RESUMO
Mannose-binding lectin plays an essential role in bacteria or virus-triggered immune response in mammals. Previous proteomic data revealed that in Eriocheir sinensis, the mannose-binding protein was differentially expressed after Spiroplasma eriocheiris infection. However, the function of mannose-binding protein against pathogen infection in invertebrates is poorly understood. In this study, a crab mannose-binding protein (EsMBP) was characterized and enhanced the host resistance to S. eriocheiris infection. The application of recombinant C-type carbohydrate recognition domain (CTLD) of EsMBP led to increased crab survival and decreased S. eriocheiris load in hemocytes. Meanwhile, the overexpression of CTLD of EsMBP in Raw264.7 cells inhibited S. eriocheiris intracellular replication. In contrast, depletion of EsMBP by RNA interference or antibody neutralization attenuated phenoloxidase activity and hemocyte phagocytosis, rendering host more susceptible to S. eriocheiris infection. Furthermore, miR-381-5p in hemocytes suppressed EsMBP expression and negatively regulated phenoloxidase activity to exacerbate S. eriocheiris invasion of hemocytes. Taken together, our findings revealed that crab mannose-binding protein was involved in host defense against S. eriocheiris infection and targeted by miR-381-5p, providing further insights into the control of S. eriocheiris spread in crabs.
Assuntos
Braquiúros , Catecol Oxidase , Precursores Enzimáticos , Lectina de Ligação a Manose , MicroRNAs , Spiroplasma , Animais , Lectina de Ligação a Manose/metabolismo , Proteômica , Monofenol Mono-Oxigenase/metabolismo , Fagocitose , MicroRNAs/genética , MicroRNAs/metabolismo , Hemócitos/metabolismo , Mamíferos/genéticaRESUMO
We aimed to find novel biomarkers associated with myocardial infarction (MI). The array data of GSE62646 were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were analyzed with limma package. Functional enrichment analyses were performed by DAVID v6.7 online tool. The micro-RNA-messenger RNA (miRNA-mRNA) pairs were predicted by miRWalk database, and the protein-mRNA interactions were predicted by StarBase. Then, miRNA-mRNA-protein regulatory network was constructed. Antigen processing and presentation were only the pathway enriched by DEG1 set such as KLRC4 (killer cell lectin-like receptor subfamily C, member 4) and KLRC2 (killer cell lectin-like receptor subfamily C, member 2). KLRC4 and KLRC2 were differentially expressed in MI patients. DLC1 (DLC1 Rho GTPase activating protein, degree = 179) was the most significant node in miRNA-mRNA-protein network. EIF4AIII (eukaryotic translation initiation factor 4A3) and FUS (FUS RNA binding protein) were the key proteins that regulated the most DEGs. KLRC4, KLRC2, and DLC1 were the biomarkers and may play important roles in the progression of MI. Furthermore, EIF4AIII and FUS may also be involved in MI progression.
Assuntos
Biomarcadores/análise , Proteínas Ativadoras de GTPase/genética , Infarto do Miocárdio/genética , Subfamília C de Receptores Semelhantes a Lectina de Células NK/genética , Proteínas Supressoras de Tumor/genética , Biomarcadores/metabolismo , Biologia Computacional , Bases de Dados Genéticas , Regulação da Expressão Gênica/genética , MicroRNAs/genética , Infarto do Miocárdio/diagnóstico , Infarto do Miocárdio/patologia , RNA Mensageiro/genéticaRESUMO
Sepsis is a type of systemic inflammatory response caused by infection. The present study aimed to identify novel targets for the treatment of sepsis. We conducted bioinformatic analysis of the microarray Gene Expression Omnibus dataset GSE12624, which includes data on 34 patients with sepsis and 36 healthy individuals without sepsis. Differentially expressed genes (DEGs) in sepsis patients were identified using Bayesian methods included in the limma package in R. Correlations among the expression values of DEGs were analyzed using the weighted gene coexpression network analysis (WGCNA) to construct a coexpression network. Subsequently, the generated coexpression network was visualized using Cytoscape 3.3 software. Additionally, a proteinprotein interaction (PPI) network was constructed based on all the DEGs using STRING. Finally, the integrated regulatory network was constructed based on DEGs, microRNAs (miRNAs) and transcription factors (TFs). A total of 407 DEGs were identified in the sepsis samples, including 227 upregulated DEGs and 180 downregulated DEGs. WGCNA grouped the DEGs into 13 coexpressed modules. Additionally, MAP3K8 and RPS6KA5 in the MEyellow module were enriched in the MAPK and TNF signaling pathways. In addition, the PPI network comprised 48 nodes and 112 edges, which included the pairs MAP3K8RPS6KA5, MAP3K8IL10, RPS6KA5EXOSC4 and EXOSC4EXOSC5. Lastly, the TFmiRNAtarget DEG regulatory network was constructed based on eight TFs (NFκB), seven miRNAs (miR152, miR148A/B), and 52 TFmiRNAtarget gene triplets (17 upregulated genes, including MAP3K8, and 10 downregulated genes, including RPS6KA5). Our analysis showed that the members of the miR148 family (miR148A/B and miR152) are candidate biomarkers for sepsis.