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Centralized Drinking Water Treatment Operations Shape Bacterial and Fungal Community Structure Xiao Ma, Amit Vikram, Leonard W. Casson, and Kyle Bibby Environ. Sci. Technol., Just Accepted Manuscript • Publication Date (Web): 31 May 2017 Downloaded from http://pubs.acs.org on June 1, 2017

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Environmental Science & Technology

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Centralized Drinking Water Treatment Operations Shape Bacterial and Fungal Community Structure

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39

Xiao Ma1, Amit Vikram1, Leonard Casson1,2, Kyle Bibby1,3*

1

Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA 15261 2 Graduate School of Public Health, University of Pittsburgh 3 Department of Computational and Systems Biology, University of Pittsburgh Medical School, Pittsburgh, PA 15261, USA

*Corresponding Author: Kyle Bibby, 709 Benedum Hall, Pittsburgh, PA 15261 [email protected], 412-624-9207

Keywords Fungi, bacteria, drinking water, microbiome, filtration, disinfection

1 ACS Paragon Plus Environment


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Abstract

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Drinking water microbial communities impact opportunistic pathogen colonization and corrosion

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of water distribution systems, and centralized drinking water treatment represents a potential

43

control for microbial community structure in finished drinking water. In this manuscript, we

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examine bacterial and fungal abundance and diversity, as well as microbial community

45

taxonomic structure following each unit operation in a conventional surface water treatment

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plant. Treatment operations drove the microbial composition more strongly than sampling time.

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Both bacterial and fungal abundance and diversity decreased following sedimentation and

48

filtration; however, only bacterial abundance and diversity was significantly impacted by free

49

chlorine disinfection. Similarly, each treatment step was found to shift bacterial and fungal

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community beta-diversity, with the exception of disinfection on fungal community structure. We

51

observed the enrichment of bacterial and fungal taxa commonly found in drinking water

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distribution systems through the treatment process, e.g. Sphingomonas following filtration and

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Leptospirillium and Penicillium following disinfection. Study results suggest that centralized

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drinking water treatment processes shape the final drinking water microbial community via

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selection of community members, and that the bacterial community is primarily driven by

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disinfection while the eukaryotic community is primarily controlled by physical treatment

57

processes.

58 59 60 61 62


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1. Introduction

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Centralized drinking water treatment processes are designed as barriers to remove primary

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pathogens from raw water and have greatly reduced the burden of waterborne infectious

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disease.1,

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treated drinking water is far from a sterile environment and hosts diverse microbial

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communities.1, 3 Previous studies have estimated bacterial abundance in finished drinking water

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to range from 103 to 105 cells/mL.3-5 Microbial eukaryotes, such as fungi and free-living amoeba,

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have also frequently been isolated from drinking water.6-8 Within this post-treatment indigenous

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drinking water microbiome, opportunistic pathogens have emerged as a significant public health

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issue, posing the greatest infectious disease risk associated with drinking water in industrialized

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countries.3,

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negative impacts, such as nitrification and biocorrosion.1, 3

2

Although drinking water treatment processes greatly reduce microbial abundance,

9

Members of the drinking water microbiome also have potential to cause other

75 76

Complete sterilization of drinking water is infeasible. On the other hand, drinking water

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treatment processes may selectively remove members of the microbial community,10,

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providing a potentially beneficial opportunity to manage the drinking water microbial

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community structure.3, 9 Previous studies of the microbial ecology of drinking water treatment

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processes have primarily investigated the bacterial community structure.10-12 A notable previous

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study demonstrated that bacteria colonizing filter media governed the downstream bacterial

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community structure, suggesting this process to be a primary driver of downstream microbial

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community composition.10 Additionally, although the drinking water microbiome also contains

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diverse fungi and potential opportunistic pathogens such as Aspergillus spp. and Fusarium

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spp.,13-15 fungal community dynamics within drinking water treatment processes have not been

11



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holistically evaluated. Examining the fungal community through drinking water treatment

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provides an opportunity to discern the role of treatment processes in shaping the composition of

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microbial eukaryotes. A knowledge gap remains regarding the dynamics of bacterial and

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eukaryote ecology along the drinking water treatment process and operational factors that shape

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the community structure.3

91 92

In the present study we evaluated bacterial and fungal diversity, taxonomy, and abundance along

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a conventional surface water treatment process train. We used high-throughput sequencing of

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bacterial 16S rRNA and fungal ITS1 gene regions to characterize the microbial community

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diversity and taxonomy. Additionally, we evaluated bacterial and fungal abundance, as well as

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presence of Acanthamoeba spp. and Aspergillus fumigatus. The goal of this study was to

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investigate the influence of drinking water treatment unit operations, including media filtration

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and disinfection, on both bacterial and fungal community structure. An enhanced understanding

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of the role of centralized drinking water treatment in driving microbial community composition

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will contribute to efforts to engineer a desirable drinking water microbiome.

101 102

2. Materials and Methods

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2.1. Sampling Site

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An anonymous U.S. drinking water treatment plant fed with river water was selected as the study

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site. The treatment process and the sampling locations are outlined in Figure S1. River water is

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pumped into the plant as raw water, and ferric chloride and cationic polymer (approximately 25

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mg/L and 1 mg/L, respectively) are added for coagulation. The water passes through a clarifier

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for initial clarification, and is settled for an additional 24 hours in a secondary sedimentation


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basin. The settled water passes through a media filter composed of coal, sand, and support

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gravel. Free chlorine disinfection using sodium hypochlorite is the last step of treatment, and a

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free chlorine residual of 0.4 to 1.0 mg/L was maintained during the study period.

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2.2. Sample Collection

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Bulk water samples were collected from four locations along the treatment process (Figure S1):

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(1) raw water at the pump to the treatment plant; (2) post-sedimentation water, the effluent from

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the secondary 24-hour sedimentation basin; (3) post-filtration water, the effluent from media

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filtration; (4) post-disinfection water, the effluent from the disinfection clear well. Eight

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duplicate monthly water samples were collected from these four locations on January, February,

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March, April, May, June, July, and September 2014. Samples were collected in duplicate using a

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1-L sterile Nalgene polypropylene sampling bottle without preservative.

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Water samples were immediately transported on ice to the laboratory and processed within four

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hours of collection. Upon arrival, 500 mL to 1900 mL of each water sample was filtered through

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a 0.2 µm Supor® 200 Polyethersulfone membrane (Pall Corporation) housed in sterile Nalgene

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analytical filter funnels (Thermo Scientific; Fisher) within a class II biological safety cabinet.

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The filter membrane with biomass was then stored in a sterile 10 mL tube at -80◦C until DNA

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extraction. The remaining subset of the water samples were measured for conductivity

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(Accument conductivity meter, Fisher Scientific), pH (Russell RL 060P pH meter, Thermo

129

Scientific), and free chlorine (Hach colorimeter, Hach) following manufacturer’s protocols. Two

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additional weekly sample sets from the described sampling locations were collected during June

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27th to July 8th 2016. These additional samples were analyzed for heterotrophic plate count (HPC)



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and total filamentous fungal colony count to evaluate the abundance of culturable bacteria and

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fungi through the studied water treatment processes train. Detailed methods for HPC and total

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filamentous fungal colony count analysis are provided in the Supplementary Information.

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2.3. DNA Extraction, PCR, and Sequencing

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Frozen filter membranes with biomass were thawed at 4ºC and subjected to DNA extraction

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using a RapidWater DNA Isolation Kit (MO BIO Laboratories) following the manufacturer’s

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protocol. All duplicate samples were subjected to PCR targeting the fungal ITS1 gene region

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using primers ITS1F and ITS2 modified for Illumina multiplex sequencing as previously

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described.16 One replicate for each sample was subjected to 16S rRNA PCR using previously

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described primers 515F and 806R.17 Detailed description of PCR conditions for ITS1 and 16S

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rRNA PCR can be found in the Supplementary Information. DNA extraction negative controls

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with a blank filter membrane were both measured by Qubit 2.0 DS DNA high sensitivity kit and

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included in PCR for sequencing library preparation and no-template PCR negative controls were

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included in all PCR runs to ensure reagents and equipment were not contaminated. All controls

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were negative for contamination. Two pure culture fungal DNA samples, Penicillium

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chrysogenum (CAES PC-1) and Aspergillus fumigatus (ATCC 34506), were included in the

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ITS1 PCR as positive controls.

150 151

PCR products were purified with Agencourt AMPure XP magnetic beads (Beckman Coulter) and

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confirmed by gel visualization. The purified PCR products were quantified with a Qubit 2.0

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Fluorometer with a Qubit dsDNA HS Assay Kit (Invitrogen), and equimolar amounts were

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pooled to construct fungal ITS amplicon and bacterial 16S rRNA amplicon libraries. One


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additional purification step was performed for each pooled library with Agencourt AMPure XP

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magnetic beads. The ITS and 16S rRNA libraries were then sequenced on an Illumina MiSeq

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Sequencer (Illumina) in two separate runs using MiSeq Reagent Kit v2 (300-cycles) (Illumina).

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2.4. Bioinformatics and Statistical Analyses

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Raw 16S rRNA sequence data was demultiplexed and trimmed based on a quality score of 20

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using QIIME 1.8.0.18 After trimming, sequences were grouped into operational taxonomic units

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(OTUs) at 97% similarity and taxonomically classified using a closed reference OTU picking

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strategy and Greengenes 13.5 as the reference database;19 17.6% of sequences did not match the

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reference database were not included in downstream analysis (details in Table S1), and

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sequences classified as chloroplast were removed from further analysis. As a previous study has

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demonstrated that grouping fungal ITS sequencing reads into OTUs limits the taxonomic

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classification accuracy for the fungal community,20 raw sequencing data of ITS1 library was

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trimmed and demultiplexed based on a quality score of 20 using QIIME 1.8.018 and compared

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directly against the reference ITS database21 using BLAST ver. 2.2.19.22 The BLASTn output

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was sorted using FHiTINGs.23 Boxplots of relative abundances were generated using R packages

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reshape2 and ggplot2 with default options; the outlier was defined as less than the first quartile -

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1.5*interquartile range or greater than the third quartile + 1.5* interquartile range.24, 25 A two

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sample t-test was conducted to compare mean relative abundance value of bacterial and fungal

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taxa at each sampling site. Additional analyses using LEfSe (Linear discriminant analysis effect

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size) tool was conducted to confirm enriched taxa at each sampling location (Kruskal-Wallis test

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p ≤ 0.05; log10 LDA score threshold equals 2.0).26 The sequencing results of the two positive

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ITS1 amplicon controls indicated accurate classification of fungal genera, as 99.9% of sequences



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of Aspergillus fumigatus were assigned to Aspergillus spp. and 99.9% of sequences of

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Penicillium chrysogenum were accurately assigned to Penicillium spp. These results are

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consistent with previous observations of ITS1 classification annotation accuracy to the genus

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level.27

182 183

Unweighted UniFrac (membership-based) and weighted UniFrac (structure-based) matrices were

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calculated to evaluate bacterial community similarity between samples.28 As UniFrac, which

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incorporates phylogenetic distance information, is impractical for the fungal ITS gene region,29

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Jaccard (membership-based) and Bray-Curtis (structure-based) matrices were calculated using R

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package Ecodist based on fungal genera.30,

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performed to evaluate variation between each sampling group and pairwise ADONIS analysis

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(permutational multivariate analysis of variance)32 implemented within QIIME18, 33 was used to

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further assess the statistical significance of differences in community structure among sampling

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groups. The raw sequencing data are publically available on figshare under DOI:

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10.6084/m9.figshare.5007743 and 10.6084/m9.figshare.5007737.

31

Principal coordinate analysis (PCoA) was

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2.5. qPCR of Total Fungi, Total Bacteria, Acanthamoeba spp., and PCR of Aspergillus

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fumigatus

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Abundances of total fungi, total bacteria, and Acanthamoeba spp. were quantified by qPCR using

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SYBR Green and fungal universal primers ITS1FI2 and ITS2,34, 35 universal bacterial primers

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targeting the 16S rRNA gene region,36 and Acanthamoeba spp. primers AcantF900 and

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AcantR1100.37 Detailed qPCR conditions can be found in the Supplementary Information. No-

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template qPCR negative controls were included in all qPCR runs to ensure reagents and


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equipment were not contaminated. Inhibition was evaluated by spiking sample DNA into a

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known amount of standard; no qPCR inhibition was observed. Based on the standard curves and

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volume of sample filtered, the lower detection limit of bacterial qPCR was 0.54 P. fluorescens

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genome equivalents/mL; the lower detection limit of fungal qPCR was 0.22 A. fumigatus

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genome equivalents/mL; and the lower detection limit of Acanthamoeba spp. qPCR was 0.002 A.

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castellanii genome equivalents/mL (1.2 A. castellanii 18S rRNA gene copies/mL).

207 208

The presence of Aspergillus fumigatus was determined using previously described PCR primers

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targeting the A. fumigatus alkaline protease gene.38,

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(ATCC 34506) genomic DNA as template, and no-template negative controls were included. All

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positive controls were positive and negative controls were negative for contamination. Detailed

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PCR conditions can be found in Supplementary Information.

39

Positive controls using A. fumigatus

213 214

3. Results and Discussion

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3.1. Sampling and Analysis Overview

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In total, 64 samples (duplicate samples of four locations over eight months) were collected from

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a single anonymous drinking water treatment plant. A process diagram of the drinking water

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treatment plant with sampling locations indicated is shown in Figure S1. All duplicate samples

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were subjected to universal fungal ITS1 PCR, with 61 out of 64 samples resulting in positive

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amplification. ADONIS analysis based on Jaccard and Bray-Curtis matrices comparing

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significance of community difference between the two replicates groups showed no significant

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community variation was caused by duplicate samples (p-values > 0.05), which is consistent

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with previous study.40 Previous research has demonstrated high reproducibility of 16S rRNA



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sequencing results between replicate drinking water samples and technical replicates.16, 40 Thus,

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one replicate of each sample (32 samples) was subjected to universal 16S rRNA PCR, with all

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samples resulting in positive amplification. All negative controls indicated no contamination of

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reagents or equipment. In total, 446,902 ITS1 sequence reads (average 7326±7866 reads per

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sample) and 548,098 16S rRNA sequence reads (average 17128±6181 reads per sample) were

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used for taxonomic classification after demultiplexing and quality trimming.

230 231

3.2. qPCR of Total Bacteria, Total Fungi, Acanthamoeba spp., and PCR of Aspergillus

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fumigatus

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Quantitative PCR was utilized to determine microbial abundance through the treatment process.

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The average total bacterial abundance was 5.5±0.4 log10 genome equivalents/mL in raw water,

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3.4±0.4 log10 genome equivalents/mL post-sedimentation, 2.9±0.3 log10 genome equivalents/mL

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post-filtration, and 1.2±0.3 log10 genome equivalents/mL post-disinfection (Table 1). Bacterial

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abundance significantly decreased after each unit operation (all p-values < 0.05). The average

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fungal abundance was 3.4±0.5 log10 genome equivalents/mL in raw water, 1.6±0.5 log10 genome

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equivalents/mL post-sedimentation, 0.9±0.4 log10 genome equivalents/mL post-filtration, and

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0.2±0.4 log10 genome equivalents/mL post-disinfection (Table 1). A significant decrease in

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fungal abundance was observed post-sedimentation and post-filtration (p-values < 0.05). The

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average post-disinfection fungal abundance was lower than post-filtration, but without a

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statistically significant change (p > 0.05).

244 245

The inability of molecular methods to discriminate DNA from viable and non-viable cells may

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inflate the bacterial and fungal abundance detected, especially in the post-disinfection samples.

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To confirm observed results, we collected additional samples and analyzed culturable


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heterotrophic bacteria and filamentous fungi. The culture analysis showed a similar decreasing

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trend through the treatment process train for both heterotrophic plate count and filamentous fungi

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colony counts (Table 1). Both the qPCR and subsequent culturing results suggest the treatment

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process significantly reduced bacterial and fungal abundance, agreeing with previous culture-

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based studies.41, 42

253 254

As the eukaryote Acanthamoeba spp. is an important human pathogen43 and may host other

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opportunistic pathogens such as Legionella spp.,44,

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presence and abundance of Acanthamoeba spp.. Six out of eight raw water samples and one out

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of eight post-filtration samples had detectable level of Acanthamoeba spp., with average

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abundances of -1.36±1.61 log10 genome equivalents/mL and -1.51 log10 genome equivalents/mL,

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respectively. None of the post-sedimentation or post-disinfection samples had detectable

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Acanthamoeba spp.. Previous studies demonstrated the isolation of Acanthamoeba spp. from

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post-filtration water samples46 and drinking water distribution systems.8,

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Acanthamoeba spp. in raw water of the current study indicate that Acanthamoeba spp. likely

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originate from an environmental source and persist within the filtration process, and further study

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investigating the diversity and association of Acanthamoeba spp. with filter media would be

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helpful for understanding the role of media filtration in Acanthamoeba spp. persistence through

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drinking water treatment. Similarly, as Aspergillus fumigatus is among the most notable human

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fungal pathogens and has previously been isolated from drinking water,14 we conducted PCR

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targeting A. fumigatus. No sample had detectable A. fumigatus based on gel visualization of PCR

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products.

45

qPCR was conducted to evaluate the

47

The presence of

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3.3. Microbial Diversity Through The Treatment Process

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We assessed the beta-diversity for both bacterial and fungal communities, which is a measure of

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the degree of microbial community similarity between samples. For both bacterial and fungal

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communities, the mean community dissimilarity distances between samples at different water

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treatment unit operations were all significantly higher than the mean distances across samples of

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different months at the same sampling location (p-values < 0.05) (Table S2). This result indicates

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that water treatment unit operations more strongly drove both bacterial and fungal community

278

structure than sampling time.

279 280

Non-metric multidimensional scaling (NMDS) analysis of the bacterial community based on

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unweighted UniFrac indicated that samples clustered within each sampling site and the bacterial

282

community structure shifted following each unit operation (Figure 1). The statistical significance

283

of the community shift following each unit operation was confirmed with pairwise ADONIS

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analysis (p-values < 0.05, Table S3). NMDS plots and ADONIS analysis based on weighted

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UniFrac demonstrated a similar trend of community variation, except that the variation between

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post-sedimentation and post-filtration samples was not statistically significant (Figure 1, Table

287

S3).

288 289

NMDS plot based on the Jaccard distance indicated that the fungal community structure shifted

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following sedimentation and media filtration. Post-disinfection samples clustered with post-

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filtration samples, indicating that the fungal community structure in post-disinfection samples

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was similar to post-filtration samples, and no further community structural variation occurred

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following disinfection (Figure 1). Pairwise ADONIS based on the Jaccard distance further


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indicated that the fungal community structure significantly shifted after sedimentation and media

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filtration (p-values < 0.05, Table S4), but no significant change occurred post-disinfection (p >

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0.05, Table S4). NMDS plots and ADONIS based on Bray-Curtis distance demonstrated the

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same pattern of fungal community variation between unit operations (Figure 1, Table S4).

298 299

We also assessed the variation in community richness (total number of taxonomic units) through

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the treatment process. The number of observed bacterial OTUs significantly decreased through

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each step of the treatment process (p-values < 0.05, Table 1). The number of fungal genera

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significantly decreased post-sedimentation and post-filtration (p-values < 0.05), but no

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significant decrease occurred post-disinfection (p > 0.05, Table 1). The decrease in bacterial

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richness through the water treatment process agrees with previous study.11 These results also

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identify a similar trend to bacteria for fungal richness with the exception of no significant change

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post-disinfection. Pearson correlation between water quality parameters (pH, conductivity, free

307

chlorine) and bacterial OTU number as well as fungal genera number were evaluated. However,

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no significant correlation was observed (- 0.5 < r-values < 0.5; p-values > 0.05). The only

309

exception was that a significant negative correlation was observed between the number of

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bacterial OTUs and free chlorine concentration (r = - 0.52; p < 0.01). Similarly, no significant

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correlation between water quality parameters and dominant taxa relative abundances was found

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(- 0.5 < r-values < 0.5; p-values > 0.05), consistent with previous studies.40, 48

313 314

Our results demonstrated that the bacterial community shifted following each treatment step, and

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disinfection shaped the final bacterial community in treated drinking water. The final fungal

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community structure in treated drinking water was primarily shaped by media filtration, as no



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significant fungal community change was observed following disinfection. Previous research

318

identified a similar trend with the bacterial community structure changing in response to addition

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of secondary disinfection in a premise plumbing system,49 with no change in fungal community

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structure found after the initiation of secondary disinfection.50 Media filtration has been

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previously shown to control the downstream bacterial community.10 The current result agrees

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with previous research, and further suggests free chlorine disinfection shifts the bacterial

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community entering the water distribution system, but does not pose a significant effect on the

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post-filtration fungal community.

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3.4. Temporal Beta-Diversity Within Water Treatment Processes

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Temporal beta-diversity following each treatment process unit operations was evaluated by

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comparing the average dissimilarity distance within each sampling location. Mean temporal

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bacterial community dissimilarity distances became apparently higher post-filtration and post-

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disinfection; and significantly higher post-disinfection regarding membership based dissimilarity

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(Unweighted UniFrac, p < 0.05) (Table S5). For the fungal community, membership based mean

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temporal community dissimilarity distance significantly increased post-filtration (Jaccard, p
0.05).

were the most

361



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Within Proteobacteria, Alpha-, Beta-, and Gammaproteobacteria were the most abundant classes

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and were each detected in all samples. The average relative abundance of Alphaproteobacteria

364

was 9.5±2.7% in raw water, 10.4±7.1% post-sedimentation, 30.5±30.1% post-filtration, and

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25.6±25.4% post-disinfection. A trend of increasing relative abundance post filtration was

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observed, but was not statistically significant (p > 0.05). Alphaproteobacteria was previously

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found to be a dominant bacterial class within filter media, suggesting media filtration may

368

provide a survival niche for Alphaproteobacteria, or conversely that Alphaproteobacteria are less

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efficiently removed by the filtration process.10, 12 In this study, Sphingomonas spp. was the most

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abundant genus within Alphaproteobacteria, and LEfSe analysis indicates that Sphingomonas

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spp. was the most enriched taxa in post-filtration samples (LDA score = 5.3, Table S6).

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Sphingomonas spp. is a common member of drinking water system biofilms and has an

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important role in biofilm formation,54-56 suggesting that Sphingomonas spp. may form a biofilm

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within filtration media and subsequently influence the post filtration bacterial community

375

structure. Betaproteobacteria had an average relative abundance of 41.1±10.1% in raw water,

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35.5±11.9% post-sedimentation, 31.3±15.8% post-filtration, and 19.6±15.0% post-disinfection,

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with a significant decreasing trend comparing raw water to post-disinfection water (p < 0.05)

378

(Figure 2). The average relative abundance of Gammaproteobacteria was 5.2±2.4% in raw water,

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8.5±11.2% post-sedimentation, 4.1±4.2% post-filtration, and significantly increased to

380

17.1±11.3% post-disinfection (p < 0.05). This increasing trend of Gammaproteobacteria relative

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abundance was confirmed by LEfSe analysis, showing Gammaproteobacteria was enriched post-

382

disinfection (LDA score > 2.0, Table S6). Acinetobacter spp. was found to be the most abundant

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Gammaproteobacteria genus to increase in relative abundance post-disinfection (LDA score =

384

4.8, Table S6). Several strains of Acinetobacter spp. were previously found to survive without


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significant inactivation when exposed to 4 mg/L free chlorine for 2 minutes.57 Previous study

386

also

387

Betaproteobacteria to the same level of free chlorine.58 This differential resistance to disinfection

388

likely drove the shift in the composition of Proteobacteria phylum during disinfection.

demonstrated

higher

resistance

by

Gammaproteobacteria

than

Alpha-

and

389 390

The Actinobacteria class was the dominant class within the Actinobacteria phylum and was

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detected in all samples with an average relative abundance of 17.0±9.4% in raw water,

392

12.3±8.6% post-sedimentation, 6.5±4.8% post-filtration, and 6.5±5.4% post-disinfection. A

393

significant trend of decreasing relative abundance was observed comparing raw water to post-

394

disinfection water (p-values < 0.05) (Figure 2). This finding is consistent with a previous study

395

which reported decreased relative abundance of Actinobacteria comparing treated drinking water

396

to raw water.10 Actinobacteria relative abundance was not decreased comparing post-filtration to

397

post-disinfection samples, suggesting the potential disinfection resistance of this bacterial group.

398 399

The Bacteroidetes phylum had an average relative abundance of 15.4±2.7% in raw water,

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24.7±8.9% post-sedimentation, 23.8±15.8% post-filtration, and 2.0±2.7% post-disinfection.

401

Bacteroidetes had significantly increased relative abundance post-sedimentation (p < 0.05), no

402

significant shift following filtration, and a significantly decreased relative abundance post-

403

disinfection (p < 0.05). Cytophagia and Saprospirae were the most abundant bacterial classes

404

within the Bacteroidetes phylum and reflected the same trend of relative abundance as the

405

Bacteroidetes phylum. The decreasing trend of relative abundance for Bacteroidetes post-

406

disinfection suggested this phylum might be more sensitive to chlorination, and further



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evaluation of disinfection kinetics would help to explain the mechanism for the observed

408

negative selection.

409 410

The Cyanobacteria and Firmicutes phyla shared similar trends of low relative abundance without

411

a statistically significant change (p-values > 0.05) before disinfection, but significantly increased

412

relative abundance after disinfection (p-values < 0.05). LEfSe analysis also indicated that

413

Cyanobacteria and Firmicutes were significantly more abundant post-disinfection (LDA scores >

414

2.0, Table S6). The average relative abundance of Cyanobacteria was 1.0±1.3% in raw water,

415

3.3±4.9% post-sedimentation, 0.7±1.1% post-filtration, and statistically significantly increased to

416

10.7±10.4% post disinfection (p < 0.05). The increase of Cyanobacteria was associated with the

417

4C0d-2 class, which had a significantly increased post-disinfection relative abundance (p < 0.05)

418

and became the dominant Cyanobacterial class post-disinfection, suggesting potential resistance

419

to chlorination by this non-photosynthetic Cyanobacteria class.59 This class has previously been

420

identified to be abundant in water systems, including drinking water treatment plants60 and a

421

simulated drinking water distribution system.61 Firmicutes had an average relative abundance of

422

1.1±0.9% in raw water, 1.0±1.3% post-sedimentation, 0.4±0.7% post-filtration, and statistically

423

significantly increased to 10.8±7.5% post-disinfection (p < 0.05). Within the Firmicutes phylum,

424

Bacilli and Clostridia were the most abundant classes and a statistically significant increase in

425

relative abundance was observed for Bacilli and Clostridia following disinfection (p-values
0.05) (Figure 2). Leptospirillum spp.

434

was identified in all eight post-disinfection samples but was only detected in 10 out of 24

435

(41.7%) of pre-disinfection samples. LEfSe analysis output also indicated that Leptospirillum

436

spp. were enriched in post-disinfection samples (LDA score = 4.7, Table S6), suggesting

437

Leptospirillum spp. to be potentially disinfection resistant. The Leptospirillum genus, which

438

contains iron-oxidizing species,64 was previously found in a cast iron water pipe biofilm and has

439

been suggested to contribute to iron pipe corrosion.65 Further evaluation of the resistance of

440

Leptospirillum spp. to disinfectant may explain its presence and inform its control in drinking

441

water distribution systems.

442 443

3.6. Fungal Taxonomy

444

Fungal ITS1 sequences were classified to 1043 fungal genera across all samples. Three phyla

445

were detected in all samples: Ascomycota, Basidiomycota, and Zygomycota. These three phyla

446

represented an average relative abundance of 98.4±1.4%. The relative abundance of fungal

447

genera detected in all samples is shown in Figure 3.

448 449

The average relative abundance of the Ascomycota phylum was 66.2±19.3% in raw water,

450


451

with no statistically significant change through the water treatment process (p-values > 0.05).

452

Within the Ascomycota phylum, the most abundant genera were Penicillium, Aspergillus,



Page 20 of 31

453

Alatospora, Taphrina, and Tuber, each detected in all samples. Penicillium spp. had an average

454

relative abundance of 1.5±1.8% in raw water, 9.3±13.5% post-sedimentation, 25.2±32.5% post-

455

filtration, and 41.3±38.3% post-disinfection, with a significant increasing trend comparing raw

456

water to post-disinfection water (p < 0.05) (Figure 3). The average relative abundance of

457

Aspergillus spp. was 0.6±0.8% in raw water, 1.2±1.1% post-sedimentation, 1.2±1.2% post-

458

filtration, and 3.6±2.1% post-disinfection. Similar to Penicillium spp., the relative abundance of

459

Aspergillus spp. significantly increased from raw water to post-disinfection water (p < 0.05);

460

including a significant increase in relative abundance from post-filtration to post-disinfection

461

samples (p < 0.05) (Figure 3). LEfSe analysis also shows Penicillium spp. and Aspergillus spp.

462

were significantly enriched post-disinfection (LDA scores > 2.0, Table S7). Alatospora spp. and

463

Taphrina spp., with overall average relative abundances of 2.1±3.9% and 2.8±5.8% respectively,

464

did not demonstrate a significant change in relative abundance through the treatment process (p-

465

values > 0.05). A decreasing trend of Tuber spp. relative abundance was observed from

466

19.0±24.5% in raw water to 8.0±7.0% post-disinfection, yet was not statistically significant (p-

467

values > 0.05).

468 469

The average relative abundance of the Basidiomycota phylum was 23.1±19.2% in raw water,

470


471

with no significant change through the treatment process (p-values > 0.05). Mrakia and

472

Peniophora were the two genera within the Basidiomycota phylum detected in all samples.

473

Peniophora spp., a soil fungus belonging to Basidiomycota,66 was previously detected in tap

474

water samples.50,

67

No significant change in relative abundance across the water treatment


Page 21 of 31


475

process was observed for either Mrakia or Peniophora (p-values > 0.05), mirroring the trend on

476

the phylum level (Figure 3).

477 478

The average relative abundance of the Zygomycota phylum was 8.4±7.1% in raw water,

479

8.6±8.7% post-sedimentation, 6.7±10.5% post-filtration, and 6.1±7.8% post-disinfection. No

480

statistically significant change was observed through the treatment process (p-values > 0.05).

481

The Schizangiella and Basidiobolus genera belonging to Zygomycota were detected in all

482

samples, and relative abundance significantly decreased through the treatment process (p-values

483

< 0.05).

484 485

Isolation of the fungal phyla Ascomycota, Basidiomycota, and Zygomycota from untreated

486

surface water has previously been reported.68-70 The effect of centralized water treatment process

487

on the fungal community structure in drinking water has not been previously evaluated; however,

488

Penicillium spp. and Aspergillus spp. have been widely isolated from drinking water.13, 71, 72 Our

489

results demonstrated that the relative abundance of Penicillium spp. and Aspergillus spp.

490

significantly increased through the water treatment process, especially post-disinfection,

491

suggesting that these two genera were less efficiently removed by the conventional water

492

treatment process or were more resistant to the selection pressure posed by water treatment

493

processes. Penicillium and Aspergillus produce unicellular spores with relatively small size (1.8-

494

6 µm × 2-4.5 µm).73-75 The small spore size may facilitate the passage of Penicillium and

495

Aspergillus through sedimentation and media filtration, while other fungal groups with larger

496

spore sizes were removed. Further research is needed to investigate the role of cell size in the

497

community structure change along the drinking water treatment process. Stronger resistance to



Page 22 of 31

498

oxidative disinfectants by waterborne fungi, especially Penicillium and Aspergillus, comparing

499

to common waterborne bacteria has been demonstrated by previous studies.51,

500

insignificant post-disinfection fungal community variation is likely related to the resistance to

501

chlorination. Further research focusing on fungal disinfection and ecology in municipal drinking

502

water could benefit the control of waterborne fungi, especially for Aspergillus as this genus

503

contains potential human pathogens such as A. fumigatus and A. flavus.77

52, 76

The

504 505

3.7. Implications

506

In the current study we applied molecular approaches to investigate bacterial and fungal

507

abundance, diversity, and taxonomy through centralized surface water treatment. Bacterial and

508

fungal abundance and richness were observed to significantly decrease following sedimentation

509

and media filtration, but only the bacterial community was affected by disinfection. Assessing

510

the bacterial and fungal community beta-diversity, each treatment step was found to shift

511

bacterial and fungal community structure, with the exception of disinfection on the fungal

512

community structure.

513 514

The current study shows disinfection by free chlorine shaped the bacterial community entering

515

the distribution system. Differential sensitivity to disinfection likely drives the selection of

516

bacterial taxa in final product water. The fungal community was found to be more influenced by

517

physical processes (sedimentation and media filtration), highlighting the difference in

518

community shifts between bacteria and microbial eukaryotes during drinking water treatment.

519

The observed difference in community drivers between bacteria and fungi suggests the necessity

520

of considering a broader microbiome including both bacteria and eukaryotes when investigating


Page 23 of 31


521

drivers of the drinking water microbiome. Ultimately, this study demonstrates that centralized

522

drinking water treatment shapes both the bacterial and eukaryotic microbiome of finished

523

drinking water, suggesting the future potential to harness these processes to select for a more

524

desirable drinking water microbial community.

525 526 527 528 529 530 531 532 533 534

Supporting Information Additional descriptions of experimental methods, statistics of 16S rRNA sequencing reads not matching reference database (Table S1), comparison of mean community dissimilarity distance across water treatment processes and time (Table S2 & S5), results of ADONIS analysis (Table S3 & S4), a water treatment processes diagram with sampling locations (Figure S1), principal coordinate analysis plots (Figure S2 & S3), detailed LEfSe analysis output (Table S6 & S7), and additional bacterial beta diversity analysis based on Jaccard and Bray-Curtis matrices (Table S8 – S10, Figure S4).

535

Acknowledgement

536 537 538 539 540 541

The authors wish to thank the staff at the anonymous drinking water treatment plant for assistance with sample collection.

TOC Art

542



543 544 545 546 547

548 549 550 551 552 553 554 555 556

Page 24 of 31

Tables

Table 1. Bacterial and fungal diversity and abundance and water quality parameters. Detection equivalent/mL for bacteria qPCR and -0.66 log10 genome equivalent/mL for fungi. PostRaw water sedimentation Bacterial community Total number of observed OTUs 6152 3736 Average number of observed OTUs per sample 1744.5±482.6 968.3±364.9 Average bacterial abundance (log10 genome equivalent/mL) 5.5±0.4 3.4±0.4 Average heterotrophic plate count (CFU/mL) 501.7±120.2 0.5±0.7 Fungal community Total number of observed genera 980 585 Average number of observed genera per sample 396.5±173.4 188.9±60.8 Average fungal abundance (log10 genome equivalent/mL) 3.4±0.5 1.6±0.5 Average total filamentous fungal colony (CFU/L) 17750±13081.5 52.5±31.8 pH 7.5±0.4 7.5±0.5 Conductivity (µS/cm) 279.7±79.7 304.5±97.8 Free chlorine residual (mg/L) ND ND ND: not detected

limits were -0.27 log10 genome Postfiltration

Postdisinfection

2207 546.4±238.1

995 192.1±68.7

2.9±0.3 ND

1.2±0.3 ND

315 91.1±51.5 0.9±0.4 7.8±1.1 7.6±0.4 311.9±66.0 ND

294 91.5±28.6 0.2±0.4 3.3±1.1 7.6±0.2 303.1±75.8 0.5±0.3


Page 25 of 31

557 558 559 560 561 562


Figures

1.2

Unweighted UniFrac (Bacterial)

Weighted UniFrac (Bacterial)

2

1

Raw water

1.5

0.8 1 0.4

NMDS2

NMDS2

0.6

0.2 0

Post-sedimentation

0.5 Raw water 0

Post-filtration

-0.5

-0.2 -1 -0.4

Post-disinfection -1.5

-0.6 -0.8

stress = 0.07

-2

stress = 0.11 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 NMDS1

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 NMDS1 2.5

2

2

1.5

1.5

1

1

0.5

NMDS2

NMDS2

Jaccard (Fungal) 2.5

0 -0.5

0

-0.5

-1

-1

-1.5

-1.5

-2

563 564 565 566 567 568

0.5

stress = 0.14 -2 -2 -1.5 -1 -0.5 0 0.5 NMDS1

1

1.5

2

2.5

Bray-Curtis (Fungal)

Ra w wa ter

stress = 0.18 -2 -1.5 -1 -0.5 0 0.5 NMDS1

1

1.5

2

Figure 1. Non-metric Multidimensional Scaling (NMDS) plot of the bacterial community (based on unweighted and weighted UniFrac distances) and fungal community (based on Jaccard and Bray-Curtis distances).



Page 26 of 31

Relative abundance (%)

80

60

Proteobacteria_Alphaproteobacteria Proteobacteria_Betaproteobacteria Proteobacteria_Gammaproteobacteria Actinobacteria_Actinobacteria Bacteroidetes_Cytophagia Bacteroidetes_[Saprospirae] Cyanobacteria_4C0d-2 Firmicutes_Bacilli Firmicutes_Clostridia Nitrospirae_Nitrospira

40

20

0 RW

569 570 571 572 573 574 575 576

P_S

P_F

P_D

Figure 2. Relative abundance of dominant bacterial classes through the water treatment processes. Boxplot range defined as the first and third quartile; outliers defined as 1.5 times the interquartile range greater than the third quartile or less than the first quartile.



Relative abundance (%)

Page 27 of 31

75 Ascomycota_Penicillium Ascomycota_Aspergillus Ascomycota_Alatospora Ascomycota_Taphrina Ascomycota_Tuber Basidiomycota_Mrakia Basidiomycota_Peniophora Zygomycota_Schizangiella Zygomycota_Basidiobolus

50

25

0 R_W

577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601

P_S

P_F

P_D

Figure 3. Relative abundance of fungal genera detected in all samples through the water treatment process. Boxplot range defined as the first and third quartile; outliers defined as 1.5 times the interquartile range greater than the third quartile or less than the first quartile.

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