Competitive and substrate limited environments drive metabolic heterogeneity for comammox Nitrospira.

Nitrospira has been revealed as a high versatile genus. Although previously considered only responsible for the conversion of nitrite to nitrate, now we know that Nitrospira can perform complete ammonia oxidation to nitrate too (comammox). Comammox activity was firstly reported as dominant in extremely limited oxygen environments, where anaerobic ammonia oxidation was also occurring (anammox). To explain the comammox selection, we developed an Individual-based Model able to describe Nitrospira and anammox growth in suspended flocs assembled in a dynamic nitrogen and oxygen-limiting environment. All known and hypothesized nitrogen transformations of Nitrospira were considered: ammonia and nitrite oxidation, comammox, nitrate-reducing ammonia oxidation, and anaerobic nitrite-reducing ammonia oxidation. Through bioenergetics analysis, the growth yield associated to each activity was estimated. The other kinetic parameters necessary to describe growth were calibrated according to the reported literature values. Our modeling results suggest that even extremely low oxygen concentrations (~1.0 µM) allow for a proportional growth of anammox versus Nitrospira similar to the one experimentally observed. The strong oxygen limitation was followed by a limitation of ammonia and nitrite, because anammox, without strong competitors, were able to grow faster than Nitrospira depleting the environment in nitrogen. These substrate limitations created an extremely competitive environment that proved to be decisive in the community assembly of Nitrospira and anammox. Additionally, a diversity of metabolic activities for Nitrospira was observed in all tested conditions, which in turn, explained the transient nitrite accumulation observed in aerobic environments with higher ammonia availability.

Multiscale models driving hypothesis and theory-based research in microbial ecology.

Hypothesis and theory-based studies in microbial ecology have been neglected in favour of those that are descriptive and aim for data-gathering of uncultured microbial species. This tendency limits our capacity to create new mechanistic explanations of microbial community dynamics, hampering the improvement of current environmental biotechnologies. We propose that a multiscale modelling bottom-up approach (piecing together sub-systems to give rise to more complex systems) can be used as a framework to generate mechanistic hypotheses and theories (in-silico bottom-up methodology). To accomplish this, formal comprehension of the mathematical model design is required together with a systematic procedure for the application of the in-silico bottom-up methodology. Ruling out the belief that experimentation before modelling is indispensable, we propose that mathematical modelling can be used as a tool to direct experimentation by validating theoretical principles of microbial ecology. Our goal is to develop methodologies that effectively integrate experimentation and modelling efforts to achieve superior levels of predictive capacity.

Environmental and ecological controls of the spatial distribution of microbial populations in aggregates.

In microbial communities, the ecological interactions between species of different populations are responsible for the spatial distributions observed in aggregates (granules, biofilms or flocs). To explore the underlying mechanisms that control these processes, we have developed a mathematical modelling framework able to describe, label and quantify defined spatial structures that arise from microbial and environmental interactions in communities. An artificial system of three populations collaborating or competing in an aggregate is simulated using individual-based modelling under different environmental conditions. In this study, neutralism, competition, commensalism and concurrence of commensalism and competition have been considered. We were able to identify interspecific segregation of communities that appears in competitive environments (columned stratification), and a layered distribution of populations that emerges in commensal (layered stratification). When different ecological interactions were considered in the same aggregate, the resultant spatial distribution was identified as the one controlled by the most limiting substrate. A theoretical modulus was defined, with which we were able to quantify the effect of environmental conditions and ecological interactions to predict the most probable spatial distribution. The specific microbial patterns observed in our results allowed us to identify the optimal spatial organizations for bacteria to thrive when building a microbial community and how this permitted co-existence of populations at different growth rates. Our model reveals that although ecological relationships between different species dictate the distribution of bacteria, the environment controls the final spatial distribution of the community.

Biochemistry shapes growth kinetics of nitrifiers and defines their activity under specific environmental conditions.

Is it possible to find trends between the parameters that define microbial growth to help us explain the vast microbial diversity? Through an extensive database of kinetic parameters of nitrifiers, we analyzed if the dominance of specific populations of nitrifiers could be predicted and explained. We concluded that, in general, higher growth yield (YXS ) and ammonia affinity (a0NH3 ) and lower growth rate (µmax ) are observed for ammonia-oxidizing archaea (AOA) than bacteria (AOB), which would explain their considered dominance in oligotrophic environments. However, comammox (CMX), with the maximum energy harvest per mole of ammonia, and some AOB, have higher a0NH3 and lower µmax than some AOA. Although we were able to correlate the presence of specific terminal oxidases with observed oxygen affinities (a0O2 ) for nitrite-oxidizing bacteria (NOB), that correlation was not observed for AOB. Moreover, the presumed dominance of AOB over NOB in O2 -limiting environments is discussed. Additionally, lower statistical variance of a0O2 values than for ammonia and nitrite affinities was observed, suggesting nitrogen limitation as a stronger selective pressure. Overall, specific growth strategies within nitrifying groups were not identified through the reported kinetic parameters, which might suggest that mostly, fundamental differences in biochemistry are responsible for underlying kinetic parameters.

A framework based on fundamental biochemical principles to engineer microbial community dynamics.

Microbial communities are complex but there are basic principles we can apply to constrain the assumed stochasticity of their activity. By understanding the trade-offs behind the kinetic parameters that define microbial growth, we can explain how local interspecies dependencies arise and shape the emerging properties of a community. If we integrate these theoretical descriptions with experimental 'omics' data and bioenergetics analysis of specific environmental conditions, predictions on activity, assembly and spatial structure can be obtained reducing the a priori unpredictable complexity of microbial communities. This information can be used to define the appropriate selective pressures to engineer bioprocesses and propose new hypotheses which can drive experimental research to accelerate innovation in biotechnology.