Novel Elements of the Chondrocyte Stress Response Identified Using

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Novel elements of the chondrocyte stress response identified using an in vitro model of mouse cartilage degradation Richard Wilson, Sue B. Golub, Lynn Rowley, Constanza Angelucci, Yuliya V Karpievitch, John F. Bateman, and Amanda J. Fosang J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.5b01115 • Publication Date (Web): 21 Jan 2016 Downloaded from http://pubs.acs.org on January 27, 2016

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Journal of Proteome Research

Novel elements of the chondrocyte stress response identified using an in vitro model of mouse cartilage degradation

Richard Wilson 1, 2*, Sue B. Golub 2, 3, Lynn Rowley 2 , Constanza Angelucci 2, Yuliya V. Karpievitch 4 , John F. Bateman 2, 5 ¥ and Amanda J. Fosang 2, 3¥

1

Central Science Laboratory, University of Tasmania, Hobart, TAS 7001, Australia

2

Murdoch Childrens Research Institute, Royal Children’s Hospital, Parkville, Melbourne, VIC 3052,

Australia 3

Department of Pediatrics, University of Melbourne, Parkville, VIC 3052, Australia

4

Department of Mathematics and Physics, University of Tasmania, Hobart, TAS 7001, Australia

5

Department of Biochemistry and Molecular Biology, University of Melbourne, Parkville, VIC 3052,

Australia ¥

Equal contribution from these two authors

* Address correspondence to: Fax: +61-3-6226 2494; Email: [email protected] Central Science Laboratory, Bag 74, University of Tasmania, Hobart, TAS 7001, Australia

Running title: The interleukin-1α induced chondrocyte stress response in cartilage Keywords: Cartilage; osteoarthritis; quantitative proteomics; chondrocyte; oxidative stress

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Abbreviations used in this manuscript: FDR – false discovery rate LFQ – label free quantitation DAVID – database for annotation, visualization and integrated discovery OA – osteoarthritis MMP – matrix metalloproteinase ADAMTS-5 - a disintegrin and metalloprotease with thrombospondin repeats-5

TS5∆cat – ADAMTS-5 lacking catalytic activity ECM – extracellular matrix IL-1α – interleukin-1α

IL-1β – interleukin-1β


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ABSTRACT The destruction of articular cartilage in osteoarthritis involves chondrocyte dysfunction and imbalanced extracellular matrix (ECM) homeostasis. Pro-inflammatory cytokines such as interleukin1α (IL-1α) contribute to osteoarthritis pathophysiology, but the effects of IL-1α on chondrocytes within their tissue microenvironment have not been fully evaluated. To redress this we used labelfree quantitative proteomics to analyse the chondrocyte response to IL-1α within a native cartilage ECM. Mouse femoral heads were cultured with and without IL-1α and both the tissue proteome and proteins released into the media were analyzed. New elements of the chondrocyte response to IL1α related to cellular stress included markers for protein misfolding (Armet, Creld2 and Hyou1), enzymes involved in glutathione biosynthesis and regeneration (Gstp1, Gsto1 and Gsr) and oxidative stress proteins (Prdx2, Txn, Atox1, Hmox1 and Vnn1). Other proteins previously not associated with the IL-1α response in cartilage included ECM components (Smoc2, Kera and Crispld1) and cysteine proteases (cathepsin Z and legumain), while chondroadherin and cartilage-derived C-type lectin (Clec3a) were identified as novel products of IL-1α induced cartilage degradation. This first proteome-level view of the cartilage IL-1α response identified candidate biomarkers of cartilage destruction and novel targets for therapeutic intervention in osteoarthritis.



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INTRODUCTION Osteoarthritis (OA) of the hip and knee is a leading cause of disability 1. Furthermore, the global prevalence of OA is increasing as a result of population ageing and the rising incidence of obesity, two predisposing risk factors. While OA is recognised as a disease affecting the whole joint 2 the major pathological hallmark of OA is destruction of the articular cartilage. Cartilage is a highly specialized tissue composed predominantly of extracellular matrix (ECM) and sparsely populated by chondrocytes. The unique composition of the ECM provides cartilage with its load-bearing properties: specifically, the osmotic swelling pressure conferred by the high charge density of aggrecan-hyaluronan aggregates is precisely balanced by the tensile strength of the collagen network. Tightly regulated maintenance of the ECM by the chondrocytes is essential for cartilage function: chondrocyte dysfunction and imbalanced homeostasis that favours ECM catabolism are central to OA pathology. Enzymes involved in ECM destruction are therefore potential candidates for therapeutic intervention 3, 4. In addition, there is increasing evidence that cartilage destruction is propagated by cytokines and other pro-inflammatory mediators, found at elevated levels in the joints of OA patients 5, 6. However, the effects of inflammatory mediators in chondrocyte dysfunction and cartilage degeneration in OA are not fully understood. Increasingly, proteomics is being used to investigate pathological mechanisms and identify new protein markers of OA 7, 8. Due to the limited availability of healthy human cartilage and the relatively high biological and clinical variability between OA patients, the major focus has been on screening synovial fluids. Despite these limitations, some progress is being made towards the use of proteomics for OA biomarker discovery in human cartilage 9, 10. Alternatively, animal models provide the opportunity to study OA pathogenesis in vivo 11, 12 including the use of genetically modified mice to explore the role of specific elements of the degradative machinery. For example, mutant mice expressing inactivated ADAMTS-5 (a disintegrin and metalloprotease with thrombospondin repeats5) and mice lacking MMP-13 (matrix metalloproteinase-13) are protected from cartilage destruction


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in experimental arthritis models 13, 14. Transcriptomics of OA-affected cartilage micro-dissected from wild-type and Adamts5∆cat mice was recently used to identify pathways and processes involved in the initiation and progression of experimental OA that were either dependent upon or independent from ADAMTS-5 activity 15. However, due to the scarcity of the OA-affected articular cartilage in this model, comprehensive analysis of mouse OA cartilage is limited to expression profiling at the transcriptional level. Cartilage explant cultures provide a feasible alternative to in vivo studies for proteomic investigation of OA pathology. Treatment of cartilage explants with pro-inflammatory cytokines, such as interleukin-1α (IL-1α), stimulates the expression of MMPs and other matrix-degrading enzymes, including ADAMTS-5 13. We and others have used proteomics to identify proteins and proteolytic fragments released into the conditioned media of IL-1α or IL-1β treated cartilage explants, focussing on the cartilage ‘secretome’ as a potential source of novel OA biomarkers 16-19. Other proteomics studies using human chondrocyte cultures have identified alterations in cellular activity in response to IL-1β, in particular remodelling of the actin cytoskeleton, induction of molecular chaperones and modified pathways for energy production 20, 21. However, in monolayer culture it is important to consider the loss of the chondrocyte phenotype 22, exposure of cells to cytokines released during cell culture expansion 23 and the potential chondroprotective role of pericellular and extracellular matrix proteoglycans 24. The aim of our current study was to investigate the response of chondrocytes exposed to IL1α within a native cartilage ECM environment, using comprehensive proteomic analysis of both the tissue extracts and soluble factors released into the conditioned media. In addition to analysing the IL-1α response in wild-type cartilage, we used genetically modified mice to further investigate the role of ADAMTS-5 with replicate experiments in Adamts5∆cat cartilage. The MaxQuant platform was used as a robust approach to label-free quantitative proteomics 25, extending the range of quantifiable proteins beyond our previous analysis of mouse cartilage using 2-D DIGE 19 or spectral



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counting 26, 27. In addition to independent experiments in wild-type and Adamts5∆cat cartilage, the expression changes in proteins significantly modulated by IL-1α were compared with corresponding mRNA expression data, providing a further level of validation. Finally, we used immunohistochemistry to verify and localize the expression of vanin-1, a pantetheinase enzyme with the capacity to influence oxidative stress via depletion of intracellular glutathione.


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EXPERIMENTAL PROCEDURES Dissection and culture of mouse femoral head cartilage explants. Proximal femoral head cartilage from 21-day-old wild-type C57BL/6 mice and Adamts5∆cat mice lacking active ADAMTS-5 13 was dissected as previously described 28. Femoral head cartilage was cultured in 0.4 ml serum-free HEPES-buffered Dulbecco’s modified Eagle’s medium in the presence or absence of 10 ng/ml IL-1α (MP Biomedicals, Irvine, CA). Biological replicates (n = 3 per treatment per genotype) comprised three femoral heads per culture well. The media for each explant culture was collected once after 48 hours and again at the end of the culture period (96 hrs). Femoral head cartilage was collected at the end of the culture period, rinsed once with PBS and stored at -80 °C. Media samples were clarified by centrifugation at 13,000 × g for 5 mins at 4 °C prior to storage at -80 °C. Preparation and SDS-PAGE analysis of protein samples. The two aliquots of conditioned media for each culture were combined and then concentrated by freeze drying. Cartilage explants were pulverized using a liquid nitrogen cooled tissue grinder. Media and cartilage samples were reconstituted in 100 µl of 100mM Tris acetate buffer pH 8.0 containing 10 mM EDTA and deglycosylated by treatment with 0.1 units of chondroitinase ABC for 6 hrs at 37 °C. Cartilage protein extracts were prepared using our published fractionation method for proteomic analysis of cartilage 26. Briefly, proteins were sequentially extracted in a non-denaturing buffer (100 mM Tris acetate, pH 8.0 containing 1 M NaCl) then a chaotropic buffer containing guanidine hydrochloride (4 M GuHCl, 65 mM DTT, 10 mM EDTA in 50 mM sodium acetate, pH 5.8). Protein samples were precipitated with nine volumes of ethanol, washed once in 70% ethanol then resuspended in 120 µl solubilisation buffer (7 M urea, 2 M thiourea and 30 mM Tris, pH 8.0) and the volume adjusted to achieve a concentration of ~1 mg/ml as estimated using the Bradford assay (Pierce). Samples were then stored at -80 °C until required. Protein samples were analyzed by SDS-PAGE and detected by silver staining as previously described 29.



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Peptide sample preparation and analysis by nano-liquid chromatography and LTQ-Orbitrap tandem mass spectrometry. Protein samples were sequentially reduced, alkylated and digested with proteomics-grade trypsin (Sigma) using established methods 26. Tryptic peptides equivalent to ~ 5 µg of digested protein were loaded at 0.03 ml/min onto a C18 capillary trapping column (Peptide CapTrap, Michrom BioResources) controlled by an Alliance 2690 Separations Module (Waters). Peptides were then separated on an analytical C18 nano-column (PicoFrit Column, 15 µm i.d. pulled tip, 10 cm, New Objective) controlled using a Surveyor MS Pump Plus (ThermoFisher Scientific) over a 4-step gradient from 100% buffer A (5% acetonitrile in 0.2% formic acid) to 100% buffer B (90% acetonitrile in 0.2% formic acid) as previously described 30. The 12 media samples were injected twice and the 24 cartilage sequential extracts were injected once, resulting in a total of 48 two-hour LC-MS runs. The LTQ-Orbitrap XL was controlled using Xcalibur 2.0 (ThermoFisher Scientific) and operated in data-dependent acquisition mode. Survey scans were acquired in the Orbitrap (resolving power of 60,000 at 400 m/z) and MS/MS spectra were concurrently acquired in the LTQ mass analyzer on the eight most intense ions. Unassigned and singly-charged precursor ions were not selected for fragmentation and 30-second dynamic exclusion of fragmented peptides (repeat count 1 exclusion list size 500) was used. Fragmentation conditions in the LTQ were: 35% normalized collision energy, activation q of 0.25, 30 ms activation time and minimum ion selection intensity of 500 counts. Database searching and criteria for protein identification. Xcalibur RAW files were imported into MaxQuant version 1.5.1.2 (http://www.maxquant.org/), an open-source software platform for LC-MS run alignment, protein identification and relative protein quantitation using the extracted ion currents of matched peptides 31, 32. In the MaxQuant experimental design table, sequential cartilage extracts were classed as fractions of the same experimental sample, as were the replicate media injections. The MS/MS data for explant culture media (secretome) and cartilage tissue extracts (proteome) were analyzed independently. MS/MS spectra were searched using the Andromeda search engine against the complete Mus musculus reference proteome (ID 000000589; 8 ACS Paragon Plus Environment

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updated on 02/10/2014) comprising 44,455 protein entries. Default group-specific and global settings for protein identification by Orbitrap MS were used, including specific digestion by trypsin/P allowing a maximum of two missed cleavages, variable oxidation of methionine and acetylation of protein N-termini, and fixed carbamidomethylation of cysteine. Mass tolerances for initial peptide searches were set to 20 ppm and reduced to 4.5 ppm for the main searches, and mass tolerances of 0.5 Da were used for fragment ions. The default false discovery rate (FDR) of 0.01 was used for peptide-spectrum matches and protein identification. Determination of relative protein abundance and statistical analysis. We utilized MaxLFQ, the MaxQuant algorithm for peptide intensity determination and normalization 25, using pair-wise comparison of unique and razor peptide intensities and a minimum ratio count of 2. The ProteinGroups output files generated by MaxQuant analysis of the culture media and cartilage extracts were processed as follows: The normalised label-free quantification (LFQ) peptide intensity values, MS/MS counts and the numbers of razor and unique peptides for each of the identified proteins were imported into Perseus software version 1.5.031 (http://perseus-framework.org/). Protein groups identified as potential contaminants (prefixed with CON_) and proteins identified only by site or by reverse database matching were removed and remaining observed intensity values were log2–transformed. Two statistical comparisons were made for both the proteome and secretome data, firstly to examine the effect of IL-1α treatment independent of genotype, and secondly to identify proteins that were differentially modulated by IL-1α in wild-type and TS5∆cat cartilage explant cultures, as follows: (1) Control and IL-1α treated cultures were defined as the two treatment groups (i.e. n = 6 in each group). Cartilage proteome and secretome proteins were filtered to include proteins detected in a minimum of six replicates of one treatment group. Missing values were replaced with random intensity values for low-abundance proteins based on a normal distribution of protein abundances. Z-scored normalized protein expression matrices were generated by calculating mean LFQ peptide



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intensities and standard deviations for each sample. The means were then subtracted from each LFQ value and the results were divided by the standard deviation. To determine proteins that were significantly altered in abundance by IL-1α treatment we applied a two-sided t-test with a permutation-based FDR of 0.05, using 250 randomizations and a stabilization parameter s0 of 0.5. The output from each data processing step is shown in supplemental Tables 4 and 5.

(2) The IL-1α response in wild-type and TS5∆cat cartilage was analyzed using separate two-sample ttests to compare control and IL-1α treated samples (i.e. n = 3 in each group). Only proteins that were detected in all 12 proteome or secretome samples were analyzed to identify proteins significantly modulated by IL-1α. Normalized protein expression data matrices for proteome and secretome samples were generated using the Z-score algorithm as described above. The t-test was then used to identify proteins that were significantly different between control and IL-1α treatment for the two genotypes (FDR of 0.05, 250 randomizations and s0 of 0). The output from each data processing step is provided in supplemental Tables 6 and 7. Bioinformatics analysis. Several bioinformatics approaches were used to extract functional information from the proteomics data. As a global evaluation of the cartilage secretome and proteome, the two lists of proteins detected on the basis of two or more matching peptides were classified according to their GO terms for cellular components using the FatiGO module of the Babelomics software suite for gene expression and functional profiling 33, 34. Identified proteins were uploaded as official gene symbols and functional annotations that were over-represented relative to term size in the complete mouse genome were identified using the two-tailed Fischer exact test. Significant GO terms (adjusted p < 0.05) were clustered into semantically similar terms using the Revigo algorithm 35 and displayed in 2-D space using Scatterplots, where bubble size represents the number of genes associated with each GO term and color shading represents the log10 p value. To identify enrichment of functional terms in the groups of proteins significantly modulated by IL-1α in the cartilage secretome and proteome, gene lists were imported into the on-line 10 ACS Paragon Plus Environment

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bioinformatics resource DAVID (version 6.7) using the mouse genome as background 36. Annotation clusters were ranked using the Functional Annotation Clustering tool based on default parameters and p values < 0.05 after adjustment using the Benjamini-Hochberg correction for multiple testing were considered significant. Interaction networks were generated using Ingenuity Pathway Analysis (IPA®,QIAGEN Redwood City, www.qiagen.com/ingenuity). Microarray analysis. Independent replicate femoral head cultures, with and without IL-1α treatment in wild-type and TS5∆cat cartilage (n = 2 biological replicates per treatment per genotype), were used for whole-genome expression profiling. After 96 hours in culture, explants were rinsed in PBS, snap-frozen using liquid nitrogen, and pulverised using a frozen mortar and pestle. Pulverised tissue was immediately collected in TRIzol reagent (Invitrogen) for RNA extraction and purification according to manufacturer’s specifications. RNA yield was quantified using a Nanodrop ND-1000 spectrophotometer (Thermo Fisher Scientific) and the purity and integrity of all RNA samples was validated by capillary electrophoresis with a Bioanalyzer 2100 (Agilent Technologies), using a Series II RNA 6000 Pico Kit (Agilent Technologies). RNA samples were amplified and labelled with Cy3 using OneColor QuickAmp Labelling Kit (Agilent Technologies). Fluorophore incorporation and yield was determined by spectrophotometry using a Nanodrop ND1000 spectrophotometer (Thermo Fisher Scientific). The aRNA samples were then hybridised onto 44K whole mouse genome oligonucleotide microarrays, according to the manufacturer’s specifications (Agilent Technologies). Arrays were scanned at 5 µm resolution on G2565BA DNA Microarray Scanner (Agilent Technologies) and microarray image files were processed by Feature Extraction 9.5.3 software (Agilent Technologies). Raw data were analyzed in Genespring GX 10.0 (Agilent Technologies) using shift to 75.0 percentile for normalization and baseline transformation based on the median of all samples. Heat maps based on hierarchical clustering of normalized log2 fold change (IL-1α vs control) were generated using Genesis software 37.



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Immunohistochemistry analysis of mouse femoral head cartilage. Tissues were paraffin – embedded and 6 µm sections cut onto ultra plus slides (Grale scientific). Slides were dewaxed in xylene (2 x 5 mins), then 100% (v/v) ethanol (2 x 5 mins), 90% (v/v) ethanol (1 min), 70% (v/v) ethanol (1 min), water (2 x 5 mins) then Tris-buffered saline (TBS, 5 min). Heat-mediated antigen retrieval was used for tissue immunostaining with anti- Vanin-1 antibodies (Proteintech Rabbit polyclonal 21745-1-AP). Slides in 10 mM citrate buffer, pH 6.0, were microwave-heated (2 mins), then maintained at 65 °C (30 mins), allowed to cool to ambient temperature and washed in TBS (3 x 5 min). Slides were processed using Vectastain Elite ABC Rabbit IgG kit reagents (Vector Labs) as follows: blocked with 2 % (v/v) normal goat serum in 1% BSA (w/v) in TBS (1 hr), incubated with antiVanin-1 antibodies (0.83 µg/ml) in 1% BSA (w/v) in TBS overnight at 4 °C, washed with PBS (3 x 5 mins), endogenous peroxidase activity blocked with 3 % (v/v) H2O2 in TBS (30 mins), washed in TBS (3 x 5mins), incubated with biotinylated secondary antibody (1 hr at ambient temperature), washed in 3x 5min in TBS, incubated with Vectastain ABC reagent (30 mins) then washed in TBS (3 x 5 mins). Color was developed using immPACTTM DAB reagent (2 mins) and stopped in tap water.


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RESULTS Analysis of femoral head cartilage proteome and secretome samples by SDS-PAGE. In our previous analysis of mouse femoral head treated with catabolic stimuli, novel cartilage proteins and protein fragments were identified in the conditioned media using 2-D gel based proteomics 19. Here, we have improved on the previous study in three important aspects: First, we analyzed the effect of IL-1α treatment on both the secretome and proteome, producing the first detailed investigation of the effect of IL-1α on cartilage at the proteome level. Second, we used gel-free proteomics to overcome limitations of sensitivity and precision associated with 2-D gels, and this is the first proteomic analysis of cartilage using MaxLFQ, a widely-implemented and robust algorithm for relative protein quantification 25. Third, our complete experiment was done in replicate using wildtype and TS5∆cat cartilage, providing insight into the IL-1α response in the absence of active ADAMTS-5, a potential target for drugs designed to limit destruction of articular cartilage. In contrast to our previous proteomic comparison of wild-type and collagen IX null cartilage tissue extracts 38, this study involved live, age-matched cartilage explants from two mouse strains. As it was not possible to precisely synchronise mating of the wild-type mice and Adamts5∆cat (TS5∆cat) mice, the explant cultures and subsequent sample collection and processing steps were done on wild-type and TS5∆cat cartilage separately. To assess the level of variation between the two independent experiments, two of the three replicate samples for all experimental treatments were resolved by SDS-PAGE and proteins detected by silver staining (Figure 1). Overall, protein band patterns were consistent across sample replicates, while clear differences were observed between control vs IL-1α treatment groups, particularly in the secretome samples (Figure 1A), and to a lesser extent in cartilage fraction #1 of the cartilage extracts (Figure 1B). Apart from these differences, SDS-PAGE analysis indicated a high degree of consistency between the wild-type and TS5∆cat protein samples, confirming that separate cultures and sample processing did not introduce major technical bias, allowing us to proceed with proteomic analysis of these samples.



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Protein identification by nanoLC MS/MS and classification using bioinformatics. Protein samples were digested and the resulting tryptic peptides were resolved by nanoLC and analyzed using an LTQ-Orbitrap high resolution tandem mass spectrometer. The MS/MS data were processed using MaxQuant as described (Experimental Procedures) and peptide-level and protein-level output files are reported in full (supplemental Tables 1 and 2). At a 1% FDR, 65,125 and 83,945 valid MS/MS peptide-spectra matches were recorded for the cartilage secretome and proteome samples, respectively. These corresponded to 2,924 and 4,641 unique peptide sequences that were assigned, respectively, to 464 and 1134 protein groups in the secretome and proteome samples. To gain an overview of the complete proteomics data set, proteins identified in cartilage extracts and culture media were compared and classified according to cellular component GO terms. Excluding proteins identified on the basis of a single matching peptide sequence resulted in filtered lists of 368 proteins in the cartilage secretome and 728 proteins in the proteome. Of these proteins, approximately 30% (n = 248 proteins) were detected in both secretome and proteome, while 120 proteins and 480 proteins were secretome- and proteome -specific, respectively. As a broad characterization of the two groups of identified proteins, we compared their subcellular distributions. Using the FatiGO on-line bioinformatics tool 33 we identified 38 and 58 significantly enriched cellular component GO terms in the secretome and proteome, respectively (supplemental Table 3). According to p value, all 20 of the highest-ranked GO terms in the secretome were also significant in the proteome. Given the abundance of extracellular matrix in cartilage, it was not surprising that these included five terms related to secreted proteins and the extracellular matrix, with “extracellular region part” (GO: 0044421) highly represented in both data sets (n = 102). To aid further interpretation of the bioinformatics data, significant GO terms were processed using ReviGO 35 and represented using the Scatterplot function to group related GO terms and visualize their significance (Figure 2). In addition to the large protein sub-group associated with the extracellular region, the cellular component terms cytosol (n = 41), vesicle (n = 29), cytoskeleton (n =


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22), cell projection (n = 28) and endoplasmic reticulum (n = 33) were all significant in the secretome dataset (Figure 2A). In comparison with the secretome, a much greater diversity of sub-cellular components was represented in the cartilage proteome (Figure 2B). In particular components of the ribonucleoprotein complex (n = 75), mitochondrion part (n = 52) and the Golgi apparatus (n = 39) were significantly enriched specifically in the proteome. This comparison of the cartilage secretome and proteome at the level of individual proteins and cellular components emphasized the importance of analysing both the cartilage tissue and conditioned media to achieve a comprehensive view of the IL-1α response in cartilage. Analysis of the IL-1α α response in the cartilage tissue proteome and secretome. To identify cartilage proteins that were significantly altered by IL-1α treatment, irrespective of the genotype, we first compared all six control samples (wild-type and TS5∆cat replicates) with all six treated samples (wild-type and TS5∆cat replicates). Only proteins that were identified in all replicates of at least one of the two treatment groups were considered, allowing for ‘missing values’ in one treatment group. These filtered groups of proteins in the secretome (n = 270) and proteome (n = 627) included proteins that were entirely absent from either the control or IL-1α sample groups. For example in the secretome, Cxcl1, Mmp12 and Saa1 were detected in none of the six control samples while Cytl1, Myoc and Pam were detected in none of the six IL-1α treated samples. As these proteins are likely to be of interest on the basis of differential expression, processing of the LFQ intensity data included imputation of missing values (see Experimental Procedures) prior to generation of normalized protein expression matrices (supplemental Tables 4 and 5). We applied ttests to identify proteins that were significantly modulated by IL-1α treatment and found 70 proteins and 96 proteins to be significantly altered in the secretome and proteome samples, respectively (FDR < 0.05). The resulting p values, log2 fold-differences and linear scale folddifferences for the significant proteins are provided in supplemental Tables 4 and 5 (Sheet 4, Columns T, U and AC, respectively). Volcano plots were used to display the log2 fold-differences vs



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the –log10 p value for each protein (Figure 3A and B). In the secretome data, the log2 folddifferences ranged from +3.2 for Hp (haptoglobin; linear scale 9-fold increased) to -1.8 for Cytl1 (cytokine like-1; linear scale 3.5-fold decreased) and in the proteome data ranged from +4.1 for Mmp3 (stromelysin-1; linear scale 17-fold increased) to -1.2 for Smoc2 (SPARC-related modular calcium-binding protein 2; linear scale 2.2-fold decreased). Details of the most highly differentially abundant proteins in the cartilage secretome and proteome are presented in Tables 1 and 2, respectively, where entry numbers correspond to the labelled data points in the two volcano plots. Differential analysis of the IL-1α α response in wild-type and TS5∆ ∆cat cartilage. We first compared the effect of IL-1 on wild-type and TS5∆cat cartilage by plotting the log2 fold-differences (IL-1α vs control) for the 166 significant proteins on 3-D bar charts, using different colors to represent the two genotypes. In the secretome (Figure 4) and proteome (Figure 5), the effect of IL1α on wild-type and TS5∆cat cartilage was generally very consistent, according to both the trend and the magnitude of protein abundance change between treated and control samples. Despite the overall similarity between the two genotypes, this comparison also revealed some notable exceptions (marked with asterisks). Three of these proteins, Acadm (medium-chain specific acyl-CoA dehydrogenase), arginase-1 (Arg1) and metalloproteinase inhibitor 1 (Timp1) were detected in all 12 proteome samples. However, as the remaining proteins were associated with missing values in some samples, we did not consider the observed data a reliable indication of differential modulation by IL-1α between the two genotypes. Specifically, Serpina1d, Ftl1, Cltc were not detected in the three secretome samples for IL-1α treated wild-type cartilage (supplemental Table 4, Sheet 1). F2 was detected in only one of the wild-type IL-1α treated proteome samples, while Tufm was not detected in the three wild-type control proteome samples (supplemental Table 5, Sheet 1). To circumvent any uncertainty associated with missing values in the dataset, further analysis of the genotype-dependent effects of IL-1α was restricted to proteins that were detected in all 12 replicates of either the secretome or proteome samples. This constrained the comparisons to 185


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proteins and 560 proteins in the secretome and proteome, respectively (supplemental Tables 6 and 7). The effect of IL-1α treatment on wild-type and TS5∆cat cartilage was evaluated using separate ttests for each genotype. The abundance of 50 proteins and 64 proteins was significantly altered in the secretome of wild-type and TS5∆cat cartilage, respectively, while 134 proteins and 170 proteins were altered in the proteome of wild-type and TS5∆cat cartilage, respectively (FDR < 0.05). Linear scale fold-changes for each protein were then calculated, based on the log2 fold-differences (IL-1α vs control). The proteins were then ranked according the ratios of the protein fold-changes in the wildtype and TS5∆cat cartilage to identify those with the greatest difference in IL-1α response between the two genotypes (supplemental Tables 6 and 7, Sheet 3, Columns AD, AE and AG respectively). In the secretome analysis, the ratio of the IL-1α vs control protein fold changes for the two genotypes (expressed as TS5∆cat: wild-type) for all 79 of the significant proteins fell within an arbitrary cut-off of 1.5-fold. In fact the ratios were < 1.2 for 56 of the proteins, indicating very high correlation between the IL-1α response in wild-type and TS5∆cat cartilage (shown in Figure 6A). Similarly, in the proteome analysis, the ratios between the two genotypes for 231 of the 234 significant proteins were < 1.5 (Figure 6B). However, three proteins, Arg1, Timp1 and Smoc2, were more highly modulated in TS5∆cat cartilage compared with wild-type cartilage, with ratio of IL-1α vs control for the two genotypes (TS5∆cat: wild-type) > 1.5. Arg1 was increased 2.1-fold in TS5∆cat cartilage in response to IL-1α but unchanged in the wild-type cartilage, while Timp1 was increased 2.0-fold in TS5∆cat cartilage but only 1.2-fold in wild-type. In contrast, Smoc2 was decreased 3.2fold in TS5∆cat cartilage in response to IL-1α and decreased 1.7-fold in wild-type cartilage. Bioinformatics and pathway analysis of significant IL-1α α modulated proteins in the cartilage secretome. The web-based integrated functional annotation software DAVID 36 was used to identify significantly enriched GO terms, protein families and biological pathways among the proteins modulated by IL-1α in the cartilage secretome. The functional annotation clusters and proteins included in each of the functional terms are presented in full in supplemental Table 8 and 17 ACS Paragon Plus Environment


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summarized in Table 3. Not surprisingly, the highest ranking annotation clusters for the secretome of IL-1α treated cartilage were dominated by terms related to secreted proteins and the extracellular matrix. Significant terms with the largest numbers of protein entries included the keyword “secreted” (n = 44; p = 2.7 x 10 -29) and cellular component GO term “extracellular region part” (n = 31; p = 9.5 x 10 -19). The biological process GO term “cell adhesion” (n = 10; p = 2.5 x 10 -2), the molecular function “carbohydrate binding” (n = 8; p = 7.1 x 10 -3) and the KEGG pathway “ECMreceptor interaction” (n = 10; p = 1.4 x 10 -9) also highlighted the effect of IL-1α on chondrocytematrix interactions, ECM structure and organization. Many of the components classified by these terms, including collagens (Col2a1, Col3a1, Col10a1, Col11a1, Col11a2, Col9a2, and Col9a3), proteoglycans (lumican and lubricin) and cell-associated matrix (matricellular) proteins (tenascin C, thrombospondin-4, SPARC and SPARC like-1) were reduced in the media of IL-1α treated cartilage. In contrast, we observed increased levels of aggrecan, link protein, chondroadherin and matrilin-3, signifying release of these proteins as a consequence of IL-1α -induced ECM catabolism.

Significant GO terms that reflected the pro-inflammatory and catabolic effects of IL-1α on cartilage included the molecular function “peptidase activity” (n = 12; p = 2.1 x 10 -3) and the biological process “inflammatory response” (n = 6; p = 2.7 x 10 -2). These groups included several known products of IL-1α stimulated chondrocytes such as the acute phase proteins haptoglobin, serum amyloid proteins Saa1 and Saa3, the chemokine Cxcl1 (growth regulated alpha protein) complement factor Cfb and matrix metalloproteinase-3 16, 20. Several other components of the protease/ inhibitor web were also modulated in the media of IL-1α stimulated cartilage. Cathepsin L (Cstl1) and the serine protease inhibitor protease nexin-2 (Serpine2) were detected at elevated levels, while alpha-1-antitrypsin 1-4 (Serpina1d) and Serpina3n were reduced in abundance. Levels of the cysteine protease legumain (Lgmn) and cystatin-C (Cst3), a recently reported inhibitor of legumain 39 were also reduced in the secretome of IL-1α treated cartilage. In addition to Mmp3, one


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of the most highly induced proteins was the macrophage metalloelastase (Mmp12), a protease reported to degrade several abundant cartilage ECM components 40. Ingenuity software for pathway analysis was used to further interrogate significant proteins that were modulated by IL-1α treatment, based on the proteins included in the significant annotation terms. Significant diseases and disorders (Fisher’s exact test, p < 0.05) associated with these proteins included “connective tissue disorders”, “developmental disorder” and “skeletal and muscular disorders” (Table 4). Together, these terms comprised the highest-scoring network function for the secretome proteins (score = 52). Proteins in the cartilage secretome were mapped onto the network and the nodes color-coded to represent IL-1α induced changes in protein abundance (Figure 7A). In particular, the secretome network highlighted the relationships between the cluster of MMPs (Mmp3, Mmp12 and Mmp13) and their substrates, including several collagen chains and other cartilage matrix components such as aggrecan, link protein and tenascin C. The pathway analysis also revealed an unanticipated link between the increased elastase activity and cysteine protease/inhibitor sub-network and the relationship between Mmp12 and Cxcl1 41, 42. Bioinformatics and pathway analysis of significant IL-1α α modulated proteins in the cartilage proteome. We then identified significantly enriched GO terms, protein families and biological pathways for the proteins modulated by IL-1α in the cartilage proteome. The complete sets of proteins and functional terms for the functional annotation clusters reported using DAVID are presented in full in supplemental Table 9 and summarized in Table 5. Enriched protein annotation clusters with the largest number of proteins were associated with extracellular matrix biosynthesis, including the significant keywords “disulphide bond” (n = 28; p = 2.1 x 10 -3) and “glycoprotein” (n = 35; p = 2.8 x 10 -3) in addition to “endoplasmic reticulum” (n = 10; ns). Key proteins associated with endoplasmic reticulum stress were more abundant, including two chaperones, 78 kDa glucoseregulated protein (Hspa5/BiP) and Hypoxia up-regulated protein 1 (Hyou1/Grp170), and the recently-described elements of the unfolded protein response in chondrocytes Manf (Armet) and



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Creld2 43, 44. In contrast, proteins related to secretory protein translocation (Rrbp1 and Sec61a1), and in particular the intracellular (Colgalt2, Fkbp9, Plod1 and P4ha1) and extracellular (Loxl2 and Pcolce) post-translational modification and assembly of collagen chains were all reduced in the proteome of IL-1α treated cartilage. These results are consistent with the reduced levels of collagen chains detected in the media of IL-1α treated cartilage. Included in the term “glycoprotein” were the proteoglycans lubricin (Prg4), decorin (Dcn) and keratocan (Kera). These matrix components were more abundant in the proteome samples of IL-1α treated cartilage, while the most highly down-regulated proteins were cysteine-rich secretory protein LCCL domain-containing 1 (Crispld1) and SPARC-related modular calcium-binding protein 2 (Smoc2), two secreted proteins currently with no known function in cartilage. The second highest-scoring annotation cluster included the keywords “oxidoreductase” (n = 15; p = 1.3 x 10 -4) and mitochondrion (n = 17; ns). The significant term “glutathione metabolic process” (n = 5; p = 2.4 x 10 -2) included several mitochondrial proteins involved in glutathione metabolism and maintenance of cell redox state that were elevated in IL-1α −stimulated cartilage, such as glutathione reductase (Gsr), glutathione S-transferase omega-1 (Gsto1) and glutathione Stransferase (Gstp1). Proteins associated with cell stress and survival were classified by the terms “negative regulation of apoptosis” (n = 9; p = 1.8 x 10 -2) and “response to oxidative stress” (n = 7; p = 5.4 x 10 -3). As shown in previous studies, mitochondrial superoxide dismutase (Sod2), a key component of the enzymatic antioxidant response was increased 21. Levels of thioredoxin-1 (Txn1) and heme oxygenase (Hmox1), two further proteins with anti-oxidative properties, were both increased. Heme oxygenase is an established marker for oxidative stress in osteoarthritic cartilage 45

, while thioredoxin-1 is associated with synovial inflammation 46 but not previously identified as

product of oxidative stress in cartilage. Consistent with the secretome results, levels of the copper/zinc-dependent extracellular superoxide dismutase (Sod3) were elevated in the proteome of IL-1α –stimulated cartilage.


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Interestingly, levels of antioxidant-1 (Atox1), a copper chaperone that stimulates both Sod3 expression and activity 47, were also significantly increased. These results provide the first evidence and a potential mechanism for cytokine-mediated Sod3 expression in cartilage. After Mmp3 and Sod3, the third most highly up-regulated protein in the proteome of both wild-type and TS5∆cat cartilage was vanin-1 (Vnn1). Vanin-1 has been identified as an oxidative stress “sensor” with the ability to mediate the redox environment via regulation of the glutathione pathway 48. Pathway analysis using Ingenuity software identified significant diseases and disorders associated with proteins modulated in IL-1α treated cartilage (Table 4). The top-scoring network functions in the cartilage proteome were related to the disease and biological functional terms “cardiovascular disease”, “post-translational modification” and “protein degradation” (score = 43) and “drug metabolism”, “protein synthesis” and “dermatological diseases and conditions” (score = 30). In network 1, key interactions between metalloproteinases, the inhibitor Timp1 and the substrates decorin and aggrecan were highlighted (Figure 7B), while network 2 highlighted connectivity between the elements of the antioxidant defence response (Hmox1, Park7, Sod2 and Sod3) and glutathione metabolism (Gstp1 and Gsr) (Figure 7C). Correlation of protein and gene expression changes in IL-1α α stimulated cartilage. In addition to validation of proteome-level data, transcriptional profiling can differentiate between regulation at the level of gene expression and post-translational mechanisms such as protein release or turnover. This is particularly valuable in the context of a tissue challenged with catabolic cytokines that increase proteolytic activity. Therefore, wild-type (n = 2) and TS5∆cat (n = 2) femoral head cartilage cultured in the presence and absence of IL-1α was used for mRNA extraction and whole-genome microarray expression profiling (see Experimental procedures). Expression levels and fold-changes (IL-1α vs control) for the mRNA probes corresponding to the IL-1α modulated proteins in the secretome and cartilage proteome are presented in supplemental Table 10. Proteins lacking equivalent mRNA probes or where changes in gene expression were inconsistent between biological



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replicates were excluded from the comparisons. Log2 differences in mRNA expression for the significant proteins were displayed on heat maps based on unsupervised hierarchical clustering of the mRNA expression data, and the corresponding log2 differences in protein abundance were shown alongside for comparison (Figure 8).

First we compared the overall changes in mRNA expression for the IL-1α modulated proteins in the cartilage secretome and proteome. At a mean (linear scale) fold-change cut-off of ± 1.3, the trends in protein and mRNA expression changes matched for 45 of the 55 secretome vs microarray comparisons and 62 of the 68 proteome vs microarray comparisons. These results indicate that the majority of protein abundance changes caused by IL-1α result from altered gene regulation and differential mRNA expression. In the secretome vs microarray comparison aggrecan, link protein, matrilin-3 and cyclophilin B were detected at elevated levels in the media of IL-1α treated cartilage but down-regulated at the gene expression level (Figure 8A), consistent with our previous analysis 19. Two additional secreted proteins, chondroadherin (Chad) and C-type lectin domain family 3 member A (Clec3a) also matched this profile. In contrast, four proteins that were reduced in the secretome of IL-1α treated cartilage relative to controls (Timp1, Ftl1, Fap and Psma3) showed the opposite trend at the mRNA expression level. The proteome vs microarray comparison (Figure 8B) identified four proteins (Hnrnpa2b1, Idh2, Clu and Myl9) that were down-regulated at the mRNA level but increased in abundance in IL-1α treated cartilage relative to controls. Second, using the microarray data, we compared the respective protein and mRNA expression changes for Timp1, Arg1 and Smoc2, the three proteins we identified as differentially modulated in wild-type and TS5∆cat cartilage (Figure 6B). In the case of Smoc2, we observed no difference in mRNA regulation between the two genotypes at the mRNA level, which was decreased ~2.5-fold by IL-1α treatment in both wild-type and TS5∆cat cartilage, respectively (supplemental Table 10). In contrast, the changes in Timp1 mRNA expression in wild-type cartilage and TS5∆cat cartilage (1.8-fold and 2.6-fold up-regulated, respectively) were consistent with the proteomic data


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(1.2-fold and 2.0-fold, respectively). The differential regulation of arginase-1 between the two genotypes was even more pronounced. Arg1 was increased 2.1-fold at the protein level in IL-1α treated TS5∆cat cartilage, while no change in Arg1 abundance was detected wild-type cartilage. Concordantly, Arg1 mRNA levels were increased 7.1-fold in IL-1α treated TS5∆cat cartilage, compared to a more modest 1.7-fold increase in wild-type cartilage. Association of vanin-1 expression with cartilage degradation in vitro. The microarray and proteomic analysis of the effect of IL-1α on cartilage revealed that Vnn1 (vanin-1) was one of the most highly induced proteins. This first report of vanin-1 expression in cartilage implicates vanin-1 as a novel component of the oxidative stress response in chondrocytes. Since vanin-1 has roles in other pathologies involving oxidative stress 49, we sought to verify the expression and tissue localization of vanin-1 protein in IL-1α treated femoral head cartilage (Figure 9). Consistent with loss of aggrecan due to destruction of the cartilage ECM, IL-1α treatment resulted in loss of toluidine blue staining, with the exception of the calcified tissue of the secondary ossification center. In contrast to non-treated controls, almost all the chondrocytes in IL-1α cartilage were positive for vanin-1. At the articular surface, staining for vanin-1 was predominantly cellular, while diffuse staining of the ECM was also evident in regions of the growth plate, suggesting that vanin-1 may be released from the cell surface, to which it is GPI-anchored 49.



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DISCUSSION In this study we report for the first time the effect of interleukin-1α, a key inflammatory mediator in OA pathology, on chondrocytes within a native cartilage tissue environment. Proteins released into the media included breakdown products of the extracellular matrix as well as newlysynthesised proteins, while the protein changes in cartilage provided a more detailed view of the cellular response to IL-1α. This complementary approach, combining secretome and cartilage proteome analysis, identified more than 150 proteins significantly modulated by IL-1α. To distil the most biological insight from our data, unbiased bioinformatics approaches were used to identify significant processes, functional terms and inter-relationships between differentially expressed proteins. Additional analysis of IL-1α treated cartilage by whole-genome microarrays enabled us to differentiate between proteins modulated by IL-1α at the gene expression level, in contrast to those that were released into the media as products of ECM degradation. Furthermore, independent studies were done using wild-type cartilage and TS5∆cat cartilage lacking activity of the aggrecanase ADAMTS-5. While few proteins were differentially modulated between the two genotypes, the overall similarity in IL-1α response between WT and TS5∆cat cartilage provided useful crossvalidation between the two datasets. Together, these aspects represent a considerable advance over previous strategies for proteomic analysis of IL-1α or IL-1β treated chondrocytes in monolayer culture 20, 21 or proteins released into the media of cartilage explants 16-19. Chondrocytes exposed to biomechanical and cellular stresses must adapt in order to maintain proper cartilage function during normal development and aging as well in pathological conditions. Chondrocyte viability depends, for example, on enzymatic and biochemical defences against excessive levels of reactive oxygen species (ROS), which are strongly implicated in the pathogenesis of OA 50. In particular, inducible nitric oxide synthase is enzymatically more active in OA cartilage 51 and nitric oxide over-production is a known effect of IL-1β on chondrocytes 52. Our data provide further insight into the protective response to ROS in chondrocytes, involving up24 ACS Paragon Plus Environment

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regulation of glutathione S-transferases and glutathione reductase to promote glutathione biosynthesis and regeneration, respectively; raising the levels and activity of superoxide dismutase through induction of Sod2, Sod3 and Atox1; and additional antioxidant enzymes such as thioredoxin1. A significant and novel outcome of this study is the discovery of vanin-1 as a major product of IL1α stimulated chondrocytes. Vanin-1 is a member of the pantetheinase gene family, of which there are three in humans (VNN1, VNN2 and VNN3) and two in mice (Vnn1 and Vnn3). Vnn1-null mice lack cysteamine, the product of pantetheinase activity, in tissues that are normally abundant in Vnn1, such as spleen, small intestine and liver 53. Cysteamine inhibits gamma-glutamyl cysteine synthetase (γ-GCS), the rate limiting enzyme in glutathione synthesis. Accordingly, further analysis revealed that elevated intracellular glutathione stores in Vnn1 null mice confer resistance to experimentallyinduced oxidative stress 48. Vanin-1 is attracting considerable interest as an important inflammatory mediator in multiple human diseases, including malaria 54 , diabetes 55, immune thrombocytopenia 56 and other conditions involving oxidative stress 49. Highlighting the emergence of this protein as a potential therapeutic target, the crystal structure of vanin-1 was recently solved 57. In addition to oxidative stress, our results shed light on the chondrocyte ER stress response in cartilage. To meet the high demand for ECM biosynthesis, ER stress pathways are active during normal chondrogenesis and cartilage development, and are also triggered by misfolding of proteins harbouring disease-causing mutations (reviewed in 58). ER stress in chondrocytes is activated by catabolic cytokines 59 and classic ER stress hallmarks, such as upregulation of BiP, are also associated with OA cartilage 60. While ER stress is central to the pathology of several human chondrodysplasias, studies in mouse models have identified interesting genotype-specific variants of the canonical “unfolded protein response” (UPR) in chondrocytes, in both the mechanism of UPR activation and downstream pathways involved 61, 62. Our data reveal for the first time that ER stress in IL-1α treated cartilage involves Manf and Creld2, two that proteins that are specifically induced by the formation of non-native disulphide bonds in misfolded proteins 43, 44, 63. Creld2 has a putative ER retention motif and PDI-like activity but is also is secreted into the extracellular milieu, in a mechanism 25 ACS Paragon Plus Environment


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recently shown to be regulated by Manf 64. A further novel product of IL-1α induced chondrocyte stress was Hyou1, a protein that was detected in chaperone complexes specifically with misfolded matrilin-3 63. In contrast to the response to misfolded matrilin-3, ER retention of misfolded COMP causes an atypical UPR involving genes related to oxidative stress 65. Interestingly, a particular characteristic of the D469del COMP knock-in mouse was the down-regulation of peroxiredoxin-2, which we also detected at reduced levels in IL-1α treated cartilage. An emerging theme in many human diseases is the intrinsic link between oxidative stress and ER stress 66. One proposed mechanism for cross-talk between the ER and mitochondria involves calcium leakage as a consequence of ER stress, resulting in excessive calcium influx into the mitochondria augmenting ROS generation. Accumulation of ROS may then disrupt the ER redox potential, leading to the formation of incorrect disulphide bonds and further contributing to ER stress 67. The proteomic data described here provides new evidence for this mechanism, involving production of Manf/Creld2 and elevation of glutathione levels to reduce erroneous disulphide bonds and restore normal proteostasis. Thus, our data support the concept of ER stress in IL-1α stimulated chondrocytes as a protective response, as previously suggested 68. Similarly, in growth plate cartilage, adaptation to ER stress facilitates chondrocyte survival, either through re-programming or reduced proliferation 61. However, under conditions of altered biomechanical loading and/or cartilage remodelling in osteoarthritis patients, chronic unresolved ER stress correlates with apoptotic chondrocyte death and disease severity 60.

Our current study provides new insight into the effects of IL-1α on cartilage catabolism. Proteins that were detected at elevated levels in the media of IL-1α treated cartilage included ECM breakdown products we identified previously 19 and novel products Clec3a and chondroadherin. Originally named cartilage-derived C-type lectin 69, Clec3a was recently reported as a candidate gene associated with OA 70, but it’s role in cartilage is unknown. Chondroadherin fragmentation is associated with intervertebral disc degeneration 71. However, the peptides identified in the present


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study spanned approximately 80% of the mature protein sequence (residues 60-331), suggesting release of intact chondroadherin from cartilage rather than proteolysis. The metalloelastase Mmp12, one of three matrix metalloproteinases that were up-regulated in both the secretome and proteome of IL-1α treated cartilage, is also associated with human intervertebral disc degeneration 72

. Our results confirm previous detection of elevated Mmp12 transcription and release from IL-1α

treated rabbit cartilage 73. In addition to elastin, Mmp12 substrates include a broad spectrum of cartilage proteoglycans, collagens and other matrix components 40 and macrophage Mmp12 activity in synovial tissue is linked to articular cartilage degradation 74. However, the value of Mmp12 as a potential therapeutic target may be limited, as mice lacking Mmp12 suffer from exacerbated inflammation-mediated cartilage destruction, due to loss of Mmp12 activity against key elements of the complement cascade 75. In addition to metalloproteinases, lysosomal cysteine proteases are also involved in cytokine-induced ECM destruction 76. Cathepsin L was detected in both the proteome and secretome of IL-1α treated cartilage, while two additional and lesser-known cysteine proteases, legumain and cathepsin Z, were detected as cartilage products for the first time.

Comparison of the proteomic data with IL-1α induced changes in gene expression revealed very consistent trends between the microarray data and IL-1α induced changes in the cartilage secretome and proteome (82% and 91% agreement, respectively). Another notable feature of our data was the overall level of consistency between the IL-1α response in wild-type and TS5∆cat cartilage, both at the level of differential protein abundance and with regard to IL-1α induced changes in mRNA expression in the two genotypes. A practical limitation of the current study is that the wild-type and TS5∆cat explant cultures could not be synchronised. Comparison between the two genotypes was therefore based on identifying the greatest difference in IL-1α induced foldchanges in protein abundance. A more refined statistical approach may have been possible, for example, by breeding heterozygous TS5∆cat -/+ mice and using cartilage from wild-type and TS5∆cat +/+

littermates for simultaneous explant cultures, reducing both biological and technical variation.



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However, interesting differences in the effect of IL-1α on Arg1 and Timp1 were found between wildtype and TS5∆cat cartilage, where both proteins were more highly up-regulated by IL-1α in TS5∆cat cartilage. Further analysis is required to verify these findings and elucidate possible mechanisms underlying these differences. It was not surprising that few differences were found between the proteome of wild-type and TS5∆cat cartilage, however, we anticipated more effects on the secretome given the importance of ADAMTS-5 in aggrecanolysis. While shotgun proteomics provides a global assessment of relative protein abundance, it is challenging to differentiate between intact proteins and truncated proteins resulting from proteolysis, as the connection between the original proteins and the peptides analysed is lost. Greater insight into the catabolic role of ADAMTS-5 in cartilage destruction, including identification of novel substrates, could be gained by specific analysis of protein neotermini in the media of wild-type and TS5∆cat cartilage, such as terminal amine isotopic labelling of substrates (TAILS) 77.

CONCLUSIONS In summary, our proteomic analysis has revealed considerable complexity in the response to IL-1α treatment on chondrocytes within a native cartilage tissue environment. Markers of the ER stress response related to protein misfolding and non-native disulphide bond formation provided new evidence for the effect of IL-1α on oxidative protein folding in the ER. The oxidative stress response involved expression of protective elements such as glutathione reductase, and vanin-1, which has a potentially detrimental effect on glutathione-mediated redox homeostasis. Further experiments in Vnn1 null mice will determine whether vanin-1 has an important role in inflammation-mediated cartilage destruction.


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ACKNOWLEDGEMENTS This work was funded in part by grants from the National Health and Medical Research Council of Australia (#1063133, #1002797, #491237). The authors acknowledge the use of equipment and infrastructure provided by the Central Science Laboratory, University of Tasmania, funded by Australian Research Council grant LE0775570.


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FIGURE LEGENDS Figure 1. SDS-PAGE analysis of (A) cartilage secretome samples, (B) the cartilage tissue NaCl-soluble fraction and (C) the cartilage tissue GuHCl-soluble fraction. Protein samples are biological replicates collected from control and interleukin-1α (IL-1α) treated wild-type cartilage and cartilage lacking ADAMTS-5 activity (TS5∆cat cartilage). Protein bands with marked differences between control and IL-1α treatment are indicated by arrowheads. The molecular weight markers in the first lane of each gel are, in descending order, 250, 150, 100, 75, 50, 37, 25, 20, 15 and 10 kDa, respectively. Figure 2. Scatter plot representation of significant GO terms enriched in the proteins identified in the cartilage secretome (A) and cartilage tissue proteome (B). Bubble size represents the number of proteins associated with each term and shading represents the log10 p value according to the key. Figure 3. Volcano plot representation of the t-test comparing IL-1α with control samples, for proteins identified in the cartilage secretome (A) and proteome (B), irrespective of genotype (n = 6 for each comparison). Each data point represents a protein, where the x-axis is the difference between the means of log2 normalized peptide LFQ intensities for control and IL-1α treated samples and the y axis is the -log10 p value. Proteins found to be significantly different between groups are plotted in red (FDR adjusted p value < 0.05). Data points corresponding to the most highly differentially abundant proteins are labelled with arrows and the numbers refer to the detailed description of those proteins in Tables 1 and 2. Figure 4. Bar graph representation of the significant proteins modulated by IL-1α in the cartilage secretome of wild-type and TS5∆cat cartilage. For each of the significant proteins, the bar height (y axis) is the log2 fold difference between mean normalized LFQ values (IL-1α treated vs control). Fold-differences for wild-type and TS5∆cat cartilage are shown in blue and red, respectively. Proteins with marked differences in response to IL-1α with respect to genotype are marked *.



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Figure 5. Bar graph representation of the significant proteins modulated by IL-1α in the cartilage proteome of wild-type and TS5∆cat cartilage. For each of the significant proteins, the bar height (y axis) is the log2 fold difference between mean normalized LFQ values (IL-1α treated vs control). Fold-differences for wild-type and TS5∆cat cartilage are shown in blue and red, respectively. Proteins with marked differences in response to IL-1α with respect to genotype are marked *. Figure 6. Scatter plot representation of the log2 fold changes for significantly modulated proteins (IL-1α treated vs control) in wild-type cartilage and TS5∆cat cartilage in (A) the secretome and (B) the proteome (n = 3 for each comparison). Each data point represents a protein, where the x-axis and y-axis are the difference between the means of log2 normalized peptide LFQ intensities (IL-1α treated vs control) for wild-type samples and TS5∆cat samples, respectively. The three labelled proteins, Smoc2, Arg1 and Timp1, all have ratios in log2 fold changes for the two genotypes (TS5∆cat: wild-type) > 1.5.

Figure 7. Top-scoring network function for the significant IL-1α modulated proteins in the cartilage (A) and proteome (B and C) identified using Ingenuity Pathway Analysis. Nodes are shaded according to increased (red) or decreased (green) abundance after IL-1α treatment. Figure 8. Comparison of gene expression changes and protein abundance changes for proteins differentially modulated by IL-1α in the cartilage secretome (A) and proteome (B). Heat maps were generated by unsupervised hierarchical cluster analysis of mRNA expression changes (IL-1α vs control) detected in wild-type (n = 2) and TS5∆cat (n = 2) cartilage using whole-genome microarray analysis (columns 1-4, respectively). Log2 fold-differences (IL-1α vs control) are color-coded according to the key, where each lane represents the results of one microarray. Corresponding changes in protein abundance in wild-type and TS5∆cat cartilage (columns 5 and 6, respectively), where each lane represents the mean log2 fold-differences (n = 3), are shown alongside for comparison. Proteins with discordant changes in protein and mRNA abundance are marked *.


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Figure 9. Immunohistochemistry analysis of vanin-1. Paraffin-embedded sections of femoral head cartilage from control (-IL-1α) and treated (+IL-1α) cultures were either stained with toluidine blue (left panel) or incubated with rabbit polyclonal antibodies against vanin-1 (right panel). Vanin-1 detection at the articular surface (AS) and growth plate (GP) is indicated by the brown staining.



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FIGURES

A

B

Figure 1


C

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Figure 2A

Figure 2B



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Figure 3A

Figure 3B 44 ACS Paragon Plus Environment

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Figure 4



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Figure 5


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Figure 6A

Figure 6B



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Figure 7A

Figure 7B


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Figure 7C



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* Microarray

Secretome *

Microarray

Figure 8B

Figure 8A


Proteome

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TABLES


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Supporting Information Supplemental Table 1 – MS peptide-level data Complete list of peptides identified by mass spectrometry imported from MaxQuant output file peptides.txt for the secretome samples (Sheet 1) and proteome samples (Sheet 2). Supplemental Table 2 – MS protein-level data Complete list of proteins identified by mass spectrometry and associated data imported from the MaxQuant output file proteinGroups.txt for the secretome samples (Sheet 1) and proteome samples (Sheet 2). Tables include proteins that were excluded from further analysis (potential contaminants, proteins identified only by site and reverse database matches). Supplemental Table 3 – bioinformatics overview of cartilage secretome and proteome data Protein lists were processed using FatiGO and output files for the secretome (Sheet 1) and proteome (Sheet 2) include the enriched cellular component terms (column A), adjusted p values (column B) and proteins associated with each significant GO term (column K). GO term descriptions (column O) specific to the secretome or proteome are highlighted in red. Supplemental Table 4 – statistical analysis of the cartilage secretome data to identify differentially-expressed proteins (IL-1α vs control) irrespective of genotype (n = 6) Normalized LFQ intensities for proteins identified in the secretome samples were imported into Perseus and processed as described in Experimental Procedures. Data were exported after log2 – transformation (Sheet 1), imputation of missing values (Sheet 2), Z-score normalization and t-test (Sheet 3). Log2 –scale fold-differences for significant proteins in wild-type and TS5∆cat samples (Sheet 4) were used to plot the bar chart (Sheet 5 and Figure 4). Supplemental Table 5 – statistical analysis of the cartilage proteome data to identify differentiallyexpressed proteins (IL-1α vs control) irrespective of genotype (n = 6) Normalized LFQ intensities for proteins identified in the proteome samples were imported into Perseus and processed as described in Experimental Procedures. Data were exported after log2 – transformation (Sheet 1), imputation of missing values (Sheet 2), Z-score normalization and t-test (Sheet 3). Log2 –scale fold-differences for significant proteins in wild-type and TS5∆cat samples (Sheet 4) were used to plot the bar chart (Sheet 5 and Figure 5). Supplemental Table 6 – statistical analysis of the cartilage secretome data based on separate t-test comparisons (IL-1α vs control) of wild-type and TS5∆cat samples (n = 3 biological replicates) Perseus export files show the results of log2 – transformation for proteins detected in all 12 secretome samples (Sheet 1), Z-score normalization and t-test (Sheet 2) and calculation of linearscale fold-differences for significant IL-1α modulated proteins in wild-type or TS5∆cat samples (Sheet 3). Proteins were ranked according to the ratios of these fold-differences (column AG; TS5/WT). Log2 –scale protein fold-differences between IL-1α and control in wild-type samples 54 ACS Paragon Plus Environment

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(column V) and TS5∆cat samples (column X) were used to generate the scatter plot (Sheet 4 and Figure 6A). Supplemental Table 7 – statistical analysis of the cartilage proteome data based on separate t-test comparisons (IL-1α vs control) of wild-type and TS5∆cat samples (n = 3 biological replicates) Perseus export files show the results of log2 – transformation for proteins detected in all 12 proteome samples (Sheet 1), Z-score normalization and t-test (Sheet 2) and calculation of linearscale fold-differences for significant IL-1α modulated proteins in wild-type or TS5∆cat samples (Sheet 3). Proteins were ranked according to the ratios of these fold-differences (column AG; TS5/WT). Log2 –scale protein fold-differences between IL-1α and control in wild-type samples (column V) and TS5∆cat samples (column X) were used to generate the scatter plot (Sheet 4 and Figure 6B). Proteins with TS5∆cat: wild-type ratios >1.5 (Arg1, Timp1 and Smoc2) highlighted in red. Supplemental Table 8 – DAVID analysis of secretome proteins significantly modulated by IL-1α The 70 IL-1α modulated proteins were mapped to 70 DAVID Id’s and used for Functional Annotation Clustering (Sheet 1). The complete output file, including all proteins associated with each ontological term, pathway or sequence feature is shown in Sheet 2. Representative significant terms (highlighted in Bold) and associated data are presented in Table 1. Supplemental Table 9 – DAVID analysis of proteome proteins significantly modulated by IL-1α The 96 IL-1α modulated proteins were mapped to 96 DAVID Id’s and used for Functional Annotation Clustering (Sheet 1). The complete output file, including all proteins associated with each ontological term, pathway or sequence feature is shown in Sheet 2. Representative significant terms (highlighted in Bold) and associated data are presented in Table 3. Supplemental Table 10 – summary of microarray mRNA expression data corresponding to proteins significantly modulated by IL-1α Table shows log2 –scale expression values, fold-difference (IL-1α vs control), probe names and full gene descriptions for the proteins modulated by IL-1α in the cartilage secretome (Sheet 1, 66 probes identified for the 70 proteins) and proteome (Sheet 2, 90 probes identified for the 96 proteins). Only genes with consistent changes in expression across the four microarrays (i.e. all positive or all negative) were used to compare gene expression changes and protein abundance changes for the cluster analysis shown in Figure 8.



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For Table of Contents only:

Interleukin-1α α >>> Cartilage Secretome + Proteome >>> ECM catabolism, ER stress & oxidative stress


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Novel Elements of the Chondrocyte Stress Response Identified Using

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