Revealing novel pyroptosis-related therapeutic targets for sepsis based on machine learning

Chen, Ying; Wang, Xingkai; Wang, Jiaxin; Zong, Junwei; Wan, Xianyao

doi:10.1186/s12920-023-01453-7

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…During AKI, the expression of the Kl gene in renal tubular cells is reduced, and its restoration can alleviate kidney damage and promote AKI recovery [26, 27]. A significant decrease in GHR expression was observed in diabetic kidney disease, and GHR antagonists protected against diabetic kidney disease [28, 29]. Interestingly, we observed an acute downregulation of Kl and Ghr in PTS1 and PTS2 cells, which was negatively correlated with Ezh2 expression.…”

Section: Discussionmentioning

confidence: 94%

Multi-omics Integrated Analysis of the Protective Effect of EZH2 inhibition in Mice with Renal Ischemia-Reperfusion Injury

Zou,

Chen,

Zhou

et al. 2024

Kidney Blood Press Res

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Introduction: Acute kidney injury (AKI) is a common clinical syndrome associated with high morbidity and mortality. Inhibition of the methyltransferase Enhancer of Zeste Homolog 2 (EZH2) by its inhibitor 3-DZNeP (3-Deazaneplanocin A) exerts renal benefits in acute renal ischemia-reperfusion injury (IRI). However, the underlying mechanisms is not completely known. This study aimed to elucidate the pathological mechanism of EZH2 in renal IRI by combination of multi-omics analysis and expression profiling in a public clinical cohort. Methods: In this study, C57BL/6J mice were used to establish acute kidney injury model, which were treated with 3-DZNeP for 24 hours. Kidney samples were collected for RNA-seq analysis, which was combined with publicly available EZH2-ChIP-seq data of mouse embryonic stem cell for a joint analysis to identify differentially expressed genes. Several selected differentially expressed genes were verified by quantitative PCR. Finally, single-nucleus sequencing data and expression profiling in public clinical datasets were used to confirm the negative correlation of the selected genes with EZH2 expression. Results: 3-DZNeP treatment significantly improved renal pathology and function in IRI mice. Through RNA-seq analysis combined with EZH2 ChIP-seq database, 162 differentially expressed genes were found, which might be involved in EZH2-mediated pathology in IRI kidneys. Four differential expressed genes (Scd1, Cidea, Ghr, and Kl) related to lipid metabolism or cell growth were selected based on GO and KEGG enrichment analysis, which were validated by quantitative PCR. Data from snRNA-seq revealed the negative correlation of these four genes with Ezh2 expression in different subpopulations of proximal tubular cells in IRI mice in different pattern. Finally, the negative correlation of these four genes with EZH2 expression was confirmed in patients with acute kidney injury in two clinical datasets. Conclusions: Our study indicates that Scd1, Cidea, Ghr, and Kl are downstream genes regulated by EZH2 in AKI. Upregulation of EZH2 in AKI inhibits the expression of these four genes in different population of proximal tubular cells to minimize normal physiological function, and promote acute or chronic cell injuries following acute kidney injury.

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Section: Discussionmentioning

confidence: 94%

Multi-omics Integrated Analysis of the Protective Effect of EZH2 inhibition in Mice with Renal Ischemia-Reperfusion Injury

Zou,

Chen,

Zhou

et al. 2024

Kidney Blood Press Res

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Advancing the Understanding of Clinical Sepsis Using Gene Expression-Driven Machine Learning to Improve Patient Outcomes

Rashid,

Al-Obeida,

Hafez

et al. 2023

Shock

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Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of Machine Learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information and its use in ML models to provide insights into sepsis pathophysiology and biomarker identification. Temporal analysis and integration of gene expression data further enhance the accuracy and predictive capabilities of ML models for sepsis. Although challenges such as interpretability and bias exist, ML research offers exciting prospects for addressing critical clinical problems, improving sepsis management, and advancing precision medicine approaches. Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. ML has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management.

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Exploring the prognostic necroptosis-related genes and underlying mechanism in sepsis using bioinformatics

Liu,

Li,

et al. 2023

Preprint

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Sepsis is a life-threatening disease due to a dysregulated host response to infection, with an unknown regulatory mechanism for prognostic necroptosis-related genes (NRGs). Using GEO datasets GSE65682 and GSE134347, we identified six NRG biomarkers (ATRX, TSC1, CD40, BACH2, BCL2, and LEF1) with survival and diagnostic significance through Kaplan-Meier (KM) and ROC analyses. The ingenuity pathway analysis (IPA) highlighted enrichment in hepatic fibrosis pathways and BEX2 protein. We examined their regulatory targets and functional links with necroptotic signaling molecules via miRDB, TargetScan, Network analyst, and GeneMANIA. Hsa-miR-5195-3p and hsa-miR-145-5p regulated ATRX, BACH2, and CD40, while TF YY1 showed strong connectivity, concurrently controlling LEF1, ATRX, BCL2, BACH2, and CD40. CD40 exhibited similar expression patterns to RIPK3 and MLKL, and LEF1 was functionally associated with MLKL. Additionally, DrugBank analysis identified Paclitaxel, Docetaxel, and Rasagiline as potential BCL2-targeting sepsis treatments. Real-Time Quantitative PCR confirmed ATRX, TSC1, and LEF1 down-regulation in sepsis samples, contrasting CD40's increased expression in case samples. Variations in BACH2 and BCL2 expression between disease and normal samples may result from sample differences. In conclusion, ATRX, TSC1, CD40, BACH2, BCL2, and LEF1 may be critical regulatory targets of necroptosis in sepsis, providing a basis for further necroptosis-related studies in sepsis.

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Revealing novel pyroptosis-related therapeutic targets for sepsis based on machine learning

Cited by 3 publications

References 38 publications

Multi-omics Integrated Analysis of the Protective Effect of EZH2 inhibition in Mice with Renal Ischemia-Reperfusion Injury

Multi-omics Integrated Analysis of the Protective Effect of EZH2 inhibition in Mice with Renal Ischemia-Reperfusion Injury

Advancing the Understanding of Clinical Sepsis Using Gene Expression-Driven Machine Learning to Improve Patient Outcomes

Exploring the prognostic necroptosis-related genes and underlying mechanism in sepsis using bioinformatics

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