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This work was supported by European Commission (FP7-MC-ERG-2009-256524 to PdE). Tool use by non-human animals has received much research attention in the last couple of decades. Nonetheless, research has focused mostly on vertebrates, particularly primates and corvids, even though tool use has also been documented in insects. One of the best documented examples involves ants using debris (e.g., sand grains, mud, leaf fragments) to collect and transport liquid food to their nest. However, little is known about the factors that determine the selection of materials to be used as tool. We investigated tool selection in two species of Aphaenogaster ants by giving them the choice between different kinds of potential tools (natural and artificial objects). Ant workers showed a clear preference for certain materials to be used as tool objects. Tool selection was also shaped by familiarity with the material as ants developed a preference for artificial tools with a good soaking capacity that cannot be found in their natural environment. Our results indicate that ants of this genus evolved unique foraging strategies and show plasticity in their behaviour. Postprint

International audience; There is tremendous diversity of interactions between plants and other species. These relationships range from antagonism to mutualism. Interactions of plants with members of their ecological community can lead to a profound metabolic reconfiguration of the plants’ physiology. This reconfiguration can favour beneficial organisms and deter antagonists like pathogens or herbivores. Determining the cellular and molecular dialogue between plants, microbes, and insects, and its ecological and evolutionary implications is important for understanding the options for each partner to adopt an adaptive response to its biotic environment. Moving forward, understanding how such ecological interactions are shaped by environmental change and how we potentially mitigate deleterious effects will be increasingly important. The development of integrative multidisciplinary approaches may provide new solutions to the major ecological and societal issues ahead of us. The rapid evolution of technology provides valuable tools and opens up novel ways to test hypotheses that were previously unanswerable, but requires that scientists master these tools, understand potential ethical problems flowing from their implementation, and train new generations of biologists with diverse technical skills. Here, we provide brief perspectives and discuss future promise and challenges for research on insect–plant interactions building on the 16th International Symposium on Insect–Plant interactions (SIP) meeting that was held in Tours, France (2–6 July 2017). Talks, posters, and discussions are distilled into key research areas in insect–plant interactions, highlighting the current state of the field and major challenges, and future directions for both applied and basic research.

International audience; In spite of their widespread occurrence, only few host jumps by plant viruses have been evidenced and the molecular bases of even fewer have been determined. A combination of three independent approaches, (i) experimental evolution followed by reverse genetics analysis, (ii) positive selection analysis and (iii) locus-by-locus analysis of molecular variance (AMOVA) allowed reconstructing the Potato virus Y (PVY; genus Potyvirus, family Potyviridae) jump to pepper (Capsicum annuum), probably from other solanaceous plants. Synthetic chimeras between infectious cDNA clones of two PVY isolates with contrasted levels of adaptation to C. annuum showed that the P3 and, to a lower extent, the CI cistron played important roles in infectivity towards C. annuum. The three analytical approaches pinpointed a single nonsynonymous substitution in the P3 and P3N-PIPO cistrons that evolved several times independently and conferred adaptation to C. annuum. In addition to increasing our knowledge of host jumps in plant viruses, this study illustrates also the efficiency of locus-by-locus AMOVA and combined approaches to reconstruct adaptive mutations in the genome of RNA viruses.

In spite of the mosquito Aedes aegypti being a vector of several infectious diseases, a limited number of studies has been undertaken on learning in this species. Moreover, larval stages have been neglected as model organisms, although they are active, aquatic and perform stereotyped behavioural responses, e.g. the escape response when disturbed. To study the learning abilities of mosquito larvae, we focused on habituation, a form of non-associative learning widely studied in vertebrates and invertebrates. Habituation was defined as the progressive and reversible decrease in response to a reiterative stimulus. We first aimed at confirming habituation of the escape response in mosquito larvae (4th instar). Then, we determined whether a mnesic trace was established. Larvae were individually stimulated with a visual danger stimulus inducing the escape response. We set up a protocol for testing larvae individually, allowing the control of different parameters that are crucial for the study of cognitive abilities. After 15 trials, the escape response of mosquitoes was significantly lower. A disturbance stimulus presented after the 15th trial, induced the escape response and reversed habituation. Retention was confirmed up to 1h after the last habituation trial. This original bioassay can be adapted for studying the physiology of learning and memory in mosquito larvae, for analysing the effects of chemicals in the water, the characterisation of the cognitive abilities related to the life history of different mosquito species across preimaginal stages.

Despite the biological importance and pharmacological potential of glycans from marine organisms, there are many unanswered questions regarding their distribution, function, and evolution. Here we describe microarray-based glycan profiling of a diverse selection of marine animals using antibodies raised against fucoidan isolated from a brown alga. We demonstrate the presence of two fucoidan epitopes in six animals belonging to three phyla including Porifera, Molusca, and Chordata. We studied the spatial distribution of these epitopes in Cliona celata ("boring sponge") and identified their restricted localization on the surface of internal chambers. Our results show the potential of high-throughput screening and probes commonly used in plant and algal cell wall biology to study the diversity and distribution of glycan structures in metazoans.

The stability of mutualistic interactions is likely to be affected by the genetic diversity of symbionts that compete for the same functional niche. Fungus-growing (attine) ants have multiple complex symbioses and thus provide ample opportunities to address questions of symbiont specificity and diversity. Among the partners are Actinobacteria of the genus Pseudonocardia that are maintained on the ant cuticle to produce antibiotics, primarily against a fungal parasite of the mutualistic gardens. The symbiosis has been assumed to be a hallmark of evolutionary stability, but this notion has been challenged by culturing and sequencing data indicating an unpredictably high diversity. We used 454 pyrosequencing of 16S rRNA to estimate the diversity of the cuticular bacterial community of the leaf-cutting ant Acromyrmex echinatior and other fungus-growing ants from Gamboa, Panama. Both field and laboratory samples of the same colonies were collected, the latter after colonies had been kept under laboratory conditions for up to 10 years. We show that bacterial communities are highly colony-specific and stable over time. The majority of colonies (25/26) had a single dominant Pseudonocardia strain, and only two strains were found in the Gamboa population across 17 years, confirming an earlier study. The microbial community on newly hatched ants consisted almost exclusively of a single strain of Pseudonocardia while other Actinobacteria were identified on older, foraging ants in varying but usually much lower abundances. These findings are consistent with recent theory predicting that mixtures of antibiotic-producing bacteria can remain mutualistic when dominated by a single vertically transmitted and resource-demanding strain.

Termites are among the few animals that themselves can digest the most abundant organic polymer, cellulose, into glucose. In mice and Drosophila , glucose can activate genes via the transcription factor carbohydrate-responsive element-binding protein (ChREBP) to induce glucose utilization and de novo lipogenesis. Here, we identify a termite orthologue of ChREBP and its downstream lipogenic targets, including acetyl-CoA carboxylase and fatty acid synthase. We show that all of these genes, including ChREBP, are upregulated in mature queens compared with kings, sterile workers and soldiers in eight different termite species. ChREBP is expressed in several tissues, including ovaries and fat bodies, and increases in expression in totipotent workers during their differentiation into neotenic mature queens. We further show that ChREBP is regulated by a carbohydrate diet in termite queens. Suppression of the lipogenic pathway by a pharmacological agent in queens elicits the same behavioural alterations in sterile workers as observed in queenless colonies, supporting that the ChREBP pathway partakes in the biosynthesis of semiochemicals that convey the signal of the presence of a fertile queen. Our results highlight ChREBP as a likely key factor for the regulation and signalling of queen fertility.

International audience; Plant scientists and farmers are facing major challenges in providing food and nutritional security for a growing population, while preserving natural resources and biodiversity. Moreover, this should be done while adapting agriculture to climate change and by reducing its carbon footprint. To address these challenges, there is an urgent need to breed crops that are more resilient to suboptimal environments. Huge progress has recently been made in understanding the physiological, genetic and molecular bases of plant nutrition and environmental responses, paving the way towards a more sustainable agriculture. In this review, we present an overview of these progresses and strategies that could be developed to increase plant nutrient use efficiency and tolerance to abiotic stresses. As illustrated by many examples, they already led to promising achievements and crop improvements. Here, we focus on nitrogen and phosphate uptake and use efficiency and on adaptation to drought, salinity and heat stress. These examples first show the necessity of deepening our physiological and molecular understanding of plant environmental responses. In particular, more attention should be paid to investigate stress combinations and stress recovery and acclimation that have been largely neglected to date. It will be necessary to extend these approaches from model plants to crops, to unravel the relevant molecular targets of biotechnological or genetic strategies directly in these species. Similarly, sustained efforts should be done for further exploring the genetic resources available in these species, as well as in wild species adapted to unfavourable environments. Finally, technological developments will be required to breed crops that are more resilient and efficient. This especially relates to the development of multiscale phenotyping under field conditions and a wide range of environments, and use of modelling and big data management to handle the huge amount of information provided by the new molecular, genetic and phenotyping techniques.

International audience; Animals are often immersed in a chemical world consisting of mixtures of many compounds rather than of single substances, and they constantly face the challenge of extracting relevant information out of the chemical landscape. To this purpose, the ability to discriminate among different stimuli with different valence is essential, but it is also important to be able to generalise, i.e. to treat different but similar stimuli as equivalent, as natural variation does not necessarily affect stimulus valence. Animals can thus extract regularities in their environment and make predictions, for instance about distribution of food resources. We studied perceptual similarity of different plant odours by conditioning individual carpenter ants to one odour, and subsequently testing their response to another, structurally different odour. We found that asymmetry in generalisation, where ants generalise from odour A to B, but not from B to A, is dependent on both chain length and functional group. By conditioning ants to a binary mixture, and testing their reaction to the individual components of the mixture, we show that overshadowing, where parts of a mixture are learned better than others, is rare. Additionally, generalisation is dependent not only on the structural similarity of odorants, but also on their functional value, which might play a crucial role. Our results provide insight into how ants make sense of the complex chemical world around them, for example in a foraging context, and provide a basis with which to investigate the neural mechanisms behind perceptual similarity.

International audience; Nestmate recognition in ants is based on perceived differences in a multi-component blend of hydrocarbons that are present on the insect cuticle. Although supplementation experiments have shown that some classes of hydrocarbons, such as methyl branched alkanes and alkenes, have a salient role in nestmate recognition, there was basically no information available on how ants detect and perceive these molecules. We used a new conditioning procedure to investigate whether individual carpenter ants could associate a given hydrocarbon (linear or methyl-branched alkane) to sugar reward. We then studied perceptual similarity between a hydrocarbon previously associated with sugar and a novel hydrocarbon. Ants learnt all hydrocarbon-reward associations rapidly and with the same efficiency, regardless of the structure of the molecules. Ants could discriminate among a large number of pairs of hydrocarbons, but also generalised. Generalisation depended both on the structure of the molecule and the animal's experience. For linear alkanes, generalisation was observed when the novel molecule was smaller than the conditioned one. Generalisation between pairs of methyl-alkanes was high, while generalisation between hydrocarbons that differed in the presence or absence of a methyl group was low, suggesting that chain length and functional group might be coded independently by the ant olfactory system. Understanding variations in perception of recognition cues in ants is necessary for the general understanding of the mechanisms involved in social recognition processes based on chemical cues.

International audience; Theoretical and experimental studies have established the dynamic nature of virulence and that, like all traits, it has evolved. Understanding parasite evolution offers a conceptual framework for diverse fields and can contribute greatly to decision-making in disease control. Recently, Grech et al. investigated the effects of host genotype-by-parasite genotype interactions on the expression of virulence in an artificial rodent-malaria system. They found that both parasite and host effects explained most of the variance in the virulence, resistance and transmission potential. These findings are a major contribution to the emerging debate on the pros and cons of a coevolutionary approach of virulence evolution; they also hold great potential for more effective control strategies.

The enemy release hypothesis (ERH) attributes the success of some exotic plant species to reduced top‐down effects of natural enemies in the non‐native range relative to the native range. Many studies have tested this idea, but very few have considered the simultaneous effects of multiple kinds of enemies on more than one invasive species in both the native and non‐native ranges. Here, we examined the effects of two important groups of natural enemies–insect herbivores and soil biota–on the performance of Tanacetum vulgare (native to Europe but invasive in the USA) and Solidago canadensis (native to the USA but invasive in Europe) in their native and non‐native ranges, and in the presence and absence of competition.In the field, we replicated full‐factorial experiments that crossed insecticide, T. vulgare–S. canadensis competition, and biogeographic range (Europe vs. USA) treatments. In greenhouses, we replicated full‐factorial experiments that crossed soil sterilization, plant–soil feedback, and biogeographic range treatments. We evaluated the effects of experimental treatments on T. vulgare and S. canadensis biomass.The effects of natural enemies were idiosyncratic. In the non‐native range and relative to populations in the native range, T. vulgare escaped the negative effects of insect herbivores but not soil biota, depending upon the presence of S. canadensis; and S. canadensis escaped the negative effects of soil biota but not insect herbivores, regardless of competition. Thus, biogeographic escape from natural enemies depended upon the enemies, the invader, and competition. Synthesis: By explicitly testing the ERH in terms of more than one kind of enemy, more than one invader, and more than one continent, this study enhances our nuanced perspective of how natural enemies can influence the performance of invasive species in their native and non‐native ranges., We examined a leading hypothesis of plant invasion, the enemy release hypothesis, in terms of multiple invasive species, multiple natural enemies, and multiple continents. We found that escape from natural enemies depends on the enemies, the invader, and competition.

Accelerating the pace of microbiome science to enhance crop productivity and agroecosystem health will require transdisciplinary studies, comparisons among datasets, and synthetic analyses of research from diverse crop management contexts. However, despite the widespread availability of crop-associated microbiome data, variation in field sampling and laboratory processing methodologies, as well as metadata collection and reporting, significantly constrains the potential for integrative and comparative analyses. Here we discuss the need for agriculture-specific metadata standards for microbiome research, and propose a list of "required" and "desirable" metadata categories and ontologies essential to be included in a future minimum information metadata standards checklist for describing agricultural microbiome studies. We begin by briefly reviewing existing metadata standards relevant to agricultural microbiome research, and describe ongoing efforts to enhance the potential for integration of data across research studies. Our goal is not to delineate a fixed list of metadata requirements. Instead, we hope to advance the field by providing a starting point for discussion, and inspire researchers to adopt standardized procedures for collecting and reporting consistent and well-annotated metadata for agricultural microbiome research. National Science FoundationNational Science Foundation (NSF) [1714276] This material was supported in part by the National Science Foundation under grant number 1714276. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Public domain – authored by a U.S. government employee

The growth and productivity of several apple rootstocks have been evaluated in various previous studies. However, limited information is available on their tolerance to osmotic stress. In the present study, the physiological and molecular responses as well as abscisic acid (ABA) levels were assessed in six apple rootstocks (M26, V3, G41, G935, B9 and B118) osmotically stressed with polyethylene glycol (PEG, 30%) application under greenhouse conditions. Our results showed that V3, G41, G935 and B9 had higher relative water content (RWC), and lower electrolyte leakage (EL) under stress conditions compared to M26 and B118. Additionally, water use efficiency (WUE) was higher in V3, G41 and B9 than M26, which might be partially due to the lower transpiration rate in these tolerant rootstocks. V3, G41 and B9 rootstocks also displayed high endogenous ABA levels which was combined with a reduction in stomatal conductance and decreased water loss. At the transcriptional level, genes involved in ABA-dependent and ABA-independent pathways, e.g., SnRK, DREB, ERD and MYC2, showed higher expression in V3, G41, G935 and B9 rootstocks compared to M26 in response to stress. In contrast, WRKY29 was down-regulated in response to stress in the tolerant rootstocks, and its expression was negatively correlated with ABA content and stomatal closure. Overall, the findings of this study showed that B9, V3 and G41 displayed better osmotic stress tolerance followed by G935 then M26 and B118 rootstocks. Published version

Hyperspectral sensors combined with machine learning are increasingly utilized in agricultural crop systems for diverse applications, including plant disease detection. This study was designed to identify the most important wavelengths to discriminate between healthy and diseased peanut (Arachis hypogaea L.) plants infected with Athelia rolfsii, the causal agent of peanut stem rot, using in-situ spectroscopy and machine learning. In greenhouse experiments, daily measurements were conducted to inspect disease symptoms visually and to collect spectral reflectance of peanut leaves on lateral stems of plants mock-inoculated and inoculated with A. rolfsii. Spectrum files were categorized into five classes based on foliar wilting symptoms. Five feature selection methods were compared to select the top 10 ranked wavelengths with and without a custom minimum distance of 20 nm. Recursive feature elimination methods outperformed the chi-square and SelectFromModel methods. Adding the minimum distance of 20 nm into the top selected wavelengths improved classification performance. Wavelengths of 501–505, 690–694, 763 and 884 nm were repeatedly selected by two or more feature selection methods. These selected wavelengths can be applied in designing optical sensors for automated stem rot detection in peanut fields. The machine-learning-based methodology can be adapted to identify spectral signatures of disease in other plant-pathogen systems. Published version

Dissecting the genetic mechanisms underlying dioecy (i.e., separate female and male individuals) is critical for understanding the evolution of this pervasive reproductive strategy. Nonetheless, the genetic basis of sex determination remains unclear in many cases, especially in systems where dioecy has arisen recently. Within the economically important plant genus Solanum (similar to 2,000 species), dioecy is thought to have evolved independently at least 4 times across roughly 20 species. Here, we generate the first genome sequence of a dioecious Solanum and use it to ascertain the genetic basis of sex determination in this species. We de novo assembled and annotated the genome of Solanum appendiculatum (assembly size: similar to 750 Mb scaffold N50: 0.92 Mb; similar to 35,000 genes), identified sex-specific sequences and their locations in the genome, and inferred that males in this species are the heterogametic sex. We also analyzed gene expression patterns in floral tissues of males and females, finding approximately 100 genes that are differentially expressed between the sexes. These analyses, together with observed patterns of gene-family evolution specific to S. appendiculatum, consistently implicate a suite of genes from the regulatory network controlling pectin degradation and modification in the expression of sex. Furthermore, the genome of a species with a relatively young sex-determination system provides the foundational resources for future studies on the independent evolution of dioecy in this Glade. National Science Foundation (USA NSF)National Science Foundation (NSF) [IOS-1127059]; Department of Biology at Indiana University Published version We are grateful to J.L. Kostyun for help in the laboratory and greenhouse, and to the editor and anonymous reviewer for helpful comments. This work was done with support from National Science Foundation (USA NSF Grant No. IOS-1127059) and the Department of Biology at Indiana University. Previous support from NSF, and the University of Connecticut, supported the field collections and greenhouse experiments with Solanum appendiculatum.

Three muscadine grape genotypes (Muscadinia rotundifolia (Michx.) Small) were evaluated for their metabolite profiling and antioxidant activities at different berry developmental stages. A total of 329 metabolites were identified using UPLC-TOF-MS analysis (Ultimate 3000LC combined with Q Exactive MS and screened with ESI-MS) in muscadine genotypes throughout different developmental stages. Untargeted metabolomics study revealed the dominant chemical groups as amino acids, organic acids, sugars, and phenolics. Principal component analysis indicated that developmental stages rather than genotypes could explain the variations among the metabolic profiles of muscadine berries. For instance, catechin, epicatechin-3-gallate, and gallic acid were more accumulated in ripening seeds (RIP-S). However, tartaric acid and malonic acid were more abundant during the fruit-set (FS) stage, and malic acid was more abundant in the veraison (V) stage. The variable importance in the projection (VIP &gt, 0.5) in partial least-squares–discriminant analysis described 27 biomarker compounds, representing the muscadine berry metabolome profiles. A heatmap of Pearson’s correlation analysis between the 27 biomarker compounds and antioxidant activities was able to identify nine antioxidant determinants, among them, gallic acid, 4-acetamidobutanoic acid, trehalose, catechine, and epicatechin-3-gallate displayed the highest correlations with different types of antioxidant activities. For instance, DPPH and FRAP conferred a similar antioxidant activity pattern and were highly correlated with gallic acid and 4-acetamidobutanoic acid. This comprehensive study of the metabolomics and antioxidant activities of muscadine berries at different developmental stages is of great reference value for the plant, food, pharmaceutical, and nutraceutical sectors.

Orchardgrass (Dactylis glomerata L.) is an important forage grass for cultivating livestock worldwide. Here, we report an similar to 1.84-Gb chromosome-scale diploid genome assembly of orchardgrass, with a contig N50 of 0.93 Mb, a scaffold N50 of 6.08 Mb and a super-scaffold N50 of 252.52 Mb, which is the first chromosome-scale assembled genome of a cool-season forage grass. The genome includes 40 088 protein-coding genes, and 69% of the assembled sequences are transposable elements, with long terminal repeats (LTRs) being the most abundant. The LTRretrotransposons may have been activated and expanded in the grass genome in response to environmental changes during the Pleistocene between 0 and 1 million years ago. Phylogenetic analysis reveals that orchardgrass diverged after rice but before three Triticeae species, and evolutionarily conserved chromosomes were detected by analysing ancient chromosome rearrangements in these grass species. We also resequenced the whole genome of 76 orchardgrass accessions and found that germplasm from Northern Europe and East Asia clustered together, likely due to the exchange of plants along the 'Silk Road' or other ancient trade routes connecting the East and West. Last, a combined transcriptome, quantitative genetic and bulk segregant analysis provided insights into the genetic network regulating flowering time in orchardgrass and revealed four main candidate genes controlling this trait. This chromosome-scale genome and the online database of orchardgrass developed here will facilitate the discovery of genes controlling agronomically important traits, stimulate genetic improvement of and functional genetic research on orchardgrass and provide comparative genetic resources for other forage grasses. National Basic Research Program (973 Program) in ChinaNational Basic Research Program of China [2014CB138705]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China [NSFC 31872997]; Earmarked Fund for Modern Agro-industry Technology Research System [CARS-34]; National Project on Sci-Tec Foundation Resources Survey [2017FY100602] This research work was funded by the National Basic Research Program (973 Program) in China (No. 2014CB138705), the National Natural Science Foundation of China (NSFC 31872997), the Earmarked Fund for Modern Agro-industry Technology Research System (No. CARS-34) and the National Project on Sci-Tec Foundation Resources Survey (2017FY100602). Public domain – authored by a U.S. government employee

Background Comparative transcriptome analysis is the comparison of expression patterns between homologous genes in different species. Since most molecular mechanistic studies in plants have been performed in model species, including Arabidopsis and rice, comparative transcriptome analysis is particularly important for functional annotation of genes in diverse plant species. Many biological processes, such as embryo development, are highly conserved between different plant species. The challenge is to establish one-to-one mapping of the developmental stages between two species. Results In this manuscript, we solve this problem by converting the gene expression patterns into co-expression networks and then apply network module finding algorithms to the cross-species co-expression network. We describe how such analyses are carried out using bash scripts for preliminary data processing followed by using the R programming language for module finding with a simulated annealing method. We also provide instructions on how to visualize the resulting co-expression networks across species. Conclusions We provide a comprehensive pipeline from installing software and downloading raw transcriptome data to predicting homologous genes and finding orthologous co-expression networks. From the example provided, we demonstrate the application of our method to reveal functional conservation and divergence of genes in two plant species. Published version

The root lesion nematode Pratylenchus penetrans is a migratory species that attacks a broad range of plants. While analysing transcriptomic datasets of P. penetrans, we have identified a full-length genome of an unknown positive-sense singlestranded RNA virus, provisionally named root lesion nematode virus 1 (RLNV1). The 8614-nucleotide genome sequence encodes a single large polyprotein with conserved domains characteristic for the families Picornaviridae, Iflaviridae and Secoviridae of the order Picornavirales. Phylogenetic, BLAST and domain search analyses showed that RLNV1 is a novel species, most closely related to the recently identified sugar beet cyst nematode virus 1 and potato cyst nematode picornalike virus. In situ hybridization with a DIG-labelled DNA probe confirmed the presence of the virus within the nematodes. A negative-strand-specific RT-PCR assay detected RLNV1 RNA in nematode total RNA samples, thus indicating that viral replication occurs in P. penetrans. To the best of our knowledge, RLNV1 is the first virus identified in Pratylenchus spp. United States Department of Agriculture, Agricultural Research ServiceUnited States Department of Agriculture (USDA) This research was supported by the United States Department of Agriculture, Agricultural Research Service. Public domain – authored by a U.S. government employee

Southern Leaf Blight (SLB), Northern Leaf Blight (NLB), and Gray Leaf Spot (GLS) caused by Cochliobolus heterostrophus, Setosphaeria turcica, and Cercospora zeae-maydis respectively, are among the most important diseases of corn worldwide. Previously, moderately high and significantly positive genetic correlations between resistance levels to each of these diseases were identified in a panel of 253 diverse maize inbred lines. The goal of this study was to identify loci underlying disease resistance in some of the most multiple disease resistant (MDR) lines by the creation of chromosome segment substitution line (CSSL) populations in multiple disease susceptible (MDS) backgrounds. Four MDR lines (NC304, NC344, Ki3, NC262) were used as donor parents and two MDS lines (Oh7B, H100) were used as recurrent parents to produce eight BC3F4:5 CSSL populations comprising 1,611 lines in total. Each population was genotyped and assessed for each disease in replicated trials in two environments. Moderate to high heritabilities on an entry mean basis were observed (0.32 to 0.83). Several lines in each population were significantly more resistant than the MDS parental lines for each disease. Multiple quantitative trait loci (QTL) for disease resistance were detected for each disease in most of the populations. Seventeen QTL were associated with variation in resistance to more than one disease (SLB/NLB: 2; SLB/GLS: 7; NLB/GLS: 2 and 6 to all three diseases). For most populations and most disease combinations, significant correlations were observed between disease scores and also between marker effects for each disease. The number of lines that were resistant to more than one disease was significantly higher than would be expected by chance. Using the results from individual QTL analyses, a composite statistic based on Mahalanobis distance (Md) was used to identify joint marker associations with multiple diseases. Across all populations and diseases, 246 markers had significant Md values. However further analysis revealed that most of these associations were due to strong QTL effects on a single disease. Together, these findings reinforce our previous conclusions that loci associated with resistance to different diseases are clustered in the genome more often than would be expected by chance. Nevertheless true MDR loci which have significant effects on more than one disease are still much rarer than loci with single disease effects. © 2019 by the Genetics Society of America. Funding for the work was provided by USDA-ARS, the Corn Growers’ Association of North Carolina and by NSF grant #1127076 to RJW and PBK. LLZ was supported by a grant from Fulbright Colombia and COLCIENCIAS. We thank Cathy Herring and the staff at Central crops for excellent field support, Shannon Sermons, David Rhyne and Greg Marshall for their technical support. We thank Daniel Gorman, David Bubeck and DuPont-Pioneer for their assistance with field trials in Andrews NC. We thank Jim Holland and Marc Cubeta for their advice and for reviewing the manuscript.

Background Apple blotch (AB) caused by Diplocarpon coronariae (Dc) has been established in Europe since 2010. AB is a serious apple disease, mostly in low input orchards and in cider production areas in Northern Italy, Switzerland, Austria and Germany. However, the epidemiology and population genetic structure of this pathogen is unknown. Methods We developed twelve Dc-specific microsatellite markers and screened DNA of both pure fungal isolates and infected apple leaves. The marker data of 313 European samples of Dc were compared to Dc isolates from Asia (n = 7) and the USA (n = 3). Results We found 31 distinct multilocus genotypes (MLGs) in European samples, and seven additional MLGs in the Asian and USA samples. The European samples had the typical genetic signature of a recently introduced species including high clonality, a low number of private alleles and one dominant MLG across all the sampling sites. All European MLGs were genetically distant from those MLGs of Asian and USA origin. Based on the lack of linkage disequilibrium observed, there is evidence that Dc undergoes regular cycles of sexual recombination in the European population, although the sexual stage (apothecia) has not been observed in Europe. Conclusions The twelve newly developed SSR markers reported here provide a useful tool to characterize the population genetic diversity and structure of Dc in Europe. Our study supports the hypothesis that Dc is a recently introduced pathogen in Europe, but of currently unknown origin. Dc has a large effective population size during field epidemics, so we believe that the pathogen has substantial evolutionary potential. Application of the SSR markers to large-scale and diverse Dc samples will help to better understand the epidemiology of AB, which has become a global apple disease, and will help guide effective mitigation strategies based on disease management and resistance breeding., CABI Agriculture and Bioscience, 2, ISSN:2662-4044

Background Metagenomics is gaining attention as a powerful tool for identifying how agricultural management practices influence human and animal health, especially in terms of potential to contribute to the spread of antibiotic resistance. However, the ability to compare the distribution and prevalence of antibiotic resistance genes (ARGs) across multiple studies and environments is currently impossible without a complete re-analysis of published datasets. This challenge must be addressed for metagenomics to realize its potential for helping guide effective policy and practice measures relevant to agricultural ecosystems, for example, identifying critical control points for mitigating the spread of antibiotic resistance. Results Here we introduce AgroSeek, a centralized web-based system that provides computational tools for analysis and comparison of metagenomic data sets tailored specifically to researchers and other users in the agricultural sector interested in tracking and mitigating the spread of ARGs. AgroSeek draws from rich, user-provided metagenomic data and metadata to facilitate analysis, comparison, and prediction in a user-friendly fashion. Further, AgroSeek draws from publicly-contributed data sets to provide a point of comparison and context for data analysis. To incorporate metadata into our analysis and comparison procedures, we provide flexible metadata templates, including user-customized metadata attributes to facilitate data sharing, while maintaining the metadata in a comparable fashion for the broader user community and to support large-scale comparative and predictive analysis. Conclusion AgroSeek provides an easy-to-use tool for environmental metagenomic analysis and comparison, based on both gene annotations and associated metadata, with this initial demonstration focusing on control of antibiotic resistance in agricultural ecosystems. Agroseek creates a space for metagenomic data sharing and collaboration to assist policy makers, stakeholders, and the public in decision-making. AgroSeek is publicly-available at https://agroseek.cs.vt.edu/.

Background: Computing genomic similarity between strains is a prerequisite for genome-based prokaryotic classification and identification. Genomic similarity was first computed as Average Nucleotide Identity (ANI) values based on the alignment of genomic fragments. Since this is computationally expensive, faster and computationally cheaper alignment-free methods have been developed to estimate ANI. However, these methods do not reach the level of accuracy of alignment-based methods. Methods: Here we introduce LINflow, a computational pipeline that infers pairwise genomic similarity in a set of genomes. LINflow takes advantage of the speed of the alignment-free sourmash tool to identify the genome in a dataset that is most similar to a query genome and the precision of the alignment-based pyani software to precisely compute ANI between the query genome and the most similar genome identified by sourmash. This is repeated for each new genome that is added to a dataset. The sequentially computed ANI values are stored as Life Identification Numbers (LINs), which are then used to infer all other pairwise ANI values in the set. We tested LINflow on four sets, 484 genomes in total, and compared the needed time and the generated similarity matrices with other tools. Results: LINflow is up to 150 times faster than pyani and pairwise ANI values generated by LINflow are highly correlated with those computed by pyani. However, because LINflow infers most pairwise ANI values instead of computing them directly, ANI values occasionally depart from the ANI values computed by pyani. In conclusion, LINflow is a fast and memory-efficient pipeline to infer similarity among a large set of prokaryotic genomes. Its ability to quickly add new genome sequences to an already computed similarity matrix makes LINflow particularly useful for projects when new genome sequences need to be regularly added to an existing dataset. National Science FoundationNational Science Foundation (NSF) [IOS-1354215]; College of Agriculture and Life Sciences at Virginia Polytechnic Institute and State University; Virginia Agricultural Experiment Station; Hatch Program of the National Institute of Food and Agriculture, US Department of Agriculture This study was supported by the National Science Foundation (IOS-1354215) and the College of Agriculture and Life Sciences at Virginia Polytechnic Institute and State University. Funding to Boris A. Vinatzer was also provided in part by the Virginia Agricultural Experiment Station and the Hatch Program of the National Institute of Food and Agriculture, US Department of Agriculture. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Subclinical doses of LPS (SD-LPS) are known to cause low-grade inflammatory activation of monocytes, which could lead to inflammatory diseases including atherosclerosis and metabolic syndrome. Sodium 4-phenylbutyrate is a potential therapeutic compound which can reduce the inflammation caused by SD-LPS. To understand the gene regulatory networks of these processes, we have generated scRNA-seq data from mouse monocytes treated with these compounds and identified 11 novel cell clusters. We have developed a machine learning method to integrate scRNA-seq, ATAC-seq, and binding motifs to characterize gene regulatory networks underlying these cell clusters. Using guided regularized random forest and feature selection, our method achieved high performance and outperformed a traditional enrichment-based method in selecting candidate regulatory genes. Our method is particularly efficient in selecting a few candidate genes to explain observed expression pattern. In particular, among 531 candidate TFs, our method achieves an auROC of 0.961 with only 10 motifs. Finally, we found two novel subpopulations of monocyte cells in response to SD-LPS and we confirmed our analysis using independent flow cytometry experiments. Our results suggest that our new machine learning method can select candidate regulatory genes as potential targets for developing new therapeutics against low grade inflammation. Jeffress Trust Awards Program in Interdisciplinary Research; NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01HL115835] This work was supported by the Jeffress Trust Awards Program in Interdisciplinary Research to SL, as well as NIH R01HL115835 to LL.

International audience; Background:Pseudomonas syringae are ubiquitous epiphytic plant pathogens infecting a wide range of important agricultural plant species. Bacteriophages has been proposed as biocontrol agents against plant pathogens, however, in order to utilize this approach, a deeper understanding of phage diversity and phage-host interactions is required.Materials and Methods: Phages targeting P. syringae GAW0113 were isolated from organic waste samples. Three distinct phage isolates were purified and subjected to whole-genome sequencing, comparative genomics, transmission electron microscopy and host-range assay using a wide selection of diverse P. syringae isolates.Results: The three phage isolates, Pseudomonas phage Bertil, Misse, and Strit, were shown to have podovirus morphology with a short tail stub and isometric head. They had double-stranded DNA ranging from 41,342 to 41,374 bp in size comprising 50–51 open reading frames. The three phage genomes were highly similar and genomic comparison analyses showed that they all belong to the Autographiviridae family of the order Caudovirales. All three phages were shown to have a narrow host-range.Conclusions: The three phages were shown to share morphological and genomic features with other phages in the Autographiviridae family, however, based on the limited nucleotide similarity we propose that the phages constitute a novel genus. All three phages were found to infect multiple strains of P. syringae covering several phylogroups.

Spray‐induced gene silencing (SIGS) using topical dsRNA applications has risen as a promising, target‐specific, and environmentally‐friendly disease management strategy against phytopathogenic fungi. We demonstrated that minicell‐encapsulated dsRNA (ME‐dsRNA) halted gray mold disease progression in strawberries. ME‐dsRNAs offer a platform that can readily be translated to large‐scale production and deployed in open‐field applications to control gray mold in strawberries., Summary Spray‐induced gene silencing (SIGS) using topical dsRNA applications has risen as a promising, target‐specific, and environmentally friendly disease management strategy against phytopathogenic fungi. However, dsRNA stability, efficacy, and scalability are still the main constraints facing SIGS broader application. Here we show that Escherichia coli‐derived anucleated minicells can be utilized as a cost‐effective, scalable platform for dsRNA production and encapsulation. We demonstrated that minicell‐encapsulated dsRNA (ME‐dsRNA) was shielded from RNase degradation and stabilized on strawberry surfaces, allowing dsRNA persistence in field‐like conditions. ME‐dsRNAs targeting chitin synthase class III (Chs3a, Chs3b) and DICER‐like proteins (DCL1 and DCL2) genes of Botryotinia fuckeliana selectively knocked‐down the target genes and led to significant fungal growth inhibition in vitro. We also observed a compensatory relationship between DCL1 and DCL2 gene transcripts, where the silencing of one gene upregulated the expression of the other. Contrary to naked‐dsRNAs, ME‐dsRNAs halted disease progression in strawberries for 12 days under greenhouse conditions. These results elucidate the potential of ME‐dsRNAs to enable the commercial application of RNAi‐based, species‐specific biocontrols comparable in efficacy to conventional synthetics. ME‐dsRNAs offer a platform that can readily be translated to large‐scale production and deployed in open‐field applications to control grey mould in strawberries.

Crimson clover (Trifolium incarnatum L.) is the most common legume cover crop in the United States. Previous research found limited genetic variation for crimson clover within the National Plant Germplasm System (NPGS) collection. The aim of this study was to assess the phenotypic and nodule microbial diversity within the NPGS crimson clover collection, focusing on traits important for cover crop performance. Experiments were conducted at the Beltsville Agricultural Research Center (Maryland, USA) across three growing seasons (2012&ndash, 2013, 2013&ndash, 2014, 2014&ndash, 2015) to evaluate 37 crimson clover accessions for six phenotypic traits: fall emergence, winter survival, flowering time, biomass per plant, nitrogen (N) content in aboveground biomass, and proportion of plant N from biological nitrogen fixation (BNF). Accession effect was significant across all six traits. Fall emergence of plant introductions (PIs) ranged from 16.0% to 70.5%, winter survival ranged from 52.8% to 82.0%, and growing degree days (GDD) to 25% maturity ranged from 1470 GDD to 1910 GDD. Biomass per plant ranged from 1.52 to 6.51 g, N content ranged from 1.87% to 2.24%, and proportion of plant N from BNF ranged from 50.2% to 85.6%. Accessions showed particularly clear differences for fall emergence and flowering time, indicating greater diversity and potential for selection in cover crop breeding programs. Fall emergence and winter survival were positively correlated, and both were negatively correlated with biomass per plant and plant N from BNF. A few promising lines performed well across multiple key traits, and are of particular interest as parents in future breeding efforts, including PIs 369045, 418900, 561943, 561944, and 655006. In 2014&ndash, 2015, accessions were also assessed for nodule microbiome diversity, and 11 genera were identified across the sampled nodules. There was large variation among accessions in terms of species diversity, but this diversity was not associated with observed plant traits, and the functional implications of nodule microbiome diversity remain unclear.

Recent advances in genomic technologies have generated data on large-scale protein–DNA interactions and open chromatin regions for many eukaryotic species. How to identify condition-specific functions of transcription factors using these data has become a major challenge in genomic research. To solve this problem, we have developed a method called ConSReg, which provides a novel approach to integrate regulatory genomic data into predictive machine learning models of key regulatory genes. Using Arabidopsis as a model system, we tested our approach to identify regulatory genes in data sets from single cell gene expression and from abiotic stress treatments. Our results showed that ConSReg accurately predicted transcription factors that regulate differentially expressed genes with an average auROC of 0.84, which is 23.5–25% better than enrichment-based approaches. To further validate the performance of ConSReg, we analyzed an independent data set related to plant nitrogen responses. ConSReg provided better rankings of the correct transcription factors in 61.7% of cases, which is three times better than other plant tools. We applied ConSReg to Arabidopsis single cell RNA-seq data, successfully identifying candidate regulatory genes that control cell wall formation. Our methods provide a new approach to define candidate regulatory genes using integrated genomic data in plants.

Plant stress is a real dilemma; it puzzles plant biologists and is a global problem that negatively affects people’s daily lives. Of particular interest is salinity, because it represents one of the major water-related stress types. We aimed to determine the signals that guide the cellular-related events where various adaptation mechanisms cross-talk to cope with salinity-related water stress in plants. In an attempt to unravel these mechanisms and introduce cellular events in the right context, we expansively discussed how salt-related signals are sensed, with particular emphasis on aquaporins, nonselective cation channels (NSCCs), and glycosyl inositol phosphorylceramide (GIPC). We also elaborated on the critical role Ca2+, H+, and ROS in mediating signal transduction pathways associated with the response and tolerance to salt stress. In addition, the fragmentary results from the literature were compiled to develop a harmonized, informational, and contemplative model that is intended to improve our perception of these adaptative mechanisms and set a common platform for plant biologists to identify intriguing research questions in this area. Published version

Iron metabolism and the plant immune system are both critical for plant vigor in natural ecosystems and for reliable agricultural productivity. Mechanistic studies of plant iron home-ostasis and plant immunity have traditionally been carried out in isolation from each other; however, our growing understanding of both processes has uncovered significant connections. For example, iron plays a critical role in the generation of reactive oxygen intermediates during immunity and has been recently implicated as a critical factor for immune-initiated cell death via ferroptosis. Moreover, plant iron stress triggers immune activation, suggesting that sensing of iron depletion is a mechanism by which plants recognize a pathogen threat. The iron deficiency response engages hormone signaling sectors that are also utilized for plant immune signaling, providing a probable explanation for iron-immunity cross-talk. Finally, interference with iron acquisition by pathogens might be a critical component of the immune response. Efforts to address the global burden of iron deficiency-related anemia have focused on classical breeding and transgenic approaches to develop crops biofortified for iron content. However, our improved mechanistic understanding of plant iron metabolism suggests that such alterations could promote or impede plant immunity, depending on the nature of the alteration and the virulence strategy of the pathogen. Effects of iron biofortification on disease resistance should be evaluated while developing plants for iron biofortification. Colleges of Agriculture and Life Sciences at Virginia Tech and North Carolina State University; National Science FoundationNational Science Foundation (NSF); Biotechnology and Biological Sciences Research Council (BBSRC)Biotechnology and Biological Sciences Research Council (BBSRC) [NSF MCB-1517058]; United States Department of Agriculture National Institute of Food and Agriculture, the Hatch Project [101090]; North Carolina State University North Carolina Agriculture and Life Sciences Research Foundation This article was supported by a joint collaborative grant (to J. M. M. and T. A. L.) from the Colleges of Agriculture and Life Sciences at Virginia Tech and North Carolina State University. T. A. L. was supported by the National Science Foundation and the Biotechnology and Biological Sciences Research Council (BBSRC) (Grant NSF MCB-1517058), the United States Department of Agriculture National Institute of Food and Agriculture, the Hatch Project (Accession Number 101090), and the North Carolina State University North Carolina Agriculture and Life Sciences Research Foundation.

Background. Synechocystis sp. PCC6803 is a model cyanobacterium that has been studied widely and is considered for metabolic engineering applications. Here, Raman spectroscopy and Raman chemometrics (Rametrix (TM)) were used to (i) study broad phenotypic changes in response to growth conditions, (ii) identify phenotypic changes associated with its circadian rhythm, and (iii) correlate individual Raman bands with biomolecules and verify these with more accepted analytical methods. Methods. Synechocystis cultures were grown under various conditions, exploring dependencies on light and/or external carbon and nitrogen sources. The Rametrix (TM) LITE Toolbox for MATLAB (R) was used to process Raman spectra and perform principal component analysis (PCA) and discriminant analysis of principal components (DAPC). The Rametrix (TM) PRO Toolbox was used to validate these models through leave-oneout routines that classified a Raman spectrum when growth conditions were withheld from the model. Performance was measured by classification accuracy, sensitivity, and specificity. Raman spectra were also subjected to statistical tests (ANOVA and pairwise comparisons) to identify statistically relevant changes in Synechocystis phenotypes. Finally, experimental methods, including widely used analytical and spectroscopic assays were used to quantify the levels of glycogen, fatty acids, amino acids, and chlorophyll a for correlations with Raman data. Results. PCA and DAPC models produced distinct clustering of Raman spectra, representing multiple Synechocystis phenotypes, based on (i) growth in the presence of 5 mM glucose, (ii) illumination (dark, light/dark [12 h/12 h], and continuous light at 20 mE), (iii) nitrogen deprivation (0-100%NaNO3 of native BG-11 medium in continuous light), and (iv) throughout a 24 h light/dark (12 h/12 h) circadian rhythm growth cycle. Rametrix (TM) PRO was successful in identifying glucose-induced phenotypes with 95.3% accuracy, 93.4% sensitivity, and 96.9% specificity. Prediction accuracy was above random chance values for all other studies. Circadian rhythm analysis showed a return to the initial phenotype after 24 hours for cultures grown in light/dark (12 h/12 h) cycles; this did not occur for cultures grown in the dark. Finally, correlation coefficients (R > 0.7) were found for glycogen, all amino acids, and chlorophyll a when comparing specific Raman bands to other experimental results. National Science FoundationNational Science Foundation (NSF) [NSF1243988]; HATCH funds This research was funded by the National Science Foundation award NSF1243988. Collakova and Senger salaries are supported by HATCH funds. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

High throughput DNA sequencing in combination with efficient algorithms could provide the basis for a highly resolved, genome phylogeny-based and digital prokaryotic taxonomy. However, current taxonomic practice continues to rely on cumbersome journal publications for the description of new species, which still constitute the smallest taxonomic units. In response, we introduce LINbase, a web server that allows users to genomically circumscribe any group of prokaryotes with measurable DNA similarity and that uses the individual isolate as smallest unit. Since LINbase leverages the concept of Life Identification Numbers (LINs), which are codes assigned to individual genomes based on reciprocal average nucleotide identity, we refer to groups circumscribed in LINbase as LINgroups. Users can associate with each LINgroup a name, a short description, and a URL to a peer-reviewed publication. As soon as a LINgroup is circumscribed, any user can immediately identify query genomes as members and submit comments about the LINgroup. Most genomes currently in LINbase were imported from GenBank, but users can upload their own genome sequences as well. In conclusion, LINbase combines the resolution of LINs with the power of crowdsourcing in support of a highly resolved, genome phylogeny-based digital taxonomy. LINbase is available at http://www.LINbase.org. National Science Foundation [IOS-1354215, in part]; College of Agriculture and Life Sciences, Virginia Polytechnic Institute and State University; Virginia Agricultural Experiment Station (to B.A.V.) (in part); Hatch Program of the National Institute of Food and Agriculture, US Department of Agriculture (to B.A.V.). Funding for open access charge: National Science Foundation; Institutional Funds.

The dataset presented here supports the research paper entitled "A calculator to quantify cover crop effects on soil health and productivity". Soil health (sometimes used synonymously with soil quality) is a concept that describes soil as a living system to sustain plants, animals, and human. Soil physical, chemical, and biological properties, along with their interactions, are required to quantify soil health. The use of cover crops in agricultural rotations may enhance soil health, yet there has been little progress in understanding how external factors such as climate, soil type, and agronomic practices affect soil and cash crop responses. In response, this dataset compiles measurements from 281 studies and provides an analysis of field-measured changes in 38 soil health indicators due to cover crop usage. Environmental and background indicators were also compiled to assess how climatic and management practices affect soil and cash crop responses to cover crops, with specific categories including climate type (tropical, arid, temperate, and continental), soil texture (coarse, medium, and fine), cover crop type (legume, grass, multi-species mixture, and other), and cash crop type (corn, soybean, wheat, vegetable, corn-soybean rotation, corn-soybean-wheat rotation, and other). An unbalanced analysis of variation was used to determine the hierarchy of most to least important factors that affected responsiveness of each soil health indicator. Based on the hierarchy structure, a soil health calculator was then developed to quantify the response of 13 parameters - erosion, runoff, weed suppression, soil aggregate stability, leaching, infiltration, microbial biomass carbon, soil bulk density, soil organic carbon, soil nitrogen, microbial biomass nitrogen, cash crop yield, and saturated hydraulic conductivity - to cover crops. The presented data in the calculator report the mean change in parameter values based on all combinations of climate, soil texture, cover crop type, and cash crop type. (c) 2020 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/) DOE Office of Biological and Environmental Research (BER), as part of BER's Terrestrial Ecosystem Science ProgramUnited States Department of Energy (DOE); Yangling Vocational & Technical College [A2019009]; U.S. Department of Agriculture NRCS Conservation Innovation Grant [693A75-14-260]; Virginia Agricultural Experiment Station, U.S. Department of Agriculture; Hatch Program of the National Institute of Food and Agriculture, U.S. Department of Agriculture The authors thank the DOE Office of Biological and Environmental Research (BER), as part of BER's Terrestrial Ecosystem Science Program, for providing funding to support the publication of this work. Xuan Du is supported by the conservation management and soil erosion project grant (A2019009) from Yangling Vocational & Technical College. Ryan Stewart received support from the U.S. Department of Agriculture NRCS Conservation Innovation Grant (No. 693A75-14-260) and the Virginia Agricultural Experiment Station and the Hatch Program of the National Institute of Food and Agriculture, U.S. Department of Agriculture The data and R code to support the analysis can be found at: https://github.com/jinshijian/SoilHealthCalculator.

BackgroundDuring their long evolution,Synechocystissp. PCC6803 developed a remarkable capacity to acclimate to diverse environmental conditions. In this study, Raman spectroscopy and Raman chemometrics tools (RametrixTM) were employed to investigate the phenotypic changes in response to external stressors and correlate specific Raman bands with their corresponding biomolecules determined with widely used analytical methods.MethodsSynechocystiscells were grown in the presence of (i) acetate (7.5–30 mM), (ii) NaCl (50–150 mM) and (iii) limiting levels of MgSO4(0–62.5 mM) in BG-11 media. Principal component analysis (PCA) and discriminant analysis of PCs (DAPC) were performed with the RametrixTMLITE Toolbox for MATLABⓇ. Next, validation of these models was realized via RametrixTMPRO Toolbox where prediction of accuracy, sensitivity, and specificity for an unknown Raman spectrum was calculated. These analyses were coupled with statistical tests (ANOVA and pairwise comparison) to determine statistically significant changes in the phenotypic responses. Finally, amino acid and fatty acid levels were measured with well-established analytical methods. The obtained data were correlated with previously established Raman bands assigned to these biomolecules.ResultsDistinguishable clusters representative of phenotypic responses were observed based on the external stimuli (i.e., acetate, NaCl, MgSO4, and controls grown on BG-11 medium) or its concentration when analyzing separately. For all these cases, RametrixTMPRO was able to predict efficiently the corresponding concentration in the culture media for an unknown Raman spectra with accuracy, sensitivity and specificity exceeding random chance. Finally, correlations (R > 0.7) were observed for all amino acids and fatty acids between well-established analytical methods and Raman bands.

SummaryDisease development in plants requires a susceptible host, a virulent pathogen, and a favourable environment. Oomycete pathogens cause many important diseases and have evolved sophisticated molecular mechanisms to manipulate their hosts. Day length has been shown to impact plant-oomycete interactions but a need exists for a tractable reference system to understand the mechanistic interplay between light regulation, oomycete pathogen virulence, and plant host immunity. Here we present data demonstrating that light is a critical factor in the interaction between Arabidopsis thaliana and its naturally occurring downy mildew pathogen Hyaloperonospora arabidopsidis (Hpa). We investigated the role of light on spore germination, mycelium development, sporulation and oospore formation of Hpa, along with defence responses in the host. We observed abundant Hpa sporulation on compatible Arabidopsis under day lengths ranging from 10 to 14 hours. Contrastingly, exposure to constant light or constant dark suppressed sporulation. Exposure to constant dark suppressed spore germination, mycelial development and oospore formation. Interestingly, exposure to constant light stimulated spore germination, mycelial development and oospore formation. A biomarker of plant immune system activation was induced under both constant light and constant dark. Altogether, these findings demonstrate that Hpa has the molecular mechanisms to perceive and respond to light and that both the host and pathogen responses are influenced by the light regime. Therefore, this pathosystem can be used for investigations to understand the molecular mechanisms through which oomycete pathogens like Hpa perceive and integrate light signals, and how light influences pathogen virulence and host immunity during their interactions.

International audience; Microbes in the atmosphere have broad ecological impacts, including the potential to trigger precipitation through species and strains that act as ice nucleation particles. To characterize spatiotemporal trends of microbial assemblages in precipitation we sequenced 16S (bacterial) and 18S (fungal) rRNA gene amplicon libraries collected from 72 precipitation events in three US states (Idaho, Louisiana, and Virginia) over four seasons. We considered these data from the perspective of a novel metacommunity framework. In agreement with our heuristic, we found evidence for distinct mechanisms underlying the composition and diversity of bacterial and fungal assemblages in precipitation. Specifically, we determined that: 1) bacterial operational taxonomic unit (OTU) composition of precipitation was strongly associated with macroscale drivers including season and high altitude characteristics of storms; 2) fungal OTU composition was strongly correlated with mesoscale drivers including particular spatial locations; 3) β‐diversity (heterogeneity of taxa among samples) for both bacteria and fungi was largely maintained by turnover of taxa; however, 4) bacterial assemblages had higher contributions to total β‐diversity from nestedness (i.e., lower richness assemblages were largely taxonomic subsets of richer assemblages), due to losses of taxa during dispersal, particularly among potential ice nucleation active bacteria; and 5) fungal assemblages had higher contributions to total β‐diversity from turnover due to OTU replacement. Spatiotemporal trends in precipitation‐borne metacommunities allowed delineation of a large number of statistically significant indicator taxa for particular sites and seasons, including trends for bacteria that are potentially ice nucleation active. Our findings advance understanding regarding the dispersion of aerosolized microbes via wet deposition, and the development of theory concerning potential assembly rules for bioaerosol assemblages.

Background Bacteriophages, the viruses infecting bacteria, are biological entities that can control their host populations. The ecological relevance of phages for microbial systems has been widely explored in aquatic environments, but the current understanding of the role of phages in terrestrial ecosystems remains limited. Here, our objective was to quantify the extent to which phages drive the assembly and functioning of soil bacterial communities. We performed a reciprocal transplant experiment using natural and sterilized soil incubated with different combinations of two soil microbial communities, challenged against native and non-native phage suspensions as well as against a cocktail of phage isolates. We tested three different community assembly scenarios by adding phages: (a) during soil colonization, (b) after colonization, and (c) in natural soil communities. One month after inoculation with phage suspensions, bacterial communities were assessed by 16S rRNA amplicon gene sequencing. Results By comparing the treatments inoculated with active versus autoclaved phages, our results show that changes in phage pressure have the potential to impact soil bacterial community composition and diversity. We also found a positive effect of active phages on the soil ammonium concentration in a few treatments, which indicates that increased phage pressure may also be important for soil functions. Conclusions Overall, the present work contributes to expand the current knowledge about soil phages and provide some empirical evidence supporting their relevance for soil bacterial community assembly and functioning.

The importance of soil age as an ecosystem driver across biomes remains largely unresolved. By combining a cross-biome global field survey, including data for 32 soil, plant, and microbial properties in 16 soil chronosequences, with a global meta-analysis, we show that soil age is a significant ecosystem driver, but only accounts for a relatively small proportion of the cross-biome variation in multiple ecosystem properties. Parent material, climate, vegetation and topography predict, collectively, 24 times more variation in ecosystem properties than soil age alone. Soil age is an important local-scale ecosystem driver; however, environmental context, rather than soil age, determines the rates and trajectories of ecosystem development in structure and function across biomes. Our work provides insights into the natural history of terrestrial ecosystems. We propose that, regardless of soil age, changes in the environmental context, such as those associated with global climatic and land-use changes, will have important long-term impacts on the structure and function of terrestrial ecosystems across biomes., This project received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement No. 702057 (CLIMIFUN). M.D.-B. is supported by a Ramón y Cajal grant from the Spanish Ministry of Science and Innovation (RYC2018-025483-I), and by the BES Grant Agreement No. LRB17\1019 (MUSGONET). F.B. is grateful to the Spanish Ministry and FEDER funds for the project AGL2017–85755-R, the i-LINK+2018 (LINKA20069) from CSIC, and received funds from “Fundación Séneca” from Murcia Province (19896/GERM/15). S.R. was supported by the US Geological Survey Ecosystems Mission Area. C.P. acknowledges support from the Spanish State Plan for Scientific and Technical Research and Innovation (2013–2016), award ref. AGL201675762-R (AEI/FEDER, UE). A.G. acknowledges support from the Spanish Ministry of Science (CGL2017-88124-R). F.A. is supported by FONDECYT 11180538 and S.A. by FONDECYT 1170995. We would like to thank Peter Vitousek for his comments on a previous draft of this paper. Moreover, we thank Matt Gebert, Jessica Henley, Fernando T. Maestre, Victoria Ochoa, and Beatriz Gozalo for their help with lab analyses, and Emilio Guirado for his advice with topographic analyses. We also want to thank Osvaldo Sala, Matthew A. Bowker, Peter Vitousek, Courtney Currier, Martin Kirchmair, Victor M. Peña-Ramírez, Lynn Riedel, Julie Larson, Katy Waechter, David Buckner, and Brian Anacker for their help with soil sampling, and to the City of Boulder Open Space and Mountain Parks for allowing us to conduct these samplings. We are also grateful to the Division of Forestry and Wildlife of the State of Hawai’i and Koke’e State Park for their logistical assistance and for allowing us access to the HA sites. The Arizona research sites were established with the support of an EPA‐STAR Graduate Fellowship (U‐916251), a Merriam‐Powell Center for Environmental Research Graduate Fellowship, an Achievement Rewards for College Scientists (ARCS) Foundation of Arizona Scholarship, and McIntire‐Stennis appropriations to Northern Arizona University and the State of Arizona. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

In Appalachian ecosystems, forest disturbance has long-term effects on microbially driven biogeochemical processes such as nitrogen (N) cycling. However, little is known regarding long-term responses of forest soil microbial communities to disturbance in the region. We used 16S and ITS sequencing to characterize soil bacterial (16S) and fungal (ITS) communities across forested watersheds with a range of past disturbance regimes and adjacent reference forests at the Coweeta Hydrologic Laboratory in the Appalachian mountains of North Carolina. Bacterial communities in previously disturbed forests exhibited consistent responses, including increased alpha diversity and increased abundance of copiotrophic (e.g., Proteobacteria) and N-cycling (e.g., Nitrospirae) bacterial phyla. Fungal community composition also showed disturbance effects, particularly in mycorrhizal taxa. However, disturbance did not affect fungal alpha diversity, and disturbance effects were not consistent at the fungal class level. Co-occurrence networks constructed for bacteria and fungi showed that disturbed communities were characterized by more connected and tightly clustered network topologies, indicating that disturbance alters not only community composition but also potential ecological interactions among taxa. Although bacteria and fungi displayed different long-term responses to forest disturbance, our results demonstrate clear responses of important bacterial and fungal functional groups (e.g., nitrifying bacteria and mycorrhizal fungi), and suggest that both microbial groups play key roles in the long-term alterations to biogeochemical processes observed following forest disturbance in the region. Coweeta LTER; National Science FoundationNational Science Foundation (NSF) [DEB-1637522] This work was funded by the Coweeta LTER, funded by National Science Foundation grant DEB-1637522. Public domain – authored by a U.S. government employee

The major nutrients available to human colonic Bacteroides species are glycans, exemplified by pectins, a network of covalently linked plant cell wall polysaccharides containing galacturonic acid (GalA). Metabolism of complex carbohydrates by the Bacteroides genus is orchestrated by polysaccharide utilization loci (PULs). In Bacteroides thetaiotaomicron, a human colonic bacterium, the PULs activated by different pectin domains have been identified; however, the mechanism by which these loci contribute to the degradation of these GalA-containing polysaccharides is poorly understood. Here we show that each PUL orchestrates the metabolism of specific pectin molecules, recruiting enzymes from two previously unknown glycoside hydrolase families. The apparatus that depolymerizes the backbone of rhamnogalacturonan-I is particularly complex. This system contains several glycoside hydrolases that trim the remnants of other pectin domains attached to rhamnogalacturonan-I, and nine enzymes that contribute to the degradation of the backbone that makes up a rhamnose-GalA repeating unit. The catalytic properties of the pectin-degrading enzymes are optimized to protect the glycan cues that activate the specific PULs ensuring a continuous supply of inducing molecules throughout growth. The contribution of Bacteroides spp. to metabolism of the pectic network is illustrated by cross-feeding between organisms., This work was supported in part by an Advanced Grant from the European Research Council (Grant No. 322820) awarded to H.J.G. and B.H. supporting A.S.L., D.N., A.C. and N.T., a Wellcome Trust Senior Investigator Award to H.J.G. (grant No. WT097907MA) that supported J.B. and E.C.L. a European Union Seventh Framework Initial Training Network Programme entitled the “WallTraC project” (Grant Agreement number 263916) awarded to M-C.R. and H.J.G, which supported X.Z. and J.S. The Biotechnology and Biological Research Council project ‘Ricefuel’ (grant numbers BB/K020358/1) awarded to H.J.G. supported A.L.

Fatty acid-and retinol-binding proteins (FARs) are unique to nematodes, and are implicated in a wide range of metabolic and parasitic related functions. Along with the in silico analyses performed in this study, three different FAR members of this family were identified in Pratylenchus penetrans. The cDNA corresponding to the fatty acid-and retinol-binding Pp-far-1 gene was cloned and herein characterized at the molecular level for the first time for this genus. The translated 185 amino acid sequence of P. penetrans FAR-1 sequence, with a predicted molecular weight of 20.82 kDa and a pI of 5.49, shares highest sequence identity to FARs of other migratory nematodes of the Pratylenchidae family (90% to P. vulnus and 80% to Radopholus similis). In situ hybridization localizes Pp-far-1 transcripts in the hypodermis of the nematode. RT-qPCR detected Pp-far-1 transcripts for all nematode developmental stages, with highest expression levels found in juveniles, adult females and adult males, respectively. Pp-far-1 is also highly expressed during infection and establishment of P. penetrans in roots of different host plants, such as corn, lily and soybean. The importance of Pp-far-1 was studied by in planta RNA interference (RNAi) assays using stable soybean hairy root lines. Targeting Pp-far-1 decreased expression of the nematode Pp-far-1 and significantly reduced the reproduction of nematodes, with a total of between 44% and 70% fewer nematodes in comparison to the average number of nematodes counted for control lines. The results indicate that suppressing the expression levels of Pp-far-1 can act as an effective target gene to control P. penetrans. National Institute of Food and Agriculture, US Department of AgricultureUnited States Department of Agriculture (USDA) [11588909] The authors acknowledge the technical assistance of Sarah Shih. This material is based upon work that is supported by the National Institute of Food and Agriculture, US Department of Agriculture, under award number 11588909. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the US Department of Agriculture. Public domain – authored by a U.S. government employee

Soybean is a major source of protein for human consumption and animal feed. Releasing new cultivars with high nutritional value is one of the major goals in soybean breeding. To achieve this goal, genome-wide association studies of seed amino acid contents were conducted based on 249 soybean accessions from China, US, Japan, and South Korea. The accessions were evaluated for 15 amino acids and genotyped by sequencing. Significant genetic variation was observed for amino acids among the accessions. Among the 231 single nucleotide polymorphisms (SNPs) significantly associated with variations in amino acid contents, fifteen SNPs localized near 14 candidate genes involving in amino acid metabolism. The amino acids were classified into two groups with five in one group and seven amino acids in the other. Correlation coefficients among the amino acids within each group were high and positive, but the correlation coefficients of amino acids between the two groups were negative. Twenty-five SNP markers associated with multiple amino acids can be used to simultaneously improve multi-amino acid concentration in soybean. Genomic selection analysis of amino acid concentration showed that selection efficiency of amino acids based on the markers significantly associated with all 15 amino acids was higher than that based on random markers or markers only associated with individual amino acid. The identified markers could facilitate selection of soybean varieties with improved seed quality. Hebei Province Natural Science Foundation for Distinguished Young Scholars [C2014301035]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China [31100880]; Key Project of the Natural Science Foundation of Hebei Province [C2012301020] The authors would like to thank Prof. Lijuan Qiu (Chinese Academy of Agricultural Sciences) for providing seeds of 249 soybean accessions. This study was supported by the Hebei Province Natural Science Foundation for Distinguished Young Scholars (C2014301035), National Natural Science Foundation of China (31100880), and Key Project of the Natural Science Foundation of Hebei Province (C2012301020).

Poor survival on plants can limit the efficacy of Biological Control Agents (BCAs) in the field. Yet bacteria survive in the atmosphere, despite their exposure to high solar radiation and extreme temperatures. If conditions in the atmosphere are similar to, or more extreme than, the environmental conditions on the plant surface, then precipitation may serve as a reservoir of robust BCAs. To test this hypothesis, two hundred and fifty-four rain-borne isolates were screened for in vitro inhibition of Erwinia amylovora, the causal agent of fire blight, as well as of other plant pathogenic bacteria, fungi and oomycetes. Two isolates showed strong activity against E. amylovora and other plant pathogenic bacteria, while other isolates showed activity against fungal and oomycete pathogens. Survival assays suggested that the two isolates that inhibited E. amylovora were able to survive on apple blossoms and branches similarly to E. amylovora. Pathogen population size and associated fire blight symptoms were significantly reduced when detached apple blossoms were treated with the two isolates before pathogen inoculation, however, disease reduction on attached blossoms within an orchard was inconsistent. Using whole genome sequencing, the isolates were identified as Pantoea agglomerans and P. ananatis, respectively. A UV-mutagenesis screen pointed to a phenazine antibiotic D-alanylgriseoluteic acid synthesis gene cluster as being at the base of the antimicrobial activity of the P. agglomerans isolate. Our work reveals the potential of precipitation as an under-explored source of BCAs, whole genome sequencing as an effective approach to precisely identify BCAs, and UV-mutagenesis as a technically simple screen to investigate the genetic basis of BCAs. More field trials are needed to determine the efficacy of the identified BCAs in fire blight control.

Green plants (Viridiplantae) include around 450,000–500,000 species1,2 of great diversity and have important roles in terrestrial and aquatic ecosystems. Here, as part of the One Thousand Plant Transcriptomes Initiative, we sequenced the vegetative transcriptomes of 1,124 species that span the diversity of plants in a broad sense (Archaeplastida), including green plants (Viridiplantae), glaucophytes (Glaucophyta) and red algae (Rhodophyta). Our analysis provides a robust phylogenomic framework for examining the evolution of green plants. Most inferred species relationships are well supported across multiple species tree and supermatrix analyses, but discordance among plastid and nuclear gene trees at a few important nodes highlights the complexity of plant genome evolution, including polyploidy, periods of rapid speciation, and extinction. Incomplete sorting of ancestral variation, polyploidization and massive expansions of gene families punctuate the evolutionary history of green plants. Notably, we find that large expansions of gene families preceded the origins of green plants, land plants and vascular plants, whereas whole-genome duplications are inferred to have occurred repeatedly throughout the evolution of flowering plants and ferns. The increasing availability of high-quality plant genome sequences and advances in functional genomics are enabling research on genome evolution across the green tree of life., The One Thousand Plant Transcriptomes Initiative provides a robust phylogenomic framework for examining green plant evolution that comprises the transcriptomes and genomes of diverse species of green plants.

Background At the molecular level, nonlinear networks of heterogeneous molecules control many biological processes, so that systems biology provides a valuable approach in this field, building on the integration of experimental biology with mathematical modeling. One of the biggest challenges to making this integration a reality is that many life scientists do not possess the mathematical expertise needed to build and manipulate mathematical models well enough to use them as tools for hypothesis generation. Available modeling software packages often assume some modeling expertise. There is a need for software tools that are easy to use and intuitive for experimentalists. Results This paper introduces PlantSimLab, a web-based application developed to allow plant biologists to construct dynamic mathematical models of molecular networks, interrogate them in a manner similar to what is done in the laboratory, and use them as a tool for biological hypothesis generation. It is designed to be used by experimentalists, without direct assistance from mathematical modelers. Conclusions Mathematical modeling techniques are a useful tool for analyzing complex biological systems, and there is a need for accessible, efficient analysis tools within the biological community. PlantSimLab enables users to build, validate, and use intuitive qualitative dynamic computer models, with a graphical user interface that does not require mathematical modeling expertise. It makes analysis of complex models accessible to a larger community, as it is platform-independent and does not require extensive mathematical expertise.

Routine strain-level identification of plant pathogens directly from symptomatic tissue could significantly improve plant disease control and prevention. Here we tested the Oxford Nanopore Technologies (ONT) MinION™ sequencer for metagenomic sequencing of tomato plants either artificially inoculated with a known strain of the bacterial speck pathogenPseudomonas syringaepv.tomato(Pto), or collected in the field and showing bacterial spot symptoms caused by either one of fourXanthomonasspecies. After species-level identification using ONT’s WIMP software and the third party tools Sourmash and MetaMaps, we used Sourmash and MetaMaps with a custom database of representative genomes of bacterial tomato pathogens to attempt strain-level identification. In parallel, each metagenome was assembled and the longest contigs were used as query with the genome-based microbial identification Web service LINbase. Both the read-based and assembly-based approaches correctly identifiedPtostrain T1 in the artificially inoculated samples. The pathogen strain in most field samples was identified as a member ofXanthomonas perforansgroup 2. This result was confirmed by whole genome sequencing of colonies isolated from one of the samples. Although in our case, metagenome-based pathogen identification at the strain-level was achieved, caution still needs to be exerted when interpreting strain-level results because of the challenges inherent to assigning reads to specific strains and the error rate of nanopore sequencing.

Identifying the global drivers of soil priming is essential to understanding C cycling in terrestrial ecosystems. We conducted a survey of soils across 86 globally-distributed locations, spanning a wide range of climates, biotic communities, and soil conditions, and evaluated the apparent soil priming effect using 13C-glucose labeling. Here we show that the magnitude of the positive apparent priming effect (increase in CO2 release through accelerated microbial biomass turnover) was negatively associated with SOC content and microbial respiration rates. Our statistical modeling suggests that apparent priming effects tend to be negative in more mesic sites associated with higher SOC contents. In contrast, a single-input of labile C causes positive apparent priming effects in more arid locations with low SOC contents. Our results provide solid evidence that SOC content plays a critical role in regulating apparent priming effects, with important implications for the improvement of C cycling models under global change scenarios., The global ecological predictors of soil priming remain unclear. Here the authors conducted a global survey of soils from 86 global locations using an isotopic approach and find that in more mesic sites with high SOC concentrations, soil priming effects are more likely to be negative.

Geminiviruses are an important group of circular, single-stranded DNA viruses that cause devastating diseases in crops. Geminiviruses replicate their genomic DNA in the nucleus. The newly-synthesized viral DNA is subsequently transported to the cytoplasm, moved to adjacent cells through plasmodesmata with the help of viral movement proteins, and, ultimately, moved long-distance to establish systemic infection. Thus, the nucleocytoplasmic transportation is crucial for a successful infection by geminiviruses. For Tomato yellow leaf curl virus (TYLCV), the V1 protein is known to bind and shuttle viral genomic DNA, but the role of V2 protein in this process is still unclear. Here, we report that the nucleus-localized V1 protein dramatically decreases when co-expressed with V2 protein, and that V2-facilitated nuclear export of V1 protein depends on host exportin-α and a specific V1-V2 interaction. Chemical inhibition of exportin-α or a substitutions at cysteine 85 of V2 protein, which abolishes the V1-V2 interaction, blocks the promoted redistribution of V1 protein to the perinuclear region and the cytoplasm. When the V2C85S mutation is incorporated into a TYLCV infectious clone, the TYLCV-C85S causes delayed onset of very mild symptoms compared to wild-type TYLCV, indicating that the V1-V2 interaction and, thus, V2-mediated nuclear export of V1 protein is crucial for viral spread and systemic infection. Our data point to a critical role of the V2 protein in promoting the nuclear export of the V1 protein, likely by promoting V1-mediated nucleocytoplasmic transportation of TYLCV genomic DNA, and in turn, promoting viral systemic infection.Author summaryAs both replication and the transcription of geminiviruses occur in the nucleus, transportation of the viral genomic DNA into and out of the nucleus of the infected cells is essential for a successful infection cycle. However, the nuclear export of geminiviruses is still little known and even less is known about the process for monopartite geminiviruses. We use TYLCV, a typical monopartite begomovirus in the family Geminiviridae, to examine the nucleocytoplasmic transportation. In this study, we found TYLCV V2 is able to redistribute the nucleus-localized V1 protein to the perinuclear region. Moreover, the nuclear export of V1 protein is dependent on the V1-V2 interaction and host exportin-α. Blocking the V1-V2 interaction impeded the V2-mediated V1 protein redistribution and decrease TYLCV infection efficiency with delayed and mild symptoms. This report shows us a new explanation for the role of V2 in the nuclear export of V1 protein and TYLCV viral systemic infection.

Harmful Algal Blooms (HABs) have been observed in all 50 states in the U.S., ranging from large freshwater lakes, such as the Great Lakes, to smaller inland lakes, rivers, and reservoirs, as well as marine coastal areas and estuaries. In 2014, a HAB on Lake Erie containing microcystin (a liver toxin) contaminated the municipal water supply in Toledo, Ohio, providing non-potable water to 400,000 people. Studying HABs is complicated as different cyanobacteria produce a range of toxins that impact human health, such as microcystins, saxitoxin, anatoxin-a, and cylindrospermopsin. HABs may be increasing in prevalence with rising temperatures and higher nutrient runoff. Consequently, new tools and technology are needed to rapidly detect, characterize, and respond to HABs that threaten our water security. A framework is needed to understand cyber threats to new and existing technologies that monitor and forecast our water quality. To properly detect, assess, and mitigate security threats on water infrastructure, it is necessary to envision water security from the perspective of a cyber-physical system (CPS). In doing so, we can evaluate risks and research needs for cyber-attacks on HAB-monitoring networks including data injection attacks, automated system hijacking attacks, node forgery attacks, and attacks on learning algorithms. Herein, we provide perspectives on the research needed to understand both the threats posed by HABs and the coupled cyber threats to water security in the context of HABs. This manuscript was supported in part by a cybersecurity grant from the College of Agriculture and Life Sciences at Virginia Tech and a grant to JW number P30 ES020957.