Genome-Wide DNA Methylation Profiles Reveal Common Epigenetic Patterns of Interferon-Related Genes in Multiple Autoimmune Diseases

Graves’ disease (GD), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE) and systemic sclerosis (SSc) are complex autoimmune diseases sharing common clinical, genetic and pathogenetic features. However, the commonalities of the DNA methylation profiles for these diseases are still unknown. We conducted an integrative analysis of the multiple-autoimmune disease methylation dataset including GD, RA, SLE, and SSc samples, to identify the common methylation patterns of autoimmune diseases. We identified 15,289 differentially methylated sites between multiple-autoimmune disease patients and controls in CD4+ T cells. We found that the most significant differentially methylated sites had a remarkable enrichment in type I interferon (IFN) pathway genes. Similarly, we identified 9,295 differentially methylated sites between GD/SSc patients and controls in CD8+ T cells. The overall IFN-related gene panel annotated by gene ontology (GO) showed an excellent diagnostic capacity in CD4+ T cells (Sensitivity = 0.82, specificity = 0.82 and AUC = 0.90), while IFI44L, another IFN-related gene not annotated by GO, showed high prediction ability in both CD4+ (AUC = 0.86) and CD8+ (AUC = 0.75) T cells. In conclusion, our study demonstrated that hypomethylation of IFN-related genes is a common feature of GD/RA/SLE/SSc patients in CD4+ T cells, and the DNA methylation profile of IFN-related genes could be promising biomarkers for the diagnosis of GD, RA, SLE, and SSc

Shotgun metagenomic sequencing reveals skin microbial variability from different facial sites

Biogeography (body site) is known to be one of the main factors influencing the composition of the skin microbial community. However, site-associated microbial variability at a fine-scale level was not well-characterized since there was a lack of high-resolution recognition of facial microbiota across kingdoms by shotgun metagenomic sequencing. To investigate the explicit microbial variance in the human face, 822 shotgun metagenomic sequencing data from Han Chinese recently published by our group, in combination with 97 North American samples from NIH Human Microbiome Project (HMP), were reassessed. Metagenomic profiling of bacteria, fungi, and bacteriophages, as well as enriched function modules from three facial sites (forehead, cheek, and the back of the nose), was analyzed. The results revealed that skin microbial features were more alike in the forehead and cheek while varied from the back of the nose in terms of taxonomy and functionality. Analysis based on biogeographic theories suggested that neutral drift with niche selection from the host could possibly give rise to the variations. Of note, the abundance of porphyrin-producing species, i.e., Cutibacterium acnes, Cutibacterium avidum, Cutibacterium granulosum, and Cutibacterium namnetense, was all the highest in the back of the nose compared with the forehead/cheek, which was consistent with the highest porphyrin level on the nose in our population. Sequentially, the site-associated microbiome variance was confirmed in American populations; however, it was not entirely consistent. Furthermore, our data revealed correlation patterns between Propionibacterium acnes bacteriophages with genus Cutibacterium at different facial sites in both populations; however, C. acnes exhibited a distinct correlation with P. acnes bacteriophages in Americans/Chinese. Taken together, in this study, we explored the fine-scale facial site-associated changes in the skin microbiome and provided insight into the ecological processes underlying facial microbial variations

knnAUC: an open-source R package for detecting nonlinear dependence between one continuous variable and one binary variable

Abstract Background Testing the dependence of two variables is one of the fundamental tasks in statistics. In this work, we developed an open-source R package (knnAUC) for detecting nonlinear dependence between one continuous variable X and one binary dependent variables Y (0 or 1). Results We addressed this problem by using knnAUC (k-nearest neighbors AUC test, the R package is available at https://sourceforge.net/projects/knnauc/ ). In the knnAUC software framework, we first resampled a dataset to get the training and testing dataset according to the sample ratio (from 0 to 1), and then constructed a k-nearest neighbors algorithm classifier to get the yhat estimator (the probability of y = 1) of testy (the true label of testing dataset). Finally, we calculated the AUC (area under the curve of receiver operating characteristic) estimator and tested whether the AUC estimator is greater than 0.5. To evaluate the advantages of knnAUC compared to seven other popular methods, we performed extensive simulations to explore the relationships between eight different methods and compared the false positive rates and statistical power using both simulated and real datasets (Chronic hepatitis B datasets and kidney cancer RNA-seq datasets). Conclusions We concluded that knnAUC is an efficient R package to test non-linear dependence between one continuous variable and one binary dependent variable especially in computational biology area.https://deepblue.lib.umich.edu/bitstream/2027.42/146514/1/12859_2018_Article_2427.pd

Identification of Hyper-Methylated Tumor Suppressor Genes-Based Diagnostic Panel for Esophageal Squamous Cell Carcinoma (ESCC) in a Chinese Han Population

DNA methylation-based biomarkers were suggested to be promising for early cancer diagnosis. However, DNA methylation-based biomarkers for esophageal squamous cell carcinoma (ESCC), especially in Chinese Han populations have not been identified and evaluated quantitatively. Candidate tumor suppressor genes (N = 65) were selected through literature searching and four public high-throughput DNA methylation microarray datasets including 136 samples totally were collected for initial confirmation. Targeted bisulfite sequencing was applied in an independent cohort of 94 pairs of ESCC and normal tissues from a Chinese Han population for eventual validation. We applied nine different classification algorithms for the prediction to evaluate to the prediction performance. ADHFE1, EOMES, SALL1 and TFPI2 were identified and validated in the ESCC samples from a Chinese Han population. All four candidate regions were validated to be significantly hyper-methylated in ESCC samples through Wilcoxon rank-sum test (ADHFE1, P = 1.7 × 10-3; EOMES, P = 2.9 × 10-9; SALL1, P = 3.9 × 10-7; TFPI2, p = 3.4 × 10-6). Logistic regression based prediction model shown a moderately ESCC classification performance (Sensitivity = 66%, Specificity = 87%, AUC = 0.81). Moreover, advanced classification method had better performances (random forest and naive Bayes). Interestingly, the diagnostic performance could be improved in non-alcohol use subgroup (AUC = 0.84). In conclusion, our data demonstrate the methylation panel of ADHFE1, EOMES, SALL1 and TFPI2 could be an effective methylation-based diagnostic assay for ESCC

Potentially Functional Variants of PLCE1 Identified by GWASs Contribute to Gastric Adenocarcinoma Susceptibility in an Eastern Chinese Population

BACKGROUND: Recent genome-wide association studies (GWAS) have found a single nucleotide polymorphism (SNP, rs2274223 A>G) in PLCE1 to be associated with risk of gastric adenocarcinoma. In the present study, we validated this finding and also explored the risk associated with another unreported potentially functional SNP (rs11187870 G>C) of PLCE1 in a hospital-based case-control study of 1059 patients with pathologically confirmed gastric adenocarcinoma and 1240 frequency-matched healthy controls. METHODOLOGY/PRINCIPAL FINDINGS: We determined genotypes of these two SNPs by the Taqman assay and used logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals (95% CI). We found that a significant higher gastric adenocarcinoma risk was associated with rs2274223 variant G allele (adjusted OR = 1.35, 95% CI = 1.14-1.60 for AG+GG vs. AA) and rs11187870 variant C allele (adjusted OR = 1.26, 95% CI = 1.05-1.50 for CG+CC vs. GG). We also found that the number of combined risk alleles (i.e., rs2274223G and rs11187870C) was associated with risk of gastric adenocarcinoma in an allele-dose effect manner (P(trend) = 0.0002). Stratification analysis indicated that the combined effect of rs2274223G and rs11187870C variant alleles was more evident in subgroups of males, non-smokers, non-drinkers and patients with gastric cardia adenocarcinoma. Further real-time PCR results showed that expression levels of PLCE1 mRNA were significantly lower in tumors than in adjacent noncancerous tissues (0.019±0.002 vs. 0.008±0.001, P<0.05). CONCLUSIONS/SIGNIFICANCES: Our results further confirmed that genetic variations in PLCE1 may contribute to gastric adenocarcinoma risk in an eastern Chinese population

Robust Reference Powered Association Test of Genome-Wide Association Studies

Genome-wide association studies (GWASs) have identified abundant genetic susceptibility loci, GWAS of small sample size are far less from meeting the previous expectations due to low statistical power and false positive results. Effective statistical methods are required to further improve the analyses of massive GWAS data. Here we presented a new statistic (Robust Reference Powered Association Test1) to use large public database (gnomad) as reference to reduce concern of potential population stratification. To evaluate the performance of this statistic for various situations, we simulated multiple sets of sample size and frequencies to compute statistical power. Furthermore, we applied our method to several real datasets (psoriasis genome-wide association datasets and schizophrenia genome-wide association dataset) to evaluate the performance. Careful analyses indicated that our newly developed statistic outperformed several previously developed GWAS applications. Importantly, this statistic is more robust than naive merging method in the presence of small control-reference differentiation, therefore likely to detect more association signals

Interleukin-22 Inhibits Bleomycin-Induced Pulmonary Fibrosis

Pulmonary fibrosis is a progressive and fatal fibrotic disease of the lungs with unclear etiology. Recent insight has suggested that early injury/inflammation of alveolar epithelial cells could lead to dysregulation of tissue repair driven by multiple cytokines. Although dysregulation of interleukin- (IL-) 22 is involved in various pulmonary pathophysiological processes, the role of IL-22 in fibrotic lung diseases is still unclear and needs to be further addressed. Here we investigated the effect of IL-22 on alveolar epithelial cells in the bleomycin- (BLM-) induced pulmonary fibrosis. BLM-treated mice showed significantly decreased level of IL-22 in the lung. IL-22 produced γδT cells were also decreased significantly both in the tissues of lungs and spleens. Administration of recombinant human IL-22 to alveolar epithelial cell line A549 cells ameliorated epithelial to mesenchymal transition (EMT) and partially reversed the impaired cell viability induced by BLM. Furthermore, blockage of IL-22 deteriorated pulmonary fibrosis, with elevated EMT marker (α-smooth muscle actin (α-SMA)) and overactivated Smad2. Our results indicate that IL-22 may play a protective role in the development of BLM-induced pulmonary fibrosis and may suggest IL-22 as a novel immunotherapy tool in treating pulmonary fibrosis