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.

Harnessing human microbiomes for disease prediction.

The human microbiome has been increasingly recognized as having potential use for disease prediction. Predicting the risk, progression, and severity of diseases holds promise to transform clinical practice, empower patient decisions, and reduce the burden of various common diseases, as has been demonstrated for cardiovascular disease or breast cancer. Combining multiple modifiable and non-modifiable risk factors, including high-dimensional genomic data, has been traditionally favored, but few studies have incorporated the human microbiome into models for predicting the prospective risk of disease. Here, we review research into the use of the human microbiome for disease prediction with a particular focus on prospective studies as well as the modulation and engineering of the microbiome as a therapeutic strategy.

The gut microbiome is a significant risk factor for future chronic lung disease.

BACKGROUND: The gut-lung axis is generally recognized, but there are few large studies of the gut microbiome and incident respiratory disease in adults. OBJECTIVE: We sought to investigate the association and predictive capacity of the gut microbiome for incident asthma and chronic obstructive pulmonary disease (COPD). METHODS: Shallow metagenomic sequencing was performed for stool samples from a prospective, population-based cohort (FINRISK02; N = 7115 adults) with linked national administrative health register-derived classifications for incident asthma and COPD up to 15 years after baseline. Generalized linear models and Cox regressions were used to assess associations of microbial taxa and diversity with disease occurrence. Predictive models were constructed using machine learning with extreme gradient boosting. Models considered taxa abundances individually and in combination with other risk factors, including sex, age, body mass index, and smoking status. RESULTS: A total of 695 and 392 statistically significant associations were found between baseline taxonomic groups and incident asthma and COPD, respectively. Gradient boosting decision trees of baseline gut microbiome abundance predicted incident asthma and COPD in the validation data sets with mean area under the curves of 0.608 and 0.780, respectively. Cox analysis showed that the baseline gut microbiome achieved higher predictive performance than individual conventional risk factors, with C-indices of 0.623 for asthma and 0.817 for COPD. The integration of the gut microbiome and conventional risk factors further improved prediction capacities. CONCLUSIONS: The gut microbiome is a significant risk factor for incident asthma and incident COPD and is largely independent of conventional risk factors.

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.

A GWAS on Helicobacter pylori strains points to genetic variants associated with gastric cancer risk.

BACKGROUND: Helicobacter pylori are stomach-dwelling bacteria that are present in about 50% of the global population. Infection is asymptomatic in most cases, but it has been associated with gastritis, gastric ulcers and gastric cancer. Epidemiological evidence shows that progression to cancer depends upon the host and pathogen factors, but questions remain about why cancer phenotypes develop in a minority of infected people. Here, we use comparative genomics approaches to understand how genetic variation amongst bacterial strains influences disease progression. RESULTS: We performed a genome-wide association study (GWAS) on 173 H. pylori isolates from the European population (hpEurope) with known disease aetiology, including 49 from individuals with gastric cancer. We identified SNPs and genes that differed in frequency between isolates from patients with gastric cancer and those with gastritis. The gastric cancer phenotype was associated with the presence of babA and genes in the cag pathogenicity island, one of the major virulence determinants of H. pylori, as well as non-synonymous variations in several less well-studied genes. We devised a simple risk score based on the risk level of associated elements present, which has the potential to identify strains that are likely to cause cancer but will require refinement and validation. CONCLUSION: There are a number of challenges to applying GWAS to bacterial infections, including the difficulty of obtaining matched controls, multiple strain colonization and the possibility that causative strains may not be present when disease is detected. Our results demonstrate that bacterial factors have a sufficiently strong influence on disease progression that even a small-scale GWAS can identify them. Therefore, H. pylori GWAS can elucidate mechanistic pathways to disease and guide clinical treatment options, including for asymptomatic carriers.

Convergent Amino Acid Signatures in Polyphyletic Campylobacter jejuni Subpopulations Suggest Human Niche Tropism.

Human infection with the gastrointestinal pathogen Campylobacter jejuni is dependent upon the opportunity for zoonotic transmission and the ability of strains to colonize the human host. Certain lineages of this diverse organism are more common in human infection but the factors underlying this overrepresentation are not fully understood. We analyzed 601 isolate genomes from agricultural animals and human clinical cases, including isolates from the multihost (ecological generalist) ST-21 and ST-45 clonal complexes (CCs). Combined nucleotide and amino acid sequence analysis identified 12 human-only amino acid KPAX clusters among polyphyletic lineages within the common disease causing CC21 group isolates, with no such clusters among CC45 isolates. Isolate sequence types within human-only CC21 group KPAX clusters have been sampled from other hosts, including poultry, so rather than representing unsampled reservoir hosts, the increase in relative frequency in human infection potentially reflects a genetic bottleneck at the point of human infection. Consistent with this, sequence enrichment analysis identified nucleotide variation in genes with putative functions related to human colonization and pathogenesis, in human-only clusters. Furthermore, the tight clustering and polyphyly of human-only lineage clusters within a single CC suggest the repeated evolution of human association through acquisition of genetic elements within this complex. Taken together, combined nucleotide and amino acid analysis of large isolate collections may provide clues about human niche tropism and the nature of the forces that promote the emergence of clinically important C. jejuni lineages.

Molecular epidemiology and comparative genomics of Campylobacter concisus strains from saliva, faeces and gut mucosal biopsies in inflammatory bowel disease.

Campylobacter concisus is an emerging pathogen associated with inflammatory bowel disease (IBD), yet little is known about the genetic diversity of C. concisus in relation to host niches and disease. We isolated 104 C. concisus isolates from saliva, mucosal biopsies and faecal samples from 41 individuals (26 IBD, 3 Gastroenteritis (GE), 12 Healthy controls (HC)). Whole genomes were sequenced and the dataset pan-genome examined, and genomic information was used for typing using multi-locus-sequence typing (MLST). C. concisus isolates clustered into two main groups/genomospecies (GS) with 71 distinct sequence types (STs) represented. Sampling site (p < 0.001), rather than disease phenotype (p = 1.00) was associated with particular GS. We identified 97 candidate genes associated with increase or decrease in prevalence during the anatomical descent from the oral cavity to mucosal biopsies to faeces. Genes related to cell wall/membrane biogenesis were more common in oral isolates, whereas genes involved in cell transport, metabolism and secretory pathways were more prevalent in enteric isolates. Furthermore, there was no correlation between individual genetic diversity and clinical phenotype. This study confirms the genetic heterogeneity of C. concisus and provides evidence that genomic variation is related to the source of isolation, but not clinical phenotype.