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

Genetic Connectivity of Gut Microbiota and Oral Ulcers: A Mendelian Randomization Study

OBJECTIVES

The aim of this study was to reveal the relationship, if any, between gut microbiota and oral ulcers.

METHODS

We performed a 2-sample Mendelian randomization (MR) study to estimate the roles of gut microbiota in mouth ulcers. The summary datasets of gut microbiota were from the largest genome-wide association study (GWAS) conducted by MiBioGen, and data of mouth ulcers were obtained from UK Biobank. Random effect inverse variance-weighted, weighted median, MR Egger, simple mode and weighted mode were used to estimate the relationship. Sensitivity analyses were conducted to assess the heterogeneity and pleiotropy of instrumental variables. MR Steiger filtering was also applied to orient the causal direction.

RESULTS

Three gut microbiota taxa were positively associated with mouth ulcers: Holdemania (odds ratio [OR] = 1.005, 95% confidence interval [CI]: 1.001-1.009, P = .019), Oxalobacter (OR = 1.004, 95% CI: 1.000-1.007, P = .032), and Ruminococcaceae UCG011 (OR = 1.006, 95% CI: 1.001-1.011, P = .029), while 4 gut microbiota taxa were negatively associated with mouth ulcers: Actinobacteria (OR = 0.992, 95% CI: 0.985-1.000, P = .042), Lactobacillales (OR = 0.995, 95% CI: 0.990-1.000, P = .034), Oscillospira (OR = 0.990, 95% CI: 0.984-0.997, P = .007) and Phascolarctobacterium (OR = 0.992, 95% CI: 0.986-0.997, P = .003). Sensitivity analyses validated the robustness of the association in between.

CONCLUSIONS

This MR study identified a strong association between the quality of gut microbiota and oral ulcers. The findings are likely to expand the therapeutic targets for mouth ulcers.

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.

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.

The diagnostic potential and barriers of microbiome based therapeutics

High throughput technological innovations in the past decade have accelerated research into the trillions of commensal microbes in the gut. The 'omics' technologies used for microbiome analysis are constantly evolving, and large-scale datasets are being produced. Despite of the fact that much of the research is still in its early stages, specific microbial signatures have been associated with the promotion of cancer, as well as other diseases such as inflammatory bowel disease, neurogenerative diareses etc. It has been also reported that the diversity of the gut microbiome influences the safety and efficacy of medicines. The availability and declining sequencing costs has rendered the employment of RNA-based diagnostics more common in the microbiome field necessitating improved data-analytical techniques so as to fully exploit all the resulting rich biological datasets, while accounting for their unique characteristics, such as their compositional nature as well their heterogeneity and sparsity. As a result, the gut microbiome is increasingly being demonstrating as an important component of personalised medicine since it not only plays a role in inter-individual variability in health and disease, but it also represents a potentially modifiable entity or feature that may be addressed by treatments in a personalised way. In this context, machine learning and artificial intelligence-based methods may be able to unveil new insights into biomedical analyses through the generation of models that may be used to predict category labels, and continuous values. Furthermore, diagnostic aspects will add value in the identification of the non invasive markers in the critical diseases like cancer.

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.

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 Gut Microbiome and Its Clinical Implications in Anorexia Nervosa]

The Gut Microbiome and Its Clinical Implications in Anorexia Nervosa Abstract. The diverse interactions of the gut microbiome with the metabolism, the immune system, and the brain of the host are increasingly becoming to the forefront of relevant research. Studies suggest a connection between an altered intestinal microbiome and somatic diseases, such as colitis ulcerosa, Crohn's disease, and diabetes, as well as mental illnesses such as anxiety and depression. Patients with anorexia nervosa (AN) also show significant changes in their gut microbiome which seem to be associated, among other things, with a different energy uptake from food, immunological and inflammatory processes, genetic predisposition, hormonal changes, and possibly increased intestinal permeability. In rats, stool transplantation from patients with AN resulted in decreased appetite and weight as well as anxious and compulsive behavior. In this review, we summarize the possible mechanisms of interaction between the microbiome and the host, and present initial findings on the microbiome in AN. Research on nutritional interventions, for example, with prebiotics and probiotics or nutritional supplements such as omega-3 fatty acids, which aim to positively influence the intestinal microbiome, could lead to additional treatment options in the therapy of patients with AN.

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.