Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data

ABSTRACT A key challenge in the analysis of longitudinal microbiome data is the inference of temporal interactions between microbial taxa, their genes, the metabolites that they consume and produce, and host genes. To address these challenges, we developed a computational pipeline, a pipeline for th...

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Autores principales:	Daniel Ruiz-Perez, Jose Lugo-Martinez, Natalia Bourguignon, Kalai Mathee, Betiana Lerner, Ziv Bar-Joseph, Giri Narasimhan
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2021
Materias:	longitudinal microbiome analysis multi-omic integration microbial composition prediction dynamic Bayesian networks temporal alignment Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/185640fd2adf400fa8253e41e5773133
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spelling	oai:doaj.org-article:185640fd2adf400fa8253e41e57731332021-12-02T19:22:27ZDynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data10.1128/mSystems.01105-202379-5077https://doaj.org/article/185640fd2adf400fa8253e41e57731332021-04-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.01105-20https://doaj.org/toc/2379-5077ABSTRACT A key challenge in the analysis of longitudinal microbiome data is the inference of temporal interactions between microbial taxa, their genes, the metabolites that they consume and produce, and host genes. To address these challenges, we developed a computational pipeline, a pipeline for the analysis of longitudinal multi-omics data (PALM), that first aligns multi-omics data and then uses dynamic Bayesian networks (DBNs) to reconstruct a unified model. Our approach overcomes differences in sampling and progression rates, utilizes a biologically inspired multi-omic framework, reduces the large number of entities and parameters in the DBNs, and validates the learned network. Applying PALM to data collected from inflammatory bowel disease patients, we show that it accurately identifies known and novel interactions. Targeted experimental validations further support a number of the predicted novel metabolite-taxon interactions. IMPORTANCE While a number of large consortia collect and profile several different types of microbiome and genomic time series data, very few methods exist for joint modeling of multi-omics data sets. We developed a new computational pipeline, PALM, which uses dynamic Bayesian networks (DBNs) and is designed to integrate multi-omics data from longitudinal microbiome studies. When used to integrate sequence, expression, and metabolomics data from microbiome samples along with host expression data, the resulting models identify interactions between taxa, their genes, and the metabolites that they produce and consume, as well as their impact on host expression. We tested the models both by using them to predict future changes in microbiome levels and by comparing the learned interactions to known interactions in the literature. Finally, we performed experimental validations for a few of the predicted interactions to demonstrate the ability of the method to identify novel relationships and their impact.Daniel Ruiz-PerezJose Lugo-MartinezNatalia BourguignonKalai MatheeBetiana LernerZiv Bar-JosephGiri NarasimhanAmerican Society for Microbiologyarticlelongitudinal microbiome analysismulti-omic integrationmicrobial composition predictiondynamic Bayesian networkstemporal alignmentMicrobiologyQR1-502ENmSystems, Vol 6, Iss 2 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	longitudinal microbiome analysis multi-omic integration microbial composition prediction dynamic Bayesian networks temporal alignment Microbiology QR1-502
spellingShingle	longitudinal microbiome analysis multi-omic integration microbial composition prediction dynamic Bayesian networks temporal alignment Microbiology QR1-502 Daniel Ruiz-Perez Jose Lugo-Martinez Natalia Bourguignon Kalai Mathee Betiana Lerner Ziv Bar-Joseph Giri Narasimhan Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data
description	ABSTRACT A key challenge in the analysis of longitudinal microbiome data is the inference of temporal interactions between microbial taxa, their genes, the metabolites that they consume and produce, and host genes. To address these challenges, we developed a computational pipeline, a pipeline for the analysis of longitudinal multi-omics data (PALM), that first aligns multi-omics data and then uses dynamic Bayesian networks (DBNs) to reconstruct a unified model. Our approach overcomes differences in sampling and progression rates, utilizes a biologically inspired multi-omic framework, reduces the large number of entities and parameters in the DBNs, and validates the learned network. Applying PALM to data collected from inflammatory bowel disease patients, we show that it accurately identifies known and novel interactions. Targeted experimental validations further support a number of the predicted novel metabolite-taxon interactions. IMPORTANCE While a number of large consortia collect and profile several different types of microbiome and genomic time series data, very few methods exist for joint modeling of multi-omics data sets. We developed a new computational pipeline, PALM, which uses dynamic Bayesian networks (DBNs) and is designed to integrate multi-omics data from longitudinal microbiome studies. When used to integrate sequence, expression, and metabolomics data from microbiome samples along with host expression data, the resulting models identify interactions between taxa, their genes, and the metabolites that they produce and consume, as well as their impact on host expression. We tested the models both by using them to predict future changes in microbiome levels and by comparing the learned interactions to known interactions in the literature. Finally, we performed experimental validations for a few of the predicted interactions to demonstrate the ability of the method to identify novel relationships and their impact.
format	article
author	Daniel Ruiz-Perez Jose Lugo-Martinez Natalia Bourguignon Kalai Mathee Betiana Lerner Ziv Bar-Joseph Giri Narasimhan
author_facet	Daniel Ruiz-Perez Jose Lugo-Martinez Natalia Bourguignon Kalai Mathee Betiana Lerner Ziv Bar-Joseph Giri Narasimhan
author_sort	Daniel Ruiz-Perez
title	Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data
title_short	Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data
title_full	Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data
title_fullStr	Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data
title_full_unstemmed	Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data
title_sort	dynamic bayesian networks for integrating multi-omics time series microbiome data
publisher	American Society for Microbiology
publishDate	2021
url	https://doaj.org/article/185640fd2adf400fa8253e41e5773133
work_keys_str_mv	AT danielruizperez dynamicbayesiannetworksforintegratingmultiomicstimeseriesmicrobiomedata AT joselugomartinez dynamicbayesiannetworksforintegratingmultiomicstimeseriesmicrobiomedata AT nataliabourguignon dynamicbayesiannetworksforintegratingmultiomicstimeseriesmicrobiomedata AT kalaimathee dynamicbayesiannetworksforintegratingmultiomicstimeseriesmicrobiomedata AT betianalerner dynamicbayesiannetworksforintegratingmultiomicstimeseriesmicrobiomedata AT zivbarjoseph dynamicbayesiannetworksforintegratingmultiomicstimeseriesmicrobiomedata AT girinarasimhan dynamicbayesiannetworksforintegratingmultiomicstimeseriesmicrobiomedata
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Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data

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