THE PHENOTYPIC ANTIMICROBIAL RESISTANCE OF CAMPYLOBACTER JEJUNI STRAINS AND WHOLE GENOME SEQUENCE-BASED PREDICTION OF RESISTANCE DETERMINANTS

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LITHUANIAN UNIVERSITY OF HEALTH SCIENCES

Jurgita Aksomaitienė

THE PHENOTYPIC ANTIMICROBIAL

RESISTANCE OF CAMPYLOBACTER

JEJUNI STRAINS AND WHOLE GENOME

SEQUENCE-BASED PREDICTION

OF RESISTANCE DETERMINANTS

Doctoral Dissertation Agricultural Sciences, Veterinary (A 002) Kaunas, 2020

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Dissertation has been prepared at the Department of Food Safety and Quality of Veterinary Academy of Lithuanian University of Health Sciences during the period of 2015–2020.

Scientific Supervisor

Prof. Dr. Mindaugas Malakauskas (Lithuanian University of Health Sciences, Agricultural Sciences, Veterinary – A 002).

Dissertation is defended at the Veterinary Research Council of Lithua-nian University of Health Sciences:

Chairperson

Prof. Dr. Modestas Ružauskas (Lithuanian University of Health Sciences, Agricultural Sciences, Veterinary – A 002).

Members:

Prof. Dr. Juozas Kupčinskas (Lithuanian University of Health Sciences, Natural Sciences, Biology – N 010);

Dr. Marius Virgailis (Lithuanian University of Health Sciences, Agri-cultural Sciences, Veterinary – A 002);

Prof. Dr. Nomeda Kuisienė (Vilnius University, Natural Sciences, Biology – N 010);

Prof. Dr. Aivars Bērziņš (Latvia University of Life Sciences and Techno-logies, Agriculture Sciences, Veterinary – A 002).

Dissertation will be defended at the open session of the Veterinary Research Council of the Lithuanian University of Health Sciences at 10 a.m. on the 3rd_{of July, 2020 in Dr. S. Jankauskas Auditorium of the Veterinary}

Academy of Lithuanian University of Health Sciences. Address: Tilžės 18, LT-47181 Kaunas, Lithuania.

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LIETUVOS SVEIKATOS MOKSLŲ UNIVERSITETAS

Jurgita Aksomaitienė

CAMPYLOBACTER JEJUNI PADERMIŲ

ATSPARUMAS ANTIMIKROBINĖMS

MEDŽIAGOMS IR GENOMO SEKOSKAITA

PAGRĮSTAS ATSPARUMO FAKTORIŲ

NUSTATYMAS

Daktaro disertacija Žemės ūkio mokslai,

veterinarija (A 002)

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Disertacija rengta 2015–2020 metais Lietuvos sveikatos mokslų universiteto Veterinarijos akademijos Veterinarijos fakulteto Maisto saugos ir kokybės katedroje.

Mokslinis vadovas

prof. dr. Mindaugas Malakauskas (Lietuvos sveikatos mokslų universi-tetas, žemės ūkio mokslai, veterinarija – A 002).

Disertacija ginama Lietuvos sveikatos mokslų universiteto Veterinari-jos mokslo krypties taryboje:

Pirmininkas

prof. dr. Modestas Ružauskas (Lietuvos sveikatos mokslų universitetas, žemės ūkio mokslai, veterinarija – A 002).

Nariai:

prof. dr. Juozas Kupčinskas (Lietuvos sveikatos mokslų universitetas, gamtos mokslai, biologija – N 010);

dr. Marius Virgailis (Lietuvos sveikatos mokslų universitetas, žemės ūkio mokslai, veterinarija – A 002);

prof. dr. Nomeda Kuisienė (Vilniaus universitetas, gamtos mokslai, biologija – N 010);

prof. dr. Aivars Bērziņš (Latvijos žemės ūkio universitetas, žemės ūkio mokslai, veterinarija – A 002).

Disertacija bus ginama viešame Veterinarijos mokslo krypties tarybos posė-dyje 2020 m. liepos 3 d. 10 val. Lietuvos sveikatos mokslų universiteto Veterinarijos akademijos Dr. S. Jankausko auditorijoje.

Disertacijos gynimo vietos adresas: Tilžės g. 18, LT-47181 Kaunas, Lietuva.

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LIST OF ABBREVATIONS

AAC – N-acetyltransferases aa-tRNA – aminoacyl tRNA

ANT – O-nucleotidyl/adenylyltransferases APH – O-phosphotransferases

blaOXA-184 – class D beta-lactamase OXA-184 blaOXA-448 – class D beta-lactamase OXA-448 blaOXA-61 – class D beta-lactamase OXA-61 BLAST – basic local alignment search tool CFU – Colony Forming Unit

cgMLST – core genome MLST

CmeABC – MDR efflux system encoded by a three-gene operon CPS – Capsular Polysaccharides

DNA – Deoxyribonucleic Acid

ERY – Erythromycin

EU – European Union

FQ – Fluoroquinolones

FQR – Fluoroquinolones Resistant HGT – Horizontal Gene Transfer LOS – Lipooligosaccharide MDR – Multidrug Resistance PBPs – Penicillin-Binding Proteins

QRDR – Quinolone Resistance Determining Region RPP – Ribosomal Protection Protein

SNP – Single Nucleotide Polymorphisms Tcr – Tetracycline resistance

tet(O) – tetracycline resistance protein wgMLST – whole genome MLST

WGS – Whole Genome Sequencing WHO – World Health Organization β-lactam – Beta lactam

µm – Micrometer

tRNA – transfer Ribonucleic Acid MLST – Multilocus Sequence tTyping AMR – Antimicrobial Resistance CO2 – Carbon Dioxide

pH – a scale of acidity or alkalinity GBS – Guillain-Barre Syndrome

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Ile – Isoleucine

gyrA – gyrase A Asp – Aspartic acid

Asn – Asparagine Val – Valine Tyr – Tyrosine Lys – Lysine Ser – Serine Gly – Glycine Ala – Alanine

Glu – Glutamic acid

Arg – Arginine

Tyr – Tyrosine

Gln – Glutamine

His – Histidine

Phe – Phenylalanine

cmeA – membrane fusion protein

cmeB – inner membrane transporter

cmeC – outer membrane channel protein

gyrB – gyrase B

parC – subunit of DNA topoisomerase IV MIC – Minimum Inhibitory Concentration

mL – millilitre

rRNA – ribosomal rRibonucleic Acid L4 – ribosomal protein L4

L22 – ribosomal protein L22

tet(M) – tetracycline resistance protein CmeDEF – MDR efflux system CmeDEF

NCTC – National Collection of Type Cultures

G – Guanine

C – Cytosine

Mbp – Megabase pair

NGS – Next Generation Sequencing

bp – base pair

NCBI – National Center for Biotechnology Information RAST – Rapid Annotation using Subsystem Technology BHI – Brain Heart Infusion broth

UK – United Kingdom

CIP – Ciprofloxacin TET – Tetracycline

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AXO – Ceftriaxone

USA – United States of America

CLSI – Clinical and Laboratory Standards Institute OD – Optical Density

PBS – Phosphate Buffered Saline

min – minute

sec – second

g – gramme

h – hour

PCR – Polymerase Chain Reaction

mPCR – multiplex Polymerase Chain Reaction TAE – Tris-Acetate-EDTA

UV – Ultraviolet

No – Number

gDNA – genomic Deoxyribonucleic Acid rpm – revolutions per minute

nm – nanometre

ng – nanogram

v – version

PGAP – Prokaryotic Genome Annotation Pipeline

ST – Sequence Type

CC – Clonal Complex

cgSNP – core genome SNP

KO – KEGG Orthology

KEGG – Kyoto Encyclopedia of Genes and Genomes BRITE – hierarchical classifications of biological entities CGView – Circular Genome Viewer

RGI – Resistance Gene Identifier ARDB – Antibiotic Resistance Database

CRISPR – Clustered Regularly Interspaced Short Palindromic Repeats

GI – Genomic Island

C – Citosine

T – Thymine

N50 – median of lengths

CDS – protein Coding Sequence CAMP – Cationic Antimicrobial Peptide

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INTRODUCTION

Campylobacteriosis is one of the most commonly identified human gastrointestinal zoonosis in the European Union (EU) [1, 2]. The predomi-nant species which has significant contribution to incidence of human infec-tions is Campylobacter jejuni which represent more than 90% of human infections [3, 4]. The main reservoir and source of transmission of

Campylo-bacer to humans is handling, preparation ant consumption of contaminated

food, notably of poultry meat. Other risk factors, including the environment and animal contact, unpasteurized milk or wild birds are potential sources of human campylobacteriosis [5–7].

While most Campylobacter infections are self-limiting and do not necessitate antibiotic treatment, but severe and prolonged cases should be treated with antimicrobials and especially in case of infection of young, elderly and in individuals with compromised immunity [8, 9]. Also, campy-lobacteriosis caused by drug-resistant strains require long treatment and are associated with an increased mortality [10]. In most cases antimicrobial agents used in the treatment of Campylobacter infections are macrolides, such as erythromycin (ERY), fluoroquinolones (FQ), or ciprofloxacin. However, in some cases aminoglycosides could be also considered as alter-native indicated for septicemia [3, 11].

For the past few years, a significant worldwide increase in antimicrobial resistance among Campylobacter strains has been noted [1, 10]. It is impor-tant to note that studies between 1997 and 2015 have revealed an 8.55% increase of fluoroquinolone (ciprofloxacin) resistant strains of C. jejuni [12]. Genetic determinants and mutations play significant roles in the transfer of resistance and suggest that natural transformation does not play a major role in the emergence of FQ-resistant (FQR) Campylobacter strains [13, 14]. Recently given scale of the problem by the World Health Organization (WHO) classified FQ-resistant Campylobacter as a high-priority antibiotic-resistant pathogen for which a new antimicrobials should be developed [15]. Moreover, the increasing level of C. jejuni resistance to other important antimicrobials, such as macrolides, aminoglycosides and β-lactams, is beco-ming a major public health concern in Europe and some other parts of the world [11, 16].

Increased antimicrobial resistance of Campylobacter jejuni is mainly associated with single point mutations in the bacteria genome, whereas the horizontal gene transfer among bacterial isolates is considered as the main mediator of acquisition of antibiotic resistance [17, 18]. Mutations in the genes encoding antibiotic resistance reduce and eliminate the efficacy of

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antibiotics to affect target sites increasing antimicrobial resistance level [14, 19]. In addition to FQR in Campylobacter, point mutations mediate resistan-ce to other antimicrobials, such as macrolides and β-lactams. Mutations in the genes encoding antibiotic targets help bacteria counteract the attack by antibiotics. A point mutation in the quinolone resistance-determining region (QRDR) of the gyrA gene is mainly responsible of resistance to fluoroquino-lones [17, 20]. Macrolide resistance in C. jejuni is mainly mediated by a point mutations occurring transition at position 2058/2059 in domain V of the 23S rRNA gene as well as amino acid change in L4/L22 ribosomal proteins [17, 21].

The point mutations regulating the expression of blaOXA-61 and

blaOXA-184 genes are linked to high level resistance to β-lactams [22, 23].

In C. jejuni, tetracycline resistance is usually associated with the gene enco-ding the ribosomal protection protein (RPP) tet(O) gene carried on trans-missible plasmids. Tetracyclines, which are the subject of RPP mediated re-sistance, bind to the ribosome and inhibit accommodation of the aminoacyl tRNA (aa-tRNA) into the ribosomal A site and, therefore, prevent the elon-gation phase of protein synthesis. The tet(O) gene also can be found in the chromosome occasionally and can confer extremely high-level of tetracy-cline resistance [11, 17, 24]. Furthermore, tetracytetracy-cline resistance in

Campy-lobacters can be conferred by a number of other different tetracycline

resi-stance (Tcr) determinants as genes, which code for energy-dependent efflux proteins, for ribosomal protection proteins, and genes which code for an inactivating enzyme or even genes with an unknown mechanism of resi-stance [25–27].

The multidrug efflux pump encoded by a three-gene operon CmeABC is the most common efflux mechanism causing antimicrobial resistance to several antimicrobials in C. jejuni [11, 28]. The CmeABC multidrug efflux pump can operate synergistically with different resistance genes spontaneous mutations in antibiotic targets conferring high-level resistance to different antimicrobials [28, 29]. In addition, CmeABC MDR efflux pumps may also play a role in C. jejuni pathogenesis and may be involved in other aspects of bacterial virulence [30, 31]. Genomic plasticy and hypervariable genomic sequences are important features for C. jejuni natural competence that encour-age development of antibiotic resistant bacterial mutants [32, 33].

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The aim of the study

The aim of the study was to determine antimicrobial resistance phenotypes of Campylobacter jejuni isolated from different sources, and to investigate the genetic mechanisms of resistance and population structure using a single nucleotide polymorphisms-based phylogenomic analysis.

Objectives of the study

1. To investigate antimicrobial resistance of C. jejuni strains to five different antimicrobial agents with special emphasis on the multi-drug resistance.

2. To analyze associations between antimicrobial resistance phenoty-pes and MLST genotyphenoty-pes.

3. To analyze genetic insights in the genetic features linked to antimic-robial resistance determinants contained in the bacterial genome. 4. To characterize C. jejuni strains using whole genome sequencing

and comparative genomics for antimicrobial resistance determi-nants and pathogenicity factors prediction.

Scientific novelty

In this study, the antimicrobial resistance of the 341 Campylobacter jejuni strains was identified using phenotypic assay based on standardized agar dilution method considered as a gold standard of AMR detection. Whole genome sequencing (WGS) based characterization of resistant C. jejuni strains provided more useful information about presence of known/unknown AMR genes or mutations, deletions, intergenic regions and other AMR genetic determinants. Comparative genomic analysis of C. jejuni strains using whole genome sequencing has provided new insights into genetic antimicro-bial resistance defined by the ability to correctly identify AMR determinants associated with an antimicrobial resistance phenotype. A DNA sequence-based study makes it possible to define preferable multidrug-resistance for rather detection of AMR determinants and to get important knowledge com-bat the increasing threat of AMR of C. jejuni.

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1. LITERATURE REVIEW

1.1. Historical background of Campylobacter

From a historical standpoint, Campylobacters were discovered in 1886 by Theodor Esherich, who observed and described a non-cultivable spiral shaped bacteria found in the colon of children who had died of an enteric disease called “cholera infantum” [34, 35]. In 1906, John McFadyean and Stewart Stockman wrote description of the first isolation about

Campylo-bacter species. The sample was from the uterine mucus of a pregnant sheep

from a flock of 150 Devon Longwoolled ewes that were experiencing an abortion [36]. In 1919, Theobald Smith and Marian Taylor isolated the same kind of organism from aborting cattle in United States. This microrganism was then named Vibrio fetus, and is now called Campylobacter fetus. In the 1930s and 1940s, veterinarians also recognized bacteria, in cattle called

Vibrio jejuni and in swine Vibrio coli, as causes of enteric infection and

diarrhea [36, 37]. In 1938, place in Illinois was regarded the first well documented case of human Campylobacter infection. In 1947, venereally transmitted “vibrio” strains were found as the cause of death of the fetus and infectious infertility. Those “vibrio” strains were then accorded a subspecies status, being nowadays named C. fetus subsp. venerealis. In the late 1950s, Elizabeth King proposed and later described that there are actually two different types of vibrios causing enteric illness, V. fetus, and related vibrios now called Campylobacter jejuni and Campylobacter coli. The crucial step is the isolation of Campylobacter which is from the blood and faeces, also was accomplished in 1968 by Dekeyser and Butzler from young woman with acute febrile hemorrhagic enteritis. A ‘related vibrio’ (C. jejuni) was isolated from blood and later, with use of a special filtration technique, from the faeces [34, 36, 38].

A significant step in the reassessing of the Campylobacter epidemio-logy was the development of the filtration technique and the selective Skirrow-media which enabled Campylobacter isolation from stool [39, 40].

1.2. General aspects of Campylobacters

Campylobacters are small, curved, S-shaped or spiral, non-spore

forming, motile Gram negative rods with size ranging from 0.2 to 0.8 μm wide and 0.5 to 5 μm long. They are microaerophilic, being neither truly anaerobic nor aerobic, but requiring an environment of reduced oxygen level for optimal growth [41]. Campylobacter spp. grows at temperatures

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between 37°C and 42°C, with C. coli and C. jejuni having an optimal growth temperature of 42°C. The majorities of Campylobacter spp. are microaerophilic and need reduced oxygen (3–10%) and raised CO2 (5–10%)

levels. Some species also require added hydrogen for optimal growth [42].

Campylobacters do not ferment carbohydrates and usually obtain energy

from amino acids or tricarboxylic acid cycle intermediates. Most species reduce nitrate and are oxidase positive but only C. jejuni is hippurate positi-ve. C. jejuni is quite sensitive to drying (4°C) and storage at room tempera-ture, but at refrigeration temperatures and appropriate humidity (70%), large number of bacteria may survive [43]. Campylobacters have a pH growth range of 6.0–8.0, but they are inactivated below pH 5.5 or above pH 9.0 [44].

1.3. Epidemiology of Campylobacter

Campylobacter jejuni infection is one of the leading bacterial causes of

acute gastroenteritis worldwide [45–47]. Since 2005 Campylobacter is the most commonly reported gastrointestinal bacterial pathogen in humans in the EU. In 2017 year, the number of reported confirmed cases of human campylobacteriosis reached 246,158 with an EU notification rate of 64.8 cases per 100,000 population. In 2017 as in previous years, the highest country specific notification rates were observed in the Czech Republic (230.0 cases per 100,000), Slovakia (127.8), Sweden (106.1) and Luxem-bourg (103.8). The lowest rates in 2017 were observed in Bulgaria, Cyprus, Latvia, Poland, Portugal and Romania (≤5.8 per 100,000) [48].

Campylo-bacter is highly infectious with reported infective doses as low as 500 to

800 cells [49]. However, it has been reported cases that the dose of C. jejuni required for the development of campylobacteriosis can be as low as 360 bacteria [3].

Campylobacter isolation rates for food-borne illness in developing

countries range are 5% to 20%. Most infections are acquired due to con-sumption of raw or undercooked poultry, unpasteurized milk, and contami-nated water [50]. Clinical disease is characterized by acute diarrhea accom-panied by intense abdominal pain. Campylobacteriosis is an inflammatory enteritis that is initially found in the small bowel and later affects the colon and the rectum [51]. The incubation period is usually between 2 and 5 days but can range from 1 to 10 days. The diarrhea can be either watery or, in almost one-third of the cases, bloody [49, 52].

Moreover, C. jejuni infection may lead to autoimmune conditions known as Guillain-Barré syndrome (GBS) and Miller Fisher syndrome [45].

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The association of C. jejuni with GBS to ensue molecular mimicry, where peripheral nerve gangliosides share epitopes with C. jejuni outer lipo-oligosaccharide (LOS) cores, leading to a misdirected and harmful immune response [52].

1.4. Antimicrobial resistance 1.4.1. Fluoroquinolone resistance

In 2017, the WHO qualified fluoroquinolone-resistant Campylobacter spp. as the fourth most important human microbial pathogen. The prevalen-ce of fluoroquinolone resistant Campylobacter spp. has reached such staggering levels that macrolides are currently recommended as the first line of treatment for human campylobacteriosis [41]. Nevertheless, fluoroquino-lones, such as ciprofloxacin, are the second-line of choice for antimicrobial treating of campylobacteriosis [42]. Moreover, fluoroquinolones have been used as first-line antibiotics against bacterial gastroenteritis in the absence of microbiological diagnosis. The quinolones target two large bacterial enzymes (DNA gyrase and topoisomerase IV) and binding to these enzymes inhibit the synthesis of bacterial DNA, causing cell death. As fluoroquino-lones play an important role in the clinical treatment of human campylobac-teriosis, antimicrobial resistance of C. jejuni strains has become a public health concern [11, 53, 54].

Resistance to fluoroquinolones is mainly due to amino acids substitu-tions in the quinolone resistance-determining region (QRDR) of the topoiso-merase. Fluoroquinolone resistance is primarily associated with a single threonine at position 86 to isoleucine (Thr86Ile) mutation in gyrA gene, which encodes A subunit of the target enzyme, in isolates from humans and animals [43, 44]. Other less common substitutions, as well as silent poly-morphisms, have been reported in the QRDR. Other modifications of the

gyrA subunit have also been reported to be associated with resistance. As

example, Asp90Asn, Thr86Val and Asp90Tyr and Thr86Lys modifications associated with moderate resistance [45].

In addition to, the gene encoding an efflux pump protein, cmeB, has been described, and inactivation of cmeB by insertional mutagenesis has been shown to increase the susceptibility of C. jejuni to several antibiotics, including ciprofloxacin [55]. The CmeABC multidrug efflux pump consists of three components: the cmeA periplasmic protein, the cmeB inner mem-brane transporter, and the cmeC outer memmem-brane channel protein The CmeABC efflux pump works in synergy with gyrA mutations in causing fluoroquinolone resistance in Campylobacters [11]. Also, additional

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muta-tions in gyrA and in gyrB or parC can further increase the level of resistance [56].

1.4.2. Macrolide resistance

Most cases of Campylobacter enteritis do not require treatment as they are of short duration, clinically mild and self-limiting. Antimicrobial treat-ment is, however, necessary for systemic Campylobacter infections, infec-tions in immune-suppressed patients and severe or long-lasting infecinfec-tions [9, 57]. Resistance to macrolides is more prevalent in Campylobacter isolates of animal origin, especially C. jejuni from poultry. Macrolides such as erythromycin act by binding to the 50S subunits of bacterial ribosomes and interfere with protein synthesis by inhibiting the elongation of peptide chains. Generally, the resistance of different bacterial species to macrolides resistance can be based on mechanisms including target modification by point mutation or methylation of 23S rRNA gene, hydrolysis of the drug, and efflux pumps [55, 58]. High‐level macrolide resistance minimum inhibi-tion concentrainhibi-tion (MIC>128 μg/mL) of C. jejuni has been attributed to nucleotide mutations at positions 2074 and 2075 in the peptidyl transferase region in domain V of the 23S rRNA target gene [21]. Mutations affecting macrolide binding have also been identified in the ribosomal proteins L4 and L22, both of which form portions of the polypeptide exit tunnel within the bacterial 70S ribosome [45].

1.4.3. Resistance to other antimicrobial agents

β-lactam antibiotics act by binding to penicillin-binding proteins (PBPs) resulting in bacterial lysis and ultimately leads to death of bacteria. [28]. Different mechanisms mediate β-lactam resistance in Campylobacter: enzy-matic inactivation by chromosomally-encoded β-lactamases, reduced uptake due to alterations in outer membrane porins and efflux [11, 28]. Mostly

Campylobacters are considered to be resistant to β-lactam antimicrobial

agents and narrow-spectrum cephalosporins due to their limited ability to bind to PBPs and their low permeability. Resistance to other β-lactam anti-biotics, such as amoxicillin and ampicillin, mainly due to the production of β-lactamase, which inactivates β-lactam antibiotics by hydrolyzing the structural lactam ring [9].

Tetracyclines are a group of broad-spectrum antibiotic compounds of natural and semisynthetic products inhibiting the bacterial protein biosyn-thesis. They are bacteriostatic agents exhibit a different structure-activity relationship against wide variety of microorganisms, but at the present they are of limited use because of acquired resistance [58, 59].

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Resistance to tetracycline of Campylobacter spp. has been reported in many various countries [60]. High rates of resistance is usually associated with the tet(O) gene carried on transmissible plasmids in C. jejuni [61]. However, there is also evidence of tet(O) being harbored on the chromo-some [24]. The major mechanism of tetracycline resistance in

Campylobac-ter is the binding and protection of ribosomal A site by the protein tet(O)

[62]. Also, resistance can be caused by tet(M), another ribosomal protecting protein, and the efflux system of tetracycline. tet(O) and tet(M) are the most common and best characterized ribosomal protection proteins, with 75% sequence similarity to each other. These proteins catalyze the GTP-dependent release of tetracycline from the ribosome thus thereby inhibit the effect of the tetracycline. Ribosomal protecting proteins and efflux pumps can also work synergistically and cause high-level tetracycline resistance [63, 64].

Several mechanisms of aminoglycoside resistance have been described in Gram-negative bacteria. Enzymatic modification and inactivation of antibiotics are the most prevalent mechanisms of aminoglycoside resistance [65, 66]. Aminoglycoside resistance genes are present in many bacterial species and commonly encode proteins that modify these antimicrobials. The enzymes are divided into three different groups: O-phosphotransferases (APH), O-nucleotidyl/adenylyltransferases (ANT) and N-acetyltransferases (AAC) based on the reaction they mediate and each enzyme having its own target drugs [9]. The main enzyme classes described for Campylobacter spp. are represented by APH(3′), APH(2″), ANT(6), ANT(9), and AAC(3). Regar-ding APH(3′), APH(3′)-IIIa is the most prevalent phosphotransferase in

Campylobacter. APH(2″) usually confers resistance to gentamicin, with

diverse phenotypes. They are generally found as monofunctional enzymes in Campylobacter but can also be found as bifunctional enzymes. Eight types of APH(2″) have been described, seven of which have already been identified in Campylobacter [67].

1.4.4. Multidrug resistance

Multidrug resistance bacteria are frequently detected in humans and animals from developed and developing countries and pose a serious threat for human health [68]. Integrons, carried by transposons, are a major vehicle for the spread of multiple‐antibiotic resistance and have a broad distribution among Gram‐negative fecal bacteria of animal origin [69, 70]. The roles of class 1 integrons and gene cassettes in the acquisition and spread of antibio-tic resistance genes are well established. The integron-borne gene cassettes confer resistance to many different antibiotics including aminoglycosides,

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cephalosporins, chloramphenicol, penicillins and trimethoprim, and for each of these antibiotic classes have several distinct gene cassettes [71].

Moreover, multidrug resistance in C. jejuni may be the result of self‐ transmissable plasmids or efflux mechanisms. An energy‐dependent efflux system with a broad specificity to be responsible for the multidrug resistan-ce profile (resistanresistan-ce to β‐lactams, erythromycin, tetracycline, chloramphe-nicol, and quinolones). CmeABC and CmeDEF the multidrug efflux pumps, have been identified in C. jejuni conferring resistance to multiple antibiotics including fluoroquinolones, erythromycin, tetracycline, chloramphenicol, and ampicillin, as well as detergents and dyes (including ethidium bromide), bile salts, and heavy metals [45, 55].

1.5. Genome characteristics

The total genome size of C. jejuni is reported to vary from approxima-tely 1.6 to 1.8 Mb. The genome of Campylobacter jejuni (NCTC 11168) comprises a circular chromosome, with G/C content of around 30%, approximately 1600 coding sequences and 1,641,481 bp [72]. The highly studied and sequenced C. jejuni 81–176 strain contains approximately 1654 genes, this are about one-half the number of genes present in Escherichia

coli, and one-third the numbers of genes present in Salmonella

Typhimu-rium. The genome of C. jejuni is relatively small compared to, for example, the 4.6 Mbp genome of Escherichia coli and it lacks the classical operon system and repetitive DNA sections. All C. jejuni strains share similar core genomes and strain dependent variable accessory genes. The genome of

C. jejuni contains hypervariable sequences that are short, homopolymeric

nucleotide runs commonly found in accessory genes associated with biosyn-thesis or modification of surface structures. These highly variable sequences are usually present in phasevariable genes encoding cell surface structures like capsular polysaccharides (CPS), lipo-oligosaccharide (LOS) locus, flagella and also restriction-modification systems and metabolism [72, 73]. There is a high rate of recombination due to horizontal gene transfer (HGT) resulting in variability of allelic diversity across C. jejuni genomes and varying degrees of disruption of the overall clonal population structure. The simultaneous introduction of large numbers of polymorphisms by HGT can rapidly generate novel phenotypes [74].

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1.6. Next generation sequencing

Next generation sequencing (NGS) is a powerful tool for microbiolo-gical surveillance helping to understand comprehensive information on the evolution of microbial pathogens and their mutational changes within and between hosts [75–77].

NGS also known as high-throughput sequencing, in-parallel DNA sequencing technologies are related terms that describe a DNA sequencing technology which has revolutionized genomic research [75]. Rapid progress in NGS technology and the simultaneous development of bioinformatics tools allows research groups to generate de novo draft genome sequences for any organism of interest [78, 79]. The distinct NGS platforms employ different approaches, all techniques make use of massive parallelization of the biochemical and sequencing steps without the need for cloning include higher speed, less labor, and lowered cost [80]. All NGS platforms perform sequencing of millions of small fragments of DNA in parallel. Bioinfor-matics analyses are used to piece together these fragments by mapping the individual reads to the microbe reference genome. NGS can be used to se-quence entire genomes or constrained to specific areas of interest, including all coding genes or small numbers of individual genes [78].

1.7. Using next-generation sequencing data of AMR detection

In the field of genomics, the advancement in whole genome sequencing using NGS has increased capacity to identify new antibiotic resistance genes and their genetic carriers, such as plasmids and AMR genetic factors [81].

An important aspect for AMR genes detection and results analysis using sequencing based methods is to understand and take into account the molecular mechanisms of antimicrobial resistance. The high level of agree-ment between phenotype and genotype coincides with the developagree-ment of new and updated versions of bioinformatics tools to predict AMR, and the maturation of well-curated AMR gene databases [82]. There exists a number accessible bioinformatics resource for detection of AMR determinants in DNA or amino acid sequence data have been developed to date [81, 82]. Bioinformatics tools such as, ARG-ANNOT, CARD, SRST2, MEGARes, ARIBA, AMRFinder, ResFinder and many others can be used to predict AMR. Bioinformatics supplies contrast for several parameters including type of accepted input data, presence/absence of software for search within a database of AMR determinants that can be specific to a tool and for the search, which can be based on mapping or on alignment [82].

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1.8. High-throughput sequence data: assembly and annotation

Next generation sequencing (NGS) is the choice for large-scale genomic of the high-throughput production with a highly memory-intensive assign-ment. NGS can be used to analyze DNA samples and is a popular tool in functional genomics [83, 84]. Today sequencing of full bacterial genome is so fast that a relatively small laboratory can easily carry out of deep whole-genome sequencing [85]. After generating 100-fold coverage in 100–150 bp reads, a scientist can assemble the data into a draft genome using any of several genome assemblers. Raw reads can be trimmed to impart a better quality and if passing quality threshold, assembled into contigs that are longer, continuous nucleotide sequences. Raw reads can be assembled to contigs either by mapping them against a known reference genome or more commonly de novo, where no reference is required and assembly is based on mathematical algorithms that use k-mers to construct the contigs [86–88].

De novo assembly for short NGS reads involves the three stages: contig assembly, scaffolding and gap filling. In the contig assembly step, the reads are assembled as long consensus sequences (called contigs) without gaps. Mainly of the de novo genome assemblers generally integrate scaffolding steps after the contig constructions. Draft genomes are fragmented with many gaps and it is important to generate methods that can close gaps on draft genomes with short sequence reads that are easily available. The gaps are carefully filled by using other independent reads to complete the as-sembly. The scaffolding and gap-filling steps can be accomplished iterati-vely to enhance the quality of the assembly until no contigs are scaffolded or no additional gaps are resolved [87, 89–91]. Microbial genome annota-tion involves of running an automatic annotaannota-tion pipeline followed by ma-nually curation of the results supported by graphical visualization tools [92]. Bacterial genome annotation is commonly performed by uploading a geno-me assembly to an automated web-based tool such as NCBI (National Center for Biotechnology Information) or RAST (Rapid Annotation using Subsystem Technology) [93, 94].

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Fig. 1.9.1. DNA sequencing scheme [88] 1.9. Comparative genomics

Comparative genomic approaches provide another avenue, which does not require the generation of additional data, thus sequencing comparison to other genomes or sequences is a principal step [78]. Any similarities between sequences are used to infer function and evolutionary relationships. One of the often used methods for examining and comparing genes is to search for similarities between newly sequenced DNA and databases of gene “query” sequences that have already been described. The functions and evolutionary relationships of a new unique genes or even whole genomes can be consider by identifying related genes or gene families with known functions [94, 95].

The one of the most common tools used to examine DNA and protein sequences is the Basic Local Alignment Search Tool (BLAST). The tool

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finds regions of local similarity between sequences. BLAST compares nucleotide or protein sequences to sequence databases and calculates the statistical significance of matches. BLAST can be used to infer functional and evolutionary relationships between sequences as well as help identify members of gene families [96].

1.9.1. SNP analysis

WGS data are frequently used to identify genetic differences by the identification of single nucleotide polymorphisms (SNPs) that vary among strains [97, 98]. WGS based strain typing on SNPs can be performed either using reference-based mapping of reads or assembled contigs. Point muta-tions involve single nucleotide polymorphisms and single nucleotide inser-tions or deleinser-tions at variable mutation rates within a gene or in a regulatory region near a gene and may affect the genes. For bacterial SNP detection requires choosing an appropriate reference genome to align reads to making it possible to detect SNPs in genes, loci, and intergenic regions present in the query genome [99, 100]. Frameshift mutations arise when the normal sequence of codons is disrupted by the nucleotide insertion or deletion in protein coding sequences. The combinations generating a nonsense codon mutation including stop codon, will be interpret from a different reading frame, leading to significant alteration of the encoded protein [77, 101].

1.9.2. Pangenome-based analysis

Pan-genome has provided knowledge on the dynamics and evolution of bacterial genome from the point of population. The pan-genome consists of the full gene pool of all strains used to build it and reflects the total number of genes that are present in a given dataset. Basically, pan-genome consists of core genome, comprised by genes shared by all genomes and usually involved in essential cellular processes; accessory/dispensable genome com-prised of genes absent in some isolates; and species specific or strain-speci-fic genes, which are those genes that are present in a single genome [102, 103]. Pan-genome analyses alleviate the interpretation of genetic diversity and evolutionary of the whole genomes of different strains in a given bacte-rium elucidate their genetic persistence and variability. It means, that micro-bial species can be described by its pan-genome [104]. A lot of bioinforma-tics tools, online datasets and web servers have been developed, to make microbial pan-genome analysis eligible and effective [105]. All bioinforma-tics tools and software suites have been developed for clustering ortholog-gous genes, identifying single nucleotide polymorphisms, constructing phy-logenies, function-based searching or analysis, and gene curation [106, 107].

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1.9.3. Phylogenomical analysis

The phylogenetic analysis of bacterial genomes based on the pan, core and accessory genomes show comparable clustering patterns with two basic clusters that principally represent genetic populations. The core genome MLST (cgMLST) and SNP for subtyping and clustering of bacterial organisms are the two most commonly used principles for the retrieval of subtyping information from WGS data [107, 108]. cgMLST is an extension of conventional MLST that aims to combine the discriminatory power of classical MLST with the extensive genetic data derived from WGS. One of the greatest advantages of the cgMLST scheme is exploiting hundreds of gene targets of the entire bacterial genome, thereby providing maximum resolution for multiple research and surveillance analyses [107, 109]. The whole genome MLST (wgMLST) is used as an extension of cgMLST and uses core genome genes/loci and all accessory genes/loci to detect lineage-specific genes/ loci. This approach might be biologically more relevant than approaches that consider only point mutations [110, 111].

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2. MATERIALS AND METHODS

2.1. Bacterial strains and growth conditions

A total of 341 C. jejuni strains of known Multilocus Sequence Types (MLST) were included in this study for comparison purposes of phenotypic antimicrobial resistance. The strain collection was composed of 41 strains isolated from dairy cattle at farm level, 98 strains isolated from retail broiler products, 101 strains isolated from wild birds and 101 strains from human clinical cases that were collected at the Microbiological Laboratory of Kaunas Clinical Hospital, Lithuanian University of Health Sciences over one year period. The strains were stored at −80°C in brain heart infusion broth (BHI) (Oxoid, Basingstoke, UK) with 30% glycerol (Stanlab, Lublin, Poland). They were recovered from frozen stocks on Blood agar base No. 2 (Oxoid, Basingstoke, Hampshire, England) supplemented with 5% defibri-nated horse blood (E&O Laboratories, Burnhouse, Bonnybridge, Scotland) and incubated under microaerophilic conditions (5% oxygen, 10% carbon dioxide and 85% nitrogen) at 37°C for 48 h.

2.2. Antimicrobial resistance testing

The C. jejuni strains were tested for phenotypic resistance against five antimicrobial agents (ciprofloxacin, CIP, tetracycline, TET, gentamicin, GEN, ceftriaxone, AXO and erythromycin, ERY), (all Sigma-Aldrich, Saint-Louis, USA) by the agar dilution method according the Clinical and Laboratory Standards Institute (CLSI) guidelines [112]. Mueller-Hinton agar (LOT 12159202, Liofilchem) plates with dilutions ranging from 0.25 to 256 mg/mL for ciprofloxacin, tetracycline, gentamicin, ceftriaxone and erythromycin were prepared. For each sample of C. jejuni, 5 μL of approximately 1 × 107

CFU/mL (OD600=0.1) bacterial suspension dissolved in phosphate-buffered

saline (PBS) (E&O Labaratories Limited, Burnhouse, Bonnybridge, Scot-land) was spotted onto Mueller-Hinton agar containing the correspondding antimicrobial agent and incubated under microaerophilic conditions at 37ºC for 48 h. The experiment for all isolates was performed in triplicate. The MIC values were defined as the lowest concentration that produces comple-te inhibition of C. jejuni growth. For quality control, the reference strain

C. jejuni NCTC 11168 was included. Following clinical breakpoints

inter-pretive criteria for resistance were used: erythromycin (≥32), tetracycline (≥16), ciprofloxacin (≥4), gentamicin (≥16) and ceftriaxone (≥16). Strains

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showing resistance to three or more classes of antimicrobials were consi-dered as multidrug resistant (MDR) [113, 114].

2.3. DNA extraction for detection of antibiotic resistance genes

C. jejuni strains were grown at 37°C on blood agar plates for 48 h under

microaerophilic conditions. After sufficient growth was obtained, one 1 μL loopful of bacteria were suspended in Eppendorf tubes containing 200 µL of PrepMan Ultra Sample Preparation Reagent (PrepMan TM Ultra, Applied Biosystems, USA). Further DNA extraction was carried out following the supplier instructions using including heating of bacterial suspension at 100°C for 10 min, centrifugation at 16,000 g for 3 min and transferring the supernatants into new tubes before storage in the freezer at –20°C until use.

2.4. Detection of antibiotic resistance genes by mPCR

All C. jejuni strains were tested for the presence of AMR genes using multiplex PCR (mPCR) as described by Obeng [115]. mPCR were perfor-med using amplification protocol: 5 min initial denaturation at 94°C, follo-wing 30 cycles of denaturation at 94°C for 30 sec, annealing at 54°C for 30 sec and extension at 72°C for 1 min. Each reaction consisted 1.5 µL of 25 pmol primer mix, 12.5 µL PCR reaction buffer, 9 µL Milli-Q water in a total reaction volume of 25 µL with 2 µL DNA templates. Each PCR product was loaded into a 1.5% TopVision agarose (LOT 00220705, Thermo Scientific) gel containing Ethidium bromide (5mg/mL) in a 1xTAE buffer at 100 volts for 50 min. The gel was visualized on an UV board (BioRAD Molecular Imager Gel Doc XR+). Band views of 559, 372, 241 pairs (bps) for genes, tet(O), blaOXA-61 and cmeB, respectively were determinate. The GeneRuler 100 bp DNA ladder (GeneRuler 100bp Plus DNA Ladder, LOT 00117273, Thermo Scientific) was used as the molecular size marker. A DNA ladder and positive control were included during electrophoresis.

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Table 2.4.1. Primers used for multiplex PCR

Gene _{Sequence (5'→3')}Primer Direction Amplicon _{size (bp)} Annealing _T°

tet(O) gcgttttgtttatgtgcg Forward 559 54°C tet(O) atggacaacccgacagaag Reverse

cmeB tcctagcagcacaatatg Forward 241 54°C cmeB agcttcgatagctgcatc Reverse

blaOXA‐61 agagtataatacaagcg Forward 372 54°C blaOXA‐61 tagtgagttgtcaagcc Reverse

2.5. Detection of antibiotic resistance determinants by MAMA PCR

The QRDR of the gyrA genes of the C. jejuni isolates were amplified by MAMA PCR as described by Zirstein [116] using GzgyrA5 and GzgyrA6 primers for amplification of the 673 bp product. Forward primer CampyMAMAgyrA1 and a reverse primer CampyMAMAgyrA5 were used to generate a 256 bp PCR product that is a positive indication of the presence of the Thr-86-Ile (ACA→ATA) mutation in the C. jejuni gyrA gene. Primer GZgyrA4, a conserved reverse primer, was used in conjugation with primer CampyMAMAgyrA1 to produce a positive PCR control product of 368 bp with any C. jejuni gyrA gene. PCR cycling conditions were as follow: 3 min initial denaturation at 94°C, following 30 cycles of denatu-ration at 94°C for 1 min, annealing at 54°C for 1 min and extension at 72°C for 1 min, with a final step at 72°C for 5 min (Table 2.5.1).

Table 2.5.1. Primers used for PCR of Thr86Ile mutation and gyrA detection

MAMA PCR

Primer Sequence (5'→3') Direction

GZgyrA4 cagtataacgcatcgcagcg Reverse

GZgyrA5 atttttagcaaagattctgat Forward

GZgyrA6 ccataaattattccacctgt Reverse

GZgyrA7 ttattataggtcgtgctttg Nested forward

GZgyrA8 tagaaggtaaaacatcaggtt Nested reverse

CampyMAMAgyrA1 tttttagcaaagattctgat Forward

CampyMAMAgyrA5 caaagcatcataaactgcaa Reverse

Sequence (5'→3')

gyrA gctgatgcaaaagkttaatatgc Forward gyrA tttgtcgccatacctacagc Reverse

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2.6. DNA sequencing and sequence analysis

The PCR amplicons were purified using the GeneJet PCR purification system (Thermo Scientific, EU). Sequencing reactions were sequenced using an ABI PRISM BigDye® Terminator 3.1 Cycle Sequencing Kit (Applied Biosystems, Foster City, CA, USA) in accordance with the manufacturer’s instructions. Duplicate forward and reverse sequencing reactions were run with forward and reverse primers, respectively, for each sample. The samp-les were analyzed with an ABI PRISM 3500 Genetic Analyzer (Applied Biosystems, Foster City, CA, USA). The sequence data were analyzed using BioNumerics program 7.0 (Applied Maths NV, USA) to evaluate the speci-fic genomic mutations associated with resistance to ciprofloxacin.

2.7. Genomic DNA preparation

The frozen C. jejuni cultures were recovered on Blood agar base No. 2 (ISO 10560, Liofilchem) supplemented with 5% defibrinated horse blood (E&O Laboratories, Bonnybridge, Scotland) and incubated under micro-aerophilic conditions (5% oxygen, 10% carbon dioxide, and 85% nitrogen) at 37°C for 48 hours. DNA was extracted using the PureLink Genomic DNA Mini Kit (Invitrogen, Carlsbad, CA, USA) based on manufacturer’s instruct-tion. A sterile loop was used to remove bacteria and resuspend in 180 μL Genomic Digestion buffer in 1.5 mL Eppendorf tube. 20 μL of Proteinase K was added and vortexed to lyse the cells. The sample was incubated at 55°C for 30 min in a heat block and vortexed occasionally during this period until lysis is complete. After that, 20 μL of RNase A was added and incubated for 2 min 200 μL of PureLink Genomic Lysis/Binding buffer was added and vortexed. This was followed by addition of 200 μL of 98% ethanol to the tube and vortexed for 5 sec to form a homogenous solution. All of the lysate was transferred to a PureLink spin column and centrifuged at 10,000 rpm for 1 min at room temperature. The column was then placed in a new collec-tion tube and 500 μL of Wash Buffer 1 prepared with ethanol was added to the column and centrifuged at 10,000 rpm for 1 min. In a new collection tube, the process was repeated with Wash Buffer 2 prepared with ethanol and centrifuged at 14,000 rpm for 3 min at room temperature. Finally, the spin column was placed in a 1.5 mL Eppendorf tube. 100 μL of Milli-Q water was added to the column membrane and incubated for 1 min at room temperature, and centrifuged at 14,000 rpm for 90 sec. The amount and integrity of gDNA (genomic DNA) was quantified using a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, USA) and 1% agarose gel, respectively. DNA samples were stored at –20°C.

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2.8. DNA quality and quantification

The quality of the DNA was evaluated by spectral analysis using a NanoDrop 2000 platform (NanoDrop Spectrophotometer, Thermo Fisher Scientific, USA). 2 μL of Milli-Q water was used to blank the initial measu-rement. After, 2 μL of a DNA sample was pipetted onto the NanoDrop 2000 platform. A ratio between absorbance readings at 260 nm and 280 nm was evaluated for DNA purity in samples. A ratio of 1.8–2.0 was considered as good quality DNA.

The DNA concentration (20 ng/μL each) was fluorimetrically quanti-fied by Qubit 3.0 Fluorometer. Qubit dsDNA BR Assay Kit (Q32850, Life technologies, Eugene, Oregon, USA) Qubit dsDNA HS Assay Kit (Q32851, Life technologies, Eugene, Oregon, USA) and Qubit ssDNA Assay Kit (Q33211, Life technologies, Eugene, Oregon, USA) was used according to the manufacturer’s protocols. Using the Qubit fluorometer with DNA standarts of known concentrations the platform calculated concentration based on the fluorescence of a dye which binds to double stranded DNA.

2.9. Genomes sequencing and assembly

A sequencing library was prepared with the Nextera XT Sample Prepa-ration Kit (Illumina, San Diego, CA, USA) following manufacturer’s guide-lines. C. jejuni genomes were sequenced at the NGS-MiSeq core facility of University of Copenhagen using an Illumina MiSeq instrument (Illumina, San Diego, CA, USA) with 250 bp paired-end reading cycles. CLC Geno-mics Workbench v.6.5.1 (CLC Genomic Workbench v.6.5.1; Denmark) was used for the adapter and quality trimming of the raw reads. De novo assembly was performed using the SPAdes version 3.10 (SPAdes v.3.10) genome assembler using assembly parameters: k automatic selection based on read length, repeat resolution, mismatch careful mode, mismatch correc-tor, and a wide range of k-mer sizes: 21, 33, 55, 77, 99, 127. The quality of the assembly was evaluated with QUAST version 2.3 (Quality Assessment Tool for Genome Assemblies, QUAST v.2.3). These assembly statistics indicated that all of the sequenced isolates yielded high-quality assemblies that contained nearly complete genomic contents and gene inventories and could be used for pan-genome analysis and phylogenetic analysis.

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2.10. Genomes annotation

The assembled sequences were annotated using Prokka version 1.13 (Prokka v.1.13) and the NCBI Prokaryotic Genomes Automatic Annotation Pipeline (PGAP) [106, 117]. The subsystems annotation was obtained using the SEED-based automated annotation system after the data were uploaded to RAST (Rapid Annotation using Subsystem Technology) [92, 118] geno-me server. The predicted protein coding sequences were annotated and protein features were predicted with BASys pipeline (BASys Annotation Manager v. 1.0) [119]. The Whole Genome Shotgun project of C. jejuni MM26-781 strain has been deposited at DDBJ/ENA/GenBank under the accession PYWF00000000. Detailed results and discussion of the whole genome sequence of C. jejuni MM26-781 obtained from common pigeon (Columbia livia) is presented in the Publication 3 (Draft Genome Sequence of Ciprofloxacin and Ceftriaxone Resistant Campylobacter jejuni MM26-781 Assigned to Novel ST Isolated From Common Pigeon in Lithuania). The version described in this publication is version PYWF01000000.

2.11. Pan-genome analysis

The annotated files were piped into Roary version 3.12.0 (Roary v.3.12.0) choosing a minimum blastp identity of 95 and core gene prevalence in all (>99%) of the isolates. The isolates metadata were visualized with Phandango tool (Phandango v.1.1.0; https://jameshadfield.github.io/

phandango/#/) [120]. The PanSeq tool (https://lfz.corefacility.ca/panseq/)

with the blastn algorithm was used to identify novel regions in the pan-genome.

2.12. cgSNPs analysis

Core genome SNPs (cgSNPs) were called for each isolate using the CSI Phylogeny 1.4 workflow accessible from the Center for Genomic Epidemio-logy (http://www.genomicepidemioEpidemio-logy.org/) and each isolate was mapped to the reference Campylobacter jejuni (AL111168.1) genome. The genera-ted phylogenetic tree and its corresponding data on recombinations were visualized using Phandango tool (Phandango v.1.1.0).

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2.13. Comparative genome analysis

BlastKOALA (https://www.kegg.jp/blastkoala/) [121] was used for genomes sequences, which perform KO (KEGG Orthology) assignments to characterize individual gene functions and reconstruct KEGG pathways, BRITE hierarchies and KEGG modules. A circular genomes graphical maps representation was generated using seven C. jejuni comparison with referen-ce genome (C. jejuni NCTC 11168; AL111168.1) using Gview (https://

server.gview.ca/) [122] and CGView (http://cgview.ca/) [123] servers.

ResFinder v.3.0 and PointFinder v.3.1.0 (https://cge.cbs.dtu.dk/services/

ResFinder/) [124] was used for identification of intrinsic genes associated

with the phenotypic antimicrobial resistance of the strain using thresholds of 90% identity and 60% gene coverage. Also, the coding sequences (CDSs) of the genome were subjected to Resistance Gene Identifier (RGI v.4.2.2; CARD v.3.0.0) (https://card.mcmaster.ca/analyze/rgi) [125] analysis infor-mation in the Antibiotic Resistance Database (ARDB). CRISPR finder (https://

crispr.i2bc.paris-saclay.fr/Server/) [126, 127] and PathogenFinder 1.1 (https:// cge.cbs.dtu.dk/services/PathogenFinder/) [128] database of the Center for

Genomic Epidemiology were used for the potential prediction of pathogeni-city. The IslandViewer version 4 server (https://www.pathogenomics.sfu.ca/

islandviewer/) [129] was used to predict the putative genomic islands (GIs).

2.14. Statistical analysis

The statistical package SPSS (Statistics 20, IBM, Armonk, NY, United States) was used for data processing and statistical analysis. Chi-square test was used to determine statistically significant associations between resistan-ce to different antimicrobial drugs and between resistanresistan-ce and MLST type. A P value of <0.05 was used to indicate statistically significant results.

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3. RESULTS AND DISSCUSION

3.1. Phenotypic antimicrobial resistance profiles of C. jejuni

The study revealed different antimicrobial resistance patterns of C. jejuni strains to five different antimicrobials. Resistance to ciprofloxacin, ceftria-xone and tetracycline was found in 91.5%, 60.4%, and 37.8%, of the tested

C. jejuni strains, respectively (Table 3.1.1). Previous studies have also

re-ported the high rates of C. jejuni resistance to ciprofloxacin and tetracycline, up to 81% and 58.4%, respectively [22]. A European Union summary report for 2017 revealed that many worldwide studies have reported about a high levels of resistance to ciprofloxacin in several countries, most noticeably in Portugal (96.5%), Lithuania (91.5%), Spain (88.6%), Estonia (84.0%) and Cyprus (80.0%) [60].

We have found a very low frequency of resistance to macrolides, which are critically important antimicrobials for treatment of Campylobacter infec-tion. Macrolides are the first choice antibiotics for the treatment of campylo-bacteriosis in humans [13]. Only three out of 341 tested strains (0.9%) were resistant to erythromycin with determined MIC of 32 μg/mL and two of them were isolated from humans and one from wild birds, respectively. This finding complements the results of other studies [48, 130].

We also revealed that all C. jejuni strains were sensitive to gentamicin. The prevalence rate of gentamicin-resistant Campylobacter was low and stable in most countries (0.5%) but higher in Italy (5.9%) and Malta (12.5%) [60].

In total out of the 341 tested C. jejuni strains, we found 133 strains (39.0%) resistant to two different classes of the tested antibiotics and 93 strains (27.3%) were resistant to three classes of antibiotics and assigned to multidrug resistant (MDR) strains.

The MIC ranges of C. jejuni strains resistant to ciprofloxacin and ceftriaxone varied between 4–256 μg/mL and 16–128 μg/mL, respectively. A significant percentage (51.1%) of tetracycline resistant strains isolated from human clinical cases (77.3%), broiler products (42.9%) and wild birds (44.5%) displayed a high-level resistance with MIC values in the range from 64 up to 256 μg/mL (Table 3.1.2). The use of tetracycline’s such as doxy-cycline in agriculture is likely responsible for the large number of tetra-cycline-resistant C. jejuni isolates. Tetracycline resistance is important due to the potential for plasmid-mediated transfer of the tet(O) gene as well as genes encoding resistance to other antimicrobials for other potential patho-gens [131].

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Tabl e 3.1 .1. A ntim ic ro bi al r es ista nc e o f C . j ej uni is ol at ed f ro m p oul try pr oduc ts, hu m ans , c at tle a nd wi ld bi rd sam pl es A B A nt imi cr ob ia l res ist an ce of C. je ju ni st ra in s H um an ca se s (n =10 1) Br oi le r pro du ct s (n =9 8) D ai ry cat tle (n =4 1) W ild b ir ds (n =10 1) A ll so urces (n =34 1) N o. o f res ist an ce st ra in s/% N o. o f se ns iti ve st ra in s/% N o. o f res ist an ce st ra in s/% N o. o f se ns iti ve st ra in s/% N o. o f res ist an ce st ra in s/% N o. o f se ns iti ve st ra in s/% N o. o f res ist an ce st ra in s/% N o. o f se ns iti ve st ra in s/% N o. o f res ist an ce st ra in s/% N o. o f se ns iti ve st ra in s/% TET 22/ 21 .8 79/ 78 .2 63/ 64 .3 35/ 35 .7 35/ 85 .4 6/ 14. 6 9/ 8. 9 92/ 91 .1 12 9/ 37. 8 21 2/ 62. 2 ER Y 2/ 2. 0 99/ 98 .0 0/ 0 98/ 10 0 0/ 0 41/ 10 0 1/ 1. 0 10 0/ 99. 0 3/ 0. 9 33 8/ 99. 1 C IP 89/ 88 .1 12/ 11 .9 98/ 10 0 0/ 0 37/ 90 .2 4/ 9. 8 88/ 87 .1 13/ 12 .9 31 2/ 91. 5 29/ 8. 5 G EN 0/ 0 101 /100 0/ 0 98/ 10 0 0/ 0 41/ 10 0 0/ 0 101 /100 0/ 0 341 /100 A X O 48/ 47 .5 53/ 52 .5 70/ 71 .4 28/ 28 .6 41/ 10 0 0/ 0 47/ 46 .5 54/ 53 .5 20 6/ 60. 4 13 5/ 39. 6 TE T, te tra cy cl in e; E RY , e ry th ro m yc in ; CI P, c ip ro flo xa ci n; G EN , g en ta m ic in ; A X O , c ef tri ax on e; A B, a nt ib io tic .

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Tabl e 3.1.2 . A nt im ic rob ial age nt m in im um inhi bi tor y c onc ent rat ion v al ue s f or C . je ju ni s tr ain s is ol ate d fr om d iffe re nt so urces Source s A nti bi oti c N o. A nti m ic ro bi al a ge nts MI C s fo r C. je ju ni st ra in s (n =3 41 ) MI C ; µ g/ m L) 0. 25 0. 5 1 2 4 8 16 32 64 128 256 H um an cas es TET 101 41/ 40. 6 32/ 31. 7 5/ 5. 0 1/ 1. 0 5/ 5. 0 10/ 9. 9 4/ 4. 0 3/ 3. 0 ERY 16/ 15. 8 54/ 53. 5 28/ 27. 7 1/ 1. 0 2/ 2. 0 CI P 2/ 2. 0 10/ 9. 9 3/ 3. 0 40/ 39. 6 27/ 26. 7 17/ 16. 8 2/ 2. 0 G EN 21/ 20. 8 69/ 68. 3 8/ 7. 9 3/ 3. 0 A XO 1/ 1. 0 1/ 1. 0 3/ 3. 0 13/ 12. 9 35/ 34. 7 38/ 37. 6 7/ 6. 9 2/ 2. 0 1/ 1. 0 Br oi le r pr oduc ts TET 98 13/ 13. 3 6/ 6. 1 6/ 6. 1 7/ 7. 1 3/ 3. 1 11/ 11. 2 25/ 25. 5 9/ 9. 2 11/ 11. 2 7/ 7. 1 ERY 18/ 18. 4 26/ 26. 5 32/ 32. 7 19/ 19. 4 3/ 3. 1 CI P 5/ 5. 1 11/ 11. 2 33/ 33. 7 12/ 12. 2 23/ 23. 5 13/ 13. 3 1/ 1. 0 G EN 36/ 36. 7 41/ 41. 8 20/ 20. 4 1/ 1. 0 A XO 1/ 1. 0 3/ 3. 1 24/ 24. 5 32/ 32. 7 21/ 21. 4 14/ 14. 3 3/ 3. 1 D ai ry ca ttl e TET 41 4/ 9. 8 1/ 2. 4 1/ 2. 4 3/ 7. 3 6/ 14. 6 22/ 53. 7 4/ 9. 8 ERY 1/ 2. 4 11/ 26. 8 15/ 36. 6 7/ 17. 1 5/ 12. 2 1/ 2. 4 1/ 2. 4 CI P 4/ 9. 8 2/ 4. 9 6/ 14. 6 18/ 43. 9 7/ 17. 1 1/ 2. 4 3/ 7. 3 G EN 18/ 43. 9 21/ 51. 2 2/ 4. 9 A XO 20/ 48 .8 15/ 36 .6 4/ 9. 8 2/ 4. 9 33

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Tabl e 3.1.2. C ont inue d So urce s A nti bi oti c N o. A nti m ic ro bi al a ge nts MI C s fo r C. je ju ni st ra in s (n =3 41 ) MI C ; µ g/ m L) 0. 25 0. 5 1 2 4 8 16 32 64 128 256 W ild bi rds TET 101 44/ 43 .6 6/ 5. 9 25/ 24 .8 9/ 8. 9 8/ 7. 9 1/ 1. 0 4/ 4. 0 1/ 1. 0 3/ 3. 0 ERY 51/ 50 .5 11/ 10 .9 32/ 31 .7 3/ 3. 0 3/ 3. 0 1/ 1. 0 CI P 13/ 12 .9 47/ 46 .5 27/ 26 .7 12/ 11 .9 2/ 2. 0 G EN 36/ 35 .6 42/ 41 .6 20/ 19 .8 2/ 2. 0 1/ 1. 0 A XO 1/ 1. 0 11/ 10 .9 6/ 5. 9 36/ 35 .6 19/ 18 .8 15/ 14 .9 11/ 10 .9 2/ 2. 0 A ll so ur ces TET 341 102/ 29. 9 44/ 12. 9 37/ 10. 9 16/ 4. 7 13/ 3. 8 15/ 4. 4 40/ 11. 7 42/ 12. 3 22/ 6. 5 10/ 2. 9 ERY 70/ 20. 5 64/ 18. 8 133/ 39. 0 57/ 16. 7 12/ 3. 5 1/ 0. 3 1/ 0. 3 3/ 0. 9 CI P 2/ 0. 6 27/ 7. 9 54/ 15. 8 47/ 13. 8 103/ 30. 2 46/ 13. 5 43/ 12. 6 18/ 5. 3 1/ 0. 3 G EN 111/ 32. 6 173/ 50. 7 50/ 14. 7 3/ 0. 9 3/ 0. 9 1/ 0. 3 A XO 2/ 0. 6 1/ 0. 3 15/ 4. 4 22/ 6. 5 95/ 27. 9 109/ 32. 0 58/ 17. 0 31/ 9. 1 8/ 2. 3 TE T, te tra cy cl in e; E RY , e ry th ro m yc in ; CI P, c ip ro flo xa ci n; G EN , g en ta m ic in ; A X O , c ef tri ax on e; M IC, m in im um in hi bi to ry c on ce nt ra tio n.

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Ten C. jejuni specific antimicrobial resistance profiles were identified (Table 3.1.3). Most of the examined C. jejuni strains (80.6%) showed resi-stance to one of three profiles: CIP+AXO (28.1%), TET+CIP+AXO (26.7%) and CIP (25.8%).

Table 3.1.3. Antimicrobial resistance profiles of C. jejuni strains isolated

from human clinical cases, broiler products, dairy cattle and wild birds

Antimicrobial resistance profiles

Antimicrobial resistance profiles of C. jejuni strains isolated from: Human

cases products Broiler Dairy cattle birds Wild All tested sources

n % n % n % n % n % CIP+AXO 34 33.7 27 27.6 4 9.7 31 30.7 96 28.1 TET+CIP+AXO 8 7.9 42 42.9 33 80.5 8 7.9 91 26.7 CIP 33 32.7 7 7.1 – – 48 47.5 88 25.8 TET+CIP 12 11.9 21 21.4 – – – – 33 9.7 AXO 5 5 1 1 2 4.9 7 6.9 15 4.4 TET+AXO – – – – 2 4.9 1 1 3 0.9 TET+CIP+ERY 1 1 – – – – – – 1 0.3 CIP+AXO+ERY 1 1 – – – – – – 1 0.3 CIP+ERY – – – – – – 1 1 1 0.3 TET 1 1 – – – – – – 1 0.3 Total resistance 95 94.2 98 100 41 100 96 95 330 96.8 Sensitive: 6 5.8 5 5 11 3.2

TET, tetracycline; ERY, erythromycin; CIP, ciprofloxacin; GEN, gentamicin; AXO, ceftri-axone.

3.2. Association between phylogenetic lineage and antimicrobial resistance profiles of C. jejuni

The study revealed that particular MLST sequence types characterized by different antimicrobial resistance. Among to 341 C. jejuni strains inclu-ded in the antimicrobial testing and MLST analysis, 146 distinct sequence types (STs) were identified. These STs were assigned to 26 previously de-scribed clonal complexes (CCs). In this study, 231 of C. jejuni strains were included, which were grouped into previously known sequence types (ST) and 110 isolates which were assigned to novel STs. All C. jejuni strains assigned to ST-464 (CC464) were resistant to tetracycline and ciprofloxacin (P<0.05). This ST was dominant among C. jejuni strains isolated from

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broi-ler products. The clonal spread of specific fluoroquinolone resistant ST-464 lineage, have been reported in other studies [132, 133]. In addition, multi-drug resistance of C. jejuni strains assigned to CC464 was significantly higher (P<0.05) than that for the other clonal complexes [134].

ST-5 (CC353) was dominant among C. jejuni strains isolated from hu-man clinical cases and 96.9% of these strains were resistant to ciprofloxacin and 84.4% showed resistance to ceftriaxone. Campylobacter has intrinsic resistance to most cephalosporin’s, therefore, beta-lactams are not recom-mended to treat Campylobacter infections [9].

The results showed that strains assigned to ST-21 (CC21) (77.8%) exhi-bited a significantly higher multidrug resistance (P<0.05). The results sho-wed that tetracycline resistance were more frequently observed in strains assigned to ST-21 (CC21) (94.4%) as well as in strains assigned to ST-257 (75%), ST-3098 (80%), and ST-353 (75%).

The TET+CIP+AXO multidrug resistance profile was confirmed for the majority of the examined C. jejuni strains assigned to 21 (77.8%), 3098 (80%), 354 (100%), 464 (53.3%), 6411 (66.7%), and ST-6391 (100%). Most of the examined C. jejuni isolates which belonged to the ST-5 (75%) were confirmed as resistant to CIP+AXO (Fig. 3.2.1).

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Fig. 3.2.1. MLST and resistance profiles of different

THE PHENOTYPIC ANTIMICROBIAL RESISTANCE OF CAMPYLOBACTER JEJUNI STRAINS AND WHOLE GENOME SEQUENCE-BASED PREDICTION OF RESISTANCE DETERMINANTS

Jurgita Aksomaitienė