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. 2010 Feb 11;11:105. doi: 10.1186/1471-2164-11-105

Fungal Secretome Database: Integrated platform for annotation of fungal secretomes

Jaeyoung Choi 1,2,3, Jongsun Park 1,2,3,4, Donghan Kim 1,2,3, Kyongyong Jung 1,2,3, Seogchan Kang 6, Yong-Hwan Lee 1,2,3,4,5,✉
PMCID: PMC2836287  PMID: 20146824

Abstract

Background

Fungi secrete various proteins that have diverse functions. Prediction of secretory proteins using only one program is unsatisfactory. To enhance prediction accuracy, we constructed Fungal Secretome Database (FSD).

Description

A three-layer hierarchical identification rule based on nine prediction programs was used to identify putative secretory proteins in 158 fungal/oomycete genomes (208,883 proteins, 15.21% of the total proteome). The presence of putative effectors containing known host targeting signals such as RXLX [EDQ] and RXLR was investigated, presenting the degree of bias along with the species. The FSD's user-friendly interface provides summaries of prediction results and diverse web-based analysis functions through Favorite, a personalized repository.

Conclusions

The FSD can serve as an integrated platform supporting researches on secretory proteins in the fungal kingdom. All data and functions described in this study can be accessed on the FSD web site at http://fsd.snu.ac.kr/.

Background

The "secretome" refers to the collection of proteins that contain a signal peptide and are processed via the endoplasmic reticulum and Golgi apparatus before secretion [1]. In organisms from bacteria to humans, secretory proteins are common and perform diverse functions. These functions include immune system [2], roles as neurotransmitters in the nervous system [3], roles as hormones/pheromones [4], acquisition of nutrients [5-7], building and remodeling of cell walls [8], signaling and environmental sensing [9], and competition with other organisms [10-13]. Some secretory proteins in pathogens function as effectors that manipulate and/or destroy host cells with special signatures. In Plasmodium and Phytophthora species, effectors carry the RXLX [EDQ] or RXLR motifs as host targeting signals [11-13].

With the aid of advanced genome sequencing technologies [14], the rapid increase of sequenced fungal genomes offers many opportunities to study the function and evolution of secretory proteins at the genome level [15,16]. The Comparative Fungal Genomics Platform (CFGP; http://cfgp.snu.ac.kr/) [16] now archives 235 genomes from 120 fungal/oomycete species. The accurate prediction of secretory proteins in sequenced genomes is the key to realizing such opportunities.

The widely used SignalP 3.0 program [17] detected 89.81% of the 2,512 experimentally verified sequences in SPdb [18], a database containing proteins with signal peptides. To improve the accuracy of prediction, we built a hierarchical identification pipeline based on nine prediction programs (Table 1). Through this pipeline, putative secretory proteins, including pathogen effectors, encoded by 158 fungal and oomycete genomes were identified. The Fungal Secretome Database (FSD; http://fsd.snu.ac.kr/) was established to support not only the archiving of fungal secretory proteins but also the management and use of the resulting data. The FSD also has a user-friendly web interface and offers several data analysis functions via Favorite, a personalized data repository implemented in the CFGP (http://cfgp.snu.ac.kr/)[16].

Table 1.

List of prediction programs used in FSD

Prediction Program Description Ref
SignalP 3.0 A program to predict whether a protein has the signal peptidase site I or not [17]
SigCleave A program to predict whether a protein has signal peptides or not [19]
SigPred A program to predict whether a protein has signal peptides or not [20]
RPSP A program to predict whether a protein has signal peptides or not [21]
TMHMM 2.0c A program to predict whether a protein has trans-membrane helix(es) or not [26]
TargetP 1.1b A program to predict a site where a protein probably resides [23]
PSort II A program to predict a site where a protein probably resides [22]
SecretomeP 1.0f A program to predict whether a protein is secreted by non-classical pathways or not [25]
predictNLS A program to predict whether a protein has nuclear localization signal or not [28]
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Construction and content

Evaluation of the pipeline for predicting secretory proteins

To evaluate the capabilities of four programs SignalP 3.0 [17], SigCleave [19], SigPred [20], and RPSP [21] for predicting signal peptides, we analyzed the secretory proteins collected in SPdb [18]. SignalP 3.0 identified 89.81% of 2,512 proteins; while adding the other three programs, in combination, 87.50% of the proteins, which were not predicted by SignalP 3.0, were identified. The remaining proteins (1.31% of 2,512 proteins) were investigated by using two programs that predicted subcellular localization: PSort II [22] and TargetP 1.1b [23]. We found that 34.38% of the proteins were predicted to be extracellular proteins, increasing the coverage to 99.16%. For the 1,093 characterized fungal/oomycete secretory proteins (Table 2), the combinatory pipeline raised the prediction coverage from 75.30% to 84.17% in comparison to SignalP 3.0. In addition, 98.14% of 24,921 experimentally unverified sequences in the SPdb were predicted as secretory proteins by the pipeline, while SignalP 3.0 caught 80.22% of them as positive. To assess robustness of the pipeline with non-secretory proteins, we prepared yeast proteins localized in cytosol, endoplasmic reticulum, nucleus, or mitochondrion [24]. When the 1,955 proteins were subjected to the FSD pipeline and SignalP 3.0, the numbers of false positives were almost same (84 and 82, respectively). Together, these results suggest that this ensemble approach could compensate for some of the weaknesses of individual programs, resulting in more robust predictions. Additionally, SecretomeP 1.0f [25], which can predict non-classical secretory proteins, was integrated into the FSD.

Table 2.

List of references and annotation results of characterized fungal secretory proteins

Title Total Identified Proteins Class SP Class SP3 Class SL Putative Secretome Ref
Crucial Role of Antioxidant Proteins and Hydrolytic Enzymes in Pathogenicity of Penicillium expansum: Analysis Based on Proteomics Approach (Secretory) 21 5 1 0 6 [43]
Crucial Role of Antioxidant Proteins and Hydrolytic Enzymes in Pathogenicity of Penicillium expansum: Analysis Based on Proteomics Approach (Non-secretory) 21 1 2 0 3 [43]
The Phanerochaete chrysosporium secretome: Database predictions and initial mass spectrometry peptide identifications in cellulose-grown medium 49 25 5 0 30 [44]
An analysis of the Candida albicans genome database for soluble secreted proteins using computer-based prediction algorithms (Secretory) 46 28 19 2 49 [45]
An analysis of the Candida albicans genome database for soluble secreted proteins using computer-based prediction algorithms (Non-secretory) 45 0 5 1 6 [45]
The secretome of the maize pathogen Ustilago maydis (Without known functions) 386 352 18 10 380 [46]
The secretome of the maize pathogen Ustilago maydis (With known functions) 168 147 15 5 167 [46]
A Catalogue of the Effector Secretome of Plant Pathogenic Oomycetes 25 22 1 0 23 [11]
Fungal degradation of wood: initial proteomic analysis of extra cellular proteins of Phanerochaete chrysosporium grown on oak substrate 11 8 0 0 8 [47]
Comparative proteomics of extracellular proteins in vitro and in planta from the pathogenic fungus Fusarium graminearum 120 63 8 0 71 [48]
Expression analysis of extracellular proteins from Phanerochaete chrysosporium grown on different liquid and solid substrates 27 16 4 0 20 [49]
Dandruff-associated Malassezia genomes reveal convergent and divergent virulence traits shared with plant and human fungal pathogens 34 28 0 0 28 [50]
Adaptive Evolution Has Targeted the C-Terminal Domain of the RXLR Effectors of Plant Pathogenic Oomycetes 79 79 0 0 79 [41]
Genome, transcriptome, and secretome analysis of wood decay fungus Postia placenta supports unique mechanisms of lignocellulose conversion. 47 29 3 1 33 [51]
Host-Microbe Interactions: Shaping the Evolution of the Plant Immune Response 14 12 0 1 13 [52]
Total 1,093 815 81 20 916 -
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The FSD contains an identification pipeline that sequentially analyzes proteomes of interest using i) SignalP 3.0; ii) a combination of SigCleave, SigPred, and RPSP to screen those proteins not considered positive by SignalP 3.0; and iii) PSort II and TargetP 1.1b to analyze the negatives from the previous step. Additionally, SecretomeP 1.0f was integrated to provide information related to non-classical secretory proteins. To eliminate potential false positives, we filtered proteins that i) contain more than one transmembrane helix predicted by TMHMM 2.0c [26] and/or ii) the endoplasmic reticulum retention signal ([KRHQSA]- [DENQ]-E-L; classified as false-positive; Figure 1A) [27]. In addition, iii) nuclear proteins predicted by both predictNLS [28] and PSort II [22] and iv) mitochondrial proteins predicted by PSort II [22] as well as TargetP 1.1b [23] were eliminated because two subcellular localizations are not related to secretory proteins.

Figure 1.

Figure 1

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FSD class definitions and the FSD pipeline. (A) Definitions of four FSD classes. The gray round rectangle indicates the total set of proteins, and the light blue arrows going outside the rectangle show the filtering out processes of the pipeline. The black rectangles show the names of the classes, the yellow arrows indicate expansion of the putative secretome boundary, and the white-bordered blue cross indicates additional information on the putative secretome. (B) Structure of the FSD pipeline. The two parallelograms are input data for the FSD pipeline. The rectangle in the middle indicates the process for identifying putative secretory proteins. The round rectangles indicate the four FSD classes. The gray square on the right represents the thirteen different analysis functions in Favorite.

Following analysis via the pipeline, the resulting putative secretory proteins after removing potential false positives are divided into four classes: i) SP contains all proteins predicted by SignalP 3.0; ii) SP3 contains the proteins predicted by SigPred, SigCleave, or RPSP but not by SignalP 3.0; iii) SL contains the proteins predicted by PSort II and/or TargetP 1.1b but not by the first two steps; and iv) NS contains the proteins predicted by SecretomeP 1.0f but not by SignalP 3.0 (Figure 1A; Table 3).

Table 3.

Class definitions used in FSD

Class Description*
Class SP Proteins which are predicted by SignalP 3.0
Class SP3 Proteins which are predicted by SigPred, SigCleave, or RPSP
Class SL Proteins which are predicted by PSort II or TargetP 1.1b, but are not predicted by SignalP 3.0, SigPred, SigCleave, RPSP, or SecretomeP 1.0f
Class NS Proteins which are predicted by SecretomeP 1.0f, but are not predicted by SignalP 3.0, SigPred, SigCleave, or RPSP
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* Proteins as follows were removed from all four classes described in this table: proteins which i) contain more than one trans-membrane helixes, ii) have ER retention signals, iii) predicted as mitochondrial proteins by PSort II and TargetP 1.1b, and iv) predicted as nuclear proteins by TargetP 1.1b and predictNLS.

System structure of the FSD

To improve the expandability and flexibility of the FSD, we adopted a three-layer structure (i.e., data warehouse, analysis pipeline, and user interface) in its design. The data warehouse was established using the standardized genome warehouse managed by the CFGP (http://cfgp.snu.ac.kr/)[16] that has been used in various bioinformatics systems [15,29-35]. The pipeline layer was built with a series of Perl programs.

In addition to the prediction programs described above, ChloroP 1.1 as well as hydropathy plots [36] were included in the FSD to provide additional information on secretory proteins. Whenever new fungal genomes become available, the automated pipeline classifies them based on the predictions of nine programs, thus keeping the FSD current (Figure 1B).

MySQL 5.0.67 and PHP 5.2.9 were used to maintain database and to develop web-based user interfaces that present complex information intuitively. Web pages were serviced through Apache 2.2.11. Favorite, a personal data repository used in the CFGP (http://cfgp.snu.ac.kr/)[16], was integrated to provide thirteen functions for further analyses.

Utility and Discussion

Discussion

Secretory proteins in 158 fungal/oomycete genomes

To survey the genome-wide distribution of secretory proteins in fungi and oomycetes, we used the pipeline to analyze all predicted proteins encoded by 158 fungal/oomycete genomes. Of the 1,373,444 open reading frames (ORFs) analyzed, 92,926 (6.77%), 103,224 (7.52%), and 12,733 (0.93%) proteins belonged to classes SP, SP3, and SL, respectively (Table 4, 5, and 6). In total, 208,883 ORFs (15.21%) were denoted putative secretory proteins. The proteins belonging to class NS were not included in the putative secretome because they represented more than 40% of whole proteome.

Table 4.

List and distribution of secretion-associated proteins of the fungal genomes belonging to the subphylum Pezizomycotina archived in FSD

Species Size (Mb) # of ORFs Class SP Class SP3 Class SL Putative Secretome Ref
Fungi (Kingdom)a
 Ascomycota (Phylum)
  Pezizomycotina (Subphylum)
   Aspergillus clavatus 27.9 9,121 754 732 81 1,567 [53,54]
   Aspergillus flavus 36.8 12,604 1,200 990 142 2,332 [55]
   Aspergillus fumigatus A1163 29.2 9,929 807 878 67 1,752 [54]
   Aspergillus fumigatus AF293 29.4 9,887 781 909 84 1,774 [56]
   Aspergillus nidulans 30.1 10,568 922 877 96 1,895 [57]
   Aspergillus niger ATCC1015 37.2 11,200 860 883 88 1,831 N
   Aspergillus niger CBS513.88 34.0 14,086 1,142 1,320 154 2,616 [58]
   Aspergillus oryzae 37.1 12,063 1,060 1,064 145 2,269 [59]
   Aspergillus terreus 29.3 10,406 934 916 81 1,931 [53]
   Botrytis cinerea 42.7 16,448 1,163 1,287 182 2,632 N
   Chaetomium globosumb 34.9 11,124 1,121 923 99 2,143 N
   Coccidioides immitis H538.4 27.7 10,663 548 957 80 1,585 N
   Coccidioides immitis RMSCC 2394 28.8 10,408 575 920 66 1,561 N
   Coccidioides immitis RMSCC 3703 27.6 10,465 539 892 65 1,496 N
   Coccidioides immitis RS 28.9 10,457 476 855 102 1,433 [60]
   Coccidioides posadasii RMSCC 3488 28.1 9,964 546 838 95 1,479 N
   Coccidioides posadasii Silveira 27.5 10,125 558 869 91 1,518 N
   Cochliobolus heterostrophus C5 34.9 9,633 932 725 83 1,740 N
   Cryphonectria parasitica 43.9 11,184 1,040 951 93 2,084 N
   Fusarium graminearum GZ3639c 15.1 6,694 373 386 47 806 [61]
   Fusarium graminearum MIPS 36.1 13,920 1,370 1,072 118 2,560 N
   Fusarium graminearum PH-1 36.6 13,339 1,282 1,004 118 2,404 [61]
   Fusarium oxysporum 61.4 17,608 1,613 1,297 147 3,057 N
   Fusarium solani 51.3 15,707 1,381 1,242 155 2,778 [62]
   Fusarium verticillioides 41.9 14,199 1,347 1,071 116 2,534 N
   Histoplasma capsulatum G186AR 29.9 7,454 357 578 96 1,031 N
   Histoplasma capsulatum G217B 41.3 8,038 393 583 103 1,079 N
   Histoplasma capsulatum H143 39.0 9,547 468 842 87 1,397 N
   Histoplasma capsulatum H88 37.9 9,445 492 832 99 1,423 N
   Histoplasma capsulatum Nam1 33.0 9,349 398 736 79 1,213 [60]
   Magnaporthe oryzae 41.7 11,069 1,573 833 64 2,470 [63]
   Microsporum canis 23.3 8,777 564 702 88 1,354 N
   Microsporum gypseum 23.3 8,876 629 669 52 1,350 N
   Mycosphaerella fijiensis 73.4 10,327 770 778 81 1,629 N
   Mycosphaerella graminicola 41.9 11,395 979 913 81 1,973 N
   Neosartorya fischerib 32.6 10,403 959 818 84 1,861 [54]
   Neurospora crassa 39.2 9,842 817 788 61 1,666 [64]
   Neurospora crassa MIPS 34.2 9,572 788 749 78 1,615 N
   Neurospora discretadiscrete 37.3 9,948 823 800 88 1,711 N
   Neurospora tetrasperma 37.8 10,640 849 895 73 1,817 N
   Paracoccidioides brasiliensis Pb01 33.0 9,136 402 808 71 1,281 N
   Paracoccidioides brasiliensis Pb03 29.1 9,264 470 823 92 1,385 N
   Paracoccidioides brasiliensis Pb18 30.0 8,741 425 743 55 1,223 N
   Penicillium chrysogenum 32.2 12,791 947 1,008 127 2,082 [65]
   Penicillium marneffei 28.6 10,638 713 792 109 1,614 N
   Podospora anserina 35.7 10,596 1,127 893 124 2,144 [66]
   Pyrenophora tritici-repentis 38.0 12,169 1,228 912 123 2,263 N
   Sclerotinia sclerotiorum 38.3 14,522 971 1,109 147 2,227 N
   Sporotrichum thermophile 38.7 8,806 697 658 66 1,421 N
   Stagonospora nodorum 37.2 15,983 1,511 1,309 142 2,962 [67]
   Talaromyces stipitatus 35.7 13,252 748 1,116 114 1,978 N
   Thielavia terrestris 37.0 9,815 877 855 67 1,799 N
   Trichoderma atroviride 36.1 11,100 907 935 86 1,928 N
   Trichoderma reesei 33.5 9,129 738 766 70 1,574 [68]
   Trichoderma virens GV29-8 38.8 11,643 933 1,009 93 2,035 N
   Trichophyton equinum 24.2 8,576 571 699 69 1,339 N
   Uncinocarpus reesii 22.3 7,798 485 626 64 1,175 [60]
   Verticillium albo-atrum VaMs. 102 32.9 10,239 1,074 815 73 1,962 N
   Verticillium dahliae VdLs. 17 33.9 10,575 1,157 861 77 2,095 N
Total 2,059.4 641,257 50,164 52,111 5,578 107,853 -
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a Taxonomy based on [69]

b Insufficient exon/intron information

c Incomplete coverage of genome information

Table 5.

List and distribution of secretion-associated proteins of the fungal genomes belonging to the subphylum Saccharomycotina and Taphrinomycotina archived in FSD

Species Size (Mb) # of ORFs Class SP Class SP3 Class SL Putative Secretome Ref
Fungi (Kingdom)a
 Ascomycota (Phylum)
  Saccharomycotina (Subphylum)
   Candida albicans SC5314 14.3 6,185 321 405 87 813 [70,71]
   Candida albicans WO-1 14.5 6,160 310 385 78 773 [72]
   Candida dubliniensisb 14.5 6,027 308 340 71 719 N
   Candida glabrata CBS138 12.3 5,165 231 290 49 570 [73]
   Candida guilliermondii 10.6 5,920 279 400 63 742 [72]
   Candida lusitaniae 12.1 5,941 310 482 50 842 [72]
   Candida parapsilosis 13.1 5,733 308 321 83 712 [72]
   Candida tropicalis 14.6 6,258 360 373 76 809 [72,74]
   Debaryomyces hansenii 12.2 6,354 254 357 74 685 [73]
   Eremothecium gossypii 8.8 4,717 204 333 35 572 [75]
   Kluyveromyces lactis 10.7 5,327 248 304 60 612 [73]
   Kluyveromyces polysporus 14.7 5,367 219 276 58 553 [76]
   Kluyveromyces waltii 10.9 4,935 187 280 41 508 [77]
   Lodderomyces elongisporus 15.5 5,802 253 351 50 654 [72]
   Pichia stipitis 15.4 5,839 263 374 58 695 [78]
   Saccharomyces bayanus 623-6C YM4911 11.9 4,966 200 275 44 519 [79]
   Saccharomyces bayanus MCYC 623 11.5 9,385 663 767 141 1571 [80]
   Saccharomyces castellii 11.4 4,677 177 240 46 463 [79]
   Saccharomyces cerevisiae 273614N 12.5 5,354 223 261 51 535 [81]
   Saccharomyces cerevisiae 322134S 12.5 5,382 224 290 53 567 [81]
   Saccharomyces cerevisiae 378604X 12.5 5,400 232 267 53 552 [81]
   Saccharomyces cerevisiae AWRI1631 11.2 5,451 220 364 63 647 N
   Saccharomyces cerevisiae BC187 12.5 5,332 226 263 47 536 [81]
   Saccharomyces cerevisiae DBVPG1106 12.5 5,318 225 253 52 530 [81]
   Saccharomyces cerevisiae DBVPG1373 12.4 5,349 229 260 48 537 [81]
   Saccharomyces cerevisiae DBVPG1788 12.4 5,347 227 263 46 536 [81]
   Saccharomyces cerevisiae DBVPG1853 12.5 5,359 224 265 51 540 [81]
   Saccharomyces cerevisiae DBVPG6040 12.6 5,364 221 271 50 542 [81]
   Saccharomyces cerevisiae DBVPG6044 12.5 5,890 224 268 48 540 [81]
   Saccharomyces cerevisiae DBVPG6765 12.2 5,377 230 263 48 541 [81]
   Saccharomyces cerevisiae K11 12.5 5,375 228 270 52 550 [81]
   Saccharomyces cerevisiae L_1374 12.4 5,346 225 264 55 544 [81]
   Saccharomyces cerevisiae L_1528 12.4 5,346 227 258 48 533 [81]
   Saccharomyces cerevisiae M22 10.8 6,755 249 399 62 710 [82]
   Saccharomyces cerevisiae NCYC110 12.5 5,408 226 264 57 547 [81]
   Saccharomyces cerevisiae NCYC361 12.6 5,360 228 261 49 538 [81]
   Saccharomyces cerevisiae RM11-1a 11.7 5,696 264 283 63 610 N
   Saccharomyces cerevisiae S288C 12.2 6,692 394 425 99 918 [83]
   Saccharomyces cerevisiae SK1 12.4 5,433 233 269 55 557 [81]
   Saccharomyces cerevisiae UWOPS03_461_4 12.6 5,329 218 268 51 537 [81]
   Saccharomyces cerevisiae UWOPS05_217_3 12.6 5,350 217 264 47 528 [81]
   Saccharomyces cerevisiae UWOPS05_227_2 12.6 5,334 220 266 51 537 [81]
   Saccharomyces cerevisiae UWOPS83_787_3 12.6 5,392 225 269 51 545 [81]
   Saccharomyces cerevisiae UWOPS87_2421 12.6 5,368 226 266 56 548 [81]
   Saccharomyces cerevisiae W303 12.4 5,467 237 271 52 560 [81]
   Saccharomyces cerevisiae Y12 12.6 5,370 223 268 57 548 [81]
   Saccharomyces cerevisiae Y55 12.3 5,415 239 262 60 561 [81]
   Saccharomyces cerevisiae Y9 12.6 5,377 223 271 49 543 [81]
   Saccharomyces cerevisiae YIIc17_E5 12.5 5,376 227 265 47 539 [81]
   Saccharomyces cerevisiae YJM789 12.0 5,903 293 303 59 655 [84]
   Saccharomyces cerevisiae YJM975 12.4 5,341 223 255 45 523 [81]
   Saccharomyces cerevisiae YJM978 12.4 5,353 224 258 47 529 [81]
   Saccharomyces cerevisiae YJM981 12.5 5,351 224 256 54 534 [81]
   Saccharomyces cerevisiae YPS128 12.4 5,364 230 269 54 553 [81]
   Saccharomyces cerevisiae YPS163 10.7 6,648 229 368 67 664 [82]
   Saccharomyces cerevisiae YPS606 12.5 5,354 224 270 51 545 [81]
   Saccharomyces cerevisiae YS2 12.6 5,383 221 254 50 525 [81]
   Saccharomyces cerevisiae YS4 12.5 5,398 215 267 54 536 [81]
   Saccharomyces cerevisiae YS9 12.6 5,373 226 265 51 542 [81]
   Saccharomyces kluyveri 11.0 2,968 120 180 29 329 [79]
   Saccharomyces kudriavzevii 11.2 3,768 187 195 28 410 [79]
   Saccharomyces mikatae 11.5 9,016 575 630 154 1359 [80]
   Saccharomyces mikatae WashU 10.8 3,100 161 154 24 339 [79]
   Saccharomyces paradoxus 11.9 8,939 581 615 138 1334 [80]
   Yarrowia lipolytica 20.5 6,524 409 464 75 948 [73]
  Taphrinomycotina (Subphylum)
   Pneumocystis cariniib, c 6.3 4,020 129 333 35 497 N
   Schizosaccharomyces japonicus 11.3 5,172 207 312 25 544 N
   Schizosaccharomyces octosporus 11.2 4,925 190 263 26 479 N
   Schizosaccharomyces pombe 12.6 5,058 192 288 36 516 [85]
Total 853.1 383,828 17,389 21,403 3,937 42,729 -
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a Taxonomy based on [69]

b Insufficient exon/intron information

c Incomplete coverage of genome information

Table 6.

List and distribution of secretion-associated proteins of the fungal genomes belonging to the phyla Basidiomycota, Chytridiomycota, and Microsporidia, the subphylum Mucoromycotina, and the phylum Peronosporomycota (oomycetes) archived in FSD

Species Size (Mb) # of ORFs Class SP Class SP3 Class SL Putative Secretome Ref
Fungi (Kingdom)a
 Basidiomycota (Phylum)
  Agricomycotina (Subphylum)
   Coprinus cinereus 36.3 13,410 1,189 1,032 119 2,340 N
   Cryptococcus neoformans Serotype A 18.9 6,980 377 549 56 982 N
   Cryptococcus neoformans Serotype B 19.0 6,870 331 529 44 904 N
   Cryptococcus neoformans Serotype D B-3501A 18.5 6,431 342 523 39 904 [86]
   Cryptococcus neoformans Serotype D JEC21 19.1 6,475 344 541 38 923 [86]
   Laccaria bicolour 64.9 20,614 1,190 2,024 256 3,470 [87]
   Moniliophthora perniciosa 26.7 13,560 843 1,127 126 2,096 N
   Phanerochaete chrysosporium 35.1 10,048 793 933 83 1,809 [88]
   Pleurotus ostreatus 34.3 11,603 1,039 1,058 106 2,203 N
   Postia placenta 90.9 17,173 1,057 1,808 202 3,067 [51]
   Schizophyllum commune 38.5 13,181 975 1,175 119 2,269 N
  Pucciniomycotina (Subphylum)
   Melampsora laricis-populina 21.9 16,694 1305 1483 233 3,021 N
   Puccinia graminis 88.7 20,567 1,931 2,020 230 4,181 N
   Sporobolomyces roseus 21.2 5,536 187 592 43 822 N
  Ustilaginomycotina (Subphylum)
   Malassezia globosa 9.0 4,286 211 378 37 626 [50]
   Ustilago maydis 521 19.7 6,689 789 583 10 1382 [89]
   Ustilago maydis FB1 19.3 6,950 481 717 34 1232 [89]
   Ustilago maydis MIPS 19.7 6,787 574 687 34 1295 N
  Chytridiomycota (Phylum)
   Batrachochytrium dendrobatidis JAM81 24.3 8,732 806 750 108 1,664 N
   Batrachochytrium dendrobatidis JEL423 23.9 8,818 650 785 91 1,526 N
  Mucoromycotina (Subphylum incertae sedis)
   Mucor circinelloides 36.6 10,930 580 623 83 1286 N
   Phycomyces blakesleeanus 55.9 14,792 642 1,085 221 1,948 N
   Rhizopus oryzae 46.1 17,482 750 994 202 1,946 [90]
  Microsporidia (Phylum)
   Antonospora locustaeb 6.1 2,606 166 208 62 436 N
   Encephalitozoon cuniculi 2.5 1,996 90 135 34 259 [91]
  Alveolata (Kingdom)
  Apicomplexa (Phylum)
   Plasmodium berghei 18.0 12,175 844 554 569 1,967 N
   Plasmodium chabaudi 16.9 15,007 1,027 643 661 2,331 N
   Plasmodium falciparum 3D7 21.0 5,387 212 283 267 762 [92]
   Plasmodium knowlesi 23.5 5,103 305 280 81 666 N
  Stramenopila (Kingdom)
  Peronosporomycota (Phylum)
   Hyaloperonospora parasitica 83.6 14,789 868 1,235 132 2,235 N
   Phytophthora capsici 107.8 17,414 1,485 1,179 136 2,800 N
   Phytophthora infestansb 228.5 22,658 1,668 1,923 153 3,744 [93]
   Phytophthora ramorum 66.7 15,743 1,670 1,372 91 3,133 [94]
   Phytophthora sojae 86.0 19,027 2,040 1,662 96 3,798 [94]
Total 1,449.1 386,513 27,761 31,470 4,796 64,027 -
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a Taxonomy based on [69]

b Insufficient exon/intron information

c Incomplete coverage of genome information

To determine the phylum-level distribution of classes SP, SP3, and SL within fungi, we investigated the proportions of the three classes among subphyla (Figure 2). Class SP3 was the largest, class SP was a little smaller, and the class SL was much smaller; this was consistent over every subphylum. Only in Plasmodium species, oomycetes, and the kingdom Metazoa class SP was dominant. Class SL did not exceeded 2.10% of the whole genome, except in Plasmodium species (4.52%). Plasmodium species also showed the lowest variance among the three classes, which may reflect signal peptide-independent types of secretory proteins such as vacuolar transport signals (VTSs) [12]. These results may be partially affected by the composition of the training data for each prediction program and inherent features of each algorithm.

Figure 2.

Figure 2

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Distribution of three classes at the phylum/subphylum level. The average ratios of the classes to the total ORFs at the subphylum and phylum levels are described. The orange circular arc represents the fungal kingdom, and the four light blue round boxes represent phyla or kingdoms. Inside the chart, the blue line represents the ratio of class SP; the red line, class SP3; and the green line, class SL.

The phylum Basidiomycota had a larger proportion of secretory proteins (17.90%) than other fungal taxonomy such as the subphylum Mucoromycotina (11.99%) and the phyla Ascomycota (12.87%) and Microsporidia (15.10%). Within the phylum Ascomycota, the subphylum Pezizomycotina showed a higher portion of class SP (7.82%) than the subphyla Saccharomycotina and Taphrinomycotina (4.57% and 3.74%, respectively). When considered that subphylum Pezizomycotina contains many pathogenic fungi (47 of 59) compared with subphylum Saccharomycotina (11 of 65), the abundance of secretory proteins in the subphylum Pezizomycotina suggests that pathogens may have larger secretome than saprophytes in general. In fact, Magnaporthe oryzae and Neurospora crassa, a closely related pair of pathogen and non-pathogen supported by recent phylogenomic studies [37-39], contain 22.31% and 16.93% of secretory proteins, respectively. Moreover, the same tendency was found in comparison with 158 fungal/oomycete genomes archived in the FSD (pathogens and saprophytes showed 14.06% and 11.70%, respectively).

Effectors encoded by fungal/oomycete and Plasmodium genomes

Phytophthora species, a group that includes many important plant pathogens, uses a RXLR signal to secrete effectors to host cells [40]. RXLR effectors were tightly co-located with signal peptides predicted by the SignalP 3.0 with high confidence values (HMM and NN for 0.93 and 0.65, respectively) [41]. With the same conditions, we identified 734 putative RXLR effectors from three Phytophthora species, similar to a previous study [42]. However, 153 fungal genomes showed that only 0.04% of the total proteome contained this motif, suggesting that the use of RXLR for secretion is oomycete-specific.

The motivation of finding the RXLR pattern in oomycetes was the RXLX [EDQ] motif of the VTS in the malaria pathogen, Plasmodium falciparum. Once P. falciparum invades the human erythrocyte, it secretes the proteins that carry the pentameric VTS of the RXLX [EDQ] motif from the parasitophorus vacuole to the host cytoplasm [12,13]. To determine how many VTSs could be detected by our pipeline, we investigated 217 proteins of P. falciparum [13]. Of these, 115 proteins (53.00%) were classified as secretory proteins, defined in the FSD by the RXLX [EDQ] motif. Comparing our result to that predicted by SignalP 3.0 alone (41 out of 217), we found that our pipeline demonstrated high fidelity in detecting proteins containing VTSs.

In class SP, the proportions of proteins possessing the RXLX [EDQ] but not the RXLR motif were 96.75%, 56.18%, and 93.21% in fungi, oomycetes, and Plasmodium species, respectively (Figure 3A). There were similar proportions of the RXLX [EDQ] motif in classes SP3 and SL across the three groups (Figure 3B and 3C). Taken together, these data show that the RXLR motif, with signal peptides predicted by SignalP 3.0, is oomycete-specific [41]. It is interesting that fungal genomes have significantly higher numbers of the RXLX [EDQ] motif than Plasmodium species (t-test based on amino acid frequency in each genome; P = 2.2e-16), suggesting that the RXLX [EDQ] motif may be one of fungal-specific signatures of effectors.

Figure 3.

Figure 3

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Composition of RXLR/RXLX [EDQ] pattern in fungi, oomycetes, and Plasmodium species. Composition of the RXLX [EDQ] (blue) and the RXLR (red) under class SP (A), class SP3 (B), and class SL (C) with the relative ratio in fungi, oomycetes, and Plasmodium species, respectively.

Utility

FSD web interfaces

To support the browsing of the global patterns of archived data, the FSD prepares diverse charts and tables. For example, intersections of prediction results are summarized in a chart for each genome (Figure 4). Despite of the many programs, all prediction results for each protein are displayed on one page, allowing users to browse them easily (Figure 5).

Figure 4.

Figure 4

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Screenshot of genome-level analysis functions for an example fungal genome. This screenshot shows the ORF numbers and ratios of each class through the pie chart in the left and the table in the right. The numbers in the table provide links to the list of putative secretory proteins belonging to each group. This figure shows the result from M. oryzae.

Figure 5.

Figure 5

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One page summary for a protein. The web page shows a one page summary of amino acid sequence, exon structure, and genome context via the SNUGB [15], along with 12 predictions, including signal peptides and subcellular localization.

The SNUGB interface (http://genomebrowser.snu.ac.kr/)[15] provides several fields: i) signal peptides predicted by four different programs; ii) effector patterns, such as RXLR and RXLX [EDQ]; iii) nucleotide localization signals predicted by predictNLS; iv) transmembrane helixes predicted by TMHMM 2.0c; and v) hydropathy plots (Figure 6). The users can readily compare secretome-related information with diverse genomic contexts.

Figure 6.

Figure 6

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SNU Genome Browser implemented in the FSD. The SNUGB (http://genomebrowser.snu.ac.kr/)[15] displays i) four types of signal peptides predicted by SignalP 3.0, SigCleave, SigPred, and RPSP, ii) amino acid patterns, iii) nucleotide localization signals predicted by predictNLS, iv) transmembrane helixes predicted by TMHMM 2.0c, and v) hydropathy plots.

The personalized virtual space, Favorite, supports in-depth analyses in the FSD

The FSD allows users to collect proteins of interest and save them into the Favorite, which provides thirteen functions: i) classes distribution of proteins; ii) comparisons of predicted signal peptides generated by the four programs; iii) distributions and lists of proteins with predicted signal peptide cleavage sites; iv) compositions of amino acids near the cleavage sites; v) analyses of subcellular localization predictions; vi) lists and ratios of proteins that have chloroplast transit peptides, as determined by ChloroP 1.1; vii) analyses of proteins detected by SecretomeP 1.0f; viii) lists and distribution charts of proteins with trans-membrane helices, as predicted by TMHMM 2.0c; ix) hydropathy plots for proteins; x) analyses of proteins believed to be targeted to the nucleus of a host cell supported by predictNLS; xi) distributions and lists of proteins with a specific amino acid patterns; xii) lists of functional domains predicted by InterPro Scan; xiii) domain architecture of InterPro Scan (Figure 7). From these result pages, users can collect and store proteins in Favorite again, for further analyses. Additionally, Favorites created in the FSD can be shared with the CFGP (http://cfgp.snu.ac.kr/)[16], permitting users to use the 22 bioinformatics tools provided in the CFGP web site.

Figure 7.

Figure 7

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Thirteen analysis functions in the Favorite browser. Six different pages of analyses, connected to the Favorite browser, are displayed. "Prediction distribution" provides a list of predicted secretory proteins with their proportion to all proteins. "Class distribution" shows the composition of the classes, with the protein numbers belonging to each class. "Frequency/Position distribution" gives a bar or pie graph and numerical values linking to proteins listed for each item. "Hydropathy plots" draws the two graphs with window sizes of 11 and 19. "Amino acid distribution" presents consensus amino acids around the cleavage sites. "Functional domain distribution" lists the domains and their architecture diagrams based on InterPro terms.

Conclusions

Given the availability of large number of fungal genomes and diverse prediction programs for secretory proteins, a three-layer classification rule was established and implemented in a web-based database, the FSD. With the aid of an automated pipeline, the FSD classifies putative secretory proteins from 158 fungal/oomycetes genomes into four different classes, three of which are defined as the putative secretome. The proportion of fungal secretory proteins and host targeting signals varies considerably by species. It is interesting that fungal genomes have high proportions of the RXLX [EDQ] motif, characterized as host targeting signal in Plasmodium species. Summaries of the complex prediction results from twelve programs help users to readily access to the information provided by the FSD. Favorite, a personalized virtual space in the CFGP, serves thirteen different analysis tools for further in-depth analyses. Moreover, 22 bioinformatics tools provided by the CFGP can be utilized via the Favorite. Given these features, the FSD can serve as an integrated environment for studying secretory proteins in the fungal kingdom.

Availability and requirements

All data and functions described in this paper can be freely accessed through the FSD web site at http://fsd.snu.ac.kr/.

Authors' contributions

JC, JP, and YHL designed this project, JC and JP constructed the database and developed the pipeline with nine prediction programs. DK generated basic data from the twelve programs and JP, JC, and DK managed genome sequences for FSD. JC developed thirteen analysis functions of FSD. JC and JP constructed web-based interfaces. JC, JP, SK, and YHL wrote the manuscript. All the authors read and confirmed the manuscript.

Contributor Information

Jaeyoung Choi, Email: amethyst1016@gmail.com.

Jongsun Park, Email: starflr@snu.ac.kr.

Donghan Kim, Email: hoppang1234@gmail.com.

Kyongyong Jung, Email: lulupon0@snu.ac.kr.

Seogchan Kang, Email: sxk55@psu.edu.

Yong-Hwan Lee, Email: yonglee@snu.ac.kr.

Acknowledgements

This work was supported by the National Research Foundation of Korea grants (2009-0063340 and 2009-0080161) and grants from the Biogreen21 (20080401-034-044-009-01-00), the TDPAF (309015-04-SB020), and the Crop Functional Genomics Center (2009K001198). JC is grateful for the graduate fellowship through the Brain Korea 21 Program.

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