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.

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.

Studies of host-associated and environmental microbiomes often incorporate longitudinal sampling or paired samples in their experimental design. Longitudinal sampling provides valuable information about temporal trends and subject/population heterogeneity, offering advantages over cross-sectional and pre-post study designs. To support the needs of microbiome researchers performing longitudinal studies, we developed q2-longitudinal, a software plugin for the QIIME 2 microbiome analysis platform (https://qiime2.org). The q2-longitudinal plugin incorporates multiple methods for analysis of longitudinal and paired-sample data, including interactive plotting, linear mixed-effects models, paired differences and distances, microbial interdependence testing, first differencing, longitudinal feature selection, and volatility analyses. The q2-longitudinal package (https://github.com/qiime2/q2-longitudinal) is open-source software released under a 3-clause Berkeley Software Distribution (BSD) license and is freely available, including for commercial use. IMPORTANCE Longitudinal sampling provides valuable information about temporal trends and subject/population heterogeneity. We describe q2-longitudinal, a software plugin for longitudinal analysis of microbiome data sets in QIIME 2. The availability of longitudinal statistics and visualizations in the QIIME 2 framework will make the analysis of longitudinal data more accessible to microbiome researchers.

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:The receptor for advanced glycation end products (RAGE) is linked to cellular stress and inflammation during Alzheimer's disease (AD). RAGE signals through Diaphanous-1 (DIAPH1); however, the expression of DIAPH1 in the healthy and AD human brain has yet to be methodically addressed. OBJECTIVE:To delineate the cell- and disease-state specific expression of DIAPH1 in the human medial temporal cortex during healthy aging and AD. METHODS:We used semi-quantitative immunohistochemistry in the human medial temporal cortex paired with widefield and confocal microscopy and automated analyses to determine colocalization and relative expression of DIAPH1 with key cell markers and molecules in the brains of subjects with AD versus age-matched controls. RESULTS:We report robust colocalization of DIAPH1 with myeloid cells and increased expression during AD, which strongly correlated to increased neutral lipids and morphology of inflamed myeloid cells. DIAPH1 moderately colocalized with markers of endothelial cells, astrocytes, neurons, and oligodendrocytes. DISCUSSION/CONCLUSIONS:Our findings localize DIAPH1 particularly to myeloid cells in the CNS, especially in AD in the locations of lipid droplet accumulation, thereby implicating RAGE-DIAPH1 signaling in dysregulated lipid metabolism and morphological changes of inflamed myeloid cells in this disorder.

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.

Islet amyloidosis is characterized by the aberrant accumulation of islet amyloid polypeptide (IAPP) in pancreatic islets, resulting in β cell toxicity, which exacerbates type 2 diabetes and islet transplant failure. It is not fully clear how IAPP induces cellular stress or how IAPP-induced toxicity can be prevented or treated. We recently defined the properties of toxic IAPP species. Here, we have identified a receptor-mediated mechanism of islet amyloidosis-induced proteotoxicity. In human diabetic pancreas and in cellular and mouse models of islet amyloidosis, increased expression of the receptor for advanced glycation endproducts (RAGE) correlated with human IAPP-induced (h-IAPP-induced) β cell and islet inflammation, toxicity, and apoptosis. RAGE selectively bound toxic intermediates, but not nontoxic forms of h-IAPP, including amyloid fibrils. The isolated extracellular ligand-binding domains of soluble RAGE (sRAGE) blocked both h-IAPP toxicity and amyloid formation. Inhibition of the interaction between h-IAPP and RAGE by sRAGE, RAGE-blocking antibodies, or genetic RAGE deletion protected pancreatic islets, β cells, and smooth muscle cells from h-IAPP-induced inflammation and metabolic dysfunction. sRAGE-treated h-IAPP Tg mice were protected from amyloid deposition, loss of β cell area, β cell inflammation, stress, apoptosis, and glucose intolerance. These findings establish RAGE as a mediator of IAPP-induced toxicity and suggest that targeting the IAPP/RAGE axis is a potential strategy to mitigate this source of β cell dysfunction in metabolic disease.

BACKGROUND:An influx of lipid-loaded macrophages characterizes visceral adipose tissue (VAT) inflammation, which is an important factor in the development of insulin resistance (IR) in obesity. Depletion of macrophage lipids accompanies increased whole body insulin sensitivity, but the underlying mechanism is unknown. Deficiency of autophagy protein ATG16L1 is associated with increases in inflammatory diseases and lipid metabolism, but the connection between ATG16L1, IR, and obesity remains elusive. We hypothesize that myeloid ATG16L1 contributes to lipid loading in macrophages and to IR. METHODS:Wild-type (WT) bone marrow derived macrophages (BMDMs) were treated with fatty acids and assessed for markers of autophagy. Myeloid-deficient Atg16l1 and littermate control male mice were fed high fat diet (HFD) or low fat diet (LFD) for 3 months starting at 8 weeks of age. Mice were assessed for body mass, fat and lean mass, glucose and insulin sensitivity, food consumption and adipose inflammation. Fluorescence-activated cell sorted VAT macrophages were assessed for lipid content and expression of autophagy related genes. RESULTS:VAT and VAT macrophages from HFD-fed WT mice did not show differences in autophagy protein and gene expression compared to tissue from LFD-fed mice. Fatty acid-treated BMDMs increased neutral lipid content but did not change autophagy protein expression. HFD-fed Atg16l1 myeloid-deficient and littermate mice demonstrated no differences in body mass, glucose or insulin sensitivity, food consumption, fat or lean mass, macrophage lipid content, or adipose tissue inflammation. CONCLUSION/CONCLUSIONS:ATG16L1 does not contribute to obesity, IR, adipose tissue inflammation or lipid loading in macrophages in mice fed HFD.