PXD033163

PXD033163 is an original dataset announced via ProteomeXchange.

Title	functional biomarker which could be used to predict connective healing based on an Achilles Tendon Model
Description	Healing process after connective tissues (CT) injury is complex but important as CT are critical for human moving, and patient outcome of CT injured patients is protracted with high individual variation. Specific markers which could be used for healing prognosis will be great help to monitor patient outcome, and furtherly improve target treatment and patient recovery. Although several common factors like age, gender and body mass index (BMI) were reported to predict ATR healing outcome [references], more specific markers which can be used as predictors of patient outcome after ATR are still lacking. Several biomarkers have been reported to associate with tendon healing, and indicated their relationship with patient outcome(1-4). These results highlight the potential of protein expression detection and analysis for tendon study in general and healing outcome prognosis in particular. Collagen type I (Col1a1) is the main component of Achilles tendon and plays vital role in tendon healing via involving in collagen fibrils production among tendon fibroblasts(5). Higher production of Col1a1 is associated with faster tendon repair(6), previous study on animal model reported the increased Col1a1 synthesis can exert a positive effect on tendon healing[?]. Thus, functional proteins with high impact on Col1a1 can be used not only to improve tendon healing, also can be as targets for healing improvement and outcome prediction. The proteome file of Achilles tendon which is important to increase understanding of tendon healing and help to identify accurate outcome-related biomarkers is however lagged behind. The identification of proteomic profile of Achilles tendon is very much limited in number of proteins and sample size(7-9), and is even few on human. Recent improvement in proteomic assay based on mass spectrometry (MS) made it possible to assemble the proteomic landscape of human tissues(10, 11) with high quality and sensitivity. To characterize the proteomic components in human Achilles tendon, we utilized biopsies from ATR patients with both good and poor 1-year healing outcome. A validated platform including quantitative MS and clinical database was used to identify proteomic landscape and potential bio-predictors of clinical outcome. Our study may provide a more comprehensive landscape of proteins for human Achilles tendon, as well as specific biomarkers which can be used for long term outcome prognosis after ATR.
HostingRepository	PRIDE
AnnounceDate	2023-11-14
AnnouncementXML	Submission_2023-11-14_08:47:24.268.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Massimiliano Gaetani
SpeciesList	scientific name: Homo sapiens (Human); NCBI TaxID: 9606;
ModificationList	monohydroxylated residue; deamidated residue; iodoacetamide derivatized residue
Instrument	Q Exactive HF; LTQ; LCQ Classic

Revision	Datetime	Status	ChangeLog Entry
0	2022-04-12 01:56:53	ID requested
1	2023-05-10 14:21:06	announced
⏵ 2	2023-11-14 08:47:30	announced	2023-11-14: Updated project metadata.

Chen J, Wang J, Wu X, Simon N, Svensson CI, Yuan J, Hart DA, Ahmed AS, Ackermann PW, eEF2 improves dense connective tissue repair and healing outcome by regulating cellular death, autophagy, apoptosis, proliferation and migration. Cell Mol Life Sci, 80(5):128(2023) [pubmed]

submitter keyword: MS, connective tissue, healing, fibroblast, eEF2, biomarker

Junyu Chen
contact affiliation	Department of Molecular Medicine and Surgery Center for Molecular Medicine, Karolinska Institutet
contact email	junyu.chen@ki.se
lab head
Massimiliano Gaetani
contact affiliation	Karolinska Institutet
contact email	massimiliano.gaetani@ki.se
dataset submitter

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PRIDE project URI

• PRIDE
  1. PXD033163
     1. Label: PRIDE project
     2. Name: functional biomarker which could be used to predict connective healing based on an Achilles Tendon Model