Growth and Maintenance of Stagonospora nodorum

S. nodorum SN15 and gna1 strains were maintained on CZV8CS agar as previously described [14]. These two strains were chosen as part of a relative quantitation analysis coupled to this proteome mapping experiment. For proteomic analysis, 100 mg of fungal mycelia were inoculated into minimal medium broth supplemented with 25 mM glucose as the sole carbon source. The fungi were grown to a vegetative state by incubation at 22°C with shaking at 150 rpm for three days. Vegetative mycelia were harvested via cheesecloth filtration and freeze-dried overnight.

Protein Extraction

Soluble intracellular proteins were extracted from freeze-dried mycelia as previously described [2]. Briefly, freeze-dried mycelia were mechanically broken with a cooled mortar and pestle and proteins were solubilised with 10 mM Tris-Cl (pH 7.5). The crude homogenate was collected and centrifuged at 20,000 g for 15 min at 4°C. The resulting supernatant was retained and treated with nucleases to remove nucleic acids. All protein samples were checked via SDS-PAGE to ensure that proteolysis was minimal during sample preparation (data not shown).

Sample Preparation

Proteins from SN15 and gna1-35 strains were precipitated individually by adding five volumes of acetone, incubating for 1 hour at -20°C and pulse centrifuging for 5-10 seconds. The protein pellets were resuspended in 0.5 M triethylammonium bicarbonate (TEAB) (pH 8.5) before reduction and alkylation according to the iTRAQ protocol (Applied Biosystems, Foster City, CA, USA). Samples were centrifuged at 13,000 g for 10 min at room temperature before the supernatant was removed and assayed for protein concentration (Bio-Rad protein assay kit, Hercules, CA, USA). A total of 55 μg of each sample was digested overnight with 5.5 μg trypsin at 37°C. Each digest was desalted on a Strata-X 33 μm polymeric reverse phase column (Phenomenex, Torrance, CA, USA) and dried. The entire experiment was performed in triplicate (including the generation of ground mycelia).

Strong Cation Exchange Chromatography

Dried peptides were dissolved in 70 μl of 2% acetonitrile and 0.05% trifluoroacetic acid (TFA) and separated by strong cation exchange chromatography on an Agilent 1100 HPLC system (Agilent Technologies, Palo Alto, CA, USA) using a PolySulfoethyl column (4.6 × 100 mm, 5 μm, 300 Å, Nest Group, Southborough, MA, USA). Peptides were eluted with a linear gradient of Buffer B (1 M KCl, 10% acetonitrile and 10 mM KH₂PO₄, pH 3). A total of 37 fractions were collected, pooled into 8 fractions, desalted, dried and resuspended in 20 μl of 2% acetonitrile and 0.05% TFA.

Reverse Phase Nano LC MALDI-MS/MS

Peptides were separated on a C18 PepMap100, 3 μm column (LC Packings, Sunnyvale, CA, USA) with a gradient of acetonitrile in 0.1% formic acid using the Ultimate 3000 nano HPLC system (LC Packings-Dionex, Sunnyvale, CA, USA). The eluent was mixed with matrix solution (5 mg/ml α-cyano-4-hydroxycinnamic acid) and spotted onto a 384 well Opti-TOF plate (Applied Biosystems, Framingham, MA, USA) using a Probot Micro Fraction Collector (LC Packings, San Francisco, CA, USA).

Peptides were analysed on a 4800 MALDI-TOF/TOF mass spectrometer (Applied Biosystems, Framingham, MA, USA) operated in reflector positive mode. MS data were acquired over a mass range of 800-4000 m/z and for each spectrum a total of 400 shots were accumulated. A job-wide interpretation method selected the 20 most intense precursor ions above a signal/noise ratio of 20 from each spectrum for MS/MS acquisition but only in the spot where their intensity was at its peak. MS/MS spectra were acquired with 4000 laser shots per selected ion with a mass range of 60 to the precursor ion -20.

Data Analysis

Mass spectral data from all three biological replicates were combined and analysed using the Mascot sequence matching software (Matrix Science, Boston, USA) with the support of the facilities at the Australian Proteomics Computational Facility (Victoria, Australia). Search parameters were: Enzyme, Trypsin; Max missed cleavages, 1; Fixed modifications, iTRAQ4plex (K), iTRAQ4plex(N-term), Methylthio(C); Variable modifications, Oxidation(M); Peptide tol, 0.6 Da; MS/MS tol, 0.6 Da. The MOWSE algorithm (MudPIT scoring) of Mascot was used to score the significance of peptide/protein matches with p < 0.05 for each protein identification. Four protein datasets were constructed for proteogenomic screening: the combination of version 1 and 2 proteins as defined from annotation of the SN15 genome sequence [12]; a between-stop codon 6-frame translation of the S. nodorum genome assembly; 6-frame translated, CAP3-generated [15] contigs of un-assembled reads of the S. nodorum assembly, and; 6-frame translated singleton un-assembled reads which did not assemble into contigs via CAP3. All 6-frame open reading frames (ORFs) were subject to a 10 amino acid minimum length threshold.

For the purpose of false discovery rate (FDR) calculation, randomised sequences from the version 1 and 2 proteins and the 6-frame translated assembly protein datasets were generated as Mascot decoy databases [16] (as detailed at http://www.matrixscience.com/help/decoy_help.html).

Characterisation of peptide-supported genes

Peptide supported genes were analysed for abundance of assigned gene ontology (GO) terms [12]. Gene counts for GO terms were compared between peptide supported and unsupported genes via Fisher's exact test. A p-value threshold of 0.05 was imposed to determine significance. Gene counts for SignalP [17] and WolfPsort [18] cellular location predictions and relative molecular mass predictions were also compared by this method.

De novo proteogenomics

MudPIT-filtered peptide matches to the 6-frame translated assembly were mapped back to their genomic location. Peptides mapping in the same orientation with either overlapping genomic coordinates or within the proximity of 200 bp were combined as peptide clusters (referred to herein as peptide clusters). The purpose of peptide cluster formation was merely to reduce the redundancy in the peptide data to aid in the interpretation of subsequent comparisons with annotated gene features, therefore clusters with a single peptide were retained. Individual peptides and peptide clusters were compared for overlap and proximity within 200 bp to S. nodorum version 1 and 2 genes.

Potential homologs to S. nodorum SN15 genes were detected by tblastn comparison of the genome assembly with the proteomes of the dothideomycete fungi Leptosphaeria maculans, Pyrenophora tritici-repentis, Cochliobolus heterostrophus, Alternaria brassicicola, Mycosphaerella graminicola and Mycosphaerella fijiensis. Tblastn high-scoring pairs (HSPs) were grouped according to hit, but also subject to additional criteria: best HSP e-value < 1e-10; individual HSP e-values < 1e-5; HSPs mapped on S. nodorum genome no further than 2 kb apart or split into sub-groups each subject to the previous criteria. Grouped HSP sequence coordinates were compared to both peptide cluster and annotated gene coordinates on the S. nodorum genome assembly for overlap (Figure 1). By this method we detected peptide clusters which could be linked back to a nearby gene model through a shared homolog or peptide clusters representing potential new gene annotations with homology support.

Gene models confirmed by Mascot analysis of the existing gene model database

Comparison of the detected peptides against a database built from both v1 and v2 predictions of SN15 genes matched a total of 2134 gene sequences with high confidence (Tables 1, 2 and 3). Of these, all but 188 were v2 genes (Figure 2; new and modified genes are listed in Additional Files 1 and 2). The results indicate the greater reliability of the v2 prediction. The proteomic analysis matched 1324 genes that were not directly supported by any of the 21,503 EST sequences (Figure 2). This clearly shows that proteomics targets a complementary set of gene products to transcriptomic approaches. The false discovery rate (FDR) for the mass spectra matched against S. nodorum proteins was determined to be 13% by the Mascot decoy method. While this is relatively high, the purpose of this study was the discovery of genes not detectable by conventional techniques. Hence we favoured sensitivity over accuracy.

The genes validated by the 2D-LC-MS/MS procedure were searched for features that were overrepresented compared to the total set of predicted genes. Peptide-supported genes predicted by WolfPsort to be localised in the cytoplasm were over-represented whereas cytoskeletal, extracellular, mitochondrial, peroxisomal and plasma membrane samples were under-represented. Peptides with predicted masses greater than 20 kDa were over-represented. Detailed analyses of GO-terms over- and under-represented are given in Additional Files 3 and 4. These analyses are consistent with the source of the peptides used in this analysis in which only soluble, intracellular proteins were isolated and analysed. The data indicates that using extracellular, membrane and cell-wall material would significantly increase the number of proteins detected. If a similar proportion of protein detection was achieved for these proteins as was for the soluble, cytoplasmic proteins, the number of extra genes detected would be about 1400.

Gene models confirmed by Mascot analysis of the 6-frame translated genome database

The proteomic data can also be used to search for gene models with errors such as inaccurate exon-intron boundaries and missed or superfluous exons as well as entirely unsuspected genes. We approached this by mapping peptides onto the 6-frame translation of the nuclear genome assembly. The coordinates of overlapping or nearby mapped peptides were amalgamated into peptide clusters Peptide clusters were defined so that internal gaps between constituent peptides of up to 200 bp were permitted, which is about four times the average intron size in S. nodorum [12].

Gene models identified by Mascot analysis of the 6 frame translated un-assembled read database

Finally, the mass spectra were compared to the collection of un-assembled DNA reads. The 15,455 reads were re-assessed for overlapping sequence missed during the first genome assembly by clustering into contigs using CAP3 [15]. 4616 reads were clustered into 939 contigs, with 10,839 singleton reads remaining. 423 contigs and 651 singleton DNA reads matched peptides that were detected by MS.

Sifting new and modified gene models by homology criteria

The probabilistic nature of the matching of the peptides to the genome is reflected in the false discovery rate, estimated at 13% for the matches against existing gene models. Thus not all the gene confirmations, the conflicts with existing models or the potential new genes can be expected to survive scrutiny.

Overall, these data suggest the confirmation of 2254 previously defined v1 and v2 genes (Figure 3). Of these, about 594 (355 + 30 + 209, Table 5) are identified as having questionable exon boundaries and 144 (86 + 58) as having questionable frame assignments (Table 6). Confirmation or rejection of the new gene models will require gene-by-gene sifting of the evidence; however this analysis remains an effective method of highlighting gene annotations with potential problems.

To sift through this large number of new gene candidates, we applied two tests. First we discarded gene candidates that mapped to known genes in the opposing orientation. This removed 113 of the 266 peptide clusters matching the assembly. As the genomes of several dothideomycetes have been released in the last three years, we were able to compare the predicted new genes to the predicted proteomes of these related organisms. Overall 68% (10899/16116) of S. nodorum genes (and 86.4% of v2 (9299/10762) genes) have a homolog among these related species. Only 18 peptide clusters (12%) corresponded to significant tblastn hits between dothideomycete proteins and the genome assembly. Of these, 15 matched known S. nodorum genes (Additional File 2A) and six of these corresponded to a single gene (SNOG_01477). The three other peptide clusters correspond to potentially novel genes (Additional File 2B). In the case of the 1074 candidate genes on the un-assembled reads, 707 (270 + 437) significantly matched via blastx to dothideomycete proteins of which 663 (241 + 422) hit existing S. nodorum genes. The remaining 44 (29 + 15) genes are dominated by transposon-related genes (as would be expected for the repeat-dominated un-assembled reads) but also include several metabolically and structurally critical gene functions (Table 8 and Additional File 2C).