Serum biomarkers discovery of steroid-induced femoral head osteonecrosis in Chinese female by comparative proteome

doi:10.21203/rs.3.rs-17162/v2

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Serum biomarkers discovery of steroid-induced femoral head osteonecrosis in Chinese female by comparative proteome

https://doi.org/10.21203/rs.3.rs-17162/v2

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Background: Steroid-induced osteonecrosis of femoral head (SONFH) is a disabling, aseptic and ischemic disease due to excessive glucocorticoids (GCs) usage. SONFH patients are commonly asymptomatic, which makes its early diagnosis a challenge, the pathological mechanisms of SONFH are not well-known, the purpose of the present study was to screen diagnostic biomarkers for SONFH.

Methods: The differential expression of serum proteins from SONFH, traumatic osteonecrosis of the femoral head (TONFH) patients and healthy volunteers (CK) in Chinese females was compared using Tandem Mass Tag (TMT) quantitative proteomics, and potential diagnostic biomarkers were verified by western blotting.

Results: Bioinformatics analyses revealed key proteins, pathways, and domains differentially regulated among SONFH, TONFH and healthy volunteers in Chinese females, the results showed that peptidase S1, fibrinogen, transferrin, lipid transport domains, hematopoietic cell lineage, fat digestion, absorption, and peroxisome proliferator-activated receptor (PPAR) pathways were associated with the development of SONFH. C-reactive protein (CRP), serum amyloid A protein (SAA1), alpha-1-acid glycoprotein 1 (ORM1), and dopamine beta-hydroxylase were selected for verification of differential expression using western blotting.

Conclusions: Our data suggest that dysfunction of hematopoietic cell lineage, adhesion, fat digestion and absorption, PPAR pathways may be involved in the pathogenesis of SONFH, serum proteins SAA1, ORM1 could be used as new potential diagnostic biomarkers for SONFH.

Osteonecrosis

Steroid-induced osteonecrosis of the femoral head

Serum proteomic

Diagnostic biomarkers

TMT

Steroid-induced osteonecrosis of the femoral head (SONFH) cases are on the rise owing to long-term or mass glucocorticoids application in patients¹, which results in microcirculation disturbance, osteocyte death, articular surface collapse, the rapid destruction of the hip joint seriously influences the patient's quality of life if not treated properly^2–6.

In the last few years, SONFH has attracted extensive attention. Various hypotheses have been put forward for the etiological factors involved in corticosteroid-induced osteonecrosis. Now it is considered that multiple factors contribute to the development of SONFH, including blood coagulation disorders, abnormal lipid metabolisms^7,8, disorders of the vascular endothelium⁹, fat embolism¹⁰, oxidative stress^11–13, apoptosis⁸, genetic variants, thrombosis, alcohol abuse, long-term corticosteroid use¹⁴, systemic lupus erythematosus, inflammatory bowel disease, and also for immunosuppression after renal transplants^15–17. Although many clinical and basic studies about femoral head necrosis have been reported, it is still unable to clarify the specific pathophysiological mechanism of the development of osteonecrosis induced by corticosteroid^18,19.

SONFH affects about 5-7.5 million people in the world and its incidence is increasing in China²⁰. Considering the unfavorable characteristics of patients with SONFH who are commonly asymptomatic, early perception of susceptible signs in patients on steroid medication is needed for prevention and early intervention. To address this problem, it is crucial and urgent for the medical community to study the pathogenesis, identify diagnostic protein biomarkers for population seems to be the key point making prevention more efficient and develop proper therapies to disrupt the progress of SONFH.

The beginning of proteomic technology applying and screening biomarkers in femoral head necrosis is relatively late and rarely reported^12,13,21,22. Thus, in this study, Tandem Mass Tag (TMT) quantitative proteomics was used to identify the differentially expressed proteins in SONFH, traumatic osteonecrosis of the femoral head (TONFH) patients and healthy volunteers (CK) in Chinese females, aim to reveal the molecular mechanism of femoral head necrosis, and screen potential diagnostic biomarkers for SONFH.

Patients

The study included a total of 15 fresh serum specimens, which were divided into three groups: SONFH, TONFH and CK group, with 5 independent individuals in each group (Table 1). All the female patients and healthy volunteers were derived from a clinical laboratory in the First Affiliated Hospital of Guangxi University of Chinese Medicine (Nanning, China) from April 2014 to June 2016. The systemic lupus erythematosus combined with SONFH and TONFH patients were selected according to the definite diagnosis by at least three orthopedists (Supplementary Table 1). Serum was distinguished from blood moderately drawn from bodies and then stored at -80 ℃ until detection.

Table 1

Sample information of experiments
Group	Number of females		Age (years, Median[range])
Group	DDA	WB	DDA	WB
CK	5	8	43.0 (32 ~ 46)(28 ~ 46)	42.0
SONFH	5	10	44.6 (39–53)(23 ~ 65)	43.5
TONFH	5	5	45.4 (30 ~ 61)(30 ~ 61)	45.4

Protein extraction and digestion

For protein extraction, 100 µl of the serum samples (each for 5 adult females) were employed for high abundant protein removal based on the ProteoPrep Blue Albumin and IgG Depletion Kit¹³. The protein eluent was precipitated with cold acetone for 3 h at -20 ℃. After centrifugation at 4 ℃ at 12000 g for 10 min, the protein deposit was redissolved in lysis buffer (8 M urea, 100 mM triethylammonium bicarbonate (TEAB)). The protein concentration was determined by a Modified Bradford Protein Assay Kit according to the manufacturer’s instructions. For digestion, 100 µg protein of each sample was first reduced with 10 mM DTT at 37 ℃ for 60 min and then alkylated with 25 mM iodoacetamide (IAM) at room temperature for 45 min in darkness. The urea concentration of the protein sample was diluted to less than 2 M by adding 100 mM TEAB. The protein pool of each sample was digested with Sequencing Grade Modified Trypsin with the ratio of protein: trypsin = 50:1 mass ratio at 37 ℃ overnight and 100:1 for continuous digestion for 4 h.

Protein isobaric labelling and sample cleanup

After trypsin digestion, peptides were desalted by Strata X SPE column and vacuum-dried. Peptides were reconstituted in 100 µL 100 mM TEAB and processed according to the protocol of manufacturer’s 6-plex tandem mass tags (TMT) kit⁴⁰. Next, one unit of TMT reagent was added to the peptide solution after thawed and dissolved in 41 µL isopropanol. The peptide mixtures were incubated for 1 h at room temperature, then pooled and dried by vacuum centrifugation. The dried and labeled peptides were reconstituted with HPLC solution A (2% ACN, pH 10) and then fractionated into fractions by high-pH reverse-phase HPLC using Waters Bridge Peptide BEH C18 (130 Å, 3.5 µm, 4.6×250 mm). Briefly, peptides were first separated into 60 fractions at a speed of 0.5 ml/min over 88 min with a gradient of 2–98% acetonitrile in pH 10. Until then, the peptides were combined into 20 fractions and dried by vacuum centrifugation. After which, the peptide fractions were desalted by Ziptip C18 according to manufacturer’s instructions and finally dried under vacuum before being kept at -20 ℃ until MS analyses were performed.

High-resolution LC-MS/MS analysis

The mass spectrometry survey was performed by Nano LC 1000 LC-MS/MS using a Proxeon EASY-nLC 1000 coupled to an LTQ-Orbitrap Elite. Trypsin digestion fractions were reconstituted in 0.1% formic acid (FA) and directly loaded onto a reversed-phase pre-column (Acclaim PepMap®100 C18, 3µm, 100Å, 75µm× 2cm) delivering at 5 µL/min in 100% solvent A (0.1 M acetic acid in water). After that, peptides eluted from the trap column were loaded onto a reversed-phase analytical column (Acclaim PepMap® RSLC C18, 2 µm, 100Å, 50 µm × 15 cm) which gradient was comprised of an increase from 10–35% solvent B (0.1% FA in 98% ACN) over 60 min, 35–50% in 10 min and climbing to 100% in 5 min at a constant flow rate of 250 nl/min on an EASY-nLC 1000 system. The eluent was sprayed via NSI source at the 1.8 kV electrospray voltage and then analyzed by MS/MS in LTQ-Orbitrap Elite. The mass spectrometer was operated in data-dependent mode, and automatically switches between MS and MS/MS. Full-scan MS spectra (from m/z 350 to 1800) were acquired in the Orbitrap with a resolution of 60,000. Ion fragments were detected in the Orbitrap at a resolution of 15,000. And the 20 most intense precursors were selected for subsequent decision tree-based ion trap higher energy collision-induced dissociation (HCD) fragmentation at the collision energy of 38% above a threshold ion count of 300 in the MS survey scan with 30.0s dynamic exclusion. The full width at half maximum (FHMW) at 400 m/z using an automatic gain control (AGC) setting of 1e6 ions. The fixed first mass was set as 100 m/z.

Data processing

The resulting MS/MS raw data were converted to mgf format profile with the software Proteome Discoverer (version 1.3, Thermo Scientific). Generated peak lists were searched against the Homo Sapiens database (Taxon identifier: 9606, including 154527 protein sequences) which consists of reviewed Swissprot database combined with unreviewed TrEMBL database. Mascot 2.3.0 is used to supplement frequently observed contaminants. Trypsin/P was chosen as the enzyme and two missed cleavages were allowed. Carbamidomethylation on cysteine was set as a fixed modification, and oxidation on methionine, acetylation in N-term were set as variable modifications. The peptide used in the search had a mass tolerance of 20 ppm, a production tolerance of 0.02 DA and a false discovery rate (FDR) of 1%. For quantitation, proteins were required to contain at least two quantitated unique peptides. Proteins with fold changes > 1.2 or < 0.83 and significance t-test p-value < 0.05 between two compared groups were considered to be differentially expressed.

Then proteins were classified by Gene Ontology annotation based on three categories: biological process, cellular component and molecular function. Gene Ontology (GO) annotation proteome was derived from the UniProt-GOA database (http://www.ebi.ac.uk/GOA/). KEGG signaling pathway is part of the Kyoto Encyclopedia of Genes and Genomes (KEGG) database^41–43, which is a reference database for pathway mapping. KEGG Pathway analyses of identified proteins were extracted using the Search pathway tool in the KEGG Mapper platform (http://www.genome.jp/kegg/mapper.html). InterProScan, a sequence analysis application was also used for protein domain annotation based on the protein sequence alignment algorithm and the InterPro domain database⁴⁴.

A two-tailed Fisher’s exact test was employed to test the GO, KEGG pathway and Domain enrichment of the differential expression protein for all identified proteins. Correction for multiple hypothesis testing was carried out under standard FDR control methods, and pathways with a corrected p-value < 0.05 were considered the most significant.

Different proteome clustering methods based on expression and function enrichment were used to explore potential relationships between different protein groups in special protein functions. Firstly, all the protein groups obtained after functional enrichment analysis along with their p values were collected. Secondly, these categories enriched were sorted in at least one protein group with a p-value < 0.05. This filtered p-value matrix was transformed by the function x = − log10 (p value). Thirdly, z-transformed applies to x values for each functional category and z scores were clustered by one-way hierarchical clustering (Euclidean distance, average linkage clustering). Finally, the expression pattern of the protein was completed using the timeclust function in the R package ‘TCseq’, and a heatmap was generated by the software Heml 1.0.3 ⁴⁵. We also present the STRING network of some known/novel protein-protein interactions as an evidence view by using the STRING Version 9.0 (Search Tool for the Retrieval of Interacting Genes/Proteins), a database of physical and functional interactions (http://string-db.org/) ⁴⁶.

Western blotting

Equal amounts of proteins (30 µg) were separated by 12% SDS-PAGE and electrophoretically transferred to a polyvinylidene fluoride (PVDF) membrane (Millipore, MA, USA). Membranes were blocked for 4 h at 4 ℃ with 5% non-fat milk in 1 × TBST buffer and rinsed 3 times (10 min each) with 1 × TBST buffer. Next, the membranes were incubated with primary antibodies: Anti-C reactive protein (1:1000, Abcam, ab32412), Anti-alpha-1-acid glycoprotein 1 (1:1000, Abcam, ab134160), Anti-SAA1 (1:1000, Abcam, ab201660), Anti-dopamine beta-hydroxylase (1:2000, Abcam, ab96615) at 4 ℃ overnight and then washed with 1 × TBST buffer for 3 times. After that, the secondary antibodies was be in progress: Peroxidase AffiniPure Goat Anti-Mouse IgG (H + L) (1:20000, Jackson, 115-035-003) and Peroxidase AffiniPure Goat Anti-Rabbit IgG (H + L) (1:20000, Jackson, 111-035-003) for 4 h at 4 ℃. After washing 3 times, the membranes were developed using Western blotting ECL Substrate (Bio-Rad) detection methods (Solarbio, PE0010) and scanned with the ChemiDoc MP imaging system using Image Lab software (Bio-Rad).

Protein identification and expression of different protein (EDPs) screening

A total of 950 proteins were identified in 5 replicates of LC-MS/MS experiments at the high confidence of peptide selection (FDR = 0.01), among which quantitative information was obtained for 529 proteins (Supplementary Table 2). The volcano diagram shows the expression of different protein (EDPs) of each comparison group, with up-regulated proteins marked by red points and down-regulated proteins marked by blue points (Fig. 1). There were 17 proteins showing a significant differential expression (fold change > 1.2 and p-value < 0.05) between the SONFH and CK groups of which 8 proteins were up-regulated and 9 proteins were down-regulated (Table 2). Meanwhile, Table 2 lists the KEGG pathway annotation and COG category of the EDPs between the SONFH and the Control groups.

Table 2

DEPs between SONFH and CK groups.
Protein	Ratio	p-value	KEGG Pathway	KOG Catagery
C-reactive protein	3.782	0.038
Anti-streptococcal/anti-myosin Ig kappa light chain variable region (Fragment)	3.719	0.011
Ig gamma−1 chain C region	3.312	0.038
Protein IGHV1OR15−1 (Fragment)	2.207	0.017
Alpha−1-acid glycoprotein 1	1.648	0.022
Complement component C9	1.435	0.036	Complement and coagulation cascades; Prion diseases; Amoebiasis; Systemic lupus erythematosus
Haptoglobin	1.388	0.040		[E] Amino acid transport and metabolism
Ceruloplasmin	1.243	0.020	Porphyrin and chlorophyll metabolism	[Q] Secondary metabolites biosynthesis, transport and catabolism
Coagulation factor XII	0.774	0.011	Complement and coagulation cascades	[E] Amino acid transport and metabolism
Sulfhydryl oxidase 1	0.769	0.038		[D] Cell cycle control, cell division, chromosome partitioning
Pigment epithelium-derived factor	0.763	0.022	Wnt signaling pathway	[V] Defense mechanisms
Kallistatin	0.712	0.001	Wnt signaling pathway	[V] Defense mechanisms
Gelsolin	0.702	0.007	Fc gamma R-mediated phagocytosis; Regulation of actin cytoskeleton; Viral carcinogenesis	[Z] Cytoskeleton
Keratinocyte differentiation-associated protein	0.680	0.001
Coagulation factor XIII A chain	0.647	0.047	Complement and coagulation cascades	[V] Defense mechanisms
Properdin	0.615	0.037	Herpes simplex infection	[R] General function prediction only; [P] Inorganic ion transport and metabolism
Insulin-like growth factor-binding protein 3	0.552	0.001	p53 signaling pathway; Transcriptional misregulation in cancer

Protein expression patterns and function clustering

To investigate the protein profile change under the two pathological situations compared to healthy volunteers, the protein Domain and KEGG pathway annotation were elaborated from InterProScan platform and KAAS online automatic annotation server (http://www.genome.jp/tools/kaas/). Then, the confidence of functional enrichment was evaluated by the Fisher's exact test, and all quantified proteins were classified into 8 clusters based on the expression level (Fig. 2). Domain enrich-based clustering of each expression cluster shows that IgG enriched expressed highly in clusters 2, 4 and 6, while the peptidase S1, fibrinogen and transferrin presents present low expression enriched level in Clusters 3 and 5 in the SONFH group. In Cluster 8, lipid transport and vitellinogen proteins were enriched higher expression in the TONFH group. KEGG pathway enrich-based function clustering analysis exhibit remarkably enriched expressed proteins in hematopoietic cell lineage, adhesion and several steroid-derived signal pathways of Cluster 4 and 6 in the SONFH group. Cluster 8 reveals high expression proteins in TONFH group was distinctly enriched in fat digestion and absorption pathways. Interestingly, the PPAR signal pathway was also found lower expression levels in SONFH and TONFH groups.

Protein-protein interaction (PPI) network analysis

STRING was then used to perform protein-protein interaction (PPI) network analysis on differentially expressed proteins between each group. From the networks, we can intuitively discover that some proteins called key protein molecules interact with many other proteins and know the importance of certain proteins. Figure 3A announced that alpha-1-acid glycoprotein 1 (P02763) and haptoglobin (P00738, HP) connect the complement pathway and the coagulation pathway that may take potential interactions. And the interaction network EDPs between SONFH and TONFH indicated that beta-2-glycoprotein 1 (P02749, APOH) employed an important role in these two pathological states (Fig. 3B).

Western blot validations

Western blot was used to selectively examine C-reactive protein, serum amyloid A protein, serum alpha-1-acid glycoprotein 1 and dopamine beta-hydroxylase based on proteomic results. As shown in Fig. 4, in SONFH versus TONFH and CK groups, significantly increased levels of C-reactive protein, serum amyloid A protein, and serum alpha-1-acid glycoprotein 1, which is consistent with TMT proteomics (based on LC-MS/MS identification and quantification). Meanwhile, western blotting also verified the decreased expression of dopamine beta-hydroxylase (DBH) in TONFH versus SONFH and CK, respectively. Western blotting was consistent with the results of the mass spectrometry analysis, all these data added confidence to the results obtained from TMT quantitative proteomics.

Currently, steroid-related SONFH is a frequently occurring disease that can lead to necrosis of bone tissue and arthritis of the hip joint in patients received high-dose corticosteroid therapy. Although there have been a lot of studies on SONFH, its pathogenesis is still unclear, and few proteomic research has been done to a high-throughput investigation of the simultaneous expression of serum proteins and diagnostic biomarkers in adult SONFH patients.

The present study investigated proteomic on SONFH, TONFH patients and healthy volunteers of Chinese females. The results show: (1) 17 proteins differentiated between SONFH and CK groups, of which 8 proteins were up-regulated (ratio > 1.2, p-value < 0.05) and 9 proteins were down-regulated (ratio < 1/1.2, p-value < 0.05). (2) In screening protein clusters, IgG enriched was up-regulated, while the peptidase S1, fibrinogen and transferrin presents were down-regulated in the SONFH group. (3) Hematopoietic cell lineage, adhesion and several steroid-derived signal pathways were up-regulated in SONFH, and the fat digestion and absorption pathways with high expression in the TONFH group. Furthermore, PPAR signal pathway was found lower expression levels in SONFH, TONFH versus CK groups. (4) Several other proteins such as C-reactive protein, serum amyloid A protein, alpha-1-acid glycoprotein 1 and dopamine beta-hydroxylase displayed similar expression in SONFH, TONFH versus CK groups.

PPARγ is believed to be the master regulator of adipogenesis and also shows well-described anti-osteoblastogenic effects, which can stimulate adipogenesis in bone marrow cells and inhibit osteogenesis²³. PPARγ-deficient embryonic stem cells differentiated into osteoblasts, and several drugs functioning on PPARγ inhibition have been confirmed efficient for SONFH^24–27. In our study, the PPAR expression pattern in the femoral head osteonecrosis in both SONFH and TONFH groups was significantly lower than CK group, which is in line with previous studies that the transcription factors PPARγ was clarified taking a crucial role in TONFH pathological process.

Two Wnt signal pathway related proteins pigment epithelium-derived factor (P36955, PEDF) and Kallistatin (P29622) were identified as down-regulated in the SONFH group. And Wnt signaling pathway has been confirmed involved in the pathogenesis of SONFH ^28,29, Prarastatin and Huogu I formula has been reported to prevent SONFH in rats through the affection in Wnt signal pathway^24,27.

An immune response is reported to be involved in the pathogenesis of many diseases, including femoral head necrosis in adult patients^30–32. In function clustering analysis, IgG domain-containing proteins were observed dramatically elevated in Chinese women with steroid-induced SONFH, while the abundance expression was significantly reduced in TONFH and CK groups, revealing that the immune response may be involved in the SONFH.

Abnormal expression of lipoproteins and apolipoproteins would lead to the imbalance of lipid metabolism, which has been reported to play an important role in the pathogenesis of SONFH^33–36. The TONFH patients showed a higher abundance of lipid transport domains in this present study, which are involved in fat digestion and absorption pathway.

Recent reports have shown that key proteins involved in signal transduction and regulation³⁷, including tissue-type plasminogen activator, plasminogen activator inhibitor type 1, Crosslaps, and anti-p53 antibody are suggested as diagnosis serum markers of nontraumatic femoral head necrosis²¹. Compared with TONFH and CK groups, patients with SONFH showed significant differences in many proteins, such as C-reactive protein, serum amyloid A protein, alpha-1-acid glycoprotein 1 and dopamine beta-hydroxylase. Furthermore, The results of four differentially expressed proteins (CRP, SAA1, ORM1 and DBH) verified by western blot were consistent with TMT. High sensitivity C-reactive protein has a drastic change under inflammation, which increased in osteoporotic/osteopenia women compared to obese normal³⁸, and it has been well known as a diagnostic marker for osteopenia, atherosclerotic cardiovascular disease and some other diseases³⁹.

In this study, the quantitative proteomic results suggest that dysfunction of hematopoietic cell lineage, adhesion, fat digestion and absorption, PPAR pathways may be involved in the pathogenesis of SONFH, and serum proteins SAA1, ORM1 may be used as new potential diagnostic biomarkers of SONFH. Although we have verified four differentially expressed proteins, this study has some limitations. The proteomic was performed analysis at the only one-time point on three quite small study populations, further analysis in a larger study population from different ethnic groups will provide more weight for the results. In summary, these findings would be helpful to advance knowledge in the field of femoral head necrosis.

C9: Complete component C9, CP: Carboxypeptidase, GSN: Gelsolin, IGFBP3: Insulin-like growth factor-binding protein 3, CFP: Properdin, KRTDAP: Keratinocyte differentiation-associated protein, SERPINF1: Pigment epithelium-derived factor, F12: Coagulation factor XII, QSOX1: Sulfhydryl oxidase 1, SERPINA4: Kallistatin, F13A1: Coagulation factor XIII A, C8B: Complement component C8 beta chain, C5: Complement C5, CPB2: Carboxypeptidase B2, CD14: Monocyte differentiation antigen CD14, IGFBP3: Insulin-like growth factor-binding protein 3.

Acknowledgements

We thank SKLCUSK-MHelix Joint Laboratory of High-Throughput Multi-omics for MS analysis.

Authors’ Contributions

ZP and ZCX conceived and designed the experiments. ZYQ, LJY and LT contributed reagents/materials/analytic tools. ZYQ, LJY, PDD and LT performed the experiments. LCR collected and analysed the data. LX, CJL and FSQ analysed the data. ZP and ZCX wrote the paper. LX helped review and edit the final paper. All authors read and approved the final manuscript.

Funding

Financial support for this study was provided by the National Natural Science Foundation of China (Grant No. 81360551), Shandong Province medical and health development science and technology project (2018WS302).

Availability of data and materials

All data generated or analyzed during this study were included in this published article and its additional files. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE⁴⁷ partner repository with the dataset identifier PXD033445.

Ethics approval and consent to participate

The study was approved by the Institutional Review Board of The First Affiliated Hospital of Guangxi University of Chinese Medicine, and informed consent was obtained from each patient and healthy volunteers.

Consent for publication

Not applicable.

Competing Interests

The authors declare no financial and non-financial competing interests.

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No competing interests reported.

SupplementaryTable1.SampleInformation.xlsx
Supplementary Table 1 Systemic lupus erythematosus combined with SONFH and TONFH patients.
SupplementaryTable2.ProteomicQuantification.xlsx
Supplementary Table 2 Differentially expression proteins identified among SONFH, TONFH and CK groups by using TMT.

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Serum biomarkers discovery of steroid-induced femoral head osteonecrosis in Chinese female by comparative proteome

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