Diversity and assembly patterns of activated sludge microbial communities: A review

Biotechnology Advances 36 (2018) 1038–1047

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Biotechnology Advances journal homepage: www.elsevier.com/locate/biotechadv

Research review paper

Diversity and assembly patterns of activated sludge microbial communities: A review

T

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Yu Xia, Xianghua Wen , Bing Zhang, Yunfeng Yang Environmental Simulation and Pollution Control State Key Joint Laboratory, School of Environment, Tsinghua University, 100084 Beijing, PR China

A R T I C LE I N FO

A B S T R A C T

Keywords: Activated sludge Microbial diversity Assembly patterns Wastewater treatment engineering Ecology

Understanding diversity and assembly patterns of microbial communities in activated sludge (AS) is pivotal for addressing fundamental ecological questions and wastewater treatment engineering. Recent applications of molecular methods especially high-throughput sequencing (HTS) have led to the explosion of information about AS community diversity, including the identification of uncultured taxa, and characterization of low-abundance but environmentally important populations such as antibiotic resistant bacteria and pathogens. Those progresses have facilitated the leverage of ecological theories in describing AS community assembly. The lognormal species abundance curve has been applied to estimate AS microbial richness. Taxa-area and taxa-time relationships (TAR and TTR) have been observed for AS microbial communities. Core AS microbial communities have been identified. Meanwhile, the roles of both deterministic and stochastic processes in shaping AS community structures have been examined. Nonetheless, it remains challenging to define tempo-spatial scales for reliable identification of community turnover, and find tight links between AS microbial structure and wastewater treatment plant (WWTP) functions. To solve those issues, we expect that future research will focus on identifying active functional populations in AS using omics- methods integrated with stable-isotope probing (SIP) with the development of bioinformatics tools. Developing mathematic models to understand AS community structures and utilize information on AS community to predict the performance of WWTPs will also be vital for advancing knowledge of AS microbial ecology and environmental engineering.

1. Introduction Wastewater treatment plant (WWTP) is an indispensable unit of modern cities, which removes wastewater pollutants resulting from anthropogenic activities. Biological treatment methods, which usually involve the activated sludge (AS) process, are the most widely applied technologies in WWTPs worldwide. Firstly demonstrated in 1914 (Ardern and Lockett, 1914), the AS process has just celebrated its 100 years' anniversary. During the past century, several modified AS processes have been developed to improve wastewater treatment efficiency and meet new objectives of waste management (Van Nieuwenhuijzen et al., 2008). Nonetheless, those technical advancements largely depend on practical experiences. The biological fundamentals behind most treatment technologies are always treated as “black boxes”. WWTPs are confronted with unpredictable performance breakdowns such as nitrification failure (Tang and Chen, 2015; Kroiss et al., 1992; Nielsen et al., 2009a) and bulking or foaming problems (Nielsen et al., 2009b; Pujol et al., 1991). It is vital to obtain a clear understanding of the underlying microbial mechanisms, to ensure the

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functional stability and benefit the engineering of new functions of WWTPs as well. Moreover, being chemically and physically well-defined engineered ecosystems amendable to experimental manipulation, WWTPs are advocated as a fertile testing ground for a range of fundamental ecological questions (Daims et al., 2006). The studies on AS microbiology typically involve inventory diversity of overall communities and important populations. Early cultivation-based studies provide initial but inaccurate compositional insights of AS communities, as the culture-dependent methods are biased by cultivation conditions preferentially selecting species rarely numerically abundant or functionally significant (Wagner et al., 1993). The use of culture-independent molecular methods since the early 1990s has circumvented this issue, especially high-throughput molecular methods which allow unprecedented access to genes and genomes. There is also highlighted interest in exploring the assembly patterns of AS microbial communities across time and space. Ecological theories originally developed for macrobial communities, including taxa-area relationship (TAR) and taxa-time relationship (TTR), have been

Corresponding author. E-mail address: [email protected] (X. Wen).

https://doi.org/10.1016/j.biotechadv.2018.03.005 Received 8 September 2017; Received in revised form 11 February 2018; Accepted 11 March 2018 Available online 15 March 2018 0734-9750/ © 2018 Elsevier Inc. All rights reserved.

Biotechnology Advances 36 (2018) 1038–1047

Y. Xia et al.

stochastic processes of birth, death, and immigration

Hydrolysers Fermenters

Log2 (taxon abundance) Log (taxa number)

Filamentous bacteria AOB NOB Denitrifiers

GAO ARB

Abundance of taxa

PAO

Pathogens

Effluent concentration

Number of taxa

Source community environmental conditions, taxa interactions … …

Log (time or area)

Frequency of taxa

COD TN TP

NH4-N Time

Fig. 1. Using knowledge on microbial ecology of AS to build predictive understanding of AS microbial structures and WWTP functions.

detection of more taxa including a large percent of uncultured organisms. They showed the overestimation of Actinobacteria and the underestimation of Acidobacteria (Lu et al., 2006) and Verrucomicrobia in AS by cultivation-based ways (Table 1), which is consistent with the experimental findings of microbial studies (Hugenholtz et al., 1998). Currently, high-throughput sequencing (HTS) allows the insights of inventory diversity of microbial community to an unprecedented level of detail. Investigations based on HTS of 16S rRNA genes with sample sizes of ~104 sequences reported the detection of over 30 phyla (Gao et al., 2016) and ~102 genera (Zhang et al., 2011a; Hu et al., 2012; Wang et al., 2012a; Gao et al., 2016) in AS. Proteobacteria and Bacteroidetes are predominant phyla in AS. Other dominant phyla (average abundance over 1%) include Acidobacteria, Firmicutes, Chloroflexi, Planctomycetes, Verrucomicrobia and Actinobacteria (Hu et al., 2012; Ju et al., 2014a; Lu et al., 2006; Wang et al., 2012a; Gao et al., 2016) (Table 1). The presence of uncultured phyla such as TM7, OD1, OP10 and WS3 have been also detected (Zhang et al., 2011a; Hu et al., 2012; Ju et al., 2014a; Gao et al., 2016). Apart from the overall microbial communities, a number of investigations have been made to specifically catalog functionally important groups in wastewater treatment. These include nitrifiers (ammonia oxidizing bacteria (AOB) (Gao et al., 2014; Wang et al., 2010; Wang et al., 2012b; Wang et al., 2012c; Pang et al., 2016) and nitrite oxidizing bacteria (NOB) (Siripong and Rittmann, 2007)), denitrifiers (Thomsen et al., 2007; Zielinska et al., 2016; Pang et al., 2016), phosphorus accumulating organisms (PAOs) (Lopez-Vazquez et al., 2008; Mielczarek et al., 2013) and filamentous bacteria (Mielczarek et al., 2012). The commonly used methods in such studies are clone library, DNA fingerprinting, fluorescent in situ hybridization (FISH) and quantitative polymerase chain reaction (qPCR), based on functional

examined since recent years (Wang et al., 2016; Hai et al., 2014; Shade et al., 2013; van der Gast et al., 2006; van der Gast et al., 2008; Wells et al., 2011), which facilitates clarification of mechanisms driving AS community assembly and predictions of their structures. In this review, we summarize current research on AS microbial richness and diversity updated especially by using high-throughput methods and community assembly patterns expanded by the test of ecological theories. In the end, we discuss the emerging trends of AS microbiology research towards the goals of building predictive understanding of AS microbial structures and WWTP functions (Fig. 1). 2. Diversity of AS microbial communities It is estimated that there are 1018 microbes in a WWTP (Woodcock et al., 2006), known to be less diverse than those in sediments, soil and sea (Curtis et al., 2002). The organisms in AS consist of bacteria, archaea, eukaryotes (fungi, algae, protozoa and metazoa), and viruses (e.g. bacteriophages). Bacteria are the main components of AS community. Alive or metabolically active bacterial cells revealed by cultivation-independent studies are typically around 80% of the total count of cells (Seviour and Nielsen, 2010). Unveiling “Who is there” is a longterm goal of the research on AS microbiology. The ability to measure microbial diversity is also a prerequisite for the studies of microbial biogeography and community assembly (Curtis et al., 2002). The richness and diversity of individual AS samples increase with the increase in sample sizes (Table 1). Typical cultivation-based research reported the detection of 4 bacterial phyla-Proteobacteria, Actinobacteria, Bacteroidetes and Firmicutes, which were classified into ~101 genera (Dias and Bhat, 1964; Benedict and Carlson, 1971; Jin et al., 2011). Molecular methods such as clone library and HTS enable the 1039

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Table 1 Microbial diversity of aerobic AS sample revealed by different methods. Method

Richness

Shannon-wiener index (H)

Composition

Unclassified organisms %

Reference

Cultivation Cultivation

39–57 isolates 60 and 69 isolates

1.06–1.44a 1.89 and 1.93a

0 3.3 and 17.4

Cultivation Clone library Clone library

63 isolates, 28 OTUs > 200 clones, 33 OTUs 53–78 clones

3.06b 3.01b 3.68–5.12b

2–4 phyla, 5–8 generad 4 and 5 phyla, 9 and 11 generae 4 phyla, 19 generad 7 phylad 8–11 phylad

(Dias and Bhat, 1964) (Benedict and Carlson 1971) (Jin et al., 2011) (Jin et al., 2011) (Yang et al. 2011)

454 pyrosequencing

493–500 sequences, 259–393 OTUs

/

454 pyrosequencing

16,528–23,288 sequences, 1183–3567 OTUs

/ b

454 pyrosequencing

7422–11,151 sequences, 2176–4123 OTUs

6.26–7.36

Illumina Miseq

23,970–46,429 sequences, 7156–10,510 OTUs

7.32–7.85b

Illumina GAII

205 million reads (72 bp), 269,385 contigs (540 bp on average) 25.4 million paired-end reads (100 bp),17–18.7 million tags (167 nt on average) 1126–1729 OTUs 38,507–40,654 coding sequences 18,847–24,268 coding sequences 24,046–32,338 coding sequences

/

Illumina PhyloChip GeoChip 4.2 GeoChip 4.2 GeoChip 4.2

/

7–12 phyla, 48–55 generad,h 17–27 phyla, 229–434 generad,i 17–24 phyla, 174–278 generad,j 30–33 phyla, 292–339 generad,k /f /

b

7.0–7.4 10.55–10.61c 9.9–10.1c 10.09–10.38 c

f

d

> 12 phyla 15 categoriesg 15 categoriesg 15 categoriesg

0 / 16–18.2 (at phylum level) /

(Lee et al. 2015)

29.5–55.5 (at genus level) 10.9–36.8 (at phylum level) < 10 (at phylum level)

(Zhang et al., 2011a)

/ /

(Albertsen et al., 2012) (Ju et al., 2014a)

/ / / /

(Xia et al., 2010) (Xia et al., 2014) (Wang et al., 2014) (Xia et al., 2016)

(Wang et al., 2012a) (Gao et al. 2016)

a

Shannon wiener index calculated based on genera. Shannon wiener index calculated based on OTUs (with a 3% nucleotide cutoff). c Shannon wiener index calculated based on coding sequences. d Bacteria was detected or characterized. e Bacteria and fungi were detected. f All organisms in activated sludge can be potentially detected by sequencing entire community genomes. g Functional genes affiliated with bacteria, archaea, fungi and viruses were characterized. h Sequences assigned to different taxa levels using the RDP's Bayesian Classifier program at an 80% cutoff. Only core OTUs identified from influent wastewater and activated sludge samples were taken into account. These OTUs accounted for 33.7–51.2% in AS samples on average. i 16,489 bacterial sequences in each sample after resampling assigned to different taxa levels using the RDP classifier at 50% threshold. j Sequences assigned to different taxa levels using the RDP classifier at 50% threshold. k Sequences assigned to different taxa levels using MOTHUR program via SILVA database with a set confidence threshold of 80%. b

pressure of the EBPR process (Albertsen et al., 2012). The characterizations of minor populations have also been achieved by metagenomics sequencing, such as heavy metal resistant bacteria (Li et al., 2014a), antibiotic resistant bacteria (ARB) (Zhang et al., 2011b; Ju et al., 2016) and bacterial pathogens (Cai and Zhang, 2013; Ju et al., 2016). Similarly, another metgenomics tool - GeoChip, which is a comprehensive functional gene array, provides a way to understand the overall diversity and functional potential of AS microbial communities (Sun et al., 2014; Wang et al., 2014; Xia et al., 2014; Xia et al., 2016) and simultaneously characterize several low-abundance organisms/ genes such as antibiotic resistant genes and pathogens (Zhang et al., 2016). Apart from understanding community diversity, several progresses have been made in exploring the temporal and spatial dynamics of AS microbial communities using DNA fingerprinting techniques (Kaewpipat and Grady, 2002; Ofiteru et al., 2010; van der Gast et al., 2006; Wang et al., 2010; Wang et al., 2012b) and high-throughput methods (Hai et al., 2014; Ju et al., 2014a; Ju et al., 2014b; Ju and Zhang, 2015; Saunders et al., 2016; Xia et al., 2016). These have facilitated the leverage of ecological theories in describing the assembly patterns of AS microbial communities, providing a way to evaluate community turnover rates and understand the mechanisms driving community structures.

genes or 16S rRNA gene segments. Currently, we have known the taxonomy of autotrophic nitrifiers and their quantitative ratio in municipal WWTPs. Nitrosomonas is abundant in AOB and Nitrospira is abundant in NOB in WWTPs. Nitrifiers typically account for 5.3–11.5% of the total bacteria in WWTPs (Harms et al., 2003; Kong et al., 2007; Nielsen et al., 2010; Reyes et al., 2015; Yao and Peng, 2017). Also, several abundant denitrifiers (e.g., Curvibacter, Azoarcus, Thauera, Zoogloea, and Accumulibacter), PAOs (e.g., Tetrasphaera and Accumulibacter) and filamentous bacteria (e.g., Microthrix parvicella and Gordonia) and their abundances in WWTPs have been revealed by quantitative analyses based on FISH and qPCR (Table 2). Low-abundance but environmentally important populations such as antibiotic resistant bacteria and potential pathogens have also received increasing attention. They have been typically characterized by qPCR (Mao et al., 2015; Lee et al., 2008; Rodriguez-Mozaz et al., 2015), molecular analysis of pathogenic bacteria 16S rRNA genes (Kumaraswamy et al., 2014; Lee et al., 2008), and cultivation-based methods as well (Ajonina et al., 2015; Cheng et al., 2012; Munir et al., 2011; Zhang et al., 2009). We are currently in the era of metagenomics. Insights of these above mentioned organisms can also be provided by metagenomics techniques without constructing separate libraries of specific populations. High-throughput methods-based metagenomics studies enable relatively unbiased snapshots of all the organisms in AS samples by directly sequencing community genomes. Metagenomics studies based on HTS are powerful in profiling diversity, genomic structures and functional potentials of community members of AS. As revealed by a metagenome generated using Illumina sequencing from a full-scale enhanced biological phosphorus removal (EBPR) plant (18.2 G), the genetic potential for community function showed enrichment of genes involved in phosphate metabolism and biofilm formation, reflecting the selective

3. Assembly patterns of AS microbial communities It has been established that microbial communities in natural ecosystems, like plants and animals, exhibit biogeographic patterns (Hanson et al., 2012, Horner-Devine et al., 2004; Fierer and Jackson, 2006; Martiny et al., 2006; Green et al., 2008; Nemergut et al., 2013). 1040

1041

Chloroflexi (phylum) TM 7 (phylum)

Gordonia

Microthrix parvicella

Tetrasphaera

Azooarcus, Thauera Zoogloea ramigera Zoogloea (excluding Z. resiniphila) Dechloromonas Accumulibacter Accumulibacter

c

b

10.1% on averageb 5.0% on averageb

11–22%b 10.3–23.5%a

18–30%b 1–3% (without foaming problems), 0 to 18% (foaming samples)a 0–3.97%a

1%b 0.2–6.2%b 15%b Up to 10%b 0.2–5.2%b 6.0–24%b 1.0–10.8%b 3.1–5.4%b 9–24%b 2–8%b 20–30%b 11–25%b

Three WWTPs. One treating domestic wastewater, one treating industrial wastewater and one treating both domestic and industrial wastewater. EBPR/non-EBPR plants (25% with potential foaming problem) Three WWTPs. One treating domestic wastewater, one treating industrial wastewater and one treating both domestic and industrial wastewater. EBPR/non-EBPR plants (25% with potential foaming problem) EBPR/non-EBPR plants (25% with potential foaming problem)

EBPR plants WWTPs with or without foaming problems

All with biological C and N removal, one EBPR plant. Both with biological C and N removal and chemical P removal. One with biological P removal EBPR plants An EBPR and two non-EBPR plants Both with biological C and N removal and chemical P removal. One with biological P removal EBPR plants An EBPR plant Both with biological C and N removal and chemical P removal. One with biological P removal An EBPR plant EBPR plants An AS plant AS plants EBPR plants A EBPR and non-EBPR plants EBPR plants EBPR plants EBPR plants EBPR plants EBPR plants 4 non-EBPR plants applying A2O, AO, UCT and Carrousel OD processes

10–30%b 11–29%b

Thauera

A2O, MAO and OD systems Single stage reactor for biological C and N removal EBPRh plants

4.02% on averageb 8.6%a 7% on averageb

0.8–9.3%b 4%b 2–11%b

Single stage reactor for C and N removal A2Oc, AOd and CASe systems A2O, MAOf and ODg systems

1.7%a 0.01–2.8%a 1.27% on averageb

Azoarcus

Data obtained by quantitative PCR. Data obtained by quantitative FISH. Anaerobic-anoxic-oxic process. d Anoxic-oxic process. e Conventional activated sludge process. f Modified anoxic-oxic process. g Oxidation ditch process. h Enhanced biological phosphorus removal process.

a

Filamentous bacteria

PAOs/denitrifiers PAO

Denitrifiers

Nitrifier

Plant description

Abundance

1.5–11.4%b 2.0–16.3%b 3–16%b

Nitrosomonas oligotropha Betaproteobacterial AOB Betaproteobacterial AOB, Nitrosomonas, and Nitrosospira Nitrospirae (Phylum), Nitrospira and Nitrobacter Nitrospira AOB-Betaproteobacterial AOB, Nitrosomonas and Nitrosospira, NOB-Nitrospira Curvibacter

AOB

NOB

Targets

Functional group

Table 2 Relative abundances of nitrifiers and abundant organisms of denitrifiers, PAOs and filamentous bacteria in WWTPs based on qPCR and FISH analyses.

(Mielczarek et al., 2012) (Mielczarek et al., 2012)

(Mielczarek et al., 2012) (Marrengane et al., 2011)

(Kumari et al., 2009)

(Kong et al., 2007) (Nielsen et al., 2010) (Reyes et al., 2015) (Rossello-Mora et al., 1995) (Nielsen et al., 2010) (Zielinska et al., 2016) (Nielsen et al., 2010) (Nguyen et al., 2011) (He et al., 2008) (Mielczarek et al., 2013) (Mielczarek et al., 2013) (Muszyński and ZałęskaRadziwiłł, 2015) (Nguyen et al., 2011) (Kaetzke et al., 2005)

(Nielsen et al., 2010) (Kong et al., 2007) (Thomsen et al., 2007)

(Nielsen et al., 2010) (Zielinska et al., 2016) (Thomsen et al., 2007)

(Thomsen et al., 2004) (Thomsen et al., 2007)

(Yao and Peng, 2017) (Harms et al., 2003) (Nielsen et al., 2010)

(Harms et al., 2003) (Limpiyakorn et al., 2005) (Yao and Peng, 2017)

Reference

Y. Xia et al.

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meta-community (logseries-like). When m decreases, the local community is more isolated and supports a smaller number of rare species, turning TAD to be lognormal-like (Hubbell, 2001). There is insufficient experimental evidence to support any particular hypothesis (McGill et al., 2007). However, the log-normal type is thought to characterize communities such as prokaryotes, which exhibit highly dynamic and random growth (Curtis et al., 2002; Engen and Lande, 1996; MacArthur, 1960; May 1975). The lognormal distribution model has been exploited to estimate the prokaryotic diversity of wastewater treatment systems based on two properties of the community: Nmax and NT, by assuming Nmin = 1 (Nmin is the number of individuals in the least abundant taxa). Nmax is the number of individuals in the most abundant species and NT is the total number of individuals in the community. It was estimated that 70 taxa resided in 1 ml of mixed liquor, according to the data generated by FISH (Curtis et al., 2002). So far, the intensively generated HTS data of AS microbes in recent years have not been used for the test of TAD curves. However, HTS data of Texas mine microbial communities showed significant deviation (D = 0.17, P < 0.001) from a log-normal model (Kang et al., 2016). A possible reason might be that incomplete sampling results in higher proportion of low-abundance populations than that of the real community (Ulrich et al., 2010).

The commonly examined patterns of microbial community assembly include relationships of taxa-area, distance-decay and taxa-time. A small number of attempts have also been done to clarify the abundance distribution curves of microbes. Here, the studies on AS community assembly patterns are summarized and the unique attributes of WWTPs are discussed. 3.1. Taxa abundance distribution A taxa abundance distribution (TAD) is a description of the abundance (number of individuals observed) for each different taxa within a community. It is well documented in macrobial communities that the majority of taxa are in low abundance and only a few are more abundant (McGill et al., 2007). There are similar observations in microbial communities, though their TAD curves tend to be skewed towards lowabundance species (Nemergut et al., 2013). In AS microbial communities, it was shown that only 25.5 ± 2.3% of the detected genera were relatively abundant, with relative abundances over 0.1%, in the samples collected from 14 WWTPs across Asia and North America. However, their cumulative abundances accounted for 89.1 ± 2.6% (Zhang et al., 2011a). The observations of a majority of low-abundance taxa and a few abundant taxa in AS were also reported by other studies (Saunders et al., 2016; Wang et al., 2012a). Abundant organisms may substantially contribute to pollutant degradation (for example, carbon turnover) in WWTPs (Saunders et al., 2016). Rare taxa may be responsible for the degradation of low concentrations of environmentally or economically important chemicals such as endocrine-disrupting compounds (EDCs) in WWTPs (Pholchan et al., 2013). Understanding the roles of low-abundance taxa can benefit the engineering of new functions of WWTPs. Documenting and explaining TADs of AS communities will benefit the understanding of mechanisms shaping community assembly (McGill et al., 2007) and ecosystem functions. Also, parameterizing the distribution curve of microbial communities provides a way for the calculation of the extent of microbial diversity (Curtis et al., 2002). Several theories have been developed to explain TADs (McGill et al., 2007). Classical models propose geometric (Motomura, 1932), logseries (Fisher et al., 1943), lognormal (Preston, 1948), and broken stick (MacArthur, 1957) distributions. The geometric model predicts extremely uneven abundances; broken stick predicts extremely even abundances; while lognormal and logseries are intermediate, with the latter predicting higher proportions of very rare species (Magurran, 1988; McGill et al., 2007). A population dynamic model - the neutral community model (NCM) proposed by Hubbell suggests that the TAD pattern of a local community is a function of the immigration rate (m). When m = 1, the TAD of the local community is the same as that of the

3.2. Spatial assembly patterns The empirical TAR was generalized in the 1920s as a power law (Arrhenius, 1921; Gleason, 1922), S = cAz, where S is the number of taxa, A is the area sampled, c is an empirically derived taxon- and location- specific constant, and z is the slope of the log-log line measuring the rate of taxa addition per unit area (community turnover rate) (Nemergut et al., 2013). The TAR has been proposed to be driven by the area per se hypothesis and habitat heterogeneity hypothesis (Kallimanis et al., 2008). The former indicates a dynamic equilibrium between extinction and immigration (mediated by dispersal), while the latter assumes the increase of habitat diversity with area (mediated by environmental selection). According to the TAR theory, a larger bioreactor harbors a more diverse microbial community. High diversity of microbial communities is commonly thought to lead to stable functioning of ecosystems. Testing the validity of TARs within wastewater treatment systems is of practical use, which helps make decisions about whether single large centralized- or several small- WWTPs are better for a town or populated area (Curtis et al., 2003) and may possibly enable predictions of microbial richness shifts of AS over space. The TAR of microbial communities in WWTPs has been limitedly reported (Table 3). It was reported that a significant taxa-volume relationship was observed within membrane bioreactors (MBRs) ranging from 2 to 3840 m3 by using denaturing gradient gel electrophoresis

Table 3 Biogeographic patterns of AS microbial communities observed in recent investigations. System

Spatial/temporal scale

Slope

Methods

Reference

Taxa-area relationship Membrane bioreactors

2–3840 m3a

0.206d

DGGE

(van der Gast et al., 2006)

Distance-decay relationship Municipal wastewater treatment plants

2777 kmb

0.008e

GeoChip 4.2

(Wang et al., 2016)

Taxa-time relationship A bioreactor treating municipal wastewater (5 L) A bioreactor treating industrial wastewater (5 L) A municipal wastewater treatment plant A pilot-scale bioreactor treating municipal wastewater A municipal wastewater treatment plant

24 weeksc 24 weeksc 12 monthsc 12 monthsc 52 weeksc

0.512d 0.162d 0.43d 0.55d 0.206d

DGGE DGGE 454-pyrosequencing 454-pyrosequencing T-RFLP

(van der Gast et al., 2008) (van der Gast et al., 2008) (Hai et al., 2014) (Hai et al., 2014) (Wells et al., 2011)

a b c d e

The volume of each membrane bioreactor investigated. The furthest distance between sampling points. The longest time interval between sampling points. Samples were taken in consecutive weeks/months. Based on bacterial 16S rRNA genes. Based on functional genes affiliated with bacteria, archaea, fungi and viruses.

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nitrifiers (Wang et al., 2011, 2012b), denitrifiers (Gentile et al., 2007), PAOs (Mielczarek et al., 2013) and filamentous bacteria (Mielczarek et al., 2012) have exhibited temporal variations. In analogy to TAR, the TTR describes how total taxa richness of a community in a fixed area increases with the length of time (Preston, 1960; White, 2004; White et al., 2006). The relationship between taxa richness (S) and time (T) can be described as S = cTw. The temporal scaling exponent w (slope) reflects taxa turnover rate (van der Gast et al., 2008). Understanding TTRs of AS microbial communities is valuable for discerning the baseline level of community temporal variability, which potentially enables predictions of richness of AS microbial communities over time (Shade et al., 2013). The TTR has been documented in several wastewater treatment systems (Table 3). For example, the community of a full-scale WWTP showed a TTR with the temporal scaling exponent w being 0.209 ± 0.007 (95% confidence interval), as revealed by terminal restriction fragment length polymorphism (T-RFLP) (Wells et al., 2011). Differences in temporal community turnover have been observed in wastewater treatment systems. The communities which are more diverse show lower temporal turnover rates, consistent with the observations in plants and animals (White et al., 2006). For instance, a full-scale bioreactor harbors a more diverse community than a pilotscale one, and the community of the former showed a smaller w value (0.43) than that of the latter (0.55). Similarly, the community of a sequencing batch reactor (SBR) system showed higher diversity and smaller temporal variations than that of an MBR system (Wittebolle et al., 2008); and a larger full-scale conventional activated sludge (CAS) system harbored a more diverse but less dynamic community than a smaller full-scale CAS system (Valentín-Vargas et al., 2012). Besides, abundant and persistent taxa have been shown to be less dynamic in wastewater treatment systems. For instance, the exponents of dominant phyla were generally lower than those of low-abundance phyla (Hai et al., 2014). Persistent OTUs were reported to exhibit a smaller temporal variability than rare taxa in a full-scale WWTP (Kim et al., 2013). In addition, taxa turnover rates may decrease with increasing selective pressure, implied by the observation that increasing proportions of industrial wastewater in the influents led to decreased w values of the microbial communities in bioreactors (van der Gast et al., 2008). The w values of wastewater treatment systems fell within the typical values determined previously by a meta-analysis of temporal dynamics in microbial communities, including 76 sites representing air, aquatic, soil, brewery wastewater treatment, human- and plant-associated microbial biomes. TTRs of these microbial communities were detected and the power law exponents ranged between 0.24 and 0.61 (Shade et al., 2013). In spite of temporal dynamics of WWTP communities, persistent taxa are reported in several studies (Ju and Zhang, 2015; Saunders et al., 2016; Xia et al., 2016). It was shown that persistent OTUs (present over 80% of the sampling months) occupied 9.7% of total OTUs and accounted for 76.6% of all of the 16S rRNA gene sequences (Saunders et al., 2016). The presence of persistent communities has valuable implications in modeling for predicting ecosystem functions, as persistent populations especially persistently abundant organisms may make indispensable contributions to WWTP performance and stability (Ju and Zhang, 2015; Saunders et al., 2016).

(DGGE) targeting bacterial 16S rRNA genes. The z value (spatial community turnover rate) was 0.206, comparable to those of insular (island) microbial communities from water-filled tree-holes and metalcutting fluid sump reservoirs (van der Gast et al., 2006). The study suggests that AS microbial communities are like island communities, to which the equilibrium theory of island biogeography (MacArthur, 1967) can be applied. Also, clone library analyses of two parallelly operated systems (treating identical wastewater and with similar operational parameters) showed that the microbial inhabitants (total bacteria, nitrifiers and denitrifiers) of a full-scale wastewater treatment system were more diverse than those of a pilot-scale one (our unpublished data). The distance-decay relationship delineates a decline in similarity with increasing geographic distance. In general, this pattern can be explained by environmental heterogeneity and dispersal history. First, environmental conditions are likely to become increasingly different with distance. Communities are expected to become increasingly different if they are mainly shaped by local conditions (Baas Becking, 1934). Second, individuals are dispersal limited. If individuals tend to colonize nearby sites, sites that are close together will tend to harbor similar communities, even without differences in environmental conditions or niche requirements (Bell, 2001; Hubbell, 2001). A number of investigations have shown that microorganisms, like plants and animals, exhibited distance-decay patterns in different habitats at various taxonomic resolutions (Horner-Devine et al., 2004; Schauer et al., 2009; Martiny et al., 2011; Astorga et al., 2012). For AS microbial communities, a distance-decay relationship was not detected in MBR systems (van der Gast et al., 2006), while a weak distance-decay relationship (slope = −0.0075) was detected within microbial functional genes from 26 full-scale AS systems located in 10 cities across China. The slope of the power law distance-decay relationship was used to calculate the exponent z value of the power law taxon-area relationships, which is about 1 to 2 orders of magnitude lower than those observed in microbial communities in other studies. The overall functional gene structure of AS was mainly shaped by environmental factors, along with geographic distance. The authors attributed the very small z value of the systems to their homogeneous environmental conditions (Wang et al., 2016). WWTPs are engineered ecosystems in which environmental conditions are affected not only by geographical locations but also influent wastewater characteristics and operational parameters. In addition, AS microbial communities originate from inoculation sludge that is artificially selected. The dissimilarities (or similarities) in environmental conditions (e.g., operational parameters and influent characteristics) and source community not resulting from the geographical distance of WWTPs may weaken or obscure the distance decay relationship of AS. Discerning the spatial assembly patterns of AS microbial communities, research on geographically different WWTPs has suggested the existence of shared communities within several wastewater treatment systems, such as 25 EBPR systems in Denmark (Nielsen et al., 2012), 5 wastewater treatment bioreactors in geographically different zones treating domestic and synthetic wastewater (Xia et al., 2010), 14 fullscale municipal wastewater treatment systems at different geographic locations of China (Wang et al., 2012a) and 13 WWTPs in Denmark during two consecutive years (Saunders et al., 2016). Specifically, the 13 WWTPs in Denmark contained a shared community of 63 abundant genus-level operational taxonomic units (OTUs) that made up 68% of the total reads. The identification of shared AS microorganisms in bioreactors might indicate a common source community amenable to the island biogeography theory. Also, the existence of shared communities can benefit modeling as the findings of one bioreactor can be extrapolated to other bioreactors.

3.4. Effects of sample size and sampling scale on the observed AS community assembly patterns The understanding of AS microbial community assembly patterns can be impaired by the sample sizes. Clone libraries of PCR-amplified 16S rRNA genes can only reveal a very small portion (typically tens to hundreds of isolates) of microorganisms from AS bioreactors, which may contain as many as 109 individuals per ml of mixed liquor (Woodcock et al., 2006). DNA fingerprinting methods only enable the detection of relatively abundant organisms. The disparity between

3.3. Temporal assembly patterns Overall AS microbial communities and functional groups such as 1043

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dramatically different communities, and biotic interactions subsequently escalated the differences to be of little overlap among 14 identical microbial electrolysis cell (MEC) reactors. However, directly illustrating the existence of stochastic assembly and its relative roles in determining community composition are very challenging because it is extremely difficult, if not impossible, to ensure identical initial conditions (e.g., initial density, initial environmental heterogeneity) and environmental factors in full-scale WWTPs. Alternatively, the role of stochastic factors has been taken into consideration using NCMs in an increasing number of studies. NCMs assume ecological equivalence of the organisms at the same trophic level in communities saturated with individuals, and only consider stochastic processes of birth, death and immigration (Ayarza and Erijman, 2011; Ofiteru et al., 2010; Sloan et al., 2006). The Sloan neutral model was shown to be successful in explaining the relative abundances and frequencies of different taxa observed in several prokaryotic communities, including AOB in domestic sewage works and archaeal communities in anaerobic digesters (Ayarza and Erijman, 2011; Sloan et al., 2006). A modified Sloan neutral model integrating environmental influence on the reproduction (birth) rate of individual taxon could well predict the abundances of heterotrophs and AOB in a wastewater treatment system (Ofiteru et al., 2010). Clarifying the relative roles of deterministic and stochastic factors and incorporating them into the predictive models of AS microbial structures is of practical use for microbial community manipulation.

sample sizes and community sizes suggests that the turnover rates of taxa (z and w) can be considerably underestimated as variations of rare taxa are undetectable. The effects of sample sizes on the power law taxa-abundance distribution of synthetic communities from homogeneous environments (for example, continually stirred bioreactors) were estimated by simulation sampling using a scheme which yielded an (n - 1)-dimensional Kolmogorov equation (n is the number of taxa in a community). When community sizes increased from 1010 (the number of individuals typically detected in tens of ml of AS) to 1018 (the number of individuals typically detected in a WWTP), analyses based on random samples of size 200 individuals (typical for clone library) and sampling 106 individuals with a 0.1% detection limit on relative abundance (typical for fingerprinting methods) did not reveal a significant relationship between community size and sample diversity. Analyses based on random samples of size 106 individuals suggested a lower exponent than that of a complete community census (Woodcock et al., 2006). HTS enables much larger sample sizes which can be theoretically unlimited. This alleviates the issue of small sample size. The spatial or temporal scales of sampling can also affect the assembly patterns of microbial communities. Different spatial scales involve differing variations in geographic distance and environmental conditions, which may lead to different spatial assembly patterns of microbial communities. Similarly, turnover rates of AS microbial communities can vary with temporal scales. It was shown that microbial communities inhabiting brewery WWTPs changed more rapidly over shorter time scales. The possible reason may be that microbial communities are likely to show ecological resilience over long terms (Shade et al., 2013). When making evaluations of community richness shifts over time and space, it is important to define the temporal/spatial scales for reliable identification of community turnover. This knowledge can be enriched with more research on the biogeography of AS microbial communities.

5. Implication of understanding AS microbial ecology for predicting WWTP performances and stability To apply the information gained from microbial diversity and assembly patterns to the understanding and even predictions of the performances and stability of WWTPs, it is crucial to elucidate the relationships between microbial community structure and ecosystem function. This calls for accurate measurements of microbial activities and functions. Currently, the links between microbial community structure and the performance of bioreactors have been documented. For example, abundances of carbon and nitrogen cycling genes were significantly associated with the removal efficiencies of influent COD and TN in full-scale AS bioreactors, respectively (Xia et al., 2014). The performance of phenol degradation was related to microbial community structure in anaerobic SBRs (Rosenkranz et al., 2013). The AS microbial community with higher diversity was more resistant to toxic shock loadings (Saikaly and Oerther, 2011). The diversity indices of polyhydroxyalkanoates (PHA)-accumulating microorganisms were positively linked to the metabolic diversity (the number of utilizable carbon sources) (Yang et al., 2011). On the other hand, it is not surprising that correlations between DNA abundance and WWTP performances can be missing (Xia et al., 2016). One of the possible reasons is that DNA is not directly related to the metabolic status of microbes. Alternatively, other biomolecules that may show tighter connections to microbial activities than DNA such as RNA and proteins can be examined. To date, the studies on RNA of AS microbial communities are in the early stage (Li et al., 2014b; Yu and Zhang, 2012). Strong nitrification activity was indicated by the high cDNA/DNA ratios of ammonia monooxygenase (AMO) and hydroxylamine oxidase (HAO) compared to those of the other enzyme subunits associated with nitrogen cycling (Yu and Zhang, 2012). No extensive meta-proteomics studies of full-scale AS bioreactors have been reported, primarily due to lack of representative reference genomes and metagenomes for reliable identification of the proteins. However, it is expected that successful application of postgenomic techniques (such as metaproteomics) will be possible in the future (Hansen et al., 2014). Another reason may be that most of the extraordinary diversity in microbial communities is redundant (Franklin and Mills, 2006). Many of the low-abundance taxa may be inactive. Selecting and measuring functional traits of microbes that are relevant to ecosystem function is of great importance (Nemergut et al., 2013). Methods such as stable-

4. Factors driving AS community assembly What processes drive microbial diversity and community assembly patterns of AS? It is a fundamental question in microbial ecology. The answer can facilitate manipulations of AS microbial communities. Typically, deterministic factors, such as environmental conditions and interspecies interactions, are considered to be important in driving AS community assembly. Highly similar environmental conditions in bioreactors can lead to high reproducibility of microbial communities, as revealed by the investigation of AOB in three parallel SBRs (Wittebolle et al., 2009). A large number of investigations have elucidated the links between environmental factors and microbial community structures of AS. Influent loadings such as chemical oxygen demand (COD) (Han et al., 2010) and total nitrogen (TN) (Liu et al., 2010), bioreactor temperatures (Alawi et al., 2009, Karkman et al., 2011 and Ma et al., 2013), pH and dissolved oxygen concentrations (DO) (Kim et al., 2011; Zheng et al., 2011), hydraulic retention time (HRT) (Han et al., 2010), geographical locations (Shanks et al., 2013; Wang et al., 2012a), treatment processes (Hu et al., 2012; Sheng et al., 2017) and membrane filtration (Hall et al., 2010) have been shown to exert important effects on AS microbial community structures. Very recently, biological interactions revealed by network analysis were shown to be dominant factors in determining the bacterial community assembly, while environmental conditions partially explained phylogenetic and abundance variances of microbes (Ju and Zhang, 2015). Deterministic factors are not alone in shaping extremely complex microbial communities. Stochasticity in community assembly (random processes, such as birth, death, migration, speciation and dispersal of microorganisms) can cause considerable site-to-site variability in species compositions (known as β-diversity), even under identical environmental conditions. A study showed that stochastic assembly can play dominant roles in shaping microbial community structure (Zhou et al., 2013). Ecological drift (i.e., initial stochastic colonization) led to 1044

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isotope probing (SIP) (Ginige et al., 2005) and combined microautoradiography and fluorescence in situ hybridization (MAR-FISH) (Lee et al., 1999) are useful tools for the identification of microbes involved in specific metabolic processes. In addition, insights of diversity and functions of community members can be easily provided by meta-omics techniques with the development of bioinformatics tools and the accumulation of reference genomes. The joint use of omicstechnology and SIP can be fruitful in in-situ identification of microbes contributive to ecosystem functions. Developing mathematic models to explain and predict AS community structures and utilize information on AS community to predict the performance of WWTPs will also be vital for environmental engineering and advancing knowledge on AS microbial ecology.

the same rules? Glob. Ecol. Biogeogr. 21, 365–375. Ayarza, J.M., Erijman, L., 2011. Balance of neutral and deterministic components in the dynamics of activated sludge floc assembly. Microb. Ecol. 61, 486–495. Baas Becking, L., 1934. In: Van Stockum, W.P., Zoon (Eds.), Baas Becking's Geobiology: Or Introduction to Environmental Science. The Hague, The Nethelands (In Dutch.). Bell, G., 2001. Neutral macroecology. Science 293, 2413–2418. Benedict, R.G., Carlson, D.A., 1971. Aerobic heterotrophic bacteria in activated sludge. Water Res. 5, 1023–1030. Cai, L., Zhang, T., 2013. Detecting human bacterial pathogens in wastewater treatment plants by a high-throughput shotgun sequencing technique. Environ. Sci. Technol. 47, 5433–5441. Cheng, H.W., Lucy, F.E., Broaders, M.A., Mastitsky, S.E., Chen, C.H., Murray, A., 2012. Municipal wastewater treatment plants as pathogen removal systems and as a contamination source of noroviruses and Enterococcus faecalis. J. Water Health 10, 380–389. 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6. Conclusions The knowledge of AS microbiology benefits the understanding of both microbial ecology and WWTP operation. Insights of diversity and assembly patterns of microbial communities in AS have been greatly enriched by using high-throughput molecular methods. The presence of uncultured phyla such as TM7 and OD1 in AS has been shown. Typical ranges of relative abundances of important functional groups have been reported. Low-abundance but environmentally important populations such as antibiotic resistant bacteria and human bacterial pathogens have been characterized. The understanding of community assembly patterns has been accelerated by applying ecological theories. High unevenness of AS communities has been reported and the lognormal distribution model has been used for diversity estimation of AS microbial communities. The detection of taxa-volume relationship enables possible prediction of microbial diversity based on system volumes. Temporal turnover of AS microbial communities has been revealed by the analyses of TTR, with more diverse AS microbial communities showing more gradual temporal turnover. Meanwhile, the core AS communities across different geographic and temporal scales have been detected, which provide robust data points for predictive models of WWTP performances. The driving forces in structuring assembly patterns of AS microbial communities are both deterministic and stochastic processes. Applying knowledge on AS microbial diversity and the new ecological concepts can be valuable but challenging. To define certain temporal and spatial scales for reliable identification of community turnover is of great importance. Identifying active microbes and microbial functional traits provides valuable information, which can be obtained from omics- methods integrated with SIP. Mathematic models and bioinformatics tools are important for data mining to understand and predict microbial community structures and functions. Acknowledgement This study was supported by the National Scientific Foundation (51678335) and Tsinghua University Initiative Scientific Research Program (No. 20161080112). References Ajonina, C., Buzie, C., Rubiandini, R.H., Otterpohl, R., 2015. Microbial pathogens in wastewater treatment plants (WWTP) in Hamburg. J. Toxicol. Env. Heal. A. 78, 381–387. Alawi, M., Off, S., Kaya, M., Spieck, E., 2009. Temperature influences the population structure of nitrite-oxidizing bacteria in activated sludge. Environ. Microb. Rep. 1, 184–190. Albertsen, M., Hansen, L.B.S., Saunders, A.M., Nielsen, P.H., Nielsen, K.L., 2012. A metagenome of a full-scale microbial community carrying out enhanced biological phosphorus removal. ISME J. 6, 1094–1106. Ardern, E., Lockett, W.T., 1914. Experiments on the oxidation of sewage without the aid of filters. J. Chem. Technol. Biot. 33, 523–539. Arrhenius, O., 1921. Species and area. J. Ecol. 9, 95–99. Astorga, A., Oksanen, J., Luoto, M., Soininen, J., Virtanen, R., Muotka, T., 2012. Distance decay of similarity in freshwater communities: do macro- and microorganisms follow

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