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PubMed2026

Dual-transcriptomic analysis of human nasal transcriptome and microbiome reveals host-bacteria associations in symptomatic respiratory infection.

BACKGROUND: The human nasopharynx is colonized by a diverse community of commensal microbiota linked to many respiratory diseases, yet their associations with the host remain unclear. RESULTS: In this study, we introduced a dual-transcriptomics analysis strategy, which can characterize the host transcriptome and microbiome from nasal samples simultaneously. We applied this workflow to a local SARS-CoV-2 cohort with 76 asymptomatic infected patients, among whom 52 (68.42%) developed symptomatic infection during a 1-week follow-up period. Nasal swabs were collected from all 76 patients at enrollment and from 73 patients at one-week later follow-up. We detected a median of 8.94% reads that did not map to the human genome across all 149 samples, among which around half (median 49.68%) were successfully mapped to microbiome genome. Meta-transcriptomic analysis detected significantly higher SARS-related coronavirus loads in samples from the symptomatic group at enrollment (P = 0.004), and both groups showed decreased loads one week later (symptomatic, P = 0.001; asymptomatic, P = 0.035). Compared with benchmarking 16 S rRNA sequencing on 53 samples, our computational strategy showed high correlation of relative abundance in all top 20 genera (median Rho = 0.90, Pmax < 0.001). A total of 670 bacteria species were identified to show a relative abundance ≥ 0.01% in at least 10% samples. Differential abundance analysis identified 76 species (DASs) from six phyla with significantly decreased abundance in samples from the symptomatic group (log2(fold change or FC) < -1 and adjusted P < 0.05) compared to the asymptomatic group at enrollment. Integrating these symptom-associated DASs with host's gene expression using an expression quantitative trait bacteria (eQTB) model, we found 45 symptom-associated DASs identified at enrollment were significantly associated with one to 14 genes (adjusted P < 0.05). GSEA showed a series of symptom-associated DASs were significantly correlated with pathways related to olfactory function, keratinocyte differentiation, and DNA methylation. CONCLUSIONS: In summary, our dual-transcriptomic analysis strategy effectively characterized host-microbiome associations, offering insights into microbial contributions to respiratory diseases.

Humans↗

PubMed2002

Transcriptome analysis of channel catfish (Ictalurus punctatus): initial analysis of gene expression and microsatellite-containing cDNAs in the skin.

Previous molecular genetic studies on channel catfish (Ictalurus punctatus) have focused on limited number of genes and gene products. Recent advancement of molecular techniques made high throughput analysis of transcriptomes possible. As part of our transcriptome analysis of channel catfish, we have analyzed 1909 expressed sequence tags (ESTs) derived from a skin library. Of the 1909 ESTs analyzed, 1376 (72.1%) ESTs representing 496 unique genes had homologies with other organisms while 478 (25.0%) ESTs had no significant homologies and were designated as unknown. The remaining 55 (2.9%) EST clones were eliminated because of their low quality or short sequences. Of the 496 unique genes, 327 (65.9%) genes were singletons while 169 (34.1%) genes represented by two or more ESTs. A total of 1007 (52.8%) ESTs representing 235 unique genes matched previously reported channel catfish ESTs while 847 (44.4%) ESTs representing 261 unique genes were newly identified from this research. Functional categorization of the channel catfish genes indicated that the largest group was ribosomal proteins with 65 unique genes represented by 500 clones. The most abundantly expressed gene, the calcium binding protein ictacalcin, accounted for almost 5% of overall expression, indicating its important function in the skin. Sequence analysis of ESTs revealed the presence of 89 microsatellite-containing genes that may be valuable for future mapping studies.

Animals↗

PubMed2026

Ten quick tips for spatial transcriptomics analysis.

Spatial transcriptomics (ST) enables genome-wide gene expression profiling while retaining spatial context within tissue sections. Since the foundational work by Ståhl et al. in 2016, the field has expanded rapidly, with diverse platforms now spanning sequencing-based (e.g., Visium, Visium HD, Slide-seq, Stereo-seq, and Seq-Scope) and imaging-based (e.g., MERFISH, Xenium, and CosMx SMI) approaches. The breadth of platforms, data structures, and computational tools, however, can be daunting for newcomers. Here, we present ten quick tips spanning the entire ST research workflow: whether ST suits a given biological question, how to select a platform aligned with study objectives, how to understand and process ST data, and which software tools to employ for analysis and visualization. We further discuss interpreting spatial patterns in biological context, integrating complementary modalities such as single-cell RNA sequencing and spatial proteomics, and leveraging public datasets and sharing results. Finally, we highlight current limitations of ST, particularly the challenge of reconstructing three-dimensional tissue architecture from serial tissue sections. This review provides biologists, bioinformaticians, and clinician-scientists with a concise, platform-neutral roadmap for incorporating ST into research, from experimental design to biological discovery.

Spatial Transcriptomics↗

PubMed2005

The role of transcriptome analysis in pre-clinical toxicology.

A major benefit of the genomics revolution in biomedical research has been the establishment of transcriptome analysis as an enabling technology in the drug development process. Nowhere in the realm of drug development has the expectation of the impact of transcriptome analysis been greater than in the area of pre-clinical toxicology. Transcriptome analysis, along with other new high-content data generating technologies, has the potential to radically improve the drug safety assessment process by allowing drug development teams to identify potential toxicity liabilities earlier, and thus proceed only with those molecules that have both efficacy at the target and a low potential for toxicity in the human population. In this review we will briefly describe the major ways in which transcriptome analysis is being applied in the pre-clinical safety assessment process, focusing primarily on four areas where transcriptome analysis has already begun to have impact. These include using transcriptome analysis to: 1) understand mechanisms of toxicity: 2) predict toxicity: 3) develop in vivo and in vitro surrogate models and screens; and, 4) develop toxicity biomarkers. We will close by briefly addressing future trends and needs in the application of transcriptome analysis to drug safety assessment.

Amyloid Precursor Protein Secretases↗

PubMed2026

MKMC enables reference-free transcriptomic analysis using k-mer representations.

Traditional RNA-seq analysis depends heavily on genome alignment and gene annotation, limiting its utility in non-model organisms and introducing biases that can obscure regulatory complexity. We present MKMC (Multi-sample Kmer Counter), a scalable, reference-free toolkit for RNA-seq analysis that leverages k-mer-based statistics to detect biological variation without requiring alignment. MKMC integrates fast k-mer counting, abundance matrix generation, normalization, dimensionality reduction, and differential analysis into a unified workflow. Across diverse datasets, MKMC recapitulates key biological signals-including sex differences in killifish liver-and matches alignment-based pipelines in differential expression analysis and transcriptomic age prediction. Notably, MKMC detects isoform-specific events missed by traditional methods, one of which we validated using in situ hybridization. These results reveal previously hidden isoform-level regulatory events that contribute to sex- and age-associated transcriptional programs. MKMC offers a robust, extensible alternative to alignment-based approaches, enabling transcriptomic discovery across both model and non-model systems. While we focus here on RNA-seq as a primary application, MKMC is broadly applicable to any k-mer-based analysis of next-generation sequencing data.

MKMC↗

PubMed2005

Transcriptome analysis and kidney research: toward systems biology.

An enormous amount of data has been generated in kidney research using transcriptome analysis techniques. In this review article, we first describe briefly the principles and major characteristics of several of these techniques. We then summarize the progress in kidney research that has been made by using transcriptome analysis, emphasizing the experience gained and the lessons learned. Several technical issues regarding DNA microarray are highlighted because of the rapidly increased use of this technology. It appears clear from this brief survey that transcriptome analysis is an effective and important tool for question-driven exploratory science. To further enhance the power of this and other high throughput, as well as conventional approaches, in future studies of the kidney, we propose a multidimensional systems biology paradigm that integrates investigation at multiple levels of biologic regulation toward the goal of achieving a global understanding of physiology and pathophysiology.

Animals↗

PubMed2006

A highly standardized, robust, and cost-effective method for genome-wide transcriptome analysis of peripheral blood applicable to large-scale clinical trials.

The use of peripheral blood mononuclear cells (PBMC) for transcriptome analysis has already been proven valuable for assessing disease-associated and drug-response-related gene signatures. While these proof-of-principle studies have been critically important, the instability of RNA within PBMC prohibits their use in large-scale multicenter trials for which samples have to be transported for a prolonged time prior to RNA isolation. Therefore, a prerequisite for transcriptome analysis of peripheral blood in clinical trials will be a standardized and valid method to stabilize the RNA profile immediately after blood withdrawal. Moreover, to be able to perform such large-scale clinical studies routinely in several hundred patients more cost-effective array technologies are required. To address these critical issues, we have combined a whole-blood RNA stabilization technology with a method to reduce globin mRNA, followed by genome-wide transcriptome analysis using a newly introduced BeadChip oligonucleotide technology. We demonstrate that the globin mRNA reduction method results in significantly improved data quality of stabilized RNA samples with low intragroup variance and a detection rate of expressed genes similar to that in PBMC. More important, even small differences in gene expression such as are observed between females and males were detected and sufficient to predict gender in whole-blood samples. We therefore propose the combination of globin mRNA reduction after whole-blood RNA stabilization with a newly introduced cost-effective BeadChip array as the preferred approach for large-scale multicenter trials, especially when establishing predictive markers for disease and treatment outcome in peripheral blood.

Blood Cells↗

PubMed2025

HUMESS: integrating quantitative transcriptomic analysis and metabolic modeling to unveil condition-specific gene signatures.

SUMMARY: Transcriptomic analysis is a key tool for exploring gene expression, but the complexity of biological systems often limits its insights. In particular, the lack of intermodal or multi-layered analysis hinders the ability to fully capture key cellular functions such as metabolism from transcriptomic data alone. Here, we introduce a novel approach that informs transcriptomic data analysis with metabolic network modeling to address this. Unlike traditional methods, HUman MEtabolism Specific Signature (HUMESS) uses genome-scale metabolic modeling and flux analysis to highlight reactions and involved genes based on their metabolic significance, offering a deeper understanding of transcriptomic data. Our computational pipeline, supported by a user-friendly Rshiny application, enhances gene expression analysis by uncovering metabolic phenotypic signatures. AVAILABILITY AND IMPLEMENTATION: HUMESS is open source and available under GitLab https://gitlab.univ-nantes.fr/bird_pipeline_registry/humess with the complete documentation available at https://gitlab.univ-nantes.fr/bird_pipeline_registry/humess/-/wikis/Home. A zenodo archive is also available at the following DOI: https://doi.org/10.5281/zenodo.15487717. An RShiny application has been developed to facilitate the exploration and analysis of HUMESS's results. The app is available online at the following address: https://shiny-bird.univ-nantes.fr/app/shinymess but can also be installed locally, available under GitLab https://gitlab.univ-nantes.fr/pare-l/shinymess.

Humans↗

PubMed2026

Mechanism of Action of Hedyotis diffusa Extract in a Rat Model of Acute Lung Injury Based on Transcriptomic Analysis.

OBJECTIVE: This study established a rat model of lipopolysaccharide (LPS)-induced acute lung injury (ALI) to evaluate pathological damage, collagen deposition, inflammatory cytokine levels, and key gene/protein expression following Hedyotis diffusa water extract (HDWE) intervention. Combined with ultra-high-performance liquid chromatography-quadrupole Orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS), transcriptomic analysis, and molecular simulation, this study identified the bioactive components of HDWE, evaluated their potential interactions with ALI-related targets, and explored the multi-omics-based protective mechanisms of HDWE. METHODS: Thirty-six Sprague-Dawley (SD) rats were randomly divided into six groups: Control group, ALI group, DXMS group, HDWE-L group (100 mg/kg), HDWE-M group (200 mg/kg), and HDWE-H group (300 mg/kg). Hematoxylin and eosin (H&E) and Masson's trichrome staining were used to evaluate lung pathological changes and collagen deposition. Enzyme-linked immunosorbent assay (ELISA) was used to measure serum tumor necrosis factor-α TNF-α interleukin-1β IL-1β, erleukin-6 (IL-6), and interleukin-10 (IL-10) levels. Transcriptomic analysis identified differentially expressed genes (DEGs), followed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), receiver operating characteristic (ROC), and immune infiltration analyses. Quantitative real-time polymerase chain reaction (qRT-PCR) detected the mRNA expression levels of SPHK1, RELA, and NFKBIA. Immunohistochemistry evaluated the expression of eight hub targets, including endothelin-1 (EDN1), sphingosine kinase 1 (SPHK1), intercellular adhesion molecule 1 (ICAM1), interleukin-17 (IL-17), prostaglandin-endoperoxide synthase 2 (PTGS2/COX-2), NF-κB p65 (encoded by RELA), WT1-associated protein (WTAP), and myeloperoxidase (MPO). UHPLC-Q-Orbitrap HRMS characterized HDWE constituents. Molecular docking analysis was performed between 22 compounds and eight hub targets, followed by 100 ns molecular dynamics simulations and molecular mechanics-Poisson-Boltzmann surface area (MM/PBSA) binding free energy calculations for five core targets. Compared with the control group, the ALI group showed increased levels of TNF-α (86%), IL-1β (107%), and IL-6 (66%), accompanied by a 43% reduction in IL-10 and a 300% increase in lung collagen deposition. All HDWE doses alleviated inflammatory responses, with medium-dose HDWE showing the most pronounced effects. Specifically, medium-dose HDWE increased IL-10 levels by 52% and reduced IL-6, TNF-α, and IL-1β levels by 18%, 22%, and 11%, respectively. Transcriptomic analysis identified 2512 DEGs between the control group and ALI groups, 832 exclusive DEGs between the ALI group and HDWE-M groups, and 876 overlapping DEGs enriched in TNF, IL-17, and NF-κB signaling pathways. The eight-hub-gene diagnostic model achieved an area under the curve (AUC) of 0.969. RELA, SPHK1, and four other hub genes showed positive correlations with Th1, Th17, and neutrophil infiltration. In the ALI group, SPHK1, RELA, and NFKBIA mRNA expression levels were 1.30-, 0.96-, and 0.71-fold of those in the control group, respectively. Compared with the ALI group, high-dose HDWE treatment and low-dose HDWE treatment reduced SPHK1 expression to 0.62- and 0.57-fold, respectively, and increased NFKBIA expression to 1.68- and 1.58-fold, respectively. High-dose HDWE treatment reduced RELA expression to 0.43-fold. The expression levels of inflammation-related proteins were increased in the ALI group and were reduced after HDWE treatment. Twenty-two HDWE components were identified, 16 of which met the docking criteria. Asperulosidic acid exhibited favorable predicted binding affinities with all eight targets, with calculated binding free energies of -14.74, -14.92, -17.58, -23.04, and -16.10 kcal/mol for MPO, IL-17, NF-κB p65, PTGS2/COX-2, and SPHK1, respectively. CONCLUSIONS: This study provides systematic in vivo pharmacodynamic and in silico component-target evidence regarding the protective effects of HDWE against LPS-induced ALI. HDWE treatment increased NFKBIA expression and reduced SPHK1, RELA, and multiple inflammatory protein levels, suggesting that HDWE may regulate the IL-17/NF-κB-associated inflammatory network, although direct causal relationships require further validation. Asperulosidic acid may represent a key bioactive component with broad target-binding potential. This study was limited by the use of an LPS-induced rat ALI model without gene knockout or target inhibitor validation; therefore, further functional experiments are required to confirm the proposed regulatory mechanisms.

Hedyotis diffusa↗

PubMed2005

Application of transcriptome analysis to clinical pharmacology studies.

Clinical pharmacology is the investigation of drug effects in humans. This review discusses the basic tenets of clinical pharmacology research, including pharmacokinetic and pharmacodynamic analysis, therapeutic window, and clinical trial design, and the issues that may arise in the application of transcriptome analysis to clinical pharmacology studies. Examples of how transcriptome analysis can be applied to clinical pharmacology research are described, including a model system of endotoxin challenge (in vitro and in vivo), and an example of a cross-over drug study in normal volunteers. Various data display and analysis methods are also illustrated, including principal component analysis, hierarchical cluster analysis, and pathways analysis.

Animals↗

PubMed2006

Transcriptome analysis of a rat PKD model: Importance of genes involved in extracellular matrix metabolism.

Transcriptome analysis of a rat polycystic kidney disease (PKD) model: importance of genes involved in extracellular matrix metabolism. PKD is a common genetic cause of chronic renal failure, and is characterized by the accumulation of fluid-filled cysts in the kidneys and other organs. Abnormalities in the expression of selected genes thought to be involved in cystogenesis have been described, but no systematic analysis of the global transcriptomal pattern has been reported. With this aim, a rat oligomicroarray was used to identify variations in gene expression in Han:Sprague-Dawley Cy/Cy rats, an animal model presenting a severe PKD phenotype. Some upregulated genes were validated using real-time polymerase chain reaction in Cy/Cy and Cy/+ rats. Among the 350 genes identified as being upregulated, we found about 30 genes involved in extracellular matrix metabolism. These genes encoded proteins or peptides that could be implicated into two different biological processes: molecules involved in fibrosis and proteins involved in adhesion to the extracellular matrix. In heterozygotes, some genes (glypican 3, fibronectin 1) were already upregulated in early stages of the disease. We conclude that differential regulation of genes linked to extracellular matrix metabolism may be one of the first events leading to tubule enlargement and subsequent cyst formation in PKD.

Animals↗

PubMed2004

Transcriptome analysis using fluorescence-labeled oligonucleotide.

In our previous studies, the fluorescence intensity at 480 nm of the bispyrene-labeled 2'-O-methyl-oligoribonucleotide (OMUpy2) was drastically enhanced only when it was hybridized with its complementary RNA. In this study, we demonstrated the potential of the OMUpy2 for transcriptome analysis in the cell free system and in the living cell. For the analysis in the cell free system, the micro-chamber was designed and used to evaluate the detection limit of the target RNA. The detection limit of oligo-RNA was approximately 15 fmol. For the analysis of the living cells, C-OMUpy2 was introduced to the cervical carcinoma cells, C4II, and the fluorescence from C-OMUpy2 was monitored. The fluorescence from the cells began to be observed 5 min after the serum stimulation, and the fluorescence was decreased after 20 min. These results seemed to be consistent with the results from the RT-PCR analysis and suggested that OMUpy2 could be a useful tool for the transcriptome analysis.

Cell Line, Tumor↗

PubMed2003

BioMeKe: an ontology-based biomedical knowledge extraction system devoted to transcriptome analysis.

Semantic interoperability between knowledge bases in medicine, and knowledge base in genomics and molecular biology will lead to advances in fundamental research as well as to improved patient care. DNA chips strategy is used for transcriptome analysis in order to identify deregulated genes in physio-pathological conditions. The objective of the BioMedical Knowledge Extraction project (BioMeKe) is to develop a knowledge warehouse in the context of transcriptome analysis during liver diseases. Knowledge sources include ontologies, related terminologies and annotations linked towards public databases (e.g., SWISSPROT). BioMeKe has been developed to have access to information using systematic investigation upon a concept, gene, gene products, pathology, or any target keyword, and is based on the combination of several relevant resources: UMLS, GeneOntology, MeSH supplementary terms, GOA, and HUGO. Current efforts are focusing on exploiting this ontology-based Knowledge Extractor, to enrich the expression data on genes delivered by a liver specific DNA microarray for better assistance of analysis.

Databases, Genetic↗

PubMed2026

S100P as a Shared Biomarker in Inflammatory Bowel Disease, Colorectal Cancer, and Pancreatic Adenocarcinoma: An Integrated Transcriptomic Analysis.

Inflammatory bowel disease (IBD) is associated with an increased risk of colorectal cancer (CRC) and pancreatic adenocarcinoma (PAAD), yet the molecular features shared among these diseases remain incompletely understood. This study aimed to identify common genes and biological pathways associated with IBD, CRC, and PAAD through integrated transcriptomic analysis and experimental validation. Gene expression datasets for IBD, CRC, and PAAD were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. Weighted gene co-expression network analysis and differential expression analysis were performed to identify disease-associated and shared genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (analyses were used to explore enriched biological functions and pathways. Immune cell infiltration was evaluated using Cell-type Identification by Estimating Relative Subsets of RNA Transcripts. Receiver operating characteristic analysis was performed to assess the diagnostic performance of common genes. Single-cell RNA sequencing analysis was conducted to examine the cellular distribution of S100P. In addition, the effects of S100P downregulation were evaluated in lipopolysaccharide (LPS)-stimulated colonic epithelial cells. A total of 162 disease-associated genes and four common genes were identified. Functional enrichment analyses indicated significant enrichment of immune- and inflammation-related pathways, including the interleukin-17 signaling pathway. Immune infiltration analysis revealed similar trends in several immune cell populations across IBD, CRC, and PAAD. Single-cell analysis showed elevated S100P expression in epithelial cells from all three diseases. Downregulation of S100P restored the proliferative capacity of LPS-stimulated colonic epithelial cells and reduced inflammatory cytokine expression. Integrated transcriptomic analysis identified S100P as a biomarker associated with IBD, CRC, and PAAD and highlighted shared immune-related features across these diseases.

Humans↗

PubMed2025

Integrative spatial transcriptomic analysis pinpoints the role of the ferroxidase, TaMCO3, in wheat root tip iron mobilization.

Roots play a critical role in the sensing and absorption of essential minerals from the rhizosphere. Iron (Fe) deficiency, for example, triggers a well-known series of physiological and molecular responses within roots that facilitate uptake, which differs between monocots and dicots. In monocots, little is known about the molecular responses that occur within specific root development zones in response to iron deprivation, and how these differences result in overall nutrient uptake. Here, we conducted a transcriptome analysis of wheat root tips under Fe deficiency (-Fe) and performed a comparative transcriptome analysis with the previous datasets generated from the whole root. Gene ontology analysis of differentially expressed genes highlighted the significance of oxidoreductase activity and metal/ion transport in the root tip, which are critical for Fe mobilization. Interestingly, wheat, an allohexaploid species consisting of three different genomes (A, B, and D) displayed varying gene expression levels arising from the three genomes that contributed to similar molecular functions. Detailed analysis of oxidoreductase function at the root tip revealed multiple multicopper oxidase (MCO) proteins, such as Fe-responsive TaMCO3, that likely contribute to the overall ferroxidase activity. Further characterization of TaMCO3 shows that it complements the yeast FET3 mutant and rescues the -Fe sensitivity phenotype of Arabidopsis atmco3 mutants by enhancing vascular Fe loading. Transgenic wheat lines overexpressing TaMCO3 exhibited increased root Fe accumulation and improved tolerance to -Fe by augmenting the expression of Fe-mobilizing genes. Our findings highlight the role of spatially resolved gene expression in -Fe responses, suggesting strategies to reprogram cells for improved nutrient stress tolerance.

Triticum↗

PubMed2026

Epigenetics and In Silico Transcriptome Analysis of Pediatric Acute Myeloid Leukemia.

Pediatric acute myeloid leukemia (AML) is a heterogeneous hematologic malignancy that accounts for about 15%-20% of childhood leukemias. Despite therapeutic advances, relapses remain common, and survival for high-risk patients is below 60%. Unlike adult AML, pediatric AML displays distinct genetic mutations, including FLT3-ITD, NPM1, KMT2A rearrangements, and core-binding factors (CBF) fusions, as well as extensive epigenetic dysregulation. Aberrant DNA methylation, histone modifications, and altered non-coding RNA expressions disrupt hematopoietic differentiation and activate oncogenic transcriptional networks. Recent advances in silico transcriptomic analysis have transformed the study of pediatric AML by integrating gene expression and epigenetic data to identify molecular drivers and regulatory networks. Computational RNA-seq pipelines and pathway analyses have highlighted key epigenetic regulators, including DNMT3A, TET2, and HDACs, as potential therapeutic targets. Multi-omics approaches combining transcriptomic, methylomic, and chromatin accessibility data are increasingly used to define biomarkers for diagnosis, prognosis, and therapeutic response. This review provides a comprehensive overview of the molecular and epigenetic landscape of pediatric AML, emphasizing the power of in silico transcriptome analysis to uncover disease mechanisms, refine patient stratification, and guide the development of precision-based epigenetic therapies aimed at improving long-term outcomes in children with AML.

Humans↗

PubMed2006

Transcriptome analysis of Neotyphodium and Epichloë grass endophytes.

Large-scale gene discovery has been performed for the grass fungal endophytes Neotyphodium coenophialum, Neotyphodium lolii, and Epichloë festucae. The resulting sequences have been annotated by comparison with public DNA and protein sequence databases and using intermediate gene ontology annotation tools. Endophyte sequences have also been analysed for the presence of simple sequence repeat and single nucleotide polymorphism molecular genetic markers. Sequences and annotation are maintained within a MySQL database that may be queried using a custom web interface. Two cDNA-based microarrays have been generated from this genome resource. They permit the interrogation of 3806 Neotyphodium genes (Nchip microarray), and 4195 Neotyphodium and 920 Epichloë genes (EndoChip microarray), respectively. These microarrays provide tools for high-throughput transcriptome analysis, including genome-specific gene expression studies, profiling of novel endophyte genes, and investigation of the host grass-symbiont interaction. Comparative transcriptome analysis in Neotyphodium and Epichloë was performed.

Computational Biology↗

PubMed2026

Integrated transcriptome analysis and machine learning to construct a homeostatic model of acetylation for bladder cancer and validate the key gene CES1.

BACKGROUND: Bladder cancer (BLCA) is one of the most common malignant tumors of the urinary system. Protein acetylation (PA) plays a critical role in regulating multiple biological processes (BPs), cellular homeostasis, and cancer-related signaling pathways. This study aimed to construct a homeostatic model of acetylation for BLCA using integrated transcriptome analysis and machine learning and to validate the key gene CES1. METHODS: RNA sequencing (RNA-seq) and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Acetylation-related differentially expressed genes (DEGs) in BLCA were screened using differential expression analysis (DEA). An acetylation homeostatic model was constructed via univariate, machine learning-based least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses, followed by validation in multiple cohorts. Single-cell RNA-seq analysis was used to explore gene expression patterns in diverse cell types. Enrichment analysis (EA), immune infiltration, and drug sensitivity analysis (DSA) were performed to characterize molecular features of different risk groups. Finally, the biological function of CES1 as the key gene was verified by in vitro knockdown experiments. RESULTS: We established a robust acetylation homeostatic model consisting of five genes, which effectively predicted overall survival (OS) and served as an independent prognostic factor in BLCA. High-risk patients showed significantly poorer prognosis, distinct immune infiltration profiles, and differential drug sensitivity. CES1 was identified and validated as the key gene in this model, which was highly expressed in BLCA and associated with poor prognosis. Knockdown of CES1 markedly suppressed cell proliferation, invasion, and migration, and reduced intracellular coenzyme A (CoA) levels, thereby regulating PA homeostasis. CONCLUSIONS: We developed and validated a novel acetylation homeostatic model for survival stratification and personalized treatment guidance in BLCA, based on integrated transcriptome analysis and machine learning. CES1 is closely associated with intracellular CoA levels and the malignant progression of BLCA. Its potential association with PA homeostasis requires further mechanistic validation, and it may act as a candidate therapeutic biomarker for BLCA.

Bladder cancer (BLCA)↗