Proteomics Analysis of Extracellular Matrix Remodeling During Zebrafish Heart Regeneration
2019; Elsevier BV; Volume: 18; Issue: 9 Linguagem: Inglês
10.1074/mcp.ra118.001193
ISSN1535-9484
AutoresAnna Garcia-Puig, José Luis Mosquera, Senda Jiménez‐Delgado, Cristina García-Pastor, Ignasi Jorba, Daniel Navajas, Françesc Canals, Ángel Raya,
Tópico(s)Cardiac Fibrosis and Remodeling
ResumoAdult zebrafish, in contrast to mammals, are able to regenerate their hearts in response to injury or experimental amputation. Our understanding of the cellular and molecular bases that underlie this process, although fragmentary, has increased significantly over the last years. However, the role of the extracellular matrix (ECM) during zebrafish heart regeneration has been comparatively rarely explored. Here, we set out to characterize the ECM protein composition in adult zebrafish hearts, and whether it changed during the regenerative response. For this purpose, we first established a decellularization protocol of adult zebrafish ventricles that significantly enriched the yield of ECM proteins. We then performed proteomic analyses of decellularized control hearts and at different times of regeneration. Our results show a dynamic change in ECM protein composition, most evident at the earliest (7 days postamputation) time point analyzed. Regeneration associated with sharp increases in specific ECM proteins, and with an overall decrease in collagens and cytoskeletal proteins. We finally tested by atomic force microscopy that the changes in ECM composition translated to decreased ECM stiffness. Our cumulative results identify changes in the protein composition and mechanical properties of the zebrafish heart ECM during regeneration. Adult zebrafish, in contrast to mammals, are able to regenerate their hearts in response to injury or experimental amputation. Our understanding of the cellular and molecular bases that underlie this process, although fragmentary, has increased significantly over the last years. However, the role of the extracellular matrix (ECM) during zebrafish heart regeneration has been comparatively rarely explored. Here, we set out to characterize the ECM protein composition in adult zebrafish hearts, and whether it changed during the regenerative response. For this purpose, we first established a decellularization protocol of adult zebrafish ventricles that significantly enriched the yield of ECM proteins. We then performed proteomic analyses of decellularized control hearts and at different times of regeneration. Our results show a dynamic change in ECM protein composition, most evident at the earliest (7 days postamputation) time point analyzed. Regeneration associated with sharp increases in specific ECM proteins, and with an overall decrease in collagens and cytoskeletal proteins. We finally tested by atomic force microscopy that the changes in ECM composition translated to decreased ECM stiffness. Our cumulative results identify changes in the protein composition and mechanical properties of the zebrafish heart ECM during regeneration. According to the World Health Organization, 1/3 of all global deaths are because of cardiovascular diseases (1Mendis S. Puska P. Norrving B. 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Biol. 2013; 382: 427-435Crossref PubMed Scopus (152) Google Scholar). A role of hyaluronic acid (HA) signaling during zebrafish heart regeneration has also been proposed, because components of this pathway were found expressed in response to injury, and blocking HA signaling impaired regeneration (32Missinato M.A. Tobita K. Romano N. Carroll J.A. Tsang M. Extracellular component hyaluronic acid and its receptor Hmmr are required for epicardial EMT during heart regeneration.Cardiovasc. Res. 2015; 107: 487-498Crossref PubMed Scopus (49) Google Scholar). These three ECM components (tnc, fn, and HA) have been shown to form a pro-regenerative matrix in newt heart (48Jaźwińska A. Sallin P. Regeneration versus scarring in vertebrate appendages and heart.J. Pathol. 2016; 238: 233-246Crossref PubMed Scopus (47) Google Scholar). Thus, a comprehensive study of the zebrafish heart ECM composition and characteristics may reveal other key points in the heart regeneration process. In the present study, we have developed a protocol to decellularize adult zebrafish hearts and applied it to noninjured and regenerating hearts at 7, 14, and 30 days postamputation (dpa). We have then characterized the composition of the zebrafish heart ECM, as well as the major changes in ECM composition that take place during heart regeneration. Finally, we have used atomic force microscopy (AFM) to analyze the effect that changes in ECM composition have on matrix stiffness. Our studies identify important changes in ECM protein composition and mechanical properties during zebrafish heart regeneration. Wild-type zebrafish of the AB strain were maintained according to Standard protocols (49Westerfield, M., (2000) The Zebrafish Book. A Guide for the Laboratory Use of Zebrafish (Danio rerio) ed University of Oregon Press E (Eugene). 4th Ed.Google Scholar). The ventricular amputations were done as previously described by Raya et al., 2003 (19Raya A. Koth C.M. Büscher D. Kawakami Y. Itoh T. Raya R.M. Sternik G. Tsai H.-J. Rodríguez-Esteban C. Izpisúa-Belmonte J.C. Activation of Notch signaling pathway precedes heart regeneration in zebrafish.Proc. Natl. Acad. Sci. U.S.A. 2003; 100: 11889-11895Crossref PubMed Scopus (275) Google Scholar). Animal procedures were performed under the approbation of the Ethics Committee on Experimental Animals of the PRBB (CEEA-PRBB). Animals were sacrificed, and hearts were extracted after an intra-abdominal injection of 20 μl of heparin (1000U/ml, Sigma-Aldrich, Madrid, Spain). Only the ventricles were decellularized by immersion in a 0.5‥ SDS solution for 4h. Then ventricles were washed with distilled H2O for 30 min. Then immersed into a 1‥ Triton-X solution for 30 min and finally washed three times with distilled H2O for 10 min each wash. All solutions were previously filtered with a 0.2 μm filter, and all incubations steps were done in a horizontal shaking plate at 30 °C and 28 rpm. Zebrafish hearts were extracted as previously mentioned and fixed overnight with 4‥ paraformaldehyde. Then the hearts were incubated overnight at 4 °C with 30‥ sucrose and frozen in OCT (Tissue-Tek, Alphen aan den Rijn, The Netherlands) for cryosectioning. The decellularized ventricles were embedded in OCT, snap frozen with isopentane and fixed after sectioning incubating them 10min in 4‥ paraformaldehyde. 10 μm thick slices of the samples were counterstained with DAPI (1:10,000) for 4 min and stained with Hematoxylin and Eosin, and Masson's Trichrome staining. Genomic DNA was extracted from nondecellularized and decellularized zebrafish ventricles. Samples were homogenized by adding 200 μl of PBS, 20 μl Proteinase K (Qiagen, Madrid, Spain) and 4 μl of RNAseA (Qiagen) and vortexed. Tissue lysis was done adding 200 μl of AL Lysis buffer (Qiagen). DNA was purified by chloroform and precipitated using isopropanol. Finally, the pellets were dried and 20 μl of TE buffer (Qiagen) was added. DNA concentration was measured with a spectrophotometer (NanoDrop® ND-100, Thermo Fisher Scientific, Barcelona, Spain). Two different proteomic analyses were performed: one to assess the decellularization protocol, and another one to assess the regeneration process. For the first one, native zebrafish ventricles, half-decellularized ventricles, and fully decellularized ventricles were analyzed. For the second one, decellularized zebrafish ventricles at 7 dpa, 14 dpa, and 30 dpa, were analyzed compared with sham operated fishes. Proteins of each sample were solubilized by mixing with 50μl of 1‥ SDS, 100 mm Tris-HCl pH 7.6, 100 mm DTT, 10 min sonication and boiling for 3 min. Protein extracts were clarified by centrifugation at 16,000 × g for 5 min and quantified using the RcDc kit (BioRad, Hercules, CA). In the first proteomic analysis, 12 μg of protein of each sample were digested with LysC and trypsin using a Filter-Aided Sample Preparation (FASP) protocol and further analyzed by mass spectrometry. The LC separation was conducted on an Easy-nLC 1000 (Thermo) using 0.1‥ formic acid as Solvent A and acetonitrile with 0.1‥ formic acid as B. Each run was carried out at 250 nL/min with a gradient of 95‥ of solvent A to 65‥ A in 180 min. Blank samples with solvent A injections were run in between each sample. Sample was concentrated in an Acclaim PepMap 100 trap column (Thermo), and fractionated in a Nikkyo Technos Co., 75 μm ID, 3 A pore size, 12.5 cm in length with built in emitter column, coupled to a Nanospray Flex (Thermo) ESI source. Shotgun LC-MS/MS analysis was performed online with an electrospray voltage of 1.9 kV using a Q Exactive HF mass spectrometer (Thermo) with HCD fragmentation using top 15 precursor with charge 2 to 5 for data-dependent acquisition (DDA). MS1 spectra were acquired in the mass range 390–1700 m/z at a resolution of 60,000 at m/z 400 with a target value of 3 × 106 ions and maximum fill time of 20 ms. MS2 spectra were collected with a target ion value of 2 × 105 and maximum 100 ms fill time using a normalized collision energy of 27. Dynamic precursor exclusion was set at 15 s. The raw files were processed with the MaxQuant software (version 1.6.2.6a) using the built-in Andromeda Search Engine. The Danio rerio TrEMBL database downloaded from www.uniprot.org (Oct, 8th 2018) (62,078 entries) was used to search for peptides. MS/MS spectra were searched with a first search precursor mass tolerance of 20 ppm. Then, the peptide masses were corrected, and a second search was performed at 4.5 ppm of mass tolerance. The fragment tolerance was set to 0.5 Da, the enzyme was trypsin and a maximum of 2 missed cleavages were allowed. The cysteine carbamidomethylation was set as fixed modification and methionine oxidation as well as protein N-terminal acetylation as variable modifications. To improve the identifications the "match between runs" was enabled among the replicates of every experimental condition. In the second proteomic analysis, the protein amount recovered was around 5 μg for the 0.5‥ SDS treated sample. The buffer was changed to 2 m Urea 50 mm Ammonium Bicarbonate using a 5kD Amicon Ultrafiltration device, and the samples were digested with trypsin. Each sample was the analyzed by LC-MS in duplicate. Five hundred nanograms of each sample was analyzed on a Maxis Impact high-resolution Q-TOF spectrometer (Bruker, Bremen, Germany), coupled to a nano-HPLC system (Proxeon, Odense, Denmark). The samples evaporated and dissolved in 5‥ acetonitrile, 0.1‥ formic acid in water, were first concentrated on a 100 mm ID, 2 cm Proxeon nanotrapping column and then loaded onto a 75 mm ID, 25 cm Acclaim PepMap nanoseparation column (Dionex, Sunnyvale, CA). Chromatography was run using a 0.1‥ formic acid - acetonitrile gradient (5–35‥ in 120min; flow rate 300 nL/min). The column was coupled to the mass spectrometer inlet through a Captive Spray (Bruker) ionization source. MS acquisition was set to cycles of MS (2 Hz), followed by Intensity Dependent MS/MS (2–20 Hz) of the 20 most intense precursor ions with an intensity threshold for fragmentation of 2500 counts, and using a dynamic exclusion time of 0.32min. All spectra were acquired on the range 100–2200Da. LC-MSMS data was analyzed using the Data Analysis 4.0 software (Bruker). Proteins were identified using Mascot (ver. 2.5; Matrix Science, London UK) to search against the Danio rerio proteins in the SwissProt 20160108 database (43,095 sequences). MS/MS spectra were searched with a precursor mass tolerance of 10 ppm, fragment tolerance of 0.05 Da, trypsin specificity with a maximum of 2 missed cleavages, cysteine carbamidomethylation set as fixed modification and methionine oxidation as variable modification. Both mass spectrometry proteomics data sets, for the decellularization protocol and for the regeneration proteome, have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository (50Vizcaíno J.A. Côté R.G. Csordas A. Dianes J.A. Fabregat A. Foster J.M. Griss J. Alpi E. Birim M. Contell J. O'Kelly G. Schoenegger A. Ovelleiro D. Pérez-Riverol Y. Reisinger F. Ríos D. Wang R. Hermjakob H. The Proteomics Identifications (PRIDE) database and associated tools: status in 2013.Nucleic Acids Res. 2012; 41: D1063-D1069Crossref PubMed Scopus (1595) Google Scholar) with the data set identifiers and , respectively. For the decellularization protocol proteomic analysis, MaxQuant software (version 1.6.2.6a) was used to validate the peptides and proteins identifications. The final list of peptides was obtained after applying a 5‥ False Discovery Rate (FDR). For proteins, only the proteins with at least 1 assigned peptide after applying a 5‥ FDR were considered. The nonunique peptides were assigned to the corresponding protein group according to the Razor peptides rule implemented in the software (principle of parsimony). Finally, the identified peptides and proteins were filtered to remove the peptides/proteins tagged as "Reverse" (significantly identified in the reverse database) and "potential contaminant" (items identified as contaminants in the "contaminants.fasta" file) as well as the proteins "Only identified by site" (proteins identified only with modified peptides). The lists can be found in the supplemental material uploaded to the PRIDE repository, with project accession code . For the regeneration process proteomic analysis, Scaffold (version Scaffold_4.0.5, Proteome Software Inc., Portland, OR) was used to validate MS/MS based peptide and protein identifications. Peptide identifications were accepted if they could be established at greater than 99‥ probability by the Peptide Prophet algorithm (51Keller A. Nesvizhskii A.I. Kolker E. Aebersold R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.Anal. Chem. 2002; 74: 5383-5392Crossref PubMed Scopus (3886) Google Scholar). Protein identifications were accepted if they could be established at greater than 98‥ probability to achieve an FDR less than 1‥ and contained at least 1 identified peptide. Protein probabilities were assigned by the Protein Prophet algorithm (52Nesvizhskii A.I. Keller A. Kolker E. Aebersold R. A statistical model for identifying proteins by tandem mass spectrometry.Anal. Chem. 2003; 75: 4646-4658Crossref PubMed Scopus (3621) Google Scholar). Protein isoforms and members of a protein family would be identified separately only if peptides that enable differentiation of isoforms had been identified based on gene
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