Abstract Staphylococcus aureus (S. aureus) has the ability to invade human cortical bones and cause intracellular infections in osteoblasts, which may lead to a long-term infection that is difficult to eliminate. It is critical to identify the underlying mechanisms of the osteoblast response to the intracellular S. aureus. More recently, multiple circular RNA (circRNA) functions have been identified, including serving as protein scaffolds or miRNA sponges and being translated into polypeptides. The role that circRNAs play in intracellular S. aureus infection of osteoblasts has not, to our knowledge, been investigated. Here, we established an intracellular infection model of S. aureus in osteoblasts and compared the circRNA expression of osteoblasts between the infected and control groups using RNA sequencing technology, by which a significant difference was found. In total, 117 upregulated and 125 down-regulated differentially expressed circRNAs (DEcircRNAs) were identified, and reverse transcription-quantitative PCR was employed to validate the results of RNA sequencing. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses demonstrated that DEcircRNAs were enriched in processes associated with macromolecule modification, cellular component organization or biogenesis, and intracellular non-membrane-bound organelles. Finally, a potentially important network of circRNA-miRNA-mRNA based on the DEcircRNAs was constructed. Overall, this study revealed the circRNA expression profile of human osteoblasts infected by intracellular S. aureus for the first time, and identified the circRNAs that may contribute to the pathogenesis of infectious diseases caused by intracellular S. aureus infection in human osteoblasts. Keywords: Osteoblast, Intracellular infection, Staphylococcus aureus, circRNAs, Orthopedic infection 1. Introduction Staphylococcus aureus (S. aureus) is a Gram-positive pathogen known for causing infections in almost all human organs, including bones and joints. While S. aureus was originally considered to be an extracellular infection pathogen, an increasing number of studies have reported that it also has the capacity of intracellular infection [[41]1,[42]2]. S. aureus may escape the phagocytosis of phagosomes, adapting to the adverse intracellular environment [[43]3]. Host cells were used as carriers and S. aureus survived intracellularly for several days [[44]4]. Once in the adaptive environment, S. aureus cells can destroy the host cells and infect other host cells [[45]5]. S. aureus, which can cause intracellular infection, is more toxic and more likely to cause serious systematic infections in humans than the ones that lead to extracellular infection [[46]6,[47]7]. Furthermore, intracellular S. aureus spreads infection and is protected from antibiotics [[48]8]. Severe bone tissue infection causes progressive inflammatory destruction of the bone, and S. aureus is the most common pathogen. The established clinical approach for chronic osteomyelitis treatment involves debridement of the infected bone, tissue reconstruction, and administration of long-term antibiotics. Due to the poor local blood supply of chronic osteomyelitis lesions and dead bone formation, it is difficult for antibiotics to attain an effective bactericidal concentration in the lesion, with a poor treatment effect and considerable side effects. The direct use of sensitive antibiotics in the local lesion has high performance requirements of the selected antibiotic carrier. This carrier must not only ensure a sufficient antibiotic load but also enable continuous and stable antibiotic releases to completely eliminate the bacterial biofilm on the surface of dead bone and scar tissue. The current treatment methods for osteomyelitis are often traumatic, costly, and frequently lead to further selection of S. aureus antibiotic-resistant strains [[49]9]. The increasing incidence of antibiotic-resistant S. aureus strains could explain the recurrent episodes of osteomyelitis in treated patients. Orthopedic infections by S. aureus can relapse even after apparent clearance of the initial infection [[50]10]. It has been shown that bacteria causing infection can enter and reside within osteoblasts [[51]10]. The intracellular bacteria proliferate and colonize adjacent areas forming biofilm aggregates, and these populations of intracellular bacteria and biofilms present a complicated clinical problem [[52]10]. It was reported that S. aureus internalization into osteoblasts plays a critical role in the persistence and recurrence of osteomyelitis, and the EGFR/FAK and c-Src signaling pathways mediate osteoblastic S. aureus internalization [[53]11]. Circular RNA (circRNA) is an emerging class of non-coding RNA molecules, which is present largely in eukaryotic cells. The expression of circRNA is tissue- and disease-specific, and affects disease development. In recent years, many reports have demonstrated the importance of circRNAs in pathophysiology in that they can function as microRNA (miRNA) sponges to regulate transcription and gene expression, some of which can even be translated into proteins [[54][12], [55][13], [56][14], [57][15], [58][16], [59][17]]. CircRNAs are involved in the pathogenesis of various diseases, with high sequence conservation, structural stability, and abundance and having certain tissue and disease specificities [[60]18], making them of considerable interest in the field of biology. They also have potential value in disease diagnosis and as prognostic biomarkers and therapeutic targets [[61]19,[62]20]. The involvement of circRNAs in the response of osteoblasts to intracellular S. aureus infection remains largely uninvestigated. In this study, we investigated the circRNA expression profile of human osteoblasts infected by intracellular S. aureus, and analyzed circRNAs which may contribute to the development of orthopedic infectious diseases caused by human osteoblastic intracellular S. aureus infection. 2. Methods and materials 2.1. Osteoblast culture Human osteoblast Hfob1.19 cells were purchased from the Cell Library of Shanghai, Chinese Academy of Sciences. Hfob1.19 cells were cultured in DMEM and F-12 medium (1:1) containing 15% fetal bovine serum and 300 g/ml G418 at 37 °C under 5% CO[2]. Histochemical staining for alkaline phosphatase was applied to investigate the functional activity of the osteoblasts. 2.2. Bacterial strains and growth conditions S.aureus strain (ATCC25923) used in this study was, stored in the clinical microbiology laboratory. To resuscitation the bacteria, S. aureus was culture on the blood plate first. Then, one colony was choosed and transferred into 1 mL Luria-Bertani (LB) isotonic liquid medium by the inoculating loop and shaked at 37 °C and 250 rpm overnight. Next day, 200 μL the overnight cultured bacterial solution was added to 20 mL LB medium and shaken at 37 °C and 250 rpm until the OD[600] reached at 0.6. 2.3. CFSE fluorescent labeling Before the invasion assay, 1 × 10^9/ml S. aureus cells were suspended in 1 μg/ml carboxyfluorescein succinimidyl ester (CFSE) and shaken for 30 min at 37 °C and 250 rpm for fluorescent labeling. 2.4. Invasion assay Hfob 1.19 cells were seeded in a six-well plate at 1 × 10^6 cells per well, and once cell fusion was achieved (6–8 days), Hfob 1.19 cells were infected with S. aureus from multiplicity of infection (MOI) 25:1 to 500:1. Before the collection of post-infection cells, gentamicin (200 μg/mL) was added into the culture to eliminate the extracellular bacteria. Then, at 30 min and 1, 2, 4, and 8 h post infection, cells were collected for further analysis after washed three times with phosphate-buffered saline. S. aureus intracellular infection in osteoblasts was detected by flow cytometry. 2.5. Preparation of total RNA and RNA sequencing of osteoblasts after S. aureus infection Both infected and control cells were extracted total RNA by TRIzol reagent (Invitrogen, USA).The quality and concentration of RNA extracted was assessed by agarose electrophoresis and NanoDrop-1000 (Thermo Fisher, USA) respectively. RNA samples were then sent to Cloudseq Biotech Inc. (Shanghai, China) for sequencing. The cDNA libraries were quantified by BioAnalyzer 2100 system (Agilent Technologies, USA), and finally sequenced on an Illumina HiSeq Sequencer NovaSeq 6000. 2.6. Identification of differentially expressed circRNAs (DEcircRNAs) Based on the above RNA sequencing data, circRNAs were detected by DCC software (v0.4.4) and identified by the circBase database and the circ2Trait circular RNA-disease database. The differentially expressed circRNAs (DEcircRNAs) were analyzed by edgeR software (v3.16.5). The DEcircRNA was calculated between two or two sets of samples. Calculate the fold change (fold change) and P-value. The fold change ≥ 2.0 and P-value <0.05 were used as the threshold for DEcircRNAs. 2.7. Verification of circRNA expression accuracy by RT-qPCR The verification of circRNA sequencing data was performed by reverse transcription-quantitative PCR (RT-qPCR). Six DEcircRNAs with good potential were randomly screened out, three of them were high expressed and three of them were low expressed. The designed specific primers for these DEcircRNAs were shown in [63]Table S1. The expression level of GAPDH was taken as an internal standard, and the relative expression levels of randomly selected DEcircRNAs were calculated by the 2^−ΔΔCt method. The experiments were performed in triplicate. 2.8. Functional analysis of DEcircRNAs Gene Ontology (GO) annotation including Molecular Function (MF), Biological Process (BP) and Cell Component (CC) was used to analysis the functions of DEcircRNAs. The GO terms with a P-value <0.05 were considered statistically significant. Then we analyzed the potential pathways associated with by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. P-value <0.05 was used as the threshold for significant enrichment. 2.9. Construction of the circRNA-miRNA interaction network CircRNA regulates mRNA expression through absorbing miRNA, which functions as a miRNA sponge. Targetscan and miRanda software were used to predict potential miRNAs targetting to DEcircRNAs. From the list of predicted circRNA-miRNA, we used Cytoscape software to analyze circRNA-miRNA interaction and generate a network diagram which may be associated with the function of DEcircRNA. 2.10. Statistical analysis Differences between the two groups were analyzed for statistical significance by the Student's t-test. P < 0.05 indicated statistical significance. 2.11. Data availability The raw RNA-seq data can be available by the accession number of [64]GSE217234 in GEO database. 3. Results 3.1. Development of an S. aureus intracellular infection model in osteoblasts In the invasion experiment, the infection dose of MOI was from 25:1 to 500:1 and the infection duration was from 30 min to 8 h. Two indicators of bacterial invasion were assessed: one was the invasion rate, which was the percentage of osteoblasts containing CFSE-labeled S. aureus among all osteoblasts, and the other was the average CFSE fluorescence intensity in osteoblasts. As the infection dose increased from an MOI of 25:1 to 500:1, the invasion rate increased from 13.58% to 97.28% after human osteoblast Hfob1.19 cells were infected with S. aureus after 2 h ([65]Fig. 1A), and the correspondent average CFSE fluorescence intensity in osteoblasts increased significantly ([66]Fig. 1B). However, when the MOI ranged from 250 to 500, there was only a slight increase in the bacterial invasion rate. Therefore, the MOI was fixed at 250:1 in this study. Fig. 1. [67]Fig. 1 [68]Open in a new tab The intracellular invasion rate by S. aureus of osteoblasts infected for 2 h at different MOI. A: The invasion rate of S. aureus into osteoblasts; B: The average CFSE fluorescence intensity of osteoblasts infected by S. aureus. The invasion rate and mean fluorescence intensity in invading osteoblasts with the different bacterial invasion times from 30 min to 8 h with the MOI of 250 were shown in [69]Fig. 2A and B, respectively. With a 30-min infection time, the bacterial invasion rate was 4.78%. When the infection time was 4 h, the bacterial invasion rate of osteoblasts reached the highest point of 51.36%, and then decreased with a longer infection time, falling to 31.65% at 8 h. Fig. 2. [70]Fig. 2 [71]Open in a new tab The intracellular invasion rate by S. aureus on infection for different times at MOI 250. A: The invasion rate of S. aureus into osteoblasts; B: The average CFSE fluorescence intensity of osteoblasts infected by S. aureus. Our study revealed that cell survival remained stable between 30 min and 4 h, with a gradually increasing number of Hfob1.19 osteoblasts containing bacteria at the infection doses ranging from a MOI of 25–500 and reaching 93.28% by an MOI of 250:1. Therefore, samples with a 4-h infection time and an MOI of 250:1 were suitable for library construction and sequencing. Finally, the infection model of S. aureus ATCC25923 interacted with the Hfob1.19 osteoblasts at an infection dose of MOI of 250:1 for 4 h was established. 3.2. Identification of the DEcircRNAs of osteoblasts on S. aureus intracellular infection We detected a total expression of 8759 circRNAs, and the length of most circRNAs was <2000 nt ([72]Fig. 3). Furthermore, we classified all the identified circRNAs based on the different locations on the chromosomes. The results showed that these circRNAs were widely distributed across all chromosomes, and among them 832 were located on Chr1 but only 14 were located on ChrY ([73]Fig. 4). Interestingly, among the 8759 identified circRNAs, 62.6% were exonic circRNAs, 15.9% were intronic circRNAs, and only 0.5% of the circRNAs were intergenic ([74]Fig. 5). Fig. 3. [75]Fig. 3 [76]Open in a new tab The length of identified circRNAs. Fig. 4. [77]Fig. 4 [78]Open in a new tab The distributions of identified circRNAs on different chromosomes. Fig. 5. [79]Fig. 5 [80]Open in a new tab Classification of identified circRNAs based on the genomic origin. Using the normalized number of reads, the differentially expressed circRNAs between the infected and control groups were calculated. Multiple changes and the P-value were calculated. In the screening analysis with a fold change ≥2.0 and P-value <0.05 as the threshold for differential circRNAs, 242 circRNAs obtained from the test samples and control were essentially differentially expressed, with 117 upregulated and 125 downregulated. The top ten circRNAs upregulated and downregulated were listed in [81]Table 1 and [82]Table 2, respectively. Table 1. The top ten upregulated circRNAs. CIRCRNA P-VALUE LOGFC CHROM TYPE SOURCE_GENE NAME hsa_circ_0085769 0.006619465 5.419799512 chr8 exonic PKT-2 hsa_circ_0004422 0.010509051 5.257841217 chr17 exonic KANSL1 hsa_circ_0027836 0.010902727 5.244490038 chr12 exonic APAF1 hsa_circ_0001777 0.011372502 5.228450418 chr7 exonic ESYT2 hsa_circ_0126072 0.016504378 5.081633679 chr4 exonic SEL1L3 hsa_circ_0093964 0.016504378 5.081633679 chr10 exonic DNA2 hsa_circ_0099480 0.016504378 5.081633679 chr12 exonic EEA1 hsa_circ_0006809 0.016504378 5.081633679 chr14 exonic CDC42BPB hsa_circ_0118871 0.016577017 5.078195332 chr2 exonic PARD3B hsa_circ_0102270 0.01885345 5.023074174 chr14 exonic RTN1 [83]Open in a new tab Table 2. The top ten downregulated circRNAs. CIRCRNA P-VALUE LOGFC CHROM TYPE SOURCE_GENE NAME hsa_circ_0005232 0.001076142 −5.896583814 chr2 exonic SLC8A1 hsa_circ_0007905 0.003607649 −5.584074167 chr1 exonic STX6 hsa_circ_0002457 0.005417129 −5.456578392 chr12 exonic ATXN2 hsa_circ_0001726 0.006080089 −5.420812827 chr7 intronic ZNF394 hsa_circ_0005332 0.006989446 −5.37673827 chr3 exonic ZBTB20 hsa_circ_0004191 0.009407276 −5.27481415 chr2 intronic PGAP1 hsa_circ_0126873 0.00993634 −5.254030886 chr4 exonic RUFY3 hsa_circ_0005881 0.012638753 −5.163517385 chr4 exonic WHSC1 hsa_circ_0002483 0.014426211 −5.115095648 chr8 exonic PKT-2 hsa_circ_0043244 0.016268519 −5.066004888 chr17 exonic ACACA [84]Open in a new tab 3.3. Verification of differential circRNA gene expression by RT-qPCR We randomly selected three DEcircRNAs upregulated and downregulated, respectively, for further verification by RT-qPCR: chr1:213,251,038-213,290,752+, chr17:44,143,903–44,172,067-,chr8:141,856,359-141,889,736-, chr2:40,655,613-40657441-, chr1:180,953,813-180,962,561-, chr8:141,874,411-141,900,868-. As shown in [85]Fig. 6, the expression of circRNAs chr17:44,143,903–44,172,067-, chr8:141,856,359-141,889,736-, and chr1:213,251,038-213,290,752+ was elevated in infected osteoblasts. By contrast, chr8:1,418,741,411-141,900,868-, chr2:40,655,613-40657441-, and chr1:180,953,813-180,962,561- were downregulated in infected osteoblasts. These results were consistent with the RNA sequencing data, and these six circRNAs were stable in normal osteoblasts infected by S. aureus, suggesting that they may serve as reliable biomarkers. Fig. 6. [86]Fig. 6 [87]Open in a new tab RT-qPCR verification of the DEcircRNAs. A: Three upregulated circRNAs; B: Three downregulated circRNAs. 3.4. Functional analysis of DEcircRNAs GO annotation divides the differently expressed genes into three categories: biological process (BP), cell component (CC), and molecular function (MF). For DEcircRNAs upregulated in the S. aureus infection group, the three most important GO processes in the BP subgroup were macromolecule modification, protein modification process, and cellular protein modification process ([88]Fig. 7A). Cellular component organization, cellular component organization or biogenesis, and cellular process were the top three processes in the BP subgroup regarding downregulated DEcircRNAs ([89]Fig. 7B). In the CC subgroup, the most enriched GO terms in upregulated DEcircRNAs were nucleoplasm part, extrinsic component of plasma membrane, and extrinsic component of membrane ([90]Fig. 7C), whereas, intracellular, intracellular part, and intracellular non-membrane-bourded organelle were the most significant GO terms regarding the downregulated DEcircRNAs ([91]Fig. 7D). The top three GO processes included catalytic activity, nucleotide binding, and nucleoside phosphate binding in the MF subgroup among the upregulated DEcircRNAs ([92]Fig. 7E). Heterocyclic compound binding, poly(A) RNA binding, and ATP binding were the top three processes in the MF subgroup regarding the downregulated DEcircRNAs ([93]Fig. 7F). Fig. 7. [94]Fig. 7 [95]Open in a new tab The GO annotation of DEcircRNAs. A: The top ten biological processes of upregulated DEcircRNAs; B: The top ten biological processes of downregulated DEcircRNAs; C: The top ten cellular components of upregulated DEcircRNAs; D: The top ten cellular components of downregulated DEcircRNAs; E: The top ten molecular functions of upregulated DEcircRNAs; F: The top ten cellular components of downregulated DEcircRNAs. In KEGG pathway enrichment analysis, chemokine signaling pathway, bacterial invasion of epithelial cells, and regulation of actin cytoskeleton were the most significant pathways enriched among the host genes of the upregulated DEcircRNAs ([96]Fig. 8A). Herpes simplex infection, endocytosis, and chemokine signaling pathway were the most significantly enriched pathways of the downregulated DEcircRNAs ([97]Fig. 8B). Fig. 8. [98]Fig. 8 [99]Open in a new tab The KEGG pathway enrichment analysis of DEcircRNAs. A: The top ten pathways for upregulated DEcircRNAs; B: The top ten pathways for downregulated DEcircRNAs. 3.5. Analysis of the circRNA-miRNA interaction networks hsa_circ_0085769 was highly expressed in S. aureus-infected osteoblasts, and its expression was positively correlated with PTK2 expression at the mRNA level based on the RNA sequencing results of this study. According to the bioinformatic prediction, hsa_circ_0085769 was found as available as the major miRNA-target site for hsa-miR-6886-5p, hsa-miR-6812-3p, hsa-miR-6769b-5p, hsa-miR-6765-5p, and hsa-miR-182-5p, of which hsa-miR-6886-5p, hsa-miR-6765-5p, and hsa-miR-182-5p can also bind hsa_circ_0002483, which was significantly downregulated in S. aureus-infected osteoblasts compared to the control in this study ([100]Fig. 9). Interestingly, hsa_circ_0002483 expression was negatively correlated with PTK2 in this study. Because PTK2 participates in the bacterial invasion pathway, hsa_circ_0085769 and hsa_circ_0002483 are probably associated with the osteogenic infection caused by S. aureus via interaction with their common miRNAs. Fig. 9. [101]Fig. 9 [102]Open in a new tab The circRNA-miRNA interaction gene network. 4. Discussion Osteocytes are the most abundant of all cell types that constitute and maintain bone, being of the osteoblast lineage derived from BMSC proliferation and differentiation. However, because they are located deep in the cortical bone, little attention has been paid to the role of osteocytes in osteomyelitis. Yang et al. showed that S. aureus is able to infiltrate both occupied and unoccupied osteocyte lacunae in cultured human bone in vitro when observed by electron microscopy, and methicillin-resistant S. aureus was internalized by osteocyte-like cells when co-cultured in vitro [[103]21,[104]22]. Relapsing osteomyelitis decades after the initial infection suggests that S. aureus can be present in human osteocytes [[105]23]. It remains for decades and can persist over most of the lifespan of an organism. The concern is that intracellular infection of osteoblasts in this way can convert a brief bacterial infection into a lasting source of infection. Ji et al. raised the potential of targeting EGFR/FAK and c-Src of osteoblasts as adjunctive therapeutics for treating chronic S. aureus osteomyelitis [[106]11]. Indeed, the difficulty of treating bone and joint infection compared with the soft tissue stems from the inherent physiological and anatomical differences of bone and joints, which makes it more difficult for some commonly used antibiotics to penetrate and achieve sufficient concentration to achieve a bactericidal effect [[107]24]. CircRNAs are noncoding RNAs, which may provide novel insights into the pathology of osteomyelitis. However, little is known about the roles of circRNAs in the pathogenesis of osteomyelitis, particularly in osteoblasts. Here, we studied the intracellular infection process of S. aureus and analyzed the circRNA expression profile of S. aureus-infected osteoblasts. In our study, an intracellular survival model of S. aureus-infected osteoblasts was established. In previous quantitative studies of S. aureus invasion into cells, infected cells were first chemically cracked, and the bacteria released extracellularly after lysis were collected [[108]25]. The collected bacteria were inoculated into bacterial solid culture plates and grown overnight by serial concentration dilution methods, and bacterial colonies were counted the next day to yield the average number of bacteria that had invaded per cell [[109]26]. However, this method can only count the bacteria that survive the lysis, and not those that die after invading the cells, making it difficult to accurately assess the actual number of invading bacteria. In addition, when calculating the number of bacteria contained in cells, the method assumes that bacteria invade all cells simultaneously, thus it cannot accurately reflect the changing process of bacterial invasion over time with the increase of infection dose and time. In our study, we developed a method to simultaneously detect invading bacteria and those that die, thus the actual number of bacteria invading the cells can be determined. In this study, we applied high-throughput sequencing to analyze intracellular S. aureus-infected osteoblasts and found 117 upregulated and 125 downregulated DEcircRNAs. The top ten circRNAs upregulated in osteoblasts during infection by S. aureus have not been reported before, except hsa_circ_0001777. It has been found that hsa_circ_0001777 might act as a miRNA sponge for miR-182 in triple-negative breast cancer [[110]27]. Among the top ten downregulated circRNAs, several have been reported, such as, hsa_circ_0005232, involved in the pathogenesis of aseptic loosening after total hip arthroplasty [[111]28], and hsa_circ_0007905, associated with cervical squamous cell carcinoma [[112]29]. The functions and pathways of the DE circRNAs involved in osteoblasts S. aureus infection require further research. Numerous studies have shown that circRNAs plays an important role in osteoblast evolution, differentiation, and functional expression. For example, Dou et al. analyzed differential expression of the entire transcriptome, including circRNAs, lncRNAs, miRNAs, and mRNAs, during osteoclast formation and found that hsa_circ_0007873 upregulated miR-103 expression in the constituted circRNA-miRNA interaction network, whereas hsa_circ_0010763 and hsa_circ_0015622 downregulated miR-103 expression [[113]30]. The expression of miR-103 has been shown to be associated with the inhibition of osteoblast proliferation [[114]31]. The present study revealed that hsa_circ_0085769 can be used as the major target site for miRNA. Because hsa_circ_0085769 expression was upregulated in S. aureus intracellular-infected osteoblasts, and was positively correlated with PTK2 protein expression, it may play a positive regulatory role in epithelial cell signaling upon bacterial invasion by binding to hsa-miR-6886-5p. Nicola et al. performed an in-depth analysis of the effect of long-term S. aureus infection on the transcriptional program of human osteoblast-like cells. Integration of energy metabolism and signaling by receptor tyrosine kinase (PTK2) categories were identified [[115]32]. The hsa_circ_0002483 can also be used as a major miRNA-target site for hsa-miR-6886-5p. Because hsa_circ_0002483 is clearly differentially expressed in S. aureus-infected osteoblasts, and its expression level is negatively correlated with PTK2 protein expression levels, hsa_circ_0002483 may negatively regulate epithelial cell signaling upon bacterial invasion by binding to hsa-miR-6886-5p, and subsequently affect the osteoblasts during infection by S. aureus. At the same time, circRNA-protein interactions received ever greater attention, and the circRNAs may interact with some proteins in the nucleus to regulate the occurrence and recurrence processes of intracellular infection. This study is purely descriptive; therefore, it is premature to come to a conclusion and we simply provide our data, but we feel that this should be recognized. However, we emphasize the need for further investigation into the specific functions of these circRNAs and the mechanisms by which they regulate intracellular infection of osteoblasts. In addition, these circRNAs will be validated in clinical samples in our next research plan. Funding This study was supported by the Suzhou Health Talents Program (2,020,092), Gusu Health Youth Talent of Suzhou (GSWS2019039, GSWS2020030), the Science and Technology Program of Suzhou (SKY2021007), Discipline Construction of The Second Affiliated Hospital of Soochow University (XKTJ-TD202001), Academic Research Fund for College Students of Soochow University (KY2022099013), and Academic Research Fund for College Students of Soochow Medical College (2022YXYKWKY065). CRediT authorship contribution statement Liubing Li: Writing – original draft, Supervision, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Min Wang: Writing – original draft, Validation, Software, Resources, Methodology, Formal analysis, Data curation. Qi Chen: Visualization, Methodology, Investigation, Data curation, Conceptualization. Mingxing Zhang: Writing – original draft, Validation, Formal analysis, Data curation, Conceptualization. Zhihao Chen: Methodology, Investigation, Data curation. Mingxiao Han: Resources, Formal analysis, Data curation. Chenhao Zhao: Methodology, Data curation. Zonggang Xie: Writing – original draft, Visualization. Qirong Dong: Writing – review & editing, Writing – original draft, Methodology, Investigation, Funding acquisition. Haifang Zhang: Writing – review & editing, Writing – original draft, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Footnotes ^Appendix A Supplementary data to this article can be found online at [116]https://doi.org/10.1016/j.heliyon.2024.e28461. Contributor Information Qirong Dong, Email: dongqirong@suda.edu.cn. Haifang Zhang, Email: haifangzhang@suda.edu.cn. Appendix A. Supplementary data The following is/are the supplementary data to this article. Multimedia component 1 [117]mmc1.docx^ (111.9KB, docx) References