Comprehensive analysis of cuproptosis-related genes in immune infiltration and diagnosis in ulcerative colitis

HSP90β shapes the fate of Th17 cells with the help of glycolysis-controlled methylation modification

BACKGROUND AND PURPOSE

Ulcerative colitis (UC) is a refractory inflammatory disease associated with immune dysregulation. Elevated levels of heat shock protein (HSP) 90 in the β but not α subtype were positively associated with disease status in UC patients. This study validated the possibility that pharmacological inhibition or reduction of HSP90β would alleviate colitis, induced by dextran sulfate sodium, in mice and elucidated its mechanisms.

EXPERIMENTAL APPROACH

Histopathological and biochemical analysis assessed disease severity, and bioinformatics and correlation analysis explained the association between the many immune cells and HSP90β. Flow cytometry was used to analyse the homeostasis and transdifferentiation of Th17 and Treg cells. In vitro inhibition and adoptive transfer assays were used to investigate functions of the phenotypically transformed Th17 cells. Metabolomic analysis, DNA methylation detection and chromatin immunoprecipitation were used to explore these mechanisms.

KEY RESULTS

The selective pharmacological inhibitor (HSP90βi) and shHSP90β significantly mitigated UC in mice and promoted transformation of Th17 to Treg cell phenotype, via Foxp3 transcription. The phenotypically-transformed Th17 cells by HSP90βi or shHSP90β were able to inhibit lymphocyte proliferation and colitis in mice. HSP90βi and shHSP90β selectively weakened glycolysis by stopping the direct association of HSP90β and GLUT1, the key glucose transporter, to accelerate ubiquitination degradation of GLUT1, and enhance the methylation of Foxp3 CNS2 region. Then, the mediator path was identified as the "lactate-STAT5-TET2" cascade.

CONCLUSION AND IMPLICATIONS

HSP90β shapes the fate of Th17 cells via glycolysis-controlled methylation modification to affect UC progression, which provides a new therapeutic target for UC.

Immune patterns of cuproptosis in ischemic heart failure: A transcriptome analysis

Cuproptosis is a recently discovered programmed cell death pattern that affects the tricarboxylic acid (TCA) cycle by disrupting the lipoylation of pyruvate dehydrogenase (PDH) complex components. However, the role of cuproptosis in the progression of ischemic heart failure (IHF) has not been investigated. In this study, we investigated the expression of 10 cuproptosis-related genes in samples from both healthy individuals and those with IHF. Utilizing these differential gene expressions, we developed a risk prediction model that effectively distinguished healthy and IHF samples. Furthermore, we conducted a comprehensive evaluation of the association between cuproptosis and the immune microenvironment in IHF, encompassing infiltrated immunocytes, immune reaction gene-sets and human leukocyte antigen (HLA) genes. Moreover, we identified two different cuproptosis-mediated expression patterns in IHF and explored the immune characteristics associated with each pattern. In conclusion, this study elucidates the significant influence of cuproptosis on the immune microenvironment in ischemic heart failure (IHF), providing valuable insights for future mechanistic research exploring the association between cuproptosis and IHF.

Artificial intelligence in cardiology: a bibliometric study

OBJECTIVES

To perform a comprehensive bibliometric analysis of global publications on the applications of artificial intelligence (AI) in cardiology.

METHODS

Documents related to AI in cardiology published between 2002 and 2022 were retrieved from Web of Science Core Collection. R package "bibliometrix", VOSviewers and Microsoft Excel were applied to perform the bibliometric analysis.

RESULTS

A total of 4332 articles were included. United States topped the list of countries publishing articles, followed by China and United Kingdom. The Harvard University was the institution that contributed the most to this field, followed by University of California System and University of London. Disease risk prediction, diagnosis, treatment, disease detection, and prognosis assessment were the research hotspots for AI in cardiology.

CONCLUSIONS

Enhancing cooperation between different countries and institutions is a critical step in leading to breakthroughs in the application of AI in cardiology. It is foreseeable that the application of machine learning and deep learning in various areas of cardiology will be a research priority in the coming years.

Novel molecular hepatocellular carcinoma subtypes and RiskScore utilizing apoptosis-related genes

Hepatocellular carcinoma (HCC) is the third leading cause of global cancer-related deaths. Despite immunotherapy offering hope for patients with HCC, only some respond to it. However, it remains unclear how to pre-screen eligible patients. Our study aimed to address this issue. In this study, we identified 13 prognostic genes through univariate Cox regression analysis of 87 apoptosis-related genes. Subsequently, these 13 genes were analyzed using ConsensusClusterPlus, and patients were categorized into three molecular types: C1, C2, and C3. A prognostic model and RiskScore were constructed using Lasso regression analysis of 132 significant genes identified between C1 and C3. We utilized quantitative polymerase chain reaction to confirm the model's transcript level in Huh7 and THLE2 cell lines. Both molecular subtypes and RiskScores effectively predicted patients benefiting from immunotherapy. Cox regression analysis revealed RiskScore as the most significant prognosis factor, suggesting its clinical application potential and providing a foundation for future experimental research.

Alleviating effect of Nexrutine on mucosal inflammation in mice with ulcerative colitis: Involvement of the RELA suppression

BACKGROUND

Nexrutine is an herbal extract derived from Phellodendron amurense, known for its anti-inflammatory, antidiarrheal, and hemostatic properties. However, its effect on ulcerative colitis (UC) remains unclear.

METHODS

A mouse model of UC was induced by 3% dextran sulfate sodium, while human colonic epithelial cells NCM-460 were exposed to lipopolysaccharide. Both models were treated with Nexrutine at 300 or 600 mg/kg, with Mesalazine applied as a positive control regimen. The disease activity index (DAI) of mice was calculated, and the pathological injury scores were assessed through hematoxylin and eosin staining. The viability of NCM-460 cells was determined using the CCK-8 method. Inflammatory cytokines were detected using ELISA kits. Expression of mucin 3 (MUC3), Claudin-1, and tight junction protein (ZO-1) was detected to analyze mucosal barrier integrity. Target genes of Nexrutine were predicted using bioinformatics tools. Expression of RELA proto-oncogene (RELA) was analyzed using qPCR and western blot assays.

RESULTS

The Nexrutine treatments significantly alleviated DAI of mice, mitigated pathological changes in their colon tissues, decreased the production of pro-inflammatory cytokines, enhanced the barrier integrity-related proteins, and increased NCM-460 cell viability in vitro. RELA, identified as a target gene of Nexrutine, showed elevated levels in UC models but was substantially suppressed by Nexrutine treatment. Adenovirus-mediated RELA upregulation in mice or the overexpression plasmid of RELA in cells counteracted the effects of Nexrutine treatments, exacerbating UC-related symptoms.

CONCLUSION

This study demonstrates that Nexrutine alleviates inflammatory mucosal barrier damage in UC by suppressing RELA transcription.

The function and immune role of cuproptosis associated hub gene in Barrett's esophagus and esophageal adenocarcinoma

Barrett's esophagus (BE) is a precancerous lesion of esophageal adenocarcinoma (EAC), with approximately 3-5% of patients developing EAC. Cuproptosis is a kind of programmed cell death phenomenon discovered in recent years, which is related to the occurrence and development of many diseases. However, its role in BE and EAC is not fully understood. We used single sample Gene Set Enrichment Analysis (ssGSEA) for differential analysis of BE in the database, followed by enrichment analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO) and GSEA, Protein-Protein Interaction (PPI), Weighted Gene Co-expression Network Analysis (WGCNA), Receiver Operating Characteristic Curve (ROC) and finally Quantitative Real Time Polymerase Chain Reaction (qRT-PCR) and immunohistochemistry (IHC) of clinical tissues. Two hub genes can be obtained by intersection of the results obtained from the cuproptosis signal analysis based on BE. The ROC curves of these two genes predicted EAC, and the Area Under the Curve (AUC) values could reach 0.950 and 0.946, respectively. The mRNA and protein levels of Centrosome associated protein E (CENPE) and Shc SH2 domain binding protein 1 (SHCBP1) were significantly increased in clinical EAC tissues. When they were grouped by protein expression levels, high expression of CENPE or SHCBP1 had a poor prognosis. The CENPE and SHCBP1 associated with cuproptosis may be a factor promoting the development of BE into EAC which associated with the regulation of NK cells and T cells.

Research hotspots and trends of artificial intelligence in rheumatoid arthritis: A bibliometric and visualized study

Artificial intelligence (AI) applications on rheumatoid arthritis (RA) are becoming increasingly popular. In this bibliometric study, we aimed to analyze the characteristics of publications relevant to the research of AI in RA, thereby developing a thorough overview of this research topic. Web of Science was used to retrieve publications on the application of AI in RA from 2003 to 2022. Bibliometric analysis and visualization were performed using Microsoft Excel (2019), R software (4.2.2) and VOSviewer (1.6.18). The overall distribution of yearly outputs, leading countries, top institutions and authors, active journals, co-cited references and keywords were analyzed. A total of 859 relevant articles were identified in the Web of Science with an increasing trend. USA and China were the leading countries in this field, accounting for 71.59% of publications in total. Harvard University was the most influential institution. Arthritis Research & Therapy was the most active journal. Primary topics in this field focused on estimating the risk of developing RA, diagnosing RA using sensor, clinical, imaging and omics data, identifying the phenotype of RA patients using electronic health records, predicting treatment response, tracking the progression of the disease and predicting prognosis and developing new drugs. Machine learning and deep learning algorithms were the recent research hotspots and trends in this field. AI has potential applications in various fields of RA, including the risk assessment, screening, early diagnosis, monitoring, prognosis determination, achieving optimal therapeutic outcomes and new drug development for RA patients. Incorporating machine learning and deep learning algorithms into real-world clinical practice will be a future research hotspot and trend for AI in RA. Extensive collaboration to improve model maturity and robustness will be a critical step in the advancement of AI in healthcare.

Screening of miR-15a-5p as a potential biomarker for intervertebral disc degeneration through RNA-sequencing

Low back pain (LBP) is a prevalent clinical condition that imposes substantial economic burdens on society. Intervertebral disc degeneration (IVDD) is recognized as a major contributing factor to LBP. Recent studies have highlighted the pivotal role of microRNAs (miRNAs) in regulating the onset and progression of IVDD. Understanding the involvement of miRNAs in IVDD will expand our knowledge of the underlying mechanisms and potentially identify novel therapeutic targets for managing LBP. However, the pathological process of IVDD and the miRNA-mediated pathomechanism in IVDD remain unclear. Herein, we comprehensively analyzed and divided the pathological process of IVDD into three stages based on the analysis by Risbud and colleagues. Results showed that IVDD was especially associated with cell death, oxidative stress, inflammatory and immune response, and extracellular matrix (ECM) metabolism. Subsequently, we obtained human normal and degenerative nucleus pulposus tissues, which were visually confirmed through histological staining techniques such as HE and TUNEL staining. RNA sequencing was then performed on these tissue samples. Additionally, miRNA (GSE116726) and mRNA (GSE56081/GSE70362/GSE23130/GSE34095) datasets were collected from the GEO database. Our analysis revealed that miR-15a-5p was significantly upregulated IVDD, as validated by both RNA sequencing and qRT-PCR experiments. To further refine our findings, bioinformatics analysis was conducted, merging the targets of miR-15a-5p and multiple mRNA datasets, ultimately identifying the overlapping IVDD-associated mRNAs. Notably, many cuproptosis-related genes (CRGs), ferroptosis-related genes, oxidative stress-related genes, and immunity-related genes were potential targets of miR-15a-5p. The miR-15a-5p-mRNA network was constructed using Cytoscape software. Additionally, PPI, functional, and pathway enrichment analyses of the CRGs were also performed. We found that MTF1, one of the CRGs, was highly expressed in IVDD and primarily localized in the nucleus of nucleus pulposus cells. These findings suggest that miR-15a-5p is a potential biomarker in IVDD, and targeting the miR-15a-5p-mRNA signaling pathway may be a promising strategy for treating IVDD diseases.

Macrophages in ulcerative colitis: A perspective from bibliometric and visual analysis

Objectives

Despite the many reported studies on macrophages in ulcerative colitis (UC), the overall research trends in this field are unclear. This study evaluates the research trends and hotspots regarding macrophages in UC using bibliometric analysis.

Methods

A systematic search was conducted in the Web of Science database to identify publications related to macrophages in UC from 2012 to 2021. R package 'bibliometrix', VOSviewers, CiteSpace and Microsoft Excel were utilised for the bibliometric analysis.

Results

1074 articles published between 2012 and 2021 were analysed. The number of publications on macrophages in UC showed a consistently increasing trend, with USA and China as the leading contributors to this field. Notably, Georgia State University and Nanjing University contributed significantly to this field. Among the authors, Wang Y had the highest productivity, while Wu X received the most citations. The journal Gut was identified as the most authoritative journal in this field. Co-citation analysis revealed that the exploration of the mechanisms of macrophages in UC through in vivo and in vitro experiments was the primary focus of research. Moreover, the emerging research hotspots included keywords such as 'macrophage polarization', 'gut microbiota' and 'NLRP3 inflammasome'.

Conclusions

Research on macrophages in UC holds significant value and practical implications. Additionally, China demonstrated prolific output in this field, while the USA had the most influential contributions. Currently, research hotspots are centred around the modulation of gut microbiota to regulate macrophage polarization and macrophage pyroptosis as potential strategies for mitigating UC.

Current Status and Future Trends of Cold Atmospheric Plasma as an Oncotherapy

Cold atmospheric plasma (CAP), a redox modulation tool, is capable of inhibiting a wide spectrum of cancers and has thus been proposed as an emerging onco-therapy. However, with incremental successes consecutively reported on the anticancer efficacy of CAP, no consensus has been made on the types of tumours sensitive to CAP due to the different intrinsic characteristics of the cells and the heterogeneous design of CAP devices and their parameter configurations. These factors have substantially hindered the clinical use of CAP as an oncotherapy. It is thus imperative to clarify the tumour types responsive to CAP, the experimental models available for CAP-associated investigations, CAP administration strategies and the mechanisms by which CAP exerts its anticancer effects with the aim of identifying important yet less studied areas to accelerate the process of translating CAP into clinical use and fostering the field of plasma oncology.

Identification of cuproptosis-related biomarkers in dilated cardiomyopathy and potential therapeutic prediction of herbal medicines

Background: Dilated cardiomyopathy (DCM) is one of the significant causes of heart failure, and the mechanisms of metabolic ventricular remodelling due to disturbances in energy metabolism are still poorly understood in cardiac pathology. Understanding the biological mechanisms of cuproptosis in DCM is critical for drug development. Methods: The DCM datasets were downloaded from Gene Expression Omnibus, their relationships with cuproptosis-related genes (CRGs) and immune signatures were analyzed. LASSO, RF, and SVM-RFE machine learning algorithms were used to identify signature genes and the eXtreme Gradient Boosting (XGBoost) model was used to assess diagnostic efficacy. Molecular clusters of CRGs were identified, and immune Infiltration analysis was performed. The WGCNA algorithm was used to identify specific genes in different clusters. In addition, AUCell was used to analyse the cuproptosis scores of different cell types in the scRNA-seq dataset. Finally, herbal medicines were predicted from an online database, and molecular docking and molecular dynamics simulations were used to support the confirmation of the potential of the selected compounds. Results: We identified dysregulated cuproptosis genes and activated immune responses between DCM and healthy controls. Two signature genes (FDX1, SLC31A1) were identified and performed well in an external validation dataset (AUC = 0.846). Two molecular clusters associated with cuproptosis were further defined in DCM, and immune infiltration analysis showed B-cell naive, Eosinophils, NK cells activated and T-cell CD4 memory resting is significant immune heterogeneity in the two clusters. AUCell analysis showed that cardiomyocytes had a high cuproposis score. In addition, 19 and 3 herbal species were predicted based on FDX1 and SLC31A1. Based on the molecular docking model, the natural compounds Rutin with FDX1 (-9.3 kcal/mol) and Polydatin with SLC31A1 (-5.5 kcal/mol) has high stability and molecular dynamics simulation studies further validated this structural stability. Conclusion: Our study systematically illustrates the complex relationship between cuproptosis and the pathological features of DCM and identifies two signature genes (FDX1 and SLC31A1) and two natural compounds (Rutin and Polydatin). This may enhance our diagnosis of the disease and facilitate the development of clinical treatment strategies for DCM.