Department of Food and Environmental Sciences University of Helsinki
Finland
AnƟ bioƟ c resistance in
human impacted environments
Antti Karkman
Academic Dissertation in Microbiology To be presented, with the permission of
the Faculty of Agriculture and Forestry of the University of Helsinki for public examination in auditorium 2, at Infocenter Korona, Viikinkaari 11,
on September 4th 2015, at 12 o’clock noon.
Helsinki 2015
Supervisors
Professor Marko Virta
Department of Food and Environmental Sciences University of Helsinki
Dr. Manu Tamminen
Department of Food and Environmental Sciences University of Helsinki
Reviewers Dr. Fiona Walsh Department of Biology Maynooth University Dr. Jenni Hultman
Department of Food Hygiene and Environmental Health University of Helsinki
Opponent
Professor Elizabeth Wellington School of Life Sciences
Th e University of Warwick
ISBN 978-951-51-1474-7 (Paperback) ISBN 978-951-51-1475-4 (PDF) ISSN 2342-5423 (Print)
ISSN 2342-5431 (Online) Cover photo: © Pertti Jarla
Layout: Tinde Päivärinta, PSWFolders Oy Hansaprint
Helsinki 2015
TABLE OF CONTENTS
List of original publications Author contribution Abbreviations Abstract Tiivistelmä
1. Introduction ...1
1.1 Brief history of antibiotics and antibiotic resistance ...1
1.2 Origins and genetics of antibiotic resistance ...2
1.3 Emergence of antibiotic resistance and the rise of superbugs ...3
1.4. Antibiotic resistance in the environment and the anthropogenic impact ...3
1.5 Challenges in detecting and quantifying antibiotic resistance in the environment ...5
2. Aims of the thesis ...7
3. Summary of the methods ...8
3.1 Samples and data used in this study...8
3.2 Methods used in this study ...9
4. Results and discussion ...10
4.1 Aquaculture ...10
4.2 Urban wastewater treatment plants ...12
4.3 Metagenomes ...14
5. Conclusions and future prospects ...15
6. Acknowledgements ...16
7. References ...17
LIST OF ORIGINAL PUBLICATIONS
Th is thesis is based on the following publications:
I Tamminen, M.; Karkman, A.; Lõhmus, A.; Muziasari, W.; Takasu, H.; Wada, S.; Suzuki, S. and Virta, M. 2011. Tetracycline resistance genes persist at aquaculture farms in the absence of selection pressure. Environmental Science and Technology, 45(2): 386-91.
**
II Laht, M.*, Karkman, A.*, Voolaid, V., Ritz, C., Tenson, T., Virta, M., Kisand, V. 2014.
Abundances of Tetracycline, Sulphonamide and Beta-Lactam Antibiotic Resistance Genes in Conventional Wastewater Treatment Plants (WWTPs) with Diff erent Waste Load. PLoS ONE 9(8): e103705.
III Karkman, A., Johnson, T., Lyra, C., Stedtfelt, R., Tamminen, M., Tiedje, J., Virta, M. High-throughput quantifi cation of antibiotic resistance genes from an urban wastewater treatment plant. Submitted.
IV Fondi, M.*, Karkman, A.*, Tamminen, M., Bosi, E., Virta, M., Fani, R., Alm, E., McInerney, J. 2015. Every gene is everywhere but the environment selects: Global geo-localization of gene sharing in environmental samples through network analysis.
Submitted.
Th e publications are referred to in the text by their roman numerals.
*Equal contribution
**Reproduced with permission from Environ. Sci. Technol., 2011, 45 (2), pp 386–391.
Copyright 2010 American Chemical Society.
AUTHOR CONTRIBUTION
I Antti Karkman participated in the design of the work, executed most of the experimental work, with the exception of the HPLC analyses. He participated in interpreting the results and writing the manuscript.
II Antti Karkman participated in the design of the study and experimental work. He analyzed the results together with the co-authors and participated in writing the manuscript.
III Antti Karkman designed the work and did the experimental work except run of samples with the qPCR array. He analyzed the results and wrote the manuscript.
IV Antti Karkman participated in the design of the study, in the experimental work, in interpreting the results and writing the manuscript.
ABBREVIATIONS
ARB Antibiotic resistant bacteria ARG Antibiotic resistance gene
CE Common Era
DNA Deoxyribonucleic acid ESBL Extended spectrum ß-lactamase HGT Horizontal gene transfer
HPLC High-performance liquid chromatography MDR Multi drug resistant
MRSA Methicillin resistant Staphylococcus aureus PCR Polymerase chain reaction
qPCR Quantitative polymerase chain reaction SSN Sequence similarity network
UWTP Urban wastewater treatment plant WHO World Health Organization
ABSTRACT
Increasing microbial resistance against antibiotics is threatening their effi ciency in the future and we might be heading back to pre-antibiotic era. Infectious diseases still treatable with antibiotics might soon become life threatening.
Th ere is a strong correlation between antibiotic use and antibiotic resistance occurrence.
Hotspots for antibiotic resistance are mainly man made such as wastewater treatment plants, animal farms and aquaculture. Aquaculture is the fastest growing food industry in the world and uses antibiotics to treat and prevent fi sh diseases. Th e use of antibiotics is linked to increase in antibiotic resistance at the farms. At urban wastewater treatment plants microbes from various sources can mix and exchange genetic material. In addition wastewaters contain antibiotics that can further select for resistant microbes.
In this work hundreds of antibiotic resistance genes were quantifi ed with cutting- edge molecular methods. Th e global patterns of antibiotic resistance gene movement were determined using publicly available metagenomic data. In addition, the famous Baas- Becking hypothesis ’everything is everywhere, but the environment selects’ was tested on gene level.
Aquaculture increases the amount of antibiotic resistance genes in the farms. Th e resistance genes persist in the aquaculture sites without a clear selection pressure, however the impact is only local. Urban wastewater treatment plants effi ciently removed antibiotic resistance genes from wastewaters. Th e release of wastewater had only a limited impact on the sediment resistome near the release site. When looking at the metagenomic data, antibiotic resistance genes were found to have diff erent dispersal pattern compared to other genes in the metagenomes. Antibiotic resistance genes can cross taxonomical and geographical barriers with ease, possibly explaining their wide dispersal in the environment and the clinic. Th ese results show that antibiotic resistance is ubiquitous in the environment and the anthropogenic activities aff ect the incidence of antibiotic resistance.
TIIVISTELMÄ
Antibioottien kasvava ja jopa holtiton käyttö terveydenhuollossa ja eläintuotannossa on johtanut antibiooteille vastustuskykyisten bakteerien yleistymiseen ja leviämiseen maailmanlaajuisesti. Pahimmassa tapauksessa olemme palaamassa aikaan, jolloin bakteerien aiheuttamille sairauksille ei ole tehokasta hoitoa ja jopa keuhkokuume voi olla kohtalokas.
Jo nyt vaarassa ovat immuunipuolustuskyvyltään heikentyneet ihmiset, kuten vanhukset ja vakavasti sairaat.
Antibioottien käyttö suosii antibiooteille vastustuskykyisiä bakteereita ja voi myös lisätä vastustuskyvyn leviämistä bakteerien välillä. Kalanviljelyssä antibiootteja käytetään sairaiden kalojen lääkitsemiseen ja sairauksien ennaltaehkäisyyn. Antibioottien käytön aiheuttaman vastustuskyvyn lisääntymisen vuoksi, EU:ssa antibioottien käyttö on sallittu vain sairaiden kalojen hoitamiseen. Jätevedenpuhdistamoilla ympäristö- ja ihmisperäiset bakteerit kohtaavat, jolloin vastustuskyky voi levitä bakteerilta toiselle. Jätevedenpuhdistamolle päätyvät ihmisten syömien antibioottien jäämät voivat lisätä vastustuskyvyn omaavia bakteereita.
Tässä työssä tutkittiin vastustuskyvyn aiheutttavien geenien esiintymistä molekyylibiologisin menetelmin kahdessa esimerkkiympäristössä sekä käyttäen maailmanlaajuisesti kerättyjä aineistoja, joissa on kartoitettu kokonaisten mikrobiyhteisöjen perimä erilaisissa ympäristöissä. Uusimmat molekyylibiologiset menetelmät mahdollistivat satojen geenien määrittämisen samanaikaisesti. Vastustuskykygeenien määrä väheni jäteveden puhdistuksessa merkittävästi ja puhdistetun jäteveden laskeminen Itämereen ei lisännyt vastustuskykygeenien määrää pohjasedimentissä. Sen sijaan kalanviljely lisäsi vastustuskykygeenien määrää kasvattamoiden pohjasedimenteissä, mutta ei kasvattamoiden ympärillä. Maailmanlaajuinen aineisto osoittaa, että vastustuskykygeenit voivat levitä bakteerilajien ja eri ympäristöjen välillä. Nämä tulokset osoittavat, että vastustuskykygeenejä löytyy kaikkialta, mutta ihmisten toiminnalla on suuri paikallinen vaikutus vastustuskykygeenien määrään.
1
1. INTRODUCTION
1.1 Brief history of anƟ bioƟ cs and anƟ bioƟ c resistance
Antibiotics are one of the most important discoveries that have aff ected human and animal health in the history of mankind. Since antibiotics are mainly natural products synthesized by microbes, it’s evident that they have existed for ages (Davies & Davies, 2010; Aminov, 2010).
Probably antibiotics have been used already in 350 – 550 CE based on traces of tetracycline on humans skeletal remains (Nelson et al, 2010). Also traditional Chinese medicine has used herbs with antimicrobial activity (Cui & Su, 2009). Th e antibiotic era began in the early 20th century with the discovery of Salvarsan, a synthetic drug against syphilis. Synthetic dyes staining microbes selectively gave Paul Ehrlich the idea of “magic bullets” that could target pathogenic microbes selectively. Erlich systematically screened chemical compounds to fi nd a cure to the common and almost untreatable disease caused by a spirochete Treponema pallidium. Th is kind of systematic screening approach became popular in drug search and was also used in the discovery of sulfa drugs in the early 1920’s.
Th e most important discovery in the history of antibiotics was the discovery of penicillin by Alexander Fleming in 1928 from Penicillium mold. Penicillin was not immediately taken into use in the clinic. Th e problems with purifi cation and stability took 12 years and Fleming already abandoned the idea in 1940. Fortunately on the same year another group in Oxford came up with a solution in purifying enough of penicillin for clinical testing. Th e purifi cation protocol of the Oxford group eventually led to the mass production of penicillin, which was further fueled by the huge need during World War II (Davies & Davies, 2010; Aminov, 2010). From there started the golden era of antibiotics and several new classes and compounds were discovered (Figure 1).
Antibiotics have revolutionized medicine and saved many lives, but it was evident from the beginning of the antibiotic era that bacteria can become resistant to antibiotics. Already Alexander Fleming observed resistant strains soon aft er the discovery of penicillin. Fleming said in 1946: ‘Th ere is probably no chemotherapeutic drug to which in suitable circumstances the bacteria cannot react by in some way acquiring fastness (resistance)” (Levy & Marshall, 2004).
It took only 5 years aft er the clinical use of penicillin had started when the fi rst clinically relevant resistant bacteria was observed. Bacteria have developed resistance to all antibiotics in use, few years aft er antibiotics are introduced to the clinic, clinically signifi cant resistance appears (Figure 1). Th e rapid evolution of microbial antibiotic resistance is ending or has already ended the golden era of antibiotics (Clatworthy et al, 2007)
Bacteria have produced antibiotics probably for over 500 million years, already from the Cambrian period (Allen et al, 2010; Baltz, 2008), but still it is unclear what are the roles of antibiotics in the natural environments. Th e concentrations in nature are far lower than what is used in the clinic and antibiotics probably have diff erent functions than warfare in nature. (Allen et al, 2010; Aminov, 2009). Low antibiotic concentrations induce biochemical pathways and the natural roles of antibiotics are related to signaling, regulation and quorum sensing (Allen et al, 2010; Aminov, 2009; Clardy et al, 2009). From anthropocentric viewpoint antibiotics have been considered to have hostile roles and act as weapons in nature (Allen et al, 2010; Davies & Davies, 2010). However there are only few examples of hostile antibiotic functions in nature (Allen et al, 2010; Currie et al, 1999; Neeno-Eckwall et al, 2001).
Introduction
2
1.2 Origins and geneƟ cs of anƟ bioƟ c resistance
Antibiotic resistance genes are thought to originate from antibiotic-producing bacteria or from bacterial housekeeping genes through mutation. Natural producers of antibiotics have a protection mechanism against the antibiotics they produce oft en located on the same genetic element as the antibiotic synthesis genes (Benveniste & Davies, 1973; Martin & Liras, 1989).
Although these bacteria can be a reservoir for new resistance determinants, no clear evidence exist for clinical antibiotic resistance to originate from natural producers (Aminov & Mackie, 2007). Strains isolated before the widespread use of antibiotics have shown that also non- producer strains had resistance determinants (Allen et al, 2010; Smith, 1967).
Bacterial antibiotic resistance can be intrinsic or acquired. Intrinsic resistance is naturally occurring in the host, such as multidrug effl ux pumps or physical barriers preventing the entry of the antibiotic. Acquired resistance involves spontaneous mutations and transfer of the resistance genes from other bacteria through transformation, conjugation or transduction (horizontal gene transfer, HGT). Th e selection pressure caused by the ever-increasing use of antibiotics fastens the evolution of resistance genes, selects for resistant phenotypes and even induces the horizontal transfer of the genes through several mechanisms (Hastings et al, 2004; Roberts & Mullany, 2009).
Antibiotic resistance genes were probably subjected to horizontal gene transfer even before antibiotic era (Allen et al, 2010; D’Costa et al, 2011). Evidence for selection and evolution of resistance genes before the antibiotic era exist (D’Costa et al, 2011). β-lactamases rose over 2 billion years ago and were already present on plasmids over million years ago. Many precursors of antibiotic resistance genes have evolved through ancient rather than recent evolution due to the selection caused by massive antibiotic use (Aminov, 2009).
Th e evolution of resistance genes can be divided into two periods, the pre-antibiotic and antibiotic. In the pre-antibiotic period the evolution of the genes was slow and did not involve Introduction
1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
Salvarsan
Prontosil (Sulfonamide)
Penicillin StreptomycinChlortetracyclineChloramphenicol
ErythromycinVancomycin RifampicinMethicillinAmpicillin Cefalotin (Cephalosporin)Ciprofloxacin (quinolone)
LinezolidDaptomycin Tigecycline (tetracycline)
Antibiotic deployment
Antibiotic resistance
The lean years Discovery void
Increasing antibiotic resistance Antibiotic resistance plasmids
Antibiotic resistance plasmids Transmissible quinolone resistanceTransmissible quinolone resistance
Pre-antibiotic era ??
SulfonamidePenicillin TetracyclineStreptomycinChloramphenicolRifampicinMethicillinCiprofloxacinCephalosporinsAmpicillin ErythromycinVancomycin LinezolidDaptomycin
Figure 1. Timeline of antibiotic deployment and the occurrence of antibiotic resistance. In the lean years between 1960-1990 antibiotic development was slow and only few new antibiotics were introduced to the market. Resistance has been increasing since the beginning of the antibiotic era. Th e capture of resistance genes by conjugative plasmids increased the emergence of antibiotic resistance genes. Figure based on: Lewis, 2013; Wright, 2007; Clatworthy et al, 2007; Davies & Davies, 2010
3 strong selection or horizontal transfer. Heavy use of antibiotics since the 1940’s accelerated the evolution of antibiotic resistance due to strong selection pressure. At some point resistance genes got captured by mobile genetic elements, which disseminated through horizontal gene transfer into commensal and pathogenic bacteria (Aminov, 2009). Th e main mechanisms behind antibiotic resistance prevalence and spread are selection pressure favoring the resistant microbes and horizontal gene transfer of the resistance genes, which is oft en induced by antibiotics (Aminov & Mackie, 2007). Plasmids have been common in bacteria already before the antibiotic era, however resistance determinants in plasmids were still rare. Today plasmids are one of the most important vectors of resistance genes (Davies & Davies, 2010)
1.3 Emergence of anƟ bioƟ c resistance and the rise of superbugs
Th ere is a strong correlation between antibiotic consumption and antibiotic resistance (Goossens et al, 2005). Both sulfa drugs and penicillin did not thrive for long before clinically relevant resistant pathogens emerged. Tetracyclines were discovered in 1948 and fi ve years later the fi rst resistant Shigella strain was isolated. Already in 1955 the fi rst multidrug resistant (MDR) Shigella was detected, but MDR strains were still rare. Few years aft er the proportion of MDR Shigella had already risen to 10% showing the remarkable pace of antibiotic resistance spread (Chopra & Roberts, 2001). Resistance can arise even against fully synthetic antibiotics like broad- spectrum fl uoroquinolones. Horizontal gene transfer has played a major role in the evolution and transmission of the resistance genes and has given the rise of extended spectrum β-lactamase- (ESBL) enterococci in community and hospital environments (Davies & Davies, 2010).
Th e most serious threat worldwide is the rise of superbugs, such as methicillin Staphylococcus aureus (MRSA). Superbugs are commensal and pathogenic bacteria that have acquired multiple resistance genes. Some strains have in addition acquired increased virulence and enhanced transmissibility (Alekshun & Levy, 2007). Many pathogens associated with epidemics of human disease have acquired multiple resistance determinants due to the massive antibiotic use. Th ese MDR strains are causing serious problems in healthcare system worldwide.
Th e World Health Organization (WHO) and recent G7 summit in Germany (2015) raised a concern about antibiotic resistance and according to WHO we are heading back to pre-antibiotic era if serious actions are not made (World Health Organization, 2014).
1.4 AnƟ bioƟ c resistance in the environment and the anthropogenic impact
Antibiotic resistance is not restricted to pathogenic or commensal microbes, but is ubiquitous in the environment. Environmental microbiome is considered to be the natural reservoir of potential antibiotic resistance genes and has more diversity and novelty than ever expected (Wright, 2010; Lin et al, 2015). Antibiotic resistance exists in ancient permafrost (D’Costa et al, 2011), deep terrestrial subsurfaces (Brown & Balkwill, 2009), in agriculture and animal husbandry (Heuer et al, 2011) and wastewaters (Auerbach et al, 2007; Czekalski et al, 2014; Rizzo et al, 2013). Since most of the antibiotics originate from soil microbes, it is obvious that soil is a huge reservoir for antibiotic resistance (Walsh & Duff y, 2013). CTX-M β-lactamases originate from environmental Kluyvera species (Humeniuk et al, 2002) and quinolone resistance genes from Klebsiella pneumoniae (Baquero et al, 2008) showing the clear link from the environment to the clinic.
Introduction
4
Antibiotics have been used in animal farming and aquaculture as growth promoters, for prophylaxis and for treatment of infections (Baquero et al, 2008; Roca et al, 2015; Cabello, 2006).
Th e use and misuse of antibiotics in the clinic, community and animal farming has resulted in the emergence and spread of resistant microbes through selection caused by the antibiotics. Even sub lethal antibiotic doses select and enrich antibiotic resistance in the environment (Andersson
& Hughes, 2012). Th e impact of increasing use of antibiotics can be seen in archived soil samples where the resistance gene abundance increases from the 1940s to present (Knapp et al, 2010).
Antibiotic resistance genes are now considered an environmental pollution and severe measures to prevent their further spread should be taken (World Health Organization, 2014;
Roca et al, 2015). Better understanding of reservoirs as well as dissemination of antibiotic resistance genes (ARGs) is needed to fi ght the resistance threat in the clinic and the environment.
Th e hotspots for environmental bacteria to mix and exchange genetic material with pathogenic bacteria are mostly man made, such as sewage, wastewater treatment plants, animal farms and aquaculture (Baquero et al, 2008; Wellington et al, 2013). Th ese genetic reactors are the main human impacted environments where resistance emerges and disseminates.
Urban wastewater treatment plants (UWTPs) are a signifi cant source of antibiotic resistance pollution (Table 1) (Rizzo et al, 2013). Sewage from households and hospitals contains antibiotics causing selective pressure for antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs) (Martinez, 2009). Bacterial biofi lms and stress caused by antibiotics and other pollutant compounds promote horizontal gene transfer in wastewaters (Aminov, 2011).
Table 1. Examples of studies quantifying antibiotic resistance genes in urban wastewater treatment plants or environments impacted by UWTPs
Sampling locations Country ARGs quantifi ed Reference River downstream
from UWTP
USA sul1, tetW (Pruden et al, 2012)
Sediment impacted with wastewater
Switzerland sul1, sul2, tetB, tetM, tetW, qnrA (Czekalski et al, 2014) Infl uent, effl uent,
biosolids, activated sludge
USA tetG, tetQ (Auerbach et al, 2007)
River infl uenced by UWTP
Spain blaTEM, blaCTX-M, blaSHV, qnrA, qnrB, qnrS, tetO, tetW, sul1, sul2, ermB
(Marti et al, 2013)
Infl uent, biosolids USA tetO, tetW, sul1 (Munir et al, 2011) Activated sludge China blaOXA-1, blaOXA-2, blaOXA-10,
ampC, blaTEM-1,blaIMP
(Yang et al, 2012) Sludge USA sul1, sul2, ermB, ermF, tetO, tetW,
tetC, tetG, tetX,
(Ma et al, 2011) River water and sed-
iments infl uenced by UWTPs
UK Int1 (Amos et al, 2015)
Introduction
5 Aquaculture is the fastest growing food industry in the world and probably will continue to grow in the future (Cabello, 2006; Miranda et al, 2013). Th e high fi sh densities and increased stress weaken the fi sh immune system and further promote the need of antibiotics. Increased use of antibiotics selects for resistance among environmental, and commensal and pathogenic fi sh bacteria making the resistance problem even worse (Cabello, 2006). One of the major concerns is the development of antibiotic resistant reservoirs from where the resistance can emerge and transfer to pathogenic bacteria. Bacteria resistant to several antibiotics, including the clinically important ones, exist in aquaculture. Resistance genes transfer horizontally between environmental aquaculture bacteria and human as well as veterinary pathogens (Cabello, 2006;
Miranda et al, 2013). However the prevalence of resistance in fi sh pathogens and the transfer routes of the resistant bacteria in the environment and food chain on global scale remain largely unknown and need further studies.
1.5 Challenges in detecƟ ng and quanƟ fying anƟ bioƟ c resistance in the environment
For the last 70 years the research on antibiotic resistance has focused mainly on pathogens.
Isolating pure cultures has been and still is the most important method in clinical microbiology.
Antibiotic susceptibility testing of bacteria is relatively inexpensive and does not require expensive equipment. Susceptibility testing gives important data on resistance patterns that is needed for designing treatments for patients. Clinical breakpoints for pathogenic bacteria are drawn based on susceptibility testing of diff erent strains. Databases of clinical breakpoints (such as EUCAST, www.eucast.org) help in monitoring antibiotic resistance worldwide. Combined with molecular techniques, whole genome sequencing as an example, data from susceptibility testing can be used to fi nd previously unknown resistance determinants acquired through mutations or horizontal gene transfer. Sequencing of whole microbial genomes gives insight about the genetic environment of the antibiotic resistance genes. Genes located on mobile genetic elements capable of horizontal transfer pose a bigger risk for resistance spread (Martinez et al, 2014). Culturing and susceptibility testing can be used for environmental bacteria (Walsh
& Duff y, 2013), but only a fraction of environmental bacteria can be grown in the laboratory, so this gives only limited information about the environmental resistome.
Culture-independent methods have their advantages when working with environmental samples. PCR and quantitative PCR (qPCR) methods can be used in detecting genes from environmental DNA without the need of culturing and even in high-throughput fashion (Szczepanowski et al, 2009), especially when using qPCR arrays for hundreds of genes (Zhu et al, 2013; Wang et al, 2014; Looft et al, 2012). PCR methods are relatively inexpensive and easy to perform, however some sophisticated equipment is required. Th e need of prior knowledge for primer design limits their use to known genes or genes with high homology to known ones.
With molecular methods it is also diffi cult to predict the functionality of the target gene.
Metagenomics, the sequencing of the whole community DNA, can overcome the need of prior knowledge of resistance genes. It is a powerful method to detect all functional genes at the same time, including antibiotic resistance genes. Metagenomics has been used to detect antibiotic resistance in diverse environments (Chen et al, 2013; Hu et al, 2013; Li et al, 2015; Nesme et al, 2014; Yang et al, 2013; Zhang et al, 2011) and is not restricted to few a priori chosen genes but through sequencing the total community DNA can capture the whole resistome. However the annotation of antibiotic resistance genes relies on known genes in public antibiotic resistance Introduction
6
Introduction
gene databases (Gibson et al, 2015; Gupta et al, 2014; Liu & Pop, 2009; Zankari et al, 2012;
McArthur et al, 2013). Still, in most environments antibiotic resistance genes are rare compared to other functional genes and therefore deep sequencing is needed to capture the whole diversity.
Most metagenomic sequencing platforms produce short reads that as such give only limited information about the sequenced genes. Assembling short reads into longer overlapping DNA segments (contigs) can give information about the phylogeny and genetic context of the genes.
Partial or even complete genomes can be reconstructed from metagenome data (Hultman et al, 2015; Albertsen et al, 2013). Th is knowledge is important in ranking the risks of antibiotic resistance genes in the environment. Antibiotic resistance genes located in mobile genetic elements in pathogenic bacteria have the highest risks for human health (Martinez et al, 2014).
Functional metagenomics, the cloning and expression of environmental DNA in a laboratory host, can overcome the limits of PCR and metagenomic sequencing in detecting mostly known resistance genes. In functional metagenomics environmental DNA is cloned in large fragments (10 – 200 kb) into a laboratory host e.g. Escherichia coli and the susceptibility of the host to diff erent antibiotics is tested. Clones with resistance phenotype are screened for the antibiotic resistance determinant by sub-cloning, mutagenesis or in-silico analysis, which can be laborious and time consuming. Cloning and expressing the genes in the host can be diffi cult and are the main disadvantages of functional metagenomics, although they can be solved to some extent by using other hosts than E. coli. Proteomics combined with functional metagenomics is promising new way to overcome the tedious screening of potential clones. Using proteomic tools the expressed proteins can be identifi ed in high-throughput manner and by comparing to a strain without the cloned DNA, the putative new resistance determinants identifi ed (Fouhy et al, 2015).
7
2. AIMS OF THE THESIS
Antibiotic resistance seems to be ubiquitous in the environment. Th e use and misuse of antibiotics has resulted in multi resistant bacteria, pollution of the environment with antibiotic resistance genes and the possible return of the pre antibiotic era with limited possibilities to treat bacterial infections. In my thesis I studied how anthropogenic pollution aff ects the antibiotic resistance abundance in the impacted environments and how antibiotic resistance genes have spread on global scale. Two example environments, aquaculture and wastewater, with high anthropogenic impact were chosen for study sites. Using publicly available metagenomic data from both human impacted and “pristine” environments the global dispersal patterns of antibiotic resistance were modeled.
Aquaculture introduces fi sh commensal bacteria and occasionally antibiotics to the sediment beneath the farms and causes eutrophication and oxygen depletion altering the bacterial community. In I my aim was to study the antibiotic resistance abundance and persistence in the sediments beneath the fi sh farms and the eff ects on the surrounding environments by sampling sediments in the close proximity to the farms.
Urban wastewater treatment plants are considered to be hotspots for antibiotic resistance and horizontal gene transfer. In II and III I investigated how diff erent urban wastewater treatment plants eliminate resistance genes from wastewaters and what is the amount of resistance genes still ending up in the environment. By screening hundreds of genes I wanted to deepen my understanding on the ARG dynamics in the UWTP and on the environmental eff ect of ARG release to the environment.
Antibiotic resistance genes are known to spread and transfer effi ciently in bacterial communities. In IV using metagenomic sequencing data my aim was to understand the global patterns of horizontal transfer of antibiotic resistance gene between diff erent environments and if biogeography or ecology aff ects the dispersal of these genes.
Aims of the Th esis
8
3. SUMMARY OF THE METHODS
3.1 Samples and data used in this study 3.1.1 Aquaculture
Sediment samples were collected in the summer time during 2006–2009 from aquaculture sites in Northern Baltic Sea in the Turku Archipelago, Finland and in August 2007 from Stockholm Archipelago, Sweden. Th e sampling locations and the sampling procedures are described in detail in I.
3.1.2 Urban wastewater treatment plants
Th e urban wastewater treatment plants were sampled in 2010–2011 for II and III. For II 24 h composite samples were collected from raw wastewater and fi nal effl uent waters from three UWTPs in Tartu, Estonia and Helsinki, Finland over a one-year period from December to December. For III the UWTP in Helsinki, Finland was sampled on four seasons over one year.
Water samples from raw wastewater and fi nal effl uents were 24 h composite samples. In addition dried sludge was studied on all seasons. Th e sediments near the fi nal effl uent discharge pipe were sampled once on Summer 2011. More detailed description can be found from II and III.
3.1.3 Metagenomes
Th e metagenomic data was collected from public databases (IMG, MG-RAST and CAMERA) and all metagenomes were assigned geographic location with GPS coordinates and habitat annotation based on the environmental metadata available. Samples from same environment and geographical location were pooled to represent that environment in that geographical location.
More detailed description of the data retrieval and processing is available in IV.
Summary of the Methods
9 Summary of the Methods
3.2 Methods used in this study
Method Study
Sediment sampling I, III
Wastewater sampling II, III
DNA extraction I, II, III
Primer design I
PCR optimization I
PCR I, II, III
Quantitative PCR optimization I, II
Quantitative PCR I, II, III
Bioavailibility measurements I Metagenomic data retrieval IV
Metagenome assembly IV
Metagenome gene calling IV
Functional gene annotation IV Antibiotic resistance gene annotation IV
Marker gene annotation IV
Statistical analysis II, III, IV
Network analysis IV
10
Results and Discussion
4. RESULTS AND DISCUSSION
To get a deeper understanding on the anthropogenic eff ect on antibiotic resistance persistence and abundance, in this thesis antibiotic resistance genes were quantifi ed in aquaculture and wastewater environments using qPCR and a high throughput qPCR assay. Aquaculture sites were sampled on several years and the eff ect of fi sh farming and antibiotic use on the resistance abundance and persistence was studied (I). Wastewaters were studied in diff erent urban wastewater treatment plants in Estonia and Finland (II). Th e UWTP in Helsinki, Finland was further monitored for one full year and the impact of wastewater release on Baltic Sea sediments was also quantifi ed with a qPCR array targeting almost 300 genes (III). Additionally, using publicly available metagenomes, I tested the famous Baas-Becking hypothesis ‘Everything is everywhere, but the environment selects.’ Th e dispersal and possible HGT of ARGs was assessed using network analysis (IV).
4.1 Aquaculture
Th e presence, quantity, dissemination and persistence of several tetracycline resistance genes were studied in four diff erent aquaculture sites in Finland and Sweden, all located in the Baltic Sea. Tetracycline resistance gene abundances were elevated in all aquaculture sites studied (I).
All seven tetracycline resistance genes were detected from aquaculture sites. No genes were detected in any of the control sites outside the farms, showing the local impact of the aquaculture on the sediment bacterial community (Figure 2). Th erefore aquaculture does not pose a direct threat on the surrounding environment on resistance gene spread. However resistance genes can transfer horizontally between bacteria from aquaculture, and commensal and pathogenic fi sh bacteria (Cabello, 2006), since antibiotic resistant bacteria and resistance genes oft en occur at aquaculture sites (Dang et al, 2007; Akinbowale et al, 2007; Nonaka et al, 2007; Miranda et al, 2003). Although my results did not show dissemination of the genes (I), persistent antibiotic resistance in the aquaculture sites might further move to the food chain and threaten human health.
Th e farmed fi sh are occasionally medicated with antibiotics. To address the possible selection pressure favoring tetracycline resistance in the sediments the tetracycline concentrations in the sediments were measured with HPLC and biosensor. Th e biosensor responds to several tetracyclines including tetracycline and oxytetracycline. Th e HPLC assay was confi gured for tetracycline and oxytetracycline. However, all samples were below the detection limit in the sediments (biosensor: 300 ng/g sediment wet weight for tetracycline, HPLC: 66 and 25 ng/g sediment wet weight for tetracycline and oxytetracycline, respectively) (I).
Tetracycline resistance genes were detected at aquaculture sites through 2006 – 2009, even though antibiotics were not found from the sediments. Th ese resistance genes persisted in the aquaculture sites without a clear selection pressure (I). Even low doses of antibiotics can select for antibiotic resistant organisms (Andersson & Hughes, 2012) and resistance genes can have low fi tness costs or even fi tness increase in the absence of selection (Enne et al, 2005; Luo et al, 2005). Th e persistence of the ARGs can also be explained by a constant infl ux from an external source. Possible sources can be the fi sh commensal and pathogenic bacteria (Cabello, 2006), wastewater or animal farming effl uents (Dang et al, 2008), fi sh hatchlings (Rhodes et al, 2000) or the fi sh feed (Seyfried et al, 2010; Kerry et al, 1995).
11 Figure 2. Presence and quantifi cation of seven tetracycline resistance genes in aquaculture sites and control sediments. A) Sediments collected from FIN1 farm site and the control sediments collected from 200 m intervals from the farm. B) FIN2 farm and Swedish farm sites and control sites. Th e mean copy numbers and standard deviations of tetM, tetC, tetA and tetH genes were calculated from three technical replicates and normalized with 16S rRNA gene copy numbers in the sediment community DNA. Figure adopted from I.
Results and Discussion
12
4.2 Urban wastewater treatment plants
In addition to the aquaculture antibiotic resistance genes were quantifi ed from urban wastewater treatment plants (II, III). In II three UWTP’s in Finland and Estonia were monitored for more than a year for extended spectrum β-lactamase genes (blactx-m-32,blaoxa-58 and blashv-34), sulphonamide resistance genes (sul1 and sul2) and tetracycline resistance genes (tetM and tetC) in infl uent and effl uent waters. In III an UWTP in Helsinki, Finland, also sampled in II, was monitored for one year for almost 300 resistance genes in the infl uent and effl uent waters and dried sludge. Th e impact of wastewater release on the environment was addressed by sampling
Figure 3. Quantifi cation of resistance genes in Infl uent and effl uent waters in three UWTP’s in Finland and Estonia. Gene copy numbers are normalized with 16S rRNA gene copy numbers. Th e results present the whole study period without seasonal comparison. Stars denote statistical diff erence between the copy numbers in infl uent and effl uent waters. *** at p<0.01, * 0.03 > p > 0.01. Figure adopted from II.
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blactx−m−32 blaoxa−58 blashv−34 sul1 sul2 tetC tetM
ARG per 16S rRNA gene
Flow IF EF Results and Discussion
13 sediments near the purifi ed wastewater discharge pipe and the resistance profi le was compared to the resistance profi le of control sediments without the impact of wastewater release (III).
Wastewater purifi cation reduced bacterial numbers and the quantifi ed antibiotic resistance genes from the infl uent to effl uent waters by several fold. However on the relative amounts when normalized with 16S rRNA gene there was no statistically signifi cant diff erence for most of the genes (Figure 3). Th e wastewater purifi cation was probably unspecifi c in removing antibiotic resistance genes and based mainly in the reduction of bacteria (Figure 3) (II, III). Antibiotic resistance is ubiquitous in sewage and also in fi nal effl uents released to the environment (II, III).
Th e Helsinki UWTP was the most effi cient in removing ARGs (Figure 3) (II). Th e resistance gene abundance decreased several fold from sewage to effl uent waters, and dried sludge, (III) and the antibiotic resistance profi les changed from raw infl uent to fi nal effl uents and dried sludge (Figure 4). Th e dried sludge was shown to be a major reservoir of resistance genes (III) that has also been seen in other UWTPs (Yang et al, 2014; Munir et al, 2011).
Only few genes accumulated in the sediments near the release site sediments (III). One of the enriched genes was related to clinical class 1 integrons, qacEΔ1 (Gillings et al, 2008; Gaze et al, 2011; Wellington et al, 2013). Clinical class 1 integrons have been proposed as a marker gene for anthropogenic pollution in the environment (Gillings et al, 2015), and my results support that. However, wastewater release did not have a big impact on the resistance gene abundance in the sediment bacterial community (III).
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Dried sludge Final effluent Raw influent
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Summer Autumn Winter Spring Sediment
Figure 4. Ordination plot from the antibiotic resistance gene (ARG) abundance profi les showing the clustering of diff erent sample types and the change from raw infl ow to the fi nal effl uents and dried sludge in the Helsinki UWTP. Th e release site sediments did not diff er substantially from the control sediments based on the ARG profi le. ARG results were normalized to relative abundances with 16S rRNA gene copy numbers. Bray-Curtis dissimilarity index was used in constructing the MDS plot.
Ellipses were drawn with 95% confi dence.
Results and Discussion
14
4.3 Metagenomes
Sequence similarity networks (SSN) were built from 97 sampled environments representing 339 metagenomes. Th e networks showed that ecology is a key factor in determining the clustering of metagenomes. Ecology seems to strongly aff ect the metagenome gene composition. Th e network analysis revealed that inland waters can connect otherwise geographically separate microbial communities (IV). Th is is in agreement with speculations about the role of aquatic environments in spreading antibiotic resistance determinants (Baquero et al, 2008). Our results also support that the Baas-Becking hypothesis, everything is everywhere, but the environment selects, applies to genes in addition to microbes for which it was originally formulated (IV).
4.3.1 Horizontal gene transfer of anƟ bioƟ c resistance genes in the environment
Antibiotic resistance genes transfer effi ciently in bacterial communities and can cross broad taxonomical boarders. To understand better the transfer of resistance genes and forces aff ecting their dispersal, putative horizontal gene transfer events including antibiotic resistance genes were studied with network analysis using publicly available metagenomes. Th e resistance genes were annotated against the Antibiotic Resistance Gene Database (Liu & Pop, 2009) and putative horizontal gene transfer events were detected by identifying blocks of nearly identical of DNA in otherwise distantly related sequences (IV). Networks were constructed and analyzed as described in IV. Antibiotic resistance genes were exceptions compared to other genes that formed ecologically homogenous clusters (IV). Tetracycline gene (tet34) was shared between inland water and host related metagenomes and made tight clusters in the network showing putative horizontal transfers between these two habitats (Figure 5). Th e same phenomenon was observed with chloramphenicol resistance gene found from soil and host related metagenomes.
Th ese two examples show that antibiotic resistance genes can overcome geographical and taxonomical barriers and spread between distinct environments and phylogenetically unrelated bacteria, possibly through few or several HGT events (Halary et al, 2010; Smillie et al, 2011). Th is might be one of the reasons for the remarkable dissemination of antibiotic resistance genes in diff erent environments.
Figure 5. Examples of putative cross-habitat HGT events among nodes embedding A) tetracycline resistance determinants and retrieved from inland waters (blue nodes) and host (red nodes) and B) chloramphenicol resistance in host (red) and soil (yellow) derived samples.
CAM140519@C136_env_aqua
I M G 9 5 5 8 2 5 @ 5 1 6 _ h o s t _ o t h e r
I M G 4 5 4 8 3 @ 1 0 7 3 9 _ e n v _ a q u a CAM142550@C136_env_aqua I M G 6 3 2 9 7 @ 1 0 7 3 9 _ e n v _ a q u a
CAM137368@C136_env_aqua CAM143101@C136_env_aqua CAM137668@C136_env_aqua
I M G 6 5 9 8 6 @ 1 0 7 3 9 _ e n v _ a q u aCAM137226@C136_env_aqua I M G 6 0 5 6 8 @ 1 0 7 3 9 _ e n v _ a q u a I M G 9 5 0 8 3 2 @ 5 1 5 _ h o s t _ o t h e r
I M G 9 6 6 4 6 4 @ 5 1 8 _ h o s t _ o t h e r I M G 6 7 4 0 6 @ 1 0 7 3 9 _ e n v _ a q u a
I M G 5 8 0 4 2 @ 1 0 7 3 9 _ e n v _ a q u a I M G 9 5 2 7 1 3 @ 5 1 5 _ h o s t _ o t h e r CAM139269@C136_env_aqua
I M G 4 4 5 3 0 @ 1 0 7 3 9 _ e n v _ a q u aCAM138339@C136_env_aqua I M G 6 3 5 9 6 @ 1 0 7 3 9 _ e n v _ a q u a
CAM142762@C136_env_aqua
I M G 9 6 4 6 0 2 @ 5 1 8 _ h o s t _ o t h e r I M G 9 4 9 3 9 3 @ 5 1 4 _ h o s t _ o t h e r I M G 6 3 5 9 5 @ 1 0 7 3 9 _ e n v _ a q u a
CAM137803@C136_env_aqua I M G 9 5 4 5 8 7 @ 5 1 5 _ h o s t _ o t h e r I M G 5 9 4 3 3 @ 1 0 7 3 9 _ e n v _ a q u a
I M G 9 5 3 7 5 1 @ 5 1 5 _ h o s t _ o t h e r I M G 6 8 1 0 4 @ 1 0 7 3 9 _ e n v _ a q u a
I M G 5 9 1 4 0 @ 1 0 7 3 9 _ e n v _ a q u a
tet34
I M G 1 0 0 0 5 9 5 @ 6 2 5 _ h o s t _ o t h e r I M G 9 7 2 8 6 9 @ 5 2 2 _ h o s t _ o t h e r I M G 9 7 1 2 6 2 @ 5 2 1 _ h o s t _ o t h e r
I M G 1 0 0 5 1 8 7 @ 7 6 5 _ h o s t _ o t h e r I M G 1 0 0 2 2 3 5 @ 7 6 5 _ h o s t _ o t h e r I M G 8 4 6 4 1 7 @ 2 2 8 5 3 _ e n v _ t e r r I M G 8 4 2 2 3 8 @ 2 2 8 5 3 _ e n v _ t e r r I M G 1 3 9 8 9 3 @ 1 1 6 5 4 _ h o s t _ o t h e r
I M G 1 0 0 2 5 4 6 @ 7 6 5 _ h o s t _ o t h e r I M G 1 0 0 0 1 1 5 @ 6 2 5 _ h o s t _ o t h e r I M G 4 9 8 4 7 3 @ 1 7 1 1 _ h o s t _ o t h e r
I M G 1 0 0 2 1 6 3 @ 7 6 5 _ h o s t _ o t h e rI M G 8 4 9 7 0 4 @ 2 2 8 5 3 _ e n v _ t e r r
I M G 1 0 0 2 2 0 8 @ 7 6 5 _ h o s t _ o t h e r I M G 9 7 2 4 3 9 @ 5 2 2 _ h o s t _ o t h e r
I M G 1 0 0 2 2 4 8 @ 7 6 5 _ h o s t _ o t h e r
I M G 8 4 1 5 0 7 @ 2 2 8 5 3 _ e n v _ t e r r
I M G 1 0 0 6 5 1 6 @ 7 6 5 _ h o s t _ o t h e r I M G 1 3 7 3 5 0 @ 1 1 6 5 4 _ h o s t _ o t h e r I M G 8 4 9 7 5 1 @ 2 2 8 5 3 _ e n v _ t e r r
I M G 8 4 9 8 1 1 @ 2 2 8 5 3 _ e n v _ t e r r I M G 4 9 8 3 7 1 @ 1 7 1 1 _ h o s t _ o t h e r I M G 1 0 0 5 7 3 7 @ 7 6 5 _ h o s t _ o t h e r
I M G 9 9 3 0 0 9 @ 6 2 5 _ h o s t _ o t h e r I M G 8 4 6 4 4 9 @ 2 2 8 5 3 _ e n v _ t e r r
I M G 1 0 0 2 1 2 5 @ 7 6 5 _ h o s t _ o t h e r I M G 1 0 0 6 1 1 7 @ 7 6 5 _ h o s t _ o t h e r I M G 8 4 2 2 3 7 @ 2 2 8 5 3 _ e n v _ t e r r
I M G 4 9 9 7 2 0 @ 1 7 1 1 _ h o s t _ o t h e r I M G 4 9 8 3 8 6 @ 1 7 1 1 _ h o s t _ o t h e r
I M G 9 7 3 0 3 6 @ 5 2 2 _ h o s t _ o t h e r I M G 1 0 0 2 1 0 5 @ 7 6 5 _ h o s t _ o t h e r
I M G 9 9 6 3 3 1 @ 6 2 5 _ h o s t _ o t h e r
chloramphenicol resistance
A B
Results and Discussion
15
5. CONCLUSIONS AND FUTURE PROSPECTS
In this work antibiotic resistance gene abundance, persistence and faith in the environment was studied using molecular methods and meta-analysis of metagenomes. Using two example environments I show that antibiotic resistance genes are abundant in environments with anthropogenic impact and that the genes can persist in the environment without a clear selection pressure. On the other hand UWTPs turned out to be eff ective in removing antibiotic resistance pollution from wastewaters. Th e impact of wastewater release on the sediments near the discharge sites is limited and aquacultures impact the antibiotic resistance in the sediments only locally. Using metagenomic data available in the public databases I show the diff erent dispersal patterns of antibiotic resistance genes in the environment, possibly explaining the huge dispersal of ARGs in bacterial communities in both the environment and the clinic.
I showed that high-throughput qPCR assay is a useful tool to describe and study the antibiotic resistance profi le in UWTP samples and to compare the fate of many genes at the same time. Th e use of only few genes gives only a limited view of the antibiotic resistance dynamics, especially in environments with high ARG diversity. High-throughput qPCR assays can be powerful in describing the resistome and to identify key marker genes that should be used for monitoring anthropogenic pollution and the emergence of antibiotic resistance in diff erent environments. However, even with a high-throughput qPCR array with hundreds of genes, the need for prior knowledge limits its use to known genes.
Meta-analysis of metagenomic sequence data was proven to be a useful way of studying ecological questions in larger, even global scale. Metagenomic sequence data from diverse environments is available to all researchers for free and combined with cloud computing, does not need big investments. Th e little amount of metadata and the diverse annotation schemes used by diff erent projects and databases makes the full scale utilization of the data still a challenge, although these issues are addressed.
In this work I have touched the surface of the antibiotic resistance problem but still many questions remain. Although the UWTPs remove the resistance genes eff ectively, there is a huge variety of microbes, resistance genes and mobile genetic elements in the sewage. Th e questions remaining include; are the genes transferred in the sewage between bacteria, what are the main mechanisms aff ecting the rate of transfer, which bacteria are carrying the resistance genes and what are the risks of resistance gene release to the environment for human health? In the forthcoming studies tools, such as high-throughput qPCR arrays and metagenomics, could be used in unraveling these questions.
Antibiotic resistance genes are considered an environmental pollution and their dissemination should be monitored. Th e methods I have used and the results presented in this thesis can be used for monitoring the reservoirs of antibiotic resistance and to predict the emergence of new resistance determinants.
Conclusions and Future Prospects
16
6. ACKNOWLEDGEMENTS
Th is work was carried out at the Division of Microbiology and Biotechnology, Department of Food and Environmental Sciences, University of Helsinki. Th e work was supported by EnSTe graduate school, MBDP graduate school and the Academy of Finland.
I wish to thank my supervisors Marko Virta and Manu Tamminen. I have learned so much from you both. I’m grateful that you have had trust on me and gave me the freedom to do what I’m interested on. Working in the MMX-group all these years has been interesting and so much fun.
Christina, without you none of this could have ever been printed on paper. I want to thank you for the endless hours you have spent improving my texts and the fruitful discussions at the offi ce.
Our lovely offi ce wouldn’t be the same without you.
Special thanks goes to my friends at work. Th e time spent with you at our coffee table has made every day so much easier. Already waiting for the next time to go out with you all.
I want to thank my custos Kaarina Sivonen. You pushed me forward to fi nish my thesis. And now I can say that I’m extremely grateful.
I wish to acknowledge my reviewers Dr. Fiona Walsh and Dr. Jenni Hultman for being kind with my thesis. Your comments really improved the quality of this work.
I also want to express my gratitude to all my co-authors, the people hosting me at their labs, members of MMX-group and the personnel in the division, especially Ritu and Mika, you were always willing to help whatever the problem.
Last but not least, a special thanks to my family and friends. Without you and the life outside of the university I wouldn’t have made it.
Acknowledgements
17
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