The whole transcriptomic analysis of local bone tissue after fracture

The whole transcriptomic analysis of local bone tissue after fracture | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The whole transcriptomic analysis of local bone tissue after fracture Shen Wang, Shuhang Guo, Shaoyun Yuan, Xinyi Gu, Jin Deng, Xinyi Zeng, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4011947/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Fracture healing is a complex physiological process involving multiple cells and signaling pathways, the potential molecular mechanisms and biological process are still unclear and need further exploration. In this study, transcriptome sequencing technology was used to detect and analyze the changes in transcriptome of the local injury tissue after fracture. Differentially expressed genes (DEGs) with the high degree were analyzed. Our results showed that different stages of fracture healing had different focuses, some important biological processes, such as the inflammatory response, mainly occurred on the 3rd day after fracture. Besides, we found that the 3rd day after fracture was a key point of transcriptome change, and neural regulation played a significant role in fracture healing at this time, and inflammatory stimulation might be an important factor affecting neural regulation after fracture. In conclusion, our research results identify some important genes and pathways in the process of fracture healing, and summarize the main characteristics of transcriptome changes in fractures. This laid the foundation for a deeper understanding of the relation between molecular mechanisms and biological processes involved in fracture healing. By identifying the specific time points at which various cellular events occur during the healing process, researchers can gain insights into the complex interplay between cells, signaling pathways, and extracellular matrix components that is required for successful bone healing. This knowledge can inform the development of new therapies and interventions to improve fracture healing outcomes and ultimately enhance the quality of life for patients affected by bone fractures. Bone Fracture healing Transcriptome KEGG GO IPA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Fracture is one of the most common types of tissue injury in humans, and it is estimated that approximately 10–15% of fracture patients will experience healing-related complications, such as delayed union or non-union[ 1 ]. The outcome of fracture healing depends on many factors, such as the severity of the trauma, the quality of fracture reduction, the quality of fracture fixation, revascularization and the presence of any comorbidities[ 2 ]. At the same time, fracture healing involves complex molecular mechanisms, and many studies have reported that various factors including BMP2/CXCL12, Cdk5, PTX3, calcitonin gene-related peptide, pain-promoting neuropeptides, and neurotrophin participate in and regulate a series of biological processes including cell recruitment, angiogenesis, osteoblast and osteoclast differentiation, chondrocyte differentiation, ossification, and bone matrix mineralization during fracture healing[ 3 – 7 ]. A previous study showed that skeletal stem cells(SSCs) expressing osteocalcin usually differentiate into osteoblasts, while the negative cells usually differentiate into adipocytes, but negative cells can also form bone in bone injuries[ 8 ]. Another study showed that there are differences in the gene expression profiles between stress fractures and complete fractures, with genes involved in inflammation, immune response, leukocyte interleukin-1 cell response, NFκB signaling, and Toll-like receptor signaling pathways significantly expressed only in complete fracture tissue, but not in stress fracture tissue[ 9 ]. In summary, various lines of evidence suggest that changes in gene expression in local tissues are involved in the entire process of bone fracture healing through downstream products that participate in various signal transduction pathways. However, the composition of local tissues in bone fractures is complex, and understanding the exact molecular mechanisms of bone fracture healing is still a challenge. Previous studies mainly focused on the expression of individual genes, so we hope to conduct a specific analysis of gene expression at various time points during bone fracture healing from the transcriptome level. Next-generation RNA-SEQ is currently the most advanced technology, and compared with other high-throughput technologies (such as microarrays), it achieves base-pair resolution, can parallelly measure the activity of thousands of genes, and analyze changes in gene expression levels in cells or tissues under different conditions in the whole genome[ 10 ]. Therefore, we used RNA-SEQ technology to study the expression profile of mRNAs in local tissues after bone fractures, screened out highly differentially expressed genes, and studied the regulation of relevant genes and the impact of the signal pathway they participate in on bone fracture healing. Methods Rats and Tissue samples. Male adult SD rats (3 months old, weighing approximately 300g, from Beijing Vital River Laboratory Animal Technology Co., Ltd) were randomly divided into five groups. Tissue samples were collected at 0(control group), 3rd, 7th, 14th, and 28th days after fracture for whole transcriptome sequencing. Rats were anesthetized with 2.5% isoflurane/oxygen. A 2cm incision was made at the tibial tuberosity, and a dental drill was used to create a hole 5mm above the tibial tuberosity. An intramedullary needle (0.8 mm×1.5 cm) was inserted into the bone marrow cavity. The tibia was fractured using three-point bending pliers 5 mm below the tibial tuberosity, taking care not to damage the intramedullary needle and the tissue was then sutured layer by layer. All procedures were conducted under sterile conditions. The animals were euthanized at 0, 3rd, 7th, 14th, and 28th days after the injury. Bone tissue, 2 mm wide, was obtained from the local fracture site of the experimental limb and processed for further analysis. RNA extraction and sequencing. Rats were anesthetized with 2.5% isoflurane/oxygen. Bone at the site of the right tibial fracture was taken from each rat, and the samples were frozen in liquid nitrogen within 3 minutes after leaving the body. The frozen samples were crushed and dissolved in TRIzol (Invitrogen). RNA was subsequently extracted from tissues and the RNA were subjected to strict quality control (Agilent 2100 bioanalyzer). At the same time, the contents of Q20, Q30 and GC in clean data were calculated. All downstream analysis was based on high-quality clean data (see “Additional file 1”). The reference genome in this study comes from the integrated database (rattus_norvegicus_Ensembl_97). Index of the reference genome was built using Bowtie v2.0.6 and paired-end clean reads were aligned to the reference genome using TopHat v2.0.9. After the completion of the library construction, Qubit 2.0 was used for preliminary quantification, diluting the library to 1.5ng/ul, and then Agilent 2100 was used to detect the Insert size of the library to ensure the quality of the library. After qualified library inspection, Illumina PE150 sequencing was conducted by pooling according to effective concentration of library and data output requirements. Differential expression analysis. The DEGs of samples were calculated by DESeq2 v.1.34.0, we chose p-value = 2 for the selecting criteria of DEGs. Volcano plots were depicted by ggplot2. Kyoto encyclopedia of genes and genomes (KEGG) and Gene Ontology (GO) analysis. KEGG pathway and GO enrichment analysis for DEGs and image depicting were conducted by clusterProfiler v.4.2.2, org.Rn.eg.db v.3.14.0, and ggplot2 v.3.4.0 used in R package. At each time point, the top10 items with smallest p-values were selected. DAVID database ( https://david.ncifcrf.gov/ ) was used for functional annotations clustering of GO enrichment. Ingenuity pathway analysis (IPA). Core analysis was performed using IPA for DEGs at each time point with inclusion criteria of p = 2. The results of the core analysis at four time points were used for comparison analysis and the clustering result of nerve biological functions in disease and function modules at four time points was exported by the QIAGEN IPA software. The result was derived directly by the QIAGEN IPA software. Results Differential genes analysis The bone tissue at the site of a local fracture was collected, and transcriptome sequencing was carried out at different time points including 0, 3rd, 7th, 14th, and 28th days post-fracture. Differential gene expression analysis was conducted to compare gene expression changes at 3rd, 7th, 14th, and 28th days post-fracture with the gene expression levels before treatment (Day 0). Day 3(up:555, down:1575), Day 7(up:754, down:1340), Day14(up:512, down:871), Day28(up:381, down:582) (Fig. 1 A-B). Based on the expression levels and the expression trends over time, we grouped the differentially expressed genes and found that they could be roughly divided into eight clusters. (Fig. 2 A). The figure showed that all 8 clusters can be broadly categorized into two types. The first type(cluster1,4,7,8) exhibits a temporal pattern of a sharp increase followed by a small decrease and then a plateau, while the second type(cluster2,3,5,6) exhibits the opposite pattern of a sharp decrease followed by a small increase and then a plateau. Both types have a critical inflection point at the 3rd day after the fracture. Then, KEGG analysis was used to cluster each group. (Fig. 2 B). The expression patterns of some pathways belong to the first type, for example ECM-receptor interaction and Protein digestion and absorption. The expression patterns of other pathways belong to the second type, such as IL-17 signaling pathway and Phagosome. Besides, top10 DEGs in each time point are also shown in the figure (Fig. 3 ). GO Enrichment Analysis GO enrichment analysis was carried out on DEGs. BP (biological process), CC (cellular component), MF (molecular function) terms were clustered at each time point (Fig. 4 ). According to the analysis, different stages of fracture healing had different focuses, Important biological processes, such as the inflammatory response, mainly occurred on the 3rd day after fracture. Osteoblast differentiation, collagen synthesis, and ossification mainly occurred on the 7th, 14th, and 28th days, while osteoclast differentiation and cartilage formation mainly occurred on the 14th and 28th days. Bone mineralization only occurred significantly on the 28th day. KEGG Pathway Analysis KEGG analysis was carried out on DEGs. At each time point, the most significant pathways with p-value < 0.01 were enriched using a bubble plot (Fig. 5 A) and clustered (Fig. 5 B). Our results showed that the protein digestion and absorption pathway and the EMC-receptor interaction pathway were the two most important pathways in the process of fracture healing. IPA Analysis We analyzed the clusters related to neurological diseases by IPA (Fig. 6 A). Almost all clusters were downregulated during the first three days, such as CREB Signaling in Neurons pathway. In addition, the top3 regulators at different time points are shown in the Fig. 6 B. Discussion The process of bone healing can be mainly divided into three stages. The first stage is the hematoma formation stage. After an acute bone injury, blood vessels from within the bone and surrounding soft tissues rupture, leading to local hematoma formation. At this time, the hematoma serves as a temporary scaffold, recruiting inflammatory cells such as macrophages and neutrophils. These inflammatory cells are activated and initiate a cascade reaction, secreting inflammatory cytokines such as IL-6 and CCL2, which play an early role in clearing damaged and inactive tissue. The second stage is the procallus formation stage. The release of various cytokines and immunogenic factors during the hematoma formation stage promotes the recruitment and migration of mesenchymal stem cells (MSCs) to the site, initiating the repair process. MSCs have the dual potential to differentiate into osteoblasts and chondrocytes. The procallus forms through intramembranous or endochondral ossification, with intramembranous ossification occurring earlier than endochondral ossification and overlapping with it, ultimately leading to bony bridging of the fracture gap. The third stage is the callus remodeling stage. Intramembranous ossification directly deposits mineralized bone through osteoblasts, while endochondral ossification involves cell differentiation, formation of a cartilaginous callus bridging the bone ends, mineralization and extension of immature callus, and coordinated activity between osteoblasts and osteoclasts to remodel the callus, replacing woven bone with mechanically stronger lamellar bone. Ultimately, the marrow cavity is reconnected, and the fracture site returns to its normal structure[ 2 , 11 , 12 ]. Our research has found that the transcriptional changes in local tissues at different time points of fracture have significant differences and correspond to the biological processes of different stages of fracture healing. Most of the top 10 DEGs (Fig. 3 ) at each time point show a significant increase in expression within 3 days of fracture, gradually reaching a peak and then slowly decreasing but still maintaining a high level of expression. This is consistent with the first type of genes identified by temporal dynamics, indicating that the molecular mechanisms involved in fracture healing begin early and run through the entire process. According to GO analysis (Fig. 4 ), only biological processes related to inflammation, such as lymphocyte activation and proliferation, adaptive immune response, leukocyte proliferation, leukocyte-mediated immunity, and monocyte proliferation, were significantly upregulated on the 3rd day, but not on the 7th day and later. According to KEGG analysis (Fig. 5 ), the AGE-RAGE signaling pathway was significantly enriched. There is evidence that advanced glycation end products (AGEs) are the products of excessive sugar and protein binding, which interact with the AGE receptor (RAGE) and activate downstream factor nuclear factor-kappaB (NF-κB) through the AGE-RAGE signaling pathway, thereby promoting the inflammatory response[ 13 ]. These findings suggest that the most prominent feature of early fracture healing is a highly inflammatory response. On the 7th day, in most of the temporal dynamics analysis, the first type of genes reached peak expression, such as collagen synthesis-related genes col1a1, col3a1, col5a1. Collagen is the main component of the extracellular matrix, and type I collagen constitutes 90% of the total organic components of bone matrix. Its synthesis, deposition, and remodeling play an important role in the development, formation, and stability of bone tissue. Mutations in the human Col1a1 gene can lead to osteogenesis imperfecta[ 14 ]. This indicates that as bone fracture healing progresses, collagen synthesis continuously increases, ensuring the toughness and mechanical strength of the newly formed bone callus. Osteogenesis-related genes Mmp2, Timp1, and ptprv also reached peak expression. Matrix metalloproteinases (MMPs) are a group of structurally related secreted and membrane-bound proteins that participate in the degradation of extracellular matrix (ECM) and other structural components of extracellular and non-matrix proteins. MMP-2 deficiency can lead to impaired osteoblast differentiation. Evidence suggests that osteoblasts and bone cells express Mmp2 and Timp1 during osteogenesis. Osteoblasts and osteocytes degrade ECM molecules together with MMPs while producing them, and regulate degradation by inhibiting MMP activity through tissue inhibitors of metalloproteinases (TIMPs). They can also reorganize ECM components, allowing the bone matrix to mature during the process of intramembranous ossification[ 15 ]. The Ptprv gene belongs to the transmembrane protein tyrosine phosphatase gene family and is specifically expressed in the cartilage and periosteum of immature long bone necks, but is hardly expressed in mature osteoblasts[ 16 ]. It may be involved in the transformation process of skeletal components from cartilage templates to bone. Platelet activation was significantly enriched according to KEGG analysis. Platelet activation often occurs in low-oxygen-induced inflammatory reactions. The substances released from activated platelet particles, such as VEGF and PDGF, have the ability to promote angiogenesis and regulate vascular maturation and stability[ 17 ], which leads to an increase in the peripheral vascular bed and its growth into the healing tissue, helping to restore the decreased blood supply in the fracture area caused by vascular injury and accelerating fracture healing. According to GO analysis, biological processes related to bone development, connective tissue development, collagen fiber tissue, and ossification began to be significantly upregulated. At the same time, the content of cell components such as collagen trimer and extracellular matrix containing collagen protein significantly increased. According to GO analysis on the 14th and 28th days, the process of cartilage generation was significantly upregulated. Cartilage cells differentiate from bone marrow mesenchymal stem cells and can produce a cartilage matrix composed of collagen and proteoglycans. At the same time, the process of bone formation was significantly accelerated, indicating that bone calluses were formed on the bone surface and in the gap of adjacent fractures through intramembranous ossification and endochondral ossification. Bone mineralization was only significantly upregulated on the 28th day. As cartilage cells differentiate, the extracellular matrix of cartilage undergoes mineralization, transforming immature primitive bone calluses into mature mineralized bone calluses. Evidence suggests that Pentraxin 3 (PTX3) plays a key role in the mineralization and deposition of bone matrix, and the genetic variation of the PTX3 gene is closely related to osteoporosis[ 18 ]. We found that the expression changes of PTX3 were not significant within 3 days but continued to increase after the 7th day, indicating that the process of bone mineralization mainly occurs in the middle and late stages of bone fracture healing. KEGG analysis showed that osteoclast differentiation was significantly enriched during this period. Osteoclasts originate from the recruitment of osteoclast precursors by bone cells expressing RANKL. The expression of RANKL and M-CSF in the bone marrow cavity initiates the differentiation of osteoclast precursors into osteoclasts[ 19 ]. As osteoclast activity gradually exceeds that of osteoblasts, completing the remolding of the bone callus. In addition, we were amazed to discover that neural regulation plays an important role in the early stages of bone fracture healing. By analyzing the temporal dynamics of gene expression clusters and conducting functional enrichment on each cluster, we excluded clusters that did not yield significant results. The first class of enriched pathway mainly includes the Cytokine-cytokine receptor interaction pathway, EMC-report interaction pathway, Protein digestion and absorption, and the PI3K-Akt signaling pathway. The second class consists of Phagosome, Neutrophil extracellular trap formation, and the Renin-angiotensin system (RAS). Interestingly, the EMC-report interaction pathway and the PI3K-Akt signaling pathway in the first class are closely related to neural function. The interaction between extracellular matrix (ECM) and neurons is an important condition for axonal regeneration after injury, and it may play a regulatory role in the regeneration of peripheral or central sensory neurons after nerve injury[ 20 , 21 ]. In addition, existing research indicates that the PI3K/Akt signaling pathway improves neuronal survival and regulates axonal growth, playing an important role in functional recovery after spinal cord injury[ 22 , 23 ]. In the second class, the RAS can participate in and affect neural activity. The massive activation of the RAS system can increase the levels of renin, angiotensin (AT), angiotensin-converting enzyme (ACE), and aldosterone in circulation and neural tissue. The excessive increase of these components can lead to neuronal damage and degeneration[ 24 , 25 ], and there is evidence that RAS inhibitors have a positive effect on spinal cord and nerve root functional activity in patients with degenerative lumbar diseases[ 26 ]. Furthermore, the KEGG results suggest that on 3rd day the Axon guidance pathway is significantly activated, and the IPA enrichment results related to neural function show that CDH1 and L1CAM exhibit the most significant expression changes. CDH1 plays a role in regulating axonal growth, and knocking down CDH1 can promote axonal growth and increase axon length, while L1CAM is of great significance for the growth, regeneration, development, and maintenance of the nervous system. It also has the ability to regulate axonal sprouting during neuronal regeneration and improve behavioral outcomes after CNS injury. In the GO-CC analysis results three days after the fracture, we found that glutamatergic synapses and gamma-aminobutyric acid (GABA) synapses are among the top components, with the former depolarizing the postsynaptic membrane and the latter hyperpolarizing it. These two are the most common excitatory and inhibitory neurotransmitters in the central nervous system[ 27 , 28 ]. Therefore, we speculate that the local intraosseous nerve repair is mainly to regulate axon growth, and the central nervous system may play an important regulatory role in the early stages. Through IPA (Fig. 6 ), we have found that most of the nerve-related pathways are in an inhibited state at the 3rd day time point after fracture, and gradually become activated over time, such as the CREB Signaling in Neurons pathway, which is closely related to various biological reactions including neuron excitation, neurogenesis, synaptic plasticity, etc. CREB must be phosphorylated to form pCREB before it can act as a transcriptional activator. Growth factor/receptor tyrosine kinase-induced pathways (Ras/Erk/RSK2) and stress or inflammatory cytokine (MAPK; PI3/Akt) pathways can all phosphorylate CREB. In our previous time kinetic results, the 3rd day after fracture was the key node for the changes in the PI3/Akt and Ras pathways. In addition, the significant IPA changes on the 7th day included FHL2 and ALP. The former can enhance CREB transcriptional activation activity, while the latter, in addition to being a sensitive indicator of osteoblast activity, can also serve as a marker of the inflammatory environment. Therefore, we speculate that the inflammatory stimulation during the bone healing process promotes CREB phosphorylation, activates CREB Signaling in Neurons, and exerts neuroregulatory effects. Furthermore, the Neuroinflammation Signaling Pathway gradually becomes activated during the bone healing process. As a key signaling pathway for maintaining central nervous system (CNS) homeostasis, its function is to destroy and clear damage factors and damaged nerve tissue. When this beneficial inflammatory response is not controlled, excessive damage to cells and tissues can lead to the destruction of normal tissue and chronic inflammation, ultimately leading to the death of glial cells and neurons. This process can be accelerated by multiple pro-inflammatory cytokines expressed by neurons in an inflammatory state, including neurotransmitters or modulators (glutamate, fractalkine, nitric oxide, GABA), and neurotoxic proteins. In summary, local inflammation during bone healing may have adverse effects on the central nervous system, and promote the regulatory role of the central nervous system during bone healing. In previous studies, many transcriptional analyses of local tissue after bone fracture have been conducted[ 29 , 30 ]. In these studies, the molecular mechanisms affecting fracture healing were explored through bioinformatics methods. In comparison, our study focuses more on summarizing the biological characteristics of each stage of fracture healing and concludes the characteristics of the changes in each stage of fracture healing, which is consistent with the biological process of fracture healing. Additionally, we found that the peripheral and central nervous systems may play an active regulatory role in the process of fracture healing, which has not been deeply explored in previous sequencing studies. This contributes to further elucidating the biological process of fracture healing and provides new ideas for clinical treatment. Declarations Ethics approval and consent to participate The experimental scheme has passed the animal ethics requirements of Peking University People's Hospital (2020PHC015). All animal experiments were performed strictly according to the requirements of the Animal Ethics Procedures and Guidelines of the People’s Republic of China and in accordance with ARRIVE guidelines. Availability of data and materials The datasets generated and/or analysed during the current study are available in the [Gene Expression Omnibus (GEO)] repository, [Accession is GSE230779 and secure token is kvuhcgwyznorlmt] Competing interests The authors declare that they have no competing interests Funding This work was supported by the Natural Science Foundation of Beijing of China [grant number 7192215] and the National Natural Science Foundation of China [grant number 82072162]. Authors' contributions XY and QL designed the research. SW and SG carried out the experiment and wrote the manuscript. SY and JD completed all data analysis. XG, CH and XZ provided valuable comments and technology supports. All authors contributed to the article and approved the submitted version. Acknowledgements Not applicable. References Cheng, C. and D. Shoback, Mechanisms Underlying Normal Fracture Healing and Risk Factors for Delayed Healing. Curr Osteoporos Rep, 2019. 17 (1): p. 36-47. 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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-4011947","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":280621279,"identity":"9172ac87-bf27-4db4-9229-f809bc07ccb8","order_by":0,"name":"Shen Wang","email":"","orcid":"","institution":"Peking University People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shen","middleName":"","lastName":"Wang","suffix":""},{"id":280621280,"identity":"5ce6ad58-73dd-486a-a8d0-2e04096ccfed","order_by":1,"name":"Shuhang Guo","email":"","orcid":"","institution":"Peking University People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shuhang","middleName":"","lastName":"Guo","suffix":""},{"id":280621281,"identity":"dc15c53d-b72e-4c00-b70c-efdc7c14bb01","order_by":2,"name":"Shaoyun Yuan","email":"","orcid":"","institution":"Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shaoyun","middleName":"","lastName":"Yuan","suffix":""},{"id":280621282,"identity":"590d7c8f-723e-4a15-aacc-df63f08d4903","order_by":3,"name":"Xinyi Gu","email":"","orcid":"","institution":"Peking University People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xinyi","middleName":"","lastName":"Gu","suffix":""},{"id":280621283,"identity":"4b16b5cc-1b9c-4564-95fc-65830aec7eb2","order_by":4,"name":"Jin Deng","email":"","orcid":"","institution":"Peking University People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Deng","suffix":""},{"id":280621284,"identity":"a71682ac-6a41-4d3e-b7ef-3319bef3ca4b","order_by":5,"name":"Xinyi Zeng","email":"","orcid":"","institution":"Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xinyi","middleName":"","lastName":"Zeng","suffix":""},{"id":280621285,"identity":"6fbace0c-237d-4e9d-b963-fc3a6f338c88","order_by":6,"name":"Qingguo Lu","email":"","orcid":"","institution":"Pizhou people’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qingguo","middleName":"","lastName":"Lu","suffix":""},{"id":280621286,"identity":"c01301c6-0bfe-49bf-8ac3-83093b1848dd","order_by":7,"name":"Xiaofeng Yin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYDACZiCugDAYHzPwgMUMCGs5A2EwGxOnhQGmhYGBTRrKx6/F4DjzwwcHKu7YbTjOe6y6QGZbYgN78zYJhpo7OLVINrMZGxw48yx5w2G+tNszeG4nNvAcK5NgOPYMpxZ+ZgYz6Y9th5MNDvOY3eYBaZHIMZNgbDiMUwsbM/s3iYP/IFqKwVrk3+DXws/MYyZxsOGwHUgLM8QWHvxaJJt5ig0OHDucIHmYx1ga6BfjNp60YouEY7i1GJw/vvHBgZrD9nznzxh+Luy5LdvPfnjjjQ81uLXAQOKCA0CSsQfoOxA3gaAGBgZ7+QYQ9YMIpaNgFIyCUTDiAAD/i1WNhjVlUQAAAABJRU5ErkJggg==","orcid":"","institution":"Peking University People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Xiaofeng","middleName":"","lastName":"Yin","suffix":""},{"id":280621287,"identity":"8f1bfd42-f73b-4f42-aa26-c1356e68fb73","order_by":8,"name":"Chen Huang","email":"","orcid":"","institution":"Peking University People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-03-04 14:06:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4011947/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4011947/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53016257,"identity":"a3b5f8bf-8b6f-49df-9916-44888617e1c6","added_by":"auto","created_at":"2024-03-19 16:05:04","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":784415,"visible":true,"origin":"","legend":"\u003cp\u003eThe DEGs of local bone tissue after fracture. (A) Volcano plots illustrating DEGs that are either upregulated or downregulated at four distinct time points. (B) Venn diagrams illustrating the overlap of DEGs across four time points, with upregulated genes represented on the left side and downregulated genes on the right side. The criteria for selecting DEGs included p\u0026lt;0.01 and FC\u0026gt;= 2.\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4011947/v1/5538e6dca32d8babacc8c431.jpg"},{"id":53016251,"identity":"91ae30ec-a088-4f95-89c8-38d5b72a8985","added_by":"auto","created_at":"2024-03-19 16:05:04","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":791644,"visible":true,"origin":"","legend":"\u003cp\u003eTime course analyses of DEGs. (A)DEGs were divided into various groups based on their expression levels and trends. (B)DEGs in each group were enriched to obtain corresponding pathway clusters through KEGG analysis.\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4011947/v1/18df2c45fc94e7bae4370fcb.jpg"},{"id":53017752,"identity":"71159fe3-7272-4ffb-9836-fe6859044cd8","added_by":"auto","created_at":"2024-03-19 16:13:04","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":677636,"visible":true,"origin":"","legend":"\u003cp\u003eThe top10 most significant DEGs at each time point.\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4011947/v1/9d27670e6e5608f9c20556c0.jpg"},{"id":53016253,"identity":"40e119d9-a510-4894-b1c3-5def70405a89","added_by":"auto","created_at":"2024-03-19 16:05:04","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":614466,"visible":true,"origin":"","legend":"\u003cp\u003eGO enrichment analysis on DEGs. BP (biological process), CC (cellular component), MF (molecular function) terms were separately clustered at each time point. The inclusion criteria were terms with top10 smallest p-value.\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4011947/v1/a2d2d36969dd9c873419a383.jpg"},{"id":53017754,"identity":"75f839c9-9b91-4d44-a37c-f6355ae23ec6","added_by":"auto","created_at":"2024-03-19 16:13:04","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":699971,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG analysis on DEGs. (A) The most significant pathways enriched at each time point and the inclusion criteria were set as p \u0026lt; 0.01. (B) Clustering the KEGG analysis results\u003c/p\u003e","description":"","filename":"Fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4011947/v1/a8dc42c8b866710a871b3d79.jpg"},{"id":53017753,"identity":"0281bf4b-af6b-48a4-9fa8-b5f00afc807c","added_by":"auto","created_at":"2024-03-19 16:13:04","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":932151,"visible":true,"origin":"","legend":"\u003cp\u003eIPA results\u003c/p\u003e\n\u003cp\u003e(A)All pathways associated with neurological diseases. (B) Top three key regulated genes at each time node.\u003c/p\u003e","description":"","filename":"Fig.6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4011947/v1/0b5b80e19078098a0d818fcf.jpg"},{"id":54882046,"identity":"e452eb01-3e30-4085-9fd0-7e2b941e9c96","added_by":"auto","created_at":"2024-04-18 05:00:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1322361,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4011947/v1/53b0eaa1-7350-4b72-a3b9-3f2a4cc54473.pdf"},{"id":53019347,"identity":"aa143ead-f586-42c7-9685-841a71c92210","added_by":"auto","created_at":"2024-03-19 16:21:04","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":16848,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4011947/v1/f53a6f720d466077af28846a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The whole transcriptomic analysis of local bone tissue after fracture","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFracture is one of the most common types of tissue injury in humans, and it is estimated that approximately 10\u0026ndash;15% of fracture patients will experience healing-related complications, such as delayed union or non-union[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The outcome of fracture healing depends on many factors, such as the severity of the trauma, the quality of fracture reduction, the quality of fracture fixation, revascularization and the presence of any comorbidities[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. At the same time, fracture healing involves complex molecular mechanisms, and many studies have reported that various factors including BMP2/CXCL12, Cdk5, PTX3, calcitonin gene-related peptide, pain-promoting neuropeptides, and neurotrophin participate in and regulate a series of biological processes including cell recruitment, angiogenesis, osteoblast and osteoclast differentiation, chondrocyte differentiation, ossification, and bone matrix mineralization during fracture healing[\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A previous study showed that skeletal stem cells(SSCs) expressing osteocalcin usually differentiate into osteoblasts, while the negative cells usually differentiate into adipocytes, but negative cells can also form bone in bone injuries[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Another study showed that there are differences in the gene expression profiles between stress fractures and complete fractures, with genes involved in inflammation, immune response, leukocyte interleukin-1 cell response, NFκB signaling, and Toll-like receptor signaling pathways significantly expressed only in complete fracture tissue, but not in stress fracture tissue[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn summary, various lines of evidence suggest that changes in gene expression in local tissues are involved in the entire process of bone fracture healing through downstream products that participate in various signal transduction pathways. However, the composition of local tissues in bone fractures is complex, and understanding the exact molecular mechanisms of bone fracture healing is still a challenge. Previous studies mainly focused on the expression of individual genes, so we hope to conduct a specific analysis of gene expression at various time points during bone fracture healing from the transcriptome level. Next-generation RNA-SEQ is currently the most advanced technology, and compared with other high-throughput technologies (such as microarrays), it achieves base-pair resolution, can parallelly measure the activity of thousands of genes, and analyze changes in gene expression levels in cells or tissues under different conditions in the whole genome[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, we used RNA-SEQ technology to study the expression profile of mRNAs in local tissues after bone fractures, screened out highly differentially expressed genes, and studied the regulation of relevant genes and the impact of the signal pathway they participate in on bone fracture healing.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cem\u003eRats and Tissue samples.\u003c/em\u003e Male adult SD rats (3 months old, weighing approximately 300g, from Beijing Vital River Laboratory Animal Technology Co., Ltd) were randomly divided into five groups. Tissue samples were collected at 0(control group), 3rd, 7th, 14th, and 28th days after fracture for whole transcriptome sequencing. Rats were anesthetized with 2.5% isoflurane/oxygen. A 2cm incision was made at the tibial tuberosity, and a dental drill was used to create a hole 5mm above the tibial tuberosity. An intramedullary needle (0.8 mm\u0026times;1.5 cm) was inserted into the bone marrow cavity. The tibia was fractured using three-point bending pliers 5 mm below the tibial tuberosity, taking care not to damage the intramedullary needle and the tissue was then sutured layer by layer. All procedures were conducted under sterile conditions. The animals were euthanized at 0, 3rd, 7th, 14th, and 28th days after the injury. Bone tissue, 2 mm wide, was obtained from the local fracture site of the experimental limb and processed for further analysis.\u003c/p\u003e \u003cp\u003e \u003cem\u003eRNA extraction and sequencing.\u003c/em\u003e Rats were anesthetized with 2.5% isoflurane/oxygen. Bone at the site of the right tibial fracture was taken from each rat, and the samples were frozen in liquid nitrogen within 3 minutes after leaving the body. The frozen samples were crushed and dissolved in TRIzol (Invitrogen). RNA was subsequently extracted from tissues and the RNA were subjected to strict quality control (Agilent 2100 bioanalyzer). At the same time, the contents of Q20, Q30 and GC in clean data were calculated. All downstream analysis was based on high-quality clean data (see \u0026ldquo;Additional file 1\u0026rdquo;). The reference genome in this study comes from the integrated database (rattus_norvegicus_Ensembl_97). Index of the reference genome was built using Bowtie v2.0.6 and paired-end clean reads were aligned to the reference genome using TopHat v2.0.9. After the completion of the library construction, Qubit 2.0 was used for preliminary quantification, diluting the library to 1.5ng/ul, and then Agilent 2100 was used to detect the Insert size of the library to ensure the quality of the library. After qualified library inspection, Illumina PE150 sequencing was conducted by pooling according to effective concentration of library and data output requirements.\u003c/p\u003e \u003cp\u003e \u003cem\u003eDifferential expression analysis.\u003c/em\u003e The DEGs of samples were calculated by DESeq2 v.1.34.0, we chose p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and fold change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;2 for the selecting criteria of DEGs. Volcano plots were depicted by ggplot2.\u003c/p\u003e \u003cp\u003e \u003cem\u003eKyoto encyclopedia of genes and genomes (KEGG) and Gene Ontology (GO) analysis.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eKEGG pathway and GO enrichment analysis for DEGs and image depicting were conducted by clusterProfiler v.4.2.2, org.Rn.eg.db v.3.14.0, and ggplot2 v.3.4.0 used in R package. At each time point, the top10 items with smallest p-values were selected. DAVID database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used for functional annotations clustering of GO enrichment.\u003c/p\u003e \u003cp\u003e \u003cem\u003eIngenuity pathway analysis (IPA).\u003c/em\u003e Core analysis was performed using IPA for DEGs at each time point with inclusion criteria of p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and FC\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;2. The results of the core analysis at four time points were used for comparison analysis and the clustering result of nerve biological functions in disease and function modules at four time points was exported by the QIAGEN IPA software. The result was derived directly by the QIAGEN IPA software.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDifferential genes analysis\u003c/h2\u003e \u003cp\u003eThe bone tissue at the site of a local fracture was collected, and transcriptome sequencing was carried out at different time points including 0, 3rd, 7th, 14th, and 28th days post-fracture. Differential gene expression analysis was conducted to compare gene expression changes at 3rd, 7th, 14th, and 28th days post-fracture with the gene expression levels before treatment (Day 0). Day 3(up:555, down:1575), Day 7(up:754, down:1340), Day14(up:512, down:871), Day28(up:381, down:582) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B). Based on the expression levels and the expression trends over time, we grouped the differentially expressed genes and found that they could be roughly divided into eight clusters. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The figure showed that all 8 clusters can be broadly categorized into two types. The first type(cluster1,4,7,8) exhibits a temporal pattern of a sharp increase followed by a small decrease and then a plateau, while the second type(cluster2,3,5,6) exhibits the opposite pattern of a sharp decrease followed by a small increase and then a plateau. Both types have a critical inflection point at the 3rd day after the fracture. Then, KEGG analysis was used to cluster each group. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The expression patterns of some pathways belong to the first type, for example ECM-receptor interaction and Protein digestion and absorption. The expression patterns of other pathways belong to the second type, such as IL-17 signaling pathway and Phagosome. Besides, top10 DEGs in each time point are also shown in the figure (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGO Enrichment Analysis\u003c/h2\u003e \u003cp\u003eGO enrichment analysis was carried out on DEGs. BP (biological process), CC (cellular component), MF (molecular function) terms were clustered at each time point (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). According to the analysis, different stages of fracture healing had different focuses, Important biological processes, such as the inflammatory response, mainly occurred on the 3rd day after fracture. Osteoblast differentiation, collagen synthesis, and ossification mainly occurred on the 7th, 14th, and 28th days, while osteoclast differentiation and cartilage formation mainly occurred on the 14th and 28th days. Bone mineralization only occurred significantly on the 28th day.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eKEGG Pathway Analysis\u003c/h2\u003e \u003cp\u003eKEGG analysis was carried out on DEGs. At each time point, the most significant pathways with p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 were enriched using a bubble plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) and clustered (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Our results showed that the protein digestion and absorption pathway and the EMC-receptor interaction pathway were the two most important pathways in the process of fracture healing.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eIPA Analysis\u003c/h2\u003e \u003cp\u003eWe analyzed the clusters related to neurological diseases by IPA (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Almost all clusters were downregulated during the first three days, such as CREB Signaling in Neurons pathway. In addition, the top3 regulators at different time points are shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe process of bone healing can be mainly divided into three stages. The first stage is the hematoma formation stage. After an acute bone injury, blood vessels from within the bone and surrounding soft tissues rupture, leading to local hematoma formation. At this time, the hematoma serves as a temporary scaffold, recruiting inflammatory cells such as macrophages and neutrophils. These inflammatory cells are activated and initiate a cascade reaction, secreting inflammatory cytokines such as IL-6 and CCL2, which play an early role in clearing damaged and inactive tissue. The second stage is the procallus formation stage. The release of various cytokines and immunogenic factors during the hematoma formation stage promotes the recruitment and migration of mesenchymal stem cells (MSCs) to the site, initiating the repair process. MSCs have the dual potential to differentiate into osteoblasts and chondrocytes. The procallus forms through intramembranous or endochondral ossification, with intramembranous ossification occurring earlier than endochondral ossification and overlapping with it, ultimately leading to bony bridging of the fracture gap. The third stage is the callus remodeling stage. Intramembranous ossification directly deposits mineralized bone through osteoblasts, while endochondral ossification involves cell differentiation, formation of a cartilaginous callus bridging the bone ends, mineralization and extension of immature callus, and coordinated activity between osteoblasts and osteoclasts to remodel the callus, replacing woven bone with mechanically stronger lamellar bone. Ultimately, the marrow cavity is reconnected, and the fracture site returns to its normal structure[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur research has found that the transcriptional changes in local tissues at different time points of fracture have significant differences and correspond to the biological processes of different stages of fracture healing. Most of the top 10 DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) at each time point show a significant increase in expression within 3 days of fracture, gradually reaching a peak and then slowly decreasing but still maintaining a high level of expression. This is consistent with the first type of genes identified by temporal dynamics, indicating that the molecular mechanisms involved in fracture healing begin early and run through the entire process. According to GO analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), only biological processes related to inflammation, such as lymphocyte activation and proliferation, adaptive immune response, leukocyte proliferation, leukocyte-mediated immunity, and monocyte proliferation, were significantly upregulated on the 3rd day, but not on the 7th day and later. According to KEGG analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), the AGE-RAGE signaling pathway was significantly enriched. There is evidence that advanced glycation end products (AGEs) are the products of excessive sugar and protein binding, which interact with the AGE receptor (RAGE) and activate downstream factor nuclear factor-kappaB (NF-κB) through the AGE-RAGE signaling pathway, thereby promoting the inflammatory response[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These findings suggest that the most prominent feature of early fracture healing is a highly inflammatory response.\u003c/p\u003e \u003cp\u003eOn the 7th day, in most of the temporal dynamics analysis, the first type of genes reached peak expression, such as collagen synthesis-related genes col1a1, col3a1, col5a1. Collagen is the main component of the extracellular matrix, and type I collagen constitutes 90% of the total organic components of bone matrix. Its synthesis, deposition, and remodeling play an important role in the development, formation, and stability of bone tissue. Mutations in the human Col1a1 gene can lead to osteogenesis imperfecta[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This indicates that as bone fracture healing progresses, collagen synthesis continuously increases, ensuring the toughness and mechanical strength of the newly formed bone callus. Osteogenesis-related genes Mmp2, Timp1, and ptprv also reached peak expression. Matrix metalloproteinases (MMPs) are a group of structurally related secreted and membrane-bound proteins that participate in the degradation of extracellular matrix (ECM) and other structural components of extracellular and non-matrix proteins. MMP-2 deficiency can lead to impaired osteoblast differentiation. Evidence suggests that osteoblasts and bone cells express Mmp2 and Timp1 during osteogenesis. Osteoblasts and osteocytes degrade ECM molecules together with MMPs while producing them, and regulate degradation by inhibiting MMP activity through tissue inhibitors of metalloproteinases (TIMPs). They can also reorganize ECM components, allowing the bone matrix to mature during the process of intramembranous ossification[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The Ptprv gene belongs to the transmembrane protein tyrosine phosphatase gene family and is specifically expressed in the cartilage and periosteum of immature long bone necks, but is hardly expressed in mature osteoblasts[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. It may be involved in the transformation process of skeletal components from cartilage templates to bone. Platelet activation was significantly enriched according to KEGG analysis. Platelet activation often occurs in low-oxygen-induced inflammatory reactions. The substances released from activated platelet particles, such as VEGF and PDGF, have the ability to promote angiogenesis and regulate vascular maturation and stability[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], which leads to an increase in the peripheral vascular bed and its growth into the healing tissue, helping to restore the decreased blood supply in the fracture area caused by vascular injury and accelerating fracture healing. According to GO analysis, biological processes related to bone development, connective tissue development, collagen fiber tissue, and ossification began to be significantly upregulated. At the same time, the content of cell components such as collagen trimer and extracellular matrix containing collagen protein significantly increased.\u003c/p\u003e \u003cp\u003eAccording to GO analysis on the 14th and 28th days, the process of cartilage generation was significantly upregulated. Cartilage cells differentiate from bone marrow mesenchymal stem cells and can produce a cartilage matrix composed of collagen and proteoglycans. At the same time, the process of bone formation was significantly accelerated, indicating that bone calluses were formed on the bone surface and in the gap of adjacent fractures through intramembranous ossification and endochondral ossification. Bone mineralization was only significantly upregulated on the 28th day. As cartilage cells differentiate, the extracellular matrix of cartilage undergoes mineralization, transforming immature primitive bone calluses into mature mineralized bone calluses. Evidence suggests that Pentraxin 3 (PTX3) plays a key role in the mineralization and deposition of bone matrix, and the genetic variation of the PTX3 gene is closely related to osteoporosis[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. We found that the expression changes of PTX3 were not significant within 3 days but continued to increase after the 7th day, indicating that the process of bone mineralization mainly occurs in the middle and late stages of bone fracture healing. KEGG analysis showed that osteoclast differentiation was significantly enriched during this period. Osteoclasts originate from the recruitment of osteoclast precursors by bone cells expressing RANKL. The expression of RANKL and M-CSF in the bone marrow cavity initiates the differentiation of osteoclast precursors into osteoclasts[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. As osteoclast activity gradually exceeds that of osteoblasts, completing the remolding of the bone callus.\u003c/p\u003e \u003cp\u003eIn addition, we were amazed to discover that neural regulation plays an important role in the early stages of bone fracture healing. By analyzing the temporal dynamics of gene expression clusters and conducting functional enrichment on each cluster, we excluded clusters that did not yield significant results. The first class of enriched pathway mainly includes the Cytokine-cytokine receptor interaction pathway, EMC-report interaction pathway, Protein digestion and absorption, and the PI3K-Akt signaling pathway. The second class consists of Phagosome, Neutrophil extracellular trap formation, and the Renin-angiotensin system (RAS). Interestingly, the EMC-report interaction pathway and the PI3K-Akt signaling pathway in the first class are closely related to neural function. The interaction between extracellular matrix (ECM) and neurons is an important condition for axonal regeneration after injury, and it may play a regulatory role in the regeneration of peripheral or central sensory neurons after nerve injury[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In addition, existing research indicates that the PI3K/Akt signaling pathway improves neuronal survival and regulates axonal growth, playing an important role in functional recovery after spinal cord injury[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In the second class, the RAS can participate in and affect neural activity. The massive activation of the RAS system can increase the levels of renin, angiotensin (AT), angiotensin-converting enzyme (ACE), and aldosterone in circulation and neural tissue. The excessive increase of these components can lead to neuronal damage and degeneration[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and there is evidence that RAS inhibitors have a positive effect on spinal cord and nerve root functional activity in patients with degenerative lumbar diseases[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Furthermore, the KEGG results suggest that on 3rd day the Axon guidance pathway is significantly activated, and the IPA enrichment results related to neural function show that CDH1 and L1CAM exhibit the most significant expression changes. CDH1 plays a role in regulating axonal growth, and knocking down CDH1 can promote axonal growth and increase axon length, while L1CAM is of great significance for the growth, regeneration, development, and maintenance of the nervous system. It also has the ability to regulate axonal sprouting during neuronal regeneration and improve behavioral outcomes after CNS injury. In the GO-CC analysis results three days after the fracture, we found that glutamatergic synapses and gamma-aminobutyric acid (GABA) synapses are among the top components, with the former depolarizing the postsynaptic membrane and the latter hyperpolarizing it. These two are the most common excitatory and inhibitory neurotransmitters in the central nervous system[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Therefore, we speculate that the local intraosseous nerve repair is mainly to regulate axon growth, and the central nervous system may play an important regulatory role in the early stages.\u003c/p\u003e \u003cp\u003eThrough IPA (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), we have found that most of the nerve-related pathways are in an inhibited state at the 3rd day time point after fracture, and gradually become activated over time, such as the CREB Signaling in Neurons pathway, which is closely related to various biological reactions including neuron excitation, neurogenesis, synaptic plasticity, etc. CREB must be phosphorylated to form pCREB before it can act as a transcriptional activator. Growth factor/receptor tyrosine kinase-induced pathways (Ras/Erk/RSK2) and stress or inflammatory cytokine (MAPK; PI3/Akt) pathways can all phosphorylate CREB. In our previous time kinetic results, the 3rd day after fracture was the key node for the changes in the PI3/Akt and Ras pathways. In addition, the significant IPA changes on the 7th day included FHL2 and ALP. The former can enhance CREB transcriptional activation activity, while the latter, in addition to being a sensitive indicator of osteoblast activity, can also serve as a marker of the inflammatory environment. Therefore, we speculate that the inflammatory stimulation during the bone healing process promotes CREB phosphorylation, activates CREB Signaling in Neurons, and exerts neuroregulatory effects. Furthermore, the Neuroinflammation Signaling Pathway gradually becomes activated during the bone healing process. As a key signaling pathway for maintaining central nervous system (CNS) homeostasis, its function is to destroy and clear damage factors and damaged nerve tissue. When this beneficial inflammatory response is not controlled, excessive damage to cells and tissues can lead to the destruction of normal tissue and chronic inflammation, ultimately leading to the death of glial cells and neurons. This process can be accelerated by multiple pro-inflammatory cytokines expressed by neurons in an inflammatory state, including neurotransmitters or modulators (glutamate, fractalkine, nitric oxide, GABA), and neurotoxic proteins. In summary, local inflammation during bone healing may have adverse effects on the central nervous system, and promote the regulatory role of the central nervous system during bone healing.\u003c/p\u003e \u003cp\u003eIn previous studies, many transcriptional analyses of local tissue after bone fracture have been conducted[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In these studies, the molecular mechanisms affecting fracture healing were explored through bioinformatics methods. In comparison, our study focuses more on summarizing the biological characteristics of each stage of fracture healing and concludes the characteristics of the changes in each stage of fracture healing, which is consistent with the biological process of fracture healing. Additionally, we found that the peripheral and central nervous systems may play an active regulatory role in the process of fracture healing, which has not been deeply explored in previous sequencing studies. This contributes to further elucidating the biological process of fracture healing and provides new ideas for clinical treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental scheme has passed the animal ethics requirements of Peking University People\u0026apos;s Hospital (2020PHC015). All animal experiments were performed strictly according to the requirements of the Animal Ethics Procedures and Guidelines of the People\u0026rsquo;s Republic of China and in accordance with ARRIVE guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the [Gene Expression Omnibus (GEO)] repository, [Accession is GSE230779 and secure token is kvuhcgwyznorlmt]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of Beijing of China [grant number 7192215] and the National Natural Science Foundation of China [grant number 82072162].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXY and QL designed the research. SW and SG carried out the experiment and wrote the manuscript. SY and JD completed all data analysis. XG, CH and XZ provided valuable comments and technology supports. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCheng, C. and D. Shoback, \u003cem\u003eMechanisms Underlying Normal Fracture Healing and Risk Factors for Delayed Healing.\u003c/em\u003e Curr Osteoporos Rep, 2019. \u003cstrong\u003e17\u003c/strong\u003e(1): p. 36-47.\u003c/li\u003e\n\u003cli\u003eClaes, L., S. Recknagel, and A. 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Conti, \u003cem\u003eGenerating diversity at GABAergic synapses.\u003c/em\u003e Trends Neurosci, 2001. \u003cstrong\u003e24\u003c/strong\u003e(3): p. 155-162.\u003c/li\u003e\n\u003cli\u003eLiu, C., et al., \u003cem\u003eScreening for potential genes associated with bone overgrowth after mid-shaft femur fracture in a rat model.\u003c/em\u003e J Orthop Surg Res, 2017. \u003cstrong\u003e12\u003c/strong\u003e(1): p. 8.\u003c/li\u003e\n\u003cli\u003eZhang, Y., et al., \u003cem\u003eTranscriptome sequencing profiling identifies miRNA-331-3p as an osteoblast-specific miRNA in infected bone nonunion.\u003c/em\u003e Bone, 2021. \u003cstrong\u003e143\u003c/strong\u003e: p. 115619.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bone, Fracture healing, Transcriptome, KEGG, GO, IPA","lastPublishedDoi":"10.21203/rs.3.rs-4011947/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4011947/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFracture healing is a complex physiological process involving multiple cells and signaling pathways, the potential molecular mechanisms and biological process are still unclear and need further exploration. In this study, transcriptome sequencing technology was used to detect and analyze the changes in transcriptome of the local injury tissue after fracture. Differentially expressed genes (DEGs) with the high degree were analyzed. Our results showed that different stages of fracture healing had different focuses, some important biological processes, such as the inflammatory response, mainly occurred on the 3rd day after fracture. Besides, we found that the 3rd day after fracture was a key point of transcriptome change, and neural regulation played a significant role in fracture healing at this time, and inflammatory stimulation might be an important factor affecting neural regulation after fracture. In conclusion, our research results identify some important genes and pathways in the process of fracture healing, and summarize the main characteristics of transcriptome changes in fractures. This laid the foundation for a deeper understanding of the relation between molecular mechanisms and biological processes involved in fracture healing. By identifying the specific time points at which various cellular events occur during the healing process, researchers can gain insights into the complex interplay between cells, signaling pathways, and extracellular matrix components that is required for successful bone healing. This knowledge can inform the development of new therapies and interventions to improve fracture healing outcomes and ultimately enhance the quality of life for patients affected by bone fractures.\u003c/p\u003e","manuscriptTitle":"The whole transcriptomic analysis of local bone tissue after fracture","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-19 16:04:59","doi":"10.21203/rs.3.rs-4011947/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bc241f46-4f65-4714-9ca6-7f121a119b63","owner":[],"postedDate":"March 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-18T04:52:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-19 16:04:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4011947","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4011947","identity":"rs-4011947","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}