Global Gene Expression Profiling and Transcription Factor Network Analysis of Cognitive Aging in Monozygotic Twins.

Cognitive aging is one of the major problems worldwide, especially as people get older. This study aimed to perform global gene expression profiling of cognitive function to identify associated genes and pathways and a novel transcriptional regulatory network analysis to identify important regulons. We performed single transcript analysis on 400 monozygotic twins using an assumption-free generalized correlation coefficient (GCC), linear mixed-effect model (LME) and kinship model and identified six probes (one significant at the standard FDR < 0.05 while the other results were suggestive with 0.18 ≤ FDR ≤ 0.28). We combined the GCC and linear model results to cover diverse patterns of relationships, and meaningful and novel genes like APOBEC3G, H6PD, SLC45A1, GRIN3B, and PDE4D were detected. Our exploratory study showed the downregulation of all these genes with increasing cognitive function or vice versa except the SLC45A1 gene, which was upregulated with increasing cognitive function. Linear models found only H6PD and SLC45A1, the other genes were captured by GCC. Significant functional pathways (FDR < 3.95e-10) such as focal adhesion, ribosome, cysteine and methionine metabolism, Huntington's disease, eukaryotic translation elongation, nervous system development, influenza infection, metabolism of RNA, and cell cycle were identified. A total of five regulons (FDR< 1.3e-4) were enriched in a transcriptional regulatory analysis in which CTCF and REST were activated and SP3, SRF, and XBP1 were repressed regulons. The genome-wide transcription analysis using both assumption-free GCC and linear models identified important genes and biological pathways implicated in cognitive performance, cognitive aging, and neurological diseases. Also, the regulatory network analysis revealed significant activated and repressed regulons on cognitive function.

EWASex: an efficient R-package to predict sex in epigenome-wide association studies.

SUMMARY: Epigenome-Wide Association Study (EWAS) has become a powerful approach to identify epigenetic variations associated with diseases or health traits. Sex is an important variable to include in EWAS to ensure unbiased data processing and statistical analysis. We introduce the R-package EWASex, which allows for fast and highly accurate sex-estimation using DNA methylation data on a small set of CpG sites located on the X-chromosome under stable X-chromosome inactivation in females. RESULTS: We demonstrate that EWASex outperforms the current state of the art tools by using different EWAS datasets. With EWASex, we offer an efficient way to predict and to verify sex that can be easily implemented in any EWAS using blood samples or even other tissue types. It comes with pre-trained weights to work without prior sex labels and without requiring access to RAW data, which is a necessity for all currently available methods. AVAILABILITY AND IMPLEMENTATION: The EWASex R-package along with tutorials, documentation and source code are available at https://github.com/Silver-Hawk/EWASex. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DNA methylome profiling of all-cause mortality in comparison with age-associated methylation patterns.

BACKGROUND: Multiple epigenome-wide association studies have been performed to identify DNA methylation patterns regulated by aging or correlated with risk of death. However, the inter-relatedness of the epigenetic basis of aging and mortality has not been well investigated. METHODS: Using genome-wide DNA methylation data from the Lothian Birth Cohorts, we conducted a genome-wide association analysis of all-cause mortality and compared this with age-associated methylation patterns reported on the same samples. RESULTS: Survival analysis using the Cox regression model identified 2552 CpG sites with genome-wide significance (false discovery rate < 0.05) for all-cause mortality. CpGs whose methylation levels are associated with increased mortality appear more distributed from the gene body to the intergenic regions whereas CpGs whose methylation levels are associated with decreased mortality is more concentrated at the promoter regions. In comparison with reported CpGs displaying significant age-dependent methylation patterns in the same samples, we observed a limited but highly significant overlap between mortality-associated and age-associated CpGs (p value 2.52e-06). Most importantly, the overlapping CpGs are dominated by those whose overall age-related methylation patterns reduce the risk of death. CONCLUSION: All-cause mortality is significantly associated with altered methylation at multiple genomic sites with differential distribution in gene regions for CpGs correlated with increased or decreased risk of death. The age-dependent methylation changes could reflect an active response to the aging process that contributes to maintain individual survival.

Interactive microbial distribution analysis using BioAtlas.

Massive amounts of 16S rRNA sequencing data have been stored in publicly accessible databases, such as GOLD, SILVA, GreenGenes (GG), and the Ribosomal Database Project (RDP). Many of these sequences are tagged with geo-locations. Nevertheless, researchers currently lack a user-friendly tool to analyze microbial distribution in a location-specific context. BioAtlas is an interactive web application that closes this gap between sequence databases, taxonomy profiling and geo/body-location information. It enables users to browse taxonomically annotated sequences across (i) the world map, (ii) human body maps and (iii) user-defined maps. It further allows for (iv) uploading of own sample data, which can be placed on existing maps to (v) browse the distribution of the associated taxonomies. Finally, BioAtlas enables users to (vi) contribute custom maps (e.g. for plants or animals) and to map taxonomies to pre-defined map locations. In summary, BioAtlas facilitates map-supported browsing of public 16S rRNA sequence data and analyses of user-provided sequences without requiring manual mapping to taxonomies and existing databases. AVAILABILITY: http://bioatlas.compbio.sdu.dk/.