Improving causality in microbiome research: can human genetic epidemiology help?

Statistical and computational methods for integrating microbiome, host genomics, and metabolomics data

Large-scale microbiome studies are progressively utilizing multiomics designs, which include the collection of microbiome samples together with host genomics and metabolomics data. Despite the increasing number of data sources, there remains a bottleneck in understanding the relationships between different data modalities due to the limited number of statistical and computational methods for analyzing such data. Furthermore, little is known about the portability of general methods to the metagenomic setting and few specialized techniques have been developed. In this review, we summarize and implement some of the commonly used methods. We apply these methods to real data sets where shotgun metagenomic sequencing and metabolomics data are available for microbiome multiomics data integration analysis. We compare results across methods, highlight strengths and limitations of each, and discuss areas where statistical and computational innovation is needed.

Mendelian randomization analysis to investigate the gut microbiome in oral and oropharyngeal cancer

Background

Evidence supports an observational association between the gut microbiome and susceptibility to extraintestinal cancers, but the causal relationship of this association remains unclear.

Methods

To identify the specific causal gut microbiota of oral and oropharyngeal cancer, we performed two-sample Mendelian randomization (MR) analysis of gut microbiota on oral and oropharyngeal cancer using a fixed-effects inverse-variance-weighted model. Gut microbiota across five different taxonomical levels from the MiBioGen genome-wide association study (GWAS) were used as exposures. Oral cancer, oropharyngeal cancer and a combination of the two cancers defined from three separate data sources were used as the outcomes. Odds ratios (ORs) and 95% confidence intervals (CIs) for disease per standard deviation (SD) higher abundance of microbiome.

Results & Conclusions

There was little evidence for a causal effect of gut microbiota on oral and oropharyngeal cancer when using a genome-wide p-value threshold for selecting instruments. Secondary analyses using a more lenient p-value threshold indicated that there were 90 causal relationships between 58 different microbial features but that sensitivity analyses suggested that these were possibly affected by violations of MR assumptions and were not consistent across MR methodologies or data sources and therefore are also to unlikely reflect causation. These findings provide new insights into gut microbiota-mediated oral and oropharyngeal cancers and warrant further investigation.

Harnessing the Microbiome: A Comprehensive Review on Advancing Therapeutic Strategies for Rheumatic Diseases

Rheumatic diseases are a group of disorders that affect the joints, muscles, and bones. These diseases, such as rheumatoid arthritis, lupus, and psoriatic arthritis, can cause pain, stiffness, and swelling, leading to reduced mobility and disability. Recent studies have identified the microbiome, the diverse community of microorganisms that live in and on the human body, as a potential factor in the development and progression of rheumatic diseases. Harnessing the microbiome offers a promising new avenue for developing therapeutic strategies for these debilitating conditions. There is growing interest in the role of oral and gut microbiomes in the management of rheumatoid arthritis and other autoimmune disease. Microbial metabolites have immunomodulatory properties that could be exploited for rheumatic disorders. A wide range of microorganisms are present in the oral cavity and are found to be vulnerable to the effects of the environment. The physiology and ecology of the microbiota become intimately connected with those of the host, and they critically influence the promotion of health or progression toward disease. This article aims to provide a comprehensive overview of the current state of knowledge on oral and gut microbiome and its potential future role in the management of rheumatic diseases. This article will also discuss newer treatment strategies such as bioinformatic analyses and fecal transplantation.

Guidelines for microbiome studies in renal physiology

Gut microbiome research has increased dramatically in the last decade, including in renal health and disease. The field is moving from experiments showing mere association to causation using both forward and reverse microbiome approaches, leveraging tools such as germ-free animals, treatment with antibiotics, and fecal microbiota transplantations. However, we are still seeing a gap between discovery and translation that needs to be addressed, so that patients can benefit from microbiome-based therapies. In this guideline paper, we discuss the key considerations that affect the gut microbiome of animals and clinical studies assessing renal function, many of which are often overlooked, resulting in false-positive results. For animal studies, these include suppliers, acclimatization, baseline microbiota and its normalization, littermates and cohort/cage effects, diet, sex differences, age, circadian differences, antibiotics and sweeteners, and models used. Clinical studies have some unique considerations, which include sampling, gut transit time, dietary records, medication, and renal phenotypes. We provide best-practice guidance on sampling, storage, DNA extraction, and methods for microbial DNA sequencing (both 16S rRNA and shotgun metagenome). Finally, we discuss follow-up analyses, including tools available, metrics, and their interpretation, and the key challenges ahead in the microbiome field. By standardizing study designs, methods, and reporting, we will accelerate the findings from discovery to translation and result in new microbiome-based therapies that may improve renal health.

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.

Approaches to discern if microbiome associations reflect causation in metabolic and immune disorders

Our understanding of microorganisms residing within our gut and their roles in the host metabolism and immunity advanced greatly over the past 20 years. Currently, microbiome studies are shifting from association and correlation studies to studies demonstrating causality of identified microbiome signatures and identification of molecular mechanisms underlying these interactions. This transformation is crucial for the efficient translation into clinical application and development of targeted strategies to beneficially modulate the intestinal microbiota. As mechanistic studies are still quite challenging to perform in humans, the causal role of microbiota is frequently evaluated in animal models that need to be appropriately selected. Here, we provide a comprehensive overview on approaches that can be applied in addressing causality of host-microbe interactions in five major animal model organisms (Caenorhabditis elegans, Drosophila melanogaster, zebrafish, rodents, and pigs). We particularly focused on discussing methods available for studying the causality ranging from the usage of gut microbiota transfer, diverse models of metabolic and immune perturbations involving nutritional and chemical factors, gene modifications and surgically induced models, metabolite profiling up to culture-based approached. Furthermore, we addressed the impact of the gut morphology, physiology as well as diet on the microbiota composition in various models and resulting species specificities. Finally, we conclude this review with the discussion on models that can be applied to study the causal role of the gut microbiota in the context of metabolic syndrome and host immunity. We hope this review will facilitate important considerations for appropriate animal model selection.

A randomization-based causal inference framework for uncovering environmental exposure effects on human gut microbiota

Statistical analysis of microbial genomic data within epidemiological cohort studies holds the promise to assess the influence of environmental exposures on both the host and the host-associated microbiome. However, the observational character of prospective cohort data and the intricate characteristics of microbiome data make it challenging to discover causal associations between environment and microbiome. Here, we introduce a causal inference framework based on the Rubin Causal Model that can help scientists to investigate such environment-host microbiome relationships, to capitalize on existing, possibly powerful, test statistics, and test plausible sharp null hypotheses. Using data from the German KORA cohort study, we illustrate our framework by designing two hypothetical randomized experiments with interventions of (i) air pollution reduction and (ii) smoking prevention. We study the effects of these interventions on the human gut microbiome by testing shifts in microbial diversity, changes in individual microbial abundances, and microbial network wiring between groups of matched subjects via randomization-based inference. In the smoking prevention scenario, we identify a small interconnected group of taxa worth further scrutiny, including Christensenellaceae and Ruminococcaceae genera, that have been previously associated with blood metabolite changes. These findings demonstrate that our framework may uncover potentially causal links between environmental exposure and the gut microbiome from observational data. We anticipate the present statistical framework to be a good starting point for further discoveries on the role of the gut microbiome in environmental health.

Environmental influences on the human gut microbiome are still to be discovered or better understood. In this paper, we contribute to the field of microbiome research and environmental epidemiology by suggesting a stage-based causal inference framework relying on the foundations of the Rubin Causal Model. A particularity of the framework is the use of randomization-based inference, which we value to be a necessary exploratory inference method when tackling untapped research questions. To illustrate the framework, we explore the effects of two inhaled environmental exposures previously hypothesized to be linked with gastrointestinal diseases and the gut microbiome: air pollution exposure and cigarette smoking.

Applying Mendelian randomization to appraise causality in relationships between nutrition and cancer

Dietary factors are assumed to play an important role in cancer risk, apparent in consensus recommendations for cancer prevention that promote nutritional changes. However, the evidence in this field has been generated predominantly through observational studies, which may result in biased effect estimates because of confounding, exposure misclassification, and reverse causality. With major geographical differences and rapid changes in cancer incidence over time, it is crucial to establish which of the observational associations reflect causality and to identify novel risk factors as these may be modified to prevent the onset of cancer and reduce its progression. Mendelian randomization (MR) uses the special properties of germline genetic variation to strengthen causal inference regarding potentially modifiable exposures and disease risk. MR can be implemented through instrumental variable (IV) analysis and, when robustly performed, is generally less prone to confounding, reverse causation and measurement error than conventional observational methods and has different sources of bias (discussed in detail below). It is increasingly used to facilitate causal inference in epidemiology and provides an opportunity to explore the effects of nutritional exposures on cancer incidence and progression in a cost-effective and timely manner. Here, we introduce the concept of MR and discuss its current application in understanding the impact of nutritional factors (e.g., any measure of diet and nutritional intake, circulating biomarkers, patterns, preference or behaviour) on cancer aetiology and, thus, opportunities for MR to contribute to the development of nutritional recommendations and policies for cancer prevention. We provide applied examples of MR studies examining the role of nutritional factors in cancer to illustrate how this method can be used to help prioritise or deprioritise the evaluation of specific nutritional factors as intervention targets in randomised controlled trials. We describe possible biases when using MR, and methodological developments aimed at investigating and potentially overcoming these biases when present. Lastly, we consider the use of MR in identifying causally relevant nutritional risk factors for various cancers in different regions across the world, given notable geographical differences in some cancers. We also discuss how MR results could be translated into further research and policy. We conclude that findings from MR studies, which corroborate those from other well-conducted studies with different and orthogonal biases, are poised to substantially improve our understanding of nutritional influences on cancer. For such corroboration, there is a requirement for an interdisciplinary and collaborative approach to investigate risk factors for cancer incidence and progression.

The Macronutrient Composition of Infant Formula Produces Differences in Gut Microbiota Maturation That Associate with Weight Gain Velocity and Weight Status

This proof-of-principle study analyzed fecal samples from 30 infants who participated in a randomized controlled trial on the effects of the macronutrient composition of infant formula on growth and energy balance. In that study, infants randomized to be fed cow milk formula (CMF) had faster weight-gain velocity during the first 4 months and higher weight-for-length Z scores up to 11.5 months than those randomized to an isocaloric extensive protein hydrolysate formula (EHF). Here we examined associations among infant formula composition, gut microbial composition and maturation, and children’s weight status. Fecal samples collected before and monthly up to 4.5 months after randomization were analyzed by shotgun metagenomic sequencing and targeted metabolomics. The EHF group had faster maturation of gut microbiota than the CMF group, and increased alpha diversity driven by Clostridia taxa. Abundance of Ruminococcus gnavus distinguished the two groups after exclusive feeding of the assigned formula for 3 months. Abundance of Clostridia at 3–4 months negatively correlated with prior weight-gain velocity and body weight phenotypes when they became toddlers. Macronutrient differences between the formulas likely led to the observed divergence in gut microbiota composition that was associated with differences in transient rapid weight gain, a well-established predictor of childhood obesity and other comorbidities.

Host Genetic Determinants of the Microbiome Across Animals: From Caenorhabditis elegans to Cattle

Animals harbor diverse communities of microbes within their gastrointestinal tracts. Phylogenetic relationship, diet, gut morphology, host physiology, and ecology all influence microbiome composition within and between animal clades. Emerging evidence points to host genetics as also playing a role in determining gut microbial composition within species. Here, we discuss recent advances in the study of microbiome heritability across a variety of animal species. Candidate gene and discovery-based studies in humans, mice, Drosophila, Caenorhabditis elegans, cattle, swine, poultry, and baboons reveal trends in the types of microbes that are heritable and the host genes and pathways involved in shaping the microbiome. Heritable gut microbes within a host species tend to be phylogenetically restricted. Host genetic variation in immune- and growth-related genes drives the abundances of these heritable bacteria within the gut. With only a small slice of the metazoan branch of the tree of life explored to date, this is an area rife with opportunities to shed light into the mechanisms governing host-microbe relationships.

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.

Chronic rejection as a persisting phantom menace in organ transplantation: a new hope in the microbiota?

PURPOSE OF REVIEW

The microbiota plays an important role in health and disease. During organ transplantation, perturbations in microbiota influence transplant outcome. We review recent advances in characterizing microbiota and studies on regulation of intestinal epithelial barrier function and mucosal and systemic immunity by microbiota and their metabolites. We discuss implications of these interactions on transplant outcomes.

RECENT FINDINGS

Metagenomic approaches have helped the research community identify beneficial and harmful organisms. Microbiota regulates intestinal epithelial functions. Signals released by epithelial cells or microbiota trigger pro-inflammatory or anti-inflammatory effects on innate and adaptive immune cells, influencing the structure and function of the immune system. Assessment and manipulation of microbiota can be used for biomarkers for diagnosis, prognosis, and therapy.

SUMMARY

The bidirectional dialogue between the microbiota and immune system is a major influence on immunity. It can be targeted for biomarkers or therapy. Recent studies highlight a close association of transplant outcomes with microbiota, suggesting exciting potential avenues for management of host physiology and organ transplantation.

The relationship between the gut microbiome and host gene expression: a review

Despite the growing knowledge surrounding host-microbiome interactions, we are just beginning to understand how the gut microbiome influences-and is influenced by-host gene expression. Here, we review recent literature that intersects these two fields, summarizing themes across studies. Work in model organisms, human biopsies, and cell culture demonstrate that the gut microbiome is an important regulator of several host pathways relevant for disease, including immune development and energy metabolism, and vice versa. The gut microbiome remodels host chromatin, causes differential splicing, alters the epigenetic landscape, and directly interrupts host signaling cascades. Emerging techniques like single-cell RNA sequencing and organoid generation have the potential to refine our understanding of the relationship between the gut microbiome and host gene expression in the future. By intersecting microbiome and host gene expression, we gain a window into the physiological processes important for fostering the extensive cross-kingdom interactions and ultimately our health.

Causal Inference in Microbiome Medicine: Principles and Applications

Microorganisms that colonize the mammalian skin and cavity play critical roles in various physiological functions of the host. Numerous studies have revealed strong associations between the microbiota and multiple diseases. However, association does not mean causation. To clarify the mechanisms underlying microbiota-mediated diseases, research is moving from associative analyses to causation studies. In this article, we first introduce the principles of the computational methods for causal inference, and then discuss the applications of these methods in microbiome medicine. Furthermore, we examine the reliability of theoretically inferred causality by the interventionist framework. Finally, we show the potential of confirmed causality in microbiota-targeted therapy, especially in personalized dietary intervention. We conclude that a comprehensive understanding of the causal relationships between diets, microbiota, host targets, and diseases is critical to future microbiome medicine.

Genome-wide association study in 8,956 German individuals identifies influence of ABO histo-blood groups on gut microbiome

The intestinal microbiome is implicated as an important modulating factor in multiple inflammatory1,2, neurologic3 and neoplastic diseases4. Recent genome-wide association studies yielded inconsistent, underpowered and rarely replicated results such that the role of human host genetics as a contributing factor to microbiome assembly and structure remains uncertain5-11. Nevertheless, twin studies clearly suggest host genetics as a driver of microbiome composition11. In a genome-wide association analysis of 8,956 German individuals, we identified 38 genetic loci to be associated with single bacteria and overall microbiome composition. Further analyses confirm the identified associations of ABO histo-blood groups and FUT2 secretor status with Bacteroides and Faecalibacterium spp. Mendelian randomization analysis suggests causative and protective effects of gut microbes, with clade-specific effects on inflammatory bowel disease. This holistic investigative approach of the host, its genetics and its associated microbial communities as a 'metaorganism' broaden our understanding of disease etiology, and emphasize the potential for implementing microbiota in disease treatment and management.

Large-scale association analyses identify host factors influencing human gut microbiome composition

To study the effect of host genetics on gut microbiome composition, the MiBioGen consortium curated and analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals (24 cohorts). Microbial composition showed high variability across cohorts: only 9 of 410 genera were detected in more than 95% of samples. A genome-wide association study of host genetic variation regarding microbial taxa identified 31 loci affecting the microbiome at a genome-wide significant (P < 5 × 10-8) threshold. One locus, the lactase (LCT) gene locus, reached study-wide significance (genome-wide association study signal: P = 1.28 × 10-20), and it showed an age-dependent association with Bifidobacterium abundance. Other associations were suggestive (1.95 × 10-10 < P < 5 × 10-8) but enriched for taxa showing high heritability and for genes expressed in the intestine and brain. A phenome-wide association study and Mendelian randomization identified enrichment of microbiome trait loci in the metabolic, nutrition and environment domains and suggested the microbiome might have causal effects in ulcerative colitis and rheumatoid arthritis.

Human genetic determinants of the gut microbiome and their associations with health and disease: a phenome-wide association study

Small-scale studies have suggested a link between the human gut microbiome and highly prevalent diseases. However, the extent to which the human gut microbiome can be considered a determinant of disease and healthy aging remains unknown. We aimed to determine the spectrum of diseases that are linked to the human gut microbiome through the utilization of its genetic determinants as a proxy for its composition. 180 single nucleotide polymorphisms (SNPs) known to influence the human gut microbiome were used to assess the association with health and disease outcomes in 422,417 UK Biobank participants. Potential causal estimates were obtained using a Mendelian randomization (MR) approach. From the total sample analysed (mean age was 57 ± 8 years), 194,567 (46%) subjects were male. Median exposure was 66-person years (interquartile range 59-72). Eleven SNPs were significantly associated with 28 outcomes (Bonferroni corrected P value < 4.63·10-6) including food intake, hypertension, atopy, COPD, BMI, and lipids. Multiple SNP MR pointed to a possible causal link between Ruminococcus flavefaciens and hypertension, and Clostridium and platelet count. Microbiota and their metabolites might be of importance in the interplay between overlapping pathophysiological processes, although challenges remain in establishing causal relationships.

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

Recent population-based1-4 and clinical studies5 have identified a range of factors associated with human gut microbiome variation. Murine quantitative trait loci6, human twin studies7 and microbiome genome-wide association studies1,3,8-12 have provided evidence for genetic contributions to microbiome composition. Despite this, there is still poor overlap in genetic association across human studies. Using appropriate taxon-specific models, along with support from independent cohorts, we show an association between human host genotype and gut microbiome variation. We also suggest that interpretation of applied analyses using genetic associations is complicated by the probable overlap between genetic contributions and heritable components of host environment. Using faecal 16S ribosomal RNA gene sequences and host genotype data from the Flemish Gut Flora Project (n = 2,223) and two German cohorts (FoCus, n = 950; PopGen, n = 717), we identify genetic associations involving multiple microbial traits. Two of these associations achieved a study-level threshold of P = 1.57 × 10-10; an association between Ruminococcus and rs150018970 near RAPGEF1 on chromosome 9, and between Coprococcus and rs561177583 within LINC01787 on chromosome 1. Exploratory analyses were undertaken using 11 other genome-wide associations with strong evidence for association (P < 2.5 × 10-8) and a previously reported signal of association between rs4988235 (MCM6/LCT) and Bifidobacterium. Across these 14 single-nucleotide polymorphisms there was evidence of signal overlap with other genome-wide association studies, including those for age at menarche and cardiometabolic traits. Mendelian randomization analysis was able to estimate associations between microbial traits and disease (including Bifidobacterium and body composition); however, in the absence of clear microbiome-driven effects, caution is needed in interpretation. Overall, this work marks a growing catalogue of genetic associations that will provide insight into the contribution of host genotype to gut microbiome. Despite this, the uncertain origin of association signals will likely complicate future work looking to dissect function or use associations for causal inference analysis.