Plant Methods最新文献

CRISPR/Cas9-mediated gene editing requires high efficiency to be routinely implemented, especially in species which are laborious and slow to transform. This requirement intensifies further when targeting multiple genes simultaneously, which is required for genetic screening or more complex genome engineering. Species in the Citrus genus fall into this category. Here we describe a series of experiments with the collective aim of improving multiplex gene editing in the Carrizo citrange cultivar using tRNA-based sgRNA arrays. We evaluate a range of promoters for their efficacy in such experiments and achieve significant improvements by optimizing the expression of both the Cas9 endonuclease and the sgRNA array. In the case of the former we find the UBQ10 or RPS5a promoters from Arabidopsis driving the zCas9i endonuclease variant useful for achieving high levels of editing. The choice of promoter expressing the sgRNA array also had a large impact on gene editing efficiency across multiple targets. In this respect Pol III promoters perform especially well, but we also demonstrate that the UBQ10 and ES8Z promoters from Arabidopsis are robust alternatives. Ultimately, this study provides a quantitative insight into CRISPR/Cas9 vector design that has practical application in the simultaneous editing of multiple genes in Citrus, and potentially other eudicot plant species.

Background: Root phenotyping is particularly challenging because of complexity and inaccessibility of root apparatus. Orientation is one of the most important architectural traits of roots and its characterization is generally addressed using multiple approaches often based on overall measurements which are difficult to correlate to plant specific physiological aspects and its genetic features. Hence, a 3D image analysis approach, based on the recent method of Straumit, is proposed in this study to obtain a local mapping of root angles.

Results: Proposed method was applied here on radicles of carrot seedlings grown in real weightlessness on the International Space Station (ISS) and on Earth simulated weightlessness by clinorotation. A reference experiment in 1 g static condition on Earth was also performed. Radicles were imaged by X-ray micro-CT and two novel root orientation traits were defined: the "root angle to sowing plane" (RASP) providing accurate angle distributions for each analysed radicle and the "root orientation changes" (ROC) number. The parameters of the RASP distributions and the ROC values did not exhibit any significant difference in orientation between radicles grown under clinorotation and on the ISS. Only a slight thickening in root corners was found in simulated vs real weightlessness. Such results showed that a simple uniaxial clinostat can be an affordable analog in experimental studies reckoning on weightless radicles growth.

Conclusions: The proposed local orientation mapping approach can be extended also to different root systems providing a contribution in the challenging task of phenotyping complex and important plant structures such as roots.

Background: Fruit storage methods such as dynamic controlled atmosphere (DCA) technology enable adjusting the level of oxygen in the storage room, according to the physiological state of the product to slow down the ripening process. However, the successful application of DCA requires precise and reliable sensors of the oxidative stress of the fruit. In this study, respiration rate and chlorophyll fluorescence (CF) signals were evaluated after introducing a novel predictors of apples' hypoxic stress based on laser speckle imaging technique (LSI).

Results: Both chlorophyll fluorescence and LSI signals were equally good for stress detection in principle. However, in an application with automatic detection based on machine learning models, the LSI signal proved to be superior, due to its stability and measurement repeatability. Moreover, the shortcomings of the CF signal appear to be its inability to indicate oxygen stress in tissues with low chlorophyll content but this does not apply to LSI. A comparison of different LSI signal processing methods showed that method based on the dynamics of changes in image content was better indicators of stress than methods based on measurements of changes in pixel brightness (inertia moment or laser speckle contrast analysis). Data obtained using the near-infrared laser provided better prediction capabilities, compared to the laser with red light.

Conclusions: The study showed that the signal from the scattered laser light phenomenon is a good predictor for the oxidative stress of apples. Results showed that effective prediction using LSI was possible and did not require additional signals. The proposed method has great potential as an alternative indicator of fruit oxidative stress, which can be applied in modern storage systems with a dynamically controlled atmosphere.

Waterlogging is expected to become a more prominent yield restricting stress for barley as rainfall frequency is increasing in many regions due to climate change. The duration of waterlogging events in the field is highly variable throughout the season, and this variation is also observed in experimental waterlogging studies. Such variety of protocols make intricate physiological responses challenging to assess and quantify. To assess barley waterlogging tolerance in controlled conditions, we present an optimal duration and setup of simulated waterlogging stress using image-based phenotyping. Six protocols durations, 5, 10, and 14 days of stress with and without seven days of recovery, were tested. To quantify the physiological effects of waterlogging on growth and greenness, we used top down and side view RGB (Red-Green-Blue) images. These images were taken daily throughout each of the protocols using the PSI PlantScreen™ imaging platform. Two genotypes of two-row spring barley, grown in glasshouse conditions, were subjected to each of the six protocols, with stress being imposed at the three-leaf stage. Shoot biomass and root imaging data were analysed to determine the optimal stress protocol duration, as well as to quantify the growth and morphometric changes of barley in response to waterlogging stress. Our time-series results show a significant growth reduction and alteration of greenness, allowing us to determine an optimal protocol duration of 14 days of stress and seven days of recovery for controlled conditions. Moreover, to confirm the reproducibility of this protocol, we conducted the same experiment in a different facility equipped with RGB and chlorophyll fluorescence imaging sensors. Our results demonstrate that the selected protocol enables the assessment of genotypic differences, which allow us to further determine tolerance responses in a glasshouse environment. Altogether, this work presents a new and reproducible image-based protocol to assess early stage waterlogging tolerance, empowering a precise quantification of waterlogging stress relevant markers such as greenness, Fv/Fm and growth rates.

Rice blast is the primary disease affecting rice yield and quality, and its effective detection is essential to ensure rice yield and promote sustainable agricultural production. To address traditional disease detection methods' time-consuming and inefficient nature, we proposed a method called Pyramid-YOLOv8 for rapid and accurate rice leaf blast disease detection in this study. The algorithm is built on the YOLOv8x network framework and features a multi-attention feature fusion network structure. This structure enhances the original feature pyramid structure and works with an additional detection head for improved performance. Additionally, this study designs a lightweight C2F-Pyramid module to enhance the model's computational efficiency. In the comparison experiments, Pyramid-YOLOv8 shows excellent performance with a mean Average Precision (mAP) of 84.3%, which is an improvement of 9.9%, 4.3%, 7.4%, 6.1%, 1.5%, 3.7%, and 8.2% compared to the models Faster-RCNN, RT-DETR, YOLOv3-SPP, YOLOv5x, YOLOv9e, and YOLOv10x, respectively. Additionally, it reaches a detection speed of 62.5 FPS; the model comprises only 42.0 M parameters. Meanwhile, the model size and Floating Point Operations (FLOPs) are reduced by 41.7% and 23.8%, respectively. These results demonstrate the high efficiency of Pyramid-YOLOv8 in detecting rice leaf blast. In summary, the Pyramid-YOLOv8 algorithm developed in this study offers a robust theoretical foundation for rice disease detection and introduces a new perspective on disease management and prevention strategies in agricultural production.