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Understanding and predicting the relationships between genotype and phenotype is often challenging, largely due to the complex nature of eukaryotic gene regulation. A step towards this goal is to map how phenotypic diversity evolves through genomic changes that modify gene regulatory interactions. Using the Prairie Rattlesnake (Crotalus viridis) and related species, we integrate mRNA-seq, proteomic, ATAC-seq and whole-genome resequencing data to understand how specific evolutionary modifications to gene regulatory network components produce differences in venom gene expression. Through comparisons within and between species, we find a remarkably high degree of gene expression and regulatory network variation across even a shallow level of evolutionary divergence. We use these data to test hypotheses about the roles of specific trans-factors and cis-regulatory elements, how these roles may vary across venom genes and gene families, and how variation in regulatory systems drive diversity in venom phenotypes. Our results illustrate that differences in chromatin and genotype at regulatory elements play major roles in modulating expression. However, we also find that enhancer deletions, differences in transcription factor expression, and variation in activity of the insulator protein CTCF also likely impact venom phenotypes. Our findings provide insight into the diversity and gene-specificity of gene regulatory features and highlight the value of comparative studies to link gene regulatory network variation to phenotypic variation.
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http://dx.doi.org/10.1093/gbe/evae110 | DOI Listing |
Med Oncol
September 2025
Division of Hematology and Blood Bank, Department of Medical Laboratory Sciences, School of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran.
Acute Myeloid Leukemia (AML) patient-derived Mesenchymal Stem Cells (MSCs) behave differently than normal ones, creating a more protective environment for leukemia cells, making relapse harder to prevent. This study aimed to identify prognostic biomarkers and elucidate relevant biological pathways in AML by leveraging microarray data and advanced bioinformatics techniques. We retrieved the GSE122917 dataset from the NCBI Gene Expression Omnibus and performed differential expression analysis (DEA) within R Studio to identify differentially expressed genes (DEGs) among healthy donors, newly diagnosed AML patients, and relapsed AML patients.
View Article and Find Full Text PDFMol Biol Rep
September 2025
Phytoveda Pvt. Ltd, Mumbai, 400022, India.
Background: The dysregulation of long-chain noncoding RNAs (lncRNAs) causes several complex human diseases including neurodegenerative disorders across the globe.
Methods And Results: This study aimed to investigate lncRNA expression profiles of Withania somnifera (WS)-treated human neuroblastoma SK-N-SH cells at different timepoints (3 & 9 h) and concentrations (50 & 100 µg/mL) using RNA sequencing. Differential gene expression analysis showed a total of 4772 differentially expressed lncRNAs, out of which 3971 were upregulated and 801 were downregulated compared to controls.
Funct Integr Genomics
September 2025
Department of Plastic Surgery, the First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, China.
Keloid scarring and Metabolic Syndrome (MS) are distinct conditions marked by chronic inflammation and tissue dysregulation, suggesting shared pathogenic mechanisms. Identifying common regulatory genes could unveil novel therapeutic targets. Methods.
View Article and Find Full Text PDFFunct Integr Genomics
September 2025
The First Clinical Medical College, Yunnan University of Chinese Medicine, Kunming, China.
Ischemic stroke (IS) has high morbidity/mortality with limited treatments. This study screened core copper homeostasis-related genes in IS and validated their function as precise intervention targets. Human IS gene chip data were retrieved from GEO, and copper homeostasis genes from multiple databases.
View Article and Find Full Text PDFACS Synth Biol
September 2025
School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona 85281, United States.
Cellular resource limitations create unintended interactions among synthetic gene circuit modules, compromising circuit modularity. This challenge is particularly pronounced in circuits with positive feedback, where uneven resource allocation can lead to Winner-Takes-All (WTA) behavior, favoring one module at the expense of others. In this study, we experimentally implemented a Negatively Competitive Regulatory (NCR) controller using CRISPR interference (CRISPRi) and evaluated its effectiveness in mitigating WTA behavior in two gene circuits: dual self-activation and cascading bistable switch.
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