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Summary Sea Island cotton (Gossypium barbadense) is the source of the world’s finest fibre quality cotton, yet relatively little is understood about genetic variations among diverse germplasms, genes underlying important traits and the effects of pedigree selection. Here, we resequenced 336 G. barbadense accessions and identified 16 million SNPs. Phylogenetic and population structure analyses revealed two major gene pools and a third admixed subgroup derived from geographical dissemination and interbreeding. We conducted a genome‐wide association study (GWAS) of 15 traits including fibre quality, yield, disease resistance, maturity and plant architecture. The highest number of associated loci was for fibre quality, followed by disease resistance and yield. Using gene expression analyses and VIGS transgenic experiments, we confirmed the roles of five candidate genes regulating four key traits, that is disease resistance, fibre length, fibre strength and lint percentage. Geographical and temporal considerations demonstrated selection for the superior fibre quality (fibre length and fibre strength), and high lint percentage in improving G. barbadense in China. Pedigree selection breeding increased Fusarium wilt disease resistance and separately improved fibre quality and yield. Our work provides a foundation for understanding genomic variation and selective breeding of Sea Island cotton.

Most commercial banana cultivars are highly susceptible to Fusarium wilt caused by soilborne fungus Fusarium oxysporum f. sp. cubense (Foc), especially tropical race 4 (TR4). Biological control using antagonistic microorganism has been considered as an alternative method to fungicide. Our previous study showed that Streptomyces sp. SCA3-4  T had a broad-spectrum antifungal activity from the rhizosphere soil of Opuntia stricta in a dry hot valley. Here, the sequenced genome of strain SCA3-4  T contained 6614 predicted genes with 72.38% of G + C content. A polymorphic tree was constructed using the multilocus sequence analysis (MLSA) of five house-keeping gene alleles ( atpD , gyrB , recA , rpoB , and trpB ). Strain SCA3-4  T formed a distinct clade with Streptomyces mobaraensis NBRC 13819  T with 71% of bootstrap. Average nucleotide identity (ANI) values between genomes of strain SCA3-4  T and S. mobaraensis NBRC 13819  T was 85.83% below 95–96% of the novel species threshold, and named after Streptomyces sichuanensis sp. nov. The type strain is SCA3-4  T (= GDMCC 4.214  T  = JCM 34964  T ). Genomic analysis revealed that strain SCA3-4  T contained 36 known biosynthetic gene clusters of secondary metabolites. Antifungal activity of strain SCA3-4  T was closely associated with the production of siderophore and its extracts induced the apoptosis of Foc TR4 cells. A total of 12 potential antifungal metabolites including terpenoids, esters, acid, macrolides etc. were obtained by the gas chromatography-mass spectrometry (GC–MS). Greenhouse experiment indicated that strain SCA3-4  T could significantly inhibit infection of Foc TR4 in the roots and corms of banana seedlings and reduce disease index. Therefore, strain SCA3-4  T is an important microbial resource for exploring novel natural compounds and developing biopesticides to manage Foc TR4. Key points • Strain SCA3-4  T was identified as a novel species of Streptomyces. • Siderophore participates in the antifungal regulation. • Secondary metabolites of strain SCA3-4  T improves the plant resistance to Foc TR4.

AIMS: Banana Fusarium wilt disease is caused by the Fusarium oxysporum f. sp. cubense race 4 fungus and is a vast problem for global banana production. Suppressive and conducive soils were analyzed to characterize important microbial populations and soil chemical properties that contribute to disease suppressiveness. METHODS: Soil bacteria communities from the two banana orchards with excellent Fusarium disease suppression (suppressive soil) after long-term monoculture and two adjacent banana orchards with serious Fusarium wilt disease (conducive soils) were compared using deep 16S RNA barcode pyrosequencing. RESULT: Compared to the conducive soils within the same field site, higher (P < 0.05) richness and diversity indices were observed in both suppressive soils. Moreover, more operational taxonomic units (OTUs) were observed in the two suppressive soils. Hierarchical cluster analyses showed that bacterial community membership and structure in disease-suppressive soils differed from disease-conducive soils. The Acidobacteria phylum was significantly (P < 0.05) elevated, but Bacteroidetes was significantly (P < 0.05) reduced in suppressive soils. The Gp4, Gp5, Chthonomonas, Pseudomonas, and Tumebacillus genera were significantly (P < 0.05) enriched in suppressive soils, but Gp2 was significantly (P < 0.05) reduced in suppressive soils. Furthermore, the enrichment of Gp5 and Pseudomonas as well as the soil physicochemical properties of available phosphorus were significantly (P < 0.05) correlated with disease suppression. CONCLUSIONS: Naturally disease suppressive soils to banana Fusarium wilt disease harbor unique bacterial communities.

In our previous work, applying biofertilizer containing Bacillus amyloliquefaciens strain NJN-6 to a banana orchard infected by a serious Fusarium wilt disease over two consecutive years effectively controlled this soil-borne disease. In this study, deep pyrosequencing of 16S ribosomal RNA (rRNA) genes and internal transcribed spacer (ITS) sequences was performed to investigate how the composition of rhizosphere microbial community responded to the application of biofertilizer (BIO), pig manure compost (PM), and chemical fertilizer (CF) and to explore the potential correlation between the microbial community composition and the Fusarium wilt disease. A total of 104,201 bacterial 16S rRNA genes and 154,953 fungal ITS sequence reads were obtained after basic quality control, and Acidobacteria, Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria, and Ascomycota were the most abundant bacterial and fungal phyla across all samples. Compared with the PM and CF control, the alpha diversity of bacteria significantly (P < 0.05) increased, whereas the value of the fungi was significantly (P < 0.05) reduced following two consecutive years of biofertilizer application. Moreover, the abundance of Acidobacteria (Gp1 and Gp3), Firmicutes, Leptosphaeria, and Phaeosphaeriopsis was significantly (P < 0.05) increased, while the abundance of Proteobacteria and Ascomycota was significantly (P < 0.05) decreased in the BIO treatment. Furthermore, the abundance of Fusarium, a causal pathogen for Fusarium wilt disease, was significantly (P < 0.05) reduced in the BIO treatment compared with the CF control and was slightly reduced (not significant) compared with the PM control. Interestingly, the disease incidence was negatively correlated with the enriched taxa of Acidobacteria (Gp1 and Gp3) and Firmicutes, Leptosphaeria, and Phaeosphaeriopsis but positively correlated with abundance of Proteobacteria, Ascomycota, Fusarium, Cylindrocarpon, Gymnascella, Monographella, Pochonia, and Sakaguchia taxa. The results from this study suggest that 2 years of biofertilizer application manipulated the composition of rhizosphere microbial community and induced the Fusarium suppression by increasing bacterial diversity and potentially stimulating microbial consortia taxa, such as Acidobacteria (Gp1 and Gp3), Firmicutes, Leptosphaeria, and Phaeosphaeriopsis.

Fusarium wilt of tomato caused by the pathogen Fusarium oxysporum f. sp. lycopersici (Fol) is one of the most devastating soilborne diseases of tomato. To evaluate whether microbial community composition associated with Fol-infected tomato is different from healthy tomato, we analyzed the tomato-associated microbes in both healthy and Fol-infected tomato plants at both the taxonomic and functional levels; both bacterial and fungal communities have been characterized from bulk soil, rhizosphere, rhizoplane, and endosphere of tomatoes using metabarcoding and metagenomics approaches. The microbial community (bacteria and fungi) composition of healthy tomato was significantly different from that of diseased tomato, despite similar soil physicochemical characteristics. Both fungal and bacterial diversities were significantly higher in the tomato plants that remained healthy than in those that became diseased; microbial diversities were also negatively correlated with the concentration of Fol pathogen. Network analysis revealed the microbial community of healthy tomato formed a larger and more complex network than that of diseased tomato, probably providing a more stable community beneficial to plant health. Our findings also suggested that healthy tomato contained significantly greater microbial consortia, including some well-known biocontrol agents (BCAs), and enriched more functional genes than diseased tomato. The microbial taxa enriched in healthy tomato plants are recognized as potential suppressors of Fol pathogen invasion.

Banana Fusarium wilt (BFW) is a devastating disease with no effective cure methods. Timely and effective detection of the disease and evaluation of its spreading trend will help farmers in making right decisions on plantation management. The main purpose of this study was to find the spectral features of the BFW-infected canopy and build the optimal BFW classification models for different stages of infection. A RedEdge-MX camera mounted on an unmanned aerial vehicle (UAV) was used to collect multispectral images of a banana plantation infected with BFW in July and August 2020. Three types of spectral features were used as the inputs of classification models, including three-visible-band images, five-multispectral-band images, and vegetation indices (VIs). Four supervised methods including Support Vector Machine (SVM), Random Forest (RF), Back Propagation Neural Networks (BPNN) and Logistic Regression (LR), and two unsupervised methods including Hotspot Analysis (HA) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) were adopted to detect the BFW-infected canopies. Comparing to the healthy canopies, the BFW-infected canopies had higher reflectance in the visible region, but lower reflectance in the NIR region. The classification results showed that most of the supervised and unsupervised methods reached excellent accuracies. Among all the supervised methods, RF based on the five-multispectral-band was considered as the optimal model, with higher overall accuracy (OA) of 97.28% and faster running time of 22 min. For the unsupervised methods, HA reached high and balanced OAs of more than 95% based on the selected VIs derived from the red and NIR band, especially for WDRVI, NDVI, and TDVI. By comprehensively evaluating the classification results of different metrics, the unsupervised method HA was recommended for BFW recognition, especially in the late stage of infection; the supervised method RF was recommended in the early stage of infection to reach a slightly higher accuracy. The results found in this study could give advice for banana plantation management and provide approaches for plant disease detection.

Aims Banana Fusarium wilt ( Fusarium oxysporum f. sp. cubense tropical race 4) is a typical destructive soil-borne disease, which was the main limiting factor for the sustainable development of the banana industry worldwide. In banana production, soil physiochemical properties and soil microbiome were effectively affected the occurrence and spread of Fusarium wilt. However, there is still a lack of systematic research, particularly in exploring the correlation between the occurrence of banana Fusarium wilt and soil properties across various climates and soil types. Methods In this study we investigated the soil physicochemical properties, bacterial and fungal community composition, and pathogenic fungal abundance in 140 banana plantations which were affected by banana Fusarium wilt in Yunnan Province, China. Results The results showed that the abundance of soil-borne pathogenic fungi was positively correlated with total phosphorus, total nitrogen, organic matter, urease activity, annual precipitation, and the alpha diversity of bacterial and fungal communities. In contrast, it showed a significant negative correlation with the annual mean temperature. As the abundance of pathogen increased, numerous potential disease-suppressive bacterial genera (such as Rhodanobacter, Gemmatimonas, Novosphingobium) and soil-borne pathogenic fungal genera (such as Plectosphaerella, Nigrospora, Cyphellophora) also increased, and the co-occurrence network showed a higher modularization index. Conclusions The results enhance the understanding of the patterns of soil-borne pathogenic fungal population dynamics in banana plantations, which would provide evidence and guidance for reducing pathogenic fungal abundance and selecting beneficial microorganisms in banana production. Furthermore, this would provide a theoretical basis for sustainable prevention and control of banana wilt disease.

Aims Continuous cropping of watermelon is known to result in the disruption of the rhizospheric bacteria and fungi that contribute to the occurrence of Fusarium wilt disease. However, the underlying changes in microbial composition and function as a response to mono-cropping are less studied. Methods In this study, differences in composition and potential function of the microbiome between healthy and diseased soils were investigated using MiSeq targeted sequencing and the functional GeoChip array, respectively. Results Twenty years of continuous watermelon monoculture was found to significantly alter the soil microbial communities by increasing bacterial diversity but decreasing fungal diversity. Compare to bacterial network, fungal co-occurrence networks were less robust and less connected in the monoculture diseased soil. Identified keystone species, belonging to the Proteobacteria, Bacteroidetesand Acidobacteria, were present in both the diseased and healthy soils. Key fungal species from the healthy soil belonged solely within the Ascomycete, while in the diseased soil Basidiomycota were dominant. As such, overall variations in the composition of the soil microbiome are accompanied by changes in the identities of the keystone species when comparing healthy versus diseased soils, further suggesting that soil function may also be altered. Relative abundances of genes associated with the degradation of hemicelluloses and chitin, the Calvin circle, ammonification, stress responses, iron uptake, and nitrogen fixation were significantly higher under long-term monoculture. Particularly, Fusarium spp. relative abundance was positively correlated with the relative abundances of genes involved in adherence, cellular metabolism, and immune evasion which may facilitate pathogen infection of plant roots. Conclusions In conclusion, these results highlight the significant compositional and functional differences in microbial communities between Fusarium wilt diseased soils and healthy soils under watermelon cultivation. This provides insight into the complex array of microorganisms in soils that suffer from Fusarium disease and illustrates potential directions towards the manipulation of the soil microbiome for suppression of this disease.

Chickpea wilt is a widespread agricultural disease that affects production worldwide every year. Rapid and accurate detection of the disease is desirable, but is difficult using traditional methods. Therefore, it is necessary to detect the disease using automatic, rapid, reliable, and simple methods before it completely damages the plant. Herein, we investigate the applicability of machine learning-based texture analysis methods to determine the severity level of Fusarium wilt in chickpea. Various procedures, such as image annotation, augmentation, resizing, and color conversion using different color spaces (RGB, HSV, and Lab*), were performed to develop the model. To perform texture feature extraction, the Gray-Level Run-Length Matrix (GLRLM) and the Gray-Level Occurrence Matrix (GLCM) feature extraction methods were used. To avoid local minima, Bayesian optimization was applied, while to train and test the effectiveness of the proposed model, 15000 images (70–20-10 ratio for training, validation and testing) were used. Finally, multi-class classification models were developed using image classification methods such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Neural Networks. The proposed GLRLM-HSV based KNN model performed well in determining the severity level of fusarium wilt of chickpea among five different severity levels, with an accuracy of 94.5%.

Panama disease (Fusarium wilt disease) caused by Fusarium oxysporum f. sp. cubense race 4 (FOC) severely threatens banana (Musa spp.) production worldwide. Intercropping of banana with Allium plants has shown a potential to reduce Panama disease. In this study, six cultivars of Chinese chive ( Allium tuberosum Rottler) were selected to compare their differences in antifungal activity and active compounds. Three cultivars Duokang Fujiu 11, Fujiuhuang 2, and Duokang Sijiqing with higher levels of antifungal compounds were further used for intercropping with banana in the pots and field to compare their effects on growth and disease incidence of banana. The six cultivars showed significant differences in antifungal activity against FOC mycelia growth in both leaf volatiles and aqueous leachates. The aqueous leachates displayed stronger antifungal activity than the volatiles. FJH cultivar showed the best inhibitory effect among all six cultivars. Contents of three main antifungal compounds dipropyl trisulfide (DPT), dimethyl trisulfide (DMT), and 2-methyl-2-pentenal (MP) in volatiles and aqueous leachates varied considerably among cultivars. Pot and field experiments showed that intercropping with three selected Chinese chive cultivars significantly improved banana vegetative growth, increased photosynthetic characteristics and yield but decreased disease incidence of Panama disease. Our results indicate that intercropping with Chinese chive shows potential to reduce banana Panama disease and selection of appropriate cultivars is vital for effective disease control.