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PubMed2026

Multiomic study of cutaneous T-cell lymphoma reveals single-cell clonal evolution in progression and therapy resistance.

Cutaneous T-cell lymphoma (CTCL) remains a challenging disease due to its significant heterogeneity, therapy resistance, and relentless progression. Multiomics technologies offer the potential to provide uniquely precise views of disease progression and response to therapy. Here, we present a comprehensive multiomics view of CTCL clonal evolution, incorporating exome, whole-genome, epigenome, bulk, single-cell T-cell receptor, and single-cell RNA sequencing of 99 clinically annotated serial skin, peripheral blood, and lymph node samples from 34 patients with CTCL. We leveraged this extensive data set to define the molecular underpinnings of CTCL progression in individual patients at single-cell resolution with the goal of identifying clinically useful biomarkers and therapeutic targets. Our studies identified recurrent progression-associated clonal genomic alterations; we highlight mutation of CCR4, phosphoinositide 3-kinase inhibitor signaling, and programmed cell death protein 1 (PD-1) checkpoint pathways as evasion tactics deployed by malignant T cells. We identified a gain-of-function mutation in STAT3 (D661Y) and demonstrated, using cleavage under targets and release using nuclease (CUT&RUN) and RNA sequencing, that it enhances binding to and transcription of genes in Rho GTPase pathways. With our previous work implicating this pathway in histone deacetylase inhibitor-resistant CTCL, these data provide further support for a previously unrecognized role for Rho GTPase pathway dysregulation in CTCL progression. Recurrent progression-associated mutations were common in the epigenetic modifier EZH2, suggesting that EZH2 inhibition may benefit patients with CTCL. Our findings support an approach in which genomic analysis is widely used for improved disease monitoring, biomarker-informed clinical trial design, and genome-guided therapeutic decision-making. Moreover, these molecular changes present new opportunities for therapeutic targeting in this challenging and incurable cancer.

Multiomics↗

PubMed2025

Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data.

Gene regulatory networks (GRNs) provide a global representation of how genetic/genomic information is transferred in living systems and are a key component in understanding genome regulation. Single-cell multiome data provide unprecedented opportunities to reconstruct GRNs at fine-grained resolution. However, the inference of GRNs is hindered by insufficient single omic profiles due to the characteristic high loss rate of single-cell sequencing data. In this study, we developed scMultiomeGRN, a deep learning framework to infer transcription factor (TF) regulatory networks via unique integration of single-cell genomic (single-cell RNA sequencing) and epigenomic (single-cell ATAC sequencing) data. We create scMultiomeGRN to elucidate these networks by conceptualizing TF network graph structures. Specifically, we build modality-specific neighbor aggregators and cross-modal attention modules to learn latent representations of TFs from single-cell multi-omics. We demonstrate that scMultiomeGRN outperforms state-of-the-art models on multiple benchmark datasets involved in diseases and health. Via scMultiomeGRN, we identified Alzheimer's disease-relevant regulatory network of SPI1 and RUNX1 for microglia. In summary, scMultiomeGRN offers a deep learning framework to identify cell type-specific gene regulatory network from single-cell multiome data.

Deep Learning↗

PubMed2026

SwinePan for pig graph-based pangenome and multiomics data mining.

Pigs are one of the most important livestock species worldwide. Although multiple high-quality reference genomes exist, reliance on a single linear reference limits the detection of structural variants (SVs) and the characterization of population-specific genetic diversity. To address this limitation, we developed SwinePan, a comprehensive and integrated multiomics database for pigs built on a graph-based pangenome framework. SwinePan incorporates a variome derived from the graph-based pangenome, covering 2,598 individuals across 35 breeds, including 185,759 SVs, 117 million SNPs, and 6.8 million indels. The database also integrates transcriptomic data from liver, loin muscle, abdominal fat, and backfat, along with over 150,000 phenotypic records. The online toolkit deployed in SwinePan enables genome-wide association studies (GWAS), expression quantitative trait locus (eQTL) mapping, and colocalization, while interactive modules visualize population structure and multiomics associations, streamlining candidate gene and variant exploration. Additionally, two proof-of-concept analyses demonstrate how SwinePan pinpoints trait-associated loci and deciphers their potential regulatory mechanisms.

Journal Article↗

PubMed2026

Multiomic approaches identify a rare CCG repeat expansion in BCLAF3 in neurodevelopmental disorders.

BACKGROUND: Tandem repeat expansions have been implicated in various neurological conditions. Here, we present a novel hypermethylated CCG repeat expansion on Xp22 in the 5'UTR of BCLAF3 in males with neurodevelopmental disorders. METHODS: We used patient-derived fibroblasts and neuronal models from a family with BCLAF3 repeat expansions to generate multiomic data and investigate downstream molecular consequences of the repeat expansion. To identify additional affected individuals with BCLAF3 repeat expansions, we screened methylation arrays (n = 12,375) and short-read genomes (n = 15,963) from probands with neurodevelopmental presentations. We also characterized BCLAF3 repeat expansions in the general population using long-read sequencing data (n = 793) and population-level short-read sequencing data (n = 410,076). RESULTS: Long-read sequencing validated hypermethylation of expanded repeats. Patient-derived cells showed repressed BCLAF3 RNA and protein expression. We show that the BCLAF3 CCG repeat expansion constitutes a previously uncharacterized fragile site (FRAXG) that shifts the surrounding chromatin compartment from open euchromatin to closed heterochromatin. Using our multiomic screening approaches, we identified three additional unrelated males and one related male cousin with long-read sequencing validated (n = 2) or short-read sequencing predicted (n = 2) repeat expansions. In one family, the BCLAF3 repeats segregate with more severe phenotypes than expected for the primary diagnoses. Long-read sequencing in three carrier mothers showed skewed X-inactivation against the repeat expansion, highlighting the potential deleterious effect of an allele with an expansion. Expansions were absent in long-read sequencing data from control populations. Assessment of the BCLAF3 repeat expansion in the UK Biobank indicates that it may be ~ 20X rarer than FMR1 repeat expansions. CONCLUSIONS: CCG repeat expansions in the 5'UTR of BCLAF3 likely constitute a novel genetic etiology associated with X-linked neurodevelopmental phenotypes in males. Future work will be essential to delineate the phenotypic spectrum and determine a disease pathomechanism.

BCLAF3↗

PubMed2026

Spatial Multiomics Reveal Insights Into ADC Efficacy.

Antibody-drug conjugates (ADCs) have transformed the therapeutic landscape of solid tumors; however, responses remain heterogeneous and complex to predict. In addition, a growing number of multiple ADC targets are either approved or in late-stage clinical development, such as NECTIN-4, HER2, or TROP2 for metastatic urothelial cancer. Spatial multiomics-representing next-generation methods that couple high-plex RNA sequencing and multiplex protein imaging with precise x-y-z coordinates within tissues-offer a direct way to correlate (ADC) antigen expression, cell state information, and micro-anatomical context with patient treatment outcomes. In this review, we highlight suitability and technological advancements in current spatial transcriptomics and proteomics approaches to decode modes of action and resistance to ADCs and extract biological insights, particularly in metastatic urothelial cancer-and propose an integrative framework that combines spatial readouts with machine and/or deep learning-driven analytics to stratify patients, forecast on- and off-target toxicities, and guide next-generation linker-payload designs or combination therapies.

Humans↗

PubMed2026

A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma.

Pediatric high-grade glioma (pHGG) is an incurable central nervous system malignancy that is a leading cause of pediatric cancer death. While pHGG shares many similarities with adult glioma, it comprises distinct disease entities. In this study, we longitudinally profile a molecularly diverse cohort of 16 pHGG patients through single-nucleus RNA and ATAC sequencing, whole-genome sequencing, and CODEX spatial proteomics to capture the evolution of neoplastic and microenvironmental features during disease progression and treatment. We define a set of core pHGG neoplastic cell states and observe differential tumor-myeloid interactions between malignant cell phenotypes. We find that essential neuromodulators and the interferon response are upregulated post-therapy, implicating them as malignant cell-intrinsic targets. We observe an increase in oligodendrocytes upon progression and that they coordinate spatial motifs with proneural tumor cells. This multiomic atlas of longitudinal pHGG captures features of therapy response and provides a scalable reference for the study of pediatric brain tumors.

Humans↗

PubMed2026

Comparative Multiomics Analysis of Cerebral Organoid-Derived Exosomes during Organoid Maturation.

Cerebral organoids derived from human pluripotent stem cells recapitulate key features of early brain development and provide a physiologically relevant model for neurogenesis. Exosomes secreted by these organoids carry bioactive cargo and offer a noninvasive means to monitor maturation and intercellular communication. We performed comprehensive multiomics profiling of exosomes collected from cerebral organoids at defined developmental stages to evaluate their utility as biomarkers of neuronal differentiation. Metabolomic analysis revealed a progressive decline in amino acids, including glutamic acid, consistent with increased metabolic demand during neurogenesis. Lipidomic and neurosteroid profiling showed dynamic increases in phosphatidylethanolamine and pregnenolone, reflecting synaptic membrane formation and signaling. Transcriptomic and proteomic analyses identified stage-specific neurodevelopmental signatures, with key markers mirroring those of parent organoids. Collectively, cerebral organoid-derived exosomes faithfully reflect organoid maturation and provide a robust platform for tracking in vitro brain development.

Humans↗

PubMed2026

Multiomics approaches to cardiovascular disease: technological innovations and clinical translation.

Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, reflecting a persistent gap between clinical phenotyping and the molecular mechanisms that govern disease initiation, progression, and interindividual variability. Recent advances in emerging technologies have fundamentally reshaped cardiovascular physiology by enabling high-resolution, cross-layer profiling of the heart and vasculature across genomic, epigenomic, transcriptomic, proteomic, metabolomic, lipidomic, glycomic, and fluxomic layers, increasingly at single-cell and spatial resolution. These approaches reveal CVD as a coordinated, multilayered process driven by dynamic interactions among cell types, regulatory programs, and metabolic states, rather than isolated gene-level defects. In this review, we synthesize how emerging multiomic, computational, and functional genomic technologies are redefining the study of cardiovascular disease across molecular, cellular, and tissue levels. We highlight recent innovations in single-cell and spatial atlases, long-read sequencing, proteomics and metabolomics, integrative data modeling, and functional omics approaches, including genome-scale perturbation screens and single-cell perturbation frameworks. These platforms enable mechanistic dissection of regulatory circuits, distinguish primary disease drivers from secondary adaptations, and directly assess therapeutic reversibility, advancing the field beyond associative biomarker discovery toward mechanism-guided target prioritization. We further discuss key methodological and translational challenges accompanying high-dimensional cardiovascular data, including preanalytical variability, control selection, temporal misalignment across molecular layers, population diversity, and reference bias. By integrating technological innovation with computational rigor and functional validation, this review frames emerging omics-enabled strategies as a unified, physiologically grounded framework for translating molecular insight into clinically meaningful cardiovascular phenotypes and advancing precision cardiovascular medicine.

Humans↗

PubMed2026

Human Variation-Informed Prioritization of MPHOSPH6 in Lung Adenocarcinoma: A Source-Aware Multiomics Evidence Framework.

Moving from an association signal to a clinically credible biomarker requires several links that are often conflated: verified variant identity, aligned allelic effects, reproducible gene-level association, relevant cellular expression, and a plausible functional consequence. We developed a source-aware multiomics framework to assess MPHOSPH6 in lung adenocarcinoma (LUAD) while keeping those evidence classes separate. Six prespecified rsIDs were recovered from the harmonized TRICL LUAD dataset, of which five reached p < 5 × 10 - 8. Only rs112333466 and rs76474922 were available with alignable alleles in FinnGen R10, and both showed concordant directions. Fixed-effect estimates were OR = 1.592 for rs112333466-T (95% CI, 1.401-1.809; p = 9.91 × 10 - 13) and OR = 0.819 for rs76474922-C (95% CI, 0.773-0.867; p = 1.03 × 10 - 11). In a prespecified two-variant GTEx v8 lung model, genetically predicted MPHOSPH6 expression was positively associated with LUAD in TRICL (Z = 3.341, p = 8.35 × 10 - 4) and FinnGen (Z = 2.697, p = 0.0070). This gene-level result did not establish colocalization or connect MPHOSPH6 to the six susceptibility rsIDs. Patient-level analysis of 89,241 immune cells from six paired tumor and normal-adjacent lung samples found no significant difference in MPHOSPH6 pseudobulk abundance (exact paired Wilcoxon p = 0.3125). None of 688 lung-lineage pharmacogenomic tests remained significant after false-discovery-rate correction. Ten recorded MPHOSPH6 missense alleles, including five ClinVar variants of uncertain significance, were curated; structural analysis identified I58 at an experimental RNA-exosome interface and defined a focused perturbation series. MPHOSPH6 is therefore supported as a human-variation-informed candidate for functional evaluation, not as a validated LUAD biomarker, pathogenic gene, drug-response predictor, or therapeutic target.

Humans↗

PubMed2026

Sparse phenotyping for wheat grain yield enabled by multiomics prediction.

Grain yield is a central target in wheat breeding, yet accurately predicting it remains challenging because it depends on many genes and responds strongly to environmental variation. Genomic selection (GS) has improved breeding efficiency by enabling genome-based prediction of genetic merit, but predictability (PA) for grain yield is often limited under stress environments. At the same time, advances in high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) provide phenomic data that capture environment-responsive plant performance and may complement genomic information. In this study, we evaluated genomic and phenomic models for predicting grain yield in elite bread wheat lines across irrigated, drought, and heat-stress environments. Using a sparse phenotyping framework, we compared parametric and non-parametric models. PA was evaluated within environments and under cross-environment sparse phenotyping scenarios. Genomic models provided a stable baseline and enabled effective information sharing across environments when phenotypic data were incomplete. Phenomics-only models captured environment-specific plant responses but were more sensitive to environmental context. Multiomics models that integrated genomic and phenomic information consistently achieved the highest PA, with the largest gains observed under stress conditions. Overall, our results demonstrate that integrating genomics and UAV-based phenomics within sparse phenotyping designs offers a practical and scalable approach to improve grain yield prediction in wheat.

Triticum↗

PubMed2025

Liquid Biopsy-Multiomics Link Adhesion Pathway Dysregulation to Kidney Injury Severity.

INTRODUCTION: Severe acute kidney injury (AKI) is strongly associated with the risk of developing chronic kidney disease; however, little is known about the cell type-specific mechanisms driving kidney injury severity. METHODS: In this multicenter observational study, we used clinically obtained liquid biopsy proteomics and machine learning (ML) to predict severe outcomes in patients with COVID-associated and non-COVID AKI. Further, we orthogonally combined 169 urine proteomics with 437 plasma proteomics samples and 40 urine sediment single-cell transcriptomics samples to identify complementary dysregulated mechanisms. RESULTS: Using a 10-fold cross-validated random forest algorithm, we identified a set of urinary proteins that demonstrate predictive power for both discovery and validation set with AUC of 87% and 76%, respectively. These predictive proteomics features obtained demonstrate that cell adhesion and autophagy-associated pathways are uniquely impacted in severe AKI. Differentially abundant proteins (DAPSs) associated with these pathways are highly expressed in cells of the juxtamedullary nephron, endothelial cells (ECs), and podocytes, indicating that these kidney cell types could be potential targets. Single-cell transcriptomic analysis in the in vitro model of kidney organoids infected with SARS-CoV-2 reveal dysregulation of extracellular matrix (ECM) organization in multiple nephron segments, recapitulating the clinically observed fibrotic response across multiomics datasets. Ligand-receptor interaction analysis of the podocyte and tubule organoid clusters shows significant reduction and loss of interaction between integrins and basement membrane receptors in the infected kidney organoids. CONCLUSION: Collectively, these data suggest that ECM degradation and adhesion-associated mechanisms could be the main driver of severe kidney injury.

AKI↗

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

Scalable, generalizable and uncertainty-aware integration of spatial multiomics across diverse modalities and platforms with SCIGMA.

Recent advances in spatial omics technologies have enabled simultaneous profiling of transcriptomic, proteomic, epigenomic, metabolomic and imaging data at high spatial resolution, offering unprecedented opportunities to dissect tissue complexity. However, integrating these diverse and large-scale spatial multimodal datasets remains a major computational challenge. We present SCIGMA, a scalable and generalizable deep learning framework for spatial multiomics integration. SCIGMA introduces an uncertainty-aware contrastive learning objective and multiview graph neural networks to preserve modality-specific signals while learning biologically meaningful joint representations. Unlike previous methods, SCIGMA provides spatially resolved uncertainty estimates, interpretably identifying regions of biological or technical heterogeneity. SCIGMA supports integration of up to five modalities, and its modular framework is extensible to future technologies with even more modalities. It also scales to more than 1 million spatial locations, enabling analysis of high-resolution datasets such as Visium HD and Xenium Prime. We evaluated SCIGMA across 19 datasets spanning 8 modalities, 10 tissues and 9 platforms. On benchmarkable datasets, SCIGMA outperformed other methods in spatial domain detection, modality preservation, feature reconstruction and reproducibility. SCIGMA identifies biologically meaningful structures, refined spatial domains and modality-specific regulatory programs, providing a robust, flexible and future-ready solution for scalable spatial multimodal integration.

Multiomics↗

PubMed2025

CrossAttOmics: multiomics data integration with cross-attention.

MOTIVATION: Advances in high throughput technologies enabled large access to various types of omics. Each omics provides a partial view of the underlying biological process. Integrating multiple omics layers would help have a more accurate diagnosis. However, the complexity of omics data requires approaches that can capture complex relationships. One way to accomplish this is by exploiting the known regulatory links between the different omics, which could help in constructing a better multimodal representation. RESULTS: In this article, we propose CrossAttOmics, a new deep-learning architecture based on the cross-attention mechanism for multiomics integration. Each modality is projected in a lower dimensional space with its specific encoder. Interactions between modalities with known regulatory links are computed in the feature representation space with cross-attention. The results of different experiments carried out in this article show that our model can accurately predict the types of cancer by exploiting the interactions between multiple modalities. CrossAttOmics outperforms other methods when there are few paired training examples. Our approach can be combined with attribution methods like LRP to identify which interactions are the most important. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/Sanofi-Public/CrossAttOmics and https://doi.org/10.5281/zenodo.15065928. TCGA data can be downloaded from the Genomic Data Commons Data Portal. CCLE data can be downloaded from the depmap portal.

Humans↗

PubMed2026

Network methods for diagonal integration of unpaired single-cell multiomics data: a review.

MOTIVATION: Advances in single-cell sequencing have enabled multiomics profiling at unprecedented resolution; however, mass spectrometry-based single-cell proteomics (scMS) remains inherently destructive, precluding simultaneous transcriptomic capture. Unlike antibody-based methods such as CITE-seq, which permit paired profiling but are restricted to targeted protein panels, scMS provides unbiased, genome-scale coverage of the intracellular proteome yet necessitates post hoc integration of unpaired datasets. This diagonal integration challenge, where transcriptomes and proteomes are measured in separate cells lacking shared anchors, remains underserved by existing reviews, which focus predominantly on vertical integration strategies enabled by non-destructive assays. RESULTS: We survey the complete computational pipeline for constructing mechanistic proteogenomic networks from unpaired single-cell data, covering: (i) unimodal network inference such as knowledge-based approaches, probabilistic graphical models, temporal directionality inference, and generative and foundation model strategies that establish the transcriptomic scaffold; (ii) cross-modal integration architectures such as network propagation, graph neural networks (scMRDR, scmFormer, scCotag), and consensus frameworks designed explicitly for the unpaired proteomics setting; and (iii) benchmarking paradigms spanning network reconstruction (BEELINE, GRETA, CausalBench) and multi-task integration evaluation (scMultiBench, SCMMIB), with guidance on metric selection under network sparsity and class imbalance. We identify three principal axes of future development: generative proteomic translation from transcriptomic precursors, inductive prior embedding in next-generation architectures, and perturbation-based causal benchmarking. AVAILABILITY AND IMPLEMENTATION: This is a review article; no novel software is distributed. A curated benchmark resource table, methods starter guide, and per-method bottleneck annotations are provided in the Supplementary Material.

Multiomics↗

PubMed2026

Multiomic single-nucleus profiling reveals cell-type-specific epigenetic and transcriptional dysregulation in major depressive disorder brain.

OBJECTIVE: Major depressive disorder (MDD) is a leading global cause of disability, marked by persistent mood disturbances, cognitive deficits, and changes in prefrontal cortex neural circuitry. In this study, we aimed to define cell-type-specific molecular and regulatory mechanisms underlying MDD by mapping gene-expression and chromatin-accessibility changes in the dorsolateral prefrontal cortex (PFC) (dlPFC). METHODS: Postmortem dlPFC (BA9) tissue from 7 MDD and 8 well-matched controls was analyzed using 10× Genomics snRNA-seq and paired ATAC+RNA multiome sequencing. Sequencing data were processed with Cell Ranger pipelines, nuclei were filtered for quality and doublets/debris, and datasets were integrated and clustered using Seurat/Signac packages. Differential gene expression, chromatin accessibility, and transcription factor motif activity were tested between MDD and controls within each cell type, followed by peak-to-gene linkage and Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and PsyGeNET enrichment to interpret dysregulated regulatory mechanisms. RESULTS: A total of 20 distinct clusters encompassing major neuronal and non-neuronal populations were identified. Differential analyses uncovered extensive cell type-specific changes in chromatin accessibility and gene expression, particularly within excitatory layer 5/6 and inhibitory Pvalb neurons, as well as glial and vascular populations. Functional enrichment indicated dysregulation of synaptic organization, neurotransmission, myelination, stress-response, and immune-regulatory pathways across neuronal and non-neuronal cells. Notably, glucocorticoid-responsive transcription factors NR3C1/NR3C2 exhibited conserved regulatory networks implicating stress signaling in MDD pathophysiology. CONCLUSIONS: Together, these findings provide a comprehensive single-nucleus atlas of gene regulation in the MDD PFC, highlighting coordinated dysfunction across neurons, glia, and vascular cells.

Major Depressive Disorder↗

PubMed2024

Multiomics analyses provide insights into the genomic basis of differentiation among four sweet osmanthus groups.

Sweet osmanthus (Osmanthus fragrans) is famous in China for its flowers and contains four groups: Albus, Luteus, Aurantiacus, and Asiaticus. Understanding the relationships among these groups and the genetic mechanisms of flower color and aroma biosynthesis are of tremendous interest. In this study, we sequenced representative varieties from two of the four sweet osmanthus groups. Multiomics and phylogenetic analyses of varieties from each of the four groups showed that Asiaticus split first within the species, followed by Aurantiacus and the sister groups Albus and Luteus. We show that the difference in flower color between Aurantiacus and the other three groups was caused by a 4-bp deletion in the promoter region of carotenoid cleavage dioxygenase 4 (OfCCD4) that leads to expression decrease. In addition, we identified 44 gene pairs exhibiting significant structural differences between the multiseasonal flowering variety "Rixianggui" in the Asiaticus group and other autumn-flowering varieties. Through correlation analysis between intermediate products of aromatic components and gene expression, we identified eight genes associated with the linalool and α- and β-ionone biosynthesis pathways. Overall, our study offers valuable genetic resources for sweet osmanthus, while also providing genetic clues for improving the flower color and multiseasonal flowering of osmanthus and other flowers.

Oleaceae↗

PubMed2026

Multiomics approaches reveal direct NF-κB p65 target genes in pancreatic islets during cytokine exposure and in type 1 diabetes.

Autoimmune diseases, including Type 1 diabetes (T1D), are often characterized by overactive inflammatory signaling pathways. The proinflammatory cytokine interleukin-1β (IL-1β) elicits global gene expression changes in islet β-cells which overlap with islets obtained from human donors with T1D. The direct transcriptional link between NF-κB subunit p65 and target genes involved with autoimmune events was investigated. We used a multiomics approach including bulk RNA-sequencing (RNA-Seq), single-cell RNA-sequencing (scRNA-Seq), and chromatin immunoprecipitation coupled to deep sequencing (ChIP-Seq), alongside molecular docking simulations, and transcriptional assays. Through the various experimental modalities, we identified early response genes driven by IL-1β that were differentially expressed in pancreatic islets from human T1D donors and also conserved across mouse, rat, and human tissues. ChIP-Seq revealed genes that are direct genomic targets of the NF-κB p65 transcription factor. Moreover, regions that gained RNA polymerase II binding following cellular exposure to IL-1β were identified, complementing the early response gene profile induced by β-cell exposure to IL-1β. Molecular docking simulations predicted that mutations reducing p65 transcriptional capacity do not alter DNA binding ability. These findings clearly show that IL-1β signaling in pancreatic β-cells directs p65 to specific genomic regions congruent with increased gene expression relevant to T1D in β-cell lines as well as mouse and human islets exposed to cytokines. Islets from human donors with T1D express genes identified as direct p65 targets using unbiased approaches, implicating heightened NF-κB activity as a critical component of autoimmune disease etiology.NEW & NOTEWORTHY Using multiple Seq-based approaches, this study identified genes expressed in human pancreatic tissue from donors with Type 1 diabetes that are regulated acutely by exposure to the cytokine interleukin-1beta. The NF-kB transcription factor p65 (RelA) was determined via ChIP-Seq to be a major control node regulating this immediate early response. These collective datasets are consistent with a paradigm of overactive NF-kB signaling as a critical component of autoimmunity in both rodents and humans.

Humans↗