A genome-wide scan for microrna-related genetic variants associated with primary open-angle glaucoma

PURPOSE: To identify microRNAs (miRNAs) involved in primary open-angle glaucoma (POAG), using genetic data. MiRNAs are small noncoding RNAs that posttranscriptionally regulate gene expression. Genetic variants in miRNAs or miRNA-binding sites within gene 3’-untranslated regions (3’UTRs) are expected to affect miRNA function and con

ARHGEF12 influences the risk of glaucoma by increasing intraocular pressure

Primary open-angle glaucoma (POAG) is a blinding disease. Two important risk factors for this disease are a positive family history and elevated intraocular pressure (IOP), which is also highly heritable. Genes found to date associated with IOP and POAG are ABCA1, CAV1/CAV2, GAS7 and TMCO1. However, these genes explain only a small part of the heritability of IOP and POAG.We performed a genome-wide association study of IOP in the population-based RotterdamStudy I and Rotterdam Study II using single nucleotide polymorphisms (SNPs) imputed to 1000 Genomes. In this discovery cohort (n = 8105), we identified a newlocus associated with IOP. The most significantly associated SNPwas rs58073046 (ß = 0.44, P-value = 1.87 × 10-8, minor allele frequency = 0.12), within the gene ARHGEF12. Independent replication in five population-based studies (n = 7471) resulted in an effect size in the same direction that was significantly associated (ß = 0.16, P-value = 0.04). The SNP was also significantly associated with POAG in two independent case-control studies [n = 1225 cases and n = 4117 controls; odds ratio (OR) = 1.53, P-value = 1.99 × 10-8], especially with high-tension glaucoma (OR = 1.66, P-value = 2.81 × 10-9; for normal-tension glaucoma OR = 1.29, P-value = 4.23 × 10-2). ARHGEF12 plays an important role in the RhoA/RhoA kinase pathway, which has been implicated in IOP regulation. Furthermore, it binds to ABCA1 and links the ABCA1, CAV1/CAV2 and GAS7 pathway to Mendelian POAG genes (MYOC, OPTN, WDR36). In conclusion, this study identified a novel association between IOP and ARHGEF12

The Wheat GENIE3 Network Provides Biologically-Relevant Information in Polyploid Wheat

Gene regulatory networks are powerful tools which facilitate hypothesis generation and candidate gene discovery. However, the extent to which the network predictions are biologically relevant is often unclear. Recently a GENIE3 network which predicted targets of wheat transcription factors was produced. Here we used an independent RNA-Seq dataset to test the predictions of the wheat GENIE3 network for the senescence-regulating transcription factor NAM-A1 (TraesCS6A02G108300). We re-analyzed the RNA-Seq data against the RefSeqv1.0 genome and identified a set of differentially expressed genes (DEGs) between the wild-type and nam-a1 mutant which recapitulated the known role of NAM-A1 in senescence and nutrient remobilisation. We found that the GENIE3-predicted target genes of NAM-A1 overlap significantly with the DEGs, more than would be expected by chance. Based on high levels of overlap between GENIE3-predicted target genes and the DEGs, we identified candidate senescence regulators. We then explored genome-wide trends in the network related to polyploidy and found that only homeologous transcription factors are likely to share predicted targets in common. However, homeologs which vary in expression levels across tissues are less likely to share predicted targets than those that do not, suggesting that they may be more likely to act in distinct pathways. This work demonstrates that the wheat GENIE3 network can provide biologically-relevant predictions of transcription factor targets, which can be used for candidate gene prediction and for global analyses of transcription factor function. The GENIE3 network has now been integrated into the KnetMiner web application, facilitating its use in future studies

Non-Coding RNAs Improve the Predictive Power of Network Medicine

Network Medicine has improved the mechanistic understanding of disease, offering quantitative insights into disease mechanisms, comorbidities, and novel diagnostic tools and therapeutic treatments. Yet, most network-based approaches rely on a comprehensive map of protein-protein interactions, ignoring interactions mediated by non-coding RNAs (ncRNAs). Here, we systematically combine experimentally confirmed binding interactions mediated by ncRNA with protein-protein interactions, constructing the first comprehensive network of all physical interactions in the human cell. We find that the inclusion of ncRNA, expands the number of genes in the interactome by 46% and the number of interactions by 107%, significantly enhancing our ability to identify disease modules. Indeed, we find that 132 diseases, lacked a statistically significant disease module in the protein-based interactome, but have a statistically significant disease module after inclusion of ncRNA-mediated interactions, making these diseases accessible to the tools of network medicine. We show that the inclusion of ncRNAs helps unveil disease-disease relationships that were not detectable before and expands our ability to predict comorbidity patterns between diseases. Taken together, we find that including non-coding interactions improves both the breath and the predictive accuracy of network medicine.Comment: Paper and S

Molecular-genetic mechanisms of the interaction between processes of cell response to mechanical stress and neuronal apoptosis in primary open-angle glaucoma

Glaucoma is a chronic and progressive disease, which affects more than 60 million people worldwide. Primary open-angle glaucoma (POAG) is one of the most common forms of glaucoma. For example, about 2.71 million people in the USA had primary open-angle glaucoma in 2011. Currently POAG is a major cause of irreversible vision loss. In patients with treated open-angle glaucoma the risk of blindness reached to be about 27 %. It is known that the death of optic nerve cells can be triggered by mechanical stress caused by increased intraocular pressure, which induces neuronal apoptosis and is observed in patients with POAG. Currently, there is a large number of scientific publications describing proteins and genes involved in the pathogenesis of POAG, including neuronal apoptosis and the cell response to mechanical stress. However, the molecular- genetic mechanisms underlying the pathophysiology of POAG are still poorly understood. Reconstruction of associative networks describing the functional interactions between these genes/proteins, including biochemical reactions, regulatory interactions, transport, etc., requires the use of methods of automated knowledge extraction from texts of scientific publications. The aim of the work was the analysis of associative networks, describing the molecular-genetic interactions between proteins and genes involved in cell response to mechanical stress (CRMS), neuronal apoptosis and pathogenesis of POAG using ANDSystem, our previous development for automated text analysis. It was shown that genes associated with POAG are statistically significantly more often represented among the genes involved in the interactions between CRMS and neuronal apoptosis than it was expected by random reasons, which can be an explanation for the effect of POAG leading to the retinal ganglion cell death

Visual Impairment and Blindness

Blindness and vision impairment affect at least 2.2 billion people worldwide with most individuals having a preventable vision impairment. The majority of people with vision impairment are older than 50 years, however, vision loss can affect people of all ages. Reduced eyesight can have major and long-lasting effects on all aspects of life, including daily personal activities, interacting with the community, school and work opportunities, and the ability to access public services. This book provides an overview of the effects of blindness and visual impairment in the context of the most common causes of blindness in older adults as well as children, including retinal disorders, cataracts, glaucoma, and macular or corneal degeneration

A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?

Artificial intelligence (AI) models are increasingly finding applications in the field of medicine. Concerns have been raised about the explainability of the decisions that are made by these AI models. In this article, we give a systematic analysis of explainable artificial intelligence (XAI), with a primary focus on models that are currently being used in the field of healthcare. The literature search is conducted following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) standards for relevant work published from 1 January 2012 to 02 February 2022. The review analyzes the prevailing trends in XAI and lays out the major directions in which research is headed. We investigate the why, how, and when of the uses of these XAI models and their implications. We present a comprehensive examination of XAI methodologies as well as an explanation of how a trustworthy AI can be derived from describing AI models for healthcare fields. The discussion of this work will contribute to the formalization of the XAI field.Comment: 15 pages, 3 figures, accepted for publication in the IEEE Transactions on Artificial Intelligenc