Integrating biogeochemistry with multiomic sequence information in a model oxygen minimum zone

Abstract

Microorganisms are the most abundant lifeform on Earth, mediating global fluxes of matter and energy. Over the past decade, high-throughput molecular techniques generating multiomic sequence information (DNA, mRNA, and protein) have transformed our perception of this microcosmos, conceptually linking microorganisms at the individual, population, and community levels to a wide range of ecosystem functions and services. Here, we develop a biogeochemical model that describes metabolic coupling along the redox gradient in Saanich Inlet-a seasonally anoxic fjord with biogeochemistry analogous to oxygen minimum zones (OMZs). The model reproduces measured biogeochemical process rates as well as DNA, mRNA, and protein concentration profiles across the redox gradient. Simulations make predictions about the role of ubiquitous OMZ microorganisms in mediating carbon, nitrogen, and sulfur cycling. For example, nitrite "leakage" during incomplete sulfide-driven denitrification by SUP05 Gammaproteobacteria is predicted to support inorganic carbon fixation and intense nitrogen loss via anaerobic ammonium oxidation. This coupling creates a metabolic niche for nitrous oxide reduction that completes denitrification by currently unidentified community members. These results quantitatively improve previous conceptual models describing microbial metabolic networks in OMZs. Beyond OMZ-specific predictions, model results indicate that geochemical fluxes are robust indicators of microbial community structure and reciprocally, that gene abundances and geochemical conditions largely determine gene expression patterns. The integration of real observational data, including geochemical profiles and process rate measurements as well as metagenomic, metatranscriptomic and metaproteomic sequence data, into a biogeochemical model, as shown here, enables holistic insight into the microbial metabolic network driving nutrient and energy flow at ecosystem scales.M icrobial communities catalyze Earth's biogeochemical cycles through metabolic pathways that couple fluxes of matter and energy to biological growth (1). These pathways are encoded in evolving genes that, over time, spread across microbial lineages and today shape the conditions for life on Earth. High-throughput sequencing and mass-spectrometry platforms are generating multiomic sequence information (DNA, mRNA, and protein) that is transforming our perception of this microcosmos, but the vast majority of environmental sequencing studies lack a mechanistic link to geochemical processes. At the same time, mathematical models are increasingly used to describe local-and global-scale biogeochemical processes or predict future changes in global elemental cycling and climate (2, 3). Although these models typically incorporate the catalytic properties of cells, they fail to integrate the information flow from DNA to mRNA, proteins, and process rates as described by the central dogma of molecular biology (4). Hence, a mechanistic framework integrating multiomic data with geochemical information has...

Microbial metabolite fluxes in a model marine anoxic ecosystem

“…Equation (1) has been used extensively to predict turbulent transport of dissolved gases and salts in various systems, especially in anoxic marine systems (Fennel and Boss, 2003;Ho et al, 2004;Li et al, 2012b;Samodurov et al, 2013;Reed et al, 2014;Louca et al, 2016). The buoyancy frequency N can be calculated from the measured salinity and temperature profiles, however the appropriate values for α and p are typically poorly constrained.…”

Microbial metabolite fluxes in a model marine anoxic ecosystem

“…Equation (1) has been used extensively to predict turbulent transport of dissolved gases and salts in various systems, especially in anoxic marine systems (Fennel & Boss, 2003;Ho et al, 2004;Li et al, 2012b;Louca et al, 2016;Reed, Algar, Huber, & Dick, 2014;Samodurov et al, 2013). The buoyancy frequency N can be calculated from the measured salinity and temperature profiles; however, the appropriate values for α and p are typically poorly constrained.…”

“…An alternative approach for estimating R that reduces estimation noise and avoids the risk of overfitting is to choose R on a finite spatiotemporal grid ("fitting grid"), such that the corresponding predicted distribution Ĉ obtained by solving the differential Equation (6) best matches the observed profile C. This approach, known as "inverse linear transport modeling" (ILTM), is widely used in oceanography and atmospheric sciences, where known distributions of compounds are used to estimate unknown sources and sinks (Berg et al, 1998;Hirsch et al, 2006;Houweling, Kaminski, Dentener, Lelieveld, & Heimann, 1999;Lettmann et al, 2012;Louca et al, 2016;Martinez-Camara, Béjar Haro, Stohl, & Vetterli, 2014;Mikaloff Fletcher et al, 2006Steinkamp, 2011). We mention that most existing studies-including those investigating metabolite fluxes in anoxic water columns or sediments (Berg et al, 1998;Lettmann et al, 2012;Louca et al, 2016)-assumed that C was at steady state even when fluxes were estimated at multiple time points; however, this assumption may be needlessly and overly restrictive. To reduce spurious oscillations in the estimated R (a common ILTM artifact), excessively high estimates of R that only marginally improve the agreement with the data are penalized, a procedure known as Tikhonov regularization (Björck, 1996;Hansen, 2000;Lettmann et al, 2012).…”

A robust, cost‐effective method for DNA, RNA and protein co‐extraction from soil, other complex microbiomes and pure cultures

“…As such, it is desired that two or more of these tools are employed simultaneously, using biomolecules extracted from any range of microbiome sample. Integrated multi-omics analyses have been successfully applied to pure culture (Christel et al, 2018), as well as mixed microbial communities (Lindemann et al, 2017;Louca et al, 2016;Muller et al, 2014;Roume et al, 2015), where they have uncovered fundamental principles in microbial ecology. For example, in situ predominance of generalist microbial populations could be attributed to the ability to redirect gene and protein expression as a function of resource availability (Muller et al, 2014).…”

“…The ultimate aim of ecosystem biology, however, is to generate predictive models based on the integration of multi-omics data (Abram, 2015). This also has started to emerge with the recent publication of a biogeochemical model accounting for DNA, transcript and protein distributions in an aquatic environment and detangling metabolic interactions underpinning carbon, sulphur and nitrogen cycling (Louca et al, 2016). The integration of multiomics remains, however, rather challenging due to the inherent high dimensionality of the data.…”