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Antibiotic Resistant Bacteria, Antibiotic Resistance Genes and Antibiotic Resistant Bacteria, Antibiotic Resistance Genes and
Potential Drivers in the Aquatic Environments Potential Drivers in the Aquatic Environments
Shuo Shen
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ANTIBIOTIC RESISTANT BACTERIA, ANTIBIOTIC RESISTANCE GENES
AND POTENTIAL DRIVERS IN THE AQUATIC ENVIRONMENTS
by
Shuo Shen
A Dissertation
Submitted to the Graduate School,
the College of Arts and Sciences
and the School of Ocean Science and Engineering
at The University of Southern Mississippi
in Partial Fulfillment of the Requirements
for the Degree of Doctor of Philosophy
Approved by:
Dr. Wei Wu, Committee Chair
Dr. D.Jay Grimes
Dr. Eric Saillant
Dr. Robert J. Griffitt
____________________ ____________________ ____________________
Dr. Wei Wu
Committee Chair
Dr. Robert J. Griffitt
Director of School
Dr. Karen S. Coats
Dean of the Graduate School
May 2020
COPYRIGHT BY
Shuo Shen
2020
Published by the Graduate School
ii
ABSTRACT
As antibiotic resistance genes in aquatic environment have been increasing across
the world, affecting water quality and public health, many studies documented
concentrations of antibiotic resistance genes and some studies discussed their potential
drivers. However, systematic and quantitative reviews that link antibiotic resistance genes
(ARGs) to anthropogenic and environmental factors are limited. Nevertheless, this
information will be important for developing regulation policy on controlling antibiotic
use and therefore reducing potential risks to antibiotic resistance. I conducted meta-
analysis of ARGs concentration at a global scale using Bayesian inference to explore
climatic and socio-economic factors as drivers. I found local-scale climatic variables
played a more important role in predicting concentrations of ARGs than the country-scale
socio-economic variables. More specifically, the concentrations of ARGs increased with
precipitation, and there existed an optimal temperature where the concentrations of ARGs
reached a maximum. Then I initialized the study on antibiotic resistance bacteria in
bottlenose dolphins Tursiops truncatus in relation to oil contamination following the
2010 BP oil spill in the northern Gulf of Mexico. Bacterial communities and antibiotic
resistance prevalence were compared between Barataria Bay (BB) and Sarasota Bay (SB)
by applying the rarefaction curve method, and linear mixed models. Finally, I collected
surface water samples at upstream, outflow, and downstream of a WWTP at lower
Pascagoula River in southeastern Mississippi from February to November in 2016. I then
tested and quantified antibiotic resistance of bacteria in the water samples to three
commonly used antibiotics: sulfamethazine, tetracycline and ciprofloxacin using both
cultural and molecular methods. I was able to detect and quantify three different ARGs
iii
commonly found in the natural environment: Suf1, Suf2, and Int1. The lowest relative
concentrations of all three ARGs occurred in the summer, and the highest relative
concentrations occurred in February or April.
The findings showed that anthropogenic pollutions increased ARB or ARGs in the
aquatic systems, meanwhile climatic factors played an important role in affecting
antibiotic resistance. The impact of temperature on antibiotic resistance shows large
spatial variability at the country and local scales. Further research is required in order to
understand how antibiotic resistance will change under global warming.
iv
ACKNOWLEDGMENTS
This work was funded by the Costal Impact Assistance Program (CIAP) MS.R.798
v
DEDICATION
Thanks to USM Gulf Coast Research Laboratory (GCRL) for accommodation.
Thanks to Dr. Wei Wu, Dr. Jay Grimes, Dr. Eric Saillant and Dr. Robert J.Griffitt for
mentoring my study and research. Thanks to everyone who help me before. And last, big
thanks to support of my family and all the GCRL beautiful smiley faces.
vi
TABLE OF CONTENTS
ABSTRACT ........................................................................................................................ ii
ACKNOWLEDGMENTS ................................................................................................. iv
DEDICATION .................................................................................................................... v
LIST OF TABLES .............................................................................................................. x
LIST OF ILLUSTRATIONS ............................................................................................. xi
CHAPTER I - INTRODUCTION ...................................................................................... 1
1.1 Introduction ............................................................................................................... 1
CHAPTER II – META-ANALYSIS OFANTIBIOTIC RESISTANCE GENES IN
AQUATIC ENVIRONMENTS .......................................................................................... 7
2.1 Introduction ............................................................................................................... 7
2.2 Methods..................................................................................................................... 9
2.2.1 Collecting literature ........................................................................................... 9
2.2.2 Collect response variable and covariates ......................................................... 11
2.2.3 Multi-level Bayesian Models ........................................................................... 13
2.2.4 Parameter and model evaluation ...................................................................... 16
2.3 Results ..................................................................................................................... 16
2.4 Discussion ............................................................................................................... 20
2.5 Conclusion .............................................................................................................. 22
vii
CHAPTER III – COMMUNITY COMPOSITION AND ANTIBIOTIC RESISTANCE
OF BACTERIA IN BOTTLENOSE DOLPHINS TURIOPS TRUNCATUS -
POTENTIAL IMPACT OF 2010 BP OIL SPILL ............................................................ 24
3.1 Introduction ............................................................................................................. 24
3.2 Methods................................................................................................................... 27
3.2.1 Study Areas ...................................................................................................... 28
3.2.2 Sample Collection ............................................................................................ 28
3.2.3 Bacteria Isolation and Identification ................................................................ 29
3.2.3.1 Purification of bacteria isolates ................................................................. 29
3.2.3.2 Molecular identification ............................................................................ 30
3.2.4 Susceptibility Test for antibiotics .................................................................... 31
3.2.5 Data Analysis ................................................................................................... 32
3.3 Results ..................................................................................................................... 36
3.3.1 Culture Isolates and Bacteria Species .............................................................. 36
3.3.2 Microbial community analysis ......................................................................... 38
3.3.3 Antibiotic Resistance Profile ........................................................................... 41
3.3.4 Generalized linear mixed model ...................................................................... 45
3.4 Discussion ............................................................................................................... 48
3.5 Conclusion .............................................................................................................. 55
viii
CHAPTER IV – ANTIBIOTIC RESISTANCE IN THE LOWER PASCAGOULA
RIVER IN THE NOTHERN GULF OF MEXICO .......................................................... 57
4.1 Introduction ............................................................................................................. 57
4.2 Methods................................................................................................................... 61
4.2.1 Study Area ....................................................................................................... 61
4.2.2 Field Sampling ................................................................................................. 62
4.2.3 Culture-based method using antibiotic-selective media .................................. 63
4.2.4 DNA Extraction and purification ..................................................................... 64
4.2.5 PCR assays for detection of resistance genes .................................................. 64
4.2.6 Real-time quantitative PCR (qPCR) to quantify ARGs ................................... 65
4.2.7 Q-PCR standard curves and quantification ...................................................... 66
4.2.8 Data analysis .................................................................................................... 68
4.3 Results ..................................................................................................................... 68
4.3.1 Culture-based methods..................................................................................... 69
4.3.2 Regular PCR test .............................................................................................. 69
4.3.3 qPCR test ......................................................................................................... 69
4.3.4 Model Analysis Results ................................................................................... 71
4.4 Discussion ............................................................................................................... 72
4.5 Conclusion .............................................................................................................. 76
CHAPTER V – CONCLUSION ....................................................................................... 77
ix
APPENDIX A --Tables ..................................................................................................... 79
REFERENCES ................................................................................................................. 80
x
LIST OF TABLES
Table 2.1 Studies in each country ..................................................................................... 12
Table 2.2 Temperature (oF) where concentrations of ARGs reach maximum ................. 20
Table 3.1 Eleven Antibiotics used in the current study and their characteristics ............. 32
Table 3.2 Asymptotic diversity estimates with related statistics ...................................... 39
Table 3.3 Antibiotic resistance of most common bacteria species cultured from dolphins
and water samples in BB (n=194) ..................................................................................... 43
Table 3.4 Antibiotic resistance of most common bacteria species cultured from dolphins
and water samples in the SB (n=100) ............................................................................... 44
Table 3.5 Profile of multi-drug resistance of bacteria ...................................................... 44
Table 3.6 Sets of linear mixed model candidates used to predict antibiotic resistance after
multicollinearity check and random effect optimization. ................................................. 47
Table 3.7 The results of mixed-effects model on the probability of resistance to each of
the nine antibiotics tested .................................................................................................. 48
Table 4.1 PCR primers and resistance genes .................................................................... 66
Table 4.2 Best model for Sul1, Sul2 and Int1 ................................................................... 72
Table A.1 Model selection ................................................................................................ 79
xi
LIST OF ILLUSTRATIONS
Figure 1.1 Spatial scopes of my research ............................................................................ 5
Figure 2.1 Selection Process of the studies (Bhaumik and Lassi, 2017) .......................... 10
Figure 2.2 Study sites form the papers used for the meta-analysis. .................................. 11
Figure 2.3 Structure of the hierarchical Bayesian model used to predict ARGs
concentration. .................................................................................................................... 13
Figure 2.4 95% credible intervals (CI) for the posteriors of the parameters. ................... 18
Figure 2.5 95% credible intervals (CI) of posterior predicted concentrations of ARGs vs.
the observations. ............................................................................................................... 19
Figure 3.1 Sampling sites .................................................................................................. 29
Figure 3.2 Heatmaps of relative abundance ...................................................................... 38
Figure 3.3 Rarefaction curve............................................................................................. 41
Figure 3.4 Frequency of resistance to individual antibiotics in samples from Sarasota Bay
and Barataria Bay. ............................................................................................................. 45
Figure 4.1 Sampling sites along lower Pascagoula River (Green Pin: Sampling Sites, Red
Pin: WWTP, Orange Star: Ingalls Shipbuilding) .............................................................. 63
Figure 4.2 Gene copy number Sul1 normalized to 16SrDNA gene copy numbers. ......... 70
Figure 4.3 Gene copy number Sul2 normalized to 16SrDNA gene copy numbers .......... 71
Figure 4.4 Gene copy number Int1 normalized to 16SrDNA gene copy numbers ........... 71
1
CHAPTER I - INTRODUCTION
1.1 Introduction
Antibiotics are substances that inhibit or kill bacteria. They could be synthesized
chemically or naturally occurring. Antibiotics are prevalent in the natural environment,
being part of self-defense systems of the living tissues against bacteria. During the long
pre-synthesized-antibiotic era, even though many cultures realized that certain
combinations of plants, mold or other life mixtures could cure wounds, fever or
infections, they did not know the active ingredients that were responsible for the cure
(Davies and Davies 2010; Gould 2016). Meanwhile, people couldn’t explain failed
treatments. Now we understand failures are mainly attributed to antibiotic resistance in
addition to individual variability. Antibiotic resistance is defined as the ability of bacteria
to resist antibiotic treatment. It is not surprising that bacteria could develop mechanisms
to protect themselves from being eliminated from their ecological niches after 3-5 billion
years evolution. Antibiotics are another pressure or selective factor (Rodríguez-Rojas et
al. 2013).
The invention and widespread use of antibiotics mark a significant accomplishment
in the history of infectious disease therapy and public health. However, the overuse of
antibiotics has increased the antibiotic resistance worldwide. It is not only a problem in
clinical settings but also a widespread emerging issue in the environment (Davies and
Davies, 2010). People have suffered from resistant microbes since the very beginning of
the synthetic antibiotic era, around the late 1930s (Davies and Davies, 2010). Since then,
the resistance of the first widely used antimicrobials, the sulfonamides, has plagued the
pharmaceutical usage for a long time (Sköld, 2000). In 1928, Sir Alexander Fleming
2
discovered penicillin and only after 20 years, penicillin-resistant staphylococcus became
a global pandemic.
As antibiotic resistance has become a more and more serious health problem in
almost every country in the world, the World Health Organization (WHO) first
announced antibiotic resistance as one of the biggest threats to global health, food
security, and development (WHO, 2017a) in 2015. More and more patients are killed by
infectious diseases due to more frequent failures of antibiotics attributed to antibiotic
resistance developed in bacteria caused by widespread use of antibiotics. In the US, CDC
in 2013 first reported that there were at least 2 million illnesses 23,000 deaths were
caused by ARB a year(CDC, 2013). The numbers continued to increase. In 2019, the
second report on antibiotic resistance threat estimated that there were 2.8 million illnesses
and 35,900 deaths caused by ARB each year (CDC, 2019). So we rely on development
and production of new antibiotics by the pharmaceutical industry, however, the recent
new drug production has slowed down due to economic and regulatory obstacles (Bartlett
et al., 2013). By the year 2019, after Novartis abandoned the research and development in
new antibiotics, there were only four pharmaceutical companies that still produce
antibiotics, including Merck and Pfizer in US, Roche in Swiss and GlaxoSmithKline in
UK (Singer et al., 2020). To make things worse, antibiotic research in academia has also
been scaled back because of the cuts of research funding (Piddock, 2012). If this trend
persists, there may never be an effective antibiotic to fight against infectious bacteria in
the future. The WHO report in 2017 confirmed the condition of a serious lack of new
antibiotics in development (WHO, 2017b).
3
After the warning on antibiotic resistance from WHO, countries all over the world
started to make new laws and build stewardship to manage the use of antibiotics to
control the widespread antibiotic resistance (Goff et al., 2017; Karanika et al., 2016; Ma
et al., 2016; Schuts et al., 2016). Even though there were some reports about the
efficiency of the global stewardship, antibiotic resistance keeps spreading all over the
world and didn’t slow down. Even the places where there are no year-long residents of
humans are not free of antibiotic resistance. A study reported antibiotic resistance gene
tet(M) from fecal of penguins in Antarctica (Rahman et al., 2015). Because of the
widespread and persistent resistance, we gave the multi-antibiotic-resistant bacteria a
nickname: Superbugs. To cure the infections caused by superbugs, colistin is the
strongest drug in the current time. However, in early 2016, a researcher in China reported
the emergence of plasmid-mediated colistin resistance MCR-1 in animals and humans
(Liu et al., 2016). Soon after that, studies about the MCR-1 were reported across the
world, including Italy (Pilato et al., 2016), China (Du et al., 2016), Denmark (Hasman et
al., 2015) and etc. The number of reports on MCR and its mutations is continuously
increasing. Just like MCR related superbugs, all kinds of superbugs might have already
spread widely in the world. In this case, incorrect usage of drugs will not cure the
infection but instead cause higher mortality rate. To conquer this antibiotic resistance,
researchers have developed different new methods, for example, using bacteriophage to
kill resistant bacteria (Edgar et al., 2012; Yosef et al., 2015), utilizing combinations of
antibiotic-non-antibiotics (Schneider et al., 2017), and creating a new combination of old
insusceptible antibiotics (Sy et al., 2017). The new methods are good and effective in
some conditions, however, none of the methods can promise no antibiotic resistance in
4
the future, and some of the new methods are not clinically proven outside the research
lab.
The mechanism and acquisition of antibiotic resistance are complex. The
mechanisms generally include 1) acquisition of resistance genes encoding enzymes, 2)
loss of enzymes involved in drug activation 3) reduction in permeability or uptake by
bacteria 4) acquisition of efflux pumps by bacteria. The most common pathways of
resistance acquisition are the uptakes of small DNA units, i.e., plasmids, transposons
integrons and tandem repeats (Blair et al., 2015a). Antibiotic resistance genes (ARGs)
help code antibiotic resistance. Even though holding ARGs don’t guarantee antibiotic
resistance, they provide a mechanism for bacteria to be able to resist antibiotics.
Therefore, we not only test pathogens’ resistance to drugs but also analyze their ARGs.
ARG analyses had generally been conducted in clinical trials until recently when people
started to realize the importance of ARGs in the natural environment and their potential
hazards to humans. In 2006, Pruden et al. firstly announced the ARGs as an
environmental contaminant (Pruden et al., 2006). Abundant ARGs were discovered in the
natural environmental reservoirs, even more than in hospitals. Since 2006, there have
been more and more studies on ARGs in the natural environment (Barcelos et al., 2018;
Bueno et al., 2018). Many of these studies were related to ARGs in the water
environment. Water bodies cover more than 70% of the world's surface, and all the
continents were connected by water. ARGs and ARB can reach any corner of the world
through the aquatic environment, including the aforementioned detection of the tetM
gene in Antarctic penguins (Rahman et al., 2015), the drug-resistant bacteria isolated in
Antarctic freshwater (Jara et al., 2020), and the detection of superbug resistance genes
5
blaNDM-1 in Arctic Circle (McCann et al., 2019). ARGs and ARBs could be enriched in
animals, plants, and sediments during the retransmission in the aquatic environment, and
spread with human transportation.
The global antibiotic resistance increases fast driven by different factors. Some
studies show that global warming increased antibiotic resistance (MacFadden et al.,
2018) while the temperature’s effect on antibiotic resistance shows large spatial
variability (Bengtsson-Palme et al., 2018). Other studies show that socioeconomic factors
contribute to global antibiotic resistance (Collignon et al., 2018). At the same time,
pollution of the water environment also led to increased concentrations of ARGs and
ARB (Al-Badaii and Shuhaimi-Othman, 2015).
Figure 1.1 Spatial scopes of my research
In my dissertation, I investigated the potential effects of the anthropogenic and
environmental factors on antibiotic-resistant bacteria (ARB) and ARGs in the aquatic
environment. My research covers global to local scales, including global-scale meta-
6
analysis of ARGs close to wastewater treatment plants or aquaculture facilities (Chapter
2), gulf-scale analysis of ARB and ARR in the bottlenose dolphins impacted by 2010 BP
Oil Spill (Chapter 3), and local-scale ARGs affected by a wastewater treatment plants
(Chapter 4).
My overall objective is to investigate the impact of water pollution on antibiotic
resistance and the potential socioeconomic and environmental drivers for its increase.
More specifically, I aim to figure out the relation between ARGs concentration and
climatic and anthropogenic factors. Then figure the effect of pollutions on the ARGs and
ARB in aquatic environments and marine mammals.
I have three chapters to address the objectives of my research.
1) In Chapter 2, I conducted meta-analysis on relative concentrations of ARGs with
uncertainties quantified using multi-level modeling in Bayesian framework across the
world. I quantified the relation between ARGs in aquatic environments and climatic
factors at the regional scale and socioeconomic factors at the country scale.
2) To zoom into the northern Gulf of Mexico, I investigated the potential impact of 2010
BP Oil Spill on antibiotic resistance of bacteria isolated from bottlenose dolphins using
the data from two contrasting bays: Barataria Bay, LA, contaminated by the oil spill,
vs. Sarasota Bay, FL. free of contamination of the oil spill.
3) At the local scale, I investigated antibiotic resistance genes in a coastal river - Lower
Pascagoula River and evaluated the influence of wastewater treatment plant (WWTP),
combined with the environmental factors on the ARGs in the river.
7
CHAPTER II – META-ANALYSIS OFANTIBIOTIC RESISTANCE GENES IN
AQUATIC ENVIRONMENTS
2.1 Introduction
Water covers 70% of the earth’s surface, and most of the water bodies are
connected. The network of water bodies potentially provides a medium to facilitate the
spread of antibiotic resistance genes (ARGs) (Baquero et al., 2008). The prevalence and
the concentration of ARGs in the aquatic environment show an increasing trend and can
be detected in many places over the last decades, including the Arctic and Antarctic
regions (Machado and Bordalo, 2014; Martinez and Olivares, 2012; Pruden et al., 2006;
Singleton et al., 2009; Zhang et al., 2015). They may be indigenous ARGs from bacteria
in the water (Jacobs and Chenia, 2007) or come from wastewater from hospitals, animal
production process, aquaculture (Liu et al., 2007), sewage water treatment plants and etc.
As ARGs are the foundation for antibiotic resistance that is a major concern of public
health, it is necessary to build global stewardship to reduce ARGs from anthropogenic
sources.
In order to investigate the widespread presence of ARGs in aquatic environments and
the effectiveness of stewardship to reduce ARGs, scientists have developed and applied
different molecular methods to study the concentrations of ARGs and local resistance
profiles, including DNA hybridization, traditional PCR, quantitative PCR, and
metagenomics. These methods are more effective than the culture-based methods, since
culturable fraction is only 1% of the total bacterial community in the environments and a
lot of environmental bacteria are viable but non-culturable (VBNC) (Colwell and Grimes,
8
2000; Pazda et al., 2019), and molecular methods could extract the total DNA from
bacterial community and keep more resistance information.
To control the increase and spread of ARGs and build effective stewardship, it is
important to understand the contributing factors. Previous studies show social-economic
variables such as population, education, gross domestic product, and climatic variables
such as temperature affect antibiotic resistance prevalence (Collignon et al., 2018;
Collignon and Beggs, 2019; Malik and Bhattacharyya, 2019; Vikesland et al., 2019).
Additionally, these impacts show spatial variability (McGough’s paper for European
countries). These quantitative analyses on antibiotic resistance most focus on a particular
region. With more and more research on antibiotic resistance available across the world
(Bueno et al., 2018; Tripathi and Cytryn, 2017), it is possible to conduct synthesis at a
global scale, the scale relevant to the problem of antibiotic resistance.
Meta-analysis is a method that comprehensively and quantitatively synthesizes the
results of a variety of studies testing the same or similar hypotheses (Stanley, 2001). It
was first proposed as a research synthesis method in 1976 (Glass, 1976), and since then,
researchers have developed and applied the method in different fields of research,
including medical research (Bell et al., 2014), ecology (Siefert et al., 2015),
environmental economics (M. Brander et al., 2012), and social research (Durlak et al.,
2011) etc.
In this study, I conducted meta-analysis to quantify the relation of concentrations of
ARGs in aquatic systems and social-ecological variables. Previous meta-analysis only
used culture-based resistance ratio of certain pathogens as response variables, and my
study is the first one to analyze concentrations of ARGs as a response variable at the
9
global scale thanks to more and more data of ARGs available across the world.
Considering multi-scale data involved, and the need to quantify the variance of the effect
of potential drivers on ARGs at a global scale, I applied multi-level models in Bayesian
statistics (Gelman and Hill, 2006). Bayesian inference is a statistical inference method
using Baye’s theorem to calculate the probability for a hypothesis (Box and Tiao, 1992).
Hierarchical Bayesian (HB) modeling is an approach that can exploit various sources of
information, can accommodate unknown effects, and can infer large numbers of potential
variables and parameters describing complex relationships (Clark, 2005). HB models
have been widely used in environmental area (Bal et al., 2014; Thuiller et al., 2008; Wu
et al., 2012, 2010).
I tested the following hypothesis:
H1: Both socio-economic and climatic variables affect the concentration of ARGs in
aquatic systems.
2.2 Methods
I applied Bayesian meta-regression models to synthesize the ARG profile (relative
concentration which normalized to 16sRNA gene concentration) as a function of climatic
variables and socio-economic variables at a global scale. I particularly focused on the
concentrations of ARGs in water bodies that were affected by wastewater treatment
plants or aquaculture facilities as both are the main contributors to ARGs in water and
most papers I have found were related to these two facilities.
2.2.1 Collecting literature
I collected peer-reviewed publications on the antibiotic resistance in aquatic
environmental at a global scale using Web of Science. The keywords I used for the search
10
were Antibiotic resistance/resistant, antibiotic resistance/resistant gene, drug
resistance/resistant, antimicrobial resistance/resistant, water, river, lake, ocean, coastal,
lagoon, estuary, aquatic. I chose studies that met the following criteria:
1) The study investigated antibiotic resistance genes in the aquatic environment, including
lagoons, coastal waters, rivers, lakes, and ocean. The studies in clinical settings and
from soil samples or other ecosystems were excluded.
2) The research should be conducted between 1994 and 2017 as there exists a
comprehensive review (not meta-analysis) on this topic up to 1994. The procedure is
shown in Fig. 2-1.
Figure 2.1 Selection Process of the studies (Bhaumik and Lassi, 2017)
11
2.2.2 Collect response variable and covariates
The papers included in the meta-regression analysis cover 149 locations in twelve
countries (Figure 2.2 & Table 2.1). They are related to wastewater treatment plants
(WWTP) or aquaculture facilities as these are where most papers focused on. We
extracted the relative concentrations of antibiotic resistance genes (Sul1 and TetW) from
the publication collections as the response variable. The socio-economic and climatic
factors collected from the publications or extracted from the database available in the
federal and state data portals (NOAA, USGS, and US census) were used as covariates.
The covariates included local air temperature, precipitation, and country-scale population
and GDP per capita.
Figure 2.2 Study sites form the papers used for the meta-analysis.
12
Table 2.1 Studies in each country
County Study Numbers
Bolivia 2
Canada 4
China 85
Cuba 18
Estonia 1
India 6
Spain 4
Sweden 1
Switzerland 2
UK 2
US 22
Romania 2
Temperature and rainfall data came from The World Bank Group. These data are
derived from NOAA’s Global Historical Climatology Network (GHCN). The
temperature and rainfall data are annual averages over 20 years from 1920 to 2017. Both
temperature and rainfall data were downloaded using the R package ‘rWBclimate’ (Hart,
2014).
Population data came from the United Nation’s World Population Prospects (WPP)
global dataset. WPP data have population demographics for each country, region (e.g.
Eastern Asia), and continent. I chose to use population data of each country in year 2010
because that was the middle of the temporal range covered by the papers I used for the
meta-analysis. These data were downloaded in R using the R package ‘wpp2017’.
GDP per capita came from The World Bank Group who provide data related to
worldwide development and poverty indicators. I chose to use the GDP in year 2010 and
downloaded the data using the R package ‘wbstats’ (Piburn, 2018).
13
2.2.3 Multi-level Bayesian Models
I developed hierarchical Bayesian models to predict the ARG concentration as a
function of temperature, precipitation, population and GDP per capita (Figure 2.3). As
climatic variables are at the local scales while socio-economic variables are at the country
scales, I implemented multi-level models. I developed a series of models with all the
different combinations of covariates, some of which’s effects on the concentrations of
ARGs differed by country and some did not. I applied both deviance information
criterion (DIC) and predictive posterior loss (PPL) functions to identify the best model(s)
during the comparison of these models. The lower the PPL or DIC, the better the model
predicts (Hooten and Hobbs, 2015). Here I used one model to explain the model
development (Figure 2.3). This model contained all the covariates and used intercept at
broader spatial scale to link to the finer-scale process.
Figure 2.3 Structure of the hierarchical Bayesian model used to predict ARGs
concentration.
The complexity was decomposed into data, process, and parameter levels (vertical) and the association of different spatial scales
(horizontal direction). ARGs (Cij) was a function of variables at the local/regional scale (r) and country scale (c). j represented each
14
region, while i represented the country j belonged to. The region process was modeled using the temperature (Tij), precipitation (Rij)
and gene type (Sij) as covariates. The country process was modeled using socioeconomic variables as covariates, including,
population (Pi), per capita GDP (Gi).
To represent the function of ARGs concentration at region j and country i (Cij), let
𝐶𝑖𝑗 ∙ 𝜇 represent the mean of Cij, and 𝜎𝑟2 represent the variance of ARGs at the region
scale Cij. I modeled Cij by assuming it was distributed as (~) a normal distribution (Eq. 1):
𝐶𝑖𝑗~𝑁(𝐶𝑖𝑗. 𝜇, 𝜎𝑅2) (1)
Mean of ARGs (𝐶𝑖𝑗 . 𝜇) was a linear function of the covariates at the region level:
temperature (𝑇𝑖𝑗), precipitation (𝑅𝑖𝑗) and gene type (Sij) (Eq.2)
𝐶𝑖𝑗 . 𝜇 = 𝑓𝑟(𝛼0𝑖, 𝛼1, 𝛼2) = 𝛼0𝑖 + 𝛼1𝑇𝑖𝑗 + 𝛼2𝑇𝑖𝑗2 + 𝛼3𝑅𝑖𝑗 + 𝛼4𝑆𝑖𝑗 (2)
Where 𝛼0 was derived from the country-scale process, 𝛼1, and 𝛼2 were parameters for
temperature, 𝛼3 was the parameter for precipitation, and 𝛼4 was the parameter for gent
type with TetW as the reference gene.
Therefore, ARGs(C) at 𝑛1 region per country for 𝑛2 countries were modeled as in
Equation 3:
𝑝(𝐶|𝛼0, 𝛼1, 𝛼2, 𝛼3, , 𝛼4, 𝜎𝑅2) ∝ ∏ ∏ 𝑁(𝐶𝑖𝑗|𝑓𝑟(𝛼0𝑖, 𝛼1, 𝛼2, 𝛼3, 𝛼4), 𝜎𝑅
2)
𝑛1
𝑗=1
𝑛2
𝑖=1
(3)
At the country scale, the intercept (𝛼0𝑖) for country i was modeled by assuming it
was distributed as (~) a normal distribution with mean of 𝛼0𝑖. 𝜇 and variance of 𝜎𝑐2
(Equation 4):
𝛼0𝑖~𝑁(𝛼0𝑖. 𝜇, 𝜎𝑐2) (4)
15
Mean of the country intercept (𝛼0𝑖. 𝜇) was modeled as a linear function of the
country-scale socioeconomic covariates (Eq.5). Socioeconomic covariates included
population (𝑃𝑖) and per capita GDP (𝐺𝑖).
𝛼0𝑖. 𝜇 = 𝑓𝑐(𝛽0, 𝛽1, 𝛽2) = 𝛽0 + 𝛽1𝑃𝑖 + 𝛽2𝐺𝑖 (5)
Therefore, the country-scale intercept 𝛼0 for all 𝑛2 countries was modeled as in
equation 6:
𝑝(𝛼0|𝛽0, 𝛽1, 𝛽2, 𝜎𝑐2) ∝ ∏ 𝑁(𝛼0𝑖|𝑓𝑐(𝛽0, 𝛽1, 𝛽2), 𝜎𝑐
2)
𝑛2
𝑖=1
(6)
To complete the Bayesian model, I defined prior distribution for unknown
parameters (𝛼0…𝛼3, 𝛽0…𝛽2, 𝜎𝑅2 and 𝜎𝐶
2). I used conjugated priors for computation
efficiency (Calder et al., 2003) therefore the priors and posteriors have the same
probability distribution forms. The priors for 𝛼0…𝛼3 and 𝛽0…𝛽2 were normally
distributed and the priors for 𝜎𝑅2 and 𝜎𝐶
2 followed the inverse gamma distribution. The
priors distributions were flat and only weakly influenced the posteriors, which reflected
the lack of knowledge on the parameters (Lambert 2005).
By combining the parameter (priors), process, and data models, I derived the joint
distribution of unknowns in Eq. 7
𝑝(𝛼0, 𝛼1, 𝛼2, 𝛼3, 𝛼4, 𝛽0, 𝛽1, 𝛽2|𝐶, 𝑇, 𝑅, 𝑆, 𝑃, 𝐺)
∝ 𝑝(𝐶|𝛼0, 𝛼1, 𝛼2, 𝛼3, 𝛼4, 𝜎𝑅2)
× 𝑝(𝛼0|𝛽0, 𝛽1, 𝛽2, 𝜎𝐶2) × 𝑝(𝛽0) × 𝑝(𝛽1)
× 𝑝(𝛽2) × 𝑝(𝛼1) × 𝑝(𝛼2) × 𝑝(𝛼3) × 𝑝(𝛼4) × 𝑝(𝜎𝑅2) × 𝑝(𝜎𝐶
2)
(7)
16
The posterior distributions were computed using Markov Chain Monte Carlo
Simulation (MCMC) (Robert et al., 2004) using the software JAGS 4.3.0 (Plummer,
2017)
2.2.4 Parameter and model evaluation
I examined the 95% credible intervals (CI) for the posteriors of the parameters to
identify the key covariates that affected ARGs. If the 95% CIs did not contain zero, then
the corresponding covariate had significant effect on ARGs. Additionally, the sign of the
95% CI dictated whether the variable had a positive or negative effect on the ARGs.
2.3 Results
Based on the model comparison, the best model included gene type, temperature,
precipitation and GDP per capita as covariates (PPL=499.88, DIC=517.5) (Table A.1).
The coefficient for temperature varied by country while the coefficients for gene type,
squared temperature and precipitation did not vary by country (Figure 2.4 a &b). As the
coefficient for squared temperature was significantly negative (Figure 2.4 b), there
existed an optimum temperature at which the concentrations of ARGs were maximum for
each country (Table 2.2). China, Sweden, and Switzerland are of major concerns as the
concentration of ARGs will likely continue to increase with temperature as the optimum
temperatures (medians) are high (Table 2.2 Highlighted in Yellow). It is not clear why
temperature had different effect on the concentrations of ARGs in each country (though
not significant) as GDP or population showed minimum influence at the country scale
process (Figure 2.4 a). Additionally, the concentrations of ARGs increased with
17
precipitation (Figure 2.4 a), and the concentrations of sul1 was significantly larger than
those of tetw in the aquatic systems (Figure 2.4 c).
In general, the best model reasonably predicted the concentrations of ARGs well as
most of the 95% predictive credible intervals included observations. It underpredicted the
concentrations when the values were large while showed overpredictions when the values
were low. Using the median predictions, the R2 was 0.41.
a
18
b
c
Figure 2.4 95% credible intervals (CI) for the posteriors of the parameters.
Pre denotes precipitation, Temp denotes temperature, Temp2 denotes temperature square and gene1 denotes sul1.
19
Figure 2.5 95% credible intervals (CI) of posterior predicted concentrations of ARGs vs.
the observations.
Points indicate medians of posterior predictions. Line indicates one to one line where predicted medians equal to observations. Error
bars crossing predicted medians indicate 95% CIs.
20
Table 2.2 Temperature (oF) where concentrations of ARGs reach maximum
Country2.5%
quantileMedian
97.5%
quantile
1 Bolivia 25.5 56.3 78.7
2 Canada -10 43.3 64.9
3 China 17.3 87.7 185.4
4 Cuba 28.4 54.1 63.5
5 Estonia 5.7 47.3 58
6 India 47.1 66.7 106.4
7 Spain -27.5 41.6 76.2
8 Sweden 46 70.9 128.8
9 Switzerland 66.9 80.3 136.1
10 UK -2.5 46.5 68.5
11 US -24.2 54.6 122.1
12 Romania 9.6 47.9 57.9
2.4 Discussion
This is the first meta-analysis to quantitatively analyze the ARGs in the natural
water environment and the potential drivers at a global scale.
My study shows that there exists an optimal temperature for the concentrations of
ARGs, meaning the concentrations of antibiotic resistance genes increases with
temperature to a maximum value before the decrease with temperature, different from
what people previously believe that antibiotic resistance increases with temperature
(MacFadden et al., 2018). The positive relation may be explained by that the proliferation
and growth of bacteria accelerated, and the spread of mobile elements also accelerated to
facilitate the spread of ARGs to non-resistant bacteria as temperature rises (Antunes et
al., 2005; Lin et al., 2017). When the temperature continues to rise, HGT may be
inhibited, bacterial activity may decrease, and decomposition of antibiotics may
accelerate. Therefore, the pressure for antibiotic selection would weaken, and non-
21
resistant bacteria could grow faster. These may explain the negative relation between the
concentrations of ARGs and temperature once the optimum temperature is passed
(Dunivin and Shade, 2018; Lin et al., 2017; Zhang et al., 2015). At the optimum
temperature, the decomposition rate of antibiotics and new additions of ARGs through
WWTP, groundwater penetration, etc. may reach a dynamic balance (Beattie et al., 2018;
Bueno et al., 2018; Li et al., 2018).
In addition to the temperature effect, I also found that temperature has different
effects on ARGs concentrations in different countries. Previous studies showed ARG
concentration was higher in warmer countries (McGough et al., 2018). My study also
showed the temperature’s effect on ARG concentrations, but in a quadratic function.
We also found that there was a positive correlation between precipitation and ARGs
concentration. The influence of rainfalls on the abundance of ARGs was investigated
(Novo et al., 2013), indirectly estimated by class 1 integrons prevalence (int1 gene copies
per 16S rDNA copies) in River Thames (Amos et al., 2015), or tested by cultural
approaches in two lakes by analyzing surface water and surface sediment samples, before
and after a storm water event (Zhang et al., 2016). Then Di Cesare et al. (2017)
determined that precipitation could significantly increase the concentration of ARGs,
probably through the increase of the overall ARG load (Amos et al., 2015). In addition,
the nutrient composition of water bodies will likely change during storm events as
precipitation is one of the main pathways for nitrogen and phosphorus compounds to
enter rivers and lakes (Hathaway et al., 2012; Janke et al., 2014), particularly after
prolonged dry periods (Chen et al., 2016; Outram et al., 2014). The nutrients showed
significant positive correlations with all of the ARGs (Su et al., 2020). In the articles I
22
collected, there were no studies relating concentrations of ARGs to precipitation, while
this relation can arise through meta-analysis.
Many studies show population and GDP per capita increased antibiotic resistance
(Bruinsma, 2003; Collignon et al., 2018; Collignon and Beggs, 2019; Klein et al., 2018;
Penders et al., 2013; Roope et al., 2019; Vikesland et al., 2019), however I did not find
they are influencing factors to the concentrations of ARGs. This may be due to a small
portion of ARGs we analyzed or the focus on wastewater treatment plant related data
cannot be explained by country-scale socio-economic variables. In the previous socio-
economic analysis, antibiotic resistance was indicated by the resistance of individual
pathogens, not ARGs, and these resistant bacteria were isolated in hospitals. The strains
were directly related to disease and infection, which was more related to population and
GDP. Secondly, the previous antibiotic resistance data were basically national averages,
aligned better with the scales of GDP and population data. In contrast, my research was
based on ARGs data collected from WWTP or aquaculture farm, showing a substantial
scale mismatch with the country-scale socio-economic variables. Finer-scale socio-
economic variables and their impact on ARGs should be explored in the future, for
example, local socio-economic variables like population, income and antibiotic
consumption of the major city along sampling sites. Also, other common ARGs should
be included in the analysis, which would give a more comprehensive resistance profile.
2.5 Conclusion
In my research, I found that climatic variables played a more important role in ARG
concentrations near WWTP or aquaculture facilities than the country-scale socio-
economic factors including population and GDP per capita across the world. There is a
23
need to expand the research by including more ARGs and more socio-economic variables
at finer spatial scales. Nevertheless, multi-scale modeling shows a powerful tool to
assimilate multi-scale data in meta-analysis.
24
CHAPTER III – COMMUNITY COMPOSITION AND ANTIBIOTIC RESISTANCE
OF BACTERIA IN BOTTLENOSE DOLPHINS TURIOPS TRUNCATUS -
POTENTIAL IMPACT OF 2010 BP OIL SPILL
3.1 Introduction
The discovery and spread of antibiotics marked a significant progress in the fight
against infectious diseases and the improvement of public health. However, the overuse
of antibiotics has led to the development of antibiotic resistance among targeted
pathogens worldwide. Bacteria can acquire resistance by two mechanisms: one is the
occurrence of new endogenous genetic mutations and the other is acquisition of
exogenous resistance factors (Blair et al., 2015b; Davies and Davies, 2010; Huddleston,
2014; Ochman et al., 2000). Once resistance is acquired, it can spread widely through
water, air, and soil, and this transfer is often facilitated by anthropogenic activities
(Davies and Davies, 2010). Common sources of antibiotic resistant bacteria (ARB)
include wastewater discharge from hospitals, sewage treatment plants, slaughterhouses,
farms, and aquaculture facilities (Colavecchio et al., 2017).
Aquatic ecosystems , in particular polluted waters, accumulate ARBs, and are
conducive to the exchange of antibiotic resistance genes (ARGs) between species through
the Horizontal Gene Transfer (HGT) process (Rizzo et al., 2013). HGT can occur through
three main ways: 1) via transformation, which is the most common pathway (Blair et al.,
2015b; Huddleston, 2014; Ochman et al., 2000) where bacteria directly uptake naked
DNA in the form of small DNA units from the environment, 2) via transduction where
bacteria get genetic elements via introduction of bacteriophages, and 3) via conjugation
where bacteria get new genes via physical contact between two cells.
25
Polluted water thus can become a reservoir of antibiotic-resistant pathogens
(Devanas et al. 1980; Baya et al. 1986; Wiggins et al. 1999) , including multidrug
resistant strains promoted by ARG transfers discussed above, causing health problems to
aquatic organisms, and potentially affecting humans through contact and food
consumption (Schneider et al., 2011). The mechanisms by which oil spill contamination
may increase ARG and ARB prevalence are still understudied but could be related to the
high abundance of heavy metals in oil contaminated waters and the selection for co-
resistance to heavy metals and antibiotics (Baker-Austin et al., 2006) and/or the
abundance of aromatic compounds in oiled waters that may also select for antibiotic
resistance or facilitate ARG transfer (Chen et al., 2017; Wang et al., 2017). Studies of
ARB and ARGs are urgently needed in the US, especially after the BP Deepwater
Horizon (DWH) Oil Spill in 2010, the largest marine oil spill in the history of the
petroleum industry. The BP DWH Oil Spill released 627,000 ton of crude oil into the
northern Gulf of Mexico (NGOM), seriously affecting ecosystems such as coastal
wetlands and coral reefs, and organisms including vegetation, birds, fish, and mammals
(Allan et al., 2012; Haney et al., 2014; Lin and Mendelssohn, 2012; Schwacke et al.,
2013; Silliman et al., 2012; White et al., 2012; Whitehead et al., 2012; Wu et al., 2012).
Many ecosystems in the NGOM have not yet recovered to pre-oil spill status and it is not
clear when or whether they will recover ( Lane et al. 2015; Takeshita et al. 2017). The
possibly large concentrations of ARBs in this highly contaminated water body, combined
with other pollutants, have affected the health condition of organisms , especially the
mammals that are long-lived and exhibit site-fidelity such as bottlenose dolphins
(Tursiops truncatus) (BNDs) (Connor and Smolker, 1985; Harzen, 1995; Mate et al.,
26
1995) . As bottlenose dolphins are sensitive to pollution, feed at a high trophic level, and
have fat that stores anthropogenic toxins, they serve a quintessential sentinel or warning
system for possible animal and human diseases originating from exposure to marine
pollution (Bossart, 2006; Stewart et al., 2008; Wells et al., 2004).
NOAA conducted capture-release health assessments of the BNDs inhabiting
Barataria Bay (BB), Louisiana, an area that was heavily contaminated by the BP DWH
oil spill (Michel et al., 2013), and Sarasota Bay (SB), Florida, where no oil was observed
following the oil spill in 2010. The results of these studies (Schwacke et al., 2013)
showed that the dolphins in water free of oil spill contamination were in better conditions
than those in oiled waters based on assessments of general health status that included
occurrence of organ disease, heart rate, and hematology. Because physiological and
immune systems of humans are more similar to those of marine mammals such as
dolphins than those of other aquatic organisms, the detection of ARBs in dolphins could
imply higher risk of contacts with ARBs for people who utilize the same water bodies for
fishing, recreation, or other purposes (Wells et al., 2004). Therefore, it is important to
assess the impact of the DWH Oil Spill on ARB and ARGs in bottlenose dolphins.
So far, the ARB isolated from the BNDs has only been studied in water bodies that
are not impacted by oil spills. Antibiotic-resistant Escherichia coli from the BNDs
inhibiting in Indian River Lagoon, Florida and Charleston Harbor Area, South Carolina
were first reported by Greig et al. (Greig et al., 2007) and later culturable resistant
bacteria from the BNDs were tested by Schaefer et al. ( Schaefer et al. 2009; Schaefer et
al. 2019). They both found that resistance differed noticeably between sampling sites
with Charleston Harbor Area having higher loads of resistant bacteria than Indian River
27
Lagoon. Most recently, a more comprehensive study was conducted to investigate ARBs
isolated from wild BNDs caught at five locations along the mid-Atlantic USA and the
Gulf of Mexico (GOM) (Stewart et al., 2014). The study showed spatial variability in the
prevalence of ARBs among sampling sites with a general trend for more urbanized bays
to display higher prevalence (Figure 3.1).
Here we initiated a study of antibiotic resistance of bacteria isolated from BNDs in
the water bodies that were contaminated by the DWH Oil Spill in 2010 and compared to
that from a water body that received minimum influence from the DWH Oil Spill. Our
hypotheses are: Bacteria isolated from the BNDs living in the water body contaminated
by the oil spill show higher antibiotic resistance and higher likelihood of multidrug
resistance than those from water bodies not contaminated by the oil spill. This study aims
to contribute to comprehensive evaluation of the impact of the Oil Spill on ecosystem
health.
3.2 Methods
We applied both culture-based and molecular methods to identify the species of
bacteria and antibiotics resistance in the bacteria isolated from tissues of BNDs and water
samples collected at the locations where the BNDs were caught. The BND and water
samples were collected in the Sarasota Bay, free of 2010 DWH Oil Spill, and Barataria
Bay, heavily contaminated by this oil spill (Figure 3.1). The bacteria communities and
prevalence of ARB and ARG in water and BND samples were compared between the two
bays which experience similar climatic and hydrological conditions using rarefaction
method and (generalized) linear mixed modelling.
28
3.2.1 Study Areas
Sarasota Bay (SB), Sarasota, Florida (27.36 N°, 82.58 W°) extends approximately
30 km along the central west coast of Florida south of Tampa, encompassing Sarasota
and associated shallow bays, and is bounded on the west by a series of narrow barrier
islands (Wells et al., 2005). Annual average water temperature ranges from 22.2 to
26.7°C, with monthly average temperature reaching 31.8°C in August. Average annual
precipitation is about 1345 mm. The salinity in the bay ranges from 8 to 15 ppt.
The inlet of the Barataria Bay (BB), Jefferson and Plaquemines Parish, Louisiana
(29.23 N°, 89.95 W°) is about 24 km long and 19 km wide in southeastern Louisiana,
largely blocked by Grand Isle and Grand Terre Islands. It is connected to the Gulf
Intracoastal Waterway system via a narrow and navigable Gulf channel (“Barataria Bay |
inlet, Louisiana, United States,” 2018). The climate and salinity are similar to those in
Sarasota Bay. Annual average water temperature ranges from 20.5 to 24.2°C, with
monthly average temperature reaching 31°C in August. Average annual precipitation is
about 1620mm. The salinity ranges from 2 to 15 ppt.
3.2.2 Sample Collection
NOAA scientists sampled a total of 14 dolphins in SB between May 16 and May 20,
2011, and 22 dolphins in BB between August 3 and August 17, 2011, as part of the 2011
NOAA Health Assessment. The samples included swabs of blowhole, genital area, skin,
feces along the anus, and blood samples (~ 8 ml). Prior to blood collection, sampled spots
on the dolphins were cleansed using Chlorhexiderm followed by methanol to prevent water
contamination of samples. Additionally, aseptically collected water samples were taken at
each unique dolphin capture site using Nasco Whirl-PAK bags (Nasco, Milton, WI). The
29
samples were kept cool on ice and shipped overnight to the University of Southern
Mississippi Gulf Coast Research Laboratory for processing.
Figure 3.1 Sampling sites
Sampling sites of this study (red points) and sampling sites of previous studies of antibiotic resistance of bacteria along the Atlantic
Coast and northern Gulf of Mexico (yellow points). Sampling sites with red circle and yellow center overlapped between this study
and others.(Greig et al., 2007; Schaefer et al., 2009, 2011; Stewart et al., 2014; Wallace et al., 2013)
3.2.3 Bacteria Isolation and Identification
3.2.3.1 Purification of bacteria isolates
Samples were kept at 4oC until processing which occurred within eight hours of
reception. Four different medias were used to grow bacteria from the swab and blood
samples: Vibrio harveyi agar (VHA) (Harris et al., 1996), Thiosulfate-citrate-bile salts-
sucrose (TCBS) agar, Marine Agar (MA) 2216E (Himedia Laboratory Private Limited,
30
Mubai, India), and Mueller-Hinton (MH) agars. The swabs samples were inoculated in 1
ml of T1N3 enrichment broth overnight for 16-18 hours at 33°C prior to placing 250 ul of
the broth onto the four medias. Plates were then incubated for 16-36 hours at 33°C. Once
there was visible growth, a single colony of each unique morphology was selected and
transferred to MA slants (MacLeod, 1965).
3.2.3.2 Molecular identification
Genomic DNA was extracted and purified using the Geno Plus Genomic DNA
Extraction Kit provided by Green BioResearch (Baton Rouge, LA), following the protocol
provided by the company.
The 16S ribosomal RNA (rRNA) gene was amplified using the universal primer set
27F (5’-AGAGTTTGATCMTGGCTCAG-3’) and 1492R (5’- ACCTTGTTACGACTT -
3’) (Lane, 1991). The PCR master mixture included 1 x PCR buffer, 2 mM MgCl2, 1.6
units Taq Polymerase, 0.2 mM dNTP mixture, 1 µM each forward and reverse primer, and
20 to 30 ng of template DNA in a volume of 25ul. PCR thermocycling, included 5 min
initial denaturation at 95 oC, followed by 30 cycles of denaturation for 45 s at 95 oC,
annealing for 45 s at 55 oC, an extension for 90 s at 72 oC, and a final elongation at 72 oC
for 10 min, were performed on a 2720 Thermal Cycler (Applied Biosystems, CA, USA).
PCR products were visualized on a 1.5% agarose gel electrophoresis to confirm successful
amplification and shipped to Eurofins Genomics Company (Louisville, KY, USA) for
sequencing.
The tested sequences were edited using Sequencher 5.2.4 (GeneCodes, Ann Arbor,
Michigan) and aligned to published sequences available in the NCBI GenBank database
(https://www.ncbi.nlm.nih.gov/genbank/) using BLAST (Basic Local Alignment Search
31
Tool) (https://blast.ncbi.nlm.nih.gov/Blast.cgi). All sequences with identity higher than 99%
were picked for alignment (Janda and Abbott, 2007). Mega 7.0 software (Kumar et al.,
2016, p. 7) was used to align sequences and generate the phylogenetic tree with neighbor-
joining method.
3.2.4 Susceptibility Test for antibiotics
Antibiotic resistance was tested by the Kirby-Bauer Disk diffusion method.
Standardized inoculums of bacteria were swabbed on Mueller-Hinton agar plates with
antibiotics and the plates were incubated for 16-24h at 33℃. The resistance was determined
following the testing standards M2-A11 published by the Clinical and Laboratory
Standards Institute (Wayne, PA: Clinical and Laboratory Standards Institute, 2012).
Particularly, if the diameter of the inhibition zone was smaller or equal to 14mm, the isolate
was recorded as resistant to a certain antibiotic. The antibiotics included Amoxicillin/K
clavulanate (AmC), Ampicillin(AM), Cefepime(FEP), Cephalothin (CF),
Piperacillin/tazobactam (TZP), Ciprofloxacin (CIP), Trimethoprim/sulfamethoxazole
(SXT), Erythromycin (E), Gentamincin (GM), Tetracycline (TE), and Tobramycin (NN),
which are commonly prescribed to cure light to serious bacteria-caused diseases and use
different mechanisms and pathways to inhibit bacterial growth (Table 3.1).
32
Table 3.1 Eleven Antibiotics used in the current study and their characteristics
Classification Year* Chemical Group
Cell wall
synthesisAmC Amoxicillin/K clavulanate 1984 Penicillin combinations
AM Ampicillin 1961 Penicillin
FEP Cefepime 1994Cephalosporins (4th
Generation)
Cephalosporins
(1st Generation)
TZP Piperacillin/tazobactam 1993 Penicillin combinations
Nucleic Acid
SynthesisCIP Ciprofloxacin 1987 Quinolones
SXT Trimethoprim/sulfamethoxazole 1974 Sulfonamides
Protein
SynthesisE Erythromycin 1967 Macrolides
GM Gentamicin 1971 Aminoglycosides
TE Tetracycline 1978 Tetracyclines
NN Tobramycin 1970 Aminoglycosides
Antibiotics
CF Cephalothin 1964
* Year denoted the year the antibiotic was first used in USA
3.2.5 Data Analysis
Using statistical analysis, we compared the bacteria communities and their antibiotic
resistance in dolphin and water samples respectively between Sarasota Bay and Barataria
Bay.
Heatmaps contrasting the relative abundance of bacteria taxa in dolphins and water
from SB and BB were generated using the “plotly” package in R (Sievert, 2018).
Bacterial diversity indices were compared using the rarefaction method to account
for different sample sizes between the two bays. The rarefaction (interpolation) and
estimation (extrapolation) of Hill number sampling curves were applied to calculate
species richness, exponential Shannon diversity and Simpson diversity using the R
33
package iNEXT (Chao et al., 2014; Hsieh et al., 2016). Hill numbers estimates the
effective numbers of species: the number of equally abundant species required to give the
same value of a diversity measure (Hill, 1973). The original diversity indices such as
Shannon-Wiener index and Gini-Simpson index could lead to misleading results, as they
represent entropies instead of diversities (Jost, 2006). On the other hand, most of the
nonparametric diversity indices could be converted to true diversities with the following
equations:
(1)
(2)
where D is the effective number of species, S is the number of species sampled, p is the
proportion of individuals belonging to a particular species (species frequency) and q is
the order of diversity. When q is equal 0, D is the richness; when q is equal to 1, the Eq.1
is undefined but its limit exists and equals Eq.2, and D is the exponential Shannon
diversity (H); and when q is equal to 2, D is the Simpson diversity. We then tested
whether exponential Shannon diversity, species richness, and Simpson diversity of
bacteria were different between the two bays using the “vegetarian” package in R
(Charney and Record, 2012).
In addition to the diversity in each bay, we calculated beta-diversity to indicate the
dissimilarity of SB and BB, with the following equation:
D(Hβ) = D(Hγ)/ D(Hα) (3)
where D(Hγ), D(Hα) and D(Hβ) represent the true gamma, alpha and beta diversity of the
Shannon index.
34
We applied generalized linear mixed models (GLMM) to predict 1) the number of
antibiotic resistance cases in any of the eleven antibiotics in the bacteria isolated from the
dolphins and water respectively, 2) the probability that a bacteria isolates the dolphin and
water samples developed multiple antibiotic resistance, and 3) probability of a bacteria
taxon that developed antibiotic resistance for each of the eleven antibiotics from the
dolphin samples. From these models, we inferred whether the two bays differ
significantly in antibiotic resistance and probability of developing antibiotic resistance.
Because the field sampling design implemented a hierarchical structure and the response
variables were not continuous variables or independent from each other, the generalized
linear mixed models was used to model response variables that violate independence
assumption required in ordinary least squares regression and do not follow normal
distribution (Wu et al., 2017). As our objective was to examine the difference of
antibiotic resistance between the two bays, the factor variable “bay” was the fixed
independent variable. The random effects were dolphin individuals nested within the
Bay, crossed with the random effects of organs (fecal, skin, blowhole, genital, or blood)
for the dolphin samples. The significance level of the coefficient for the variable “bay”
indicated whether the antibiotic resistance or probability was significantly different
between the two bays or not. We implemented model selection method to choose the
optimal random effect structure based on Akaike Information Criterion (AIC) (Burnham
and Anderson, 2004). In the first set of models that predicted the number of antibiotic
resistance cases in the dolphin samples, we tallied the number of positive antibiotic
resistance cases across the eleven antibiotics for each bacterium isolate as the response
variable (Table 3.6). As the response variable was nonnegative integers, we chose
35
Poisson distribution and log link function in the mixed effects models. In the second set
of models, the response variable took the values of 0 or 1 representing single-drug or
multi-drug resistance respectively, and as such we chose binomial distribution and logit
link function to model the probability of multi-drug resistance in the bacteria isolates. In
the third set of models, the response variable took the values of 0 or 1 representing
absence or existence of antibiotic resistance respectively, therefore we chose binomial
distribution and logit link function to model the probability of antibiotic resistance of
bacteria isolates for each of the eleven antibiotics. We implemented “glmer” function in
the “lmertest” package in R (Kuznetsova et al., 2017; R: A Language and Environment
for Statistical Computing, 2018) to develop the GLMMs.
For the water samples, we applied a generalized linear mixed model (GLMM) with
Poisson error distribution and a log link function. The fixed effect of this model was the
variable “bay”. The random effect was “dolphin” around which the water samples were
collected. We implemented “glmer” function in the “stats” package in R (R: A Language
and Environment for Statistical Computing, 2018).
Because the number of bacteria isolates from dolphins differed much between the
two bays (71 for SB, 163 for BB), we randomly selected the same number of bacteria
isolates as SB (no replacement) from the BB samples to investigate whether the number
of bacteria isolates affected significance level of the Bay effect. After the random
selection, we applied the GLMMs and repeated the procedure 200 times and summarized
the number of times when the two bays demonstrated significant difference in antibiotic
resistance. To further verify the results whether the two bays had significant different
antibiotic resistant cases, we developed models for the proportion of antibiotic resistance
36
of bacteria isolates that are weighted by total number of antibiotic resistance cases in each
bay. The models had bays as a fixed effect with binomial distribution (response variable
from 0 to 1) and logit link function.
3.3 Results
Results showed there was no significant difference in biodiversity for the microbial
communities in dolphin or water samples between SB and BB. The beta diversities were
0.301 in dolphin samples and 0.549 in water samples, indicating low to moderate
difference of microbial communities of these two bays. The antibiotic resistance in water
samples was not significantly different between the two bays. However, there was a
significant difference in the antibiotic resistance in dolphins’ samples, with higher
resistance and multi-drug resistance in BB than SB, mainly driven by resistance to E and
CF among the eleven antibiotics tested.
3.3.1 Culture Isolates and Bacteria Species
In total, 294 bacterial colonies were isolated, and all were identified at the species
level. There were 194 isolates from BB including 163 isolates (41 species) from dolphin
and 31 isolates (19 species) from water samples (Figure 3.2). There were one hundred
isolates from SB including 71 from dolphin samples assigned to 28 species and 29
isolates from water samples assigned to 15 species. The most frequently cultured
organisms were Vibrio, including Vibrio alginolyticus (15.31%), Vibrio
parahaemolyticus (6.80%) and Vibrio fluvialis (5.78%). Other species included
Shewanella algae, Bacillus cereus, Micrococcus luteus, Pseudomonas aeruginosa,
Pseudomonas pseudoalcaligenes, Vibrio owensii, Vibrio harveyi, Shewanella haliotis,
Photobacterium damselae, Vibrio campbellii, and Stenotrophomonas maltophilia. Most
37
of the species are indigenous marine bacteria, however, some of them can be pathogens,
such as S. maltophilia and P. aeruginosa. BB had higher richness of bacteria species than
SB in both dolphin and water samples (Figure 3.2).
(a
)
38
Figure 3.2 Heatmaps of relative abundance
(a) Heatmaps of the relative abundance of bacterial species isolated from dolphins and water in BB and SB, (b) heatmaps of relative
abundance of bacterial classes from BB and SB, the unit of the legend is %
3.3.2 Microbial community analysis
In addition to species difference, bacterial communities in SB and BB also showed
class-level difference (Fig.2b). The main classes present in both SB and BB included
Actinobacteria, Alphaproteobacteria, Bacilli, and Gammaproteobacteria, while BB also
had a high frequency of Betaproteobacteria.
Though bacteria compositions on dolphins and water in SB and BB were different,
the 95% confidence intervals of the richness, Shannon diversity and Simpson diversity of
(b)
39
SB and BB overlapped indicating lack of significant difference for these indices (Figure
3.3 & Table 3.2).
The beta diversities of dolphin and water samples across BB and SB were 0.301 and
0.549. As the beta diversity ranges from 0 (no difference) to 1 (completely different), the
value indicated low to moderate level of difference of bacterial composition between
these two bays.
Table 3.2 Asymptotic diversity estimates with related statistics
S.E. denotes standard error of estimates, LCL denotes lower confidence level, and UCL denotes upper confidence level
Site Origin Diversity Observed Estimator S.E. LCL(2.5%) UCL(97.5%)
Species Richness 41.000 63.424 13.825 48.372 109.212
BB Dolphin Shannon Diversity 23.004 28.454 2.718 23.126 33.781
Simpson Diversity 15.712 17.281 1.701 15.712 20.616
Species Richness 28.000 75.488 36.467 40.504 208.349
SB Dolphin Shannon Diversity 16.761 27.082 6.335 16.761 39.498
Simpson Diversity 9.676 11.044 2.531 9.676 16.006
Species Richness 19.000 36.419 13.944 23.386 88.182
BB Water Shannon Diversity 16.166 30.164 7.703 16.166 45.260
Simpson Diversity 13.535 23.250 6.740 13.535 36.459
Species Richness 15.000 30.448 15.940 17.911 96.969
SB Water Shannon Diversity 12.375 21.336 5.555 12.675 32.223
Simpson Diversity 10.922 16.917 4.339 10.922 25.421
40
(a)
41
Figure 3.3 Rarefaction curve
Sample-size-based rarefaction (solid line segment) and extrapolation (dotted line segments) sampling curves with 95% confidence
intervals (shaded areas) for the bacteria from dolphins (a) and water (b) of SB and BB: q=0 (species richness), q=1 (Shannon
diversity), q=2 (Simpson diversity) from left to right.
3.3.3 Antibiotic Resistance Profile
Among the 71 bacteria isolates from the dolphin samples in SB, 59 showed antibiotic
resistance (83.1%). Forty-one isolates (57.8%) were resistant to only one antibiotic and
18 isolates (25.3%) were resistant to more than one antibiotic. Among the 29 bacteria
isolates from the water samples in SB, 22 isolates (75.9%) were resistant to antibiotics,
(b)
42
15 (51.8%) resistant to one antibiotic and seven (24.1%) were resistant to multiple
antibiotics. In the 163 bacterial isolates from the dolphin samples in BB, 142 isolates
(87.1%) showed resistance to antibiotics with 69 (42.3%) resistant to only one antibiotic
and 73 (44.8%) resistant to more than one antibiotic. Among the 31 bacteria isolates from
the water samples in BB, 24 (77.4%) were ARBs with 19 (61.3%) resistant to one
antibiotic and five (16.1%) resistant to multiple antibiotics. The proportion of multi-drug
resistance was lower than single-drug resistance in most of the samples except in the
dolphin’s samples in the BB. In addition, there was a much higher proportion of multi-
drugs resistant bacteria (MDRB) from the dolphins in the BB (44.8%) than in SB (25.3%)
(Table 3.5). Noticeably, S. maltophilia (three isolates) and P. aeruginosa, isolates from
BB samples only, were resistant to six, seven, eight and five antibiotics respectively. We
did not isolate any strains from SB that were resistant to more than four drugs. Even
though there were no significant differences in the resistance of the water sample
between SB and BB (p-value = 0.993), SB water (24.1%) had higher proportion of
MDRB than that of BB (16.1%) and it was due to the extremely high resistance to AM
(68.9%) in SB water (Table 3.5 & Figure 3.4 ).
Samples showed differences in resistance to specific bacteria too. Resistance to AM
was the most frequent, followed by resistance to E. The most effective antibiotics were
CIP, GM, and TZP in SB with very few isolates showing resistance to them (Figure 3.4).
While no bacteria isolates were resistant to CIP in SB samples, CIP, TZP, and TE were
the most effective antibiotics in BB (Figure 3.4).
43
Table 3.3 Antibiotic resistance of most common bacteria species cultured from dolphins
and water samples in BB (n=194)
Species # Isolates AmC AM FEP CF CIP E GM TZP TE NN SXT
Vibrio alginolyticus 25 1 21 0 6 0 5 0 0 0 0 0
Resistance % 4.0 84.0 0.0 24.0 0.0 20.0 0.0 0.0 0.0 0.0 0.0
Shewanella algae 19 6 12 0 14 0 2 0 0 0 0 0
Resistance % 31.6 63.2 0.0 73.7 0.0 10.5 0.0 0.0 0.0 0.0 0.0
Bacillus cereus 16 1 16 15 1 0 0 0 0 0 1 3
Resistance % 6.3 100.0 93.8 6.3 0.0 0.0 0.0 0.0 0.0 6.3 18.8
Vibrio parahaemolyticus 13 0 11 0 6 0 5 0 0 0 0 0
Resistance % 0.0 84.6 0.0 46.2 0.0 38.5 0.0 0.0 0.0 0.0 0.0
Vibrio fluvialis 13 4 4 0 11 0 0 0 0 0 0 0
Resistance % 30.8 30.8 0.0 84.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Pseudomonas
pseudoalcaligenes13 1 10 0 13 0 13 0 0 0 0 2
Resistance % 7.7 76.9 0.0 100.0 0.0 100.0 0.0 0.0 0.0 0.0 15.4
Pseudomonas stutzeri 8 0 1 0 7 0 0 0 0 0 0 0
Resistance % 0.0 12.5 0.0 87.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Micrococcus luteus 8 0 0 0 0 0 1 0 0 0 5 0
Resistance % 0.0 0.0 0.0 0.0 0.0 12.5 0.0 0.0 0.0 62.5 0.0
Bacillus pumilus 7 0 0 7 0 0 0 0 0 0 0 0
Resistance % 0.0 0.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Shewanella haliotis 6 5 2 0 5 0 1 0 0 1 0 0
Resistance % 83.3 33.3 0.0 83.3 0.0 16.7 0.0 0.0 16.7 0.0 0.0
Total Culturable Bateria 194 170 93 157 118 193 152 189 192 190 181 178
Resistance % 12.4 52.1 19.1 39.2 0.5 21.6 2.6 1.0 2.1 6.7 8.2
44
Table 3.4 Antibiotic resistance of most common bacteria species cultured from dolphins
and water samples in the SB (n=100)
Species # Isolates AmC AM FEP CF CIP E GM TZP TE NN SXT
Vibrio alginolyticus 20 0 20 0 7 0 1 0 0 0 0 0
Resistance % 0.0 100.0 0.0 35.0 0.0 5.0 0.0 0.0 0.0 0.0 0.0
Vibrio parahaemolyticus 7 0 6 0 1 0 0 0 0 0 0 0
Resistance % 0.0 85.7 0.0 14.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Micrococcus luteus 7 0 2 0 0 0 1 0 0 0 4 0
Resistance % 0.0 28.6 0.0 0.0 0.0 14.3 0.0 0.0 0.0 57.1 0.0
Vibrio owensii 7 0 7 0 2 0 0 0 0 0 0 0
Resistance % 0.0 100.0 0.0 28.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Vibrio harveyi 6 0 6 0 0 0 0 0 0 0 0 0
Resistance % 0.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Photobacterium damselae 5 0 1 0 0 0 0 0 0 0 0 0
Resistance % 0.0 20.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Vibrio campbellii 5 0 5 0 0 0 0 0 0 0 0 0
Resistance % 0.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Vibrio fluvialis 4 0 3 0 4 0 0 0 0 0 0 0
Resistance % 0.0 75.0 0.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Shewanella corallii 4 0 0 0 0 0 0 1 0 0 1 0
Resistance % 0.0 0.0 0.0 0.0 0.0 0.0 25.0 0.0 0.0 25.0 0.0
Vibrio neptunius 3 0 3 0 3 0 0 0 0 0 0 0
Resistance % 0.0 100.0 0.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Total Culturable Bateria 100 96 38 91 77 100 97 99 99 99 92 98
Resistance % 4.0 62.0 9.0 23.0 0.0 3.0 1.0 1.0 1.0 8.0 2.0
Table 3.5 Profile of multi-drug resistance of bacteria
Resistant
antibioticsSB Dolphin % BB Dolphin % SB Water % BB Water %
2 12 16.9 27 16.6 6 20.7 2 6.5
3 5 7 33 20.2 1 3.4 3 9.7
4 1 1.4 6 3.7 0 0 0 0
5 0 0 2 1.2 0 0 0 0
6 0 0 3 1.8 0 0 0 0
7 0 0 1 0.6 0 0 0 0
8 0 0 1 0.6 0 0 0 0
9 0 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0 0
11 0 0 0 0 0 0 0 0
Total 18 25.3 73 44.7 7 24.1 5 16.2
45
Figure 3.4 Frequency of resistance to individual antibiotics in samples from Sarasota Bay
and Barataria Bay.
3.3.4 Generalized linear mixed model
The optimum random effect structure to model antibiotic resistance cases in bacteria
isolates from the dolphin samples based on AIC was dolphin (D) nested in bay (B) and
crossed random effect of organ (O) (Table 3.6). Based on this model, the antibiotic
resistance of water samples did not differ significantly between SB and BB (p-value =
0.993), but the antibiotic resistance of the dolphin samples was significantly greater in
BB than in SB (p-value = 0.019). Similar results were obtained in the models to predict
the probability of multi-drug resistance in the bacteria isolates. The probability of multi-
drug resistance in water samples did not differ significantly between SB and BB (p-
value=0.533), but the probability from the dolphin samples was significantly higher in
BB than in SB (p-value=0.009). Compelling evidence showed that the significantly
46
higher numbers of antibiotic resistance cases or higher probability of multi-drug
resistance from dolphin samples in BB than in SB was not due to the larger number of
bacteria isolates in BB. When the model was rerun 200 times on random subsamples of
the number of bacteria isolates from BB to match the number recorded in the SB samples
the difference between the two bays was significant in 86% of runs. This result was
consistent with findings during modelling of the proportion of antibiotic resistance, i.e.,
the proportion of antibiotic resistance of bacteria isolates in the BB was significantly
larger than in the SB (p-value = 0.024).
Since the bacteria isolates from the dolphin samples showed higher antibiotic
resistance in BB than in SB, we further studied how the antibiotic resistance differed
between the two bays for each antibiotic.
The resistance to each single drug varied widely (Figure 3.4 & Table 3.7).
Resistances to E (p-value=0.031) were significantly higher in BB than in SB while the
differences in resistance to CF (p-value=0.085), FEP (p-value=0.087) and SXT (p-value
=0.083) were marginally significant. As there was only one isolate of Nitratireductor
kimnyeongensis and 7 isolates (1 Nitratireductor aquibiodomus, 1 Halomonas
aquamarina and 5 S. maltophilia) from BB that were respectively resistant to CIP and
GM and no bacteria were resistant to CIP and GM in SB, there were not enough data to
compare the different resistance to CIP and GM between BB and SB. The resistance to
other antibiotics did not differ significantly between the two bays (Table 3.7).
47
Table 3.6 Sets of linear mixed model candidates used to predict antibiotic resistance after
multicollinearity check and random effect optimization.
Samples Multi ModelFixed
EffectsEstimate p-Value AIC
Dolphin NM R~B+(1|B/D)+(1|O) B -0.361 0.019 718.8
R~B+(1|B/D) B -0.379 0.014 720.1
R~B+(1|O) B -0.384 0.002 720.6
R~B B -0.405 0.001 722.4
Water NM R~B+(1|D) B 0.002 0.993 151.4
R~B B 0.002 0.993 149.4
Dolphin M R~B+(1|B/D)+(1|O) B -0.931 0.027 276.0
R~B+(1|B/D) B -0.931 0.021 275.6
R~B+(1|O) B -0.871 0.009 274.3
R~B B -0.880 0.008 273.3
Water M R~B+(1|D) B 0.526 0.533 57.4
R~B B 0.573 0.399 56.1
Dolphin NM P~B+(1|B/D)+(1|O) B -0.402 0.024 719.9
P~B+(1|B/D) B -0.436 0.012 722.3
P~B+(1|O) B -0.431 0.001 726.1
P~B B -0.467 4x10-5 728.4
Water NM P~B+D B 0.002 0.993 151.2
P~B B 0.002 0.993 147.2
Probability
Proportion
R denotes antibiotic resistance, P denotes antibiotic resistance proportion, B denotes bay, D denotes dolphin individuals, O denotes
organs, NM denotes not multi-resistance, M denotes multi-resistance, / denotes nested, and ~ denotes “as a function of”
48
Table 3.7 The results of mixed-effects model on the probability of resistance to each of
the nine antibiotics tested
Drug Model Fixed Estimate Std.Error Z-Value Pr(>|Z|)
AmC R~B+(1|B/D) B -1.222 0.804 -1.520 0.128
AM R~B+(1|B/D) B 0.201 0.430 0.468 0.64
FEP R~B+(1|B/D)+(1|O) B -1.732 1.013 -1.711 0.087
CF R~B+(1|B/D)+(1|O) B -1.051 0.610 -1.722 0.085
E R~B+(1|B/D)+(1|O) B -2.013 0.933 -2.158 0.031
TZP R~B+(1|O) B 0.140 1.233 0.113 0.910
TE R~B+(1|O) B -0.566 1.127 -0.502 0.616
NN R~B+(1|O) B 0.319 0.498 0.641 0.522
SXT R~B+(1|O) B -1.323 0.764 -1.732 0.083
R denotes antibiotic resistance, B denotes bay, D denotes dolphin individuals, O denotes organs, / denotes nested, and ~ denotes “as a
function of”
3.4 Discussion
We compared antibiotic resistance of bacteria in a heavily oil contaminated bay to
that in a climatically and hydrologically similar bay but free of oil contamination. We are
not aware of any previous studies of the effects of the 2010 BP DWH oil spill on
antibiotic resistance though studies exist on evaluating antibiotic-resistant bacteria in
BND along the southeast coast of the United States without influences of oil spills (Greig
et al., 2007; Schaefer et al., 2009, 2019; Stewart et al., 2014), including Florida, Georgia,
South Carolina and North Carolina’s coastlines (Figure 3.1). These studies showed
dolphins living in urbanized watersheds with high anthropogenic influences tend to
promote antibiotic resistance. The relationship between the increase of aquatic antibiotic
resistance and urbanization has been reported throughout the world (Almakki et al., 2019;
Honda et al., 2016; Ouyang et al., 2015). Therefore we would expect higher antibiotic
resistance in SB as the percentage of impervious areas (high, medium and low developed
49
areas), an indicator for urbanization (Li et al., 2016; Schueler et al., 2009), was much
larger in SB (16.14%) than in BB which was close to zero based on watershed boundary
data (USGS Watershed Boundary Dataset) and land use/land cover data of 2010 (NOAA
C-CAP data). In general, degradation of water quality would be expected when the
watershed had 10 to 25% impervious cover (IC) (Schueler et al., 2009). The opposite
findings from our study – higher antibiotic resistance in BB than SB - is, we hypothesize,
due to oil spill impacts though no causal inference can be made as no antibiotic resistance
data exist for BB before the BP DWH Oil Spill or other time. The oil spills can be very
intense like 2010 BP DWH Oil Spill or oil leaks at much smaller scale as many oil rigs,
oil platforms and pipelines are nearby though one would expect the impact from BP
DWH Oil Spill was at a much larger magnitude. Not only the bacteria found on dolphins
in BB had higher antibiotic resistance across the antibiotics tested, but the pathogenic
ones had higher abundance in BB or were only isolated from this location, including S.
maltophilia (4.29%) and P. aeruginosa (0.98%). Bacteria with higher resistance can
cause more serious infection and higher resistance can indicate poor health conditions of
the hosts. Therefore our result is consistent with the research that showed that BB's
dolphins were in worse health condition than those in SB with more lung disease and
impaired stress response in BB in 2011 (Schwacke et al., 2013). The data analysis of the
NOAA Health Assessment in 2013-2014 also found that the dolphins in BB had very low
reproductive success, high mortality (Lane et al., 2015), serious lung disease and
impaired stress response (Smith et al., 2017) and worse immune functions (De Guise et
al., 2017). On the other hand, the dolphins from non-oil contaminated areas, such as SB,
50
Indian River lagoon, FL and Charleston Harbor, SC had higher reproductive ratios
(Kellar et al., 2017).
Crude oil usually contains a number of toxic compounds including heavy metals and
aromatic compounds (Hudson et al., 2008). Therefore, intensive oil pollution like DWH
Oil Spill likely led to selective pressure on bacteria, so that the strains more resistant to
these toxic compounds were more likely to survive. This could favor the accumulation of
antibiotic resistance through the co-selection of bacterial resistance to heavy metals and
antibiotics. The co-selection could involve three mechanism: (a) co-resistance, i.e. heavy
metal and antibiotic resistance genes are carried by the same mobile elements, such as
plasmids, integrons and transposons; (b) cross-resistance, i.e. heavy metals stimulate the
antibiotic resistance efflux pumps; (c) co-regulation, i.e. heavy metals and antibiotic
resistance regulatory pathway are stimulated by either of them (Baker-Austin et al.,
2006). Under the pressure of heavy metals contained in crude oil, the bacteria with dual
resistance could survive (P. Gao et al., 2015a; Oyetibo et al., 2010; Seiler and Berendonk,
2012). The other mechanism could relate to the increase of aromatics in the environment.
The relationship between aromatics and antibiotic resistance is not as documented as the
co-resistance mechanisms discussed for heavy metals and thus still needs formal
investigation. Aromatics could increase ARB and ARG prevalence as follows 1)
aromatics might promote HGT of ARGs to spread resistance (Chen et al., 2017; Ghaima
et al., 2013; Hemala et al., 2014; Jiao et al., 2017; Máthé et al., 2012; Wang et al., 2017);
2) aromatics can shift the microbial community composition and diversity to change
antibiotic resistance in a selection process (Wang et al., 2014); this hypothesis is
supported by a recent study that reported that bacteria bearing aromatic degradation genes
51
(ADGs) harbored more ARGs than others, suggesting a new mechanism of aromatic-
induced antibiotic resistance (Xia et al., 2019).
The differences in antibiotic resistance observed between the two bays were mainly
contributed by the resistance to E, CF, FEP, followed by SXT. Resistance to other drugs
was also observed but did not differ between the two bays. This may be because oil
pollution promoted specific resistance genes, particularly aromatics could enrich ARGs
for ß-lactams (CF, FEP), macrolides (E) and sulfonamide (SXT) (Chen et al., 2017; Jiao
et al., 2017; Xia et al., 2019).
Some strains with resistance may have been missed using the culture-based method.
Culture-independent methods including qPCR and metagenomic method have been used
more widely to identify microbial communities and detect antibiotic resistance genes (Jia
et al., 2017; Su et al., 2017). With these methods, microbial communities and antibiotic
resistance prevalence can be estimated more accurately. This approach would be worth
investigating in future studies on comparing ARB profiles.
Our study showed much higher antibiotic resistance proportion in SB and BB in
2011 which were 81% and 85.5% respectively compared to the previous studies on the
Gulf Coast. The average antibiotic resistance prevalence in SB for 2004, 2005, 2006, and
2009 was 54.4% (Stewart et al., 2014). This increase of resistance might indicate a
temporal increasing trend of resistance at the SB. A study conducted in Indian River
Lagoon (IRL), Florida, close to SB, found that the Multiple Antibiotic Resistance (MAR)
index significantly increased from 2003 to 2015 for P. aeruginosa (0.54~0.63) and V.
alginolyticus (0.32 ~ 0.37) and had an overall 88.2% resistance prevalence ( Schaefer et
al. 2019).
52
There was no significant difference in the antibiotic resistance in water samples
between SB and BB, though the antibiotic resistance from the dolphin samples was
significantly different. The different results from water and dolphin samples may be due
to quicker recovery of water quality compared to recovery of dolphins. Previous research
showed the bacteria diversity declined significantly in the early stage of oil pollution,
probably due to the rapid proliferation of crude oil-degrading bacteria during
bioremediation treatments. But as soon as the concentration of crude oil in water
decreased, the bacterial community diversity recovered rapidly to pre-oiling levels (Y.
Gao et al., 2015; Röling et al., 2002). Thus, the lack of significant difference in bacteria
diversity between SB and BB could be due to a recovery of the bacterial fauna in BB.
On the other hand, dolphins who are long-lived and more advanced marine animals
may have more opportunities for marine resistant bacteria to attach and accumulate,
making dolphins recover much more slowly than water quality from the DWH Oil spill.
Also, the health of dolphins was still impaired and they could not fight pathogenic
including ARBs (De Guise et al., 2017; Handel et al., 2009).
A variety of bacteria were isolated from this study, and most were species that were
previously isolated from other studies (Buck et al., 2006; Greig et al., 2007; Morris et al.,
2011; Schaefer et al., 2009; Stewart et al., 2014; VennWatson et al., 2008). Among the
bacteria isolates, there are many opportunistic pathogens that may cause disease in
humans. When the humans are immunocompromised, they are more likely to get
infection. These bacteria were either found only in BB or, if they were found in dolphins
in both bays, in equal abundance and higher abundance in BB. V. alginolyticus is a
ubiquitous bacterium of marine and estuarine environments that can cause severe sepsis,
53
soft tissue infections, ear infections and extraintestinal infections. Since 2007, V.
alginolyticus has been the second most common species causing vibriosis in human. In
the USA, 1170 infections and 12 deaths were attributed to V. alginolyticus from 1988 to
2012 (Slifka et al., 2017). V. parahaemolyticus is also a common bacterium in water that
can cause gastroenteritis, wound infections, and sepsis. The main methods of
parahaemolyticus are edible raw seafood and open wound infections in seawater (Daniels
et al., 2000; Huang et al., 2018; Shaw et al., 2015). Both V. alginolyticus and V.
parahaemolyticus (Table 3.3 & Table 3.4) had similar abundance in SB and BB. In
contrast, abundance of P. aeruginosa and S. algae was higher in BB than in SB. P.
aeruginosa is an important nosocomial pathogen that commonly colonizes hospital water
supplies but is also ubiquitous in natural aquatic environments. P. aeruginosa mainly
leads to three types of diseases, bacteremia in burns, cystic fibrosis of chronic lung
infections, and acute keratitis (Lyczak et al., 2000). S. algae is also a common bacterium
in food, sewage, and natural water environments. S. algae was not a common pathogen in
the past, but now more and more infections due to S. algae are occurring globally. When
the climate gets warmer, S. algae will multiply in water. Infections by S. algae may be
fatal to humans if not treated in time. S. algae usually causes gastrointestinal illness, skin
and soft tissue infection. Severe soft tissue infection can require amputation treatment
(Sharma and Kalawat, 2010).
Besides the common species, there were some unique isolates that were not found in
dolphins before such as S. maltophilia that was found in BB only. Five strains of S.
maltophilia were resistant to 2, 3, 6, 7 and 8 antibiotics respectively. Although this
bacterium is common in nature, it has been mostly found together with P. aeruginosa in
54
medical centers. The WHO also lists S. maltophilia as the leading hospital-resistant
pathogen (Brooke, 2014). According to the available medical data, one of the biggest
features of S. maltophilia is its extensive resistance spectrum that includes ß-lactams,
carbapenems, macrolides, cephalosporines, fluoroquinolones, aminoglycosides,
chloramphenicol, tetracyclines, and polymixins (Brooke, 2012). The current effective
drug for the treatment of S. maltophilia is SXT, but in recent years reports of strains
resistant to SXT have been on the rise (Al-Jasser, 2006; Toleman et al., 2007). It is worth
noting that all five S. maltophilia strains isolated in the BB samples are resistant to SXT
in this study. As far as we know, this is the first study to detect SXT resistance in the
Gulf of Mexico and previous reports were of ubiquitous presence in clinical facilities,
farmed animals or wastewater treatments. Thus the resistance of S. maltophilia has spread
to the natural environment, and it can cause various serious infections, including
pneumonia, pulmonary disease, bloodstream bacteremia, myositis, soft skin and tissue,
urinary tract infection and etc. (Bin Abdulhak et al., 2009; Downhour et al., 2002; Ewig
et al., 2000; Fujita et al., 1996; Vartivarian et al., 1996). It has caused several infections
in recent years that are difficult to cure (Brooke, 2012; Furushita et al., 2005). The
isolation of such pathogenic bacteria that are difficult to treat indicates risks to humans
living on the coast of BB. While the species discussed above are known pathogens, the
virulence of the strains detected in this study is not known.
As antibiotic resistance is a global health problem, we suggest antibiotic resistance
be considered as a bioindicator for measuring the health of marine animals and water
quality impacted by pollution. In fact, people have been testing the drug-resistant bacteria
in water, and used it as one of the indicators for water quality, though they focus on
55
E.coli concentration and its drug resistance (Ng et al., 2018; Overbey et al., 2015). Other
bacteria’s infectivity and drug resistance may be higher than E. coli and more abundant in
polluted ocean than wastewater. For example, the Vibrio, including V. alginolyticus, V.
parahaemolyticus and Vibrio vulnificus. They caused infections to humans along the
GOM every year. With the spread of global drug resistance, Vibrio in seawater is also
more resistant now than in the past. Detecting antibiotic resistant bacteria in water bodies
and mammals could inform more effective controls of water quality and provide better
guidance on beach closure decisions etc. to protect human health.
Also, there were limitations of this study. To our knowledge, there was no data on
ARBs and ARGs of bottlenose dolphins before DWH at BB (control groups), so we
couldn’t compare the data before and after DWH at BB. However, the data of this study
provided a baseline for future ARBs and ARGs researches on bottlenose dolphins at BB.
3.5 Conclusion
We found that bacteria isolated from the bottlenose dolphins in Barataria Bay, a
location heavily contaminated by 2010 BP DWH Oil Spill, had higher antibiotic
resistance and higher abundance of pathogens, one year after the incident, compared to
those from Sarasota Bay, a reference location, though bacteria diversity in water and
dolphin samples, or antibiotic resistance in water samples did not show significant
difference between the two bays. The difference of antibiotic resistance in the dolphin
samples was mainly attributed to resistance to E, CF, FEP and SXT. Bacteria isolated
from Barataria Bay also had more multi-drug resistance than those isolated from Sarasota
Bay. The antibiotic-resistant profiles can reflect the health of animals and water quality
and therefore potential risks to human health; thus, we suggest it routinely used as a
56
metric for ecosystem health in the future. The antibiotic resistance data gathered in this
research will fill in the important data gaps and contributes to the broader spatial-scale
emerging studies on antibiotic resistance in aquatic environment.
57
CHAPTER IV – ANTIBIOTIC RESISTANCE IN THE LOWER PASCAGOULA
RIVER IN THE NOTHERN GULF OF MEXICO
4.1 Introduction
In the aquatic environment, the spread of antibiotics and resistant bacteria is a
growing concern with implications in negatively affecting ecosystem functions and
public health (Costanzo et al., 2005; Schwartz et al., 2003). Antibiotics affect the
diversity of natural bacterial communities and could shift the structure of local microbial,
which could affect the degradation of waste and toxin in the water (Aminov, 2009;
Grenni et al., 2018). Resistant bacteria could be harder to be treated after causing
infections. Research has shown that the abundance of antibiotics in the coastal and
marine environment has increased in the past decades due to more effluent from WWTPs,
antibiotics used in aquaculture, oil pollution and transportation (Allen et al., 2010;
Cabello, 2006; Di Cesare et al., 2012; Zou et al., 2011). The sewage finally got most of
the antibiotics of human antibiotic used in households. Therefore, urban wastewater
treatment plants (WWTPs) are among the main sources of both antibiotic-resistant
bacteria (ARB) and antibiotic-resistance genes (ARGs) released into the environment
(Karkman et al., 2017, Barancheshme and Munir, 2019; Luczkiewicz et al., 2015; Su et
al., 2020). Aquaculture and mariculture is another point of source of ARB and ARGs,
especially in Asian countries that have huge aquaculture industries (Muziasari et al.,
2017; Wang et al., 2018; Xiong et al., 2015). Besides ARGs and ARB directly from
WWTPs and aquaculture farms, selective pressure can accelerate the growth of ARB and
spread of the ARGs, such as water pollution due to oil spills (Chapter 3), heavy metals,
and marine transportation (Singer et al., 2016; Wang et al., 2017). Meanwhile, cases of
58
fish-borne and water-borne human infections have increased, and antibiotic drugs show
less and less effective in treating these bacteria driven diseases which can lead to lethal
illness and even death.
ARGs are the foundation of resistance (Pruden et al., 2006), and therefore the
abundance and diversity of ARGs can serve as indicators of the fitness of antibiotic-
resistant bacteria and their potential threat to humans and other organisms. Evidence
suggests that more polluted areas had higher ARG abundance and higher resistance ratio
of the bacteria isolates (P. Gao et al., 2015b; Pei et al., 2006; Wang et al., 2013).
Therefore, examining ARG profiles in waters potentially impacted by sources of
pollution is important to understand water quality and related potential risks to the local
business and economy, and public health, especially in coastal water bodies which link
the upland and marine environment. It is suggested that ARGs should be regularly tested
to indicate water quality in addition to the Enterococci monitoring (see Chapter 2).
Aquatic ecosystems continuously accumulate human pathogens released into the
environment and boost the exchange of ARGs between bacterial species via the
horizontal gene transfer (HGT) (Rizzo et al., 2013). In general, HGT has three ways: 1)
transformation: bacteria directly take up naked DNA from the environment, 2)
transduction: bacteria get genetic elements via introduction of bacteriophages, and 3)
conjugation: bacteria get new genes via physical contact between two cells. Among the
three ways, transformation is the most common pathway (Blair et al., 2015b; Huddleston,
2014; Ochman et al., 2000). The selection pressure like more antibiotics in the water,
heavy metals and oil pollutions of antibiotic-resistant strains further increases ARGs’
concentration (Bergeron et al., 2015).
59
Resistances to sulfonamides are the most common antibiotic resistance as
sulfonamides are the first antibiotic developed for large-scale clinical use. Sulfonamides
target dihydropteroate synthase (DHPS) (Alekshun and Levy, 2007), a type of bio-
enzyme responsible for folate bio-synthesis that is associated with thymine production
and microbial growth (Brochet et al., 2008), often encoded by mutations located at highly
conserved areas of DHPS genes (sul) (Sköld, 2000). Sulfonamide resistance was mostly
generated by changes in the sul genes and mediation by mobile elements (Antunes et al.,
2007; Huovinen et al., 1995). Four kinds of sul genes (sul1, 2, 3, and 4) have been found
in the bacteria in the environment that are resistant to antibiotics. Sul1 and sul2 have been
detected from different places including fecal slurry of dairy farms (Srinivasan et al.,
2005), water and sediments close to aquaculture facilities (Agersø and Petersen, 2007;
Akinbowale et al., 2007), clinic facilities (Blahna et al., 2006; Phuong Hoa et al., 2008),
and unpolluted river or seawater (Hu et al., 2008; Lin and Biyela, 2005) across the world.
Compared to Sul1 and Sul2, Sul3 is more frequently discovered from non-culturable
communities in seawater than from bacterial isolates (Grape et al., 2003; Suzuki and Hoa,
2012). Sul1, as a part of class 1 integron, can be disseminated and transferred
horizontally within and between bacterial species in wastewater (Tennstedt et al., 2003),
river water (Mukherjee and Chakraborty, 2006), and sea water (Taviani et al., 2008). Sul4
is the latest mobile sulfonamide resistance gene discovered from sediments of a
wastewater treatment in India in 2017 (Razavi et al., 2017).
Tetracycline-resistance is another common antibiotic resistance found to emerge in
the natural environments with the introduction of tetracycline (Dancer et al., 1997). There
have been at least 45 different tetracycline resistance (tet) genes characterized to date
60
(Roberts et al., 2012; Thompson et al., 2007), including 29 genes for efflux proteins
(efflux pump mechanism), 12 genes for ribosomal protection proteins (target
modification mechanism), three genes for an inactivating enzyme, and one gene with
unknown resistance mechanism (Alekshun and Levy, 2007; Roberts et al., 2012).
In addition to resistance genes, mobile elements are another major mediator of
ARGs’ transmission. Class 1 integron is a very important mobile element for Sul1.
Integrons are an ancient and common feature of bacterial genomes, and they usually
reside at chromosomes (Gillings, 2014). They have three core features: an integron-
integrase gene (intI), a recombination site (attI) and a promoter (Pc). These features allow
capture and expression of exogenous genes as part of gene cassettes that are recombined
into the attI sited using the integrase activity encoded by intI (Boucher 2007: Cambray
2010). This allows genes to be acquired and expressed with minimal disturbance to the
existing genome. Integrons sample cassettes from an extraordinary diverse pool that
encodes functions of potential adaptive ability of bacteria. Consequently, they are a hot
spot of genomic diversity in a range of genera (Gillings et al 2005; Boucher et al.2007).
Here I aim to investigate the seasonal occurrence of ARGs potentially impacted by a
municipal wastewater treatment plant (WWTP) in the lower Pascagoula River on the
Mississippi Gulf Coast. This research specifically targeted sulfonamide and tetracycline
ARGs because corresponding antibiotics were widely detected in clinical facilities and
natural environment in the USA (McKinney et al., 2010). The hypotheses are as follows:
H1: Wastewater treatment plant (WWTP) contributes to ARGs. More specifically,
the further away from the WWTP in the downstream, the lower the concentration of
ARGs.
61
Hypothesis 2: The concentrations of ARGs show seasonality, no matter where they
are relative to the WWTP.
As there have been few studies on the ARG abundance along the north-central
coastlines in the Gulf of Mexico (GOM) (Cetina et al., 2010), this study filled in critical
data gap on ARG profiles in this region.
4.2 Methods
I applied both culture-dependent and molecular methods to determine the existence
of ARGs and their relative concentrations. I collected surface water samples at upstream,
outflow, and downstream of a WWTP at lower Pascagoula River in southeastern
Mississippi, USA from February to November in 2016. I examined the antibiotic
resistance profiles of bacteria isolated from these water samples using R2A mediums. I
also applied traditional PCR to confirm the existence of selected ARGs (Table 4.1). I
further quantified concentrations of existing ARGs using qPCR. With these molecular
methods, I analyzed all the bacteria in the samples, including both culturable and
nonculturable ones, which enabled a more comprehensive analysis of antibiotic resistance
profiles.
4.2.1 Study Area
My study area is eastern Pascagoula River in southeastern Mississippi, USA (Figure
4.1). Pascagoula River is the largest undammed river in the continental US with an
average discharge of 11520 ft3/sec from 1994 to 2007 (https://waterdata.usgs.gov/nwis)
(Wu et al., 2020). It is about 80 miles (130 km) long, draining an area of about 8,800
square miles (23,000 km²) and flows into the Mississippi Sound (Scientific Investigations
Report, 2019). The eastern river is adjacent to the City of Pascagoula and has one
62
municipal wastewater treatment plant located at the riverbank. The river provides plenty
of recreation activities like boating, fishing etc., and is regularly dredged for maritime
transportation. As the water body is heavily utilized by humans, the ARGs in it pose a
threat to humans’ health.
4.2.2 Field Sampling
I systematically selected 5 sites along the eastern lower Pascagoula River: two sites
located at upstream of the Pascagoula wastewater treatment plant, one at the outflow of the
plant, and the other two sites at the downstream of the plant. I collected 1000 ml of surface
water from each site for five months in 2016 (February, April, May, August, and
November). I collected duplicate samples at each sampling site. One sample was processed
using culture-dependent methods and the other was processed using molecular methods. I
eventually processed and analyzed the water samples of five months that represent four
seasons at five sites (Site 1- Site5 in Figure 4.1). With two months in spring, I could derive
how different ARGs are within the same season. While I sampled the surface waters, I also
measured surface water temperature, pH, and salinity using YSI Model 30 (YSI
Incorporated, OH USA) and Orion Start ph Meter (Thermo Scientific, MA USA).
63
Figure 4.1 Sampling sites along lower Pascagoula River (Green Pin: Sampling Sites, Red Pin: WWTP,
Orange Star: Ingalls Shipbuilding)
4.2.3 Culture-based method using antibiotic-selective media
The water samples were spread-plated in onto (50ul) three R2A plates (Reasoner and
64
Geldreich, 1985) containing antibiotics of 80 mg/L sulfamethazine, 50 mg/L Ciprofloxacin,
and 20 mg/L tetracycline respectively. Plates were incubated at 37 oC for 48 h at room
temperature, shielded from light to prevent antibiotic degradation, and then for another
week to ensure that slow-glowing organisms were included.
4.2.4 DNA Extraction and purification
Water samples (1.0L) were filtered through a 0.22um polycarbonate membranes
(GTTP, Millipore, USA) filter using a vacuum filtration apparatus and DNA was
extracted from water samples using the E.Z.N.A.® Water DNA Kit (Omega Bio-tek,
Norcross, GA) following the manufacturer’s protocol. Concentrations and quality of the
extracted DNA were checked by spectrophotometric analysis on a NanoDrop 2000
(Nanodrop, USA). The extracted DNA was stored at -80 °C for PCR and qPCR analysis.
4.2.5 PCR assays for detection of resistance genes
Traditional PCR assays were performed to test the existence of two sulfonamide
ARGs, two tetracycline ARGs and two integron genes commonly found in aquatic
environment. The PCR mixtures (25ul total volume) consisted of 1 x PCR buffer, 2 mM
MgCl2, 1.6 units Taq Polymerase, 0.2 mM dNTP mixture, 1 µM each forward and
reverse primer, and 1ul (20 to 30 ng) of genomic DNA extracted earlier as template in a
volume of 25ul. PCR reactions included 5-min initial denaturation at 95oC, followed by
35 cycles of denaturation for 30s at 95oC, 30s at the annealing temperature (55.9 oC for
sul1, 60.8oC for sul2, 60oC for tet(W), 50.3 oC for tet(O), 57oC for intI, 56oC for intII and
55oC for 16s), an extension for 30 s at 72oC, and a final elongation at 72oC for 10 min in
Thermal Cycler (Bio-Rad Laboratory, CA, USA). I further ran all the PCR products on
65
1.5% agarose gels containing 0.1 % ethidium bromide for visualization of the fragments
and shipped to Eurofins Genomics Company (Louisville, KY, USA) for sequencing.
4.2.6 Real-time quantitative PCR (qPCR) to quantify ARGs
While traditional PCR was used to test the existence of the ARGs, I used qPCR to
quantify the concentrations of existing ARGs (Pei et al., 2006). Most bacterial species
could be distinguished by partial V4 region in 16s rRNA gene (Kozich et al., 2013).
I performed the qPCR using a PowerUp SYBR Green Master Mix (Thermo Fisher
Scientific Baltics UAB, Lithuania) and a 7500 Fast Real-time PCR system (Applied
Biosystems, Foster City, CA, USA). Reactions were conducted in 10ul volumes on 96-
well plates containing 1x SYBR Green Master mix, 0.3uM of each primer, and 1ml of
templated DNA. The thermal cycling conditions were as follows: 50oC for 2 minutes
(UDG activation), 90oC for 2 minutes (Dual-lock DNA polymerase), 40 cycles consisting
of the following: (i) 95 oC for 15 seconds (Denature), (ii) optimal annealing temperature
(Table 1) for 15 seconds , (iii) 72 oC for 1 minute (Extend), and melting curve stage
including: (i) 95 oC for 15 seconds, (ii) 60 oC for 1 minute, (iii) 95 oC for 15 seconds.
The optimal annealing temperature for the qPCR was determined using PCR
machine with thermal gradient feature. I tested a range of temperatures above and below
the calculated annealing temperature of the primers (5oC lower than the lower melting
temperature (Tm) of the primer pair). The annealing temperature that yielded the lowest
quantification cycle (Cq) and no nonspecific amplification was selected for assays for
each locus.
66
Table 4.1 PCR primers and resistance genes
PrimerGene
TargetedSequences
Annealing
Temp(°C)
Amplicon
size(bp)
sulI-FW sul(I) CGCACCGGAAACATCGCTGCAC 55.9163(Pei et al.,
2006)
sulI-RV TGAAGTTCCGCCGCAAGGCTCG
sulII-FW sul(II) TCCGGTGGAGGCCGGTATCTGG 60.8191(Pei et al.,
2006)
sulII-RV CGGGAATGCCATCTGCCTTGAG
tetO-FW tetO TGCAACACTGGACTACGCAT 50.3171(Aminov et
al., 2001)
tetO-RV AACACCGACCATTACGCCAT
tetW-FW tetW GAGAGCCTGCTATATGCCAGC 60168(Aminov et
al., 2001)
tetW-RV GGGCGTATCCACAATGTTAAC
tetM-FW tetM ACAGAAAGCTTATTATATAAC 60171(Aminov et
al., 2001)
tetM-RV TGGCGTGTCTATGATGTTCAC
tetS-FW tetS GAAAGCTTACTATACAGTAGC 60169(Aminov et
al., 2001)
tetS-RV AGGAGTATCTACAATATTTAC
intI-FW intI GGCTTCGTGATGCCTGCTT 57146(Luo et al.,
2010)
intI-RV CATTCCTGGCCGTGGTTCT
intII-FW intII GTTATTTTATTGCTGGGATTAGGC 56164(Luo et al.,
2010)
intII-RV TTTTACGCTGCTGTATGGTGC
qnrA-F qnrA ATTTCTCACGCCAGGATTTG 56516(Robicsek et
al., 2006)
qnrA-R GATCGGCAAAGGTTAGGTCA
1369F 16s CGGTGAATACGTTCYCGG 55123(Suzuki et
al., 2000)
1492R GGWTACCTTGTTACGACTT
4.2.7 Q-PCR standard curves and quantification
I generated the standard curves of each ARG and 16s using recombinant plasmids in
order to determine the concentrations of ARGs. Product from the traditional PCR was
purified from the agarose gel with Gel Extraction kit (Biogreen, USA) and cloned into
67
the linearized pMiniT 2.0 vector using the NEB PCR Cloning Kit (New England
Biolabs). Recombinant plasmids were transformed into competent Escherichia coli. The
E.coli cultures containing positive plasmid controls were selected via Blue-White Screen
method. Also, the positive isolates were inoculated into 10ml Luria-Bertani (LB) broth
with ampicillin (100ug/ml) and cultured overnight at 37oC with agitation (200rpm). All
plasmids were extracted with the Plasmid kit (Biogreen, USA) and used for producing
qPCR standard curves after determination of concentrations of the standard plasmids
(ng/ul) with the Nanodrop 2000 (Nanodrop, USA). The copy concentration (copies/ul)
was calculated by the following formula(Pei et al., 2006) (Eq. 1):
𝐶𝑜𝑝𝑦 𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛(𝑐𝑜𝑝𝑖𝑒𝑠/𝑢𝑙) =𝑎
𝑏× 𝑐 × 10−9 Eq 1
where a is the DNA mass concentration (ng/ul), b is the DNA molecular weight
(g/mol) and c is Avogadro’s constant (6.022x 1023/mol).
To determine the qPCR primer efficiency, the target amplicons was quantified in five
dilutions of the extracted DNA (1:1, 1:2, 1:3, 1:4, 1:5) following the qPCR protocols
mentioned above. The efficiency of PCR reached 90% to obtain reliable quantification.
At the optimal annealing temperatures, the R2 of the standard curves over the five
concentrations and amplification efficiencies based on slopes from 90% to 110% (slope
of 100% indicates that concentration is equal to efficiency) should be higher than 0.99.
Ten-fold dilutions of purified recombined plasmid DNA containing known
concentrations were used as qPCR templates to generate standard curve using the
software of ABI7500fast qPCR machine.
The ratio between absolute concentration ARGs and 16s rRNA was defined as the
relative concentration of the ARGs (from 0 to 1). Compared to the absolute
68
concentration, the relative concentration was more reliable and could show the real shift
of the gene in the microbial community. Therefore, I used relative concentrations in my
data analysis.
4.2.8 Data analysis
I first applied logit transformation on the relative concentrations and then developed
a suite of mixed-effects models to examine how the transformed relative concentrations
of ARGs responded to distance from WWTP, temperature, salinity, and pH with month
as a random effect. The models contained different combinations of these covariates and
their two-way interactions. I applied Variance Inflation Factor (VIF) to detect
multicollinearity in the models. Any variable with a large VIF value (above 5 or 10) was
removed from the models. I implemented “VIF” function in the “CAR” package in R
(Fox and Weisberg, 2019; R Core Team, 2018). I then selected the best model candidates
based on Akaike Information Criterion corrected for small sample size (AICc) (Hurvich
and Tsai, 1989). The lower the AIC, the better the model predicts. To implement the
mixed-effects models, I applied “lmer” function in the “lmerTest” package in R
(Kuznetsova et al., 2017; R Core Team, 2018). To calculate AICc, I implemented “AICc”
function in the “AICcmodavg” package in R (Mazerolle, 2019).
4.3 Results
I was able to detect and quantify three different ARGs commonly found in the
natural environment: Suf1, Suf2, and Int1. The lowest relative concentrations of all three
ARGs occurred in the summer with the highest temperature. The best models show that
the relative concentrations of all three genes decreased with distance from the WWTP.
However, the relative concentration of Sulf1 also decreased with salinity or temperature,
69
so it is not clear whether the decrease of Sulf1 was due to being away from the WWTP,
or due to higher salinity or temperature. The models for the Suf2 and Int1 showed that the
WWTP was likely a source of ARGs.
The water temperature ranged from 11oC in February to 29.4oC in August. The
salinity ranged from 0.6 ppt in April to 29.5 ppt in November. The pH ranged from 6.3 in
April to 7.7 in November.
4.3.1 Culture-based methods
All the water samples showed resistance to sulfamethazine and tetracycline. None of
them showed resistance ciprofloxacin.
4.3.2 Regular PCR test
In all of the water samples, I detected sul1, sul2, and int1. I did not detect Tet genes
(tetW, tetM, tetO, tetS), int2, or qnrA. Some of the samples had tetW or tetO genes,
however, the results were no stable and not included here.
4.3.3 qPCR test
The relative concentrations of Sul1, sul2 and int1 varied with temperature. When the
temperature was highest in August, the concentration reached the lowest levels at most
sites. After that, the concentration rose back as the temperature dropped (Figure
4.2~Figure 4.4). Within the same season, different months showed different
concentrations of ARGs (April and May), showing the need to sample more frequently.
Generally, the highest concentrations of resistance genes relative occurred at Site 2
and 3 – the outflows of the WWTT and the closest site in the downstream of the
outflows. Then the concentrations decreased along the downstream (site 4 and site 5)
70
except Suf2 in February and August. With the exception of the pattern in August, the
farthest sample point, site 5, had a lower concentration than that at site 4.
Figure 4.2 Gene copy number Sul1 normalized to 16SrDNA gene copy numbers.
1.00E-04
1.00E-03
1.00E-02
1.00E-01
1 2 3 4 5
Rel
ativ
e co
nce
ntr
atio
n o
f Su
l1(c
op
ies/
16
srD
NA
)
Sampling Site
Sul1 Mean+SD
FEB
APR
MAY
AUG
NOV
71
Figure 4.3 Gene copy number Sul2 normalized to 16SrDNA gene copy numbers
Figure 4.4 Gene copy number Int1 normalized to 16SrDNA gene copy numbers
4.3.4 Model Analysis Results
The pH was removed from the models due to multicollinearity. The pH was highly
correlated to distance. Sul1 dominated the change when we analyzed three genes
1.00E-04
1.00E-03
1.00E-02
1.00E-01
1.00E+00
1 2 3 4 5R
elat
ive
con
cen
trat
ion
of
Sul2
(co
pie
s/1
6sr
DN
A)
Smapling Site
Sul2 Mean+SD
FEB
APR
MAY
AUG
NOV
1.00E-04
1.00E-03
1.00E-02
1.00E-01
1.00E+00
1 2 3 4 5
Rel
ativ
e co
nce
ntr
atio
n o
f In
t1(c
op
ies/
16
srD
NA
)
Sampling Site
Int1 Mean+SD
FEB
APR
MAY
AUG
NOV
72
together. All the models for Suf1 performed similarly based on the AICs. They are all
single-covariate model with distance from the outflows of the WWTP, or temperature, or
salinity respectively (Table 4.2), indicating the relative concentration of Suf1 decreased
with distance from the WWTT , salinity, or temperature. Only temperature’s effect was
significant. None of the two-way interactions were included, indicating the interactions of
these covariates may not be important to predict concentrations of ARGs based on the
data I have. The best models for Suf2 and Int1 both have distance from the outflows as
covariate, indicating the relative concentrations of Suf2 and Int1 were affect by distance
from the WWTT, though not significantly, probably due to small sample size.
Table 4.2 Best model for Sul1, Sul2 and Int1
Gene Model Fixed Estimate p-Value AICc
All genes C~G+L+(1|M) GSul1 -1.3626 0.0001 276.6
Gsul2 -0.585 0.1204
L -0.5618 0.0908
C~G+(1|M) GSul1 -1.3626 0.0001 276.76
Gsul2 -0.585 0.1163
Sul1 C~L+(1|M) L -0.676 0.1641 92.97
C~T+(1|M) T -0.2207 0.0182 90.63
Sul2 C~L+(1|M) L -0.108 0.887 107.97
Int1 C~L+(1|M) L -0.9014 0.0806 94.03
C denotes antibiotic resistance gene relative concentration, L denotes distance, G denotes gene types, P denotes pH, T denotes
temperature, M denotes month, B denote location and ~ denotes “as a function of”
4.4 Discussion
In the Gulf of Mexico, the research of ARBs and ARGs mainly concentrated in
Louisiana (LA) or Florida (FL) (Belding and Boopathy, 2018; Bergeron et al., 2016;
Unger et al., 2014). In the North Gulf of Mexico, studies on ARB before 2010 were
available in FL, Alabama (AL), and LA, but in the past decade, only the research on
73
ARGs related to WWTP was done in LA (Moore et al., 2005; Naquin et al., 2015, 2017;
Parveen et al., 1997). Mississippi did not report any studies on ARB or ARGs of WWTP.
In the past 10 years, the studies on WWTP-related ARGs have kept increasing globally
(Pazda et al., 2019). People have realized the importance of WWTP in spreading of
ARGs, and the research in North Gulf of Mexico obviously lags the global research.
I found the ARBs in the lower Pascagoula River were mainly resistant to
sulfamethazine and tetracycline, not to ciprofloxacin using the cultural method. In the
subsequent traditional PCR tests, sul1, sul2 and Int1 genes related to sulfamethazine were
detected, while qnrA gene related to ciprofloxacin was not detected in all water samples,
confirming the findings on resistance to sulfamethazine using the cultural method. This
may be due to the more widespread use of sulfamethazine and tetracycline in the area.
Sulfonamides are used both in animals and in humans, though generally more-so in
humans. They are used primarily as therapeutic agents, rather than as routine growth-
promoters. The target of sulfonamide antibiotics is the enzyme dihydropteroate synthase
(DHPS) in the folic acid pathway. Sul(I) and sul(II) are two alternative sulfonamide
resistant DHPS genes found in Gram-negative bacteria (Sköld, 2000). In a case study of
pathogenic E. coli from various livestock in Switzerland by Lanz et al. (2003), about 70%
of the sulfonamide-resistant isolates from pigs could be explained by the presence of
sul(I) and sul(II). Both genes have also been detected in human pathogens such as
Salmonella typhimurium, E. coli, and Streptococcus, Pneumoniae. The most commonly
detected sulfonamide resistance genes in this study were also mainly sul(I) and sul(II).
However, although bacteria from all the water samples had tetracycline resistance
based on the cultural method, the target genes tetO, tetW, tetM and tetS were not
74
detected. The other target genes tetA, tetX, tetQ and tetG may exist but I did not check
them as they were not common in the natural environment. In the future studies the other
target genes need to be examined.
The WWTP at the lower Pascagoula River implemented the traditional processing
method, including (1) preliminary treatment (removing floating materials and settleable
inorganic solids) (2) primary treatment (removing fine suspended organic solids), and (3)
secondary or biological treatment (removing dissolved and fine colloidal organic matter
with activated sludge, lagoon, sequencing batch reactor and aerobic digestion). It will
implement tertiary or advanced treatment (removing solids, nitrogen, phosphorus and
disinfecting with chlorine, ozone, ultra-violet rays, gamma rays, Fenton-reaction and
solar-driven Fenton oxidation) only under high sewage discharge (EPA, 2004; Kumar,
2015). My study showed that the ARB and ARGs were not removed completely after
wastewater treatment, as in many other studies (Brooks et al., 2007; Munir et al., 2011;
Reinthaler et al., 2003). The residues will finally enter the aquatic and terrestrial
environment via wastewater discharge and land application of biosolids. The routine
monitoring of sewage and surface water quality takes into account neither the presence of
antibiotic resistant bacteria nor ARGs (Munir et al., 2011; Novo and Manaia, 2010), so it
is not clear how efficient the WWTT reduced ARGs. Regular monitoring of ARB and
ARGs is necessary to fill in the data gap. It may require the WWTP to implement some
effective methods, such as high-concentration UV irradiation (now limited to laboratory)
or Membrane Bioreactor (MBR) technology (Pazda et al., 2019) to remove ARGs.
The relation between relative concentrations of ARGs and distance could be
explained by dilution effect or ARGs depositing into the sediments. The fluctuations of
75
ARGs with the distance may be due to the river being heavily influenced by human
activities other than WWTP. For example, it is regularly dredged to allow maritime
transportation that likely bring in external ARGs. Also, the Ingalls Shipbuilding is on the
west bank of the river and the during the building of ships, heavy metals could get into
the water around it. Heavy metal could be a selective pressure for ARGs and ARB due to
co-selection function of heavy metal resistance genes and ARGs (Seiler and Berendonk,
2012).
I also found salinity decreased relative concentrations of Sulf1. The current literature
on the relation between salinity and ARGs can go different directions. Bergeron
(Bergeron et al., 2016) found that salinity did not affect the existence of ARB, but it
affected the types and abundance of ARGs. When the salinity increased from 0 to 12 ppt,
the prevalence of ARGs increased, and then the prevalence decreased when the salinity
continued to increase. Liu's study in 2018 (Liu et al., 2018) reported sul1 gene
concentration decreased from 1.04x108 copies / ml to 8.34x105copies / ml when salinity
increased from 20 to 45 ppt in water. This change might be due to that bacteria had
reduced the entry of foreign substances in order to maintain osmotic pressure balance.
Other studies also show that suf1 in water and soil decreased with salinity by promoting
elimination of resistant plasmids or hindering conjugation transfer of resistant plasmids
(Tan et al., 2019).
My study in this study showed decreased ARGs with increase of temperature.
Different relations existed in the previous studies. Some reported that summer was the
optimal season for ARGs proliferation (Chen et al., 2013; Mao et al., 2015; Sui et al.,
2011), while others reported higher concentration of int1 in winter (Koczura et al., 2016).
76
Furthermore, there are studies that reported only slightly variation or inconsistent
between the seasons (Chen et al., 2013; Yuan et al., 2014) or ARGs increased with
temperature (MacFadden et al., 2018). In my Chapter 1 of meta-analysis, I found there
existed an optimum temperature for relative concentrations of ARGs below which
relative concentrations of ARGs increased with temperature while beyond which they
increased with temperature. All these indicate large variability of this relation, which may
depend on the gene types, variation in precipitation and temperature, antibiotic usage,
geographical location, hydrodynamic conditions and disposal practice (Awad et al., 2015;
Devarajan et al., 2016). In addition, I did not find any interactions between different
environmental factors could predict the concentrations of ARGs, which may be due to the
relatively small sample size.
4.5 Conclusion
This study showed that WWTP contributed ARGs to the receiving river. In addition,
temperature and salinity could affect ARGs. We suggest ARB and ARGs at the WWTP
be monitored regularly and the risks to residents be formally assessed to determine if
removal treatments need to be implemented in the WWTP.
77
CHAPTER V – CONCLUSION
In this study, I investigated the possible effects of the man-made disaster (2010 BP
Oil Spill) and daily activities (waste water treatment plants), combined with
environmental factors, on antibiotic-resistant bacteria (ARB), antibiotic resistance ratio
(ARR), and antibiotic-resistant genes (ARGs) in the aquatic environments. My research
covers global to local scales, including global-scale meta-analysis of ARGs close to
wastewater treatment plants or aquaculture facilities, gulf-scale analysis of ARB and
ARR in the bottlenose dolphins impacted by 2010 BP Oil Spill, and local-scale ARGs
affected by a wastewater treatment plants. The comprehensive study advances the
knowledge of emerging conditions of ARB and ARGs and their potential drivers.
Specifically, my major findings are as follows:
1) Across the world, regional-scale climatic variables played a more important role in
predicting concentrations of ARGs than the country-scale socio-economic variables.
More specifically, the concentrations of ARGs increased with precipitation, and there
existed an optimal temperature where the concentrations of ARGs reached a maximum.
While precipitation showed a similar impact on the concentrations of ARGs globally,
the impact of temperature varied by country. The dominant role of climatic variables
in ARGs demonstrates the difficulty in controlling the spread of antibiotic resistance,
especially considering climate change.
2) In the northern Gulf of Mexico (NGOM), bacteria isolated from the bottlenose dolphins
in Barataria Bay, a location heavily contaminated by 2010 BP DWH Oil Spill, had
higher antibiotic resistance and higher abundance of pathogens, one year after the
incident, compared to those from Sarasota Bay, a reference location, though bacteria
78
diversity in water and dolphin samples, or antibiotic resistance in water samples did
not show significant difference between the two bays. The difference of antibiotic
resistance in the dolphin samples was mainly attributed to resistance to E, CF, FEP and
SXT. Bacteria isolated from Barataria Bay also had more multi-drug resistance than
those isolated from Sarasota Bay. This is the first research related to the impact of 2010
BP Oil Spill on ARB in dolphins along the NGOM.
3) In the eastern Pascagoula River at the local scale, I was able to detect and quantify three
different ARGs commonly found in the natural environment: Suf1, Suf2, and Int1. The
lowest relative concentrations of all three ARGs occurred in the summer, and the
highest relative concentrations occurred in February or April. The relative
concentrations of all three genes decreased with downstream distance from the WWTP.
Additionally, the relative concentration of Sulf1 also decreased with salinity or
temperature.
The findings showed that anthropogenic pollutions increased ARB, ARR or
ARGs in the aquatic systems, meanwhile climatic factors played an important role in
affecting antibiotic resistance. The impact of temperature on antibiotic resistance
shows large spatial variability at the country and local scales. Further research is
required in order to understand how antibiotic resistance will change under global
warming.
79
APPENDIX A --Tables
Table A.1 Model selection
80
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