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Single-nucleus transcriptomics reveals the cellular immune responses to Candidatus Liberibacter asiaticus in rough lemon.
Citrus Huanglongbing (HLB) is the most destructive disease in citriculture, mainly caused by Candidatus Liberibacter asiaticus (CLas). However, the immune response of citrus to CLas at the cellular level remains to be elucidated. In this study, the first single-cell atlas of rough lemon (Citrus jambhiri Lush.) root apexes were generated using single-nucleus RNA sequencing at 20 weeks postinoculation with CLas. According to gene expression patterns, the single-cell atlas was partitioned into 20 transcriptionally distinct clusters, and five cell types were identified within these clusters. A significant number of defense-related genes were co-upregulated across the five cell types following CLas infection, whereas genes involved in signal transduction pathways, such as tubulin beta-6 chain (TUBB1) and the phospholipase D alpha 1 (PLD1), were concurrently downregulated. Based on pseudotime trajectory analysis, the key pathways and genes involved in the coordination of cell differentiation and resistance in citrus under CLas infection were characterized. Following CLas infection, the development of phloem cells was significantly delayed, and the differentiation of cambium cells into xylem cells was evident. The expression of genes associated with lignin synthesis was significantly upregulated in these cells. The reduction in phloem cell differentiation and the enhanced differentiation of cambium cells into defense-related xylem cells may represent the primary vascular immune mechanisms exhibited by citrus plants in response to CLas infection. Additionally, DNA-binding one zinc finger transcription factor DOF2.4 was found to potentially serve dual roles in regulating vascular cell development and inducing plant resistance against CLas. In conclusion, this study collectively provides insights into the cellular innate immunity responses of citrus to CLas infection. These findings hold significant implications for the sustainable development of citriculture amidst the ongoing global HLB epidemic, and offer novel insights into vascular immunity and plant defense responses.
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Comprehensive analysis of NT5DC family prognostic and immune significance in breast cancer.
Among the most common malignancies, breast cancer has a high incidence and mortality rate. NT5DC family is a highly well-conserved 5'-nucleotidase. Previous studies showed that the progression of tumors was associated with some NT5DC family members. However, there are no studies about the comprehensive analysis such as expression, prognosis, and immune properties of NT5DC family in breast cancer. Based on the data from The Cancer Genome Atlas database, we used UALCAN, Tumor Immune Estimation Resource, Breast cancer gene-expression miner (Bc-GenExMiner), Kaplan-Meier Plotter, TISIDB, cBioPortal, GeneMANIA, Search Tool for the Retrieval of Interacting Genes, Metascape, Tumor Immune Single-cell Hub, The Database for Annotation, Visualization and Integrated Discovery, and Gene Set Cancer Analysis databases to explore expression, prognostic and diagnostic value, genetic alterations, biological function, immune value and drug sensitivity of NT5DC family in breast cancer patients. There was a downregulation of NT5C2, NT5DC1, and NT5DC3 in breast cancer compared to normal tissues, and NT5DC2 instead. All NT5DC family members were associated with the clinicopathological parameters of breast cancer patients. Survival and ROC analysis revealed that NT5DC family genes were related to the prognosis and diagnosis of breast cancer. NT5DC family were mainly involved in nucleotide metabolism. Moreover, NT5DC family were significantly associated with tumor immune microenvironment, diverse immune cells, and immune checkpoints in breast cancer. This research showed that NT5DC family might be novel prognostic biomarkers and immunotherapeutic targets of breast cancer.
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Long non-coding RNA NEAT1 promotes colorectal cancer progression via interacting with SIRT1.
Nuclear-enriched abundant transcript 1 (NEAT1), a long noncoding RNA, is found to be significantly dysregulated in different types of cancer, including colorectal cancer (CRC). Nevertheless, there is still much to learn about the precise functions and processes of NEAT1 in the progression of CRC. Using The Cancer Genome Atlas (TCGA) database and 50 CRC specimens from the First Affiliated Hospital of Dali University, we assessed the expression of NEAT1 to determine its clinical impact. Through gene set enrichment analysis (GSEA), Cancer Single-cell State Atlas (CancerSEA), and immune infiltration studies, we elucidated key functions of NEAT1. We utilized Cell Counting Kit-8 (CCK8), wound healing, and Transwell assays to investigate the role of NEAT1 in the progression of CRC. Through the use of GSEA and immunohistochemistry, additional investigations were conducted to unveil the downstream targets of NEAT1 and gain insights into their regulatory dynamics. Our in vitro studies confirmed the regulatory role of NEAT1 in CRC. Findings indicate that increased NEAT1 expression correlates with adverse outcomes in colorectal tissues. In the CRC model, reduced levels of NEAT1 lead to reduced cell proliferation, invasion, and migration. Additionally, NEAT1 influenced immune cell infiltration in CRC and functioned as an oncogene by upregulating Sirtuin 1 (SIRT1) expression. This study demonstrates that NEAT1 promotes CRC progression and metastasis through a SIRT1-mediated mechanism, suggesting its potential as a prognostic biomarker and therapeutic target for CRC.
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Characterization of ccl20a.3 and ccl20l as gene markers for Th17 cell in turbot.
T-helper 17 lymphocytes (Th17) are the most common inflammatory cells identified in mammals. However, the identification of Th17 cells and the clarification of their function in teleost fish remain largely unknown. In this study, we took advantage of the single-cell RNA sequencing-based immune cell atlas that was identified in turbot (Scophthalmus maximus), and revealed two chemokine-related genes, ccl20a.3 and ccl20l, that were specifically expressed in Th17 cells. Moreover, through immuno-fluorescence analysis, we found that CCL20a.3 or CCL20l was co-expressed with the classical makers in Th17 cells, including IL17a/f1 and IL22. Furthermore, through a Th17 lineage-specific transcription factor RORc inhibitor GSK805 treatment, we found that the expression of ccl20a.3 and ccl20l was significantly impaired, compared with other T cell markers. Besides, we also found that ccl20a.3 and ccl20l exhibited the same dynamic response with the classical markers that were identified in Th17 cells during bacterial infection. Taken together, these results provide potential gene markers for better understanding of the dynamic immune responses of Th17 cells in teleost fish.
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Integrated post-GWAS, single-cell, and functional analyses prioritize TCTN2 at an osteoarthritis locus associated with innate immunity-related cartilage remodeling.
BACKGROUND: Post-genome-wide association study (GWAS) interpretation of osteoarthritis (OA) increasingly requires integration with single-cell biology because many risk loci likely act through sterile inflammatory remodeling, regulatory and inflammatory chondrocyte states, and tissue-stress biology rather than through large disease-stage expression shifts. We therefore asked whether a published Transformer-based post-GWAS prioritization strategy could refine a multi-gene chr12 susceptibility locus into a tractable TCTN2-centered effector-gene hypothesis. METHODS: Using combined hip and knee osteoarthritis summary statistics (213,839 cases and 1,080,481 controls), we built a transfer-learning variant-prioritization layer that scored 20,651,210 variants with 21 engineered features, combined these with LDSC, positional MAGMA, fibroblast and muscle eMAGMA, six-tissue SMR, and fine-mapping, and then integrated the prioritized locus with a 56,000-cell chondrocyte atlas, independent OA cartilage expression resources, OA primary-tissue eQTL maps, and ATDC5 perturbation experiments. RESULTS: The prioritization model, used solely as a ranking layer, assigned a high prioritization score (≥ 0.99) to 17,150 variants, including the chr12 lead rs11611450 (prioritization score 0.999853), and highlighted 6,275 additional non-genome-wide-significant high-priority variants. Positional MAGMA nominated eight Bonferroni-significant genes, and eMAGMA nominated four genes in skeletal muscle and seven in fibroblasts. Within the rs11611450-linked five-gene cluster, TCTN2 showed the strongest cross-layer convergence, with SMR support in five of six tissues, fibroblast eMAGMA support (P = 8.34 × 10-10), hypertrophic-chondrocyte enrichment (z = 2.20), and localization to regulatory and inflammatory chondrocyte states. Independent validation showed modestly higher TCTN2 abundance in OA than in normal cartilage (delta = 0.45, P = 2.8 × 10-2), no high-grade-versus-low-grade cartilage shift (logFC = 0.053, FDR = 1.0), and strong OA-cartilage eQTL support in both low-grade and high-grade cartilage (q = 4.86 × 10-4 and 1.13 × 10-2). In ATDC5 cells, Tctn2 knockdown reduced expression to 0.21-fold of Mock and increased Col10a1, Mmp13, and Runx2 to 2.71-, 2.98-, and 3.53-fold, respectively, whereas overexpression and rescue reversed the same markers. CONCLUSIONS: This staged analysis nominates TCTN2 as a plausible candidate within an unresolved chr12 osteoarthritis locus; ATDC5 perturbation indicates that TCTN2 can modulate hypertrophic markers. Because the human-tissue association is directionally discordant, we present a locus-to-cell-state hypothesis rather than a complete genetics-to-mechanism chain, with cross-sectional shifts remaining modest and context-dependent.
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Spatial Transcriptomic Analysis of Focal and Normal Areas of Myocyte Disarray in Human Hypertrophic Cardiomyopathy.
Hypertrophic Cardiomyopathy (HCM) is a common inherited disorder that can lead to heart failure and sudden cardiac death, characterized at the histological level by focal areas of myocyte disarray, hypertrophy and fibrosis, and only a few disease-targeted therapies exist. To identify the focal and spatially restricted alterations in the transcriptional pathways and reveal novel therapeutic targets, we performed a spatial transcriptomic analysis of the areas of focal myocyte disarray compared to areas of normal tissue using a commercially available platform (GeoMx, nanoString). We analyzed surgical myectomy tissue from four patients with HCM and the control interventricular septum tissue from two unused organ donor hearts that were free of cardiovascular disease. Histological sections were reviewed by an expert pathologist, and 72 focal areas with varying degrees of myocyte disarray (normal, mild, moderate, severe) were chosen for analysis. Areas of interest were interrogated with the Human Cancer Transcriptome Atlas designed to profile 1800 transcripts. Differential expression analysis revealed significant changes in gene expression between HCM and the control tissue, and functional enrichment analysis indicated that these genes were primarily involved in interferon production and mitochondrial energetics. Within the HCM tissue, differentially expressed genes between areas of normal and severe disarray were enriched for genes related to mitochondrial energetics and the extracellular matrix in severe disarray. An analysis of the gene expression of the ligand-receptor pair revealed that the HCM tissue exhibited downregulation of platelet-derived growth factor (PDGF), NOTCH, junctional adhesion molecule, and CD46 signaling while showing upregulation of fibronectin, CD99, cadherin, and amyloid precursor protein signaling. A deconvolution analysis utilizing the matched single nuclei RNA-sequencing (snRNA-seq) data to determine cell type composition in areas of interest revealed significant differences in fibroblast and vascular cell composition in areas of severe disarray when compared to normal areas in HCM samples. Cell composition in the normal areas of the control tissue was also divergent from the normal areas in HCM samples, which was consistent with the differential expression results. Overall, our data identify novel and potential disease-modifying targets for therapy in HCM.
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Analysis of Intracellular Communication Reveals Consistent Gene Changes Associated with Early-Stage Acne Skin.
A comprehensive understanding of the intricate cellular and molecular changes governing the complex interactions between cells within acne lesions is currently lacking. Herein, we analyzed early papules from six subjects with active acne vulgaris, utilizing single-cell and high-resolution spatial RNA sequencing. We observed significant changes in signaling pathways across seven different cell types when comparing lesional skin samples (LSS) to healthy skin samples (HSS). Using CellChat, we constructed an atlas of signaling pathways for the HSS, identifying key signal distributions and cell-specific genes within individual clusters. Further, our comparative analysis revealed changes in 49 signaling pathways across all cell clusters in the LSS- 4 exhibited decreased activity, whereas 45 were upregulated, suggesting that acne significantly alters cellular dynamics. We identified ten molecules, including GRN, IL-13RA1 and SDC1 that were consistently altered in all donors. Subsequently, we focused on the function of GRN and IL-13RA1 in TREM2 macrophages and keratinocytes as these cells participate in inflammation and hyperkeratinization in the early stages of acne development. We evaluated their function in TREM2 macrophages and the HaCaT cell line. We found that GRN increased the expression of proinflammatory cytokines and chemokines, including IL-18, CCL5, and CXCL2 in TREM2 macrophages. Additionally, the activation of IL-13RA1 by IL-13 in HaCaT cells promoted the dysregulation of genes associated with hyperkeratinization, including KRT17, KRT16, and FLG. These findings suggest that modulating the GRN-SORT1 and IL-13-IL-13RA1 signaling pathways could be a promising approach for developing new acne treatments.
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Single cell spatial transcriptomic profiling of childhood-onset lupus nephritis reveals complex interactions between kidney stroma and infiltrating immune cells.
Children with systemic lupus erythematosus (SLE) are at increased risk of developing kidney disease, termed childhood-onset lupus nephritis (cLN). Single cell transcriptomics of dissociated kidney tissue has advanced our understanding of LN pathogenesis, but loss of spatial resolution prevents interrogation of in situ cellular interactions. Using a technical advance in spatial transcriptomics, we generated a spatially resolved, single cell resolution atlas of kidney tissue (>400,000 cells) from eight cLN patients and two controls. Annotated cells were assigned to 35 reference cell types, including major kidney subsets and infiltrating immune cells. Analysis of spatial distribution demonstrated that individual immune lineages localize to specific regions in cLN kidneys, including myeloid cells trafficking to inflamed glomeruli and B cells clustering within tubulointerstitial immune hotspots. Notably, gene expression varied as a function of tissue location, demonstrating how incorporation of spatial data can provide new insights into the immunopathogenesis of SLE. Alterations in immune phenotypes were accompanied by parallel changes in gene expression by resident kidney stromal cells. However, there was little correlation between histologic scoring of cLN disease activity and glomerular cell transcriptional signatures at the level of individual glomeruli. Finally, we identified modules of spatially-correlated gene expression with predicted roles in induction of inflammation and the development of tubulointerstitial fibrosis. In summary, single cell spatial transcriptomics allows unprecedented insights into the molecular heterogeneity of cLN, paving the way towards more targeted and personalized treatment approaches.
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Integrated bioinformatics analysis and experimental validation identified CDCA families as prognostic biomarkers and sensitive indicators for rapamycin treatment of glioma.
The cell division cycle associated (CDCA) genes regulate the cell cycle; however, their relationship with prognosis in glioma has been poorly reported in the literature. The Cancer Genome Atlas (TCGA) was utilized to probe the CDCA family in relation to the adverse clinical features of glioma. Glioma single-cell atlas reveals specific expression of CDCA3, 4, 5, 8 in malignant cells and CDCA7 in neural progenitor cells (NPC)-like malignant cells. Glioma data from TCGA, the China Glioma Genome Atlas Project (CGGA) and the gene expression omnibus (GEO) database all demonstrated that CDCA2, 3, 4, 5, 7 and 8 are prognostic markers for glioma. Further analysis identified CDCA2, 5 and 8 as independent prognostic factors for glioma. Lasso regression-based risk models for CDCA families demonstrated that high-risk patients were characterized by high tumor mutational burden (TMB), low levels of microsatellite instability (MSI), and low tumor immune dysfunction and rejection (TIDE) scores. These pointed to immunotherapy for glioma as a potentially viable treatment option Further CDCA clustering suggested that the high CDCA subtype exhibited a high macrophage phenotype and was associated with a higher antigen presentation capacity and high levels of immune escape. In addition, hsa-mir-15b-5p was predicted to be common regulator of CDCA3 and CDCA4, which was validated in U87 and U251 cells. Importantly, we found that CDCAs may indicate response to drug treatment, especially rapamycin, in glioma. In summary, our results suggest that CDCAs have potential applications in clinical diagnosis and as drug sensitivity markers in glioma.
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Construction and validation of immune-related LncRNAs classifier to predict prognosis and immunotherapy response in laryngeal squamous cell carcinoma.
BACKGROUND: Rapid advances in transcriptomic profiles have resulted in recognizing IRLs (immune-related long noncoding RNAs), as modulators of the expression of genes related to immune cells that mediate immune inhibition as well as immune stimulatory, indicating LncRNAs play fundamental roles in immune modulation. Hence, we establish an IRL classifier to precisely predict prognosis and immunotherapeutic efficiency in laryngeal squamous cell carcinoma (LSCC). METHODS: LSCC RNA-seq (RNA sequencing) datasets, somatic mutation data, and corresponding clinicopathologic information were acquired from TCGA (the Cancer Genome Atlas) and Gene Expression Omnibus (GEO) databases. Spearman correlation analysis identified LncRNAs associated with immune-related genes (IRG). Based on Lasso penalized regression and random forest (RF), we constructed an IRL classifier associated with prognosis. GEO database was utilized to validate the IRL classifier. The predictive precision and clinical application of the IRL classifier were assessed and compared to clinicopathologic features. The immune cell infiltration of LSCC was calculated via CIBERSORTx tools and ssGSEA (single-sample gene set enrichment analysis). Then, we systematically correlated the IRL classifier with immunological characteristics from multiple perspectives, such as immune-related cells infiltrating, tumor microenvironment (TME) scoring, microsatellite instability (MSI), tumor mutation burden (TMB), and chemokines. Finally, the TIDE (tumor immune dysfunction and exclusion) algorithm was used to predict response to immunotherapy. RESULTS: Based on machine learning approach, three prognosis-related IRLs (BARX1-DT, KLHL7-DT, and LINC02154) were selected to build an IRL classifier. The IRL classifier could availably classify patients into the low-risk and high-risk groups based on the different endpoints, including recurrence-free survival (RFS) and overall survival (OS). In terms of predictive ability and clinical utility, the IRL classifier was superior to other clinical characteristics. Encouragingly, similar results were observed in the GEO databases. Immune infiltration analysis displayed immune cells that are significantly richer in low-risk group, CD8 T cells and activated NK cells via CIBERSORTx algorithm as well as activated CD8 T cell via ssGSEA. Additionally, compared with the high-risk group, immune score, CD8 T effector was higher in the low-risk group, yet stromal score, score of p53 signaling pathway and TGFher in the Tx algorithm, was lower in the low-risk group. Corresponding results were confirmed in GEO dataset. Finally, TIDE analysis uncovered that the IRL classifier may be effectually predict the clinical response of immunotherapy in LSCC. CONCLUSION: Based on BARX1-DT, KLHL7-DT, and LINC02154, the IRL classifier was established, which can be used to predict the prognosis, immune infiltration status, and immunotherapy response in LSCC patients and might facilitate personalized counseling for immunotherapy.