Analysis of alterations in serum vitamins and correlations with gut microbiome, microbial metabolomics in patients with sepsis.

BACKGROUND: Vitamins are essential micronutrients that play key roles in many biological pathways associated with sepsis. The gut microbiome plays a pivotal role in the progression of sepsis and may contribute to the onset of multi-organ dysfunction syndrome (MODS). The aim of this study was to investigate the changes in serum vitamins, and their correlation with intestinal flora and metabolomic profiles in patients with sepsis. METHODS: The serum levels of vitamins were determined by Ultra Performance Liquid Chromatography (UPLC). 16S rRNA gene sequencing and Liquid Chromatography-tandem Mass Spectrometry (LC-MS/MS) targeted metabolomics were used for microbiome and metabolome analysis. RESULTS: In the training cohort: After univariate, multivariate (OPLS-DA) and Spearman analyses, it was concluded that vitamin levels of 25 (OH) VD3 and (VD2 + VD3), as well as vitamins A and B9, differed significantly among healthy controls (HC), non-septic critical patients (NS), and sepsis patients (SS) (P < 0.05). The validation cohort confirmed the differential vitamin findings from the training cohort. Moreover, analyses of gut flora and metabolites in septic patients and healthy individuals revealed differential flora, metabolites, and metabolic pathways that were linked to alterations in serum vitamin levels. We found for the first time that vitamin B9 was negatively correlated with g_Sellimonas. CONCLUSION: Sepsis patients exhibited significantly lower levels of 25 (OH) VD3 and (VD2 + VD3), vitamins A and B9, which hold potential as predictive markers for sepsis prognosis. The changes in these vitamins may be associated with inflammatory factors, oxidative stress, and changes in gut flora.

Gastrointestinal Characteristics of Constipation from the Perspectives of Microbiome and Metabolome.

BACKGROUND: Constipation is one of the most common gastrointestinal complaints. Yet, the underlying mechanisms of constipation remain to be explored deeply. Integration of microbiome and metabolome is powerful and promising to demonstrate characteristics of constipation. AIM OF STUDY: This study aimed to characterize intestinal microbiome and metabolome of constipation. In addition, this study revealed the correlations among behaviors, intestinal microbiota, and metabolites interrupted by constipation. METHODS: Firstly, the constipation model was successfully applied. At the macro level, the ability of learning, memory, locomotor activity, and the defecation index of rats with constipation-like phenotype were characterized. At the micro-level, 16S rRNA sequencing was applied to analyze the intestinal microbiota in rats with constipation-like phenotype. 1H nuclear magnetic resonance (NMR)-based metabolomics was employed to investigate the metabolic phenotype of constipation. In addition, we constructed a correlation network, intuitively showing the correlations among behaviors, intestinal microbiota, and metabolites. RESULTS: Constipation significantly attenuated the locomotor activity, memory recognition, and frequency of defecation of rats, while increased the time of defecation. Constipation significantly changed the diversity of intestinal microbial communities, which correspondingly involved in 5 functional pathways. Besides, 28 fecal metabolites were found to be associated with constipation, among which 14 metabolites were further screened that can be used to diagnose constipation. On top of this, associated networks intuitively showed the correlations among behaviors, intestinal microbiota, and metabolites. CONCLUSIONS: The current findings are significant in terms of not only laying a foundation for understanding characteristics of constipation, but also providing accurate diagnosis and treatments of constipation clinically.

Genetic investigation into the broad health implications of caffeine: evidence from phenome-wide, proteome-wide and metabolome-wide Mendelian randomization.

BACKGROUND: Caffeine is one of the most utilized drugs in the world, yet its clinical effects are not fully understood. Circulating caffeine levels are influenced by the interplay between consumption behaviour and metabolism. This study aimed to investigate the effects of circulating caffeine levels by considering genetically predicted variation in caffeine metabolism. METHODS: Leveraging genetic variants related to caffeine metabolism that affect its circulating levels, we investigated the clinical effects of plasma caffeine in a phenome-wide association study (PheWAS). We validated novel findings using a two-sample Mendelian randomization framework and explored the potential mechanisms underlying these effects in proteome-wide and metabolome-wide Mendelian randomization. RESULTS: Higher levels of genetically predicted circulating caffeine among caffeine consumers were associated with a lower risk of obesity (odds ratio (OR) per standard deviation increase in caffeine = 0.97, 95% confidence interval (CI) CI: 0.95-0.98, p = 2.47 × 10-4), osteoarthrosis (OR = 0.97, 95% CI: 0.96-0.98, P=1.10 × 10-8) and osteoarthritis (OR: 0.97, 95% CI: 0.96 to 0.98, P = 1.09 × 10-6). Approximately one third of the protective effect of plasma caffeine on osteoarthritis risk was estimated to be mediated through lower bodyweight. Proteomic and metabolomic perturbations indicated lower chronic inflammation, improved lipid profiles, and altered protein and glycogen metabolism as potential biological mechanisms underlying these effects. CONCLUSIONS: We report novel evidence suggesting that long-term increases in circulating caffeine may reduce bodyweight and the risk of osteoarthrosis and osteoarthritis. We confirm prior genetic evidence of a protective effect of plasma caffeine on risk of overweight and obesity. Further clinical study is warranted to understand the translational relevance of these findings before clinical practice or lifestyle interventions related to caffeine consumption are introduced.

Effects of radiofrequency field from 5G communication on fecal microbiome and metabolome profiles in mice.

With the rapid development of 5G networks, the influence of the radiofrequency field (RF) generated from 5G communication equipment on human health is drawing increasing attention in public. The study aimed at assessing the effects of long-term exposure to 4.9 GHz (one of the working frequencies of 5G communication) RF field on fecal microbiome and metabolome profiles in adult male C57BL/6 mice. The animals were divided into Sham group and radiofrequency group (RF group). For RF group, the mice were whole body exposed to 4.9 GHz RF field for three weeks, 1 h/d, at average power density (PD) of 50 W/m2. After RF exposure, the mice fecal samples were collected to detect gut microorganisms and metabolites by 16S rRNA gene sequencing and LC-MS method, respectively. The results showed that intestinal microbial compositions were altered in RF group, as evidenced by reduced microbial diversity and changed microbial community distribution. Metabolomics profiling identified 258 significantly differentially abundant metabolites in RF group, 57 of which can be classified to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Besides, functional correlation analysis showed that changes in gut microbiota genera were significantly correlated with changes in fecal metabolites. In summary, the results suggested that altered gut microbiota and metabolic profile are associated with 4.9 GHz radiofrequency exposure.

Integration of transcriptome and metabolome reveals the accumulation of related metabolites and gene regulation networks during quinoa seed development.

Quinoa seeds are gluten- and cholesterol-free, contain all amino acids required by the human body, have a high protein content, provide endocrine regulation, protein supplementation, and cardiovascular protection effects. However, metabolite accumulation and transcriptional regulatory networks in quinoa seed development are not well understood. Four key stages of seed development in Dianli-3260 and Dianli-557 were thus analyzed and 849 metabolites were identified, among which sugars, amino acids, and lipids were key for developmental processes, and their accumulation showed a gradual decrease. Transcriptome analysis identified 40,345 genes, of which 20,917 were differential between the M and F phases, including 8279 and 12,638 up- and down-regulated genes, respectively. Grain development processes were mainly enriched in galactose metabolism, pentose and glucuronate interconversions, the biosynthesis of amino acids, and carbon metabolism pathways, in which raffinose, phosphoenolpyruvate, series and other metabolites are significantly enriched, gene-LOC110689372, Gene-LOC110710556 and gene-LOC110714584 are significantly expressed, and these metabolites and genes play an important role in carbohydrate metabolism, lipid and Amino acid synthesis of quinoa. This study provides a theoretical basis to expand our understanding of the molecular and metabolic development of quinoa grains.

Genome scale metabolic network modelling for metabolic profile predictions.

Metabolic profiling (metabolomics) aims at measuring small molecules (metabolites) in complex samples like blood or urine for human health studies. While biomarker-based assessment often relies on a single molecule, metabolic profiling combines several metabolites to create a more complex and more specific fingerprint of the disease. However, in contrast to genomics, there is no unique metabolomics setup able to measure the entire metabolome. This challenge leads to tedious and resource consuming preliminary studies to be able to design the right metabolomics experiment. In that context, computer assisted metabolic profiling can be of strong added value to design metabolomics studies more quickly and efficiently. We propose a constraint-based modelling approach which predicts in silico profiles of metabolites that are more likely to be differentially abundant under a given metabolic perturbation (e.g. due to a genetic disease), using flux simulation. In genome-scale metabolic networks, the fluxes of exchange reactions, also known as the flow of metabolites through their external transport reactions, can be simulated and compared between control and disease conditions in order to calculate changes in metabolite import and export. These import/export flux differences would be expected to induce changes in circulating biofluid levels of those metabolites, which can then be interpreted as potential biomarkers or metabolites of interest. In this study, we present SAMBA (SAMpling Biomarker Analysis), an approach which simulates fluxes in exchange reactions following a metabolic perturbation using random sampling, compares the simulated flux distributions between the baseline and modulated conditions, and ranks predicted differentially exchanged metabolites as potential biomarkers for the perturbation. We show that there is a good fit between simulated metabolic exchange profiles and experimental differential metabolites detected in plasma, such as patient data from the disease database OMIM, and metabolic trait-SNP associations found in mGWAS studies. These biomarker recommendations can provide insight into the underlying mechanism or metabolic pathway perturbation lying behind observed metabolite differential abundances, and suggest new metabolites as potential avenues for further experimental analyses.

Comparative Metabolome and Transcriptome Analyses Reveal the Regulatory Mechanism of Purple Leafstalk Production in Taro (Colocasia esculenta L. Schott).

Taro is a plant in the Araceae family, and its leafstalk possesses significant botanical and culinary value owing to its noteworthy medicinal and nutritional attributes. Leafstalk colour is an essential attribute that significantly influences its desirability and appeal to both breeders and consumers. However, limited information is available about the underlying mechanism responsible for the taro plant's colouration. Thus, the purpose of the current study was to elucidate the information on purple leafstalks in taro through comprehensive metabolome and transcriptome analysis. In total, 187 flavonoids, including 10 anthocyanins, were identified. Among the various compounds analysed, it was observed that the concentrations of five anthocyanins (keracyanin chloride (cyanidin 3-O-rutinoside chloride), cyanidin 3-O-glucoside, tulipanin (delphinidin 3-rutinoside chloride), idaein chloride (cyanidin 3-O-galactoside), and cyanidin chloride) were found to be higher in purple taro leafstalk compared to green taro leafstalk. Furthermore, a total of 3330 differentially expressed genes (DEGs) were identified by transcriptome analysis. Subsequently, the correlation network analysis was performed to investigate the relationship between the expression levels of these differentially expressed genes and the content of anthocyanin. There were 18 DEGs encoding nine enzymes detected as the fundamental structural genes contributing to anthocyanin biosynthesis, along with seven transcription factors (3 MYB and 4 bHLH) that may be promising candidate modulators of the anthocyanin biosynthesis process in purple taro leafstalk. The findings of the current investigation not only provide a comprehensive transcriptional code, but also give information on anthocyanin metabolites as well as beneficial insights into the colour mechanism of purple taro leafstalk.

RefMetaPlant: a reference metabolome database for plants across five major phyla.

Plants are unique with tremendous chemical diversity and metabolic complexity, which is highlighted by estimates that green plants collectively produce metabolites numbering in the millions. Plant metabolites play crucial roles in all aspects of plant biology, like growth, development, stress responses, etc. However, the lack of a reference metabolome for plants, and paucity of high-quality standard compound spectral libraries and related analytical tools, have hindered the discovery and functional study of phytochemicals in plants. Here, by leveraging an advanced LC-MS platform, we generated untargeted mass spectral data from >150 plant species collected across the five major phyla. Using a self-developed computation protocol, we constructed reference metabolome for 153 plant species. A 'Reference Metabolome Database for Plants' (RefMetaPlant) was built to encompass the reference metabolome, integrated standard compound mass spectral libraries for annotation, and related query and analytical tools like 'LC-MS/MS Query', 'RefMetaBlast' and 'CompoundLibBlast' for searches and profiling of plant metabolome and metabolite identification. Analogous to a reference genome in genomic research, RefMetaPlant provides a powerful platform to support plant genome-scale metabolite analysis to promote knowledge/data sharing and collaboration in the field of metabolomics. RefMetaPlant is freely available at https://www.biosino.org/RefMetaDB/.

PMhub 1.0: a comprehensive plant metabolome database.

The Plant Metabolome Hub (PMhub), available at https://pmhub.org.cn, is a valuable resource designed to provide scientists with comprehensive information on plant metabolites. It offers extensive details about their reference spectra, genetic foundations, chemical reactions, metabolic pathways and biological functions. The PMhub contains chemical data for 188 837 plant metabolites gathered from various sources, with 1 467 041 standard/in-silico high-resolution tandem mass-spectrometry (HRMS/MS) spectra corresponding to these metabolites. Beyond its extensive literature-derived data, PMhub also boasts a sizable collection of experimental metabolites; it contains 144 366 detected features in 10 typical plant species, with 16 423 successfully annotated by using standard/in-silico HRMS/MS data, this collection is further supplemented with thousands of features gathered from reference metabolites. For each metabolite, the PMhub enables the reconstructed of a simulated network based on structural similarities and existing metabolic pathways. Unlike previous plant-specific metabolome databases, PMhub not only contains a vast amount of metabolic data but also assembles the corresponding genomic and/or transcriptomic information, incorporating multiple methods for the comprehensive genetic analysis of metabolites. To validate the practicality, we verified a synthetic pathway for N-p-coumaroyltyramine by in vitro enzymatic activity experiments. In summary, the robust functionality provided by the PMhub will make it an indispensable tool for studying plant metabolomics.

Transcriptome and metabolome analyses of Streptococcus gordonii DL1 under acidic conditions.

OBJECTIVES: Streptococcus gordonii is associated with the formation of biofilms, especially those that comprise dental plaque. Notably, S. gordonii DL1 causes infective endocarditis (IE). Colonization of this bacterium requires a mechanism that can tolerate a drop in environmental pH by producing acid via its own sugar metabolism. The ability to survive acidic environmental conditions might allow the bacterium to establish vegetative colonization even in the endocardium due to inflammation-induced lowering of pH, increasing the risk of IE. At present, the mechanism by which S. gordonii DL1 survives under acidic conditions is not thoroughly elucidated. The present study was thus conducted to elucidate the mechanism(s) by which S. gordonii DL1 survives under acidic conditions. METHODS: We analyzed dynamic changes in gene transcription and intracellular metabolites in S. gordonii DL1 exposed to acidic conditions, using transcriptome and metabolome analyses. RESULTS: Transcriptome analysis revealed upregulation of genes involved in heat shock response and glycolysis, and down regulation of genes involved in phosphotransferase systems and biosynthesis of amino acids. The most upregulated genes were a beta-strand repeat protein of unknown function (SGO_RS06325), followed by copper-translocating P-type ATPase (SGO_RS09470) and malic enzyme (SGO_RS01850). The latter two of these contribute to cytoplasmic alkalinization. S. gordonii mutant strains lacking each of these genes showed significantly reduced survival under acidic conditions. Metabolome analysis revealed that cytoplasmic levels of several amino acids were reduced. CONCLUSIONS: S. gordonii survives the acidic conditions by recovering the acidic cytoplasm using the various activities, which are regulated at the transcriptional level.

Integrated transcriptome and metabolome revealed the drought responsive metabolic pathways in Oriental Lily (Lilium L.).

Objective: Lily is an essential ornamental flowering species worldwide. Drought stress is a major constraint affecting the morphology and physiology and lily leaves and flowers. Therefore, understanding the molecular mechanism underlying lily response to drought stress is important. Method: Transcriptome and metabolome analysis were performed on Oriental Lily subjected to drought stress. Result: Most transcription factors and metabolites yielded by the conjoint analysis displayed a downregulated expression pattern. Differential genes and metabolites mainly co-enriched in glycolic pathways related to sugars, such as galactose, and sucrose, glycolysis and gluconeogenesis, indicating that drought stress reduced the sugar metabolism level of Oriental Lily. Combined with transcriptome and metabolome data, nine pairs of differentially expressed metabolites and the genes (p < 0.05) were obtained. Interestingly, a gene named TRINITY_DN2608 (encoding a type of alpha-D-glucose) cloned and its overexpression lines in Arabidopsis thaliana was generated. Overexpression of TRINITY_DN2608 gene elevated the susceptibility to drought stress possibly by suppressing the glucose level. Conclusion: The enrichment of sugar-related pathways advocates the potential role of glucose metabolism in drought stress. Our study provides theoretical information related to the glucose-mediated drought response and would be fruitful in future lily breeding programs.

Integrative analyses of transcriptomes and metabolomes provide insight into salinity adaption in Bangia (Rhodaphyta).

The salinity of the external environment poses a serious threat to most land plants. Although seaweeds can adapt to this, intertidal species are subject to wide fluctuations in salinity, including hypo- and hyper-saline conditions. The red algal genus Bangiales is a typical example; it is one of the oldest eukaryotes with sexual reproduction and has successfully adapted to both marine and freshwater environments. However, there is a dearth of research focused on elucidating the mechanism by which marine Bangia (Bangia fuscopurpurea) adapts to hypo-salinity, as well as the mechanism by which freshwater Bangia (Bangia atropurpurea) adapts to hyper-salinity. The objective of this study is to employ third-generation full-length transcriptome data and untargeted metabolome data, to provide insights into the salinity adaptation mechanism of as well as the evolutionary relationship between both Bangia species. B. fuscopurpurea and B. atropurpurea exhibited 9112 and 8772 differentially expressed genes (DEGs), respectively, during various periods of hyper-saline condition. These genes were primarily enriched in secondary metabolites and energy-related metabolic pathways. Additionally, B. fuscopurpurea displayed 16,285 DEGs during different periods of hypo-saline condition, which were mainly enriched in metabolic pathways related to ion transport and membrane proteins. In the hyper- and hypo-saline adapt response processes of B. fuscopurpurea, a total of 303 transcription factors were identified, which belonged to 26 families. Among these, 85 and 142 differential transcription factors were identified, respectively, mainly belonging to the C2H2 and MYB family. Similarly, in the response process of B. atropurpurea to hyper-saline condition, a total of 317 transcription factors were identified, mainly belonging to 17 families. Among these, 121 differential transcription factors were identified, mainly belonging to the C2H2 and bZIP family. Furthermore, a correlation analysis was conducted to examine the relationship between the transcriptional and metabolic levels of both species under saline adaptation. The findings demonstrated that Bangia exhibits intricate adaptations to salinity, which involve swift regulation of its photosynthetic processes, alternations in membrane contents, and a robust anti-oxidation system to mitigate the effects of excess redox energy during exposure to varying salinity. Notably, the unsaturated fat and glutathione metabolic pathways were found to be significantly enriched in this context.

Integrated Analysis of Metabolome and Transcriptome Reveals Insights for Low Phosphorus Tolerance in Wheat Seedling.

Low phosphorus (LP) stress leads to a significant reduction in wheat yield, primarily in the reduction of biomass, the number of tillers and spike grains, the delay in heading and flowering, and the inhibition of starch synthesis and grouting. However, the differences in regulatory pathway responses to low phosphorus stress among different wheat genotypes are still largely unknown. In this study, metabolome and transcriptome analyses of G28 (LP-tolerant) and L143 (LP-sensitive) wheat varieties after 72 h of normal phosphorus (CK) and LP stress were performed. A total of 181 and 163 differentially accumulated metabolites (DAMs) were detected for G28CK vs. G28LP and L143CK vs. L143LP, respectively. Notably, the expression of pilocarpine (C07474) in G28CK vs. G28LP was significantly downregulated 4.77-fold, while the expression of neochlorogenic acid (C17147) in L143CK vs. L143LP was significantly upregulated 2.34-fold. A total of 4023 differentially expressed genes (DEGs) were acquired between G28 and L143, of which 1120 DEGs were considered as the core DEGs of LP tolerance of wheat after LP treatment. The integration of metabolomics and transcriptomic data further revealed that the LP tolerance of wheat was closely related to 15 metabolites and 18 key genes in the sugar and amino acid metabolism pathway. The oxidative phosphorylation pathway was enriched to four ATPases, two cytochrome c reductase genes, and fumaric acid under LP treatment. Moreover, PHT1;1, TFs (ARFA, WRKY40, MYB4, MYB85), and IAA20 genes were related to the Pi starvation stress of wheat roots. Therefore, the differences in LP tolerance of different wheat varieties were related to energy metabolism, amino acid metabolism, phytohormones, and PHT proteins, and precisely regulated by the levels of various molecular pathways to adapt to Pi starvation stress. Taken together, this study may help to reveal the complex regulatory process of wheat adaptation to Pi starvation and provide new genetic clues for further study on improving plant Pi utilization efficiency.

Integrated metabolome, full-length sequencing, and transcriptome analyses unveil the molecular mechanisms of color formation of the canary yellow and red bracts of Bougainvillea × buttiana 'Chitra'.

Bougainvillea is a typical tropical flower of great ornamental value due to its colorful bracts. The molecular mechanism behind color formation is not well-understood. Therefore, this research conducted metabolome analysis, transcriptome analysis, and multi-flux full-length sequencing in two color bracts of Bougainvillea × buttiana 'Chitra' to investigate the significantly different metabolites (SDMs) and differentially expressed genes (DEGs). Overall, 261 SDMs, including 62 flavonoids and 26 alkaloids, were detected, and flavonols and betalains were significantly differentially accumulated among the two bracts. Furthermore, the complete-length transcriptome of Bougainvillea × buttiana was also developed, which contained 512 493 non-redundant isoforms. Among them, 341 210 (66.58%) displayed multiple annotations in the KOG, GO, NR, KEGG, Pfam, Swissprot, and NT databases. RNA-seq findings revealed that 3610 DEGs were identified between two bracts. Co-expression analysis demonstrated that the DEGs and SDMs involved in flavonol metabolism (such as CHS, CHI, F3H, FLS, CYP75B1, kaempferol, and quercetin) and betacyanin metabolism (DODA, betanidin, and betacyanins) were the main contributors for the canary yellow and red bract formation, respectively. Further investigation revealed that several putative transcription factors (TFs) might interact with the promoters of the genes mentioned above. The expression profiles of the putative TFs displayed that they may positively and negatively regulate the structural genes' expression profiles. The data revealed a potential regulatory network between important genes, putative TFs, and metabolites in the flavonol and betacyanin biosynthesis of Bougainvillea × buttiana 'Chitra' bracts. These findings will serve as a rich genetic resource for future studies that could create new color bracts.

Circulating metabolites modulated by diet are associated with depression.

Metabolome reflects the interplay of genome and exposome at molecular level and thus can provide deep insights into the pathogenesis of a complex disease like major depression. To identify metabolites associated with depression we performed a metabolome-wide association analysis in 13,596 participants from five European population-based cohorts characterized for depression, and circulating metabolites using ultra high-performance liquid chromatography/tandem accurate mass spectrometry (UHPLC/MS/MS) based Metabolon platform. We tested 806 metabolites covering a wide range of biochemical processes including those involved in lipid, amino-acid, energy, carbohydrate, xenobiotic and vitamin metabolism for their association with depression. In a conservative model adjusting for life style factors and cardiovascular and antidepressant medication use we identified 8 metabolites, including 6 novel, significantly associated with depression. In individuals with depression, increased levels of retinol (vitamin A), 1-palmitoyl-2-palmitoleoyl-GPC (16:0/16:1) (lecithin) and mannitol/sorbitol and lower levels of hippurate, 4-hydroxycoumarin, 2-aminooctanoate (alpha-aminocaprylic acid), 10-undecenoate (11:1n1) (undecylenic acid), 1-linoleoyl-GPA (18:2) (lysophosphatidic acid; LPA 18:2) are observed. These metabolites are either directly food derived or are products of host and gut microbial metabolism of food-derived products. Our Mendelian randomization analysis suggests that low hippurate levels may be in the causal pathway leading towards depression. Our findings highlight putative actionable targets for depression prevention that are easily modifiable through diet interventions.

Statistical methods and resources for biomarker discovery using metabolomics.

Metabolomics is a dynamic tool for elucidating biochemical changes in human health and disease. Metabolic profiles provide a close insight into physiological states and are highly volatile to genetic and environmental perturbations. Variation in metabolic profiles can inform mechanisms of pathology, providing potential biomarkers for diagnosis and assessment of the risk of contracting a disease. With the advancement of high-throughput technologies, large-scale metabolomics data sources have become abundant. As such, careful statistical analysis of intricate metabolomics data is essential for deriving relevant and robust results that can be deployed in real-life clinical settings. Multiple tools have been developed for both data analysis and interpretations. In this review, we survey statistical approaches and corresponding statistical tools that are available for discovery of biomarkers using metabolomics.

Integrating genetics and metabolomics from multi-ethnic and multi-fluid data reveals putative mechanisms for age-related macular degeneration.

Age-related macular degeneration (AMD) is a leading cause of blindness in older adults. Investigating shared genetic components between metabolites and AMD can enhance our understanding of its pathogenesis. We conduct metabolite genome-wide association studies (mGWASs) using multi-ethnic genetic and metabolomic data from up to 28,000 participants. With bidirectional Mendelian randomization analysis involving 16,144 advanced AMD cases and 17,832 controls, we identify 108 putatively causal relationships between plasma metabolites and advanced AMD. These metabolites are enriched in glycerophospholipid metabolism, lysophospholipid, triradylcglycerol, and long chain polyunsaturated fatty acid pathways. Bayesian genetic colocalization analysis and a customized metabolome-wide association approach prioritize putative causal AMD-associated metabolites. We find limited evidence linking urine metabolites to AMD risk. Our study emphasizes the contribution of plasma metabolites, particularly lipid-related pathways and genes, to AMD risk and uncovers numerous putative causal associations between metabolites and AMD risk.

Dynamic genome-based metabolic modeling of the predominant cellulolytic rumen bacterium Fibrobacter succinogenes S85.

Fibrobacter succinogenes is a cellulolytic bacterium that plays an essential role in the degradation of plant fibers in the rumen ecosystem. It converts cellulose polymers into intracellular glycogen and the fermentation metabolites succinate, acetate, and formate. We developed dynamic models of F. succinogenes S85 metabolism on glucose, cellobiose, and cellulose on the basis of a network reconstruction done with the automatic reconstruction of metabolic model workspace. The reconstruction was based on genome annotation, five template-based orthology methods, gap filling, and manual curation. The metabolic network of F. succinogenes S85 comprises 1,565 reactions with 77% linked to 1,317 genes, 1,586 unique metabolites, and 931 pathways. The network was reduced using the NetRed algorithm and analyzed for the computation of elementary flux modes. A yield analysis was further performed to select a minimal set of macroscopic reactions for each substrate. The accuracy of the models was acceptable in simulating F. succinogenes carbohydrate metabolism with an average coefficient of variation of the root mean squared error of 19%. The resulting models are useful resources for investigating the metabolic capabilities of F. succinogenes S85, including the dynamics of metabolite production. Such an approach is a key step toward the integration of omics microbial information into predictive models of rumen metabolism. IMPORTANCE F. succinogenes S85 is a cellulose-degrading and succinate-producing bacterium. Such functions are central for the rumen ecosystem and are of special interest for several industrial applications. This work illustrates how information of the genome of F. succinogenes can be translated to develop predictive dynamic models of rumen fermentation processes. We expect this approach can be applied to other rumen microbes for producing a model of rumen microbiome that can be used for studying microbial manipulation strategies aimed at enhancing feed utilization and mitigating enteric emissions.

A metabolome-wide Mendelian randomization study prioritizes potential causal circulating metabolites for multiple sclerosis.

To prioritize circulating metabolites that likely play causal roles in the pathogenesis of multiple sclerosis (MS). Two-sample Mendelian randomization analysis was performed to estimate the causal effects of 571 circulating metabolites on the risk of MS. Genetic instruments for circulating metabolites were obtained from three previous genome-wide association studies (GWAS) of the blood metabolome (N = 7824; 24,925; and 115,078; respectively), while genetic associations with MS were from a large GWAS by the International Multiple Sclerosis Genetics Consortium (14,802 cases and 26,703 control). The primary analysis was performed with the multiplicative random-effect inverse variance-weighted method, while multiple sensitivity analyses were conducted with the weighted median, weighted mode, MR-Egger, and MR-PRESSO. A total of 29 metabolites had suggestive evidence of causal associations with MS. Genetically instrumented levels of serine (OR = 1.56, 95% CI = 1.25-1.95), lysine (OR = 1.18, 95% CI = 1.01-1.38), acetone (OR = 2.45, 95% CI = 1.02-5.90), and acetoacetate (OR = 2.47, 95% CI = 1.14-5.34) were associated with a higher MS risk. Total cholesterol and phospholipids in large very-low-density lipoprotein were associated with a lower MS risk (OR = 0.83, 95% CI = 0.69-1.00; OR = 0.80, 95% CI = 0.68-0.95), but risk-increasing associations (OR = 1.20, 95% CI = 1.04-1.40; OR = 1.13, 95% CI = 1.00-1.28) were observed for the same two lipids in very large high-density lipoprotein. Our metabolome-wide Mendelian randomization study prioritized a list of circulating metabolites, such as serine, lysine, acetone, acetoacetate, and lipids, that likely have causal associations with MS.

Whole-Genome Sequencing Analysis of Human Metabolome in Multi-Ethnic Populations.

Circulating metabolite levels may reflect the state of the human organism in health and disease, however, the genetic architecture of metabolites is not fully understood. We have performed a whole-genome sequencing association analysis of both common and rare variants in up to 11,840 multi-ethnic participants from five studies with up to 1666 circulating metabolites. We have discovered 1985 novel variant-metabolite associations, and validated 761 locus-metabolite associations reported previously. Seventy-nine novel variant-metabolite associations have been replicated, including three genetic loci located on the X chromosome that have demonstrated its involvement in metabolic regulation. Gene-based analysis have provided further support for seven metabolite-replicated loci pairs and their biologically plausible genes. Among those novel replicated variant-metabolite pairs, follow-up analyses have revealed that 26 metabolites have colocalized with 21 tissues, seven metabolite-disease outcome associations have been putatively causal, and 7 metabolites might be regulated by plasma protein levels. Our results have depicted the genetic contribution to circulating metabolite levels, providing additional insights into understanding human disease.