Microbial network inference for longitudinal microbiome studies with LUPINE.

IF 13.8 1区生物学 Q1 MICROBIOLOGY Microbiome Pub Date : 2025-03-03 DOI:10.1186/s40168-025-02041-w

Saritha Kodikara, Kim-Anh Lê Cao

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Abstract

Background: The microbiome is a complex ecosystem of interdependent taxa that has traditionally been studied through cross-sectional studies. However, longitudinal microbiome studies are becoming increasingly popular. These studies enable researchers to infer taxa associations towards the understanding of coexistence, competition, and collaboration between microbes across time. Traditional metrics for association analysis, such as correlation, are limited due to the data characteristics of microbiome data (sparse, compositional, multivariate). Several network inference methods have been proposed, but have been largely unexplored in a longitudinal setting.

Results: We introduce LUPINE (LongitUdinal modelling with Partial least squares regression for NEtwork inference), a novel approach that leverages on conditional independence and low-dimensional data representation. This method is specifically designed to handle scenarios with small sample sizes and small number of time points. LUPINE is the first method of its kind to infer microbial networks across time, while considering information from all past time points and is thus able to capture dynamic microbial interactions that evolve over time. We validate LUPINE and its variant, LUPINE_single (for single time point analysis) in simulated data and four case studies, where we highlight LUPINE's ability to identify relevant taxa in each study context, across different experimental designs (mouse and human studies, with or without interventions, and short or long time courses). To detect changes in the networks across time and groups or in response to external disturbances, we used different metrics to compare the inferred networks.

Conclusions: LUPINE is a simple yet innovative network inference methodology that is suitable for, but not limited to, analysing longitudinal microbiome data. The R code and data are publicly available for readers interested in applying these new methods to their studies. Video Abstract.

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背景：微生物组是一个由相互依存的类群组成的复杂生态系统，传统上通过横断面研究对其进行研究。然而，纵向微生物组研究正变得越来越流行。这些研究使研究人员能够推断分类群之间的关联，从而了解微生物之间在不同时期的共存、竞争和协作。由于微生物组数据的数据特征（稀疏、组成复杂、多变量），传统的关联分析指标（如相关性）受到限制。目前已经提出了几种网络推断方法，但在纵向环境中基本上还没有得到探索：我们介绍了 LUPINE（LongitUdinal modelling with Partial least squares regression for NEtwork inference），这是一种利用条件独立性和低维数据表示的新方法。这种方法专门用于处理样本量小、时间点数量少的情况。LUPINE 是同类方法中第一种跨时间推断微生物网络的方法，同时考虑了过去所有时间点的信息，因此能够捕捉到随时间演变的动态微生物相互作用。我们在模拟数据和四个案例研究中验证了 LUPINE 及其变体 LUPINE_single（用于单个时间点分析），在这些案例研究中，我们强调了 LUPINE 在不同实验设计（小鼠和人体研究、干预或不干预、短时间或长时间程）的每个研究环境中识别相关类群的能力。为了检测网络在不同时间、不同组别或外部干扰下的变化，我们使用了不同的指标来比较推断出的网络：LUPINE是一种简单而创新的网络推断方法，适用于但不限于分析纵向微生物组数据。R代码和数据已公开，有兴趣的读者可将这些新方法应用于自己的研究。视频摘要。

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来源期刊

Microbiome MICROBIOLOGY-

CiteScore

21.90

自引率

2.60%

发文量

198

审稿时长

4 weeks

期刊介绍： Microbiome is a journal that focuses on studies of microbiomes in humans, animals, plants, and the environment. It covers both natural and manipulated microbiomes, such as those in agriculture. The journal is interested in research that uses meta-omics approaches or novel bioinformatics tools and emphasizes the community/host interaction and structure-function relationship within the microbiome. Studies that go beyond descriptive omics surveys and include experimental or theoretical approaches will be considered for publication. The journal also encourages research that establishes cause and effect relationships and supports proposed microbiome functions. However, studies of individual microbial isolates/species without exploring their impact on the host or the complex microbiome structures and functions will not be considered for publication. Microbiome is indexed in BIOSIS, Current Contents, DOAJ, Embase, MEDLINE, PubMed, PubMed Central, and Science Citations Index Expanded.