A number of recent studies on AOB in wastewater treatment systems have suggested that different plants support different populations and different levels of species richness. For instance a domestic wastewater biofilm from a lab-scale reactor in Japan was dominated by N. europaea-like AOB  while AOB populations from lab- and full-scale plants in Germany were dominated by Nitrosospira-like bacteria or N. mobilis-like AOB respectively [27,12]. Other lab- and full-scale reactors in Germany contained more diverse AOB populations [13,28,29]. To examine whether particular AOB are selected in full-scale reactors of different configuration receiving identical wastewater, the AOB present in reactors treating waste of the same origin were characterised using culture-independent methods. PCR, DGGE and sequence analysis were combined to determine the dominant AOB populations in the BAF and trickling filter reactors.
3.1DGGE analysis of the BAF and trickling filter reactors
Analysis of duplicate samples by DGGE revealed that the profiles obtained were reproducible and for clarity, comparisons of single samples from each part of the reactor are shown. Visual comparison of the DGGE profiles of bacterial and AOB 16S rRNA gene fragments from the filter beds and the BAF reactor revealed some different populations in different sections of each of the reactors and differences between the reactors (Figs. 1 and 2).
Figure 1. DGGE profile of AOB communities from the trickling filters and BAF reactors. Lanes 1–3: primary filter bed; 1. top, 2. middle, 3. bottom. Lanes 4–6: secondary filter bed; 4. top, 5. middle, 6. bottom. Lanes 7–9: BAF; 7. nitrification basin, 8. C removal basin, 9. denitrification basin. The predominant band present in all samples is marked X.
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Figure 2. DGGE profile of eubacterial communities from the trickling filters and BAF reactors. Lanes 1–3: primary filter bed; 1. top, 2. middle, 3. bottom. Lanes 4–6: secondary filter bed; 4. top, 5. middle, 6. bottom. Lanes 7–9: BAF; 7. nitrification basin, 8. C removal basin, 9. denitrification basin.
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Bacterial and AOB DGGE data for the BAF suggest that the conditions within the reactor differentially select for some different populations (Figs. 1 and 2, lanes 7–9). The BAF reactor is composed of three linked basins each of which is optimised for different processes. A change in the level of aeration appears to have an effect on the bacterial populations present. This is evident by a visual comparison of the banding pattern of each of the three stages (Figs. 1 and 2, lanes 7–9).
Considerable differences in bacterial and AOB DGGE data were also observed between different depths of the filter bed and between the primary and secondary filter bed (Figs. 1 and 2, lanes 1–6). For example, more bands were detected in samples from the bottom of the primary filter (Fig. 1, lane 3) and in the secondary filter bed (Fig. 1, lanes 4–6) than at the top of the primary filter (Fig. 1, lanes 1 and 2). Furthermore, particularly in the AOB DGGE profiles, there appears to be a successional change in bacterial populations through the filter beds. This is apparent by the loss of some bands down the filter bed profile and the appearance of others (Fig. 1, lanes 1–6).
A comparison of the DGGE profiles between the filter beds and the BAF reactor revealed that although both reactors were fed the same waste they harboured distinct bacterial and AOB populations (Figs. 1 and 2). The BAF reactor harboured a lower detectable diversity of AOB compared with the filter beds. In particular, a number of bands that migrated further in the DGGE gel were noted in samples from the filter beds that were not present in the BAF reactor. Nevertheless both reactors appeared to have a common predominant population (marked X on Fig. 1). To confirm the identity of AOB represented by bands in DGGE gels PCR-amplified 16S rDNA from the WWTP samples was cloned and sequenced.
3.2Characterisation of AOB in the BAF and trickling filter reactors
Clone libraries of betaproteobacterial AOB 16S rRNA genes were constructed from samples from each of the reactors selected as those containing the greatest diversity on the basis of DGGE profiles (top and bottom, 0 and 1.9 m depth, of the secondary filter bed) or exhibiting active nitrification (the nitrification unit of the BAF). Two methods, DGGE and ARDRA, were employed to screen 30 clones from each sample. Both methods resulted in similar groupings of clones; however, DGGE proved more discriminatory than ARDRA, hence ARDRA appeared to underestimate the AOB diversity in these reactors.
The DGGE screening of the clone libraries indicated that sequences that co-migrated with most of the predominant bands from the original DGGE analysis were recovered in clone libraries (Fig. 3). However, some components of the DGGE profiles were not recovered in our screening of the clone libraries. Conversely some clones present at low frequencies in the clone libraries did not have a corresponding band in the DGGE gel (e.g. clone 19Fb). Nevertheless, the intensity of most of the bands reflected the frequency of different clones in the libraries (e.g. 45 BAF dominant DGGE band and clone Figs. 3 and 4).
Figure 3. DGGE community profile from the top (A) and bottom (B) of the secondary filter bed and nitrification unit (C) of the BAF system. Arrows indicates clones that co-migrate with DGGE band. Some clones did not have a corresponding DGGE band (e.g. 1BAF, 40Fb). Designations in bold represent AOB-like sequences. Clone frequencies are shown in brackets.
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Figure 4. Clone frequencies from filter bed and BAF samples. Clones 43Ft, 47Ft, 1BAF, 67Ft and 40Fb grouped with Betaproteobacteria other than the ammonia-oxidisers (non-AOB).
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Nucleotide sequences were determined for each clone type from the clone libraries and were compared to the GenBank database using FASTA 3 . All AOB sequences recovered had between 96 and 99% identity with previously identified AOB 16S rRNA gene sequences with the exception of clones 21Fb and 55Fb which had 95% identity with N. cryotolerans Nm55 and an environmental clone related to Nitrosomonas spp. respectively (Table 2).
Table 2. Nearest neighbour of the cloned sequences from the top and bottom of the secondary trickling filter and the nitrification unit of the BAF reactor
|Clone typea||Closest relative and accession number||Percent identity||Origin|
|9BAF||Nitrosomonas sp. Nm107 (AF272416)||97.2||Activated sludge, rendering plant Kraftisried, Germany|
|21Fb||Nitrosomonas crylotolerans Nm55 AF272423)||95.3||Kasitsna Bay, Alaska (Marine)|
|26Ft||Clone Rw5 (AJ3763)||99.0||Sediment (Lake Reeuwijk) Man-made lake|
|19Fb||Nitrosomonas sp. AL212 (AB000699)||99.4||Culture from activated sludge (NH4)2SO4 sensitive strain|
|17Ft||Nitrosomonas ureae Nm10 (AF272414)||98.5||Soil, Sardinia, Italy|
|25Ft 36Fb 45BAF||Nitrosomonas sp. Nm107 (AF272416)||99.0||Activated sludge, rendering plant Kraftisried, Germany|
|11Fb 50Ft||Nitrosomonas sp. Nm51 (AF272424)||96.0||Seawater, Peru|
|67Ft||Uncultured betaproteobacterium CRE-Fl40 (AF141461)||93.6||Columbian river system|
|47Ft||Uncultured betaproteobacterium SBR1011 (AF204253)||94.9||Full-scale EBPR reactor|
|55Fb||Culture AEM-5 (UB09546)||95.0||Seawater enrichment (Aberdeen beach)|
|43Ft||Uncultured betaproteobacterium 16S–8 (NSP238205)||98.0||Arable soil, most closely related to D. agitatus|
|40Fb||Uncultured bacterium Phos-He26 (AF314447)||94.0||Aerobic phosphorus system|
|1BAF||Uncultured gammaproteobacterium BioIuz K38 (AF324538)||92.0||Bacteriophages in natural habitats|
A detailed analysis of the sequences recovered from the BAF and filter bed revealed that all were derived from the Betaproteobacteria (except clone 1BAF which grouped most closely, but with low identity (92%) to an uncultured Gammaproteobacterium). Furthermore, most of the sequences (97% BAF, and 80% top filter and 85% bottom filter) were most closely related to the betaproteobacterial ammonia oxidisers. The rest of the sequences recovered were usually closely related to either Thauera spp. (e.g. clone 47Ft) or Dechlorimonas agitatus-like bacteria (e.g. clone 43Ft, Table 2).
AOB-like sequences from the nitrification unit of the BAF and top and bottom of the filter bed reactors were all recovered with the genus Nitrosomonas (Fig. 5). The clone library from the BAF reactor had a preponderance of N. mobilis-like sequences (clone 45BAF; 90%, n=30, Fig. 4 and Table 2) (Nitrosomonas cluster 7). Whereas the filter bed samples were more diverse and three or four different predominant clone types were identified; N. mobilis-like sequences, (clone 25Ft, 33%; clone 36Fb, 27%; n=30), Nitrosomonas spp. cluster 6b sequences (clone 50Ft, 10%; clone 11Fb, 23%; n=30), cluster 6a sequences (clones 19Fb, 10%; 21Fb, 7%; 26Ft, 23%; 17Ft, 14%; n=30) and Nitrosomonas spp. Cluster 5 sequences (clone 55Fb, 20%; n=30, Fig. 4 and Table 2). The sequences most closely related to cluster 6a (50Ft, 11Fb), 6b (21Fb) and 5 (54Fb, 55Fb) had relatively low sequence identity with the most similar sequences in the sequence databases and their association with these groups based on bootstrap analysis was not robust. This may be a consequence of the relatively short sequences used in the analysis. Analysis of longer 16S rRNA gene sequences would help place these sequences more confidently with one of the recognised AOB clusters. The nearest neighbours to the sequences recovered were originally reported from a range of environments but a number group with sequences from halotolerant AOB or sequences recovered from saline environments or enrichments (36Fb-N. mobilis and 55Fb-clone AEM-5). N. mobilis-like sequences were found in all the reactors, but were proportionally more abundant in the clone library from the BAF (90% of sequences) than in the filter beds (33% top of secondary, 27% bottom of secondary, Fig. 4 and Table 2). It should be noted that the AOB-selective primers that were used in this study are not completely universal for all betaproteobacterial AOB and primer CTO189f and CTO654r have mismatches with some sequences from Nitrosomonas cluster 7 and cluster 6a, and CTO654r has more than two mismatches with all cultured members of the N. communis cluster (cluster 8) . This might to some extent explain why we did not detect any sequences closely related to Nitrosomonas cluster 8, while we did detect sequences that were related to all other currently described Nitrosomonas clusters, albeit in some cases with relatively low sequence identity. However, in clone libraries generated from some of the samples analysed in this study, using primers Nso190 and Nso1225, which have no mismatches with the N. communis cluster , no members of this group were detected (unpublished data).
Figure 5. Phylogenetic distance tree based on comparison of 464 position of 16S rRNA gene sequences from the genus Nitrosomonas of the Betaproteobacteria and other members of the Betaproteobacteria. BAF – BAF plant, Fb – filter bottom, Ft – filter top.
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The cloning and sequencing analysis indicated that comparable levels of AOB diversity were identified when the amplified rRNA gene fragments were analysed by DGGE or cloning. DGGE and sequencing analysis therefore appeared to indicate that particular AOB were selected for in the different reactors. To examine whether the selection of particular AOB in the different reactors was statistically significant Raup and Crick simulations  were employed.
3.4Comparison of AOB communities down the profile of the trickling filter beds
The similarity between the total pooled AOB data and any given sampling point in the trickling filter was no greater than could be accounted for by chance alone (P=0.501). Neither was there any significant similarity between the top of the reactor and the subsequent depths (Fig. 6). Taken in isolation these data suggest that the similarities in the AOB communities present at different depths are no greater than can be accounted for by chance. However, regression analysis demonstrated a statistically significant (P=0.006) relationship between the depth in the reactor and the SRC value.
Moreover, adjacent sampling points were similar (SRC>0.95; Fig. 7) suggesting that selection for different AOB with depth in the filter was non-random and the similarities in AOB communities were statistically greater than could be expected from chance matching of bands in the DGGE profiles. Clearly there is a significant gradient in AOB diversity down the reactor. A similar pattern of succession was also observed in the bacterial DGGE profiles (Figs. 6 and 7). This may be due to the stratification that exists in unmixed biofilm reactors as the waste is degraded and changes in composition as it passes through the filter bed . The waste entering the primary filter bed will contain a high amount of organic matter, and as the waste is degraded the level of organic matter will be reduced and concomitantly oxygen will be consumed. In the presence of high levels of organic matter heterotrophs can out-compete autotrophic ammonia oxidisers for oxygen [4,41] and ammonia [42,43]. As the organic matter declines, the number of heterotrophs decreases and consequently AOB can proliferate. Hence there should be a higher number of AOB at the base of the primary filter bed and in the secondary filter bed. AOB were not quantified in this study but operational data suggest that the secondary filter beds remove more ammonia than the primary beds (04/01/99 to 16/08/01 NH4+ removal – primary 45%±9.0; secondary 87%±4; mean±95% CI); shortly prior to sampling primary 74%; secondary 81%) suggesting the presence of a higher number or metabolically more active AOB in the secondary filter.
Interestingly, no statistically significant differences or gradients were observed in the BAF even though this is also a fixed film reactor. This may be, at least in part, attributable to the more homogenous conditions engendered by the vigorous mixing and recycle regime in the BAF reactor.
3.5Comparison of AOB communities between the BAF and trickling filter reactors
Comparison of DGGE data from the BAF and trickling filter revealed that for all but two of the reactor comparisons the similarities observed were no greater than would be expected by random association of bands in the DGGE profiles (0.05<SRC<0.95; Fig. 8). Therefore any differences observed between the BAF and filter beds could be explained by random processes without the need to invoke differences in reactor design as a causal factor.
Nevertheless, the similarity values for each comparison of the BAF with the primary filter bed were generally all very high (SRC>0.943) whereas the values for each comparison of the BAF with the secondary filter bed were lower and successively decreased with depth (e.g. BAF nitrification B vs St, SRC=0.734; B vs Sm SRC=0.473; B vs Sb SRC=0.083; Fig. 8).
Although not statistically significant on the basis of the Raup and Crick analysis, it is apparent that the similarities between the BAF and the primary filter bed are considerably greater than the similarities between the BAF and the secondary filter bed. This may reflect the fact that the primary filter and the BAF reactor receive identical influent whereas the influent to the secondary filter is the effluent from the primary filter and as such the composition will be different.
Interestingly, reports from practitioners suggest that the filter beds are more robust and suffer less from nitrification failure than other systems . The comparison of pooled DGGE data from the BAF with the secondary filter bed suggested that the AOB populations in the BAF and secondary filter bed were significantly divergent (SRC=0.044). This was reflected in the extent of AOB diversity within each plant. The filter beds had a greater AOB diversity (four or five different AOB identified) than the BAF (two different AOB identified). This could explain why the filter beds perform better and are inherently more stable than the BAF reactor.
It has been suggested that the level of AOB diversity within a WWTP has a major influence on process stability ; the greater the diversity, the more stable the process. A plant with greater diversity should cope better with changing conditions since a reduction in numbers of one organism may not mean process failure as other organisms better adapted to the new conditions may proliferate resulting in a more functionally stable system. Our data support this notion since the filter beds where AOB diversity was greater are the inherently more stable reactors. If diversity does play a major role in wastewater treatment then as previously suggested by Daims and his co-workers , the engineering of plant to have higher diversity may make processes such as nitrification more stable. However, the reason why different AOB populations are present under different conditions must be understood to permit the development of engineering solutions to obtain the most suitable diverse AOB population in WWTPs.
The observation of gradients in the diversity of the AOB communities suggests that environmental selection is partly influencing the diversity observed. However, an N. mobilis-like organism appears to be ubiquitous at this site. This must mean that either this organism is well adapted to the environmental conditions at all points in all reactors or it is so abundant in the source community that it appears, by chance, at all locations . Traditionally microbial ecologists have taken a more deterministic stance . However, we cannot distinguish between these two possibilities in this, or other, purely observational, studies. It is likely a number of mechanisms are at work and the relative importance of each mechanism will vary. In our study for example, the N. mobilis-like AOB would be dominant at all depths in the filter bed, if community assembly was random and the N. mobilis-like AOB were abundant in the source population. In reality, other AOB sequences increase in proportional abundance deeper in the reactor, an observation consistent with deterministic selection. The reactors studied here were fed with effluent from the same HRAS reactor. Interestingly, although we have no data on the composition of the AOB communities in the effluent from the HRAS reactor, analysis of cloned 16S rRNA gene sequences obtained from this reactor using AOB-selective primers indicated that the clone libraries contained a low frequency of AOB sequences (26%, n=30), but those that were identified were most closely related to N. mobilis (data not shown).
In this study we have shown important and significant differences in the AOB diversity of two full-scale WWTP of different conformation. The differences in diversity may very well be related to the differences in performance. The challenge now is to elucidate the mechanism underlying the differences so that these mechanisms may be incorporated into WWTP design.