Comparison of four commercial EBV DNA quantitative tests to a new test at an early stage of development.

BACKGROUND: Regular screening for Epstein-Barr virus (EBV) DNA using quantitative RT-PCR is recommended for early intervention in at-risk patients. Harmonization of quantitative RT-PCR assays is critical to avoid misinterpretation of results. Here, we compare quantitative results of the cobas® EBV assay to four commercial RT-qPCR assays. METHODS: The cobas EBV, EBV R-Gene, artus EBV RG PCR, RealStar EBV PCR kit 2.0 and Abbott EBV RealTime assays were compared for analytic performance using a 10-fold dilution series of EBV reference material, normalized to the WHO standard. For clinical performance, their quantitative results were compared using anonymized, leftover EBV-DNA-positive EDTA plasma samples. RESULTS: For analytic accuracy, the cobas EBV deviated -0.0097 log10 from target values. The other tests showed deviations between 0.0037 and -0.12 log10. For clinical performance, accuracy and linearity of cobas EBV data from both study sites were excellent. Bland-Altman bias and Deming regression analyses showed statistical correlation for cobas EBV to both EBV R-Gene and Abbott RealTime assays but an offset of cobas EBV to artus EBV RG PCR and RealStar EBV PCR kit 2.0. CONCLUSION: The cobas EBV showed the closest correlation to the reference material, followed closely by EBV R-Gene and Abbott EBV RealTime. Values obtained are stated in IU/mL, facilitating comparison across testing sites and potentially improving utilization of guidelines for diagnosis, monitoring, and treatment of patients.

Global regulatory architecture of human, mouse and rat tissue transcriptomes.

BACKGROUND: Predicting molecular responses in human by extrapolating results from model organisms requires a precise understanding of the architecture and regulation of biological mechanisms across species. RESULTS: Here, we present a large-scale comparative analysis of organ and tissue transcriptomes involving the three mammalian species human, mouse and rat. To this end, we created a unique, highly standardized compendium of tissue expression. Representative tissue specific datasets were aggregated from more than 33,900 Affymetrix expression microarrays. For each organism, we created two expression datasets covering over 55 distinct tissue types with curated data from two independent microarray platforms. Principal component analysis (PCA) revealed that the tissue-specific architecture of transcriptomes is highly conserved between human, mouse and rat. Moreover, tissues with related biological function clustered tightly together, even if the underlying data originated from different labs and experimental settings. Overall, the expression variance caused by tissue type was approximately 10 times higher than the variance caused by perturbations or diseases, except for a subset of cancers and chemicals. Pairs of gene orthologs exhibited higher expression correlation between mouse and rat than with human. Finally, we show evidence that tissue expression profiles, if combined with sequence similarity, can improve the correct assignment of functionally related homologs across species. CONCLUSION: The results demonstrate that tissue-specific regulation is the main determinant of transcriptome composition and is highly conserved across mammalian species.