DNA Sequence-Based Approach for Classifying the Mold Status of Buildings
DNA Sequence-Based Approach for Classifying the Mold Status of Buildings
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基于 DNA 序列的建筑物霉菌状态分类方法
DOI：
10.1021/acs.est.0c03904
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发表时间：
2020
影响因子：
11.4
通讯作者：
Peccia, Jordan
中科院分区：
文献类型：
--
作者：
Hegarty, Bridget;Pan, Annabelle;Haverinen-Shaughnessy, Ulla;Shaughnessy, Richard;Peccia, Jordan
建筑物中的潮湿或水损害以及人类暴露在由此产生的霉菌生长中是一个始终存在的公共卫生问题。这项研究提供了定量证据，表明已知霉菌生长的房屋的空气真菌生态不同于没有潮湿、水损害或可见霉菌史的房屋的正常空气真菌生态(没有霉菌)。来自美国11个城市的室内空气和室外空气中的沉降尘以及有霉菌生长的建筑材料的直接样本在美国境内干燥、温带和大陆性气候地区的家庭中进行了检测。基于真菌核糖体RNA编码基因内部转录间隔区序列的群落分析表明，经检查员验证有水损害和可见霉菌的房屋中的尘埃与没有潮湿、水损害或可见霉菌史的房屋中的尘埃之间的真菌生态存在一致且可量化的差异。这些差异包括霉变房屋与未霉变房屋的尘土中的群落丰富度较低(PADJ=0.01)，以及霉变房屋与未霉变房屋之间存在明显的群落分类结构(ANOSIM，R=0.15，P=0.001)。我们从子囊菌门、担子菌门和接合菌中鉴定出11个子囊菌在霉菌中高度浓缩，14个分类群在无霉菌中高度浓缩。在所考虑的所有三个气候区域中，霉变房屋和无霉变房屋之间的室内空气差异是显著的。这些发霉和未发霉房屋尘埃样本之间的明显但复杂的差异被用来训练基于机器学习的模型，以对房屋的霉菌状态进行分类。该模型能够准确地对100%的霉变房屋和90%的无霉变房屋进行分类。将基于DNA的真菌生态学与先进的计算方法相结合，可以用来准确地对住宅中是否存在霉菌生长进行分类，帮助做出检查和补救决策，并有可能减少室内有害微生物的暴露。
Dampness or water damage in buildings and human exposure to the resultant mold growth is an ever-present public health concern. This study provides quantitative evidence that the airborne fungal ecology of homes with known mold growth (“moldy”) differs from the normal airborne fungal ecology of homes with no history of dampness, water damage, or visible mold (“no mold”). Settled dust from indoor air and outdoor air and direct samples from building materials with mold growth were examined in homes from 11 cities across dry, temperate, and continental climate regions within the United States. Community analysis based on the sequence of the internal transcribed spacer region of fungal ribosomal RNA encoding genes demonstrated consistent and quantifiable differences between the fungal ecology of settled dust in homes with inspector-verified water damage and visible mold versus the settled dust of homes with no history of dampness, water damage, or visible mold. These differences include lower community richness (padj= 0.01) in the settled dust of moldy homes versus no mold homes, as well as distinct community taxonomic structures between moldy and no mold homes (ANOSIM, R = 0.15,p= 0.001). We identified 11Ascomycotataxa that were more highly enriched in moldy homes and 14 taxa fromAscomycota,Basidiomycota,andZygomycotathat were more highly enriched in no mold homes. The indoor air differences between moldy versus no mold homes were significant for all three climate regions considered. These distinct but complex differences between settled dust samples from moldy and no homes were used to train a machine learning-based model to classify the mold status of a home. The model was able to accurately classify 100% of moldy homes and 90% of no mold homes. The integration of DNA-based fungal ecology with advanced computational approaches can be used to accurately classify the presence of mold growth in homes, assist with inspection and remediation decisions, and potentially lead to reduced exposure to hazardous microbes indoors.