The Arabidopsis DEETIOLATED2 (DET2) gene has been cloned and shown to encode a protein that shares significant sequence identity with mammalian steroid 5 alpha-reductases. Loss of DET2 function causes many defects in Arabidopsis development that can be rescued by the application of brassinolide; therefore, we propose that DET2 encodes a reductase that acts at the first step of the proposed biosynthetic pathway--in the conversion of campesterol to campestanol. Here, we used biochemical measurements and biological assays to determine the precise biochemical defect in det2 mutants. We show that DET2 actually acts at the second step in brassinolide biosynthesis in the 5 alpha-reduction of (24R)-24-methylcholest-4-en-3-one, which is further modified to form campestanol. In feeding experiments using 2H6-labeled campesterol, no significant level of 2H6-labeled campestanol was detected in det2, whereas the wild type accumulated substantial levels. Using gas chromatography-selected ion monitoring analysis, we show that several presumed null alleles of det2 accumulated only 8 to 15% of the wild-type levels of campestanol. Moreover, in det2 mutants, the endogenous levels of (24R)-24-methylcholest-4-en-3-one increased by threefold, whereas the levels of all other measured brassinosteroids accumulated to < 10% of wild-type levels. Exogenously applied biosynthetic intermediates of brassinolide were found to rescue both the dark- and light-grown defects of det2 mutants. Together, these results refine the original proposed pathway for brassinolide and indicate that mutations in DET2 block the second step in brassinosteroid biosynthesis. These results reinforce the utility of combining genetic and biochemical analyses to studies of biosynthetic pathways and strengthen the argument that brassinosteroids play an essential role in Arabidopsis development.
The amount of biomedical literature is vast and growing quickly, and accurate text mining techniques could help researchers to efficiently extract useful information from the literature. However, existing named entity recognition models used by text mining tools such as tmTool and ezTag are not effective enough, and cannot accurately discover new entities. Also, the traditional text mining tools do not consider overlapping entities, which are frequently observed in multi-type named entity recognition results. We propose a neural biomedical named entity recognition and multi-type normalization tool called BERN. The BERN uses high-performance BioBERT named entity recognition models which recognize known entities and discover new entities. Also, probability-based decision rules are developed to identify the types of overlapping entities. Furthermore, various named entity normalization models are integrated into BERN for assigning a distinct identifier to each recognized entity. The BERN provides a Web service for tagging entities in PubMed articles or raw text. Researchers can use the BERN Web service for their text mining tasks, such as new named entity discovery, information retrieval, question answering, and relation extraction. The application programming interfaces and demonstrations of BERN are publicly available at https://bern.korea.ac.kr.
An entomopathogenic bacterium, Xenorhabdus nematophila, is known to have potent antibiotic activities to maintain monoxenic condition in its insect host for effective pathogenesis and ultimately for optimal development of its nematode symbiont, Steinernema carpocapsae. In this study we assess its antibacterial activity against plant-pathogenic bacteria and identify its unknown antibiotics. The bacterial culture broth had significant antibacterial activity that increased with development of the bacteria and reached its maximum at the stationary growth phase. The antibiotic activities were significant against five plant-pathogenic bacterial strains: Agrobacterium vitis, Pectobacterium carotovorum subsp. atrosepticum, P. carotovorum subsp. carotovorum, Pseudomonas syringae pv. tabaci, and Ralstonia solanacearum. The antibacterial factors were extracted with butanol and fractionated using column chromatography with the eluents of different hydrophobic intensities. Two active antibacterial subfractions were purified, and the higher active fraction was further fractionated and identified as a single compound of benzylideneacetone (trans-4-phenyl-3-buten-2-one). With heat stability, the synthetic compound showed equivalent antibiotic activity and spectrum to the purified compound. This study reports a new antibiotic compound synthesized by X. nematophila, which is a monoterpenoid compound and active against some Gram-negative bacteria.
Motivation Traditional drug discovery approaches identify a target for a disease and find a compound that binds to the target. In this approach, structures of compounds are considered as the most important features because it is assumed that similar structures will bind to the same target. Therefore, structural analogs of the drugs that bind to the target are selected as drug candidates. However, even though compounds are not structural analogs, they may achieve the desired response. A new drug discovery method based on drug response, which can complement the structure-based methods, is needed. Results We implemented Siamese neural networks called ReSimNet that take as input two chemical compounds and predicts the CMap score of the two compounds, which we use to measure the transcriptional response similarity of the two compounds. ReSimNet learns the embedding vector of a chemical compound in a transcriptional response space. ReSimNet is trained to minimize the difference between the cosine similarity of the embedding vectors of the two compounds and the CMap score of the two compounds. ReSimNet can find pairs of compounds that are similar in response even though they may have dissimilar structures. In our quantitative evaluation, ReSimNet outperformed the baseline machine learning models. The ReSimNet ensemble model achieves a Pearson correlation of 0.518 and a precision@1% of 0.989. In addition, in the qualitative analysis, we tested ReSimNet on the ZINC15 database and showed that ReSimNet successfully identifies chemical compounds that are relevant to a prototype drug whose mechanism of action is known. Availability and implementation The source code and the pre-trained weights of ReSimNet are available at https://github.com/dmis-lab/ReSimNet. Supplementary information Supplementary data are available at Bioinformatics online.
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