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Lecluze E, Rolland AD, Filis P, Evrard B, Leverrier-Penna S, Maamar MB, Coiffec I, Lavoué V, Fowler PA, Mazaud-Guittot S, Jégou B, Chalmel F. Dynamics of the transcriptional landscape during human fetal testis and ovary development. Hum Reprod 2021; 35:1099-1119. [PMID: 32412604 DOI: 10.1093/humrep/deaa041] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 02/10/2020] [Indexed: 12/17/2022] Open
Abstract
STUDY QUESTION Which transcriptional program triggers sex differentiation in bipotential gonads and downstream cellular events governing fetal testis and ovary development in humans? SUMMARY ANSWER The characterization of a dynamically regulated protein-coding and non-coding transcriptional landscape in developing human gonads of both sexes highlights a large number of potential key regulators that show an early sexually dimorphic expression pattern. WHAT IS KNOWN ALREADY Gonadal sex differentiation is orchestrated by a sexually dimorphic gene expression program in XX and XY developing fetal gonads. A comprehensive characterization of its non-coding counterpart offers promising perspectives for deciphering the molecular events underpinning gonad development and for a complete understanding of the etiology of disorders of sex development in humans. STUDY DESIGN, SIZE, DURATION To further investigate the protein-coding and non-coding transcriptional landscape during gonad differentiation, we used RNA-sequencing (RNA-seq) and characterized the RNA content of human fetal testis (N = 24) and ovaries (N = 24) from 6 to 17 postconceptional week (PCW), a key period in sex determination and gonad development. PARTICIPANTS/MATERIALS, SETTING, METHODS First trimester fetuses (6-12 PCW) and second trimester fetuses (13-14 and 17 PCW) were obtained from legally induced normally progressing terminations of pregnancy. Total RNA was extracted from whole human fetal gonads and sequenced as paired-end 2 × 50 base reads. Resulting sequences were mapped to the human genome, allowing for the assembly and quantification of corresponding transcripts. MAIN RESULTS AND THE ROLE OF CHANCE This RNA-seq analysis of human fetal testes and ovaries at seven key developmental stages led to the reconstruction of 22 080 transcripts differentially expressed during testicular and/or ovarian development. In addition to 8935 transcripts displaying sex-independent differential expression during gonad development, the comparison of testes and ovaries enabled the discrimination of 13 145 transcripts that show a sexually dimorphic expression profile. The latter include 1479 transcripts differentially expressed as early as 6 PCW, including 39 transcription factors, 40 long non-coding RNAs and 20 novel genes. Despite the use of stringent filtration criteria (expression cut-off of at least 1 fragment per kilobase of exon model per million reads mapped, fold change of at least 2 and false discovery rate adjusted P values of less than <1%), the possibility of assembly artifacts and of false-positive differentially expressed transcripts cannot be fully ruled out. LARGE-SCALE DATA Raw data files (fastq) and a searchable table (.xlss) containing information on genomic features and expression data for all refined transcripts have been submitted to the NCBI GEO under accession number GSE116278. LIMITATIONS, REASONS FOR CAUTION The intrinsic nature of this bulk analysis, i.e. the sequencing of transcripts from whole gonads, does not allow direct identification of the cellular origin(s) of the transcripts characterized. Potential cellular dilution effects (e.g. as a result of distinct proliferation rates in XX and XY gonads) may account for a few of the expression profiles identified as being sexually dimorphic. Finally, transcriptome alterations that would result from exposure to pre-abortive drugs cannot be completely excluded. Although we demonstrated the high quality of the sorted cell populations used for experimental validations using quantitative RT-PCR, it cannot be totally excluded that some germline expression may correspond to cell contamination by, for example, macrophages. WIDER IMPLICATIONS OF THE FINDINGS For the first time, this study has led to the identification of 1000 protein-coding and non-coding candidate genes showing an early, sexually dimorphic, expression pattern that have not previously been associated with sex differentiation. Collectively, these results increase our understanding of gonad development in humans, and contribute significantly to the identification of new candidate genes involved in fetal gonad differentiation. The results also provide a unique resource that may improve our understanding of the fetal origin of testicular and ovarian dysgenesis syndromes, including cryptorchidism and testicular cancers. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by the French National Institute of Health and Medical Research (Inserm), the University of Rennes 1, the French School of Public Health (EHESP), the Swiss National Science Foundation [SNF n° CRS115_171007 to B.J.], the French National Research Agency [ANR n° 16-CE14-0017-02 and n° 18-CE14-0038-02 to F.C.], the Medical Research Council [MR/L010011/1 to P.A.F.] and the European Community's Seventh Framework Programme (FP7/2007-2013) [under grant agreement no 212885 to P.A.F.] and from the European Union's Horizon 2020 Research and Innovation Programme [under grant agreement no 825100 to P.A.F. and S.M.G.]. There are no competing interests related to this study.
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Affiliation(s)
- Estelle Lecluze
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France
| | - Antoine D Rolland
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France
| | - Panagiotis Filis
- Institute of Medical Sciences, School of Medicine, Medical Sciences & Nutrition, University of Aberdeen, Foresterhill, Aberdeen, AB25 2ZD, UK
| | - Bertrand Evrard
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France
| | - Sabrina Leverrier-Penna
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France.,Univ Poitiers, STIM, CNRS ERL7003, Poitiers Cedex 9, CNRS ERL7003, France
| | - Millissia Ben Maamar
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France
| | - Isabelle Coiffec
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France
| | - Vincent Lavoué
- Service Gynécologie et Obstétrique, CHU Rennes, F-35000 Rennes, France
| | - Paul A Fowler
- Institute of Medical Sciences, School of Medicine, Medical Sciences & Nutrition, University of Aberdeen, Foresterhill, Aberdeen, AB25 2ZD, UK
| | - Séverine Mazaud-Guittot
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France
| | - Bernard Jégou
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France
| | - Frédéric Chalmel
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France
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2
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Gamble J, Chick J, Seltzer K, Graber JH, Gygi S, Braun RE, Snyder EM. An expanded mouse testis transcriptome and mass spectrometry defines novel proteins. Reproduction 2020; 159:15-26. [PMID: 31677600 DOI: 10.1530/rep-19-0092] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Accepted: 10/31/2019] [Indexed: 12/18/2022]
Abstract
The testis transcriptome is exceptionally complex. Despite its complexity, previous testis transcriptome analyses relied on a reductive method for transcript identification, thus underestimating transcriptome complexity. We describe here a more complete testis transcriptome generated by combining Tuxedo, a reductive method, and spliced-RUM, a combinatorial transcript-building approach. Forty-two percent of the expanded testis transcriptome is composed of unannotated RNAs with novel isoforms of known genes and novel genes constituting 78 and 9.8% of the newly discovered transcripts, respectively. Across tissues, novel transcripts were predominantly expressed in the testis with the exception of novel isoforms which were also highly expressed in the adult ovary. Within the testis, novel isoform expression was distributed equally across all cell types while novel genes were predominantly expressed in meiotic and post-meiotic germ cells. The majority of novel isoforms retained their protein-coding potential while most novel genes had low protein-coding potential. However, a subset of novel genes had protein-coding potentials equivalent to known protein-coding genes. Shotgun mass spectrometry of round spermatid total protein identified unique peptides from four novel genes along with seven annotated non-coding RNAs. These analyses demonstrate the testis expresses a wide range of novel transcripts that give rise to novel proteins.
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Affiliation(s)
- Jaya Gamble
- Department of Animal Sciences, Rutgers, The State University of New Jersey, New Brunswick, New Jersey, USA
| | - Joel Chick
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts, USA
| | - Kelly Seltzer
- Department of Animal Sciences, Rutgers, The State University of New Jersey, New Brunswick, New Jersey, USA
| | | | - Steven Gygi
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Elizabeth M Snyder
- Department of Animal Sciences, Rutgers, The State University of New Jersey, New Brunswick, New Jersey, USA
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3
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Braschi B, Denny P, Gray K, Jones T, Seal R, Tweedie S, Yates B, Bruford E. Genenames.org: the HGNC and VGNC resources in 2019. Nucleic Acids Res 2020; 47:D786-D792. [PMID: 30304474 PMCID: PMC6324057 DOI: 10.1093/nar/gky930] [Citation(s) in RCA: 217] [Impact Index Per Article: 54.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Accepted: 10/04/2018] [Indexed: 12/26/2022] Open
Abstract
The HUGO Gene Nomenclature Committee (HGNC) based at EMBL's European Bioinformatics Institute (EMBL-EBI) assigns unique symbols and names to human genes. There are over 40 000 approved gene symbols in our current database of which over 19 000 are for protein-coding genes. The Vertebrate Gene Nomenclature Committee (VGNC) was established in 2016 to assign standardized nomenclature in line with human for vertebrate species that lack their own nomenclature committees. The VGNC initially assigned nomenclature for over 15000 protein-coding genes in chimpanzee. We have extended this process to other vertebrate species, naming over 14000 protein-coding genes in cow and dog and over 13 000 in horse to date. Our HGNC website https://www.genenames.org has undergone a major design update, simplifying the homepage to provide easy access to our search tools and making the site more mobile friendly. Our gene families pages are now known as 'gene groups' and have increased in number to over 1200, with nearly half of all named genes currently assigned to at least one gene group. This article provides an overview of our online data and resources, focusing on our work over the last two years.
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Affiliation(s)
- Bryony Braschi
- HUGO Gene Nomenclature Committee, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Paul Denny
- HUGO Gene Nomenclature Committee, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Kristian Gray
- HUGO Gene Nomenclature Committee, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Tamsin Jones
- HUGO Gene Nomenclature Committee, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Ruth Seal
- HUGO Gene Nomenclature Committee, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Susan Tweedie
- HUGO Gene Nomenclature Committee, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Bethan Yates
- HUGO Gene Nomenclature Committee, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Elspeth Bruford
- HUGO Gene Nomenclature Committee, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
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Saha S, Chatzimichali EA, Matthews DA, Bessant C. PITDB: a database of translated genomic elements. Nucleic Acids Res 2019; 46:D1223-D1228. [PMID: 30053269 PMCID: PMC5753392 DOI: 10.1093/nar/gkx906] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Accepted: 09/28/2017] [Indexed: 12/02/2022] Open
Abstract
PITDB is a freely available database of translated genomic elements (TGEs) that have been observed in PIT (proteomics informed by transcriptomics) experiments. In PIT, a sample is analyzed using both RNA-seq transcriptomics and proteomic mass spectrometry. Transcripts assembled from RNA-seq reads are used to create a library of sample-specific amino acid sequences against which the acquired mass spectra are searched, permitting detection of any TGE, not just those in canonical proteome databases. At the time of writing, PITDB contains over 74 000 distinct TGEs from four species, supported by more than 600 000 peptide spectrum matches. The database, accessible via http://pitdb.org, provides supporting evidence for each TGE, often from multiple experiments and an indication of the confidence in the TGE’s observation and its type, ranging from known protein (exact match to a UniProt protein sequence), through multiple types of protein variant including various splice isoforms, to a putative novel molecule. PITDB’s modern web interface allows TGEs to be viewed individually or by species or experiment, and downloaded for further analysis. PITDB is for bench scientists seeking to share their PIT results, for researchers investigating novel genome products in model organisms and for those wishing to construct proteomes for lesser studied species.
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Affiliation(s)
- Shyamasree Saha
- School of Biological and Chemical Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK
| | - Eleni A Chatzimichali
- School of Biological and Chemical Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK
| | - David A Matthews
- School of Cellular and Molecular Medicine, University of Bristol, University Walk, Bristol BS8 1TD, UK
| | - Conrad Bessant
- School of Biological and Chemical Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK.,Centre for Computational Biology, Life Science Institute, Queen Mary University of London, Mile End, London E1 4NS, UK
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Gao Z, Zhou H, Wang Y, Chen J, Ou Y. Regulatory effects of lncRNA ATB targeting miR-200c on proliferation and apoptosis of colorectal cancer cells. J Cell Biochem 2019; 121:332-343. [PMID: 31222825 DOI: 10.1002/jcb.29180] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 05/22/2019] [Accepted: 05/23/2019] [Indexed: 01/17/2023]
Abstract
This investigation was intended to elucidate whether long noncoding RNA (lncRNA)-activated by transforming growth factor-β (ATB) interacting with miR-200c could mediate colorectal cancer (CRC) progression, offering potential strategies for diagnosing and treating CRC. Here totally 315 patients with CRC were recruited, and their CRC tissues and adjacent normal tissues were gathered. Concurrently, four colon cancer cell lines (ie, SW620, Lovo, HCT116, and SW480) and the human colon mucosal epithelial cell line (NCM460) were also purchased. Moreover, si-ATB, si-NC, miR-200c mimic, miR-200c inhibitor, and miR-NC were prepared for transfection into the CRC cells, and their effects on CRC cell lines were evaluated based on the conduction of 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay, colony formation assay, and flow cytometry assay. Eventually, the Luciferase reporter gene assay was carried out to judge if there existed a targeted relationship between ATB and miR-200c. The results of Cox regression analyses suggested that overexpressed lncRNA ATB, underexpressed miR-200c, poor tumor differentiation, lymph-vascular invasion, and perineural invasion were symbolic of shortened survival of the patients with CRC (all P < .05). Besides, transfection of pcDNA3.1-ATB and miR-200c inhibitor could boost the viability and proliferation of Lovo and SW620 cell lines (all P < .05). Meanwhile, the expressions of p53 and p21 were also reduced under treatments of pcDNA3.1-ATB and miR-200c inhibitor (P < .05). In addition, CDK2 seemed to reverse the contribution of miR-200c to intensifying viability and proliferation of Lovo and SW420 cell lines (P < .05). Furthermore, ATB might downregulate miR-200c expression by targeting it (P < .05), and CDK2 was subjected to dual regulation of both ATB and miR-200c (P < .05). In conclusion, the lncRNA ATB/miR-200c/CDK2 signaling was responsible for intensified proliferation and prohibited apoptosis of CRC cells, which might provide effective approaches for diagnosing and treating CRC.
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Affiliation(s)
- Zhenyuan Gao
- Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, China
| | - Hairong Zhou
- Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, China
| | - Yaping Wang
- Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, China
| | - Juan Chen
- Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, China
| | - Yimei Ou
- Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, China
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Ma WT, Liu ZY, Chen XZ, Lin ZL, Zheng ZB, Miao WG, Xie SQ. A protein identification algorithm for tandem mass spectrometry by incorporating the abundance of mRNA into a binomial probability scoring model. J Proteomics 2019; 197:53-59. [PMID: 30790687 DOI: 10.1016/j.jprot.2019.02.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2018] [Revised: 02/15/2019] [Accepted: 02/17/2019] [Indexed: 12/17/2022]
Abstract
Peptide-spectrum matches (PSM) scoring between the experimental and theoretical spectrum is a key step in the identification of proteins using mass spectrometry (MS)-based proteomics analyses. Efficient protein identification using MS/MS data remains a challenge. The strategy of using RNA-seq data increases the number of proteins identified by re-constructing the custom search database and integrating mRNA abundance into the false discovery rate of post-PSM. However, this process lacks an algorithm that can allow the incorporation of mRNA abundance into the key scoring model of PSM. Therefore, we developed a novel PSM scoring model, which incorporates mRNA abundance for improved peptide and protein identification. In the new algorithm, abundance information of mRNA was transformed to the prior probability of protein identification and integrated to re-score in PSM using the binomial probability distribution model. Compared with other algorithms using five MS/MS datasets, the results showed that the least improvement ratios of peptide and protein groups were 3.39%-9.79% and 0.48%-8.16% in different datasets (human, rat, zebrafish, yeast, and Arabidopsis thaliana). The new strategy offers an effective solution for MS-based identification of peptides and proteins. SIGNIFICANCE: The new algorithm identifies proteins by quantifying mRNA abundance (FPKM) and incorporating it into a scoring model for peptide-spectrum matches. It is important to improve peptide and protein identification from MS/MS datasets in proteomics research.
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Affiliation(s)
- Wen-Tai Ma
- Institute of Tropical Agriculture and Forestry, Hainan University, Haikou 570228, China
| | - Zhao-Yu Liu
- Institute of Tropical Agriculture and Forestry, Hainan University, Haikou 570228, China
| | - Xiao-Zhou Chen
- School of Mathematics and Computer science, Yunnan Minzu University, Kunming 650031, China
| | - Zhen-Liang Lin
- Department of General Surgery, The Affiliated Cangnan Hospital of Wenzhou Medical University, Wenzhou 325800, China
| | - Zhong-Bing Zheng
- Institute of Tropical Agriculture and Forestry, Hainan University, Haikou 570228, China
| | - Wei-Guo Miao
- Institute of Tropical Agriculture and Forestry, Hainan University, Haikou 570228, China.
| | - Shang-Qian Xie
- Institute of Tropical Agriculture and Forestry, Hainan University, Haikou 570228, China.
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7
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Guillot L, Delage L, Viari A, Vandenbrouck Y, Com E, Ritter A, Lavigne R, Marie D, Peterlongo P, Potin P, Pineau C. Peptimapper: proteogenomics workflow for the expert annotation of eukaryotic genomes. BMC Genomics 2019; 20:56. [PMID: 30654742 PMCID: PMC6337836 DOI: 10.1186/s12864-019-5431-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Accepted: 01/03/2019] [Indexed: 01/02/2023] Open
Abstract
Background Accurate structural annotation of genomes is still a challenge, despite the progress made over the past decade. The prediction of gene structure remains difficult, especially for eukaryotic species, and is often erroneous and incomplete. We used a proteogenomics strategy, taking advantage of the combination of proteomics datasets and bioinformatics tools, to identify novel protein coding-genes and splice isoforms, assign correct start sites, and validate predicted exons and genes. Results Our proteogenomics workflow, Peptimapper, was applied to the genome annotation of Ectocarpus sp., a key reference genome for both the brown algal lineage and stramenopiles. We generated proteomics data from various life cycle stages of Ectocarpus sp. strains and sub-cellular fractions using a shotgun approach. First, we directly generated peptide sequence tags (PSTs) from the proteomics data. Second, we mapped PSTs onto the translated genomic sequence. Closely located hits (i.e., PSTs locations on the genome) were then clustered to detect potential coding regions based on parameters optimized for the organism. Third, we evaluated each cluster and compared it to gene predictions from existing conventional genome annotation approaches. Finally, we integrated cluster locations into GFF files to use a genome viewer. We identified two potential novel genes, a ribosomal protein L22 and an aryl sulfotransferase and corrected the gene structure of a dihydrolipoamide acetyltransferase. We experimentally validated the results by RT-PCR and using transcriptomics data. Conclusions Peptimapper is a complementary tool for the expert annotation of genomes. It is suitable for any organism and is distributed through a Docker image available on two public bioinformatics docker repositories: Docker Hub and BioShaDock. This workflow is also accessible through the Galaxy framework and for use by non-computer scientists at https://galaxy.protim.eu. Data are available via ProteomeXchange under identifier PXD010618. Electronic supplementary material The online version of this article (10.1186/s12864-019-5431-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Laetitia Guillot
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35042, Rennes cedex, France.,Protim, Univ Rennes, F-35042, Rennes cedex, France
| | - Ludovic Delage
- Sorbonne Université, UPMC, CNRS, UMR 8227, Integrative Biology of Marine Models, Biological Station, CS 90074, F-29688, Roscoff, France
| | - Alain Viari
- INRIA Grenoble-Rhône-Alpes, F-38330, Montbonnot-Saint-Martin, France
| | - Yves Vandenbrouck
- University Grenoble Alpes, CEA, Inserm, BIG-BGE, 38000, Grenoble, France
| | - Emmanuelle Com
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35042, Rennes cedex, France.,Protim, Univ Rennes, F-35042, Rennes cedex, France
| | - Andrés Ritter
- Sorbonne Université, UPMC, CNRS, UMR 8227, Integrative Biology of Marine Models, Biological Station, CS 90074, F-29688, Roscoff, France.,Present address: Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, Laboratory of Computational and Quantitative Biology, F-75005, Paris, France
| | - Régis Lavigne
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35042, Rennes cedex, France.,Protim, Univ Rennes, F-35042, Rennes cedex, France
| | - Dominique Marie
- Sorbonne Université, UPMC, CNRS, UMR 8227, Integrative Biology of Marine Models, Biological Station, CS 90074, F-29688, Roscoff, France
| | | | - Philippe Potin
- Sorbonne Université, UPMC, CNRS, UMR 8227, Integrative Biology of Marine Models, Biological Station, CS 90074, F-29688, Roscoff, France
| | - Charles Pineau
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35042, Rennes cedex, France. .,Protim, Univ Rennes, F-35042, Rennes cedex, France.
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8
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Lecluze E, Jégou B, Rolland AD, Chalmel F. New transcriptomic tools to understand testis development and functions. Mol Cell Endocrinol 2018; 468:47-59. [PMID: 29501799 DOI: 10.1016/j.mce.2018.02.019] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Revised: 02/26/2018] [Accepted: 02/27/2018] [Indexed: 12/16/2022]
Abstract
The testis plays a central role in the male reproductive system - secreting several hormones including male steroids and producing male gametes. A complex and coordinated molecular program is required for the proper differentiation of testicular cell types and maintenance of their functions in adulthood. The testicular transcriptome displays the highest levels of complexity and specificity across all tissues in a wide range of species. Many studies have used high-throughput sequencing technologies to define the molecular dynamics and regulatory networks in the testis as well as to identify novel genes or gene isoforms expressed in this organ. This review intends to highlight the complementarity of these transcriptomic studies and to show how the use of different sequencing protocols contribute to improve our global understanding of testicular biology.
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Affiliation(s)
- Estelle Lecluze
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, Environnement et travail) - UMR_S1085, F-35000 Rennes, France
| | - Bernard Jégou
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, Environnement et travail) - UMR_S1085, F-35000 Rennes, France
| | - Antoine D Rolland
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, Environnement et travail) - UMR_S1085, F-35000 Rennes, France
| | - Frédéric Chalmel
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, Environnement et travail) - UMR_S1085, F-35000 Rennes, France.
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9
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Roumaud P, Haché J, Martin LJ. Expression profiles of Sox transcription factors within the postnatal rodent testes. Mol Cell Biochem 2018; 447:175-187. [PMID: 29383560 DOI: 10.1007/s11010-018-3302-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2017] [Accepted: 01/25/2018] [Indexed: 12/22/2022]
Abstract
SRY-related box (Sox) transcription factors are conserved among vertebrate species. These proteins regulate multiple processes including sex determination and testis differentiation of the male embryo. Members of the Sox family have been identified in pre- and postnatal testis and are known to play an important role in sex determination (Sry, Sox9), male gonadal development, and fertility (Sox4, Sox8, Sox30). However, their expression profiles per cell types remain elusive. The objectives of this research were to characterize the expression profiles of Sox family members within adult testes using publically available datasets and to determine whether these findings are consistent with literature as well as immunofluorescence and in situ hybridization results. We have found that Sox4, Sox8, Sox9, and Sox12 are highly expressed in Sertoli cells, whereas Sox5, Sox6, and Sox30 were typically expressed in spermatocytes and spermatids. Spermatogonia were characterized by the expressions of Sox3, Sox4, Sox12, Sox13, and Sox18. Hence, these results suggest that Sox transcription factors may play different roles according to cell types of the adult mammalian testis.
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Affiliation(s)
- Pauline Roumaud
- Biology Department, Université de Moncton, 18, avenue Antonine Maillet, Moncton, NB, E1A 3E9, Canada
| | - Josée Haché
- Biology Department, Université de Moncton, 18, avenue Antonine Maillet, Moncton, NB, E1A 3E9, Canada
| | - Luc J Martin
- Biology Department, Université de Moncton, 18, avenue Antonine Maillet, Moncton, NB, E1A 3E9, Canada.
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10
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Soffientini U, Rebourcet D, Abel MH, Lee S, Hamilton G, Fowler PA, Smith LB, O'Shaughnessy PJ. Identification of Sertoli cell-specific transcripts in the mouse testis and the role of FSH and androgen in the control of Sertoli cell activity. BMC Genomics 2017; 18:972. [PMID: 29246116 PMCID: PMC5731206 DOI: 10.1186/s12864-017-4357-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Accepted: 11/29/2017] [Indexed: 11/29/2022] Open
Abstract
Background The Sertoli cells act to induce testis differentiation and subsequent development in fetal and post-natal life which makes them key to an understanding of testis biology. As a major step towards characterisation of factors involved in Sertoli cell function we have identified Sertoli cell-specific transcripts in the mouse testis and have used the data to identify Sertoli cell-specific transcripts altered in mice lacking follicle-stimulating hormone receptors (FSHRKO) and/or androgen receptors (AR) in the Sertoli cells (SCARKO). Results Adult iDTR mice were injected with busulfan to ablate the germ cells and 50 days later they were treated with diphtheria toxin (DTX) to ablate the Sertoli cells. RNAseq carried out on testes from control, busulfan-treated and busulfan + DTX-treated mice identified 701 Sertoli-specific transcripts and 4302 germ cell-specific transcripts. This data was mapped against results from microarrays using testicular mRNA from 20 day-old FSHRKO, SCARKO and FSHRKO.SCARKO mice. Results show that of the 534 Sertoli cell-specific transcripts present on the gene chips, 85% were altered in the FSHRKO mice and 94% in the SCARKO mice (mostly reduced in both cases). In the FSHRKO.SCARKO mice additive or synergistic effects were seen for most transcripts. Age-dependent studies on a selected number of Sertoli cell-specific transcripts, showed that the marked effects in the FSHRKO at 20 days had largely disappeared by adulthood although synergistic effects of FSHR and AR knockout were seen. Conclusions These studies have identified the Sertoli cell-specific transcriptome in the mouse testis and have shown that most genes in the transcriptome are FSH- and androgen-dependent at puberty although the importance of FSH diminishes towards adulthood. Electronic supplementary material The online version of this article (10.1186/s12864-017-4357-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- U Soffientini
- Institute of Biodiversity, Animal Health & Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, G61 1QH, Glasgow, UK
| | - D Rebourcet
- Institute of Biodiversity, Animal Health & Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, G61 1QH, Glasgow, UK.,MRC Centre for Reproductive Health, University of Edinburgh, The Queen's Medical Research Institute, 47 Little France Crescent, Edinburgh, EH16 4TJ, UK
| | - M H Abel
- Department of Physiology, Anatomy and Genetics, University of Oxford, Le Gros Clarke Building, Oxford, OX1 3QX, UK
| | - S Lee
- Department of Physiology, Anatomy and Genetics, University of Oxford, Le Gros Clarke Building, Oxford, OX1 3QX, UK
| | - G Hamilton
- Glasgow Polyomics, College of Medical, Veterinary and Life Sciences, University of Glasgow, G61 1QH, Glasgow, UK
| | - P A Fowler
- Institute of Medical Sciences, School of Medicine, Medical Sciences & Nutrition, University of Aberdeen, Foresterhill, Aberdeen, AB25 2ZD, UK
| | - L B Smith
- MRC Centre for Reproductive Health, University of Edinburgh, The Queen's Medical Research Institute, 47 Little France Crescent, Edinburgh, EH16 4TJ, UK.,School of Environmental and Life Sciences, University of Newcastle, Callaghan, Newcastle, 2308, Australia
| | - P J O'Shaughnessy
- Institute of Biodiversity, Animal Health & Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, G61 1QH, Glasgow, UK.
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11
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Oleaga A, Obolo-Mvoulouga P, Manzano-Román R, Pérez-Sánchez R. A proteomic insight into the midgut proteome of Ornithodoros moubata females reveals novel information on blood digestion in argasid ticks. Parasit Vectors 2017; 10:366. [PMID: 28764815 PMCID: PMC5540513 DOI: 10.1186/s13071-017-2300-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Accepted: 07/18/2017] [Indexed: 11/16/2022] Open
Abstract
Background The argasid tick Ornithodoros moubata is the main African vector of the human relapsing fever agent Borrelia duttoni and the African swine fever virus. Together with saliva, the tick midgut forms part of the host-tick-pathogen interface, and numerous midgut proteins play key functions in the blood digestion-related process and the infection and transmission of pathogens. This work explores the composition of the midgut proteome of unfed and fed O. moubata females with the aim to complete the biological information already obtained from the midgut transcriptome and provide a more robust and comprehensive perspective of this biological system. Methods Midgut tissues taken from females before feeding and 48 h after feeding were subjected to LC/MS-MS analysis. After functional characterization and classification of the proteins identified, the differences in the proteome between unfed and fed females were analysed and discussed. Additionally, a detailed analysis of particular groups of proteins that are involved in the processes of nutrient digestion and responses to the oxidative stress was carried out. Results 1491 non-redundant tick proteins were identified: 1132 of them in the midgut of unfed ticks, 1138 in the midgut of fed ticks, and up to 779 shared by both physiological conditions. Overall, the comparative analysis of the midgut proteomes of O. moubata females before and after feeding did not reveal great differences in the number or class of proteins expressed, enzymatic composition or functional classification. Conclusions The hemoglobinolytic system in ixodids and argasids is very similar in spite of the fact that they display very different feeding and reproductive strategies. Although the main source of nutrients in ticks are proteins, lipids and carbohydrates also constitute significant nutritional sources and play an important part in the process of blood digestion. The genes and proteins involved in intracellular transport mechanisms, defensive responses, detoxifying responses and stress responses seem to be closely regulated, highlighting the complexity and importance of these processes in tick biology, which in turn assigns them a great interest as targets for therapeutic and immunological interventions. Electronic supplementary material The online version of this article (doi:10.1186/s13071-017-2300-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ana Oleaga
- Parasitology Laboratory, Institute of Natural Resources and Agrobiology (IRNASA, CSIC), Cordel de Merinas, 40-52, 37008, Salamanca, Spain.
| | - Prosper Obolo-Mvoulouga
- Parasitology Laboratory, Institute of Natural Resources and Agrobiology (IRNASA, CSIC), Cordel de Merinas, 40-52, 37008, Salamanca, Spain
| | - Raúl Manzano-Román
- Parasitology Laboratory, Institute of Natural Resources and Agrobiology (IRNASA, CSIC), Cordel de Merinas, 40-52, 37008, Salamanca, Spain
| | - Ricardo Pérez-Sánchez
- Parasitology Laboratory, Institute of Natural Resources and Agrobiology (IRNASA, CSIC), Cordel de Merinas, 40-52, 37008, Salamanca, Spain
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12
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Becker E, Com E, Lavigne R, Guilleux MH, Evrard B, Pineau C, Primig M. The protein expression landscape of mitosis and meiosis in diploid budding yeast. J Proteomics 2017; 156:5-19. [DOI: 10.1016/j.jprot.2016.12.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Revised: 12/14/2016] [Accepted: 12/26/2016] [Indexed: 12/12/2022]
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13
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Prasad TSK, Mohanty AK, Kumar M, Sreenivasamurthy SK, Dey G, Nirujogi RS, Pinto SM, Madugundu AK, Patil AH, Advani J, Manda SS, Gupta MK, Dwivedi SB, Kelkar DS, Hall B, Jiang X, Peery A, Rajagopalan P, Yelamanchi SD, Solanki HS, Raja R, Sathe GJ, Chavan S, Verma R, Patel KM, Jain AP, Syed N, Datta KK, Khan AA, Dammalli M, Jayaram S, Radhakrishnan A, Mitchell CJ, Na CH, Kumar N, Sinnis P, Sharakhov IV, Wang C, Gowda H, Tu Z, Kumar A, Pandey A. Integrating transcriptomic and proteomic data for accurate assembly and annotation of genomes. Genome Res 2016; 27:133-144. [PMID: 28003436 PMCID: PMC5204337 DOI: 10.1101/gr.201368.115] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2015] [Accepted: 11/10/2016] [Indexed: 01/05/2023]
Abstract
Complementing genome sequence with deep transcriptome and proteome data could enable more accurate assembly and annotation of newly sequenced genomes. Here, we provide a proof-of-concept of an integrated approach for analysis of the genome and proteome of Anopheles stephensi, which is one of the most important vectors of the malaria parasite. To achieve broad coverage of genes, we carried out transcriptome sequencing and deep proteome profiling of multiple anatomically distinct sites. Based on transcriptomic data alone, we identified and corrected 535 events of incomplete genome assembly involving 1196 scaffolds and 868 protein-coding gene models. This proteogenomic approach enabled us to add 365 genes that were missed during genome annotation and identify 917 gene correction events through discovery of 151 novel exons, 297 protein extensions, 231 exon extensions, 192 novel protein start sites, 19 novel translational frames, 28 events of joining of exons, and 76 events of joining of adjacent genes as a single gene. Incorporation of proteomic evidence allowed us to change the designation of more than 87 predicted “noncoding RNAs” to conventional mRNAs coded by protein-coding genes. Importantly, extension of the newly corrected genome assemblies and gene models to 15 other newly assembled Anopheline genomes led to the discovery of a large number of apparent discrepancies in assembly and annotation of these genomes. Our data provide a framework for how future genome sequencing efforts should incorporate transcriptomic and proteomic analysis in combination with simultaneous manual curation to achieve near complete assembly and accurate annotation of genomes.
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Affiliation(s)
- T S Keshava Prasad
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,YU-IOB Center for Systems Biology and Molecular Medicine, Yenepoya University, Mangalore 575018, India.,NIMHANS-IOB Proteomics and Bioinformatics Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka 560029, India
| | - Ajeet Kumar Mohanty
- National Institute of Malaria Research, Field Station, Goa 403001, India.,Department of Zoology, Goa University, Taleigao Plateau, Goa 403206, India
| | - Manish Kumar
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India
| | - Sreelakshmi K Sreenivasamurthy
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India
| | - Gourav Dey
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India
| | - Raja Sekhar Nirujogi
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Centre for Bioinformatics, Pondicherry University, Puducherry 605014, India
| | - Sneha M Pinto
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,YU-IOB Center for Systems Biology and Molecular Medicine, Yenepoya University, Mangalore 575018, India
| | - Anil K Madugundu
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Centre for Bioinformatics, Pondicherry University, Puducherry 605014, India
| | - Arun H Patil
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Jayshree Advani
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India
| | - Srikanth S Manda
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Centre for Bioinformatics, Pondicherry University, Puducherry 605014, India
| | - Manoj Kumar Gupta
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India
| | - Sutopa B Dwivedi
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India
| | - Dhanashree S Kelkar
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India
| | - Brantley Hall
- Department of Biochemistry, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA
| | - Xiaofang Jiang
- Department of Biochemistry, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA
| | - Ashley Peery
- Department of Entomology, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA
| | - Pavithra Rajagopalan
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Soujanya D Yelamanchi
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Hitendra S Solanki
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Remya Raja
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India
| | - Gajanan J Sathe
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India
| | - Sandip Chavan
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India
| | - Renu Verma
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Krishna M Patel
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India
| | - Ankit P Jain
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Nazia Syed
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Department of Biochemistry and Molecular Biology, Pondicherry University, Puducherry 605014, India
| | - Keshava K Datta
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Aafaque Ahmed Khan
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Manjunath Dammalli
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Department of Biotechnology, Siddaganga Institute of Technology, Tumkur, Karnataka 572103, India
| | - Savita Jayaram
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India
| | - Aneesha Radhakrishnan
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Department of Biochemistry and Molecular Biology, Pondicherry University, Puducherry 605014, India
| | - Christopher J Mitchell
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
| | - Chan-Hyun Na
- Department of Neurology, Johns Hopkins University, Baltimore, Maryland 21205, USA
| | - Nirbhay Kumar
- Department of Tropical Medicine, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana 70112, USA
| | - Photini Sinnis
- Malaria Research Institute, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA
| | - Igor V Sharakhov
- Department of Entomology, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA
| | - Charles Wang
- Center for Genomics and Department of Basic Sciences, School of Medicine, Loma Linda University, Loma Linda, California 92350, USA
| | - Harsha Gowda
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,YU-IOB Center for Systems Biology and Molecular Medicine, Yenepoya University, Mangalore 575018, India
| | - Zhijian Tu
- Department of Biochemistry, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA
| | - Ashwani Kumar
- National Institute of Malaria Research, Field Station, Goa 403001, India
| | - Akhilesh Pandey
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.,Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.,Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.,Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
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14
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Chalmel F, Rolland AD. Linking transcriptomics and proteomics in spermatogenesis. Reproduction 2016; 150:R149-57. [PMID: 26416010 DOI: 10.1530/rep-15-0073] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Spermatogenesis is a complex and tightly regulated process leading to the continuous production of male gametes, the spermatozoa. This developmental process requires the sequential and coordinated expression of thousands of genes, including many that are testis-specific. The molecular networks underlying normal and pathological spermatogenesis have been widely investigated in recent decades, and many high-throughput expression studies have studied genes and proteins involved in male fertility. In this review, we focus on studies that have attempted to correlate transcription and translation during spermatogenesis by comparing the testicular transcriptome and proteome. We also discuss the recent development and use of new transcriptomic approaches that provide a better proxy for the proteome, from both qualitative and quantitative perspectives. Finally, we provide illustrations of how testis-derived transcriptomic and proteomic data can be integrated to address new questions and how the 'proteomics informed by transcriptomics' technique, by combining RNA-seq and MS-based proteomics, can contribute significantly to the discovery of new protein-coding genes or new protein isoforms expressed during spermatogenesis.
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Affiliation(s)
- Frédéric Chalmel
- Inserm U1085-IrsetUniversité de Rennes 1, F-35042 Rennes, France
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15
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Locard-Paulet M, Pible O, Gonzalez de Peredo A, Alpha-Bazin B, Almunia C, Burlet-Schiltz O, Armengaud J. Clinical implications of recent advances in proteogenomics. Expert Rev Proteomics 2016; 13:185-99. [DOI: 10.1586/14789450.2016.1132169] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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16
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Taylor DH, Chu ETJ, Spektor R, Soloway PD. Long non-coding RNA regulation of reproduction and development. Mol Reprod Dev 2015; 82:932-56. [PMID: 26517592 PMCID: PMC4762656 DOI: 10.1002/mrd.22581] [Citation(s) in RCA: 122] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2015] [Accepted: 09/03/2015] [Indexed: 12/13/2022]
Abstract
Noncoding RNAs (ncRNAs) have long been known to play vital roles in eukaryotic gene regulation. Studies conducted over a decade ago revealed that maturation of spliced, polyadenylated coding mRNA occurs by reactions involving small nuclear RNAs and small nucleolar RNAs; mRNA translation depends on activities mediated by transfer RNAs and ribosomal RNAs, subject to negative regulation by micro RNAs; transcriptional competence of sex chromosomes and some imprinted genes is regulated in cis by ncRNAs that vary by species; and both small-interfering RNAs and piwi-interacting RNAs bound to Argonaute-family proteins regulate post-translational modifications on chromatin and local gene expression states. More recently, gene-regulating noncoding RNAs have been identified, such as long intergenic and long noncoding RNAs (collectively referred to as lncRNAs)--a class totaling more than 100,000 transcripts in humans, which include some of the previously mentioned RNAs that regulate dosage compensation and imprinted gene expression. Here, we provide an overview of lncRNA activities, and then review the role of lncRNAs in processes vital to reproduction, such as germ cell specification, sex determination and gonadogenesis, sex hormone responses, meiosis, gametogenesis, placentation, non-genetic inheritance, and pathologies affecting reproductive tissues. Results from many species are presented to illustrate the evolutionarily conserved processes lncRNAs are involved in.
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Affiliation(s)
- David H. Taylor
- Field of Genetics, Genomics and Development, Cornell University, Ithaca, New York
| | - Erin Tsi-Jia Chu
- Field of Comparative Biomedical Sciences, Cornell University, Ithaca, New York
| | - Roman Spektor
- Field of Genetics, Genomics and Development, Cornell University, Ithaca, New York
| | - Paul D. Soloway
- Field of Genetics, Genomics and Development, Cornell University, Ithaca, New York
- Field of Comparative Biomedical Sciences, Cornell University, Ithaca, New York
- Division of Nutritional Sciences, Cornell University, Ithaca, New York
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17
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Darde TA, Sallou O, Becker E, Evrard B, Monjeaud C, Le Bras Y, Jégou B, Collin O, Rolland AD, Chalmel F. The ReproGenomics Viewer: an integrative cross-species toolbox for the reproductive science community. Nucleic Acids Res 2015; 43:W109-16. [PMID: 25883147 PMCID: PMC4489245 DOI: 10.1093/nar/gkv345] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2015] [Accepted: 04/06/2015] [Indexed: 12/23/2022] Open
Abstract
We report the development of the ReproGenomics Viewer (RGV), a multi- and cross-species working environment for the visualization, mining and comparison of published omics data sets for the reproductive science community. The system currently embeds 15 published data sets related to gametogenesis from nine model organisms. Data sets have been curated and conveniently organized into broad categories including biological topics, technologies, species and publications. RGV's modular design for both organisms and genomic tools enables users to upload and compare their data with that from the data sets embedded in the system in a cross-species manner. The RGV is freely available at http://rgv.genouest.org.
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Affiliation(s)
- Thomas A Darde
- Inserm U1085-Irset, Université de Rennes 1, F-35042 Rennes, France Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA/INRIA) - GenOuest platform, Université de Rennes 1, F-35042 Rennes, France
| | - Olivier Sallou
- Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA/INRIA) - GenOuest platform, Université de Rennes 1, F-35042 Rennes, France
| | | | - Bertrand Evrard
- Inserm U1085-Irset, Université de Rennes 1, F-35042 Rennes, France
| | - Cyril Monjeaud
- Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA/INRIA) - GenOuest platform, Université de Rennes 1, F-35042 Rennes, France
| | - Yvan Le Bras
- Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA/INRIA) - GenOuest platform, Université de Rennes 1, F-35042 Rennes, France
| | - Bernard Jégou
- Inserm U1085-Irset, Université de Rennes 1, F-35042 Rennes, France Ecole des Hautes Études en Santé Publique, Avenue du Professeur Léon-Bernard, F-35043 Rennes, France
| | - Olivier Collin
- Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA/INRIA) - GenOuest platform, Université de Rennes 1, F-35042 Rennes, France
| | | | - Frédéric Chalmel
- Inserm U1085-Irset, Université de Rennes 1, F-35042 Rennes, France
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18
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Trapp J, Armengaud J, Pible O, Gaillard JC, Abbaci K, Habtoul Y, Chaumot A, Geffard O. Proteomic Investigation of Male Gammarus fossarum, a Freshwater Crustacean, in Response to Endocrine Disruptors. J Proteome Res 2014; 14:292-303. [DOI: 10.1021/pr500984z] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Judith Trapp
- Irstea, Unité
de Recherche MALY, Laboratoire d’écotoxicologie, CS70077, F-69626 Villeurbanne, France
- CEA, DSV, IBEB,
Lab Biochim System Perturb, F-30207 Bagnols-sur-Cèze, France
| | - Jean Armengaud
- CEA, DSV, IBEB,
Lab Biochim System Perturb, F-30207 Bagnols-sur-Cèze, France
| | - Olivier Pible
- CEA, DSV, IBEB,
Lab Biochim System Perturb, F-30207 Bagnols-sur-Cèze, France
| | | | - Khedidja Abbaci
- Irstea, Unité
de Recherche MALY, Laboratoire d’écotoxicologie, CS70077, F-69626 Villeurbanne, France
| | - Yassine Habtoul
- Irstea, Unité
de Recherche MALY, Laboratoire d’écotoxicologie, CS70077, F-69626 Villeurbanne, France
| | - Arnaud Chaumot
- Irstea, Unité
de Recherche MALY, Laboratoire d’écotoxicologie, CS70077, F-69626 Villeurbanne, France
| | - Olivier Geffard
- Irstea, Unité
de Recherche MALY, Laboratoire d’écotoxicologie, CS70077, F-69626 Villeurbanne, France
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