The host range of a parasite has a central influence on the emergence and spread of disease (Woolhouse and Gowtage-Sequeria, 2005). There is a clear demarcation between specialist parasites that can only infect one or a few closely related host species and generalists that can infect more than a hundred unrelated host species (Woolhouse et al., 2001; Barrett et al., 2009). The host range is a trait constrained by ecological and physiological factors and determined by the evolutionary history of a parasite and its potential hosts (Poulin and Keeney, 2008). Host specialization is a frequent pattern in living systems that was proposed to result from tradeoffs in performance on multiple hosts (Joshi and Thompson, 1995). At the molecular level, optimization by natural selection of parasite proteins functioning in the interaction with a given host may be detrimental to their function in another species (Dodds and Rathjen, 2010; Dong et al., 2014). Nevertheless, generalists able to thrive on a broad range of hosts are found in most parasite lineages. The molecular mechanisms associated with the evolution of generalist parasitism remain, however, elusive. Notably, the extent to which interaction with multiple hosts affects the evolution of parasite genomes remains unknown.

Genomic analyses of obligate specialist parasites revealed massive losses of enzymes unnecessary for growth and reproduction on living hosts (Schirawski et al., 2010; Spanu et al., 2010). Instead, such specialists rely largely on a sets of small secreted protein (SSPs) subverting host cell function. For instance, the genome of the powdery mildew pathogen Blumeria graminis f. sp. hordei harbors 180 SSPs of about 100–150 amino acids (Pedersen et al., 2012). SSPs of specialist fungi often show evidence of diversifying-selection leading to extensive changes in their protein sequence (Stukenbrock et al., 2011; Pedersen et al., 2012; Hacquard et al., 2013). By contrast, the secretion of a battery of degrading enzymes is crucial for the parasitic success of generalists (Hu et al., 2014; Kubicek et al., 2014). The genomes of generalist fungi notably contain in average ~3 times more carbohydrate activity enzymes (CAZYmes) than specialist fungi (Zhao et al., 2013). For instance, Rhizoctonia solani AG2-2IIIB contains over 1000 CAZYme genes (Wibberg et al., 2016). Protein translation is the largest consumer of energy during cellular proliferation, and because secreted proteins are not recycled like other cellular proteins, strong constraints exist to reduce the synthetic cost of secreted proteins in microbes (Smith and Chapman, 2010). Considering the distinct properties of secreted proteins in specialist and generalist parasites, the impact of protein translation efficiency on the cost of secreted protein synthesis likely differ in these organisms.

The differential use of synonymous codons in a genome, or codon usage bias, affects gene expression level, protein folding, translation efficiency and accuracy (Plotkin and Kudla, 2011). In particular, the co-evolution of codons with the genomic tRNA complement, leading to codon optimization, results from the combination of neutral processes and selection for the optimization of protein translation (Hershberg and Petrov, 2008; Shah and Gilchrist, 2011). Natural selection for the optimization of protein translation, through ribosome pausing time, translation error rates and co-translational protein folding, has been widely associated with codon usage biases (Drummond and Wilke, 2008; Tuller et al., 2010; Shah and Gilchrist, 2011). Because the probability of protein translation error increases with protein length, selection for translation accuracy can be higher in genes encoding long proteins, as was observed in E. coli (Eyre-Walker, 1996). Codon usage bias also increases with the evolutionary age of genes, the frequency of codons optimal for translation being significantly higher at codons for conserved amino acids (Marais and Duret, 2001; Prat et al., 2009). Considering that highly expressed secreted proteins are often longer and more conserved in generalist than in specialist parasites, we expect natural selection on the optimization of codons to be stronger in generalist parasites than in host specialists.

The cost of protein translation leads to a tradeoff between protein production and cell growth rate (Scott et al., 2010; Shah et al., 2013; Kafri et al., 2016). Codon optimization therefore impacts cell growth in selective environments (Botzman and Margalit, 2011; Krisko et al., 2014), and could positively impact on the performances of parasites regardless of the host they encounter. This hypothesis would be consistent with generalist lineages performing better on average on their set of available hosts than co-occurring specialist competitors to persist in the biota, as predicted by the 'jack of all trades, master of none' theoretical model (Futuyma and Moreno, 1988). Here, we have examined the strength of natural selection on the optimization of codons in parasites with contrasted host range across the fungal kingdom. Because the genomes of broad host range parasites generally encode longer proteins, an in silico model of the cell translation machinery predicts that natural selection on codon optimization should be stronger in these species. Consistent with this prediction, we found that codon optimization at the genome scale correlates with fungal parasites host range. High codon optimization and broad host ranges co-evolved multiple times independently through fungal phylogeny. To document the molecular bases of codon optimization at the species level in generalist fungi, we compared single-nucleotide polymorphism (SNP) patterns in natural populations of a generalist and a specialist fungal parasite. We detected signatures of purifying natural selection acting on optimal codons in the generalist parasite only. Finally, we show that genes associated with virulence are better codon-optimized in generalist than in specialist parasite genomes supporting codon optimization as an adaptation to the colonization of multiple hosts. Together, our results reveal patterns of adaptive genome evolution associated with generalism that are conserved through the fungal Kingdom. Furthermore, we establish a link between translational regulation and host range variation through genomic patterns of codon optimization, contributing to our understanding of the dynamics of disease epidemics.

Our study reveals that codon optimization through biased synonymous substitutions is a common feature in the evolution of generalist parasites and is associated with the colonization of multiple hosts. Because host colonization by generalist often requires diverse and relatively complex secreted proteins, these organisms are expected to require higher protein translation efficiency to compete with co-occuring specialist microbes. In agreement with this model, we found that natural selection for codon optimization correlates with host range in fungal parasites. We describe patterns of purifying selection acting on optimal codons in generalist parasites, providing a molecular basis for genome scale adaptation to the colonization of multiple hosts.

The increasing amount of genome sequences available for specialist fungal parasites revealed massive losses of enzymes associated with the ability to thrive and reproduce on living hosts (Schirawski et al., 2010; Spanu et al., 2010). Specialists rely largely on SSPs subverting host cell functions. Functional analyses have shown that SSPs from filamentous pathogens can function as effectors, facilitating the colonization of susceptible hosts while triggering resistance in some host genotypes by activating resistance (R) proteins (Dodds and Rathjen, 2010). In specialist species, the one-to-one relationship, either direct or indirect, between effectors and R proteins favors evolution through non-synonymous mutations, to increase effector diversity and escape R protein recognition (Raffaele and Kamoun, 2012). By contrast, pathogen populations exposed to weaker host selection, caused by quantitative resistance genes, or disruptive selection due to heterogeneity in the host population, are expected to evolve adapted genetic variants less rapidly (McDonald and Linde, 2002; Roux et al., 2014). Our analyses indeed support the view that selection driven by multiple hosts is associated with relatively low rates of evolution of protein variants but instead associates with the directional evolution of synonymous genetic variants. Translational selection may, nevertheless, be active in the genome of specialists but on limited gene sets only, resulting in a low signal in genome-scale analyses (dos Reis et al., 2004).

We noted a trend toward longer proteins encoded in the genome of generalist fungi, notably secreted proteins, consistent with the abundance of SSPs in specialists (Spanu et al., 2010) and previous comparative analyses (Kim et al., 2016). We associated longer proteomes in generalists with higher selection on codon optimization at the whole genome scale. The relative strength of translational selection on individual genes may, however, vary significantly within a given genome. Gene length increases in relation to evolutionary age, with conserved genes under purifying selection generally being longer and harboring higher frequency of optimal codons (Prat et al., 2009; Wolf et al., 2009). Neofunctionalization in duplicated genes has also been associated with increased gene length (He and Zhang, 2005). Duplications frequently underlie domain loss and gain in eukaryotic proteins, which is an important mechanism for the evolution of new functions (Buljan et al., 2010; Peisajovich et al., 2010). Natural selection may promote protein domains recombination to increase the versatility of fungal proteins functions, and thereby contribute to host range expansion.

The adaptive evolution of effectors that increase the colonization of one host species also increases the risk of detection in other host species by triggering their immune system. Furthermore, natural selection that maximizes parasite fitness on one particular host species might result in decreased fitness on an alternative host, leading to specialization (reduced host range) or host shift (Dong et al., 2014). The evolution of generalism has been associated to hybridization events (McMullan et al., 2015; Menardo et al., 2016), horizontal transfer of genetic elements (Ma et al., 2010; Hu et al., 2014), and an overall increase in the repertoire of protein-coding genes (Hu et al., 2014). This observation is consistent with the prominent role in pathogenic success of large repertoires of host-degrading enzymes encoded by the genome of generalist parasites (King et al., 2011; Zhao et al., 2013). Catalytic efficiency of these enzymes is crucial to perform in host environment. Although the catalytic activity of enzymes is typically modulated by adaptation of protein sequences, high reaction rates for efficient host colonization may be reached by modulating the amount of protein synthesized during colonization. However, an increase in proteins production may lead to a metabolic or structural burden of the translation machinery. For instance, the massive production of virulence factors by the generalist bacterial pathogen Ralstonia solanacearum reduces its metabolic versatility and thus its host colonization capacity (Perrier et al., 2016; Peyraud et al., 2016). Natural selection for the optimization of protein translation, through ribosome pausing time, translation error rates and co-translational protein folding, has been widely associated with codon usage biases (Drummond and Wilke, 2008; Tuller et al., 2010; Shah and Gilchrist, 2011). The adaptive evolution of the translation machinery described here is predicted to favor generalists’ performances in heterogeneous host populations, by reducing the cost of large and diverse sets of secreted enzymes production on proliferation. The mitigation of the tradeoff between cell proliferation and protein synthesis (Li et al., 2014) by codon optimization could explain how generalist lineages have maintained or gained the ability to produce diverse sets of secreted enzymes, allowing the colonization of genetically diverse hosts. Codon optimization may affect the expression of effectors and other virulence factors, modifying the degree of infection or enabling the colonization of new hosts opposing quantitative disease resistance mechanisms. These findings reveal that parasite host range variation is a prime example of an adaptive phenotype related to codon optimality and translational regulation.

1. 1. Mbengue M
   2. Navaud O
   3. Raffaele S

   (2016) Sclerotinia sclerotiorum isolates genome sequence

   Publicly available at the NCBI BioProject (accession no: PRJNA342788).

   https://www.ncbi.nlm.nih.gov/bioproject/342788

1. 1. de Bekker C
   2. Ohm RA
   3. Loreto RG
   4. Sebastian A
   5. Albert I
   6. Merrow M
   7. Brachmann A
   8. Hughes DP

   (2015) Ophiocordyceps unilateralis genome sequence

   Publicly available at NCBI Nucleotide (accession no. LAZP00000000.1).

   http://www.ncbi.nlm.nih.gov/nuccore/LAZP00000000.1/
2. 1. James TY
   2. Pelin A
   3. Bonen L
   4. Ahrendt S
   5. Sain D
   6. Corradi N
   7. Stajich JE

   (2013) Rozella allomycis genome sequence

   Publicly available at genome.jgi.doe.gov.

   http://genome.jgi.doe.gov/Rozal1_1/Rozal1_1.home.html
3. 1. Ma LJ
   2. Ibrahim AS
   3. Skory C
   4. Grabherr MG
   5. Burger G
   6. Butler M
   7. Elias M
   8. Idnurm A
   9. Lang BF
   10. Sone T
   11. Abe A
   12. Calvo SE
   13. Corrochano LM
   14. Engels R
   15. Fu J
   16. Hansberg W
   17. Kim JM
   18. Kodira CD
   19. Koehrsen MJ
   20. Liu B
   21. Miranda-Saavedra D
   22. O'Leary S
   23. Ortiz-Castellanos L
   24. Poulter R
   25. Rodriguez-Romero J
   26. Ruiz-Herrera J
   27. Shen YQ
   28. Zeng Q
   29. Galagan J
   30. Birren BW
   31. Cuomo CA
   32. Wickes BL

   (2009) Rhizopus oryzae genome sequence

   Publicly available at genome.jgi.doe.gov.

   http://genome.jgi.doe.gov/Rhior3/Rhior3.home.html
4. 1. Cornman RS
   2. Chen YP
   3. Schatz MC
   4. Street C
   5. Zhao Y
   6. Desany B
   7. Egholm M
   8. Hutchison S
   9. Pettis JS
   10. Lipkin WI
   11. Evans JD

   (2009) Nosema ceranae genome sequence

   Publicly available at the EBI European Nucleotide Archive (accession no: GCA_000988165.1).

   http://www.ebi.ac.uk/ena/data/view/GCA_000988165.1
5. 1. Corradi N
   2. Pombert JF
   3. Farinelli L
   4. Didier ES
   5. Keeling PJ

   (2010) Encephalitozoon intestinalis genome sequence

   Publicly available at genome.jgi.doe.gov.

   http://genome.jgi.doe.gov/Encin1/Encin1.home.html
6. 1. Batrachochytrium dendrobatidis Sequencing Project
   2. Broad Institute of Harvard and MIT

   (2016) Batrachochytrium dendrobatidis genome sequence

   Publicly available at www.broadinstitute.org.

   https://www.broadinstitute.org/fungal-genome-initiative/batrachochytrium-genome-project
7. 1. Chang Y
   2. Wang S
   3. Sekimoto S
   4. Aerts AL
   5. Choi C
   6. Clum A
   7. LaButti KM
   8. Lindquist EA
   9. Yee Ngan C
   10. Ohm RA
   11. Salamov AA
   12. Grigoriev IV
   13. Spatafora JW
   14. Berbee ML

   (2015) Gonapodya prolifera genome sequence

   Publicly available at genome.jgi.doe.gov.

   http://genome.jgi.doe.gov/Ganpr1/Ganpr1.home.html
8. 1. Schirawski J
   2. Mannhaupt G
   3. Münch K
   4. Brefort T
   5. Schipper K
   6. Doehlemann G
   7. Di Stasio M
   8. Rössel N
   9. Mendoza-Mendoza A
   10. Pester D
   11. Müller O
   12. Winterberg B
   13. Meyer E
   14. Ghareeb H
   15. Wollenberg T
   16. Münsterkötter M
   17. Wong P
   18. Walter M
   19. Stukenbrock E
   20. Güldener U
   21. Kahmann R

   (2010) Sporisorium reilianum genome sequence

   Publicly available at genome.jgi.doe.gov.

   http://genome.jgi.doe.gov/Spore1/Spore1.home.html
9. 1. Zhu Z
   2. Zhang S
   3. Liu H
   4. Shen H
   5. Lin X
   6. Yang F
   7. Zhou YJ
   8. Jin G
   9. Ye M
   10. Zou H
   11. Zhao ZK

   (2012) Rhodotorula toruloides genome sequence

   Publicly available at genome.jgi.doe.gov.

   http://genome.jgi.doe.gov/Rhoto1/Rhoto1.home.html
10. 1. Puccinia Group Sequencing Project
    2. Broad Institute of Harvard and MIT

    (2016) Puccinia triticina genome sequence

    Publicly available at www.broadinstitute.org.

    https://www.broadinstitute.org/scientific-community/science/projects/fungal-genome-initiative/puccinia-comparative-genomic-projects
11. 1. Duplessis S
    2. Cuomo CA
    3. Lin YC
    4. Aerts A
    5. Tisserant E
    6. Veneault-Fourrey C
    7. Joly DL
    8. Hacquard S
    9. Amselem J
    10. Cantarel BL
    11. Chiu R
    12. Coutinho PM
    13. Feau N
    14. Field M
    15. Frey P
    16. Gelhaye E
    17. Goldberg J
    18. Grabherr MG
    19. Kodira CD
    20. Kohler A
    21. Kues U
    22. Lindquist EA
    23. Lucas SM
    24. Mago R
    25. Mauceli E
    26. Morin E
    27. Murat C
    28. Pangilinan JL
    29. Park R
    30. Pearson M
    31. Quesneville H
    32. Rouhier N
    33. Sakthikumar S
    34. Salamov AA
    35. Schmutz J
    36. Selles B
    37. Shapiro H
    38. Tanguay P
    39. Tuskan GA
    40. Henrissat B
    41. Van de Peer Y
    42. Rouzé P
    43. Ellis JG
    44. Dodds PN
    45. Schein JE
    46. Zhong S
    47. Hamelin RC
    48. Grigoriev IV
    49. Szabo LJ
    50. Martin F

    (2011) Puccinia graminis genome sequence

    Publicly available at genome.jgi.doe.gov.

    http://genome.jgi.doe.gov/Pucgr2/Pucgr2.home.html
12. 1. Loftus BJ
    2. Fung E
    3. Roncaglia P
    4. Rowley D
    5. Amedeo P
    6. Bruno D
    7. Vamathevan J
    8. Miranda M
    9. Anderson IJ
    10. Fraser JA
    11. Allen JE
    12. Bosdet IE
    13. Brent MR
    14. Chiu R
    15. Doering TL
    16. Donlin MJ
    17. D'Souza CA
    18. Fox DS
    19. Grinberg V
    20. Fu J
    21. Fukushima M
    22. Haas BJ
    23. Huang JC
    24. Janbon G
    25. Jones SJ
    26. Koo HL
    27. Krzywinski MI
    28. Kwon-Chung JK
    29. Lengeler KB
    30. Maiti R
    31. Marra MA
    32. Marra RE
    33. Mathewson CA
    34. Mitchell TG
    35. Pertea M
    36. Riggs FR
    37. Salzberg SL
    38. Schein JE
    39. Shvartsbeyn A
    40. Shin H
    41. Shumway M
    42. Specht CA
    43. Suh BB
    44. Tenney A
    45. Utterback TR
    46. Wickes BL
    47. Wortman JR
    48. Wye NH
    49. Kronstad JW
    50. Lodge JK
    51. Heitman J
    52. Davis RW
    53. Fraser CM
    54. Hyman RW

    (2005) Cryptococcus neoformans genome sequence

    Publicly available at genome.jgi.doe.gov.

    http://genome.jgi.doe.gov/Cryne_JEC21_1/Cryne_JEC21_1.home.html
13. 1. Floudas D
    2. Binder M
    3. Riley R
    4. Barry K
    5. Blanchette RA
    6. Henrissat B
    7. Martinez AT
    8. Otillar R
    9. Spatafora JW
    10. Yadav JS
    11. Aerts A
    12. Benoit I
    13. Boyd A
    14. Carlson A
    15. Copeland A
    16. Coutinho PM
    17. de Vries RP
    18. Ferreira P
    19. Findley K
    20. Foster B
    21. Gaskell J
    22. Glotzer D
    23. Gorecki P
    24. Heitman J
    25. Hesse C
    26. Hori C
    27. Igarashi K
    28. Jurgens JA
    29. Kallen N
    30. Kersten P
    31. Kohler A
    32. Kues U
    33. Kumar TK
    34. Kuo A
    35. LaButti K
    36. Larrondo LF
    37. Lindquist E
    38. Ling A
    39. Lombard V
    40. Lucas S
    41. Lundell T
    42. Martin R
    43. McLaughlin DJ
    44. Morgenstern I
    45. Morin E
    46. Murat C
    47. Nagy LG
    48. Nolan M
    49. Ohm RA
    50. Patyshakuliyeva A
    51. Rokas A
    52. Ruiz-Duenas FJ
    53. Sabat G
    54. Salamov A
    55. Samejima M
    56. Schmutz J
    57. Slot JC
    58. St John F
    59. Stenlid J
    60. Sun H
    61. Sun S
    62. Syed K
    63. Tsang A
    64. Wiebenga A
    65. Young D
    66. Pisabarro A
    67. Eastwood DC
    68. Martin F
    69. Cullen D
    70. Grigoriev IV
    71. Hibbett DS

    (2012) Wolfiporia cocos genome sequence

    Publicly available at genome.jgi.doe.gov.

    http://genome.jgi-psf.org/Wolco1/Wolco1.home.html
14. 1. Cubeta MA
    2. Thomas E
    3. Dean RA
    4. Jabaji S
    5. Neate SM
    6. Tavantzis S
    7. Toda T
    8. Vilgalys R
    9. Bharathan N
    10. Fedorova-Abrams N
    11. Pakala SB
    12. Pakala SM
    13. Zafar N
    14. Joardar V
    15. Losada L
    16. Nierman WC.

    (2014) Rhizoctonia solani genome sequence

    Publicly available at the EBI European Nucleotide Archive (accession no:GCA_000524645.1).

    http://www.ebi.ac.uk/ena/data/view/GCA_000524645.1
15. 1. Eastwood DC
    2. Floudas D
    3. Binder M
    4. Majcherczyk A
    5. Schneider P
    6. Aerts A
    7. Asiegbu FO
    8. Baker SE
    9. Barry K
    10. Bendiksby M
    11. Blumentritt M
    12. Coutinho PM
    13. Cullen D
    14. de Vries RP
    15. Gathman A
    16. Goodell B
    17. Henrissat B
    18. Ihrmark K
    19. Kauserud H
    20. Kohler A
    21. LaButti K
    22. Lapidus A
    23. Lavin JL
    24. Lee YH
    25. Lindquist E
    26. Lilly W
    27. Lucas S
    28. Morin E
    29. Murat C
    30. Oguiza JA
    31. Park J
    32. Pisabarro AG
    33. Riley R
    34. Rosling A
    35. Salamov A
    36. Schmidt O
    37. Schmutz J
    38. Skrede I
    39. Stenlid J
    40. Wiebenga A
    41. Xie X
    42. Kues U
    43. Hibbett DS
    44. Hoffmeister D
    45. Hogberg N
    46. Martin F
    47. Grigoriev IV
    48. Watkinson SC

    (2011) Serpula lacrymans genome sequence

    Publicly available at genome.jgi-psf.org.

    http://genome.jgi-psf.org/SerlaS7_9_2/SerlaS7_9_2.home.html
16. 1. Meinhardt LW
    2. Costa GG
    3. Thomazella DP
    4. Teixeira PJ
    5. Carazzolle MF
    6. Schuster SC
    7. Carlson JE
    8. Guiltinan MJ
    9. Mieczkowski P
    10. Farmer A
    11. Ramaraj T
    12. Crozier J
    13. Davis RE
    14. Shao J
    15. Melnick RL
    16. Pereira GA
    17. Bailey BA

    (2014) Moniliophthora roreri genome sequence

    Publicly available at www.ncbi.nlm.nih.gov.elis.tmu.edu.tw.

    ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/488/995/GCA_000488995.1_M_roreri_MCA_2997_v1/
17. 1. Morin E
    2. Kohler A
    3. Baker AR
    4. Foulongne-Oriol M
    5. Lombard V
    6. Nagy LG
    7. Ohm RA
    8. Patyshakuliyeva A
    9. Brun A
    10. Aerts AL
    11. Bailey AM
    12. Billette C
    13. Coutinho PM
    14. Deakin G
    15. Doddapaneni H
    16. Floudas D
    17. Grimwood J
    18. Hilden K
    19. Kues U
    20. Labutti KM
    21. Lapidus A
    22. Lindquist EA
    23. Lucas SM
    24. Murat C
    25. Riley RW
    26. Salamov AA
    27. Schmutz J
    28. Subramanian V
    29. Wosten HA
    30. Xu J
    31. Eastwood DC
    32. Foster GD
    33. Sonnenberg AS
    34. Cullen D
    35. de Vries RP
    36. Lundell T
    37. Hibbett DS
    38. Henrissat B
    39. Burton KS
    40. Kerrigan RW
    41. Challen MP
    42. Grigoriev IV
    43. Martin F

    (2012) Agaricus bisporus genome sequence

    Publicly available at genome.jgi.doe.gov.

    http://genome.jgi.doe.gov/Agabi_varbisH97_2/Agabi_varbisH97_2.home.html
18. 1. Cisse OH
    2. Almeida JM
    3. Fonseca A
    4. Kumar AA
    5. Salojarvi J
    6. Overmeyer K
    7. Hauser PM
    8. Pagni M

    (2013) Taphrina deformans genome sequence

    Publicly available at genome.jgi.doe.gov.

    http://genome.jgi.doe.gov/Tapde1_1/Tapde1_1.home.html
19. 1. Martin F
    2. Aerts A
    3. AhrÃ{copyright
    4. serif}n D
    5. Brun A
    6. Danchin EG
    7. Duchaussoy F
    8. Gibon J
    9. Kohler A
    10. Lindquist E
    11. Pereda V
    12. Salamov A
    13. Shapiro HJ
    14. Wuyts J
    15. Blaudez D
    16. BuÃ{copyright
    17. serif}e M
    18. Brokstein P
    19. Canbäck B
    20. Cohen D
    21. Courty PE
    22. Coutinho PM
    23. Delaruelle C
    24. Detter JC
    25. Deveau A
    26. DiFazio S
    27. Duplessis S
    28. Fraissinet-Tachet L
    29. Lucic E
    30. Frey-Klett P
    31. Fourrey C
    32. Feussner I
    33. Gay G
    34. Grimwood J
    35. Hoegger PJ
    36. Jain P
    37. Kilaru S
    38. LabbÃ{copyright
    39. serif} J
    40. Lin YC
    41. LeguÃ{copyright
    42. serif} V
    43. Le Tacon F
    44. Marmeisse R
    45. Melayah D
    46. Montanini B
    47. Muratet M
    48. Nehls U
    49. Niculita-Hirzel H
    50. Oudot-Le Secq MP
    51. Peter M
    52. Quesneville H
    53. Rajashekar B
    54. Reich M
    55. Rouhier N
    56. Schmutz J
    57. Yin T
    58. Chalot M
    59. Henrissat B
    60. KÃ1/4es U
    61. Lucas S
    62. Van de Peer Y
    63. Podila GK
    64. Polle A
    65. Pukkila PJ
    66. Richardson PM
    67. RouzÃ{copyright
    68. serif} P
    69. Sanders IR
    70. Stajich JE
    71. Tunlid A
    72. Tuskan G
    73. Grigoriev IV

    (2008) Tuber melanosporum genome sequence

    Publicly available at genome.jgi.doe.gov.

    http://genome.jgi.doe.gov/Tubme1/Tubme1.home.html
20. 1. Marcet-Houben M
    2. Ballester AR
    3. de la Fuente B
    4. Harries E
    5. Marcos JF
    6. Gonzalez-Candelas L
    7. Gabaldon T

    (2012) Penicillium digitatum genome sequence

    Publicly available at genome.jgi.doe.gov.

    http://genome.jgi.doe.gov/Pendi1/Pendi1.home.html
21. 1. Nierman WC
    2. Pain A
    3. Anderson MJ
    4. Wortman JR
    5. Kim HS
    6. Arroyo J
    7. Berriman M
    8. Abe K
    9. Archer DB
    10. Bermejo C
    11. Bennett J
    12. Bowyer P
    13. Chen D
    14. Collins M
    15. Coulsen R
    16. Davies R
    17. Dyer PS
    18. Farman M
    19. Fedorova N
    20. Feldblyum TV
    21. Fischer R
    22. Fosker N
    23. Fraser A
    24. Garcia JL
    25. Garcia MJ
    26. Goble A
    27. Goldman GH
    28. Gomi K
    29. Griffith-Jones S
    30. Gwilliam R
    31. Haas B
    32. Haas H
    33. Harris D
    34. Horiuchi H
    35. Huang J
    36. Humphray S
    37. Jimenez J
    38. Keller N
    39. Khouri H
    40. Kitamoto K
    41. Kobayashi T
    42. Konzack S
    43. Kulkarni R
    44. Kumagai T
    45. Lafon A
    46. Latge JP
    47. Li W
    48. Lord A
    49. Lu C
    50. Majoros WH
    51. May GS
    52. Miller BL
    53. Mohamoud Y
    54. Molina M
    55. Monod M
    56. Mouyna I
    57. Mulligan S
    58. Murphy L
    59. O'Neil S
    60. Paulsen I
    61. Penalva MA
    62. Pertea M
    63. Price C
    64. Pritchard BL
    65. Quail MA
    66. Rabbinowitsch E
    67. Rawlins N
    68. Rajandream MA
    69. Reichard U
    70. Renauld H
    71. Robson GD
    72. Rodriguez de Cordoba S
    73. Rodriguez-Pena JM
    74. Ronning CM
    75. Rutter S
    76. Salzberg SL
    77. Sanchez M
    78. Sanchez-Ferrero JC
    79. Saunders D
    80. Seeger K
    81. Squares R
    82. Squares S
    83. Takeuchi M
    84. Tekaia F
    85. Turner G
    86. Vazquez de Aldana CR
    87. Weidman J
    88. White O
    89. Woodward J
    90. Yu JH
    91. Fraser C
    92. Galagan JE
    93. Asai K
    94. Machida M
    95. Hall N
    96. Barrell B
    97. Denning DW

    (2005) Aspergillus fumigatus genome sequence

    Publicly available at genome.jgi.doe.gov.

    http://genome.jgi.doe.gov/Aspfu1/Aspfu1.home.html
22. 1. Hane JK
    2. Lowe RG
    3. Solomon PS
    4. Tan KC
    5. Schoch CL
    6. Spatafora JW
    7. Crous PW
    8. Kodira C
    9. Birren BW
    10. Galagan JE
    11. Torriani SF
    12. McDonald BA
    13. Oliver RP

    (2007) Stagonospora nodorum genome sequence

    Publicly available at genome.jgi.doe.gov.

    http://genome.jgi.doe.gov/Stano2/Stano2.home.html
23. 1. Ohm RA
    2. Feau N
    3. Henrissat B
    4. Schoch CL
    5. Horwitz BA
    6. Barry KW
    7. Condon BJ
    8. Copeland AC
    9. Dhillon B
    10. Glaser F
    11. Hesse CN
    12. Kosti I
    13. LaButti K
    14. Lindquist EA
    15. Lucas S
    16. Salamov AA
    17. Bradshaw RE
    18. Ciuffetti L
    19. Hamelin RC
    20. Kema GH
    21. Lawrence C
    22. Scott JA
    23. Spatafora JW
    24. Turgeon BG
    25. de Wit PJ
    26. Zhong S
    27. Goodwin SB
    28. Grigoriev IV

    (2012) Alternaria brassicicola genome sequence

    Publicly available at genome.jgi.doe.gov.

    http://genome.jgi.doe.gov/Altbr1/Altbr1.home.html
24. 1. Manning VA
    2. Pandelova I
    3. Dhillon B
    4. Wilhelm LJ
    5. Goodwin SB
    6. Berlin AM
    7. Figueroa M
    8. Freitag M
    9. Hane JK
    10. Henrissat B
    11. Holman WH
    12. Kodira CD
    13. Martin J
    14. Oliver RP
    15. Robbertse B
    16. Schackwitz W
    17. Schwartz DC
    18. Spatafora JW
    19. Turgeon BG
    20. Yandava C
    21. Young S
    22. Zhou S
    23. Zeng Q
    24. Grigoriev IV
    25. Ma LJ
    26. Ciuffetti LM

    (2013) Pyrenophora tritici-repentis genome sequence

    Publicly available at www.broadinstitute.org.

    https://www.broadinstitute.org/scientific-community/science/projects/fungal-genome-initiative/pyrenophora-genome-project
25. 1. de Wit PJ
    2. van der Burgt A
    3. Okmen B
    4. Stergiopoulos I
    5. Abd-Elsalam KA
    6. Aerts AL
    7. Bahkali AH
    8. Beenen HG
    9. Chettri P
    10. Cox MP
    11. Datema E
    12. de Vries RP
    13. Dhillon B
    14. Ganley AR
    15. Griffiths SA
    16. Guo Y
    17. Hamelin RC
    18. Henrissat B
    19. Kabir MS
    20. Jashni MK
    21. Kema G
    22. Klaubauf S
    23. Lapidus A
    24. Levasseur A
    25. Lindquist E
    26. Mehrabi R
    27. Ohm RA
    28. Owen TJ
    29. Salamov A
    30. Schwelm A
    31. Schijlen E
    32. Sun H
    33. van den Burg HA
    34. van Ham RC
    35. Zhang S
    36. Goodwin SB
    37. Grigoriev IV
    38. Collemare J
    39. Bradshaw RE

    (2012) Dothistroma septosporum genome sequence

    Publicly available at genome.jgi.doe.gov.

    http://genome.jgi.doe.gov/Dotse1/Dotse1.home.html
26. 1. Ohm RA
    2. Feau N
    3. Henrissat B
    4. Schoch CL
    5. Horwitz BA
    6. Barry KW
    7. Condon BJ
    8. Copeland AC
    9. Dhillon B
    10. Glaser F
    11. Hesse CN
    12. Kosti I
    13. LaButti K
    14. Lindquist EA
    15. Lucas S
    16. Salamov AA
    17. Bradshaw RE
    18. Ciuffetti L
    19. Hamelin RC
    20. Kema GH
    21. Lawrence C
    22. Scott JA
    23. Spatafora JW
    24. Turgeon BG
    25. de Wit PJ
    26. Zhong S
    27. Goodwin SB
    28. Grigoriev IV

    (2012) Pseudocercospora fijiensis genome sequence

    Publicly available at ftp.ncbi.nlm.nih.gov.

    ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/340/215/GCA_000340215.1_Mycfi2/
27. 1. Goodwin SB
    2. M'barek SB
    3. Dhillon B
    4. Wittenberg AH
    5. Crane CF
    6. Hane JK
    7. Foster AJ
    8. Van der Lee TA
    9. Grimwood J
    10. Aerts A
    11. Antoniw J
    12. Bailey A
    13. Bluhm B
    14. Bowler J
    15. Bristow J
    16. van der Burgt A
    17. Canto-Canche B
    18. Churchill AC
    19. Conde-Ferraez L
    20. Cools HJ
    21. Coutinho PM
    22. Csukai M
    23. Dehal P
    24. De Wit P
    25. Donzelli B
    26. van de Geest HC
    27. van Ham RC
    28. Hammond-Kosack KE
    29. Henrissat B
    30. Kilian A
    31. Kobayashi AK
    32. Koopmann E
    33. Kourmpetis Y
    34. Kuzniar A
    35. Lindquist E
    36. Lombard V
    37. Maliepaard C
    38. Martins N
    39. Mehrabi R
    40. Nap JP
    41. Ponomarenko A
    42. Rudd JJ
    43. Salamov A
    44. Schmutz J
    45. Schouten HJ
    46. Shapiro H
    47. Stergiopoulos I
    48. Torriani SF
    49. Tu H
    50. de Vries RP
    51. Waalwijk C
    52. Ware SB
    53. Wiebenga A
    54. Zwiers LH
    55. Oliver RP
    56. Grigoriev IV
    57. Kema GH

    (2011) Zymoseptoria tritici genome sequence

    Publicly available at genome.jgi.doe.gov.

    http://genome.jgi.doe.gov/Mycgr3/Mycgr3.home.html
28. 1. de Wit PJ
    2. van der Burgt A
    3. Okmen B
    4. Stergiopoulos I
    5. Abd-Elsalam KA
    6. Aerts AL
    7. Bahkali AH
    8. Beenen HG
    9. Chettri P
    10. Cox MP
    11. Datema E
    12. de Vries RP
    13. Dhillon B
    14. Ganley AR
    15. Griffiths SA
    16. Guo Y
    17. Hamelin RC
    18. Henrissat B
    19. Kabir MS
    20. Jashni MK
    21. Kema G
    22. Klaubauf S
    23. Lapidus A
    24. Levasseur A
    25. Lindquist E
    26. Mehrabi R
    27. Ohm RA
    28. Owen TJ
    29. Salamov A
    30. Schwelm A
    31. Schijlen E
    32. Sun H
    33. van den Burg HA
    34. van Ham RC
    35. Zhang S
    36. Goodwin SB
    37. Grigoriev IV
    38. Collemare J
    39. Bradshaw RE

    (2012) Passalora fulva genome sequence

    Publicly available at genome.jgi.doe.gov.

    http://genome.jgi.doe.gov/Clafu1/Clafu1.home.html
29. 1. Jones L
    2. Riaz S
    3. Morales-Cruz A
    4. Amrine KC
    5. McGuire B
    6. Gubler WD
    7. Walker MA
    8. Cantu D

    (2014) Erysiphe necator genome sequence

    Publicly available at www.ncbi.nlm.nih.gov.

    http://www.ncbi.nlm.nih.gov/Traces/wgs/?val=JNVN01#contigs
30. 1. van Kan JA
    2. Stassen JH
    3. Mosbach A
    4. van der Lee TA
    5. Faino L
    6. Farmer AD
    7. Papasotiriou D
    8. Zhou S
    9. Seidl MF
    10. Cottam E
    11. Edel D
    12. Hahn M
    13. Schwartz DC
    14. Dietrich RA
    15. Widdison S
    16. Scalliet G

    (2016) Botrytis cinerea genome sequence

    Publicly available at fungi.ensembl.org.

    http://fungi.ensembl.org/Botrytis_cinerea/Info/Index
31. 1. Kohler A
    2. Kuo A
    3. Nagy LG
    4. Morin E
    5. Barry KW
    6. Buscot F
    7. Canback B
    8. Choi C
    9. Cichocki N
    10. Clum A
    11. Colpaert J
    12. Copeland A
    13. Costa MD
    14. Dore J
    15. Floudas D
    16. Gay G
    17. Girlanda M
    18. Henrissat B
    19. Herrmann S
    20. Hess J
    21. Hogberg N
    22. Johansson T
    23. Khouja HR
    24. LaButti K
    25. Lahrmann U
    26. Levasseur A
    27. Lindquist EA
    28. Lipzen A
    29. Marmeisse R
    30. Martino E
    31. Murat C
    32. Ngan CY
    33. Nehls U
    34. Plett JM
    35. Pringle A
    36. Ohm RA
    37. Perotto S
    38. Peter M
    39. Riley R
    40. Rineau F
    41. Ruytinx J
    42. Salamov A
    43. Shah F
    44. Sun H
    45. Tarkka M
    46. Tritt A
    47. Veneault-Fourrey C
    48. Zuccaro A
    49. Tunlid A
    50. Grigoriev IV
    51. Hibbett DS
    52. Martin F

    (2015) Oidiodendron maius genome sequence

    Publicly available at genome.jgi.doe.gov.

    http://genome.jgi.doe.gov/Oidma1/Oidma1.home.html
32. 1. Geomyces destructans Sequencing Project
    2. Broad Institute of Harvard and MIT

    (2016) Pseudogymnoascus destructans genome sequence

    Publicly available at www.broadinstitute.org.

    https://www.broadinstitute.org/fungal-genome-initiative/geomyces-destructans-genome-project
33. 1. Dean RA
    2. Talbot NJ
    3. Ebbole DJ
    4. Farman ML
    5. Mitchell TK
    6. Orbach MJ
    7. Thon M
    8. Kulkarni R
    9. Xu JR
    10. Pan H
    11. Read ND
    12. Lee YH
    13. Carbone I
    14. Brown D
    15. Oh YY
    16. Donofrio N
    17. Jeong JS
    18. Soanes DM
    19. Djonovic S
    20. Kolomiets E
    21. Rehmeyer C
    22. Li W
    23. Harding M
    24. Kim S
    25. Lebrun MH
    26. Bohnert H
    27. Coughlan S
    28. Butler J
    29. Calvo S
    30. Ma LJ
    31. Nicol R
    32. Purcell S
    33. Nusbaum C
    34. Galagan JE
    35. Birren BW

    (2005) Magnaporthe oryzae genome sequence

    Publicly available at genome.jgi.doe.gov.

    http://genome.jgi.doe.gov/Maggr1/Maggr1.home.html
34. 1. Berka RM
    2. Grigoriev IV
    3. Otillar R
    4. Salamov A
    5. Grimwood J
    6. Reid I
    7. Ishmael N
    8. John T
    9. Darmond C
    10. Moisan MC
    11. Henrissat B
    12. Coutinho PM
    13. Lombard V
    14. Natvig DO
    15. Lindquist E
    16. Schmutz J
    17. Lucas S
    18. Harris P
    19. Powlowski J
    20. Bellemare A
    21. Taylor D
    22. Butler G
    23. de Vries RP
    24. Allijn IE
    25. van den Brink J
    26. Ushinsky S
    27. Storms R
    28. Powell AJ
    29. Paulsen IT
    30. Elbourne LD
    31. Baker SE
    32. Magnuson J
    33. Laboissiere S
    34. Clutterbuck AJ
    35. Martinez D
    36. Wogulis M
    37. de Leon AL
    38. Rey MW
    39. Tsang A

    (2011) Myceliophthora thermophila genome sequence

    Publicly available at genome.jgi.doe.gov.

    http://genome.jgi.doe.gov/Spoth2/Spoth2.home.html
35. 1. Berka RM
    2. Grigoriev IV
    3. Otillar R
    4. Salamov A
    5. Grimwood J
    6. Reid I
    7. Ishmael N
    8. John T
    9. Darmond C
    10. Moisan MC
    11. Henrissat B
    12. Coutinho PM
    13. Lombard V
    14. Natvig DO
    15. Lindquist E
    16. Schmutz J
    17. Lucas S
    18. Harris P
    19. Powlowski J
    20. Bellemare A
    21. Taylor D
    22. Butler G
    23. de Vries RP
    24. Allijn IE
    25. van den Brink J
    26. Ushinsky S
    27. Storms R
    28. Powell AJ
    29. Paulsen IT
    30. Elbourne LD
    31. Baker SE
    32. Magnuson J
    33. Laboissiere S
    34. Clutterbuck AJ
    35. Martinez D
    36. Wogulis M
    37. de Leon AL
    38. Rey MW
    39. Tsang A

    (2011) Chaetomium globosum genome sequence

    Publicly available at genome.jgi.doe.gov.

    http://genome.jgi.doe.gov/Chagl_1/Chagl_1.home.html
36. 1. Faino L
    2. Seidl MF
    3. Datema E
    4. van den Berg GC
    5. Janssen A
    6. Wittenberg AH
    7. Thomma BP

    (2015) Verticilium dahliae genome sequence

    Publicly available at NCBI Genome (accession no. GCA_000952015.1).

    http://www.ncbi.nlm.nih.gov/genome/?term=GCA_000952015.1
37. 1. O'Connell RJ
    2. Thon MR
    3. Hacquard S
    4. Amyotte SG
    5. Kleemann J
    6. Torres MF
    7. Damm U
    8. Buiate EA
    9. Epstein L
    10. Alkan N
    11. Altmuller J
    12. Alvarado-Balderrama L
    13. Bauser CA
    14. Becker C
    15. Birren BW
    16. Chen Z
    17. Choi J
    18. Crouch JA
    19. Duvick JP
    20. Farman MA
    21. Gan P
    22. Heiman D
    23. Henrissat B
    24. Howard RJ
    25. Kabbage M
    26. Koch C
    27. Kracher B
    28. Kubo Y
    29. Law AD
    30. Lebrun MH
    31. Lee YH
    32. Miyara I
    33. Moore N
    34. Neumann U
    35. Nordstrom K
    36. Panaccione DG
    37. Panstruga R
    38. Place M
    39. Proctor RH
    40. Prusky D
    41. Rech G
    42. Reinhardt R
    43. Rollins JA
    44. Rounsley S
    45. Schardl CL
    46. Schwartz DC
    47. Shenoy N
    48. Shirasu K
    49. Sikhakolli UR
    50. Stuber K
    51. Sukno SA
    52. Sweigard JA
    53. Takano Y
    54. Takahara H
    55. Trail F
    56. van der Does HC
    57. Voll LM
    58. Will I
    59. Young S
    60. Zeng Q
    61. Zhang J
    62. Zhou S
    63. Dickman MB
    64. Schulze-Lefert P
    65. Ver Loren van Themaat E
    66. Ma LJ
    67. Vaillancourt LJ

    (2012) Colletotrichum higginsianum genome sequence

    Publicly available at genome.jgi.doe.gov.

    http://genome.jgi.doe.gov/Colhi1/Colhi1.home.html
38. 1. Colletotrichum Sequencing Project
    2. Broad Institute of Harvard and MIT

    (2016) Colletotrichum graminicola genome sequence

    Publicly available at www.broadinstitute.org.

    https://www.broadinstitute.org/scientific-community/science/projects/fungal-genome-initiative/colletotrichum-genome-project
39. 1. Xiao G
    2. Ying SH
    3. Zheng P
    4. Wang ZL
    5. Zhang S
    6. Xie XQ
    7. Shang Y
    8. St Leger RJ
    9. Zhao GP
    10. Wang C
    11. Feng MG

    (2012) Beauveria bassiana genome sequence

    Publicly available at genome.jgi.doe.gov.

    http://genome.jgi.doe.gov/Beaba1/Beaba1.home.html
40. 1. Ma LJ
    2. van der Does HC
    3. Borkovich KA
    4. Coleman JJ
    5. Daboussi MJ
    6. Di Pietro A
    7. Dufresne M
    8. Freitag M
    9. Grabherr M
    10. Henrissat B
    11. Houterman PM
    12. Kang S
    13. Shim WB
    14. Woloshuk C
    15. Xie X
    16. Xu JR
    17. Antoniw J
    18. Baker SE
    19. Bluhm BH
    20. Breakspear A
    21. Brown DW
    22. Butchko RA
    23. Chapman S
    24. Coulson R
    25. Coutinho PM
    26. Danchin EG
    27. Diener A
    28. Gale LR
    29. Gardiner DM
    30. Goff S
    31. Hammond-Kosack KE
    32. Hilburn K
    33. Hua-Van A
    34. Jonkers W
    35. Kazan K
    36. Kodira CD
    37. Koehrsen M
    38. Kumar L
    39. Lee YH
    40. Li L
    41. Manners JM
    42. Miranda-Saavedra D
    43. Mukherjee M
    44. Park G
    45. Park J
    46. Park SY
    47. Proctor RH
    48. Regev A
    49. Ruiz-Roldan MC
    50. Sain D
    51. Sakthikumar S
    52. Sykes S
    53. Schwartz DC
    54. Turgeon BG
    55. Wapinski I
    56. Yoder O
    57. Young S
    58. Zeng Q
    59. Zhou S
    60. Galagan J
    61. Cuomo CA
    62. Kistler HC
    63. Rep M

    (2010) Fusarium graminearum genome sequence

    Publicly available at genome.jgi.doe.gov.

    http://genome.jgi.doe.gov/Fusgr1/Fusgr1.home.html
41. 1. Gao Q
    2. Jin K
    3. Ying SH
    4. Zhang Y
    5. Xiao G
    6. Shang Y
    7. Duan Z
    8. Hu X
    9. Xie XQ
    10. Zhou G
    11. Peng G
    12. Luo Z
    13. Huang W
    14. Wang B
    15. Fang W
    16. Wang S
    17. Zhong Y
    18. Ma LJ
    19. St Leger RJ
    20. Zhao GP
    21. Pei Y
    22. Feng MG
    23. Xia Y
    24. Wang C

    (2011) Metarhizium acridum genome sequence

    Publicly available at genome.jgi.doe.gov.

    http://genome.jgi.doe.gov/pages/dynamicOrganismDownload.jsf?organism=Metac1
42. 1. Croll D
    2. Zala M
    3. McDonald BA

    (2013) Zymoseptoria tritici variant call file

    Publicly available at fungi.ensembl.org.

    http://fungi.ensembl.org/Zymoseptoria_tritici/Info/Index
43. 1. Farr DF
    2. Rossman AY

    (2016) List of hosts for fungal plant pathogens

    Publicly available at nt.ars-grin.gov.

    http://nt.ars-grin.gov/fungaldatabases/fungushost/FungusHost.cfm
44. 1. Spanu PD
    2. Abbott JC
    3. Amselem J
    4. Burgis TA
    5. Soanes DM
    6. Stüber K
    7. Ver Loren van Themaat E
    8. Brown JK
    9. Butcher SA
    10. Gurr SJ
    11. Lebrun MH
    12. Ridout CJ
    13. Schulze-Lefert P
    14. Talbot NJ
    15. Ahmadinejad N
    16. Ametz C
    17. Barton GR
    18. Benjdia M
    19. Bidzinski P
    20. Bindschedler LV
    21. Both M
    22. Brewer MT
    23. Cadle-Davidson L
    24. Cadle-Davidson MM
    25. Collemare J
    26. Cramer R
    27. Frenkel O
    28. Godfrey D
    29. Harriman J
    30. Hoede C
    31. King BC
    32. Klages S
    33. Kleemann J
    34. Knoll D
    35. Koti PS
    36. Kreplak J
    37. López-Ruiz FJ
    38. Lu X
    39. Maekawa T
    40. Mahanil S
    41. Micali C
    42. Milgroom MG
    43. Montana G
    44. Noir S
    45. O'Connell RJ
    46. Oberhaensli S
    47. Parlange F
    48. Pedersen C
    49. Quesneville H
    50. Reinhardt R
    51. Rott M
    52. Sacristán S
    53. Schmidt SM
    54. Schön M
    55. Skamnioti P
    56. Sommer H
    57. Stephens A
    58. Takahara H
    59. Thordal-Christensen H
    60. Vigouroux M
    61. Wessling R
    62. Wicker T
    63. Panstruga R.

    (2010) Blumeria graminis genome sequence

    Publicly available at genome.jgi.doe.gov.

    http://genome.jgi.doe.gov/Blugr1/Blugr1.home.html
45. 1. Duplessis S
    2. Cuomo CA
    3. Lin YC
    4. Aerts A
    5. Tisserant E
    6. Veneault-Fourrey C
    7. Joly DL
    8. Hacquard S
    9. Amselem J
    10. Cantarel BL
    11. Chiu R
    12. Coutinho PM
    13. Feau N
    14. Field M
    15. Frey P
    16. Gelhaye E
    17. Goldberg J
    18. Grabherr MG
    19. Kodira CD
    20. Kohler A
    21. Kües U
    22. Lindquist EA
    23. Lucas SM
    24. Mago R
    25. Mauceli E
    26. Morin E
    27. Murat C
    28. Pangilinan JL
    29. Park R
    30. Pearson M
    31. Quesneville H
    32. Rouhier N
    33. Sakthikumar S
    34. Salamov AA
    35. Schmutz J
    36. Selles B
    37. Shapiro H
    38. Tanguay P
    39. Tuskan GA
    40. Henrissat B
    41. Van de Peer Y
    42. Rouzé P
    43. Ellis JG
    44. Dodds PN
    45. Schein JE
    46. Zhong S
    47. Hamelin RC
    48. Grigoriev IV
    49. Szabo LJ
    50. Martin F.

    (2011) Melampsora larici-populina genome sequence

    Publicly available at genome.jgi.doe.gov.

    http://genome.jgi.doe.gov/Mellp2_3/Mellp2_3.home.html
46. 1. Martin F
    2. Aerts A
    3. Ahrén D
    4. Brun A
    5. Danchin EG
    6. Duchaussoy F
    7. Gibon J
    8. Kohler A
    9. Lindquist E
    10. Pereda V
    11. Salamov A
    12. Shapiro HJ
    13. Wuyts J
    14. Blaudez D
    15. Buée M
    16. Brokstein P
    17. Canbäck B
    18. Cohen D
    19. Courty PE
    20. Coutinho PM
    21. Delaruelle C
    22. Detter JC
    23. Deveau A
    24. DiFazio S
    25. Duplessis S
    26. Fraissinet-Tachet L
    27. Lucic E
    28. Frey-Klett P
    29. Fourrey C
    30. Feussner I
    31. Gay G
    32. Grimwood J
    33. Hoegger PJ
    34. Jain P
    35. Kilaru S
    36. Labbé J
    37. Lin YC
    38. Legué V
    39. Le Tacon F
    40. Marmeisse R
    41. Melayah D
    42. Montanini B
    43. Muratet M
    44. Nehls U
    45. Niculita-Hirzel H
    46. Oudot-Le Secq MP
    47. Peter M
    48. Quesneville H
    49. Rajashekar B
    50. Reich M
    51. Rouhier N
    52. Schmutz J
    53. Yin T
    54. Chalot M
    55. Henrissat B
    56. Kües U
    57. Lucas S
    58. Van de Peer Y
    59. Podila GK
    60. Polle A
    61. Pukkila PJ
    62. Richardson PM
    63. Rouzé P
    64. Sanders IR
    65. Stajich JE
    66. Tunlid A
    67. Tuskan G
    68. Grigoriev IV.

    (2008) Laccaria bicolor genome sequence

    Publicly available at genome.jgi.doe.gov.

    http://genome.jgi.doe.gov/Lacbi2/Lacbi2.home.html
47. 1. Amselem J
    2. Cuomo CA
    3. van Kan JA
    4. Viaud M
    5. Benito EP
    6. Couloux A
    7. Coutinho PM
    8. de Vries RP
    9. Dyer PS
    10. Fillinger S
    11. Fournier E
    12. Gout L
    13. Hahn M
    14. Kohn L
    15. Lapalu N
    16. Plummer KM
    17. Pradier JM
    18. Quévillon E
    19. Sharon A
    20. Simon A
    21. ten Have A
    22. Tudzynski B
    23. Tudzynski P
    24. Wincker P
    25. Andrew M
    26. Anthouard V
    27. Beever RE
    28. Beffa R
    29. Benoit I
    30. Bouzid O
    31. Brault B
    32. Chen Z
    33. Choquer M
    34. Collémare J
    35. Cotton P
    36. Danchin EG
    37. Da Silva C
    38. Gautier A
    39. Giraud C
    40. Giraud T
    41. Gonzalez C
    42. Grossetete S
    43. Güldener U
    44. Henrissat B
    45. Howlett BJ
    46. Kodira C
    47. Kretschmer M
    48. Lappartient A
    49. Leroch M
    50. Levis C
    51. Mauceli E
    52. Neuvéglise C
    53. Oeser B
    54. Pearson M
    55. Poulain J
    56. Poussereau N
    57. Quesneville H
    58. Rascle C
    59. Schumacher J
    60. Ségurens B
    61. Sexton A
    62. Silva E
    63. Sirven C
    64. Soanes DM
    65. Talbot NJ
    66. Templeton M
    67. Yandava C
    68. Yarden O
    69. Zeng Q
    70. Rollins JA
    71. Lebrun MH
    72. Dickman M

    (2011) Gene expression data for Botrytis cinerea

    Publicly available at urgi.versailles.inra.fr.

    http://urgi.versailles.inra.fr/Data/Transcriptome

1. 1. Smith DR
   2. Chapman MR

   (2010) Economical evolution: microbes reduce the synthetic cost of extracellular proteins

   mBio 1:e00131-10–e00131-18.

   https://doi.org/10.1128/mBio.00131-10

   • PubMed
   • Google Scholar
2. 1. Amselem J
   2. Cuomo CA
   3. van Kan JA
   4. Viaud M
   5. Benito EP
   6. Couloux A
   7. Coutinho PM
   8. de Vries RP
   9. Dyer PS
   10. Fillinger S
   11. Fournier E
   12. Gout L
   13. Hahn M
   14. Kohn L
   15. Lapalu N
   16. Plummer KM
   17. Pradier JM
   18. Quévillon E
   19. Sharon A
   20. Simon A
   21. ten Have A
   22. Tudzynski B
   23. Tudzynski P
   24. Wincker P
   25. Andrew M
   26. Anthouard V
   27. Beever RE
   28. Beffa R
   29. Benoit I
   30. Bouzid O
   31. Brault B
   32. Chen Z
   33. Choquer M
   34. Collémare J
   35. Cotton P
   36. Danchin EG
   37. Da Silva C
   38. Gautier A
   39. Giraud C
   40. Giraud T
   41. Gonzalez C
   42. Grossetete S
   43. Güldener U
   44. Henrissat B
   45. Howlett BJ
   46. Kodira C
   47. Kretschmer M
   48. Lappartient A
   49. Leroch M
   50. Levis C
   51. Mauceli E
   52. Neuvéglise C
   53. Oeser B
   54. Pearson M
   55. Poulain J
   56. Poussereau N
   57. Quesneville H
   58. Rascle C
   59. Schumacher J
   60. Ségurens B
   61. Sexton A
   62. Silva E
   63. Sirven C
   64. Soanes DM
   65. Talbot NJ
   66. Templeton M
   67. Yandava C
   68. Yarden O
   69. Zeng Q
   70. Rollins JA
   71. Lebrun MH
   72. Dickman M

   (2011) Genomic analysis of the necrotrophic fungal pathogens Sclerotinia sclerotiorum and Botrytis cinerea

   PLoS Genetics 7:e1002230.

   https://doi.org/10.1371/journal.pgen.1002230

   • PubMed
   • Google Scholar
3. 1. Bailey BA
   2. Melnick RL
   3. Strem MD
   4. Crozier J
   5. Shao J
   6. Sicher R
   7. Phillips-Mora W
   8. Ali SS
   9. Zhang D
   10. Meinhardt L

   (2014) Differential gene expression by Moniliophthora roreri while overcoming cacao tolerance in the field

   Molecular Plant Pathology 15:711–729.

   https://doi.org/10.1111/mpp.12134

   • PubMed
   • Google Scholar
4. 1. Barrett LG
   2. Kniskern JM
   3. Bodenhausen N
   4. Zhang W
   5. Bergelson J

   (2009) Continua of specificity and virulence in plant host-pathogen interactions: causes and consequences

   New Phytologist 183:513–529.

   https://doi.org/10.1111/j.1469-8137.2009.02927.x

   • PubMed
   • Google Scholar
5. 1. Beg QK
   2. Vazquez A
   3. Ernst J
   4. de Menezes MA
   5. Bar-Joseph Z
   6. Barabási AL
   7. Oltvai ZN

   (2007) Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity

   PNAS 104:12663–12668.

   https://doi.org/10.1073/pnas.0609845104

   • PubMed
   • Google Scholar
6. 1. Blomberg SP
   2. Garland T
   3. Ives AR

   (2003) Testing for phylogenetic signal in comparative data: behavioral traits are more labile

   Evolution 57:717–745.

   https://doi.org/10.1111/j.0014-3820.2003.tb00285.x

   • PubMed
   • Google Scholar
7. 1. Botzman M
   2. Margalit H

   (2011) Variation in global codon usage bias among prokaryotic organisms is associated with their lifestyles

   Genome Biology 12:R109.

   https://doi.org/10.1186/gb-2011-12-10-r109

   • PubMed
   • Google Scholar
8. 1. Bradshaw RE
   2. Guo Y
   3. Sim AD
   4. Kabir MS
   5. Chettri P
   6. Ozturk IK
   7. Hunziker L
   8. Ganley RJ
   9. Cox MP

   (2016) Genome-wide gene expression dynamics of the fungal pathogen Dothistroma septosporum throughout its infection cycle of the gymnosperm host Pinus radiata

   Molecular Plant Pathology 17:210–224.

   https://doi.org/10.1111/mpp.12273

   • PubMed
   • Google Scholar
9. 1. Bremer H
   2. Dennis PP

   (2008) Modulation of chemical composition and other parameters of the cell at different exponential growth rates

   EcoSal Plus 3:.

   https://doi.org/10.1128/ecosal.5.2.3

   • PubMed
   • Google Scholar
10. 1. Britton T
    2. Anderson CL
    3. Jacquet D
    4. Lundqvist S
    5. Bremer K

    (2007) Estimating divergence times in large phylogenetic trees

    Systematic Biology 56:741–752.

    https://doi.org/10.1080/10635150701613783

    • PubMed
    • Google Scholar
11. 1. Buljan M
    2. Frankish A
    3. Bateman A

    (2010) Quantifying the mechanisms of domain gain in animal proteins

    Genome Biology 11:R74.

    https://doi.org/10.1186/gb-2010-11-7-r74

    • PubMed
    • Google Scholar
12. 1. Chen SL
    2. Lee W
    3. Hottes AK
    4. Shapiro L
    5. McAdams HH

    (2004) Codon usage between genomes is constrained by genome-wide mutational processes

    PNAS 101:3480–3485.

    https://doi.org/10.1073/pnas.0307827100

    • PubMed
    • Google Scholar
13. 1. Chen Y
    2. Toffaletti DL
    3. Tenor JL
    4. Litvintseva AP
    5. Fang C
    6. Mitchell TG
    7. McDonald TR
    8. Nielsen K
    9. Boulware DR
    10. Bicanic T
    11. Perfect JR

    (2014) The Cryptococcus neoformans Transcriptome at the site of human meningitis

    mBio 5:e01087-13–01013.

    https://doi.org/10.1128/mBio.01087-13

    • PubMed
    • Google Scholar
14. 1. Conesa A
    2. Götz S
    3. García-Gómez JM
    4. Terol J
    5. Talón M
    6. Robles M

    (2005) Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research

    Bioinformatics 21:3674–3676.

    https://doi.org/10.1093/bioinformatics/bti610

    • PubMed
    • Google Scholar
15. 1. Crick FH

    (1966) Codon--anticodon pairing: the wobble hypothesis

    Journal of Molecular Biology 19:548–555.

    https://doi.org/10.1016/S0022-2836(66)80022-0

    • PubMed
    • Google Scholar
16. 1. Croll D
    2. Zala M
    3. McDonald BA

    (2013) Breakage-fusion-bridge cycles and large insertions contribute to the rapid evolution of accessory chromosomes in a fungal pathogen

    PLoS Genetics 9:e1003567.

    https://doi.org/10.1371/journal.pgen.1003567

    • PubMed
    • Google Scholar
17. 1. de Bekker C
    2. Ohm RA
    3. Loreto RG
    4. Sebastian A
    5. Albert I
    6. Merrow M
    7. Brachmann A
    8. Hughes DP

    (2015) Gene expression during zombie ant biting behavior reflects the complexity underlying fungal parasitic behavioral manipulation

    BMC Genomics 16:620.

    https://doi.org/10.1186/s12864-015-1812-x

    • PubMed
    • Google Scholar
18. 1. Derbyshire M
    2. Denton-Giles M
    3. Hegedus D
    4. Seifbarghy S
    5. Rollins JA
    6. Van Kan JA
    7. Seidl MF
    8. Faino L
    9. Mbengue M
    10. Navaud O
    11. Raffaele S
    12. Hammond-Kosack K
    13. Heard S
    14. Oliver RP

    (2017)

    The complete genome sequence of the phytopathogenic fungus Sclerotinia sclerotiorum reveals insights into the genome architecture of broad host range pathogens

    Genome Biology and Evolution In press.

    • Google Scholar
19. 1. Dodds PN
    2. Rathjen JP

    (2010) Plant immunity: towards an integrated view of plant-pathogen interactions

    Nature Reviews Genetics 11:539–548.

    https://doi.org/10.1038/nrg2812

    • PubMed
    • Google Scholar
20. 1. Dong S
    2. Stam R
    3. Cano LM
    4. Song J
    5. Sklenar J
    6. Yoshida K
    7. Bozkurt TO
    8. Oliva R
    9. Liu Z
    10. Tian M
    11. Win J
    12. Banfield MJ
    13. Jones AM
    14. van der Hoorn RA
    15. Kamoun S

    (2014) Effector specialization in a lineage of the irish potato famine pathogen

    Science 343:552–555.

    https://doi.org/10.1126/science.1246300

    • PubMed
    • Google Scholar
21. 1. dos Reis M
    2. Savva R
    3. Wernisch L

    (2004) Solving the riddle of Codon usage preferences: a test for translational selection

    Nucleic Acids Research 32:5036–5044.

    https://doi.org/10.1093/nar/gkh834

    • PubMed
    • Google Scholar
22. 1. Drummond DA
    2. Wilke CO

    (2008) Mistranslation-induced protein misfolding as a dominant constraint on coding-sequence evolution

    Cell 134:341–352.

    https://doi.org/10.1016/j.cell.2008.05.042

    • PubMed
    • Google Scholar
23. 1. Duplessis S
    2. Cuomo CA
    3. Lin YC
    4. Aerts A
    5. Tisserant E
    6. Veneault-Fourrey C
    7. Joly DL
    8. Hacquard S
    9. Amselem J
    10. Cantarel BL
    11. Chiu R
    12. Coutinho PM
    13. Feau N
    14. Field M
    15. Frey P
    16. Gelhaye E
    17. Goldberg J
    18. Grabherr MG
    19. Kodira CD
    20. Kohler A
    21. Kües U
    22. Lindquist EA
    23. Lucas SM
    24. Mago R
    25. Mauceli E
    26. Morin E
    27. Murat C
    28. Pangilinan JL
    29. Park R
    30. Pearson M
    31. Quesneville H
    32. Rouhier N
    33. Sakthikumar S
    34. Salamov AA
    35. Schmutz J
    36. Selles B
    37. Shapiro H
    38. Tanguay P
    39. Tuskan GA
    40. Henrissat B
    41. Van de Peer Y
    42. Rouzé P
    43. Ellis JG
    44. Dodds PN
    45. Schein JE
    46. Zhong S
    47. Hamelin RC
    48. Grigoriev IV
    49. Szabo LJ
    50. Martin F

    (2011a) Obligate biotrophy features unraveled by the genomic analysis of rust fungi

    PNAS 108:9166–9171.

    https://doi.org/10.1073/pnas.1019315108

    • PubMed
    • Google Scholar
24. 1. Duplessis S
    2. Hacquard S
    3. Delaruelle C
    4. Tisserant E
    5. Frey P
    6. Martin F
    7. Kohler A

    (2011b) Melampsora larici-populina transcript profiling during germination and timecourse infection of poplar leaves reveals dynamic expression patterns associated with virulence and biotrophy

    Molecular Plant-Microbe Interactions 24:808–818.

    https://doi.org/10.1094/MPMI-01-11-0006

    • PubMed
    • Google Scholar
25. 1. Eyre-Walker A

    (1996) Synonymous Codon bias is related to gene length in Escherichia coli: selection for translational accuracy?

    Molecular Biology and Evolution 13:864–872.

    https://doi.org/10.1093/oxfordjournals.molbev.a025646

    • PubMed
    • Google Scholar
26. 1. Farr DF
    2. Rossman AY

    (2016) Fungal databases, systematic mycology and microbiology laboratory, ARS, USDA

    Fungal databases, systematic mycology and microbiology laboratory, ARS, USDA, http://nt.ars-grin.gov/fungaldatabases/.

    http://nt.ars-grin.gov/fungaldatabases/

    • Google Scholar
27. 1. Felsenstein J

    (1985) Phylogenies and the comparative method

    The American Naturalist 125:1–15.

    https://doi.org/10.1086/284325

    • Google Scholar
28. 1. Futuyma DJ
    2. Moreno G

    (1988) The evolution of ecological specialization

    Annual Review of Ecology and Systematics 19:207–233.

    https://doi.org/10.1146/annurev.es.19.110188.001231

    • Google Scholar
29. 1. Gao Q
    2. Jin K
    3. Ying SH
    4. Zhang Y
    5. Xiao G
    6. Shang Y
    7. Duan Z
    8. Hu X
    9. Xie XQ
    10. Zhou G
    11. Peng G
    12. Luo Z
    13. Huang W
    14. Wang B
    15. Fang W
    16. Wang S
    17. Zhong Y
    18. Ma LJ
    19. St Leger RJ
    20. Zhao GP
    21. Pei Y
    22. Feng MG
    23. Xia Y
    24. Wang C

    (2011) Genome sequencing and comparative transcriptomics of the model entomopathogenic fungi Metarhizium anisopliae and M. acridum

    PLoS Genetics 7:e1001264.

    https://doi.org/10.1371/journal.pgen.1001264

    • PubMed
    • Google Scholar
30. 1. Gardin J
    2. Yeasmin R
    3. Yurovsky A
    4. Cai Y
    5. Skiena S
    6. Futcher B

    (2014) Measurement of average decoding rates of the 61 sense codons in vivo

    eLife 3:e03735.

    https://doi.org/10.7554/eLife.03735

    • Google Scholar
31. 1. Gilbert GS
    2. Webb CO

    (2007) Phylogenetic signal in plant pathogen-host range

    PNAS 104:4979–4983.

    https://doi.org/10.1073/pnas.0607968104

    • Google Scholar
32. 1. Guindon S
    2. Dufayard JF
    3. Lefort V
    4. Anisimova M
    5. Hordijk W
    6. Gascuel O

    (2010) New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0

    Systematic Biology 59:307–321.

    https://doi.org/10.1093/sysbio/syq010

    • PubMed
    • Google Scholar
33. 1. Hacquard S
    2. Kracher B
    3. Maekawa T
    4. Vernaldi S
    5. Schulze-Lefert P
    6. Ver Loren van Themaat E

    (2013) Mosaic genome structure of the barley powdery mildew pathogen and conservation of transcriptional programs in divergent hosts

    PNAS 110:E2219–E2228.

    https://doi.org/10.1073/pnas.1306807110

    • PubMed
    • Google Scholar
34. 1. He X
    2. Zhang J

    (2005) Gene complexity and gene duplicability

    Current Biology 15:1016–1021.

    https://doi.org/10.1016/j.cub.2005.04.035

    • PubMed
    • Google Scholar
35. 1. Hedges SB
    2. Marin J
    3. Suleski M
    4. Paymer M
    5. Kumar S

    (2015) Tree of life reveals clock-like speciation and diversification

    Molecular Biology and Evolution 32:835–845.

    https://doi.org/10.1093/molbev/msv037

    • PubMed
    • Google Scholar
36. 1. Hershberg R
    2. Petrov DA

    (2008) Selection on codon bias

    Annual Review of Genetics 42:287–299.

    https://doi.org/10.1146/annurev.genet.42.110807.091442

    • PubMed
    • Google Scholar
37. 1. Hershberg R
    2. Petrov DA

    (2009) General rules for optimal codon choice

    PLoS Genetics 5:e1000556.

    https://doi.org/10.1371/journal.pgen.1000556

    • PubMed
    • Google Scholar
38. 1. Hoops S
    2. Sahle S
    3. Gauges R
    4. Lee C
    5. Pahle J
    6. Simus N
    7. Singhal M
    8. Xu L
    9. Mendes P
    10. Kummer U

    (2006) COPASI--a COmplex PAthway SImulator

    Bioinformatics 22:3067–3074.

    https://doi.org/10.1093/bioinformatics/btl485

    • PubMed
    • Google Scholar
39. 1. Hu X
    2. Xiao G
    3. Zheng P
    4. Shang Y
    5. Su Y
    6. Zhang X
    7. Liu X
    8. Zhan S
    9. Leger RJ
    10. Wang C

    (2014) Trajectory and genomic determinants of fungal-pathogen speciation and host adaptation

    PNAS 111:16796–16801.

    https://doi.org/10.1073/pnas.1412662111

    • PubMed
    • Google Scholar
40. 1. Ingolia NT
    2. Ghaemmaghami S
    3. Newman JR
    4. Weissman JS

    (2009) Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling

    Science 324:218–223.

    https://doi.org/10.1126/science.1168978

    • PubMed
    • Google Scholar
41. 1. Joshi A
    2. Thompson JN

    (1995) Trade-offs and the evolution of host specialization

    Evolutionary Ecology 9:82–92.

    https://doi.org/10.1007/BF01237699

    • Google Scholar
42. 1. Kafri M
    2. Metzl-Raz E
    3. Jona G
    4. Barkai N

    (2016) The cost of protein production

    Cell Reports 14:22–31.

    https://doi.org/10.1016/j.celrep.2015.12.015

    • PubMed
    • Google Scholar
43. 1. Katoh K
    2. Standley DM

    (2013) MAFFT multiple sequence alignment software version 7: improvements in performance and usability

    Molecular Biology and Evolution 30:772–780.

    https://doi.org/10.1093/molbev/mst010

    • PubMed
    • Google Scholar
44. 1. Keck F
    2. Rimet F
    3. Bouchez A
    4. Franc A

    (2016) Phylosignal: an R package to measure, test, and explore the phylogenetic signal

    Ecology and Evolution 6:2774–2780.

    https://doi.org/10.1002/ece3.2051

    • PubMed
    • Google Scholar
45. 1. Kellner R
    2. Bhattacharyya A
    3. Poppe S
    4. Hsu TY
    5. Brem RB
    6. Stukenbrock EH

    (2014) Expression profiling of the wheat pathogen Zymoseptoria tritici reveals genomic patterns of transcription and host-specific regulatory programs

    Genome Biology and Evolution 6:1353–1365.

    https://doi.org/10.1093/gbe/evu101

    • PubMed
    • Google Scholar
46. 1. Kim KT
    2. Jeon J
    3. Choi J
    4. Cheong K
    5. Song H
    6. Choi G
    7. Kang S
    8. Lee YH

    (2016) Kingdom-wide analysis of fungal small secreted proteins (SSPs) reveals their potential role in host association

    Frontiers in Plant Science 7:186.

    https://doi.org/10.3389/fpls.2016.00186

    • PubMed
    • Google Scholar
47. 1. King BC
    2. Waxman KD
    3. Nenni NV
    4. Walker LP
    5. Bergstrom GC
    6. Gibson DM

    (2011) Arsenal of plant cell wall degrading enzymes reflects host preference among plant pathogenic fungi

    Biotechnology for Biofuels 4:4.

    https://doi.org/10.1186/1754-6834-4-4

    • PubMed
    • Google Scholar
48. 1. Krisko A
    2. Copic T
    3. Gabaldón T
    4. Lehner B
    5. Supek F

    (2014) Inferring gene function from evolutionary change in signatures of translation efficiency

    Genome Biology 15:R44.

    https://doi.org/10.1186/gb-2014-15-3-r44

    • PubMed
    • Google Scholar
49. 1. Kubicek CP
    2. Starr TL
    3. Glass NL

    (2014) Plant cell wall-degrading enzymes and their secretion in plant-pathogenic fungi

    Annual Review of Phytopathology 52:427–451.

    https://doi.org/10.1146/annurev-phyto-102313-045831

    • PubMed
    • Google Scholar
50. 1. Li GW
    2. Burkhardt D
    3. Gross C
    4. Weissman JS

    (2014) Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources

    Cell 157:624–635.

    https://doi.org/10.1016/j.cell.2014.02.033

    • PubMed
    • Google Scholar
51. 1. Ma LJ
    2. van der Does HC
    3. Borkovich KA
    4. Coleman JJ
    5. Daboussi MJ
    6. Di Pietro A
    7. Dufresne M
    8. Freitag M
    9. Grabherr M
    10. Henrissat B
    11. Houterman PM
    12. Kang S
    13. Shim WB
    14. Woloshuk C
    15. Xie X
    16. Xu JR
    17. Antoniw J
    18. Baker SE
    19. Bluhm BH
    20. Breakspear A
    21. Brown DW
    22. Butchko RA
    23. Chapman S
    24. Coulson R
    25. Coutinho PM
    26. Danchin EG
    27. Diener A
    28. Gale LR
    29. Gardiner DM
    30. Goff S
    31. Hammond-Kosack KE
    32. Hilburn K
    33. Hua-Van A
    34. Jonkers W
    35. Kazan K
    36. Kodira CD
    37. Koehrsen M
    38. Kumar L
    39. Lee YH
    40. Li L
    41. Manners JM
    42. Miranda-Saavedra D
    43. Mukherjee M
    44. Park G
    45. Park J
    46. Park SY
    47. Proctor RH
    48. Regev A
    49. Ruiz-Roldan MC
    50. Sain D
    51. Sakthikumar S
    52. Sykes S
    53. Schwartz DC
    54. Turgeon BG
    55. Wapinski I
    56. Yoder O
    57. Young S
    58. Zeng Q
    59. Zhou S
    60. Galagan J
    61. Cuomo CA
    62. Kistler HC
    63. Rep M

    (2010) Comparative genomics reveals mobile pathogenicity chromosomes in Fusarium

    Nature 464:367–373.

    https://doi.org/10.1038/nature08850

    • PubMed
    • Google Scholar
52. 1. Marais G
    2. Duret L

    (2001) Synonymous codon usage, accuracy of translation, and gene length in Caenorhabditis elegans

    Journal of Molecular Evolution 52:275–280.

    https://doi.org/10.1007/s002390010155

    • PubMed
    • Google Scholar
53. 1. Maxwell DP

    (1970) Oxalic acid production by sclerotinia sclerotiorum in infected bean and in culture

    Phytopathology 60:1395–1398.

    https://doi.org/10.1094/Phyto-60-1395

    • Google Scholar
54. 1. McDonald BA
    2. Linde C

    (2002) Pathogen population genetics, evolutionary potential, and durable resistance

    Annual Review of Phytopathology 40:349–379.

    https://doi.org/10.1146/annurev.phyto.40.120501.101443

    • PubMed
    • Google Scholar
55. 1. McMullan M
    2. Gardiner A
    3. Bailey K
    4. Kemen E
    5. Ward BJ
    6. Cevik V
    7. Robert-Seilaniantz A
    8. Schultz-Larsen T
    9. Balmuth A
    10. Holub E
    11. van Oosterhout C
    12. Jones JD

    (2015) Evidence for suppression of immunity as a driver for genomic introgressions and host range expansion in races of Albugo Candida, a generalist parasite

    eLife 4:e04550.

    https://doi.org/10.7554/eLife.04550

    • PubMed
    • Google Scholar
56. 1. Menardo F
    2. Praz CR
    3. Wyder S
    4. Ben-David R
    5. Bourras S
    6. Matsumae H
    7. McNally KE
    8. Parlange F
    9. Riba A
    10. Roffler S
    11. Schaefer LK
    12. Shimizu KK
    13. Valenti L
    14. Zbinden H
    15. Wicker T
    16. Keller B

    (2016) Hybridization of powdery mildew strains gives rise to pathogens on novel agricultural crop species

    Nature Genetics 48:201–205.

    https://doi.org/10.1038/ng.3485

    • PubMed
    • Google Scholar
57. 1. Morton CO
    2. Varga JJ
    3. Hornbach A
    4. Mezger M
    5. Sennefelder H
    6. Kneitz S
    7. Kurzai O
    8. Krappmann S
    9. Einsele H
    10. Nierman WC
    11. Rogers TR
    12. Loeffler J

    (2011) The temporal dynamics of differential gene expression in aspergillus fumigatus interacting with human immature dendritic cells in vitro

    PLoS One 6:e16016.

    https://doi.org/10.1371/journal.pone.0016016

    • PubMed
    • Google Scholar
58. 1. O'Connell RJ
    2. Thon MR
    3. Hacquard S
    4. Amyotte SG
    5. Kleemann J
    6. Torres MF
    7. Damm U
    8. Buiate EA
    9. Epstein L
    10. Alkan N
    11. Altmüller J
    12. Alvarado-Balderrama L
    13. Bauser CA
    14. Becker C
    15. Birren BW
    16. Chen Z
    17. Choi J
    18. Crouch JA
    19. Duvick JP
    20. Farman MA
    21. Gan P
    22. Heiman D
    23. Henrissat B
    24. Howard RJ
    25. Kabbage M
    26. Koch C
    27. Kracher B
    28. Kubo Y
    29. Law AD
    30. Lebrun MH
    31. Lee YH
    32. Miyara I
    33. Moore N
    34. Neumann U
    35. Nordström K
    36. Panaccione DG
    37. Panstruga R
    38. Place M
    39. Proctor RH
    40. Prusky D
    41. Rech G
    42. Reinhardt R
    43. Rollins JA
    44. Rounsley S
    45. Schardl CL
    46. Schwartz DC
    47. Shenoy N
    48. Shirasu K
    49. Sikhakolli UR
    50. Stüber K
    51. Sukno SA
    52. Sweigard JA
    53. Takano Y
    54. Takahara H
    55. Trail F
    56. van der Does HC
    57. Voll LM
    58. Will I
    59. Young S
    60. Zeng Q
    61. Zhang J
    62. Zhou S
    63. Dickman MB
    64. Schulze-Lefert P
    65. Ver Loren van Themaat E
    66. Ma LJ
    67. Vaillancourt LJ

    (2012) Lifestyle transitions in plant pathogenic Colletotrichum fungi deciphered by genome and transcriptome analyses

    Nature Genetics 44:1060–1065.

    https://doi.org/10.1038/ng.2372

    • PubMed
    • Google Scholar
59. 1. O'Neill PK
    2. Or M
    3. Erill I

    (2013) scnRCA: a novel method to detect consistent patterns of translational selection in mutationally-biased genomes

    PLoS One 8:e76177.

    https://doi.org/10.1371/journal.pone.0076177

    • PubMed
    • Google Scholar
60. 1. Pagel M

    (1999) Inferring the historical patterns of biological evolution

    Nature 401:877–884.

    https://doi.org/10.1038/44766

    • PubMed
    • Google Scholar
61. 1. Pedersen C
    2. Ver Loren van Themaat E
    3. McGuffin LJ
    4. Abbott JC
    5. Burgis TA
    6. Barton G
    7. Bindschedler LV
    8. Lu X
    9. Maekawa T
    10. Wessling R
    11. Cramer R
    12. Thordal-Christensen H
    13. Panstruga R
    14. Spanu PD

    (2012) Structure and evolution of barley powdery mildew effector candidates

    BMC Genomics 13:694.

    https://doi.org/10.1186/1471-2164-13-694

    • PubMed
    • Google Scholar
62. 1. Peisajovich SG
    2. Garbarino JE
    3. Wei P
    4. Lim WA

    (2010) Rapid diversification of cell signaling phenotypes by modular domain recombination

    Science 328:368–372.

    https://doi.org/10.1126/science.1182376

    • PubMed
    • Google Scholar
63. 1. Perrier A
    2. Peyraud R
    3. Rengel D
    4. Barlet X
    5. Lucasson E
    6. Gouzy J
    7. Peeters N
    8. Genin S
    9. Guidot A

    (2016) Enhanced in planta fitness through adaptive mutations in EfpR, a Dual Regulator of Virulence and Metabolic Functions in the Plant Pathogen Ralstonia solanacearum

    PLoS Pathogens 12:e1006044.

    https://doi.org/10.1371/journal.ppat.1006044

    • PubMed
    • Google Scholar
64. 1. Petersen TN
    2. Brunak S
    3. von Heijne G
    4. Nielsen H

    (2011) SignalP 4.0: discriminating signal peptides from transmembrane regions

    Nature Methods 8:785–786.

    https://doi.org/10.1038/nmeth.1701

    • PubMed
    • Google Scholar
65. 1. Peyraud R
    2. Cottret L
    3. Marmiesse L
    4. Gouzy J
    5. Genin S

    (2016) A resource allocation Trade-Off between virulence and proliferation drives metabolic versatility in the plant pathogen ralstonia solanacearum

    PLoS Pathogens 12:e1005939.

    https://doi.org/10.1371/journal.ppat.1005939

    • PubMed
    • Google Scholar
66. 1. Plotkin JB
    2. Kudla G

    (2011) Synonymous but not the same: the causes and consequences of codon bias

    Nature Reviews Genetics 12:32–42.

    https://doi.org/10.1038/nrg2899

    • PubMed
    • Google Scholar
67. 1. Poulin R
    2. Keeney DB

    (2008) Host specificity under molecular and experimental scrutiny

    Trends in Parasitology 24:24–28.

    https://doi.org/10.1016/j.pt.2007.10.002

    • PubMed
    • Google Scholar
68. 1. Prat Y
    2. Fromer M
    3. Linial N
    4. Linial M

    (2009) Codon usage is associated with the evolutionary age of genes in metazoan genomes

    BMC Evolutionary Biology 9:285.

    https://doi.org/10.1186/1471-2148-9-285

    • PubMed
    • Google Scholar
69. 1. Raffaele S
    2. Kamoun S

    (2012) Genome evolution in filamentous plant pathogens: why bigger can be better

    Nature Reviews Microbiology 10:417–430.

    https://doi.org/10.1038/nrmicro2790

    • PubMed
    • Google Scholar
70. 1. Revell LJ

    (2012) Phytools: an R package for phylogenetic comparative biology (and other things)

    Methods in Ecology and Evolution 3:217–223.

    https://doi.org/10.1111/j.2041-210X.2011.00169.x

    • Google Scholar
71. 1. Rice P
    2. Longden I
    3. Bleasby A

    (2000) EMBOSS: the European molecular biology open software suite

    Trends in Genetics 16:276–277.

    https://doi.org/10.1016/S0168-9525(00)02024-2

    • PubMed
    • Google Scholar
72. 1. Roux F
    2. Voisin D
    3. Badet T
    4. Balagué C
    5. Barlet X
    6. Huard-Chauveau C
    7. Roby D
    8. Raffaele S

    (2014) Resistance to phytopathogens e tutti quanti: placing plant quantitative disease resistance on the map

    Molecular Plant Pathology 15:427–432.

    https://doi.org/10.1111/mpp.12138

    • PubMed
    • Google Scholar
73. 1. Schattner P
    2. Brooks AN
    3. Lowe TM

    (2005) The tRNAscan-SE, snoscan and snoGPS web servers for the detection of tRNAs and snoRNAs

    Nucleic Acids Research 33:W686–W689.

    https://doi.org/10.1093/nar/gki366

    • PubMed
    • Google Scholar
74. 1. Schirawski J
    2. Mannhaupt G
    3. Münch K
    4. Brefort T
    5. Schipper K
    6. Doehlemann G
    7. Di Stasio M
    8. Rössel N
    9. Mendoza-Mendoza A
    10. Pester D
    11. Müller O
    12. Winterberg B
    13. Meyer E
    14. Ghareeb H
    15. Wollenberg T
    16. Münsterkötter M
    17. Wong P
    18. Walter M
    19. Stukenbrock E
    20. Güldener U
    21. Kahmann R

    (2010) Pathogenicity determinants in smut fungi revealed by genome comparison

    Science 330:1546–1548.

    https://doi.org/10.1126/science.1195330

    • PubMed
    • Google Scholar
75. 1. Scott M
    2. Gunderson CW
    3. Mateescu EM
    4. Zhang Z
    5. Hwa T

    (2010) Interdependence of cell growth and gene expression: origins and consequences

    Science 330:1099–1102.

    https://doi.org/10.1126/science.1192588

    • PubMed
    • Google Scholar
76. 1. Shah P
    2. Ding Y
    3. Niemczyk M
    4. Kudla G
    5. Plotkin JB

    (2013) Rate-limiting steps in yeast protein translation

    Cell 153:1589–1601.

    https://doi.org/10.1016/j.cell.2013.05.049

    • PubMed
    • Google Scholar
77. 1. Shah P
    2. Gilchrist MA

    (2011) Explaining complex codon usage patterns with selection for translational efficiency, mutation bias, and genetic drift

    PNAS 108:10231–10236.

    https://doi.org/10.1073/pnas.1016719108

    • PubMed
    • Google Scholar
78. 1. Sharp PM
    2. Li WH

    (1987) The codon adaptation index-a measure of directional synonymous codon usage bias, and its potential applications

    Nucleic Acids Research 15:1281–1295.

    https://doi.org/10.1093/nar/15.3.1281

    • PubMed
    • Google Scholar
79. 1. Spanu PD
    2. Abbott JC
    3. Amselem J
    4. Burgis TA
    5. Soanes DM
    6. Stüber K
    7. Ver Loren van Themaat E
    8. Brown JK
    9. Butcher SA
    10. Gurr SJ
    11. Lebrun MH
    12. Ridout CJ
    13. Schulze-Lefert P
    14. Talbot NJ
    15. Ahmadinejad N
    16. Ametz C
    17. Barton GR
    18. Benjdia M
    19. Bidzinski P
    20. Bindschedler LV
    21. Both M
    22. Brewer MT
    23. Cadle-Davidson L
    24. Cadle-Davidson MM
    25. Collemare J
    26. Cramer R
    27. Frenkel O
    28. Godfrey D
    29. Harriman J
    30. Hoede C
    31. King BC
    32. Klages S
    33. Kleemann J
    34. Knoll D
    35. Koti PS
    36. Kreplak J
    37. López-Ruiz FJ
    38. Lu X
    39. Maekawa T
    40. Mahanil S
    41. Micali C
    42. Milgroom MG
    43. Montana G
    44. Noir S
    45. O'Connell RJ
    46. Oberhaensli S
    47. Parlange F
    48. Pedersen C
    49. Quesneville H
    50. Reinhardt R
    51. Rott M
    52. Sacristán S
    53. Schmidt SM
    54. Schön M
    55. Skamnioti P
    56. Sommer H
    57. Stephens A
    58. Takahara H
    59. Thordal-Christensen H
    60. Vigouroux M
    61. Wessling R
    62. Wicker T
    63. Panstruga R

    (2010) Genome expansion and gene loss in powdery mildew fungi reveal tradeoffs in extreme parasitism

    Science 330:1543–1546.

    https://doi.org/10.1126/science.1194573

    • PubMed
    • Google Scholar
80. 1. Stukenbrock EH
    2. Bataillon T
    3. Dutheil JY
    4. Hansen TT
    5. Li R
    6. Zala M
    7. McDonald BA
    8. Wang J
    9. Schierup MH

    (2011) The making of a new pathogen: insights from comparative population genomics of the domesticated wheat pathogen Mycosphaerella graminicola and its wild sister species

    Genome Research 21:2157–2166.

    https://doi.org/10.1101/gr.118851.110

    • PubMed
    • Google Scholar
81. 1. Tamura K
    2. Stecher G
    3. Peterson D
    4. Filipski A
    5. Kumar S

    (2013) MEGA6: molecular evolutionary genetics analysis version 6.0

    Molecular Biology and Evolution 30:2725–2729.

    https://doi.org/10.1093/molbev/mst197

    • PubMed
    • Google Scholar
82. 1. Tuller T
    2. Carmi A
    3. Vestsigian K
    4. Navon S
    5. Dorfan Y
    6. Zaborske J
    7. Pan T
    8. Dahan O
    9. Furman I
    10. Pilpel Y

    (2010) An evolutionarily conserved mechanism for controlling the efficiency of protein translation

    Cell 141:344–354.

    https://doi.org/10.1016/j.cell.2010.03.031

    • PubMed
    • Google Scholar
83. 1. Vleugels T
    2. Baert J
    3. van Bockstaele E

    (2013) Morphological and pathogenic characterization of genetically diverse sclerotinia Isolates from European Red Clover Crops ( Trifolium Pratense L.)

    Journal of Phytopathology 161:254–262.

    https://doi.org/10.1111/jph.12056

    • Google Scholar
84. 1. Wibberg D
    2. Andersson L
    3. Tzelepis G
    4. Rupp O
    5. Blom J
    6. Jelonek L
    7. Pühler A
    8. Fogelqvist J
    9. Varrelmann M
    10. Schlüter A
    11. Dixelius C

    (2016) Genome analysis of the sugar beet pathogen Rhizoctonia solani AG2-2IIIB revealed high numbers in secreted proteins and cell wall degrading enzymes

    BMC Genomics 17:1.

    https://doi.org/10.1186/s12864-016-2561-1

    • PubMed
    • Google Scholar
85. 1. Wolf YI
    2. Novichkov PS
    3. Karev GP
    4. Koonin EV
    5. Lipman DJ

    (2009) The universal distribution of evolutionary rates of genes and distinct characteristics of eukaryotic genes of different apparent ages

    PNAS 106:7273–7280.

    https://doi.org/10.1073/pnas.0901808106

    • PubMed
    • Google Scholar
86. 1. Woolhouse ME
    2. Gowtage-Sequeria S

    (2005) Host range and emerging and reemerging pathogens

    Emerging Infectious Diseases 11:1842–1847.

    https://doi.org/10.3201/eid1112.050997

    • PubMed
    • Google Scholar
87. 1. Woolhouse ME
    2. Taylor LH
    3. Haydon DT

    (2001) Population biology of multihost pathogens

    Science 292:1109–1112.

    https://doi.org/10.1126/science.1059026

    • PubMed
    • Google Scholar
88. 1. Xiao G
    2. Ying SH
    3. Zheng P
    4. Wang ZL
    5. Zhang S
    6. Xie XQ
    7. Shang Y
    8. St Leger RJ
    9. Zhao GP
    10. Wang C
    11. Feng MG

    (2012) Genomic perspectives on the evolution of fungal entomopathogenicity in Beauveria bassiana

    Scientific Reports 2:483.

    https://doi.org/10.1038/srep00483

    • PubMed
    • Google Scholar
89. 1. Zhang XW
    2. Jia LJ
    3. Zhang Y
    4. Jiang G
    5. Li X
    6. Zhang D
    7. Tang WH

    (2012) In planta stage-specific fungal gene profiling elucidates the molecular strategies of Fusarium graminearum growing inside wheat coleoptiles

    The Plant Cell 24:5159–5176.

    https://doi.org/10.1105/tpc.112.105957

    • PubMed
    • Google Scholar
90. 1. Zhao Z
    2. Liu H
    3. Wang C
    4. Xu JR

    (2013) Comparative analysis of fungal genomes reveals different plant cell wall degrading capacity in fungi

    BMC Genomics 14:274.

    https://doi.org/10.1186/1471-2164-14-274

    • PubMed
    • Google Scholar

Author details

1. Thomas Badet

   LIPM, Université de Toulouse, INRA, CNRS, Castanet-Tolosan, France

   Contribution

   TB, Data curation, Formal analysis, Investigation, Methodology, Writing—review and editing

   Contributed equally with

   Remi Peyraud and Malick Mbengue

   Competing interests

   The authors declare that no competing interests exist.

2. Remi Peyraud

   LIPM, Université de Toulouse, INRA, CNRS, Castanet-Tolosan, France

   Contribution

   RP, Data curation, Formal analysis, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Thomas Badet and Malick Mbengue

   Competing interests

   The authors declare that no competing interests exist.

3. Malick Mbengue

   LIPM, Université de Toulouse, INRA, CNRS, Castanet-Tolosan, France

   Contribution

   MM, Resources, Formal analysis, Investigation, Methodology, Writing—review and editing

   Contributed equally with

   Thomas Badet and Remi Peyraud

   Competing interests

   The authors declare that no competing interests exist.

4. Olivier Navaud

   LIPM, Université de Toulouse, INRA, CNRS, Castanet-Tolosan, France

   Contribution

   ON, Formal analysis, Methodology, Writing—review and editing

   Competing interests

   The authors declare that no competing interests exist.

5. Mark Derbyshire

   Centre for Crop and Disease Management, Department of Environment and Agriculture, Curtin University, Perth, Australia

   Contribution

   MD, Resources, Writing—review and editing

   Competing interests

   The authors declare that no competing interests exist.

6. Richard P Oliver

   Centre for Crop and Disease Management, Department of Environment and Agriculture, Curtin University, Perth, Australia

   Contribution

   RPO, Resources, Writing—review and editing

   Competing interests

   The authors declare that no competing interests exist.

7. Adelin Barbacci

   LIPM, Université de Toulouse, INRA, CNRS, Castanet-Tolosan, France

   Contribution

   AB, Software, Formal analysis, Methodology, Writing—review and editing

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0003-3156-272X

8. Sylvain Raffaele

   LIPM, Université de Toulouse, INRA, CNRS, Castanet-Tolosan, France

   Contribution

   SR, Conceptualization, Formal analysis, Supervision, Funding acquisition, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   sylvain.raffaele@toulouse.inra.fr

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-2442-9632

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