243 Synthesis and discussion for ANME) and pmoA (for aerobic methane oxidation) could aid future modeling studies in better validating predicted methane oxidation rates and gene-based abundances. Similarly, coupling qPCR with deep metatranscriptomics could provide a more accurate estimate of cell metabolic activity, as demonstrated by Louca et al. (2016) (Figure 1). Cell specific reaction rates 2. Rates: Michaelis-Menten (Vmax) Km Vmax + 1. Biogeochemistry No inhibitor H2S/O2/SO4 -2 (salinity) 3.Specific Microbial growth Vmax Km OUTPUT = PREDICTIONS GENE BASED MICROBIAL ABUNDANCE RATE of PROCESSES CH4 CO2 SO4 -2 O2 Fe(OH)3 MnO2 CH4 CO2 SO4 -2 Fe(OH)3 MnO2 ANME-2 4. Model validation Functional marker qPCR Meta-omics CH4 CO2 mcrA Figure 1. Gene-based predictive biogeochemical modelling schematic. Inputs (1-3) and validation (4). Methyl coenzyme M reductase A (mcrA). COASTAL ECOSYSTEMS AS PANDORA BOX FOR METABOLICALLY FLEXIBLE CHEMOLITHO(AUTO)TROPHS (CHAPTER 3) Microaerophilic methanotrophs: low oxygen, high sulfide, and potential for EET In Chapter 3, we explored the metabolic flexibility of microaerophilic methanotrophs in coastal ecosystems, derived from anoxic sediments at Sandöfjärden in the Stockholm Archipelago. This site had oxic bottom water redox conditions, in contrast to the anoxic sediments with more hypoxic redox conditions used in Chapter 2 (Lilla Värtan). Similar to Chapter 2, we selected specific sediment depths with high anaerobic methane oxidation (AOM) rates, 8
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