How the diverse neural cell types emerge from multipotent neural progenitor cells during central nervous system development remains poorly understood. Recent scRNA-seq studies have delineated the developmental trajectories of individual neural cell types in many neural systems including the neural retina. Further understanding of the formation of neural cell diversity requires knowledge about how the epigenetic landscape shifts along individual cell lineages and how key transcription factors regulate these changes. In this study, we dissect the changes in the epigenetic landscape during early retinal cell differentiation by scATAC-seq and identify globally the enhancers, enriched motifs, and potential interacting transcription factors underlying the cell state/type specific gene expression in individual lineages. Using CUT&Tag, we further identify the enhancers bound directly by four key transcription factors, Otx2, Atoh7, Pou4f2 and Isl1, including those dependent on Atoh7, and uncover the sequential and combinatorial interactions of these factors with the epigenetic landscape to control gene expression along individual retinal cell lineages such as retinal ganglion cells (RGCs). Our results reveal a general paradigm in which transcription factors collaborate and compete to regulate the emergence of distinct retinal cell types such as RGCs from multipotent retinal progenitor cells (RPCs).
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http://dx.doi.org/10.1093/nar/gkad026 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
State Key Laboratory of Wheat Improvement, College of Life Science, Shandong Agricultural University, Tai'an 271018, China.
In many plants, the asymmetric division of the zygote sets up the apical-basal body axis. In the cress , the zygote coexpresses regulators of the apical and basal embryo lineages, the transcription factors WOX2 and WRKY2/WOX8, respectively. WRKY2/WOX8 activity promotes nuclear migration, cellular polarity, and mitotic asymmetry of the zygote, which are hallmarks of axis formation in many plant species.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Institute of Science and Technology Austria, AT-3400 Klosterneuburg, Austria.
Biophysical constraints limit the specificity with which transcription factors (TFs) can target regulatory DNA. While individual nontarget binding events may be low affinity, the sheer number of such interactions could present a challenge for gene regulation by degrading its precision or possibly leading to an erroneous induction state. Chromatin can prevent nontarget binding by rendering DNA physically inaccessible to TFs, at the cost of energy-consuming remodeling orchestrated by pioneer factors (PFs).
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH 43210.
The homo-dodecameric ring-shaped RNA binding attenuation protein (TRAP) from binds up to twelve tryptophan ligands (Trp) and becomes activated to bind a specific sequence in the 5' leader region of the operon mRNA, thereby downregulating biosynthesis of Trp. Thermodynamic measurements of Trp binding have revealed a range of cooperative behavior for different TRAP variants, even if the averaged apparent affinities for Trp have been found to be similar. Proximity between the ligand binding sites, and the ligand-coupled disorder-to-order transition has implicated nearest-neighbor interactions in cooperativity.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Department of Computer Science, Colorado State University, Fort Collins, Colorado, United States of America.
Complex deep learning models trained on very large datasets have become key enabling tools for current research in natural language processing and computer vision. By providing pre-trained models that can be fine-tuned for specific applications, they enable researchers to create accurate models with minimal effort and computational resources. Large scale genomics deep learning models come in two flavors: the first are large language models of DNA sequences trained in a self-supervised fashion, similar to the corresponding natural language models; the second are supervised learning models that leverage large scale genomics datasets from ENCODE and other sources.
View Article and Find Full Text PDFDifferentiation of antigen-activated B cells into pro-proliferative germinal center (GC) B cells depends on the activity of the transcription factors MYC and BCL6, and the epigenetic writers DOT1L and EZH2. GCB-like Diffuse Large B Cell Lymphomas (GCB-DLBCLs) arise from GCB cells and closely resemble their cell of origin. Given the dependency of GCB cells on DOT1L and EZH2, we investigated the role of these epigenetic regulators in GCB-DLBCLs and observed that GCB-DLBCLs synergistically depend on the combined activity of DOT1L and EZH2.
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