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Insight into c-di-GMP Regulation in Anammox Aggregation in response to Alternating Feed Loadings Yongzhao Guo, Sitong Liu, Xi Tang, Chao Wang, Zhao Niu, and Ying Feng Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b06396 • Publication Date (Web): 24 Jul 2017 Downloaded from http://pubs.acs.org on July 25, 2017

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Environmental Science & Technology

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Insight into c-di-GMP Regulation in Anammox Aggregation in

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response to Alternating Feed Loadings

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Yongzhao Guo 1,2, Sitong Liu 1, 2, *, Xi Tang 1, Chao Wang 3, Zhao Niu 1,2, Ying Feng 1

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University, Beijing 100871, China

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518055, China

Key Laboratory of Water and Sediment Sciences, Ministry of Education of China, Peking

School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen

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Science and Technology, Dalian University of Technology, Dalian 116024, China

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*Corresponding author: Sitong Liu

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Address: College of Environmental Science and Engineering, Peking University, Yiheyuan Road,

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No.5, Haidian District, Beijing 100871, China.

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E-mail: [email protected]

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Tel/Fax: 0086-10-62754290.

Key Laboratory of Industrial Ecology and Environmental Engineering, School of Environmental

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ACS Paragon Plus Environment


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Abstract

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Substrate concentrations generally fluctuate in wastewaters. However, how anammox

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biomass behaves to overcome the stress of alternating feed loadings remains unclear.

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Here, we combined long-term reactor operation, batch tests, 16S rRNA transcript

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sequencing, and metabolomics analysis to investigate the aggregation of anammox

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biomass under the regulation of c-di-GMP, a key second messenger, in response to

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alternating feed loadings. We demonstrated that the aggregation process was

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significantly faster under alternating loadings and was significantly correlated with

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higher levels of c-di-GMP and extracellular polymeric substances (EPS) production.

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The increase in c-di-GMP was positively correlated with a higher relative transcript

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expression level in the c-di-GMP pathway-dependent community. The targeted

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metabolomics results indicated that the increased production of fructose 6-phosphate

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and UDP-N-acetyl-D-glucosamine, the precursor substances for the synthesis of

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exopolysaccharides, was induced by higher levels of c-di-GMP. Consequently, the

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granulation process was accelerated via EPS production. Higher levels of intracellular

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hydrophobic amino acids were also positively correlated with increased extracellular

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protein levels, considering the significant increase in peptides under alternating

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loadings. Based on our findings, we believe that c-di-GMP regulation and EPS

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production of the anammox biomass are important mechanisms to enhance its

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tolerance against unfavorable feed stress. These results highlight the role of c-di-GMP

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in anammox biomass as it works to survive in unfavorable niches.

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Key words: Anammox; c-di-GMP; 16S rRNA transcripts; Metabolomics; Alternating loadings; Granulation 2


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1 Introduction

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The anaerobic ammonium oxidation (anammox) process is a shortcut in the nitrogen

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cycle that directly converts nitrite and ammonium to nitrogen gas.1 Many applications

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of the anammox process have emerged worldwide as alternatives for wastewater

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treatment because of its high nitrogen removal, low energy consumption, no

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requirement for external organic carbon, and low sludge yield.2 Despite these

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significant advantages, application of the anammox process has been limited

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primarily because of a slow growth rate (doubling time of 11-13 d)3 and the high

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sensitivity to operational conditions such as dissolved oxygen, organic content,

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temperature, pH, and especially substrates.4, 5 The fluctuation of substrate

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concentrations occurs commonly and naturally both in actual industrial and domestic

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wastewater and in natural environments. Therefore, gaining further insight into the

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phenotype and metabolic response to the nutrient changes is a subject of great interest

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for applications involving the anammox process.

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Anammox bacteria are not currently available as pure cultures;6 they exist as

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matrix-encased species-rich communities with other species as surface-associated

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biofilms or surface-independent aggregates in natural or bioreactor habitats.7 Thus,

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the characteristics of an anammox biofilm will be closely related to the performance

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of the anammox process. Nutrient concentration or availability, as an important

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environmental factor, can impact biofilm growth, development, and dispersal behavior.

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8-10

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dissolved organic carbon, glucose or nitrogen concentrations decrease 11, and biofilms

It has been demonstrated that Pseudomonas fluorescens biofilms detach when

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of Pseudomonas putida rapidly disperse in response to carbon starvation.12 For E. coli,

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when the carbon/nitrogen ratio in the nutrient supply is increased, the extracellular

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polysaccharide/protein ratio also increases which has a strong effect on its biofilm

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formation.13

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For the molecular mechanisms triggered by nutrient changes, the ubiquitous

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intracellular second messenger c-di-GMP (Bis-(3’-5’)-cyclic dimeric guanosine

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monophosphate) represents an intriguing area of research, particularly with respect to

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the behavior and ecology of microbial assemblages.14 It has been shown that an

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increase in c-di-GMP facilitates the biofilm mode, whereas a decrease results in a

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switch to dispersal and the planktonic mode of existence.10 For instance, carbon

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starvation of Pseudomonas putida results in a decrease in c-di-GMP levels, which will

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induce the protease LapG-mediated cleavage of the surface adhesion LapA and lead

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to dispersal.15 Similarly, biofilms of Pseudomonas aeruginosa undergo dispersal in

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response to a sudden decrease or increase in carbon-dependent nutrients by

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modulating c-di-GMP levels;16 17 the sensor regulator for this process in P.

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aeruginosa is BdlA,18 a chemotaxis regulator that is affected by c-di-GMP levels. For

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complex microbial communities, a biofilm composed of species-rich communities

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represents a completely different organism status compared with a pure culture,

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including different living styles, metabolic behaviors, and resistance capability. Yang

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et al. 19 used strategies involving different organic loading rates to achieve accelerated

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aerobic granulation under the regulation of c-di-GMP. When nutrients were increased

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suddenly, the constituent cells were stimulated to secrete higher levels of c-di-GMP to 4


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produce alginate-like exopolysaccharides (ALE), which served as the precursor for

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aerobic granules. This study produced meaningful results for wastewater treatment,

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but the mechanism by which c-di-GMP affects community assembly and the

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metabolic properties of the community members are still worth being explored.

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16S rRNA sequencing is a widely used method for exploring bacterial community

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compositions. Unlike the 16S rRNA gene set from genomic DNA that includes

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dormant or dead bacteria, the 16S rRNA transcript set from mRNA can be used to

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analyze the metabolically active community composition.20 In addition, metabolomics

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has emerged as a technique for defining abundant small molecules from a complex

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network of chemical and biochemical pathways; the resulting spectrum of masses

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offers a chemical fingerprint of the microbiota functional status.

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To improve our understanding of the aggregation mechanism of anammox sludge

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under nutrient stress, two SBRs were operated for 19 weeks. Particle size, c-di-GMP

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levels, and EPS contents were subsequently determined, along with reactor

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performance. Furthermore, short-term batch assays were combined with 16S rRNA

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transcript sequencing and metabolomics analysis to identify the community assembly

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mechanism regulated by c-di-GMP.

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2 Materials and Methods

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2.1 Bioreactor Operation.

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To study how c-di-GMP levels and EPS change along with anammox sludge

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aggregation under alternating feed loadings, two identical sequencing batch reactors

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(SBRs) with a 1.5-L working volume were operated for 135 days (19 weeks). R1, as

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control reactor, was operated at conventional stepwise nitrogen loading rates (NLRs)

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when the concentrations of effluent NO2--N and NH4+-N were both lower than 20

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mg-N/L (Figure 1A). In accordance with this condition, R2 was operated at a NLR of

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0.13 kg-N/m3/d, alternating weekly to be higher or lower than that of R1 (Figure 1A).

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(Figure 1A). Both reactors were fed with the same medium solution mainly consisting

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of ammonium and nitrite in the form of (NH4)2SO4 and NaNO2 as the nitrogen source.

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The details of the medium compositions are presented in the Supporting Information21.

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The reactors were maintained at 37 ± 1 °C, pH of 7.5 to 8.0, and under anaerobic

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conditions. The concentrations of ammonium, nitrite, and nitrate in the SBRs were

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measured every two days using American Public Health Association (APHA) standard

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engineering methods.22 The particle size was determined weekly using a laser particle

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analysis system (Malvern Mastersizer 2000, UK). Sludge samples were taken from

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the reactor once a week and immediately stored at -80 °C for the determination of

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extracellular polymeric substances (EPS) and intracellular c-di-GMP content.

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2.2 Batch Assays.

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Batch assays were used to explore the mechanism of aggregation regulated by

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c-di-GMP. The anammox sludge was first pulverized and sieved through a 100-mesh 6


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screen to form a uniform particle size and then harvested by centrifugation at 6,000

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rpm for 10 min. Batch assays were performed in 100-mL serum bottles with 50 mL of

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anammox sludge suspensions at a final concentration of 1.45 gVSS/L (VSS: volatile

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suspended solids). The normal load test (B1) contained 25 mg NO2--N/L and 25 mg

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NH4+-N/L and the other components shown in Text S1. B2-low and B2-high

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contained NO2--N/L and NH4+-N/L at concentrations 8 mg/L lower and higher,

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respectively, than that of B1. Every 30 min, 1/3 of the supernatant was replaced with

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the same volume of fresh medium to obtain alternating loadings (Figure S2A). The

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vials were made anoxic by flushing with a gas mixture of N2/CO2 (95%/5%, v/v) for

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10 min, sealed tightly with rubber caps, and shaken at 150 rpm and 37 ± 1 °C. The

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assays were performed for 2 h. Supernatant samples were taken every 10 min for

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nitrogen (NO2-, NH4+, and NO3-) detection. Biomass samples were taken at the

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beginning, 1 h and 2 h and immediately stored at -80 °C for c-di-GMP extraction, EPS

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determination, 16S rRNA transcripts sequencing and metabolomics analysis. Each

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loading pattern was conducted in triplicate.

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2.3 EPS Extraction and Determination.

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EPS were extracted using the cation exchange resin (CER) method proposed by Hou

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et al.23 CER was added at a dosage of 70 g/g VSS. The suspensions were stirred for 3

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h at 200 rpm and 4 °C. More details are provided in Supporting Information Text S2.

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After the final centrifugation, the bacteria without EPS were collected for intracellular

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c-di-GMP extraction. The cellular proteins were extracted using a Bacterial Protein

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Extraction Kit (Sigma, USA). The extracellular polysaccharides and proteins were 7



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determined using the Anthrone method with glucose as a standard 24and the

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Bicinchoninic Acid (BCA) assay 25, respectively. Each was performed in triplicate.

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More detailed information is provided in Texts S3 and S4.

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2.4 Intracellular c-di-GMP Detection.

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Intracellular c-di-GMP was extracted using ultrasonication and acetonitrile/methanol

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(50/50, v/v) extraction following the method of Christian Spangler et al. 26 with some

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modifications. Quantitative analysis of intracellular c-di-GMP concentrations was

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performed using xanthosine 3’,5’-cyclic monophosphate (cXMP) (Sigma-Aldrich,

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Steinheim, Germany) as the internal standard. LC-MS/MS analysis was performed

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using a Dionex Ultimate 3000 UPLC system coupled to a TSQ Quantiva Ultra

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triple-quadrupole mass spectrometer (Thermo Fisher, CA, USA) and equipped with a

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heated electrospray ionization (HESI) probe. Detailed procedures are described in

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Supporting Information Text S5.

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2.5 16S rRNA-transcript Sequencing Analysis.

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Sequencing of 16S rRNA transcripts was conducted to analyze the metabolically

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active community composition under alternating feed loadings. Total RNA was

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extracted from anammox sludge using a RiboPureTM RNA Purification Kit (Ambion,

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Life Technologies, Lithuania) according to the manufacturer’s guidelines. Then, the

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extracted RNA was subjected to DNA removal and complimentary DNA (cDNA)

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synthesis (Tiangen, Beijing, China). The quality and quantity of RNA were checked

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using a Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific, USA); DNA

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contamination was detected by performing PCR reactions using the isolated RNA

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samples. No PCR products were detected after 27 cycles of PCR (Figure S3). (Figure

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S3). The cDNA with bacterial 16S rRNA transcripts was amplified with the primers

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338F and 806R targeting the V3–V4 region.27 High-throughput sequencing was

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conducted at Majorbio Co., Ltd. (Shanghai, China) using the Illumina MiSeq platform

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according to the manufacturer’s instructions. The raw 16S rRNA sequences

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determined by MiSeq sequencing were quality-filtered using QIIME v.1.7.0 with the

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following criteria: (1) the sequences with Ns, lengths shorter than 200 bp, or average

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quality scores less than 25 were discarded; (2) two nucleotide mismatches in primer

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matching and reads containing ambiguous characters were removed; and (3) only

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sequences that overlapped >10 bp were assembled according to their overlap

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sequence. The sequences were clustered into operational taxonomy units (OTUs) with

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97% similarity cutoff using the UPARSE pipeline. 29 The detailed sequencing

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methods are presented in Text S6. All 16S rRNA sequences from Miseq sequencing

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have been deposited into the NCBI Sequence Read Archive database under the

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accession number SRP095288.

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2.6 LC-MS-based Metabolomic Profiling and Quantification Analysis.

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Metabolomics analysis was applied to provide deeper insights into the metabolic

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responses of anammox sludge to environmental alterations. Intracellular metabolites

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were extracted using ultrasonication and methanol (80%) extraction. Untargeted

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metabolomic analysis was performed using the Q Exactive Orbitrap (Thermo, CA).

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Targeted metabolomics profiles were analyzed by TSQ Quantiva (Thermo, CA, USA).

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Details are presented in Text S7.

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2.7 Data Analysis.

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Means and standard deviations were calculated using Microsoft Excel. ANOVA tests

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determine the significance and differences between experimental and control groups,

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and p < 0.05 was considered statistically significant. Pearson correlation coefficients

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were determined using SPSS software. Prior to analysis, data sets of abundance were

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normalized by log10 transformation for 16S rRNA transcript sequencing and log2

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transformation for metabolomics profiling, followed by mean centering.32 Differences

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in active microbial community composition were visualized through unconstrained

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non-metric multidimensional scaling (NMDS) ordination, based on Bray-Curtis

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distance in R with the Vegan package. All the heatmaps in this study were generated

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in R (Package “pheatmap”) using the Pearson correlation as the distance measure.

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Principal component analysis (PCA) was performed using R (Package

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“pcaMethods”).

and Bonferroni corrections 31 were conducted using PASW (SPSS version 18.0) to

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3 Results

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3.1 Anammox Sludge Granulation Process in SBRs.

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Two SBRs (R1 and R2) were operated for 135 d (19 weeks) with two different

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loading strategies (Figure 1A). Three distinct phases were defined according to the

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developmental trends for particle size (Figure 1B). At the point of inoculation, the

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biomass had a mean particle diameter of 100 µm (50th percentile distribution). This

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was followed by different granulation processes due to different nitrogen loading rate

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(NLR) strategies. For R2 as shown in Figure 1B, weeks 0-4 (phase I) were

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characterized by a dramatic increase in the mean particle size from 100 to 1100 µm.

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Granule size remained steady in the range of 1000 to 1100 µm for the subsequent 15

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weeks (phase II, weeks 4-13; phase III, weeks 13-19).

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More obvious granulation occurred under alternating loadings. Compared to R2,

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R1 had an even slower granulation process from 100 to 800 µm (phases I-II, weeks

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0-13), followed by a maintenance phase (granule size: 772 ± 31 µm, phase III, weeks

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13-19) (Figure 1B).

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3.2 c-di-GMP and EPS Production in response to Alternating Feed Loadings.

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The amount of c-di-GMP in the sludge of R2 increased markedly during phase I,

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which was normalized to 11.07 ± 0.42 to 36.70 ± 1.39 pmol/mg bacterial protein. For

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R1, in contrast, there was a smooth uptrend within phases I-II, from 11.07 ± 0.42 to

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21.96 ± 1.11 pmol/mg bacterial protein (Figure 2). The Pearson analysis results

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showed that c-di-GMP levels in both reactors were strongly and positively correlated 11



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with anammox sludge particle size (as a granulation measure) during the granulation

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processes (R1: r = 0.8907, p < 0.01 for phases I-II; R2: r = 0.9797, p < 0.01 for phase

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I) (Table S1). The maintenance phases (no obvious changes in particle size) in R1

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(phases II-III) and R2 (phase III) were characterized by granule maintenance with a

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steady trend of particle size development, during which c-di-GMP levels showed a

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weak correlation and no statistical difference (Table S1).

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EPS contents (PN, PS, and ratio of PN/PS) increased during the granulation

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process in R1 and R2, and showed a strong positive Pearson correlation with particle

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size (Figure 1B and Figure 3, Table S1). Interestingly, EPS contents showed no

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significant correlation with particle size during the maintenance phases in R1 and R2

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(r < 0.3469, p > 0.0615; minimum absolute value ‘r’ and maximum ‘p’ values for

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most PNs, PSs, and PN/PS ratios).

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Additionally, statistical analysis indicated that the EPS contents correlated with

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the c-di-GMP levels of anammox biomass in the SBRs (Figure 2 and Figure 3, Table

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S2). For R2, especially for phase I, PN, PS, and the PN/PS ratio showed strong

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positive correlations with the c-di-GMP levels (PN: r = 0.9894, p < 0.01; PS: r =

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0.8975, p < 0.01; PN/PS ratio: r = 0.8289, p < 0.01). However, the c-di-GMP levels

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and EPS contents were not significantly correlated (maximum ‘r’ absolute value=

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0.5487) or statistically correlated (minimum ‘p’ = 0.2547) during phases II-III. For R1,

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phases I-II, as expected, were emphasized. We found that PN and the PN/PS ratio

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both showed strong positive correlations with the c-di-GMP levels (PN: r = 0.9098, p

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< 0.01; PN/PS ratio: r = 0.8530, p < 0.01). Even the Pearson correlation coefficient 12


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between PS and c-di-GMP was statistically significant (p < 0.01), but not significantly

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correlated (r = 0.3407).

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Furthermore, we made attempt to explore the cause and effect between c-di-GMP

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and EPS. The short-term batch test showed that there was a significant increase in

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c-di-GMP was produced at the 1 h time point under alternating loadings; EPS

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production did not increase until the 2 h time point (Figure S4). Although the increase

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in the amount of EPS in the batch culture was not as high as that observed in the

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natural granulation course, the EPS increase was statistically significant (B2-high or

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B2-low vs B1; PS: p < 0.0006; PN: p < 0.0021) and likely to be of biological

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importance.

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3.3 Metabolically Active Community Composition Analysis.

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Sequencing of 16S rRNA transcripts offers support in uncovering the composition of

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the metabolically active community. A total of 330,314 raw sequences were obtained

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initially. After filtering for low quality sequences, an average of 35,212 sequences

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were yielded per sample (n = 9, SD = 2,461). Individual samples contained OTU

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number from 248 to 307. The NMDS plot, based on sequences at an OTU level with >

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97% similarity, showed a clear separation of the community composition between

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normal loading (B1) and alternating loadings (B2-low or B2-high) (Figure 4),

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revealing obvious changes in the community structure under different loadings. A

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close examination of the community changes based on 16S rRNA transcript

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sequencing revealed a total of 46 community members that significantly changed in

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abundance (p < 0.05) under alternating loadings, as shown in Figure 5. The

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metabolically active members were divided into two clusters based on their

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abundance under different feed loading patterns (Figure 5). Under alternating loadings,

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the members in cluster 1 decreased significantly in abundance compared with those

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under normal loading (B1). Importantly, it was obvious that 39 out of the 46

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community members (cluster 2) (over 80%) exhibited an increase in abundance under

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alternating loadings. From these clusters, we found that metabolically-active

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community structural composition shifts occurred at the phylum level mainly for

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Proteobacteria (up regulation, approximately 57% in cluster 2) and Planctomycetes

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(down regulation) (Table S3).

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Up-regulation of the proteobacterial branch bacteria, Nitrosomonas europaea, an

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aerobic ammonium oxidizer (AOB) belonging to the phylum Proteobacteria (Tag 4 in

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Figure 4), was found to be significantly higher under alternating loadings [2.7-fold

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change, p = 0.0052 (B2-low) and 2.9-fold change, p = 0.0034 (B2-high)]. Moreover,

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within cluster 2 as shown in Figure 5, the nitrite-oxidizing bacteria (NOB)

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Nitrospira_sp. species (Tag 38) [2.5-fold change, p = 0.012 (B2-high)] and

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Denitrifiers Denitratisoma genus (Tag 7 and Tag 32) presented significantly to be

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higher under alternating loadings [Tag 7: 2.8-fold change, p = 0.0091 (B2-low) and

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2.9-fold change, p = 0.027 (B2-high); Tag 32: 3.6-fold change, p = 0.0011 (B2-low)

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and 3.2-fold change, p = 0.012 (B2-high)].

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3.4 Metabolomics Response to Alternating Feed Loadings.

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An untargeted approach was used for the global profiling of metabolites, and 331

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metabolites belonging to the following Kyoto Encyclopedia of Genes and Genomes

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(KEGG) metabolic pathways were identified: amino acids, carbohydrates, lipids,

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peptides, cofactors and vitamins, nucleotides, and xenobiotics (Supporting Data S1).

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The PCA scatter plot presented a sufficient separation between B1 and B2-low or

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B2-high (Figure 6A), which indicated that the metabolome associated with alternating

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loadings (B2-low and B2-high) was drastically different from that with normal

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loading (B1). In addition, the distance between B2-low and B2-high was close in the

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PCA scatter plot (Figure 6A), indicating that the two samples harvested from the

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alternating loading assays (B2-low and B2-high) had similar metabolic profiles.

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The abundance of amino acids appeared as a polarization state (Figures S5A).

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The alterations of amino acids in the batch assays coincided with a significant

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increase in almost all the peptides detected for B2-low, and B2-high (Figures S5B)

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compared to the levels in B1. The B2-low and B2-high states were characterized by a

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slight decrease in abundance for the majority of the metabolites in the carbohydrate

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metabolic pathway. Interestingly, the exceptions were fructose 6-phosphate and

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UDP-N-acetyl-D-glucosamine, which showed obvious increases in abundance (Figure

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S5C).

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To more precisely define the changes in the metabolome identified using the

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untargeted metabolomics approach, we used a targeted metabolomics approach to

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measure the levels of amino acids and carbohydrates. The direct measurement of

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amino acids confirmed these results (Figure 6B). The total amounts of amino acids in 15



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B1, B2-low, and B2-high were calculated, and the results showed that significantly

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higher levels of amino acids were produced in B2-low and B2-high than in B1

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(B2-low > B1, p = 0.00036; B2-high > B1, p = 0.00032) (Table S4).

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For the carbohydrate metabolic pathway, fructose 6-phosphate and

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poly-β-1,6-N-acetylglucosamine (PNAG) were emphasized. Fructose 6-phosphate

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was found to be up regulated by 21.55% in B2-low and by 50.91% in B3 compared to

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the level in B1. Similar to fructose 6-phosphate, the amount of

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UDP-N-acetyl-D-glucosamine also increased by 29.36 % and 19.38 % in B2-low and

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B2-high, respectively (Figure 6C).

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4 Discussion

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4.1 Response of c-di-GMP Levels to Alternating Feed Loadings.

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In this study, two long-term SBRs with different feed loading strategies were operated,

323

and the intracellular c-di-GMP levels were determined for anammox sludge. Two

324

dramatically different aggregation processes were found under different operational

325

strategies. Moreover, this treatment has been successfully replicated within the first

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month of SBR operation, indicative of its repeatability (Figure S8). Additionally, the

327

discrepancy within the initial stage of reactor operation appeared once the nitrogen

328

loading changed, indicating that the feed variability was in fact the main factor

329

responsible for inducing the observed differences between the two treatments (Figures

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2, 3 and S8). It should be emphasized that under alternating feed loadings, anammox

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biomass formed larger particles in a shorter time interval to reach a stable level

332

(almost 4 weeks vs 13 weeks, R2 vs R1) (Figure 1). The c-di-GMP levels of R2 kept

333

pace with its granulation process, especially during the quick maturation period

334

(phase I) (Figures 1 and 2). Additionally, c-di-GMP levels were weakly correlated in

335

phase III of R1 and in phases II-III of R2. The reason for this result could be that

336

anammox biomass reached a relatively stable granule status to adapt to the

337

environment after long-term development, during which c-di-GMP probably no

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longer played a major role. Based on this, we hypothesized that the anammox sludge

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community made use of the second messenger c-di-GMP to regulate its aggregation

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under alternating loadings.

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Here the community levels of c-di-GMP signaling and responses to alternating

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loadings were analyzed using anammox batch assays. An 16S rRNA data set was

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generated from reverse-transcribed RNA and used for sequencing to obtain insights

344

into the changes in the metabolically active community composition in response to

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alternating feed loading.20 Within cluster 2 of the community members with a

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statistically significant change, we found that the Proteobacteria phylum was

347

obviously up regulated (Figure 5). Such shifts could indicate that metabolically active

348

microbial species were most likely to adapt or respond to the nutrient stress. Bacteria,

349

especially those in the proteobacterial branch, contain numerous enzymes involved in

350

c-di-GMP turnover that are used to monitor various environmental and intracellular

351

inputs and adjust c-di-GMP levels in a precise manner 33, 34. In addition, the receptor

352

PilZ domain, which has been well studied, is extensively involved in the

353

proteobacterial branch bacteria 33-35. Hence, it was likely that c-di-GMP levels were

354

increased under alternating loadings by the regulation of certain community

355

compositions, especially of the Proteobacteria. To better understand the up-regulation

356

of proteobacterial branch bacteria, an AOB species, N. europaea, was evaluated.

357

Hydroxylamine, an intermediate in the oxidation of ammonia to nitrite for AOB,36

358

was found to be consumed under alternating loadings (Figure S6). Additionally, N.

359

europaea is capable of growing mixotropically on ammonia and hydroxylamine.

360

Under anoxic conditions, hydroxylamine is oxidized with nitrite as an electron

361

acceptor, and nitrous oxide is produced.37 Therefore, during alternating loadings in the

362

absence of oxygen, AOB are likely to be activated when using hydroxylamine and 18


Page 19 of 38


363

ammonia as substrates. The hydroxylamine might be released from anammox species

364

through the ladderane lipids that are involved.38 Hydroxylamine-dependent metabolic

365

pathways were demonstrated within Candidatus Brocadia sinica,39 and hydrazine, an

366

intermediate in Candidatus Kuenenia species and Candidatus Jettenia species, was

367

found to be able to diffuse through ladderane lipids though its unusual density.40 Thus,

368

some c-di-GMP-dependent community members could be activated and induce higher

369

levels of c-di-GMP when responding to alternating loadings.

370

Moreover, we also found that some NOB and denitrifiers were presented at

371

significantly higher levels under alternating loadings (Figure 5). The intra-taxon

372

relation between AOB and NOB was syntrophic in which nitrite was released by the

373

former and used by the latter. In addition, the NO3- that accumulated from nitrifiers

374

(AOB and NOB) can be further used by denitrifiers. The coexisting nitrifying and

375

putative heterotrophic bacteria in the anammox biofilm might consume a trace

376

amount of O2 or organic by-products of anammox bacteria, which would

377

consequently establish suitable microenvironments for anammox bacteria to resist the

378

unfavorable conditions.41

379

4.2 Manipulation of EPS Production in response to Alternating Feed Loadings.

380

EPS as a key component of biofilms and granules were characterized and quantified

381

based on extracellular protein (PN) and polysaccharide (PS) contents.42 In this study,

382

EPS production was determined to have a strong positive correlation with granulation,

383

especially for the extracellular protein content during the granular formation phases

19



Page 20 of 38

384

(phases I-II for R1 and phase I for R2) (Figures 1 and 3, Table S1). It has been

385

proposed that extracellular proteins are likely to be strongly associated with the

386

aggregation of suspended flocs into granules by affecting the surface properties of

387

sludge. 23, 43 Thus, there was a strong correlation during the rapid granulation phases,

388

and weak correlations between EPS contents and granulation were found in phase III

389

of R1 and phases II-III of R2 when the granulation process stopped and reached its

390

maintenance stage.

391

Importantly, it can be concluded that EPS production increased significantly

392

faster under alternating loadings than during normal anammox SBR operation (Figure

393

3). When comparing different feed loadings, it could be concluded that PN production

394

was markedly more rapid and higher in amount at the granulation stage during phase I

395

of R2 (10-fold in amount; week 4 vs week 0) than during phases I-II of R1 (6-fold in

396

amount; week 13 vs week 0). These observations are consistent with a previous report

397

showing that excess EPS form the interiors of aerobic granules that are secreted by

398

bacteria under environmental stress.44 Hence, we hypothesized that the increased

399

production of EPS was likely to induce faster granular formation under unfavorable

400

conditions (that is, alternating loadings in this study).

401

To further examine the levels of the metabolome using the targeted metabolomics

402

approach, amino acids were grouped by their hydrophobicity. The ratio of

403

hydrophobic amino acids to hydrophilic amino acids was determined for each loading

404

pattern (Table S4). The average ratios of B2-low and B2-high were significantly

405

higher than that of B1, and the p values were 0.00018 and 0.00022 relative to B1, 20


Page 21 of 38


406

respectively. More importantly, as indicated by the metabolomics results for amino

407

acids and peptides (Figures S5), an increasing trend was not found for all amino acids,

408

but there was a polarization state as mentioned above, which indicated that bacterial

409

protein was likely not degraded on a large scale but utilized for synthesis considering

410

the up-regulation of peptides under alternating loadings. Therefore, the levels of

411

cellular proteins, especially hydrophobic amino acid-rich proteins, would increase.

412

Anammox bacteria have a slow growth rate, with a doubling time of 11-13 d.3

413

Thus, after a very short-term batch cultivation of anammox sludge (2 h), the biomass

414

would barely increase. Additionally, the growth might even be slower under

415

alternating loadings because of the unfavorable stress. Thus, intracellular proteins

416

may remain stable due to the negligible cellular growth in the batch assays. Based on

417

that conclusion, a higher level of cellular protein synthesis likely implied that more

418

extracellular proteins were produced. To verify this, the naked bacteria after EPS

419

extraction were subjected to cellular protein detection. Significantly more

420

extracellular proteins for the same amount of total bacterial proteins were produced

421

(Figure S7), indicating that more hydrophobic amino acids were probably used for the

422

synthesis of extracellular proteins. This result was also in accordance with the results

423

of Hou et al. 23 in which higher levels of hydrophobic amino acids in extracellular

424

proteins significantly contributed to the high aggregation ability of anammox sludge.

425

Previous studies have shown that the transcription of genes encoding matrix

426

proteins in Vibrio cholerae was increased when intracellular levels of c-di-GMP were

427

elevated 45-47. In addition, through the manipulation of c-di-GMP metabolism, cAMP 21



Page 22 of 38

428

signaling was found to affect biofilm formation in V. cholera by directly affecting

429

polysaccharide biosynthesis genes and matrix proteins 48. However, it should be noted

430

that there is no direct evidence that cAMP signaling also participated in the process of

431

anammox sludge. Further studies are needed to clarify whether extracellular proteins

432

were also regulated by c-di-GMP in the anammox community.

433

4.3 Functional Pathways of c-di-GMP for Anammox Biomass Aggregation.

434

Previous studies have shown that c-di-GMP signaling is important for biofilm

435

development, and in some species, this is mediated partly through the regulation of

436

EPS production, 49-51 although, to the best of our knowledge, most previous studies

437

used pure culture laboratory systems. Based on these results, we used the short-term

438

batch test as described above to clarify the potential cause and effect from these data.

439

The c-di-GMP levels under nutrient stress increased significantly first at the 1 h time

440

point of the batch test, followed by an increase in EPS (Figure S4). Hence, the EPS

441

induction observed in the batch experiments was likely to be partly a consequence of

442

the regulation of c-di-GMP.

443

The decrease for most carbohydrates under alternating loadings was a logical

444

response to the nutrient stress and unfavorable growth environment. In addition,

445

fructose 6-phosphate and UDP-N-acetyl-D-glucosamine were emphasized here

446

because they have been found to participate in the production of alginate and PNAG,

447

which are two important exopolysaccharide (PS) components. 52-54 The synchronous

448

increased levels of fructose 6-phosphate and c-di-GMP were confirmed at the end of

22


Page 23 of 38


449

the batch assays (Figure 6C and Figure S4). Fructose 6-phosphate is a substrate used

450

for the formation of GDP-mannuronic acid, which is the precursor of alginate, an

451

important component of EPS. 53 Notably, the process of the conversion of

452

GDP-mannuronic acid into alginate is regulated by c-di-GMP. 52, 53 At the same time,

453

alginate concentrations also increased significantly under alternating feed loadings

454

(Figure 6D). Similar trends were also determined for UDP-N-acetyl-D-glucosamine.

455

Under the regulation of c-di-GMP, it can act as a monomer in the assembly of PNAG,

456

which is another important exopolysaccharide and an essential intercellular

457

polysaccharide adhesin for adherence and biofilm formation.54 In parallel, PNAG

458

production was about 1.5-fold for B2-low and 2.0-fold for B2-high higher than that of

459

B1 (Figure 6E). Overall, the results above may support the conclusion that c-di-GMP

460

plays an important role in EPS production for anammox biomass.

461

4.4 Significance of this Study and Prospects for the Future.

462

The long-term operation of bioreactors and short-term batch assays combined with

463

16S rRNA transcript sequencing and metabolomics analysis were used for the first

464

time to investigate the aggregation behaviors of anammox sludge under alternating

465

feed loadings. This study provides strong evidence that a more active aggregation

466

process is strongly and positively correlated with c-di-GMP levels and EPS content

467

under alternating loadings. When subjected to nutrient stress, anammox sludge tended

468

to produce higher levels of c-di-GMP through shifts in the metabolically active

469

community, according to the 16S rRNA transcript sequencing results. Metabolomics

470

analysis revealed that hydrophobic amino acids were significantly up regulated and 23



471

likely to be used for the synthesis of more extracellular proteins in accordance with

472

the characteristics of proteins within anammox sludge EPS. In addition, the EPS

473

production process was probably partly under the regulation of c-di-GMP.

474

Page 24 of 38

c-di-GMP, a ubiquitous second messenger in bacteria, can play an important role

475

in the regulation of the behaviors of anammox sludge in response to nutrient stress, as

476

discussed here. Because anammox is a very promising wastewater treatment process,

477

further studies are needed to improve its performance via c-di-GMP regulation

478

especially under unfavorable conditions.

24


Page 25 of 38


479

Acknowledgments

480

The authors are grateful to the National Natural Science Foundations of China (No.

481

51308007 and No. 51478006) for financial support. The financial support from

482

Shenzhen Science and Technology Innovation Committee (No.

483

JSGG20160429162015597) should also be highly appreciated.

484

Supporting Information

485

The supporting information contains (1) tables of Pearson correlation analysis results

486

and relative abundance of 16S rRNA samples at phylum level; (2) details of EPS

487

content detection procedures, c-di-GMP detection methods, 16S rRNA transcript

488

sequencing, metabolites detection, hydroxylamine determination, alginate and PNAG

489

detection, and replicated reactor operation; (3) figures illustrating the details of SBRs

490

operation and batch assays, heatmaps and bar graph of metabolites with different

491

pathways, and the results of the replicated reactor operation within the first month; (4)

492

one file (excel format) about the details of untargeted metabolomics results.

25



Page 26 of 38

493

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625

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Figure captions

627

Figure 1. (A) Alternating feed loadings in R1 and R2 during long-term operation; (B)

628

development process of particle size distribution (measured on a volume basis) under

629

different feed loadings. 50th percentile indicates that 50% of total particles were

630

below the corresponding size distribution. The dotted line divides the different

631

developmental phases of granulation, namely phases I-III.

632

Figure 2. HPLC-MS/MS profiling of c-di-GMP extracted from anammox sludge

633

during aggregation under different feed loadings. The data were normalized to the

634

respective sample biomass total cellular proteins. Error bars are indicated as s.e.m. (n

635

= 3, technical replicates). The dotted line separates the different developmental phases

636

of granulation.

637

Figure 3. The concentrations of (A) extracellular proteins (PN) and (B) extracellular

638

polysaccharides components of EPS within different development phases of

639

granulation under different feed loadings. Each EPS component was normalized to the

640

respective sample biomass; (C) the ratio of extracellular proteins to polysaccharides

641

(PN/PS). Error bars are defined as s.e.m. (n = 3, technical replicates). The dotted line

642

separates the different developmental phases of granulation.

643

Figure 4. The NMDS plot based on community members at OUT level with > 97%

644

similarity of normal loading (B1) or alternating loadings (B2-low and B2-high).

645

Figure 5. Unsupervised clustering of community members based on 16S rRNA

646

transcript sequences showing statistically significant changes (p < 0.05) following 30


Page 31 of 38


647

normal loading (B1) or alternating loadings (B2-low and B2-high). Unsupervised

648

clustering was conducted using Pearson correlation as the distance metric for the

649

purpose of discerning differences between sample classes. The heatmap scale

650

represents the abundance of microbial members normalized by log10 transformation

651

and then mean centering. Tags refer to bacteria at the highest taxonomic resolution

652

identified in the RDP database. Each column represents one biological replicate,

653

which was represented as one colored boxed at the bottom of each column. These

654

community members were colored on the right according to the phylum that they

655

belong to.

656

Figure 6. (A) Principal component analysis (PCA) scatter plot of untargeted

657

metabolomics under different feed loadings in the batch assays. The colored regions

658

in PCA plots indicated 95% confidence region. The abundance of (B) amino acids

659

with statistical significance, (C) fructose 6-phosphate and

660

UDP-N-acetyl-D-glucosamine from targeted metabolomic analysis, (D) alginate

661

concentrations and (E) PNAG detected using WGA-biotin (visualized by

662

chemiluminescence detection) in the batch assays. Error bars are defined as s.e.m. (n

663

= 3, biological replicates). Two-way ANOVA was performed and Bonferroni

664

post-tests were conducted to compare each treatment to normal load (as control)

665

where significant differences are indicated as follows: *p < 0.05 and **p < 0.01.

31



Page 32 of 38

666 667

Figure 1. (A) Alternating feed loadings in R1 and R2 during long-term operation; (B)

668

development process of particle size distribution (measured on a volume basis) under

669

different feed loadings. 50th percentile indicates that 50% of total particles were

670

below the corresponding size distribution. The dotted line divides the different

671

developmental phases of granulation, namely phases I-III.

32


Page 33 of 38


672 673

Figure 2. HPLC-MS/MS profiling of c-di-GMP extracted from anammox sludge

674

during aggregation under different feed loadings. The data were normalized to the

675

respective sample biomass total cellular proteins. Error bars are indicated as s.e.m. (n

676

= 3, technical replicates). The dotted line separates the different developmental phases

677

of granulation.

33



Page 34 of 38

678 679

Figure 3. The concentrations of (A) extracellular proteins (PN) and (B) extracellular

680

polysaccharides components of EPS within different development phases of

681

granulation under different feed loadings. Each EPS component was normalized to the

682

respective sample biomass; (C) the ratio of extracellular proteins to polysaccharides

683

(PN/PS). Error bars are defined as s.e.m. (n = 3, technical replicates). The dotted line

684

separates the different developmental phases of granulation. 34


Page 35 of 38


685 686

Figure 4. The NMDS plot based on community members at OUT level with > 97%

687

similarity of normal loading (B1) or alternating loadings (B2-low and B2-high).

688

35



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689 690

Figure 5. Unsupervised clustering of community members based on 16S rRNA

691

transcript sequences showing statistically significant changes (p < 0.05) following

692

normal loading (B1) or alternating loadings (B2-low and B2-high). Unsupervised

693

clustering was conducted using Pearson correlation as the distance metric for the

694

purpose of discerning differences between sample classes. The heatmap scale

695

represents the abundance of microbial members normalized by log10 transformation

696

and then mean centering. Tags refer to bacteria at the highest taxonomic resolution

697

identified in the RDP database. Each column represents one biological replicate,

698

which was represented as one colored boxed at the bottom of each column. These

699

community members were colored on the right according to the phylum that they

700

belong to. 36


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701 702

Figure 6. (A) Principal component analysis (PCA) scatter plot of untargeted

703

metabolomics under different feed loadings in the batch assays. The colored regions

704

in PCA plots indicated 95% confidence region. The abundance of (B) amino acids

705

with statistical significance, (C) fructose 6-phosphate and

706

UDP-N-acetyl-D-glucosamine from targeted metabolomic analysis, (D) alginate

707

concentrations and (E) PNAG detected using WGA-biotin (visualized by

708

chemiluminescence detection) in the batch assays. Error bars are defined as s.e.m. (n

709

= 3, biological replicates). Two-way ANOVA was performed and Bonferroni

710

post-tests were conducted to compare each treatment to normal load (as control)

711

where significant differences are indicated as follows: *p < 0.05 and **p < 0.01.

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TOC/Abstract graphic

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For Table of Contents Only

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