Identifying prokaryotes and eukaryotes disintegrated by a high-pressure jet device for excess activated sludge reduction

Journal Pre-proof Identifying prokaryotes and eukaryotes disintegrated by a high-pressure jet device for excess activated sludge reduction Hiroyuki Yoshino, Tomoyuki Hori, Masaaki Hosomi, Akihiko Terada

PII:

S1369-703X(20)30010-3

DOI:

https://doi.org/10.1016/j.bej.2020.107495

Reference:

BEJ 107495

To appear in:

Biochemical Engineering Journal

Received Date:

8 January 2020

Accepted Date:

11 January 2020

Please cite this article as: Yoshino H, Hori T, Hosomi M, Terada A, Identifying prokaryotes and eukaryotes disintegrated by a high-pressure jet device for excess activated sludge reduction, Biochemical Engineering Journal (2020), doi: https://doi.org/10.1016/j.bej.2020.107495

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Identifying prokaryotes and eukaryotes disintegrated by a high-pressure jet device for excess activated sludge reduction

Hiroyuki Yoshinoa, Tomoyuki Horib, Masaaki Hosomia, Akihiko Teradaa*

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Department of Chemical Engineering, Tokyo University of Agriculture and Technology, Naka

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2-24-16 Koganei, Tokyo, 184-8588 Japan

Environmental Management Research Institute, National Institute of Advanced Industrial Science

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

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*Corresponding author: [email protected] Tel/Fax: +81-42-388-7069/+81-42-388-7731

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Graphical abstract

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Highlights:

A sludge reduction method to damage and identify prokaryotes was proposed.

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A high-pressure jet device (HPJD) was applied for sludge reduction.

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Eluted DNA from damaged bacteria was sequenced for taxonomic identification.

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Nitrifying bacteria were not damaged by HPJD treatment.

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Protozoa were severely damaged by HPJD treatment.

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Abstract Changes in microbial communities are observed during the long-term operation of activated sludge systems employing an excess sludge reduction technology. Short-term changes are also significant to confirm if key microbes are damaged, but they have not been investigated due to the absence of an appropriate method. We report a methodology to identify prokaryotic and eukaryotic species damaged almost instantaneously by a high-pressure jet device (HPJD), a cost-effective sludge

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reduction technology. 16S rRNA gene amplicon sequencing and microscopic inspection were implemented to identify damaged microorganisms. A sludge injection ratio (R), activated sludge

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suspension from the top port relative to that of the lateral port of the HPJD, was set at 0, 1, and 3. The DNA eluted by the HPJD treatment at these R-values was successfully retrieved. Nitrifying

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bacteria were not detected in the eluted DNA at the R-values, indicating that the essential guilds for

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nitrogen removal can be retained. The dominant phylum was Proteobacteria (47.8%), followed by Bacteroidetes (18.2%), Chloroflexi (9.0%), and Verrucomicrobia (5.8%) before HPJD treatment.

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These phyla retained their dominance after HPJD treatment; however, species in the phylum Actinobacteria (6.9-19.9%) were intensively damaged. A higher degree of sludge destruction by the HPJD increased the species destroyed, but the composition was not changed. The microscopic inspection indicated the HPJD treatment pronouncedly destroyed the eukaryotic metazoa in

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activated sludge.

Keywords Activated sludge; Amplicon sequencing; High-pressure jet device; Lysis-cryptic growth system; Microbial community composition; Nitrifying bacteria

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1. Introduction An activated sludge system is most widely used for wastewater treatment. One of the challenges regarding the sustainability of a conventional activated sludge system (CAS) is excess sludge management. To reduce the amount of excess sludge, a sludge reduction process, where a sludge solubilization unit is installed into a biological system, e.g., CAS, has been proposed. The system incorporating the sludge solubilization unit has achieved 30–100% of excess sludge reduction in comparison with systems devoid of this unit [1–3]. Among different systems that minimize excess

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sludge production, the lysis-cryptic growth system, which facilitates sludge solubilization by mechanical destruction of microorganism cells, has been often applied because of its simple

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operation and high-throughput for excess sludge treatment.

A high-pressure jet device (HPJD), as one of these technologies based on lysis-cryptic growth,

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provides a promising solution to achieve cost-effective and small-footprint excess sludge reduction

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[4]. The HPJD consists of a high-pressure pump and pipe with two narrow and broader diameters in the middle and at both edges, respectively, to disrupt the bacterial cell walls and membranes.

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Excess activated sludge was partially conveyed by the high-pressure pump at a high-lateral velocity with a high-applied pressure (6 MPa), whereas it was also supplied from the top port concomitant with air aspiration. The two entries of excess activated sludge at one end of the pipe generate fine bubbles, high friction of bacterial cells in the activated sludge and collision with the steel wall at the

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other side of the pipe [5]. Because of the cost-effectiveness and high-throughput for excess sludge reduction, the HPJD has demonstrated an advantage in short-term laboratory-scale batch [4] and long-term pilot-scale reactor studies [4,6]. On the other hand, the excessive use of a sludge reduction system potentially exacerbates the functions of activated sludge for contaminant removal. Nitrifying bacteria, chemolithoautotrophs responsible for ammonia oxidation to nitrate, are potentially damaged by an HPJD. The disruption 1

of these may impair a potential for nitrification in activated sludge, deteriorating nitrification performance. Our previous study on the long-term pilot-scale experiment demonstrated that microbial community compositions of activated sludge introducing an HPJD were distinct from that without an HPJD [6]. Nevertheless, the microbial community shift by an HPJD in the short term has not been investigated. Monitoring a long-term transition of microbial community shifts does not provide whether the transition is derived from the sludge reduction technology per se or the change in operating conditions, e.g., a solid retention time of the system. Identifying microorganisms

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instantaneously damaged by HPJD treatment in an exhaustive way paves the way for the optimization of an HPJD operation, capable of reducing excess activated sludge whereas keeping

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high organic carbon and nitrogen removal performances by indigenous activated sludge. Understanding damaged microorganisms is, therefore, of importance for the implementation of a

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lysis-cryptic growth system in practice.

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The goals of this work were, therefore, (i) the development of a methodology to identify damaged microorganisms by immediate HPJD treatment, as a promising lysis-cryptic growth

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process, and (ii) the taxonomic identification of DNA eluted into the bulk solution by an HPJD. The study specifically focused on the enigma if HPJD treatment under different operating conditions compromises nitrifying bacteria or not. To this end, the HPJD was applied to an activated sludge sample as a representative biomass consisting of an intricate microbial community. As an operating

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parameter of an HPJD, a sludge injection ratio (R), defined as the amount of activated sludge supplied from the top port over relative to that from the lateral port, was chosen because it determines a throughput of activated sludge treated per time. The DNA samples eluted in the supernatant and remaining activated sludge were subject to 16S rRNA gene amplicon sequencing. Additionally, the eukaryotic protozoa and metazoa damaged by the HPJD were identified by microscopy. 2

2. Materials and Methods 2.1 HPJD treatment An activated sludge sample was taken from a municipal wastewater treatment plant implementing an anaerobic-anoxic-oxic process (Saitama, Japan). Ten-liters of activated sludge was used for the HPJD treatment after centrifuging at 5,000 rpm to decant and re-suspend with the same

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volume of 0.02 × phosphate-buffered saline (PBS). The HPJD consisted of a 1 m length of pipe and 1 mm nozzle diameter at one edge with the other dimension identical to that of the one previously

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used [6]. The HPJD received activated sludge from the two entries, i.e., the ports at the top and lateral points with different volumes. Concerning the HPJD operation for the activated sludge

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treatment, a sludge injection ratio, R, defined as the amount of activated sludge supplied from the

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top port over relative to that from the lateral port, was set at 0, 1, and 3. Note that R of 0 indicates no injection of activated sludge from the top port. The value R mainly determines the amount of

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reduced excess activated sludge per unit time because an increase in R correlates with the throughput of the applied activated sludge and substantially affects the degree of excess sludge reduction [4]. The range of R-values in this study is realistic for excess sludge reduction, given the cost and the maximum amount of excess sludge supplied from the top port [4]. Time for activated

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sludge disruption by the HPJD treatment was 5 s for all experiments. 2.2 DNA extraction from sludge samples DNA was extracted from 50 mg of activated sludge using an ISOIL Bead Beading Kit (Nippon Gene, Tokyo, Japan) according to the manufacturer's instructions. To identify the damaged microorganisms, DNA was directly purified from 500 μL of the supernatant without bead beating. The concentrations and purities of the extracted and purified DNA samples were measured with a 3

Qubit 2.0 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA). 2.3 Prokaryotic community structure analysis The extracted DNA samples were subject to the amplification of the 16S rRNA gene. The primer set targeting the V4 hypervariable region of the 16S rRNA gene for all prokaryotes, 515F (5′-GTGCCAGCMGCCGCGG-3′) and 806R (5′-CCGTCAATTCMTTTRAGTTT-3′), was used. The reverse primer included barcodes of 12 bp [7]. The PCR conditions were as follows [8]: initial

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denaturation at 98C for 20 s, 28 cycles of amplification consisting of 98C for 10 s, 53C for 30 s, 72C for 120 s, and a final extension at 72C for 7 min. PCR amplicons were purified by an AMPure XP Kit (Beckman Coulter, Brea, CA, USA) and a wizard PCR clean-up system (Promega

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Corporation, Madison, WI). The DNA concentration and purity of the amplicon were measured with a Qubit 2.0 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA). A 300-cycle MiSeq

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Reagent kit (Illumina, San Diego, CA, USA) and a MiSeq sequencer (Illumina, San Diego, CA,

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USA) were applied to the adjusted samples and initial control (bacteriophage phiX; Illumina). Subsequently, low-quality (Q < 30) and chimeric sequences were removed, and paired-end

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sequences were performed [9]. Retrieved sequences were assigned as an operational taxonomic unit (OTU), a pragmatic proxy for microbial species, with 97% similarity using QIIME version 1.7.0 [10]. The equal number of sequences (11,131 per sample) were used to calculate alpha-diversity, comprised of Shannon index [11], Chao1 index [12], Simpson’s reciprocal [13], and Simpson

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evenness [14] using QIIME. Ten-time random sub-sampling was performed for the calculation of the alpha-diversity index. Shannon index (0 ≤) provides a species diversity in a community. High values indicate that the structure of population is balanced. Chao1 index estimates the number of total species, denoted by OTUs, present in a sample. This index represents the richness of a microbial community. Simpson’s reciprocal, i.e., the reciprocal number of Simpson index, taking richness and evenness in a community into account. Simpson evenness is calculated from 4

Simpson’s diversity index divided by the number of total species (OTUs). 2.4 Statistical analysis A one-way analysis of variance (one-way ANOVA) was employed to detect a significant difference (p < 0.05) in the extracted DNA concentrations and alpha-diversity index. 2.5 Protozoa and metazoan community structure analysis Protozoan and metazoan community structure analyses were performed by phase-contrast microscopy (Eclipse 80i, Nikon, Japan). The numbers of protozoans and metazoans under

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microscopy were counted using 25 μL sub-samples taken before and after the HPJD treatment. Protozoa and metazoa were identified by phase-contrast microscopy based on their morphological

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features, according to a previous study [6]. 2.6 Accession numbers

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The sequence data acquired in this study were deposited in the GenBank/EMBL/DDBJ accession

3. Results

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number for the 16S rRNA gene sequence as ID: DRA006940.

3.1 DNA released from activated sludge by the HPJD treatment The supernatant DNA concentrations released from activated sludge after the HPJD treatment at

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R = 0, 1, and 3 are shown in Fig. 1. An extracellular DNA concentration in the supernatant before the HPJD treatment was below the detection limit (< 0.50 g/L). At all tested R-values, DNA concentrations noticeably increased in the supernatant. The elution of DNA indicated the microbial cell decomposition resulting in the release of the intracellular components as previously reported [15]. The eluted DNA concentrations were different for each R-value. The DNA concentration was the highest at R of 3 (18.1 ± 0.11 mg/L), followed by R of 0 (11.0 ± 0.16 mg/L), and R of 1 (4.30 ± 5

0.05 mg/L) in the descending order. The order of the eluted DNA concentrations based on R-values agreed with that of the solubilized biomass concentration as previously reported [4]. Therefore, the eluted DNA concentration could be used as an index to sludge reduction efficiency. 3.2 Detection of prokaryotic community structure using MiSeq sequencing The results of the alpha-diversity (Shannon, Chao1, Simpson reciprocal, and Simpson evenness) analysis based on 16S rRNA gene amplicons are summarized in Table 1. PCR amplification of the DNA sample before the HPJD treatment originally from activated sludge supernatant was not

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successful, which is excluded from the analysis. Concerning microbial communities in activated sludge, the diversity (Shannon) and the number of species (Chao1) at R-values of 0 and 3 altered,

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whereas those at R of 1 were kept unchanged (p < 0.05).

With respect to microbial communities in activated sludge supernatant, the diversity indices

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listed in Table 1 were more distinct among the tested R-values than those in activated sludge. At R

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of 1, where the degree of DNA elusion was the least, the Chao1 index in the supernatant, indicating the detected OTUs number disrupted by an HPJD, was the lowest. More OTUs number was

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detected at R values of 0 and 3, supporting a higher degree of bacterial cell disruption. The Simpson reciprocal in the supernatant, representing species richness and evenness, were lower at R-value of 1 than R-values of 0 and 3, underscoring that the higher intensity of the HPJD treatment instantaneously disrupted broader species of microorganisms in activated sludge.

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Prokaryotic community structure analysis was performed based on 16S rRNA gene amplicons

from the DNA extracted from activated sludge and the DNA purified from the supernatant before and after HPJD treatment (Fig. 2). The most abundant phylum was Proteobacteria (relative abundance: 47.8%), followed by Bacteroidetes (18.2%), Chloroflexi (9.0%), and Verrucomicrobia (5.8%) in activated sludge before the HPJD treatment (Fig. 2). These dominant phyla maintained their dominance ranking in activated sludge after the HPJD treatment (Fig. 2). The genera 6

Nitrosomonas and Nitrospira, which are microbial guilds responsible for ammonia and nitrite oxidation, were kept the relative abundances of 0.9 and 0.5%, respectively, which did not change before and after the HPJD treatment. Nitrobacter, another representative nitrite-oxidizer occasionally detected in activated sludge, was not detected in this study. Therefore, the HPJD treatment did not immediately change the dominant and essential taxon responsible for nitrogen removal in activated sludge. Regarding the supernatant samples after the HPJD treatment, the phyla Proteobacteria

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(46.7-62.3%) and Bacteroidetes (5.7-9.4%) were dominant as observed in the activated sludge samples. The difference was found in the dominant fraction of the phylum Actinobacteria

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(6.9–19.9%), which was only detected in the supernatant samples (Fig. 2). At the class level, Alphaproteobacteria was dominant in the supernatant fraction after the HPJD treatment, whereas

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Betaproteobacteria was dominant in the activated sludge fraction (Fig. 3). No noticeable and

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common trend was attained, depending on the differences in the cell wall and membrane structures of Gram-positive and Gram-negative bacteria. Almost all the Gram-positive bacterial 16S rRNA

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genes present in activated sludge were retrieved in the supernatant irrespective of the applied R values in the HPJD treatment (Fig. S1), whereas no discernible trend on the eluted DNA for Gram-negative bacteria was observed. Filamentous bacteria, e.g., the phylum Chloroflexi, was not detected in the supernatant under all the tested conditions.

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The degree of the activated sludge supply with aspiration majorly affected the damaged bacteria

and their diversity. At the R value of 1 where the lowest DNA elusion was observed (Fig. 1), most of the DNA released in the supernatant belonged to the phyla Alphaproteobacteria and Actinobacteria, followed by Deltaproteobacteria, Anaerolineae, and Bacteroidea at the R values of 0 and 3. For the further systematic analysis of the damaged prokaryotic community, the top 10 bacterial 7

OTUs in relative abundance and their taxonomic positions are displayed (Fig. 4) and summarized in Table 2, respectively. Irrespective of the HPJD treatment conditions, the five most predominant OTUs were common and comprised of de novo 16174 (family Caulobacteraceae), de novo 6844 (genus Sphingomonas), de novo 18916 (family Micrococcaceae in phylum Actinobacteria), de novo 11346 (class betaproteobacteria unclassified species), and de novo 6025 (genus Hylemonella). No 16S rRNA genes derived from the identified nitrifying bacteria in activated sludge were detected in the supernatant (the relative abundance of Nitrosomonas and Nitrospira were below 0.1% in the

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supernatant). 3.3 Microscopic observation of eukaryotic cells

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Eukaryotic cells in activated sludge before and after the HPJD treatment were enumerated under microscopy (Fig. 5). The HPJD treatment severely decreased the abundance of protozoa and

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metazoa, regardless of the tested R values. Approximately 4,500 /mL of protozoa was detected in

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activated sludge before each HPJD treatment; however, the number noticeably decreased to approximately 1,500 /mL after the HPJD treatment at the R values of 0 and 1, and 2,800 /mL at the

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R value of 3. As for protozoa, Amoebida retained a high relative abundance (60 /mL) at the R value of 0, whereas Peritrichida slightly increased at the R value of 3 (27 /mL). More pronounced damage on the metazoan communities by the HPJD treatment was observed than that on the protozoan communities. The order Chaetonotoidea, dominant in activated sludge

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prior to the HPJD treatment (67 /mL), was not detectable after the HPJD treatment at any tested R values. The remaining metazoans were Ploima (40 /mL) at the R-value of 1 and Bdelloidea (27 /mL) at the R-value of 3.

4. Discussion This study is the first to identify the damaged bacterial species in a sludge destruction device, which 8

is a high-pressure jet device (HPJD), with high sensitivity. As shown in Fig. 1, DNA as intracellular compounds was eluted from activated sludge by the HPJD treatment, and then 16S rRNA genes from the eluted DNA were amplified for microbial community analysis. The ensuing amplicon sequencing identified the bacteria instantaneously (within 5 s) destroyed by the HPJD treatment (Fig. 2). Additionally, the DNA eluted in the supernatant from activated sludge allowed the detection of 16S rRNA genes with extremely low (0.01%) relative abundances, highlighting the high sensitivity of the employed method that was capable of identifying the damaged bacterial

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species. There have been no reports to identify microorganisms that are destroyed and eluted by sludge solubilization treatment, except the implementation of a cavitation-induced device for DNA

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extraction, e.g., French press [16]. Consequently, a new analytical method enabling the elucidation of a microbial community transition in a lysis-cryptic system described herein.

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This study also underpinned the correlation of the amount of extracted DNA and diversities of

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damaged bacteria. By monitoring DNA eluted from activated sludge but not DNA indigenously present in activated sludge, diversities and species of damaged bacteria were successfully attained.

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The diversity indices in supernatant allowed the distinct effect of sludge disintegration at different sludge injection ratios (R), i.e., the relative amount of activated sludge supply from the top over the lateral ports. It has been known that R-values determine the degree of bacterial cell disintegration [4]. As shown in Fig. 1, the amount of eluted DNA and Chao1 in the supernatant at R = 1 were

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lower than those at R = 0 and R = 3, respectively (Table 1). Our study, therefore, added a finding that R value affects the number of microorganism species damaged by the HPJD treatment. On the other hand, the correlation of community evenness with the intensity of excess sludge disruption by an HPJD cannot be explicitly obtained, as shown in Table 1. A more thorough investigation is required to attain an in-depth understanding of the HPJD treatment effect on the damaged bacterial species. 9

The lower DNA elusion at the R value of 1 agrees with the trend wherein the organic carbon released from bacterial cells was minimal at the R value [4]. Meanwhile, the highest DNA elution concomitant with the highest organic carbon elusion [4] plausibly affects the resultant microbial community composition because more bacterial species were damaged. Indeed, the distinct community composition in a CAS incorporating the HPJD from that in a CAS [6] is contradictory to the report on the comparable microbial community compositions attained in a CAS with or without ozonation [17]. Our result in this study suggests the former observation in that the

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lysis-cryptic growth system allowed the bacterial cell disintegration, which was linked with the resultant microbial community structure. Although the contradictory findings cannot be rationally

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explained, possible differences in the principles of the sludge reduction technologies could be a driver for either drastic or marginal changes in the microbial community structure.

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Regardless of the injection ratio (R) applied in this study, the taxonomy of bacterial species

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damaged by the HPJD treatment were comparable, as shown in Figure 4. Meanwhile, the morphologies of these damaged bacteria, as listed in Table 2, were not necessarily analogous (Table

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S1). Our results showed the pronounced cell destruction of the Actinobacteria and Firmicutes as Gram-positive bacteria in the supernatant with their relative abundances of over 1% under all the tested conditions, implying that some Gram-positive bacteria were susceptible to the HPJD treatment. This observation is not consistent with that from a previous study [18], reporting that

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Bacillus subtilis, a Gram-positive bacterium harboring a thick peptidoglycan layer, was less susceptible to cell destruction than Escherichia coli, a Gram-negative bacterium. This antithetical result to Xie et al. [18] indicates that the inherent cell wall robustness is not essential for the disruption of bacterial cells in activated sludge by the HPJD treatment. It is rather likely that the floc architectures and spatial locations of prokaryotes determine the species damaged by the HPJD treatment because the physical destruction of a microbial cell by an HPJD is deemed to favorably 10

occur on the surface of activated sludge flocs. Given this notion, prokaryotes dominantly present in the exterior of activated sludge floc are more amenable to disruption by an HPJD. Future intensive research should focus on identifying the bacterial species juxtaposed to the outermost layer of the floc and clarifying their relationship with the extent of the cell damage caused by the HPJD treatment. Besides, the identification of damaged bacteria elucidated that nitrifying bacteria, indispensable for biological nitrogen removal in a CAS, were not disrupted by an HPJD. Excessive bacterial cell

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destruction by a lysis-cryptic growth process potentially offsets the demise of carbon and nitrogen removal performances. In particular, the disruption of nitrifying microorganism cells, also known as

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slow-growing chemolithoautotrophs, impairs nitrification potential of activated sludge, which is fatal for effective nitrogen removal and takes a long time to recover nitrifying activity. The

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OTU-level analysis did not detect the 16S rRNA genes of nitrifying bacteria in the supernatant

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(Table 2). Most of the ammonia-oxidizing bacteria (AOB) in activated sludge, affiliated with the class beta-proteobacteria, are tempted to form a robust cluster-like structure [19]. The tightly dense

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AOB cluster is plausibly less susceptible to a physical impact induced by an HPJD, maintaining AOB activity in activated sludge. This study unraveled that the application of an HPJD allowed to rapidly confirm if nitrifying microorganisms that are guilds for nitrogen removal are intact or damaged by a lysis-cryptic growth system. The rapid confirmation of the non-damaged nitrifying

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bacteria coincides with the long-term monitoring where the abundances of AOB were comparable in activated sludge systems with or without an HPJD (Yoshino et al., unpublished data). The management of prokaryotic community composition potentially permits the prevention of bulking caused by the overgrowth of filamentous bacteria [20] and the selection of metabolically active microbial guilds responsible for xenobiotic compound degradation [21]; hence, the methodology advocated in this study could aid in the comprehensive understanding of mechanisms 11

underlying the selection of the functionally favorable microbial communities. A combination of the amplicon sequencing and microscopic identification employed in this study elucidated the prokaryotic and eukaryotic community compositions after the introduction of the lysis-cryptic growth excess sludge reduction. The microscopic observations revealed that the metazoan community in activated sludge was severely damaged by the mechanical disruption. We confirmed that the HPJD treatment at all tested R values eliminated most of the metazoan Chaetonotoidea, frequently detected in the original activated sludge (Fig. 5). Given the facts that

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metazoans represent the system performance of a wastewater bioreactor [22] and that the inoculation of aquatic worms [23] contributes to excess sludge reduction, the extensive damage of

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the metazoan community in activated sludge does not provide an opportunity to synergistically reduce the excess activated sludge because of prey reduction in the lysis-cryptic growth system with

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the HPJD. Further studies are needed to systematically demonstrate the relationship among

lysis-cryptic growth system.

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metazoan abundance, the performance of wastewater treatment, and excess sludge reduction in the

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A future perspective for the precise methodology to identify the microbes mechanically damaged by the HPJD treatment is to refine the microscopic method. It was somewhat unusual to document an increase in the abundance of the order Peritrichida, known as a prokaryote predator in activated sludge [25] at the R value of 3 by the HPJD treatment. The seemingly unreasonable result occurred

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because the condition of R = 3 provided a favorable environment for the selective destruction of the order Peritrichida. Given that the doubling time of Vorticella sp. in the order Peritrichida was 6.2 h [25], its growth putatively occurred during the microscopic observation. In addition to the refinement of the methodology advocated in this study, the investigation of 18S rRNA gene-based amplicon sequencing for identification of eukaryotic communities could improve the reliability of the microscopic results. A primer set that can holistically detect eukaryotic communities has been 12

designed [26], and its usefulness for analyzing eukaryotic community structure has been reported [27]. The application of 18S rRNA gene-based amplicon sequencing warrants future study.

5. Conclusion This study examined a methodology to identify the prokaryotic and eukaryotic species damaged by the treatment of a high-pressure jet device (HPJD), as an excess sludge reduction technology in a lysis-cryptic growth system. A combination of the 16S rRNA gene amplicon sequencing and

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microscopic observation allowed for a better understanding of the prokaryotic and eukaryotic species specifically susceptible to the HPJD treatment. A vital operation parameter, i.e., the sludge

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injection ratio (R) in the HPJD, determined the degree of the microbial cell destruction and the number of the damaged bacterial species, which potentially affects the long-term transition of

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microbial community composition in the lysis-cryptic growth system. The analysis of the DNA

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eluted in the supernatant revealed that nitrifying bacteria were not destructed, ensuring the maintenance of nitrification potential in activated sludge. The selective destruction of bacterial

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species by the HPJD treatment was possibly driven not by the bacterial cell wall thickness but by the spatial localization of the damaged microorganisms in activated sludge, such as the sludge floc surface. The microscopic observation after the HPJD treatment confirmed the severe damage to the metazoan community, which likely affects the degree of sludge predation and the resultant

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microbial community composition.

Acknowledgments We acknowledge Mr. Tadahiro Fujii (DPK) for the technical support of HPJD. We would like to thank Editage (www.editage.jp) for English language editing.

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Funding:

This work was supported by the Adaptable and Seamless Technology Transfer Program through the target-driven R&D of Japan Science and Technology Agency (AS2311489E), a Grant-in-Aid for Scientific Research (A) (26249076), and a Grant-in-Aid for JSPS Fellows (201508427) from the

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Ministry of Education, Culture, Sports, Science and Technology of Japan.

Conflicts of Interest

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The authors declare that there are no conflicts of interest related to this manuscript.

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

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18

Figure captions Fig. 1 DNA concentration eluted from activated sludge by HPJD treatment as a function of R-value. N. D. represents “not detected” (The detection limit: < 0.50 μg/L). The tested mixed liquor suspended solid concentrations were 2,057-2,193 mg/L.

Fig. 2 Prokaryotic community compositions in activated sludge and the supernatant at a phylum

ro of

level after HPJD treatment at each injection ratio (R).

Fig. 3 Prokaryotic community compositions in activated sludge and the supernatant at a class level

-p

after HPJD treatment at each injection ratio (R).

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Fig. 4 Top 10 OTUs detected before and after HPJD treatment at different R values of (a) R = 0, (b)

lP

R = 1 and (c) R = 3.

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Fig. 5 The effect of HPJD treatment on compositions and abundances of (a) protozoan and (b)

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metazoan communities in activated sludge

19

20 18

Concentration [mg/L]

16 14 12 10 8 6

2 0

N. D. Before

R=0

R=1

R=3

Jo

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lP

re

-p

Fig. 1

ro of

4

20

1

1

Other WPS-2

WPS-2

Euryarchaeota Euryarchaeota

0.90.9

0.80.8

WS3

WS3

TM7

TM7

SR1 0.7

Elusimicrobia

0.7

SR1 Elusimicrobia

GN02

GN02

0.6

Spirochaetes

0.6

Spirochaetes

Gemmatimonadetes 0.5

Cyanobacteria Gemmatimonadetes

0.5 0.4

0.4

Chlorobi

Cyanobacteria

Nitrospirae

Chlorobi

ro of

Relative abundance [-] Relative abundance [-] [-] Relativeabundance

Other

Firmicutes

0.3

OD1

0.3

Nitrospirae

Firmicutes

Acidobacteria

OD1

0.2

Planctomycetes

Acidobacteria

Verrucomicrobia

0.10.2

00.1

R0 R=0

R1 R=1

R3 R=3

R0 R=0

Sludge

0

Before

R0 R=0

R1 R=1

Supernatant

R1 R=1

R3 R=3

R0 R=0

R1 R=1

Supernatant

lP

Sludge

R=3R3

re

Before

-p

Chloroflexi

Jo

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Fig. 2

21

R=3R3

Planctomycetes

Actinobacteria Verrucomicrobia Bacteroidetes Chloroflexi

Proteobacteria Actinobacteria

Bacteroidetes

Proteobacteria

100%1

100%

Other Other

Other

TK17 TK17

WPS-2

70%

60%

50%

40%

0.4 40%

OM190 ZB2 30%

0.3 30%

0.2 20%

20%

Nitrospirae

Before

R=0 R=0

R=1 R=1

R=3 R=3

R=0 R=0

R=1

R=3 R=3

R0 R=0 R=0 R=0

Sludge

Supernatant

Supernatant

Jo

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lP

Fig. 3

22

Elusimicrobia

Phycisphaerae

Phycisphaerae [Leptospirae] [Leptospirae] 4C0d-2

4C0d-2 Opitutae Clostridia Opitutae

[Pedosphaerae]

Chloroflexi Planctomycetes

Actinobacteria

Anaerolineae Verrucomicrobia

Gammaproteobacteria

Deltaproteobacteria Chloroflexi

Sphingobacteriia Actinobacteria

Flavobacteriia Bacteroidia

[Pedosphaerae] Actinobacteria

Alphaproteobacteria

Gammaproteobacteria

Betaproteobacteria

Deltaproteobacteria Bacteroidetes

Alphaproteobacteria

Sphingobacteriia Proteobacteria

Betaproteobacteria

re

Sludge

OPB56 Elusimicrobia

Verrucomicrobiae Acidobacteria

-p

Before Before

Sludge Supernatant R1 R3 R0 R1 R=1 R=1 R=3 R=0 R=1 R=3 R=1 R=3 R=3 R=0 R=0 R=1R=3R3R=3

Gemmatimonadetes OPB56

Bacteroidia

Anaerolineae

0%0

PRR-12

Gemmatimonadetes

Planctomycetia OD1 Nitrospira

Chloroflexi

0%

iii1-8

PRR-12

Flavobacteriia Clostridia

Verrucomicrobiae

0.1 10%

iii1-8

Nitrospira OM190 Firmicutes

Planctomycetia

10%

SJA-28 SJA-28

ro of

0.5 50%

Relative abundance [-]

abundance Relative [-] abundance Relative [-] [-] abundance Relative

0.6 60%

Euryarchaeota Epsilonproteobacteria Epsilonproteobacteria WS3 Solibacteres Solibacteres TM7 Bacilli Bacilli SR1 BD1-5 BD1-5 Elusimicrobia Acidobacteria-6 GN02 Acidobacteria-6 Chloracidobacteria Spirochaetes Chloracidobacteria Holophagae Gemmatimonadetes Holophagae Acidimicrobiia Cyanobacteria Acidimicrobiia ZB2 Chlorobi

80%

0.8 80%

0.7 70%

SC3 SC3

90%

0.9 90%

0.25

( a ) R= 0

ab u n d an Re lative [-]ce [-] Abundance

ab u n d an Re lative [-]ce [-] Abundance

0.25 0.2 0.15

0.1 0.05 0

( b) R= 1 0.2 0.15

0.1 0.05 0

After supernatant

Before sludge

0.25

( c) R= 3

Initia l a ctiva ted slud g e

0.2 0.15

-p

Sup erna ta nt a fter H PJD tre a tm e n t

0.1

0.05

re

ab u n d an Re lative [-]ce [-] Abundance

After supernatant

ro of

Before sludge

After supernatant

Fig. 4

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Before sludge

lP

0

23

(a) Protozoa

(b) Metazoa

Testacida

160

4000

Amoebida

3500

Euglenida

3000

Protomastigida

2500

Hypotrichida

2000

Heterotrichida

1500

Peritrichida

1000

Hymenostomatida

Chaetonotoidea

120

Ploima

100

Suctorida

500

Macrobiotus

140

Metazoa abundance [/mL]

Protozoa densities [/mL]

4500

Bdelloidea

80 60

40 20

0

0 Before R=0 R0

R1 R=1

R3 R=3

Before R=0 R0

R1 R=1

R3 R=3

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lP

re

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Fig. 5

24

Table captions Table 1. Alpha diversities of prokaryotic communities in activated sludge and eluted DNA

Table 2. Taxonomies of frequently detected top ten ranked OTUs eluted from activated sludge

alphabet means remarkable difference. Sludge Before

ro of

Table 1. Alpha diversities of prokaryotic communities in activated sludge and eluted DNA. Different

Supernatant

R=0

R=1

R=3

R=0

8.99

±

0.022a

9.05

±

0.013b

9.00

±

0.028ac

8.94

±

0.023bc

8.59

Chao1§

4204

±

246.8a

4774

±

192.7b

4280

±

182.7a

4495

±

250.3c

1690

198.3

±

3.8a

201.1

±

3.3a

202.1

±

4.4a

185.2

±

3.6b

0.106

±

0.002a

0.100

±

0.002b

0.107

±

0.002a

0.097

Simpson

Simpson evenness#

*

A species diversity in a community The number of total species based on OTUs ¶ Species richness and evenness in a community # Species evenness

±

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§

66.5

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Reciprocal¶

25

0.002c

0.049

R=3

±

0.02

5.41

±

0.016

7.40

±

0.013

±

46.8

604

±

38.4

1253

±

36.0

-p

Shannon*

R=1

±

1.7

10.6

±

0.1

21.4

±

0.3

±

0.001

0.027

±

0.000

0.021

±

0.000

Table 2. Taxonomies of frequently detected top ten ranked OTUs eluted from activated sludge Taxonomy

Note

denovo Kingdom

Phylum

Class

Order

Family

denovo16174

Bacteria

Proteobacteria

Alphaproteobacteria

Caulobacterales

Caulobacteraceae

denovo6844

Bacteria

Proteobacteria

Alphaproteobacteria

Sphingomonadales

Sphingomonadaceae

denovo18916

Bacteria

Actinobacteria

Actinobacteria

Actinomycetales

Micrococcaceae

denovo11346

Bacteria

Proteobacteria

Betaproteobacteria

denovo11795

Bacteria

Proteobacteria

Betaproteobacteria

Rhodocyclales

Rhodocyclaceae

denovo19735

Bacteria

denovo6602

Bacteria

Proteobacteria

Gammaproteobacteria

Pseudomonadales

Moraxellaceae

denovo6025

Bacteria

Proteobacteria

Betaproteobacteria

Burkholderiales

Comamonadaceae

denovo22275

Bacteria

Proteobacteria

Betaproteobacteria

Rhodocyclales

Rhodocyclaceae

Genus

Species Gram-negative

Sphingomonas

Gram-negative Gram-positive Gram-negative

Zoogloea

Gram-negative

ro of

Gram-negative

Hylemonella

Gram-negative

Candidatus

Gram-negative

Accumulibacter

Bacteria

GN02

BD1-5

Gram-negative

denovo16174

Bacteria

Proteobacteria

Alphaproteobacteria

Caulobacterales

Caulobacteraceae

Gram-negative

denovo22252

Bacteria

Proteobacteria

Alphaproteobacteria

Rhizobiales

Phyllobacteriaceae

Gram-negative

denovo19135

Bacteria

Proteobacteria

Alphaproteobacteria

Rhizobiales

Bradyrhizobiaceae

Gram-negative

denovo12889

Bacteria

Proteobacteria

Alphaproteobacteria

Rhizobiales

Xanthobacteraceae

Gram-negative

denovo18561

Bacteria

Proteobacteria

Alphaproteobacteria

Rhizobiales

Bradyrhizobiaceae

Gram-negative

denovo18711

Bacteria

Proteobacteria

Gammaproteobacteria

Xanthomonadales

Xanthomonadaceae

denovo20249

Bacteria

Proteobacteria

Alphaproteobacteria

Rhodobacterales

Rhodobacteraceae

Amaricoccus

Gram-negative

denovo3888

Bacteria

Actinobacteria

Actinobacteria

Actinomycetales

Mycobacteriaceae

Mycobacterium

Gram-positive

denovo19135

Bacteria

Proteobacteria

Alphaproteobacteria

Rhizobiales

Bradyrhizobiaceae

Jo

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lP

re

-p

denovo14674

26

Gram-negative

Gram-negative