TaxiBGC: a Taxonomy-Guided Approach for Profiling Experimentally Characterized Microbial Biosynthetic Gene Clusters and Secondary Metabolite Production Potential in Metagenomes.
TaxiBGC: a Taxonomy-Guided Approach for Profiling Experimentally Characterized Microbial Biosynthetic Gene Clusters and Secondary Metabolite Production Potential in Metagenomes.
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DOI：
10.1128/msystems.00925-22
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发表时间：
2022-12-20
期刊：
影响因子：
6.4
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中科院分区：
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--
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微生物基因组中的生物合成基因簇（BGC）编码具有生物活性的次生代谢物（SM），在微生物与微生物以及宿主与微生物的相互作用中发挥重要作用。鉴于 SM 的生物学意义以及当前对微生物组代谢功能的浓厚兴趣，从高通量宏基因组数据中公正地鉴定 BGC 可以为微生物群落复杂的化学生态学提供新的见解。目前用于从鸟枪法宏基因组预测 BGC 的可用工具有一些局限性，包括需要计算要求较高的读取组装、预测 BGC 类别的范围较窄以及不提供 SM 产品。为了克服这些限制，我们开发了分类学引导的生物合成基因簇识别（TaxiBGC），这是一种命令行工具，用于通过首先查明可能含有 BGC 的微生物物种来预测宏基因组中实验表征的 BGC（并推断其已知的 SM）。我们在各种模拟宏基因组上对 TaxiBGC 进行了基准测试，结果表明，与通过将测序读数映射到 BGC 基因（平均 F1 得分，0.49；平均 PPV 得分，0.41）直接识别 BGC 相比，我们的分类引导方法可以预测 BGC，其性能大大提高（平均 F1 得分，0.56；平均 PPV 得分，0.80）。接下来，通过将 TaxiBGC 应用到人类微生物组计划的 2,650 个宏基因组和各种病例对照肠道微生物组研究中，我们能够将 BGC（及其 SM）与不同的人体部位和多种疾病（包括克罗恩病和肝硬化）联系起来。总之，TaxiBGC 提供了一个计算机平台来预测实验表征的 BGC 及其在宏基因组数据中的 SM 生产潜力，同时展示了相对于现有技术的重要优势。重要性 目前用于从宏基因组测序数据中识别 BGC 的生物信息学工具在预测能力或易用性方面受到限制，即使是面向计算的研究人员也是如此。我们提出了一个名为 TaxiBGC 的自动化计算管道，它通过首先考虑微生物物种来源来预测鸟枪法宏基因组中实验表征的 BGC（并推断其已知的 SM）。通过对模拟宏基因组的严格基准测试技术，我们表明 TaxiBGC 比现有方法具有显着优势。当在数千个人类微生物组样本上演示 TaxiBGC 时，我们将编码细菌素的 BGC 与不同的人体部位和疾病联系起来，从而阐明了此类抗生素在维持整个人体微生物生态系统稳定性方面可能的新作用。此外，我们首次报告了多种病理学之间共享的肠道微生物 BGC 关联。最终，我们希望我们的工具能够促进未来对不同生态位和病理学微生物群落化学生态学的研究。
Biosynthetic gene clusters (BGCs) in microbial genomes encode bioactive secondary metabolites (SMs), which can play important roles in microbe-microbe and host-microbe interactions. Given the biological significance of SMs and the current profound interest in the metabolic functions of microbiomes, the unbiased identification of BGCs from high-throughput metagenomic data could offer novel insights into the complex chemical ecology of microbial communities. Currently available tools for predicting BGCs from shotgun metagenomes have several limitations, including the need for computationally demanding read assembly, predicting a narrow breadth of BGC classes, and not providing the SM product. To overcome these limitations, we developed taxonomy-guided identification of biosynthetic gene clusters (TaxiBGC), a command-line tool for predicting experimentally characterized BGCs (and inferring their known SMs) in metagenomes by first pinpointing the microbial species likely to harbor them. We benchmarked TaxiBGC on various simulated metagenomes, showing that our taxonomy-guided approach could predict BGCs with much-improved performance (mean F1 score, 0.56; mean PPV score, 0.80) compared with directly identifying BGCs by mapping sequencing reads onto the BGC genes (mean F1 score, 0.49; mean PPV score, 0.41). Next, by applying TaxiBGC on 2,650 metagenomes from the Human Microbiome Project and various case-control gut microbiome studies, we were able to associate BGCs (and their SMs) with different human body sites and with multiple diseases, including Crohn’s disease and liver cirrhosis. In all, TaxiBGC provides an in silico platform to predict experimentally characterized BGCs and their SM production potential in metagenomic data while demonstrating important advantages over existing techniques. IMPORTANCE Currently available bioinformatics tools to identify BGCs from metagenomic sequencing data are limited in their predictive capability or ease of use to even computationally oriented researchers. We present an automated computational pipeline called TaxiBGC, which predicts experimentally characterized BGCs (and infers their known SMs) in shotgun metagenomes by first considering the microbial species source. Through rigorous benchmarking techniques on simulated metagenomes, we show that TaxiBGC provides a significant advantage over existing methods. When demonstrating TaxiBGC on thousands of human microbiome samples, we associate BGCs encoding bacteriocins with different human body sites and diseases, thereby elucidating a possible novel role of this antibiotic class in maintaining the stability of microbial ecosystems throughout the human body. Furthermore, we report for the first time gut microbial BGC associations shared among multiple pathologies. Ultimately, we expect our tool to facilitate future investigations into the chemical ecology of microbial communities across diverse niches and pathologies.
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