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      NIN-like protein 7 transcription factor is a plant nitrate sensor

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          Abstract

          Nitrate is an essential nutrient and signaling molecule for plant growth. Plants sense intracellular nitrate to adjust their metabolic and growth responses. Here we identify the primary nitrate sensor in plants. We found that mutation of all seven Arabidopsis NIN-like protein (NLP) transcription factors abolished plants’ primary nitrate responses and developmental programs. Analyses of NIN-NLP7 chimeras and nitrate binding revealed that NLP7 is derepressed upon nitrate perception via its amino terminus. A genetically encoded fluorescent split biosensor, mCitrine-NLP7, enabled visualization of single-cell nitrate dynamics in planta. The nitrate sensor domain of NLP7 resembles the bacterial nitrate sensor NreA. Substitutions of conserved residues in the ligand-binding pocket impaired the ability of nitrate-triggered NLP7 to control transcription, transport, metabolism, development, and biomass. We propose that NLP7 represents a nitrate sensor in land plants.

          Transcription-activating nitrate sensor

          Plants depend on nitrogen, responding with changes in growth and metabolism when nitrogen supplies change. Indeed, nitrogen fertilizer underlies a good deal of agricultural crop productivity. Studying a family of seven similar genes, Liu et al . identified the nitrate sensor in the small mustard plant Arabidopsis thaliana . The protein’s nitrate-binding pocket resembles that found in bacteria nitrate sensors. Conformation change upon nitrate binding allows the protein to then function as a transcriptional activator, triggering the plant’s responses to nitrogen availability. —PJH

          Abstract

          A single bifunctional protein links nitrate sensing to changes in transcription in the small mustard plant Arabidopsis thaliana .

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          Most cited references44

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            Enzymatic assembly of DNA molecules up to several hundred kilobases.

            We describe an isothermal, single-reaction method for assembling multiple overlapping DNA molecules by the concerted action of a 5' exonuclease, a DNA polymerase and a DNA ligase. First we recessed DNA fragments, yielding single-stranded DNA overhangs that specifically annealed, and then covalently joined them. This assembly method can be used to seamlessly construct synthetic and natural genes, genetic pathways and entire genomes, and could be a useful molecular engineering tool.
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              Floral dip: a simplified method forAgrobacterium-mediated transformation ofArabidopsis thaliana

              The Agrobacterium vacuum infiltration method has made it possible to transform Arabidopsis thaliana without plant tissue culture or regeneration. In the present study, this method was evaluated and a substantially modified transformation method was developed. The labor-intensive vacuum infiltration process was eliminated in favor of simple dipping of developing floral tissues into a solution containing Agrobacterium tumefaciens, 5% sucrose and 500 microliters per litre of surfactant Silwet L-77. Sucrose and surfactant were critical to the success of the floral dip method. Plants inoculated when numerous immature floral buds and few siliques were present produced transformed progeny at the highest rate. Plant tissue culture media, the hormone benzylamino purine and pH adjustment were unnecessary, and Agrobacterium could be applied to plants at a range of cell densities. Repeated application of Agrobacterium improved transformation rates and overall yield of transformants approximately twofold. Covering plants for 1 day to retain humidity after inoculation also raised transformation rates twofold. Multiple ecotypes were transformable by this method. The modified method should facilitate high-throughput transformation of Arabidopsis for efforts such as T-DNA gene tagging, positional cloning, or attempts at targeted gene replacement.
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                Author and article information

                Contributors
                Journal
                Science
                Science
                American Association for the Advancement of Science (AAAS)
                0036-8075
                1095-9203
                September 23 2022
                September 23 2022
                : 377
                : 6613
                : 1419-1425
                Affiliations
                [1 ]State Key Laboratory of Crop Stress Biology for Arid Areas and College of Life Sciences, Northwest Agriculture and Forestry University, Yangling, Shaanxi 712100, China.
                [2 ]Institute of Future Agriculture, Northwest Agriculture and Forestry University, Yangling, Shaanxi 712100, China.
                [3 ]Department of Molecular Biology and Centre for Computational and Integrative Biology, Massachusetts General Hospital, and Department of Genetics, Harvard Medical School, Boston, MA 02114, USA.
                [4 ]Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA.
                [5 ]Agro-Biotechnology Research Center, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Yayoi 1-1-1, Bunkyo-ku, Tokyo 113-8657, Japan.
                Article
                10.1126/science.add1104
                36137053
                8dea35aa-b9e3-49f6-8dd7-716be5857ad7
                © 2022
                History

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