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A catalog of genetic loci associated with kidney function from analyses of a million individuals
(2019)
Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through transancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these,147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research.
microRNAs (miRNAs) have been identified as high-value drug targets. A widely applied strategy in miRNA inhibition is the use of antisense agents. However, it has been shown that oligonucleotides are poorly cell permeable because of their complex chemical structure and due to their negatively charged backbone. Consequently, the general application of oligonucleotides in therapy is limited. Since miRNAs’ functions are executed exclusively by the Argonaute 2 protein, we therefore describe a protocol for the design of a novel miRNA inhibitor class: antagonists of the miRNA-Argonaute 2 protein complex, so-called anti-miR-AGOs, that not only block the crucial binding site of the target miRNA but also bind to the protein’s active site. Due to their lower molecular weight and, thus, more drug-like chemical structure, the novel inhibitor class may show better pharmacokinetic properties than reported oligonucleotide inhibitors, enabling them for potential therapeutic use.
miRNA Targeting Drugs
(2017)
Only 20 years after the discovery of small non-coding, single-stranded ribonucleic acids, so-called microRNAs (miRNAs), as post-transcriptional gene regulators, the first miRNA-targeting drug Miravirsen for the treatment of hepatitis C has been successfully tested in clinical Phase II trials. Addressing miRNAs as drug targets may enable the cure, or at least the treatment of diseases, which presently seems impossible. However, due to miRNAs’ chemical structure, generation of potential drug molecules with necessary pharmacokinetic properties is still challenging and requires a re-thinking of the drug discovery process. Therefore, this chapter highlights the potential of miRNAs as drug targets, discusses the challenges, and tries to give a complete overview of recent strategies in miRNA drug discovery.
Preface
(2017)
Polygenic risk scores (PRS) aggregating results from genome-wide association studies are the state of the art in the prediction of susceptibility to complex traits or diseases, yet their predictive performance is limited for various reasons, not least of which is their failure to incorporate the effects of gene-gene interactions. Novel machine learning algorithms that use large amounts of data promise to find gene-gene interactions in order to build models with better predictive performance than PRS. Here, we present a data preprocessing step by using data-mining of contextual information to reduce the number of features, enabling machine learning algorithms to identify gene-gene interactions. We applied our approach to the Parkinson's Progression Markers Initiative (PPMI) dataset, an observational clinical study of 471 genotyped subjects (368 cases and 152 controls). With an AUC of 0.85 (95% CI = [0.72; 0.96]), the interaction-based prediction model outperforms the PRS (AUC of 0.58 (95% CI = [0.42; 0.81])). Furthermore, feature importance analysis of the model provided insights into the mechanism of Parkinson's disease. For instance, the model revealed an interaction of previously described drug target candidate genes TMEM175 and GAPDHP25. These results demonstrate that interaction-based machine learning models can improve genetic prediction models and might provide an answer to the missing heritability problem.
Moving in the Anthropocene
(2018)
Animal movement is fundamental for ecosystem functioning and species survival, yet the effects of the anthropogenic footprint on animal movements have not been estimated across species. Using a unique GPS-tracking database of 803 individuals across 57 species, we found that movements of mammals in areas with a comparatively high human footprint were on average one-half to one-third the extent of their movements in areas with a low human footprint. We attribute this reduction to behavioral changes of individual animals and to the exclusion of species with long-range movements from areas with higher human impact. Global loss of vagility alters a key ecological trait of animals that affects not only population persistence but also ecosystem processes such as predator-prey interactions, nutrient cycling, and disease transmission.