Tzu‐Ming Chu

Geminiviruses are small DNA viruses that use plant replication machinery to amplify their genomes. Microarray analysis of the Arabidopsis (Arabidopsis thaliana) transcriptome in response to cabbage leaf curl virus (CaLCuV) infection uncovered 5,365 genes (false discovery rate <0.005) differentially expressed in infected rosette leaves at 12 d postinoculation. Data mining revealed that CaLCuV triggers a pathogen response via the salicylic acid pathway and induces expression of genes involved in programmed cell death, genotoxic stress, and DNA repair. CaLCuV also altered expression of cell cycle-associated genes, preferentially activating genes expressed during S and G2 and inhibiting genes active in G1 and M. A limited set of core cell cycle genes associated with cell cycle reentry, late G1, S, and early G2 had increased RNA levels, while core cell cycle genes linked to early G1 and late G2 had reduced transcripts. Fluorescence-activated cell sorting of nuclei from infected leaves revealed a depletion of the 4C population and an increase in 8C, 16C, and 32C nuclei. Infectivity studies of transgenic Arabidopsis showed that overexpression of CYCD3;1 or E2FB, both of which promote the mitotic cell cycle, strongly impaired CaLCuV infection. In contrast, overexpression of E2FA or E2FC, which can facilitate the endocycle, had no apparent effect. These results showed that geminiviruses and RNA viruses interface with the host pathogen response via a common mechanism, and that geminiviruses modulate plant cell cycle status by differentially impacting the CYCD/retinoblastoma-related protein/E2F regulatory network and facilitating progression into the endocycle.

BACKGROUND: Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. RESULTS: We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. CONCLUSIONS: We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.