Introduction

Fracture is a disease in which the continuity of the bone is interrupted or integrity of the bone is disrupted due to various reasons, and its main clinical symptoms are pain, swelling, and mobility disorders. Unique signs include deformities, abnormal movements, bone frictions, bone friction sensation, et al.1. The main reason is that the force borne by the bones exceeds the maximum strength they can withstand, and in severe cases, shock can occur, endangering life. Research has shown that the global number of fractures in 2019 was approximately 455 million2, which has a significant impact on the quality of human life. At present, the treatment of fractures in clinical mainly relies on conservative or surgical methods to restore the para-position and alignment of the fracture ends as much as possible. However, the healing time of fractures is relatively long, and specific parts of fractures are prone to delayed union or non union3. Long term bed rest is prone to complications such as pressure ulcers and infections.

The process of fracture healing pertains to a repair reaction that occurs between the fractured ends of the bone, characterized by a series of healing stages that ultimately restore the normal structure and function of the bone3. The process of fracture healing includes hematoma formation, primitive callus formation, neurovascular regeneration, bone formation, bone crawling replacement, bone reconstruction, and bone remodeling, et al.4. Each stage of fracture healing involves complex molecular changes. It is a huge challenge that global scholars want to solve, which is how to accelerate fracture healing.

The fibrillins are large extracellular matrix molecules that polymerize to form microfibrils which include FBN1, FBN2. Fibrillin microfibrils are ubiquitous in the connective tissue space, provide mechanical and functional support to human cells, tissues, and organs5,6. Research indicated that FBN2 was intricately involved in the regulation of elastin deposition within adult skin models7. Notably, FBN2 exhibited heightened expression in lung cancer, and its suppression has been shown to curtail the invasive and migratory capabilities of lung cancer cells8. The proteolytic degradation of FBN2 microfibers was pivotal for the normal development of bone9. Wu et al. employed bioinformatics analysis to identify six central genes, including FBN2, associated with bone marrow stromal cells (BMSCs) and osteoporosis, further highlighting its importance in skeletal health10. However, the role of FBN2 in the fracture healing process is not clear.

In this study, we screened human or model animal fracture samples which had been sequenced for RNA-seq or high-throughput sequencing, and selected the GSE157460 and GSE152677 datasets for analysis to explore differentially expressed genes (DEGs) between fractures and fracture healing, and identified FBN2 as the hub gene involved in fracture healing. Then, we conducted histological analysis on fractured mice to experimentally validate the results of bioinformatics analysis, and explored the effects of FBN2 on the proliferation and differentiation of osteoblast in vitro, laying the foundation for subsequent research on the correlation between drug targets and facilitating more advanced studies in future stages.

Materials and methods

RNA-seq datasets and screening of DEGs

In this study, the gene expression profiles were downloaded from the GEO database of the National Center for Biotechnology Information (NCBI, https://www.ncbi.nlm.nih.gov/geo/). Datasets related to “Fracture” were retrieved from the database, and after screening, two RNA-seq datasets, GSE157460 and GSE152677, were identified.

The datasets were analyzed by GEO2R (www.ncbi.nlm.nih.gov/geo/geo2r). Benjamini & Hochberg (False discovery rate) algorithm was applied to correct p-values. In GSE157460 and GSE152677, DEGs were filtered based on the condition of adj.p ≤ 0.05 and |Log2FC|≥1. DEGs were visualized by heatmap and volcano plot, and Venn diagram was drawn.

GO and KEGG functional analysis of DEGs

GO and KEGG functional analysis of common DEGs were performed by bioinformatics (http://www.bioinformatics.com.cn/srplot)11. Then, the R (version 4.4.1) was used to analyze the results of the GO and KEGG enrichment analysis of DEGs. GO and KEGG pathway with the minimum p-value was selected for display, and bubble plots of GO and KEGG enrichment analysis were drawn.

Construction of protein-protein Interaction (PPI) network and identification of hub genes

STRING (https://www.string-db.org/, version 12.0) was utilized to analyze DEGs12 and predict the interaction relationships between proteins encoded by genes that may play an important role in fracture healing. For significance criteria, the confidence interaction score was set to 0.40. Subsequently, PPI network was visualized using Cytoscape software (www.cytoscape.org/, version 3.10.3)13. MCODE (Molecular Complex Detection, version 2.0.3) was used to identify hub genes from the PPI network of DEGs14. Metascape (version 3.5) was performed to illustrate the enriched pathways of hub genes15, and a matrix heatmap was generated for visualizing the strength of correlations between hub genes.

Construction of fracture mice model

Male C57BL/6J mice (12 weeks) were obtained from SiPeiFu Biotechnology (Beijing, China) and assorted into the sham group and model group (Fracture) (n = 5). To create a mouse fracture model, as previous reported, following the anesthesia of the mouse and immobilization of its limbs, a 5 mm incision was made approximately 12 mm proximal to the bone prominence on the right lateral leg16. Soft tissues and muscles surrounding the fibula were separated through blunt dissection to expose the bone, with hemostasis applied as necessary. A transverse cut was then performed to induce the fracture, which was subsequently stabilized using a 23-gauge intramedullary needle. In the sham surgery group, mice did not undergo the cut for fracture induction. Mice that exhibited malformation of the fracture were excluded from the study. The mice were then subjected to inhalational anesthesia using isoflurane and euthanized on post-injury Days 1 and 3, respectively. The tissues surrounding the fracture site and the fracture ends were collected for subsequent experiments.

The animal experiment conformed to the Guide for the Care and Use of Laboratory Animals, and was approved by the Medical Ethics Committee of Haikou Hospital of Traditional Chinese Medicine (Approve No. HKSZYYYLL-20221202).

Hematoxylin-eosin (HE) staining

The fibula tissues of mice were fixed in a 4% paraformaldehyde solution for 48 h. Tissues were decalcified with 14% EDTA (Thermo Fisher Scientific, Waltham, MA, USA) for 14 days, and the decalcification solution was replaced every 4–5 days. Decalcification was complete when a surgical suture needle can pass through the tissue easily without resistance or can be lightly cut with a scalpel. The slides were rinsed with distilled water at room temperature, then stained in hematoxylin for 3–5 min. After stained, they were rinsed, differentiated in hydrochloric alcohol for 1–3 s, and rinsed for 15 min for bluing. The slides were stained in eosin for 40 s–1 min. Subsequently, the slides were placed in 80%, 95%, and 100% alcohol, each for 2 min, dehydrated in xylene for 2 min, and dried at room temperature for 10 min. The slides were sealed with neutral balsam, covered with a cover slide, and observed under an optical microscope.

Enzyme-linked immunosorbent assay (ELISA)

The expressions of TNF-α, IL-1β, and IL-6 were detected by ELISA kits (Esebio, Shanghai, China) according to the manufacturer’s instructions. Each well was added with 50 µL of sample, followed by an addition of 100 µL horseradish peroxidase (HRP)-labeled detection antibody. The plate was incubated at 37℃ for 1 h. After that, the wells were thoroughly washed. Subsequently, 50 µL of Substrates A and B were appended to each well, incubated in the dark at 37℃ for 15 min. The reaction was terminated by the addition of 50 µL of stop solution to each well. The OD value of each well was measured at a wavelength of 450 nm within 15 min.

Quantitative fluorogenic real-time PCR (qRT-PCR)

Total RNA was extracted from each group of tissues using TRIZOL reagent (Invitrogen, USA). The concentration and purity of RNA were detected by ultraviolet spectrophotometry. cDNA template was synthesized by Hiscript II QRT Supermix for qPCR kit (Vazyme, Nanjing, China). RT-PCR was conducted using an ABI7500 quantitative PCR (Applied Biosystems, USA). The reaction conditions were as follows: pre-denaturation at 95 °C for 30 s, followed by 40 cycles of denaturation at 95 °C for 10 s and annealing at 60 °C for 30 s. GAPDH was used as an internal reference. The obtained Ct values were analyzed by the 2−ΔΔCt method. The experiment was repeated three times. Primer sequences were shown in Table S1.

Cell culture and transfection

MC3T3-E1 cell line was obtained from iCell (Shanghai, China) and cultured in osteogenic differentiation medium containing DMEM (Thermo Fisher Scientific), 10% fetal bovine serum (FBS, Thermo Fisher Scientific), 4 mM glycerophosphate (Sigma-Aldrich, St. Louis, MO, USA), and 25 µg/ml ascorbic acid (Sigma-Aldrich) for 14 days when they reached 70% confluence17. The culture medium was replaced every two days with 2 mL of fresh medium. Cells were divided into the Control, pcDNA-NC, pcDNA-FBN2, si-NC, and si-FBN2 groups. The coding sequence of FBN2 was cloned into the pcDNA3.1 plasmid vector to overexpress FBN2. Cells were transfected with pcDNA-NC, pcDNA-FBN2, si-NC, and si-FBN2 for 24 h using Lipo3000™ (Thermo Fisher Scientific, Waltham, MA, USA), respectively. Subsequently, cells were cultured in DMEM with 10% FBS for 48 h.

Cell counting kit-8 (CCK-8) assay

MC3T3-E1 (2 × 104/well) were seeded and incubated at 96-well plate for 24 h under 37℃, CO2. Total 10 µL of CCK-8 solution were supplemented into each well and incubated for 2 h. The optical density (OD) was read once every hour, with the optimal value being selected to determine the best cultivation time. OD value was obtained at 450 nm using a microplate reader.

Flow cytometry

According to the protocol, the apoptosis of MC3T3E1 cells was assessed by Annexin V-FITC/PI Apoptosis Detection Kit (MedChemExpress, New Jersey, USA). Cells were incubated with FITC-labeled Annexin V and propidium iodide for 15 min at room temperature in the dark. Subsequently, the samples were analyzed by flow cytometry (Cytoflex, BD Biosciences, USA).

Alizarin Red S (ARS) staining

After the MC3T3E1 cells were induced to undergo osteogenic differentiation, they were rinsed three times with PBS. The cells were fixed in 4% paraformaldehyde at 37˚C for 30 min in the dark, followed by staining with 1% ARS (Sigma-Aldrich) at room temperature for 35 min. Mineralization was observed and images were captured using an inverted light microscope.

Alkaline phosphatase (ALP) activity

In accordance with the manufacturer’s instructions, ALP detection was carried out using corresponding ALP kit (Beyotime, Shanghai, China). The final formula for calculating the relative ALP activity was structured as follows:

U/L = (OD value at 405 nm of the test sample-OD value at 405 nm of the blank)/(OD value at 405 nm of the standard-OD value at 405 nm of the blank) * (concentration of the standard sample/protein concentration of the sample).

Western blot

Total protein was extracted from the cells and an equal amount of protein was separated using 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). The protein was then transferred into a polyvinylidene fluoride (PVDF) membrane. The membrane was blocked with 5% skim milk at room temperature for 2 h, followed by incubation with the primary antibodies (1/1000, Abcam, Cambridge, UK) overnight at 4 °C, including anti-ALP (ab305306) and anti-RUNX2 (ab192256). The membrane was washed with TBST and incubated with goat anti-rabbit secondary antibody (Abcam) at room temperature for 1 h. After washing with TBST, the membrane was placed on the Tanon 5200 chemiluminescence imaging system incubation plate, and ECL solution was used for developing, followed by exposure to collect images. The gray value of the protein bands was calculated using Image J software.

Statistical analysis

The data was analyzed using Prism 9.0 and expressed as means ± SD. Comparison between two groups was conducted using a t-test. Comparisons between multiple groups were made using one-way ANOVA and Tukey’s multiple comparisons test. P < 0.05 indicated a statistically significant difference.

Results

Identification of DEGs

In this study, two gene expression profile datasets, GSE157460 and GSE152677, were selected. From the GSE157460 dataset, 6 samples were selected as initial fracture samples (Day1) and 6 samples as early fracture healing samples (Day3). Limma was conducted, resulting in 142 DEGs, including 100 up-regulated and 42 down-regulated genes. The GSE152677 dataset also contained 5 cases as initial fracture samples (Day1) and 4 cases as early fracture healing samples (Day3), and 169 DEGs were identified, with 148 up-regulated and 21 down-regulated genes. Cluster analysis is a static classification method that divides similar objects into different groups or more subsets, so that all member objects in the same subset have similar attributes. Cluster analysis was performed on these DEGs from both datasets, yielding a volcano plot of DEGs (Fig. 1A). Additionally, the expression of the top 15 upregulated and downregulated DEGs in GSE157460 and GSE152677 was displayed separately, visualized through heatmaps (Fig. 1B). The Venn diagram was drawn to intersect the DEGs screened in the two datasets to obtain common DEGs, showing that there were 78 common DEGs in the two datasets, including 71 up-regulated and 7 down-regulated DEGs (Fig. 1C).

Fig. 1
figure 1

Analysis of differentially expressed genes (DEGs) in GSE157460 and GSE152677 datasets. (A) Volcano plots of DEGs in GSE157460 and GSE152677 datasets. The x-axis represents log2FoldChange, the y-axis represents -log10(p-value). The red dots indicate up-regulated genes in the group, and the blue dots represent down-regulated genes in the group. (B) Heatmaps of DEGs in GSE157460 and GSE152677 datasets. The horizontal axis represents DEGs, with each column corresponding to an individual sample. Red indicates genes with high expression levels, while blue signifies genes with low expression levels. (C) Venn diagrams of DEGs in GSE157460 and GSE152677 datasets. The sum of the numbers within each circle represents the total number of DEGs in that dataset. The overlapping sections of the circles denote the common DEGs between the two datasets.

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Functional analysis of common DEGs

GO and KEGG functional analysis were performed on the 78 common DEGs. GO enrichment analysis of DEGs were classified to Biological Process (BP), Cellular Component (CC) and Molecular Function (MF). The top 10 GO items and KEGG pathways with the smallest p-value were selected and shown as bubble charts (Fig. 2A-D). The results shown that DEGs were mainly concentrated in extracellular matrix and structure organization, collagen-containing extracellular matrix, extracellular matrix structural constituent and protein digestion and absorption pathway.

Fig. 2
figure 2

GO and KEGG enrichment analysis of common differentially expressed genes (DEGs). (A–C). Bubble chart of GO-BP, GO-CC and GO-MF enrichment analysis, where the depth of the node color indicates the adjusted p-value, and the size of the node refers to the number of genes involved. (D) Bubble chart of KEGG pathway enrichment analysis.

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Hub genes identified by PPI network

A PPI network based on DEGs was constructed using the STRING tool (Fig. 3A). The PPI network was visualized by Cytoscape software, and then MCODE was used to identify four highly interconnected clusters as potential functional molecular complexes for fractures from the PPI network. Hub genes of cluster 1, the highest scoring subnetwork based on MCODE, including COL3A1, P3H1, P4HA3, COL24A1, COL5A2, COL11A1, FBN2, FKBP10, P4HA1, and COL5A1, were identified (Fig. 3B). Metascape was performed to create an enrichment chord diagram for 10 hub genes (Fig. 3C). To reveal the strength of correlations among the selected genes within different sample data, the expression profile data from GSE152677 was utilized to construct a matrix heatmap (Fig. 3D).

Fig. 3
figure 3

(A) Protein-Protein Interaction network (PPI) panorama of common DEGs in GSE157460 and GSE152677 datasets. (B) Hub genes identified based on MCODE analysis. (C) Metascape illustrates the enriched pathways of hub genes in GSE152677 dataset. Below is the GO term, and the connecting line in the middle indicates subordination. (D) A matrix analysis is employed to impeccably demonstrate and visualize the correlation between hub genes across matrices.

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Establishment of fracture mouse model

HE staining was employed to observe changes in tissue structure. As depicted in the results, demonstrating that in the sham group, the cortical bone morphology of mice remained stable and dense at both Day 1 and Day 3. In contrast, fracture mice exhibited a decrease in cortical bone thickness on Day 1, concurrent with the formation of woven bone characterized by a profusion of trabeculae and osteoblasts. By Day 3, the condition of the fractured bones had notably improved (Fig. 4A).

Fig. 4
figure 4

Effects of bone fracture on inflammation in mice models. (A) The pathological characteristics of bone tissue was observed by hematoxylin-eosin staining (Magnification: 200×, Scale: 100 μm). (B) RT-PCR was executed to the mRNA expression level of DEGs. (C) ELISA was performed to detect the expression of Tumor Necrosis Factor α (TNF-α), Interleukin 1β (IL-1β), and IL-6. *P < 0.05 **P < 0.01 ***P < 0.001 vs. Sham #P<0.05 ##P<0.01 ###P<0.001 vs. Fracture (Day1).

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DEGs were subjected to qRT-PCR for mRNA expression analysis, including ANKRD2, CSRP3, MT2, COL3A1, FBN2, and COL11A1, revealing that compared to the Sham (Day 1) group, the Fracture (Day 1) group showed significantly reduced expression of FBN2, COL3A1, and COL11A1, while ANKRD2, CSRP3, and MT2 expression was markedly increased. When comparing the Fracture (Day 1) group to the Fracture (Day 3) group, there was a noticeable increase in FBN2, COL3A1, and COL11A1 expression, and a significant decrease in ANKRD2, CSRP3, and MT2 expression in the Fracture (Day 3) group (Fig. 4B).

The levels of inflammatory cytokines (IL-1β, IL-6 and TNF-α) were quantified using ELISA, showing that the Fracture (Day 1) group demonstrated significantly elevated expression of IL-1β, IL-6, and TNF-α compared to the Sham (Day 1) group. In contrast, the expression of IL-1β, IL-6, and TNF-α was notably reduced in the Fracture (Day 3) group when compared to the Fracture (Day 1) group (Fig. 4C).

FBN2 promotes osteoblast proliferation and differentiation

By consulting the database, we found that FBN2 plays a significant role in bone related diseases and can inhibit cell apoptosis, so we chose FBN2 for further research18,19,20. qRT-PCR was used to detect the transfection efficiency, showing no significant difference in FBN2 levels between the control group and the si-NC or pcDNA-NC groups. Compared to the si-NC group, FBN2 levels in the si-FBN2-1, 2, and 3 groups were significantly reduced. In comparison to the pcDNA-NC group, the mRNA expression of FBN2 was markedly increased in the pcDNA-FBN2 group (Fig. 5A-B).

Fig. 5
figure 5

FBN2 promotes osteoblast proliferation and differentiation. (A,B). The level of FBN2 was detected by qRT-PCR. (C) CCK-8 assay was used to detect cell viability. (D) Cell apoptosis was measured by flow cytometry. (E) Alizarin red staining was performed to observe cell mineralization. (F) The expression of ALP was detected by ELISA kit. (G) Western blot was used to detect the expression of osteogenic marker proteins ALP and RUNX2. *P < 0.05 **P < 0.01 ***P < 0.001 vs. pcDNA-NC #P<0.05 ##P<0.01 ###P<0.001 vs. si-NC.

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Cell viability and apoptosis were assessed using the CCK-8 assay and flow cytometry, respectively, which revealed no significant difference in cell viability or apoptosis rate between the control group and the si-NC or pcDNA-NC groups. Compared to the si-NC group, the si-FBN2 group exhibited a significant decrease in cell viability and promoted apoptosis. Conversely, the pcDNA-FBN2 group showed a significant increase in cell viability and inhibited apoptosis relative to the pcDNA-NC group (Fig. 5C-D).

ARS staining was employed to evaluate cell mineralization ability, which indicated that there was no significant change in mineralization in the si-NC and pcDNA-NC groups when compared to the Control group. However, the si-FBN2 group showed a significant decrease in cell mineralization compared to the si-NC group. In contrast, the pcDNA-FBN2 group displayed a significant increase in cell mineralization compared to the pcDNA-NC group (Fig. 5E).

Additionally, the si-FBN2 construct reduced the expression levels of the ALP marker; in contrast, the pcDNA-FBN2 group exhibited a significant increase in ALP expression compared to the pcDNA-NC group (Fig. 5F). To further investigate the role of FBN2 in osteogenic differentiation, western blot analysis was employed to assess the expression of the osteogenic markers ALP and RUNX2. In comparison to the Control group, there were no notable changes in the expression of ALP and RUNX2 in either the si-NC or pcDNA-NC groups. However, relative to the si-NC group, the si-FBN2 group showed a marked decrease in the expression of both ALP and RUNX2. Conversely, the pcDNA-FBN2 group displayed significantly higher levels of ALP and RUNX2 when compared to the pcDNA-NC group (Fig. 5G).

Discussion

The process of fracture healing involves multiple genes and related signaling pathways at the molecular to cellular levels, with complex mechanisms21. Finding new key targets in the process of fracture healing is crucial for conducting research on drug component, targets, and fracture healing. In this study, RNA-seq datasets (GSE157460 and GSE152677) were screened. Through PPI, hub genes COL3A1, P3H1, P4HA3, COL24A1, COL5A2, COL11A1, FBN2, FKBP10, P4HA1, and COL5A1 were screened. Collagen, a pivotal constituent of the extracellular matrix, serves as a structural scaffold for connective tissues and is crucial for facilitating fracture healing22. Among the collagen family, members such as COL3A1, COL24A1, COL5A2, COL11A1, and COL5A1 are potentially involved in enhancing bone repair. Through bioinformatic analysis, Jiang discovered that COL3A1 and COL11A1 exhibit upregulated expression during the initiation and early stages of fracture healing, suggesting their potential as biomarkers for this process23. A homozygous mutation in P3H1 leads to osteogenesis imperfecta24. Prolyl 4-hydroxylase (P4H), essential for collagen stability, is an enzyme whose subunit, P4HA3, has been implicated in various cancer progressions25, and P4HA1 is associated with poor prognoses and invasive metastasis in cancers26,27. FKBP10 encodes FKBP65, a chaperone molecule with peptidyl-prolyl cis-trans isomerase activity that interacts with collagens and elastin within the endoplasmic reticulum28. Research has revealed that deletion of Fkbp10 in osteoblasts results in qualitative skeletal defects29. Mutations in FBN2 are associated with Marfan syndrome and congenital contractural arachnodactyly30, as well as with severe adolescent idiopathic scoliosis31. Consequently, FBN2 likely plays a significant role in bone-related disorders. So in the following research, we chose FBN2 for further investigation. In this study, FBN2 was downregulated on Day 1 post-fracture but increased by Day 3, indicating that FBN2 may play a pivotal role in fracture healing.

The proliferation and differentiation of osteoblasts promote the release of skeleton growth factor (SGF)32, which can promote the mitosis of osteoblasts and the functional differentiation of chondrocytes33,34. Growth hormon (GH) can directly affect the role of cartilage35. GH target cells on the germ cell layer and epiblast of the bone growth plate can promote the release of many bone forming growth factors and promote fracture healing36. The release of BMP can induce the migration of undifferentiated mesenchymal cells around the blood vessels to differentiate and proliferate into osteoblasts or chondroblasts, and can induce mesenchymal cells to differentiate into osteoblast cell lines, which determines the differentiation method of mesenchymal cells to promote its differentiation into bone, cartilage, ligament, tendon and nerve tissue37,38. The transcription factor Runx2 stands as a cornerstone in the development and differentiation of osteoblasts, concurrently exerting a pivotal influence on the repair processes of bone tissue39. ALP, a key enzyme in bone mineralization, presents significant therapeutic implications for the treatment and management of bone-related disorders40. Irisin has been demonstrated to stimulate ALP expression and mineralization, upregulate osteogenic gene expression (including Runx2, ALP, and BMP2), and expedite the healing of fractures41. Additionally, miR-181a-5p facilitates the upregulation of osteogenic markers, notably ALP and RUNX2, by targeting BMP3; this mechanism is critical for the prevention of osteoporosis and the effective management of associated fractures42. Herein, this study has shown that overexpression of FBN2 promoted osteoblast proliferation and mineralization, enhancing the protein expression of osteogenic markers, and knockdown of FBN2 showed opposing results, indicating that FBN2 facilitated the process of fracture healing.

In this study, we screened and identified the hub gene FBN2. Through in vivo and in vitro assays, FBN2 was down-regulated in Fracture Day 1, and increased in Fracture Day 3. Overexpression of FBN2 contributed to proliferation, mineralization and differentiation of MC3T3-E1, while inhibited cell apoptosis, which layed the foundation for the development and application of subsequent fracture treatment drugs. It was also found that FBN2 can significantly increase the expression of osteogenic markers ALP and RUNX2. Compared with the pcDNA-NC group, the cell viability of the pcDNA-FBN2 group was significantly increased, and apoptosis was inhibited. Therefore, we believe that the mechanism by which FBN2 promotes fracture healing may be related to the inhibition of osteoblast apoptosis. Next, we will further expand our research scope and explore in depth the mechanism by which FBN2 acts on osteoblasts to promote their proliferation and differentiation.