METAGENOMIC INSIGHTS INTO MICROBIAL DIVERSITY AND RESISTANCE TO
ANTIBIOTICS IN WASTEWATER TREATMENT PLANTS
By
Mariya Munir

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Environmental Engineering - Doctor of Philosophy
2014

ABSTRACT
METAGENOMIC INSIGHTS INTO MICROBIAL DIVERSITY AND RESISTANCE TO
ANTIBIOTICS IN WASTEWATER TREATMENT PLANTS
By
Mariya Munir
Our water environment is greatly impacted by the presence of microbial contaminants
which is a great concern it terms of public health exposure. Full-scale conventional and state-ofthe-art wastewater utilities have been found to release pathogens and antibiotic resistant bacteria
in the environment. Management and minimization of microbial pathogens and resistant bacteria
in wastewater treatment plants is critical since the spread of pathogens and antibiotic resistant
genes in the environment poses a significant challenge to diverse aspects of our global
community. The overall aim of this study is to provide metagenomic insights into bacterial, viral
and phage diversity and resistance to antibiotics and metal compounds in wastewater utilities.
Samples were collected from two different wastewater treatment systems, a conventional
activated sludge utility and a membrane bioreactor (MBR), in Michigan. Metagenomic analyses
were conducted on Illumina Miseq and Hiseq generated sequences using MGRAST and
METAVIR analysis software. The findings suggest that there is a substantial shift in the phage
community over the course of the activated sludge process. Phage populations are dynamic and
phage DNA was associated with antibiotic resistant genes in wastewater. It was observed that
there are differences in the abundance of functional genes related to resistance (antibiotic
resistance and metal resistance) in different samples. Genes coding for antibiotic resistance were
identified in all bacterial samples along with genes coding for resistance to metals. The MBR
utility samples showed slightly higher number of hits for all the functional categories compared
to conventional wastewater treatment samples. Diverse viral and bacterial human pathogens were

observed in treated wastewater samples. Diversity analysis does not provide quantitative data on
pathogen loads or infectivity but it provides a list of potentially pathogenic viruses and bacteria
that need to be considered during treatment management decisions. This study provided a
bioinformatics approach for identifying microbial diversity in different wastewater treatment
stages and technologies. The results of this work provide significant information that will
contribute to sustainable wastewater management decisions.

iii

I would like to dedicate my thesis to my beloved father Late Mr. Muniruddin Hasan, my
mother Mrs Shabnam Mirza and my husband Mr. Azhar Ahamad, who inspired me, had faith in
me and made me an achiever. I always admire them for their hardwork, intelligence and
kindness. I am blessed to have them in my life.

iv

ACKNOWLEDGEMENTS

I would like to express my sincere thanks to my advisor and mentor Dr. Irene Xagoraraki
for her constant guidance, encouragements, research ideas and support throughout my Ph.D
program and research. A special thank to Dr. Terence Marsh for guiding me and helping me
during my PhD research.
I would also like to thank all my committee members, Dr. Syed Hashsham and Dr. Alison
Cupples for guidance and thoughts on my research. I also thank my labmates for their help and
cooperation.
Words can not describe the support from my parents, my in-laws and family members.
They have always supported me in my personal and professional career and have been there in
every thick and thin moment. I thank them all for their endless Love. I also want to thank my
friends for their continuous help throughout my stay at Michigan State University.
And special thanks to my husband Mr. Azhar Ahamad, my strength, my support, my
courage. He has been extremely patient, caring and encouraging; making me realize this dream is
possible and achievable. This has been truly a memorable journey and would not have been
complete with out the support of everyone.

v

TABLE OF CONTENTS

LIST OF TABLES ......................................................................................................................... ix
LIST OF FIGURES ........................................................................................................................ x
CHAPTER 1 ................................................................................................................................... 1
INTRODUCTION ....................................................................................................................... 1
REFERENCES ............................................................................................................................ 6
CHAPTER 2 ................................................................................................................................. 10
PHAGE AND ANTIBIOTIC RESISTANCE GENES IN A CONVENTIONAL
WASTEWATER TREATMENT PLANT ................................................................................... 10
Abstract ..................................................................................................................................... 10
1. Introduction ........................................................................................................................... 11
2. Materials and Methods .......................................................................................................... 13
2.1. Sample Collection: ......................................................................................................... 13
2.2. Sample Processing: ......................................................................................................... 14
2.3. DNA Extraction: ............................................................................................................. 15
2.4. Quantification: ................................................................................................................ 15
2.5. Metagenomic analyses:................................................................................................... 15
2.5.1. Blast analyses: .......................................................................................................... 16
2.5.2. MG-RAST analyses: ................................................................................................ 16
3. Results and Discussion:......................................................................................................... 16
3.1. Metagenome sequencing and assembly results: ............................................................. 16
3.2. Phage Diversity: ............................................................................................................. 18
3.3. Antibiotic Resistant Gene Diversity: .............................................................................. 19
3.4. Concentration of ARGs: ................................................................................................. 20
4. Conclusions: .......................................................................................................................... 21
5. Acknowledgement:................................................................................................................ 22
APPENDIX ............................................................................................................................... 23
REFERENCES .......................................................................................................................... 32
CHAPTER 3 ................................................................................................................................. 37
METAGENOMIC INSIGHTS INTO MICROBIAL RESISTANCE TO ANTIBIOTIC AND
METAL COMPOUNDS IN WASTEWATER UTILITIES......................................................... 37
Abstract ..................................................................................................................................... 37
1. Introduction ........................................................................................................................ 38
2. Materials and Methods .......................................................................................................... 41
2.1. Sample Collection: ......................................................................................................... 41
2.2. Sample Processing: ......................................................................................................... 41
2.3. Nucleic acid Extraction: ................................................................................................. 41
2.5. Metagenomic sequencing and analyses: ......................................................................... 42
2.6. MG-RAST analyses:....................................................................................................... 42
2.7. Statistical analysis: ......................................................................................................... 43
3. Results and Discussion:......................................................................................................... 44

vi

3.1. Metagenome sequencing and assembly results: ............................................................. 44
3.2. Resistance to antibiotics: Antibiotic Resistant Gene Diversity: ..................................... 46
3.3. Resistance to metal compounds:..................................................................................... 48
4. Conclusions: .......................................................................................................................... 50
5. Acknowledgement:................................................................................................................ 51
APPENDIX ............................................................................................................................... 52
REFERENCES .......................................................................................................................... 62
CHAPTER 4 ................................................................................................................................. 68
SCREENING FOR POTENTIAL VIRAL AND BACTERIAL PATHOGENS IN
WASTEWATER EFFLUENT RELEASED FROM AN MBR AND A CONVENTIONAL
TREATMENT UTILITY USING METAGENOMICS ANALYSIS .......................................... 68
Abstract ..................................................................................................................................... 68
1. Introduction ........................................................................................................................ 69
2. Materials and Methods .......................................................................................................... 72
2.1. Sample Collection: ......................................................................................................... 72
2.2. Sample Processing: ......................................................................................................... 72
2.3. Nucleic acid Extraction: ................................................................................................. 74
2.4. Metagenomic analyses:................................................................................................... 74
2.4.1. MG-RAST analyses: ................................................................................................ 75
2.4.2. METAVIR 2 analyses: ............................................................................................. 75
3. Results and Discussion:......................................................................................................... 76
3.1. Metagenome sequencing and assembly results: ............................................................. 76
3.2. Microbial diversity in WWTP: ....................................................................................... 78
3.2.1. Virus diversity in WWTP: ....................................................................................... 79
3.2.2. Bacterial diversity in WWTP: .................................................................................. 80
3.3. Potential Pathogens in WWTP: ...................................................................................... 81
4. Conclusions: .......................................................................................................................... 84
5. Acknowledgement:................................................................................................................ 85
APPENDIX ............................................................................................................................... 86
REFERENCES ........................................................................................................................ 103
CHAPTER 5 ............................................................................................................................... 110
VIRUS AND PHAGE DIVERSITY IN ACTIVATED SLUDGE FROM A CONVENTIONAL
AND A MBR WASTEWATER UTILITY USING METAGENOMIC ANALYSES .............. 110
Abstract ................................................................................................................................... 110
1. Introduction ...................................................................................................................... 110
2. Materials and Methods ........................................................................................................ 112
2.1. Sample Collection: ....................................................................................................... 112
2.2. Sample Processing: ....................................................................................................... 112
2.2.1. Virus elution/isolation process: ................................................................................. 113
2.2.2. Phage isolation: ...................................................................................................... 113
2.3. Nucleic acid Extraction: ............................................................................................... 114
2.4. Metagenomic sequencing and analyses: ....................................................................... 114
2.5. MG-RAST analyses:..................................................................................................... 115
3. Results and Discussion:....................................................................................................... 116
3.1. Metagenomic statistics: ................................................................................................ 116
vii

3.2. Virus and phage diversity in sludge: ............................................................................ 118
4. Conclusions: ........................................................................................................................ 121
5. Acknowledgement:.............................................................................................................. 122
APPENDIX ............................................................................................................................. 123
REFERENCES ........................................................................................................................ 139
CHAPTER 6 ............................................................................................................................... 143
CONCLUSIONS AND SIGNIFICANCE .................................................................................. 143

viii

LIST OF TABLES

Table 2.1: Metagenome analysis statistics (generated by MGRAST)…………………………..24
Table 2.2: Presence of Phage lineages using BLAST searches…………………………………25
Table 3.1: Wastewater Treatment Characterstics.........................................................................53
Table 3.2a: Metagenome analysis statistics in bacterial samples……………………………….54
Table 3.2b: Functional category Hit distribution ……………………………………………….55
Table 3.3: Resistance groups for bacterial samples (MGRAST)……………………………......60
Table 4.1: Microbial Contaminant Candidate List from EPA………………………………....87
Table 4.2: Virus and bacterial Pathogens detected in wastewater effluent……………...……..88
Table 4.3: Virus and bacterial Pathogens detected in raw and sludge samples ………....……...90
Table 4.4: Wastewater Treatment Plant Characterstics................................................................92
Table 4.5: Metagenome analysis statistics for virus samples (by MetaVir).................................92
Table 4.6a: Metagenome analysis statistics for bacterial samples (generated by MGRAST)......93
Table 4.6b: Functional category Hit distribution (Bacterial samples)...............................….......93
Table 4.7: Taxonomic comparison heat map based contigs best BLAST hit ratios (number of
hits for the genome divided by total number of hits in the metagenome)………….97
Table 4.8: Organism Abundance (Bacteria Phylum Distribution)………………………………99
Table 5.1: Characterstics of WWTPs..........................................................................................125
Table 5.2: Metagenome analysis statistics for sludge samples (generated by MGRAST)…….125
Table 5.3: Virus abundance in ELWWTP and TCWWTP activated sludge samples………....130
Table 5.4: Phage abundance in ELWWTP and TCWWTP activated sludge samples………...132

ix

LIST OF FIGURES

Figure 2.1: (a) Subsystem functional barchart, (b) Functional distribution of “Virulence, Disease
and Defense” subsystem, (c) Functional distribution of “Resistance to
antibiotics”…………………………………………………………………………. 27
Figure 2.2: Organism (genus) Tree……………………………………………………………...29
Figure 2.3: Concentration (copies/100mL) of (a) tetracycline resistant gene (Tet W), and (b)
sulfonamide resistant gene (Sul I) abundance in Phage DNA from sludge samples..30
Figure 3.1: Map showing location of sampling and sampling schematic……………………….53
Figure 3.2: Schematic flowchart showing the procedure and methodology………………….....54
Figure 3.3: Schematic showing the analysis step for the functional abundance in MGRAST….55
Figure 3.4: Bacterial resistance to antibiotics and metals in activated sludge for East Lansing and
Traverse City wastewater utilities…………………………………………………..56
Figure 3.5: Bacterial resistance to antibiotics and metals in before disinfection water samples for
East Lansing and Traverse City wastewater utility…………………...………….....57
Figure 3.6: Bacterial resistance to antibiotics and metals in after disinfection water samples for
East Lansing and Traverse City wastewater utility………………………………....58
Figure 3.7: Relative abundance of resistance to antibiotics and metal compounds obtained from
MGRAST……………………………………………………………………...…….59
Figure 4.1: Location of two wastewater treatment plants selected for effluent sampling for
investigation of the viral and bacterial community……………………………........94
Figure 4.2: Schematic flowchart showing the procedure followed for metagenomic analysis…95
Figure 4.3: Rarefaction curve of species richness in virus and bacterial DNA enriched samples
from effluents of two different WWTPs……………………………........................96
Figure 4.4: Potentially pathogenic virus abundance in effluent samples…………………...….101
Figure 4.5: Potentially pathogenic bacteria diversity abundance in effluent samples…...…….102
Figure 5.1: Schematic of methodology and location of sampling……………………………..124
Figure 5.2: Metagenome Summary in phage and virus DNA enriched samples from sludge of
two different WWTPs……………………………………………………………...126

x

Figure 5.3: Rarefaction curve of species richness in phage and virus DNA enriched samples
from sludge of two different WWTPs……………………………….…………….127
Figure 5.4: Virus diversity/organism abundance (family-level) based on best hit
classification……………………………………………………………………….128
Figure 5.5: Phage diversity/organism abundance based on best hit classification.....................129

xi

CHAPTER 1
INTRODUCTION

Wastewater presents a time dynamic collection spot where many substances of physical,
chemical and biological nature are brought together at one point (Sinclair et al. 2008).
Wastewater treatment varies from one plant to another, however in general it is a multi-stage
process that treats wastewater before it is discharged to a body of water, applied to land or reused (Shannon et al. 2007). The efficiency of the wastewater treatment process depends on
several factors like the type of biological treatment (for example conventional activated sludge
process or MBRs (Membrane Bioreactor)), hydraulic residence time and solids retention time
(Ma et al. 2011, Saikaly et al. 2005, Tchobanoglous et al. 2003). Changes in the bacterial
community were also observed in response to changes in operational parameters of activatedsludge systems (Saikaly et al. 2005).
Studies have shown that potentially pathogenic bacteria were detected in the activated
sludge and effluents from WWTPs (Ye and Zhang, 2011, Odjadjare, 2010). Conventional
utilities and even state of the art WWTPs such as MBRs have been proven to release pathogenic
viruses in the environment (Simmons and Xagoraraki, 2011, Bibby and Peccia, 2013). According
to a recent study, human enteric viruses were detected in effluent from two different WWTPs
using RT-QPCR (Kitajima et al. 2014). Another study based on characterizing effluent water
quality from satellite MBRs facilities reported that adenoviruses were detected in effluent from
all nine MBR facilities sampled (Hirani et al. 2013). There have been reports of finding
pathogens in the effluent from different WWTPs even after disinfection treatment (Kitajima et al.
2014, Hirani et al. 2013, Simmon et al. 2011, Fong et al. 2010, Okoh et al. 2007).

1

Additionally, Antibiotic resistant bacteria and genes encoding antibiotic resistance are
commonly detected at high rates and concentrations in wastewater samples (Munir et al. 2011,
Zhang and Zhang 2011, Borjesson et al. 2009, Zhang et al. 2009a, Auerbach et al. 2007, Brooks
et al. 2007, Kim and Aga 2007, Pruden et al. 2006, Reinthaler et al. 2003). Large numbers of
antibiotic resistant organisms can survive in sewage and reach the wastewater treatment plant
(Reinthaler et al. 2003, Guardabassi et al. 2002). Wastewater treatment plants (WWTPs) can be
considered as an important reservoirs for the spread of antibiotic resistance to opportunistic
pathogens and can stimulate horizontal gene transfer among microbial species.
Occurrence of antibiotic resistance bacteria (ARB) and antibiotic resistant genes (ARG)
in our environment is a growing global health problem. Due to increasing evidences of antibiotic
resistance in pathogenic and benign bacteria in our environment, an emerging threat to public
and environmental health has been reported (Munir and Xagoraraki 2011, Knapp et al. 2009,
Blasco et al. 2008). The use of numerous antimicrobial agents as treatments in animal, human,
and plant health maintenance, is a worldwide practice providing both desirable and undesirable
consequences (Munir et al. 2011). Links have been found to exist between antibiotic use and the
emergence of antibiotic resistant genes (Gao et al. 2012). Studies have proven increase in
antibiotic resistance strains that belong to pathogenic bacteria (Blasco et al., 2008, Peak et al.,
2007).
Horizontal gene transfer in bacteria is an important process in accelerating the dispersal
of ARGs in the environment (Colomer-Lluch et al. 2011a, Baquero et al. 2008, Sander and
Schmieger 2001). Until the 1950s, when antibiotic resistance emerged worldwide, the
significance of horizontal gene transfer for bacterial evolution was not recognized (Ochman et al.
2000). Horizontal gene transfer is the movement of genetic material among bacterial species
2

without cell division. In recent years, efforts have been made to study various gene transfer
mechanisms involved in the spread of antibiotic resistance. Transformation is the direct uptake of
naked DNA from the surroundings. It is the most common and widespread means of horizontal
gene transfer. Conjugation is the transfer of DNA mediated by a conjugative or mobilizable
genetic element (plasmids or transposons). It requires cell to cell contact and long fragments of
DNA can be transferred through this mechanism. The transfer of DNA mediated by
bacteriophage is known as transduction.
Bacteriophages play a major role in bacterial evolution facilitated by transferring
virulence and antibiotic resistant genes to new bacterial hosts via the process of transduction
(Mazaheri Nezhad Fard et al. 2011, Canchaya et al. 2004, Boyd and Brüssow 2002, Weinbauer
and Rassoulzadegan 2003). Bacteriophages, also called phages, are viruses that infect bacteria.
They all contain nucleic acid surrounded by a protein coat that makes them stick on to bacterial
cell walls. When attached, they inject the DNA into the bacteria. Only few studies have been
conducted to determine antibiotic resistant genes present in bacteriophage isolated from
wastewater environments (Parsley et al. 2010, Colomer-Lluch et al. 2011a, Mazaheri Nezhad
Fard et al. 2011, Prescott 2004, Muniesa et al. 2004a). Recently the role of phages in the spread
of ARGs in the environment has been studied (Colomer-Lluch etal. 2011). That study highlights
the potential role of phages in the spread of β lactamase genes in urban sewage and river water
samples and found that phages are a suitable candidate to act as reservoir for the spread of ARGs
in the environment. Another study was done on enterococcal bacteriophages which have been
shown to play a role in successful transfer of antibiotic resistant genes as tetracycline (tetM) and
gentamicin (ant2-I) resistance between the same and different enterococcal species (Mazaheri
Nezhad Fard et al. 2011).
3

Wastewater microbial diversity including potential pathogens and antibiotic resistant
bacteria is vast and still not clearly characterized. Molecular biology is currently being
revolutionized with the emergence of next generation technology. It has been reported that these
next-generation DNA sequencing methods have the ability to significantly help to accelerate
biological research (Shendure and Ji 2008). A field known as metagenomics is fast evolving and
provides a way of characterizing the entire microbial communities (microbiome). Environmental
metagenomics is the study of organisms in a microbial community based on analyzing the DNA
within an environmental sample. Environmental metagenomics as a field was extremely limited
prior to the advent of next-generation sequencing (NGS). Next-generation sequencing has
substantially widened the scope of metagenomic analysis of environmentally derived samples
(Mardis 2008). The high demand for low-cost sequencing methods has motivated the
improvement of sequencing technologies. The advent of NGS has decrease the time required and
the cost of complete genome sequencing (Subramanian et al. 2010, Mardis 2008, Soni and
Meller 2007, Shendure and Ji 2008). These recent technologies allow us to sequence DNA and
RNA much more quickly and cheaply than the previously used Sanger sequencing. NGS
provides researchers the capability to profile entire microbial communities from complex
samples, discover new organisms, and explore the dynamic nature of microbial populations
under changing conditions.
Metagenomic technologies present an opportunity for generating an improved
understanding of the water microbiome and thus enhancing microbial water quality and water
safety (Aw and Rose, 2013; Edwards and Rohwer, 2005). Various methods have been applied to
investigate microbial community in wastewater but they provide only limited information
compared to latest emerging high throughput sequencing technologies. According to Zhang et al
4

(2011), a comprehensive characterization of the vast microbial community present in activated
sludge systems is hindered by the low sequencing depth of the traditional PCR-cloning approach
(Zhang et al. 2011). Next-generation DNA sequencing has recently been applied to study viral
metagenomes in different environmental samples (Alhamlan et al. 2013, Gomez-Alvarez et al.
2012, Hu et al. 2012, Bibby et al. 2011, Wommack et al. 2011, Tamaki et al. 2011, Rosario et al.
2009). With the help of metagenomic tools, microbial communities related with wastewater
systems could easily be analyzed. The objective of this study is to provide metagenomic insights
into bacterial, viral and phage diversity and resistance to antibiotics and metal compounds in
wastewater utilities.

5

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6

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9

CHAPTER 2
PHAGE AND ANTIBIOTIC RESISTANCE GENES IN A CONVENTIONAL
WASTEWATER TREATMENT PLANT
Munir M., T. Marsh, and I. Xagoraraki. Submitted for consideration to Water Research.
Abstract
Wastewater treatment plants (WWTPs) can be considered as an important reservoirs for the
spread of antibiotic resistance to opportunistic pathogens and can stimulate horizontal gene
transfer among microbial species. Bacteriophages exist in most environments and may play a
major role in the dissemination of antibiotic resistant genes (ARGs) within WWTPs. Phage
diversity was studied by next generation sequencing on sludge samples (before and after DNase
treatment) with Illumina (Miseq). Sludge samples were collected from a conventional WWTP in
Michigan. A method for phage DNA isolation was optimized using PEG (polyethylene glycol)
precipitation and DNase (deoxyribonuclease) treatment. Metagenome data analysis revealed that
after DNase treatment and assembly of contigs, the activated returned sludge (RAS) sample
contained 21,985 contigs totaling 17,227,533 basepairs with an average length of 783 bps and
primary sludge (PS) contained 2,870 contigs sequences totaling 2,292,422 basepairs with an
average length of 798 bps. On a genus level, Burkholderia phage, Coliphage, Enterobacteria
phage, and Pseudomonas phage are present in all the samples. Phages infecting Burkholderia
cepacia, Edwardsiella, Mycobacterium, Salmonella, Vibrioe and Xanthomonas citri were
detected only in RAS samples while phages infecting Bacillus, Brochothrix, Lactobacillus,
Listeria, Phormidium and Staphylococcus were found only in PS samples. Additionally, phage
DNA was isolated and screened for ARGs (tetracycline resistant genes (Tet-W and Tet-O) and
sulfonamide resistant gene (Sul-I)) using real-time Q-PCR. We have detected ARGs in phage

10

DNA with concentrations ranging from 3.84x102-8.14x103 copies/100mL for Tet-W gene and
5.89x104-7.9x104 copies/100mL for Sul-I gene. In additon, phage metagenomes were searched
for functional signatures of resistance genes. Metagenomics analysis revealed that most of the
antibiotic resistance belongs to methicillin, fluoroquinolones and beta-lactamase group of
antibiotics. This work presents the diversity and occurance of phages in sludge samples and
indicates that there is a substantial shift in the phage community over the course of the activated
sludge process, thus suggesting that within the activated sludge the phage populations are
dynamic. This work indicates that phage DNA was associated with antibiotic resistant genes in
wastewater.
Keywords: wastewater, activated sludge, antibiotic resistant gene, next-generation sequencing,
Bacteriophage metagenome
1. Introduction
Viruses are the most abundant and most diverse group of biological entities.
Bacteriophage, viruses that attack bacteria (hereafter referred to as phage), have abundance and
distribution that in most cases reflects that of their host organisms. Contemporary investigations
focusing on the ecology and genetics of phage take advantage of metagenomics that can yield
useful information based on the amount of coverage of particular phages/gene sets present in
environmental samples (Clokie et al. 2011). Phages contain nucleic acid surrounded by a protein
coat that makes them stick on to bacterial cell walls. When attached, they inject the DNA into
bacteria where transcription, replication and assembly of new phage take place. Horizontal gene
transfer in bacteria is an important process in accelerating the dispersal of ARGs in the
environment (Colomer-Lluch et al. 2011a, Baquero et al. 2008, Sander and Schmieger 2001).
Phages play a major role in bacterial evolution by transferring antibiotic resistant genes to new
11

bacterial hosts via the process of transduction, which is one of the mechanism of horizontal gene
transfer (Muniesa et al. 2013a,b, Mazaheri Nezhad Fard et al. 2011, Canchaya et al. 2004,
Muniesa et al. 2004, Boyd and Brüssow 2002, Weinbauer and Rassoulzadegan 2003).
Due to the increasing evidence of antibiotic resistance in pathogenic and benign bacteria
in our environment, an emerging threat to public and environmental health has been postulated
(Munir and Xagoraraki 2011, Knapp et al. 2009, Blasco et al. 2008). Antibiotic resistant bacteria
and genes encoding antibiotic resistance are commonly detected at high rates and concentrations
in wastewater samples (Munir et al. 2011, Zhang and Zhang 2011, Borjesson et al. 2009, Zhang
et al. 2009, Auerbach et al. 2007, Brooks et al. 2007, Kim and Aga 2007, Pruden et al. 2006,
Reinthaler et al. 2003). Large numbers of antibiotic resistant organisms can survive in sewage
and reach the wastewater treatment plant (Reinthaler et al. 2003, Guardabassi et al. 2002). A
recent study suggested that multidrug resistant genes even survive through several wastewater
treatment units, including disinfection (Luo et al. 2014). A strong link has been reported between
wastewater and antibiotic resistance (Börjesson S et al. 2009, Volkmann et al. 2004, and
Schwartz et al. 2003).
Our understanding of the role that phage play in the dissemination of antibiotic
resistances is at an early stage with only a few studies addressing this (Parsley et al. 2010,
Colomer-Lluch et al. 2011a, Mazaheri Nezhad Fard et al. 2011, Prescott 2004, Muniesa et al.
2004). According to recent literature review, ARGs related with phages have been identified in
different environmental samples. For example phages have been termed a reservoir for the
spread of β lactamase genes in urban sewage and river water samples (Colomer-Lluch et al.
2011a). Gene resistant to β-lactam antibiotics have also been identified in fecal waste from cattle,
pigs and poultry using PCR and QPCR (Colomer-Lluch et al. 2011b). A group of antibiotic
12

resistance genes were detected in bacteriophage DNA isolated from human fecal samples
(Quiros et al. 2014). Thus, bacteriophages have been regarded as potential vector for ARG
transfer. Wastewater can provide favorable conditions for the growth and propagation of
antibiotic resistant bacteria and their genes. Large amounts of antibiotics are released into
municipal wastewater due to incomplete metabolism in humans or to disposal of unused
antibiotics. Once in the wastewater stream they can exert selective pressure for or maintain
resistance among microorganisms (Allen et al. 2010, Nagulapally et al. 2009). Activated sludge
has been referred to be a “hot-bed” for horizontal gene transfer and selection of antibiotic
resistant genes among aquatic bacteria (Guardabassi et al. 2002). The design of activated sludge
in WWTPs is primarily focused to maximize biological substrate removal by promoting factors
for nurturing bacterial retention and growth (Kim et al. 2010) and promoting cellular interactions
among diverse microorganisms. Thus, it provides great potential for the lateral transfer of ARGs
between microbes in activated sludge (Parsley et al. 2010) and is characterized with high
concentration of microbial community that facilitates horizontal gene transfer (HGT) of ARG via
mobile genetic elements (Zhang and Zhang 2011).
The objective of this study was to describe and compare the diversity of phages present in
primary sludge and returned activated sludge using metagenomic investigations. Further, the
goal was to detect ARGs in bacteriophage in order to assess the likely occurrence of
transductional transfer within wastewater treatment plants.
2. Materials and Methods
2.1. Sample Collection: Returned activated sludge (RAS) and primary sludge (PS) samples were
collected from East Lansing WWTP in Michigan (U.S.A.) in 2012. Samples were kept on ice

13

and were transported to the Laboratory at Michigan State University (East Lansing, U.S.A.) for
immediate processing.
2.2. Sample Processing: Samples were induced with Mitomycin C (1µg/ml) and incubated at
room temperature for 24 hrs while gently shaking (150 rpm). Several drops of chloroform were
added to the samples to complete lysis and incubation was continued for another 15-30 mins.
The samples (250-300mL of sludge) were then centrifuged at 3396 xg for 45 minutes in F10S6X500Y rotor and the supernatant was carefully decanted. The pellet was saved for bacterial
DNA extraction. Each supernatant was filtered through a 0.22 µm filter (Millipore, Billerica,
MA) and then the bacteriophages were precipitated with PEG-NaCl (Colomer-Lluch et al.
2011a, Sander and Schmieger 2001, Muniesa et al. 2004). The PEG precipitate was collected by
centrifugation at 10000xg in an aerosol-tight fixed-angle rotor at 4°C for 40 minutes. The
supernatant was carefully decanted and the pellet was resuspended in 1.0 ml of SM buffer
(Yamamota et al., 1970). Any free DNA that co-purified with bacteriophage was removed by
digestion with DNase I (100 Units/mL) (Colomer-Lluch et al. 2011a) in half of each of the
samples. The DNase-treated phage preparations and the ones without DNase were stored at 80°C until DNA extraction was performed for molecular analysis. The volume of all the samples
initially collected for processing was taken into account when calculating final concentrations.
A positive control test was conducted initially using Coliphage T4 and E.coli BREC607 on a M9
supplement media in a PlaqueAssay. A high titre of phages (108 - 1010 pfu/mL) was grown. The
phage was tested after phage isolation by PEG precipitation using plaque assay to confirm the
method.

14

2.3. DNA Extraction: Phage DNA and bacterial DNA was extracted (from samples with and
without DNase treatment) using a MagNA Pure Compact DNA extractor (Roche Applied
Science, Indianapolis, IN, USA) following the protocol in the manufacturer’s manual. The
MagNA Pure Compact utilizes a magnetic-bead technology for the isolation process. Sample
amount of 400 µL was loaded in the system and the elution volume was 100µL. The extracts
were stored in a freezer at -20°C.
2.4. Quantification: Antibiotic resistance genes were quantified in these samples using
previously developed assays in our lab (Munir et al. 2011). In these samples, tetracycline
resistance genes (tetO and tetW) and sulfonamide resistance gene (sulI) were detected using realtime qPCR with SYBR Green method, which was optimized using previously described primers
(Aminov et al. 2001, Pei et al. 2006). All the qPCR reactions were performed with a Roche
LightCycler 1.5 (Roche Applied Science, Indianapolis, IN, USA). All reactions were done in
triplicate.
2.5. Metagenomic analyses: Bacteriophage-enriched DNA isolated from the sludge samples
was sequenced on an Illumina platform (Illumina MiSeq, Roche Technologies) at The Research
Technology Support Facility (RTSF) genomic center at Michigan State University generating
150 bp paired-end reads. The sequences were assembled using an integrated pipeline for de
NOVO

assembly

of

microbial

genomes.

An

assembly

pipeline

called

A5

(http://ged.msu.edu/angus/2013-04-assembly-workshop/assembly-with-a5.html) was applied that
simplifies the entire genome assembly process by automating sequence data cleaning, error
correction, assembly, and quality control and automated assembly parameter selection (Tritt et
al. 2012).

15

2.5.1. Blast analyses: Standard nucleotide BLAST analyses were conducted against the NCBI

non-redundant nucleotide sequence (nr/nt) database to identify members of gene families. Blast
(http://www.ncbi.nlm.nih.gov/blast) analysis was performed on the contigs file generated from
the A5 assembler to determine the phage metagenome of the activated sludge.
2.5.2. MG-RAST analyses: The assembled data from the two samples were also analyzed using

MG-RAST 3.3.1 (Meyer et al. 2008). Data was analyzed based on organism abundance and on
the functional distribution at the subsystem hierarchy with maximum E- value cutoff of 1E-5,
minimum percent identity cutoff of 60% and minimum alignment length cutoff of 15bps. The
displayed data has been normalized to values between 0 and 1 to allow for comparison of
differently sized samples. Each of the categories was further studied for detailed analysis further
exploring each category in more detail.
3. Results and Discussion:
3.1. Metagenome sequencing and assembly results:

In order to investigate the phage community in activated sludge, next-generation sequencing was
used. Bacteriophage-enriched DNA isolated from the sludge samples at two different locations
within the wastewater treatment plant were sequenced using an Illumina Miseq and assembled
using A5 pipeline. Both the DNase-treated and non- treated phage preparations were used in
metagenomics analyses to observe the activity of DNase on the samples in detecting free DNA
that co-purified with bacteriophage during the phage isolation process. Metagenome analysis
revealed that after DNase treatment the returned activated sludge (RAS) sample contained
21,985 sequences totaling 17,227,533 basepairs (bps) with an average length of 783 bps and the
primary sludge (PS) sample contained 2,870 contigs sequences totaling 2,292,422 bps with an
16

average length of 798 bps. The RAS samples without the DNase treatment contained 23,663
sequences totaling 18,332,554 bps with an average length of 774 bps and PS samples without
DNase treatment contained 7,796 sequences totaling 5,511,578 bps with an average length of
706 bps. The decline in the number of contigs in each set of samples indicates that some free
DNA was lost as a result of DNase treatment indicating that there was some extracellular DNA
present in the PEG precipitation process for phage isolation. Analysis statistics for all the
samples are shown in Table 2.1.
Annotation of all the reads for the functional distribution at the subsystem hierarchy
showed that 63.93 % and 46.56% of sequences were predicted as “Phages or, Prophages” in
themetagenome from RAS and PS samples after DNase treatment respectively. Figure 2.1
illustrates the distribution of functional categories at the highest level supported by subsystems
analysis. The presence of phages is indicated in the bar chart with phages occupying the
majority on 0 to 1 scale, in both RAS and PS samples (Figure 2.1a). In this figure, the membrane
transport functional category, along with cell division and cell cycle function showed lesser
values after DNase treatment in both in RAS and PS samples suggesting that there was loss of
free DNA after DNase treatment. The data also demonstrate the occurrence of virulence, disease
and defense factors in the sludge samples occupying 10-38% of functional hits. Analysis of just
the ‘virulence, disease and defense’ functional category, revealed a higher resistance to
antibiotics in PS samples compared to RAS sample (Figure 2.1). Deeper analysis of the
metagenomic data revealed that most of the antibiotic resistance belonged to methicillin,
fluoroquinolones and beta-lactamase group of antibiotics (Figure 2.1c).

17

3.2. Phage Diversity:

The diversity of phages present in RAS and PS was studied using MGRAST v3.0
pipeline. The MG-RAST pipeline analysis includes the phylogenetic comparisons and functional
annotations against the database. Standard nucleotide BLAST analyses were also conducted
against the NCBI non-redundant nucleotide sequence (nr/nt) database to identify members of
gene families. Figure 2.2 shows the phage diversity present in RAS and PS samples on a genus
level classification. The stacked bar chart indicates the abundance of each genus in each of the
samples analyzed. Based on MGRAST analysis, all the samples showed presence of
Chlorovirus, Microvirus, Siphoviridae, Lambda-like viruses, Podoviridae, P22-like viruses, T4like viruses, SPO1-like viruses and Myoviridae (Figure2). The detailed list of types of phages
present in all the samples in this study is presented in Table 2.2 (Blast search analysis).
Burkholderia phage, coliphage, Enterobacteria phage, and Pseudomonas phage were present in
both RAS and PS sample before and after treatment. Burkholderia cepacia phage, Edwardsiella
phage, Mycobacterium phage, Salmonella phage, Vibrio phage and Xanthomonas citri phage
were detected only in returned activated sludge (RAS) samples. Bacillus phage, Brochothrix
phage, Lactobacillus phage, Listeria phage, Phormidium phage, and Staphylococcus phage were
found only in primary sludge (PS) samples. The trend in the abundance of phages in sludge
samples indicates that there was a substantial shift in the phage community over the course of the
activated sludge process, indicating that within the activated sludge the phage populations are
dynamic. Reasoning for some of the phages that are only detected in RAS but not in PS, this may
be due to the fact that PS sample have generated significantly small sequence size compared to
RAS samples with sequencing.

18

The phage diversity detected in this study using Miseq (Illumina) sequencing platform is
similar to the diversity presented in the literature. Myoviridae, Podoviridae, Siphoviridae, are the
most common group of phages found in wastewater samples (Colomer-Lluch et al. 2011).
Parsley and his team followed shotgun library approach to study activated sludge samples
finding Myoviridae (40.3%), Siphoviridae (31.9%), Podoviridae (25.6%) and considered
unclassified phages (2.2%) (Parsley et al. 2010). Myoviridae and Siphoviridae were also detected
when electron microscopy was used on sewage and river water sample (Colomer-Lluch et al.
2011).

Pyrosequencing discovered similar results on dairy manure wastewater lagoons

(Alhamlan et al. 2013).

3.3. Antibiotic Resistant Gene Diversity:

The phage metagenome was searched for functional signatures of resistance genes using
MGRAST. Greater percentage of antibiotic resistant genes was observed in PS samples
compared to RAS sample when analyzing the ‘virulence, disease and defense’ functional
category (Figure 2.1b) according to MGRAST analysis. It was found that most of the antibiotic
resistance genes conferred resistance to methicillin, fluoroquinolones and beta-lactamase group
of antibiotics (Figure 2.1c). Further exploring the phage metagenome for ARGs in MGRAST, it
was found that RAS sample with DNase treatment contain proteins for Oxetanocin resistance
and Vancomycin resistance whereas RAS samples without DNase treatment contain proteins for
multiple antibiotic resistance, Oxetanocin resistance, quaternary ammonium compoundresistance and Tellurium resistance. PS sample with DNase treatment contain proteins for only
Oxetanocin resistance whereas no data was returned for abundance of ARGs in the metagenome
for PS sample with DNase treatment An interesting copper resistance protein was also detected

19

in phage fraction obtained from RAS sample without DNase treatment. It has been suggested
that presence of metals in wastewater treatment can also drive for selection of antibiotic
resistance among bacteria (Peltier et al. 2010; Knapp et al. 2011; Wright et al. 2006). Wright et
al. (2006) concluded that metal exposure can directly select for metal-resistance while coselecting for antibiotic-resistant bacteria. Also, the presence of Staphylococcus phage indicated
by BLAST analysis and the presence of methicillin resistance in Staphylococci from MGRAST
analysis showed that the results are related (Figure 2.1).

3.4. Concentration of ARGs:

Antibiotic resistance genes were quantified with QPCR using previously developed
assays in our lab (Munir et al. 2011). Phage isolate (with- and without-DNase treatment) and
bacterial isolate from returned activated sludge and primary sludge samples collected from East
Lansing WWTP in Michigan were tested for detectable tetracycline resistance genes (tetO and
tetW) and sulfonamide resistance gene (sulI). Concentrations of ARGs in phage DNA with
DNase treatment for RAS and PS samples were found to be 3.84x102 and 8.14x103
copies/100mL for Tet-W gene and 5.89x104 and 7.9x104 copies/100mL for Sul-I gene,
respectively (Figure 3). Whereas, concentrations of ARGs in phage DNA of RAS and PS
samples without DNase treatment was found to be 2.14x103 and 2.5x104 copies/100mL for TetW gene and 4.17x105 and 1.19x105 copies/100mL for Sul-I gene , respectively (Figure 2.3). TetO gene was not detected in these samples. Concentrations of ARGs in bacterial DNA of RAS
and PS samples waere found to be 1.48x107and 1.33x109 copies/100mL for Tet-W gene and
1.63x109 and 1.55x109 copies/100mL for Sul-I gene respectively (Figure 2.3). The concentration
of phage associated ARGs detected in phage DNA was much lower than that present in the

20

fraction of bacterial DNA based on the same volume comparison. There is about 4-5 log
difference in the concentration of ARGs between the two fraction of DNA in each of RAS and
PS samples. A remarkable difference was seen in phage DNA before and after DNase treatment
indicating the presence of free DNA containing ARGs that have been digested by DNase.
Our study also detected oxetanocin and vancomycin resistance (MGRAST) along with
sulfonamide resistant gene Sul-I. A recent study using shotgun library approach found that
phages appear to carry partial genes that may be responsible for resistance to tetracycline,
ampicillin, acriflavine, and bleomycin, few others in activated sludge sample (Parsley et al.
2010). Our work is consistent with repeated isolations of antibiotic resistant bacteria from
wastewater treatment plants and the detection of resistance determinants using cultivation
independent techniques. More work is needed to understand the importance of phages and their
role in ARG transfer among bacterial community in wastewater treatment plants.
4. Conclusions:


Phage diversity was studied by next generation sequencing on sludge samples (before and
after DNase treatment) with Illumina (Miseq).



On a genus level, Burkholderia phage, Coliphage, Enterobacteria phage, and Pseudomonas
phage are present in all the samples. Burkholderia cepacia phage, Edwardsiella phage,
Mycobacterium phage, Salmonella phage, Vibrio phage and Xanthomonas citri phage were
detected only in RAS samples. Bacillus phage, Brochothrix phage, Lactobacillus phage,
Listeria phage, Phormidium phage, Staphylococcus phage and Sugarcane mosaic virus were
found only in PS samples.

21



Concentration of ARGs detected in phage DNA in all samples ranges from 3.84x1028.14x103 copies/100mL for Tet-W gene and 5.89x104-7.9x104 copies/100mL for Sul-I gene.



This work presents the diversity of phages in sludge samples and indicates that phage DNA
was associated with antibiotic resistant genes in wastewater.

5. Acknowledgement:
We would like to thank the manager of the East Lansing Wastewater Treatment Plant for
providing the samples and information needed for this study. Also, we would like to
acknowledge bioinformatic support and asisstance provided by Bioinformatic team at High
Performance Computing Center (HPCC) at Michigan State University and a special thank to
Bioinformatic Research Specialist Dr. Tracy Teal.

22

APPENDIX

23

Table 2.1: Metagenome analysis statistics (generated by MGRAST).

Raw bp Count

RAS w/

RAS w/o

PS w/

PS w/o

DNase

DNase

DNase

DNase

17,227,533 bp

18,332,554 bp

2,292,422 bp

5,511,578
bp

No. of contigs

21,985

23,663

2870

7,796

Mean Sequence Length

783 ± 1053 bp

774 ± 1017 bp

798 ± 926 bp

706 ± 696
bp

Artificial Duplicate Reads:

884

677

98

229

13,742,435 bp

14,561,037 bp

1,727,518 bp

4,452,805

Sequence Count
Post QC: bp Count

bp
Post QC: No. of contigs

20,523

22,301

2650

7,287

Post QC: Mean Sequence Length

669 ± 456 bp

652 ± 445 bp

651 ± 439 bp

611 ± 364
bp

Note: Sequences were assembled using A5 pipeline assembly and contigs generated were analyzed on
MGRAST; Abbreviation: bp= base pair

24

Table 2.2: Presence of Phage lineages using BLAST searches.
RAS w/
DNase

RAS w/o
DNase

PS w/
DNase

PS w/o
DNase

Burkholderia phage









Coliphage









Enterobacteria phage









Pseudomonas phage









EBPR podovirus





Burkholderia cepacia phage





Edwardsiella phage





Vibrio phage





Xanthomonas citri phage





Mycobacterium phage





Salmonella phage





Genus names



Bacillus phage





Brochothrix phage





Lactobacillus phage





Listeria phage





Phormidium phage





Staphylococcus phage





Klebsiella phage





Environmental Halophage





Escherichia phage





Burkholderia cenocepacia phage





Persicivirga phage



Helicobacter phage



Iodobacteriophage



25



Table 2.2 (cont’d)
Aeromonas phage



Bordetella phage



Caulobacter phage




Cronobacter phage
Erwinia amylovora phage



Enterobacter phage



Leptospira
bacteriophage

biflexa



temperate

Pseudomonas aeruginosa phage



Rhizobium phage



Rhodobacter phage



Streptococcus phage



Synechococcus phage







Thermus phage



Geobacillus virus
Acanthamoeba castellanii mamavirus



Lactobacillus johnsonii prophage



Lactobacillus plantarum bacteriophage



Listeria bacteriophage




Megavirus


Rhodococcus phage

26

(a)

Figure 2.1: (a) Subsystem functional barchart, (b) Functional distribution of “Virulence,
Disease and Defense” subsystem, (c) Functional distribution of “Resistance to antibiotics”.
Note: The data was compared to Subsystems using a maximum e-value of 1e-5, a minimum
identity of 60 %, and a minimum alignment length of 15 measured in aa for protein and bp for
RNA databases.

27

Figure 2.1 (cont’d)

(b)

(c)

28

Figure 2.2: Organism (genus) Tree. The data was compared to M5NR using a maximum evalue of 1e-5, a minimum identity of 60 %, and a minimum alignment length of 15 measured in
aa for protein and bp for RNA databases. Color shading of the names indicates genus
membership. Domain: viruses

29

(a)

Figure 2.3: Concentration (copies/100mL) of (a) tetracycline resistant gene (Tet W), and
(b) sulfonamide resistant gene (Sul I) abundance in Phage DNA from sludge samples. Note:
DNase indicate purified phage DNA after DNase treatment; Bacterial indicate overall bacterial
DNA in the sample; RAS= Returned activated sludge, PS=Primary Sludge

30

Figure 2.3 (cont’d)

(b)

31

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25. Munir, M. and Xagoraraki, I. (2011). Levels of Antibiotic Resistance Genes in Manure,
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1.

Yamamoto, K. R., Alberts, B. M., Benzinger, R., Lawhorne, L., and Treiber, G. (1970).
Rapid bacteriophage sedimentation in the presence of polyethylene glycol and its
application to large-scale virus purification. Virology, 40(3), 734-744.

2.

Zhang, X.-X. and Zhang, T. (2011). Occurrence, abundance, and diversity of tetracycline
resistance genes in 15 sewage treatment plants across China and other global locations.
Environmental science and technology 45(7), 2598-2604.

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Zhang, Y., Marrs, C.F., Simon, C. and Xi, C. (2009). Wastewater treatment contributes to
selective increase of antibiotic resistance among Acinetobacter spp. Science of the Total
Environment 407(12), 3702-3706.

36

CHAPTER 3
METAGENOMIC INSIGHTS INTO MICROBIAL RESISTANCE TO ANTIBIOTIC
AND METAL COMPOUNDS IN WASTEWATER UTILITIES
Mariya Munir, Terence Marsh, and Irene Xagoraraki. (in preparation)
Abstract
Over the past few years resistance to antibiotics has increased. Co-existence of antibiotics and

metals may increase antibiotic resistance gene development in the environment. Wastewater
treatment plants (WWTPs) can be considered as important reservoirs for the spread of antibiotic
resistance to opportunistic pathogens and can stimulate horizontal gene transfer among microbial
species. Metal exposure can directly select for metal-resistant bacteria while co-selecting for
antibiotic-resistance. This study aimed to describe and compare the diversity and abundance of
antibiotics and metal resistance in a conventional and MBR (membrane bioreactor) utility using
metagenomic investigations. Illumina Hiseq sequencing was applied on six samples from two
different WWTPs in Michigan. Bacterial DNA was isolated from three different sampling points
(activated sludge (AS), before disinfection effluent (BD) and after disinfection effluent (AD))
from a conventional and MBR utility.
Sequencing reads from all the samples revealed differences in the abundance of
functional genes within the WWTPs. Genes coding for antibiotic resistance were identified in all
the samples. Most of the antibiotic resistance genes conferred resistance to fluoroquinolones,
beta-lactamase, methicillin, and erythromycin and vancomycin. Genes coding for resistance to
metals were also observed in all our samples. High resistance to metals (including Cobalt-zinccadmium resistance, zinc resistance, arsenic resistance, copper tolerance, and resistance to
37

chromium compounds, mercury and cadmium resistance) was detected in most of the samples.
The MBR utility showed slightly higher number of hits for all the functional categories
compared to the conventional WWTP samples. The incidence of multiple metal and antibiotic
resistances among bacterial populations in WWTP poses a potential threat to human health.
Keywords: Metagenomics, wastewater, effluents, activated sludge, antibiotic resistance, metal resistance,
Illumina Hiseq

1. Introduction
The occurrence of antibiotic resistance bacteria (ARB) and antibiotic resistant genes
(ARG) in our environment is a growing global health problem. The microbial quality of water
itself is of great concern; however, if the trace levels of antibiotics and antibiotic resistant
bacteria are present, they may also greatly affect public health and is an emerging issue for the
general public and water industries (Xi et al. 2009). Development of novel antibiotics is being
outpaced by rapid propagation of antibiotic resistance thus calling for effective strategies to
mitigate the spread of antibiotic resistance (Carlet et al., 2012).
Wastewater treatment plants (WWTPs) can be considered as an important reservoirs for
the spread of antibiotic resistance to opportunistic pathogens and can stimulate horizontal gene
transfer among microbial species. Large amounts of antibiotics are released into municipal
wastewater due to incomplete metabolism in humans or to disposal of unused antibiotics. Once
in the wastewater stream they can exert selective pressure for or maintain resistance among
microorganisms (Allen et al. 2010, Nagulapally et al. 2009).
Due to the increasing evidence of antibiotic resistance in pathogenic and benign bacteria
in our environment, an emerging threat to public and environmental health has been postulated
(LaPara et al., 2011; Munir and Xagoraraki 2011, Knapp et al. 2009, Blasco et al. 2008).
38

Antibiotic resistance genes (ARGs) which code for specific antimicrobial functions such as
efflux pumps is considered to play a major role in conferring antibiotic resistance to microbial
community (Webber and Piddock, 2003). Antibiotic resistant bacteria and genes encoding
antibiotic resistance are commonly detected at high rates and concentrations in wastewater
samples (Munir et al. 2011, Zhang and Zhang 2011, Borjesson et al. 2009, Zhang et al. 2009,
Auerbach et al. 2007, Brooks et al. 2007, Kim and Aga 2007, Pruden et al. 2006). A strong link
has been reported between wastewater and antibiotic resistance (Börjesson S et al. 2009,
Volkmann et al. 2004, Reinthaler et al. 2003, Guardabassi et al. 2002).
Disinfection methods including chlorine or UV disinfection are capable of reacting with
nucleic acids during treatment and therefore may potentially reduce ARGs (Dodd, 2012).
Previous studies have shown that disinfection process did not contribute much in the reduction of
ARGs and ARBs in wastewater effluents from full-scale WWTPs (Fahrenfeld et al. 2013; Munir
and Xagoraraki 2011, Auerbach et al., 2007). Studies have suggested that multidrug resistant
genes even survive through several wastewater treatment units, including disinfection (Luo et al.
2014, Shi et al. 2013, Odjadjare et al. 2012). In a recent controlled lab study led by Mckinney
and Pruden (2013), it was demonstrated that UV disinfection at WWTPs is capable of reducing
strains of bacteria that are resistant to antibiotics but not ARGs.
Heavy metals along with antibiotics also create a selective pressure in the environment
that leads to resistance (Baquero et al. 1998). It is believed that multiple stresses can provide
more ecologically favorable conditions for a bacterium to survive and acquire resistance in an
environment, for example antibiotics and heavy metals (Spain and Alm 2003). Recently it has
been reported that chemical contaminants like metals can influence the selection of antibiotic
resistance among bacteria (Deredjian et al. 2011, Chandra and Sankhwar 2011). Sub-toxic levels

39

of zinc have shown to increase antibiotic resistance in wastewater treatment plant’s microbial
communities at comparatively low levels of antibiotic. The reason for such an observation was
suggested to be development of cross-resistance (Peltier et al. 2010). Presence of metals in
wastewater treatment can also be one of the factors responsible for selection of antibiotic
resistance among exposed bacteria in the environment (Peltier et al. 2010, Knapp et al. 2011,
Kamala-Kannan and Lee 2008, Tuckfield and McArthur 2008, Baker-Austin et al. 2006, Wright
et al. 2006, Stepanauskas et al. 2005). Wright et al. (2006) concluded that metal exposure can
directly select for metal-resistant bacteria while co-selecting for antibiotic-resistant bacteria.
Recently one study has shown that ARG concentration significantly correlate with presence of
metals (Knapp et al. 2011).
Next-generation sequencing (NGS) has substantially widened the scope of metagenomic
analysis of environmentally derived samples (Mardis 2008). Studies have demonstrated NGS to
be effective in detecting gene modifications responsible for antibiotic resistance (La Scola et al.
2008). High-throughput sequencing was used successfully to highlight the prevalence of ARGs
and mobile genetic elements in microbial population of sewage treatment plants and is
considered as a promising tool for analyzing ARG and other functional diversity in the
environmental samples (Zhang et al. 2011).
The objective of this study was to describe and compare the diversity of microbial
resistances to antibiotics and metal compounds in a conventional and MBR (membrane
bioreactor) utility using metagenomic investigations. This study provided a bioinformatics
approach for identifying microbial population and functional features like antibiotic resistance
and metal resistance in wastewater utilities. This is the first study investigating microbial
resistance patterns along with metal resistance in bacterial isolated samples in WWTPs.

40

2. Materials and Methods
2.1. Sample Collection: Effluent samples before and after disinfection along with
activated sludge were collected from East Lansing (EL) WWTP and Traverse City (TC) WWTP
in Michigan (U.S.A.) in 2013. Table 3.1 provides the characteristics of these WWTPs based on
wastewater treatment processes and disinfection methods. The schematic of the sampling along
with location of WWTPs is shown in Figure 3.1. The schematic of the methods used in this study
is also presented in Figure 3.2. Two liters of grab effluent samples were collected for bacterial
isolation from each WWTP. Activated sludge (AS) sample was collected in two 1L nalgene
bottles, mixed together in laboratory and then bacteria isolation from each WWTP was done.
Samples were kept on ice and were transported to the Water Quality Engineering Laboratory at
Michigan State University (East Lansing, U.S.A.) for further immediate processing.
2.2. Sample Processing: Bacteria in the effluent samples were concentrated by filtration with
0.45 μm HA filters (Millipore, Billerica, MA). The volume of effluent samples filtered was 1 L
for each of the four samples. The filters were collected in a 50 mL tubes and 50 mL Phosphate
Buffer Water (PBW) was added in each tube containing a filter. The tubes were then vortexed
for 5 min to allow the biomass layer on the filters to mix with water. 50mL of AS samples were
also collected in a centrifuge tubes. All the tubes were then centrifuged for 20 min at 4500 rpm
to concentrate the sample down to 2 mL. Supernatant was discarded and the concentrates were
stored at −80 °C until the DNA extraction was performed for further molecular analysis.
2.3. Nucleic acid Extraction: Bacterial DNA was extracted (from samples before and after
disinfection) using a MagNA Pure Compact DNA extractor (Roche Applied Science,
Indianapolis, IN, USA) following the protocol in the manufacturer’s manual. The MagNA Pure
Compact utilizes a magnetic-bead technology for the isolation process. Sample amount of 400
41

µL was loaded in the system and the elution volume was 100µL. The extracts were stored in a
freezer at -20°C. Following extraction the quantity of bacterial nucleic acid extracts from all
samples were checked using the NanoDrop Spectrophotometer (NanoDrop® ND-1000,
Wilmington, DE).
2.5. Metagenomic sequencing and analyses: All samples including Bacterial enriched DNA
isolated from the sludge and the effluent samples was sequenced on an Illumina platform
(Illumina HiSeq, Roche Technologies) at The Research Technology Support Facility (RTSF)
genomic center at Michigan State University generating 250 bp paired-end reads. Approximately
1 μg DNA (per core sample) was sent to the sequencing facility. The sequencing results were
returned as .fastq.gz files. Further files were converted to fastq files by processing them in MSU
HPCC (High Performance Computing Center) secure shell (SSH) connection. PuTTY, a freely
available piece of software was used to establish this SSH connection. In order to quality filter
the illumina data, a flexible read trimming tool for Illumina NGS data called Trimmomatic was
used for trimming Illumina data and removing adapters (Bolget et al. 2014). Finally, the trimmed
sequences were assembled using an iterative De Bruijn Graph De Novo Assembler for Short
Reads Sequencing data with Highly Uneven Sequencing Depth called IDBA-UD (Peng et al.
2012).
2.6. MG-RAST analyses: The assembled data from all the samples were analyzed using
MetaGenome Rapid Annotation Subsystems Technology server (MG-RAST 3.3.1) (Meyer et al.
2008). Each assembled data file underwent quality control (QC) process, which included quality
filtering (removing sequences with ≥5 ambiguous base pairs), length filtering (removing
sequences with a length ≥2 standard deviations from the mean), and de-replication (removing
similar sequences that are artifacts of sequencing). Analysis includes the phylogenetic
42

comparisons and functional annotations against the database and the results are expressed in the
form of abundance profiles. The “abundance” in the MG-RAST presents the estimate of the
number of sequences that contain a given annotation, found by multiplying each selected
database match (hit) by the number of representatives in each cluster. Hits refer to the number of
unique database sequences that were found in the similarity search, not the number of reads. The
hit count can be smaller than the number of reads because of clustering or larger due to double
counting (Wilke et al. 2014). Data was analyzed based on organism abundance and on the
functional distribution at the subsystem hierarchy with maximum E- value cutoff of 1E-5,
minimum percent identity cutoff of 60% and minimum alignment length cutoff of 15bps. The
MG-RAST pipeline analysis includes the comparisons and functional annotations against the
database. Each of the categories was further studied for detailed analysis and data was
downloaded in excel sheet for further analysis.
2.7. Statistical analysis: Abundance data was downloaded from MGRAST and statistical
analysis was performed using Microsoft Office Excel. Relative abundance is defined as the
number of sequences mapped to specific function divided by the total number of sequences in
that sample. The relative abundance data is then normalized using the following formula:
The normalized value of ei for variable E in the ith row is calculated as:

Where Emin = the minimum value for variable E and
Emax = the maximum value for variable E.

43

3. Results and Discussion:
3.1. Metagenome sequencing and assembly results:

To analyze the wastewater metagenomes, Illumina Hiseq sequencing was applied on
samples from two different WWTPs in Michigan. Bacterial DNA was isolated from three
different sampling points (activated sludge (AS), before disinfection effluent (BD) and after
disinfection effluent (AD)) from a conventional and MBR utility. Sequences generated were
assembled using an IDBA-UD assembler. Metagenome analysis revealed that a total of 2355
Mbp (mega base pairs) of assembled sequence data was generated. Bacterial DNA samples
contained contigs ranging from 238657 to 380106 sequences totaling 1250 Mbp of sequences.
Exploring the metagenome sequence breakdown, it was found that 6,741 sequences
(1.9%) failed to pass the QC pipeline for EL activated sludge sample. Of the sequences that
passed QC, 460 sequences (0.1%) contain ribosomal RNA genes. Of the remainder, 223,213
sequences (64.0%) contain predicted proteins with known functions and 117,626 sequences
(33.7%) contain predicted proteins with unknown function. 795 (0.2%) of the sequences that
passed QC have no rRNA genes or predicted proteins. Out of the 342,096 sequences (totaling
204,012,220 bps) that passed quality control, 340,839 (99.6%) produced a total of 391,844
predicted protein coding regions. Of these 391,844 predicted protein features, 213,941 (54.6% of
features) have been assigned an annotation using at least one of our protein databases (M5NR)
and 165,694 features (77.4% of annotated features) were assigned to functional categories. For
TC sludge sample, 7,927 sequences (2.1%) failed to pass the QC pipeline. Of the sequences that
passed QC, 543 sequences (0.1%) contain ribosomal RNA genes. Of the remainder, 276,740
sequences (72.8%) contain predicted proteins with known functions and 94,297 sequences
44

(24.8%) contain predicted proteins with unknown function. 597 (0.2%) of the sequences that
passed QC have no rRNA genes or predicted proteins. Of the 372,179 sequences (totaling
227,949,491 bps) that passed quality control, 371,037 (99.7%) produced a total of 441,132
predicted protein coding regions. Of these 441,132 predicted protein features, 266,439 (60.4% of
features) have been assigned an annotation using at least one of our protein databases (M5NR)
and 215,875 features (81.0% of annotated features) were assigned to functional categories.
In before disinfection effluent samples from EL, 5,765 sequences (2.2%) failed to pass
the QC pipeline. Of the sequences that passed QC, 702 sequences (0.3%) contain ribosomal
RNA genes. Of the remainder, 176,685 sequences (68.8%) contain predicted proteins with
known functions and 70,878 sequences (27.6%) contain predicted proteins with unknown
function. 2,845 (1.1%) of the sequences that passed QC have no rRNA genes or predicted
proteins. Within 251,116 sequences (totaling 153,515,277 bps) that passed quality control,
247,563 (98.6%) produced a total of 297,712 predicted protein coding regions. Of these 297,712
predicted protein features, 167,031 (56.1% of features) have been assigned an annotation using at
least one of the protein databases (M5NR) and 137,654 features (82.4% of annotated features)
were assigned to functional categories. Similarly, in bacterial isolated effluent sample after
disinfection from EL, 4,302 sequences (1.8%) failed to pass the Quality Control (QC) pipeline.
Of the sequences that passed QC, 841 sequences (0.4%) contain ribosomal RNA genes as
computed by MGRAST analysis software. Of the remainder, 184,215 sequences (77.2%) contain
predicted proteins with known functions and 48,954 sequences (20.5%) contain predicted
proteins with unknown function. 339 (0.1%) of the sequences that passed QC have no rRNA
genes or predicted proteins. Out the 234,355 sequences (totaling 147,771,487 bps) that passed
quality control, 233,169 (99.5%) produced a total of 270,966 predicted protein coding regions.
45

Of these 270,966 predicted protein features, 172,766 (63.8% of features) have been assigned an
annotation using at least one of the protein databases (M5NR) and 148,347 features (85.9% of
annotated features) were assigned to functional categories.
In TC, before disinfection effluent samples, 6,703 sequences (2.6%) failed to pass the QC
pipeline. Of the sequences that passed QC, 552 sequences (0.2%) contain ribosomal RNA genes.
Of the remainder, 202,533 sequences (78.1%) contain predicted proteins with known functions
and 49,351 sequences (19.0%) contain predicted proteins with unknown function. 77 (0.0%) of
the sequences that passed QC have no rRNA genes or predicted proteins. Whereas in effluent
sample after disinfection from TC, 3,695 sequences (1.3%) failed to pass the QC pipeline. Of the
sequences that passed QC, 624 sequences (0.2%) contain ribosomal RNA genes. Of the
remainder, 231,252 sequences (80.0%) contain predicted proteins with known functions and
53,202 sequences (18.4%) contain predicted proteins with unknown function. 153 (0.1%) of the
sequences that passed QC have no rRNA genes or predicted proteins. Detailed metagenome
statistics for all the samples is shown in Table 3.2 a & b.

3.2. Resistance to antibiotics: Antibiotic Resistant Gene Diversity:

All six wastewater metagenomes were searched for functional signatures of resistance
genes using MGRAST. Around three percent (3.6-3.8%) of the sequences of the bacterial
enriched metagenomic library could be mapped to “virulence, disease and defense” genes using
the subsystem functional database classification in MGRAST server. The ‘virulence, disease and
defense’ functional category was analyzed further for resistance to antibiotic and toxic
compounds category. In all the datasets of bacterial samples from TCWWTP and ELWWTPs,
74-76% of sequences were associated with resistance to antibiotic and toxic compounds
46

(RATC). A schematic of the process followed to analyze these metagenomes is presented in
Figure 3.3.
Going deeper into these categories for all the metagenomes, cobalt-zinc-cadmium
resistance is the most frequently occurring functional group followed by multidrug resistance
efflux pumps and resistance to fluoroquinolones. It has been reported that the fluoroquinolone
resistance genes are of great concern as they may contribute to the development of resistance to
this class of antibiotics in humans because of their use in veterinary medicine, particularly for
food animals (Durso et al., 2011).
To further analyze these metagenomes, different resistance hits were grouped according
to their function (Table 3.3) provided in supplement data. Analysis based on each grouping
category was conducted and results are presented in Figure 3.4 to 3.6. Around 18-23% of RATC
sequences in all bacterial samples were associated with resistance to antibiotics. Figure 3.4
shows the abundance of bacterial resistance to antibiotics in sludge samples from EL and TC
WWTPs. It was found that most of the antibiotic resistance genes conferred resistance to
fluoroquinolones, beta-lactamase, methicillin, and erythromycin and vancomycin group of
antibiotics (Table 3.3). The level of abundance of resistance to anitbiotics was higher in TC
sludge samples compared to EL WWTP. Similar results were observed for all the effluent
samples (Figure 3.5 and 3.6).
Also, the chlorination effects on microbial antibiotic resistance in a wastewater treatment
plants were investigated. Using metagenomic analysis, no significant difference was observed in
functional categories in before and after disinfection samples from both the treatment plant. In
order to have a comparison between different samples, normalization of the sequence hits was

47

done by total number of sequences in each dataset based on functional grouping (Table 3.3).
Figure 3.7 shows the relative abundance of different functional resistance in bacterial isolated
samples. According to the relative abundance of each bacterial sample analyzed with MGRAST,
no significant difference was observed between before and after disinfection samples from both
ELWWTP and TCWWTP. Although a slight reduction in resistance is observed within TC
WWTP. From the figure, it looks like disinfection further concentrated the antibiotic resistance
and resistance to metal signatures. Similar higher proportion of resistance was observed after
chlorination in drinking water treatment (Shi et al. 2013).
A significant percentage (18-24%) of metagenome sequences are associated with
“multidrug resistance efflux pump” and were prevalent in all the bacterial samples (Figure 3.4 to
3.6). Efflux pumps play an important defensive role against different toxic compounds that
bacteria may encounter in their environment. The role of efflux pumps antibiotic resistance is
their ability to export antibiotics and other drugs out of bacterial cells (Fernandez and Hancock,
2012). Further, high-throughput sequencing was used successfully to highlight the prevalence of
ARGs in wastewater samples. Metagnenomic analysis can also help in identifying the novel ARG
determinants within WWTPs.

3.3. Resistance to metal compounds:

Functional classifications of the metagenomic sequences for analyzing the resistance to
metals were determined by annotating sequences to the functional gene database using MGRAST program. Different functional hits were grouped according to their function role and the
grouping as shown in table (Table 3.3) provided in supplement data. Based on literature review,
metals are usually categorized as either essential metals (important for life) or non essential
48

metals (with no known physiological functions to humans) (Krasnici et al., 2013; Ishaque at al.
2006; Francisco et al. 2002). Metals that are generally regarded as essential for human health in
trace amounts include iron, zinc, copper, manganese. They are essential because they form an
integral part of one or more enzymes involved in a metabolic or biochemical process. Nonessential metals (lead, mercury, cadmium) are of main concern pollutants because they pose
potential risks to human health and the environment (Ishaque at al. 2006).
Further exploring the RATC gene category within all the metagenomes, cobalt-zinccadmium resistance is the most frequently occurring functional group in bacterial metagenomes.
In all the datasets, 47-50% of these sequences were associated with resistance to metals. Figure
3.4, 3.5 and 3.6 shows the abundance of bacterial resistance to metals in sludge, before
disinfection effluents and after disinfection effluent samples from EL and TC WWTPs
respectively. An interesting high resistance to essential metals (including Cobalt-zinc-cadmium
resistance, copper tolerance, and zinc resistance) was detected in all the samples. Around 5-8%
of these samples were annotated to resistance to non-essential metals (resistance to arsenic,
chromium compounds, mercury and cadmium). The level of abundance of Cobalt-zinc-cadmium
resistance was usually higher in TC compared to ELWWTP for most of the samples.
Wright et al. (2006) concluded that metal exposure can directly select for metalresistance while co-selecting for antibiotic-resistant bacteria. Contaminations due to heavy
metals are widespread. As it has been suggested that presence of metals in wastewater treatment
can also drive for selection of antibiotic resistance among bacteria, then they could be a more
long-lasting source of resistance than are antibiotics themselves (Peltier et al. 2010; Knapp et al.
2011; Wright et al. 2006). Heavy metals cannot be degraded and thus, difficult to remove from
the environment (Sinha et al. 2013). Presence of essential metals in high concentrations can be
49

toxic or can exert a selective pressure in bacterial communities. This selective pressure could
also select for antibiotic resistant bacteria, and thus may play an important role in the
maintenance and proliferation of antibiotic resistance (Francisco et al. 2013). According to
Knapp et al. (2009), defense-associated metal resistance genes are often closely related with
antibiotic resistance genes which can either encode for generic detoxifying mechanisms (e.g.,
efflux pumps) where intracellular concentrations of both metals and antibiotics is reduced nonspecifically (cross resistance), or they may involve separate genes integrated on the same genetic
element (co-resistance).
These results present a unique opportunity to examine the WWTPS with respect to
diversity and presence of ARG along with other metal resistances, thus adding the knowledge
gap to the number of metagenomic datasets previously available. Metagenome RATC data on
different samples are important and can be further used to connect antibiotic and metal resistance
information with microbial community in wastewater environment. In order to comprehensively
and deeply characterize these metagenomes it is necessary to increase the sequencing depth.
Although the lack of replication makes it difficult to draw wide conclusions regarding the effects
treatment type have on microbial community about ARGs or metal resistances, these
metagenomes provide important data to make baseline observations that will need to be
examined more thoroughly in future studies.
4. Conclusions:
In the present work, we analyzed activated sludge and effluent samples using
metagenomic analysis in an effort to better understand the composition and diversity of antibiotic
resistance and resistance to metal compounds in wastewater utilities. Most of the antibiotic
resistance genes conferred resistance to fluoroquinolones, beta-lactamase, methicillin, and
50

erythromycin and vancomycin group of antibiotics. The abundance of resistance to fluroquinones
was higher in TC WWWTP samples compared to EL WWTP.
High resistance to metals (including Cobalt-zinc-cadmium resistance, copper tolerance,
zinc resistance, Arsenic, chromium compounds, mercury and cadmium resistance) was also
detected in all samples. For sludge samples, MBR utility showed slightly higher number of hits
for all the functional categories as described in the grouping. Further research is needed to
provide better understanding of the comparison between treatments.
5. Acknowledgement:
We would like to thank the managers of the East Lansing Wastewater Treatment Plant and
Traverse City Wastewater Treatment Plant for providing the samples and information needed for this
study. We would like to acknowledge bioinformatic support and asisstance provided by Bioinformatic
team at High Performance Computing Center (HPCC) at Michigan State University.

51

APPENDIX

52

Figure 3.1: Map showing location of sampling and sampling schematic.

Table 3.1: Wastewater Treatment Characterstics

Wastewater treatment process
(Biological treatment)
Sludge Retention Time (SRT)
Capacity
Average flow
Discharge Rate
Disinfection
*MGD-Millions gallon per day

EAST LANSING WWTP

TRAVERSE CITY WWTP

Activated Sludge (AS)

Membrane BioReactor
(MBR)
7.58 days
17.0 MGD
8.5 MGD
4.0 MGD
Ultra-Violet (UV)

14 days
18.8 MGD*
13.4 MGD
14.1 MGD
Chlorine (Cl)

53

Figure 3.2: Schematic flowchart showing the procedure and methodology.

Table 3.2a: Metagenome analysis statistics in bacterial samples.
Mean
Sequence
Length (bp)
East Lansing Waste Water Treatment Plant
EL_AS
237008412 348837 679 ± 1053
EL_BD
184817243 256881 719 ± 1374
EL_AD
176512330 238657 739 ± 1927
TC_AS
TC_BD
TC_AD

Metagenome
Name

Raw bp
count

265323338
179323360
206030297

No. of
contigs

380106
259220
288930

698 ± 993
691 ± 1048
713 ± 2198

Post QC
No. of
contigs

Post QC: Mean
Sequence
Length (bp)

204012220
153515277
147771487

342096
251116
234355

596 ± 345
611 ± 411
630 ± 482

227949491
14,645886
176537661

372179
252517
285235

612 ± 357
584 ± 339
618 ± 484

Post QC:
bp Count

Note: Sequences were assembled and contigs generated were analyzed on MGRAST; Abbreviation: bp= base pair

54

Table 3.2b: Functional category Hit distribution.
Processed: Processed:
Predicted
Predicted
Protein
rRNA
Features
Features
East Lansing Waste Water Treatment Plant
EL_AS
391,844
29,185
EL_BD
297,712
22,251
EL_AD
270,966
17,827
Traverse City Waste Water Treatment Plant
TC_AS
441,132
28,367
TC_BD
297,052
19,752
TC_AD
341,222
23,529
Metagenome
Name

Alignment
:Identified
Protein
Features

Alignment
:Identified
rRNA
Features

Annotation:
Identified
Functional
Categories

213,941
167,031
172,766

414
625
758

165,694
137,654
148,347

266,439
191,448
218,046

500
499
546

215,875
156,342
179,026

Figure 3.3: Schematic showing the analysis step for the functional abundance in MGRAST.

55

Figure 3.4: Bacterial resistance to antibiotics and metals in activated sludge for East
Lansing and Traverse City wastewater utilities.

56

Figure 3.5: Bacterial resistance to antibiotics and metals in before disinfection effluent
samples for East Lansing and Traverse City wastewater utility.

57

Figure 3.6: Bacterial resistance to antibiotics and metals in after disinfection effluent
samples for East Lansing and Traverse City wastewater utility.

58

Figure 3.7: Relative abundance of resistance to antibiotics and metal compounds obtained
from MGRAST.

59

Table 3.3: Abundance of Resistance groups for bacterial samples (MGRAST)
East Lansing and Traverse City – Before and After Disinfection and Activated Sludge
(Bacteria)
Resistance
Groups

Resistance
to Metals

Multidrug
Resistance
Efflux
Pump

Antibiotic and Toxic Compound
Resistance Distribution

East Lansing (EL)

Traverse City (TC)

AS

BD

AD

AS

Cobalt-zinc-cadmium resistance

1400

1079

1406

1847 1460

1632

Copper homeostasis

876

629

742

1097

752

888

Zinc resistance

331

130

51

266

143

220

Arsenic resistance

231

167

144

260

187

218

Copper homeostasis: copper
tolerance

71

90

123

152

119

134

Resistance to chromium compounds

60

70

74

138

100

135

Mercury resistance operon

56

56

130

61

64

88

Mercuric reductase

51

51

116

79

54

73

Cadmium resistance

12

8

11

13

5

7

MexA-MexB-OprM Multidrug
Efflux System

2

1

0

4

2

3

MexC-MexD-OprJ Multidrug Efflux
System

0

1

0

1

0

0

MexE-MexF-OprN Multidrug
Efflux System

50

51

55

71

33

29

Multidrug efflux pump in
Campylobacter jejuni (CmeABC
operon)

130

173

208

231

228

261

Multidrug Resistance Efflux Pumps

874

740

1002

1128

952

1022

Multiple Antibiotic Resistance MAR
locus

0

0

1

1

0

2

60

BD

AD

Table 3.3 (cont’d)

Resistance
to
Antibiotics

The mdtABCD multidrug resistance
cluster

106

112

124

143

140

131

Beta-lactamase

409

236

264

592

384

399

Methicillin resistance in
Staphylococci

251

148

217

307

216

207

Erythromycin resistance

65

39

54

77

53

68

Fosfomycin resistance

3

4

6

1

0

1

Resistance to fluoroquinolones

649

502

613

900

517

554

Resistance to Vancomycin

22

4

1

17

11

8

Adaptation to d-cysteine

1

3

0

2

5

3

Aminoglycoside
adenylyltransferases

3

3

3

2

0

0

679

386

496

840

546

614

0

2

0

1

0

1

2

0

1

0

1

0

22

28

28

51

24

24

BlaR1 Family Regulatory Sensortransducer Disambiguation
Resistance
Lysozyme inhibitors
to Other
Toxic
Polymyxin Synthetase Gene Cluster
Compounds in Bacillus
Bile hydrolysis

61

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67

CHAPTER 4
SCREENING FOR POTENTIAL VIRAL AND BACTERIAL PATHOGENS IN
WASTEWATER EFFLUENT RELEASED FROM AN MBR AND A CONVENTIONAL
TREATMENT UTILITY USING METAGENOMICS ANALYSIS
Mariya Munir, Terence Marsh, and Irene Xagoraraki. (in preparation)

Abstract
Despite recent rapid advancements in water and wastewater treatment technologies,
waterborne pathogens still remain as one of the major environmental threats to human health.
Monitoring of all pathogens with conventional methods is not feasible due to time and cost
constraints. In this paper, virus and bacterial diversity of two wastewater treatment plants, a
conventional activated sludge and membrane bioreactor (MBR), are investigated using
metagenomics. Effluent samples (before and after disinfection effluents) have been analyzed to
reveal microbial diversity. Diversity analysis does not provide quantitative data on pathogen
loads or infectivity but it provides a list of potentially pathogenic viruses and bacteria that need
to be considered in more detail. Caudovirales is the most dominating order in the viral
community detected in our samples, consisting of families: Siphoviridae, Myoviridae and
Podoviridae. Analyzing the bacterial community, Proteobacteria was the highly abundant
bacterial phylum followed by Bacteroidetes, Planctomycetes, Actinobacteria and Firmicutes.
Further, the most abundant potential human viral pathogen observed in our study belongs
to taxonomic order Herpesviridales. Other potentially pathogenic viruses detected in this study
include Adenoviridae and Coronaviridae. While all the bacterial pathogens described in the
Contaminant Candidate List from EPA were detected in our study which include Cyanobacteria,
68

Legionella pneumophila, Salmonella enterica and Aeromonas hydrophila, Mycobacterium avium
intracellulare (MAC), Helicobacter pylori, Campylobacter jejuni, Shigella sonnei and
Escherichia coli (0157). Other common bacterial pathogens found in our samples are Leptospira,
Mycobacterium tuberculosis, Vibrio cholera, Yersinia pestis, and Yersinia enterocolitica.
Metagenomic analyses in this study also revealed that a large proportion of sequences could not
be assigned to taxonomic affiliations even at the phylum/class levels and thus are most likely to
be derived from novel, uncharacterized microbes. This study provides a complete description of
virus and bacterial diversity in effluents from a conventional utility and an MBR utility. This
paper provides guidance on which pathogens to monitor in the effluents and suggest WWTPS as
are reservoirs of microbial populations of public health relevance
Keywords: Metagenomics, wastewater, pathogenic virus, pathogenic bacteria, Illumina Hiseq
1. Introduction
Despite recent advances in water and wastewater treatment technology, waterborne
diseases still pose a serious threat to public health across the world. List of contaminants (called
the Contaminant Candidate List or CCL) generated by US EPA (Environmental Protection
Agency) are the contaminants that are known or anticipated to occur in public water systems,
and which may require regulation under the Safe Drinking Water Act (SDWA). Table 4.1
describes a list of microbes on CCL. Table 4.2 and 4.3 provide some other common pathogens
detected in effluents and raw samples respectively from WWTPs.
Viruses are potentially the most important and most hazardous among the pathogens
found in wastewater (Sidhu et al., 2008, Toze, 1997). They are also generally more difficult to
detect in environmental samples. A high diversity of human viral pathogens is present in our
69

environment (approximately 200 recognized viral pathogen species) which are further elevated
in environments affected by pollution, and additional species are continuously discovered every
year (Bibby, 2013). The most commonly detected pathogenic viruses are the enteroviruses. Other
viruses which have been detected in wastewater include adenoviruses, rotaviruses, reoviruses,
astroviruses and caliciviruses (such as Norwalk virus). Bacteria is the most common type of
microorganism found in wastewater and bacterial indicators like E.coli and fecal coliforms are
the required microbial measures of effluents for wastewater-discharge (Francy et al. 2011). The
list of pathogenic bacteria is quite large and can be further classified as waterborne faecal
pathogens and non-faecal pathogens. Waterborne faecal pathogens are microorganisms that are
result of contamination from human or animal faeces and are often associated with bacteria from
the Enterobacteriaceae family. They are well established as having a history of being responsible
for waterborne outbreaks of gastrointestinal illness and include pathogenic Escherichia coli,
Salmonella, Shigella, Campylobacter and Yersinia. Non-faecal pathogens are usually bacteria
naturally found in source waters and have a public health impact. They include Legionella,
Mycobacterium avium complex, Aeromonas and Helicobacter pylori (Health Canada, 2013).
Even state of the art WWTPs such as MBRs (Membrane Biological Reactor) have been
proven to release pathogenic viruses in the environment (Simmons and Xagoraraki, 2011).
Conventional utilities release pathogenic viruses (Simmons and Xagoraraki, 2011) in the effluent
and potentially pathogenic viruses in sludge (Bibby and Peccia, 2013). Similarly, potentially
pathogenic bacteria were also detected in the activated sludge and effluents from WWTPs (Ye
and Zhang, 2011, Odjadjare, 2010). According to a recent study, human enteric viruses were
detected in effluent from two different WWTPs using RT-QPCR (Kitajima et al. 2014). Another
study based on characterizing effluent water quality from satellite MBRs facilities reported that
70

adenoviruses were detected in effluent from all nine MBR facilities sampled (Hirani et al. 2013).
There have been reports of finding pathogens in the effluent from different WWTPs even after
disinfection treatment (Kitajima et al. 2014, Hirani et al. 2013, Simmons et al. 2011, Fong et al.
2010, Okoh et al. 2007). Further research is required to identify a complete list of potentially
infectious viruses and bacteria in the effluent of WWTPs.
Next-generation DNA sequencing has recently been applied to study viral metagenomes
(viromes) and bacterial diversity in different environmental samples (Aw et al. 2014; Alhamlan
et al. 2013, Gomez-Alvarez et al. 2012, Hu et al. 2012, Bibby et al. 2011, Wommack et al. 2011,
Tamaki et al. 2011, Rosario et al. 2009). The emergence of next-generation sequencing as a new
research tool has aided in the detection of genetically diverse and rapidly evolving novel
pathogens (Malik and Matthijnssens, 2014; Naccache et al. 2014). With the help of metagenomic
tools, microbial communities related with wastewater could easily be analyzed. According to
Zhang et al (2011), a comprehensive characterization of the vast microbial community present in
activated sludge systems is hindered by the low sequencing depth of the traditional PCR-cloning
approach (Zhang et al. 2011). Previous methods, however, were limited by a requirement that a
researcher must select the microbes (pathogenic viruses and bacteria) that will be searched for,
but in contrast, the metagenomic analyses produces a list of microbes that is based on abundance
and is independent of researcher bias (Bibby et al. 2011). Further, it has been found that many
species are difficult to isolate because they fail to grow in laboratory culture, depend on other
organisms for critical processes, or have become extinct. Next-generation sequencing methods
overcome these obstacles, as DNA can be isolated directly from living or dead cells in various
contexts (Tringe et al. 2005). It is expected that these powerful new methods will open up new
questions to genomic exploration and will also allow high-throughput sequencing to be more
71

than just a discovery exercise but also a routine assay for hypothesis testing. However, these
methods also have certain limitations. They can’t provide infectivity results, are good for family
level identification and identification is as good as the assembly methods and database used.
The goal of this study was to describe and compare the overall diversity of viruses and
bacteria and screen for potentially pathogenic viruses and bacteria present in a conventional and
MBR wastewater treatment plant effluents using metagenomic investigations. Metagenomic
sequencing has been adapted for detection and characterization of viruses in complex wastewater
environments (Aw et al., 2014). This whole genome sequencing approach can be considered as
good as database comparison and is expected to be more than just a discovery exercise and also a
routine assay for hypothesis testing. Usage of assembled sequence data (contigs) for analysis
reduces the chances of false positive detection in our study.
2. Materials and Methods
2.1. Sample Collection: Effluent samples before and after disinfection were collected from East
Lansing (EL) WWTP and Traverse City (TC) WWTP in Michigan (U.S.A.) in 2013. Table 4.4
provides the characteristics of these WWTPs based on wastewater treatment processes and
disinfection methods. Two liters of grab effluent samples were collected for bacterial isolation
two samples from each WWTP while for viral isolation, Argonite filters were used.
Approximately 400 Litres of effluent samples were passed through the filter pumped through a
sampler at a rate of about 11-12L/min. Samples were kept on ice and were transported to the
Water Quality Engineering Laboratory at Michigan State University (East Lansing, U.S.A.) for
further immediate processing.

2.2. Sample Processing:
72

Bacteria in the effluent samples were concentrated by filtration with 0.45 μm HA filters
(Millipore, Billerica, MA). The volume of effluent samples filtered was 1 L for each of the four
samples. The filters were collected in a 50 mL tubes and 50 mL Phosphate Buffer Water (PBW)
was added in each tube containing a filter. The tubes were then vortexed for 5 min to allow the
biomass layer on the filters to mix with water. All the tubes were then centrifuged for 20 min at
4500rpm to concentrate the sample down to 2 mL. Supernatant was discarded and the
concentrates were stored at −80 °C until the DNA extraction was performed for further
molecular analysis.
All virus samples collected were eluted 12–24 h after initial sampling according to the
Concentration and Processing of Waterborne Viruses by Positive Charge 1MDS Cartridge Filters
and Organic Flocculation (USEPA, 2001). Briefly, a 1.5% w/v beef extract (0.05 M glycine, pH
9.0–9.5) solution was used for eluting the filters. The filters were submerged for a total of 2 min
(2 separate 1 min elutions) in filter housings with 1 L of beef extract added to the pressure
vessel. The filter housing was disinfected between filters using 0.17% bleach solution for a 1 min
contact time and then dechlorinated using 2% sodium thiosulfate for another 1 min contact time.
After the beef extract was passed through each filter, the 1 L of beef extract and eluted particles
had the pH adjusted to 3.5 ± 0.1 using 1 M HCl and slowly flocculated for 30 min. Further
concentrated of the solution was done by placing 500 mL into a centrifuge bottle centrifuge for
15 min at 2500×g at 4 °C. The supernatant was then slowly poured off and the process was
repeated until all the beef extract solution was centrifuged. The accumulated pellets were
resuspended using 30 mL of 0.15 M sodium phosphate (pH 9.0–9.5), mixed until the pellet was
mostly dissolved and the pH was adjusted to 9.0–9.5 using 1 M HCl. Next, the solution was
placed into a 40 mL centrifuge tube and centrifuge for 10 min at 4 °C at 7000×g. The supernatant
73

was poured off into a 50 mL centrifuge bottle, the pH was adjusted to 7.0–7.5 for stabilization of
the virus particles and the pellet was discarded. The supernatant was loaded into a 60 mL syringe
and passed through a 0.22 μm sterilized filter for removal of bacteria, fungi and other
contaminating agents. All samples were completely mixed and placed into 2 mL cryogenic tubes
and stored at −80 °C until further analysis.
2.3. Nucleic acid Extraction: Bacterial DNA and virus DNA was extracted (from samples
before and after disinfection) using a MagNA Pure Compact DNA extractor (Roche Applied
Science, Indianapolis, IN, USA) following the protocol in the manufacturer’s manual. The
MagNA Pure Compact utilizes a magnetic-bead technology for the isolation process. Sample
amount of 400 µL was loaded in the system and the elution volume was 100µL. The extracts
were stored in a freezer at -20°C. Following extraction the quantity of bacterial and viral nucleic
acid extracts from all samples were checked using the NanoDrop Spectrophotometer
(NanoDrop® ND-1000, Wilmington, DE).
2.4. Metagenomic analyses: Bacterial and Virus-enriched DNA isolated from the effluent
samples was sequenced on an Illumina platform (Illumina HiSeq, Roche Technologies) at The
Research Technology Support Facility (RTSF) genomic center at Michigan State University
generating 250 bp paired-end reads. A flexible read trimming tool for Illumina NGS data called
Trimmomatic was used for trimming Illumina data and removing adapters (Bolger et al. 2014).
Running Trimmomatic is a good first step in quality filtering the Illumina data. The sequences
were assembled using an iterative De Bruijn Graph De Novo Assembler for Short Reads
Sequencing data with Highly Uneven Sequencing Depth called IDBA-UD (Peng et al. 2012).
Once the sequences were assembled into contigs, diversity analysis was done for bacteria and
viruses using MGRAST and METAVIR analysis platform respectively. After the diversity
74

analysis done, search was done for CCL pathogens listed in Table 4.1 along with some other
emerging pathogens.
2.4.1. MG-RAST analyses: The assembled data from all the samples were analyzed using MG-

RAST 3.3.1 (Meyer et al. 2008). Data was analyzed based on organism abundance and on the
functional distribution at the subsystem hierarchy with maximum E- value cutoff of 1E-5,
minimum percent identity cutoff of 60% and minimum alignment length cutoff of 15bps. The
MG-RAST pipeline is a fully automated pipeline providing quality control, feature prediction
and functional annotation (Thomas et al. 2012). Analysis includes the phylogenetic comparisons
and functional annotations against the database and the results are expressed in the form of
abundance profiles. MG-RAST searches the non-redundant M5NR and M5RNA databases in
which each sequence is unique. The best hit classification option was used which reports the
functional and taxonomic annotation of the best hit in the M5NR for each feature. The number of
hits is defined as “occurrences of the input sequence in the database”. The “abundance” in the
MG-RAST presents the estimate of the number of sequences that contain a given annotation,
found by multiplying each selected database match (hit) by the number of representatives in each
cluster. Hits refer to the number of unique database sequences that were found in the similarity
search, not the number of reads (Wilke et al. 2014). Some of the displayed data has been
normalized to values between 0 and 1 to allow for comparison of differently sized samples. Each
of the categories was further studied for detailed analysis and data was downloaded in Microsoft
Office Excel. MG-RAST version 3.0 works with reads of 75bp or longer.
2.4.2. METAVIR 2 analyses: The assembled data from the six virus samples were analyzed using

METAVIR 2 (Roux et al. 2014). METAVIR 2 is a web server designed to annotate viral
metagenomic sequences (raw reads or assembled contigs). The MetaVir server uses the
75

taxonomic composition tool, which gives taxonomic affiliations of the viral sequence reads.
Taxonomic composition is computed from a BLAST comparison with the Refseq complete viral
genomes protein sequences database from NCBI (release of 2014-03-13) using BLASTp.
Maximum E- value cutoff of 1E-5 was used. Predicted proteins from contigs are compared to
Refseq through BLAST, and then each protein is affiliated to its best BLAST hit (if any). Then a
contig is affiliated to its best BLAST hit, i.e. to the affiliation of the predicted protein with the
higher BLAST score. The number of hits is defined as “occurrences of the input sequence in the
database”. Best hit ratio is defined as the number of hits for one category divided by total number
of hits. Metavir only select for sequences longer than 300 bp.
3. Results and Discussion:
3.1. Metagenome sequencing and assembly results:

To describe the microbial community in wastewater treatment plant (before and after
disinfection effluents), Illumina Hiseq sequencing was applied on eight samples from two
different WWTPs in Michigan. Virus and bacterial DNA was isolated from two different
locations (before disinfection effluent (BD) and after disinfection effluent (AD)) from a
conventional and MBR utility.

Sequences generated were assembled using an IDBA-UD

assembler. Virus metagenomes were analyses using METAVIR 2 software tool while bacterial
metagenomes were analyses using MGRAST v3 analysis tool. Metagenomic analysis details for
virus isolated samples are presented in Table 5. As computed by METAVIR, virus isolated DNA
samples contained contigs ranging from 151992 to 256064 sequences. Virus isolated effluent
sample after disinfection from EL contains 11.6% of affiliated sequences, while before
disinfection effluent showed 19.39% of affiliated sequences. In virus isolated effluent sample

76

after disinfection from TC, 16.36 % of the total sequences are affiliated while before disinfection
sample contains 19.85 % of affiliated sequences.
Metagenome analysis revealed bacterial DNA samples contained contigs ranging from
238657 to 288930 sequences. In bacterial isolated effluent sample after disinfection from EL,
4,302 sequences (1.8%) failed to pass the Quality Control (QC) pipeline. Of the sequences that
passed QC, 841 sequences (0.4%) contain ribosomal RNA genes as computed by MGRAST
analysis software. Of the remainder, 184,215 sequences (77.2%) contain predicted proteins with
known functions and 48,954 sequences (20.5%) contain predicted proteins with unknown
function. 339 (0.1%) of the sequences that passed QC have no rRNA genes or predicted proteins.
Out the 234,355 sequences (totaling 147,771,487 bps) that passed quality control, 233,169
(99.5%) produced a total of 270,966 predicted protein coding regions. Of these 270,966
predicted protein features, 172,766 (63.8% of features) have been assigned an annotation using at
least one of the protein databases (M5NR). 148,347 features (85.9% of annotated features) were
assigned to functional categories. Similarly, in before disinfection effluent samples, 5,765
sequences (2.2%) failed to pass the QC pipeline. Of the sequences that passed QC, 702
sequences (0.3%) contain ribosomal RNA genes. Of the remainder, 176,685 sequences (68.8%)
contain predicted proteins with known functions and 70,878 sequences (27.6%) contain
predicted proteins with unknown function. 2,845 (1.1%) of the sequences that passed QC have
no rRNA genes or predicted proteins. Of the 251,116 sequences (totaling 153,515,277 bps) that
passed quality control, 247,563 (98.6%) produced a total of 297,712 predicted protein coding
regions. Of these 297,712 predicted protein features, 167,031 (56.1% of features) have been
assigned an annotation using at least one of the protein databases (M5NR). 137,654 features
(82.4% of annotated features) were assigned to functional categories.
77

In bacterial isolated effluent sample after disinfection from TC, 3,695 sequences (1.3%)
failed to pass the QC pipeline. Of the sequences that passed QC, 624 sequences (0.2%) contain
ribosomal RNA genes. Of the remainder, 231,252 sequences (80.0%) contain predicted proteins
with known functions and 53,202 sequences (18.4%) contain predicted proteins with unknown
function. 153 (0.1%) of the sequences that passed QC have no rRNA genes or predicted proteins.
While in before disinfection effluent samples, 6,703 sequences (2.6%) failed to pass the QC
pipeline. Of the sequences that passed QC, 552 sequences (0.2%) contain ribosomal RNA genes.
Of the remainder, 202,533 sequences (78.1%) contain predicted proteins with known functions
and 49,351 sequences (19.0%) contain predicted proteins with unknown function. 77 (0.0%) of
the sequences that passed QC have no rRNA genes or predicted proteins. Analysis statistics for
all the bacterial isolated sequenced samples are shown in Table 4.6 (a &b).

3.2. Microbial diversity in WWTP:

The diversity of viruses and bacteria present in effluent samples from a full-scale
conventional activated sludge wastewater treatment plant and a membrane bioreactor (MBR)
utility was studied using Illumina Hiseq sequencing. Analysis software METAVIR 2 and
MGRAST v3.0 pipeline was used for analyzing the assembled sequences. Virus and bacterial
species richness is presented in the rarefaction curve in Figure 4.3. The rarefaction curve of
annotated species richness is a plot of the total number of distinct species annotations as a
function of the number of sequences sampled (Meyer et al. 2008). These rarefaction curves are
calculated from the table of species abundance. Rarefaction curve also demonstrate whether a
sample has been sequenced to saturation. The slope of the right-hand part of the curve is related
to the fraction of sampled species that are rare (Wilke et al. 2013). A steeper slope indicates that

78

the sample has not yet been fully sequenced (that is large fraction of the species diversity
remains to be discovered. Figure 4.3 shows that diversity of bacteria is higher from a
conventional (EL WWTP) compared to MBR utility (TC WWTP) in effluent. This is as expected
because MBR utilities are said to provide better treatment comparatively. Also, bacterial and
virus diversity is higher in samples from before disinfection compared to after disinfection which
is how the situation should be. Based on alpha diversity (Figure 4.3), virus diversity is higher in
conventional (EL WWTP) compared to MBR utility (TC WWTP) in effluent samples. The alpha
diversity estimate summarizes the distribution of species-level annotations in each metagenome
dataset; it is the diversity of organisms in a sample with a single number. In simpler word, it is
used to describe the number of distinct species in a given sample.

3.2.1. Virus diversity in WWTP:

Viral diversity was explored by Illumina Hiseq sequencing on virus-enriched DNA
obtained from effluent samples. Table 4.7 provides the taxonomic comparison of viruses based
on Baltimore classification. This classification groups viruses into families, depending on their
type of genome (DNA, RNA, single-stranded (ss), double-stranded (ds), etc.) and their method of
replication. Most of the viruses found in all our samples belong to group of dsDNA viruses with
no RNA stage. Table 7 shows the viral taxonomic composition of the all the assembled datasets
from the wastewater viromes obtained from METAVIR server. The taxonomic compositions are
computed for each dataset using the BLASTp based on best BLAST hit affiliation of each
contigs (threshold of 50 on the BLAST bitscore). The best hit ratios are calculated based on all
hits to Refseq Virus, and is calculated from dividing the number of hits from one category (e.g.
dsDNA viruses) by the total number of hits. In order to relate these ratios to the complete
metagenome they can be further normalized with ratio of affiliated sequences.
79

Virus families are classified according to the NCBI taxonomy; each column represents a
dataset and each row a group of virus families. From the results we found that Caudovirales is
the most dominating order accounting for 56.10% - 78.11% of total virus sequences. Among this
order, Siphoviridae is the most dominating virus family closely followed by Myoviridae and
Podoviridae. These three families belong to order Caudovirales which are dsDNA group are also
known as tailed bacteriophages. Phycodnaviridae and Mimiviridae are other two families of
viruses that are present in high numbers in all the samples. Most of the sequence reads were
unclassified that must be derived from putatively novel viruses. There are more than 3,000
different viruses are recognized, but it has been suggested using metagenomic studies that these
are a small fraction of the total that exist in nature (Cantalupo et al. 2011). It is very important to
identify the virus diversity as the lack of knowledge on characteristics of the viral universe and
the diversity of viral genomes is a roadblock to understanding important issues, like the origin of
emerging pathogens and the extent of gene exchange among viruses (Cantalupo et al. 2011).
Most sequences showed no sequence relation to any known sequences in the databases and thus
are most likely to be derived from novel, uncharacterized viruses.

3.2.2. Bacterial diversity in WWTP:

Diversity of bacteria in effluent samples was explored at the phylum taxonomic level
using the MG-RAST server. Table 4.8 shows the organism abundance based in best hit
classification. The displayed data was compared to M5NR using a maximum e-value of 1e-5, a
minimum identity of 60 %, and a minimum alignment length of 15 and has been normalized to
values between 0 and 1 to allow for comparison of differently sized samples. Normalization
allows for correction based on the sample size. Proteobacteria was the most abundant phylum in
all samples, accounting for (71-81%), of total effective bacterial sequences. This is consistent
80

with the results of bacterial communities found in sewage (Zhang et al., 2011; McLellan et al.,
2010) and soil (Roesch et al., 2007). This phylum was followed by Bacteroidetes accounting (814%) in phylum distribution in all the samples. The other dominant phyla were Planctomycetes
(1-3.4%), Actinobacteria (1.2–2.8%), Firmicutes (1.6-2.1%) and Verrucomicrobia (1-3%). This
is very similar to few other studies on wastewater and specially sludge (Zhang et al. 2011; Xia et
al., 2010; Snaidr et al., 1997). Apart from these four dominating groups in this study, a few other
major (average abundance >1%) phyla includes Chloroflexi, Cynobacteria, Nitrospira and
Acidobacteria. The four classes of the Proteobacteria abundant in all the samples are
Betaproteobacteria, Alphaproteobacteria, Gammaproteobacteria and Deltaproteobacteria.
Metagenomic analysis showed that the bacterial community composition of effluent samples was
distinct between the two wastewater treatment systems.
Difference in the abundance hit with respect to each sample is observed mostly because
of different sample size (sequence length), so the diversity comparison among different sample is
not possible from this data. Bacterial diversity was similar in EL WWTP and TC WWTP in all
the samples but some genus was more abundant in ELWWTP and some in TC WWTP, thus a
slight variation in population is observed.

3.3. Potential Pathogens in WWTP:

In order to explore pathogenic viruses and bacteria in wastewater, Illumina Hiseq
sequencing has proven to be an effective tool. Among the groups of pathogenic viruses tested
based on Table 4.1, four different pathogenic viruses were detected in our samples. Pathogenic
bacteria abundance based on CCL and other common potential pathogens is presented in Figure
4.4. The most abundant potential human pathogenic virus observed in our study belongs to
81

taxonomic order Herpesviridales (Table 4.7). Herpesviridae is a large family of DNA viruses
that cause diseases in humans and animals. Herpes viruses have been regarded as the leading
cause of human viral disease, along with influenza and cold viruses and it is said that once a
patient has become infected by herpes virus, the infection remains for life (Hunt, 2011). More
than eight types of Herpes viruses are known to infect humans including Herpes simplex virus
Type 1 (HSV-1), Herpes simplex virus Type 2 (HSV-2), Human herpes virus 3 (Varicella Zoster
Virus (VZV)), Human herpes virus 4 (Epstein Barr virus (EBV)), Human herpes virus 5
(Cytomegalovirus (CMV)), Human herpes virus 6/7 (exanthum subitum or roseola infantum),
Human herpes virus 8 (Kaposi's sarcoma-associate herpes virus) (Hunt, 2011). All of these
human herpes viruses except Human herpes virus 5 were detected in our samples. Apart from
Human Herpesvirus (1, 2, 3, 4, 6B, 7and 8), several other bovine Herpesviruses were also
identified.
The other pathogenic viruses detected were Adenoviridae, followed by Coronaviridae.
Adenoviridae are the dsDNA viruses having no RNA stage. They usually cause respiratory
infections, but they can also cause conjunctivitis, gastroenteritis, cystitis, and rash illness The
family Adenoviridae consists of five genera among which Aviadenoviruses (which infects birds)
and Mastoadenoviruses (which infect mammals) are present in our samples. Coronaviruses
belong to the group of single strand (ss) positive strand viruses having no DNA stage.
Coronaviruses primarily infect the upper respiratory and gastrointestinal tract and are the cause
all common colds in human adults. These results are consistent with previous studies as shown in
Table 4.2. Our results closely matches with studies conducted on sewage sludge (Bibby and
Peccia, 2013a, Bibby and Peccia, 2013b). It is possible that the presence and abundance of viral
pathogens in wastewater will also vary seasonally. And therefore it is recommended that samples
82

should be collected and analyzed throughout the year to compare seasonal occurrence of
pathogenic viruses and the changing virome (Aw et al., 2014; Katayama et al., 2008).
Occurrences of human adenoviruses in wastewater treatment plants and drinking water treatment
plants have previously been confirmed by QPCR methods (La Rosa et al. 2010; AlbinanaGimenez et al. 2009).
Pathogenic bacteria abundance based on CCL and other common potential pathogens is
presented in Figure 4.5. All the pathogens presented in CCL as mentioned in Table 1 are detected
in all our effluent samples. Cyanobacteria are the most abundant member from the CCL present
in all the samples. They occur worldwide and some species of cyanobacteria produce toxins that
affect animals and humans. Exposure to cyanobacterial toxins can occur by number of ways
(drinking or bathing in contaminated water or recreational water contact). A gram-negative
bacteria belonging to genus Legionella named as Legionella pneumophila is an important human
pathogen present in all our samples with highest abundance. This was followed by other CCL
pathogens like Mycobacterium avium, Salmonella enterica and Aeromonas hydrophila,
Mycobacterium avium intracellulare (MAC, Helicobacter pylori, Campylobacter jejuni, Shigella
sonnei and Escherichia coli (0157) (figure 4.5). Other common bacterial pathogens found in our
samples are Leptospira, Mycobacterium tuberculosis, Vibrio cholera, Yersinia pestis, and
Yersinia enterocolitica. These results are consistent with previous studies as shown in Table 4.2.
Metagenomic analyses of wastewater samples in this study also revealed that a large
proportion of sequences could not be assigned to taxonomic affiliations even at the phylum/class
levels. The minority populations that are hard to be explored by traditional molecular methods
can easily be detected using metagenomic analyses. The effluent quality varied greatly due to
differences in sewage composition, organic loading, pH, temperature, dissolved oxygen, sludge
83

retention time applied at the aeration tank and different disinfection methods. There is a
possibility of difference in the organism load in the human population for each viruses and
bacteria and that the occurrences of these microbes fluctuate daily and seasonally in raw sewage.
Samples analyzed in this study represent only a single time point at each WWTP and the
diversity could vary if sampling is done in different seasons. Further, it is important to
acknowledge the limitations of this research in terms of available resources. Still, this is first of
the study to provide complete description of virus and bacterial diversity in effluents from two
different wastewater treatment plants. More comparative studies are needed based on welldesigned sampling plans to find out the main factors influencing the microbial communities.
4. Conclusions:
In the present work, we analyzed effluent samples using metagenomic analysis in an
effort to better understand the composition and diversity of viruses and bacteria in wastewater.
The analyses unveiled that the communities are highly diverse. In this paper, we found that
Caudovirales is the most dominating order in the viral community detected in our samples,
consisting of families: Siphoviridae, Myoviridae and Podoviridae. Analyzing the bacterial
community, Proteobacteria was the highly abundant bacterial phylum followed by
Bacteroidetes, Planctomycetes, Actinobacteria and Firmicutes.
This study provided a bioinformatics approach for identifying potential microbial
pathogens in wastewater. The most abundant potential human pathogen observed in our study
belongs to taxonomic order Herpesviridales. It is recommended that further study should be
conducted to look into Herpesviruses since they are present in such a high number. Other
pathogenic viruses detected in this study include Adenoviridae, and Coronaviridae.

84

While all the bacterial pathogens described in the Contaminant Candidate List from EPA
(Cyanobacteria, Legionella pneumophila Mycobacterium avium, Salmonella enterica and
Aeromonas hydrophila, Mycobacterium avium intracellulare (MAC, Helicobacter pylori,
Campylobacter jejuni, Shigella sonnei and Escherichia coli (0157). Other bacterial pathogens
observed in our samples are Leptospira, Mycobacterium tuberculosis, Vibrio cholera, Yersinia
pestis, and Yersinia enterocolitica. As the emphasis of this study was on pathogenic species, the
abundance of pathogens found in these samples suggests that these systems are reservoirs of
microbial populations of public health relevance. This paper provides guidance on which
pathogens to monitor in the effluents. Further, it is very much recommended to test all potential
pathogens detected with QPCR or culture methods when wastewater reuse is done example for
irrigation purposes etc. This is very important for microbial risk assessment. This study provided
an overview of microbial diversity and metagenomics as a basic screening tool for detecting
potential pathogens in wastewater environment.
5. Acknowledgement:
We would like to thank the managers of the East Lansing Wastewater Treatment Plant
and Traverse City Wastewater Treatment Plant for providing the samples and information
needed for this study. A very special thank to Bioinformatic Research Specialist Dr. Tracy Teal.
Also, we would like to acknowledge bioinformatic support and asisstance provided by
Bioinformatic team at High Performance Computing Center (HPCC) at Michigan State
University.

85

APPENDIX

86

Table 4.1: Microbial Contaminant Candidate List from EPA.
Microbial Contaminant
Candidates
(Adapted from U.S. EPA
1998, 2005,2009)
VIRUSES
Adenoviruses
Caliciviruses

Coxsackieviruses

Echoviruses
Enterovirus
Hepatitis A virus
BACTERIA
Aeromonas hydrophila

Campylobacter jejuni

Cyanobacteria
Escherichia coli (0157)
Helicobacter pylori
Legionella
pneumophila
Mycobacterium avium
(MAC)
Salmonella enterica
Shigella sonnei

Diseases/ Symptoms

Respiratory illness, and occasionally
gastrointestinal illness
Gastrointestinal illness (Diarrhea,
vomiting, nausea and stomach pain)
Respiratory illness, meningitis (an
infection of the linings of the spinal cord
and brain), encephalitis (inflammation
of the brain), pleurodynia (chest pain),
and myopericarditis (inflammation of
the heart)
Aseptic meningitis (Pneumonia like
symptoms)
Respiratory illness
Liver disease and jaundice
Gastroenteritis (inflammation of the
stomach and intestines), hemolytic
syndrome and kidney disease
Gastrointestinal illness, Guillain-Barré
syndrome (disorder affecting peripheral
nervous system)
Toxins to cause poisoning (affect the
nervous and respiratory systems), liver
damage
Gastrointestinal illness and kidney
failure
Ulcers and stomach cancer
Lung diseases (severe form of
pneumonia)
Lung disease (pulmonary pathogen,
tuberculosis and gastrointestinal
symptoms)
Salmonellosis (food poisoning), enteric
fever, gastroenteritis
Shigellosis (food-borne illness),
gastrointestinal illness, bloody diarrhea

87

CCL-1

CCL-2

CCL-3
















































Table 4.2: Virus and bacterial Pathogens detected in wastewater effluent.
Methods
of
detection

Full scale
Conventional
or MBR

References

Adenoviruses,
enterovirus, Norovirus GI

QPCR/RTQPCR

MBR and
conventional

Francy et al.
2012

Adenovirus (HAdV) and
Enterovirus (EV)

QPCR

MBR and
conventional

Simmons &
Xagoraraki,
2011

QPCR

MBR and
conventional

Simmons et al.
2011

QPCR/RTQPCR

conventional

Hewitt et al.
2011

QPCR

MBR

Kuo et al. 2010

BGM cell
culture

conventional

QPCR

conventional

RT-QPCR

MBR and
conventional

Salmonella typhi, Shigella

QPCR

-

Escherichia coli (E. coli)

RT-PCR

MBR

Clostridium perfringens

-

conventional

C. perfringens, E.coli

culture

conventional

Mycobacterium
tuberculosis, Bacillus
Anthracis, Yersinia pestis
and Vibrio cholerae

llumina
MiSeq

conventional

M.tuberculosis, S. flexneri

Illumina
Hiseq 2000

conventional

culture

conventional

DNA
microarray,
QPCR,

conventional

Pathogens Detected
Viruses

Bacteria

Adenovirus (HAdV),
Enterovirus (EV) and
Norovirus
Enterovirus, Norovirus,
Adenovirus
Human Adenoviruses
Enteroviruses
Adenovirus, Norovirus
GI & GII, enterovirus
Noroviruses
Enteroviruses
Calicivirus (Norovirus
and Sapovirus),
Adenovirus and
Enterovirus
Adenovirus
Human enteric viruses

Listeria strains.
Aeromonas hydrophila, B.
cereus, C. perfringens, E.
faecalis, E. coli, K.
pneumoniae, P.
aeruginosa, Salmonella
spp.

88

CostánLongares et al.
2008
La Rosa et al.
2010
DaSilva et al.
2007
Zhang et al.
2014
Sima et al.
2011
Jacangelo et al.
2003
Payment et al.
2001
Kumaraswamy
et al. 2014
Cai and Zhang,
2013
Odjadjare et al.
2010

Lee et al. 2008

Table 4.2 (cont’d)
Clostridium perfringens,
Escherichia coli,
Enterococcus faecalis,
Klebsiella pneumoniae
A. hydrophila, B. cereus,
C. perfringens, E. faecalis,
E. coli, and K. pneumonia,
P. aeruginosa.

89

QPCR

conventional

Shannon et al.
2007

DNA
microarray,
QPCR,

conventional

Lee et al. 2006

Table 4.3: Virus and bacterial Pathogens detected in raw and sludge samples.
Methods
of
detection

Pathogens Detected
Viruses

Bacteria

Full scale
Conventional
or MBR

References

Adenovirus, Aichi virus,
Astroviruses, Bocavirus,
Coronavirus, Cosavirus,
Echovirus, Hepatitis C,
Herpesvirus, Human Immuno
deficiency virus, Klassevirus,
Norovirus, Norwalk virus,
Papillomavirus, Parechovirus,
Parvovirus, Poliovirus,
Rhinovirus, Rotavirus, Rubella
virus, T-Lymph virus, Torque
Teno Virus

Illumina
Hiseq 2000

Adenovirus species B and C

Ion Torrent
sequencing

conventional

Bibby et al.
2013 b

Pyrosequen
cing

conventional

Bibby et al.
2011

QPCR

conventional

La Rosa et
al. 2010

Adenovirus, Parechovirus,
Coronavirus, Torque teno
virus, Herpes virus and Aichi
virus
Adenovirus, Norovirus GI &
GII, and Enterovirus.

Bibby et al.
2013

Enterovirus, rotavirus and
norovirus

Salmonella and Shigella

QPCR

MBR

Zhou et al.
2014

Adenoviruses, Calciviruses
(noroviruses), Enteroviruses,
hepatitis A viruses and
hepatitis E viruses

Escherichia coli O157:H7,
Helicobacter pylori, Legionella
pneumophila, Campylobacter
jejuni, Mycobacterium avium
complex, Salmonella enterica,
Shigella spp

QPCR

-

Aw and
Rose, 2012

Yates, 2011
Griffin et
al. 2003
Enteric viruses

Legionella spp., Mycobacterium
spp., and Leptospira

Enteric viruses

-

-

Toze, 2006

Clostridium perfringens,
Escherichia coli

culture

conventional

Payment et
al. 2001

Salmonella enterica, E. coli, V.
cholerae, Yersinia pestis, M.
tuberculosis, B. anthracis,
Streptococcus agalactiae

llumina
MiSeq

conventional

Kumaraswa
my et al.
2014

90

Table 4.3 (cont’d)
M.tuberculosis, C. perfringens, E.
faecalis, Legionella pneumophila,
S. flexneri, S. pyogenes, V.
cholerae, S. enterica, S.
pneumonia, S. dysenteriae, N.
meningitides, and Y. pestis

Illumina
Hiseq 2000

conventional

Cai and
Zhang,
2013

Aeromonas, Arcobacter,
Clostridium, Corynebacterium,
Legionella, Leptospira,
Pseudomonas, Streptococcus

PCR,
pyrosequen
cing

MBR and
conventional

Ye and
Zhang,
2011

Mycobacterium forituitum,
Mycobacterium phlei,
Mycobacterium chelonae,
Clostridium perfringens

pyrosequen
cing

conventional

Bibby et al.
2010

A. hydrophila, B. cereus, C.
perfringens, E. faecalis, E. coli, K.
pneumoniae, P. aeruginosa,
Salmonella spp.

DNA
microarray,
QPCR,

conventional

Lee et al.
2008

A. hydrophila, C.perfringens,
Enterococcus faecalis, Escherichia
coli, Klebsiella pneumoniae,
Pseudomonas aeruginosa

QPCR

conventional

Shannon et
al. 2007

DNA
microarray,
QPCR,

conventional

Lee et al.
2006

A. hydrophila, B. cereus, C.
perfringens, E. faecalis, E. coli
O158:H7, K. pneumoniae, P.
aeruginosa, Salmonella sp.

91

Table 4.4: Wastewater Treatment Plant Characterstics
EAST LANSING WWTP

TRAVERSE CITY WWTP

Activated Sludge

Membrane Biological Reactor

process (Biological treatment)

(AS)

(MBR)

Sludge Retention Time (SRT)

14 days

7.58 days

Capacity

18.8 MGD*

17.0 MGD

Average flow

13.4 MGD

8.5 MGD

Discharge Rate

14.1 MGD

4.0 MGD

Chlorine (Cl)

Ultra-Violet (UV)

Wastewater treatment

Disinfection
*MGD-Millions gallon per day

Table 4.5: Metagenome analysis statistics for virus samples (by MetaVir).
Metagenome
Name

Number of
contigs

S2

No. of genes
Ratio of affiliated
predicted
sequences
East Lansing Waste Water Treatment Plant (EL)
256064
11.6
365870
EL_AD_VIRUS

S4

EL_BD_VIRUS

151994

S9

Traverse City Waste Water Treatment Plant (TC)
197517
16.36
309715
TC_AD_VIRUS

S11

TC_BD_VIRUS

Sample
No.

19.39

151992

19.85

92

258135

258140

Table 4.6a: Metagenome analysis statistics for bacterial samples (generated by MGRAST).
Sample

Metagenome

Raw bp

No. of

No.

Name

count

contigs

Mean
Sequence
Length (bp)

Post QC:
bp Count

Post QC

Post QC: Mean

No. of

Sequence

contigs

Length (bp)

East Lansing Waste Water Treatment Plant (EL)
S1

EL_AD

176,512,330

238657

739 ± 1927

147771487

234355

630 ± 482

S3

EL_BD

184,817,243

256881

719 ± 1374

153515277

251116

611 ± 411

Traverse City Waste Water Treatment Plant (TC)
S8

TC_AD

206,030,297

288930

713 ± 2198

176537661

285235

618 ± 484

S10

TC_BD

179,323,360

259220

691 ± 1048

14,645886

252517

584 ± 339

Table 4.6b: Functional category Hit distribution (Bacterial samples).
Processed:

Processed:

Alignment:

Alignment:I

Metagenome

Predicted

Predicted

Identified

dentified

Name

Protein

rRNA

Protein

rRNA

Features

Features

Features

Features

EL_AD

270,966

17,827

172,766

758

EL_BD

297,712

22,251

167,031

625

TC_AD

341,222

23,529

218,046

546

TC_BD

297,052

19,752

191,448

499

Note: Sequences were assembled and contigs generated were analyzed on MGRAST; Abbreviation: bp= base pair;
AD=After Disinfection Effluent; BD= Before Disinfection Effluent

93

Figure 4.1: Location of two wastewater treatment plants selected for effluent sampling for
investigation of the viral and bacterial community.

94

Figure 4.2: Schematic flowchart showing the procedure followed for metagenomic analysis

95

(a) Rarefaction curve for virus enriched samples in effluent samples

(b) Rarefaction curve for bacterial enriched samples in effluent samples
Figure 4.3: Rarefaction curve of species richness in virus and bacterial DNA enriched
samples from effluents of two different WWTPs. Note: S2: EL_AD_VIRUS=East Lansing after disinfection
effluent Virus sample, S4: EL_BD_VIRUS= East Lansing before disinfection effluent Virus sample; S9: TC_AD_VIRUS= East
Lansing after disinfection effluent Virus sample; S11: TC_BD_VIRUS= East Lansing before disinfection effluent Virus sample

96

Table 4.7: Taxonomic comparison heat map based contigs best BLAST hit ratios (number
of hits for the genome divided by total number of hits in the metagenome).
S2
EL_AD
0.27
0.06
0.21
96.39
0.01
0.83
0.06
0.56
0.12
61.2
0.01
0.4
0.7
0.08
0.07
0.8
10.26
0.01
0.12
12.26
0.01
0.02
1.57
0.01
0.04
2.76
4.57
0.01
0.01
0.55
0
0.52

Taxonomy
Retro-transcribing viruses
Caulimoviridae
Retroviridae
dsDNA viruses, no RNA stage
Adenoviridae
Ascoviridae
Asfarviridae
Baculoviridae
Bicaudaviridae
Caudovirales
Corticoviridae
Herpesvirales
Iridoviridae
Ligamenvirales
Lipothrixviridae
Marseilleviridae
Mimiviridae
Nimaviridae
Nudiviridae
Phycodnaviridae
Plasmaviridae
Polydnaviridae
Poxviridae
Rudiviridae
Tectiviridae
unclassified dsDNA phages
unclassified dsDNA viruses
dsRNA viruses
Cystoviridae
ssDNA viruses
Circoviridae
Inoviridae
97

S4
EL_BD
0.08
0.03
0.05
97.45
0.02
0.15
0.13
0.2
0.05
78.11
0.01
0.21
0.44
0
0.02
0
2.76
0.03
0
4.85
0
0.01
0.54
0.01
0.03
5.82
4.06
0.01
0.01
0.15
0
0.12

S9
TC_AD
0.08
0.04
0.05
97.01
0
0.29
0.2
0.31
0.04
75.05
0.01
0.14
0.24
0
0.03
0
3.3
0.01
0
5.97
0.01
0.01
0.77
0.01
0.08
5.81
4.71
0.01
0.01
0.46
0.01
0.22

S11
TC_BD
0.07
0.03
0.04
96.71
0.01
0.14
0.09
0.17
0.04
78.08
0.01
0.22
0.49
0.04
0.02
0.34
3.75
0.03
0.06
4.62
0
0.01
0.49
0.01
0.04
5.52
2.56
0.01
0.01
0.14
0
0.11

Table 4.7 (cont’d)
Microviridae
Parvoviridae
unclassified ssDNA viruses
ssRNA viruses
ssRNA positive-strand viruses, no DNA
stage
unassigned viruses
unclassified archaeal viruses
unclassified phages
unclassified virophages
unclassified viruses

0.03
0
0.01
0.07

0.02
0
0.01
0.03

0.03
0.15
0.05
0.03

0.02
0
0.01
0.03

0.07

0.03

0.03

0.03

0.05
0.02
2.22
0.01
0.42

0.02
0.01
2.22
0.02
0

0.02
0
2.36
0.03
0

0.02
0.01
2.74
0.02
0.25

Note: Comparison of the taxonomic compositions computed from a BLAST comparison with ncbi refseq complete
viral genomes proteins using BLASTp (threshold of 50 on the BLAST bitscore). Abbreviation: S2: EL_AD =East
Lansing after disinfection effluent virus sample, S4: EL_BD = East Lansing before disinfection effluent virus sample; S9:
TC_AD = East Lansing after disinfection effluent virus sample; S11: TC_BD = East Lansing before disinfection effluent virus
sample

98

Table 4.8: Organism Abundance (Bacteria Phylum Distribution).
Sample Type

EL_AD EL_BD TC_AD TC_BD

Proteobacteria

0.989

0.973

1

0.983

acteroidetes

0.777

0.735

0.802

0.811

Firmicutes

0.571

0.591

0.605

0.582

Actinobacteria

0.541

0.623

0.654

0.62

Cyanobacteria

0.517

0.539

0.564

0.541

Planctomycetes

0.515

0.644

0.617

0.617

Verrucomicrobia

0.513

0.542

0.659

0.65

Nitrospirae

0.492

0.345

0.582

0.59

Acidobacteria

0.433

0.482

0.517

0.495

Chloroflexi

0.43

0.509

0.537

0.495

Chlorobi

0.406

0.436

0.471

0.456

unclassified (derived from Bacteria)

0.386

0.411

0.428

0.408

Deinococcus-Thermus

0.34

0.39

0.416

0.386

Chlamydiae

0.34

0.371

0.251

0.225

Gemmatimonadetes

0.316

0.449

0.358

0.303

Spirochaetes

0.286

0.362

0.444

0.384

Aquificae

0.247

0.253

0.306

0.279

Poribacteria

0.236

0.085

0.317

0.327

Fusobacteria

0.229

0.234

0.221

0.217

Synergistetes

0.191

0.204

0.219

0.17

Deferribacteres

0.175

0.182

0.195

0.175

Thermotogae

0.155

0.228

0.264

0.231

99

Table 4.8 (cont’d)
Lentisphaerae

0.127 0.205 0.245 0.243

Chrysiogenetes 0.119 0.136 0.131

0.12

Dictyoglomi

0.087 0.087 0.104 0.104

Elusimicrobia

0.057 0.029 0.087 0.062

Tenericutes

0.042 0.101

Fibrobacteres

0

0.05

0.08

0.069

0.039

0.05

100

Figure 4.4: Potentially pathogenic virus abundance in effluent samples. Adenoviruses are the
only CCL pathogens found in our samples. Note: The scale has been log transformed for better visual.

101

Figure 4.5: Potentially pathogenic bacteria diversity abundance in effluent samples. Note:
The scale has been log transformed for better visual.

102

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69. Zhang, T., Zhang, X.-X. and Ye, L. (2011). Plasmid metagenome reveals high levels of
antibiotic resistance genes and mobile genetic elements in activated sludge. PLoS One
6(10), e26041.
70. Zhou, J., Wang, X. C., Ji, Z., Xu, L., & Yu, Z. (2014). Source identification of bacterial
and viral pathogens and their survival/fading in the process of wastewater treatment,
reclamation, and environmental reuse. World Journal of Microbiology and
Biotechnology, 1-12.

109

CHAPTER 5
VIRUS AND PHAGE DIVERSITY IN ACTIVATED SLUDGE FROM A
CONVENTIONAL AND A MBR WASTEWATER UTILITY USING METAGENOMIC
ANALYSES
Mariya Munir, Camille McCall, Terence Marsh, and Irene Xagoraraki. (in preparation)
Abstract
In this paper, phage and virus diversity of two different wastewater treatment systems,
conventional and membrane bioreactor (MBR) utilities were investigated through metagenomics.
Samples collected from activated sludge were studied using Illumina Hiseq sequencing. Analysis
software MGRAST v3.0 pipeline was used for analyzing the assembled sequences.
Phycodnaviridae, Herpesviridae, Mimiviridae and Lipothrixviridae were present in high
numbers in sludge samples. Most of the sequences detected were uncharacterized; indicating a
greater number of viral diversity is yet to be discovered.
Siphoviridae, Podoviridae and Myoviridae are the most common group of phages found
in both the phage enriched samples from sludge obtained from the two treatment plants. On
genus and species level, our results showed differences in viral community compositions
between two different WWTPs.
Keywords: Metagenomics, activated sludge, virus, phage, diversity, Illumina Hiseq, MBR
1. Introduction
Viruses are small infectious particles consisting of a nucleic acid core (single or double
stranded RNA or DNA) typically 20–200 nm in size, enclosed by a protein coat (capsid) and in
some cases a lipid envelope (Carter and Saunders, 2007). Viruses are potentially the most
110

important and most hazardous among the pathogens found in wastewater (Sidhu et al., 2008,
Toze, 1997). They are also generally more difficult to detect in environmental samples. The huge
diversity of viruses that exist in human populations are potentially excreted and concentrated in
wastewater (Bibby and Peccia, 2013). It has been shown that viruses persist in the water
environment for extended periods and are also more resistant to removal by wastewater
treatment systems compared to bacteria (Aw et al. 2014, Gomila et al., 2008). According to
Xagoraraki et al (2014), presence of viruses in water and wastewater is a complex problem for
environmental engineers because of prevalence, infectivity, and resistance of viruses to
disinfection.
Bacteriophages are the most abundant biological entities on the planet (Bergh et al.,
1989) and are regarded as an active part of the activated sludge microbial ecosystem (Khan et al.
2002). Bacteriohphages (phages) are the viruses that infect bacteria. They are the most
dominating and abundant among viral diversity (Bibby, 2014). They are associated with the large
bacterial (host) populations. Like all viruses, phages are obligate intracellular parasites and
require the metabolic machinery of the host cell to support their reproduction (Withey et al.
2005). The role of bacteriophages in the emergence of novel bacterial pathogens by horizontal
gene transfer is noteworthy. Very little information is available regarding phage-mediated
transduction (Colomer-Lluch et al. 2011, Sander and Schmieger 2001). The significance of
bacteriophages for the process of transduction among bacterial population has been undermined
for long. Concentration of 108 to

109

phage-like-particles per ml was reported in different full

scale activated sludge bioreactors (Otawa et al. 2007).
It is suggested that phage abundance in activated sludge is higher than any other
environment (Otawa et al. 2007, Shapiro and Kushmaro 2011, Rosenberg et al. 2010, Wu and
111

Liu 2009). Occurrences of different viable bacteriophages in the wastewater environment
(somatic coliphage range: 103 ml-1 to 105 ml-1) have been reported to be typically lower (Muniesa
et al. 2004b, Mandilara et al. 2006, Khan et al. 2002, Havelaar et al. 1990). An increase in total
phage concentration was observed during an activated sludge process, suggesting active
replication was occurring via host infection and lysis (Ewert and Paynter 1980). It was shown
that in WWTP the phage to bacterial cell ratio was approximately 10:1 (Rosenberg et al. 2010).
The objective of this study was to describe and compare the diversity viruses and phages
in a conventional and MBR (membrane biological Reactor) utility using metagenomics with high
throughput sequencing technology (Hiseq Illumina sequencing). Not many studies have provided
complete diversity in wastewater using metagenomics based on our literature review. This study
provides a complete diversity characterization of viruses and phage- enriched DNA in activated
sludge from two different wastewater treatment plants.
2. Materials and Methods
2.1. Sample Collection: Effluent samples before and after disinfection and activated sludge were
collected from East Lansing (EL) WWTP and Traverse City (TC) WWTP in Michigan (U.S.A.)
in 2013. The characteristic of these WWTPs is shown in Table 5.1. Activated sludge (AS)
sample was collected in two 1L nalgene bottles, mixed together in laboratory and then separated
into sections for phage and virus isolation from each WWTP. Samples were kept on ice and were
transported to the Water Quality Engineering Laboratory at Michigan State University (East
Lansing, U.S.A.) for further immediate processing. The schematic of the all the methods used in
this study is presented in Figure 5.1.

2.2. Sample Processing:
112

2.2.1. Virus elution/isolation process:

The virus elution and concentration from sludge samples were performed according to the
ASTM-4994 using 10% beef extract (USEPA, 2001). Briefly, sludge samples were conditioned
with 0.05M AlCl3 and the pH adjusted to 3.5 ± 0.1 using 1 M HCl with 30 min mixing, to ensure
all viruses adsorbed to the solids, before elution process. Conditioned samples were then poured
into centrifuge bottles and centrifuge for 15 min at 2500×g at 4 °C. To each decanted bottle, 10%
of beef extract was added and mixed for 30 min. This was followed by another centrifuge at
10000×g at 4 °C for 30 min. This procedure was followed by organic flocculation where the pH
of the eluted beef extract is brought down to 3.5 ± 0.1with constant slow stirring for 30 min.
Another round of centrifuge at 2500×g for 15 min at 4 °C was done, discarding the supernatant
and the pellets were resuspended and dissolved in 0.01M phosphate buffer saline (PBS). The resuspension was transferred to a small sterile beaker with stir bar for vigorously stirring to
dissolve the flocculate. Antibiotics (Kanamycin, Gentamicon or Antimucotic: 1mL) is added at
this step and pH is adjusted to 7.0-7.5 with 1N HCL or 1N NaOH. One last optional centrifuge,
to remove debris for easy filtering, was done for 10 min at 1850×g in 50 mL centrifuge tubes. As
before, the supernatant was loaded into a 60 mL syringe and passed through a 0.22 μm sterilized
filter for removal of bacteria, fungi and other contaminating agents. All samples were completely
mixed and placed into 2 mL cryogenic tubes and stored at −80 °C until further analysis.
2.2.2. Phage isolation:
Phage isolation from activated sludge samples was carried out using previously developed
method in our lab. Mitomycin C (1µg/ml) was added to induce the samples and the samples were
incubated at room temperature for 24 hrs while gently shaking (150 rpm). Several drops of

113

chloroform were added to the samples to complete lysis and incubation was continued for
another 15-30 mins. The samples (250-300mL of sludge) were then centrifuged at 3396 xg for 45
minutes in F10S-6X500Y rotor and the supernatant was carefully decanted. Each supernatant
was filtered through a 0.22 µm filter (Millipore, Billerica, MA) and then the bacteriophages
were precipitated with PEG-NaCl (Colomer-Lluch et al. 2011, Sander and Schmieger 2001,
Muniesa et al. 2004a). The PEG precipitate was collected by centrifugation at 10000xg in an
aerosol-tight fixed-angle rotor at 4°C for 40 minutes. The supernatant was carefully decanted and
the pellet was resuspended in 1.0 ml of SM buffer (Yamamota et al., 1970). Any free DNA that
co-purified with bacteriophage was removed by digestion with DNase I (100 Units/mL)
(Colomer-Lluch et al. 2011). The phage preparations were stored at -80°C until DNA extraction
was performed for molecular analysis.
2.3. Nucleic acid Extraction: Virus DNA and Phage DNA was extracted (from samples before
and after disinfection) using a MagNA Pure Compact DNA extractor (Roche Applied Science,
Indianapolis, IN, USA) following the protocol in the manufacturer’s manual. The MagNA Pure
Compact utilizes a magnetic-bead technology for the isolation process. Sample amount of 400
µL was loaded in the system and the elution volume was 100µL. The extracts were stored in a
freezer at -20°C. Following extraction the quantity of bacterial and viral nucleic acid extracts
from all samples were checked using the NanoDrop Spectrophotometer (NanoDrop ® ND-1000,
Wilmington, DE).
2.4. Metagenomic sequencing and analyses: Isolated DNA samples including phage-enriched
DNA and virus-enriched DNA isolated from the sludge was sequenced on an Illumina platform
(Illumina HiSeq, Roche Technologies) at The Research Technology Support Facility (RTSF)
genomic center at Michigan State University generating 250 bp paired-end reads. Approximately
114

1 μg DNA (per core sample) was sent to the sequencing facility. The sequencing results were
returned as .FASTQ.GZ files and they were converted to FASTQ files by processing them in
MSU High Performance Computing Center (HPCC) secure shell (SSH) connection. The SSH
connection was established by a widely used software known as PuTTY. In order to quality
filter the illumina data, a flexible read trimming tool for Illumina NGS data called Trimmomatic
was used for trimming Illumina data and removing adapters (Bolger et al. 2014). Finally, the
trimmed sequences were assembled using an iterative De Bruijn Graph De Novo Assembler for
Short Reads Sequencing data with Highly Uneven Sequencing Depth called IDBA-UD (Peng et
al. 2012).
2.5. MG-RAST analyses: MetaGenome Rapid Annotation Subsystems Technology server (MGRAST 3.3.1) was used to analyze the assembled data from each sample (Meyer et al. 2008). Each
of the assembled data files were underwent quality control (QC) process. This included quality
filtering, which involves removing sequences with ≥5 ambiguous base pairs, length filtering,
removing sequences with length ≥2 standard deviations from the mean, and de-replication, which
involves removing similar sequences that are artifacts of sequencing. The analysis obtained from
MG-RAST consists of the phylogenetic comparison and functional annotation compared with the
database. The results are expressed in the form of abundance profiles. The abundance represents
an estimate of a number of sequences that contain a given annotation. This is found by
multiplying each selected database match, or hit, by the number of representatives in each
cluster. Hit refers to the number of unique database sequences that were found in the similarity
search. The hit count can be smaller than the number of reads because of clustering, or larger
than the number of reads due to double counting (Wilke et al. 2014). Data was analyzed based
on organism abundance and on the functional distribution at the subsystem hierarchy with
115

maximum E- value cutoff of 1E-5, minimum percent identity cutoff of 60% and minimum
alignment length cutoff of 15bps. The MG-RAST pipeline analysis includes the comparisons and
functional annotations against the database. Each of the categories was further studied for
detailed analysis and data was downloaded in excel sheet for further analysis.
3. Results and Discussion:
3.1. Metagenomic statistics:

Virus and phage diversity in wastewater treatment plants was analyzed using next
generation sequencing (NGS) on the Illumina HiSeq platform. The study was conducted on four
samples from two different WWTPs in Michigan. Virus and phage DNA was isolated from
activated sludge (AS) from a conventional and MBR utility. Sequences generated were
assembled using an IDBA-UD assembler. Metagenome analysis revealed that virus-enriched
DNA sample contained 297613 and 244963 contigs sequences from ELWWTP and TCWWTP
respectively, totaling 348 Mbp and phage-enriched DNA samples contained 154095 and 138306
ontigs sequences totaling 238 Mbp. Bioinformatic analysis statistics pre and post quality control
process obtained from MGRAST server is presented in Table 5.2.

In virus isolated sample from EL, 7763 sequences (2.6%) failed to pass the QC pipeline.
Of the sequences that passed QC, 363 sequences (0.1%) contain ribosomal RNA genes. Of the
remainder, 185,201 sequences (62.2%) contain predicted proteins with known functions and
103,941 sequences (34.9%) contain predicted proteins with unknown function. 343 (0.1%) of the
sequences that passed QC have no rRNA genes or predicted proteins. In TC virus isolated
sample, 4211 sequences (1.7%) failed to pass the QC pipeline, 331 sequences (0.1%) contain
ribosomal RNA genes, 152,188 sequences (62.1%) contain predicted proteins with known
116

functions and 87,863 sequences (35.9%) contain predicted proteins with unknown function. 368
(0.2%) of the sequences that passed QC have no rRNA genes or predicted proteins. Similarly, in
phage isolated sample (EL), 16,775 sequences (10.9%) failed to pass the QC pipeline, 575
sequences (0.4%) contain ribosomal RNA genes, 57,544 sequences (37.3%) contain predicted
proteins with known functions and 78,937 sequences (51.2%) contain predicted proteins with
unknown function. 204 (0.1%) of the sequences that passed QC have no rRNA genes or
predicted proteins. In TC phage isolated sample, 12,774 sequences (9.2%) failed to pass the QC
pipeline, 364 sequences (0.3%) contain ribosomal RNA genes, 48,588 sequences (35.1%)
contain predicted proteins with known functions and 76,405 sequences (55.2%) contain
predicted proteins with unknown function. Only 144 (0.1%) of the sequences that passed QC
have no rRNA genes or predicted proteins. Figure 5.2 shows the metagenomic summary division
for all the four samples analyzed in this study.

Figure 5.3 shows the rarefaction curve of annotated species richness in virus and phage
enriched DNA samples. This curve is a plot of the total number of distinct species annotations as
a function of the number of sequences sampled. These rarefaction curves are calculated from the
table of species abundance. These curves generally rise very quickly at first and then level off
towards an asymptote as fewer new species are found per unit of individuals collected. A steeper
slope on the left side of the curve indicates that a large fraction of the species diversity remains
to be discovered. If the curve becomes flatter to the right, a reasonable number of individuals are
sampled: more intensive sampling is likely to yield only few additional species (Meyer et al.
2008, Wilke et al. 2014). The alpha diversity for each sample is also presented in these graphs.
Alpha diversity summarizes the diversity of organisms in a sample with a single number; it can
be estimated from the distribution of the species-level annotations (Wilke et al. 2014).
117

According to figure 5.3, diversity within the virus isolated samples is slightly lower in
MBR treatment facility as compared to conventional activated sludge (CAS) process. The MBR
facility had lower sludge retention time (SRT) compared to conventional WWTP during the
month of sampling.

3.2. Virus and phage diversity in sludge:

In order to analyze the diversity of viruses and phages present in sludge samples from a
full-scale conventional activated sludge wastewater treatment plant and a membrane bioreactors
(MBRs) utility MGRAST v3.0 pipeline was used. Figure 5.4 shows the virus abundance of the
all the assembled datasets from the wastewater viromes obtained from MGRAST server based on
best hit classification on a family level. The best hit classification option was used which reports
the functional and taxonomic annotation of the best hit in the M5NR database for each feature.
The number of hits is defined as “occurrences of the input sequence in the database”.
Phycodnaviridae is the family of viruses present in high numbers (54% and 66% in EL and TC
respectively) in sludge samples in both the WWTPs. The Phycodnaviridae family consists of
large double-stranded-DNA (dsDNA) viruses infecting eukaryotic algae and they are some of the
largest known viruses, and have great ecological importance (Larsen et al. 2008). Evidence
suggests that these viruses are active players in the formation and termination of algal blooms
(Larsen et al. 2008). In EL sludge sample; this family is followed by Herpesviridae (10%),
Mimiviridae (10%) and Lipothrixviridae (10%) that are also present in high numbers. Further
exploring the Phycodnaviridae family on a genus level, Cholorvirus is present in higher
percentage (57%) followed by Prasinovirus (29%) and Phaeovirus (14%) (Figure 5.5). Among
other virus families, groups of viruses on a genus-level present in EL sludge include

118

Chlamydiamicrovirus (50%), Alphalipothrixvirus (17%), Rhadinovirus (17%), and Mimivirus
(16%).

In TC sludge samples, the dominating family of viruses following the Phycodnaviridae is
Mimiviridae (16%). Other virus families present are Asfarviridae (6%), Lipothrixviridae (3%),
Nimaviridae (3%), Parvoviridae (3%), and Alloherpesviridae (3%). Further classifications on a
genus level, within the Phycodnaviridae family, 83% of the viruses are Chlorovirus and 17% are
Prasinovirus. Among other virus families, groups of viruses on a genus-level present in TC
sludge include Mimivirus (50%), Asfivirus (20%), Betalipothrixvirus (10%), and Whispovirus
(10%) and Parvovirus (10%). A vast difference in viral diversity at genus level is observed
between the two treatment plants. Most of the sequence reads were unclassified that must be
derived from putatively novel viruses, therefore has not been taken into percentage analysis.
Further classification of virus-isolated samples from EL and TC sludge on a species level is
shown in Table 5.3. It is very important to identify the virus diversity as the lack of knowledge
on characteristics of the viral universe and the diversity of viral genomes is a roadblock to
understanding important issues, like the origin of emerging pathogens and the extent of gene
exchange among viruses (Cantalupo et al. 2011). This work presents the diversity viruses in
sludge and indicates that there is a difference in the virus community between different treatment
process, thus suggesting that within different WWTPs the virus populations are dynamic.

Phage diversity is shown in Figure 5.5. Siphoviridae, Podoviridae and Myoviridae are the
most common group of phages found in both the phage enriched samples from sludge obtained
from two different treatment plant. Further analyzing each family on a genus level classification
as shown in the figure, the dominating genus were Lambda-like viruses, bpp1-like viruses, and

119

T4-like viruses respectively in each of the above families. Myoviridae, Podoviridae,
Siphoviridae, are also the most common families of phages found in wastewater samples as
reported by other studies (Alhamlan et al. 2013; Colomer-Lluch et al. 2011, Parsley et al. 2010).
Parsley and his team followed shotgun library approach to study activated sludge samples
finding Myoviridae (40.3%), Siphoviridae (31.9%), Podoviridae (25.6%) and considered
unclassified phages (2.2%) (Parsley et al. 2010). Pyrosequencing discovered similar results on
dairy manure wastewater lagoons where the majority belongs to the family Siphoviridae (dsDNA
tailed viruses) (Alhamlan et al. 2013). Myoviridae and Siphoviridae were also detected when
electron microscopy was used on sewage and river water sample (Colomer-Lluch et al. 2011). A
link between the presence of siphophages and fecal pollution has been suggested implying that
these phages could be used as bioindicators of fecal contamination (Rosario et al., 2009).

Further classification in phage enriched samples is shown in Table 5.4 on a species level.
Not much difference in the diversity between the two treatment plants is observed with few
exceptions like Streptomyces phage is only present in TC sample and Pseudomonas phage is
more abundant in EL sludge.
It has been suggest that bacteriophage may play a significant role in determining the
structure and function of bacterial communities in activated sludge (Barr et al. 2010).
Considering the potential that many phages kill bacteria, it has been suggested to be used to
eradicate bacteria from water (Alhamlan et al. 2013). More research is needed in this direction to
further explore this possibility. Despite this critical role in ecosystem processes, the study of
virus/phage diversity has lagged far behind parallel studies of the Bacterial and Eukaryotic
kingdoms.

120

Metagenomic analyses of wastewater samples in this study also revealed that a large
proportion of sequences could not be assigned to taxonomic affiliations even at the phylum/class
levels. Most sequences showed no sequence relation to any known sequences in the databases
and thus are most likely to be derived from novel, uncharacterized viruses or phages. This study
provided a bioinformatics approach for identifying viral diversity thus adding the knowledge gap
to the number of metagenomic datasets previously available. These results present a unique
opportunity to examine the WWTPS with respect to virus and phage diversity; these
metagenomes provide important data to make baseline observations that will need to be
examined more thoroughly in future studies.

4. Conclusions:
In order to evaluate virus and phage community composition in a conventional and MBR
utility, metagenomics coupled with Illumina sequencing was used. It provided a broader outlook
of the microbial composition in sludge and effluent samples. The analyses unveiled that the
communities are highly diverse.


Virus diversity: Phycodnaviridae are highly abundant followed by Herpesviridae,
Lipothrixviridae and Mimiviridae in sludge samples. Difference in viral diversity on a genus
level is observed between different treatment plants.



Siphoviridae, Podoviridae and Myoviridae are the most common group of phages found in
both the phage enriched samples from sludge obtained from two different treatment plant.



Most of the sequences detected were uncharacterized; indicating a greater number of viral
diversity is yet to be discovered.

121

The results presented here revealed the feasibility of metagenomic approaches to characterize
viral communities in complex environmental samples, thus describing the role of WWTP in
returning safe and healthy water.
5. Acknowledgement:
We would like to thank the managers of the East Lansing Wastewater Treatment Plant
and Traverse City Wastewater Treatment Plant for providing the samples and information
needed for this study. We would like to acknowledge bioinformatic support and asisstance
provided by Bioinformatic team at High Performance Computing Center (HPCC) at Michigan
State University. A very special thank to Bioinformatic Research Specialist Dr. Tracy Teal.

122

APPENDIX

123

Figure 5.1: Schematic of methodology and location of sampling.

124

Table 5.1: Characterstics of WWTPs.
TRAVERSE CITY
EAST LANSING WWTP

WWTP

Activated Sludge

Membrane BioReactor

process (Biological treatment)

(AS)

(MBR)

Sludge Retention Time (SRT)

14 days

7.58 days

Capacity

18.8 MGD*

17.0 MGD

Average flow

13.4 MGD

8.5 MGD

Discharge Rate

14.1 MGD

4.0 MGD

Chlorine (Cl)

Ultra-Violet (UV)

Wastewater treatment

Disinfection

Table 5.2: Metagenome analysis statistics for sludge samples (generated by MGRAST).
Metagenome
Name

Raw bp
count

No. of
contigs

297613

Mean
Sequence
Length
(bp)
630 ± 652

EL_VIRUS

187710045

TC_VIRUS

Post QC:
bp Count

Post QC
No. of
contigs

162566539

289850

Post QC:
Mean
Sequence
Length (bp)
560 ± 272

160406503

244963

654 ± 1063

141269383

240752

586 ± 348

EL_PHAGE

118452544

154095

768 ± 1557

90081784

137320

655 ± 551

TC_PHAGE

119319471

138306

862 ± 1763

91302789

125532

727 ± 617

Note: Sequences were assembled and contigs generated were analyzed on MGRAST;
Abbreviation: bp= base pair

125

Figure 5.2: Metagenome Summary in phage and virus DNA enriched samples from sludge
of two different WWTPs.

126

(a) Rarefaction curve for viruses in sludge samples

(b) Rarefaction curve for phage in sludge samples
Figure 5.3: Rarefaction curve of species richness in phage and virus DNA enriched samples
from sludge of two different WWTPs.

127

Figure 5.4: Virus diversity/organism abundance (family-level) based on best hit
classification: Virus diversity in activated sludge on a family level, further classified into genus
level for East Lansing and Traverse City wastewater utilities.

128

Figure 5.5: Phage diversity/organism abundance based on best hit classification. (a) Diversity of phages on a family level in
activated sludge for Traverse City and East Lansing wastewater utilities, (b) Phage diversity (genus-level) within the Siphoviridae,
Podoviridae and Myoviridae family for East Lansing and Traverse City wastewater utilities
129

Table 5.3: Virus abundance in ELWWTP and TCWWTP activated sludge samples.

Family (host)

Phycodnaviridae
(algae)

Mimiviridae
(acanthamoeba)
Asfarviridae
(animal)

Virus Abundance
Genome
Group
Genus
Chlorovirus

East
Lansing AS
4

Traverse
City AS
15

Phaeovirus

1

0

Prasinovirus

2

3

unclassified (derived
from Phycodnaviridae)

0

3

dsDNA

Mimivirus

1

5

dsDNA

Asfivirus

0

2

Alphalipothrixvirus

1

0

Betalipothrixvirus

0

1

dsDNA

Lipothrixviridae
(archaea)

dsDNA

Nimaviridae (animal)

dsDNA

Whispovirus

0

1

Parvoviridae (vertebrates,
Insects)

ssDNA

Parvovirus

0

1

Alloherpesviridae (fish)

dsDNA

unclassified (derived
from Alloherpesviridae)

0

1

Herpesviridae (vertebrates)

dsDNA

Rhadinovirus

1

0

20

136

unclassified (derived from Viruses)

Virus Abundance_Mimiviridae (Genus/Species) Adundance
East Lansing
Traverse City
Genus
Species
AS
AS
Acanthamoeba polyphaga
Mimivirus
1
5
mimivirus
Virus Abundance_Asfarviridae (Genus/Species) Adundance
East Lansing
Traverse City
Genus
Species
AS
AS
Asfivirus
African swine fever virus
0
2

130

Table 5.3 (cont’d)

Virus Abundance_Lipothrixviridae (Genus/Species) Adundance
East Lansing
Traverse City
Genus
Species
AS
AS
Alphalipothrixvirus
Thermoproteus tenax virus 1
1
0
Betalipothrixvirus
Acidianus filamentous virus 7
0
1
Virus Abundance_Nimaviridae (Genus/Species) Adundance
East Lansing
Traverse City
Genus
Species
AS
AS
Whispovirus
White spot syndrome virus 1
0
1
Virus Abundance_Parvoviridae (Genus/Species) Adundance
East Lansing
Traverse City
Genus
Species
AS
AS
Parvovirus
Porcine parvovirus
0
1
Virus Abundance_Alloherpesviridae (Genus/Species) Adundance
East Lansing
Traverse City
Genus
Species
AS
AS
unclassified (derived
Anguillid herpesvirus 1
0
1
from Alloherpesviridae)
Virus Abundance_Herpesviridae (Genus/Species) Adundance
East Lansing
Traverse City
Genus
Species
AS
AS
Rhadinovirus
Leporid herpesvirus 1
1
0

131

Table 5.4: Phage abundance in ELWWTP and TCWWTP activated sludge samples.
Phage Abundance
Family

Siphoviridae

Podoviridae

unclassified

East Lansing
AS
1769

Traverse City
AS
2106

Lambda-like phages

111

167

N15-like phages

8

9

T1-like phages

8

11

T5-like phages

5

10

L5-like phages

1

2

SPbeta-like phages

2

0

PhiC31-like phages

3

8

unclassified

558

546

Bpp-1-like phages

257

299

N4-like phages

123

23

P22-like phages

67

76

Epsilon15-like phages

48

49

LUZ24-like phages

36

39

T7-like phages

35

16

phiKMV-like phages

9

8

VP2-like phages

3

1

SP6-like phages

3

7

Phieco32-like phages

1

2

unclassified

744

708

T4-like phages

152

132

I3-like phages

14

4

SPO1-like phages

19

20

Genus

Myoviridae

132

Table 5.4 (cont’d)
phiKZ-like phages

9

7

P2-like phages

10

29

P1-like phages

6

16

Mu-like phages

3

0

Phage Abundance_Siphoviridae (Genus/Species)
(Family) Genus

Lambda-like
phages

T1-like phages

Bacillus phage SPP1

East Lansing
AS
2

Traverse City
AS
1

Burkholderia phage KS9

2

16

Burkholderia phage phi1026b

16

11

Burkholderia phage phi644-2

4

7

Burkholderia phage phiE125

67

70

Enterobacteria phage HK022

4

4

Enterobacteria phage HK97

0

18

Enterobacteria phage cdtI

2

0

Escherichia Stx1 converting
bacteriophage

12

21

Pseudomonas phage DMS3

0

1

Streptomyces phage VWB

0

20

Stx2 converting phage II

0

1

Stx2-converting phage 86

2

0

Enterobacteria phage RTP

3

4

Enterobacteria phage T1

5

2

Enterobacteria phage TLS

0

5

Enterobacteria phage SPC35

0

1

Enterobacteria phage T5

5

9

Enterobacteria phage N15

8

9

Species

T5-like phages
N15-like phages

133

Table 5.4 (cont’d)
PhiC31-like
phages

Streptomyces phage phiBT1

0

3

Streptomyces phage phiSASD1

3

5

Mycobacterium phage Bxb1

1

0

Mycobacterium phage D29

0

2

Bacillus phage SPbeta

2

0

L5-like phages
SPbeta-like phages

Phage Abundance_Podoviridae (Genus/Species)
East Lansing
(Family) Genus
Species
AS
Bordetella phage BIP-1
178

167

Bordetella phage BMP-1

173

166

Bordetella phage BPP-1

186

184

Burkholderia phage BcepC6B

66

114

Enterobacter phage EcP1

27

7

Enterobacteria phage N4

39

9

Pseudomonas phage LIT1

10

1

Pseudomonas phage LUZ7

32

2

Roseovarius sp. 217 phage 1

0

1

Silicibacter phage DSS3phi2

8

3

Sulfitobacter phage EE36phi1

7

0

Enterobacteria phage CUS-3

14

17

Enterobacteria phage P22

3

13

Enterobacteria phage ST104

19

22

Myxococcus phage Mx9

1

2

Salmonella phage HK620

10

5

Salmonella phage SE1

1

1

Salmonella phage ST160

5

7

Salmonella phage ST64T

11

8

Bpp-1-like phages

N4-like phages

Traverse City
AS

P22-like phages

134

Table 5.4 (cont’d)

Epsilon15-like
phages
LUZ24-like
phages

Salmonella phage epsilon34

1

0

Salmonella phage g341c

5

2

Shigella phage Sf6

0

3

Escherichia phage phiV10

19

31

Salmonella phage epsilon15

29

18

Pseudomonas phage LUZ24

24

25

Pseudomonas phage PaP3

12

14

Enterobacteria phage 13a

0

3

Enterobacteria phage BA14

3

5

Enterobacteria phage EcoDS1

5

4

Enterobacteria phage K1F

0

1

Enterobacteria phage T3

3

0

Enterobacteria phage T7

4

0

Klebsiella phage K11

3

0

Kluyvera phage Kvp1

1

0

Pseudomonas phage gh-1

2

0

Pseudomonas phage phiIBB-PF7A

1

0

Salmonella phage phiSG-JL2

1

0

Vibriophage VP4

1

0

Yersinia pestis phage phiA1122

4

0

Yersinia phage Berlin

0

1

Yersinia phage Yepe2

1

1

Yersinia phage phiYeO3-12

6

1

Pseudomonas phage LKA1

0

2

Ralstonia phage RSB1

4

4

Vibrio phage VP93

5

2

Vibrio phage VP2

2

1

T7-like phages

phiKMV-like
phages
VP2-like phages

135

Table 5.4 (cont’d)
Vibrio phage VP5

3

1

Enterobacteria phage Era103

3

6

Enterobacteria phage K1-5

0

1

Erwinia phage phiEa100

3

4

Erwinia phage phiEa1H

3

4

Enterobacteria phage Phieco32

1

2

SP6-like phages

Phieco32-like
phages

(Family)
Genus

Phage Abundance_Myoviridae (Genus/Species)
East Lansing
Species
AS
Acinetobacter phage 133
4

Traverse City
AS
4

Acinetobacter phage Ac42

2

8

Acinetobacter phage Acj61

3

5

Acinetobacter phage Acj9

2

5

Aeromonas phage 25

2

2

Aeromonas phage 31

1

0

Aeromonas phage 44RR2.8t

1

1

Aeromonas phage 65

7

15

Aeromonas phage Aeh1

9

0

Aeromonas phage PX29

1

0

Aeromonas phage phiAS4

1

0

Aeromonas phage phiAS5

7

6

Enterobacteria phage JSE

3

0

Enterobacteria phage Phi1

0

1

Enterobacteria phage RB32

1

0

Enterobacteria phage RB43

4

1

Enterobacteria phage RB49

5

0

Enterobacteria phage RB69

0

1

T4-like phages

136

Table 5.4 (cont’d)
Enterobacteria phage T4 sensu lato

5

5

Enterobacteria phage vB_EcoMVR7

9

4

Klebsiella phage KP15

0

1

Prochlorococcus phage P-SSM2

22

13

Prochlorococcus phage P-SSM4

30

25

Synechococcus phage S-PM2

11

12

Synechococcus phage syn9

19

13

Vibrio phage nt-1 sensu lato

3

10

Aeromonas phage phiO18P

0

3

Enterobacteria phage P2

1

4

Pseudomonas phage phiCTX

0

1

Ralstonia phage phiRSA1

9

21

Bacillus phage Bastille

3

4

Bacillus phage SPO1

4

0

Lactobacillus phage LP65

4

6

Listeria phage A511

2

6

Staphylococcus phage G1

4

1

Staphylococcus phage K

0

1

Staphylococcus phage Twort

2

3

Enterobacteria phage P1

6

16

Mycobacterium phage Bxz1

7

1

Mycobacterium phage Cali

13

1

Mycobacterium phage Catera

1

1

Mycobacterium phage ET08

8

1

Mycobacterium phage Rizal

7

3

Mycobacterium phage Spud

7

1

P2-like phages

SPO1-like
phages

P1-like phages

I3-like phages

137

Table 5.4 (cont’d)

phiKZ-like
phages
Mu-like phages

Pseudomonas phage 201phi2-1

5

5

Pseudomonas phage EL

4

0

Pseudomonas phage phiKZ

0

2

Burkholderia phage BcepMu

3

0

138

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139

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CHAPTER 6
CONCLUSIONS AND SIGNIFICANCE
Our environment is greatly impacted by the presence of emerging microbial
contaminants. The overall objective of this study was to provide metagenomic insights into
bacterial, viral and phage diversity and resistance to antibiotics and metal compounds in
wastewater utilities.
Chapter 1 focuses on phage diversity and antibiotic resistance genes in a conventional
wastewater treatment plant in Michigan. A method for phage DNA isolation was optimized using
PEG (polyethylene glycol) precipitation and DNase (deoxyribonuclease) treatment. Phage DNA
was screened for ARGs (tetracycline resistant genes (Tet-W and Tet-O) and sulfonamide
resistant gene (Sul-I)) using real-time Q-PCR. Diversity of phages was studied by next
generation sequencing with Illumina (Miseq). Phage metagenomes were searched for functional
signatures of antibiotic resistance genes. Metagenomics analysis revealed that most of the
observed antibiotic resistance was resistance to methicillin, fluoroquinolones and beta-lactamase
group of antibiotics. The findings of the study also suggest that there is a substantial shift in the
phage community over the course of the activated sludge process between primary and returned
activated sludge, thus suggesting that within the activated sludge the phage populations are
dynamic and that phage DNA was associated with antibiotic resistant genes in wastewater.
Chapter 2 focuses on the diversity of microbial resistances to antibiotics and metal
compounds in samples from a conventional and an MBR (membrane biological Reactor) utility
using metagenomic investigations. Illumina Hiseq sequencing was applied on six samples from
the two different WWTPs in Michigan where bacterial DNA was isolated from three different
143

sampling points (activated sludge, before disinfection effluent and after disinfection effluent.
Findings of this study reveal that genes coding for antibiotic resistance were identified in all
bacterial samples, along with genes coding for resistance to metals. The MBR utility showed
slightly higher number of hits for all the functional categories compared to conventional WWTP
samples. The incidence of multiple metal and antibiotic resistances among bacterial populations
poses a potential threat to human health.
Chapter 3 focuses on identification of viral and bacterial pathogen diversity in wastewater
effluent released from an MBR and a conventional treatment utility using metagenomics
analysis. Effluent samples (before and after disinfection) have been collected and analyzed to
reveal microbial pathogenic diversity. Findings of this study show that a potential human viral
pathogen observed in our samples belongs to taxonomic order Herpesviridales. Other potentially
pathogenic viruses detected in this study include Adenoviridae, Coronaviridae and Hepatitis C
viruses. Whereas all the bacterial pathogens described in the Contaminant Candidate List from
EPA were detected in our study. Diversity analysis does not provide quantitative data on
pathogen loads or infectivity but it provides a list of potentially pathogenic viruses and bacteria
that need to be considered in more detail.
Chapter 4 focuses on investigating the phage and virus diversity of two distinct
wastewater treatment systems, conventional and MBR using metagenomics. Samples collected
from activated sludge were studied using Illumina Hiseq sequencing. Analysis software
MGRAST v3.0 pipeline was used for analyzing the assembled sequences. Our results showed
differences in viral community compositions on a genus level between two different WWTPs,
suggesting there is a considerable difference in the community between different treatment

144

processes. This study provided a bioinformatics approach for identifying viral diversity which
was an important gap in knowledge.
The work presented in chapter 1-4 is significant since it characterizes the microbial
diversity in wastewater. Increasing population and urbanization will result into increasing
wastewater quantities. Management of ARGs and microbial pathogens in WWTPs will be
critical since the problem of spread of pathogens and antibiotic resistant genes in the
environment poses a significant challenge to public health management. Full-scale conventional
and state of the art wastewater utilities have been found to release pathogens and resistant
bacteria in the environment. Understanding the microbial diversity and linkage between diversity
and wastewater treatment methods and practices will lead to sustainable wastewater
management. More research is needed to study links between engineering design, microbial
diversity and operational parameters.

145