Plants have evolved adaptive strategies that involve transcriptional networks to cope with and survive environmental challenges. Key transcriptional regulators that mediate responses to environmental fluctuations in nitrate have been identified; however, little is known about how these regulators interact to orchestrate nitrogen (N) responses and cell-cycle regulation. Here we report that teosinte branched1/cycloidea/proliferating cell factor1-20 (TCP20) and NIN-like protein (NLP) transcription factors NLP6 and NLP7, which act as activators of nitrate assimilatory genes, bind to adjacent sites in the upstream promoter region of the nitrate reductase gene, , and physically interact under continuous nitrate and N-starvation conditions. Regions of these proteins necessary for these interactions were found to include the type I/II Phox and Bem1p (PB1) domains of NLP6&7, a protein-interaction module conserved in animals for nutrient signaling, and the histidine- and glutamine-rich domain of TCP20, which is conserved across plant species. Under N starvation, TCP20-NLP6&7 heterodimers accumulate in the nucleus, and this coincides with TCP20 and NLP6&7-dependent up-regulation of nitrate assimilation and signaling genes and down-regulation of the G/M cell-cycle marker gene, TCP20 and NLP6&7 also support root meristem growth under N starvation. These findings provide insights into how plants coordinate responses to nitrate availability, linking nitrate assimilation and signaling with cell-cycle progression.
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http://dx.doi.org/10.1073/pnas.1615676114 | DOI Listing |
Sci Rep
January 2025
National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, No.11 Fucheng Road, Beijing, 100048, China.
Promoters are essential DNA sequences that initiate transcription and regulate gene expression. Precisely identifying promoter sites is crucial for deciphering gene expression patterns and the roles of gene regulatory networks. Recent advancements in bioinformatics have leveraged deep learning and natural language processing (NLP) to enhance promoter prediction accuracy.
View Article and Find Full Text PDFbioRxiv
December 2024
Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Pre-trained language models have transformed the field of natural language processing (NLP), and their success has inspired efforts in genomics to develop domain-specific foundation models (FMs). However, creating high-quality genomic FMs from scratch is resource-intensive, requiring significant computational power and high-quality pre-training data. The success of large language models (LLMs) in NLP has largely been driven by industrial-scale efforts leveraging vast, diverse corpora and massive computing infrastructure.
View Article and Find Full Text PDFInt J Mol Sci
December 2024
College of Agronomy, Gansu Agricultural University, Lanzhou 730070, China.
Nitrogen is a critical factor in plant growth, development, and crop yield. NODULE-INCEPTION-like proteins (NLPs), which are plant-specific transcription factors, function as nitrate sensors and play a vital role in the nitrogen response of plants. However, the genome-wide identification of the gene family, the elucidation of the underlying molecular mechanism governing nitrogen response, and haplotype mining remain elusive in millet.
View Article and Find Full Text PDFSci Rep
December 2024
Clinical Psychology Department, PFH University of Applied Sciences, Göttingen, Germany.
ArXiv
November 2024
Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065.
Objectives: The vast and complex nature of human genomic sequencing data presents challenges for effective analysis. This review aims to investigate the application of Natural Language Processing (NLP) techniques, particularly Large Language Models (LLMs) and transformer architectures, in deciphering genomic codes, focusing on tokenization, transformer models, and regulatory annotation prediction. This review aims to assess data and model accessibility in the most recent literature, gaining a better understanding of the existing capabilities and constraints of these tools in processing genomic sequencing data.
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