Comparative evaluation of multiple methods to quantify and characterise granular anammox biomass

w a t e r r e s e a r c h 6 8 ( 2 0 1 5 ) 1 9 4 e2 0 5

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Comparative evaluation of multiple methods to quantify and characterise granular anammox biomass Podmirseg Sabine Marie a,b,*, Thomas Pu¨mpel a, Rudolf Markt a,b, Sudhir Murthy c, Charles Bott d, Bernhard Wett e a

Institute of Microbiology, University of Innsbruck, Technikerstraße 25d, 6020 Innsbruck, Austria alpS GmbH, Grabenweg 83, 6020 Innsbruck, Austria c DCWATER, 5000 Overlook Ave., SW, Washington, DC 20032, USA d HRSD, 1436 Air Rail Ave., Virginia Beach, VA 23455, USA e ARAconsult GmbH, Unterbergerstraße 1, 6020 Innsbruck, Austria b

article info

abstract

Article history:

Six methodologically different approaches were evaluated and compared regarding their

Received 2 June 2014

suitability to quantify and characterise granular anammox biomass. The investigated tech-

Received in revised form

niques were gravimetric analysis (GA), activity measurements (AM), Coulter counter analysis

1 October 2014

(CC), quantitative PCR (qPCR), heme protein quantification (HQ) and the novel image analysis

Accepted 5 October 2014

technique Particle Tracking (PT). The focus was set on the development of fast, economic and

Available online 15 October 2014

user-friendly approaches for potential implementation in regular wastewater treatment plant (WWTP) monitoring. To test the effectiveness of each technique, two sample matrices

Keywords:

were chosen at the WWTP Strass (Austria): i) sludge liquor of the DEMON® tank, treating

Particle tracking

ammonium-rich reject water of anaerobic digestion via the deammonification process and

Coulter counter

rich in anammox biomass (SL), and ii) the mainstream biological stage, that has been enriched

qPCR

with anammox biomass for more than two years (B). In both of these plants hydro-cyclones

Activity measurements

are installed for density-fractioning of the sludge into a low- and a high-density fraction,

Heme protein

thus leading to a characteristic anammox distribution in the investigated sample set. All

Granule volume estimation

investigated methods could statistically discriminate the SL samples. Heme quantification and qPCR were also able to correctly classify the B-samples and both methods showed a Pearson's correlation coefficient of 0.81. An asset of the PT and CC method is the additional qualitative characterization of granule size distribution that can help to better understand and optimise general process operation (cyclone operation duration and construction characteristics). In combination these two methods were able to elucidate the relationship of gross granule volume and actual biomass, excluding the dead volume of inner cavities and exopolymers. We found a linear sphere-equivalent-radius correction factor (3.96 ± 0.15) for investigated anammox granules, that can be used for the fast and reliable PT technique to avoid biomass overestimation. We also recommend routine HQ and PT analysis as ideal monitoring strategy for anammox abundance in wastewater facilities with the HQ technique entailing the further advantage of being also suited for non-granular anammox biomass. © 2014 Elsevier Ltd. All rights reserved.

* Corresponding author. Institute of Microbiology, University of Innsbruck, Technikerstraße 25d, 6020, Innsbruck, Austria. E-mail address: [email protected] (P. Sabine Marie). http://dx.doi.org/10.1016/j.watres.2014.10.005 0043-1354/© 2014 Elsevier Ltd. All rights reserved.

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1.

Introduction

The existence of a microbe capable of direct ammonium conversion to dinitrogen gas has already been hypothesized in the 1970s (Broda, 1977), their discovery, however, only took place in the late 1990s (Strous et al., 1999). Unraveling the characteristic physiological and structural properties of these anoxic ammonium oxidizing bacteria (AnAOB) (Kartal et al., 2012; Strous et al., 2002, 1999; van Niftrik and Jetten, 2012) quickly led to their biotechnological implementation. In 2014 approximately 100 wastewater treatment plants (WWTP) have incorporated this energy- and resource-saving nitrogenelimination process and the number of full-scale installations is steadily increasing (Lackner et al., 2014). AnAOB belong to the order of Brocadiales (phylum Planctomycetes) (Jetten et al., 2001) and exhibit unique physiological and morphological features, such as the use of hydrazine (N2H4) as metabolic intermediate, internal cell compartmentalisation, ladderane lipids, absence of peptidoglycan in their cell walls, budding granule reproduction and their characteristic reddish colour, due to high intracellular levels of cytochrome c (Francis et al., 2007; Jetten et al., 2001; Strous, 2011). They are able to perform anaerobic ammonium oxidation with nitrite to form dinitrogen gas and small amounts of nitrate (Strous et al., 1999). The actual aggregate structure is dependent on process conditions, reactor type, inoculum and the encountered granulation phase (formation/replication). Thus different shapes and sizes of anammox aggregates (granules, flocs, film-like structures, microcolonies) can emerge (Vlaeminck et al., 2010). In general these microbial complexes are a consortium of AnAOB, aerobic ammonium oxidizing bacteria (AerAOB) and depending on dissolved oxygen levels also aerobic nitrite oxidizing bacteria (NOB), all embedded in a matrix of extracellular polymeric substances (Vlaeminck et al., 2010). Additionally, in some granular anammox-biomass, Chloroflexilike bacteria were detected that probably help with biodegradation of macromolecules and the reinforcement of the granule structure (Bossier and Verstraete, 1996; Cho et al., 2010). In this study the term AC (¼Anammox Consortium) is used to describe the whole consortium of AnAOB, AerAOB and other potential microbial groups present on/in the granules, whereas AnAOB is strictly used for representatives of the order Brocadiales (e.g. Brocadia sp., Kuenenia sp., etc.). Due to slow growth rates with doublings times of 11e20 days (Jetten et al., 2001), compared to 0.73 days for aerobic nitrifiers (Jetten et al., 2001), long sludge retention times (SRT) are essential for optimal AnAOB biomass generation. Density selecting hydrocyclones can be used to achieve this long SRT and are integrated in the DEMON® process (Wett et al., 2013). Besides the strict control of process parameters (Wett, 2007; Wett et al., 2007), the monitoring of the microbial abundance and population characteristics is a helpful tool to guarantee for stable reactor operation. Fortunately, physiological and structural features of AnAOB biomass allow for the application or development of completely different technical approaches targeting:

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 functional- and phylogenetic genes via qPCR and FISH (Daims et al., 1999; Hu et al., 2009, Neef et al., 1998),  characteristic membrane components (e.g. ladderanes) (Lindsay et al., 2001; Sinninghe Damste et al., 2005),  characteristic molecules like heme, essential for specific enzymes (e.g. hydroxylamine-oxidoreductase (HAO) or hydrazine-oxidoreductase (HZO) (Klotz et al., 2008; Schalk et al., 2000; Shimamura et al., 2007; Tang et al., 2011), or  the granular structure of AC/AnAOB biomass per se ((Chen et al., 2012; Lu et al., 2012; Volcke et al., 2012) and this study). Despite all these possibilities, AnAOB abundance is commonly described only by the two molecular techniques FISH and qPCR (Hu et al., 2009, Lahav et al., 2009; Pathak et al., 2007; Tsushima et al., 2007) and further through batch activity determination, derived from concentration trends of dissolved N-species or N2-production (e.g. Isotope pairing techniques (IPT) (Bettazzi et al., 2010; Carvajal-Arroyo et al., 2013; Dalsgaard and Thamdrup, 2002; Dapena-Mora et al., 2007; Li and Gu, 2011; Risgaard-Petersen et al., 2003). There is still a need for fast and reliable methods to monitor AnAOB population. In this study two different sample matrices, a DEMON® tank treating ammonium-rich sludge liquor (SL) and a mainstream low-rate biological stage (B) were characterised regarding their AC/AnAOB abundance, activity and granule size distribution. Samples were analysed with six methodological approaches, i.e. gravimetric analysis (GA), heme protein quantification (HQ), Coulter counter analysis (CC), quantitative PCR (qPCR), activity measurements (AM) and a novel image analysis approach, called Particle Tracking (PT) (Fig. 1). For a better evaluation of the techniques' reliabilities a sample set with varying AC/AnAOB abundances was obtained through density fractioning of the two sample matrices via hydro-cyclones into an enriched (high-density) and depleteded (low-density) AC/AnAOB sample. Despite its high precision and ability to characterise spatial distribution of microbial groups, FISH analysis was not included in this experimental design as it is better suited for relative abundance estimation than for absolute AC/AnAOB quantification. Some of these above mentioned methods, however have already been optimized for the characterization of AC/AnAOB biomass in samples originating from various WWTP and especially the AM-, qPCR- and PT-technique are currently in use for the monitoring of novel process schemes (Podmirseg et al. (in prep.)). The aim of this study was to establish new and to optimize existing protocols for different sample matrices (i.e. SL and B) and to summarize each technique's specific advantages and drawbacks. A combination of instrumentally challenging but also straightforward methods was used in order to target a broad range of AnAOB characteristics. The goal was to develop a reliable, reproducible and user-friendly technique or combination of techniques that allows for a fast and simple quantification and characterization of AC/AnAOB biomass. In the end good applicability in standard lab-facilities of WWTPs

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Fig. 1 e Experimental design of the method evaluation approach, depicting the six different sample types (SL; SL-OF; SL-UF; B; B-OF; B-UF) and methodological approaches used in this study.

and low costs per analysis was considered as further benefit of a method.

2.

Material & methods

2.1.

Sample origin

Samples were obtained from WWTP AIZ (Tyrol, AT) that is known as a net energy positive plant providing mainstream treatment by a high- and low-rate mainstream process. Sidestream sludge liquor treatment is conducted through the pH-controlled DEMON® technology that was implemented in the year 2004 (Wett, 2007). Since 2011, AC biomass, produced from this sidestream treatment is seeded to the mainstream and further retained and enriched by a hydro-cyclone classifier, selecting for the high density sludge fraction from the waste stream (Wett et al., 2013). In this study, for the evaluation of each technique towards AnAOB quantification and sample characterization, six different sample types were chosen, obtained from two different origins, the DEMON®tank (SL) and the biological low-rate stage (B). At both sample sources hydro-cyclones are installed that allowed for a further density-fractioning of the respective sample into the lowdensity overflow (OF) and the high-density underflow (UF). For a detailed specification of each sample type and general information on the Strass WWTP refer to Fig. 1 and Wett et al. (2013).

2.2.

Sampling and gravimetric analysis

Samples were collected in three replicates (1 L each) and special care for partitioning of samples was taken as AC granules settle very fast in insufficiently mixed suspensions due to their compactness and size. Therefore a mixer with Rushton impeller in a baffled vessel was used to guarantee fully stirred liquors and hence representative partition. Aliquots for activity measurement (AM), particle tracking (PT), gravimetric analysis (GA) and heme quantification (HQ) were used right away and samples used for Coulter counter analysis (CC), or qPCR were stored at 4  C and 20  C, respectively.

Gravimetric analyses were performed with membrane filtered-samples, i.e suspended solids (SS) (Machery-Nagel GF5 e diameter 25 mm) that were dried at 105  C overnight (O/ N). The organic fraction of the SS (i.e. volatile suspended solids; VSS) was calculated after processing of dried samples in a muffle furnace (550  C, 3 h). Granular suspended solids (granular-SS) were collected and gently washed on an analysis sieve (mesh size 0.125 mm), and dried at 105  C O/N.

2.3.

Activity measurement

The activity of the anaerobic ammonium oxidizing population (AnAOB) was characterised as the measured conversion rates of NH4, NO2 and NO3 in mixed liquor samples. For this, initial ammonium was adjusted to 100 mg N L1 with NH4HCO3 and nitrite to 70 mg N L1 with NaNO2, respectively. Phosphate buffer was added in non-inhibitory concentration (10 mM) and the pH adjusted to 7.2. Representative 100 mL-aliquots of samples were transferred to 100 mL-Erlenmeyer flasks with screw caps. These caps were equipped with a silicon rubber septum, perforated by a 2 mm immerged sampling-tube and a hypodermic needle as gas exit. The flasks were purged for 3 min with nitrogen gas via the sampling tube to remove oxygen, and then incubated at 30  C and slowly mixed with a magnetic stirrer. Samples (0.5 mL) were retrieved in 30 min intervals over a period of 4 h and immediately centrifuged in a pre-cooled rotor at 5.000 rfc for 3 min. The supernatant was then used for further analysis. Nitrite and nitrate were quantified by ion pair RP-HPLC (column: standard-C18, 5 mm, 50  4 mm; eluent: n-octylamine (1 g L1) in 10% (v/v) methanol, pH 4 adjusted with phosphoric acid; flow-rate: 1.2 mL min1; detection: UV 210 nm; modified protocol based on Doblander and Lackner (1996)). Ammonium was analyzed colorimetrically by the improved Berthelot reaction according to Rhine et al. (1998). AnAOB activity was characterised as nitrogen turnover of the extracellularly available ions of NH4, NO2 and NO3 (sum of all three species). Activity rates were calculated from time intervals not characterized by substrate scarceness (i.e. linear concentration trends). Commercially available cuvette tests for NH4-, NO2- and NO3eN are equally suited for the monitoring of AC/AnAOB activity in wastewater treatment facilities.

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2.4.

Particle tracking

Particle tracking was conducted through image analysis of the different sample types to determine the number of AC granules per liter, the total sectional area of granules per liter and to capture the distribution of granule size fractions. This novel technique is based on the analysis of two-dimensional image information of scanned samples, taking into account the density of the particles (gray scale - granules are generally darker than flocs), and reddish colour of AnAOB biomass. It is a fast and cheap method that requires no other equipment than a conventional flatbed scanner. Due to changes in floc morphology samples containing activated sludge should be scanned within 2 days after sampling and not be frozen before analysis. The samples were diluted accordingly (from 1:6 to 1:120) with distilled water in a total volume of 15 mL and transferred into standardized plastic Petri dishes. Determination of the ideal dilution factor for each sample is crucial to avoid superimposition of granules and thus overestimation of granule size or underestimation of granule abundance. Petri dishes were scanned at 600 dpi with a flatbed scanner (Plustek OpticPro 640, Germany) using a uniform white background. The open source software Fiji (www.fiji.sc) was chosen for image analysis. A specific protocol was elaborated for the AC granules, allowing for a distinct separation of backgroundand granule area in the images. For each analysed sample the number of granules and each specific granule-area were obtained. A detailed explanation of the performed steps can be found in the Supplementary material.

2.5.

Heme analysis

Prior to this study several protocols for heme quantification in mostly clinical samples (Sinclair et al., 2001) were tested and evaluated for use with AnAOB. These tests included different sample homogenisation approaches, either through ultrasonification, pestling or disintegration with a bead mill. Extraction of heme was tested with various ratios of acetic acid and sodium hydroxide at varying temperatures and incubation times. Hot alkaline extraction was in the end best suited regarding extraction yield and reproducibility. Spectrophotometric measurement of the reduced hemochrome was performed at 550 nm. The procedure is based on the method for heme quantification in animal tissue described by (Sinclair et al., 2001) which was optimized for heme extraction from AnAOB by using increased concentrations of NaOH of up to 2 M at 100  C. Calibration of the analysis was performed with the 1-heme cytochrome c from horse heart (Sigma C2506; M ¼ 12.384 g mol1), possessing an approximate molar extinction coefficient of ε550 ¼ 15.000 AU M1 cm1.

2.6.

Coulter counter analysis

To define the abundance, total volume and particle size distribution of AC granules a Coulter counter approach was chosen. The principle of this method is the measurement of drops in electrical conductivity, occurring whenever a particle passes a defined pore that separates two electrodes immersed in an isotonic buffer solution. This alteration in electrical conductivity is proportional to the electrically isolating

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granule volume and allows for a detailed sample characterization (Coulter, 1956). Here the Multisizer II (Coulter Beckman Inc.) was used with a 1 mm aperture. For all performed measurements, a specific isotonic buffer solution [0.73 g L1 KH2PO4, 0.72 g L1 NaH2PO4 * H2O, 0.79 g L1 NaCl, in distilled water adjusted to pH 7.2 with 10 M NaOH] was used. Calibration of the 256 allocable measuring channels was performed with Dowex 50 W (Sigma Aldrich) ion-exchange particles, made of styrol-divinyl-benzol. These spherically shaped particles with an average diameter of 320 mm exhibit normal size distribution and were thus ideally suited for this purpose. They were suspended in isotonic solution and visualized under a stereo microscope (Leica MSV 266). Then, the normal size distribution and average particle size were determined through image analysis [Fiji software; for details on the protocol see Appendix (excluding the colour channel subtraction step)]. These data allowed for the determination of the modal particle radius that could then be applied for the size definition of the 256 measuring channels. The instrument software automatically extrapolated size ranges of unknown measurement channels. To avoid blocking of the aperture and to reduce the probability of coincidental measurements, samples were prepared as follows: Samples were first sieved through a 1 mm meshsize sieve, collecting the flow-through and subsequently the latter was liberated from particles <125 mm by rinsing the granules over a 0.125 mm sieve at a gentle water jet. This way also excess, non-granular sludge from the DEMON® tank could be eliminated. After this the AC granules were resuspended into an isotonic buffer solution and washed three times (i.e. suspended in the solution, sedimented (ca. 3 min), supernatant discarded). Finally, the concentrated granule sample was resuspended in the desired volume (depending on the neccessary dilution) and measured with the Coulter counter.

2.7.

DNA extraction and quantitative real-time PCR

To determine the abundance of 16S rRNA gene copies of the AnAOB population quantitative PCR was performed. For this, DNA extraction was conducted using a modified DNeasy® Blood & Tissue protocol (QIAGEN; Handbook 07/2006), according to the “Pretreatment for Gram-Positive Bacteria” work-flow. However, an initial physical break-up was added to the protocol. For details on the exact extraction procedure refer to the supplementary information. Real-time PCR was performed with the 1X Sensimix™ SYBR® Hi-rox (Bioline, USA) based on the DNA-intercalating dye SYBR Green I. The Rotorgene 6000 Real Time Thermal Cycler (Corbett Research, Sydney, Australia) was used in combination with the RotorGene Series Software 1.7. Standard construction was performed from an enriched anammox-granule sample with endpoint PCR and the primer set Pla46f and Amx667r (van der Star et al., 2007). Freshly prepared, ten-fold dilutions ranging from 106 to 102 and 101.2 gene copies were used for standard curve construction. Quantitative PCR was performed in 20 mL assays with each reaction mix containing 1X Sensimix™ SYBR® Hi-rox (Bioline, USA), 250 nM of each primer, 0.4 mL BSA 2% (w/v), distilled water (RNase/DNase free, Gibco™, UK) and 2 mL of either 1:10 diluted DNA-extract, or standard DNA. Thermocycling was conducted in technical duplicates as

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3.72 0.24 2.22 0.24 10.9 0.52 0.01 0.01 0.01 0.01 ¡0.01 0.02 ¡22.7 0.77 ¡14.6 1.15 54.7 1.93 ¡4.11 0.31 ¡3.78 0.55 ¡7.22 1.70 ¡17.2 4.27 ¡10.8 1.39 ¡71.3 2.95 3.65 2.53 2.57 2.64 4.00 1.18 ¡36.1 4.51 ¡23.1 2.20 ¡115 2.33 ¡0.45 2.83 ¡1.20 2.43 ¡3.23 2.77 9.32Eþ11 7.50Eþ11 7.01Eþ11 2.48Eþ11 3.72Eþ12 1.19Eþ12 4.00Eþ09 1.27Eþ08 2.70Eþ09 4.03Eþ08 2.88Eþ10 4.45Eþ10 n.a.

n.a.

164 12.2 86.4 7.03 1143 8.68 12.3 0.33 11.6 0.51 25.1 2.14 40 0.94 35.8 1.61 113 3.00 n.a.

2.98 105 3.3 104 1.61 105 8.0 103 3.96 106 4.9 105 6.0 103 2.0 103 1.4 104 2.0 103 7.6 104 1.9 104 B-UF

B-OF

B

SL-UF

SL-OF

In this work the applicability and reliability of the selected methods should be exemplified by the six tested sample types. Results of each method and additional characterization data (e.g. SS, VSS, granule abundance) are summarized in Table 1. The structure of sample matrices are shown in Fig. 2 (upper row). It could be shown, that AC granules from Strass (A) exhibit a clear reddish colour and a very heterogenous structure. Cryotome sections revealed that a great extent is occupied by interstitial cavities (data not shown). Through the hydrocyclone enrichment of AC biomass into the underflow (UF), the following fundamental AC/AnAOB abundance can be hypothesized in the sample types: SL-UF > SL > SL-OF > BUF > B > B-OF. All six methods combined were able to discriminate each sample type regarding sample origin and hydro-cyclone fraction as shown by the position and nonoverlapping of convex hull areas in the NM-MDS (Fig. 3). NM-MDS showed R2 values of 0.99 (coordinate 1) and 0.00007 (coordinate 2), respectively and rendered a low Stressevalue of <0.02, indicating an accurate representation of original data. SL-sample matrix had less variation between replicate results,

3.26 105 3.6 104 1.84 105 9.0 103 4.25 106 6.91 105 9.0 103 5.0 102 4.7 104 4.0 103 1.13 105 2.2 104

General results and comparison of the six methods

280 33.2 129 11.2 4965 997 3.46 0.66 4.21 0.82 50.1 16.4

3.1.

283 35.6 131 11.3 4985 984 3.62 0.55 5.97 0.94 52.3 16.6

Results and discussion

0.79 0.12 0.44 0.00 4.69 0.19 0.07 0.01 0.07 0.01 0.79 0.10

3.

2.32 0.07 1.77 0.05 6.59 0.32 3.05 0.44 3.03 0.07 4.53 0.06

All statistical analyses were performed with the PAST software (Version 2.17) (Hammer et al., 2001). For the visualization of sample type differentiation, non-metric multidimensional scaling (NM-MDS) was calculated including all the investigated methods, using the BrayeCurtis distance index on lognormalized data. The following parameters were included for this analysis: GA data, PT total sample sectional area, PT sectional area for particles >0.125 mm and <1 mm diameter, HQ-, AM-, qPCR- and CC-data (for CC-data SL, SL-OF, SL-UF only). Correlation between the different methodological approaches was performed by MANTEL permutation analyses (Mantel, 1967; Mantel and Valand, 1970) of distance matrices. Matrices (BrayeCurtis distance) for each method-approach were constructed for either all sample types, SL-samples or B-samples only. For each investigated method the coefficient of variation (CV%), i.e. the quotient of standard deviation (SD) and the mean value (xmean) was calculated and is given for each sample type as a general index of reproducibility and reliability. To compare the suitability for either the B- or SL-matrix within a method, CV-values were calculated from raw data, mean values generated for each sample matrix, and compared. Intervariable (different methods) comparison of CV (%) was performed on z-transformed data ((x1-xmean)/SD) and rated through ranking from the lowest to the highest result.

2.95 0.09 2.19 0.06 8.95 0.51 4.25 0.61 4.20 0.09 6.71 0.11

Statistical analysis

SL

2.8.

SS VSS GA PTsieved CC HQ qPCR PTtot sectional PTsieved PTtot AM NH4 NO2 NO3 [g L1] [g L-1] granular-SS sectional area [particles L1] [particles L1] [mL L1] [mAU L1] [gene area [mg N [mg NH4eN [mg NO2eN [mg NO3eN [g L1]* copies L1] L1*h-1] L1 h1] L1 h1] L1 h1] [mm2 L1] [mm2 L1] sieved

follows: initial 95  C for 10 min, 40 cycles of 20 s at 95  C, 20 s at 57  C, and 20 s at 72  C. To check for product specificity and potential primer dimer formation runs were completed with a melt-analysis starting from 60  C to 99  C with 0.25  C increments and a transition rate of 5s. The R2 value of the standard curve was >0.999.

Table 1 e Sample characterisation through TSS, VSS, granular-SS (particles >0.125 mm diameter), PTtot sectional area (total sample), PTsieved sectional area (particles > 0.125 mm and <1 mm diameter), PTtot (particle abundance of total sample), PTsieved (abundance of particles >0.125 mm and <1 mm diameter), CC (AC granule volume of sieved sample >0.125 mm and < 1 mm diameter), HQ, qPCR, AM (total N-turnover), NH4eN-, NO2eN-, and NO3eN-production rate; values are given as mean (bold) and standard deviation; n ¼ 3 (for CC n ¼ 9 (3 technical replicates)), n.a. ¼ not analysed.

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Fig. 2 e Upper row: AC granules submerged in sludge liquor (SL) (left) and in B-stage activated sludge matrix (right); Lower row: image of B-UF sample (left) and highlighted (red) AC granules (middle) of the same sample after PT analysis; image of SL-UF sample used for PT analysis (right). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

explained through more concise areas of convex hulls. The ordination indicates a weaker discrimination between B- and B-OF-samples as can be seen by the superimposition of convex hull areas along the x-axis (R2 ¼ 0.99).

Fig. 3 e Non-metric multidimensional scaling ordination of AC/AnAOB biomass quantification for all six sample types, SL, SL-OF, SL-UF, B, B-OF and B-UF. All six quantitative methods, GA, PT, HQ, AM, qPCR and CC were included for the calculation. Samples from the same group are connected by lines defining convex hull surface area of NM-MDS scores.

Looking at the sample discrimination in more detail, differentiation of the three SL-sample types was possible with all tested methods, leading to the conclusion that a high overall AC abundance and the sample matrix of the DEMON® tank allow for reproducible results with the tested methods. For the B samples, especially the differentiation of B and B-OF was not distinct for the GA, PT and AM approach. As will be discussed further on in chapter 3.5., AM in the B-matrix is often hampered by concurrent metabolism of several microbial groups. Best results for all samples (B- and SL-matrix) were obtained with the HQ- and qPCR method that were both able to group samples according to the hypothesized AC/AnAOB abundance (see Table 2). The CC method was performed and evaluated only for SL samples, as activated sludge separation from B-stage granules was very complex and led to a bias in the volume determination. For an estimation of the precision of each method, the coefficients of variation were calculated (CV [%] of raw data) and are depicted in Table S1 (Supplementary material). Additionally within-method comparison was performed for average CV-values of both matrices and showed that the SLmatrix generally led to a higher precision of data but HQ [mAU L1] and PT abundance (PTtot [particles L1]) determination were equally suited for both sample matrices (see Table S2 (Supplementary material)). The inter-method comparison showed that for the B-matrix AM [mg N L1*h1], PTtot [particles L1] and GA [g L1] rendered the most precise data, while for the SL-matrix SS [g L1], NO2-determination [mg N L1*h1] and qPCR [gene copies L1] were most reproducible. All comparisons are listed in Table S2 (Supplementary material). Nevertheless it needs to be mentioned that this

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Table 2 e Summary of the sample distinction by six different approaches and compared to expected results for the six investigated sample types; discrepancies highlighted in gray; n.a. ¼ not analysed. Labeling SL SL-OF SL-UF B B-OF B-UF AnAOB specificity

Expected AC/AnAOB abundancea

GA

PT

HQ

AM

qPCR

CC

2 3 1 5 6 4 Accuracy to measure abundance of AnAOB only

2 3 1 5 5 3 6

2 3 1 6 5 4 3

2 3 1 5 6 4 2

2 3 1 6 5 4 5

2 3 1 5 6 4 1

2 3 1 n.a. n.a. n.a. 4

a

Numbers are reflecting the ranked expected abundance with 1 representing the highest and 6, the lowest AC/AnAOB abundance, respectively; numbers in the bottom line are ranked from 1 (best suited method) to 6 (least suited method).

inter-method comparison does not imply correctness of the results but only best reproducibility. The qPCR approach, targeting the 16S rRNA of Planctomycetes is clearly the most accurate technique to measure explicitly AnAOB. If, however the whole AC is to be quantified, also HQ, PT and CC are clearly justified approaches. The PT method exhibits the big advantage of further colour selection and thus more accurate AC identification, compared to the CC method. This is why PT can also be applied to more complex matrices, e.g. AC granules embedded in activated sludge (B). Both methods, PT and CC alike, entail the big advantage of an additional qualitative sample characterization. The least accurate method was the simple gravimetric analysis with three discrepancies in the sample ranking (Table 2). Table 3 summarizes the most important assets and drawbacks of each method and gives amongst others, valuable information on average duration, price and detection limits of each technique, if possible. Correlation results between all methods are depicted in Tables 4e6 , respectively and can be summarized as follows: Highest correlations were achieved for SL-matrix only, with significant Pearson's correlation values >0.71 for all methods. Without taking the qPCR method into account even values >0.96 were obtained. Applying the correlation calculation to all six sample types at once still rendered significant and positively correlated results, however at a lower degree. Here best correlation was achieved for the GA and PT approach (all > 0.94) and lowest values for the correlation with the AM technique (Pearson's correlation coefficient between 0.30 and 0.96). When looking at the results for B-matrix only, correlation amongst methods was significant and high only for GA, PT and HQ (all >0.94; CC not analysed), whereas qPCR correlation with other methods was considerably reduced but positive (between 0.41 and 0.47). No positive correlation could be detected for correlation between AM and other methods. Reasons for this result are further discussed in chapter 3.5. Summarizing, the correlation results should be seen as a guideline on the ability of different methods to group samples in a similar way, again not implying correctness of the results. Nevertheless, HQ and qPCR were the only two techniques that were able to clearly classify sample types according to the expected abundance (Table 2), and these two methods achieved a Pearson's coefficient of 0.81. A high correlation of any other method with either HQ or qPCR could thus signify high reliability of the results.

The following observations could be made with samples of other WWTPs (data not shown) that suggest high applicability of the presented methods to WWTPs other than Strass: i) at the WWTP Tobl (Bruneck, Italy) the DEMON®-reactor is fed with digester filtrate together with exhaust vapour condensate resulting from sludge incineration. Samples from this specific Demon®-tank, contained red and black AC/AnAOB granules. For the analysis these granules were separated into the two colour fractions - this was possible through slightly different densities of particles - and analysed with AM and HQ. Both methods revealed high activity and heme concentration in the red fraction and drastically reduced values in the black fraction. This suggested that HQ, and PT (i.e. colourbased approach) cannot only be used as abundance-, but also as activity parameter. ii) a second WWTP (ARA Glarnerland, Switzerland) was investigated that also operates sidestream deammonification with a Demon®-tank. In addition this plant has two completely separated mainstream biological stages, one of these lines was bioaugmented with AC/ AnAOB biomass and further equipped with a cyclone to increase SRT for AC/AnAOB biomass, while the second line was kept as control line (no AC/AnAOB bioaugmentation from the sidestream). Only few particles could be detected with PT in the control line (less than 0.1%e1% compared to results of Bsamples (this study)), while AC/AnAOB granules were clearly detected in the bioaugmented line. iii) Although heme proteins are ubiquitous in nature (Gumiero et al., 2011), several HQ-results for other WWTP-samples (WWTP Innsbruck (Austria): biological stage and digester sludge and WWTP Zirl (Austria): biological stage and digester sludge) that were not operating a sidestream deammonification process, were below the detection limit. This indicated reduced probability of false positive HQ detection in environmental samples. Apart from the methodological approaches presented here, also ladderane lipids are specific for Planctomycetales. These linearly concatenated cyclobutane rings play a major role in the membrane composition of the so-called anammoxosome (Rattray et al., 2008; Sinninghe Damste et al., 2002), and enable the containment of products such as hydrazine and hydroxylamine during the anammox process (Sinninghe Damste et al., 2005). To our knowledge, these molecules were until now only used for qualitative analyses and not for AC quantification. Sinninghe Damste and co-workers were characterising the membrane composition of Canditatus Brocadia anammoxidans and showed that the compound class originating from specific lipids, amongst others ladderanes,

Table 3 e Summary of the used techniques and their suitability for anammox/AnAOB quantification. Method

Methodological background

Advantages

Drawbacks

Mass determination of granular biomass >0.125 mm diameter;

Fast, cheap little equipment needed;

Not applicable to smallest fraction (<0.125 mm); unspecific; sieve clogging with (slimy) mainstream sludge Redc granule colour necessary for activated sludge-matrix;

PT

Particle number/sectional area determination through image analysis;

HQ

Extraction and colorimetric quantification of hemochrome

Exact granular AC abundance and biomass area/-volumeb; granule size distribution; little equipment needed; Relatively fast; exact; cheap; measures mostly AnAOB; little equipment needed;

AM

Measures activity not Sum of NO2-, NO3-, NH4eN depletion/production- rates; only presence;

Biased through denitrifier community and ammonification (B);

qPCR

16S rRNA gene quantification;

AnAOB specific; if RNA-based also suitable as activity parameter;

Extraction-bias; special equipment needed;

CC

Particle number/-volume determination; granules assigned to size channels;

Exact AC-granule Special equipment abundance and -volumeb; needed; sample granule size distribution; matrix dependent;

None known

Appropiateness Time needed for granular and per samplea non-granular biomass

Non-basic lab-equipment needed

Price per sample (only materials)

Not defined

Only granular

5e10 min

Analytical sieves, drying balance or drying oven

<0.50 V

With conventional flat-bed scanner ca. 50 mm particle diameter;

Only granular

10 min

Flat-bed scanner, image analysis software

<0.50 V

Here the protocol was adjusted that AnAOB-range of both sample matrices was captured; n.d. if rate is low, incubation can be prolonged;

Any

5e10 min

Standard-centrifuge, water bath or heating block, photometer

1e2 V

Any

6h

ca. 50 V (with commercial kits)

1.2 104 gene copies per L;

Any, but adjusted extraction protocol important

8e10 h/3 hd

Ideal granule size 2%e60% of the aperture (here 1000 mm diameter);

only granular

30e45 min

System-devices for commercial kits (photometer) or alternative analytical equipment Molecular lab facilities, PCR-cycler, qPCR-cycler Coulter Counter

ca. 10e15 V

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GA

Detection limit

<1 V

a

Approximate net working time for one sample; depending on the method the total time needed for more samples is generally less than a multiple. The Coulter counter renders the net biomass volume, whereas Particle Tracking the gross volume. c In this case red is the discriminating characteristic, but also other colours or gray-scales (darker, densly packed granules compared to WAS) can be chosen as target and protocols modified accordingly. d First time including standard construction and DNA-extraction, second qPCR run only. b

201

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Table 4 e Correlation of AC/AnAOB quantification results between each tested method for all sample types; Pearson's correlation coefficients shown together with their significance levels, defined as superscript letters: a (p < 0.01), b (p < 0.001) and c (p < 0.0005).

GA PT sieved PT total HQ qPCR AM

GA

PT sieved

PT total

HQ

qPCR

AM

e 0.95c 0.94c 0.71c 0.54b 0.37b

0.95c e 0.99c 0.78c 0.69c 0.49c

0.94c 0.99c e 0.78c 0.68c 0.53c

0.71c 0.78c 0.78c e 0.81c 0.30a

0.54b 0.69c 0.68c 0.81c e 0.38c

0.37b 0.49c 0.53c 0.30a 0.38c e

encompassed up to 15% of the total lipid extract (Sinninghe Damste et al., 2005). Drawback of this methodological approach, if used for AnAOB quantification, is a temperature dependence of ladderane composition and abundance within AnAOB cells that could be demonstrated for Candidatus Brocadia fulgida (Rattray et al., 2010). Furthermore, apart from a rather complicated extraction procedure and possible contamination with humic- and fulvic acids during purification, ladderanes can also be found in non-living organic matter and thus bias results (Li and Gu, 2011). Nevertheless there has been a considerable progress in detection techniques (Hopmans et al., 2006; Li and Gu, 2011) and further experiments including this parameter for AnAOB quantification should be considered.

3.2.

Table 6 e Correlation of AC/AnAOB quantification results between each tested method for B-samples only; Pearson's correlation coefficients shown together with their significance levels, defined as superscript letters: a (p < 0.05), b (p < 0.001) and c (p < 0.0005).

GA PT sieved PT total HQ qPCR AM

PT sieved

PT total

HQ

qPCR

AM

0.99b e 1c 0.96a 0.47a 0.12

0.97b 1c e 0.94a 0.45a 0.06

0.99b 0.96a 0.94a e 0.41a 0.17

0.44a 0.47a 0.45a 0.41a e 0.20

0.15 0.12 0.06 0.17 0.20 e

Hemoproteins are essential components of the AnAOB redox system and therefore a suitable parameter for estimation of AnAOB biomass. It could be demonstrated that up to 20% of total proteins in AnAOB cells are heme proteins (Jetten et al. 2009), clearly discriminating this biomass from other microbes. It is hypothesized that AnAOB biomass colour can be subjected to changes and vary from carmine red to pale red, brownish and even black and that this characteristic is directly correlated with anammox activity (Ali et al., 2013). Thus a monitoring of heme quantity, combined with PT abundance measurement could be used as further WWT routine parameter to estimate AnAOB efficiency and health. A further advantage of heme quantification is that it is not dependent on granular structure of AnAOB biomass.

Gravimetric analysis (GA) 3.4.

Gravimetric analysis was the simplest approach used in this study and allows only for a very rough estimation of granular AC biomass. Nevertheless discrimination of SL-samples was possible and implementation of a first sieving step with a mesh-size of >2 mm could further remove non-AC debris present in the tank and optimize results. Depending on structural characteristics, i.e. the average granule size found in a tank, this approach can be adjusted accordingly.

3.3.

GA e 0.99b 0.97b 0.99b 0.44a 0.15

Heme quantification (HQ)

Hemes are prosthetic groups of a variety of proteins in all forms of life but only cells with extremely elevated heme concentration are reddish, like red blood cells and AnAOB.

Table 5 e Correlation of AC/AnAOB quantification results between each tested method for SL-samples only; Pearson's correlation coefficients shown together with their significance levels, defined as superscript letters: a (p < 0.01), b (p < 0.001) and c (p < 0.0005). GA GA PT sieved PT total HQ qPCR AM CC

e 0.99c 0.99c 0.99c 0.75b 0.99c 0.96c

PT sieved PT total 0.99c e 1c 1c 0.73b 0.99c 0.98c

0.99c 1c e 0.99c 0.73b 0.99c 0.98c

HQ 0.99c 1c 0.99c e 0.71b 0.99 0.97c

qPCR AM 0.75b 0.73b 0.73b 0.71b e 0.75b 0.72b

0.99c 0.99c 0.99c 0.99c 0.75b e 0.96c

CC 0.96c 0.98c 0.98c 0.97c 0.72b 0.96c e

Quantitative real-time PCR analysis (qPCR)

Quantification of microbial communities via 16S rRNA gene specific primers is commonly performed in microbial ecology. Quantification of AnAOB biomass bears the great advantage that all representatives of this group belong to the same monophyletic clade of Planctomycetales (Kuenen, 2008; Li and Gu, 2011). Drawbacks of this approach are the bias that can occur during DNA-extraction, PCR amplification and through the utilization of non-specific primer sets. When working with granular biomass, like AC aggregates, the choice of a reliable DNA-extraction protocol with adequate chemical and mechanical cell-lysis, as shown in this study, is of crucial importance to avoid underestimation of AnAOB biomass.

3.5.

Activity measurements (AM)

Measurement of AC/AnAOB activity through the assessment of anaerobic nitrogen turnover - i.e. the sum of the major dissolved nitrogen species NH4eN, NO2eN and NO3eN - is widely used in various studies and is generally not dependent on granular structure of the AC biomass. Here we could demonstrate that this method is ideally suited for AC-rich samples like the sludge liquor treating Demon® tank. More heterogeneous sample matrices such as the AC-enriched Bstage were, however, hard to distinguish. Reasons for this are the presence of the denitrifier population in the activated sludge matrix that bias total nitrogen turnover rate via denitrification processes. The low-density overflow fraction (B-OF) of the hydro-cyclone might further select for this group and

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makes discrimination even more complicated. Another factor that is generally encountered in environmental samples and that has to be taken into account is ammonium release from organic compounds (ammonification), which is further increased at higher temperatures and that might lead to underestimation of actual ammonium depletion rates (Wett et al., 2013).

3.6. Comparison of PT and CC regarding particle size characterization The big advantage of both methods, compared to the other methodological approaches, is that they allow for a qualitative investigation of the granular AC biomass through the size distribution pattern. Each granule is assigned to an exact sectional area (PT) or pre-determined volume category (CC). This size distribution can be of special relevance for plant operators when it comes to evaluate biomass growth or the efficiency of hydro-cyclones, regarding density separation and thus the increase of sludge retention time for AC biomass. Furthermore the granule size is of crucial importance for the determination of community distribution within the granules and the preferential metabolic pathways taking place. Smaller granules are more often populated by NOB, due to the higher fractional aerobic volume, whereas bigger granules are fostering the growth of AnAOB (Volcke et al., 2010; Winkler et al., 2011). This higher concentration of AnAOB leads to low nitrite levels eventually outcompeting NOB ((Nielsen et al., 2005; Volcke et al., 2010; Winkler et al., 2011). Particle tracking has originally been developed for a three year lasting monitoring project, where mainstream bioaugmentation with AC biomass was performed for the first time at full-scale. It was then designed as a fast tool for AC quantification, but in addition the classification of AC granules into size categories allowed for an evaluation of cyclone efficiency and ideal cyclone construction for optimal separation. In combination with the Coulter counter techniques we could elucidate the relationship between visible gross granule volume and actual net biomass. To compare both methods, the calibration of the Coulter counter was performed with the Dowex50W-particles whose size distribution was initially characterised with the PT method. Both methods led to a similar distribution pattern. The PT mode diameter for Dowex-particles was 320 mm and could be assigned to the measurement channel 162 of the Coulter counter. Figure S1 (Supplementary material) depicts the result of this particle size distribution analysis. The comparison of size distributions of the investigated sample types (SL, SL-OF, SL-UF) with either PT or CC led to a low correlation. This at first glance low conformity is due to the following methodological discrepancies. Particle Tracking is generating a two-dimensional image of each granule, neglecting the porous structure of such aggregates, whereas Coulter counter analysis measures only the net biomass of the granule, acting as electrical insulator. To estimate the difference in net- and gross-volume measured by both methods, mode values of sphere equivalent radii for different size fractions were calculated. The size fractions (diameter) were 0.125e0.25 mm, 0.25e0.5 mm and 0.5e1.0 mm. Radius discrepancy between CC and PT

203

exhibited a stable factor of 3.96 ± 0.15 for the whole size range (0.125 mme1.0 mm), thus leading to a biomass overestimation factor of 62.2 ± 7.18 for the PT approach. In other words, if AC granule biomass-volume needs to be estimated from measured PT sectional area, a correction value for the radius should be taken into consideration, in order to avoid severe overestimation. In the case of the here investigated AC granules from WWTP Strass (Austria) only 1.61% of an AC granule, would according to these findings, consist of electrically insolated material, i.e. living cells, whereas most of the volume is made up from exopolysaccharides (EPS), pores and caves. Introducing this correction factor to the PT analysis led to a significant improvement of PT and CC comparability. As shown in Figure 4, size distributions of both methods were now very similar. This conformity is even more obvious when correlating the CC and PT curves of accumulated relative abundances (Spearman's r ¼ 0.94 p < 0.00001). Summarizing an initially purely method-oriented comparison could demonstrate a drastic discrepancy between visible granule size and net microbial biomass, a fact that is even more pronounced considering the high amount of EPS that can be produced by these bacteria, encompassing up to 50% of the autotrophic space (Vlaeminck et al., 2010).

4.

Conclusion

All investigated techniques use different approaches to target AC/AnAOB biomass, but were all suited to correctly discriminate the analysed SL samples. For quantification of AC/AnAOB in both sample matrices, heme quantification and qPCR proved most reliable. The instrumentally more challenging but widely recognized techniques Coulter counter and qPCR could help to confirm results and reliability of more straightforward and user-friendly approaches such as Particle Tracking and heme quantification. Generally, a positive relationship was found amongst all methods, i.e. the sample with the highest AnAOB abundance rendered the highest values for all techniques. In addition, the choice of different technical approaches helped to elucidate structural characteristics of AC/AnAOB biomass, such as the discrepancy between granule volume and actual biomass. Summarizing we recommend heme quantification and Particle Tracking in combination with occasional activity measurements as ideal monitoring strategy for AnAOB abundance and vitality in wastewater facilities.

Acknowledgements We would like to thank the AIZ waterboard for their kind cooperation throughout this study and for allowing us to sample at their WWT facility. Many thanks go to Thorsten Schwerte for valuable information during the establishment of the particle tracking method with the Fiji software. We  Stres for help would further like to show our gratitude to Blaz with the statistical analysis protocol and to Sebastian Waldhuber for support during the PT analysis.

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Appendix A. Supplementary data Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.watres.2014.10.005.

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