STAT3 is the quintessential pleiotropic transcription factor with many biological roles throughout development as well as in multiple adult tissues. Its functional heterogeneity is encoded in the range of genome-wide binding patterns that specify different regulatory networks in distinct cell types. However, STAT3 does not display remarkable DNA binding preferences that may help correlate specific motifs with individual biological functions or cell types. Therefore, achieving a detailed understanding of the regulatory mechanisms that endow STAT3 (or any other pleiotropic transcription factor) with such a rainbow of functions is not only a central problem in biology but also a fiendishly difficult one. Here we describe key genomic and computational approaches that have shed light into this question, and present the two current models of STAT3 binding (universal and cell type-specific). We also discuss the role that the local epigenetic environment plays in the selection of STAT3 binding sites.
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http://dx.doi.org/10.4161/jkst.25097 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Institute of Medical Microbiology, Rheinisch-Westfälische Technische Hochschule Aachen University Hospital, Aachen 52074, Germany.
Postnatal establishment of enteric metabolic, host-microbial and immune homeostasis is the result of precisely timed and tightly regulated developmental and adaptive processes. Here, we show that infection with the invasive enteropathogen Typhimurium results in accelerated maturation of the neonatal epithelium with premature appearance of antimicrobial, metabolic, developmental, and regenerative features of the adult tissue. Using conditional Myd88-deficient mice, we identify the critical contribution of immune cell-derived mediators.
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 PDFMedicine (Baltimore)
January 2025
Department of Endoscopy, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
This study enrolled 10 patients diagnosed with premalignant lesions and early-stage gastric cardia adenocarcinoma (GCA), confirmed through endoscopic examination. These patients were subjected to next-generation sequencing (NGS) using a customized 1123-gene panel to identify genetic alterations and signaling pathways. The results were compared to stage IIB to IV GCA samples from the cancer genome atlas (TCGA) and a cohort of Hong Kong patients.
View Article and Find Full Text PDFSci Adv
January 2025
Center for Physical Genomics and Engineering, Northwestern University, Evanston, IL 60208, USA.
In single cells, variably sized nanoscale chromatin structures are observed, but it is unknown whether these form a cohesive framework that regulates RNA transcription. Here, we demonstrate that the human genome is an emergent, self-assembling, reinforcement learning system. Conformationally defined heterogeneous, nanoscopic packing domains form by the interplay of transcription, nucleosome remodeling, and loop extrusion.
View Article and Find Full Text PDFNicotine Tob Res
January 2025
Department of Population Health Sciences, University of Leicester, Leicester, UK.
Introduction: Varenicline is an α4β2 nicotinic acetylcholine receptor partial agonist with the highest therapeutic efficacy of any pharmacological smoking cessation aid and a 12-month cessation rate of 26%. Genetic variation may be associated with varenicline response, but to date no genome-wide association studies of varenicline response have been published.
Methods: In this study, we investigated the genetic contribution to varenicline effectiveness using two electronic health record-derived phenotypes.
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