Microbial biodiversity monitoring through the analysis of DNA extracted from environmental samples is increasingly popular because it is perceived as being rapid, cost-effective, and flexible concerning the sample types studied. DNA can be extracted from diverse media before high-throughput sequencing of the prokaryotic 16S rRNA gene is used to characterize the taxonomic diversity and composition of the sample (known as metabarcoding). While sources of bias in metabarcoding methodologies are widely acknowledged, previous studies have focused mainly on the effects of these biases within a single substrate type, and relatively little is known of how these vary across substrates. We investigated the effect of substrate type (water, microbial mats, lake sediments, stream sediments, soil and a mock microbial community) on the relative performance of DNA metabarcoding in parallel with phospholipid fatty acid (PLFA) analysis. Quantitative estimates of the biomass of different taxonomic groups in samples were made through the analysis of PLFAs, and these were compared to the relative abundances of microbial taxa estimated from metabarcoding. Furthermore, we used the PLFA-based quantitative estimates of the biomass to adjust relative abundances of microbial groups determined by metabarcoding to provide insight into how the biomass of microbial taxa from PLFA analysis can improve understanding of microbial communities from environmental DNA samples. We used two sets of PLFA biomarkers that differed in their number of PLFAs to evaluate how PLFA biomarker selection influences biomass estimates. Metabarcoding and PLFA analysis provided significantly different views of bacterial composition, and these differences varied among substrates. We observed the most notable differences for the Gram-negative bacteria, which were overrepresented by metabarcoding in comparison to PLFA analysis. In contrast, the relative biomass and relative sequence abundances aligned reasonably well for Cyanobacteria across the tested freshwater substrates. Adjusting relative abundances of microbial taxa estimated by metabarcoding with PLFA-based quantification estimates of the microbial biomass led to significant changes in the microbial community compositions in all substrates. We recommend including independent estimates of the biomass of microbial groups to increase comparability among metabarcoding libraries from environmental samples, especially when comparing communities associated with different substrates.

通过分析从环境样本中提取的 DNA 进行微生物生物多样性监测越来越受欢迎，因为它被认为快速、经济高效且在研究的样本类型方面具有灵活性。在使用原核 16S rRNA 基因的高通量测序来表征样品的分类多样性和组成（称为元条形码）之前，可以从多种培养基中提取 DNA。虽然元条形码方法中的偏差来源已被广泛承认，但之前的研究主要关注这些偏差在单一基质类型中的影响，而对于这些偏差如何在不同基质中变化知之甚少。我们研究了底物类型（水、微生物垫、湖泊沉积物、河流沉积物、土壤和模拟微生物群落）与磷脂脂肪酸 (PLFA) 分析并行的 DNA 宏条形码的相对性能。通过对 PLFA 的分析，对样本中不同分类群的生物量进行了定量估计，并将这些与通过元条形码估计的微生物分类群的相对丰度进行了比较。此外，我们使用基于 PLFA 的生物量定量估计来调整由元条形码确定的微生物群的相对丰度，以深入了解来自 PLFA 分析的微生物类群的生物量如何提高对环境 DNA 样本中微生物群落的理解。我们使用两组 PLFA 数量不同的 PLFA 生物标志物来评估 PLFA 生物标志物选择如何影响生物量估计。Metabarcoding 和 PLFA 分析提供了显着不同的细菌组成观点，并且这些差异因底物而异。我们观察到革兰氏阴性菌最显着的差异，与 PLFA 分析相比，通过元条形码进行过度表达。相比之下，在测试的淡水基质中，蓝藻的相对生物量和相对序列丰度相当好。使用基于 PLFA 的微生物生物量量化估计调整通过元条形码估计的微生物分类群的相对丰度，导致所有基质中微生物群落组成发生显着变化。我们建议包括对微生物群生物量的独立估计，以增加来自环境样本的元条形码库之间的可比性，