Microbial network inference for longitudinal microbiome studies with LUPINE

被引:1
作者
Kodikara, Saritha [1 ]
Le Cao, Kim-Anh [1 ]
机构
[1] Univ Melbourne, Sch Math & Stat, Melbourne Integrat Genom, Parkville, Vic 3052, Australia
基金
英国医学研究理事会;
关键词
Longitudinal; Network; 16S; Partial correlation; GUT MICROBIOTA; REGRESSION;
D O I
10.1186/s40168-025-02041-w
中图分类号
Q93 [微生物学];
学科分类号
071005 ; 100705 ;
摘要
BackgroundThe 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.ResultsWe 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.ConclusionsLUPINE 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.DkUAt2ZH6pgY76Tc7tgdsnVideo AbstractConclusionsLUPINE 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.DkUAt2ZH6pgY76Tc7tgdsnVideo Abstract
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页数:32
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共 70 条
[1]   Aberrant intestinal microbiota in individuals with prediabetes [J].
Allin, Kristine H. ;
Tremaroli, Valentina ;
Caesar, Robert ;
Jensen, Benjamin A. H. ;
Damgaard, Mads T. F. ;
Bahl, Martin I. ;
Licht, Tine R. ;
Hansen, Tue H. ;
Nielsen, Trine ;
Dantoft, Thomas M. ;
Linneberg, Allan ;
Jorgensen, Torben ;
Vestergaard, Henrik ;
Kristiansen, Karsten ;
Franks, Paul W. ;
Hansen, Torben ;
Backhed, Fredrik ;
Pedersen, Oluf .
DIABETOLOGIA, 2018, 61 (04) :810-820
[2]   Applications and Comparison of Dimensionality Reduction Methods for Microbiome Data [J].
Armstrong, George ;
Rahman, Gibraan ;
Martino, Cameron ;
McDonald, Daniel ;
Gonzalez, Antonio ;
Mishne, Gal ;
Knight, Rob .
FRONTIERS IN BIOINFORMATICS, 2022, 2
[3]  
Berg G, 2020, MICROBIOME, V8, DOI 10.1186/s40168-020-00875-0
[4]   Gut Microbial Diversity Assessment of Indian Type-2-Diabetics Reveals Alterations in Eubacteria, Archaea, and Eukaryotes [J].
Bhute, Shrikant S. ;
Suryavanshi, Mangesh V. ;
Joshi, Suyog M. ;
Yajnik, Chittaranjan S. ;
Shouche, Yogesh S. ;
Ghaskadbi, Saroj S. .
FRONTIERS IN MICROBIOLOGY, 2017, 8
[5]   MITRE: inferring features from microbiota time-series data linked to host status [J].
Bogart, Elijah ;
Creswell, Richard ;
Gerber, Georg K. .
GENOME BIOLOGY, 2019, 20 (01)
[6]   Microbiota-activated PPAR-γ signaling inhibits dysbiotic Enterobacteriaceae expansion [J].
Byndloss, Mariana X. ;
Olsan, Erin E. ;
Rivera-Chavez, Fabian ;
Tiffany, Connor R. ;
Cevallos, Stephanie A. ;
Lokken, Kristen L. ;
Torres, Teresa P. ;
Byndloss, Austin J. ;
Faber, Franziska ;
Gao, Yandong ;
Litvak, Yael ;
Lopez, Christopher A. ;
Xu, Gege ;
Napoli, Eleonora ;
Giulivi, Cecilia ;
Tsolis, Renee M. ;
Revzin, Alexander ;
Lebrilla, Carlito B. ;
Baumler, Andreas J. .
SCIENCE, 2017, 357 (6351) :570-+
[7]  
Callahan BJ, 2016, NAT METHODS, V13, P581, DOI [10.1038/nmeth.3869, 10.1038/NMETH.3869]
[8]  
Cao K-AL., 2021, Multivariate data integration using R: methods and applications with the mixOmics package, DOI [10.1201/9781003026860, DOI 10.1201/9781003026860]
[9]   Gut Microbiota in Patients with Prediabetes [J].
Chang, Wei-Lin ;
Chen, Yu-En ;
Tseng, Hsiang-Tung ;
Cheng, Ching-Feng ;
Wu, Jing-Hui ;
Hou, Yi-Cheng .
NUTRIENTS, 2024, 16 (08)
[10]   The Role of Butyrate in Attenuating Pathobiont-Induced Hyperinflammation [J].
Chen, Jiezhong ;
Vitetta, Luis .
IMMUNE NETWORK, 2020, 20 (02)