Identification of critical genetic variants associated with metabolic phenotypes of the Japanese population

6Pgdh polymorphism in wild bulb mite populations: prevalence, environmental correlates and life history trade-offs

Genetic polymorphism in key metabolic genes plays a pivotal role in shaping phenotypes and adapting to varying environments. Polymorphism in the metabolic gene 6-phosphogluconate dehydrogenase (6Pgdh) in bulb mites, Rhizoglyphus robini is characterized by two alleles, S and F, that differ by a single amino acid substitution and correlate with male reproductive fitness. The S-bearing males demonstrate a reproductive advantage. Although the S allele rapidly fixes in laboratory settings, the persistence of polymorphic populations in the wild is noteworthy. This study examines the prevalence and stability of 6Pgdh polymorphism in natural populations across Poland, investigating potential environmental influences and seasonal variations. We found widespread 6Pgdh polymorphism in natural populations, with allele frequencies varying across locations and sampling dates but without clear geographical or seasonal clines. This widespread polymorphism and spatio-temporal variability may be attributed to population demography and gene flow between local populations. We found some correlation between soil properties, particularly cation content (Na, K, Ca, and Mg) and 6Pgdh allele frequencies, showcasing the connection between mite physiology and soil characteristics and highlighting the presence of environment-dependent balancing selection. We conducted experimental fitness assays to determine whether the allele providing the advantage in male-male competition has antagonistic effects on life-history traits and if these effects are temperature-dependent. We found that temperature does not differentially influence development time or juvenile survival in different 6Pgdh genotypes. This study reveals the relationship between genetic variation, environmental factors, and reproductive fitness in natural bulb mite populations, shedding light on the dynamic mechanisms governing 6Pgdh polymorphism.

Genetic risk score for insulin resistance based on gene variants associated to amino acid metabolism in young adults

Circulating concentration of arginine, alanine, aspartate, isoleucine, leucine, phenylalanine, proline, tyrosine, taurine and valine are increased in subjects with insulin resistance, which could in part be attributed to the presence of single nucleotide polymorphisms (SNPs) within genes associated with amino acid metabolism. Thus, the aim of this work was to develop a Genetic Risk Score (GRS) for insulin resistance in young adults based on SNPs present in genes related to amino acid metabolism. We performed a cross-sectional study that included 452 subjects over 18 years of age. Anthropometric, clinical, and biochemical parameters were assessed including measurement of serum amino acids by high performance liquid chromatography. Eighteen SNPs were genotyped by allelic discrimination. Of these, ten were found to be in Hardy-Weinberg equilibrium, and only four were used to construct the GRS through multiple linear regression modeling. The GRS was calculated using the number of risk alleles of the SNPs in HGD, PRODH, DLD and SLC7A9 genes. Subjects with high GRS (≥ 0.836) had higher levels of glucose, insulin, homeostatic model assessment- insulin resistance (HOMA-IR), total cholesterol and triglycerides, and lower levels of arginine than subjects with low GRS (p < 0.05). The application of a GRS based on variants within genes associated to amino acid metabolism may be useful for the early identification of subjects at increased risk of insulin resistance.

Identification of predictive biomarkers for endometrial cancer diagnosis and treatment response monitoring using plasma metabolome profiling

BACKGROUND

Endometrial cancer (EMC) is the most common female genital tract malignancy with an increasing prevalence in many countries including Japan, a fact that renders early detection and treatment necessary to protect health and fertility. Although early detection and treatment are necessary to further improve the prognosis of women with endometrial cancer, biomarkers that accurately reflect the pathophysiology of EMC patients are still unclear. Therefore, it is clinically critical to identify biomarkers to assess diagnosis and treatment efficacy to facilitate appropriate treatment and development of new therapies for EMC.

METHODS

In this study, wide-targeted plasma metabolome analysis was performed to identify biomarkers for EMC diagnosis and the prediction of treatment responses. The absolute quantification of 628 metabolites in plasma samples from 142 patients with EMC was performed using ultra-high-performance liquid chromatography with tandem mass spectrometry.

RESULTS

The concentrations of 111 metabolites increased significantly, while the concentrations of 148 metabolites decreased significantly in patients with EMC compared to healthy controls. Specifically, LysoPC and TGs, including unsaturated fatty acids, were reduced in patients with stage IA EMC compared to healthy controls, indicating that these metabolic profiles could be used as early diagnostic markers of EMC. In contrast, blood levels of amino acids such as histidine and tryptophan decreased as the risk of recurrence increased and the stages of EMC advanced. Furthermore, a marked increase in total TG and a decrease in specific TGs and free fatty acids including polyunsaturated fatty acids levels were observed in patients with EMC. These results suggest that the polyunsaturated fatty acids in patients with EMC are crucial for disease progression.

CONCLUSIONS

Our data identified specific metabolite profiles that reflect the pathogenesis of EMC and showed that these metabolites correlate with the risk of recurrence and disease stage. Analysis of changes in plasma metabolite profiles could be applied for the early diagnosis and monitoring of the course of treatment of EMC patients.

Quantitative trait loci mapping of circulating metabolites in cerebrospinal fluid to uncover biological mechanisms involved in brain-related phenotypes

Genomic studies of molecular traits have provided mechanistic insights into complex disease, though these lag behind for brain-related traits due to the inaccessibility of brain tissue. We leveraged cerebrospinal fluid (CSF) to study neurobiological mechanisms in vivo , measuring 5,543 CSF metabolites, the largest panel in CSF to date, in 977 individuals of European ancestry. Individuals originated from two separate cohorts including cognitively healthy subjects (n=490) and a well-characterized memory clinic sample, the Amsterdam Dementia Cohort (ADC, n=487). We performed metabolite quantitative trait loci (mQTL) mapping on CSF metabolomics and found 126 significant mQTLs, representing 65 unique CSF metabolites across 51 independent loci. To better understand the role of CSF mQTLs in brain-related disorders, we performed a metabolome-wide association study (MWAS), identifying 40 associations between CSF metabolites and brain traits. Similarly, over 90% of significant mQTLs demonstrated colocalized associations with brain-specific gene expression, unveiling potential neurobiological pathways.

Genetic influences on human blood metabolites in the Japanese population

An increase in ethnic diversity in genetic studies has the potential to provide unprecedented insights into how genetic variations influence human phenotypes. In this study, we conducted a quantitative trait locus (QTL) analysis of 121 metabolites measured using gas chromatography-mass spectrometry with plasma samples from 4,888 Japanese individuals. We found 60 metabolite-gene associations, of which 13 have not been previously reported. Meta-analyses with another Japanese and a European study identified six and two additional unreported loci, respectively. Genetic variants influencing metabolite levels were more enriched in protein-coding regions than in the regulatory regions while being associated with the risk of various diseases. Finally, we identified a signature of strong negative selection for uric acid ( S ˆ  = -1.53, p = 6.2 × 10^-18). Our study expanded the knowledge of genetic influences on human blood metabolites, providing valuable insights into their physiological, pathological, and selective properties.

Indolepropionic Acid, a Gut Bacteria-Produced Tryptophan Metabolite and the Risk of Type 2 Diabetes and Non-Alcoholic Fatty Liver Disease

An intricate relationship between gut microbiota, diet, and the human body has recently been extensively investigated. Gut microbiota and gut-derived metabolites, especially, tryptophan derivatives, modulate metabolic and immune functions in health and disease. One of the tryptophan derivatives, indolepropionic acid (IPA), is increasingly being studied as a marker for the onset and development of metabolic disorders, including type 2 diabetes (T2D) and non-alcoholic fatty liver disease (NAFLD). The IPA levels heavily depend on the diet, particularly dietary fiber, and show huge variations among individuals. We suggest that these variations could partially be explained using genetic variants known to be associated with specific diseases such as T2D. In this narrative review, we elaborate on the beneficial effects of IPA in the mitigation of T2D and NAFLD, and further study the putative interactions between IPA and well-known genetic variants (TCF7L2, FTO, and PPARG), known to be associated with the risk of T2D. We have investigated the long-term preventive value of IPA in the development of T2D in the Finnish prediabetic population and the correlation of IPA with phytosterols in obese individuals from an ongoing Kuopio obesity surgery study. The diversity in IPA-linked mechanisms affecting glucose metabolism and liver fibrosis makes it a unique small metabolite and a promising candidate for the reversal or management of metabolic disorders, mainly T2D and NAFLD.

Gut microbiota accelerates obesity in peri-/post-menopausal women via Bacteroides fragilis and acetic acid

OBJECTIVE

Many animal experiments and epidemiological studies have shown that the gut microbiota (GM) plays an important role in the development of obesity, but the specific biological mechanism involved in the pathogenesis of disease remain unknown. We aimed to examine the relationships and functional mechanisms of GM on obesity in peri- and post-menopausal women.

METHODS

We recruited 499 Chinese peri- and post-menopausal women and performed comprehensive analyses of the gut microbiome, targeted metabolomics for short-chain fatty acids in serum, and host whole-genome sequencing by various association analysis methods.

RESULTS

Through constrained linear regression analysis, we found that an elevated abundance of Bacteroides fragilis (B. fragilis) was associated with obesity. We also found that serum levels of acetic acid were negatively associated with obesity, and that B. fragilis was negatively associated with serum acetic acid levels by partial Spearman correlation analysis. Mendelian randomization analysis indicated that B. fragilis increases the risk of obesity and may causally down-regulate acetic acid levels.

CONCLUSIONS

We found the gut with B. fragilis may accelerate obesity, in part, by suppressing acetic acid levels. Therefore, B. fragilis and acetic acid may represent important therapeutic targets for obesity intervention in peri- and post-menopausal women.

Transcriptional changes in orthotopic liver transplantation and ischemia/reperfusion injury

Background There are few effective targeting strategies to reduce liver ischemia-reperfusion injury (IRI), which is one of the reasons for the poor prognosis of liver transplant recipients. Methods A systematic approach combining gene expression with protein interaction (PPI) network was used to screen the characteristic genes and related biological functions of post-transplant. Differentially expressed genes (DEGs) between IRI+ and IRI- were identified. Logistic regression model and receiver operating characteristic (ROC) curve were used to identify potential target genes of IRI. The expression of key genes was verified by qRT-PCR and Western-blot experiments. Finally, the ssGSEA was used to identify the immune cell infiltration in patients with IRI. Results The 283 common DEGs in GSE87487 and GSE151648 were mainly related to apoptosis and IL-17 signaling pathway. Through PPI network and logistic regression analysis, we identified that IL6, CCL2 and CXCL8 may be involved in the ischemia/reperfusion (IR) process. In addition, 32 genes were showed associated with IRI through inflammatory and metabolic pathways. Among the key genes identified, the differential expression of AGBL4, CILP2 and IL4I1 was verified by molecular experiments. Th17 cells of differentially infiltrated immune cells were positively correlated with CILP2 and IL4I1. The difference of Th17 cells between IRI+ and IRI- was verified by flow cytometry. Conclusion The study showed that AGBL4, CILP2 and IL4I1 were associated with IRI. Th17 cells may be associated with the regulation of IRI by key genes. These genes and related pathways may be targets for improving IRI.

Comparison of Kit-Based Metabolomics with Other Methodologies in a Large Cohort, towards Establishing Reference Values

Metabolic profiling is an omics approach that can be used to observe phenotypic changes, making it particularly attractive for biomarker discovery. Although several candidate metabolites biomarkers for disease expression have been identified in recent clinical studies, the reference values of healthy subjects have not been established. In particular, the accuracy of concentrations measured by mass spectrometry (MS) is unclear. Therefore, comprehensive metabolic profiling in large-scale cohorts by MS to create a database with reference ranges is essential for evaluating the quality of the discovered biomarkers. In this study, we tested 8700 plasma samples by commercial kit-based metabolomics and separated them into two groups of 6159 and 2541 analyses based on the different ultra-high-performance tandem mass spectrometry (UHPLC-MS/MS) systems. We evaluated the quality of the quantified values of the detected metabolites from the reference materials in the group of 2541 compared with the quantified values from other platforms, such as nuclear magnetic resonance (NMR), supercritical fluid chromatography tandem mass spectrometry (SFC-MS/MS) and UHPLC-Fourier transform mass spectrometry (FTMS). The values of the amino acids were highly correlated with the NMR results, and lipid species such as phosphatidylcholines and ceramides showed good correlation, while the values of triglycerides and cholesterol esters correlated less to the lipidomics analyses performed using SFC-MS/MS and UHPLC-FTMS. The evaluation of the quantified values by MS-based techniques is essential for metabolic profiling in a large-scale cohort.

Quality Assessment of Untargeted Analytical Data in a Large-Scale Metabolomic Study

Large-scale metabolomic studies have become common, and the reliability of the peak data produced by the various instruments is an important issue. However, less attention has been paid to the large number of uncharacterized peaks in untargeted metabolomics data. In this study, we tested various criteria to assess the reliability of 276 and 202 uncharacterized peaks that were detected in a gathered set of 30 plasma and urine quality control samples, respectively, using capillary electrophoresis-time-of-flight mass spectrometry (CE-TOFMS). The linear relationship between the amounts of pooled samples and the corresponding peak areas was one of the criteria used to select reliable peaks. We used samples from approximately 3000 participants in the Tsuruoka Metabolome Cohort Study to investigate patterns of the areas of these uncharacterized peaks among the samples and clustered the peaks by combining the patterns and differences in the migration times. Our assessment pipeline removed substantial numbers of unreliable or redundant peaks and detected 35 and 74 reliable uncharacterized peaks in plasma and urine, respectively, some of which may correspond to metabolites involved in important physiological processes such as disease progression. We propose that our assessment pipeline can be used to help establish large-scale untargeted clinical metabolomic studies.

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.