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Variation of Bacterial Communities with Water Quality in an Urban Tropical Catchment Jean Pierre Nshimyimana, Adam Joshua Ehrich Freedman, Peter Shanahan, Lloyd C.H. Chua, and Janelle Renee Thompson Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b04737 • Publication Date (Web): 17 Apr 2017 Downloaded from http://pubs.acs.org on April 18, 2017

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Environmental Science & Technology

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Variation of Bacterial Communities with Water Quality in an Urban Tropical Catchment

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Jean Pierre Nshimyimana1, 2, 4, 5, Adam Joshua Ehrich Freedman2,4, Peter Shanahan2, 4, Lloyd C.

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H. Chua3, and Janelle R. Thompson2, 4, *

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1. School of Civil and Environmental Engineering, Nanyang Technological University (NTU),

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50 Nanyang Avenue, Singapore 639798, Singapore 2. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology (MIT), 77 Massachusetts Avenue, Cambridge, MA 02139, USA

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3. School of Engineering, Deakin University, Waurn Ponds, Geelong, Victoria 3216, Australia

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4. Centre for Environmental Sensing and Modeling (CENSAM), Singapore-MIT Alliance for

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Research and Technology (SMART), 1 Create Way, Singapore 138602, Singapore 5. Singapore Center on Environmental Life Sciences Engineering (SCELSE), NTU, 60 Nanyang Drive, Singapore 637551

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*Corresponding author: Janelle Thompson: [email protected]

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1 ACS Paragon Plus Environment


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Abstract A major challenge for assessment of water quality in tropical environments is the natural

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occurrence and potential growth of Fecal Indicator Bacteria (FIB). To gain a better

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understanding of the relationship between measured levels of FIB and the distribution of sewage-

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associated bacteria including potential pathogens in the tropics this study compared the

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abundance of FIB (Total coliforms and E. coli) and the Bacteroidales (HF183 marker) with

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bacterial community structure determined by next-generation amplicon sequencing. Water was

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sampled twice over 6 months from 18 sites within a tropical urban catchment and reservoir,

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followed by extraction of DNA from microorganisms, and sequencing targeting the V3-V4

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region of the 16S rRNA gene. Multivariate statistical analyses indicated that bacterial

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community composition (BCC) varied between reservoir and catchment, within catchment land-

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uses, and with E. coli concentration. Beta-regression indicated that the proportion of sequences

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from sewage-associated taxa (SAT) or pathogen-like sequences (PLS) were predicted most

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significantly by measured levels of E. coli(log MPN/100ml) (χ2>8.7; p400bp which enable more confident taxonomic assignment. NGS now provides the opportunity

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to examine how microbial diversity at the genus and species-level varies with water quality

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predicted by the abundance of FIB and source tracking markers, especially in tropical

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environments. While numerous studies have attempted to relate concentrations of specific

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pathogen targets to FIB measured in a water body e.g. 35-38 such studies generally showed poor to

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moderate correlation, likely due to highly variable dynamics of a specific pathogen target in a

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complex environment. A survey-based approach such as NGS provides the opportunity to

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simultaneously evaluate a diversity of microorganisms and identify potential risk agents without

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pre-defining targeted groups.

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The tropical urban island of Singapore has an advanced and reputable water and

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wastewater management infrastructure where stormwater is collected through engineered drains

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and stored in reservoirs. Studies of surface microbial water quality in Singapore have noted

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elevated levels of FIB (E. coli, total coliforms, and Enterococci) at sites from various catchments

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during dry weather (i.e. defined as >48 hours after a rain event) 39-42.

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The goal of this study was to apply IlluminaTM MiSeq 16S rRNA gene amplitag

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sequencing to characterize the bacterial community composition (BCC) including sequences

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related to sewage-associated taxa (SAT) and human pathogens (i.e. pathogen-like sequences,

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PLS) in water samples collected from an urban reservoir and catchment in Singapore. We

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hypothesized that bacterial communities in samples would vary with site, land-use, and sample

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date, reflecting seasonal and spatial ecology. We also hypothesized that sites with high measured



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levels of E. coli and/or HF183 marker would harbor bacterial communities enriched in pathogen-

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like bacteria or SAT across land-uses and sample dates. Our findings will be useful for

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evaluating the utility of next-generation sequencing to identify impaired tropical waters and to

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identify specific bacterial targets that may be relevant for further monitoring using quantitative

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methods.

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2. Methodology

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2.1 Study design and sample collection

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2.1.1 Sampling and Site Characterization

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Water samples from 18 sites in an urban reservoir and catchment in the northwest of

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Singapore Island were collected during dry weather in January and July 2009 (Figure S5) 39. Dry

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weather was defined as >48 hours following a rainfall event based on rain gauges distributed

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around the watershed and monitored by the Public Utilities Board41. Two additional samples

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were obtained from municipal sewers in a high-density residential area within the catchment in

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January 2010. The catchment covers 61 square kilometers with mixed land uses where the

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residential areas (R) (19%) are distinguished by a high-density population in high-rise buildings

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and farming areas (F) (5%) are characterized by horticultural and agricultural activities including

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small scale production of flowers, vegetables, ornamental fish, and chicken eggs 39, 40. The

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undeveloped area (U), the largest of the land-use categories covering 76% of the catchment, is

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maintained as limited-access land dominated by native vegetation (Figure S5). Land use data was

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provided by the Singapore Public Utilities Board in the form of a GIS shapefile43.

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Storm water and sewage are transported in the catchment via separate conveyance

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systems. An underground system conveys sewage to wastewater treatment plants, while storm

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water and surface runoff are drained by open concrete-lined channels that discharge into rivers 4 ACS Paragon Plus Environment

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and reservoirs. Some of the farming areas are served by on-site sewage and wastewater treatment

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systems, while the rest of the farming areas and residential area is served by the underground

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sewerage system. Catchment water samples were collected from open concrete-lined channels

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conveying water to the reservoir. The majority of catchment collection sites were drains in small

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upstream watersheds with uniform land use. Exceptions were sites F9, F10, and R2 classified as

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residential or horticultural, which also drained a minor proportion of undeveloped lands.

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Reservoir water samples were collected at four stations approximately 800m and 1,200m apart to

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provide spatial coverage of the reservoir surface.

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The climate is tropical with seasons defined by prevailing wind directions and weather

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patterns corresponding to the Northeast Monsoon (December to March), Southwest Monsoon

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(June to September), and two-inter Monsoon seasons, with year-round temperatures ranging

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from highs of 29 to 31°C during the day to lows of 23 to 24°C at night. Water temperatures vary

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between 27 and 29°C throughout the year 39.

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2.1.2 Quantification of HF183 and IDEXX-based enumeration of E. coli and Total

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coliforms

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Analysis of samples by DNA extraction and qPCR-based quantification of the HF183

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marker according to MIQE standards has been reported in a prior publication40. In brief,

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particulates from water samples were concentrated onto 0.2-µm-pore-size cartridge filters

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(Millipore, Billerica, MA, USA), subjected to extraction of environmental DNA, and

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quantification of the Bacteroidales HF183 marker in units of genome equivalents (GE) by

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qPCR40. Enumeration of FIB (E. coli, Total Coliforms) by the most-probable-number (MPN)

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method (IDEXX Laboratories, Inc., Westbrook, ME, USA) was carried out with 100 ml volumes

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of undiluted sample, or with 1:10 or 1:100 sample dilutions in sterile deionized water40. The



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detection limit for HF183 was 150 GE/100ml while for E. coli the detection limit was 1

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MPN/100ml40.

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2.2 Illumina sequencing

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2.2.1 Library preparation Environmental DNA was used as template for PCRs for Illumina library preparation

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targeting the V3 to V4 16S rRNA region as described in Preheim et al. 44 with modification of

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primers (Table 1). Briefly, the 16S rRNA gene was amplified using Taq polymerase (New

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England BioLabs® Inc, Ipswich, MA, USA) in 20 µl of reaction volume containing 100 µM each

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of primers 357F 45 and 806R 46, 10 mM dNTPs, 50 mM MgCl2, and bovine serum albumin

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(BSA). To avoid cycling templates past the mid-log phase and to normalize template

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concentration, samples were subjected to Real-time qPCR to determine the optimum PCR cycles

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for library construction (15 to 27 cycles). A no-sample DNA extraction control was included as

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template and did not amplify during qPCR or during library construction, therefore was not

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included in sequencing. Illumina adaptors and barcodes were added as previously described 44

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(Table 1). Barcoded PCR products at the predicted size of 550-650 bp were gel purified

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(QIAQuick Gel extraction kit, QIAGEN®, Valencia, CA, USA) and sequenced using the

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Illumina MiSeq platform at the MIT BioMicro Center (Cambridge, MA, USA) (Table S5). All

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DNA sequences generated in this study have been deposited in Genbank (accession numbers

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KX967493-KX976459).

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2.2.2

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Next generation 16S rRNA gene sequencing Base-calling and quality filtering were implemented by Illumina MiSeq software to

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generate FASTQ files containing sequences and quality scores. Resulting FASTQ files were

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demultiplexed based on barcode sequence and were processed through the UPARSE pipeline for


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additional quality control and identification of operational taxonomic units (OTUs) at 97%

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nucleotide identity 47. Overlapping regions of each paired-end sequence were merged to create a

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single read. Sequences were then quality filtered by adjusted Q score, globally trimmed to 400bp

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(sequences shorter than 400bp were discarded), and were de-replicated. Following OTU

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clustering all singleton sequences were discarded per recommended settings and chimeric

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sequences were identified using UCHIME 47. OTUs were taxonomically classified based on

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representative sequences (cluster centroids) from kingdom to species using Silva ARB software

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mapped to OTUs to create a matrix of sequence abundance.

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2.3 Identification of pathogen-like sequences (PLS) and sewage-associated taxa (SAT)

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with a bootstrap value of 60 % as assignment cut off. Trimmed and filtered sequences were

OTUs were screened to identify genera and species corresponding to human etiological

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agents as indicated by the US National Institute of Health (US NIH) 49, the Pathosystems

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Resources Integration Center (PATRIC) in collaboration with the National Institute of Allergy

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and Infectious Diseases (NIAID) 50, and a database of emerging infectious diseases 51. In

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addition, all OTUs assigned to pathogen-bearing genera were screened for species-level

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relatedness to potential bacterial pathogens obtained from clinical specimens associated with

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human disease using BlastN with the criterion of ≥99% sequence identity where the best-hit

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sequence was confirmed via BLAST distance-based clustering. Sewage-associated taxa (SAT)

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were identified by one of two criteria: 1) as OTUs annotated to a genus previously determined as

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sewage-associated by McClellan and co-workers 1 , or 2) OTUs shared by two municipal sewage

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samples from Singapore with annotations indicating that they were derived from sewage or the

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human gut (Table S2).



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2.4 Data Analysis Multivariate analysis of bacterial community composition (BCC) and the diversity of

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pathogen-like sequences (PLS) and sewage-associated taxa (SAT) was conducted in

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PERMANOVA+ for Plymouth Routines In Multivariate Ecological Research (PRIMER) V7 52.

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Principal Coordinate Analysis (PCO) and ANOSIM (analysis of similarity) of Bray-Curtis

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similarity indices were used to identify samples with similar bacterial community composition.

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Permutational multivariate analysis of variance (PERMANOVA) was used to explore how BCC,

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SAT, and PLS varied with land use or sampling dates and was implemented for OTUs (BCC,

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and PLSs) or genera (SAT, PLSg). Concentrations of fecal indicator bacteria (Total coliforms

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and E. coli, MPN/100 ml) or HF183 marker (GE/100 ml) were log-transformed prior to all

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statistical analyses and modeling. The relationship between bacterial community composition

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(BCC) and log E. coli concentration or log HF183 GE/100ml was determined using the BIONEV

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best selection procedure routine with AIC (Akaike information criterion) as the selection

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criterion based on 999 permutations in PERMANOVA+. The variation in BCC, SAT, or PLS

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explained by the abundance of the log HF183 marker or log E. coli concentration was

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determined by distance-based linear modeling (DistLM) routine implementing the AIC selection

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criteria and Best procedure followed by application of the marginal test 53. Similarity Percentages

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(SIMPER) calculated by decomposing average Bray-Curtis dissimilarity between all pairs of

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samples into percentage contributions from each taxa, were used to identify taxa contributing to

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the similarity or dissimilarity of bacterial communities sampled in the catchment and reservoir.

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The sequence diversity in samples was compared at different sampling efforts by

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rarefaction analysis through the permute, lattice, and vegan packages in R Version 3.2.454-57. The

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distribution of log10-transformed indicator bacteria (E. coli and total coliforms) and HF183


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marker levels across samples was examined by Pearson's correlation and hierarchical clustering

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using Ward's method on standardized data (JMP Pro v.12). The extent to which log E. coli

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concentration, log HF183, land use, and sample date accounted for variability in the proportion

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of either PLS or SAT sequences observed across all samples in the dataset was modeled using

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beta-regression implemented via maximum likelihood in JMP Pro v.12 (SAS Institute Inc., Cary,

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NC, USA). To confirm the robustness of observed trends models were also run for catchment

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samples only. Beta-regression was selected as it models a continuous dependent variable

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restricted to the interval (0, 1) with respect to continuous and/or categorical predictor variables

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through a regression structure58. The statistical significance of individual predictors was assessed

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via the Wald Chi Squared test.

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3. Results

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3.1 Bacterial Community Composition (BCC) in an Urban Reservoir and Catchment

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A total of 3,810,864 paired-end Illumina MiSeq reads were quality filtered and

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overlapping paired ends were merged into 1,189,972 sequences ranging from 17,986 to 67,583

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sequences per sample (also referred to herein as “reads”). Sequences were mapped onto 9,205

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OTUs using the UPARSE pipeline (Table 2). All OTUs classified as bacterial (8, 967) were

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classified according to phylum and 96.4%, 94.0%, 89.3%, and 67.0% were classified to class,

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order, family, and genus, respectively. Overall, sequences from the Proteobacteria phyla

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dominated most samples (57% of sequences) followed by Bacteriodetes (16%), Cyanobacteria

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(9%), Firmicutes (6%), and Actinobacteria (4%) (Figure 1A). Rarefaction analysis of OTU

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richness indicated that, despite > 17,000 reads per sample, most sites were not sampled to

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saturation suggesting undiscovered diversity (Figure 1B).



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To evaluate the potential role of spatial and seasonal ecology in structuring bacterial

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communities in the reservoir and catchment samples the effects of land-use, sample site and

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sample month was examined. Catchment samples collected from the same site on two different

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dates harbored bacterial communities that were significantly correlated (ANOSIM R=0.32,

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p=0.03). The distribution of bacterial OTUs varied significantly between reservoir and catchment

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samples (PERMANOVA p=0.001, F =8.7) (Figure 2A-B) and among catchment land uses

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(PERMANOVA p=0.009, F=2) but did not vary significantly between months of sample

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collection (PERMANOVA p=0.16, F=1.2). Reservoir samples clustered away from samples

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collected from the catchment by PCO (Figure 2A) and were enriched in sequences from

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Cyanobacteria, Planctomycetes, Chlorobi, Bacteroidetes, and Chloroflexi (Spearman R=0.65 to

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0.9 to PCO1). Horticultural and residential samples were enriched in Proteobacteria (Spearman

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R=0.50 to PCO2) and Firmicutes (Spearman R=-0.98 to PCO1) (Figure 2A). Taken together

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these results suggest that characteristics of the sampling locations played a stronger role

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influencing the overall bacterial community composition than temporal variation.

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3.2 Relationship between BCC, Fecal Indicator Bacteria (FIB), and HF183

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To examine the hypothesis that sites with elevated FIB would harbor distinct bacterial

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communities, the relationship between BCC, FIB (E. coli, total coliform), and HF183 was

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examined in the reservoir and catchment samples. Total coliform was highly co-linear with E.

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coli(R=0.84), thus E. coli was used to represent both in subsequent analysis. As previously

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reported40, the majority of catchment samples were associated with E. coli levels greater than the

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US EPA single grab sample threshold of 235 MPN/100ml and E. coli concentrations were

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significantly related to land-use. The range of E. coli in samples considered in this study was

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below detection to 2.0 x 105 MPN/100ml, while HF183 concentrations ranged from 4.6 x 102 to


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9.5 x 105 GE/100ml. The composition and diversity of bacterial communities in the samples was

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correlated to a combination of log E. coli (MPN/100ml) and log HF183 (GE/100ml) (BIOENV

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Spearman R=0.48), and explained a combined cumulative variance of the bacterial community

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structure of 25.3% (DistLM, R2=0.25). E. coli concentrations explained more variation in the

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composition of bacterial communities than HF183 (E. coli, variation of 12%, p=0.001 compared

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to HF183, variation of 5.5%, p=0.016) (Table 3).

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3.3 Distribution and Composition of Sewage-Associated Taxa (SAT)

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To examine the hypothesis that sites with elevated levels of FIB would harbor signatures

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of sewage-associated taxa (SAT), we identified (30,087) reads (n=16 genera) that corresponded

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to OTUs shared by sewage samples analyzed as part of this study (n=2) or to bacterial groups

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proposed by McLellan et al. (2010) as associated with human fecal pollution. Sewage-associated

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sequences from this study shared substantial overlap with SAT described by McLellan et al.

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(2010) including the shared genera Bifidobacterium, Bacteroides, Parabacteroides,

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Faecalibacterium, Roseburia, Ruminococcus, Akkermansia, Subdoligranulum, Papillibacter, and

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Sutterella (Table S2)1.

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The composition of SAT sequences varied with catchment land use (PERMANOVA, p =

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0.004) but not sample month (Figure 2C, Table S1) with the genera Prevotella,

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Faecalibacterium, and Bifidobacterium enriched in horticultural areas and Papillibacter enriched

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in residential areas (Table S4). The composition of SAT was moderately correlated to measured

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levels of E. coli and weakly correlated with HF183 levels (BIOENV: E. coli, R=0.55 and HF183,

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R=0.1). E. coli and HF183 explained a combined cumulative variance of 37.6% (R2=0.37) in

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SAT composition with E. coli explaining more variation than HF183 (E. coli: 22.3%, p=0.001

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compared to HF183: 14.4%, p=0.001) (Table 3).



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The proportion of SAT sequences were highest in horticultural areas (0.05% to 16.5% of

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total reads; n=14) and lowest in the reservoir (0.2).

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3. 4 Distribution and Composition of Pathogen-like Sequences

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To examine the hypothesis that samples with elevated levels of FIB would also harbor

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signatures of potential human pathogens, we classified sequences as pathogen-like based on

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named genus or species-level identity to known or emerging pathogens by BlastN. Out of 75,687

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sequences, 6.3% were classified to 33 genera harboring known pathogens (PLSg) (Table 2)

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while 2.3% of sequences matched pathogens at the species level (PLSs). The most highly

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represented PLSg were Acinetobacter (38%), Arcobacter (22%), Pseudomonas (8.2%),

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Aeromonas (7.4%), and Clostridium (7%). Samples with the highest and lowest contribution

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from PLSg sequences were respectively F10_7 (33%) and K4_1 (0.43%). The composition of

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PLSg and PLSs in samples clustered distinctly with reservoir or catchment origin (Figure 2D and


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Figure S3) and varied with catchment land use and collection month (PERMANOVA, p0.1

χ2=3.35, p=0.067

SAT

All Samples

0.66

χ2=14.0, p=0.0002

p>0.1

χ2=7.6, p=0.0057

SAT

Catchment only

0.61

χ2=12.0, p=0.0005

p>0.1 χ2=13.1, p=0.0003 χ2=12.8, p=0.0003

p>0.1

χ2=6.0, p=0.014

*Datasets considered were all samples (n=36) or catchment-only samples (n=30).


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Figure 1: (A) Highly represented phyla across samples collected from the reservoir and catchment. Sample codes indicate land use: R=Residential, U=Undeveloped, K=Reservoir, F=Horticultural/Farming, sample number, and collection date “_1” identifies samples collected January 2009, and the rest were collected in July 2009. (B) Rarefaction analysis of species richness in individual samples. Line color corresponds to land use: Red=Reservoir, Blue=Residential, Cyan=Undeveloped, Green=Horticultural, and Purple = Reference samples. Reference samples 114_Sw and 115_Sw were collected January 2010 from sewage infrastructure



Figure 2. Multivariate analysis of bacterial community composition based on Principal Coordinate Analysis (PCO) of Bray-Curtis resemblance between samples. Sample codes indicate land use: R=Residential, U=Undeveloped, K=Reservoir, and F=Horticultural/Farming. Bacterial communities are distinguished by (A) bacterial phyla (B) OTUs, (C) SAT OTUs, and (D) PLS OTUs in the catchment (horticultural, residential, and undeveloped) and reservoir sites. (A) Individual bacterial phyla contributing to variation were determined by Spearman correlation (R>0.65) to the first two PCO axis and are represented by vectors.


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Figure 3. Draftsman plot of quantities considered in this study: Log HF183 (GE/100ml and Copies/ng), E. coli (MPN/100ml), and proportions of sequences corresponding to SAT, PLSg, PLSs, and B. dorei OTU45. Significant Pearson correlations (p

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