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

Gene-Gene Interaction Between Factor-XI and ABO Genes in Cerebral Venous Thrombosis: The BEAST Study

BACKGROUND AND OBJECTIVES

Gene-gene interactions likely contribute to the etiology of multifactorial diseases such as cerebral venous thrombosis (CVT) and could be one of the main sources of known missing heritability. We explored Factor XI (F11) and ABO gene interactions among patients with CVT.

METHODS

Patients with CVT of European ancestry from the large Bio-Repository to Establish the Aetiology of Sinovenous Thrombosis (BEAST) international collaboration were recruited. Codominant modelling was used to determine interactions between genome-wide identified F11 and ABO genes with CVT status.

RESULTS

We studied 882 patients with CVT and 1,205 ethnically matched control participants (age: 42 ± 15 vs 43 ± 12 years, p = 0.08: sex: 71% male vs 68% female, p = 0.09, respectively). Individuals heterozygous (AT) for the risk allele (T) at both loci (rs56810541/F11 and rs8176645/ABO) had a 3.9 (95% CI 2.74-5.71, p = 2.75e-13) increase in risk of CVT. Individuals homozygous (TT) for the risk allele at both loci had a 13.9 (95% CI 7.64-26.17, p = 2.0e-15) increase in risk of CVT. The presence of a non-O blood group (A, B, AB) combined with TT/rs56810541/F11 increased CVT risk by OR = 6.8 (95% CI 4.54-10.33, p = 2.00e15), compared with blood group-O combined with AA.

DISCUSSION

Interactions between factor XI and ABO genes increase risk of CVT by 4- to 14-fold.

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.

Node-adaptive graph Transformer with structural encoding for accurate and robust lncRNA-disease association prediction

BACKGROUND

Long noncoding RNAs (lncRNAs) are integral to a plethora of critical cellular biological processes, including the regulation of gene expression, cell differentiation, and the development of tumors and cancers. Predicting the relationships between lncRNAs and diseases can contribute to a better understanding of the pathogenic mechanisms of disease and provide strong support for the development of advanced treatment methods.

RESULTS

Therefore, we present an innovative Node-Adaptive Graph Transformer model for predicting unknown LncRNA-Disease Associations, named NAGTLDA. First, we utilize the node-adaptive feature smoothing (NAFS) method to learn the local feature information of nodes and encode the structural information of the fusion similarity network of diseases and lncRNAs using Structural Deep Network Embedding (SDNE). Next, the Transformer module is used to capture potential association information between the network nodes. Finally, we employ a Transformer module with two multi-headed attention layers for learning global-level embedding fusion. Network structure coding is added as the structural inductive bias of the network to compensate for the missing message-passing mechanism in Transformer. NAGTLDA achieved an average AUC of 0.9531 and AUPR of 0.9537 significantly higher than state-of-the-art methods in 5-fold cross validation. We perform case studies on 4 diseases; 55 out of 60 associations between lncRNAs and diseases have been validated in the literatures. The results demonstrate the enormous potential of the graph Transformer structure to incorporate graph structural information for uncovering lncRNA-disease unknown correlations.

CONCLUSIONS

Our proposed NAGTLDA model can serve as a highly efficient computational method for predicting biological information associations.

Influence of perinatal and childhood exposure to tobacco and mercury in children's gut microbiota

Background

Early life determinants of the development of gut microbiome composition in infants have been widely investigated; however, if early life pollutant exposures, such as tobacco or mercury, have a persistent influence on the gut microbial community, its stabilization at later childhood remains largely unknown.

Objective

In this exposome-wide study, we aimed at identifying the contribution of exposure to tobacco and mercury from the prenatal period to childhood, to individual differences in the fecal microbiome composition of 7-year-old children, considering co-exposure to a width of established lifestyle and clinical determinants.

Methods

Gut microbiome was studied by 16S rRNA amplicon sequencing in 151 children at the genus level. Exposure to tobacco was quantified during pregnancy through questionnaire (active tobacco consumption, second-hand smoking -SHS) and biomonitoring (urinary cotinine) at 4 years (urinary cotinine, SHS) and 7 years (SHS). Exposure to mercury was quantified during pregnancy (cord blood) and at 4 years (hair). Forty nine other potential environmental determinants (12 at pregnancy/birth/infancy, 15 at 4 years and 22 at 7 years, such as diet, demographics, quality of living/social environment, and clinical records) were registered. We used multiple models to determine microbiome associations with pollutants including multi-determinant multivariate analysis of variance and linear correlations (wUnifrac, Bray-Curtis and Aitchison ß-diversity distances), single-pollutant permutational multivariate analysis of variance adjusting for co-variates (Aitchison), and multivariable association model with single taxa (MaAsLin2; genus). Sensitivity analysis was performed including genetic data in a subset of 107 children.

Results

Active smoking in pregnancy was systematically associated with microbiome composition and ß-diversity (R2 2-4%, p < 0.05, Aitchison), independently of other co-determinants. However, in the adjusted single pollutant models (PERMANOVA), we did not find any significant association. An increased relative abundance of Dorea and decreased relative abundance of Akkermansia were associated with smoking during pregnancy (q < 0.05).

Discussion

Our findings suggest a long-term sustainable effect of prenatal tobacco exposure on the children's gut microbiota. This effect was not found for mercury exposure or tobacco exposure during childhood. Assessing the role of these exposures on the children's microbiota, considering multiple environmental factors, should be further investigated.

Early-life microbiota-immune homeostasis

As the prevalence of allergy and autoimmune disease in industrialized societies continues to rise, improving our understanding of the mechanistic roles behind microbiota-immune homeostasis has become critical for informing therapeutic interventions in cases of dysbiosis. Of particular importance, are alterations to intestinal microbiota occurring within the critical neonatal window, during which the immune system is highly vulnerable to environmental exposures. This review will highlight recent literature concerning mechanisms of early-life microbiota-immune homeostasis as well as discuss the potential for therapeutics in restoring dysbiosis in early life.

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.

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

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

Gut Microbiota and Microbial Metabolism in Early Risk of Cardiometabolic Disease

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

Human Genes Involved in the Interaction between Host and Gut Microbiome: Regulation and Pathogenic Mechanisms

Introduction: The umbrella term “human gut microbiota” describes the complex ecosystem harboring our gut. It includes bacteria, viruses, protozoa, archaea, fungi, and yeasts. This taxonomic classification does not describe its functions, which encompass nutrients digestion and absorption, immune system regulation, and host metabolism. “Gut microbiome” indicates instead the genome belonging to these “microbes” actively involved in these functions. However, the interaction between the host genome and the microbial ones determines the fine functioning of our organism. Methods: We reviewed the data available in the scientific literature on the definition of gut microbiota, gut microbiome, and the data on human genes involved in the interaction with the latter. We consulted the main medical databases using the following keywords, acronyms, and their associations: gut microbiota, gut microbiome, human genes, immune function, and metabolism. Results: Candidate human genes encoding enzymes, inflammatory cytokines, and proteins show similarity with those included in the gut microbiome. These findings have become available through newer artificial intelligence (AI) algorithms allowing big data analysis. From an evolutionary point of view, these pieces of evidence explain the strict and sophisticated interaction at the basis of human metabolism and immunity regulation in humans. They unravel more and more physiopathologic pathways included in human health and disease. Discussion: Several lines of evidence also obtained through big data analysis support the bi-directional role of gut microbiome and human genome in host metabolism and immune system regulation.

Association between Microbiome-Related Human Genetic Variants and Fasting Plasma Glucose in a High-Cardiovascular-Risk Mediterranean Population

Background and Objectives: The gut microbiota has been increasingly recognized as a relevant factor associated with metabolic diseases. However, directly measuring the microbiota composition is a limiting factor for several studies. Therefore, using genetic variables as proxies for the microbiota composition is an important issue. Landmark microbiome–host genome-wide association studies (mbGWAS) have identified many SNPs associated with gut microbiota. Our aim was to analyze the association between relevant microbiome-related genetic variants (Mi-RSNPs) and fasting glucose and type 2 diabetes in a Mediterranean population, exploring the interaction with Mediterranean diet adherence. Materials and Methods: We performed a cross-sectional study in a high-cardiovascular-risk Mediterranean population (n = 1020), analyzing the association of Mi-RSNPs (from four published mbGWAS) with fasting glucose and type 2 diabetes. A single-variant approach was used for fitting fasting glucose and type 2 diabetes to a multivariable regression model. In addition, a Mendelian randomization analysis with multiple variants was performed as a sub-study. Results: We obtained several associations between Mi-RSNPs and fasting plasma glucose involving gut Gammaproteobacteria_HB, the order Rhizobiales, the genus Rumminococcus torques group, and the genus Tyzzerella as the top ranked. For type 2 diabetes, we also detected significant associations with Mi-RSNPs related to the order Rhizobiales, the family Desulfovibrionaceae, and the genus Romboutsia. In addition, some Mi-RSNPs and adherence to Mediterranean diet interactions were detected. Lastly, the formal Mendelian randomization analysis suggested combined effects. Conclusions: Although the use of Mi-RSNPs as proxies of the microbiome is still in its infancy, and although this is the first study analyzing such associations with fasting plasma glucose and type 2 diabetes in a Mediterranean population, some interesting associations, as well as modulations, with adherence to the Mediterranean diet were detected in these high-cardiovascular-risk subjects, eliciting new hypotheses.