After the outbreak of the severe acute respiratory syndrome (SARS) in the world in 2003, human coronaviruses (HCoVs) have been reported as pathogens that cause severe symptoms in respiratory tract infections. Recently, a new emerged HCoV isolated from the respiratory epithelium of unexplained pneumonia patients in the Wuhan seafood market caused a major disease outbreak and has been named the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This virus causes acute lung symptoms, leading to a condition that has been named as “coronavirus disease 2019” (COVID-19). The emergence of SARS-CoV-2 and of SARS-CoV caused widespread fear and concern and has threatened global health security. There are some similarities and differences in the epidemiology and clinical features between these two viruses and diseases that are caused by these viruses. The goal of this work is to systematically review and compare between SARS-CoV and SARS-CoV-2 in the context of their virus incubation, originations, diagnosis and treatment methods, genomic and proteomic sequences, and pathogenic mechanisms.

Background: Genetic factors affecting multiple biomedical traits in mice have been identified when GWAS data that measured responses in panels of inbred mouse strains was analyzed using haplotype-based computational genetic mapping (HBCGM). Although this method was previously used to analyze one dataset at a time; but now, a vast amount of mouse phenotypic data is now publicly available, which could lead to many more genetic discoveries. Results: HBCGM and a whole genome SNP map covering 53 inbred strains was used to analyze 8462 publicly available datasets of biomedical responses (1.52M individual datapoints) measured in panels of inbred mouse strains. As proof of concept, causative genetic factors affecting susceptibility for eye, metabolic and infectious diseases were identified when structured automated methods were used to analyze the output. One analysis identified a novel genetic effector mechanism; allelic differences within the mitochondrial targeting sequence affected the subcellular localization of a protein. We also found allelic differences within the mitochondrial targeting sequences of many murine and human proteins, and these could affect a wide range of biomedical phenotypes. Implications: These initial results indicate that genetic factors affecting biomedical responses could be identified through analysis of very large datasets, and they provide an early indication of how this type of ‘ augmented intelligence ’ can facilitate genetic discovery.

Background: Uterine carcinosarcoma (UCS) is a type of rare and aggressive tumor. The standard treatment for UCS involves surgical treatment followed by radiochemotherapy. Clinical outcomes of UCS patients are poor due to high metastasis and relapse rate. Therefore, new targeted therapy strategies for UCS are needed. Because UCS is highly heterogenous, it is critical to identify and develop prognostic biomarkers to distinguish molecular subtypes of UCS for better treatment guidance. Methods: Using gene expression profiles and clinical follow-up data, we developed an online consensus survival analysis tool named OSucs. This web tool allows researchers to conveniently analyze the prognostic abilities of candidate genes in UCS. Results: To test the reliability of this server, we analyzed five previously reported prognostic biomarkers, all of which showed significant prognostic impacts. In addition, ETV4 (ETS variant transcription factor 4), ANGPTL4 (Angiopoietin-like protein 4), HIST1H1C (Histone cluster 1 H1 family member c) and CTSV (Cathepsin V) showed prognostic potential in a molecular subtype-specific manner. Conclusion: We built a platform for researchers to analyze if genes have prognostic potentials in UCS.

Myxofibrosarcoma is a complex genetic disease with poor prognosis. However, more effective biomarkers that forebode poor prognosis in Myxofibrosarcoma remain to be determined. Herein, utilizing gene expression profiling data and clinical follow-up data of Myxofibrosarcoma cases in three independent cohorts with a total of 128 Myxofibrosarcoma samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we constructed an easy-to-use web tool, named Online consensus Survival analysis for Myxofibrosarcoma (OSmfs) to analyze the prognostic value of certain genes. Through retrieving the database, users generate a Kaplan–Meier plot with log-rank test and hazard ratio (HR) to assess prognostic-related genes or discover novel Myxofibrosarcoma prognostic biomarkers. The effectiveness and availability of OSmfs were validated using genes in ever reports predicting the prognosis of Myxofibrosarcoma patients. Furthermore, utilizing the cox analysis data and transcriptome data establishing OSmfs, seven genes were selected and considered as more potentially prognostic biomarkers through overlapping and ROC analysis. In conclusion, OSmfs is a promising web tool to evaluate the prognostic potency and reliability of genes in Myxofibrosarcoma, which may significantly contribute to the enrichment of novelly potential prognostic biomarkers and therapeutic targets for Myxofibrosarcoma.

Rationale: Mutation in human arteriovenous malformation (AVM) causative genes in a fraction of endothelial cells (ECs) causes AVMs in mice. It is unclear how a small number of mutant ECs can lead to AVM formation. Objective: To understand how a fraction of mutant ECs causes AVM, we tested the following hypotheses: (1) activin receptor-like kinase 1 ( Alk1 or Acvlr1 ) mutant brain ECs undergo clonal expansion upon angiogenic stimulation, (2) Alk1 mutant ECs display growth advantage, (3) the burden of Alk 1 mutant ECs correlates with AVM severity, and (4) Alk1 mutant bone marrow (BM) derived ECs alone is sufficient to cause AVM. Methods and Results: We used Pdgfb iCreER; Alk1 ^f/f ;confetti ^+/− mice which express an EC-specific tamoxifen (TM)-inducible Cre recombinase, a Cre-regulated confetti transgene, and Alk1 floxed alleles. Brain AVMs were induced by direct brain injection of an adeno-associated viral vector expressing vascular endothelial growth factor (AAV-VEGF) followed with intra-peritoneal injection of TM two weeks later. Color-predominance of confetti reporter in AVMs compared to control brain ECs suggested that clonal expansion was associated with AVM development. We treated Pdgfb iCreER; Alk1 ^f/f with different doses of TM to create a mosaic of wild-type (WT) and mutant ECs and found that equal numbers of Alk1 ⁺ and Alk1 ⁻ ECs were proliferating. Increase of TM dose increased the number of Alk1 ⁻ ECs, the abnormal vessels in brain AVMs, the number of arteriovenous shunts in the intestines, and mouse mortality. To test if mutation of Alk1 in BM-derived ECs can cause brain AVM, we transplanted WT mice with BM of Pdgfb iCreER; Alk1 ^f/f mice. After AAV-VEGF and TM treatment, these mice developed AVMs in their brains and arteriovenous shunts in their intestines. Conclusion: Clonal expansion of Alk1 mutant ECs could partly explain why a fraction of mutant ECs causes AVM. Mutation of AVM causal genes in BM-derived ECs is sufficient to cause AVM formation.