We report the sequence and analysis of the 814-megabase genome of the sea urchin Strongylocentrotus purpuratus , a model for developmental and systems biology. The sequencing strategy combined whole-genome shotgun and bacterial artificial chromosome (BAC) sequences. This use of BAC clones, aided by a pooling strategy, overcame difficulties associated with high heterozygosity of the genome. The genome encodes about 23,300 genes, including many previously thought to be vertebrate innovations or known only outside the deuterostomes. This echinoderm genome provides an evolutionary outgroup for the chordates and yields insights into the evolution of deuterostomes.

Sequence variation in regulatory DNA alters gene expression and shapes genetically complex traits. However, the identification of individual, causal regulatory variants is challenging. Here, we used a massively parallel reporter assay to measure the cis-regulatory consequences of 5832 natural DNA variants in the promoters of 2503 genes in the yeast Saccharomyces cerevisiae. We identified 451 causal variants, which underlie genetic loci known to affect gene expression. Several promoters harbored multiple causal variants. In five promoters, pairs of variants showed non-additive, epistatic interactions. Causal variants were enriched at conserved nucleotides, tended to have low derived allele frequency, and were depleted from promoters of essential genes, which is consistent with the action of negative selection. Causal variants were also enriched for alterations in transcription factor binding sites. Models integrating these features provided modest, but statistically significant, ability to predict causal variants. This work revealed a complex molecular basis for cis-acting regulatory variation.

One of the main challenges in personalized medicine is to establish and apply a large number of variants from genomic databases into clinical diagnostics and further facilitate genome-driven drug repurposing. By utilizing biological chronic hepatitis B infection (CHB) risk genes, our study proposed a systematic approach to use genomic variants to drive drug repurposing for CHB. The genomic variants were retrieved from the Genome-Wide Association Study (GWAS) and Phenome-Wide Association Study (PheWAS) databases. Then, the biological CHB risk genes crucial for CHB progression were prioritized based on the scoring system devised with five strict functional annotation criteria. A score of ≥ 2 were categorized as the biological CHB risk genes and further shed light on drug target genes for CHB treatments. Overlapping druggable targets were identified using two drug databases (DrugBank and Drug-Gene Interaction Database (DGIdb)). A total of 44 biological CHB risk genes were screened based on the scoring system from five functional annotation criteria. Interestingly, we found 6 druggable targets that overlapped with 18 drugs with status of undergoing clinical trials for CHB, and 9 druggable targets that overlapped with 20 drugs undergoing preclinical investigations for CHB. Eight druggable targets were identified, overlapping with 25 drugs that can potentially be repurposed for CHB. Notably, CD40 and HLA-DPB1 were identified as promising targets for CHB drug repurposing based on the target scores. Through the integration of genomic variants and a bioinformatic approach, our findings suggested the plausibility of CHB genomic variant-driven drug repurposing for CHB.

Multiple sclerosis (MS) is a chronic autoimmune disease in the central nervous system (CNS) marked by inflammation, demyelination, and axonal loss. Currently available MS medication is limited, thereby calling for a strategy to accelerate new drug discovery. One of the strategies to discover new drugs is to utilize old drugs for new indications, an approach known as drug repurposing. Herein, we first identified 421 MS-associated SNPs from the Genome-Wide Association Study (GWAS) catalog (p-value < 5 × 10⁻⁸), and a total of 427 risk genes associated with MS using HaploReg version 4.1 under the criterion r² > 0.8. MS risk genes were then prioritized using bioinformatics analysis to identify biological MS risk genes. The prioritization was performed based on six defined categories of functional annotations, namely missense mutation, cis-expression quantitative trait locus (cis-eQTL), molecular pathway analysis, protein-protein interaction (PPI), genes overlap with knockout mouse phenotype, and primary immunodeficiency (PID). A total of 144 biological MS risk genes were found and mapped into 194 genes within an expanded PPI network. According to the DrugBank and the Therapeutic Target Database, 27 genes within the list targeted by 68 new candidate drugs were identified. Importantly, the power of our approach is confirmed with the identification of a known approved drug (dimethyl fumarate) for MS. Based on additional data from ClinicalTrials.gov, eight drugs targeting eight distinct genes are prioritized with clinical evidence for MS disease treatment. Notably, CD80 and CD86 pathways are promising targets for MS drug repurposing. Using in silico drug repurposing, we identified belatacept as a promising MS drug candidate. Overall, this study emphasized the integration of functional genomic variants and bioinformatic-based approach that reveal important biological insights for MS and drive drug repurposing efforts for the treatment of this devastating disease.

Introduction: Atherosclerosis (AS) is a major risk factor for cardiovascular disease (CVD), which is the leading cause of death worldwide. Genetic factors are an integral factor in the cause of AS. This study aims to correlate epidemiological data, prevalence and mortality of AS worldwide with Single Nucleotide Polymorphisms (SNPs) associated with susceptibility and severity in different populations. Hence, utilized the gene variants to discover potential drug targets using bioinformatics. Material and methods: This study used a secondary data-driven bioinformatics approach to analyze the correlation between genetic variants associated with AS and epidemiological data from different regions of the world. Genetic data were obtained from the GWAS Catalog, while epidemiological data were collected from WHO and Our World in Data. The analysis was performed by fi ltering SNPs based on several criteria, namely the missense variant category, signifi cant p-value < 10-5, and prediction of variant impact using PolyPhen (Possibly Damaging) and SIFT (Deleterious). The correlation between allele frequency and AS prevalence and mortality was analyzed using the Pearson test. Results: The results of the study obtained three missense variants rs11466653 in TLR10, rs2296172 in MACF1 and rs6025 in F5. at rs11466653 has a role in the pathogenesis of AS with analysis using SNPnexus which shows the possibility of damaging (PolyPhen) and deleterious (SIFT) properties in addition to the results of the correlation test signifi cant to the prevalence and mortality of global AS (p < 0.05). Due to the challenge of drugging all potential target genes, our study was only able to identify the F5 gene as a viable target. The results of the identifi cation of candidate drugs targeting the F5 gene indicate that copper has the potential to be used in the treatment of AS. This assessment is based on a scoring analysis where the drug obtained a score of 6 indicating excellent quality or suitability in the context of drug repurposing for AS treatment. Conclusion: The fi ndings in this study will encourage efforts to improve AS diagnosis and early treatment, as well as increase public awareness of the importance of genetic factors and lifestyle in preventing this disease. This study can also be a reference for further research focusing on genetic intervention and development of personalized therapy for AS. Therefore it can provide broader benefi ts for global public health.

Background/Aim: Leukaemia is a malignant disease of blood cells found in the bone marrow, which can be divided into acute lymphocytic leukaemia and myelocytic leukaemia. Current management of acute leukaemia still uses chemo therapy as the main therapy but has many side effects, therefore a new approach is needed to identify genetic factors involved in leukaemia. The aim of this study was to investigate gene variations that have potential pathogenic properties in leukaemia. Methods: This study used genome-wide association study (GWAS) data obtained from the National Human Genome Research Institute (NHGRI) to search for genomic variants associated with leukaemia. The data was then screened using SNPnexus to detect potentially protein-damaging variants. Furthermore, the gene expression of these variants was analysed using the GTEx portal. Results: Of the 2115 genomic variants found, four were deleterious, namely rs12140153, rs140386498, rs757110 and rs2066827, representing four different genes, namely PATJ, MINDY1, ABCC8 and CDKN1B. Alterations in the expression of PATJ, MINDY1, CDKN1B and ABCC8 genes affect the brain and leukaemia development. PATJ maintains brain cell integrity, MINDY1 regulates gene expression, CDKN1B controls the cell cycle and ABCC8 regulates glucose levels. Their deregulation is associated with neurological dysfunction and leukaemia. Variation in allele frequencies showed differences between continents, with rs757110 and rs2066827 having higher expression than rs12140153 and rs140386498. Variant gene expression also varied between tissues, with rs757110 and rs2066827 showing higher expression than rs12140153 and rs140386498. Conclusion: This study successfully identified four genomic variants by harnessing a genomic and bioinformatic database, which are associated with leukemia and demonstrated variations in gene distribution and expression across different populations and tissues.