Environmental Exposures Influence Nasal Microbiome Composition in a Longitudinal Study of Division I Collegiate Athletes

Kask, Oliver; Kyman, Shari; Conn, Kathryn A; Gormley, Jenny M.; Gardner, Julia; Johns, Robert; Caporaso, J. Gregory; Bycura, Dierdra; Sutliffe, Jay T.; Cope, Emily K.

doi:10.1101/2020.02.13.946475

Cited by 2 publications

(3 citation statements)

References 54 publications

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“…However, a large-scale morphological study on benthic invertebrates showed that ecological status classifications based on abundance and P–A data showed only minor variations [105]. In a microbial context, multiple HTS studies showed similar correlations of both abundance and P–A data with environmental variables [106–108], while some studies showed that correlations differed between data types [109,110]. These results indicate that the impact of data types might depend on the studied environmental variables, but if further research shows that both data types have similar predictive power for environmental assessments, as our results suggest, then P–A data could be used exclusively in future environmental assessment studies.…”

Section: Discussionmentioning

confidence: 99%

Section: Impact Of Data Types On Sppmentioning

confidence: 99%

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Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned data

Hempel

Buchner

Mack

et al. 2022

Preprint

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Background: Microbes are increasingly (re)considered for environmental assessments because they are powerful indicators for the health of ecosystems. The complexity of microbial communities necessitates powerful novel tools to derive conclusions for environmental decision-makers, and machine learning is a promising option in that context. While amplicon sequencing is typically applied to assess microbial communities, metagenomics and total RNA sequencing (herein summarized as omics-based methods) can provide a more holistic picture of microbial biodiversity at sufficient sequencing depths. Despite this advantage, amplicon sequencing and omics-based methods have not yet been compared for taxonomy-based environmental assessments with machine learning. In this study, we applied 16S and ITS-2 sequencing, metagenomics, and total RNA sequencing to samples from a stream mesocosm experiment that investigated the impacts of two aquatic stressors, insecticide and increased fine sediment deposition, on stream biodiversity. We processed the data using similarity clustering and denoising (only applicable to amplicon sequencing) as well as multiple taxonomic levels, data types, feature selection, and machine learning algorithms and evaluated the stressor prediction performance of each generated model for a total of 1,536 evaluated combinations of taxonomic datasets and data-processing methods. Results: Sequencing and data-processing methods had a substantial impact on stressor prediction. While omics-based methods detected much more taxa than amplicon sequencing, 16S sequencing outperformed all other sequencing methods in terms of stressor prediction based on the Matthews Correlation Coefficient. However, even the highest observed performance for 16S sequencing was still only moderate. Omics-based methods performed poorly overall, but this was likely due to insufficient sequencing depth. Data types had no impact on performance while feature selection significantly improved performance for omics-based methods but not for amplicon sequencing. Conclusion: Amplicon sequencing might be a better candidate for machine-learning-based environmental stressor prediction than omics-based methods, but the latter require further research at higher sequencing depths to confirm this conclusion. More sampling could improve stressor prediction performance, and while this was not possible in the context of our study, thousands of sampling sites are monitored for routine environmental assessments, providing an ideal framework to further refine the approach for possible implementation in environmental diagnostics.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Impact Of Data Types On Sppmentioning

confidence: 99%

Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned data

Hempel

Buchner

Mack

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…However, a large-scale morphological study on benthic invertebrates showed that ecological status classifications based on abundance and P-A data showed only minor variations (Buchner et al, 2019). In a microbial context, multiple HTS studies showed similar correlations of both abundance and P-A data with environmental variables (Muletz Wolz et al, 2018;Knowles et al, 2019;Farinella et al, 2022), while some studies showed that correlations differed between data types (Kask et al, 2020;Tavalire et al, 2021). These results indicate that the impact of data types might depend on the studied environmental variables, but if further research shows that both data types have similar predictive power for environmental assessments, as our results suggest, then P-A data could be used exclusively in future environmental assessment studies.…”

Section: Impact Of Data Types On Sppmentioning

confidence: 99%

Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned data

Hempel,

Buchner,

Mack

et al. 2023

Front. Microbiol.

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IntroductionMicrobes are increasingly (re)considered for environmental assessments because they are powerful indicators for the health of ecosystems. The complexity of microbial communities necessitates powerful novel tools to derive conclusions for environmental decision-makers, and machine learning is a promising option in that context. While amplicon sequencing is typically applied to assess microbial communities, metagenomics and total RNA sequencing (herein summarized as omics-based methods) can provide a more holistic picture of microbial biodiversity at sufficient sequencing depths. Despite this advantage, amplicon sequencing and omics-based methods have not yet been compared for taxonomy-based environmental assessments with machine learning.MethodsIn this study, we applied 16S and ITS-2 sequencing, metagenomics, and total RNA sequencing to samples from a stream mesocosm experiment that investigated the impacts of two aquatic stressors, insecticide and increased fine sediment deposition, on stream biodiversity. We processed the data using similarity clustering and denoising (only applicable to amplicon sequencing) as well as multiple taxonomic levels, data types, feature selection, and machine learning algorithms and evaluated the stressor prediction performance of each generated model for a total of 1,536 evaluated combinations of taxonomic datasets and data-processing methods.ResultsSequencing and data-processing methods had a substantial impact on stressor prediction. While omics-based methods detected a higher diversity of taxa than amplicon sequencing, 16S sequencing outperformed all other sequencing methods in terms of stressor prediction based on the Matthews Correlation Coefficient. However, even the highest observed performance for 16S sequencing was still only moderate. Omics-based methods performed poorly overall, but this was likely due to insufficient sequencing depth. Data types had no impact on performance while feature selection significantly improved performance for omics-based methods but not for amplicon sequencing.DiscussionWe conclude that amplicon sequencing might be a better candidate for machine-learning-based environmental stressor prediction than omics-based methods, but the latter require further research at higher sequencing depths to confirm this conclusion. More sampling could improve stressor prediction performance, and while this was not possible in the context of our study, thousands of sampling sites are monitored for routine environmental assessments, providing an ideal framework to further refine the approach for possible implementation in environmental diagnostics.

show abstract

Environmental Exposures Influence Nasal Microbiome Composition in a Longitudinal Study of Division I Collegiate Athletes

Cited by 2 publications

References 54 publications

Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned data

Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned data

Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned data

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