Faculty Bibliography

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.

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.

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.

Patients with complex chronic diseases usually must make multiple lifestyle changes to limit and manage their conditions. Numerous studies have shown that education alone is insufficient for engaging people in lifestyle behavior change, and that theory-based behavioral approaches also are necessary. However, even the most motivated individual may have difficulty with making lifestyle changes because of the information complexity associated with multiple behavior changes. The goal of the current Healthy Hearts and Kidneys study was to evaluate, different mobile health (mHealth)-delivered intervention approaches for engaging individuals with type 2 diabetes (T2D) and concurrent chronic kidney disease (CKD) in behavior changes. Participants were randomized to 1 of 4 groups, receiving: (1) a behavioral counseling, (2) technology-based self-monitoring to reduce information complexity, (3) combined behavioral counseling and technology-based self-monitoring, or (4) baseline advice. We will determine the impact of randomization assignment on weight loss success and 24-hour urinary excretion of sodium and phosphorus. With this report we describe the study design, methods, and approaches used to assure information security for this ongoing clinical trial. Clinical Trials.gov Identifier: NCT02276742.

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.

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.

Human microbiome is the collection of microbes living in and on the various parts of our body. The microbes living on our body in nature do not live alone. They act as integrated microbial community with massive competing and cooperating and contribute to our human health in a very important way. Most current analyses focus on examining microbial differences at a single time point, which do not adequately capture the dynamic nature of the microbiome data. With the advent of high-throughput sequencing and analytical tools, we are able to probe the interdependent relationship among microbial species through longitudinal study. Here, we propose a multivariate distance-based test to evaluate the association between key phenotypic variables and microbial interdependence utilizing the repeatedly measured microbiome data. Extensive simulations were performed to evaluate the validity and efficiency of the proposed method. We also demonstrate the utility of the proposed test using a well-designed longitudinal murine experiment and a longitudinal human study. The proposed methodology has been implemented in the freely distributed open-source R package and Python code.

Aims/UNASSIGNED:The transformation of the United Arab Emirates (UAE) from a semi-nomadic to a high income society has been accompanied by increasing rates of obesity and Type 2 diabetes mellitus. We examined if the AGE-RAGE (receptor for advanced glycation endproducts) axis is associated with obesity and diabetes mellitus in the pilot phase of the UAE Healthy Futures Study (UAEHFS). Methods/UNASSIGNED:517 Emirati subjects were enrolled and plasma/serum levels of AGE, carboxy methyl lysine (CML)-AGE, soluble (s)RAGE and endogenous secretory (es)RAGE were measured along with weight, height, waist and hip circumference (WC/HC), blood pressure, HbA1c, Vitamin D levels and routine chemistries. The relationship between the AGE-RAGE axis and obesity and diabetes mellitus was tested using proportional odds models and linear regression. Results/UNASSIGNED:After covariate adjustment, AGE levels were significantly associated with diabetes status. Levels of sRAGE and esRAGE were associated with BMI and levels of sRAGE were associated with WC/HC. Conclusions/UNASSIGNED:The AGE-RAGE axis is associated with diabetes status and obesity in this Arab population. Prospective serial analysis of this axis may identify predictive biomarkers of obesity and cardiometabolic dysfunction in the UAEHFS.