Analysis of Zika Virus gene expression by Ribosome profiling and RNA sequencing
ABSTRACT: Ribosome profiling (Ribo-Seq) (maps positions of translating ribosomes on the transcriptome) and RNA-Seq (quantifies the transcriptome) analysis of African green monkey (Vero E6) cells and Aedes albopictus (C6/36) cells infected with Zika Virus (ZIKV) strain PE243. Cells were harvested at 24 h post infection (p.i.) and Ribo-Seq and RNA-Seq libraries were prepared and deep sequenced.
Project description:Ribosome profiling (Ribo-Seq) (maps positions of translating ribosomes on the transcriptome) and RNA-Seq (quantifies the transcriptome) analysis of chicken (Gallus gallus) cells infected with Infectious Bronchitis Virus (IBV) strains Beaudette and M41.
Project description:Ribosome profiling (Ribo-Seq) (maps positions of translating ribosomes on the transcriptome) and RNA-Seq (quantifies the transcriptome) analysis of Rattus norvegicus cells infected with Moloney Murine Leukemia Virus (Mo-MuLV).
Project description:The effects of RyhB expression were examined by Ribo-seq and RNA-seq after 10 min to avoid indirect effects. Expression of RyhB was induced by arabinose from cells carrying pBAD-ryhB plasmid. The RyhB expression was confirmed by real-time PCR. As a control, cells with vector pNM12 were grown and induced. The cells were pulverized and total mRNAs were extracted from the pulverized cells and processed for Ribo-seq and RNA-seq.
Project description:Ribosome profiling (RiboSeq) (maps positions of translating ribosomes on the transcriptome) and RNASeq (quantifies the transcriptome) analysis of murine 17 clone 1 (17Cl-1) cells infected with Murine coronavirus strain A59 (MHV-A59). Samples comprise 1 and 8 h mocks, 1, 2.5, 5 and 8 h post infection timecourse, for each of RiboSeq with cycloheximide (CHX), RiboSeq with harringtonine (HAR), and RNASeq, performed in duplicate (6 x 3 x 2 libraries); RiboSeq CHX, RiboSeq HAR and RNASeq at 1 h post infection for high multiplicity of infection (3 libraries); and 1 long-read library for 5 h post infection RiboSeq CHX to test for larger-than-normal ribosome footprints.
Project description:Ribosome profiling (Ribo-Seq) and RNA-Seq analysis of eEF3 depletion in yeast (Saccharomyces cerevisiae). eEF3 depletion was induced by methionine in a modified strain where the native promoter was replaced by methionine repressible MET25 promoter. Conditional depletion enables us to study global effects of an essential gene.
Project description:Methods: RNA-sequencing was performed on matched samples obtained across several different gene expression measurement methods including: (a) fresh-frozen (FF) RNA samples by mRNA-seq, Ribo-zero and DSN and (b) FFPE samples by Ribo-zero and DSN. We also assayed the matched samples with Agilent microarray. RNA-seq data was compared on the rRNA-removal efficiency, genome profile, library complexity, coverage uniformity and quantitative cosinstency across protocols and with microarray data. Results: Compared to mRNA-seq, Ribo-zero provides equivalent percentage of rRNA component, genome-based mapped reads, and consistent quantification of transcripts. Moreover, Ribo-zero and DSN protocols achieve concordant transcript profiling in FFPE samples, and provide substantially more information on non-poly(A) RNA, which cannot be captured by mRNA-seq. Therefore, our study provides evidence that RNA-sequencing can generate accurate and reproducible transcript quantification using FFPE tissues. mRNA profile of 11 breast tumors were assayed by Agilent microarray, and by RNA-sequencing on libraries including: (a) fresh-frozen (FF) RNA samples by mRNA-seq, Ribo-zero and DSN and (b) FFPE samples by Ribo-zero and DSN, using Illunia HiSeq2000 2x50bp. RNA-Seq raw data is to be made available through dbGaP (controlled access) due to patient privacy concerns: http://www.ncbi.nlm.nih.gov/gap/?term=phs000676
Project description:Ribosome profiling is a widespread tool for studying translational dynamics in human cells. Its central assumption is that ribosome footprint density on a transcript quantitatively reflects protein synthesis. Here, we test this assumption using pulsed-SILAC (pSILAC) high-accuracy targeted proteomics. We focus on multiple myeloma cells exposed to bortezomib, a first-line chemotherapy and proteasome inhibitor. In the absence of drug effects, we found that direct measurement of protein synthesis by pSILAC correlated well with indirect measurement of synthesis from ribosome footprint density. This correlation, however, broke down under bortezomib-induced stress. By developing a statistical model integrating longitudinal proteomic and mRNA-seq measurements, we found that proteomics could directly detect global alterations in translational rate caused by bortezomib; these changes are not detectable by ribosomal profiling alone. Further, by incorporating pSILAC data into a gene expression model, we predict cell-stress specific proteome remodeling events. These results demonstrate that pSILAC provides an important complement to ribosome profiling in measuring proteome dynamics. Timecourse experiment with six points over 48hr after bortezomib exposure in MM.1S myeloma cells. mRNA-seq and ribosome profiling data at each time point.