Flagellar plays an important role in producing the toxin of
bacteria, which is often mixed in the outer membrane protein when an
antigen is isolated to make a Salmonella vaccine. Understanding the
expression genes associated with flagellar will be very helpful for
purifying this component. For example, inactivating these genes will
create pure outer membrane vesicles, as antigens to make the vaccine.
This is also one of the new contributions of the thesis
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elevant drug resistance.
1.3. Contamination and antibiotic resistance of Salmonella in food
1.3.1. In the world
There have been many different studies on Salmonella
contamination rate in food which have been published. In general, the
research results show that Salmonella strains are distributed
differently depending on the geographical region and the food source.
6
The common serotypes vary by geographic region and their rates of
antibiotic resistance in Salmonella are increasing each year.
1.3.2. In Vietnam
Recent reports show that Vietnam food is contaminated
Salmonella at different rates, including multiple antibiotic-resistant
species. The prevalence of Salmonella in food and the rate of multi-
antibiotic resistance in Salmonella isolates are increasing every year.
Therefore, it is necessary to isolate and determine the antibiotic
resistance characteristics of Salmonella in each food type in a
particular geographical region at different time periods.
1.4. Methods are commonly used in gene expression research
Currently, there are four methods are used: Reverse
transcription PCR (RT-PCR), Real-time PCR (qPCR), Microarray,
and next generation sequencing (NGS) to study gene expression.
Among the above techniques, the next generation sequencing has the
most advantage, this technique can overcome the disadvantages of the
remaining three techniques and is the only one capable of detecting
new genes.
Next generation sequencing
NGS is a direct measurement of nucleic acid sequences
present in the sample. There is the linear relationship between the
number of sequences and the concentration of nucleic acid sequences
present in the sample. Moreover, NGS is not dependent on the
information of nucleic acid sequences and highly homologous genes
that can be expressed in the sample. Thus new genes can be detected.
Among the next generation sequencing technologies, the Illumina
technology produces the most accurate data, the procedure is simple,
and is widely applied in many different research fields. Currently,
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more than 90% of the sequence data in the world is generated by
Illumina technology.
Chapter 2. MATERIAL AND METHODS
2.1. MATERIAL
2.1.1. Samples
A total of 90 meat samples, including 30 porks (from TL-1 to
TL-30), 30 chicken meats (from TG-1 to TG-30), and 30 beef samples
(from TL-1 to TL-30). symbols from TB-1 to TB-30), randomly
collected at 10 markets in Hanoi.
2.1.2. Culture media, chemicals, antibiotics, and kits
Culture media, antibiotics, and kits are purchased from
reputable companies globally.
2.1.3. Research equipment
The equipments are used at prestigious lab in Vietnam, such
as Vietnam Academy of Science and Technology, National Institute
of Burn, Vietnam Medical Military University.
2.2. METHODS
2.2.1. Sampling
Samples were collected according to TCVN 4833-2002, from
7-8 am in the winter season, from October to December 2016.
2.2.2. Identification of Salmonella
Salmonella was detected according to ISO 6579: 2002.
2.2.3. Antibiotics susceptibility
Salmonella’s antibiotics susceptibility was testing using
Kirby-Bauer diffuse method.
2.2.4. Nex generation sequencing
We choosed some of multidrug resistant Salmonella to
transcriptome sequence using Illumina's technology.
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2.2.5. Bioinformatics methods
The sequence quality is checked by FastQC software. The
adapter sequences and noise sequences were removed by
Trimmomatic 0.32 software. The sequence at Q20 quality score was
de novo assembled using Geneious R11 software. The de novo
sequence was annotated by different databases such as RAST,
PATRIC 3.5.2, BASys, and Geneious R11 software. Identification of
antibiotic resistance genes using: ResFinder, ARG-ANNOT, CARD,
and PATRIC 3.5.2. Identify the gene mutation resistance to quinolone,
by ResFinder tool.
2.2.6. Confirm antimicrobial resistant gene mutations using Sanger
sequencing
To confirm the predicted results of antibiotic resistance gene
mutations obtained from the next generation sequencing method, the
new gene mutations related to quinolone resistance will be confirmed
using Sanger sequencing from cDNA of the samples.
2.2.7. Statistical analyzed
Using SPSS 16.0 software to calculate the χ2, Fisher exact
test, p values.
Chapter 3. RESULTS AND DISCUSSION
3.1. Isolate and identify the Salmonella serotype
3.1.1. Identify Salmonella results
After non-selective enrichment, selective enrichment and
biochemical confirmation, we have obtained Salmonella spp. from the
research samples. The results are presented in Table 3.1
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Table 3.1. Results of identify Salmonella in the research samples
Sample
Total
(number)
Positive Negative
number rate (%) number rate (%)
Chicken 30 11 36,7 19 63,3
Pork 30 9 30,0 21 70,0
Beef 30 5 16,7 25 83,3
Total 90 25 27,8 65 72,2
χ2 = 3,102; df = 2; p = 0,212
The list of Salmonella positive isolated sources is presented in
Table 3.2
Table 3.2. List of samples positive for Salmonella
The prevalence of Salmonella in this study was 27.8%, this
result is in line to that of Do Ngoc Thuy et. al. (30%). However, this
rate is lower than other studies such as Ta et. al. (48.7%), Nguyen et.
al. (69.7%), Boomar et. al. (80%). This lower result may be due to
collecting samples in the morning, when meat was fresh, limiting
bacterial infection. Moreover, the time of sample collection is in the
winter season, the low temperature and humidity, combined with the
dry air, these factors pose inhibited to the growth of bacteria.
Among Salmonella positive samples, chicken samples were
the most prevalence (36.7%), followed by pork samples (30%), and
beef samples were the lowest rate (16.7%). This result is in line with
the research results of Do Ngoc Thuy et. al., Zhao et. al., Miranda et.
al. However, this result is different from other studies such as Phan et.
al. (pork, beef, chicken) and Nguyen et. al. (pork, chicken, beef). The
Salmonella positive rate in chicken is often higher than pork, which
Sample sources Sample ID
1 Chicken (11 samples) TG-1, 2, 3, 4, 5, 6, 14, 25, 28, 29, 30
2 Pork (9 samples ) TL-1, 2, 3, 4, 5, 15, 26, 29, 30
3 Beef (5 samples ) TB-1, 2, 3, 15, 29
10
can be due to chickens are plucked, slaughter and bleeding directly on
the cement floor, there is no separation between these areas, so
bacteria from feces, environment easily contaminate into meat. From
the above research results it can be seen that the prevalence of
Salmonella in retail meats is very different, depending on the
geographic area of sample collection.
3.1.2. Salmonella serotype results
Identify serotype from the Salmonella spp. we obtained, we
obtained 9 different serovars. The most common was S. Typhimurium
(11/25 strains). Following by S. Derby (4/25 strains), S. Warragul, S.
Indiana, S. Rissen (2/25 strains), S. London, S. Meleagridis, S. Give,
S. Assine (1/25 strains). We found that in different studies the common
serovar was also different. For example, Moe et. al. (S. Albany),
Patchanee et. al. (S. Rissen). From the above research results, it is
shown that the common serovars in the studies are different,
depending on the geographical area and time of sample collection.
3.2. Antibiotic resistance characteristics of Salmonella isolated
3.2.1. The level of antibiotic resistance of Salmonella isolated
Base on the antibiotic susceptibility results, we obtained 52%
(13/25) strains resistant to at least one antibiotic, of which the multiple
resistance rate was 36%. The rate of resistance to Streptomycin (STR)
and tetracycline (TE) was highest (44%). This is understandable
because these two antibiotics are widely used in Vietnam, both in
treatment and animal husbandry. All Salmonella are susceptible to
ceftazidime (CAZ), thus this is a good antibiotic that can be used to
treat Salmonella infection. It is needed to monitor the use of CAZ.
Antibiotic-resistant Salmonella is a source of transmission of
resistance genes to other organisms, more dangerous is to humans
11
through the consumption of food. The rate of antibiotic resistance in
this study (52%) is lower than the results of some studies in Vietnam
(62.2%) and Japan (89.9%). The rate of multi-resistance (36%) is
lower than some other research results such as Nguyen et. al. (41.1%)
and Katoh et. al. (90.2%). This difference may be due to the overuse
of antibiotics in animal husbandry and treatment between nations,
which increases selective pressures, resulting in emergence different
rates of antimicrobial resistance Salmonella. The number of samples
in this study is small (25 samples). Therefore, further research on
resistance rates and multiple antibiotic resistance should be conducted
with a larger number of samples.
3.2.2. The number of each Salmonella resistant to antibiotics
according to the isolation source
Determining the rate of antibiotic resistance of Salmonella
according to isolated sources helps to evaluate in detail their antibiotic
resistance characteristics by food source. The results showed that
Salmonella isolates from pork were most resistant to antibiotics, with
66.7% (6/9 strains), of which 44.4% (4/9 strains) were multi-antibiotic
resistant. Following by the isolated from chicken with the rate of
36.4% (4/11 strains) of which 27.3% (3/11 strains) are resistant. There
is only one S. Typhimurium from beef that is antibiotic resistant and
is multi-resistant. S. Typhimurium accounts for a large proportion of
all three isolates (11 strains), but only 3 strains of antibiotic resistance
are also multi-resistant.
3.2.3. Number of Salmonella resistant to each antibiotic according
to the isolation source
The determination of Salmonella antibiotic resistance
according to isolated sources helps to evaluate in detail their antibiotic
12
resistance characteristics by food source. Accordingly, all Salmonella
isolated from chicken were antibiotic resistant (except CAZ). No
strains were isolated from pork and beef resistant to CIP. Salmonella
isolated from three meat sources that are resistant to AM, STR, C, TE
and SXT, among which isolates from pork have the highest rate of
antibiotic resistance. All Salmonella isolates from beef are sensitive to
CAZ, GN and CIP. Salmonella was isolated from pork resistant to
AM, STR, and TE with the largest percentage. Salmonella from
chicken is the most C resistant, especially the CIP resistant strains
were found only in chicken. Beef is the least contaminated Salmonella
and these strains are most susceptible to antibiotics.
3.2.4. Antibiotic resistance phenotypic
Base upon antibiotic susceptibility results, we have identified
the antibiotic resistance pattern of Salmonella studied. Two common
phenotypes of antibiotic resistance are TE, STR, AM (2/9), and C, TE,
SXT, STR, AM (3/9). TE, STR, AM phenotypes are only found in
Salmonella isolated from pork. The two common antibiotic resistant
phenotypes in this study differ from the antibiotic resistance
phenotypes published in the Miranda et. al., Kim et. al. studies. From
the above results it can be said that the common patterns of antibiotic
resistance are different between the studies.
Identifying the antibiotic resistance phenotype in the research
samples is important. This phenotypic result combined with genomic
analysis results will show antibiotic resistance genes, mutations
related to antibiotic resistance phenotypes. From the phenotypic
results, we have obtained 9 multi-resistant Salmonella, of which S.
Typhimurium is the most common. Pork is the most isolated source of
13
multi-resistant Salmonella (5 serovars), followed by chicken (3
serovars), beef (1 serovar).
Antibiotic resistance rates, multidrug resistance rates, and
antibiotic resistance patterns are different among published studies.
This difference, according to some researchers, may be due to the
overuse of antibiotics in treatment and animal husbandry, increasing
the selection pressure on bacteria leading to the emergence of different
strains of multi-antibiotic resistant Salmonella by region. geography.
3.3. Results of analyzing gene categories in some multidrug
resistance Salmonella
Transcriptome sequencing is rarely used to identify antibiotic
resistance genes. Instead, researchers often use the entire whole
genome sequencing. Transcriptome sequencing allows us to study the
function of genes better than DNA sequencing. In this study, we want
to focus on the functional genome of bacteria. This is important
because the unnecessary genes will not be expressed and tend to be
lost or degraded. Moreover, the whole genome sequencing technique
could not distinguish the genes that were inactivated in the genome
and other related functions of that gene. In addition, in our opinion,
the expression gene will be related to the phenotype, particular
resistant or sensitive phenotypes, higher than the non-expression gene.
We have sequenced transcriptome of multidrug resistant
Salmonella using method which was published by Marcelino et. al.
(the author also sequenced the transcriptome of bacteria in bird gut in
Australia to identify of antibiotic resistance genes). It is very important
that antibiotic resistance genes are also expressed in antibiotic
sensitive bacteria under normal culture conditions. Therefore, in this
study we did not use antibiotic sensitive strains for comparison.
14
Instead, we use the same phenotype that is sensitive and resistant to
each antibiotic in the strains to compare. From that, we predict which
genes, or mutations, might be related to antibiotic resistance in this
bacterium.
In order to analyze the gene groups in multidrug-resistant
Salmonella, several resistant serovars should be selected to sequence
transcriptome. However, in the framework of this thesis, we selected
only 6 strains according to the criteria of high infection rate, resistance
to as many antibiotics as possible and by isolated sources, including S.
Derby, S. Give, S. Indiana, S. Typhimurium S384, S. Typhimurium
S360, and S. Typhimurium S181.
After extracted RNA from 6 research samples, we conducted
quality control by concentration measurement at OD260/280, and
electrophoresis. Results showed that these RNA samples were of good
quality. This sample was then synthesized cDNA and tested for
integrity. The results showed that the RIN (RNA Integrity Number)
was above 8.0, qualified for sequencing. The results are as follows:
3.3.1. Number of read
From the raw sequence results, we conducted trimming
sequence by Trimmomatic software: this yield total of 160,043,486
read, total number of read at Q20 score is 146,080,642. The number
of read is highest in Sal 4 and at least in Sal 6. The read sequences at
Q20 will be used for de novo assembly and used for further analysis.
3.3.2. De novo assembly
Results of de novo assembly showed that the transcriptome
size ranged from 4.69 Mb (Sal 4) to 5.1 Mb (Sal 11), GC values around
52%, N50 values are high and L50 values are low. This result is similar
to the sequencing result of S. Derby 07CR553 published by
15
Kérouanton et. al. From that, it can be concluded that the sequence of
6 research samples is of good quality, eligible for subsequent analysis.
3.3.3. Genes annotation
To avoid the missing annotation genes, gene analysis tools
have been used as many as possible. The number of genes detected in
these tools is different. The findings of these tools' genes differ
because of their different methods of analysis, and there is currently
no standard method for annotating genes accepted among researchers
around the world. The number of coding sequences found by different
databases is shown in Table 3.3.
Table 3.3. Coding sequences in 6 research samples
Gene
analysis tool
Number of coding sequences
Sal 4 Sal 6 Sal 7 Sal 8 Sal 11 Sal 12
RAST 4.807 4.917 5.154 5.097 5.357 5.000
PATRIC 4.807 4.917 5.154 5.049 5.357 5.000
BASys 4.973 5.100 5.336 5.253 5.566 5.209
Genious R11 4.400 4.513 5.022 5.207 5.304 5.309
In 2017, Baek et. al. announced that many genes that encode
proteins less than 100 amino acids undetectable when annotating the
bacterial genome. Therefore, further studies of the above genes are
needed in the research samples.
3.3.4. Gene categories analysis in multidrug resistant Salmonella
In addition to housekeeping genes...We have identified
important gene categories expressed in multi-resistant Salmonella:
3.3.4.1. Antibiotic resistance genes
Results of antibiotic resistance genes and antibiotic resistance
phenotype are presented in Table 3.4. Accordingly, a total of 107
16
antibiotic resistance genes (list of genes not shown in this summary).
Including 22 β-lactam resistance genes, 46 aminoglycoside resistance
genes, 8 quinolone resistance genes, 7 phenicol resistance genes, 6
cycline resistance genes, 3 sulfonamide resistance genes, 3
trimethoprim resistance genes. Furthermore, we have found 12
antibiotic resistance genes which resistant to macrolides, rifamycin,
fosfomycin, lincosamide, polymyxin, and peptides. The number and
diversity of antibiotic resistance genes in this study are similar to the
results of the study by Saskia et. al. (2018).
A total of 42 phenotypes were identified from antibiotic
susceptibility results. There are 29 antibiotic resistant phenotypes have
expression of antibiotic resistance genes. There are 12 antibiotic-
sensitive phenotypes have expression of antibiotic resistance gene.
The only sensitive phenotype is that there is no expression of the
antibiotic resistance gene, Sal 6 is susceptible to SXT and there is no
expression of SXT resistance gene. Thus, the genotype and phenotype
accordant is (29 + 1) / 42 = 71.4%. And the genotype and antibiotic
resistance phenotype not accordant is 12/42 = 28.6%. The correlation
between genotype and phenotype is similar to the study results of
Owen et al., 2017 (72.7%). Our research results are lower than those
of some other authors like McDermott et. al, 2016 (99%), Zankari et.
al., 2013 (99.74%). The genotypes and phenotypes not accordant in
this study can be explained by the inadequate expression of antibiotic
resistance genes, due to the multiple antibiotic resistance mechanisms
involved in resistance to one antibiotic, and the other mechanisms of
antibiotic resistance have not been found.
17
Table 3.4. Summarize results of antibiotic resistance genes and antibiotic resistance phenotype in the research samples
KS
Mẫu nghiên cứu (Kiểu hình kháng kháng sinh/gen kháng kháng sinh)
Sal 4 Sal 6 Sal 7 Sal 8 Sal 11 Sal 12
AM
(R)
blaOXA-1
blaTEM family, PBPE**
(R)
blaTEM
family
(R)
blaTEM family
PBPE**
(R)
blaTEM family
(R)
blaTEM family
PBPE**
(R)
blaTEM family
PBPE**
GN
(R)
aac family*
aph family*
ant family*, kdpE
(S)
aac(6')-Iy
kdpE
(S)
aac (6')-Iy, aac6-Iy,
aadA8, aadA17;
kdpE
(S)
aac (6')-Iaa, aac3-IIa,
aadA17, aadA8b
aph3-IIa, kdpE
(R)
aac family*
aph family*
kdpE
(S)
aac(6')-Iaa, aac6-
Iaa, aph(6)-Id,
aph(3'')-Ib
strA, strB, kdpE
STR
(R)
aac family*
aph family*
ant family*, kdpE
(R)
aac(6')-Iy
kdpE
(R)
aac (6')-Iy, aac6-Iy
aadA8, aadA17
kdpE
(R)
aac (6')-Iaa, aac3-IIa
aadA17, aadA8b
aph3-IIa, kdpE
(R)
aac family*
aph family*
kdpE
(R)
aac(6')-Iaa, aac6-Iaa
aph(6)-Id, aph(3'')-Ib
strA, strB, kdpE
CIP
(R)
aac(6')Ib-cr, gyrA,
gyrB, parC
(S)
gyrA, gyrB,
parC
(S)
qnr-S1, qnr-S3,
gyrA, gyrB, parC,
parE
(S)
qnrS1, qnr-S3, qnr-S5,
gyrB, parC
(S)
gyrA, gyrB, parC
(S)
gyrA, gyrB, parC
C
(R)
floR, cmlA1, catB3,
catB4, catB8
(S)
floR, cmlA1
(R)
floR, cmlA1, cmlA5,
cat2
(R)
floR, cmlA1, cmlA5,
cat2
(R)
floR, cmlA1
(S)
cmlA1
TE
(R)
tet(A), tet(R)
(R)
tet(A),
et(M), tet(S)
(R)
tet(A), tet(M),
tet(R), tet(S)
(R)
tet(A), tet(B), tet(C),
tet(M), tet(R), tet(S)
(R)
tet(A), tet(B),
tet(C), tet(R)
(R)
tet(A)
SXT
(R)
sul1, sul2, dfrA12
(S)
(R)
sul2, sul3, dfrA12
(R)
sul2, sul3, dfrA12
(R)
sul2, dfrA14, dfrA5
(S)
sul2
Abbreviation: *Aac (Acetylation) family; Aph (Phosphorylation) family; Ant (Adenylylation) family; ** Penicillin
Binding Protein E. coli. KS (kháng sinh), AM (ampicillin), GN (gentamycin), STR (streptomycin), CIP (ciprofloxacin),
C (chloramphenicol), TE (tetracyclin), SXT (sulfamethoxazol/trimetoprim).
18
3.3.4.2. Quinolone resistance gene mutations
Quinolone is commonly used in the treatment of Salmonella
infections in humans. For food-borne Salmonella, quinolone
resistance is the most concerned and has been mentioned in the list of
the most important antibiotics in the field of medicine in 2016 by
WHO. One of the quinolone resistance mechanisms in Salmonella is
caused by mutations of the gyrA, gyrB and parC genes. Thus, we
found mutations of these genes, the results are presented in Table 3.5.
Table 3.5. Results of the quinolone resistance gene mutations
Samples SR
Gene mutations
gyrA parC parE
Sal 4 R S83F;D87G T57S; S80R; T255S; A628S
Sal 6 S T57S; T255S; N395S
Sal 7 S S83Y T57S; T255S; A352V S592N
Sal 8 S T255S; N395S; S469A; A620T
Sal 11 S T255S; N395S; S469A; A620T
Sal 12 S T255S; N395S; S469A; A620T
Abbreviation: A (Alanine), N (Asparagine), R (Arginine), S (Serine),
T (Threonine), V (Valine), SR (susceptibility results), R (resistant), S
(sensitive).
The results of Table 3.5 show that the list of mutations was
identified: S83F, S83Y, D87G, S80R, T57S, T255S, N395S, S469A,
A620T, A628S, S592N. To date, there are no reports of mutations
A628S, T255S, N395S, S469A, and A620T have been published.
However, the mutations T255S, N395S, S469A, A620T were
identified in CIP-sensitive strains Sal 6, Sal 7, Sal 8, Sal 11, and Sal
19
12 proving that these mutations have no role in CIP resistance in
research samples. The parC mutation (A628S) may have a role in CIP
resistance because it appears only in Sal 4, the only CIP resistant
strain. This result is not only new but also has high scientific
significance, paving the way for further research on antibiotic
resistance in Salmonella.
3.3.4.3. The gene categories involved in the efflux pumps
We identified the expression of 41 efflux pumps-related
genes, including 37 genes in S. Typhimurium S181, 25 genes in S.
Typhimurium S384, 27 genes in S. Typhimurium S360, 27 genes in S.
Give, 23 genes in S. Derby, 26 genes in S. Indiana. Efflux pumps were
detected in research samples belonging to 3 families: MFS (MefB,
EmrAB, tetA, tetB, MdtD), SMR (QacE), and RND (AcrAB-TolC,
AcrAD-TolC, AcrEF-TolC, MdtABC-TolC, MexPQ-OpmE).
Understanding of efflux pumps is essential for the development of
interventions to limit antibiotic resistance in Salmonella.
Some strains of Salmonella are susceptible to antibiotics but
still have expression of efflux pumps, like AcrAB-TolC, AcrEF-TolC,
and MdfA in C-sensitive strains. AcrD in GN-sensitive strains.
MexPQ-OpmE in CIP-sensitive and C-sensitive strains. Therefore
these channels do not play a role in resistant to these antibiotics.
Some antibiotic resistant strains have the expression of efflux
pumps, like AcrD in AM, STR-resistant strains, MexPQ-OpmE in TE-
resistance strains, EmrAB in TE, STR and AM-resistant strains.
Therefore, we predicted that these channels may be related to
resistance to the respective antibiotics. Further studies on the drug
dispensing canal system in Salmonella need to be conducted in the
future.
20
New finding the role of the above efflux pumps on antibiotic
resistance is the novelty of the thesis.
3.3.4.4. Analysis results of some other gene categories
In addition to the antibiotic resistance gene category and the
genes related to efflux pumps, we identified many other gene groups
related to antibiotic resistance and bacterial toxins. The number of
genes in each of these functional categories is shown in Table 3.6.
Table 3.6. Expression of related gene groups.
Gene category
Number of genes
Sal 4 Sal 6 Sal 7 Sal 8 Sal 11 Sal 12
Toxin genes 118 124 125 130 131 133
flagellar 38 38 38 38 38 38
Cell wall and LPS 52 39 48 52 51 51
Response to Selenium 2 2 2 2 2 2
Response to Tellurite 3 3 3 3 3 3
Response to
Formaldehyde
2 2 2 2 2 2
Mobile elements, phage
and prophage
12 24 22 27 26 26
ABC transporter 21 21 21 21 21 21
Abbreviation: Sal 4 (S. Indiana), Sal 6 (S. De
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