Faculty Bibliography

INTRODUCTION/BACKGROUND:Epidemiological studies that investigate alterations in the gut microbial composition associated with smoking are lacking. This study examined the composition of the gut microbiome in smokers compared with non-smokers. METHODS:Stool samples were collected in a cross-sectional study of 249 participants selected from the Health Effects of Arsenic Longitudinal Study (HEALS) in Bangladesh. Microbial DNA was extracted from the fecal samples and sequenced by 16S rRNA gene sequencing. The associations of smoking status and intensity of smoking with the relative abundance or the absence and presence of individual bacterial taxon from phylum to genus levels were examined. RESULTS:The relative abundance of bacterial taxa along the Erysipelotrichi-to-Catenibacterium lineage was significantly higher in current smokers compared to never smokers. The odds ratio comparing the mean relative abundance in current smokers with that in never smokers was 1.91 (95% confidence interval [CI] = 1.36 to 2.69) for the genus Catenibacterium and 1.89 (95% CI = 1.39 to 2.56) for the family Erysipelotrichaceae, the order Erysipelotrichale, and the class Erysipelotrichi ((FDR-adjusted p-values = 0.0008 to 0.01). A dose-response association was observed for each of these bacterial taxa. The presence of Alphaproteobacteria was significantly greater comparing current with never smokers (OR = 4.85, FDR-adjusted p-values = 0.04). CONCLUSIONS:Our data in a Bangladeshi population are consistent with evidence of an association between smoking status and dosage with change in the gut bacterial composition. IMPLICATIONS/CONCLUSIONS:This study for the first time examined the relationship between smoking and the gut microbiome composition. The data suggest that smoking status may play an important role in the composition of the gut microbiome, especially among individuals with higher levels of tobacco exposure.

Despite advances in lipid-lowering therapies, people with diabetes continue to experience more limited cardiovascular benefits. In diabetes, hyperglycemia sustains inflammation and preempts vascular repair. We tested the hypothesis that the receptor for advanced glycation end-products (RAGE) contributes to these maladaptive processes. We report that transplantation of aortic arches from diabetic, Western diet-fed Ldlr-/- mice into diabetic Ager-/- (Ager, the gene encoding RAGE) versus WT diabetic recipient mice accelerated regression of atherosclerosis. RNA-sequencing experiments traced RAGE-dependent mechanisms principally to the recipient macrophages and linked RAGE to interferon signaling. Specifically, deletion of Ager in the regressing diabetic plaques downregulated interferon regulatory factor 7 (Irf7) in macrophages. Immunohistochemistry studies colocalized IRF7 and macrophages in both murine and human atherosclerotic plaques. In bone marrow-derived macrophages (BMDMs), RAGE ligands upregulated expression of Irf7, and in BMDMs immersed in a cholesterol-rich environment, knockdown of Irf7 triggered a switch from pro- to antiinflammatory gene expression and regulated a host of genes linked to cholesterol efflux and homeostasis. Collectively, this work adds a new dimension to the immunometabolic sphere of perturbations that impair regression of established diabetic atherosclerosis and suggests that targeting RAGE and IRF7 may facilitate vascular repair in diabetes.

MOTIVATION/BACKGROUND:Recent microbiome association studies have revealed important associations between microbiome and disease/health status. Such findings encourage scientists to dive deeper to uncover the causal role of microbiome in the underlying biological mechanism, and have led to applying statistical models to quantify causal microbiome effects and to identify the specific microbial agents. However, there are no existing causal mediation methods specifically designed to handle high dimensional and compositional microbiome data. RESULTS:We propose a rigorous Sparse Microbial Causal Mediation Model (SparseMCMM) specifically designed for the high dimensional and compositional microbiome data in a typical three-factor (treatment, microbiome and outcome) causal study design. In particular, linear log-contrast regression model and Dirichlet regression model are proposed to estimate the causal direct effect of treatment and the causal mediation effects of microbiome at both the community and individual taxon levels. Regularization techniques are used to perform the variable selection in the proposed model framework to identify signature causal microbes. Two hypothesis tests on the overall mediation effect are proposed and their statistical significance is estimated by permutation procedures. Extensive simulated scenarios show that SparseMCMM has excellent performance in estimation and hypothesis testing. Finally, we showcase the utility of the proposed SparseMCMM method in a study which the murine microbiome has been manipulated by providing a clear and sensible causal path among antibiotic treatment, microbiome composition and mouse weight. AVAILABILITY/BACKGROUND:https://sites.google.com/site/huilinli09/software and https://github.com/chanw0/SparseMCMM. SUPPLEMENTARY INFORMATION/BACKGROUND:Supplementary data are available at Bioinformatics online.

Over the past decade, there has been a change in the epidemiology of oral cavity squamous cell cancer (OC-SCC). Many new cases of OC-SCC lack the recognized risk factors of smoking, alcohol and human papilloma virus. The aim of this study was to determine if the oral microbiome may be associated with OC-SCC in nonsmoking HPV negative patients. We compared the oral microbiome of HPV-negative nonsmoker OC-SCC( n=18), premalignant lesions(PML) (n=8) and normal control patients (n=12). Their oral microbiome was sampled by oral wash and defined by 16S rRNA gene sequencing. We report that the periodontal pathogens Fusobacterium, Prevotella, Alloprevotella were enriched while commensal Streptococcus depleted in OC-SCC. Based on the four genera plus a marker genus Veillonella for PML, we classified the oral microbiome into two types. Gene/pathway analysis revealed a progressive increase of genes encoding HSP90 and ligands for TLRs 1, 2 and 4 along the controls→PML→OC-SCC progression sequence. Our findings suggest an association between periodontal pathogens and OC-SCC in non smoking HPV negative patients.

Background/UNASSIGNED:(the gene encoding RAGE), have been reported to be associated with T2D and its complications, we tested for potential relationships between these factors and T2D status in Emirati subjects. Methods/UNASSIGNED:In a case-control study, we recruited Emirati subjects with T2D and controls from the Sheikh Khalifa Medical City in Abu Dhabi. Anthropomorphic characteristics, levels of plasma sRAGE and esRAGE, and routine chemistry variables were measured. Results/UNASSIGNED: = 0.01, after adjustment for age and sex). Conclusion/discussion/UNASSIGNED:Levels of sRAGE, but not esRAGE, were associated with T2D status in Abu Dhabi, but not after correction for eGFR. Elevated levels of plasma insulin in both control and T2D subjects suggests the presence of metabolic dysfunction, even in subjects without diabetes.

Objectives/UNASSIGNED:Hemoglobin A1c (HbA1c) is associated with hypertension in prediabetes (PD) and type 2 diabetes (T2D), both of which are associated with increased cardiovascular disease (CVD) risk. Traditionally, HbA1c is the standard approach to assessing glycemic status, however, glycemic variability (GV)-the daily fluctuations in blood glucose concentrations-may be more predictive of CVD. Little is known regarding the relationship between GV and blood pressure (BP). Therefore, we examined GV and HbA1c and their associations with BP in overweight adults with PD and early stage T2D enrolled in a weight loss study. Methods/UNASSIGNED:Participants had a history of PD or T2D treated with lifestyle alone or lifestyle and metformin. Data for this report were obtained at baseline and included sociodemographics, height, weight, BP, and HbA1c. Up to two weeks of continuous glucose monitoring data were collected using the Abbott Freestyle Libre Pro, and mean amplitude of glycemic excursions (MAGE) was computed using EasyGV. Linear mixed models were used to test the associations of MAGE and HbA1c with BP, with adjustment for sociodemographic characteristics, as well as to compare MAGE on weekdays versus weekends. Repeated measures ANOVA was used to investigate the within-subject and between-subject variation of MAGE. All analyses were performed using R software. Results/UNASSIGNED:Â =Â 0.03). Conclusions/UNASSIGNED:Among overweight adults with PD and early-stage T2D, MAGE was found to be associated with SBP. This suggests that a measure of daily GV may be a good alternative measure of metabolic health outcomes in this population. Funding Sources/UNASSIGNED:This work was funded by the American Heart Association.

Our objective was to investigate the relationship between the gut microbiota and anthropometric measurements among 248 participants from the Health Effects of Arsenic Longitudinal Study (HEALS) in Bangladesh. Our cohort represents a unique population that allows for the investigation of the gut microbiota and anthropometric measurements in lean individuals. We measured height, weight, arm, thigh, hip, and waist circumferences, and collected fecal samples. Microbial DNA was extracted from the stool samples and sequenced by 16S rRNA gene sequencing. We examined associations between relative abundance of individual bacterial taxa from phylum to genus levels and anthropometric measurements. We found that higher BMI, mid-upper arm circumference, waist circumference, and waist-to-hip ratio were associated with a lower alpha diversity of fecal bacteria. Relative abundance of the genus Oscillospira and the family S24-7 were inversely related to all measurements after correction for multiple testing. Relative abundance of genus Acidaminococcus and family Ruminococcaceae were also associated with several measurements. The positive associations of the genus Acidaminococcus with BMI, as well as waist and hip circumferences, were stronger in women than in men. Our data in this lean Bangladeshi population found a correlation between Oscillospira and leanness, as measured using multiple anthropometric measures.

The high-fat, high-calorie diets of westernized cultures contribute to the global obesity epidemic, and early life exposure to antibiotics may potentiate those dietary effects. Previous experiments with mice had shown that sub-therapeutic antibiotic treatment (STAT)-even restricted to early life-affected the gut microbiota, altered host metabolism, and increased adiposity throughout the lifetime of the animals. Here we carried out a large-scale cohousing experiment to investigate whether cohousing STAT and untreated (Control) mice would transfer the STAT-perturbed microbiota and transmit its impact on weight. We exposed pregnant dams and their young offspring to either low-dose penicillin (STAT) or water (Control) until weaning, and then followed the offspring as they grew and endured a switch from normal to high-fat diet at week 17 of life. Cohousing, which started at week 4, rapidly approximated the microbiota within cages, lowering the weight of STAT mice relative to non-cohoused mice. The effect, however, varied between cages, and was restricted to the first 16 weeks when diet consisted of normal chow. Once mice switched to high-fat diet, the microbiota α- and β-diversity expanded and the effect of cohousing faded: STAT mice, again, were heavier than control mice independently of cohousing. Metabolomics revealed serum metabolites associated with STAT exposure, but no significant differences were detected in glucose or insulin tolerance. Our results show that cohousing can partly ameliorate the impact of STAT on the gut microbiota but not prevent increased weight with high-fat diet. These observations have implications for microbiota therapies aimed to resolve the collateral damage of antibiotics and their load on human obesity.

Weight loss reduces the risk of type 2 diabetes mellitus (T2D) in overweight and obese individuals. Although the physiological response to food varies among individuals, standard dietary interventions use a "one-size-fits-all" approach. The Personal Diet Study aims to evaluate two dietary interventions targeting weight loss in people with prediabetes and T2D: (1) a low-fat diet, and (2) a personalized diet using a machine-learning algorithm that predicts glycemic response to meals. Changes in body weight, body composition, and resting energy expenditure will be compared over a 6-month intervention period and a subsequent 6-month observation period intended to assess maintenance effects. The behavioral intervention is delivered via mobile health technology using the Social Cognitive Theory. Here, we describe the design, interventions, and methods used.