Current and future perspectives of functional metabolomics in disease studies-A review

Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products

Covering: 1995 to 2023Advances in bioanalytical methods, particularly mass spectrometry, have provided valuable molecular insights into the mechanisms of life. Non-targeted metabolomics aims to detect and (relatively) quantify all observable small molecules present in a biological system. By comparing small molecule abundances between different conditions or timepoints in a biological system, researchers can generate new hypotheses and begin to understand causes of observed phenotypes. Functional metabolomics aims to investigate the functional roles of metabolites at the scale of the metabolome. However, most functional metabolomics studies rely on indirect measurements and correlation analyses, which leads to ambiguity in the precise definition of functional metabolomics. In contrast, the field of natural products has a history of identifying the structures and bioactivities of primary and specialized metabolites. Here, we propose to expand and reframe functional metabolomics by integrating concepts from the fields of natural products and chemical biology. We highlight emerging functional metabolomics approaches that shift the focus from correlation to physical interactions, and we discuss how this allows researchers to uncover causal relationships between molecules and phenotypes.

Exploration on the effect of anserine on the alleviation of DVT and its molecular mechanism

Background

This study aimed to explore the regulatory effect of anserine on HUVEC cell injury and thrombosis in deep venous thrombosis (DVT) rats, and to elucidate the underlying molecular mechanisms.

Methods

Non-targeted metabolomics data analyses were conducted using an ultra-performance liquid chromatography system Vanquish UHPLC and mass spectrometer to detect plasma metabolism profiles. The transcriptome sequencing and gene intervention experiments were performed to verify the regulatory effect. Further in vivo and in vitro experiments were performed. Enzyme-linked immunosorbent assay was used to detect the levels of P-selectin, E-selectin, and vWF, hematoxylin-eosin (HE) staining was performed to observe thrombotic and inflammatory cell infiltration, flow cytometry and TUNEL assays were performed to detect apoptosis, and qPCR and WB assays were conducted to determine the gene and protein expression.

Results

Anserine alleviated HUVECs injury, reduced adhesion molecule expression, and inflammation. It decreased P-selectin, E-selectin, vWF, THBD, TFPI levels, and apoptosis while promoting NOS3, ET-1, and NO release in HUVECs. In DVT rats, anserine reduced P-selectin, E-selectin, vWF, thrombosis, cell infiltration, apoptosis, and promoted NO release. Transcriptome sequencing and gene intervention confirmed anserine's regulation of the PI3K-Akt pathway and coagulation via MYB. CARNMT1, a regulatory enzyme for anserine metabolism, increased anserine content, inhibiting coagulation, thrombosis, cell infiltration, and promoting NO release in rats.

Conclusion

This study confirmed anserine could alleviate DVT by improving the inflammatory response, inhibiting blood agglutination, and promoting vasodilation, providing new potential therapeutic targets, important scientific evidence for the development of DVT management, and new clues for an in-depth understanding of its molecular mechanisms.

Influence of Cold Stress on Physiological and Phytochemical Characteristics and Secondary Metabolite Accumulation in Microclones of Juglans regia L

The current study investigated the impact of cold stress on the morphological, physiological, and phytochemical properties of Juglans regia L. (J. regia) using in vitro microclone cultures. The study revealed significant stress-induced changes in the production of secondary antioxidant metabolites. According to gas chromatography-mass spectrometry (GC-MS) analyses, the stress conditions profoundly altered the metabolism of J. regia microclones. Although the overall spectrum of metabolites was reduced, the production of key secondary antioxidant metabolites significantly increased. Notably, there was a sevenfold (7×) increase in juglone concentration. These findings are crucial for advancing walnut metabolomics and enhancing our understanding of plant responses to abiotic stress factors. Additionally, study results aid in identifying the role of individual metabolites in these processes, which is essential for developing strategies to improve plant resilience and tolerance to adverse conditions.

Vacancy-Driven High-Performance Metabolic Assay for Diagnosis and Therapeutic Evaluation of Depression

Depression is one of the most common mental illnesses and is a well-known risk factor for suicide, characterized by low overall efficacy (<50%) and high relapse rate (40%). A rapid and objective approach for screening and prognosis of depression is highly desirable but still awaits further development. Herein, a high-performance metabolite-based assay to aid the diagnosis and therapeutic evaluation of depression by developing a vacancy-engineered cobalt oxide (Vo-Co3O4) assisted laser desorption/ionization mass spectrometer platform is presented. The easy-prepared nanoparticles with optimal vacancy achieve a considerable signal enhancement, characterized by favorable charge transfer and increased photothermal conversion. The optimized Vo-Co3O4 allows for a direct and robust record of plasma metabolic fingerprints (PMFs). Through machine learning of PMFs, high-performance depression diagnosis is achieved, with the areas under the curve (AUC) of 0.941-0.980 and an accuracy of over 92%. Furthermore, a simplified diagnostic panel for depression is established, with a desirable AUC value of 0.933. Finally, proline levels are quantified in a follow-up cohort of depressive patients, highlighting the potential of metabolite quantification in the therapeutic evaluation of depression. This work promotes the progression of advanced matrixes and brings insights into the management of depression.

Gentiopicroside improves non-alcoholic steatohepatitis by activating PPARα and suppressing HIF1

Gentiopicroside (GPS) is a highly water-soluble small-molecule drug and the main bioactive secoiridoid glycoside of Gentiana scabra that has been shown to have hepatoprotective effects against non-alcoholic steatohepatitis (NASH), a form of non-alcoholic fatty liver disease (NAFLD) that can progress to cirrhosis and hepatocellular carcinoma. However, the effects of GPS on NASH and the underlying mechanisms remain obscure. Firstly, a high-fat, high-cholesterol (HFHC) diet and a high-sugar solution containing d-fructose and d-glucose were used to establish a non-alcoholic steatohepatitis (NASH) mice model. Secondly, we confirmed GPS supplementation improve metabolic abnormalities and reduce inflammation in NASH mice induced by HFHC and high-sugar solution. Then we used metabolomics to investigate the mechanisms of GPS in NASH mice. Metabolomics analysis showed GPS may work through the Peroxisome Proliferator-Activated Receptor (PPAR) signaling pathway and glycine, serine, and threonine metabolism. Functional metabolites restored by GPS included serine, glycine, eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA). Western blot and qRT-PCR analysis confirmed GPS improve NASH by regulating PPARα and Hypoxia-Inducible Factor-1α (HIF-1α) signaling pathways. In vitro, studies further demonstrated EPA and DHA enhance fatty acid oxidation through the PPARα pathway, while serine and glycine inhibit oxidative stress through the HIF-1α pathway in palmitic acid-stimulated HepG2 cells. Our results suggest GPS's anti-inflammatory and anti-steatosis effects in NASH progression are related to the suppression of HIF-1α through the restoration of L-serine and glycine and the activation of PPARα through increased EPA and DHA.

Metabolic profile of follicular fluid in patients with ovarian endometriosis undergoing IVF: a pilot study

RESEARCH QUESTION

What are the metabolic characteristics of follicular fluid in patients with ovarian endometriosis undergoing IVF?

DESIGN

This was an exploratory cohort study on endometriosis. In total, 19 infertile patients with ovarian endometriosis diagnosed by laparoscopy, and 23 controls matched in terms of age and body mass index (women with infertility due to male or tubal factors) were enrolled in this study. All patients underwent IVF treatment with a gonadotrophin-releasing hormone antagonist protocol, and follicular fluid was collected at oocyte retrieval. The metabolomics of follicular fluid samples was analysed using an ultra-high-performance liquid chromatography Orbitrap Exploris mass spectrometer (UHPLC-OE-MS). The best combination of biomarkers was selected by performing stepwise logistic regression analysis with backward elimination.

RESULTS

Fifteen metabolites were identified as biomarkers associated with endometriosis. A final model containing 8-hydroxy-2-deoxyguanosine, biotin, n-acetyl-L-methionine and n-methylnicotinamide was constructed. Receiver operating characteristic analysis confirmed the value of these parameters in diagnosing endometriosis, with sensitivity of 94.7% and specificity of 95.7%. Enrichment analysis via the Kyoto Encyclopedia of Genes and Genome showed that 15 metabolites were enriched in eight metabolic pathways.

CONCLUSION

Metabolomics based on UHPLC-OE-MS effectively characterized the metabolomics analysis of follicular fluid in patients with ovarian endometriosis. These findings may provide a new basis for better understanding of how diseases progress, and for the discovery of new biomarkers.

Toward an understanding of glucose metabolism in radial glial biology and brain development

Decades of research have sought to determine the intrinsic and extrinsic mechanisms underpinning the regulation of neural progenitor maintenance and differentiation. A series of precise temporal transitions within progenitor cell populations generates all the appropriate neural cell types while maintaining a pool of self-renewing progenitors throughout embryogenesis. Recent technological advances have enabled us to gain new insights at the single-cell level, revealing an interplay between metabolic state and developmental progression that impacts the timing of proliferation and neurogenesis. This can have long-term consequences for the developing brain's neuronal specification, maturation state, and organization. Furthermore, these studies have highlighted the need to reassess the instructive role of glucose metabolism in determining progenitor cell division, differentiation, and fate. This review focuses on glucose metabolism (glycolysis) in cortical progenitor cells and the emerging focus on glycolysis during neurogenic transitions. Furthermore, we discuss how the field can learn from other biological systems to improve our understanding of the spatial and temporal changes in glycolysis in progenitors and evaluate functional neurological outcomes.

Marine indole alkaloid diversity and bioactivity. What do we know and what are we missing?

Covering: marine indole alkaloids (n = 2048) and their reported bioactivities up to the end of 2021Despite increasing numbers of marine natural products (MNPs) reported each year, most have only been examined for cytotoxic, antibacterial, and/or antifungal biological activities with the majority found to be inactive in these assays. In this context, why are natural products continuing to be examined in assays they are unlikely to show significant activity in, and what targets might be more useful for expanding knowledge of their biologically relevant chemical space? We have undertaken a meta-analysis of the biological activities for 2048 marine indole alkaloids (MIAs), a diverse sub-class of MNPs reported up to the end of 2021, and this has highlighted that the bioactivity potentials for up to 86% of published MIAs remains underexplored and/or undefined. Although most published MIAs are not cytotoxic or antimicrobial, there is a continued focus on using these assays to evaluate new structurally related analogues. Using cheminformatics analyses, the chemical diversity of the 2048 MIAs were clustered using fragment based fingerprints and their reported bioactivity potency towards specific disease targets was assessed for structure activity trends. These analyses showed that there are groups of MIAs that possess potent and diverse activities and that many analogues, previously tested only in cellular toxicity assays, could be better exploited to generate structure activity relationships associated with leads to treat emerging diseases. A collection of indole drug and drug-lead structures from non-natural sources were also incorporated into the dataset providing complementary bioactivity profiles that were further used to predict underexplored areas of potential new activity and to better direct future testing of MIAs. Our findings clearly suggest the biological evaluation of MIAs continues to be conducted on a narrow range of bioassays and disease targets, and that shifting the focus to non-toxic disease targets should provide expanded knowledge of biologically relevant chemical space aimed at maximising the potential of MIAs for drug discovery.

Metabolomics acts as a powerful tool for comprehensively evaluating vaccines approved under emergency: a CoronaVac retrospective study

Introduction

To control the COVID-19 pandemic, great efforts have been made to realize herd immunity by vaccination since 2020. Unfortunately, most of the vaccines against COVID-19 were approved in emergency without a full-cycle and comprehensive evaluation process as recommended to the previous vaccines. Metabolome has a close tie with the phenotype and can sensitively reflect the responses to stimuli, rendering metabolomic analysis have the potential to appraise and monitor vaccine effects authentically.

Methods

In this study, a retrospective study was carried out for 330 Chinese volunteers receiving recommended two-dose CoronaVac, a vaccine approved in emergency in 2020. Venous blood was sampled before and after vaccination at 5 separate time points for all the recipients. Routine clinical laboratory analysis, metabolomic and lipidomic analysis data were collected.

Results and discussion

It was found that the serum antibody-positive rate of this population was around 81.82%. Most of the laboratory parameters were slightly perturbated within the relevant reference intervals after vaccination. The metabolomic and lipidomic analyses showed that the metabolic shift after inoculation was mainly in the glycolysis, tricarboxylic acid cycle, amino acid metabolism, urea cycle, as well as microbe-related metabolism (bile acid metabolism, tryptophan metabolism and phenylalanine metabolism). Time-course metabolome changes were found in parallel with the progress of immunity establishment and peripheral immune cell counting fluctuation, proving metabolomics analysis was an applicable solution to evaluate immune effects complementary to traditional antibody detection. Taurocholic acid, lysophosphatidylcholine 16:0 sn-1, glutamic acid, and phenylalanine were defined as valuable metabolite markers to indicate the establishment of immunity after vaccination. Integrated with the traditional laboratory analysis, this study provided a feasible metabolomics-based solution to relatively comprehensively evaluate vaccines approved under emergency.

Quantitative plasma profiling by 1H NMR-based metabolomics: impact of sample treatment

Introduction: There is evidence that sample treatment of blood-based biosamples may affect integral signals in nuclear magnetic resonance-based metabolomics. The presence of macromolecules in plasma/serum samples makes investigating low-molecular-weight metabolites challenging. It is particularly relevant in the targeted approach, in which absolute concentrations of selected metabolites are often quantified based on the area of integral signals. Since there are a few treatments of plasma/serum samples for quantitative analysis without a universally accepted method, this topic remains of interest for future research. Methods: In this work, targeted metabolomic profiling of 43 metabolites was performed on pooled plasma to compare four methodologies consisting of Carr-Purcell-Meiboom-Gill (CPMG) editing, ultrafiltration, protein precipitation with methanol, and glycerophospholipid solid-phase extraction (g-SPE) for phospholipid removal; prior to NMR metabolomics analysis. The effect of the sample treatments on the metabolite concentrations was evaluated using a permutation test of multiclass and pairwise Fisher scores. Results: Results showed that methanol precipitation and ultrafiltration had a higher number of metabolites with coefficient of variation (CV) values above 20%. G-SPE and CPMG editing demonstrated better precision for most of the metabolites analyzed. However, differential quantification performance between procedures were metabolite-dependent. For example, pairwise comparisons showed that methanol precipitation and CPMG editing were suitable for quantifying citrate, while g-SPE showed better results for 2-hydroxybutyrate and tryptophan. Discussion: There are alterations in the absolute concentration of various metabolites that are dependent on the procedure. Considering these alterations is essential before proceeding with the quantification of treatment-sensitive metabolites in biological samples for improving biomarker discovery and biological interpretations. The study demonstrated that g-SPE and CPMG editing are effective methods for removing proteins and phospholipids from plasma samples for quantitative NMR analysis of metabolites. However, careful consideration should be given to the specific metabolites of interest and their susceptibility to the sample treatment procedures. These findings contribute to the development of optimized sample preparation protocols for metabolomics studies using NMR spectroscopy.

Exploring the Metabolic Differences between Cisplatin- and UV Light-Induced Apoptotic Bodies in HK-2 Cells by an Untargeted Metabolomics Approach

Among the extracellular vesicles, apoptotic bodies (ABs) are only formed during the apoptosis and perform a relevant role in the pathogenesis of different diseases. Recently, it has been demonstrated that ABs from human renal proximal tubular HK-2 cells, either induced by cisplatin or by UV light, can lead to further apoptotic death in naïve HK-2 cells. Thus, the aim of this work was to carry out a non-targeted metabolomic approach to study if the apoptotic stimulus (cisplatin or UV light) affects in a different way the metabolites involved in the propagation of apoptosis. Both ABs and their extracellular fluid were analyzed using a reverse-phase liquid chromatography-mass spectrometry setup. Principal components analysis showed a tight clustering of each experimental group and partial least square discriminant analysis was used to assess the metabolic differences existing between these groups. Considering the variable importance in the projection values, molecular features were selected and some of them could be identified either unequivocally or tentatively. The resulting pathways indicated that there are significant, stimulus-specific differences in metabolites abundancies that may propagate apoptosis to healthy proximal tubular cells; thus, we hypothesize that the share in apoptosis of these metabolites might vary depending on the apoptotic stimulus.

AntDAS-DDA: A New Platform for Data-Dependent Acquisition Mode-Based Untargeted Metabolomic Profiling Analysis with Advantage of Recognizing Insource Fragment Ions to Improve Compound Identification

Data-dependent acquisition (DDA) mode in ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) can provide massive amounts of MS¹ and MS/MS information of compounds in untargeted metabolomics and can thus facilitate compound identification greatly. In this work, we developed a new platform called AntDAS-DDA for the automatic processing of UHPLC-HRMS data sets acquired under the DDA mode. Several algorithms, including extracted ion chromatogram extraction, feature extraction, MS/MS spectrum construction, fragment ion identification, and MS¹ spectrum construction, were developed within the platform. The performance of AntDAS-DDA was investigated comprehensively with a mixture of standard and complex plant data sets. Results suggested that features in complex sample matrices can be extracted effectively, and the constructed MS¹ and MS/MS spectra can benefit in compound identification greatly. The efficiency of compound identification can be improved by about 20%. AntDAS-DDA can take full advantage of MS/MS information in multiple sample analyses and provide more MS/MS spectra than single sample analysis. A comparison with advanced data analysis tools indicated that AntDAS-DDA may be used as an alternative for routine UHPLC-HRMS-based untargeted metabolomics. AntDAS-DDA is freely available at http://www.pmdb.org.cn/antdasdda.

Comparative plasma and urine metabolomics analysis of juvenile and adult canines

Background and aims

The metabolomic profile of a biofluid can be affected by age, and thus provides detailed information about the metabolic alterations in biological processes and reflects the in trinsic rule regulating the growth and developmental processes.

Methods

To systemically investigate the characteristics of multiple metabolic profiles associated with canine growth, we analyzed the metabolomics in the plasma and urine samples from 15 young and 15 adult beagle dogs via UHPLC-Q-TOFMS-based metabolomics. Blood routine and serum biochemical analyses were also performed on fasting blood samples.

Results

The metabolomics results showed remarkable differences in metabolite fingerprints both in plasma and urine between the young and adult groups. The most obvious age-related metabolite alterations include decreased serumlevels of oxoglutaric acid and essential amino acids and derivatives but increased levels of urine levels of O-acetylserine. These changes primarily involved in amino acid metabolism and bile secretion pathways. We also found that the levels of glutamine were consistently higher in both serum and urine of adults, while N-acetylhistamine and uracil concentrations were much lower in the adult group compared to younger ones.

Conclusion

Our study provides a whole metabolic profile of serum and urine characteristics of young and adult canines, identifying several metabolites that were significantly associated with age change, which provides theoretical support for the nutrition-related research and age-related homeostasis maintenance in dogs.

Data Processing and Analysis in Mass Spectrometry-Based Metabolomics

Metabolomics is the latest of the omics sciences. It attempts to measure and characterize metabolites-small chemical compounds <1500 Da-on cells, tissue, or biofluids, which are usually products of biological reactions. As metabolic reactions are closer to the phenotype, metabolomics has emerged as an attractive science for various areas of research, including personalized medicine. However, due to the complexity of data obtained and the absence of curated databases for metabolite identification, data processing is the major bottleneck in this area since most technicians lack the required bioinformatics expertise to process datasets in a reliable and fast manner. The aim of this chapter is to describe the available tools for data processing that makes an inexperienced researcher capable of obtaining reliable results without having to undergo through huge parametrization steps.

Metabolomic transition trajectory and potential mechanisms of N-nitrosomethylbenzylamine induced esophageal squamous cell carcinoma in rats

Esophageal squamous cell carcinoma (ESCC) is an environment-relevant malignancy with a high mortality. Nitrosamines, a class of nitrogen-containing environmental carcinogens, are widely suggested as a risk factor for ESCC. However, how nitrosamines affect metabolic regulation to promote ESCC tumorigenesis is largely unknown. In this study, the transition trajectory of serum metabolism in the course of ESCC induced by N-nitrosomethylbenzylamine (NMBA) in rats was depicted by an untargeted metabolomic analysis, and the potential molecular mechanisms were revealed. The results showed that the metabolic alteration in rats was slight at the basal cell hyperplasia (BCH) stage, while it became apparent when the esophageal lesion developed into dysplasia (DYS) or more serious conditions. Moreover, serum metabolism of severe dysplasia (S-DYS) showed more similar characteristics to that of carcinoma in situ (CIS) and invasive cancer (IC). Aberrant nicotinate (NA) and nicotinamide (NAM) metabolism, tryptophan (TRP) metabolism, and sphingolipid metabolism could be the key players favoring the malignant transformation of esophageal epithelium induced by NMBA. More particularly, NA and NAM metabolism in the precancerous stages and TRP metabolism in the cancerous stages were demonstrated to replenish NAD⁺ in different patterns. Furthermore, both the IDO1-KYN-AHR axis mediated by TRP metabolism and the SPHK1-S1P-S1PR1 axis by sphingolipid metabolism provided an impetus to create the pro-inflammatory yet immune-suppressive microenvironment to facilitate the esophageal tumorigenesis and progression. Together, these suggested that NMBA exerted its carcinogenicity via more than one pathway, which may act together to produce combination effects. Targeting these pathways may open up the possibility to attenuate NMBA-induced esophageal carcinogenesis. However, the interconnection between different metabolic pathways needs to be specified further. And the integrative and multi-level systematic research will be conducive to fully understanding the mechanisms of NMBA-induced ESCC.

Native metabolomics identifies the rivulariapeptolide family of protease inhibitors

The identity and biological activity of most metabolites still remain unknown. A bottleneck in the exploration of metabolite structures and pharmaceutical activities is the compound purification needed for bioactivity assignments and downstream structure elucidation. To enable bioactivity-focused compound identification from complex mixtures, we develop a scalable native metabolomics approach that integrates non-targeted liquid chromatography tandem mass spectrometry and detection of protein binding via native mass spectrometry. A native metabolomics screen for protease inhibitors from an environmental cyanobacteria community reveals 30 chymotrypsin-binding cyclodepsipeptides. Guided by the native metabolomics results, we select and purify five of these compounds for full structure elucidation via tandem mass spectrometry, chemical derivatization, and nuclear magnetic resonance spectroscopy as well as evaluation of their biological activities. These results identify rivulariapeptolides as a family of serine protease inhibitors with nanomolar potency, highlighting native metabolomics as a promising approach for drug discovery, chemical ecology, and chemical biology studies.

Bioactivity-guided isolation of specialized metabolites is an iterative process. Here, the authors demonstrate a native metabolomics approach that allows for fast screening of complex metabolite extracts against a protein of interest and simultaneous structure annotation.

Metabolomics: A New Approach in the Evaluation of Effects in Human Beings and Wildlife Associated with Environmental Exposition to POPs

Human beings and wild organisms are exposed daily to a broad range of environmental stressors. Among them are the persistent organic pollutants that can trigger adverse effects on these organisms due to their toxicity properties. There is evidence that metabolomics can be used to identify biomarkers of effect by altering the profiles of endogenous metabolites in biological fluids or tissues. This approach is relatively new and has been used in vitro studies mainly. Therefore, this review addresses those that have used metabolomics as a key tool to identify metabolites associated with environmental exposure to POPs in wildlife and human populations and that can be used as biomarkers of effect. The published results suggest that the metabolic pathways that produce energy, fatty acids, and amino acids are commonly affected by POPs. Furthermore, these pathways can be promoters of additional effects. In the future, metabolomics combined with other omics will improve understanding of the origin, development, and progression of the effects caused by environmental exposure.

Urinary metabolic modulation in human participants residing in Siachen: a 1H NMR metabolomics approach

The main physiological challenge in high altitude environment is hypoxia which affects the aerobic metabolism reducing the energy supply. These changes may further progress toward extreme environment-related diseases. These are further reflected in changes in small molecular weight metabolites and metabolic pathways. In the present study, metabolic changes due to chronic environmental hypoxia were assessed using 1H NMR metabolomics by analysing the urinary metabolic profile of 70 people at sea level and 40 people at Siachen camp (3700 m) for 1 year. Multivariate statistical analysis was carried out, and PLSDA detected 15 metabolites based on VIP score > 1. ROC analysis detected cis-aconitate, Nicotinamide Mononucleotide, Tyrosine, Choline and Creatinine metabolites with a high range of sensitivity and specificity. Pathway analysis revealed 16 pathways impact > 0.05, and phenylalanine tyrosine and tryptophan biosynthesis was the most prominent altered pathway indicating metabolic remodelling to meet the energy requirements. TCA cycle, Glycine serine and Threonine metabolism, Glutathione metabolism and Cysteine alterations were other metabolic pathways affected during long-term high-altitude hypoxia exposure. Present findings will help unlock a new dimension for the potential application of NMR metabolomics to address extreme environment-related health problems, early detection and developing strategies to combat high altitude hypoxia.

Explore potential disease related metabolites based on latent factor model

Background

In biological systems, metabolomics can not only contribute to the discovery of metabolic signatures for disease diagnosis, but is very helpful to illustrate the underlying molecular disease-causing mechanism. Therefore, identification of disease-related metabolites is of great significance for comprehensively understanding the pathogenesis of diseases and improving clinical medicine.

Results

In the paper, we propose a disease and literature driven metabolism prediction model (DLMPM) to identify the potential associations between metabolites and diseases based on latent factor model. We build the disease glossary with disease terms from different databases and an association matrix based on the mapping between diseases and metabolites. The similarity of diseases and metabolites is used to complete the association matrix. Finally, we predict potential associations between metabolites and diseases based on the matrix decomposition method. In total, 1,406 direct associations between diseases and metabolites are found. There are 119,206 unknown associations between diseases and metabolites predicted with a coverage rate of 80.88%. Subsequently, we extract training sets and testing sets based on data increment from the database of disease-related metabolites and assess the performance of DLMPM on 19 diseases. As a result, DLMPM is proven to be successful in predicting potential metabolic signatures for human diseases with an average AUC value of 82.33%.

Conclusion

In this paper, a computational model is proposed for exploring metabolite-disease pairs and has good performance in predicting potential metabolites related to diseases through adequate validation. The results show that DLMPM has a better performance in prioritizing candidate diseases-related metabolites compared with the previous methods and would be helpful for researchers to reveal more information about human diseases.

Metabolomics-based molecular signatures reveal the toxic effect of co-exposure to nitrosamines in drinking water

Nitrosamines, a group of emerging nitrogenous pollutants, are ubiquitously found in the drinking water system. However, less is known about how systemic biological responses resist or tolerate nitrosamines, especially long-term co-exposure at low concentrations. In this study, untargeted metabolomics was used to investigate the metabolic perturbations in human esophageal epithelial Het-1A cells induced by a mixture of nine common nitrosamines in drinking water at environmentally relevant, human-internal-exposure, and genotoxic concentrations. Generally, the disrupted metabolic spectrum became complicated with nitrosamines dose increasing. Notably, two inflammation-associated pathways, namely, cysteine (Cys) and methionine (MET) metabolism, and nicotinate and nicotinamide metabolism, changed significantly under the action of nitrosamines, even at the environmentally relevant level. Furthermore, targeted metabolomics and molecular biology indicators in cells were identified in mice synchronously. For one thing, the up-regulated Cys and MET metabolism provided methyl donors for histone methylation in the context of pro-inflammatory response. For another, the down-regulated NAD⁺/NADH ratio inhibited the deacetylation of NF-кB p65 and eventually activated the NF-кB signaling pathway. Taken collectively, the metabolomics molecular signatures were important indicative markers for nitrosamines-induced inflammation. The potential crosstalk between the inflammatory cascade and metabolic regulation also requires further studies. These findings suggest that more attention should be paid to long-term co-exposure at low concentrations in the control of nitrosamines pollution in drinking water. Additionally, this study also highlights a good prospect of the combined metabolomic-molecular biology approach in environmental toxicology.

Metabolomics study identified bile acids as potential biomarkers for gastric cancer: A case control study

Gastric cancer (GC) is a common lethal malignancy worldwide. Gastroscopy is an effective screening technique for decreasing mortality. However, there are still limited useful non-invasive markers for early detection of GC. Bile acids are important molecules for the modulation of energy metabolism. With an in-depth targeted method for accurate quantitation of 80 bile acids (BAs), we aimed to find potential biomarkers for the early screening of GC. A cohort with 280 participants was enrolled, including 113 GC, 22 benign gastric lesions (BGL) and 145 healthy controls. Potential markers were identified using a random forest machine algorithm in the discovery cohort (n=180), then validated in an internal validation cohort (n=78) and a group with 22 BGL. The results represented significant alterations in the circulating BA pool between GC and the controls. BAs also exhibited significant correlations with various clinical traits. Then, we developed a diagnostic panel that comprised six BAs or ratios for GC detection. The panel showed high accuracy for the diagnosis of GC with AUC of 1 (95%CI: 1.00-1.00) and 0.98 (95%CI: 0.93-1.00) in the discovery and validation cohort, respectively. This 6-BAs panel was also able to identify early GC with AUC of 1 (95%CI: 0.999-1.00) and 0.94 (95%CI: 0.83-1.00) in the discovery and validation cohort, respectively. Meanwhile, this panel achieved a good differential diagnosis between GC and BGL and the AUC was 0.873 (95%CI: 0.812-0.934). The alternations of serum bile acids are characteristic metabolic features of GC. Bile acids could be promising biomarkers for the early diagnosis of GC.

Application of metabolomics in osteoporosis research

Osteoporosis (OP) is a systemic disease characterized by bone metabolism imbalance and bone microstructure destruction, which causes serious social and economic burden. At present, the diagnosis and treatment of OP mainly rely on imaging combined with drugs. However, the existing pathogenic mechanisms, diagnosis and treatment strategies for OP are not clear and effective enough, and the disease progression that cannot reflect OP further restricts its effective treatment. The application of metabolomics has facilitated the study of OP, further exploring the mechanism and behavior of bone cells, prevention, and treatment of the disease from various metabolic perspectives, finally realizing the possibility of a holistic approach. In this review, we focus on the application of metabolomics in OP research, especially the newer systematic application of metabolomics and treatment with herbal medicine and their extracts. In addition, the prospects of clinical transformation in related fields are also discussed. The aim of this study is to highlight the use of metabolomics in OP research, especially in exploring the pathogenesis of OP and the therapeutic mechanisms of natural herbal medicine, for the benefit of interdisciplinary researchers including clinicians, biologists, and materials engineers.

Application of the Metabolomics Approach in Food Authentication

The authentication of food products is essential for food quality and safety. Authenticity assessments are important to ensure that the ingredients or contents of food products are legitimate and safe to consume. The metabolomics approach is an essential technique that can be utilized for authentication purposes. This study aimed to summarize food authentication through the metabolomics approach, to study the existing analytical methods, instruments, and statistical methods applied in food authentication, and to review some selected food commodities authenticated using metabolomics-based methods. Various databases, including Google Scholar, PubMed, Scopus, etc., were used to obtain previous research works relevant to the objectives. The review highlights the role of the metabolomics approach in food authenticity. The approach is technically implemented to ensure consumer protection through the strict inspection and enforcement of food labeling. Studies have shown that the study of metabolomics can ultimately detect adulterant(s) or ingredients that are added deliberately, thus compromising the authenticity or quality of food products. Overall, this review will provide information on the usefulness of metabolomics and the techniques associated with it in successful food authentication processes, which is currently a gap in research that can be further explored and improved.

Distinct lipid profiles of radiation-induced carotid plaques from atherosclerotic carotid plaques revealed by UPLC-QTOF-MS and DESI-MSI

BACKGROUND AND PURPOSE

Radiotherapy is a standard treatment for head and neck tumors that significantly increases patients' long-term survival rates. However, late cerebrovascular complications, especially carotid artery stenosis (CAS), have gained increasing attention. Investigation of biomarkers of radiation-induced CAS may help to elucidate the mechanism by which radiation induces damage to blood vessels and identify possible preventive measures against such damage.

MATERIALS AND METHODS

In this study, we used lipidomics strategy to characterize the lipids present in 8 radiation-induced carotid plaques (RICPs) and 12 atherosclerotic carotid plaques (ASCPs). We also used desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) to map the spatial distribution of the screened lipids from 2 RICPs samples and 2 ASCPs samples.

RESULTS

The results showed that 31 metabolites in RICPs were significantly higher than that in ASCPs, 24 of which were triglycerides (TGs). We used four machine learning models to select potential indicators from the 31 metabolites. Six TGs [TG(17:2/17:2/18:0), TG(17:1/17:2/18:0), TG(17:0/17:2/18:0), TG(17:2/17:2/20:0), TG(17:1/17:2/20:0), TG(15:0/22:0/22:2)] were found to be the potential markers for distinguishing RICPs and ASCPs (AUC = 0.83). The DESI-MSI results suggested that the 6 TGs were localized in the collagen fiber regions and confirmed the differences of these TGs between the two kinds of plaques.

CONCLUSIONS

The 6 TGs primarily localized in the collagen fiber regions of plaques are likely to be potential indicators for the differentiation of RICPs from ASCPs which may have implications in the mechanisms and possible preventive measures against RICPs.

LC-MS Based Metabolomics Study of the Effects of EGCG on A549 Cells

(−)-Epigallocatechin-3-gallate (EGCG) is the main bioactive catechin in green tea. The antitumor activity of EGCG has been confirmed in various types of cancer, including lung cancer. However, the precise underlying mechanisms are still largely unclear. In the present study, we investigated the metabolite changes in A549 cells induced by EGCG in vitro utilizing liquid chromatography-mass spectrometry (LC-MS)-based metabolomics. The result revealed 33 differentially expressed metabolites between untreated and 80 μM EGCG-treated A549 cells. The altered metabolites were involved in the metabolism of glucose, amino acid, nucleotide, glutathione, and vitamin. Two markedly altered pathways, including glycine, serine and threonine metabolism and alanine, aspartate and glutamate metabolism, were identified by MetaboAnalyst 5.0 metabolic pathway analysis. These results may provide potential clues for the intramolecular mechanisms of EGCG’s effect on A549 cells. Our study may contribute to future molecular mechanistic studies of EGCG and the therapeutic application of EGCG in cancer management.

Distinct metabolic alterations in different Caenorhabditis elegans mitochondrial mutants

Mitochondria play an essential role in various biochemical processes that maintain cellular homeostasis. Minor defects in the mitochondrial genome can lead to aversive behavioral responses in an organism. Nevertheless, little is known about the impact of mitochondrial mutations on the metabolome of Caenorhabditis elegans (C. elegans). In this study, an untargeted metabolomics approach was employed to elucidate the metabolic aberrant caused by mitochondrial DNA mutations in C. elegans. Specifically, three mutant strains of C. elegans, including clk-1, mev-1, and phb-2, were adopted to study corresponding metabolic signatures. Adult worms were collected, and metabolites were extracted and analyzed by gas chromatography-mass spectrometry. Uni- and multivariate analyses were performed to elucidate perturbed metabolism between wildtype worms and mutant strains, and metabolic differences among the mutants. The tricarboxylic acid cycle intermediates, amino acids, and sugars were significantly affected in the mitochondrial mutants. Overall, each mitochondrial DNA mutation exhibited a different pattern of metabolic alterations. The shift of metabolome appeared to be associated with the lifespan of C. elegans. In particular, clk-1 and mev-1 strains, which had the opposite phenotypes of lifespan, had apparently different metabolomes. Our findings set light on the metabolic consequences of mitochondrial genetic variants, which may help better understand mitochondrial disease mechanisms.

Exploratory Metabolomic Analysis Based on Reversed-Phase Liquid Chromatography-Mass Spectrometry to Study an In Vitro Model of Hypoxia-Induced Metabolic Alterations in HK-2 Cells

Oxygen deficiency in cells, tissues, and organs can not only prevent the proper development of biological functions but it can also lead to several diseases and disorders. In this sense, the kidney deserves special attention since hypoxia can be considered an important factor in the pathophysiology of both acute kidney injury and chronic kidney disease. To provide better knowledge to unveil the molecular mechanisms involved, new studies are necessary. In this sense, this work aims to study, for the first time, an in vitro model of hypoxia-induced metabolic alterations in human proximal tubular HK-2 cells because renal proximal tubules are particularly susceptible to hypoxia. Different groups of cells, cultivated under control and hypoxia conditions at 0.5, 5, 24, and 48 h, were investigated using untargeted metabolomic approaches based on reversed-phase liquid chromatography–mass spectrometry. Both intracellular and extracellular fluids were studied to obtain a large metabolite coverage. On the other hand, multivariate and univariate analyses were carried out to find the differences among the cell groups and to select the most relevant variables. The molecular features identified as affected metabolites were mainly amino acids and Amadori compounds. Insights about their biological relevance are also provided.

The investigation of the volatile metabolites of lung cancer from the microenvironment of malignant pleural effusion

For malignant pleural effusions, pleural fluid cytology is a diagnostic method, but sensitivity is low. The pleural fluid contains metabolites directly released from cancer cells. The objective of this study was to diagnose lung cancer with malignant pleural effusion using the volatilomic profiling method. We recruited lung cancer patients with malignant pleural effusion and patients with nonmalignant diseases with pleural effusion as controls. We analyzed the headspace air of the pleural effusion by gas chromatography-mass spectrometry. We used partial least squares discriminant analysis (PLS-DA) to identify metabolites and the support vector machine (SVM) to establish the prediction model. We split data into a training set (80%) and a testing set (20%) to validate the accuracy. A total of 68 subjects were included in the final analysis. The PLS-DA showed high discrimination with an R² of 0.95 and Q² of 0.58. The accuracy of the SVM in the test set was 0.93 (95% CI 0.66, 0.998), the sensitivity was 83%, the specificity was 100%, and kappa was 0.85, and the area under the receiver operating characteristic curve was 0.96 (95% CI 0.86, 1.00). Volatile metabolites of pleural effusion might be used in patients with cytology-negative pleural effusion to rule out malignancy.

Metabolomics, a Powerful Tool for Understanding Plant Abiotic Stress

Metabolomics is a technology that generates large amounts of data and contributes to obtaining wide and integral explanations of the biochemical state of a living organism. Plants are continuously affected by abiotic stresses such as water scarcity, high temperatures and high salinity, and metabolomics has the potential for elucidating the response-to-stress mechanisms and develop resistance strategies in affected cultivars. This review describes the characteristics of each of the stages of metabolomic studies in plants and the role of metabolomics in the characterization of the response of various plant species to abiotic stresses.

Bioinformatic analysis identifying FGF1 gene as a new prognostic indicator in clear cell Renal Cell Carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) has been the commonest renal cell carcinoma (RCC). Although the disease classification, diagnosis and targeted therapy of RCC has been increasingly evolving attributing to the rapid development of current molecular pathology, the current clinical treatment situation is still challenging considering the comprehensive and progressively developing nature of malignant cancer. The study is to identify more potential responsible genes during the development of ccRCC using bioinformatic analysis, thus aiding more precise interpretation of the disease METHODS: Firstly, different cDNA expression profiles from Gene Expression Omnibus (GEO) online database were used to screen the abnormal differently expressed genes (DEGs) between ccRCC and normal renal tissues. Then, based on the protein-protein interaction network (PPI) of all DEGs, the module analysis was performed to scale down the potential genes, and further survival analysis assisted our proceeding to the next step for selecting a credible key gene. Thirdly, immunohistochemistry (IHC) and quantitative real-time PCR (QPCR) were conducted to validate the expression change of the key gene in ccRCC comparing to normal tissues, meanwhile the prognostic value was verified using TCGA clinical data. Lastly, the potential biological function of the gene and signaling mechanism of gene regulating ccRCC development was preliminary explored.

RESULTS

Four cDNA expression profiles were picked from GEO database based on the number of containing sample cases, and a total of 192 DEGs, including 39 up-regulated and 153 down-regulated genes were shared in four profiles. Based on the DEGs PPI network, four function modules were identified highlighting a FGF1 gene involving PI3K-AKT signaling pathway which was shared in 3/4 modules. Further, both the IHC performed with ccRCC tissue microarray which contained 104 local samples and QPCR conducted using 30 different samples confirmed that FGF1 was aberrant lost in ccRCC. And Kaplan-Meier overall survival analysis revealed that FGF1 gene loss was related to worse ccRCC patients survival. Lastly, the pathological clinical features of FGF1 gene and the probable biological functions and signaling pathways it involved were analyzed using TCGA clinical data.

CONCLUSIONS

Using bioinformatic analysis, we revealed that FGF1 expression was aberrant lost in ccRCC which statistical significantly correlated with patients overall survival, and the gene's clinical features and potential biological functions were also explored. However, more detailed experiments and clinical trials are needed to support its potential drug-target role in clinical medical use.

Metabolomic-based clinical studies and murine models for acute pancreatitis disease: A review

Acute pancreatitis (AP) is one of the most common gastroenterological disorders requiring hospitalization and is associated with substantial morbidity and mortality. Metabolomics nowadays not only help us to understand cellular metabolism to a degree that was not previously obtainable, but also to reveal the importance of the metabolites in physiological control, disease onset and development. An in-depth understanding of metabolic phenotyping would be therefore crucial for accurate diagnosis, prognosis and precise treatment of AP. In this review, we summarized and addressed the metabolomics design and workflow in AP studies, as well as the results and analysis of the in-depth of research. Based on the metabolic profiling work in both clinical populations and experimental AP models, we described the metabolites with potential utility as biomarkers and the correlation between the altered metabolites and AP status. Moreover, the disturbed metabolic pathways correlated with biological function were discussed in the end. A practical understanding of current and emerging metabolomic approaches applicable to AP and use of the metabolite information presented will aid in designing robust metabolomics and biological experiments that result in identification of unique biomarkers and mechanisms, and ultimately enhanced clinical decision-making.

Functional metabolomics innovates therapeutic discovery of traditional Chinese medicine derived functional compounds

Traditional Chinese medicines (TCMs) produce chemically diverse functional compounds that are importantly chemical resource for facilitating new drug discovery and development against a diversity of diseases. However, modern exploration of TCM derived functional compounds is significantly hindered by the inefficient elucidation of pharmacological functions over past decades, because conventional research methods are incapable of efficiently elucidating therapeutic potential of TCM conferred by multiple functional compounds. Functional metabolomics has the priority-capacity to characterize systems therapeutic actions of TCM by precisely capturing molecular interactions between disease response metabolite biomarkers (DRMB) and functional compounds (secondary metabolites), which underline pharmacological efficiency and associated therapeutic mechanisms. In this critical review, we innovatively summarize systems therapeutic feature of TCM derived functional compounds from a functional-metabolism perspective, then systems metabolic targets (SMT) identified by functional metabolomics method are strategically proposed to better understanding of therapeutic discovery of TCM derived functional compounds. In addition, we propose the perspective strategy as Spatial Temporal Operative Real Metabolomics (STORM) to considerably improve analytical capacity of functional metabolomics method by selectively incorporating the cutting edge technologies of mass spectrometry imaging, isotope-metabolic fluxomics, synthetic and biosynthetic chemistry, which could considerably enhance the precision and resolution of elucidating pharmacological efficiency and associated therapeutic mechanisms of TCM derived functional compounds. Collectively, such critical review is expected to provide novel perspective-strategy that could significantly improve modern exploration and exploitation of TCM derived functional compounds that further promote new drug discovery and development against the complex diseases.

Temporal metabolomic alteration in rat brains of experimental intracerebral hemorrhage

BACKGROUND

Intracerebral hemorrhage (ICH) is the top lethal and disabling form of stroke. The pathophysiology of ICH is not fully understood yet. Metabolites are indicators and regulators of cellular processes. However, the overall brain metabolic pattern and the temporal alterations after ICH remain unknown.

METHODS

A total of 40 male rats were randomly assigned to sham group and ICH group. ICH was induced by collagenase Ⅶ. Body weight was assessed. Neurological deficits were evaluated by modified neurological severity score. Then, the perihematomal brain tissues were collected for metabolites detection using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS). The metabolic profiles were displayed by principal component analysis (PCA), partial least-squares-discriminant analysis (PLS-DA) and cluster analysis. The significant differential metabolites were screened by fold change > 2.0, the false discovery rate (FDR) < 0.05 and Variable Importance of Projection (VIP) > 1. Next, the relevant metabolic pathways were discerned by MetaboAnalyst website. A metabolite-protein interaction network was subsequentially constructed to further annotate the function of differential metabolites.

RESULTS

Rats suffered from compromised body weight increasement and impaired neurological function. The metabolomics profiles of brain tissues in the post-ICH rats were markedly different from those in the sham group on days 3 and 14. Thirty-four metabolites (bilirubin, uric acid, 6-Methylnicotinamide et al.) were abnormally upregulated in the acute stage, while 27 metabolites were disturbed in the recovery stage, including bilirubin, uric acid, and histamine et al. Seven and three metabolic pathways altered in the acute and recovery stage, respectively. Metabolite-protein interaction analysis revealed that the disturbed metabolites may participate in ICH pathophysiology by altering amino acid metabolism, peroxisome proliferators-activated receptor signaling pathway, fatty acid metabolism and urea cycle in the acute stage, while influencing amino acid metabolism, urea cycle and peroxisome in the recovery stage.

CONCLUSIONS

Our study mapped the pathological metabolomics profiles of the post-ICH rat brains in the acute and recovery phases. This work will assist in discovering novel therapeutic targets and treatments for ICH.

A Novel UHPLC-MS Method Targeting Urinary Metabolomic Markers for Autism Spectrum Disorder

Autism spectrum disorder is a heterogeneous neurodevelopmental disease. Currently, no biomarker of this disease is known. Diagnosis is performed through observation, standardized behavioral scales, and interviews with parents. In practice, diagnosis is often delayed to the average age of four years or even more which adversely affects a child’s perspective. A laboratory method allowing to detect the disorder at earlier stages is of a great need, as this could help the patients to start with treatment at a younger age, even prior to the clinical diagnosis. Recent evidence indicates that metabolomic markers should be considered as diagnostic markers, also serving for further differentiation and characterization of different subgroups of the autism spectrum. In this study, we developed an ultra-high performance liquid chromatography-tandem triple quadrupole mass spectrometry method for the simultaneous determination of six metabolites in human urine. These metabolites, namely methylguanidine, N-acetyl arginine, inosine, indole-3-acetic acid, indoxyl sulfate and xanthurenic acid were selected as potential biomarkers according to prior metabolomic studies. The analysis was carried out by means of reversed-phase liquid chromatography with gradient elution. Separation of the metabolites was performed on a Phenomenex Luna^® Omega Polar C18 (100 × 1.0 mm, 1.6 µm) column at a flow rate of 0.15 mL/min with acetonitrile/water 0.1% formic acid aqueous as the mobile phase. The analysis was performed on a group of children with autism spectrum disorder and age-matched controls. In school children, we have detected disturbances in the levels of oxidative stress markers connected to arginine and purine metabolism, namely methylguanidine and N-acetylargine. Also, products of gut bacteria metabolism, namely indoxyl sulfate and indole-3-acetic acid, were found to be elevated in the patients’ group. We can conclude that this newly developed method is fast, sensitive, reliable, and well suited for the quantification of proposed markers.

2,2',4,4'-tetrabromodiphenyl ether (BDE-47) induces wide metabolic changes including attenuated mitochondrial function and enhanced glycolysis in PC12 cells

Polybrominated diphenyl ethers (PBDEs) are extensively used as brominated flame retardants in various factory products. As environmental pollutants, the adverse effects of PBDEs on human health have been receiving considerable attention. However, the precise fundamental mechanisms of toxicity induced by PBDEs are still not fully understood. In this study, the mechanism of cytotoxicity induced by 2,2',4,4'-tetrabromodiphenyl ether (BDE-47) was investigated by combining Seahorse XFp analysis and mass spectrometry-based metabolomics and flux approaches in PC12 cells, one of the most widely used neuron-like cell lines for investigating cytotoxic effects. The Seahorse results suggest that BDE-47 significantly attenuated mitochondrial respiration and enhanced glycolysis in PC12 cells. Additionally, metabolomics results revealed the reduction of TCA metabolites such as citrate, succinate, aconitate, malate, fumarate, and glutamate after BDE-47 exposure. Metabolic flux analysis showed that BDE-47 exposure reduced the oxidative metabolic capacity of mitochondria in PC12 cells. Furthermore, various altered metabolites were found in multiple metabolic pathways, especially in glycine-serine-threonine metabolism and glutathione metabolism. A total of 17 metabolic features were determined in order to distinguish potentially disturbed metabolite markers of BDE-47 exposure. Our findings provide possible biomarkers of cytotoxic effects induced by BDE-47 exposure, and elicit a deeper understanding of the intramolecular mechanisms that could be used in further studies to validate the potential neurotoxicity of PBDEs in vivo. Based on our results, therapeutic approaches targeting mitochondrial function and the glycolysis pathway may be a promising direction against PBDE exposure.

Synthesizing Systems Biology Knowledge from Omics Using Genome-Scale Models

Omic technologies have enabled the complete readout of the molecular state of a cell at different biological scales. In principle, the combination of multiple omic data types can provide an integrated view of the entire biological system. This integration requires appropriate models in a systems biology approach. Here, genome-scale models (GEMs) are focused upon as one computational systems biology approach for interpreting and integrating multi-omic data. GEMs convert the reactions (related to metabolism, transcription, and translation) that occur in an organism to a mathematical formulation that can be modeled using optimization principles. A variety of genome-scale modeling methods used to interpret multiple omic data types, including genomics, transcriptomics, proteomics, metabolomics, and meta-omics are reviewed. The ability to interpret omics in the context of biological systems has yielded important findings for human health, environmental biotechnology, bioenergy, and metabolic engineering. The authors find that concurrent with advancements in omic technologies, genome-scale modeling methods are also expanding to enable better interpretation of omic data. Therefore, continued synthesis of valuable knowledge, through the integration of omic data with GEMs, are expected.

FUNCTIONAL METABOLOMICS DECIPHER BIOCHEMICAL FUNCTIONS AND ASSOCIATED MECHANISMS UNDERLIE SMALL-MOLECULE METABOLISM

Metabolism is the collection of biochemical reactions enabled by chemically diverse metabolites, which facilitate different physiological processes to exchange substances and synthesize energy in diverse living organisms. Metabolomics has emerged as a cutting-edge method to qualify and quantify the metabolites in different biological matrixes, and it has the extraordinary capacity to interrogate the biological significance that underlies metabolic modification and modulation. Liquid chromatography combined with mass spectrometry (LC/MS), as a robust platform for metabolomics analysis, has increased in popularity over the past 10 years due to its excellent sensitivity, throughput, and versatility. However, metabolomics investigation currently provides us with only phenotype data without revealing the biochemical functions and associated mechanisms. This limitation indeed weakens the core value of metabolomics data in a broad spectrum of the life sciences. In recent years, the scientific community has actively explored the functional features of metabolomics and translated this cutting-edge approach to be used to solve key multifaceted questions, such as disease pathogenesis, the therapeutic discovery of drugs, nutritional issues, agricultural problems, environmental toxicology, and microbial evolution. Here, we are the first to briefly review the history and applicable progression of LC/MS-based metabolomics, with an emphasis on the applications of metabolic phenotyping. Furthermore, we specifically highlight the next era of LC/MS-based metabolomics to target functional metabolomes, through which we can answer phenotype-related questions to elucidate biochemical functions and associated mechanisms implicated in dysregulated metabolism. Finally, we propose many strategies to enhance the research capacity of functional metabolomics by enabling the combination of contemporary omics technologies and cutting-edge biochemical techniques. The main purpose of this review is to improve the understanding of LC/MS-based metabolomics, extending beyond the conventional metabolic phenotype toward biochemical functions and associated mechanisms, to enhance research capability and to enlarge the applicable scope of functional metabolomics in small-molecule metabolism in different living organisms.

Urine metabolomic responses to aerobic and resistance training in rats under chronic unpredictable mild stress

Background

It is known that bioenergetics of aerobic and resistance exercise are not the same but both can effectively improve depression. However, it is not clear whether and how different types of exercise can influence depression through the same metabolic regulatory system. Metabolomics provides a way to study the correlation between metabolites and changes in exercise and/or diseases through the quantitative analysis of all metabolites in the organism. The objective of this study was to investigate the effects of aerobic and resistance training on urinary metabolites by metabolomics analysis in a rodent model of depression.

Methods

Male Sprague-Dawley rats were given chronic unpredictable mild stress (CUMS) for eight weeks. The validity of the modeling was assessed by behavioral indices. After four weeks of CUMS, the rats that developed depression were randomly divided into a depression control group, an aerobic training group and a resistance training group. There was also a normal control group. From week 5, the rats in the exercise groups were trained for 30 min per day, five days per week, for four weeks. The urine samples were collected pre and post the training program, and analyzed by proton nuclear magnetic resonance (¹H-NMR) spectroscopy.

Results

Both types of training improved depression-like behavior in CUMS rats. Compared with normal control, 21 potential biomarkers were identified in the urine of CUMS rats, mainly involved in energy, amino acids and intestinal microbial metabolic pathways. Common responses to the training were found in the two exercise groups that the levels of glutamine, acetone and creatine were significantly recalled (all P<0.05) Aerobic training also resulted in changes in pyruvate and trigonelline, while resistance training modified α-Oxoglutarate, citric acid, and trimethylamine oxide (all P<0.05).

Conclusions

Aerobic and resistance training resulted in common effects on the metabolic pathways of alanine-aspartate-glutamate, TCA cycle, and butyric acid. Aerobic training also had effects on glycolysis or gluconeogenesis and pyruvate metabolism, while resistance training had additional effect on intestinal microbial metabolism.

OMICS insights into cancer histology; Metabolomics and proteomics approach

Metabolomics as a post-genomic research area comprising different analytical methods for small molecules analysis. One of the underlying applications of metabolomics technology for better disease diagnosis and prognosis is discovering the metabolic pathway differences between healthy individuals and patients. On the other hand, the other noteworthy applications of metabolomics include its effective role in biomarker screening for cancer detection, monitoring, and prediction. In other words, emerging of the metabolomics field can be hopeful to provide a suitable alternative for the common current cancer diagnostic methods especially histopathological tests. Indeed, cancer as a major global issue places a substantial burden on the health care system. Hence, proper management can be beneficial. In this respect, formalin-fixed paraffin-embedded tissue specimens (in histopathological tests) are considered as a valuable source for metabolomics investigations. Interestingly, formalin-fixed paraffin-embedded tissue specimens can provide informative data for cancer management. In general, using these specimens, determining the cancer stage, individual response to the different therapies, personalized risk prediction are possible and high-quality clinical services are the promise of OMICS technologies for cancer disease. However, considering all of these beneficial characteristics, there are still some limitations in this area that need to be addressed in order to optimize the metabolomics utilizations and advancement.

Comparative Blood and Urine Metabolomics Analysis of Healthy Elderly and Young Male Singaporeans

Comparative metabolomics analysis of biofluids could provide information about the metabolic alterations in aging. To investigate the signature of multiple metabolic profiles associated with aging in an Asian population, we performed a pilot study in healthy Singaporeans, including 33 elderly and 33 young males. Fasting whole bloods were analyzed by routine hematology; the serum and urine metabolome profiles were obtained using NMR-based nontargeted metabolomics analysis and targeted lipoprotein analysis. Among the 90 identified compounds in serum and urine samples, 32 were significantly different between the two groups. The most obvious age-related metabolic signatures include decreased serum levels of albumin lysyl and essential amino acids and derivatives but increased levels of N-acetyl glycoproteins and several lipids and elevated urine levels of trimethylamine N-oxide, scyllo-inositol, citrate, and ascorbic acid but decreased levels of several amino acids, acetate, etc. Among 112 lipoprotein subfractions, 65 were elevated, and 2 were lower in the elderly group. These significantly age-varying metabolites, especially in the amino acid and fatty acid metabolism pathways, suggest that the regulation of these pathways contributes to the aging process in Chinese Singaporeans. Further multiomics studies including the gut microbiome and intervention studies in a larger cohort are needed to elucidate the possible mechanisms in the aging process.

Serum Metabolomics Revealed the Differential Metabolic Pathway in Calves with Severe Clinical Diarrhea Symptoms

Simple Summary

The present study focuses on the metabolic changes in the diarrhea of calves, which are manifested with the following symptoms: a thin water-like stool, cold ears and nose, throbbing bowels, oliguria, a pale or yellowish complexion, a smooth mouth, and a slow pulse. The differential metabolic pathways in calves with diarrhea were screened by metabolomics. There were nine biomarkers in the serum of healthy calves and calves with diarrhea. On the basis of these biomarkers, their associated mineral absorption, protein digestion and absorption, and other metabolic pathways, the targeted regulation of the metabolic differences of calves with diarrhea may contribute to the diagnosis, treatment, and discussion of the mechanism of calf diarrhea.

Abstract

The complex etiology, higher morbidity and mortality, poor prognosis, and expensive cost of calf diarrhea have made it a catastrophic disease in the dairy industry. This study aims to assess the biomarkers in calves with diarrhea and to predict the biomarkers related to the pathway. As subjects, nine calves with diarrhea and nine healthy calves were enrolled, according to strict enrollment criteria. The serum metabolites were detected by a liquid chromatographic tandem mass spectrometry (LC-MS/MS), and then analyzed by online multivariate statistical analysis software to further screen the biomarkers. In addition, the biomarkers involved in the metabolic pathways of calves with diarrhea and healthy calves were analyzed. In the serum of calves with diarrhea, nine biomarkers were found to which several biomarkers exhibited a certain relation. Moreover, these biomarkers were involved in important metabolic pathways, including protein digestion and absorption, ABC transporters, aminoacyl-tRNA biosynthesis, mineral absorption, and fatty acid biosynthesis. All these findings suggested that the imbalance of these markers was closely related to the occurrence and development of calf diarrhea. The targeted regulation of metabolic pathways involved in these biomarkers may facilitate the diagnosis, treatment, and discussion of the mechanism of calf diarrhea.

Circulating metabolite profiles to predict response to cardiac resynchronization therapy

Background

Heart failure is associated with ventricular dyssynchrony and energetic inefficiency, which can be alleviated by cardiac resynchronization therapy (CRT) with approximately one-third of non-response rate. Thus far, there is no specific biomarker to predict the response to CRT in patients with heart failure. In this study, we assessed the role of the blood metabolomic profile in predicting the response to CRT.

Methods

A total of 105 dilated cardiomyopathy patients with severe heart failure who received CRT were included in our two-stage study. Baseline blood samples were collected prior to CRT implantation. The response to CRT was defined according to echocardiographic criteria. Metabolomic profiling of serum samples was carried out using ultrahigh performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry.

Results

Seventeen metabolites showed significant differences in their levels between responders and non-responders, and these metabolites were primarily involved in six pathways, including linoleic acid metabolism, Valine, leucine and isoleucine biosynthesis, phenylalanine metabolism, citrate cycle, tryptophan metabolism, and sphingolipid metabolism. A combination of isoleucine, tryptophan, and linoleic acid was identified as an ideal metabolite panel to distinguish responders from non-responders in the discovery set (n = 51 with an AUC of 0.981), and it was confirmed in the validation set (n = 54 with an AUC of 0.929).

Conclusions

Mass spectrometry based serum metabolomics approach provided larger coverage of metabolome which can help distinguish CRT responders from non-responders. A combination of isoleucine, tryptophan, and linoleic acid may associate with significant prognostic values for CRT.

Metabolomic applications for understanding complex tripartite plant-microbes interactions: Strategies and perspectives

Phytopathogens from the Alternaria sp., Fusarium sp., Penicillium sp., and Pseudomonas sp. and their toxigenic metabolites - alternariol, fumonisin, citrinin, and coronatine respectively, negatively impact crop yields and sales by eliciting plant diseases and/or causing human and veterinary toxicoses upon the consumption of contaminated food. These phytopathogens and their associated toxins, however, are present and most likely in undetectable concentrations pre-harvest and post-harvest of many major staple crops. Metabolomic approaches have been used extensively for better characterizing and diagnosing human disease, plant disease and, their etiological agents. Their use in agro-industrial research focusing specifically on tripartite (plant - toxicogenic microbe - beneficial microbe) interactions is, however, limited. Since new approaches for eradicating food-borne pathogens, increasing crop productivity and improving agro-international trade are being sought worldwide, the consequent integration of metabolomic approaches and perspectives in crop protection strategies for better understanding plant - toxicogenic microbe - beneficial microbe interaction in tandem is discussed.

Statistically Driven Metabolite and Lipid Profiling of Patients from the Undiagnosed Diseases Network

Advancements in molecular separations coupled with mass spectrometry have enabled metabolome analyses for clinical cohorts. A population of interest for metabolome profiling is patients with rare disease for which abnormal metabolic signatures may yield clues into the genetic basis, as well as mechanistic drivers of the disease and possible treatment options. We undertook the metabolome profiling of a large cohort of patients with mysterious conditions characterized through the Undiagnosed Diseases Network (UDN). Due to the size and enrollment procedures, collection of the metabolomes for UDN patients took place over 2 years. We describe the study designed to adjust for measurements collected over a long time scale and how this enabled statistical analyses to summarize the metabolome of individual patients. We demonstrate the removal of time-based batch effects, overall statistical characteristics of the UDN population, and two case studies of interest that demonstrate the utility of metabolome profiling for rare diseases.