Healthy trees, healthy future: Phytophthora systems biology
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Apoplastic effectors secreted by two unrelated eukaryotic plant pathogens target the tomato defense protease Rcr3

Current models of plant–pathogen interactions stipulate that pathogens secrete effector proteins that disable plant defense components known as virulence targets. Occasionally, the perturbations caused by these effectors trigger innate immunity via plant disease resistance proteins as described by the “guard hypothesis.” This model is nicely illustrated by the interaction between the fungal plant pathogenCladosporium fulvum and tomato. C. fulvum secretes a protease inhibitor Avr2 that targets the tomato cysteine protease Rcr3pim. In plants that carry the resistance protein Cf2, Rcr3pim is required for resistance to C. fulvum strains expressing Avr2, thus fulfilling one of the predictions of the guard hypothesis. Another prediction of the guard hypothesis has not yet been tested. Considering that virulence targets are important components of defense, different effectors from unrelated pathogens are expected to evolve to disable the same host target. In this study we confirm this prediction using a different pathogen of tomato, the oomycete Phytophthora infestans that is distantly related to fungi such as C. fulvum. This pathogen secretes an array of protease inhibitors including EPIC1 and EPIC2B that inhibit tomato cysteine proteases. Here we show that, similar to Avr2, EPIC1 and EPIC2B bind and inhibit Rcr3pim. However, unlike Avr2, EPIC1 and EPIC2B do not trigger hypersensitive cell death or defenses on Cf-2/Rcr3pim tomato. We also found that thercr3–3 mutant of tomato that carries a premature stop codon in the Rcr3 gene exhibits enhanced susceptibility to P. infestans, suggesting a role for Rcr3pim in defense. In conclusion, our findings fulfill a key prediction of the guard hypothesis and suggest that the effectors Avr2, EPIC1, and EPIC2B secreted by two unrelated pathogens of tomato target the same defense protease Rcr3pim. In contrast to C. fulvumP. infestans appears to have evolved stealthy effectors that carry inhibitory activity without triggering plant innate immunity.

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Different pathogens, same target.

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Healthy trees, healthy future: Phytophthora systems biology
The ‘Healthy trees, healthy future’ programme is an exciting collaboration that is consolidating New Zealand’s capacity in Phytophthora, plant breeding, molecular biology and analytical chemistry to tackle a group of pathogens that impacts plant systems across horticulture, forestry and our natural forest estates.  Our programme is using a multi-host-pathogen ‘Systems Biology’ model that is driving increased research focus on Phytophthora species nationally. This page hilights some of the exciting research and activity in this field internationally.
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Statisticians Found One Thing They Can Agree On: It’s Time To Stop Misusing P-Values

Statisticians Found One Thing They Can Agree On: It’s Time To Stop Misusing P-Values | Healthy trees, healthy future: Phytophthora systems biology | Scoop.it
Little p-value What are you trying to say Of significance? — Stephen Ziliak, Roosevelt University economics professor How many statisticians does it take to en…

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Deep Learning for Population Genetic Inference

Deep learning is an active area of research in machine learning which has been applied to various challenging problems in computer science over the past several years, breaking long-standing records of classification accuracy. Here, we apply deep learning to develop a novel likelihood-free inference framework to estimate population genetic parameters and learn informative features of DNA sequence data. As a concrete example, we focus on the challenging problem of jointly inferring natural selection and demographic history.


Via Pierre Gladieux, Niklaus Grunwald
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PhySortR: a fast, flexible tool for sorting phylogenetic trees in R

PhySortR: a fast, flexible tool for sorting phylogenetic trees in R | Healthy trees, healthy future: Phytophthora systems biology | Scoop.it
A frequent bottleneck in interpreting phylogenomic output is the need to screen often thousands of trees for features of interest, particularly robust clades of specific taxa, as evidence of monophyletic relationship and/or reticulated evolution. Here we present PhySortR, a fast, flexible R package for classifying phylogenetic trees. Unlike existing utilities, PhySortR allows for identification of both exclusive and non-exclusive clades uniting the target taxa based on tip labels (i.e., leaves) on a tree, with customisable options to assess clades within the context of the whole tree. Using simulated and empirical datasets, we demonstrate the potential and scalability of PhySortR in analysis of thousands of phylogenetic trees without a priori assumption of tree-rooting, and in yielding readily interpretable trees that unambiguously satisfy the query. PhySortR is a command-line tool that is freely available and easily automatable.

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Can NMR solve some significant challenges in metabolomics?

Can NMR solve some significant challenges in metabolomics? | Healthy trees, healthy future: Phytophthora systems biology | Scoop.it

The field of metabolomics continues to witness rapid growth driven by fundamental studies, methods development, and applications in a number of disciplines that include biomedical science, plant and nutrition sciences, drug development, energy and environmental sciences, toxicology, etc. NMR spectroscopy is one of the two most widely used analytical platforms in the metabolomics field, along with mass spectrometry (MS). NMR’s excellent reproducibility and quantitative accuracy, its ability to identify structures of unknown metabolites, its capacity to generate metabolite profiles using intact bio-specimens with no need for separation, and its capabilities for tracing metabolic pathways using isotope labeled substrates offer unique strengths for metabolomics applications. However, NMR’s limited sensitivity and resolution continue to pose a major challenge and have restricted both the number and the quantitative accuracy of metabolites analyzed by NMR. Further, the analysis of highly complex biological samples has increased the demand for new methods with improved detection, better unknown identification, and more accurate quantitation of larger numbers of metabolites. Recent efforts have contributed significant improvements in these areas, and have thereby enhanced the pool of routinely quantifiable metabolites. Additionally, efforts focused on combining NMR and MS promise opportunities to exploit the combined strength of the two analytical platforms for direct comparison of the metabolite data, unknown identification and reliable biomarker discovery that continue to challenge the metabolomics field. This article presents our perspectives on the emerging trends in NMR-based metabolomics and NMR’s continuing role in the field with an emphasis on recent and ongoing research from our laboratory.

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bioRxiv: Arabidopsis late blight: Infection of a nonhost plant by Albugo laibachii enables full colonization by Phytophthora infestans (2015)

bioRxiv: Arabidopsis late blight: Infection of a nonhost plant by Albugo laibachii enables full colonization by Phytophthora infestans (2015) | Healthy trees, healthy future: Phytophthora systems biology | Scoop.it

The oomycete pathogen Phytophthora infestans causes potato late blight, and as a potato and tomato specialist pathogen, is seemingly poorly adapted to infect plants outside the Solanaceae. Here, we report the unexpected finding that P. infestans can infect Arabidopsis thaliana when another oomycete pathogen, Albugo laibachii, has colonized the host plant. The behaviour and speed of P. infestans infection in Arabidopsis pre-infected with A. laibachii resemble P. infestans infection of susceptible potato plants. Transcriptional profiling of P. infestans genes during infection revealed a significant overlap in the sets of secreted-protein genes that are induced in P. infestans upon colonisation of potato and susceptible Arabidopsis, suggesting major similarities in P. infestans gene expression dynamics on the two plant species. Furthermore, we found haustoria of A. laibachii and P. infestanswithin in the same Arabidopsis cells. This Arabidopsis - A. laibachii - P. infestanstripartite interaction opens up various possibilities to dissect the molecular mechanisms of P. infestans infection and the processes occurring in co-infected Arabidopsis cells.


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New Phytol: The microbiome of the leaf surface of Arabidopsis protects against a fungal pathogen

New Phytol: The microbiome of the leaf surface of Arabidopsis protects against a fungal pathogen | Healthy trees, healthy future: Phytophthora systems biology | Scoop.it

We have explored the importance of the phyllosphere microbiome in plant resistance in the cuticle mutants bdg (BODYGUARD) or lacs2.3 (LONG CHAIN FATTY ACID SYNTHASE 2) that are strongly resistant to the fungal pathogen Botrytis cinerea.
The study includes infection of plants under sterile conditions, 16S ribosomal DNA sequencing of the phyllosphere microbiome, and isolation and high coverage sequencing of bacteria from the phyllosphere.
When inoculated under sterile conditions bdg became as susceptible as wild-type (WT) plants whereas lacs2.3 mutants retained the resistance. Adding washes of its phyllosphere microbiome could restore the resistance of bdg mutants, whereas the resistance of lacs2.3 results from endogenous mechanisms. The phyllosphere microbiome showed distinct populations in WT plants compared to cuticle mutants. One species identified as Pseudomonas sp isolated from the microbiome of bdg provided resistance to B. cinerea on Arabidopsis thaliana as well as on apple fruits. No direct activity was observed against B. cinerea and the action of the bacterium required the plant.
Thus, microbes present on the plant surface contribute to the resistance to B. cinerea. These results open new perspectives on the function of the leaf microbiome in the protection of plants.


Via Stéphane Hacquard, Francis Martin
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New insight into a complex plant-fungal pathogen interaction : Nature Genetics : Nature Publishing Group

New insight into a complex plant-fungal pathogen interaction : Nature Genetics : Nature Publishing Group | Healthy trees, healthy future: Phytophthora systems biology | Scoop.it

“ The coevolution of plants and microbes has shaped plant mechanisms that detect and repel pathogens. A newly identified plant gene confers partial resistance to a fungal pathogen not by preventing initial infection but by limiting its spread through the plant.”


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Eric Larson's curator insight, December 29, 2015 7:36 AM

Interesting insight.

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Disentangling the factors shaping microbiota composition across the plant holobiont

Disentangling the factors shaping microbiota composition across the plant holobiont | Healthy trees, healthy future: Phytophthora systems biology | Scoop.it

Healthy and asymptomatic plants in nature are colonized by a rich diversity of microbes comprising bacteria, fungi, protists and viruses (i.e. the plant microbiota), forming complex microbial consortia that impact plant growth and productivity. Consequently, plants must not be viewed as autonomous entities but rather as holobionts (a macrobe and its numerous microbial associates), within which all interacting organisms contribute to the overall stability of the system (Vandenkoornhuyse et al., 2015). More than a century ago, Hiltner hypothesized that the resistance of plants towards pathogenesis is dependent on the composition of plant microflora and that root exudates of different plants could support development of different microbial communities (Hartmann et al., 2008). The development of next generation sequencing technologies and associated computational analytical tools now allows the detailed investigation of these important concepts (Bulgarelli et al., 2012; Lundberg et al., 2012). However, despite the fact that the plant microbiota research field shows exponential growth (Fig. 1), most of the studies published so far have focused on one particular microbial kingdom and/or specific host niches. There is consequently a need for a more holistic understanding of the microbial communities associated with different plant compartments and discerning which factors shape these microbial assemblages across the plant holobiont. In this issue of New Phytologist, Coleman-Derr et al. (pp. 798–811) provide a comprehensive analysis of the structure of both fungal and bacterial communities in the rhizosphere, phyllosphere, leaf and root endosphere, as well as proximal and distal soil samples from cultivated and native agaves. Since agaves spp. are adapted to nutrient-poor environments, extreme drought and elevated temperatures, these plants represent important models for the plant microbiota research field because they are likely to host an important reservoir of beneficial microbes that may support their survival.


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Stéphane Hacquard's curator insight, December 22, 2015 3:37 AM

My commentary on the nice manuscript by Coleman-Derr et al. (Plant compartment and biogeography affect microbiome composition in cultivated and native Agave species, New Phytologist 209, January 2016, Pages 798–811)

Eric Larson's curator insight, December 29, 2015 7:37 AM

Pathogen protections???

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Systemic above- and belowground cross talk: hor...

Systemic above- and belowground cross talk: hor... | Healthy trees, healthy future: Phytophthora systems biology | Scoop.it
Above- and belowground plant parts are simultaneously attacked by different pests and pathogens. The host mediates these interactions and physiologically reacts, e.g. with local and systemic alterations of endogenous hormone levels coupled with coordinated transcriptional changes. This in turn af...
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Eric Larson's curator insight, December 29, 2015 7:39 AM

ISR and attacks from various challenges.

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New and Improved Techniques for the Study of Pathogenic Fungi: Trends in Microbiology

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Tree pests and diseases: the threat to biodiversity and the delivery of ecosystem services - Online First - Springer

Tree pests and diseases: the threat to biodiversity and the delivery of ecosystem services - Online First - Springer | Healthy trees, healthy future: Phytophthora systems biology | Scoop.it
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Australasian Plant Pathology: Globalisation, the founder effect, hybrid Phytophthora species and rapid evolution: new headaches for biosecurity (2015)

Australasian Plant Pathology: Globalisation, the founder effect, hybrid Phytophthora species and rapid evolution: new headaches for biosecurity (2015) | Healthy trees, healthy future: Phytophthora systems biology | Scoop.it

The oomycete genus Phytophthora contains a large number of plant pathogens that cause significant damage to natural and agricultural systems. Until recently species have been distinguished using a limited set of morphological characters. The development of DNA-based technologies has revealed much broader and more complex diversity than previously recognised, and has led to the recent description of many new species. This review looks at the underlying mechanisms for the generation of diversity within the genus. The intercontinental movement and transplantation of infected plant material partially explains the appearance of new species in unexpected places. However, it is also likely that novel species arise as a result of the hybridisation and rapid evolution of introduced species under episodic selection pressures. Hybrid progeny may possess equal or greater virulence than parent species, thereby posing an increasing risk to our natural environment and agricultural production systems. These discoveries amplify the threats posed by the introduction of plant pathogens into new environments, and expose a crucial weakness in current evidence-based biosecurity regimes. Further work is required to identify hybrids, anticipate and understand the occurrence of hybridisation, and to implement appropriate quarantine and risk management measures.


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Eric Larson's curator insight, December 29, 2015 7:40 AM

New biosecurity challenges.

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Genome Biology | Full text | Wheat rusts never sleep but neither do sequencers: will pathogenomics transform the way plant diseases are managed?

Field pathogenomics adds highly informative data to surveillance surveys by enabling rapid evaluation of pathogen variability, population structure and host genotype.
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News: How plant sensors detect pathogens (2015)

News: How plant sensors detect pathogens (2015) | Healthy trees, healthy future: Phytophthora systems biology | Scoop.it

In the mid-20th century, an American scientist named Harold Henry Flor helped explain how certain varieties of plants can fight off some plant killers (pathogens), but not others, with a model called the “gene-for-gene” hypothesis. Seventy years later, an international team of scientists describes precisely how a plant senses a pathogen, bringing an unprecedented level of detail to Flor’s model.

 

“We know that plants have sensors to detect pathogens but we knew little about how they work,” says Professor Banfield from the John Innes Centre (UK).

 

In a study published in eLife, the team led by Professor Mark Banfield, in collaboration with the Iwate Biotechnology Research Centre (Japan) and The Sainsbury Laboratory (UK), investigated how one sensor protein from rice called Pik binds AVR-Pik, a protein from the rice blast pathogen. This fungus causes the most devastating disease of rice crops. Using X-ray crystallography facilities at Diamond Light Source in Oxfordshire, the team succeeded in imaging the contact points between the plant and pathogen proteins at the molecular level – the first time this has been done for a pair of plant and pathogen proteins that follow the gene-for-gene model.

 

Dr Abbas Maqbool from the JIC, first author of the study added, “Harold Flor predicted that plant sensors discriminate between different pathogen types, but at the time he had no knowledge of the molecules involved. It is remarkable that his ideas have now crystallized into detailed molecular models.”

 

Dr Maqbool, Professor Banfield and colleagues went on to discover that the strength at which the Pik sensor binds the pathogen AVR-Pik protein correlates with the strength of the plant’s response. This opens up new avenues for engineering better plant responses against pathogens by building sensors with increased strength of binding to pathogen proteins, and therefore conferring enhanced resistance to disease.

 

“Once we understand how these plant sensors detect invading pathogens, we can devise strategies to ‘boost’ the plant immune system and help protect rice and other important food crops from disease,” says Professor Banfield.

 

Maqbool et al. eLife http://elifesciences.org/content/4/e08709


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FOOD SERVICES NO.1 TESTING/CERTIFICATION/INSPEC/ GIREESAN's curator insight, September 22, 2015 9:19 AM

Harold Flor predicted that plant sensors discriminate between different pathogen types, but at the time he had no knowledge of the molecules involved. It is remarkable that his ideas have now crystallized into detailed molecular models.”

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Plant chemical defense: at what cost?

Plants are sessile organisms and dependent on deployment of secondary metabolites for their response to biotic and abiotic challenges. A trade-off is envisioned between resources allocated to growth, development, and reproduction and to the biosynthesis, storage, and maintenance of secondary metabolites. However, increasing evidence suggests that secondary metabolites serve auxiliary roles, including functions associated with primary metabolism. In this opinion article, we examine how the costs of plant chemical defense can be offset by multifunctional biosynthesis and the optimization of primary metabolism. These additional benefits may negate the trade-off between primary and secondary metabolism, and provide plants with an innate plasticity required for growth, development, and interactions with their environment.

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PLoS ONE: RNA-Seq of Early-Infected Poplar Leaves by the Rust Pathogen Melampsora larici-populina Uncovers PtSultr3;5, a Fungal-Induced Host Sulfate Transporter (2012)

PLoS ONE: RNA-Seq of Early-Infected Poplar Leaves by the Rust Pathogen Melampsora larici-populina Uncovers PtSultr3;5, a Fungal-Induced Host Sulfate Transporter (2012) | Healthy trees, healthy future: Phytophthora systems biology | Scoop.it

Biotroph pathogens establish intimate interactions with their hosts that are conditioned by the successful secretion of effectors in infected tissues and subsequent manipulation of host physiology. The identification of early-expressed pathogen effectors and early-modulated host functions is currently a major goal to understand the molecular basis of biotrophy. Here, we report the 454-pyrosequencing transcriptome analysis of early stages of poplar leaf colonization by the rust fungus Melampsora larici-populina. Among the 841,301 reads considered for analysis, 616,879 and 649 were successfully mapped to Populus trichocarpa and M. larici-populina genome sequences, respectively. From a methodological aspect, these results indicate that this single approach is not appropriate to saturate poplar transcriptome and to follow transcript accumulation of the pathogen. We identified 19 pathogen transcripts encoding early-expressed small-secreted proteins representing candidate effectors of interest for forthcoming studies. Poplar RNA-Seq data were validated by oligoarrays and quantitatively analysed, which revealed a highly stable transcriptome with a single transcript encoding a sulfate transporter (herein named PtSultr3;5, POPTR_0006s16150) showing a dramatic increase upon colonization by either virulent or avirulent M. larici-populina strains. Perspectives connecting host sulfate transport and biotrophic lifestyle are discussed.

 

Benjamin Petre, Emmanuelle Morin, Emilie Tisserant, Stéphane Hacquard, Corinne Da Silva, Julie Poulain, Christine Delaruelle, Francis Martin, Nicolas Rouhier, Annegret Kohler, Sébastien Duplessis


Via Kamoun Lab @ TSL, Nicolas Denancé, Biswapriya Biswavas Misra
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Dual RNA-seq of the plant pathogen Phytophthora ramorum and its tanoak host - Springer

Dual RNA-seq of the plant pathogen Phytophthora ramorum and its tanoak host - Springer | Healthy trees, healthy future: Phytophthora systems biology | Scoop.it
Abstract

Emergent diseases are an ever-increasing threat to forests and forest ecosystems and necessitate the development of research tools for species that often may have few pre-existing resources. We sequenced the mRNA expressed by the sudden oak death pathogen Phytophthora ramorum and its most susceptible forest host, tanoak, within the same tissue at two time points after inoculation, and in uninfected tanoak controls. Using the P. ramorum genome to differentiate host and pathogen transcripts, we detected more than 850 P. ramorum transcripts at 5 days post-inoculation and a concurrent upregulation of host genes usually associated with pathogenicity. At 1 day, in contrast, we did not detect pathogen expression or significant enrichment of functional categories of host transcripts relative to controls, highlighting the importance of sequencing depth for in planta studies of host–pathogen interactions. This study highlights processes in molecular host–pathogen interactions in forest trees and provides a first reference transcriptome for tanoak, allowing the preliminary identification of disease-related genes in this study and facilitating future work for this and other members of the family Fagaceae.

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PLoS Pathogens: Sequence Divergent RXLR Effectors Share a Structural Fold Conserved across Plant Pathogenic Oomycete Species

PLoS Pathogens: Sequence Divergent RXLR Effectors Share a Structural Fold Conserved across Plant Pathogenic Oomycete Species | Healthy trees, healthy future: Phytophthora systems biology | Scoop.it

Our laboratories have employed structural biology to investigate the molecular basis of RXLR effector function. A total of four structures have recently been published, those of AVR3a4 and AVR3a11 (paralogues from Phytophthora capsici), PexRD2 (from P. infestans), and ATR1 (from H. arabidopsidis) [21]–[23]. Each publication focused on a different aspect of structure/function analysis including phospholipid binding, protein folding, and effector recognition by the host.

   The studies of Boutemy et al. and Chou et al. independently described the structural homology of AVR3a11 and a domain of ATR1, respectively, to the cyanobacterial four-helix bundle protein KaiA [24]. This strongly implied they would also be structurally related to each other. This is unexpected, as these Phytophthora and H. arabidopsidis effectors do not share any significant sequence similarity: the conservation was only apparent after the structures were determined and compared.


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Curr Opin Microbiol: How eukaryotic filamentous pathogens evade plant recognition (2015)

Curr Opin Microbiol: How eukaryotic filamentous pathogens evade plant recognition (2015) | Healthy trees, healthy future: Phytophthora systems biology | Scoop.it

• A broad spectrum of fungal and oomycete mechanisms facilitate disease development.

• Biotrophic interfaces contain complex sets of components to evade host plant defences.
• Pathogen effector secretion is a key feature for reprograming host metabolism.
• Highly specialized effectors suppress pathogen recognition by host plants.
• Pathogen cell wall remodeling supports the host invasion process.

Plant pathogenic fungi and oomycetes employ sophisticated mechanisms for evading host recognition. After host penetration, many fungi and oomycetes establish a biotrophic interaction. It is assumed that different strategies employed by these pathogens to avoid triggering host defence responses, including establishment of biotrophic interfacial layers between the pathogen and host, masking of invading hyphae and active suppression of host defence mechanisms, are essential for a biotrophic parasitic lifestyle. During the infection process, filamentous plant pathogens secrete various effectors, which are hypothesized to be involved in facilitating effective host infection. Live-cell imaging of fungi and oomycetes secreting fluorescently labeled effector proteins as well as functional characterization of the components of biotrophic interfaces have led to the recent progress in understanding how eukaryotic filamentous pathogens evade plant recognition.


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BMC Genomics | Full text | Transcriptome dynamics of Arabidopsis thaliana root penetration by the oomycete pathogen Phytophthora parasitica

Oomycetes are a group of filamentous microorganisms that includes both animal and plant pathogens and causes major agricultural losses. Phytophthora species can infect most crops and plants from natural ecosystems.
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Microbiology and Molecular Biology Reviews: Oomycete Interactions with Plants: Infection Strategies and Resistance Principles (2015)

Microbiology and Molecular Biology Reviews: Oomycete Interactions with Plants: Infection Strategies and Resistance Principles (2015) | Healthy trees, healthy future: Phytophthora systems biology | Scoop.it

The Oomycota include many economically significant microbial pathogens of crop species. Understanding the mechanisms by which oomycetes infect plants and identifying methods to provide durable resistance are major research goals. Over the last few years, many elicitors that trigger plant immunity have been identified, as well as host genes that mediate susceptibility to oomycete pathogens. The mechanisms behind these processes have subsequently been investigated and many new discoveries made, marking a period of exciting research in the oomycete pathology field. This review provides an introduction to our current knowledge of the pathogenic mechanisms used by oomycetes, including elicitors and effectors, plus an overview of the major principles of host resistance: the established R gene hypothesis and the more recently defined susceptibility (S) gene model. Future directions for development of oomycete-resistant plants are discussed, along with ways that recent discoveries in the field of oomycete-plant interactions are generating novel means of studying how pathogen and symbiont colonizations overlap.


Via Kamoun Lab @ TSL, Niklaus Grunwald
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Genome Biology: Wheat rusts never sleep but neither do sequencers: will pathogenomics transform the way plant diseases are managed? (2015)

Genome Biology: Wheat rusts never sleep but neither do sequencers: will pathogenomics transform the way plant diseases are managed? (2015) | Healthy trees, healthy future: Phytophthora systems biology | Scoop.it

Field pathogenomics adds highly informative data to surveillance surveys by enabling rapid evaluation of pathogen variability, population structure and host genotype.


Yellow rust, caused by Puccinia striiformis f. sp. tritici (PST), is a major disease of wheat and, together with stem rust (Puccinia graminis) and leaf rust (Puccinia triticina), causes some of the most devastating epidemics on wheat worldwide [1]. Control of these rust pathogens relies predominantly on breeding and deployment of resistant varieties of wheat. To date, nearly 200 wheat-rust-resistance genes have been catalogued [2]; however, resistance has often proved to be ephemeral owing to changes in the pathogen population. In order to increase the durability of resistance, gene-deployment strategies need to consider extant and potential pathogen variability. Although these concepts are not new [3], their implementation was difficult until the advent of high-throughput sequencing (HTS) and genotyping technologies.


Next-generation sequencing technologies provide new opportunities to study pathogens and the hosts they infect. The increasing availability of crop and pathogen genomes [4] is providing new insights into pathogen biology, population structure and pathogenesis. This provides new opportunities for disease management. An important input into resistance breeding programs should be surveillance of the pathogen population. High-throughput pathogenomics offers the possibility for analyzing a large number of pathogen isolates and host varieties rapidly and at low cost.


In an article published in Genome Biology, Hubbard and colleagues [5] implemented a robust and rapid method to screen field isolates of PST and their host cultivars. In this particular version of pathogenomics, a selected set of 39 samples of infected wheat and triticale leaf tissue were collected directly from the field in 2013 and analyzed using RNAseq. In addition, the genomes of 21 archived PST isolates from the UK and France were also sequenced. Transcriptome analysis restricted the amount of sequence necessary to obtain diagnostic information for both host and pathogen; this not only accelerated genetic analysis of PST populations in situ but also allowed simultaneous assessment of the host genotype in the same sequencing runs. Another advantage of transcriptome analysis is that it detects genes being expressed and therefore the determinants of the interaction; thus, non-expressed genes present in the genome do not obscure genotype-phenotype correlations.


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Molecular Genetics and Genomics: Full-genome identification and characterization of NBS-encoding disease resistance genes in wheat (2014)

Molecular Genetics and Genomics: Full-genome identification and characterization of NBS-encoding disease resistance genes in wheat (2014) | Healthy trees, healthy future: Phytophthora systems biology | Scoop.it

Host resistance is the most economical, effective and ecologically sustainable method of controlling diseases in crop plants. In bread wheat, despite the high number of resistance loci that have been cataloged to date, only few have been cloned, underlying the need for genomics-guided investigations capable of providing a prompt and acute knowledge on the identity of effective resistance genes that can be used in breeding programs. Proteins with a nucleotide-binding site (NBS) encoded by the major plant disease resistance (R) genes play an important role in the responses of plants to various pathogens. In this study, a comprehensive analysis of NBS-encoding genes within the whole wheat genome was performed, and the genome scale characterization of this gene family was established. From the recently published wheat genome sequence, we used a data mining and automatic prediction pipeline to identify 580 complete ORF candidate NBS-encoding genes and 1,099 partial-ORF ones. Among complete gene models, 464 were longer than 200 aa, among them 436 had less than 70 % of sequence identity to each other. This gene models set was deeply characterized. (1) First, we have analyzed domain architecture and identified, in addition to typical domain combinations, the presence of particular domains like signal peptides, zinc fingers, kinases, heavy-metal-associated and WRKY DNA-binding domains. (2) Functional and expression annotation via homology searches in protein and transcript databases, based on sufficient criteria, enabled identifying similar proteins for 60 % of the studied gene models and expression evidence for 13 % of them. (3) Shared orthologous groups were defined using NBS-domain proteins of rice and Brachypodium distachyon. (4) Finally, alignment of the 436 NBS-containing gene models to the full set of scaffolds from the IWGSC’s wheat chromosome survey sequence enabled high-stringence anchoring to chromosome arms. The distribution of the R genes was found balanced on the three wheat sub-genomes. In contrast, at chromosome scale, 50 % of members of this gene family were localized on 6 of the 21 wheat chromosomes and ~22 % of them were localized on homeologous group 7. The results of this study provide a detailed analysis of the largest family of plant disease resistance genes in allohexaploid wheat. Some structural traits reported had not been previously identified and the genome-derived data were confronted with those stored in databases outlining the functional specialization of members of this family. The large reservoir of NBS-type genes presented and discussed will, firstly, form an important framework for marker-assisted improvement of resistance in wheat, and, secondly, open up new perspectives for a better understanding of the evolution dynamics of this gene family in grass species and in polyploid systems.


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PLOS Pathogens: Advances and Challenges in Computational Prediction of Effectors from Plant Pathogenic Fungi

PLOS Pathogens: Advances and Challenges in Computational Prediction of Effectors from Plant Pathogenic Fungi | Healthy trees, healthy future: Phytophthora systems biology | Scoop.it

With the rising number of sequenced pathogen genomes, computational prediction of effector proteins holds promise as a fast and economical technique to define candidates for subsequent laboratory work. Bacterial effectors delivered to the host via dedicated pathogen-derived delivery mechanisms, such as the type III secretion system, can be predicted using machine learning approaches based on protein sequence information. In oomycetes, consensus sequence motifs implicated in host translocation, such as RXLR, can be exploited for effector prediction. However, computational effector prediction in fungi is challenging due to a lack of known protein features that are common to fungal effectors and the low number of characterized effectors for individual species, which limits the use of machine learning approaches.


Via Bradford Condon, Niklaus Grunwald
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