Identification of Immune Infiltration and Effective Immune Biomarkers in Acute Lung Injury by Bioinformatics Analysis

Anti-Inflammatory Effects of Clematis terniflora Leaf on Lipopolysaccharide-Induced Acute Lung Injury

For centuries, natural products are regarded as vital medicines for human survival. Clematis terniflora var. mandshurica (Rupr.) Ohwi is an ingredient of the herbal medicine, Wei Ling Xian, which has been used in Chinese medicine to alleviate pain, fever, and inflammation. In particular, C. terniflora leaves have been used to cure various inflammatory diseases, including tonsillitis, cholelithiasis, and conjunctivitis. Based on these properties, this study aimed to scientifically investigate the anti-inflammatory effect of an ethanol extract of leaves of C. terniflora (EELCT) using activated macrophages that play central roles in inflammatory response. In this study, EELCT inhibited the essential inflammatory mediators, such as nitric oxide, cyclooxygenase-2, tumor necrosis factor-α, interleukin- (IL-) 6, IL-1β, and inducible nitric oxide synthase, by suppressing the nuclear factor-κB and mitogen-activated protein kinase activation in macrophages. Acute lung injury (ALI) is a fatal respiratory disease accompanied by serious inflammation. With high mortality rate, the disease has no effective treatments. Therefore, new therapeutic agents must be developed for ALI. We expected that EELCT can be a promising therapeutic agent for ALI by reducing inflammatory responses and evaluated its action in a lipopolysaccharide- (LPS-) induced ALI model. EELCT alleviated histological changes, immune cell infiltration, inflammatory mediator production, and protein-rich pulmonary edema during ALI. Collectively, our results may explain the traditional usage of C. terniflora in inflammatory diseases and suggest the promising potential of EELCT as therapeutic candidate for ALI.

Identification and validation of ferroptosis-related genes in lipopolysaccharide-induced acute lung injury

BACKGROUND

Emerging evidence reveals the important role of ferroptosis in the pathophysiological process of acute lung injury (ALI). We aimed to identify and validate the potential ferroptosis-related genes of ALI through bioinformatics analysis and experimental validation.

METHODS

Murine ALI model was established via intratracheal instillation with LPS and confirmed by H&E staining and transmission electronic microscopy (TEM). RNA sequencing (RNA-seq) was used to screen differentially expressed genes (DEGs) between control and ALI model mice. The potential differentially expressed ferroptosis-related genes of ALI were identified using the limma R package. Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, gene set enrichment analysis (GSEA), and protein-protein interactions (PPI) were applied for the differentially expressed ferroptosis-related genes. CIBERSORT tool was used to conduct immune cell infiltration analysis. Finally, protein expressions and RNA expression of ferroptosis DEGs were validated in vivo and in vitro by western blots and RT-qPCR.

RESULTS

Among 5009 DEGs, a total of 86 differentially expressed ferroptosis-related genes (45 up-regulated genes and 41 down-regulated genes) were identified in the lungs between control and ALI. GSEA analysis showed that the genes enriched were mainly involved in response to molecule of bacterial origin and fatty acid metabolic process. The GO and KEGG enrichment analysis indicated that the top 40 ferroptosis DEGs were mainly enriched in reactive oxygen species metabolic process, HIF-1signaling pathway, lipid and atherosclerosis, and ferroptosis. The PPI results and Spearman correlation analysis suggested that these ferroptosis-related genes interacted with each other. Immune infiltration analysis confirmed that ferroptosis DEGs were closely related to immune response. Consistent with the RNA-seq data, the western blot and RT-qPCR unveiled increased mRNA expressions of Cxcl2, Il-6, Il-1β, and Tnfα, and protein expressions of FTH1, TLR4 as well as decreased ACSL3 in LPS-induced ALI. In vitro, the upregulated mRNA levels of CXCL2, IL-6, SLC2A1, FTH1, TNFAIP3, and downregulated NQO1 and CAV1 in LPS-stimulated BEAS-2B and A549 cells were verified.

CONCLUSION

We identified 86 potential ferroptosis-related genes of LPS-induced ALI through RNA-seq. Several pivotal ferroptosis-related genes involved in lipid metabolism and iron metabolism were implicated in ALI. This study may be helpful to expand our understanding of ALI and provide some potential targets to counteract ferroptosis in ALI.

Single-cell RNA analysis to identify five cytokines signaling in immune-related genes for melanoma survival prognosis

Melanoma is one of the deadliest skin cancers. Recently, developed single-cell sequencing has revealed fresh insights into melanoma. Cytokine signaling in the immune system is crucial for tumor development in melanoma. To evaluate melanoma patient diagnosis and treatment, the prediction value of cytokine signaling in immune-related genes (CSIRGs) is needed. In this study, the machine learning method of least absolute selection and shrinkage operator (LASSO) regression was used to establish a CSIRG prognostic signature of melanoma at the single-cell level. We discovered a 5-CSIRG signature that was substantially related to the overall survival of melanoma patients. We also constructed a nomogram that combined CSIRGs and clinical features. Overall survival of melanoma patients can be consistently predicted with good performance as well as accuracy by both the 5-CSIRG signature and nomograms. We compared the melanoma patients in the CSIRG high- and low-risk groups in terms of tumor mutation burden, infiltration of the immune system, and gene enrichment. High CSIRG-risk patients had a lower tumor mutational burden than low CSIRG-risk patients. The CSIRG high-risk patients had a higher infiltration of monocytes. Signaling pathways including oxidative phosphorylation, DNA replication, and aminoacyl tRNA biosynthesis were enriched in the high-risk group. For the first time, we constructed and validated a machine-learning model by single-cell RNA-sequencing datasets that have the potential to be a novel treatment target and might serve as a prognostic biomarker panel for melanoma. The 5-CSIRG signature may assist in predicting melanoma patient prognosis, biological characteristics, and appropriate therapy.