Roles of transducin-like enhancer of split (TLE) family proteins in tumorigenesis and immune regulation

TLE4 downregulation identified by WGCNA and machine learning algorithm promotes papillary thyroid carcinoma progression via activating JAK/STAT pathway

Background: Papillary Thyroid Carcinoma (PTC), a common type of thyroid cancer, has a pathogenesis that is not fully understood. This study utilizes a range of public databases, sophisticated bioinformatics tools, and empirical approaches to explore the key genetic components and pathways implicated in PTC, particularly concentrating on the Transducin-Like Enhancer of Split 4 (TLE4) gene. Methods: Public databases such as TCGA and GEO were utilized to conduct differential gene expression analysis in PTC. Hub genes were identified using Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning techniques, including Random Forest, LASSO regression, and SVM-RFE, were employed for biomarker identification. The clinical impact of the TLE4 gene was assessed in terms of diagnostic accuracy, prognostic value, and its functional enrichment analysis in PTC. Additionally, the study focused on understanding the role of TLE4 in the dynamics of immune cell infiltration, gene function enhancement, and behaviors of PTC cells like growth, migration, and invasion. To complement these analyses, in vivo studies were performed using a xenograft mouse model. Results: 244 genes with significant differential expression across various databases were identified. WGCNA indicated a strong link between specific gene modules and PTC. Machine learning analysis brought the TLE4 gene into focus as a key biomarker. Bioinformatics studies verified that TLE4 expression is lower in PTC, linking it to immune cell infiltration and the JAK-STAT signaling pathways. Experimental data revealed that decreased TLE4 expression in PTC cell lines leads to enhanced cell growth, migration, invasion, and activates the JAK/STAT pathway. In contrast, TLE4 overexpression in these cells inhibited tumor growth and metastasis. Conclusions: This study sheds light on TLE4's crucial role in PTC pathogenesis, positioning it as a potential biomarker and target for therapy. The integration of multi-omics data and advanced analytical methods provides a robust framework for understanding PTC at a molecular level, potentially guiding personalized treatment strategies.

Foxq1 Promotes Alveolar Epithelial Cell Death through Tle1-mediated Inhibition of the NF-κB Signaling Pathway

Acute lung injury (ALI) is a common respiratory disease characterized by diffuse alveolar injury and interstitial edema, as well as a hyperinflammatory response, lung cell damage, and oxidative stress. Foxq1, a member of the FOX family of transcription factors, is expressed in various tissues, such as the lungs, liver, and kidneys, and contributes to various biological processes, such as stress, metabolism, cell cycle arrest, and aging-related apoptosis. However, the role of Foxq1 in ALI is unknown. We constructed ex vivo and in vivo ALI models by LPS tracheal perfusion of ICR mice and conditioned medium stimulation of injured MLE-12 cells. Foxq1 expression was increased, and its localization was altered, in our ALI model. In normal or injured MLE-12 cells, knockdown of Foxq1 promoted cell survival, and overexpression had the opposite effect. This regulatory effect was likely mediated by Tle1 and the NF-κB/Bcl2/Bax signaling pathway. These data suggest a potential link between Foxq1 and ALI, indicating that Foxq1 can be used as a biomarker for the diagnosis of ALI. Targeted inhibition of Foxq1 expression could promote alveolar epithelial cell survival and may provide a strategy for mitigating ALI.

Sox10 Activity and the Timing of Schwann Cell Differentiation Are Controlled by a Tle4-Dependent Negative Feedback Loop

The HMG-domain containing transcription factor Sox10 plays a crucial role in regulating Schwann cell survival and differentiation and is expressed throughout the entire Schwann cell lineage. While its importance in peripheral myelination is well established, little is known about its role in the early stages of Schwann cell development. In a search for direct target genes of Sox10 in Schwann cell precursors, the transcriptional co-repressor Tle4 was identified. At least two regions upstream of the Tle4 gene appear involved in mediating the Sox10-dependent activation. Once induced, Tle4 works in tandem with the bHLH transcriptional repressor Hes1 and exerts a dual inhibitory effect on Sox10 by preventing the Sox10 protein from transcriptionally activating maturation genes and by suppressing Sox10 expression through known enhancers of the gene. This mechanism establishes a regulatory barrier that prevents premature activation of factors involved in differentiation and myelin formation by Sox10 in immature Schwann cells. The identification of Tle4 as a critical downstream target of Sox10 sheds light on the gene regulatory network in the early phases of Schwann cell development. It unravels an elaborate regulatory circuitry that fine-tunes the timing and extent of Schwann cell differentiation and myelin gene expression.

Bioinformatics and Experimental Validation for Identifying Biomarkers Associated with AMG510 (Sotorasib) Resistance in KRASG12C-Mutated Lung Adenocarcinoma

The Kirsten rat sarcoma viral oncogene homolog (KRAS)G12C mutation is prevalent in lung adenocarcinoma (LUAD), driving tumor progression and indicating a poor prognosis. While the FDA-approved AMG510 (Sotorasib) initially demonstrated efficacy in treating KRASG12C-mutated LUAD, resistance emerged within months. Data from AMG510 treatment-resistant LUAD (GSE204753) and single-cell datasets (GSE149655) were analyzed. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were used to explore enriched signaling pathways, nomogram models were constructed, and transcription factors predicting resistance biomarkers were predicted. CIBERSORT identified immune cell subpopulations, and their association with resistance biomarkers was assessed through single-cell analysis. AMG510-resistant LUAD cells (H358-AR) were constructed, and proliferative changes were evaluated using a CCK-8 assay. Key molecules for AMG510 resistance, including SLC2A1, TLE1, FAM83A, HMGA2, FBXO44, and MTRNR2L12, were recognized. These molecules impacted multiple signaling pathways and the tumor microenvironment and were co-regulated by various transcription factors. Single-cell analysis revealed a dampening effect on immune cell function, with associations with programmed cell death ligand 1 (PDL1) expression, cytokine factors, and failure factors. The findings indicate that these newly identified biomarkers are linked to the abnormal expression of PDL1 and have the potential to induce resistance through immunosuppression. These results highlight the need for further research and therapeutic intervention to address this issue effectively.

Regulation of phosphosignaling pathways involved in transcription of cell cycle target genes by TRH receptor activation in GH1 cells

Thyrotropin-releasing hormone (TRH) is known to activate several cellular signaling pathway, but the activation of the TRH receptor (TRH-R) has not been reported to regulate gene transcription. The aim of this study was to identify phosphosignaling pathways and phosphoprotein complexes associated with gene transcription in GH1 pituitary cells treated with TRH or its analog, taltirelin (TAL), using label-free bottom-up mass spectrometry-based proteomics. Our detailed analysis provided insight into the mechanism through which TRH-R activation may regulate the transcription of genes related to the cell cycle and proliferation. It involves control of the signaling pathways for β-catenin/Tcf, Notch/RBPJ, p53/p21/Rbl2/E2F, Myc, and YY1/Rb1/E2F through phosphorylation and dephosphorylation of their key components. In many instances, the phosphorylation patterns of differentially phosphorylated phosphoproteins in TRH- or TAL-treated cells were identical or displayed a similar trend in phosphorylation. However, some phosphoproteins, especially components of the Wnt/β-catenin/Tcf and YY1/Rb1/E2F pathways, exhibited different phosphorylation patterns in TRH- and TAL-treated cells. This supports the notion that TRH and TAL may act, at least in part, as biased agonists. Additionally, the deficiency of β-arrestin2 resulted in a reduced number of alterations in phosphorylation, highlighting the critical role of β-arrestin2 in the signal transduction from TRH-R in the plasma membrane to transcription factors in the nucleus.

From viral load to survival: Unveiling new genetic links for resistance against PMCV in Atlantic Salmon (Salmo salar)

The infectious agent piscine myocarditis virus (PMCV) causes cardiomyopathy syndrome (CMS) and is responsible for substantial mortality and economic losses in the Atlantic salmon (Salmo salar) farming industry. Previous research has demonstrated that breeding for resistance against PMCV is an effective approach to mitigate the disease's impact. In this study, a new quantitative trait locus (QTL) is described on chromosome 23, together with previously described QTLs on chromosomes 12 and 27. The findings are based on two genome-wide association studies conducted on two different year-classes of Atlantic salmon of the Rauma strain. In this study, we utilized data from an experimental challenge trial with the viral load as the phenotype and a field outbreak of CMS with survival data as the phenotype. The estimated SNP-based heritability was 0.55 and 0.44 in the two studies, respectively. In the infection trial, the top associated SNP on chromosome 23 accounted for approximately 46% of the genetic and 25.53% of the phenotypic variations in the viral load. In the field outbreak, we identified a QTL on the same genomic region of chromosome 23. The most significantly associated marker on this chromosome explained 13.57% and 5.97% of the genetic and phenotypic variations. The QTL on chromosome 23 is in proximity to delta-5 fatty acyl desaturase and fatty acid desaturase 2 genes, both of which play a role in the production of polyunsaturated fatty acids. This proximity is particularly interesting as it offers valuable insights into enhancing our understanding of resistance against PMCV.

Identification of anoikis-related genes classification patterns and immune infiltration characterization in ischemic stroke based on machine learning

Introduction

Ischemic stroke (IS) is a type of stroke that leads to high mortality and disability. Anoikis is a form of programmed cell death. When cells detach from the correct extracellular matrix, anoikis disrupts integrin junctions, thus preventing abnormal proliferating cells from growing or attaching to an inappropriate matrix. Although there is growing evidence that anoikis regulates the immune response, which makes a great contribution to the development of IS, the role of anoikis in the pathogenesis of IS is rarely explored.

Methods

First, we downloaded GSE58294 set and GSE16561 set from the NCBI GEO database. And 35 anoikis-related genes (ARGs) were obtained from GSEA website. The CIBERSORT algorithm was used to estimate the relative proportions of 22 infiltrating immune cell types. Next, consensus clustering method was used to classify ischemic stroke samples. In addition, we used least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms to screen the key ARGs in ischemic stroke. Next, we performed receiver operating characteristics (ROC) analysis to assess the accuracy of each diagnostic gene. At the same time, the nomogram was constructed to diagnose IS by integrating trait genes. Then, we analyzed the correlation between gene expression and immune cell infiltration of the diagnostic genes in the combined database. And gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analysis were performed on these genes to explore differential signaling pathways and potential functions, as well as the construction and visualization of regulatory networks using NetworkAnalyst and Cytoscape. Finally, we investigated the expression pattern of ARGs in IS patients across age or gender.

Results

Our study comprehensively analyzed the role of ARGs in IS for the first time. We revealed the expression profile of ARGs in IS and the correlation with infiltrating immune cells. And The results of consensus clustering analysis suggested that we can classify IS patients into two clusters. The machine learning analysis screened five signature genes, including AKT1, BRMS1, PTRH2, TFDP1 and TLE1. We also constructed nomogram models based on the five risk genes and evaluated the immune infiltration correlation, gene-miRNA, gene-TF and drug-gene interaction regulatory networks of these signature genes. The expression of ARGs did not differ by sex or age.

Discussion

This study may provide a beneficial reference for further elucidating the pathogenesis of IS, and render new ideas for drug screening, individualized therapy and immunotherapy of IS.