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A Unique Signal Distorts the Perception of Species Richness and Composition in High-Throughput Sequencing Surveys of Microbial Communities: a Case Study of Fungi in Indoor Dust

机译:在微生物群落高通量测序调查中一个独特的信号扭曲了物种丰富度和成分的感知:室内灰尘中真菌的案例研究

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摘要

Sequence-based surveys of microorganisms in varied environments have found extremely diverse assemblages. A standard practice in current high-throughput sequence (HTS) approaches in microbial ecology is to sequence the composition of many environmental samples at once by pooling amplicon libraries at a common concentration before processing on one run of a sequencing platform. Biomass of the target taxa, however, is not typically determined prior to HTS, and here, we show that when abundances of the samples differ to a large degree, this standard practice can lead to a perceived bias in community richness and composition. Fungal signal in settled dust of five university teaching laboratory classrooms, one of which was used for a mycology course, was surveyed. The fungal richness and composition in the dust of the nonmycology classrooms were remarkably similar to each other, while the mycology classroom was dominated by abundantly sporulating specimen fungi, particularly puffballs, and appeared to have a lower overall richness based on rarefaction curves and richness estimators. The fungal biomass was three to five times higher in the mycology classroom than the other classrooms, indicating that fungi added to the mycology classroom swamped the background fungi present in indoor air. Thus, the high abundance of a few taxa can skew the perception of richness and composition when samples are sequenced to an even depth. Next, we used in silico manipulations of the observed data to confirm that a unique signature can be identified with HTS approaches when the source is abundant, whether or not the taxon identity is distinct. Lastly, aerobiology of indoor fungi is discussed.Electronic supplementary materialThe online version of this article (doi:10.1007/s00248-013-0266-4) contains supplementary material, which is available to authorized users.
机译:在不同环境中对微生物进行的基于序列的调查发现了极其多样的组合。在微生物生态学中,当前高通量序列(HTS)方法的标准做法是,在一次测序平台上进行处理之前,通过以相同的浓度合并扩增子文库,立即对许多环境样品的组成进行测序。但是,目标分类单元的生物量通常在HTS之前不会确定,在这里,我们表明,当样品的丰度差异很大时,这种标准做法会导致社区的丰富程度和组成方面出现明显的偏差。调查了五个大学教学实验室教室中沉降尘埃中的真菌信号,其中一个用于真菌学课程。非真菌学教室尘埃中的真菌丰富度和组成彼此非常相似,而真菌学教室则以大量散布的标本真菌(尤其是马勃菌)为主导,并且根据稀疏度曲线和丰富度估计值,其总体丰富度较低。真菌学教室的真菌生物量是其他教室的三到五倍,这表明添加到真菌学教室的真菌淹没了室内空气中的背景真菌。因此,当样本被测序到均匀深度时,几个分类单元的高丰度可能会使对丰富度和成分的感知产生偏差。接下来,我们使用计算机对观察到的数据进行操作,以确认当来源丰富时,无论分类群身份是否不同,都可以使用HTS方法识别唯一的签名。最后,讨论了室内真菌的生物学。电子补充材料本文的在线版本(doi:10.1007 / s00248-013-0266-4)包含补充材料,授权用户可以使用。

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