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Management of Microbial Communities through Transient Disturbances Enhances the Functional Resilience of Nitrifying GasBiofilters to Future Disturbances Léa Cabrol,*,†,‡,§,∇ Franck Poly,∥ Luc Malhautier,† Thomas Pommier,∥ Catherine Lerondelle,∥ Willy Verstraete,⊥ Anne-Sophie Lepeuple,‡ Jean-Louis Fanlo,† and Xavier Le Roux∥ †

Laboratoire Génie de l’Environnement Industriel, Ecole des Mines d’Alès, Rue Jules Renard, 30100 Alès, France Veolia Environnement Recherche et Innovation, Chemin de la Digue, BP76, 78600, Maisons Laffitte, France § Pontificia Universidad Católica de Valparaíso, Escuela de Ingeniería Bioquímica, Avenida Brasil 2185, Valparaíso, Chile ∥ Laboratoire d’Ecologie Microbienne, Université de Lyon, Université Lyon 1, CNRS, INRA, UMR CNRS 5557, USC INRA 1364, Bâtiment Gregor Mendel, 16, rue Raphael Dubois, 69622, Villeurbanne Cedex, France ⊥ LabMET, Faculty Bio-Science Engineering, Ghent University, Coupure L 653, 9000 Gent, Belgium ‡

S Supporting Information *

ABSTRACT: Microbial communities have a key role for the performance of engineered ecosystems such as waste gas biofilters. Maintaining constant performance despite fluctuating environmental conditions is of prime interest, but it is highly challenging because the mechanisms that drive the response of microbial communities to disturbances still have to be disentangled. Here we demonstrate that the bioprocess performance and stability can be improved and reinforced in the face of disturbances, through a rationally predefined strategy of microbial resource management (MRM). This strategy was experimentally validated in replicated pilot-scale nitrifying gasbiofilters, for the two steps of nitrification. The associated biological mechanisms were unraveled through analysis of functions, abundances and community compositions for the major actors of nitrification in these biofilters, that is, ammonia-oxidizing bacteria (AOB) and Nitrobacter-like nitrite-oxidizers (NOB). Our MRM strategy, based on the application of successive, transient perturbations of increasing intensity, enabled to steer the nitrifier community in a favorable way through the selection of more resistant AOB and NOB sharing functional gene sequences close to those of, respectively, Nitrosomonas eutropha and Nitrobacter hamburgensis that are well adapted to high N load. The induced community shifts resulted in significant enhancement of nitrification resilience capacity following the intense perturbation.

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INTRODUCTION Microbial communities, through the unique metabolic capacities they harbor and their ability to organize and sustain themselves in variable environments, offer numerous applications for environmental biotechnology and provide valuable services to the society, such as the removal of contaminants from wastewater and waste-gases.1,2 Engineered microbial ecosystems represent an efficient and economical alternative to chemical processes, as long as they are maintained under stable regimes. However, full-scale industrial waste streams are usually characterized by inherent erratic variability.2,26 Engineered microbial ecosystems used for air or water purification exhibit hard-to-predict performance under variable regimes,3 which can hinder their full-scale implementation. Maintaining stable performance of engineered microbial ecosystems in spite of a fluctuating environment is of great concern, in particular for bioprocess operators, and much attention is currently paid to unraveling the basic microbial © XXXX American Chemical Society

mechanisms underlying the stability, resistance and resilience properties of microbial ecosystems facing disturbing conditions.4−6 Resistance is defined here as the degree to which an ecosystem characteristic (i.e., overall performance or community structure) remains unchanged in the face of a disturbance, and the resilience as the rate at which the given ecosystem characteristic returns to its original state after being disturbed.7,8 Even though microbial communities are key players of the functional outcome of waste gas and wastewater treatment bioprocesses, managing microbial resources in order to enhance a desired functional property of the ecosystem still remains a challenge,2,3,9 because microbial communities exhibit tremenReceived: January 19, 2015 Revised: November 30, 2015 Accepted: December 9, 2015

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DOI: 10.1021/acs.est.5b02740 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Environmental Science & Technology

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dous diversity,10,11 high metabolic flexibility and functional redundancy,12 and a substantial level of dynamism and interactions.13 Therefore, stronger synergism between microbial ecology and environmental engineering should help disentangling the microbial processes and improve the ecological engineering of bioreactor design and operation.1,2,13,14 More particularly, hypothesis-driven research is needed to go beyond the descriptive approach of the microbial component in engineered ecosystems, and to evaluate operational strategies such as microbial resource management (MRM), which would increase the functional potential and stability of engineered ecosystems in a predictable way.15−17 Through a better understanding of microbial communities’ structure and organization in environmental ecosystems, MRM approaches aim to provide tools for controlling and/or steering the microbial capabilities associated with complex microbial communities, for improved biotechnological applications.9,18 Managing stability and instability issues in bioprocesses gives rise to a puzzling dilemma which has not been solved to date and is an interesting challenge for MRM approaches. On the short-term (e.g., over a few days), disturbancessuch as fluctuations of contaminant loadcommonly have to be avoided since they may affect the microbial component and induce functional breakdown.19,20 This leads bioprocess operators to prescribe environmental conditions as constant as possible. However, on a longer term perspective (e.g., over a few months), these disturbances may select for a more tolerant and/or resistant microbiota, suited to better withstand variable conditions.21,22 In other words, the functional stability of engineered microbial ecosystems could be favorably shaped by setting up ad hoc historical contingencies.23 Therefore, a rational trade-off has to be found between the costs and benefits of previous disturbance history for optimal bioprocess functioning. In gas biofilters, the biological removal of gaseous nitrogen is carried out by nitrification, that is, the sequential oxidation of ammonia to nitrite by ammonia-oxidizing bacteria, AOB, and nitrite to nitrate by nitrite-oxidizing bacteria, NOB. As remediation processes, nitrifying biofilters have an important environmental significance in terms of atmospheric pollution control, given the well-known environmental toxicity of ammonia emissions. Besides, nitrifying populations are considered as a relevant model to test a MRM strategy, due to their functional importance in biogeochemical cycles, their limited diversity and their thorough characterization at physiological and phylogenetical levels,41 paving the way to their possible management. Here we evaluated whether the performance and stability of engineered ecosystems such as nitrifying gas-biofilters can be successfully and rationally enhanced through a MRM strategy. We hypothesized that prescribed mild-disturbances of increasing intensity that do not jeopardize the overall functioning of biofilters could progressively select for resistant/tolerant nitrifier populations, ultimately reinforcing the nitrification activity level in face of future -possibly intense- disturbances. The biological bases of the outcome of this strategy were investigated. Although a fine characterization of microbial succession was beyond the scope of this study, we tested if the observed functional enhancement was due to the acclimation of existing microbial populations, for example, through physiological modifications, or if it was due to the selection of renewed dominant populations with specific functional traits24 providing better robustness to withstand disturbances.

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MATERIALS AND METHODS Conceptual Framework. In this study, we assumed that, for a given microbial engineered ecosystem exposed to a given type of disturbance, our capacity to orient favorably the functional outcome of the ecosystem through microbial resource management is governed by a resistance-to-resilience trade-off (Supporting Information (SI) Figure S1), characteristic of the community stress response. In what we call “proneto-strengthening” ecosystems, the resistance of the microbial component is affected by mild-intensity disturbances (i.e., microbial abundance drops and some species are lost and replaced), while the functional resilience is highly conserved (situation in black in SI Figure S1a). We hypothesize that in these ecosystems, the microbial resource can be managed by applying repeated perturbations of increasing intensity which would induce a progressive selection of resistant/tolerant populations, ultimately leading to a reinforced functional resilience in face of future disturbances, even of high intensity (SI Figure S1b). However, this MRM strategy cannot be applied to “prone-to-collapse” ecosystems, where the microbial activity is too much jeopardized by mild-intensity disturbances and eventually collapses in face of high-intensity disturbances (situations in light and dark gray in SI Figure S1a). Biofilter Setup. As previously described,20 the pilot-scale unit was composed of four biofiltration columns (15 cm internal diameter, 230 cm height), each filled with 26.5 L of packing material consisting of pine bark woodchips screened between 6−16 mm with a void fraction of 58% (SI Figure S2). The ratio of media size/filter diameter was around 1/10 so as to avoid death zones and preferential channeling.81 The biofiltration columns were separated into five successive compartments of 15 cm height each, providing access to gas and biomass sampling ports longitudinally distributed across the biofilter height. The biofilters were inoculated with activated sludge from wastewater treatment plant (WWTP). For moisturizing and neutral pH control purposes, they were periodically sprayed by a mineral nutrient solution without carbon or nitrogen sources25 (15 min every 2 h, corresponding to a leaching flow of 2.5 ± 1 L·d−1). A biofilm developed at the surface of the packing material within the first half of the biofilter columns in less than one month. Operation Parameters. The biofilters were operated for one year at ambient temperature, in a cocurrent downward flow mode. They were fed continuously with a synthetic gaseous stream, qualitatively and quantitatively representative of sludge composting emissions,26 composed of ammonia mainly (21 ± 3 mg·m−3 basal level), as well as six volatile organic compounds (VOCs) at lower concentration (4.6 ± 0.5 mgVOC·m−3 for each VOC): butyraldehyde, acetone, methyl ethyl ketone (MEK), methyl butanoate, methyl pentanoate, and dimethyl disulfide (DMDS). The gas generation system allowed to maintain stable and reproducible concentrations during the whole experiment, with relative standard deviations ranging from 10% to 12% of the mean value depending on compounds. The gas flow across the biofilters was set up at 30 L min−1, in order to achieve a superficial gas velocity of 100 m·h−1 and an empty bed retention time (EBRT) of 54 s, which are representative of full-scale biofilter operation.27 The theoretical mean residence time taking into account the media porosity was 31 s. The composition of the influent gas mixture resulted in nitrogen and organic carbon loads of, respectively, 1.2 ± 0.1 g-N·m−3filter·h−1 B


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Environmental Science & Technology (corresponding to an inlet mass flow rate of 747 ± 59 mgN·d−1) and 1.1 ± 0.1 g-C·m−3filter·h−1 during steady states. Perturbation Strategy. A high-intensity shock-load (876− 899 mg.m−3 of ammonia) was applied to the four biofilters around day 230, after two contrasted management strategies: two control biofilters had been maintained at a constant feeding-load during 230 days, whereas two perturbed biofilters had been exposed to five previous temporary shock loads of increasing intensity (from 2 to 43 times the standard concentration level, as indicated in SI Table S1). During a shock, the inlet gas concentration was increased, without changing the flow rate or the gas composition. A shock was applied only when all biofilters experienced steady state. Each shock lasted for 6 days, after which the standard concentrations were restored. The magnitude of the shock loads was chosen to mimic possible perturbations observed at full-scale composting facilities.26 Especially, the high intensity shock is characteristic of the desorption of volatile compounds which can seriously impair biofilter performance and typically occurs (i) during start-up of the fermentation phase, upon process recovery after accidental interruption, or (ii) during the prefermentation phase, after a sudden change of composting-substrate toward high nitrogen-content compounds.26 Due to the length of the experiment and the complexity of the pilot-scale implementation and operation, the number of replicates was limited to two for each treatment. The evaluation of the MRM strategy presented in this paper focused on nitrification activity and nitrifying community, since ammonia was the major component of the influent mixture (5times more concentrated than the VOCs, in order to mimic the typical waste-gas composition observed at full-scale composting facilities26). Analytical Methods. Gas-sampling ports were located at 0, 30, 60, 90, 120, and 150 cm from the gas inlet. Ammonia concentration was quantified daily online at each gas-sampling port in all biofilters, by a nondispersive infrared sensor (MIR 9000; Environnement S.A., Poissy, France) equipped with a heated transfer line. Based on ammonia concentration (C) at different sampling levels h, the functional performance was defined as ammonia removal efficiency (RE), calculated as follows for the biofilter height h (eq 1). RE h =

C inlet − C h C inlet

by vigorous shaking in physiological buffer, and DNA was extracted using a commercial extraction kit (FastDNA SPIN Kit for Soil; MP Biomedicals, Irvine, CA). Abundance of Nitrifiers. The abundances of four bacterial groups potentially involved in nitrification were quantified by quantitative PCR for two sampling heights (15 and 75 cm from the gas inlet), at 13 sampling times along the study (i.e., on the day before each shock, on the last day of each shock, and 100 days after the last shock) in the four biofilters. These two sampling heights were selected because nitrate concentration measured after suspension of the biofilm attached to the carrier at different biofilter heights was maximal within the first half of the column and then decreased to insignificant levels (data not shown). Ammonia oxidizing bacteria (AOB) were quantified by targeting a sequence of their amoA gene coding for a subunit of the ammonia monooxygenase, using the primers amoA1F and amoA2R.29 The final reaction volume was 20 μL, with 10 μL of Master Mix SsoFast EvaGreen Supermix (Biorad, Hercules, CA), 0.4 μM of each primer and 5−15 ng of template DNA. Triplicate samples were run on a thermocycler Realplex 2 (Eppendorf, Hamburg, Germany) as follows: 30 s at 95 °C, and 40 cycles at 95 °C for 5 s and 57 °C for 10 s. A synthetic amoA fragment from Nitrosomonas europaea (Eurofins MWG Operon, Ebersberg, Germany) cloned in pCR2.1 plasmid (Invitrogen, Carlsbad, CA) was used as standard. The qPCR specificity was verified by the melting curve, and the qPCR efficiency was higher than 90% for all samples. Ammonia oxidizing archaea (AOA) were quantified by targeting a sequence of their amoA gene, using the primers amoA19F and CrenamoA616r48x,30,31 with the same protocol as above. Linearized fosmid 54d9 containing the AOA-amoA gene was used as standard.32 AOA quantification was validated on AOA-containing DNA samples from a nitrogen-removing reactor33 and from drinking water distribution systems.34 Nitrobacter-like nitrite oxidizing bacteria (NOB) were quantified by targeting a sequence of the nxrA gene coding for the catalytic subunit of the nitrite oxidoreductase, using the primers F1norA35 and R2norA,36 according to a previously described protocol.37 The standard curve ranged from 2 × 101 to 2 × 108 copies/μL of nxrA gene from Nitrobacter hamburgensis X14 SC used as standard. Nitrospira-like nitrite oxidizing bacteria (NOB) were quantified by targeting a sequence of Nitrospira 16S rDNA rather than functional gene, using the primers Nspra675f and Nspra746r,38 according to a previously described protocol.37 Composition of Nitrifying Communities. Given the low to insignificant abundances of AOA and Nitrospira communities (see Results section), the compositions of AOB and Nitrobacter communities at the layer of maximal nitrification activity (75 cm from the gas inlet) were determined by 454-pyrosequencing analysis of bacterial amoA and Nitrobacter nxrA sequences, with the Roche GS-Junior System (Biofidal/platform DTAMB, Villeurbanne, France), using the same primers as for the qPCR assays, at two sampling times in control and perturbed biofilters: once at the beginning of the experiment (days 42 and 47, respectively) and once at the end of the experiment before the last shock (days 173 and 224, respectively). AmoA and nxrA sequences were analyzed using the pipeline Fungene,39 with the exception that HMM model was not used to align nxrA sequences. Putative functional units (PFUs) were defined as clusters of sequences distant of maximum 0.05 substitutions/ nucleotides. Phylogenetic consensus trees of the PFUs for each

(1)

The pH and the flow-rate of the biofilters leachate were measured daily at the liquid outlet. Ammonia oxidation (AO) and nitrite oxidation (NO) activities were determined weekly by monitoring nitrogenous compound concentrations in leachate samples from the four biofilters. Concentrations of ammonium NH4+ (salicylate method), nitrite NO2− (diazotization method) and nitrate NO3− (chromotropic acid method) were quantified using colorimetric reagent kits (AmVer high range TNT Ammonia Nitrogen Reagent Set, NitriVer3 low range TNT Nitrite reagent set, and NitraVerX TNT Nitrate reagent set, Hach Lange, Marne-La-Vallée, France) and colorimeter (DR/890, Hach Lange France, Marne-La-Vallée, France). Microbial Community Analysis. Biomass-sampling ports were located at 15, 45, 75, 105, and 135 cm from the gas inlet. DNA was recovered from the biofilm which developed on the packing material, according to a previously optimized protocol.28 Briefly, cells were dislodged from the woodchips C


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Figure 1. Temporal dynamics of biofilter functioning and bacterial abundances in (left) biofilters exposed to disturbances of increasing intensity and (right) control biofilters exposed only to the last disturbance. (a) Inlet mass flow-rate of ammonia in the gas feed; (b) Outlet mass flow-rate of nitrite and nitrate in the leachate; (c) AOB abundance and (d) NOB abundance in the middle layer of biofilters. Values are averaged among duplicate biofilters and error bars represent standard deviations. AOB and NOB abundances were obtained by qPCR. The beginning and end of the disturbances are represented by vertical dotted lines.

time is normalized by shock intensity to better compare shocks of different intensities; and tr* is divided by (tr*)max and subtracted from 1 in order to scale the resilience index between 0 (no resilience capacity) and 1 (excellent resilience capacity). An example of computation of this resilience index is given in SI Figure S3. The biological resistance index was an indicator of the level of microbial survival upon stress. It was computed for both AOB and Nitrobacter as the ratio of bacterial abundance at the end of the shock compared to the preshock abundance (the abundances being estimated from qPCR results for each population) in a given biofilter layer, according to eq 3.

gene were constructed according maximum likelihood using RAxML.40 The trees were built in relation to 75 and 22 reference amoA and nxrA sequences, respectively, obtained from GenBank, in order to determine the affiliation of the sequences retrieved from the biofilters (Figure 3). Clusters A1−A3 and B1−B6 (Figure 3) were defined as the major sequence groups, containing always more than 8% of total sequences and/or featuring abundance patterns significantly discriminant between control and perturbed biofilters. Computation of Resistance and Resilience Indexes. The biofilter response to perturbations was evaluated in terms of functional resilience and biological resistance. The functional resilience index was an indicator of the rate of return to preshock activity.20 It was computed during transient states for both ammonia-oxidation (AO) and nitrite-oxidation (NO) activities according to eq 2.

resistance index =

population abundanceend of shock population abundance pre‐shock

(3)

The resistance index ranges from 0 (complete abundance loss) to 1 (abundance maintained).

tr* resilience index = 1 − , with = tr* = tr /NH3 (tr*)max

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RESULTS Experimental Validation of the Conceptual Framework. The effect of a high intensity shock load was compared in the pilot-scale nitrifying biofilters previously exposed to different MRM strategies. Before the high-intensity shock, while ammonia was fed at 21 ± 3 mg·m−3, control and previously disturbed biofilters reached similar levels of RENH3 (SI Figure S4) and nitrification activity (Figure 1). The complete elimination of ammonia from the gas phase (i.e., RE 100%) occurred within the first biofilter layers (between 0 and 90 cm from the gas inlet) in all reactors. Nitrogen was recovered in the leachate mainly in the form of ammonium-N and nitrate-N, which represented, respectively, 44 ± 7% and 13 ± 3% of the mass flow of ammonia-N removed from the gas stream

(2)

For AO activity, tr is the recovery time required to reach the maximum of the nitrite concentration at the biofilter outlet after a given shock. For NO activity, tr is the recovery time required to recover high and stable nitrate concentration at the biofilter outlet after a given shock. In both cases, tr is expressed in days, since the beginning of each shock. tr* is the recovery time normalized by shock intensity, that is, divided by the mean ammonia concentration at the inlet during the given shock (NH3). (tr*)max represents the maximal value of the measured normalized recovery time, among the six perturbations under study. Overall, eq 2 is a modified version of the index proposed by Cabrol et al.20 with the following modifications: the recovery D


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Figure 2. (a) Relation between the resilience of ammonia-oxidation (AO) activity and the resistance of ammonia oxidizing bacteria (AOB) abundance, for perturbations P1−P6 (in increasingly perturbed biofilters) and P6C (in control biofilters). (b) Same representation for nitriteoxidation (NO) activity and Nitrobacter abundance. Resilience and resistance index were computed according to eqs 2 and (3), respectively. The initial state is indicated by t0 and the symbol size is proportional to the inlet ammonia concentration applied. The dotted arrow represents the biofilter performance without microbial resource management (MRM), and the continuous arrow shows how the performance tipped over with MRM. (c) Percentage of AOB belonging to clusters A1 and A2+A3 in control and increasingly perturbed biofilters, at the beginning and at the end of the experiment. (d) Percentage of Nitrobacter belonging to clusters B1+B2+B3 and B4+B5+B6 in control and increasingly perturbed biofilters, at the beginning and at the end of the experiment. AOB and Nitrobacter sequence abundances were obtained from pyrosequencing results, average and standard deviations were calculated among duplicated biofilters. Clusters are presented in Figure 3.

level of nitrite (Figure 1). As shown in Figure 1 and SI Figure S4, this functional behavior was highly reproducible between replicated biofilters. Structural Effect of a Transient Intense Disturbance on Nitrifier Abundances. In the biofilters, the first step of nitrification was driven by AOB, since AOA were not detected, whatever the biofilter, sampling date and biofilter layer considered. The key players of the second step of nitrification were Nitrobacter, since Nitrospira abundance was always low to undetectable (SI Figure S5). In all biofilters, the highest AOB and Nitrobacter abundances were found in the middle layer of the filter (75 cm from the gas inlet) (SI Figure S6). AOB and Nitrobacter were, respectively, 36 and 197 times less abundant in the inlet layer (15 cm from the gas inlet), probably because of the higher (potentially toxic) ammonia concentrations. Before the intense disturbance, control and previously perturbed biofilters contained similar abundances of AOB (7−9 × 106 amoA copies·gDW−1) and Nitrobacter (7−9 × 107 nxrA copies·gDW−1) in the middle layer of the filters. In control biofilters, AOB and Nitrobacter were severely affected by the intense disturbance. Their abundances dropped by a factor of 240 for AOB (decreasing down to 6.1 × 103 ± 4.0 × 103 amoA copies·gDW−1) and 8360 for Nitrobacter (decreasing down to 6.5 × 103 ± 3.4 × 103 nxrA copies·gDW−1) 6 days after the shock. One hundred days after the intense perturbation, the AOB abundance had recovered its preshock level at 75 cm from the gas inlet, while the Nitrobacter abundance only reached 1.2% of its preshock level, which could explain the lack of nitrification recovery at long-term in control biofilters. In contrast, in previously disturbed biofilters, AOB and Nitrobacter abundan-

(average and standard deviation calculated during steady states in absence of perturbation). There was no accumulation of nitrite during steady states. The nonclosure of the N balance has been already reported in similar gas biofilters treating ammonia78−80 and can be explained by biomass incorporation for cell growth, denitrification in anaerobic or anoxic zones of the biofilm, adsorption on the organic packing material. Functional Effect of a Transient Intense Disturbance on Nitrification Activity. In response to the high-intensity shock (876−899 mg·m−3 of ammonia), nitrification activity (Figure 1) and RENH3 (SI Figure S4) immediately dropped close to zero in both types of biofilters, while pH sharply increased (up to 9.7 ± 0.1) and ammonium concentration peaked (up to 2700 ± 500 mg·L−1) (SI Figure S4). This effect was transitory and the biofilters were resilient. Once the biofilters had recovered a stable function, the same longitudinal stratification of RENH3 was observed, with complete ammonia elimination occurring between 0 and 90 cm from the gas inlet in all reactors. However, the time necessary to recover complete and stable RENH3 at the gas outlet (after 150 cm of filter) was considerably longer for control (48 ± 1 days) than for previously perturbed biofilters (24 ± 5 days). This positive effect of the MRM strategy was even stronger when considering the resilience of nitrification activity. In control biofilters, nitrate production only partially recovered 117 days after the shock, at 44% of its preshock level, with still high levels of nitrite-intermediate (28−74 mg·L−1) indicating incomplete nitrification. In contrast, in previously perturbed biofilters, nitrate production had fully recovered 41 days after the shock, and stabilized at 73% of its preshock level, with undetectable E


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Figure 3. Phylogenetic consensus trees of (a) bacterial amoA sequences and (b) Nitrobacter nxrA sequences (grouped in PFUs at 97% similarity) retrieved at 75 cm from the gas inlet in control and perturbed biofilters at the beginning and at the end of the experiment. The number of PFUs/ number of total sequences per cluster is indicated next to each cluster. The taxonomic affiliation (and number) of GenBank reference sequences included in each cluster are indicated next to the cluster. A1−A3, and B1−B6, refer to the main clusters taken into account for abundance analysis in Figure 2.

perturbations was highly reproducible between replicate biofilters. In parallel, nitrification activity, and in particular the nitriteoxidation (NO) step, was progressively affected by the first shocks, as evidenced by the progressive accumulation of nitrite and drops of nitrate production (Figure 1), consecutively to the increases in pH values (above 8.6−8.9) and ammonium concentrations (above 500−700 mgN‑NH4+·L−1). The resilience capacities of AO and NO progressively decreased with the first three perturbations, during the acclimatization to the perturbation regime. However, afterward, despite the application of increasing intensity perturbations, the AO and NO resilience no longer decreased but rather stabilized (Figure 2). Globally, the resilience capacities of AO and NO activities were less affected than the resistance of nitrifier abundances. Therefore, in response to disturbances, these biofilters exhibited a resistance-to-resilience trajectory characteristic of “prone-tostrenghtening” ecosystems (Figure 2 and SI Figure S1). Without MRM strategy (i.e., by rigorously maintaining constant conditions), the biofilters could not face a high intensity perturbation and their performance collapsed, as observed in control biofilters. However, upon the fourth/fifth perturbation, the MRM strategy enabled to tip over the resistance-toresilience trade-off and pull out the biofilter response toward

ces stayed remarkably stable in response to the same intense disturbance (around 7.0 × 106 ± 0.2 × 106 amoA copies·gDW−1 and 4.4 × 107 ± 1.0 × 107 nxrA copies·gDW−1, respectively) (Figure 1). Therefore, the better resistance capacity of nitrifying populations in previously disturbed biofilters likely accounted for the improved functional resilience of nitrification activity observed in response to an intense perturbation. The Success of the MRM Strategy, And Underlying Mechanisms. Previous Disturbance History Tipped over the Resistance-to-Resilience Trade-Off. The improved resistance of nitrifiers to disturbances appeared progressively in previously disturbed biofilters. AOB abundance was increasingly affected by the first disturbances, immediately dropping by a factor of 2−25 after shocks 1−4 (Figure 1). It was then only marginally affected by the last two−more intense−disturbances. AOB abundance stayed remarkably stable in response to the last, most intense disturbance (Figure 2). A similar pattern was observed for Nitrobacter, their abundance dropping by a factor of 2−10 from shocks 1−5 and being then only marginally affected by the sixth disturbance (Figure 1). Despite the drastic abundance losses induced at short-term (6 days) by successive disturbances, AOB and Nitrobacter abundances always recovered after 2−7 weeks up to similar or higher levels than before the shocks. The dynamics of nitrifier abundances in response to F


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Environmental Science & Technology enhanced resistance of AOB and Nitrobacter abundance (along x-axis) and reinforced resilience capacity of nitrification activity (along y-axis). The dramatic functional and abundance losses observed in response to high-intensity disturbance in control biofilters without MRM strategy could therefore be avoided (Figure 2). Nitrifier Population Shifts Induced by the MRM. The enhanced resistance of AOB and Nitrobacter abundance and the enhanced resilience of nitrification were linked to marked composition shifts within both nitrifier groups, as revealed by pyrosequencing analysis of amoA and nxrA sequences (Figures 2 and 3). In control biofilters, the AOB community exhibited a rather stable composition along time, mainly dominated by sequences from two clusters (A2 and A3) including amoA sequences from Nitrosospira briensis, N. multiformis, and N. tenuis. These two clusters always represented more than 80% of the total amoA sequences in control biofilters. In contrast, sequences from the same (A2 and A3) clusters decreased in previously perturbed biofilters, from 62 ± 7 to 36 ± 1% of all amoA sequences during the whole experiment. Meanwhile, amoA sequences from one cluster (A1), including amoA sequences from Nitrosomonas eutropha, specifically emerged in previously perturbed biofilters (Figure 3). These emerging PFUs increased from 5 ± 3% of the total amoA sequences at day 47 (beginning of the experimental period) to 59 ± 3% at day 224, that is, just before the last shock (Figure 2). In contrast, amoA sequences from this cluster A1 remained at low level during the whole study in control biofilters (0.7 ± 0% to 1.6 ± 0.7% of the total amoA sequences). Similarly, nxrA sequences from three clusters (B1 to B3), including nxrA sequences from Nitrobacter hamburgensis, specifically emerged in perturbed biofilters, increasing from 9 ± 4 to 47 ± 2% of the total nxrA sequences between days 47 and 224 (Figures 2 and 3). In contrast, these PFUs remained at a low level during the whole study in control biofilters (10 ± 3 to 12 ± 1% of the total sequences) (Figure 2). Clusters B4 (including nxrA sequences from Nitrobacter vulgaris) to B6 corresponded to dominant Nitrobacter populations that remain stable in control biofilters (around 36 ± 4% of the total nxrA sequences during the whole experiment). In contrast the same clusters B4−B6 decreased from 43 ± 1% to 14 ± 2% of the total Nitrobacter sequences in perturbed biofilters between the beginning and the end of the study. The successions of AOB and Nitrobacter populations in response to perturbations were highly reproducible between replicated biofilters.

A drawback of choosing this biological model could be that nitrification is a sensitive process which can sometimes be impaired by unreliable performance linked to unpredictable dynamics. Several experiments in chemostats revealed the nonlinear dynamics of AOB and NOB abundances, showing that AOB-NOB interaction can be a fragile mutualism prone to chaotic instability. In particular, subtle differences in initial conditions can result in totally different trajectories,38 and small instabilities in the AOB guild can lead to irregular NO2− supply for NOB, resulting in amplified destabilization of NOB with huge and unpredictable abundance variability, eventually leading to dramatic process failure.42 However, during the whole experiment, we observed a remarkable reproducibility of the nitrifier dynamics in the replicated biofilters in terms of function (nitrifying activity), abundances of AOB and NOB, and dominant AOB and NOB populations. Furthermore, the patterns of stress-response (i.e., resistance and resilience dynamics) of nitrifiers were also similar between replicated biofilters. This demonstrates that nonlinear dynamics of AOB and NOB did not restrict the success of the MRM strategy. Because the experiment was performed at pilot-scale under operating conditions as close as possible from full-scale operation26 in terms of both feeding parameters (contaminant composition, basal concentrations, perturbation type and intensity) and reactor configuration (packing media and hydrodynamic conditions), this proof of principle demonstrates the potential for MRM at full scale. Previous Transient Disturbances Can Increase Biofilter Performance and Resilience. Ammonia removal efficiency and nitrification activity were affected by the first shocks, in parallel to the increases in pH values and ammonium concentrations. This confirms the inhibiting effect of high pH and ammonium concentrations on ammonia-oxidation (AO) and nitriteoxidation (NO) activities, as previously reported.43,44 However, following the application of several transient perturbations of increasing intensity, the resilience capacities of ammonia removal and of AO and NO activities in response to an intense disturbance were much better than those of control biofilters permanently maintained under stable conditions. The influence of contrasting disturbance histories on microbial communities, and the fact that they can “learn” from past events in order to better face subsequent disturbances, had been suggested in previous studies. After pre-exposition to multiple perturbations (e.g., minor physical and chemical stresses, low-level metallic contamination, fluctuating particulate-carbon concentration, volatile fatty acid accumulation, periodic feeding pattern), improved functional stability, or at least delayed and/or reduced impact, has been observed in soil,45,46 marine chemostats,47 and anaerobic digesters48,49 facing a subsequent shock. In nitrifying trickling filters exposed to wastewater with low ammonium concentration, alternating high and low ammonium loads was used to improve the nitrification potential.50 Gas biofilters regularly submitted to alternating feast-famine conditions were also reported to provide better functional stability in response to shock-loading.51 However, while these studies suggested some links between historical contingency and ecosystem stability, they did not disentangle the biological bases of this “memory effect” and did not further evaluate the possibility and the benefits to use a controlled perturbation strategy fed by microbial ecology principles as a tool to manage the microbial resource and promote the stability and service delivery in engineered

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DISCUSSION This work demonstrated that the stability of the two key microbial functions involved in nitrification can be successfully and rationally strengthened through a MRM strategy that can successfully guide bioprocess understanding and operation for biofilters facing disturbances. Suitability of Pilot-Scale Nitrifying Biofilters to Test the MRM Strategy. The four pilot-scale biofilters under study proved to be model engineered ecosystems well suited to evaluate how microbial communities can be managed to improve service delivery, as their operation parameters and performance can be reliably controlled and monitored.13 In addition, nitrifiers are relevant models because they correspond to two microbial groups cooperating in a mutualistic relation, having a high functional importance for biogeochemical cycles, a rather limited diversity, and ecophysiological traits that have been partly characterized.41 G


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dominated by members of the Nitrosomonas europaea−eutropha lineages, as demonstrated in pilot- and full-scale WWTPs.69,70 Consistently to our results, AOB affiliated to N. eutropha were dominant in lab-scale reactors performing ammonia oxidation at high ammonium concentration (>1000 mg·m−3).71,72 However, in the present study, ammonium concentration was not the only driving force of the community. Indeed, perturbed-biofilter communities were exposed to transient periods of excess substrate availability and high pH followed by periods of possible substrate limitation. Selected populations in perturbed biofilters were thus able to cope with these fluctuations, in contrast with control-biofilter communities. The dominant AOB belonged to the N. europaea/eutropha lineage in highly ammonium-loaded nitrifying biofilms from sequencing batch biofilm reactor (SBBR) where conditions are continuously changing due to the SBBR cycling operation.73 Actually, different AOB species have developed different molecular and physiological strategies to cope with nutrient and oxygen fluctuations.74 In particular, differences of ammonia oxidation activity, half-saturation constant for substrate, and regulation gene expression in response to toxic metabolites like nitrite underlie different adaptive and regulatory strategies between AOB species, even though closely related, such as Nitrosomonas europaea, Nitrosospira multiformis, and Nitrosomonas eutropha.75 Therefore, in the present study, the emergence and dominance of Nitrosomonas eutropha and Nitrobacter hamburgensis in perturbed biofilters is consistent with their better capacity to withstand transient periods of high resource availability and high pH. This selection supports the assumption underlying our MRM strategy and provides the biological basis to explain the significant enhancement of nitrification resilience capacity in periodically perturbed biofilters. The “prone to strengthening” or “prone to collapse” state of bioprocesses most usually depends on the operational and environmental conditions. Since a given ecosystem can shift from “prone-to-collapse” to “prone-to-strengthening” state through the application of an appropriate operation strategy (MRM), it seems critical to determine which kind of MRM strategy (if any) can ensure this shift (SI Figure S1). The applicability of the MRM strategy depends on the interaction between the considered ecosystem and type of stress. The functional diversity level of the microbial community performing the service of interest, and how it is related to the variability of environmental parameters, appears to be a key factor to speculate the “prone-to-collapse” or “prone-to-strengthening” nature of a given microbial process, and thus the applicability of the MRM strategy. Here, AOB and NOB communities were shaped by pH and ammonia concentrations, resulting in differentially adapted functional diversity along the environmental gradient. The MRM strategy should be applicable to other ecosystems where different taxa can perform the same function under stable conditions, but respond in different ways to environmental perturbations, as suggested by previous preliminary results in anaerobic digestion.49 Further investigation will be required to generalize the proposed conceptual framework (SI Figure S1) to other microbial functional groups and other disturbances, and to determine if the stress response mechanism is specific to a given ecosystem and type of stress, or has broader significance. Especially, to ensure the applicability of the MRM strategy, further work should define criteria for determining the minimal

ecosystems. Previous studies evaluating the impact of variable conditions on the microbial component have been reported in methanogenic bioreactors (where functional stability was correlated to flexible community structure52) or activated sludge (where the complete recovery of microbial diversity and composition after a disturbance was not necessary for functional recovery53). More generally, the history of perturbations has been described as an important factor to select for microorganisms over a range of life-history strategies53 and to shape fermentation reactor microbiomes in order to maximize the production of molecules of interest.54 However, there was still a need to understand how deliberate and controlled perturbations can steer the microbial community toward better functional stability through replacement of dominant populations. the Functional Traits of the Nitrifiers Favored by the MRM Strategy Can Explain Improved Biofilter Performance. Overall, nitrification in the biofilters under study was mainly due to AOB and Nitrobacter rather than AOA and Nitrospira, which is consistent with the known ecophysiology of these bacteria. Indeed, AOB have been reported to dominate over AOA in marine environments with high ammonium concentration,55 and previous studies suggested that nitrification is mainly driven by bacterial rather than archaeal ammoniaoxidizers in nitrogen-rich grassland soils,56 highly aerated activated sludge57 and constructed wastewater treatment wetlands.58 Similarly, Nitrobacter are known to be the key players of the second step of nitrification under high nitrogen availability.37,59 The functional gene sequences retrieved from our biofilters were close to those from nitrifiers reported in similar gas treatment bioprocesses. Indeed, the dominant AOB in biofilters treating ammonia gas alone or in mixture, at different concentrations, are mainly Nitrosospira sp.60 and/or Nitrosomonas sp.61−63 Nitrosomonas europaea have been immobilized in biotrickling filters to treat high ammonia loads64 and Nitrosomonas eutropha played a key role in biotrickling filters treating a real gaseous effluent from pig facility.65 In such biofilters, the NOB community is usually dominated by Nitrobacter.63,65 The proposed MRM strategy enabled to steer the nitrifier community in a favorable way through the release of poorly adapted and sensitive PFUs (i.e., population losses induced by initial disturbances) and the emergence of new dominant PFUs of ammonia- and nitrite-oxidizing bacteria, related to, respectively, Nitrosomonas eutropha and Nitrobacter hamburgensis. Different ammonium concentrations select for different types of AOB and NOB, essentially through differences in substrate affinity, growth rate and inhibition level. The selection of N. eutropha and N. hamburgensis was consistent with the known physiological traits of their cultivated representatives. Indeed, Nitrosomonas eutropha is known to be favored under high ammonium concentrations66,67 and Nitrobacter hamburgensis is known to perform well under high nitrite and organic carbon concentrations.68 These targeted community shifts thus suggest that biofilter strengthening was mainly due to the selection of resistant populations able to cope with high load levels, rather than increased tolerance of existing populations. The shift in AOB and NOB populations observed in our gasbiofilters can be compared with nitrifier population dynamics reported for other natural and engineered ecosystems. In general, members of the genus Nitrosospira and Nitrosomonas oligotropha prevail in environments with low ammonia concentrations, while environments richer in ammonia are H



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ACKNOWLEDGMENTS We thank Pr S. Kjelleberg, University of New South Wales, Australia, for critical reading and comments on the manuscript and M. Jovic, N. Brack and R. Gagneux for technical support with qPCR at VERI. We also thank T. Zhang, University of Hong Kong, and P. W. J. J. van der Wielen, KWR Watercycle Research Institute Nieuwegein, The Netherlands, for kindly providing AOA-containing DNA samples for validation of archaeal amoA quantification by qPCR. This work was funded by Veolia Environnement Recherche et Innovation (VERI), Maisons Laffitte, France through ANRT agreement CIFRE No. 2006/497 and through a ‘Contrat de Coopération dans le Cadre d’Etudes et de Recherches’ established in 2006 between Veolia, LEM-CNRS and ARMINES-EMA.

number and intensity of the preshocks that are required to trigger a memory effect and how long this effect would last. The conceptual framework and experimental design developed in the present study provide a general guideline that can be applied to validate the MRM strategy in different ecosystems, by determining which conditions entail the shift from “prone to collapse” to “prone to strengthening” communities. This work thus paves the way to further challenging studies aiming to figure out the predictability of the tipping point in the stress-response trajectory of microbial engineered ecosystems. Recent studies demonstrated that community structural and functional changes over time can be reliably controlled by deterministic processes and that historical preconditioning of microbial communities may help to reduce unpredictability in the design of microbial communities for biotechnological applications.76,77 Being able to predict when the resistance-toresilience trade off will shift toward strengthening state will determine our capacity to effectively manage the stress responses of microbial communities and the services they deliver. These future studies should provide tools for process engineers to (i) prescribe the application of appropriate mild disturbances during the start-up phase (“training period”) or (ii) let the system adapt itself to the natural fluctuations of the influent without imposing constant inlet concentrations at all cost. The underlying microbial mechanisms have been broadly disentangled here, but a deeper phylogenetic and functional characterization of microbial successions following perturbations would help to validate our conceptual framework and further develop a robust MRM strategy.

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ABBREVIATIONS AOA ammonium oxidizing archaea AOB ammonium oxidizing bacteria AO ammonia oxidation BF biofilter DW dry weight MRM microbial resource management NO nitrite oxidation NOB nitrite oxidizing bacteria PCR polymerase chain reaction RE removal efficiency VOC volatile organic compound WWTP wastewater treatment plant

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ASSOCIATED CONTENT

S Supporting Information *

REFERENCES

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The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.5b02740. Table S1 (Inlet concentrations of gaseous ammonia in the four biofilters during perturbations), Figure S1 (The resistance to resilience trade-off in perturbed microbial ecosystems), Figure S2 (Schematic representation of the pilot-scale biofiltration unit), Figure S3 (Schematic representation of the determination of functional resilience index), SI Figure S4 (Functional and operational parameters of the biofilters), Figure S5 (Dynamics of Nitrospira abundance), Figure S6 (Dynamics of AOB and Nitrobacter abundances) (PDF)

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AUTHOR INFORMATION

Corresponding Author

*Phone: 0056 (32) 237 2025; fax: 0056 (32) 227 3806; e-mail: [email protected]). Present Address

́ (L.C.) Escuela de Ingenieriá Bioquimica, Pontificia Uní versidad Catól ica de Valparaiso, Avenida Brasil 2185, ́ Chile. Valparaiso, ∇

Author Contributions

All authors have given approval to the final version of the manuscript. Notes

The authors declare no competing financial interest. I


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