Identification of CCL20 as a prognostic predictor for severe fever with thrombocytopenia syndrome based on plasma proteomics.

BACKGROUND: Severe fever with thrombocytopenia syndrome (SFTS), a lethal tick-borne hemorrhagic fever, prompted our investigation into prognostic predictors and potential drug targets using plasma Olink Proteomics. METHODS: Employing the Olink assay, we analyzed 184 plasma proteins in 30 survivors and 8 non-survivors of SFTS. Validation was performed in a cohort of 154 SFTS patients using enzyme-linked immunosorbent assay. We utilized the Drug Gene Interaction database to identify protein-drug interactions. RESULTS: Non-survivors exhibited 110 differentially expressed proteins (DEPs) compared to survivors, with functional enrichment in the cell chemotaxis-related pathway. Thirteen DEPs, including C-C motif chemokine 20 (CCL20), calcitonin gene-related peptide alpha and Pleiotrophin, were associated with multiple organ dysfunction syndrome. CCL20 emerged as the top predictor of death, demonstrating an area under the curve of 1 (P = .0004) and 0.9033 (P < .0001) in the discovery and validation cohort, respectively. Patients with CCL20 levels exceeding 45.74 pg/mL exhibited a fatality rate of 45.65%, while no deaths occurred in those with lower CCL20 levels. Furthermore, we identified 202 FDA-approved drugs targeting 37 death-related plasma proteins. CONCLUSIONS: Distinct plasma proteomic profiles characterize SFTS patients with different outcomes, with CCL20 emerging as a novel, sensitive, accurate, and specific biomarker for predicting SFTS prognosis.

Serum mAST/ALT ratio had high predictive value for adverse outcome of severe fever with thrombocytopenia syndrome with severe condition.

BACKGROUND: Severe fever with thrombocytopenia syndrome (SFTS) usually demonstrates multi-organ injury with a high mortality rate. This study aimed to investigate associations of serum aspartate/alanine aminotransferase (AST)/ALT, cytosolic AST (cAST)/ALT and mitochondrial AST (mAST)/ALT ratios with the prognosis of SFTS patients. METHODS: A total of 355 confirmed SFTS patients were included. Clinical and laboratory data were compared between survivors and nonsurvivors. Logistic regression analysis was used to assess the independent risk factors for fatality in all patients and those admitted to the intensive care unit (ICU). The predictive values of the risk factors and constructed risk models were evaluated. RESULTS: Mean age and biochemical parameters were significantly greater in nonsurvivors than in survivors. In ICU patients, the three ratios, high-sensitivity troponin I (hsTnI), creatine kinase (CK), lactate dehydrogenase (LDH) and α-hydroxybutyrate dehydrogenase (α-HBDH) were elevated markedly in nonsurvivors than in survivors. Multivariate logistic regression analysis showed that age, three ratios and α-HBDH were independent risk factors for mortality in all patients. Only the three ratios were independent risk factors for death in ICU patients. Risk Models (M1, M2 and M3) and simplified models (sMs) containing the three ratios respectively had comparatively high predictive values for fatality in all patients with area under ROC curves (AUCs) > 0.85. In ICU patients, mAST/ALT ratio had the highest predictive value, sensitivity and odds ratio (OR) for mortality among three ratios. CONCLUSION: AST/ALT, cAST/ALT and mAST/ALT ratios were associated with unfavorable clinical outcome of SFTS. The prognostic value of mAST/ALT ratio was higher in severe cases.

Down-regulation of KLF9 ameliorates LPS-caused acute lung injury and inflammation in mice via reducing GSDMD expression.

Acute lung injury (ALI) is considered as a severe respiratory disease with aggravated inflammatory responses. Krüppel-like factor 9 (KLF9), a member of KLF family, has been reported to be involved in inflammatory disorders. However, the effect of KLF9 in ALI has not been elucidated. Here the present study was to clarify the role of KLF9 and its mechanism in ALI. The ALI in vitro model was established with lipopolysaccharide (LPS)-treated RAW264.7 cells. Mice were injected with LPS to conduct an ALI in vivo model. The expression of KLF9 and gasdermin D (GSDMD) was examined using quantitative reverse transcription-PCR, haematoxylin-eosin/immunohistochemistry staining and western blot assays. Enzyme-linked immunosorbent assay was employed to detect the levels of inflammatory cytokines. JASPAR database was used to predict the recognition motif of KLF9, and the relationship between KLF9 and GSDMD was determined by luciferase reporter assay and chromatin immunoprecipitation analysis. The number of neutrophils in bronchoalveolar lavage fluid as well as the wet/dry weight ratio was caculated. The results showed that The expression of KLF9 in lung was significantly increased in LPS-stimulated mice. Moreover, KLF9 knockout relieved the lung injury in ALI mice. GSDMD is one of targets genes of the transcription factor KLF9. KLF9 knockout induced a decreased expression of GSDMD in LPS-treated mice. Furthermore, in RAW264.7 cells after LPS administration, KLF9 knockdown reduced the levels of inflammatory factors and suppressed the expression of GSDMD. In summarise, these findings exhibited that KLF9 knockout could mitigate the lung injury and inflammatory responses in ALI mice by directly regulating GSDMD.

Identification of Novel COVID-19 Biomarkers by Multiple Feature Selection Strategies.

Coronavirus disease 2019 (COVID-19) arising from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a global pandemic since its first report in December 2019. So far, SARS-CoV-2 nucleic acid detection has been deemed as the golden standard of COVID-19 diagnosis. However, this detection method often leads to false negatives, thus triggering missed COVID-19 diagnosis. Therefore, it is urgent to find new biomarkers to increase the accuracy of COVID-19 diagnosis. To explore new biomarkers of COVID-19 in this study, expression profiles were firstly accessed from the GEO database. On this basis, 500 feature genes were screened by the minimum-redundancy maximum-relevancy (mRMR) feature selection method. Afterwards, the incremental feature selection (IFS) method was used to choose a classifier with the best performance from different feature gene-based support vector machine (SVM) classifiers. The corresponding 66 feature genes were set as the optimal feature genes. Lastly, the optimal feature genes were subjected to GO functional enrichment analysis, principal component analysis (PCA), and protein-protein interaction (PPI) network analysis. All in all, it was posited that the 66 feature genes could effectively classify positive and negative COVID-19 and work as new biomarkers of the disease.

Immune response- and viral control-related pathways in the progression of chronic hepatitis B.

BACKGROUND: Chronic hepatitis B (CHB) is a complicated and dynamic course, and is associated with advanced liver disease. Host immune response against viral infection plays a pivotal role in the progression of CHB. However, it is still uncharted that how the hepatic transcriptomes in patients with CHB are correlated with the clinical phases. OBJECTIVE: This study aimed to identify the specific sub-networks across various phases of CHB and infer potential pathways for phenotypic outcome prediction. METHODS: In this study, we performed the pairwise comparisons of the hepatic transcriptomes of CHB patients under different phases, and constructed the differential co-expression networks (DCNs). We firstly identified the critical genes from each DCN according to the adjacency matrix of the network. Then, the specific sub-networks were digged by iteratively affiliating genes that can increase the classification accuracy, using a snow-ball sampling strategy. Permutation test was implemented to determine the statistical significance of these sub-networks. Finally, each sub-network was given a most significant functional pathway. RESULTS: We constructed 3 DCNs by pairwise comparing the hepatic transcriptomes among three CHB phases, and systemically tracked 1, 1 and 2 specific sub-networks and pathways, respectively. Relative to immune tolerant phase, TGF-beta receptor signaling in EMT (epithelial to mesenchymal transition) pathway was significantly changed in the immune clearance phase, and nuclear receptor transcription pathway and adenylate cyclase activating pathway were altered in inactive carrier state. The host genes related to DNA strand elongation showed significant difference between the immune clearance phase and inactive carrier state. CONCLUSIONS: By pairwise comparing the hepatic transcriptomes of CHB patients under a network view, several immune- and viral control-related pathways were identified in this study. These results might serve as a foundation for characterizing the host transcriptomes responded to CHB infection, and hold clues for the development of potential targets for disease control.