Critical roles of S100A12, MMP9, and PRTN3 in sepsis diagnosis: Insights from multiple microarray data analyses.

BACKGROUND: Sepsis, characterized by systemic inflammatory response syndrome and life-threatening organ dysfunction, remains a significant global cause of disability and death. Despite its impact, reliable biomarkers for sepsis diagnosis are yet to be identified. OBJECTIVE: This study aims to investigate and identify key genes and pathways in sepsis through the analysis of multiple microarray datasets, providing potential treatment targets for future clinical trials. METHODS: Two independent gene expression profiles (GSE54514 and GSE69528) were downloaded from the Gene Expression Omnibus (GEO) database. After merging and batch normalization, differentially expressed genes (DEGs) were obtained using the "limma" package. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) were performed using "R" software. A Protein-Protein Interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING). The top 10 hub genes were identified using Cytoscape. A Nomogram model for predicting sepsis occurrence was constructed and evaluated. RESULTS: Bioinformatic analysis of 210 sepsis and 91 control blood samples identified 72 DEGs. GO analyses revealed associations with immune response processes. GSEA indicated involvement in key signaling pathways. S100A12, MMP9, and PRTN3 were identified as independent risk factors for sepsis. CONCLUSION: This study unveils critical genes and pathways in sepsis through bioinformatic methods. S100A12, MMP9, and PRTN3 may play essential roles in the immune response to infection, influencing sepsis prognosis.

A two-gene marker for the two-tiered innate immune response in COVID-19 patients.

For coronavirus disease 2019 (COVID-19), a pandemic disease characterized by strong immune dysregulation in severe patients, convenient and efficient monitoring of the host immune response is critical. Human hosts respond to viral and bacterial infections in different ways, the former is characterized by the activation of interferon stimulated genes (ISGs) such as IFI27, while the latter is characterized by the activation of anti-bacterial associated genes (ABGs) such as S100A12. This two-tiered innate immune response has not been examined in COVID-19. In this study, the activation patterns of this two-tiered innate immune response represented by IFI27 and S100A12 were explored based on 1421 samples from 17 transcriptome datasets derived from the blood of COVID-19 patients and relevant controls. It was found that IFI27 activation occurred in most of the symptomatic patients and displayed no correlation with disease severity, while S100A12 activation was more restricted to patients under severe and critical conditions with a stepwise activation pattern. In addition, most of the S100A12 activation was accompanied by IFI27 activation. Furthermore, the activation of IFI27 was most pronounced within the first week of symptom onset, but generally waned after 2-3 weeks. On the other hand, the activation of S100A12 displayed no apparent correlation with disease duration and could last for several months in certain patients. These features of the two-tiered innate immune response can further our understanding on the disease mechanism of COVID-19 and may have implications to the clinical triage. Development of a convenient two-gene protocol for the routine serial monitoring of this two-tiered immune response will be a valuable addition to the existing laboratory tests.

Identification of hub genes and potential biomarkers of neutrophilic asthma: evidence from a bioinformatics analysis.

OBJECTIVE: Asthma is a chronic airway inflammatory disease caused by multiple genetic and environmental factors. This study mainly sought to provide potential therapeutic targets and biomarkers for neutrophilic asthma (NA). METHODS: Three gene expression profiling datasets were obtained from the Genome Expression Omnibus (GEO) database. GSE45111 and GSE41863 were used to identify hub genes and potential biomarkers, and GSE137268 was used for data verification. We verified the repeatability of intragroup data and identified differentially expressed genes (DEGs). Then, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the DEGs, and a protein-protein interaction (PPI) network was constructed to identify the hub genes. Finally, receiver operating characteristic (ROC) analysis was used to verify the ability of the hub genes to differentiate between NA and eosinophilic asthma (EA). RESULTS: In this study, we identified 411 DEGs by comprehensive analysis of NA/EA patients and NA/healthy controls (HCs). Ten hub genes (CXCR1, FCGR3B, CXCR2, SELL, S100A12, CSF3R, IL6R, JAK3, CD48, and GNG2) were identified from the PPI network. Finally, based on the ROC analysis, 7 genes showed good diagnostic value for discriminating NA from EA-CXCR1, FCGR3B, CXCR2, SELL, S100A12, CSF3R, and IL6R (AUC > 0.7). CONCLUSION: We identified 7 hub genes that can distinguish NA from EA. The IL-8-mediated signaling may be the primary pathway to determine the NA phenotype in asthma. CXCR1/2 and S100A12 may be the primary genes determining the NA phenotype. CXCR1/2 and S100A12 might be biomarkers and new therapeutic targets for NA.Supplemental data for this article is available online at at.

Analysis of signature genes and association with immune cells infiltration in pediatric septic shock.

Background: Early diagnosis of septic shock in children is critical for prognosis. This study committed to investigate the signature genes and their connection with immune cells in pediatric septic shock. Methods: We screened a dataset of children with septic shock from the GEO database and analyzed differentially expressed genes (DEGs). Functional enrichment analysis was performed for these DEGs. Weighted gene co-expression network analysis (WCGNA) was used to screen the key modules. Least absolute shrinkage and selection operator (LASSO) and random forest analysis were finally applied to identify the signature genes. Then gene set enrichment analysis (GSEA) was exerted to explore the signaling pathways related to the hub genes. And the immune cells infiltration was subsequently classified via using CIBERSORT. Results: A total of 534 DEGs were screened from GSE26440. The data then was clustered into 17 modules via WGCNA, which MEgrey module was significantly related to pediatric septic shock (cor=-0.62, p<0.0001). LASSO and random forest algorithms were applied to select the signature genes, containing UPP1, S100A9, KIF1B, S100A12, SLC26A8. The receiver operating characteristic curve (ROC) of these signature genes was 0.965, 0.977, 0.984, 0.991 and 0.989, respectively, which were verified in the external dataset from GSE13904. GSEA analysis showed these signature genes involve in positively correlated fructose and mannose metabolism and starch and sucrose metabolism signaling pathway. CIBERSORT suggested these signature genes may participate in immune cells infiltration. Conclusion: UPP1, S100A9, KIF1B, S100A12, SLC26A8 emerge remarkable diagnostic performance in pediatric septic shock and involved in immune cells infiltration.

Identification and validation of the common pathogenesis and hub biomarkers in Hirschsprung disease complicated with Crohn's disease.

Background: Although increasing evidence has supported that Hirschsprung disease (HSCR) is the risk factor for children developing Crohn's disease (CD), the common mechanism of its co-occurrence remains unknown. The purpose of this study is to further explore the underlying mechanism and biomarkers for the co-occurrence of HSCR and CD. Methods: The Gene Expression Omnibus (GEO) database was used to obtain gene expression profiles for CD (GSE95095) and HSCR (GSE98502). Following the identification of the shared differentially expressed genes (DEGs) of CD and HSCR, functional annotation, protein-protein interaction (PPI) network creation, and module assembly were performed to discover hub genes. RT-qPCR was performed to validate the expression of the hub genes in HSCR samples. The receiver operating characteristic (ROC) curve was utilized to assess the accuracy of the hub genes as biomarkers in predicting CD in both the training dataset and test dataset. Results: A total of 103 common DEGs (50 downregulated genes and 53 upregulated genes) were chosen for further investigation. The importance of chemokines and cytokines in these two disorders is highlighted by functional analysis. MCODE plug identified three important modules, which functionally enriched the immune system process. Finally, nine hub genes were identified using cytoHubba, including IL1B, IL10, CXCL10, ICAM1, EGR1, FCGR3A, S100A12, S100A9, and FPR1. The nine hub genes were mainly enriched in immune- and inflammation-related pathways. External data profiles and RT-qPCR confirmed the expression of the nine hub genes in HSCR and CD. ROC analysis revealed that the nine hub genes had a strong diagnostic value. Conclusion: Our study reveals the common pathogenesis of HSCR and CD. These hub genes and diagnostic models may provide novel insight for the diagnosis and treatment of HSCR complicated with CD.

Identification of Key Gene Targets for Periodontitis Treatment by Bioinformatics Analysis.

Background: Periodontitis is considered to be the leading cause of tooth loss in adults, and it interacts with some serious systemic diseases. Periodontal basic therapy is the cornerstone of periodontal disease treatment and long-term maintenance and has a positive impact on the treatment of systemic diseases. Aim: To explore the potential gene targets of periodontitis therapies by bioinformatics method. Methods: We analyzed the expression database (GSE6751) downloaded from the Gene Expression Omnibus (GEO) with weighted gene coexpression network analysis (WGCNA) to confirm the functional gene modules. Pathway enrichment network analyses the key genes in functional modules and verified the candidate genes from the samples in peripheral blood sources of GSE43525. Moreover, we confirmed the expression of target protein in the periodontal tissues of experimental periodontitis-afflicted mice using western blotting. Results: The functional gene modules were found to have biological processes, and ARRB2, BIRC3, CD14, DYNLL1, FCER1G, FCGR1A, FCGR2B, FGR, HCK, and PRKCD were screened as candidates' genes in functional modules. The 921 DEG from GSE43525 and 418 DEG is from the green module of GSE6751 and identified AMICA1, KDELR1, DHRS7B, LMNB1, CTSA, S100A12, and FCGR1A as target genes. Finally, FCGR1A (CD64) was confirmed as the key gene that affects periodontal treatment. Western blot analysis showed an increasing trend in the expression level of FCGR1A protein in the periodontal tissues of experimental periodontitis mice compared to normal mice. Conclusions: FCGR1A (CD64) may be a key gene target for periodontal therapy in patients with periodontitis and other systemic diseases.

Comprehensive transcriptome analysis of sika deer antler using PacBio and Illumina sequencing.

Antler is the fastest growing and ossifying tissue in animals and it is a valuable model for cartilage/bone development. To understand the molecular mechanisms of chondrogenesis and osteogenesis of antlers, the PacBio Sequel II and Illumina sequencing technology were combined and used to investigate the mRNA expression profiles in antler tip, middle, and base at six different developmental stages, i.e., at 15th, 25th, 45th, 65th, 100th and 130th growth days. Consequently, we identified 24,856 genes (FPKM > 0.1), including 8778 novel genes. Besides, principal component analysis (PCA) revealed a significant separation between the growth stage (25th, 45th and 65th days) and ossification stage (100th and 130th days). COL2A1 gene was significantly abundant in the growth stage, whereas S100A7, S100A12, S100A8, and WFDC18 genes were abundant at the ossification stage. Subsequently screened to 14,765 significantly differentially expressed genes (DEGs), WGCNA and GO functional enrichment analyses revealed that genes related to cell division and chondrocyte differentiation were up-regulated, whereas those with steroid hormone-mediated signaling pathways were down-regulated at ossification stages. Additionally, 25 tumor suppressor genes and 11 oncogenes were identified and were predicted to interact with p53. Co-regulation of tumor suppressor genes and oncogenes is responsible for the special growth pattern of antlers. Together, we constructed the most complete sika deer antler transcriptome database so far. The database provides data support for subsequent studies on the molecular mechanism of sika deer antler chondrogenesis and osteogenesis.

Inflammation and Oxidative Stress Role of S100A12 as a Potential Diagnostic and Therapeutic Biomarker in Acute Myocardial Infarction.

Acute myocardial infarction (AMI) is one of the most serious cardiovascular diseases with high morbidity and mortality. Numerous studies have indicated that S100A12 may has an essential role in the occurrence and development of AMI, and in-depth studies are currently lacking. The purpose of this study is to investigate the effect of S100A12 on inflammation and oxidative stress and to determine its clinical applicability in AMI. Here, AMI datasets used to explore the expression pattern of S100A12 in AMI were derived from the Gene Expression Omnibus (GEO) database. The pooled standard average deviation (SMD) was calculated to further determine S100A12 expression. The overlapping differentially expressed genes (DEGs) contained in all included datasets were recognized by the GEO2R tool. Then, functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, were carried out to determine the molecular function of overlapping DEGs. Gene set enrichment analysis (GSEA) was conducted to determine unrevealed mechanisms of S100A12. Summary receiver operating characteristic (SROC) curve analysis and receiver operating characteristic (ROC) curve analysis were carried out to identify the diagnostic capabilities of S100A12. Moreover, we screened miRNAs targeting S100A12 using three online databases (miRWalk, TargetScan, and miRDB). In addition, by comprehensively using enzyme-linked immunosorbent assay (ELISA), real-time quantitative PCR (RT-qPCR), Western blotting (WB) methods, etc., we used the AC16 cells to validate the expression and underlying mechanism of S100A12. In our study, five datasets related to AMI, GSE24519, GSE60993, GSE66360, GSE97320, and GSE48060 were included; 412 overlapping DEGs were identified. Protein-protein interaction (PPI) network and functional analyses showed that S100A12 was a pivotal gene related to inflammation and oxidative stress. Then, S100A12 overexpression was identified based on the included datasets. The pooled standard average deviation (SMD) also showed that S100A12 was upregulated in AMI (SMD = 1.36, 95% CI: 0.70-2.03, p = 0.024). The SROC curve analysis result suggested that S100A12 had remarkable diagnostic ability in AMI (AUC = 0.90, 95% CI: 0.87-0.92). And nine miRNAs targeting S100A12 were also identified. Additionally, the overexpression of S100A12 was further confirmed that it maybe promote inflammation and oxidative stress in AMI through comprehensive in vitro experiments. In summary, our study suggests that overexpressed S100A12 may be a latent diagnostic biomarker and therapeutic target of AMI that induces excessive inflammation and oxidative stress. Nine miRNAs targeting S100A12 may play a crucial role in AMI, but further studies are still needed. Our work provides a positive inspiration for the in-depth study of S100A12 in AMI.

CKS2 and S100A12: Two Novel Diagnostic Biomarkers for Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a chronic systematicness autoimmunity disease with joint inflammation. RA etiology is still unknown. Early and exact diagnosing is still hard to reach. In the paper, we purposed to discover novel diagnosis biological marker for RA. Two open, usable gene expression profiles of human RA as well as controlled specimens (dataset GSE17755 as well as GSE93272) were downloaded from the GEO database. Differentially expressed genes (DEGs) were screened between 331 RA and 88 control samples. Functional enrichment analysis was applied to explore the possible function of DEGs. Expression levels as well as diagnosis values of biological marker in RA were further verified in our cohort by the use of RT-PCR and ROC assays. We identified 13 DEGs between RA samples and control samples. 13 DEGs were remarkably abundant in NF-kappa B signal pathway. Among the 13 DEGs, CKS2, S100A12, LY96, and ANXA3 exhibited a strong diagnostic ability in screening RA specimens from normal specimens using all AUC > 0.8. Moreover, we confirmed that the expression of CKS2 and S100A12 was distinctly upregulated in RA specimens contrasted to normal specimens. Overall, serum CKS2 and S100A12 could be used as novel diagnosis biological markers for RA patients.

LPS and oxLDL-induced S100A12 and RAGE expression in carotid arteries of atherosclerotic Yucatan microswine.

BACKGROUND: S100A12, also known as Calgranulin C, is a ligand for the receptor for advanced glycation end products (RAGE) and plays key roles in cardiovascular and other inflammatory diseases. Interactions between S100A12 and RAGE initiate downstream signaling activating extracellular signal-regulated kinases (ERK1/2), mitogen activated protein kinases (MAPK), and transcription factor NF-κB. This increases the expression of pro-inflammatory cytokines to induce the inflammatory response. S100A12, and RAGE play a critical role in the development and progression of atherosclerosis. There is a well-known relationship between the bacterial endotoxin lipopolysaccharide (LPS) and the lipid antigens oxidized low-density lipoprotein (oxLDL) in driving the immune response in atherosclerosis. METHODS AND RESULTS: Our study aimed to compare the potential of LPS and oxLDL in regulating the expression of S100A12 and RAGE in atherosclerosis. The expression of these proteins was assessed in the harvested carotid arteries from LPS- and oxLDL-treated atherosclerotic Yucatan microswine. Tissues were collected from five different treatment groups: (i) angioplasty alone, (ii) LPS alone, (iii) oxLDL alone, (iv) angioplasty with LPS, and (v) angioplasty with oxLDL. Immunohistochemical findings revealed that angioplasty with LPS induced higher expression of S100A12 and RAGE compared to other treatment groups. The results were further corroborated by testing their gene expression through qPCR in cultured vascular smooth muscle cells (VSMCs) isolated from control carotid arteries and LPS- and oxLDL-treated arteries. CONCLUSIONS: The results of this study suggest that LPS induces the expression of S100A12 and RAGE more than oxLDL in atherosclerotic artery and both S100A12 and RAGE could be therapeutic targets.

Identification and Verification of Potential Core Genes in Pediatric Septic Shock.

BACKGROUND: Septic shock is a frequent and costly problem among patients in the pediatric intensive care unit (PICU) and is associated with high mortality and devastating survivor morbidity. In this study, we aimed to screen candidate biomarkers and potential therapeutic targets for septic shock. METHODS: GSE26440 dataset was downloaded from Gene Expression Omnibus (GEO), including 32 normal controls and 98 children with septic shock RNA samples from whole blood. The pathways and functional annotations of differentially expressed genes (DEGs) in the two types of samples were examined by GO and KEGG pathway enrichment analyses using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) tool. Protein-protein interactions (PPI) of the above-described DEGs were investigated using the Search Tool for the Retrieval of Interacting Genes (STRING) and Hub gene identification was performed by the plug-in cytoHubba in Cytoscape software. RESULTS: A total of 140 genes were identified as DEGs, of which 98 genes were up-regulated and 42 genes were down-regulated. GO function analysis showed that DEGs were significantly enriched in biological processes, including immune response, leukocyte activation involved in immune response, and so on. The top hub genes, namely MMP9, CEACAM8, ARG1, MCEMP1, LCN2, RETN, S100A12, GPR97, and TRAT1 were recognized from the protein-protein interaction (PPI) network. Furthermore, qRT-PCR results demonstrated that the mRNA level of MMP9, CEACAM8, ARG1, MCEMP1, LCN2, RETN, and S100A12 was elevated while GPR97 was decreased in involved mouse and human models. However, TRAT1 expression is species-dependent which was decreased in the mouse septic shock model but elevated in the human LPS-treated macrophages model. CONCLUSION: Taken together, the identification and validation of several novel hub genes, especially GPR97 and TRAT1, deepen our comprehension of the molecular mechanisms of septic shock progression. These genes may be therapeutic molecular targets or diagnostic biomarkers in patients with septic shock.

S100A12 as Biomarker of Disease Severity and Prognosis in Patients With Idiopathic Pulmonary Fibrosis.

Background: Idiopathic pulmonary fibrosis (IPF) is one of interstitial lung diseases (ILDs) with poor prognosis. S100 calcium binding protein A12 (S100A12) has been reported as a prognostic serum biomarker in the IPF, but its correlation with IPF remains unclear in the lung tissue and bronchoalveolar lavage fluids (BALF). Methods: Datasets were collected from the Gene Expression Omnibus (GEO) database. Person correlation coefficient, Kaplan-Meier analysis, Cox regression analysis, functional enrichment analysis and so on were used. And single cell RNA-sequencing (scRNA-seq) analysis was also used to explore the role of S100A12 and related genes in the IPF. Results: S100A12 was mainly and highly expressed in the monocytes, and its expression was downregulated in the lung of patients with IPF according to scRNA-seq and the transcriptome analysis. However, S100A12 expression was upregulated both in blood and BALF of patients with IPF. In addition, 10 genes were found to interact with S100A12 according to protein-protein interaction (PPI) network, and the first four transcription factors (TF) targeted these genes were found according to hTFtarget database. Two most significant co-expression genes of S100A12 were S100A8 and S100A9. The 3 genes were significantly negatively associated with lung function and positively associated with the St. George's Respiratory Questionnaire (SGRQ) scores in the lung of patients with IPF. And, high expression of the 3 genes was associated with higher mortality in the BALF, and shorter transplant-free survival (TFS) and progression-free survival (PFS) time in the blood. Prognostic predictive value of S100A12 was more superior to S100A8 and S100A9 in patients with IPF, and the composited variable [S100A12 + GAP index (gender, age, and physiological index)] may be a more effective predictive index. Conclusion: These results imply that S100A12 might be an efficient disease severity and prognostic biomarker in patients with IPF.

The alarmin S100A12 causes sterile inflammation of the human chorioamniotic membranes as well as preterm birth and neonatal mortality in mice†.

Sterile inflammation is triggered by danger signals, or alarmins, released upon cellular stress or necrosis. Sterile inflammation occurring in the amniotic cavity (i.e. sterile intra-amniotic inflammation) is frequently observed in women with spontaneous preterm labor resulting in preterm birth, the leading cause of neonatal morbidity and mortality worldwide; this condition is associated with increased amniotic fluid concentrations of alarmins. However, the mechanisms whereby alarmins induce sterile intra-amniotic inflammation are still under investigation. Herein, we investigated the mechanisms whereby the alarmin S100A12 induces inflammation of the human chorioamniotic membranes in vitro and used a mouse model to establish a causal link between this alarmin and adverse perinatal outcomes. We report that S100A12 initiates sterile inflammation in the chorioamniotic membranes by upregulating the expression of inflammatory mediators such as pro-inflammatory cytokines and pattern recognition receptors. Importantly, S100A12 induced the priming and activation of inflammasomes, resulting in caspase-1 cleavage and the subsequent release of mature IL-1ß by the chorioamniotic membranes. This alarmin also caused the activation of the chorioamniotic membranes by promoting MMP-2 activity and collagen degradation. Lastly, the ultrasound-guided intra-amniotic injection of S100A12 at specific concentrations observed in the majority of women with sterile intra-amniotic inflammation induced preterm birth (rates: 17% at 200 ng/sac; 25% at 300 ng/sac; 25% at 400 ng/sac) and neonatal mortality (rates: 22% at 200 ng/sac; 44% at 300 ng/sac; 31% at 400 ng/sac), thus demonstrating a causal link between this alarmin and adverse perinatal outcomes. Collectively, our findings shed light on the inflammatory responses driven by alarmins in the chorioamniotic membranes, providing insight into the immune mechanisms leading to preterm birth in women with sterile intra-amniotic inflammation.

A single transcript for the prognosis of disease severity in COVID-19 patients.

With many countries strapped for medical resources due to the COVID-19 pandemic, it is highly desirable to allocate the precious resources to those who need them the most. Several markers have been found to be associated with the disease severity in COVID-19 patients. However, the established markers only display modest prognostic power individually and better markers are urgently needed. The aim of this study is to investigate the potential of S100A12, a prominent marker gene for bacterial infection, in the prognosis of disease severity in COVID-19 patients. To ensure the robustness of the association, a total of 1695 samples from 14 independent transcriptome datasets on sepsis, influenza infection and COVID-19 infection were examined. First, it was demonstrated that S100A12 was a marker for sepsis and severity of sepsis. Then, S100A12 was found to be a marker for severe influenza infection, and there was an upward trend of S100A12 expression as the severity level of influenza infection increased. As for COVID-19 infection, it was found that S100A12 expression was elevated in patients with severe and critical COVID-19 infection. More importantly, S100A12 expression at hospital admission was robustly correlated with future quantitative indexes of disease severity and outcome in COVID-19 patients, superior to established prognostic markers including CRP, PCT, d-dimer, ferritin, LDH and fibrinogen. Thus, S100A12 is a valuable novel prognostic marker for COVID-19 severity and deserves more attention.

A host-based two-gene model for the identification of bacterial infection in general clinical settings.

OBJECTIVES: In this study, we aimed to develop a simple gene model to identify bacterial infection, which can be implemented in general clinical settings. METHODS: We used a clinically availablereal-time quantitative polymerase chain reaction platform to conduct focused gene expression assays on clinical blood samples. Samples were collected from 2 tertiary hospitals. RESULTS: We found that the 8 candidate genes for bacterial infection were significantly dysregulated in bacterial infection and displayed good performance in group classification, whereas the 2 genes for viral infection displayed poor performance. A two-gene model (S100A12 and CD177) displayed 93.0% sensitivity and 93.7% specificity in the modeling stage. In the independent validation stage, 87.8% sensitivity and 96.6% specificity were achieved in one set of case-control groups, and 93.6% sensitivity and 97.1% specificity in another set. CONCLUSIONS: We have validated the signature genes for bacterial infection and developed a two-gene model to identify bacterial infection in general clinical settings.

Associations between COVID-19 and skin conditions identified through epidemiology and genomic studies.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is commonly associated with skin manifestations, and may also exacerbate existing skin diseases, yet the relationship between COVID-19 and skin diseases remains unclear. OBJECTIVE: By investigating this relationship through a multiomics approach, we sought to ascertain whether patients with skin conditions are more susceptible to COVID-19. METHODS: We conducted an epidemiological study and then compared gene expression across 9 different inflammatory skin conditions and severe acute respiratory syndrome coronavirus 2-infected bronchial epithelial cell lines, and then performed a genome-wide association study transdisease meta-analysis between COVID-19 susceptibility and 2 skin diseases (psoriasis and atopic dermatitis). RESULTS: Skin conditions, including psoriasis and atopic dermatitis, increase the risk of COVID-19 (odds ratio, 1.55; P = 1.4 × 10-9) but decrease the risk of mechanical ventilation (odds ratio, 0.22; P = 8.5 × 10-5). We observed significant overlap in gene expression between the infected normal bronchial epithelial cells and inflammatory skin diseases, such as psoriasis and atopic dermatitis. For genes that are commonly induced in both the severe acute respiratory syndrome coronavirus 2 infection and skin diseases, there are 4 S100 family members located in the epidermal differentiation complex, and we also identified the "IL-17 signaling pathway" (P = 4.9 × 10-77) as one of the most significantly enriched pathways. Furthermore, a shared genome-wide significant locus in the epidermal differentiation complex was identified between psoriasis and severe acute respiratory syndrome coronavirus 2 infection, with the lead marker being a significant expression quantitative trait locus for S100A12 (P = 3.3 × 10-7). CONCLUSIONS: Together our findings suggest association between inflammatory skin conditions and higher risk of COVID-19, but with less severe course, and highlight shared components involved in anti-COVID-19 immune response.

Serum S100A12 levels: Possible association with skin sclerosis and interstitial lung disease in systemic sclerosis.

Damage-associated molecular patterns (DAMPs) have drawn much attention as a member of disease-associated molecules in systemic sclerosis (SSc). In this study, we investigated the potential contribution of S100A12, a member of DAMPs, to the development of SSc by evaluating S100A12 expression in the lesional skin and the clinical correlation of serum S100A12 levels. S100A12 expression was markedly elevated in the epidermis of SSc-involved skin at protein levels and in the bulk skin at mRNA levels. The deficiency of transcription factor Fli1, a predisposing factor of SSc, enhanced S100A12 expression and Fli1 occupied the S100A12 promoter in normal human keratinocytes. Serum S100A12 levels were higher in SSc patients, especially in those with diffuse cutaneous involvement, than in healthy controls and positively correlated with skin score. Furthermore, the presence of interstitial lung disease significantly augmented serum levels of S100A12. Importantly, serum S100A12 levels correlated inversely with both per cent forced vital capacity and per cent diffusing capacity for carbon monoxide and positively with serum levels of KL-6 and surfactant protein-D. Collectively, these results indicate a possible contribution of S100A12 to skin sclerosis and interstitial lung disease associated with SSc, further supporting the critical roles of DAMPs in the pathogenesis of this disease.

Genetic landscape and autoimmunity of monocytes in developing Vogt-Koyanagi-Harada disease.

Vogt-Koyanagi-Harada (VKH) disease is a systemic autoimmune disorder affecting multiple organs, including eyes, skin, and central nervous system. It is known that monocytes significantly contribute to the development of autoimmune disease. However, the subset heterogeneity with unique functions and signatures in human circulating monocytes and the identity of disease-specific monocytic populations remain largely unknown. Here, we employed an advanced single-cell RNA sequencing technology to systematically analyze 11,259 human circulating monocytes and genetically defined their subpopulations. We constructed a precise atlas of human blood monocytes, identified six subpopulations-including S100A12, HLA, CD16, proinflammatory, megakaryocyte-like, and NK-like monocyte subsets-and uncovered two previously unidentified subsets: HLA and megakaryocyte-like monocyte subsets. Relative to healthy individuals, cellular composition, gene expression signatures, and activation states were markedly alternated in VKH patients utilizing cell type-specific programs, especially the CD16 and proinflammatory monocyte subpopulations. Notably, we discovered a disease-relevant subgroup, proinflammatory monocytes, which showed a discriminative gene expression signature indicative of inflammation, antiviral activity, and pathologic activation, and converted into a pathologic activation state implicating the active inflammation during VKH disease. Additionally, we found the cell type-specific transcriptional signature of proinflammatory monocytes, ISG15, whose production might reflect the treatment response. Taken together, in this study, we present discoveries on accurate classification, molecular markers, and signaling pathways for VKH disease-associated monocytes. Therapeutically targeting this proinflammatory monocyte subpopulation would provide an attractive approach for treating VKH, as well as other autoimmune diseases.

Epigenetic Regulation of S100A9 and S100A12 Expression in Monocyte-Macrophage System in Hyperglycemic Conditions.

The number of diabetic patients in Europe and world-wide is growing. Diabetes confers a 2-fold higher risk for vascular disease. Lack of insulin production (Type 1 diabetes, T1D) or lack of insulin responsiveness (Type 2 diabetes, T2D) causes systemic metabolic changes such as hyperglycemia (HG) which contribute to the pathology of diabetes. Monocytes and macrophages are key innate immune cells that control inflammatory reactions associated with diabetic vascular complications. Inflammatory programming of macrophages is regulated and maintained by epigenetic mechanisms, in particular histone modifications. The aim of our study was to identify the epigenetic mechanisms involved in the hyperglycemia-mediated macrophage activation. Using Affymetrix microarray profiling and RT-qPCR we identified that hyperglycemia increased the expression of S100A9 and S100A12 in primary human macrophages. Expression of S100A12 was sustained after glucose levels were normalized. Glucose augmented the response of macrophages to Toll-like receptor (TLR)-ligands Palmatic acid (PA) and Lipopolysaccharide (LPS) i.e., pro-inflammatory stimulation. The abundance of activating histone Histone 3 Lysine 4 methylation marks (H3K4me1, H3K4me3) and general acetylation on histone 3 (AceH3) with the promoters of these genes was analyzed by chromatin immunoprecipitation. Hyperglycemia increased acetylation of histones bound to the promoters of S100A9 and S100A12 in M1 macrophages. In contrast, hyperglycemia caused a reduction in total H3 which correlated with the increased expression of both S100 genes. The inhibition of histone methyltransferases SET domain-containing protein (SET)7/9 and SET and MYND domain-containing protein (SMYD)3 showed that these specifically regulated S100A12 expression. We conclude that hyperglycemia upregulates expression of S100A9, S100A12 via epigenetic regulation and induces an activating histone code on the respective gene promoters in M1 macrophages. Mechanistically, this regulation relies on action of histone methyltransferases SMYD3 and SET7/9. The results define an important role for epigenetic regulation in macrophage mediated inflammation in diabetic conditions.

Downregulation of S100 calcium binding protein A12 inhibits the growth of glioma cells.

BACKGROUND: S100 calcium binding protein A12 (S100A12) is a member of the S100 protein family and is widely expressed in neutrophil and low expressed in lymphocytes and monocyte. However, the role of S100A12 in glioma has not yet been identified. METHODS: In the present study, we carried out immunohistochemical investigation of S100A12 in 81 glioma tissues to determine the expression of S100A12 in glioma cells, and evaluate the clinical significance of S100A12 in glioma patients. Futher we knockdown the S100A12 by shRNA, and evaluated cell proliferation, cell migration and cell apoptosis by MTT, colony formation assay, transwell assay,flow cytometry assa and western blot. RESULTS: We found that S100A12 was upregulated in tissues of glioma patients and the expression was correlated to WHO stage and tumor size. Further, we found that knockdown S100A12 inhibits the proliferation, migration and invasion of glioma cells through regulating cell apoptosis and EMT. CONCLUSION: S100A12 plays a vital role in glioma progression, and may be an important regulatory molecule for biological behaviors of glioma cell lines.