Gene-centric approach to integrating environmental genomics and biogeochemical models

Abstract

Rapid advances in molecular microbial ecology have yielded an unprecedented amount of data about the evolutionary relationships and functional traits of microbial communities that regulate global geochemical cycles. Biogeochemical models, however, are trailing in the wake of the environmental genomics revolution, and such models rarely incorporate explicit representations of bacteria and archaea, nor are they compatible with nucleic acid or protein sequence data. Here, we present a functional gene-based framework for describing microbial communities in biogeochemical models by incorporating genomics data to provide predictions that are readily testable. To demonstrate the approach in practice, nitrogen cycling in the Arabian Sea oxygen minimum zone (OMZ) was modeled to examine key questions about cryptic sulfur cycling and dinitrogen production pathways in OMZs. Simulations support previous assertions that denitrification dominates over anammox in the central Arabian Sea, which has important implications for the loss of fixed nitrogen from the oceans. Furthermore, cryptic sulfur cycling was shown to attenuate the secondary nitrite maximum often observed in OMZs owing to changes in the composition of the chemolithoautotrophic community and dominant metabolic pathways. Results underscore the need to explicitly integrate microbes into biogeochemical models rather than just the metabolisms they mediate. By directly linking geochemical dynamics to the genetic composition of microbial communities, the method provides a framework for achieving mechanistic insights into patterns and biogeochemical consequences of marine microbes. Such an approach is critical for informing our understanding of the key role microbes play in modulating Earth's biogeochemistry.

Fig. 1.

A comparison of model results and data from Pitcher et al. (18). Solid lines represents model output, whereas individual points and dotted line are observations. Chemical profiles are as labeled, and hydrazine oxidoreductase (hzo) and ammonia monooxygenase (amoA) genes are associated with anammox and aerobic ammonia oxidation pathways, respectively. Error bars represent SDs.

Fig. 2.

Model output from simulations with (black dashed lines) and without (red solid lines) cryptic sulfur cycling active. The half-saturation constant for nitrate in the dissimilatory nitrate reduction to nitrite pathway is increased to 80 μM to induce sulfate reduction and, consequently, initiate cryptic sulfur cycling. Chemical profiles are as labeled, and sox, nap, narG, nirK, and dsr genes correspond to aerobic sulfide oxidation, sulfide oxidation by nitrate, organotrophic dissimilatory nitrate reduction, organotrophic dissimilatory nitrite reduction, and dissimilatory sulfate reduction, respectively.

Fig. 3.

Profiles of oxygen and relative gene abundances from model simulation without and with cryptic sulfur cycling active. At three depths, gene abundances are plotted in pie charts demonstrating how direct comparisons can be made between model output and metagenomic data, which are typically expressed in relative terms at discrete points in space. cox, cytochrome-c oxidase.