Preparation of functional metagenomic libraries from low biomass samples using METa assembly and their application to capture antibiotic resistance genes.

IF 4.6 2区 生物学 Q1 MICROBIOLOGY
mSystems Pub Date : 2025-09-10 DOI:10.1128/msystems.01039-25
H M Allman, E P Bernate, E Franck, F J Oliaro, E M Hartmann, T S Crofts
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引用次数: 0

Abstract

A significant challenge in the field of microbiology is the functional annotation of novel genes from microbiomes. The increasing pace of sequencing technology development has made solving this challenge in a high-throughput manner even more important. Functional metagenomics offers a sequence-naive and cultivation-independent solution. Unfortunately, most methods for constructing functional metagenomic libraries require large input masses of metagenomic DNA, putting many sample types out of reach. Here, we show that our functional metagenomic library preparation method, METa assembly, can be used to prepare useful libraries from much lower input DNA quantities. Standard methods of functional metagenomic library preparation generally call for 5-60 µg of input metagenomic DNA. We demonstrate that the threshold for input DNA mass can be lowered at least to 30.5 ng, a 3-log decrease from prior art. We prepared functional metagenomic libraries using between 30.5 ng and 100 ng of metagenomic DNA and found that despite their limited input mass, they were sufficient to link MFS transporters lacking substrate-specific annotations to tetracycline resistance and capture a gene encoding a novel GNAT family acetyltransferase that represents a new streptothricin acetyltransferase, satB. Our preparation of functional metagenomic libraries from aquatic samples and a human stool swab demonstrates that METa assembly can be used to prepare functional metagenomic libraries from microbiomes that were previously incompatible with this approach.IMPORTANCEBacterial genes in microbial communities, including those that give resistance to antibiotics, are often so novel that sequencing-based approaches cannot predict their functions. Functional metagenomic libraries offer a high-throughput, sequence-naive solution to this problem, but their use is often held back due to their need for large quantities of metagenomic DNA. We demonstrate that our functional metagenomic library preparation method, METa assembly, can prepare these libraries using as little as ~30 ng of DNA, approximately 1,000-fold less than other methods. We use METa assembly to prepare functional metagenomic libraries from low-biomass aquatic and fecal swab microbiomes and show that they are home to novel tetracycline efflux pumps and a new family of streptothricin resistance gene, respectively. The efficiency of the METa assembly library preparation method makes many otherwise off-limits, low-biomass microbiome samples compatible with functional metagenomics.

METa组装低生物量样品功能宏基因组文库的制备及其在抗生素耐药基因捕获中的应用。
微生物学领域的一个重大挑战是来自微生物组的新基因的功能注释。测序技术发展的步伐越来越快,以高通量的方式解决这一挑战变得更加重要。功能宏基因组学提供了一个序列朴素和培养无关的解决方案。不幸的是,大多数构建功能性宏基因组文库的方法需要大量输入宏基因组DNA,这使得许多样本类型遥不可及。在这里,我们展示了我们的功能宏基因组文库制备方法,METa组装,可以用更低的输入DNA量制备有用的文库。功能宏基因组文库制备的标准方法通常需要输入5-60µg宏基因组DNA。我们证明,输入DNA质量的阈值可以降低到至少30.5 ng,比现有技术降低了3倍。我们使用30.5 ng至100 ng的宏基因组DNA制备了功能性宏基因组文库,发现尽管它们的输入质量有限,但它们足以将缺乏底物特异性注释的MFS转运蛋白与四环素耐药性联系起来,并捕获编码新型GNAT家族乙酰转移酶的基因,该基因代表一种新的链脲霉素乙酰转移酶satB。我们从水生样本和人类粪便拭子中制备的功能宏基因组文库表明,METa组装可以用于从微生物组中制备功能宏基因组文库,而以前这种方法是不兼容的。微生物群落中的细菌基因,包括那些对抗生素产生耐药性的基因,往往是如此新颖,以至于基于测序的方法无法预测它们的功能。功能性宏基因组文库为这一问题提供了高通量、无序列的解决方案,但由于需要大量的宏基因组DNA,它们的使用经常受到阻碍。我们证明了我们的功能宏基因组文库制备方法,METa组装,可以用大约30 ng的DNA制备这些文库,比其他方法少约1000倍。我们利用METa组装方法从低生物量的水生和粪便微生物组中制备了功能宏基因组文库,并表明它们分别是新的四环素外排泵和新的链霉素耐药基因家族的家园。METa组装库制备方法的效率使许多其他禁止的低生物量微生物组样品与功能宏基因组学兼容。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
mSystems
mSystems Biochemistry, Genetics and Molecular Biology-Biochemistry
CiteScore
10.50
自引率
3.10%
发文量
308
审稿时长
13 weeks
期刊介绍: mSystems™ will publish preeminent work that stems from applying technologies for high-throughput analyses to achieve insights into the metabolic and regulatory systems at the scale of both the single cell and microbial communities. The scope of mSystems™ encompasses all important biological and biochemical findings drawn from analyses of large data sets, as well as new computational approaches for deriving these insights. mSystems™ will welcome submissions from researchers who focus on the microbiome, genomics, metagenomics, transcriptomics, metabolomics, proteomics, glycomics, bioinformatics, and computational microbiology. mSystems™ will provide streamlined decisions, while carrying on ASM''s tradition of rigorous peer review.
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