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. 2013 Apr 26:13:91.
doi: 10.1186/1471-2180-13-91.

Comprehensive annotation of secondary metabolite biosynthetic genes and gene clusters of Aspergillus nidulans, A. fumigatus, A. niger and A. oryzae

Affiliations

Comprehensive annotation of secondary metabolite biosynthetic genes and gene clusters of Aspergillus nidulans, A. fumigatus, A. niger and A. oryzae

Diane O Inglis et al. BMC Microbiol. .

Abstract

Background: Secondary metabolite production, a hallmark of filamentous fungi, is an expanding area of research for the Aspergilli. These compounds are potent chemicals, ranging from deadly toxins to therapeutic antibiotics to potential anti-cancer drugs. The genome sequences for multiple Aspergilli have been determined, and provide a wealth of predictive information about secondary metabolite production. Sequence analysis and gene overexpression strategies have enabled the discovery of novel secondary metabolites and the genes involved in their biosynthesis. The Aspergillus Genome Database (AspGD) provides a central repository for gene annotation and protein information for Aspergillus species. These annotations include Gene Ontology (GO) terms, phenotype data, gene names and descriptions and they are crucial for interpreting both small- and large-scale data and for aiding in the design of new experiments that further Aspergillus research.

Results: We have manually curated Biological Process GO annotations for all genes in AspGD with recorded functions in secondary metabolite production, adding new GO terms that specifically describe each secondary metabolite. We then leveraged these new annotations to predict roles in secondary metabolism for genes lacking experimental characterization. As a starting point for manually annotating Aspergillus secondary metabolite gene clusters, we used antiSMASH (antibiotics and Secondary Metabolite Analysis SHell) and SMURF (Secondary Metabolite Unknown Regions Finder) algorithms to identify potential clusters in A. nidulans, A. fumigatus, A. niger and A. oryzae, which we subsequently refined through manual curation.

Conclusions: This set of 266 manually curated secondary metabolite gene clusters will facilitate the investigation of novel Aspergillus secondary metabolites.

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Figures

Figure 1
Figure 1
Genomic context of the predicted An03g05680 cluster of A. niger viewed with the Sybil multiple genome browser. Boundary predictions for A. niger CBS 513.88 species identifies predicted clusters in A. niger ATCC 1015, A. acidus and A. brasiliensis by matching orthologous protein clusters in Sybil. The red bar delineates the manually predicted cluster boundary based on cluster synteny between 2 A. niger strains and 2 additional Aspergillus species. The blue bar indicates the extent of the SMURF cluster prediction and the green bar indicates the antiSMASH-predicted boundaries.
Figure 2
Figure 2
A. nidulans AN3497 gene cluster predicted based of gene expression analysis of Andersen et al. 2013. Red bar indicates manually predicted cluster boundary (AN3490-AN3497) based on expression pattern and aligned with orthologous clusters of A. versicolor and A. sydowii. Blue bar indicates SMURF boundary prediction (AN3491-AN3506) and green bar indicates the antiSMASH-predicted boundary (AN3485-AN3503).
Figure 3
Figure 3
Conserved cluster synteny between the gliotoxin cluster of A. fumigatus and the orthologous cluster of Neosartorya fischeri. The predicted gene cluster is indicated with a red bar. The left border of the Afu6g09650 cluster shows a small increase in intergenic distance while the right border shows a large change in intergenic distance. Both borders are examples of interspecies cluster synteny. Red bar indicates experimentally determined cluster boundary (Afu6g09630 - Afu6g09740). Blue bar indicates SMURF boundary prediction (Afu6g09580 - Afu6g09740) and green bar indicates the antiSMASH-predicted boundary (Afu6g09520 - Afu6g09745).

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