Investigation on the molecular mechanism of SPA interference with osteogenic differentiation of bone marrow mesenchymal stem cells

doi:10.21203/rs.3.rs-3754554/v1

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Investigation on the molecular mechanism of SPA interference with osteogenic differentiation of bone marrow mesenchymal stem cells

https://doi.org/10.21203/rs.3.rs-3754554/v1

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The binding of Staphylococcus aureus protein A (SPA) to osteoblasts induces apoptosis and inhibits bone formation. Bone marrow derived mesenchymal stem cells (BMSC) has the ability to differentiate into bone, fat and cartilage. Hence, it was vital to analyze the molecular mechanism of SPA affecting osteogenic differentiation. We introduced transcript sequence data to screen out differentially expressed genes (DEGs) related to SPA interfered BMSC. Protein-protein interaction (PPI) network of DEGs was established to screen biomarkers associated with BMSC with SPA interference. ROC curve was plotted to evaluate the ability of biomarkers to distinguish between two groups of samples. We finally performed GSEA and regulatory analysis based on biomarkers. We identified 321 DEGs. Subsequently, 6 biomarkers (Cenpf, Kntc1, Nek2, Asf1b, Troap and Kif14) were identified via hubba algorithm in PPI. ROC analysis showed that six biomarkers could clearly distinguish normal differentiated and SPA interfered BMSC. Moreover, we found that these biomarkers was mainly enriched in the ‘Pyrimidine metabolism’ pathway. We also constructed ‘71 circRNAs-14 miRNAs-5 mRNAs’ and ‘10 lncRNAs-5 miRNAs-2 mRNAs’ networks. Kntc1 and Asf1b genes were associated with rno-miR-3571. Nek2 and Asf1b genes were associated with rno-miR-497-5p. Finally, we found significant lower expression of six biomarkers in SPA interfered group compared to the normal group by RT-qPCR. Overall, we obtained 6 biomarkers (Cenpf, Kntc1, Nek2, Asf1b, Troap and Kif14) related to SPA interfered BMSC, which laid a theoretical foundation for exploring the key factors of SPA affecting osteogenic differentiation.

Health sciences/Diseases

Health sciences/Medical research

Staphylococcus aureus protein A

Bone marrow derived mesenchymal stem cells

Biomarkers

Bioinformatics

osteogenic differentiation

Osteomyelitis is one of the most challenging and tricky diseases in orthopedic. Osteomyelitis can easily the induce of bone defects, bone nonunion and other relative diseases, leading to limb dysfunction, amputation, and even life-threatening, which seriously threaten the physical and mental health of patients (1). So it is important to deeply explore the mechanism related to osteomyelitis and bone defects. Staphylococcus aureus is the most common pathogenic microorganism in osteomyelitis, which can cause increased inflammation and progressive bone destruction (2). Staphylococcus aureus protein A (SPA), which is expressed in most S. aureus, is an important virulence factor in the cell wall of S. aureus that interacts with human immunoglobulins (3). When SPA binds to osteoblasts, it has been reported to induce apoptosis and cell death, thereby inhibiting bone formation and mineralization (4).

Bone marrow derived mesenchymal stem cells (BMSCs) are considered as a promising cellular resource and potential therapeutic tools for improving transplantation-related function and pathological recovery from a variety of diseases (5, 6). The development of osteomyelitis with bone defects is closely related to the reduced osteogenic differentiation capacity of BMMSCs. It has been found that during the development of osteomyelitis, SPA not only directly stimulates the apoptosis of osteoblasts in the focal area, which in turn causes bone destruction and bone loss in the focal area, but also downregulates the osteogenic differentiation ability of BMMSCs and upregulates their lipogenic differentiation ability. However, the decrease in the differentiation ability of BMMSCs toward osteoblasts directly affects osteogenesis and bone union, which ultimately leads to infected bone nonunion or the development of bone defects (7). Interactions of chemokines and chemokine receptors mediate the migration of mesenchymal stem cells to the impaired site in the brain after hypoglossal nerve injury, suggesting that SPA plays a key role in the development of osteomyelitis (8). Therefore, an in-depth study of the causes of the reduced osteogenic differentiation of BMMSCs under the effect of SPA is important for the treatment of osteomyelitis with bone defects.

In our previous study, we found that the occurrence of infectious bone defects is related to the osteogenic differentiation of BMSCs, and SPA inhibits the osteogenic differentiation of BMSCs, but the specific mechanism of action is unknown. In this study, by constructing an osteogenic differentiation cell model of BMSCs under the effect of SPA and performing bioinformatics analysis, we identified 6 biomarkers (Cenpf, Kntc1, Nek2, Asf1b, Troap and Kif14) related to SPA interfered BMSC, which laid a theoretical foundation for exploring the key factors of SPA affecting osteogenic differentiation. And we tried to explore the specific mechanism of action by constructing non-coding RNA interactions network. This is a new theoretical basis and research direction for further understanding and treatment of osteomyelitis and bone defects.

Cell culture and sequencing. All cells were acquired from Cell Bank of the Chinese Academy of Sciences (Shanghai, China) and were osteogenically differentiated for 14 d. SPA was added to the experimental group and no SPA was added to the control group. 3 experimental and 3 control samples were separated from total RNA using TRIzol. Then, RNA quality assay, rRNA removal, RNA fragmentation, reverse transcription to cDNA, end-filling, A-tailing, PCR amplification with junction were performed, and finally sequencing on Illumina platform.

Data processing. The whole transcript sequence data of 6 bone marrow derived mesenchymal stem cells (BMSCs) form Rattus norvegicus was obtained, of which 3 normal differentiated and 3 SPA interfered samples. For transcriptome (mRNA) sequencing data, Trimmomatic (version 0.36) was conducted to obtain clean data. Then, the hisat2 software (version 2.1.0) was applied to compare Reads with the reference genome. The alignment results of sequencing data were counted using the samtools (version 0.1.19). The RNA was quantified according to the position in the annotation file using the hisat2 software (version 2.1.0). Finally, ‘scatterplot3d’ package was used to implement PCA analysis in different groups.

Analysis of differential genes. The ‘Deseq2’ R package (9) was applied to mine DEGs between normal differentiated group and SPA interfered group. The P.Value < 0.05 & |log2fold change (FC)| >2.5 was determined as the signifcance criteria. Volcano plot and heatmap were applied to show DEGs. GO and KEGG enrichment analysis of DEGs was performed using ‘clusterProfiler’ (10). p < 0.05 was used as screening criteria.

Protein-protein interaction (PPI) network. PPI network which depicted the interactions among DEGs was generated using STRING website (https://string-db.org). MCODE algorithm of cytoscape plug-in was applied to screen core gene cluster (11). Subsequently, we applied the hubba plug-in to score the degree, edge filtered component (EPC), betweenness and closeness. The biomarkers were obtained by overlapping 10 genes with the highest scores.

ROC and Gene set enrichment analysis (GSEA). ROC curve was plotted to evaluate the ability of biomarkers to distinguish normal differentiated cells from SPA-interfered cells by ‘pROC’ package (12). GSEA was conducted to explore the potential GO items and KEGG pathways associated with biomarkers through ‘clusterProfiler’ package (13). p.adjust < 0.05 was used as screening criteria.

The circRNA/lncRNA-miRNA-mRNA network construction. In order to explore the regulatory relationship, miRDB (https://mirdb.org/index.html) was utilized to forecast the miRNAs of biomarkers. circRNAs that interacted with miRNAs were predicted through the miranda (www.microrna.org/microrna/ home.do) database. Further, circRNAs with consistent expression trends with biomarkers were applied for subsequent analysis. The same method was yielded to obtain the lncRNAs. Cytoscape software (14) was applied to optimize the ‘circRNA-miRNA-mRNA’ and ‘lncRNA-miRNA-mRNA’ network.

The analysis of the expression of biomarkers. In order to confirm the expression of biomarkers, we implemented RT-qPCR. 5 SD-BMSC1 normal cells and 5 intervention cells were obtained with the consent from The Affiliated Hospital of Yunnan University, and this study was approved by the ethics committee of The Affiliated Hospital of Yunnan University. Total RNA of 20 samples was separated by the TRIzol (Ambion, Austin, USA) based on the manufacturer’s guidance. The inverse transcription of total RNA into cDNA was implemented by using the First-strand-cDNA-synthesis-kit (Servicebio, Wuhan, China) based on the producer’s indication. Then, RT-qPCR was carried out utilizing the 2xUniversal Blue SYBR Green qPCR Master Mix (Servicebio, Wuhan, China) under the direction of the manual. The primer sequences for PCR were tabulated in Table 1. GAPDH was used as an internal reference gene, and the expression was calculated according to the 2 − ΔΔCt method (15).

Table 1

The primer sequences of biomarkers for RT-qPCR.
Primer	Sequences
Cenpf F	GTTTGAATCGCTCGTGCTGG
Cenpf R	TCCTTCCACTCTTCCAACGC
Kntc1 F	CTGAGAAGACACTGACGTGGA
Kntc1 R	CGAGACTCCGGTAAGTACGC
Nek2 F	GGCCTCAGCAGAAAGGGATT
Nek2 R	AGGAGTCTGCGTGTTTAGCC
Asf1b F	CCTGTCTGACGACCTTGAGTG
Asf1b R	GGTGCAGGTGATGAGAACCA
Troap F	GCTTGTCTCACCACCATCCA
Troap R	GGAATGAAACGCAGGGCATC
Kif14 F	CTCAGCGACCAATCGGGAAG
Kif14 R	CTCAGCCTACCGGCTCTCTG
GAPDH F	GACCCCTTCATTGACCTCAAC
GAPDH R	GCCATCACGCCACAGCTTTCC

Statistical analysis. All P values of statistical results were based on two-sided statistical tests, and a P value < 0.05 was considered statistically significant.

Identification of DEGs related to SPA interfered BMSC.

Using the mouse reference genome alignment, the mapping rate of all samples was above 88.15%, indicating that the quality of sequencing was very good. PCA analysis suggested that there was obvious separation between the two sample groups (Fig. 1A). 321 DEGs were identified in SPA interfered vs normal differentiated group, including 260 down-regulated and 61 up-regulated genes (Fig. 1B-C). To further probe the function of the DEGs, functional enrichment analysis was conducted. GO results indicated that these DEGs were principally involved in ‘mitotic nuclear division’, ‘chromosome separation’ and ‘nuclear division’ (Fig. 1D). Additionally, the KEGG analysis demonstrated that these DEGs were mainly enriched in the ‘Complement and coagulation cascades’ and ‘p53 signaling pathway’ (Fig. 1E).

Screening of biomarkers associated with SPA interfered BMSC.

In order to explore the interaction regulation relationship, the PPI network of the DEGs was constructed, including 295 nodes and 1423 edges (Fig. 2A). We then screened an important gene cluster, which include 48 genes (Fig. 2B). After that, six biomarkers associated with SPA interference, including Cenpf, Kntc1, Nek2, Asf1b, Troap and Kif14, were obtained by overlapping 10 genes with the highest scores based on four algorithms in the 48 gene cluster (Fig. 2C). At the transcription level, we observed lower expression of Cenpf, Kntc1, Nek2, Asf1b, Troap, and Kif14 in SPA interfered group compared to the normal differentiated group (Fig. 2D). The AUC values of biomarkers were all 1, indicating an excellent ability to distinguish normal differentiated cells from SPA-interfered cells (Fig. 2E).

Functional enrichment analysis.

To further study the potential roles of Cenpf, Kntc1, Nek2, Asf1b, Troap and Kif14 related to SPA interference in BMSC, we performed single-gene GSEA on biomarkers. The results showed that Cenpf was mainly enriched in the ‘regulation of autophagy’ and ‘Lysosome’ (Fig. 3A-B). Kntc1 and Nek2 were mainly related to the ‘autophagosome’ and ‘Pyrimidine metabolism’ (Fig. 3C-F). Asf1b and Troap was mainly enriched in the ‘process utilizing autophagic mechanism’ and ‘Biosynthesis of nucleotide sugars’ (Fig. 4A-D). Kif14 was mainly enriched in the ‘macroautophagy’ and ‘Pyrimidine metabolism’ (Fig. 4E-F).

Construction of the regulatory network.

To explore the regulatory mechanism of biomarkers associated with SPA interfered BMSC, 71 circRNAs-14 miRNAs-5 mRNAs network was constructed (Fig. 5A). The network had 90 nodes and 107 edges, in which Kntc1, and Asf1b genes were associated with rno-miR-3571. Additional, 10 lncRNAs-5 miRNAs-2 mRNAs network was constructed (Fig. 5B).The network had 34 nodes and 17 edges, in which Nek2, and Asf1b genes were associated with rno-miR-497-5p.

Experimental verification of marker expression level.

We verified the expression in clinical cell samples by RT-Qpcr, which in agreement with the results of the public database data analysis. The expression of Cenpf, Kntc1, Asf1b and Kif14 were notably reduced in clinical SPA interfered group versus normal group. However, no significant differences were observed between the two groups for Nek2 and Troap (Fig. 6).

Our previous study and other related studies have shown that SPA can affect the osteogenic differentiation of BMSCs. In this study, bioinformatic analysis of transcriptome sequencing data revealed that the osteogenic differentiation of BMSCs under the effect of SPA did caused differential expression of molecular markers Cenpf, Kntc1, Nek2, Asf1b, Troap and Kif14. And these molecular biomarkers formed a network of interactions with nRNA. There were few studies on this aspect.

Kinetochore associated 1(Kntc1) encoded a protein that was one of many involved in mechanisms to ensure proper chromosome segregation during cell division. The functional enrichment analysis showed that Kntc1 was mainly related to the ‘autophagosome’ and ‘Pyrimidine metabolism’. Recent studies suggested that mitogenic proteins might be potential biomarkers and might contribute to the development of human malignancies (16). It was often associated with tumors of the digestive and genitourinary systems (17). It has been shown that Kntc1 was highly expressed in hepatocellular carcinoma (HCC) tissues and was associated with poor prognosis, suggesting a key role for Kntc1 in HCC development (18). Wnt pathway, MAPK pathway, c-Jun NH2-terminal kinase (JNK) pathway, PI3K/Akt pathway, Hedgehog signaling and other pathways are closely related to osteogenic differentiation (19–21). Kntc1 has been reported to function in a variety of diseases by participating in the PI3K/Akt signaling pathway (18), and we speculated that it may also be involved in the regulation of osteogenic differentiation in BMSCs. Centromere protein F (Cenpf) was a protein coding gene. The functional enrichment analysis showed that Cenpf was mainly enriched in the ‘regulation of autophagy’ and ‘Lysosome’. Over-expression of Cenpf was associated with tumorigenesis of many human malignant tumors (22–24). Moreover, Cenpf was a cancer stem cell (CSCs)-specific marker gene, and the latter played a key role in promoting bone destruction (25). Cenpf has a close relationship with MAPK (26, 27) and Wnt pathway (28). Antisilencing function 1b (Asf1b) had effects on cell proliferation, leading to abnormal nuclear structure and unique transcriptional features (29) and was often associated with various malignancies (30, 31). According to the functional enrichment analysis, Asf1b was mainly enriched in the ‘process utilizing autophagic mechanism’ and ‘biosynthesis of nucleotide sugars’. Furthermore, several studies have shown that Asf1b played an important role in the PI3K/Akt signaling pathway (32–34). Never in mitosis gene A-related kinase 2 (Nek2) was highly associated with drug resistance, rapid recurrence and poor outcome in a variety of cancers (35). The functional enrichment analysis showed that Nek2 was mainly related to the ‘autophagosome’ and ‘pyrimidine metabolism’. It had been shown that the over-expression of Nek2 was associated with the development of bone damage (36) and that it regulated osteoblast gene expression and affected osteoblast growth and activity (37). In addition, Nek2 induced osteoclast differentiation and bone destruction via heparanase in multiple myeloma (38). Nek2 has been reported to plays an important regulatory role in MAPK (39, 40), Wnt/β-Catenin pathway (41–43), PI3K/Akt pathway (44), and other pathways. Experimental evidence suggested that troponin-associated protein (Troap) played a key role in regulating cell proliferation in multiple tumors (45, 46). One study found that Troap accelerates glioma progression through the Wnt/β-Catenin pathway (45). Finally, Kinesin family member 14 (Kif14) was a mitotic kinesin whose abnormal function was associated with developmental defects in the brain and kidney as well as multiple cancers (47). The The functional enrichment analysis showed that Kif14 was mainly enriched in the ‘macroautophagy’ and ‘pyrimidine metabolism’. Moreover, Kif14 was also active in signaling pathways such as Wnt pathway (48, 49), Hedgehog signaling (50, 51) and PI3K/Akt pathway (52). Currently, there were no studies on the direct involvement of the above biomarkers in the osteogenic differentiation of MSCs. In conclusion, combining the current literature and the results of the present study, we suggest that Cenpf, Kntc1, Nek2, Asf1b, Troap and Kif14 might be involved in the regulation of osteogenic differentiation of BMCSs under the action of SPA.

Currently, studies on the regulation of osteogenic differentiation of BMSCs by non-coding RNAs had been reported. However, there were few studies on the regulation of osteogenic differentiation of BMSCs by non-coding RNAs in SPA mimicking inflammatory environment. From the lncRNA-miRNA-mRNA network in this study, it could be found that miR-497-5p and miR-322-5p had an action relationship with both Asf1b and Nek2. And one study showed that miR-497-5p was significantly down-regulated in bone tissue of aging and osteoporosis mouse models and up-regulated during osteogenic differentiation of MC3T3-E1 cells. The miR-497-5p over-expression promoted osteoblast differentiation and mineralization (53). In addition, one study showed that miR-322-5p was significantly down-regulated during osteogenic differentiation of rat bone marrow mesenchymal stem cells (54). Secondly, it had been shown that miR-455-3p could promote osteogenic differentiation, which might be related to fracture healing (55), while the present study found a regulatory relationship between miR-455-3p and Troap. Finally, miR-207 was significantly down-regulated during FK506-induced osteogenic differentiation of rat bone marrow mesenchymal stem cells, while the present study showed an association between miR-207 and Nek2 (56). In conclusion, the present study identified some potential molecular networks of action, and the potential significance of which was to be clarified by further studies.

Bioinformatics had been widely used for differential analysis of osteogenic differentiation at the genomic level, allowing the identification of functional pathways of differentially expressed genes (DEGs) associated with osteogenic differentiation in BMSC. In this study, bioinformatic analysis was performed to obtain some key biomarkers, which were hypothesized to be involved in the regulation of osteogenic differentiation of BMSCs in an inflammatory environment. It provides some reference to explore the key factors of SPA affecting osteogenic differentiation. There were some shortcomings in this study: first, we used SPA to simulate the inflammatory environment, which is somewhat different from the real inflammatory environment in the clinic; in addition, two of the biomarkers identified in this study were not validated successfully, which may be related to the sample quality. However, we will clarify the roles of these biomarkers through further experiments and analyze their molecular mechanisms of action in depth.

Overall, we obtained 6 biomarkers (Cenpf, Kntc1, Nek2, Asf1b, Troap and Kif14) related to SPA interfered BMSC, which laid a theoretical foundation for exploring the key factors of SPA affecting osteogenic differentiation.

Acknowledgments

Not application.

Author Contributions

Conceptualization: HJW; methodology: SYZ; formal analysis: HGY; investigation: SYZ, HGY and HJW; resources: FYG; data curation: SYZ and HGY; writing-original draft preparation: HJW; writing-reviewing, and editing: FYG and SYZ; supervision: FYG; project administration: FYG. All authors contributed to the article and approved the submitted version.

Funding

This study was supported by the Yunnan Provincial Science and Technology Department Kunming Medical University Joint Special Project (No. 202201AY070001-274).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Data availability

The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) in National Genomics Data Center (Nucleic Acids Res 2022), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA014184) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa.

Zeller JL, Burke AE, Glass RM. JAMA patient page. Osteomyelitis. Jama (2008) 299:858. doi: 10.1001/jama.299.7.858.
Kavanagh N, Ryan EJ, Widaa A, Sexton G, Fennell J, O'Rourke S, et al. Staphylococcal Osteomyelitis: Disease Progression, Treatment Challenges, and Future Directions. Clin Microbiol Rev (2018) 31. doi: 10.1128/cmr.00084-17.
Widaa A, Claro T, Foster TJ, O'Brien FJKerrigan SW. Staphylococcus aureus protein A plays a critical role in mediating bone destruction and bone loss in osteomyelitis. PLoS One (2012) 7:e40586. doi: 10.1371/journal.pone.0040586.
Claro T, Widaa A, O'Seaghdha M, Miajlovic H, Foster TJ, O'Brien FJ, et al. Staphylococcus aureus protein A binds to osteoblasts and triggers signals that weaken bone in osteomyelitis. PLoS One (2011) 6:e18748. doi: 10.1371/journal.pone.0018748.
Huang Y, Zhu M, Liu Z, Hu R, Li F, Song Y, et al. Bone marrow mesenchymal stem cells in premature ovarian failure: Mechanisms and prospects. Front Immunol (2022) 13:997808. doi: 10.3389/fimmu.2022.997808.
Chen Q, Zhou R, Zhang Y, Zhu S, Xiao C, Gong J, et al. Bone marrow mesenchymal stromal cells attenuate liver allograft rejection may via upregulation PD-L1 expression through downregulation of miR-17-5p. Transpl Immunol (2018) 51:21–29. doi: 10.1016/j.trim.2018.08.004.
Jiang X, Zou S, Ye B, Zhu S, Liu YHu J. bFGF-Modified BMMSCs enhance bone regeneration following distraction osteogenesis in rabbits. Bone (2010) 46:1156–61. doi: 10.1016/j.bone.2009.12.017.
Ji JF, He BP, Dheen STTay SS. Interactions of chemokines and chemokine receptors mediate the migration of mesenchymal stem cells to the impaired site in the brain after hypoglossal nerve injury. Stem Cells (2004) 22:415–27. doi: 10.1634/stemcells.22-3-415.
Love MI, Huber WAnders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol (2014) 15:550. doi: 10.1186/s13059-014-0550-8.
Yu G, Wang LG, Han YHe QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics (2012) 16:284–7. doi: 10.1089/omi.2011.0118.
Chen XM, Zhao Y, Wu XD, Wang MJ, Yu H, Lu JJ, et al. Novel findings from determination of common expressed plasma exosomal microRNAs in patients with psoriatic arthritis, psoriasis vulgaris, rheumatoid arthritis, and gouty arthritis. Discov Med (2019) 28:47–68.
Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, et al. pROC: an open-source package for R and S + to analyze and compare ROC curves. BMC Bioinformatics (2011) 12:77. doi: 10.1186/1471-2105-12-77.
Kumar LM EF. Mfuzz: a software package for soft clustering of microarray data. Bioinformation (2007) 2:5–7. doi: 10.6026/97320630002005.
Ru Y, Kechris KJ, Tabakoff B, Hoffman P, Radcliffe RA, Bowler R, et al. The multiMiR R package and database: integration of microRNA-target interactions along with their disease and drug associations. Nucleic Acids Res (2014) 42:e133. doi: 10.1093/nar/gku631.
Livak KJSchmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods (2001) 25:402–8. doi: 10.1006/meth.2001.1262.
Zhang W, Long J, Tang P, Chen K, Guo G, Yu Z, et al. SYT7 regulates the progression of chronic lymphocytic leukemia through interacting and regulating KNTC1. Biomark Res (2023) 11:58. doi: 10.1186/s40364-023-00506-4.
Liu R, Liu R, Guo Z, Ren J, Huang J, Luo Q, et al. shRNA–mediated knockdown of KNTC1 inhibits non-small-cell lung cancer through regulating PSMB8. Cell Death Dis (2022) 13:685. doi: 10.1038/s41419-022-05140-w.
Tong H, Liu X, Peng C, Shen BZhu Z. Silencing of KNTC1 inhibits hepatocellular carcinoma cells progression via suppressing PI3K/Akt pathway. Cell Signal (2023) 101:110498. doi: 10.1016/j.cellsig.2022.110498.
He Y, Li Z, Ding X, Xu B, Wang J, Li Y, et al. Nanoporous titanium implant surface promotes osteogenesis by suppressing osteoclastogenesis via integrin β1/FAKpY397/MAPK pathway. Bioact Mater (2022) 8:109–123. doi: 10.1016/j.bioactmat.2021.06.033.
Yi C, Liu D, Fong CC, Zhang JYang M. Gold nanoparticles promote osteogenic differentiation of mesenchymal stem cells through p38 MAPK pathway. ACS Nano (2010) 4:6439–48. doi: 10.1021/nn101373r.
Wang T, Zhao H, Jing S, Fan Y, Sheng G, Ding Q, et al. Magnetofection of miR-21 promoted by electromagnetic field and iron oxide nanoparticles via the p38 MAPK pathway contributes to osteogenesis and angiogenesis for intervertebral fusion. J Nanobiotechnology (2023) 21:27. doi: 10.1186/s12951-023-01789-3.
Sun J, Huang J, Lan J, Zhou K, Gao Y, Song Z, et al. Overexpression of CENPF correlates with poor prognosis and tumor bone metastasis in breast cancer. Cancer Cell Int (2019) 19:264. doi: 10.1186/s12935-019-0986-8.
Lokody I. Signalling: FOXM1 and CENPF: co-pilots driving prostate cancer. Nat Rev Cancer (2014) 14:450–1. doi: 10.1038/nrc3772.
Aytes A, Mitrofanova A, Lefebvre C, Alvarez MJ, Castillo-Martin M, Zheng T, et al. Cross-species regulatory network analysis identifies a synergistic interaction between FOXM1 and CENPF that drives prostate cancer malignancy. Cancer Cell (2014) 25:638–651. doi: 10.1016/j.ccr.2014.03.017.
Pan XW, Zhang H, Xu D, Chen JX, Chen WJ, Gan SS, et al. Identification of a novel cancer stem cell subpopulation that promotes progression of human fatal renal cell carcinoma by single-cell RNA-seq analysis. Int J Biol Sci (2020) 16:3149–3162. doi: 10.7150/ijbs.46645.
Xu P, Yang J, Chen Z, Zhang X, Xia Y, Wang S, et al. N6-methyladenosine modification of CENPF mRNA facilitates gastric cancer metastasis via regulating FAK nuclear export. Cancer Commun (Lond) (2023) 43:685–705. doi: 10.1002/cac2.12443.
Zhang M, Zhang Q, Bai J, Zhao ZZhang J. Transcriptome analysis revealed CENPF associated with glioma prognosis. Math Biosci Eng (2021) 18:2077–2096. doi: 10.3934/mbe.2021107.
Sánchez-Tilló E, Fanlo L, Siles L, Montes-Moreno S, Moros A, Chiva-Blanch G, et al. The EMT activator ZEB1 promotes tumor growth and determines differential response to chemotherapy in mantle cell lymphoma. Cell Death Differ (2014) 21:247–57. doi: 10.1038/cdd.2013.123.
Corpet A, De Koning L, Toedling J, Savignoni A, Berger F, Lemaître C, et al. Asf1b, the necessary Asf1 isoform for proliferation, is predictive of outcome in breast cancer. Embo j (2011) 30:480 – 93. doi: 10.1038/emboj.2010.335.
Liu X, Song J, Zhang Y, Wang H, Sun H, Feng X, et al. ASF1B promotes cervical cancer progression through stabilization of CDK9. Cell Death Dis (2020) 11:705. doi: 10.1038/s41419-020-02872-5.
Chen Z, Ou D, Huang ZShen P. Identification of hsa_circ_0002024 as a prognostic competing endogenous RNA (ceRNA) through the hsa_miR_129-5p/Anti-Silencing Function 1B Histone Chaperone (ASF1B) axis in renal cell carcinoma. Bioengineered (2021) 12:6579–6593. doi: 10.1080/21655979.2021.1974650.
Han G, Zhang X, Liu P, Yu Q, Li Z, Yu Q, et al. Knockdown of anti-silencing function 1B histone chaperone induces cell apoptosis via repressing PI3K/Akt pathway in prostate cancer. Int J Oncol (2018) 53:2056–2066. doi: 10.3892/ijo.2018.4526.
Wang K, Hao Z, Fu X, Li W, Jiao AHua X. Involvement of elevated ASF1B in the poor prognosis and tumorigenesis in pancreatic cancer. Mol Cell Biochem (2022) 477:1947–1957. doi: 10.1007/s11010-022-04404-5.
Chen C, Bao H, Lin W, Chen X, Huang Y, Wang H, et al. ASF1b is a novel prognostic predictor associated with cell cycle signaling pathway in gastric cancer. J Cancer (2022) 13:1985–2000. doi: 10.7150/jca.69544.
Zhou W, Yang Y, Xia J, Wang H, Salama ME, Xiong W, et al. NEK2 induces drug resistance mainly through activation of efflux drug pumps and is associated with poor prognosis in myeloma and other cancers. Cancer Cell (2013) 23:48–62. doi: 10.1016/j.ccr.2012.12.001.
Gu X, Wang ZPan Q. Overexpression of NIMA related kinase 2 in multiple myeloma and its relevance with disease features and prognosis to bortezomib treatment. J Clin Pharm Ther (2022) 47:1690–1697. doi: 10.1111/jcpt.13723.
Zhou X, Qiu YH, He P, Jiang F, Wu LF, Lu X, et al. Why SNP rs227584 is associated with human BMD and fracture risk? A molecular and cellular study in bone cells. J Cell Mol Med (2019) 23:898–907. doi: 10.1111/jcmm.13991.
Hao M, Franqui-Machin R, Xu H, Shaughnessy J, Jr., Barlogie B, Roodman D, et al. NEK2 induces osteoclast differentiation and bone destruction via heparanase in multiple myeloma. Leukemia (2017) 31:1648–1650. doi: 10.1038/leu.2017.115.
Di Agostino S, Rossi P, Geremia RSette C. The MAPK pathway triggers activation of Nek2 during chromosome condensation in mouse spermatocytes. Development (2002) 129:1715–27. doi: 10.1242/dev.129.7.1715.
Zhang MX, Xu XM, Zhang P, Han NN, Deng JJ, Yu TT, et al. Effect of silencing NEK2 on biological behaviors of HepG2 in human hepatoma cells and MAPK signal pathway. Tumour Biol (2016) 37:2023–35. doi: 10.1007/s13277-015-3993-y.
Xu T, Zeng Y, Shi L, Yang Q, Chen Y, Wu G, et al. Targeting NEK2 impairs oncogenesis and radioresistance via inhibiting the Wnt1/β-catenin signaling pathway in cervical cancer. J Exp Clin Cancer Res (2020) 39:183. doi: 10.1186/s13046-020-01659-y.
Chen Y, Wu N, Liu L, Dong HLiu X. microRNA-128-3p overexpression inhibits breast cancer stem cell characteristics through suppression of Wnt signalling pathway by down-regulating NEK2. J Cell Mol Med (2020) 24:7353–7369. doi: 10.1111/jcmm.15317.
Zhou J, Lai J, Cheng YQu W. NEK2 Serves as a Novel Biomarker and Enhances the Tumorigenicity of Clear-CellRenal-Cell Carcinoma by Activating WNT/β-Catenin Pathway. Evid Based Complement Alternat Med (2022) 2022:1890823. doi: 10.1155/2022/1890823.
Das TK, Dana D, Paroly SS, Perumal SK, Singh S, Jhun H, et al. Centrosomal kinase Nek2 cooperates with oncogenic pathways to promote metastasis. Oncogenesis (2013) 2:e69. doi: 10.1038/oncsis.2013.34.
Zhao ZQ, Wu XJ, Cheng YH, Zhou YF, Ma XM, Zhang J, et al. TROAP regulates cell cycle and promotes tumor progression through Wnt/β-Catenin signaling pathway in glioma cells. CNS Neurosci Ther (2021) 27:1064–76. doi: 10.1111/cns.13688.
Li L, Wei JR, Song Y, Fang S, Du Y, Li Z, et al. TROAP switches DYRK1 activity to drive hepatocellular carcinoma progression. Cell Death Dis (2021) 12:125. doi: 10.1038/s41419-021-03422-3.
Benoit M, Asenjo AB, Paydar M, Dhakal S, Kwok BHSosa H. Structural basis of mechano-chemical coupling by the mitotic kinesin KIF14. Nat Commun (2021) 12:3637. doi: 10.1038/s41467-021-23581-3.
Wang D, Dai J, Suo C, Wang S, Zhang YChen X. Molecular subtyping of esophageal squamous cell carcinoma by large-scale transcriptional profiling: Characterization, therapeutic targets, and prognostic value. Front Genet (2022) 13:1033214. doi: 10.3389/fgene.2022.1033214.
Yang T, Li XN, Li L, Wu QM, Gao PZ, Wang HL, et al. Sox17 inhibits hepatocellular carcinoma progression by downregulation of KIF14 expression. Tumour Biol (2014) 35:11199–207. doi: 10.1007/s13277-014-2398-7.
Pejskova P, Reilly ML, Bino L, Bernatik O, Dolanska L, Ganji RS, et al. KIF14 controls ciliogenesis via regulation of Aurora A and is important for Hedgehog signaling. J Cell Biol (2020) 219. doi: 10.1083/jcb.201904107.
Liu J, Li D, Zhang X, Li YOu J. Histone Demethylase KDM3A Promotes Cervical Cancer Malignancy Through the ETS1/KIF14/Hedgehog Axis. Onco Targets Ther (2020) 13:11957–11973. doi: 10.2147/ott.S276559.
Yang T, Zhang XBZheng ZM. Suppression of KIF14 expression inhibits hepatocellular carcinoma progression and predicts favorable outcome. Cancer Sci (2013) 104:552–7. doi: 10.1111/cas.12128.
Ma J, Lin X, Chen C, Li S, Zhang S, Chen Z, et al. Circulating miR-181c-5p and miR-497-5p Are Potential Biomarkers for Prognosis and Diagnosis of Osteoporosis. J Clin Endocrinol Metab (2020) 105. doi: 10.1210/clinem/dgz300.
Liu J, Yao Y, Huang J, Sun H, Pu Y, Tian M, et al. Comprehensive analysis of lncRNA-miRNA-mRNA networks during osteogenic differentiation of bone marrow mesenchymal stem cells. BMC Genomics (2022) 23:425. doi: 10.1186/s12864-022-08646-x.
Ma H, Li M, Jia Z, Chen XBu N. MicroRNA-455-3p promotes osteoblast differentiation via targeting HDAC2. Injury (2022) 53:3636–3641. doi: 10.1016/j.injury.2022.08.047.
Zhang J, Yu X, Yu YGong Y. MicroRNA expression analysis during FK506-induced osteogenic differentiation in rat bone marrow stromal cells. Mol Med Rep (2017) 16:581–590. doi: 10.3892/mmr.2017.6655.

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Investigation on the molecular mechanism of SPA interference with osteogenic differentiation of bone marrow mesenchymal stem cells

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