Nitrososphaera viennensis gen. nov., sp. nov., an aerobic and mesophilic, ammonia-oxidizing archaeon from soil and a member of the archaeal phylum Thaumarchaeota.
Bottom Line: The organism gained energy by oxidizing ammonia to nitrite aerobically, thereby fixing CO2, but growth depended on the addition of small amounts of organic acids.The optimal growth temperature was 42 °C and the optimal pH was 7.5, with ammonium and pyruvate concentrations of 2.6 and 1 mM, respectively.Additionally, we propose the family Nitrososphaeraceae fam. nov., the order Nitrososphaerales ord. nov. and the class Nitrososphaeria classis nov.
Affiliation: University of Vienna, Department of Ecogenomics and Systems Biology, Archaea Biology and Ecogenomics Division, Althanstr. 14, 1090 Vienna, Austria.Show MeSH
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Mentions: In order to investigate optimal growth conditions for EN76T, a design of experiments (DoE) strategy was applied, using the factors temperature, pyruvate concentration and ammonium concentration. Based on our preliminary knowledge of the strain’s growth requirements (Tourna et al., 2011), the range for each factor (design space) was set as follows: 37–47 °C, 0.1–1.5 mM sodium pyruvate and 1–4 mM NH4Cl. As nitrite production was shown to follow biomass production (Tourna et al., 2011), it was used to calculate the growth rate (μ) and maximum growth rate (μmax), which were eventually used to develop the model (Design-Expert 8 software; Stat-Ease Inc.). Experiments were conducted in triplicate, except for the centre points of the initial two-level factorial screening design, which were set up in fivefold replicates. The two-level factorial design was applied in order to screen the design space rapidly. Due to a low model significance of data obtained from the initial two-level factorial screening design space, an augmented matrix was used in order to account for putative interactions of individual factors. Thus, the two-level factorial design space was extended by using a face-centred augmented matrix. Eventually, data points of all experiments (n = 51) were used to establish a response surface model (RSM). Data were analysed with the software Design-Expert 8. ANOVA, based on a stepwise regression elimination procedure, was used to develop the model. The desirability approach, as described elsewhere (Derringer & Suich, 1980), was used to maximize μ or μmax (variable) based on variation of quantitative factors, here c(ammonium), c(pyruvate) and temperature (within the design space). A score is given to each quantitative factor setting that can be used to maximize the variable. In this approach, desirability between 0 and 1 (corresponding to 0–100 %) can be assigned to a variable for optimization; factors identified as being outside a certain desirability function will not be considered for model generation. To verify the calculated optimal growth conditions identified by the established RSM model design space, one additional growth experiment (fivefold-replicated closed-batch cultures) was performed (Fig. 1a).
Affiliation: University of Vienna, Department of Ecogenomics and Systems Biology, Archaea Biology and Ecogenomics Division, Althanstr. 14, 1090 Vienna, Austria.