DISEASES 2.0: a weekly updated database of disease–gene associations from text mining and data integration

The scientific knowledge about which genes are involved in which diseases grows rapidly, which makes it difficult to keep up with new publications and genetics datasets. The DISEASES database aims to provide a comprehensive overview by systematically integrating and assigning confidence scores to evidence for disease–gene associations from curated databases, genome-wide association studies (GWAS), and automatic text mining of the biomedical literature. Here, we present a major update to this resource, which greatly increases the number of associations from all these sources. This is especially true for the text-mined associations, which have increased by at least 9-fold at all confidence cutoffs. We show that this dramatic increase is primarily due to adding full-text articles to the text corpus, secondarily due to improvements to both the disease and gene dictionaries used for named entity recognition, and only to a very small extent due to the growth in number of PubMed abstracts. DISEASES now also makes use of a new GWAS database, TIGA, which considerably increased the number of GWAS-derived disease–gene associations. DISEASES itself is also integrated into several other databases and resources, including GeneCards/MalaCards, Pharos/TCRD, and the Cytoscape stringApp. All data in DISEASES is updated on a weekly basis and is available via a web interface at https://diseases.jensenlab.org, from where it can also be downloaded under open licenses.

Hypothetical molecular interconnection between type 2 diabetes and dyslexia

Background Dyslexia is one of the most common learning disabilities, especially among children. Type 2 diabetes is a metabolic disorder that affects a large population globally, with metabolic disorders. There have been several genes that are identified as causes of Dyslexia, and in recent studies, it has been found out that some of those genes are also involved in several metabolic pathways. For several years, it has been known that type 2 diabetes causes several neurodegenerative disorders, such as Alzheimer’s disease and Parkinson’s disease. Furthermore, in several studies, it was suggested that type 2 diabetes also has some associations with learning disabilities. This raises the question of whether “Is there a connection between type 2 diabetes and dyslexia?”. In this study, this question is elaborated by linking their developmental processes via bioinformatics analysis about these two diseases individually and collectively. Result The literature review for dyslexia and type two diabetes was completed. As the result of this literature review, the genes that are associated to type 2 diabetes and dyslexia were identified. The biological pathways of dyslexia, and dyslexia associated genes, type 2 diabetes, and type 2 diabetes associated genes were identified. The association of these genes, regarding to their association with pathways were analysed, and using STRING database the gene associations were analysed and identified. Conclusion The findings of this research included the interaction analysis via gene association, co-expression and protein–protein interaction. These findings clarified the interconnection between dyslexia and type 2 diabetes in molecular level and it will be the beginning of an answer regarding to the relationship between T2D and dyslexia. Finally, by improving the understanding this paper aims to open the way for the possible future approach to examine this hypothesis.

Dysregulation of ribosome-related genes in ankylosing spondylitis: a systems biology approach and experimental method

Background Ankylosing spondylitis (AS) is an autoimmune rheumatic disease. Few candidate gene associations have been reported for AS and the current understanding of its pathogenesis remains still poor. Thus, the exact mechanism of AS is needed to urgently be disclosed. The purpose of this study was to identify candidate genes involving in AS disease. Methods and results GSE25101 publicly available microarray and GSE117769 RNA-seq datasets of AS patients were obtained for bioinformatics analyses. Gene set enrichment analysis showed that in the microarray dataset, the ribosome pathway was significantly up-regulated in AS compared with controls. Furthermore, some ribosomal components demonstrated overexpression in patients in the RNA-seq dataset. To confirm the findings, 20 AS patients and 20 matching controls were selected from the Rheumatology Research Center clinic, Shariati Hospital. PBMCs were separated from whole blood and RNA contents were extracted. Following the results of datasets analysis, the expression level of rRNA5.8S pseudogene, rRNA18S pseudogene, RPL23, RPL7, and RPL17 genes were measured through real-time PCR. Our findings showed dysregulation of rRNA5.8S and rRNA18S pseudogenes, and also the RPL17 gene in patients. Conclusion Considering that genes involved in ribosome biogenesis contributed to some AS-associated biological processes as well as diseases that have comorbidities with AS, our results might advance our understanding of the pathological mechanisms of ankylosing spondylitis.

Recent advancements in molecular marker-assisted selection and applications in plant breeding programmes

Background DNA markers improved the productivity and accuracy of classical plant breeding by means of marker-assisted selection (MAS). The enormous number of quantitative trait loci (QTLs) mapping read for different plant species have given a plenitude of molecular marker-gene associations. Main body of the abstract In this review, we have discussed the positive aspects of molecular marker-assisted selection and its precise applications in plant breeding programmes. Molecular marker-assisted selection has considerably shortened the time for new crop varieties to be brought to the market. To explore the information about DNA markers, many reviews have been published in the last few decades; all these reviews were intended by plant breeders to obtain information on molecular genetics. In this review, we intended to be a synopsis of recent developments of DNA markers and their application in plant breeding programmes and devoted to early breeders with little or no knowledge about the DNA markers. The progress made in molecular plant breeding, plant genetics, genomics selection, and editing of genome contributed to the comprehensive understanding of DNA markers and provides several proofs on the genetic diversity available in crop plants and greatly complemented plant breeding devices. Short conclusion MAS has revolutionized the process of plant breeding with acceleration and accuracy, which is continuously empowering plant breeders around the world.

GCSENet: A GCN, CNN and SENet ensemble model for microRNA-disease association prediction

Recently, an increasing number of studies have demonstrated that miRNAs are involved in human diseases, indicating that miRNAs might be a potential pathogenic factor for various diseases. Therefore, figuring out the relationship between miRNAs and diseases plays a critical role in not only the development of new drugs, but also the formulation of individualized diagnosis and treatment. As the prediction of miRNA-disease association via biological experiments is expensive and time-consuming, computational methods have a positive effect on revealing the association. In this study, a novel prediction model integrating GCN, CNN and Squeeze-and-Excitation Networks (GCSENet) was constructed for the identification of miRNA-disease association. The model first captured features by GCN based on a heterogeneous graph including diseases, genes and miRNAs. Then, considering the different effects of genes on each type of miRNA and disease, as well as the different effects of the miRNA-gene and disease-gene relationships on miRNA-disease association, a feature weight was set and a combination of miRNA-gene and disease-gene associations was added as feature input for the convolution operation in CNN. Furthermore, the squeeze and excitation blocks of SENet were applied to determine the importance of each feature channel and enhance useful features by means of the attention mechanism, thus achieving a satisfactory prediction of miRNA-disease association. The proposed method was compared against other state-of-the-art methods. It achieved an AUROC score of 95.02% and an AUPR score of 95.55% in a 10-fold cross-validation, which led to the finding that the proposed method is superior to these popular methods on most of the performance evaluation indexes.

Sitagliptin: a potential drug for the treatment of COVID-19?

Recently, an outbreak of a fatal coronavirus, SARS-CoV-2, has emerged from China and is rapidly spreading worldwide. Possible interaction of SARS-CoV-2 with DPP4 peptidase may partly contribute to the viral pathogenesis. An integrative bioinformatics approach starting with mining the biomedical literature for high confidence DPP4-protein/gene associations followed by functional analysis using network analysis and pathway enrichment was adopted. The results indicate that the identified DPP4 networks are highly enriched in viral processes required for viral entry and infection, and as a result, we propose DPP4 as an important putative target for the treatment of COVID-19. Additionally, our protein-chemical interaction networks identified important interactions between DPP4 and sitagliptin. We conclude that sitagliptin may be beneficial for the treatment of COVID-19 disease, either as monotherapy or in combination with other therapies, especially for diabetic patients and patients with pre-existing cardiovascular conditions who are already at higher risk of COVID-19 mortality.

Genome size evolution in the diverse insect order Trichoptera

Genome size can vary widely over relatively short evolutionary time scales and is implicated in form, function and ecological success of a species. Here, we generated 17 new de novo whole genome assemblies and present a holistic view on genome size diversity of the highly diversified, non-model insect order, Trichoptera (caddisflies). We detect large variation in genome size and find strong evidence that transposable element (TE) expansions are the primary driver of genome size evolution: TE expansions contribute to larger genomes in clades with higher ecological diversity and have a major impact on protein-coding gene regions. These TE-gene associations show a linear relationship with increasing genome size. Our findings suggest new hypotheses for future testing, especially the effects of TE activity and TE-gene associations on genome stability, gene expression, phenotypes, and their potential adaptive advantages in groups with high species, ecological, and functional diversities.