Search

Abstract Background Sex is increasingly recognized as a significant factor contributing to the biological and clinical heterogeneity in AD. There is also growing evidence for the prominent role of DNA methylation (DNAm) in Alzheimer’s disease (AD). Methods We studied sex-specific DNA methylation differences in the blood samples of AD subjects compared to cognitively normal subjects, by performing sex-specific meta-analyses of two large blood-based epigenome-wide association studies (ADNI and AIBL), which included DNA methylation data for a total of 1284 whole blood samples (632 females and 652 males). Within each dataset, we used two complementary analytical strategies, a sex-stratified analysis that examined methylation to AD associations in male and female samples separately, and a methylation-by-sex interaction analysis that compared the magnitude of these associations between different sexes. After adjusting for age, estimated immune cell type proportions, batch effects, and correcting for inflation, the inverse-variance fixed-effects meta-analysis model was used to identify the most consistent DNAm differences across datasets. In addition, we also evaluated the performance of the sex-specific methylation-based risk prediction models for AD diagnosis using an independent external dataset. Results In the sex-stratified analysis, we identified 2 CpGs, mapped to the PRRC2A and RPS8 genes, significantly associated with AD in females at a 5% false discovery rate, and an additional 25 significant CpGs (21 in females, 4 in males) at P-value < 1×10−5. In methylation-by-sex interaction analysis, we identified 5 significant CpGs at P-value < 10−5. Out-of-sample validations using the AddNeuroMed dataset showed in females, the best logistic prediction model included age, estimated immune cell-type proportions, and methylation risk scores (MRS) computed from 9 of the 23 CpGs identified in AD vs. CN analysis that are also available in AddNeuroMed dataset (AUC = 0.74, 95% CI: 0.65–0.83). In males, the best logistic prediction model included only age and MRS computed from 2 of the 5 CpGs identified in methylation-by-sex interaction analysis that are also available in the AddNeuroMed dataset (AUC = 0.70, 95% CI: 0.56–0.82). Conclusions Overall, our results show that the DNA methylation differences in AD are largely distinct between males and females. Our best-performing sex-specific methylation-based prediction model in females performed better than that for males and additionally included estimated cell-type proportions. The significant discriminatory classification of AD samples with our methylation-based prediction models demonstrates that sex-specific DNA methylation could be a predictive biomarker for AD. As sex is a strong factor underlying phenotypic variability in AD, the results of our study are particularly relevant for a better understanding of the epigenetic architecture that underlie AD and for promoting precision medicine in AD.

DNA methylation differences in Alzheimer’s disease have been previously reported, although the interpretation of the differences is unclear. Here, the authors performed epigenome-wide meta-analyses of DNA methylation in blood and brain, and developed a methylation-based risk prediction model for AD.

Abstract Background Little is known about the role of global DNA methylation in recurrence and chemoresistance of high grade serous ovarian cancer (HGSOC). Methods We performed whole genome bisulfite sequencing and transcriptome sequencing in 62 primary and recurrent tumors from 28 patients with stage III/IV HGSOC, of which 11 patients carried germline, pathogenic BRCA1 and/or BRCA2 mutations. Results Landscapes of genome-wide methylation (on average 24.2 million CpGs per tumor) and transcriptomes in primary and recurrent tumors showed extensive heterogeneity between patients but were highly preserved in tumors from the same patient. We identified significant differences in the burden of differentially methylated regions (DMRs) in tumors from BRCA1/2 compared to non-BRCA1/2 carriers (mean 659 DMRs and 388 DMRs in paired comparisons respectively). We identified overexpression of immune pathways in BRCA1/2 carriers compared to non-carriers, implicating an increased immune response in improved survival (P = 0.006) in these BRCA1/2 carriers. Conclusion These findings indicate methylome and gene expression programs established in the primary tumor are conserved throughout disease progression, even after extensive chemotherapy treatment, and that changes in methylation and gene expression are unlikely to serve as drivers for chemoresistance in HGSOC.

Background and Aims: Lamina propria phagocytes are key mediators of inflammatory bowel disease (IBD). We aimed to understand the transcriptomic and functional differences in these cells based on location, disease type, inflammation state, and medication use in patients with IBD. Methods: Phagocytic immune cells in the lamina propria, as defined by the marker CD11b, were isolated from 54 unique patients (n = 111 gut mucosal biopsies). We performed flow cytometry for cell phenotyping (n = 30) and RNA sequencing with differential gene expression analysis (n = 58). We further cultured these cells in vitro and exposed them to janus kinase inhibitors to measure cytokine output (n = 27). Finally, we matched patient genomic data to our RNA sequencing data to perform candidate gene expression quantitative trait locus analysis (n = 34). Results: We found distinct differences in gene expression between CD11b+ cells from the colon vs ileum, as well as in different inflammatory states and, to a lesser degree, IBD types (Crohn’s disease or ulcerative colitis). These genes mapped to targetable immune pathways and metabolic and cancer pathways. We further explored the janus kinase-signal transducer and activator of transcription pathway, which was upregulated across many comparisons including in biopsies from anti–tumor necrosis factor refractory patients. We found that isolated CD11b+ cells treated with janus kinase inhibitors had decreased secretion of cytokines tumor necrosis factorα and interleukin-8 (P ≤ .05). We also found 3 genetic variants acting as expression quantitative trait loci (P ≤ .1) within our CD11b+ data set. Conclusions: Lamina propria phagocytes from IBD mucosa provide pathogenetic clues on the nature of treatment refractoriness and inform new targets for therapy.

Triple negative breast cancer can be divided into additional subtypes. Here, using omics analyses, the authors show that in the mesenchymal subtype expression of MHC-1 is repressed and that this can be restored by using drugs that target subunits of the epigenetic modifier PRC2.

Abstract Sex is an important factor that contributes to the clinical and biological heterogeneities in Alzheimer’s disease (AD), but the regulatory mechanisms underlying sex disparity in AD are still not well understood. DNA methylation is an important epigenetic modification that regulates gene transcription and is known to be involved in AD. We performed the first large-scale sex-specific meta-analysis of DNA methylation differences in AD neuropathology, by re-analyzing four recent epigenome-wide association studies totaling more than 1000 postmortem prefrontal cortex brain samples using a uniform analytical pipeline. For each cohort, we employed two complementary analytical strategies, a sex-stratified analysis that examined methylation-Braak stage associations in male and female samples separately, and a sex-by-Braak stage interaction analysis that compared the magnitude of these associations between different sexes. Our analysis uncovered 14 novel CpGs, mapped to genes such as TMEM39A and TNXB that are associated with the AD Braak stage in a sex-specific manner. TMEM39A is known to be involved in inflammation, dysregulated type I interferon responses, and other immune processes. TNXB encodes tenascin proteins, which are extracellular matrix glycoproteins demonstrated to modulate synaptic plasticity in the brain. Moreover, for many previously implicated genes in AD neuropathology, such as MBP and AZU1, our analysis provided the new insights that they were predominately driven by effects in only one sex. These sex-specific DNA methylation differences were enriched in divergent biological processes such as integrin activation in females and complement activation in males. Our study implicated multiple new loci and biological processes that affected AD neuropathology in a sex-specific manner.

A subset of CpG Island promoter genes are regulated by Polycomb-Repressive Complex 2 (PRC2+-CGI), which become DNA hypermethylated and silenced in cancer. Here, the authors investigate the transcriptomic and epigenomic characteristics of PRC2-occupied CGI and free CGI across pan-cancer types.

Previous studies have identified DNA methylation changes in Alzheimer’s disease. Here the authors perform a meta-analysis of DNA methylation datasets to identify epigenetic correlates of Alzheimer’s disease in the prefrontal cortex.

Recent advances in technology have made multi-omics datasets increasingly available to researchers. To leverage the wealth of information in multi-omics data, a number of integrative analysis strategies have been proposed recently. However, effectively extracting biological insights from these large, complex datasets remains challenging. In particular, matched samples with multiple types of omics data measured on each sample are often required for multi-omics analysis tools, which can significantly reduce the sample size. Another challenge is that analysis techniques such as dimension reductions, which extract association signals in high dimensional datasets by estimating a few variables that explain most of the variations in the samples, are typically applied to whole-genome data, which can be computationally demanding. Here we present pathwayMultiomics, a pathway-based approach for integrative analysis of multi-omics data with categorical, continuous, or survival outcome variables. The input of pathwayMultiomics is pathway p-values for individual omics data types, which are then integrated using a novel statistic, the MiniMax statistic, to prioritize pathways dysregulated in multiple types of omics datasets. Importantly, pathwayMultiomics is computationally efficient and does not require matched samples in multi-omics data. We performed a comprehensive simulation study to show that pathwayMultiomics significantly outperformed currently available multi-omics tools with improved power and well-controlled false-positive rates. In addition, we also analyzed real multi-omics datasets to show that pathwayMultiomics was able to recover known biology by nominating biologically meaningful pathways in complex diseases such as Alzheimer’s disease.

Cell–cell interactions (CCIs) and cell–cell communication (CCC) are critical for maintaining complex biological systems. The availability of single-cell RNA sequencing (scRNA-seq) data opens new avenues for deciphering CCIs and CCCs through identifying ligand-receptor (LR) gene interactions between cells. However, most methods were developed to examine the LR interactions of individual pairs of genes. Here, we propose a novel approach named LR hunting which first uses random forests (RFs)-based data imputation technique to link the data between different cell types. To guarantee the robustness of the data imputation procedure, we repeat the computation procedures multiple times to generate aggregated imputed minimal depth index (IMDI). Next, we identify significant LR interactions among all combinations of LR pairs simultaneously using unsupervised RFs. We demonstrated LR hunting can recover biological meaningful CCIs using a mouse cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) dataset and a triple-negative breast cancer scRNA-seq dataset.

Identification of cancer driver genes, especially those that can act as tumour suppressors or oncogenes depending on context, remains a challenge. Here, the authors introduce Moonlight, a tool that integrates multi-omic data to address this challenge and identify numerous dual-role cancer genes.

Abstract Background The development of next generation sequencing (NGS) methods led to a rapid rise in the generation of large genomic datasets, but the development of user-friendly tools to analyze and visualize these datasets has not developed at the same pace. This presents a two-fold challenge to biologists; the expertise to select an appropriate data analysis pipeline, and the need for bioinformatics or programming skills to apply this pipeline. The development of graphical user interface (GUI) applications hosted on web-based servers such as Shiny can make complex workflows accessible across operating systems and internet browsers to those without programming knowledge. Results We have developed GENAVi (Gene Expression Normalization Analysis and Visualization) to provide a user-friendly interface for normalization and differential expression analysis (DEA) of human or mouse feature count level RNA-Seq data. GENAVi is a GUI based tool that combines Bioconductor packages in a format for scientists without bioinformatics expertise. We provide a panel of 20 cell lines commonly used for the study of breast and ovarian cancer within GENAVi as a foundation for users to bring their own data to the application. Users can visualize expression across samples, cluster samples based on gene expression or correlation, calculate and plot the results of principal components analysis, perform DEA and gene set enrichment and produce plots for each of these analyses. To allow scalability for large datasets we have provided local install via three methods. We improve on available tools by offering a range of normalization methods and a simple to use interface that provides clear and complete session reporting and for reproducible analysis. Conclusion The development of tools using a GUI makes them practical and accessible to scientists without bioinformatics expertise, or access to a data analyst with relevant skills. While several GUI based tools are currently available for RNA-Seq analysis we improve on these existing tools. This user-friendly application provides a convenient platform for the normalization, analysis and visualization of gene expression data for scientists without bioinformatics expertise.

Biotechnological advances in sequencing have led to an explosion of publicly available data via large international consortia such as The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE), and The NIH Roadmap Epigenomics Mapping Consortium (Roadmap). These projects have provided unprecedented opportunities to interrogate the epigenome of cultured cancer cell lines as well as normal and tumor tissues with high genomic resolution. The Bioconductor project offers more than 1,000 open-source software and statistical packages to analyze high-throughput genomic data. However, most packages are designed for specific data types (e.g. expression, epigenetics, genomics) and there is no one comprehensive tool that provides a complete integrative analysis of the resources and data provided by all three public projects. A need to create an integration of these different analyses was recently proposed. In this workflow, we provide a series of biologically focused integrative analyses of different molecular data. We describe how to download, process and prepare TCGA data and by harnessing several key Bioconductor packages, we describe how to extract biologically meaningful genomic and epigenomic data. Using Roadmap and ENCODE data, we provide a work plan to identify biologically relevant functional epigenomic elements associated with cancer. To illustrate our workflow, we analyzed two types of brain tumors: low-grade glioma (LGG) versus high-grade glioma (glioblastoma multiform or GBM). This workflow introduces the following Bioconductor packages: AnnotationHub, ChIPSeeker, ComplexHeatmap, pathview, ELMER, GAIA, MINET, RTCGAToolbox, TCGAbiolinks.

The advent of Next-Generation Sequencing (NGS) technologies has opened new perspectives in deciphering the genetic mechanisms underlying complex diseases. Nowadays, the amount of genomic data is massive and substantial efforts and new tools are required to unveil the information hidden in the data. The Genomic Data Commons (GDC) Data Portal is a platform that contains different genomic studies including the ones from The Cancer Genome Atlas (TCGA) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiatives, accounting for more than 40 tumor types originating from nearly 30000 patients. Such platforms, although very attractive, must make sure the stored data are easily accessible and adequately harmonized. Moreover, they have the primary focus on the data storage in a unique place, and they do not provide a comprehensive toolkit for analyses and interpretation of the data. To fulfill this urgent need, comprehensive but easily accessible computational methods for integrative analyses of genomic data that do not renounce a robust statistical and theoretical framework are required. In this context, the R/Bioconductor package TCGAbiolinks was developed, offering a variety of bioinformatics functionalities. Here we introduce new features and enhancements of TCGAbiolinks in terms of i) more accurate and flexible pipelines for differential expression analyses, ii) different methods for tumor purity estimation and filtering, iii) integration of normal samples from other platforms iv) support for other genomics datasets, exemplified here by the TARGET data. Evidence has shown that accounting for tumor purity is essential in the study of tumorigenesis, as these factors promote confounding behavior regarding differential expression analysis. With this in mind, we implemented these filtering procedures in TCGAbiolinks. Moreover, a limitation of some of the TCGA datasets is the unavailability or paucity of corresponding normal samples. We thus integrated into TCGAbiolinks the possibility to use normal samples from the Genotype-Tissue Expression (GTEx) project, which is another large-scale repository cataloging gene expression from healthy individuals. The new functionalities are available in the TCGAbiolinks version 2.8 and higher released in Bioconductor version 3.7.