Global profiling of alternative RNA splicing events provides insights into molecular differences between various types of hepatocellular carcinoma

Protein families encoded by transcripts that are differentially spliced in various types of HCC. Table S2. Bioinformatical prediction of functional changes caused by some of ASEs identified. Table S3. List of tumor suppressors for which AS is dysregulated in various types of HCC. Table S4. List of oncogenes for which AS is dysregulated in various types of HCC. Table S5. List of kinases for which AS is dysregulated in various types of HCC. Table S6. List of transcription factors for which AS is dysregulated in various types of HCC. Table S7. List of genes for which AS is dysregulated in all types of HCC. Table S8. List of genes uniquely dysregulated in HBV-associated HCC. Table S9. List of genes uniquely dysregulated in HCV-associated HCC. Table S10. List of genes uniquely dysregulated in HBV&HCV-associated HCC. Table S11. List of genes uniquely dysregulated in virus-free HCC. Figure S1. Characterization of splicing mysregulation in HCC. Figure S2. Characterization of ASEs that are modified in HBV- and HCV-associated HCC. Figure S3. AS modifications in transcripts encoded by kinases and transcriptions factores in HBV- and HCV-associated HCC. Figure S4. Global profiling of ASE modifications in both HBV&HCV-associated HCC and virus-free-associated HCC. Figure S5. RNA splicing factors in HCC. Figure S6. Modifications to AS of 96 transcripts in response to knockdown of splicing factors with specific siRNAs (PDF 6675 kb

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

The Epstein-Barr virus EBNA1 protein modulates the alternative splicing of cellular genes

Abstract Background Alternative splicing (AS) is an important mRNA maturation step that allows increased variability and diversity of proteins in eukaryotes. AS is dysregulated in numerous diseases, and its implication in the carcinogenic process is well known. However, progress in understanding how oncogenic viruses modulate splicing, and how this modulation is involved in viral oncogenicity has been limited. Epstein-Barr virus (EBV) is involved in various cancers, and its EBNA1 oncoprotein is the only viral protein expressed in all EBV malignancies. Methods In the present study, the ability of EBNA1 to modulate the AS of cellular genes was assessed using a high-throughput RT-PCR approach to examine AS in 1238 cancer-associated genes. RNA immunoprecipitation coupled to RNA sequencing (RIP-Seq) assays were also performed to identify cellular mRNAs bound by EBNA1. Results Upon EBNA1 expression, we detected modifications to the AS profiles of 89 genes involved in cancer. Moreover, we show that EBNA1 modulates the expression levels of various splicing factors such as hnRNPA1, FOX-2, and SF1. Finally, RNA immunoprecipitation coupled to RIP-Seq assays demonstrate that EBNA1 immunoprecipitates specific cellular mRNAs, but not the ones that are spliced differently in EBNA1-expressing cells. Conclusion The EBNA1 protein can modulate the AS profiles of numerous cellular genes. Interestingly, this modulation protein does not require the RNA binding activity of EBNA1. Overall, these findings underline the novel role of EBNA1 as a cellular splicing modulator

Global Profiling of the Cellular Alternative RNA Splicing Landscape during Virus-Host Interactions.

Alternative splicing (AS) is a central mechanism of genetic regulation which modifies the sequence of RNA transcripts in higher eukaryotes. AS has been shown to increase both the variability and diversity of the cellular proteome by changing the composition of resulting proteins through differential choice of exons to be included in mature mRNAs. In the present study, alterations to the global RNA splicing landscape of cellular genes upon viral infection were investigated using mammalian reovirus as a model. Our study provides the first comprehensive portrait of global changes in the RNA splicing signatures that occur in eukaryotic cells following infection with a human virus. We identify 240 modified alternative splicing events upon infection which belong to transcripts frequently involved in the regulation of gene expression and RNA metabolism. Using mass spectrometry, we also confirm modifications to transcript-specific peptides resulting from AS in virus-infected cells. These findings provide additional insights into the complexity of virus-host interactions as these splice variants expand proteome diversity and function during viral infection

Transcriptome-wide analysis of alternative RNA splicing events in Epstein-Barr virus-associated gastric carcinomas

<div><p>Multiple human diseases including cancer have been associated with a dysregulation in RNA splicing patterns. In the current study, modifications to the global RNA splicing landscape of cellular genes were investigated in the context of Epstein-Barr virus-associated gastric cancer. Global alterations to the RNA splicing landscape of cellular genes was examined in a large-scale screen from 295 primary gastric adenocarcinomas using high-throughput RNA sequencing data. RT-PCR analysis, mass spectrometry, and co-immunoprecipitation studies were also used to experimentally validate and investigate the differential alternative splicing (AS) events that were observed through RNA-seq studies. Our study identifies alterations in the AS patterns of approximately 900 genes such as tumor suppressor genes, transcription factors, splicing factors, and kinases. These findings allowed the identification of unique gene signatures for which AS is misregulated in both Epstein-Barr virus-associated gastric cancer and EBV-negative gastric cancer. Moreover, we show that the expression of Epstein–Barr nuclear antigen 1 (EBNA1) leads to modifications in the AS profile of cellular genes and that the EBNA1 protein interacts with cellular splicing factors. These findings provide insights into the molecular differences between various types of gastric cancer and suggest a role for the EBNA1 protein in the dysregulation of cellular AS.</p></div

Comparison between EBV-negative and EBV-associated gastric cancer.

<p><b>(A)</b> Heatmap representation of isoform ratios (PSI values) for EBV-negative gastric adenocarcinomas tissues. EBV-negative gastric adenocarcinomas tissues (TNoV) are shown in blue, and the comparative healthy tissues are shown in green (NNoV). <b>(B)</b> Comparison of the cellular genes with dysregulated ASEs between EBVaGC and EBV-negative GC tissues. <b>(C)</b> The list displays common differentially spliced transcripts for both EBV-negative and EBVaGC with the corresponding Delta PSI values, the associated gene expression (in Log₂), and the related biological processes.</p

Identification of alternative splicing events in gastric cancer.

<p><b>(A)</b> Overview of the strategy used to identify the changes in alternative splicing in GC. <b>(B)</b> Classification of the TCGA RNA sequencing data for GC and healthy tissues. <b>(C)</b> The splicing events list for both EBV-negative gastric adenocarcinomas (TNoV, Tumors, no virus) and EBV-associated gastric carcinomas (EBVaGC, TEBV, Tumors with EBV) compared the global splicing patterns with normal tissues (NNoV, Normal tissues, no virus) was filtered to keep only significant ASEs.</p

Protein families encoded by transcripts that are differentially spliced in GC.

<p>Protein families encoded by transcripts that are differentially spliced in GC.</p

Modifications to AS of 96 transcripts in response to knockdown of specific splicing factors with siRNAs.

<p>Using specific siRNAs, eighteen splicing factors (U2AF2, U2AF1, SYNCRIP, SFRS9, SFRS6, SFRS2, NOVA1, KHSRP, KHDRBS1, HNRPU, HNRPR, HNRPM, HNRPK, HNRPH1, HNRPF, HNRPD, HNRPC, and HNRPA1) were individually knocked-down in various cell lines to evaluate their implication in splicing of 96 different transcripts. Asterisks (top) indicate transcripts for which AS was altered in GC. Individual knockdowns and ASEs are presented to indicate which knockdowns produced a shift in AS in various cell lines (PC-3, SKOV3, NIH:OVCAR-3, MDA-MB-231, MCF7). Each individual column represents a different knockdown performed with specific siRNAs. The changes in PSI values are indicated. The map displays the changes in PSI values in a color-coded scale. White areas indicate no shifts.</p