The RNA helicase Mtr4p modulates polyadenylation in the TRAMP complex.

Many steps in nuclear RNA processing, surveillance, and degradation require TRAMP, a complex containing the poly(A) polymerase Trf4p, the Zn-knuckle protein Air2p, and the RNA helicase Mtr4p. TRAMP polyadenylates RNAs designated for decay or trimming by the nuclear exosome. It has been unclear how polyadenylation by TRAMP differs from polyadenylation by conventional poly(A) polymerase, which produces poly(A) tails that stabilize RNAs. Using reconstituted S. cerevisiae TRAMP, we show that TRAMP inherently suppresses poly(A) addition after only 3-4 adenosines. This poly(A) tail length restriction is controlled by Mtr4p. The helicase detects the number of 3'-terminal adenosines and, over several adenylation steps, elicits precisely tuned adjustments of ATP affinities and rate constants for adenylation and TRAMP dissociation. Our data establish Mtr4p as a critical regulator of polyadenylation by TRAMP and reveal that an RNA helicase can control the activity of another enzyme in a highly complex fashion and in response to features in RNA.

Critical Assessment of Metagenome Interpretation: the second round of challenges.

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to assess methods on realistic and complex datasets with long- and short-read sequences, created computationally from around 1,700 new and known genomes, as well as 600 new plasmids and viruses. Here we analyze 5,002 results by 76 program versions. Substantial improvements were seen in assembly, some due to long-read data. Related strains still were challenging for assembly and genome recovery through binning, as was assembly quality for the latter. Profilers markedly matured, with taxon profilers and binners excelling at higher bacterial ranks, but underperforming for viruses and Archaea. Clinical pathogen detection results revealed a need to improve reproducibility. Runtime and memory usage analyses identified efficient programs, including top performers with other metrics. The results identify challenges and guide researchers in selecting methods for analyses.

Division of Labor in an Oligomer of the DEAD-Box RNA Helicase Ded1p.

Most aspects of RNA metabolism involve DEAD-box RNA helicases, enzymes that bind and remodel RNA and RNA-protein complexes in an ATP-dependent manner. Here we show that the DEAD-box helicase Ded1p oligomerizes in the cell and in vitro, and unwinds RNA as a trimer. Two protomers bind the single-stranded region of RNA substrates and load a third protomer to the duplex, which then separates the strands. ATP utilization differs between the strand-separating protomer and those bound to the single-stranded region. Binding of the eukaryotic initiation factor 4G to Ded1p interferes with oligomerization and thereby modulates unwinding activity and RNA affinity of the helicase. Our data reveal a strict division of labor between the Ded1p protomers in the oligomer. This mode of oligomerization fundamentally differs from other helicases. Oligomerization represents a previously unappreciated level of regulation for DEAD-box helicase activities.

The Baseline Gut Microbiota Directs Dieting-Induced Weight Loss Trajectories.

BACKGROUND & AIMS: Elucidating key factors affecting personal responses to food is the first step toward implementing personalized nutrition strategies in for example weight loss programs. Here, we aimed to identify factors of importance for individual weight loss trajectories in a natural setting where participants were provided dietary advice but otherwise asked to self-manage the daily caloric intake and data reporting. METHODS: A 6-month weight-reduction program with longitudinal collection of dietary, physical activity, body weight, and fecal microbiome data as well as single-nucleotide polymorphism genotypes in 83 participants was conducted, followed by integration of the high-dimensional data to define the most determining factors for weight loss in a dietician-guided, smartphone-assisted dieting program. RESULTS: The baseline gut microbiota was found to outperform other factors as a predieting predictor of individual weight loss trajectories. Weight loss was also linked to the magnitude of changes in abundances of certain bacterial species during dieting. Ruminococcus gnavus (MGS0160) was significantly enriched in obese individuals and decreased during weight loss. Akkermansia muciniphila (MGS0120) and Alistipes obesi (MGS0342) were significantly enriched in lean individuals, and their abundance increased during dieting. Finally, Blautia wexlerae (MGS0575) and Bacteroides dorei (MGS0187) were the strongest predictors for weight loss when present in high abundance at baseline. CONCLUSION: Altogether, the baseline gut microbiota was found to excel as a central personal factor in capturing the relationship between dietary factors and weight loss among individuals on a dieting program.

Gut Microbiota Perturbation in IgA Deficiency Is Influenced by IgA-Autoantibody Status.

BACKGROUND & AIMS: IgA exerts its primary function at mucosal surfaces, where it binds microbial antigens to regulate bacterial growth and epithelial attachment. One third of individuals with IgA deficiency (IgAD) suffers from recurrent mucosal infections, possibly related to an altered microbiota. We aimed to delineate the impact of IgAD and the IgA-autoantibody status on the composition and functional capacity of the gut microbiota. METHODS: We performed a paired, lifestyle-balanced analysis of the effect of IgA on the gut microbiota composition and functionality based on fecal samples from individuals with IgAD and IgA-sufficient household members (n = 100), involving quantitative shotgun metagenomics, species-centric functional annotation of gut bacteria, and strain-level analyses. We supplemented the data set with 32 individuals with IgAD and examined the influence of IgA-autoantibody status on the composition and functionality of the gut microbiota. RESULTS: The gut microbiota of individuals with IgAD exhibited decreased richness and diversity and was enriched for bacterial species encoding pathogen-related functions including multidrug and antimicrobial peptide resistance, virulence factors, and type III and VI secretion systems. These functional changes were largely attributed to Escherichia coli but were independent of E coli strain variations and most prominent in individuals with IgAD with IgA-specific autoreactive antibodies. CONCLUSIONS: The microbiota of individuals with IgAD is enriched for species holding increased proinflammatory potential, thereby potentially decreasing the resistance to gut barrier-perturbing events. This phenotype is especially pronounced in individuals with IgAD with IgA-specific autoreactive antibodies, thus warranting a screening for IgA-specific autoreactive antibodies in IgAD to identify patients with IgAD with increased risk for gastrointestinal implications.

New genetic variants associated with major adverse cardiovascular events in patients with acute coronary syndromes and treated with clopidogrel and aspirin.

Although a few studies have reported the effects of several polymorphisms on major adverse cardiovascular events (MACE) in patients with acute coronary syndromes (ACS) and those undergoing percutaneous coronary intervention (PCI), these genotypes account for only a small fraction of the variation and evidence is insufficient. This study aims to identify new genetic variants associated with MACE end point during the 18-month follow-up period by a two-stage large-scale sequencing data, including high-depth whole exome sequencing of 168 patients in the discovery cohort and high-depth targeted sequencing of 1793 patients in the replication cohort. We discovered eight new genotypes and their genes associated with MACE in patients with ACS, including MYOM2 (rs17064642), WDR24 (rs11640115), NECAB1 (rs74569896), EFR3A (rs4736529), AGAP3 (rs75750968), ZDHHC3 (rs3749187), ECHS1 (rs140410716), and KRTAP10-4 (rs201441480). Notably, the expressions of MYOM2 and ECHS1 are downregulated in both animal models and patients with phenotypes related to MACE. Importantly, we developed the first superior classifier for predicting 18-month MACE and achieved high predictive performance (AUC ranged between 0.92 and 0.94 for three machine-learning methods). Our findings shed light on the pathogenesis of cardiovascular outcomes and may help the clinician to make a decision on the therapeutic intervention for ACS patients.

Transplantation of microbiota from drug-free patients with schizophrenia causes schizophrenia-like abnormal behaviors and dysregulated kynurenine metabolism in mice.

Accumulating evidence suggests that gut microbiota plays a role in the pathogenesis of schizophrenia via the microbiota-gut-brain axis. This study sought to investigate whether transplantation of fecal microbiota from drug-free patients with schizophrenia into specific pathogen-free mice could cause schizophrenia-like behavioral abnormalities. The results revealed that transplantation of fecal microbiota from schizophrenic patients into antibiotic-treated mice caused behavioral abnormalities such as psychomotor hyperactivity, impaired learning and memory in the recipient animals. These mice also showed elevation of the kynurenine-kynurenic acid pathway of tryptophan degradation in both periphery and brain, as well as increased basal extracellular dopamine in prefrontal cortex and 5-hydroxytryptamine in hippocampus, compared with their counterparts receiving feces from healthy controls. Furthermore, colonic luminal filtrates from the mice transplanted with patients' fecal microbiota increased both kynurenic acid synthesis and kynurenine aminotransferase II activity in cultured hepatocytes and forebrain cortical slices. Sixty species of donor-derived bacteria showed significant difference between the mice colonized with the patients' and the controls' fecal microbiota, highlighting 78 differentially enriched functional modules including tryptophan biosynthesis function. In conclusion, our study suggests that the abnormalities in the composition of gut microbiota contribute to the pathogenesis of schizophrenia partially through the manipulation of tryptophan-kynurenine metabolism.

Metagenomic profiling of the pro-inflammatory gut microbiota in ankylosing spondylitis.

OBJECTIVE: Gut dysbiosis has been reported implicated in ankylosing spondylitis (AS), a common chronic inflammatory disease mainly affects sacroiliac joints and spine. Utilizing deep sequencing on the feces of untreated AS patients, our study aimed at providing an in-depth understanding of AS gut microbiota. METHODS: We analyzed the fecal metagenome of 85 untreated AS patients and 62 healthy controls by metagenomic shotgun sequencing, and 23 post-treatment feces of those AS patients were collected for comparison. Comparative analyses among different cohorts including AS, rheumatoid arthritis and Behcet's disease were performed to uncover some common signatures related to inflammatory arthritis. Molecular mimicry of a microbial peptide was also demonstrated by ELISpot assay. RESULTS: We identified AS-enriched species including Bacteroides coprophilus, Parabacteroides distasonis, Eubacterium siraeum, Acidaminococcus fermentans and Prevotella copri. Pathway analysis revealed increased oxidative phosphorylation, lipopolysaccharide biosynthesis and glycosaminoglycan degradation in AS gut microbiota. Microbial signatures of AS gut selected by random forest model showed high distinguishing accuracy. Some common signatures related to autoimmunity, such as Bacteroides fragilis and type III secretion system (T3SS), were also found. Finally, in vitro experiments demonstrated an increased amount of IFN-γ producing cells triggered by a bacterial peptide of AS-enriched species, mimicking type II collagen. CONCLUSIONS: These findings collectively indicate that gut microbiota was perturbed in untreated AS patients with diagnostic potential, and some AS-enriched species might be triggers of autoimmunity by molecular mimicry. Additionally, different inflammatory arthritis shared some common microbial signatures.

Sequencing of the MHC region defines HLA-DQA1 as the major genetic risk for seropositive rheumatoid arthritis in Han Chinese population.

OBJECTIVE: The strong genetic contribution of the major histocompatibility complex (MHC) region to rheumatoid arthritis (RA) has been generally attributed to human leukocyte antigen (HLA)-DRB1. However, due to the high polymorphisms and linkage disequilibrium within MHC, it is difficult to define novel and/or independent genetic risks using conventional HLA genotyping or chip-based microarray technology. This study aimed to identify novel RA risk variants by performing deep sequencing for MHC. METHODS: We first conducted target sequencing for the entire MHC region in 357 anticitrullinated protein antibodies (ACPA)-positive patients with RA and 1001 healthy controls, and then performed HLA typing in an independent case-control cohort consisting of 1415 samples for validation. All study subjects were Han Chinese. Genetic associations for RA susceptibility and severity were analysed. Comparative modelling was constructed to predict potential functions for the newly discovered RA association variants. RESULTS: HLA-DQα1:160D conferred the strongest and independent susceptibility to ACPA-positive RA (p=6.16×10-36, OR=2.29). DRß1:37N had an independent protective effect (p=5.81×10-16, OR=0.49). As predicted by comparative modelling, the negatively charged DQα1:160D stabilises the dimer of dimers, thus may lead to an increased T cell activation. The negatively charged DRß1:37N encoding alleles preferentially bind with epitope P9 arginine, thus may result in a decreased RA susceptibility. CONCLUSIONS: We provide the first evidence that HLA-DQα1:160D, instead of HLA-DRB1*0405, is the strongest and independent genetic risk for ACPA-positive RA in Han Chinese. Our study also illustrates the value of deep sequencing for fine-mapping disease risk variants in the MHC region.

A metagenome-wide association study of gut microbiota in asthma in UK adults.

BACKGROUND: Asthma, one of the most common chronic respiratory disorders, is associated with the hyper-activation of the T-cell subset of adaptive immunity. The gut microbiota may be involved in the development of asthma through the production of short-chain fatty acids (SCFAs), exhibiting modulatory effects on Th. So, we performed a metagenome-wide association study (MWAS) of the fecal microbiota from individuals with asthma and healthy controls. And that was the first case to resolve the relationship between asthma and microbiome among UK adults. RESULTS: The microbiota of the individuals with asthma consisted of fewer microbial entities than the microbiota of healthy individuals. Faecalibacterium prausnitzii, Sutterella wadsworthensis and Bacteroides stercoris were depleted in cases, whereas Clostridiums with Eggerthella lenta were over-represented in individuals with asthma. Functional analysis shows that the SCFAs might be altered in the microbiota of asthma patients. CONCLUSION: In all, the adult human gut microbiome of asthma patients is clearly different from healthy controls. The functional and taxa results showed that the change of asthma patients might related to SCFAs.

Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer.

OBJECTIVE: To evaluate the potential for diagnosing colorectal cancer (CRC) from faecal metagenomes. DESIGN: We performed metagenome-wide association studies on faecal samples from 74 patients with CRC and 54 controls from China, and validated the results in 16 patients and 24 controls from Denmark. We further validated the biomarkers in two published cohorts from France and Austria. Finally, we employed targeted quantitative PCR (qPCR) assays to evaluate diagnostic potential of selected biomarkers in an independent Chinese cohort of 47 patients and 109 controls. RESULTS: Besides confirming known associations of Fusobacterium nucleatum and Peptostreptococcus stomatis with CRC, we found significant associations with several species, including Parvimonas micra and Solobacterium moorei. We identified 20 microbial gene markers that differentiated CRC and control microbiomes, and validated 4 markers in the Danish cohort. In the French and Austrian cohorts, these four genes distinguished CRC metagenomes from controls with areas under the receiver-operating curve (AUC) of 0.72 and 0.77, respectively. qPCR measurements of two of these genes accurately classified patients with CRC in the independent Chinese cohort with AUC=0.84 and OR of 23. These genes were enriched in early-stage (I-II) patient microbiomes, highlighting the potential for using faecal metagenomic biomarkers for early diagnosis of CRC. CONCLUSIONS: We present the first metagenomic profiling study of CRC faecal microbiomes to discover and validate microbial biomarkers in ethnically different cohorts, and to independently validate selected biomarkers using an affordable clinically relevant technology. Our study thus takes a step further towards affordable non-invasive early diagnostic biomarkers for CRC from faecal samples.

Influenza H7N9 and H9N2 viruses: coexistence in poultry linked to human H7N9 infection and genome characteristics.

UNLABELLED: Avian influenza virus A of the novel H7N9 reassortant subtype was recently found to cause severe human respiratory infections in China. Live poultry markets were suspected locations of the human H7N9 infection sources, based on the cases' exposure histories and sequence similarities between viral isolates. To explore the role of live poultry markets in the origin of the novel H7N9 virus, we systematically examined poultry and environmental specimens from local markets and farms in Hangzhou, using real-time reverse transcription-PCR (RT-PCR) as well as high-throughput next-generation sequencing (NGS). RT-PCR identified specimens positive for the H7 and N9 genomic segments in all of the 12 poultry markets epidemiologically linked to 10 human H7N9 cases. Chickens, ducks, and environmental specimens from the markets contained heavily mixed subtypes, including H7, N9, H9, and N2 and sometimes H5 and N1. The idea of the coexistence of H7N9 and H9N2 subtypes in chickens was further supported by metagenomic sequencing. In contrast, human H7N9 infection cases (n = 31) were all negative for H9N2 virus according to real-time RT-PCR. The six internal segments were indistinguishable for the H7N9 and H9N2 viruses. The H9, N2, and internal-segment sequences were very close to the sequence of the H9N2 virus circulating in chickens in China recently. Our results provide direct evidence that H9N2 strains coexisted with the novel human-pathogenic H7N9 influenza virus in epidemiologically linked live poultry markets. Avian influenza A virus of the H9N2 subtype likely made a recent contribution to the evolution of the H7N9 virus and continues to do so. IMPORTANCE: Our results suggest that avian influenza A virus of the H9N2 subtype likely made a recent contribution to the evolution of the H7N9 virus, a novel reassortant avian influenza virus A subtype, and continues to do so. The finding helps shed light on how the H7N9 virus emerged, spread, and transmitted to humans. It is of considerable interest for assessing the risk of the possible emergence of novel reassortant viruses with enhanced transmissibility to humans.

RNA unwinding by the Trf4/Air2/Mtr4 polyadenylation (TRAMP) complex.

Many RNA-processing events in the cell nucleus involve the Trf4/Air2/Mtr4 polyadenylation (TRAMP) complex, which contains the poly(A) polymerase Trf4p, the Zn-knuckle protein Air2p, and the RNA helicase Mtr4p. TRAMP polyadenylates RNAs designated for processing by the nuclear exosome. In addition, TRAMP functions as an exosome cofactor during RNA degradation, and it has been speculated that this role involves disruption of RNA secondary structure. However, it is unknown whether TRAMP displays RNA unwinding activity. It is also not clear how unwinding would be coordinated with polyadenylation and the function of the RNA helicase Mtr4p in modulating poly(A) addition. Here, we show that TRAMP robustly unwinds RNA duplexes. The unwinding activity of Mtr4p is significantly stimulated by Trf4p/Air2p, but the stimulation of Mtr4p does not depend on ongoing polyadenylation. Nonetheless, polyadenylation enables TRAMP to unwind RNA substrates that it otherwise cannot separate. Moreover, TRAMP displays optimal unwinding activity on substrates with a minimal Mtr4p binding site comprised of adenylates. Our results suggest a model for coordination between unwinding and polyadenylation activities by TRAMP that reveals remarkable synergy between helicase and poly(A) polymerase.

AID/APOBEC deaminases disfavor modified cytosines implicated in DNA demethylation.

Activation-induced deaminase (AID)/APOBEC-family cytosine deaminases, known to function in diverse cellular processes from antibody diversification to mRNA editing, have also been implicated in DNA demethylation, a major process for transcriptional activation. Although oxidation-dependent pathways for demethylation have been described, pathways involving deamination of either 5-methylcytosine (5mC) or 5-hydroxymethylcytosine (5hmC) have emerged as alternatives. Here we address the biochemical plausibility of deamination-coupled demethylation. We found that purified AID/APOBECs have substantially reduced activity on 5mC relative to cytosine, their canonical substrate, and no detectable deamination of 5hmC. This finding was explained by the reactivity of a series of modified substrates, where steric bulk was increasingly detrimental to deamination. Further, upon AID/APOBEC overexpression, the deamination product of 5hmC was undetectable in genomic DNA, whereas oxidation intermediates remained detectable. Our results indicate that the steric requirements for cytosine deamination are one intrinsic barrier to the proposed function of deaminases in DNA demethylation.

Identifying keystone species in microbial communities using deep learning.

Previous studies suggested that microbial communities can harbour keystone species whose removal can cause a dramatic shift in microbiome structure and functioning. Yet, an efficient method to systematically identify keystone species in microbial communities is still lacking. Here we propose a data-driven keystone species identification (DKI) framework based on deep learning to resolve this challenge. Our key idea is to implicitly learn the assembly rules of microbial communities from a particular habitat by training a deep-learning model using microbiome samples collected from this habitat. The well-trained deep-learning model enables us to quantify the community-specific keystoneness of each species in any microbiome sample from this habitat by conducting a thought experiment on species removal. We systematically validated this DKI framework using synthetic data and applied DKI to analyse real data. We found that those taxa with high median keystoneness across different communities display strong community specificity. The presented DKI framework demonstrates the power of machine learning in tackling a fundamental problem in community ecology, paving the way for the data-driven management of complex microbial communities.

A genome-wide association study reveals the relationship between human genetic variation and the nasal microbiome.

The nasal cavity harbors diverse microbiota that contributes to human health and respiratory diseases. However, whether and to what extent the host genome shapes the nasal microbiome remains largely unknown. Here, by dissecting the human genome and nasal metagenome data from 1401 healthy individuals, we demonstrated that the top three host genetic principal components strongly correlated with the nasal microbiota diversity and composition. The genetic association analyses identified 63 genome-wide significant loci affecting the nasal microbial taxa and functions, of which 2 loci reached study-wide significance (p < 1.7 × 10-10): rs73268759 within CAMK2A associated with genus Actinomyces and family Actinomycetaceae; and rs35211877 near POM121L12 with Gemella asaccharolytica. In addition to respiratory-related diseases, the associated loci are mainly implicated in cardiometabolic or neuropsychiatric diseases. Functional analysis showed the associated genes were most significantly expressed in the nasal airway epithelium tissue and enriched in the calcium signaling and hippo signaling pathway. Further observational correlation and Mendelian randomization analyses consistently suggested the causal effects of Serratia grimesii and Yokenella regensburgei on cardiometabolic biomarkers (cystine, glutamic acid, and creatine). This study suggested that the host genome plays an important role in shaping the nasal microbiome.

Identifying keystone species in microbial communities using deep learning.

Previous studies suggested that microbial communities harbor keystone species whose removal can cause a dramatic shift in microbiome structure and functioning. Yet, an efficient method to systematically identify keystone species in microbial communities is still lacking. This is mainly due to our limited knowledge of microbial dynamics and the experimental and ethical difficulties of manipulating microbial communities. Here, we propose a Data-driven Keystone species Identification (DKI) framework based on deep learning to resolve this challenge. Our key idea is to implicitly learn the assembly rules of microbial communities from a particular habitat by training a deep learning model using microbiome samples collected from this habitat. The well-trained deep learning model enables us to quantify the community-specific keystoneness of each species in any microbiome sample from this habitat by conducting a thought experiment on species removal. We systematically validated this DKI framework using synthetic data generated from a classical population dynamics model in community ecology. We then applied DKI to analyze human gut, oral microbiome, soil, and coral microbiome data. We found that those taxa with high median keystoneness across different communities display strong community specificity, and many of them have been reported as keystone taxa in literature. The presented DKI framework demonstrates the power of machine learning in tackling a fundamental problem in community ecology, paving the way for the data-driven management of complex microbial communities.

Mendelian randomization analyses reveal causal relationships between the human microbiome and longevity.

Although recent studies have revealed the association between the human microbiome especially gut microbiota and longevity, their causality remains unclear. Here, we assess the causal relationships between the human microbiome (gut and oral microbiota) and longevity, by leveraging bidirectional two-sample Mendelian randomization (MR) analyses based on genome-wide association studies (GWAS) summary statistics of the gut and oral microbiome from the 4D-SZ cohort and longevity from the CLHLS cohort. We found that some disease-protected gut microbiota such as Coriobacteriaceae and Oxalobacter as well as the probiotic Lactobacillus amylovorus were related to increased odds of longevity, whereas the other gut microbiota such as colorectal cancer pathogen Fusobacterium nucleatum, Coprococcus, Streptococcus, Lactobacillus, and Neisseria were negatively associated with longevity. The reverse MR analysis further revealed genetically longevous individuals tended to have higher abundances of Prevotella and Paraprevotella but lower abundances of Bacteroides and Fusobacterium species. Few overlaps of gut microbiota-longevity interactions were identified across different populations. We also identified abundant links between the oral microbiome and longevity. The additional analysis suggested that centenarians genetically had a lower gut microbial diversity, but no difference in oral microbiota. Our findings strongly implicate these bacteria to play a role in human longevity and underscore the relocation of commensal microbes among different body sites that would need to be monitored for long and healthy life.

A consortium of three-bacteria isolated from human feces inhibits formation of atherosclerotic deposits and lowers lipid levels in a mouse model.

By a survey of metagenome-wide association studies (MWAS), we found a robust depletion of Bacteroides cellulosilyticus, Faecalibacterium prausnitzii, and Roseburia intestinalis in individuals with atherosclerotic cardiovascular disease (ACVD). From an established collection of bacteria isolated from healthy Chinese individuals, we selected B. cellulosilyticus, R. intestinalis, and Faecalibacterium longum, a bacterium related to F. prausnitzii, and tested the effects of these bacteria in an Apoe/- atherosclerosis mouse model. We show that administration of these three bacterial species to Apoe-/- mice robustly improves cardiac function, reduces plasma lipid levels, and attenuates the formation of atherosclerotic plaques. Comprehensive analysis of gut microbiota, plasma metabolome, and liver transcriptome revealed that the beneficial effects are associated with a modulation of the gut microbiota linked to a 7α-dehydroxylation-lithocholic acid (LCA)-farnesoid X receptor (FXR) pathway. Our study provides insights into transcriptional and metabolic impact whereby specific bacteria may hold promises for prevention/treatment of ACVD.

Sex differences in the oral microbiome, host traits, and their causal relationships.

The oral microbiome has been implicated in a growing number of diseases; however, determinants of the oral microbiome and their roles remain elusive. Here, we investigated the oral (saliva and tongue dorsum) metagenome, the whole genome, and other omics data in a total of 4,478 individuals and demonstrated that the oral microbiome composition and its major contributing host factors significantly differed between sexes. We thus conducted a sex-stratified metagenome-genome-wide-association study (M-GWAS) and identified 11 differential genetic associations with the oral microbiome (p sex-difference  < 5 × 10-8). Furthermore, we performed sex-stratified Mendelian randomization (MR) analyses and identified abundant causalities between the oral microbiome and serum metabolites. Notably, sex-specific microbes-hormonal interactions explained the mostly observed sex hormones differences such as the significant causalities enrichments for aldosterone in females and androstenedione in males. These findings illustrate the necessity of sex stratification and deepen our understanding of the interplay between the oral microbiome and serum metabolites.