Role of genomics and RNA-seq in studies of fungal virulence

Current Fungal Infection Reports

Volumn 6, Issue 4, 2012, Pages 267-274

  Riccombeni, Alessandro ^a   Butler, Geraldine ^a  

^a UNIVERSITY COLLEGE DUBLIN   (Ireland)

Author keywords

Aspergillus; Bioinformatics; Candida; ChIP seq; Cryptococcus; Dermatophytes; Genomics; Next generation sequencing; RNA seq

Indexed keywords

ARTICLE; ASPERGILLUS; CANDIDA; CANDIDA ALBICANS; CANDIDA PARAPSILOSIS; CRYPTOCOCCUS; CRYPTOCOCCUS GATTII; CRYPTOCOCCUS NEOFORMANS; DATA ANALYSIS; FUNGAL GENETICS; FUNGAL VIRULENCE; GENOMICS; NONHUMAN; RNA SEQUENCE;

EID: 84886722186     PISSN: 19363761     EISSN: 1936377X     Source Type: Journal    
DOI: 10.1007/s12281-012-0104-z     Document Type: Article

References (97)

1. Nagalakshmi U, Wang Z, Waern K, et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science. 2008;320:1344-9.
2. Mortazavi A, Williams BA, McCue K, et al. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5:621-8.
3. Parkhomchuk D, Borodina T, Amstislavskiy V, et al. Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic Acids Res. 2009;37:e123.
4. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11:R106.
5. Blankenberg D, Gordon A, Von Kuster G, et al. Manipulation of FASTQ data with Galaxy. Bioinformatics. 2010;26:1783-5.
6. Wang X, Wu Z, Zhang X. Isoform abundance inference provides a more accurate estimation of gene expression levels in RNA-seq. J Bioinforma Comput Biol. 2010;8 Suppl 1:177-92.
7. •• Trapnell C, Roberts A, Goff L, et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012;7:562-78. Description of the Tuxedo pipeline and its application for de novo transcriptome assembly, differential gene expression and visualization of results.
8. Goffeau A, Barrell BG, Bussey H, et al. Life with 6000 genes. Science. 1996;274:546-67.
9. Jones T, Federspiel NA, Chibana H, et al. The diploid genome sequence of Candida albicans. Proc Natl Acad Sci U S A. 2004;101:7329-34.
10. Butler G, Rasmussen MD, Lin MF, et al. Evolution of pathogenicity and sexual reproduction in eight Candida genomes. Nature. 2009;459:657-62.
11. Nierman WC, Pain A, Anderson MJ, et al. Genomic sequence of the pathogenic and allergenic filamentous fungus Aspergillus fumigatus. Nature. 2005;438:1151-6.
12. Pel HJ, de Winde JH, Archer DB, et al. Genome sequencing and analysis of the versatile cell factory Aspergillus niger. CBS 513.88. Nat Biotechnol. 2007;25:221-31.
13. Loftus BJ, Fung E, Roncaglia P, et al. The genome of the basidiomycetous yeast and human pathogen Cryptococcus neoformans. Science. 2005;307:1321-4.
14. Abadio AK, Kioshima ES, Teixeira MM, et al. Comparative genomics allowed the identification of drug targets against human fungal pathogens. BMC Genomics. 2011;12:75.
15. Cheng SC, Joosten LA, Kullberg BJ, et al. Interplay between Candida albicans and the mammalian innate host defense. Infect Immun. 2012;80:1304-13.
16. • Silva S, Negri M, Henriques M, et al. Candida glabrata, Candida parapsilosis and Candida tropicalis: biology, epidemiology, pathogenicity and antifungal resistance. FEMS Microbiol Rev. 2012;36:2880-305. Review of biology of Candida albicans, C. parapsilosis and C. Glabrata, including recent information about epidemiology and methods for laboratory identification.
17. Jackson AP, Gamble JA, Yeomans T, et al. Comparative genomics of the fungal pathogens Candida dubliniensis and Candida albicans. Genome Res. 2009;19:2231-44.
18. • Riccombeni A, Vidanes G, Proux-Wéra E, et al. Sequence and analysis of the genome of the pathogenic yeast Candida orthopsilosis. PLoS One. 2012;7:e35750. Recent example of hybrid de novo genome assembly, combining Sanger, 454 and Illumina sequencing technologies.
19. Pfaller MA, Diekema DJ. Epidemiology of invasive mycoses in North America. Crit Rev Microbiol. 2010;36:1-53.
20. Tavanti A, Davidson AD, Gow NA, et al. Candida orthopsilosis and Candida metapsilosis spp. nov. to replace Candida parapsilosis groups II and III. J Clin Microbiol. 2005;43:284-92.
21. Gomez-Lopez A, Alastruey-Izquierdo A, Rodriguez D, et al. Prevalence and susceptibility profile of Candida metapsilosis and Candida orthopsilosis: results from population-based surveillance of candidemia in Spain. Antimicrob Agents Chemother. 2008;52:1506-9.
22. Tay ST, Na SL, Chong J. Molecular differentiation and antifungal susceptibilities of Candida parapsilosis isolated from patients with bloodstream infections. J Med Microbiol. 2009;58:185-91.
23. Zordan RE, Galgoczy DJ, Johnson AD. Epigenetic properties of white-opaque switching in Candida albicans are based on a self-sustaining transcriptional feedback loop. Proc Natl Acad Sci U S A. 2006;1032:12807-12.
24. Zhang A, Petrov KO, Hyun ER, et al. The Tlo proteins are stoichiometric components of Candida albicans mediator anchored via the Med3 subunit. Eukaryot Cell. 2012;11:874-84.
25. Silva AP, Miranda IM, Guida A, et al. Transcriptional profiling of azole-resistant Candida parapsilosis strains. Antimicrob Agents Chemother. 2011;55:3546-56.
26. • Achterman RR, White TC. A foot in the door for dermatophyte research. PLoS Pathog. 2012;8:e1002564. Description of ongoing experiments in genome sequencing and transcriptional profiling of dermatophyte species.
27. •• Burmester A, Shelest E, Glöckner G, et al. Comparative and functional genomics provide insights into the pathogenicity of dermatophytic fungi. Genome Biol. 2011;12:R7. Example of integration of RNA-seq, secretome analysis and comparative genomics in the analysis of virulence in dermatophytes.
28. Roemer T, Jiang B, Davison J, et al. Large-scale essential gene identification in Candida albicans and applications to antifungal drug discovery. Mol Microbiol. 2003;50:167-81.
29. Hu W, Sillaots S, Lemieux S, et al. Essential gene identification and drug target prioritization in Aspergillus fumigatus. PLoS Pathog. 2007;3:e24.
30. Buurman ET, Westwater C, Hube B, et al. Molecular analysis of CaMnt1p, a mannosyl transferase important for adhesion and virulence of Candida albicans. Proc Natl Acad Sci U S A. 1998;95:7670-5.
31. Jensen-Pergakes KL, Kennedy MA, Lees ND, et al. Sequencing, disruption, and characterization of the Candida albicans sterol methyltransferase (ERG6) gene: drug susceptibility studies in erg6 mutants. Antimicrob Agents Chemother. 1998;42:1160-7.
32. Williams CH, Arscott LD, Müller S, et al. Thioredoxin reductase two modes of catalysis have evolved. Eur J Biochem. 2000;267:6110-7.
33. Peñalva MA, Arst Jr HN. Regulation of gene expression by ambient pH in filamentous fungi and yeasts. Microbiol Mol Biol Rev. 2002;66:426-46.
34. Davis D, Edwards Jr JE, Mitchell AP, Ibrahim AS. Candida albicans RIM101 pH response pathway is required for host-pathogen interactions. Infect Immun. 2000;10:5953-9.
35. Wagener J, Echtenacher B, Rohde M, et al. The putative alpha-1,2-mannosyltransferase AfMnt1 of the opportunistic fungal pathogen Aspergillus fumigatus is required for cell wall stability and full virulence. Eukaryot Cell. 2008;7:1661-73.
36. Paul SM, Mytelka DS, Dunwiddie CT, et al. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nat Rev Drug Discov. 2010;9:203-14.
37. Louis E. Saccharomyces cerevisiae: gene annotation and genome variability, state of the art through comparative genomics. Methods Mol Biol. 2011;759:31-40.
38. Liti G, Schacherer J. The rise of yeast population genomics. C R Biol. 2011;334:612-9.
39. • Bruno VM, Wang Z, Marjani SL, et al. Comprehensive annotation of the transcriptome of the human fungal pathogen Candida albicans using RNA-seq. Genome Res. 2010;20:1451-8. Example of the application of strand-specific RNA-seq.
40. • Tuch BB, Mitrovich QM, Homann OR, et al. The transcriptomes of two heritable cell types illuminate the circuit governing their differentiation. PLoS Genet. 2010;6:e1001070. Characterization of an epigenetic switch in C. albicans using RNA-seq, and identification of nTARs.
41. •• Nobile CJ, Fox EP, Nett JE, et al. A recently evolved transcriptional network controls biofilm development in Candida albicans. Cell. 2012;148:126-38. Outstanding example of integrated analysis, combining animal models, microarrays, RNA-seq, ChIP/chip and computational molecular evolution to describe the core regulatory network for biofilm formation in C. albicans.
42. Harriott MM, Noverr MC. Importance of Candida-bacterial polymicrobial biofilms in disease. Trends Microbiol. 2011;19:557-63.
43. Sudbery PE. Growth of Candida albicans hyphae. Nat Rev Microbiol. 2011;9:737-48.
44. Martin R, Wächtler B, Schaller M, et al. Host-pathogen interactions and virulence-associated genes during Candida albicans oral infections. Int J Med Microbiol. 2011;301:417-22.
45. Andes D, Nett J, Oschel P, et al. Development and characterization of an in vivo central venous catheter Candida albicans biofilm model. Infect Immun. 2004;72:6023-31.
46. Nett J, Andes D. Candida albicans biofilm development, modeling a host-pathogen interaction. Curr Opin Microbiol. 2006;9:340-5.
47. •• Tierney L, Linde J, Müller S, et al. An interspecies regulatory network inferred from simultaneous RNA-seq of Candida albicans invading innate immune cells. Front Microbiol. 2012;3:85. Example of using RNA-seq to profile both host and pathogen response to infection.
48. Diniz SN, Nomizo R, Cisalpino PS, et al. PTX3 function as an opsonin for the dectin-1-dependent internalization of zymosan by macrophages. J Leukoc Biol. 2004;75:649-56.
49. Feng Q, Zhang Y. The NuRD complex: linking histone modification to nucleosome remodeling. Curr Top Microbiol Immunol. 2003;274:269-90.
50. Lu X, Kovalev GI, Chang H, et al. Inactivation of NuRD component Mta2 causes abnormal T cell activation and lupus-like autoimmune disease in mice. J Biol Chem. 2008;283:13825-33.
51. Altwasser R, Linde J, Buyko E, et al. Genome-wide scale-free network inference for Candida albicans. Front Microbiol. 2012;3:51.
52. • Guida A, Lindstädt C, Maguire SL, et al. Using RNA-seq to determine the transcriptional landscape and the hypoxic response of the pathogenic yeast Candida parapsilosis. BMC Genomics. 2011;12:628. First report of RNA-seq analysis in C. parapsilosis, including characterization of the hypoxic response.
53. Ernst JF, Tielker D. Responses to hypoxia in fungal pathogens. Cell Microbiol. 2009;11:183-90.
54. Rossignol T, Ding C, Guida A, et al. Correlation between biofilm formation and the hypoxic response in Candida parapsilosis. Eukaryot Cell. 2009;8:550-9.
55. Synnott JM, Guida A, Mulhern-Haughey S, et al. Regulation of the hypoxic response in Candida albicans. Eukaryot Cell. 2010;9:1734-46.
56. Vik A, Rine J. Upc2p and Ecm22p, dual regulators of sterol biosynthesis in Saccharomyces cerevisiae. Mol Cell Biol. 2001;21:6395-405.
57. Marr KA, Carter RA, Crippa F, et al. Epidemiology and outcome of mould infections in hematopoietic stem cell transplant recipients. Clin Infect Dis. 2002;34:909-17.
58. Morgan J, Wannemuehler KA, Marr KA, et al. Incidence of invasive aspergillosis following hematopoietic stem cell and solid organ transplantation: interim results of a prospective multicenter surveillance program. Med Mycol. 2005;43 Suppl 1:S49-58.
59. Bennett JW, Klich M. Mycotoxins. Clin Microbiol Rev. 2003;16:497-516.
60. Paterson RR, Lima N. Toxicology of mycotoxins. EXS. 2010;100:31-63.
61. Yu J, Chang PK, Ehrlich KC, et al. Clustered pathway genes in aflatoxin biosynthesis. Appl Environ Microbiol. 2004;70:1253-62.
62. Yu J, Fedorova ND, Montalbano BG, et al. Tight control of mycotoxin biosynthesis gene expression in Aspergillus flavus by temperature as revealed by RNA-Seq. FEMS Microbiol Lett. 2011;322:145-9.
63. O'Brian GR, Georgianna DR, Wilkinson JR, et al. The effect of elevated temperature on gene transcription and aflatoxin biosynthesis. Mycologia. 2007;99:232-9.
64. • Gibbons JG, Beauvais A, Beau R, et al. Global transcriptome changes underlying colony growth in the opportunistic human pathogen Aspergillus fumigatus. Eukaryot Cell. 2012;11:68-78. Using RNA-seq to determine the transcriptional profile of Aspergillus biofilms.
65. Loussert C, Schmitt C, Prevost MC, et al. In vivo biofilm composition of Aspergillus fumigatus. Cell Microbiol. 2010;12:405-10.
66. Machida M, Asai K, Sano M, et al. Genome sequencing and analysis of Aspergillus oryzae. Nature. 2005;438:1157-61.
67. Wang B, Guo G, Wang C, et al. Survey of the transcriptome of Aspergillus oryzae via massively parallel mRNA sequencing. Nucleic Acids Res. 2010;38:5075-87.
68. Garcia-Hermoso D, Janbon G, Dromer F. Epidemiological evidence for dormant Cryptococcus neoformans infection. J Clin Microbiol. 1999;37:3204-9.
69. Brizendine KD, Baddley JW, Pappas PG. Pulmonary cryptococcosis. Semin Respir Crit Care Med. 2011;32:727-34.
70. Martinez LR, Casadevall A. Cryptococcus neoformans biofilm formation depends on surface support and carbon source and reduces fungal cell susceptibility to heat, cold, and UV light. Appl Environ Microbiol. 2007;73:4592-601.
71. Chang YC, Miller GF, Kwon-Chung KJ. Importance of a developmentally regulated pheromone receptor of Cryptococcus neoformans for virulence. Infect Immun. 2003;71:4953-60.
72. Doering TL. How sweet it is! Cell wall biogenesis and polysaccharide capsule formation in Cryptococcus neoformans. Annu Rev Microbiol. 2009;63:223-47.
73. Rivera J, Feldmesser M, Cammer M, Casadevall A. Organ-dependent variation of capsule thickness in Cryptococcus neoformans during experimental murine infection. Infect Immun. 1998;66:5027-30.
74. •• Haynes BC, Skowyra ML, Spencer SJ, et al. Toward an integrated model of capsule regulation in Cryptococcus neoformans. PLoS Pathog. 2011;7:e1002411. An example of integrating RNA-seq with ChIP-seq to identify genes directly involved in capsule regulation.
75. Koutelou E, Hirsch CL, Dent SY. Multiple faces of the SAGA complex. Curr Opin Cell Biol. 2010;22:374-82.
76. Jung WH, Sham A, White R, et al. Iron regulation of the major virulence factors in the AIDS-associated pathogen Cryptococcus neoformans. PLoS Biol. 2006;4:e410.
77. Carriconde F, Gilgado F, Arthur I, et al. Clonality and α-a recombination in the Australian Cryptococcus gattii VGII population - an emerging outbreak in Australia. PLoS One. 2011;6:e16936.
78. Galanis E, Hoang L, Kibsey P, et al. Clinical presentation, diagnosis and management of Cryptococcus gattii cases: lessons learned from British Columbia. Can J Infect Dis Med Microbiol. 2009;20:23-8.
79. Bovers M, Hagen F, Boekhout T. Diversity of the Cryptococcus neoformans-Cryptococcus gattii species complex. Rev Iberoam Micol. 2008;25:S4-S12.
80. Chaturvedi V, Nierman WC. Cryptococcus gattii comparative genomics and transcriptomics: a NIH/NIAID white paper. Mycopathologia. 2012;173:367-73.
81. •• Thompson JF, Milos PM. The properties and applications of single-molecule DNA sequencing. Genome Biol. 2011;12:217. Extensive review of next-generation sequencing, describing benefits, drawbacks and applications of the main technologies.
82. Rizzo JM, Buck MJ. Key principles and clinical applications of next-generation DNA sequencing. Cancer Prev Res (Phila). 2012;5:887-900.
83. Levin JZ, Yassour M, Adiconis X, et al. Comprehensive comparative analysis of strand-specific RNA sequencing methods. Nat Methods. 2010;7:709-15.
84. Hansen KD, Irizarry RA, Wu Z. Removing technical variability in RNA-seq data using conditional quantile normalization. Biostatistics. 2012;13:204-16.
85. Hardcastle TJ, Kelly KA. baySeq: empirical bayesian methods for identifying differential expression in sequence count data. BMC Bioinforma. 2010;11:422.
86. Wang L, Feng Z, Wang X, et al. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics. 2010;26:136-8.
87. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139-40.
88. Brown JA, Sherlock G, Myers CL, et al. Global analysis of gene function in yeast by quantitative phenotypic profiling. Mol Syst Biol. 2006;2:2006.0001.
89. Li H, Ruan J, Durbin R. Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res. 2008;18:1851-8.
90. Jiang H, Wong WH. SeqMap: mapping massive amount of oligonucleotides to the genome. Bioinformatics. 2008;15:2395-6.
91. Jiang H, Wong WH. Statistical inferences for isoform expression in RNA-Seq. Bioinformatics. 2009;25:1026-32.
92. Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004;3:Article3
93. Lo K, Gottardo R. Flexible empirical bayes models for differential gene expression. Bioinformatics. 2007;23:328-35.
94. Li R, Li Y, Kristiansen K, Wang J. SOAP: short oligonucleotide alignment program. Bioinformatics. 2008;24:713-4.
95. Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci USA. 2003;100:9440-5.
96. Kent WJ. BLAT - the BLAST-like alignment tool. Genome Res. 2002;12:656-64.
97. Zhang M, Gish W. Improved spliced alignment from an information theoretic approach. Bioinformatics. 2006;22:13-20.