Metabolic signatures of population exposure to metal mixtures: A metabolome-wide association study.

Numerous studies have explored the health impacts of individual metal exposures, yet the effects of metal mixtures on human endogenous metabolism remain largely unexplored. We aimed to assess the serum metabolic signatures of people exposed to metal mixtures. Serum and urine samples were collected from 186 workers at a steel factory in Anhui, China, in September 2019. Inductively coupled plasma mass spectrometry was used to analyze the concentrations of 23 metal elements. The serum metabolome was determined by liquid chromatography-mass spectrometry (LC-MS). A metabolome-wide association study (MWAS) was performed across the metal exposures and metabolism using quantile g-computation modeling. Pathway enrichment analysis was performed using MetaboAnalyst. We identified 226 metabolites associated with metal mixtures, primarily involving lipid metabolism (glycerophospholipids, sphingolipids), amino acid metabolism (arginine and proline, alanine, aspartate and glutamate metabolism) and caffeine metabolic pathways. Exposure to metal mixtures is mainly associated with alterations in lipid metabolism and amino acid metabolism, particularly in the glycerophospholipid and arginine and proline metabolism pathways.

MIMOSA: a resource consisting of improved methylome prediction models increases power to identify DNA methylation-phenotype associations.

Although DNA methylation (DNAm) has been implicated in the pathogenesis of numerous complex diseases, from cancer to cardiovascular disease to autoimmune disease, the exact methylation sites that play key roles in these processes remain elusive. One strategy to identify putative causal CpG sites and enhance disease etiology understanding is to conduct methylome-wide association studies (MWASs), in which predicted DNA methylation that is associated with complex diseases can be identified. However, current MWAS models are primarily trained using the data from single studies, thereby limiting the methylation prediction accuracy and the power of subsequent association studies. Here, we introduce a new resource, MWAS Imputing Methylome Obliging Summary-level mQTLs and Associated LD matrices (MIMOSA), a set of models that substantially improve the prediction accuracy of DNA methylation and subsequent MWAS power through the use of a large summary-level mQTL dataset provided by the Genetics of DNA Methylation Consortium (GoDMC). Through the analyses of GWAS (genome-wide association study) summary statistics for 28 complex traits and diseases, we demonstrate that MIMOSA considerably increases the accuracy of DNA methylation prediction in whole blood, crafts fruitful prediction models for low heritability CpG sites, and determines markedly more CpG site-phenotype associations than preceding methods. Finally, we use MIMOSA to conduct a case study on high cholesterol, pinpointing 146 putatively causal CpG sites.

Investigating causal relationships between the gut microbiota and allergic diseases: A mendelian randomization study.

Observational studies revealed altered gut microbial composition in patients with allergic diseases, which illustrated a strong association between the gut microbiome and the risk of allergies. However, whether such associations reflect causality remains to be well-documented. Two-sample mendelian randomization (2SMR) was performed to estimate the potential causal effect between the gut microbiota and the risk of allergic diseases. 3, 12, and 16 SNPs at the species, genus, and family levels respectively of 15 microbiome features were obtained as the genetic instruments of the exposure dataset from a previous study. GWAS summary data of a total of 17 independent studies related to allergic diseases were collected from the IEU GWAS database for the outcome dataset. Significant causal relationships were obtained between gut microbiome features including Ruminococcaceae, Eggerthella, Bifidobacterium, Faecalibacterium, and Bacteroides and the risk of allergic diseases. Furthermore, our results also pointed out a number of putative associations between the gut microbiome and allergic diseases. Taken together, this study was the first study using the approach of 2SMR to elucidate the association between gut microbiome and allergic diseases.

Pathway-based integration of multi-omics data reveals lipidomics alterations validated in an Alzheimer's disease mouse model and risk loci carriers.

Alzheimer's disease (AD) is a highly prevalent neurodegenerative disorder. Despite increasing evidence of the importance of metabolic dysregulation in AD, the underlying metabolic changes that may impact amyloid plaque formation are not understood, particularly for late-onset AD. This study analyzed genome-wide association studies (GWAS), transcriptomics, and proteomics data obtained from several data repositories to obtain differentially expressed (DE) multi-omics elements in mouse models of AD. We characterized the metabolic modulation in these data sets using gene ontology, transcription factor, pathway, and cell-type enrichment analyses. A predicted lipid signature was extracted from genome-scale metabolic networks (GSMN) and subsequently validated in a lipidomic data set derived from cortical tissue of ABCA-7 null mice, a mouse model of one of the genes associated with late-onset AD. Moreover, a metabolome-wide association study (MWAS) was performed to further characterize the association between dysregulated lipid metabolism in human blood serum and genes associated with AD risk. We found 203 DE transcripts, 164 DE proteins, and 58 DE GWAS-derived mouse orthologs associated with significantly enriched metabolic biological processes. Lipid and bioenergetic metabolic pathways were significantly over-represented across the AD multi-omics data sets. Microglia and astrocytes were significantly enriched in the lipid-predominant AD-metabolic transcriptome. We also extracted a predicted lipid signature that was validated and robustly modeled class separation in the ABCA7 mice cortical lipidome, with 11 of these lipid species exhibiting statistically significant modulations. MWAS revealed 298 AD single nucleotide polymorphisms-metabolite associations, of which 70% corresponded to lipid classes. These results support the importance of lipid metabolism dysregulation in AD and highlight the suitability of mapping AD multi-omics data into GSMNs to identify metabolic alterations.

Meta-Analysis and Validation of a Colorectal Cancer Risk Prediction Model Using Deep Sequenced Fecal Metagenomes.

The gut microbiome is a potential modifiable risk factor for colorectal cancer (CRC). We re-analyzed all eight previously published stool sequencing data and conducted an MWAS meta-analysis. We used cross-validated LASSO predictive models to identify a microbiome signature for predicting the risk of CRC and precancerous lesions. These models were validated in a new study, Colorectal Cancer Screening (COLSCREEN), including 156 participants that were recruited in a CRC screening context. The MWAS meta-analysis identified 95 bacterial species that were statistically significantly associated with CRC (FDR < 0.05). The LASSO CRC predictive model obtained an area under the receiver operating characteristic curve (aROC) of 0.81 (95%CI: 0.78−0.83) and the validation in the COLSCREEN dataset was 0.75 (95%CI: 0.66−0.84). This model selected a total of 32 species. The aROC of this CRC-trained model to predict precancerous lesions was 0.52 (95%CI: 0.41−0.63). We have identified a signature of 32 bacterial species that have a good predictive accuracy to identify CRC but not precancerous lesions, suggesting that the identified microbes that were enriched or depleted in CRC are merely a consequence of the tumor. Further studies should focus on CRC as well as precancerous lesions with the intent to implement a microbiome signature in CRC screening programs.

Exposure to perfluoroalkyl substances and risk of hepatocellular carcinoma in a multiethnic cohort.

Background & Aims: Exposure to poly- and perfluoroalkyl substances (PFAS), a class of persistent organic pollutants, is ubiquitous. Animal studies suggest that PFAS may increase risk of fatty liver and hepatocellular carcinoma (HCC) via impacts on hepatic lipid, amino acid, and glucose metabolism, but human data is lacking. We examined associations between PFAS exposure, altered metabolic pathways, and risk of non-viral HCC. Methods: In this nested case-control study, pre-diagnostic plasma PFAS and metabolomics were measured in 50 incident HCC cases and 50 individually matched controls from the Multiethnic Cohort (MEC) study. Cases/controls were matched by age, sex, race, and study area. PFAS exposure and risk of HCC were examined using conditional logistic regression. A metabolome-wide association study and pathway enrichment analysis was performed for PFAS exposure and HCC risk, and key metabolites/metabolic pathways were identified using a meet in the middle approach. Results: High perfluorooctane sulfonic acid (PFOS) levels (90th percentile from NHANES; >55 µg/L) were associated with 4.5-fold increased risk of HCC (odds ratio 4.5, 95% CI 1.2-16.0). Pathway enrichment analysis showed that PFOS exposure was associated with alterations in amino acid and glycan biosynthesis pathways, which were also associated with HCC risk. We identified 4 metabolites linking PFOS exposure with HCC, including glucose, butyric acid (a short-chain fatty acid), α-ketoisovaleric acid (a branched-chain α-keto acid), and 7α-hydroxy-3-oxo-4-cholestenoate (a bile acid), each of which was positively associated with PFOS exposure and risk of HCC. Conclusion: This proof-of-concept analysis shows that exposure to high PFOS levels was associated with increased risk of non-viral HCC, likely via alterations in glucose, amino acid, and bile acid metabolism. Larger studies are needed to confirm these findings. Lay summary: Per- and polyfluoroalkyl substances (PFAS), often referred to as "forever chemicals" because they are difficult to break down and stay in the human body for years, are extremely common and can cause liver damage. In a first of its kind study, we found that exposure to high levels of perfluorooctanesulfonic acid, one of the most common PFAS chemicals, was linked to increased risk of hepatocellular carcinoma in humans. Hepatocellular carcinoma is difficult to treat and is one of the most common forms of liver cancer, and these findings may provide new avenues for helping to prevent this disease.

Investigating the prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits.

As popularised by PrediXcan (and related methods), transcriptome-wide association studies (TWAS), in which gene expression is imputed from single-nucleotide polymorphism (SNP) genotypes and tested for association with a phenotype, are a popular approach for investigating the role of gene expression in complex traits. Like gene expression, DNA methylation is an important biological process and, being under genetic regulation, may be imputable from SNP genotypes. Here, we investigate prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits. We start by examining how well CpG methylation can be predicted from SNP genotypes, comparing three penalised regression approaches and examining whether changing the window size improves prediction accuracy. Although methylation at most CpG sites cannot be accurately predicted from SNP genotypes, for a subset it can be predicted well. We next apply our methylation prediction models (trained using the optimal method and window size) to carry out a methylome-wide association study (MWAS) of primary biliary cholangitis. We intersect the regions identified via MWAS with those identified via TWAS, providing insight into the interplay between CpG methylation, gene expression and disease status. We conclude that MWAS has the potential to improve understanding of biological mechanisms in complex traits.

Genetic Architecture of Untargeted Lipidomics in Cardiometabolic-Disease Patients Combines Strong Polygenic Control and Pleiotropy.

Analysis of the genetic control of small metabolites provides powerful information on the regulation of the endpoints of genome expression. We carried out untargeted liquid chromatography−high-resolution mass spectrometry in 273 individuals characterized for pathophysiological elements of the cardiometabolic syndrome. We quantified 3013 serum lipidomic features, which we used in both genome-wide association studies (GWAS), using a panel of over 2.5 M imputed single-nucleotide polymorphisms (SNPs), and metabolome-wide association studies (MWAS) with phenotypes. Genetic analyses showed that 926 SNPs at 551 genetic loci significantly (q-value < 10−8) regulate the abundance of 74 lipidomic features in the group, with evidence of monogenic control for only 22 of these. In addition to this strong polygenic control of serum lipids, our results underscore instances of pleiotropy, when a single genetic locus controls the abundance of several distinct lipid features. Using the LIPID MAPS database, we assigned putative lipids, predominantly fatty acyls and sterol lipids, to 77% of the lipidome signals mapped to the genome. We identified significant correlations between lipids and clinical and biochemical phenotypes. These results demonstrate the power of untargeted lipidomic profiling for high-density quantitative molecular phenotyping in human-genetic studies and illustrate the complex genetic control of lipid metabolism.

Genome-wide analysis of genetic predisposition to common polygenic cancers.

Lung, breast, prostate, and colorectal cancers are among the most common and fatal malignancies worldwide. They are mainly caused by multifactorial mechanisms and are genetically heterogeneous. We investigated the genetic architecture of these cancers through genome-wide association, pathway-based, and summary-based transcriptome-/methylome-wide association analyses using three independent cohorts. Our genome-wide association analyses identified the associations of 33 single-nucleotide polymorphisms (SNPs) at P < 5E - 06, of which 32 SNPs were not previously reported and did not have proxy variants within their ± 1 Mb flanking regions. Moreover, other polymorphisms mapped to their closest genes were not previously associated with the same cancers at P < 5E - 06. Our pathway enrichment analyses revealed associations of 32 pathways; mainly related to the immune system, DNA replication/transcription, and chromosomal organization; with the studied cancers. Also, 60 probes were associated with these cancers in our transcriptome-wide and methylome-wide analyses. The ± 1 Mb flanking regions of most probes had not attained P < 5E - 06 in genome-wide association studies. The genes corresponding to the significant probes can be considered as potential targets for further functional studies. Two genes (i.e., CDC14A and PMEL) demonstrated stronger evidence of associations with lung cancer as they had significant probes in both transcriptome-wide and methylome-wide association analyses. The novel cancer-associated SNPs and genes identified here would advance our understanding of the genetic heterogeneity of the common cancers.

Methylome-wide association study of different responses to risperidone in schizophrenia.

Background: Accumulating evidence shows that DNA methylation plays a role in antipsychotic response. However, the mechanisms by which DNA methylation changes are associated with antipsychotic responses remain largely unknown. Methods: We performed a methylome-wide association study (MWAS) to evaluate the association between DNA methylation and the response to risperidone in schizophrenia. Genomic DNA methylation patterns were assessed using the Agilent Human DNA Methylation Microarray. Results: We identified numerous differentially methylated positions (DMPs) and regions (DMRs) associated with antipsychotic response. CYP46A1, SPATS2, and ATP6V1E1 had the most significant DMPs, with p values of 2.50 × 10-6, 3.53 × 10-6, and 5.71 × 10-6, respectively. The top-ranked DMR was located on chromosome 7, corresponding to the PTPRN2 gene with a Sidák-corrected p-value of 9.04 × 10-13. Additionally, a significant enrichment of synaptic function and neurotransmitters was found in the differentially methylated genes after gene ontology and pathway analysis. Conclusion: The identified DMP- and DMR-overlapping genes associated with antipsychotic response are related to synaptic function and neurotransmitters. These findings may improve understanding of the mechanisms underlying antipsychotic response and guide the choice of antipsychotic in schizophrenia.

Captivity Influences the Gut Microbiome of Rhinopithecus roxellana.

Ex situ (captivity in zoos) is regarded as an important form of conservation for endangered animals. Many studies have compared differences in the gut microbiome between captive and wild animals, but few have explained those differences at the functional level due to the limited amount of 16S rRNA data. Here, we compared the gut microbiome of captive and wild Rhinopithecus roxellana, whose high degree of dietary specificity makes it a good subject to observe the effects of the captive environment on their gut microbiome, by performing a metagenome-wide association study (MWAS). The Chao1 index was significantly higher in the captive R. roxellana cohort than in the wild cohort, and the Shannon index of captive R. roxellana was higher than that of the wild cohort but the difference was not significant. A significantly increased ratio of Prevotella/Bacteroides, which revealed an increased ability to digest simple carbohydrates, was found in the captive cohort. A significant decrease in the abundance of Firmicutes and enrichment of genes related to the pentose phosphate pathway were noted in the captive cohort, indicating a decreased ability of captive monkeys to digest fiber. Additionally, genes required for glutamate biosynthesis were also significantly more abundant in the captive cohort than in the wild cohort. These changes in the gut microbiome correspond to changes in the composition of the diet in captive animals, which has more simple carbohydrates and less crude fiber and protein than the diet of the wild animals. In addition, more unique bacteria in captive R. roxellana were involved in antibiotic resistance (Acinetobacter) and diarrhea (Desulfovibrio piger), and in the prevention of diarrhea (Phascolarctobacterium succinatutens) caused by Clostridioides difficile. Accordingly, our data reveal the cause-and-effect relationships between changes in the exact dietary composition and changes in the gut microbiome on both the structural and functional levels by comparing of captive and wild R. roxellana.

DNA Methylation Changes Associated With Type 2 Diabetes and Diabetic Kidney Disease in an East Asian Population.

CONTEXT: There is a growing body of evidence that epigenetic changes including DNA methylation influence the risk of type 2 diabetes (T2D) and its microvascular complications. OBJECTIVE: We conducted a methylome-wide association study (MWAS) to identify differentially methylated sites (DMSs) of T2D and diabetic kidney disease (DKD) in a Korean population. METHODS: We performed an MWAS in 232 participants with T2D and 197 nondiabetic controls with the Illumina EPIC bead chip using peripheral blood leukocytes. The T2D group was subdivided into 87 DKD patients and 80 non-DKD controls. An additional 819 individuals from 2 population-based cohorts were used to investigate the association of identified DMSs with quantitative metabolic phenotypes. A mendelian randomization (MR) approach was applied to evaluate the causal effect of metabolic phenotypes on identified DMSs. RESULTS: We identified 8 DMSs (each at BMP8A, NBPF20, STX18, ZNF365, CPT1A, and TRIM37, and 2 at TXNIP) that were significantly associated with the risk of T2D (P < 9.0 × 10-8), including 3 that were previously known (DMSs in TXNIP and CPT1A). We also identified 3 DMSs (in COMMD1, TMOD1, and FHOD1) associated with DKD. With our limited sample size, we were not able to observe a significant overlap between DMSs of T2D and DKD. DMSs in TXNIP and CTP1A were associated with fasting glucose and glycated hemoglobin A1c. In MR analysis, fasting glucose was causally associated with DMS in CPT1A. CONCLUSION: In an East Asian population, we identified 8 DMSs, including 5 novel CpG loci, associated with T2D and 3 DMSs associated with DKD at methylome-wide statistical significance.

Cell-type-specific meQTLs extend melanoma GWAS annotation beyond eQTLs and inform melanocyte gene-regulatory mechanisms.

Although expression quantitative trait loci (eQTLs) have been powerful in identifying susceptibility genes from genome-wide association study (GWAS) findings, most trait-associated loci are not explained by eQTLs alone. Alternative QTLs, including DNA methylation QTLs (meQTLs), are emerging, but cell-type-specific meQTLs using cells of disease origin have been lacking. Here, we established an meQTL dataset by using primary melanocytes from 106 individuals and identified 1,497,502 significant cis-meQTLs. Multi-QTL colocalization with meQTLs, eQTLs, and mRNA splice-junction QTLs from the same individuals together with imputed methylome-wide and transcriptome-wide association studies identified candidate susceptibility genes at 63% of melanoma GWAS loci. Among the three molecular QTLs, meQTLs were the single largest contributor. To compare melanocyte meQTLs with those from malignant melanomas, we performed meQTL analysis on skin cutaneous melanomas from The Cancer Genome Atlas (n = 444). A substantial proportion of meQTL probes (45.9%) in primary melanocytes is preserved in melanomas, while a smaller fraction of eQTL genes is preserved (12.7%). Integration of melanocyte multi-QTLs and melanoma meQTLs identified candidate susceptibility genes at 72% of melanoma GWAS loci. Beyond GWAS annotation, meQTL-eQTL colocalization in melanocytes suggested that 841 unique genes potentially share a causal variant with a nearby methylation probe in melanocytes. Finally, melanocyte trans-meQTLs identified a hotspot for rs12203592, a cis-eQTL of a transcription factor, IRF4, with 131 candidate target CpGs. Motif enrichment and IRF4 ChIP-seq analysis demonstrated that these target CpGs are enriched in IRF4 binding sites, suggesting an IRF4-mediated regulatory network. Our study highlights the utility of cell-type-specific meQTLs.

Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox.

The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce, and overoptimistic evaluation and limited cross-study generalization are prevailing issues. To address these, we developed SIAMCAT, a versatile R toolbox for ML-based comparative metagenomics. We demonstrate its capabilities in a meta-analysis of fecal metagenomic studies (10,803 samples). When naively transferred across studies, ML models lost accuracy and disease specificity, which could however be resolved by a novel training set augmentation strategy. This reveals some biomarkers to be disease-specific, with others shared across multiple conditions. SIAMCAT is freely available from siamcat.embl.de .

Multiple-testing correction in metabolome-wide association studies.

BACKGROUND: The search for statistically significant relationships between molecular markers and outcomes is challenging when dealing with high-dimensional, noisy and collinear multivariate omics data, such as metabolomic profiles. Permutation procedures allow for the estimation of adjusted significance levels without assuming independence among metabolomic variables. Nevertheless, the complex non-normal structure of metabolic profiles and outcomes may bias the permutation results leading to overly conservative threshold estimates i.e. lower than those from a Bonferroni or Sidak correction. METHODS: Within a univariate permutation procedure we employ parametric simulation methods based on the multivariate (log-)Normal distribution to obtain adjusted significance levels which are consistent across different outcomes while effectively controlling the type I error rate. Next, we derive an alternative closed-form expression for the estimation of the number of non-redundant metabolic variates based on the spectral decomposition of their correlation matrix. The performance of the method is tested for different model parametrizations and across a wide range of correlation levels of the variates using synthetic and real data sets. RESULTS: Both the permutation-based formulation and the more practical closed form expression are found to give an effective indication of the number of independent metabolic effects exhibited by the system, while guaranteeing that the derived adjusted threshold is stable across outcome measures with diverse properties.

A metabolomic study of cervical dystonia.

INTRODUCTION: Cervical dystonia is the most common of the adult-onset focal dystonias. Most cases are idiopathic. The current view is that cervical dystonia may be caused by some combination of genetic and environmental factors. Genetic contributions have been studied extensively, but there are few studies of other factors. We conducted an exploratory metabolomics analysis of cervical dystonia to identify potentially abnormal metabolites or altered biological pathways. METHODS: Plasma samples from 100 cases with idiopathic cervical dystonia and 100 controls were compared using liquid chromatography coupled with mass spectrometry-based metabolomics. RESULTS: A total of 7346 metabolic features remained after quality control, and up to 289 demonstrated significant differences between cases and controls, depending on statistical criteria chosen. Pathway analysis revealed 9 biological processes to be significantly associated at p < 0.05, 5 pathways were related to carbohydrate metabolism, 3 pathways were related to lipid metabolism. CONCLUSION: This is the first large scale metabolomics study for any type of dystonia. The results may provide potential novel insights into the biology of cervical dystonia.

Understanding mixed environmental exposures using metabolomics via a hierarchical community network model in a cohort of California women in 1960's.

Even though the majority of population studies in environmental health focus on a single factor, environmental exposure in the real world is a mixture of many chemicals. The concept of "exposome" leads to an intellectual framework of measuring many exposures in humans, and the emerging metabolomics technology offers a means to read out both the biological activity and environmental impact in the same dataset. How to integrate exposome and metabolome in data analysis is still challenging. Here, we employ a hierarchical community network to investigate the global associations between the metabolome and mixed exposures including DDTs, PFASs and PCBs, in a women cohort with sera collected in California in the 1960s. Strikingly, this analysis revealed that the metabolite communities associated with the exposures were non-specific and shared among exposures. This suggests that a small number of metabolic phenotypes may account for the response to a large class of environmental chemicals.

MBD-seq - realities of a misunderstood method for high-quality methylome-wide association studies.

The majority of methylome-wide association studies (MWAS) have been performed using commercially available array-based technologies such as the Infinium Human Methylation 450K and the Infinium MethylationEPIC arrays (Illumina). While these arrays offer a convenient and relatively robust assessment of the probed sites they only allow interrogation of 2-4% of all CpG sites in the human genome. Methyl-binding domain sequencing (MBD-seq) is an alternative approach for MWAS that provides near-complete coverage of the methylome at similar costs as the array-based technologies. However, despite publication of multiple positive evaluations, the use of MBD-seq for MWAS is often fiercely criticized. Here we discuss key features of the method and debunk misconceptions using empirical data. We conclude that MBD-seq represents an excellent approach for large-scale MWAS and that increased utilization is likely to result in more discoveries, advance biological knowledge, and expedite the clinical translation of methylome-wide research findings.

Independent Methylome-Wide Association Studies of Schizophrenia Detect Consistent Case-Control Differences.

Methylome-wide association studies (MWASs) are promising complements to sequence variation studies. We used existing sequencing-based methylation data, which assayed the majority of all 28 million CpGs in the human genome, to perform an MWAS for schizophrenia in blood, while controlling for cell-type heterogeneity with a recently generated platform-specific reference panel. Next, we compared the MWAS results with findings from 3 existing large-scale array-based schizophrenia methylation studies in blood that assayed up to ~450 000 CpGs. Our MWAS identified 22 highly significant loci (P < 5 × 10-8) and 852 suggestively significant loci (P < 1 × 10-5). The top finding (P = 5.62 × 10-11, q = 0.001) was located in MFN2, which encodes mitofusin-2 that regulates Ca2+ transfer from the endoplasmic reticulum to mitochondria in cooperation with DISC1. The second-most significant site (P = 1.38 × 10-9, q = 0.013) was located in ALDH1A2, which encodes an enzyme for astrocyte-derived retinoic acid-a key neuronal morphogen with relevance for schizophrenia. Although the most significant MWAS findings were not assayed on the arrays, we observed significant enrichment of overlapping findings with 2 of the 3 array datasets (P = 0.0315, 0.0045, 0.1946). Overrepresentation analysis of Gene Ontology terms for the genes in the significant overlaps suggested high similarity in the biological functions detected by the different datasets. Top terms were related to immune and/or stress responses, cell adhesion and motility, and a broad range of processes essential for neurodevelopment.

Relationship between the Gut Microbiome and Energy/Nutrient Intake in a Confined Bioregenerative Life Support System.

Recent studies have suggested that the gut microbiome is modified in space analogs and that human health can be affected during actual spaceflight. However, the relationship between the gut microbiome and dietary intake in simulator subjects and astronauts remains unclear. Bioregenerative life support systems (BLSSs) are confined and self-sufficient ecosystems that enable exploration of this issue. Here, we correlate changes in gut microbes to the nutrient types present in controlled diets within subjects cohabitating in a BLSS. A metagenome-wide association study (MWAS) was performed on 55 shotgun-sequenced fecal samples longitudinally obtained from healthy Chinese subjects (n = 4 in total, n = 2 per sex) subjected to a 60-day BLSS stay and a specialized diet. Each food item was categorized based on nutrient type according to the Chinese Food Ingredients List (https://wenku.baidu.com/view/3f2b628488eb172ded630b1c59eef8c75fbf9514.html?from=search). The physical parameters of each subject fluctuated within normal medical ranges. Sex- and individual-specific differences and a trend of individual convergence of the gut microbiome in the BLSS were observed. Depletion of bacterial taxa such as Faecalibacterium prausnitzii, Bifidobacterium longum, and Escherichia coli and functional modules such as short-chain fatty acid (SCFA) production, as well as an increase in an unidentified Lachnospiraceae and glutamate/tryptophan synthesis, were observed in the BLSS. Correlation analysis showed that these compositional and functional changes were associated with energy/nutrient intake during the BLSS stay. Our findings suggest that the gut microbiota is a useful indicator for monitoring health and that individual nutritive diets should be considered according to sex and individual differences in simulations or in spaceflight.IMPORTANCE The gut microbiome shows individual specificity and is affected by sex, environment, and diet; gut microbiome imbalance is related to cancer, cardiovascular diseases, and autoimmune diseases. Astronauts are faced with a challenging environment and limited diet in outer space. Recent studies indicate that the gut microbiome is altered in space simulators and space, but what happens to intestinal microorganisms when astronauts cohabitate in a self-sufficient ecosystem in which they plant and cook food is unclear. Bioregenerative life support systems (BLSSs) are ideal devices to investigate the above issues because they are closed and self-sufficient. Four healthy Chinese subjects cohabitated in a confined BLSS for 60 days, during which their physical parameters and energy/nutrient intake were recorded. We performed a metagenome-wide association study (MWAS) on 55 shotgun-sequenced fecal samples longitudinally obtained from the subjects. Alterations occurred in the gut microbial composition and function, and their relationships with energy/nutrient intake were explored.