Prevalence and determinants of hepatitis B co-infection among a United States Veterans Administration cohort with hepatitis C

Background and aim. Hepatitis B virus (HBV) and hepatitis C virus (HCV) co-infection is associated with increased risk of cirrhosis, decompensation, hepatocellular carcinoma, and death. Yet, there is sparse epidemiologic data on co-infection in the United States. Therefore, the aim of this study was to determine the prevalence and determinants of HBV co-infection in a large United States population of HCV patients. ^ Methods. The National Veterans Affairs HCV Clinical Case Registry was used to identify patients tested for HCV during 1997–2005. HCV exposure was defined as two positive HCV tests (antibody, RNA or genotype) or one positive test combined with an ICD-9 code for HCV. HCV infection was defined as only a positive HCV RNA or genotype. HBV exposure was defined as a positive test for hepatitis B core antibodies, hepatitis B surface antigen, HBV DNA, hepatitis Be antigen, or hepatitis Be antibody. HBV infection was defined as only a positive test for hepatitis B surface antigen, HBV DNA, or hepatitis Be antigen within one year before or after the HCV index date. The prevalence of exposure to HBV in patients with HCV exposure and the prevalence of HBV infection in patients with HCV infection were determined. Multivariable logistic regression was used to identify demographic and clinical determinants of co-infection. ^ Results. Among 168,239 patients with HCV exposure, 58,415 patients had HBV exposure for a prevalence of 34.7% (95% CI 34.5–35.0). Among 102,971 patients with HCV infection, 1,431 patients had HBV co-infection for a prevalence of 1.4% (95% CI 1.3–1.5). The independent determinants for an increased risk of HBV co-infection were male sex, positive HIV status, a history of hemophilia, sickle cell anemia or thalassemia, history of blood transfusion, cocaine and other drug use. Age >50 years and Hispanic ethnicity were associated with a decreased risk of HBV co-infection. ^ Conclusions. This is the largest cohort study in the United States on the prevalence of HBV co-infection. Among veterans with HCV, exposure to HBV is common (∼35%), but HBV co-infection is relatively low (1.4%). There is an increased risk of co-infection with younger age, male sex, HIV, and drug use, with decreased risk in Hispanics.^

New methods for quantification and analysis of quantitative real-time polymerase chain reaction data

Quantitative real-time polymerase chain reaction (qPCR) is a sensitive gene quantitation method that has been widely used in the biological and biomedical fields. The currently used methods for PCR data analysis, including the threshold cycle (CT) method, linear and non-linear model fitting methods, all require subtracting background fluorescence. However, the removal of background fluorescence is usually inaccurate, and therefore can distort results. Here, we propose a new method, the taking-difference linear regression method, to overcome this limitation. Briefly, for each two consecutive PCR cycles, we subtracted the fluorescence in the former cycle from that in the later cycle, transforming the n cycle raw data into n-1 cycle data. Then linear regression was applied to the natural logarithm of the transformed data. Finally, amplification efficiencies and the initial DNA molecular numbers were calculated for each PCR run. To evaluate this new method, we compared it in terms of accuracy and precision with the original linear regression method with three background corrections, being the mean of cycles 1-3, the mean of cycles 3-7, and the minimum. Three criteria, including threshold identification, max R2, and max slope, were employed to search for target data points. Considering that PCR data are time series data, we also applied linear mixed models. Collectively, when the threshold identification criterion was applied and when the linear mixed model was adopted, the taking-difference linear regression method was superior as it gave an accurate estimation of initial DNA amount and a reasonable estimation of PCR amplification efficiencies. When the criteria of max R2 and max slope were used, the original linear regression method gave an accurate estimation of initial DNA amount. Overall, the taking-difference linear regression method avoids the error in subtracting an unknown background and thus it is theoretically more accurate and reliable. This method is easy to perform and the taking-difference strategy can be extended to all current methods for qPCR data analysis.^

RNA-SEQUENCING APPLICATIONS: GENE EXPRESSION QUANTIFICATION AND METHYLATOR PHENOTYPE IDENTIFICATION

My dissertation focuses on two aspects of RNA sequencing technology. The first is the methodology for modeling the overdispersion inherent in RNA-seq data for differential expression analysis. This aspect is addressed in three sections. The second aspect is the application of RNA-seq data to identify the CpG island methylator phenotype (CIMP) by integrating datasets of mRNA expression level and DNA methylation status. Section 1: The cost of DNA sequencing has reduced dramatically in the past decade. Consequently, genomic research increasingly depends on sequencing technology. However it remains elusive how the sequencing capacity influences the accuracy of mRNA expression measurement. We observe that accuracy improves along with the increasing sequencing depth. To model the overdispersion, we use the beta-binomial distribution with a new parameter indicating the dependency between overdispersion and sequencing depth. Our modified beta-binomial model performs better than the binomial or the pure beta-binomial model with a lower false discovery rate. Section 2: Although a number of methods have been proposed in order to accurately analyze differential RNA expression on the gene level, modeling on the base pair level is required. Here, we find that the overdispersion rate decreases as the sequencing depth increases on the base pair level. Also, we propose four models and compare them with each other. As expected, our beta binomial model with a dynamic overdispersion rate is shown to be superior. Section 3: We investigate biases in RNA-seq by exploring the measurement of the external control, spike-in RNA. This study is based on two datasets with spike-in controls obtained from a recent study. We observe an undiscovered bias in the measurement of the spike-in transcripts that arises from the influence of the sample transcripts in RNA-seq. Also, we find that this influence is related to the local sequence of the random hexamer that is used in priming. We suggest a model of the inequality between samples and to correct this type of bias. Section 4: The expression of a gene can be turned off when its promoter is highly methylated. Several studies have reported that a clear threshold effect exists in gene silencing that is mediated by DNA methylation. It is reasonable to assume the thresholds are specific for each gene. It is also intriguing to investigate genes that are largely controlled by DNA methylation. These genes are called “L-shaped” genes. We develop a method to determine the DNA methylation threshold and identify a new CIMP of BRCA. In conclusion, we provide a detailed understanding of the relationship between the overdispersion rate and sequencing depth. And we reveal a new bias in RNA-seq and provide a detailed understanding of the relationship between this new bias and the local sequence. Also we develop a powerful method to dichotomize methylation status and consequently we identify a new CIMP of breast cancer with a distinct classification of molecular characteristics and clinical features.