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

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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 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction 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. Materials and methods 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 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. Results 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. 1 A). 321 DEGs were identified in SPA interfered vs normal differentiated group, including 260 down-regulated and 61 up-regulated genes (Fig. 1 B-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. 1 D). Additionally, the KEGG analysis demonstrated that these DEGs were mainly enriched in the ‘Complement and coagulation cascades’ and ‘p53 signaling pathway’ (Fig. 1 E). 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. 2 A). We then screened an important gene cluster, which include 48 genes (Fig. 2 B). 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. 2 C). 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. 2 D). The AUC values of biomarkers were all 1, indicating an excellent ability to distinguish normal differentiated cells from SPA-interfered cells (Fig. 2 E). 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. 3 A-B). Kntc1 and Nek2 were mainly related to the ‘autophagosome’ and ‘Pyrimidine metabolism’ (Fig. 3 C-F). Asf1b and Troap was mainly enriched in the ‘process utilizing autophagic mechanism’ and ‘Biosynthesis of nucleotide sugars’ (Fig. 4 A-D). Kif14 was mainly enriched in the ‘macroautophagy’ and ‘Pyrimidine metabolism’ (Fig. 4 E-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. 5 A). 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. 5 B).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 ). Discussion 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. Conclusion 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. Declarations 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. References 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 . 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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 . Additional Declarations No competing interests reported. Supplementary Files countsjunctionreads.xls FPKManno.xls mRNAcount.csv HubbaTable.csv Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 May, 2024 Reviews received at journal 26 Apr, 2024 Reviews received at journal 24 Apr, 2024 Reviewers agreed at journal 11 Apr, 2024 Reviewers agreed at journal 11 Apr, 2024 Reviewers invited by journal 07 Jan, 2024 Editor assigned by journal 07 Jan, 2024 Editor invited by journal 06 Jan, 2024 Submission checks completed at journal 06 Jan, 2024 First submitted to journal 14 Dec, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3754554","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":265723980,"identity":"1a6936bd-e461-4ad5-bb13-dd58a31f6247","order_by":0,"name":"Hong-jie Wen","email":"","orcid":"","institution":"The Affiliated Hospital of Yunnan University","correspondingAuthor":false,"prefix":"","firstName":"Hong-jie","middleName":"","lastName":"Wen","suffix":""},{"id":265723981,"identity":"80173116-ce68-4a7f-b1af-6855a2d5251c","order_by":1,"name":"Shou-yan Zhu","email":"","orcid":"","institution":"The Affiliated Hospital of Yunnan University","correspondingAuthor":false,"prefix":"","firstName":"Shou-yan","middleName":"","lastName":"Zhu","suffix":""},{"id":265723982,"identity":"d5a8425d-70d9-4032-95d7-b8122c6453ba","order_by":2,"name":"Hua-gang Yang","email":"","orcid":"","institution":"The Affiliated Hospital of Yunnan University","correspondingAuthor":false,"prefix":"","firstName":"Hua-gang","middleName":"","lastName":"Yang","suffix":""},{"id":265723983,"identity":"d9eade2a-eed3-47b6-8f26-74cc87e196ea","order_by":3,"name":"Feng-yong Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYDCCAzAGewOYYmwgQgtUEc8BkrVIJBCphe948/MHH/ccljOXfGP2mIfBRnbDAeZnD/BpkTxzzLBxxrPDxpazc8yNeRjSjDccYDM3wKfF4EYOYzPPgduJG27nmEnzMBxO3HCAh02CGC31G26eAWn5T7yWBIMbPCAtBwhrAfll5owD/w03nEkrk5xjkGw88zCbGV4twBB78OHDgTR5g+OHt0m8qbCT7Tve/AyvFiTAAQwnUFAxE6keCNgfEK92FIyCUTAKRhQAAJbYToGmncT6AAAAAElFTkSuQmCC","orcid":"","institution":"The Affiliated Hospital of Yunnan University","correspondingAuthor":true,"prefix":"","firstName":"Feng-yong","middleName":"","lastName":"Guo","suffix":""}],"badges":[],"createdAt":"2023-12-14 16:29:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3754554/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3754554/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49321158,"identity":"1597bf79-e3a2-423a-8f93-89a8f0289403","added_by":"auto","created_at":"2024-01-08 16:29:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":375850,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of DEGs and functional enrichment analysis. (A) PCA analysis of normal differentiated group and SPA interfered group. (B,C) The volcano map (B) and heat map (C) of DEGs between normal differentiated group and SPA interfered group. (D,E) The GO terms (D) and KEGG pathways (E) enriched in DEGs. DEGs, differentially expressed genes; PCA, principal component analysis; SPA, Staphylococcus aureus protein A; GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3754554/v1/9101f1c6f4c3093a9ecd58f1.png"},{"id":49322077,"identity":"fb1f1d83-1a46-4e2a-bc87-ae8732090fa7","added_by":"auto","created_at":"2024-01-08 16:45:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3248003,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of biomarkers for SPA interfered BMSC. (A) The PPI network of DEGs. (B) The interaction of the important gene cluster. (C) The Venn diagram of six biomarkers. (D) The expression of biomarkers in normal differentiated group and SPA interfered group. (E) The ROC curves of biomarkers. BMSC, bone marrow mesenchymal stem cells; PPI, protein-protein interaction; ROC, receiver operating characteristic; AUC, area under the curve. ** p\u0026lt;0.01; ***p\u0026lt;0.001; ****p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3754554/v1/c168b9519e000497dbd0b193.png"},{"id":49321155,"identity":"449582eb-dbc2-4edc-92d9-84f9cb32820e","added_by":"auto","created_at":"2024-01-08 16:29:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":982631,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis. GO terms and KEGG pathways enriched in Cenpf (A,B), Kntc1 (C,D), Nek2 (E,F).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3754554/v1/ae54a9e19f3af8f741a48d2c.png"},{"id":49321154,"identity":"f0ea5af7-76c3-4bdf-9f6f-1b021760e04e","added_by":"auto","created_at":"2024-01-08 16:29:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":730201,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis. GO terms and KEGG pathways enriched inAsf1b (A,B), Troap (C,D), and Kif14 (E,F).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3754554/v1/cb0259ea97b80a05f1c138d6.png"},{"id":49321153,"identity":"91f22007-2925-4969-9ddc-dc3ea625e298","added_by":"auto","created_at":"2024-01-08 16:29:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1171957,"visible":true,"origin":"","legend":"\u003cp\u003eExploration of regulatory mechanism for biomarkers. (A) The network of circRNA- miRNAs-mRNA.The green squares represent circRNAs, orange squares represent miRNAs, and red circles represent mRNA. (B) Construction of lncRNA-miRNA-mRNA network. Blue hexagons represent lncRNAs, orange squares represent miRNAs, and red circles represent mRNA. circRNA, circular RNA; miRNA, microRNA; lncRNA, long non-coding RNA.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3754554/v1/e7a0ef37e3511b293dce2684.png"},{"id":49321157,"identity":"30ef744a-c633-4fc3-a007-d1afac227d64","added_by":"auto","created_at":"2024-01-08 16:29:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":230982,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the expression of biomarkers by qRT-PCR. ns, not significant; *p\u0026lt;0.05; ** p\u0026lt;0.01; ****p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3754554/v1/e1ddcbba766383f072bdb393.png"},{"id":49322443,"identity":"73a764fe-2e08-4733-bf63-18f6d5bed3e3","added_by":"auto","created_at":"2024-01-08 16:53:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2826001,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3754554/v1/d3d42c3d-270d-4a28-9885-76526fa02eda.pdf"},{"id":49321483,"identity":"b0d20475-83ca-4aec-b55f-f194769413ca","added_by":"auto","created_at":"2024-01-08 16:37:25","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":215879,"visible":true,"origin":"","legend":"","description":"","filename":"countsjunctionreads.xls","url":"https://assets-eu.researchsquare.com/files/rs-3754554/v1/89bc7874ecb886d8b0b2afd3.xls"},{"id":49321162,"identity":"e89a13ef-05d1-4c63-bef6-29b07450c1a3","added_by":"auto","created_at":"2024-01-08 16:29:26","extension":"xls","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":11447218,"visible":true,"origin":"","legend":"","description":"","filename":"FPKManno.xls","url":"https://assets-eu.researchsquare.com/files/rs-3754554/v1/842695097354d4b742993db4.xls"},{"id":49321163,"identity":"4b3991cd-d572-4a84-af6c-12c898132f18","added_by":"auto","created_at":"2024-01-08 16:29:26","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":14571418,"visible":true,"origin":"","legend":"","description":"","filename":"mRNAcount.csv","url":"https://assets-eu.researchsquare.com/files/rs-3754554/v1/abe2881e64c77e8af51b58d2.csv"},{"id":49321482,"identity":"78ff89f5-b942-4ef8-b6ff-d9113d22da5b","added_by":"auto","created_at":"2024-01-08 16:37:25","extension":"csv","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":3679,"visible":true,"origin":"","legend":"","description":"","filename":"HubbaTable.csv","url":"https://assets-eu.researchsquare.com/files/rs-3754554/v1/750a8ff8651a942a1a4e82ac.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigation on the molecular mechanism of SPA interference with osteogenic differentiation of bone marrow mesenchymal stem cells","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOsteomyelitis 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 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). 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 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). 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 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). When SPA binds to osteoblasts, it has been reported to induce apoptosis and cell death, thereby inhibiting bone formation and mineralization (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBone 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 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). 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 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). 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 (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). 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.\u003c/p\u003e \u003cp\u003eIn 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.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eCell 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.\u003c/p\u003e\n\u003cp\u003eData 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, \u0026lsquo;scatterplot3d\u0026rsquo; package was used to implement PCA analysis in different groups.\u003c/p\u003e\n\u003cp\u003eAnalysis of differential genes. The \u0026lsquo;Deseq2\u0026rsquo; R package (\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e) was applied to mine DEGs between normal differentiated group and SPA interfered group. The P.Value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 \u0026amp; |log2fold change (FC)| \u0026gt;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 \u0026lsquo;clusterProfiler\u0026rsquo; (\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e). p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used as screening criteria.\u003c/p\u003e\n\u003cp\u003eProtein-protein interaction (PPI) network. PPI network which depicted the interactions among DEGs was generated using STRING website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org\u003c/span\u003e\u003c/span\u003e). MCODE algorithm of cytoscape plug-in was applied to screen core gene cluster (\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e). 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.\u003c/p\u003e\n\u003cp\u003eROC 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 \u0026lsquo;pROC\u0026rsquo; package (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e). GSEA was conducted to explore the potential GO items and KEGG pathways associated with biomarkers through \u0026lsquo;clusterProfiler\u0026rsquo; package (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e). p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used as screening criteria.\u003c/p\u003e\n\u003cp\u003eThe circRNA/lncRNA-miRNA-mRNA network construction. In order to explore the regulatory relationship, miRDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mirdb.org/index.html\u003c/span\u003e\u003c/span\u003e) was utilized to forecast the miRNAs of biomarkers. circRNAs that interacted with miRNAs were predicted through the miranda (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.microrna.org/microrna/\u003c/span\u003e\u003c/span\u003e 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 (\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e) was applied to optimize the \u0026lsquo;circRNA-miRNA-mRNA\u0026rsquo; and \u0026lsquo;lncRNA-miRNA-mRNA\u0026rsquo; network.\u003c/p\u003e\n\u003cp\u003eThe 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\u0026rsquo;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\u0026rsquo;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 \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. GAPDH was used as an internal reference gene, and the expression was calculated according to the 2\u0026thinsp;\u0026minus;\u0026thinsp;\u0026Delta;\u0026Delta;Ct method (\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe primer sequences of biomarkers for RT-qPCR.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrimer\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSequences\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCenpf F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGTTTGAATCGCTCGTGCTGG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCenpf R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTCCTTCCACTCTTCCAACGC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKntc1 F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCTGAGAAGACACTGACGTGGA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKntc1 R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCGAGACTCCGGTAAGTACGC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNek2 F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGGCCTCAGCAGAAAGGGATT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNek2 R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAGGAGTCTGCGTGTTTAGCC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsf1b F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCTGTCTGACGACCTTGAGTG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsf1b R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGGTGCAGGTGATGAGAACCA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTroap F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGCTTGTCTCACCACCATCCA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTroap R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGGAATGAAACGCAGGGCATC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKif14 F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCTCAGCGACCAATCGGGAAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKif14 R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCTCAGCCTACCGGCTCTCTG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGAPDH F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGACCCCTTCATTGACCTCAAC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGAPDH R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGCCATCACGCCACAGCTTTCC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis. All P values of statistical results were based on two-sided statistical tests, and a P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification of DEGs related to SPA interfered BMSC. \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing 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. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). 321 DEGs were identified in SPA interfered vs normal differentiated group, including 260 down-regulated and 61 up-regulated genes (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB-C). To further probe the function of the DEGs, functional enrichment analysis was conducted. GO results indicated that these DEGs were principally involved in \u0026lsquo;mitotic nuclear division\u0026rsquo;, \u0026lsquo;chromosome separation\u0026rsquo; and \u0026lsquo;nuclear division\u0026rsquo; (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). Additionally, the KEGG analysis demonstrated that these DEGs were mainly enriched in the \u0026lsquo;Complement and coagulation cascades\u0026rsquo; and \u0026lsquo;p53 signaling pathway\u0026rsquo; (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScreening of biomarkers associated with SPA interfered BMSC. \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to explore the interaction regulation relationship, the PPI network of the DEGs was constructed, including 295 nodes and 1423 edges (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). We then screened an important gene cluster, which include 48 genes (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). 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. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). 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. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). The AUC values of biomarkers were all 1, indicating an excellent ability to distinguish normal differentiated cells from SPA-interfered cells (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis. \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo 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 \u0026lsquo;regulation of autophagy\u0026rsquo; and \u0026lsquo;Lysosome\u0026rsquo; (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA-B). Kntc1 and Nek2 were mainly related to the \u0026lsquo;autophagosome\u0026rsquo; and \u0026lsquo;Pyrimidine metabolism\u0026rsquo; (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC-F). Asf1b and Troap was mainly enriched in the \u0026lsquo;process utilizing autophagic mechanism\u0026rsquo; and \u0026lsquo;Biosynthesis of nucleotide sugars\u0026rsquo; (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA-D). Kif14 was mainly enriched in the \u0026lsquo;macroautophagy\u0026rsquo; and \u0026lsquo;Pyrimidine metabolism\u0026rsquo; (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE-F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the regulatory network. \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the regulatory mechanism of biomarkers associated with SPA interfered BMSC, 71 circRNAs-14 miRNAs-5 mRNAs network was constructed (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). 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. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB).The network had 34 nodes and 17 edges, in which Nek2, and Asf1b genes were associated with rno-miR-497-5p.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperimental verification of marker expression level. \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe 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. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur 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.\u003c/p\u003e \u003cp\u003eKinetochore 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 \u0026lsquo;autophagosome\u0026rsquo; and \u0026lsquo;Pyrimidine metabolism\u0026rsquo;. Recent studies suggested that mitogenic proteins might be potential biomarkers and might contribute to the development of human malignancies (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). It was often associated with tumors of the digestive and genitourinary systems (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). 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 (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). 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 (\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Kntc1 has been reported to function in a variety of diseases by participating in the PI3K/Akt signaling pathway (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), 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 \u0026lsquo;regulation of autophagy\u0026rsquo; and \u0026lsquo;Lysosome\u0026rsquo;. Over-expression of Cenpf was associated with tumorigenesis of many human malignant tumors (\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Moreover, Cenpf was a cancer stem cell (CSCs)-specific marker gene, and the latter played a key role in promoting bone destruction (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Cenpf has a close relationship with MAPK (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) and Wnt pathway (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Antisilencing function 1b (Asf1b) had effects on cell proliferation, leading to abnormal nuclear structure and unique transcriptional features (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) and was often associated with various malignancies (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). According to the functional enrichment analysis, Asf1b was mainly enriched in the \u0026lsquo;process utilizing autophagic mechanism\u0026rsquo; and \u0026lsquo;biosynthesis of nucleotide sugars\u0026rsquo;. Furthermore, several studies have shown that Asf1b played an important role in the PI3K/Akt signaling pathway (\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). 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 (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). The functional enrichment analysis showed that Nek2 was mainly related to the \u0026lsquo;autophagosome\u0026rsquo; and \u0026lsquo;pyrimidine metabolism\u0026rsquo;. It had been shown that the over-expression of Nek2 was associated with the development of bone damage (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) and that it regulated osteoblast gene expression and affected osteoblast growth and activity (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). In addition, Nek2 induced osteoclast differentiation and bone destruction via heparanase in multiple myeloma (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Nek2 has been reported to plays an important regulatory role in MAPK (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), Wnt/β-Catenin pathway (\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), PI3K/Akt pathway (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e), and other pathways. Experimental evidence suggested that troponin-associated protein (Troap) played a key role in regulating cell proliferation in multiple tumors (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). One study found that Troap accelerates glioma progression through the Wnt/β-Catenin pathway (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). 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 (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). The The functional enrichment analysis showed that Kif14 was mainly enriched in the \u0026lsquo;macroautophagy\u0026rsquo; and \u0026lsquo;pyrimidine metabolism\u0026rsquo;. Moreover, Kif14 was also active in signaling pathways such as Wnt pathway (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), Hedgehog signaling (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e) and PI3K/Akt pathway (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). 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.\u003c/p\u003e \u003cp\u003eCurrently, 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 (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). In addition, one study showed that miR-322-5p was significantly down-regulated during osteogenic differentiation of rat bone marrow mesenchymal stem cells (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Secondly, it had been shown that miR-455-3p could promote osteogenic differentiation, which might be related to fracture healing (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e), 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 (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). 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.\u003c/p\u003e \u003cp\u003eBioinformatics 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.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall, 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.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot application.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: HJW; methodology: SYZ; formal analysis: HGY; investigation: SYZ, HGY\u0026nbsp;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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by \u0026nbsp;the Yunnan Provincial Science and Technology Department Kunming Medical University Joint Special Project (No. 202201AY070001-274).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics \u0026amp; 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.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZeller JL, Burke AE, Glass RM. 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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 (\u003cem\u003eCenpf\u003c/em\u003e, \u003cem\u003eKntc1\u003c/em\u003e, \u003cem\u003eNek2\u003c/em\u003e, \u003cem\u003eAsf1b\u003c/em\u003e, \u003cem\u003eTroap\u003c/em\u003e and \u003cem\u003eKif14\u003c/em\u003e) 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 \u0026lsquo;Pyrimidine metabolism\u0026rsquo; pathway. We also constructed \u0026lsquo;71 circRNAs-14 miRNAs-5 mRNAs\u0026rsquo; and \u0026lsquo;10 lncRNAs-5 miRNAs-2 mRNAs\u0026rsquo; networks. \u003cem\u003eKntc1\u003c/em\u003e and \u003cem\u003eAsf1b\u003c/em\u003e genes were associated with rno-miR-3571. \u003cem\u003eNek2\u003c/em\u003e and \u003cem\u003eAsf1b\u003c/em\u003e 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 (\u003cem\u003eCenpf\u003c/em\u003e, \u003cem\u003eKntc1\u003c/em\u003e, \u003cem\u003eNek2\u003c/em\u003e, \u003cem\u003eAsf1b\u003c/em\u003e, \u003cem\u003eTroap\u003c/em\u003e and \u003cem\u003eKif14\u003c/em\u003e) related to SPA interfered BMSC, which laid a theoretical foundation for exploring the key factors of SPA affecting osteogenic differentiation.\u003c/p\u003e","manuscriptTitle":"Investigation on the molecular mechanism of SPA interference with osteogenic differentiation of bone marrow mesenchymal stem cells","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-08 16:29:21","doi":"10.21203/rs.3.rs-3754554/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-02T05:58:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-26T04:43:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-24T21:41:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5b44c86e-e92b-431e-baa4-4fe385c4a96f","date":"2024-04-11T17:26:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"07ff3a2c-a06a-41bd-9233-2f0b0f6cddf4","date":"2024-04-11T15:47:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-07T15:13:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-07T15:05:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-01-06T05:46:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-06T05:43:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2023-12-14T16:15:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2f02f4e4-ae6b-4cbc-9d08-692023e9eae3","owner":[],"postedDate":"January 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":27994479,"name":"Health sciences/Diseases"},{"id":27994480,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2024-07-02T05:26:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-08 16:29:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3754554","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3754554","identity":"rs-3754554","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}