Induction of Microbial Oxidative Stress as a New Strategy to

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Environmental Processes

Induction of microbial oxidative stress as a new strategy to enhance the enzymatic degradation of organic micropollutants in synthetic wastewater Amrita Bains, Octavio Perez-Garcia, Gavin Lear, David R. Greenwood, Simon Swift, Martin J Middleditch, Edward P. Kolodziej, and Naresh Singhal Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.9b02219 • Publication Date (Web): 29 Jul 2019 Downloaded from pubs.acs.org on July 31, 2019

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Environmental Science & Technology

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Induction of microbial oxidative stress as a new strategy to enhance the enzymatic

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degradation of organic micropollutants in synthetic wastewater

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Amrita Bains †, Octavio Perez-Garcia†, Gavin Lear‡, David Greenwood‡, Simon Swift§, Martin

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Middleditch‡, Edward P. Kolodziej┴♯ꜞ, Naresh Singhal*†

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†Department

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‡School

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§Faculty

of Civil and Environmental Engineering, University of Auckland, New Zealand

of Biological Sciences, University of Auckland, New Zealand of Medical and Health Sciences, University of Auckland, New Zealand

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┴Division

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United States

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♯Department

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United States

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ꜞCenter

of Sciences and Mathematics, University of Washington-Tacoma, Washington, of Civil & Environmental Engineering, University of Washington, Washington,

for Urban Waters, Tacoma, Washington, United States.

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ABSTRACT:

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Organic micropollutants (OMPs) are pervasive anthropogenic contaminants of receiving

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waters where they can induce various adverse effects to aquatic life. Their ubiquitous

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environmental occurrence is primarily attributed to discharge from wastewater treatment plants

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due to incomplete removal by common biological wastewater treatment processes. Here, we

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assess a new strategy for promoting the degradation of six representative OMPs (i.e.

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sulfamethoxazole, carbamazepine, tylosin, atrazine, naproxen and ibuprofen) by intentionally

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stimulating the production of microbial oxidoreductases to counter oxidative stress caused by

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oxygen perturbations. Mixed microbial cultures from a dairy farm wastewater were subjected

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to cyclic perturbations of dissolved oxygen (DO). A distance-based redundancy analysis was

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used to show that DO perturbations correlate with the abundance of Pseudomonadaceae and

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Rhodocyclaceae families, activities of peroxidases and cytochromes, and the degradation of

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OMPs. DO perturbation of 0.25 and 0.5 cycles/hr led to most abundance of Pseudomonadaceae

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and Rhodocyclaceae families, showed higher activity of peroxidase and cytochrome, and gave

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largest removal of OMPs (removal of 92±3% for sulfamethoxazole, 84±3% for naproxen,

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82±3% for ibuprofen, 66±2% for carbamazepine, 57±15% for tylosin, and 88±1% for atrazine).

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INTRODUCTION

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The discharge of trace levels of pharmaceuticals, industrial chemicals, pesticides and personal

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care products into aquatic environments is of global environmental concern.1 The resistance of

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such organic micropollutants (OMPs) to conventional wastewater treatment and their

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environmental persistence risks adverse ecological impacts to biota via direct toxicity and

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endocrine and metabolic disruption2, DNA fragmentation and immune deficiency.3–5 For

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example, exposure to the potent synthetic estrogen 17α-ethinylestradiol and the antidepressant

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fluoxetine even at concentrations below 5 ng/L and 28 ng/L, respectively, causes reproductive

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endocrine malfunction (e.g., production of vitellogenin mRNA and protein and disruption of

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gonad development) in fathead minnow (Pimephales promelas) males.6,7 OMP exposure

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effects may be additive; for example, exposure of female zebrafish (Danio rerio) to

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pharmaceutical mixtures of acetaminophen, carbamazepine, gemfibrozil and venlafaxine

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significantly reduces fecundity.8 Such concerns about the occurrence of OMPs have triggered

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attempts to enhance their biodegradation during municipal wastewater treatment.9 Although

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conventional activated sludge processes are not specifically optimised to eliminate OMPs 10,

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exploiting the diverse enzymatic potential of microbial consortia has been identified as a

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critical pathway for enhancing OMP removal.11

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In the period since the first oxidative conditions arose on Earth due to early photosynthesis,

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microbes have evolved to survive exposure to a variety of harmful oxidative stresses.12 For

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instance, oxidative stress generated by high concentrations of intracellular reactive oxygen

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species (ROS), including superoxide (O2-), hydrogen peroxide (H2O2) and hydroxyl radicals

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(OH.), induces oxidative enzyme gene expression 13 and consequent synthesis of antioxidative

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enzymes such as oxidoreductases (peroxidases and cytochromes) to protect against such

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oxidative stress.14 From an engineered wastewater treatment perspective, intentional

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manipulations of DO concentrations may be able to induce the production of such enzymes,

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some of which are also capable of degrading pollutants like polycyclic aromatic hydrocarbons

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and organophosphorus contaminants.15–17 Therefore, DO perturbations might pre-dispose cells,

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through transcriptional responses of their oxidoreductases to protect against iterative oxidative

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stress, to enhance OMP degradation. This proposed mechanism implies that simple oxygen

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control strategies within wastewater treatment bioreactors may provide a cost-effective

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solution for enhanced OMP removal.

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In this study, we investigate the impact of dynamic variation in DO concentration on OMP

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biotransformation by representative microbial consortia. We hypothesise that varying DO 2 ACS Paragon Plus Environment

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concentrations alter the intracellular metabolic pathways to disrupt the prevailing equilibrium

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in electron flow, leading to enhanced ROS formation and thereby upregulating ROS

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scavenging and defence enzymes. We evaluate OMP removal under three oxygenation patterns

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– constant aeration, continuous cyclic aeration, and intermittent cyclic aeration for both high

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and low (~10 and ~4 mg-DO/L, respectively) aerobic conditions. Our objective is to assess

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whether cyclic patterns enhance OMP degradation by inducing oxidative stress and forcing

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microbial oxidoreductase production (superoxide dismutase (SOD), catalases, peroxidases

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etc.).

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MATERIALS AND METHODS

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Reactor operation and sampling Fed-batch experiments were performed using bioreactors inoculated with a mixed microbial

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culture (sludge from a dairy wastewater settling pond, Alfriston, New Zealand). This inoculum

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represents a complex source of microbial communities potentially exposed to antibiotics and

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different carbon sources typical of intensive animal agricultural operations. The sludge was

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diluted to 3 g/L volatile suspended solids (VSS) with distilled water. A 10x concentrated stock

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synthetic wastewater with the following composition was used: 4000 mg-COD/L acetate, 1000

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mg-COD/L methanol, and 600 mg-N/L ammonium along with 1 mg/L of each OMP

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(sulfamethoxazole, carbamazepine, tylosin, atrazine, naproxen and ibuprofen) (detailed in

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supplementary information). Each 1 L bioreactor was continuously fed with this synthetic

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wastewater at 0.034 mL/min over 48 hours using a low flow syringe pump (KDS Legato 210-

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788210 Syringe Pumps, KD Scientific, USA). Following other studies2,18, a higher than

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environmentally realistic nominal OMP concentration of 100 µg/L was used in the bioreactors

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to more easily monitor their concentration changes during the study and account for the

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relatively limited selectivity of the LC/MS detector.

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Oxidative stress was imposed on cultures by iteratively exposing them to ON-OFF cycles of

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an oxygen (20%) enriched air supply during the 48h cultivation period. (Table 1, Figure 1). Air

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delivery to reactors was controlled by a flow meter in combination with Millenium 3 CD20

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logic controller (Crouzet, New Zealand) connected to solenoid valve operating on compressed

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air lines. DO concentrations in reactors were continuously measured using Visiferm DO ARC

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425 probes (Hamilton Bonaduz AG, Switzerland) connected to a PC using Hamilton Device

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Manager 1.0.0 software (Hamilton Bonaduz AG, Switzerland). Additionally, constant (non-

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perturbed) high (10 mg-DO/L) and low (4 mg-DO/L) DO conditions and autoclaved (dead)

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biomass were evaluated as controls under constant aeration at 10 mg-DO/L. 3 ACS Paragon Plus Environment


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Samples were collected at 1, 24 and 48 hours, with sampling at the end of any perturbation

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cycles. The samples collected at 48 hours were analysed for enzyme activity, proteins,

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microbial speciation, and OMP concentrations, while the samples collected at 1 and 24 hours

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were analysed for enzyme activity (Table S1). Each analysis was performed in duplicate (two

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biological and two analytical replicates). Additional experimental details are described in the

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supplementary information.

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Enzymes activity assays

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Oxidoreductase activity in crude biomass extracts was determined spectrophotometrically by

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measuring the degradation (oxidation and hydrolysis) of various chromogenic substrates used

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as surrogate xenobiotics (Table S2). The oxidoreductase (lignin peroxidase, horseradish

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peroxidase, laccase and cytochrome P450) and hydrolase (-glucosaminidase, -glucosidase)

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activities in bioreactors were detected spectrophotometrically by the oxidation of their

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respective chromogenic substrates (Methylene Blue [MB], Azure B [AB], 3,4-Dihydroxy-L-

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phenylalanine [L-DOPA], 2,2’-Azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid)[ABTS],

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Sudan Orange G [SO], 4-nitrophenyl-dodecanoate [pNP-12], Indole and 4-Aminoantipyrine

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[4-AAP], 4-nitrophenyl N-acetyl-β-D-glucosaminide [pNP-A] and 4-nitrophenyl- β-D-

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glucopyranoside [pNP-G]

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selected as they are synthesised during intracellular oxidative stress (generated by cyclic

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oxygen shocks here) and their ability to degrade OMPs22. Oxidoreductases catalyse OMP

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biotransformation by both oxidation and reduction reactions22–24. Except for Cyt P450 activity,

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estimated using three substrates, and hydrolases β-glu and β-glcNAc, estimated using one

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substrate, all enzyme activities were estimated using two chromogenic substrates. Standard

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normal distribution (N(0,1) was used to obtain the autoscaled enzyme activities (Figure 2a). In

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brief, 20 ml aliquots of microbial cultures samples were centrifuged at 20,000 x g for 15 min.

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at 4˚C in 50 ml falcon tubes. The pellets from each tube were resuspended in either 10 ml

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acetate buffer or 10 ml phosphate buffer (detailed in supporting information). Each well of a

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96 well microplate was filled with 100 µl aliquots of the respective buffers, 100 µl chromogenic

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dye and 100 µl resuspended biomass. Dyes and samples resuspended in double the amount of

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buffer served as controls. The microplates were mixed and incubated at 30˚C for 1 hour 10 µl

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of 0.3% H2O2 at 30% as primary oxidant was added to start the reactions for lignin peroxidase

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(AB, MB) and horseradish peroxidase (ABTS, L-DOPA) enzymes25,26 and 10 µl of 1M NaOH

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was added to quench the para-nitrophenol dye reactions19 (i.e. -glucosaminidase (pNP-A), -

19–21

(Table S2). These oxidoreductases and hydrolases were


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glucosidase (pNP-G) and cytochrome P450 (pNP-12)) only. Changes in absorbance caused by

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chromogenic reactions were quantified using a Victor X3 Multimode Plate Reader

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(PerkinElmer, USA) at different wavelengths (refer to supplementary information).

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Protein Analysis

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Proteomic analysis was performed to screen for oxidative stress associated enzymes as well as

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non-ROS enzymes. Protein extraction was performed by cell lysis followed by precipitation of

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proteins and separation of low and high stringency protein fractions by 1D SDS PAGE. Briefly,

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30 ml of the DO treated sample was washed with 50 ml of 0.9% sodium chloride and spun

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down at 20,000g (20 min at -4°C). The pellet was rewashed in 40 ml Tris-HCl (pH 7) and

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centrifuged at 20,000g (20 min at -4°C) to get a another pellet, which was resuspended in

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sample buffer, pulse-vortexed and placed on ice for 2 h with regular mixing at 15 min intervals.

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Samples were sonicated for 15 s (six rounds on ice) and centrifuged at 20,000g (3 min at 4°C).

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The supernatant was mixed with trichloroacetic acid (100% w/v) and centrifuged at 23,400g

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(30 min at -4°C) after which the pellet was washed with 5 ml cold acetone twice. The final

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pellet was heat dried and re-suspended in 400 µl low stringency buffer (see supplementary

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information) to generate a low stringency fraction (LSF) in the supernatant after,

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centrifugation, then extracting the pellet in 400 µl high stringency buffer to generate a soluble

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high stringency fraction (HSF) after recentrifugation.

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Proteins in both fractions were quantified spectrophotometrically by fluorescence. Crude

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separation of LSF and HSF proteins was achieved using 1D SDS PAGE. Individual bands were

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excised with a sharp razor blade and placed into low-binding, siliconized microcentrifuge tubes

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for destaining reduction, alkylation and trypsinolysis. Finally, 10 µL of the generated tryptic

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peptides of the microbial proteins were injected onto a SCIEX 6600 tripleTOF mass

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spectrometer. Protein identification was carried out by comparing the obtained peptide

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sequences against those of the UniProt database. Additional details can be found in the

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Supplementary Information.

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Organic micropollutant analysis

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The individual 1L bioreactors were continuously fed with OMPs mixture (sulfamethoxazole,

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carbamazepine, tylosin, atrazine, naproxen and ibuprofen) at a feed rate of 0.034 mL/min to

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the bioreactors for 48 h. Residual OMPs were extracted using solid phase extraction (SPE) on

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OASIS HLB cartridges (Waters, Milford, MA, USA) following the described method.27

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Briefly, 200 mL of aqueous bioreactor culture was collected at the end of the cultivation cycle 5 ACS Paragon Plus Environment


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and centrifuged to remove suspended particles (20,400 g, 20 min, -4°C). The supernatant was

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passed through the cartridges (pre-conditioned with 5 mL each of tert-methyl butyl ether,

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methanol (Merck; laboratory grade purity) and deionised water) and washed with water. The

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OMPs were eluted with methanol (10 mL) under vacuum.

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OMP analysis was performed by liquid chromatography coupled with mass spectrometry (LC-

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MS) using a Shimadzu 2020 Series LC-MS (Shimadzu, Japan) with an Agilent ZORBAX

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Eclipse Plus C18 column (2.1 mm × 100 mm, particle size 1.8 µm, Agilent Technologies,

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Germany) following EPA Method 1694.28 A binary gradient system of mobile phase A, 0.1%

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formic acid in deionised water, and mobile phase B, 0.1% formic acid in acetonitrile were used

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to separate analytes in ESI+ mode, while 5 mM ammonium acetate, pH-5.5 (mobile phase A)

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and methanol (mobile phase B) were used for analysis in ESI- mode. The solvent gradient for

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ESI+ mode was 10% B to 60% B in 24 min and then maintained at 100% B to 30 min. For

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ESI- mode, the gradient started at 40% B and was increased linearly to 100% B over 13.5 min,

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and then maintained at 40% B to 17 min. The flow rate was 0.2 ml/min. in both modes and the

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injection volume was set to 2 µl and 10 µl for +/- polarity modes, respectively. Limits of

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detection and quantification were determined using signal/noise ratios of 3 and 10,

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respectively. OMP removal efficiencies were calculated as [(OMP0 – OMP48)/OMP0] *100,

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where OMP0 and OMP48 are the OMP concentrations at the start of the study (0 hour) and after

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48 hours. The quality assurance and quality control were checked within each measurement

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series by spike recovery experiments (spiking at 100 µg/L, n ≥ 3) and repeated injections of

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matrix recovery samples. Recovery of the matrix spikes ranged from 54 to 100% and was

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consistent between analytes. Replicate samples showed a maximum range of relative standard

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deviation (RSD%) within 15%. OMPs were never detected in distilled water blanks.

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Estimating the removal efficiency and degradation rate constant

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The OMP removal efficiencies (%) for fed-batch mode involving a continuous addition of

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OMPs to bioreactors, were calculated as follows:

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 48hr    Qin Cin dt  VCresidual   x100 % Removal =  0 48hr   Q C dt   in in 0  

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Where, Qin is the flow rate and Cin is the OMP concentration in the feed from syringe pump, V

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is the volume of synthetic media, and Cresidual is the OMP concentration after 48 hours of reactor

(Eq. 1)


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operation. The first-order degradation rate constants were estimated for the fed batch mode

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using the following equation, obtained by applying the mass balance principle.

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Ct 

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where, Ct is the OMP concentration in reactor after time t and k is the first-order degradation

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rate constant (/hr).

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The estimates for first-order rate constants were obtained by fitting the data using Solver in MS

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Excel. These values were then used to predict the decay that would have occurred in batch

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mode, i.e., for a spike addition of OMPs to bioreactors, using the following equation.

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Qin Cin 1  e  k*t kV





(Eq. 2)

C t  C0 e  k*t

(Eq. 3)

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where, C0 is the initial OMP concentration (100 µg/L).

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Microbial DNA isolation and bacterial species identification

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A PowerSoil DNA isolation kit (MoBio, Carlsbad, USA) was used for the isolation of bacterial

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total genomic DNA extracted from samples (1 mL) following the manufacturer’s protocol.

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Bacterial community composition was characterised by amplifying and sequencing bacterial

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16S ribosomal RNA (rRNA) gene fragments with the universal 16S PCR forward primer (5'-

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TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3')

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and

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GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAA

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TCC-3') following standard protocols29; nucleotide bases in bold are Illumina overhang adapter

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sequences for high throughput sequencing. Amplified PCR products were purified using an

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AMPure XP beads kit (Beckman Coulter Inc.) and concentrations recorded using a Qubit®

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dsDNA HS Assay Kit (Life Technologies) and sequenced using an Illumina MiSeq machine

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(New Zealand Genomics Ltd., Auckland, New Zealand) using 2-by-300 bp chemistry. Prior to

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DNA sequencing, the sequencing provider attached a unique combination of Nextera XT dual

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indices (Illumina Inc., USA) to the DNA from each sample to allow multiplex sequencing. The

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resulting paired-end read DNA sequence data were merged and quality filtered using the

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USEARCH sequence analysis tool.30 Data were dereplicated so that only one copy of each

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sequence was reported. Sequence data were then checked for chimeric sequences and clustered

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into groups of operational taxonomic units based on a sequence identity threshold equal to or

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greater than 97% (thereafter referred to as 97% OTUs) using the clustering pipeline UPARSE

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30

16S

PCR

reverse

primer

(5'-

in QIIME v.1.6.0, as described.31 Prokaryote phylotypes were classified to their 7 ACS Paragon Plus Environment


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corresponding taxonomy by implementing the RDP classifier routine 32 in QIIME v. 1.6.0 33 to

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interrogate the Greengenes 13˙8 database.34 All sequences of chloroplast and mitochondrial

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DNA were removed. Finally, DNA sequence data were rarefied to a depth of 5,600 randomly

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selected reads per sample and two samples per treatment to achieve a standard number of

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sequencing reads across all samples.

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Statistical analyses

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Enzyme activities were plotted using the heat map function within the R package ‘gplots’.

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Similarities in bacterial community composition, enzyme activity and OMP remaining in

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cultures were investigated using multivariate statistical analyses. Bray-Curtis distance matrices

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of relative bacterial abundance, enzyme synthesis and OMP residual concentrations were

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calculated and significant differences across all sample groups assessed using permutational

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multivariate analysis of variance (PERMANOVA). The RELATE function within the

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PRIMER package was used to calculate the Spearman rank correlation between data matrices

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constructed from the bacterial community, enzyme activity and OMP residual concentrations.

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All multivariate statistical analyses were performed in PRIMER 6 (Plymouth Marine

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Laboratory). Significant differences in OMP removal and residual concentrations were

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calculated by GraphPad Prism 7 using one-way ANOVA and post-hoc Tukey tests.

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RESULTS

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Influence of oxygen perturbations on microbial enzymatic activity

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The cyclic DO exposure to representative mixed microbial communities induces the activation

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of different carbon metabolic pathways and oxidative stress. The shift in the intracellular

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metabolism and ROS generation results in the synthesis of oxidoreductases and other enzymes

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essential for metabolic regulation. To test this hypothesis, five frequencies (2, 1, 0.5, 0.25 and

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0.16 cycles per hour) and constant controls (0 cycles/hr) were tested under two aeration regimes

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(high-DO, up to 10 mg/L DO and low-DO, up to 4 mg/L DO) that generated cyclic patterns

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(both continuous and intermittent) of DO concentrations in bioreactors (Figure 1, Table 1). The

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activities of oxidoreductases (lignin peroxidase, horseradish peroxidase, laccase and

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cytochrome P450) and hydrolases (-glucosidase and -glucosaminidase) in bioreactors were

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determined spectrophotometrically.

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Cultures perturbed with 0.25 and 0.5 cycles/hr at both low and high-DO regimes showed

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significantly higher (ANOVA, p < 0.05) cytochrome P450 (Indole, 4-AAP) and horseradish

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peroxidase (L-DOPA, ABTS) compared to constant DO non-perturbed controls. The 8 ACS Paragon Plus Environment

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horseradish peroxidase (L-DOPA, ABTS) activity remained significantly higher in 0.16

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cycles/hr DO perturbed samples but decreased in cultures perturbed with 1 or 2 cycle/hr at

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high-DO aeration where -glucosidase (pNP-G) activity was high. This indicated that the DO

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transitions activate the enzymes associated with regulation of carbon metabolism and the

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oxidative stress caused by ROS formation. Lignin peroxidase (AB, MB) activity was evident

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in all DO conditions (perturbed and non-perturbed). By contrast, under constant non-perturbed

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oxygen concentrations, the high-DO aerobic cultures showed greater (ANOVA, p < 0.05)

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cytochrome P450 (pNP-12), -glucosaminidase (pNP-A), -glucosidase (pNP-G) activities

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while the low-DO aerobic samples exhibited greater laccase (SO) and horseradish peroxidase

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(ABTS) activity (Figure 2a).

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The influence of varying DO conditions on protein abundance in biomass samples was

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illustrated by proteomic analysis. Proteins were resolved for perturbed (0.25 and 0.5 cycles/hr

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at low-DO regime) and non-perturbed (constant high and low-DO aerobic regime; Figure 2b).

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Overall, 436 targeted proteins (>1.3 unused score and 95% confidence interval) were identified,

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of which 245 proteins were detected in perturbed (0.25 and 0.5 cycle/hr low-DO aerobic)

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cultures and 191 proteins in non-perturbed (constant high and low-DO aerobic) cultures.

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Relatively higher abundance of expressed cytochrome c, cbb3 cytochrome oxidase,

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flavocytochrome c, NADH-quinone oxidoreductase and putative oxidoreductase were

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observed in cultures perturbed with 0.25 and 0.5 cycles/hr at low-DO aerobic regime. This

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observation demonstrates that exposing microbes to transitioning aerobic-microaerobic-anoxic

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conditions will induce the activation of different cellular central metabolic pathways leading

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to differential enzyme synthesis35. The non-perturbed constant high and low-DO aerobic

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cultures showed higher abundances for superoxide dismutase (SOD) and putative

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oxidoreductases, synthesised by the intracellular ROS formation (Figure 2b). SOD, as the

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primary ROS scavenging enzyme indicates the first line of defence against oxidative stress

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caused by ROS formation14,36. Table S3 and S4 provides the autoscaled enzyme activities and

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proteins relative abundance at 48 hours under different DO perturbed and non-perturbed

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conditions, respectively. Figure S1 shows the change in the enzyme activities in DO perturbed

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and non-perturbed cultures at 1 and 24 hours.

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Micropollutant biotransformation by mixed cultures

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Minimal (3±3%) OMP removal was observed in autoclaved controls (Figure S1) and is

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attributed to abiotic factors such as hydrophobic partitioning and sorption to solids. In

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biologically active reactors, we reasoned that synthesised oxidoreductases might enhance OMP 9 ACS Paragon Plus Environment


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biotransformation and increase removals. To further validate this hypothesis, we observed that

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OMP removal (at 100 µg/L initial nominal concentrations) was significantly (ANOVA test at

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p

307

0.05 by post-hoc Tukey test) for sulfamethoxazole, carbamazepine and tylosin between DO

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perturbed and non-perturbed constant high and low-DO aerobic cultures. Tables S5, S6 and S7

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present OMP removal efficiencies, standard deviation using pair of two replicates and quality

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control measures, respectively. The estimated first-order rate constants for the observed OMP

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removals are presented in Table S8 and the corresponding half-lives of OMP in DO perturbed,

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non-perturbed, autoclaved biomass and wastewater treatment systems are presented in Table

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S9. Figure S2 shows predictions for OMP removals in reactors operated in batch mode. Figure

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S3 represents the OMP residual concentrations after 48 hours in the bioreactors operated in

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fed-batch mode.

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Influence of oxidative stress on microbial speciation

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Next, we assessed the effect of oxidative stress on the microbial community composition and

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whether bacterial species presence correlated with oxidoreductases synthesis and OMP

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biotransformation. A total of 527 operational taxonomic units (97% OTUs) were identified in

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biomass samples, of which 513 represented Bacteria and 13 were Archaea. The occurrence of

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bacteria belonging to the phylum Proteobacteria, which are linked to the biotransformation of

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recalcitrant contaminants in wastewaters, 37, predominated and were correlated to ecological 10 ACS Paragon Plus Environment

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variables like process operations (changing oxygen conditions), influent wastewater

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characteristics or geographical location.38 Comamonadaceae (40%) was the dominant family

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in cultures perturbed with 0.25 cycles/hr in high-DO regime while a higher relative abundance

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of Methylophilaceae (25%) was found in 1 and 2 cycles/hr treated cultures at high-DO range

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(Figure S2). Cultures exposed to 0.16 cycles/hr in low-DO regime showed a higher relative

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abundance of Rhodocyclaceae (15%). Flavobacteriaceae (10-12%) and Pseudomonadaceae

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(14%) were dominant in cultures perturbed with 0.25 and 0.5 cycles/hr at low-DO regime. The

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non-perturbed constant high and low-DO regime subjected cultures resulted in higher

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abundances (30-45%) for Comamonadaceae (Figure S4). A dbRDA constrained ordination

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based on Bray-Curtis similarity was conducted to determine the extent to which bacterial

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community variations at family level are explained by the DO conditions (Figure 4). A

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relationship was confirmed between dependent variables (bacterial families, enzyme activity

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and residual OMP concentration) and changes in predictor variables (perturbed and non-

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perturbed DO conditions). This indicates that DO modulations can cause physiological and

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biochemical changes, greatly affecting bacterial community composition, enzymatic activities

338

and OMP biotransformations. The DO treatments (perturbed and non-perturbed)

339

[permutational multivariate ANOVA (PERMANOVA): F = 3.26; df = 10, 11; p < 0.001] had

340

significant effects on the bacterial community composition (Figure 4). The variation in

341

bacterial

342

Sphingobacteriaceae, Methylophilaceae, Xanthomonadaceae, Campylobacteriacea and

343

Aeromonadaceae), expressed enzyme activities (laccase, lignin, horseradish peroxidase and

344

cytochrome P450) and OMPs (sulfamethoxazole, carbamazepine, naproxen and ibuprofen)

345

removal were assessed in both perturbed and constant non-perturbed DO conditions (Figure 4).

346

These data indicate that microbial species that from oxidative stress showed more enzyme

347

activities and OMP biotransformation. Variation in the composition of bacterial communities

348

was positively correlated with microbial enzyme activity (relate analysis, R = 0.46, p < 0.05)

349

and residual concentrations of OMPs (relate analysis, R = 0.54, p < 0.05). Pseudomonas39 and

350

Rhodococcus40

351

Xanthomonadaceae42 synthesise laccases, which can catalytically degrade atrazine43,

352

carbamazepine44, estradiols45 and other OMPs22.

families

(Rhodocyclaceae,

synthesise

cytochrome

Syntrophaceae,

P450,

whereas

Syntrophobacteraceae

Methylophilaceae41

and

353 354

DISCUSSION

355

Our results show that DO perturbations can rapidly alter the functional performance and

356

composition of bacterial communities. These findings are consistent with other studies that 11 ACS Paragon Plus Environment


357

exposed microbial communities to shock treatments,46, and reflect the toxic effects of ROS47–

358

49

359

ROS formation and control are illustrated in Figure 5. The arcA and fnr genes play a significant

360

role in regulating the central carbon metabolism51 and are only activated under micro-aerobic

361

or anoxic52–54 conditions. Oxygen modulation between aerobic and anoxic conditions seems to

362

reprogram central carbon metabolism,35, disrupt intracellular electron flow55 and generate O2•-,

363

one the primary ROS molecules within cells.

364

SoxRS genes to produce superoxide dismutase (SOD), which converts O2•- to H2O2. The

365

formation of H2O2 activates OxyR genes, which produce catalases and peroxidases to convert

366

H2O2 to H2O.13 Enzymes such as catalase and peroxidases form activated enzyme-oxygen

367

complexes during H2O2 attenuation that are not completely selective to H2O2 and incidentally

368

degrade contaminants like OMPs reduction of H2O2 to H2O.56 Peroxidases, laccases and

369

cytochromes have been reported to hydroxylate and oxidise aromatic organic substrates via

370

one electron oxidations.22,23,24 In addition, the metal centres of the peroxidases and laccases

371

have reactive affinities for various functional groups in OMPs.57

372

Microbes that are better able to cope with dynamic changes to central carbon metabolism and

373

at scavenging toxic ROS species are preferentially able to survive and grow under oxygen

374

modulation47. Constant aeration, continuous cyclic DO perturbation, and intermittent cyclic

375

DO perturbation will challenge microbial survival in different ways: under constant aeration,

376

high and low DO levels will lead to different rates of carbon metabolism; for continuous cyclic

377

DO perturbations, the frequency will affect the rate of transitioning between aerobic, micro-

378

aerobic and anoxic regimes, thereby inducing shifts in carbon metabolism and altering ROS

379

formation.The introduction of anoxic phase in intermittent cyclic modulations alters carbon

380

metabolism (e.g., potentially inactivating the TCA cycle) and potentially activates anaerobic

381

pathways (e.g. nitrate reduction) along with impacts to ROS formation55. The most common

382

bacterial families surviving the ROS stress caused by constant aeration, continuous cyclic DO

383

perturbation, and intermittent cyclic DO perturbation belong to the proteobacterial phylum

384

(Pseudomonadacaeae, Xanthomonadacaeae, Rhodocyclacea, Comamonadaceace) concordant

385

with previous studies58,59.

386

DO perturbations led to the production of a more diverse protein pool. The significant

387

formation of superoxide dismutase only under constant aeration suggests the activation of Sox

388

genes due to the formation of more O2•-. The production of cytochrome c, cbb3 cytochrome

389

oxidase and NADH-quinone oxidoreductase under cyclic DO perturbations is indicative of a

along with their role as signalling molecules50. Key regulatory mechanisms of intracellular

47

Microbes control O2•- levels by activating


Page 12 of 26

Page 13 of 26


390

change in carbon metabolism and the activation of FNR and ArcAB regulatory systems.51 The

391

formation of putative oxidoreductases occurred under all conditions, but their concentrations

392

were higher for cyclic DO perturbations, with the intermittent cyclic DO perturbation showing

393

the largest enzyme concentration. Furthermore, catalase and peroxidase formation were only

394

seen for intermittent cyclic DO perturbation. The enzyme activity assay results were consistent

395

with proteomics data, with DO perturbations exhibiting more enzyme activity relative to

396

constant aeration conditions. All DO perturbations, however, showed a similar level of enzyme

397

activity, indicating that enzyme assays have reduced sensitivity than proteomics analysis in

398

discriminating between DO treatments

399

The observed OMP data illustrate that cyclic DO perturbations typically resulted in more OMP

400

removal as compared to constant aeration. Additionally, intermittent cyclic DO perturbations

401

showed larger OMP removal than continuous cyclic DO perturbation. DO perturbation

402

characteristics affected OMP removal, but the relationship is complicated and requires further

403

characterization60. The frequency and range of DO perturbation also influenced OMP removal.

404

For example, for continuous cyclic high DO perturbations, the 0.25 cycles/hr treatment (in 0-

405

10 mg/L DO range) showed more OMP removal than 1 cycle/hr (in 5-7 mg/L DO range) and

406

2 cycles/hr (in 6-8 mg/L DO range). Similarly, for the 0.25 cycles/hr treatments, more OMP

407

removal occurred at low DO compared to the high DO condition. Continuous OMP addition in

408

our fed batch mode of reaction operation (i.e., continuous OMP addition) seemed to result in a

409

plateau of OMP removal at ~70%. However, using the fitted first-order rate constants (Table

410

S6) to predict OMP removal in batch mode (i.e., OMPs only added at the start of study) yields

411

an expected 80-100% removal for most OMPs under DO perturbation conditions (Figure S1).

412

Practical Implications

413

This study suggests that exposing wastewater microbial communities to oscillating oxygen

414

concentrations in different aeration regimes can affect bacterial species selection, induce de

415

novo synthesis of oxidative biocatalysts and subsequently increase OMP removal. The use of

416

DO perturbations to improve treatment performance implies the possibility of new and

417

underutilized mechanisms for wastewater treatment plants to use existing infrastructure to

418

improve their treatment efficiencies for problematic organic contaminants.11 With a potential

419

to catalyse OMP biotransformations, the use of induced oxidoreductases for directed

420

transformations in wastewater treatment has just started to be investigated at the laboratory

421

scale.23 In the future, the biodegradation of OMPs may be achieved by improving the

422

performance of existing wastewater treatment systems under conditions that provide directed 13 ACS Paragon Plus Environment


423

environmental stresses that result in over-production of beneficial OMP-degrading biocatalyst

424

enzymes. We recommend further investigation of these possibilities.

425 426

ASSOCIATED CONTENT

427

Supporting Information

428

Detailed materials and methods, tables of chemicals used and residual OMPs concentrations

429

and figures of OMPs removal efficiency, microbial speciation and bacterial regulatory system.

430 431

AUTHOR INFORMATION

432

Corresponding Authors

433

*Phone: +64 9 923 4512. Fax: +64 9 373 7462. Email: [email protected] (Naresh

434

Singhal).

435

*Phone: +64 9 923 4512. Fax: +64 9 373 7462. Email: [email protected] (Amrita

436

Bains).

437 438

ORCID

439

Amrita Bains: 0000-0003-2286-6596

440 441

Notes

442

The authors declare no competing financial interest.

443 444 445 446

ACKNOWLEDGEMENTS The study was funded by FRDF grant 3707510/9572 from the Faculty of Engineering to

447

Singhal, Lear and Greenwood. Bains thanks the University of Auckland for a Doctoral

448

Scholarship during her PhD study. Mabel Smith and Matthew Fung, students visiting our lab

449

from the University of Alberta, assisted with the development of analytical methods.

450 451

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Table 1: Different temporal dissolved oxygen (DO) supply regimes Operational condition

High-DO Aerobic

Low-DO Aerobic

Dissolved Oxygen range (mg/L) 8-9 0-10 4-6 6-8 2-3 0-3 0-4 2-5

Perturbation frequency (cycles/hr)

Dissolved Oxygen shock durations (min.) Oxygenation duration (min.) Constant 30 10 8 Constant 6 6 12

0 0.25 1 2 0 0.16 0.25 0.5

Deoxygenation duration (min.) Constant 210 50 22 Constant 354 234 106

a)

b)

633 634 635 636 637

Figure 1. Dissolved oxygen profiles of microbial mixed cultures under: (a) high-DO and (b) low DO perturbation in comparison to non-perturbed conditions (constant). Stable DO profiles were achieved in the reactors that were provided constant aerobic (high and low) DO conditions, while aerobic-anaerobic DO transitions were induced with perturbed conditions.

638 639

(Note: DO Conditions; CHA- constant high-DO aerobic, CLA- constant low-DO aerobic, PHA -perturbed high-DO aerobic, PLA- perturbed low-DO aerobic). 21 ACS Paragon Plus Environment


a)

b) 640

641 642 643 644 645 646 647 648 649 650

Figure 2. Autoscaled enzyme activity at 48 hour and quantification in cultures; (a) Oxidoreductase activities in DO perturbed and non-perturbed samples. Different rows represent DO perturbed frequencies in high and low-DO regimes; different columns represent expressed oxidoreductase activities (Lacc- laccase, HRP- horseradish peroxidase, LiPlignin peroxidase, Cyt P450- cytochrome P450, β-glcNAc- beta-glucosaminidase, β-glubeta-glucosidase), each detected with more than one dye. The color intensity in each panel shows the auto-scaled enzyme activity (µmol mL-1 min.-1) across the gradient from red (highest enzyme activity) to green (lowest enzyme activity), (b) Relative peptide abundances of expressed oxidoreductases under non-perturbed and perturbed DO conditions.

651 652

(Note: DO Conditions; CHA- constant high-DO aerobic, CLA- constant low-DO aerobic, PHA -perturbed high-DO aerobic, PLA- perturbed low-DO aerobic).

653


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Figure 3. Observed removal efficiency of OMPs under different DO conditions in mixed culture fed-batch bioreactors; constant non-perturbed (controls at high aerobic (8-9 mg/L) and low aerobic (2-3 mg/L)) and perturbed (different frequencies (2,1,0.5,0.25,0.16)). Different letters above bars in the same graph indicate significant statistical differences between datasets via ANOVA (p = 0.05) and post hoc Tukey tests; bars indicated with the same letter are not significantly different. The error bars represent standard deviations from four measurements (duplicate analytical results for two biological replicates; n=4).

663 664 665

(Note: DO Conditions; AB- autoclaved biomass control, CHA- constant high-DO aerobic, CLA- constant low-DO aerobic, PHA -perturbed high-DO aerobic, PLA- perturbed low-DO aerobic)



666 667 668 669 670 671 672 673 674

Figure 4. Variation in bacterial community composition (Bray-Curtis similarity) as revealed by distance-based redundancy analysis (dbRDA) of the data, illustrating relationships between bacterial families, enzyme activity and OMPs concentrations remaining in DO perturbed and non-perturbed control cultures at different frequencies. The dbRDA was constrained by the best fitting explanatory variables from multivariate multiple regression analysis (DISTLM) and vectors are shown for predictor variables explaining significant proportions of bacterial community variation (p = 0.001) at 97% OTU richness. Dotted black ellipses indicate 95% confidence interval for clusters of each DO condition (perturbed and non-perturbed).

675 676

(Note: DO Conditions; CHA- constant high-DO aerobic, CLA- constant low-DO aerobic, PHA -perturbed high-DO aerobic, PLA- perturbed low-DO aerobic).


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Figure 5. Cellular mechanism for ROS formation, gene activation, enzyme production and pollutant degradation. Three sections explains the effect of cyclic oxygen perturbations between aerobic and anaerobic conditions has on: (1) carbon metabolism, NADPH and O2●– formation; (2) gene activation and production of enzymes (SOD, catalase, peroxidase); (3) lowering of hydrogen peroxide, formation enzyme-oxidant complex; and (4) pollutant degradation by enzymes.



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Induction of Microbial Oxidative Stress as a New Strategy to

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