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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

Protocol to predict gene expression from transcriptomic data using PREDICT.

Linking DNA sequence variation to context-specific transcriptional programs is a critical challenge in regulatory genomics, especially for non-model organisms. Here, we present PREDICT, a modular Python package for discovering cis-regulatory elements and transcription factor binding motifs. We describe steps to identify enriched k-mers from differentially expressed genes, map them to known motifs, quantify their impact on gene expression, and visualize motif co-occurrences. PREDICT provides a robust, k-mer-based approach to uncover regulatory logic in diverse genomic systems. For complete details on the use and execution of this protocol, please refer to Yen et al. and Liu et al.1,2.

Gene Expression Profiling↗

PubMed2026

transfactor: transcription factor activity estimation via probabilistic gene expression deconvolution.

Gene expression is a primary modality being studied to differentiate between biological cells. Contemporary single-cell studies simultaneously measure genome-wide transcription levels for thousands of individual cells in a single experiment. While the characterization of cell population differences has often occurred through differential gene expression analysis, tiny effect sizes become statistically significant when thousands of cells are available for each population, compromising biological interpretation. Moreover, these large studies have spurred the development of methods to infer gene regulatory networks (GRNs) directly from the data, and GRN databases are becoming more comprehensive. In this work, we propose a statistical model for gene expression measures and an inference method that leverage GRNs to deconvolve transcription factor (TF) activity from gene expression, by probabilistically assigning mRNA molecules to TFs. This shifts the paradigm from investigating gene expression differences to regulatory differences at the level of TF activity, aiding interpretation and allowing prioritization of a limited number of TFs responsible for significant contributions to the observed gene expression differences. The inferred TF activities result in intuitive prioritization of TFs in terms of the (difference in) estimated number of molecules they produce, in contrast to other widely used methods relying on arbitrary enrichment scores. Our model allows the incorporation of prior information on the regulatory potential between each TF and target gene and is able to deal with both repressing and activating interactions. We compare our approach to other TF activity estimation methods using two simulation experiments and two case studies. Single-cell RNA-sequencing; TF activity; bioinformatics; GRN.

Transcription Factors↗

PubMed2025

Exploring Potential Causality and Molecular Mechanisms between Heart Failure and Renal Failure: Insights from Mendelian Randomization Studies, the MIMIC-IV Database and the Gene Expression Omnibus Database.

UNLABELLED: Introduction: Heart failure (HF) and renal failure (RF) frequently coexist as cardiorenal syndrome, but their underlying causal mechanisms remain poorly defined. METHODS: This study applied Mendelian randomization (MR) using genome-wide association study (GWAS) datasets to investigate the causal effect of HF on RF. The inverse variance weighted method assessed causality, and summary-data-based MR (SMR) was used to identify therapeutic targets. Additional analyses included 211 gut microbiota traits and 1,400 serum metabolites. Validation was performed using the MIMIC-IV database. Transcriptomic data were analyzed to identify differentially expressed genes (DEGs) and key transcription factors (TFs). RESULTS: This study found that HF significantly increases the risk of RF (OR = 1.54, 95% CI: 1.07-2.23, p = 0.020). SMR analysis identified SURF1 and MAP3K11 as potential therapeutic targets for HF and RF. One gut microbiota genus and one serum metabolite showed causal associations with both diseases. MIMIC-IV data supported the HF-RF association (OR = 2.94, 95% CI: 2.81-3.07, p < 0.001). A total of 11 overlapping DEGs were enriched in the MAPK cascade, with RELA identified as a key TF. CONCLUSION: This study provides genetic and molecular evidence supporting a causal role of HF in RF, highlighting microbial, metabolic, and immune mechanisms as potential therapeutic targets. .

Humans↗

PubMed2026

Chromosome engineering to correct a complex rearrangement on Chromosome 8 reveals the effects of 8p syndrome on gene expression and neural differentiation.

Chromosomal rearrangements on the short arm of Chromosome 8 cause 8p syndrome, a rare developmental disorder characterized by neurodevelopmental delays, epilepsy, and cardiac abnormalities. Although significant progress has been made in managing the symptoms of 8p syndrome and other conditions caused by large-scale chromosomal aneuploidies, no therapeutic approach has yet been demonstrated to target the underlying disease-causing chromosome. Here, we establish a two-step approach to eliminate the abnormal copy of Chromosome 8 and restore euploidy in cells derived from an individual with a complex rearrangement of Chromosome 8p. Transcriptomic analysis revealed 361 differentially expressed genes between the proband and the euploid revertant, highlighting genes both within and outside the 8p region that may contribute to 8p syndrome pathology. Furthermore, we demonstrate that the proband exhibits a significant defect in neural differentiation that could be partially rescued by treatment with small-molecule inhibitors of cell death. Our work demonstrates the feasibility of using chromosome engineering to correct complex aneuploidies in vitro and establishes a platform to further dissect the pathophysiology of 8p syndrome and other conditions caused by chromosomal rearrangements.

Humans↗

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 < 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

Integrating histology and spatial transcriptomics via multimodal transformers and contrastive representation learning for accurate gene expression prediction.

Predicting spatial gene expression from Histological images is a fundamental task in understanding tissue organization and molecular phenotypes. However, existing methods often rely on single-model representations or lack effective alignment between image and transcriptomic features. To address these limitations, we propose a unified multimodal learning framework that integrates histological imaging and spatial transcriptomics through a shared latent representation space. Specifically, histological H&E images are encoded by a ResNet50-based convolutional stem and a MobileViT Transformer backbone to extract hierarchical visual representations. Both modalities are projected into a shared latent space via linear-GELU-dropout transformation blocks, enabling cross-modal alignment through a contrastive learning objective that maximizes agreement between the corresponding image and the spot embeddings. Experimental results on the 10x Genomics Visium dataset of human liver tissue demonstrate that MViTGene achieves significantly higher prediction accuracy than existing methods across multiple gene subsets, with improvements of 20%, 33%, and 12% in predicting marker genes, highly expressed genes, and highly variable genes, respectively. The significant improvement in relevance indicates that the model can more accurately capture the true correspondence between tissue morphology and gene expression, therefore enabling more reliable biological interpretation. It provides a computational tool for high-throughput spatial gene expression prediction that balances performance and interpretability.

Humans↗

PubMed2025

Integrating mutation, copy number, and gene expression data to identify driver genes of recurrent chromosome-arm losses.

Aneuploidy is a hallmark of cancer, yet the genes driving recurrent chromosome-arm losses remain largely unknown. We present a systematic framework integrating mutation, copy number, and gene expression data to identify candidate driver genes of cancer type-specific recurrent chromosome-arm losses across 20 cancer types, using ∼7,500 tumors from The Cancer Genome Atlas. By analyzing focal deletions and point mutations that co-occur, or are mutually exclusive, with chromosome-arm losses, we pinpoint 322 candidate drivers associated with 159 recurring events. Our approach identifies known aneuploidy drivers such as TP53 and PTEN, while revealing multiple additional candidates, including tumor suppressors not previously linked to aneuploidy. We leverage expression changes associated with chromosome-arm losses to propose cancer-promoting pathway-level alterations. Integrating these findings highlights key candidate drivers that underlie the observed expression alterations, reinforcing their biological relevance. We provide a comprehensive catalog of candidate driver genes for recurrently lost chromosome-arms in human cancer.

Humans↗

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↗

PubMed2024

Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast.

Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein coexpression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship.

Saccharomyces cerevisiae↗

PubMed2025

The offonome reveals on and off states of gene expression near the detection limit of RNA-seq.

RNA-seq, widely used for gene expression profiling, provides nucleotide level genome coverage and summary gene expression values. Generally, low-expressed genes are ignored due to their unfavorable signal-to-noise ratio, however, these genes may offer crucial information, such as detecting rare cells in bulk tissues. In this study, we applied an approach that transforms the expression levels of low-expressed genes into a robust dichotomized on/off state by leveraging similarities in transcript coverage shape. Applied to three human cancer cohorts from the Cancer Genome Atlas (TCGA), chosen based on tissue morphology and anatomic site, we identified genes, the "offonome" near the detection limit, consistently or occasionally off across samples. Genes in the offonome spectrum proved useful for supervised and unsupervised applications, including characterizing oncogenic pathways, and identifying rare populations of cells in bulk tissue. Interrogating the offonome is relevant to bulk tumor analyses like TCGA, potentially expediting gene investigation in low-input situations like single cell RNA-seq.

Humans↗

PubMed2011

Identification of transcriptome SNPs between Xiphophorus lines and species for assessing allele specific gene expression within F₁ interspecies hybrids.

Variations in gene expression are essential for the evolution of novel phenotypes and for speciation. Studying allelic specific gene expression (ASGE) within interspecies hybrids provides a unique opportunity to reveal underlying mechanisms of genetic variation. Using Xiphophorus interspecies hybrid fishes and high-throughput next generation sequencing technology, we were able to assess variations between two closely related vertebrate species, Xiphophorus maculatus and Xiphophorus couchianus, and their F(1) interspecies hybrids. We constructed transcriptome-wide SNP polymorphism sets between two highly inbred X. maculatus lines (JP 163 A and B), and between X. maculatus and a second species, X. couchianus. The X. maculatus JP 163 A and B parental lines have been separated in the laboratory for ≈70 years and we were able to identify SNPs at a resolution of 1 SNP per 49 kb of transcriptome. In contrast, SNP polymorphisms between X. couchianus and X. maculatus species, which diverged ≈5-10 million years ago, were identified about every 700 bp. Using 6524 transcripts with identified SNPs between the two parental species (X. maculatus and X. couchianus), we mapped RNA-seq reads to determine ASGE within F(1) interspecies hybrids. We developed an in silico X. couchianus transcriptome by replacing 90,788 SNP bases for X. maculatus transcriptome with the consensus X. couchianus SNP bases and provide evidence that this procedure overcomes read mapping biases. Employment of the in silico reference transcriptome and tolerating 5 mismatches during read mapping allow direct assessment of ASGE in the F(1) interspecies hybrids. Overall, these results show that Xiphophorus is a tractable vertebrate experimental model to investigate how genetic variations that occur during speciation may affect gene interactions and the regulation of gene expression.

Alleles↗

PubMed2025

The release of sexual conflict after sex loss is associated with evolutionary changes in gene expression.

Sexual conflict can arise because males and females, while sharing most of their genome, can have different phenotypic optima. Sexually dimorphic gene expression may help reduce conflict, but the expression of many genes may remain sub-optimal owing to unresolved tensions between the sexes. Asexual lineages lack such conflict, making them relevant models for understanding the extent to which sexual conflict influences gene expression. We investigate the evolution of sexual conflict subsequent to sex loss by contrasting the gene expression patterns of sexual and asexual lineages in the pea aphid Acyrthosiphon pisum. Although asexual lineages of this aphid produce a small number of males in autumn, their mating opportunities are limited because of geographic isolation between sexual and asexual lineages. Therefore, gene expression in parthenogenetic females of asexual lineages is no longer constrained by that of other morphs. We found that the expression of genes in males from asexual lineages tended towards the parthenogenetic female optimum, in agreement with theoretical predictions. Surprisingly, males and parthenogenetic females of asexual lineages overexpressed genes normally found in the ovaries and testes of sexual morphs. These changes in gene expression in asexual lineages may arise from the relaxation of selection or the dysregulation of gene networks otherwise used in sexual lineages.

Animals↗

PubMed2025

Perinatal Lead (Pb) Exposure Increases Mouse Embryonic Weight and Alters Neuronal Gene Expression.

Acute and chronic exposure to lead (Pb) during pregnancy is linked to adverse health outcomes, including delayed neurodevelopment in offspring. However, the pathways by which Pb exposure influences long-term health remain poorly understood. To address this, we measured the effects of perinatal Pb exposure on gene expression including imprinted genes, X-linked genes, and sexually dimorphic genes. Female mice were given control or Pb acetate dosed (32 ppm) drinking water two weeks prior to timed mating until embryonic day (E)10-12, upon which whole embryos were collected, weighed, and sexed at E13-15. From a subset of embryo heads (n≥9 per sex per group), we extracted and sequenced RNA. We used linear regression to assess Pb impacts on embryonic weight and gene expression across all mice and stratified by sex. Among the differentially expressed genes, we identified significantly enriched pathways. Pb-exposed embryos weighed more than controls (p=0.007), across both sexes. Collectively, we identified 2,920 differentially expressed genes (FDR<0.05), including 31 imprinted genes and 120 X-linked genes upon Pb exposure. Pb exposure altered expression in gene pathways related to neuronal structure and function as well as sexually dimorphic genes (44 for females; 76 for males). These findings highlight perinatal Pb-linked alterations that may drive later-life health outcomes.

DOHaD↗

PubMed2026

Predicted brain-regional gene expression patterns in individuals living with Alzheimer's disease.

Studying brain gene expression in Alzheimer's Disease (AD) remains difficult as postmortem brain is difficult to access, cannot be used to guide donor treatment, may be confounded by environmental factors before and after death, and is difficult to link to early AD states or disease progression. To circumvent these limitations, several studies have tested blood transcriptome biomarkers for AD. However, gene-expression levels in the blood have limited correlation with those in the brain. To evaluate the potential of monitoring Alzheimer's progression with peripheral data, we used transcriptome-imputation to identify brain-region-specific AD-associated gene-expression differences in cohorts with blood-based transcriptome data. This approach provides a high-resolution image of AD-associated molecular differences in the brains of individuals actively living with disease. We analyzed eight AD studies (777 AD cases, 779 cognitively unimpaired controls), imputing transcriptomes in 10 brain regions via the Brain Gene Expression and Network Imputation Engine (BrainGENIE). Hundreds of differentially expressed genes (DEGs) associated with AD were identified in nine brain regions, with anterior cingulate cortex and amygdala showing the most differential expression. AD-associated genes were enriched in pathways such as proteostasis, mitochondrial dysfunction, and immune activation. We observed significant yet moderate concordance between imputed AD-associated changes and those directly measured in the dorsolateral prefrontal cortex and cerebellum. These transcriptomic changes can guide future in vitro studies focused on pathogenesis or be targets of novel therapeutic development. In conclusion, we demonstrated the scope and utility of brain expression imputation from the peripheral transcriptome, laying the groundwork for biomarker discovery and prospective AD studies.

Alzheimer Disease↗

PubMed2018

Convergent evolution of gene expression in two high-toothed stickleback populations.

Changes in developmental gene regulatory networks enable evolved changes in morphology. These changes can be in cis regulatory elements that act in an allele-specific manner, or changes to the overall trans regulatory environment that interacts with cis regulatory sequences. Here we address several questions about the evolution of gene expression accompanying a convergently evolved constructive morphological trait, increases in tooth number in two independently derived freshwater populations of threespine stickleback fish (Gasterosteus aculeatus). Are convergently evolved cis and/or trans changes in gene expression associated with convergently evolved morphological evolution? Do cis or trans regulatory changes contribute more to gene expression changes accompanying an evolved morphological gain trait? Transcriptome data from dental tissue of ancestral low-toothed and two independently derived high-toothed stickleback populations revealed significantly shared gene expression changes that have convergently evolved in the two high-toothed populations. Comparing cis and trans regulatory changes using phased gene expression data from F1 hybrids, we found that trans regulatory changes were predominant and more likely to be shared among both high-toothed populations. In contrast, while cis regulatory changes have evolved in both high-toothed populations, overall these changes were distinct and not shared among high-toothed populations. Together these data suggest that a convergently evolved trait can occur through genetically distinct regulatory changes that converge on similar trans regulatory environments.

Alleles↗

PubMed2009

Similarities and differences between smoking-related gene expression in nasal and bronchial epithelium.

Previous studies have shown that physiological responses to cigarette smoke can be detected via bronchial airway epithelium gene expression profiling and that heterogeneity in this gene expression response to smoking is associated with lung cancer. In this study, we sought to determine the similarity of the effects of tobacco smoke throughout the respiratory tract by determining patterns of smoking-related gene expression in paired nasal and bronchial epithelial brushings collected from 14 healthy nonsmokers and 13 healthy current smokers. Using whole genome expression arrays, we identified 119 genes whose expression was affected by smoking similarly in both bronchial and nasal epithelium, including genes related to detoxification, oxidative stress, and wound healing. While the vast majority of smoking-related gene expression changes occur in both bronchial and nasal epithelium, we also identified 27 genes whose expression was affected by smoking more dramatically in bronchial epithelium than nasal epithelium. Both common and site-specific smoking-related gene expression profiles were validated using independent microarray datasets. Differential expression of select genes was also confirmed by RT-PCR. That smoking induces largely similar gene expression changes in both nasal and bronchial epithelium suggests that the consequences of cigarette smoke exposure can be measured in tissues throughout the respiratory tract. Our findings suggest that nasal epithelial gene expression may serve as a relatively noninvasive surrogate to measure physiological responses to cigarette smoke and/or other inhaled exposures in large-scale epidemiological studies.

Adult↗

PubMed2026

Genotype-dependent DNA methylation patterns are negatively associated with allelic variation rather than heat-induced gene expression in two contrasting potato genotypes.

Potato (Solanum tuberosum L.) is an important food crop that is sensitive to high temperatures, which cause major changes in the transcriptome and a reduction in yield. In several plant species, DNA methylation has been reported to influence gene expression, particularly under abiotic stress conditions. However, the role of DNA methylation in regulating gene expression in heat-tolerant and heat-sensitive potato genotypes is still poorly understood. In this study, we conducted genome-wide DNA methylome and transcriptome analyses of leaves from two contrasting potato cultivars, Annabelle (moderately heat-tolerant) and Camel (heat-sensitive), before and after heat stress (HS). Genome-wide differential methylation analysis revealed that most identified differentially methylated regions (DMRs) were constitutive, reflecting variation between cultivars rather than being induced by HS. While thousands of heat-responsive differentially expressed genes (DEGs) were identified, only a small fraction coincided with heat-induced DMRs. Despite substantial constitutive DNA methylation and transcriptome differences between the cultivars, we found no consistent association between DMRs and DEGs, indicating that DNA methylation does not play a widespread direct regulatory role in gene expression. Surprisingly, hypermethylated genomic regions were associated with lower alternative allele frequencies, whereas hypomethylated regions showed the opposite trend. These findings indicate that the potato DNA methylome is largely stable under HS and that constitutive DNA methylation variation contributes rather to genetic diversity than to the direct regulation of gene expression.

DNA Methylation↗