Plant Methods最新文献

{"title":"Advanced screening methods for assessing motility and hatching in plant-parasitic nematodes.","authors":"Alena Kadlecová, Romana Hendrychová, Tomáš Jirsa, Václav Čermák, Mengmeng Huang, Florian M W Grundler, A Sylvia S Schleker","doi":"10.1186/s13007-024-01233-z","DOIUrl":"10.1186/s13007-024-01233-z","url":null,"abstract":"<p><strong>Background: </strong>Plant-parasitic nematodes are economically important pests responsible for substantial losses in agriculture. Researchers focusing on plant-parasitic nematodes, especially on finding new ways of their control, often need to assess basic parameters such as their motility, viability, and reproduction. Traditionally, these assays involve visually counting juveniles and eggs under a dissecting microscope, making this investigation time-consuming and laborious.</p><p><strong>Results: </strong>In this study, we established a procedure to efficiently determine the motility of two plant-parasitic nematode species, Heterodera schachtii and Ditylenchus destructor, using the WMicrotracker ONE platform. Additionally, we demonstrated that hatching of the cyst nematode H. schachtii can be evaluated using both the WMicrotracker ONE and by assessing the enzymatic activity of chitinase produced during hatching.</p><p><strong>Conclusions: </strong>We present fast and straightforward protocols for studying nematode motility and hatching that allow us to draw conclusions about viability and survival. Thus, these methods are useful tools for facilitating fast and efficient evaluation in various fields of research focused on plant-parasitic nematodes.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"108"},"PeriodicalIF":4.7,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11264999/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141734912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Revisiting superiority and stability metrics of cultivar performances using genomic data: derivations of new estimators.","authors":"Humberto Fanelli Carvalho, Simon Rio, Julian García-Abadillo, Julio Isidro Y Sánchez","doi":"10.1186/s13007-024-01231-1","DOIUrl":"10.1186/s13007-024-01231-1","url":null,"abstract":"","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"106"},"PeriodicalIF":4.7,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11253387/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141627329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MAPtools: command-line tools for mapping-by-sequencing and QTL-Seq analysis and visualization.","authors":"César Martínez-Guardiola, Ricardo Parreño, Héctor Candela","doi":"10.1186/s13007-024-01222-2","DOIUrl":"10.1186/s13007-024-01222-2","url":null,"abstract":"<p><strong>Background: </strong>Classical mutagenesis is a powerful tool that has allowed researchers to elucidate the molecular and genetic basis of a plethora of processes in many model species. The integration of these methods with modern massively parallel sequencing techniques, initially in model species but currently also in many crop species, is accelerating the identification of genes underlying a wide range of traits of agronomic interest.</p><p><strong>Results: </strong>We have developed MAPtools, an open-source Python3 application designed specifically for the analysis of genomic data from bulked segregant analysis experiments, including mapping-by-sequencing (MBS) and quantitative trait locus sequencing (QTL-seq) experiments. We have extensively tested MAPtools using datasets published in recent literature.</p><p><strong>Conclusions: </strong>MAPtools gives users the flexibility to customize their bioinformatics pipeline with various commands for calculating allele count-based statistics, generating plots to pinpoint candidate regions, and annotating the effects of SNP and indel mutations. While extensively tested with plants, the program is versatile and applicable to any species for which a mapping population can be generated and a sequenced genome is available.</p><p><strong>Availability and implementation: </strong>MAPtools is available under GPL v3.0 license and documented as a Python3 package at https://github.com/hcandela/MAPtools .</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"107"},"PeriodicalIF":4.7,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11253474/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141627331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying rice field weeds from unmanned aerial vehicle remote sensing imagery using deep learning.","authors":"Zhonghui Guo, Dongdong Cai, Yunyi Zhou, Tongyu Xu, Fenghua Yu","doi":"10.1186/s13007-024-01232-0","DOIUrl":"10.1186/s13007-024-01232-0","url":null,"abstract":"<p><strong>Background: </strong>Rice field weed object detection can provide key information on weed species and locations for precise spraying, which is of great significance in actual agricultural production. However, facing the complex and changing real farm environments, traditional object detection methods still have difficulties in identifying small-sized, occluded and densely distributed weed instances. To address these problems, this paper proposes a multi-scale feature enhanced DETR network, named RMS-DETR. By adding multi-scale feature extraction branches on top of DETR, this model fully utilizes the information from different semantic feature layers to improve recognition capability for rice field weeds in real-world scenarios.</p><p><strong>Methods: </strong>Introducing multi-scale feature layers on the basis of the DETR model, we conduct a differentiated design for different semantic feature layers. The high-level semantic feature layer adopts Transformer structure to extract contextual information between barnyard grass and rice plants. The low-level semantic feature layer uses CNN structure to extract local detail features of barnyard grass. Introducing multi-scale feature layers inevitably leads to increased model computation, thus lowering model inference speed. Therefore, we employ a new type of Pconv (Partial convolution) to replace traditional standard convolutions in the model.</p><p><strong>Results: </strong>Compared to the original DETR model, our proposed RMS-DETR model achieved an average recognition accuracy improvement of 3.6% and 4.4% on our constructed rice field weeds dataset and the DOTA public dataset, respectively. The average recognition accuracies reached 0.792 and 0.851, respectively. The RMS-DETR model size is 40.8 M with inference time of 0.0081 s. Compared with three classical DETR models (Deformable DETR, Anchor DETR and DAB-DETR), the RMS-DETR model respectively improved average precision by 2.1%, 4.9% and 2.4%.</p><p><strong>Discussion: </strong>This model is capable of accurately identifying rice field weeds in complex real-world scenarios, thus providing key technical support for precision spraying and management of variable-rate spraying systems.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"105"},"PeriodicalIF":4.7,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11253438/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141627330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An ensemble deep learning models approach using image analysis for cotton crop classification in AI-enabled smart agriculture","authors":"Muhammad Farrukh Shahid, Tariq J. S. Khanzada, Muhammad Ahtisham Aslam, Shehroz Hussain, Souad Ahmad Baowidan, Rehab Bahaaddin Ashari","doi":"10.1186/s13007-024-01228-w","DOIUrl":"https://doi.org/10.1186/s13007-024-01228-w","url":null,"abstract":"Agriculture is one of the most crucial assets of any country, as it brings prosperity by alleviating poverty, food shortages, unemployment, and economic instability. The entire process of agriculture comprises many sectors, such as crop cultivation, water irrigation, the supply chain, and many more. During the cultivation process, the plant is exposed to many challenges, among which pesticide attacks and disease in the plant are the main threats. Diseases affect yield production, which affects the country’s economy. Over the past decade, there have been significant advancements in agriculture; nevertheless, a substantial portion of crop yields continues to be compromised by diseases and pests. Early detection and prevention are crucial for successful crop management. To address this, we propose a framework that utilizes state-of-the-art computer vision (CV) and artificial intelligence (AI) techniques, specifically deep learning (DL), for detecting healthy and unhealthy cotton plants. Our approach combines DL with feature extraction methods such as continuous wavelet transform (CWT) and fast Fourier transform (FFT). The detection process involved employing pre-trained models such as AlexNet, GoogLeNet, InceptionV3, and VGG-19. Implemented models performance was analysed based on metrics such as accuracy, precision, recall, F1-Score, and Confusion matrices. Moreover, the proposed framework employed ensemble learning framework which uses averaging method to fuse the classification score of individual DL model, thereby improving the overall classification accuracy. During the training process, the framework achieved better performance when features extracted from CWT were used as inputs to the DL model compared to features extracted from FFT. Among the learning models, GoogleNet obtained a remarkable accuracy of 93.4% and a notable F1-score of 0.953 when trained on features extracted by CWT in comparison to FFT-extracted features. It was closely followed by AlexNet and InceptionV3 with an accuracy of 93.4% and 91.8% respectively. To further improve the classification accuracy, ensemble learning framework achieved 98.4% on the features extracted from CWT as compared to feature extracted from FFT. The results show that the features extracted as scalograms more accurately detect each plant condition using DL models, facilitating the early detection of diseases in cotton plants. This early detection leads to better yield and profit which positively affects the economy.","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"29 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141612073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GeCKO: user-friendly workflows for genotyping complex genomes using target enrichment capture. A use case on the large tetraploid durum wheat genome.","authors":"Morgane Ardisson, Johanna Girodolle, Stéphane De Mita, Pierre Roumet, Vincent Ranwez","doi":"10.1186/s13007-024-01210-6","DOIUrl":"10.1186/s13007-024-01210-6","url":null,"abstract":"<p><strong>Background: </strong>Genotyping of individuals plays a pivotal role in various biological analyses, with technology choice influenced by multiple factors including genomic constraints, number of targeted loci and individuals, cost considerations, and the ease of sample preparation and data processing. Target enrichment capture of specific polymorphic regions has emerged as a flexible and cost-effective genomic reduction method for genotyping, especially adapted to the case of very large genomes. However, this approach necessitates complex bioinformatics treatment to extract genotyping data from raw reads. Existing workflows predominantly cater to phylogenetic inference, leaving a gap in user-friendly tools for genotyping analysis based on capture methods. In response to these challenges, we have developed GeCKO (Genotyping Complexity Knocked-Out). To assess the effectiveness of combining target enrichment capture with GeCKO, we conducted a case study on durum wheat domestication history, involving sequencing, processing, and analyzing variants in four relevant durum wheat groups.</p><p><strong>Results: </strong>GeCKO encompasses four distinct workflows, each designed for specific steps of genomic data processing: (i) read demultiplexing and trimming for data cleaning, (ii) read mapping to align sequences to a reference genome, (iii) variant calling to identify genetic variants, and (iv) variant filtering. Each workflow in GeCKO can be easily configured and is executable across diverse computational environments. The workflows generate comprehensive HTML reports including key summary statistics and illustrative graphs, ensuring traceable, reproducible results and facilitating straightforward quality assessment. A specific innovation within GeCKO is its 'targeted remapping' feature, specifically designed for efficient treatment of targeted enrichment capture data. This process consists of extracting reads mapped to the targeted regions, constructing a smaller sub-reference genome, and remapping the reads to this sub-reference, thereby enhancing the efficiency of subsequent steps.</p><p><strong>Conclusions: </strong>The case study results showed the expected intra-group diversity and inter-group differentiation levels, confirming the method's effectiveness for genotyping and analyzing genetic diversity in species with complex genomes. GeCKO streamlined the data processing, significantly improving computational performance and efficiency. The targeted remapping enabled straightforward SNP calling in durum wheat, a task otherwise complicated by the species' large genome size. This illustrates its potential applications in various biological research contexts.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"103"},"PeriodicalIF":4.7,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11246579/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141604045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Terrestrial laser scanning and low magnetic field digitization yield similar architectural coarse root traits for 32-year-old Pinus ponderosa trees.","authors":"Antonio Montagnoli, Andrew T Hudak, Pasi Raumonen, Bruno Lasserre, Mattia Terzaghi, Carlos A Silva, Benjamin C Bright, Lee A Vierling, Bruna N de Vasconcellos, Donato Chiatante, R Kasten Dumroese","doi":"10.1186/s13007-024-01229-9","DOIUrl":"10.1186/s13007-024-01229-9","url":null,"abstract":"<p><strong>Background: </strong>Understanding how trees develop their root systems is crucial for the comprehension of how wildland and urban forest ecosystems plastically respond to disturbances such as harvest, fire, and climate change. The interplay between the endogenously determined root traits and the response to environmental stimuli results in tree adaptations to biotic and abiotic factors, influencing stability, carbon allocation, and nutrient uptake. Combining the three-dimensional structure of the root system, with root morphological trait information promotes a robust understanding of root function and adaptation plasticity. Low Magnetic Field Digitization coupled with AMAPmod (botAnique et Modelisation de l'Architecture des Plantes) software has been the best-performing method for describing root system architecture and providing reliable measurements of coarse root traits, but the pace and scale of data collection remain difficult. Instrumentation and applications related to Terrestrial Laser Scanning (TLS) have advanced appreciably, and when coupled with Quantitative Structure Models (QSM), have shown some potential toward robust measurements of tree root systems. Here we compare, we believe for the first time, these two methodologies by analyzing the root system of 32-year-old Pinus ponderosa trees.</p><p><strong>Results: </strong>In general, at the total root system level and by root-order class, both methods yielded comparable values for the root traits volume, length, and number. QSM for each root trait was highly sensitive to the root size (i.e., input parameter PatchDiam) and models were optimized when discrete PatchDiam ranges were specified for each trait. When examining roots in the four cardinal direction sectors, we observed differences between methodologies for length and number depending on root order but not volume.</p><p><strong>Conclusions: </strong>We believe that TLS and QSM could facilitate rapid data collection, perhaps in situ, while providing quantitative accuracy, especially at the total root system level. If more detailed measures of root system architecture are desired, a TLS method would benefit from additional scans at differing perspectives, avoiding gravitational displacement to the extent possible, while subsampling roots by hand to calibrate and validate QSM models. Despite some unresolved logistical challenges, our results suggest that future use of TLS may hold promise for quantifying tree root system architecture in a rapid, replicable manner.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"102"},"PeriodicalIF":4.7,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11232291/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141564103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep migration learning-based recognition of diseases and insect pests in Yunnan tea under complex environments.","authors":"Zhaowen Li, Jihong Sun, Yingming Shen, Ying Yang, Xijin Wang, Xinrui Wang, Peng Tian, Ye Qian","doi":"10.1186/s13007-024-01219-x","DOIUrl":"10.1186/s13007-024-01219-x","url":null,"abstract":"<p><strong>Background: </strong>The occurrence, development, and outbreak of tea diseases and pests pose a significant challenge to the quality and yield of tea, necessitating prompt identification and control measures. Given the vast array of tea diseases and pests, coupled with the intricacies of the tea planting environment, accurate and rapid diagnosis remains elusive. In addressing this issue, the present study investigates the utilization of transfer learning convolution neural networks for the identification of tea diseases and pests. Our objective is to facilitate the accurate and expeditious detection of diseases and pests affecting the Yunnan Big leaf kind of tea within its complex ecological niche.</p><p><strong>Results: </strong>Initially, we gathered 1878 image data encompassing 10 prevalent types of tea diseases and pests from complex environments within tea plantations, compiling a comprehensive dataset. Additionally, we employed data augmentation techniques to enrich the sample diversity. Leveraging the ImageNet pre-trained model, we conducted a comprehensive evaluation and identified the Xception architecture as the most effective model. Notably, the integration of an attention mechanism within the Xeption model did not yield improvements in recognition performance. Subsequently, through transfer learning and the freezing core strategy, we achieved a test accuracy rate of 98.58% and a verification accuracy rate of 98.2310%.</p><p><strong>Conclusions: </strong>These outcomes signify a significant stride towards accurate and timely detection, holding promise for enhancing the sustainability and productivity of Yunnan tea. Our findings provide a theoretical foundation and technical guidance for the development of online detection technologies for tea diseases and pests in Yunnan.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"101"},"PeriodicalIF":4.7,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11229499/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141538361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A high-efficiency transient expression system mediated by Agrobacterium tumefaciens in Spinacia oleracea leaves.","authors":"Yumeng Zhang, Liuliu Qiu, Yongxue Zhang, Yiran Wang, Chunxiang Fu, Shaojun Dai, Meihong Sun","doi":"10.1186/s13007-024-01218-y","DOIUrl":"10.1186/s13007-024-01218-y","url":null,"abstract":"<p><strong>Background: </strong>Optimization of a highly efficient transient expression system is critical for the study of gene function, particularly in those plants in which stable transformation methods are not widely available. Agrobacterium tumefaciens‑mediated transient transformation is a simple and low-cost method that has been developed and applied to a wide variety of plant species. However, the transient expression in spinach (Spinacia oleracea L.) is still not reported.</p><p><strong>Results: </strong>We developed a transient expression system in spinach leaves of the Sp75 and Sp73 varieties. Several factors influencing the transformation efficiency were optimized such as Agrobacterium strain, spinach seedling stage, leaf position, and the expression time after injection. Agrobacterium strain GV3101 (pSoup-p19) was more efficient than AGL1 in expressing recombinant protein in spinach leaves. In general, Sp75 leaves were more suitable than Sp73 leaves, regardless of grow stage. At four-leaf stage, higher intensity and efficiency of transient expression were observed in group 1 (G1) of Sp75 at 53 h after injection (HAI) and in G1 of Sp73 at 64 HAI. At six-leaf stage of Sp75, group 3 (G3) at 72 HAI were the most effective condition for transient expression. Using the optimized expression system, we detected the subcellular localization of a transcriptional co-activator SoMBF1c and a NADPH oxidase SoRbohF. We also detected the interaction of the protein kinase SoCRK10 and the NADPH oxidase SoRbohB.</p><p><strong>Conclusion: </strong>This study established a method of highly efficient transient expression mediated by Agrobacterium in spinach leaves. The transient expression system will facilitate the analysis of gene function and lay a solid foundation for molecular design breeding of spinach.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"100"},"PeriodicalIF":4.7,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11220957/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141493028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An improved bacterial mRNA enrichment strategy in dual RNA sequencing to unveil the dynamics of plant-bacterial interactions.","authors":"Jayabalan Shilpha, Junesung Lee, Ji-Su Kwon, Hyun-Ah Lee, Jae-Young Nam, Hakgi Jang, Won-Hee Kang","doi":"10.1186/s13007-024-01227-x","DOIUrl":"10.1186/s13007-024-01227-x","url":null,"abstract":"<p><strong>Background: </strong>Dual RNA sequencing is a powerful tool that enables a comprehensive understanding of the molecular dynamics underlying plant-microbe interactions. RNA sequencing (RNA-seq) poses technical hurdles in the transcriptional analysis of plant-bacterial interactions, especially in bacterial transcriptomics, owing to the presence of abundant ribosomal RNA (rRNA), which potentially limits the coverage of essential transcripts. Therefore, to achieve cost-effective and comprehensive sequencing of the bacterial transcriptome, it is imperative to devise efficient methods for eliminating rRNA and enhancing the proportion of bacterial mRNA. In this study, we modified a strand-specific dual RNA-seq method with the goal of enriching the proportion of bacterial mRNA in the bacteria-infected plant samples. The enriched method involved the sequential separation of plant mRNA by poly A selection and rRNA removal for bacterial mRNA enrichment followed by strand specific RNA-seq library preparation steps. We assessed the efficiency of the enriched method in comparison to the conventional method by employing various plant-bacterial interactions, including both host and non-host resistance interactions with pathogenic bacteria, as well as an interaction with a beneficial rhizosphere associated bacteria using pepper and tomato plants respectively.</p><p><strong>Results: </strong>In all cases of plant-bacterial interactions examined, an increase in mapping efficiency was observed with the enriched method although it produced a lower read count. Especially in the compatible interaction with Xanthmonas campestris pv. Vesicatoria race 3 (Xcv3), the enriched method enhanced the mapping ratio of Xcv3-infected pepper samples to its own genome (15.09%; 1.45-fold increase) and the CDS (8.92%; 1.49-fold increase). The enriched method consistently displayed a greater number of differentially expressed genes (DEGs) than the conventional RNA-seq method at all fold change threshold levels investigated, notably during the early stages of Xcv3 infection in peppers. The Gene Ontology (GO) enrichment analysis revealed that the DEGs were predominantly enriched in proteolysis, kinase, serine type endopeptidase and heme binding activities.</p><p><strong>Conclusion: </strong>The enriched method demonstrated in this study will serve as a suitable alternative to the existing RNA-seq method to enrich bacterial mRNA and provide novel insights into the intricate transcriptomic alterations within the plant-bacterial interplay.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"99"},"PeriodicalIF":4.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11218159/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141477216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}