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Results for “differential expression”

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At least 19 recordsLinked to original sources

PubMed2025

LimROTS: a hybrid method integrating empirical Bayes and reproducibility-optimized statistics for robust differential expression analysis.

MOTIVATION: Differential expression analysis plays a vital role in omics research enabling precise identification of features that associate with different phenotypes. This process is critical for uncovering biological differences between conditions, such as disease versus healthy states. In proteomics, several statistical methods have been used, ranging from simple t-tests to more advanced methods like DEqMS, limma and ROTS. However, a flexible method for reproducibility-optimized statistics tailored for clinical omics data has been lacking. RESULTS: In this study, we developed LimROTS, a hybrid method that integrates a linear regression model and the empirical Bayes approach with reproducibility optimized statistics, to create a novel moderated ranking statistic, for robust and flexible analysis of proteomics data. We validated its performance using twenty-one proteomics gold standard spike-in datasets with different protein mixtures, MS instruments, and techniques for benchmarking. This hybrid approach improves accuracy and reproducibility of complex proteomics data, making LimROTS a powerful tool for high-dimensional omics data analysis. AVAILABILITY AND IMPLEMENTATION: LimROTS has been implemented as an R/Bioconductor package, available at https://doi.org/doi:10.18129/B9.bioc.LimROTS. Additionally, the code used in this study is available in GitHub repository https://github.com/AliYoussef96/LimROTSmanuscript.

Bayes Theorem↗

PubMed2026

Comprehensive analysis of differentially expressed mRNAs, lncRNAs, and miRNAs involved in ovarian differentiation and development in Qihe gibel carp (Carassius gibelio var. Qihe).

Qihe gibel carp (Carassius gibelio var. Qihe) exhibits diverse reproductive modes including gynogenesis and sexual reproduction, yet the molecular mechanisms of ovarian differentiation remain poorly understood. Ovarian tissues at 20, 30, and 60 days after hatching (dah), representing key stages covering early ovarian differentiation and primary oocyte growth, were subjected to whole-transcriptome sequencing. A total of 27,259 mRNAs, 2622 lncRNAs, and 2467 miRNAs were differentially expressed. Cell cycle, transcription, translation, and DNA replication pathways were significantly upregulated from 20 to 60 dah. Oocyte meiosis was enriched from 20 and 30 dah, whereas metabolic pathways (lipid, carbohydrate, and nucleotide metabolism) were enriched from 30 to 60 dah, indicating sequential progression from meiosis initiation to primary oocyte growth with nutrient synthesis. Hub lncRNAs and key ceRNA networks (e.g., MSTRG.28669.5-miR-221-ccnb2) were identified. This study provides the first comprehensive characterization of ncRNA-mediated regulation and ceRNA networks during ovarian development in Qihe gibel carp, establishing a foundation for understanding ovarian differentiation in this species.

Animals↗

PubMed2025

Bayesian identification of differentially expressed isoforms using a novel joint model of RNA-seq data.

We develop a Bayesian approach, BayesIso, to identify differentially expressed isoforms from RNA-seq data. The approach features a novel joint model of the sample variability and the deferential state of isoforms. Specifically, the within-sample variability and the between-sample variability of each isoform are modeled by a Poisson-Lognormal model and a Gamma-Gamma model, respectively. Using a Bayesian framework, the differential state of each isoform and the model parameters are jointly estimated by a Markov Chain Monte Carlo (MCMC) method. Extensive studies using simulation and real data demonstrate that BayesIso can effectively detect isoforms of less differentially expressed and differential transcripts for genes with multiple isoforms. We applied the approach to breast cancer RNA-seq data and uncovered a unique set of isoforms that form key pathways associated with breast cancer recurrence. First, PI3K/AKT/mTOR signaling and PTEN signaling pathways are identified as being involved in breast cancer development. Further integrated with protein-protein interaction data, pathways of Jak-STAT, mTOR, MAPK and Wnt signaling are revealed in association with breast cancer recurrence. Finally, several pathways are activated in the early recurrence of breast cancer. In tumors that occur early, members of pathways of cellular metabolism and cell cycle (such as CD36 and TOP2A) are upregulated, while immune response genes such as NFATC1 are downregulated.

Humans↗

PubMed1979

Host dependence of RP1-specified resistance to ampicillin: differential expression in Escherichia coli and Rhizobium leguminosarum.

Rhizobium leguminosarum L4 is able to serve as a host for the plasmid RP1. Properties of R. leguminosarum [RP1] plasmid carrier suggest that the expression of RP1-coded Apr gene(s) is inhibited in this host, although the determinants of transfer and resistance to kanamycin and tetracycline are expressed. This system exemplifies a differential expression of plasmid genes in a new host.

Ampicillin↗

PubMed2025

Analysis of differentially expressed genes in schizophrenia based on bioinformatics and corresponding mRNA expression levels.

OBJECTIVE: This study aimed to use bioinformatics analysis to identify differentially expressed genes (DEGs) involved in the pathogenesis of schizophrenia and validate their mRNA expression levels through real-time quantitative PCR (qPCR). MATERIAL/METHODS: Datasets from the publicly available Gene Expression Omnibus (GEO) database were analyzed using R software to identify DEGs. Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, were conducted. A protein-protein interaction (PPI) network was constructed using Cytoscape software to identify key genes with notable expression changes. The expression levels of these key genes were subsequently validated in schizophrenia patients using qPCR to assess potential susceptibility genes. RESULTS: In total, 813 DEGs were identified, with six key genes highlighted through GO analysis and PPI network screening. Among these, HDAC1, UBA52, and FYN demonstrated statistically significant differences in mRNA expression between schizophrenia patients and healthy controls (P < 0.05). CONCLUSIONS: This study identified several DEGs potentially linked to the pathogenesis of schizophrenia, suggesting that HDAC1, UBA52, and FYN could serve as candidate susceptibility genes and diagnostic biomarkers. These findings provide new insights and directions for future schizophrenia research.

Humans↗

PubMed2026

Comprehensive Analysis of Differentially Expressed Genes and Immune Infiltration in Burn Injury: Key Biomarkers and Pathways.

BACKGROUND: Burn injuries trigger complex immune responses and gene expression changes, impacting wound healing and systemic inflammation. Understanding these changes is crucial for identifying biomarkers and therapeutic targets. METHODS: We analyzed two gene expression omnibus datasets (wound tissue [GSE8056] and blood [GSE37069]) to identify differentially expressed genes (DEGs) in burn injury samples versus controls. Immune cell proportions were assessed using CIBERSORT. Functional enrichment analyses (Gene Ontology and Kyoto Encyclopedia of Genes and Genomes) and protein-protein interaction networks were constructed to identify key genes and pathways. RESULTS: We identified 1170 upregulated and 1227 downregulated DEGs. Gene Ontology analysis revealed enrichment in neutrophil activation, inflammatory response, and extracellular matrix organization. Kyoto Encyclopedia of Genes and Genomes analysis highlighted cytokine-cytokine receptor interaction, TNF, and IL-17 signaling pathways. Immune infiltration analysis showed significant changes in neutrophils, macrophages (M1/M2), and T-cell subsets. Protein-protein interaction network analysis identified five hub genes: JUN, STAT1, Bcl2, MMP9, and TLR2. CONCLUSIONS: This study provides a comprehensive bioinformatic analysis of gene expression and immune responses in burn injuries. The identified DEGs, hub genes, and pathways offer insights into the immune response mechanisms and suggest potential targets for diagnostic and therapeutic interventions in burn injury management.

Burns↗

PubMed2026

The prognostic value and molecular mechanisms of Porphyromonas gingivalis infection-associated differentially expressed genes in oral squamous cell carcinoma.

BACKGROUND: Increasing evidence suggests that Porphyromonas gingivalis (Pg) is associated with oral squamous cell carcinoma (OSCC) development and progression. This study aimed to identify Pg-associated genes with prognostic relevance in OSCC through integrated bioinformatics analysis. METHODS: OSCC-related differentially expressed genes (DEGs) were identified from the The Cancer Genome Atlas (TCGA)-OSCC cohort and intersected with Pg supernatant-associated DEGs from GSE192887. Raw count data were analyzed with DESeq2, whereas transcripts per million (TPM)-transformed expression values were used for downstream visualization and model construction. Weighted gene co-expression network analysis (WGCNA), univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariable Cox modeling were used to develop a seven-gene prognostic signature, which was externally evaluated in GSE41613. Additional analyses examined treatment-associated expression changes in the seven model genes, pairwise correlations among the model genes, and correlations between Pg supernatant-associated differentially expressed gene (PgSDEG)-derived module eigengenes and immune-cell fractions. Quantitative reverse-transcription polymerase chain reaction (qRT-PCR) was performed in eight paired OSCC and adjacent non-tumor tissues and in supplemented-brain heart infusion (BHI) vehicle-control and Pg culture-supernatant-treated HOK, HSC-3, and CAL-27 cells. RESULTS: A prognostic signature comprising CXCL8, GAST, HBQ1, PADI3, STC1, TEX19, and TMEM92 was established. The signature showed limited-to-moderate discrimination in the TCGA training cohort, with 1-, 3-, and 5-year areas under the curve (AUCs) of 0.68, 0.69, and 0.69, respectively, and limited discrimination in the GSE41613 external cohort (AUCs: 0.66, 0.67, and 0.61). Kaplan-Meier analysis showed poorer survival in the high-risk group in both cohorts. The GSE192887 analysis showed significant treatment-associated expression changes in all seven genes after Pg culture-supernatant exposure. In paired tissues, CXCL8 and TMEM92 were significantly higher in OSCC tissues, whereas STC1 was not significant after Holm correction. In CAL-27 cells, CXCL8, STC1, and TMEM92 increased significantly after culture-supernatant treatment, whereas the corresponding comparisons were not significant in HOK or HSC-3 cells after adjustment. CONCLUSIONS: This study developed a seven-gene Pg-associated prognostic signature for OSCC and provided complementary transcriptomic, immune-correlation, tissue, and cell-based evidence that placed the signature in biological context. The model showed limited-to-moderate discrimination and is not ready for clinical use. The enrichment, gene-correlation, and immune-correlation findings are hypothesis-generating rather than mechanistic evidence. Further independent validation and dedicated functional studies are required.

Oral squamous cell carcinoma (OSCC)↗

PubMed2024

Revealing differential expression patterns of piRNA in FACS blood cells of SARS-CoV-2 infected patients.

Non-coding RNA expression has shown to have cell type-specificity. The regulatory characteristics of these molecules are impacted by changes in their expression levels. We performed next-generation sequencing and examined small RNA-seq data obtained from 6 different types of blood cells separated by fluorescence-activated cell sorting of severe COVID-19 patients and healthy control donors. In addition to examining the behavior of piRNA in the blood cells of severe SARS-CoV-2 infected patients, our aim was to present a distinct piRNA differential expression portrait for each separate cell type. We observed that depending on the type of cell, different sorted control cells (erythrocytes, monocytes, lymphocytes, eosinophils, basophils, and neutrophils) have altering piRNA expression patterns. After analyzing the expression of piRNAs in each set of sorted cells from patients with severe COVID-19, we observed 3 significantly elevated piRNAs - piR-33,123, piR-34,765, piR-43,768 and 9 downregulated piRNAs in erythrocytes. In lymphocytes, all 19 piRNAs were upregulated. Monocytes were presented with a larger amount of statistically significant piRNA, 5 upregulated (piR-49039 piR-31623, piR-37213, piR-44721, piR-44720) and 35 downregulated. It has been previously shown that piR-31,623 has been associated with respiratory syncytial virus infection, and taking in account the major role of piRNA in transposon silencing, we presume that the differential expression patterns which we observed could be a signal of indirect antiviral activity or a specific antiviral cell state. Additionally, in lymphocytes, all 19 piRNAs were upregulated.

Humans↗

PubMed2025

Differential expression of plasma proteins and pathway enrichments in pediatric diabetic ketoacidosis.

BACKGROUND: In children with type 1 diabetes (T1D), diabetic ketoacidosis (DKA) triggers a significant inflammatory response; however, the specific effector proteins and signaling pathways involved remain largely unexplored. This pediatric case-control study utilized plasma proteomics to explore protein alterations associated with severe DKA and to identify signaling pathways that associate with clinical variables. METHODS: We conducted a proteome analysis of plasma samples from 17 matched pairs of pediatric patients with T1D; one cohort with severe DKA and another with insulin-controlled diabetes. Proximity extension assays were used to quantify 3072 plasma proteins. Data analysis was performed using multivariate statistics, machine learning, and bioinformatics. RESULTS: This study identified 214 differentially expressed proteins (162 upregulated, 52 downregulated; adj P < 0.05 and a fold change > 2), reflecting cellular dysfunction and metabolic stress in severe DKA. We characterized protein expression across various organ systems and cell types, with notable alterations observed in white blood cells. Elevated inflammatory pathways suggest an enhanced inflammatory response, which may contribute to the complications of severe DKA. Additionally, upregulated pathways related to hormone signaling and nitrogen metabolism were identified, consistent with increased hormone release and associated metabolic processes, such as glycogenolysis and lipolysis. Changes in lipid and fatty acid metabolism were also observed, aligning with the lipolysis and ketosis characteristic of severe DKA. Finally, several signaling pathways were associated with clinical biochemical variables. CONCLUSIONS: Our findings highlight differentially expressed plasma proteins and enriched signaling pathways that were associated with clinical features, offering insights into the pathophysiology of severe DKA.

Humans↗

PubMed2026

Integrated transcriptomic and metabolomic analysis of fluoride tolerance-related pathways and differentially expressed genes in silkworm strain XSKD.

XueSong KD (XSKD) silkworm strain exhibits prominent fluoride tolerance, yet the underlying molecular mechanisms of fluoride tolerance remains unclear. In the present study, fourth-instar pre-molting XSKD silkworms were used as experimental materials for integrated transcriptomic and untargeted metabolomic analyses. In total, 572 differentially expressed genes and 90 differential metabolites were screened. GO enrichment and KEGG enrichment based on the hypergeometric distribution model revealed that 13-Hydroxy-9Z,11E-octadecadienoic acid (13-(S)-HODE) acts as the core differential metabolite, which is significantly enriched in the linoleic acid metabolism pathway. Within this pathway, LOC101737302 and CYP338A1 display opposite expression trends and show correlations with pathway metabolites. Based on multi-omics data, this study preliminarily characterizes the lipid metabolic response under fluoride stress, providing omics dataset support for further in-depth exploration of the molecular mechanism of fluoride tolerance in silkworms.

Animals↗

PubMed2025

Differential Expression of Erythrocyte Proteins in Patients with Alcohol Use Disorder.

Alcohol Use Disorder (AUD) poses global health challenges, and causes hematological alterations such as macrocytosis and oxidative stress. Disruption of protein structures by alcohol and/or its metabolites may exacerbate AUDs; proteomics can elucidate the underlying biological mechanisms. This study examined the proteins differentially expressed in the cytosol and membrane fractions of erythrocytes obtained from 30 male patients with AUD, comparing them to samples from 15 age- and BMI-matched social drinkers (SDs) and 15 non-drinkers (control). The analysis aimed to identify the molecular differences related to alcohol consumption. The AUD patient subgrouping was based on mean corpuscular volume (MCV), with 16 individuals classified as having a normal MCV and 14 having a high MCV. Proteins were separated via two-dimensional(2D)-gel electrophoresis, digested with trypsin, and identified via Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (TOF) mass spectrometry (MALDI-TOF/TOF). Additionally, levels of malondialdehyde and 4-hydroxyalkenals (MDA + HAE), reduced glutathione (GSH), oxidized glutathione (GSSG), serum carbohydrate-deficient transferrin (%CDT), disialotransferrin (%DST), and sialic acid (SA) were analyzed. The results showed increased MDA + HAE and decreased total thiols in AUD patients, with GSSG elevated and the GSH/GSSG ratio reduced in the AUD MCV-high subgroup. Serum %CDT, %DST, and SA were significantly higher in AUD. Compared to the control profiles, the AUD group exhibited differential protein expression. Few proteins, such as bisphosphoglycerate mutase, were downregulated in AUD versus control and SD, as well as in the MCV-high AUD subgroup. Conversely, endoplasmin and gelsolin were upregulated in AUD relative to control. Cytoskeletal proteins, including spectrin-alpha chain, actin cytoplasmic 2, were overexpressed in the AUD group and MCV-high AUD subgroup. Several proteins, such as 14-3-3 isoforms, alpha-synuclein, translation initiation factors, heat shock proteins, and others, were upregulated in the MCV-high AUD subgroup. Under-expressed proteins in this subgroup include band 3 anion transport protein, bisphosphoglycerate mutase, tropomyosin alpha-3 chain, uroporphyrinogen decarboxylase, and WD repeat-containing protein 1. Our findings highlight the specific changes in protein expression associated with oxidative stress, cytoskeletal alterations, and metabolic dysregulation, specifically in AUD patients with an elevated MCV. Understanding these mechanisms is crucial for developing targeted interventions and identifying biomarkers of alcohol-induced cellular damage. The complex interplay between oxidative stress, membrane composition, and cellular function illustrates how chronic alcohol exposure affects cellular physiology.

Humans↗

PubMed2024

PoweREST: Statistical Power Estimation for Spatial Transcriptomics Experiments to Detect Differentially Expressed Genes Between Two Conditions.

Recent advancements in Spatial Transcriptomics (ST) have significantly enhanced biological research in various domains. However, the high cost of current ST data generation techniques restricts its application in large-scale population studies. Consequently, there is a pressing need to maximize the use of available resources to achieve robust statistical power. One fundamental question in ST analysis is to detect differentially expressed genes (DEGs) among different conditions using ST data. Such DEG analysis is often performed but the associated power calculation is rarely discussed in the literature. To address this gap, we introduce, PoweREST (https://github.com/lanshui98/PoweREST), a power estimation tool designed to support power calculation of DEG detection with 10X Genomics Visium data. PoweREST enables power estimation both before any ST experiments or after preliminary data are collected, making it suitable for a wide variety of power analyses in ST studies. We also provide a user-friendly, program-free web application (https://lanshui.shinyapps.io/PoweREST/), allowing users to interactively calculate and visualize the study power along with relevant the parameters.

Differentially expressed genes↗

PubMed2026

Atlas-level single-cell integration and clustering-free differential expression analysis with GEDI 2.0.

MOTIVATION: GEDI is a generative framework for multi-sample, multi-condition single-cell analysis that performs batch correction, latent representation learning, and clustering-free differential expression within a unified model. However, the original implementation suffered from prohibitive memory use and runtime, preventing its application to modern atlas-scale datasets. RESULTS: We present GEDI 2.0, a complete high-performance reimplementation featuring a standalone C++ computational core with pre-allocated workspaces, strict sparse-matrix preservation, optimized BLAS routines, and multi-threaded block-coordinate descent. Across extensive benchmarks spanning up to 500 000 cells and 10 000 features, GEDI 2.0 achieves 40%-63.6% mean reduction in peak memory, 2.98× mean single-threaded speedups, and up to 11.5× acceleration with parallel execution, while maintaining full numerical equivalence to the original method. These improvements enable GEDI 2.0 to analyze million-cell datasets, a scale not achievable with the legacy implementation. GEDI 2.0 provides R and Python interfaces and seamless interoperability with common single-cell workflows. AVAILABILITY AND IMPLEMENTATION: Source code, documentation, reproducible codebase, and tutorials are available at https://github.com/csglab/gedi2.

Single-Cell Analysis↗

PubMed2026

DiaReport: reproducible workflow for differential expression analysis and interactive reporting in DIA-based proteomics.

MOTIVATION: Data-independent acquisition (DIA) has become the preferred data acquisition method for mass spectrometry-based proteomics, yet, reproducible workflows for differential expression (DE) analysis and results reporting remain limited. We present DiaReport, an R package that performs precursor- and protein-level DE analysis from DIA-NN output using MSqRob and QFeatures, while generating high-quality, interactive HTML reports through Quarto. DiaReport integrates precursor data, filtering of missing values, normalization, protein summarization and statistical modeling within a single function, supporting both simple pairwise as well as complex experimental designs. The package provides structured outputs and configuration files to ensure computational reproducibility across different studies. To accommodate diverse research needs, DiaReport includes multiple reporting templates tailored to different proteomic applications. Applying DiaReport to an extracellular vesicle (EV) proteomics dataset demonstrates its ability to efficiently analyze DIA data and provide rapid insights into sample quality and protein level differences. AVAILABILITY: DiaReport is an open-source R package available at https://github.com/Gevaert-Lab/diareport (DOI: 10.5281/zenodo.20120604). The package is platform-independent and distributed under the MIT license. Reports are generated using Quarto and require only standard R dependencies. Detailed documentation, installation guides and usage vignettes are provided within the repository. The interactive HTML reports discussed in this study, including the UPS2 benchmark and EV case study, are archived on Zenodo (10.5281/zenodo.20122506 and 10.5281/zenodo.20123378).

Proteomics↗

PubMedDate not supplied

[Differential expression of relevant Rous sarcoma tumor antigens in cultured cells].

Chickens bearing tumours induced by Rous sarcoma virus (RSV) are able to mount a cellular immune response against antigens associated with these neoplasms. Cultured Rous sarcoma (RS) cells, derived from the wings of chickens bearing tumors induced by RSV, were consistently more susceptible in cytotoxicity assays to killing by the splenic lymphocytes of RS-bearing chickens than were chicken embryo fibroblast (CEF) cells which had been transformed in vitro by RSV. In contrast, extracts (3M KCI-derived) and supernatant fluids of transformed CEF were equally or more capable of eliciting blastogenic responsiveness in circulating lymphocytes of birds with tumors than were similar extracts and supernatant fluids obtained from cultured RS cells. As measured by lymphocyte stimulation, the expression of this anti-tumour response was in some cases only short-lived, and varied between animals in terms of time following inoculation with RSV. These findings point to a differential expression of relevant RS-tumour antigens in these various cell types, and indicate that the use of various experimental techniques may yield quite different results.

Animals↗

PubMed2025

Proteomics of Duchenne Muscular Dystrophy Patient iPSC-Derived Skeletal Muscle Cells Reveal Differential Expression of Cytoskeletal and Extracellular Matrix Proteins.

Proteomics of dystrophic muscle samples is limited by the amount of protein that can be extracted from patient biopsies. Cells and tissues derived from patient-derived induced pluripotent stem cells (iPSCs) can be an expandable alternative source. We have patterned iPSCs from three Duchenne muscular dystrophy (DMD) patient lines into skeletal muscle cells using a two-dimensional as well as our three-dimensional organoid differentiation system. Probes with sufficient protein amounts could be extracted and prepared for mass spectrometry. In total, 3007 proteins in 2D and 2709 proteins in 3D were detected in DMD patient probes. A total of 83 proteins in 2D and 338 proteins in 3D can be described as differentially expressed between DMD and control patient probes in a post hoc test. We have identified and we propose Myosin-9, Collagen 18A, Tropomyosin 1, BASP1, RUVBL1, and NCAM1 as proteins specifically altered in their expression in DMD for further investigation. Proteomics of skeletal muscle organoids resulted in greater consistency of results between cell lines in comparison to the two-dimensional myogenic differentiation protocol.

Humans↗

PubMed1978

Molecular mechanisms involved in the differential expression of gag gene products by clonal isolates of a primate sarcoma virus.

Clonal isolates of an early passage stock of woolly monkey sarcoma virus (WSV) have been shown to code for different numbers of woolly monkey helper leukemia virus gag gene products. In the present report, the molecular mechanisms responsible for their differential expression of gag gene products have been analyzed. Three WSV RNA genomes were shown to possess sedimentation coefficients consistent with the differences demonstrated in their allotments of helper viral sequences. The WSV variant (WSV clone 9) that expressed no detectable proteins was shown to contain the largest amount of helper viral information. Moreover, there was no additive hybridization of the WLV complementary DNA probe by RNA of this WSV clone and that of a WSV clone coding for several gag gene products. These results suggest that the lack of expression of gag gene products by WSV clone 9 is not due to a major deletion of helper viral gag gene sequences. Similar levels of WLV-specific RNA were demonstrated in cells nonproductively transformed by each WSV clone, arguing that the ability to express gag gene proteins was not related to the magnitude of viral RNA transcription. Taken together, the results are most consistent with a mechanism by which small deletions or point mutations in the genomes of some WSV variants result in premature termination of translation or synthesis of immunologically nonreactive gag gene proteins. The present findings have implications concerning the effects of evolutionary selective pressures on helper viral genetic information in mammalian transforming viruses.

Base Sequence↗

PubMed2026

Differential expression of neuronal function genes follows a tissue-specific temporal dynamic during Deformed Wing Virus infection in honey bees.

Deformed Wing Virus type A (DWV-A) is one of the primary threats to honeybees (Apis mellifera), significantly impacting their nervous system physiology and behavior. While its neurotropic nature is well-recognized, the temporal dynamics of the neuronal transcriptomic response following oral infection, the natural transmission route, remains poorly understood. In this study, we analyzed gene expression in the heads of worker bees orally inoculated with DWV-A over a 16-day time course (1, 4, 7, 10, 13, and 16 days post-inoculation). RNA-seq analysis at day 10 identified 147 differentially expressed genes associated with different biological processes that are critical to the organism, including cellular metabolism and neuronal activity. RT-qPCR validation revealed a persistent downregulation of key genes related to glutamatergic system (eaat-2, neto, and kainate) and sensory perception-related genes in the antennae. Notably, the simultaneous co-expression of nurse-associated and forager-associated marker genes suggests that DWV-A infection induces an asynchrony in behavioral maturation. Our findings demonstrate that DWV-A disrupts neuronal homeostasis and peripheral sensory perception in a tissue-specific and time-dependent manner, providing a molecular framework to understand the behavioral impairment and the loss of coordination at the colony level.

Animals↗