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Site: phys.org

The LEAFY protein assembling in small chains on DNA (white and red) thanks to its DNA binding domain (pale or dark green) and oligomerization domain (pale or dark blue). Credit: Camille Sayou et al., Nature Communications The LEAFY protein, a transcription factor responsible for flower development, is able to assemble itself in small chains made up of several proteins. This mechanism allows it to bind to and activate regions of the genome that are inaccessible to a single protein. These results were obtained by scientists in the Laboratoire de Physiologie Cellulaire Végétale (CNRS/Inra/CEA/Université Grenoble Alpes) and the Institut de Biologie Structurale (CNRS/CEA/Université Grenoble Alpes), working in collaboration with their international partners. Published on 21 April 2016 in Nature Communications, they open the way to new research opportunities regarding the regulation of gene expression. The LEAFY protein plays an essential role in the beauty of the plant kingdom: it governs the development of flower buds and their different organs (sepals, petals, stamens and pistils). This protein is a transcription factor that is necessary to decipher the genetic code and is endowed with two important domains: one that binds to DNA to activate floral genes, and the second of a hitherto unknown type. The scientists have demonstrated that the latter is a so-called "oligomerization" domain that allows the LEAFY proteins to assemble in small chains. In this form, they become capable of binding to compacted regions of the chromatin while a single LEAFY protein cannot achieve this. The experiments performed suggest that once assembled in chains, the different DNA binding domains cooperate, thus improving their binding to regions that are normally too condensed to be recognized. LEAFY can thus activate the genes at the origin of the development of flower organs. LEAFY may therefore play the role of a "pioneer factor" capable of binding to the dense chromatin structure of certain genomic regions and initiating epigenetic changes that lead to gene expression. Human beings and animals do not have the LEAFY protein but other transcription factors containing oligomerization domains. These new findings therefore suggest that these domains may also contribute to giving pioneering properties to transcription factors in other kingdoms. This work thus opens new avenues for understanding the role of these factors in the regulation of gene expression. Explore further: New insights into cooperativity in gene regulation More information: Camille Sayou et al. A SAM oligomerization domain shapes the genomic binding landscape of the LEAFY transcription factor, Nature Communications (2016). DOI: 10.1038/NCOMMS11222


Roy R.N.,Grenoble Alpes | Roy R.N.,CEA Grenoble | Bonnet S.,Grenoble Alpes | Bonnet S.,CEA Grenoble | And 5 more authors.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | Year: 2015

Mental workload estimation is of crucial interest for user adaptive interfaces and neuroergonomics. Its estimation can be performed using event-related potentials (ERPs) extracted from electroencephalographic recordings (EEG). Several ERP spatial filtering methods have been designed to enhance relevant EEG activity for active brain-computer interfaces. However, to our knowledge, they have not yet been used and compared for mental state monitoring purposes. This paper presents a thorough comparison of three ERP spatial filtering methods: principal component analysis (PCA), canonical correlation analysis (CCA) and the xDAWN algorithm. Those methods are compared in their performance to allow for an accurate classification of mental workload when applied in an otherwise similar processing chain. The data of 20 healthy participants that performed a memory task for 10 minutes each was used for classification. Two levels of mental workload were considered depending on the number of digits participants had to memorize (2/6). The highest performances were obtained using the CCA filtering and the xDAWN algorithm respectively with 98% and 97% of correct classification. Their performances were significantly higher than that obtained using the PCA filtering (88%). © 2015 IEEE.

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