Mapping Microbial Abundance and Prevalence to Changing Oxygen Concentration in Deep-Sea Sediments Using Machine Learning and Differential Abundance

• Published in: Frontiers in microbiology 2022-05, Vol.13, p.804575-804575
• Main Authors: Møller, Tor Einar, Le Moine Bauer, Sven, Hannisdal, Bjarte, Zhao, Rui, Baumberger, Tamara, Roerdink, Desiree L, Dupuis, Amandine, Thorseth, Ingunn H, Pedersen, Rolf Birger, Jørgensen, Steffen Leth
• Format: Article
• Language: English
• Subjects: Arctic Mid-Ocean Ridge; classification; Compositional Data Analysis; Microbiology; Norwegian-Greenland Sea; Support Vector Machines; threshold response
• ISSN: 1664-302X; 1664-302X
• DOI: 10.3389/fmicb.2022.804575

Oxygen constitutes one of the strongest factors explaining microbial taxonomic variability in deep-sea sediments. However, deep-sea microbiome studies often lack the spatial resolution to study the oxygen gradient and transition zone beyond the oxic-anoxic dichotomy, thus leaving important questions regarding the microbial response to changing conditions unanswered. Here, we use machine learning and differential abundance analysis on 184 samples from 11 sediment cores retrieved along the Arctic Mid-Ocean Ridge to study how changing oxygen concentrations (1) are predicted by the relative abundance of higher taxa and (2) influence the distribution of individual Operational Taxonomic Units. We find that some of the most abundant classes of microorganisms can be used to classify samples according to oxygen concentration. At the level of Operational Taxonomic Units, however, representatives of common classes are not differentially abundant from high-oxic to low-oxic conditions. This weakened response to changing oxygen concentration suggests that the abundance and prevalence of highly abundant OTUs may be better explained by other variables than oxygen. Our results suggest that a relatively homogeneous microbiome is recruited to the benthos, and that the microbiome then becomes more heterogeneous as oxygen drops below 25 μM. Our analytical approach takes into account the oft-ignored compositional nature of relative abundance data, and provides a framework for extracting biologically meaningful associations from datasets spanning multiple sedimentary cores.