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Characterization of a drinking water distribution pipeline terminally colonized by Naegleria fowleri Matthew J. Morgan, Samuel Halstrom, Jason Wylie, Tom Walsh, Anna Kaksonen, David Sutton, Kalan Braun, and Geoffrey Puzon Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b05657 • Publication Date (Web): 06 Feb 2016 Downloaded from http://pubs.acs.org on February 12, 2016

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Environmental Science & Technology

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Characterization of a drinking water distribution pipeline terminally colonized by Naegleria

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fowleri

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Matthew J. Morgan1#, Samuel Halstrom2,3#, Jason T. Wylie2, Tom Walsh1, Anna H. Kaksonen2,3, David

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Sutton3, Kalan Braun4 and *Geoffrey J. Puzon2

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1 CSIRO Land and Water, Black Mountain Laboratories, Acton, ACT, 2601, P.O. Box 1700,

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Canberra, ACT 2601, Australia

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2

CSIRO Land and Water, Centre for Environment and Life Sciences, Private Bag No.5, Wembley,

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Western Australia 6913, Australia

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3

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35 Stirling Highway, Crawley, Western Australia 6009, Australia,

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4

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Australia

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#

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*Corresponding author: Telephone: +61 8 9333 6174. Email: [email protected]

School of Pathology and Laboratory Medicine and Oceans Institute, University of Western Australia,

Water Corporation of Western Australia, 629 Newcastle St, Leederville Western Australia 6007,

Authors have contributed equally to this manuscript

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Abstract

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Free-living amoebae, such as Naegleria fowleri, Acanthamoeba spp. and Vermamoeba spp., have been

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identified as organisms of concern due to their role as hosts for pathogenic bacteria and as agents of

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human disease. In particular N. fowleri is known to cause the disease primary amoebic

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meningoencephalitis (PAM) and can be found in drinking water systems in many countries.

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Understanding the temporal dynamics in relation to environmental and biological factors is vital in

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order to develop management tools for mitigating the risks of PAM. Characterizing drinking water

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systems in Western Australia with a combination of physical, chemical and biological measurements

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over the course of a year showed a close association of N. fowleri with free chlorine and distance from

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treatment over the course of a year. This information can be used to help design optimal management

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strategies for the control of N. fowleri in drinking water distribution systems.

1 ACS Paragon Plus Environment


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Keywords: amoeba, chlorine, Naegleria fowleri, biofilm, drinking water

2 ACS Paragon Plus Environment

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Introduction

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The presence of harmful microbial eukaryotes in drinking water distribution systems (DWDSs) is an

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emerging area of concern. Several microbial eukaryotes are known human pathogens (e.g. Giardia,

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Cryptosporidium) and thus present a risk to water consumers and utilities. Free-living amoebae such

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as Naegleria fowleri, Acanthamoeba spp. and Vermamoeba spp., have been identified as organisms of

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concern due both to their role as hosts for pathogenic bacteria and as agents of human disease

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themselves. Substantial research effort has been directed towards understanding the relationships

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between pathogenic bacteria and their host amoeba such as Acanthamoeba spp. and Vermamoeba

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spp.1,2, but less effort has been directed towards understanding the factors in DWDSs that may

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promote the growth and persistence of specific pathogenic amoeba species, such as Naegleria fowleri,

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over closely-related non-pathogenic species.

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N. fowleri is a free-living amoeba that was first identified as a human pathogen in Australia in

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19653. The Naegleria genus is composed of more than 40 members, of which N. fowleri is the only

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species known to cause the disease primary amoebic meningoencephalitis (PAM). PAM is a rare but

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fatal central nervous system condition resulting from the aspiration of water containing amoeboid

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trophozoites into the nasal cavity, which migrate to the brain and CNS where they multiply and

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destroy the host tissue. No reliable treatment is currently available, however the drug miltefosine

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shows promise 4. N. fowleri is thermophilic and is found globally in naturally warm or artificially

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heated freshwater environments including hot springs, lakes, ponds and swimming pools 5-8. As a

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result, N. fowleri is more prevalent during the summer months when recreational water use also peaks

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and the likelihood of incidental contact with humans increases. N. fowleri is also found in DWDS,

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particularly in pipes with elevated temperatures in summer months 9,10. Biofilms that form on the

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internal surfaces of DWDSs provide a bacterial food source and protection from environmental

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stresses, including disinfection agents such as chlorine and chloramine 11. Amoebae can slough off or

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migrate into the bulk water from these biofilm reservoirs and make their way into contact with humans

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through the DWDS. In the USA, up to 8 PAM cases each year have been recorded that originate from

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exposure to recreational waters or contaminated drinking water 12. N. fowleri has been isolated from

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treated municipal drinking water systems during PAM outbreaks in Pakistan13, the 2011 and 2013

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PAM deaths in Louisiana, USA9,14 and continued detections in the state of Louisiana DWDSs15. Detecting and predicting the occurrence of N. fowleri in DWDS are important for mitigating

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the risks of PAM. N. fowleri detection in DWDS water and biofilms has been enhanced by the

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development of molecular methods such as qPCR assays for diagnostic gene sequences 10,16-18, but

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these methods can only detect amoebae already in the system. While research has identified N. fowleri

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in both the bulk water and the pipe wall biofilms in DWDSs 10, no work has been done to examine the

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relationship between microbial ecology within DWDS biofilms and bulk water, and successful N.

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fowleri colonization. Understanding this relationship will be crucial to identifying biological and

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physical-chemical factors that predict DWDS susceptibility to N. fowleri colonization.

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Here we report on a year-long study of a section of a DWDS in rural Western Australia which

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is colonized with N. fowleri. Changes in the physical-chemical conditions as well as the microbial

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activity, concentrations and ecology in the bulk water and biofilm were measured on a seasonal basis.

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To our knowledge this report constitutes the first study of a DWDS colonized with N. fowleri and

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offers insight into the biological (bulk water and biofilm) and physical-chemical dynamics occurring

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in a pipeline susceptible to N. fowleri colonization thus aiding water utilities in their management

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practices for N. fowleri and other amoebae.

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Materials and Methods

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Sample collection and analysis

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The DWDS pipeline was previously studied by Puzon et al. and known to be seasonally colonized by

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N. fowleri at the terminal point 10,11. A preliminary survey for viable amoebae along the DWDS was

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conducted prior to initiation of this study by the Water Corporation of Western Australia

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(Supplementary Figure 1). For this study, bulk water samples (1 X 250 mL and 1 X 10 L) were

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obtained from six sample points along the > 40 km length of the pipeline. Samples points were heat

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sterilized and flushed under constant flow for 5 min before sample collection in order to obtain a

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sample representative of the bulk water in the DWDS. The first sample point was located 10 km post

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chlorination with the remaining sample points placed at 5 km intervals. Two Kiwa biofilm monitors 19


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were directly connected to the DWDS pipeline. The first was connected at 25 km post chlorination and

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the second located at the terminal endpoint (40 km post chlorination). DWDS bulk water samples (1 X

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250 mL and 1 X 10 L) and triplicate biofilm samples were collected four times over the course of the

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year to correspond to the seasons in the southern hemisphere: spring (October 2012), summer

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(February 2013), autumn (May 2013) and winter (July 2013). Physical-chemical analyses of water

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samples were completed in the field at the time of sampling. Water temperature was measured using a

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digital thermometer (TPS, Australia). Chlorine residuals (free and total) were measured using a pocket

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colorimeter II (Hach, U.S.) according to the manufacturer’s protocol. Turbidity was measured using an

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Orion AQ4500 turbidity meter (Thermo Scientific, U.S.).

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Biological samples (biofilm and bulk water) were collected as previously described 10. Total

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microbial cell concentrations of biofilm and bulk water were enumerated using a previously published

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method11 on a Quanta flow cytometer (Beckman Coulter Quanta, U.S.) as follows: 200 µL of each

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sample was stained with 2 µL (10 ×) of SYBR Green 1 (Invitrogen, U.S.) and incubated for 15 min in

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the dark. Samples were diluted with filtered Milli-Q water to fit into the counting range of the

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Beckman Coulter Quanta flow cytometer. The Data was analyzed using Cell Lab Quanta SC MPL

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analysis program (Beckman Coulter Quanta, U.S.) ATP was measured following the manufacture’s

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protocol with a Promicol biomass detection kit (Promical, Netherlands) on a LUMAC Biocounter

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(Lumac, Netherlands). Triplicate analysis of 100 µL field samples (bulk water or biofilm) were

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compared to freshly produced ATP standard curves (10 ng, 100 ng, 500 ng and 1000 ng) at each

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sample time.

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Detection of amoebae

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Viable amoeba detection was conducted on all samples using methods described previously10. Bulk

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water (10L) samples were first concentrated by tangential flow filtration (TFF) with the use of FX-80

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ultra-filters (Fresenius Medical Care) following the method of Hill et al. (2005)20 and further

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concentrated by centrifugation at 3,500 × g for 10 min. The 250 mL samples bulk water samples were

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directly concentrated by centrifugation at 3,500 × g for 10 min. Biofilm samples were dislodged and



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concentrated from the biofilm monitors glass tubes by sonication and centrifugation as previously

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described10. For the viability assay, previously concentrated bulk water or biofilm samples (500 µL)

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were plated onto non-nutrient agar (NNA) covered with 100 µL of Escherichia coli culture (5.39 ×

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108 cells/mL) and incubated at 42°C for a minimum of 48 h. Viable amoebic growth, visualized as

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plaques on the E. coli lawn, were scraped and added to 100 µL of InstaGene Matrix (BioRad, USA)

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for DNA extraction following the manufacture’s protocol. Extracted DNA was then used in

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quantitative PCR melt curve analysis (qPCR) for the detection and identification of amoebae using

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general primers and for N. fowleri using specific primers10,21. For direct analysis, total DNA was

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extracted from a subsample of each biofilm or bulk water using the Zymo ZR Soil Microbe DNA

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MiniPrep™ kit (Zymo, USA) and analyzed by qPCR melt curve analysis. All qPCR assays were

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performed as previously described using a BioRad iQ5 (Bio-Rad, USA)10.

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16S rRNA and 18S rRNA gene pyrosequencing

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Variations in microbial composition along the length of the DWDS pipeline were assessed by am-

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plicon pyrosequencing. First, total DNA was directly extracted from concentrated bulk water or bio-

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film as mentioned previously. DNA from concentrated 10 L bulk water and replicate biofilm samples

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was screened by pyrosequencing to detect bacterial 16S rRNA (primer: 27Fmod (5’-

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AGRGTTTGATCMTGGCTCAG-3′), 530R (5’-CCGCNGCNGCTGGCAC-3’) and eukaryotic 18S

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rRNA (primers: Euk7F (5′- AACCTGGTTGATCCTGCCAGT -3′) and Euk570R (5’-

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GCTATTGGAGCTGGAATTAC-3’))22-25 genes (Molecular Research LP, USA). The 18S rRNA gene

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primer set was selected specifically to exclude amoebae sequences, in order to more efficiently identi-

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fy the broader eukaryotic population present in the samples (detection of amoebae was performed us-

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ing specific assays described above). Amplicon pyrosequencing was performed by Molecular Re-

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search following a previously published standard protocol26. In short samples were first amplified for

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30 cycles (94°C for 3 minutes, followed by 28 cycles of 94°C for 30 seconds; 53°C for 40 seconds

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and 72°C for 1 minute) followed by a final elongation step at 72°C for 5 minutes. All amplicon prod-

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ucts were then mixed in equal concentrations and purified using Agencourt Ampure beads (Agencourt


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Bioscience Corporation, USA). Purified products were then sequenced utilizing Roche 454 FLX tita-

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nium instruments following manufacturer’s guidelines (Roche, USA).

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Pyrosequences from the bacterial 16S rRNA and eukaryote 18S rRNA were processed

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separately. Errors were removed from the raw pyrosequences using the Amplicon Pyrosequence

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Denoising Program (APDP) pipeline (v1.1)27. The steps in the amplicon sequencing protocol generate

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a significant number of errors that can mislead biodiversity inferences28. APDP accurately identifies

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sequences in the dataset that cannot be confidently distinguished from these errors, enabling more

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robust inferences from ecogenomic data29. Raw pyrosequences were de-multiplexed by binning

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sequences with a perfect match to the same barcode and forward primer sequence, truncated at 250 bp,

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and retained if observed with at least two reads. All retained unique sequences were assigned to a

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group based on the best hit of a BLASTn search of the NCBI non-redundant nucleotide database30.

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Default parameters for BLASTn were used for within and between-group comparisons to detect errors

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and de novo chimeras. The final set of sequences comprised all unique sequences that passed

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validation in at least one sample.

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Denoised sequences were analyzed using the Quantitative Insights in to Microbial Ecology

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(QIIME) pipeline software (v1.6.0)31. Bacterial 16S rRNA gene sequences were clustered into OTUs

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of 97% similarity using uclust. Representative sequences for each OTU were classified using the

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Ribosomal Database Project (RDP) naive Bayes classifier against the greengenes May 2013 database

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clustered at the 97% level. Eukaryote 18S rRNA gene sequences were clustered in to 100% OTUs.

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Representative sequences for each OTU were aligned using MUSCLE and classified using the

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Ribosomal Database Project (RDP) classifier the using the SILVA v111 database clustered at the 99%

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level. Phylogenetic trees for 16S rRNA and 18S rRNA genes were constructed from MUSCLE

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alignments using Fasttree32.

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Richness (number of OTUs) and beta-diversity estimates were calculated based on rarefied

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OTU tables with QIIME. The R statistical package was used to generate non-metric multidimensional

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scaling (NMDS) plots using Jaccard distances and to perform all statistical tests of richness, beta-

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diversity and environmental data, including representative clade identification. Differences in

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environmental variable values between N. fowleri positive (Nf+) and N. fowleri negative (Nf-) samples



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were analyzed using t-tests. Taxa associated with the distribution of N. fowleri across samples were

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identified using a phylogenetic tree method33. The aim of this approach was to find the most inclusive

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clades whose distributions were predicted by one or more variables. Briefly, a binomial linear model

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was fitted to the presence or absence of each clade and tip sequence across the samples as predicted by

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the presence or absence of N. fowleri. Significance was assessed using a likelihood ratio test

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comparing the fits of the full model and a null model including just the intercept, and the p-value

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adjusted using the Bonferroni correction for multiple tests. The clade with the lowest p-value was

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retained and all less-inclusive sub-clades discarded from further consideration. This procedure was

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repeated until all clades and tip sequences had been retained or discarded. Retained clades with

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corrected p-values less than 0.05 were considered to be significantly associated with N. fowleri.

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Significant clades were assigned the consensus taxonomy of component sequences.

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The multivariate relationships between the assemblage structures of both prokaryotes and

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eukaryotes, and environmental variables were analyzed using Distance-Based Redundancy Analysis

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(dbRDA) with a stepwise forward selection of model parameters. Biological assemblage data were

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presence/absence transformed and inter-sample distances measured using the Jaccard coefficient. The

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significance level of all inferential analyses was set at p < 0.05.

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Results

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Bulk water

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Changes in the biological and physical chemical conditions of the bulk water were measured during

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each sampling trip on a seasonal basis (Table 1). The chlorine concentration (both free and total)

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decreased along the DWDS pipeline with distance from the chlorinator. The free chlorine

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concentration was reduced to < 0.2 mg/L, approximately 28 km post chlorination. Free chlorine

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concentrations were generally lowest during the spring and summer months. Water turbidity ranged

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from 0.4 to 0.6 Nephelometric Turbidity Units (NTU) through most of the pipeline with increased

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readings above 1.0 NTU at site SP5, approximately 48 km post chlorination. The water temperature

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was influenced by seasonal factors with the highest temperatures recorded during the summer. The


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highest recorded temperature was 41°C detected at site SP4, approximately 37 km post chlorination.

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The temperature remained fairly consistent along the pipeline with a slight increase in the overall

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average temperature at site SP5. Total cell numbers and microbial activity in the bulk water increased

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with increasing distance from chlorination with the highest activity recorded at site SP3,

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approximately 28 km post chlorination. No consistent correlation of total cell concentrations and

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biological activity was noted.

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Biofilm

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The total cell concentration of the DWDS biofilm samples ranged from 105 to 106 cells/cm2 at site SP3

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with biological activity ranging from 101 ATP ng/cm2 detected in the spring, summer and winter

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samples to 102 ATP ng/cm2 in the autumn sample (Table 2). Site SP5 had slightly higher total cell

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concentrations ranging from 105 to 107 cells/cm2 with an elevated biological activity of 102-103 ATP

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ng/cm2.

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Amoebae detection

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No amoeba were detected in the bulk water samples at sites SP0 and SP1 throughout the study, while a

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single Vermamoeba was detected in the autumn samples from sites SP2 and SP3 (Table 3). The site

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SP3 biofilm sample was positive for Vermamoeba (spring and summer only) and an unknown

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thermophilic amoebae (TA) throughout the study. Site SP4 bulk water samples were positive for viable

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N. fowleri in the summer and autumn as well as Vermamoeba in the summer. Site SP5 bulk water

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samples were positive for N. fowleri in the summer, autumn and winter, but not in the spring. In

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addition, Vermamoeba was detected in the summer and winter samples from site SP5. SP5 biofilm

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samples were positive for viable N. fowleri for all seasons except for spring. Vermamoeba was also

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found in the SP5 biofilm samples in summer and autumn.

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Molecular characterization of bacterial and eukaryotic communities



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The microbial communities of biofilms and bulk water along a chlorine concentration gradient in the

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DWDS were characterized using amplicon pyrosequencing of barcoded 16S rRNA and 18S rRNA

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genes for Bacteria and Eukaryota. A total of 159,246 eubacterial and 139,917 eukaryote pyrosequence

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reads remained after processing with APDP, corresponding to 1287 bacterial and 336 eukaryotic

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OTUs. Analyses were performed on rarefied data sets (1,783 reads per bacterial sample and 1,646

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reads per eukaryote sample) to control for sampling and sequencing effort, which led to the exclusion

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of three N. fowleri negative (Nf-) samples in each dataset. The normalized bacterial dataset comprised

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60,622 reads clustered into 1206 OTUs across 34 samples. The normalized eukaryote dataset

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comprised 60,902 reads clustered into 321 OTUs across 37 samples.

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Differences in environmental variables and OTU richness between Nf+ and Nf- samples.

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A total of eleven samples (including duplicate biofilm samples) were positive for N. fowleri (Nf+). N.

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fowleri was only detected in bulk water samples from the two sites furthest from the initial chlorinated

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water storage tank (sites SP4 and SP5), and was only detected in biofilm samples from the site furthest

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from the chlorination tank (site SP5) (Figure 1). Distance from chlorination tank was significantly

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associated (P < 0.001) with chlorine concentration in both bulk water and biofilm samples (not

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shown). Nf+ bulk water samples had significantly higher bacterial richness and lower free and total

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chlorine concentrations than Nf- bulk water samples. Nf+ biofilm samples had significantly higher

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bacterial richness and lower total chlorine than Nf- biofilm samples. Most environmental variables

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were not useful predictors of Nf+ status in bulk water or biofilm samples (Table 4). For example,

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bacterial richness was significantly different between Nf+ and Nf- samples for both bulk water and

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biofilm sample types (Table 4). Closer examination of the bacterial richness data showed a trend

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towards increasing richness along the length of the pipe, but no clear trend in bacterial richness

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differences between Nf+ and Nf- samples at different sites (Figure 1). For example bacterial richness

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was higher in Nf+ samples than Nf- samples at site SP4, but lower in Nf+ samples than Nf- samples at

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site SP5. Indeed, the highest bacterial richness was observed in an Nf- sample. Thus low bacterial

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richness is correlated with N. fowleri absence, but high bacterial richness is not itself predictive of N.

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fowleri presence.


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Taxon Co-occurrence with N. fowleri

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The representative clade method identified eleven clades from the bacterial 16S rRNA gene OTU

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phylogeny that had significant co-occurrence with N. fowleri in bulk water samples (Table 5). These

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eleven clades represented ten distinct taxonomic entities (Chloracidobacterium PK29 was represented

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by two OTU clades). Many of these taxa have an ecology similar to N. fowleri

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(thermophilic/extremophile-like). A further single clade (assigned to Sphingomonadaceae) was

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identified with a negative relationship (i.e. it was present in Nf- samples and absent in Nf+ samples).

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Five bacterial clades representing four distinct taxonomic entities and two eukaryote taxa were

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identified from biofilm samples. No taxa with significant co-occurrence with N. fowleri in bulk water

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samples were identified from the eukaryote 18S rRNA gene OTU data.

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Multivariate analysis of beta-diversity

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Bacterial 16S rRNA genes

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There was a strong pattern of change in the prokaryotic assemblages along the chlorine-distance

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gradient, with samples from sites close to the chlorination point (high total chlorine concentration)

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grouped at the right of the NMDS plot (Figure 2). Biofilm samples were clustered with bulk water

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samples from the same sites along the first axis, but separated based on sample type (biofilm-bulk

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water) along the second axis. Bacterial communities in the Nf+ bulk water samples from site SP4

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(filled red circles) were more similar to bulk water samples from the site SP5 (black circles) than to

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Nf- samples from site SP4 (empty red circles).

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In individual marginal tests all environmental variables were significantly correlated with

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bacterial community structure, with variables explaining between 4.8 and 8.2% of the variation in

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beta-diversity (Table 6). The stepwise forward selection model identified total chlorine concentration,

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N. fowleri status and water temperature as significant variables which together explained 19.1% of the

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variation in bacterial beta-diversity. Distance-based RDA plot confirmed the observation from the

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NMDS that Nf+ samples from site SP4 were biologically and environmentally more similar to Nf+

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samples from site SP5 than to the Nf- sample from site SP4.



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Eukaryotic 18S rRNA genes

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Patterns in the eukaryote NMDS ordination were less distinct, although the first axis appeared to

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separate samples along the same chlorine gradient observed for the bacterial samples (Figure 2).

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Compared to the bacterial samples there was a greater separation between Nf+ and Nf- samples from

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the site SP5. Site SP4 bulk water samples were not well clustered, although Nf+ samples from site SP4

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were more similar to site SP5 samples than to Nf- samples from site SP4 as was seen in the bacterial

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NMDS. The eukaryote dbRDA showed little clear separation of samples from different sites or Nf+

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and Nf- samples.

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In marginal tests, all environmental variables except water temperature were significantly

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correlated with eukaryote community structure (Table 6). Significant variables explained between 4.4

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and 11% of variation in eukaryote beta-diversity. The stepwise forward selection model identified total

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chlorine concentration, total cell number and water temperature as significant variables predicting

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eukaryote beta-diversity patterns, together explaining 20.9% of the total variance in eukaryote

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community structure.

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Discussion

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This study surveyed multiple points along an operational drinking water distribution system, using a

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combination of bulk water and biofilm samples, to determine the ability of environmental variables to

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predict the presence or absence of N. fowleri over the course of a year. Bacterial and eukaryotic

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richness and beta-diversity within each bulk water and biofilm sample was assessed along with water

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temperature, chlorine residual (free and total), total cell counts, ATP concentrations and water

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turbidity.

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Our results indicate that maintaining high chlorine concentrations in DWDS is an effective

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control for N. fowleri, consistent with current water management practice. Environmental variables

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that associated significantly with the presence of viable N. fowleri in the bulk water included distance

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from the last chlorine treatment point, chlorine residual in the bulk water and high bacterial

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community richness in the bulk water (p < 0.001). In the biofilm samples, only site distance from the


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treatment plant could be significantly linked to viable N. fowleri presence (p < 0.001). Although the

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site distance from the last treatment plant would not be directly affecting the presence of N. fowleri, it

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is likely that this correlation was due to the resulting reduction of the chlorine residual in the pipeline.

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Chlorine residual has a negative correlation with distance covered by the pipeline due to the

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accumulating effect of chlorine binding to and being consumed by reactive species34. While chlorine is

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known to kill N. fowleri (cyst, trophozoite and flagellate forms) if maintained consistently throughout

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the DWDS, the loss of disinfectant residual with distance is an ongoing operational issue for water

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utilities making poorly chlorinated sections susceptible to colonization by N. fowleri. Miller et al.11

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demonstrated that N. fowleri associated with pipe wall biofilms are able to survive chlorination events

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and re-emerge from biofilms after disinfectant residuals have dropped. In addition, N. fowleri present

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within flocs sloughed off pipe wall biofilms can be shielded. This can prevent the disinfectant residual

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from inactivating the N. fowleri11 and may promote colonization of the DWDS and possibly premise

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plumbing with N. fowleri.

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Comparison of the other environmental variables with N. fowleri presence resulted in few

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significant correlations. Environmental factors including eukaryotic richness, turbidity, water

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temperature, total cell count and ATP concentration were poor indicators of N. fowleri presence or

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absence. A recent study by Sifuentes et al. (2014)5 was also unable to find any significant correlations

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between the physical, chemical or microbial parameters in surface waters and the presence of N.

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fowleri, however the microbial community composition was not analyzed. Although N. fowleri are

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known thermophiles, the temperature of the water within the ranges observed in this study did not

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show significant differences between N. fowleri positive and negative sites. Interestingly, Sifuentes et

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al. reported that N. fowleri in surface waters in Arizona had higher presence in the winter and spring

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months than in summer and autumn, when water temperatures were highest5, while other surface water

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studies have found a higher prevalence of N. fowleri during the summer months7,8,35. The different

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results in detecting N. fowleri in relation to water temperature indicate that elevated temperatures, i.e.

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> 20 °C, alone are not a good predictor for N. fowleri presence or absence and suggest that other

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factors likely contribute to the presence of N. fowleri.



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While the increase in bacterial richness was significantly associated with the presence of

333

viable N. fowleri in the bulk water (p < 0.001), it was likely due to decreasing chlorine residuals in the

334

DWDS with distance from the treatment plant. However, N. fowleri was likely benefiting from the

335

bacterial richness, as an increase in bacterial richness in the DWDS would increase the potential of

336

suitable bacterial food for N. fowleri to be present.

337

Analysis of the 454-pyrosequencing of 16S rRNA and 18S rRNA gene sequences identified

338

several clades that had significant co-occurrence with N. fowleri in the bulk water and biofilm samples

339

(Table 5). All bacterial clades with a significant positive association with N. fowleri presence were

340

found to be Gram negative. This may have implications relating to the preferred food sources of N.

341

fowleri. Although N. fowleri are known to consume Gram negative bacteria in vitro, the specifics of

342

their food source in the DWDS environment are largely unknown36. Alternatively, these may be the

343

bacteria which share the same ideal growth conditions as N. fowleri (i.e. elevated temperature and low

344

chlorine residuals). For example, a Nitrospira clade was positively correlated with N. fowleri presence,

345

and previous work suggests that Nitrospirae are highly senstivite of disinfection treatments including

346

chloramination37 and chlorination38. In addition, the clade assigned to Sphingomonadaceae displayed a

347

significant negative relationship with N. fowleri presence. Recent studies of bacterial community

348

dynamics in DWDS have found an increase in the relative abundance of 16S rRNA sequences

349

assigned to Sphingomonadaceae in response to increased chlorine levels but no direct evidence linked

350

this increase to the presence of N. fowleri or other pathogenic amoebae in the system32. Whether or not

351

these bacteria are capable of inhibiting N. fowleri growth is uncertain and requires further study.

352

Analyses of the 18S rRNA gene sequences (eukaryotes) displayed positive associations between N.

353

fowleri presence and 2 distinct clades of freshwater flatworms (Catenulida and Stenostomum) and a

354

class of rotifer, Monogononta. Currently no studies in the literature have investigated associations

355

between these organisms and N. fowleri.

356

In all these cases, the cause underlying the observed patterns is unclear and it is uncertain if

357

the correlations between increased bacterial richness or abundance of specific groups, are due to a

358

causal relationship to the presence of N. fowleri, or are due to similar underlying environmental

359

conditions that promote both microbial groups. These effects cannot be separated by the current data,


Page 14 of 26

Page 15 of 26


360

and future studies are needed to help clarify this relationship, and to further determine the predictive

361

value of specific bacterial taxa for N. fowleri management.

362

In conclusion, this study characterized the physical-chemical and microbial parameters of an

363

operational DWDS in rural Western Australia. The results identified that the pipeline was seasonally

364

(summer-winter) colonized by N. fowleri at the terminal end. Certain environmental variables

365

(distance from treatment plant, chlorine residual in the bulk water and high bacterial community

366

richness) correlated significantly with N. fowleri presence. In addition, ecogenomic analysis identified

367

specific bacterial and eukaryotic clades which were positively associated with N. fowleri presence.

368

This is the first case where bacterial community richness has been demonstrated to have a positive

369

correlation with N. fowleri in DWDS. These factors could potentially be used in the further

370

development of a risk assessment system to predict the presence of N. fowleri within DWDS.

371 372

Acknowledgements

373

Water Corporation of Western Australia and CSIRO Land and Water are acknowledged for the

374

funding. Haylea C. Miller and Chris Hardy are thanked for their critical review of this manuscript.

375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396

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Table: 1 Biological and physical-chemical parameters of bulk water samples collected from a drinking water distribution system in rural Western Australia. Free

Total

Sample

Sample

chlorine

chlorine

Turbidity

Temperature

Total cell count*

ATP#

location

time

(mg/L)

(mg/L)

(NTU)

(°C)

(cells/mL)

(ng/mL)

Spring

0.6

0.78

1.53

16

1.00E+03

1.66 + 0.12

Summer

0.54

0.77

0.87

32

1.00E+03

5.39 + 1.3

Autumn

0.73

1.08

0.45

15

1.00E+03

7.22 + 0.47

Winter

0.54

0.75

0.48

15

1.00E+03

2.51 + 0.89

Spring

0.31

0.47

0.49

16

1.80E+04

1.96 + 0.20

Summer

0.39

0.62

0.53

32

5.00E+03

4.31 + 0.61

Autumn

0.72

0.97

0.36

16

1.00E+03

7.67 + 0.52

Winter

0.4

0.68

0.78

15

1.00E+03

1.83 + 0.48

Spring

0.19

0.36

0.4

16

2.50E+04

6.45 + 0.20

Summer

0.23

0.41

0.46

34

4.82E+05

6.55 + 0.75

Autumn

0.56

0.84

0.63

16

1.00E+03

10.1 + 1.13

Winter

0.36

0.54

0.59

16

3.00E+03

2.96 + 1.07

Spring

0.02

0.08

0.44

16

1.43E+05

3.7 + 0.29

Summer

0.03

0.15

0.49

34

3.00E+05

79.6 + 12

Autumn

0.11

0.47

0.43

15

1.87E+05

11.5 + 0.45

Winter

0.06

0.29

0.41

15

3.90E+04

2.49 + 0.20

Spring

0.03

0.07

0.7

18

2.46E+05

6.48 + 0.64

Summer

0.02

0.07

0.58

41

2.96E+05

7.22 + 0.41

Autumn

0.04

0.09

0.29

18

5.70E+04

12.0 + 0.52

Winter

0.01

0.1

0.44

19

1.19E+05

5.85 + 1.3

Spring

0.03

0.06

1.52

20

1.70E+05

6.03 + 0.22

Summer

0.02

0.04

0.35

28

3.00E+03

6.15 + 0.22

Autumn

0.04

0.05

1.63

20

2.60E+06

17.9 + 7.8

Winter

0.02

0.03

3.67

15

2.10E+05

49.1 + 6.8

SP0

SP1

SP2

SP3

SP4

SP5

*

Total cell counts are averages of duplicates and have a 5% standard error.

#

ATP are averages of triplicates with deviation.

504



Page 20 of 26

Table 2: Total microbial cell counts and ATP activity of biofilm samples. Sample location

Sample time

Cell count (cells/cm2)*

ATP (ng/cm2)#

Spring

1.60E+06

8.56E+01 + 1.58E+01

Summer

1.24E+06

6.18E+01 + 3.24E+00

Autumn

7.14E+06

2.22E+02 + 2.86E+01

Winter

5.40E+05

2.99E+01 + 8.96E+00

Spring

7.25E+05

6.18E+03 + 2.36E+02

Summer

2.54E+06

3.29E+02 + 8.59E+01

Autumn

4.67.E+06

2.06E+03 + 4.59E+02

Winter

1.47E+07

1.67E+03 + 3.96E+01

SP3 Biofilm

SP5 Biofilm

*

Total cell counts are averages of duplicates and have a 5% standard error.

#

ATP are averages of triplicates with deviation.

Table 3: Amoeba detection in bulk water and biofilm samples. Sample SP0

SP1

SP2

SP3

SP4

SP5

SP3 biofilm

SP5 biofilm

Spring

Neg

Neg

Neg

Neg

Neg

Neg

H*

Neg

Summer

Neg

Neg

Neg

Neg

Nf + V

Nf + V

H + TA

Nf + V

Autumn

Neg

Neg

V

V

Nf

Nf

TA

Nf

Winter

Neg

Neg

Neg

Neg

TA

Nf + V

TA

Nf

Time

505

* Samples were either plated on NNA-E. coli plates and further identified by qPCR melt-curve analysis or identified by

506

direct qPCR melt-curve analysis. Nf = N. fowleri, V = Vermamoeba, TA = unknown thermophilic amoebae and Neg =

507

Negative for viable amoebae.


Page 21 of 26

508


Table 4. Differences in environmental variables and OTU richness between Nf+ and Nf- samples. Bulk water Nf-

Biofilms

Nf+

Nf-

Nf+

Variable

Mean

SE

Mean

SE

p

Mean

SE

Mean

SE

p

Distance (km)

25.786

2.219

37.8

0.49

>0.001

30.750

1.800

39.000

0.000

>0.001

0.248

0.07

0.03

0.004

>0.001

0.051

0.014

0.027

0.004

0.071

(mg/L)

0.433

0.093

0.064

0.009

>0.001

0.208

0.063

0.040

0.004

0.009

Turbidity (NTU)

0.791

0.235

0.71

0.242

0.801

0.710

0.177

1.883

0.611

0.103

Temperature (°C)

18.714

1.777

25.000

4.405

0.304

18.875

2.287

21.000

2.394

0.773

12.014

3.816

64.04

49.299

0.327

268.900

98.797

729.333

287.352

0.144

14.362

5.924

9.948

2.245

0.772

1633.313

991.880

1353.000

342.056

0.966

70.5

16.922

164.6

5.278

>0.001

175.375

20.008

265.500

5.390

0.002

27.714

4.726

47.000

8.526

0.065

27.125

4.344

29.667

5.886

0.666

Free chlorine (mg/L) Total chlorine

Total Cells (cells/mL)* or (cells/cm2)# ATP (ng/mL)* or (ng/cm2) # Bacterial Richness Eukaryote Richness

509

*

510

#

Measurements are from bulk water samples. Measurements are from biofilm samples.



Page 22 of 26

511

Table 5. Consensus taxonomy of clades identified as having significant co-occurrence with N. fowleri in bulk water and

512

biofilm samples. See text for methods and databases used to assign taxonomy to bacterial and eukaryotic sequences.

513

indicates a clade with a significant negative correlation with N. fowleri presence. Kingdom

Sample Type

Taxonomy of Indicator Clades

Bacteria

Biofilm

[Thermi];Deinococci;Thermales;Thermaceae;Meiothermus (Gram negative)

1

Acidobacteria;[Chloracidobacteria];PK29 (Gram negative) Acidobacteria;[Chloracidobacteria];PK29; (Gram negative) Acidobacteria;[Chloracidobacteria];RB41;Ellin6075 (Gram negative) Acidobacteria;Solibacteres;Solibacterales (Gram negative) Bacteroidetes;Cytophagia;Cytophagales;Cytophagaceae (Gram negative) Chloroflexi;Anaerolineae (Gram negative) Chloroflexi;Anaerolineae;SBR1031;A4b (Gram negative) Nitrospirae;Nitrospira;Nitrospirales;Nitrospiraceae;Nitrospira (Gram negative) Planctomycetes;Planctomycetia;Pirellulales;Pirellulaceae (Gram negative) Proteobacteria;Deltaproteobacteria;FAC87 (Gram negative) 1

Proteobacteria;Alphaproteobacteria;Sphingomonadales;Sphingomonadaceae (Gram

negative) Bacteria

Bulk water Bacteroidetes;Cytophagia;Cytophagales;Cytophagaceae (Gram negative) Bacteroidetes;Cytophagia;Cytophagales;Cytophagaceae (Gram negative) Proteobacteria (Gram negative) Proteobacteria;Alphaproteobacteria (Gram negative)

Eukaryote

Biofilm

Metazoa;Platyhelminthes;Turbellaria;Catenulida;Stenostomum Metazoa;Rotifera;Monogononta

514


Page 23 of 26

515


Table 6. Relationship between environmental variables and bacterial and eukaryote community structure. Marginal

Stepwise forward variable selection

r2

p

Partial r2

Cumulative r2

p

Total Chlorine

0.082

0.010

0.082

0.082

0.010

N. fowleri status

0.076

0.010

0.058

0.140

0.010

Water Temperature

0.053

0.020

0.051

0.191

0.020

ATP

0.048

0.020

Turbidity

0.065

0.020

Total Cells

0.057

0.010

Total Chlorine

0.110

0.010

0.110

0.110

0.010

Total Cells

0.063

0.010

0.054

0.164

0.010

Water Temperature

0.037

0.120

0.045

0.209

0.030

Turbidity

0.057

0.030

N. fowleri status

0.057

0.020

ATP

0.044

0.040

Variable Bacterial 16S

Eukaryote 18S

516



517

Figure 1. Boxplots of bacterial and eukaryotic OTU richness in Nf- and Nf+ samples at sites arranged from left to right in

518

order of increasing distance from the chlorination tank. Colors indicate samples from each site: dark blue = SP0, light blue =

519

SP1, green = SP2, orange = SP3, red = SP4, grey = SP5.

520 521 522 523 524 525 526 527 528 529


Page 24 of 26

Page 25 of 26


530

Figure 2. Distance-based redundancy analysis of bacterial and eukaryote community structure. Only vectors for variables

531

determined to be significant are shown. Point shape, color and fill indicate sample type, site and Nf+ status. Circles = bulk

532

water, squares = biofilms. Site colors match those in Figure 1, with the exception that black = SP5. Filled = Nf+, no fill =

533

Nf-.

534




Page 26 of 26

Characterization of a Drinking Water Distribution ... - ACS Publications

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