Dataset Information

Proteomics of regenerated tissue in response to a titanium implant with a bioactive surface in a rat tibial defect model

ABSTRACT: Background: Due to their excellent mechanical and biocompatibility properties, titanium-based implants are successfully used as biomedical devices. However, when new bone formation fails for different reasons, impaired fracture healing will become a clinical problem and will affect the patient's quality of life. This study proposes to design a new bioactive surface of titanium implants with a synergetic PEG biopolymer-based composition for gradual delivery of growth factors (FGF2, VEGF, and BMP4) during bone healing. Methods: The optimal architecture of non-cytotoxic polymeric coatings deposited by dip coating under controlled parameters was assessed both in cultured cells and on rat in vivo animal model (100% viability). Results: Notably, the titanium adsorbed polymer matrix induced an improved healing process when compared with the individual action of each biomolecules. High-performance mass spectrometry analysis demonstrated that recovery after a traumatic event is governed by specific differentially regulated proteins, acting in a coordinated response to the external stimulus. Predicted protein interactions shown by STRING analysis were well organized in hub-based networks related with response to chemical, wound healing and response to stress pathways. Conclusions: The described functional polymer coatings of the titanium implants demonstrated the significant improvement of bone healing process after injury.

INSTRUMENT(S): LTQ Orbitrap Velos

ORGANISM(S): Rattus Norvegicus (rat)

TISSUE(S): Callus, Tibia

DISEASE(S): Wounds And Injuries,Disease Free

SUBMITTER: Felicia Antohe

LAB HEAD: Felicia Antohe

PROVIDER: PXD016135 | Pride | 2021-04-29

REPOSITORIES: Pride

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Publications

Proteomics of regenerated tissue in response to a titanium implant with a bioactive surface in a rat tibial defect model.

Boteanu Raluca M RM Suica Viorel I VI Ivan Luminita L Safciuc Florentina F Uyy Elena E Dragan Emanuel E Croitoru Sorin M SM Grumezescu Valentina V Chiritoiu Marioara M Sima Livia E LE Vlagioiu Constantin C Socol Gabriel G Antohe Felicia F

Scientific reports 20201028 1

Due to their excellent mechanical and biocompatibility properties, titanium-based implants are successfully used as biomedical devices. However, when new bone formation fails for different reasons, impaired fracture healing becomes a clinical problem and affects the patient's quality of life. We aimed to design a new bioactive surface of titanium implants with a synergetic PEG biopolymer-based composition for gradual delivery of growth factors (FGF2, VEGF, and BMP4) during bone healing. The opti ...[more]

PMID: 33116264

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Project description:Tissue response following implantation determines the success of the healing process. This response is not only dependent on the chemical properties of the implant surface but also by the surface topography or its roughness. Although in vitro and in vivo studies show improved results with rough- and fluoride-modified implants, the mechanisms behind these findings are still unknown. Here, we have used a two step procedure to identify novel genes related to the early cell response of primary human osteoblasts to roughness and fluoride-modified titanium implants. 217 genes were identified by microarray analysis as response genes to roughness and 198 genes as response genes to fluoride. 11 of these identified genes have been related to bone and mineralization and were further investigated by real-time RT-PCR. After one day of culture, TLR3, ANKH, DCN, OC and RUNX2 were classified as responsive genes to roughness; DLX2 and TUFT1 as responsive genes to fluoride treatment. COLL-I, PTHLH, HES1, FST, ENPP1 and THRA as responsive genes to both, roughness and fluoride treatment. In conclusion, our strategy was useful for identifying novel genes that might be involved in the early response of osteoblasts to roughness and fluoride treatment of titanium implants. Tissue response following implantation determines the success of the healing process. This response is not only dependent on the chemical properties of the implant surface but also by the surface topography or its roughness. Although in vitro and in vivo studies show improved results with rough- and fluoride-modified implants, the mechanisms behind these findings are still unknown. Here, we have used a two step procedure to identify novel genes related to the early cell response of primary human osteoblasts to roughness and fluoride-modified titanium implants. 217 genes were identified by microarray analysis as response genes to roughness and 198 genes as response genes to fluoride. 11 of these identified genes have been related to bone and mineralization and were further investigated by real-time RT-PCR. After one day of culture, TLR3, ANKH, DCN, OC and RUNX2 were classified as responsive genes to roughness; DLX2 and TUFT1 as responsive genes to fluoride treatment. COLL-I, PTHLH, HES1, FST, ENPP1 and THRA as responsive genes to both, roughness and fluoride treatment. In conclusion, our strategy was useful for identifying novel genes that might be involved in the early response of osteoblasts to roughness and fluoride treatment of titanium implants. Human osteoblasts were cultured for 24 hours on titanium disks modified either with polished surfaces (P), grit-blasted surfaces (GB), or grit-blasted High Fluor treated surfaces (GB-HF). After 24 hours of culture RNA was isolated. 3 arrays were hybridized, one with the isolated RNA from cells cultured on titanium polished surfaces (P), one with the isolated RNA from cells cultured on titanium grit-blasted surfaces (GB) and one with the isolated RNA from cells cultured on titanium grit-blasted HF treated surfaces (GB-HF).

Dataset Information

Proteomics of regenerated tissue in response to a titanium implant with a bioactive surface in a rat tibial defect model

Publications

Proteomics of regenerated tissue in response to a titanium implant with a bioactive surface in a rat tibial defect model.

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