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Profile and fate of bacterial pathogens in sewage treatment plants revealed by high-throughput metagenomic approach Bing Li, Feng Ju, Lin Cai, and Tong Zhang Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b02345 • Publication Date (Web): 07 Aug 2015 Downloaded from http://pubs.acs.org on August 13, 2015

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Environmental Science & Technology

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Profile and fate of bacterial pathogens in sewage treatment plants

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revealed by high-throughput metagenomic approach

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Bing Li†#, Feng Ju‡#, Lin Cai‡, Tong Zhang‡*

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†

#

Bing Li and Feng Ju contributed equally to this work

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*

Corresponding author: Tong Zhang; email: [email protected]; phone: +852-28591968; fax: +852-25595337

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‡

Division of Energy & Environment, Graduate School at Shenzhen, Tsinghua University, China Environmental Biotechnology Lab, The University of Hong Kong, Hong Kong SAR, China

Bing Li: [email protected]; Feng Ju: [email protected]; Lin Cai: [email protected]；Tong Zhang：[email protected]

Abi ot r op h ia d ef ec ti va Ac hr o m o b ac te r p ie c hau di i Ac h r om o ba ct er xy lo s ox i da ns Ac id a m i no co c cu s fer m en ta n s A ci neto ba c te r b au m an n ii Ac in et ob ac te r h a em ol y tic u s Ac ine tob ac te r j oh n so n ii A ci ne to b ac te r j un ii Aci ne to b ac te r l w of fii Ac in et ob ac ter r a d io r es is te n s A ct in om yc e s od o nt ol yt ic us Aer o c oc c us v ir i da ns Ae ro m on a s hy dr oph i la Ar c o ba c te r b ut zl er i B ac il lu s an th r a ci s B ac il lu s cer e us Baci llu s p um il us

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Ba ci ll u s s ub ti lis Bac il lu s th ur i ng i en si s Ba ct er o i de s ca c ca e Ba ct er o id e s eg g er t hi i B ac te r o id es f r ag il is

Metagenomic Phylogenetic Analysis

Sewage Treatment Plants (Influent, Effluent, AS, Biofilm, ADS) High-throughput Sequences

Metagenomic DNA

Illumina high-throughput sequencing (Hiseq 2000)

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B ac te r o id es o v at us B ac te r oi de s p ec ti no p hi lu s Bac te r oi d es s te r c or i s B ac te r o id es th e ta io ta om i c r o n Bac te r o id es u ni fo r m is Bac te r oi d es v u lg at us B i fi do bac ter iu m de n ti u m B il oph B iloradw e ad te l slawaor v tiuhima Bo r de te ll a p a r ap er tu ss is Bo r d et el la p er t us s is C hr om obac te r iu m v i ol a ce u m C itr o b ac te r k o se r i C los tr i di um b otu lin u m C lo st r id iu m di ffi ci l e C l os tr id iu m p er f r in ge ns C oll in se ll a ae r ofa ci en s C om am o na s tes tost er o ni D e l f ti a a c id ov o r an s Eg ge r th e lla l en ta Ei ke n el la c or r o d en s En te r ob ac te r c a nc e r og e nu s En te r ob a ct er cl oac ae En te r o co cc u s ca s se li fl av us Ente r oc occ u s fa ec a l is Ente r oc occ u s fa ec i u m En te r oc o cc u s ga ll i n ar u m E sc h er i ch i a c ol i E ubac te r iu m li m os um Euba ct er i um r e c ta le Fi n ego ld ia m a gn a F u s ob ac ter iu m m or t ifer u m F u so b ac te r i um nu c l e at um F us ob a ct er i um u lc e r an s F us ob a ct er i um v a r iu m G ar dn er e l l a va g in al is G o r do ni a b r on c hi al is H ae m op hi lu s pa r a in flu e nz a e Kle bs ie ll a pne um o ni ae M a nnhe im ia h a em o ly tic a M e g am ona s hy p er m e ga le M y co b ac te r iu m ab s ce s su s M y co b ac te r i u m a vi um M y c ob act er i um bo vi s M y co bac ter iu m ka n sa s i i M yc ob a ct er i um l ep r ae M yc ob a ct er i um m a r in um M y c ob ac te r iu m s m e g m a ti s M yc ob a ct er i um u lc e r an s N ei ss e r ia el o ng a ta N e is se r ia f la ve s ce ns N eis s er i a gon or r h o ea e N ei ss e r ia m en in g iti di s N ei ss e r ia m uc o sa N e i s s er i a si cc a N e is s er i a su b fl a v a N o ca r d ia fa r c in i c a O c hr o b ac tr u m ant hr o pi P r op io ni ba c te r iu m ac n es P se u do m o n as f lu or e s ce n s P se ud o m o n as s tu tz e r i R a ls to n ia p ic ke tt i i R ho doc o cc u s eq ui R ho d oc oc c us e r y thr opo l is Ro th ia d e nt oc a r io sa Seba l del la te r m iti di s Sh i ge l la b o yd ii Sh ig el la d ys ent er i ae Shi g el la fl ex n er i Sh ig el l a s on n ei Sta ph y lo co c cu s a ur e u s Sta p hy lo c oc cus s ap r o ph y tic u s St en o tr o ph om o na s m al to phi l i a Str e p toc o cc u s ag al a ct ia e Str e p toc o cc u s an gi n os u s Str e pt oc o cc us bo v is S tr ep toc oc c us g o r do n ii Str e p to coc cu s m i ti s S tr ep to c oc c us m u ta ns Str e pt oc o cc u s sa li var iu s St r ep to c oc c us s u is Su tt er el la w a d sw o r th e ns is T suk am u re l l a pa u r om e ta bo la Ve il lo nel l a at yp ic a Ve il l o ne ll a di sp ar Ve ilVloi bnel a r iol ac pa ho rlev rulae V ib r io fu r n is si i Vi br i o m i m i cu s V i b r io pa r aha e m o ly ti cu s Vi br i o vu ln if ic us S ha t in - E ff . S h a ti n - In f. S h a tin - A S F o a m i ng

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Abstract: The broad-spectrum profile of bacterial pathogens and their fate in sewage treatment plants (STPs)

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were investigated using high-throughput sequencing based metagenomic approach. This novel approach could

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provide a united platform to standardize bacterial pathogen detection and realize direct comparison among

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different samples. Totally, 113 bacterial pathogen species were detected in 8 samples including influent, effluent,

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activated sludge (AS), biofilm, and anaerobic digestion sludge with the abundances ranging from 0.000095% to

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4.89%. Among these 113 bacterial pathogens, 79 species were reported in STPs for the first time. Specially,

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compared to AS in bulk mixed liquor, more pathogen species and higher total abundance were detected in upper

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foaming layer of AS. This suggests that the foaming layer of AS might impose more threat to onsite workers

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and citizens in the surrounding areas of STPs because pathogens in foaming layer are easily transferred into air

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and cause possible infections. The high removal efficiency (98.0%) of total bacterial pathogens suggests that AS

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treatment process is effective to remove most bacterial pathogens. Remarkable similarities of bacterial pathogen

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compositions between influent and human gut indicated that bacterial pathogen profiles in influents could well

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reflect the average bacterial pathogen communities of urban resident guts within the STP catchment area.

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INTRODUCTION

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Despite the remarkable improvement achieved in water and wastewater treatment technology as well as

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management level, waterborne diseases which mainly result from the infection by pathogens are responsible for

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4.0% of total death rate and 5.7% of the total burden of disease approximately. 1 Therefore, pathogens, including

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bacteria, viruses, protozoa and helminths are considered to pose a major threat to human health around the 2


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world.2,3 Sewage treatment plant (STP) is one of the major sources for pathogens to enter into water

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supplies/recreational waters and soil via water reuse and land application of sludge. 2-4,5 Considering the nature

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of pathogens, that is, low infectious dose of some pathogens would associate with high level of community

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risks,6 it is imperative to establish a rapid, accurate and universal detection method for the broad-spectrum

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monitoring of pathogens in wastewater treatment plants.

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Conventionally, simultaneous detection and identification of multiple kinds of pathogens using culture-based

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methods is a time consuming and labor intensive undertaking. To get rid of such heavy work load, indicator

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microorganisms, such as Escherichia coli and Clostridium perfringens, are widely used to estimate the overall

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contamination level of pathogens in water and wastewater samples indirectly. However, due to the different

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survival characteristics between indicator microorganisms and the majority of other pathogens, this method

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could neither reflect the actual quantity of pathogens nor identify the pathogens explicitly. 4,7,8 In addition, the

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ability of some pathogens to persist in a viable-but-non-culturable state will lead to a great underestimation of

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pathogen contamination. 7 During the past 15 years, the development of molecular biology techniques provided

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researchers some novel and sensitive approaches for pathogen detection. PCR, quantitative real-time PCR

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(qPCR) and microarray assays have been successfully established to identify and quantify pathogens in water

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and wastewater. 3,4,7,9 Nevertheless, PCR, qPCR approaches and microarray assays suffer from inherent low

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throughput design; thereby numerous reactions are needed to detect diverse pathogens in environmental samples.

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On the other hand, since the limited available primers or probes usually could not cover all known pathogens,

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these methods cannot reveal the broad profiles on the diversity and abundance of the overall pathogens.

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Currently, the breakthrough of next-generation sequencing technology launched a new era for the

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broad-spectrum monitoring and rapid quantification of pathogens. For example, 454 pyrosequencing of the 16S

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rRNA gene was used to investigate the occurrence and quantity of more than 30 bacterial pathogens in biosolids,

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activated sludge (AS), anaerobic digestion sludge (ADS), influent and effluent simultaneously.5,10-12

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Undoubtedly, 454 pyrosequencing provides people new and important insights into the diversity and

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contamination level of bacterial pathogens in various environmental samples. Nonetheless, this novel pathogen

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detection approach still has the following limitations. Just like all the other PCR-based methods, the inherent

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amplification biases during PCR step could not be avoided.13 Additionally, pyrosequencing of the 16S rRNA

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gene will generate biased quantification due to the wide variability in copy numbers of the 16S gene even within

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the same genus.

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resolution at genus level which is neither accurate nor specific enough to investigate pathogens, 15 since not all

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of the species under the pathogenic genera are pathogens.

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Finally, the relatively short sequence length (~400 bp) can only provide the taxonomic

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Metagenomic shotgun sequencing using high-throughput sequencing (HTS) technology, which typically

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generates millions to billions of reads for the metagenomic DNA extracted from each environmental sample

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directly, could provide a broad profile of microbial communities.15 Compared to PCR-based pyrosequencing of

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the 16S rRNA gene, metagenomic approach is able to analyze the microbial community and estimate their 4


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abundances without PCR bias.14 Furthermore, metagenomic approach using unique clade-specific marker genes

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can investigate the microbial composition at lower taxonomic level, i.e., species level. 15 This is especially

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crucial for pathogen detection since the investigation of pathogens at genus level usually leads to some

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inaccurate estimate.

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approach to identify and evaluate the diversity of human viruses in freshwater lake, 18 reclaimed water

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biosolid collected from STP. 20,21 However, so far, the detection of bacterial pathogens using metagenomic

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approach is very limited although bacterial pathogens accounted for as high as 43% of the total pathogens

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concerned by World Health Organization.22 Our previous study mainly focused on the establishment of bacterial

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pathogen detection approach based on HTS-based metagenomic technique. 23 Compared to BLAST searching

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against several databases using 16S rRNA genes and virulence factors, MetaPhlAn tool (Metagenomic

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Phylogenetic Analysis) is the most reliable method because it relies on unique clade-specific marker genes

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identified from 3,000 reference genomes for taxonomic classification (species level) and abundance

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estimation.15,23 In addition, all the pathogen species detected by MetaPhlAn tool were also verified by the other

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two approaches, further indicating the reliability of MetaPhlAn tool. 23 Therefore, MetaPhlAn tool was selected

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in this study to investigate the profile of bacterial pathogens and their fate in STPs.

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A few pioneer studies demonstrated the successful application of metagenomic 19

and

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The objectives of this study were (1) to reveal the diversity and abundances of the bacterial pathogens in

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various samples including influent, effluent, AS (foaming and non-foaming), biofilm, and anaerobic digestion

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sludge (ADS); (2) to evaluate the fate of multiple bacterial pathogens simultaneously in secondary/biological

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treatment process and (3) to compare the overall bacterial pathogen community compositions in different

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samples collected from STPs and human guts.

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MATERIALS AND METHODS

Sample Collection

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Two grab influent samples (~500 mL) and two grab effluent samples (~1000 mL) were collected from

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Hong Kong Shatin STP (22.407° N, 114.214° E) in December of 2012 and January of 2013. Two DNA samples

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of influents and effluents were pooled together respectively as composite samples named “Shatin-Inf.” and

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“Shatin-Eff.”. AS samples (~25 mL) including “Shatin-AS Foaming” and “Shatin-AS” were collected from the

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same aeration tank of Shatin STP in March of 2012 when the serious foaming phenomenon occurred.

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“Shatin-AS Foaming” was gathered from the upper foaming layer while “Shatin-AS” was obtained from the

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bulk mixed liquor which was about 30 centimeters below the wastewater surface. Biofilm (“Stanley-BF”，~25

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mL) and the counterpart AS sample (“Stanley-AS”，~25 mL) were collected in March of 2012 from the hybrid

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aeration tank of Hong Kong Stanley STP (22.219° N, 114.21° E). Biofilms grew on poly ethylene carriers with

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25 mm diameter (Figure S1). Anaerobic digestion sludge samples (“SWH-ADS” and “Shatin-ADS”，~25 mL)

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were obtained from the mesophilic anaerobic digestion tanks of Hong Kong Shek Wu Hui STP (22.51° N,

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114.119° E) and Shatin STP in March of 2012, respectively. At sampling, AS, biofilm and ADS were kept in 50

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ml sterilized centrifuge tubes and mixed with 100% ethanol at a 1:1 volume ratio for fixation. Influent and

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effluent were kept in sterilized containers which were placed in an ice box and transported to the laboratory 6


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within 2 hours after sampling. When returned to the laboratory, influent samples were firstly centrifuged at 4000

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rpm for 20 min to collect the cell pellets and then suspended and fixed using 50% ethanol. Effluent samples

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were firstly filtered with 0.2 µm membrane to collect cell pellets and then fixed using 50% ethanol. At last, all

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the fixed samples were stored at -20 °C before DNA extraction.23 The characteristics and treatment processes of

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these three STPs were summarized in the Supporting Information (Table S1).

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DNA Extraction and HTS

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The DNA extraction was conducted using the FastDNA® Spin Kit for Soil (Q-biogene, CA, USA) and

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DNA concentrations as well as purity were determined using microspectrophotometry (NanoDrop® ND-1000,

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NanDrop Technologies, Willmington, DE). DNA extracted from two separate aliquots of each sample was

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pooled to minimize the bias caused by variation of DNA extraction. Totally, eight prepared DNA samples (1

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from influent, one from effluent, three from AS, one from biofilm and two from ADS) were sent out to Beijing

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Genomics Institute (BGI) for shotgun library construction and Illumina HTS on HiSeq2000 platform. HTS was

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performed using the strategy of index PE101+8+101 cycle (Paired-End sequencing, 101-bp reads and 8-bp

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index sequence). The base-calling pipeline (Version Illumina Pipeline - 0.3) was used to process the raw

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fluorescence images and call sequences. 24

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Bioinformatics Analysis

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To guarantee the quality of downstream analysis, raw reads containing three or more ambiguous

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nucleotides, or with average quality score below 20, or with length less than 100 bp were removed using a 7



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custom-made script. On the other hand, the HiSeq2000 platform produces artificial replicates that are nearly

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identical and failure to remove these replicated sequences could result in biased estimate.25-28 Therefore, the

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reads after the above quality control processing were then filtered by another custom-made script to remove

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replicated sequences.23,29 Subsequently, the remaining clean paired-end reads were paired-aligned by the third

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custom-made script to merge 10-50 bp overlapped paired-end reads into 150-190 bp illumina Tags (iTags).23 The

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numbers of iTags belonging to each sample before and after normalization was summarized in Table S2. After

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that, MetaPhlAn (Version 1.6.0) was applied to conduct taxonomic classification at species level and quantify

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abundance by mapping metagenomic reads against a catalog of clade-specific marker sequences currently

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spanning the bacterial and archaeal phylogenies.15 Except for the threshold of the e-value of 1 × 10–15, all the

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other parameters of MetaPhlAn utilized default settings. Finally, the output file of MetaPhlAn including

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taxonomic results and the corresponding abundance of each identified species will be compared to our bacterial

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pathogen database which contains 538 pathogenic species.30 The mapped pathogenic species names and their

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abundances will be extracted as a csv file using the fourth custom-made script. The accuracy of all these four

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custom-made scripts (https://github.com/RichieJu520/Metagenomics.git) has been validated before use to avoid

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both false-positive and false-negative errors (S1, Supporting Information). One data set named “Shatin AS” was

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previously used to evaluate different bacterial pathogen identification methods in our previous study.23 Based on

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the optimized identification method, this data set was compared with seven new data sets using the most

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updated and comprehensive bacterial pathogen database in the present study to investigate the broad-spectrum

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profile and fate of bacterial pathogens in STPs. 8


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Bacterial Pathogen List

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The bacterial pathogen database used in the present study includes 538 pathogenic species (Table

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S3),30much more than the 35 pathogenic species contained in a previous database, 23 and thus could guarantee

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broad-spectrum monitoring of bacterial pathogens in various environmental samples.

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RESULTS AND DISCUSSION

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The Occurrence and Abundance of Bacterial Pathogens in Sewage Treatment Plants

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To distinguish the bacterial pathogen community composition in various types of sludge and wastewater

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samples via metagenomic approach, it is essential to evaluate the reproducibility of the whole methodology,

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including DNA extraction, shotgun library construction, Illumina HTS, taxonomic classification using

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MetaPhlAn. The results shown in Figure S2 and S2 (Supporting Information, SI) revealed that the established

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metagenomic approach was reliable enough to compare the abundance and variation of bacterial pathogen

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community among different types of samples. To guarantee the subsequent comparison results, the abundance

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ratio of 4.0 was selected as the threshold for the significant difference in the following analysis. That is, the

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abundances for a specific bacterial pathogen occurred in different samples are considered to be significantly

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different when the corresponding ratio is greater than 4.0 (Figure S2 and S2).

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Figure 1 demonstrates the diversity and abundance of the overall bacterial pathogens in various samples

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including influent, effluent, AS (foaming and non-foaming), biofilm and anaerobic digestion sludge. Totally,

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113 bacterial pathogen species were detected in the above samples. Only three bacterial pathogens with total 9



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abundance of 0.11% were detected in SWH ADS which had the lowest bacterial pathogen diversity and

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abundance. Not surprisingly, influent samples possessed the highest bacterial pathogen diversity, i.e. 91 species,

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with total abundances of 23.2%. The bacterial pathogen composition in influent could reflect the overall

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bacterial pathogen profiles in human gut of the population within the STP catchment area to the greatest extent.

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This will be further discussed in detail in Subsection “PCoA and CA Analysis of Bacterial Pathogen in

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Sewage Treatment Plants”.

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Shatin Influent

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Among the 91 species of bacterial pathogens existed in the influent sample, nine dominant bacterial

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pathogens, including Megamonas hypermegale, Eubacterium rectale, Escherichia coli, Collinsella aerofaciens,

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Streptococcus salivarius, Klebsiella pneumonia, Bacteroides vulgatus, Streptococcus suis and Streptococcus

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bovis were detected with high abundances greater than 0.5%. Megamonas hypermegale, Eubacterium rectale,

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and Escherichia coli were the three top abundant pathogens, i.e., 4.89%, 4.05% and 2.65% within the total

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prokaryotic community of influent. To the best of our knowledge, 79 bacterial pathogen species were first found

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in influents,4,7,9,23,31-33 mainly benefiting from the innovative metagenomic approach which could provide the

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broad-spectrum scan of bacterial pathogens simultaneously (Table S4). Lee et al. 4 and Shannon et al.9 utilized

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qPCR/oligonucleotide microarray for bacterial pathogen detection in STPs. Compared to the present study,

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much less bacterial pathogens, i.e., only 7 and 9 species were detected in raw sewage of a Canada STP,

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respectively. One of the major reasons for such great difference might be due to that the final detection results in

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the previous study were limited by the primers or probes which only covered 17 target bacterial pathogen 10


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species. Escherichia coli population was considered to dominate the bacterial pathogen community in influent,

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that is, more abundant than other pathogens by 2~4 orders of magnitude.4 However, except for Escherichia coli,

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the other 8 species, i.e., Megamonas hypermegale, Eubacterium rectale, Collinsella aerofaciens, Streptococcus

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salivarius, Klebsiella pneumonia, Bacteroides vulgatus, Streptococcus suis and Streptococcus bovis were also

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reported as the dominant bacterial pathogen species with comparable abundances in this study. Therefore, the

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HTS-based metagenomic approach could provide us new insights into broad-spectrum profiles of bacterial

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pathogens in influents. On the other hand, our previous study using 454 pyrosequencing approach revealed that

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Arcobacter and Streptococcus with the abundance of 2.88% and 3.30% were the most abundant bacterial

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pathogen genera in the influent collected from the same STP.10 The abundance of Streptococcus in this study

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was 3.69%, very similar to that obtained via 454 pyrosequencing method. Nevertheless, Arcobacter only

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accounted for 0.22% in the present study, one order of magnitude lower than the 454 pyrosequencing result.

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One of the possible reasons might be due to the inherent amplification biases during PCR step conducted before

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454 pyrosequencing process.13 In addition, pyrosequencing of the 16S rRNA gene will also generate bias

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because of the highly variable copy number of the 16S rRNA gene, ranging from 1 to 15. 14

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Shatin Effluent

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37 bacterial pathogen species were detected in effluent with the total abundances of 11.8%. Compared with

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previous studies, 4,9,23,31-33 32 bacterial pathogen species were discovered in effluents of STPs for the first time

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(Table S5). For example, except for Escherichia coli, other 8 dominant bacterial pathogens, Bacillus cereus,

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Bacillus thuringiensis, Megamonas hypermegale, Collinsella aerofaciens, Bacillus subtilis, Streptococcus 11



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salivarius, Eubacterium rectale and Bacteroides vulgatus were also found with high abundance more than

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0.10% in STP effluent. In addition, the number of disease types caused by Escherichia coli only accounts for

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~10% of the number of total disease types caused by all the pathogens existed in effluents (Table S6). Therefore,

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the profile of bacterial pathogens in STP effluent obtained in the present study could facilitate us to conduct a

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comprehensive risk assessment for wastewater quality as well as public health. Undoubtedly, this is the essential

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anchor for setting appropriate regulatory guidelines of wastewater reclamation and reuse. 2

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Among the 37 bacterial pathogen species totally detected in STP effluent, 3 species, including Escherichia

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coli, Mycobacterium avium and Shigella boydii belong to bacterial pathogens in water environment and are

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highly concerned by WHO.22 Additionally, Mycobacterium avium which could cause pulmonary tuberculosis

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was also selected as the microbial contaminant candidate in Contaminant Candidate List 3 (CCL3) compiled by

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United

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http://water.epa.gov/scitech/drinkingwater/dws/ccl/ccl3.cfm). These 3 bacterial pathogen species deserve to be

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paid more attention in effluents because more and more wastewater reuse projects have been carried out around

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the world and these pathogens are highly concerned in water quality control.34-36

States

Environmental

Protection

Agency

(US

EPA,

218

Shatin AS

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Compared to influent and effluent, much less bacterial pathogen species and total abundances were found

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in “Shatin-AS” and

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(Arcobacter butzleri, Collinsella aerofaciens, Eubacterium rectale, Gordonia bronchialis, Streptococcus

“Shatin-AS Foaming” (Figure 1). To the best of our knowledge, all the pathogen species

12


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salivarius, Vibrio furnissii and Vibrio mimicus) detected in “Shatin-AS” or “Shatin-AS Foaming” were first

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reported in AS system of STPs. 9,10, 23, 32 Shannon et al.

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and Escherichia coli as well as Klebsiella pneumonia were found in AS of a Canada STP. Although Escherichia

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coli and Klebsiella pneumonia were also involved in our pathogen database used in this study, they were not

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detected in the activated sludge samples. The possible reason might be due to the lower detection limit of qPCR

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compared to the metagenomic approach at current sequencing depth. That is, the metagenomic approach is less

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sensitive than qPCR approach and some rare pathogens could not be covered at the sequencing depth applied in

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this study. On the other hand, the previous study found no pathogens at species level in Shatin AS although

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metagenomic approach was also utilized.23 The major reason should be due to the different sizes of pathogen

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databases, i.e., 538 pathogenic species in the database used in this study, much more comprehensive than the 35

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pathogenic species in the database used in the previous study.23

9

applied qPCR to detect 13 bacterial pathogens in AS

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Specially, three pathogens, i.e., Arcobacter butzleri, Gordonia bronchialis and Vibrio furnissii, only existed

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in the “Shatin-AS Foaming” which was collected from the upper foaming layer of AS. This is consistent with

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previous studies which indicated that Gordonia spp. was responsible for AS foaming.37,38 Nevertheless, up to

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now, there are no reports about the existence of Arcobacter butzleri or Vibrio furnissii in foaming AS and thus

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their roles during AS foaming were not well understood currently. More pathogen species and higher total

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abundance of detected pathogens were found in “Shatin-AS Foaming” than “Shatin AS”, which was obtained

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from the bulk mixed liquor. This implies the foaming layer of AS might impose more threat to workers in STP

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and the people in the surrounding areas of STPs because pathogens existed in foaming layer of AS are easily 13



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transferred into air and cause infections accordingly.39

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Stanley AS and Biofilm

243

27 and 24 bacterial pathogen species were detected in “Stanley AS” and “Stanley BF” with the total

244

abundances of 0.64% and 0.56%, respectively. Although the diversity and total abundance of the overall

245

bacterial pathogens in these two samples were comparable with each other, the occurrence of some specific

246

pathogens exhibited significant difference between AS and BF samples (Table S7). Among the 9 bacterial

247

pathogen species which occurred with higher abundances in “Stanley BF”, Mycobacterium abscessus,

248

Mycobacterium avium, Mycobacterium bovis, Mycobacterium kansasii, Mycobacterium marinum, Nocardia

249

farcinica and Ralstonia pickettii were commonly found to be able to form biofilm under various

250

environments.40-46 It should be noted that most studies on Mycobacterium pathogens are specific to drinking

251

water or hospital water distribution systems due to their close relationship with the public health.42,44,48 However,

252

the related studies for activated sludge or biofilm of wastewater system are very limited currently.47 Therefore,

253

the results of this study expanded the horizons of Mycobacterium pathogens into different environments. It

254

should be emphasized that, compared to planktonic bacterial pathogens, the pathogens existed in the biofilm

255

might impose more threat to human health because they could demonstrate stronger and more persistent

256

resistance to disinfectant when they disperse into the effluent via the detached biofilm.48-52 Therefore, these

257

pathogens should deserve special attention for reclamation and reuse of wastewater which are treated using

258

attached growth biological treatment processes.

259 14


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260

Acinetobacter johnsonii, one of the ten pathogens which occurred with much higher abundance in “Stanley

261

AS” was regarded as an important bridging microorganism for sludge floc formation in AS.53-55 This pathogen

262

showed a high affinity for many of settled sewage bacteria and activated sludge flocs.55 The extent and pattern

263

of coaggregation as well as the aggregate size exhibited good correlation with cellular characteristics of the

264

coaggregating partners.53 FISH result indicates that the abundance of Acinetobacter spp. in AS of a Japan STP is

265

2.2%,55 much higher than that of Acinetobacter johnsonii (0.19%) in the present study. The possible reasons for

266

such differences might be due to different samples and different detection methods (FISH vs. Metagenomic

267

approach) applied in these two studies. In addition, the abundance of 2.2% represents the total abundance for all

268

the Acinetobacter species, rather than Acinetobacter johnsonii only.55

269

Shatin ADS and SWH ADS

270

Six and three bacterial pathogen species were detected in “Shatin-ADS” and “SWH-ADS” with the total

271

abundances of 1.23% and 0.11%, respectively (Figure 1). This is comparable with the previous study which

272

reported that the bacterial pathogen abundance ranged between 0.55% and 1.57% for the mesophilic ADS.

273

Bibby et al.5 used 454 pyrosequencing of the 16S rRNA gene to reveal bacterial pathogen diversity and

274

abundances in mesophilic and thermophilic anaerobic digestion sludge. Due to the limitation of PCR based 16S

275

pyrosequencing and RDP taxonomic assignment approach, bacterial pathogens existed in digestion sludge could

276

only be identified at genus level rather than species level. The dominant pathogen genera were Clostridium and

277

Mycobacterium and the total abundances of all the pathogens in mesophilic and thermophilic anaerobic

278

digestion sludge were 0.08% and 0.022%, respectively.5 Very similar pathogen species composition and total 15


12


279

abundances were found between “SWH-ADS” and the mesophilic anaerobic digestion sludge reported by Bibby

280

et al..5 However, the total abundance of pathogen in “Shatin-ADS” exhibited one order of magnitude higher than

281

that of “SWH-ADS”. Additionally, the pathogen compositions of “Shatin-ADS” and “SWH-ADS” also

282

exhibited remarkable difference (Table S8). The possible reason is due to that “Shatin-ADS” mainly derived

283

from AS treating saline sewage (salinity of ~1.2%) while “SWH-ADS” primarily originated from AS treating

284

freshwater sewage. These total different environmental conditions would probably lead to the distinct difference

285

of pathogen species and abundance in these ADS samples.

286 287

Among the eight bacterial pathogen species occurred in “Shatin-ADS” and “SWH-ADS”, Bacteroides vulgatus,

288

Collinsella aerofaciens, Eubacterium rectale, Streptococcus salivarius, and Streptococcus suis were discovered

289

in ADS of STP for the first time.5,56 Arcobacter butzleri was frequently found in ADS collected from an Italy

290

STP in different seasons, and the mean abundance is 7649 MPN/g dry ADS.5 Collinsella aerofaciens and

291

Eubacterium rectale are commonly found in the human gut.57-59 It was reported that Collinsella aerofaciens, a

292

high G+C, Gram-positive member of the Actinobacteria exhibited salt tolerance due to the presence of galE,

293

murB and mazG genes. This is consistent with the phenomenon that higher abundance of Collinsella

294

aerofaciens was observed in ADS collected from Shatin STP which treats saline sewage. Some indicator

295

microorganisms, such as Escherichia coli and Clostridium perfringens were not detected in ADS. However, it

296

does not mean the absence of potential risk for the ADS due to the occurrence of other pathogens (Table S8).

297

Instead, this further indicates the limitation of indicator microorganism method which might underestimate, 16


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298


sometimes even misestimate the potentially infectious risk for human health.

299

Although the final disposal for ADS in Hong Kong is landfill, application of ADS to agriculture is

300

widespread in the USA and Europe. Considering the strong survival ability of these bacterial pathogens56 and

301

the various diseases caused by them (Table S9), it is believed that the land application of ADS could result in

302

serious pollution in soil, farm produce and underground water. Even for landfill treatment, there is still potential

303

risk to transfer these pathogens into underground water and thus cause the spread of multiple diseases due to the

304

leachate leakage. Therefore, the full understanding of the bacterial pathogens profile in ADS is the crucial step

305

for the safe disposal and reuse of ADS.

306 307

Emerging/Re-emerging Bacterial Pathogens in Sewage Treatment Plants

308

Emerging/re-emerging pathogen species are notable for their taxonomic, biological, epidemiological and

309

geographical diversity. Emerging bacterial pathogen is defined as the bacterial infectious agents whose

310

incidences are increasing following their first introduction into the new host population. Re-emerging bacterial

311

pathogens are bacterial infectious agents whose incidences are increasing in the existing host population as a

312

result of long-term changes in their underlying epidemiology.30 It was reported that a significant number of

313

emerging and re-emerging bacterial pathogens had been recognized and water transmission had caused

314

outbreaks including Escherichia coli O157:H7, Helicobacter pylori, Legionella pneumophila, Campylobacter

315

jejuni as well as Mycobacterium avium complex.3 17 and 8 emerging/re-emerging bacterial pathogens were

316

found in influent/effluent and AS/ADS, respectively (Figure 2). The abundances of these emerging/re-emerging 17



Page 18 of 34

317

pathogens were 3.84% in STP influent and 0.33% in STP effluent. Emerging/re-emerging pathogens accounted

318

for 16.6% and 2.79% of the total bacterial pathogens in influent and effluent, respectively. The decrease of

319

percentages of the emerging/re-emerging pathogens in the total bacterial pathogens after the secondary

320

biological treatment implies that, compared to other non-emerging/re-emerging pathogens, some

321

emerging/re-emerging pathogens are easier to be removed in AS treatment process. The detailed removal

322

efficiency for the specific pathogen will be discussed in Subsection “Removal of Bacterial Pathogen in

323

Activated Sludge Process”.

324 325

As shown in Figure 2a, Escherichia coli and Klebsiella pneumoniae were most abundant, accounting for

326

88.9~92.0% of the total emerging/re-emerging pathogens in effluent and influent. Except for Escherichia coli

327

and

328

emerging/re-emerging pathogens) in STP effluent. Escherichia coli is usually used as an indicator

329

microorganism to estimate the presence of pathogenic microorganisms in wastewater. Klebsiella pneumoniae

330

was previously detected in the influents of a constructed soil filter in India and the raw sewage of a STP with

331

bioreactor “BIO-PAK” in Poland.31,39 As shown in Figure 2b, emerging/re-emerging pathogens accounted for

332

14.8%, 56.5% and 82.7 % of the total bacterial pathogens in “Stanley-AS”, “Stanley-BF” and “SWH-ADS”,

333

respectively. The abundances of these emerging/re-emerging pathogens ranged from 0.092% to 0.32%. No

334

emerging/re-emerging bacterial pathogens were detected in “Shatin-AS foaming”, “Shatin-AS” and

335

“Shatin-ADS”. Mycobacterium spp. were widely found in AS, BF and ADS samples. In “Stanley-BF”,

Klebsiella

pneumoniae,

Mycobacterium

ulcerans

was

also

18


abundant

(7.6%

of

the

total

Page 19 of 34


336

Mycobacterium bovis is the most abundant emerging/re-emerging pathogen followed by Bordetella pertussis.

337

Although Mycobacterium bovis is able to form biofilm,44 there was no report on its occurrence in STP

338

previously. Bordetella pertussis, Mycobacterium ulcerans and Aeromonas hydrophila which were first reported

339

in AS in this study are the dominant emerging/re-emerging pathogen species in “Stanley-AS”. Mycobacterium

340

ulcerans and Clostridium botulinum made up the total emerging/re-emerging pathogens in “SWH-ADS”.

341

Clostridium botulinum, an anaerobic spore-former, was detected in sludge samples from biogas plants before

342

digestion, but disappeared after digestion.58 It implies that digestion is an effective process to remove

343

Clostridium botulinum. However, we could not calculate the removal efficiency of Clostridium botulinum in the

344

present study since we have no corresponding abundance data in “SWH-AS”.

345 346 347 348

Removal of Bacterial Pathogen in Activated Sludge Process

Removal efficiency of each specific bacterial pathogen was calculated based on Equation (1):

Re moval efficiency (%) = (1 −

VSSeffluent × Abundance pathogen,eff VSSinfluent × Abundance pathogen,inf

) × 100

(1)

349

Where VSSinfluent and VSSeffluent represent the volatile suspended solids (VSS) in influent and effluent,

350

respectively. Abundance pathogen,eff and Abundance pathogen,inf are the abundance of one specific bacterial pathogen

351

in effluent and influent, respectively. From the point view of engineering, we used Equation (1) to calculate the

352

removal efficiencies due to the fact that increasing volatile suspended solids removal also increases bacterial

353

removal as bacteria are often solid-associated. Additionally, we assumed that the pathogenic bacteria represent

354

only some percentage of total bacteria in VSS. VSS of influent and effluent during the sampling period were 19



355

summarized in Table S10.

356 357

The removal efficiency of total bacterial pathogens was 98.0%. This suggests that AS treatment process is

358

efficient and effective to reduce the potential health risk to public health caused by bacterial pathogens. Based

359

on the removal efficiency results, bacterial pathogens in influents were divided into five groups (Figure 3).

360

Group I referred to that these 3 pathogen species could not be removed efficiently and their remove efficiencies

361

were lower than 1-log. Group II indicated that the 12 pathogen species could be removed with efficiencies

362

ranging from 1-log to 2-log. For Group III, the 14 pathogens were removed by 2-log to 3-log. Group IV

363

included 4 pathogens with removal efficiencies of 3-log ~ 4-log. For Group V, the 58 pathogens could be

364

reduced by >4-log during AS treatment process. As mentioned in Subsection “Emerging/Re-emerging

365

Bacterial Pathogens in Sewage Treatment Plants”, the decrease of percentages of the emerging/re-emerging

366

pathogens in the total bacterial pathogens after the secondary biological treatment implies that some

367

emerging/re-emerging pathogens are easier to be removed in AS treatment process. As shown in Figure 3, 13

368

out of 15 emerging/re-emerging pathogen species belong to Group III ~ Group V with high removal efficiencies

369

ranging from >99% to >99.99%. Additionally, the detected bacterial pathogen listed in CCL3 (Shigella sonnei)

370

and transmitted through drinking-water concerned by WHO (Shigella boydii, Escherichia coli, Vibrio cholera,

371

Shigella sonnei, Shigella flexneri and Shigella dysenteriae) were well removed by the activated sludge process

372

(>99% ~ >99.99%). Since only limited bacterial pathogens have been detected in previous studies, the

373

comparison of removal efficiencies between the present study and previous references could not cover all the 20


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374

pathogens illustrated in Figure 3. Shannon et al.9 used qPCR to detect selected bacterial pathogens during

375

municipal wastewater treatment and found that Escherichia coli, Klebsiella pneumoniae, Clostridium

376

perfringens as well as Enterococcus faecalis were all efficiently removed with high removal efficiencies

377

(99.93~99.99%) calculated based on the gene copy numbers per 100 mL wastewater.9 In this study, Escherichia

378

coli and Klebsiella pneumoniae exhibited similar removal pattern, i.e., 99.34%~99.42% of them could be

379

removed during AS treatment process. For Clostridium perfringens and Enterococcus faecalis, the removal

380

efficiencies were as high as >99.9% and >99.99%, respectively in the present study. Wen et al. 2 once detected

381

Clostridium perfringens in Bolivar STP utilizing secondary AS treatment process in Australia and the

382

corresponding removal efficiency was 94.22%, much lower than those reported by Shannon et al.9 and our study.

383

The possible reasons might be as follows. Clostridium perfringens is an anaerobe but is able to survive under

384

aerobic stage as spores. 6 h of anoxic stirring for nitrogen removal would cause the detachment of Clostridium

385

perfringens spores from the sludge flocs and consequently reducing the removal efficiency.2 Aeromonas

386

hydrophila was only detected in raw wastewater and was undetectable thereafter in Shannon et al.’s study.9

387

Similarly, the removal efficiency of Aeromonas hydrophila in this study was 99.999%, also consistent with the

388

results of 99.98% reported by Lee et al..4

389 390

PCoA and CA Analysis of Bacterial Pathogen in Sewage Treatment Plants

391

Principal coordinate analysis (PCoA) and cluster analysis (CA) were conducted to compare the overall

392

bacterial pathogen community compositions for different types of samples collected from STPs. PCoA results 21



393

revealed that bacterial pathogen communities in these various samples could be clustered into five groups

394

(Figure 4): (1) Group I influent sample obtained from Shatin STP as well as two human gut samples published

395

in MG-RAST (http://metagenomics.anl.gov/, Table S11); (2) Group II contains effluent sample collected from

396

Shatin STP; (3) Group III is ADS from Shek Wu Hui SPT which treats freshwater sewage containing

397

slaughterhouse wastewater; (4) Group IV contains AS and BF samples from Stanley STP which is located inside

398

a cave and treats freshwater sewage; (5) Group V contains AS, foaming AS and ADS samples from Shatin STP

399

which treats saline sewage (salinity of ~1.2%). This grouping pattern was reasonable and in consistent with the

400

CA results shown in Figure S3. That is, the clustering has strong relatedness with both the sample types

401

(influent vs. effluent vs. AS/ADS samples) and the STP characteristics (freshwater STP or saline STP). It should

402

be pointed out that SWH ADS and Shatin ADS were clustered into two separate groups although they belong to

403

the same sample type, i.e., anaerobic digestion sludge. In addition, “SWH-ADS” clustered more closely to

404

“Stanley-AS” and “Stanley-BF” while “Shatin-ADS” clustered more tightly with “Shatin-AS” and “Shatin-AS

405

foaming”. Similarly, “Stanley-AS” and “Shatin-AS” were also separated into Group IV and Group V,

406

respectively. As mentioned above, both Stanley and Shek Wu Hui STPs treat freshwater sewage while Shatin

407

STP treats saline sewage. Therefore, it might imply that STP characteristic (salinity effect) is the main

408

determinant for this grouping pattern.

409 410

We hypothesized that there would be remarkable similarities of bacterial pathogen compositions between

411

influent samples and human gut samples. In other words, the bacterial pathogen profiles in influents could 22


Page 22 of 34

Page 23 of 34


412

reflect the average bacterial pathogen communities of the urban resident guts within the STP catchment area to a

413

great extent. The grouping pattern demonstrated by the PCoA and CA results effectively confirmed this

414

assumption. Compared to other samples, the influent sample indeed clustered much more tightly with two

415

human gut samples (Figure 4). This result might be driven primarily by the shared presence of 14 dominant

416

pathogens in both influent and human gut samples, i.e., Eubacterium rectale, Bacteroides vulgatus, Collinsella

417

aerofaciens, Bacteroides stercoris, Escherichia coli, Streptococcus salivarius, Bacteroides ovatus, Sutterella

418

wadsworthensis, Bacteroides caccae, Bacteroides uniformis, Bilophila wadsworthia, Eggerthella lenta,

419

Bacteroides thetaiotaomicron and Bacteroides fragilis (Table S12). Assessing average bacterial community

420

structure in urban residents from catchment areas has also been studied in previous works using 16S rRNA.

421

The results indicated that influent sewage bacterial profile reflected the human microbiome and human gut

422

bacterial community was the dominant force shaping influent sewage bacterial profile. These findings were

423

consistent with the results obtained in our study.

11

424 425

Advantage and Disadvantage of Metagenomic Approach

426

The breakthrough of next-generation sequencing technology launched a new era for the broad-spectrum

427

monitoring and rapid detection of pathogens. To the best of our knowledge, this is the first study to conduct the

428

broad-spectrum monitoring of bacterial pathogens in various environmental samples simultaneously using

429

metagenomic approach combined with HTS technology. The full understanding of the bacterial pathogens

430

profile in effluent and ADS could facilitate us to conduct a comprehensive risk assessment for public health 23



431

during the wastewater reclamation/reuse as well as application of ADS to agriculture. Compared to other

432

molecular biology detection method of bacterial pathogen (PCR/qPCR or 454 pyrosequencing of the 16S rRNA

433

gene), the metagenomic approach used in this study not only provides us a broad picture of the occurrence of

434

bacterial pathogens, but also shows the following advantages. Firstly, it is able to accurately analyze the

435

bacterial pathogen community and quantify the abundance without PCR bias and copy number variability.11,31

436

Secondly, it can investigate the bacterial pathogen composition at a lower taxonomic resolution, i.e., species

437

level. This is especially crucial to characterize and quantify pathogens since the investigation of pathogens at

438

genus level usually leads to some inaccurate estimate.15 Finally but more importantly, it could standardize the

439

bacterial pathogen detection method in different laboratories and realize the direct comparison of results among

440

different samples and studies on a united platform.

441 442

However, the metagenomic approach used in this study still has its limitations. Firstly, MetaPhlAn tool which

443

is used for pathogen annotation only applies to bacterial pathogens and thus could not cover the other pathogen

444

types including viruses/prions, fungi, protozoa and helminthes.15 These four pathogen types account for as high

445

as 61.8% of the total reported human pathogen species.26 In addition, some novel bacterial pathogens which was

446

excluded in the database of MetaPhlAn could not be annotated even they actually existed in the samples (Table

447

S13). Secondly, metagenomic approach might be less sensitive than qPCR approach at the current sequencing

448

depth and rare pathogens might not be detected, especially for complicated environmental samples, such as AS

449

and ADS. Thirdly, similar to the other molecular biology techniques, this metagenomic approach might 24


Page 24 of 34

Page 25 of 34


450

overestimate the abundance of metabolically active bacterial pathogens since it is based on the total DNA

451

extracted from both metabolically active bacteria and dead bacterial cells. Moreover, it should be pointed out

452

that the strains within the identified pathogen species vary greatly in pathogenicity and therefore the bacterial

453

species identified in this study should be “potential pathogens”. Finally, HTS for metagenomics is still costly at

454

present and cannot be applied to routine analysis in STPs, although the cost has been greatly decreased in recent

455

years.61

456 457

Acknowledgment

458

This work was supported by the Research Grants Council of Hong Kong (HKU7201/11E). Dr. Lin Cai thank

459

The University of Hong Kong for the postdoctoral fellowships. Mr. Feng Ju thanks The University of Hong

460

Kong for the postgraduate studentship. The authors also wish to thank Dr. Feng Guo for his valuable

461

suggestions and discussion.

462

Supporting Information Available

463

Supplementary data includes S1, S2, Fig. S1- Fig. S3 and Table S1－Table S13. This information is available

464

free of charge via the Internet at http://pubs.acs.org.

465

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Abiotrophia defectiva Achromobacter piechaudii Achromobacter xylosoxidans Acidaminococcus fermentans Acinetobacter baumannii Acinetobacter haemolyticus Acinetobacter johnsonii Acinetobacter junii Acinetobacter lwoffii Acinetobacter radioresistens Actinomyces odontolyticus Aerococcus viridans Aeromonas hydrophila Arcobacter butzleri Bacillus anthracis Bacillus cereus Bacillus pumilus Bacillus subtilis Bacillus thuringiensis Bacteroides caccae Bacteroides eggerthii Bacteroides fragilis Bacteroides ovatus Bacteroides pectinophilus Bacteroides stercoris Bacteroides thetaiotaomicron Bacteroides uniformis Bacteroides vulgatus Bifidobacterium dentium Bilophila wadsworthia Bordetella avium Bordetella parapertussis Bordetella pertussis Chromobacterium violaceum Citrobacter koseri Clostridium botulinum Clostridium difficile Clostridium perfringens Collinsella aerofaciens Comamonas testosteroni Delftia acidovorans Eggerthella lenta Eikenella corrodens Enterobacter cancerogenus Enterobacter cloacae Enterococcus casseliflavus Enterococcus faecalis Enterococcus faecium Enterococcus gallinarum Escherichia coli Eubacterium limosum Eubacterium rectale Finegoldia magna Fusobacterium mortiferum Fusobacterium nucleatum Fusobacterium ulcerans Fusobacterium varium Gardnerella vaginalis Gordonia bronchialis Haemophilus parainfluenzae Klebsiella pneumoniae Mannheimia haemolytica Megamonas hypermegale Mycobacterium abscessus Mycobacterium avium Mycobacterium bovis Mycobacterium kansasii Mycobacterium leprae Mycobacterium marinum Mycobacterium smegmatis Mycobacterium ulcerans Neisseria elongata Neisseria flavescens Neisseria gonorrhoeae Neisseria meningitidis Neisseria mucosa Neisseria sicca Neisseria subflava Nocardia farcinica Ochrobactrum anthropi Propionibacterium acnes Pseudomonas fluorescens Pseudomonas stutzeri Ralstonia pickettii Rhodococcus equi Rhodococcus erythropolis Rothia dentocariosa Sebaldella termitidis Shigella boydii Shigella dysenteriae Shigella flexneri Shigella sonnei Staphylococcus aureus Staphylococcus saprophyticus Stenotrophomonas maltophilia Streptococcus agalactiae Streptococcus anginosus Streptococcus bovis Streptococcus gordonii Streptococcus mitis Streptococcus mutans Streptococcus salivarius Streptococcus suis Sutterella wadsworthensis Tsukamurella paurometabola Veillonella atypica Veillonella dispar Veillonella parvula Vibrio cholerae Vibrio furnissii Vibrio mimicus Vibrio parahaemolyticus Vibrio vulnificus

Pathogen species number Total abundance (%)

Abiotrophia_defectiva Achromobacter_piechaudii Achromobacter_xylosoxidans Acidaminococcus_fermentans Acinetobacter_baumannii Acinetobacter_haemolyticus Acinetobacter_johnsonii Acinetobacter_junii Acinetobacter_lwoffii Acinetobacter_radioresistens Actinomyces_odontolyticus Aerococcus_viridans Aeromonas_hydrophila Arcobacter_butzleri Bacillus_anthracis Bacillus_cereus Bacillus_licheniformis Bacillus_mycoides Bacillus_pumilus Bacillus_subtilis Bacillus_thuringiensis Bacteroides_caccae Bacteroides_eggerthii Bacteroides_fragilis Bacteroides_ovatus Bacteroides_pectinophilus Bacteroides_stercoris Bacteroides_thetaiotaomicron Bacteroides_uniformis Bacteroides_vulgatus Bifidobacterium_dentium Bilophila_wadsworthia Bordetella_avium Bordetella_parapertussis Bordetella_pertussis Brevibacillus_brevis Chromobacterium_violaceum Citrobacter_koseri Clostridium_botulinum Clostridium_difficile Clostridium_perfringens Collinsella_aerofaciens Comamonas_testosteroni Corynebacterium_amycolatum Delftia_acidovorans Eggerthella_lenta Eikenella_corrodens Enterobacter_cancerogenus Enterobacter_cloacae Enterococcus_casseliflavus Enterococcus_faecalis Enterococcus_faecium Enterococcus_gallinarum Escherichia_coli Eubacterium_limosum Eubacterium_rectale Finegoldia_magna Fusobacterium_mortiferum Fusobacterium_nucleatum Fusobacterium_ulcerans Fusobacterium_varium Gardnerella_vaginalis Gordonia_bronchialis Haemophilus_parainfluenzae Kingella_denitrificans Klebsiella_pneumoniae Legionella_pneumophila Mannheimia_haemolytica Megamonas_hypermegale Mycobacterium_abscessus Mycobacterium_avium Mycobacterium_bovis Mycobacterium_kansasii Mycobacterium_leprae Mycobacterium_marinum Mycobacterium_smegmatis Mycobacterium_ulcerans Neisseria_elongata Neisseria_flavescens Neisseria_gonorrhoeae Neisseria_meningitidis Neisseria_mucosa Neisseria_sicca Neisseria_subflava Nocardia_farcinica Ochrobactrum_anthropi Propionibacterium_acnes Pseudomonas_fluorescens Pseudomonas_stutzeri Pseudoramibacter_alactolyticus Ralstonia_pickettii Rhodococcus_equi Rhodococcus_erythropolis Rothia_dentocariosa Sebaldella_termitidis Shigella_boydii Shigella_dysenteriae Shigella_flexneri SWH-ADS Shatin-Eff. Shatin-Inf. Shatin-AS Shatin-AS Stanley-AS Stanley-BFShatin-ADS Shigella_sonnei Foaming Staphylococcus_aureus Staphylococcus_epidermidis Staphylococcus_saprophyticus 91 7 4 37 27 24 6 3 Stenotrophomonas_maltophilia Streptococcus_agalactiae Streptococcus_anginosus 11.8 23.2 0.63 0.33 0.64 0.56 1.23 0.11 Streptococcus_bovis Streptococcus_gordonii Streptococcus_mitis Streptococcus_mutans Streptococcus_salivarius Streptococcus_suis Sutterella_wadsworthensis T sukamurella_paurometabola Veillonella_atypica Veillonella_dispar Veillonella_parvula Vibrio_cholerae Vibrio_furnissii Vibrio_mimicus Vibrio_parahaemolyticus Vibrio_vulnificus

5.0

1

1.0

0

0.1

-1

0.01

-2

0.001

-3

Abundance （%）

Page 31 of 34

0.0001

-4

ND

-5

Figure 1 Species and relat ive abundance of bacterial pathogen in influent, effluent, activated sludge, biofilm and anaerobic digestion sludge ( listed in CCL3;

represents emerg ing/re-emerg ing bacterial pathogen;

represents bacterial pathogen

represents bacterial pathogen transmitted through drinking-water concerned by WHO). Blue color

in the heat map indicates the relative abundance of zero.

SWH.ADS

Shatin.ADS

Stanley.BF

Stanley.AS

Shatin.AS

Shatin.AS.Foaming

Shatin.Inf.Winter

Shatin.Inf.Summer

Shatin.Eff.Winter

Shatin.Eff.Summer



100

(a)

4

Non-emerging/re-emerging pathogens Vibrio vulnificus Vibrio parahaemolyticus Vibrio cholerae Staphylococcus aureus Shigella dysenteriae Neisseria meningitidis Neisseria gonorrhoeae Mycobacterium ulcerans Mycobacterium marinum Mycobacterium avium Klebsiella pneumoniae Escherichia coli Enterococcus faecium Enterococcus faecalis Clostridium difficile Bacillus anthracis Aeromonas hydrophila

80

2 40

Abundance (%)

Percentage (%)

3 60

1 20

0

0

Abundance

Shatin-Eff.

Shatin-Inf.

100

0.4

(b)

80

60 0.2 40

Abundance (%)

0.3 Percentage (%)

Page 32 of 34

Non-emerging/re-emerging pathogens Mycobacterium ulcerans Mycobacterium marinum Mycobacterium leprae Mycobacterium bovis Mycobacterium avium Clostridium botulinum Bordetella pertussis Aeromonas hydrophila

0.1 Abundance

SWH-ADS

Shatin-ADS

Stanley-BF

Stanley-AS

Shatin-AS

0

Shatin-AS Foaming

20

0.0

Figure 2 Emerging/re-emerging bacterial pathogens in (a) influent/effluent and (b) AS/BF/ADS. Outer ring: Percentage of emerging/re-emerg ing bacterial pathogens in total bacterial pathogen. Inner ring: Relat ive abundance of emerg ing/re-emerging bacterial pathogens in the corresponding samples . The “abundance” obtained via MetaPhlAn was estimated by weighting read counts assigned using the direct method with the total nucleotide size of all the markers in the clade and normalizing by the sum of all direct ly estimated weighted read counts. The “percentage” represents the ratio of abundance of each individual pathogen divided by the total pathogen abundance in one specific sample.


Page 33 of 34


Figure 3 Removal of bacterial pathogens in activated sludge process . pathogens.

represents bacterial pathogens listed in CCL3.

represents emerg ing/re-emerg ing bacterial

represents bacterial pathogens transmitted through

drinking-water concerned by WHO.



Page 34 of 34

0.5

IV

P2-Percent variation explained 22.0%

0.4

Stanley-BF Stanley-AS

III

0.3

SWH-ADS

0.2 0.1

I

II

Shatin-Eff.

0.0

Human gut-1 Shatin-Inf.

-0.1 Human gut-2 -0.2

V Shatin-AS Shatin-ADS Shatin-AS Foaming

-0.3 -0.4 -0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

P1- Percent variation explained 31.8% Figure 4 Principal coordinate analysis (PCoA) of bacterial pathogen profiles in influent, effluent, activated sludge, biofilm, anaerobic digestion sludge and human gut samples. PCoA was conducted based on the Bray-Curtis distance calculated from the matrix of bacterial pathogen relative abundance using PAST software.
