Background

Network construction and analysis algorithms provide scientists with the ability to sift through high-throughput biological outputs, such as transcription microarrays, for small groups of genes (modules) that are relevant for further research. Most of these algorithms ignore the important role of non-linear interactions in the data, and the ability for genes to operate in multiple functional groups at once, despite clear evidence for both of these phenomena in observed biological systems.

Results

We have created a novel co-expression network analysis algorithm that incorporates both of these principles by combining the information-theoretic association measure of the maximal information coefficient (MIC) with an Interaction Component Model. We evaluate the performance of this approach on two datasets collected from a large panel of mice, one from macrophages and the other from liver by comparing the two measures based on a measure of module entropy, Gene Ontology (GO) enrichment, and scale-free topology (SFT) fit. Our algorithm outperforms a widely used co-expression analysis method, weighted gene co-expression network analysis (WGCNA), in the macrophage data, while returning comparable results in the liver dataset when using these criteria. We demonstrate that the macrophage data has more non-linear interactions than the liver dataset, which may explain the increased performance of our method, termed Maximal Information Component Analysis (MICA) in that case.

Conclusions

In making our network algorithm more accurately reflect known biological principles, we are able to generate modules with improved relevance, particularly in networks with confounding factors such as gene by environment interactions.

Rationale

Only a small portion of the known heritability of cardiovascular diseases, such as heart failure, can be explained based on single-gene mutations. Chromatin structure and regulation provide a substrate through which genetic differences in noncoding regions may affect cellular function and response to disease, but the mechanisms are unknown.

Objective

We conducted genome-wide measurements of DNA methylation in different strains of mice that are susceptible and resistant to isoproterenol-induced dysfunction to test the hypothesis that this epigenetic mark may play a causal role in the development of heart failure.

Methods and results

BALB/cJ and BUB/BnJ mice, determined to be susceptible and resistant to isoproterenol-induced heart failure, respectively, were administered the drug for 3 weeks via osmotic minipump. Reduced representational bisulfite sequencing was then used to compare the differences between the cardiac DNA methylomes in the basal state between strains and then after isoproterenol treatment. Single-base resolution DNA methylation measurements were obtained and revealed a bimodal distribution of methylation in the heart, enriched in lone intergenic CpGs and depleted from CpG islands around genes. Isoproterenol induced global decreases in methylation in both strains; however, the basal methylation pattern between strains shows striking differences that may be predictive of disease progression before environmental stress. The global correlation between promoter methylation and gene expression (as measured by microarray) was modest and revealed itself only with focused analyses of transcription start site and gene body regions (in contrast to when gene methylation was examined in toto). Modules of comethylated genes displayed correlation with other protein-based epigenetic marks, supporting the hypothesis that chromatin modifications act in a combinatorial manner to specify transcriptional phenotypes in the heart.

Conclusions

This study provides the first single-base resolution map of the mammalian cardiac DNA methylome and the first case-control analysis of the changes in DNA methylation with heart failure. The findings demonstrate marked genetic differences in DNA methylation that are associated with disease progression.

Exposure to ambient particulate matter has been shown to promote a variety of disorders, including cardiovascular diseases predominantly of ischemic etiology. However, the mechanisms linking inhaled particulates with systemic vascular effects, resulting in worsened atherosclerosis, are not well defined. We assessed the potential role of macrophages in translating these effects by analyzing gene expression patterns in response to diesel exhaust particles (DEP) at the average cell level, using Affymetrix microarrays in peritoneal macrophages in culture (in vitro), and at the individual cell level, using single-cell RNA sequencing (scRNA-seq) in alveolar macrophages collected from exposed mice (in vivo). Peritoneal macrophages were harvested from C57BL/6J mice and treated with 25 μg/mL of a DEP methanol extract (DEPe). These cells exhibited significant (FDR < 0.05) differential expression of a large number of genes and enrichment in pathways, especially engaged in immune responses and antioxidant defense. DEPe led to marked upregulation of heme oxygenase 1 (Hmox1), the most significantly upregulated gene (FDR = 1.75E-06), and several other antioxidant genes. For the in vivo work, C57BL/6J mice were subjected to oropharyngeal aspiration of 200 μg of whole DEP. The gene expression profiles of the alveolar macrophages harvested from these mice were analyzed at the single-cell level using scRNA-seq, which showed significant dysregulation of a broad number of genes enriched in immune system pathways as well, but with a large heterogeneity in how individual alveolar macrophages responded to DEP exposures. Altogether, DEP pollutants dysregulated immunological pathways in macrophages that may mediate the development of pulmonary and systemic vascular effects.

Background

DNA methylation is a contributing factor to both rare and common human diseases, and plays a major role in development and gene silencing. While the variation of DNA methylation among individuals has been partially characterized, the degree to which methylation patterns are preserved across generations is still poorly understood. To determine the extent of methylation differences between two generations of mice we examined DNA methylation patterns in the livers of eight parental and F1 mice from C57BL/6J and DBA/2J mouse strains using bisulfite sequencing.

Results

We find a large proportion of reproducible methylation differences between C57BL/6J and DBA/2J chromosomes in CpGs, which are highly heritable between parent and F1 mice. We also find sex differences in methylation levels in 396 genes, and 11% of these are differentially expressed between females and males. Using a recently developed approach to identify allelically methylated regions independently of genotypic differences, we identify 112 novel putative imprinted genes and microRNAs, and validate imprinting at the RNA level in 10 of these genes.

Conclusions

The majority of DNA methylation differences among individuals are associated with genetic differences, and a much smaller proportion of these epigenetic differences are due to sex, imprinting or stochastic intergenerational effects. Epigenetic differences can be a determining factor in heritable traits and should be considered in association studies for molecular and clinical traits, as we observed that methylation differences in the mouse model are highly heritable and can have functional consequences on molecular traits such as gene expression.

Many common diseases have an important inflammatory component mediated in part by macrophages. Here we used a systems genetics strategy to examine the role of common genetic variation in macrophage responses to inflammatory stimuli. We examined genome-wide transcript levels in macrophages from 92 strains of the Hybrid Mouse Diversity Panel. We exposed macrophages to control media, bacterial lipopolysaccharide (LPS), or oxidized phospholipids. We performed association mapping under each condition and identified several thousand expression quantitative trait loci (eQTL), gene-by-environment interactions, and eQTL "hot spots" that specifically control LPS responses. We used siRNA knockdown of candidate genes to validate an eQTL hot spot in chromosome 8 and identified the gene 2310061C15Rik as a regulator of inflammatory responses in macrophages. We have created a public database where the data presented here can be used as a resource for understanding many common inflammatory traits that are modeled in the mouse and for the dissection of regulatory relationships between genes.

Most epigenome-wide association studies to date have been conducted in blood. However, metabolic syndrome is mediated by a dysregulation of adiposity and therefore it is critical to study adipose tissue in order to understand the effects of this syndrome on epigenomes. To determine if natural variation in DNA methylation was associated with metabolic syndrome traits, we profiled global methylation levels in subcutaneous abdominal adipose tissue. We measured association between 32 clinical traits related to diabetes and obesity in 201 people from the Metabolic Syndrome in Men cohort. We performed epigenome-wide association studies between DNA methylation levels and traits, and identified associations for 13 clinical traits in 21 loci. We prioritized candidate genes in these loci using expression quantitative trait loci, and identified 18 high confidence candidate genes, including known and novel genes associated with diabetes and obesity traits. Using methylation deconvolution, we examined which cell types may be mediating the associations, and concluded that most of the loci we identified were specific to adipocytes. We determined whether the abundance of cell types varies with metabolic traits, and found that macrophages increased in abundance with the severity of metabolic syndrome traits. Finally, we developed a DNA methylation-based biomarker to assess type 2 diabetes risk in adipose tissue. In conclusion, our results demonstrate that profiling DNA methylation in adipose tissue is a powerful tool for understanding the molecular effects of metabolic syndrome on adipose tissue, and can be used in conjunction with traditional genetic analyses to further characterize this disorder.

Background

Complex diseases are characterized by multiple subtle perturbations to biological processes. New omics platforms can detect these perturbations, but translating the diverse molecular and statistical information into testable mechanistic hypotheses is challenging. Therefore, we set out to create a public tool that integrates these data across multiple datasets, platforms, study designs and species in order to detect the most promising targets for further mechanistic studies.

Results

We developed Mergeomics, a computational pipeline consisting of independent modules that 1) leverage multi-omics association data to identify biological processes that are perturbed in disease, and 2) overlay the disease-associated processes onto molecular interaction networks to pinpoint hubs as potential key regulators. Unlike existing tools that are mostly dedicated to specific data type or settings, the Mergeomics pipeline accepts and integrates datasets across platforms, data types and species. We optimized and evaluated the performance of Mergeomics using simulation and multiple independent datasets, and benchmarked the results against alternative methods. We also demonstrate the versatility of Mergeomics in two case studies that include genome-wide, epigenome-wide and transcriptome-wide datasets from human and mouse studies of total cholesterol and fasting glucose. In both cases, the Mergeomics pipeline provided statistical and contextual evidence to prioritize further investigations in the wet lab. The software implementation of Mergeomics is freely available as a Bioconductor R package.

Conclusion

Mergeomics is a flexible and robust computational pipeline for multidimensional data integration. It outperforms existing tools, and is easily applicable to datasets from different studies, species and omics data types for the study of complex traits.

Heritable epigenetic factors can contribute to complex disease etiology. Here we examine the contribution of DNA methylation to complex traits that are precursors to heart disease, diabetes, and osteoporosis. We profiled DNA methylation in the liver using bisulfite sequencing in 90 mouse inbred strains, genome-wide expression levels, proteomics, metabolomics, and 68 clinical traits and performed epigenome-wide association studies (EWAS). We found associations with numerous clinical traits including bone density, insulin resistance, expression, and protein and metabolite levels. A large proportion of associations were unique to EWAS and were not identified using GWAS. Methylation levels were regulated by genetics largely in cis, but we also found evidence of trans regulation, and we demonstrate that genetic variation in the methionine synthase reductase gene Mtrr affects methylation of hundreds of CpGs throughout the genome. Our results indicate that natural variation in methylation levels contributes to the etiology of complex clinical traits.

Anti-integrins are therapeutically effective for inflammatory bowel disease, yet the relative contribution of α4β7 and αEβ7 to gut lymphocyte trafficking is not fully elucidated. Here, we evaluate the effect of α4β7 and αEβ7 blockade using a combination of murine models of gut trafficking and longitudinal gene expression analysis in etrolizumab-treated patients with Crohn's disease (CD). Dual blockade of α4β7 and αEβ7 reduces CD8⁺ T cell accumulation in the gut to a greater extent than blockade of either integrin alone. Anti-αEβ7 reduces epithelial:T cell interactions and promotes egress of activated T cells from the mucosa into lymphatics. Inflammatory gene expression is greater in human intestinal αEβ7⁺ T cells. Etrolizumab-treated patients with CD display a treatment-specific reduction in inflammatory and cytotoxic intraepithelial lymphocytes (IEL) genes. Concurrent blockade of α4β7 and αEβ7 promotes reduction of cytotoxic IELs and inflammatory T cells in the gut mucosa through a stepwise inhibition of intestinal tissue entry and retention.

Genomic studies in age-related macular degeneration (AMD) have identified genetic variants that account for the majority of AMD risk. An important next step is to understand the functional consequences and downstream effects of the identified AMD-associated genetic variants. Instrumental for this next step are 'omics' technologies, which enable high-throughput characterization and quantification of biological molecules, and subsequent integration of genomics with these omics datasets, a field referred to as systems genomics. Single cell sequencing studies of the retina and choroid demonstrated that the majority of candidate AMD genes identified through genomic studies are expressed in non-neuronal cells, such as the retinal pigment epithelium (RPE), glia, myeloid and choroidal cells, highlighting that many different retinal and choroidal cell types contribute to the pathogenesis of AMD. Expression quantitative trait locus (eQTL) studies in retinal tissue have identified putative causal genes by demonstrating a genetic overlap between gene regulation and AMD risk. Linking genetic data to complement measurements in the systemic circulation has aided in understanding the effect of AMD-associated genetic variants in the complement system, and supports that protein QTL (pQTL) studies in plasma or serum samples may aid in understanding the effect of genetic variants and pinpointing causal genes in AMD. A recent epigenomic study fine-mapped AMD causal variants by determing regulatory regions in RPE cells differentiated from induced pluripotent stem cells (iPSC-RPE). Another approach that is being employed to pinpoint causal AMD genes is to produce synthetic DNA assemblons representing risk and protective haplotypes, which are then delivered to cellular or animal model systems. Pinpointing causal genes and understanding disease mechanisms is crucial for the next step towards clinical translation. Clinical trials targeting proteins encoded by the AMD-associated genomic loci C3, CFB, CFI, CFH, and ARMS2/HTRA1 are currently ongoing, and a phase III clinical trial for C3 inhibition recently showed a modest reduction of lesion growth in geographic atrophy. The EYERISK consortium recently developed a genetic test for AMD that allows genotyping of common and rare variants in AMD-associated genes. Polygenic risk scores (PRS) were applied to quantify AMD genetic risk, and may aid in predicting AMD progression. In conclusion, genomic studies represent a turning point in our exploration of AMD. The results of those studies now serve as a driving force for several clinical trials. Expanding to omics and systems genomics will further decipher function and causality from the associations that have been reported, and will enable the development of therapies that will lessen the burden of AMD.