The complete mitochondrial genome of <i>Sarcophaga angarosinica</i> (Diptera: Sarcophagidae)

Sarcophaga (Liosarcophaga) angarosinica (Rohdendorf, 1937) (Diptera: Sarcophagidae) is a species of both medical and ecological significance. In this study, the complete mitochondrial genome (mitogenome) of S. angarosinica was sequenced and characterized. The mitogenome has a total length of 15,215 bp, including 13 protein-coding genes, two ribosomal RNAs, 22 transfer RNAs, and an adenine and thymine-rich region. This mitogenome comprises 39.5% adenine, 9.4% guanine, 14.4% cytosine, and 36.8% thymine. Phylogenetic analysis revealed that S. angarosinica is closely related to Sarcophaga similis. This study enriches the genetic data on S. angarosinica and will contribute to establishing the phylogenetic relationships among flesh flies.</p

DataSheet_1_A new framework for host-pathogen interaction research.zip

COVID-19 often manifests with different outcomes in different patients, highlighting the complexity of the host-pathogen interactions involved in manifestations of the disease at the molecular and cellular levels. In this paper, we propose a set of postulates and a framework for systematically understanding complex molecular host-pathogen interaction networks. Specifically, we first propose four host-pathogen interaction (HPI) postulates as the basis for understanding molecular and cellular host-pathogen interactions and their relations to disease outcomes. These four postulates cover the evolutionary dispositions involved in HPIs, the dynamic nature of HPI outcomes, roles that HPI components may occupy leading to such outcomes, and HPI checkpoints that are critical for specific disease outcomes. Based on these postulates, an HPI Postulate and Ontology (HPIPO) framework is proposed to apply interoperable ontologies to systematically model and represent various granular details and knowledge within the scope of the HPI postulates, in a way that will support AI-ready data standardization, sharing, integration, and analysis. As a demonstration, the HPI postulates and the HPIPO framework were applied to study COVID-19 with the Coronavirus Infectious Disease Ontology (CIDO), leading to a novel approach to rational design of drug/vaccine cocktails aimed at interrupting processes occurring at critical host-coronavirus interaction checkpoints. Furthermore, the host-coronavirus protein-protein interactions (PPIs) relevant to COVID-19 were predicted and evaluated based on prior knowledge of curated PPIs and domain-domain interactions, and how such studies can be further explored with the HPI postulates and the HPIPO framework is discussed.</p

Additional file 1 of Genome-wide association analyses identified novel susceptibility loci for pulmonary embolism among Han Chinese population

Additional file 1: Fig. S1. Regional association plot at genome-wide association study (GWAS) genome-wide significant loci. Fig. S2. Principal component analysis (PCA) plot of Han Chinese PE cohort. Fig. S3. FUMA Manhattan plot and QQ plot of genome-wide association study (GWAS) meta-analysis. Fig. S4. The transfection efficiency of cellular experiments for FABP2. Fig. S5. Low-density lipoprotein cholesterol (LDL-C) levels of patients with different genotypes of rs1799883. Fig. S6. Forest plot for the association of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and triglyceride (TG) with PE. Fig. S7. Ancestry-specific polygenic risk score (PRS) ROC plot. Fig. S8. Performance of different PRSVTE in the CURES testing set

Additional file 2 of Genome-wide association analyses identified novel susceptibility loci for pulmonary embolism among Han Chinese population

Additional file 2: Table S1. Independent genome-wide significant lead SNPs associated with pulmonary embolism (PE) in the discovery stage and replication stage. Table S2. The allele frequency of identified loci in 1000Genomes. Table S3. Replication of associations for the known loci in our cohort. Table S4. Association results for genes that were significant in FUMA gene-based analysis. Table S5. Polygenic risk score variants. Table S6. Polygenic risk score (PRS) quantile and odds ratio (OR)