Hemorrhagic fever with renal syndrome (HFRS) has been reported from greater than 20 provinces in China. The number of reported cases has increased markedly in recent years and surpassed 80,000 human cases in 1983. All of the cases reported before 1981 were from rural areas and were attributed to Apodemus rats. In 1981, outbreaks of cases associated with house rats were first reported. Cases associated with Apodemus agrarius were more severe than those associated with the house rat Rattus norvegicus. The rate of inapparent infection in the rural population of areas endemic for Apodemus-associated disease was lower than that of Rattus-associated urban disease. After the onset of the disease, IgG antibody levels increase rapidly, peak after one week, and persist for as long as 25 years. Lung tissues from 16 species of rodent, from two species of sorex, and from cats and weasels in the epidemic areas have been found to carry antigen. A. agrarius, Apodemus peninsulae, and R. norvegicus serve as the main reservoirs of HFRS in rural areas, forest areas, and urban areas, respectively.
The complete genome of a novel adult diarrhoea rotavirus strain J19 was cloned and sequenced using an improved single-primer sequence-independent method. The complete genome is 17 961 bp and is . Northern blot analysis and genomic sequence analysis indicated that segments 1-11 encode 11 viral proteins, respectively. Protein alignments with the corresponding proteins of J19 with B219, and groups A, B and C rotaviruses, produced higher per cent sequence identities to B219. Among groups A, B and C rotaviruses, 10 proteins from group B rotaviruses exhibited slightly higher amino acid sequence identity to the J19 proteins, but proteins of J19 showed low amino acid sequence identity with groups A and C rotaviruses. Construction of unrooted phylogenetic trees using a set of known proteins and representatives of three known rotavirus groups revealed that six structural proteins were positioned close to B219 and the basal nodes of groups A, B and C lineages, although with a preferred association with group B lineages. Phylogenetic analysis of the five non-structural proteins showed a similar trend. The results of the serological analysis, protein sequence analysis and phylogenetic analysis suggested that J19 would be a novel rotavirus strain with great significance to the evolution and origin of group B rotaviruses. INTRODUCTIONRotaviruses are important aetiological agents of disease in humans and animals. The viral genome is composed of 11 segments of double-stranded (ds) RNA that encode structural and non-structural proteins. The group A rotavirus strain SA11 genome, sequenced in 1990, is AUrich and has 18 555 bp (Both et al., 1984;Estes et al., 1984;Mitchell & Both, 1990). Its RNA segments encode six structural proteins (VP1, VP2, VP3, VP4, VP6 and VP7) and five non-structural proteins (NSP1, NSP2, NSP3, NSP4 and NSP5) (Estes, 2001). VP6 is the group antigen determinant, making up approximately 50 % of the viral protein (Estes et al., 1984;Estes & Cohen, 1989). The antigenic properties of VP6 are used to define seven antigenically different rotavirus groups, named groups A-G. Groups A, B and C rotaviruses infect humans, while other groups have been found only in animal species (Saif & Jiang, 1994). Group A rotaviruses cause severe diarrhoea in infants and young children, but they can also infect adults. Group C rotavirus infection occurs both in children and in adults, usually in sporadic cases or clustered outbreaks (Rodger et al., 1982;Caul et al., 1990;Jiang et al.,1995; Kuzuya et al., 1996; Nilsson et al., 2000;Adah et al., 2002;Chen et al., 2002;Ji et al., 2002;Schnagl et al., 2004).In China, large waterborne epidemics caused by the human group B rotavirus strain adult diarrhoea rotavirus (ADRV) infected thousands of people aged between 10 and 40 years of age in the 1980s (Hung et al., 1983(Hung et al., , 1984Chen et al., 1985Chen et al., , 1990 et al., 1987). The same rotavirus was implicated in gastroenteritis outbreaks in Beijing city in 1994 and in Shijiazhuang city in 1997. Partial genes of NADRV were clon...
Subpopulations of tumor cells characterized by mutation profiles may confer differential fitness and consequently influence prognosis of cancers. Understanding subclonal architecture has the potential to provide biological insight in tumor evolution and advance precision cancer treatment. Recent methods comprehensively integrate single nucleotide variants (SNVs) and copy number aberrations (CNAs) to reconstruct subclonal architecture using whole-genome or whole-exome sequencing (WGS, WES) data from bulk tumor samples. However, the commonly used Bayesian methods require a large amount of computational resources, a prior knowledge of the number of subclones, and extensive post-processing. Regularized likelihood modeling approach, never explored for subclonal reconstruction, can inherently address these drawbacks. We therefore propose a model-based method, Clonal structure identification through pair-wise Penalization, or CliP, for clustering subclonal mutations without prior knowledge or post-processing. The CliP model is applicable to genomic regions with or without CNAs. CliP demonstrates high accuracy in subclonal reconstruction through extensive simulation studies. Utilizing the well-established regularized likelihood framework, CliP takes only 16 hours to process WGS data from 2,778 tumor samples in the ICGC-PCAWG study, and 38 hours to process WES data from 9,564 tumor samples in the TCGA study. In summary, a penalized likelihood framework for subclonal reconstruction will help address intrinsic drawbacks of existing methods and expand the scope of computational analysis for cancer evolution in large cancer genomic studies. The associated software tool is freely available at: https://github.com/wwylab/CliP.
Cancers can vary greatly in their transcriptomes. In contrast to alterations in specific genes or pathways, differences in tumor cell total mRNA content have not been comprehensively assessed. Technical and analytical challenges have impeded examination of total mRNA expression at scale across cancers. To address this, we developed a model for quantifying tumor-specific total mRNA expression (TmS) from bulk sequencing data, which performs transcriptomic deconvolution while adjusting for mixed genomes. We used single-cell RNA sequencing data to demonstrate total mRNA expression as a feature of tumor phenotype. We estimated and validated TmS in 5,015 patients across 15 cancer types identifying significant inter-individual variability. At a pan-cancer level, high TmS is associated with increased risk of disease progression and death. Cancer type-specific patterns of genetic alterations, intra-tumor genetic heterogeneity, as well as pan-cancer trends in metabolic dysregulation and hypoxia contribute to TmS. Taken together, our results suggest that measuring cell-type specific total mRNA expression offers a broader perspective of tracking cancer transcriptomes, which has important biological and clinical implications.
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