Search Results (2,204)

Background: Polycystic ovary syndrome (PCOS) is a complex and multifactorial disorder affecting reproductive, endocrine, and metabolic functions in women of reproductive age. While environmental and lifestyle factors play a role, increasing evidence highlights the contribution of genetic and epigenetic mechanisms to its pathogenesis. Objective: This narrative review aims to provide an updated overview of the current evidence regarding the role of genetic variants, gene expression patterns, and epigenetic modifications in the etiopathogenesis of PCOS, with a focus on their impact on ovarian function, fertility, and systemic alterations. Methods: A comprehensive search was conducted across MEDLINE, EMBASE, PubMed, Web of Science, and the Cochrane Library using MeSH terms including “PCOS”, “Genes involved in PCOS”, and “Etiopathogenesis of PCOS” from January 2015 to June 2025. The selection process followed the SANRA quality criteria for narrative reviews. Seventeen studies published in English were included, focusing on original data regarding gene expression, polymorphisms, and epigenetic changes associated with PCOS. Results: The studies analyzed revealed a wide array of molecular alterations in PCOS, including the dysregulation of SIRT and estrogen receptor genes, altered transcriptome profiles in cumulus cells, and the involvement of long non-coding RNAs and circular RNAs in granulosa cell function and endometrial receptivity. Epigenetic mechanisms such as the DNA methylation of TGF-β1 and inflammation-related signaling pathways (e.g., TLR4/NF-κB/NLRP3) were also implicated. Some genetic variants—particularly in DENND1A, THADA, and MTNR1B—exhibit signs of positive evolutionary selection, suggesting possible ancestral adaptive roles. Conclusions: PCOS is increasingly recognized as a syndrome with a strong genetic and epigenetic background. The identification of specific molecular signatures holds promise for the development of personalized diagnostic markers and therapeutic targets. Future research should focus on large-scale genomic studies and functional validation to better understand gene–environment interactions and their influence on phenotypic variability in PCOS. Full article

Background: Accumulating evidence indicates that SARS-CoV-2 infection results in long-term multiorgan complications, with the kidney being a primary target. This study aimed to characterize the long-term transcriptomic changes in the kidney following coronavirus infection using a murine model of MHV-1-induced SARS-like illness and to evaluate the therapeutic efficacy of SPIKENET (SPK). Methods: A/J mice were infected with MHV-1. Renal tissues were collected and subjected to immunofluorescence analysis and Next Generation RNA Sequencing to identify differentially expressed genes associated with acute and chronic infection. Bioinformatic analyses, including PCA, volcano plots, and GO/KEGG pathway enrichment, were performed. A separate cohort received SPK treatment, and comparative transcriptomic profiling was conducted. Gene expression profile was further confirmed using real-time PCR. Results: Acute infection showed the upregulation of genes involved in inflammation and fibrosis. Long-term MHV-1 infection led to the sustained upregulation of genes involved in muscle regeneration, cytoskeletal remodeling, and fibrotic responses. Notably, both expression and variability of SLC22 and SLC22A8, key proximal tubule transporters, were reduced, suggesting a loss of segment-specific identity. Further, SLC12A1, a critical regulator of sodium reabsorption and blood pressure, was downregulated and is associated with the onset of polyuria and hydronephrosis. SLC transporters exhibited expression patterns consistent with tubular dysfunction and inflammation. These findings suggest aberrant activation of myogenic pathways and structural proteins in renal tissues, consistent with a pro-fibrotic phenotype. In contrast, SPK treatment reversed the expression of most genes, thereby restoring the gene profiles to those observed in control mice. Conclusions: MHV-1-induced long COVID is associated with persistent transcriptional reprogramming in the kidney, indicative of chronic inflammation, cytoskeletal dysregulation, and fibrogenesis. SPK demonstrates robust therapeutic potential by normalizing these molecular signatures and preventing long-term renal damage. These findings underscore the relevance of the MHV-1 model and support further investigation of SPK as a candidate therapy for COVID-19-associated renal sequelae. Full article

Furunculosis caused by Aeromonas salmonicida poses a significant challenge to the sustainable production of maraena whitefish (Coregonus maraena). This case report outlines a multi-year disease management strategy at a European whitefish facility with two production departments, each specialising in different life-cycle stages. Recurrent outbreaks of A. salmonicida necessitated the development of effective vaccination protocols. Herd-specific immersion vaccines failed to confer protection, while injectable formulations with plant-based adjuvants caused severe adverse reactions and mortality rates exceeding 30%. In contrast, the bivalent vaccine Alpha Ject 3000, containing inactivated A. salmonicida and Vibrio anguillarum with a mineral oil adjuvant, yielded high tolerability and durable protection in over one million whitefish. Post-vaccination mortality remained low (3.3%), aligning with industry benchmarks, and furunculosis-related losses were fully prevented in both departments. Transcriptomic profiling of immune-relevant tissues revealed distinct gene expression signatures depending on vaccine type and time post-vaccination. Both the herd-specific vaccine and Alpha Ject 3000 induced the expression of immunoglobulin and inflammatory markers in the spleen, contrasted by reduced immunoglobulin transcript levels in the gills and head kidney together with the downregulated expression of B-cell markers. These results demonstrate that an optimised injectable vaccination strategy can significantly improve health outcomes and disease resilience in maraena whitefish aquaculture. Full article

Background/Objective: Exertional rhabdomyolysis (ER) is primarily driven by mechanical stress on muscles during strenuous or unaccustomed exercise, often exacerbated by environmental factors like heat and dehydration. While the general cellular pathway involving energy depletion and calcium overload is understood in horse ER models, the underlying mechanisms specific to the ER are not universally known within humans. This study aimed to evaluate whether patients with ER exhibited transcriptional signatures that were significantly different from those of healthy individuals. Methods: This study utilized RNA sequencing on skeletal muscle samples from 19 human patients with ER history, collected at a minimum of six months after the most recent ER event, and eight healthy controls to investigate the transcriptomic landscape of ER. To identify any alterations in biological processes between the case and control groups, functional pathway analyses were conducted. Results: Functional pathway enrichment analyses of differentially expressed genes revealed strong suppression of mitochondrial function. This suppression included the “aerobic electron transport chain” and “oxidative phosphorylation” pathways, indicating impaired energy production. Conversely, there was an upregulation of genes associated with adhesion and extracellular matrix-related pathways, indicating active restoration of muscle function in ER cases. Conclusions: The study demonstrated that muscle tissue exhibited signs of suppressed mitochondrial function and increased extracellular matrix development. Both of these facilitate muscle recovery within several months after an ER episode. Full article

Leather biodegradation is a complex microbial process with increasing relevance for sustainable waste management. In this study, we investigated bacterial communities responsible for the degradation of leather treated with different tanning agents (chrome, Zeolite, Biole^®) using high-throughput 16S rRNA gene sequencing and metatranscriptomic analysis. Proteobacteria, Bacteroidetes, and Patescibacteria emerged as the dominant phyla, while genera such as Acinetobacter, Pseudomonas, and Sphingopyxis were identified as key contributors to enzymatic activity and potential metal resistance. A total of 1302 enzymes were expressed across all the conditions, including 46 proteases, with endopeptidase La, endopeptidase Clp, and methionyl aminopeptidase being the most abundant. Collagen samples exhibited the highest functional diversity and total enzyme expression, whereas chrome-treated samples showed elevated protease activity, indicating selective pressure from heavy metals. Differential enzyme expression patterns were linked to both the microbial identity and tanning chemistry, revealing genus- and treatment-specific enzymatic signatures. These findings deepen our understanding of how tanning agents modulate the microbial structure and function and identify proteases with potential applications in the bioremediation and eco-innovation of leather waste processing. Full article

Posterior capsule opacification (PCO), a frequent complication of cataract surgery, arises from dysregulated wound healing and fibrotic transformation of residual lens epithelial cells. While transcriptomic and machine learning (ML) approaches have elucidated fibrosis-related pathways in other tissues, the molecular divergence between regenerative and fibrotic outcomes in the lens remains unclear. Here, we used an ex vivo chick lens injury model to simulate post-surgical conditions, collecting RNA from lenses undergoing either regenerative wound healing or fibrosis between days 1–3 post-injury. Bulk RNA sequencing data were normalized, log-transformed, and subjected to univariate filtering prior to training LASSO, SVM, and RF ML models to identify discriminatory gene signatures. Each model was independently validated using a held-out test set. Distinct gene sets were identified, including fibrosis-associated genes (VGLL3, CEBPD, MXRA7, LMNA, gga-miR-143, RF00072) and wound-healing-associated genes (HS3ST2, ID1), with several achieving perfect classification. Gene Set Enrichment Analysis revealed divergent pathway activation, including extracellular matrix remodeling, DNA replication, and spliceosome associated with fibrosis. RT-PCR in independent explants confirmed key differential expression levels. These findings demonstrate the utility of supervised ML for discovering lens-specific fibrotic and regenerative gene features and nominate biomarkers for targeted intervention to mitigate PCO. Full article

Chronic kidney disease (CKD) is a heterogeneous condition with various etiologies, including type 2 diabetes mellitus (T2D), hypertension, and autoimmune disorders. Both diabetic CKD (CKD_T2D) and non-diabetic CKD (CKD_nonT2D) share overlapping clinical features, but understanding the molecular mechanisms underlying each subtype and distinguishing diabetic from non-diabetic forms remain poorly defined. To identify differentially expressed genes (DEGs) and enriched biological pathways between CKD_T2D and CKD_nonT2D cohorts, including autoimmune (CKD_nonT2D_AI) and hypertensive (CKD_nonT2D_HT) subtypes, through integrative transcriptomic analysis. Publicly available gene expression datasets from human glomerular and tubulointerstitial kidney tissues were curated and analyzed from GEO and ArrayExpress. Differential expression analysis and Gene Set Enrichment Analysis (GSEA) were conducted to assess cohort-specific molecular signatures. A considerable overlap in DEGs was observed between CKD_T2D and CKD_nonT2D, with CKD_T2D exhibiting more extensive gene expression changes. Hypertensive-CKD shared greater transcriptomic similarity with CKD_T2D than autoimmune-CKD. Key DEGs involved in fibrosis, inflammation, and complement activation—including Tgfb1, Timp1, Cxcl6, and C1qa/B—were differentially regulated in diabetic samples, where GSEA revealed immune pathway enrichment in glomeruli and metabolic pathway enrichment in tubulointerstitium. The transcriptomic landscape of CKD_T2D reveals stronger immune and metabolic dysregulation compared to non-diabetic CKD. These findings suggest divergent pathological mechanisms and support the need for tailored therapeutic approaches. Full article

Glioblastoma (GBM) is a highly aggressive and heterogeneous brain tumor. Glioma stem-like cells (GSCs) play a central role in tumor progression, therapeutic resistance, and recurrence. Although immune cells are known to shape the GBM microenvironment, the impact of antigen-presenting-cell (APC) signature genes on tumor-intrinsic phenotypes remains underexplored. We analyzed both bulk- and single-cell RNA sequencing datasets of GBM to investigate the association between APC gene expression and tumor-cell states, including stemness and metabolic reprogramming. Signature scores were computed using curated gene sets related to APC activity, KEGG metabolic pathways, and cancer hallmark pathways. Protein–protein interaction (PPI) networks were constructed to examine the links between immune regulators and metabolic programs. The high expression of APC-related genes, such as HLA-DRA, CD74, CD80, CD86, and CIITA, was associated with lower stemness signatures and enhanced inflammatory signaling. These APC-high states (mean difference = –0.43, adjusted p < 0.001) also showed a shift in metabolic activity, with decreased oxidative phosphorylation and increased lipid and steroid metabolism. This pattern suggests coordinated changes in immune activity and metabolic status. Furthermore, TNF-α and other inflammatory markers were more highly expressed in the less stem-like tumor cells, indicating a possible role of inflammation in promoting differentiation. Our findings revealed that elevated APC gene signatures are associated with more differentiated and metabolically specialized GBM cell states. These transcriptional features may also reflect greater immunogenicity and inflammation sensitivity. The APC metabolic signature may serve as a useful biomarker to identify GBM subpopulations with reduced stemness and increased immune engagement, offering potential therapeutic implications. Full article

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, projected to affect 55% globally by 2040. Up to one-third of NAFLD patients develop non-alcoholic steatohepatitis (NASH), with 40% progressing to fibrosis. However, there are currently few reliable tools to predict disease progression. Impaired mitochondrial dynamics, characterized by dysregulated fission, fusion, and mitophagy, have emerged as key events in NAFLD pathophysiology, contributing to hepatocyte death and inflammation. This study explored the transition from steatosis to NASH through transcriptomic analyses, including data from patients with steatosis and those with NASH at different fibrosis stages. By identifying a transcriptomic signature associated with disease progression, the study revealed increased expression of genes involved in mitochondrial dynamics in NASH compared to steatosis and during NASH-related fibrosis. Histological analyses highlighted the central role of Dynamin-related protein 1 (Drp1), a dynamin GTPase essential for mitochondrial fission and mitophagy. In human liver biopsies, Drp1 expression progressively increased from NAFLD to NASH and NASH-related fibrosis and cirrhosis, predominantly in Kupffer cells. These finding suggest Drp1 is a potential driver of the transition to more severe liver damage, making it a promising biomarker for NASH development and progression and a potential therapeutic target in metabolic disorders. Full article

Prognostic markers such as overall survival (OS) and tertiary lymphoid structure (TLS) ratios, alongside diagnostic signatures like primary cancer-type classification, provide critical information for treatment selection, risk stratification, and longitudinal care planning across the oncology continuum. However, extracting these signals solely from sparse, high-dimensional multi-omics data remains a major challenge due to heterogeneity and frequent missingness in patient profiles. To address this challenge, we present SeNMo, a self-normalizing deep neural network trained on five heterogeneous omics layers—gene expression, DNA methylation, miRNA abundance, somatic mutations, and protein expression—along with the clinical variables, that learns a unified representation robust to missing modalities. Trained on more than 10,000 patient profiles across 32 tumor types from The Cancer Genome Atlas (TCGA), SeNMo provides a baseline that can be readily fine-tuned for diverse downstream tasks. On a held-out TCGA test set, the model achieved a concordance index of 0.758 for OS prediction, while external evaluation yielded 0.73 on the CPTAC lung squamous cell carcinoma cohort and 0.66 on an independent 108-patient Moffitt Cancer Center cohort. Furthermore, on Moffitt’s cohort, baseline SeNMo fine-tuned for TLS ratio prediction aligned with expert annotations (p < 0.05) and sharply separated high- versus low-TLS groups, reflecting distinct survival outcomes. Without altering the backbone, a single linear head classified primary cancer type with 99.8% accuracy across the 33 classes. By unifying diagnostic and prognostic predictions in a modality-robust architecture, SeNMo demonstrated strong performance across multiple clinically relevant tasks, including survival estimation, cancer classification, and TLS ratio prediction, highlighting its translational potential for multi-omics oncology applications. Full article

Background/Objectives: Alterations in DNA damage repair mechanisms can impair the therapeutic effectiveness of cisplatin. MicroRNAs (miRNAs), key regulators of DNA damage repair processes, have been proposed as promising biomarkers for predicting the response to platinum-based chemotherapy (CT) in non-small cell lung cancer (NSCLC). In this study, by using a bioinformatics approach, we identified six miRNAs, which were differentially expressed (DE) between NSCLC patients characterized as responders and non-responders to platinum-based CT. We further validated the differential expression of the selected miRNAs on tumor and matched normal tissues from patients with resected NSCLC. Methods: Two miRNA microarray expression datasets were retrieved from the Gene Expression Omnibus (GEO) repository, comprising a total of 69 NSCLC patients (N = 69) treated with CT and annotated data from their response to treatment. Differential expression analysis was performed using the Linear Models for Microarray Analysis (Limma) package in R to identify DE miRNAs between responders (N = 33) and non-responders (N = 36). Quantitative real-time PCR (qRT-PCR) was used to assess miRNA expression levels in clinical tissue samples (N = 20). Results: Analysis with the Limma package revealed 112 DE miRNAs between responders and non-responders. A random-effects meta-analysis further identified 24 miRNAs that were consistently up- or downregulated in at least two studies. Survival analysis using the Kaplan–Meier plotter (KM plotter) indicated that 22 of these miRNAs showed significant associations with prognosis in NSCLC. Functional and pathway enrichment analysis revealed that several of the identified miRNAs were linked to key pathways implicated in DNA damage repair, including the p53, Hippo, PI3K and TGF-β signaling pathways. We finally distinguished a six-miRNA signature consisting of miR-26a, miR-29c, miR-34a, miR-30e-5p, miR-30e-3p and miR-497, which were downregulated in non-responders and are involved in at least three DNA damage repair pathways. Comparative expression analysis on tumor and matched normal tissues from surgically treated NSCLC patients confirmed their differential expression in clinical samples. Conclusions: In summary, we identified a signature of six miRNAs that are suppressed in NSCLC and may serve as a predictor of cisplatin response in NSCLC. Full article

Background: Acute myeloid leukemia (AML) is a common and aggressive adults hematological malignancies. This study explored megakaryocyte–erythroid progenitors (MEPs) signature genes and constructed a prognostic model. Methods: Uniform manifold approximation and projection (UMAP) identified distinct cell types, with differential analysis between AML-MEP and normal MEP groups. Univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression selected biomarkers to build a risk model and nomogram for 1-, 3-, and 5-year survival prediction. Results: Ten differentially expressed genes (DEGs) related to overall survival (OS), six (AHSP, MYB, VCL, PIM1, CDK6, as well as SNHG3) were retained post-LASSO. The model exhibited excellent efficiency (the area under the curve values: 0.788, 0.77, and 0.847). Pseudotime analysis of UMAP-defined subpopulations revealed that MYB and CDK6 exert stage-specific regulatory effects during MEP differentiation, with MYB involved in early commitment and CDK6 in terminal maturation. Finally, although VCL, PIM1, CDK6, and SNHG3 showed significant associations with AML survival and prognosis, they failed to exhibit pathological differential expression in quantitative real-time polymerase chain reaction (qRT-PCR) experimental validations. In contrast, the downregulation of AHSP and upregulation of MYB in AML samples were consistently validated by both qRT-PCR and Western blotting, showing the consistency between the transcriptional level changes and protein expression of these two genes (p < 0.05). Conclusions: In summary, the integration of single-cell/transcriptome analysis with targeted expression validation using clinical samples reveals that the combined AHSP-MYB signature effectively identifies high-risk MEP-AML patients, who may benefit from early intensive therapy or targeted interventions. Full article

Background: Extremely premature neonates are at increased risk for respiratory complications, often resulting in recurrent hospitalizations during early childhood. Early identification of preterm infants at highest risk of respiratory hospitalizations could enable targeted preventive interventions. While clinical and demographic factors offer some prognostic value, integrating transcriptomic data may improve predictive accuracy. Objective: To determine whether early-life gene expression profiles can predict respiratory-related hospitalizations within the first four years of life in extremely preterm neonates. Methods: We conducted a retrospective cohort study of 58 neonates born at <32 weeks’ gestational age, using publicly available transcriptomic data from peripheral blood samples collected on days 5, 14, and 28 of life. Random forest models were trained to predict unplanned respiratory readmissions. Model performance was evaluated using sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC). Results: All three models, built using transcriptomic data from days 5, 14, and 28, demonstrated strong predictive performance (AUC = 0.90), though confidence intervals were wide due to small sample size. We identified 31 genes and eight biological pathways that were differentially expressed between preterm neonates with and without subsequent respiratory readmissions. Conclusions: Transcriptomic data from the neonatal period, combined with machine learning, accurately predicted respiratory-related rehospitalizations in extremely preterm neonates. The identified gene signatures offer insight into early biological disruptions that may predispose preterm neonates to chronic respiratory morbidity. Validation in larger, diverse cohorts is needed to support clinical translation. Full article

Military personnel deployed to Iraq and Afghanistan were exposed to emissions from open-air burn pits, where plastics, metals, and medical waste were incinerated. These exposures have been linked to deployment-related respiratory diseases (DRRD) and may also impact neurological health via the lung–brain axis. To investigate molecular mechanisms, adult male rats were exposed to filtered air, naphthalene (a representative volatile organic compound), or a combination of naphthalene and carbon black (surrogate for particulate matter; CBN) via whole-body inhalation (six hours/day, three consecutive days). Lung, brain, and plasma samples were collected 24 h after the final exposure. Pro-inflammatory biomarkers were assessed using multiplex electrochemiluminescence and western blot. Differentially expressed genes (DEGs) were identified by RNA sequencing, and elastic net modeling was used to define exposure-predictive gene signatures. CBN exposure altered inflammatory biomarkers across tissues, with activation of nuclear factor kappa B (NF-κB) signaling. In the lung, gene set enrichment revealed activated pathways related to proliferation and inflammation, while epithelial–mesenchymal transition (EMT) and oxidative phosphorylation were suppressed. In the brain, EMT, inflammation, and senescence pathways were activated, while ribosomal function and oxidative metabolism were downregulated. Elastic net modeling identified a lung gene signature predictive of CBN exposure, including Kcnq3, Tgfbr1, and Tm4sf19. These findings demonstrate that inhalation of a surrogate burn pit mixture induces inflammatory and metabolic gene expression changes in both lung and brain tissues, supporting the utility of this animal model for understanding systemic effects of airborne military toxicants and for identifying potential biomarkers relevant to DRRD and Veteran health. Full article

Plants are constantly exposed to environmental stressors such as drought, salinity, and extreme temperatures, which threaten their growth and productivity. To counter these challenges, they employ complex molecular defense systems, including epigenetic modifications that regulate gene expression without altering the underlying DNA sequence. This review comprehensively examines the emerging roles of reactive oxygen species (ROS) and reactive nitrogen species (RNS) as central signaling molecules orchestrating epigenetic changes in response to abiotic stress. In addition, biotic factors such as pathogen infection and microbial interactions are considered for their ability to trigger ROS/RNS generation and epigenetic remodeling. It explores how ROS and RNS influence DNA methylation, histone modifications, and small RNA pathways, thereby modulating chromatin structure and stress-responsive gene expression. Mechanistic insights into redox-mediated regulation of DNA methyltransferases, histone acetyltransferases, and microRNA expression are discussed in the context of plant stress resilience. The review also highlights cutting-edge epigenomic technologies such as whole-genome bisulfite sequencing (WGBS), chromatin immunoprecipitation sequencing (ChIP-seq), and small RNA sequencing, which are enabling precise mapping of stress-induced epigenetic landscapes. By integrating redox biology with epigenetics, this work provides a novel framework for engineering climate-resilient crops through the targeted manipulation of stress-responsive epigenomic signatures. Full article