Jana Sperschneider

EffectorP: predicting fungal effector proteins from secretomes using machine learning

Eukaryotic filamentous plant pathogens secrete effector proteins that modulate the host cell to facilitate infection. Computational effector candidate identification and subsequent functional characterization delivers valuable insights into plant-pathogen interactions. However, effector prediction in fungi has been challenging due to a lack of unifying sequence features such as conserved N-terminal sequence motifs. Fungal effectors are commonly predicted from secretomes based on criteria such as small size and cysteine-rich, which suffers from poor accuracy. We present EffectorP which pioneers the application of machine learning to fungal effector prediction. EffectorP improves fungal effector prediction from secretomes based on a robust signal of sequence-derived properties, achieving sensitivity and specificity of over 80%. Features that discriminate fungal effectors from secreted noneffectors are predominantly sequence length, molecular weight and protein net charge, as well as cysteine, serine and tryptophan content. We demonstrate that EffectorP is powerful when combined with in planta expression data for predicting high-priority effector candidates. EffectorP is the first prediction program for fungal effectors based on machine learning. Our findings will facilitate functional fungal effector studies and improve our understanding of effectors in plant-pathogen interactions. EffectorP is available at http://effectorp.csiro.au.

Genome Sequencing and Comparative Genomics of the Broad Host-Range Pathogen Rhizoctonia solani AG8

Rhizoctonia solani is a soil-borne basidiomycete fungus with a necrotrophic lifestyle which is classified into fourteen reproductively incompatible anastomosis groups (AGs). One of these, AG8, is a devastating pathogen causing bare patch of cereals, brassicas and legumes. R. solani is a multinucleate heterokaryon containing significant heterozygosity within a single cell. This complexity posed significant challenges for the assembly of its genome. We present a high quality genome assembly of R. solani AG8 and a manually curated set of 13,964 genes supported by RNA-seq. The AG8 genome assembly used novel methods to produce a haploid representation of its heterokaryotic state. The whole-genomes of AG8, the rice pathogen AG1-IA and the potato pathogen AG3 were observed to be syntenic and co-linear. Genes and functions putatively relevant to pathogenicity were highlighted by comparing AG8 to known pathogenicity genes, orthology databases spanning 197 phytopathogenic taxa and AG1-IA. We also observed SNP-level “hypermutation” of CpG dinucleotides to TpG between AG8 nuclei, with similarities to repeat-induced point mutation (RIP). Interestingly, gene-coding regions were widely affected along with repetitive DNA, which has not been previously observed for RIP in mononuclear fungi of the Pezizomycotina. The rate of heterozygous SNP mutations within this single isolate of AG8 was observed to be higher than SNP mutation rates observed across populations of most fungal species compared. Comparative analyses were combined to predict biological processes relevant to AG8 and 308 proteins with effector-like characteristics, forming a valuable resource for further study of this pathosystem. Predicted effector-like proteins had elevated levels of non-synonymous point mutations relative to synonymous mutations (dN/dS), suggesting that they may be under diversifying selection pressures. In addition, the distant relationship to sequenced necrotrophs of the Ascomycota suggests the R. solani genome sequence may prove to be a useful resource in future comparative analysis of plant pathogens.

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

1 CSIRO Agriculture Flagship, Centre for Environment and Life Sciences, Perth, Western Australia, Australia, 2 CSIRO Agriculture Flagship, Black Mountain Laboratories, Canberra, Australian Capital Territory, Australia, 3 CSIRO Agriculture Flagship, Queensland Bioscience Precinct, Brisbane, Queensland, Australia, 4 University of Western Australia Institute of Agriculture, University of Western Australia, Crawley, Western Australia, Australia

Genome-Wide Analysis in Three Fusarium Pathogens Identifies Rapidly Evolving Chromosomes and Genes Associated with Pathogenicity

Pathogens and hosts are in an ongoing arms race and genes involved in host–pathogen interactions are likely to undergo diversifying selection. Fusarium plant pathogens have evolved diverse infection strategies, but how they interact with their hosts in the biotrophic infection stage remains puzzling. To address this, we analyzed the genomes of three Fusarium plant pathogens for genes that are under diversifying selection. We found a two-speed genome structure both on the chromosome and gene group level. Diversifying selection acts strongly on the dispensable chromosomes in Fusarium oxysporum f. sp. lycopersici and on distinct core chromosome regions in Fusarium graminearum, all of which have associations with virulence. Members of two gene groups evolve rapidly, namely those that encode proteins with an N-terminal [SG]-P-C-[KR]-P sequence motif and proteins that are conserved predominantly in pathogens. Specifically, 29 F. graminearum genes are rapidly evolving, in planta induced and encode secreted proteins, strongly pointing toward effector function. In summary, diversifying selection in Fusarium is strongly reflected as genomic footprints and can be used to predict a small gene set likely to be involved in host–pathogen interactions for experimental verification.

LOCALIZER: Subcellular localization prediction of both plant and effector proteins in the plant cell

Pathogens secrete effector proteins and many operate inside plant cells to enable infection. Some effectors have been found to enter subcellular compartments by mimicking host targeting sequences. Although many computational methods exist to predict plant protein subcellular localization, they perform poorly for effectors. We introduce LOCALIZER for predicting plant and effector protein localization to chloroplasts, mitochondria, and nuclei. LOCALIZER shows greater prediction accuracy for chloroplast and mitochondrial targeting compared to other methods for 652 plant proteins. For 107 eukaryotic effectors, LOCALIZER outperforms other methods and predicts a previously unrecognized chloroplast transit peptide for the ToxA effector, which we show translocates into tobacco chloroplasts. Secretome-wide predictions and confocal microscopy reveal that rust fungi might have evolved multiple effectors that target chloroplasts or nuclei. LOCALIZER is the first method for predicting effector localisation in plants and is a valuable tool for prioritizing effector candidates for functional investigations. LOCALIZER is available at http://localizer.csiro.au/.

Comparative genomics of Australian isolates of the wheat stem rust pathogen Puccinia graminis f. sp. tritici reveals extensive polymorphism in candidate effector genes.

The wheat stem rust fungus Puccinia graminis f. sp. tritici (Pgt) is one of the most destructive pathogens of wheat. In this study, a draft genome was built for a founder Australian Pgt isolate of pathotype (pt.) 21-0 (collected in 1954) by next generation DNA sequencing. A combination of reference-based assembly using the genome of the previously sequenced American Pgt isolate CDL 75-36-700-3 (p7a) and de novo assembly were performed resulting in a 92 Mbp reference genome for Pgt isolate 21-0. Approximately 13 Mbp of de novo assembled sequence in this genome is not present in the p7a reference assembly. This novel sequence is not specific to 21-0 as it is also present in three other Pgt rust isolates of independent origin. The new reference genome was subsequently used to build a pan-genome based on five Australian Pgt isolates. Transcriptomes from germinated urediniospores and haustoria were separately assembled for pt. 21-0 and comparison of gene expression profiles showed differential expression in ∼10% of the genes each in germinated spores and haustoria. A total of 1,924 secreted proteins were predicted from the 21-0 transcriptome, of which 520 were classified as haustorial secreted proteins (HSPs). Comparison of 21-0 with two presumed clonal field derivatives of this lineage (collected in 1982 and 1984) that had evolved virulence on four additional resistance genes (Sr5, Sr11, Sr27, SrSatu) identified mutations in 25 HSP effector candidates. Some of these mutations could explain their novel virulence phenotypes.

Diversifying selection in the wheat stem rust fungus acts predominantly on pathogen-associated gene families and reveals candidate effectors

Plant pathogens cause severe losses to crop plants and threaten global food production. One striking example is the wheat stem rust fungus, Puccinia graminis f. sp. tritici, which can rapidly evolve new virulent pathotypes in response to resistant host lines. Like several other filamentous fungal and oomycete plant pathogens, its genome features expanded gene families that have been implicated in host-pathogen interactions, possibly encoding effector proteins that interact directly with target host defense proteins. Previous efforts to understand virulence largely relied on the prediction of secreted, small and cysteine-rich proteins as candidate effectors and thus delivered an overwhelming number of candidates. Here, we implement an alternative analysis strategy that uses the signal of adaptive evolution as a line of evidence for effector function, combined with comparative information and expression data. We demonstrate that in planta up-regulated genes that are rapidly evolving are found almost exclusively in pathogen-associated gene families, affirming the impact of host-pathogen co-evolution on genome structure and the adaptive diversification of specialized gene families. In particular, we predict 42 effector candidates that are conserved only across pathogens, induced during infection and rapidly evolving. One of our top candidates has recently been shown to induce genotype-specific hypersensitive cell death in wheat. This shows that comparative genomics incorporating the evolutionary signal of adaptation is powerful for predicting effector candidates for laboratory verification. Our system can be applied to a wide range of pathogens and will give insight into host-pathogen dynamics, ultimately leading to progress in strategies for disease control.

Comparative genomics and prediction of conditionally dispensable sequences in legume–infecting Fusarium oxysporum formae speciales facilitates identification of candidate effectors

BackgroundSoil-borne fungi of the Fusarium oxysporum species complex cause devastating wilt disease on many crops including legumes that supply human dietary protein needs across many parts of the globe. We present and compare draft genome assemblies for three legume-infecting formae speciales (ff. spp.): F. oxysporum f. sp. ciceris (Foc-38-1) and f. sp. pisi (Fop-37622), significant pathogens of chickpea and pea respectively, the world’s second and third most important grain legumes, and lastly f. sp. medicaginis (Fom-5190a) for which we developed a model legume pathosystem utilising Medicago truncatula.ResultsFocusing on the identification of pathogenicity gene content, we leveraged the reference genomes of Fusarium pathogens F. oxysporum f. sp. lycopersici (tomato-infecting) and F. solani (pea-infecting) and their well-characterised core and dispensable chromosomes to predict genomic organisation in the newly sequenced legume-infecting isolates. Dispensable chromosomes are not essential for growth and in Fusarium species are known to be enriched in host-specificity and pathogenicity-associated genes. Comparative genomics of the publicly available Fusarium species revealed differential patterns of sequence conservation across F. oxysporum formae speciales, with legume-pathogenic formae speciales not exhibiting greater sequence conservation between them relative to non-legume-infecting formae speciales, possibly indicating the lack of a common ancestral source for legume pathogenicity. Combining predicted dispensable gene content with in planta expression in the model legume-infecting isolate, we identified small conserved regions and candidate effectors, four of which shared greatest similarity to proteins from another legume-infecting ff. spp.ConclusionsWe demonstrate that distinction of core and potential dispensable genomic regions of novel F. oxysporum genomes is an effective tool to facilitate effector discovery and the identification of gene content possibly linked to host specificity. While the legume-infecting isolates didn’t share large genomic regions of pathogenicity-related content, smaller regions and candidate effector proteins were highly conserved, suggesting that they may play specific roles in inducing disease on legume hosts.

Evaluation of Secretion Prediction Highlights Differing Approaches Needed for Oomycete and Fungal Effectors

The steadily increasing number of sequenced fungal and oomycete genomes has enabled detailed studies of how these eukaryotic microbes infect plants and cause devastating losses in food crops. During infection, fungal and oomycete pathogens secrete effector molecules which manipulate host plant cell processes to the pathogens advantage. Proteinaceous effectors are synthesized intracellularly and must be externalized to interact with host cells. Computational prediction of secreted proteins from genomic sequences is an important technique to narrow down the candidate effector repertoire for subsequent experimental validation. In this study, we benchmark secretion prediction tools on experimentally validated fungal and oomycete effectors. We observe that for a set of fungal SwissProt protein sequences, SignalP 4 and the neural network predictors of SignalP 3 (D-score) and SignalP 2 perform best. For effector prediction in particular, the use of a sensitive method can be desirable to obtain the most complete candidate effector set. We show that the neural network predictors of SignalP 2 and 3, as well as TargetP were the most sensitive tools for fungal effector secretion prediction, whereas the hidden Markov model predictors of SignalP 2 and 3 were the most sensitive tools for oomycete effectors. Thus, previous versions of SignalP retain value for oomycete effector prediction, as the current version, SignalP 4, was unable to reliably predict the signal peptide of the oomycete Crinkler effectors in the test set. Our assessment of subcellular localization predictors shows that cytoplasmic effectors are often predicted as not extracellular. This limits the reliability of secretion predictions that depend on these tools. We present our assessment with a view to informing future pathogenomics studies and suggest revised pipelines for secretion prediction to obtain optimal effector predictions in fungi and oomycetes.

A comparative hidden Markov model analysis pipeline identifies proteins characteristic of cereal-infecting fungi

BackgroundFungal pathogens cause devastating losses in economically important cereal crops by utilising pathogen proteins to infect host plants. Secreted pathogen proteins are referred to as effectors and have thus far been identified by selecting small, cysteine-rich peptides from the secretome despite increasing evidence that not all effectors share these attributes.ResultsWe take advantage of the availability of sequenced fungal genomes and present an unbiased method for finding putative pathogen proteins and secreted effectors in a query genome via comparative hidden Markov model analyses followed by unsupervised protein clustering. Our method returns experimentally validated fungal effectors in Stagonospora nodorum and Fusarium oxysporum as well as the N-terminal Y/F/WxC-motif from the barley powdery mildew pathogen. Application to the cereal pathogen Fusarium graminearum reveals a secreted phosphorylcholine phosphatase that is characteristic of hemibiotrophic and necrotrophic cereal pathogens and shares an ancient selection process with bacterial plant pathogens. Three F. graminearum protein clusters are found with an enriched secretion signal. One of these putative effector clusters contains proteins that share a [SG]-P-C-[KR]-P sequence motif in the N-terminal and show features not commonly associated with fungal effectors. This motif is conserved in secreted pathogenic Fusarium proteins and a prime candidate for functional testing.ConclusionsOur pipeline has successfully uncovered conservation patterns, putative effectors and motifs of fungal pathogens that would have been overlooked by existing approaches that identify effectors as small, secreted, cysteine-rich peptides. It can be applied to any pathogenic proteome data, such as microbial pathogen data of plants and other organisms.