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Tiberi S, Meili J, Cai P, Soneson C, He D, Sarkar H, Avalos-Pacheco A, Patro R, Robinson MD. DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes. Biostatistics 2024:kxae017. [PMID: 38887902 DOI: 10.1093/biostatistics/kxae017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 03/21/2024] [Accepted: 05/15/2024] [Indexed: 06/20/2024] Open
Abstract
Although transcriptomics data is typically used to analyze mature spliced mRNA, recent attention has focused on jointly investigating spliced and unspliced (or precursor-) mRNA, which can be used to study gene regulation and changes in gene expression production. Nonetheless, most methods for spliced/unspliced inference (such as RNA velocity tools) focus on individual samples, and rarely allow comparisons between groups of samples (e.g. healthy vs. diseased). Furthermore, this kind of inference is challenging, because spliced and unspliced mRNA abundance is characterized by a high degree of quantification uncertainty, due to the prevalence of multi-mapping reads, ie reads compatible with multiple transcripts (or genes), and/or with both their spliced and unspliced versions. Here, we present DifferentialRegulation, a Bayesian hierarchical method to discover changes between experimental conditions with respect to the relative abundance of unspliced mRNA (over the total mRNA). We model the quantification uncertainty via a latent variable approach, where reads are allocated to their gene/transcript of origin, and to the respective splice version. We designed several benchmarks where our approach shows good performance, in terms of sensitivity and error control, vs. state-of-the-art competitors. Importantly, our tool is flexible, and works with both bulk and single-cell RNA-sequencing data. DifferentialRegulation is distributed as a Bioconductor R package.
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Affiliation(s)
- Simone Tiberi
- Department of Statistical Sciences, University of Bologna, Via delle Belle Arti 41, Bologna, 40126, Italy
- Department of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland
| | - Joël Meili
- Department of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland
| | - Peiying Cai
- Department of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland
| | - Charlotte Soneson
- Computational Biology Platform, Friedrich Miescher Institute for Biomedical Research and SIB Swiss Institute of Bioinformatics, Fabrikstrasse 24, Basel, 4056, Switzerland
| | - Dongze He
- Department of Cell Biology and Molecular Genetics, University of Maryland, 4062 Campus Drive, College Park, MD 20742, United States
- Center for Bioinformatics and Computational Biology, University of Maryland, 8125 Paint Branch Dr, College Park, MD 20742, United States
| | - Hirak Sarkar
- Department of Computer Science, Princeton University, 35 Olden St, Princeton, NJ 08540, United States
| | - Alejandra Avalos-Pacheco
- Research Unit of Applied Statistics, TU Wien, Wiedner Hauptstrabe 8-10/105, Wien 1040, Austria
- Harvard-MIT Center for Regulatory Science, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115200 Longwood Avenue, Boston, MA 02115, United States
| | - Rob Patro
- Center for Bioinformatics and Computational Biology, University of Maryland, 8125 Paint Branch Dr, College Park, MD 20742, United States
- Department of Computer Science, University of Maryland, 8125 Paint Branch Dr, College Park, MD 20742, United States
| | - Mark D Robinson
- Department of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland
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Tiberi S, Meili J, Cai P, Soneson C, He D, Sarkar H, Avalos-Pacheco A, Patro R, Robinson MD. DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.17.553679. [PMID: 37645841 PMCID: PMC10462127 DOI: 10.1101/2023.08.17.553679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Motivation Although transcriptomics data is typically used to analyse mature spliced mRNA, recent attention has focused on jointly investigating spliced and unspliced (or precursor-) mRNA, which can be used to study gene regulation and changes in gene expression production. Nonetheless, most methods for spliced/unspliced inference (such as RNA velocity tools) focus on individual samples, and rarely allow comparisons between groups of samples (e.g., healthy vs. diseased). Furthermore, this kind of inference is challenging, because spliced and unspliced mRNA abundance is characterized by a high degree of quantification uncertainty, due to the prevalence of multi-mapping reads, i.e., reads compatible with multiple transcripts (or genes), and/or with both their spliced and unspliced versions. Results Here, we present DifferentialRegulation, a Bayesian hierarchical method to discover changes between experimental conditions with respect to the relative abundance of unspliced mRNA (over the total mRNA). We model the quantification uncertainty via a latent variable approach, where reads are allocated to their gene/transcript of origin, and to the respective splice version. We designed several benchmarks where our approach shows good performance, in terms of sensitivity and error control, versus state-of-the-art competitors. Importantly, our tool is flexible, and works with both bulk and single-cell RNA-sequencing data. Availability and implementation DifferentialRegulation is distributed as a Bioconductor R package.
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Affiliation(s)
- Simone Tiberi
- Department of Statistical Sciences, University of Bologna, Bologna, Italy
- Department of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
| | - Joël Meili
- Department of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
| | - Peiying Cai
- Department of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
| | - Charlotte Soneson
- Computational Biology Platform, Friedrich Miescher Institute for Biomedical Research and SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Dongze He
- Department of Cell Biology and Molecular Genetics, University of Maryland, MD, USA
- Center for Bioinformatics and Computational Biology, University of Maryland, MD, USA
| | - Hirak Sarkar
- Department of Computer Science, Princeton University, NJ, USA
| | - Alejandra Avalos-Pacheco
- Research Unit of Applied Statistics, TU Wien, Vienna, Austria
- Harvard-MIT Center for Regulatory Science, Harvard Medical School, Boston, MA, USA
| | - Rob Patro
- Department of Computer Science, University of Maryland, MD, USA
- Center for Bioinformatics and Computational Biology, University of Maryland, MD, USA
| | - Mark D Robinson
- Department of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
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Crews LA, Ma W, Ladel L, Pham J, Balaian L, Steel SK, Mondala PK, Diep RH, Wu CN, Mason CN, van der Werf I, Oliver I, Reynoso E, Pineda G, Whisenant TC, Wentworth P, La Clair JJ, Jiang Q, Burkart MD, Jamieson CHM. Reversal of malignant ADAR1 splice isoform switching with Rebecsinib. Cell Stem Cell 2023; 30:250-263.e6. [PMID: 36803553 PMCID: PMC10134781 DOI: 10.1016/j.stem.2023.01.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 11/15/2022] [Accepted: 01/20/2023] [Indexed: 02/18/2023]
Abstract
Adenosine deaminase acting on RNA1 (ADAR1) preserves genomic integrity by preventing retroviral integration and retrotransposition during stress responses. However, inflammatory-microenvironment-induced ADAR1p110 to p150 splice isoform switching drives cancer stem cell (CSC) generation and therapeutic resistance in 20 malignancies. Previously, predicting and preventing ADAR1p150-mediated malignant RNA editing represented a significant challenge. Thus, we developed lentiviral ADAR1 and splicing reporters for non-invasive detection of splicing-mediated ADAR1 adenosine-to-inosine (A-to-I) RNA editing activation; a quantitative ADAR1p150 intracellular flow cytometric assay; a selective small-molecule inhibitor of splicing-mediated ADAR1 activation, Rebecsinib, which inhibits leukemia stem cell (LSC) self-renewal and prolongs humanized LSC mouse model survival at doses that spare normal hematopoietic stem and progenitor cells (HSPCs); and pre-IND studies showing favorable Rebecsinib toxicokinetic and pharmacodynamic (TK/PD) properties. Together, these results lay the foundation for developing Rebecsinib as a clinical ADAR1p150 antagonist aimed at obviating malignant microenvironment-driven LSC generation.
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Affiliation(s)
- Leslie A Crews
- Department of Medicine, Division of Regenerative Medicine, Sanford Stem Cell Institute, University of California, San Diego, La Jolla, CA 92037, USA; Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Wenxue Ma
- Department of Medicine, Division of Regenerative Medicine, Sanford Stem Cell Institute, University of California, San Diego, La Jolla, CA 92037, USA
| | - Luisa Ladel
- Department of Medicine, Division of Regenerative Medicine, Sanford Stem Cell Institute, University of California, San Diego, La Jolla, CA 92037, USA
| | - Jessica Pham
- Department of Medicine, Division of Regenerative Medicine, Sanford Stem Cell Institute, University of California, San Diego, La Jolla, CA 92037, USA
| | - Larisa Balaian
- Department of Medicine, Division of Regenerative Medicine, Sanford Stem Cell Institute, University of California, San Diego, La Jolla, CA 92037, USA; Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - S Kathleen Steel
- Department of Medicine, Division of Regenerative Medicine, Sanford Stem Cell Institute, University of California, San Diego, La Jolla, CA 92037, USA
| | - Phoebe K Mondala
- Department of Medicine, Division of Regenerative Medicine, Sanford Stem Cell Institute, University of California, San Diego, La Jolla, CA 92037, USA
| | - Raymond H Diep
- Department of Medicine, Division of Regenerative Medicine, Sanford Stem Cell Institute, University of California, San Diego, La Jolla, CA 92037, USA
| | - Christina N Wu
- Department of Medicine, Division of Regenerative Medicine, Sanford Stem Cell Institute, University of California, San Diego, La Jolla, CA 92037, USA
| | - Cayla N Mason
- Department of Medicine, Division of Regenerative Medicine, Sanford Stem Cell Institute, University of California, San Diego, La Jolla, CA 92037, USA
| | - Inge van der Werf
- Department of Medicine, Division of Regenerative Medicine, Sanford Stem Cell Institute, University of California, San Diego, La Jolla, CA 92037, USA
| | - Isabelle Oliver
- Department of Medicine, Division of Regenerative Medicine, Sanford Stem Cell Institute, University of California, San Diego, La Jolla, CA 92037, USA
| | - Eduardo Reynoso
- Department of Medicine, Division of Regenerative Medicine, Sanford Stem Cell Institute, University of California, San Diego, La Jolla, CA 92037, USA
| | - Gabriel Pineda
- Department of Medicine, Division of Regenerative Medicine, Sanford Stem Cell Institute, University of California, San Diego, La Jolla, CA 92037, USA
| | - Thomas C Whisenant
- Center for Computational Biology & Bioinformatics (CCBB), Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Peggy Wentworth
- Department of Medicine, Division of Regenerative Medicine, Sanford Stem Cell Institute, University of California, San Diego, La Jolla, CA 92037, USA
| | - James J La Clair
- Departments of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093, USA
| | - Qingfei Jiang
- Department of Medicine, Division of Regenerative Medicine, Sanford Stem Cell Institute, University of California, San Diego, La Jolla, CA 92037, USA
| | - Michael D Burkart
- Departments of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093, USA
| | - Catriona H M Jamieson
- Department of Medicine, Division of Regenerative Medicine, Sanford Stem Cell Institute, University of California, San Diego, La Jolla, CA 92037, USA; Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, USA.
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Yang Q, Fan W, Lai B, Liao B, Deng M. lncRNA-TCONS_00008552 expression in patients with pulmonary arterial hypertension due to congenital heart disease. PLoS One 2023; 18:e0281061. [PMID: 36893166 PMCID: PMC9997923 DOI: 10.1371/journal.pone.0281061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 01/16/2023] [Indexed: 03/10/2023] Open
Abstract
Long noncoding RNAs (lncRNAs) are potential regulators of a variety of cardiovascular diseases. Therefore, there is a series of differentially expressed lncRNAs in pulmonary arterial hypertension (PAH) that may be used as markers to diagnose PAH and even predict the prognosis. However, their specific mechanisms remain largely unknown. Therefore, we investigated the biological role of lncRNAs in patients with PAH. First, we screened patients with PAH secondary to ventricular septal defect (VSD) and those with VSD without PAH to assess differences in lncRNA and mRNA expression between the two groups. Our results revealed the significant upregulation of 813 lncRNAs and 527 mRNAs and significant downregulation of 541 lncRNAs and 268 mRNAs in patients with PAH. Then, we identified 10 hub genes in a constructed protein-protein interaction network. Next, we performed bioinformatics analyses, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis and subsequently constructed coding-noncoding co-expression networks. We screened lncRNA-TCONS_00008552 and lncRNA-ENST00000433673 as candidate genes and verified the expression levels of the lncRNAs using quantitative reverse-transcription PCR. Although expression levels of lncRNA-TCONS_00008552 in the plasma from the PAH groups were significantly increased compared with the control groups, there was no significant difference in the expression of lncRNA-ENST00000433673 between the two groups. This study bolsters our understanding of the role of lncRNA in PAH occurrence and development and indicates that lncRNA-TCONS_00008552 is a novel potential molecular marker for PAH.
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Affiliation(s)
- Qi Yang
- Department of Cardiovascular Surgery, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
- Metabolic Vascular Diseases Key Laboratory of Sichuan Province, Luzhou, China
- Key Laboratory of Medical Electrophysiology, Ministry of Education & Medical Electrophysiological Key Laboratory of Sichuan Province, (Collaborative Innovation Center for Prevention of Cardiovascular Diseases) Institute of Cardiovascular Research, Southwest Medical University, Luzhou, China
| | - Wei Fan
- Department of Cardiovascular Surgery, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
- Metabolic Vascular Diseases Key Laboratory of Sichuan Province, Luzhou, China
- Key Laboratory of Medical Electrophysiology, Ministry of Education & Medical Electrophysiological Key Laboratory of Sichuan Province, (Collaborative Innovation Center for Prevention of Cardiovascular Diseases) Institute of Cardiovascular Research, Southwest Medical University, Luzhou, China
| | - Banghui Lai
- Department of Cardiovascular Surgery, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
- Metabolic Vascular Diseases Key Laboratory of Sichuan Province, Luzhou, China
- Key Laboratory of Medical Electrophysiology, Ministry of Education & Medical Electrophysiological Key Laboratory of Sichuan Province, (Collaborative Innovation Center for Prevention of Cardiovascular Diseases) Institute of Cardiovascular Research, Southwest Medical University, Luzhou, China
| | - Bin Liao
- Department of Cardiovascular Surgery, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
- Metabolic Vascular Diseases Key Laboratory of Sichuan Province, Luzhou, China
- Key Laboratory of Medical Electrophysiology, Ministry of Education & Medical Electrophysiological Key Laboratory of Sichuan Province, (Collaborative Innovation Center for Prevention of Cardiovascular Diseases) Institute of Cardiovascular Research, Southwest Medical University, Luzhou, China
- * E-mail: (BL); (MD)
| | - Mingbin Deng
- Department of Cardiovascular Surgery, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
- Metabolic Vascular Diseases Key Laboratory of Sichuan Province, Luzhou, China
- Key Laboratory of Medical Electrophysiology, Ministry of Education & Medical Electrophysiological Key Laboratory of Sichuan Province, (Collaborative Innovation Center for Prevention of Cardiovascular Diseases) Institute of Cardiovascular Research, Southwest Medical University, Luzhou, China
- * E-mail: (BL); (MD)
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Potemkin N, Cawood SMF, Treece J, Guévremont D, Rand CJ, McLean C, Stanton JAL, Williams JM. A method for simultaneous detection of small and long RNA biotypes by ribodepleted RNA-Seq. Sci Rep 2022; 12:621. [PMID: 35022475 PMCID: PMC8755727 DOI: 10.1038/s41598-021-04209-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 11/24/2021] [Indexed: 11/09/2022] Open
Abstract
RNA sequencing offers unprecedented access to the transcriptome. Key to this is the identification and quantification of many different species of RNA from the same sample at the same time. In this study we describe a novel protocol for simultaneous detection of coding and non-coding transcripts using modifications to the Ion Total RNA-Seq kit v2 protocol, with integration of QIASeq FastSelect rRNA removal kit. We report highly consistent sequencing libraries can be produced from both frozen high integrity mouse hippocampal tissue and the more challenging post-mortem human tissue. Removal of rRNA using FastSelect was extremely efficient, resulting in less than 1.5% rRNA content in the final library. We identified > 30,000 unique transcripts from all samples, including protein-coding genes and many species of non-coding RNA, in biologically-relevant proportions. Furthermore, the normalized sequencing read count for select genes significantly negatively correlated with Ct values from qRT-PCR analysis from the same samples. These results indicate that this protocol accurately and consistently identifies and quantifies a wide variety of transcripts simultaneously. The highly efficient rRNA depletion, coupled with minimized sample handling and without complicated and high-loss size selection protocols, makes this protocol useful to researchers wishing to investigate whole transcriptomes.
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Affiliation(s)
- Nikita Potemkin
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin, New Zealand
- Brain Health Research Centre, Brain Research New Zealand-Rangahau Roro Aotearoa, University of Otago, Dunedin, New Zealand
| | - Sophie M F Cawood
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin, New Zealand
- Brain Health Research Centre, Brain Research New Zealand-Rangahau Roro Aotearoa, University of Otago, Dunedin, New Zealand
| | - Jackson Treece
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin, New Zealand
| | - Diane Guévremont
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin, New Zealand
- Brain Health Research Centre, Brain Research New Zealand-Rangahau Roro Aotearoa, University of Otago, Dunedin, New Zealand
| | - Christy J Rand
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin, New Zealand
| | - Catriona McLean
- Victorian Brain Bank, The Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia
- Anatomical Pathology, The Alfred Hospital, Melbourne, VIC, Australia
| | - Jo-Ann L Stanton
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin, New Zealand
| | - Joanna M Williams
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin, New Zealand.
- Brain Health Research Centre, Brain Research New Zealand-Rangahau Roro Aotearoa, University of Otago, Dunedin, New Zealand.
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Deschamps-Francoeur G, Simoneau J, Scott MS. Handling multi-mapped reads in RNA-seq. Comput Struct Biotechnol J 2020; 18:1569-1576. [PMID: 32637053 PMCID: PMC7330433 DOI: 10.1016/j.csbj.2020.06.014] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 06/06/2020] [Accepted: 06/07/2020] [Indexed: 11/07/2022] Open
Abstract
Many eukaryotic genomes harbour large numbers of duplicated sequences, of diverse biotypes, resulting from several mechanisms including recombination, whole genome duplication and retro-transposition. Such repeated sequences complicate gene/transcript quantification during RNA-seq analysis due to reads mapping to more than one locus, sometimes involving genes embedded in other genes. Genes of different biotypes have dissimilar levels of sequence duplication, with long-noncoding RNAs and messenger RNAs sharing less sequence similarity to other genes than biotypes encoding shorter RNAs. Many strategies have been elaborated to handle these multi-mapped reads, resulting in increased accuracy in gene/transcript quantification, although separate tools are typically used to estimate the abundance of short and long genes due to their dissimilar characteristics. This review discusses the mechanisms leading to sequence duplication, the biotypes affected, the computational strategies employed to deal with multi-mapped reads and the challenges that still remain to be overcome.
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Affiliation(s)
- Gabrielle Deschamps-Francoeur
- Département de Biochimie et Génomique Fonctionnelle, Faculté de médecine et des sciences de la santé, Université de Sherbrooke, Sherbrooke, QC J1E 4K8, Canada
| | - Joël Simoneau
- Département de Biochimie et Génomique Fonctionnelle, Faculté de médecine et des sciences de la santé, Université de Sherbrooke, Sherbrooke, QC J1E 4K8, Canada
| | - Michelle S. Scott
- Département de Biochimie et Génomique Fonctionnelle, Faculté de médecine et des sciences de la santé, Université de Sherbrooke, Sherbrooke, QC J1E 4K8, Canada
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