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PubMed2025

Spatial mutual nearest neighbors for spatial transcriptomics data.

MOTIVATION: Mutual nearest neighbors (MNN) is a widely used computational tool to perform batch correction for single-cell RNA-sequencing data. However, in applications such as spatial transcriptomics, it fails to take into account the 2D spatial information. RESULTS: Here, we present spatialMNN, an algorithm that integrates multiple spatial transcriptomic samples and identifies spatial domains. Our approach begins by building a k-nearest neighbors (kNN) graph based on the spatial coordinates, prunes noisy edges, and identifies niches to act as anchor points for each sample. Next, we construct a MNN graph across the samples to identify similar niches. Finally, the spatialMNN graph can be partitioned using existing algorithms, such as the Louvain algorithm to predict spatial domains across the tissue samples. We demonstrate the performance of spatialMNN using large datasets, including one with N = 31 10x Genomics Visium samples. We also evaluate the computing performance of spatialMNN to other popular spatial clustering methods. AVAILABILITY AND IMPLEMENTATION: Our software package is available on GitHub (https://github.com/Pixel-Dream/spatialMNN). The code is available on Zenodo (https://doi.org/10.5281/zenodo.15073963).

Algorithms↗
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

SpatialRNA: a Python package for easy application of Graph Neural Network models on single-molecule spatial transcriptomics dataset.

SUMMARY: Image-based spatial transcriptomics (iST) deliver gene expression measurements of RNA transcripts in tissue slices with single-molecule resolution and spatial context preserved. Modern Graph Neural Network (GNN) models are promising methods for capturing the complex molecular and cellular phenotypes in tissues at single-transcript and single-cell levels. A key application of GNNs is the detection of spatial domains or niches, that is, groups of molecules and/or cells that collaboratively work together to produce complex phenotypes. Due to the vast number of detected transcripts in (iST) dataset, applying GNNs on RNA molecule graphs is not trivial. We present a Python package, SpatialRNA, for easy (sub)graph generation from tissue samples and provide comprehensive tutorials for convenient and efficient application of Graph Neural Network models under the PyG framework. This highly scalable tool comprehensively segments tissue into spatial domains, aiding in biological interpretation of iST data and its underlying molecular microenvironments. AVAILABILITY AND IMPLEMENTATION: The SpatialRNA package is freely accessible from online repository https://github.com/ruqianl/spatialrna and can be installed via pip. Comprehensive tutorials, guidance on parameter selection, and complete workflows of case studies are available from the documentation website https://ruqianl.github.io/spatialrna_docs/, and uploaded on Zenodo with a DOI 10.5281/zenodo.17339575.

Neural Networks, Computer↗
PubMed2025

Unsupervised multiscale clustering of single-cell transcriptomes to identify hierarchical structures of cell subtypes.

BACKGROUND: Cell clustering is an essential step in uncovering cellular architectures in single-cell RNA sequencing (scRNA-seq) data. However, the existing cell clustering approaches are not well designed to dissect complex structures of cellular landscapes at a finer resolution. RESULTS: Here, we develop a multiscale clustering (MSC) approach to construct a sparse cell-cell correlation network for unsupervised identification of de novo cell types and subtypes across multiple resolutions. Based upon simulated silver- and gold-standard data as well as real scRNA-seq data in diseases, MSC demonstrates significantly improved performance compared to established benchmark methods and reveals a biologically meaningful cell hierarchy to facilitate the discovery of novel disease-associated cell subtypes and mechanisms. CONCLUSIONS: We present MSC as a new single-cell multiscale clustering framework as a powerful tool for advancing discoveries in disease-associated cell populations using single-cell sequencing data.

Single-Cell Analysis↗
PubMed2025

Dynamic allelic expression in mouse mammary glands across the adult developmental cycle.

The mammary gland, which primarily develops postnatally, undergoes significant changes during pregnancy and lactation to facilitate milk production. Through the generation and analysis of 480 transcriptomes, we provide the most detailed allelic expression map of the mammary gland, cataloguing cell-type-specific expression from ex-vivo purified cell populations over 10 developmental stages, enabling comparative analysis. The work identifies genes involved in the mammary gland cycle, parental-origin-specific and genetic background-specific expression at cellular and temporal resolution, genes associated with human lactation disorders and breast cancer. Genomic imprinting, a mechanism regulating gene expression based on parental origin, is crucial for controlling gene dosage and stem cell potential throughout development. The analysis identified 25 imprinted genes monoallelically expressed in the mammary gland, with several showing allele-specific expression in distinct cell types. No novel imprinted genes were identified and the absence of biallelically expressed imprinted genes suggests that, unlike in brain, selective absence of imprinting does not regulate gene dosage in the mammary gland. This research highlights transcriptional dynamics within mammary gland cells and identifies novel candidate genes potentially significant in the tissue during pregnancy and lactation. Overall, this comprehensive atlas represents a valuable resource for future studies on expression and transcriptional dynamics in mammary cells.

Animals↗
PubMed2025

A Functionally Conserved yet Dynamically Evolving Toolkit Underpinning Molluscan Biomineralization: Insights From Shell and Radula.

The molluscan shell and radula constitute pivotal molluscan innovations, each characterized by distinct functions and diverse forms, regulated by the highly specific biomineralization regulatory networks. Despite their paramount importance, the conserved components and adaptive evolutionary processes governing these regulatory networks remain unresolved. To address this knowledge gap, we advocate for the integration of data from less-explored lineages, such as Scaphopoda, as an essential step. This study presents the inaugural comprehensive transcriptome analysis of Pictodentalium vernedei, a representative species of Scaphopoda distinguished by a unique and evolutionarily conserved shell morphology and radula structure. Furthermore, comparative transcriptome/genome analyses are employed to unravel the conservatism and evolutionary innovation of the involved biomineralization regulatory elements. Our findings underscore the central role of secretomes in governing biomineralization processes, and we identified a fundamental set of 26 domains within molluscan secretomes, forming an essential functional protein domain repertoire necessary for the transformation of inorganic ions into biomineralized structures. This core biomineralization toolkit has undergone independent expansion and lineage-specific recruitment, giving rise to novel, modular domain architectures. This may be essential for the functional specialization and morphological diversification of shell and radula structures. These evolutionary processes are driven by the independent co-option of ancient genes and the emergence of novel de novo genes. This comprehensive investigation not only contributes insights into the evolution of molluscan biomineralization structures but also establishes avenues for further scholarly exploration.

Animals↗
PubMed2026

MAdLandExpression: integrating sexual reproduction into the Physcomitrium patens expression atlas.

Physcomitrium patens is a bryophyte model system particularly valuable for evolutionary developmental and comparative genomics studies. Sexual reproduction in bryophytes offers unique insights into the evolution of land plant reproduction. Unlike seed plants, bryophytes have a dominant gametophyte phase and provide significant advantages for studying sexual reproduction, such as the possibility to maintain embryo-lethal mutants through vegetative propagation or the presence of motile male gametes. More than 25 years after the first publications of transcriptomic data for P. patens, expression data of most developmental stages of P. patens as well as its responses to various biotic and abiotic perturbations have been represented by microarrays or RNA-seq datasets. To facilitate the use of such data, we introduce the MAdLandExpression atlas as a successor of PEATmoss (Physcomitrium Expression Atlas Tool), integrating its 109 P. patens expression experiments and expanding it with 20 recently published RNA-seq samples of sexual reproduction stages, thus completing the coverage of the P. patens life cycle. The MAdLandExpression atlas also introduces new features for data visualization and analysis, such as the comparison of samples from multiple datasets and gene set normalization. Using this tool, the sexual reproduction dataset was analyzed, identifying genes potentially important for egg and sperm cell development, and confirming the behavior of known key genes in sexual development observed in previous studies.

Bryopsida↗
PubMed2026

Insights into glandular trichome biology from analysis of organ-specific gene expression programmes in cannabis, hop and tomato.

Glandular trichomes (GTs) are epidermal outgrowths in which diverse specialised (secondary) metabolites are synthesised and stored. Cannabis (Cannabis sativa L.) and its close relative hop (Humulus lupulus L.) have pharmaceutical and industrial significance due to the presence of these metabolites in their GTs. We examined the conservation or divergence of the specific transcriptional programmes underlying GT biology. To achieve this, we generated transcriptome atlases of trichomes, flower, leaf, stem and root for cannabis, hop and tomato. We found that 12.9, 10.1 and 16.8% of cannabis, hop and tomato genes, respectively, were expressed organ/tissue specifically across all organs/tissues. Transcription factors (TFs) on average accounted for 7.5% of the organ-specific transcriptome and likely regulate organ-specific functions. We also conducted weighted gene co-expression network analysis and gene regulatory network (GRN) analysis to identify key regulators of GT function across the species and validated our predictions by DNA affinity purification sequencing for a subset of the cannabis and tomato GT TFs. The GRNs specific to cannabis or hop GTs were enriched for TFs and target genes associated with specialised metabolism, reflecting their species-specific nature. Conversely, the shared GRN components (identified via orthology analysis) were involved in highly conserved processes, such as flavonoid biosynthesis, solute transport and metabolite storage. Together, these GRNs and the associated transcriptome atlases are valuable resources to improve our knowledge of GT function and organ-specific genome regulation.

Solanum lycopersicum↗
PubMed2025

Exploring phage-host interactions in Burkholderia cepacia complex bacterium to reveal host factors and phage resistance genes using CRISPRi functional genomics and transcriptomics.

Complex interactions of bacteriophages with their bacterial hosts determine phage host range and infectivity. While phage defense systems and host factors have been identified in model bacteria, they remain challenging to predict in non-model bacteria. In this paper, we integrate functional genomics and transcriptomics to investigate phage-host interactions, revealing active phage resistance and host factor genes in Burkholderia cenocepacia K56-2. Burkholderia cepacia complex species are commonly found in soil and are opportunistic pathogens in immunocompromised patients. We studied infection of B. cenocepacia K56-2 with Bcep176, a temperate phage isolated from Burkholderia multivorans. A genome-wide dCas9 knockdown library targeting B. cenocepacia K56-2 was constructed, and a pooled infection experiment identified 63 novel genes or operons coding for candidate host factors or phage resistance genes. The activities of a subset of candidate host factor and resistance genes were validated via single-gene knockdowns. Transcriptomics of B. cenocepacia K56-2 during Bcep176 infection revealed that expression of genes coding for host factor and resistance candidates identified in this screen was significantly altered during infection by 4 h post-infection. Identifying which bacterial genes are involved in phage infection is important to understand the ecological niches of B. cenocepacia and its phages, and for designing phage therapies.IMPORTANCEBurkholderia cepacia complex bacteria are opportunistic pathogens inherently resistant to antibiotics, and phage therapy is a promising alternative treatment for chronically infected patients. Burkholderia bacteria are also ubiquitous in soil microbiomes. To develop improved phage therapies for pathogenic Burkholderia bacteria, or engineer phages for applications, such as microbiome editing, it's essential to know the bacterial host factors required by the phage to kill bacteria, as well as how the bacteria prevent phage infection. This work identified 65 genes involved in phage-host interactions in Burkholderia cenocepacia K56-2 and tracked their expression during infection. These findings establish a knowledge base to select and engineer phages infecting or transducing Burkholderia bacteria.

Bacteriophages↗
PubMed2025

An Annotated Biobank of Triple-Negative Breast Cancer Patient-Derived Xenografts Features Treatment-Naïve and Longitudinal Samples during Neoadjuvant Chemotherapy.

UNLABELLED: Triple-negative breast cancer (TNBC) that fails to respond to neoadjuvant chemotherapy (NACT) can be lethal. Developing effective strategies to eradicate chemoresistant disease requires experimental models that recapitulate the heterogeneity characteristic of TNBC. To that end, we established a biobank of 92 orthotopic patient-derived xenograft (PDX) models of TNBC from the tumors of 75 patients enrolled in A Robust TNBC Evaluation fraMework to Improve Survival clinical trial (ARTEMIS, NCT02276443), including 12 longitudinal sets generated from serial patient biopsies collected throughout NACT treatment and from metastatic disease. Models were established from both chemosensitive and chemoresistant tumors, and nearly 30% of the PDX models were capable of metastasizing to the lungs. Comprehensive molecular profiling demonstrated conservation of genomes and transcriptomes between patient and corresponding PDX tumors, with representation of all major transcriptional subtypes. Transcriptional changes observed in the longitudinal PDX models highlighted dysregulation in pathways associated with DNA integrity, extracellular matrix interactions, the ubiquitin-proteasome system, epigenetics, and inflammatory signaling. These alterations revealed a complex network of adaptations associated with chemoresistance. Overall, this PDX biobank provides a valuable tool for tackling the most pressing issues facing the clinical management of TNBC. SIGNIFICANCE: The development of a patient-derived xenograft biobank that comprehensively captures the genomic and transcriptional diversity of triple-negative breast cancer promises to be a robust resource to investigate and overcome chemoresistance and metastasis.

Animals↗
PubMed2025

Multi‑omics identification of a novel signature for serous ovarian carcinoma in the context of 3P medicine and based on twelve programmed cell death patterns: a multi-cohort machine learning study.

BACKGROUND: Predictive, preventive, and personalized medicine (PPPM/3PM) is a strategy aimed at improving the prognosis of cancer, and programmed cell death (PCD) is increasingly recognized as a potential target in cancer therapy and prognosis. However, a PCD-based predictive model for serous ovarian carcinoma (SOC) is lacking. In the present study, we aimed to establish a cell death index (CDI)-based model using PCD-related genes. METHODS: We included 1254 genes from 12 PCD patterns in our analysis. Differentially expressed genes (DEGs) from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were screened. Subsequently, 14 PCD-related genes were included in the PCD-gene-based CDI model. Genomics, single-cell transcriptomes, bulk transcriptomes, spatial transcriptomes, and clinical information from TCGA-OV, GSE26193, GSE63885, and GSE140082 were collected and analyzed to verify the prediction model. RESULTS: The CDI was recognized as an independent prognostic risk factor for patients with SOC. Patients with SOC and a high CDI had lower survival rates and poorer prognoses than those with a low CDI. Specific clinical parameters and the CDI were combined to establish a nomogram that accurately assessed patient survival. We used the PCD-genes model to observe differences between high and low CDI groups. The results showed that patients with SOC and a high CDI showed immunosuppression and hardly benefited from immunotherapy; therefore, trametinib_1372 and BMS-754807 may be potential therapeutic agents for these patients. CONCLUSIONS: The CDI-based model, which was established using 14 PCD-related genes, accurately predicted the tumor microenvironment, immunotherapy response, and drug sensitivity of patients with SOC. Thus this model may help improve the diagnostic and therapeutic efficacy of PPPM.

Humans↗
PubMed2025

Developing a disease-specific accessible transcriptional signature as a biomarker for ataxia with oculomotor apraxia type 2.

BACKGROUND: Genetic ataxias are clinically heterogenous neurodegenerative conditions often involving rare or private mutations and it is often difficult to assign pathogenicity to rare gene variants solely based on DNA sequencing. An effective functional assay from an easy-to-obtain biospecimen would aid this assessment and be of high clinical value. SETX encodes a ubiquitous DNA/RNA helicase crucial for resolving R-loops and maintaining genome stability. Loss-of-function mutations cause a recessive disorder, Ataxia with Oculomotor Apraxia Type 2 (AOA2). METHODS: Here we utilize Weighted Gene Co-expression Network Analysis (WGCNA) from patient blood to construct an AOA2-specific transcriptomic signature as a biomarker to evaluate SETX variants in patients clinically suspected of having AOA2. RESULTS: WGCNA from peripheral blood RNA of 11 AOA2 patients from 7 families initially identified a single gene module that was modestly effective in distinguishing individuals with AOA2 from controls (sensitivity 73%, specificity 97%) and was able to robustly differentiate AOA2 patients from those with genetically distinct, yet phenotypically similar, neurological disorders (sensitivity 100%, specificity 100%). An independent derivation of the transcriptional biomarker identified a dual module model that was able to better distinguish individuals with AOA2 from controls (sensitivity 100%, specificity 97%). As validation, we examined a second cohort of 21 patients from 13 families and demonstrate that this dual module transcriptional biomarker could discriminate patients clinically suspected of AOA2 from controls (57%, 95%CI: 34%-78%). Overall, the transcriptional biomarker was able to separate AOA2 subjects (n = 32) from controls (n = 35) with 72% sensitivity and 97% specificity. Notably, this transcriptomic biomarker enabled verification of the first pathogenic SETX mutation found in a non-canonical transcript, expanding the spectrum of mutations that contribute to AOA2. CONCLUSIONS: Our study identified a transcriptional biomarker that was able to differentiate AOA2 from controls and from other related neurological disorders, consequently expanding the spectrum of known pathogenic mutations. This proof-of-concept study illustrates that transcriptional biomarkers may be used to validate variants of uncertain significance in known genetic diseases.

Humans↗
PubMed2026

Physiological and transcriptomic responses of sunflower to combined saline-alkali stress.

BACKGROUND: Sunflower (Helianthus annuus L.), an important oilseed crop, is often used as a pioneer species for improving saline-alkali soils. However, the molecular mechanisms underlying sunflower seedling responses to combined saline-alkali stress remain unclear. This study aimed to elucidate the molecular basis of saline-alkali tolerance at the seedling stage by comparing physiological and transcriptomic responses between tolerant and sensitive sunflower hybrids. The saline-alkali tolerant hybrid K-27 and the sensitive hybrid K-7 were used as experimental materials. Root samples were collected at 0, 3, 12, 24, 48, and 96 h after exposure to combined saline-alkali stress (0.5% NaCl + Na2CO3, adjusted to pH 9.0). Physiological parameters, including antioxidant enzyme activities, osmolyte contents, ion concentrations, membrane damage levels, and cell wall components, were measured, followed by transcriptome sequencing analysis. RESULTS: Phenotypic analysis showed that the root length inhibition rate and fresh weight loss rate of K-27 were significantly lower than those of K-7, indicating stronger tolerance. Physiological analysis revealed that K-27 exhibited an inducible antioxidant enzyme response pattern. In addition, K-27 achieved osmotic adjustment through sustained proline accumulation (peaking at 12 h and remaining significantly higher than that of K-7 at 96 h) and exhibited higher basal levels of lignin and hemicellulose. Transcriptome analysis showed that the number of upregulated genes in K-27 was consistently higher than in K-7 at all time points, with 5,283 genes upregulated as early as 3 h after stress exposure. Venn analysis identified 44 core differentially expressed genes (cDEGs) shared between the two genotypes, which were mainly enriched in auxin biosynthesis regulation, phenylpropanoid biosynthesis, and glutathione metabolism. Among them, the benzoic acid carboxyl methyltransferase gene (BAMT) was continuously upregulated in K-27 but persistently downregulated in K-7. In addition, five other genes (encoding fatty aldehyde dehydrogenase, pectin methylesterase inhibitor, glutathione S-transferase, INPP5E, and HXXXD-type acyltransferase) exhibited significantly higher expression levels in K-27. CONCLUSION: K-27 tolerates combined saline-alkali stress through coordinated multi-layered response mechanisms, including inducible antioxidant defense, maintenance of ion homeostasis, sustained osmotic adjustment, and activation of the phenylpropanoid metabolic pathway. Candidate genes such as BAMT may provide potential targets for molecular breeding of saline-alkali tolerant sunflower, although their functions require further experimental validation.

Helianthus↗
PubMed2025

RNAcare: integrating clinical data with transcriptomic evidence using rheumatoid arthritis as a case study.

BACKGROUND: Gene expression analysis is a crucial tool for uncovering the biological mechanisms that underlie differences between patient subgroups, offering insights that can inform clinical decisions. However, despite its potential, gene expression analysis remains challenging for clinicians due to the specialised skills required to access, integrate, and analyse large datasets. Existing tools primarily focus on RNA-Seq data analysis, providing user-friendly interfaces but often falling short in several critical areas: they typically do not integrate clinical data, lack support for patient-specific analyses, and offer limited flexibility in exploring relationships between gene expression and clinical outcomes in disease cohorts. Users, including clinicians with a general knowledge of transcriptomics, however, who may have limited programming experience, are increasingly seeking tools that go beyond traditional analysis. To overcome these issues, computational tools must incorporate advanced techniques, such as machine learning, to better understand how gene expression correlates with patient symptoms of interest. RESULTS: Our RNAcare platform, addresses these limitations by offering an interactive and reproducible solution specifically designed for analysing transcriptomic data from patient samples in a clinical context. This enables researchers to directly integrate gene expression data with clinical features, perform exploratory data analysis, and identify patterns among patients with similar diseases. By enabling users to integrate transcriptomic and clinical data, and customise the target label, the platform facilitates the analysis of the relationships between gene expression and clinical symptoms like pain and fatigue. This allows users to generate hypotheses and illustrative visualisations/reports to support their research. As proof of concept, we use RNAcare to link inflammation-related genes to pain and fatigue in rheumatoid arthritis (RA) and detect signatures in the drug response group, confirming previous findings. CONCLUSION: We present a novel computational platform allowing the interpretation of clinical and transcriptomics data in real-time. The platform can be used for data generated by the user, such as the patient data presented here or using published datasets. The platform is available at https://rna-care.mvls.gla.ac.uk/ , and its source code is https://github.com/sii-scRNA-Seq/RNAcare/ .

Humans↗
PubMed2025

Sparse deconvolution of cell type medleys in spatial transcriptomics.

Mapping cell distributions across spatial locations with whole-genome coverage is essential for understanding cellular responses and signaling However, current deconvolution models aim to estimate the proportions of distinct cell types in each spatial transcriptomics spot by integrating reference single-cell data. These models often assume strong overlap between the reference and spatial datasets, neglecting biology-grounded constraints such as sparsity and cell-type variations, as well as technical sparsity. As a result, these methods rely on over-permissive algorithms that ignore given constraints leading to inaccurate predictions, particularly in heterogeneous or unmatched datasets. We introduce Weight-Induced Sparse Regression (WISpR), a machine learning algorithm that integrates spot-specific hyperparameters and sparsity-driven modeling. Unlike conventional approaches that neglect biology-grounded constraints, WISpR accurately predicts cell-type distributions while preserving biological coherence, i.e., spatially and functionally consistent cell-type localization, even in unmatched datasets. Benchmarking against five alternative methods across ten datasets, WISpR consistently outperformed competitors and predicted cellular landscapes in both normal and cancerous tissues. By leveraging sparse cell-type arrangements, WISpR provides biologically informed, high-resolution cellular maps. Its ability to decode tissue organization in both healthy and diseased states highlights WISpR's practical utility for spatial transcriptomics, particularly in challenging settings involving noise, sparsity, or reference mismatches.

Humans↗
PubMed2024

Comparative transcriptomics of Venus flytrap (Dionaea muscipula) across stages of prey capture and digestion.

The Venus flytrap, Dionaea muscipula, is perhaps the world's best-known botanical carnivore. The act of prey capture and digestion along with its rapidly closing, charismatic traps make this species a compelling model for studying the evolution and fundamental biology of carnivorous plants. There is a growing body of research on the genome, transcriptome, and digestome of Dionaea muscipula, but surprisingly limited information on changes in trap transcript abundance over time since feeding. Here we present the results of a comparative transcriptomics project exploring the transcriptomic changes across seven timepoints in a 72-hour time series of prey digestion and three timepoints directly comparing triggered traps with and without prey items. We document a dynamic response to prey capture including changes in abundance of transcripts with Gene Ontology (GO) annotations related to digestion and nutrient uptake. Comparisons of traps with and without prey documented 174 significantly differentially expressed genes at 1 hour after triggering and 151 genes with significantly different abundances at 24 hours. Approximately 50% of annotated protein-coding genes in Venus flytrap genome exhibit change (10041 of 21135) in transcript abundance following prey capture. Whereas peak abundance for most of these genes was observed within 3 hours, an expression cluster of 3009 genes exhibited continuously increasing abundance over the 72-hour sampling period, and transcript for these genes with GO annotation terms including both catabolism and nutrient transport may continue to accumulate beyond 72 hours.

Droseraceae↗
PubMed2024

Role of the Pseudomonas plecoglossicida fliL gene in immune response of infected hybrid groupers (Epinephelus fuscoguttatus ♀ × Epinephelus lanceolatus ♂).

Pseudomonas plecoglossicida, a gram-negative bacterium, is the main pathogen of visceral white-point disease in marine fish, responsible for substantial economic losses in the aquaculture industry. The FliL protein, involved in torque production of the bacterial flagella motor, is essential for the pathogenicity of a variety of bacteria. In the current study, the fliL gene deletion strain (ΔfliL), fliL gene complement strain (C-ΔfliL), and wild-type strain (NZBD9) were compared to explore the influence of the fliL gene on P. plecoglossicida pathogenicity and its role in host immune response. Results showed that fliL gene deletion increased the survival rate (50%) and reduced white spot disease progression in the hybrid groupers. Moreover, compared to the NZBD9 strain, the ΔfliL strain was consistently associated with lower bacterial loads in the grouper spleen, head kidney, liver, and intestine, coupled with reduced tissue damage. Transcriptomic analysis identified 2 238 differentially expressed genes (DEGs) in the spleens of fish infected with the ΔfliL strain compared to the NZBD9 strain. Based on Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, the DEGs were significantly enriched in seven immune system-associated pathways and three signaling molecule and interaction pathways. Upon infection with the ΔfliL strain, the toll-like receptor (TLR) signaling pathway was activated in the hybrid groupers, leading to the activation of transcription factors (NF-κB and AP1) and cytokines. The expression levels of proinflammatory cytokine-related genes IL-1β, IL-12B, and IL-6 and chemokine-related genes CXCL9, CXCL10, and CCL4 were significantly up-regulated. In conclusion, the fliL gene markedly influenced the pathogenicity of P. plecoglossicida infection in the hybrid groupers. Notably, deletion of fliL gene in P. plecoglossicida induced a robust immune response in the groupers, promoting defense against and elimination of pathogens via an inflammatory response involving multiple cytokines.

Animals↗
PubMed2024

Developing a machine learning-based prognosis and immunotherapeutic response signature in colorectal cancer: insights from ferroptosis, fatty acid dynamics, and the tumor microenvironment.

INSTRUCTION: Colorectal cancer (CRC) poses a challenge to public health and is characterized by a high incidence rate. This study explored the relationship between ferroptosis and fatty acid metabolism in the tumor microenvironment (TME) of patients with CRC to identify how these interactions impact the prognosis and effectiveness of immunotherapy, focusing on patient outcomes and the potential for predicting treatment response. METHODS: Using datasets from multiple cohorts, including The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we conducted an in-depth multi-omics study to uncover the relationship between ferroptosis regulators and fatty acid metabolism in CRC. Through unsupervised clustering, we discovered unique patterns that link ferroptosis and fatty acid metabolism, and further investigated them in the context of immune cell infiltration and pathway analysis. We developed the FeFAMscore, a prognostic model created using a combination of machine learning algorithms, and assessed its predictive power for patient outcomes and responsiveness to treatment. The FeFAMscore signature expression level was confirmed using RT-PCR, and ACAA2 progression in cancer was further verified. RESULTS: This study revealed significant correlations between ferroptosis regulators and fatty acid metabolism-related genes with respect to tumor progression. Three distinct patient clusters with varied prognoses and immune cell infiltration were identified. The FeFAMscore demonstrated superior prognostic accuracy over existing models, with a C-index of 0.689 in the training cohort and values ranging from 0.648 to 0.720 in four independent validation cohorts. It also responses to immunotherapy and chemotherapy, indicating a sensitive response of special therapies (e.g., anti-PD-1, anti-CTLA4, osimertinib) in high FeFAMscore patients. CONCLUSION: Ferroptosis regulators and fatty acid metabolism-related genes not only enhance immune activation, but also contribute to immune escape. Thus, the FeFAMscore, a novel prognostic tool, is promising for predicting both the prognosis and efficacy of immunotherapeutic strategies in patients with CRC.

Ferroptosis↗