Relationship of Microbiota and Cyanobacterial Secondary Metabolites

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Relationship of microbiota and cyanobacterial secondary metabolites in Planktothricoides-dominated bloom Shu Harn Te, Boon Fei Tan, Janelle Renee Thompson, and Karina Yew-Hoong Gin Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b05767 • Publication Date (Web): 27 Mar 2017 Downloaded from http://pubs.acs.org on March 28, 2017

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Environmental Science & Technology

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Relationship of microbiota and cyanobacterial

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secondary metabolites in Planktothricoides-

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dominated bloom

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Shu Harn, Te1, Boon Fei, Tan2, Janelle, R. Thompson2, 3and Karina Yew-Hoong, Gin1,4*

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1

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Drive 1, #02-01 T-Lab Building, Singapore 117411

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Technology Centre, 1 CREATE Way, #09-03 CREATE Tower, Singapore 138602

NUS Environmental Research Institute, National University of Singapore, 5A Engineering

Centre for Environmental Sensing and Modelling, Singapore-MIT Alliance for Research and

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Massachusetts Avenue, Cambridge, MA 02139-4307

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4

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Engineering Drive 2, E1A 07-03, Singapore 117576

Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77

Department of Civil and Environmental Engineering, National University of Singapore, 1

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* corresponding author: Karina Yew-Hoong, Gin; [email protected]; +656516 8104 (Tel)

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ABSTRACT GRAPHICS

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19 20 21 22

KEYWORDS

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Cyanobacterial bloom, 16S rRNA OTU (operational taxonomy unit), microbial community,

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Planktothricoides, off-flavours.

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ABSTRACT

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Identification of phytoplankton species and microbial biodiversity is necessary to assess water

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ecosystem health and the quality of water resources. We investigated the short-term (2 days)

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vertical and diel variations in bacterial community structure and microbially-derived secondary

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metabolites during a cyanobacterial bloom that emerged in a highly urbanized tropical reservoir.

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The waterbody was largely dominated by the cyanobacteria Planktothricoides spp., together with

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the Synechococcus, Pseudanabaena, Prochlorothrix and Limnothrix. Spatial differences (i.e.

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water depth) rather than temporal differences (i.e day vs night) better explained the short-term

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variability in water quality parameters and bacterial community composition. Difference in

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bacterial structure suggested a resource driven distribution pattern for the community. We found

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that the freshwater bacterial community associated with cyanobacterial blooms is largely

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conserved at the phylum level, with Proteobacteria (β-proteobateria), Bacteroidetes and

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Actinobacteria as the main taxa despite the cyanobacterial species present and geographical

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(Asia, Europe, Australia and North America) or climatic distinctions. Through multivariate

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statistical analyses of the bacterial community, environmental parameters and secondary

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metabolite concentrations, we observed positive relationships between the occurrences of

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cyanobacterial groups and off-flavour compounds (2-methyisoborneol and β-ionone), suggesting

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a cyanobacterial origin. This study demonstrates the potential of 16S rRNA gene amplicon

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sequencing as a supporting tool in algal bloom monitoring or water resource management.

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1.

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Proliferation of cyanobacteria in freshwater systems has increased in scale and frequency in past

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decades as a consequence of eutrophication and climate change, becoming one of the major

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challenges in protecting aquatic ecosystem health and maintaining safety of water resources 1.

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The production of secondary metabolites and exudates that are toxic to organisms and humans or

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having unpleasant smells is one of the key concerns related to cyanobacterial blooms 2. These

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compounds can be synthesized by different taxa or genera 3, 4 which complicates the water

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management strategy in controlling the sources of these pollutants.

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Extensive effort has been carried out to explain bloom formation with regards to nutrients and

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physiochemical factor 5. Recently, more studies have focused on the bacterial community that

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thrives alongside the cyanobacterial population 6. Indeed, heterotrophic bacteria can be

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influenced by biotic and abiotic factors induced by blooms such as nutrients, temperature, pH,

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alkalinity and organic carbon 7, 8. Similarly, heterotrophic bacteria can also alter the water

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environments 9 leading to positive or negative impacts on blooms. For instance, Flavobacterium,

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Streptomyces and Oxalobacteraceae can enhance cyanobacterial growth, Pedobacter and

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Arthrobacter may cause negative impacts 6, 10, while Limnobacter, Rhizobium and Lacibacter

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develop symbiotic relationships with cyanobacteria 11. Previous studies demonstrated that

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bloom-associated microbial communities are highly varied at phylum level 8, 12. However, the

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functional potential revealed by genes could be highly conserved despite differences in

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community assemblage 12, such as nitrogen fixing / assimilation 8, organic matter degradation 6,

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11

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Riding on the development of high-throughput sequencing (HTS), information regarding

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bacterial groups and microbial diversity in bloom-affected water bodies is fast expanding but the

INTRODUCTION

and iron uptake 13.


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existing literatures report only a few genera such as Microcystis, Anabaena and

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Cylindrospermopsis 8, 11, 14, 15. In addition, most of the relevant studies focused on temperate or

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subtropical regions with very limited investigations done in the tropical climate zone 16. In the

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current study, we carried out an intensive short-term sampling in one of the reservoirs in an

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urbanized area of Singapore, during which a rarely reported filamentous cyanobacterium,

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Planktothricoides, dominated the waterbody. This study aims to: (i) determine the bacterial

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community associated with Planktothricoides bloom (ii) investigate diel and vertical variations

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of physiochemical variables, cyanobacterial metabolites and microbial community, and (iii)

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identify potential metabolite producers using 16S rRNA amplicon sequencing combined with

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correlation analysis.

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2.

MATERIALS AND METHODS

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2.1.

Site description and sampling

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This study was conducted in a freshwater reservoir located in a fully urbanized catchment

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covered mainly by commercial, residential and industrial areas. Having a total catchment area of

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100 km2, water body surface area of 2.4 km2 and average depth of nearly 4 m, the water body is

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the largest multi-functional reservoir in Singapore providing recreation, flood control and water

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storage to the country. Sampling was carried out over two days from 7 to 9 January 2014 at the

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confluence of three main tributaries feeding water to the reservoir. To study possible diel vertical

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variations caused by thermal or nutrient stratifications 17, 18, samples were collected at different

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times of the day (early morning, late morning, afternoon, evening and night, i.e. 6am, 10am,



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2pm, 6pm, 10pm, 2am) from 2 depths (surface, 0.1 m; bottom, 3.2 m) using a subsurface grab

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sampler. A 4-hr sampling interval was used to improve detection of fast-degrading off-flavour

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compounds which may have half-lives on the order of hours to days 19. All samples were kept in

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sterile polypropylene bottles and transported to the lab immediately for processing or storage.

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2.2.

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Temperature, pH, conductivity, total dissolved solid (TDS), salinity and dissolved oxygen (DO)

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were measured with a YSI 6600 V2 multi-parameter water quality sonde (YSI Inc.). Other water

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quality including chlorophyll-a (chl-a), total phosphorus (TP), orthophosphate (PO4), total

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nitrogen (TN), ammonium (NH4), nitrate (NO3), nitrite (NO2), total organic carbon (TOC),

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dissolved organic carbon (DOC), calcium (Ca) and magnesium (Mg) were determined with

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APHA standard methods (10200H, 4500-P (B,E), 5310B, 4110B, 3120B) and JIS method (K

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0102). Light intensity was measured with a LI-250A light meter and LI-192 sensor (LICOR).

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2.3.

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Water samples for cyanotoxin analysis were stored at -20 °C upon arrival to the lab.

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Concentrations of two bloom-associated cyanotoxins, i.e. microcystin and cylindrospermopsin,

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were measured using the Microcystin/Nodularin (ADDA) and Cylindrospermopsin ELISA kits

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(Abraxis LLC), respectively. Water samples were sonicated without heat for 15 min to release

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cell-bound toxin and final development of colour was measured with a microtiter plate reader.

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Four off-flavour compounds, i.e. 2-methylisoborneol (2-MIB), geosmin, β-cyclocitral and β-

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ionone, were determined with HS-SPME/GC-MS/MS method using an Agilent GC 7890A

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coupled with a 7000B Triple Quad series mass spectral detector as described previously 20. All

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secondary metabolite measurements were run in duplicate.

Physical and chemical water quality variables

Cyanobacterial secondary metabolites


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2.4.

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Microbial biomass was harvested within 3 hours after each sampling by filtering reservoir water

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through a 0.45 µm cellulose nitrate filter (Sartorius) and frozen at -20 °C. DNA was extracted

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from the filter using a PowerWater ® DNA Isolation Kit (MOBIO) following manufacturer’s

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instructions.

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The abundance of bacteria and cyanobacteria in water were inferred from the 16S rRNA gene

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copy numbers (GNC) in the samples. Total bacterial 16S rRNA gene was quantified using a

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droplet digital PCR system (ddPCR) (QX200, Bio-Rad), while cyanobacterial 16S rRNA gene

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was quantified with a StepOnePlus real-time PCR (qPCR) system. Two most commonly reported

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cyanobacterial bloom genera – Microcystis (MIC) and Cylindrospermopsis (CYL) were also

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quantified using qPCR assays developed previously 21. Detailed methods are provided in

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supporting information SI (A). To investigated the major off-flavor producers, we amplified and

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sequenced partial 2-MIB biosynthesis genes using primer sets reported previously 22, and

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searched for sequence homologues against the database of NCBI by using BLAST 23.

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2.5.

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Isolated genomic DNA (gDNA) was amplified using the universal pyrotaq primer set (926wF

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and 1392R) targeting the small subunit 16 rRNA variable regions 6 – 8 of bacterial communities

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24

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rRNA sequences have been deposited at the National Centre for Biotechnology Information

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(NCBI) Sequence Read Archive (SRA) under accession numbers SRR3996072-3996095.

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The standardized biological data (relative abundance of bacterial OTUs and cyanobacterial

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genera) were square-root-transformed to reduce the dominance of OTUs in high abundances in

Quantitative analysis of bacterial and cyanobacteria abundance

16S rRNA amplicon sequencing and data analyses

. Details on Illumina Miseq sequencing and sequence pre-processing are in SI (B). All 16S



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analysis, while all environmental data sets were normalized using means and standard deviations

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of the variables. The similarity (resemblance) of each pair of samples in terms of biological and

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physico-chemical characteristics was calculated using the Bray-Curtis similarity and Euclidean

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distance, respectively. Relationships between OTUs and environmental variables were analyzed

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using RELATE-BEST and a simple model indicating variance explained by selected

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environmental variables was calculated using the distance-based linear model (DistLM)

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implemented in Primer-7 25. To detect spatial and temporal differences in the microbial

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community between samples, permutational multivariate analysis of variance (PERMANOVA)

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and Analysis of Similarity test (ANOSIM) were performed. Samples were separated into 4

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groups and compared according to the sampling time and depth – day versus night; surface

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versus bottom. This was followed by similarity percentage calculations (SIMPER) to indicate

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OTUs that contributed the most to the variation.

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Diel and vertical differences in water quality variables and biological variables (qPCR and

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ddPCR) were determined using paired-T test and general linear model (GML) in the statistical

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software package PASW18 (SPSS Inc.). Non-normally distributed data sets were transformed

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with a 2-steps transformation approach 26 prior to the tests. Day and night samplings were

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determined from the ambient light intensity of which q quantum measurement > 8 umol /m2.s

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was considered as day time.

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To investigate changes of bacterial community composition, non-metric multi-dimensional

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scaling (nMDS) was conducted for sample comparisons based on OTUs, followed by Spearman

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correlation to determine environmental variables that correlated most highly with the first and

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second ordination axes. Results were visualized using nMDS and vector plots.


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3.

RESULTS

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3.1.

Water quality characteristics, total bacterial and cyanobacterial abundance

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The reservoir was ranked between mesotrophic and eutrophic (18-month chl-a: 7.6 – 99.7 µg/L)

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states and susceptible to occasional algal bloom outbreaks driven by monsoon changes and

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nutrient loadings. During the 2-day sampling duration, the weather was cloudy with temperature

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recorded (26.6) lower than average (28.4 oC), while ambient light intensity was below 1000

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µmol photon/m2.s around noon time. In general, windy conditions were observed in the day

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while calm conditions were observed at night.

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Physicochemical water characteristics measured in this study are summarized in Figure 1, S1 and

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Table S1. During the study, the waterbody experienced a bloom attributed to a prolonged period

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of drought lasting for three months from December 2013 to February 2014. Compared to non-

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bloom periods (Chl-a < 20 µg/L; phytoplankton count < 60,000 cell/mL), an increase in

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phytoplankton biomass was observed in these three consecutive months (Chl-a from 14.1 to 73

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µg/L; phytoplankton count from 43,000 to 540,000 cell/mL). For nutrient analyses, the

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geometric mean concentration of total nitrogen (TN) was consistent at 0.78±1.053 mg/L, while

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nitrate (NO3) was higher at the surface than bottom waters (paired T-test and GLM, P < 0.05)

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and the opposite was true for ammonia (NH4). Total phosphorus (TP) showed greater fluctuation

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between 0.005 and 0.07 mg/L, but orthophosphate was consistently below the detection limit of

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5 µg/L. An average TN:TP ratio of 32.5 ± 2.23 (geometric) indicated that the reservoir was

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phosphorus-limited, and trophic status based on Chl-a and TP concentrations still classified the

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system as eutrophic 27. The water temperature was higher at the surface than bottom throughout

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the day-night cycle (0.6 °C, paired T-test and GLM, P < 0.01). Light intensity at the surface 9 ACS Paragon Plus Environment


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during mid-day was 35.8 – 184.1 µmol photon/m2.s and decreased with depth with virtually no

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light at the bottom (3.4 m depth) at mid-day (i.e. 0 – 0.4 µmol photon/m2.s). Physical parameters

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showing significant vertical variation were turbidity and pH (paired T-test and GLM, P < 0.05).

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Notable levels of 2-MIB and β-ionone were found in surface and bottom samples (2-MIB: 12-39

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ng/L, β-ionone: 10-48 ng/L), exceeding their olfactory threshold concentrations of 10 and 7 ng/L,

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respectively. 2-MIB showed significant depth variation where its concentration at the surface

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was 32% higher than the bottom.

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The total bacterial 16S rRNA GCN (including cyanobacteria) showed a stable community

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abundance ranging from 1.3 – 2.6 × 106 GCN/mL. Cyanobacterial population abundance

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fluctuated at a slightly greater magnitude that varied from 3.7 × 105 – 1.7 × 106 GCN/mL (Figure

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2). Significant diurnal differences in phytoplankton and bacterial abundance were observed, with

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daytime samples showing higher Chl-a, Cyan 16S rRNA and Bact 16S rRNA gene copies

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(paired T-test and GLM, P < 0.05), consistent with the result from an online multi-parameter

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sensor probe located near the sampling site (Figure S2). Cyanobacteria, including the targeted

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populations of Cylindrospermopsis and Microcystis, formed denser assemblages at the top layer

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(paired T-test and GLM, P < 0.05) but did not exhibit a diurnal trend in abundance. With

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concentrations ranging from 36 – 1356 and 377 – 3630 GCN/mL respectively,

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Cylindrospermopsis and Microcystis spp. accounted for not more than 0.5% of the total

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cyanobacterial population (16S rRNA equivalent), indicating that they were not the main

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phytoplankton taxa.

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The raw sequencing output showed good evenness across samples based on the number of reads

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where all samples had a minimum of 40k raw paired-end reads (Table S2). The average copy of

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artefactual OTU was 1.39 ± 0.89 (Table S2b) calculated from three blanks sequenced together 10 ACS Paragon Plus Environment

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with samples. Proportions of sequence removed by quality filtering for length & ambiguity,

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alignment and predicted chimeras were 30%, 5% and 30%, respectively, generating a total of

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680,581 quality-controlled sequences for 24 samples (Table S2a). We obtained a total of 27,975

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unique OTUs at 95% cut-off distance, however, approximately 60% of the OTUs were rare taxa

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contributing less than 5 reads in the whole dataset. Owing to a high percentage of chimeric

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sequences and large amount of rare OTUs, singletons and doubletons were removed to avoid

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overestimation of species, following the average copies of artefactual OTU obtained from the

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blanks. The sample standardization (subsampled to 17,679 reads) resulted in a loss of no more

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than 1% of reads, which is in agreement with the rarefaction curves (Figure S3) that plateaued

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after 15,000 reads. A total of 3,577 final OTUs were obtained, of which 3,447 were bacterial

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OTUs and 15.6% of them were unclassified. OTUs of archaeal and eukaryotic origin were not

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included in the analysis. 361 out of 3,447 (10.5%) bacterial OTUs were assigned as

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Cyanobacteria based on Silva reference.

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Of the 37 phyla detected in the system, phyla with highest relative sequence abundance were

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Cyanobacteria (39%), Proteobacteria (29%), Bacteroidetes (15%) and Actinobacteria (10%)

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(Figure 3) comparable to other studies of freshwater systems 28, 29. Other bacterial phyla detected

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including Planctomycetes, Verrucomicrobia, Chloroflexi, Acidobacteria and Armatimonadetes,

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in sum contributing 6.6% of the total sequences. Classification at family level revealed nine

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major non-cyanobacterial taxa (≥ 1% of the total non-cyanobacteria community) belonging to

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Alphaproteobacteria (Sphingomonadaceae, Rhodobacteraceae), Betaproteobacteria

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(Comamonadaceae, Burkholderiaceae, Oxalobacteraceae), Bacteroidetes (Chitinophagaceae,

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Cytophagaceae) and Actinobacteria (Sporichthyaceae, Acidimicrobiaceae). Cyanobacteria was

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the most abundant phylum in the upper water layers (39%) and co-dominated the lower layers 11 ACS Paragon Plus Environment


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with Proteobacteria, with similar proportions for both. Planktothricoides was the key genus

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within cyanobacterial populations varying from 39 to 92% in relative abundance. This was

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followed by Synechococcus, Pseudanabaena, Prochlorothrix and Limnothrix, making up 96.6 to

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99.2% of the entire phylum.

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3.2.

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Richness and diversity (Chao1 and Inverse-Simpson’s diversity indices) calculated from

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normalized OTUs showed that samples from all four groups (day vs night, surface vs bottom)

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shared similar richness while higher alpha diversity was found for bottom samples (T-test, P
0.1 mg/L; TN:TP = 1:10) were strongly associated with higher cell

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biomass and relative abundance of Planktothricoides spp. 4, 8. It is not surprising that our

Planktothricoides bloom



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sampling site which is located in a tropical urban catchment where water temperatures are warm

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throughout the year, is of favourable range for Planktothricoides spp. 52. However, a significant

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connection between phosphorus and total cyanobacteria / Planktothricoides was absent in our

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study, likely due to low levels of phosphorus measured during the short sampling period.

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Interestingly, the bloom detected in Brazil coincided with a La Nina period with low

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precipitation, while a prolonged drought with slightly lower temperature was recorded when

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samples were taken for this study (rainfall < 50% of average; monthly temperature 26.6°C

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compared to annual average of 28.4°C). Our findings suggest that Planktothricoides may share

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features akin to Oscillatoriales which can explain their ecological succession and disappearance

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in waters.

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4.4.

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We present here one of the few studies describing freshwater microbial community structure in

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an equatorial region. To our knowledge, this is also the first study on a bacterial community

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associated with a Planktothricoides bloom. Given the highly variable freshwater

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bacterioplankton composition responding to a diverse range of physiochemical and biological

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variables, a question arises as to whether cyanobacterial blooms would shape the non-

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cyanobacterial bacterial community. A substantial portion of bacterial taxa recovered from this

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study are known as typical freshwater or river taxa covering Proteobacteria, Cyanobacteria,

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Actinobacteria, Bacteriodetes and Verrucomicrobia 28, 29. Generally, bacterial taxa discovered

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were comparable to those reported in other bloom studies with different dominating

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cyanobacterial species (Table 3). In those studies, bacterial clones, DGGE bands or OTUs

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isolated were mainly affiliated with Proteobacteria, Bacteriodetes, Actinobacteria,

Microbial community


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Verrucomicrobia and Planctomycetes of varied orders or dominance. Comparing our findings

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with other studies, we conclude that the freshwater bacterial community associated with

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cyanobacterial blooms are largely conserved at phylum level with Proteobacteria

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(Betaproteobacteria), Bacteroidetes and Actinobacteria as the main taxa despite differences in the

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phytoplankton species, geography and climate. We speculate that this could be due to the core

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functions of the cyanobacterial bloom associated community being conserved across a wide

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variety of taxa, as demonstrated in several studies 12, 16. Further investigation on the

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metagenomes of cyanobacterial blooms of other dominant species could shed light on the

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functional association of the bacterial consortium in cyanobacteria-dominant ecology.

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Vertical stratification of the bacterial community was also demonstrated, although not to a large

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extent. Moving down the water column, relative abundance of Sphingomonadaceae

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(Proteobacteria), Chitinophagaceae, Cytophagaceae (Bacteriodetes) and Acidimicrobiaceae

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(Actinobacteria) were enriched among non-Cyanobacterial taxa in the community composition

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(paired T-test, P< 0.05), which is in agreement with their natural habitats of soil, sediment or

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deep water layers. Further examination revealed that the aquatic microbial community

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comprised of only several major families (>1%) (Table 3) which made up 65% of the non-

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cyanobacterial microbial portion. The alphaproteobacteria, Sphingomonadaceae, are common

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bacteria having widespread distribution in the soil and aquatic systems and even drinking water

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57

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cyanobacterial infected waters 11 and are capable of breaking down complex organic compounds

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and recalcitrant by products released by cyanobacteria, such as microcystin 7. Comamonadaceae

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was the most abundant Betaproteobacteria (10.1%) in the system. Little is known about this

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group of bacteria but they exhibit diverse physiological mechanisms including denitrification 59

, and are typically connected to anthropogenic influence 58. They are frequently detected in



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and degradation of aromatic compounds 60. Although evidence indicates that they co-occur with

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several filamentous cyanobacteria (e.g. Anabaena circinalis & Phormidium) 15, and are likely

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involved in the decomposition of Microcystis biomass and production of volatile organic

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compounds 61, their implication in the current tropical lake is unclear given the scope of study.

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Chitanophagaceae (Bacteroidetes) was the second most abundant family comprising of major

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genera classified as sediment or deep water taxa such as Sediminibacterium 62, Lacibacter 63 and

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Ferruginibacter 64, comparable to the relative abundance found for Comamonadaceae (major

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Proteobacteria). Members of the phylum, Bacteroidetes, are specialized in decomposition of

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organic matter in food chains 29. Along with Cytophagaceae and Flavobacteriaceae of the same

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family group, Chitanophagaceae are chemoorganotrophs that can lyse complex organic

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compounds such as cellulose 65 and high molecular weight organic matter in water 66,

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emphasizing their role in regulating the carbon-cycle in aquatic environments. This feature

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implies a carbon-rich layer near the bottom, which is favourable to these bacterial groups,

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emphasizing a resource-driven distribution pattern for the bacteria. Actinobacteria is a typical

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freshwater phylum 29 consisting of globally ubiquitous surface dwellers 67 although some of its

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members live in bottom waters 68. Association of Actinobacteria with phytoplankton seems to be

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complicated where their abundance could either be enhanced or decreased by a bloom 10, 69. The

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Actinobacterial clades, hgcl and CL-500_29, belonging to the most prevalent lineages (acI and

427

acIV) among freshwater Actinobacteria 29, were also the two most abundant groups detected in

428

the current system.

429

It is worth noting that several major bacterial taxa, i.e. hgcl-clade (Actinobacteria),

430

Polynucleobacter (Betaproteobacteria), and Sediminibacterium (Bacteroidetes), detected in our

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study matched the members of core microbiomes identified in three constructed water bodies of 20 ACS Paragon Plus Environment

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different trophic levels spanning from oligotrophic to eutrophic 7. This supports the cosmopolitan

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nature of these taxa 29 and suggest that they are less susceptible to eutrophication pressure.

434 435

Funding Sources

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This research grant is supported by the Singapore National Research Foundation under its

437

Environmental & Water Technologies Strategic Research Programme and administered by PUB,

438

the Singapore’s National Water Agency (Grant number: 1102-IRIS-14-02). BFT and JRT were

439

supported by the National Research Foundation Singapore through the Singapore MIT Alliance

440

for Research and Technology’s (SMART) Center for Environmental Sensing and Modeling

441

(CENSAM) research program.

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ACKNOWLEDGMENT

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We thank Yueat Tin Wong and Grace Chan for logistics support and Jin Zhi Lim for off-flavour

444

compound analysis.

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SUPPORTING INFORMATION

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SI (A). qPCR and ddPCR methods

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SI (B). 16S rRNA sequence pre-processing

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Table S1. Water physicochemical characteristics.

450

Table S2a. Summary on numbers sequence and OTU.

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Table S2b. Artefacts from sequencing blanks.

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Table S3. PERMANOVA analysis.

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Table S4. Marginal test for DISTLM analyses.

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Table S5. Primer and probe sequences.

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Figure S1. Bloxplot of water physicochemical characteristics measured at day and night times.

456

Figure S2. Cyanobacterial concentration estimated with sensor.

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Figure S3. Rarefaction curves of samples.

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Figure S4. Regression between the relative abundances of Microcystis and Cylindrospermopsis

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estimated by qPCR and 16S rRNA sequence.

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646


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647 648

OTU

1 3 4 2 21 51 57 5 9 23 26 649 650


Table 1. Bacterial OTUs responsible for dissimilarity between surface and bottom communities calculated using SIMPER. Total average dissimilarity = 34.84 (Bray-Curtis). Only OTUs ≥ 0.3 % contribution were shown.

Classification

With Cyanobacterial OTUs Surface Bottom Diss/SD Av Abund Av Abund

Contrib%

Without Cyanobacterial OTUs Surface Bottom Diss/SD Av Abund Av Abund

Contrib%

Planktothricoides, Cyanobacteria 72.39 60.17 1.4 1.67 Synechococcus, Cyanobacteria 32.61 24 1.5 0.99 hgcl_Clade, Actinobacteria 27.6 27.6 1.4 0.61 27.60 27.6 1.41 0.70 Comamonas serinivorans, Proteobacteria 36.83 38.13 1.49 0.51 36.83 38.13 1.50 0.58 Opitutae, Verrucomicrobia 8.96 12.79 1.45 0.46 8.96 12.79 1.46 0.53 Methylomonas, Proteobacteria 4.14 9.21 2.54 0.45 4.14 9.21 2.53 0.51 Acinetobacter, Proteobacteria 6.6 2.89 0.87 0.42 6.60 2.89 0.87 0.48 Sediminibacterium, Bacteroidetes 20.93 23.91 1.42 0.41 20.93 23.91 1.43 0.47 CL500-29, Actinobacteria 13.85 18.03 1.28 0.39 13.85 18.03 1.28 0.45 Pseudanabaena, Cyanobacteria 12.15 10.36 1.48 0.3 CL500-29, Actinobacteria 9.06 11.64 0.91 0.3 9.06 11.64 0.91 0.34 Abbreviations: Av. – Average; Abund – abundance; Diss – dissimilarity; SD – standard deviation; Contrib% – percentage contribution to the total average dissimilarity.



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Table 2. Sequential test results of environmental variables on the composition of bacterial community and cyanobacterial population predicted by distance-based linear model (DistLM) using forward procedure, adjusted R2 criterion and 4999 permutations. Significant values are in bold.

Variable Depth SpCond Ca TP NO3 MIB Turbidity Chl-a Salinity TN NH4 bionone TOC Mcyst Temp Cyr

Bacteria Adj R2 0.0849 0.1516 0.1791 0.1890 0.1978 0.2033 0.2086 0.2137 0.2183 0.2232

SS(trace) 1642.2 1326.8 801.4 577.8 554.5 513.2 505.2 496.9 487.0 484.6

Pseudo-F 3.133 2.730 1.705 1.244 1.207 1.125 1.115 1.103 1.088 1.089

P 0.0002 0.0002 0.0016 0.0854 0.1352 0.2650 0.2954 0.3264 0.3570 0.3674

Prop. 0.1247 0.1007 0.0608 0.0439 0.0421 0.0390 0.0384 0.0377 0.0370 0.0368

Cyanobacteria Adj R2 SS(trace)

Pseudo-F

0.1695

836.5

5.693

0.0050

0.2056

0.5208 0.3960

86.1 337.1

1.015 3.155

0.3820 0.0240

0.0212 0.0829

0.3340 0.4343 0.4639 0.4900 0.5131 0.5203

758.2 235.7 194.3 173.2 155.7 105.3

6.435 2.355 2.048 1.920 1.807 1.240

0.0010 0.0880 0.0990 0.1250 0.1320 0.2930

0.1863 0.0579 0.0477 0.0426 0.0383 0.0259

P

Prop.

Best Solution Adj R2 R2

Best Solution RSS Adj R2 R2 RSS 0.5207 0.22319 0.56093 5784.2 5 0.70828 1187.0 Table 3. Comparison of major bacterial taxa associated with blooms dominated by different cyanobacterial species. 28

ACS Paragon Plus Environment

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Blooming cyanobacteria taxa

Geographical region, climate

Taxon description

Planktothricoides

Southeast Asia, Proteobacteria tropical/equatorial

Alphaproteobacteria Betaproteobacteria

Bacteroidetes Bacteroidetes Bacteroidetes Actinobacteria Actinobacteria Microcystis

(culture)

Anabaena / Aphanizomenon

Sphingobacteriia Cytophagia Flavobacteriia Actinobacteria Acidimicrobiia

Sphingomonadaceae** Rhodobacteraceae Comamonadaceae Burkholderiaceae* Oxalobacteraceae Chitinophagaceae** Cytophagaceae** Flavobacteriaceae Sporichthyaceae Acidimicrobiaceae**

Proportion Reference in community This study 2.55% 1.52% 10.13% 2.68% 1.51% 9.93% 1.11% 0.97% 5.71% 3.37%

East Asia, Proteobacteria (α,β,γ), Bacteriodetes, Actinobacteria, Firmicutes+ subtropical East Asia, Proteobacteria (β,α,γ,δ), Actinobacteria, Bacteriodetes, Planctomycetes, subtropical Verrucomicrobia+ Southeast Asia, Proteobacteria, Bacteroidetes, Actinobacteria tropical/equatorial Western Europe, Proteobacteria (α,β,γ), Bacteroidetes temperate

11

Northern Europe, cold temperate Northern Europe, cold temperate

70

Verrucomicrobia, Actinobacteria, Chloroflexi, Proteobacteria, Bacteriodetes Bacteroidetes, Proteobacteria (β,α,γ), Actinobacteria, Planctomycetes, Verrucomicrobia, Firmicutes

Aphanizomenon and Western Europe, Proteobacteria , Actinobacteria , Verrucomicrobia, Bacteroidetes,

56

16

8

14

8

29



Cylindrospermopsis temperate

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Planctomycetes, Chloroflexi and Candidate Division OP10

Cylindrospermopsis Western Europe, Proteobacteria (α,β,γ), Bacteroidetes (culture) temperate

8

Microcystis / Oscillatoria / Anabeana

Actinobacteria, Bacteroidetes, Proteobacteria (β,α,γ)

15

Actinobacteria, Proteobacteria (β,α,γ), Verrucomicrobia, Firmicutes

71

Proteobacteria, Bacteroidetes

12

Australia, temperate North America, temperate

Cyanothece / North America, Trichodesmium-like temperate +

Abundance in descending order

30


Page 31 of 35


Figure 1. Boxplot of water physicochemical characteristics measured at upper and lower water layers (surface and bottom). Unit for parameters: µg/L (Cyn, Mcyst, Chl-a); mg/L (TN, TP, DOC, TOC, NO2, NO3, NH4, Salinity, Ca, Mg, DO); m.S/cm (Cond); g/L (TDS); ng/L (2-MIB, β-cyclocitral, Geosmin, β-ionone); µmol/m2.s (light); °C (Temp), pH unit (pH) and NTU (Turbidity). Asterisks (*) indicate parameters with significant differences determined using paired T-test and GLM, P < 0.05.



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Figure 2. Abundance of total bacteria, total cyanobacteria and two common cyanobacteria genera analysed based on molecular quantification methods for different sampling times. First number denotes the day (1 – Day 1, 2 – Day 2), second number denotes the time, D and N represent day and night sampling, respectively.


Page 33 of 35


Figure 3. A) OTU Richness (Chao1) and diversity (Inverse Simpson Index) and B) relative abundance of major bacterial phyla in all samples.



A

Page 34 of 35

B

Figure 4. (A) Non-metric multi-dimensional scaling (nMDS) plot of the bacterial community dissimilarity (Bray-Curtis) between samples. Cyan circles represent surface (0.1 m) samples, while blue squares represent bottom (3.2m) samples, other markers indicate OTUs assigned to different taxa groups: Planktothricoides (Cyanobacteria); ∅ Other Cyanobacteria;  Proteobacteria; Bacteroidetes; Actinobacteria; Others and | Unclassified. (B) Vector plot of environmental variables correlate (Spearman correlation) significantly to the changes in bacterial community.


Page 35 of 35


16S rRNA Sequencing

80%

60%

40%

20%

y = 0.6497x + 0.0617; R² = 0.8129 0% 0%

20%

40%

60%

80%

qPCR

Figure 5. Regression showing comparison between the relative abundance of cyanobacterial populations in the bacterial community (16S rRNA cyanobacteria / 16S rRNA total bacteria) estimated by qPCR and amplicon sequence classification.


Relationship of Microbiota and Cyanobacterial Secondary Metabolites

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