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Aging weakens Th17 cell pathogenicity and ameliorates experimental autoimmune uveitis in mice.
Aging-induced changes in the immune system are associated with a higher incidence of infection and vaccination failure. Lymph nodes, which filter the lymph to identify and fight infections, play a central role in this process. However, careful characterization of the impact of aging on lymph nodes and associated autoimmune diseases is lacking. We combined single-cell RNA sequencing (scRNA-seq) with flow cytometry to delineate the immune cell atlas of cervical draining lymph nodes (CDLNs) of both young and old mice with or without experimental autoimmune uveitis (EAU). We found extensive and complicated changes in the cellular constituents of CDLNs during aging. When confronted with autoimmune challenges, old mice developed milder EAU compared to young mice. Within this EAU process, we highlighted that the pathogenicity of T helper 17 cells (Th17) was dampened, as shown by reduced GM-CSF secretion in old mice. The mitigated secretion of GM-CSF contributed to alleviation of IL-23 secretion by antigen-presenting cells (APCs) and may, in turn, weaken APCs' effects on facilitating the pathogenicity of Th17 cells. Meanwhile, our study further unveiled that aging downregulated GM-CSF secretion through reducing both the transcript and protein levels of IL-23R in Th17 cells from CDLNs. Overall, aging altered immune cell responses, especially through toning down Th17 cells, counteracting EAU challenge in old mice.
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Dissecting PTPN7-driven aggressiveness in IDH-wildtype astrocytomas: multi-omics, clinical validation, and spatial transcriptomics for prognostic insights.
BACKGROUND: Gliomas, particularly IDH-wildtype astrocytomas, remain highly aggressive and resistant to current therapies. Despite advances in molecular classification, effective therapeutic targets are still limited. Consequently, identifying new targets is essential to improve patient survival. PTPN7, a tyrosine phosphatase implicated in MAPK signaling, is known to play roles in various malignancies but remains underexplored in gliomas. This study examines the prognostic significance, spatial distribution, and immune-related functions of PTPN7, aiming to elucidate its potential as a prognostic role and therapeutic target in glioma treatment. MATERIALS AND METHODS: We analyzed PTPN7 mRNA expression in gliomas via TCGA, CGGA, and single-cell RNA sequencing (GSE131928 and GSE89567). Kaplan determined prognostic significance-Meier and uni-/multi-variate Cox survival analyses. Gene set enrichment analysis (GSEA) was used to identify dysregulated pathways, immune signatures, and cell-type enrichments. We also applied CIBERSORT to evaluate the relationships between PTPN7 expression and 12-principal cell states and 22 immune populations. Spatial transcriptomics (Ivy Glioblastoma Atlas, 10 × Genomics Visium) mapped PTPN7 distribution; these findings were corroborated by immunohistochemistry-validated protein expression in 70 cases. RESULTS: Pan-cancer analysis revealed PTPN7 overexpression in multiple malignancies, including glioma. Notably, PTPN7 was significantly elevated in IDH-wildtype astrocytomas, correlating with higher tumor grades and poorer overall survival. GSEA indicated that high PTPN7 is linked to T-cell differentiation, macrophage/monocyte activation, and dendritic cell-associated pathways. Both immune deconvolution and single-cell analyses showed that PTPN7 positively correlates with myeloid series and T-cell populations, supported by additional GSEA findings. In the Ivy dataset and spatial transcriptomics, PTPN7 was concentrated in peri-necrotic, cellular tumor, and slightly lower in the infiltrating border regions, consistent with immune interaction sites. Immunohistochemical data further demonstrated high PTPN7 expression tracks with increased tumor grade, reaching statistical significance in IDH-wildtype astrocytomas and confirming its clinical relevance. CONCLUSION: This study positions PTPN7 as a prognostic biomarker and immune modulator in gliomas, particularly IDH-wildtype astrocytomas. Its expression correlates with tumor aggressiveness and immune infiltration, potentially driving glioma progression. Targeting PTPN7 may disrupt immune evasion and support tumor eradication, indicating a promising therapeutic avenue in immunotherapy-based strategies.
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Systematic identification of post-transcriptional regulatory modules.
In our cells, a limited number of RNA binding proteins (RBPs) are responsible for all aspects of RNA metabolism across the entire transcriptome. To accomplish this, RBPs form regulatory units that act on specific target regulons. However, the landscape of RBP combinatorial interactions remains poorly explored. Here, we perform a systematic annotation of RBP combinatorial interactions via multimodal data integration. We build a large-scale map of RBP protein neighborhoods by generating in vivo proximity-dependent biotinylation datasets of 50 human RBPs. In parallel, we use CRISPR interference with single-cell readout to capture transcriptomic changes upon RBP knockdowns. By combining these physical and functional interaction readouts, along with the atlas of RBP mRNA targets from eCLIP assays, we generate an integrated map of functional RBP interactions. We then use this map to match RBPs to their context-specific functions and validate the predicted functions biochemically for four RBPs. This study provides a detailed map of RBP interactions and deconvolves them into distinct regulatory modules with annotated functions and target regulons. This multimodal and integrative framework provides a principled approach for studying post-transcriptional regulatory processes and enriches our understanding of their underlying mechanisms.
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Network toxicology integrated with machine learning and SHAP analysis identifies overlapping immune signatures between Di(2-ethylhexyl) phthalate (DEHP) and Sjögren's syndrome.
BACKGROUND: As a prevalent plasticizer in industrial manufacturing and daily-use products, di(2-ethylhexyl) phthalate (DEHP) has raised growing concerns regarding its safety and potential role in disease pathogenesis. Although direct epidemiological evidence linking DEHP exposure to Sjögren’s syndrome (SS) remains limited, DEHP has been reported to exert immunomodulatory and endocrine-disrupting effects that are highly relevant to the core pathological mechanisms of SS. Therefore, this study aimed to systematically explore the potential links between DEHP and SS using an integrated strategy combining toxicity prediction, network toxicology, machine learning, molecular docking, single-cell atlas interrogation, and in vitro validation. METHODS: The toxicity profile of DEHP was predicted using two complementary online platforms (ProTox 3.0 and ADMETlab 2.0). SS-related transcriptomic datasets were obtained from GEO, and potential DEHP targets were collected from CTD, SwissTargetPrediction, and SEA. Network toxicology analyses (intersection screening, PPI construction, and GO/KEGG enrichment) were performed to identify key genes and pathways. Machine learning models were trained to classify SS versus controls using the DEHP–SS intersect genes, and SHAP was applied to interpret key predictors. Molecular docking (AutoDock Vina) was conducted to estimate the binding propensity between DEHP and prioritized proteins. A single-cell atlas of mouse submandibular gland tissue (PanglaoDB; Mus musculus; SRA693675:SRS3206192) was queried to localize gene expression. Finally, in vitro experiments were performed in human submandibular gland epithelial cells (HSG) to validate STAT1 expression changes following DEHP exposure. RESULTS: In silico toxicity predictions suggested that DEHP may exhibit immunotoxic potential among multiple predicted toxicity endpoints. Network toxicology analysis identified 27 DEHP–SS intersect targets, and the PPI network highlighted STAT1, CCL2, CXCL10, MYD88, and IRF7 as highly connected hub genes. Across 113 machine learning models, Ridge/Elastic Net–based models demonstrated stable classification performance, and SHAP analysis prioritized five core predictors (STAT1, PHGDH, ISG15, CXCL10, and CCL2). Molecular docking suggested favorable binding energies ( ≤ − 5.0 kcal/mol) between DEHP and these five proteins. Single-cell atlas interrogation indicated that STAT1 is expressed in salivary mucous cells. In vitro experiments further demonstrated that DEHP exposure increases STAT1 mRNA and protein expression in HSG cells in a dose-dependent manner. Collectively, these results provide mechanistically plausible—yet non-causal—evidence linking DEHP to SS-relevant immune signatures. CONCLUSION: This study identifies an overlapping immune-response signature between predicted DEHP-associated targets and SS-related transcriptomic features, highlighting STAT1 as a prioritized node within this intersection. In vitro experiments demonstrate that DEHP exposure upregulates STAT1 expression in HSG cells. These findings provide hypothesis-generating evidence that DEHP may modulate interferon-related immune pathways relevant to SS, while not establishing causality.
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Cuproptosis-related lncRNAs predict prognosis and immune response of thyroid carcinoma.
Objective: To estimate the survival and prognosis of patients with thyroid carcinoma (THCA) based on the Long non-coding RNA (lncRNA) traits linked to cuproptosis and to investigate the connection between the immunological spectrum of THCA and medication sensitivity. Methods: RNA-Seq data and clinical information for THCA were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We built a risk prognosis model by identifying and excluding lncRNAs associated with cuproptosis using Cox regression and LASSO methods. Both possible biological and immune infiltration functions were investigated using Principal Component Analysis (PCA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and immunoassays. The sensitivity of the immune response to possible THCA medicines was assessed using ratings for tumor immune dysfunction and exclusion (TIDE) and tumor mutational burden (TMB). Results: Seven cuproptosis-related lncRNAs were used to construct our prognostic prediction model: AC108704.1, DIO3OS, AL157388.1, AL138767.3, STARD13-AS, AC008532.1, and PLBD1-AS1. Using data from TCGA's training, testing, and all groups, Kaplan-Meier and ROC curves demonstrated this feature's adequate predictive validity. Different clinical characteristics have varying effects on cuproptosis-related lncRNA risk models. Further analysis of immune cell infiltration and single sample Gene Set Enrichment Analysis (ssGSEA) supported the possibility that cuproptosis-associated lncRNAs and THCA tumor immunity were closely connected. Significantly, individuals with THCA showed a considerable decline in survival owing to the superposition effect of patients in the high-risk category and high TMB. Additionally, the low-risk group had a higher TIDE score compared with the high-risk group, indicating that these patients had suboptimal immune checkpoint blocking responses. To ensure the accuracy and reliability of our results, we further verified them using several GEO databases. Conclusion: The clinical and risk aspects of cuproptosis-related lncRNAs may aid in determining the prognosis of patients with THCA and improving therapeutic choices.
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Integrated Multiomics Analysis and Machine Learning Approaches in Bladder Cancer: Unveiling the Impact of Immunogenic Cell Death and Its Key Gene SLC2A3 on Prognosis and Personalized Treatment Strategies.
Recently, immunogenic cell death (ICD) has been identified as a regulatory cell death mechanism that induces an adaptive immune response, thereby improving enhancing the efficacy of immunotherapy and contributing to improved prognosis in bladder cancer (BLCA). This study established a risk signature based on ICD and identified ICD-related genes as diagnostic markers and therapeutic targets for BLCA. Thirty-two key ICD-risk genes (IRGs) were screened from correlation and univariate Cox regression analyses. Data obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases regarding BLCA and normal samples were categorized based on IRGs. ICD-based prognostic signature was built based on IRGs, stratifying BLCA patients into high- and low-risk groups. SLC2A3 was identified as a hub gene by 101 combinatorial machine learning algorithms and 10 individual machine learning algorithms. Based on single-cell sequencing data, we identified the cancer-associated fibroblasts (CAFs), the key cell population exhibiting high SLC2A3 expression. Functional analyses were performed to explore the potential value of SLC2A3 as a target for precision therapy. A prognostic signature was constructed using IRGs, indicating significant differences in the tumor microenvironment (TME) and treatment response between different risk groups. We identified SLC2A3 as the most critical IRG exhibiting high expression in the fibroblast population of patients with BLCA, especially in CAFs, which play an important role in BLCA progression. We found that inhibiting SLC2A3 expression may enhance the effectiveness of immunotherapy and the identified potential drugs targeting SLC2A3. We demonstrated that the identified IRGs serve as risk factors for clinical prognosis in BLCA and successfully constructed an ICD-based prognostic signature. Additionally, SLC2A3 holds potential as a therapeutic target to advance precision and personalized treatment strategies for BLCA, in combination with immunotherapy.
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Dissecting the impact of transcription factor dose on cell reprogramming heterogeneity using scTF-seq.
Reprogramming often yields heterogeneous cell fates, yet the underlying mechanisms remain poorly understood. To address this, we developed single-cell transcription factor sequencing (scTF-seq), a single-cell technique that induces barcoded, doxycycline-inducible TF overexpression and quantifies TF dose-dependent transcriptomic changes. Applied to mouse embryonic multipotent stromal cells, scTF-seq generated a gain-of-function atlas for 384 mouse TFs, identifying key regulators of lineage specification, cell cycle control and their interplay. Leveraging single-cell resolution, we uncovered how TF dose shapes reprogramming heterogeneity, revealing both dose-dependent and stochastic cell state transitions. We classified TFs into low-capacity and high-capacity groups, with the latter further subdivided by dose sensitivity. Combinatorial scTF-seq demonstrated that TF interactions can shift from synergistic to antagonistic depending on the relative dose. Altogether, scTF-seq enables the dissection of TF function, dose and cell fate control, providing a high-resolution framework to understand and predict reprogramming outcomes, advancing gene regulation research and the design of cell engineering strategies.
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A consensus spinal cord cell type atlas across mouse, macaque, and human.
The spinal cord contains evolutionarily conserved cell types critical for motor function, sensory processing, and autonomic regulation, many of which are implicated in diverse neurological diseases and injuries. Yet the field lacks a comprehensive molecular characterization of cellular diversity in human, macaque, and mouse spinal cord. Here, we present a unified, cross-species cell type atlas based on the integration of single-nucleus gene expression, chromatin accessibility, and spatial transcriptomic data from segments within cervical, thoracic, lumbar, and sacral regions, including motor neurons (MNs) sampled across the entire rostro-caudal axis of the macaque spinal cord. Leveraging the spatial distributions of our molecularly defined cell types, we generated a cell type-guided anatomical map of spinal cord laminae and nuclei. We identified both conserved and species-specific cellular features, including gene expression patterns across distinct MN subtypes in the primate spinal cord. Cross-species cis-regulatory analysis and deep learning sequence models dissected the enhancer logic underlying viral targeting, uncovering conserved transcription factor grammar encoding cellular identity. Together, these results establish a unifying molecular and anatomical taxonomy of spinal cord cell types across species.
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Association of CDKN2A overexpression with poor prognosis, radioresistance, and immunosuppression in colorectal cancer.
BACKGROUND: In colorectal cancer (CRC), dysregulated cyclin-dependent kinase inhibitor 2A (CDKN2A) contributes to disease progression. This investigation sought to explore the link between CDKN2A expression and the effectiveness of radiotherapy in CRC, and to investigate the underlying mechanisms. METHODS: Transcriptomic and clinical datasets were procured from the Cancer Genome Atlas and Gene Expression Omnibus databases. Immune cell infiltration was assessed using xCell, CIBERSORT, and EPIC methods. Biological mechanisms were investigated through the utilization of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. HCT 116 cells were used for in vitro and in vivo experiments. RESULTS: Bulk and single-cell transcriptome analyses and immunohistochemistry consistently revealed elevated CDKN2A expression in tumor specimens from patients with CRC. Patients with tumors that were nonresponsive to neoadjuvant chemoradiotherapy had significantly higher CDKN2A expression than those of responders (P = 0.039). CDKN2A knockdown in tumor led to a marked decrease in the proliferation, invasion, and migration capabilities, and this effect was enhanced by combined X-ray irradiation. Elevated CDKN2A expression was linked to markedly worse survival. In the tumor immune microenvironment, CDKN2A expression was negatively linked to CD8+ cell infiltration and positively correlated with the levels of immunosuppressive factors. Functional enrichment analysis indicated that elevated CDKN2A expression is linked to transcriptional regulation, amino acid metabolism, protein folding, and the TGF-β signaling pathway. CONCLUSION: In CRC, CDKN2A overexpression is associated with unfavorable responses to radiotherapy, an immunosuppressive tumor microenvironment, and poor prognosis. Suppression of CDKN2A expression could potentially improve therapeutic outcomes in CRC patients. More experimental and clinical studies are still needed to verify and further distinguish the similarities and differences between colon cancer and rectal cancer.
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Establishment and validation of a prognostic model for pancreatic cancer utilizing genes of tumor-associated neutrophils.
BACKGROUND: Dysregulation in chemotaxis and activation of neutrophils may trigger cancer. Nevertheless, the function of neutrophils and their mechanism during the prognosis of pancreatic cancer (PC) remain unclear. METHODS: From the databases of The Cancer Genome Atlas (TGCA) and GeneCards, the genes of tumor-associated neutrophils (TANs) were screened out leveraging the differential expression analysis. The constructed prognostic model for PC was analyzed through the least absolute shrinkage and selection operator (LASSO) regression and Cox univariate and multivariate regression. The dataset (GSE62452) provided by the Gene Expression Omnibus (GEO) database served as the validation cohort, and its potential mechanistic pathways and biological functions were analyzed leveraging Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The immune infiltration analysis, as well as the drug sensitivity prediction, were conducted. The gene expression in the prognostic model was checked through online databases, including Human Protein Atlas (HPA) and Tumor Immune Single-Cell Hub (TISCH). Finally, the genes were experimentally confirmed by immunohistochemistry (IHC) and qPCR in combination with clinical samples from patients with PC. RESULTS: AIM2, PSCA, IL18BP, and LIPE were four genes of TANs closely linked to the prognosis of PC. A model for the prognosis of PC was established utilizing these 4 genes. The AUC (area under the receiver operating characteristic curve (ROC curve)) values of this model for forecasting the survival rates of patients at 1, 2, and 3 years were 0.774, 0.841, and 0.953, respectively. The immune infiltration analysis revealed that resting dendritic cells (resting DCs), naive B cells, CD8T cells, as well as follicular helper T cells, were highly infiltrated among patients suffering from PC. According to the drug sensitivity analysis, the high-risk pancreatic ductal adenocarcinoma (PAAD) group had a significantly higher inhibitory concentration 50 (IC50) for dactolisib, docetaxel, gemcitabine, and ulixertinib compared to the group by patients with low-risk PAAD. The results of IHC and qPCR confirmed those of bioinformatics analysis. CONCLUSION: New TANs-related biomarkers have been found that effectively forecast the prognosis of patients suffering from PAAD.