Association of Immunosuppression with DR6 Expression during the Development and Progression of Spontaneous Ovarian Cancer in Laying Hen Model

Identifying Lipid Metabolism-Related Therapeutic Targets and Diagnostic Markers for Lung Adenocarcinoma by Mendelian Randomization and Machine Learning Analysis

BACKGROUND

Lipid metabolic disorders are emerging as a recognized influencing factors of lung adenocarcinoma (LUAD). This study aims to investigate the influence of lipid metabolism-related genes (LMRGs) on the diagnosis and treatment of LUAD and to identify significant biomarkers.

METHODS

DESeq2 and robust rank aggregation (RRA) analyses were employed to determine the differential expression of LMRGs from TCGA-LUAD and five GEO datasets. Mendelian randomization (MR) was conducted utilizing protein quantitative trait loci (pQTLs) in the deCODE, prot-a, and UKB-PPP Study to estimate causal relationships between plasma proteins and LUAD within the ieu-a-984, ieu-a-965, and FinnGen R10 cohorts as potential drug targets of LUAD. Subsequently, an optimal machine learning model for diagnosing LUAD was established by comparing four models: support vector machine, random forest (RF), glmBoost, and eXtreme Gradient Boosting. Finally, the diagnostic performance of five plasma proteins was validated through nomogram analysis, calibration curve assessment, decision curve analysis (DCA), independent internal and external datasets.

RESULT

A total of five biomarkers were identified from 1034 LMRGs via MR and differential expression analysis. TNFRSF21 exhibited a positive association with LUAD risk; conversely, BCHE, FABP4, LPL, and PLBD1 demonstrated negative correlations with this risk. The RF machine learning model was determined to be the optimal model for diagnosing LUAD using these five plasma proteins. Ultimately, nomogram construction, calibration curve analysis, DCA, as well as independent internal and external dataset validation confirmed that these biomarkers exhibit excellent diagnostic performance.

CONCLUSIONS

BCHE, FABP4, LPL, PLBD1, and TNFRSF21 represent potential novel reliable diagnostic markers as well as therapeutic targets for LUAD.

Amyloid precursor protein promotes MASH progression by upregulating death receptor 6-mediated hepatocyte apoptosis

Metabolic dysfunction-associated steatohepatitis (MASH) is a complicated process that contributes to end-stage liver disease and, eventually, hepatocellular carcinoma. Hepatocyte apoptosis, a well-defined form of cell death in MASH, is considered the primary cause of liver inflammation and fibrosis. However, the mechanisms underlying the regulation of hepatocyte apoptosis in MASH remain largely unclear. We explored the proapoptotic effect of hepatocyte amyloid precursor protein (APP) in MASH. C57BL/6J mice were fed a Western diet plus sugar water, a high-fat high-fructose diet, or a methionine and choline deficiency diet to induce MASH. APP expression was analyzed in murine MASH specimens. App-/- mice and mice with adeno-associated virus-mediated APP overexpression were established to study the role of APP in MASH. Palmitic acid was used to mimic lipotoxicity-induced MASH in AML12 cells. We identified a dramatic increase in APP expression in hepatocytes of patients with MASH and three different mouse models. Suppression of APP attenuated hepatic steatosis, inflammation, and fibrosis in MASH mice, whereas its restoration activated MASH pathogenesis. Furthermore, increased death receptor 6 (DR6) was observed in MASH mouse livers. Mechanistically, APP interacted with DR6, a tumor necrosis factor receptor, to facilitate DR6 expression and activation. Activated DR6 increased apoptosis in hepatocytes, which was associated with an increase in proapoptotic effectors (cleaved-caspase 3/7). Our results highlight the role of the APP-DR6 axis in hepatocyte apoptosis, inflammation activation, and fibrosis formation in murine MASH model, providing new insights into therapeutic strategies for MASH.

LPA suppresses HLA-DR expression in human melanoma cells: a potential immune escape mechanism involving LPAR1 and DR6-mediated release of IL-10

While immune checkpoint inhibitors (ICIs) are promising in the treatment of metastatic melanoma, about half of patients do not respond well to them. Low levels of human leukocyte antigen-DR (HLA-DR) in tumors have been shown to negatively influence prognosis and response to ICIs. Lysophosphatidic acid (LPA) is produced in large amounts by melanoma and is abundantly present in the tumor microenvironment. LPA induces the release of various cytokines and chemokines from tumor cells, which affect cancer development, metastasis, and tumor immunity. In the present study, we investigated the role of LPA-induced IL-10 release in regulating HLA-DR expression and the underlying mechanisms in human melanoma cells. We showed that LPA (0.001-10 μM) dose-dependently increased DR6 transcript levels through activating LPAR1 in HEK293T cells. Knockdown of NF-κB1 abrogated the LPA-increased DR6 expression without affecting basal DR6 expression in both A2058 and A375 melanoma cell lines. LPA (10 µM) significantly increased IL-10 transcripts in A2058 and A375 melanoma cells, the effect was abolished by pharmacological inhibition of LPAR1 or knockdown of DR6. We found a statistically significant correlation between the expression of LPAR1, DR6 and IL-10 in human melanoma tissue and an association between increased expression of LPAR1 and reduced effectiveness of ICI therapy. We demonstrated that LPA (10 µM) markedly suppressed HLA-DR expression in both A375 and A2058 melanoma cells via activating the LPAR1-DR6-IL-10 pathway. These data suggest that the LPAR1-DR6-IL-10 autocrine loop could constitute a novel mechanism used by tumor cells to evade immunosurveillance by decreasing HLA-DR expression.

The Dual Role of ACOD1 in Inflammation

Immunometabolism is an interdisciplinary field that focuses on the relationship between metabolic pathways and immune responses. Dysregulated immunometabolism contributes to many pathological settings, such as cytokine storm or immune tolerance. Aconitate decarboxylase 1 (ACOD1, also known as immunoresponsive gene 1), the mitochondrial enzyme responsible for catalyzing itaconate production, was originally identified as a bacterial LPS-inducible gene involved in innate immunity in mouse macrophages. We now know that the upregulation of ACOD1 expression in immune or nonimmune cells plays a context-dependent role in metabolic reprogramming, signal transduction, inflammasome regulation, and protein modification. The emerging function of ACOD1 in inflammation and infection is a double-edged sword. In this review, we discuss how ACOD1 regulates anti-inflammatory or proinflammatory responses in an itaconate-dependent or -independent manner. Further understanding of ACOD1 expression and function may pave the way for the development of precision therapies for inflammatory diseases.

A Structural and Functional Perspective of Death Receptor 6

As a member of the tumor necrosis factor receptor superfamily (TNFRSF), death receptor 6 (DR6) has a similar structural architecture to other family members. The extracellular region of DR6 contains four cysteine-rich domains, followed by a single-pass transmembrane domain and an intracellular region. Since its discovery, DR6 has become an orphan receptor ubiquitously expressed to transduce unique signaling pathways. Although the free ectodomains of β-amyloid precursor protein (APP) can bind to DR6 to induce apoptotic signals, the natural ligands of DR6 still remain largely unknown. In this review, we focus on recent research progress of structural and functional studies on DR6 for better understanding DR6-mediated signaling and the treatment of DR6-related diseases.

ACOD1 in immunometabolism and disease

Immunometabolism plays a fundamental role in health and diseases and involves multiple genes and signals. Aconitate decarboxylase 1 (ACOD1; also known as IRG1) is emerging as a regulator of immunometabolism in inflammation and infection. Upregulation of ACOD1 expression occurs in activated immune cells (e.g., macrophages and monocytes) in response to pathogen infection (e.g., bacteria and viruses), pathogen-associated molecular pattern molecules (e.g., LPS), cytokines (e.g., TNF and IFNs), and damage-associated molecular patterns (e.g., monosodium urate). Mechanistically, several immune receptors (e.g., TLRs and IFNAR), adapter proteins (e.g., MYD88), ubiquitin ligases (e.g., A20), and transcription factors (e.g., NF-κB, IRFs, and STATs) form complex signal transduction networks to control ACOD1 expression in a context-dependent manner. Functionally, ACOD1 mediates itaconate production, oxidative stress, and antigen processing and plays dual roles in immunity and diseases. On the one hand, activation of the ACOD1 pathway may limit pathogen infection and promote embryo implantation. On the other hand, abnormal ACOD1 expression can lead to tumor progression, neurodegenerative disease, and immune paralysis. Further understanding of the function and regulation of ACOD1 is important for the application of ACOD1-based therapeutic strategies in disease.

Ovarian Cancer Immunotherapy: Preclinical Models and Emerging Therapeutics

Immunotherapy has emerged as one of the most promising approaches for ovarian cancer treatment. The tumor microenvironment (TME) is a key factor to consider when stimulating antitumoral responses as it consists largely of tumor promoting immunosuppressive cell types that attenuate antitumor immunity. As our understanding of the determinants of the TME composition grows, we have begun to appreciate the need to address both inter- and intra-tumor heterogeneity, mutation/neoantigen burden, immune landscape, and stromal cell contributions. The majority of immunotherapy studies in ovarian cancer have been performed using the well-characterized murine ID8 ovarian carcinoma model. Numerous other animal models of ovarian cancer exist, but have been underutilized because of their narrow initial characterizations in this context. Here, we describe animal models that may be untapped resources for the immunotherapy field because of their shared genomic alterations and histopathology with human ovarian cancer. We also shed light on the strengths and limitations of these models, and the knowledge gaps that need to be addressed to enhance the utility of preclinical models for testing novel immunotherapeutic approaches.