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White CR, Dungan M, Carrithers MD. Activation of human macrophage sodium channels regulates RNA processing to increase expression of the DNA repair protein PPP1R10. Immunobiology 2019; 224:80-93. [PMID: 30391100 DOI: 10.1016/j.imbio.2018.10.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Revised: 10/12/2018] [Accepted: 10/16/2018] [Indexed: 11/26/2022]
Abstract
Prior work demonstrated that a splice variant of SCN5A, a voltage-gated sodium channel gene, acts as a cytoplasmic sensor for viral dsRNA in human macrophages. Expression of this channel also polarizes macrophages to an anti-inflammatory phenotype in vitro and in vivo. Here we utilized global expression analysis of splice variants to identify novel channel-dependent signaling mechanisms. Pharmacological activation of voltage-gated sodium channels in human macrophages, but not treatment with cytoplasmic poly I:C, was associated with splicing of a retained intron in transcripts of PPP1R10, a regulator of phosphatase activity and DNA repair. Microarray analysis also demonstrated expression of a novel sodium channel splice variant, human macrophage SCN10A, that contains a similar exon deletion as SCN5A. SCN10A localizes to cytoplasmic and nuclear vesicles in human macrophages. Simultaneous expression of human macrophage SCN5A and SCN10A was required to decrease expression of the retained intron and increase protein expression of PPP1R10. Channel activation also increased protein expression of the splicing factor EFTUD2, and knockdown of EFTUD2 prevented channel dependent splicing of the retained PPP1R10 intron. Knockdown of the SCN5A and SCN10A variants in human macrophages reduced the severity of dsDNA breaks induced by treatment with bleomycin and type 1 interferon. These results suggested that human macrophage SCN5A and SCN10A variants mediate an innate immune signaling pathway that limits DNA damage through increased expression of PPP1R10. The functional significance of this pathway is that it may prevent cytotoxicity during inflammatory responses.
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Affiliation(s)
- Chelsea R White
- Department of Neurology, University of Illinois College of Medicine, Chicago, IL 60612, United States
| | - Matthew Dungan
- Department of Neurology, University of Illinois College of Medicine, Chicago, IL 60612, United States
| | - Michael D Carrithers
- Department of Neurology, University of Illinois College of Medicine, Chicago, IL 60612, United States; Program in Neuroscience, University of Illinois College of Medicine, Chicago, IL 60612, United States; Jesse Brown Veterans Affairs Medical Center, Chicago, IL 60612, United States.
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3
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Romero JP, Ortiz-Estévez M, Muniategui A, Carrancio S, de Miguel FJ, Carazo F, Montuenga LM, Loos R, Pío R, Trotter MWB, Rubio A. Comparison of RNA-seq and microarray platforms for splice event detection using a cross-platform algorithm. BMC Genomics 2018; 19:703. [PMID: 30253752 PMCID: PMC6156849 DOI: 10.1186/s12864-018-5082-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Accepted: 09/17/2018] [Indexed: 12/13/2022] Open
Abstract
Background RNA-seq is a reference technology for determining alternative splicing at genome-wide level. Exon arrays remain widely used for the analysis of gene expression, but show poor validation rate with regard to splicing events. Commercial arrays that include probes within exon junctions have been developed in order to overcome this problem. We compare the performance of RNA-seq (Illumina HiSeq) and junction arrays (Affymetrix Human Transcriptome array) for the analysis of transcript splicing events. Three different breast cancer cell lines were treated with CX-4945, a drug that severely affects splicing. To enable a direct comparison of the two platforms, we adapted EventPointer, an algorithm that detects and labels alternative splicing events using junction arrays, to work also on RNA-seq data. Common results and discrepancies between the technologies were validated and/or resolved by over 200 PCR experiments. Results As might be expected, RNA-seq appears superior in cases where the technologies disagree and is able to discover novel splicing events beyond the limitations of physical probe-sets. We observe a high degree of coherence between the two technologies, however, with correlation of EventPointer results over 0.90. Through decimation, the detection power of the junction arrays is equivalent to RNA-seq with up to 60 million reads. Conclusions Our results suggest, therefore, that exon-junction arrays are a viable alternative to RNA-seq for detection of alternative splicing events when focusing on well-described transcriptional regions. Electronic supplementary material The online version of this article (10.1186/s12864-018-5082-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Juan P Romero
- CEIT and Tecnun, University of Navarra, Parque Tecnológico de San Sebastián, Paseo Mikeletegi 48, 20009, San Sebastián, Gipuzkoa, Spain
| | - María Ortiz-Estévez
- Celgene Institute for Translational Research Europe, Celgene Corporation, Parque Científico y Tecnológico Cartuja 93, Centro de Empresas Pabellón de Italia, Isaac Newton, 4, E-41092, Seville, Spain
| | - Ander Muniategui
- CEIT and Tecnun, University of Navarra, Parque Tecnológico de San Sebastián, Paseo Mikeletegi 48, 20009, San Sebastián, Gipuzkoa, Spain
| | - Soraya Carrancio
- Celgene Institute for Translational Research Europe, Celgene Corporation, Parque Científico y Tecnológico Cartuja 93, Centro de Empresas Pabellón de Italia, Isaac Newton, 4, E-41092, Seville, Spain
| | - Fernando J de Miguel
- Program in Solid Tumors and Biomarkers, CIMA, University of Navarra, Avda. Pío XII, 55, E-31008, Pamplona, Navarra, Spain
| | - Fernando Carazo
- CEIT and Tecnun, University of Navarra, Parque Tecnológico de San Sebastián, Paseo Mikeletegi 48, 20009, San Sebastián, Gipuzkoa, Spain
| | - Luis M Montuenga
- Program in Solid Tumors and Biomarkers, CIMA, University of Navarra, Avda. Pío XII, 55, E-31008, Pamplona, Navarra, Spain.,Department of Histology and Pathology, University of Navarra, Campus Universitario, 31009, Pamplona, Navarra, Spain.,IdiSNA, Navarra Institute for Health Research, Recinto de Complejo Hospitalario de Navarra, Irunlarrea 3, 31008, Pamplona, Navarra, Spain.,CIBERONC, Centro de Investigación Biomédica en Red, Instituto de Salud Carlos III, Calle Monforte de Lemos 3-5, Pabellón 11. Planta 0, 28029, Madrid, Spain
| | - Remco Loos
- Celgene Institute for Translational Research Europe, Celgene Corporation, Parque Científico y Tecnológico Cartuja 93, Centro de Empresas Pabellón de Italia, Isaac Newton, 4, E-41092, Seville, Spain
| | - Rubén Pío
- Program in Solid Tumors and Biomarkers, CIMA, University of Navarra, Avda. Pío XII, 55, E-31008, Pamplona, Navarra, Spain.,IdiSNA, Navarra Institute for Health Research, Recinto de Complejo Hospitalario de Navarra, Irunlarrea 3, 31008, Pamplona, Navarra, Spain.,Department of Biochemistry and Genetics, University of Navarra, Campus Universitario, 31009, Pamplona, Navarra, Spain.,CIBERONC, Centro de Investigación Biomédica en Red, Instituto de Salud Carlos III, Calle Monforte de Lemos 3-5, Pabellón 11. Planta 0, 28029, Madrid, Spain
| | - Matthew W B Trotter
- Celgene Institute for Translational Research Europe, Celgene Corporation, Parque Científico y Tecnológico Cartuja 93, Centro de Empresas Pabellón de Italia, Isaac Newton, 4, E-41092, Seville, Spain
| | - Angel Rubio
- CEIT and Tecnun, University of Navarra, Parque Tecnológico de San Sebastián, Paseo Mikeletegi 48, 20009, San Sebastián, Gipuzkoa, Spain.
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Van Moerbeke M, Kasim A, Shkedy Z. The Usage of Exon-Exon Splice Junctions for the Detection of Alternative Splicing using the REIDS model. Sci Rep 2018; 8:8331. [PMID: 29844567 PMCID: PMC5974242 DOI: 10.1038/s41598-018-26695-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Accepted: 05/17/2018] [Indexed: 02/08/2023] Open
Abstract
Alternative gene splicing is a common phenomenon in which a single gene gives rise to multiple transcript isoforms. The process is strictly guided and involves a multitude of proteins and regulatory complexes. Unfortunately, aberrant splicing events have been linked to genetic disorders. Therefore, understanding mechanisms of alternative splicing regulation and differences in splicing events between diseased and healthy tissues is crucial in advancing personalized medicine and drug developments. We propose a linear mixed model, Random Effects for the Identification of Differential Splicing (REIDS), for the identification of alternative splicing events using Human Transcriptome Arrays (HTA). For each exon, a splicing score is calculated based on two scores, an exon score and an array score. The junction information is used to rank the identified exons from strongly confident to less confident candidates for alternative splicing. The design of junctions was also discussed to highlight the complexity of exon-exon and exon-junction interactions. Based on a list of Rt-PCR validated probe sets, REIDS outperforms AltAnalyze and iGems in the % recall rate.
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Affiliation(s)
- Marijke Van Moerbeke
- Hasselt University, Interuniversity institute for biostatistics and statistical bioinformatics, Hasselt, 3500, Belgium.
| | - Adetayo Kasim
- Durham University, Wolfson Research Institute for Health and Wellbeing, Durham, United Kingdom
- Durham University, Department of Anthropology, Durham, United Kingdom
| | - Ziv Shkedy
- Hasselt University, Interuniversity institute for biostatistics and statistical bioinformatics, Hasselt, 3500, Belgium
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5
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Dapas M, Kandpal M, Bi Y, Davuluri RV. Comparative evaluation of isoform-level gene expression estimation algorithms for RNA-seq and exon-array platforms. Brief Bioinform 2017; 18:260-269. [PMID: 26944083 PMCID: PMC5444266 DOI: 10.1093/bib/bbw016] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Indexed: 01/04/2023] Open
Abstract
Given that the majority of multi-exon genes generate diverse functional products, it is important to evaluate expression at the isoform level. Previous studies have demonstrated strong gene-level correlations between RNA sequencing (RNA-seq) and microarray platforms, but have not studied their concordance at the isoform level. We performed transcript abundance estimation on raw RNA-seq and exon-array expression profiles available for common glioblastoma multiforme samples from The Cancer Genome Atlas using different analysis pipelines, and compared both the isoform- and gene-level expression estimates between programs and platforms. The results showed better concordance between RNA-seq/exon-array and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) platforms for fold change estimates than for raw abundance estimates, suggesting that fold change normalization against a control is an important step for integrating expression data across platforms. Based on RT-qPCR validations, eXpress and Multi-Mapping Bayesian Gene eXpression (MMBGX) programs achieved the best performance for RNA-seq and exon-array platforms, respectively, for deriving the isoform-level fold change values. While eXpress achieved the highest correlation with the RT-qPCR and exon-array (MMBGX) results overall, RSEM was more highly correlated with MMBGX for the subset of transcripts that are highly variable across the samples. eXpress appears to be most successful in discriminating lowly expressed transcripts, but IsoformEx and RSEM correlate more strongly with MMBGX for highly expressed transcripts. The results also reinforce how potentially important isoform-level expression changes can be masked by gene-level estimates, and demonstrate that exon arrays yield comparable results to RNA-seq for evaluating isoform-level expression changes.
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Affiliation(s)
| | - Manoj Kandpal
- Department of Veterinary Surgery & Radiology, College of Veterinary & Animal Sciences, GBPUAT, Pantnagar - 263 145, Uttarakhand, India
| | - Yingtao Bi
- Center for Systems and Computational Biology, Molecular and Cellular Oncogenesis Program, The Wistar Institute, 19104 Philadelphia, PA, USA
| | - Ramana V Davuluri
- Center for Systems and Computational Biology, Molecular and Cellular Oncogenesis Program, The Wistar Institute, 19104 Philadelphia, PA, USA
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Nazarov PV, Muller A, Kaoma T, Nicot N, Maximo C, Birembaut P, Tran NL, Dittmar G, Vallar L. RNA sequencing and transcriptome arrays analyses show opposing results for alternative splicing in patient derived samples. BMC Genomics 2017; 18:443. [PMID: 28587590 PMCID: PMC5461714 DOI: 10.1186/s12864-017-3819-y] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Accepted: 05/25/2017] [Indexed: 01/29/2023] Open
Abstract
Background RNA sequencing (RNA-seq) and microarrays are two transcriptomics techniques aimed at the quantification of transcribed genes and their isoforms. Here we compare the latest Affymetrix HTA 2.0 microarray with Illumina 2000 RNA-seq for the analysis of patient samples - normal lung epithelium tissue and squamous cell carcinoma lung tumours. Protein coding mRNAs and long non-coding RNAs (lncRNAs) were included in the study. Results Both platforms performed equally well for protein-coding RNAs, however the stochastic variability was higher for the sequencing data than for microarrays. This reduced the number of differentially expressed genes and genes with predictive potential for RNA-seq compared to microarray data. Analysis of this variability revealed a lack of reads for short and low abundant genes; lncRNAs, being shorter and less abundant RNAs, were found especially susceptible to this issue. A major difference between the two platforms was uncovered by analysis of alternatively spliced genes. Investigation of differential exon abundance showed insufficient reads for many exons and exon junctions in RNA-seq while the detection on the array platform was more stable. Nevertheless, we identified 207 genes which undergo alternative splicing and were consistently detected by both techniques. Conclusions Despite the fact that the results of gene expression analysis were highly consistent between Human Transcriptome Arrays and RNA-seq platforms, the analysis of alternative splicing produced discordant results. We concluded that modern microarrays can still outperform sequencing for standard analysis of gene expression in terms of reproducibility and cost. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3819-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Petr V Nazarov
- Proteome and Genome Research Unit, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg.
| | - Arnaud Muller
- Proteome and Genome Research Unit, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Tony Kaoma
- Proteome and Genome Research Unit, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Nathalie Nicot
- Proteome and Genome Research Unit, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Cristina Maximo
- Proteome and Genome Research Unit, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | | | - Nhan L Tran
- Departments of Cancer Biology and Neurosurgery, Mayo Clinic Arizona, Phoenix, USA
| | - Gunnar Dittmar
- Proteome and Genome Research Unit, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Laurent Vallar
- Proteome and Genome Research Unit, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg
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7
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Van Moerbeke M, Kasim A, Talloen W, Reumers J, Göhlmann HWH, Shkedy Z. A random effects model for the identification of differential splicing (REIDS) using exon and HTA arrays. BMC Bioinformatics 2017; 18:273. [PMID: 28545391 PMCID: PMC5445373 DOI: 10.1186/s12859-017-1687-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Accepted: 05/15/2017] [Indexed: 12/17/2022] Open
Abstract
Background Alternative gene splicing is a common phenomenon in which a single gene gives rise to multiple transcript isoforms. The process is strictly guided and involves a multitude of proteins and regulatory complexes. Unfortunately, aberrant splicing events do occur which have been linked to genetic disorders, such as several types of cancer and neurodegenerative diseases (Fan et al., Theor Biol Med Model 3:19, 2006). Therefore, understanding the mechanism of alternative splicing and identifying the difference in splicing events between diseased and healthy tissue is crucial in biomedical research with the potential of applications in personalized medicine as well as in drug development. Results We propose a linear mixed model, Random Effects for the Identification of Differential Splicing (REIDS), for the identification of alternative splicing events. Based on a set of scores, an exon score and an array score, a decision regarding alternative splicing can be made. The model enables the ability to distinguish a differential expressed gene from a differential spliced exon. The proposed model was applied to three case studies concerning both exon and HTA arrays. Conclusion The REIDS model provides a work flow for the identification of alternative splicing events relying on the established linear mixed model. The model can be applied to different types of arrays. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1687-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Marijke Van Moerbeke
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Hasselt, 3500, Belgium.
| | - Adetayo Kasim
- Wolfson Research Institute for Health and Wellbeing, Durham University, Durham, UK
| | | | | | | | - Ziv Shkedy
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Hasselt, 3500, Belgium
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