A genome-wide association study for gut metagenome in Chinese adults illuminates complex diseases

Advances in multi-omics integrated analysis methods based on the gut microbiome and their applications

The gut microbiota actually shares the host's physical space and affects the host's physiological functions and health indicators through a complex network of interactions with the host. However, its role as a determinant of host health and disease is often underestimated. With the emergence of new technologies including next-generation sequencing (NGS) and advanced techniques such as microbial community sequencing, people have begun to explore the interaction mechanisms between microorganisms and hosts at various omics levels such as genomics, transcriptomics, metabolomics, and proteomics. With the enrichment of multi-omics integrated analysis methods based on the microbiome, an increasing number of complex statistical analysis methods have also been proposed. In this review, we summarized the multi-omics research analysis methods currently used to study the interaction between the microbiome and the host. We analyzed the advantages and limitations of various methods and briefly introduced their application progress.

Unraveling the impact of host genetics and factors on the urinary microbiome in a young population

The significance of the urinary microbiome in maintaining health and contributing to disease development is increasingly recognized. However, a comprehensive understanding of this microbiome and its influencing factors remains elusive. Utilizing whole metagenomic and whole-genome sequencing, along with detailed metadata, we characterized the urinary microbiome and its influencing factors in a cohort of 1,579 Chinese individuals. Our findings unveil the distinctiveness of the urinary microbiome from other four body sites, delineating five unique urotypes dominated by Gardnerella vaginalis, Sphingobium fluviale, Lactobacillus iners, Variovorax sp. PDC80, and Acinetobacter junii, respectively. We identified 108 host factors significantly influencing the urinary microbiome, collectively explaining 12.92% of the variance in microbial composition. Notably, gender-related factors, including sex hormones, emerged as key determinants in defining urotype groups, microbial composition and pathways, with the urinary microbiome exhibiting strong predictive ability for gender (area under the curve [AUC] = 0.843). Furthermore, we discovered 43 genome-wide significant associations between host genetic loci and specific urinary bacteria, Acinetobacter in particular, linked to eight host loci (P < 5 × 10-8). These associations were also modulated by gender and sex hormone levels. In summary, our study provides novel insights into the impact of host genetics and other factors on the urinary microbiome, shedding light on its implications for host health and disease.

IMPORTANCE

The urinary microbiome, essential to human health, reveals its unique qualities in our study of 1,579 Chinese individuals. We identified distinctive microbial profiles, or "urotypes," and uncovered strong gender-related influences, particularly from sex hormones, on these microbial communities. Our research highlights significant genetic associations affecting specific urinary bacteria, indicating a deep interaction between our genetics and our microbiome. These insights not only enhance our understanding of the urinary microbiome's role in health and disease but also open new pathways for personalized medical strategies, making our findings crucial for future diagnostic and therapeutic innovations. This work underscores the intricate relationship between our body's biological processes and the microorganisms within, providing valuable knowledge for both scientific and medical communities.

GEMimp: An Accurate and Robust Imputation Method for Microbiome Data Using Graph Embedding Neural Network

Microbiome research has increasingly underscored the profound link between microbial compositions and human health, with numerous studies establishing a strong correlation between microbiome characteristics and various diseases. However, the analysis of microbiome data is frequently compromised by inherent sparsity issues, characterized by a substantial presence of observed zeros. These zeros not only skew the abundance distribution of microbial species but also undermine the reliability of scientific conclusions drawn from such data. Addressing this challenge, we introduce GEMimp, an innovative imputation method designed to infuse robustness into microbiome data analysis. GEMimp leverages the node2vec algorithm, which incorporates both Breadth-First Search (BFS) and Depth-First Search (DFS) strategies in its random walks sampling process. This approach enables GEMimp to learn nuanced, low-dimensional representations of each taxonomic unit, facilitating the reconstruction of their similarity networks with unprecedented accuracy. Our comparative analysis pits GEMimp against state-of-the-art imputation methods including SAVER, MAGIC and mbImpute. The results unequivocally demonstrate that GEMimp outperforms its counterparts by achieving the highest Pearson correlation coefficient when compared to the original raw dataset. Furthermore, GEMimp shows notable proficiency in identifying significant taxa, enhancing the detection of disease-related taxa and effectively mitigating the impact of sparsity on both simulated and real-world datasets, such as those pertaining to Type 2 Diabetes (T2D) and Colorectal Cancer (CRC). These findings collectively highlight the strong effectiveness of GEMimp, allowing for better analysis on microbial data. With alleviation of sparsity issues, it could be greatly facilitated in downstream analyses and even in the field of microbiology.

Investigating the link between gut microbiome and bone mineral density: The role of genetic factors

Osteoporosis is a complex metabolic bone disease that severely undermines the quality of life and overall health of the elderly. While previous studies have established a close relationship between gut microbiome and host bone metabolism, the role of genetic factors has received less scrutiny. This research aims to identify potential taxa associated with various bone mineral density states, incorporating assessments of genetic factors. Fecal microbiome profiles from 605 individuals (334 females and 271 males) aged 55-65 from the Taizhou Imaging Study with osteopenia (n = 270, 170 women) or osteoporosis (n = 94, 85 women) or normal (n = 241, 79 women) were determined using shotgun metagenomic sequencing. The linear discriminant analysis was employed to identify differentially enriched taxa. Utilizing the Kyoto Encyclopedia of Genes and Genomes for annotation, functional pathway analysis was conducted to identify differentially metabolic pathways. Polygenic risk score for osteoporosis was estimated to represent genetic susceptibility to osteoporosis, followed by stratification and interaction analyses. Gut flora diversity did not show significant differences among various bone mineral groups. After multivariable adjustment, certain species, such as Clostridium leptum, Fusicatenibacter saccharivorans and Roseburia hominis, were enriched in osteoporosis patients. Statistically significant interactions between the polygenic risk score and taxa Roseburia faecis, Megasphaera elsdenii were observed (P for interaction = 0.005, 0.018, respectively). Stratified analyses revealed a significantly negative association between Roseburia faecis and bone mineral density in the low-genetic-risk group (β = -0.045, P < 0.05), while Turicimonas muris was positively associated with bone mineral density in the high-genetic-risk group (β = 4.177, P < 0.05) after multivariable adjustments. Functional predictions of the gut microbiome indicated an increase in pathways related to structural proteins in high-genetic-risk patients, while low-genetic-risk patients exhibited enrichment in enzyme-related pathways. This study emphasizes the association between gut microbes and bone mass, offering new insights into the interaction between genetic background and gut microbiome.

Unveiling the hidden link between oral flora and colorectal cancer: a bidirectional Mendelian randomization analysis and meta-analysis

Objective

The impact of oral flora on intestinal micro-environment and related diseases has been widely reported, but its role in colorectal cancer (CRC) remains elusive.

Methods

A Two-sample Mendelian Randomization (TSMR) analysis was conducted to explore the causal relationship between oral flora and CRC, with the Inverse-Variance Weighted (IVW) serving as the primary method for evaluating this causal relationship. Data on the oral flora were derived from human samples from the tongue and saliva, with all cohort populations originating from Asia. In addition, 2 independent external cohorts were used to validate the positive results and perform a meta-analysis of the final results. Lastly, to balance the effect of positive oral flora on CRC, a Multivariate Mendelian Randomization (MVMR) analysis was also performed.

Results

The TSMR analysis revealed that 17 oral flora may have a causal relationship with CRC in the training cohort. Among them, s Haemophilus, g Fusobacterium, s Metamycoplasma salivarium, and s Mogibacterium pumilum were validated in two testing cohorts. Intriguingly, after integrating the results of the 3 cohorts for meta-analysis, 16 associations remained significant. In the training cohort, MVMR analysis demonstrated that s Capnocytophaga ochracea and s Metamycoplasma salivarium retained statistical significance. In one of the testing cohorts, s Metamycoplasma salivarium, s Streptococcus anginosus, and s Streptococcus sanguinis retained statistical significance. In the other testing cohort, s Metamycoplasma salivarium, s Haemophilus, and g Fusobacterium remained significant.

Conclusion

s Haemophilus, g Fusobacterium, s Metamycoplasma salivarium, and s Mogibacterium pumilum have a solid causal relationship with the occurrence and development of CRC.

Exploring microbial worlds: a review of whole genome sequencing and its application in characterizing the microbial communities

The classical microbiology techniques have inherent limitations in unraveling the complexity of microbial communities, necessitating the pivotal role of sequencing in studying the diversity of microbial communities. Whole genome sequencing (WGS) enables researchers to uncover the metabolic capabilities of the microbial community, providing valuable insights into the microbiome. Herein, we present an overview of the rapid advancements achieved thus far in the use of WGS in microbiome research. There was an upsurge in publications, particularly in 2021 and 2022 with the United States, China, and India leading the metagenomics research landscape. The Illumina platform has emerged as the widely adopted sequencing technology, whereas a significant focus of metagenomics has been on understanding the relationship between the gut microbiome and human health where distinct bacterial species have been linked to various diseases. Additionally, studies have explored the impact of human activities on microbial communities, including the potential spread of pathogenic bacteria and antimicrobial resistance genes in different ecosystems. Furthermore, WGS is used in investigating the microbiome of various animal species and plant tissues such as the rhizosphere microbiome. Overall, this review reflects the importance of WGS in metagenomics studies and underscores its remarkable power in illuminating the variety and intricacy of the microbiome in different environments.

The mediating role of metabolites between gut microbiome and Hirschsprung disease: a bidirectional two-step Mendelian randomization study

BACKGROUND

Gut microbiome (GM) was observed to be associated with the incidence of Hirschsprung disease (HD). However, the effect and mechanism of GM in HD is still unclear. To investigate the relationship between GM and HD and the effect of metabolites as mediators, a bidirectional two-step Mendelian randomization (MR) study was conducted.

METHODS

The study selected instrument variables (IVs) from summary-level genome-wide association studies (GWAS). The MiBioGen consortium provided the GWAS data for GM, while the GWAS data for metabolites and HD were obtained from the GWAS Catalog consortium. Two-sample MR analyses were performed to estimate bidirectional correlations between IVs associated with GM and HD. Then, genetic variants related to 1,400 metabolite traits were selected for further mediation analyses using the Product method.

RESULTS

This study found that seven genus bacteria had a significant causal relationship with the incidence of HD but not vice versa. 27 metabolite traits were significantly correlated with HD. After combining the significant results, three significant GM-metabolites-HD lines have been identified. In the Peptococcus-Stearoyl sphingomyelin (d18:1/18:0)-HD line, the Stearoyl sphingomyelin (d18:1/18:0) levels showed a mediation proportion of 14.5%, while in the Peptococcus-lysine-HD line, the lysine levels had a mediation proportion of 12.9%. Additionally, in the Roseburia-X-21733-HD line, the X-21733 levels played a mediation proportion of 23.5%.

CONCLUSION

Our MR study indicates a protective effect of Peptococcus on HD risk that is partially mediated through serum levels of stearoyl sphingomyelin (d18:1/18:0) and lysine, and a risk effect of Roseburia on HD that is partially mediated by X-21733 levels. These findings could serve as novel biomarkers and therapeutic targets for HD.

Tumour-associated and non-tumour-associated bacteria co-abundance groups in colorectal cancer

BACKGROUND & AIMS

Gut microbiota is closely related to the occurrence and development of colorectal cancer (CRC). However, the differences in bacterial co-abundance groups (CAGs) between tumor tissue (TT) and normal tissue (NT), as well as their associations with clinical features, are needed to be clarified.

METHODS

Bacterial 16 S rRNA sequencing was performed by using TT samples and NT samples of 251 patients with colorectal cancer. Microbial diversity, taxonomic characteristics, microbial composition, and functional pathways were compared between TT and NT. Hierarchical clustering was used to construct CAGs.

RESULTS

Four CAGs were grouped in the hierarchical cluster analysis. CAG 2, which was mainly comprised of pathogenic bacteria, was significantly enriched in TT samples (2.27% in TT vs. 0.78% in NT, p < 0.0001). CAG 4, which was mainly comprised of non-pathogenic bacteria, was significantly enriched in NT samples (0.62% in TT vs. 0.79% in NT, p = 0.0004). In addition, CAG 2 was also significantly associated with tumor microsatellite instability (13.2% in unstable vs. 2.0% in stable, p = 0.016), and CAG 4 was positively correlated with the level of CA199 (r = 0.17, p = 0.009).

CONCLUSIONS

Our research will deepen our understanding of the interactions among multiple bacteria and offer insights into the potential mechanism of NT to TT transition.

Causal link between gut microbiome and schizophrenia: a Mendelian randomization study

OBJECTIVE

Some observational studies have shown that gut microbiome is significantly changed in patients with schizophrenia. We aim to identify the genetic causal link between gut microbiome and schizophrenia.

METHODS

A two-sample Mendelian randomization (MR) study was used to evaluate the causal link between gut microbiome and schizophrenia with 28 gut microbiome-associated genetic instrumental variants chosen from recent MR reports and the largest schizophrenia genome-wide association studies (8-Apr-22 release).

RESULTS

Inverse variance weighted method showed that genetically increased Bacteroidales_S24-7 (per SD) resulted in increased risk of schizophrenia (OR = 1.110, 95% CI: [1.012-1.217], P  = 0.027). Similarly, genetically increased Prevotellaceae promoted schizophrenia risk (OR = 1.124, 95% CI: [1.030-1.228], P  = 0.009). However, genetically increased Lachnospiraceae reduced schizophrenia risk (OR = 0.878, 95% CI: [0.785-0.983], P  = 0.023). In addition, schizophrenia risk was also suppressed by genetically increased Lactobacillaceae (OR = 0.878, 95% CI: [0.776-0.994], P  = 0.040) and Verrucomicrobiaceae (OR = 0.860, 95% CI: [0.749-0.987], P  = 0.032). Finally, we did not find any significant results in the causal association of other 23 gut microbiome with schizophrenia.

CONCLUSION

Our analysis suggests that genetically increased Bacteroidales_S24-7 and Prevotellaceae promotes schizophrenia risk, whereas genetically increased Lachnospiraceae, Lactobacillaceae, and Verrucomicrobiaceae reduces schizophrenia risk. Thus, regulation of the disturbed intestinal microbiota may represent a new therapeutic strategy for patients with schizophrenia.

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.

Dynamics of the optimality control of transmission of infectious disease: a sensitivity analysis

Over the course of history global population has witnessed deterioration of unprecedented scale caused by infectious transmission. The necessity to mitigate the infectious flow requires the launch of a well-directed and inclusive set of efforts. Motivated by the urge for continuous improvement in existing schemes, this article aims at the encapsulation of the dynamics of the spread of infectious diseases. The objectives are served by the launch of the infectious disease model. Moreover, an optimal control strategy is introduced to ensure the incorporation of the most feasible health interventions to reduce the number of infected individuals. The outcomes of the research are facilitated by stratifying the population into five compartments that are susceptible class, acute infected class, chronic infected class, recovered class, and vaccinated class. The optimal control strategy is formulated by incorporating specific control variables namely, awareness about medication, isolation, ventilation, vaccination rates, and quarantine level. The developed model is validated by proving the pivotal delicacies such as positivity, invariant region, reproduction number, stability, and sensitivity analysis. The legitimacy of the proposed model is delineated through the detailed sensitivity analysis along with the documentation of local and global features in a comprehensive manner. The maximum sensitivity index parameters are disease transmission and people moved from acute stages into chronic stages whose value is (0.439, 1) increase in parameter by 10 percent would increase the threshold quantity by (4.39, 1). Under the condition of a stable system, we witnessed an inverse relationship between susceptible class and time. Moreover, to assist the gain of the fundamental aim of this research, we take the control variables as time-dependent and obtain the optimal control strategy to minimize infected populations and to maximize the recovered population, simultaneously. The objectives are attained by the employment of the Pontryagin maximum principle. Furthermore, the efficacy of the usual health interventions such as quarantine, face mask usage, and hand sanitation are also noticed. The effectiveness of the suggested control plan is explained by using numerical evaluation. The advantages of the new strategy are highlighted in the article.

Host genetic regulation of human gut microbial structural variation

Although the impact of host genetics on gut microbial diversity and the abundance of specific taxa is well established1-6, little is known about how host genetics regulates the genetic diversity of gut microorganisms. Here we conducted a meta-analysis of associations between human genetic variation and gut microbial structural variation in 9,015 individuals from four Dutch cohorts. Strikingly, the presence rate of a structural variation segment in Faecalibacterium prausnitzii that harbours an N-acetylgalactosamine (GalNAc) utilization gene cluster is higher in individuals who secrete the type A oligosaccharide antigen terminating in GalNAc, a feature that is jointly determined by human ABO and FUT2 genotypes, and we could replicate this association in a Tanzanian cohort. In vitro experiments demonstrated that GalNAc can be used as the sole carbohydrate source for F. prausnitzii strains that carry the GalNAc-metabolizing pathway. Further in silico and in vitro studies demonstrated that other ABO-associated species can also utilize GalNAc, particularly Collinsella aerofaciens. The GalNAc utilization genes are also associated with the host's cardiometabolic health, particularly in individuals with mucosal A-antigen. Together, the findings of our study demonstrate that genetic associations across the human genome and bacterial metagenome can provide functional insights into the reciprocal host-microbiome relationship.

Single cell dynamics of tumor specificity vs bystander activity in CD8+ T cells define the diverse immune landscapes in colorectal cancer

CD8+ T cell activation via immune checkpoint blockade (ICB) is successful in microsatellite instable (MSI) colorectal cancer (CRC) patients. By comparison, the success of immunotherapy against microsatellite stable (MSS) CRC is limited. Little is known about the most critical features of CRC CD8+ T cells that together determine the diverse immune landscapes and contrasting ICB responses. Hence, we pursued a deep single cell mapping of CRC CD8+ T cells on transcriptomic and T cell receptor (TCR) repertoire levels in a diverse patient cohort, with additional surface proteome validation. This revealed that CRC CD8+ T cell dynamics are underscored by complex interactions between interferon-γ signaling, tumor reactivity, TCR repertoire, (predicted) TCR antigen-specificities, and environmental cues like gut microbiome or colon tissue-specific 'self-like' features. MSI CRC CD8+ T cells showed tumor-specific activation reminiscent of canonical 'T cell hot' tumors, whereas the MSS CRC CD8+ T cells exhibited tumor unspecific or bystander-like features. This was accompanied by inflammation reminiscent of 'pseudo-T cell hot' tumors. Consequently, MSI and MSS CRC CD8+ T cells showed overlapping phenotypic features that differed dramatically in their TCR antigen-specificities. Given their high discriminating potential for CD8+ T cell features/specificities, we used the single cell tumor-reactive signaling modules in CD8+ T cells to build a bulk tumor transcriptome classification for CRC patients. This "Immune Subtype Classification" (ISC) successfully distinguished various tumoral immune landscapes that showed prognostic value and predicted immunotherapy responses in CRC patients. Thus, we deliver a unique map of CRC CD8+ T cells that drives a novel tumor immune landscape classification, with relevance for immunotherapy decision-making.

Gut microbiota: A magical multifunctional target regulated by medicine food homology species

BACKGROUND

The relationship between gut microbiota and human health has gradually been recognized. Increasing studies show that the disorder of gut microbiota is related to the occurrence and development of many diseases. Metabolites produced by the gut microbiota are responsible for their extensive regulatory roles. In addition, naturally derived medicine food homology species with low toxicity and high efficiency have been clearly defined owing to their outstanding physiological and pharmacological properties in disease prevention and treatment.

AIM OF REVIEW

Based on supporting evidence, the current review summarizes the representative work of medicine food homology species targeting the gut microbiota to regulate host pathophysiology and discusses the challenges and prospects in this field. It aims to facilitate the understanding of the relationship among medicine food homology species, gut microbiota, and human health and further stimulate the advancement of more relevant research.

KEY SCIENTIFIC CONCEPTS OF REVIEW

As this review reveals, from the initial practical application to more mechanism studies, the relationship among medicine food homology species, gut microbiota, and human health has evolved into an irrefutable interaction. On the one hand, through affecting the population structure, metabolism, and function of gut microbiota, medicine food homology species maintain the homeostasis of the intestinal microenvironment and human health by affecting the population structure, metabolism, and function of gut microbiota. On the other hand, the gut microbiota is also involved in the bioconversion of the active ingredients from medicine food homology species and thus influences their physiological and pharmacological properties.

Causal Link between Gut Microbiota, Neurophysiological States, and Bone Diseases: A Comprehensive Mendelian Randomization Study

Increasing evidence highlights a robust correlation between the gut microbiota and bone diseases; however, the existence of a causal relationship between them remains unclear. In this study, we thoroughly examined the correlation between gut microbiota and skeletal diseases using genome-wide association studies. Linkage disequilibrium score regression and Mendelian randomization were used to probe genetic causality. Furthermore, the potential mediating role of neuropsychological states (i.e., cognition, depression, and insomnia) between the gut microbiota and bone diseases was evaluated using mediation analysis, with genetic colocalization analysis revealing potential targets. These findings suggest a direct causal relationship between Ruminococcaceae and knee osteoarthritis (OA), which appears to be mediated by cognitive performance and insomnia. Similarly, a causal association was observed between Burkholderiales and lumbar pelvic fractures, mediated by cognitive performance. Colocalization analysis identified a shared causal variant (rs2352974) at the TRAF-interacting protein locus for cognitive ability and knee OA. This study provides compelling evidence that alterations in the gut microbiota can enhance cognitive ability, ameliorate insomnia, and potentially reduce the risk of site-specific fractures and OA. Therefore, strategies targeting gut microbiota optimization could serve as novel and effective preventive measures against fractures and OA.

The triangle relationship between human genome, gut microbiome, and COVID-19: opening of a Pandora's box

Since the pandemic started, the coronavirus disease 2019 (COVID-19) has spread worldwide. In patients with COVID-19, the gut microbiome (GM) has been supposed to be closely related to the progress of the disease. The gut microbiota composition and human genetic variation are also connected in COVID-19 patients, assuming a triangular relationship between the genome, GM, and COVID-19. Here, we reviewed the recent developments in the study of the relationship between gut microbiota and COVID-19. The keywords "COVID-19," "microbiome," and "genome" were used to search the literature in the PubMed database. We first found that the composition of the GM in COVID-19 patients varies according to the severity of the illness. Most obviously, Candida albicans abnormally increased while the probiotic Bifidobacterium decreased in severe cases of COVID-19. Interestingly, clinical studies have consistently emphasized that the family Lachnospiraceae plays a critical role in patients with COVID-19. Additionally, we have demonstrated the impact of microbiome-related genes on COVID-19. Specially, we focused on angiotensin-converting enzyme 2's dual functions in SARS-CoV-2 infection and gut microbiota alternation. In summary, these studies showed that the diversity of GMs is closely connected to COVID-19. A triangular relationship exists between COVID-19, the human genome, and the gut flora, suggesting that human genetic variations may offer a chance for a precise diagnosis of COVID-19, and the important relationships between genetic makeup and microbiome regulation may affect the therapy of COVID-19.

Gut Microbiota and Microbial Metabolism in Early Risk of Cardiometabolic Disease

Cardiometabolic disease comprises cardiovascular and metabolic dysfunction and underlies the leading causes of morbidity and mortality, both within the United States and worldwide. Commensal microbiota are implicated in the development of cardiometabolic disease. Evidence suggests that the microbiome is relatively variable during infancy and early childhood, becoming more fixed in later childhood and adulthood. Effects of microbiota, both during early development, and in later life, may induce changes in host metabolism that modulate risk mechanisms and predispose toward the development of cardiometabolic disease. In this review, we summarize the factors that influence gut microbiome composition and function during early life and explore how changes in microbiota and microbial metabolism influence host metabolism and cardiometabolic risk throughout life. We highlight limitations in current methodology and approaches and outline state-of-the-art advances, which are improving research and building toward refined diagnosis and treatment options in microbiome-targeted therapies.

Cross-talks between gut microbiota and tobacco smoking: a two-sample Mendelian randomization study

BACKGROUND

Considerable evidence has been reported that tobacco use could cause alterations in gut microbiota composition. The microbiota-gut-brain axis also in turn hinted at a possible contribution of the gut microbiota to smoking. However, population-level studies with a higher evidence level for causality are lacking.

METHODS

This study utilized the summary-level data of respective genome-wide association study (GWAS) for 211 gut microbial taxa and five smoking phenotypes to reveal the causal association between the gut microbiota and tobacco smoking. Two-sample bidirectional Mendelian randomization (MR) design was deployed and comprehensively sensitive analyses were followed to validate the robustness of results. We further performed multivariable MR to evaluate the effect of neurotransmitter-associated metabolites on observed associations.

RESULTS

Our univariable MR results confirmed the effects of smoking on three taxa (Intestinimonas, Catenibacterium, and Ruminococcaceae, observed from previous studies) with boosted evidence level and identified another 13 taxa which may be causally affected by tobacco smoking. As for the other direction, we revealed that smoking behaviors could be potential consequence of specific taxa abundance. Combining with existing observational evidence, we provided novel insights regarding a positive feedback loop of smoking through Actinobacteria and indicated a potential mechanism for the link between parental smoking and early smoking initiation of their children driven by Bifidobacterium. The multivariable MR results suggested that neurotransmitter-associated metabolites (tryptophan and tyrosine, also supported by previous studies) probably played a role in the action pathway from the gut microbiota to smoking, especially for Actinobacteria and Peptococcus.

CONCLUSIONS

In summary, the current study suggested the role of the specific gut microbes on the risk for cigarette smoking (likely involving alterations in metabolites) and in turn smoking on specific gut microbes. Our findings highlighted the hazards of tobacco use for gut flora dysbiosis and shed light on the potential role of specific gut microbiota for smoking behaviors.

Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity

BACKGROUND

Understanding human genetic influences on the gut microbiota helps elucidate the mechanisms by which genetics may influence health outcomes. Typical microbiome genome-wide association studies (GWAS) marginally assess the association between individual genetic variants and individual microbial taxa. We propose a novel approach, the covariate-adjusted kernel RV (KRV) framework, to map genetic variants associated with microbiome beta-diversity, which focuses on overall shifts in the microbiota. The KRV framework evaluates the association between genetics and microbes by comparing similarity in genetic profiles, based on groups of variants at the gene level, to similarity in microbiome profiles, based on the overall microbiome composition, across all pairs of individuals. By reducing the multiple-testing burden and capturing intrinsic structure within the genetic and microbiome data, the KRV framework has the potential of improving statistical power in microbiome GWAS.

RESULTS

We apply the covariate-adjusted KRV to the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) in a two-stage (first gene-level, then variant-level) genome-wide association analysis for gut microbiome beta-diversity. We have identified an immunity-related gene, IL23R, reported in a previous microbiome genetic association study and discovered 3 other novel genes, 2 of which are involved in immune functions or autoimmune disorders. In addition, simulation studies show that the covariate-adjusted KRV has a greater power than other microbiome GWAS methods that rely on univariate microbiome phenotypes across a range of scenarios.

CONCLUSIONS

Our findings highlight the value of the covariate-adjusted KRV as a powerful microbiome GWAS approach and support an important role of immunity-related genes in shaping the gut microbiome composition. Video Abstract.

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.

Different roles of microbiota and genetics in the prediction of treatment response in major depressive disorder

The roles of gut microbiota and susceptibility genes in patients with major depression disorder (MDD) are not well understood. Examining the microbiome and host genetics might be helpful for clinical decision-making. Patients with MDD were recruited in this study and subsequently treated for eight weeks. We identified the differences between the population with a response after two weeks and those with a response after eight weeks. The factors that were significantly correlated with efficacy were used to predict the treatment response. The differences in the importance of microbiota and genetics in prediction were analyzed. Our study identified rs58010457 as a potentially key locus affecting the treatment effect. Different microbiota and enriched pathways might play different roles in the response after two and eight weeks. We found that the area under the curve (AUC) value was greater than 0.8 for both random forest models. The contribution of different components to the AUC was evaluated by removing genetic information, microbiota abundance, and pathway data. The gut microbiome was an important predictor of the response after eight weeks, while genetics was an important predictor of the response after two weeks. These results suggested a dynamic effect of interaction among genetics and gut microbes on treatment. Furthermore, these results provide new guidance for clinical decisions: in cases of inadequate treatment effects after two weeks, the composition of the intestinal flora can be improved by diet therapy, which could ultimately affect the efficacy.

Monitoring Longitudinal Trends and Assessment of the Health Risk of Shigella flexneri Antimicrobial Resistance

Shigella flexneri infection is the main cause of diarrhea in humans worldwide. The emergence of antimicrobial resistance (AMR) of S. flexneri is a growing public health threat worldwide, while large-scale studies monitoring the longitudinal AMR trends of isolates remain scarce. Here, the AMR gene (ARG) profiles of 717 S. flexneri isolates from 1920 to 2020 worldwide were determined. The results showed that the average number of ARGs in isolates has increased significantly, from 19.2 ± 2.4 before 1970 to 29.6 ± 5.3 after 2010. In addition, mobile genetic elements were important contributors to ARGs in S. flexneri isolates. The results of the structural equation model showed that the human development index drove the consumption of antibiotics and indirectly promoted the antibiotic resistance. Finally, a machine learning algorithm was used to predict the antibiotic resistance risk of global terrestrial S. flexneri isolates and successfully map the antibiotic resistance threats in global land habitats with over 80% accuracy. Collectively, this study monitored the longitudinal AMR trends, quantitatively surveilled the health risk of S. flexneri AMR, and provided a theoretical basis for mitigating the threat of antibiotic resistance.

GWAS Central: an expanding resource for finding and visualising genotype and phenotype data from genome-wide association studies

The GWAS Central resource gathers and curates extensive summary-level genome-wide association study (GWAS) data and puts a range of user-friendly but powerful website tools for the comparison and visualisation of GWAS data at the fingertips of researchers. Through our continued efforts to harmonise and import data received from GWAS authors and consortia, and data sets actively collected from public sources, the database now contains over 72.5 million P-values for over 5000 studies testing over 7.4 million unique genetic markers investigating over 1700 unique phenotypes. Here, we describe an update to integrate this extensive data collection with mouse disease model data to support insights into the functional impact of human genetic variation. GWAS Central has expanded to include mouse gene-phenotype associations observed during mouse gene knockout screens. To allow similar cross-species phenotypes to be compared, terms from mammalian and human phenotype ontologies have been mapped. New interactive interfaces to find, correlate and view human and mouse genotype-phenotype associations are included in the website toolkit. Additionally, the integrated browser for interrogating multiple association data sets has been updated and a GA4GH Beacon API endpoint has been added for discovering variants tested in GWAS. The GWAS Central resource is accessible at https://www.gwascentral.org/.

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.

Causal relationship between gut microbes and cardiovascular protein expression

Evidence supports associations between gut microbiota and cardiovascular protein levels in plasma. However, it is unclear whether these associations reflect a causal relationship. To reveal the causal relationship between gut microbiota and cardiovascular protein levels in plasma, we estimated their causal effects using two-sample Mendelian randomization (MR) analysis. Sensitivity analysis was also performed to assess the robustness of our results. Genome-wide association study (GWAS) of microbiomes in the MiBioGen study included 211 bacterial taxa (18,473 individuals), and GWAS of 90 cardiovascular proteins included 30,931 individuals. There were 196 bacterial taxa from five levels available for analysis. The following 14 causal relationships were identified: phylum Euryarchaeota and carbohydrate antigen 125 (β = 0.289), order Bacillales and CSF-1 (β = -0.211), genus Dorea and HSP-27 (β = 0.465), phylum Actinobacteria and IL-8 (β = 0.274), order Enterobacteriales and KIM-1 (β = -0.499), class Actinobacteria, genus Bifidobacterium, phylum Actinobacteria and LEP (β = -0.219, β = -0.201, and β = -0.221), genus Methanobrevibacter and NT-proBNP (β = 0.371), family Peptostreptococcaceae and SRC (β = 0.191), order Verrucomicrobiales, phylum Verrucomicrobia and TNF-R2 (β = 0.251 and β = 0.233), family Veillonellaceae and t-PA (β = 0.271), and class Erysipelotrichia and VEGF-D (β = 0.390). Sensitivity analysis showed no evidence of pleiotropy or heterogeneity. The results of the reverse MR analysis showed no reverse causality for any of the 13 gut microbes and 11 cardiovascular proteins. Mendelian randomization estimates provide strong evidence for a causal effect of gut microbiota-mediated alterations on cardiovascular protein expression.

The road not taken: host genetics in shaping intergenerational microbiomes

The early-life gut microbiome is linked to human phenotypes as an imbalanced microbiome of this period is implicated in diseases throughout life. Several determinants of early-life gut microbiome are explored, however, mechanisms of acquisition, colonization, and stability of early-life gut microbiome and their interindividual variability remain elusive. Host genetics play a vital role to shape the gut microbiome and interact with it to modulate individual phenotypes in human studies and animal models. Given the microbial linkage between host generations, we discuss the current state of roles of host genetics in forming intergenerational microbiomes associated with mothers, offspring, and those vertically transmitted, providing a basis for taking into account host genetics in future early-life microbiome research. We further expand our discussion to the bidirectional interactions between host gene expression and microbiome in human health.

Role of gut microbe-derived metabolites in cardiometabolic diseases: Systems based approach

BACKGROUND

The gut microbiome influences host physiology and cardiometabolic diseases by interacting directly with intestinal cells or by producing molecules that enter the host circulation. Given the large number of microbial species present in the gut and the numerous factors that influence gut bacterial composition, it has been challenging to understand the underlying biological mechanisms that modulate risk of cardiometabolic disease.

SCOPE OF THE REVIEW

Here we discuss a systems-based approach that involves simultaneously examining individuals in populations for gut microbiome composition, molecular traits using "omics" technologies, such as circulating metabolites quantified by mass spectrometry, and clinical traits. We summarize findings from landmark studies using this approach and discuss future applications.

MAJOR CONCLUSIONS

Population-based integrative approaches have identified a large number of microbe-derived or microbe-modified metabolites that are associated with cardiometabolic traits. The knowledge gained from these studies provide new opportunities for understanding the mechanisms involved in gut microbiome-host interactions and may have potentially important implications for developing novel therapeutic approaches.

Apolipoprotein E knockout may affect cognitive function in D-galactose-induced aging mice through the gut microbiota–brain axis

The gut microbiota plays an important role in central nervous system (CNS) disorders. Apolipoprotein E (ApoE) can affect the composition of the gut microbiota and is closely related to the CNS. However, the mechanism by which ApoE affects cognitive dysfunction through the gut microbiota–brain axis has thus far not been investigated. In this study, we used wild-type mice and ApoE knockout (ApoE^–/–) mice to replicate the aging model and examined the effects of ApoE deletion on cognitive function, hippocampal ultrastructure, synaptophysin (SYP) and postsynaptic density 95 (PSD-95) in aging mice. We also explored whether ApoE deletion affects the gut microbiota and the metabolite profile of the hippocampus in aging mice and finally examined the effect of ApoE deletion on lipids and oxidative stress in aging mice. The results showed that the deletion of ApoE aggravated cognitive dysfunction, hippocampal synaptic ultrastructural damage and dysregulation of SYP and PSD-95 expression in aging mice. Furthermore, ApoE deletion reduced gut microbial makeup in aging mice. Further studies showed that ApoE deletion altered the hippocampal metabolic profile and aggravated dyslipidemia and oxidative stress in aging mice. In brief, our findings suggest that loss of ApoE alters the composition of the gut microbiota, which in turn may affect cognitive function in aging mice through the gut microbiota–brain axis.

Gut microbiome associations with host genotype vary across ethnicities and potentially influence cardiometabolic traits

Previous studies in mainly European populations have reported that the gut microbiome composition is associated with the human genome. However, the genotype-microbiome interaction in different ethnicities is largely unknown. We performed a large fecal microbiome genome-wide association study of a single multiethnic cohort, the Healthy Life in an Urban Setting (HELIUS) cohort (N = 4,117). Mendelian randomization was performed using the multiethnic Pan-UK Biobank (N = 460,000) to dissect potential causality. We identified ethnicity-specific associations between host genomes and gut microbiota. Certain microbes were associated with genotype in multiple ethnicities. Several of the microbe-associated loci were found to be related to immune functions, interact with glutamate and the mucus layer, or be expressed in the gut or brain. Additionally, we found that gut microbes potentially influence cardiometabolic health factors such as BMI, cholesterol, and blood pressure. This provides insight into the relationship of ethnicity and gut microbiota and into the possible causal effects of gut microbes on cardiometabolic traits.

Liuwei Dihuang Decoction Alleviates Cognitive Dysfunction in Mice With D-Galactose-Induced Aging by Regulating Lipid Metabolism and Oxidative Stress via the Microbiota-Gut-Brain Axis

Background

Aging is an important cause of cognitive dysfunction. Liuwei Dihuang decoction (LW), a commonly applied Chinese medicine formula, is widely used for the treatment of aging-related diseases in China. Previously, LW was confirmed to be effective in prolonging life span and reducing oxidative stress in aged mice. Unfortunately, the underlying mechanism of LW remains unclear. The aim of this study was to interpret the mechanism by which LW alleviates cognitive dysfunction related to aging from the perspective of the microbiota-gut-brain axis.

Method

All C57BL/6 mice (n = 60) were randomly divided into five groups: the control, model, vitamin E (positive control group), low-dose LW and high-dose LW groups (n = 12 in each group). Except for those in the control group, D-galactose was subcutaneously injected into mice in the other groups to induce the aging model. The antiaging effect of LW was evaluated by the water maze test, electron microscopy, 16S rRNA sequencing, combined LC–MS and GC–MS metabolomics, and ELISA.

Results

Liuwei Dihuang decoction ameliorated cognitive dysfunction and hippocampal synaptic ultrastructure damage in aging mice. Moreover, LW decreased Proteobacteria abundance and increased gut microbiota diversity in aging mice. Metabolomic analysis showed that LW treatment was associated with the significantly differential abundance of 14 metabolites, which were mainly enriched in apelin signaling, sphingolipid metabolism, glycerophospholipid and other metabolic pathways. Additionally, LW affected lipid metabolism and oxidative stress in aging mice. Finally, we also found that LW-regulated microbial species such as Proteobacteria and Fibrobacterota had potential relationships with lipid metabolism, oxidative stress and hippocampal metabolites.

Conclusion

In brief, LW improved cognitive function in aging mice by regulating lipid metabolism and oxidative stress through restoration of the homeostasis of the microbiota-gut-brain axis.

Host gene effects on gut microbiota in type 1 diabetes

Type 1 diabetes (T1D) is an organ-specific autoimmune disease characterized by progressive pancreatic β-cell loss. Both a predisposing genetic background, that may encompass mutations in several genes, as well as exposure to environmental factors can affect the progression of autoimmune responses to multiple pancreatic islet autoantigens. Many genetic variants that increase the risk of T1D are found in immunity genes involved in sensing and responding to microorganisms. Although increasing evidence indicates that the gut microbiome composition may promote or prevent T1D development, little is known about the link between gut microbiota and T1D susceptibility genes in patients with T1D. Recent studies in the inbred non-obese diabetic (NOD) mouse, a widely used model of T1D, have suggested that many genetic loci can influence gut microbiome composition to modulate islet autoimmunity. This review summarizes evidence that examines the effect of host genes on gut microbiota diversity and function during T1D development. Knowledge of the host gene-gut microbiota interactions at play during T1D progression may help us identify new diagnostic and prognostic tools and help also design effective strategies for disease treatment.

Microbiome-associated human genetic variants impact phenome-wide disease risk

Human genetic variation associates with the composition of the gut microbiome, yet its influence on clinical traits remains largely unknown. We analyzed the consequences of nearly a thousand gut microbiome-associated variants (MAVs) on phenotypes reported in electronic health records from tens of thousands of individuals. We discovered and replicated associations of MAVs with neurological, metabolic, digestive, and circulatory diseases. Five significant MAVs in these categories correlate with the relative abundance of microbes down to the strain level. We also demonstrate that these relationships are independently observed and concordant with microbe by disease associations reported in case-control studies. Moreover, a selective sweep and population differentiation impacted some disease-linked MAVs. Combined, these findings establish triad relationships among the human genome, microbiome, and disease. Consequently, human genetic influences may offer opportunities for precision diagnostics of microbiome-associated diseases but also highlight the relevance of genetic background for microbiome modulation and therapeutics.

Bovine host genome acts on rumen microbiome function linked to methane emissions

Our study provides substantial evidence that the host genome affects the comprehensive function of the microbiome in the rumen of bovines. Of 1,107/225/1,141 rumen microbial genera/metagenome assembled uncultured genomes (RUGs)/genes identified from whole metagenomics sequencing, 194/14/337 had significant host genomic effects (heritabilities ranging from 0.13 to 0.61), revealing that substantial variation of the microbiome is under host genomic control. We found 29/22/115 microbial genera/RUGs/genes host-genomically correlated (|0.59| to |0.93|) with emissions of the potent greenhouse gas methane (CH4), highlighting the strength of a common host genomic control of specific microbial processes and CH4. Only one of these microbial genes was directly involved in methanogenesis (cofG), whereas others were involved in providing substrates for archaea (e.g. bcd and pccB), important microbial interspecies communication mechanisms (ABC.PE.P), host-microbiome interaction (TSTA3) and genetic information processes (RP-L35). In our population, selection based on abundances of the 30 most informative microbial genes provided a mitigation potential of 17% of mean CH4 emissions per generation, which is higher than for selection based on measured CH4 using respiration chambers (13%), indicating the high potential of microbiome-driven breeding to cumulatively reduce CH4 emissions and mitigate climate change.

Over 50,000 Metagenomically Assembled Draft Genomes for the Human Oral Microbiome Reveal New Taxa

The oral cavity of each person is home to hundreds of bacterial species. While taxa for oral diseases have been studied using culture-based characterization as well as amplicon sequencing, metagenomic and genomic information remains scarce compared to the fecal microbiome. Here, using metagenomic shotgun data for 3346 oral metagenomic samples together with 808 published samples, we obtain 56,213 metagenome-assembled genomes (MAGs), and more than 64% of the 3589 species-level genome bins (SGBs) contain no publicly available genomes. The resulting genome collection is representative of samples around the world and contains many genomes from candidate phyla radiation (CPR) that lack monoculture. Also, it enables the discovery of new taxa such as a genus Candidatus Bgiplasma within the family Acholeplasmataceae. Large-scale metagenomic data from massive samples also allow the assembly of strains from important oral taxa such as Porphyromonas and Neisseria. The oral microbes encode genes that could potentially metabolize drugs. Apart from these findings, a strongly male-enriched Campylobacter species was identified. Oral samples would be more user-friendly collected than fecal samples and have the potential for disease diagnosis. Thus, these data lay down a genomic framework for future inquiries of the human oral microbiome.

OMARU: a robust and multifaceted pipeline for metagenome-wide association study

Microbiome is an essential omics layer to elucidate disease pathophysiology. However, we face a challenge of low reproducibility in microbiome studies, partly due to a lack of standard analytical pipelines. Here, we developed OMARU (Omnibus metagenome-wide association study with robustness), a new end-to-end analysis workflow that covers a wide range of microbiome analysis from phylogenetic and functional profiling to case–control metagenome-wide association studies (MWAS). OMARU rigorously controls the statistical significance of the analysis results, including correction of hidden confounding factors and application of multiple testing comparisons. Furthermore, OMARU can evaluate pathway-level links between the metagenome and the germline genome-wide association study (i.e. MWAS-GWAS pathway interaction), as well as links between taxa and genes in the metagenome. OMARU is publicly available (https://github.com/toshi-kishikawa/OMARU), with a flexible workflow that can be customized by users. We applied OMARU to publicly available type 2 diabetes (T2D) and schizophrenia (SCZ) metagenomic data (n = 171 and 344, respectively), identifying disease biomarkers through comprehensive, multilateral, and unbiased case–control comparisons of metagenome (e.g. increased Streptococcus vestibularis in SCZ and disrupted diversity in T2D). OMARU improves accessibility and reproducibility in the microbiome research community. Robust and multifaceted results of OMARU reflect the dynamics of the microbiome authentically relevant to disease pathophysiology.

Challenges and future directions for studying effects of host genetics on the gut microbiome

The human gut microbiome is a complex ecosystem that is involved in its host's metabolism, immunity and health. Although interindividual variations in gut microbial composition are mainly driven by environmental factors, some gut microorganisms are heritable and thus can be influenced by host genetics. In the past 5 years, 12 microbial genome-wide association studies (mbGWAS) with >1,000 participants have been published, yet only a few genetic loci have been consistently confirmed across multiple studies. Here we discuss the state of the art for mbGWAS, focusing on current challenges such as the heterogeneity of microbiome measurements and power issues, and we elaborate on potential future directions for genetic analysis of the microbiome.

Effect of host genetics on the gut microbiome in 7,738 participants of the Dutch Microbiome Project

Host genetics are known to influence the gut microbiome, yet their role remains poorly understood. To robustly characterize these effects, we performed a genome-wide association study of 207 taxa and 205 pathways representing microbial composition and function in 7,738 participants of the Dutch Microbiome Project. Two robust, study-wide significant (P < 1.89 × 10-10) signals near the LCT and ABO genes were found to be associated with multiple microbial taxa and pathways and were replicated in two independent cohorts. The LCT locus associations seemed modulated by lactose intake, whereas those at ABO could be explained by participant secretor status determined by their FUT2 genotype. Twenty-two other loci showed suggestive evidence (P < 5 × 10-8) of association with microbial taxa and pathways. At a more lenient threshold, the number of loci we identified strongly correlated with trait heritability, suggesting that much larger sample sizes are needed to elucidate the remaining effects of host genetics on the gut microbiome.

Combined effects of host genetics and diet on human gut microbiota and incident disease in a single population cohort

Human genetic variation affects the gut microbiota through a complex combination of environmental and host factors. Here we characterize genetic variations associated with microbial abundances in a single large-scale population-based cohort of 5,959 genotyped individuals with matched gut microbial metagenomes, and dietary and health records (prevalent and follow-up). We identified 567 independent SNP-taxon associations. Variants at the LCT locus associated with Bifidobacterium and other taxa, but they differed according to dairy intake. Furthermore, levels of Faecalicatena lactaris associated with ABO, and suggested preferential utilization of secreted blood antigens as energy source in the gut. Enterococcus faecalis levels associated with variants in the MED13L locus, which has been linked to colorectal cancer. Mendelian randomization analysis indicated a potential causal effect of Morganella on major depressive disorder, consistent with observational incident disease analysis. Overall, we identify and characterize the intricate nature of host-microbiota interactions and their association with disease.

Mendelian randomization analyses support causal relationships between blood metabolites and the gut microbiome

The gut microbiome has been implicated in a variety of physiological states, but controversy over causality remains unresolved. Here, we performed bidirectional Mendelian randomization analyses on 3,432 Chinese individuals with whole-genome, whole-metagenome, anthropometric and blood metabolic trait data. We identified 58 causal relationships between the gut microbiome and blood metabolites, and replicated 43 of them. Increased relative abundances of fecal Oscillibacter and Alistipes were causally linked to decreased triglyceride concentration. Conversely, blood metabolites such as glutamic acid appeared to decrease fecal Oxalobacter, and members of Proteobacteria were influenced by metabolites such as 5-methyltetrahydrofolic acid, alanine, glutamate and selenium. Two-sample Mendelian randomization with data from Biobank Japan partly corroborated results with triglyceride and with uric acid, and also provided causal support for published fecal bacterial markers for cancer and cardiovascular diseases. This study illustrates the value of human genetic information to help prioritize gut microbial features for mechanistic and clinical studies.

KOMB: K-core based de novo characterization of copy number variation in microbiomes

Characterizing metagenomes via kmer-based, database-dependent taxonomic classification has yielded key insights into underlying microbiome dynamics. However, novel approaches are needed to track community dynamics and genomic flux within metagenomes, particularly in response to perturbations. We describe KOMB, a novel method for tracking genome level dynamics within microbiomes. KOMB utilizes K-core decomposition to identify Structural variations (SVs), specifically, population-level Copy Number Variation (CNV) within microbiomes. K-core decomposition partitions the graph into shells containing nodes of induced degree at least K, yielding reduced computational complexity compared to prior approaches. Through validation on a synthetic community, we show that KOMB recovers and profiles repetitive genomic regions in the sample. KOMB is shown to identify functionally-important regions in Human Microbiome Project datasets, and was used to analyze longitudinal data and identify keystone taxa in Fecal Microbiota Transplantation (FMT) samples. In summary, KOMB represents a novel graph-based, taxonomy-oblivious, and reference-free approach for tracking CNV within microbiomes. KOMB is open source and available for download at https://gitlab.com/treangenlab/komb.

Metagenome-genome-wide association studies reveal human genetic impact on the oral microbiome

The oral microbiota contains billions of microbial cells, which could contribute to diseases in many body sites. Challenged by eating, drinking, and dental hygiene on a daily basis, the oral microbiota is regarded as highly dynamic. Here, we report significant human genomic associations with the oral metagenome from more than 1915 individuals, for both the tongue dorsum (n = 2017) and saliva (n = 1915). We identified five genetic loci associated with oral microbiota at study-wide significance (p < 3.16 × 10-11). Four of the five associations were well replicated in an independent cohort of 1439 individuals: rs1196764 at APPL2 with Prevotella jejuni, Oribacterium uSGB 3339 and Solobacterium uSGB 315; rs3775944 at the serum uric acid transporter SLC2A9 with Oribacterium uSGB 1215, Oribacterium uSGB 489 and Lachnoanaerobaculum umeaense; rs4911713 near OR11H1 with species F0422 uSGB 392; and rs36186689 at LOC105371703 with Eggerthia. Further analyses confirmed 84% (386/455 for tongue dorsum) and 85% (391/466 for saliva) of host genome-microbiome associations including six genome-wide significant associations mutually validated between the two niches. As many of the oral microbiome-associated genetic variants lie near miRNA genes, we tentatively validated the potential of host miRNAs to modulate the growth of specific oral bacteria. Human genetics accounted for at least 10% of oral microbiome compositions between individuals. Machine learning models showed that polygenetic risk scores dominated over oral microbiome in predicting risk of dental diseases such as dental calculus and gingival bleeding. These findings indicate that human genetic differences are one explanation for a stable or recurrent oral microbiome in each individual.

Rare variant MX1 alleles increase human susceptibility to zoonotic H7N9 influenza virus

Zoonotic avian influenza A virus (IAV) infections are rare. Sustained transmission of these IAVs between humans has not been observed, suggesting a role for host genes. We used whole-genome sequencing to compare avian IAV H7N9 patients with healthy controls and observed a strong association between H7N9 infection and rare, heterozygous single-nucleotide variants in the MX1 gene. MX1 codes for myxovirus resistance protein A (MxA), an interferon-induced antiviral guanosine triphosphatase known to control IAV infections in transgenic mice. Most of the MxA variants identified lost the ability to inhibit avian IAVs, including H7N9, in transfected human cell lines. Nearly all of the inactive MxA variants exerted a dominant-negative effect on the antiviral function of wild-type MxA, suggesting an MxA null phenotype in heterozygous carriers. Our study provides genetic evidence for a crucial role of the MX1-based antiviral defense in controlling zoonotic IAV infections in humans.