Department of Plant & Microbial Biology, University of California, Berkeley, CA, 94720, USA,
Microb Ecol. 2013 Nov;66(4):735-41. doi: 10.1007/s00248-013-0266-4. Epub 2013 Jul 24.
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.
基于序列的微生物多样性调查发现,环境中存在着极其多样化的微生物群落。在当前微生物生态学高通量测序(HTS)方法中,一种标准做法是将许多环境样本的扩增子文库在一个测序平台上进行一次处理前,按共同的浓度混合,从而一次性对其组成进行测序。然而,在 HTS 之前,目标分类群的生物量通常是未知的,在这里,我们表明,当样本丰度差异很大时,这种标准做法可能导致群落丰富度和组成的感知偏差。我们对五个大学教学实验室教室的沉降灰尘中的真菌信号进行了调查,其中一个教室用于真菌学课程。非真菌学教室的灰尘中的真菌丰富度和组成非常相似,而真菌学教室则以大量产孢的标本真菌为主,特别是马勃菌,并且根据稀疏曲线和丰富度估计值,似乎总体丰富度较低。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存在的背景真菌。因此,当以相同的深度对样本进行测序时,少数几个类群的高丰度可能会扭曲对丰富度和组成的感知。接下来,我们使用观察数据的计算机模拟操作来证实,当来源丰富时,无论分类群的身份是否不同,都可以通过 HTS 方法识别出独特的特征。最后,讨论了室内真菌的空气生物学。