Department of Bioinformatics, University of British Columbia, Vancouver, British Columbia, Canada.
Department of Cellular and Physiological Sciences, University of British Columbia, Vancouver, British Columbia, Canada.
mSphere. 2024 Jul 30;9(7):e0036024. doi: 10.1128/msphere.00360-24. Epub 2024 Jul 9.
Characterizing microbial communities at high resolution and with absolute quantification is crucial to unravel the complexity and diversity of microbial ecosystems. This can be achieved with PCR assays, which enable highly selective detection and absolute quantification of microbial DNA. However, a major challenge that has hindered PCR applications in microbiome research is the design of highly specific primer sets that exclusively amplify intended targets. Here, we introduce Phylogenetically Unique Primers in python (PUPpy), a fully automated pipeline to design microbe- and group-specific primers within a given microbial community. PUPpy can be executed from a user-friendly graphical user interface, or two simple terminal commands, and it only requires coding sequence files of the community members as input. PUPpy-designed primers enable the detection of individual microbes and quantification of absolute microbial abundance in defined communities below the strain level. We experimentally evaluated the performance of PUPpy-designed primers using two bacterial communities as benchmarks. Each community comprises 10 members, exhibiting a range of genetic similarities that spanned from different phyla to substrains. PUPpy-designed primers also enable the detection of groups of bacteria in an undefined community, such as the detection of a gut bacterial family in a complex stool microbiota sample. Taxon-specific primers designed with PUPpy showed 100% specificity to their intended targets, without unintended amplification, in each community tested. Lastly, we show the absolute quantification of microbial abundance using PUPpy-designed primers in droplet digital PCR, benchmarked against 16S rRNA and shotgun sequencing. Our data shows that PUPpy-designed microbe-specific primers can be used to quantify substrain-level absolute counts, providing more resolved and accurate quantification in defined communities than short-read 16S rRNA and shotgun sequencing.
Profiling microbial communities at high resolution and with absolute quantification is essential to uncover hidden ecological interactions within microbial ecosystems. Nevertheless, achieving resolved and quantitative investigations has been elusive due to methodological limitations in distinguishing and quantifying highly related microbes. Here, we describe Phylogenetically Unique Primers in python (PUPpy), an automated computational pipeline to design taxon-specific primers within defined microbial communities. Taxon-specific primers can be used to selectively detect and quantify individual microbes and larger taxa within a microbial community. PUPpy achieves substrain-level specificity without the need for computationally intensive databases and prioritizes user-friendliness by enabling both terminal and graphical user interface applications. Altogether, PUPpy enables fast, inexpensive, and highly accurate perspectives into microbial ecosystems, supporting the characterization of bacterial communities in both in vitro and complex microbiota settings.
以高分辨率和绝对定量的方式描述微生物群落对于揭示微生物生态系统的复杂性和多样性至关重要。这可以通过聚合酶链式反应（PCR）检测来实现，该方法能够高度选择性地检测和绝对定量微生物 DNA。然而，阻碍 PCR 在微生物组研究中应用的一个主要挑战是设计高度特异性的引物，这些引物只能扩增预期的目标。在这里，我们引入了 Phylogenetically Unique Primers in python（PUPpy），这是一个完全自动化的管道，用于在给定的微生物群落中设计微生物和群体特异性引物。PUPpy 可以通过用户友好的图形用户界面或两个简单的终端命令执行，它只需要群落成员的编码序列文件作为输入。PUPpy 设计的引物能够检测单个微生物，并在低于菌株水平的定义群落中定量绝对微生物丰度。我们使用两个细菌群落作为基准实验评估了 PUPpy 设计的引物的性能。每个群落由 10 个成员组成，表现出从不同门到亚菌株的遗传相似性范围。PUPpy 设计的引物还能够检测未定义群落中的细菌群体，例如在复杂粪便微生物群样本中检测肠道细菌家族。在每个测试的群落中，使用 PUPpy 设计的分类特异性引物对其预期目标具有 100%的特异性，没有非预期的扩增。最后，我们展示了使用 PUPpy 设计的引物在液滴数字 PCR 中进行微生物丰度的绝对定量，与 16S rRNA 和 shotgun 测序相基准。我们的数据表明，PUPpy 设计的微生物特异性引物可用于量化亚菌株水平的绝对计数，在定义的群落中提供比短读 16S rRNA 和 shotgun 测序更具分辨率和更准确的定量结果。
以高分辨率和绝对定量的方式描述微生物群落对于揭示微生物生态系统中的隐藏生态相互作用至关重要。然而，由于在区分和量化高度相关的微生物方面存在方法学限制，实现分辨率和定量研究一直具有挑战性。在这里，我们描述了 Phylogenetically Unique Primers in python（PUPpy），这是一个用于在定义的微生物群落中设计分类特异性引物的自动化计算管道。分类特异性引物可用于选择性地检测和定量微生物群落中的单个微生物和更大的分类群。PUPpy 在不需要计算密集型数据库的情况下实现亚菌株水平的特异性，并通过支持终端和图形用户界面应用程序来优先考虑用户友好性。总的来说，PUPpy 能够快速、廉价且高度准确地了解微生物生态系统，支持在体外和复杂微生物群环境中对细菌群落进行特征描述。
