Comparative analysis of serum proteome in congenital scoliosis patients with TBX6 haploinsufficiency – a first report pointing to lipid metabolism

Abstract Congenital scoliosis (CS) is a three‐dimensional deformity of the spine affecting quality of life. We have demonstrated TBX6 haploinsufficiency is the most important contributor to CS. However, the pathophysiology at the protein level remains unclear. Therefore, this study was to explore the differential proteome in serum of CS patients with TBX6 haploinsufficiency. Sera from nine CS patients with TBX6 haploinsufficiency and nine age‐ and gender‐matched healthy controls were collected and analysed by isobaric tagged relative and absolute quantification (iTRAQ) labelling coupled with mass spectrometry (MS). In total, 277 proteins were detected and 20 proteins were designated as differentially expressed proteins, which were submitted to subsequent bioinformatics analysis. Gene Ontology classification analysis showed the biological process was primarily related to ‘cellular process’, molecular function ‘structural molecule activity’ and cellular component ‘extracellular region’. IPA analysis revealed ‘LXR/RXR activation’ was the top pathway, which is a crucial pathway in lipid metabolism. Hierarchical clustering analysis generated two clusters. In summary, this study is the first proteomic research to delineate the total and differential serum proteins in TBX6 haploinsufficiency‐caused CS. The proteins discovered in this experiment may serve as potential biomarkers for CS, and lipid metabolism might play important roles in the pathogenesis of CS.


Introduction
CS is a complex three-dimensional deformity of the spine with a lateral curvature of more than ten degrees [1]. The overall incidence of CS has been estimated at approximately one of 2000 live births [2]. CS originates from defects of the axial skeletal development during embryogenesis. Disruption of normal somitogenesis in the embryonic period which is regulated by a multitude of genes can result in vertebral malformations [3]. If left untreated, the scoliotic deformity of the CS, specifically in the thoracic region, can progress to serious conditions devastating the pulmonary function, hence augmenting the mortality [4,5].
The T-box genes are an evolutionarily conserved family of genes encoding transcription factors that play a key role in regulating the development processes [6]. The T-box gene TBX6, located at the 16p11.2 region, with 6092 bp in size, is accountable for normal somitogenesis by interacting with the Notch pathway in the clock-wavefront model [7,8]. More recently, our team reported that TBX6 haploinsufficiency is associated with sporadic cases of CS [1]. Involvement of TBX6 haploinsufficiency in CS cases is endorsed by the identification of TBX6 null mutation in conjunction with a hypomorphic allele to account for 8-11% of CS cases [1]. To date, the TBX6 compound inheritance model has shed light on the maximum ratio of CS [9], suggesting TBX6 one of the most important genes in the occurrence and pathogenesis of CS.
Although the involvement of TBX6 gene in CS has been well identified, further biological researches are needed to clarify the exact mechanisms of TBX6 in CS from more aspects. While gene is the carrier for genetic information, protein is the final output of gene and the executor of exact biological functions [10,11]. In this regard, it is quite compelling to elucidate the role of TBX6 in CS from the protein perspective. One desirable way is to utilize the proteomics technique to detect the total proteins, namely the global proteome profiles, thus complementing our previous findings on TBX6. However, to the best of our knowledge, there are no publications using proteomics techniques to study the CS disease. Therefore, this study is conceived to establish the serum proteome profiles of CS cases with TBX6 haploinsufficiency by iTRAQ technique.

Patients and sample obtainment
In the previous genetic experiment, genome-wide analyses of copy number variants were performed by Agilent oligonucleotide comparative genomic hybridization (CGH) microarrays. Additionally, a custom 4 9 180k CGH microarray with approximately 180,000 oligonucleotide probes was utilized. DNA processing and microarray experiment were following the Agilent oligonucleotide CGH instruction (Protocol version 6.0). Quantitative PCR analysis was conducted through ΔCt method using SYBR Green Realtime PCR Master Mix (TOYOBO, Osaka, Japan) [1].
As for the proteomic experiment, a case-control study was adopted. The sera of the nine patients and nine healthy controls were drawn from the previous cohort. The exclusion criteria were exactly the same as previously mentioned [1], in which known syndromes (including Alagille syndrome, Goldenhar's syndrome, hemifacial microsomia, Klippel-Feil syndrome, spondylocostal dysostosis, spondylothoracic dysostosis and VACTERL syndrome) were excluded clinically. Herein, the inclusion criteria for case group were as follows: (i) clinical diagnosis of CS; and (ii) carrying a genotype of 16p11.2 deletion and a common TBX6 haplotype of TCA (defined by the non-reference alleles of three common single-nucleotide polymorphisms [SNPs]-rs2289292, rs3809624 and rs3809627, respectively). Age and gender were matched between the case and control groups to guarantee there was no statistical significance. This work had been approved by the ethical committee of Peking Union Medical College Hospital, and written informed consent had been obtained from the participants or their guardians.

Depletion of high-abundance serum proteins
Serum IgG and albumin were depleted using ProteoPrep Blue Albumin and IgG Depletion kit (PROTBA, Sigma-Aldrich Company, Darmstadt, Germany), according to the manufacturer's instruction. A total of 600 ll serum was used for the depletion, and finally, 400 ll was eluted at the end of the procedure. The protein concentration after depletion was evaluated by Bradford method.

Trypsin digestion and iTRAQ labelling
Peptides from 100 lg of each depleted serum were labelled with 8-plex iTRAQ reagents (Applied Biosystems, Foster City, CA, USA), according to the manufacturer's protocol. In short, the proteins were denatured, alkylated and trypsin-digested at 37°C overnight. Then the peptides were labelled with one unit of iTRAQ reagent that was reconstituted in 150 ll isopropanol. The labelling arrangement was shown in Table 1. After the labelling, the samples were combined into one tube and dried in vacuo. Dried peptides were resuspended in 100 ll of mobile phase A and were centrifuged at 14,000 g for 20 min. The supernatants were loaded on the column (Durashell-C18,4.6 9 250 mm, 5 lm, 100 A, Agela, DC952505-0) and eluted stepwise by injecting mobile B in the RIGOL L-3000 system (RIGOL, Beijing, China). Mobile phase A consisted of 2% (v/v) acetonitrile, 98% (v/v) ddH 2 O and pH 10, and phase B consisted of 98% (v/v) acetonitrile, 2% (v/v) ddH 2 O and pH 10. The 60-min. gradients comprised 5% mobile B for 5 min., 5-30% mobile B for 35 min., 30-95% mobile B for 10 min. and equilibrated with 5% mobile B for 10 min. at a 300 nl/min. flow rate. The fractions were eluted at 1.5-min. intervals and collected using step gradients of mobile B. acetonitrile, 0.1% formic acid (phase B). Linear gradients of 8-30% B for 77 min., 30-50% B for 10 min., 50-95% B for 15 min. and finally 5% B for 11 min., with a flow rate of 350 nl/min., were employed. The eluent was submitted to Q-Exactive mass spectrometer through EASY-Spray ion source with parameters as follows: spray voltage, 2.3 kv; capillary temperature, 320°C; and declustering potential, 100 V. The mass spectrometer was adjusted automatically between MS and MS/MS mode. The fullscan MS mode was operated with the following parameters: automatic gain control (AGC) target, 3e6; maximum ion transfer, 20 ms; resolution, 70,000 FWHM; and scan range, 300-1800 m/z. The MS/MS mode was set as follows: AGC target, 1e5; maximum ion transfer, 120 ms; intensity threshold, 8.30E+03; fragmentation methods, high-energy collisional dissociation (HCD); normalized collision energy, 29%; and top number, 20.

Database searching and protein identification
We used ProteoWizard (version 3.0.8789) to extract the raw data files from MS. Charge state deconvolution and deisotoping were not performed. The MS/MS samples were searched using the Mascot search engine against the SwissProt database (2016_03, selected for Homo sapiens, unknown version, 20200 entries). The search was governed by the following parameters: trypsin was assumed as the digestion enzyme; two missed cleavages were allowed at maximum; fragment ion mass tolerance of 20 mmu; parent ion tolerance of 15 ppm; carbamidomethylation of cysteine was selected as fixed modification; oxidation of methionine, iTRAQ 8 plex of lysine and the N-terminus were set as variable modifications. Protein quantification was performed by Scaffold Q+ (4.6.1, Proteome Software Inc., Portland, OR, USA) to calculate the relative abundance of iTRAQ-labelled peptides and the corresponding proteins. Peptide identification was accepted at greater than 95% probability to achieve FDR < 1.0%. Proteins identifications were accepted if more than 96% probability to achieve FDR < 1.0%. Only the proteins with at least two identified peptides were considered for quantification. Protein probabilities were calculated by the Protein Prophet algorithm [12]. Principle of parsimony was applied for grouping of proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone. Normalization calculation was performed with median protein ratio iteratively on intensities. Spectral data were log-transformed, pruned of those that can match to multiple proteins and with a missing value.

Bioinformatics analysis
The distribution description of the data in each group is generally presented as mean AE standard deviation (S.D.) The total proteins were submitted to Student's t-tests to determine the significance between two groups by Perseus software (version 1.5.0.31, Berlin, Germany).
The q values were calculated on the basis of permutation-based FDR default setting in Perseus. Ratio is defined as 2 difference , while difference equals the mean of CS log-transformed spectral intensities minus the mean of control log-transformed spectral intensities. The significance threshold of q values was 0.05. Ratio cut-off values were set as 1.42 or 0.70. Differentially expressed proteins were designated when meeting the two requirements simultaneously: (i) q values < 0.05; and (ii) ratios ≥ 1.42 (up-regulated) or ≤0.70 (down-regulated). Gene Ontology (GO) analysis was performed on the PANTHER platform (http://www.pa ntherdb.org/), in which the biological process, molecular function and cellular component were analysed. The Ingenuity Pathway Analysis software (IPA @ , v01-04, QIANGEN Redwood City, CA, USA) was utilized to conduct the network and pathway analysis. In the IPA parameters, no expression cut-off value was selected, and both up-regulated and downregulated proteins were focused on. Hierarchical clustering analysis with Euclidean distance was carried out after Z-score transformation, and K means pre-processing by Perseus.

Results
Detection and identification of total proteins and differentially expressed proteins In total, we identified 277 proteins that were expressing in both CS and control groups. Among the identified total proteins, 65 proteins showed a q value < 0.05. There are 108 up-regulated (ratio > 1) and 169 down-regulated proteins (ratio < 1) in the CS group in comparison with the control group, with ratio (CS/control) ranging from 0.573 to 2.052 (Table S1). Taking into account both the q value and the ratio, we designated 20 differentially expressed proteins (q < 0.05 and ratio ≥ 1.42 or ≤0.70) ( Table 2) (Fig. 1). The differentially expressed proteins included apolipoprotein C-I, sex hormonebinding globulin, complement C4-A and transgelin-2.

GO classification of identified proteins
We classified the 20 differentially expressed proteins according to GO information (PANTHER platform, http://www.pantherdb.org/) concerning biological process, molecular function and cellular

IPA network and pathway analysis
Network analyses of 20 differentially expressed proteins in the serum of CS samples versus controls were assessed through the use of IPA software. The IPA calculates the P-values by Fisher's exact test and the p score ðp score ¼ Àlog Pvalue 10 Þ to assess the probability of matching the submitted proteins in a protein-protein interaction by random chance. Generally, we found a total of four significant molecular networks (Table S2). The first remarkable networks are depicted in Figure 3. The first network with the highest score (37) consists of 15 'focus molecules', and its related   (Table S2). The canonical pathways assigned by IPA which involve the 20 differentially expressed proteins indicated that the top seven pathways are LXR/RXR activation, acute phase response signalling, FXR/RXR activation, atherosclerosis signalling, IL-12 signalling and production in macrophages, coagulation system and complement system ( Table 3). The interactive relationship between proteins in the first canonical pathway is denoted in Figure 4. The whole information on the canonical pathway output is displayed in Table S3. It is noteworthy to mention that the top one network is intimately associated with lipid metabolism (Fig. 4).

Hierarchical clustering analysis
We conducted the hierarchical clustering analysis on the basis of the expression values for the 65 proteins having q values < 0.05. The data were Z-score transformed and were clustered in both the row and the column directions. The results showed that most of these samples were correctly classified into two groups corresponding to disease or normal control condition, apart from one sample (CS 4) that was incorrectly classified (Fig. 5A). This suggests that CS may have particular expression profiles, distinct from the normal state profiles. The 65 submitted proteins were separated into two clusters: cluster 61 (16 proteins) and cluster 62 (49 proteins) ( Table S4). The corresponding expressional pattern in cluster 61 is delineated in Figure 5B.

Discussion
In the present study, to elucidate the mechanisms of TBX6-induced CS at the protein level, we employed a comparative proteomics strategy of iTRAQ to explore the differential protein expression profiles, GO terms, IPA network and pathway and hierarchical clustering between normal control and CS patients with both TBX6 deletion and T-C-A haplotype. To the best of our knowledge, this investigation is the first proteomics research on CS, following our latest genetic revelation of TBX6 in the onset of CS [1].
Our results indicated that 277 total proteins were expressed in both CS and normal controls. Furthermore, 65 proteins showed q values < 0.05 and 20 proteins were designated as differentially expressed proteins (q < 0.05, and ratio ≥ 1.42 or ≤0.70). We then conducted bioinformatics analyses on these proteins. Generally, the GO classification suggested that annotation with highest percentage in biological process, molecular function and cellular component was cellular process, structural molecule activity and extracellular region, respectively. IPA output showed that the most significant canonical pathway was the 'LXR/RXR activation' pathway. Hierarchical clustering analysis separated the 65 protein (q < 0.05) into two clusters (16 versus 49 proteins). Due to the significant expression modulations as well as potential biological and functional relevance of the differentially expressed proteins in the pathogenesis and aetiology of CS, we herein choose several of the proteins to discuss in the following section.
The most appealing finding to emerge from our study is that we, for the first time, hypothesize lipid metabolism to play vital roles in the pathogenesis of CS. Supporting evidence not only arises from proteomic revelation of APOC1 and LXR/RXR pathway in this study, as illustrated in following text, but also comes from other discoveries including the doctoral thesis of Dr. Sun from our team who conducted a preliminary proteomics analysis on CS by 2-D gel electrophoresis in 2011 (http://d.g.wanfangdata.com.cn/Thesis_Y1985714.aspx). Part of the serum proteins he found, such as APOA1 and APOA4, also point to lipid metabolism. But his results have not been submitted to any journal for publishing so far. LXR encodes highly homologous transcription factors of nuclear receptors and forms functional   heterodimers with RXR via LXR-responsive element within the targeted regulators [13]. The LXR/RXR functions as sensors of cholesterol homoeostasis by regulating genes that control sterol and fatty acid metabolism [14]. Another supporting evidence is that in adolescent idiopathic scoliosis, which is another type of scoliosis and may have overlapping pathogenesis with CS [15,16], lipid metabolism was identified to be the most remarkable metabolic changes in the serum [17]. Taken together, our results and other evidence strongly point to disrupted lipid metabolism in CS. More generalized studies with larger sample sizes should be conducted to investigate whether lipid metabolism is associated with CS.
Sex hormone-binding globulin (SHBG) is a homodimeric glycoprotein, the most prominent feature of which is to bind sex steroids with equally high affinity as well as specificity. It is synthesized in the liver, and its molecular weight is approximately 90 kD [18]. Each dimer of the SHBG, consisting of 373 amino acids, exhibits a tertiary structure of carboxy-terminal laminin G-like domain (LGLD), which is joined to an amino-terminal LGLD by a linker region [19]. SHBG has been reported to be implicated in various physiological disturbance states and diseases. In polycystic ovary syndrome (PCOS), Wassell et al. [20] found that PCOS patients exhibiting a positive response to metformin treatment showed a significantly lower pre-treatment SHBG levels, implying the SHBG may be a predictor of response to pharmacological treatment for PCOS. In thyroid disorders, Hampl et al. [21] observed that low SHBG levels in patients treated with total thyroidectomy because of thyroid cancer were significantly elevated to physiological values after reaching normal thyroid function. In pituitary diseases, hyperprolactinemia was associated with decreased SHBG concentrations, and SHBG levels returned to normal limits following bromocriptine administration [22]. In metabolic syndrome (METS) and type 2 diabetes mellitus (T2DM), SHBG levels have been demonstrated to have an inverse association with an increased risk for the development of METS and T2DM [23][24][25], suggesting that SHBG is likely to represent a sensitive and early biomarker for metabolic disturbances. The related molecular mechanism explaining the observational studies may be partially attributed to that monosaccharide-induced hepatic de novo lipogenesis can reduce hepatic HNF-4a levels and subsequently SHBG gene expression. In liver disorders, SHBG was significantly increased in patients with cirrhosis, a result and final path of various liver disorders [26]. The elevation of SHBG in cirrhosis was less pronounced with progression to the decompensated state. As liver cirrhosis is inclined to develop into hepatocellular carcinoma (HCC), SHBG might constitute a potential risk marker for early detection of HCC [26]. In the realm of orthopaedic researches, SHBG was found abated in rheumatoid arthritis patients than control volunteers, in both premenopausal and postmenopausal status [27]. SHBG was also associated with bone loss, vertebral fractures and kyphosis [28]. Interestingly, Chilean scholars, Albala et al. [29], evaluated the bone mineral density and the SHBG in obese and non-obese postmenopausal women, and concluded that obesity exerts protection  against osteoporosis via decreased SHBG. Furthermore, another publication suggested that high serum SHBG levels have an increased risk of fractures in older Swedish men [30]. Low bone mineral density has been observed in CS patients [31], and the distribution of bone mineral density in CS mice was highest in the curve apex [32]. Our study is the first to account for SHBG protein as differentially expressed protein in CS patients. We believe the finding of SHBG may be helpful in elaborating the low bone mineral density in CS patients when taking into account the reported role of SHBG in osteoporosis.
Apolipoprotein C-I (APOC1), with a 6.6 kD molecular mass, is one constituent of chylomicrons, high-density lipoprotein (HDL) and triglyceride-rich lipoproteins, which can blunt the metabolism of triglyceride-rich lipoproteins [33]. APOC1 functions as an inhibitor of lipoprotein binding to the low-density lipoprotein (LDL) receptor and very low-density lipoprotein (VLDL) receptor. APOC1 is also a major inhibitor of cholesterol ester transfer protein and is able to affect fatty acid uptake in a direct manner [34]. High expression of apolipoproteins in embryogenesis would be helpful for transporting material, such as lipid. Therefore, it is reasonable to speculate APOC1, the smallest member of apolipoprotein family, may have an important role in embryogenesis, in addition to the well-documented expression of other lipoproteins (APOE and APOA1) in embryonic development [35] that, potentially, may be associated with bone forming. One publication confirmed our ratiocination. Wang and his colleagues [36] observed in zebrafish that apoc1 gene is expressed in gradient along the ventral-dorsal axis with the highest expression on the ventral side, and apoc1 gene expression occurs in tail paraxial mesoderm that originates from the blastoderm margin in somitogenesis. They also revealed that apoc1 gene expression can be activated by bone morphogenetic proteins (BMPs) signalling, and this effect can be dampened by retinoic acid (RA) signalling suppression. This serves a new cynosure because RA and its receptors are demonstrated to be expressed in the paraxial mesoderm and are critical for regulating key genes in the somitogenesis clock and the HOX gene induction [37]. RA inhibits the ability of the paraxial mesoderm to respond to asymmetric signalling downstream of the left-side determinant Nodal, which could cause asymmetry of somitogenesis [38]. In addition, BMP signalling has been shown to be involved in the development of the initial vertebrate body and somite [39,40] and BMP inhibitor, dorsomorphin, produces split-body phenotypes including bilaterally split somites [41]. Considering the roles of BMP and RA in somitogenesis and their demonstrated downstream effects on APOC1 protein [36], we can infer that the down-regulated expression of APOC1 protein in our study and the occurrence of CS are two concurrent phenotypes caused, at least in part, by dysregulation of some common upstream signallings such as RA and BMP. Furthermore, apoc1 gene expression in murine macrophages can be up-regulated by ligands of LXR/ RXR pathway [42], which is in agreement with the appearance of LXR/RXR pathway as the most remarkable canonical pathway of IPA output in our study. This further strengthens the link between APOC1 and CS. As LXR/RXR pathway is the key regulator for lipid and cholesterol metabolism [14], it is reasonable to generalize a step further from APOC1 and LXR/RXR to lipid metabolism -it is lipid metabolism that may share a common molecular background with CS under partial contributions from RA and BMPs. Given the previously demonstrated aetiological documentation of CS, we can even propose a more radical supposition that lipid metabolism may have interactions with the known mechanisms of CS, and future investigations on how the lipid metabolism is involved in the onset of CS, especially from an embryological point of view, may shed new light on the early prevention and treatment of CS.
Complement C4-A (CO4A) is a component of the complement system and is indispensable for its classical activation pathway [43]. CO4A is derived from the proteolytic degradation of complement factor 4 (C4). CO4A acts in the form of a single chain precursor, and then is cleaved into a trimer before secretion [44]. Anaphylatoxin C4a, the cleaved product of alpha chain of C4, is a mediator of local inflammatory response. The gene encoding CO4A is localized at the major histocompatibility complex (MHC) class III region. Recent studies implicated that CO4A was related to neurological disorders. In schizophrenia, the level of C4 protein was observed to be significantly increased in patients' sera [45,46], and CO4A RNA expression was significantly higher in the brain tissue [47]. Besides, C4A gene structural diversity can in part serve as a bridge between MHC variation and schizophrenia [47]. It is known that C4 protein lies in neuronal synapses, dendrites, axons and cell bodies. Thus, the schizophrenic phenotype can be attributed to the potential role of C4 protein in synapse remodelling and pruning during the neurodevelopmental process [48]. In our study, the level of CO4A protein was significantly elevated in CS patients, all of which carry a 16p11.2 deletion. In fact, 16p11.2 deletion has demonstrated to be a likely cause for developmental delay, mental retardation and autism spectrum disorder [49]. However, the detailed neurodevelopmental records of the patients in this study were not available for us to determine whether they had neurological disorders or not. Further investigations are needed to clarify this point. As CS and neurological disorder (such as schizophrenia) are two well-demonstrated phenotypes caused by 16p11.2 deletion, one phenotype may, to some extent, be indicative of the other phenotype. Hence, we propose that CO4A, and thus neurological disorder (which has to do with CO4A) may be coincidental with some CS cases that have 16p11.2 deletion. In line with our reasoning pattern in the hypothesis, previous publications revealed that two other phenotypes of 16p11.2 deletion, obesity and low cognitive functioning, showed remarkable interrelations with each other: people who have learning disabilities have a greater chance of being obese [50], and the obese people in turn exhibit a substantially decreased cognitive ability [51]. However, the altered level of CO4A in our study might be caused by 16p11.2 deletion, but not CS. Whether CO4A and CS can manifest simultaneously depends on the coverage of deletion.
Transgelin-2 is a homolog of the protein transgelin, a 22 kD actinbinding protein of the calponin family. Transgelin was first identified in 1987 in smooth muscle tissues of birds and therefore named as smooth muscle 22 (SM22) [52]. Transgelin is widely expressed in vascular and visceral smooth muscles and is one of the earliest markers of smooth muscle differentiation [53]. Transgelin-2 has been suggested to be a tumour suppressor demonstrated by its discovery as differentially expressed protein in several proteomics studies. adenocarcinoma [54]. Validation by immunohistochemistry revealed the exclusive expression of transgelin and transgelin-2 in the stromal tissue compartment and neoplastic glandular compartment, respectively, suggesting its important role in active stromal remodelling of invasive carcinomas. Similarly, in a highly metastatic variant of human breast cancer cell, transgelin-2 was found to be the only down-regulated protein, and verification experiments implied a significant association between lymph node metastasis and expression of transgelin-2 [55]. In antineoplastic drug-resistant breast cancer cell line, transgelin-2 was reported to be a mediator for drug resistance by up-regulating the adenosine triphosphate-binding cassette transporter proteins [56]. In colorectal cancer, transgelin-2 was significantly up-regulated, which was also discovered by quantitative proteome analysis. Further validation indicated transgelin-2 was tissue-specific in colorectal cancer cells, and overexpression of transgelin-2 was associated with tumour prognostic factors [57]. Apart from its identification in cancer cells, transgelin-2 was also significantly differentially expressed in proteome of other diseases, including rheumatoid arthritis and meniscal injury. Transgelin-2 was selected as one of the candidate markers for rheumatoid arthritis due to its elevated expression in the synovial fluid of erosive rheumatoid arthritis in comparison with non-erosive rheumatoid arthritis [58]. In injured menisci, proteomic analysis suggested transgelin-2 was one of the interest proteins after evaluating the expressional trends between case and control groups [59]. In spite of its discovery and potential biological value in many diseases, transgelin-2 has never been reported in the threshold of scoliosis research. Our study for the first time reports the involvement of transgelin-2 as serum differentially expressed protein in CS. As such, transgelin-2 may have been suggested as a candidate marker for CS possibly indicating potential co-existing smooth muscle deformity with the malformation of spine. However, more observational investigations, especially on abnormal embryogenesis, are necessary to confirm the hypothesis.
In addition to the above-mentioned proteins, other significantly differentially expressed proteins, including serum amyloid A-1 protein, serum amyloid A-2 protein, leucine-rich a-2-glycoprotein, creatine kinase M-type and a-1-acid glycoprotein 1, are also worth our concern. Serum amyloid A proteins belong to the apolipoproteins family and are acute phase proteins during inflammatory response. Serum amyloid A-1 and A-2, two isoforms of serum amyloid A, are produced by hepatocyte and released into blood up to a 1000-fold during inflammation [60]. Leucine-rich a-2-glycoprotein is a potential diagnostic marker for inflammation-associated diseases for its augmented expression in ulcerative colitis [61,62], asthma [63], acute appendicitis [64] and rheumatoid arthritis [65]. Creatine kinase Mtype, a subtype of creatine kinase, reversibly catalyses the conversion of creatine into phosphocreatine and adenosine diphosphate (ADP) by facilitating the transfer of adenosine triphosphate (ATP) [66]. Because it is mainly expressed in sarcomeric muscles such as skeletal and cardiac muscles, creatine kinase M-type is a potential diagnostic marker for myocardial infarction [67], rhabdomyolysis [68] and myositis [69]. a-1-acid glycoprotein 1 is an acute phase protein involved in immune system modulation, and elevated serum a-1-acid glycoprotein 1 is seen in systemic inflammatory response or infection [70]. Although these proteins are firstly identified in the proteome of CS, future researches on the possible biological merits of the observed proteins are still necessary for the aetiological elucidation of CS.
In summary, we for the first time depicted the differential protein expression profiles of CS in serum. These previously unidentified proteins may offer invaluable clue for not only the aetiological mechanism but also the onset and development of CS. The application of these proteins may serve effective diagnostic tools for early detection of CS. The underlying interactions between the differential proteins and CS need further investigation.
Table S1 List of total proteins identified in sera from CS and control samples.