The role of S100A9 in the progression of tuberculosis

The role of S100A9 in the progression of tuberculosis | 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 role of S100A9 in the progression of tuberculosis Ruichao Liu, Shujuan Duan, Jing Tong, Siyu Yao, Qiuyue Liu, Liang Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4237009/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 Objective: Elevated plasma levels of S100A9 have been observed in patients with severe tuberculosis, with further increases in patients with poor prognosis, suggesting that S100A9 is a potential biomarker for disease progression and prognosis. However, the molecular mechanism underlying its potential remains unclear, highlighting the importance of exploring its function. Methods: To further investigate the role of S100A9 in severe tuberculosis, we constructed S100A9 gene knockout or overexpression models and analyzed the transcriptome changes in THP-1 cells following S100A9 overexpression or shRNA silencing using next-generation sequencing. Through the analysis of transcriptome sequencing results, we identified eight genes that may be involved in the regulation of S100A9 expression. We also detected the expression of the S100A9 gene and related differentially expressed genes after Mycobacterium tuberculosis infection, as well as their enrichment and related pathways. It was inferred that S100A9 may be involved in the mechanism by which tuberculosis progresses to severe tuberculosis. Results: FOSB and IL17c are potentially related to the IL-17 signaling pathway, while calcium/calmodulin-dependent protein kinase II beta (CAMK2B) may be related to the ErbB signaling pathway. These findings indicate that these genes may promote the progression of tuberculosis through different mechanisms. Conclusion: Our study explored the potential role and mechanism of S100A9 in the development of tuberculosis, providing a new perspective for the development of treatment strategies for this disease. Mycobacterium tuberculosis THP-1 cell S100A9 Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Tuberculosis (TB) is a bacterial infectious disease caused by Mycobacterium tuberculosis (MTB) infection. It spreads through the respiratory tract, is common in the lungs, and can also damage other tissues and organs. The World Health Organization reported that 1.6 million people died of TB in 2021. The COVID-19 pandemic further increased the burden of patients and had a significant impact on the diagnosis and treatment of TB. The most direct impact was a sharp decline in the number of newly diagnosed tuberculosis patients, an increase in the number of undiagnosed and untreated patients, an increase in the case fatality rate, and an increase in the spread of community infection, thereby increasing the number of patients with TB. By 2022, tuberculosis has become the world's second largest cause of death from a single source of infection, second only to coronavirus disease (COVID-19), and its death toll is almost twice that of HIV/AIDS [ 1 , 2 ] . Due to the influence of human immunodeficiency virus infection and the increasing number of drug-resistant strains, the diagnosis and treatment of pulmonary tuberculosis have become more difficult. Once patients have one or more tissue and organ failures in the clinic, they can progress to severe pulmonary tuberculosis, resulting in a variety of complications, such as massive hemoptysis, which threatens people's lives. The mortality of patients with pulmonary tuberculosis depends on the severity of the disease [ 3 ] . Early warning, timely diagnosis, and targeted treatment of patients with severe pulmonary TB have a direct impact on the mortality of patients, suggesting that the severity of lesions, rapid progression of lesions, and timely targeted treatment are important causes of death in patients with pulmonary TB [ 4 , 5 ] . Although significant progress has been made in the diagnosis and treatment of tuberculosis, there are still few studies on severe pulmonary tuberculosis. Early judgment of the patient's condition and the development of effective prevention and treatment measures have certain clinical significance in reducing patient mortality. S100A9, a Ca2+-binding protein belonging to the S100 family, is primarily expressed in neutrophils and monocytes and plays a pivotal role in regulating various inflammatory responses and inflammation-related diseases. Studies have shown that MTB Rv1768 regulates NF through S100A9-κB-TNF-α signaling and arachidonic acid metabolism, thereby promoting mycobacterial survival in macrophages [ 6 , 7 ] . Our previous studies showed that compared with that of healthy controls, the plasma S100A9 level of severe TB patients was significantly increased, and the plasma S100A9 level of nonsurvivors of severe TB patients was further increased compared with that of survivors of severe TB patients, which may be a marker for predicting TB exacerbation [ 8 ] . S100A9 can also be expressed in macrophages; studies have shown that S100A9 can impair macrophage differentiation through TLR4-NF-κB signaling, leading to worsening inflammation and wound healing [ 9 ] . These findings support the use of S100A9 as a diagnostic indicator of severe TB, with increased S100A9 mRNA expression in the whole blood of patients and patients with advanced-stage TB and a rapid reduction in serum S100A9 protein levels following TB treatment [ 10 , 11 ] . Therefore, S100A9 can be used not only for the diagnosis of TB but also for detecting the severity of the disease. In this study, we used transcriptome sequencing to analyze the transcriptome gene expression changes in THP-1 cells induced by S100A9 knockdown or overexpression. In addition, we identified genes involved in the IL-17 and ErbB signaling pathways as targets for S100A9 regulation in THP-1 cells. These results provide new insights into the role of S100A9 in the mechanism underlying the progression of TB. 2. Materials and methods 2.1 Cell culture The human monocytic leukemia cell line (THP-1), provided by the laboratory, was cultured in Roswell Park Memorial Institute-1640 (RPMI-1640; Gibco, Carlsbad, CA, USA) medium supplemented with 10% fetal bovine serum (FBS; Gibco, USA) in a humidified incubator supplied with 5% CO2 at 37°C. The THP-1 cells were incubated with phorbol 12-myristate 13-acetate (PMA; Multi Sciences, Hangzhou, China; 100 nM) for 36 hours to obtain macrophage-like THP-1 cells. 2.2 Bacterial culture and solution preparation The reference MTB strain H37Rv was collected at -80°C, freeze-thawed at room temperature (the strain was stored in the bacterial immune room of Beijing Chest Hospital), and cultured in neutral Roche solid media at 37°C for 3–4 weeks. Colonies were scraped from the solid medium with a sterile scraping ring and placed in a grinding flask. To disperse the culture equally, an ultrasonic disperser was used to produce a single bacterial suspension. Then, the bacteria were diluted with RPMI (10% FBS) medium, the OD at 600 nm was adjusted to 0.3 (1 × 10 7 ), and the bacterial concentration was adjusted according to an MOI of 10. 2.3 MTB-infected macrophages THP-1 cells were seeded into 12-well plates at 5×10 5 cells/well, and the THP-1 cells were incubated with PMA for 36 hours to obtain macrophage-like THP-1 cells. At an MOI of 10, the macrophages were infected with bacteria for 4 hours. The excess bacteria were removed, and the plates were washed three times with PBS (Solarbio; Beijing, China). Total RNA was collected from the cells before infection (0 h) and after infection (4 h, 8 h, 12 h, 24 h). 2.4 S100A9 interference and overexpression 2.4.1 S100A9 interference To knock down S100A9 gene expression in THP-1 cells, a human S100A9 shRNA lentiviral interference vector (id: 6280, NCBI RefSeq transcript: nm_002965.4) was generated. We cloned human S100A9 shRNA (target sequence: 5'- CATCAACACCTTCCACCAATACTCGAGTATTGGTGGAAGGTGTTGATG-3') and control scrambled shRNA into a lentiviral transfer plasmid (vector name: LV-U6 > hS100A9 [shRNA#1]-PGK > EGFP/T2A/Puro; LV-U6 > Scramble-shRNA-PGK > EGFP/T2A/Puro. The virus batch number was 220405LVH606;201224LVH605. The lentivirus and plasmid were transfected into THP-1 cells at the same time. After 48 h of culture, the cell culture supernatant was collected, and the virus particles were precipitated by ultracentrifugation. The virus titer was quantified by RT‒qPCR. Then, the collected lentivirus was infected with THP-1 cells at an MOI of 10 in complete medium for 48 hours, and the transduced cells and the blank cell group without transduced virus were changed to contain a concentration of 1.0 µg/ml of complete medium containing the antibiotic Puro for drug screening to generate stable cell lines. 2.4.2 S100A9 overexpression To overexpress the S100A9 gene in THP-1 cells, a lentiviral overexpression vector expressing the human S100A9 gene under the control of the EFS promoter was generated. THP-1 cells will be transduced with lentivirus and similarly treated with a concentration of 1.0 µg/ml of the antibiotic Puro to screen for drug-resistant cells and obtain stable cell lines. 2.5 RT‒qPCR A Tiangen animal tissue total RNA extraction kit was used to extract the total RNA from the samples subjected to examination, during which the DNA was removed. A Takara PrimeScript RT reagent kit with a gDNA eraser (perfect real time) kit was used for reverse transcription, and total RNA was used as a template to synthesize cDNA according to the manufacturer's instructions. The human GAPDH (hGAPDH) gene was used as an internal reference, and SYBR Green I dye was used for relative quantitative analysis of the expression of the target gene in the samples. Three parallel samples were made for each sample, and the 2 − △△ CT method was used for relative quantification. The sequences of the primers used in our study are shown in Table 1 . Table 1 ༎Primer Information Gene Name Primers Primer sequences Fragment size hGAPDH Forward GAGTCCACTGGCGTCTTCAC 123 bp Reverse ATGGTTCACACCCATGACGA hS100A9 Forward TCAAAGAGCTGGTGCGAAAAG 185 bp Reverse GTCACCCTCGTGCATCTTCT Forward, forward primer for the gene (5′ to 3′ on the plus strand); reverse, reverse primer for the gene (5′ to 3′ on the minus strand). 2.6 Transcriptome sequencing and gene expression quantification Total RNA extracted from S100A9-silenced and S100A9-overexpressing THP-1 cells was subjected to transcriptome sequencing using TRIzol reagent (Invitrogen, CA, USA). RNA purity and quantification were evaluated using a NanoDrop 2000 spectrophotometer (Thermo Scientific, USA). RNA integrity was assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Then, the libraries were constructed using the VAHTS Universal V6 RNA-seq Library Prep Kit according to the manufacturer's instructions. Transcriptome sequencing and analysis were conducted by Fanxing Biotech Co., Ltd. (Beijing, China). Libraries were sequenced on the Illumina NovaSeq 6000 platform, raw reads in fastq format were processed using fastp1, low-quality reads were removed to obtain clean reads, and clean reads were mapped to the reference genome using HISAT2 2. The FPKM 3 value for each gene was calculated, and the read counts for each gene were obtained via HTSeq-count 4. Differential expression analysis was performed using DESeq2 5. A Q value 2 or < 0.5 were set as the thresholds for significantly differentially expressed genes (DEGs). Hierarchical cluster analysis of DEGs was performed using R (v3.2.0) to determine the expression patterns of genes in different groups and samples. Based on the hypergeometric distribution, GO 6 and KEGG 7 pathway enrichment analyses of DEGs were performed to screen the significantly enriched terms using R (v3.2.0). R (v3.2.0) was used to construct the column diagram and bubble diagram of the significantly enriched terms. 2.7 Statistical analysis All the data are presented as the means ± standard deviations. Statistical analysis was performed using GraphPad Prism 8 software. Unpaired two-tailed t tests (for comparisons between two groups) or one-way ANOVA were used. A p value < 0.05 indicated a significant difference, and an asterisk indicates a significant difference (*, P < 0.05; **, P < 0.01; ****, P < 0.001; and *, *, P < 0.0001). 3. Results 3.1 Transcriptome differential gene analysis To explore the role of S100A9 in regulating the progression and aggravation of TB, we used THP-1 cells overexpressing the S100A9 gene (over group) or in which the S100A9 gene was silenced by shRNA for transcriptome sequencing (knockdown group) and normal control (control group) THP-1 cells. Our results showed that 1688 and 1369 genes were significantly upregulated and downregulated, respectively, in the knockdown group compared with the control group (Fig. 1 a). Compared with those in the control group, the expression levels of 68 and 66 genes were significantly upregulated and downregulated, respectively (Fig. 1 a). Compared with those in the knockdown group, 1612 and 1601 genes were significantly upregulated and downregulated, respectively, in the overexpression group (Fig. 1 a). In addition, many DEGs overlapped between different pairs (Fig. 1 b). The results of the differential gene expression level clustering analysis are shown in Fig. 1 c. 4.3 Results of differential gene function and pathway enrichment analysis After obtaining the DEGs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the DEGs revealed that the DEGs in the THP-1 cells after S100A9 gene overexpression or silencing could be considered their own regulatory genes. The GO functional enrichment results suggested that S100A9 is related to a variety of biological processes, such as cytoskeleton rearrangement, biological adhesion, cell killing, immune processes and regulation of biological processes (Fig. 2 ab). S100A9 gene overexpression mainly affects cell adhesion and transmembrane signal transduction and regulates protein synthesis, ubiquitination, vascular permeability and neuroskeletal muscle system function (Fig. 2 c, d). S100A9 gene silencing mainly affects the synthesis and transport of proteins, participates in the body's immune response, induces or inhibits the inflammatory response, and regulates cytokine activity (Fig. 2 .e,f). KEGG enrichment analysis revealed that the main upregulated pathways related to S100A9 gene overexpression were the IL-17 signaling pathway, ErbB signaling pathway, and several metabolism-related pathways, such as aldosterone and glucocorticoid metabolism (Fig. 3 .a), and the main downregulated pathway was axon guidance (Fig. 3 .b), which was consistent with the GO analysis results. S100A9 gene silencing mainly affected oxidative phosphorylation, lipid metabolism, and some nervous system diseases (Fig. 3 .c), and the enrichment and downregulation pathways were mainly the NF-κB pathway and rheumatoid- and diabetes-related pathways (Fig. 3 .d). To further clarify the relationship between the S100A9 protein and infection, we screened the DEGs related to infection and the immune system. In the S100A9 gene overexpression group, according to a p value < 0.05, we found eight DEGs, as shown in Table 2 . Table 2 Expression of differentially expressed genes in THP-1 cells overexpressing S100A9 Gene name Over group Knockdown group P value Expression level P value Expression level FOSB 3.31E-16 Upregulated - Downregulated IL17C 0.001 Upregulated 0.05 Downregulated EGR3 8.08E-5 Upregulated 1.84E-51 Downregulated FCER2 1.87E-05 Downregulated 1.20E-26 Downregulated SERPINE1 0.0008 Upregulated 4.20E-08 Upregulated IL1RAP 1.79E-07 Downregulated 2.40E-05 Upregulated MYO1B 0.0001 Downregulated 0.000118 Downregulated CAMK2B 4.07E-12 Upregulated - - -, sequencing data not provided 4.4. Validation results of differentially expressed genes To further clarify the changes in the S100A9 gene in THP-1 cells after MTB infection, we performed RT‒qPCR on samples obtained at 0 h, 4 h, 8 h and 12 h after MTB infection. The results are shown in Fig. 4 a. The expression of S100A9 increased after MTB infection, the highest expression was at 8 h, and the expression decreased after 24 h of infection. Therefore, we used the 8 h after MTB infection to perform RT‒qPCR on the selected 8 genes. The results, including those for EGR3, FCER2, and IL1RAP, are shown in Fig. 4 b. The results of MYO1B infection were inconsistent with the results of omics. The results for FOSB, IL17c, SERPINE1, and CAMK2B were consistent with the omics results. The expression of FOSB, IL17c, and serpine1 increased in the overexpression group and decreased in the knockdown group, while the expression of serpine1 increased in the knockdown group, and CAMK2B had no abnormal expression. Therefore, FOSB, IL17c, and CAMK2B may have some regulatory relationship with the S100A9 gene. According to the KEGG enrichment results, FOSB and IL17c may be related to the IL-17 signaling pathway, and CAMK2B may be related to the ErbB signaling pathway. 5. Discussion The severity of tuberculosis is closely linked to factors such as the number of Mycobacterium tuberculosis infections, virulence, and host immunity. The interaction between the host and pathogen affects various cellular processes, including cell apoptosis, autophagy, cytokine production, and macrophage polarization, all of which play a role in limiting the spread of MTB. However, in the state of immunosuppression or overactivation, MTB virulence factors affect macrophage homeostasis, which may lead to the growth of pathogens [ 12 ] . The weakened immune system defense of patients, failure to receive timely formal treatment, or the presence of other chronic diseases can also cause a large amount of MTB replication and reproduction, leading to worsening of TB. However, the relevant mechanisms are currently less studied. To further explore the molecular mechanism of S100A9 and TB exacerbation, this study established S100A9 knockdown and overexpression models in THP-1 cells in vitro, completed transcriptome sequencing, and finally screened and verified relevant DEGs. Transcriptome sequencing revealed that the expression levels of many genes were significantly altered due to overexpression or silencing of the S100A9 gene in THP-1 cells. GO functional enrichment results showed that S100A9 has complex functions and participates in the regulation of various biological processes in the body, with an important function being its involvement in the inflammatory response. S100A9 mainly originates from immune cells, and inflammation caused by infection is one of the main sources of S100A9 secretion. After infection with bacteria, neutrophils, macrophages, and monocytes strongly express and secrete S100A9, which regulates the inflammatory process by inducing inflammatory cytokines, reactive oxygen species (ROS), and nitric oxide (NO) [ 13 ] . FOSB is a member of the multigene Fos family and plays an important role in regulating cell growth and proliferation. Previous studies have shown that FOSB is highly expressed in glioma tissues. When FOSB is downregulated, glioma cell viability decreases, and the proliferation and migration ability of glioma cells decreases. This may promote the development and migration of glioma cells and decrease the survival of non-small cell lung cancer (NSCLC) patients [ 14 , 15 ] . There are also studies showing that FOSB is associated with inflammation after myocardial cell infarction [ 16 ] . IL17C is a member of the IL-17 family and is mainly expressed on CD4 + T cells, DCs, and macrophages. It may be involved in proinflammatory cytokine induction and neutrophil recruitment, but its specific mechanism of action is currently unclear. Research has shown that it plays a pathogenic role in kidney disease. IL-17C neutralizing antibodies significantly inhibited the expression of proinflammatory cytokines, inflammatory cell infiltration, and Th17/IL-17A activation. Hypoxia- or high glucose-induced upregulation of IL-17C is mainly caused by the NF-κB pathway [ 17 , 18 ] . The transcriptome sequencing results indicate that when S100A9 is overexpressed, the expression of both can increase, and S100A9 silencing is significantly inhibited. In THP-1 cells, after infection with Mycobacterium tuberculosis , the expression of S100A9 increases, but the expression level also changes depending on the infection time. The expression of FOSB and IL17C can also increase, which is consistent with the omics sequencing results. CAMK2B belongs to the serine/threonine protein kinase family and the Ca (2+)/calmodulin-dependent protein kinase subfamily. Studies have shown that after infection with MTB macrophages, the expression of CAMK2B increases, which may be related to ROS [ 19 ] , consistent with the results of this study. Bioinformatics analysis revealed that these DEGs are significantly enriched in multiple biological processes associated with infection, including multiple signaling pathways such as the IL-17 and ErbB pathways. Consistent with the homology of Toll-like receptors and IL-1 receptor signaling, IL-17 signaling can activate inflammatory transcription factors through the NF-κB and mitogen-activated protein kinase (MAPK) pathways to induce gene expression [ 17 ] . ErbB belongs to the epidermal growth factor receptor family, which includes an extracellular region, transmembrane region, and cytoplasmic tyrosine kinase region. Cell proliferation, migration, differentiation, apoptosis, and motility are regulated by the PI3K/Akt signaling pathway, JAK/STAT signaling pathway, and MAPK signaling pathway [ 20 ] . In conclusion, the sequencing results of the S100A9 knockdown or overexpression transcriptome in THP-1 cells revealed that the gene expression of many genes changed after S100A9 gene expression changed. These changes are related to various biological processes and signaling pathways. For the first time, our study revealed changes in the expression of genes related to IL-17, the ErbB signaling pathway and related genes, which may be related to the pathogenesis of TB. Although we found that MTB infection of macrophages can change the expression of DEGs related to the S100A9 gene, we selected only 8 DEGs for RT‒qPCR verification and did not verify them at the protein level, nor did we explore the specific molecular mechanism related to disease progression. In future research, it will be necessary to carry out further relevant experiments, refine the above related research content, and collect clinical samples for further improvement. 6. Conclusion Our study provides new insight into the further progression of this disease after S100A9 gene expression regulates MTB infection in host macrophages. In future studies, the functions of many genes identified in this study need to be further explored. Declarations Disclosure statement The authors declare no conflicts of interest. Funding This work was supported by the Beijing Hospitals Authority Ascent Plan (DFL20221401). Author Contribution Ruichao Liu: Writing-original draft, Project administration, Funding acquisition, Conceptualization. Shujuan Duan: Investigation. Jing Tong: Conceptualization. Siyu Yao: Methodology, Investigation. Qiuyue Liu: Review & editing, Writing – original draft, Supervision.Liang Li: Review & editing, Writing – original draft, Supervision, Funding acquisition, Conceptualization. Acknowledgments We are thankful to the participants in this study. Data Availability 'The data that support the findings of this study are openly available in GSA in the human database at https://ngdc.cncb.ac.cn/gsub/; the reference number is HRA006609 References BAGCCHI S. WHO's Global Tuberculosis Report 2022 [J]. Lancet Microbe. 2023;4(1):e20. Global tuberculosis report 2023. License: CC BY-NC-SA 3.0 IGO [J]. Geneva: World Health Organization; 2023. HU C, DAI X, WEI H, et al. Analysis of the clinical features of lethal cases in different intensive care units [J]. 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Overexpression of S100A9 in obesity impairs macrophage differentiation via TLR4-NFkB-signaling worsening inflammation and wound healing [J]. Theranostics. 2022;12(4):1659–82. SCOTT N R, SWANSON R V, AL-HAMMADI N, et al. S100A8/A9 regulates CD11b expression and neutrophil recruitment during chronic tuberculosis [J]. J Clin Invest. 2020;130(6):3098–112. XU D, LI Y, LI X, et al. Serum protein S100A9, SOD3, and MMP9 as new diagnostic biomarkers for pulmonary tuberculosis by iTRAQ-coupled two-dimensional LC–MS/MS [J]. Proteomics. 2015;15(1):58–67. AHMAD F, RANI A, ALAM A, et al. Macrophage: A Cell With Many Faces and Functions in Tuberculosis [J]. Front Immunol. 2022;13:747799. WANG S, SONG R, WANG Z, et al. S100A8/A9 in Inflammation [J]. Front Immunol. 2018;9:1298. QI M, SUN L-A, ZHENG L-R, et al. Expression and potential role of FOSB in glioma [J]. Front Mol Neurosci. 2022;15:972615. TING, C-H, LEE K-Y, WU S-M et al. FOSB⁻PCDHB13 Axis Disrupts the Microtubule Network in Non-Small Cell Lung Cancer [J]. Cancers (Basel), 2019, 11(1). HUANG C-K DAID, XIE H, et al. Lgr4 Governs a Pro-Inflammatory Program in Macrophages to Antagonize Post-Infarction Cardiac Repair [J]. Circ Res. 2020;127(8):953–73. IWAKURA Y, ISHIGAME H. Functional specialization of interleukin-17 family members [J]. Immunity. 2011;34(2):149–62. ZHANG F, YIN J, LIU L, et al. IL-17C neutralization protects the kidney against acute injury and chronic injury [J]. EBioMedicine. 2023;92:104607. SU R, YUAN J, GAO T, et al. Selection and validation of genes related to oxidative stress production and clearance in macrophages infected with Mycobacterium tuberculosis [J]. Front Cell Infect Microbiol. 2023;13:1324611. HO J, MOYES D L, TAVASSOLI M, et al. The Role of ErbB Receptors in Infection [J]. Trends Microbiol. 2017;25(11):942–52. Additional Declarations No competing interests reported. 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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-4237009","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":290212367,"identity":"41eae494-66ec-4777-8f97-f1e02ccee674","order_by":0,"name":"Ruichao Liu","email":"","orcid":"","institution":"Capital Medical University, Thoracic Tumor Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Ruichao","middleName":"","lastName":"Liu","suffix":""},{"id":290212368,"identity":"64b14e05-308a-4a12-b780-e1f9b34a8162","order_by":1,"name":"Shujuan Duan","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shujuan","middleName":"","lastName":"Duan","suffix":""},{"id":290212369,"identity":"a1a2151d-244a-45e8-ac0a-01eade6dd791","order_by":2,"name":"Jing Tong","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Tong","suffix":""},{"id":290212370,"identity":"e490578c-2474-4380-8cb9-0c668674a141","order_by":3,"name":"Siyu Yao","email":"","orcid":"","institution":"Capital Medical University, Thoracic Tumor Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Siyu","middleName":"","lastName":"Yao","suffix":""},{"id":290212371,"identity":"bf88702c-1219-4150-8f20-4928f395af48","order_by":4,"name":"Qiuyue Liu","email":"","orcid":"","institution":"Capital Medical University, Thoracic Tumor Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Qiuyue","middleName":"","lastName":"Liu","suffix":""},{"id":290212372,"identity":"3116698c-c110-4f71-a056-268a271f5215","order_by":5,"name":"Liang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBACPmbmhgNAWo6fvYEBxOAhqIWNmRGsxViy5wCxWhgYG0B0osGNBCIdxsbO2Hjg547aBIObbwwPFzDUyZizH2D88DEHv8MO9p45nid5O8fg8AyGwzyWPQnMkjO3EfALb9uxYr7baQmHeRgO8BgcSGBj5iWg5eDftmOJDTePgbTU8Ricf0BYy2HetprECTeYDwC1MPMAw4EILbJtB4CBnHzg8AyDw0AtD5vx+oWf//Dhj2/b6oBRebD5c0FFnb3B+eSDHz7i0QIFh8EkM4MBiILEFCFQB9UyCkbBKBgFowALAACuPFMhcWlauwAAAABJRU5ErkJggg==","orcid":"","institution":"Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Liang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-04-08 14:01:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4237009/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4237009/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54928246,"identity":"cb62a660-849e-4eff-bd19-d3b210b4c845","added_by":"auto","created_at":"2024-04-18 17:37:17","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":930862,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential genes after knockdown or overexpression. \u003c/strong\u003e(a) Knockdown and control groups, over- and control groups, and over- and knockdown groups are shown as volcano plots. (b) Venn diagrams of the knockdown and control groups, the overexpressionand control groups, and the overexpression and knockdown groups. (c) The differentially expressed and silenced genes of S100A9 are shown in the heatmap.\u003c/p\u003e","description":"","filename":"Figure1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4237009/v1/f99e34b26180ccb619465d57.jpeg"},{"id":54929202,"identity":"66c4a929-96b0-47f7-8d86-9324c3624d3e","added_by":"auto","created_at":"2024-04-18 17:45:17","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":451390,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS100A9 overexpression and silencing regulated GO functional enrichment in THP-1 cells. \u003c/strong\u003e(a, b) Biological processes related to S100A9 overexpression and silencing were significantly enriched in THP-1 cells. (c, d) Biological functions related toS100A9 overexpression. (e, f) Biological functions related toS100A9 silencing.\u003c/p\u003e","description":"","filename":"Figure2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4237009/v1/e0753032aa74763274d9ce6d.jpeg"},{"id":54928249,"identity":"4a809449-553a-48ed-8825-a897fa764aeb","added_by":"auto","created_at":"2024-04-18 17:37:17","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":300137,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS100A9 overexpression and silencing regulated KEGG functionalenrichment in THP-1 cells. \u003c/strong\u003e(a) Signaling pathways with upregulated genesenriched\u003c/p\u003e\n\u003cp\u003eand (b) signaling pathways with downregulated genesenriched related to differential gene enrichment during S100A9 overexpression. (c) Signaling pathways with upregulated genesenriched and (d) signaling pathways with downregulated genes enriched related to differential gene enrichment during S100A9 silencing.\u003c/p\u003e","description":"","filename":"Figure3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4237009/v1/cfb27dbe55ac279a35822543.jpeg"},{"id":54928247,"identity":"779b153c-e737-410c-bbf6-5c5964f0f91e","added_by":"auto","created_at":"2024-04-18 17:37:17","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":125932,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Changes in S100A9 gene expression over time in THP-1 cells infected with MTB. (b) Expression of differentially expressed genes in THP-1 cells hours after MTB infection.\u003c/p\u003e","description":"","filename":"Figure4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4237009/v1/e79245e893ae742513f083d3.jpeg"},{"id":59992399,"identity":"86d9e293-1cdb-4515-a435-428be61c98e6","added_by":"auto","created_at":"2024-07-10 08:47:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2328513,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4237009/v1/2ab02cc1-d46e-4d30-b017-09bc9e3aacfb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The role of S100A9 in the progression of tuberculosis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTuberculosis (TB) is a bacterial infectious disease caused by \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (MTB) infection. It spreads through the respiratory tract, is common in the lungs, and can also damage other tissues and organs. The World Health Organization reported that 1.6\u0026nbsp;million people died of TB in 2021. The COVID-19 pandemic further increased the burden of patients and had a significant impact on the diagnosis and treatment of TB. The most direct impact was a sharp decline in the number of newly diagnosed tuberculosis patients, an increase in the number of undiagnosed and untreated patients, an increase in the case fatality rate, and an increase in the spread of community infection, thereby increasing the number of patients with TB. By 2022, tuberculosis has become the world's second largest cause of death from a single source of infection, second only to coronavirus disease (COVID-19), and its death toll is almost twice that of HIV/AIDS\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Due to the influence of human immunodeficiency virus infection and the increasing number of drug-resistant strains, the diagnosis and treatment of pulmonary tuberculosis have become more difficult. Once patients have one or more tissue and organ failures in the clinic, they can progress to severe pulmonary tuberculosis, resulting in a variety of complications, such as massive hemoptysis, which threatens people's lives. The mortality of patients with pulmonary tuberculosis depends on the severity of the disease \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Early warning, timely diagnosis, and targeted treatment of patients with severe pulmonary TB have a direct impact on the mortality of patients, suggesting that the severity of lesions, rapid progression of lesions, and timely targeted treatment are important causes of death in patients with pulmonary TB\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough significant progress has been made in the diagnosis and treatment of tuberculosis, there are still few studies on severe pulmonary tuberculosis. Early judgment of the patient's condition and the development of effective prevention and treatment measures have certain clinical significance in reducing patient mortality.\u003c/p\u003e \u003cp\u003eS100A9, a Ca2+-binding protein belonging to the S100 family, is primarily expressed in neutrophils and monocytes and plays a pivotal role in regulating various inflammatory responses and inflammation-related diseases. Studies have shown that MTB Rv1768 regulates NF through S100A9-κB-TNF-α signaling and arachidonic acid metabolism, thereby promoting mycobacterial survival in macrophages\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Our previous studies showed that compared with that of healthy controls, the plasma S100A9 level of severe TB patients was significantly increased, and the plasma S100A9 level of nonsurvivors of severe TB patients was further increased compared with that of survivors of severe TB patients, which may be a marker for predicting TB exacerbation \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. S100A9 can also be expressed in macrophages; studies have shown that S100A9 can impair macrophage differentiation through TLR4-NF-κB signaling, leading to worsening inflammation and wound healing\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. These findings support the use of S100A9 as a diagnostic indicator of severe TB, with increased S100A9 mRNA expression in the whole blood of patients and patients with advanced-stage TB and a rapid reduction in serum S100A9 protein levels following TB treatment\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Therefore, S100A9 can be used not only for the diagnosis of TB but also for detecting the severity of the disease.\u003c/p\u003e \u003cp\u003eIn this study, we used transcriptome sequencing to analyze the transcriptome gene expression changes in THP-1 cells induced by S100A9 knockdown or overexpression. In addition, we identified genes involved in the IL-17 and ErbB signaling pathways as targets for S100A9 regulation in THP-1 cells. These results provide new insights into the role of S100A9 in the mechanism underlying the progression of TB.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Cell culture\u003c/h2\u003e \u003cp\u003eThe human monocytic leukemia cell line (THP-1), provided by the laboratory, was cultured in Roswell Park Memorial Institute-1640 (RPMI-1640; Gibco, Carlsbad, CA, USA) medium supplemented with 10% fetal bovine serum (FBS; Gibco, USA) in a humidified incubator supplied with 5% CO2 at 37\u0026deg;C. The THP-1 cells were incubated with phorbol 12-myristate 13-acetate (PMA; Multi Sciences, Hangzhou, China; 100 nM) for 36 hours to obtain macrophage-like THP-1 cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Bacterial culture and solution preparation\u003c/h2\u003e \u003cp\u003eThe reference MTB strain H37Rv was collected at -80\u0026deg;C, freeze-thawed at room temperature (the strain was stored in the bacterial immune room of Beijing Chest Hospital), and cultured in neutral Roche solid media at 37\u0026deg;C for 3\u0026ndash;4 weeks. Colonies were scraped from the solid medium with a sterile scraping ring and placed in a grinding flask. To disperse the culture equally, an ultrasonic disperser was used to produce a single bacterial suspension. Then, the bacteria were diluted with RPMI (10% FBS) medium, the OD at 600 nm was adjusted to 0.3 (1 \u0026times; 10\u003csup\u003e7\u003c/sup\u003e), and the bacterial concentration was adjusted according to an MOI of 10.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 MTB-infected macrophages\u003c/h2\u003e \u003cp\u003eTHP-1 cells were seeded into 12-well plates at 5\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells/well, and the THP-1 cells were incubated with PMA for 36 hours to obtain macrophage-like THP-1 cells. At an MOI of 10, the macrophages were infected with bacteria for 4 hours.\u003c/p\u003e \u003cp\u003eThe excess bacteria were removed, and the plates were washed three times with PBS (Solarbio; Beijing, China). Total RNA was collected from the cells before infection (0 h) and after infection (4 h, 8 h, 12 h, 24 h).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 S100A9 interference and overexpression\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 S100A9 interference\u003c/h2\u003e \u003cp\u003eTo knock down S100A9 gene expression in THP-1 cells, a human S100A9 shRNA lentiviral interference vector (id: 6280, NCBI RefSeq transcript: nm_002965.4) was generated. We cloned human S100A9 shRNA (target sequence: 5'- CATCAACACCTTCCACCAATACTCGAGTATTGGTGGAAGGTGTTGATG-3') and control scrambled shRNA into a lentiviral transfer plasmid (vector name: LV-U6\u0026thinsp;\u0026gt;\u0026thinsp;hS100A9 [shRNA#1]-PGK\u0026thinsp;\u0026gt;\u0026thinsp;EGFP/T2A/Puro; LV-U6\u0026thinsp;\u0026gt;\u0026thinsp;Scramble-shRNA-PGK\u0026thinsp;\u0026gt;\u0026thinsp;EGFP/T2A/Puro. The virus batch number was 220405LVH606;201224LVH605. The lentivirus and plasmid were transfected into THP-1 cells at the same time. After 48 h of culture, the cell culture supernatant was collected, and the virus particles were precipitated by ultracentrifugation. The virus titer was quantified by RT‒qPCR. Then, the collected lentivirus was infected with THP-1 cells at an MOI of 10 in complete medium for 48 hours, and the transduced cells and the blank cell group without transduced virus were changed to contain a concentration of 1.0 \u0026micro;g/ml of complete medium containing the antibiotic Puro for drug screening to generate stable cell lines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 S100A9 overexpression\u003c/h2\u003e \u003cp\u003eTo overexpress the S100A9 gene in THP-1 cells, a lentiviral overexpression vector expressing the human S100A9 gene under the control of the EFS promoter was generated. THP-1 cells will be transduced with lentivirus and similarly treated with a concentration of 1.0 \u0026micro;g/ml of the antibiotic Puro to screen for drug-resistant cells and obtain stable cell lines.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5 RT‒qPCR\u003c/h2\u003e \u003cp\u003e \u003cb\u003eA\u003c/b\u003e Tiangen animal tissue total RNA extraction kit was used to extract the total RNA from the samples subjected to examination, during which the DNA was removed. A Takara PrimeScript RT reagent kit with a gDNA eraser (perfect real time) kit was used for reverse transcription, and total RNA was used as a template to synthesize cDNA according to the manufacturer's instructions. The human GAPDH (hGAPDH) gene was used as an internal reference, and SYBR Green I dye was used for relative quantitative analysis of the expression of the target gene in the samples. Three parallel samples were made for each sample, and the 2\u003csup\u003e\u0026minus; △△ CT\u003c/sup\u003e method was used for relative quantification. The sequences of the primers used in our study are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e༎Primer Information\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimer sequences\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFragment size\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehGAPDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGAGTCCACTGGCGTCTTCAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e123 bp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATGGTTCACACCCATGACGA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehS100A9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCAAAGAGCTGGTGCGAAAAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e185 bp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTCACCCTCGTGCATCTTCT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eForward, forward primer for the gene (5\u0026prime; to 3\u0026prime; on the plus strand); reverse, reverse primer for the gene (5\u0026prime; to 3\u0026prime; on the minus strand).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Transcriptome sequencing and gene expression quantification\u003c/h2\u003e \u003cp\u003eTotal RNA extracted from S100A9-silenced and S100A9-overexpressing THP-1 cells was subjected to transcriptome sequencing using TRIzol reagent (Invitrogen, CA, USA). RNA purity and quantification were evaluated using a NanoDrop 2000 spectrophotometer (Thermo Scientific, USA). RNA integrity was assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Then, the libraries were constructed using the VAHTS Universal V6 RNA-seq Library Prep Kit according to the manufacturer's instructions. Transcriptome sequencing and analysis were conducted by Fanxing Biotech Co., Ltd. (Beijing, China). Libraries were sequenced on the Illumina NovaSeq 6000 platform, raw reads in fastq format were processed using fastp1, low-quality reads were removed to obtain clean reads, and clean reads were mapped to the reference genome using HISAT2 2. The FPKM 3 value for each gene was calculated, and the read counts for each gene were obtained via HTSeq-count 4.\u003c/p\u003e \u003cp\u003eDifferential expression analysis was performed using DESeq2 5. A Q value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a fold change\u0026thinsp;\u0026gt;\u0026thinsp;2 or \u0026lt;\u0026thinsp;0.5 were set as the thresholds for significantly differentially expressed genes (DEGs). Hierarchical cluster analysis of DEGs was performed using R (v3.2.0) to determine the expression patterns of genes in different groups and samples. Based on the hypergeometric distribution, GO 6 and KEGG 7 pathway enrichment analyses of DEGs were performed to screen the significantly enriched terms using R (v3.2.0). R (v3.2.0) was used to construct the column diagram and bubble diagram of the significantly enriched terms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll the data are presented as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations. Statistical analysis was performed using GraphPad Prism 8 software. Unpaired two-tailed t tests (for comparisons between two groups) or one-way ANOVA were used. A p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated a significant difference, and an asterisk indicates a significant difference (*, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ****, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; and *, *, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Transcriptome differential gene analysis\u003c/h2\u003e \u003cp\u003eTo explore the role of S100A9 in regulating the progression and aggravation of TB, we used THP-1 cells overexpressing the S100A9 gene (over group) or in which the S100A9 gene was silenced by shRNA for transcriptome sequencing (knockdown group) and normal control (control group) THP-1 cells. Our results showed that 1688 and 1369 genes were significantly upregulated and downregulated, respectively, in the knockdown group compared with the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Compared with those in the control group, the expression levels of 68 and 66 genes were significantly upregulated and downregulated, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Compared with those in the knockdown group, 1612 and 1601 genes were significantly upregulated and downregulated, respectively, in the overexpression group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). In addition, many DEGs overlapped between different pairs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). The results of the differential gene expression level clustering analysis are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Results of differential gene function and pathway enrichment analysis\u003c/h2\u003e \u003cp\u003eAfter obtaining the DEGs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the DEGs revealed that the DEGs in the THP-1 cells after S100A9 gene overexpression or silencing could be considered their own regulatory genes. The GO functional enrichment results suggested that S100A9 is related to a variety of biological processes, such as cytoskeleton rearrangement, biological adhesion, cell killing, immune processes and regulation of biological processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eab). S100A9 gene overexpression mainly affects cell adhesion and transmembrane signal transduction and regulates protein synthesis, ubiquitination, vascular permeability and neuroskeletal muscle system function (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, d). S100A9 gene silencing mainly affects the synthesis and transport of proteins, participates in the body's immune response, induces or inhibits the inflammatory response, and regulates cytokine activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.e,f). KEGG enrichment analysis revealed that the main upregulated pathways related to S100A9 gene overexpression were the IL-17 signaling pathway, ErbB signaling pathway, and several metabolism-related pathways, such as aldosterone and glucocorticoid metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.a), and the main downregulated pathway was axon guidance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.b), which was consistent with the GO analysis results. S100A9 gene silencing mainly affected oxidative phosphorylation, lipid metabolism, and some nervous system diseases (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.c), and the enrichment and downregulation pathways were mainly the NF-κB pathway and rheumatoid- and diabetes-related pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.d). To further clarify the relationship between the S100A9 protein and infection, we screened the DEGs related to infection and the immune system. In the S100A9 gene overexpression group, according to a p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, we found eight DEGs, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExpression of differentially expressed genes in THP-1 cells overexpressing S100A9\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGene name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOver group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eKnockdown group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExpression level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExpression level\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFOSB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.31E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUpregulated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDownregulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL17C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUpregulated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDownregulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.08E-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUpregulated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.84E-51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDownregulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCER2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.87E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDownregulated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20E-26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDownregulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSERPINE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUpregulated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.20E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUpregulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL1RAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.79E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDownregulated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.40E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUpregulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMYO1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDownregulated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDownregulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAMK2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.07E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUpregulated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e-, sequencing data not provided\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Validation results of differentially expressed genes\u003c/h2\u003e \u003cp\u003eTo further clarify the changes in the S100A9 gene in THP-1 cells after MTB infection, we performed RT‒qPCR on samples obtained at 0 h, 4 h, 8 h and 12 h after MTB infection. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea. The expression of S100A9 increased after MTB infection, the highest expression was at 8 h, and the expression decreased after 24 h of infection. Therefore, we used the 8 h after MTB infection to perform RT‒qPCR on the selected 8 genes. The results, including those for EGR3, FCER2, and IL1RAP, are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb. The results of MYO1B infection were inconsistent with the results of omics. The results for FOSB, IL17c, SERPINE1, and CAMK2B were consistent with the omics results. The expression of FOSB, IL17c, and serpine1 increased in the overexpression group and decreased in the knockdown group, while the expression of serpine1 increased in the knockdown group, and CAMK2B had no abnormal expression. Therefore, FOSB, IL17c, and CAMK2B may have some regulatory relationship with the S100A9 gene. According to the KEGG enrichment results, FOSB and IL17c may be related to the IL-17 signaling pathway, and CAMK2B may be related to the ErbB signaling pathway.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe severity of tuberculosis is closely linked to factors such as the number of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e infections, virulence, and host immunity. The interaction between the host and pathogen affects various cellular processes, including cell apoptosis, autophagy, cytokine production, and macrophage polarization, all of which play a role in limiting the spread of MTB. However, in the state of immunosuppression or overactivation, MTB virulence factors affect macrophage homeostasis, which may lead to the growth of pathogens \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. The weakened immune system defense of patients, failure to receive timely formal treatment, or the presence of other chronic diseases can also cause a large amount of MTB replication and reproduction, leading to worsening of TB. However, the relevant mechanisms are currently less studied. To further explore the molecular mechanism of S100A9 and TB exacerbation, this study established S100A9 knockdown and overexpression models in THP-1 cells in vitro, completed transcriptome sequencing, and finally screened and verified relevant DEGs. Transcriptome sequencing revealed that the expression levels of many genes were significantly altered due to overexpression or silencing of the S100A9 gene in THP-1 cells. GO functional enrichment results showed that S100A9 has complex functions and participates in the regulation of various biological processes in the body, with an important function being its involvement in the inflammatory response. S100A9 mainly originates from immune cells, and inflammation caused by infection is one of the main sources of S100A9 secretion. After infection with bacteria, neutrophils, macrophages, and monocytes strongly express and secrete S100A9, which regulates the inflammatory process by inducing inflammatory cytokines, reactive oxygen species (ROS), and nitric oxide (NO) \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFOSB is a member of the multigene Fos family and plays an important role in regulating cell growth and proliferation. Previous studies have shown that FOSB is highly expressed in glioma tissues. When FOSB is downregulated, glioma cell viability decreases, and the proliferation and migration ability of glioma cells decreases. This may promote the development and migration of glioma cells and decrease the survival of non-small cell lung cancer (NSCLC) patients\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. There are also studies showing that FOSB is associated with inflammation after myocardial cell infarction \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. IL17C is a member of the IL-17 family and is mainly expressed on CD4\u0026thinsp;+\u0026thinsp;T cells, DCs, and macrophages. It may be involved in proinflammatory cytokine induction and neutrophil recruitment, but its specific mechanism of action is currently unclear. Research has shown that it plays a pathogenic role in kidney disease. IL-17C neutralizing antibodies significantly inhibited the expression of proinflammatory cytokines, inflammatory cell infiltration, and Th17/IL-17A activation. Hypoxia- or high glucose-induced upregulation of IL-17C is mainly caused by the NF-κB pathway \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The transcriptome sequencing results indicate that when S100A9 is overexpressed, the expression of both can increase, and S100A9 silencing is significantly inhibited. In THP-1 cells, after infection with \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e, the expression of S100A9 increases, but the expression level also changes depending on the infection time. The expression of FOSB and IL17C can also increase, which is consistent with the omics sequencing results. CAMK2B belongs to the serine/threonine protein kinase family and the Ca (2+)/calmodulin-dependent protein kinase subfamily. Studies have shown that after infection with MTB macrophages, the expression of CAMK2B increases, which may be related to ROS\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, consistent with the results of this study.\u003c/p\u003e \u003cp\u003eBioinformatics analysis revealed that these DEGs are significantly enriched in multiple biological processes associated with infection, including multiple signaling pathways such as the IL-17 and ErbB pathways. Consistent with the homology of Toll-like receptors and IL-1 receptor signaling, IL-17 signaling can activate inflammatory transcription factors through the NF-κB and mitogen-activated protein kinase (MAPK) pathways to induce gene expression \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. ErbB belongs to the epidermal growth factor receptor family, which includes an extracellular region, transmembrane region, and cytoplasmic tyrosine kinase region. Cell proliferation, migration, differentiation, apoptosis, and motility are regulated by the PI3K/Akt signaling pathway, JAK/STAT signaling pathway, and MAPK signaling pathway\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. In conclusion, the sequencing results of the S100A9 knockdown or overexpression transcriptome in THP-1 cells revealed that the gene expression of many genes changed after S100A9 gene expression changed. These changes are related to various biological processes and signaling pathways. For the first time, our study revealed changes in the expression of genes related to IL-17, the ErbB signaling pathway and related genes, which may be related to the pathogenesis of TB. Although we found that MTB infection of macrophages can change the expression of DEGs related to the S100A9 gene, we selected only 8 DEGs for RT‒qPCR verification and did not verify them at the protein level, nor did we explore the specific molecular mechanism related to disease progression. In future research, it will be necessary to carry out further relevant experiments, refine the above related research content, and collect clinical samples for further improvement.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eOur study provides new insight into the further progression of this disease after S100A9 gene expression regulates MTB infection in host macrophages. In future studies, the functions of many genes identified in this study need to be further explored.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no\u0026nbsp;conflicts\u0026nbsp;of interest.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Beijing Hospitals Authority Ascent Plan (DFL20221401).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRuichao Liu: Writing-original draft, Project administration, Funding acquisition, Conceptualization. Shujuan Duan: Investigation. Jing Tong: Conceptualization. Siyu Yao: Methodology, Investigation. Qiuyue Liu: Review \u0026amp; editing, Writing \u0026ndash; original draft, Supervision.Liang Li: Review \u0026amp; editing, Writing \u0026ndash; original draft, Supervision, Funding acquisition, Conceptualization.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe are thankful to the participants in this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003e'The data that support the findings of this study are openly available in GSA in the human database at https://ngdc.cncb.ac.cn/gsub/; the reference number is HRA006609\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBAGCCHI S. WHO's Global Tuberculosis Report 2022 [J]. Lancet Microbe. 2023;4(1):e20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal tuberculosis report 2023. License: CC BY-NC-SA 3.0 IGO [J]. Geneva: World Health Organization; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHU C, DAI X, WEI H, et al. Analysis of the clinical features of lethal cases in different intensive care units [J]. Chin J Emerg Med. 2017;46(7):1307\u0026ndash;12. ().\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePENNER C, ROBERTS D. Tuberculosis as a primary cause of respiratory failure requiring mechanical ventilation [J]. Am J Respir Crit Care Med. 1995;151(3 Pt 1):867\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePASSI N N. Tuberculosis on the intensive care unit [J]. Br J Hosp Med (Lond). 2018;79(3):142\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLIU S, XIE Y, LUO W, et al. PE_PGRS31-S100A9 Interaction Promotes Mycobacterial Survival in Macrophages Through the Regulation of NF-κB-TNF-α Signaling and Arachidonic Acid Metabolism [J]. Front Microbiol. 2020;11:845.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCHEN Y, OUYANG Y, LI Z, et al. S100A8 and S100A9 in Cancer [J]. Biochim Biophys Acta Rev Cancer. 2023;1878(3):188891.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLIU Q, LI R, LI Q, et al. High levels of plasma S100A9 at admission indicate an increased risk of death in severe tuberculosis patients [J]. J Clin Tuberc Other Mycobact Dis. 2021;25:100270.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFRANZ S, ERTEL A, ENGEL K M, et al. Overexpression of S100A9 in obesity impairs macrophage differentiation via TLR4-NFkB-signaling worsening inflammation and wound healing [J]. Theranostics. 2022;12(4):1659\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSCOTT N R, SWANSON R V, AL-HAMMADI N, et al. S100A8/A9 regulates CD11b expression and neutrophil recruitment during chronic tuberculosis [J]. 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FOSB⁻PCDHB13 Axis Disrupts the Microtubule Network in Non-Small Cell Lung Cancer [J]. Cancers (Basel), 2019, 11(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHUANG C-K DAID, XIE H, et al. Lgr4 Governs a Pro-Inflammatory Program in Macrophages to Antagonize Post-Infarction Cardiac Repair [J]. Circ Res. 2020;127(8):953\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIWAKURA Y, ISHIGAME H. Functional specialization of interleukin-17 family members [J]. Immunity. 2011;34(2):149\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZHANG F, YIN J, LIU L, et al. IL-17C neutralization protects the kidney against acute injury and chronic injury [J]. EBioMedicine. 2023;92:104607.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSU R, YUAN J, GAO T, et al. Selection and validation of genes related to oxidative stress production and clearance in macrophages infected with \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e [J]. Front Cell Infect Microbiol. 2023;13:1324611.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHO J, MOYES D L, TAVASSOLI M, et al. The Role of ErbB Receptors in Infection [J]. Trends Microbiol. 2017;25(11):942\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\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":"Mycobacterium tuberculosis, THP-1 cell, S100A9","lastPublishedDoi":"10.21203/rs.3.rs-4237009/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4237009/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eElevated plasma levels of S100A9 have been observed in patients with severe tuberculosis, with further increases in patients with poor prognosis, suggesting that S100A9 is a potential biomarker for disease progression and prognosis. However, the molecular mechanism underlying its potential remains unclear, highlighting the importance of exploring its function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e To further investigate the role of S100A9 in severe tuberculosis, we constructed S100A9 gene knockout or overexpression models and analyzed the transcriptome changes in THP-1 cells following S100A9 overexpression or shRNA silencing using next-generation sequencing. Through the analysis of transcriptome sequencing results, we identified eight genes that may be involved in the regulation of S100A9 expression. We also detected the expression of the S100A9 gene and related differentially expressed genes after \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003einfection, as well as their enrichment and related pathways. It was inferred that S100A9 may be involved in the mechanism by which tuberculosis progresses to severe tuberculosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e FOSB and IL17c are potentially related to the IL-17 signaling pathway, while calcium/calmodulin-dependent protein kinase II beta (CAMK2B) may be related to the ErbB signaling pathway. These findings indicate that these genes may promote the progression of tuberculosis through different mechanisms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Our study explored the potential role and mechanism of S100A9 in the development of tuberculosis, providing a new perspective for the development of treatment strategies for this disease.\u003c/p\u003e","manuscriptTitle":"The role of S100A9 in the progression of tuberculosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-18 17:37:12","doi":"10.21203/rs.3.rs-4237009/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":"f11e6bbb-08cf-4fad-94e9-c90b2977060b","owner":[],"postedDate":"April 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-10T08:39:46+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-18 17:37:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4237009","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4237009","identity":"rs-4237009","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}