The GC and window-averaged DNA curvature profile of secondary metabolite gene cluster in Aspergillus fumigatus genome
The GC and window-averaged DNA curvature profile of secondary metabolite gene cluster in Aspergillus fumigatus genome
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DOI：
10.1007/s00253-008-1638-4
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发表时间：
2008-10-01
影响因子：
5
通讯作者：
Miyano, Satoru
中科院分区：
文献类型：
--
作者：
Do, Jin Hwan;Miyano, Satoru
丝状真菌产生大量复杂的次生代谢物，包括烟曲霉，一种侵袭性曲霉病的主要诱导剂。真菌次生代谢产物基因簇的鉴定是通过基因破坏和克隆等重组技术从遗传学和生物化学角度研究真菌次生代谢的基础。大多数次生代谢物基因簇的预测方法严重依赖于同源性搜索。然而，基于同源性的方法对未知或新的基因簇有内在的局限性。通过对烟曲霉基因组中26个次生代谢物基因簇的GC和窗平均DNA曲率谱分析，探讨了烟曲霉次生代谢物基因簇的潜在保守特征。15个次级代谢基因簇在窗口平均DNA曲率谱上呈现保守模式，即含有次级代谢特征基因（如聚酮合成酶、非核糖体肽合成酶和/或二甲基烯丙基色氨酸合成酶）的DNA区域的窗口平均DNA曲率值小于0.18，且这些DNA区域至少为20 kb。40%具有这种保守模式的次级代谢物基因簇与转录因子LaeA的严格调控有关。本研究结果可用于真菌其他次生代谢物基因簇的鉴定，特别是受LaeA或其他与LaeA功能相似的蛋白严重调控的次生代谢物基因簇。
An immense variety of complex secondary metabolites is produced by filamentous fungi including Aspergillus fumigatus, a main inducer of invasive aspergillosis. The identification of fungal secondary metabolite gene cluster is essential for the characterization of fungal secondary metabolism in terms of genetics and biochemistry through recombinant technologies such as gene disruption and cloning. Most of the prediction methods for secondary metabolite gene cluster severely depend on homology searches. However, homology-based approach has intrinsic limitation to unknown or novel gene cluster. We analyzed the GC and window-averaged DNA curvature profile of 26 secondary metabolite gene clusters in the A. fumigatus genome to find out potential conserved features of secondary metabolite gene cluster. Fifteen secondary metabolite gene clusters showed a conserved pattern in window-averaged DNA curvature profile, that is, the DNA regions including secondary metabolic signature genes such as polyketide synthase, nonribosomal peptide synthase, and/or dimethylallyl tryptophan synthase consisted of window-averaged DNA curvature values lower than 0.18 and these DNA regions were at least 20 kb. Forty percent of secondary metabolite gene clusters with this conserved pattern were related to severe regulation by a transcription factor, LaeA. Our result could be used for identification of other fungal secondary metabolite gene clusters, especially for secondary metabolite gene cluster that is severely regulated by LaeA or other proteins with similar function to LaeA.