Dataset Information

Large-Scale Phenomics Identifies Primary and Fine-Tuning Roles for CRKs in Responses Related to Oxidative Stress.

ABSTRACT: Cysteine-rich receptor-like kinases (CRKs) are transmembrane proteins characterized by the presence of two domains of unknown function 26 (DUF26) in their ectodomain. The CRKs form one of the largest groups of receptor-like protein kinases in plants, but their biological functions have so far remained largely uncharacterized. We conducted a large-scale phenotyping approach of a nearly complete crk T-DNA insertion line collection showing that CRKs control important aspects of plant development and stress adaptation in response to biotic and abiotic stimuli in a non-redundant fashion. In particular, the analysis of reactive oxygen species (ROS)-related stress responses, such as regulation of the stomatal aperture, suggests that CRKs participate in ROS/redox signalling and sensing. CRKs play general and fine-tuning roles in the regulation of stomatal closure induced by microbial and abiotic cues. Despite their great number and high similarity, large-scale phenotyping identified specific functions in diverse processes for many CRKs and indicated that CRK2 and CRK5 play predominant roles in growth regulation and stress adaptation, respectively. As a whole, the CRKs contribute to specificity in ROS signalling. Individual CRKs control distinct responses in an antagonistic fashion suggesting future potential for using CRKs in genetic approaches to improve plant performance and stress tolerance.

SUBMITTER: Bourdais G

PROVIDER: S-EPMC4511522 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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Large-Scale Phenomics Identifies Primary and Fine-Tuning Roles for CRKs in Responses Related to Oxidative Stress.

Bourdais Gildas G Burdiak Paweł P Gauthier Adrien A Nitsch Lisette L Salojärvi Jarkko J Rayapuram Channabasavangowda C Idänheimo Niina N Hunter Kerri K Kimura Sachie S Merilo Ebe E Vaattovaara Aleksia A Oracz Krystyna K Kaufholdt David D Pallon Andres A Anggoro Damar Tri DT Glów Dawid D Lowe Jennifer J Zhou Ji J Mohammadi Omid O Puukko Tuomas T Albert Andreas A Lang Hans H Ernst Dieter D Kollist Hannes H Brosché Mikael M Durner Jörg J Borst Jan Willem JW Collinge David B DB Karpiński Stanisław S Lyngkjær Michael F MF Robatzek Silke S Wrzaczek Michael M Kangasjärvi Jaakko J

PLoS genetics 20150721 7

Cysteine-rich receptor-like kinases (CRKs) are transmembrane proteins characterized by the presence of two domains of unknown function 26 (DUF26) in their ectodomain. The CRKs form one of the largest groups of receptor-like protein kinases in plants, but their biological functions have so far remained largely uncharacterized. We conducted a large-scale phenotyping approach of a nearly complete crk T-DNA insertion line collection showing that CRKs control important aspects of plant development an ...[more]

PMID: 26197346

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Project description:BACKGROUND:The bidirectional encoder representations from transformers (BERT) model has achieved great success in many natural language processing (NLP) tasks, such as named entity recognition and question answering. However, little prior work has explored this model to be used for an important task in the biomedical and clinical domains, namely entity normalization. OBJECTIVE:We aim to investigate the effectiveness of BERT-based models for biomedical or clinical entity normalization. In addition, our second objective is to investigate whether the domains of training data influence the performances of BERT-based models as well as the degree of influence. METHODS:Our data was comprised of 1.5 million unlabeled electronic health record (EHR) notes. We first fine-tuned BioBERT on this large collection of unlabeled EHR notes. This generated our BERT-based model trained using 1.5 million electronic health record notes (EhrBERT). We then further fine-tuned EhrBERT, BioBERT, and BERT on three annotated corpora for biomedical and clinical entity normalization: the Medication, Indication, and Adverse Drug Events (MADE) 1.0 corpus, the National Center for Biotechnology Information (NCBI) disease corpus, and the Chemical-Disease Relations (CDR) corpus. We compared our models with two state-of-the-art normalization systems, namely MetaMap and disease name normalization (DNorm). RESULTS:EhrBERT achieved 40.95% F1 in the MADE 1.0 corpus for mapping named entities to the Medical Dictionary for Regulatory Activities and the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT), which have about 380,000 terms. In this corpus, EhrBERT outperformed MetaMap by 2.36% in F1. For the NCBI disease corpus and CDR corpus, EhrBERT also outperformed DNorm by improving the F1 scores from 88.37% and 89.92% to 90.35% and 93.82%, respectively. Compared with BioBERT and BERT, EhrBERT outperformed them on the MADE 1.0 corpus and the CDR corpus. CONCLUSIONS:Our work shows that BERT-based models have achieved state-of-the-art performance for biomedical and clinical entity normalization. BERT-based models can be readily fine-tuned to normalize any kind of named entities.

Dataset Information

Large-Scale Phenomics Identifies Primary and Fine-Tuning Roles for CRKs in Responses Related to Oxidative Stress.

Publications

Large-Scale Phenomics Identifies Primary and Fine-Tuning Roles for CRKs in Responses Related to Oxidative Stress.

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