ABSTRACT
There is increasing interest in harnessing the microbiome to improve cropping systems. With the availability of high-throughput and low-cost sequencing technologies, gathering microbiome data is becoming more routine. However, the analysis of microbiome data is challenged by the size and complexity of the data, and the incomplete nature of many microbiome databases. Further, to bring microbiome data value, it often needs to be analyzed in conjunction with other complex data that impact on crop health and disease management, such as plant genotype and environmental factors. Artificial intelligence (AI), boosted through deep learning (DL), has achieved significant breakthroughs and is a powerful tool for managing large complex datasets such as the interplay between the microbiome, crop plants, and their environment. In this review, we aim to provide readers with a brief introduction to AI techniques, and we introduce how AI has been applied to areas of microbiome sequencing taxonomy, the functional annotation for microbiome sequences, associating the microbiome community with host traits, designing synthetic communities, genomic selection, field phenotyping, and disease forecasting. At the end of this review, we proposed further efforts that are required to fully exploit the power of AI in studying phytomicrobiomes.
ABSTRACT
Leptosphaeria biglobosa is a less virulent Leptosphaeria spp. that causes blackleg disease in canola. Previous studies from our lab have shown that inoculation with the less virulent L. biglobosa can boost the resistance of canola plants against the highly virulent L. maculans. The objective of this study was to confirm the effectiveness of L. biglobosa as a biocontrol agent against L. maculans utilizing morphology, fluorescence microscopy, gene quantification, and transcriptomic analysis. The in planta development of two Leptosphaeria species inoculated at different time points was assessed using fluorescent protein-tagged isolates which are GFP-tagged L. maculans and DsRed-tagged L. biglobosa. The growth inhibition of L. maculans by pre-and co-inoculated L. biglobosa was supported by no lesion development on cotyledons and no or weak fluorescence protein-tagged mycelia under the confocal microscope. The host defense-related genes, WRKY33, PR1, APX6, and CHI, were upregulated in L. biglobosa inoculated Westar cotyledons compared to L. maculans inoculated cotyledons. The quantification of each pathogen through qPCR assay and gene expressions analysis on host defense-related genes by RT-qPCR confirmed the potential of L. biglobosa "brassicae' in the management of the blackleg disease pathogen, L. maculans 'brassicae', in canola.
ABSTRACT
Leptosphaeria maculans causes blackleg disease, which is one of the most destructive diseases of canola (Brassica napus L.). Due to the erosion of the current resistance in B. napus, it is pivotal to introduce new resistant genotypes to the growers. This study evaluated the potential of Rlm7 gene as resistance to its corresponding avirulence AvrLm7 gene is abundant. The Rlm7 line was inoculated with L. maculans isolate with AvrLm7; UMAvr7; and the CRISPR/Cas9 knockout AvrLm7 mutant, umavr7, of the same isolate to cause incompatible and compatible interactions, respectively. Dual RNA-seq showed differential gene expressions in both interactions. High expressions of virulence-related pathogen genes-CAZymes, merops, and effector proteins after 7-dpi in compatible interactions but not in incompatible interaction-confirmed that the pathogen was actively virulent only in compatible interactions. Salicyclic and jasmonic acid biosynthesis and signaling-related genes, defense-related PR1 gene (GSBRNA2T00150001001), and GSBRNA2T00068522001 in the NLR gene family were upregulated starting as early as 1- and 3-dpi in the incompatible interaction and the high upregulation of those genes after 7-dpi in compatible interactions confirmed the early recognition of the pathogen by the host and control it by early activation of host defense mechanisms in the incompatible interaction.
Subject(s)
Ascomycota , Brassica napus , Brassica napus/genetics , Leptosphaeria/genetics , Plant Diseases/geneticsABSTRACT
Phytopathologists are actively researching the molecular basis of plant-pathogen interactions. The mechanisms of responses to pathogens have been studied extensively in model crop plant species and natural populations. Today, with the rapid expansion of genomic technologies such as DNA sequencing, transcriptomics, proteomics, and metabolomics, as well as the development of new methods and protocols, data analysis, and bioinformatics, it is now possible to assess the role of genetic variation in plant-microbe interactions and to understand the underlying molecular mechanisms of plant defense and microbe pathogenicity with ever-greater resolution and accuracy. Genetic variation is an important force in evolution that enables organisms to survive in stressful environments. Moreover, understanding the role of genetic variation and mutational events is essential for crop breeders to produce improved cultivars. This review focuses on genetic variations and mutational events associated with plant-pathogen interactions and discusses how these genome compartments enhance plants' and pathogens' evolutionary processes.
ABSTRACT
Blackleg is a serious disease in Brassica plants, causing moderate to severe yield losses in rapeseed worldwide. Although China has not suffered from this disease yet (more aggressive Leptosphaeria maculans is not present yet), it is crucial to take provisions in breeding for disease resistance to have excellent blackleg-resistant cultivars already in the fields or in the breeding pipeline. The most efficient strategy for controlling this disease is breeding plants with identified resistance genes. We selected 135 rapeseed accessions in Sichuan, including 30 parental materials and 105 hybrids, and we determined their glucosinolate and erucic acid content and confirmed 17 double-low materials. A recently developed single-nucleotide polymorphism (SNP) marker, SNP_208, was used to genotype allelic Rlm1/rlm1 on chromosome A07, and 87 AvrLm1-resistant materials. Combined with the above-mentioned seed quality data, we identified 11 AvrLm1-resistant double-low rapeseed accessions, including nine parental materials and two hybrids. This study lays the foundation of specific R gene-oriented breeding, in the case that the aggressive Leptosphaeria maculans invades and establishes in China in the future and a robust and less labor consuming method to identify resistance in canola germplasm.
ABSTRACT
Temperature is considered one of the crucial environmental elements in plant pathological interactions, and previous studies have indicated that there is a relationship between temperature change and host-pathogen interactions. The objective of this research is to investigate the link between temperature and the incompatible interactions of the host and pathogen. In this study, two Leptosphaeria maculans isolates (HCRT75 8-1 and HCRT77 7-2) and two Brassica napus genotypes (Surpass400 and 01-23-2-1) were selected. The selected B. napus genotypes displayed intermediate and resistant phenotypes. The inoculated seedlings were tested under three temperature conditions: 16 °C/10 °C, 22 °C/16 °C and 28 °C/22 °C (day/night: 16 h/8 h). Lesion measurements demonstrated that the necrotic lesions from the 28 °C/22 °C treatment were enlarged compared with the other two temperature treatments (i.e., 16 °C/10 °C and 22 °C/16 °C). The results of expression analysis indicated that the three temperature treatments displayed distinct differences in two marker genes (PATHOGENESIS-RELATED (PR) 1 and 2) for plant defense and one temperature-sensitive gene BONZAI 1 (BON1). Additionally, seven dpi at 22 °C/16 °C appeared to be the optimal pre-condition for the induction of PR1 and 2. These findings suggest that B. napus responds to temperature changes when infected with L. maculans.
ABSTRACT
Blackleg disease of Brassica napus caused by Leptosphaeria maculans (Lm) is largely controlled by the deployment of race-specific resistance (R) genes. However, selection pressure exerted by R genes causes Lm to adapt and give rise to new virulent strains through mutation and deletion of effector genes. Therefore, a knowledge of effector gene function is necessary for the effective management of the disease. Here, we report the cloning of Lm effector AvrLm9 which is recognized by the resistance gene Rlm9 in B. napus cultivar Goéland. AvrLm9 was mapped to scaffold 7 of the Lm genome, co-segregating with the previously reported AvrLm5 (previously known as AvrLmJ1). Comparison of AvrLm5 alleles amongst the 37 re-sequenced Lm isolates and transgenic complementation identified a single point mutation correlating with the AvrLm9 phenotype. Therefore, we renamed this gene as AvrLm5-9 to reflect the dual specificity of this locus. Avrlm5-9 transgenic isolates were avirulent when inoculated on the B. napus cultivar Goéland. The expression of AvrLm5-9 during infection was monitored by RNA sequencing. The recognition of AvrLm5-9 by Rlm9 is masked in the presence of AvrLm4-7, another Lm effector. AvrLm5-9 and AvrLm4-7 do not interact, and AvrLm5-9 is expressed in the presence of AvrLm4-7. AvrLm5-9 is the second Lm effector for which host recognition is masked by AvrLm4-7. An understanding of this complex interaction will provide new opportunities for the engineering of broad-spectrum recognition.
