Host Genetic Effects Research Articles

Host Genetic Associations with Salivary Microbiome in Oral Cancer

Despite the growing recognition of a host genetic effect on shaping gut microbiota composition, the genetic determinants of oral microbiota remain largely unexplored, especially in the context of oral diseases. Here, we performed a microbiome genome-wide association study in 2 independent cohorts of patients with oral squamous cell carcinoma (OSCC, n = 144 and 67) and an additional group of noncancer individuals (n = 104). Besides oral bacterial dysbiosis and signatures observed in OSCC, associations of 3 loci with the abundance of genus-level taxa and 4 loci with β diversity measures were detected (q < 0.05) at the discovery stage. The most significant hit (rs10906082 with the genus Lachnoanaerobaculum, P = 3.55 × 10–9 at discovery stage) was replicated in a second OSCC cohort. Moreover, the other 2 taxonomical associations, rs10973953 with the genus Kingella (P = 1.38 × 10–9) and rs4721629 with the genus Parvimonas (P = 3.53 × 10–8), were suggestive in the meta-analysis combining 2 OSCC cohorts. Further pathway analysis revealed that these loci were enriched for genes in regulation of oncogenic and angiogenic responses, implicating a genetic anchor to the oral microbiome in estimation of casual relationships with OSCC. Our findings delineate the role of host genotypes in influencing the structure of oral microbial communities.

A field experiment reveals seasonal variation in the Daphnia gut microbiome

The gut microbiome is increasingly recognized for its impact on host fitness, but it remains poorly understood how naturally variable environments influence gut microbiome diversity and composition. We studied changes in the gut microbiome of ten genotypes of water fleas Daphnia magna in submerged mesocosm enclosures in a eutrophic lake over a period of 16 weeks, from early summer to autumn. The microbial diversity increased when Daphnia were reintroduced from the laboratory to the lake, and the composition of gut microbes drastically changed. Both gut microbiome diversity and composition continued to change over the 16‐week period, with alpha diversity peaking in late summer. The gut microbiome community was clearly distinct from that of the surrounding water, and temporal changes in the two communities were independent of each other. There were no consistent differences in the gut microbiomes among Daphnia genotypes in the lake environment. The change in gut microbiome over the season was accompanied by a decline in reproductive output and survival. There were weak, but statistically supported, effects of microbiota composition on Daphnia fitness, but there was no evidence that natural variation in microbiome diversity or composition was associated with tolerance to the cyanotoxin microcystin. We conclude that the gut microbiome of Daphnia is highly dynamic in a natural lake environment, but that host genetic effects on microbiome diversity and composition between genotypes within a population can be vanishingly small. These results emphasize that establishing the ecological effects of gut microbiota will require large‐scale experiments under natural conditions.

Impact of the gut microecology on Campylobacter presence revealed by comparisons of the gut microbiota from chickens raised on litter or in individual cages

BackgroundPoultry is the major reservoir of Campylobacter that contributes to human campylobacteriosis and threatens food safety. Litter contact has been linked to Campylobacter colonization, but the gut microecological impact underlying this link remains not fully clear. Here, we sought to investigate the impact of the gut microecology on the presence of Campylobacter by examining the microbiota in the duodenum, jejunum, ileum, ceca, and feces from chickens raised on commercial litter and in individual cages at 0–57 days of age.ResultsThrough litter contact, the presence of Campylobacter was found to benefit from microecological competition among Lactobacillus, Helicobacter, and genera that are halotolerant and aerobic or facultatively anaerobic in the upper intestine, such as Corynebacterium and Brachybacterium. The presence was also promoted by the increased abundance in obligate anaerobic fermentation microbes, especially members of the orders Clostridiales and Bacteroidales. The longitudinal analysis supported the vertical or pseudo-vertical transmission but suggested that colonization might occur immensely at 7–28 days of age. We observed a host genetic effect on the gut microecology, which might lead to increased heterogeneity of the microecological impact on Campylobacter colonization.ConclusionsThe findings advance the understanding of the gut microecological impact on Campylobacter presence in the chicken gut under conditions of litter contact and suggest that manipulations of the gut microecology, as well as the microbes identified in the Campylobacter association networks, might be important for the development of intervention strategies.

Prioritizing host phenotype to understand microbiome heritability in plants.

Breeders and evolutionary geneticists have grappled with the complexity of the 'genotype-to-phenotype map' for decades. Now, recent studies highlight the relevance of this concept for understanding heritability of plant microbiomes. Because host phenotype is a more proximate cause of microbiome variation than host genotype, microbiome heritability varies across plant anatomy and development. Fine-scale variation of plant traits within organs suggests that the well-established concept of 'microbiome compartment' should be refined. Additionally, recent work shows that the balance of deterministic processes (including host genetic effects) vs stochastic processes also varies over time and space. Together, these findings suggest that re-centering plant phenotype - both as a predictor and a readout of microbiome function - will accelerate new insights into microbiome heritability.

Opportunities and limits of combining microbiome and genome data for complex trait prediction

BackgroundAnalysis and prediction of complex traits using microbiome data combined with host genomic information is a topic of utmost interest. However, numerous questions remain to be answered: how useful can the microbiome be for complex trait prediction? Are estimates of microbiability reliable? Can the underlying biological links between the host’s genome, microbiome, and phenome be recovered?MethodsHere, we address these issues by (i) developing a novel simulation strategy that uses real microbiome and genotype data as inputs, and (ii) using variance-component approaches (Bayesian Reproducing Kernel Hilbert Space (RKHS) and Bayesian variable selection methods (Bayes C)) to quantify the proportion of phenotypic variance explained by the genome and the microbiome. The proposed simulation approach can mimic genetic links between the microbiome and genotype data by a permutation procedure that retains the distributional properties of the data.ResultsUsing real genotype and rumen microbiota abundances from dairy cattle, simulation results suggest that microbiome data can significantly improve the accuracy of phenotype predictions, regardless of whether some microbiota abundances are under direct genetic control by the host or not. This improvement depends logically on the microbiome being stable over time. Overall, random-effects linear methods appear robust for variance components estimation, in spite of the typically highly leptokurtic distribution of microbiota abundances. The predictive performance of Bayes C was higher but more sensitive to the number of causative effects than RKHS. Accuracy with Bayes C depended, in part, on the number of microorganisms’ taxa that influence the phenotype.ConclusionsWhile we conclude that, overall, genome-microbiome-links can be characterized using variance component estimates, we are less optimistic about the possibility of identifying the causative host genetic effects that affect microbiota abundances, which would require much larger sample sizes than are typically available for genome-microbiome-phenome studies. The R code to replicate the analyses is in https://github.com/miguelperezenciso/simubiome.

Geography, Host Genetics, and Cross-Domain Microbial Networks Structure the Skin Microbiota of Fragmented Brazilian Atlantic Forest Frog Populations.

The host‐associated microbiome plays a significant role in health. However, the roles of factors such as host genetics and microbial interactions in determining microbiome diversity remain unclear. We examined these factors using amplicon‐based sequencing of 175 Thoropa taophora frog skin swabs collected from a naturally fragmented landscape in southeastern Brazil. Specifically, we examined (1) the effects of geography and host genetics on microbiome diversity and structure; (2) the structure of microbial eukaryotic and bacterial co‐occurrence networks; and (3) co‐occurrence between microeukaryotes with bacterial OTUs known to affect growth of the fungal pathogen Batrachochytrium dendrobatidis (Bd). While bacterial alpha diversity varied by both site type and host MHC IIB genotype, microeukaryotic alpha diversity varied only by site type. However, bacteria and microeukaryote composition showed variation according to both site type and host MHC IIB genotype. Our network analysis showed the highest connectivity when both eukaryotes and bacteria were included, implying that ecological interactions may occur among domains. Lastly, anti‐Bd bacteria were not broadly negatively co‐associated with the fungal microbiome and were positively associated with potential amphibian parasites. Our findings emphasize the importance of considering both domains in microbiome research and suggest that for effective probiotic strategies for amphibian disease management, considering potential interactions among all members of the microbiome is crucial.

Joint contributions of the gut microbiota and host genetics to feed efficiency in chickens

BackgroundFeed contributes most to livestock production costs. Improving feed efficiency is crucial to increase profitability and sustainability for animal production. Host genetics and the gut microbiota can both influence the host phenotype. However, the association between the gut microbiota and host genetics and their joint contribution to feed efficiency in chickens is largely unclear.ResultsHere, we examined microbial data from the duodenum, jejunum, ileum, cecum, and feces in 206 chickens and their host genotypes and confirmed that the microbial phenotypes and co-occurrence networks exhibited dramatic spatial heterogeneity along the digestive tract. The correlations between host genetic kinship and gut microbial similarities within different sampling sites were weak, with coefficients ranging from − 0.07 to 0.08. However, microbial genome-wide analysis revealed that genetic markers near or inside the genes MTHFD1L and LARGE1 were associated with the abundances of cecal Megasphaera and Parabacteroides, respectively. The effect of host genetics on residual feed intake (RFI) was 39%. We further identified three independent genetic variations that were related to feed efficiency and had a modest effect on the gut microbiota. The contributions of the gut microbiota from the different parts of the intestinal tract on RFI were distinct. The cecal microbiota accounted for 28% of the RFI variance, a value higher than that explained by the duodenal, jejunal, ileal, and fecal microbiota. Additionally, six bacteria exhibited significant associations with RFI. Specifically, lower abundances of duodenal Akkermansia muciniphila and cecal Parabacteroides and higher abundances of cecal Lactobacillus, Corynebacterium, Coprobacillus, and Slackia were related to better feed efficiency.ConclusionsOur findings solidified the notion that both host genetics and the gut microbiota, especially the cecal microbiota, can drive the variation in feed efficiency. Although host genetics has a limited effect on the entire microbial community, a small fraction of gut microorganisms tends to interact with host genes, jointly contributing to feed efficiency. Therefore, the gut microbiota and host genetic variations can be simultaneously targeted by favoring more-efficient taxa and selective breeding to improve feed efficiency in chickens.EooUS9Np68nMFAp4xE4K1kVideo abstract

Host genetics exerts lifelong effects upon hindgut microbiota and its association with bovine growth and immunity

The gut microbiota is a complex ecological community that plays multiple critical roles within a host. Known intrinsic and extrinsic factors affect gut microbiota structure, but the influence of host genetics is understudied. To investigate the role of host genetics upon the gut microbiota structure, we performed a longitudinal study in which we evaluated the hindgut microbiota and its association with animal growth and immunity across life. We evaluated three different growth stages in an Angus-Brahman multibreed population with a graduated spectrum of genetic variation, raised under variable environmental conditions and diets. We found the gut microbiota structure was changed significantly during growth when preweaning, and fattening calves experienced large variations in diet and environmental changes. However, regardless of the growth stage, we found gut microbiota is significantly influenced by breed composition throughout life. Host genetics explained the relative abundances of 52.2%, 40.0%, and 37.3% of core bacterial taxa at the genus level in preweaning, postweaning, and fattening calves, respectively. Sutterella, Oscillospira, and Roseburia were consistently associated with breed composition at these three growth stages. Especially, butyrate-producing bacteria, Roseburia and Oscillospira, were associated with nine single-nucleotide polymorphisms (SNPs) located in genes involved in the regulation of host immunity and metabolism in the hindgut. Furthermore, minor allele frequency analysis found breed-associated SNPs in the short-chain fatty acids (SCFAs) receptor genes that promote anti-inflammation and enhance intestinal epithelial barrier functions. Our findings provide evidence of dynamic and lifelong host genetic effects upon gut microbiota, regardless of growth stages. We propose that diet, environmental changes, and genetic components may explain observed variation in critical hindgut microbiota throughout life.

Acquisition of oral microbiota is driven by environment, not host genetics

BackgroundThe oral microbiota is acquired very early, but the factors shaping its acquisition are not well understood. Previous studies comparing monozygotic (MZ) and dizygotic (DZ) twins have suggested that host genetics plays a role. However, all twins share an equal portion of their parent’s genome, so this model is not informative for studying parent-to-child transmission. We used a novel study design that allowed us to directly examine the genetics of transmission by comparing the oral microbiota of biological versus adoptive mother-child dyads.ResultsNo difference was observed in how closely oral bacterial community profiles matched for adoptive versus biological mother-child pairs, indicating little if any effect of host genetics on the fidelity of transmission. Both adopted and biologic children more closely resembled their own mother as compared to unrelated women, supporting the role of contact and environment. Mother-child strain similarity increased with the age of the child, ruling out early effects of host genetic influence that are lost over time. No effect on the fidelity of mother-child strain sharing from vaginal birth or breast feeding was seen. Analysis of extended families showed that fathers and mothers were equally similar to their children, and that cohabitating couples showed even greater strain similarity than mother-child pairs. These findings support the role of contact and shared environment, and age, but not genetics, as determinants of microbial transmission, and were consistent at both species and strain level resolutions, and across multiple oral habitats. In addition, analysis of individual species all showed similar results.ConclusionsThe host is clearly active in shaping the composition of the oral microbiome, since only a few of the many bacterial species in the larger environment are capable of colonizing the human oral cavity. Our findings suggest that these host mechanisms are universally shared among humans, since no effect of genetic relatedness on fidelity of microbial transmission could be detected. Instead our findings point towards contact and shared environment being the driving factors of microbial transmission, with a unique combination of these factors ultimately shaping the highly personalized human oral microbiome.1Xj9JJxyfatkyACGqE8HPMVideo abstract

Studying host genetic background effects on multimorbidity of intestinal cancer development, type 2 diabetes and obesity in response to oral bacterial infection and high-fat diet using the collaborative cross (CC) lines.

BackgroundMultimorbidity of intestinal cancer (IC), type 2 diabetes (T2D) and obesity is a complex set of diseases, affected by environmental and genetic risk factors. High‐fat diet (HFD) and oral bacterial infection play important roles in the etiology of these diseases through inflammation and various biological mechanisms.MethodsTo study the complexity of this multimorbidity, we used the collaborative cross (CC) mouse genetics reference population. We aimed to study the multimorbidity of IC, T2D, and obesity using CC lines, measuring their responses to HFD and oral bacterial infection. The study used 63 mice of both sexes generated from two CC lines (IL557 and IL711). For 12 weeks, experimental mice were maintained on specific dietary regimes combined with co‐infection with oral bacteria Porphyromonas gingivalis and Fusobacterium nucleatum, while control groups were not infected. Body weight (BW) and results of a intraperitoneal glucose tolerance test (IPGTT) were recorded at the end of 12 weeks, after which length and size of the intestines were assessed for polyp counts.ResultsPolyp counts ranged between 2 and 10 per CC line. The combination of HFD and infection significantly reduced (P < .01) the colon polyp size of IL557 females to 2.5 cm2, compared to the other groups. Comparing BW gain, IL557 males on HFD gained 18 g, while the females gained 10 g under the same conditions and showed the highest area under curve (AUC) values of 40 000‐45 000 (min mg/dL) in the IPGTT.ConclusionThe results show that mice from different genetic backgrounds respond differently to a high fat diet and oral infection in terms of polyp development and glucose tolerance, and this effect is gender related.

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.

Host genetic effects in pneumonia

SummaryGiven the coronavirus disease 2019 (COVID-19) pandemic, investigations into host susceptibility to infectious diseases and downstream sequelae have never been more relevant. Pneumonia is a lung disease that can cause respiratory failure and hypoxia and is a common complication of infectious diseases, including COVID-19. Few genome-wide association studies (GWASs) of host susceptibility and severity of pneumonia have been conducted. We performed GWASs of pneumonia susceptibility and severity in the Vanderbilt University biobank (BioVU) with linked electronic health records (EHRs), including Illumina Expanded Multi-Ethnic Global Array (MEGAEX)-genotyped European ancestry (EA, n= 69,819) and African ancestry (AA, n = 15,603) individuals. Two regions of large effect were identified: the CFTR locus in EA (rs113827944; OR = 1.84, p value = 1.2 × 10−36) and HBB in AA (rs334 [p.Glu7Val]; OR = 1.63, p value = 3.5 × 10−13). Mutations in these genes cause cystic fibrosis (CF) and sickle cell disease (SCD), respectively. After removing individuals diagnosed with CF and SCD, we assessed heterozygosity effects at our lead variants. Further GWASs after removing individuals with CF uncovered an additional association in R3HCC1L (rs10786398; OR = 1.22, p value = 3.5 × 10−8), which was replicated in two independent datasets: UK Biobank (n = 459,741) and 7,985 non-overlapping BioVU subjects, who are genotyped on arrays other than MEGAEX. This variant was also validated in GWASs of COVID-19 hospitalization and lung function. Our results highlight the importance of the host genome in infectious disease susceptibility and severity and offer crucial insight into genetic effects that could potentially influence severity of COVID-19 sequelae.

Drug response in association with pharmacogenomics and pharmacomicrobiomics: towards a better personalized medicine.

Researchers have long been presented with the challenge imposed by the role of genetic heterogeneity in drug response. For many years, Pharmacogenomics and pharmacomicrobiomics has been investigating the influence of an individual's genetic background to drug response and disposition. More recently, the human gut microbiome has proven to play a crucial role in the way patients respond to different therapeutic drugs and it has been shown that by understanding the composition of the human microbiome, we can improve the drug efficacy and effectively identify drug targets. However, our knowledge on the effect of host genetics on specific gut microbes related to variation in drug metabolizing enzymes, the drug remains limited and therefore limits the application of joint host-microbiome genome-wide association studies. In this paper, we provide a historical overview of the complex interactions between the host, human microbiome and drugs. While discussing applications, challenges and opportunities of these studies, we draw attention to the critical need for inclusion of diverse populations and the development of an innovative and combined pharmacogenomics and pharmacomicrobiomics approach, that may provide an important basis in personalized medicine.

Mechanisms governing avian phylosymbiosis: Genetic dissimilarity based on neutral and MHC regions exhibits little relationship with gut microbiome distributions of Galápagos mockingbirds.

The gut microbiome of animals, which serves important functions but can also contain potential pathogens, is to varying degrees under host genetic control. This can generate signals of phylosymbiosis, whereby gut microbiome composition matches host phylogenetic structure. However, the genetic mechanisms that generate phylosymbiosis and the scale at which they act remain unclear. Two non‐mutually exclusive hypotheses are that phylosymbiosis is driven by immunogenetic regions such as the major histocompatibility complex (MHC) controlling microbial composition, or by spatial structuring of neutral host genetic diversity via founder effects, genetic drift, or isolation by distance. Alternatively, associations between microbes and host phylogeny may be generated by their spatial autocorrelation across landscapes, rather than the direct effects of host genetics. In this study, we collected MHC, microsatellite, and gut microbiome data from separate individuals belonging to the Galápagos mockingbird species complex, which consists of four allopatrically distributed species. We applied multiple regression with distance matrices and Bayesian inference to test for correlations between average genetic and microbiome similarity across nine islands for which all three levels of data were available. Clustering of individuals by species was strongest when measured with microsatellite markers and weakest for gut microbiome distributions, with intermediate clustering of MHC allele frequencies. We found that while correlations between island‐averaged gut microbiome composition and both microsatellite and MHC dissimilarity existed across species, these relationships were greatly weakened when accounting for geographic distance. Overall, our study finds little support for large‐scale control of gut microbiome composition by neutral or adaptive genetic regions across closely related bird phylogenies, although this does not preclude the possibility that host genetics shapes gut microbiome at the individual level.

Assessing the host genetic background effects on type 2 diabetes and obesity development in response to mixed-oral bacteria and high-fat diet using the collaborative cross mouse model.

BackgroundHost genetic background and sex, play central roles in defining the pathogenesis of type 2 diabetes (T2D), obesity and infectious diseases. Our previous studies demonstrated the utilization of genetically highly diverse inbred mouse lines, namely collaborative cross (CC), for dissecting host susceptibility for the development of T2D and obesity, showing significant variations following high‐fat (42% fat) diet (HFD). Here, we aimed to assessing the host genetic background and sex effects on T2D and obesity development in response to oral‐mixed bacterial infection and HFD using the CC lines.Materials and MethodsStudy cohort consists of 97 mice from 2 CC lines (both sexes), maintained on either HFD or Standard diet (CHD) for 12 weeks. At week 5 a group of mice from each diet were infected with Porphyromonas gingivalis (Pg) and Fusobacterium nucleatum (Fn) bacteria (control groups without infection). Body weight (BW) and glucose tolerance ability were assessed at the end time point of the experiment.ResultsThe CC lines varied (P < .05) at their BW gain and glucose tolerance ability (with sex effect) in response to diets and/or infection, showing opposite responses despite sharing the same environmental conditions. The combination of diet and infection enhances BW accumulation for IL1912, while restraints it for IL72. As for glucose tolerance ability, only females (both lines) were deteriorated in response to infection.ConclusionsThis study emphasizes the power of the CC mouse population for the characterization of host genetic makeup for defining the susceptibility of the individual to development of obesity and/or impaired glucose tolerance.

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

Evidence supports associations between human gut microbiome variation and multiple health outcomes and diseases. Despite compelling results from in vivo and in vitro models, few findings have been translated into an understanding of modifiable causal relationships. Furthermore, epidemiological studies have been unconvincing in their ability to offer causal evidence due to their observational nature, where confounding by lifestyle and behavioural factors, reverse causation and bias are important limitations. Whilst randomized controlled trials have made steps towards understanding the causal role played by the gut microbiome in disease, they are expensive and time-consuming. This evidence that has not been translated between model systems impedes opportunities for harnessing the gut microbiome for improving population health. Therefore, there is a need for alternative approaches to interrogate causality in the context of gut microbiome research. The integration of human genetics within population health sciences have proved successful in facilitating improved causal inference (e.g., with Mendelian randomization [MR] studies) and characterising inherited disease susceptibility. MR is an established method that employs human genetic variation as natural "proxies" for clinically relevant (and ideally modifiable) traits to improve causality in observational associations between those traits and health outcomes. Here, we focus and discuss the utility of MR within the context of human gut microbiome research, review studies that have used this method and consider the strengths, limitations and challenges facing this research. Specifically, we highlight the requirements for careful examination and interpretation of derived causal estimates and host (i.e., human) genetic effects themselves, triangulation across multiple study designs and inter-disciplinary collaborations. Meeting these requirements will help support or challenge causality of the role played by the gut microbiome on human health to develop new, targeted therapies to alleviate disease symptoms to ultimately improve lives and promote good health.

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

Evidence supports associations between human gut microbiome variation and multiple health outcomes and diseases. Despite compelling results from in vivo and in vitro models, few findings have been translated into an understanding of modifiable causal relationships. Furthermore, epidemiological studies have been unconvincing in their ability to offer causal evidence due to their observational nature, where confounding by lifestyle and behavioural factors, reverse causation and bias are important limitations. Whilst randomized controlled trials have made steps towards understanding the causal role played by the gut microbiome in disease, they are expensive and time-consuming. This evidence that has not been translated between model systems impedes opportunities for harnessing the gut microbiome for improving population health. Therefore, there is a need for alternative approaches to interrogate causality in the context of gut microbiome research. The integration of human genetics within population health sciences have proved successful in facilitating improved causal inference (e.g., with Mendelian randomization [MR] studies) and characterising inherited disease susceptibility. MR is an established method that employs human genetic variation as natural “proxies” for clinically relevant (and ideally modifiable) traits to improve causality in observational associations between those traits and health outcomes. Here, we focus and discuss the utility of MR within the context of human gut microbiome research, review studies that have used this method and consider the strengths, limitations and challenges facing this research. Specifically, we highlight the requirements for careful examination and interpretation of derived causal estimates and host (i.e., human) genetic effects themselves, triangulation across multiple study designs and inter-disciplinary collaborations. Meeting these requirements will help support or challenge causality of the role played by the gut microbiome on human health to develop new, targeted therapies to alleviate disease symptoms to ultimately improve lives and promote good health.

Evaluation of the Host Genetic Effects of Tuberculosis-Associated Variants Among Patients With Type 1 and Type 2 Diabetes Mellitus.

BackgroundUnderstanding the link between tuberculosis (TB) and diabetes is increasingly important as public health responds to the growing global burden of noncommunicable diseases. Genetic association studies have identified numerous host genetic variants linked to TB; however, potential host genetic mechanisms linking TB and diabetes remain unexplored.MethodsWe used genetic and phenotypic data from the UK Biobank to evaluate the association of 6 previously reported TB-related host genetic variants (genome-wide significant associations from published studies) with diabetes. The study included 409 692 adults of European ancestry including 2177 with type 1 diabetes mellitus (T1DM) and 13 976 with type 2 diabetes mellitus (T2DM), defined by ICD-10 diagnosis codes.ResultsOf the 6 TB-associated single nucleotide polymorphisms (SNPs), 2 were associated with T1DM and 3 with T2DM, after adjusting for age, sex, body mass index, smoking, alcohol use, and population structure. After correction for multiple testing, SNPs rs2894257 and rs3135359 (HLA-DRA-DQA1) were associated with T1DM (rs2894257: odds ratio [OR], 1.32; 95% confidence interval [CI], 1.21–1.45; rs3135359: OR, 1.72; 95% CI, 1.57–1.88) and T2DM (rs2894257: OR, 1.11; 95% CI, 1.08–1.15; rs3135359: OR, 1.06; 95% CI, 1.025–1.096). The associations with T2DM weakened for rs2894257 and rs3135359 after further exclusion of probable T1DM cases defined by International Statistical Classification of Diseases and Related Health Problems (ICD-10) codes. SNP rs4733781 on chromosome 8 (ASAP1 gene) was associated with T2DM after exclusion of T1DM cases.ConclusionsOur findings suggest that common host genetic effects may play a role in the molecular mechanism linking TB and diabetes. Future large genetic studies of TB and diabetes should focus on developing countries with high burdens of infectious and chronic diseases.

Cohort Profile: The Christchurch IBS cOhort to investigate Mechanisms FOr gut Relief and improved Transit (COMFORT)

Background and aims: This cross-sectional observational case-control study was initiated in July 2016 with the aim of increasing an understanding of the underlying disease mechanisms in functional gastrointestinal disorders (FGIDs) including irritable bowel syndrome (IBS), functional diarrhoea (FD), and functional constipation (FC). Specific areas of interest include the effect of food, microbiome, host and microbial genetics, metabolome, and psychological variables on unexplained chronic gastrointestinal (GI) symptoms. Methods: This study recruited consecutive patients who were attending one of two endoscopy centres in Christchurch, New Zealand, for colonoscopy and a subgroup of participants from the general public who did not undergo colonoscopy. Participants with known GI disease other than an FGID were excluded. Those with symptoms were recruited as cases, whilst those without symptoms were recruited as controls. In the days prior to preparation for colonoscopy, or an agreeable time for those not undergoing colonoscopy, demographic, symptom, psychological, dietary, and health data were collected in addition to biological samples (breath, faeces, blood, and urine). Colonic biopsies were taken at the time of colonoscopy from participants in the colonoscopy subgroup. Results: Between July 2016 and December 2018, 349 participants were recruited, 315 of whom completed the study, 220 participants were from the colonoscopy subgroup, and 95 from the non-colonoscopy subgroup. This included 129 controls and 186 cases (57 IBS-diarrhoea predominant, 30 IBS-constipation predominant, 41 IBS-mixed, 42 FC, and 16 FD). The mean age of FGID cases was 53.4 years and controls 54.4 years. Cases (149/186, 80.1%) and controls (57/72, 55.8%) were predominantly female. Education levels were similar across the cohort. Smoking and alcohol rates were also similar. Biological samples were collected as planned from participants. Conclusions: The COMFORT cohort is a unique clinical cohort of FGID cases and controls with a wide range of demographic, dietary, clinical, psychological, and health data in addition to biological samples. Future research will aim to use a systems biology approach to establish the potential role of diet, host-microbiome interactions, and other factors in the pathogenesis of FGIDs.

Probiotic potential of lactic acid bacteria isolates from indigenous calves is superior to isolates from crossbred dairy calves

To investigate the effect of host genetics on gut microbiota probiotic potential, we performed a comparative study between the probiotic attributes of the Lactobacilli isolated from the faeces of indigenous (Tharparkar) as well as crossbred (Vrindavani) cattle. Accordingly, 69 lactic acid bacteria (LAB) strains were isolated from faeces of new-born calves (40 from Tharparkar and 29 from Vrindavani), out of which 4 strains from each breed having auto-aggregation >40%, and cell surface hydrophobicity >70% were short-listed. The auto-aggregation and cell surface hydrophobicity values were significantly (P 0.05) to form biofilm and had comparable (P>0.05) antagonistic activity. Further assessments indicated that the per cent co-aggregation was significantly (P<0.05) higher in isolates from indigenous compared to that from the crossbred cattle. Thus, it can be concluded that LAB isolates from the indigenous Tharparkar cattle are superior over the isolates from crossbred cattle in terms of their in vitro probiotic efficacy.