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At least 19 recordsLinked to original sources
PubMed2025

Differential gene expression study in whole blood identifies candidate genes for psychosis in African American individuals.

Genome-wide association has identified regions of the genome that mediate risk for psychosis. It is possible that variants in these regions confer risk by altering gene expression. This work has predominantly been conducted in individuals of European descent and has focused narrowly on schizophrenia rather than psychosis as a syndrome. In the present study we investigated alterations in gene expression in African American individuals with a range of psychotic diagnoses to increase understanding of the etiology in an underserved population. We performed RNA-seq in whole bloody to survey the transcriptome in 126 patients with a psychosis-spectrum disorder and 217 healthy controls and applied differential gene expression analyses across the genome while controlling for age, sex, population stratification and batch. We found 18 differentially expressed genes (DEGs), some of the locations of the corresponding genes overlap with previously implicated regions for psychosis, but many of which were novel associations. Enrichment analysis of nominally significant genes (p&#xa0;<&#xa0;0.05) revealed overrepresentation of biological processes relating to platelet, immune and cellular function, and sensory perception. Weighted gene co-expression network analysis, applied to identify modules of co-expressed genes associated with psychosis, revealed 10 modules, one of which was significantly associated with psychosis. This module was significantly enriched for DEGs, and for platelet function. These results support the potential role of immune function in the etiology of psychosis, identify novel candidate gene expression phenotypes that correspond to both established and new genomic regions, in individuals of African American ancestry.

Humans↗
PubMed2026

Enterococcus faecalis GP1764 induces an early differential gene expression in the intestine on key pathways related to cellular immune response and gut barrier function in chickens.

The aim of the present study was to elucidate the mode of action of Enterococcus faecalis GP1764 in improving performance traits during the starter phase by analyzing genome-wide gene expression and its interaction with microbial populations in the intestine of chickens challenged with an NSP-rich diet. At day 7, microbiota populations from ileal and cecal contents and transcriptomics from jejunal and cecal mucosa were analyzed between Control (Ctrl) and Enterococcus faecalis GP1764 (EntF) groups. Results from microbiota analysis demonstrated that EntF shifted &#x3b2;-diversity indices in ileum (neutral (p= 0.006) and phylogenetic (p= 0.006)) and caecum (phylogenetic (p= 0.017)). Transcriptomics revealed 43 differentially expressed genes for EntF vs. Ctrl in the jejunal mucosa. Of these, MHCY-36 (MHC-I-Related), RAG2 and MUC19-like genes were upregulated in EntF vs. Ctrl, protein-coding genes with immunomodulatory capacities as supported by GSEA and Cytoscape-ClueGo pathway analyses. Results suggest an intestinal immunomodulation induced through presentation of B vitamins metabolites, synthetized by EntF, to an undescribed subset of innate-like unconventional T lymphocytes in chickens, similar to MAIT cells in mammals. These cells could contribute to antibacterial responses and repair of damaged barrier tissue after inflammatory processes. The upregulation of the MUC19-like gene expression observed in the jejunal mucosa can protect gut integrity via the promotion of mucus production by goblet cells. Finally, RAG2, involved in V(D)J coding segments recombination in B- and T-cells may provide a greater recognition of foreign invaders, allowing the animals to efficiently fight against pathogenic infections. Collectively, these results suggest an important role of EntF in promoting the capacity of animals to rapidly act against pathogenic challenges, herein, inducing resilience towards dietary ingredients with anti-nutritional activity that impart moderate inflammation in chickens.

Enterococcus faecalis↗
PubMed2025

PseudotimeDE-fast: fast testing of differential gene expression along cell pseudotime.

SUMMARY: Identifying differentially expressed (DE) genes along cell pseudotime is crucial for understanding dynamic biological processes captured by single-cell RNA sequencing. However, existing DE methods either produce invalid P-values by ignoring the uncertainty in pseudotime inference or struggle to scale with the growing size of modern datasets. To address these limitations, we introduce PseudotimeDE-fast, a scalable method for detecting DE genes along pseudotime with well-calibrated P-values. Through comprehensive simulations and real-data analyses, we demonstrate that PseudotimeDE-fast delivers comparable or superior performance to existing approaches while offering substantial improvements in computational efficiency. AVAILABILITY AND IMPLEMENTATION: PseudotimeDE-fast is implemented in R with Rcpp acceleration and released under the MIT license. The source code is available at: https://github.com/dsong-lab/PseudotimeDE.

Single-Cell Analysis↗
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)↗
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&#xa0;<&#xa0;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

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↗
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↗
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↗
PubMed1976

Differential gene expression in multilocus isozyme systmes of the developing green sunfish.

The patterns of expression of eight multilocous isozyme systems were investigated in the differentiated adult tissues and the early embryonic stages (0-210 hours after fertilization) of the green sunfish, Lepomis cyanellus. Enzymes encoded by approximately 23 gene loci were resolved by starch-gel electrophoresis and detected by specific histochemical staining. The developmental patterns of these isozyme systems appear to be the result of the diffential expression of the multiple gene loci. Isozymic forms of glucoseophosphate isomerase (GPI-A2), malate dehydrogenase (MDH-A2), and creatine kinase (CK-C2) were present in most differentiated tissues, in the unfertilized eggs, and in all stages of embryonic development. Closely homologous forms of these isozymes (GPI-B2, MDH-B2, and CK-A2) were expressed predominantly in skeletal muscle and were first detected at around the time of hatching (38-42 hours). The similar temporal and spatial patterns of gene expressions for the GPI, LDH, MDH, and CK loci suggest that the duplicates loci encoding enzymes, diverged in their regulation to patterns of differential gene expression which are similar for each enzyme system.

Adenylate Kinase↗
PubMed2025

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 for current ST data generation techniques restricts the large-scale application of ST. Consequently, maximization of the use of available resources to achieve robust statistical power for ST data is a pressing need. One fundamental question in ST analysis is detection of differentially expressed genes (DEGs) under different conditions using ST data. Such DEG analyses are performed frequently, but their power calculations are rarely discussed in the literature. To address this gap, we developed PoweREST, a power estimation tool designed to support the power calculation for DEG detection with 10X Genomics Visium data. PoweREST enables power estimation both before any ST experiments and 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 that allows users to interactively calculate and visualize study power along with relevant parameters.

Gene Expression Profiling↗
PubMed1979

Hormonal control of gene expression: differential activation of rat bone marrow RNA polymerases by erythropoietin and testosterone.

Hormones play a role in the regulation of gene expression by inducing changes in enzyme patterns in target cells mediated by the synthesis of specific RNA molecules. Erythropoiesis has been used as a system for studying the molecular mechanism of regulation of gene action by means of two hormones: erythropoietin and testosterone. Experiments designed to correlate the biochemical action of both hormones on rat marrow cells are herein reported. Both factors seems to act at different biochemical and citological levels. Erythropoietin triggers the erythropoietic process acting on the erythropoietin sensitive cells (ESC), in which the hormone induces the synthesis of a high molecular weight RNA, which is the precursor of a functional 9 S messenger RNA. Testosterone seems to act on polychromatophilic erythroblasts, in which the synthesis of ribosomal RNA or its precursor is stimulated. The steroid enhances the nuclear ribonuclease activity, which could represent a control mechanism for the processing (maturation) of high molecular weight RNAs. The incorporation of 3H-GTP and 3H-UTP into RNA by isolated rat bone marrow nuclei is stimulated by erythropoietin and testosterone. Using alpha-amanitine and different ionic strength conditions it was found that erythropoietin enhances preferentially RNA polymerase II activity while testosterone increases RNA polymerase I activity. It is postulated that erythropoietin and testosterone act synergically to create the biochemical machinery for hemoglobin synthesis, the macromolecule that characterizes the erythropoietic process.

Amanitins↗
PubMed2025

Systematic evaluation of metatranscriptomic differential gene expression in silico, in vitro, and in vivo enables elucidation of inter-species cross-feeding.

Metatranscriptomic (MTX) sequencing quantifies gene expression from the collective genomes of microbial communities (microbiomes), enabling assessment of functional activity rather than functional potential. While differential expression testing is instrumental to RNA-sequencing analysis, current metatranscriptomic approaches have been benchmarked only on simulated data and not under real operating conditions, resulting in a lack of standard practices. Here, we evaluate the performance of statistical differential expression methods on both simulated datasets and data collected from real bacterial 'mock communities' designed for this purpose. We assess the robustness of individual methods to organisms' low relative abundance, differential abundance, low prevalence, and transcription rate changes, showing that no existing methods perform adequately across all confounding conditions. We then apply the same approaches to metatranscriptomic datasets generated from gnotobiotic mice colonized with defined consortia of human bacterial strains and show that the method nominated by our mock community comparisons successfully inferred cross-feeding dynamics which were validated in vitro. We conclude that MTX method benchmarking on real, not simulated, datasets can and should optimize model implementation, enabling inference and validation of cross-feeding and other inter-species and host-microbe dynamics from in vivo studies.

Journal Article↗
PubMed1977

Influence of 9-beta-D-arabinofuranosyladenine on total protein synthesis and on differential gene expression of unique proteins in the rodent malarial parasite Plasmodium berghei.

The antibiotic 9-beta-D-arabinofuranosyladenine is a drug with a broad spectrum of activity against animal viruses, with little or no effect on mammalian cells, when administered in vivo or in vitro. Here we report that the antibiotic markedly inhibited the incorporation of [35S]methionine into malarial protein. Inhibition was apparent when the parasites were either exposed to the drug in vivo during the course of infection or incubated with the drug in vitro. Moreover, the antibiotic induced pronounced changes in the spectrum of proteins synthesized. Some proteins that are prominently apparent in the control disappear from the drug-treated parasites; others specific for drug-treated parasites appear, indicating changes in the commitment for gene expression as manifested by the appearance of the final protein product. Proteins synthesized were analyzed by two-dimensional polyacrylamide gels; the first dimension used isoelectric focusing in cylinder gels and the second dimension used electrophoresis in a lithium dodecyl sulfate slab gel. Proteins were visualized by radioautography.

Animals↗
PubMed1977

Effects of cytosine arabinoside on differential gene expression in embryonic neural retina. II. Immunochemical studies on the accumulation of glutamine synthetase.

Cytosine arabinoside (Ara-C) elicits a significant increase in the level of the enzyme glutamine synthetase (GS) while it markedly reduces overall RNA and protein synthesis in cultures of embryonic chick neural retina. This increase was analyzed by radioimmunochemical procedures and compared with the induction of GS by hydrocortisone (HC). Accumulation of GS in Ara-C-treated retinas was found to be due to de novo synthesis of the enzyme; however, unlike the induction of GS by HC, Ara-C caused no measurable increase in the rate of GS synthesis. The results indicate that Ara-C facilitates GS accumulation largely by preventing degradation of the enzyme. Even though Ara-C inhibits the bulk of RNA synthesis in the retina, it does not stop the formation of GS-specific RNA templates. However, the progressive accumulation of these templates does not result in an increased rate of GS synthesis unless Ara-C is withdrawn from such cultures under suitable experimental conditions. Thus, it is suggested that the continuous presence of Ara-C imposes a reversible hindrance at the translational level which limits the rate of GS synthesis. The results demonstrate that the increase in retinal GS elicited by Ara-C is achieved through mechanisms which are quite different from those involved in the hydrocortisone-mediated induction of this enzyme.

Animals↗
PubMed2025

Structural basis of differential gene expression at eQTLs loci from high-resolution ensemble models of 3D single-cell chromatin conformations.

MOTIVATION: Techniques such as high-throughput chromosome conformation capture (Hi-C) have provided a wealth of information on nucleus organization and genome important for understanding gene expression regulation. Genome-Wide Association Studies have identified numerous loci associated with complex traits. Expression quantitative trait loci (eQTL) studies have further linked the genetic variants to alteration in expression levels of associated target genes across individuals. However, the functional roles of many eQTLs in noncoding regions remain unclear. Current joint analyses of Hi-C and eQTLs data lack advanced computational tools, limiting what can be learned from these data. RESULTS: We developed a computational method for simultaneous analysis of Hi-C and eQTL data, capable of identifying a small set of nonrandom interactions from all Hi-C interactions. Using these nonrandom interactions, we reconstructed large ensembles (&#xd7;105) of high-resolution single-cell 3D chromatin conformations with thorough sampling, accurately replicating Hi-C measurements. Our results revealed many-body interactions in chromatin conformation at the single-cell level within eQTL loci, providing a detailed view of how 3D chromatin structures form the physical foundation for gene regulation, including how genetic variants of eQTLs affect the expression of associated eGenes. Furthermore, our method can deconvolve chromatin heterogeneity and investigate the spatial associations of eQTLs and eGenes at subpopulation level, revealing their regulatory impacts on gene expression. Together, ensemble modeling of thoroughly sampled single-cell chromatin conformations combined with eQTL data, helps decipher how 3D chromatin structures provide the physical basis for gene regulation, expression control, and aid in understanding the overall structure-function relationships of genome organization. AVAILABILITY AND IMPLEMENTATION: It is available at https://github.com/uic-liang-lab/3DChromFolding-eQTL-Loci.

Quantitative Trait Loci↗
PubMed2026

A Computational Workflow for Prioritizing Microbial Metabolite-Associated Host Genes in Constipation-Predominant Irritable Bowel Syndrome.

No standardized computational pipeline exists for systematically prioritizing microbial metabolite-associated host genes and protein-ligand complexes from publicly available chemical, genomic, and structural databases. This article describes an eight-stage workflow that accepts a user-defined set of gut microbiota-derived metabolites and produces a ranked shortlist of candidate metabolite-associated host genes, enriched biological pathways, and structurally prioritized protein-ligand complexes for experimental follow-up. The pipeline integrates (i) chemoinformatic metabolite profiling; (ii) multi-database candidate target prediction using protein-chemical interaction and ligand-based target-prediction tool and a molecular docking program; (iii) differential gene expression analysis of publicly available transcriptomic data; (iv) target-differentially expressed gene overlap; (v) protein-protein interaction network construction and pathway enrichment; (vi) molecular docking with a molecular docking program; (vii) 200 ns molecular dynamics simulation using a molecular dynamics engine with a protein force field used for molecular dynamics simulations; and (viii) MM-PBSA binding free-energy estimation. As a worked example, nine gut microbiota-derived or microbiota-modified metabolites representing short-chain fatty acids, bile acids, tryptophan-derived metabolites, and urolithin A were processed using the public IBS-C rectal mucosal transcriptomic dataset GSE36701. The workflow ranked 17 unique predicted metabolite-associated genes that were differentially expressed in this dataset. Docking, molecular dynamics simulation, and MM-PBSA analyses structurally prioritized five metabolite-protein complexes: lithocholic acid-VDR, lithocholic acid-NR1H4/FXR, ursodeoxycholic acid-NR1H4/FXR, tryptamine-HTR2A (simulated in an explicit 1-Palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) lipid bilayer), and urolithin A-CASP3. The protocol is designed to be adaptable to other metabolite sets, disease transcriptomic datasets, and target classes; all outputs are hypothesis-generating computational predictions that require independent transcriptomic replication, protein-level validation, and functional ligand-response assays before causal or therapeutic conclusions can be drawn.

Irritable Bowel Syndrome↗