Viruses, particularly those infecting bacteria (bacteriophages), have the potential to alter the function and structure of the microbiome via gene transfer and host lysis. Viral-induced microbiome changes can directly and indirectly influence host health and disease. The contribution of viruses towards disease pathogenesis is therefore an important area for research in ME/CFS. Recent advancements in sequencing technology and bioinformatics now allow more comprehensive and inclusive investigations of human microbiomes. However, as the number of microbiome studies increases, the need for greater consistency in study design and analysis also increases. Comparisons between different ME/CFS microbiome studies are difficult because of differences in patient selection and diagnosis criteria, sample processing, genome sequencing and downstream bioinformatics analysis. It is therefore important that microbiome studies adopt robust, reproducible and consistent study design to enable more reliable and valid comparisons and conclusions to be made between studies. This article provides a comprehensive review of the current evidence supporting microbiome alterations in ME/CFS patients. Additionally, the pitfalls and challenges associated with microbiome studies are discussed.

Combined Metabolomic Analysis of Plasma and Urine Reveals AHBA, Tryptophan and Serotonin Metabolism as Potential Risk Factors in Gestational Diabetes Mellitus (GDM)

Gestational diabetes mellitus during pregnancy has severe implications for the health of the mother and the fetus. Therefore, early prediction and an understanding of the physiology are an important part of prenatal care. Metabolite profiling is a long established method for the analysis and prediction of metabolic diseases. Here, we applied untargeted and targeted metabolomic protocols to analyze plasma and urine samples of pregnant women with and without GDM. Univariate and multivariate statistical analyses of metabolomic profiles revealed markers such as 2-hydroxybutanoic acid (AHBA), 3-hydroxybutanoic acid (BHBA), amino acids valine and alanine, the glucose-alanine-cycle, but also plant-derived compounds like sitosterin as different between control and GDM patients. PLS-DA and VIP analysis revealed tryptophan as a strong variable separating control and GDM. As tryptophan is biotransformed to serotonin we hypothesized whether serotonin metabolism might also be altered in GDM. To test this hypothesis we applied a method for the analysis of serotonin, metabolic intermediates and dopamine in urine by stable isotope dilution direct infusion electrospray ionization mass spectrometry (SID-MS). Indeed, serotonin and related metabolites differ significantly between control and GDM patients confirming the involvement of serotonin metabolism in GDM. Clustered correlation coefficient visualization of metabolite correlation networks revealed the different metabolic signatures between control and GDM patients. Eventually, the combination of selected blood plasma and urine sample metabolites improved the AUC prediction accuracy to 0.99. The detected GDM candidate biomarkers and the related systemic metabolic signatures are discussed in their pathophysiological context. Further studies with larger cohorts are necessary to underpin these observations.

Application of Metabolomics to Quality Control of Natural Product Derived Medicines

Metabolomics has been used as a powerful tool for the analysis and quality assessment of the natural product (NP)-derived medicines. It is increasingly being used in the quality control and standardization of NP-derived medicines because they are composed of hundreds of natural compounds. The most common techniques that are used in metabolomics consist of NMR, GC-MS, and LC-MS in combination with multivariate statistical analyses including principal components analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). Currently, the quality control of the NP-derived medicines is usually conducted using HPLC and is specified by one or two indicators. To create a superior quality control framework and avoid adulterated drugs, it is necessary to be able to determine and establish standards based on multiple ingredients using metabolic profiling and fingerprinting. Therefore, the application of various analytical tools in the quality control of NP-derived medicines forms the major part of this review. Veregen^® (Medigene AG, Planegg/Martinsried, Germany), which is the first botanical prescription drug approved by US Food and Drug Administration, is reviewed as an example that will hopefully provide future directions and perspectives on metabolomics technologies available for the quality control of NP-derived medicines.

Simultaneous determination of intracellular nucleotides and coenzymes in Yarrowia lipolytica producing lipid and lycopene by capillary zone electrophoresis

Yarrowia lipolytica is an oleaginous yeast with promise in producing terpenoids such as lycopene. Though methods for analyzing primary metabolic intermediates have been established, further work is needed to better analyze nucleotides and coenzymes. Here, we presented an optimized method for the separation of nucleotides and coenzymes in Y. lipolytica using the capillary electrophoresis. The separation of twelve metabolites including four coenzymes, five nucleotides and three nucleosides was achieved within 32min using a voltage of 15kV and 70mM sodium carbonate/hydrogencarbonate buffer with 1.0% β-CD at pH 10. The results show that the concentrations of adenosine triphosphate and nicotinamide adenine dinucleotide phosphate changed significantly between lycopene producing strain and the control, indicating that these two metabolites may be closely related with lycopene production. The optimized method provides a useful approach for future metabolic analysis of fermentation process as well as industrial strain improvement.

A countermeasure development pipeline

We have developed an integrated pipeline for countermeasure discovery that, under the auspices of the National Institutes of Health Countermeasures Against Chemical Threats network, is one of the few efforts within academia that by design spans the spectrum from discovery to phase I. The successful implementation of this approach for cyanide would enable efficient proof-of-concept studies that would lay the foundation for a generalizable strategy for parallel mechanistic studies and accelerated countermeasure development in the face of new and emerging chemical threats.