The human hepatitis B virus （HBV） and the duck hepatitis B virus （DHBV） share several fundamental features. Both viruses have a partially double-stranded DNA genome that is replicated via a RNA intermediate and th...The human hepatitis B virus （HBV） and the duck hepatitis B virus （DHBV） share several fundamental features. Both viruses have a partially double-stranded DNA genome that is replicated via a RNA intermediate and the coding open reading frames （ORFs） overlap extensively. In addition, the genomic and structural organization, as well as replication and biological characteristics, are very similar in both viruses. Host of the key features of hepadnaviral infection were first discovered in the DHBV model system and subsequently confirmed for HBV. There are, however, several differences between human HBV and DHBV. This review will focus on the molecular and cellular biology, evolution, and host adaptation of the avian hepatitis B viruses with particular emphasis on DHBV as a model system.展开更多
Inflammatory bowel disease(IBD),including Crohn’s disease(CD)and ulcerative colitis(UC),is a chronic inflammatory disease of the digestive tract with increasing prevalence globally.Although venous thromboembolism(VTE...Inflammatory bowel disease(IBD),including Crohn’s disease(CD)and ulcerative colitis(UC),is a chronic inflammatory disease of the digestive tract with increasing prevalence globally.Although venous thromboembolism(VTE)is a major complication in IBD patients,it is often underappreciated with limited tools for risk stratification.AIM To estimate the proportion of VTE among IBD patients and assess genetic risk factors(monogenic and polygenic)for VTE.METHODS Incident VTE was followed for 8465 IBD patients in the UK Biobank(UKB).The associations of VTE with F5 factor V leiden(FVL)mutation,F2 G20210A prothrombin gene mutation(PGM),and polygenic score(PGS003332)were tested using Cox hazards regression analysis,adjusting for age at IBD diagnosis,gender,and genetic background(top 10 principal components).The performance of genetic risk factors for discriminating VTE diagnosis was estimated using the area under the receiver operating characteristic curve(AUC).RESULTS The overall proportion of incident VTE was 4.70%in IBD patients and was similar for CD(4.46%),UC(4.49%),and unclassified(6.42%),and comparable to that of cancer patients(4.66%)who are well-known at increased risk for VTE.Mutation carriers of F5/F2 had a significantly increased risk for VTE compared to non-mutation carriers,hazard ratio(HR)was 1.94,95%confidence interval(CI):1.42-2.65.In contrast,patients with the top PGS decile had a considerably higher risk for VTE compared to those with intermediate scores(middle 8 deciles),HR was 2.06(95%CI:1.57-2.71).The AUC for differentiating VTE diagnosis was 0.64(95%CI:0.61-0.67),0.68(95%CI:0.66-0.71),and 0.69(95%CI:0.66-0.71),respectively,for F5/F2 mutation carriers,PGS,and combined.CONCLUSION Similar to cancer patients,VTE complications are common in IBD patients.PGS provides more informative risk information than F5/F2 mutations(FVL and PGM)for personalized thromboprophylaxis.展开更多
Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw...Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent “noise” within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV （gene by variety） interaction using the adjusted unbiased prediction （AUP） method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.展开更多
基金Supported by the Freie und Hansestadt Hamburg and the Bundesministcrium für Gesundheit und Soziale Sicherung grants from DFG and by the German Competence Network for Viral Hepatitis （Hop-Net）, funded by the German Ministry of Education and Research （BMBF）, Grant No. TFI3. IWe apologize to those authors whose work we could not cite directly due to space limitations. The authors are indebted to Claudia Franke （Heinrich-Pette-Institute, Hamburg, Germany） for providing the picture of core protein phosphorylation.
文摘The human hepatitis B virus （HBV） and the duck hepatitis B virus （DHBV） share several fundamental features. Both viruses have a partially double-stranded DNA genome that is replicated via a RNA intermediate and the coding open reading frames （ORFs） overlap extensively. In addition, the genomic and structural organization, as well as replication and biological characteristics, are very similar in both viruses. Host of the key features of hepadnaviral infection were first discovered in the DHBV model system and subsequently confirmed for HBV. There are, however, several differences between human HBV and DHBV. This review will focus on the molecular and cellular biology, evolution, and host adaptation of the avian hepatitis B viruses with particular emphasis on DHBV as a model system.
基金The UK Biobank was approved by North West-Haydock Research Ethics Committee(REC reference:16/NW/0274,IRAS project ID:200778).
文摘Inflammatory bowel disease(IBD),including Crohn’s disease(CD)and ulcerative colitis(UC),is a chronic inflammatory disease of the digestive tract with increasing prevalence globally.Although venous thromboembolism(VTE)is a major complication in IBD patients,it is often underappreciated with limited tools for risk stratification.AIM To estimate the proportion of VTE among IBD patients and assess genetic risk factors(monogenic and polygenic)for VTE.METHODS Incident VTE was followed for 8465 IBD patients in the UK Biobank(UKB).The associations of VTE with F5 factor V leiden(FVL)mutation,F2 G20210A prothrombin gene mutation(PGM),and polygenic score(PGS003332)were tested using Cox hazards regression analysis,adjusting for age at IBD diagnosis,gender,and genetic background(top 10 principal components).The performance of genetic risk factors for discriminating VTE diagnosis was estimated using the area under the receiver operating characteristic curve(AUC).RESULTS The overall proportion of incident VTE was 4.70%in IBD patients and was similar for CD(4.46%),UC(4.49%),and unclassified(6.42%),and comparable to that of cancer patients(4.66%)who are well-known at increased risk for VTE.Mutation carriers of F5/F2 had a significantly increased risk for VTE compared to non-mutation carriers,hazard ratio(HR)was 1.94,95%confidence interval(CI):1.42-2.65.In contrast,patients with the top PGS decile had a considerably higher risk for VTE compared to those with intermediate scores(middle 8 deciles),HR was 2.06(95%CI:1.57-2.71).The AUC for differentiating VTE diagnosis was 0.64(95%CI:0.61-0.67),0.68(95%CI:0.66-0.71),and 0.69(95%CI:0.66-0.71),respectively,for F5/F2 mutation carriers,PGS,and combined.CONCLUSION Similar to cancer patients,VTE complications are common in IBD patients.PGS provides more informative risk information than F5/F2 mutations(FVL and PGM)for personalized thromboprophylaxis.
基金This research was partially supported by the National Natural Science Foundation of China(No.30470916).
文摘Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent “noise” within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV （gene by variety） interaction using the adjusted unbiased prediction （AUP） method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.
