Open Access

Fungal Secretome Database: Integrated platform for annotation of fungal secretomes

BMC Genomics201011:105

DOI: 10.1186/1471-2164-11-105

Received: 16 May 2009

Accepted: 11 February 2010

Published: 11 February 2010

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 [57], building and remodeling of cell walls [8], signaling and environmental sensing [9], and competition with other organisms [1013]. 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 [1113].

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]

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

-

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.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-11-105/MediaObjects/12864_2009_Article_2699_Fig1_HTML.jpg
Figure 1

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

* 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, 2935]. 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 globosum b

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 fischeri b

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

-

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 dubliniensis b

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 carinii b, 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

-

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 locustae b

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 infestans b

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

-

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.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-11-105/MediaObjects/12864_2009_Article_2699_Fig2_HTML.jpg
Figure 2

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 [3739], 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.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-11-105/MediaObjects/12864_2009_Article_2699_Fig3_HTML.jpg
Figure 3

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).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-11-105/MediaObjects/12864_2009_Article_2699_Fig4_HTML.jpg
Figure 4

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.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-11-105/MediaObjects/12864_2009_Article_2699_Fig5_HTML.jpg
Figure 5

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.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-11-105/MediaObjects/12864_2009_Article_2699_Fig6_HTML.jpg
Figure 6

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.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-11-105/MediaObjects/12864_2009_Article_2699_Fig7_HTML.jpg
Figure 7

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/.

Declarations

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.

Authors’ Affiliations

(1)
Fungal Bioinformatics Laboratory, Seoul National University
(2)
Department of Agricultural Biotechnology, Seoul National University
(3)
Center for Fungal Pathogenesis, Seoul National University
(4)
Center for Fungal Genetic Resources, Seoul National University
(5)
Center for Agricultural Biomaterials, Seoul National University
(6)
Department of Plant Pathology, The Pennsylvania State University

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