Dataset Information

Integrated Single Cell and Bulk RNA-Seq Analysis Revealed Immunomodulatory Effects of Ulinastatin in Sepsis: A Multicenter Cohort Study.

ABSTRACT: Sepsis is a leading cause of morbidity and mortality in the intensive care unit, which is caused by unregulated inflammatory response leading to organ injuries. Ulinastatin (UTI), an immunomodulatory agent, is widely used in clinical practice and is associated with improved outcomes in sepsis. But its underlying mechanisms are largely unknown. Our study integrated bulk and single cell RNA-seq data to systematically explore the potential mechanisms of the effects of UTI in sepsis. After adjusting for potential confounders in the negative binomial regression model, there were more genes being downregulated than being upregulated in the UTI group. These down-regulated genes were enriched in the neutrophil involved immunity such as neutrophil activation and degranulation, indicating the immunomodulatory effects of UTI is mediated via regulation of neutrophil activity. By deconvoluting the bulk RNA-seq samples to obtain fractions of cell types, the Myeloid-derived suppressor cells (MDSC) were significantly expanded in the UTI treated samples. Further cell-cell communication analysis revealed some signaling pathways such as ANEEXIN, GRN and RESISTIN that might be involved in the immunomodulatory effects of UTI. The study provides a comprehensive reference map of transcriptional states of sepsis treated with UTI, as well as a general framework for studying UTI-related mechanisms.

SUBMITTER: Chen L

PROVIDER: S-EPMC9130465 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Integrated Single Cell and Bulk RNA-Seq Analysis Revealed Immunomodulatory Effects of Ulinastatin in Sepsis: A Multicenter Cohort Study.

Chen Lin L Jin Senjun S Yang Min M Gui Chunmei C Yuan Yingpu Y Dong Guangtao G Zeng Weizhong W Zeng Jing J Hu Guoxin G Qiao Lujun L Wang Jinhua J Xi Yonglin Y Sun Jian J Wang Nan N Wang Minmin M Xing Lifeng L Yang Yi Y Teng Yan Y Hou Junxia J Bi Qiaojie Q Cai Huabo H Zhang Gensheng G Hong Yucai Y Zhang Zhongheng Z

Frontiers in immunology 20220511

Sepsis is a leading cause of morbidity and mortality in the intensive care unit, which is caused by unregulated inflammatory response leading to organ injuries. Ulinastatin (UTI), an immunomodulatory agent, is widely used in clinical practice and is associated with improved outcomes in sepsis. But its underlying mechanisms are largely unknown. Our study integrated bulk and single cell RNA-seq data to systematically explore the potential mechanisms of the effects of UTI in sepsis. After adjusting ...[more]

PMID: 35634310

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Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly contagious and pathogenic coronavirus that emerged in late 2019 and caused a pandemic of respiratory illness termed as coronavirus disease 2019 (COVID-19). Cancer patients are more susceptible to SARS-CoV-2 infection. The treatment of cancer patients infected with SARS-CoV-2 is more complicated, and the patients are at risk of poor prognosis compared to other populations. Patients infected with SARS-CoV-2 are prone to rapid development of acute respiratory distress syndrome (ARDS) of which pulmonary fibrosis (PF) is considered a sequelae. Both ARDS and PF are factors that contribute to poor prognosis in COVID-19 patients. However, the molecular mechanisms among COVID-19, ARDS and PF in COVID-19 patients with cancer are not well-understood. In this study, the common differentially expressed genes (DEGs) between COVID-19 patients with and without cancer were identified. Based on the common DEGs, a series of analyses were performed, including Gene Ontology (GO) and pathway analysis, protein-protein interaction (PPI) network construction and hub gene extraction, transcription factor (TF)-DEG regulatory network construction, TF-DEG-miRNA coregulatory network construction and drug molecule identification. The candidate drug molecules (e.g., Tamibarotene CTD 00002527) obtained by this study might be helpful for effective therapeutic targets in COVID-19 patients with cancer. In addition, the common DEGs among ARDS, PF and COVID-19 patients with and without cancer are TNFSF10 and IFITM2. These two genes may serve as potential therapeutic targets in the treatment of COVID-19 patients with cancer. Changes in the expression levels of TNFSF10 and IFITM2 in CD14+/CD16+ monocytes may affect the immune response of COVID-19 patients. Specifically, changes in the expression level of TNFSF10 in monocytes can be considered as an immune signature in COVID-19 patients with hematologic cancer. Targeting N6-methyladenosine (m6A) pathways (e.g., METTL3/SERPINA1 axis) to restrict SARS-CoV-2 reproduction has therapeutic potential for COVID-19 patients.

Project description:ObjectiveOvarian cancer has the highest mortality rate among gynecological malignant tumors, and it preferentially metastasizes to omental tissue, leading to intestinal obstruction and death. scRNA-seq is a powerful technique to reveal tumor heterogeneity. Analyzing omentum metastasis of ovarian cancer at the single-cell level may be more conducive to exploring and understanding omentum metastasis and prognosis of ovarian cancer at the cellular function and genetic levels.MethodsThe omentum metastasis site scRNA-seq data of GSE147082 were acquired from the GEO (Gene Expression Omnibus) database, and single cells were clustered by the Seruat package and annotated by the SingleR package. Cell differentiation trajectories were reconstructed through the monocle package. The ovarian cancer microarray data of GSE132342 were downloaded from GEO and were clustered by using the ConsensusClusterPlus package into omentum metastasis-associated clusters according to the marker genes gained from single-cell differentiation trajectory analysis. The tumor microenvironment (TME) and immune infiltration differences between clusters were analyzed by the estimate and CIBERSORT packages. The expression matrix of genes used to cluster GSE132342 patients was extracted from bulk RNA-seq data of TCGA-OV (The Cancer Genome Atlas ovarian cancer), and least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were performed to establish an omentum metastasis-associated gene (OMAG) signature. The signature was then tested by GSE132342 data. Finally, the clinicopathological characteristics of TCGA-OV were screened by univariate and multivariate Cox regression analysis to draw the nomogram.ResultsA total of 9885 cells from 6 patients were clustered into 18 cell clusters and annotated into 14 cell types. Reconstruction of differentiation trajectories divided the cells into 5 branches, and a total of 781 cell trajectory-related characteristic genes were obtained. A total of 3769 patients in GSE132342 were subtyped into 3 clusters by 74 cell trajectory-related characteristic genes. Kaplan-Meier (K-M) survival analysis showed that the prognosis of cluster 2 was the worst, P < 0.001. The TME analysis showed that the ESTIMATE score and stromal score in cluster 2 were significantly higher than those in the other two clusters, P < 0.001. The immune infiltration analysis showed differences in the fraction of 8 immune cells among the 3 clusters, P < 0.05. The expression data of 74 genes used for GEO clustering were extracted from 379 patients in TCGA-OV, and combined with survival information, 10 candidates for OMAGs were filtered by LASSO. By using multivariate Cox regression, the 6-OMAGs signature was established as RiskScore = 0.307*TIMP3 + 3.516*FBN1-0.109*IGKC + 0.209*RPL21 + 0.870*UCHL1 + 0.365*RARRES1. Taking TCGA-OV as the training set and GSE132342 as the test set, receiver operating characteristic (ROC) curves were drawn to verify the prognostic value of 6-OMAGs. Screened by univariate and multivariate Cox regression analysis, 3 (age, cancer status, primary therapy outcome) of 5 clinicopathological characteristics were used to construct the nomogram combined with risk score.ConclusionWe constructed an ovarian cancer prognostic model related to omentum metastasis composed of 6-OMAGs and 3 clinicopathological features and analyzed the potential mechanism of these 6-OMAGs in ovarian cancer omental metastasis.

Dataset Information

Integrated Single Cell and Bulk RNA-Seq Analysis Revealed Immunomodulatory Effects of Ulinastatin in Sepsis: A Multicenter Cohort Study.

Publications

Integrated Single Cell and Bulk RNA-Seq Analysis Revealed Immunomodulatory Effects of Ulinastatin in Sepsis: A Multicenter Cohort Study.

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