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PubMed2026

TMEM176B is co-expressed with TMEM176A and upregulated in peripheral blood monocytes of patients with primary Sjögren's syndrome.

OBJECTIVES: This study aims to determine the role of acid‑sensitive nonspecific cation channels transmembrane protein 176A (TMEM176A) and TMEM176B in autoimmune diseases, with a focus on primary Sjögren's Syndrome (pSS). METHODS: We examined the expression patterns of TMEM176A and TMEM176B across tissues and cells utilizing bulk RNA-seq and scRNA-seq datasets. Immunophenotyping analysis was performed by flow cytometry to compare CD62L expression between TMEM176B⁺ and TMEM176B⁻ monocytes. The proportion of TMEM176B+ cells in monocytes was interrogated in both pSS patients and healthy controls. Clinical correlations of TMEM176B with anti‑SSB antibody and complement C4 levels were also evaluated. RESULTS: TMEM176A and TMEM176B showed conserved co‑expression and were significantly upregulated in autoimmune diseases. TMEM176B⁺ monocytes displayed higher CD62L positivity rate than TMEM176B- monocytes. In pSS patients, the proportion of TMEM176B⁺ monocytes was elevated in total monocytes, classical monocytes (cMo) and intermediate monocytes (iMo). The proportion of TMEM176B+ monocytes positively correlated with anti‑SSB levels, while several TMEM176B-associated monocyte subset markers inversely correlated with C4. CONCLUSION: TMEM176A and TMEM176B are highly correlated. TMEM176B expression in monocytes is linked to pSS and may serve as a novel auxiliary diagnostic biomarker.

Humans↗

PubMed2025

Distinct transcriptional and epigenomic programs define Hofbauer cells in term placenta.

Hofbauer cells (HBCs) are fetal macrophages located in the placenta that contribute to antimicrobial defense, angiogenesis, tissue remodeling, and metabolic processes within the chorionic villi. Although their roles in placental biology are increasingly recognized, the mechanisms that regulate HBC identity and function are not yet fully defined. This study aimed to define the core transcriptomic and epigenomic features of HBCs in term placentas and to examine their capacity for transcriptional responsiveness and phenotypic variation. Using chromatin accessibility profiling and bulk RNA-seq, we found that HBCs exhibit a unique gene expression and chromatin accessibility profile compared with other fetal and adult macrophages. We identified a coordinated transcriptional network involving nuclear receptors (NRs) NR4A1-3, the glucocorticoid receptor, and RFX family members (RFX1, RFX2, RFX5) that appears to shape HBC identity, particularly through pathways linked to lipid metabolism and angiogenesis. Although exploratory in nature, in vitro stimulation studies showed that HBCs exhibited increased transcriptional activity in response to combined IL-4 and rosiglitazone treatment, including induction of the lipid transporter CD36. Mass cytometry analysis revealed surface markers indicative of both immature and mature macrophage states. These results together indicate that HBCs are a distinct and diverse population of macrophages with a specialized, adaptable regulatory program in the human placenta.

Female↗

PubMed2026

SCMO: a deep learning model integrating the single-cell resolution TME ecosystem and multi-omics for survival prediction in CRC patients.

BACKGROUND: Colorectal cancer (CRC) remains a leading cause of global cancer mortality, highlighting the need for precise survival prediction to guide clinical decisions. Although tissue-level multi-omics is widely utilized for survival prediction, its limited resolution cannot capture tumor heterogeneity. Single-cell RNA sequencing (scRNA-seq) enables dissection of the tumor microenvironment (TME) at cellular resolution, supporting personalized prognostic assessment. METHODS: We collected 213 CRC scRNA-seq samples and established a CRC-specific TME atlas comprising 339,060 cells. Using this atlas as a reference, we deconvolved bulk RNA-seq data from TCGA-CRC cohort with the EcoTyper algorithm to reconstruct TME features. Clinical, genomic, and transcriptomic data were obtained from the Xena platform; microbial data were sourced from the BIC database. We integrated TME and multi-omics features through a self-normalizing neural network to construct a deep learning model (single-cell resolution TME ecosystem with multi-omics data [SCMO]) for survival prediction. To enhance interpretability, we utilized the Integrated Gradients algorithm and spatial transcriptomic data to analyze multi-omics and TME features. We performed anticancer drug screening with tumor necrosis factor receptor-associated protein 1 (TRAP1), a critical feature according to the Integrated Gradients algorithm, as a potential target. RESULTS: We identified 13 survival-related TME features from the CRC-specific atlas: 12 cell states and one multi-cellular ecosystem. SCMO, which combined TME and multi-omics features, improved survival prediction and outperformed existing methods, achieving a concordance index of 0.762. The SCMO demonstrated robust performance for long-term predictions, achieving areas under the curve (AUCs) of 0.752, 0.772, and 0.869 for 1-, 3-, and 5-year predictions in the training set, with corresponding test set AUCs of 0.639, 0.756, and 0.772. TME features from the SCMO model revealed that ecosystem density increased with CRC malignancy. Multi-omics features included TRAP1 as a potential drug target. Drug screening identified saikosaponin A as a novel TRAP1 inhibitor, and its anticancer activity was validated in vitro. We developed SCMO-Lite, a simplified model incorporating 12 high-attribution-weight multi-omics features, which demonstrated robust risk stratification. CONCLUSIONS: SCMO combines analytical precision with biological interpretability, offering novel insights for oncology survival prediction.

Humans↗

PubMed2025

OmnibusX: A unified platform for accessible multi-omics analysis.

OmnibusX is an integrated, privacy-centric platform that enables code-free multi-omics data analysis by bridging computational methodologies with user-friendly interfaces. Designed to overcome challenges posed by fragmented analytical tools and high computational barriers, OmnibusX consolidates workflows for diverse technologies - including bulk RNA-seq, single-cell RNA-seq, single-cell ATAC-seq, and spatial transcriptomics - into a single, cohesive application. The application integrates established open-source tools such as Scanpy, DESeq2, SciPy, and scikit-learn into transparent, reproducible pipelines, offering users control over analytical parameters. Additionally, OmnibusX features proprietary modules, including a highly accurate cell-type prediction engine and an interactive plotting editor for generating publication-quality visualizations. Available as a standalone desktop application and an enterprise edition for centralized server deployment, OmnibusX ensures all data processing is conducted locally, eliminating external data transfer and usage tracking. By lowering technical barriers and enhancing reproducibility, OmnibusX aims to accelerate biological discovery and foster robust, data-driven collaborations. A fully documented trial version is accessible at: https://omnibusx.com/apps.

Computational Biology↗

PubMed2026

Genome-wide cis-expression Quantitative Trait Loci (eQTL) and transcriptomic signals reveal distinct molecular regulation across correlated feed efficiency traits.

INTRODUCTION: Feed efficiency (FE) is a complex trait which determines livestock production profitability, yet the molecular mechanisms behind it remain unclear. This study investigated the blood transcriptomic profile of lambs, alongside genotype data with the aim to uncover the genetic basis of FE traits such as absolute dry matter intake (DMIabsolute), DMI adjusted for body size (DMIadjusted), average daily live weight gain (ADG), and residual feed intake (RFI). MATERIALS AND METHODS: Bulk RNA-Seq and genotype data were analysed using three complementary approaches: differential gene expression (DGE) analysis, weighted gene co-expression network analysis (WGCNA), and cis-expression Quantitative Trait Loci (cis-eQTL) mapping. These methods were used independently to identify genes and regulatory networks associated with FE traits and to investigate evidence supporting multi-trait candidate gene selection. RESULTS: DGE analysis revealed 2, 24, 85 and 4 differentially expressed genes for DMIabsolute, DMIadjusted, ADG, and RFI (Padjusted < 0.05), functionally enriched in sensory perception, ATP-dependent chromatin remodeling, Notch signaling and immune response pathways. 9 gene modules significantly associated with the FE traits (P ≤ 0.05) with correlations ranging from r = -0.56 to 0.49, were identified using WGCNA. Single nucleotide polymorphism (SNP)-level cis-eQTL analysis identified 93 eSNPs associated with 74 genes (false discovery rate (FDR) < 0.05), while permutation-derived gene level analysis identified 280 eGenes (FDR < 0.2, empirical P < 0.03). Across the three analyses, applying thresholds of DGE (Padjusted < 0.05), WGCNA (correlation, P ≤ 0.05), and cis-eQTL gene-level significance (empirical P < 0.05), multiple overlapping genes were identified including DNMT3A, KANSL1, NCOR1 for DMIadjusted, ACOX2, FANCF, CIMIP2B, LOC101115106, ARMH2, LOC132657496 for ADG, and LOC114114576 for RFI representing regulators of variations in FE. DISCUSSION: The integration of DGE, WGCNA, and cis-eQTL analyses identified key genes and regulatory mechanisms associated with variation in FE traits. These results highlight that integrated multi-trait candidate gene identification approaches can reveal key genes that lower feed intake while maintaining animal growth, supporting breeding strategies aimed at improving efficiency and long-term economic sustainability in sheep.

average daily gain (ADG)↗

PubMed2024

A comprehensive meta-analysis of tissue resident memory T cells and their roles in shaping immune microenvironment and patient prognosis in non-small cell lung cancer.

Tissue-resident memory T cells (TRM) are a specialized subset of long-lived memory T cells that reside in peripheral tissues. However, the impact of TRM-related immunosurveillance on the tumor-immune microenvironment (TIME) and tumor progression across various non-small-cell lung cancer (NSCLC) patient populations is yet to be elucidated. Our comprehensive analysis of multiple independent single-cell and bulk RNA-seq datasets of patient NSCLC samples generated reliable, unique TRM signatures, through which we inferred the abundance of TRM in NSCLC. We discovered that TRM abundance is consistently positively correlated with CD4+ T helper 1 cells, M1 macrophages, and resting dendritic cells in the TIME. In addition, TRM signatures are strongly associated with immune checkpoint and stimulatory genes and the prognosis of NSCLC patients. A TRM-based machine learning model to predict patient survival was validated and an 18-gene risk score was further developed to effectively stratify patients into low-risk and high-risk categories, wherein patients with high-risk scores had significantly lower overall survival than patients with low-risk. The prognostic value of the risk score was independently validated by the Cancer Genome Atlas Program (TCGA) dataset and multiple independent NSCLC patient datasets. Notably, low-risk NSCLC patients with higher TRM infiltration exhibited enhanced T-cell immunity, nature killer cell activation, and other TIME immune responses related pathways, indicating a more active immune profile benefitting from immunotherapy. However, the TRM signature revealed low TRM abundance and a lack of prognostic association among lung squamous cell carcinoma patients in contrast to adenocarcinoma, indicating that the two NSCLC subtypes are driven by distinct TIMEs. Altogether, this study provides valuable insights into the complex interactions between TRM and TIME and their impact on NSCLC patient prognosis. The development of a simplified 18-gene risk score provides a practical prognostic marker for risk stratification.

Humans↗

PubMed2026

Parietal Cortex Transcriptomics Refines Parkinson Disease GWAS Nomination and Highlights STAT3 as a Putative Upstream Glial Regulator.

Parkinson disease (PD) affects more than 1.1 million individuals in the United States and around 12 million worldwide. Although Genome Wide Association Studies (GWAS) have substantially advanced our understanding of PD genetic architecture, the regulatory mechanisms linking PD risk loci to disease-relevant gene expression remain incompletely characterized, limiting our ability to infer disease mechanisms from genetic associations. Here, we integrated disease-state parietal cortex transcriptomics with the International Parkinson's Disease Genomics Consortium (iPDGC) locus prioritization to refine PD gene nomination and identify biologically plausible candidates missed by GWAS-only approaches. Using bulk RNA-seq from 99 neuropathologically confirmed PD cases and 30 neuropathologically confirmed controls, we prioritized candidate genes across 78 loci and classified them according to concordance between genetic evidence and differential expression in diseased cortices. This integrative approach recovered candidate genes not captured by external GWAS-based prioritization methods and highlighted synaptic, lysosomal, and proteostasis pathways as major components of PD risk biology. Network and transcription factor analyses further suggested coordinated regulation of these genes, with STAT3 emerging as a putative upstream glial regulator. Together, these findings suggest that integrating disease-state transcriptomics with genetic prioritization can refine PD risk-gene nomination and uncover regulatory programs that may be missed by GWAS alone.

Journal Article↗

PubMed2026

Integrated Bulk and Single-Cell RNA-Seq Analysis Reveals Transcriptional Activation of PTGS2 by FOS in Progression From T2DM to T2DM-Associated NAFLD.

Type 2 diabetes mellitus (T2DM) and nonalcoholic fatty liver disease (NAFLD) frequently coexist, exacerbating disease burden. However, the molecular mechanisms underlying the progression from T2DM to T2DM-associated NAFLD remain unclear. This study investigated the regulatory function of FOS-mediated PTGS2 activation in this transition. We integrated bulk RNA-seq data from GEO, single-cell transcriptomic data and transcriptomes from patients with T2DM-associated NAFLD. Differentially expressed genes were identified using the limma package, and T2DM-related gene modules were defined by weighted gene co-expression network analysis. LASSO regression and random forest identified 14 candidate genes, with PTGS2 and FOS prioritised. Single-cell analysis showed increased FOS and PTGS2 expression in monocytes, CD8+ T cells and Kupffer cells. Transcription factor prediction and dual-luciferase assays confirmed that FOS directly binds the PTGS2 promoter and drives its transcription. In vitro, FOS silencing decreased PTGS2 expression, cytokine secretion and apoptosis under high-glucose and free fatty acid conditions, whereas PTGS2 overexpression exacerbated inflammation and apoptosis independently of FOS expression. These findings demonstrate that FOS transcriptionally activates PTGS2, contributing to hepatic inflammation and apoptosis during the progression from T2DM to NAFLD. PTGS2 may serve as a promising biomarker and therapeutic target for T2DM-associated NAFLD.

Single-Cell Gene Expression Analysis↗

PubMed2024

Meningioma transcriptomic landscape demonstrates novel subtypes with regional associated biology and patient outcome.

Meningiomas, although mostly benign, can be recurrent and fatal. World Health Organization (WHO) grading of the tumor does not always identify high-risk meningioma, and better characterizations of their aggressive biology are needed. To approach this problem, we combined 13 bulk RNA sequencing (RNA-seq) datasets to create a dimension-reduced reference landscape of 1,298 meningiomas. The clinical and genomic metadata effectively correlated with landscape regions, which led to the identification of meningioma subtypes with specific biological signatures. The time to recurrence also correlated with the map location. Further, we developed an algorithm that maps new patients onto this landscape, where the nearest neighbors predict outcome. This study highlights the utility of combining bulk transcriptomic datasets to visualize the complexity of tumor populations. Further, we provide an interactive tool for understanding the disease and predicting patient outcomes. This resource is accessible via the online tool Oncoscape, where the scientific community can explore the meningioma landscape.

Meningioma↗

PubMed2026

A novel deep learning-driven framework for improving lncRNA comprehensive annotation with LncADeep 2.0.

MOTIVATION: Long non-coding RNAs (lncRNAs) have emerged as crucial players in diverse physiological and pathological processes, yet the biological mechanisms of the vast majority of lncRNAs remain elusive. To fill this gap, it is necessary to improve the accuracy of lncRNA identification and functional annotation. RESULTS: Here, we introduce LncADeep 2.0, an integrated deep learning framework designed to meet these needs. In the identification module, LncADeep 2.0 incorporated novel peptide features along with sequence and structural information, demonstrating superior performance over our previous LncADeep and other existing tools on both annotated transcripts from GENCODE and RNA-seq data. For functional annotation, LncADeep 2.0 leveraged lncRNA-centric interaction networks and gene ontology terms through the transfer learning strategy to achieve robust annotation performance with limited functional data. Compared to LncADeep, LncADeep 2.0 could accurately elucidate the general functions of given lncRNA sequences, predict tissue- or cell-type-specific functions from bulk and single-cell RNA-seq data, and establish connections between tumor-associated lncRNAs and genomic markers. Overall, LncADeep 2.0 stands out as an efficient and reliable tool for lncRNA identification and functional annotation across a wide spectrum of biological processes. AVAILABILITY AND IMPLEMENTATION: LncADeep 2.0 is available for use at https://github.com/Jefferson-Chou/LncADeep2 and https://doi.org/10.5281/zenodo.17164767.

RNA, Long Noncoding↗

PubMed2026

Activation of transposable elements is linked to a region- and cell type-specific interferon response in Parkinson's disease.

Parkinson's disease (PD) is a neurodegenerative disorder involving a neuroinflammatory response, the cause of which remains unclear. Transposable elements (TEs) have been linked to inflammation, but their potential role in PD remains unexplored. Using bulk- and single-nuclei RNA-seq of postmortem brain tissue from four brain regions, we studied TE transcription and its correlation with PD neuroinflammation. Over a thousand TEs, including LINE-1 s and ERVs, were expressed in a cell type- and region-specific manner in the human brain. Increased TE expression was found in microglia and neurons in the substantia nigra and putamen of PD brains, but not amygdala or prefrontal cortex, compared to controls. This TE activation correlated with an innate immune response in the same brain regions. The link between an interferon response and TE activation was mechanistically confirmed using human pluripotent stem cell-derived microglia and neurons. Our findings provide insights into TE transcription in the PD brain and suggest that TEs may contribute to neuroinflammation and pathological progression in PD.

Humans↗

PubMed2025

Twisted Sister1: an agravitropic mutant of bread wheat (Triticum aestivum) with altered root and shoot architectures.

We identified a mutant of hexaploid wheat (Triticum aestivum) with impaired responses to gravity. The mutant, named Twisted Sister1 (TS1), had agravitropic roots that were often twisted along with altered shoot phenotypes. Roots of TS1 were insensitive to externally applied auxin, with the genetics and physiology suggestive of a mutated AUX/IAA transcription factor gene. Hexaploid wheat possesses over 80 AUX/IAA genes, and sequence information did not identify an obvious candidate. Bulked segregant analysis of an F2 population mapped the mutation to chromosome 5A, and subsequent mapping located the mutation to a 41 Mbp region. RNA-seq identified the TraesCS5A03G0149800 gene encoding a TaAUX/IAA protein to be mutated in the highly conserved domain II motif. We confirmed TraesCS5A03G0149800 as underlying the mutant phenotype by generating transgenic Arabidopsis thaliana. Analysis of RNA-seq data suggested broad similarities between Arabidopsis and wheat for the role of AUX/IAA genes in gravity responses, although there were marked differences. Here we show that the sequenced wheat genome, along with previous knowledge of the physiology of gravity responses from other plant species, gene mapping, RNA-seq, and expression in Arabidopsis have enabled the cloning of a key wheat gene that defines plant architecture.

Triticum↗

PubMed2026

Single-cell transcriptional profiling identifies the swimming crab Portunus trituberculatus in response to bacterial infection.

Crustaceans rely entirely on innate immunity, yet the cellular composition, functional specialization, and pathogen-induced remodeling of their immune system remain poorly resolved. Here, we generated a high-resolution single-cell transcriptomic atlas of hemocytes from the swimming crab Portunus trituberculatus following Vibrio parahaemolyticus infection using 10× Genomics scRNA-seq. Seven putatively distinct hemocyte clusters were identified, including granulocytes, semigranular hemocytes, prohemocytes, unresolved hemocytes, hyalinocyte-like hemocytes, biosynthetically active secretory hemocytes, and regulatory hemocytes. Although the overall cellular composition remained relatively stable after infection, hemocytes exhibited pronounced cluster-specific transcriptional reprogramming involving Toll/NF-κB signaling, antimicrobial peptide synthesis and metabolic rewiring. By integrating single-cell and bulk transcriptomes, we identified multiple anti-lipopolysaccharide factors (ALFs) as key secretory effectors and experimentally validated their antibacterial activities. FITC-based bacterial engulfment assays and RNA-seq of sorted phagocytes demonstrated that phagocytic capability was shared across multiple hemocyte clusters. Notably, the immunoglobulin superfamily receptor DSCAM displayed extensive alternative splicing and strong infection-induced activation in unresolved hemocytes. Immune-training experiments showed that prior bacterial exposure was associated with altered DSCAM expression and reduced early cumulative mortality upon secondary challenge, suggesting a memory-like immune phenotype. These findings provide a foundational framework for understanding crustacean immunity and advancing disease-resistant breeding in aquaculture.

Antimicrobial peptides↗

PubMed2026

FANCI promotes esophageal squamous cell carcinoma progression and cell cycle regulation and interacts with FANCD2.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is an aggressive malignancy with poor clinical outcomes, and reliable molecular biomarkers and therapeutic targets remain limited. Fanconi anemia group I protein (FANCI) is a core component of the Fanconi anemia (FA) pathway, but its expression pattern, clinical significance, and functional role in ESCC have not been comprehensively defined. This study aimed to investigate FANCI expression and prognostic value in ESCC, assess its effects on malignant cellular phenotypes and tumor growth, and explore its potential mechanistic relationship with Fanconi anemia group D2 protein (FANCD2) and cell-cycle regulation. METHODS: Multi-cohort analyses were performed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, together with ESCC single-cell RNA sequencing (RNA-seq) data. FANCI functions were assessed by bidirectional gain- and loss-of-function experiments in vitro (proliferation, colony formation, migration, invasion, apoptosis, and cell-cycle assays) and by xenograft models in vivo. Mechanistic studies included protein-protein interaction (PPI) analyses, co-immunoprecipitation (Co-IP), and immunofluorescence (IF) colocalization. RESULTS: FANCI was consistently upregulated in ESCC across bulk transcriptomic datasets and was further supported by quantitative polymerase chain reaction (qPCR), Western blotting, and immunohistochemistry (IHC). FANCI discriminated ESCC from normal tissues in TCGA-ESCC and was independently validated in GSE53624 [area under the curve (AUC) =0.940 and 0.975, respectively]. FANCI was associated with poorer overall survival (OS) and shorter disease-free interval (DFI), and these findings were validated in an independent GEO cohort. Functionally, FANCI promoted ESCC cell proliferation, migration, and invasion, while inhibiting apoptosis; FANCI knockdown suppressed tumor growth in vivo and induced G2/M cell-cycle arrest. Mechanistically, FANCI physically interacted with FANCD2, colocalized with FANCD2 in the nucleus, and was associated with altered FANCD2 protein abundance, consistent with cell-cycle and DNA repair-related programs. Single-cell analysis indicated that FANCI was enriched in epithelial cells and associated with higher activity of malignant functional programs. In TCGA-ESCC, FANCI-high tumors showed distinct mutation profiles, a trend toward increased tumor mutation burden (TMB), and altered immune-associated signatures. CONCLUSIONS: FANCI is upregulated in ESCC and is associated with diagnostic and prognostic value. It promotes malignant phenotypes and tumor growth, potentially through a FANCI-FANCD2-linked cell-cycle/DNA repair program, supporting FANCI as a candidate biomarker and therapeutic target in ESCC.

Esophageal squamous cell carcinoma (ESCC)↗

PubMed2026

A contextual activity score (CAS) for inferring ADAR-associated transcriptional activity across RNA-seq, single-cell, and spatial transcriptomics.

BACKGROUND AND OBJECTIVE: Adenosine-to-inosine RNA editing, catalyzed by Adenosine Deaminases Acting on RNA (ADARs), is a widespread modification involved in neural function, immune regulation, and cancer. The Alu Editing Index (AEI) is the standard metric to estimate ADAR activity but requires raw sequencing reads and is poorly suited for single-cell and spatial transcriptomic data. This study aimed to develop an alternative framework for inferring ADAR-associated transcriptional activity from gene expression data across diverse transcriptomic technologies. METHODS: We developed the Contextual Activity Score (CAS), a framework based on transcriptional signatures from ADAR perturbation experiments. Context-specific signatures were generated for human neurons, mouse neurons, and cancer models to infer ADAR1 and ADAR2 activity. CAS was computed from normalized gene expression matrices using regulon-based enrichment analysis. Performance was evaluated by comparing with the Alu Editing Index across bulk RNA sequencing datasets, simulated sequencing depths, and library preparation protocols. RESULTS: CAS showed strong concordance with the Alu Editing Index across multiple datasets, while remaining robust to reduced sequencing depth and different library protocols. Unlike the Alu Editing Index, CAS can be applied to single-cell and spatial transcriptomic data and enables the independent assessment of ADAR2 activity. In cancer and neuronal contexts, CAS captured biologically meaningful variations in ADAR-associated transcriptional activity at sample, cell-type, and spatial levels. CONCLUSION: CAS provides a scalable approach applicable across multiple RNA-seq protocols for estimating ADAR-associated transcriptional activity using gene expression data. This method, implemented in an open-source R package for broad adoption, expands the ability to study ADAR-associated transcriptional activity across transcriptomic modalities where direct editing quantification is challenging, such as single-cell and spatial transcriptomics.

Adenosine Deaminase↗

PubMed2026

Dysregulation of U12-Type Splicing in Lupus Neutrophils.

OBJECTIVE: Neutrophil dysfunction is a hallmark of systemic lupus erythematosus (SLE), but its molecular basis remains unclear. This study explores transcriptional and posttranscriptional changes in low-density granulocytes (LDGs), a proinflammatory neutrophil subset expanded in SLE, focusing on NADPH oxidase (Nox) function and minor intron splicing. METHODS: LDGs and normal-density granulocytes (NDGs) were isolated from patients with SLE and healthy controls (HCs). CYBA (p22phox) expression was evaluated at transcript and protein levels. Nox activity was measured using luminol assays. Bulk RNA sequencing (RNA-seq) and rMATS software were used to assess alternative splicing, particularly of U12-type intron-containing genes. RESULTS: CYBA expression was reduced in SLE LDGs (n = 11) compared to SLE and HC NDGs (n = 6), with levels resembling those in chronic granulomatous disease neutrophils. SLE LDGs exhibited impaired Nox activity (n = 7 SLE, n = 12 HC). CYBA is a U12 intron-containing gene, and transcriptomic analysis revealed broad down-regulation of this gene class in SLE LDGs, suggesting minor spliceosome dysfunction. rMATS analysis showed increased U12-type intron retention and widespread splicing defects-including exon skipping and mutually exclusive exon use-in genes such as GBP5, MAEA, and STX10. These abnormalities were validated in an independent long-read RNA-seq data set from SLE peripheral blood mononuclear cells. Importantly, splicing disruptions correlated with disease activity and autoantibody profiles. CONCLUSION: Impaired U12-dependent splicing may contribute to neutrophil dysfunction in SLE, potentially via defective oxidative burst and altered immune regulation. These findings highlight the minor spliceosome as a novel player in lupus pathogenesis.

Humans↗

PubMed2026

Single-cell multiomics reveals exosome-mediated reprogramming and clonotypic remodeling of T cells in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is an aggressive and immunogenic subtype lacking targeted therapies. While tumor-derived exosomes are known to modulate immune function, their direct impact on human T cell plasticity and antigen specificity remains poorly defined. Here, we conducted a comprehensive single-cell multiomic analysis of primary human T cells exposed to exosomes derived from 17 genomically diverse TNBC cell lines and 35 patient samples. Integrating single-cell RNA-seq, V(D)J sequencing, non-coding RNA profiling, bulk and single-cell cytokine analyses, we uncovered conserved and subtype-specific immunomodulatory programs induced by TNBC exosomes. Exosome-treated T cells displayed skewing toward regulatory and dysfunctional phenotypes, including Th17-like, Treg, and PD-1⁺/PD-L1⁺ Tfh cells. Functional profiling revealed suppression of early activation markers and cytokine responses, alongside selective preservation of cytotoxic features in γδ T and NKT subsets. Transcriptomic and miRNA network analyses demonstrated widespread downregulation of immune effector genes (e.g., HBEGF and TNFSF9) mediated by exosome-delivered regulatory miRNAs (has-miR-98-5p). Notably, exosome-stimulated T cells displayed distinct clonotypic expansions, characterized by the emergence of five tumor-specific γδ TCR clonotypes and 30 unique αβ TCR CDR3 sequences that were absent in mock-treated controls, underscoring the role of exosomes in shaping TCR repertoire dynamics.

Humans↗

PubMed2026

Radiogenomics predicts immune microenvironment heterogeneity and response to combination immunotherapy in hepatocellular carcinoma.

BACKGROUND: The combination of immune checkpoint inhibitors (ICIs) with anti-angiogenic agents is the preferred first-line therapy option for patients with advanced hepatocellular carcinoma (HCC), yet only a subset of patients responds, urging the quest for prediction biomarkers. We aimed to integrate genomics with radiology to propose an immune-derived radiogenomics biomarker of response to such combination immunotherapy and evaluate its added value in clinical context. METHODS: We integrated bulk RNA sequencing (RNA-seq) and proteomics data of 994 HCC patients with single-cell RNA-seq data of 11 samples across multiple datasets to identify an immune-related signature (IRS) that may influence sensitivity or resistance to such combined immunotherapy strategy, followed by verification of selected marker genes using immunohistochemistry and cytological experiments. We then trained/validated a cross-modality radiogenomics biomarker using machine learning based on TCIA database that was further tested in multi-scale independent cohorts covering 754 HCC patients. RESULTS: Integrative multi-omics analysis identifed a parsimonious 2-gene prognostic signature including KPNA2 and SMG5 that was significantly associated with immune heterogeneity and response to combination immunotherapy. Machine-learning pipeline exported the optimal 4-feature radiogenomics biomarker using support vector machine that significantly discriminated prognosis (hazard ratio 1.415–1.890; p < 0.05 for all) and modestly predicted response to ICI plus anti-angiogenic therapy (area under the curve 0.720–0.829) in independent retrospective series across major imaging modalities (computed tomography/magnetic resonance imaging). In a prospective neoadjuvant cohort, this biomarker also showed favorable performance for predicting pathological response and tumor recurrence, accompanied by biological validation through single-cell RNA-seq analysis of pre-treatment biopsies. CONCLUSIONS: Our study provides a cross-device-cross-modal radiogenomics biomarker that can improve patient selection for emerging ICI plus anti-angiogenic therapy with novel potential therapeutic targets in HCC.

Humans↗