| 1. |
- Karlsson, Milla, et al.
(author)
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CRISPR/Cas9 genome editing of potato StDMR6-1 results in plants less affected by different stress conditions
- 2024
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In: Horticulture Research. - 2052-7276. ; 11
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Journal article (peer-reviewed)abstract
- Potato is the third most important food crop, but cultivation is challenged by numerous diseases and adverse abiotic conditions. To combat diseases, frequent fungicide application is common. Knocking out susceptibility genes by genome editing could be a durable option to increase resistance. DMR6 has been described as a susceptibility gene in several crops, based on data that indicates increased resistance upon interruption of the gene function. In potato, Stdmr6-1 mutants have been described to have increased resistance against the late blight pathogen Phytophthora infestans in controlled conditions. Here, we present field evaluations of CRISPR/Cas9 mutants, in a location with a complex population of P. infestans, during four consecutive years that indicate increased resistance to late blight without any trade-off in terms of yield penalty or tuber quality. Furthermore, studies of potato tubers from the field trials indicated increased resistance to common scab, and the mutant lines exhibit increased resistance to early blight pathogen Alternaria solani in controlled conditions. Early blight and common scab are problematic targets in potato resistance breeding, as resistance genes are very scarce. The described broad-spectrum resistance of Stdmr6-1 mutants may further extend to some abiotic stress conditions. In controlled experiments of either drought simulation or salinity, Stdmr6-1 mutant plants are less affected than the background cultivar. Together, these results demonstrate the prospect of the Stdmr6-1 mutants as a useful tool in future sustainable potato cultivation without any apparent trade-offs.
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| 2. |
- Tian, Xue-Chan, et al.
(author)
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Plant-LncPipe: a computational pipeline providing significant improvement in plant lncRNA identification
- 2024
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In: Horticulture Research. - 2662-6810 .- 2052-7276. ; 11:4
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Journal article (peer-reviewed)abstract
- Long non-coding RNAs (lncRNAs) play essential roles in various biological processes, such as chromatin remodeling, post-transcriptional regulation, and epigenetic modifications. Despite their critical functions in regulating plant growth, root development, and seed dormancy, the identification of plant lncRNAs remains a challenge due to the scarcity of specific and extensively tested identification methods. Most mainstream machine learning-based methods used for plant lncRNA identification were initially developed using human or other animal datasets, and their accuracy and effectiveness in predicting plant lncRNAs have not been fully evaluated or exploited. To overcome this limitation, we retrained several models, including CPAT, PLEK, and LncFinder, using plant datasets and compared their performance with mainstream lncRNA prediction tools such as CPC2, CNCI, RNAplonc, and LncADeep. Retraining these models significantly improved their performance, and two of the retrained models, LncFinder-plant and CPAT-plant, alongside their ensemble, emerged as the most suitable tools for plant lncRNA identification. This underscores the importance of model retraining in tackling the challenges associated with plant lncRNA identification. Finally, we developed a pipeline (Plant-LncPipe) that incorporates an ensemble of the two best-performing models and covers the entire data analysis process, including reads mapping, transcript assembly, lncRNA identification, classification, and origin, for the efficient identification of lncRNAs in plants. The pipeline, Plant-LncPipe, is available at: https://github.com/xuechantian/Plant-LncRNA-pipline.
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