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J Orthop Surg Res
J Orthop Surg Res
Journal of Orthopaedic Surgery and Research
1749-799X
BioMed Central London

5034
10.1186/s13018-024-05034-x
Research Article
Identification of novel drug targets for osteoarthritis by integrating genetics and proteomes from blood
Song Shan 12
Qiao Jun 12
Zhao Rong 12
Lu Yu-Jie 12
Wang Can 3
Chang Min-Jing 4
Zhang He-Yi 12
Li Xiao-Feng 12
Wang Cai-Hong snwch@sina.com

1
1 https://ror.org/03tn5kh37 grid.452845.a Department of Rheumatology, Second Hospital of Shanxi Medical University, Taiyuan, 030001 China
2 https://ror.org/0265d1010 grid.263452.4 0000 0004 1798 4018 Ministry of Education Key Laboratory of Cellular Physiology, Shanxi Medical University, Taiyuan, China
3 https://ror.org/0265d1010 grid.263452.4 0000 0004 1798 4018 School of Management, Shanxi Medical University, Taiyuan, China
4 https://ror.org/0265d1010 grid.263452.4 0000 0004 1798 4018 Shanxi Key Laboratory of Big Data for Clinical Decision, Shanxi Medical University, Taiyuan, China
11 9 2024
11 9 2024
2024
19 55931 1 2024
27 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

Osteoarthritis (OA) is a degenerative osteoarticular disease, involving genetic predisposition. How the risk variants confer the risk of OA through their effects on proteins remains largely unknown. Therefore, we aimed to discover new and effective drug targets for OA and its subtypes.

Methods

A proteome-wide association study (PWAS) was performed based on OA and its subtypes genome-wide association studies (GWAS) summary datasets and the protein quantitative trait loci (pQTL) data. Subsequently, Mendelian randomization (MR) and colocalization analysis was conducted to estimate the associations between protein and OA risk. The replication analysis was performed in an independent dataset of human plasma pQTL data.

Results

The abundance of seven proteins was causally related to OA, two proteins to knee OA and six proteins to hip OA, respectively. We replicated 2 of these proteins using an independent pQTL dataset. With the further support of colocalization, and higher ECM1 level was causally associated with a higher risk of OA and hip OA. Higher PCSK1 level was causally associated with a lower risk of OA. And higher levels of ITIH1, EFEMP1, and ERLEC1 were associated with decreased risk of hip OA.

Conclusion

Our study provides new insights into the genetic component of protein abundance in OA and a promising therapeutic target for future drug development.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13018-024-05034-x.

Keywords

Colocalization
Mendelian randomization
Osteoarthritis
Proteomics
Therapeutic target
http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 81971543 Key Research and Development (R&D) Projects of Shanxi Province201803D31119 Four “Batches” Innovation Project of Invigorating Medical through Science and Technology of Shanxi Province2022XM05 issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcIntroduction

Osteoarthritis (OA) is a degenerative joint disease characterized by progressive degradation of articular cartilage, subchondral bone thickening, and osteophyte formation [1]. It can involve almost any joint but commonly affects knee and hip joints. Globally prevalent cases of OA increased to 595 million in 2020 [2]. No therapy is currently available to completely prevent the initiation or progression of the disease, partly due to a poor understanding of the mechanisms of the disease pathology. Although the pathogenesis of OA is not well understood, a complex interplay of genetic predisposition and environmental factors is thought to contribute to the disease [3].

With the rapid advances in human genomics technology, genome-wide association studies (GWAS) have identified numerous genetic variations linked to OA, such as rs2521349 in an intron of MAP2K6 and rs375575359 in an intron of the zinc finger protein 345 (ZNF345) gene [4]. However, this approach is limited in interpreting the genetic effects because most GWAS-identified SNPs are located in non-coding regions of the genome [5].

Expression quantitative trait loci (eQTL) analyses have been used to identify single nucleotide polymorphisms (SNPs) associated with a gene’s expression level [6]. As a supplement to traditional GWAS analyses, transcriptome-wide association studies (TWASs) have emerged as a powerful strategy that integrates GWASs and eQTL reference datasets to explore gene-trait relationships [7]. Recently, TWASs have been successfully utilized to identify genetic loci associated with various diseases and traits, including osteoarthritis [8]. Indeed, the regulation of gene expression occurs at various levels including post-transcriptional, translational, and post-translational levels, and proteins are the final products of gene expression, which highlights the importance and the necessity of investigating the risk proteins [9].

Plasma proteins play critical roles in various biological processes and represent the major effective biomarkers and druggable targets [9]. To identify potentially causal proteins in OA, we hypothesized that specific genetic variants influence OA by altering blood protein expression levels. Wingo et al. developed a novel framework called proteome-wide association studies (PWAS), which leverage expression reference panels protein quantitative trait loci (pQTL) to discover protein–trait associations in GWAS datasets [10]. The causal inference of this integrated analytical approach has been applied to various diseases and has shown to be reliable, including Alzheimer’s disease, ischemic stroke, depression, and sarcopenia-related traits [10–13].

Accordingly, we sought to discover new and effective drug targets for OA by integrating genetic and proteomic data in the plasma. We integrate a plasma pQTL discovery dataset from Atherosclerosis Risk in Communities (ARIC) study and OA and its subtypes GWAS to perform a PWAS analysis. Next, we used Mendelian randomization (MR) and Bayesian colocalization analysis to evaluate the causal relationships between proteins and OA. The potential causal proteins identified were further subjected to explore potential repurposing opportunities targeting risk-related proteins. Additionally, we performed a replication analysis in an independent dataset of human plasma proteomic and genetic data from INTERCAL study. Together, we presented here the first PWAS of OA and its subtypes to identify novel plasma proteins as potential treatment targets for OA.

Materials and methods

GWAS summary datasets for OA and its subtypes

We used summary statistics from recent GWAS from the UK Biobank and Arthritis Research UK Osteoarthritis Genetics (arcOGEN) based on participants of European ancestry, including 39,427 cases with hip and/or knee OA patients, 24,955 cases with knee OA patients, 15,704 cases with hip OA patients, and 378,169 controls [14]. Cases were ascertained based on clinical evidence of disease to a level requiring joint replacement or radiographic evidence of disease (Kellgren–Lawrence grade ≥ 2), and gender, age, over third-degree relatedness and ethnicity were adjusted. In all original studies, ethical approval, and consent to participate had been obtained. More details about the samples, genotyping, and imputation are available in original publication. Additionally, quality control of SNPs was further performed to GWAS summary data: (a) aligning based on 1000 Genomes Project phase 3 data; (b) screening out rare variants with a threshold of minor allele frequency [MAF] at 1%; (c) removing unmapped or sex chromosome SNP; and (d) removing duplicated SNPs.

Plasma proteins quantitative trait loci (pQTL) data

Plasma proteins’ pQTL data were generated from genome-wide association testing. The human plasma pQTL (i.e., protein weights) discovery cohort was obtained from the study by Zhang J. et al., which analyzed the proteomes of 1,344 plasma proteins from 7,213 European American individuals from the ARIC study. Account for confounding factors, the levels of proteins were adjusted for sex, age, study site, and 10 genetic principal components (PCs) and 90 Probabilistic Estimation of Expression Residuals (PEER). More details about the quality control of the proteomic and genomic data have been described in previously [15]. The weights of plasma proteins expressions imputation models were obtained from previous study. The cis-acting pQTLs located in the vicinity of the encoding gene, are considered to have a higher biological prior and have been widely employed in relation to some phenome-wide scans of drug targets [16]. We restricted proposed instrumental variants to cis-pQTLs, where cis is defined as variant within 500-kb upstream and downstream of the target protein-coding genes’ transcription start site (TSS). From these SNPs, only those with a minor allele frequency (MAF) of ≥ 1% that were genome-wide significant (P < 5.0 × 10 − 8) and considered independent (LD R2 < 0.1 in 1000G) were retained.

The replication pQTL cohort was profiled from 3,301 healthy blood donors with European descent recruited by the INTERCAL study [17]. Proteomic profiling followed similar steps as the discovery proteomes. The relative protein abundances was adjusted using age, sex, body mass index, natural log of estimated glomerular filtration rate (eGFR) and subcohort. Finally, 1,031 cis-pQTLs were included in the next analysis.

Proteome-wide association studies

A proteome-wide association study (PWAS) was performed by the FUnctional Summary-based ImputatiON (FUSION) approach [7]. Using the pre-computed protein expression weights of plasma together with GWAS summary data, FUSION is capable of estimating the associations of each protein with target diseases. Briefly, we utilized FUSION to compute the effect of SNPs on protein abundance and constructed prediction models of protein expression. Then, the prediction models implemented in FUSION were integrated with the genetic effect of OA (i.e., OA GWAS Z-score), that is, calculating the linear sum of Z score × weight for the independent SNPs at the locus to impute the association between protein expression and OA. The predictive models in the ARIC study were fitted using elastic net [Regularization and variable selection via the elastic net] and for INTERVAL study fitting the Sum-of-Single-Effects model [18] (i.e. SuSiE). Due to the complex linkage disequilibrium (LD) patterns, we excluded the PWAS associations from major histocompatibility complex (MHC) regions [19]. Bonferroni-corrected p value was used to reduce the instance of a false positive. The significance threshold of discovery PWAS associations was P < 3.72 × 10 − 05 (0.05/1,344). The significance thresholds of replicated PWAS associations was P < 4.85 × 10 − 05 (0.05/1,031).

Mendelian randomization analysis

MR is a powerful method to detect the causal effect of exposure on the outcome using genetic variants extracted from GWAS summary statistics as instrumental variables. And it is increasingly being used to ascertain the health effects of potential therapeutic targets. To estimate the effect of each circulating protein on OA risk, we conducted the MR study. Where more than one SNP was available, the multiplicative random-effects inverse-variance weighted (IVW) analysis was used as primary analysis, to estimate the risk effect of protein expression on OA [20, 21]. The Wald ratio was employed when only a single independent QTL was available [22]. MR-Egger was also employed as complementary approach, which could test the presence of potential pleiotropy and account for this potential pleiotropy using the MR-Egger intercept test [23]. The association with P-value in the MR-Egger pleiotropy test < 0.05 and P-value in the MR-Egger causality test > 0.05 was considered likely caused by pleiotropy and thus removed from further analysis. Bonferroni correction was used to control for the total number of distinct proteins tested in our MR experiments. The results were presented as MR odds ratio (OR) and 95% confidence intervals (CIs) for risk of OA per genetically predicted 1-SD change in circulating protein level.

Colocalization analysis

To differentiate whether any putative causal association detected by MR is driven by a common causal variant across multiple traits (i.e., protein expressed/disease), we subsequently applied COLOC to assess the probability of the same SNP being responsible for both changing OA risk and modulating the protein levels [24]. We used the default Coloc priors: p1 is the probability that a given SNP is associated with OA (p1 = 10 − 4), p2 is the probability that a given SNP is a significant pQTL (p2 = 10 − 4), p12 is the probability that a given SNP is both a PD result and a pQTL (p12 = 10 − 5). We extracted the summary association statistics and computed approximation Bayes factors to estimate posterior probabilities (PP). This approach has five mutually exclusive hypotheses: no associated SNP either GWAS or pQTL (H0); associated SNP with GWAS only (H1); associated SNP with pQTL only (H2); distinct associated SNPs, one for GWAS and another for pQTL (H3); and common SNP to both GWAS and pQTL (H4). Support for each of the hypotheses is quantified by the posterior probability (PP). Evidence for colocalization was defined as PPH4 ≥ 0.8, representing the posterior probability that the association with OA and protein expression is due to the same underlying variant—colocalization analysis in “coloc” R package.

Druggable target analysis

To assess the druggability of identified proteins, we obtained the list of druggable genes from the study by Finan et al. to assess the druggability of each protein candidate [25]. Genes were classified into three categories according to their druggability: Tier 1 includes the targets of approved, in clinical trials or preclinical pipelines small molecules and biotherapeutic drugs. Tier 2 includes the targets with high sequence homology to approved drug targets. Tier 3 includes secreted or extracellular proteins, members of key drug target families, the targets with more distant similarity to approved drug targets. This tier was further subdivided to prioritize those genes that were in proximity (+/- 50 kbp) to a GWAS SNP and had an extracellular location (Tier 3 A). The remainder of the genes was assigned to Tier 3B.

Result

Identification of candidate proteins associated with OA using discovery PWAS and replication PWAS

We performed discovery PWAS by integrating GWAS and 1344 pQTL results from ARIC, and replicated PWAS by integrating GWAS and 1031 pQTL results from INTERVAL. In OA data set, discovery PWAS identified 12 proteins whose cis-regulated plasma protein abundances/levels were associated with OA (Fig. 1A; Table 1, Supplemental Table 1). Four proteins (C-terminal Src kinase [CSK], Inter-alpha-trypsin inhibitor heavy chain 1 [ITIH1], proprotein convertase subtilisin/kexin type 1 [PCSK1] and semaphorin 3G [SEMA3G]) could be replicated in the independent PWAS, providing a higher confidence level (Fig. 1B; Table 1, Supplemental Table 2). In knee OA data set, discovery PWAS identified 4 proteins (P < 3.72 × 10 − 05) whose genetically regulated protein abundance levels in the plasma were associated with knee OA risk (Fig. 1C; Table 1, Supplemental Table 3). In replicated PWAS 5 proteins were identified, in which only copine 1 [CPNE1] both demonstrated significance in discovery and replicated PWAS (Fig. 1D, Supplemental Table 4). In hip OA data set, discovery PWAS identified 9 plasma proteins (P < 3.72 × 10 − 05) associated with hip OA risk (Fig. 1E, Supplemental Table 5), and 3 proteins (haptoglobin [HP], ITIH1 and transmembrane protein 190 TMEM190]) were further verified in replicated PWAS (Fig. 1F; Table 1, Supplemental Table 6).

Table 1 PWAS results for significant proteins in the discovery cohort and some of which were replicated in the replication cohort

Outcome	ID	Discovery PWAS	Replicated PWAS	Evidence for replication	
Z.score	PWAS.P	Z.score	PWAS.P	
Knee/Hip OA	COL6A2	4.24	2.24E-05	/	/	/	
CSK	-4.13	3.71E-05	-4.70	2.59E-06	YES	
DDX19A	-4.40	1.07E-05	/	/	/	
DDX19B	-4.64	3.41E-06	/	/	/	
ECM1	4.86	1.17E-06	/	/	/	
EFEMP1	-4.38	1.20E-05	/	/	/	
HP	-4.50	6.95E-06	/	/	/	
ITIH1	-6.06	1.35E-09	-6.05	1.43E-09	YES	
ITIH3	5.54	2.98E-08	/	/	/	
PCSK1	-4.39	1.15E-05	-4.48	7.35E-06	YES	
SAT2	4.73	2.19E-06	3.90	9.74E-05	NO	
SEMA3G	-5.44	5.33E-08	-6.09	1.15E-09	YES	
Knee OA	CPNE1	4.20	2.70E-05	4.08	4.50E-05	YES	
DDX19A	-4.88	1.06E-06	/	/	/	
DDX19B	-4.79	1.68E-06	/	/	/	
PFKM	4.41	1.03E-05	/	/	/	
Hip OA	ECM1	5.57	2.54E-08	/	/	/	
EFEMP1	-4.97	6.74E-07	/	/	/	
ERLEC1	-4.81	1.48E-06	0.07	9.44E-01	NO	
HHIP	4.26	2.03E-05	/	/	/	
HP	-6.04	1.54E-09	-5.58	2.40E-08	YES	
ITIH1	-7.03	2.10E-12	-6.95	3.76E-12	YES	
ITIH3	5.59	2.31E-08	/	/	/	
ITIH4	4.29	1.83E-05	/	/	/	
TMEM190	-5.21	1.84E-07	-4.74	2.18E-06	YES	

Fig. 1 Manhattan plots showing proteome-wide association study (PWAS)–identified proteins. Each point represents a single association test between a protein and outcome. The x-axis represents the genomic position (based on NCBI Build 37), and the y-axis shows the -log10 (P.value). The red line represents the significant threshold (0.05 / number of proteins analyzed), and the names of statistically significant PWAS proteins are labeled. (A) Discovery PWAS was performed by integrating plasma pQTL from ARIC study and OA GWAS; (B) Replicated PWAS was performed by plasma pQTL from INTERCAL study and OA GWAS; (C) Discovery PWAS was performed by integrating plasma pQTL from ARIC study and knee OA GWAS; (D) Replicated PWAS was performed by plasma pQTL from INTERCAL study and knee OA GWAS; (E) Discovery PWAS was performed by integrating plasma pQTL from ARIC study and hip OA GWAS; (F) Replicated PWAS was performed by plasma pQTL from INTERCAL study and hip OA GWAS

MR reveals causal proteins for OA using plasma pQTL

Using the 1,344 cis-pQTLs SNPs from ARIC as genetic instruments for their respective proteins, we identified 7 protein biomarkers that provided strong evidence of an association (P < 3.72 × 10 − 05) in the OA, including 2 risk factors and 5 protective factors (Table 2, Supplemental Table 7). Genetically predicted higher protein levels of collagen type VI alpha 2 chain (COL6A2) and extracellular matrix protein 1 (ECM1) were associated with higher risk of OA. As well as higher levels of ITIH1, DEAD-box helicase 19B (DDX19B), DEAD-box helicase 19 A (DDX19A), HP, and PCSK1 were associated with lower risk of OA.

Table 2 Causal proteins were identified in the discovery and replication MR

Outcome	Protein	Discovery MR	Evidence for COLOC	Evidence for Replication MR	
OR (95% CI)	P.value	
Knee/Hip OA	ITIH1	0.897 (0.846, 0.951)	2.715E-04	NO	NO	
ECM1	1.048 (1.021, 1.076)	3.815E-04	YES	/	
DDX19B	0.819 (0.754, 0.890)	2.358E-06	NO	/	
HP	0.970 (0.952, 0.988)	1.200E-03	NO	NO	
DDX19A	0.897 (0.848, 0.949)	1.567E-04	NO	/	
PCSK1	0.964 (0.946, 0.982)	1.086E-04	YES	NO	
COL6A2	1.076 (1.037, 1.116)	1.040E-04	NO	/	
Knee OA	DDX19A	0.873 (0.815, 0.935)	1.034E-04	NO	/	
DDX19B	0.791 (0.715, 0.875)	5.612E-06	NO	/	
Hip OA	ITIH1	0.793 (0.7265, 0.867)	3.670E-07	YES	NO	
HP	0.929 (0.903, 0.956)	2.887E-07	NO	YES	
ECM1	1.085 (1.049, 1.123)	2.166E-06	YES	/	
TMEM190	0.945 (0.913, 0.978)	1.325E-03	NO	YES	
EFEMP1	0.882 (0.808, 0.963)	5.217E-03	YES	/	
ERLEC1	0.796 (0.707, 0.897)	1.699E-04	YES	/	

Genetically predicted SD increase in DDX19A and DDX19B were associated with decreased risk of knee OA (MR DDX19A OR and 95% CI = 0.873 [0.815, 0.935], P = 1.034E-04; DDX19B OR and 95% CI = 0.791 [0.715, 0.875], P = 5.612E-06) (Table 2, Supplemental Table 8). However, due to they do not exist in the INTERNAL study, they cannot be verified. Interestingly, these 2 plasma proteins were both negatively associated with the risk of knee OA and OA.

For hip OA, a genetically predicted SD increase in ECM1 (OR and 95% CI = 1.085 [1.049, 1.123], P = 2.166E-06) showed associations with higher OA risk (Table 2, Supplemental Table 9). Higher levels of ITIH1, HP, TMEM190, EGF containing fibulin extracellular matrix protein 1 (EFEMP1) and endoplasmic reticulum lectin 1 (ERLEC1) were associated with decreased risk of hip OA. ECM1, HP and ITIH1 showed significant effects in hip OA and OA.

Colocalization between OA risk proteins and pQTL and integrative analysis

Colocalization analyses were performed to identify the underlying single causal variant that the GWAS signal and pQTL shared. We identified co-localization evidence with the expression of plasma protein based on the high posterior probability of colocalization (PPH4 ≥ 0.8) (Supplementary Table 10). We first tested whether the identified associations of the circulating protein with OA shared causal variants, two proteins (ECM1 and PCSK1) with high support of colocalization evidence were identified as tier 1 targets (Table 2; Fig. 2A-B). For knee OA, no protein was observed with high support of colocalization evidence. The 2 proteins (DDX19A and DDX19B) were identified as tier 3 targets in the treatment of knee OA. In addition, four proteins including ECM1, EFEMP1, ERLEC1 and ITIH1 were colocalized with hip OA associations with high support of evidence (Table 2; Fig. 2C-G), which were also identified as tier 1. Proteins (HP and TMEM190) that yielded replicated MR evidence in the INTERCAL study MR were identified as tier 2 targets in the treatment of hip OA. The remaining proteins–outcome pairs with limited evidence of colocalization and MR were ascertained as tier 3 targets.

Fig. 2 The locus-compare scatter plot show the association signals with strong evidence of colocalization. The gene prioritized in each locus is shown on the y-axis of the corresponding figure label. The small purple squares represent an SNP site, which suggests that pQTL and outcome are affected by the same SNP locus variation. ECM1 (A) and PCSK1 (B) showed suggestive evidence of colocalization between OA and pQTL signals. ECM1 (C), EFEMP1 (D), ERLEC1 (E) and ITIH1 (F) showed suggestive evidence of colocalization between hip OA and pQTL signals. Chr. chromosome. GWAS, the genome-wide association study

Druggability of the identified proteins

As shown in Supplementary Table 11, a total of 10 proteins putative causal proteins of OA were listed in the druggable genome, 4 proteins in tier 3 A group (ITIH1, HP, PCSK1, and EFEMP1), 2 proteins in tier 3B group (COL6A2 and ECM1).

Discussion

This is the first study to our knowledge that explores the causality between plasma proteins and osteoarthritis and its subtypes by integrating genetic and proteomic data. In this study, we sought to identify plasma proteins that contribute to the pathogenesis of osteoarthritis to find potential novel treatment targets for osteoarthritis. We identified 7 causal protein-OA associations, 2 causal protein-knee OA associations and 6 causal protein-hip OA associations. With the support of colocalization, we identify 2 proteins (ECM1 and PCSK1) as potential targets for intervention for the primary prevention of OA, and 4 proteins (ECM1, EFEMP1, ERLEC1 and ITIH1) of hip OA. None of these causal relationships has been reported before, to our knowledge.

Cartilage is a highly specialized extracellular matrix rich in collagens, in which the protease production and inhibition of cartilage tissue synthesis are implicated in the pathogenesis of osteoarthritis [26]. Our study demonstrated that ECM1 related to the inhibition of cartilage tissue synthesis is a risk factor for OA and hip OA. ECM1, a secreted glycoprotein, has been shown to regulate cartilage formation, promote endothelial cell proliferation, and induce angiogenesis [27]. As a direct targeting molecule of parathyroid hormone–related peptide, ECM1 negatively regulates chondrogenesis and endochondral ossification via its interaction with the progranulin growth factor [28]. Previous studies have demonstrated that overexpression of ECM1 suppresses chondrocyte hypertrophy, matrix mineralization, and endochondral bone formation in vitro [29]. These discoveries were subsequently validated in vivo by Kong L et al., and they found that overexpressed ECM1 in osteoblasts led to dwarf-like changes, delayed bone growth, and declining bone quality in vivo [28]. Feng D et al. also foung that downregulation of extracellular matrix protein 1 effectively ameliorates osteoarthritis progression in OA mice [30]. Therefore, ECM1 inhibitors may serve as a potential therapeutic drug for OA.

Except ECM1, another 3 proteins (EFEMP1, ERLEC1 and ITIH1) were identified as causal proteins in hip OA. Among them, EFEMP1 and ITIH1 also play key roles in the regulation of extracellular matrix. EFEMP1, a member of the fibulin family of extracellular matrix glycoproteins, contains a series of epidermal growth factor-like modules followed by a C-terminus fibulin-type module. It was initially identified as a senescence protein [31]. Our findings suggest that genetically predicted circulating EFEMP1 levels were associated with a decreased risk of hip OA. The anti-hip OA effects of EFEMP1 are most likely accounted for by its anti-angiogenic effect are most likely accounted for its function in normal tissue to inhibit the expression and activities of matrix metalloproteinase.

ITIH1, a component of the inter-alpha-trypsin inhibitor proteins, is mostly secreted by hepatocytes into the blood. And it also can be synthesized by the chondrocytes [32]. ITIH1 binds to hyaluronic acid and other extracellular matrix components providing stability to the cartilage [33]. Our analysis suggests that genetically predicted higher levels of circulating ITIH1 were associated with a reduced risk of hip OA. It appears to conflict with a recent proteomic analysis30 reporting a massive increase in HC1 in OA tissues; however, no other studies have supported them. This difference needs the further study in different tissues [34].

Our study demonstrated that genetically predicted higher levels of PCSK1 were associated with a reduced risk of OA. PCSK1 is an enzyme involved in the proteolytic conversion of proinsulin to insulin, which is necessary for proinsulin processing. In recent years an increasing number of PCSK1 variants have been discovered to be associated with severe obesity, alterations in increased circulating proinsulin levels, and defects in glucose homeostasis [35, 36]. There seems to be a synergistic and similar effect between PCSK1 and GLP-1. Wei et al. reported that dapagliflozin upregulated the expression of PCSK1 while increasing GLP-1 content and secretion [37]. And PCSK1 may promote intestinal glucagon-like peptide-1 (GLP-1) release in gut L cells and islet α cells [38]. In turn, GLP-1 receptor signaling increases PCSK1 in human α cells. Obesity is a well-known primary risk factor for the incidence and progression of OA, and in clinical treatments for obesity, the utilization of glucagon-like peptide one receptor agonists (GLP-1RA) is mature [39]. Therefore, given the similarity between PCSK1 and GLP-1, It seems reasonable to suppose that PCSK1 agonists with the potential for improved therapeutic outcomes in obesity and OA.

We also found that genetically predicted ERLEC1 levels were inversely associated with hip OA risk; however, literature on this association was scarce. Endoplasmic reticulum lectin 1 (ERLEC1) a resident endoplasmic reticulum protein that functions in N-glycan recognition. This protein is thought to be involved in ER-associated degradation via its interaction with the membrane-associated ubiquitin ligase complex. The new findings associated deserves further study [40, 41].

However, our findings should be interpreted in the context of their limitations. First of all, only individuals of European ancestry were included in the analysis, and therefore it should be cautious about applying our study results to other populations. Secondly, we discussed the relationships between plasma proteins and OA. However, some proteins are expressed locally and are not secreted into the circulation. Follow-up studies should focus on investigating the proteins expressed in articular cartilage further. Moreover, further verification experiments are required to confirm our findings and clarify the potential biological mechanisms underlying OA.

Conclusion

In summary, we found some novel proteins causally related to OA and its subtypes by a series of genetic approaches, in which ECM1, PCSK1, EFEMP1, ERLEC1 and ITIH1 were prioritized biomarkers and targets. By deciphering these genetic effects, our results provide novel clues for exploring the pathogenesis of OA and identifying new drug targets.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Acknowledgements

The authors acknowledge all GWAS participants and investigators for their contributions to the summary statistics data.

Author contributions

(I) Conceptualization and Project administration: SS, JQ, C-HW. (II) Funding acquisition and Supervision: X-FL, C-HW. (III) Data curation: RZ, Y-J L. (IV) Visualization: CW, M-J C, RZ. (V) Formal analysis: SS, JQ, RZ. (VI) Writing - original draft: SS, JQ. All authors were involved in editing or revising the article critically for important intellectual content, and all authors approved the final version to be published.

Funding

This work was supported by the National Natural Science Foundation of China (No. 81971543, No. 81471618), Key Research and Development (R&D) Projects of Shanxi Province (No. 201803D31119), and Four “Batches” Innovation Project of Invigorating Medical through Science and Technology of Shanxi Province (No. 2022XM05).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

This study was approved by the Ethics Committee of the Second Hospital of Shanxi Medical University (Approval (2019) KY No. (105)).

Competing interests

The authors declare no competing interests.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Shan Song and Jun Qiao contributed equally to this study.
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