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Article

Network of Nitrifying Bacteria in Aquarium Biofilters: An Unfaltering Cooperation Between Comammox Nitrospira and Ammonia-Oxidizing Archaea

by
Martyna Godzieba
1,
Piotr Hliwa
2 and
Slawomir Ciesielski
1,*
1
Department of Environmental Biotechnology, University of Warmia and Mazury in Olsztyn, Sloneczna St. 45G, 10-719 Olsztyn, Poland
2
Department of Ichthyology and Aquaculture, University of Warmia and Mazury in Olsztyn Warszawska St. 117A, 10-719 Olsztyn, Poland
*
Author to whom correspondence should be addressed.
Water 2025, 17(1), 52; https://doi.org/10.3390/w17010052
Submission received: 28 November 2024 / Revised: 20 December 2024 / Accepted: 25 December 2024 / Published: 28 December 2024
(This article belongs to the Special Issue Advances in Biological Technologies for Drinking and Wastewater Treatment)
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Abstract

:
Nitrification plays a crucial role in aquatic ecosystems and in the biofilters used in fish farms. Despite their importance, the role of canonical nitrifiers, comammox bacteria, and archaea has not yet been sufficiently investigated. The aim of this study was to characterize the microbiome of the external canister biofilter in a freshwater fish aquarium, with particular focus on the role of comammox Nitrospira and their competition with other nitrifiers. To achieve this, a comprehensive approach combining metagenome sequencing and co-occurrence network analysis was used to study the interactions between microorganisms in portable biofilter. The fish were subjected to a changing feeding regime that affected the ecological relationships and abundance of different microbial taxa. The results showed the presence of two types of nitrifiers in the biofilter: comammox Nitrospira and ammonia-oxidizing archaea (AOA). Five comammox Nitrospira genomes were reconstructed, with comammox clade B being the most abundant with an average abundance of 7.8 ± 0.4%. In addition, two families of archaea were identified: Nitrosopumilaceae and Nitrososphaeraceae, with an average abundance of 4.3 ± 0.4%. Heterotrophs were also abundant in the bacterial community, particularly in the genera Actinomycetota, Planctomycetota, and Pseudomonadota. Network analysis indicated competitive interactions between comammox and heterotrophs, whereas no competition was observed between comammox and AOA. The predominance of comammox Nitrospira, and AOA over canonical nitrifiers emphasizes their better adaptation to oligotrophic environments. This study highlights the importance of competition within the biofilter microbiome and the role of ecological interaction networks, which can contribute to the optimization of water purification systems in RASs.

1. Introduction

Aquaculture, i.e., the farming of aquatic organisms, is playing an increasingly important role in ensuring global food security. According to statistics from the Food and Agriculture Organization of the United Nations, the global consumption of seafood has increased more than fivefold in the last sixty years [1]. In view of the growing demand for aquatic products and the increasing problem of overfishing, it is necessary to constantly increase the amount of fish from different types of farms on the market. This requires the development of farming technologies that are not only efficient but also ecologically sustainable. Conventional aquaculture systems such as the well-known and widely used ponds or cages have numerous disadvantages, including a strong dependence on water resources, the risk of pollution, and limited control over farming conditions [2,3]. For this reason, the development of recirculating aquaculture systems (RASs) has gained interest as a more sustainable alternative. In recirculating systems, water is repeatedly treated and purified, reducing water consumption while allowing more the precise control of environmental conditions such as temperature, pH, oxygen concentration, and contaminant levels [4].
One of the biggest challenges in fish farming in recirculation systems is the effective management of water quality, which is crucial for the health and growth of the fish [5]. In particular, fish metabolites such as ammonia (NH₃), which are produced as a by-product of protein metabolism, can accumulate in the water and lead to serious health problems or even the death of the farmed organisms [6]. Ammonia occurs in water in two forms: the more toxic non-ionized form and the less toxic ionized form. The proportion of the non-ionic fraction grows as the pH and temperature increase, and, at the same time, the risk of fish poisoning increases [7]. The tolerable concentrations of these compounds depend on the species and the age of the organisms [8]. Aquatic invertebrates are the most sensitive to elevated ammonia concentrations [9], particularly filter-feeding freshwater mussels of the family Unionidae, four of which have been classified by the United States Environmental Protection Agency as the most sensitive to acute ammonia toxicity, with a 96 h LC50 even as low as 0.06 mg/L [10]. Freshwater and marine fish are considered generally less sensitive, with similar tolerance levels, ranging from 0.068 to 2.0 NH3-N mg/L and 0.09 to 3.35 NH3-N mg/L (96 h LC50), respectively [11]. The duration of exposure to elevated ammonia concentrations is also important [12].
Biofilters, an integral part of recirculation systems, are responsible for biological water purification via the aforementioned nitrogen conversion. The microbiome of the biofilter, which consists of different groups of bacteria, plays a fundamental role in this process [13,14]. Among the microorganisms involved in nitrification, ammonia-oxidizing bacteria (AOB—Nitrosomonas sp. and Nitrosococcus sp.) and nitrite-oxidizing bacteria (NOB—Nitrobacter sp., Nitrotoga sp., and Nitrospira sp.) play important roles, oxidizing ammonia to nitrites and nitrites to nitrates, respectively [15]. In recent years, however, new groups of microorganisms have been discovered that are also involved in this process, including complete ammonia oxidizing bacteria (comammox), which are able to oxidize ammonia directly to nitrates, and archaea from the phylum Thaumarchaeota [16].
According to the ecological theory of r/K strategies, comammox and ammonia oxidizing archaea (AOA) exhibit characteristics of organisms favoring the K strategy, which means that they are adapted to low-resource conditions [17]. Aquacultures, where it is imperative to ensure low ammonia concentrations, are a good example of such an oligotrophic environment. These microorganisms develop slowly but utilize the available resources more efficiently. In general, they are characterized by a high affinity for ammonia, although different genera of AOA exhibit varying levels of affinity [18].
In contrast, r-strategists, represented by species such as those of the genus Nitrosomonas, are faster-growing and display a lower affinity for ammonia than K-strategists [19]. However, there is also diversity within this taxon, meaning that different Nitrosomonas species can thrive in a wide range of environments. For example, Nitrosomonas oligotropha, as the name suggests, thrives in oligotrophic environments, while Nitrosomonas europaea is adapted to eutrophic environments [20].
Many different types of nitrifiers are found in aquaculture biofilters, and the formation of the microbiome in these biofilters is influenced by many factors. Key influences are known to include water temperature, oxygen concentration, and pH [21], as well as the availability of organic substances and the water retention time [22]. Additionally, the design of the biofilter and the materials used in its construction can play crucial roles, as the properties of different surfaces can favor the colonization and growth of specific microorganisms. Changes in environmental conditions, e.g., fluctuations in the amount of nitrogen compounds resulting from changes in dosing and feeding schedules, can disrupt the ecological balance and lead to shifts in the abundance of certain bacterial groups, affecting the efficiency of the nitrification process. This is why it is so important to understand the processes that determine the effectiveness of the nitrogen compounds degradation in the fish breeding tank.
Unfortunately, the factors that determine the niche differentiation of nitrifiers and the shape of bacterial communities in RASs are not well-understood. In addition, most research has focused on large RASs with biofilters similar to those in wastewater treatment plants, while smaller portable canister filters remain largely unexplored. Portable canister biofilters are rarely studied, although unique processes can develop in this unusual environment that lead to the removal of pollutants. Furthermore, in such an environment, unique relationships may develop between the bacteria responsible for these processes. Due to the cyclical feeding of the fish, the microbiome of the biofilter is subject to constant change, which also makes it an extremely interesting research object for biological processes. This type of feeding harbors the risk that the activity and abundance of nitrifying bacteria will be significantly reduced at times when the food substrate is scarce. As a result, after feeding, when the level of nitrogen compounds increases, the risk of accumulation of toxic nitrogen compounds also increases, as the bacteria may not be able to adapt and process these compounds quickly enough. To our knowledge, this aspect of the biofilter function has not yet been investigated.
One approach to gaining greater insight into factors influencing nitrifier niche-differentiation and the shape of bacterial communities in RASs is to combine metagenome sequencing with network analysis. In recent years, metagenome-sequencing techniques have opened up new possibilities for studying the complexities of microbial communities in biofilters. Most bacteria cannot be cultivated in laboratory settings, making molecular and bioinformatics tools essential for studying them. Shotgun metagenome sequencing produces numerous reads, from which metagenome-assembled genomes (MAGs) are created. These MAGs represent the genomes of the bacteria present in the environment being examined. Metagenome sequencing enables the analysis of the whole metagenome of microorganisms present in environmental samples, facilitating the identification of both known and unknown bacterial and archaeal species [23]. This technique provides detailed information about the composition and function of the microbiome, enabling a better understanding of the factors that influence its formation. In combination with network analysis of the bacterial community, it is possible to identify complex interactions between different microbial species, which can lead to the discovery of new ecological relationships that would not be visible using traditional research methods [24]. Combining metagenome sequencing with network analysis provides a powerful research tool that enables a more comprehensive understanding of how the microbiome functions in recirculating systems.
Therefore, this study combined these techniques with the objectives of (i) testing the hypothesis that K-strategists dominate in an external freshwater canister biofilter, (ii) identifying the bacterial groups that dominate the competition for nutrients in the biofilter, and (iii) determining whether alternating the feeding regime affects the ecological relationships and abundance of different microbial taxa. This combination of metagenome sequencing with network analysis enabled a more detailed investigation of the complexity of the microbiome of portable biofilters, which can contribute to a better understanding and optimization of the processes occurring within them. In turn, this knowledge could enable engineers to improve existing filters or design new ones for use in aquaculture recirculation systems.

2. Materials and Methods

2.1. Description of the Fish Farming Tank and Filtration System

All biomass samples for the microbial community analysis were taken from an EHEIM Professionel 3 (model 1200XLT) external canister filter with a volume of 13.5 L (EHEIM GmbH & Co. KG, Deizisau, Germany). The filter was equipped with both a mechanical filter and an ultraviolet light chamber, which inactivated heterotrophic and coliform bacteria. The biofilter contained three types of filter media. The bottom layer was filled with spherical PVC shapes, approximately 1.5 cm in diameter, with surfaces featuring numerous channels. The middle layer consisted of hollow ceramic cylinders, while the top layer was filled with a porous sponge. Water from the aquarium was pumped into the lower part of the filter, and the cleaned water was drained from the upper part back into the aquarium. A diagram of the filter can be seen in Figure 1. The maximum flow rate through the biofilter was 400 L per hour. This biofilter was designed to handle up to 0.5 kg of feed per day, based on the predicted ammonia production from fish protein catabolism at this feeding rate.
The RAS was a glass tank with a volume of 0.35 m3, in which 50 Amur sleeper fish (Percottus glenii Dybowski, 1877) were reared, with an average weight of 17.0 ± 5.7 g. The fish were fed every third day with commercial feed (Aller Aqua Ltd., Christiansfeld, Denmark) at a rate of approximately 2% of the total fish biomass. The typical water temperature in the system was 19.3 ± 0.2 °C, and the dissolved oxygen concentration was 8.98 ± 0.06 mg/L. The water’s pH was stable, with an average value of 7.8 ± 0.1. The photoperiod was consistent with natural day–night cycles.

2.2. Sampling and DNA Extracion

To assess the composition of the biofilter microbiome and any potential changes caused by alternating feeding, biomass samples were collected for microbiological analysis both the day before and the day after fish feeding. To reduce variability, sampling was conducted during two distinct periods (Period I and Period II), each consisting of these two sampling days. On each sampling day, biomass samples were obtained from different layers of the biofilter and collected into sterile 50 mL Falcon tubes. The samples were promptly frozen and stored at −20 °C until further analysis.
Before isolating DNA, the samples were centrifuged to eliminate excess water, and 500 mg of semi-dry biomass was used for DNA extraction. DNA samples isolated separately from each layer of the biofilter on the same sampling day were pooled together. This process ensured that the resulting DNA libraries provided a comprehensive representation of the microbiome throughout the entire biofilter.
DNA was isolated using the FastDNA® SPIN Kit for Soil (MP Bio, Solon, OH, USA) according to the manufacturer’s instructions. The concentration of the isolated nucleic acids was measured with a Qubit fluorimeter (Invitrogen, Waltham, MA, USA) using Qubit™ Broad Range Quantification Assay Kits (Invitrogen, Waltham, MA, USA).

2.3. Concentration of Nitrogen Compounds and Dissolved Oxygen

Water samples from both the inflow and outflow of the filter were collected at the same time as the biomass samples to determine the concentrations of various nitrogen compounds. The concentrations of ammonia, nitrite, and nitrate were measured on the day of sampling using photometric LCK cuvette tests (Hach Lange GmbH, Düsseldorf, Germany). The temperature and oxygen concentrations were measured using an HQ40D digital multimeter (Hach Lange GmbH, Düsseldorf, Germany).

2.4. Metagenomic Sequencing and Data Analysis

DNA sequencing was performed by Macrogen Inc. (Seoul, Korea). A TruSeq DNA PCR-Free Kit (Illumina, San Diego, CA, USA) was used to prepare the metagenomic library, which was sequenced on a NovaSeq 6000 S4 (Illumina, San Diego, CA, USA) using paired-end sequencing (2 × 150 bp). The raw sequences were deposited in the NCBI Sequence Read Archive (SRA) under accession number PRJNA1175723.
Read preprocessing, metagenomic assembly, binning, and taxonomic classification of the MAGs were carried out using tools from the KBase platform [26]. Briefly, raw reads underwent quality assessment, including trimming and filtering based on quality and length, using Trimmomatic (v.0.36) [27] and FastQC (v.0.12.1) [28]. The four metagenomic datasets were co-assembled using two different assemblers: MEGAHIT (v.1.2.9) [29] and metaSPAdes (v.3.15.3) [30], with a minimum contig length of 2000 bp. The two assemblies were compared using the KBase app “Compare Assembled Contig Distributions” (v.1.1.2), which generates a report similar to MetaQUAST. Based on assembly statistics (number of contigs, longest contig, N50, L50), the metaSPAdes assembly was selected for further analysis. MAGs were binned using three tools: MetaBAT2 [31], MaxBin2 [32], and CONCOCT [33]. To enhance binning quality, consensus assignments from these tools were integrated using DAS Tool [34] to create an optimized, non-redundant set of bins. Completeness and contamination of each MAG were assessed with CheckM [35].
MAGs that met quality thresholds (completeness ≥50% and contamination <10%) were taxonomically classified using the GTDB-Tk toolkit [36]. Genome abundance was quantified across samples by mapping reads to the MAGs, with normalized abundance calculated using the formula from Yang et al. (2020) [37].
To investigate the metabolic roles of MAGs in the microbial community of the biofilter and identify bacteria involved in the nitrogen cycle, the DRAM [38] annotation pipeline on the KBase platform was used. DRAM uses Prodigal for gene prediction, with annotation based on the KOfam, Pfam, CAZy, and MEROPS databases.
Alpha and beta biodiversity analyses were conducted on the obtained MAGs. Bacterial community indices (Simpson index (1-D) and Shannon index (H)) and principal coordinate analysis based on Bray–Curtis dissimilarity were calculated using the PAST software (version 4.03) [39].

2.5. Construction of Microbial Co-Occurrence Network

The co-occurrence network of individual bacterial genera was constructed based on a correlation analysis. Spearman’s correlation coefficients were calculated for bacterial genera that had an abundance of at least 0.01% in at least one sample. Spearman’s rank correlation was calculated using Statistica (v.13.1, StatSoft, Inc., Tulsa, OK, USA). The resulting correlation matrix was used to visualize the network with Gephi software (v.0.10.1) [40], applying the “Force Atlas” layout. The network included only highly correlated taxa, defined as those with correlation coefficients ≥0.75 or ≤−0.75.

3. Results

3.1. Results of Physicochemical Measurements

The concentrations of nitrite and ammonia were very low in both the water that entered the filter and the purified water (at the detection limit). In contrast, the nitrate concentrations were higher than ammonia and nitrite concentrations in all the samples tested (Table 1). At the same time, there were no significant differences between the samples before and after feeding. Low ammonia and nitrite concentrations and the presence of nitrates indicate that the biofiltration system was functioning effectively and that the nitrification process was stable.

3.2. Genome Reconstruction and Biofilter Microbiome Composition

The metagenome sequencing of biomass samples from the biofilter yielded a total of 71.5 Gbp of Illumina short-read raw data. The processing of these raw reads led to the reconstruction of 203 MAGs meeting the assumed quality requirements, i.e., at least 50% completeness and at most 10% contamination. Phylogenetic analysis of these MAGs based on 120 bacterial and 53 archaeal marker genes revealed that 200 of them represent bacterial genomes, while 3 represent archaeal genomes. All reconstructed genomes were assigned to one of 23 phyla, of which Planctomycetota (45 MAGs), Bacteroidota (32 MAGs), Pseudomonadota (29 MAGs), and Chloroflexota (22 MAGs) had the most representatives (Figure 2). Five MAGs were assigned to the phylum Nitrospirota, while three MAGs were assigned to the archaea phylum Thermoproteota. The large number of representatives of these phyla does not necessarily mean that they were very abundant in the microbial community. Of all 203 MAGs, only 37 reached an abundance of at least 1% in at least one sample. Both before and after feeding, Actinomycetota dominated the biofilter microbiome, although their average abundance in the AF samples decreased significantly (from 22.4 ± 1.8% to 15.6 ± 0.2%) (Figure 3). Planctomycetota (BF—14.2 ± 0.7%, AF—14.0 ± 0.3%) and Pseudomonadota (BF—10.6 ± 0.4%, AF—12.3 ± 0.9%) ranked second and third, respectively. The phylum Nitrospirota, which was represented by only five MAGs, ranked fourth (BF—9.5 ± 0.2%, AF—11.9 ± 1.4%). The abundance of archaea was also quite high: BF—4.5 ± 0.7%, AF—4.0 ± 0.2%.
The alpha diversity was assessed using the Simpson index of diversity (1-D) and Shannon index (H) (Table S1). The Simpson index highlights the evenness of the community and can be interpreted as the probability that two randomly selected individuals from a sample belong to different species. High values of this index indicate greater diversity within the community. Meanwhile, the Shannon index focuses on the richness of the community. The values of both of these indices did not change significantly before and after feeding. The beta biodiversity was analyzed using principal coordinate analysis based on the Bray–Curtis dissimilarity. Figure S1 showed that no pairs grouped together and the considerable distances between the samples suggest that the microbial communities of the biofilter experienced significant fluctuations due to changing feeding conditions.
At lower taxonomic levels, the abundance of identified MAGs decreased. At the family level, nearly 40% of MAGs remained unclassified (BF: 38.4 ± 1.0%, AF: 35.3 ± 1.0%), while, at the genus level, as many as 80% of MAGs were unclassified (BF: 80.5 ± 1.2%, AF: 75.7 ± 1.4%). Due to this, the family level was chosen as the lowest taxonomic level for analysis (Figure 3). The most abundant family was Gaiellaceae, represented by two MAGs: 198 and 225. MAG 198 was the most abundant of all MAGs, with a relative abundance of 12.7 ± 1.0% and 9.0 ± 0.2% in BF and AF samples, respectively, whereas the abundance of MAG 225 ranged from 0.2% to 0.4%. The second most abundant family was Nitrospiraceae, with abundances of 9.5 ± 0.2% (BF) and 11.9 ± 1.4% (AF). Similar to Gaiellaceae, one of the five MAGs belonging to Nitrospiraceae (MAG 119) had a significantly (t-test p < 0.05) higher abundance than the others, with values of 7.3 ± 0.1% in BF samples and 8.3 ± 0.5% in AF samples. Two families of archaea were identified: Nitrosopumilaceae (MAGs 164 and 199) and Nitrososphaeraceae (MAG 092). Among the Nitrosopumilaceae, MAG 164 was several times more abundant than MAG 199 and ranked fifth in abundance at 2.7 ± 0.2% (BF) and 2.2 ± 0.4% (AF). MAG 092 had a slightly lower abundance, at 1.6% in both BF and AF groups.
A phylogenetic analysis of the MAGs classified as Nitrospira sp. was also carried out (Figure 4). It showed that both clades of comammox Nitrospira were present in the biofilter. Clade A comprised four MAGs—MAG 143, MAG 144, MAG 201, and MAG 219—while clade B comprised one—MAG 119.

3.3. Biofilter Microbial Network

The structure of the microbial community of the biofilter was modelled using a co-occurrence network (Figure 5). The nodes in the network represent MAGs with a minimum abundance of 0.5% in at least one sample, and the edges connecting the nodes indicate the existence of strong correlations in co-occurrence and possible ecological dependence. The resulting network consists of 62 nodes connected by 844 edges. In the network, 415 edges have a negative correlation coefficient (red), while 429 have a positive one (green). The presence of two modules was detected in the network, which are marked with two colors. The modularity class depends on the density of connections between the microorganisms in the network and reflects a particular group of microorganisms that have more interactions with each other than with others. In the case of the studied biofilter microbiome, the pink module was slightly larger and contained 32 nodes (51.6%), while the green module contained 30 nodes (48.4%). MAGs representing Nitrospira sp. and Nitrosocosmicus sp. were assigned to the pink cluster, while Nitrosotenuis sp. was assigned to the green cluster. Most of the 402 edges connecting members of different modules had a negative value (385). In turn, edges between members of the same module had a predominantly positive value. Such a distribution could indicate the existence of two competing subpopulations.
The size of the nodes in the network is directly proportional to the value of betweenness centrality. This is a measure of how often a particular node is on the shortest paths between pairs of other nodes in the network. High betweenness centrality values of nodes representing bacterial genera indicate that they have a substantial effect on the connections between different bacterial groups and that ecological or metabolic dependencies exist between these genera. In the network, the highest values of betweenness centrality corresponded to MAG 126 (Luteolibacter sp.), 48.7; MAG 224 (Acidimicrobiia sp.), 40.5; and MAG 127 (Ferruginibacter sp.), 38.7. The nitrifiers had moderate betweenness centrality values (from 16.7 to 19.2), with the exception of MAG 164 (Nitrosotenuis sp.), which had a value of 30.6. However, the nodes representing Nitrospira sp. had very high values of the degree parameter (39–40), which measures the number of direct connections that a given node has with other nodes in the network.

3.4. Nitrogen-Cycle-Related Genes

Genes related to the nitrogen cycle, i.e., amo, hao, nxr, napA/narG, nirK/nirS, nrfA, norB, and nosZ, were found in most MAGs (Figure 6). All Nitrospira sp. MAGs had genes encoding ammonium monooxygenase, suggesting that they can be categorized as comammox Nitrospira. MAGs 119, 143, and 219 had a complete set of genes related to nitrification (amo, hao, and nxr). The Nitrospira sp. MAGs also differed in terms of other genes related to the nitrogen cycle. The nirK/nirS gene was not detected in MAGs 201 and 219, while norB was detected in MAGs 143, 144, and 219. In addition to comammox bacteria, archaea also played an important role in the nitrification process. The presence of the amo gene was detected in two of the three MAGs: MAG 092 (Nitrosocosmicus sp.) and MAG 164 (Nitrosotenuis sp.). The nxr gene was more frequent and was detected in a total of 30 MAGs. The most frequent genes were those related to the denitrification process: napA/narG was present in 46 MAGs, nirK/nirS in 73, nrfA/nrfH in 39, norB in 40, and nosZ in 42.

4. Discussion

4.1. The Diversity and Role of Nitrifiers in the Biofilter

The nitrifier community in the tested biofilter consisted of bacteria from the genus Nitrospira and ammonia-oxidizing archaea from the genus Thermoproteota. The presence of the amo, hao, and nxr genes in all Nitrospira sp. MAGs clearly indicates that these bacteria are capable of complete nitrification. However, no MAGs were found from other nitrifying organisms, such as Nitrosomonas sp., which are commonly present in biofilters. Earlier studies attributed the major role in ammonia oxidation in biofilters to AOB [42], and, later on, to archaea [43]. In recent years, however, this perspective has evolved with the discovery of complete ammonia oxidizers within Nitrospira genus and extensive studies on their ecophysiology. Research indicates that comammox Nitrospira are highly abundant in oligotrophic environments, such as biofilters [44,45,46]. The advantage of these groups over canonical AOB could be their higher affinity for ammonia. The kinetic Michaelis–Menten equations, which yield the apparent half-saturation constant Km(app) and the maximum reaction rate Vmax, are used to determine and compare the substrate affinity of different microorganisms. Km(app) is defined as the effective substrate concentration needed to achieve half of the maximum reaction rate. Thus, a lower Km(app) value indicates more effective substrate binding, even at low concentrations. Vmax, on the other hand, represents the reaction rate when the enzyme is saturated with substrate [47]. Kits et al. (2017) [18] found that the Km(app) of a pure culture of Nitrospira inopinata ranged from 0.65 to 1.1 μM total ammonium (equivalent to ~0.053–0.083 μM NH3), with Vmax reached at total ammonium concentrations as low as 5 μM. Currently, there are no other pure cultures of comammox bacteria available, so no precise kinetic data for other species of comammox Nitrospira exist. However, enrichment cultures are under investigation. In a study conducted by Sakoula et al. (2021) [48], the enrichment culture of Nitrospira kreftii demonstrated a very high affinity for ammonia, with a Km(app) of 0.040 μM NH3. In general, the affinity for ammonia is typically lower in most representatives of AOB, although there is some variation. For instance, representatives of cluster 6a within the Nitrosomonas genus, such as Nitrosomonas oligotropha, prefer oligotrophic environments and possess a relatively high affinity for ammonia, with Km(app) values ranging from ~ 0.24 to 4.4 μM NH3 [20,49,50]. In contrast, Nitrosomonas species from cluster 7 thrive better in eutrophic environments and at higher ammonia concentrations, with Km(app) values ranging from 12.5 to as much as 160 μM NH3 [50,51]. Archaea also display varying levels of ammonia affinity. Notably, the AOA model strain Nitrosopumilus maritimus SCM1, found in saltwater and marine RAS biofilters, exhibited a very low half-saturation constant (Km(app) = 0.132 μM total ammonium, equivalent to ~3 nM NH3). This led researchers to initially believe that most archaea would also demonstrate a high affinity for ammonia [52]. However, later studies challenged this view. For example, Jung et al. (2022) [47] reported two strains of the genus Nitrosocosmicus sp. with significantly lower ammonia affinities, comparable to most AOB, with Km(app) values of approximately 12.37 μM NH3 and 16.32 μM NH3. This indicates that Nitrosocosmicus sp. may occupy similar ecological niches to AOB.
In this study, Nitrosocosmicus sp. was relatively abundant but less than Nitrosotenuis sp., the latter of which has been identified in freshwater biofilters, showing an affinity for ammonium with Km(app) values ranging from 38.7 to 41.9 μM total ammonium (approximately 4.86 to 5.26 μM NH3) [18]. Low ammonium concentrations in biofilters favor the growth of bacteria exhibiting an “K” strategy with high substrate affinity. Considering the differences in ammonium affinity among various nitrifiers, the proportions observed in the tested biofilter align with the findings of the kinetic studies. Comammox bacteria were the most abundant, followed by AOA Nitrosotenuis sp. and Nitrosocosmicus sp., while canonical AOB, which typically follow an “r” strategy, were suppressed due to intense competition. However, in systems with higher ammonia concentrations, the abundance of AOB may also increase. This was evident in the work of Roalkvam et al. (2021) [53], which investigated the microbial communities of two biofilters with different fish stocking densities. In the first biofilter, ammonia levels reached up to 17.6 mg N/L, with the abundance of Nitrosomonas sp. ranging from 2.3% to 2.9%. In the second biofilter, where ammonia concentrations reached up to 8.3 mg N/L, a high abundance of Nitrosococcus sp. and Nitrosomonas sp. was observed, with percentages of up to 3.3% and 2.7%, respectively.
The coexistence of potentially competing AOA and comammox is common in the microbiome of a single biofilter, although their proportions can vary. In this study, both groups remained abundant, although the total abundance of archaea decreased slightly after feeding. This change was not statistically significant (Wilcoxon test, p > 0.05). In contrast, the total abundance of Nitrospira sp. increased significantly (Wilcoxon test, p < 0.05), likely due to the greater availability of nitrogen and organic compounds. The concentrations of ammonia, nitrate, and nitrite were very low both before and after feeding. This is probably attributable to the high activity and rapid processing of nitrogen compounds by the bacteria in the biofilter. Consequently, any increase in nitrogen compound concentrations after feeding was not detectable at the time of water sampling for physico-chemical tests. This means that the microorganisms present in the biofilter had sufficient adaptability to react quickly to the increase in pollution.
Based on the phylogeny of the amoA gene, comammox Nitrospira can be divided into clades A and B, and further into subclades. A phylogenetic analysis of Nitrospira sp. MAGs revealed a diverse array of these organisms, indicating their affiliation with both comammox clades. Unlike previous biofilter studies that reported the presence of comammox bacteria, our findings showed that clade B was the most abundant in this study. A recent investigation of biofilters in freshwater aquaria found that comammox clade A was dominant, with 38 biofilters analyzed [45]. In that study, PCR was used to detect comamox, and clade A comammox Nitrospira was abundant across all samples, while clade B was not found. Overall, many studies suggest that comammox clade A is typically more common in various environments, with subclade A.1 frequently identified in aquatic and engineered systems, whereas subclade A.2 and clade B are often associated with soils and sediments [54,55,56,57]. However, the significant presence of comammox clade B in the freshwater biofilter examined in this study indicates that these patterns may not be universal. This highlights the fact that, despite a substantial advancement in our understanding of complete nitrifiers in recent years, it remains challenging to accurately determine the ecological niche preferences of these microorganisms.

4.2. Diversity and Role of Heterotrophic Bacteria in the Biofilter

One of the objectives of this study was to determine the composition of the microbial population in the biofilter and the roles of its individual members in the ecosystem, particularly regarding the purification of recirculating water. Nitrifying bacteria are crucial for the effectiveness of the water purification system. However, the majority of the biofilter community consists of heterotrophic bacteria, which are much more diverse. Such proportions of these two groups of microorganisms are typical for systems in which the C/N ratio is high, e.g., in fish farming systems, but also in wastewater treatment systems. As autotrophs, nitrifying bacteria prefer to utilize inorganic carbon [58]. Organic carbon, on the other hand, which in RAS systems comes from uneaten feed and fish feces, favors the development of heterotrophic bacteria. The autotrophic metabolism of carbon compounds is energetically less efficient than the heterotrophic metabolism, which is the reason for the higher maximum growth rates and growth yields of the heterotrophs compared to the autotrophs. A sudden increase in the amount of organic matter in the period after feeding can lead to an intensification of the competitive relationship between heterotrophs and autotrophs [59]. Fast-growing heterotrophs consume the oxygen resources necessary for nitrification, which can lead to a reduction in nitrification rates and the accumulation of toxic fish metabolites in the recirculating water. However, heterotrophic bacteria also require nitrogen compounds for their growth, so, in the environment of RAS systems, the amount of available N is a factor limiting their growth, even under conditions of periodic growth of C compounds. The high affinity of comammox bacteria and AOA for ammonia gives them an advantage in competition with heterotrophs for this substrate, which, in this study, was probably the reason for the increase in abundance of nitrifiers and the decrease in abundance of the main representative of heterotrophs—Gaiellaceae (MAG 198)—in the samples after feeding.
Typically, bacteria from the phyla Pseudomonadota and Bacteroidota dominate in biofilters [13,60]. In the biofilter tested, these groups were present in significant numbers, but Actinomycetota and Planctomycetota were even more abundant.
Bacteria from the phylum Actinomycetota are commonly found in aquatic environments. Their presence in fish culture systems can positively influence both water quality and fish health, as they produce a wide range of secondary metabolites, including antibacterial compounds. Many genera within the Actinomycetota have the potential to be used as probiotics and in bioremediation [61]. As saprophytic heterotrophs, they produce various hydrolytic enzymes that decompose organic material, thereby improving water quality. In the study conducted by Babu et al. (2018) [62], the addition of Actinomycetota bacteria led to a significant reduction in biochemical oxygen demand (BOD) in pond shrimp culture systems. In this study, the phylum Actinomycetota was represented by 16 members, with the most prevalent member being MAG 198 from the Gaiellaceae family. This bacterium was first isolated from a deep mineral water aquifer [63] and has since been found mainly in various extreme environments, including aquatic settings such as the water-sediment interface of an acidic lake [64], mangrove wetlands [65], and the deep sea [66]. In this study, MAG 198 played a role in the denitrification process, as genes coding for nitrate and nitrite reductase were identified. This finding is consistent with existing literature, which states that the only known member of the Gaiellaceae family, Gaiella occulta, possesses genes coding for the narGHIJ nitrate reductase complex and the MFS-type nitrate/nitrite transporter (narK/nasA) [67]. The abundance of Gaiellaceae decreased significantly in the samples after feeding. However, due to the limited information available on their ecophysiology, it is difficult to determine whether this decline is a direct consequence of environmental changes or the result of strong competitive pressure from growing comammox bacteria. Other families of the phylum Actinomycetota, including Miltoncostaeaceae and Nocardiidaceae, were found in much lower numbers.
In contrast to Actinomycetota, the abundance of Planctomycetota remained stable and did not change significantly in the samples after feeding. This phylum was the most abundant, represented by 45 MAGs. However, none of these MAGs dominated as strongly as MAG 198 in the Actinomycetota phylum. Like Actinomycetota, Planctomycetota are widespread in biofilter communities. Burut-Archanai et al. 2021 [68] also found that Planctomycetota developed more in biofilters acclimatized to low total ammonia nitrogen (TAN) concentrations (5 mg N/L), while Actinomycetota thrived at high TAN concentrations (100 mg N/L); however, in this study both groups were abundant despite low ammonia concentrations. This study identified several families of Planctomycetota: Gemmataceae, Lacipirellulaceae, Pirellulaceae, Planctomycetaceae, and Tepidisphaeraceae. Planctomycetota is a highly diverse group of aerobic or facultatively anaerobic heterotrophs. The only exceptions are anaerobic autotrophic anammox bacteria, which were not detected in this study. The genomes of bacteria from the phylum Planctomycetota contain genes related to the metabolism of nitrogen, oxygen, sulfur, and metal(loids), highlighting their metabolic versatility and explaining their widespread occurrence in various environments.
In the analyzed biofilter, representatives of Planctomycetota likely played a significant role in several stages of the nitrogen transformation process. The nxr gene was identified in two MAGs, while the hao gene was found in eleven. At least one key gene associated with denitrification (napA/narG, nirK/nirS, norB, and nosZ) was present in thirty-five MAGs, and the nrfAH gene, essential for dissimilatory nitrate reduction (DNRA), was detected in twenty MAGs. Although the biofilter was aerobic, deeper layers of the biofilm, where oxygen diffusion is limited, may have facilitated anaerobic processes. Notably, nitrate reductase in the Planctomycetota MAGs was primarily encoded by the napA gene, which can function under both aerobic and anaerobic conditions. This contrasts with nitrate reductase encoded by narG, whose expression is inhibited by oxygen [69]. This suggests that aerobic denitrification could also occur in the biofilter. However, without gene expression studies, it is impossible to ascertain whether aerobic or anaerobic processes predominate.
Pseudomonadota and Bacteroidota are typically the most abundant phyla found in biofilters within the studied systems; however, their presence was lower in this analysis. An examination of the genes present in MAGs from these groups indicated their involvement in denitrification processes. The unusual proportions of the dominant bacterial groups may result from various environmental and ecological factors, as well as stochastic processes influencing the formation and differentiation of the microbiome. Furthermore, biofilter ecosystems in fish farms represent unique environments, as each system differs significantly from the others, which subsequently impacts the diversity of their microbiomes. Indeed, it is often suggested that this diversity is so extensive that each system possesses its own unique microbiological “fingerprint” [44]. This variability can be attributed to significant differences among individual recirculating aquaculture systems, such as the species and developmental stages of the fish being reared, the type of feed utilized, and the frequency and method of disinfection, as well as the design, filter material, and capacity of the biofilter itself [70]. The formation of the microbial community is also influenced by physicochemical factors such as the pH value, the total dissolved solids, or the oxygen concentration. For example, Bayer et al. 2019 [71], when studying several compartments of the same RAS system, found that the bacterial population in these compartments was modelled by physico-chemical parameters such as temperature, pH, and salinity, although they originated from the same reservoir. These diverse bacterial groups play important roles in various biological processes and, together with nitrifiers, help maintain the desired quality of recirculating water. For example, numerous species of denitrifiers not only engage in the conversion of nitrogen compounds but also degrade organic matter. Due to the differing environmental requirements of the various bacterial groups, particularly their varying tolerances to oxygen, bacterial biofilms in biofilters exhibit spatial organization [72]. In canister filters, where aerobic conditions prevail, anaerobic bacteria can develop in the deeper layers of the biofilm, while aerobic nitrifiers grow in the outer layers.

4.3. Competitive and Symbiotic Relationships Within the Bacterial Network of the Biofilter

In the plotted network, nitrifiers were characterized by high betweenness centrality and degree values. Nodes exhibiting these characteristics are likely to play a central role in the community, and their removal could disrupt the network structure unless new connections form between the remaining nodes [24]. The high values of these parameters in nitrifiers, especially in environments with large populations of denitrifiers, are likely due to their role as nitrate producers. However, this mutually beneficial cooperation can be much more complex.
The mutual positive influence between heterotrophs and comammox bacteria or AOA has been observed in various studies. For instance, in the research conducted by Xu et al. (2022) [73], which examined the nitrifying community within a wastewater nutrient removal system supplemented with volatile fatty acids, comammox bacteria were found to be abundant. The authors noted that comammox bacteria can provide vitamins and cofactors, such as cobalamin and biotin, to heterotrophs (e.g., Burkholderiaceae). In return, these heterotrophs supply amino acids, such as phenylalanine and tyrosine, which comammox bacteria cannot synthesize on their own.
Mehrani et al. (2022) [74] revised the conventional two-stage nitrification model by incorporating comammox and heterotrophic denitrification based on soluble microbial products (SMP). SMPs are organic compounds produced as a result of normal metabolic processes in microorganisms and the decomposition of biomass. These products, which can originate from autotrophic bacteria, can be effectively used as a carbon source by heterotrophic organisms, even at low concentrations.
Another example of symbiotic cooperation is between AOA and heterotrophs, as described by Bayer et al. 2019 [71]. The authors highlight that many AOA strains are sensitive to oxidative stress and lack the catalase gene, which encodes an enzyme that detoxifies hydrogen peroxide—a by-product of oxygen metabolism. The organic compounds released by the archaea can be utilized by the heterotrophs, which, in turn, neutralize the hydrogen peroxide present in the environment. This relationship benefits both groups of microorganisms.
The significant number of positive correlations identified in the network analyzed in this study suggests beneficial dependencies between autotrophs and heterotrophs. However, the division of the network into two distinct clusters, characterized primarily by negative correlations, indicates the presence of two competing populations. The cluster labelled in green primarily included those MAGs that had higher abundance before feeding, while the cluster labelled in pink consisted mainly of those with greater abundance following feeding. In a context of persistent nitrogen deficiency, this separation may arise from intensified competition for substrate during periods of greater availability.
All Nitrospira sp. MAGs were located in the pink cluster, while the most abundant AOA were found in the green cluster. Notably, none of the Nitrospira sp. MAGs displayed direct negative correlations with one another or with the AOA MAGs, despite potentially competing for the same substrate. It is important to recognize that ecological relationships among microorganisms are complex and influenced by a variety of factors. While network analysis has its limitations and cannot provide definitive conclusions, it serves as a valuable preliminary tool for modelling potential interactions among microorganisms and between these organisms and their environment. The relationships observed in this analysis may suggest new avenues for further research.

5. Conclusions

By studying the bacterial biofilter community and the role of its members in nitrogen transformation processes under varying availability of nitrogen compounds, we found that the role of canonical AOB in freshwater canister biofilters is much smaller than previously thought; in the case of the biofilter tested, it turned out to be negligible. In addition, comammox bacteria thrive much better than AOB in this type of biofilter due to their high affinity for ammonia. AOA can also significantly influence the nitrification process in the biofilter, although they are under competitive pressure from the comammox bacteria, which leads to a reduction in their abundance. Furthermore, fluctuations in the availability of food substrates shape the microbiome of the biofilter and affect the competitive relationships between the microorganisms.
The conclusions drawn may be useful for users of this type of aquarium biofilter. Often, when setting up smaller, non-commercial aquaria, users opt for ready-made preparations containing cultures of nitrifying bacteria, typically from the genera Nitrosomonas or Nitrobacter. However, this study suggests that aquarium biofilters are not an ideal environment for these bacteria, which should be taken into account when producing and using such preparations. Furthermore, the impact of feeding regimes on the abundance and competitive relationships among bacteria indicates that a planned feeding schedule for fish is essential in order to maintain a consistent level of food substrates for microorganisms.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w17010052/s1, Figure S1: Principal coordinates analysis based on the Bray—Curtis dissimilarity for the samples before (BF) and after feeding (AF).; Table S1: Values of alpha biodiversity indices in microbial samples before (BF) and after feeding (AF).

Author Contributions

Conceptualization, M.G. and S.C.; methodology, S.C. and P.H.; formal analysis, M.G. and P.H.; investigation, M.G.; resources, M.G., S.C. and P.H.; data curation, M.G.; writing—original draft preparation, M.G. and P.H.; writing—review and editing, S.C. and P.H.; visualization, M.G.; supervision, S.C.; project administration, M.G.; funding acquisition, M.G. and S.C. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the Polish National Science Center under the project no. UMO-2021/41/N/NZ9/01749.

Data Availability Statement

The raw sequences were deposited in the NCBI Sequence Read Archive (SRA) under accession number PRJNA1175723.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Figure 1. Schematic diagram of the fish farm and the biofilter under investigation. Red arrows indicate the direction of water flow. Figure 1 was created by modifying the schematic drawing of the biofilter from the enclosed instructions for use [25].
Figure 1. Schematic diagram of the fish farm and the biofilter under investigation. Red arrows indicate the direction of water flow. Figure 1 was created by modifying the schematic drawing of the biofilter from the enclosed instructions for use [25].
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Figure 2. Phylogenetic maximum likelihood tree with the reconstructed bacterial MAGs. The tree was based on the concatenated alignment of 120 bacterial marker genes created by GTDB—Tk [36] with Genome Taxonomy Database (GTDB) release 207 and 214 and visualized using iTOL software (v. 7) [41].
Figure 2. Phylogenetic maximum likelihood tree with the reconstructed bacterial MAGs. The tree was based on the concatenated alignment of 120 bacterial marker genes created by GTDB—Tk [36] with Genome Taxonomy Database (GTDB) release 207 and 214 and visualized using iTOL software (v. 7) [41].
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Figure 3. Abundance of the most abundant phyla (left) and families (right). Taxa whose abundance did not exceed 0.5 in any of the samples were summed and labelled “Other”. At the family level, the unclassified part of the bacterial community was omitted for better readability.
Figure 3. Abundance of the most abundant phyla (left) and families (right). Taxa whose abundance did not exceed 0.5 in any of the samples were summed and labelled “Other”. At the family level, the unclassified part of the bacterial community was omitted for better readability.
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Figure 4. Phylogenetic maximum likelihood tree of the five reconstructed comammox Nitrospira genomes (MAG 119, MAG 143, MAG 144, and MAG 201). Nitrospira MAGs obtained in this study are highlighted in bold. Red and yellow boxes indicate comammox clades A and B, respectively. The tree was based on the concatenated alignment of 120 bacterial marker genes created by GTDB—Tk [36] with Genome Taxonomy Database (GTDB) release 207 and 214 and visualized using iTOL software (v.7) [41].
Figure 4. Phylogenetic maximum likelihood tree of the five reconstructed comammox Nitrospira genomes (MAG 119, MAG 143, MAG 144, and MAG 201). Nitrospira MAGs obtained in this study are highlighted in bold. Red and yellow boxes indicate comammox clades A and B, respectively. The tree was based on the concatenated alignment of 120 bacterial marker genes created by GTDB—Tk [36] with Genome Taxonomy Database (GTDB) release 207 and 214 and visualized using iTOL software (v.7) [41].
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Figure 5. Microbial community network with nodes representing the most abundant MAGs (with a relative abundance of at least 0.5% in at least one sample). Each node represents a single MAG. The size of the nodes is directly proportional to the size of the betweenness centrality parameter. Red edges indicate negative correlations, while green edges indicate positive correlations. The thickness of the edges corresponds to the strength of the correlation. Using the “Modularity” function of the Gephi software, two subpopulations (marked with pink and green) were identified, which are connected by numerous negative edges. All negative edges connecting pink and green clusters were omitted to improve the readability of the graph.
Figure 5. Microbial community network with nodes representing the most abundant MAGs (with a relative abundance of at least 0.5% in at least one sample). Each node represents a single MAG. The size of the nodes is directly proportional to the size of the betweenness centrality parameter. Red edges indicate negative correlations, while green edges indicate positive correlations. The thickness of the edges corresponds to the strength of the correlation. Using the “Modularity” function of the Gephi software, two subpopulations (marked with pink and green) were identified, which are connected by numerous negative edges. All negative edges connecting pink and green clusters were omitted to improve the readability of the graph.
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Figure 6. Presence of genes related to the nitrogen cycle in the reconstructed genomes. Only MAGs with an abundance of at least 1% in at least one sample are shown in the graph.
Figure 6. Presence of genes related to the nitrogen cycle in the reconstructed genomes. Only MAGs with an abundance of at least 1% in at least one sample are shown in the graph.
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Table 1. Values of physicochemical characteristics measured in water flowing into and out of the biofilter.
Table 1. Values of physicochemical characteristics measured in water flowing into and out of the biofilter.
Before Feeding I (BF_I)After Feeding I (AF_I)Before Feeding II (BF_II)After Feeding II (AF_II)
Ammonia (mg N/L)inflow0.010.0160.0070.008
outflow0.0110.0110.0070.009
Nitrite (mg N/L)inflow0.0150.0090.0650.01
outflow0.0140.0110.060.011
Nitrate (mg N/L)inflow4.054.664.392.49
outflow4.334.564.374.41
Dissolved oxygen (mg/L) 8.969.028.959.07
Temperature (°C) 19.119.219.719.2
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Godzieba, M.; Hliwa, P.; Ciesielski, S. Network of Nitrifying Bacteria in Aquarium Biofilters: An Unfaltering Cooperation Between Comammox Nitrospira and Ammonia-Oxidizing Archaea. Water 2025, 17, 52. https://doi.org/10.3390/w17010052

AMA Style

Godzieba M, Hliwa P, Ciesielski S. Network of Nitrifying Bacteria in Aquarium Biofilters: An Unfaltering Cooperation Between Comammox Nitrospira and Ammonia-Oxidizing Archaea. Water. 2025; 17(1):52. https://doi.org/10.3390/w17010052

Chicago/Turabian Style

Godzieba, Martyna, Piotr Hliwa, and Slawomir Ciesielski. 2025. "Network of Nitrifying Bacteria in Aquarium Biofilters: An Unfaltering Cooperation Between Comammox Nitrospira and Ammonia-Oxidizing Archaea" Water 17, no. 1: 52. https://doi.org/10.3390/w17010052

APA Style

Godzieba, M., Hliwa, P., & Ciesielski, S. (2025). Network of Nitrifying Bacteria in Aquarium Biofilters: An Unfaltering Cooperation Between Comammox Nitrospira and Ammonia-Oxidizing Archaea. Water, 17(1), 52. https://doi.org/10.3390/w17010052

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