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Combining chemical and genetic approaches to increase drought resistance in plants.

ABSTRACT: Drought stress is a major threat to crop production, but effective methods to mitigate the adverse effects of drought are not available. Here, we report that adding fluorine atoms in the benzyl ring of the abscisic acid (ABA) receptor agonist AM1 optimizes its binding to ABA receptors by increasing the number of hydrogen bonds between the compound and the surrounding amino acid residues in the receptor ligand-binding pocket. The new chemicals, known as AMFs, have long-lasting effects in promoting stomatal closure and inducing the expression of stress-responsive genes. Application of AMFs or transgenic overexpression of the receptor PYL2 in Arabidopsis and soybean plants confers increased drought resistance. The greatest increase in drought resistance is achieved when AMFs are applied to the PYL2-overexpression transgenic plants. Our results demonstrate that the combining of potent chemicals with transgenic overexpression of an ABA receptor is very effective in helping plants combat drought stress.

SUBMITTER: Cao MJ

PROVIDER: S-EPMC5662759 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Combining chemical and genetic approaches to increase drought resistance in plants.

Cao Min-Jie MJ Zhang Yu-Lu YL Liu Xue X Huang Huan H Zhou X Edward XE Wang Wen-Long WL Zeng Ai A Zhao Chun-Zhao CZ Si Tong T Du Jiamu J Wu Wen-Wu WW Wang Fu-Xing FX Xu H Eric HE Zhu Jian-Kang JK

Nature communications 20171030 1

Drought stress is a major threat to crop production, but effective methods to mitigate the adverse effects of drought are not available. Here, we report that adding fluorine atoms in the benzyl ring of the abscisic acid (ABA) receptor agonist AM1 optimizes its binding to ABA receptors by increasing the number of hydrogen bonds between the compound and the surrounding amino acid residues in the receptor ligand-binding pocket. The new chemicals, known as AMFs, have long-lasting effects in promotin ...[more]

PMID: 29084945

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Project description:BACKGROUND:The past decade has seen an upsurge in the number of publications in chemistry. The ever-swelling volume of available documents makes it increasingly hard to extract relevant new information from such unstructured texts. The BioCreative CHEMDNER challenge invites the development of systems for the automatic recognition of chemicals in text (CEM task) and for ranking the recognized compounds at the document level (CDI task). We investigated an ensemble approach where dictionary-based named entity recognition is used along with grammar-based recognizers to extract compounds from text. We assessed the performance of ten different commercial and publicly available lexical resources using an open source indexing system (Peregrine), in combination with three different chemical compound recognizers and a set of regular expressions to recognize chemical database identifiers. The effect of different stop-word lists, case-sensitivity matching, and use of chunking information was also investigated. We focused on lexical resources that provide chemical structure information. To rank the different compounds found in a text, we used a term confidence score based on the normalized ratio of the term frequencies in chemical and non-chemical journals. RESULTS:The use of stop-word lists greatly improved the performance of the dictionary-based recognition, but there was no additional benefit from using chunking information. A combination of ChEBI and HMDB as lexical resources, the LeadMine tool for grammar-based recognition, and the regular expressions, outperformed any of the individual systems. On the test set, the F-scores were 77.8% (recall 71.2%, precision 85.8%) for the CEM task and 77.6% (recall 71.7%, precision 84.6%) for the CDI task. Missed terms were mainly due to tokenization issues, poor recognition of formulas, and term conjunctions. CONCLUSIONS:We developed an ensemble system that combines dictionary-based and grammar-based approaches for chemical named entity recognition, outperforming any of the individual systems that we considered. The system is able to provide structure information for most of the compounds that are found. Improved tokenization and better recognition of specific entity types is likely to further improve system performance.

Dataset Information

Combining chemical and genetic approaches to increase drought resistance in plants.

Publications

Combining chemical and genetic approaches to increase drought resistance in plants.

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