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Results for “RNA-seq transcriptomics”

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PubMed2026

Firemaster 550 differentially alters gene expression underlying synaptic function in amygdala of prairie voles after gestational or lactational exposure.

Neurodevelopmental disorders often share similar behavioral diagnostic criteria including socioemotional and cognitive deficits. The prairie vole is a uniquely suitable model to study these deficits because they demonstrate strong social affiliation, bi-parental care, and partner attachment. Previously, we have shown that developmental exposure to the flame-retardant mixture Firemaster 550 (FM 550) impairs socioemotional behavior in the prairie vole and alters underlying neuroanatomy and function. However, the mechanisms for impaired pair bonding in males and increased anxiety in females remain unknown, along with the specific critical window(s) of vulnerability. Herein, we exposed prairie vole dams to FM 550 during gestation or lactation, and performed bulk RNA-seq on the amygdala, a hub of socioemotional processing, in their adult offspring. Two mathematically orthogonal methods were utilized for analysis, a linear statistical method and an ensemble machine learning method, incorporating sex as a biological variable. Gene ontology (GO) pathway analysis was performed following both and results compared to identify potential mechanisms of toxicity. GO results indicated consistent expression changes in the Synapse cellular component in all conditions, and implicated glutamatergic signaling specifically. Additionally, gestational exposure (GE) altered genes underlying modulation of synaptic transmission and neural development, while lactational exposure (LE) impacted genes underlying synaptic plasticity, axon guidance, and mitophagy. Machine learning identified disruption of endocrine system development, regulation of biosynthetic processes in GE animals, and suppression of various neuroinflammatory genes across multiple groups. Finally, we performed RNA expression analysis using Nanostring and demonstrated stronger correlation with the differentially expressed genes (DEG) of interest in females than males. Overall, this study demonstrates both the intersecting and distinct impacts of FM 550 exposure on amygdalar gene expression depending on sex and timing of exposure.

Animals↗

PubMed2026

A latent activated olfactory stem cell state revealed by single-cell transcriptomic and epigenomic profiling.

The olfactory epithelium is one of the few regions of the nervous system that sustains neurogenesis throughout life. Its experimental accessibility makes it especially tractable for studying molecular mechanisms that drive neural regeneration in response to injury. In this study, we used single-cell sequencing to identify transcriptional and epigenetic processes involved in determining olfactory epithelial stem cell fate during injury-induced regeneration. By combining gene expression and accessible chromatin profiles of individual lineage-traced olfactory stem cells, we identified transcriptional heterogeneity among activated stem cells at a stage when cell fates are being specified. We further identified a subset of resting cells that appears poised for activation, characterized by accessible chromatin around silent genes prior to their expression in response to injury. These results provide evidence for a latent activated stem cell state in which a subset of quiescent olfactory epithelial stem cells are epigenetically primed to support injury-induced regeneration.

Animals↗

PubMed2026

Integrated single-cell and bulk transcriptomic analysis identifies a novel senescent fibroblast subtype associated with poor prognosis in acral melanoma.

BACKGROUND: Acral melanoma (AM) exhibits significant intratumoral heterogeneity, but its tumor microenvironment (TME) and immune regulation remain unclear. This study aims to dissect TME heterogeneity and establish a prognostic model based on key cell subpopulations. METHODS: We collected AM single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA). Unsupervised clustering, CellChat, and Scissor analysis were performed to characterize cellular heterogeneity, cell-cell communication, and prognosis-related cell subpopulations. Kaplan-Meier analysis was used to assess the prognostic value of key genes, which were further validated by multiplex immunohistochemistry (mIHC). RESULTS: In AM, Mel_C2, C7, and C9 with high SEMA6A and KIT expression were strongly linked to poor prognosis. We further identified a senescent fibroblast subpopulation (sCAF_CDKN2A) characterized by high fibroblast senescence signature (FSS) scores. Integrating Scissor analysis of fibroblast subtypes with bulk prognostic data, we identified COL3A1, VCAN, and KIT as prognosis-associated genes upregulated in poor-outcome-related fibroblast subsets. Cell-cell communication analysis revealed that sCAF_CDKN2A engages in an immunosuppressive network, interacting with regulatory T cells (Tregs) via MIF signaling and receiving signals from exhausted CD8+ T cells through PPIA-BSG interactions. Using transcription factor expression patterns from these fibroblast subtypes, we constructed a prognostic model that effectively stratified patients into distinct risk groups with significant differences in overall survival (OS). mIHC confirmed significantly higher protein levels of SEMA6A and COL3A1 in tumor tissues compared to matched normal tissues. CONCLUSIONS: We established a novel prognostic model for AM and identified sCAF_CDKN2A as an immunosuppressive senescent fibroblast subpopulation driving poor prognosis.

Acral melanoma↗

PubMed2024

Complementation testing identifies genes mediating effects at quantitative trait loci underlying fear-related behavior.

Knowing the genes involved in quantitative traits provides an entry point to understanding the biological bases of behavior, but there are very few examples where the pathway from genetic locus to behavioral change is known. To explore the role of specific genes in fear behavior, we mapped three fear-related traits, tested fourteen genes at six quantitative trait loci (QTLs) by quantitative complementation, and identified six genes. Four genes, Lamp, Ptprd, Nptx2, and Sh3gl, have known roles in synapse function; the fifth, Psip1, was not previously implicated in behavior; and the sixth is a long non-coding RNA, 4933413L06Rik, of unknown function. Variation in transcriptome and epigenetic modalities occurred preferentially in excitatory neurons, suggesting that genetic variation is more permissible in excitatory than inhibitory neuronal circuits. Our results relieve a bottleneck in using genetic mapping of QTLs to uncover biology underlying behavior and prompt a reconsideration of expected relationships between genetic and functional variation.

Animals↗

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

The CTNNB1-TRIM28 complex governs hormone-induced RNA polymerase II dynamics in kidney epithelial cells.

Arginine vasopressin maintains water homeostasis by regulating epithelial water permeability through complex transcriptional mechanisms in kidney collecting duct cells. Although CTNNB1 (β-catenin) functions as a transcriptional coregulator in vasopressin-responsive gene transcription, its role remains poorly understood. To identify CTNNB1-dependent components mediating the vasopressin-responsive transcription, we profiled transcriptomic changes following Ctnnb1 knockdown in mouse kidney collecting duct cells using RNA sequencing (RNA-Seq). RNA-Seq and promoter enrichment analyses identified bromodomain-containing proteins (TRIM28, TRIM33, BRD4, CREBBP, and EP300) as components of a CTNNB1-dependent complex regulating RNA Polymerase II (Pol II) activity. Biochemical analyses revealed physical interactions between TRIM28, CTNNB1, Pol II, and CDK9. Functionally, Trim28 knockdown blunted vasopressin-induced expression of the Aqp2 gene. Quantitative genomic binding assays demonstrated that TRIM28 is required for robust genomic occupancy and stabilization of Pol II at the transcription start site of Aqp2. Additionally, dynamic formation of phase-separated nuclear TRIM28 condensates in response to vasopressin suggests that TRIM28-associated machinery functions at specialized chromatin hubs. These findings reveal that TRIM28 facilitates Pol II recruitment, pause release, and elongation upon vasopressin stimulation. Our study establishes the CTNNB1-TRIM28 machinery as a critical transcriptional scaffold that controls Pol II dynamics and chromatin structure, thereby driving osmotic water reabsorption and urine concentration.

Animals↗

PubMed2025

Alevin-fry-atac enables rapid and memory frugal mapping of single-cell ATAC-seq data using virtual colors for accurate genomic pseudoalignment.

SUMMARY: Ultrafast mapping of short reads via lightweight mapping techniques such as pseudoalignment has significantly accelerated transcriptomic and metagenomic analyses with minimal accuracy loss compared to alignment-based methods. However, applying pseudoalignment to large genomic references, like chromosomes, is challenging due to their size and repetitive sequences. We introduce a new and modified pseudoalignment scheme that partitions each reference into "virtual colors." These are essentially overlapping bins of fixed maximal extent on the reference sequences that are treated as distinct "colors" from the perspective of the pseudoalignment algorithm. We apply this modified pseudoalignment procedure to process and map single-cell ATAC-seq data in our new tool alevin-fry-atac. We compare alevin-fry-atac to both Chromap and Cell Ranger ATAC. Alevin-fry-atac is highly scalable and, when using 32 threads, is 2.8 times faster than Chromap (the second fastest approach) while using only 33% of the memory required by Chromap. The resulting peaks and clusters generated from alevin-fry-atac show high concordance with those obtained from both Chromap and the Cell Ranger ATAC pipeline, demonstrating that virtual color-enhanced pseudoalignment directly to the genome provides a fast, memory-frugal, and accurate alternative to existing approaches for single-cell ATAC-seq processing. The development of alevin-fry-atac brings single-cell ATAC-seq processing into a unified ecosystem with single-cell RNA-seq processing (via alevin-fry) to work toward providing a truly open alternative to many of the varied capabilities of CellRanger. AVAILABILITY AND IMPLEMENTATION: Alevin-fry-atac is written in Rust and C++17, and is freely-available under a BSD 3-clause license. It is integrated into piscem (https://github.com/COMBINE-lab/piscem) and alevin-fry (https://github.com/COMBINE-lab/alevin-fry), and is also supported directly as part of simpleaf (https://github.com/COMBINE-lab/simpleaf).

Single-Cell Analysis↗

PubMed2025

Enhancing and accelerating cell type deconvolution of large-scale spatial transcriptomics slices with dual network model.

MOTIVATION: Cell type deconvolution deciphers spatial distribution of mRNA transcripts at single cell level by integrating single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data to infer mixture of cell types of spots in slices. Current algorithms are criticized for neglecting connection between scRNA-seq and spatial transcriptomics data, as well as time-consuming, hampering their application to large-scale datasets. RESULTS: In this study, we propose a joint learning nonnegative matrix factorization algorithm for fast cell type deconvolution (aka jMF2D), which integrates scRNA-seq and spatial transcriptomics data with network models. To bridge scRNA-seq and spatial transcriptomics data, jMF2D jointly learns cell type similarity network to enhance quality of signatures of cell types, thereby promoting accuracy and efficiency of deconvolution. Experiments demonstrate that jMF2D outperforms state-of-the-art baselines in terms of accuracy by saving about 90% running time on various datasets generated by different platforms. Furthermore, it can also facilitates the identification of spatial domains and bio-marker genes, providing an efficient and effective model for analyzing spatial transcriptomics data. AVAILABILITY AND IMPLEMENTATION: The software is coded using python, and is free available for academic https://github.com/xkmaxidian/jMF2D.

Algorithms↗

PubMed2026

Predicting gene-specific regulation with transcriptomic and epigenetic single-cell data.

MOTIVATION: Analysis of single cell ATAC-seq and RNA-seq data has allowed to gain unprecedented insights into gene regulation by allowing to define cell type-specific regulatory regions and their effects on gene expression. While powerful, such analysis is challenging due to the inherent sparsity of single cell data. RESULTS: We present a new approach, MetaFR, to learn gene-specific models that link open-chromatin variation from scATAC-seq data to gene expression from scRNA-seq. Using efficient regression trees, we illustrate that accurate expression prediction models can be learned on the single-cell or meta-cell level. Validation was done using fine-mapped eQTLs. Meta-cell models were found to outperform single-cell models for most genes. Comparison to the SOTA method SCARlink revealed advantages of MetaFR in terms of runtime and prediction performance. MetaFR thus allows time-efficient analysis and obtains reliable models of gene expression prediction, which can be used to study gene regulation in any organism for which scRNA-seq and scATAC-seq data is available. AVAILABILITY AND IMPLEMENTATION: MetaFR is available under https://github.com/SchulzLab/MetaFR.

Single-Cell Analysis↗

PubMed2026

ARCADIA reveals spatially dependent transcriptional programs through integration of scRNA-seq and spatial proteomics.

MOTIVATION: Cellular states are strongly influenced by spatial context, but single-cell RNA sequencing (scRNA-seq) loses information about local tissue organization, while spatial proteomic assays capture limited marker panels that constrain transcriptomic inference. Integrating these modalities can elucidate how spatial niches shape transcriptional programs, yet existing approaches depend on either feature-level correspondence such as gene-protein linkage or cell-level barcode pairing, which is often unavailable. RESULTS: We present ARCADIA (ARchetype-based Clustering and Alignment with Dual Integrative Autoencoders), a generative framework for cross-modal integration that operates without cell barcode pairing and does not assume direct feature-to-feature correspondence. ARCADIA identifies modality-specific archetypes, that is, convex combinations of cells representing extreme phenotypic states, and aligns these anchors across modalities by minimizing the discrepancy between their cell-type composition profiles. The aligned archetypes define a shared coordinate system that anchors dual variational autoencoders (VAEs) trained with cross-modal geometric regularization, preserving archetype structure and spatial neighborhood information while enabling bidirectional translation between modalities. On semi-synthetic CITE-seq data, ARCADIA outperforms existing weak-linkage methods. Applied to independent human tonsil scRNA-seq and CODEX data, ARCADIA reconstructs known tissue architecture and reveals spatially dependent transcriptional programs linking B-cell maturation and T-cell activation or exhaustion to microenvironmental niches. AVAILABILITY AND IMPLEMENTATION: Source code is accessible at https://github.com/azizilab/ARCADIA_public. Reproducibility scripts and data are available at https://github.com/azizilab/arcadia_reproducibility.

Proteomics↗

PubMed2026

The Small Noncoding RNA, RsaC, Is Essential for Staphylococcus aureus Virulence.

BACKGROUND: Bacterial small noncoding RNAs (sRNAs) play critical roles in virulence, stress adaptation, and host-pathogen interactions. Transcriptomic analyses during infection can help reveal pathogen-derived sRNAs required for pathogenesis, providing valuable insights for the development of novel therapeutic strategies. However, the low abundance of pathogen biomass within the host tissues poses a significant challenge for such analyses. METHODS: We employed 2-step cell disruption to enrich Staphylococcus aureus cells from infected mouse organs and conducted RNA sequencing (RNA-seq) analysis to examine staphylococcal sRNAs expressed during infection. qRT-PCR was used to confirm the gene expression. A knockout mutant of highly expressed sRNA, RsaC, was generated, and RNA-seq under in vivo as well as in vitro aerobic and anaerobic conditions were compared between the wild-type and ΔrsaC strains. Virulence of S. aureus was assessed using both mouse and silkworm survival assays. RESULTS: We identified RsaC as one of the most highly expressed sRNAs in mouse organs with consistent increment over time postinfection. Through gene disruption and complementation, we demonstrated that RsaC is an independent virulence determinant required for full pathogenicity of S. aureus in a murine infection model. In addition, RsaC influenced gene expression in response to oxygen availability and host-associated stress. Further analysis revealed that mutation of 2 genes downregulated in ΔrsaC in vivo, NWMN_RS03420 (sodium: proton antiporter) and NWMN_RS12015 (hypothetical protein), reduced S. aureus virulence in a silkworm model. CONCLUSIONS: These findings identify RsaC as a novel independent virulence determinant that supports S. aureus adaptation within the host.

Animals↗

PubMed2025

Alevin-fry-atac enables rapid and memory frugal mapping of single-cell ATAC-seq data using virtual colors for accurate genomic pseudoalignment.

Ultrafast mapping of short reads via lightweight mapping techniques such as pseudoalignment has significantly accelerated transcriptomic and metagenomic analyses, often with minimal accuracy loss compared to alignment-based methods. However, applying pseudoalignment to large genomic references, like chromosomes, is challenging due to their size and repetitive sequences. We introduce a new and modified pseudoalignment scheme that partitions each reference into "virtual colors…. These are essentially overlapping bins of fixed maximal extent on the reference sequences that are treated as distinct "colors" from the perspective of the pseudoalignment algorithm. We apply this modified pseudoalignment procedure to process and map single-cell ATAC-seq data in our new tool alevin-fry-atac . We compare alevin-fry-atac to both Chromap and Cell Ranger ATAC . Alevin-fry-atac is highly scalable and, when using 32 threads, is approximately 2.8 times faster than Chromap (the second fastest approach) while using approximately one third of the memory and mapping slightly more reads. The resulting peaks and clusters generated from alevin-fry-atac show high concordance with those obtained from both Chromap and the Cell Ranger ATAC pipeline, demonstrating that virtual colorenhanced pseudoalignment directly to the genome provides a fast, memory-frugal, and accurate alternative to existing approaches for single-cell ATAC-seq processing. The development of alevin-fry-atac brings single-cell ATAC-seq processing into a unified ecosystem with single-cell RNA-seq processing (via alevin-fry ) to work toward providing a truly open alternative to many of the varied capabilities of CellRanger . Furthermore, our modified pseudoalignment approach should be easily applicable and extendable to other genome-centric mapping-based tasks and modalities such as standard DNA-seq, DNase-seq, Chip-seq and Hi-C.

Journal Article↗

PubMed2026

Trichoderma reesei Nsd3 transcription factor: pleiotropic roles in development, stress response, secondary metabolism, and cellulase production.

Trichoderma reesei is known for its ability to secrete high amounts of cellulases, enzymes of fundamental importance in generating products from lignocellulosic biomass. Diverse signaling pathways and transcription factors (TFs) control the cellulolytic repertoire in T. reesei to ensure correct adaptation to the environment. Here, we analyzed RNA-Seq data and identified a new potential regulator of cellulase production in T. reesei: a novel TF named Nsd3, a homolog of NsdC from Aspergilli. Deletion of nsd3 reduced vegetative growth and conidiation on solid medium. Phenotypic characterization of the Δnsd3 strain showed that it is more sensitive to osmotic stress, but more resistant to cell wall and oxidative stresses. Our results showed that Nsd3 is a repressor of cellulase expression by directly regulating key genes in the cellulolytic pathway, an unreported role for this TF in fungi. Loss of nsd3 leads to a faster and more robust induction of cellulolytic genes, and higher cellulase and hemicellulase activities. Transcriptional profiling by RNA-Seq, chromatin accessibility profiling by ATAC-Seq, and protein-DNA interaction assays showed that sugar transporters are important targets of Nsd3 during cellulase expression regulation. Combined with microscopy and gene expression analyses, the ATAC-Seq data also highlighted Nsd3 as a central regulator of cell wall remodeling and organization. Furthermore, the transcriptomics also showed that Nsd3 regulates genes involved in secondary metabolism. These results showed that Nsd3 regulates several physiological processes and provide novel insights into the regulatory system of cellulases in T. reesei that can be used in the design of high-performance strains for biorefinery.IMPORTANCETrichoderma reesei is a key player in the production of hydrolytic enzymes for the degradation of lignocellulose biomass, and transcription factors are important targets for genetic engineering to construct cellulase-hyperproducing strains. Here, we identified the transcription factor Nsd3 and characterized its role as a regulator of cellulase production in T. reesei. We applied two powerful genomics methods (transcriptome sequencing and chromatin accessibility sequencing) to unravel the global role of Nsd3 and its regulatory mechanism. Nsd3 participates in various biological processes in T. reesei, including cell wall remodeling, calcium metabolism, and secondary metabolism, in addition to regulating the expression of sugar transporters. Protein-DNA interaction assays demonstrate that Nsd3 acts through important genes to regulate cellulase expression, including ace4, crt1, stp1, and cel1b. Our study provides mechanistic insights about how Nsd3 regulates diverse physiological processes in T. reesei. This work also applied ATAC-Seq for the first time to study chromatin accessibility in T. reesei.

ATAC-Seq↗

PubMed2026

Quantitative trait loci mapping of gene expression and chromatin accessibility in primary fibroblasts reveals shared allelic effects between Latin American and European ancestries.

BACKGROUND: Quantitative Trait Locus (QTL) analysis of molecular data has identified genetic variants associated with traits such as gene expression, and colocalization of these functional QTL with GWAS risk loci has offered insights into the genetic basis of human disease. We employed gene expression (RNA-seq) and chromatin accessibility (ATAC-seq) obtained from human primary fibroblasts to investigate quantitative trait loci (QTLs) in cohorts ascertained for bipolar disorder of European (n = 150) and Latin American (n = 96) ancestries. RESULTS: Leveraging data from three countries of origin (The Netherlands, Colombia, Costa Rica) within our cohort, we characterized differences among individuals at the SNP, gene, and accessible-chromatin levels to compute ancestry-specific expression (e)QTLs and chromatin-accessibility (ca)QTLs. Across ancestries, we observed R2 ≥ 0.93 for eQTL effect sizes and R2 ≥ 0.95 for caQTLs, indicating a high degree of concordance. Integrating chromatin data with expression and genotype information enabled precise fine-mapping of eQTLs, yielding 203 genes with high-confidence (posterior probability > 90%) candidate regulatory pathways. In downstream analyses, transcriptome-wide (TWAS) and chromatin-wide (CWAS) association studies with brain- and skin-related GWAS identified 36 TWAS-significant genes and 77 CWAS-significant open chromatin regions. CONCLUSIONS: These findings underscore the shared genetic regulatory mechanisms across European and Latin American ancestries, while demonstrating that ancestry-specific reference panels enhance the accuracy of TWAS and CWAS in diverse populations. More broadly, this study highlights the value of paired multi-omic datasets from diverse cohorts for interpreting disease-associated genetic variation.

Humans↗

PubMed2024

Transcriptome analysis of the diseased intervertebral disc tissue in patients with spinal tuberculosis.

OBJECTIVE: To investigate the differential expression genes (DEGs) in spinal tuberculosis using transcriptomics, with the aim of identifying novel therapeutic targets and prognostic indicators for the clinical management of spinal tuberculosis. METHODS: Patients who visited the Department of Orthopedics at the Second Hospital, Lanzhou University from January 2021 to May 2023 were enrolled. Based on the inclusion and exclusion criteria, there were 5 patients in the test group and 5 patients in the control group. Total RNA was extracted and paired-end sequencing was conducted on the sequencing platform. After processing the sequencing data with clean reads and annotating the reference genome, FPKM normalization and differential expression analysis were performed. The DEGs and long non-coding RNAs (LncRNAs) were analyzed for Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment. The cis-regulation of differentially expressed mRNAs (DE mRNAs) by LncRNAs was predicted and analyzed to establish a co-expression network. RESULTS: This study identified 2366 DEGs, with 974 genes significantly upregulated and 1392 genes significantly downregulated. The upregulated genes are associated with cytokine-cytokine receptor interactions, tuberculosis, and TNF-α signaling pathways, primarily enriched in biological processes such as immunity and inflammation. The downregulated genes are related to muscle development, contraction, fungal defense response, and collagen metabolism processes. Analysis of LncRNAs from bone tuberculosis RNA-seq data detected a total of 3652 LncRNAs, with 356 significantly upregulated and 184 significantly downregulated. Further analysis identified 311 significantly different LncRNAs that could cis-regulate 777 target genes, enriched in pathways such as muscle contraction, inflammatory response, and immune response, closely related to bone tuberculosis. There are 51 genes enriched in the immune response pathway regulated by cis-acting LncRNAs. LncRNAs that regulate immune response-related genes, such as upregulated RP11-451G4.2, RP11-701P16.5, AC079767.4, AC017002.1, LINC01094, CTA-384D8.35, and AC092484.1, as well as downregulated RP11-2C24.7, may serve as potential prognostic and therapeutic targets. CONCLUSION: The DE mRNAs and LncRNAs in spinal tuberculosis are both associated with immune regulatory pathways. These pathways promote or inhibit the tuberculosis infection and development at the mechanistic level and play an important role in the process of tuberculosis transferring to bone tissue.

Humans↗

PubMed2026

BHLHE40 and ChREBP associate with hepatic enhancer clusters containing PPARα, RXRα, and HNF4 nuclear receptors.

BHLHE40/DEC1 is a basic helix-loop-helix transcription factor (TF) that regulates circadian rhythm and T-cell responses. In hepatocytes, its function and interplay with other TFs are poorly understood. Employing a genome-wide approach, we show that its genomic binding strongly overlapped with that of carbohydrate response-element binding protein, a sugar-sensing TF and known inducer of BHLHE40 expression. Transcriptomic analysis of primary mouse hepatocytes revealed reduced expression of genes involved in genomic stability on Bhlhe40 knockdown by siRNA. Bhlhe40 depletion potentiated fructose responsiveness of genes involved in cell-cycle regulation. Strikingly, genomic binding of BHLHE40 extensively overlapped with enhancers occupied by PPARα, RXRα, and HNF4 nuclear receptors and BHLHE40 fine-tuned the expression of PPARα target genes. Using HEK293 cells, we further observed that BHLHE40 physically interacted with RXRα and PPARα cofactors. Collectively, our data suggest that through cooperation with carbohydrate response-element binding protein and nuclear receptors, BHLHE40 is a central regulator of hepatic gene expression with potential to integrate inputs from nutrient signals contributing to the metabolic flexibility of the liver.

Animals↗

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

The role of stem cells in pituitary tumour formation.

Pituitary tumours are intracranial neoplasms that pose significant clinical challenges due to their potential for recurrence, therapeutic resistance and resultant endocrine dysfunction and mass effects. In the normal anterior pituitary, resident pituitary stem cells (PSCs) contribute to tissue homeostasis and cellular turnover. The extent to which PSCs contribute to tumourigenesis is not known, but an increasing number of studies have been aiming to address this. In this review, we summarise current evidence implicating PSCs and tumour stem-like populations in pituitary tumour biology, including potential roles in tumour initiation, maintenance and progression. We outline practical criteria for defining tumour stem cells and evaluate findings from functional studies of human tumours, emerging single-cell and spatial transcriptomic datasets and murine lineage-tracing models. We also provide a curated overview of published single-cell RNA sequencing studies of pituitary tumours, highlighting reported stem/progenitor populations and transcriptional signatures across tumour subtypes and propose a framework for future genomic analyses. Finally, we discuss the translational implications of these findings, including the potential for targeting stem-like populations and their associated signalling pathways.

Humans↗