Fisher Matthew C, Aanensen David, de Hoog Sybren, Vanittanakom Nongnuch
Department of Infectious Disease Epidemiology, Imperial College Faculty of Medicine, London W2 1PG, UK.
J Clin Microbiol. 2004 Nov;42(11):5065-9. doi: 10.1128/JCM.42.11.5065-5069.2004.
For eukaryotic pathogens that have low levels of genetic variation, multilocus microsatellite typing (MLMT) offers an accurate and reproducible method of characterizing genetic diversity. Here, we describe the application of an MLMT system to the emerging pathogenic fungus Penicillium marneffei. Isolates used for this study were those held in the culture collections of the Centraalbureau voor Schimmelcultures, Utrecht, The Netherlands, and the Chiang Mai University Department of Microbiology, Chang Mai, Thailand. High genetic diversity and extensive spatial structure were observed among clinical isolates, with the geographical area of origin for each isolate strongly correlating with the occurrence of two deeply divided clades. Within each clade, multilocus linkage associations were highly significant and could be explained by genetically differentiated populations or by an exclusively clonal reproductive mode, or both. Our results show that southeast Asian penicilliosis is caused by a fungus with a complex population genetic structure. Furthermore, this MLMT system generates digital data that can be easily queried against a centrally held database via the internet (http://pmarneffei.multilocus.net/); this provides a powerful epidemiological tool for analyzing the underlying parameters that are responsible for the emergence of P. marneffei in human immunodeficiency virus-positive populations.
对于遗传变异水平较低的真核病原体,多位点微卫星分型(MLMT)提供了一种准确且可重复的遗传多样性表征方法。在此,我们描述了一种MLMT系统在新兴致病真菌马尔尼菲青霉中的应用。本研究使用的菌株保存在荷兰乌得勒支的中央真菌培养中心以及泰国清迈大学微生物学系的菌种保藏中心。临床分离株中观察到了高遗传多样性和广泛的空间结构,每个分离株的地理起源区域与两个深度分化的进化枝的出现密切相关。在每个进化枝内,多位点连锁关联非常显著,这可以通过遗传分化的群体或完全的克隆繁殖模式,或两者来解释。我们的结果表明,东南亚青霉病是由一种具有复杂群体遗传结构的真菌引起的。此外,这种MLMT系统生成的数字数据可以通过互联网轻松地与集中保存的数据库(http://pmarneffei.multilocus.net/)进行比对;这为分析导致马尔尼菲青霉在人类免疫缺陷病毒阳性人群中出现的潜在参数提供了一个强大的流行病学工具。