Epigenome-wide association study identifies Vogt-Koyanagi-Harada disease-associated DNA methylation loci in Chinese.

DNA methylation is one of the important epigenetic mechanisms for modulating gene expression. By performing a genome-wide methylation association analysis of whole peripheral blood from 60 Vogt-Koyanagi-Harada disease (VKH) patients and 60 healthy controls, we depicted the global DNA methylation status of VKH disease. Further pyrosequencing validation in 160 patients and 159 controls identified 3 aberrant CpG sites in HLA gene regions including cg04026937 and cg18052547 (located in HLA-DRB1 region), and cg13778567 (HLA-DQA1). We also identified 9 aberrant CpG sites in non-HLA gene regions including cg13979407, cg21075643, cg24290586, cg10135747 and cg22707857 (BTNL2), cg22155039 (NOTCH4), cg02605387 (TNXB), cg06255004 (AGPAT2) and cg18855195 (RIBC2). Increased mRNA levels of BTNL2, NOTCH4 and TNXB were identified in VKH patients when compared with healthy controls, consistent with the hypomethylated CpG status in these gene regions. Moreover, seven aberrantly methylated CpG sites may serve as a diagnostic marker for VKH disease (AUC = 84.95%, 95%CI: 79.49%-90.41%).

Gut metagenomics-derived genes as potential biomarkers of Parkinson's disease.

Identification of the gut microbiome compositions associated with disease has become a research focus worldwide. Emerging evidence has revealed the presence of gut microbiota dysbiosis in Parkinson's disease. In this study, we aimed to identify the gut microbiome associated with Parkinson's disease and subsequently to screen and to validate potential diagnostic biomarkers of Parkinson's disease. This case-control study investigated gut microbial genes in faeces from 40 volunteer Chinese patients with Parkinson's disease and their healthy spouses using shotgun metagenomic sequencing. Furthermore, the identified specific gut microbial gene markers were validated with real-time PCR in an independent Chinese cohort of 78 Parkinson's disease patients, 75 control subjects, 40 patients with multiple system atrophy and 25 patients with Alzheimer's disease. We developed the first gut microbial gene catalogue associated with Parkinson's disease. Twenty-five gene markers were identified that distinguished Parkinson's disease patients from healthy control subjects, achieving an area under the receiver operating characteristic curve (AUC) of 0.896 (95% confidence interval: 83.1-96.1%). A highly accurate Parkinson's disease index, which was not influenced by disease severity or Parkinson's disease medications, was created. Testing these gene markers using quantitative PCR distinguished Parkinson's disease patients from healthy controls not only in the 40 couples (AUC = 0.922, 95% confidence interval: 86.4-98.0%), but also in an independent group of 78 patients with Parkinson's disease and 75 healthy control subjects (AUC = 0.905, 95% confidence interval: 86.0-95.1%). This classifier also performed a differential diagnosis power in discriminating these 78 patients with Parkinson's disease from a cohort of 40 patients with multiple system atrophy and 25 patients with Alzheimer's disease based on the panel of 25 biomarkers. Based on our results, the identified Parkinson's disease index based on the gene set from the gut microbiome may be a potential diagnostic biomarker of Parkinson's disease.

Integrated multi-omics profiling of nonfunctioning pituitary adenomas.

PURPOSE: Genetic and epigenetic alterations are involved in pituitary adenoma pathogenesis, however the molecular basis of proliferative nonfunctioning pituitary adenomas (NFPAs) remains unclear. Here, we analyzed integrated multi-omics profiling including copy number variation (CNV), DNA methylation and gene expression of 8 NFPAs. METHODS: We collected 4 highly proliferative (hpNFPA, Ki-67 ≥ 3) and 4 lowly proliferative (Ki-67 ≤ 1) NFPAs, and comprehensively assessed CNV, DNA methylation, and gene expression by Illumina HumanMethylation450 BeadChip and Affymetrix GeneChip PrimeView Human Gene Expression Array. We performed Ingenuity Pathway Analysis (IPA) for differentially expressed genes to illustrate aberrant pathways and delineated protein-protein networks of selected key genes in dysregulated pathways. RESULTS: Aberrant arm level CNV, dysregulated DNA methylation, and associated impacts on gene expressions were observed in 2 early occurring hpNFPAs. Chromosomal losses were associated with attenuated expression of DNA methyltransferases, further altering global methylation in these 2 samples. Correlation analysis between DNA methylation and gene expression in 8 NFPAs indicates methylation in promoter and gene body regions are both involved in gene regulation. IPA showed PPARα/RXRα, dopamine receptor signaling, cAMP-mediated signaling, and calcium signaling were all activated, while p38 MAPK and ERK5 signaling were inhibited in hpNFPAs. Moreover, selected key gene networks in hpNFPAs exhibited concurrent methylation status and expression levels of adenylate cyclase genes, G protein subunits, HLA genes, CXCL12, and CCL2. CONCLUSION: This study presents comprehensive multi-omics views of CNV, DNA methylation, and gene expression in 8 NFPAs. Pathway analysis and network maps of key genes provide clues to elucidate the molecular basis of hpNFPA.

Integrated metagenomic data analysis demonstrates that a loss of diversity in oral microbiota is associated with periodontitis.

BACKGROUND: Periodontitis is an inflammatory disease affecting the tissues supporting teeth (periodontium). Integrative analysis of metagenomic samples from multiple periodontitis studies is a powerful way to examine microbiota diversity and interactions within host oral cavity. METHODS: A total of 43 subjects were recruited to participate in two previous studies profiling the microbial community of human subgingival plaque samples using shotgun metagenomic sequencing. We integrated metagenomic sequence data from those two studies, including six healthy controls, 14 sites representative of stable periodontitis, 16 sites representative of progressing periodontitis, and seven periodontal sites of unknown status. We applied phylogenetic diversity, differential abundance, and network analyses, as well as clustering, to the integrated dataset to compare microbiological community profiles among the different disease states. RESULTS: We found alpha-diversity, i.e., mean species diversity in sites or habitats at a local scale, to be the single strongest predictor of subjects' periodontitis status (P < 0.011). More specifically, healthy subjects had the highest alpha-diversity, while subjects with stable sites had the lowest alpha-diversity. From these results, we developed an alpha-diversity logistic model-based naive classifier able to perfectly predict the disease status of the seven subjects with unknown periodontal status (not used in training). Phylogenetic profiling resulted in the discovery of nine marker microbes, and these species are able to differentiate between stable and progressing periodontitis, achieving an accuracy of 94.4%. Finally, we found that the reduction of negatively correlated species is a notable signature of disease progression. CONCLUSIONS: Our results consistently show a strong association between the loss of oral microbiota diversity and the progression of periodontitis, suggesting that metagenomics sequencing and phylogenetic profiling are predictive of early periodontitis, leading to potential therapeutic intervention. Our results also support a keystone pathogen-mediated polymicrobial synergy and dysbiosis (PSD) model to explain the etiology of periodontitis. Apart from P. gingivalis, we identified three additional keystone species potentially mediating the progression of periodontitis progression based on pathogenic characteristics similar to those of known keystone pathogens.

CoreProbe: A Novel Algorithm for Estimating Relative Abundance Based on Metagenomic Reads.

With the rapid development of high-throughput sequencing technology, the analysis of metagenomic sequencing data and the accurate and efficient estimation of relative microbial abundance have become important ways to explore the microbial composition and function of microbes. In addition, the accuracy and efficiency of the relative microbial abundance estimation are closely related to the algorithm and the selection of the reference sequence for sequence alignment. We introduced the microbial core genome as the reference sequence for potential microbes in a metagenomic sample, and we constructed a finite mixture and latent Dirichlet models and used the Gibbs sampling algorithm to estimate the relative abundance of microorganisms. The simulation results showed that our approach can improve the efficiency while maintaining high accuracy and is more suitable for high-throughput metagenomic data. The new approach was implemented in our CoreProbe package which provides a pipeline for an accurate and efficient estimation of the relative abundance of microbes in a community. This tool is available free of charge from the CoreProbe's website: Access the Docker image with the following instruction: sudo docker pull panhongfei/coreprobe:1.0.