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iPSC-Derived Microglia as a Model to Study Inflammation in Idiopathic Parkinson's Disease.
Parkinson's disease (PD) is a neurodegenerative disease with unknown cause in the majority of patients, who are therefore considered "idiopathic" (IPD). PD predominantly affects dopaminergic neurons in the substantia nigra pars compacta (SNpc), yet the pathology is not limited to this cell type. Advancing age is considered the main risk factor for the development of IPD and greatly influences the function of microglia, the immune cells of the brain. With increasing age, microglia become dysfunctional and release pro-inflammatory factors into the extracellular space, which promote neuronal cell death. Accordingly, neuroinflammation has also been described as a feature of PD. So far, studies exploring inflammatory pathways in IPD patient samples have primarily focused on blood-derived immune cells or brain sections, but rarely investigated patient microglia in vitro. Accordingly, we decided to explore the contribution of microglia to IPD in a comparative manner using, both, iPSC-derived cultures and postmortem tissue. Our meta-analysis of published RNAseq datasets indicated an upregulation of IL10 and IL1B in nigral tissue from IPD patients. We observed increased expression levels of these cytokines in microglia compared to neurons using our single-cell midbrain atlas. Moreover, IL10 and IL1B were upregulated in IPD compared to control microglia. Next, to validate these findings in vitro, we generated IPD patient microglia from iPSCs using an established differentiation protocol. IPD microglia were more readily primed as indicated by elevated IL1B and IL10 gene expression and higher mRNA and protein levels of NLRP3 after LPS treatment. In addition, IPD microglia had higher phagocytic capacity under basal conditions-a phenotype that was further exacerbated upon stimulation with LPS, suggesting an aberrant microglial function. Our results demonstrate the significance of microglia as the key player in the neuroinflammation process in IPD. While our study highlights the importance of microglia-mediated inflammatory signaling in IPD, further investigations will be needed to explore particular disease mechanisms in these cells.
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Integrated explainable machine learning and multi-omics analysis for survival prediction in cancer with immunotherapy response.
To demonstrate the efficacy of machine learning models in predicting mortality in melanoma cancer, we developed an interpretability model for better understanding the survival prediction of cancer. To this end, the optimal features were identified, ten different machine learning models were utilized to predict mortality across various datasets. Then we have utilized the important features identified by those machines learning methods to construct a new model named NKECLR to forecast mortality of patient with cancer. To explicitly clarify the model's decision-making process and uncover novel findings, an interpretable technique incorporating machine learning and SHapley Additive exPlanations (SHAP), as well as LIME, has been employed, and four genes EPGN, PHF11, RBM34, and ZFP36 were identified from those machine learning(ML). The experimental analysis conducted on training and validation datasets demonstrated that the proposed model has a good performance com- pared to existing methods with AUC value 81.8%, and 79.3%, respectively. Moreover, when combined our NKECLR with PD-L1, PD-1, and CTLA-4 the AUC value was 83%0. Finally, these findings have been applied to comprehend the response of drugs and immunotherapy. Our research introduced an innovative predictive NKECLR model utilizing natural killer(NK) cell marker genes for cohorts with melanoma cancer. The NKECLR model can effectively predict the survival of melanoma cancer cohorts and treatment results, revealing distinct immune cell infiltration in the high-risk group.
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CXCL13 as a Prognostic Biomarker and Modulator of the Tumor Microenvironment in Colorectal Cancer.
OBJECTIVES: Immunotherapy yields limited results in patients with colorectal cancer (CRC), emphasizing the need for a deeper understanding of the immune landscape within the tumor microenvironment. Although the C-X-C motif chemokine ligand 13 (CXCL13) recruits B cells and promotes tertiary lymphoid structure (TLS) formation, its immune function and prognostic value in CRC remain unclear. This study investigated the impact of CXCL13 on patient outcomes and CRC immune landscape. METHODS: Four independent cohorts were recruited in this study. The Cancer Genome Atlas Program (TCGA) cohort evaluated survival differences as well as immune contexture associated with CXCL13 expression in CRC. Immunohistochemistry (IHC) in the Renji Hospital (RJ) cohort was used to validate CXCL13 and CD8 levels, while multiplex IHC assessed their spatial correlation. Two single-cell RNA sequencing (scRNA-seq) cohorts were used for evaluating the potential roles of CXCL13 and CXCL13+CD8+T cells in the immune context of CRC. RESULTS: Our study revealed a positive correlation between CXCL13 expression and improved survival among CRC patients. Elevated CXCL13 levels and CXCL13+CD8+T cells were linked to a favorable immune context that impeded tumor growth in CRC. Moreover, CXCL13 expression was more prevalent in microsatellite instability-high (MSI-H)/mismatch repair-deficient (MMRd) tumors, demonstrating its potential role in enhancing the response to anti-programmed death-1 ligand (PD-L1) treatment. CONCLUSIONS: CXCL13 plays a critical role in shaping a favorable immune tumor microenvironment in CRC. Further research should elucidate how CXCL13 modulates CD8+T cell function to improve antitumor immunity.
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The expression of PD-1 ligands in the immune microenvironment was altered in TTF-1-negative lung adenocarcinoma.
Thyroid transcription factor-1 (TTF-1) is a lineage-specific marker for lung adenocarcinoma (LUAD), whereas the relatively minor subset of TTF-1-negative LUADs shows a poor prognosis and a limited response to therapy. However, its relationship with the tumor immune microenvironment remains poorly defined. How TTF-1 expression affects the immune context in LUAD was investigated, focusing on tumor-associated macrophages (TAMs) and T-cell infiltration. Immunohistochemical (IHC) analysis of 226 LUAD specimens showed that TTF-1-negative tumors were associated with epidermal growth factor receptor wild-type status, advanced stage, and worse progression-free and cancer-specific survivals. Notably, PD-L1 (programmed death-ligand 1) and PD-L2 expression in TAMs, but not in cancer cells, was significantly reduced in TTF-1-negative tumors. Public single-cell RNA sequencing data confirmed downregulation of CD274 (PD-L1) in TAMs from tumors with low expression of TTF-1-related genes. In contrast, PDCD1LG2 (PD-L2) expression showed less consistent patterns. On IHC analysis, infiltration of CD8+ and CD4+ T cells was modestly lower in TTF-1-negative tumors, accompanied by decreased HLA class I and II expressions. Transcriptomic analysis of The Cancer Genome Atlas LUAD cohort further showed lower interferon gamma (IFN-γ) signaling and decreased T cell-inflamed gene signatures in the low TTF-1-negative tumors. These findings suggest that TTF-1-negative LUAD exhibits more immune-suppressive features, with a relatively reduced antitumor immune response characterized by decreased T-cell infiltration and INF-γ signaling, which are related to PD-L1 and PD-L2 expressions in TAMs.
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Construction of chronic inflammation and mitochondrial energy metabolism-associated predictive and therapeutic models for lung adenocarcinoma patients.
BACKGROUND: Lung adenocarcinoma (LUAD) remains the most common subtype of non–small cell lung cancer with poor survival. Dysregulation of mitochondrial energy metabolism (MEM) can be considered as major driver for LUAD pathogenesis among Chronic obstructive pulmonary disease (COPD) patients. Hence, deeper understanding of MEM carcinogenic role for LUAD patients can provide novel insights into LUAD pathogenesis for COPD patients. METHODS: By employing integrative bioinformatic approaches, such as Limma and WGCNA in LUAD bulk profile (GSE32863), we first identified COPD and MEM (CM)-associated DEGs for LUAD patients, and these DEGs can divide LUAD patients from TCGA-LUAD cohort into 2 risk groups. Next, Lasso-cox regression and multi-variate cox regression identified CM-associated predictive model and hub gene for LUAD patients both in TCGA-LUAD cohort and GSE13213. Indeed, hub gene molecular and immune characters were estimated at LUAD bulk level (TCGA-LUAD cohort) and single-cell level (GSE203360). Besides, drug sensitivity and molecular docking analysis confirmed potential therapeutic agent targeting hub gene for the treatment of LUAD. RESULTS: 8 CM-associated gene signatures can divide LUAD patients into 2 molecular subgroups and guide the prognostic model construction for LUAD patients. TPI1 can be considered as CM-associated hub gene involved in LUAD progression. 17-AAG can be considered as drug reproposing framework for the treatment of LUAD. CONCLUSION: This study first highlighted the predictive and therapeutic potentials of CM, and highlighted TPI1 pathogenic role for LUAD patients.
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The activator protein-1 complex governs a vascular degenerative transcriptional programme in smooth muscle cells to trigger aortic dissection and rupture.
BACKGROUND AND AIMS: Stanford type A aortic dissection (AD) is a degenerative aortic remodelling disease marked by an exceedingly high mortality without effective pharmacologic therapies. Smooth muscle cells (SMCs) lining tunica media adopt a range of states, and their transformation from contractile to synthetic phenotypes fundamentally triggers AD. However, the underlying pathomechanisms governing this population shift and subsequent AD, particularly at distinct disease temporal stages, remain elusive. METHODS: Ascending aortas from nine patients undergoing ascending aorta replacement and five individuals undergoing heart transplantation were subjected to single-cell RNA sequencing. The pathogenic targets governing the phenotypic switch of SMCs were identified by trajectory inference, functional scoring, single-cell regulatory network inference and clustering, regulon, and interactome analyses and confirmed using human ascending aortas, primary SMCs, and a β-aminopropionitrile monofumarate-induced AD model. RESULTS: The transcriptional profiles of 93 397 cells revealed a dynamic temporal-specific phenotypic transition and marked elevation of the activator protein-1 (AP-1) complex, actively enabling synthetic SMC expansion. Mechanistically, tumour necrosis factor signalling enhanced AP-1 transcriptional activity by dampening mitochondrial oxidative phosphorylation (OXPHOS). Targeting this axis with the OXPHOS enhancer coenzyme Q10 or AP-1-specific inhibitor T-5224 impedes phenotypic transition and aortic degeneration while improving survival by 42.88% (58.3%-83.3% for coenzyme Q10 treatment), 150.15% (33.3%-83.3% for 2-week T-5224), and 175.38% (33.3%-91.7% for 3-week T-5224) in the β-aminopropionitrile monofumarate-induced AD model. CONCLUSIONS: This cross-sectional compendium of cellular atlas of human ascending aortas during AD progression provides previously unappreciated insights into a transcriptional programme permitting aortic degeneration, highlighting a translational proof of concept for an anti-remodelling intervention as an attractive strategy to manage temporal-specific AD by modulating the tumour necrosis factor-OXPHOS-AP-1 axis.
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Blood DNA methylation markers are associated with diabetic kidney disease progression in type 1 diabetes.
AIMS/HYPOTHESIS: DNA methylation has been shown to be associated with kidney function and diabetic kidney disease (DKD), but prospective studies are scarce. Therefore, we conducted epigenome-wide association studies (EWASs) on early- and late-stage DKD progression using DNA methylation data obtained by analysing baseline blood samples from participants in the Finnish Diabetic Nephropathy Study type 1 diabetes cohort. METHODS: We included 403 individuals with normal AER (early-stage progression group) and 372 individuals with severe albuminuria (late-stage progression group), and followed up DKD progression, defined as a decrease in eGFR to <60 ml/min per 1.73 m2 in the early-stage progression group, and end-stage kidney disease (ESKD) in the late-stage group. Replication was conducted in two type 1 diabetes cohorts in addition to publicly available EWAS summary statistics from diabetes and general population cohorts. Significant loci were further characterised by integration with genetic and proteomic data. RESULTS: We identified 11 methylation sites associated with DKD progression (p<9.4 × 10-8). Methylation at cg01730944 near the podocyte-specific gene CDKN1C and three other CpGs associated with early-stage DKD progression were independent of baseline eGFR, whereas late-stage progression CpGs were strongly associated with eGFR. The identified lead ESKD risk locus cg17944885 (chr19p13.2, p=2.6 × 10-17) and several novel methylation sites associated with late-stage DKD progression were supported by the results of previous studies. Proteomic analysis of cis proteins identified potential target genes for two CpGs: cg14999724 methylation was associated with PRG3 and PRG2, and cg12272104 was associated with BSG, FSTL3 and PALM. Furthermore, UK Biobank data show associations between these proteins and severe kidney endpoints. Finally, survival models that included methylation markers in addition to clinical risk factors significantly improved the identification of individuals at risk of early-stage DKD progression. CONCLUSIONS/INTERPRETATION: The current study detected 11 loci associated with DKD progression, identifying methylation changes predictive of early-stage DKD progression in type 1 diabetes for the first time. Future research is needed to establish prognostic DNA methylation markers for DKD progression.
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Cellular dynamics in cerebrospinal fluid unveils the key regulators of intracranial response to immune checkpoint inhibitors in NSCLC brain metastases.
BACKGROUND: Brain metastases (BrM) in non-small cell lung cancer (NSCLC) present a significant challenge due to poor prognosis. While immune checkpoint inhibitors (ICIs) have been standard treatments for NSCLC, their efficacy in BrM is variable, emphasizing the urgent need for predictive biomarkers and fundamental mechanisms. METHODS: Patients with NSCLC and BrM planning to receive ICI therapy were enrolled in the prospective cohort, dynamic cerebrospinal fluid (CSF) samples (n=20) were collected through lumbar puncture and used for single-cell RNA sequencing (scRNA-seq), and BrM tumors were collected if they have the indications of surgery, with integrating scRNA-seq data from external database (total n=20). Three independent cohorts underwent flow cytometry (n=8), proteomics analysis (n=31), and multiplex immunohistochemistry (n=25) for validation. RESULTS: Our study provided a high-resolution atlas of cellular dynamics in the CSF and BrM during ICI therapy in patients with NSCLC and BrM. Notably, we identified a key immune cell subset, CD4+PDCD1+CXCR6+ T cells, as a positive predictor of ICI intracranial tumor responses, which presented highly functional and transcriptomic similarities in both CSF and BrM tumor environment. Moreover, CXCR6 could serve as a specific marker for CD4+PDCD1+ T cells linked to ICI response. Further, we revealed that the novel cluster of CD4+PDCD1+CXCR6+ T cells was closely associated with lymphocyte activation and aggregation in CSF and BrM of ICI responders, and classical dendritic cells of ICI responders interacted with CD4+PDCD1+CXCR6+ T cells for enhanced antigen presentation and inflammatory activation. CONCLUSION: Our findings revealed critical insights into the immune landscape of NSCLC BrM under ICI therapy, highlighting CD4+PDCD1+CXCR6+ T cells in CSF as a promising biomarker and illuminating fundamental mechanisms underlying ICI efficacy.
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Exploring the potential mechanisms of Yi-Yi-Fu-Zi-Bai-Jiang-San therapy on the immune-inflamed phenotype of colorectal cancer via combined network pharmacology and bioinformatics analyses.
BACKGROUND: The development and progression of colorectal cancer (CRC) is closely associated with its complex tumor microenvironment (TME). Assessment of the modified pattern of immune cell infiltration (ICI) will help increase knowledge regarding the characteristics of TME infiltration. Yi-Yi-Fu-Zi-Bai-Jiang-San (YYFZBJS) has been shown to have positive effects on the regulation of the immune microenvironment of CRC. However, its pharmacological targets and molecular mechanisms remain to be elucidated. METHODS: Network pharmacological analysis was used to identify the target of YYFZBJS in the TME of CRC. Patients with the immune-inflamed phenotype (IIP) were identified using CRC samples from The Cancer Genome Atlas (TCGA) database. Consensus genes were identified by intersecting YYFZBJS targets, CRC disease targets and differentially expressed genes in the CRC microenvironment. Then, least absolute shrinkage and selection operator (LASSO) Cox analyses were used to identify a prognostic signature from the consensus genes. Cytoscape software was further used to build a unique herb-compound-target network diagram of the important components of YYFZBJS and prognostic gene targets. In addition, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was performed using the prognostic gene sets to explore the molecular mechanism of the prognostic genes in drug therapy for CRC IIP patients. Finally, single-cell analysis was performed to validate the expression of the prognostic genes in the TME of CRC using the TISCH2 database. RESULTS: A total of 284 IIP patients were identified from 480 patients with CRC. A total of 35 consensus genes were identified as targets of YYFZBJS in the TME of CRC patients. An eleven-gene prognostic signature, including PIK3CG, C5AR1, PRF1, CAV1, HPGDS, PTGS2, SERPINE1, IDO1, TGFB1, CXCR2 and MMP9, was identified from the consensus genes, with areas under the receiver operating characteristic (ROC) curve (AUCs) values of 0.84 and 0.793 for the training and test cohorts, respectively. In the herb-compound-target network, twenty-four compounds were shown to interact with the 11 prognostic genes, which were significantly enriched in the IL-17 signaling, arachidonic acid metabolism and metabolic pathways. Single-cell analysis of the prognostic genes confirmed that their abnormal expression was associated with the TME of CRC. CONCLUSION: This study organically integrated network pharmacology and bioinformatics analyses to identify prognostic genes in CRC IIP patients from the targets of YYFZBJS. Although this data mining work was limited to the study of mechanisms related to prognosis based on the immune microenvironment, the methodology provides new perspectives in the search for novel therapeutic targets of traditional Chinese medicines (TCMs) and accurate diagnostic indicators of cancers targeted by TCMs.
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Multi-omics and spatial transcriptomics reveal that S100A10 drives CD8+ T-cell exhaustion and immune evasion in hepatocellular carcinoma through cPLA2-5-LOX-mediated arachidonic acid metabolism and ferroptosis.
Immune evasion in hepatocellular carcinoma (HCC) represents a major biological barrier limiting the efficacy of immunotherapy, yet its molecular basis remains incompletely understood. Increasing evidence indicates that tumor metabolic reprogramming and ferroptosis-related signaling play critical roles in shaping an immunosuppressive tumor microenvironment (TME); however, the specific regulatory factors involved remain unclear. This study aims to systematically elucidate the functional role of S100 calcium-binding protein A10 (S100A10) in immune evasion in HCC, with a particular focus on the molecular mechanisms by which S100A10 regulates CD8+ T-cell exhaustion through arachidonic acid (AA) metabolism and ferroptosis, as well as its potential therapeutic implications. To this end, data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort are integrated to analyze the expression patterns of S100A10, its prognostic value, and its association with the immune microenvironment. S100A10 overexpression and knockout models are established in HCCLM3 and MHCC97L cell lines, and S100A10-mediated metabolic pathway reprogramming is characterized using transcriptomic profiling, untargeted metabolomics, and ferroptosis-related functional assays. In parallel, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics are employed to delineate the cell-type specificity and spatial distribution of S100A10. Furthermore, human CD8+ T-cell co-culture systems and orthotopic mouse HCC models are used to evaluate the impact of S100A10 on immune function and responsiveness to anti-programmed cell death protein 1 (anti-PD-1) therapy. The results demonstrate that S100A10 is significantly upregulated in HCC and is closely associated with poor prognosis and an immunosuppressive state. Mechanistically, S100A10 activates cytosolic phospholipase A2-arachidonate 5-lipoxygenase (cPLA2-5-LOX)-mediated AA oxidative metabolism, leading to the accumulation of lipid peroxidation products and ferroptosis-associated signals, thereby driving CD8+ T-cell exhaustion and promoting immune evasion. Significantly, inhibition of S100A10 reshapes the tumor immune microenvironment (TIME) and enhances the therapeutic efficacy of anti-PD-1 treatment. Collectively, these findings identify S100A10 as a critical regulator of metabolic-immune coupling in HCC and provide a theoretical basis for combinatorial strategies targeting metabolism and immunotherapy.