Genome-wide associations of human gut microbiome variation and implications for causal inference analyses

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.

Gut microbiome and obesity in late adolescence: A case-control study in "Children of 1997" birth cohort

PURPOSE

Although the gut microbiome is important in human health, its relation to adolescent obesity remains unclear. Here we assessed the associations of the gut microbiome with adolescent obesity in a case-control study.

METHODS

In the "Children of 1997" birth cohort, participants with and without obesity at ∼17.4 years were 1:1 matched on sex, physical activity, parental education and occupation (n = 312). Fecal gut microbiome composition and pathways were assessed via shotgun metagenomic sequencing. The association of microbiota species with obesity was evaluated using conditional logistic regression. We explored the association of the obesity-relevant species with adolescent metabolomics using multivariable linear regression, and causal relationships with type 2 diabetes using Mendelian randomization analysis.

RESULTS

Gut microbiota in the adolescents with obesity exhibited lower richness (p = 0.031) and evenness (p = 0.014) compared to controls. Beta diversity revealed differences in the microbiome composition in two groups (p = 0.034). Lower relative abundance of Clostridium spiroforme, Clostridium phoceensis and Bacteroides uniformis were associated with higher obesity risk (q<0.15). Lower Bacteroides uniformis was associated with higher branched-chain amino acid, potentially contributing to higher type 2 diabetes risk.

CONCLUSION

Adolescents with obesity had a distinct gut microbiota profile compared to the controls, possibly linked to metabolic pertubation and related diseases.

Examining the healthy human microbiome concept

Human microbiomes are essential to health throughout the lifespan and are increasingly recognized and studied for their roles in metabolic, immunological and neurological processes. Although the full complexity of these microbial communities is not fully understood, their clinical and industrial exploitation is well advanced and expanding, needing greater oversight guided by a consensus from the research community. One of the most controversial issues in microbiome research is the definition of a 'healthy' human microbiome. This concept is complicated by the microbial variability over different spatial and temporal scales along with the challenge of applying a unified definition to the spectrum of healthy microbiome configurations. In this Perspective, we examine the progress made and the key gaps that remain to be addressed to fully harness the benefits of the human microbiome. We propose a road map to expand our knowledge of the microbiome-health relationship, incorporating epidemiological approaches informed by the unique ecological characteristics of these communities.

Unfolding and de-confounding: biologically meaningful causal inference from longitudinal multi-omic networks using METALICA

A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state of the art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel unrolling and de-confounding techniques used to uncover multi-omic entities that are believed to act as confounders for some of the relationships that may be inferred using standard causal inferencing tools. The results lend support to predictions about biological models and processes by which microbial taxa interact with each other in a microbiome. The unrolling process helps identify putative intermediaries (genes and/or metabolites) to explain the interactions between microbes; the de-confounding process identifies putative common causes that may lead to spurious relationships to be inferred. METALICA was applied to the networks inferred by existing causal discovery, and network inference algorithms were applied to a multi-omics data set resulting from a longitudinal study of IBD microbiomes. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.

IMPORTANCE

We have developed a suite of tools and techniques capable of inferring interactions between microbiome entities. METALICA introduces novel techniques called unrolling and de-confounding that are employed to uncover multi-omic entities considered to be confounders for some of the relationships that may be inferred using standard causal inferencing tools. To evaluate our method, we conducted tests on the inflammatory bowel disease (IBD) dataset from the iHMP longitudinal study, which we pre-processed in accordance with our previous work. From this dataset, we generated various subsets, encompassing different combinations of metagenomics, metabolomics, and metatranscriptomics datasets. Using these multi-omics datasets, we demonstrate how the unrolling process aids in the identification of putative intermediaries (genes and/or metabolites) to explain the interactions between microbes. Additionally, the de-confounding process identifies potential common causes that may give rise to spurious relationships to be inferred. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.

Gut microbiome and serum metabolome alterations associated with lactose intolerance (LI): a case‒control study and paired-sample study based on the American Gut Project (AGP)

Lactose intolerance (LI) is a prevalent condition characterized by gastrointestinal symptoms that arise following lactose consumption. Recent evidence suggests that the gut microbiome may influence lactose levels in the gut. However, there is limited understanding regarding the alterations in microbiota and metabolism between individuals with LI and non-LI. This study conducted a paired-sample investigation utilizing data from the American Gut Project (AGP) and performed metagenomic and untargeted metabolomic analyses in a Chinese cohort to explore the interaction between the gut microbiome and serum metabolites. In addition, fecal microbiota transplantation (FMT) experiments were conducted to further examine the impact of the LI-associated gut microbiome on inflammatory outcomes. We identified 14 microbial genera that significantly differed between LI and controls from AGP data. Using a machine learning approach, group separation was predicted based on seven species and nine metabolites in the Chinese cohort. Notably, increased levels of Escherichia coli in the LI group were negatively correlated with several metabolites, including PC (22:6/0:0), indole, and Lyso PC, while reduced levels of Faecalibacterium prausnitzii and Eubacterium rectale were positively correlated with indole and furazolidone. FMT-LI rats displayed visceral hypersensitivity and an altered gut microbiota composition compared to FMT-HC rats. Metagenomic and metabolomic analyses revealed an enrichment of MAPK signaling in LI, which was confirmed by FMT-LI rats showing higher expression of ERK and RAS, along with increased concentrations of proinflammatory cytokines. This study provides valuable insights into the disrupted microbial and metabolic traits associated with LI, emphasizing potential microbiome-based approaches for its prevention and treatment.

IMPORTANCE

Lactose intolerance (LI) is a prevalent condition characterized by gastrointestinal symptoms after lactose consumption due to a deficiency of lactase. There is limited understanding regarding the microbiota and metabolic alterations between individuals with LI and non-LI. This study represents the first exploration to investigate metagenomic and metabolomic signatures among subjects with lactose intolerance as far as our knowledge. We identified 14 microbial genera in the Western cohort and 7 microbial species, along with 9 circulating metabolites in the Chinese cohort, which significantly differed in LI patients. Metagenomic and metabolomic analyses revealed an enrichment of MAPK signaling in LI patients. This finding was confirmed by FMT-LI rats, exhibiting increased expression of ERK and RAS, along with higher concentrations of pro-inflammatory cytokines. Our study provides insights into the disrupted functional and metabolic traits of the gut microbiome in LI, highlighting potential microbiome-based approaches for preventing and treating LI.

Calcium deposition in chicken eggshells: role of host genetics and gut microbiota

Eggshell is predominantly composed of calcium carbonate, making up about 95% of its composition. Eggshell quality is closely related to the amount of calcium deposition in the shell, which requires chickens to maintain a robust state of calcium metabolism. In this study, we introduced a novel parameter, Total Eggshell Weight (TESW), which measures the total weight of eggshells produced by chickens over a period of 10 consecutive d, providing valuable information on the intensity of calcium metabolism in chickens. Genome-wide association study (GWAS) was conducted to explore the genetic determinants of eggshell calcification in a population of 570 Rhode Island Red laying hens at 90 wk of age. This study revealed a significant association between a specific SNP (rs14249431) and TESW. Additionally, using random forest modeling and 2-tailed testing, we identified 3 genera, Lactobacillus in the jejunum, Lactobacillus, and Fournierella in the cecum, that exhibited a significant association with TESW. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis of claudin-1 and occludin genes in individuals with low TESW and high abundance of jejunal Lactobacillus confirmed that the inhibitory effect of jejunal Lactobacillus on calcium uptake was achieved through the up-regulation of tight junctions in intestinal epithelial cells. Notably, both host and microbial factors influence TESW, displaying a mutually influential relationship between them. The microbiome-wide Genome-Wide Association Study (mb-GWAS) identified significant associations between these 3 genera and specific genomic variants, such as rs316115020 and rs316420452 on chromosome 5, rs313198529 on chromosome 11, linked to Lactobacillus in the cecum. Moreover, rs312552529 on chromosome 1 exhibited potential association with Fournierella in the cecum. This study highlights the influence of host genetics and gut microbiota on calcium deposition in eggshells during the late laying phase, providing a foundational reference for studying calcium metabolism in hens.

Causal Inference Meets Deep Learning: A Comprehensive Survey

Deep learning relies on learning from extensive data to generate prediction results. This approach may inadvertently capture spurious correlations within the data, leading to models that lack interpretability and robustness. Researchers have developed more profound and stable causal inference methods based on cognitive neuroscience. By replacing the correlation model with a stable and interpretable causal model, it is possible to mitigate the misleading nature of spurious correlations and overcome the limitations of model calculations. In this survey, we provide a comprehensive and structured review of causal inference methods in deep learning. Brain-like inference ideas are discussed from a brain-inspired perspective, and the basic concepts of causal learning are introduced. The article describes the integration of causal inference with traditional deep learning algorithms and illustrates its application to large model tasks as well as specific modalities in deep learning. The current limitations of causal inference and future research directions are discussed. Moreover, the commonly used benchmark datasets and the corresponding download links are summarized.

Mendelian randomization as a cornerstone of causal inference for gut microbiota and related diseases from the perspective of bibliometrics

Gut microbiota, a special group of microbiotas in the human body, contributes to health in a way that can't be ignored. In recent years, Mendelian randomization, which is a widely used and successful method of analyzing causality, has been investigated for the relationship between the gut microbiota and related diseases. Unfortunately, there seems to be a shortage of systematic bibliometric analysis in this field. Therefore, this study aims to investigate the research progress of Mendelian randomization for gut microbiota through comprehensive bibliometric analysis. In this study, publications about Mendelian randomization for gut microbiota were gathered from 2013 to 2023, utilizing the Web of Science Core Collection as our literature source database. The search strategies were as follows: TS = (intestinal flora OR gut flora OR intestinal microflora OR gut microflora OR intestinal microbiota OR gut microbiota OR bowel microbiota OR bowel flora OR gut bacteria OR intestinal tract bacteria OR bowel bacteria OR gut metabolites OR gut microbiota) and TS = (Mendelian randomization). VOSviewer (version 1.6.18), CiteSpace (version 6.1.R1), Microsoft Excel 2021, and Scimago Graphica were employed for bibliometric and visualization analysis. According to research, from January 2013 to August 2023, 154 publications on Mendelian randomization for gut microbiota were written by 1053 authors hailing from 332 institutions across 31 countries and published in 86 journals. China had the highest number of publications, with 109. Frontiers in Microbiology is the most prolific journal, and Lei Zhang has published the highest number of significant articles. The most popular keywords were "Mendelian randomization," "gut microbiota," "instruments," "association," "causality," "gut microbiome," "risk," "bias," "genome-wide association," and "causal relationship." Moreover, the current research hotspots in this field focus on utilizing a 2-sample Mendelian randomization to investigate the relationship between gut microbiota and associated disorders. This research systematically reveals a comprehensive overview of the literature that has been published over the last 10 years about Mendelian randomization for gut microbiota. Moreover, the knowledge of key information in the field from a bibliometric perspective may greatly facilitate future research in the field.

Caenorhabditis elegans as a Convenient Animal Model for Microbiome Studies

Microbes constitute the most prevalent life form on Earth, yet their remarkable diversity remains mostly unrecognized. Microbial diversity in vertebrate models presents a significant challenge for investigating host-microbiome interactions. The model organism Caenorhabditis elegans has many advantages for delineating the effects of host genetics on microbial composition. In the wild, the C. elegans gut contains various microbial species, while in the laboratory it is usually a host for a single bacterial species. There is a potential host-microbe interaction between microbial metabolites, drugs, and C. elegans phenotypes. This mini-review aims to summarize the current understanding regarding the microbiome in C. elegans. Examples using C. elegans to study host-microbe-metabolite interactions are discussed.

The intestine microbiota of shrimp and its impact on cultivation

Intestinal microbiome contains several times of functional genes compared to the host and mediates the generation of multiple metabolic products, and therefore it is called "second genome" for host. Crustaceans rank second among the largest subphylum of aquaculture animals that are considered potentially satisfy global substantial food and nutrition security, among which the Pacific white shrimp (Litopenaeus vannamei) ranks the first in the production. Currently, increasing evidences show that outbreaks of some most devastating diseases in shrimp, including white feces syndrome (WFS) and acute hepatopancreatic necrosis disease (AHPND), are related to intestinal microbiota dysbiosis. Importantly, the intestine microbial composition can be altered by environmental stress, diet, and age. In this review, we overview the progress of intestinal microbiota dysbiosis and WFS or ANPHD in shrimp, and how the microbial composition is altered by external factors. Hence, developing suitable microbial micro-ecological prevention and control strategy to maintain intestinal balance may be a feasible solution to reduce the risk of disease outbreaks. Moreover, we highlight that defining the "healthy intestine microbiota" and evaluating the causality of intestinal microbiota dysbiosis and diseases following the logic of "Microecological Koch's postulates" should be the key goal in future shrimp intestinal field, which help to guide disease diagnosis and prevent disease outbreaks in shrimp farming. KEY POINTS: • Intestinal microbiota dysbiosis is relevant to multiple shrimp diseases. • Microecological Koch's postulates help to evaluate the causality of shrimp diseases.

Sugar types, genetic predictors of the gut microbiome, and the risk of chronic kidney disease: a prospective cohort study

Background: Emerging studies suggest that focusing on the intake of specific types or sources of sugars may yield greater benefits in preventing chronic kidney disease (CKD). Objective: We aimed to investigate the associations between free and non-free sugar intakes and CKD risk as well as the potential sugar type-gut microbiome interactions. Methods: A total of 138 064 participants from the UK Biobank were included in this prospective study. The free and non-free sugar intakes were assessed using repeated web-based 24-hour dietary recalls. A cause-specific competing risk model was used to estimate hazard ratios (HRs) and the corresponding confidence intervals (CIs) of incident CKD, treating deaths before incident CKD as competing events. Results: During a median follow-up of 10.5 years, 2,923 participants (2.1%) developed CKD. The free sugar intake was positively associated with the risk of CKD (HRquartile 4 vs. quartile 1 = 1.32, 95% CI = 1.18, 1.47), with a nonlinear relationship (P for nonlinearity = 0.01, the risk increased rapidly after free sugars made up 10% of the total energy). The non-free sugar intake was inversely associated with CKD risk (HRquartile 4 vs. quartile 1 = 0.68, 95% CI = 0.60, 0.77), with an L-shaped nonlinear curve (p for nonlinearity = 0.01, the turning point was at 13.5% of the total energy). We found that the associations between free sugar and non-free sugar intakes and CKD risk were more pronounced in participants with high genetically predicted gut microbial abundance. Furthermore, a significant interaction was observed between the genetically predicted gut microbial abundance and non-free sugar intake (P for interaction = 0.04). Conclusion: A higher intake of free sugars was associated with an elevated risk of CKD, whereas a higher intake of non-free sugars was associated with a reduced risk of CKD. The impact of free sugar intake and non-free sugar intake may be modified by the gut microbial abundance.

Human genetic associations of the airway microbiome in chronic obstructive pulmonary disease

Little is known about the relationships between human genetics and the airway microbiome. Deeply sequenced airway metagenomics, by simultaneously characterizing the microbiome and host genetics, provide a unique opportunity to assess the microbiome-host genetic associations. Here we performed a co-profiling of microbiome and host genetics with the identification of over 5 million single nucleotide polymorphisms (SNPs) through deep metagenomic sequencing in sputum of 99 chronic obstructive pulmonary disease (COPD) and 36 healthy individuals. Host genetic variation was the most significant factor associated with the microbiome except for geography and disease status, with its top 5 principal components accounting for 12.11% of the microbiome variability. Within COPD individuals, 113 SNPs mapped to candidate genes reported as genetically associated with COPD exhibited associations with 29 microbial species and 48 functional modules (P < 1 × 10-5), where Streptococcus salivarius exhibits the strongest association to SNP rs6917641 in TBC1D32 (P = 9.54 × 10-8). Integration of concurrent host transcriptomic data identified correlations between the expression of host genes and their genetically-linked microbiome features, including NUDT1, MAD1L1 and Veillonella parvula, TTLL9 and Stenotrophomonas maltophilia, and LTA4H and Haemophilus influenzae. Mendelian randomization analyses revealed a potential causal link between PARK7 expression and microbial type III secretion system, and a genetically-mediated association between COPD and increased relative abundance of airway Streptococcus intermedius. These results suggest a previously underappreciated role of host genetics in shaping the airway microbiome and provide fresh hypotheses for genetic-based host-microbiome interactions in COPD.

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease

To understand the dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune, and clinical markers of microbiomes from four body sites in 86 participants over 6 years. We found that microbiome stability and individuality are body-site specific and heavily influenced by the host. The stool and oral microbiome are more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. We identify individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlate across body sites, suggesting systemic dynamics influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals show altered microbial stability and associations among microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease.

Integration of polygenic and gut metagenomic risk prediction for common diseases

Multiomics has shown promise in noninvasive risk profiling and early detection of various common diseases. In the present study, in a prospective population-based cohort with ~18 years of e-health record follow-up, we investigated the incremental and combined value of genomic and gut metagenomic risk assessment compared with conventional risk factors for predicting incident coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer disease and prostate cancer. We found that polygenic risk scores (PRSs) improved prediction over conventional risk factors for all diseases. Gut microbiome scores improved predictive capacity over baseline age for CAD, T2D and prostate cancer. Integrated risk models of PRSs, gut microbiome scores and conventional risk factors achieved the highest predictive performance for all diseases studied compared with models based on conventional risk factors alone. The present study demonstrates that integrated PRSs and gut metagenomic risk models improve the predictive value over conventional risk factors for common chronic diseases.

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease

To understand dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune and clinical markers of microbiomes from four body sites in 86 participants over six years. We found that microbiome stability and individuality are body-site-specific and heavily influenced by the host. The stool and oral microbiome were more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. Also, we identified individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlated across body sites, suggesting systemic coordination influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals showed altered microbial stability and associations between microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease.

Study Highlights

The stability of the human microbiome varies among individuals and body sites.Highly individualized microbial genera are more stable over time.At each of the four body sites, systematic interactions between the environment, the host and bacteria can be detected.Individuals with insulin resistance have lower microbiome stability, a more diversified skin microbiome, and significantly altered host-microbiome interactions.

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.

Meta-analysis identifying gut microbial biomarkers of Qinghai-Tibet Plateau populations and the functionality of microbiota-derived butyrate in high-altitude adaptation

The extreme environmental conditions of a plateau seriously threaten human health. The relationship between gut microbiota and human health at high altitudes has been extensively investigated. However, no universal gut microbiota biomarkers have been identified in the plateau population, limiting research into gut microbiota and high-altitude adaptation. 668 16s rRNA samples were analyzed using meta-analysis to reduce batch effects and uncover microbiota biomarkers in the plateau population. Furthermore, the robustness of these biomarkers was validated. Mendelian randomization (MR) results indicated that Tibetan gut microbiota may mediate a reduced erythropoietic response. Functional analysis and qPCR revealed that butyrate may be a functional metabolite in high-altitude adaptation. A high-altitude rat model showed that butyrate reduced intestinal damage caused by high altitudes. According to cell experiments, butyrate may downregulate hypoxia-inducible factor-1α (HIF-1α) expression and blunt cellular responses to hypoxic stress. Our research found universally applicable biomarkers and investigated their potential roles in promoting human health at high altitudes.

miMatch: a microbial metabolic background matching tool for mitigating host confounding in metagenomics research

Metagenomic research faces a persistent challenge due to the low concordance across studies. While matching host confounders can mitigate the impact of individual differences, the influence of factors such as genetics, environment, and lifestyle habits on microbial profiles makes it exceptionally challenging to create fully matched cohorts. The microbial metabolic background, which modulates microbial composition, reflects a cumulative impact of host confounders, serving as an ideal baseline for microbial sample matching. In this study, we introduced miMatch, an innovative metagenomic sample-matching tool that uses microbial metabolic background as a comprehensive reference for host-related variables and employs propensity score matching to build case-control pairs, even in the absence of host confounders. In the simulated datasets, miMatch effectively eliminated individual metabolic background differences, thereby enhancing the accuracy of identifying differential microbial patterns and reducing false positives. Moreover, in real metagenomic data, miMatch improved result consistency and model generalizability across cohorts of the same disease. A user-friendly web server (https://www.biosino.org/iMAC/mimatch) has been established to promote the integration of multiple metagenomic cohorts, strengthening causal relationships in metagenomic research.

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.

Unfolding and De-confounding: Biologically meaningful causal inference from longitudinal multi-omic networks using METALICA

A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state-of-the-art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel unrolling and de-confounding techniques used to uncover multi-omic entities that are believed to act as confounders for some of the relationships that may be inferred using standard causal inferencing tools. The results lend support to predictions about biological models and processes by which microbial taxa interact with each other in a microbiome. The unrolling process helps to identify putative intermediaries (genes and/or metabolites) to explain the interactions between microbes; the de-confounding process identifies putative common causes that may lead to spurious relationships to be inferred. METALICA was applied to the networks inferred by existing causal discovery and network inference algorithms applied to a multi-omics data set resulting from a longitudinal study of IBD microbiomes. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.

Host genetics and gut microbiota jointly regulate blood biochemical indicators in chickens

Blood biochemical indicators play a crucial role in assessing an individual's overall health status and metabolic function. In this study, we measured five blood biochemical indicators, including total cholesterol (CHOL), low-density lipoprotein cholesterol (LDL-CH), triglycerides (TG), high-density lipoprotein cholesterol (HDL-CH), and blood glucose (BG), as well as 19 growth traits of 206 male chickens. By integrating host whole-genome information and 16S rRNA sequencing of the duodenum, jejunum, ileum, cecum, and feces microbiota, we assessed the contributions of host genetics and gut microbiota to blood biochemical indicators and their interrelationships. Our results demonstrated significant negative phenotypic and genetic correlations (r =  - 0.20 ~  - 0.67) between CHOL and LDL-CH with growth traits such as body weight, abdominal fat content, muscle content, and shin circumference. The results of heritability and microbiability indicated that blood biochemical indicators were jointly regulated by host genetics and gut microbiota. Notably, the heritability of HDL-CH was estimated to be 0.24, while the jejunal microbiability for BG and TG reached 0.45 and 0.23. Furthermore, by conducting genome-wide association study (GWAS) with the single-nucleotide polymorphism (SNPs), insertion/deletion (indels), and structural variation (SV), we identified RAP2C, member of the RAS oncogene family (RAP2C), dedicator of cytokinesis 11 (DOCK11), neurotensin (NTS) and BOP1 ribosomal biogenesis factor (BOP1) as regulators of HDL-CH, and glycerophosphodiester phosphodiesterase domain containing 5 (GDPD5), dihydrodiol dehydrogenase (DHDH), and potassium voltage-gated channel interacting protein 1 (KCNIP1) as candidate genes of BG. Moreover, our findings suggest that cecal RF39 and Clostridia_UCG_014 may be linked to the regulation of CHOL, and jejunal Streptococcaceae may be involved in the regulation of TG. Additionally, microbial GWAS results indicated that the presence of gut microbiota was under host genetic regulation. Our findings provide valuable insights into the complex interaction between host genetics and microbiota in shaping the blood biochemical profile of chickens. KEY POINTS: • Multiple candidate genes were identified for the regulation of CHOL, HDL-CH, and BG. • RF39, Clostridia_UCG_014, and Streptococcaceae were implicated in CHOL and TG modulation. • The composition of gut microbiota is influenced by host genetics.

Causal effects of gut microbiome on endometriosis: a two-sample mendelian randomization study

BACKGROUND

Previous studies have shown observational associations between the gut microbiota and endometriosis; however, the causal nature of such associations remains unclear. This study aimed to analyze the genetic causal relationship between the two.

METHODS

A gut microbiome genome-wide association study conducted by the MiBioGen consortium was used as exposure data, and summary statistics of endometriosis were obtained from the FinnGen consortium R8 release data. Inverse variance weighted, MR-Egger, weighted median, weighted model, and simple model analyses were applied to examine the causal relationship, and sensitivity analyses were conducted to validate the robustness of the results.

RESULTS

The results showed that, out of 211 gut microbiome taxa, Clostridiales_vadin_BB60_group, Oxalobacteraceae, Desulfovibrio, Haemophilus, and Holdemania had protective effects on endometriosis, while Porphyromonadaceae and Anaerotruncus might contribute to the development of endometriosis. Heterogeneity and pleiotropy analyses confirmed the robustness of the results.

CONCLUSION

The two-sample Mendelian randomization analysis conducted in this study identified specific intestinal flora with a causal relationship with endometriosis at the genetic level, offering new insights into the gut microbiota-mediated development mechanism of endometriosis.

Analysis of gut microbiome, host genetics, and plasma metabolites reveals gut microbiome-host interactions in the Japanese population

Interaction between the gut microbiome and host plays a key role in human health. Here, we perform a metagenome shotgun-sequencing-based analysis of Japanese participants to reveal associations between the gut microbiome, host genetics, and plasma metabolome. A genome-wide association study (GWAS) for microbial species (n = 524) identifies associations between the PDE1C gene locus and Bacteroides intestinalis and between TGIF2 and TGIF2-RAB5IF gene loci and Bacteroides acidifiaciens. In a microbial gene ortholog GWAS, agaE and agaS, which are related to the metabolism of carbohydrates forming the blood group A antigen, are associated with blood group A in a manner depending on the secretor status determined by the East Asian-specific FUT2 variant. A microbiome-metabolome association analysis (n = 261) identifies associations between bile acids and microbial features such as bile acid metabolism gene orthologs including bai and 7β-hydroxysteroid dehydrogenase. Our publicly available data will be a useful resource for understanding gut microbiome-host interactions in an underrepresented population.

Causal associations between gut microbiota, gut microbiota-derived metabolites, and cerebrovascular diseases: a multivariable Mendelian randomization study

Background

Mounting evidence has demonstrated the associations between gut microbiota, gut microbiota-derived metabolites, and cerebrovascular diseases (CVDs). The major categories of CVD are ischemic stroke (IS), intracerebral hemorrhage (ICH), and subarachnoid hemorrhage (SAH). However, the causal relationship is still unclear.

Methods

A two-sample Mendelian randomization (MR) study was conducted leveraging the summary data from genome-wide association studies. The inverse variance-weighted, maximum likelihood, weighted median, and MR.RAPS methods were performed to detect the causal relationship. Several sensitivity analyses were carried out to evaluate potential horizontal pleiotropy and heterogeneity. Finally, reverse MR analysis was conducted to examine the likelihood of reverse causality, and multivariable MR was performed to adjust the potential confounders.

Results

We collected 1,505 host single nucleotide polymorphisms (SNPs) linked to 119 gut microbiota traits and 1,873 host SNPs associated with 81 gut metabolite traits as exposure data. Among these, three gut bacteria indicated an elevated risk of IS, two of ICH, and one of SAH. In contrast, five gut bacteria were associated with a reduced risk of IS, one with ICH, and one with SAH. Our study also demonstrated the potential causal associations between 11 gut microbiota-derived metabolites and CVD.

Conclusions

This study provided evidence of the causal relationship between gut microbiota, gut microbiota-derived metabolites, and CVD, thereby offering novel perspectives on gut biomarkers and targeted prevention and treatment for CVD.

Geographic social vulnerability is associated with the alpha diversity of the human microbiome

IMPORTANCE

As a risk factor for conditions related to the microbiome, understanding the role of SVI on microbiome diversity may assist in identifying public health implications for microbiome research. Here we found, using a sub-sample of the Human Microbiome Project phase 1 cohort, that SVI was linked to microbiome diversity across body sites and that SVI may influence race/ethnicity-based differences in diversity. Our findings, build on the current knowledge regarding the role of human geography in microbiome research, suggest that measures of geographic social vulnerability be considered as additional contextual factors when exploring microbiome alpha diversity.

A fast non-parametric test of association for multiple traits

The increasing availability of multidimensional phenotypic data in large cohorts of genotyped individuals requires efficient methods to identify genetic effects on multiple traits. Permutational multivariate analysis of variance (PERMANOVA) offers a powerful non-parametric approach. However, it relies on permutations to assess significance, which hinders the analysis of large datasets. Here, we derive the limiting null distribution of the PERMANOVA test statistic, providing a framework for the fast computation of asymptotic p values. Our asymptotic test presents controlled type I error and high power, often outperforming parametric approaches. We illustrate its applicability in the context of QTL mapping and GWAS.

Overview of data preprocessing for machine learning applications in human microbiome research

Although metagenomic sequencing is now the preferred technique to study microbiome-host interactions, analyzing and interpreting microbiome sequencing data presents challenges primarily attributed to the statistical specificities of the data (e.g., sparse, over-dispersed, compositional, inter-variable dependency). This mini review explores preprocessing and transformation methods applied in recent human microbiome studies to address microbiome data analysis challenges. Our results indicate a limited adoption of transformation methods targeting the statistical characteristics of microbiome sequencing data. Instead, there is a prevalent usage of relative and normalization-based transformations that do not specifically account for the specific attributes of microbiome data. The information on preprocessing and transformations applied to the data before analysis was incomplete or missing in many publications, leading to reproducibility concerns, comparability issues, and questionable results. We hope this mini review will provide researchers and newcomers to the field of human microbiome research with an up-to-date point of reference for various data transformation tools and assist them in choosing the most suitable transformation method based on their research questions, objectives, and data characteristics.

The interactions between host genome and gut microbiome increase the risk of psychiatric disorders: Mendelian randomization and biological annotation

BACKGROUND

The correlation between human gut microbiota and psychiatric diseases has long been recognized. Based on the heritability of the microbiome, genome-wide association studies on human genome and gut microbiome (mbGWAS) have revealed important host-microbiome interactions. However, establishing causal relationships between specific gut microbiome features and psychological conditions remains challenging due to insufficient sample sizes of previous studies of mbGWAS.

METHODS

Cross-cohort meta-analysis (via METAL) and multi-trait analysis (via MTAG) were used to enhance the statistical power of mbGWAS for identifying genetic variants and genes. Using two large mbGWAS studies (7,738 and 5,959 participants respectively) and12 disease-specific studies from the Psychiatric Genomics Consortium (PGC), we performed bidirectional two-sample mendelian randomization (MR) analyses between microbial features and psychiatric diseases (up to 500,199 individuals). Additionally, we conducted downstream gene- and gene-set-based analyses to investigate the shared biology linking gut microbiota and psychiatric diseases.

RESULTS

METAL and MTAG conducted in mbGWAS could boost power for gene prioritization and MR analysis. Increases in the number of lead SNPs and mapped genes were witnessed in 13/15 species and 5/10 genera after using METAL, and MTAG analysis gained an increase in sample size equivalent to expanding the original samples from 7% to 63%. Following METAL use, we identified a positive association between Bacteroides faecis and ADHD (OR, 1.09; 95 %CI, 1.02-1.16; P = 0.008). Bacteroides eggerthii and Bacteroides thetaiotaomicron were observed to be positively associated with PTSD (OR, 1.11; 95 %CI, 1.03-1.20; P = 0.007; OR, 1.11; 95 %CI, 1.01-1.23; P = 0.03). These findings remained stable across statistical models and sensitivity analyses. No genetic liabilities to psychiatric diseases may alter the abundance of gut microorganisms.Using biological annotation, we identified that those genes contributing to microbiomes (e.g., GRIN2A and RBFOX1) are expressed and enriched in human brain tissues.

CONCLUSIONS

Our statistical genetics strategy helps to enhance the power of mbGWAS, and our genetic findings offer new insights into biological pleiotropy and causal relationship between microbiota and psychiatric diseases.

Human genetics influences microbiome composition involved in asthma exacerbations despite inhaled corticosteroid treatment

BACKGROUND

The upper-airway microbiome is involved in asthma exacerbations despite inhaled corticosteroid (ICS) treatment. Although human genetics regulates microbiome composition, its influence on asthma-related airway bacteria remains unknown.

OBJECTIVE

We sought to identify genes and biological pathways regulating airway-microbiome traits involved in asthma exacerbations and ICS response.

METHODS

Saliva, nasal, and pharyngeal samples from 257 European patients with asthma were analyzed. The association of 6,296,951 genetic variants with exacerbation-related microbiome traits despite ICS treatment was tested through microbiome genome-wide association studies. Variants with 1 × 10-4 

RESULTS

Genes associated with exacerbation-related airway-microbiome traits were enriched in asthma comorbidities development (ie, reflux esophagitis, obesity, and smoking), and were likely regulated by trichostatin A and the nuclear factor-κB, the glucocorticosteroid receptor, and CCAAT/enhancer-binding protein transcription factors (7.8 × 10-13 ≤ false discovery rate ≤ 0.022). Enrichment in smoking, trichostatin A, nuclear factor-κB, and glucocorticosteroid receptor were replicated in the saliva samples from diverse populations (4.42 × 10-9 ≤ P ≤ .008). The ICS-response-associated single nucleotide polymorphisms rs5995653 (APOBEC3B-APOBEC3C), rs6467778 (TRIM24), and rs5752429 (TPST2) were identified as microbiome quantitative trait loci of Streptococcus, Tannerella, and Campylobacter in the upper airway (0.027 ≤ false discovery rate ≤ 0.050).

CONCLUSIONS

Genes associated with asthma exacerbation-related microbiome traits might influence asthma comorbidities. We reinforced the therapeutic interest of trichostatin A, nuclear factor-κB, the glucocorticosteroid receptor, and CCAAT/enhancer-binding protein in asthma exacerbations.

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Key Words

• Airway microbiome
• CEBP
• NF-κB
• NR3C1
• gastroesophageal reflux disease
• inhaled corticosteroids
• mGWAS
• obesity
• smoking
• trichostatin A

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MESH Headings

• Humans
• Anti-Asthmatic Agents/therapeutic use
• Genome-Wide Association Study
• NF-kappa B/genetics
• Administration, Inhalation
• Asthma/drug therapy
• Asthma/genetics
• Adrenal Cortex Hormones/therapeutic use
• Human Genetics
• Cytidine Deaminase
• Minor Histocompatibility Antigens
• Carrier Proteins/genetics

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Grants

• R01 ES015794 NIEHS NIH HHS
• UL1 TR001872 NCATS NIH HHS
• R01 HL135156 NHLBI NIH HHS
• R21 ES024844 NIEHS NIH HHS
• R01 HL117004 NHLBI NIH HHS
• P60 MD006902 NIMHD NIH HHS
• RL5 GM118984 NIGMS NIH HHS
• R01 MD010443 NIMHD NIH HHS

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Affiliation(s)

Javier Perez-Garcia Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), La Laguna, Tenerife, Spain
Antonio Espuela-Ortiz Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), La Laguna, Tenerife, Spain
José M Hernández-Pérez Pulmonary Medicine Service, Hospital Universitario N.S de Candelaria, La Laguna, Tenerife, Spain; Pulmonary Medicine Section, Hospital Universitario de La Palma, La Palma, Spain
Ruperto González-Pérez Severe Asthma Unit, Allergy Department, Hospital Universitario de Canarias, La Laguna, Tenerife, Spain
Paloma Poza-Guedes Severe Asthma Unit, Allergy Department, Hospital Universitario de Canarias, La Laguna, Tenerife, Spain
Elena Martin-Gonzalez Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), La Laguna, Tenerife, Spain
Celeste Eng Department of Medicine, University of California San Francisco (UCSF), San Francisco, Calif
Olaia Sardón-Prado Division of Pediatric Respiratory Medicine, Hospital Universitario Donostia, San Sebastián, Spain; Department of Pediatrics, University of the Basque Country (UPV/EHU), San Sebastián, Spain
Elena Mederos-Luis Allergy Department, Hospital Universitario de Canarias, La Laguna, Tenerife, Spain
Paula Corcuera-Elosegui Division of Pediatric Respiratory Medicine, Hospital Universitario Donostia, San Sebastián, Spain
Inmaculada Sánchez-Machín Allergy Department, Hospital Universitario de Canarias, La Laguna, Tenerife, Spain
Javier Korta-Murua Division of Pediatric Respiratory Medicine, Hospital Universitario Donostia, San Sebastián, Spain
Jesús Villar CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain; Multidisciplinary Organ Dysfunction Evaluation Research Network, Research Unit, Hospital Universitario Dr. Negrín, Las Palmas de Gran Canaria, Spain; Li Ka Shing Knowledge Institute at the St. Michael's Hospital, Toronto, Ontario, Canada
Esteban G Burchard Department of Medicine, University of California San Francisco (UCSF), San Francisco, Calif; Department of Bioengineering and Therapeutic Sciences, University of California San Francisco (UCSF), San Francisco, Calif
Fabian Lorenzo-Diaz Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), La Laguna, Tenerife, Spain; Instituto Universitario de Enfermedades Tropicales y Salud Pública de Canarias, Universidad de La Laguna (ULL), La Laguna, Tenerife, Spain.
Maria Pino-Yanes Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), La Laguna, Tenerife, Spain; CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain; Instituto de Tecnologías Biomédicas, Universidad de La Laguna (ULL), La Laguna, Tenerife, Spain.

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Variable microbiomes between mosquito lines are maintained across different environments

The composition of the microbiome is shaped by both environment and host in most organisms, but in the mosquito Aedes aegypti the role of the host in shaping the microbiome is poorly understood. Previously, we had shown that four lines of Ae. aegypti harbored different microbiomes when reared in the same insectary under identical conditions. To determine whether these lines differed from each other across time and in different environments, we characterized the microbiome of the same four lines of Ae. aegypti reared in the original insectary and at another institution. While it was clear that the environment influenced the microbiomes of these lines, we did still observe distinct differences in the microbiome between lines within each insectary. Clear differences were observed in alpha diversity, beta diversity, and abundance of specific bacterial taxa. To determine if the line specific differences in the microbiome were maintained across environments, pair-wise differential abundances of taxa was compared between insectaries. Lines were most similar to other lines from the same insectary than to the same line reared in a different insectary. Additionally, relatively few differentially abundant taxa identified between pairs of lines were shared across insectaries, indicating that line specific properties of the microbiome are not conserved across environments, or that there were distinct microbiota within each insectary. Overall, these results demonstrate that mosquito lines under the same environmental conditions have different microbiomes across microbially- diverse environments and host by microbe interactions affecting microbiome composition and abundance is dependent on environmentally available bacteria.

Association between gut microbiome and intracerebral hemorrhage based on genome-wide association study data

OBJECTIVES

Intracerebral hemorrhage (ICH) has the highest mortality and disability rates among various subtypes of stroke. Previous studies have shown that the gut microbiome (GM) is closely related to the risk factors and pathological basis of ICH. This study aims to explore the causal effect of GM on ICH and the potential mechanisms.

METHODS

Genome wide association study (GWAS) data on GM and ICH were obtained from Microbiome Genome and International Stroke Genetics Consortium. Based on the GWAS data, we first performed Mendelian randomization (MR) analysis to evaluate the causal association between GM and ICH. Then, a conditional false discovery rate (cFDR) method was conducted to identify the pleiotropic variants.

RESULTS

MR analysis showed that Pasteurellales, Pasteurellaceae, and Haemophilus were negatively correlated with the risk of ICH, whileVerrucomicrobiae, Verrucomicrobiales, Verrucomicrobiaceae, Akkermansia, Holdemanella, and LachnospiraceaeUCG010 were positively correlated with ICH. By applying the cFDR method, 3 pleiotropic loci (rs331083, rs4315115, and rs12553325) were found to be associated with both GM and ICH.

CONCLUSIONS

There is a causal association and pleiotropic variants between GM and ICH.

Statistical modeling of gut microbiota for personalized health status monitoring

BACKGROUND

The gut microbiome is closely associated with health status, and any microbiota dysbiosis could considerably impact the host's health. In addition, many active consortium projects have generated many reference datasets available for large-scale retrospective research. However, a comprehensive monitoring framework that analyzes health status and quantitatively present bacteria-to-health contribution has not been thoroughly investigated.

METHODS

We systematically developed a statistical monitoring diagram for personalized health status prediction and analysis. Our framework comprises three elements: (1) a statistical monitoring model was established, the health index was constructed, and the health boundary was defined; (2) healthy patterns were identified among healthy people and analyzed using contrast learning; (3) the contribution of each bacterium to the health index of the diseased population was analyzed. Furthermore, we investigated disease proximity using the contribution spectrum and discovered multiple multi-disease-related targets.

RESULTS

We demonstrated and evaluated the effectiveness of the proposed monitoring framework for tracking personalized health status through comprehensive real-data analysis using the multi-study cohort and another validation cohort. A statistical monitoring model was developed based on 92 microbial taxa. In both the discovery and validation sets, our approach achieved balanced accuracies of 0.7132 and 0.7026, and AUC of 0.80 and 0.76, respectively. Four health patterns were identified in healthy populations, highlighting variations in species composition and metabolic function across these patterns. Furthermore, a reasonable correlation was found between the proposed health index and host physiological indicators, diversity, and functional redundancy. The health index significantly correlated with Shannon diversity ([Formula: see text]) and species richness ([Formula: see text]) in the healthy samples. However, in samples from individuals with diseases, the health index significantly correlated with age ([Formula: see text]), species richness ([Formula: see text]), and functional redundancy ([Formula: see text]). Personalized diagnosis is achieved by analyzing the contribution of each bacterium to the health index. We identified high-contribution species shared across multiple diseases by analyzing the contribution spectrum of these diseases.

CONCLUSIONS

Our research revealed that the proposed monitoring framework could promote a deep understanding of healthy microbiomes and unhealthy variations and served as a bridge toward individualized therapy target discovery and precise modulation. Video Abstract.

Using the collaborative cross to identify the role of host genetics in defining the murine gut microbiome

BACKGROUND

The human gut microbiota is a complex community comprised of trillions of bacteria and is critical for the digestion and absorption of nutrients. Bacterial communities of the intestinal microbiota influence the development of several conditions and diseases. We studied the effect of host genetics on gut microbial composition using Collaborative Cross (CC) mice. CC mice are a panel of mice that are genetically diverse across strains, but genetically identical within a given strain allowing repetition and deeper analysis than is possible with other collections of genetically diverse mice.

RESULTS

16S rRNA from the feces of 167 mice from 28 different CC strains was sequenced and analyzed using the Qiime2 pipeline. We observed a large variance in the bacterial composition across CC strains starting at the phylum level. Using bacterial composition data, we identified 17 significant Quantitative Trait Loci (QTL) linked to 14 genera on 9 different mouse chromosomes. Genes within these intervals were analyzed for significant association with pathways and the previously known human GWAS database using Enrichr analysis and Genecards database. Multiple host genes involved in obesity, glucose homeostasis, immunity, neurological diseases, and many other protein-coding genes located in these regions may play roles in determining the composition of the gut microbiota. A subset of these CC mice was infected with Salmonella Typhimurium. Using infection outcome data, an increase in abundance of genus Lachnospiraceae and decrease in genus Parasutterella correlated with positive health outcomes after infection. Machine learning classifiers accurately predicted the CC strain and the infection outcome using pre-infection bacterial composition data from the feces.

CONCLUSION

Our study supports the hypothesis that multiple host genes influence the gut microbiome composition and homeostasis, and that certain organisms may influence health outcomes after S. Typhimurium infection. Video Abstract.

Genome-wide association studies: utility and limitations for research in physiology

Physiological systems are subject to interindividual variation encoded by genetics. Genome-wide association studies (GWAS) operate by surveying thousands of genetic variants from a substantial number of individuals and assessing their association to a trait of interest, be it a physiological variable, a molecular phenotype (e.g. gene expression), or even a disease or condition. Through a myriad of methods, GWAS downstream analyses then explore the functional consequences of each variant and attempt to ascertain a causal relationship to the phenotype of interest, as well as to delve into its links to other traits. This type of investigation allows mechanistic insights into physiological functions, pathological disturbances and shared biological processes between traits (i.e. pleiotropy). An exciting example is the discovery of a new thyroid hormone transporter (SLC17A4) and hormone metabolising enzyme (AADAT) from a GWAS on free thyroxine levels. Therefore, GWAS have substantially contributed with insights into physiology and have been shown to be useful in unveiling the genetic control underlying complex traits and pathological conditions; they will continue to do so with global collaborations and advances in genotyping technology. Finally, the increasing number of trans-ancestry GWAS and initiatives to include ancestry diversity in genomics will boost the power for discoveries, making them also applicable to non-European populations.

Preterm Prelabor Rupture of Membranes Linked to Vaginal Bacteriome of Pregnant Females in the Early Second Trimester: a Case-Cohort Design

Preterm prelabor rupture of membranes (PPROM) is a major cause of spontaneous preterm birth (sPTB), one of the greatest challenges facing obstetrics with complicated pathogenesis. This case-cohort study investigated the association between vaginal bacteriome of singleton pregnant females in the early second trimester and PPROM. The study included 35,255 and 180 pregnant females with PPROM as cases and term-birth without prelabor rupture of membranes (TWPROM) and term prelabor rupture of membranes (TPROM) pregnant females as controls, respectively. Using 16S rRNA sequencing, the vaginal microbiome traits were analyzed. Females with PPROM had higher alpha and beta diversity (P < 0.05) than TWPROM and TPROM. The presence of L. mulieris was associated with a decreased risk of PPROM (adjusted odds ratio [aOR] = 0.35; 95% confidence interval [CI]: 0.17-0.72) compared with TWPROM. Meanwhile, the presence of Megasphaera genus (aOR = 2.27; 95% CI: 1.09-4.70), Faecalibacterium genus (aOR = 3.29; 95% CI: 1.52-7.13), Bifidobacterium genus (aOR = 3.26; 95% CI: 1.47-7.24), Xanthomonadales genus (aOR = 2.76; 95% CI: 1.27-6.01), Gammaproteobacteria class (aOR = 2.36; 95% CI: 1.09-5.14), and Alphaproteobacteria class (aOR = 2.45; 95% CI: 1.14-5.26) was associated with an increased risk of PPROM compared with TWPROM. Our results indicated that the risk of PPROM can decrease with vaginal L. mulieris but increase with high alpha or beta diversity, and several vaginal bacteria in pregnant females may be involved in the occurrence of PPROM.

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 microbiome dysbiosis is associated with host genetics in the Norwegian Lundehund

A group of diseases have been shown to correlate with a phenomenon called microbiome dysbiosis, where the bacterial species composition of the gut becomes abnormal. The gut microbiome of an animal is influenced by many factors including diet, exposures to bacteria during post-gestational growth, lifestyle, and disease status. Studies also show that host genetics can affect microbiome composition. We sought to test whether host genetic background is associated with gut microbiome composition in the Norwegian Lundehund dog, a highly inbred breed with an effective population size of 13 individuals. The Lundehund has a high rate of a protein-losing enteropathy in the small intestine that is often reported as Lundehund syndrome, which negatively affects longevity and life-quality. An outcrossing project with the Buhund, Norrbottenspets, and Icelandic sheepdog was recently established to reintroduce genetic diversity to the Lundehund and improve its health. To assess whether there was an association between host genetic diversity and the microbiome composition, we sampled the fecal microbiomes of 75 dogs of the parental (Lundehund), F1 (Lundehund x Buhund), and F2 (F1 x Lundehund) generations. We found significant variation in microbiome composition from the parental Lundehund generation compared to the outcross progeny. The variation observed in purebred Lundehunds corresponded to dysbiosis as seen by a highly variable microbiome composition with an elevated Firmicutes to Bacteroidetes ratio and an increase in the prevalence of Streptococcus bovis/Streptococcus equinus complex, a known pathobiont that can cause several diseases. We tracked several other environmental factors including diet, the presence of a cat in the household, living in a farm and the use of probiotics, but we did not find evidence of an effect of these on microbiome composition and alpha diversity. In conclusion, we found an association between host genetics and gut microbiome composition, which in turn may be associated with the high incidence of Lundehund syndrome in the purebred parental dogs.

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.

Evaluating the interactive effects of dietary habits and human gut microbiome on the risks of depression and anxiety

BACKGROUND

Gut microbiome and dietary patterns have been suggested to be associated with depression/anxiety. However, limited effort has been made to explore the effects of possible interactions between diet and microbiome on the risks of depression and anxiety.

METHODS

Using the latest genome-wide association studies findings in gut microbiome and dietary habits, polygenic risk scores (PRSs) analysis of gut microbiome and dietary habits was conducted in the UK Biobank cohort. Logistic/linear regression models were applied for evaluating the associations for gut microbiome-PRS, dietary habits-PRS, and their interactions with depression/anxiety status and Patient Health Questionnaire (PHQ-9)/Generalized Anxiety Disorder-7 (GAD-7) score by R software.

RESULTS

We observed 51 common diet-gut microbiome interactions shared by both PHQ score and depression status, such as overall beef intake × genus Sporobacter [hurdle binary (HB)] (PPHQ = 7.88 × 10-4, Pdepression status = 5.86 × 10-4); carbohydrate × genus Lactococcus (HB) (PPHQ = 0.0295, Pdepression status = 0.0150). We detected 41 common diet-gut microbiome interactions shared by GAD score and anxiety status, such as sugar × genus Parasutterella (rank normal transformed) (PGAD = 5.15 × 10-3, Panxiety status = 0.0347); tablespoons of raw vegetables per day × family Coriobacteriaceae (HB) (PGAD = 6.02 × 10-4, Panxiety status = 0.0345). Some common significant interactions shared by depression and anxiety were identified, such as overall beef intake × genus Sporobacter (HB).

CONCLUSIONS

Our study results expanded our understanding of how to comprehensively consider the relationships for dietary habits-gut microbiome interactions with depression and anxiety.

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.

Application of Mendelian randomization to explore the causal role of the human gut microbiome in colorectal cancer

The role of the human gut microbiome in colorectal cancer (CRC) is unclear as most studies on the topic are unable to discern correlation from causation. We apply two-sample Mendelian randomization (MR) to estimate the causal relationship between the gut microbiome and CRC. We used summary-level data from independent genome-wide association studies to estimate the causal effect of 14 microbial traits (n = 3890 individuals) on overall CRC (55,168 cases, 65,160 controls) and site-specific CRC risk, conducting several sensitivity analyses to understand the nature of results. Initial MR analysis suggested that a higher abundance of Bifidobacterium and presence of an unclassified group of bacteria within the Bacteroidales order in the gut increased overall and site-specific CRC risk. However, sensitivity analyses suggested that instruments used to estimate relationships were likely complex and involved in many potential horizontal pleiotropic pathways, demonstrating that caution is needed when interpreting MR analyses with gut microbiome exposures. In assessing reverse causality, we did not find strong evidence that CRC causally affected these microbial traits. Whilst our study initially identified potential causal roles for two microbial traits in CRC, importantly, further exploration of these relationships highlighted that these were unlikely to reflect causality.

Novel genetic variants linked to prelabor rupture of membranes among Chinese pregnant women

INTRODUCTION

The etiology of prelabor rupture of membranes (PROM), either preterm or term PROM (PPROM or TPROM), remains largely unknown. This study aimed to investigate the association between maternal genetic variants (GVs) and PROM and further establish a GV-based prediction model for PROM.

METHODS

In this case-cohort study (n = 1166), Chinese pregnant women with PPROM (n = 51), TPROM (n = 283) and controls (n = 832) were enrolled. A weighted Cox model was applied to identify the GVs (single nucleotide polymorphisms [SNPs], insertions/deletions, and copy number variants) associated with either PPROM or TPROM. Gene set enrichment analysis (GSEA) was to explore the mechanisms. The suggestively significant GVs were applied to establish a random forest (RF) model.

RESULTS

PTPRT variants (rs117950601, P = 4.37 × 10^-9; rs147178603, P = 8.98 × 10^-9) and SNRNP40 variant (rs117573344, P = 2.13 × 10^-8) were associated with PPROM. STXBP5L variant (rs10511405, P = 4.66 × 10^-8) was associated with TPROM. GSEA results showed that genes associated with PPROM were enriched in cell adhesion, and TPROM in ascorbate and glucuronidation metabolism. The area under the receiver operating characteristic curve of SNP-based RF model for PPROM was 0.961, with a sensitivity of 100.0% and specificity of 83.3%.

DISCUSSION

Maternal GVs in PTPRT and SNRNP40 were associated with PPROM, and GV in STXBP5L was associated with TPROM. Cell adhesion participated in PPROM, while ascorbate and glucuronidation metabolism contributed in TPROM. The PPROM might be well predicted using the SNP-based RF model.

Evaluating the genetic interaction effects of gut microbiome and diet on the risk of neuroticism in the UK Biobank cohort

OBJECTIVES

In this study designed to investigate the effect of diet and gut microbiome on neuropsychiatric disorders, we explored the mechanisms of the interaction between diet and gut microbiome on the risk of neuroticism.

METHODS

First, using the individual genotype data from the UK Biobank cohort (N = 306 165), we calculated the polygenic risk score (PRS) based on 814 dietary habits single nucleotide polymorphisms (SNPs), 21 diet compositions SNPs and 1001 gut microbiome SNPs, respectively. Gut microbiome and diet-associated SNPs were collected from three genome-wide association studies (GWAS), including the gut microbiome (N = 3890), diet compositions (over 235 000 subjects) and dietary habits (N = 449 210). The neuroticism score was calculated by 12 questions from the Eysenck Personality Inventory Neuroticism scale. Then, regression analysis was performed to evaluate the interaction effects between diet and the gut microbiome on the risk of neuroticism.

RESULTS

Our studies demonstrated multiple candidate interactions between diet and gut microbiome, such as protein vs. Bifidobacterium (β = 4.59 × 10-3; P = 9.45 × 10-3) and fat vs. Clostridia (β = 3.67 × 10-3; P = 3.90 × 10-2). In addition, pieces of fresh fruit per day vs. Ruminococcus (β = -5.79 × 10-3, P = 1.10 × 10-3) and pieces of dried fruit per day vs. Clostridiales (β = -5.63 × 10-3, P = 1.49 × 10-3) were found to be negatively associated with neuroticism in fruit types. We also identified several positive interactions, such as tablespoons of raw vegetables per day vs. Veillonella (β = 5.92 × 10-3, P = 9.21 × 10-4) and cooked vegetables per day vs. Acidaminococcaceae (β = 5.69 × 10-3, P = 1.24 × 10-3).

CONCLUSIONS

Our results provide novel clues for understanding the roles of diet and gut microbiome in the development of neuroticism.

Bacterial gut microbiome differences in adults with ADHD and in children with ADHD on psychostimulant medication

Recent evidence suggests that there is a link between neurodevelopmental disorders, such as attention deficit hyperactivity disorder (ADHD), and the gut microbiome. However, most studies to date have had low sample sizes, have not investigated the impact of psychostimulant medication, and have not adjusted for potential confounders, including body mass index, stool consistency and diet. To this end, we conducted the largest, to our knowledge, fecal shotgun metagenomic sequencing study in ADHD, with 147 well-characterized adult and child patients. For a subset of individuals, plasma levels of inflammatory markers and short-chain fatty acids were also measured. In adult ADHD patients (n=84), compared to controls (n=52), we found a significant difference in beta diversity both regarding bacterial strains (taxonomic) and bacterial genes (functional). In children with ADHD (n=63), we found that those on psychostimulant medication (n=33 on medication vs. n=30 not on medication) had (i) significantly different taxonomic beta diversity, (ii) lower functional and taxonomic evenness, (iii) lower abundance of the strain Bacteroides stercoris CL09T03C01 and bacterial genes encoding an enzyme in vitamin B₁₂ synthesis, and (iv) higher plasma levels of vascular inflammatory markers sICAM-1 and sVCAM-1. Our study continues to support a role for the gut microbiome in neurodevelopmental disorders and provides additional insights into the effects of psychostimulant medication. However, additional studies are needed to replicate these findings and examine causal relationships with the disorder.

The gut microbiota links disease to human genome evolution

A large number of studies have established a causal relationship between the gut microbiota and human disease. In addition, the composition of the microbiota is substantially influenced by the human genome. Modern medical research has confirmed that the pathogenesis of various diseases is closely related to evolutionary events in the human genome. Specific regions of the human genome known as human accelerated regions (HARs) have evolved rapidly over several million years since humans diverged from a common ancestor with chimpanzees, and HARs have been found to be involved in some human-specific diseases. Furthermore, the HAR-regulated gut microbiota has undergone rapid changes during human evolution. We propose that the gut microbiota may serve as an important mediator linking diseases to human genome evolution.

Interactions between microbiome and underlying mechanisms in asthma

Microbiome primes host innate immunity in utero and play fundamental roles in the development, training, and function of the immune system throughout the life. Interplay between the microbiome and immune system maintains mucosal homeostasis, while alterations of microbial community dysregulate immune responses, leading to distinct phenotypic features of immune-mediated diseases including asthma. Microbial imbalance within the mucosal environments, including upper and lower airways, skin, and gut, has consistently been observed in asthma patients and linked to increased asthma exacerbations and severity. Microbiome research has increased to uncover hidden microbial members, function, and immunoregulatory effects of bacterial metabolites within the mucosa. This review provides an overview of environmental and genetic factors that modulate the composition and function of the microbiome, and the impacts of microbiome metabolites and skin microbiota on immune regulation in asthma.

The gut microbiome and hypertension

A large body of evidence has emerged in the past decade supporting a role for the gut microbiome in the regulation of blood pressure. The field has moved from association to causation in the last 5 years, with studies that have used germ-free animals, antibiotic treatments and direct supplementation with microbial metabolites. The gut microbiome can regulate blood pressure through several mechanisms, including through gut dysbiosis-induced changes in microbiome-associated gene pathways in the host. Microbiota-derived metabolites are either beneficial (for example, short-chain fatty acids and indole-3-lactic acid) or detrimental (for example, trimethylamine N-oxide), and can activate several downstream signalling pathways via G protein-coupled receptors or through direct immune cell activation. Moreover, dysbiosis-associated breakdown of the gut epithelial barrier can elicit systemic inflammation and disrupt intestinal mechanotransduction. These alterations activate mechanisms that are traditionally associated with blood pressure regulation, such as the renin-angiotensin-aldosterone system, the autonomic nervous system, and the immune system. Several methodological and technological challenges remain in gut microbiome research, and the solutions involve minimizing confounding factors, establishing causality and acting globally to improve sample diversity. New clinical trials, precision microbiome medicine and computational methods such as Mendelian randomization have the potential to enable leveraging of the microbiome for translational applications to lower blood pressure.

Genetic mapping of microbial and host traits reveals production of immunomodulatory lipids by Akkermansia muciniphila in the murine gut

The molecular bases of how host genetic variation impacts the gut microbiome remain largely unknown. Here we used a genetically diverse mouse population and applied systems genetics strategies to identify interactions between host and microbe phenotypes including microbial functions, using faecal metagenomics, small intestinal transcripts and caecal lipids that influence microbe-host dynamics. Quantitative trait locus (QTL) mapping identified murine genomic regions associated with variations in bacterial taxa; bacterial functions including motility, sporulation and lipopolysaccharide production and levels of bacterial- and host-derived lipids. We found overlapping QTL for the abundance of Akkermansia muciniphila and caecal levels of ornithine lipids. Follow-up in vitro and in vivo studies revealed that A. muciniphila is a major source of these lipids in the gut, provided evidence that ornithine lipids have immunomodulatory effects and identified intestinal transcripts co-regulated with these traits including Atf3, which encodes for a transcription factor that plays vital roles in modulating metabolism and immunity. Collectively, these results suggest that ornithine lipids are potentially important for A. muciniphila-host interactions and support the role of host genetics as a determinant of responses to gut microbes.

Impact of diet and host genetics on the murine intestinal mycobiome

The mammalian gut is home to a diverse microbial ecosystem, whose composition affects various physiological traits of the host. Next-generation sequencing-based metagenomic approaches demonstrated how the interplay of host genetics, bacteria, and environmental factors shape complex traits and clinical outcomes. However, the role of fungi in these complex interactions remains understudied. Here, using 228 males and 363 females from an advanced-intercross mouse line, we provide evidence that fungi are regulated by host genetics. In addition, we map quantitative trait loci associated with various fungal species to single genes in mice using whole genome sequencing and genotyping. Moreover, we show that diet and its' interaction with host genetics alter the composition of fungi in outbred mice, and identify fungal indicator species associated with different dietary regimes. Collectively, in this work, we uncover an association of the intestinal fungal community with host genetics and a regulatory role of diet in this ecological niche.

Network-based quantitative trait linkage analysis of microbiome composition in inflammatory bowel disease families

Introduction: Inflammatory bowel disease (IBD) is characterized by a dysbiosis of the gut microbiome that results from the interaction of the constituting taxa with one another, and with the host. At the same time, host genetic variation is associated with both IBD risk and microbiome composition. Methods: In the present study, we defined quantitative traits (QTs) from modules identified in microbial co-occurrence networks to measure the inter-individual consistency of microbial abundance and subjected these QTs to a genome-wide quantitative trait locus (QTL) linkage analysis. Results: Four microbial network modules were consistently identified in two cohorts of healthy individuals, but three of the corresponding QTs differed significantly between IBD patients and unaffected individuals. The QTL linkage analysis was performed in a sub-sample of the Kiel IBD family cohort (IBD-KC), an ongoing study of 256 German families comprising 455 IBD patients and 575 first- and second-degree, non-affected relatives. The analysis revealed five chromosomal regions linked to one of three microbial module QTs, namely on chromosomes 3 (spanning 10.79 cM) and 11 (6.69 cM) for the first module, chr9 (0.13 cM) and chr16 (1.20 cM) for the second module, and chr13 (19.98 cM) for the third module. None of these loci have been implicated in a microbial phenotype before. Discussion: Our study illustrates the benefit of combining network and family-based linkage analysis to identify novel genetic drivers of microbiome composition in a specific disease context.