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PXD008321

PXD008321 is an original dataset announced via ProteomeXchange.

Dataset Summary
TitleTwo independent proteomic approaches provide the most comprehensive analysis of the synovial fluid proteome in responders and non-responders to Autologous Chondrocyte Implantation
DescriptionBackground: Results obtained from our previous study using a label-free quantitation (LF) proteomic approach in which dynamic range compression was used to profile lower abundance proteins, demonstrated few proteins that were differentially abundant within the synovial fluid (SF) when comparing Autologous Chondrocyte Implantation (ACI) responders and non-responders at baseline (1). This study builds upon our previous findings by assessing higher abundance proteins within these SFs; providing a more global proteome analysis from which we can understand more fully the biology underlying ACI success or failure. Methods: Isobaric tagging for relative and absolute quantitation (iTRAQ) proteomics was used to assess SFs 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 were investigated using pathway analyses and the iTRAQ proteomic dataset was combined with our published proteomic dataset of dynamically compressed SFs, from which an interactome network model of systemic protein interactions was generated. Results: iTRAQ proteomics has confirmed our previous finding that there is a marked proteome shift in response to cartilage harvest (70 and 54 proteins demonstrating ≥2.0 fold change between Stages I and II in responders and non-responders, respectively) and has highlighted 28 proteins that were differentially abundant between responders and non-responders to ACI, that were not found in the LF study; 16 of which were altered at baseline. Two protein abundance changes (Complement C1S subcomponent and Matrix metalloproteinase 3 (MMP3), have been biochemically validated. Combination of the iTRAQ and LF proteomic datasets has generated in-depth SF proteome information that has been used to generate interactome networks representing ACI success or failure, from which functional pathways that are dysregulated in ACI non-responders have been identified. Conclusions: Several candidate biomarkers for baseline prediction of ACI outcome have been 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.
HostingRepositoryPRIDE
AnnounceDate2018-05-03
AnnouncementXMLSubmission_2018-05-23_00:36:58.xml
DigitalObjectIdentifier
ReviewLevelPeer-reviewed dataset
DatasetOriginOriginal dataset
RepositorySupportUnsupported dataset by repository
PrimarySubmitterSally Shirran
SpeciesList scientific name: Homo sapiens (Human); NCBI TaxID: 9606;
ModificationListiTRAQ4plex-116 reporter+balance reagent acylated residue
InstrumentTripleTOF 5600
Dataset History
RevisionDatetimeStatusChangeLog Entry
02017-11-29 02:30:42ID requested
12018-05-03 04:13:48announced
22018-05-23 00:36:59announcedUpdated publication reference for PubMed record(s): 29720234.
Publication List
Hulme CH, Wilson EL, Fuller HR, Roberts S, Richardson JB, Gallacher P, Peffers MJ, Shirran SL, Botting CH, Wright KT, Two independent proteomic approaches provide a comprehensive analysis of the synovial fluid proteome response to Autologous Chondrocyte Implantation. Arthritis Res Ther, 20(1):87(2018) [pubmed]
Keyword List
curator keyword: Biomedical
submitter keyword: Autologous Chondrocyte Implantation (ACI)
iTRAQ proteomics
Label-free quantification proteomics
Synovial Fluid
Cartilage repair
Osteoarthritis
Contact List
Karina Wright
contact affiliationInstitute of Science and Technology in Medicine, Keele Univeristy, Keele, Staffordshire, UK Robert Jones and Agnes Hunt Orthopaedic Hospital, Oswestry, Shropshire, UK
contact emailkarina.wright@rjah.nhs.uk
lab head
Sally Shirran
contact affiliationUniversity of St Andrews
contact emailss101@st-andrews.ac.uk
dataset submitter
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