Metabolomics platforms for genome wide association studies--linking the genome to the metabolome

Metabolic and immune-related gene signatures: Predictive stratification and prognostic implications in gastric cancer

BACKGROUND

Gastric cancer, marked by its heterogeneous nature, showcases various molecular subtypes and clinical trajectories. This research delves into the significance of metabolic and immune-driven pathways in gastric cancer, constructing a prognostic signature derived from differentially expressed metabolic and immune-correlated genes (DE-MIGs).

METHODS

Metabolic and immune-associated gene were sourced from the GeneCards database. Differential expression analysis on the TCGA-STAD dataset was executed using the limma package, unveiling 51 DE-MIGs that underwent functional enrichment scrutiny. The LASSO Cox regression methodology guided the creation of the prognostic signature, and individual patient risk scores were determined. Assessment tools like CIBERSORT, ESTIMATE and ssGSEA were deployed to study the immune microenvironment, while mutation profiles, genomic stability, resistance to chemotherapy and immunotherapy responsiveness were scrutinized across distinct signature categorizations.

RESULTS

Among the identified DE-MIGs, 26 were significantly tied to the overall survival of gastric cancer patients. The developed prognostic signature proficiently differentiated patients into high-risk and low-risk cohorts, with the latter showing markedly better outcomes. The study underscored the centrality of the immune microenvironment in influencing gastric cancer outcomes. Key pathways such as TGF-Beta, TP53 and NRF2 dominated the high-risk group, whereas the LRTK-RAS and WNT pathways characterized the low-risk group. Interestingly, the low-risk segment also manifested a heightened tumor mutation burden and enhanced susceptibility to immunotherapy.

CONCLUSIONS

Our findings introduce a pivotal prognostic signature, rooted in DE-MIGs, that effectively segregates gastric cancer patients into distinct risk-based segments. Insights into the influential role of the immune microenvironment in gastric cancer progression pave the way for more refined therapeutic interventions.

Metabolomic Analysis of the Effect of Freezing on Leaves of Malus sieversii (Ledeb.) M.Roem. Histoculture Seedlings

Malus sieversii (Ledeb.) M.Roem. is the ancestor of cultivated apples, and is an excellent germplasm resource with high resistance to cold. Artificial refrigerators were used to simulate the low temperature of -3 °C to treat Malus sieversii (Ledeb.) M.Roem. histoculture seedlings. Observations were performed to find the effects of freezing stress on the status of open or closed stomata, photosystems, and detection of metabolomic products in leaves of Malus sieversii (Ledeb.) M.Roem. histoculture seedlings. The percentage of closed stomata in the Malus sieversii (Ledeb.) M.Roem. histoculture seedlings increased, the maximum fluorescence (Fm') excited by a strong light (saturating pulse) was weakened relative to the real-time fluorescence in its vicinity, and the quantum yield of unregulated energy dissipation was increased in PSII under freezing stress. The metabolites in the leaves of the Malus sieversii (Ledeb. M.Roem.) histoculture seedlings were analyzed by ultra-performance liquid chromatography-tandem mass spectrometry using CK, T12h, T36 h, and HF24h. Results demonstrated that cold stress in the Malus sieversii (Ledeb.) M.Roem. histoculture seedlings led to wilting, leaf stomatal closure, and photosystem damage. There were 1020 metabolites identified as lipids (10.2%), nucleotides and their derivatives (5.2%), phenolic acids (19.12%), flavonoids (24.51%), amino acids and their derivatives (7.75%), alkaloids (5.39%), terpenoids (8.24%), lignans (3.04%), organic acids (5.88%), and tannins (0.88%). There were 110 differential metabolites at CKvsT12h, 113 differential metabolites at CKvsT36h, 87 differential metabolites at T12hvsT36h, 128 differential metabolites at CKvsHF24h, 121 differential metabolites at T12hvsHF24h, and 152 differential metabolites at T36hvsHF24h. The differential metabolites in the leaves of the Malus sieversii (Ledeb.) M.Roem. seedlings grown under low-temperature stress mainly involved glycolysis, amino acid metabolism, lipid metabolism, pyrimidine metabolism, purine metabolism, and secondary metabolite metabolism. The Malus sieversii (Ledeb.) M.Roem. seedlings responded to the freezing stress by coordinating with each other through these metabolic pathways. The metabolic network of the leaves of the Malus sieversii (Ledeb.) M.Roem. histoculture seedlings under low temperature stress was also proposed based on the above pathways to deepen understanding of the response of metabolites of Malus sieversii (Ledeb.) M.Roem. to low-temperature stress and to lay a theoretical foundation for the development and utilization of Malus sieversii (Ledeb.) M.Roem. cultivation resources.

Association of lipid metabolism-related metabolites with overweight/obesity based on the FTO rs1421085

Globally, obesity is a severe health issue. A more precise and practical approach is required to enhance clinical care and drug development. The FTO (fat mass and obesity-associated) gene variant rs1421085 is strongly associated with an increased susceptibility to obesity in numerous populations; however, the precise mechanism behind this association concerning metabolomics is still not understood. This study aims to examine the association between metabolites and obesity-related anthropometric traits based on the variant FTO rs1421085. This study was based on a case-control design involving a total of 542 participants including overweight/obese cases and healthy controls. The blood samples were collected from all the participants. The isolated serum samples were subjected to untargeted metabolomics using GC-MS. The isolated DNA samples were genotyped for the FTO rs1421085 variant. Initially, a total of 42 metabolites were identified on GC-MS, which were subjected to further association analyses. The study observed a significant association of two metabolites, glycerol and 2,3-dihydroxypropyl stearate with FTO gene variant rs1421085 and obesity-related anthropometric traits including % BF, WHtR, WC, and HC. The CT genotype of FTO rs1421085 may greatly increase the risk of overweight/obesity by changing the lipid metabolism-related metabolites. Therefore, this study highlights the significance of biochemical networks in the progression of obesity in carriers of the FTO rs1421085 risk genotype.

Exploring the Prognosis-Related Genetic Variation in Gastric Cancer Based on mGWAS

The use of metabolome genome-wide association studies (mGWAS) has been shown to be effective in identifying functional genes in complex diseases. While mGWAS has been applied to biomedical and pharmaceutical studies, its potential in predicting gastric cancer prognosis has yet to be explored. This study aims to address this gap and provide insights into the genetic basis of GC survival, as well as identify vital regulatory pathways in GC cell progression. Genome-wide association analysis of plasma metabolites related to gastric cancer prognosis was performed based on the Generalized Linear Model (GLM). We used a log-rank test, LASSO regression, multivariate Cox regression, GO enrichment analysis, and the Cytoscape software to visualize the complex regulatory network of genes and metabolites and explored in-depth genetic variation in gastric cancer prognosis based on mGWAS. We found 32 genetic variation loci significantly associated with GC survival-related metabolites, corresponding to seven genes, VENTX, PCDH 7, JAKMIP1, MIR202HG, MIR378D1, LINC02472, and LINC02310. Furthermore, this study identified 722 Single nucleotide polymorphism (SNP) sites, suggesting an association with GC prognosis-related metabolites, corresponding to 206 genes. These 206 possible functional genes for gastric cancer prognosis were mainly involved in cellular signaling molecules related to cellular components, which are mainly involved in the growth and development of the body and neurological regulatory functions related to the body. The expression of 23 of these genes was shown to be associated with survival outcome in gastric cancer patients in The Cancer Genome Atlas (TCGA) database. Based on the genome-wide association analysis of prognosis-related metabolites in gastric cancer, we suggest that gastric cancer survival-related genes may influence the proliferation and infiltration of gastric cancer cells, which provides a new idea to resolve the complex regulatory network of gastric cancer prognosis.

HSA Adductomics Reveals Sex Differences in NHL Incidence and Possible Involvement of Microbial Translocation

BACKGROUND

The higher incidence of non-Hodgkin lymphoma (NHL) in males is not well understood. Although reactive oxygen species (ROS) have been implicated as causes of NHL, they cannot be measured directly in archived blood.

METHODS

We performed untargeted adductomics of stable ROS adducts in human serum albumin (HSA) from 67 incident NHL cases and 82 matched controls from the European Prospective Investigation into Cancer and Nutrition-Italy cohort. Regression and classification methods were employed to select features associated with NHL in all subjects and in males and females separately.

RESULTS

Sixty seven HSA-adduct features were quantified by liquid chromatography-high-resolution mass spectrometry at Cys34 (n = 55) and Lys525 (n = 12). Three features were selected for association with NHL in all subjects, while seven were selected for males and five for females with minimal overlap. Two selected features were more abundant in cases and seven in controls, suggesting that altered homeostasis of ROS may affect NHL incidence. Heat maps revealed differential clustering of features between sexes, suggesting differences in operative pathways.

CONCLUSIONS

Adduct clusters dominated by Cys34 oxidation products and disulfides further implicate ROS and redox biology in the etiology of NHL. Sex differences in dietary and alcohol consumption also help to explain the limited overlap of feature selection between sexes. Intriguingly, a disulfide of methanethiol from enteric microbial metabolism was more abundant in male cases, thereby implicating microbial translocation as a potential contributor to NHL in males.

IMPACT

Only two of the ROS adducts associated with NHL overlapped between sexes and one adduct implicates microbial translocation as a risk factor.

Biochemical phenotyping of paroxysmal nocturnal hemoglobinuria reveals solute carriers and β-oxidation deficiencies

INTRODUCTION

Paroxysmal nocturnal hemoglobinuria (PNH) is a clonal disease of hematopoietic cells with a variable clinical spectrum characterized by intravascular hemolysis, high risk of thrombosis, and cytopenias. To understand the biochemical shifts underlying PNH, this study aimed to search for the dysfunctional pathways involved in PNH physiopathology by comparing the systemic metabolic profiles of affected patients to healthy controls and the metabolomic profiles before and after the administration of eculizumab in PNH patients undergoing treatment.

METHODS

Plasma metabolic profiles, comprising 186 specific annotated metabolites, were quantified using targeted quantitative electrospray ionization tandem mass spectrometry in 23 PNH patients and 166 population-based controls. In addition, samples from 12 PNH patients on regular eculizumab maintenance therapy collected before and 24 hours after eculizumab infusion were also analyzed.

RESULTS

In the PNH group, levels of the long-chain acylcarnitines metabolites were significantly higher as compared to the controls, while levels of histidine, taurine, glutamate, glutamine, aspartate and phosphatidylcholines were significantly lower in the PNH group. These differences suggest altered acylcarnitine balance, reduction in the amino acids participating in the glycogenesis pathway and impaired glutaminolysis. In 12 PNH patients who were receiving regular eculizumab therapy, the concentrations of acylcarnitine C6:1, the C14:1/C6 ratio (reflecting the impaired action of the medium-chain acyl-Co A dehydrogenase), and the C4/C6 ratio (reflecting the impaired action of short-chain acyl-Co A dehydrogenase) were significantly reduced immediately before eculizumab infusion, revealing impairments in the Acyl CoA metabolism, and reached levels similar to those in the healthy controls 24 hours after infusion.

CONCLUSIONS

We demonstrated significant differences in the metabolomes of the PNH patients compared to healthy controls. Eculizumab infusion seemed to improve deficiencies in the acyl CoA metabolism and may have a role in the mitochondrial oxidative process of long and medium-chain fatty acids, reducing oxidative stress, and inflammation.

Metabolomics of infectious diseases in the era of personalized medicine

Infectious diseases continue to be a major cause of morbidity and mortality worldwide. Diseases cause perturbation of the host's immune system provoking a response that involves genes, proteins and metabolites. While genes are regulated by epigenetic or other host factors, proteins can undergo post-translational modification to enable/modify function. As a result, it is difficult to correlate the disease phenotype based solely on genetic and proteomic information only. Metabolites, however, can provide direct information on the biochemical activity during diseased state. Therefore, metabolites may, potentially, represent a phenotypic signature of a diseased state. Measuring and assessing metabolites in large scale falls under the omics technology known as "metabolomics". Comprehensive and/or specific metabolic profiling in biological fluids can be used as biomarkers of disease diagnosis. In addition, metabolomics together with genomics can be used to differentiate patients with differential treatment response and development of host targeted therapy instead of pathogen targeted therapy where pathogens are more prone to mutation and lead to antimicrobial resistance. Thus, metabolomics can be used for patient stratification, personalized drug formulation and disease control and management. Currently, several therapeutics and in vitro diagnostics kits have been approved by US Food and Drug Administration (FDA) for personalized treatment and diagnosis of infectious diseases. However, the actual number of therapeutics or diagnostics kits required for tailored treatment is limited as metabolomics and personalized medicine require the involvement of personnel from multidisciplinary fields ranging from technological development, bioscience, bioinformatics, biostatistics, clinicians, and biotechnology companies. Given the significance of metabolomics, in this review, we discussed different aspects of metabolomics particularly potentials of metabolomics as diagnostic biomarkers and use of small molecules for host targeted treatment for infectious diseases, and their scopes and challenges in personalized medicine.

Development and validation of robust metabolism-related gene signature in the prognostic prediction of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignant tumours worldwide. Given metabolic reprogramming in tumours was a crucial hallmark, several studies have demonstrated its value in the diagnostics and surveillance of malignant tumours. The present study aimed to identify a cluster of metabolism-related genes to construct a prediction model for the prognosis of HCC. Multiple cohorts of HCC cases (466 cases) from public datasets were included in the present analysis. (GEO cohort) After identifying a list of metabolism-related genes associated with prognosis, a risk score based on metabolism-related genes was formulated via the LASSO-Cox and LASSO-pcvl algorithms. According to the risk score, patients were stratified into low- and high-risk groups, and further analysis and validation were accordingly conducted. The results revealed that high-risk patients had a significantly worse 5-year overall survival (OS) than low-risk patients in the GEO cohort. (30.0% vs. 57.8%; hazard ratio [HR], 0.411; 95% confidence interval [95% CI], 0.302-0.651; p < 0.001) This observation was confirmed in the external TCGA-LIHC cohort. (34.5% vs. 54.4%; HR 0.452; 95% CI, 0.299-0.681; p < 0.001) To promote the predictive ability of the model, risk score, age, gender and tumour stage were integrated into a nomogram. According to the results of receiver operating characteristic curves and decision curves analysis, the nomogram score possessed a superior predictive ability than conventional factors, which indicate that the risk score combined with clinicopathological features was able to achieve a robust prediction for OS and improve the individualized clinical decision making of HCC patients. In conclusion, the metabolic genes related to OS were identified and developed a metabolism-based predictive model for HCC. Through a series of bioinformatics and statistical analyses, the predictive ability of the model was approved.

Widely Targeted Metabolomics Reveals Metabolite Diversity in Jalapeño and Serrano Chile Peppers (Capsicum annuum L.)

Chile peppers (Capsicum annuum L.) are good sources of vitamins and minerals that can be included in the diet to mitigate nutritional deficiencies. Metabolomics examines the metabolites involved in biological pathways to understand the genes related to complex phenotypes such as the nutritional quality traits. The current study surveys the different metabolites present in jalapeño ('NuMex Pumpkin Spice') and serrano ('NuMex LotaLutein') type chile peppers grown in New Mexico using a widely targeted metabolomics approach, with the 'NuMex LotaLutein' as control. A total of 1088 different metabolites were detected, where 345 metabolites were differentially expressed; 203 (59%) were downregulated and 142 (41%) were upregulated (i.e., relative metabolite content is higher in 'NuMex Pumpkin Spice'). The upregulated metabolites comprised mostly of phenolic acids (42), flavonoids (22), and organic acids (13). Analyses of principal component (PC) and orthogonal partial least squares demonstrated clustering based on cultivars, where at least 60% of variation was attributed to the first two PCs. Pathway annotation identified 89 metabolites which are involved in metabolic pathways and the biosynthesis of secondary metabolites. Altogether, metabolomics provided insights into the different metabolites present which can be targeted for breeding and selection towards the improvement of nutritional quality traits in Capsicum.

Metabolomics analysis reveal the molecular responses of high CO2 concentration improve resistance to Pb stress of Oryza sativa L. seedlings

Rice seedlings were exposed to two CO₂ concentrations (400 ± 20 and 800 ± 20 μmol mol^-1) and three PbNO₃ concentrations (0, 50 and 100 µmol L^-1) for 10 days to explore the regulatory mechanisms of elevated CO₂ for Pb stress resistance. Electrical conductivity, MDA content, SOD, POD, CAT activities and metabolomics changes were studied. Results showed that: Pb stress damaged cell membrane system, electrical conductivity and MDA content increased 49.34 % and 73.27 %, respectively, and some antioxidant enzymes activities increased. Sugar, polyol, amino acid metabolism and fatty acid β-oxidation were all enhanced to improve osmotic adjustments, maintain cell membrane stability, supply energy, nitrogen assimilates and antioxidant capacity; Under composite treatments, cell membrane damage was reduced, activities of protective enzymes increased compared with only Pb stress, POD activity increased the most (49.14 %) under severe Pb composite treatment. High CO₂ caused the enhance of cells antioxidant capacity, TCA cycle intermediate products contents and fatty acid desaturation under mild Pb stress. Many sugars, polyols and amino acids contents were increased as osmotic regulatory substances by high CO₂ under severe Pb stress; Secondary metabolites played an important role under Pb stress and composite treatments. The object of this study is to provide a possible molecular mechanism of rice response to Pb stress under high CO₂ in the future.

Construction and validation of a metabolic-related genes prognostic model for oral squamous cell carcinoma based on bioinformatics

BACKGROUND

Oral squamous cell carcinoma (OSCC) accounts for a frequently-occurring head and neck cancer, which is characterized by high rates of morbidity and mortality. Metabolism-related genes (MRGs) show close association with OSCC development, metastasis and progression, so we constructed an MRGs-based OSCC prognosis model for evaluating OSCC prognostic outcome.

METHODS

This work obtained gene expression profile as well as the relevant clinical information from the The Cancer Genome Atlas (TCGA) database, determined the MRGs related to OSCC by difference analysis, screened the prognosis-related MRGs by performing univariate Cox analysis, and used such identified MRGs for constructing the OSCC prognosis prediction model through Lasso-Cox regression. Besides, we validated the model with the GSE41613 dataset based on Gene Expression Omnibus (GEO) database.

RESULTS

The present work screened 317 differentially expressed MRGs from the database, identified 12 OSCC prognostic MRGs through univariate Cox regression, and then established a clinical prognostic model composed of 11 MRGs by Lasso-Cox analysis. Based on the optimal risk score threshold, cases were classified as low- or high-risk group. As suggested by Kaplan-Meier (KM) analysis, survival rate was obviously different between the two groups in the TCGA training set (P < 0.001). According to subsequent univariate and multivariate Cox regression, risk score served as the factor to predict prognosis relative to additional clinical features (P < 0.001). Besides, area under ROC curve (AUC) values for patient survival at 1, 3 and 5 years were determined as 0.63, 0.70, and 0.76, separately, indicating that the prognostic model has good predictive accuracy. Then, we validated this clinical prognostic model using GSE41613. To enhance our model prediction accuracy, age, gender, risk score together with TNM stage were incorporated in a nomogram. As indicated by results of ROC curve and calibration curve analyses, the as-constructed nomogram had enhanced prediction accuracy compared with clinicopathological features alone, besides, combining clinicopathological characteristics with risk score contributed to predicting patient prognosis and guiding clinical decision-making.

CONCLUSION

In this study, 11 MRGs prognostic models based on TCGA database showed superior predictive performance and had a certain clinical application prospect in guiding individualized.

Metabolomic profile in patients with primary warm autoimmune haemolytic anaemia

Autoimmune haemolytic anaemia (AIHA) is a rare clinical condition with immunoglobulin fixation on the surface of erythrocytes, with or without complement activation. The pathophysiology of AIHA is complex and multifactorial, presenting functional abnormalities of T and B lymphocytes that generate an imbalance between lymphocyte activation, immunotolerance and cytokine production that culminates in autoimmune haemolysis. In AIHA, further laboratory data are needed to predict relapse and refractoriness of therapy, and thus, prevent adverse side-effects and treatment-induced toxicity. The metabolomic profile of AIHA has not yet been described. Our group developed a cross-sectional study with follow-up to assess the metabolomic profile in these patients, as well as to compare the metabolites found depending on the activity and intensity of haemolysis. We analysed the plasma of 26 patients with primary warm AIHA compared to 150 healthy individuals by mass spectrometry. Of the 95 metabolites found in the patients with AIHA, four acylcarnitines, two phosphatidylcholines (PC), asymmetric dimethylarginine (ADMA) and three sphingomyelins were significantly increased. There was an increase in PC, spermine and spermidine in the AIHA group with haemolytic activity. The PC ae 34:3/PC ae 40:2 ratio, seen only in the 12-month relapse group, was a predictor of relapse with 81% specificity and 100% sensitivity. Increased sphingomyelin, ADMA, PC and polyamines in patients with warm AIHA can interfere in autoantigen and autoimmune recognition mechanisms in a number of ways (deficient action of regulatory T lymphocytes on erythrocyte recognition as self, negative regulation of macrophage nuclear factor kappa beta activity, perpetuation of effector T lymphocyte and antibody production against erythrocyte antigens). The presence of PC ae 34:3/PC ae 40:2 ratio as a relapse predictor can help in identifying cases that require more frequent follow-up or early second-line therapies.

Gamma-glutamyl-leucine levels are causally associated with elevated cardio-metabolic risks

Objective

Gamma-glutamyl dipeptides are bioactive peptides involved in inflammation, oxidative stress, and glucose regulation. Gamma-glutamyl-leucine (Gamma-Glu-Leu) has been extensively reported to be associated with the risk of cardio-metabolic diseases, such as obesity, metabolic syndrome, and type 2 diabetes. However, the causality remains to be uncovered. The aim of this study was to explore the causal-effect relationships between Gamma-Glu-Leu and metabolic risk.

Materials and methods

In this study, 1,289 subjects were included from a cross-sectional survey on metabolic syndrome (MetS) in eastern China. Serum Gamma-Glu-Leu levels were measured by untargeted metabolomics. Using linear regressions, a two-stage genome-wide association study (GWAS) for Gamma-Glu-Leu was conducted to seek its instrumental single nucleotide polymorphisms (SNPs). One-sample Mendelian randomization (MR) analyses were performed to evaluate the causality between Gamma-Glu-Leu and the metabolic risk.

Results

Four SNPs are associated with serum Gamma-Glu-Leu levels, including rs12476238, rs56146133, rs2479714, and rs12229654. Out of them, rs12476238 exhibits the strongest association (Beta = -0.38, S.E. = 0.07 in discovery stage, Beta = -0.29, S.E. = 0.14 in validation stage, combined P-value = 1.04 × 10^-8). Each of the four SNPs has a nominal association with at least one metabolic risk factor. Both rs12229654 and rs56146133 are associated with body mass index, waist circumference (WC), the ratio of WC to hip circumference, blood pressure, and triglyceride (5 × 10^-5 < P < 0.05). rs56146133 also has nominal associations with fasting insulin, glucose, and insulin resistance index (5 × 10^-5 < P < 0.05). Using the four SNPs serving as the instrumental SNPs of Gamma-Glu-Leu, the MR analyses revealed that higher Gamma-Glu-Leu levels are causally associated with elevated risks of multiple cardio-metabolic factors except for high-density lipoprotein cholesterol and low-density lipoprotein cholesterol (P > 0.05).

Conclusion

Four SNPs (rs12476238, rs56146133, rs2479714, and rs12229654) may regulate the levels of serum Gamma-Glu-Leu. Higher Gamma-Glu-Leu levels are causally linked to cardio-metabolic risks. Future prospective studies on Gamma-Glu-Leu are required to explain its role in metabolic disorders.

The potential for mitigation of methane emissions in ruminants through the application of metagenomics, metabolomics, and other -OMICS technologies

Ruminant supply chains contribute 5.7 gigatons of CO_2-eq per annum, which represents approximately 80% of the livestock sector emissions. One of the largest sources of emission in the ruminant sector is methane (CH₄), accounting for approximately 40% of the sectors total emissions. With climate change being a growing concern, emphasis is being put on reducing greenhouse gas emissions, including those from ruminant production. Various genetic and environmental factors influence cattle CH₄ production, such as breed, genetic makeup, diet, management practices, and physiological status of the host. The influence of genetic variability on CH₄ yield in ruminants indicates that genomic selection for reduced CH₄ emissions is possible. Although the microbiology of CH₄ production has been studied, further research is needed to identify key differences in the host and microbiome genomes and how they interact with one another. The advancement of “-omics” technologies, such as metabolomics and metagenomics, may provide valuable information in this regard. Improved understanding of genetic mechanisms associated with CH₄ production and the interaction between the microbiome profile and host genetics will increase the rate of genetic progress for reduced CH₄ emissions. Through a systems biology approach, various “-omics” technologies can be combined to unravel genomic regions and genetic markers associated with CH₄ production, which can then be used in selective breeding programs. This comprehensive review discusses current challenges in applying genomic selection for reduced CH₄ emissions, and the potential for “-omics” technologies, especially metabolomics and metagenomics, to minimize such challenges. The integration and evaluation of different levels of biological information using a systems biology approach is also discussed, which can assist in understanding the underlying genetic mechanisms and biology of CH₄ production traits in ruminants and aid in reducing agriculture’s overall environmental footprint.

The remodel of the "central dogma": a metabolomics interaction perspective

BACKGROUND

In 1957, Francis Crick drew a linear diagram on a blackboard. This diagram is often called the "central dogma." Subsequently, the relationships between different steps of the "central dogma" have been shown to be considerably complex, mostly because of the emerging world of small molecules. It is noteworthy that metabolites can be generated from the diet through gut microbiome metabolism, serve as substrates for epigenetic modifications, destabilize DNA quadruplexes, and follow Lamarckian inheritance. Small molecules were once considered the missing link in the "central dogma"; however, recently they have acquired a central role, and their general perception as downstream products has become reductionist. Metabolomics is a large-scale analysis of metabolites, and this emerging field has been shown to be the closest omics associated with the phenotype and concomitantly, the basis for all omics.

AIM OF REVIEW

Herein, we propose a broad updated perspective for the flux of information diagram centered in metabolomics, including the influence of other factors, such as epigenomics, diet, nutrition, and the gut- microbiome.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Metabolites are the beginning and the end of the flux of information.

Association of metabolites with obesity based on two gene variants, MC4R rs17782313 and BDNF rs6265

Previous genome-wide association analyses for obesity related genes demonstrated the association of BDNF gene variant rs6265 and MC4R gene variant rs17782313 with body mass index (BMI). However, the associated metabolite pathways are still behind the curtain. The aim of the current study is to investigate the associations of metabolic changes in obesity with MC4R gene variant rs17782313 and BDNF variant rs6265. Gas chromatography-mass spectrometry based untargeted metabolomics approach was used and 42 identified serum metabolites were selected for statistical analyses. Significant association of seven metabolites with MC4R gene variant rs17782313 based on obesity and thirty metabolites with obesity dependent BDNF variant rs6265 using additive model (adjusted p < 0.05) was observed. This study highlights the importance of alteration of fatty acid biosynthesis, probably due to high consumption of fats may cause to develop obesity. But obesity is a complex disorder and the full clarification of this complex machinery is still distant. To understand the obesity in a better way, more studies are required to identify remaining metabolites and also mechanism of these metabolic entities.

Metabolite Analysis of Jerusalem Artichoke (Helianthus tuberosus L.) Seedlings in Response to Polyethylene Glycol-Simulated Drought Stress

Jerusalem artichokes are a perennial crop with high drought tolerance and high value as a raw material to produce biofuels, functional feed, and food. However, there are few comprehensive metabolomic studies on Jerusalem artichokes under drought conditions. Methods: Ultra-performance liquid chromatography and tandem mass spectrometry were used to identify differential metabolites in Jerusalem artichoke seedling leaves under polyethylene glycol (PEG) 6000-simulated drought stress at 0, 18, 24, and 36 h. Results: A total of 661 metabolites and 236 differential metabolites were identified at 0 vs. 18, 18 vs. 24, and 24 vs. 36 h. 146 differential metabolites and 56 common were identified and at 0 vs. 18, 24, and 36 h. Kyoto Encyclopedia of Genes and Genomes enrichment identified 236 differential metabolites involved in the biosynthesis of secondary metabolites and amino acids. Metabolites involved in glycolysis, phenolic metabolism, tricarboxylic cycle, glutamate-mediated proline biosynthesis, urea cycle, amino acid metabolism, unsaturated fatty acid biosynthesis, and the met salvage pathway responded to drought stress. Conclusion: A metabolic network in the leaves of Jerusalem artichokes under drought stress is proposed. These results will improve understanding of the metabolite response to drought stress in Jerusalem artichokes and develop a foundation for breeding drought-resistant varieties.

Metabolomics bridging proteomics along metabolites/oncometabolites and protein modifications: Paving the way toward integrative multiomics

Systems biology adopted functional and integrative multiomics approaches enable to discover the whole set of interacting regulatory components such as genes, transcripts, proteins, metabolites, and metabolite dependent protein modifications. This interactome build up the midpoint of protein-protein/PTM, protein-DNA/RNA, and protein-metabolite network in a cell. As the key drivers in cellular metabolism, metabolites are precursors and regulators of protein post-translational modifications [PTMs] that affect protein diversity and functionality. The precisely orchestrated core pattern of metabolic networks refer to paradigm 'metabolites regulate PTMs, PTMs regulate enzymes, and enzymes modulate metabolites' through a multitude of feedback and feed-forward pathway loops. The concept represents a flawless PTM-metabolite-enzyme(protein) regulomics underlined in reprogramming cancer metabolism. Immense interconnectivity of those biomolecules in their spectacular network of intertwined metabolic pathways makes integrated proteomics and metabolomics an excellent opportunity, and the central component of integrative multiomics framework. It will therefore be of significant interest to integrate global proteome and PTM-based proteomics with metabolomics to achieve disease related altered levels of those molecules. Thereby, present update aims to highlight role and analysis of interacting metabolites/oncometabolites, and metabolite-regulated PTMs loop which may function as translational monitoring biomarkers along the reprogramming continuum of oncometabolism.

Integrative analysis of transcriptome and metabolome reveal mechanism of tolerance to salt stress in oat (Avena sativa L.)

Soil salinity is among the crucial factors that impact on crop productivity, including oat (Avena sativa L.). Herein, we used two distinct oat cultivars with varied salt tolerance levels to unravel adaptive responses to salt stress by metabolomic and transcriptomic characterization. Metabolomic profiling revealed 201 metabolites, including saccharides, amino acids, organic acids, and secondary metabolites. The levels of most saccharides and amino acids were elevated in Baiyan 2 (BY2) as well as in Baiyan 5 (BY5) exposed to salt stress. In the tolerant cultivar BY2 exposed to 150 mM NaCl, concentrations of most of the metabolites increased significantly, with sucrose increased by 38.34-fold, Sophorose increased by 314.15-fold and Isomaltose 2 increased by 25.76-fold. In the sensitive cultivar BY5, the concentrations of most metabolites increased after the plant was exposed to 150 mM NaCl but decreased after the plant was exposed to 300 mM NaCl. Transcriptomic analysis revealed that gene expressions in BY5 were significantly affected under exposure to 300 mM NaCl (34040 genes up-regulated and 14757 genes down-regulated). Assessment of metabolic pathways as well as KEGG enrichment revealed that salt stress interferes with the biosynthesis of two oat cultivars, including capacity expenditure and sugar metabolism. Most of the BY2 genes enhanced energy consumption (for example, glycolysis) and biosynthesis (for instance, starch and sugar metabolism) under salt stress. In contrast, genes in BY5 were found to be down-regulated, leading to the inhibition of energy consumption and biosynthesis, which may also be attributed to salt sensitivity in BY5. In addition, the modified Na⁺/K⁺ transporter genes expression is associated with the predominant ionic responses in BY2, which leads low concentration of Na⁺ and high K⁺ when exposed to high salt situations. These findings suggest that the varied defensive capacities of these two oat cultivars in response to salt stress are due to their variations in energy-expenditure strategy, synthesis of energy substances and ion transport in roots. Our present study offers a crucial reference for oat cultivation under saline soil.

Investigating metabolism by mass spectrometry: From steady state to dynamic view

Metabolism is the set of life-sustaining reactions in organisms. These biochemical reactions are organized in metabolic pathways, in which one metabolite is converted through a series of steps catalyzed by enzymes in another chemical compound. Metabolic reactions are categorized as catabolic, the breaking down of metabolites to produce energy, and/or anabolic, the synthesis of compounds that consume energy. The balance between catabolism of the preferential fuel substrate and anabolism defines the overall metabolism of a cell or tissue. Metabolomics is a powerful tool to gain new insights contributing to the identification of complex molecular mechanisms in the field of biomedical research, both basic and translational. The enormous potential of this kind of analyses consists of two key aspects: (i) the possibility of performing so-called targeted and untargeted experiments through which it is feasible to verify or formulate a hypothesis, respectively, and (ii) the opportunity to run either steady-state analyses to have snapshots of the metabolome at a given time under different experimental conditions or dynamic analyses through the use of labeled tracers. In this review, we will highlight the most important practical (e.g., different sample extraction approaches) and conceptual steps to consider for metabolomic analysis, describing also the main application contexts in which it is used. In addition, we will provide some insights into the most innovative approaches and progress in the field of data analysis and processing, highlighting how this part is essential for the proper extrapolation and interpretation of data.

Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics

Metabolomics has been widely used for investigating the biological functions of disease expression and has the potential to discover biomarkers in circulating biofluids or tissue extracts that reflect in phenotypic changes. Metabolic profiling has advantages because of the use of unbiased techniques, including multivariate analysis, and has been applied in pharmacological studies to predict therapeutic and adverse reactions of drugs, which is called pharmacometabolomics (PMx). Nuclear magnetic resonance (NMR)- and mass spectrometry (MS)-based metabolomics has contributed to the discovery of recent disease biomarkers; however, the optimal strategy for the study purpose must be selected from many established protocols, methodologies and analytical platforms. Additionally, information on molecular localization in tissue is essential for further functional analyses related to therapeutic and adverse effects of drugs in the process of drug development. MS imaging (MSI) is a promising technology that can visualize molecules on tissue surfaces without labeling and thus provide localized information. This review summarizes recent uses of MS-based global and wide-targeted metabolomics technologies and the advantages of the MSI approach for PMx and highlights the PMx technique for the biomarker discovery of adverse drug effects.

Development and validation of metabolism-related gene signature in prognostic prediction of gastric cancer

Gastric cancer is one of the most common malignant tumours in the world. As one of the crucial hallmarks of cancer reprogramming of metabolism and the relevant researches have a promising application in the diagnosis treatment and prognostic prediction of malignant tumours. This study aims to identify a group of metabolism-related genes to construct a prediction model for the prognosis of gastric cancer.

A large cohort of gastric cancer cases (1121 cases) from public database was included in our analysis and classified patients into training and testing cohorts at a ratio of 7: 3. After identifying a list of metabolism-related genes having prognostic value, we constructed a risk score based on metabolism-related genes using LASSO-COX method. According to the risk score, patients were divided into high- and low-risk groups. Our results revealed that high-risk patients had a significantly worse prognosis than low-risk patients in both the training (high-risk vs low-risk patients; five years overall survival: 37.2% vs 72.2%; p < 0.001) and testing cohorts (high-risk vs low-risk patients; five years overall survival: 42.9% vs 62.9%; p < 0.001). This observation was validated in the external validation cohort (high-risk vs. low-risk patients; five years overall survival: 30.2% vs 40.4%; p = 0.007).

To reinforce the predictive ability of the model, we integrated risk score, age, adjuvant chemotherapy, and TNM stage into a nomogram. According to the result of receiver operating characteristic curves and decision curves analysis, we found that the nomogram score had a superior predictive ability than conventional factors, indicating that the risk score combined with clinicopathological features can develop a robust prediction for survival and improve the individualized clinical decision making of the patient.

In conclusion, we identified a list of metabolic genes related to survival and developed a metabolism-based predictive model for gastric cancer. Through a series of bioinformatics and statistical analyses, the predictive ability of the model was confirmed.

Maternal Liver Metabolic Response to Chronic Vitamin D Deficiency Is Determined by Mouse Strain Genetic Background

BACKGROUND

Liver metabolite concentrations have the potential to be key biomarkers of systemic metabolic dysfunction and overall health. However, for most conditions we do not know the extent to which genetic differences regulate susceptibility to metabolic responses. This limits our ability to detect and diagnose effects in heterogeneous populations.

OBJECTIVES

Here, we investigated the extent to which naturally occurring genetic differences regulate maternal liver metabolic response to vitamin D deficiency (VDD), particularly during perinatal periods when such changes can adversely affect maternal and fetal health.

METHODS

We used a panel of 8 inbred Collaborative Cross (CC) mouse strains, each with a different genetic background (72 dams, 3-6/treatment group, per strain). We identified robust maternal liver metabolic responses to vitamin D depletion before and during gestation and lactation using a vitamin-D-deficient (VDD; 0 IU vitamin D3/kg) or -sufficient diet (1000 IU vitamin D3/kg). We then identified VDD-induced metabolite changes influenced by strain genetic background.

RESULTS

We detected a significant VDD effect by orthogonal partial least squares discriminant analysis (Q2 = 0.266, pQ2 = 0.002): primarily, altered concentrations of 78 metabolites involved in lipid, amino acid, and nucleotide metabolism (variable importance to projection score ≥1.5). Metabolites in unsaturated fatty acid and glycerophospholipid metabolism pathways were significantly enriched [False Discovery Rate (FDR) <0.05]. VDD also significantly altered concentrations of putative markers of uremic toxemia, acylglycerols, and dipeptides. The extent of the metabolic response to VDD was strongly dependent on genetic strain, ranging from robustly responsive to nonresponsive. Two strains (CC017/Unc and CC032/GeniUnc) were particularly sensitive to VDD; however, each strain altered different pathways.

CONCLUSIONS

These novel findings demonstrate that maternal VDD induces different liver metabolic effects in different genetic backgrounds. Strains with differing susceptibility and metabolic response to VDD represent unique tools to identify causal susceptibility factors and further elucidate the role of VDD-induced metabolic changes in maternal and/or fetal health for ultimately translating findings to human populations.

Metabogenomics reveals four candidate regions involved in the pathophysiology of Equine Metabolic Syndrome

An analogous condition to human metabolic syndrome, Equine Metabolic Syndrome (EMS) is defined by several clinical signs including obesity, hyperinsulinemia, and peripheral insulin dysregulation (ID). Affected horses may also exhibit hypertension, hyperlipemia and systemic inflammation. Measures of ID typically comprise the gold-standard for diagnosis in veterinary care. Yet, the dynamic nature of insulin homeostasis and complex procedures of typical assays make accurate quantification of ID and EMS challenging. This work aimed to investigate new strategies for identification of biochemical markers and correlated genes in EMS. To quantify EMS risk within this population, we utilized a composite score derived from nine common diagnostic variables. We applied a global liquid chromatography/mass spectroscopy approach (HPLC/MS) to whole plasma collected from 49 Arabian horses, resulting in 3392 high-confidence features and identification of putative metabolites in public databases. We performed a genome wide association analysis with genotypes from the 670k Affymetrix Equine SNP array utilizing EMS-correlated metabolites as phenotypes. We discovered four metabolite features significantly correlated with EMS score (P < 1.474 × 10^-5). GWAs for these features results (P = 6.787 × 10^-7, Bonferroni) identified four unique candidate regions (r² > 0.4) containing 63 genes. Significant genomic markers capture 43.52% of the variation in the original EMS score phenotype. The identified genomic loci provide insight into the pathways controlling variation in EMS and the origin of genetic predisposition to the condition. Rapid, feasible and accurate diagnostic tools derived from metabogenomics can be translated into measurable benefits in the timeline and quality of preventative management practices to preserve health in horses.

Genetics and Not Shared Environment Explains Familial Resemblance in Adult Metabolomics Data

Metabolites are small molecules involved in cellular metabolism where they act as reaction substrates or products. The term 'metabolomics' refers to the comprehensive study of these molecules. The concentrations of metabolites in biological tissues are under genetic control, but this is limited by environmental factors such as diet. In adult mono- and dizygotic twin pairs, we estimated the contribution of genetic and shared environmental influences on metabolite levels by structural equation modeling and tested whether the familial resemblance for metabolite levels is mainly explained by genetic or by environmental factors that are shared by family members. Metabolites were measured across three platforms: two based on proton nuclear magnetic resonance techniques and one employing mass spectrometry. These three platforms comprised 237 single metabolic traits of several chemical classes. For the three platforms, metabolites were assessed in 1407, 1037 and 1116 twin pairs, respectively. We carried out power calculations to establish what percentage of shared environmental variance could be detected given these sample sizes. Our study did not find evidence for a systematic contribution of shared environment, defined as the influence of growing up together in the same household, on metabolites assessed in adulthood. Significant heritability was observed for nearly all 237 metabolites; significant contribution of the shared environment was limited to 6 metabolites. The top quartile of the heritability distribution was populated by 5 of the 11 investigated chemical classes. In this quartile, metabolites of the class lipoprotein were significantly overrepresented, whereas metabolites of classes glycerophospholipids and glycerolipids were significantly underrepresented.

MEATabolomics: Muscle and Meat Metabolomics in Domestic Animals

In the past decades, metabolomics has been used to comprehensively understand a variety of food materials for improvement and assessment of food quality. Farm animal skeletal muscles and meat are one of the major targets of metabolomics for the characterization of meat and the exploration of biomarkers in the production system. For identification of potential biomarkers to control meat quality, studies of animal muscles and meat with metabolomics (MEATabolomics) has been conducted in combination with analyses of meat quality traits, focusing on specific factors associated with animal genetic background and sensory scores, or conditions in feeding system and treatments of meat in the processes such as postmortem storage, processing, and hygiene control. Currently, most of MEATabolomics approaches combine separation techniques (gas or liquid chromatography, and capillary electrophoresis)-mass spectrometry (MS) or nuclear magnetic resonance (NMR) approaches with the downstream multivariate analyses, depending on the polarity and/or hydrophobicity of the targeted metabolites. Studies employing these approaches provide useful information to monitor meat quality traits efficiently and to understand the genetic background and production system of animals behind the meat quality. MEATabolomics is expected to improve the knowledge and methodologies in animal breeding and feeding, meat storage and processing, and prediction of meat quality.

The metabolome as a link in the genotype-phenotype map for peroxide resistance in the fruit fly, Drosophila melanogaster

BACKGROUND

Genetic association studies that seek to explain the inheritance of complex traits typically fail to explain a majority of the heritability of the trait under study. Thus, we are left with a gap in the map from genotype to phenotype. Several approaches have been used to fill this gap, including those that attempt to map endophenotype such as the transcriptome, proteome or metabolome, that underlie complex traits. Here we used metabolomics to explore the nature of genetic variation for hydrogen peroxide (H2O2) resistance in the sequenced inbred Drosophila Genetic Reference Panel (DGRP).

RESULTS

We first studied genetic variation for H2O2 resistance in 179 DGRP lines and along with identifying the insulin signaling modulator u-shaped and several regulators of feeding behavior, we estimate that a substantial amount of phenotypic variation can be explained by a polygenic model of genetic variation. We then profiled a portion of the aqueous metabolome in subsets of eight 'high resistance' lines and eight 'low resistance' lines. We used these lines to represent collections of genotypes that were either resistant or sensitive to the stressor, effectively modeling a discrete trait. Across the range of genotypes in both populations, flies exhibited surprising consistency in their metabolomic signature of resistance. Importantly, the resistance phenotype of these flies was more easily distinguished by their metabolome profiles than by their genotypes. Furthermore, we found a metabolic response to H2O2 in sensitive, but not in resistant genotypes. Metabolomic data further implicated at least two pathways, glycogen and folate metabolism, as determinants of sensitivity to H2O2. We also discovered a confounding effect of feeding behavior on assays involving supplemented food.

CONCLUSIONS

This work suggests that the metabolome can be a point of convergence for genetic variation influencing complex traits, and can efficiently elucidate mechanisms underlying trait variation.

Physiological characteristics and metabolomics reveal the tolerance mechanism to low nitrogen in Glycine soja leaves

To explore the regulatory mechanisms involved in the adaption to nitrogen (N) deficiency of wild soybean, the ion balance, photosynthetic characteristics, metabolic and transcriptional changes in leaves of common and low N (LN)-tolerant wild soybean seedlings under LN stress were determined. The LN-tolerant wild soybean seedlings showed a stronger ability to maintain photosynthesis and nutrient balance than common wild soybean. A total of 52 differentially accumulated metabolites, mainly related to carbon and N metabolism, were identified between the control and the LN treatment group. In general, tricarboxylic acid (TCA) cycle, shikimic acid pathway, synthetase/glutamate synthase (GS/GOGAT) cycle and accumulation of most organic acids were enhanced in LN-tolerant wild soybean, while reduced in common wild soybean under LN stress compared with their respective control group. Moreover, glycolysis, sugar and polyol and fatty acid metabolism increased in both wild soybean genotypes, and increased more in LN-tolerant wild soybean. A total of 3381 differentially expressed genes (DEGs) were identified in leaves of both wild soybean genotypes and the expressed level of DEGs associated with sugars, polyols, fatty acids and energy metabolism was significantly higher in LN-tolerant wild soybean than in common wild soybean, consistent with changes in metabolite level. Our results suggest new ideas for the study of LN tolerance of wild soybean and provide a theoretical basis for development and utilization of wild soybean resources.

Alteration of intracellular metabolome in osteosarcoma stem cells revealed by liquid chromatography-tandem mass spectrometry

Cancer stem cells (CSCs) are the origin of many malignant tumours, including osteosarcoma that mainly affects adolescents and is accompanied by a poor prognosis. However, little is known about the intrinsic biological information of osteosarcoma stem cells, particularly for the metabolomics features. Hence, an ultra-high performance liquid chromatography coupled with tandem Q-Exactive Orbitrap mass spectrometer (UHPLC-QE-MS)-based metabolomics approach was developed to investigate the metabolism changes in the human osteosarcoma (HOS) cell line in order to understand its possible mechanism. HMDB, METLIN and m/z Cloud databases were used to identify the metabolic markers. Additionally, the compounds were further identified using standards of the metabolites. Comparing HOS-CSCs with non-CSCs, 154 different metabolites were identified in both the positive and negative modes. Based on the clearly distinct metabolites, the changed metabolic pathways were analysed using MetaboAnalyst. The top five altered pathways included alanine, aspartate and glutamate metabolism; arginine and proline metabolism; glutathione metabolism; cysteine and methionine metabolism; and the citrate cycle (TCA cycle). The downregulation of the TCA cycle and elevation of oxidized glutathione levels suggested a decline of mitochondrial metabolism, while most of the amino acid metabolisms were upregulated. Further biological experiments including an analysis of mitochondrial activity confirmed the above hypotheses that were deduced from metabolomics results. These findings not only enhance our understanding of the altered metabolome in osteosarcoma stem cells but also demonstrate the great potential of such a metabolomics method based on UHPLC-QE-MS in large-scale cell studies.

Genetic Regulation of Liver Metabolites and Transcripts Linking to Biochemical-Clinical Parameters

Given the central metabolic role of the liver, hepatic metabolites and transcripts reflect the organismal physiological state. Biochemical-clinical plasma biomarkers, hepatic metabolites, transcripts, and single nucleotide polymorphism (SNP) genotypes of some 300 pigs were integrated by weighted correlation networks and genome-wide association analyses. Network-based approaches of transcriptomic and metabolomics data revealed linked of transcripts and metabolites of the pentose phosphate pathway (PPP). This finding was evidenced by using a NADP/NADPH assay and HDAC4 and G6PD transcript quantification with the latter coding for first limiting enzyme of this pathway and by RNAi knockdown experiments of HDAC4. Other transcripts including ARG2 and SLC22A7 showed link to amino acids and biomarkers. The amino acid metabolites were linked with transcripts of immune or acute phase response signaling, whereas the carbohydrate metabolites were highly enrich in cholesterol biosynthesis transcripts. Genome-wide association analyses revealed 180 metabolic quantitative trait loci (mQTL) (p < 10^-4). Trans-4-hydroxy-L-proline (p = 6 × 10^-9), being strongly correlated with plasma creatinine (CREA), showed strongest association with SNPs on chromosome 6 that had pleiotropic effects on PRODH2 expression as revealed by multivariate analysis. Consideration of shared marker association with biomarkers, metabolites, and transcripts revealed 144 SNPs associated with 44 metabolites and 69 transcripts that are correlated with each other, representing 176 mQTL and expression quantitative trait loci (eQTL). This is the first work to report genetic variants associated with liver metabolite and transcript levels as well as blood biochemical-clinical parameters in a healthy porcine model. The identified associations provide links between variation at the genome, transcriptome, and metabolome level molecules with clinically relevant phenotypes. This approach has the potential to detect novel biomarkers displaying individual variation and promoting predictive biology in medicine and animal breeding.

A Systems-Level View of Renal Metabolomics

The measurement of select circulating metabolites such as creatinine, glucose, and cholesterol are integral to clinical medicine, with implications for diagnosis, prognosis, and treatment. Metabolomics studies in nephrology research seek to build on this paradigm, with the goal to identify novel markers and causal participants in the pathogenesis of kidney disease and its complications. This article reviews three themes pertinent to this goal. Each is rooted in long-established principles of human physiology, with recent updates enabled by metabolomics and other tools. First, the kidney has a broad and heterogeneous impact on circulating metabolites, with progressive loss of kidney function resulting in a multitude of small molecule alterations. Second, an increasing number of circulating metabolites have been shown to possess functional roles, in some cases acting as ligands for specific G-protein-coupled receptors. Third, circulating metabolites traffic through varied, and sometimes complex, interorgan circuits. Taken together, these themes emphasize the importance of viewing renal metabolomics at the systems level, recognizing the diverse origins and physiologic effects of blood metabolites. However, how to synthesize these themes and how to establish clinical relevance remain uncertain and will require further investigation.

ProGeM: a framework for the prioritization of candidate causal genes at molecular quantitative trait loci

Quantitative trait locus (QTL) mapping of molecular phenotypes such as metabolites, lipids and proteins through genome-wide association studies represents a powerful means of highlighting molecular mechanisms relevant to human diseases. However, a major challenge of this approach is to identify the causal gene(s) at the observed QTLs. Here, we present a framework for the 'Prioritization of candidate causal Genes at Molecular QTLs' (ProGeM), which incorporates biological domain-specific annotation data alongside genome annotation data from multiple repositories. We assessed the performance of ProGeM using a reference set of 227 previously reported and extensively curated metabolite QTLs. For 98% of these loci, the expert-curated gene was one of the candidate causal genes prioritized by ProGeM. Benchmarking analyses revealed that 69% of the causal candidates were nearest to the sentinel variant at the investigated molecular QTLs, indicating that genomic proximity is the most reliable indicator of 'true positive' causal genes. In contrast, cis-gene expression QTL data led to three false positive candidate causal gene assignments for every one true positive assignment. We provide evidence that these conclusions also apply to other molecular phenotypes, suggesting that ProGeM is a powerful and versatile tool for annotating molecular QTLs. ProGeM is freely available via GitHub.

Application of omics technologies for a deeper insight into quali-quantitative production traits in broiler chickens: A review

The poultry industry is continuously facing substantial and different challenges such as the increasing cost of feed ingredients, the European Union's ban of antibiotic as growth promoters, the antimicrobial resistance and the high incidence of muscle myopathies and breast meat abnormalities. In the last decade, there has been an extraordinary development of many genomic techniques able to describe global variation of genes, proteins and metabolites expression level. Proper application of these cutting-edge omics technologies (mainly transcriptomics, proteomics and metabolomics) paves the possibility to understand much useful information about the biological processes and pathways behind different complex traits of chickens. The current review aimed to highlight some important knowledge achieved through the application of omics technologies and proteo-genomics data in the field of feed efficiency, nutrition, meat quality and disease resistance in broiler chickens.

LC-HRMS Metabolomics for Untargeted Diagnostic Screening in Clinical Laboratories: A Feasibility Study

Today’s high-resolution mass spectrometers (HRMS) allow bioanalysts to perform untargeted/global determinations that can reveal unexpected compounds or concentrations in a patient’s sample. This could be performed for preliminary diagnosis attempts when usual diagnostic processes and targeted determinations fail. We have evaluated an untargeted diagnostic screening (UDS) procedure. UDS is a metabolome analysis that compares one sample (e.g., a patient) with control samples (a healthy population). Using liquid chromatography (LC)-HRMS full-scan analysis of human serum extracts and unsupervised data treatment, we have compared individual samples that were spiked with one xenobiotic or a higher level of one endogenous compound with control samples. After the use of different filters that drastically reduced the number of metabolites detected, the spiked compound was eventually revealed in each test sample and ranked. The proposed UDS procedure appears feasible and reliable to reveal unexpected xenobiotics (toxicology) or higher concentrations of endogenous metabolites. HRMS-based untargeted approaches could be useful as preliminary diagnostic screening when canonical processes do not reveal disease etiology nor establish a clear diagnosis and could reduce misdiagnosis. On the other hand, the risk of overdiagnosis of this approach should be reduced with mandatory biomedical interpretation of the patient’s UDS results and with confirmatory targeted and quantitative determinations.

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

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

Systems Biology in Aging Research

Systems biology is an approach to collect high-dimensional data and analyze in an integrated manner. As aging is a complicated physiological functional decline in biological system, the methods in systems biology could be utilized in aging studies. Here we reviewed recent advances in systems biology in aging research and divide them into two major parts. One is the data resource, which includes omics data from DNA, RNA, proteins, epigenetic changes, metabolisms, and recently single-cell-level variations. The other is the data analysis methods consisting of network and modeling approaches. With all the data and the tools to analyze them, we could further promote our understanding of the systematic aging.

Comparative Metabolomic Analysis of the Cambium Tissue of Non-transgenic and Multi-Gene Transgenic Poplar (Populus × euramericana 'Guariento')

Poplar, a model for woody plant research, is the most widely distributed tree species in the world. Metabolites are the basis of phenotypes, allowing an intuitive and effective understanding of biological processes and their mechanisms. However, metabolites in non-transgenic and multi-gene transgenic poplar remains poorly characterized, especially in regards of the influences on quantity and in the analysis of the relative abundance of metabolites after the introduction of multi stress-related genes. In this study, we investigated the cambium metabolomes of one non-transgenic (D5-0) and two multi-gene (vgb, SacB, ERF36, BtCry3A, and OC-I) transgenic lines (D5-20 and D5-21) of hybrid poplar (Populus × euramericana 'Guariento') using both gas chromatography-mass spectrometry (GC-MS) and ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). We aimed to explore the effects of the exogenous genes on metabolite composition and to screen out metabolites with important biological functions. Finally, we identified 239 named metabolites and determined their relative abundance. Among these, 197 metabolites had a different abundance across the three lines. These methabolites spanned nine primary and 44 secondary metabolism pathways. Arginine and glutamate, as substrates and intermediates in nitrogen metabolism, and important in growth and stress-related processes, as well as sucrose, uridine diphosphate glucose, and their derivatives, precursors in cell wall pathways, and catechol, relevant to insect resistance, differed greatly between the genetically modified and non-transgenic poplar. These findings may provide a basis for further study of cambium metabolism, and fully understand metabolites associated with stress response.

From Discovery to Translation: Characterization of C-Mannosyltryptophan and Pseudouridine as Markers of Kidney Function

Using a non-targeted metabolomics platform, we recently identified C-mannosyltryptophan and pseudouridine as non-traditional kidney function markers. The aims of this study were to obtain absolute concentrations of both metabolites in blood and urine from individuals with and without CKD to provide reference ranges and to assess their fractional excretions (FE), and to assess the agreement with their non-targeted counterparts. In individuals without/with CKD, mean plasma and urine concentrations for C-mannosyltryptophan were 0.26/0.72 µmol/L and 3.39/4.30 µmol/mmol creatinine, respectively. The respective concentrations for pseudouridine were 2.89/5.67 µmol/L and 39.7/33.9 µmol/mmol creatinine. Median (25^th, 75^th percentiles) FEs were 70.8% (65.6%, 77.8%) for C-mannosyltryptophan and 76.0% (68.6%, 82.4%) for pseudouridine, indicating partial net reabsorption. Association analyses validated reported associations between single metabolites and eGFR. Targeted measurements of both metabolites agreed well with the non-targeted measurements, especially in urine. Agreement for composite nephrological measures FE and urinary metabolite-to-creatinine ratio was lower, but could be improved by replacing non-targeted creatinine measurements with a standard clinical creatinine test. In summary, targeted quantification and additional characterization in relevant populations are necessary steps in the translation of non-traditional biomarkers in nephrology from non-targeted discovery to clinical application.

Gastric cancer: Metabolic and metabolomics perspectives (Review)

Gastric cancer is one of the most malignant tumors worldwide and remains a major health threat in Asia-Pacific regions, while its pathological mechanism is generally unknown. Recent research has advanced the understanding of the relationship between metabolic reprogramming and carcinogenesis. In particular, metabolic regulation and cancer research are being further brought into sharp focus with the emergence of metabolomics. Not only can metabolomics provide global information on metabolic profiles of specific tumors, but it can also act as a promising tool to discover biomarkers regarding diagnosis, metastatic surveillance and chemotherapeutic sensitivity prediction. Meanwhile, metabolism-based anticancer therapies will be further discovered. Up to now, accumulative studies have highlighted the application of metabolomics in gastric cancer research regarding different aspects; therefore we summarized the current available results of how metabolic changes are linked to gastric carcinogenesis, and how metabolomics holds promise for the diagnosis, metastatic surveillance, treatment and prognosis prediction of gastric cancer.

Metabolite profiling of yam (Dioscorea spp.) accessions for use in crop improvement programmes

INTRODUCTION

Ninety-seven percent of yam (Dioscorea spp.) production takes place in low income food deficit countries (LIFDCs) and the crop provides 200 calories a day to approximately 300 million people. Therefore, yams are vital for food security. Yams have high-yield potential and high market value potential yet current breeding of yam is hindered by a lack of genomic information and genetic resources. New tools are needed to modernise breeding strategies and unlock the potential of yam to improve livelihood in LIFDCs.

OBJECTIVES

Metabolomic screening has been undertaken on a diverse panel of Dioscorea accessions to assess the utility of the approach for advancing breeding strategies in this understudied crop.

METHODS

Polar and lipophilic extracts from tubers of accessions from the global yam breeding program have been comprehensively profiled via gas chromatography-mass spectrometry.

RESULTS

A visual pathway representation of the measured yam tuber metabolome has been delivered as a resource for biochemical evaluation of yam germplasm. Over 200 compounds were routinely measured in tubers, providing a major advance for the chemo-typing of this crop. Core biochemical redundancy concealed trends that were only elucidated following detailed mining of global metabolomics data. Combined analysis on leaf and tuber material identified a subset of metabolites which allow accurate species classification and highlighted the potential of predicting tuber composition from leaf profiles. Metabolic variation was accession-specific and often localised to compound classes, which will aid trait-targeting for metabolite markers.

CONCLUSIONS

Metabolomics provides a standalone platform with potential to deliver near-future crop gains for yam. The approach compliments the genetic advancements currently underway and integration with other '-omics' studies will deliver a significant advancement to yam breeding strategies.

Pathway analysis of complex diseases for GWAS, extending to consider rare variants, multi-omics and interactions

BACKGROUND

Genome-wide association studies (GWAS) is a major method for studying the genetics of complex diseases. Finding all sequence variants to explain fully the aetiology of a disease is difficult because of their small effect sizes. To better explain disease mechanisms, pathway analysis is used to consolidate the effects of multiple variants, and hence increase the power of the study. While pathway analysis has previously been performed within GWAS only, it can now be extended to examining rare variants, other "-omics" and interaction data.

SCOPE OF REVIEW

1. Factors to consider in the choice of software for GWAS pathway analysis. 2. Examples of how pathway analysis is used to analyse rare variants, other "-omics" and interaction data.

MAJOR CONCLUSIONS

To choose appropriate software tools, factors for consideration include covariate compatibility, null hypothesis, one- or two-step analysis required, curation method of gene sets, size of pathways, and size of flanking regions to define gene boundaries. For rare variants, analysis performance depends on consistency between assumed and actual effect distribution of variants. Integration of other "-omics" data and interaction can better explain gene functions.

GENERAL SIGNIFICANCE

Pathway analysis methods will be more readily used for integration of multiple sources of data, and enable more accurate prediction of phenotypes.

Association of Metabolites with Obesity and Type 2 Diabetes Based on FTO Genotype

The single nucleotide polymorphism rs9939609 of the gene FTO, which encodes fat mass and obesity–associated protein, is strongly associated with obesity and type 2 diabetes (T2D) in multiple populations; however, the underlying mechanism of this association is unclear. The present study aimed to investigate FTO genotype–dependent metabolic changes in obesity and T2D. To elucidate metabolic dysregulation associated with disease risk genotype, genomic and metabolomic datasets were recruited from 2,577 participants of the Korean Association REsource (KARE) cohort, including 40 homozygous carriers of the FTO risk allele (AA), 570 heterozygous carriers (AT), and 1,967 participants carrying no risk allele (TT). A total of 134 serum metabolites were quantified using a targeted metabolomics approach. Through comparison of various statistical methods, seven metabolites were identified that are significantly altered in obesity and T2D based on the FTO risk allele (adjusted p < 0.05). These identified metabolites are relevant to phosphatidylcholine metabolic pathway, and previously reported to be metabolic markers of obesity and T2D. In conclusion, using metabolomics with the information from genome-wide association studies revealed significantly altered metabolites depending on the FTO genotype in complex disorders. This study may contribute to a better understanding of the biological mechanisms linking obesity and T2D.

Plant Metabolomics: An Indispensable System Biology Tool for Plant Science

As genomes of many plant species have been sequenced, demand for functional genomics has dramatically accelerated the improvement of other omics including metabolomics. Despite a large amount of metabolites still remaining to be identified, metabolomics has contributed significantly not only to the understanding of plant physiology and biology from the view of small chemical molecules that reflect the end point of biological activities, but also in past decades to the attempts to improve plant behavior under both normal and stressed conditions. Hereby, we summarize the current knowledge on the genetic and biochemical mechanisms underlying plant growth, development, and stress responses, focusing further on the contributions of metabolomics to practical applications in crop quality improvement and food safety assessment, as well as plant metabolic engineering. We also highlight the current challenges and future perspectives in this inspiring area, with the aim to stimulate further studies leading to better crop improvement of yield and quality.

Metabolomics in rheumatic diseases: desperately seeking biomarkers

Metabolomics enables the profiling of large numbers of small molecules in cells, tissues and biological fluids. These molecules, which include amino acids, carbohydrates, lipids, nucleotides and their metabolites, can be detected quantitatively. Metabolomic methods, often focused on the information-rich analytical techniques of NMR spectroscopy and mass spectrometry, have potential for early diagnosis, monitoring therapy and defining disease pathogenesis in many therapeutic areas, including rheumatic diseases. By performing global metabolite profiling, also known as untargeted metabolomics, new discoveries linking cellular pathways to biological mechanisms are being revealed and are shaping our understanding of cell biology, physiology and medicine. These pathways can potentially be targeted to diagnose and treat patients with immune-mediated diseases.

Metabolomics in epidemiology: from metabolite concentrations to integrative reaction networks

Metabolomics is becoming feasible for population-scale studies of human disease. In this review, we survey epidemiological studies that leverage metabolomics and multi-omics to gain insight into disease mechanisms. We outline key practical, technological and analytical limitations while also highlighting recent successes in integrating these data. The use of multi-omics to infer reaction rates is discussed as a potential future direction for metabolomics research, as a means of identifying biomarkers as well as inferring causality. Furthermore, we highlight established analysis approaches as well as simulation-based methods currently used in single- and multi-cell levels in systems biology.

Genetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms

Metabolomic profiling is a powerful approach to characterize human metabolism and help understand common disease risk. Although multiple high-throughput technologies have been developed to assay the human metabolome, no technique is capable of capturing the entire human metabolism. Large-scale metabolomics data are being generated in multiple cohorts, but the datasets are typically profiled using different metabolomics platforms. Here, we compared analyses across two of the most frequently used metabolomic platforms, Biocrates and Metabolon, with the aim of assessing how complimentary metabolite profiles are across platforms. We profiled serum samples from 1,001 twins using both targeted (Biocrates, n = 160 metabolites) and non-targeted (Metabolon, n = 488 metabolites) mass spectrometry platforms. We compared metabolite distributions and performed genome-wide association analyses to identify shared genetic influences on metabolites across platforms. Comparison of 43 metabolites named for the same compound on both platforms indicated strong positive correlations, with few exceptions. Genome-wide association scans with high-throughput metabolic profiles were performed for each dataset and identified genetic variants at 7 loci associated with 16 unique metabolites on both platforms. The 16 metabolites showed consistent genetic associations and appear to be robustly measured across platforms. These included both metabolites named for the same compound across platforms as well as unique metabolites, of which 2 (nonanoylcarnitine (C9) [Biocrates]/Unknown metabolite X-13431 [Metabolon] and PC aa C28:1 [Biocrates]/1-stearoylglycerol [Metabolon]) are likely to represent the same or related biochemical entities. The results demonstrate the complementary nature of both platforms, and can be informative for future studies of comparative and integrative metabolomics analyses in samples profiled on different platforms.