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Gene Expression Analysis of Blood Reveals S100A11 and AQP9 as Potential Biomarkers of Infective Endocarditis

ABSTRACT: Background—Diagnosis and pronostic assessment are challenging in infective endocarditis (IE). To investigate the host response during IE and identify potential biomarkers, we determined the circulating gene expression profile through a whole genome microarray analysis. Methods and Results—A transcriptomic case-control study was performed on blood samples from patients with native valve IE (n=39), excluded IE after an initial suspicion (n=10) at patient’s admission, and age-matched healthy controls (n=10). The whole genome microarray analysis showed that patients with IE exhibited a specific transcriptional program with a predominance of gene categories associated with cell activation, innate immune and inflammatory responses. These categories were organized in a dense network from which arose numerous subnetworks including major histocompatibily complex and natural killer cell network, type 1 interferon pathway and intracellular traffic. Quantitative real-time RT-PCR performed on a selection of highly modulated genes showed that the expression of the gene encoding S100 calcium binding protein A11 (S100A11) was significantly increased in patients with IE in comparison with controls (P<0.001) and patients with excluded IE (P<0.05). Interestingly, the upregulated expression of S100A11 gene was more pronounced in staphylococcal IE than in streptococcal IE (P<0.01). These results were confirmed by serum concentrations of the S100A11 protein. Finally, we showed that, in patients with IE, the upregulation of aquaporin-9 gene (AQP9) was significantly related to the occurrence of acute heart failure (P=0.02). Conclusions— Using transcriptional signatures of blood samples, we identified S100A11 as a potential diagnostic marker of IE. In addition, the determination of AQP9 may improve the prognostic assessment of IE. The transcriptomic case-control study was performed in 39 consecutive patients with native valve IE (IE group) diagnosed by a multidisciplinary team who applied the modified Duke criteria,12 10 patients admitted for a suspicion of IE but with a final excluded IE diagnosis. Ten IE patients and five controls were arbitrary selected and investigated with microarrays.

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PROVIDER: S-DIXA-D-1094 | biostudies-other |

REPOSITORIES: biostudies-other

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Project description:BackgroundM-bM-^@M-^TDiagnosis and pronostic assessment are challenging in infective endocarditis (IE). To investigate the host response during IE and identify potential biomarkers, we determined the circulating gene expression profile through a whole genome microarray analysis. Methods and ResultsM-bM-^@M-^TA transcriptomic case-control study was performed on blood samples from patients with native valve IE (n=39), excluded IE after an initial suspicion (n=10) at patientM-bM-^@M-^Ys admission, and age-matched healthy controls (n=10). The whole genome microarray analysis showed that patients with IE exhibited a specific transcriptional program with a predominance of gene categories associated with cell activation, innate immune and inflammatory responses. These categories were organized in a dense network from which arose numerous subnetworks including major histocompatibily complex and natural killer cell network, type 1 interferon pathway and intracellular traffic. Quantitative real-time RT-PCR performed on a selection of highly modulated genes showed that the expression of the gene encoding S100 calcium binding protein A11 (S100A11) was significantly increased in patients with IE in comparison with controls (P<0.001) and patients with excluded IE (P<0.05). Interestingly, the upregulated expression of S100A11 gene was more pronounced in staphylococcal IE than in streptococcal IE (P<0.01). These results were confirmed by serum concentrations of the S100A11 protein. Finally, we showed that, in patients with IE, the upregulation of aquaporin-9 gene (AQP9) was significantly related to the occurrence of acute heart failure (P=0.02). ConclusionsM-bM-^@M-^T Using transcriptional signatures of blood samples, we identified S100A11 as a potential diagnostic marker of IE. In addition, the determination of AQP9 may improve the prognostic assessment of IE. The transcriptomic case-control study was performed in 39 consecutive patients with native valve IE (IE group) diagnosed by a multidisciplinary team who applied the modified Duke criteria,12 10 patients admitted for a suspicion of IE but with a final excluded IE diagnosis. Ten IE patients and five controls were arbitrary selected and investigated with microarrays.

Project description:Purpose: Currently, the early diagnosis and treatment of osteoarthritis (OA) remain a challenge. In the present study, we attempted to explore potential biomarkers for the diagnosis and treatment of OA. Methods: The differentially expressed genes (DEGs) were identified based on three mRNA datasets of synovial tissues for OA patients and normal controls downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used for evaluating gene function related categories. Then, miRNA sequencing was performed for differentially expressed miRNAs' identification. Finally, weighted gene co-expression network analysis (WGCNA) was performed for genes detected by the three mRNA datasets and a competing endogenous RNA (ceRNA) network with DEGs and differentially expressed microRNAs (miRNAs) was constructed for central genes identification. In addition, the relationship between central gene expression and immune infiltration was analyzed, and the candidate agents for OA were predicted based on the Connectivity Map database. Quantitative RT-PCR (qRT-PCR), Western blotting analysis, and immunofluorescent staining were performed to validate the expression levels of differentially expressed miRNAs and differentially expressed target genes in normal and OA tissues and chondrocytes. MiRNA-mRNA network was also validated in chondrocytes in vitro. Results: A total of 259 DEGs and 26 differentially expressed miRNAs were identified, among which 94 miRNA-mRNA interactions were predicted. The brown module in WGCNA was most closely correlated with the clinical traits of OA. After overlapping the brown module genes with miRNA-mRNA pairs, 27 miRNA-mRNA pairs were obtained. A ceRNA network was constructed with 5505 lncRNA-miRNA-mRNA interactions. B-cell translocation gene 2(BTG2), Abelson-related gene (ABL2), and vascular endothelial growth factor A (VEGFA) were identified to be the central genes with good predictive performance, which were significantly correlated with immune cell infiltration in OA, reflected by declined activated dendritic cells (aDCs), and elevated contents of B cells, macrophages, neutrophils, and T helper cells. Anisomycin, MG-132, thapsigargin, and lycorine were predicted to be the potential candidate agents for OA intervention. In vitro, the expression levels of differentially expressed miRNAs and biomarkers identified in the present study were consistent with the results obtained in normal or OA knee cartilage tissues and chondrocytes. Furthermore, BTG2 was identified to be negatively regulated by miR-125a-5p. Conclusion: BTG2, ABL2, and VEGFA can be regarded as potential predictive and treatment biomarkers for OA, which might guide the clinical therapy of OA.

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Gene Expression Analysis of Blood Reveals S100A11 and AQP9 as Potential Biomarkers of Infective Endocarditis

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