Defining and Evaluating Microbial Contributions to Metabolite Variation in Microbiome-Metabolome Association Studies
ABSTRACT Correlation-based analysis of paired microbiome-metabolome data sets is becoming a widespread research approach, aiming to comprehensively identify microbial drivers of metabolic variation. To date, however, the limitations of this approach and other microbiome-metabolome analysis methods h...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
American Society for Microbiology
2019-12-01
|
| Series: | mSystems |
| Subjects: | |
| Online Access: | https://journals.asm.org/doi/10.1128/mSystems.00579-19 |
| _version_ | 1818345246398873600 |
|---|---|
| author | Cecilia Noecker Hsuan-Chao Chiu Colin P. McNally Elhanan Borenstein |
| author_facet | Cecilia Noecker Hsuan-Chao Chiu Colin P. McNally Elhanan Borenstein |
| author_sort | Cecilia Noecker |
| collection | DOAJ |
| description | ABSTRACT Correlation-based analysis of paired microbiome-metabolome data sets is becoming a widespread research approach, aiming to comprehensively identify microbial drivers of metabolic variation. To date, however, the limitations of this approach and other microbiome-metabolome analysis methods have not been comprehensively evaluated. To address this challenge, we have introduced a mathematical framework to quantify the contribution of each taxon to metabolite variation based on uptake and secretion fluxes. We additionally used a multispecies metabolic model to simulate simplified gut communities, generating idealized microbiome-metabolome data sets. We then compared observed taxon-metabolite correlations in these data sets to calculated ground truth taxonomic contribution values. We found that in simulations of both a representative simple 10-species community and complex human gut microbiota, correlation-based analysis poorly identified key contributors, with an extremely low predictive value despite the idealized setting. We further demonstrate that the predictive value of correlation analysis is strongly influenced by both metabolite and taxon properties, as well as by exogenous environmental variation. We finally discuss the practical implications of our findings for interpreting microbiome-metabolome studies. IMPORTANCE Identifying the key microbial taxa responsible for metabolic differences between microbiomes is an important step toward understanding and manipulating microbiome metabolism. To achieve this goal, researchers commonly conduct microbiome-metabolome association studies, comprehensively measuring both the composition of species and the concentration of metabolites across a set of microbial community samples and then testing for correlations between microbes and metabolites. Here, we evaluated the utility of this general approach by first developing a rigorous mathematical definition of the contribution of each microbial taxon to metabolite variation and then examining these contributions in simulated data sets of microbial community metabolism. We found that standard correlation-based analysis of our simulated microbiome-metabolome data sets can identify true contributions with very low predictive value and that its performance depends strongly on specific properties of both metabolites and microbes, as well as on those of the surrounding environment. Combined, our findings can guide future interpretation and validation of microbiome-metabolome studies. |
| first_indexed | 2024-12-13T16:59:19Z |
| format | Article |
| id | doaj.art-ed19ccfc5f994f1db0463cb9975f1f52 |
| institution | Directory Open Access Journal |
| issn | 2379-5077 |
| language | English |
| last_indexed | 2024-12-13T16:59:19Z |
| publishDate | 2019-12-01 |
| publisher | American Society for Microbiology |
| record_format | Article |
| series | mSystems |
| spelling | doaj.art-ed19ccfc5f994f1db0463cb9975f1f522022-12-21T23:37:50ZengAmerican Society for MicrobiologymSystems2379-50772019-12-014610.1128/mSystems.00579-19Defining and Evaluating Microbial Contributions to Metabolite Variation in Microbiome-Metabolome Association StudiesCecilia Noecker0Hsuan-Chao Chiu1Colin P. McNally2Elhanan Borenstein3Department of Genome Sciences, University of Washington, Seattle, Washington, USADepartment of Genome Sciences, University of Washington, Seattle, Washington, USADepartment of Genome Sciences, University of Washington, Seattle, Washington, USADepartment of Genome Sciences, University of Washington, Seattle, Washington, USAABSTRACT Correlation-based analysis of paired microbiome-metabolome data sets is becoming a widespread research approach, aiming to comprehensively identify microbial drivers of metabolic variation. To date, however, the limitations of this approach and other microbiome-metabolome analysis methods have not been comprehensively evaluated. To address this challenge, we have introduced a mathematical framework to quantify the contribution of each taxon to metabolite variation based on uptake and secretion fluxes. We additionally used a multispecies metabolic model to simulate simplified gut communities, generating idealized microbiome-metabolome data sets. We then compared observed taxon-metabolite correlations in these data sets to calculated ground truth taxonomic contribution values. We found that in simulations of both a representative simple 10-species community and complex human gut microbiota, correlation-based analysis poorly identified key contributors, with an extremely low predictive value despite the idealized setting. We further demonstrate that the predictive value of correlation analysis is strongly influenced by both metabolite and taxon properties, as well as by exogenous environmental variation. We finally discuss the practical implications of our findings for interpreting microbiome-metabolome studies. IMPORTANCE Identifying the key microbial taxa responsible for metabolic differences between microbiomes is an important step toward understanding and manipulating microbiome metabolism. To achieve this goal, researchers commonly conduct microbiome-metabolome association studies, comprehensively measuring both the composition of species and the concentration of metabolites across a set of microbial community samples and then testing for correlations between microbes and metabolites. Here, we evaluated the utility of this general approach by first developing a rigorous mathematical definition of the contribution of each microbial taxon to metabolite variation and then examining these contributions in simulated data sets of microbial community metabolism. We found that standard correlation-based analysis of our simulated microbiome-metabolome data sets can identify true contributions with very low predictive value and that its performance depends strongly on specific properties of both metabolites and microbes, as well as on those of the surrounding environment. Combined, our findings can guide future interpretation and validation of microbiome-metabolome studies.https://journals.asm.org/doi/10.1128/mSystems.00579-19correlationevaluationmetabolic modelingmetabolomicsmicrobiome |
| spellingShingle | Cecilia Noecker Hsuan-Chao Chiu Colin P. McNally Elhanan Borenstein Defining and Evaluating Microbial Contributions to Metabolite Variation in Microbiome-Metabolome Association Studies mSystems correlation evaluation metabolic modeling metabolomics microbiome |
| title | Defining and Evaluating Microbial Contributions to Metabolite Variation in Microbiome-Metabolome Association Studies |
| title_full | Defining and Evaluating Microbial Contributions to Metabolite Variation in Microbiome-Metabolome Association Studies |
| title_fullStr | Defining and Evaluating Microbial Contributions to Metabolite Variation in Microbiome-Metabolome Association Studies |
| title_full_unstemmed | Defining and Evaluating Microbial Contributions to Metabolite Variation in Microbiome-Metabolome Association Studies |
| title_short | Defining and Evaluating Microbial Contributions to Metabolite Variation in Microbiome-Metabolome Association Studies |
| title_sort | defining and evaluating microbial contributions to metabolite variation in microbiome metabolome association studies |
| topic | correlation evaluation metabolic modeling metabolomics microbiome |
| url | https://journals.asm.org/doi/10.1128/mSystems.00579-19 |
| work_keys_str_mv | AT cecilianoecker definingandevaluatingmicrobialcontributionstometabolitevariationinmicrobiomemetabolomeassociationstudies AT hsuanchaochiu definingandevaluatingmicrobialcontributionstometabolitevariationinmicrobiomemetabolomeassociationstudies AT colinpmcnally definingandevaluatingmicrobialcontributionstometabolitevariationinmicrobiomemetabolomeassociationstudies AT elhananborenstein definingandevaluatingmicrobialcontributionstometabolitevariationinmicrobiomemetabolomeassociationstudies |