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Quantitative Proteomic Study Reveals Up-regulation of cAMP Signaling Pathway-related Proteins in Mild Traumatic Brain Injury Hai Song, Shanhua Fang, Jing Gao, Jiaxong Wang, Zhenzhen Cao, Zeyun Guo, Qiongping Huang, Yongqang Qu, Hu Zhou, and Jianyun Yu J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.7b00618 • Publication Date (Web): 07 Dec 2017 Downloaded from http://pubs.acs.org on December 9, 2017

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Journal of Proteome Research

Quantitative Proteomic Study Reveals Up-regulation of cAMP Signaling Pathway-related Proteins in Mild Traumatic Brain Injury

Hai Song1, 5#, Shanhua Fang3#, Jing Gao4, Jiaxong Wang1, 5，Zhenzhen Cao2，Zeyun Guo2， Qiongping Huang4, Yongqang Qu1, Hu Zhou3,4*, Jianyun Yu1* 1. Department of Forensic Medicine of Kunming Medical University, Kunming,

Yunnan 650032, China 2. Department of Anatomy and histology of Kunming Medical University, Kunming,

Yunnan 650032, China 3. E-institute of Shanghai Municipal Education Committee, Shanghai University of

Traditional Chinese Medicine, 1200 Cai Lun Road, Shanghai, China 201203 4. Department of Analytical Chemistry and CAS Key Laboratory of Receptor

Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China 5. Department of Neurosurgery, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, China

Corresponding Authors *Jianyun Yu, Email: [email protected], Tel.: +86-871-65922961, The Department of Forensic Medicine, Kunming Medical University, 1168 West Chunrong Road, Yuhua Avenue, Chenggong Zone, Kunming, Yunnan, China,650032 *Hu Zhou, Email: [email protected], Tel.: +86-21-50806706, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, China201203

#These authors made equal contributions to this work.

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ABSTRACT Traumatic brain injury (TBI), as a neurological injury, becomes a leading cause of disability and mortality due to lacking effective therapy. About 75% of TBI is mild traumatic brain injury (mTBI). However, the complex molecular mechanisms underlying mTBI pathophysiology remains to be elucidated. In this study, iTRAQ-based quantitative

proteomic approach was employed to measure

temporal-global proteome changes of rat brain tissues from different time points (1 day, 7 day and 6 months) post single mTBI (smTBI) and repetitive mTBI (rmTBI). A total of 5,169 proteins were identified, of which, 237 proteins were significantly changed between control rats and mTBI model rats. Fuzzy c-means (FCM) clustering analysis classified these 237 proteins into six clusters according to their temporal pattern of protein abundance. Functional bioinformatics analysis and protein-protein interaction (PPI) network mapping of these FCM clusters showed that phosphodiesterase 10A (Pde10a) and guanine nucleotide-binding protein G (olf) subunit alpha (Gnal) were the node proteins in the cAMP signaling pathway. Other biological processes, such as cell adhesion, autophagy, myelination, microtubule depolymerization and brain development, were also over-represented in FCM clusters. Further Western Blot experiments confirmed that Pde10a and Gnal were acutely up-regulated in severity-dependent manner by mTBI, but these two proteins could not be down-regulated to basal level at the time point of 6 months post repetitive mTBI. Our study demonstrated that different severity of mTBI cause significant temporal profiling change at the proteomic level and pointed out the cAMP signaling pathway-related proteins, Pde10a and Gnal, may play important roles in the pathogenesis and recovery of mTBI.

Keywords: mTBI, iTRAQ, proteomic analysis, cAMP signal process, Pde10a, Gnal

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INTRODUCTION Traumatic brain injury (TBI), recognized as a brain damage due to a traumatic force to head, poses a major public-health problem with high morbidity and mortality worldwide. In the Unite State, approximately 2.5 million TBI-related emergency department visits and approximately 56,000 TBI-related deaths occurred in 20131. In Europe, it was reported that annual incidence of TBI was nearly 235 per 100,000 and average mortality had reached 15 per 100,0002. TBI is classified into three grades: mild, moderate, and severe, according to the Glasgow Coma Score (GCS)3. Mild TBI (mTBI, also referred to concussion) is the most common subtype of TBI that accounts for approximately 75% of all TBI patients. It frequently occurs in contact sports, military service and physical abuse4. The acute symptoms of mTBI, including headache, vomiting and unconsciousness, appear to be automatically resolved, however, some patients undergo long-term lasting sequela such as prolonged cognitive, emotional and functional disabilities that substantially affect quality of life5. In addition, repetitive brain trauma is recognized as a risk factor of neurodegenerative disorders including Alzheimer’s-like dementia and Parkinsonism6. Therefore, the growing awareness of socioeconomic burden of mTBI has brought new urgency to develop effective mTBI diagnostic and treatment assays. Improving the efficacy of treatment of mTBI is dependent on an intensive understanding of the complex molecular mechanisms that drive transient and long-term changes in brain after mTBI. Proteomic research using brain tissues or biofluids from either patients or animal models have made it feasible to identify the large-scale proteins associated with TBI7,8. Proteomic approach can be used to discover potential biomarkers for diagnosis, to unravel novel biological processes underlying degeneration and repair, or to represent complex protein-protein interactions. For example, proteomic analysis of consecutive ventricular cerebrospinal fluid (CSF) samples of TBI patients using MALDI-TOF-MS/MS showed up-regulation of acute phase reactants (APRs),

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fibrinogens (FIB), and glial fibrillary acid protein (GFAP) in TBI patients, which were considered as potential candidate biomarkers of TBI9. With proteomic analysis of extracellular fluids from TBI patients, Lakshmanan et al.10 reported that significant alteration of cytoskeleton proteins and novel metabolic distress-associated peptides had not been well explored. However, elucidation of comprehensive molecular events in response to mTBI is a big challenge due to its heterogeneity caused by distinct injury degree and temporal evolution of TBI pathobiology11. Using TMT-based proteomic approach, Tzekov et al.12 found negative effect of repetitive mTBI (rmTBI) on some cellular processes, such as depolymerization of microtubules. Evans et al.13 contrasted normal versus mTBI mice at 1, 7, 30 and 120 days post-injury using microwave & magnetic (M2) proteomics. They found gradual loss of myelin basic protein (MBP) and increase of myelin-associated glycoprotein (MAG) over the long time after mTBI. Thus, it is desirable to take the two factors, injury degree and time course, into account in proteomic study of mTBI. Quantitative proteomic approaches have been used for neurological diseases in our previous studies14-16. Label-free quantitative proteomic strategy was employed to reveal the protein expression changes in dorsal hippocampus of heroin self-administering rats, suggesting that CDK5 and RhoB are two important molecules involved in heroin addiction. In addition, using the same approach, we performed proteomic and phosphoproteomic analysis to investigate the pharmacological mechanism of the Alzheimer’s disease-relatedneuroprotective compounds Huperzine A and GFKP-19 in the neuronal cells, demonstrating that p53 and phosphorylated Tau may be involved in the drug responses of these two compounds, respectively. However, label-free quantitative proteomic approach has its limits, such as low accuracy on quantifying the proteins with low molecular weight or small fold changes17. The isobaric tags for relative and absolute quantification (iTRAQ) proteomics provides an alternative approach for achieving high quantitation accuracy and reproducibility, which allows the simultaneous quantification of proteins in up to

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8-plex samples18. This technique is suitable for proteomic comparison of different samples with multiple treatments or time series19. In this study, we employed iTRAQ-labeling coupled with liquid chromatography tandem mass spectrometry (LC-MS/MS) approach to investigate differentially expressed proteins in rat brain samples collected at different time points (1day,7 days and 6 months) after single or repetitive mTBI. And the cAMP signaling pathway-related proteins, Pde10a and Gnal, were found to be up-regulated in mTBI models, indicating the cAMP signaling pathway may be activated during the mTBI pathogenesis.

MATERIALS AND METHODS mTBI animal model This timeline of the experimental design was illustrated in Figure 1A. A total of 272-month old male Sprague-Dawley rats (270-300g, purchased from the Laboratory Animal Center of Kunming Medical University) were randomly divided into 9 experimental subgroups (n=3 per group): 1 day after single mTBI (smTBI) group (D1S), 1 day after the third strike of repetitive mTBI (D1R), 7 days after single mTBI (D7S), 7 days after the third strike of repetitive mTBI (D7R)，6 months after single mTBI (M6S), 6 months after the third strike of repetitive mTBI (M6R), 1 day control sham rats (D1C), 7 days control sham rats (D7C) and 6 months control sham rats paired for 6 months post injury groups (M6C). D7C mice were only used for Morris water maze (MWM) experiments, but not for proteomic experiments. Because we believed that there was no obviously developmental difference during only 7 days, D1C mice were paired for both 1 day and 7 days post injury groups in the proteomic study. The mTBI model rats were generated using a custom-designed device of metallic pendulum-striker as previously described20. Briefly, each rat was mounted in a stereotactic frame and a single metallic pendulum (1450 g weight) impacted on the parietal-occipital skull. The single mTBI groups were impacted just one time, while

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repetitive mTBI groups were impacted once per day for three consecutive days. For the sham injury group, rats were kept under the same environmental conditions but not subjected to impact injury. At 1 day, 7 days or 6 months time points after the final strike (after the third strike for rmTBI models), rats were sacrificed and the whole brain tissues were collected. Then, prefrontal cortex tissues were dissected, snap frozen in liquid nitrogen and stored at -80 °C, until the samples from all of the time points were collected. All procedures related to animal care and processing were conducted in accordance with guidelines set forth by the National Institutes of Health Guide for Care and Use of Laboratory Animals and were approved by the Ethics Committee of Kunming Medical University. Spatial learning and working memory assessments For rats from day 7 and 6 months group, Morris water maze (MWM) paradigm was tested for 7 times before tissue collection (Figure 1A) to assess spatial learning and working memory. For latency test, each rat was trained to locate hidden, submerged platform in the circular pool (2 m diameter) using visual cues. During testing days (1-7 days), rat was placed at random location of pool and the time taken (latency) to locate platform was recorded. For the probe trial, the platform was removed from the maze, and rat was allowed to search for location of the removed platform area (target quadrant). Time spent in each 4 quadrants of maze was recorded using an automated tracking system (Kunming Institute of Zoology, Chinese Academyof Sciences). Sample preparation and iTRAQ labeling Prefrontal cortex tissues from each group were lysed with 4% SDS buffer containing 1 mM dithiothreitol, 1% (v/v) protease inhibitor cocktail (Roche, Germany), and homogenized using a small mortar and pestle set on ice. Brain tissue lysates were centrifuged at 14,000 g for 20 min at 4 °C. The clarified supernatants were collected to be used as samples for iTRAQ labeling. Protein concentrations were determined by tryptophan fluorescence emission at 350 nm using an excitation wave length of 295 nm21 and confirmed by Coomassie-stained gel (Figure S-1). 100 µg of protein from

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each sample was processed by the Filter Assisted Sample Preparation (FASP) method as previously described22. Briefly, each sample was transferred to a 10 kDa filter (Millipore Corporation) and centrifuged at 14,000 g for 40 min at 20 °C. Then, 200 µL of urea buffer (8 M urea, 0.1 M Tris-HCl, pH 8.5) was added and followed by another centrifugation at 15,000 g for 40 min. This step was repeated one more time. The concentrate was then mixed with 100 µL of 50 mM iodoacetamide (IAA) in urea buffer and incubated for an additional 40 min at room temperature in darkness. After that, IAA was removed by centrifugation at 14,000 g for 40 min. Next, the sample was diluted with 200 µL of urea buffer and centrifuged two more times. Then, 200 µL of 50 mM tetraethyl-ammonium bromide (TEAB) was added and the sample was centrifuged at 14,000 g for 40 min. This step was repeated twice. Finally, samples were digested with trypsin (1:50, enzyme to protein in 50 mM TEAB) by incubating at 37 °C for 16 h. Peptides were labeled with iTRAQ reagents according to the manufacturer’s instructions (AB Sciex, Foster City, CA). To quantify the 24 samples, 4 batches of 8-plex iTRAQ labeling experiment was performed, with a mixture of 24 samples in equal amount as the reference for comparison between different batches. Fifty µg of peptide from each sample was reacted with one tube of iTRAQ reagent. The iTRAQ reagent was dissolved in 50 µL of isopropanol, and

the peptide sample was

dissolved in 15 µL of 0.5 M TEAB solution (pH 8.5) . Then, the iTRAQ reagent-containing solution was added to the peptide solution, mixed by vortex, and the resulting mixture was incubated at room temperature for 2 h. The 8-plex iTRAQ-labeled peptide samples from the same batch were pooled together and lyophilized. High pH reverse phase fractionation (HPRP) iTRAQ-labeled peptides mixture was fractionated using a Waters XBridge BEH130 C18 3.5 µm 2.1 × 150 mm column on a Agilent 1260 HPLC operating at 0.2 mL/min. Buffer A consisted of 10 mM ammonium formate and buffer B consisted of 10 mM

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ammonium formate with 90% acetonitrile; both buffers were adjusted to pH 10 with ammonium hydroxide as described previously23. A CBS-B programed multifunction automatic fraction collecting instrument (Huxi instrument, Shanghai, China) was coupled to the HPLC and used to collect eluted peptides. A total 28 fractions were collected, and then concatenated to 14 (pooling equal interval RPLC fractions). The fractions were dried for nano LC–MS/MS analysis. LC-MS/MS analysis The reverse phase high-performance liquid chromatography (RP-HPLC) separation was achieved on the Easy nano-LC system (Thermo Fisher Scientific) using a self-packed column (75 µm × 150 mm; 3 µm ReproSil-Pur C18 beads, 120 Å, Dr. Maisch GmbH, Ammerbuch, Germany) at a flow rate of 300 nL/min. The mobile phase A of RP-HPLC was 0.1% formic acid in water, and B was 0.1% formic acid in acetonitrile. The peptides were eluted using a gradient (2 - 90% mobile phase B) over 90 min period into a nano-ESI Orbitrap Elite mass spectrometer (Thermo Fisher Scientific). The mass spectrometer was operated in data-dependent mode with each full MS scan (m/z 300 - 1500) followed by MS/MS for the 12 most intense ions with the parameters: ≥ +2 precursor ion charge, 2 Da precursor ion isolation window, 80 first mass and 38 normalized collision energy of HCD. Dynamic Exclusion™ was set for 30 s. The full mass and the subsequent MS/MS analyses were scanned in the Orbitrap analyzer with R = 60,000 and R= 15,000, respectively. Database searching Data were processed by search against the UniProt/SwissProt Rat database containing 532,146 sequence entries using Maxquant (1.5.1.0), with default settings including the allowance of one missed cleavage and 8-plex iTRAQ fixed modifications. minimum 7 amino acids for peptide, > 2 peptides were required per protein. For peptide and protein identification, false discovery rate (FDR) was set to 1%. iTRAQ reporter ion intensity were used for quantification. To be able to control the normalization of the data across multiple 8-plex reactions from three independent biological replicates, an

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indirect design was used by including a mixture of 24 samples in equal amount in the 113 channel of every 8-plex labeling reaction. We first adjusted the median intensity of each channel to unity. In order to make consistent comparisons across all samples from four 8-plex iTRAQ experiment batches, each sample iTRAQ reporter (channel 114, 115, 116, 117, 118, 119 and 121) was divided by the reference iTRAQ reporter (channel 113). Then, the resultant ratios were transformed to the relative intensity through multiplying by the median intensity of 113 channel. Data analysis Hierarchical clustering of proteins was performed on logarithmized data, using Euclidean distances and Ward clustering method by Package of ‘pheatmap’ in language R. Fold changes for all experiment treatment of altered proteins (log Ratio values) were normalized to obtain a standard deviation of 1 and a mean of 0 for each protein. The transformed profiles were classified utilizing the M fuzz toolbox (Futschik and Carlisle). In this study, the optimal parameters number of clusters ‘c’ and fuzzification parameter ‘m’ were 6 and 2, respectively, and the distance metric was Euclidean distance24. One way analysis of variance (ANOVA) and Tukey's honestly significant difference (HSD) test was performed with language R. p 1.2

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, a total of 237 proteins were significantly changed in mTBI groups

compared to sham injury group (Table S-2). There were more differential proteins at each time point in rmTBI models than in smTBI ones (Figure S-2). Heatmap of a hierarchical clustering analysis (HCA) was generated to distinguish the changed protein patterns across all the control and model groups. As shown in the Figure 3C, each group showed a unique and diverse change pattern of protein abundance. Interestingly, five mTBI groups (D1S, D7S, M6R, D1R, D7R) were clearly separated from two sham groups (D1C, M6C), while the change pattern of M6S group showed a high similarity to M6C group. This result indicated that proteins had been significantly changed in the acute (1 day) and sub-acute (7 days) phase following mTBI. However, the expression levels of most proteins in smTBI groups went back to their basal levels after 6 months, which had not been observed in repetitive mTBI group. In addition, three replicates of each group revealed good

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reproducibility in respective Hierarchical clustering generated from all the individual samples at the same time point（Figure S-3）. Bioinformatics analysis of the differentially expressed proteins To understand the biological characterization of differentially expressed proteins obtained from iTRAQ data, the cellular component, molecular function and biological process of the 237 proteins were analyzed by Gene Ontology (GO) annotation. In the cellular component category of GO, the most over-represented term is extracellular exosome, followed by myelin sheath, axon, membrane, mitochondrion, neuronal cell body and cytosol (Figure 4A). In the molecular function category, terms of glutathione binding, extracellular matrix constituent, protein homodimerization activity, calcium-dependent protein binding, Rab GTPase binding, integrin binding, microtubule binding were found to be significantly over-represented (Figure 4C). The highly enriched biological processes were related to cell adhesion, glutathione metabolic process and myelination. Of note, several neuron-specific processes including central nervous system development, axon guidance, and locomotory behavior were also significantly enriched (Figure 4B). These characteristics were further reflected by the KEGG pathway analysis, where highly enriched terms included glutathione metabolism, cell adhesion molecules, morphine addiction, dopaminergic synapse, retrograde endocannabinoid signaling. In addition, KEGG analysis also revealed several crucial pathways including PI3K-AKT signaling pathway, carbon metabolism and pathway in cancer (Figure 4D). The detailed information was listed in Table S-3. Classification of differentially expressed proteins into temporal profiles To further describe the dynamic proteomic profiles in response to different extent of mTBI over time, Fuzzy c-means (FCM) clustering analysis was performed on all of 237 significantly changed proteins. According to the change pattern of proteins abundance and the average probability of the clusters, 6 principal clusters were generated (Figure 5A). Cluster 1 and 4 showed that both smTBI and rmTBI resulted

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in significantly down-regulation and up-regulation of proteins in acute and subacute phase, respectively, and these proteins went back to basal levels at 6 months post mTBI. Proteins in cluster 2 and cluster 5 exhibited nonsignificant changes over time after smTBI, but remarkable down-regulation and up-regulation at 6months post rmTBI, respectively. Change pattern of smTBI in cluster 3 seemed to be similar to that in cluster 1, however, prolonged down-regulation of proteins at 6months was observed in rmTBI groups. Notably, proteins in cluster 6 were characterized by significant up-regulation after mTBI, with higher magnitude of change over times in response to rmTBI compared to smTBI, indicating that change pattern of these proteins is dependent upon the degree of brain injury and leading us to focus on their function regarding to mTBI. In addition, over-represented biological processes of proteins in individual clusters were analyzed using GO annotation to understandfunction feature of proteins that changed in diverse patterns. The most enriched GO term was indicated. Proteins in our interested cluster 6 were mainly involved in cAMP signal pathway. Protein-protein interaction network analysis of differentially expressed proteins To understand functional links among the 237 differential proteins as well as related biological processes in context of mTBI, the protein-protein interaction (PPI) network based on STRING action scores was constructed. The FCM cluster of each protein and biological process based on GO analysis were also indicated in this view. As shown in Figure 5B, many proteins participating in different biological processes were connected to each other. For instance, the interaction between cell adhesion-related proteins and myelination related proteins can be observed, both of which were down-regulated following mTBI. Additionally, proteins belonging to different clusters coordinated each other to regulate same biological process (e.g. cAMP signaling pathway, protein folding). A number of interacted proteins in PPI map were involved in neurological processes, such as synaptic transmission, brain development, axon guidance and myelination, as well as other important processes

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such as autophagy process, oxidative stress, and metabolic process. Interestingly, the network comprising several proteins of cluster 6 (e.g. Pde10a, Gnal, Pde1a, Gng7) were involved in cAMP signaling pathway, which simultaneously interacted with other networks related to neural disease, such as autophagy, microtubule depolymerization and synaptic transmission. This indicated that mTBI may affect some neurologic biochemical processes via regulating cAMP signaling pathway. Immunoblots analysis of Pde10a and Gnal protein levels in rat tissue In this study, candidate proteins were selected according to several factors including change pattern of protein levels, protein-protein interaction network and literature relevance. As such, two of cluster 6 proteins, phosphodiesterase 10a (Pde10a) and Guanine nucleotide-binding protein G(olf) subunit alpha (Gnal), were selected to further verification. As shown in Figure 6 A and B, iTRAQ data revealed that the protein levels of Pde10a and Gnal were continuously up-regulated after rmTBI (Pde10a, D1R: D1C ratio= 1.70, D7C: D1C ratio = 1.92, M6R: M6C ratio = 1.81; Gnal, D1R: D1C ratio = 2.56, D7R: D1C ratio = 2.60, M6R: M6C ratio = 2.01). Western blotting assay and corresponding densitometric analysis showed significant increase of Pde10a and Gnal proteins (p

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