1
|
Velasco-Álvarez JR, Torres y Torres N, Chairez I, Castrejón-Flores JL. Microbiome distribution modeling using gradient descent strategies for mock, in vitro and clinical community distributions. PLoS One 2023; 18:e0290082. [PMID: 37603566 PMCID: PMC10441787 DOI: 10.1371/journal.pone.0290082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 08/01/2023] [Indexed: 08/23/2023] Open
Abstract
The human gut is home to a complex array of microorganisms interacting with the host and each other, forming a community known as the microbiome. This community has been linked to human health and disease, but understanding the underlying interactions is still challenging for researchers. Standard studies typically use high-throughput sequencing to analyze microbiome distribution in patient samples. Recent advancements in meta-omic data analysis have enabled computational modeling strategies to integrate this information into an in silico model. However, there is a need for improved parameter fitting and data integration features in microbial community modeling. This study proposes a novel alternative strategy utilizing state-of-the-art dynamic flux balance analysis (dFBA) to provide a simple protocol enabling accurate replication of abundance data composition through dynamic parameter estimation and integration of metagenomic data. We used a recurrent optimization algorithm to replicate community distributions from three different sources: mock, in vitro, and clinical microbiome. Our results show an accuracy of 98% and 96% when using in vitro and clinical bacterial abundance distributions, respectively. The proposed modeling scheme allowed us to observe the evolution of metabolites. It could provide a deeper understanding of metabolic interactions while taking advantage of the high contextualization features of GEM schemes to fit the study case. The proposed modeling scheme could improve the approach in cases where external factors determine specific bacterial distributions, such as drug intake.
Collapse
Affiliation(s)
- Juan Ricardo Velasco-Álvarez
- Instituto Politécnico Nacional, Unidad Profesional Interdisciplinaria de Biotecnología, Gustavo A. Madero, Mexico City, Mexico
| | - Nimbe Torres y Torres
- Departamento de Fisiólogía de la Nutrición, Instituto Nacional Ciencias Médicas y Nutrición(“Salvador Zubirán”, Tlalpan, Mexico City, Mexico
| | - Isaac Chairez
- Instituto Politécnico Nacional, Unidad Profesional Interdisciplinaria de Biotecnología, Gustavo A. Madero, Mexico City, Mexico
- School of Engineering and Sciences, Técnologico de Monterrey-Campus Guadalajara, Zapopan, Jalisco, Mexico
| | - José Luis Castrejón-Flores
- Instituto Politécnico Nacional, Unidad Profesional Interdisciplinaria de Biotecnología, Gustavo A. Madero, Mexico City, Mexico
| |
Collapse
|
2
|
Yousefi B, Melograna F, Galazzo G, van Best N, Mommers M, Penders J, Schwikowski B, Van Steen K. Capturing the dynamics of microbial interactions through individual-specific networks. Front Microbiol 2023; 14:1170391. [PMID: 37256048 PMCID: PMC10225591 DOI: 10.3389/fmicb.2023.1170391] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 04/21/2023] [Indexed: 06/01/2023] Open
Abstract
Longitudinal analysis of multivariate individual-specific microbiome profiles over time or across conditions remains dauntin. Most statistical tools and methods that are available to study microbiomes are based on cross-sectional data. Over the past few years, several attempts have been made to model the dynamics of bacterial species over time or across conditions. However, the field needs novel views on handling microbial interactions in temporal analyses. This study proposes a novel data analysis framework, MNDA, that combines representation learning and individual-specific microbial co-occurrence networks to uncover taxon neighborhood dynamics. As a use case, we consider a cohort of newborns with microbiomes available at 6 and 9 months after birth, and extraneous data available on the mode of delivery and diet changes between the considered time points. Our results show that prediction models for these extraneous outcomes based on an MNDA measure of local neighborhood dynamics for each taxon outperform traditional prediction models solely based on individual-specific microbial abundances. Furthermore, our results show that unsupervised similarity analysis of newborns in the study, again using the notion of a taxon's dynamic neighborhood derived from time-matched individual-specific microbial networks, can reveal different subpopulations of individuals, compared to standard microbiome-based clustering, with potential relevance to clinical practice. This study highlights the complementarity of microbial interactions and abundances in downstream analyses and opens new avenues to personalized prediction or stratified medicine with temporal microbiome data.
Collapse
Affiliation(s)
- Behnam Yousefi
- Computational Systems Biomedicine Lab, Institut Pasteur, University Paris City, Paris, France
- École Doctorale Complexite du vivant, Sorbonne University, Paris, France
- BIO3—Laboratory for Systems Medicine, Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Federico Melograna
- BIO3—Laboratory for Systems Medicine, Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Gianluca Galazzo
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, Netherlands
| | - Niels van Best
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, Netherlands
- Institute of Medical Microbiology, Rhine-Westphalia Technical University of Aachen, RWTH University, Aachen, Germany
| | - Monique Mommers
- Department of Epidemiology, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, Netherlands
| | - John Penders
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, Netherlands
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, Care and Public Health Research Institute (CAPHRI), Maastricht University Medical Center+, Maastricht, Netherlands
| | - Benno Schwikowski
- Computational Systems Biomedicine Lab, Institut Pasteur, University Paris City, Paris, France
| | - Kristel Van Steen
- BIO3—Laboratory for Systems Medicine, Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, Belgium
- BIO3—Laboratory for Systems Genetics, GIGA-R Medical Genomics, University of Lièvzge, Liège, Belgium
| |
Collapse
|
3
|
Ye M, Jiang L, Chen C, Zhu X, Wang M, Wu R. np 2 QTL: networking phenotypic plasticity quantitative trait loci across heterogeneous environments. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 99:796-806. [PMID: 31009134 DOI: 10.1111/tpj.14355] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 03/29/2019] [Accepted: 04/04/2019] [Indexed: 05/18/2023]
Abstract
Despite its critical importance to our understanding of plant growth and adaptation, the question of how environment-induced plastic response is affected genetically remains elusive. Previous studies have shown that the reaction norm of an organism across environmental index obeys the allometrical scaling law of part-whole relationships. The implementation of this phenomenon into functional mapping can characterize how quantitative trait loci (QTLs) modulate the phenotypic plasticity of complex traits to heterogeneous environments. Here, we assemble functional mapping and allometry theory through Lokta-Volterra ordinary differential equations (LVODE) into an R-based computing platform, np2 QTL, aimed to map and visualize phenotypic plasticity QTLs. Based on LVODE parameters, np2 QTL constructs a bidirectional, signed and weighted network of QTL-QTL epistasis, whose emergent properties reflect the ecological mechanisms for genotype-environment interactions over any range of environmental change. The utility of np2 QTL was validated by comprehending the genetic architecture of phenotypic plasticity via the reanalysis of published plant height data involving 3502 recombinant inbred lines of maize planted in multiple discrete environments. np2 QTL also provides a tool for constructing a predictive model of phenotypic responses in extreme environments relative to the median environment.
Collapse
Affiliation(s)
- Meixia Ye
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
| | - Libo Jiang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
| | - Chixiang Chen
- Departments of Public Health Sciences and Statistics, Center for Statistical Genetics, Pennsylvania State University, Hershey, PA, 17033, USA
| | - Xuli Zhu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
| | - Ming Wang
- Departments of Public Health Sciences and Statistics, Center for Statistical Genetics, Pennsylvania State University, Hershey, PA, 17033, USA
| | - Rongling Wu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
- Departments of Public Health Sciences and Statistics, Center for Statistical Genetics, Pennsylvania State University, Hershey, PA, 17033, USA
| |
Collapse
|
4
|
Chung M, Binois M, Gramacy RB, Bardsley JM, Moquin DJ, Smith AP, Smith AM. PARAMETER AND UNCERTAINTY ESTIMATION FOR DYNAMICAL SYSTEMS USING SURROGATE STOCHASTIC PROCESSES. SIAM JOURNAL ON SCIENTIFIC COMPUTING : A PUBLICATION OF THE SOCIETY FOR INDUSTRIAL AND APPLIED MATHEMATICS 2019; 41:A2212-A2238. [PMID: 31749599 PMCID: PMC6867882 DOI: 10.1137/18m1213403] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Inference on unknown quantities in dynamical systems via observational data is essential for providing meaningful insight, furnishing accurate predictions, enabling robust control, and establishing appropriate designs for future experiments. Merging mathematical theory with empirical measurements in a statistically coherent way is critical and challenges abound, e.g., ill-posedness of the parameter estimation problem, proper regularization and incorporation of prior knowledge, and computational limitations. To address these issues, we propose a new method for learning parameterized dynamical systems from data. We first customize and fit a surrogate stochastic process directly to observational data, front-loading with statistical learning to respect prior knowledge (e.g., smoothness), cope with challenging data features like heteroskedasticity, heavy tails, and censoring. Then, samples of the stochastic process are used as "surrogate data" and point estimates are computed via ordinary point estimation methods in a modular fashion. Attractive features of this two-step approach include modularity and trivial parallelizability. We demonstrate its advantages on a predator-prey simulation study and on a real-world application involving within-host influenza virus infection data paired with a viral kinetic model, with comparisons to a more conventional Markov chain Monte Carlo (MCMC) based Bayesian approach.
Collapse
Affiliation(s)
- Matthias Chung
- Department of Mathematics, Computational Modeling and Data Analytics Division, Academy of Integrated Science, Virginia Tech, Blacksburg, VA 24061
| | - Mickaël Binois
- Booth School of Business, University of Chicago, Chicago, IL 60637
| | | | | | - David J Moquin
- Department of Internal Medicine, University of Tennessee Health Science Center, Memphis, TN 38103
| | - Amanda P Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN 38103
| | - Amber M Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN 38103
| |
Collapse
|
5
|
Lugo-Martinez J, Ruiz-Perez D, Narasimhan G, Bar-Joseph Z. Dynamic interaction network inference from longitudinal microbiome data. MICROBIOME 2019; 7:54. [PMID: 30940197 PMCID: PMC6446388 DOI: 10.1186/s40168-019-0660-3] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Accepted: 03/11/2019] [Indexed: 05/21/2023]
Abstract
BACKGROUND Several studies have focused on the microbiota living in environmental niches including human body sites. In many of these studies, researchers collect longitudinal data with the goal of understanding not only just the composition of the microbiome but also the interactions between the different taxa. However, analysis of such data is challenging and very few methods have been developed to reconstruct dynamic models from time series microbiome data. RESULTS Here, we present a computational pipeline that enables the integration of data across individuals for the reconstruction of such models. Our pipeline starts by aligning the data collected for all individuals. The aligned profiles are then used to learn a dynamic Bayesian network which represents causal relationships between taxa and clinical variables. Testing our methods on three longitudinal microbiome data sets we show that our pipeline improve upon prior methods developed for this task. We also discuss the biological insights provided by the models which include several known and novel interactions. The extended CGBayesNets package is freely available under the MIT Open Source license agreement. The source code and documentation can be downloaded from https://github.com/jlugomar/longitudinal_microbiome_analysis_public . CONCLUSIONS We propose a computational pipeline for analyzing longitudinal microbiome data. Our results provide evidence that microbiome alignments coupled with dynamic Bayesian networks improve predictive performance over previous methods and enhance our ability to infer biological relationships within the microbiome and between taxa and clinical factors.
Collapse
Affiliation(s)
- Jose Lugo-Martinez
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213 Pennsylvania USA
| | - Daniel Ruiz-Perez
- Bioinformatics Research Group (BioRG), Florida International University, 11200 SW 8th Street, Miami, 33199 Florida USA
| | - Giri Narasimhan
- Bioinformatics Research Group (BioRG), Florida International University, 11200 SW 8th Street, Miami, 33199 Florida USA
- Biomolecular Sciences Institute, Florida International University, Miami, 33199 Florida USA
| | - Ziv Bar-Joseph
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213 Pennsylvania USA
| |
Collapse
|