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DREAM complexes are transcriptional regulators that control the expression of hundreds to thousands of target genes involved in the cell cycle, quiescence, differentiation, and apoptosis. These complexes contain many subunits that can vary according to the considered target genes. Depending on their composition and the nature of the partners they recruit, DREAM complexes control gene expression through diverse mechanisms, including chromatin remodeling, transcription cofactor and factor recruitment at various genomic binding sites. This complexity is particularly high in mammals. Since the discovery of the first dREAM complex (drosophila Rb, E2F, and Myb) in Drosophila melanogaster, model organisms such as Caenorhabditis elegans, and plants allowed a deeper understanding of the processes regulated by DREAM-like complexes. Here, we review the conservation of these complexes. We discuss the contribution of model organisms to the study of DREAM-mediated transcriptional regulatory mechanisms and their relevance in characterizing novel activities of DREAM complexes.
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http://dx.doi.org/10.1002/bies.202300125 | DOI Listing |
Genome Biol
September 2025
Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, 100101, Beijing, China.
Background: Centromeres are crucial for precise chromosome segregation and maintaining genome stability during cell division. However, their evolutionary dynamics, particularly in polyploid organisms with complex genomic architectures, remain largely enigmatic. Allopolyploid wheat, with its well-defined hierarchical ploidy series and recent polyploidization history, serves as an excellent model to explore centromere evolution.
View Article and Find Full Text PDFMetabolomics
September 2025
Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France.
Introduction: Initially developed for transcriptomics data, pathway analysis (PA) methods can introduce biases when applied to metabolomics data, especially if input parameters are not chosen with care. This is particularly true for exometabolomics data, where there can be many metabolic steps between the measured exported metabolites in the profile and internal disruptions in the organism. However, evaluating PA methods experimentally is practically impossible when the sample's "true" metabolic disruption is unknown.
View Article and Find Full Text PDFNat Aging
September 2025
Aging Biomarker Consortium (ABC), Beijing, China.
The global surge in the population of people 60 years and older, including that in China, challenges healthcare systems with rising age-related diseases. To address this demographic change, the Aging Biomarker Consortium (ABC) has launched the X-Age Project to develop a comprehensive aging evaluation system tailored to the Chinese population. Our goal is to identify robust biomarkers and construct composite aging clocks that capture biological age, defined as an individual's physiological and molecular state, across diverse Chinese cohorts.
View Article and Find Full Text PDFMol Syst Biol
September 2025
TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, France.
Overflow metabolism refers to the widespread phenomenon of cells excreting metabolic by-products into their environment. Although overflow is observed in virtually all living organisms, it has been studied independently and given different names in different species. This review highlights emerging evidence that overflow metabolism is governed by common principles in prokaryotic and eukaryotic organisms.
View Article and Find Full Text PDFEMBO Rep
September 2025
Institute for Stem Cell Science and Regenerative Medicine (inStem), GKVK post, Bellary Road, Bangalore, Karnataka, 560065, India.
Immune cells are increasingly recognized as nutrient sensors; however, their developmental role in regulating growth under homeostasis or dietary stress remains elusive. Here, we show that Drosophila larval macrophages, in response to excessive dietary sugar (HSD), reprogram their metabolic state by activating glycolysis, thereby enhancing TCA-cycle flux, and increasing lipogenesis-while concurrently maintaining a lipolytic state. Although this immune-metabolic configuration correlates with growth retardation under HSD, our genetic analyses reveal that enhanced lipogenesis supports growth, whereas glycolysis and lipolysis are growth-inhibitory.
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