Faculty Bibliography

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.

BACKGROUND:Emerging data suggest that inorganic arsenic exposure and gut microbiome are associated with the risk of cardiovascular disease. The gut microbiome may modify disease risk associated with arsenic exposure. Our aim was to examine the inter-relationships between arsenic exposure, the gut microbiome, and carotid intima-media thickness (IMT)-a surrogate marker for atherosclerosis. METHODS:We recruited 250 participants from the Health Effects of Arsenic Longitudinal Study in Bangladesh, measured IMT and collected fecal samples in year 2015-2016. 16S rRNA gene sequencing was conducted on microbial DNA extracted from the fecal samples. Arsenic exposure was measured using data on arsenic concentration in drinking water wells over time to derive a time-weighted water arsenic index. Multivariable linear regression models were used to test the inter-relationships between arsenic exposure, relative abundance of selected bacterial taxa from phylum to genus levels, and IMT. RESULTS:We identified nominally significant associations between arsenic exposure, measured using either time-weighted water arsenic or urinary arsenic, and the relative abundances of several bacterial taxa from the phylum Tenericutes, Proteobacteria, and Firmicutes. However, none of the associations retained significance after correction for multiple testing. The relative abundances of the family Aeromonadaceae and genus Citrobacter were significantly associated with IMT after correction for multiple testing (P-value = 0.02 and 0.03, respectively). Every 1% increase in the relative abundance of Aeromonadaceae and Citrobacter was related to an 18.2-μm (95% CI: 7.8, 28.5) and 97.3-μm (95% CI: 42.3, 152.3) difference in IMT, respectively. These two taxa were also the only selected family and genus using the LASSO variable selection method. There was a significant interaction between Citrobacter and time-weighted water arsenic in IMT (P for interaction = 0.04). CONCLUSIONS:Our findings suggest a role of Citrobacter in the development of atherosclerosis, especially among individuals with higher levels of arsenic exposure.

There has been increasing interest in the human anaerobic colonic bacterium Oxalobacter formigenes because of its ability to metabolize oxalate, and its potential contribution to protection from calcium oxalate kidney stones. Prior studies examining the prevalence of this organism have focused on subjects in developed countries and on adults. Now using O. formigenes-specific PCR, we have compared the prevalence of these organisms among subjects in two remote areas in which modern medical practices have hardly been present with a USA group of mothers and their infants for the first three years of life. Among the Amerindians of the Yanomami-Sanema and Yekwana ethnic groups in Venezuela and the Hadza in Tanzania, O. formigenes was detected in 60-80% of the adult subjects, higher than found in adults from USA in this and prior studies. In young children, the prevalence was much lower in USA than in either tribal village. These data extend our understanding of the epidemiology of O. formigenes carriage, and are consistent with the hypothesis that the rising incidence of kidney stones is associated with the progressive loss of O. formigenes colonization in populations that have been highly impacted by modern medical practices.

The use of depot medroxyprogesterone acetate (DMPA), a 3-monthly injectable hormonal contraceptive, is associated with an increased risk of HIV acquisition possibly through alteration of the vaginal microbiome. In this longitudinal interventional study, we investigated the impact of DMPA administration on the vaginal microbiome in Hispanic White and Black women at the baseline (visit 1), 1 month (visit 2), and 3 months (visit 3) following DMPA treatment by using 16S rRNA gene sequencing. No significant changes in the vaginal microbiome were observed after DMPA treatment when Hispanic White and Black women were analysed as a combined group. However, DMPA treatment enriched total vaginosis-associated bacteria (VNAB) and Prevotella at visit 2, and simplified the correlational network in the vaginal microbiome in Black women, while increasing the network size in Hispanic White women. The microbiome in Black women became more diversified and contained more VNAB than Hispanic White women after DMPA treatment. While the Firmicutes to Bacteroidetes (F/B) ratio and Lactobacillus to Prevotella (L/P) ratio were comparable between Black and Hispanic White women at visit 1, both ratios were lower in Black women than in Hispanic White women at visit 2. In conclusion, DMPA treatment altered the community network and enriched VNAB in Black women but not in Hispanic White women. The Lactobacillus deficiency and enrichment of VNAB may contribute to the increased risk of HIV acquisition in Black women. Future studies on the impact of racial differences on the risk of HIV acquisition will offer insights into developing effective strategies for HIV prevention. Abbreviations: DMPA: depot medroxyprogesterone acetate; PCR: polymerase chain reaction; OTU: operational taxonomic unit; STI: sexually transmitted infections; VNAB: vaginosis-associated bacteria.

Proteasomes are a class of protease that carry out the degradation of a specific set of cellular proteins. While essential for eukaryotic life, proteasomes are found only in a small subset of bacterial species. In this chapter, we present the current knowledge of bacterial proteasomes, detailing the structural features and catalytic activities required to achieve proteasomal proteolysis. We describe the known mechanisms by which substrates are doomed for degradation, and highlight potential non-degradative roles for components of bacterial proteasome systems. Additionally, we highlight several pathways of microbial physiology that rely on proteasome activity. Lastly, we explain the various gaps in our understanding of bacterial proteasome function and emphasize several opportunities for further study.

The early-life intestinal microbiota plays a key role in shaping host immune system development. We found that a single early-life antibiotic course (1PAT) accelerated type 1 diabetes (T1D) development in male NOD mice. The single course had deep and persistent effects on the intestinal microbiome, leading to altered cecal, hepatic, and serum metabolites. The exposure elicited sex-specific effects on chromatin states in the ileum and liver and perturbed ileal gene expression, altering normal maturational patterns. The global signature changes included specific genes controlling both innate and adaptive immunity. Microbiome analysis revealed four taxa each that potentially protect against or accelerate T1D onset, that were linked in a network model to specific differences in ileal gene expression. This simplified animal model reveals multiple potential pathways to understand pathogenesis by which early-life gut microbiome perturbations alter a global suite of intestinal responses, contributing to the accelerated and enhanced T1D development.

BACKGROUND:In microbiome studies, it is important to detect taxa which are associated with pathological outcomes at the lowest definable taxonomic rank, such as genus or species. Traditionally, taxa at the target rank are tested for individual association, followed by the Benjamini-Hochberg (BH) procedure to control for false discovery rate (FDR). However, this approach neglects the dependence structure among taxa and may lead to conservative results. The taxonomic tree of microbiome data represents alignment from phylum to species rank and characterizes evolutionary relationships across microbial taxa. Taxa that are closer on the tree usually have similar responses to the exposure (environment). The statistical power in microbial association tests can be enhanced by efficiently employing the prior evolutionary information via the taxonomic tree. METHODS:We propose a two-stage microbial association mapping framework (massMap) which uses grouping information from the taxonomic tree to strengthen statistical power in association tests at the target rank. massMap first screens the association of taxonomic groups at a pre-selected higher taxonomic rank using a powerful microbial group test OMiAT. The method then proceeds to test the association for each candidate taxon at the target rank within the significant taxonomic groups identified in the first stage. Hierarchical BH (HBH) and selected subset testing (SST) procedures are evaluated to control the FDR for the two-stage structured tests. RESULTS:Our simulations show that massMap incorporating OMiAT and the advanced FDR controlling methodologies largely alleviates the multiplicity issue. It is statistically more powerful than the traditional association mapping directly at the target rank while controlling the FDR at desired levels under most scenarios. In our real data analyses, massMap detects more or the same amount of associated species with smaller adjusted p values compared to the traditional method, which further illustrates the efficiency of the proposed framework. The R package of massMap is publicly available at https://sites.google.com/site/huilinli09/software and https://github.com/JiyuanHu/ . CONCLUSIONS:massMap is a novel microbial association mapping framework and achieves additional efficiency by utilizing the intrinsic taxonomic structure of microbiome data.

BACKGROUND:There has been increasing interest in discovering microbial taxa that are associated with human health or disease, gathering momentum through the advances in next-generation sequencing technologies. Investigators have also increasingly employed prospective study designs to survey survival (i.e., time-to-event) outcomes, but current item-by-item statistical methods have limitations due to the unknown true association pattern. Here, we propose a new adaptive microbiome-based association test for survival outcomes, namely, optimal microbiome-based survival analysis (OMiSA). OMiSA approximates to the most powerful association test in two domains: 1) microbiome-based survival analysis using linear and non-linear bases of OTUs (MiSALN) which weighs rare, mid-abundant, and abundant OTUs, respectively, and 2) microbiome regression-based kernel association test for survival traits (MiRKAT-S) which incorporates different distance metrics (e.g., unique fraction (UniFrac) distance and Bray-Curtis dissimilarity), respectively. RESULTS:We illustrate that OMiSA powerfully discovers microbial taxa whether their underlying associated lineages are rare or abundant and phylogenetically related or not. OMiSA is a semi-parametric method based on a variance-component score test and a re-sampling method; hence, it is free from any distributional assumption on the effect of microbial composition and advantageous to robustly control type I error rates. Our extensive simulations demonstrate the highly robust performance of OMiSA. We also present the use of OMiSA with real data applications. CONCLUSIONS:OMiSA is attractive in practice as the true association pattern is unpredictable in advance and, for survival outcomes, no adaptive microbiome-based association test is currently available.