Metagenomics Shows That Low-Energy AnaerobicâAerobic Treatment

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Metagenomics shows low energy anaerobic-aerobic treatment reactors reduce antibiotic resistance gene dissemination from domestic wastewater Beate Christgen, Ying Yang, Ziauddin Ahamed, Bing Li, D Catalina Rodriguez, Tong Zhang, and David W. Graham Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/es505521w • Publication Date (Web): 20 Jan 2015 Downloaded from http://pubs.acs.org on January 30, 2015

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Environmental Science & Technology

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Metagenomics shows low-energy anaerobic-aerobic treatment reactors

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reduce antibiotic resistance gene dissemination from domestic

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wastewater

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Beate Christgen1#, Ying Yang2#, S. Ziauddin Ahammad1,3, Bing Li2, D. Catalina Rodriquez1,4,

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Tong Zhang2* & David W. Graham1*

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1- School of Civil Engineering & Geosciences, Newcastle University, Newcastle upon

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Tyne, United Kingdom

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2- Environmental Biotechnology Laboratory, Department of Civil Engineering, University

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of Hong Kong, Hong Kong SAR, China

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3- Department of Biochemical Engineering and Biotechnology, Indian Institute of

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Technology Delhi, Hauz Khas, New Delhi, India

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4- University of Antioquia, Laboratory Diagnostics and Pollution Control (GDCON),

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Medellin, Colombia

#

*

These authors contributed equally to the manuscript.

Corresponding Authors:

Prof David Graham School of Civil Engineering and Geosciences Newcastle University Newcastle upon Tyne NE1 7RU United Kingdom Phone: (44)-0-191-222-7930 Fax: (44)-0-191-222-6502 E-mail: [email protected] Dr Tong Zhang Environmental Biotechnology Laboratory Department of Civil Engineering University of Hong Kong, Hong Kong SAR, China Phone: 0-852-2857-8551 Fax: 0-852-2559-5337 E-mail: [email protected]

ACS Paragon Plus Environment


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Graphic Abstract

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Abstract

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Effective domestic wastewater treatment is among our primary defences against the

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dissemination of infectious waterborne disease. However, reducing the amount of energy

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used in treatment processes has become essential for the future. One low-energy treatment

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option is anaerobic-aerobic sequence (AAS) bioreactors, which use an anaerobic pre-

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treatment step (e.g., anaerobic hybrid reactors) to reduce carbon levels followed by some

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form of aerobic treatment. Although AAS is common in warm climates, it is not known how

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its compares with other treatment options relative to disease transmission, including its

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influence on antibiotic resistance (AR) in treated effluents. Here we used metagenomic

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approaches to contrast the fate of AR genes (ARG) in anaerobic, aerobic and AAS

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bioreactors treating domestic wastewater. Five reactor configurations were monitored for six

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months, and treatment performance, energy use and ARG abundance and diversity were

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compared in influents and effluents. AAS and aerobic reactors were superior to anaerobic

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units in reducing ARG-like sequence abundances, with effluent ARG levels of 29, 34 and 74

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ppm (198 ppm influent), respectively. AAS and aerobic systems especially reduced

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aminoglycoside, tetracycline, and β-lactam ARG levels relative to anaerobic units, although

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63 persistent ARG subtypes were detected in effluents from all systems (of 234 assessed).

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Sulfonamide and chloramphenicol ARG levels were largely unaffected by treatment,

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whereas a broad shift from target-specific ARGs to ARGs associated with multidrug-

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resistance was seen across influents and effluents. AAS reactors show promise for future

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applications because they can reduce more ARGs for less energy (32% less energy here),

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but all three treatment options have limitations and need further study.

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Keywords:

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minimization

Wastewater

treatment,

metagenomes,

antibiotic

67 68


resistance,

energy


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Introduction

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Domestic biological wastewater treatment is among our most effective defences against the

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transmission of infectious waterborne disease and poor water quality. Contemporary

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wastewater treatment plants (WWTPs) effectively reduce waste organic matter (as COD),

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nitrogen (N) and fecal bacterial levels,1 often employing aerobic microbiological processes

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with active aeration (e.g., activated sludge; AS). However, AS processes often have high

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operating costs due to energy-consuming aeration, which will almost certainly increase if

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energy prices rise in the future. This is a worldwide concern, but it is especially problematic

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in emerging countries like China and India, which have rapidly growing economies,

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increased urbanization, chronic shortages of energy, and higher baseline levels of endemic

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disease.

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One possible energy-saving treatment option is anaerobic-aerobic sequence (AAS) reactors,

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which pre-treat the wastewater anaerobically to reduce COD loads and potentially produce

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biogas, followed by aerobic processes that polish the anaerobic effluents to meet effluent

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discharge standards. Depending on the design, AAS systems can have lower capital costs

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(relative to conventional treatment systems), smaller footprints, lower sludge production,

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possible biogas production, and reduced energy use because of lower oxygen demand.1-4 In

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fact, AAS treatment systems are now quite common for domestic waste treatment in warmer

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climate regions (e.g. Brazil, Egypt, India, and Israel),5-7 although AAS systems are not typical

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in temperate and colder parts of the world.

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Although a shift towards low-energy waste treatment has become critical, treatment

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technologies must still protect against disease transmission, such as reducing pathogen

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levels in treated effluents. However, there is strong evidence that WWTPs are reservoirs for

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and can promote antibiotic resistant bacteria (ARB) and genes (ARG) in their microbial

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communities,8-11 which then can be released to the environment via liquid effluents and

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biosolids.8,12-15 The increased potential for ARBs/ARGs in WWTPs has implications to all


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plants and their ability to protect against disease because acquired AR in pathogenic and

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other bacteria is increasing on a global scale.16 Therefore, for low-energy waste treatment

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options to be implemented, they must be at least as effective as current treatment

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approaches (ideally better) in terms of ARG levels or they will not effectively protect against

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resistant disease dissemination.

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Here we assessed ARG dissemination in the treated effluents of three different domestic

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wastewater treatment options that use different amounts of energy. Specifically, anaerobic,

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aerobic, and AAS reactors treating domestic wastes were compared in terms of energy use,

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treatment performance, and ARG abundance and diversity over six months. High-throughput

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sequencing and metagenomics were employed to characterise ARG occurrence and

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diversity levels between influents and effluents, which were compared with relative energy

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consumption levels during treatment.

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Methods

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Reactor Set-up and Operations: Reactor configurations are shown in Figure S1 (see

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Supporting Information; SI). Fresh settled wastewater was collected weekly from a local

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domestic WWTP in Northern England and stored in 18-L carboys at 4oC for use as reactor

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feeds. Mean waste influent characteristics were as follows (± standard error): soluble COD

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(sCOD) = 265.9 ± 14.9 mg/L, Total Kejdahl Nitrogen (TKN) = 82.7 ± 8.41 mg-N/L, ammonia

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(NH3) = 47.2 ± 3.57 mg-N/L, and pH = 7.2 ± 0.02. Wastewater was fed using peristaltic

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pumps (Watson Marlow 520S, UK) directly into three initial reactors, which included an

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upflow anaerobic sludge blanket (UASB) reactor, an anaerobic hybrid reactor (AHR), and a

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completely-mixed aerobic reactor (AER1); all reactor designs used in domestic wastewater

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treatment.17 Effluents from the AHR and UASB units, respectively, were pumped into

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second-stage aerobic units (AER2 and AER3) for further treatment (i.e., the two AAS

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systems were AHR-AER2 and UASB-AER3). Two anaerobic reactor types were tested

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because a sub-goal was to contrast UASB vs AHR designs relative to ARG/ARB mitigation.



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UASB and AHR reactors had working volumes of 1.5 L and height-diameter ratios of 4.0

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(H/D). Wastewater influent was introduced at the bottom of the units and both units were

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operated at three-day hydraulic retention times (HRT). Recirculation flow rates for the UASB

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and AHR reactors were 133 mL/min and 155 mL/min, respectively. A three-phase separator

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(gas–liquid–solid) was located at the top of each reactor to reduce washout of suspended

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solids and also to trap biogas in attached Tedlar sampling bags. The anaerobic units were

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maintained at 35 ± 1°C by circulating warm water around their cores. pH was maintained

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between 6.8 and 7.2.

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The three aerobic reactors had 1.04 L operating volumes. Wastewater was fed by peristaltic

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pump in the aerobic units, either directly from the wastewater source (AER1) or from AHR

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(AER2) or UASB (AER3) reactor effluent lines. The aerobic reactors were operated at two-

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day HRTs, which were chosen based on preliminary system testing. No recycle was

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provided because it was desired to keep operations simple to minimise the chance of

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mechanical or other failures disturbing the systems. Air supply was tightly regulated and

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monitored, which was essential for energy calculations. Mixing was provided using air

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diffusers, stir plates and magnetic stir bars. Aerobic reactors were maintained at room

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temperature (20 to 22 oC).

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Sample Collection and Routine Sample Analysis: Wastewater influent and effluent

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samples from the UASB, AHR and AER1 units, and effluents from the two AAS systems

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(UASB-AER2 and AHR-AER3) were collected and analysed weekly. These reactors were

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actually operated for over six months; however, sample collection for metagenomic analysis

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was only performed over a six-week window of time after the reactors had been very stable

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for six weeks. Our goal was to quantify ARG abundances and diversity under pseudo-steady

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state operating conditions. Routine monitoring included sCOD, TKN, NH3, and pH for all

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units; total suspended solids (TSS) and volatile suspended solids (VSS) for aerobic units;


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and methane (CH4) production in the anaerobic units. Influent and effluent samples for DNA

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extraction and metagenomic analyses were collected weekly and frozen immediately.

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All chemical analyses were performed according to standard methods for wastewater

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characterisation,18 except biogas and CH4 analysis. Gas bag volumes were recorded

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regularly and CH4 levels were quantified using direct injection into a Carlo Erba 5160 (UK)

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Mega Gas Chromatograph (GC) equipped with a Flame Ionisation Detector (FID) and a HP-

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PLOT Q capillary column (30m X 0.32 mm internal diameter) packed with 20µm Q phase.

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The GC oven temperature was maintained at 35°C, and the injector and detector were kept

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at 300°C. Hydrogen was used as the carrier gas (1ml/min, 65kPa). Methane analysis

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standards were prepared using a 70:30% analytical grade CH4:CO2 standard gas mixture

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(BOC Gases, UK). All GC injections were performed in duplicate.

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DNA extraction, sequencing and bioinformatics analysis: DNA was extracted from the

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weekly samples after completion of all sampling, using the Fast Soil DNA extraction kit (MP

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Biomedicals, USA) and a Ribolyzer (MP Biomedicals, USA) according manufacturer’s

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instructions. After extraction, DNA were sorted and combined into two tri-weekly groups for

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sequencing (i.e., the first three weeks were combined and the second three weeks were

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combined). Combining DNA from sequential sampling windows provided two independent

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samples for influent and effluent microbial meta-communities in each reactor.

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To assess the diversity and relative abundances of ARGs in the combined samples, DNA

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extracts were provided (in duplicate) to the Beijing Genomics Institute (BGI) for shot-gun

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libraries construction (insert size 170 bp) and high-throughput sequencing using the Illumina

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HiSeq 2000 platform. Approximately 3 Gb (giga base pairs) of data was generated for each

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DNA sample. Quality filtering was conducted for all metagenomic data to ensure validity in

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downstream analysis. Raw reads that contained three or more ambiguous nucleotides,

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quality scores below 20 for more than 36 bases, or with adapter contamination were

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removed prior to subsequent data processing. Quality-filtered metagenomic data was



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searched for putative ARGs against the clean Antibiotic Resistance Database (ARDB),

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which had non-redundant sequences using BLASTX with E-value ≤ 10-5.19,20 A read was

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identified as an ARG-like sequence according to its best BLASTX hit with amino acid identity

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≥ 90% and alignment length ≥ 25 amino acids.21,22

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Identified ARG-like sequences were sorted into ARG types and sub-types, using a structured

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ARDB and customized python script.20 ARG definitions, which included presumed resistance

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mechanisms for each ARG, were based on previous work and are simplifications because

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ARGs often code for proteins with multiple effects.20,22 However, simplification is reasonable

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because over 230 ARGs were assessed and overarching trends were more important than

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individual details. To compare ARG levels among samples, the number of ARG-like

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sequences was normalized to the number of metagenome and ARG-sequences in each

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sample. As such, ARG levels are reported either as “total metagenome sequences” (in ppm;

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one read in one million reads) or as “total ARG-like sequences” as percentages (%).

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Energy Calculations: Energy use was calculated for all systems, and included energy used

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for aeration, mixing, heating, and pumping, and potential energy gained from biogas

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production.2 To produce realistic energy estimates, calculations for each case were based

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on extrapolated energy use in geometrically identical, scaled-up treatment units treating

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1000 L/d wastewater. However, air supply rate, mixing and pumping rate data were based

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upon measured lab reactor data.

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Energy used in the aerated units was dominated by aeration and mixing needs, which was

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calculated using measured air flow rate data and equivalent paddle-mixing requirements for

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scaled-up aerobic reactors. Power ratings for characteristic industrial-scale centrifugal

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pumps were used to estimate feed and effluent recycle pump energy needs.2 Energy

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estimates for anaerobic units were based on required mixing energy as well as energy

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required for heating anaerobic units to 35°C. Potential energy recovery from biogas


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production was estimated based on previous CH4 data and use of industrial-scale gas

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turbines. Net energy used by the anaerobic units was the difference between energy used

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for pumping, heating and mixing minus the potential energy gained from biogas production.

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Energy calculations for AAS systems included the energy used for mixing and aeration in the

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aerobic units (less air was used due to lower sCODs), and mixing, heating and biogas

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production in the anaerobic units, which varied slightly between the UASB and AHR

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reactors.

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Other Data Analysis: This study contrasted aerobic, anaerobic and AAS treatment systems

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relative to ARG fate and energy use using two independent influent and effluent

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metagenomes per system (except AHR where one metagenome was available; see SI

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Tables S1-S3). However, to allow statistical comparisons among the three treatment options,

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it was first necessary to determine whether ARG characteristics in each duplicate-pair were

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statistically the same in paired DNA samples. One-way ANOVA and t-tests were performed

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on relative ARG levels in each duplicate for the 19 AR groups (identified in the metagenomic

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analysis; see Table S1). If the duplicate-pairs were statistically the same (i.e., p < 0.05), data

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from the duplicates were clumped as averages to allow more rigorous statistical

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comparisons among treatment opens. Follow-on tests included two-sample testing, such as

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ANOVA among reactors under each treatment, and statistical comparisons between

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performance data for each reactor system. All analyses were conducted using SPSS

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(Chicago,

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(http://bioinfogp.cnb.csic.es/tools/venny/), and the relatedness of influents and treated

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effluents was visualized using Circos.23

IL;

v.

17.0).

Venn

Diagram

was

plotted

using

Venny

232 233

Results and Discussion

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Bioreactor Performance, Operational Data and Energy Used per Design. Two anaerobic

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(UASB and AHR), one aerobic (AER1), and two AAS (AHR-AER2 and UASB-AER3)

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treatment systems (see Figure S1) were monitored to compare treatment performance and



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ARG metagenomes before and after treatment of domestic wastes. Relative to treatment

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performance, Table 1 data show effluent sCOD, TKN and NH3 levels did not significantly

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differ between the two anaerobic reactors or between the two AAS systems (p > 0.05).

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Therefore, these data were grouped under ‘anaerobic’ (UASB and AHR combined) and

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‘AAS’ (AHR-AER2 and UASB-AER3 combined), which were then statistically compared with

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each other and ‘aerobic’ treatment unit (AER1), relative to effluent sCOD, TKN and NH3

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levels.

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The AAS systems had significantly lower effluent sCOD levels than aerobic or anaerobic

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units alone (p < 0.027 and p < 0.002, respectively). In contrast, aerobic units had

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significantly lower effluent TKN and NH3 levels than the AAS systems (p < 0.024 and p

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