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Microarray hybridization analysis of light‐dependent gene expression in Penicillium chrysogenum identifies bZIP transcription factor PcAtfA
Author(s) -
Wolfers Simon,
Kamerewerd Jens,
Nowrousian Minou,
Sigl Claudia,
Zadra Ivo,
Kürnsteiner Hubert,
Kück Ulrich,
Bloemendal Sandra
Publication year - 2015
Publication title -
journal of basic microbiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.58
H-Index - 54
eISSN - 1521-4028
pISSN - 0233-111X
DOI - 10.1002/jobm.201400588
Subject(s) - biology , penicillium chrysogenum , gene , microarray analysis techniques , transcription factor , velvet , in silico , genetics , gene expression , complementation , microarray , transcription (linguistics) , mutant , microbiology and biotechnology , chemistry , linguistics , philosophy , organic chemistry
The fungal velvet complex is a light‐dependent master regulator of secondary metabolism and development in the major penicillin producer, Penicillium chrysogenum . However, the light‐dependent mechanism is unclear. To identify velvet‐dependent transcriptional regulators that show light‐regulated expression, we performed microarray hybridizations with RNA isolated from P. chrysogenum ΔPcku70 cultures grown under 13 different long‐term, light‐dependent growth conditions. We compared these expression data to data from two velvet complex deletion mutants; one lacked a subunit of the velvet complex (ΔPcvelA), and the other lacked a velvet‐associated protein (ΔPclaeA). We sought to identify genes that were up‐regulated in light, but down‐regulated in ΔPcvelA and ΔPclaeA. We identified 148 co‐regulated genes that displayed this regulatory pattern. In silico analyses of the co‐regulated genes identified six proteins with fungal‐specific transcription factor domains. Among these, we selected the bZIP transcription factor, PcAtfA, for functional characterization in deletion and complementation strains. Our data clearly indicates that PcAtfA governs spore germination. This comparative analysis of different microarray hybridization data sets provided results that may be useful for identifying genes for future functional analyses.