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Bravo S, Moya J, Leiva F, Guzman O, Vidal R. Transcriptome analyses reveal key roles of alternative splicing regulation in atlantic salmon during the infectious process of Piscirickettsiosis disease. Heliyon 2023; 9:e22377. [PMID: 38058636 PMCID: PMC10696053 DOI: 10.1016/j.heliyon.2023.e22377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 09/26/2023] [Accepted: 11/10/2023] [Indexed: 12/08/2023] Open
Abstract
In the Chilean salmon farming industry, infection by Piscirickettsia salmonis is the primary cause of the main bacterial disease known as Piscirickettsiosis, which has an overwhelming economic impact. Although it has been demonstrated that Piscirickettsiosis modifies the expression of numerous salmonids genes, it is yet unknown how alternative splicing (AS) contributes to salmonids bacterial infection. AS, has the potential to create heterogeneity at the protein and RNA levels and has been associated as a relevant molecular mechanism in the immune response of eukaryotes to several diseases. In this study, we used RNA data to survey P. salmonis-induced modifications in the AS of Atlantic salmon and found that P. salmonis infection promoted a substantial number (158,668) of AS events. Differentially spliced genes (DSG) sensitive to Piscirickettsiosis were predominantly enriched in genes involved in RNA processing, splicing and spliceosome processes (e.g., hnRNPm, hnRPc, SRSF7, SRSF45), whereas among the DSG of resistant and susceptible to Piscirickettsiosis, several metabolic and immune processes were found, most notably associated to the regulation of GTPase, lysosome and telomere organization-maintenance. Furthermore, we found that DSG were mostly not differentially expressed (5-7 %) and were implicated in distinct biological pathways. Therefore, our results underpin AS achieving a significant regulatory performance in the response of salmonids to Piscirickettsiosis.
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Affiliation(s)
- Scarleth Bravo
- Laboratory of Genomics, Molecular Ecology and Evolutionary Studies, Department of Biology, Universidad de Santiago de Chile, Santiago, Chile
| | - Javier Moya
- Benchmark Animal Health Chile, Santa Rosa 560 of.26, Puerto Varas, Chile
| | - Francisco Leiva
- Laboratory of Genomics, Molecular Ecology and Evolutionary Studies, Department of Biology, Universidad de Santiago de Chile, Santiago, Chile
| | - Osiel Guzman
- IDEVAC SpA, Francisco Bilbao 1129 of. 306, Osorno, Chile
| | - Rodrigo Vidal
- Laboratory of Genomics, Molecular Ecology and Evolutionary Studies, Department of Biology, Universidad de Santiago de Chile, Santiago, Chile
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2
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Zhu Y, Wang MJ, Crawford KM, Ramírez-Tapia JC, Lussier AA, Davis KA, de Leeuw C, Takesian AE, Hensch TK, Smoller JW, Dunn EC. Sensitive period-regulating genetic pathways and exposure to adversity shape risk for depression. Neuropsychopharmacology 2022; 47:497-506. [PMID: 34689167 PMCID: PMC8674315 DOI: 10.1038/s41386-021-01172-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 07/23/2021] [Accepted: 08/30/2021] [Indexed: 01/03/2023]
Abstract
Animal and human studies have documented the existence of developmental windows (or sensitive periods) when experience can have lasting effects on brain structure or function, behavior, and disease. Although sensitive periods for depression likely arise through a complex interplay of genes and experience, this possibility has not yet been explored in humans. We examined the effect of genetic pathways regulating sensitive periods, alone and in interaction with common childhood adversities, on depression risk. Guided by a translational approach, we: (1) performed association analyses of three gene sets (60 genes) shown in animal studies to regulate sensitive periods using summary data from a genome-wide association study of depression (n = 807,553); (2) evaluated the developmental expression patterns of these genes using data from BrainSpan (n = 31), a transcriptional atlas of postmortem brain samples; and (3) tested gene-by-development interplay (dGxE) by analyzing the combined effect of common variants in sensitive period genes and time-varying exposure to two types of childhood adversity within a population-based birth cohort (n = 6254). The gene set regulating sensitive period opening associated with increased depression risk. Notably, 6 of the 15 genes in this set showed developmentally regulated gene-level expression. We also identified a statistical interaction between caregiver physical or emotional abuse during ages 1-5 years and genetic risk for depression conferred by the opening genes. Genes involved in regulating sensitive periods are differentially expressed across the life course and may be implicated in depression vulnerability. Our findings about gene-by-development interplay motivate further research in large, more diverse samples to further unravel the complexity of depression etiology through a sensitive period lens.
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Affiliation(s)
- Yiwen Zhu
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Min-Jung Wang
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | | | - Alexandre A Lussier
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Center for Brain Science, Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Kathryn A Davis
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Christiaan de Leeuw
- Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Department of Complex Trait Genetics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Anne E Takesian
- Eaton-Peabody Laboratories, Massachusetts Eye & Ear and Department of Otorhinolaryngology and Head/Neck Surgery, Harvard Medical School, Boston, MA, USA
| | - Takao K Hensch
- Center for Brain Science, Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
- F.M. Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jordan W Smoller
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Erin C Dunn
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
- Stanley Center for Psychiatric Research, The Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Harvard Center on the Developing Child, Cambridge, MA, USA.
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Mehmood A, Laiho A, Elo LL. Exon-level estimates improve the detection of differentially expressed genes in RNA-seq studies. RNA Biol 2021; 18:1739-1746. [PMID: 33522408 PMCID: PMC8582999 DOI: 10.1080/15476286.2020.1868151] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Detection of differentially expressed genes (DEGs) between different biological conditions is a key data analysis step of most RNA-sequencing studies. Conventionally, computational tools have used gene-level read counts as input to test for differential gene expression between sample condition groups. Recently, it has been suggested that statistical testing could be performed with increased power at a lower feature level prior to aggregating the results to the gene level. In this study, we systematically compared the performance of calling the DEGs when using read count data at different levels (gene, transcript, and exon) as input, in the context of two publicly available data sets. Additionally, we tested two different methods for aggregating the lower feature-level p-values to gene-level: Lancaster and empirical Brown’s method. Our results show that detection of DEGs is improved compared to the conventional gene-level approach regardless of the lower feature-level used for statistical testing. The overall best balance between accuracy and false discovery rate was obtained using the exon-level approach with empirical Brown’s aggregation method, which we provide as a freely available Bioconductor package EBSEA (https://bioconductor.org/packages/release/bioc/html/EBSEA.html).
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Affiliation(s)
- Arfa Mehmood
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Institute of Biomedicine, University of Turku, Turku, Finland
| | - Asta Laiho
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Institute of Biomedicine, University of Turku, Turku, Finland
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Chen J, Mi X, Ning J, He X, Hu J. A tail-based test to detect differential expression in RNA-sequencing data. Stat Methods Med Res 2020; 30:261-276. [PMID: 32867604 DOI: 10.1177/0962280220951907] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
RNA sequencing data have been abundantly generated in biomedical research for biomarker discovery and other studies. Such data at the exon level are usually heavily tailed and correlated. Conventional statistical tests based on the mean or median difference for differential expression likely suffer from low power when the between-group difference occurs mostly in the upper or lower tail of the distribution of gene expression. We propose a tail-based test to make comparisons between groups in terms of a specific distribution area rather than a single location. The proposed test, which is derived from quantile regression, adjusts for covariates and accounts for within-sample dependence among the exons through a specified correlation structure. Through Monte Carlo simulation studies, we show that the proposed test is generally more powerful and robust in detecting differential expression than commonly used tests based on the mean or a single quantile. An application to TCGA lung adenocarcinoma data demonstrates the promise of the proposed method in terms of biomarker discovery.
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Affiliation(s)
- Jiong Chen
- Data Science, LinkedIn, Mountain View, CA, USA
| | - Xinlei Mi
- Department of Biostatistics, Columbia University, New York, NY, USA
| | - Jing Ning
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xuming He
- Department of Statistics, University of Michigan at Ann Arbor, Ann Arbor, MI, USA
| | - Jianhua Hu
- Department of Biostatistics, Columbia University, New York, NY, USA
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Odhams CA, Cunninghame Graham DS, Vyse TJ. Profiling RNA-Seq at multiple resolutions markedly increases the number of causal eQTLs in autoimmune disease. PLoS Genet 2017; 13:e1007071. [PMID: 29059182 PMCID: PMC5695635 DOI: 10.1371/journal.pgen.1007071] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Revised: 11/02/2017] [Accepted: 10/11/2017] [Indexed: 01/12/2023] Open
Abstract
Genome-wide association studies have identified hundreds of risk loci for autoimmune disease, yet only a minority (~25%) share genetic effects with changes to gene expression (eQTLs) in immune cells. RNA-Seq based quantification at whole-gene resolution, where abundance is estimated by culminating expression of all transcripts or exons of the same gene, is likely to account for this observed lack of colocalisation as subtle isoform switches and expression variation in independent exons can be concealed. We performed integrative cis-eQTL analysis using association statistics from twenty autoimmune diseases (560 independent loci) and RNA-Seq data from 373 individuals of the Geuvadis cohort profiled at gene-, isoform-, exon-, junction-, and intron-level resolution in lymphoblastoid cell lines. After stringently testing for a shared causal variant using both the Joint Likelihood Mapping and Regulatory Trait Concordance frameworks, we found that gene-level quantification significantly underestimated the number of causal cis-eQTLs. Only 5.0-5.3% of loci were found to share a causal cis-eQTL at gene-level compared to 12.9-18.4% at exon-level and 9.6-10.5% at junction-level. More than a fifth of autoimmune loci shared an underlying causal variant in a single cell type by combining all five quantification types; a marked increase over current estimates of steady-state causal cis-eQTLs. Causal cis-eQTLs detected at different quantification types localised to discrete epigenetic annotations. We applied a linear mixed-effects model to distinguish cis-eQTLs modulating all expression elements of a gene from those where the signal is only evident in a subset of elements. Exon-level analysis detected disease-associated cis-eQTLs that subtly altered transcription globally across the target gene. We dissected in detail the genetic associations of systemic lupus erythematosus and functionally annotated the candidate genes. Many of the known and novel genes were concealed at gene-level (e.g. IKZF2, TYK2, LYST). Our findings are provided as a web resource.
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Affiliation(s)
- Christopher A. Odhams
- Department of Medical & Molecular Genetics, King’s College London, London, United Kingdom
| | - Deborah S. Cunninghame Graham
- Department of Medical & Molecular Genetics, King’s College London, London, United Kingdom
- Academic Department of Rheumatology, Division of Immunology, Infection and Inflammatory Disease, King’s College London, London, United Kingdom
| | - Timothy J. Vyse
- Department of Medical & Molecular Genetics, King’s College London, London, United Kingdom
- Academic Department of Rheumatology, Division of Immunology, Infection and Inflammatory Disease, King’s College London, London, United Kingdom
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Accurate Detection of Differential Expression and Splicing Using Low-Level Features. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2016; 1507:141-151. [PMID: 27832538 DOI: 10.1007/978-1-4939-6518-2_11] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Gene expression can be quantified in high throughput using microarray technology. Here we describe how to accurately detect differential expression and splicing using a probe-level expression change averaging (PECA) method. PECA is available as an R package from Bioconductor ( https://www.bioconductor.org ), and it supports multiple operating systems.
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Nguyen T, Bhatti A, Yang S, Nahavandi S. RNA-Seq Count Data Modelling by Grey Relational Analysis and Nonparametric Gaussian Process. PLoS One 2016; 11:e0164766. [PMID: 27783633 PMCID: PMC5082617 DOI: 10.1371/journal.pone.0164766] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2016] [Accepted: 09/30/2016] [Indexed: 11/28/2022] Open
Abstract
This paper introduces an approach to classification of RNA-seq read counts using grey relational analysis (GRA) and Bayesian Gaussian process (GP) models. Read counts are transformed to microarray-like data to facilitate normal-based statistical methods. GRA is designed to select differentially expressed genes by integrating outcomes of five individual feature selection methods including two-sample t-test, entropy test, Bhattacharyya distance, Wilcoxon test and receiver operating characteristic curve. GRA performs as an aggregate filter method through combining advantages of the individual methods to produce significant feature subsets that are then fed into a nonparametric GP model for classification. The proposed approach is verified by using two benchmark real datasets and the five-fold cross-validation method. Experimental results show the performance dominance of the GRA-based feature selection method as well as GP classifier against their competing methods. Moreover, the results demonstrate that GRA-GP considerably dominates the sparse Poisson linear discriminant analysis classifiers, which were introduced specifically for read counts, on different number of features. The proposed approach therefore can be implemented effectively in real practice for read count data analysis, which is useful in many applications including understanding disease pathogenesis, diagnosis and treatment monitoring at the molecular level.
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Affiliation(s)
- Thanh Nguyen
- Institute for Intelligent Systems Research and Innovation, Deakin University, Victoria, Australia
- * E-mail:
| | - Asim Bhatti
- Institute for Intelligent Systems Research and Innovation, Deakin University, Victoria, Australia
| | - Samuel Yang
- Department of Emergency Medicine, Stanford University, California, United States of America
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation, Deakin University, Victoria, Australia
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Schuierer S, Roma G. The exon quantification pipeline (EQP): a comprehensive approach to the quantification of gene, exon and junction expression from RNA-seq data. Nucleic Acids Res 2016; 44:e132. [PMID: 27302131 PMCID: PMC5027495 DOI: 10.1093/nar/gkw538] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2015] [Accepted: 06/04/2016] [Indexed: 01/24/2023] Open
Abstract
The quantification of transcriptomic features is the basis of the analysis of RNA-seq data. We present an integrated alignment workflow and a simple counting-based approach to derive estimates for gene, exon and exon–exon junction expression. In contrast to previous counting-based approaches, EQP takes into account only reads whose alignment pattern agrees with the splicing pattern of the features of interest. This leads to improved gene expression estimates as well as to the generation of exon counts that allow disambiguating reads between overlapping exons. Unlike other methods that quantify skipped introns, EQP offers a novel way to compute junction counts based on the agreement of the read alignments with the exons on both sides of the junction, thus providing a uniformly derived set of counts. We evaluated the performance of EQP on both simulated and real Illumina RNA-seq data and compared it with other quantification tools. Our results suggest that EQP provides superior gene expression estimates and we illustrate the advantages of EQP's exon and junction counts. The provision of uniformly derived high-quality counts makes EQP an ideal quantification tool for differential expression and differential splicing studies. EQP is freely available for download at https://github.com/Novartis/EQP-cluster.
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Affiliation(s)
- Sven Schuierer
- Novartis Institutes for Biomedical Research, CH-4056 Basel, Switzerland
| | - Guglielmo Roma
- Novartis Institutes for Biomedical Research, CH-4056 Basel, Switzerland
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Suomi T, Corthals GL, Nevalainen OS, Elo LL. Using Peptide-Level Proteomics Data for Detecting Differentially Expressed Proteins. J Proteome Res 2015; 14:4564-70. [PMID: 26380941 DOI: 10.1021/acs.jproteome.5b00363] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
The expression of proteins can be quantified in high-throughput means using different types of mass spectrometers. In recent years, there have emerged label-free methods for determining protein abundance. Although the expression is initially measured at the peptide level, a common approach is to combine the peptide-level measurements into protein-level values before differential expression analysis. However, this simple combination is prone to inconsistencies between peptides and may lose valuable information. To this end, we introduce here a method for detecting differentially expressed proteins by combining peptide-level expression-change statistics. Using controlled spike-in experiments, we show that the approach of averaging peptide-level expression changes yields more accurate lists of differentially expressed proteins than does the conventional protein-level approach. This is particularly true when there are only few replicate samples or the differences between the sample groups are small. The proposed technique is implemented in the Bioconductor package PECA, and it can be downloaded from http://www.bioconductor.org.
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Affiliation(s)
| | - Garry L Corthals
- Van't Hoff Institute for Molecular Sciences, University of Amsterdam , 1090 GD Amsterdam , The Netherlands
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