Revealing sludge and biofilm microbiomes in membrane bioreactor treating piggery wastewater by non-destructive microscopy and 16S rRNA gene sequencing

Accepted Manuscript Revealing sludge and biofilm microbiomes in membrane bioreactor treating piggery wastewater by non-destructive microscopy and 16S rRNA gene sequencing Tomohiro Inaba, Tomoyuki Hori, Ronald R. Navarro, Atsushi Ogata, Dai Hanajima, Hiroshi Habe PII: DOI: Reference:

S1385-8947(17)31433-X http://dx.doi.org/10.1016/j.cej.2017.08.095 CEJ 17546

To appear in:

Chemical Engineering Journal

Received Date: Revised Date: Accepted Date:

16 June 2017 17 August 2017 18 August 2017

Please cite this article as: T. Inaba, T. Hori, R.R. Navarro, A. Ogata, D. Hanajima, H. Habe, Revealing sludge and biofilm microbiomes in membrane bioreactor treating piggery wastewater by non-destructive microscopy and 16S rRNA gene sequencing, Chemical Engineering Journal (2017), doi: http://dx.doi.org/10.1016/j.cej.2017.08.095

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Revealing sludge and biofilm microbiomes in membrane bioreactor treating piggery wastewater by non-destructive microscopy and 16S rRNA gene sequencing Tomohiro Inaba1, Tomoyuki Hori1*, Ronald R. Navarro1, Atsushi Ogata1, Dai Hanajima2, and Hiroshi Habe1 1

Environmental Management Research Institute, National Institute of Advanced Industrial

Science and Technology (AIST), 16-1 Onogawa, Tsukuba, Ibaraki 305-8569, Japan 2

Dairy Research Division, National Agricultural Research Center for Hokkaido Region,

National Agricultural and Food Research Organization, 1 Hitsujigaoka, Sapporo, Hokkaido 062-8555, Japan

*Corresponding author. Tomoyuki Hori Mailing address: Environmental Management Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Onogawa 16-1, Tsukuba, Ibaraki 305-8569, Japan. Phone: +81-29-849-1107, Fax: +81-29-861-8326 E-mail: [email protected]

Running title: Microbiomes in piggery wastewater-treating MBRs

Key words: Membrane bioreactor; Biofouling; Biofilm; Microbiome; piggery wastewater treatment

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Abstract Membrane bioreactors (MBRs) are used for the treatment of piggery wastewater. In this study, we investigated the microbiomes of sludge and membrane-attached biofilm in MBRs treating piggery wastewater by non-destructive confocal reflection microscopy and high-throughput sequencing of 16S rRNA genes. Microscopic visualization indicated that amine-containing extracellular matrix accumulating on the membrane caused membrane fouling, especially in the absence of microbiome acclimatization. The principal coordinate and phylogenetic analyses of the sequence data showed that the sludge and biofilm microbiomes were different, but that both changed in response to reactor conditions. The non-acclimatized sludge microbiomes were composed of operational taxonomic units (OTUs) that were abundant in piggery wastewater. The

acclimatized

microbiomes,

including

the

polysaccharide-degrading

Mitsuaria

chitosanitabida and protein-degrading Reyranella massiliensis, were found under stable conditions. An OTU related to the bacteriolytic myxobacterium Enhygoromyxa salina increased under the deteriorative condition induced by overloading. Meanwhile, the biofilm microbiomes comprised mainly phylogenetically novel bacteria under all conditions. In addition, the anaerobic Clostridium cellulovorans became dominant in the thick, dense biofilms observed under the deteriorative condition. The results of this study demonstrated that sludge and biofilm microbiomes are associated with reactor performance, especially with membrane fouling, during MBR treatment of piggery wastewater.

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1. Introduction Piggeries produce large amounts of wastewater, which mainly consists of pig manure. The difficulty of treating piggery wastewater is one of the most serious issues in the livestock industry. Membrane bioreactors (MBRs) are drawing attention as one efficient treatment method for high-strength wastewaters [1]. MBRs provide a smaller installation area, more efficient solid–liquid separation, less excess sludge, and a higher quality of treated wastewater compared to more traditional activated sludge methods [2, 3]. Due to these advantages, use of MBRs for the treatment of piggery wastewater is increasing [4-8]. However, the microbiological mechanisms underlying stable piggery wastewater treatment by MBR remain unclear. Moreover, membrane filtration is affected by a persistent problem called membrane fouling, which results in decreased quality and quantity of treated wastewater as well as increased operational cost [9]. One main cause of membrane fouling is the deposition and accumulation of microbial cells and metabolites (i.e., biocake build-up) on the filtration membrane. However, the mechanisms of membrane fouling have not yet been clarified due to the complexity of the biological factors involved [10, 11]. Membrane fouling occurs as a result of the formation of biofilms on the filtration membranes [12-15]. We recently revealed architectures, components, and microbiomes of the fouling-related biofilms at naturally occurring states in MBRs treating artificial wastewater [12]. It has been reported that various environmental factors can affect biofilm formation and development, so that biofilms with highly diverse structures emerge [16]. Yet the effect of high-strength wastewaters, such as piggery wastewater, on fouling-related biofilm remains unclear. Our previous studies showed that the sludge microbiomes in MBRs changed in response to organic loading conditions [17-19]. Meanwhile, there have been a few studies that investigated both the microbiomes of the sludge and fouling-related biofilm [12, 20]. Moreover, there is not much information in the literature regarding specific microbiological data associated with reactor performance during the treatment of piggery wastewater in MBR. The objective in this study was to investigate the microbiomes of the sludge and membrane-attached biofilm during the treatment of piggery wastewater in MBRs. Cross sections of three-dimensional structures of fouling-related biofilms were visualized by non-destructive confocal microscopy, and the compositions of the sludge and biofilm microbiomes were examined using high-throughput Illumina sequencing of 16S rRNA genes. These analyses were applied to both the unstable and stable conditions of the MBRs, established in the absence and presence, respectively, of microbiomes acclimatized to piggery wastewater. We also investigated the deteriorative condition induced by overloading after acclimatization.

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2. Materials and Methods 2. 1. Operation of laboratory-scale MBRs treating pig manure wastewater A schematic view of the aerobic MBR and a detailed description of the experimental setting used in this study have been presented in previous studies [12, 18]. Detailed information regarding the MBR reactor and operational procedures were provided in the Supporting Information. Fresh pig feces were collected from a pig-fattening facility at the National Institute of Livestock and Grassland Science (Tsukuba, Ibaraki, Japan). The preparation of the influent piggery wastewater was performed as described in our previous report [21]. Briefly, real pig feces were filtered through a metallic sieve with a 0.5-mm mesh, and the feces were mixed with distilled water (dry feces : distilled water =1:14 [w/w]). Urea was added as urinary constituent at a final concentration of 2 g/L. Generally, mammalian urine is sterile and mostly composed of water and urea. The mixture of real pig feces, urea and water was used as simulated piggery wastewater. This wastewater was not the exactly same but highly relevant to real piggery wastewater [21, 22]. The chemical oxygen demand (COD) and biological oxygen demand (BOD) values of piggery wastewater were determined to be 34,200 mg/L and 10,165 mg/L, respectively. The prepared piggery wastewater was stored at 4°C, and supplied into the reactor. The flow rates of influent and effluent were adjusted to 4.8 and 9.6 L/day, resulting in hydraulic retention times (HRTs) of 6 and 3 days, respectively. To evaluate the effect of wastewater loading conditions, the reactor was operated under two different organic loads (704 and 1,407 mg-COD/L/day). Under the low organic load, twice-diluted piggery wastewater was used as influent. The reactor was started up with the inoculation of an activated sludge obtained from a municipal wastewater treatment plant (Kinu aqua-station, Ibaraki, Japan). Before microbiome acclimatization, the MBR was operated for 11 days and then stopped, due to severe levels of membrane fouling, which is defined as the unstable condition. The sludge microorganisms were acclimatized to piggery wastewater for 29 days, during which the wastewater feeding was maintained but the membrane filtration was stopped, and the sludge inside the reactor was pumped out at 4.8 L/day. The period required for reactor stabilization was reported to be set at more than three HRT, according to previous studies dealing with treatment of piggery wastewater using sludge from a municipal wastewater plant as seed sludge [23, 24]. After the acclimatization, operation with stable reactor performance was continued for 14 days, which is defined as the stable condition. Because the sludge was retained during MBR treatment and the period required for the stabilization of reactor conditions was not defined based on the

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employed HRT, the operation period was determined owing to the experimental result that the sludge microbiomes were almost stabilized after 14 days (data not shown). The MBR operated under overloading was defined as the deteriorative condition. The overloading operation was ended after 12 days because it could not continue due to the severe levels of membrane fouling, defined as the deteriorative condition.

2. 2. Chemical analysis procedures The mixed liquor suspended solid (MLSS), temperature, dissolved oxygen (DO) and pH in all three compartments of the MBR, as well as the transmembrane pressure (TMP) of the membrane module, were monitored throughout the operation. Activated sludges collected from the second compartment were separated by centrifugation (15,000 × g, 15 min, 4°C) and the supernatants were filtered using a cellulose acetate membrane (ø0.20 µm; ADVANTEC, Tokyo, Japan). The total organic carbon (TOC) and total nitrogen (TN) concentrations in the supernatant and treated effluent were analyzed using a TOC-TN analyzer (TOC-L/TNM-L; Shimadzu, Kyoto, Japan). The concentration of ammonium ion (NH4+) was determined using a capillary electrophoresis system (CE; Agilent, Santa Clara, CA, USA). The COD value was measured by the closed reflux colorimetric method with a COD analyzer (DR2800 and DRB200; Hach, CO, USA) and an appropriate kit (TNT820 or TNT821; Hach). COD removal rate was calculated based on the concentration of piggery wastewater. BOD (20°C, 5 days) was determined with a pressure-sensor method using BOD Track II (Hach Company), with a nitrification inhibitor.

2. 3. Biofilm visualization The fouled membrane was collected at the final phase of the operation under each reactor condition; the TMP values of the unstable, stable, and deteriorative conditions were 87, 27 and 60 kPa, respectively. After retrieving the membrane module from the MBR, whole filtration membranes were detached, and fresh membranes were installed for the next sampling. The sampled fouled membranes were temporarily stored at 4°C, and treated for microscopic visualization. Direct confocal reflection microscopy for the visualization of biofilms and their attached substratum [12, 25, 26] was performed to observe membrane-attached biofilms. The procedures of microscopic observation are provided in the Supporting Information. Illumination with 488-nm and 514-nm multi-argon lasers was conducted to detect the SYTO9/FITC and FM4-64 fluorescence, respectively. The PI fluorescence was detected with a 543-nm He-Ne laser. Reflected lights were obtained using a 488-nm argon laser. A main beam splitter MBS T80/R20 filter (Carl Zeiss) was used to detect the reflected light. The obtained raw images were

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analyzed using the ZEN software (Carl Zeiss). At least 5 microscopic images were taken per sample, and representative images are presented. Amount of live and dead cells were calculated from the fluorescent signal intensities of SYTO9 and PI, respectively, using the Comstat2 program (www.comstat.dk) [27, 28]; the same program was also used to calculate the amount of extracellular matrix was also calculated from the FITC fluorescent intensity. Values were determined on the basis of 3-10 independent images analyses, and the standard deviation was obtained.

2. 4. DNA extraction, PCR amplification, and high-throughput Illumina sequencing In this study, approximately 100 mm2 pieces of the fouled membranes and activated sludges were sampled and stored at -20°C until DNA extraction. Preparation of the fouled membrane and the detailed procedures for DNA extraction and PCR amplification targeting the V4 region of 16S rRNA genes were described in a previous study [12]. Details regarding total DNA sample preparation and PCR condition are provided in the Supporting Information. High-throughput Illumina sequencing was performed as described in a previous study [29], and the procedure is briefly explained in the Supporting Information. Removal of the internal control, low-quality (Q < 30) and chimeric sequences, and assembly of the paired-end reads were performed as described in a previous report [30]. The determined sequences in each library were phylogenetically characterized using the QIIME software [31]. Alpha-diversity indices (i.e., Chao1, Shannon and Simpson reciprocal) and the unweighted UniFrac distances for the principal coordinate analysis (PCoA) were calculated based on an equal number (n = 12556) of sequences using the QIIME software. The closest relative of the operational taxonomic unit (OTU) was determined by an NCBI BLAST search (http://blast.ncbi.nlm.nih.gov/). The raw sequence data in this study were deposited in the sequence read archive in the DDBJ database (http://www.ddbj.nig.ac.jp/) under the accession number DRA005750.

3. Results and Discussion 3. 1. Physicochemical profile during the treatment of piggery wastewater in MBR Table 1 presents the physicochemical profile of wastewater treated during the MBR operation. After starting the operation of piggery wastewater treatment in the MBR using non-acclimatized sludge, the COD concentration was 134.6 mg/L (removal rate: 99.4%). The NH4 + nitrogen was degraded at 92.8% and accumulated at 0.36 mM/day in the treated wastewater. The amount of effluent was 2.2 mL/min (59.5% of the set value 3.7 mL/min), and the TMP increased at a rate of 7.2 kPa/day. Despite the high removal rates of organic carbon and nitrogen, the membrane was rapidly fouled during the operation. These results indicated that membrane filtration was

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not performed appropriately, and the MBR operation became unstable during this period (the unstable condition). To stabilize the treatment of piggery wastewater, the sludge microorganisms were acclimatized by the continuous operation of the MBR accompanied by sludge withdrawal for 29 days (i.e., more than three times of HRT). During the period of acclimatization, membrane filtration was stopped. It has been reported that microbiome acclimatization to piggery wastewater requires a period of at least three times HRT [23, 24]. After acclimatization, the COD concentration of treated water was detected to be 106.0 mg/L (removal rate: 99.6%). The concentration of NH4+ nitrogen in treated wastewater was 2.27 mM, exhibiting 95.8% NH4 + removal. During the operation, NH4+ nitrogen decreased at a rate of 0.05 mM/day. Moreover, the effluent flow rate was 3.4 mL/min (91.9% of the set value 3.7 mL/min), and the TMP increased at a rate of 0.9 kPa/day. These results indicated that reactor performance, including nitrogen removal and membrane filtration, were improved considerably by acclimatization, which designated the stable condition. We further assessed the impact of wastewater overloading on reactor performance in the MBR. Under an organic load of 1407 mg-COD/L-day at an HRT of 3 days, the concentration of NH4+ nitrogen increased (accumulation rate: 0.06 mM/day). The TMP increasing rate dynamically increased to 11.7 kPa/day, suggesting that the rate of membrane fouling is the critical factor for evaluating reactor performance of piggery wastewater treatment in an MBR. The results indicated that the reactor performance deteriorated during the operation, and was designated a deteriorative condition. To help evaluate the reactor performance here, previous studies on MBR treatment of piggery wastewater are summarized in Supplemental Table S1. Compared to prior investigations, our results showed higher or comparable levels of the COD removal rate under the stable condition. Moreover, even though there was no anaerobic/anoxic reactor, the nitrogen removal rate was sufficiently high compared to other studies performing nitrogen removal processes in reactors before MBR. These results showed that the piggery wastewater treatment was performed with high efficiency in this study.

3. 2. The cross sections of three-dimensional structures of fouling-related biofilms The confocal reflection microscopy was applied to non-destructively visualize the fouling-related biofilms. Biofilm samples were treated with SYTO9 and PI to stain the live and dead cells, respectively (Figure 1), and with FITC to stain the amine groups of extracellular matrixes (Figure 2). Under the unstable condition, the TMP value reached a maximum of 87 kPa. The biofilms were markedly thick (>1000 µm) and the largest parts were the non-cell

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region (Figures 1A, 1D and 2A). Microbial cells were sparsely scattered over the biofilm, and 97.6 ± 0.6% of cells were alive (Figures 1A and 1D). Meanwhile, the non-cell region was mainly labeled with FITC (Figure 2A). These results suggested that an amine-containing extracellular matrix was the causative agent of the membrane fouling in the piggery wastewater treating MBR. Under the stable condition, the maximum value of TMP was 27 kPa. The TMP increasing rate was notably low (0.9 kPa/day) compared to that under the unstable condition (7.2 kPa/day). Biofilm thicknesses were less than 300 µm (Figures 1B, 1E and 2B). In contrast to the unstable condition, the biofilms mainly consisted of living microbial cells (Figures 1B and 1E). Moreover, the amine-containing extracellular matrixes accounted for 11.3 ± 5.4% of the total biofilm amount. These thin biofilm structures apparently contribute to the stability of reactor performance, i.e., mitigation of membrane fouling in the MBR. Under the deteriorative condition, the TMP reached a maximum of 60 kPa, and TMP increasing rate was 11.7 kPa/day at an organic load of 1,407 mg COD/L-day and HRT of 3 days. The biofilm thicknesses ranged from 500–700 µm, and most parts of biofilms were also labeled with FITC (80.6 ± 5.2% of total biofilm) (Figures 1C, 1D and 2C). As a way of marking the differences from the unstable and stable conditions, consider dead cells, which were frequently detected in the biofilms under the deteriorative condition (ratio of living cells: 71.1 ± 4.4%). The density of the FITC-labeled extracellular matrixes under the deteriorative condition was higher (80.6 ± 5.2%) than those under the unstable (56.3 ± 18.6%) and stable (11.3 ± 5.4%) conditions. These results strongly suggested that the thick and dense biofilms composed mainly of amine-containing extracellular matrixes caused the rapid membrane fouling in the MBR.

3. 3. Dynamics and diversity of the sludge and biofilm microbiomes To investigate the dynamics of the sludge and biofilm microbiomes under the different reactor conditions, high-throughput Illumina sequencing of 16S rRNA genes was performed. Supplemental Table S2 summarizes the Illumina sequence data. A total of 837,518 sequences in 24 libraries were obtained in this study, corresponding to an average of 34,897 sequences per library (minimum, 12,132; maximum, 54,967). It should be noted that both live and dead cells could be detected. To differentiate DNAs from the live and dead cells, the recently developed procedure [32] can be employed in future studies. Alpha-diversity indices were calculated to evaluate the diversity of the sludges and biofilm microbiomes. Supplemental Figure S1 shows the alpha-diversity rarefaction curves for the sludge and biofilm samples. The rarefaction curves indicate that the sequence numbers obtained from all the samples were sufficient to reflect the diversity of microbiomes. For the sludge microbiomes, all of the α-diversity indices changed,

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depending on the reactor conditions (Supplemental Table S2). The α-diversity indices values were highest under the stable condition, compared to those under the other two conditions. For the biofilm microbiomes, similar fluctuation trends of the α-diversity indices were observed. However, the α-diversity indices under the deteriorative condition showed the highest values among the reactor conditions. This result suggested that the diversities of the sludge and biofilm microbiomes responded differently to reactor performance in the MBR. PCoA of the Illumina sequence data, based on unweighted UniFrac distances, was used to compare the whole structure of the sludges and biofilm microbiomes under the reactor conditions (Figure 3A). The distances on the PCoA plot between the sludge and biofilm microbiomes were long, indicating that these microbiomes were different. Moreover, the differentiated location of the microbiomes under the three reactor conditions indicated that a distinctive microbiome structure was formed under each reactor condition. The PCoA plot positions of the sludge and biofilm microbiomes under the unstable condition were closer to those of the pig feces microorganisms than to those of the seed microorganisms, suggesting that the inflow of pig feces microorganisms influenced the microbiome compositions in the MBR. The microbiomes under the stable condition were distant from those of the unstable condition in the PCoA plot. Meanwhile, the microbiomes of the deteriorative condition were scattered over more to the lower right of the plot compared to those of the stable condition. These results indicated that the whole structures of sludge and biofilm microbiomes were different from each other, but that both changed in response to the reactor conditions.

3. 4. Class-level distribution of the sludge and biofilm microbiomes A class-level phylogenetic analysis of the Illumina sequence data was performed using the QIIME software to determine the composition of sludge and biofilm microbiomes under the reactor conditions (Figure 3B). For the sludge microbiomes, γ-Proteobacteria was the main component (89.9% of the total) under the unstable condition. This class was the most abundant taxon in the pig feces microorganisms (48.8%), whereas it was only a small constituent in the seed microorganisms (5.2%). The sludge microbiome under the unstable condition in the MBR reflected the pig feces microorganisms, which agreed with the results of PCoA (Figure 3A). β-Proteobacteria was the most abundant taxon (49.7%) under the stable condition, while δ-Proteobacteria was detected as the main component (14.5-33.9%) under the deteriorative condition. These findings suggest that high frequencies of γ-, β-, and δ-Proteobacteria were possible indicators of the unstable, stable and deteriorative conditions, respectively, in the MBR. The biofilm microbiomes under the three reactor conditions were distinct from each other (Figure 3B), which was consistent with the α-diversity indices and PCoA plot (Supplemental

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Table S2 and Figure 3A). Under the unstable condition, the classes Chlamydiia and γ-Proteobacteria were the main components, accounting for 28.6% and 19.9%, respectively. The highly frequent and specific detection of Chlamydiia suggested a strong relationship with the unstable conditions before the acclimatization in the MBR. Under the stable condition, the class Clostridia became one of the main components (12.7%), although it was minor under the unstable condition (1.1%). Similarly, the class δ-Proteobacteria was detected more frequently under the stable condition (4.5%) than under the unstable condition (< 0.1%). Furthermore, the classes Clostridia and δ-Proteobacteria showed higher relative abundances of 29.0% and 8.6%, respectively, under the deteriorative condition.

3. 5. The sludge microbial species responding to the reactor conditions The detailed OTU-level compositions of the sludge microbiomes were determined. The top 5 most abundant OTUs are shown in Tables 2 and 3, in which their increasing ratios based on the data from the former reactor condition are shown. The increasing ratios indicate the increase or decrease of specific OTUs associated with the change in reactor condition. For instance, the increasing ratio of OTU under the stable condition was based on the relative abundances under the unstable and stable conditions. Under the unstable condition, the most abundant constituent was the γ-proteobacterial OTU 78238 (Pseudomonas proteolytica [accession no. KU647673; 100% sequence similarity]) which accounted for 86.14% of the total (Table 2). OTU 78238 was also detected among the top 10 most abundant OTUs (2.31%) in pig feces microorganisms (Supplemental Table S3), whereas its abundance in the seed microorganisms was extremely low (<0.001%). Similarly, the abundant OTUs 97429 (Turicibacter sanguinis [HQ646364; 99% similarity]) and 85719 (Clostridium cellulovorans [KF528156; 98% similarity]) were also main components of the pig feces microorganisms (11.03% and 11.90%, respectively), but not frequently detected in the seed microorganisms (<0.01%). These results strongly suggest that the top abundant OTUs of the sludge microbiomes under the unstable condition originated from the pig feces microorganisms. The replacement of the seed microorganisms by the pig feces microorganisms might facilitate membrane fouling in the non-acclimatized MBR. Under the stable condition, there were a few OTUs that were common between the sludge microbiomes and pig feces microorganisms (Table 2, Supplemental Tables S3 and S4). The most abundant constituent was the β-proteobacterial OTU 134827 (Mitsuaria chitosanitabida [JQ659937; 99% similarity]), accounting for 39.64% of the total. OTU 134827 was a very small constituent in the pig feces microorganisms (<0.0001%) and microbiomes under the unstable condition (0.007%), but it was definitely detected in the seed microorganisms

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(0.472%). This result suggests that OTU 134827 was derived from the seed microorganisms, and it would proliferate during the acclimatization of the microbiomes in piggery wastewater. Similar population trends were also observed for OTUs 131299 (Dokdonella immobilis [NR108377; 99% similarity]) and 4823 (Chyseolinea serpens [NR108511; 94% similarity]), which were the second and third most abundant species. It has been reported that the Chryseolinea bacteria [related species of out 4823 and 87394] were enriched in piggery wastewater treating bioreactor [33]. Taken together, these findings suggest that the microbial species that existed in the seed microorganisms became acclimated and proliferated, and were thus involved in the effective treatment under the stable condition in the MBR. Mitsuaria chitosanitabida, related to the markedly increased OTU 134827, is known as a chitosanase-producing bacterium, and can degrade polysaccharide with D-glucosamine residues in the molecule such as chitosan [34, 35]. Apart from OTU 134827, OTUs 67393 (Pseudomonas caeni [NR116388; 100% similarity]) and 118925 (Reyranella massiliensis [NR116005; 98% similarity]) also notably increased (Table 2 and Supplemental Table S4). These bacteria were reported to possess the positive activity of aminopeptidase [36, 37]. Extracellular polysaccharides and proteins have generally been known as the main components of biofilm matrixes [38], and the most causative agents of membrane fouling [39]. Therefore, the existence of the polysaccharide and protein degraders, such as M. chitosanitabida, P. caeni and R. massiliensis, would contribute to the stabilization of MBR, i.e., by mitigating the membrane fouling, and degrading organic substances such as the amine-group extracellular matrix (Figure 2). Together with diminishing the membrane fouling, nitrogen removal efficiency was also improved under the stable condition (Table 1). Nitrifying bacteria were not detected as abundant OTUs; however, OTUs 44873 (Nitrosomonas europaea [NR117649; 100% similarity]) and 93680 (Candidatus Nitrospira defluvii [KM052506; 100% similarity]), relatives of well-known nitrifying bacteria, became more abundant under the stable condition than under the unstable condition (Supplemental Table S5). NH4+ nitrogen removal amounts were high, with relative abundance of these bacterial species in the sludge and biofilm microbiomes (Supplemental Figure S2). Under the deteriorative condition, the top 10 most abundant OTUs exhibited different compositions compared to those found under the unstable and stable conditions (Table 2 and Supplemental Table S4). Among the top 10 abundant species, the δ-proteobacterial OTUs 120466 (Enhygromyxa salina [HM769728; 94% similarity]), 144828 (Nanocystis exedens [KF267739; 94% similarity]) and 106791 (Polyangium spumosum [GU207881; 97% similarity]) are known as myxobacteria [40-42]. These OTUs were not main components in the pig feces microorganisms (Supplemental Table S3), indicating that the sludge microbiome under

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the deteriorative condition was not similar to the pig feces microorganisms, unlike the situation observed under the unstable condition. The myxobacteria Enhygromyxa and Nannocystis has been reported to be able to lyse other bacteria [40, 43]. Our previous study showed that bacterial predation by δ-Proteobacterial OTUs was directly connected to membrane fouling under high organic loads [12]. Thus, the results of this study also confirm that the increase in bacteriolytic bacteria facilitated membrane fouling in MBRs treating high-strength wastewaters.

3. 6. The biofilm microbial species’ response to reactor conditions Details regarding the composition of the biofilm microbiomes are shown in Table 3 and Supplemental Table S6, and increasing ratios based on the data from the sludge microbiomes are also presented. Under the unstable condition, the top 10 most abundant OTUs identified were specific to the biofilm microbiomes. In particular, the top 3 abundant species, OTUs 7923 (Simkania negevensis [NR029194; 89% similarity]), 120598 (Arhodomonas recens [KX351860; 84% similarity]) and 64586 (Brevibacillus thermoruber [KX583604; 86% similarity]), showed 2,500-16,000 times higher relative abundance than in the sludge microbiomes. The ecophysiological roles of these OTUs in the biofilms were difficult to estimate, because their sequence similarities to the cultured relatives were quite low (<89%). Under the stable condition, the top 10 most abundant species were also biofilm-specific, and most of these were not detected under the unstable condition (Table 3 and Supplemental Table S6). Moreover, the anaerobic clostridial OTUs 85719 (Clostridium cellulovorans [KF528156; 98% similarity]), 122220 (Alkaliphilus oremlandii [NR074435; 74% similarity] and 77897 (Clostridium sardiniense [KU306930; 99% similarity]) were detected as the most abundant species. Other major OTUs showed quite low similarities to cultured relatives, as was found under the unstable condition, suggesting that the fouling-related biofilm was the remaining source of phylogenetically novel bacteria. Under the deteriorative condition, the composition of the biofilm microbiomes was different from those under the stable condition (Table 3 and Supplemental Table S6). The most abundant OTU 85719 exhibited 98% similarity to the obligately anaerobic Clostridium cellulovorans, indicating that the environment inside the biofilms became anoxic. Anaerobic bacteria were not detected in the biofilm microbiomes under the unstable condition, despite the rapid membrane fouling observed under both conditions. These results indicated that membrane-fouling processes differed under unstable and deteriorative conditions. Out of the top 10 most abundant species, four OTUs 114193, 106791, 134827 and 120466 were common in the sludge microbiomes but not detectable under the stable condition, suggesting that the sludge microbiome contributed significantly to the occurrence of membrane fouling under the

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deteriorative condition. The δ-proteobacterial OTU 120466, related to the predatory Enhygromyxa salina, was also detected in the biofilm microbiomes, strengthening the argument that bacterial predator-prey interaction was the crucial factor in the development of membrane fouling.

4. Conclusions MBR is one of the most effective methods for the treatment of piggery wastewater. In this study, the fouling-related biofilms in the simulated piggery wastewater were visualized by direct confocal reflection microscopy. Furthermore, the microbiomes of the sludge and membrane-attached biofilm were phylogenetically identified using high-throughput Illumina sequencing. Physicochemical analyses revealed that the instability and deterioration of reactor performance in the MBR were mainly caused by the rapid progress of membrane fouling. Biofilm visualization elucidated that membrane fouling was induced by the accumulation of amine-containing extracellular matrix, as well as by live and dead microbial cells on the filtration membrane. Furthermore, high-throughput sequencing showed that the sludge and biofilm microbiomes were different, but that both changed in response to the reactor conditions. High-resolution phylogenies of the microbiomes revealed that a variety of bacterial species, including polysaccharide and protein degraders, bacteriolytic predators, and obligate anaerobes were involved in the mitigation and/or development of the membrane-fouling processes. Consequently, the sludge and biofilm microbiomes were tightly associated with the reactor performance, especially with the membrane fouling, during the treatment of the high-strength wastewater in the MBR. Because the composition and concentration of real piggery wastewater change depending on the facility size, equipment and operation of piggeries, the verification test using the real wastewater should be carried out.

Acknowledgements This study was financially supported in part by a Grant-in-aid for Encouragement of Young Scientists (90760439, to T. I.) from the Ministry of Education, Culture, Sports and Technology of Japan. We would like to acknowledge the Kinu-aqua station for kindly providing the activated sludge. We also appreciate the contribution of Ms. Yumiko Kayashima, Ms. Maki Yanagisawa, Mr. Kazuyuki Matsuo and Mr. Hideaki Takahashi (National Institute of Advanced Industrial Science and Technology (AIST)) for the operation of the bioreactor.

Conflict of Interest The authors declare no conflicts of interest.

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Figure legend Figure 1. The cross sections of three-dimensional structures and living cell rates of membrane-attached biofilms. The gray areas indicate the physical body reflected by light, while green and red indicate live and dead microbial cells, respectively. The red arrows indicate the positions of the filtration membrane surfaces. Average living cell rates of the biofilms are listed across the top of the figure, and the differences observed between values was significant (P<0.01, t-test). The lower panels show only fluorescent images of the biofilms. Biofilms under the unstable (A and D, TMP; 87 kPa), stable (B and E, TMP; 27 kPa) and deteriorative conditions (C and F, TMP; 60 kPa) are shown.

Figure 2. Extracellular matrixes of the membrane-attached biofilms. Green indicates amine-containing compounds probed by FITC, and gray indicates the physical body. Red arrows point to the positions of membrane surfaces. Fluorescent images were obtained from the 87 kPa TMP biofilm under the unstable condition (A), 27 kPa TMP biofilm under the stable condition (B), and 60 kPa TMP biofilm under the deteriorative condition (C).

Figure 3. Comparison and compositional changes of the sludge and biofilm microbiomes during piggery wastewater treatment. (A) Principal coordinate analysis (PCoA) scatter plot of 16S rRNA genes obtained from Illumina sequencing. The unweighted UniFrac distances were calculated based on an equal number (n = 12556) of sequences. Closed and open symbols represent the sludge and biofilm microbiomes, respectively. The symbol type indicates the reactor conditions. (B) Class-level distribution of the sludge and biofilm microbiomes. The relative abundance of each bacterial class is shown.

Supplemental Figure S1. Alpha-diversity rarefaction plots for the sludge and biofilm samples. (A) Rarefaction curve, and (B) Shannon-index curve based on the Illumina high-throughput sequencing of microbial communities.

Supplemental Figure S2. Relative abundance of nitrifying bacteria in the sludge and biofilm microbiomes. Green and blue bars indicate the relative abundances of nitrifying bacteria in the sludge and biofilm microbiomes, respectively. Closed diamonds indicate NH4+-N concentrations (mM) of treated water. The averages are based on two independent determinations, and the bars indicate minimum and maximum values.

14

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Ultra-high-sensitivity stable-isotope probing of rRNA by high-throughput sequencing of isopycnic centrifugation gradients, Environ. Microbiol. Rep. 7 (2015) 282-287 [30] H. Itoh, R. Navarro, K. Takeshita, K. Tago, M. Hayatsu, T. Hori, Y. Kikuchi, Bacterial population succession and adaptation affected by insecticide application and soil spraying history, Front. Microbiol. 5 (2014) [31] J.G. Caporaso, J. Kuczynski, J. Stombaugh, K. Bittinger, F.D. Bushman, E.K. Costello, N. Fierer, A.G. Pena, J.K. Goodrich, J.I. Gordon, G.A. Huttley, S.T. Kelley, D. Knights, J.E. Koenig, R.E. Ley, C.A. Lozupone, D. McDonald, B.D. Muegge, M. Pirrung, J. Reeder, J.R. Sevinsky, P.J. Turnbaugh, W.A. Walters, J. Widmann, T. Yatsunenko, J. Zaneveld, R. Knight, QIIME allows analysis of high-throughput community sequencing data, Nat. Meth. 7 (2010) 335-336 [32] T.H. Chiao, T.M. Clancy, A. Pinto, C. Xi, L. Raskin, Differential Resistance of Drinking Water Bacterial Populations to Monochloramine Disinfection, Environ. Sci. Technol. 48 (2014) 4038-4047 [33] Z. Zhong, X. Wu, L. Gao, X. Lu, B. Zhang, Efficient and microbial communities for pollutant removal in a distributed-inflow biological reactor (DBR) for treating piggery wastewater, RSC Advances 6 (2016) 95987-95998 [34] D. Amakata, Y. Matsuo, K. Shimono, J.K. Park, C.S. Yun, H. Matsuda, A. Yokota, M. Kawamukai, Mitsuaria chitosanitabida gen. nov., sp. nov., an aerobic, chitosanase-producing member of the ‘Betaproteobacteria’, Int. J. Syst. Evol. Microbiol. 55 (2005) 1927-1932 [35] F. Duan, X. Lu, Enzymatic properties and kinetics of an endo-β-1,3-glucanase of Mitsuaria chitosanitabida H12 and preparation of 1,3-β-d-glucooligosaccharides from yeast β-glucan, Ann. Microbiol. 62 (2012) 307-312 [36] S.J. Kim, J.-H. Ahn, T.-H. Lee, H.-Y. Weon, S.-B. Hong, S.-J. Seok, K.-S. Whang, S.-W. Kwon, Reyranellasoli sp. nov., isolated from forest soil, and emended description of the genus Reyranella Pagnier et al. 2011, Int. J. Syst. Evol. Microbiol. 63 (2013) 3164-3167 [37] Y.P. Xiao, W. Hui, Q. Wang, S.W. Roh, X.-Q. Shi, J.-H. Shi, Z.-X. Quan, Pseudomonas caeni sp. nov., a denitrifying bacterium isolated from the sludge of an anaerobic ammonium-oxidizing bioreactor, Int. J. Syst. Evol. Microbiol. 59 (2009) 2594-2598 [38] H.C. Flemming, J. Wingender, The biofilm matrix, Nat. Rev. Microbiol. 8 (2010) 623-633 [39] H.P. Chu, X.Y. Li, Membrane fouling in a membrane bioreactor (MBR): Sludge cake formation and fouling characteristics, Biotechnol. Bioeng. 90 (2005) 323-331 [40] T. Iizuka, Y. Jojima, R. Fudou, M. Tokura, A. Hiraishi, S. Yamanaka, Enhygromyxa salina gen. nov., sp nov., a slightly halophilic myxobacterium isolated from the coastal areas of Japan, Syst. Appl. Microbiol. 26 (2003) 189-196 [41] E.K. Lang, H. Reichenbach, Designation of type strains for seven species of the order Myxococcales and proposal for neotype strains of Cystobacter ferrugineus, Cystobacter minus and Polyangium fumosum, Int. J. Syst. Evol. Microbiol. 63 (2013) 4354-4360 [42] W. Ludwig, K.H. Schleifer, H. Reichenbach, E. Stackebrandt, A phylogenetic analysis of the myxobacteria Myxococcus-fulvus, Stigmatella-aurantica,

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Cystobacter-fuscus, Sorangium-cellulosum and Nannocystis-exedens, Arch. Microbiol. 135 (1983) 58-62 [43] H. Reichenbach, Nannocystis exedens gen. nov., spec. nov., a new myxobacterium of the family Sorangiaceae, Arch. Mikrobiol. 70 (1970) 119-138

18

Table 1. Reactor performance of piggery wastewater-treating MBR Loading conditions Volumetric load HRT (mgCOD/L/day) (day)

Physicochemical data TOC CODCr (mg/L) (mg/L)

TN (mg/L)

NH4+-N (mM)

Ammonia accumulation rate (mM/day)

Effluent (mL/min)

Clogging rate (kPa/day)

Unstable (non-acclimatized)

704

6

53.8

134.6

211.1

3.94

0.36

2.2

7.2

Stable (acclimatized)

704

6

43.1

106.0

133.2

2.27

-0.05

3.4

0.9

3

30.1

71.2

82.6

1.23

-0.31

6.8

4.5

3

42.2

99.6

126.8

2.31

0.06

6.5

11.7

Deteriorative (acclimatized 704 but overloading) 1407

19

Table 2. Top 5 most abundant OTUs in the sludge under the unstable, stable and deteriorative conditions Bacteria Class Genus species Unstable (non-acclimatized) 78238b γ-Proteobacteria Pseudomonas proteolytica 97429 Erysipelotrichia Turicibacter sanguinis 85719 Clostridia Clostridium cellulovorans 117005 γ-Proteobacteria Lysobacter ximonensis 101961 γ-Proteobacteria Lysobacter lycopersici Stable (acclimatized) 134827c β-Proteobacteria Mitsuaria chitosanitabida 131299 γ-Proteobacteria Dokdonella immobilis 4823c Cytophagia Chryseolinea serpens 67393 γ-Proteobacteria Pseudomonas caeni 78238b γ-Proteobacteria Pseudomonas proteolytica Deteriorative (acclimatized but overloading) 120466 δ-Proteobacteria Enhygromyxa salina 134827c β-Proteobacteria Mitsuaria chitosanitabida 121206 Bacteroidia Prolixibacter bellariivorans 87394 Cytophagia Chryseolinea serpens 4823c Cytophagia Chryseolinea serpens OTU ID

Accession No.

Identity (%)

Relative abundance (%)

KU647673 HQ646364 KF528156 AB682647 NR136845

100 99 98 100 99

86.14 1.40 0.66 0.60 0.52

JQ659937 NR108377 NR108511 NR116388 KU647673

99 99 94 100 100

39.64 5.69 4.79 4.04 2.84

1134.1 13.2 10.6 91.0 0.03

HM769728 JQ659937 LC015091 NR108511 NR108511

94 99 85 94 94

28.84 25.03 4.15 2.58 2.44

65.3 0.6 2.5 1.4 0.5

Fold changea

a

Fold changes relative to the previous conditions (relative to the seed microorganisms in the case of the unstable condition). OTUs that were detected at both the unstable and stable conditions c OTUs that were detected at both the stable and deteriorative conditions. b

20

Table 3. Top 5 most abundant OTUs in the biofilm under the unstable, stable and deteriorative conditions Bacteria Class Genus species Unstable (non-acclimatized) 7293 Clamydiae Simkania negevensis 120598 γ-Proteobacteria Arhodomonas recens 64586 Bacilli Brevibacillus thermoruber 12689 γ-Proteobacteria Thermomonas brevis 98812 Clamydiae Simkania negevensis Stable (acclimatized) 2392 Clamydiae Simkania negevensis 29217 α-Proteobacteria Oceanibacterium hippocampi 67393 γ-Proteobacteria Pseudomonas caeni 85719b Clostridia Clostridium cellulovorans Deteriorative (acclimatized but overloading) 85719b Clostridia Clostridium cellulovorans 103109 β-Proteobacteria Dechloromonas aromatica 97429 Erysipelotrichia Turicibacter sanguinis 114193 Bacilli Agitococcus lubricus 122220 Clostridia Alkaliphilus oremlandii a Fold changes relative to the planktonic state (sludge) are shown. b OTUs that were detected at both the stable and deteriorative conditions. OTU ID

Accession No.

Identity (%)

Relative abundance (%)

Fold change (relative to) Sludge Previous conditiona

NR029194 KX351860 KX583604 NR025578 NR074932

89 84 86 97 94

24.69 22.12 8.67 7.59 3.45

3338.0 16487.7 2574.1 95.1 −

NR074932 NR117037 NR116388 KF528156

86 84 100 98

17.18 14.52 11.48 5.69

4193.5 − 2.8 15.8

550.7 2949.4 268.9 14.1

KF528156 CP000089 KM269034 NR104868 NR074435

98 93 99 99 74

19.19 10.83 9.35 5.47 5.03

30.6 98.0 23.7 3.1 2035.6

3.4 3380.9 2.4 3.8 1.1

21

Supplemental Table S1. Treatment efficiencies of piggery wastewater-treating MBR Influent Piggery wastewater (pig feces and urea) Piggery wastewater Piggery wastewater Piggery wastewater Digested piggery wastewater

Treatment method

Pre-treatment for influenta

AO treatment and aerobic MBR CP (chemical precipitation)-MBR AO2 reactor with membrane module (MBR)

Particle rejection by sieve with 0.5-mm mesh Solid/liquid separation by centrifugation Particle rejection by sieve with 1.0-mm mesh and chemical precipitation Solid/liquid separation and biological pre-treatment

Aerobic MBR

Mesophilic anaerobic digestion

Aerobic MBR

Organic loading rate (mg-COD/L/day)

HRT (days)

Removal rates (%) COD NH4+-H TN

References

700-1400

3-6

>99

92-95

93-95

This study

310-2200

3-4.5

~93

NA

90

Prado et al., 2009

570

2

>99

>98%

NA

Kornboonraksa et al., 2009

NA

5

~95

97-98

93-95

Kim et al., 2008

770

4-5

~90

~83

~13.5

Song et al., 2017

NA: not applicable

22

Supplemental Table S2. Summary of the Illumina sequencing data Loading conditions Volumetric load (mgCOD/L-day)

Sample

Diversity indicesb HRT (day)

Piggery wastewater Seed

Sludge

Biofilm

Number of sequences

Number of OTUs

CXa

22630±10167

135±19

40876±3014

Chao1

Shannon

1/Simpson

0.999±0.0005

1800±326

3.80±0.030

4.34±0.19

931±31

0.995±0.0005

4956±90

7.33±0.087

35.90±1.68

Unstable

704

6

43287±6026

437±33

0.996±0.0011

1928±413

1.89±0.281

1.44±0.08

Stable

704

6

36359±255

955±35

0.993±0.0002

7706±918

6.18±0.300

7.77±1.17

Deteriorative

1,407

3

37551±2933

933±43

0.993±0.0008

6040±153

5.66±0.270

7.72±1.30

Unstable

704

6

16223±4091

290±61

0.993±0.0005

2743±787

4.94±0.535

9.56±2.46

Stable

704

6

48148±3312

512±32

0.997±0.0003

2302±162

5.17±0.177

10.91±1.51

Deteriorative

1,407

3

31651±1985

548±58

0.995±0.0012

2982±529

5.50±0.335

15.57±4.39

a

Calculated from the equation CX = 1 - (n/N), where “n” is the number of OTUs composed of singletons, and N is the total number of sequences. Each index was calculated based on an equal number of sequences (n = 12556). All data were based on the average of 2 replicates.

b

23

Supplemental Table S3. Top 10 most abundant OTUs in the seed-activated sludge and piggery wastewater OTU ID

Piggery wastewater 67393 85719 97429 77897 23192 120540 78238 136720 107818 87017 Seed 24461 13832 115665 86364 14545 113442 131299 63044 75777 141156

Bacteria Class

Genus species

γ-Proteobacteria Clostridia Erysipelotrichia Clostridia Clostridia Clostridia γ-Proteobacteria Clostridia Bacilli Clostridia β-Proteobacteria β-Proteobacteria Sphingobacteriia β-Proteobacteria β-Proteobacteria Sphingobacteriia γ-Proteobacteria β-Proteobacteria Sphingobacteriia β-Proteobacteria

Accession no.

Identity (%)

Relative abundance (%)

Pseudomonas caeni Clostridium cellulovorans Turicibacter sanguinis Clostridium sardiniense Terrisporobacter glycolicus Clostridium disporicum Pseudomonas proteolytica Clostridium butyricum Lactobacillus amylovorus Ruminiclostridium thermocellum

NR116388 KF528156 HQ646364 KU306930 KJ722507 DQ855943 KU647673 CP016332 KR055506 KR047885

100 98 99 99 99 100 100 100 100 91

47.88 11.90 11.03 7.44 3.00 2.65 2.31 2.21 0.95 0.62

Azospira restricta Collimonas fungivorans Ferruginibacter yonginensis Georgfuchsia toluolica Acidovorax facilis Phaeodactylibacter xiamenensis Dokdonella immobilis Piscinibacter aquaticus Lewinella nigricans Zoogloea caeni

NR044023 CP013232 NR133743 NR115995 KR827440 NR134132 NR108377 NR043921 NR115013 KM083699

99 96 95 98 100 89 99 99 92 100

13.30 8.41 4.02 3.47 2.40 2.39 2.36 2.32 1.80 1.78

24

Supplemental Table S4. Top 6–10 most abundant OTUs in the sludge under the unstable, stable and deteriorative conditions Bacteria Class Genus species Unstable (non-acclimatized) 4823 Cytophagia Chryseolinea serpens 131299b γ-Proteobacteria Dokdonella immobilis 93347 γ-Proteobacteria Xanthomonas axonopodis 77897 Clostridia Clostridium sardiniense 115176 Sphingobacteriia Terrimonas lutea Stable (acclimatized) 87394 Cytophagia Chryseolinea serpens 121206 Bacteroidia Prolixibacter bellariivorans 24461 β-Proteobacteria Azospira restricta 118925 α-Proteobacteria Reyranella massiliensis 63044b β-Proteobacteria Piscinibacter aquaticus Deteriorative (acclimatized but overloading) 144828 δ-Proteobacteria Nannocystis exedens 114193 Bacilli Agitococcus lubricus 73160 Bacteroidia Prolixibacter denitrificans 63044b β-Proteobacteria Piscinibacter aquaticus 106791 δ-Proteobacteria Polyangium spumosum OTU ID

Identity (%)

Relative abundance (%)

NR108511 NR108377 AB101447 KU306930 NR041250

94 99 100 99 99

0.45 0.43 0.41 0.39 0.32

NR108511 LC015091 NR044023 NR116005 NR043921

94 85 99 98 99

1.79 1.63 1.46 1.35 1.23

− 167.2 1090.4 252.3 5.0

KF267739 NR104868 NR137212 NR043921 GU207881

94 99 87 99 97

2.21 1.74 1.17 0.87 0.85

8.4 2.7 60.8 0.7 5.7

Accession No.

Fold changea

a

Fold changes relative to the previous conditions (relative to the seed microorganisms in the case of the unstable condition). OTUs that were detected at both the stable and deteriorative conditions.

b

25

Supplemental Table S5. Relative abundance of nitrification bacterial OTUs Bacteria OUT ID 93608 107692 856 23343 27392 27425 36694 37744 44873 75847 82131 97781 100952 101924 110169 114378 129985 132052 143459

Class

Genus species

Accession No.

Nitrospira Nitrospira β-Proteobacteria β-Proteobacteria β-Proteobacteria β-Proteobacteria β-Proteobacteria β-Proteobacteria β-Proteobacteria β-Proteobacteria β-Proteobacteria β-Proteobacteria β-Proteobacteria β-Proteobacteria β-Proteobacteria β-Proteobacteria β-Proteobacteria β-Proteobacteria β-Proteobacteria

Candidatus Nitrospira defluvii Candidatus Nitrospira defluvii Nitrosomonas communis Nitrosospira multiformis Nitrosospira briensis Nitrosospira multiformis Nitrosospira multiformis Nitrosospira briensis Nitrosomonas europaea Nitrosovibrio tenuis Nitrosospira multiformis Nitrosovibrio tenuis Nitrosovibrio tenuis Nitrosospira multiformis Nitrosospira multiformis Nitrosospira briensis Nitrosospira briensis Nitrosospira multiformis Nitrosovibrio tenuis

KM052506 KM052506 CP011451 NR074736 CP012371 NR074736 NR074736 CP012371 NR117649 AY123803 AF408634 AY123803 AY123803 NR074736 NR074736 CP012371 CP012371 NR074736 AY123803

Identity (%) 100% 96% 96% 95% 95% 95% 96% 95% 100% 95% 95% 96% 95% 96% 97% 96% 95% 95% 95%

Relative abundance (%) Piggery Seed wastewater 0 0.109 0 0 0 0.002 0 0.010 0 0.009 0 0 0 0.174 0 0.014 0 0.007 0 0.035 0 0.083 0 0.003 0 0.011 0 0.168 0 0.010 0 0.070 0 0.048 0 0.016 0 0

Unstable Sludge 0.009 0 0 0 0 0 0.007 0 0 0 0 0 0 0 0 0 0 0 0

Biofilm 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Stable Sludge 0.019 0.004 0 0 0 0.012 0.001 0 0.007 0 0 0 0 0 0 0.001 0.008 0 0

Biofilm 0.004 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Deteriorative Sludge Biofilm 0.012 0.009 0 0 0 0 0 0 0 0 0.005 0.003 0 0 0 0 0.004 0 0 0 0 0 0 0 0 0.001 0 0 0 0 0 0 0 0 0 0 0.001 0

26

Supplemental Table S6. Top 6–10 most abundant OTUs in the biofilm under the unstable, stable and deteriorative conditions Bacteria Class Genus species Unstable (non-acclimatized) 101961 γ-Proteobacteria Lysobacter lycopersici 97429bc Erysipelotrichia Turicibacter sanguinis 25912 β-Proteobacteria Chromobacterium violaceum 140569 Flavobacteriia Fluviicola hefeinensis 117005 γ-Proteobacteria Pseudoxanthomonas spadix Stable (acclimatized) 46249 δ-Proteobacteria Spirobacillus cienkowskii 97429bc Erysipelotrichia Turicibacter sanguinis 24397 Nitrospira Leptospirillum ferrooxidans 71077 Cyanophyceae Oscillatoria acuminata 77897c Clostridia Clostridium sardiniense Deteriorative (acclimatized but overloading) 106791 δ-Proteobacteria Polyangium spumosum 126506 Flavobacteriia Phaeocystidibacter luteus 134827 β-Proteobacteria Mitsuaria chitosanitabida 120466 δ-Proteobacteria Enhygromyxa salina 77897c Clostridia Clostridium sardiniense OTU ID

Fold change (relative to) Sludge Previous conditiona

Identity (%)

Relative abundance (%)

NR136845 HQ646364 KM269034 NR133750 NC016147

99 99 97 99 95

2.59 2.56 1.85 1.77 1.57

5.0 1.8 14.5 105.3 2.6

EU220836 KM269034 NC017094 NC019693 KU306930

90 99 74 75 99

4.01 3.95 2.91 2.78 2.43

965.2 19.5 − 2037.6 18.0

− 1.5 − − 38.3

GU207881 NR132329 JQ659937 HM769728 KU306930

97 85 99 94 99

4.11 3.35 3.33 3.18 3.13

4.8 18.0 0.1 0.1 11.2

344.3 7.8 2.2 218.0 1.3

Accession No.

a

Fold changes relative to the planktonic state (sludge). OTUs that were detected at both the unstable and stable conditions c OTUs that were detected at both the stable and deteriorative conditions b

27

28

29

30

31

32

Highlights ・Fouled membranes of pig manure treating MBRs were visualized by confocal microscopy ・Large amounts of amine-containing biopolymer were detected from the fouled membranes ・High-resolution dynamics and diversity of microbiomes in the MBRs were revealed. ・Sludge and biofilm microbiomes were tightly associated with the membrane fouling

33

Actual pig feces

Membrane bioreactor Biofilm

Sludge 100% 90%

Non-destructive visualization of biofilms

1. Non-acclimatized 2. Acclimatized 3. Overload

1400

1400

1400

1200

1200

1200

1000

1000

1000

800

800

800

50%

Z (µm)

60%

Z (µm)

70% Z (µm)

Relative abundance (%)

80%

600

600

600

400

400

400

200

200

200

40% 30% 20% 10% 0%

0

.1.

2.

3.

.1.

2.

Phylogenetic distribution of microbiomes

3.

0

110 220 X (µm)

.1.

0

0

110 220 X (µm)

2.

0

0

110 220 X (µm)

3.

Fouling-related biofilm structure