A network-based, integrative approach to identify genes with aberrant co-methylation in colorectal cancer

Genome-wide methylation and gene-expression analyses in thalassemia

Thalassemia is the most common autosomal genetic disorder in humans. The pathogenesis of thalassemia is principally due to the deletion or mutation of globin genes that then leads to disorders in globin-chain synthesis, and its predominant clinical manifestations include chronic forms of hemolytic anemia. However, research on the epigenetics and underlying pathogenesis of thalassemia is in its nascency and not yet been systematically realized. In this study, we compared the results of RNA-seq and the whole-genome bisulfite sequencing (WGBS) on 22 peripheral blood samples from 14 thalassemic patients and eight healthy individuals revealed a genome-wide methylation landscape of differentially methylated regions (DMRs). And functional-enrichment analysis revealed the enriched biological pathways with respect to the differentially expressed genes (DEGs) and differentially methylated genes (DMGs) to include hematopoietic lineage, glucose metabolism, and ribosome. To further analyze the interaction between the transcriptome and methylome, we implemented a comprehensive analysis of overlaps between DEGs and DMGs, and observed that biological processes significantly enriched the immune-related genes (i.e., our hypermethylated and down-regulated gene group). Hypermethylated and hypomethylated regions of thalassemia-related genes exhibited different distribution patterns. We thus, further identified and validated thalassemia-associated DMGs and DEGs by multi-omics integrative analyses of DNA methylation and transcriptomics data, and provided a comprehensive genomic map of thalassemia that will facilitate the exploration of the epigenetics mechanisms and pathogenesis underlying thalassemia.

DNA co-methylation analysis of lincRNAs across nine cancer types reveals novel potential epigenetic biomarkers in cancer

Aim: The potential functions and prognostic value of lincRNAs with co-methylation events are explored in 9 cancer types. Materials & methods: Here, we evaluated the co-methylation events in promoter and gene-body regions between two lincRNAs across 9 cancer types by constructing a systematic biological framework. Results: The co-methylation events in both promoter and gene-body regions tended to be highly cancer specific. Patient samples could be separated by tumor and normal types according to the eigengenes of universal co-methylation clusters. Functional enrichment results revealed the lincRNAs that brought promoter and gene-body co-methylation events that affected cancer progress through participating in different pathways and could serve as potential prognostic biomarkers. Conclusion: The study provides new insight into the epigenetic regulation in cancer and leads to a potential new direction for epigenetic biomarker discovery.

Identifying DNA methylation biomarkers for non-endoscopic detection of Barrett's esophagus

We report a biomarker-based non-endoscopic method for detecting Barrett's esophagus (BE) based on detecting methylated DNAs retrieved via a swallowable balloon-based esophageal sampling device. BE is the precursor of, and a major recognized risk factor for, developing esophageal adenocarcinoma. Endoscopy, the current standard for BE detection, is not cost-effective for population screening. We performed genome-wide screening to ascertain regions targeted for recurrent aberrant cytosine methylation in BE, identifying high-frequency methylation within the CCNA1 locus. We tested CCNA1 DNA methylation as a BE biomarker in cytology brushings of the distal esophagus from 173 individuals with or without BE. CCNA1 DNA methylation demonstrated an area under the curve of 0.95 for discriminating BE-related metaplasia and neoplasia cases versus normal individuals, performing identically to methylation of VIM DNA, an established BE biomarker. When combined, the resulting two biomarker panel was 95% sensitive and 91% specific. These results were replicated in an independent validation cohort of 149 individuals who were assayed using the same cutoff values for test positivity established in the training population. To progress toward non-endoscopic esophageal screening, we engineered a well-tolerated, swallowable, encapsulated balloon device able to selectively sample the distal esophagus within 5 min. In balloon samples from 86 individuals, tests of CCNA1 plus VIM DNA methylation detected BE metaplasia with 90.3% sensitivity and 91.7% specificity. Combining the balloon sampling device with molecular assays of CCNA1 plus VIM DNA methylation enables an efficient, well-tolerated, sensitive, and specific method of screening at-risk populations for BE.

Integrating network, sequence and functional features using machine learning approaches towards identification of novel Alzheimer genes

Background

Alzheimer’s disease (AD) is a complex progressive neurodegenerative disorder commonly characterized by short term memory loss. Presently no effective therapeutic treatments exist that can completely cure this disease. The cause of Alzheimer’s is still unclear, however one of the other major factors involved in AD pathogenesis are the genetic factors and around 70 % risk of the disease is assumed to be due to the large number of genes involved. Although genetic association studies have revealed a number of potential AD susceptibility genes, there still exists a need for identification of unidentified AD-associated genes and therapeutic targets to have better understanding of the disease-causing mechanisms of Alzheimer’s towards development of effective AD therapeutics.

Results

In the present study, we have used machine learning approach to identify candidate AD associated genes by integrating topological properties of the genes from the protein-protein interaction networks, sequence features and functional annotations. We also used molecular docking approach and screened already known anti-Alzheimer drugs against the novel predicted probable targets of AD and observed that an investigational drug, AL-108, had high affinity for majority of the possible therapeutic targets. Furthermore, we performed molecular dynamics simulations and MM/GBSA calculations on the docked complexes to validate our preliminary findings.

Conclusions

To the best of our knowledge, this is the first comprehensive study of its kind for identification of putative Alzheimer-associated genes using machine learning approaches and we propose that such computational studies can improve our understanding on the core etiology of AD which could lead to the development of effective anti-Alzheimer drugs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3108-1) contains supplementary material, which is available to authorized users.

Gene Networks in Plant Biology: Approaches in Reconstruction and Analysis

Even though vast amounts of genome-wide gene expression data have become available in plants, it remains a challenge to effectively mine this information for the discovery of genes and gene networks, for instance those that control agronomically important traits. These networks reflect potential interactions among genes and, therefore, can lead to a systematic understanding of the molecular mechanisms underlying targeted biological processes. We discuss methods to analyze gene networks using gene expression data, specifically focusing on four common statistical approaches used to reconstruct networks: correlation, feature selection in supervised learning, probabilistic graphical model, and meta-prediction. In addition, we discuss the effective use of these methods for acquiring an in-depth understanding of biological systems in plants.

Identification and functional annotation of lncRNA genes with hypermethylation in colorectal cancer

Colorectal cancer (CRC) is one of the leading causes of mortality worldwide. DNA methylation is an important epigenetic modification for CRC. Although currently a number of studies about DNA methylation of protein coding genes have been carried out, only a few are about the methylation of genes encoding the long noncoding RNAs (lncRNAs). In this study, we identified 761 lncRNA genes with DNA hypermethylation in CRC using a free MethylCap-seq dataset. Integration of lncRNA expression and methylation datasets showed that the expression of lncRNAs is negatively correlated with DNA methylation (p<0.01). Co-methylation network was also constructed to annotate the functions of unknown lncRNAs. Our results showed that a total of 364 lncRNAs were annotated with at least one GO biological process term. The current data-mining work is likely to provide informative clues for biological researchers to further understand the role of lncRNAs in the development of CRC.