Search Results (204)

High-throughput sequencing generally results in a list of genes. Which functional groups of genes among the DEGs are meaningful underlying factors to the differential biological/biomedical conditions under investigation? The process to find answers to this question can be called biological functional class enrichment analysis (FunCEA). R is a robust platform for FunCEA due to its accessibility by general users and availability of well-developed R packages for enrichment analysis and visualization, as well as for knowledge databases. Bench scientists in biomedical sciences need accessible and easy-to-understand protocols for FunCEA. This R tutorial provides detailed R scripts or command lines for FunCEA, as well as for data processing and visualization of the enrichment results. It keeps bench scientists in mind and provides supportive and apprehensible descriptions of the R scripts for each task (enrichment analysis, enrichment data processing, and visualization). It describes detailed procedures for the two popular FunCEA methods, the so-called over-representation analysis (ORA) and functional class scoring (FCS). The introduced FunCEA here uses three basic knowledge databases: gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and reactome. R codes for various visualizations (dot plot, term-gene network plot, enrichment map plot, ridge plot, and GSEA plot) are presented and annotated. Since all analyses are conducted in R, no commercial software is needed, yet clusterProfiler can directly access the latest KEGG knowledge database. Full article

Background: The hairy ear (Eh) mutation in heterozygous mice (Eh/+) results in elongated and additional ear hairs, along with altered pinna morphology compared to wild-type (+/+) mice. Previous studies suggest that disruption of the Hoxc gene cluster caused by the Eh inversion influences the hair growth cycle. Methods: To elucidate the molecular basis of this phenotype, we performed RNA-seq analysis on ear tissues from four-week-old Eh/+ and +/+ mice and compared their transcriptomic profiles. Results: Differential expression analysis identified 2092 genes, and subsequent Gene Ontology (GO) and overrepresentation analysis revealed significant alterations in hair growth-related processes, including the hair cycle and canonical keratinization in Eh/+ ears. Notably, numerous hair keratin and keratin-associated protein (Krtap) genes were markedly upregulated in Eh/+ mice. Validation by quantitative real-time PCR confirmed increased expression of randomly selected keratin genes (Krt34, Krt39, Krt71, Krt81, Krt84) and keratin-associated proteins (Krtap4-16 and Krtap22-2). In contrast, epithelial keratin genes such as Krt2 and Krt14 were downregulated in Eh/+ ears. In addition, genes associated with hair follicle growth, Car6 and Gprc5d, showed elevated expression, while Dab2, a telogen–anagen transition marker linked to hair follicle stem cell activation, was slightly increased at the telogen stage in Eh/+ compared with +/+ mice. Conclusions: These findings provide new insights into the role of Hoxc cluster genes in orchestrating the expression of hair keratin and Krtap genes and highlight potential regulatory mechanisms underlying the hairy ear phenotype. Full article

Background: Triple-negative breast cancers (TNBCs) are among the most aggressive breast tumors, due not only to the absence of clinically functional biomarkers used in other molecular subtypes, but also their marked heterogeneity and pronounced migratory and invasive behavior. The search for new molecules of interest for risk prediction, diagnosis and therapy stems from the class of long non-coding RNAs (lncRNAs), which often display context-dependent (“dual”) functions and tissue specificity. Among them, lncRNA LINC01133 stands out for its dysregulation across cancer, although its molecular role in TNBC remains unclear. Methods: In the present study, we used the human TNBC cell line Hs578T to generate a cell panel comprising the parental line (Hs578T_wt), the control line (Hs578T_ctr), and the LINC01133 knockout line (Hs578T_ko). Subsequently, we performed bulk RNA-Seq to identify KO-associated Differentially Expressed Genes (DEGs) using ko_vs_ctr as the primary contrast. Functional interpretation was achieved by Over-Representation Analysis (ORA) using Gene Ontology. We then conducted a comparative patient-cohort analysis using TCGA-BRCA Basal-like/TNBC cases (TCGA/BRCA n = 1098; Basal-like/TNBC n = 199), classified with the AIMS algorithm, and evaluated concordance between KO-associated signatures and patient tumor expression patterns via trend-based analyses across the LINC01133 expression levels and associated genes. Results: A total of 265 KO-dominant DEGs were identified in Hs578T_ko, reflecting transcriptional changes consistent with tumor progression, with enrichment of pathways associated with LINC01133 knockout including cell adhesion, cell–cell interactions, epithelial–mesenchymal transition (EMT), and extracellular matrix (ECM) remodeling. The main DEGs included ITIH5, GLUL, CACNB2, PDX1, ASPN, PTGER3, MFAP4, PI15, EPHB6, and CPA3 with additional candidates, such as KAZN and the lncRNA gene SSC4D, which have been implicated in migration/invasion, ECM remodeling, or signaling across multiple tumor contexts. Translational analyses in TCGA-BRCA basal-like tumors suggested a descriptive association in which lower LINC01133 levels were accompanied by shifts in the expression trends of genes linked to ECM/EMT programs and modulation of genes related to cell adhesion and protease inhibition. Conclusions: These results suggest a transcriptional model in which LINC01133 is associated with TNBC-related gene expression programs in a concentration-dependent manner, with loss of LINC01133 being associated with a transcriptomic shift toward pro-migratory/ECM remodeling signatures. While functional validation is required to establish causality, these data support LINC01133 as a molecule of interest in breast cancer research. Full article

We developed OmicIntegrator, a broadly adaptable pipeline designed to standardize and integrate publicly available transcriptomic, proteomic, and phosphoproteomic datasets. We applied this workflow to Arabidopsis thaliana etiolated seedlings to identify protein kinases and phosphatases relevant to skotomorphogenic development, a phase during which seedlings rely on tightly regulated signaling networks to ensure survival in darkness. This meta-analysis provided a comprehensive view of gene and protein expression, revealing discrepancies between transcript and protein abundance, suggesting post-transcriptional and post-translational regulation. By integrating multiple datasets, OmicIntegrator reduces experimental bias and enables the detection of phosphorylation events that may be missed in single-condition studies. Distinct phosphorylation patterns were detected across different protein kinase families. Motif enrichment analysis showed a strong overrepresentation of RxxS motifs among phosphosites in protein phosphatases and microtubule-associated proteins, consistent with potential regulation by calcium-dependent protein kinases (CPKs). Across omics layers, CPK3 and CPK9 repeatedly emerged as prominent candidates, highlighting them as priorities for future functional studies in skotomorphogenesis. Overall, our results demonstrate the power of OmicIntegrator as a flexible framework to contextualize signaling landscapes and identify robust patterns and candidate genes and for generating testable hypotheses from integrated multi-omics data in plant developmental biology. Full article

Non-coding RNAs (ncRNAs) have emerged as promising biomarkers for prostate cancer (PCa), yet evidence remains dispersed across heterogeneous studies and their regulatory context is seldom analyzed in an integrated manner. This study systematically maps ncRNAs reported as diagnostic biomarkers for PCa and characterizes their molecular interactions through in silico analyses. A comprehensive evidence-mapping strategy across major bibliographic databases identified 693 studies, of which 58 met eligibility criteria. Differentially expressed ncRNAs were extracted and classified by RNA type. Subsequently, miRNA–target prediction, miRNA–protein interaction network construction, and functional enrichment analyses were performed to explore the regulatory landscape of miRNA-associated proteins. Results: The final dataset included 4500 participants (2871 PCa cases and 2093 controls) and reported 94 differentially expressed miRNAs, eight lncRNAs, and several circRNAs, snoRNAs, snRNAs, and piRNAs. In silico analyses predicted 13,493 miRNA–mRNA interactions converging on 4916 unique target genes, with an additional 2481 prostate tissue-specific targets. The miRNA–protein network comprised 845 nodes and 2335 edges, revealing highly connected miRNAs (e.g., hsa-miR-16-5p, hsa-miR-20a-5p) and protein hubs (QKI, YOD1, TBL1XR1; prostate-specific CDK6, ACVR2B). Enrichment analysis showed strong overrepresentation of metabolic process-related GO terms and cancer-associated KEGG pathways. Conclusions: These findings refine the list of promising ncRNA biomarkers and highlight candidates for future clinical validation. Full article

This study examines the relationship between social sustainability and sustainable food consumption through bibliometric analysis and an empirical approach. A bibliometric study of 211 scientific publications was conducted in accordance with the PRISMA 2020 framework, alongside concept mapping using the Biblioshiny tool. The results indicate a lack of theoretical coherence, low integration of social parameters such as gender, labor, and migration, as well as a geographical imbalance in the literature, with an overrepresentation of high-income countries. Overall, the study highlights the need for a theoretically grounded framework of social sustainability, based on local socio-cultural contexts, fostering participation and the active engagement of local stakeholders. Additionally, the findings underscore the importance of utilizing Artificial Intelligence tools and social innovation for a just transition towards sustainable food systems. The contribution of this research lies in formulating a framework for social sustainability grounded in locality and empirical evidence, enhancing the integration of the social dimension into sustainable food systems. Finally, an interdisciplinary approach is proposed, centered on participation and providing evidence-based directions for policy and educational strategies. Full article

Background/Objectives: Parkinson’s disease (PD) is an adult-onset neurodegenerative disorder whose pathogenesis is still not completely understood. Several lines of evidence suggest that alterations in epigenetic architecture may contribute to the development of this condition. Here, we present a pilot DNA methylation study from peripheral blood in a cohort of Sicilian PD patients and matched controls. Peripheral tissue analysis has previously been shown to reflect molecular and functional profiles relevant to neurological diseases, supporting their validity as a proxy for studying brain-related epigenetic mechanisms. Methods: We analyzed 20 PD patients and 20 healthy controls (19 males and 21 females overall), matched for sex, with an age range of 60–87 years (mean 72.3 years). Peripheral blood DNA was extracted and processed using the Illumina Infinium MethylationEPIC v2.0 BeadChip, which interrogates over 935,000 CpG sites across the genome, including promoters, enhancers, CpG islands, and other regulatory elements. The assay relies on sodium bisulfite conversion of DNA to detect methylation status at single-base resolution. Results: Epigenome-wide association study (EWAS) data allowed for multiple levels of analysis, including immune cell-type deconvolution, estimation of biological age (epigenetic clocks), quantification of stochastic epigenetic mutations (SEMs) as a measure of epigenomic stability, and differential methylation profiling. Immune cell-type inference revealed an increased but not significant proportion of monocytes in PD patients, consistent with previous reports. In contrast, epigenetic clock analysis did not reveal significant differences in biological age acceleration between cases and controls, partially at odds with earlier studies—likely due to the limited sample size. SEMs burden did not differ significantly between groups. Epivariations reveal genes involved in pathways known to be altered in dopaminergic neuron dysfunction and α-synuclein toxicity. Differential methylation analysis, however, yielded 167 CpG sites, of which 55 were located within genes, corresponding to 54 unique loci. Gene Ontology enrichment analysis highlighted significant overrepresentation of pathways with neurological relevance, including regulation of synapse structure and activity, axonogenesis, neuron migration, and synapse organization. Notably, alterations in KIAA0319, a gene involved in neuronal migration, synaptic formation, and cortical development, have previously been associated with Parkinson’s disease at the gene expression level, while methylation changes in FAM50B have been reported in neurotoxic and cognitive contexts; our data suggest, for the first time, a potential epigenetic involvement of both genes in Parkinson’s disease. Conclusions: This pilot study on a Sicilian population provides further evidence that DNA methylation profiling can yield valuable molecular insights into PD. Despite the small sample size, our results confirm previously reported findings and highlight biological pathways relevant to neuronal structure and function that may contribute to disease pathogenesis. These data support the potential of epigenetic profiling of peripheral blood as a tool to advance the understanding of PD and generate hypotheses for future large-scale studies. Full article

We recently identified significant differences between porcine arterial and venous smooth muscle cells (ApSMCs and VpSMCs) in the expression of numerous genes and activity of several important signaling pathways. To understand the mechanisms that are responsible for these differences, we performed a genome-wide comparison of VpSMCs and ApSMCs using bulk RNA sequencing. A principal component analysis (PCA) plot and heatmaps revealed a clear separation of the two groups of samples. Using a standard cutoff (≥2-fold change, false discovery rate (FDR) ≤ 0.05), 466 genes were highly expressed in ApSMCs, and 358 genes were highly expressed in VpSMCs. Functional pathway analyses were conducted using the Gene Set Enrichment Analysis (GSEA) tool. The top 15 enriched pathways of the GSEA and Overrepresentation Analysis (ORA) results were detected by comparing the dataset against the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO) biological process, GO cellular component, GO molecular function, and WikiPathways databases. Both the GSEA and ORA results revealed that the top enriched pathways are mostly linked to cell cycle, cell structure, and cell differentiation. Further analysis of differentially expressed genes (DEGs) in a specific pathway identified that different sets of genes were utilized to regulate the same pathway between ApSMCs and VpSMCs. For example, in the cell cycle pathway, TGFB1, GADD45A, and TP53 were expressed highly in ApSMCs, while SKP2, PCK1, CDK1, and PPP2CA were expressed highly in VpSMCs. This study identified key differences in the gene expression patterns of two subsets of VSMCs and found that different sets of genes are utilized in specific signaling pathways within the different subtypes of cells, which provides crucial information for developing vein- or artery-specific strategies to prevent corresponding vascular diseases. Full article

The rapid advancements in high-throughput technologies have led to a dramatic increase in diverse -omics data types, enabling comprehensive analyses, especially for complex diseases like cancer. Despite the development of multi-omics approaches, the challenges of scaling integration to massive, heterogeneous -omics datasets suggest that novel computational tools need to be designed. In this study, we propose an approach for integrating microRNA (miRNA) and messenger RNA (mRNA) expression data, incorporating prior biological knowledge (PBK). This approach scores and ranks groups of miRNAs and their associated genes using cross-validation iterations. The proposed method incorporates a Pattern detection (P) component to identify molecular motifs unique to each biological group. The analysis also facilitates the visualization of the groups, facilitating the identification of co-occurring groups and their characteristic features across iterations. Furthermore, the groups are scored using an over-representation analysis through a new Enrichment (E) component in each iteration. The clusters of the groups based on the Enrichment Scores (ESs) are visualized in a heatmap to obtain novel insights into the collective behavior and dependencies of the groups, aiming to understand the molecular mechanisms of complex diseases. The developed G-S-M-E tool not only provides performance metrics and biological scores at the group level but also offers comprehensive insights into intricate multi-omics interactions. In summary, our study emphasizes the importance of mathematical and data science methodologies in elucidating intricate multi-omics integration, yielding a formalized approach that deepens our comprehension of complex diseases. Full article

Cardiometabolic phenotypes such as obesity and impaired insulin action are key determinants of type 2 diabetes (T2D). Growing evidence highlights the postprandial state as a critical window in metabolic regulation, where epigenetic mechanisms, particularly DNA methylation in insulin-sensitive tissues, may play pivotal roles. However, their dynamics across prandial states in subcutaneous adipose tissue (SAT) remain unclear. We analyzed genome-wide DNA methylation in paired fasting and postprandial SAT biopsies from 29 asymptomatic, drug-naïve individuals classified as controls (n = 8), prediabetes n = 9), or T2D (n = 12). Postprandial samples followed a standardized mixed-meal test. DNA methylation was quantified using the Illumina MethylationEPIC array and analyzed through the Chip Analysis Methylation Pipeline (ChAMP) pipeline. Differential methylation was more pronounced postprandially, especially in the T2D group. After adjusting for age and sex, 4599 differentially methylated CpG sites (DMCs) were identified, with increased hypermethylation in T2D. A total of 130 DMCs across 99 genes, including LCLAT1, HLA-C, ZNF714, and HOOK2, were shared by prediabetes and T2D groups. Over-representation analysis revealed 202 enriched pathways related to insulin resistance, AMPK signaling, and immune responses. Additionally, 110 Differentially Methylated Regions (DMRs), including ZNF577 and AGPAT1, were detected. These findings reveal early, prandial-dependent epigenetic alterations in SAT that precede overt dysglycemia, offering insights into personalized prevention in T2D. Full article

Network pharmacology is a powerful approach for studying complex drug–target interactions and biological pathways. However, existing tools often require extensive manual intervention and lack integrated analysis capabilities. Here, we present NeXus v1.2, an automated platform for network pharmacology and multi-method enrichment analysis including Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) that addresses these limitations. NeXus v1.2 enables the seamless integration of multi-layer biological relationships, handling complex interactions between genes, compounds, and plants while maintaining analytical rigor. The platform implements three enrichment methodologies: Over-Representation Analysis (ORA), GSEA, and GSVA, circumventing limitations associated with arbitrary threshold-based approaches. NeXus v1.2 was validated using multiple datasets spanning 111 to 10,847 genes, demonstrating robust scalability and performance across dataset sizes. The platform was initially tested using a representative dataset comprising 111 genes, 32 compounds, and 3 plants, showing consistent performance in processing various relationship patterns, including shared compounds between plants and multitargeted genes. The processing time for this dataset was 4.8 s with peak memory usage of 480 MB. Large-scale validation with datasets up to 10,847 genes confirmed scalability, with linear time complexity and completion times under 3 min. NeXus v1.2 automatically generates comprehensive visualizations, including network maps, enrichment analyses, and relationship patterns, while maintaining the biological context of interactions. The tool successfully processed and analyzed enrichment patterns across multiple functional domains, generating publication-quality visualization outputs at 300 DPI resolution. The platform demonstrated enhanced automation in handling incomplete relationship data and maintaining analytical integrity across different biological layers. Compared to manual workflows requiring 15–25 min, NeXus v1.2 reduced the analysis time to under 5 s (>95% reduction) while ensuring the comprehensive coverage of biological relationships. NeXus v1.2 provides improved automation and integration for network pharmacology analysis, offering an efficient and user-friendly platform for complex biological network analysis. Its modular architecture enables the future integration of AI technologies and expansion into various therapeutic applications. Full article

Background/Objectives: Due to the numerical dominance of environmental and commensal strains, understanding antimicrobial resistance (AMR) transmission in Escherichia coli requires consideration of non-clinical as well as pathogenic isolates. In this cross-sectional study, associations between the genetic context of non-clinical E. coli and AMR carriage are examined in isolates sampled from different niches within a One Health continuum. Methods: Two hundred eighty-eight E. coli isolates collected in Alberta, Canada (2018–2019) from wastewater, well water, feces of broiler chickens and feedlot cattle, and retail beef and chicken meat were selected from existing surveillance collections using a stratified random sampling structure. Using short-read whole genome assemblies, phylogenetic relationships were inferred from pan-genome multiple sequence alignments. Principal coordinate analysis and permutational analysis of variance (PERMANOVA) of a Jaccard dissimilarity matrix derived from gene presence/absence data were used to investigate contributions of source and AMR strata to observe genetic dissimilarity. Population clustering and gene under- or over-representation by source and cluster were also explored. Results: Minimal phylogenetic segregation of isolates was noted based on source or AMR strata, and both contributed significant but small proportions of observed genetic dissimilarity, with the largest proportion attributed to phylogroup. There was notable diversity of E. coli within and between sources; however, in some larger clusters, differential gene presence/absence was potentially linked to ecological niche rather than source of isolation. Conclusions: This study highlights the ecological complexity of AMR in E. coli in non-clinical contexts, offering a novel lens on how niche-specific factors can influence population structure and AMR carriage. It also provides insight into apparent discrepancies in the literature regarding clustering of E. coli by source. These findings support a more integrative One Health approach to AMR surveillance, emphasizing the need to account for microbial diversity and niche-specific adaptation across interconnected systems. Full article

Background: Glaucoma is the leading cause of global irreversible blindness, and it disproportionately affects people of African descent, in addition to having slightly higher prevalence rates in females. Glaucoma is a group of diseases that are characterized by progressive and irreversible damage to the optic nerve, leading to eventual blindness without proper treatment. There are a number of interventions available to treat glaucoma, including MIGS, of which usage has drastically increased due to its safety and efficacy. However, with minority populations, such as people of African descent, having the highest disease burden, it remains critical to evaluate the diversity of clinical trial populations that are used in the study of glaucoma treatments. The objective of this study is to compare the representation of Black and other ethnic minorities, as well as female participants, between cataract surgery (CS), minimally invasive glaucoma surgery (MIGS), and MIGS and cataract surgery (MACS) trials. Methods: This analysis consisted of publicly available data on MIGS, CS, and MACS clinical trials from 2005 to 2017, using ClinicalTrials.gov as well as prevalence data sourced from the CDC. Data reporting and synthesis adhered to PRISMA guidelines. This study focuses on sex rather than gender, as this is how data was reported on ClinicalTrials.gov. The primary outcome was the participation-to-prevalence ratio (PPR) of each clinical trial. A PPR between 0.8 and 1.2 represents adequate representation, while a PPR less than 0.8 or greater than 1.2 can signify under- or over-representation, respectively. Results: A total of 21 trials were included in this review, comprising 3330 clinical trial participants: 7 CS trials (N = 570), 13 MIGS trials (N = 1577), and 9 MACS trials (N = 1183). All of the clinical trials included data on sex, while only 14 reported race data and 7 reported ethnicity data. The overall PPR of female participants was 1.00, with CS, MIGS, and MACS clinical trials having PPRs of 0.99, 1.00, and 1.00, respectively. On the other hand, the overall PPR of Black participants was 0.44, with CS, MIGS, and MACS clinical trials having PPRs of 0.27, 0.62, and 0.22, respectively. Further analysis demonstrated that the PPR of Black participants in trials sponsored by medical device companies and medical centers or universities was 0.41 and 1.25, respectively. The study was registered with Prospero CRD420251152586. Conclusions: Cataract surgery, MIGS, and MIGS and cataract surgery clinical trials under-represent Black individuals and appropriately represent females. Due to the disproportionate amount of Black individuals impacted by glaucoma, this lack of representation raises concerns about the applicability of the clinical trials to these populations. Understanding clinical trial disparities in the representation of minority races is a key first step toward promoting advancements in diversity and equitable healthcare. Clinical trials in the future need to make a genuine effort to include minority groups to improve the generalizability of results. Full article

Cell–cell interactions play a pivotal role in maintaining tissue homeostasis and driving disease progression. Conventional Cell–cell interactions modeling approaches depend on ligand–receptor databases, which often fail to capture context-specific or newly emerging signaling mechanisms. To address this limitation, we propose a data-driven computational framework, data-driven cell–cell interaction inference (DD-CC-II), which employs a graph-based model using eigen-cells to represent cell groups. DD-CC-II uses eigen-cells (i.e., functional module within the cell population) to characterize cell groups and construct correlation coefficient networks to model between-group associations. Correlation coefficient networks between eigen-cells are constructed, and their statistical significance is evaluated via over-representation analysis and hypergeometric testing. Monte Carlo simulations demonstrate that DD-CC-II achieves superior performance in inferring CCIs compared with ligand–receptor-based methods. The application to whole-blood RNA-seq data from the Japan COVID-19 Task Force revealed severity stage-specific interaction patterns. Markers such as FOS, CXCL8, and HLA-A were associated with high severity, whereas IL1B, CD3D, and CCL5 were related to low severity. The systemic lupus erythematosus pathway emerged as a potential immune mechanism underlying disease severity. Overall, DD-CC-II provides a data-centric approach for mapping the cellular communication landscape, facilitating a better understanding of disease progression at the intercellular level. Full article

Iron plays a key role in oxygen transport, hematopoiesis, and hypoxia adaptation. This study aimed to explore the dynamic response mechanism of the iron regulatory network and key genes in Duroc piglets. Eighteen weaned piglets were randomly divided into three dietary intervention groups: low iron (0 mg/kg), conventional (100 mg/kg), and high iron (200 mg/kg). Transcriptomics technology was used to screen key liver iron regulatory genes under the influence of different dietary iron concentrations, and the expression of related genes was verified using primary pig liver cells. Fasting serum iron metabolism parameters were detected and iron content in organs was quantified. The results show, enrichment analysis highlighted immune–metabolic signaling, including NF-κB, PI3K-Akt, and TGF-β, and a total of 14 candidate genes (such as FGF21, SAA2/3, FNDC1, ETNPPL, TFR1) were identified. The study observed that these genes showed obvious dosage differentiation and nonlinear patterns. However, findings reflect mRNA-level changes and GO/KEGG over-representation, protein-level validation is planned in follow-up studies. Through the integration of in vitro and in vivo data, this study discovered new liver genes that may be related to pig iron homeostasis function, providing a theoretical basis for analyzing the regulatory mechanism of piglet iron response. Full article