Biology subjects

Rathjen, J. P.

Publications and source records attributed to Rathjen, J. P..

7 recordsLinked to original sources

bioRxiv2025

A conserved stripe rust effector elicits variable resistance responses in wheat

Plant resistance (R) and pathogen avirulence (Avr) gene interactions are central to pathogen recognition and disease resistance in crops. Functional characterisation of recognised Avr effectors of Puccinia striiformis f. sp. tritici (Pst) lags other key fungal pathogens of wheat. Here, we used a wheat protoplast-based screen to identify Avr/R interactions via the proxy of effector-induced defence responses in a set of diverse wheat cultivars. We identified an Avr candidate, termed AvrPstB48, that triggers defence responses in 16 out of 24 cultivars tested. AvrPstB48 is hemizygous, and the Pst genome carries four divergent paralogs within a gene cluster. Analysis of these paralogs revealed partial redundancy in their ability to activate wheat defences and enabled us to identify a single amino acid in AvrPstB48 that is necessary but not sufficient for defence activation. Notably, the activation of defence signalling by AvrPstB48 in protoplasts did not directly correlate with disease outcomes. Whole-plant infection assays revealed that some cultivars which exhibited strong defence activation in the protoplast assay are susceptible to the Pst isolate Pst104E137A- from which AvrPstB48 is derived. Comparison of infection dynamics of two wheat cultivars that differ in their AvrPstB48 recognition capacity revealed a delay in disease progression in the recognising cultivar Avocet S compared to the non-recognising cultivar Morocco. While correlative only, our observations, combined with other recent reports, support a recognize-then-suppress model of plant-pathogen interaction where disease outcomes are driven not only by simple Avr/R interactions but also by pathogen effectors that suppress defence signalling downstream of effector recognition.

plant biology↗

bioRxiv2025

Long-term surveillance suggests multiple hybridization events by nuclear reassortment and accelerated intercontinental spread of wheat yellow rust

O_LIEvolutionary forces affecting crop pathogens, including hybridization and long-distance dispersal (LDD), may have strong implications for food security and sustainable plant disease control at global scales. However, consolidated evidence is often lacking due to absence of consistent pathogen surveys beyond national capacities. C_LIO_LIOur study documents world-wide connectivity between populations of Puccinia striiformis, causing yellow rust on cereals and grasses, when analysing 3240 pathogen samples collected in 41 countries on six continents from 2009-2023. Our analyses revealed twelve cases of inter-continental spread of Puccinia striiformis, including seven cases with major impact on disease epidemics in recipient areas. C_LIO_LISomatic hybridization by nuclear reassortment between co-existing multi-locus genotypes (MLGs) on a common host was the most plausible mechanism for the emergence of three novel clonal groups of global relevance, first detected in Europe. Subsequently, onwards spread to South America and Australia was observed. Several high-impact incursions from South Asia into East Africa were also observed, including a genotype with a dramatic impact on wheat breeding programs of global relevance. C_LIO_LIOur study stresses an urgent need for coordinated crop pathogens monitoring across borders. Only global efforts will enable prevention and control of pathogens that represent major challenges for food security at regional and global scales. C_LI

evolutionary biology↗

bioRxiv2024

Long-read genomics reveal extensive nuclear-specific evolution and allele-specific expression in a dikaryotic fungus

Phased telomere to telomere (T2T) genome assemblies are revolutionising our understanding of long hidden genome biology "dark matter" such as centromeres, rDNA repeats, inter-haplotype variation, and allele specific expression (ASE). Yet insights into dikaryotic fungi that separate their haploid genomes into distinct nuclei is limited. Here we explore the impact of dikaryotism on the genome biology of a long-term asexual clone of the wheat pathogenic fungus Puccinia striiformis f. sp. tritici. We use Oxford Nanopore (ONT) duplex sequencing combined with Hi-C to generate a T2T nuclear-phased assembly with >99.999% consensus accuracy. We show that this fungus has large regional centromeres enriched in LTR retrotransposons, with a single centromeric dip in methylation that suggests one kinetochore attachment site per chromosomes. The centromeres of chromosomes pairs are most often highly diverse in sequence and kinetochore attachment sites are not always positionally conserved. Each nucleus carries a unique array of rDNAs with >200 copies that harbour nucleus-specific sequence variations. The inter-haplotype diversity between the two nuclear genomes is caused by large-scale structural variations linked to transposable elements. Nanopore long-read cDNA analysis across distinct infection conditions revealed pervasive allele specific expression for nearly 20% of all heterozygous gene pairs. Genes involved in plant infection were significantly enriched in ASE genes which appears to be mediated by elevated CpG gene body methylation of the lower expressed pair. This suggests that epigenetically regulated ASE is likely a previously overlooked mechanism facilitating plant infection. Overall, our study reveals how dikaryotism uniquely shapes key eukaryotic genome features.

genomics↗

bioRxiv2024

Translocation of effector proteins into plant cells by the flax rust pathogen Melampsora lini

During infection, rust fungi secrete effector proteins into host plant cells from haustoria to aid their colonisation. How rust effectors are secreted from the haustorium and delivered into the cytoplasm of host cells remains poorly understood. We used an Agrobacterium-mediated transformation procedure to generate stable transgenic flax rust strains expressing the effectors AvrM and AvrP123 fused to yellow fluorescent protein (YFP). We showed that both AvrM-YFP and AvrP123-YFP fusion proteins were secreted by the fungus into the extrahaustorial matrix (EHMx); however only AvrM-YFP was delivered into host cells, triggering a typical resistance phenotype in plants carrying the corresponding resistance (R) gene M. The signal peptide of AvrM was sufficient to direct YFP secretion into the EHMx; however, delivery into the host cell required a larger 105 amino acid N-terminal fragment of AvrM. These results indicate that translocation of this protein into the host cell from the EHMx is a separate process from secretion into the EHMx and requires a signal present in AvrM between amino acids 34 and 105. This is in contrast to previous observations of AvrM localisation after transient expression in plants, highlighting the necessity for analysis in the natural infection system.

plant biology↗

bioRxiv2024

Identification of a key gain-of-function residue for effector binding by in vitro shuffling of barley Mla NLR genes

Natural plant populations maintain high resistance (R) gene diversities that provide effective pathogen resistance; however, such diversity has been reduced significantly by the genetic bottlenecks associated with plant domestication and breeding. Agricultural crops typically contain limited R gene diversity so resistance is often short-lived as pathogens evolve rapidly to evade recognition. The mildew resistance locus A (Mla) R gene family of barley and wheat represents a rich source of natural genetic variation that is ideal for mining disease resistance specificities. Mla R genes encode immune receptor proteins of the nucleotide-binding leucine-rich repeat (NLR) class that recognise unrelated plant pathogens by binding secreted virulence proteins termed effectors. The barley NLRs MLA13 and MLA7 confer resistance to different strains of the barley powdery mildew pathogen through direct interaction with the pathogen effectors AVRA13 and AVRA7 respectively. Using DNA shuffling, we generated a variant library by recombining the Mla7 and Mla13 genes in vitro. The variant library was cloned into yeast generating [~]4,000 independent clones and was screened for interaction with AVRA13 and AVRA7 using a yeast-two-hybrid (Y2H) assay. This yielded a number of clones that encode NLR proteins that interacted with AVRA13. Sequence analysis showed that the interacting MLA proteins can be clustered into three groups, all of which contain a critical residue from MLA13. While MLA13 and MLA7 differ by 30 residues across the LRR domain, the replacement of leucine to serine at this position in MLA7 facilitated interaction with AVRA13 in yeast and AVRA13-dependent immune signalling in planta. We have established a pipeline that evolves MLAs to recognise distinct pathogen effectors without the requirement for protein structural knowledge and the use of rationale design. We suggest these findings represent a step towards evolving novel recognition capabilities rapidly in vitro.

plant biology↗

bioRxiv2023

Plant TIR domains physically interact with EDS1 family proteins to propagate immune signalling.

Plant Toll/interleukin-1 receptor/resistance protein (TIR) type nucleotide-binding and leucine-rich repeat immune receptors (NLRs) require Enhanced Disease Susceptibility 1 (EDS1) family proteins and the helper NLRs NRG1 and ADR1 for immune activation. TIR signalling domains possess NADase activity, producing NAM and v-cADPR from NAD+ in vitro. However, after TIR activation different small non-cyclic signalling molecules have been detected bound to EDS1/SAG101 and EDS1/PAD4 heterodimers. These molecules have not been detected in in vitro assays or as free molecules in planta and it is not clear how they are delivered to the EDS1 complexes. Here we investigate physical and functional interactions between TIR signalling domains, EDS1 family proteins and helper NLRs to clarify these signalling transduction pathways. We show that the NbEDS1-NbSAG101b-NbNRG1 signalling pathway in N. benthamiana is necessary and sufficient for cell death signalling induced by six different TIR-containing NLRs from a range of plant species, suggesting this module is likely a universal requirement for TIR-NLR mediated cell death in N. benthamiana. We also find that TIR domains physically interact with NbEDS1, NbPAD4 and NbSAG101 in planta, independently of each other. We also find evidence for direct interaction of NbNRG1 with NbSAG101b via its C-terminal EP domain, but not with other EDS1 family members. These data suggest a model in which physical interaction between activated TIRs and EDS1 signalling complexes facilitates efficient transfer of low abundance products of TIR catalytic activity directly to EDS1 heterocomplexes. The interaction could also alter TIR catalytic activity to favor production of the ligands recognised by EDS1/SAG101 and EDS1/PAD4.

plant biology↗

bioRxiv2021

Linked machine learning classifiers improve species classification of fungi when using error-prone long-reads on extendedmetabarcodes

BackgroundThe increased usage of error-prone long-read sequencing for metabarcoding of fungi has not been matched with adequate public databases and concomitant analysis approaches. We address this gap and present a proof-of-concept study for classifying fungal taxa using linked machine learning classifiers. We demonstrate the capability of linked machine learning classifiers to accurately classify species and strains using real-world and simulated fungal ribosomal DNA datasets, including plant and human pathogens. We benchmark our new approach in comparison to current alignment and k-mer based methods based on synthetic mock communities. We also assess real world applications of species identification in complex unlabelled datasets. ResultsOur machine learning approach assigned individual nanopore long-read amplicon sequences to fungal species with high recall rates and low false positive rates. Importantly, our approach successfully distinguished between closely-related species and strains when individual read errors were higher than the genetic distance between individual taxa, which the alignment and k-mer methods could not do. The machine learning approach showed an ability to identify key species with high recall rates, even in complex samples of unknown species composition. ConclusionsA proof of concept machine learning approach using a tree-descent approach on a decision tree of classifiers can identify known taxa with high accuracy, and precisely detect known target species from complex samples with high recall rates. We propose this approach is suitable for detecting the known knowns of pathogens or invasive species in any environment of mostly unknown composition, including agriculture and wild ecosystems.

bioinformatics↗