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Article Cite This: J. Proteome Res. 2018, 17, 1300−1308

pubs.acs.org/jpr

Dynamics of Zebrafish Heart Regeneration Using an HPLC−ESI−MS/ MS Approach Danjun Ma,*,†,◆ Chengjian Tu,‡,◆ Quanhu Sheng,§,◆ Yuxi Yang,∥,◆ Zhisheng Kan,⊥ Yan Guo,# Yu Shyr,§ Ian C. Scott,▽,○ and Xin Lou*,∥ †

College of Mechanical Engineering, Dongguan University of Technology, 1 Daxue Road, Dongguan, Guangdong 523808, China Department of Pharmaceutical Sciences, State University of New York at Buffalo, 285 Kapoor Hall, Buffalo, New York 14260, United States § Center for Quantitative Sciences, Department of Biostatistics, Vanderbilt University School of Medicine, 2220 Pierce Avenue, Nashville, Tennessee 37232, United States ∥ Model Animal Research Center, Nanjing University, Nanjing 210093, China ⊥ Department of Neurosurgery, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China # Department of Internal Medicine, University of New Mexico, Comprehensive Cancer Center, Albuquerque, New Mexico 87131, United States ▽ Program in Developmental and Stem Cell Biology, The Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada ○ Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada ‡

S Supporting Information *

ABSTRACT: Failure to properly repair damaged due to myocardial infarction is a major cause of heart failure. In contrast with adult mammals, zebrafish hearts show remarkable regenerative capabilities after substantial damage. To characterize protein dynamics during heart regeneration, we employed an HPLC−ESI−MS/ MS (mass spectrometry) approach. Myocardium tissues were taken from shamoperated fish and ventricle-resected sample at three different time points (2, 7, and 14 days); dynamics of protein expression were analyzed by an ion-current-based quantitative platform. More than 2000 protein groups were quantified in all 16 experiments. Two hundred and nine heart-regeneration-related protein groups were quantified and clustered into six time-course patterns. Functional analysis indicated that multiple molecular function and metabolic pathways were involved in heart regeneration. Interestingly, Ingenuity Pathway Analysis revealed that P53 signaling was inhibited during the heart regeneration, which was further verified by real-time quantitative polymerase chain reaction (Q-PCR). In summary, we applied systematic proteomics analysis on regenerating zebrafish heart, uncovered the dynamics of regenerative genes expression and regulatory pathways, and provided invaluable insight into design regenerative-based strategies in human hearts. KEYWORDS: heart regeneration, quantitative proteomics, zebrafish, P53, ion-current-based quantitative platform

1. INTRODUCTION Mammals are not equipped with significant natural capacity to repair injured heart muscle; this deficiency leaves millions heart failure patients vulnerable to pathological hypertrophy, serious secondary heart failure, and other issues. Unlike the mammalian heart, the injured zebrafish heart normally undergoes minimal scarring, and in 30 days the transient fibrin clot is replaced with new contractile muscle.1 A molecular understanding of endogenous regenerative mechanisms will help to project a future in which cardiac muscle regeneration can be therapeutically stimulated in human hearts. Researchers have found that zebrafish heart regeneration involves two fundamental components: (i) an environment that stimulates and support myocardium generation and (ii) existing cardiomyocytes proliferating as the primary cellular source.2 © 2018 American Chemical Society

There is mounting evidence that cardiac nonmuscle cells create an environment that enables myocardium regrowth. Injury to the zebrafish heart initiates an organ-wide reaction: endocardium, the endothelial lining of the lumen and epicardium, the outer lining of the heart, release signals that facilitate cardiomyocyte proliferation, and neo-angiogenesis. RA, Tgf-β ligands, Igf2, Shh, and platelet-derived growth factor (Pdgf) ligands all are pumped into the vicinity of cardiomyocytes to cause positive influences on muscle regeneration.3−5 Genetic lineage tracing experiments in zebrafish have shown that the regenerative ability of the zebrafish heart mainly relies on existing cardiomyocytes re-entering into mitosis.6 Upon Received: December 24, 2017 Published: January 25, 2018 1300

DOI: 10.1021/acs.jproteome.7b00915 J. Proteome Res. 2018, 17, 1300−1308

Article

Journal of Proteome Research

10% glycerol, 1 mM MgCl2, 1 mM CaCl2) with a mixture of phosphatase and protease inhibitors in 5 μL of extraction buffer per milligram of tissue.13 Muscle lysates were then incubated on ice for 20 min, followed by centrifugation at 4 °C for 20 min at 15 000g. Protein concentrations in the supernatant fractions were determined by the Bradford. These protein mixtures were subsequently digested in-solution by trypsin,14 and the resulting protein digests were dried by vacuum centrifugation and stored at −80 °C for further use.

receiving the regeneration signal, these cardiomyocytes show characteristics of dedifferentiation, including a reduction in contractile structure, and start to express a group of embryonic cardiac factors including gata4.6,7 In addition to cardiomyocyte proliferation, it has been reported that chemokine-mediated cardiomyocyte migration to the injury site is a critical step in the regenerative process.8 Thus far, most of the studies have analyzed the mechanisms of regeneration by determining the gene expression via Q-PCR, in situ hybridization (ISH), microarray, and deep sequencing approaches.5,9−11 However, analyses of transcriptome and proteome data sets have shown that mRNA expression levels do not necessarily reflect protein abundances. During the past decade, MS-based proteomics have helped tremendously to systematically analyze protein dynamics in various biological processes. Moreover, the development of quantitative methods for processing MS data has greatly enhanced the accuracy of unbiased proteome screens. For example, a recent study successfully identified proteins to be differentially regulated during zebrafish fin regeneration using MS technology.12 In the present study, we applied an ion-current-based quantitative platform to characterize proteomic profiling change during zebrafish heart regeneration. Myocardium tissues were taken from sham-operated fish and ventricle resected sample at three different time points (2, 7, and 14 days). There are 209 differential protein groups involved in different regeneration stages from stress response to restore the shape of heart chamber. Functional and pathways analysis by Ingenuity Pathway Analysis (IPA) on the 209 differential protein groups identified multiple significantly enriched pathways pertaining to heart regeneration. Interestingly, IPA analysis revealed that P53 signaling was inhibited during the heart regeneration, which was further verified by Q-PCR. This new finding may facilitate the design of experiments to better understand mechanisms underlying heart regeneration and to design regenerative-based strategies in humans.

2.5. HPLC−ESI−MS/MS Analysis

The peptide mixture was separated with a linear gradient of 5− 35% buffer B (99.9% ACN and 0.1% FA) in 180 min at a flow rate of 250 nL/min on a C18 reversed-phase column (75 μm ID, 15 cm length) packed in-house with ReproSil-Pur C18-AQ μm resin (Dr. Maisch) in buffer A (0.1% FA). A nanoflow Easy−nLC system (Thermo Scientific) was online coupled to a Thermo Finnigan LTQ-Orbitrap Elite fitted with a nanospray flex Ion source (Thermo Fisher, San Jose, CA). MS data were acquired in survey scan with 240 000 resolution at m/z 400 and ion trap rapid collision-induced dissociation (CID) scans of the 20 most abundant ions (Top-20-rCID). Dynamic exclusion was set at 60 s. 2.6. Peptides/Protein Identification and Quantitation

Peptides/proteins identification was performed using the MaxQuant,15−18 one of the most popular quantitative proteomics software packages. In brief, raw MS files were processed using the MaxQuant (ver. 1.3.0.5) against a database with forward and reversed UniProt Danio rerio protein sequences, downloaded from www.uniprot.org (2015.3). Standard settings in the MaxQuant were applied. Parent mass tolerance was 6 ppm, and fragment mass tolerance was 0.5 Da. Two missing trypsin cleavage sites were allowed, carbamidomethylation was searched as a fixed modification, and methionine oxidation (Mox), phosphorylation (STY), and acetylation (Protein N-term) were allowed as variable modifications. The false discovery rate (FDR) for both proteins and peptides (with minimum six amino acids) was set to 0.01. Ion-current-based quantitative analysis was applied to perform the relative quantification of these confidently identified proteins as previously described.19 The LC−MS/ MS analyses were aligned with the ChromAlign algorithm in SIEVE package (v2.1, Thermo Scientific, San Jose, CA); then, quantitative frames were determined based on m/z (width: 15 ppm) and retention time (width: 2.5 min). Peptide ion current areas were calculated for each frame to assess relative expression ratios. Subsequent to frame determination, the peptide identifications were linked to each frame based on the scan number using an in-house script. Peptides or frames shared among different protein groups were excluded from quantitative analysis. The ion current (IC) intensities of each peptide were normalized against the total ion current intensities of all peptides in individual runs according to the following formula: normalized IC = (IC/total IC). Intensities of frames with the identical sequence were combined to be the unique peptide intensity. Then, intensities of unique peptides from same protein were further combined to be the protein intensity with outlier removing based on Grubbs’ test in both steps. The relative expression ratio of a protein in different groups was calculated based on the average ion current intensities of all replicates in each group.

2. MATERIALS AND METHODS 2.1. Chemicals and Regents

LC−MS-grade buffer was purchased from Thermo Fisher (Fair Lawn, NJ). Sequencing-grade trypsin was obtained from Promega (Madison, WI). Protease and phosphatase inhibitor mixtures and other chemicals used were purchased from Sigma (St. Louis, MO). 2.2. Zebrafish Strains and Maintenance

Animal density was maintained at approximately four fish per liter in all experiments. Zebrafish were housed and handled as per Canadian Council on Animal Care and Hospital for Sick Children Laboratory Animal Services guidelines. 2.3. Zebrafish Cardiac Injures

Outbred TU zebrafish strains (4−10 months of age) were used for ventricular resection surgeries.1 All protocols involving the use of animals were in accordance with approved guidelines of the Institutional Animal Care and Use Committee of the Nanjing University. 2.4. Heart Muscle Sample Preparation

Approximately 10−20 mg heart muscle samples were homogenized on ice using a Brinkmann homogenizer (model PT 10/35) in detergent-containing lysis buffer A (50 mM HEPES, pH 7.6, 150 mM NaCl, 20 mM sodium pyrophosphate, 20 mM β-glycerophosphate, 2 mM EDTA, 1% Triton, 1301

DOI: 10.1021/acs.jproteome.7b00915 J. Proteome Res. 2018, 17, 1300−1308

Article

Journal of Proteome Research

Figure 1. Experimental design and data summary. (A) Schematic overview of the experimental proteomics workflow. At 2, 7, and 14 days past surgery, regrowing and control hearts were collected and processed for mass spectrometric analysis. (B) Data analysis flow chart. A total of 16 samples from four sets of experiments were analyzed: sham, 2 DPA, 7 DPA, and 14 DPA (n = 4). In total, 2174 unique protein groups were quantified in all 16 experiments, with at least two unique peptides at a protein FDR of 1%. Of them, 209 proteins showed the significant changes (fold change >1.5, p value 0.7). (G−L) The other six genes, including oxsr1a, stmn1, anxa2a, hk2, anxa1a, and shmt1, have no correlation between gene expression on mRNA level and protein level (correl 1.5, p value 0.7) (Figure 5A−F). This suggested that the six genes had similar regulation on mRNA and protein levels during heart regeneration. The other six genes, oxsr1a, stmn1, anxa2a, hk2, anxa1a, and shmt1, had no correlation between gene expression on mRNA level and protein level (Correl