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

Christopher A Hempel; Dominik Buchner; Leoni Mack; Marie V Brasseur; Dan Tulpan; Florian Leese; Dirk Steinke

doi:10.3389/fmicb.2023.1217750

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

Front Microbiol. 2023 Nov 24:14:1217750. doi: 10.3389/fmicb.2023.1217750. eCollection 2023.

Authors

Christopher A Hempel^{1

2}, Dominik Buchner³, Leoni Mack⁴, Marie V Brasseur⁵, Dan Tulpan^{6

7}, Florian Leese^{3

8}, Dirk Steinke^{1

2}

Affiliations

¹ Department of Integrative Biology, University of Guelph, Guelph, ON, Canada.
² Centre for Biodiversity Genomics, University of Guelph, Guelph, ON, Canada.
³ Aquatic Ecosystem Research, University of Duisburg-Essen, Essen, Germany.
⁴ Faculty of Aquatic Ecology, University of Duisburg-Essen, Essen, Germany.
⁵ Leibniz Institute for the Analysis of Biodiversity Change, Zoological Research Museum A. Koenig, Bonn, Germany.
⁶ School of Computer Science, University of Guelph, Guelph, ON, Canada.
⁷ Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada.
⁸ Centre for Water and Environmental Research (ZWU), University of Duisburg-Essen, Essen, Germany.

Abstract

Introduction: 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.

Methods: 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 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.

Discussion: We 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.

Keywords: ExStream; bioinformatics; environmental assessment; freshwater; mesocosm; metabarcoding; metatranscriptomics; stressor prediction.

Grants and funding

CH was funded through the Canada First Research Excellence Fund to the program CFREF–Food from Thought at the University of Guelph. DB, MB, and the field experiment were funded through the DFG grants LE 2323/9-1, MA, and SCHA. LM was funded through the Land2Sea project (Aquatic Ecosystem Services in a Changing World, https://land2sea.ucd.ie/; funded under the Joint BiodivERsA-Belmont Forum call and the DFG) and the DFG project LE2323/9-1/MA XXXXX 418091530.