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: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.

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.

MOTIVATION:Accurate detection of differentially expressed genes between tumor and normal samples is a primary approach of cancer-related biomarker identification. Due to the infiltration of tumor surrounding normal cells, the expression data derived from tumor samples would always be contaminated with normal cells. Ignoring such cellular contamination would deflate the power of detecting DE genes and further confound the biological interpretation of the analysis results. For the time being, there does not exists any differential expression analysis approach for RNA-seq data in literature that can properly account for the contamination of tumor samples. RESULTS:Without appealing to any extra information, we develop a new method 'contamDE' based on a novel statistical model that associates RNA-seq expression levels with cell types. It is demonstrated through simulation studies that contamDE could be much more powerful than the existing methods that ignore the contamination. In the application to two cancer studies, contamDE uniquely found several potential therapy and prognostic biomarkers of prostate cancer and non-small cell lung cancer. AVAILABILITY AND IMPLEMENTATION:An R package contamDE is freely available at http://homepage.fudan.edu.cn/zhangh/softwares/ CONTACT:zhanghfd@fudan.edu.cn SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

Most existing statistical methods developed for calling single nucleotide polymorphisms (SNPs) using next-generation sequencing (NGS) data are based on Bayesian frameworks, and there does not exist any SNP caller that produces p-values for calling SNPs in a frequentist framework. To fill in this gap, we develop a new method MAFsnp, a Multiple-sample based Accurate and Flexible algorithm for calling SNPs with NGS data. MAFsnp is based on an estimated likelihood ratio test (eLRT) statistic. In practical situation, the involved parameter is very close to the boundary of the parametric space, so the standard large sample property is not suitable to evaluate the finite-sample distribution of the eLRT statistic. Observing that the distribution of the test statistic is a mixture of zero and a continuous part, we propose to model the test statistic with a novel two-parameter mixture distribution. Once the parameters in the mixture distribution are estimated, p-values can be easily calculated for detecting SNPs, and the multiple-testing corrected p-values can be used to control false discovery rate (FDR) at any pre-specified level. With simulated data, MAFsnp is shown to have much better control of FDR than the existing SNP callers. Through the application to two real datasets, MAFsnp is also shown to outperform the existing SNP callers in terms of calling accuracy. An R package "MAFsnp" implementing the new SNP caller is freely available at http://homepage.fudan.edu.cn/zhangh/softwares/.

A classical approach to combine independent test statistics is Fisher's combination of $p$-values, which follows the $\chi ^2$ distribution. When the test statistics are dependent, the gamma distribution (GD) is commonly used for the Fisher's combination test (FCT). We propose to use two generalizations of the GD: the generalized and the exponentiated GDs. We study some properties of mis-using the GD for the FCT to combine dependent statistics when one of the two proposed distributions are true. Our results show that both generalizations have better control of type I error rates than the GD, which tends to have inflated type I error rates at more extreme tails. In practice, common model selection criteria (e.g. Akaike information criterion/Bayesian information criterion) can be used to help select a better distribution to use for the FCT. A simple strategy of the two generalizations of the GD in genome-wide association studies is discussed. Applications of the results to genetic pleiotrophic associations are described, where multiple traits are tested for association with a single marker.