Variations of SARS-CoV-2 in the Iranian population and candidate putative drug-like compounds to inhibit the mutated proteins.

The first cases of the novel coronavirus, SARS-CoV-2, were detected in December 2019 in Wuhan, China. Nucleotide substitutions and mutations in the SARS-CoV-2 sequence can result in the evolution of the virus and its rapid spread across the world. Therefore, understanding genetic variants of SARS-CoV-2 and targeting the conserved elements responsible for viral replication have great benefits for detecting its infection sources and diagnosing and treating COVID-19. In this study, we used the SARS-CoV-2 sequence isolated from a 59-year-old man in Ardabil, Iran, in April 2020 and sequenced using Oxford Nanopore technology. A meta-analysis comparing the sequence under study with other sequences from Iran indicated long nucleotide insertions/deletions (indels) that code for NSP15, the NSP14-NSP10 complex, open reading frame ORF9b, and ORF1ab polyproteins. In addition, replicating the NSP8 protein in the study sequence is another topic that can affect viral replication. Then using the DNA structure of NSP8, NSP15, NSP14-NSP10 complex, and ORF1ab as a genetic target can help find drug-like compounds for COVID-19. Potential drug-like compounds reported in this study for their mechanism of action and interactions with SARS-CoV-2 genes using drug repurposing are resveratrol, erythromycin, chloramphenicol, indomethacin, ciclesonide, and PDE4 inhibitor. Ciclesonide appears to show the best results when docked with chosen viral proteins. Therefore, different proteins isolated from nucleotide mutations in the virus sequence can indicate distinct inducers for antibodies and are important in vaccine design.

Recent innovations and in-depth aspects of post-genome wide association study (Post-GWAS) to understand the genetic basis of complex phenotypes.

In the past decade, the high throughput and low cost of sequencing/genotyping approaches have led to the accumulation of a large amount of data from genome-wide association studies (GWASs). The first aim of this review is to highlight how post-GWAS analysis can be used make sense of the obtained associations. Novel directions for integrating GWAS results with other resources, such as somatic mutation, metabolite-transcript, and transcriptomic data, are also discussed; these approaches can help us move beyond each individual data point and provide valuable information about complex trait genetics. In addition, cross-phenotype association tests, when the loci detected by GWASs have significant associations with multiple traits, are reviewed to provide biologically informative results for use in real-time applications. This review also discusses the challenges of identifying interactions between genetic mutations (epistasis) and mutations of loci affecting more than one trait (pleiotropy) as underlying causes of cross-phenotype associations; these challenges can be overcome using post-GWAS analysis. Genetic similarities between phenotypes that can be revealed using post-GWAS analysis are also discussed. In summary, different methodologies of post-GWAS analysis are now available, enhancing the value of information obtained from GWAS results, and facilitating application in both humans and nonhuman species. However, precise methods still need to be developed to overcome challenges in the field and uncover the genetic underpinnings of complex traits.

Considering smoking status, coexpression network analysis of non-small cell lung cancer at different cancer stages, exhibits important genes and pathways.

Non-small cell lung cancer (NSCLC) is the most common subtype of lung cancer among smokers, nonsmokers, women, and young individuals. Tobacco smoking and different stages of the NSCLC have important roles in cancer evolution and require different treatments. Existence of poorly effective therapeutic options for the NSCLC brings special attention to targeted therapies by considering genetic alterations. In this study, we used RNA-Seq data to compare expression levels of RefSeq genes and to find some genes with similar expression levels. We utilized the "Weighted Gene Co-expression Network Analysis" method for three different datasets to create coexpressed genetic modules having relations with the smoking status and different stages of the NSCLC. Our results indicate seven important genetic modules having important associations with the smoking status and cancer stages. Based on investigated genetic modules and their biological explanation, we then identified 13 newly candidate genes and 7 novel transcription factors in association with the NSCLC, the smoking status, and cancer stages. We then examined those results using other datasets and explained our results biologically to illustrate some important genes in relation with the smoking status and metastatic stage of the NSCLC that can bring some crucial information about cancer evolution. Our genetic findings also can be used as some therapeutic targets for different clinical conditions of the NSCLC.

Novel putative drugs and key initiating genes for neurodegenerative disease determined using network-based genetic integrative analysis.

Understanding the genetic causes of neurodegenerative disease (ND) can be useful for their prevention and treatment. Among the genetic variations responsible for ND, heritable germline variants have been discovered in genome-wide association studies (GWAS), and nonheritable somatic mutations have been discovered in sequencing projects. Distinguishing the important initiating genes in ND and comparing the importance of heritable and nonheritable genetic variants for treating ND are important challenges. In this study, we analysed GWAS results, somatic mutations and drug targets of ND from large databanks by performing directed network-based analysis considering a randomised network hypothesis testing procedure. A disease-associated biological network was created in the context of the functional interactome, and the nonrandom topological characteristics of directed-edge classes were interpreted. Hierarchical network analysis indicated that drug targets tend to lie upstream of somatic mutations and germline variants. Furthermore, using directed path length information and biological explanations, we provide information on the most important genes in these created node classes and their associated drugs. Finally, we identified nine germline variants overlapping with drug targets for ND, seven somatic mutations close to drug targets from the hierarchical network analysis and six crucial genes in controlling other genes from the network analysis. Based on these findings, some drugs have been proposed for treating ND via drug repurposing. Our results provide new insights into the therapeutic actionability of GWAS results and somatic mutations for ND. The interesting properties of each node class and the existing relationships between them can broaden our knowledge of ND.

Candidate novel long noncoding RNAs, MicroRNAs and putative drugs for Parkinson's disease using a robust and efficient genome-wide association study.

Parkinson's disease (PD) is a neurodegenerative disorder with serious symptoms of which, are not clearly demonstrated at the beginning stages of the disease, making treatment challenging. Understanding the genetic causes of PD can be useful for determining its mechanisms and proposing treatments and preventive methods. For different populations with different genetic backgrounds and lifestyles, genome-wide association studies (GWASs) represent a crucial approach for genetic analysis. In this study, a robust and efficient GWAS without dimensionality reduction applied to evaluate heritability and genetic causes of PD in the German and US populations. The results show higher rate of PD heritability in the German population. Moreover, 25 significant SNPs have been determined, as well as five newly identified candidate genes associated with PD and some potential drug candidates. Analysis also reveals various long noncoding RNAs (lncRNAs), microRNAs and transcription-factor binding sites (TFBSs) with potential in the prevention and treatment of PD.