PF-IND: probability algorithm and software for separation of plant and fungal sequences

Computational techniques for elucidating plant-pathogen interactions from large-scale experiments on fungi and oomycetes

Eukaryotic plant pathogens are responsible for the destruction of billions of dollars worth of crops each year. With large-scale genomics of both pathogens and hosts and the corresponding computational analysis, biologists are now able to gain knowledge about many pathogenic and defense genes concurrently. To study the interactions between these two organism groups, it is necessary to design experiments to elucidate the genes being expressed during the invasion of the pathogen into the host. For the most part, this does not require new software development, though it does require the use of existing software in novel ways. This article provides a broad overview of several key and illustrative experiments and the corresponding computational analyses, outlining the knowledge gained in each. It goes on to describe databases for plant-pathogen data and important initiatives such as Plant-Associated Microbe Gene Ontology. It discusses how various emerging approaches will increase the power of computers in host-pathogen interaction studies.

Candidate effector gene identification in the ascomycete fungal phytopathogen Venturia inaequalis by expressed sequence tag analysis

The hemi-biotrophic fungus Venturia inaequalis infects members of the Maloideae, causing the economically important apple disease, scab. The plant-pathogen interaction of Malus and V. inaequalis follows the gene-for-gene model. cDNA libraries were constructed, and bioinformatic analysis of the resulting expressed sequence tags (ESTs) was used to characterize potential effector genes. Effectors are small proteins, secreted in planta, that are assumed to facilitate infection. Therefore, a cDNA library was constructed from a compatible interaction. To distinguish pathogen from plant sequences, the library was probed with genomic DNA from V. inaequalis to enrich for pathogen genes, and cDNA libraries were constructed from in vitro-grown material. A suppression subtractive hybridization library enriched for cellophane-induced genes was included, as growth on cellophane may mimic that in planta, with the differentiation of structures resembling those formed during plant colonization. Clustering of ESTs from the in planta and in vitro libraries indicated a fungal origin of the resulting non-redundant sequence. A total of 937 ESTs was classified as putatively fungal, which could be assembled into 633 non-redundant sequences. Sixteen new candidate effector genes were identified from V. inaequalis based on features common to characterized effector genes from filamentous fungi, i.e. they encode a small, novel, cysteine-rich protein, with a putative signal peptide. Three of the 16 candidates, in particular, conformed to most of the protein structural characteristics expected of fungal effectors and showed significant levels of transcriptional up-regulation during in planta growth. In addition to candidate effector genes, this collection of ESTs represents a valuable genomic resource for V. inaequalis.

Generation of a wheat leaf rust, Puccinia triticina, EST database from stage-specific cDNA libraries

Thirteen cDNA libraries constructed from small amounts of leaf rust mRNA using optimized methods served as the source for the generation of 25 558 high-quality DNA sequence reads. Five life-cycle stages were sampled: resting urediniospores, urediniospores germinated over water or plant extract, compatible, interactive stages during appressorium or haustorium formation just before sporulation, and an incompatible interaction. mRNA populations were subjected to treatments such as full-length cDNA production, subtractive and normalizing hybridizations, and size selection methods combined with PCR amplification. Pathogen and host sequences from interactive libraries were differentiated in silico using cereal and fungal sequences, codon usage analyses, and by means of a partial prototype cDNA microarray hybridized with genomic DNAs. This yielded a non-redundant unigene set of 9760 putative fungal sequences consisting of 6616 singlets and 3144 contigs, representing 4.7 Mbp. At an E-value 10(-5), 3670 unigenes (38%) matched sequences in various databases and collections but only 694 unigenes (7%) were similar to genes with known functions. In total, 296 unigenes were identified as most probably wheat and ten as rRNA sequences. Annotation rates were low for germinated urediniospores (4%) and appressoria (2%). Gene sets obtained from the various life-cycle stages appear to be remarkably different, suggesting drastic reprogramming of the transcriptome during these major differentiation processes. Redundancy within contigs yielded information about possible expression levels of certain genes among stages. Many sequences were similar to genes from other rusts such as Uromyces and Melampsora species; some of these genes have been implicated in pathogenicity and virulence.

Separation of sequences from host-pathogen interface using triplet nucleotide frequencies

The identification of genes involved in host-pathogen interactions is important for the elucidation of mechanisms of disease resistance and host susceptibility. A traditional way to classify the origin of genes sampled from a pool of mixed cDNA is through sequence similarity to known genes from either the pathogen or host organism or other closely related species. This approach does not work when the identified sequence has no close homologues in the sequence databases. In our previous studies, we classified genes using their codon frequencies. This method, however, explicitly required the prediction of CDS regions and thus could not be applied to sequences composed from the non-coding regions of genes. In this study, we show that the use of sliding-window triplet frequencies extends the application of the algorithm to both coding and non-coding sequences and also increases the prediction accuracy of a Support Vector Machine classifier from 95.6+/-0.3 to 96.5+/-0.2. Thus the use of the triplet frequencies increased the prediction accuracy of the new method by more than 20% compared to our previous approach. A functional analysis of sequences detected gene families having significantly higher or lower probability to be correctly classified compared to the average accuracy of the method is described. The server to perform classification of EST sequences using triplet frequencies is available at (URL: http://mips.gsf.de/proj/est3).

Eclair--a web service for unravelling species origin of sequences sampled from mixed host interfaces

The identification of the genes that participate at the biological interface of two species remains critical to our understanding of the mechanisms of disease resistance, disease susceptibility and symbiosis. The sequencing of complementary DNA (cDNA) libraries prepared from the biological interface between two organisms provides an inexpensive way to identify the novel genes that may be expressed as a cause or consequence of compatible or incompatible interactions. Sequence classification and annotation of species origin typically use an orthology-based approach and require access to large portions of either genome, or a close relative. Novel species- or clade-specific sequences may have no counterpart within existing databases and remain ambiguous features. Here we present a web-service, Eclair, which utilizes support vector machines for the classification of the origin of expressed sequence tags stemming from mixed host cDNA libraries. In addition to providing an interface for the classification of sequences, users are presented with the opportunity to train a model to suit their preferred species pair. Eclair is freely available at http://eclair.btk.fi.

Support vector machines for separation of mixed plant-pathogen EST collections based on codon usage

MOTIVATION

Discovery of host and pathogen genes expressed at the plant-pathogen interface often requires the construction of mixed libraries that contain sequences from both genomes. Sequence identification requires high-throughput and reliable classification of genome origin. When using single-pass cDNA sequences difficulties arise from the short sequence length, the lack of sufficient taxonomically relevant sequence data in public databases and ambiguous sequence homology between plant and pathogen genes.

RESULTS

A novel method is described, which is independent of the availability of homologous genes and relies on subtle differences in codon usage between plant and fungal genes. We used support vector machines (SVMs) to identify the probable origin of sequences. SVMs were compared to several other machine learning techniques and to a probabilistic algorithm (PF-IND) for expressed sequence tag (EST) classification also based on codon bias differences. Our software (Eclat) has achieved a classification accuracy of 93.1% on a test set of 3217 EST sequences from Hordeum vulgare and Blumeria graminis, which is a significant improvement compared to PF-IND (prediction accuracy of 81.2% on the same test set). EST sequences with at least 50 nt of coding sequence can be classified using Eclat with high confidence. Eclat allows training of classifiers for any host-pathogen combination for which there are sufficient classified training sequences.

AVAILABILITY

Eclat is freely available on the Internet (http://mips.gsf.de/proj/est) or on request as a standalone version.

CONTACT

friedel@informatik.uni-muenchen.de.