A systematic SNP selection approach to identify mechanisms underlying disease aetiology: Linking height to post-menopausal breast and colorectal cancer risk

Data from GWAS suggest that SNPs associated with complex diseases or traits tend to co-segregate in regions of low recombination, harbouring functionally linked gene clusters. This phenomenon allows for selecting a limited number of SNPs from GWAS repositories for large-scale studies investigating shared mechanisms between diseases. For example, we were interested in shared mechanisms between adult-attained height and post-menopausal breast cancer (BC) and colorectal cancer (CRC) risk, because height is a risk factor for these cancers, though likely not a causal factor. Using SNPs from public GWAS repositories at p-values < 1 × 10-5 and a genomic sliding window of 1 mega base pair, we identified SNP clusters including at least one SNP associated with height and one SNP associated with either post-menopausal BC or CRC risk (or both). SNPs were annotated to genes using HapMap and GRAIL and analysed for significantly overrepresented pathways using ConsensuspathDB. Twelve clusters including 56 SNPs annotated to 26 genes were prioritised because these included at least one height- and one BC risk- or CRC risk-associated SNP annotated to the same gene. Annotated genes were involved in Indian hedgehog signalling (p-value = 7.78 × 10-7) and several cancer site-specific pathways. This systematic approach identified a limited number of clustered SNPs, which pinpoint potential shared mechanisms linking together the complex phenotypes height, post-menopausal BC and CRC

Evolutionary Dynamics of Co-Segregating Gene Clusters Associated with Complex Diseases

BACKGROUND: The distribution of human disease-associated mutations is not random across the human genome. Despite the fact that natural selection continually removes disease-associated mutations, an enrichment of these variants can be observed in regions of low recombination. There are a number of mechanisms by which such a clustering could occur, including genetic perturbations or demographic effects within different populations. Recent genome-wide association studies (GWAS) suggest that single nucleotide polymorphisms (SNPs) associated with complex disease traits are not randomly distributed throughout the genome, but tend to cluster in regions of low recombination. PRINCIPAL FINDINGS: Here we investigated whether deleterious mutations have accumulated in regions of low recombination due to the impact of recent positive selection and genetic hitchhiking. Using publicly available data on common complex diseases and population demography, we observed an enrichment of hitchhiked disease associations in conserved gene clusters subject to selection pressure. Evolutionary analysis revealed that these conserved gene clusters arose by multiple concerted rearrangements events across the vertebrate lineage. We observed distinct clustering of disease-associated SNPs in evolutionary rearranged regions of low recombination and high gene density, which harbor genes involved in immunity, that is, the interleukin cluster on 5q31 or RhoA on 3p21. CONCLUSIONS: Our results suggest that multiple lineage specific rearrangements led to a physical clustering of functionally related and linked genes exhibiting an enrichment of susceptibility loci for complex traits. This implies that besides recent evolutionary adaptations other evolutionary dynamics have played a role in the formation of linked gene clusters associated with complex disease traits

Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers

<p>Abstract</p> <p>Background</p> <p>Maturation inhibitors such as Bevirimat are a new class of antiretroviral drugs that hamper the cleavage of HIV-1 proteins into their functional active forms. They bind to these preproteins and inhibit their cleavage by the HIV-1 protease, resulting in non-functional virus particles. Nevertheless, there exist mutations in this region leading to resistance against Bevirimat. Highly specific and accurate tools to predict resistance to maturation inhibitors can help to identify patients, who might benefit from the usage of these new drugs.</p> <p>Results</p> <p>We tested several methods to improve Bevirimat resistance prediction in HIV-1. It turned out that combining structural and sequence-based information in classifier ensembles led to accurate and reliable predictions. Moreover, we were able to identify the most crucial regions for Bevirimat resistance computationally, which are in line with experimental results from other studies.</p> <p>Conclusions</p> <p>Our analysis demonstrated the use of machine learning techniques to predict HIV-1 resistance against maturation inhibitors such as Bevirimat. New maturation inhibitors are already under development and might enlarge the arsenal of antiretroviral drugs in the future. Thus, accurate prediction tools are very useful to enable a personalized therapy.</p

DNA methylation in an engineered heart tissue model of cardiac hypertrophy: common signatures and effects of DNA methylation inhibitors

DNA methylation affects transcriptional regulation and constitutes a drug target in cancer biology. In cardiac hypertrophy, DNA methylation may control the fetal gene program. We therefore investigated DNA methylation signatures and their dynamics in an in vitro model of cardiac hypertrophy based on engineered heart tissue (EHT). We exposed EHTs from neonatal rat cardiomyocytes to a 12-fold increased afterload (AE) or to phenylephrine (PE 20 µM) and compared DNA methylation signatures to control EHT by pull-down assay and DNA methylation microarray. A 7-day intervention sufficed to induce contractile dysfunction and significantly decrease promoter methylation of hypertrophy-associated upregulated genes such as Nppa (encoding ANP) and Acta1 (α-skeletal actin) in both intervention groups. To evaluate whether pathological consequences of AE are affected by inhibiting de novo DNA methylation we applied AE in the absence and presence of DNA methyltransferase (DNMT) inhibitors: 5-aza-2′-deoxycytidine (aza, 100 µM, nucleosidic inhibitor), RG108 (60 µM, non-nucleosidic) or methylene disalicylic acid (MDSA, 25 µM, non-nucleosidic). Aza had no effect on EHT function, but RG108 and MDSA partially prevented the detrimental consequences of AE on force, contraction and relaxation velocity. RG108 reduced AE-induced Atp2a2 (SERCA2a) promoter methylation. The results provide evidence for dynamic DNA methylation in cardiac hypertrophy and warrant further investigation of the potential of DNA methylation in the treatment of cardiac hypertrophy

A systematic SNP selection approach to identify mechanisms underlying disease aetiology: linking height to post-menopausal breast and colorectal cancer risk

Data from GWAS suggest that SNPs associated with complex diseases or traits tend to co-segregate in regions of low recombination, harbouring functionally linked gene clusters. This phenomenon allows for selecting a limited number of SNPs from GWAS repositories for large-scale studies investigating shared mechanisms between diseases. For example, we were interested in shared mechanisms between adult-attained height and post-menopausal breast cancer (BC) and colorectal cancer (CRC) risk, because height is a risk factor for these cancers, though likely not a causal factor. Using SNPs from public GWAS repositories at p-values < 1 × 10-5 and a genomic sliding window of 1 mega base pair, we identified SNP clusters including at least one SNP associated with height and one SNP associated with either post-menopausal BC or CRC risk (or both). SNPs were annotated to genes using HapMap and GRAIL and analysed for significantly overrepresented pathways using ConsensuspathDB. Twelve clusters including 56 SNPs annotated to 26 genes were prioritised because these included at least one height- and one BC risk- or CRC risk-associated SNP annotated to the same gene. Annotated genes were involved in Indian hedgehog signalling (p-value = 7.78 × 10-7) and several cancer site-specific pathways. This systematic approach identified a limited number of clustered SNPs, which pinpoint potential shared mechanisms linking together the complex phenotypes height, post-menopausal BC and CRC. © The Author(s) 2017

Maternal Western-Style High Fat Diet Induces Sex-Specific Physiological and Molecular Changes in Two-Week-Old Mouse Offspring

<p>Maternal diet is associated with the development of metabolism-related and other non-communicable diseases in offspring. Underlying mechanisms, functional profiles, and molecular markers are only starting to be revealed. Here, we explored the physiological and molecular impact of maternal Western-style diet on the liver of male and female offspring. C57BL/6 dams were exposed to either a low fat/low cholesterol diet (LFD) or a Western-style high fat/high cholesterol diet (WSD) for six weeks before mating, as well as during gestation and lactation. Dams and offspring were sacrificed at postnatal day 14, and body, liver, and blood parameters were assessed. The impact of maternal WSD on the pups' liver gene expression was characterised by whole-transcriptome microarray analysis. Exclusively male offspring had significantly higher body weight upon maternal WSD. In offspring of both sexes of WSD dams, liver and blood parameters, as well as hepatic gene expression profiles were changed. In total, 686 and 604 genes were differentially expressed in liver (p</p>