A mathematical study of the ecology that shapes microbial communities of nitrifiers

Martínez Rabert, Eloy (2024) A mathematical study of the ecology that shapes microbial communities of nitrifiers. PhD thesis, University of Glasgow.

Abstract

Nitrification, the biotic transformation of ammonia to nitrate via nitrite, plays a fundamental role in natural and engineered systems. In the last decades, our understanding of nitrification has changed significantly – from the collaboration between two aerobic microbial cohorts, ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB), to the complex ecological network involving five microbial cohorts from both bacterial and archaeal domains and including anaerobic microbes: AOB, NOB, ammonia-oxidizing archaea (AOA), comammox bacteria (CMX) and anammox bacteria (AMX). Although this complexity has hindered the full comprehension of nitrification, new opportunities could arise for novel biotechnology designs to remove nitrogen compounds from sewage in a more efficient and sustainable way. The comprehension of the complex ecological network of nitrifiers is the first step to achieve this goal. In order to elucidate the ecological mechanisms that shape the nitrifying community, statistical methods (meta-analysis and nonparametric statistics) and multiscale modelling (Individual-based Model framework) were combined.

The meta-analysis, reviewing approximately 100 references in literature and more than 300 data points, found that AOA and CMX have higher growth yield, higher ammonia affinity and lower maximum specific growth rate than AOB, in accordance with the conventional life strategy theory (r-strategy, K-strategy and Y-strategy). This would explain their dominance in oligotrophic environments. However, CMX, with the maximum energy harvest per mole of ammonia, and some AOB (especially from Nitrosospira genus) have higher ammonia affinity than some AOA species. Moreover, similar oxygen affinity between AOB and AOA was found, and the presumed dominance of AOB over NOB in oxygen-limiting environments was discussed. Although Nitrobacter have the lowest oxygen affinity, Nitrospira have a similar affinity than AOB and AOA. Moreover, lower statistical variance of oxygen affinity than ammonia and nitrite affinities was observed, suggesting that nitrogen availability (ammonia and nitrite) is stronger selective pressure than oxygen. This meta-analysis also showed that the measured kinetic parameters (and potential niche specializations) are mainly defined by the fundamental differences in the biochemistry of nitrifying populations.

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. The modelling studies presented in this thesis were designed following the guidelines of in-silico bottom-up methodology, in which the simulated domain is piecing together sub-systems (i.e., key elements and processes) to give rise to more complex systems. Ruling out the belief that experimentation before modelling is indispensable, this thesis shows that mathematical modelling can be used as a tool to direct experimentation by validating theoretical principles and generating new hypotheses on microbial ecology. Two modelling studies is presented in this work. The first in-silico study aimed to explore the underlying mechanism that control the microbial community assembly in aggregates. For this, an artificial microbial community noninteracting (neutralism), collaborating (commensalism) and/or competing was simulated under different environmental conditions. This study identified specific spatial distributions of populations in function of the ecological interaction considered. When multiple ecological interactions were considered, the resultant spatial distribution was the one controlled by the most limiting substrate. Based on this, a theoretical modulus was defined, called eco-interaction modulus. With this, we are able to quantify the effect of environmental conditions and ecological interactions on the resultant microbial community. Although competition for space is generally overlooked, the in-silico results show its role on the assembly of microbial communities in aggregates. The second modelling study aimed to investigate the resilience of CMX under different nitrogen and/or oxygen limited environments considering all their reported catabolic activities. The in-silico results from this study suggest that even extremely low oxygen concentrations (~1.0 μM) allow for a proportional growth of AMX and CMX similar to the one experimentally observed. Additionally, a diversity of metabolic activities for CMX was observed in all tested conditions (i.e., metabolic heterogeneity), being essential for the survival of comammox Nitrospira under hypoxic conditions together with AMX. Moreover, metabolic heterogeneity would also explain the transient accumulation of nitrite experimentally observed in aerobic environments with higher ammonia availability.

Overall, the meta-analysis presented in this thesis highlights the importance of considering the microbial taxonomy, the biochemistry of populations, and the metabolic versatility of microbes for the definition of ecological niches of nitrifying populations. Additionally, the lack of defined ecological niches in nitrification might not be only because we are not considering key environmental variables that establish the ecological niches, but we are observing niche overlapping, that is, distinct nitrifying cohorts co-dominate in the same ecological niche. On the other hand, the in-silico studies reveal that (i) although ecological relationships between different species dictates the distribution of microbes in aggregates, the environment controls the final spatial distribution of the community, (ii) the specific microbial patterns observed are, in turn, the optimal spatial organization for microbes to thrive in aggregates and how columned stratification allows the co-existence of populations at different growth rates, and (iii) metabolic heterogeneity mechanistically explains the early findings on comammox Nitrospira – the coexistence with anammox bacteria under hypoxic conditions, the dominance of complete nitrification activity in nitrogen limiting environments and the transient accumulation of nitrite under aerobic conditions. Finally, this thesis shows that the systematic attribute of the in-silico bottom-up methodology, together with the gradual increasing in complexity of the simulated system, allows to draw stronger hypothesis on mechanisms, which accelerates the research task.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Additional Information: Supported by funding from the Engineering and Physical Sciences Research Council (EPSRC).
• Subjects: Q Science > QH Natural history > QH345 Biochemistry; Q Science > QR Microbiology; T Technology > T Technology (General)
• Colleges/Schools: College of Science and Engineering > School of Engineering
• Funder's Name: Engineering and Physical Sciences Research Council (EPSRC)
• Supervisor's Name: Smith, Professor Cindy, Sloan, Professor William and Gonzalez-Cabaleiro, Dr. Rebeca
• Date of Award: 2024
• Depositing User: Theses Team
• Unique ID: glathesis:2024-84125
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 10 Apr 2024 13:05
• Last Modified: 10 Apr 2024 13:30
• Thesis DOI: 10.5525/gla.thesis.84125
• URI: https://theses.gla.ac.uk/id/eprint/84125

Related URLs

• Article DOI