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dc.contributor.authorHulme, Charlotte H.*
dc.contributor.authorWilson, Emma L.*
dc.contributor.authorFuller, Heidi R.*
dc.contributor.authorRoberts, Sally*
dc.contributor.authorRichardson, James B.*
dc.contributor.authorGallacher, Pete*
dc.contributor.authorPeffers, Mandy J.*
dc.contributor.authorShirran, Sally L.*
dc.contributor.authorBotting, Catherine H.*
dc.contributor.authorWright, Karina T.*
dc.date.accessioned2018-06-11T15:05:09Z
dc.date.available2018-06-11T15:05:09Z
dc.date.issued2018-05-02
dc.identifier.citationHulme, C. H., et al. (2018). Two independent proteomic approaches provide a comprehensive analysis of the synovial fluid proteome response to Autologous Chondrocyte Implantation. Arthritis Research & Therapy, 20(1), 87. https://doi.org/10.1186/s13075-018-1573-4
dc.identifier.doi10.1186/s13075-018-1573-4
dc.identifier.urihttp://hdl.handle.net/10034/621178
dc.description.abstractBackground: Autologous chondrocyte implantation (ACI) has a failure rate of approximately 20%, but it is yet to be fully understood why. Biomarkers are needed that can pre-operatively predict in which patients it is likely to fail, so that alternative or individualised therapies can be offered. We previously used label-free quantitation (LF) with a dynamic range compression proteomic approach to assess the synovial fluid (SF) of ACI responders and non-responders. However, we were able to identify only a few differentially abundant proteins at baseline. In the present study, we built upon these previous findings by assessing higher-abundance proteins within this SF, providing a more global proteomic analysis on the basis of which more of the biology underlying ACI success or failure can be understood. Methods: Isobaric tagging for relative and absolute quantitation (iTRAQ) proteomic analysis was used to assess SF from ACI responders (mean Lysholm improvement of 33; n = 14) and non-responders (mean Lysholm decrease of 14; n = 13) at the two stages of surgery (cartilage harvest and chondrocyte implantation). Differentially abundant proteins in iTRAQ and combined iTRAQ and LF datasets were investigated using pathway and network analyses. Results: iTRAQ proteomic analysis confirmed our previous finding that there is a marked proteomic shift in response to cartilage harvest (70 and 54 proteins demonstrating ≥ 2.0-fold change and p < 0.05 between stages I and II in responders and non-responders, respectively). Further, it highlighted 28 proteins that were differentially abundant between responders and non-responders to ACI, which were not found in the LF study, 16 of which were altered at baseline. The differential expression of two proteins (complement C1s subcomponent and matrix metalloproteinase 3) was confirmed biochemically. Combination of the iTRAQ and LF proteomic datasets generated in-depth SF proteome information that was used to generate interactome networks representing ACI success or failure. Functional pathways that are dysregulated in ACI non-responders were identified, including acute-phase response signalling. Conclusions: Several candidate biomarkers for baseline prediction of ACI outcome were identified. A holistic overview of the SF proteome in responders and non-responders to ACI  has been profiled, providing a better understanding of the biological pathways underlying clinical outcome, particularly the differential response to cartilage harvest in non-responders.
dc.language.isoenen
dc.publisherBioMed Central
dc.relation.urlhttps://arthritis-research.biomedcentral.com/articles/10.1186/s13075-018-1573-4en
dc.subjectAutologous chondrocyte implantation (ACI)en
dc.subjectiTRAQ proteomicsen
dc.subjectLabel-free quantitation proteomicsen
dc.subjectSynovial fluiden
dc.subjectCartilage repairen
dc.subjectComplement C1S subcomponenten
dc.subjectMatrix metalloproteinase 3en
dc.subjectMMP3en
dc.titleTwo independent proteomic approaches provide a comprehensive analysis of the synovial fluid proteome response to Autologous Chondrocyte Implantationen
dc.typeArticleen
dc.identifier.eissn1478-6362
dc.contributor.departmentKeele University; Robert Jones and Agnes Hunt Orthopaedic Hospital; University of Chester; University of Liverpool; University of St Andrews
dc.identifier.journalArthritis Research & Therapyen
dc.language.rfc3066en
dc.rights.holderThe Author(s).
dc.date.updated2018-06-01T11:40:45Z
dc.date.accepted2018-03-21
or.grant.openaccessYesen
rioxxterms.funderArthritis Research UKen
rioxxterms.identifier.projectArthritis Research UKen
rioxxterms.identifier.projectWellcome Trust Clinical Intermediate Fellowship 094476/Z/10/Zen
rioxxterms.versionVoRen
rioxxterms.licenseref.startdate2018-05-02
refterms.dateFCD2019-07-17T08:51:56Z
refterms.versionFCDVoR
refterms.dateFOA2018-08-13T14:55:35Z
html.description.abstractBackground: Autologous chondrocyte implantation (ACI) has a failure rate of approximately 20%, but it is yet to be fully understood why. Biomarkers are needed that can pre-operatively predict in which patients it is likely to fail, so that alternative or individualised therapies can be offered. We previously used label-free quantitation (LF) with a dynamic range compression proteomic approach to assess the synovial fluid (SF) of ACI responders and non-responders. However, we were able to identify only a few differentially abundant proteins at baseline. In the present study, we built upon these previous findings by assessing higher-abundance proteins within this SF, providing a more global proteomic analysis on the basis of which more of the biology underlying ACI success or failure can be understood. Methods: Isobaric tagging for relative and absolute quantitation (iTRAQ) proteomic analysis was used to assess SF from ACI responders (mean Lysholm improvement of 33; n = 14) and non-responders (mean Lysholm decrease of 14; n = 13) at the two stages of surgery (cartilage harvest and chondrocyte implantation). Differentially abundant proteins in iTRAQ and combined iTRAQ and LF datasets were investigated using pathway and network analyses. Results: iTRAQ proteomic analysis confirmed our previous finding that there is a marked proteomic shift in response to cartilage harvest (70 and 54 proteins demonstrating ≥ 2.0-fold change and p < 0.05 between stages I and II in responders and non-responders, respectively). Further, it highlighted 28 proteins that were differentially abundant between responders and non-responders to ACI, which were not found in the LF study, 16 of which were altered at baseline. The differential expression of two proteins (complement C1s subcomponent and matrix metalloproteinase 3) was confirmed biochemically. Combination of the iTRAQ and LF proteomic datasets generated in-depth SF proteome information that was used to generate interactome networks representing ACI success or failure. Functional pathways that are dysregulated in ACI non-responders were identified, including acute-phase response signalling. Conclusions: Several candidate biomarkers for baseline prediction of ACI outcome were identified. A holistic overview of the SF proteome in responders and non-responders to ACI  has been profiled, providing a better understanding of the biological pathways underlying clinical outcome, particularly the differential response to cartilage harvest in non-responders.


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