Disease variants alter transcription factor levels and methylation of their binding sites

Most disease-associated genetic variants are noncoding, making it challenging to design experiments to understand their functional consequences. Identification of expression quantitative trait loci (eQTLs) has been a powerful approach to infer the downstream effects of disease-associated variants, but most of these variants remain unexplained. The analysis of DNA methylation, a key component of the epigenome, offers highly complementary data on the regulatory potential of genomic regions. Here we show that disease-associated variants have widespread effects on DNA methylation in trans that likely reflect differential occupancy of trans binding sites by cis-regulated transcription factors. Using multiple omics data sets from 3,841 Dutch individuals, we identified 1,907 established trait-associated SNPs that affect the methylation levels of 10,141 different CpG sites in trans (false discovery rate (FDR) < 0.05). These included SNPs that affect both the expression of a nearby transcription factor (such as NFKB1, CTCF and NKX2-3) and methylation of its respective binding site across the genome. Trans methylation QTLs effectively expose the downstream effects of disease-associated variants.

Identification of context-dependent expression quantitative trait loci in whole blood

Genetic risk factors often localize to noncoding regions of the genome with unknown effects on disease etiology. Expression quantitative trait loci (eQTLs) help to explain the regulatory mechanisms underlying these genetic associations. Knowledge of the context that determines the nature and strength of eQTLs may help identify cell types relevant to pathophysiology and the regulatory networks underlying disease. Here we generated peripheral blood RNA-seq data from 2,116 unrelated individuals and systematically identified context-dependent eQTLs using a hypothesis-free strategy that does not require previous knowledge of the identity of the modifiers. Of the 23,060 significant cis-regulated genes (false discovery rate (FDR) ≤ 0.05), 2,743 (12%) showed context-dependent eQTL effects. The majority of these effects were influenced by cell type composition. A set of 145 cis-eQTLs depended on type I interferon signaling. Others were modulated by specific transcription factors binding to the eQTL SNPs.

Age-related accrual of methylomic variability is linked to fundamental ageing mechanisms

BACKGROUND

Epigenetic change is a hallmark of ageing but its link to ageing mechanisms in humans remains poorly understood. While DNA methylation at many CpG sites closely tracks chronological age, DNA methylation changes relevant to biological age are expected to gradually dissociate from chronological age, mirroring the increased heterogeneity in health status at older ages.

RESULTS

Here, we report on the large-scale identification of 6366 age-related variably methylated positions (aVMPs) identified in 3295 whole blood DNA methylation profiles, 2044 of which have a matching RNA-seq gene expression profile. aVMPs are enriched at polycomb repressed regions and, accordingly, methylation at those positions is associated with the expression of genes encoding components of polycomb repressive complex 2 (PRC2) in trans. Further analysis revealed trans-associations for 1816 aVMPs with an additional 854 genes. These trans-associated aVMPs are characterized by either an age-related gain of methylation at CpG islands marked by PRC2 or a loss of methylation at enhancers. This distinct pattern extends to other tissues and multiple cancer types. Finally, genes associated with aVMPs in trans whose expression is variably upregulated with age (733 genes) play a key role in DNA repair and apoptosis, whereas downregulated aVMP-associated genes (121 genes) are mapped to defined pathways in cellular metabolism.

CONCLUSIONS

Our results link age-related changes in DNA methylation to fundamental mechanisms that are thought to drive human ageing.

Blood lipids influence DNA methylation in circulating cells

Background

Cells can be primed by external stimuli to obtain a long-term epigenetic memory. We hypothesize that long-term exposure to elevated blood lipids can prime circulating immune cells through changes in DNA methylation, a process that may contribute to the development of atherosclerosis. To interrogate the causal relationship between triglyceride, low-density lipoprotein (LDL) cholesterol, and high-density lipoprotein (HDL) cholesterol levels and genome-wide DNA methylation while excluding confounding and pleiotropy, we perform a stepwise Mendelian randomization analysis in whole blood of 3296 individuals.

Results

This analysis shows that differential methylation is the consequence of inter-individual variation in blood lipid levels and not vice versa. Specifically, we observe an effect of triglycerides on DNA methylation at three CpGs, of LDL cholesterol at one CpG, and of HDL cholesterol at two CpGs using multivariable Mendelian randomization. Using RNA-seq data available for a large subset of individuals (N = 2044), DNA methylation of these six CpGs is associated with the expression of CPT1A and SREBF1 (for triglycerides), DHCR24 (for LDL cholesterol) and ABCG1 (for HDL cholesterol), which are all key regulators of lipid metabolism.

Conclusions

Our analysis suggests a role for epigenetic priming in end-product feedback control of lipid metabolism and highlights Mendelian randomization as an effective tool to infer causal relationships in integrative genomics data.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-016-1000-6) contains supplementary material, which is available to authorized users.

Determining the quality and complexity of next-generation sequencing data without a reference genome

We describe an open-source kPAL package that facilitates an alignment-free assessment of the quality and comparability of sequencing datasets by analyzing k-mer frequencies. We show that kPAL can detect technical artefacts such as high duplication rates, library chimeras, contamination and differences in library preparation protocols. kPAL also successfully captures the complexity and diversity of microbiomes and provides a powerful means to study changes in microbial communities. Together, these features make kPAL an attractive and broadly applicable tool to determine the quality and comparability of sequence libraries even in the absence of a reference sequence. kPAL is freely available at https://github.com/LUMC/kPAL webcite.

Exome sequencing of germline DNA from non-BRCA1/2 familial breast cancer cases selected on the basis of aCGH tumor profiling

The bulk of familial breast cancer risk (∼70%) cannot be explained by mutations in the known predisposition genes, primarily BRCA1 and BRCA2. Underlying genetic heterogeneity in these cases is the probable explanation for the failure of all attempts to identify further high-risk alleles. While exome sequencing of non-BRCA1/2 breast cancer cases is a promising strategy to detect new high-risk genes, rational approaches to the rigorous pre-selection of cases are needed to reduce heterogeneity. We selected six families in which the tumours of multiple cases showed a specific genomic profile on array comparative genomic hybridization (aCGH). Linkage analysis in these families revealed a region on chromosome 4 with a LOD score of 2.49 under homogeneity. We then analysed the germline DNA of two patients from each family using exome sequencing. Initially focusing on the linkage region, no potentially pathogenic variants could be identified in more than one family. Variants outside the linkage region were then analysed, and we detected multiple possibly pathogenic variants in genes that encode DNA integrity maintenance proteins. However, further analysis led to the rejection of all variants due to poor co-segregation or a relatively high allele frequency in a control population. We concluded that using CGH results to focus on a sub-set of families for sequencing analysis did not enable us to identify a common genetic change responsible for the aggregation of breast cancer in these families. Our data also support the emerging view that non-BRCA1/2 hereditary breast cancer families have a very heterogeneous genetic basis.

Directed adenovirus evolution using engineered mutator viral polymerases

Adenoviruses (Ads) are the most frequently used viruses for oncolytic and gene therapy purposes. Most Ad-based vectors have been generated through rational design. Although this led to significant vector improvements, it is often hampered by an insufficient understanding of Ad’s intricate functions and interactions. Here, to evade this issue, we adopted a novel, mutator Ad polymerase-based, ‘accelerated-evolution’ approach that can serve as general method to generate or optimize adenoviral vectors. First, we site specifically substituted Ad polymerase residues located in either the nucleotide binding pocket or the exonuclease domain. This yielded several polymerase mutants that, while fully supportive of viral replication, increased Ad’s intrinsic mutation rate. Mutator activities of these mutants were revealed by performing deep sequencing on pools of replicated viruses. The strongest identified mutators carried replacements of residues implicated in ssDNA binding at the exonuclease active site. Next, we exploited these mutators to generate the genetic diversity required for directed Ad evolution. Using this new forward genetics approach, we isolated viral mutants with improved cytolytic activity. These mutants revealed a common mutation in a splice acceptor site preceding the gene for the adenovirus death protein (ADP). Accordingly, the isolated viruses showed high and untimely expression of ADP, correlating with a severe deregulation of E3 transcript splicing.

Genome-wide assessment of differential roles for p300 and CBP in transcription regulation

Despite high levels of homology, transcription coactivators p300 and CREB binding protein (CBP) are both indispensable during embryogenesis. They are largely known to regulate the same genes. To identify genes preferentially regulated by p300 or CBP, we performed an extensive genome-wide survey using the ChIP-seq on cell-cycle synchronized cells. We found that 57% of the tags were within genes or proximal promoters, with an overall preference for binding to transcription start and end sites. The heterogeneous binding patterns possibly reflect the divergent roles of CBP and p300 in transcriptional regulation. Most of the 16 103 genes were bound by both CBP and p300. However, after stimulation 89 and 1944 genes were preferentially bound by CBP or p300, respectively. Target genes were found to be primarily involved in the regulation of metabolic and developmental processes, and transcription, with CBP showing a stronger preference than p300 for genes active in negative regulation of transcription. Analysis of transcription factor binding sites suggest that CBP and p300 have many partners in common, but AP-1 and Serum Response Factor (SRF) appear to be more prominent in CBP-specific sequences, whereas AP-2 and SP1 are enriched in p300-specific targets. Taken together, our findings further elucidate the distinct roles of coactivators p300 and CBP in transcriptional regulation.

Deep sequencing to reveal new variants in pooled DNA samples

We evaluated massive parallel sequencing and long-range PCR (LRP) for rare variant detection and allele frequency estimation in pooled DNA samples. Exons 2 to 16 of the MUTYH gene were analyzed in breast cancer patients with Illumina's (Solexa) technology. From a pool of 287 genomic DNA samples we generated a single LRP product, while the same LRP was performed on 88 individual samples and the resulting products then pooled. Concentrations of constituent samples were measured with fluorimetry for genomic DNA and high-resolution melting curve analysis (HR-MCA) for LRP products. Illumina sequencing results were compared to Sanger sequencing data of individual samples. Correlation between allele frequencies detected by both methods was poor in the first pool, presumably because the genomic samples amplified unequally in the LRP, due to DNA quality variability. In contrast, allele frequencies correlated well in the second pool, in which all expected alleles at a frequency of 1% and higher were reliably detected, plus the majority of singletons (0.6% allele frequency). We describe custom bioinformatics and statistics to optimize detection of rare variants and to estimate required sequencing depth. Our results provide directions for designing high-throughput analyses of candidate genes.

CORE_TF: a user-friendly interface to identify evolutionary conserved transcription factor binding sites in sets of co-regulated genes

BACKGROUND

The identification of transcription factor binding sites is difficult since they are only a small number of nucleotides in size, resulting in large numbers of false positives and false negatives in current approaches. Computational methods to reduce false positives are to look for over-representation of transcription factor binding sites in a set of similarly regulated promoters or to look for conservation in orthologous promoter alignments.

RESULTS

We have developed a novel tool, "CORE_TF" (Conserved and Over-REpresented Transcription Factor binding sites) that identifies common transcription factor binding sites in promoters of co-regulated genes. To improve upon existing binding site predictions, the tool searches for position weight matrices from the TRANSFAC R database that are over-represented in an experimental set compared to a random set of promoters and identifies cross-species conservation of the predicted transcription factor binding sites. The algorithm has been evaluated with expression and chromatin-immunoprecipitation on microarray data. We also implement and demonstrate the importance of matching the random set of promoters to the experimental promoters by GC content, which is a unique feature of our tool.

CONCLUSION

The program CORE_TF is accessible in a user friendly web interface at http://www.LGTC.nl/CORE_TF. It provides a table of over-represented transcription factor binding sites in the users input genes' promoters and a graphical view of evolutionary conserved transcription factor binding sites. In our test data sets it successfully predicts target transcription factors and their binding sites.