Biomarkers of inflammation and innate immunity in atrophic nonunion fracture

Abstract

Background: Nonunion is a failure of healing following a bone fracture. Its physiopathology remains partially unclear and the discovery of new mediators could promote the understanding of bone healing.

Methods: Thirty-three atrophic nonunion (NU) patients that failed to demonstrate any radiographic improvement for 6 consecutive months were recruited for providing serum samples. Thirty-five healthy volunteers (HV) served as the control group. Proteomics studies were performed using SELDI-TOF-MS and 2D-DIGE approaches, associated or not with Proteominer® preprocessing, to highlight biomarkers specific to atrophic nonunion pathology. Peak intensities were analyzed by two statistical approaches, a nonparametric Mann-Whitney U tests (univariate approach) and a machine-learning algorithm called extra-trees (multivariate approach). Validation of highlighted biomarkers was performed by alternative approaches such as microfluidic LC-MS/MS, nephelometry, western blotting or ELISA assays.

Results: From the 35 HV and 33 NU crude serum samples and Proteominer® eluates, 136 spectra were collected by SELDI-TOF-MS using CM10 and IMAC-Cu(2+) ProteinChip arrays, and 665 peaks were integrated for extra-trees multivariate analysis. Accordingly, seven biomarkers and several variants were identified as potential NU biomarkers. Their levels of expression were found to be down- or up-regulated in serum of HV vs NU. These biomarkers are inter-α-trypsin inhibitor H4, hepcidin, S100A8, S100A9, glycated hemoglobin β subunit, PACAP related peptide, complement C3 α-chain. 2D-DIGE experiment allowed to detect 14 biomarkers as being down- or up-regulated in serum of HV vs NU including a cleaved fragment of apolipoprotein A-IV, apolipoprotein E, complement C3 and C6. Several biomarkers such as hepcidin, complement C6, S100A9, apolipoprotein E, complement C3 and C4 were confirmed by an alternative approach as being up-regulated in serum of NU patients compared to HV controls.

Conclusion: Two proteomics approaches were used to identify new biomarkers up- or down-regulated in the nonunion pathology, which are involved in bone turn-over, inflammation, innate immunity, glycation and lipid metabolisms. High expression of hepcidin or S100A8/S100A9 by myeloid cells and the presence of advanced glycation end products and complement factors could be the result of a longstanding inflammatory process. Blocking macrophage activation and/or TLR4 receptor could accelerate healing of fractured bone in at-risk patients.

Keywords: 2D-DIGE; Apolipoprotein; Biomarkers; Complement; Hepcidin; Innate immunity; Nonunion; Proteomics; S100A8; SELDI.

Fig. 1

SELDI-TOF–MS gel view spectra for samples collected from at least 15 healthy controls and 15 nonunion patients. Illustration of identified biomarkers: hepcidin-25, pituitary adenylate cyclase-activating polypeptide (PACAP)—related peptide, hemoglobin β subunit, S100A8, S100A9* variant, S100A9, inter-α-trypsin inhibitor heavy chain H4 (ITIH4), complement factor C3a and apolipoprotein A1. HV healthy volunteers; NU nonunion patients

Fig. 2

Immunodepletion for hepcidin identification. Purification from serum and identification of the protein fragment at m/z value of 2792 as hepcidin-25. Identification by depletion of serum using specific and non specific antibodies followed by elution from the immune complexes. FT flow-through; IP immunoprecipitation

Fig. 3

Gel master and location of the protein spots picked: contours are protein spots with at least 1.2-fold expression change (P < 0.05, student t-test) and proteins significantly identified with the Ultraflex II MALDI mass spectrometer and Biotools v.3.1.software with an in-house hosted Mascot v2.2.2 server

Fig. 4

Comparative analysis of selected protein spot intensities with the BVA module of the Decyder 6.5 software. Spot intensities obtained for each pool of healthy volunteers (HV) and nonunion (NU) patients were plotted on a graph. 3-D images represent the 3D view of selected spots obtained from one representative HV and NU pool for each biomarker of interest

Fig. 5

Validation of selected biomarkers by alternative approaches: Hepcidin (MS/MS quantification), complement C6 (western blotting), S100A9 (western blotting), apolipoprotein E (ELISA), Complement C3 (nephelometry) and Complement C4 (nephelometry). Statistical analysis by Mann–Whitney U test was performed to compare biomarkers intensity in serum of healthy volunteers (HV) vs nonunion (NU) patients. For western blotting, band intensities were quantified by ImageQuant LAS 4000 software and are expressed as pixel counts representing integrated signal intensities