Faculty Bibliography

OBJECTIVE: Diabetic subjects are at higher risk of ischemic peripheral vascular disease. We tested the hypothesis that advanced glycation end products (AGEs) and their receptor (RAGE) block neoangiogenesis and blood flow recovery after hindlimb ischemia induced by femoral artery ligation through modulation of immune/inflammatory mechanisms. APPROACH AND RESULTS: Wild-type mice rendered diabetic with streptozotocin and subjected to unilateral femoral artery ligation displayed increased accumulation and expression of AGEs and RAGE in ischemic muscle. In diabetic wild-type mice, femoral artery ligation attenuated neoangiogenesis and impaired blood flow recovery, in parallel with reduced macrophage content in ischemic muscle and suppression of early inflammatory gene expression, including Ccl2 (chemokine [C-C motif] ligand-2) and Egr1(early growth response gene-1) versus nondiabetic mice. Deletion of Ager (gene encoding RAGE) or transgenic expression of Glo1 (reduces AGEs) restored adaptive inflammation, neoangiogenesis, and blood flow recovery in diabetic mice. In diabetes mellitus, deletion of Ager increased circulating Ly6Chi monocytes and augmented macrophage infiltration into ischemic muscle tissue after femoral artery ligation. In vitro, macrophages grown in high glucose display inflammation that is skewed to expression of tissue damage versus tissue repair gene expression. Further, macrophages grown in high versus low glucose demonstrate blunted macrophage-endothelial cell interactions. In both settings, these adverse effects of high glucose were reversed by Ager deletion in macrophages. CONCLUSIONS: These findings indicate that RAGE attenuates adaptive inflammation in hindlimb ischemia; underscore microenvironment-specific functions for RAGE in inflammation in tissue repair versus damage; and illustrate that AGE/RAGE antagonism may fill a critical gap in diabetic peripheral vascular disease.

The surgical robot, a costly technology for treatment of prostate cancer with equivocal marginal benefit, rapidly diffused into clinical practice. We sought to evaluate the role of teaching in the early adoption phase of the surgical robot. Teaching hospitals were the primary early adopters: data from the Healthcare Cost and Utilization Project showed that surgical robots were acquired by 45.5% of major teaching, 18.0% of minor teaching and 8.0% of non-teaching hospitals during the early adoption phase. However, teaching hospital faculty produced little comparative effectiveness research: By 2008, only 24 published studies compared robotic prostatectomy outcomes to those of conventional techniques. Just ten of these studies (41.7%) were more than minimally powered, and only six (25%) involved cross-institutional collaborations. In adopting the surgical robot, teaching hospitals fulfilled their mission to innovate, but failed to generate corresponding scientific evidence.

BACKGROUND: The role of the microbiota in human health and disease has been increasingly studied, gathering momentum through the use of high-throughput technologies. Further identification of the roles of specific microbes is necessary to better understand the mechanisms involved in diseases related to microbiome perturbations. METHODS: Here, we introduce a new microbiome-based group association testing method, optimal microbiome-based association test (OMiAT). OMiAT is a data-driven testing method which takes an optimal test throughout different tests from the sum of powered score tests (SPU) and microbiome regression-based kernel association test (MiRKAT). We illustrate that OMiAT efficiently discovers significant association signals arising from varying microbial abundances and different relative contributions from microbial abundance and phylogenetic information. We also propose a way to apply it to fine-mapping of diverse upper-level taxa at different taxonomic ranks (e.g., phylum, class, order, family, and genus), as well as the entire microbial community, within a newly introduced microbial taxa discovery framework, microbiome comprehensive association mapping (MiCAM). RESULTS: Our extensive simulations demonstrate that OMiAT is highly robust and powerful compared with other existing methods, while correctly controlling type I error rates. Our real data analyses also confirm that MiCAM is especially efficient for the assessment of upper-level taxa by integrating OMiAT as a group analytic method. CONCLUSIONS: OMiAT is attractive in practice due to the high complexity of microbiome data and the unknown true nature of the state. MiCAM also provides a hierarchical association map for numerous microbial taxa and can also be used as a guideline for further investigation on the roles of discovered taxa in human health and disease.

Recent advancements in next-generation DNA sequencing technologies have made it plausible to study the association of rare variants with complex diseases. Due to the low frequency, rare variants need to be aggregated in association tests to achieve adequate power with reasonable sample sizes. Hierarchical modeling/kernel machine methods have gained popularity among many available methods for testing a set of rare variants collectively. Here, we propose a new score statistic based on a hierarchical model by additionally modeling the distribution of rare variants under the case-control study design. Results from extensive simulation studies show that the proposed method strikes a balance between robustness and power and outperforms several popular rare-variant association tests. We demonstrate the performance of our method using the Dallas Heart Study.

Oral microbiome dysbiosis is associated with oral disease and potentially with systemic diseases; however, the determinants of these microbial imbalances are largely unknown. In a study of 1204 US adults, we assessed the relationship of cigarette smoking with the oral microbiome. 16S rRNA gene sequencing was performed on DNA from oral wash samples, sequences were clustered into operational taxonomic units (OTUs) using QIIME and metagenomic content was inferred using PICRUSt. Overall oral microbiome composition differed between current and non-current (former and never) smokers (P<0.001). Current smokers had lower relative abundance of the phylum Proteobacteria (4.6%) compared with never smokers (11.7%) (false discovery rate q=5.2 x 10-7), with no difference between former and never smokers; the depletion of Proteobacteria in current smokers was also observed at class, genus and OTU levels. Taxa not belonging to Proteobacteria were also associated with smoking: the genera Capnocytophaga, Peptostreptococcus and Leptotrichia were depleted, while Atopobium and Streptococcus were enriched, in current compared with never smokers. Functional analysis from inferred metagenomes showed that bacterial genera depleted by smoking were related to carbohydrate and energy metabolism, and to xenobiotic metabolism. Our findings demonstrate that smoking alters the oral microbiome, potentially leading to shifts in functional pathways with implications for smoking-related diseases.The ISME Journal advance online publication, 25 March 2016; doi:10.1038/ismej.2016.37.

We consider the non-inferiority (or equivalence) test of the odds ratio (OR) in a crossover study with binary outcomes to evaluate the treatment effects of two drugs. To solve this problem, Lui and Chang (2011) proposed both an asymptotic method and a conditional method based on a random effects logit model. Kenward and Jones (1987) proposed a likelihood ratio test (LRTM ) based on a log linear model. These existing methods are all subject to model misspecification. In this paper, we propose a likelihood ratio test (LRT) and a score test that are independent of model specification. Monte Carlo simulation studies show that, in scenarios considered in this paper, both the LRT and the score test have higher power than the asymptotic and conditional methods for the non-inferiority test; the LRT, score, and asymptotic methods have similar power, and they all have higher power than the conditional method for the equivalence test. When data can be well described by a log linear model, the LRTM has the highest power among all the five methods (LRTM , LRT, score, asymptotic, and conditional) for both non-inferiority and equivalence tests. However, in scenarios for which a log linear model does not describe the data well, the LRTM has the lowest power for the non-inferiority test and has inflated type I error rates for the equivalence test. We provide an example from a clinical trial that illustrates our methods

BACKGROUND: Patients with atopic dermatitis (AD) are prone to skin infections, with microbes such as Staphylococcus aureus suspected of contributing to pathogenesis. Bleach baths might improve AD by reducing skin microbial burden. OBJECTIVE: We sought to characterize the microbiota of lesional and nonlesional skin in young children with AD and control subjects and compare changes after treatment with a topical corticosteroid (TCS) alone or TCS + dilute bleach bath. METHODS: In a randomized, placebo-controlled, single-blinded clinical trial in 21 children with AD and 14 healthy children, lesional and nonlesional AD skin was examined at baseline and after 4-week treatment with TCS alone or TCS plus bleach bath. Microbial DNA was extracted for quantitative polymerase chain reaction of predominant genera and 16S rRNA sequencing. RESULTS: At baseline, densities of total bacteria and Staphylococcus, including Staphylococcus aureus, were significantly higher at the worst AD lesional site than nonlesional (P = .001) or control (P < .001) skin; bacterial communities on lesional and nonlesional AD skin significantly differed from each other (P = .04) and from control (P < .001). After TCS + bleach bath or TCS alone, bacterial compositions on lesional skin normalized (P < .0001), resembling nonlesional skin, with microbial diversity restored to control skin levels. LIMITATIONS: The 4-week time period and/or the twice-weekly baths may not have been sufficient for additional impact on the cutaneous microbiome. More detailed sequencing may allow better characterization of the distinguishing taxa with bleach bath treatment. CONCLUSIONS: Treatment with a TCS cream suffices to normalize the cutaneous microbiota on lesional AD; after treatment, bacterial communities on lesional skin resemble nonlesional skin but remain distinct from control.

The early life microbiome plays important roles in host immunological and metabolic development. Because the incidence of type 1 diabetes (T1D) has been increasing substantially in recent decades, we hypothesized that early-life antibiotic use alters gut microbiota, which predisposes to disease. Using non-obese diabetic mice that are genetically susceptible to T1D, we examined the effects of exposure to either continuous low-dose antibiotics or pulsed therapeutic antibiotics (PAT) early in life, mimicking childhood exposures. We found that in mice receiving PAT, T1D incidence was significantly higher, and microbial community composition and structure differed compared with controls. In pre-diabetic male PAT mice, the intestinal lamina propria had lower Th17 and Treg proportions and intestinal SAA expression than in controls, suggesting key roles in transducing the altered microbiota signals. PAT affected microbial lipid metabolism and host cholesterol biosynthetic gene expression. These findings show that early-life antibiotic treatments alter the gut microbiota and its metabolic capacities, intestinal gene expression and T-cell populations, accelerating T1D onset in non-obese diabetic mice.