Jinxing Maa,
Zhiwei Wang*a,
Lili Zangb,
Jian Huanga and
Zhichao Wua
aState Key Laboratory of Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, P. R. China. E-mail: zwwang@tongji.edu.cn; Fax: +86-21-65980400; Tel: +86-21-65980400
bShanghai Zizheng Environmental Technology Co Ltd, Shanghai 200437, P. R. China
First published on 27th February 2015
One of the primary concerns on wastewater reuse is the presence of pathogenic bacteria. Considering that indicator bacteria might only offer limited information, we applied high-throughput pyrosequencing in this study to reveal bacterial pathogen diversity in a full-scale membrane bioreactor (MBR) treating restaurant wastewater. The results showed that fecal indicator bacteria could provide a rough estimation rather than an accurate characterization of the potential pathogenic bacteria in wastewaters, particularly from non-fecal sources. In general, MBR treatment showed good removal of potential pathogenic bacteria. The bacterial count of Arcobacter was decreased by nearly seven orders of magnitude, from (8.35 ± 0.87) × 107 to <10 counts per mL, and Aeromonas, Enterobacter, Enterococcus, and Pseudomonas were not detected in the treated wastewater. The most dominant potential pathogens in activated sludge and treated wastewater were affiliated to the genera of Legionella, Clostridium and Mycobacterium. Species-specific comparison showed that only a small portion (0.0–1.6%) of the corresponding sequences had identities of > 99% to the neighbor pathogenic species, including Arcobacter butzleri and Arcobacter cryaerophilus. This study, therefore, provides insights into the occurrence and fate of potential bacterial pathogens in restaurant wastewater treatment and reclamation using MBRs.
On the condition that treated wastewater utilization is expected, contaminant removal should be sufficient to meet stringent regulatory standards, because of public health concerns.6 In MBRs, bacteria play an important role in the biochemical process, consuming nutrients and organic matter. To date, numerous studies have been conducted to improve the efficiency of the biochemical process, but most of them failed to attach importance to the potential hazard of the bacteria accordingly. One of the primary concerns about bacteria is the community of pathogenic bacteria originating from the excrement of disease-carrying humans and animals or other sources.7 In many public places (e.g., general merchandise stores and restaurants), the outbreak of gastroenteritis or other infections due to access of reused water could actually be masked by the background levels of assumed sources, such as food-borne and community-based infections.8 Microbial assessment of pathogenic bacteria in treated wastewater is thereby important in view of consequent health risks.
Historically, fecal indicator bacteria, including total and fecal coliforms and enterococci, have been widely used as a monitoring tool to predict the presence of potential bacterial, viral and protozoan pathogens.9 The major drawback of fecal indicator bacteria arises from their poor correlation with pathogens, especially those from non-fecal sources.8,10 Moreover, membranes have a size-selective retention of different bacteria, and the abundance of pathogenic bacteria in the permeate could be underestimated or overestimated when referenced to certain indicator bacteria.11 In recent years, real-time qPCR assays have been proposed and these assays are now used in many diagnostic and reference laboratories for the detection of pathogenic bacteria in clinical fluids.8,12,13 Compared to indicator bacteria methods, the qPCR assay enables quantitative and highly specific detection, which could target the 16S ribosomal RNA, encoding genes or housekeeping genes of actual pathogens.12 Nevertheless, the application of this technology is still hindered due to its limited throughput capacity. In environmental samples, bacterial pathogen diversity can be extremely high, as reflected by the more than thirty phylogenetic genera and thousands of strains. Clueless one-by-one detection is definitely time-consuming, and might also miss a potential infectious risk. Therefore, efficient solutions are urgently required for elucidating bacterial pathogen diversity in MBRs and assessing the full microbial risk of treated wastewater reuse in public places.
In this study, 454 high-throughput pyrosequencing was used to investigate the occurrence and fate of potential pathogenic bacteria in a full-scale MBR treating restaurant wastewater. Pyrosequencing is a high-throughput analytical method that generates a large number of DNA reads through a massive parallel sequencing-by-synthesis approach, and this technology can provide an adequate resolution of the microbial diversity of different environmental samples.7,14–16 In the present work, 258438 reads of the hypervariable V1∼V3 regions of the bacterial 16S rRNA gene were obtained. Sequence subsets with capacities of 10
000 and 100 were generated from the maternal gene libraries by a semi-random extraction method, and comprehensive comparison of these datasets was then carried out. Bacterial pathogen diversity was analyzed at genus level using the Ribosomal Database Project (RDP) Classifier.17 Alignment of the corresponding sequences to the known pathogen was further conducted by phylogenetic analysis.
Influent wastewater, activated sludge and treated wastewater samples, termed as A1, A2 and A3 samples, were taken from the inlet pipe, aerobic tank and outlet pipe of the MBR, respectively (see Fig. S1†). After DNA extraction and PCR amplification (see Section II of the ESI†), amplicons from A1, A2 and A3 were mixed at equal concentration, and the mixture was used for pyrosequencing on a Roche 454 FLX Titanium platform at Majorbio Bio-Pharm Technology Co., Ltd (Shanghai, China).
Sample ID | Raw reads | High-quality reads | Assigned readsa | OTU | Chao | Shannon | Fo |
---|---|---|---|---|---|---|---|
a Assigned reads are the reads that match the OTU in each sample. Some high-quality reads may not match any OTU for these reasons: (1) the read is chimeric, and (2) the read that has a singleton sequence is discarded.b A1, A2 and A3 represent pyrosequencing results of influent wastewater, activated sludge and treated wastewater samples; B1, B2 and B3 represent the subsets with 10![]() |
|||||||
A1b | 34![]() |
24![]() |
19![]() |
897 | 1323 | 3.70 | 0.92 |
B1 | n.a.c | 10![]() |
8162 | 864 | 1886 | 3.88 | 0.89 |
C1 | n.a. | 100 | 89 | 40 | 164 | 2.90 | 0.64 |
A2 | 158![]() |
113![]() |
63![]() |
1712 | 2026 | 4.85 | 0.90 |
B2 | n.a. | 10![]() |
6163 | 1132 | 2664 | 5.04 | 0.81 |
C2 | n.a. | 100 | 63 | 46 | 187 | 3.65 | 0.42 |
A3 | 64![]() |
54![]() |
36![]() |
1362 | 1670 | 5.20 | 0.89 |
B3 | n.a. | 10![]() |
7132 | 1063 | 2092 | 5.34 | 0.83 |
C3 | n.a. | 100 | 75 | 59 | 190 | 3.95 | 0.37 |
For a comprehensive understanding of the impacts of sequencing depth, subsets with capacities of 10000 and 100 were generated from the high-quality maternal sets of A1∼A3 by a semi-random extraction method. Initially, the sub.sample command of the MOTHUR program (http://www.mothur.org/wiki/Sub.sample) was used for A1∼A3 to create 30 subsets comprised of 10
000 sequences, i.e., B1i = {xj | xj ∈ A1, j = 1–1000} (i = 1–10), B2i = {xj | xj ∈ A2, j = 1–1000} (i = 1–10) and B3i = {xj | xj ∈ A3, j = 1–1000} (i = 1–10). Principal coordinates analysis (PCoA) with the Bray–Curtis index (R package, http://www.r-project.org/) was then performed to evaluate the relationship between A1∼A3 and B1i∼B3i (i = 1–10), and the subsets with the highest homology were retained and specified as B1∼B3 (Fig. S2 in the ESI†). Afterwards, a similar procedure was applied to create 30 subsets containing 100 sequences from B1∼B3, and the subsets with the highest homology with B1∼B3 were specified as C1∼C3. Despite the debate that semi-random extraction is reliable enough compared to independent sequencing, this method is similar to pyrosequencing run in reverse; and in practice the final gene libraries (e.g., A1∼A3) can be obtained based on the deficient datasets (e.g., B1∼B3) by further sequencing of the amplicons. Nevertheless, evaluation of pathogenic bacteria diversity was mainly based on the original pyrosequencing results.
Venn diagrams with shared and unique OTUs were utilized to depict the similarity and difference between microbial communities. A pairwise statistical comparison of taxonomy at phylum level between maternal sets and subsets was carried out using STAMP.21 Biological relevance between samples at the genus level was evaluated using linear regression of SigmaPlot software (version 12.5, Systat Software, Inc., U.S.). Furthermore, the LDA Effect Size (LEfSe) algorithm was introduced herein to identify taxa that characterize the differences among the three environmental samples.22 A1∼A3, B1∼B3 and C1∼C3 were grouped according to the source (e.g., influent wastewater, activated sludge or treated wastewater sample), and each sample was first normalized to the sum of the values of 0.05 M. The parameters for data processing were set as follows: ‘alpha value for the factorial Kruskal–Wallis test among classes’ = 0.05, ‘threshold on the logarithmic LDA score for discriminative features’ = 2.7, and ‘set the strategy for multi-class analysis’ = all-against-all.
Alignment of microbial communities to pathogenic genera was first evaluated using the taxonomic results of the RDP Classifier. The lists of known pathogenic genera summarized by Ye and Zhang and Bibby et al. were used as in ref. 7 and 23. Representative sequences from OTUs that were assigned as Arcobacter, Clostridium, Legionella, and Mycobacterium were further separated for phylogenetic analysis at the species level. The 16S rRNA gene of known pathogens and non-pathogens from the four genera were achieved from the NCBI Genbank (Table S2 of the ESI†), and merged with the corresponding sequences of this study into a fasta file. ClustalW was used for aligning and bootstrapping of the phylogenetic tree, which was then viewed, edited and published with MEGA 6.24 Default settings were used. Furthermore, bacteria assigned to the families of Enterobacteriaceae and Enterococcaceae were regarded as the representative fecal indicators in this study.
Table 1 indicates that insufficient resolution reduced the accuracy of alpha-diversity analysis. For instance, at the sequencing depth of 100, only 40, 46 and 59 OTUs were predicted for the whole microbial communities in influent wastewater, activated sludge and treated wastewater, respectively. Furthermore, we compared the taxonomic results of the nine datasets at phylum and genus levels (Fig. 2). In total, 25 phyla were classified at the threshold of 80%. Proteobacteria was the most dominant phylum, accounting for 53.1–68.3% of the total communities, respectively. Pairwise comparison using STAMP shows that there is no significant dissimilarity of taxonomic results between Aj and Cj (j = 1, 2, 3) at phylum level (Fig. 2a, c and e). However, the reliability was significantly reduced at the genus level. Most taxa of A1∼A3 could not be predicted by the taxonomic results of C1∼C3 in a 95% predication band. In contrast, linear regression showed that except for a few categories, B1∼B3 supplied a credible characterization of the microbial communities of A1∼A3 at the genus level (Fig. 2b, d and f). The results suggested that compared to low-throughput sequencing methods, pyrosequencing could provide a more valid estimation of the population structure of diverse communities, especially at terminal taxonomic levels (e.g., genus level). Increasing the library size from 10000 to 100
000, however, did not improve the taxonomic results as expected, probably due to the abundance of singletons at high sequencing depth, which were always discarded after de-noising (Table 1).
In this study, the depth of pyrosequencing also had a significant influence on the detection and characterization of potential pathogenic bacteria. For B1∼B3, the dominant pathogenic genera could be well identified, while those with low abundances (e.g., Enterobacter, Enterococcus and Vibrio) were neglected (Fig. 3). It is worth noting that sequencing failed to reveal the majority of potential pathognic bacteria in environmental samples when the library size was reduced to 100. Specifically, no pathogenic bacteria were detected in C2 and Mycobacterium were obviously overestimated in C3 (Fig. 3).
Overall, the corresponding sequences had good alignment with the potential pathogenic genera by using the RDP Classifier, and most had a bootstrap confidence over 80% (Fig. 3 and Table S3†). Since species-specific comparison with known pathogenic bacteria could give a more accurate estimation of the potential pathogens in the samples, representative sequences of concerned OTUs assigned into Arcobacter, Clostridium, Legionella and Mycobacterium genera (Fig. 3) were retrieved from the datasets. Phylogenetic analysis was then conducted by building a library with representative 16S rRNA gene sequences of pathogenic and non-pathogenic bacterial species. As shown in Fig. 4, 15 OTUs out of the total (37 OTUs) had an identity of over 95% with neighbor pathogens, including 8 OTUs assigned into Arcobacter, 3 OTUs into Clostridium and 4 OTUs into Mycobacterium (Table S4 of the ESI†). In the Arcobacter genus, the most abundant taxon OTU2964, accounting for 32.9% of the population in A1, showed a low alignment with known pathogens. Without additional information, it could not be concluded whether these sequences referred to nonpathogenic strains, because variants might be also associated with disease but not yet identified.23 Of particular importance is that only two strains (OTU2202 and OTU2091) were recognized as potential pathogenic species by species-specific comparison. It could be deduced that phylogenetic analysis at the genus level might lead to an overestimation of the pathogenic bacteria in environmental samples.
Since fecal indicator bacteria are still widely used to predict the presence of bacterial, viral and protozoan pathogens,9 the abundances of typical indicators were further evaluated based on the taxonomic results in the present work. Because there is no full taxonomic definition of fecal indicators yet, bacteria assigned to the families of Enterobacteriaceae and Enterococcaceae were regarded as the representative fecal indicators herein. As shown in Table 2, 41 sequences from A1 were classified, which contributed 0.21% of the dataset. In A2, only 10 sequences got a valid match from the RDP Classifier, including 6 sequences assigned into Enterobacteriaceae and 4 sequences into Enterococcaceae. Probably due to the sound separation of the 0.20 μm poly(vinylidene fluoride) membranes, no Enterobacteriaceae or Enterococcaceae were detected in the treated wastewater (A3) in this study.
From the view of pathology, it is of great concern to focus on the pathogenic bacteria emerging in the aerobic tank of the MBR because not only do these tolerant microorganisms have a competitive advantage with the biomarkers involved in contaminant degradation (e.g., Zoogloea and Dechloromonas, as shown in Fig. S4†) but also aerosols containing pathogens could be generated from the aeration tank and further transported and dispersed by wind. A genus of gram-negative coccobacilli, Legionella, was well recognized in the activated sludge sample (A2). It has been reported that Legionella prefer to inhabit man-made aquatic environments where the water temperature is higher than ambient temperature, and that the growth of Legionella spp. can be aided by co-existing micro-organisms (e.g., protozoa).25,29 Although this bacterial genus was enriched in activated sludge, the Legionella found herein seemed non-pathogenic; all the representative sequences had a low alignment (87–92%) with the foremost pathogenic species L. pneumophila, L. longbeachae, L. micdadei and L. bozemanii. Fig. 3 indicates that MBR removal of Legionella from restaurant wastewater was mainly attributed to membrane retention, which could efficiently eliminate the hosts (e.g., amoebae) in the treated wastewater. Moreover, a recent study on bacterial pathogen diversity in biosolids (digested sludge) using pyrosequencing has revealed that most of the pathogenic sequences belonged to the genera of Mycobacterium and Clostridium.23 In the present work, our results showed that despite low relative abundances, all sequences belonging to the Mycobacterium genus had more than 95% similarity to a ‘freak’ pathogenic species, M. abscessus. The gene order phylogeny of M. abscessus groups the organisms into rapid and slow-growers.30,31 M. abscessus is closer to the non-pathogens in terms of its growth characteristics and is placed away from the pathogens (Fig. 4), which could lead to taxonomic bias based on 16S rRNA gene pyrosequencing. As a result, virulence assays that target the functional genes are further required to support the relevant conclusions.
This study also reinvigorates the debate that the indicator bacteria are inefficient in representing the potential pathogenic bacteria from non-fecal sources. The relative abundance of fecal indicator bacteria did not show a good relationship with that of potential pathogenic species, though provided a rough evaluation on the occurrence of potential pathogens in the restaurant wastewater (Tables 2 and S4†). Notably, Enterobacteriaceae and Enterococcaceae were not detected in A3 but 97 of the total 36644 sequences were classified into the pathogenic genera, including 25 sequences with identities of >95% with M. abscessus, C. difficile and C. botulinum. Furthermore, the Clostridium genus was an important group in A3, which was resistant to the MBR treatment (Fig. 3). In this study, the ambiguously defined taxon contained Clostridium cluster sensu stricto (Clostridiaceae 1), Clostridium cluster IV (Ruminococcaceae) and Clostridium cluster XI (Peptostreptococcaceae). Three strains from Clostridium cluster XI and Clostridium cluster sensu stricto had a good phylogenetic alignment with C. difficile and C. botulinum, respectively (Fig. 4 and Table S4†). Since Clostridia (spores) are highly resistant to chlorination,32 disinfection efficiency could be easily overestimated when referenced to the elimination of intolerant indicator bacteria (e.g., Enterobacteriaceae).
In this study, 454 pyrosequencing was introduced for a comprehensive understanding of bacterial pathogens in restaurant wastewater. Compared to conventional culture-based methods and qPCR assays, this technology is high-throughput for mining potential pathogenic bacteria in environmental samples, which avoids the mis-estimation of pathogens by using a certain group of indicator bacteria. Molecular biology methods that offer ≈100 tags could only provide rough information on the structure of microbial communities at the phylum level (Fig. 2). By contrast, the 10000-sequence datasets were generally valid in forecasting individuals within microbial communities, but overrepresentation and underrepresentation were still noted regarding the highly likely bacterial pathogens (Table S4†). For accurately reaping the rare strains, exponential growth of the library size (1–2 orders of magnitude) might be unwise since a large number of singletons were generated at a sequencing depth of 30
000–150
000 (Table 1). DNA fragment pretreatment (e.g., the use of multiple genus level PCR primers) should therefore be considered in pathogenic studies. Furthermore, 16S rRNA gene pyrosequencing provided the opportunity to discover the important strains that have not been cultured yet (e.g., OTU2964). Short-gun metagenomic and metatranscriptomic sequencing could be used to predict their functions. Phylogenic analysis of the concerned pathogenic and non-pathogenic bacterial species herein gave a more accurate evaluation of the abundance and diversity of bacterial pathogens. Virulence of relevant communities could be further analyzed using qPCR or microarrays that target the functional genes. Overall, the present work showed that restaurant wastewater is suitable for reclamation using MBR technology. Pathogenic bacteria were efficiently removed in the hybrid systems, and the membrane filtration process retained the communities that were resistant to biological treatment. The tolerant bacterial pathogens in treated wastewater revealed by pyrosequencing provide insights into the selection of specific tertiary treatment and also proper disinfection methods.
Footnote |
† Electronic supplementary information (ESI) available. See DOI: 10.1039/c4ra10220g |
This journal is © The Royal Society of Chemistry 2015 |