Improving gut virome comparisons using predicted phage host information.

IF 5 2区 生物学 Q1 MICROBIOLOGY
mSystems Pub Date : 2025-04-08 DOI:10.1128/msystems.01364-24
Michael Shamash, Anshul Sinha, Corinne F Maurice
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引用次数: 0

Abstract

The human gut virome is predominantly made up of bacteriophages (phages), viruses that infect bacteria. Metagenomic studies have revealed that phages in the gut are highly individual specific and dynamic. These features make it challenging to perform meaningful cross-study comparisons. While several taxonomy frameworks exist to group phages and improve these comparisons, these strategies provide little insight into the potential effects phages have on their bacterial hosts. Here, we propose the use of predicted phage host families (PHFs) as a functionally relevant, qualitative unit of phage classification to improve these cross-study analyses. We first show that bioinformatic predictions of phage hosts are accurate at the host family level by measuring their concordance to Hi-C sequencing-based predictions in human and mouse fecal samples. Next, using phage host family predictions, we determined that PHFs reduce intra- and interindividual ecological distances compared to viral contigs in a previously published cohort of 10 healthy individuals, while simultaneously improving longitudinal virome stability. Lastly, by reanalyzing a previously published metagenomics data set with >1,000 samples, we determined that PHFs are prevalent across individuals and can aid in the detection of inflammatory bowel disease-specific virome signatures. Overall, our analyses support the use of predicted phage hosts in reducing between-sample distances and providing a biologically relevant framework for making between-sample virome comparisons.

Importance: The human gut virome consists mainly of bacteriophages (phages), which infect bacteria and show high individual specificity and variability, complicating cross-study comparisons. Furthermore, existing taxonomic frameworks offer limited insight into their interactions with bacterial hosts. In this study, we propose using predicted phage host families (PHFs) as a higher-level classification unit to enhance functional cross-study comparisons. We demonstrate that bioinformatic predictions of phage hosts align with Hi-C sequencing results at the host family level in human and mouse fecal samples. We further show that PHFs reduce ecological distances and improve virome stability over time. Additionally, reanalysis of a large metagenomics data set revealed that PHFs are widespread and can help identify disease-specific virome patterns, such as those linked to inflammatory bowel disease.

利用预测噬菌体宿主信息改进肠道病毒组比较。
人类肠道病毒组主要由噬菌体(噬菌体)组成,噬菌体是感染细菌的病毒。元基因组研究表明,肠道中的噬菌体具有高度的个体特异性和动态性。这些特点使得进行有意义的交叉研究比较具有挑战性。虽然有几种分类框架可以对噬菌体进行分组并改进这些比较,但这些策略对噬菌体对细菌宿主的潜在影响提供的洞察力甚微。在这里,我们建议使用预测的噬菌体宿主家族(PHF)作为噬菌体分类的功能相关定性单位,以改进这些交叉研究分析。首先,我们通过测量人类和小鼠粪便样本中噬菌体宿主的生物信息学预测与基于 Hi-C 测序的预测之间的一致性,证明生物信息学预测的噬菌体宿主在宿主家族水平上是准确的。接下来,利用噬菌体宿主家族预测,我们确定 PHF 与之前发表的 10 个健康个体队列中的病毒等位基因相比,减少了个体内和个体间的生态距离,同时提高了病毒组的纵向稳定性。最后,通过重新分析以前发表的、包含超过 1000 个样本的元基因组学数据集,我们确定 PHFs 在不同个体中普遍存在,有助于检测炎症性肠病特异性病毒组特征。总之,我们的分析支持使用预测的噬菌体宿主来减少样本间的距离,并为进行样本间病毒组比较提供一个生物学相关的框架:人类肠道病毒组主要由噬菌体(phage)组成,噬菌体感染细菌,具有高度的个体特异性和变异性,使得跨研究比较变得复杂。此外,现有的分类框架对噬菌体与细菌宿主的相互作用提供的洞察力有限。在这项研究中,我们建议使用预测的噬菌体宿主家族(PHFs)作为更高层次的分类单元,以加强功能性交叉研究比较。我们证明,生物信息学预测的噬菌体宿主与人类和小鼠粪便样本中宿主家族水平的 Hi-C 测序结果一致。我们还进一步证明,PHFs 可以减少生态距离,并随着时间的推移提高病毒组的稳定性。此外,对大型元基因组学数据集的重新分析表明,PHFs 广泛存在,有助于识别疾病特异性病毒组模式,如与炎症性肠病相关的病毒组模式。
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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