Disaster Plant Pathology: Smart Solutions for Threats to Global Plant Health from Natural and Human-Driven Disasters.

Disaster plant pathology addresses how natural and human-driven disasters impact plant diseases and the requirements for smart management solutions. Local to global drivers of plant disease change in response to disasters, often creating environments more conducive to plant disease. Most disasters have indirect effects on plant health through factors such as disrupted supply chains and damaged infrastructure. There is also the potential for direct effects from disasters, such as pathogen or vector dispersal due to floods, hurricanes, and human migration driven by war. Pulse stressors such as hurricanes and war require rapid responses, whereas press stressors such as climate change leave more time for management adaptation but may ultimately cause broader challenges. Smart solutions for the effects of disasters can be deployed through digital agriculture and decision support systems supporting disaster preparedness and optimized humanitarian aid across scales. Here, we use the disaster plant pathology framework to synthesize the effects of disasters in plant pathology and outline solutions to maintain food security and plant health in catastrophic scenarios. We recommend actions for improving food security before and following disasters, including (i) strengthening regional and global cooperation, (ii) capacity building for rapid implementation of new technologies, (iii) effective clean seed systems that can act quickly to replace seed lost in disasters, (iv) resilient biosecurity infrastructure and risk assessment ready for rapid implementation, and (v) decision support systems that can adapt rapidly to unexpected scenarios. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

The persistent threat of emerging plant disease pandemics to global food security.

Plant disease outbreaks are increasing and threaten food security for the vulnerable in many areas of the world. Now a global human pandemic is threatening the health of millions on our planet. A stable, nutritious food supply will be needed to lift people out of poverty and improve health outcomes. Plant diseases, both endemic and recently emerging, are spreading and exacerbated by climate change, transmission with global food trade networks, pathogen spillover, and evolution of new pathogen lineages. In order to tackle these grand challenges, a new set of tools that include disease surveillance and improved detection technologies including pathogen sensors and predictive modeling and data analytics are needed to prevent future outbreaks. Herein, we describe an integrated research agenda that could help mitigate future plant disease pandemics.

Integration of Phenotypes in Microbiome Networks for Designing Synthetic Communities: a Study of Mycobiomes in the Grafted Tomato System.

Understanding factors influencing microbial interactions, and designing methods to identify key taxa that are candidates for synthetic communities, or SynComs, are complex challenges for achieving microbiome-based agriculture. Here, we study how grafting and the choice of rootstock influences root-associated fungal communities in a grafted tomato system. We studied three tomato rootstocks (BHN589, RST-04-106, and Maxifort) grafted to a BHN589 scion and profiled the fungal communities in the endosphere and rhizosphere by sequencing the internal transcribed spacer (ITS2). The data provided evidence for a rootstock effect (explaining ~2% of the total captured variation, P < 0.01) on the fungal community. Moreover, the most productive rootstock, Maxifort, supported greater fungal species richness than the other rootstocks or controls. We then constructed a phenotype-operational taxonomic unit (OTU) network analysis (PhONA) using an integrated machine learning and network analysis approach based on fungal OTUs and associated tomato yield as the phenotype. PhONA provides a graphical framework to select a testable and manageable number of OTUs to support microbiome-enhanced agriculture. We identified differentially abundant OTUs specific to each rootstock in both endosphere and rhizosphere compartments. Subsequent analyses using PhONA identified OTUs that were directly associated with tomato fruit yield and others that were indirectly linked to yield through their links to these OTUs. Fungal OTUs that are directly or indirectly linked with tomato yield may represent candidates for synthetic communities to be explored in agricultural systems. IMPORTANCE The realized benefits of microbiome analyses for plant health and disease management are often limited by the lack of methods to select manageable and testable synthetic microbiomes. We evaluated the composition and diversity of root-associated fungal communities from grafted tomatoes. We then constructed a phenotype-OTU network analysis (PhONA) using these linear and network models. By incorporating yield data in the network, PhONA identified OTUs that were directly predictive of tomato yield and others that were indirectly linked to yield through their links to these OTUs. Follow-up functional studies of taxa associated with effective rootstocks, identified using approaches such as PhONA, could support the design of synthetic fungal communities for microbiome-based crop production and disease management. The PhONA framework is flexible for incorporation of other phenotypic data, and the underlying models can readily be generalized to accommodate other microbiome or 'omics data.

The Use of Synthetic Microbial Communities to Improve Plant Health.

Despite the numerous benefits plants receive from probiotics, maintaining consistent results across applications is still a challenge. Cultivation-independent methods associated with reduced sequencing costs have considerably improved the overall understanding of microbial ecology in the plant environment. As a result, now, it is possible to engineer a consortium of microbes aiming for improved plant health. Such synthetic microbial communities (SynComs) contain carefully chosen microbial species to produce the desired microbiome function. Microbial biofilm formation, production of secondary metabolites, and ability to induce plant resistance are some of the microbial traits to consider when designing SynComs. Plant-associated microbial communities are not assembled randomly. Ecological theories suggest that these communities have a defined phylogenetic organization structured by general community assembly rules. Using machine learning, we can study these rules and target microbial functions that generate desired plant phenotypes. Well-structured assemblages are more likely to lead to a stable SynCom that thrives under environmental stressors as compared with the classical selection of single microbial activities or taxonomy. However, ensuring microbial colonization and long-term plant phenotype stability is still one of the challenges to overcome with SynComs, as the synthetic community may change over time with microbial horizontal gene transfer and retained mutations. Here, we explored the advances made in SynCom research regarding plant health, focusing on bacteria, as they are the most dominant microbial form compared with other members of the microbiome and the most commonly found in SynCom studies.

Ralstonia Strains from Potato-Growing Regions of Kenya Reveal Two Phylotypes and Epidemic Clonality of Phylotype II Sequevar 1 Strains.

Bacterial wilt, caused by the Ralstonia solanacearum species complex (RSSC), is the most destructive potato disease in Kenya. Studies were conducted to (i) determine the molecular diversity of RSSC strains associated with bacterial wilt of potato in Kenya, (ii) generate an RSSC distribution map for epidemiological inference, and (iii) determine whether phylotype II sequevar 1 strains exhibit epidemic clonality. Surveys were conducted in 2018 and 2019, in which tubers from wilting potato plants and stem samples of potential alternative hosts were collected for pathogen isolation. The pathogen was phylotyped by multiplex PCR and 536 RSSC strains typed at a sequevar level. Two RSSC phylotypes were identified, phylotype II (98.4%, n = 506 [sequevar 1 (n = 505) and sequevar 2 (n = 1)]) and phylotype I (1.6%, n = 30 [sequevar 13 (n = 9) and a new sequevar (n = 21)]). The phylotype II sequevar 1 strains were haplotyped using multilocus tandem repeat sequence typing (TRST) schemes. The TRST scheme identified 51 TRST profiles within the phylotype II sequevar 1 strains with a modest diversity index (HGDI = 0.87), confirming the epidemic clonality of RSSC phylotype II sequevar 1 strains in Kenya. A minimum spanning tree and mapping of the TRST profiles revealed that TRST27 '8-5-12-7-5' is the primary founder of the clonal complex of RSSC phylotype II sequevar 1 and is widely distributed via latently infected seed tubers. [Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Assessing Changes and Associations in the Xanthomonas perforans Population Across Florida Commercial Tomato Fields Via a Statewide Survey.

Before 1991, Xanthomonas euvesicatoria was the causal agent of bacterial spot of tomato in Florida but was quickly replaced by X. perforans. The X. perforans population has changed in genotype and phenotype despite lack of a clear selection pressure. To determine the current Xanthomonas population in Florida, we collected 585 Xanthomonas strains from 70 tomato fields, representing 22 farms across eight counties, in the Florida tomato production region. Strains were isolated from 23 cultivars across eight seed producers and were associated with eight transplant facilities during the fall 2017 season. Our collection was phenotypically and genotypically characterized. Only X. perforans was identified, and all strains except one (99.8%) were tolerant to copper sulfate and 25% of strains were resistant to streptomycin sulfate. Most of the strains (99.3%) that were resistant to streptomycin sulfate were sequence type 1. The X. perforans population consisted of tomato races 3 (8%) and 4 (92%) and all three previously reported sequence types, ranging from 22 to 46% frequency. Approximately half of all strains, none of which were sequence type 2, produced bacteriocins against X. euvesicatoria. Effector profiles were highly variable among strains, which could impact the strains' host range. The effector xopJ4, which was previously thought to be conserved in X. perforans tomato pathogens, was absent in 19 strains. Nonmetric multidimensional scaling and network analyses show how strains and strain traits were associated with production system variables, including anonymized farms and transplant facilities. These analyses show that the composition of the Florida X. perforans population is diverse and complex.

Three pillars of sustainability in fisheries.

Sustainability of global fisheries is a growing concern. The United Nations has identified three pillars of sustainability: economic development, social development, and environmental protection. The fisheries literature suggests that there are two key trade-offs among these pillars of sustainability. First, poor ecological health of a fishery reduces economic profits for fishers, and second, economic profitability of individual fishers undermines the social objectives of fishing communities. Although recent research has shown that management can reconcile ecological and economic objectives, there are lingering concerns about achieving positive social outcomes. We examined trade-offs among the three pillars of sustainability by analyzing the Fishery Performance Indicators, a unique dataset that scores 121 distinct fishery systems worldwide on 68 metrics categorized by social, economic, or ecological outcomes. For each of the 121 fishery systems, we averaged the outcome measures to create overall scores for economic, ecological, and social performance. We analyzed the scores and found that they were positively associated in the full sample. We divided the data into subsamples that correspond to fisheries management systems with three categories of access-open access, access rights, and harvest rights-and performed a similar analysis. Our results show that economic, social, and ecological objectives are at worst independent and are mutually reinforcing in both types of managed fisheries. The implication is that rights-based management systems should not be rejected on the basis of potentially negative social outcomes; instead, social considerations should be addressed in the design of these systems.

"Breaking through the 40% adoption ceiling: Mind the seed system gaps." A perspective on seed systems research for development in One CGIAR.

Seed systems research is central to achieving the United Nations Sustainable Development Goals. Improved varieties with promise for ending hunger, improving nutrition, and increasing livelihood security may be released, but how do they reach and benefit different types of farmers? Without widespread adoption the genetic gains achieved with improved crop varieties can never be actualized. Progress has been made toward demand responsive breeding, however the draft CGIAR 2030 Research and Innovation Strategy fails to recognize the complexity of seed systems and thus presents a narrow vision for the future of seed systems research. This points to the lack of evidence-based dialogue between seed systems researchers and breeders. This perspective paper presents findings from an interdisciplinary group of more than 50 CGIAR scientists who used a suite of seed systems tools to identify four knowledge gaps and associated insights from work on the seed systems for vegetatively propagated crops (VPCs), focusing on bananas (especially cooking bananas and plantains), cassava, potato, sweetpotato, and yam. We discuss the implications for thinking about and intervening in seed systems using a combined biophysical and socioeconomic perspective and how this can contribute to increased varietal adoption and benefits to farmers. The tools merit wider use, not only for the seed systems of VPCs, but for the seed of crops facing similar adoption challenges. We argue for deeper collaboration between seed systems researchers, breeders and national seed system stakeholders to address these and other knowledge gaps and generate the evidence and innovations needed to break through the 40% adoption ceiling for modern varieties, and ensure good quality seed once the new varieties have been adopted. Without this, the achievements of breeders may remain stuck in the seed delivery pipeline.

Global Cropland Connectivity: A Risk Factor for Invasion and Saturation by Emerging Pathogens and Pests.

The geographic pattern of cropland is an important risk factor for invasion and saturation by crop-specific pathogens and arthropods. Understanding cropland networks supports smart pest sampling and mitigation strategies. We evaluate global networks of cropland connectivity for key vegetatively propagated crops (banana and plantain, cassava, potato, sweet potato, and yam) important for food security in the tropics. For each crop, potential movement between geographic location pairs was evaluated using a gravity model, with associated uncertainty quantification. The highly linked hub and bridge locations in cropland connectivity risk maps are likely priorities for surveillance and management, and for tracing intraregion movement of pathogens and pests. Important locations are identified beyond those locations that simply have high crop density. Cropland connectivity risk maps provide a new risk component for integration with other factors-such as climatic suitability, genetic resistance, and global trade routes-to inform pest risk assessment and mitigation.

Rootstocks Shape the Rhizobiome: Rhizosphere and Endosphere Bacterial Communities in the Grafted Tomato System.

Root-associated microbes are critical to plant health and performance, although understanding of the factors that structure these microbial communities and the theory to predict microbial assemblages are still limited. Here, we use a grafted tomato system to study the effects of rootstock genotypes and grafting in endosphere and rhizosphere microbiomes that were evaluated by sequencing 16S rRNA. We compared the microbiomes of nongrafted tomato cultivar BHN589, self-grafted BHN589, and BHN589 grafted to Maxifort or RST-04-106 hybrid rootstocks. Operational taxonomic unit (OTU)-based bacterial diversity was greater in Maxifort compared to the nongrafted control, whereas bacterial diversity in the controls (self-grafted and nongrafted) and the other rootstock (RST-04-106) was similar. Grafting itself did not affect bacterial diversity; diversity in the self-graft was similar to that of the nongraft. Bacterial diversity was higher in the rhizosphere than in the endosphere for all treatments. However, despite the lower overall diversity, there was a greater number of differentially abundant OTUs (DAOTUs) in the endosphere, with the greatest number of DAOTUs associated with Maxifort. In a permutational multivariate analysis of variance (PERMANOVA), there was evidence for an effect of rootstock genotype on bacterial communities. The endosphere-rhizosphere compartment and study site explained a high percentage of the differences among bacterial communities. Further analyses identified OTUs responsive to rootstock genotypes in both the endosphere and rhizosphere. Our findings highlight the effects of rootstocks on bacterial diversity and composition. The influence of rootstock and plant compartment on microbial communities indicates opportunities for the development of designer communities and microbiome-based breeding to improve future crop production.IMPORTANCE Understanding factors that control microbial communities is essential for designing and supporting microbiome-based agriculture. In this study, we used a grafted tomato system to study the effect of rootstock genotypes and grafting on bacterial communities colonizing the endosphere and rhizosphere. To compare the bacterial communities in control treatments (nongrafted and self-grafted plants) with the hybrid rootstocks used by farmers, we evaluated the effect of rootstocks on overall bacterial diversity and composition. These findings indicate the potential for using plant genotype to indirectly select bacterial taxa. In addition, we identify taxa responsive to each rootstock treatment, which may represent candidate taxa useful for biocontrol and in biofertilizers.

Penicillin Trunk Injection Affects Bacterial Community Structure in Citrus Trees.

Huanglongbing (HLB), caused by Candidatus Liberibacter asiaticus (CLas), an uncultured α-proteobacterium, is the most destructive disease of citrus trees worldwide. In previous studies, trunk injections of penicillin reduced CLas titers and HLB symptoms in citrus. However, antibiotic effects on the whole plant microbial community, which include effects on taxa that interact with CLas, have not yet been addressed. In this study, we investigated the effects of penicillin injection (0, 1000, and 6000 mg L-1) on rhizospheric and endophytic bacterial communities of grapefruit trees in field and greenhouse experiments through culture-independent high-throughput sequencing. DNA extractions from petioles and roots were subjected to 16S rRNA high-throughput sequencing, and reads were clustered by sequence similarity into operational taxonomic units (OTUs). Principal coordinates analysis based on weighted-UniFrac distances did not reveal differences in bacterial communities among treatments in any of the sample sources. However, pairwise linear discriminant analysis indicated significant differences in relative abundance of some taxa (including CLas) among treatments. Network analysis showed that penicillin produced major changes in root bacterial community structure by affecting interspecific microbial associations. This study provides new knowledge of the effect of antimicrobial treatments on interspecific relationships in citrus microbial communities.

A Framework for Optimizing Phytosanitary Thresholds in Seed Systems.

Seedborne pathogens and pests limit production in many agricultural systems. Quarantine programs help prevent the introduction of exotic pathogens into a country, but few regulations directly apply to reducing the reintroduction and spread of endemic pathogens. Use of phytosanitary thresholds helps limit the movement of pathogen inoculum through seed, but the costs associated with rejected seed lots can be prohibitive for voluntary implementation of phytosanitary thresholds. In this paper, we outline a framework to optimize thresholds for seedborne pathogens, balancing the cost of rejected seed lots and benefit of reduced inoculum levels. The method requires relatively small amounts of data, and the accuracy and robustness of the analysis improves over time as data accumulate from seed testing. We demonstrate the method first and illustrate it with a case study of seedborne oospores of Peronospora effusa, the causal agent of spinach downy mildew. A seed lot threshold of 0.23 oospores per seed could reduce the overall number of oospores entering the production system by 90% while removing 8% of seed lots destined for distribution. Alternative mitigation strategies may result in lower economic losses to seed producers, but have uncertain efficacy. We discuss future challenges and prospects for implementing this approach.

Modeling Virus Coinfection to Inform Management of Maize Lethal Necrosis in Kenya.

Maize lethal necrosis (MLN) has emerged as a serious threat to food security in sub-Saharan Africa. MLN is caused by coinfection with two viruses, Maize chlorotic mottle virus and a potyvirus, often Sugarcane mosaic virus. To better understand the dynamics of MLN and to provide insight into disease management, we modeled the spread of the viruses causing MLN within and between growing seasons. The model allows for transmission via vectors, soil, and seed, as well as exogenous sources of infection. Following model parameterization, we predict how management affects disease prevalence and crop performance over multiple seasons. Resource-rich farmers with large holdings can achieve good control by combining clean seed and insect control. However, crop rotation is often required to effect full control. Resource-poor farmers with smaller holdings must rely on rotation and roguing, and achieve more limited control. For both types of farmer, unless management is synchronized over large areas, exogenous sources of infection can thwart control. As well as providing practical guidance, our modeling framework is potentially informative for other cropping systems in which coinfection has devastating effects. Our work also emphasizes how mathematical modeling can inform management of an emerging disease even when epidemiological information remains scanty. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Ecological Networks in Stored Grain: Key Postharvest Nodes for Emerging Pests, Pathogens, and Mycotoxins.

Wheat is at peak quality soon after harvest. Subsequently, diverse biota use wheat as a resource in storage, including insects and mycotoxin-producing fungi. Transportation networks for stored grain are crucial to food security and provide a model system for an analysis of the population structure, evolution, and dispersal of biota in networks. We evaluated the structure of rail networks for grain transport in the United States and Eastern Australia to identify the shortest paths for the anthropogenic dispersal of pests and mycotoxins, as well as the major sources, sinks, and bridges for movement. We found important differences in the risk profile in these two countries and identified priority control points for sampling, detection, and management. An understanding of these key locations and roles within the network is a new type of basic research result in postharvest science and will provide insights for the integrated pest management of high-risk subpopulations, such as pesticide-resistant insect pests.

Ecotypes of an ecologically dominant prairie grass (Andropogon gerardii) exhibit genetic divergence across the U.S. Midwest grasslands' environmental gradient.

Big bluestem (Andropogon gerardii) is an ecologically dominant grass with wide distribution across the environmental gradient of U.S. Midwest grasslands. This system offers an ideal natural laboratory to study population divergence and adaptation in spatially varying climates. Objectives were to: (i) characterize neutral genetic diversity and structure within and among three regional ecotypes derived from 11 prairies across the U.S. Midwest environmental gradient, (ii) distinguish between the relative roles of isolation by distance (IBD) vs. isolation by environment (IBE) on ecotype divergence, (iii) identify outlier loci under selection and (iv) assess the association between outlier loci and climate. Using two primer sets, we genotyped 378 plants at 384 polymorphic AFLP loci across regional ecotypes from central and eastern Kansas and Illinois. Neighbour-joining tree and PCoA revealed strong genetic differentiation between Kansas and Illinois ecotypes, which was better explained by IBE than IBD. We found high genetic variability within prairies (80%) and even fragmented Illinois prairies, surprisingly, contained high within-prairie genetic diversity (92%). Using Bayenv2, 14 top-ranked outlier loci among ecotypes were associated with temperature and precipitation variables. Six of seven BayeScanFST outliers were in common with Bayenv2 outliers. High genetic diversity may enable big bluestem populations to better withstand changing climates; however, population divergence supports the use of local ecotypes in grassland restoration. Knowledge of genetic variation in this ecological dominant and other grassland species will be critical to understanding grassland response and restoration challenges in the face of a changing climate.

Climate change may have limited effect on global risk of potato late blight.

Weather affects the severity of many plant diseases, and climate change is likely to alter the patterns of crop disease severity. Evaluating possible future patterns can help focus crop breeding and disease management research. We examined the global effect of climate change on potato late blight, the disease that caused the Irish potato famine and still is a common potato disease around the world. We used a metamodel and considered three global climate models for the A2 greenhouse gas emission scenario for three 20-year time-slices: 2000-2019, 2040-2059 and 2080-2099. In addition to global analyses, five regions were evaluated where potato is an important crop: the Andean Highlands, Indo-Gangetic Plain and Himalayan Highlands, Southeast Asian Highlands, Ethiopian Highlands, and Lake Kivu Highlands in Sub-Saharan Africa. We found that the average global risk of potato late blight increases initially, when compared with historic climate data, and then declines as planting dates shift to cooler seasons. Risk in the agro-ecosystems analyzed, varied from a large increase in risk in the Lake Kivu Highlands in Rwanda to decreases in the Southeast Asian Highlands of Indonesia.

Network typologies predict future molecular linkages in the network of HIV transmission.

OBJECTIVE: HIV molecular transmission network typologies have previously demonstrated associations to transmission risk; however, few studies have evaluated their predictive potential in anticipating future transmission events. To assess this, we tested multiple models on statewide surveillance data from the Florida Department of Health. DESIGN: This was a retrospective, observational cohort study examining the incidence of new HIV molecular linkages within the existing molecular network of persons with HIV (PWH) in Florida. METHODS: HIV-1 molecular transmission clusters were reconstructed for PWH diagnosed in Florida from 2006 to 2017 using the HIV-TRAnsmission Cluster Engine (HIV-TRACE). A suite of machine-learning models designed to predict linkage to a new diagnosis were internally and temporally externally validated using a variety of demographic, clinical, and network-derived parameters. RESULTS: Of the 9897 individuals who received a genotype within 12 months of diagnosis during 2012-2017, 2611 (26.4%) were molecularly linked to another case within 1 year at 1.5% genetic distance. The best performing model, trained on two years of data, was high performing (area under the receiving operating curve = 0.96, sensitivity = 0.91, and specificity = 0.90) and included the following variables: age group, exposure group, node degree, betweenness, transitivity, and neighborhood. CONCLUSIONS: In the molecular network of HIV transmission in Florida, individuals' network position and connectivity predicted future molecular linkages. Machine-learned models using network typologies performed superior to models using individual data alone. These models can be used to more precisely identify subpopulations for intervention.

Translating virome analyses to support biosecurity, on-farm management, and crop breeding.

Virome analysis via high-throughput sequencing (HTS) allows rapid and massive virus identification and diagnoses, expanding our focus from individual samples to the ecological distribution of viruses in agroecological landscapes. Decreases in sequencing costs combined with technological advances, such as automation and robotics, allow for efficient processing and analysis of numerous samples in plant disease clinics, tissue culture laboratories, and breeding programs. There are many opportunities for translating virome analysis to support plant health. For example, virome analysis can be employed in the development of biosecurity strategies and policies, including the implementation of virome risk assessments to support regulation and reduce the movement of infected plant material. A challenge is to identify which new viruses discovered through HTS require regulation and which can be allowed to move in germplasm and trade. On-farm management strategies can incorporate information from high-throughput surveillance, monitoring for new and known viruses across scales, to rapidly identify important agricultural viruses and understand their abundance and spread. Virome indexing programs can be used to generate clean germplasm and seed, crucial for the maintenance of seed system production and health, particularly in vegetatively propagated crops such as roots, tubers, and bananas. Virome analysis in breeding programs can provide insight into virus expression levels by generating relative abundance data, aiding in breeding cultivars resistant, or at least tolerant, to viruses. The integration of network analysis and machine learning techniques can facilitate designing and implementing management strategies, using novel forms of information to provide a scalable, replicable, and practical approach to developing management strategies for viromes. In the long run, these management strategies will be designed by generating sequence databases and building on the foundation of pre-existing knowledge about virus taxonomy, distribution, and host range. In conclusion, virome analysis will support the early adoption and implementation of integrated control strategies, impacting global markets, reducing the risk of introducing novel viruses, and limiting virus spread. The effective translation of virome analysis depends on capacity building to make benefits available globally.

Whole genome sequences reveal the Xanthomonas perforans population is shaped by the tomato production system.

Modern agricultural practices increase the potential for plant pathogen spread, while the advent of affordable whole genome sequencing enables in-depth studies of pathogen movement. Population genomic studies may decipher pathogen movement and population structure as a result of complex agricultural production systems. We used whole genome sequences of 281 Xanthomonas perforans strains collected within one tomato production season across Florida and southern Georgia fields to test for population genetic structure associated with tomato production system variables. We identified six clusters of X. perforans from core gene SNPs that corresponded with phylogenetic lineages. Using whole genome SNPs, we found genetic structure among farms, transplant facilities, cultivars, seed producers, grower operations, regions, and counties. Overall, grower operations that produced their own transplants were associated with genetically distinct and less diverse populations of strains compared to grower operations that received transplants from multiple sources. The degree of genetic differentiation among components of Florida's tomato production system varied between clusters, suggesting differential dispersal of the strains, such as through seed or contaminated transplants versus local movement within farms. Overall, we showed that the genetic variation of a bacterial plant pathogen is shaped by the structure of the plant production system.

The Role of Plant-Associated Bacteria, Fungi, and Viruses in Drought Stress Mitigation.

Drought stress is an alarming constraint to plant growth, development, and productivity worldwide. However, plant-associated bacteria, fungi, and viruses can enhance stress resistance and cope with the negative impacts of drought through the induction of various mechanisms, which involve plant biochemical and physiological changes. These mechanisms include osmotic adjustment, antioxidant enzyme enhancement, modification in phytohormonal levels, biofilm production, increased water and nutrient uptake as well as increased gas exchange and water use efficiency. Production of microbial volatile organic compounds (mVOCs) and induction of stress-responsive genes by microbes also play a crucial role in the acquisition of drought tolerance. This review offers a unique exploration of the role of plant-associated microorganisms-plant growth promoting rhizobacteria and mycorrhizae, viruses, and their interactions-in the plant microbiome (or phytobiome) as a whole and their modes of action that mitigate plant drought stress.