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Blanco M, El Khattabi L, Gobé C, Crespo M, Coulée M, de la Iglesia A, Ialy-Radio C, Lapoujade C, Givelet M, Delessard M, Seller-Corona I, Yamaguchi K, Vernet N, Van Leeuwen F, Lermine A, Okada Y, Daveau R, Oliva R, Fouchet P, Ziyyat A, Pflieger D, Cocquet J. DOT1L regulates chromatin reorganization and gene expression during sperm differentiation. EMBO Rep 2023:e56316. [PMID: 37099396 DOI: 10.15252/embr.202256316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 03/31/2023] [Accepted: 04/13/2023] [Indexed: 04/27/2023] Open
Abstract
Spermatozoa have a unique genome organization. Their chromatin is almost completely devoid of histones and is formed instead of protamines, which confer a high level of compaction and preserve paternal genome integrity until fertilization. Histone-to-protamine transition takes place in spermatids and is indispensable for the production of functional sperm. Here, we show that the H3K79-methyltransferase DOT1L controls spermatid chromatin remodeling and subsequent reorganization and compaction of the spermatozoon genome. Using a mouse model in which Dot1l is knocked-out (KO) in postnatal male germ cells, we found that Dot1l-KO sperm chromatin is less compact and has an abnormal content, characterized by the presence of transition proteins, immature protamine 2 forms and a higher level of histones. Proteomic and transcriptomic analyses performed on spermatids reveal that Dot1l-KO modifies the chromatin prior to histone removal and leads to the deregulation of genes involved in flagellum formation and apoptosis during spermatid differentiation. As a consequence of these chromatin and gene expression defects, Dot1l-KO spermatozoa have less compact heads and are less motile, which results in impaired fertility.
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Affiliation(s)
- Mélina Blanco
- Université Paris Cité, INSERM, CNRS, Institut Cochin, Paris, France
| | - Laila El Khattabi
- Université Paris Cité, INSERM, CNRS, Institut Cochin, Paris, France
- Chromosomal Genomics Unit, Medical Genetics Department, Sorbonne Université and APHP, Hôpital Armand Trousseau, Paris, France
| | - Clara Gobé
- Université Paris Cité, INSERM, CNRS, Institut Cochin, Paris, France
| | - Marion Crespo
- University Grenoble Alpes, CEA, INSERM, UA13 BGE, CNRS, CEA, FR2048, Grenoble, France
- ADLIN Science, Pépinière « Genopole Entreprises », Evry, France
| | - Manon Coulée
- Université Paris Cité, INSERM, CNRS, Institut Cochin, Paris, France
| | - Alberto de la Iglesia
- Université Paris Cité, INSERM, CNRS, Institut Cochin, Paris, France
- Molecular Biology of Reproduction and Development Research Group, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, Universitat de Barcelona (UB), Barcelona, Spain
| | - Côme Ialy-Radio
- Université Paris Cité, INSERM, CNRS, Institut Cochin, Paris, France
| | - Clementine Lapoujade
- Université de Paris and Université Paris-Saclay, iRCM/IBFJ CEA, UMR Stabilité Génétique Cellules Souches et Radiations, Laboratoire des Cellules Souches Germinales, Fontenay-aux-Roses, France
| | - Maëlle Givelet
- Université de Paris and Université Paris-Saclay, iRCM/IBFJ CEA, UMR Stabilité Génétique Cellules Souches et Radiations, Laboratoire des Cellules Souches Germinales, Fontenay-aux-Roses, France
| | - Marion Delessard
- Université Paris Cité, INSERM, CNRS, Institut Cochin, Paris, France
| | | | - Kosuke Yamaguchi
- Institute for Quantitative Biosciences, The University of Tokyo, Tokyo, Japan
| | - Nadège Vernet
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Département de Génétique Fonctionnelle et Cancer, CNRS, INSERM, Université de Strasbourg, Illkirch, France
| | - Fred Van Leeuwen
- Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Alban Lermine
- MOABI-APHP Bioinformatics Platform-I&D-DSI, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Yuki Okada
- Institute for Quantitative Biosciences, The University of Tokyo, Tokyo, Japan
| | - Romain Daveau
- MOABI-APHP Bioinformatics Platform-I&D-DSI, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Rafael Oliva
- Molecular Biology of Reproduction and Development Research Group, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, Universitat de Barcelona (UB), Barcelona, Spain
- Biochemistry and Molecular Genetics Service, Clinic Barcelona, Barcelona, Spain
| | - Pierre Fouchet
- Université de Paris and Université Paris-Saclay, iRCM/IBFJ CEA, UMR Stabilité Génétique Cellules Souches et Radiations, Laboratoire des Cellules Souches Germinales, Fontenay-aux-Roses, France
| | - Ahmed Ziyyat
- Université Paris Cité, INSERM, CNRS, Institut Cochin, Paris, France
- Service d'Histologie, d'Embryologie, Biologie de la Reproduction, AP-HP, Hôpital Cochin, Paris, France
| | - Delphine Pflieger
- University Grenoble Alpes, CEA, INSERM, UA13 BGE, CNRS, CEA, FR2048, Grenoble, France
| | - Julie Cocquet
- Université Paris Cité, INSERM, CNRS, Institut Cochin, Paris, France
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Jehl F, Degalez F, Bernard M, Lecerf F, Lagoutte L, Désert C, Coulée M, Bouchez O, Leroux S, Abasht B, Tixier-Boichard M, Bed'hom B, Burlot T, Gourichon D, Bardou P, Acloque H, Foissac S, Djebali S, Giuffra E, Zerjal T, Pitel F, Klopp C, Lagarrigue S. RNA-Seq Data for Reliable SNP Detection and Genotype Calling: Interest for Coding Variant Characterization and Cis-Regulation Analysis by Allele-Specific Expression in Livestock Species. Front Genet 2021; 12:655707. [PMID: 34262593 PMCID: PMC8273700 DOI: 10.3389/fgene.2021.655707] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 06/01/2021] [Indexed: 12/19/2022] Open
Abstract
In addition to their common usages to study gene expression, RNA-seq data accumulated over the last 10 years are a yet-unexploited resource of SNPs in numerous individuals from different populations. SNP detection by RNA-seq is particularly interesting for livestock species since whole genome sequencing is expensive and exome sequencing tools are unavailable. These SNPs detected in expressed regions can be used to characterize variants affecting protein functions, and to study cis-regulated genes by analyzing allele-specific expression (ASE) in the tissue of interest. However, gene expression can be highly variable, and filters for SNP detection using the popular GATK toolkit are not yet standardized, making SNP detection and genotype calling by RNA-seq a challenging endeavor. We compared SNP calling results using GATK suggested filters, on two chicken populations for which both RNA-seq and DNA-seq data were available for the same samples of the same tissue. We showed, in expressed regions, a RNA-seq precision of 91% (SNPs detected by RNA-seq and shared by DNA-seq) and we characterized the remaining 9% of SNPs. We then studied the genotype (GT) obtained by RNA-seq and the impact of two factors (GT call-rate and read number per GT) on the concordance of GT with DNA-seq; we proposed thresholds for them leading to a 95% concordance. Applying these thresholds to 767 multi-tissue RNA-seq of 382 birds of 11 chicken populations, we found 9.5 M SNPs in total, of which ∼550,000 SNPs per tissue and population with a reliable GT (call rate ≥ 50%) and among them, ∼340,000 with a MAF ≥ 10%. We showed that such RNA-seq data from one tissue can be used to (i) detect SNPs with a strong predicted impact on proteins, despite their scarcity in each population (16,307 SIFT deleterious missenses and 590 stop-gained), (ii) study, on a large scale, cis-regulations of gene expression, with ∼81% of protein-coding and 68% of long non-coding genes (TPM ≥ 1) that can be analyzed for ASE, and with ∼29% of them that were cis-regulated, and (iii) analyze population genetic using such SNPs located in expressed regions. This work shows that RNA-seq data can be used with good confidence to detect SNPs and associated GT within various populations and used them for different analyses as GTEx studies.
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Affiliation(s)
- Frédéric Jehl
- INRAE, INSTITUT AGRO, PEGASE UMR 1348, Saint-Gilles, France
| | - Fabien Degalez
- INRAE, INSTITUT AGRO, PEGASE UMR 1348, Saint-Gilles, France
| | - Maria Bernard
- INRAE, SIGENAE, Genotoul Bioinfo MIAT, Castanet-Tolosan, France.,INRAE, AgroParisTech, Université Paris-Saclay, GABI UMR 1313, Jouy-en-Josas, France
| | | | | | - Colette Désert
- INRAE, INSTITUT AGRO, PEGASE UMR 1348, Saint-Gilles, France
| | - Manon Coulée
- INRAE, INSTITUT AGRO, PEGASE UMR 1348, Saint-Gilles, France
| | - Olivier Bouchez
- INRAE, US 1426, GeT-PlaGe, Genotoul, Castanet-Tolosan, France
| | - Sophie Leroux
- INRAE, INPT, ENVT, Université de Toulouse, GenPhySE UMR 1388, Castanet-Tolosan, France
| | - Behnam Abasht
- Department of Animal and Food Sciences, University of Delaware, Newark, DE, United States
| | | | - Bertrand Bed'hom
- INRAE, AgroParisTech, Université Paris-Saclay, GABI UMR 1313, Jouy-en-Josas, France
| | | | | | - Philippe Bardou
- INRAE, SIGENAE, Genotoul Bioinfo MIAT, Castanet-Tolosan, France
| | - Hervé Acloque
- INRAE, AgroParisTech, Université Paris-Saclay, GABI UMR 1313, Jouy-en-Josas, France
| | - Sylvain Foissac
- INRAE, INPT, ENVT, Université de Toulouse, GenPhySE UMR 1388, Castanet-Tolosan, France
| | - Sarah Djebali
- INRAE, INPT, ENVT, Université de Toulouse, GenPhySE UMR 1388, Castanet-Tolosan, France
| | - Elisabetta Giuffra
- INRAE, AgroParisTech, Université Paris-Saclay, GABI UMR 1313, Jouy-en-Josas, France
| | - Tatiana Zerjal
- INRAE, AgroParisTech, Université Paris-Saclay, GABI UMR 1313, Jouy-en-Josas, France
| | - Frédérique Pitel
- INRAE, INPT, ENVT, Université de Toulouse, GenPhySE UMR 1388, Castanet-Tolosan, France
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Merot‐L'anthoene V, Tournebize R, Darracq O, Rattina V, Lepelley M, Bellanger L, Tranchant‐Dubreuil C, Coulée M, Pégard M, Metairon S, Fournier C, Stoffelen P, Janssens SB, Kiwuka C, Musoli P, Sumirat U, Legnaté H, Kambale J, Ferreira da Costa Neto J, Revel C, de Kochko A, Descombes P, Crouzillat D, Poncet V. Development and evaluation of a genome-wide Coffee 8.5K SNP array and its application for high-density genetic mapping and for investigating the origin of Coffea arabica L. Plant Biotechnol J 2019; 17:1418-1430. [PMID: 30582651 PMCID: PMC6576098 DOI: 10.1111/pbi.13066] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Accepted: 12/10/2018] [Indexed: 06/09/2023]
Abstract
Coffee species such as Coffea canephora P. (Robusta) and C. arabica L. (Arabica) are important cash crops in tropical regions around the world. C. arabica is an allotetraploid (2n = 4x = 44) originating from a hybridization event of the two diploid species C. canephora and C. eugenioides (2n = 2x = 22). Interestingly, these progenitor species harbour a greater level of genetic variability and are an important source of genes to broaden the narrow Arabica genetic base. Here, we describe the development, evaluation and use of a single-nucleotide polymorphism (SNP) array for coffee trees. A total of 8580 unique and informative SNPs were selected from C. canephora and C. arabica sequencing data, with 40% of the SNP located in annotated genes. In particular, this array contains 227 markers associated to 149 genes and traits of agronomic importance. Among these, 7065 SNPs (~82.3%) were scorable and evenly distributed over the genome with a mean distance of 54.4 Kb between markers. With this array, we improved the Robusta high-density genetic map by adding 1307 SNP markers, whereas 945 SNPs were found segregating in the Arabica mapping progeny. A panel of C. canephora accessions was successfully discriminated and over 70% of the SNP markers were transferable across the three species. Furthermore, the canephora-derived subgenome of C. arabica was shown to be more closely related to C. canephora accessions from northern Uganda than to other current populations. These validated SNP markers and high-density genetic maps will be useful to molecular genetics and for innovative approaches in coffee breeding.
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