Comparative transcriptomics of multidrug-resistant Acinetobacter baumannii in response to antibiotic treatments - Nature
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OPEN Comparative transcriptomics of
multidrug-resistant Acinetobacter
baumannii in response to antibiotic
Received: 11 May 2016
Accepted: 5 February 2018 treatments
Published: xx xx xxxx
Hao Qin1,2,6, Norman Wai-Sing Lo3, Jacky Fong-Chuen Loo1, Xiao Lin1,2, Aldrin Kay-Yuen Yim1,2,7,
Stephen Kwok-Wing Tsui4, Terrence Chi-Kong Lau 5, Margaret Ip3 & Ting-Fung Chan 1,2
Multidrug-resistant Acinetobacter baumannii, a major hospital-acquired pathogen, is a serious health
threat and poses a great challenge to healthcare providers. Although there have been many genomic
studies on the evolution and antibiotic resistance of this species, there have been very limited
transcriptome studies on its responses to antibiotics. We conducted a comparative transcriptomic study
on 12 strains with different growth rates and antibiotic resistance profiles, including 3 fast-growing
pan-drug-resistant strains, under separate treatment with 3 antibiotics, namely amikacin, imipenem,
and meropenem. We performed deep sequencing using a strand-specific RNA-sequencing protocol,
and used de novo transcriptome assembly to analyze gene expression in the form of polycistronic
transcripts. Our results indicated that genes associated with transposable elements generally showed
higher levels of expression under antibiotic-treated conditions, and many of these transposon-
associated genes have previously been linked to drug resistance. Using co-expressed transposon
genes as markers, we further identified and experimentally validated two novel genes of which
overexpression conferred significant increases in amikacin resistance. To the best of our knowledge,
this study represents the first comparative transcriptomic analysis of multidrug-resistant A. baumannii
under different antibiotic treatments, and revealed a new relationship between transposons and
antibiotic resistance.
Acinetobacter baumannii is a nosocomial pathogen that can grow in diverse environments, including intensive
care units. Immunocompromised patients are vulnerable to A. baumannii infection even during therapy. These
bacteria enter the body through moist tissues and can colonize various sites, such as the respiratory tract, cen-
tral nervous system, skin, and eyes. Pneumonia, blood stream infections, and meningitis are the most frequent
adverse effects of infection1. The major challenge in the treatment of A. baumannii infection is multidrug resist-
ance. Pan-drug-resistant A. baumannii strains have been reported across Asia and Europe2–4.
Several comparative genomic studies of A. baumannii have discussed the relationships among the variability of
drug resistance islands and drug-resistant phenotypes2–4. Multidrug-resistant strains possess a higher metabolic
capacity in environments with limited resources5. Nonetheless, information from genome sequences is not suffi-
cient to capture the dynamics of gene expression. Several transcriptome studies have probed the transcriptional
changes in the A. baumannii reference strain ATCC17978 when grown under harsh environmental conditions,
such as ethanol treatment6, high salinity7, and iron deficiency8. These studies particularly noted the strong surviv-
ability of A. baumannii under such conditions. Recent transcriptome studies have also investigated the response
of this species to antibiotics. For example, a study in which the A. baumannii reference strain ATCC19606 was
1
School of Life Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China. 2Partner State Key
Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong SAR, China. 3Department of
Microbiology, The Chinese University of Hong Kong, Hong Kong SAR, China. 4School of Biomedical Sciences, The
Chinese University of Hong Kong, Hong Kong SAR, China. 5Department of Biomedical Sciences, City University of
Hong Kong, Hong Kong SAR, China. 6Present address: 3D Medicines Corporation, Shanghai, China. 7Present address:
Washington University School of Medicine, Saint Louis, MO, USA. Norman Wai-Sing Lo and Jacky Fong-Chuen Loo
contributed equally to this work. Correspondence and requests for materials should be addressed to T.-F.C. (email:
tf.chan@cuhk.edu.hk)
SCieNTiFiC ReporTs | (2018) 8:3515 | DOI:10.1038/s41598-018-21841-9 1www.nature.com/scientificreports/
treated with colistin and doripenem revealed the critical genes involved in the killing process of colistin9, while
in another study the multidrug-resistant A. baumannii strain MDR-ZJ was treated with tigecycline and identified
an efflux pump and several transcriptional regulators involved in the drug resistance response10. These studies
were performed using only single strains, which may limit the generalizability of their conclusions. Due to the
large variations in genome sizes and sequences among different strains, especially between multidrug-resistant
and -sensitive ones, a more systematic analysis that is free from strain bias is needed to study and compare their
transcriptomes under antibiotic-free and -treated environments.
Herein, to better understand the transcriptomic response of drug resistance in A. baumannii, we conducted
a comparative study of nine multidrug-resistant strains, five of which are pan-drug-resistant, and three sensitive
strains using a strand-specific RNA-sequencing protocol. We adopted a de novo transcriptome assembly and anal-
ysis pipeline to study gene expression in the form of polycistronic transcripts. Differentially expressed genes were
analyzed according to the corresponding strain properties. Three multidrug-resistant strains were further treated
with three antibiotics, namely amikacin, imipenem and meropenem, and the corresponding transcriptomes were
generated and analyzed. Our results indicated that genes associated with transposable elements in general were
up-regulated under antibiotic treatment. Many of these genes are known to be drug resistance-related. Among
several previously unknown transposon-associated genes, we experimentally validated two of them. Expression
of these two genes in E. coli BL21 conferred increased resistance to amikacin. In summary, we believe our com-
parative transcriptomic study and polycistronic transcript analysis revealed new insights into the drug resistance
response in A. baumannii. Our analytical approach should also be applicable to similar studies of other bacterial
pathogens.
Results
A. baumannii strains with different drug resistance profiles exhibited differential growth rates
in antibiotic-containing media. Twelve strains with different pulsed-field gel electrophoresis (PFGE) pat-
terns (Fig. S1) and antibiotic resistance profiles were collected (Table S1). These include nine multidrug-resistant
strains (R1 to R9), five of which are pan-drug-resistant (R1, R5, R6, R7, and R8), and three sensitive strains (S1 to
S3) (Table S1). Seven multidrug-resistant strains (R1 to R7) of the Ia PFGE pattern were clustered to the Global
Clone II (GC II)4 of multidrug-resistant A. baumannii (Fig. 1a). The other strains belonged to clones other than
GC I and GC II. Three multidrug-resistant strains (R7, R8, and R9) grew significantly slower than the other nine
strains after 4 hours (Fig. 1b) based on a pooled two-tailed Student’s t-test (p < 0.01).
De novo transcriptome assembly revealed gene-gene relationships in polycistronic transcripts.
All 12 strains were cultured in antibiotic-free conditions until mid-log phase. Three multidrug-resistant strains,
R3, R4, and R5, which are from GC II and show similar PFGE patterns, were also cultured in antibiotic-containing
media. Three antibiotics, amikacin (an aminoglycoside), imipenem, and meropenem (both of the carbapenem
class) were chosen, because the 12 strains had exhibited various degrees of resistance to these three antibiotics. In
total, 18 antibiotic-treated samples were prepared from either mid-log or stationary phases, but only 17 samples,
excluding the amikacin-treated R4 at mid-log phase, were successfully sequenced. RNA sequencing generated
a read depth of over 1,000× per sample. A total of 177,939 transcripts were assembled by Trinity11 across 29
samples. 38.8% of the assembled transcripts could be annotated based on known genes from 15 complete A. bau-
mannii genomes on the transcribing strand, while the other 61.2% of the transcripts could be classified into ERCC
spike-in sequences, antisense transcripts, or mis-assembled transcripts. Among these annotated transcripts, over
75% were polycistronic, along which more than one gene could be annotated (Fig. S2). Polycistronic mRNAs
are produced by prokaryotes to regulate functionally related genes in operons12. In our results, over 97.5% of
the genes were annotated from at least one polycistronic transcript (Fig. S3a). Genes that were on the same tran-
scripts, particularly those within three loci of one another, had a higher chance of being annotated under the
same gene ontology (GO) hierarchy (biological process) compared with those that were on different transcripts
(p < 0.01) (Fig. S3b). Many of these pairs of genes encoded on the same transcript had significantly positive
Spearman correlations in terms of expression levels. The median Spearman correlation between genes on the
same transcripts was significantly greater than 0 (p < 0.01) (Fig. S3c).
Amino acid metabolism and membrane transporters were up-regulated in fast-growing pan-drug-
resistant strains. To investigate the major pathways associated with growth rates, the transcriptomes
were compared between fast-growing and slow-growing strains at the antibiotic-free mid-log phase. In total,
133 up-regulated and 53 down-regulated genes were identified in fast-growing strains when compared with
slow-growing strains. Based on their GO annotations, six GO terms were functionally correlated with the growth
rate based on Pearson’s chi-square test (q < 0.01) followed by the Benjamini–Hochberg procedure (Fig. 1c).
Genes that are involved in amino acid metabolic processes and that exhibited the most expression variability
among multidrug-resistant strains were all significantly up-regulated in fast-growing strains. Contrary to the two
slow-growing pan-drug-resistant strains R7 and R8, the two fast-growing strains, R5 and R6, exhibited a high
degree of similarity in expression patterns to the fast-growing, sensitive strains S2 and S3, especially for genes
involved in amino acid metabolism, glycerol lipid metabolism, siderophore biosynthesis, and transmembrane
transporters.
Metabolic pathways centered at and around the TCA cycle were consistently up-regulated in all
three antibiotic treatments. To investigate which genes and pathways were significantly modulated under
antibiotic treatment, the transcriptomes of the antibiotic-free mid-log phase were compared with those of the
antibiotic-treated mid-log phase and antibiotic-treated stationary phase from three multidrug-resistant strains
(R3, R4, and R5). A total of 559 genes were up-regulated and 910 genes were down-regulated in treatment with
SCieNTiFiC ReporTs | (2018) 8:3515 | DOI:10.1038/s41598-018-21841-9 2www.nature.com/scientificreports/
a R5
b
1.6
R7 ** **
R6
R4
R1 1.4 R1, R2, R4, S1, S2
R3 R3
R6, S3
R2 Global Clone II R5
BJAB07104 1.2
MDR-TJ
MDR-ZJ06
BJAB0868 1
R7, R9
TCDC-AB0715
O.D. 600
R8
TYTH-1
1656-2 0.8
ACICU
R8
R9 0.6
S1
AB0057
AB307-0294 Global Clone I 0.4
AYE
S2 n=3
SDF 0.2 ** P-value < 0.01
ATCC17978
S3
BJAB0715
D1279779
0 1 2 3 4 5 6
0.002 Time (hours)
c Low High
G420 G3936
G4166
G322
G1468
G1767 Group III
G496
G1827
G3350
G3934 Siderophore biosynthetic
G323 G1269
G1081
G321
G3351 process
G3707
G3708 G1118
G1192
G2902 Group I
G980
G1533
Group IV
G4076
G425
Amino acid
G2012
G4239 Transmembrane transporter
G4179 G1897
G426
G589 metabolic process
G1594 activity
G3695
G1235
G427 G4169
G4203 G4167
G81 G3928
G4106 G4162
G3802 G3927
G3851 G4164
G4161 Group V
G3646
G2144 G3888
G4158 Ribosome biogenesis
G1732
G3923
G292 G4171
G362 G4172
G2126
G2161 Group II G4184
G4159
G2266
G1868 Glycerol lipid
G1407
G1625 Group VI
G1384
G959 metabolic process G1622
G1619 Proton transporting
G1813
G4253 G1620
S1 S2 S3 R1 R2 R3 R4 R5 R6 R7 R8 R9 S1 S2 S3 R1 R2 R3 R4 R5 R6 R7 R8 R9 ATP synthase
Fast-growing Slow-growing Fast-growing Slow-growing
strains strains strains strains
Figure 1. Fast-growing pan-drug-resistant strains exhibited higher nutrition uptake and utilization ability.
(a) Phylogenetic tree including collected strains and 15 published complete genomes. (b) Growth curves of 12
strains in BHI broth over 5 hours. Error bars: standard error. p-values were calculated by Student’s t-test.
(c) Heat map of differentially expressed genes between fast-growing strains and slow-growing strains, classified
by functional groups.
at least one of the three antibiotics (Fig. S4a). Among them, 172 genes were commonly up-regulated in all three
antibiotic treatments, with genes involved in metabolism constituting the largest group of these (Fig. 2). Genes
coding for enzymes involved in the TCA cycle, and in the biosynthesis of some amino acids, purines, and pyri-
midines whose raw materials originate from the TCA cycle, were up-regulated. The complete operons encoding
enzyme complexes responsible for ATP synthesis, including NADH dehydrogenase, cytochrome C oxidase, and
ATP synthase, and ribosomal protein operons, were also up-regulated.
Antibiotic-specific responses were also studied. The three A. baumannii strains exhibited distinct responses
to the two classes of antibiotics (Fig. S4a). Amikacin specifically induced up-regulation of 149 genes, but only 4
genes were down-regulated. Those up-regulated genes were significantly enriched in unfolded protein binding,
protein folding, and proteolysis based on a hypergeometric test of enrichment (q-value < 0.01), including two
protein-folding chaperons (DnaK and HtpX), several peptidyl-prolyl isomerases, and the CLP protease system
(Fig. 2). In contrast, more than 400 genes were commonly down-regulated when treated with either of the two
carbapenem-related antibiotics, and only 17 genes were commonly up-regulated. Many transcription factors were
commonly down-regulated in treatment with imipenem or meropenem. However, up to 60% of the genes that
were commonly down-regulated in carbapenem do not have any GO-term annotation.
The three strains also exhibited strain-specific responses under different antibiotic treatments (Fig. S4b). Many
more genes were down-regulated in the pan-drug-resistant strain R5 when compared with R3 and R4. About 24%
of these R5-specific down-regulated genes were affected by all three antibiotic treatments, of which many were
transmembrane transporters and other membrane proteins involved in signal transductions (Fig. S4c).
SCieNTiFiC ReporTs | (2018) 8:3515 | DOI:10.1038/s41598-018-21841-9 3www.nature.com/scientificreports/
H+
NADH Cytochrome C
Dyhydrogenase ATP synthase
oxidase
IlvH IlvC
(G4490) (G4491)
2-Oxo-butanoate 2-Oxo-butanoate 2-Oxo-butanoate Isoleucine NADH ADP
Unfolded Mature NAD+ ATP
Threonine Glycine Serine Cysteine H+
Proteins Proteins Pi
Peptidyl-prolyl isomerases ThrC
PpiC PpiD SecB SurA SlyD (G175)
O-phospho- H+
(G888) (G4249) (G2171) (G246) (G2316) Cystathionine Homocysteine Methionine
Chaperons L-homoserine
DnaK Eno PGK Carbapenems
(G4516) (G3387)
(G1334)
Phosphoenol-pyruvate Glycerate-2P Glycerate-3P Glycerate-1,3P2 down-regulated
HtpX Homoserine
(G4413)
CLP protease system transcription factors
Amikacin ClpA ClpP ClpS ClpX IlvH
IlvC AdeR family transcription factors:
(G65) (G1679) (G64) (G1681) (G4491)
Erroneous (G4490)
Glycerone-P Glyceraldehyde-3P G269
2,3-Dihydroxy-3-
piptides L-Aspartate Pyruvate 2-Acetolactate
Other proteases 4-Semialdehyde PckG
Methylbutanoate
PbpG PrlC (G4376)
Fda AraC family transcription factors:
Asd (G3389)
(G4561) (G4371)
(G181) G1887, G3801, G4432
β-D-Fructose-6P
Valine Leucine
Amino acids 4-Phospho-
FadA FadB
L-aspartate
Alanine (G79) (G78)
AsnC family transcription factors:
LysC
Acetyl-CoA Fatty acids
Prephenate G1205, G3869
(G1644) D-Xylulose-5P
AraT AcnB AraT
L-Aspartate
(G4468)
Oxaloacetate Citrate
(G2235)
Isocitrate
(G4468) GntR family transcription factors:
Ribosome Mqo Mdh
G298, G504
PurA ArgG Tyrosine Phenylalanine
(G176) (G4034)
(G19) (G249)
Malate D-Ribulose-5P Lrp family transcription factors:
G3613
tRNA mRNA
Adenylo-succinate L-Arginino-succinate
TCA cycle 2-oxo-glutarate L-Glutamate L-Glutamine
Asd
Fumarate (G181) LysR family transcription factors:
rRNA PurB
2-Acetamido-5- CarB G1348, G1558, G2050, G3455, G3782,
(G2155)
SucC SucB oxopentanoate (G2286) D-Ribose-5P G3805, G4152
(G4316) (G4314) S-Succinyl-
Succinate Succinyl-CoA
Other RNA dihyrolipoamide-E
Ribosomal proteins SucD
(G4317) MerR family transcription factors:
Proline Ornithine
ArgG G1315
(G249)
Arginine L-Argininosuccinate Citruline
RpoA MocR family transcription factors:
(G4187)
Dihydro- N-Carbamoyl- Carbamoyl- G284
RpoB UDP UMP Orotidine-5P Orotate
orotate L-aspartate phosphate
(G3905)
RpoC NdK
NikR family transcription factors:
UTP
(G3906)
CTP (G2128) G2879
RNA ADP PurB PurM PurL PurD PurF
ATP
GTP (G2155) (G2139) (G2111) (G2426) (G2261)
5-Phospho-
IMP FAICAIR AICAR SAICAR CAIR AIR FGAM FGAR GAR
ribosylamine
PRPP TetR family transcription factors:
G1475, G2198, G2251, G2438, G2890,
GDP
G3668, G3809, G3867
Ribosomal protein operons
RplC RplD RplW RplB RpsS RplV RpsC RplP RpmC RpsQ RplN RplX RplE RpsN RpsH RplF RplR RpsE RpmD RplO SecY RpmJ RpsM RpsK RpsD RpoA RplQ
(G2803) (G4157) (G4158) (G4159) (G4160) (G4161) (G4162) (G4164) (G4165) (G4167) (G4168) (G4169) (G4170) (G4171) (G4172) (G4173) (G4175) (G4176) (G4177) (G4180) (G4181) (G4182) (G4183) (G4184) (G4186) (G4187) (G4188)
RpsG FusA TufA tRNA-Trp SecE NusG RplK RplA RplJ RplL RpoB RpoC
(G3931) (G3932) (G3919) (G3920) (G3921) (G3922) (G3923) (G3925) (G3927) (G3928) (G3905) (G3906) Operons in metabolisms
RplU RpmA SucB Lpd SucC SucD CLP protease system
(G1715) (G1716) (G4314) (G4315) (G4316) (G4317)
ClpS ClpA
(G64) (G65)
PGK Fda
Cytochrome C oxidase operons (G3387)
(G3388)
(G3389)
CyoA CyoB CyoC CyoD CyoE RpsF RpsR RplI ClpP ClpX
(G1726) (G1727) (G1728) (G1729) (G1730) (G1732) (G1733) (G1734) AlaS LysC (G1679) (G1681)
(G356) (G1644)
ATP synthase operons IlvH IlvC
AtpI
(G1615)
AtpB
(G1616)
(G1617)
AtpE
(G1618)
AtpF
(G1619)
AtpH
(G1620)
AtpA
(G1622)
AtpG
(G1623)
AtpD
(G1625)
AtpC
(G1626)
(G4490) (G4491)
Words & arrows in Red: Consistant up-regulated genes in all three antibiotics treatments
FadB FadA Words & arrows in Purple: Consistant up-regulated genes only in amikacin treatment
NADH dehydrogenase operons (G78) (G79)
NuoA NuoB NuoCD NuoE NuoF NuoG NuoH NuoI NuoJ NuoK NuoL NuoM NuoN Words & arrows in Green: Consistant down-regulated genes in two carbapenems treatment
(G1660) (G1661) (G1662) (G1663) (G1664) (G1665) (G1666) (G1667) (G1670) (G1672) (G1673) (G1674) (G1675)
Words & arrows in Black: Not consistantly differentially expressed genes
Figure 2. Genes that were differentially expressed under antibiotic treatment. Red genes or arrows: genes that
were consistently up-regulated under treatment with all three antibiotics. Purple genes or arrows: genes that
were consistently up-regulated only under treatment with amikacin. Green genes or arrows: genes that were
consistently down-regulated only under treatment with the two carbapenem-related antibiotics.
Antibiotic resistance genes were expressed in both antibiotic-resistant and -sensitive strains.
The Comprehensive Antibiotic Resistant Database13 includes a total of 48 known antibiotic resistance-related
genes that were identified in at least one strain (Fig. 3), which include antibiotic inactivation enzymes, multidrug
efflux pumps, and direct antibiotic targets. Some of the well-documented resistance-related mutations were also
identified in the multidrug-resistant strains, such as the two fluoroquinolone-resistance-conferring mutations:
serine-to-leucine at the 83rd residue in the DNA gyrase subunit A, and serine-to-isoleucine at the 80th residue in
the DNA topoisomerase IV subunit A14. Moreover, the expression patterns of the drug resistance genes attributed
to the phenotype of the antibiotic-resistant strains. For example, beta-lactamase which is an enzyme to break
down the structure of beta-lactam and multidrug efflux pump which transports the antibiotics out of the cell
(Fig. 3) in particular meropenem were highly expressed in R5 and R6 strains (Table S1) and these two strains were
resistant to MEM and IPM. Intriguingly, on the other hand, many antibiotic resistance genes were also found to
be expressed in the sensitive strains (Fig. 3). On this basis, the presence of antibiotic resistance in the resistant
strains may not be solely due to the expression of one particular antibiotic inactivation enzyme or efflux pump.
This phenomenon also suggested the combinational and synergic effects of the drug resistance genes expressed
and functioned with each other in antibiotic resistant bacteria.
Transposon-associated antibiotic resistance genes are major contributors to antibiotic resistance.
To investigate the driving factors, other than the expression of antibiotic resistance genes, of drug resistance, we
also examined the extent to which antibiotic resistance genes appeared in polycistronic transcripts. Known anti-
biotic resistance genes were annotated in over 67% of the polycistronic transcripts (Fig. 4a). Over 50% of the other
co-transcribed genes could be annotated based on their GO terms. These genes are enriched in several functions,
including amino acid metabolic processes, cell division, and transposition, revealing the complex transcriptional
regulation of the antibiotic response, with significant q-values as calculated by the Benjamini–Hochberg proce-
dure (q-values < 0.05).
To further identify the genes and pathways that contribute the most to antibiotic resistance, differential gene
expression analysis was performed between multidrug-resistant and -sensitive strains at the antibiotic-free
mid-log phase. Four groups of genes that were differentially expressed between multidrug-resistant and -sensitive
strains were, as expected, functionally correlated with antibiotic resistance based on Pearson’s chi-square
SCieNTiFiC ReporTs | (2018) 8:3515 | DOI:10.1038/s41598-018-21841-9 4www.nature.com/scientificreports/
Figure 3. Antibiotic resistance genes were widely distributed in resistant and sensitive strains. Each square
represents the expression level of a gene (across the row) in a given strain (down the column). Genes are grouped
according to protein functions, followed by the type of antibiotic resistance that the gene is associated with.
statistical test (q < 0.01) followed by the Benjamini–Hochberg procedure (Fig. 4b). Responses to antibiotics that
involve either many known antibiotic resistance genes or DNA-mediated transposons were both up-regulated in
multidrug-resistant strains.
Some genes can be assembled with an annotated transposable element on the same transcript, and will be
referred to as “transposon-associated” throughout this study. To globally study the difference in expression
patterns between transposon-associated genes and non-transposon-associated genes, the cumulative density
curves of the expressions of both groups of genes were plotted. Gene expressions of three multidrug-resistant
strains, namely R3, R4, and R5, with or without antibiotic treatment, were pooled according to the conditions
(antibiotic-free mid-log phase, antibiotic-treated mid-log phase, or antibiotic-treated stationary phase) to be
analyzed. The expression distributions of transposon-associated genes did not show significant differences from
non-transposon-associated genes at the mid-log phase under antibiotic-free conditions (Fig. 4c). However,
transposon-associated genes generally maintained higher expression levels in both mid-log and stationary
phases under antibiotic treatment, indicated by the significant shift of the curves to the right (higher expression)
below the 70th percentile based on Wilcoxon’s rank-sum test (p < 0.01) (Fig. 4d,e). This observation also held
true for individual genes when the cumulative density curves were separately plotted with different antibiotic
treatments and strains (Fig. S5). To confirm whether antibiotic resistance had a statistically significant correla-
tion with transposon-associated genes by the chi-square test, 39 known drug resistance genes were categorized
according to their functions, expression patterns, and their co-transcribed genes (Fig. S6a). Transposable ele-
ments showed a higher probability of being co-transcribed with drug inactivation enzymes, and their associ-
ated drug resistance genes were more likely to be up-regulated in multidrug-resistant strains, indicated by the
significant p-values (p < 0.01). These findings led to the observation that up-regulated drug resistance genes
were preferentially those coding for drug inactivation enzymes (p = 0.007) (Fig. S6b–d). It was also noticeable
that all transposon-associated up-regulated drug resistance genes were drug inactivation enzymes (Table S7).
Furthermore, comparing the expressions of known drug resistance genes between the antibiotic-free mid-log
phase and the antibiotic-treated mid-log phase of each of the three multidrug-resistant strains (R3, R4, and R5)
showed that under the antibiotic-treated conditions, transposon-associated resistance genes generally exhibited
up-regulation compared with antibiotic-free conditions in all three multidrug-resistant strains, with significant
p-values (p < 0.01) based on the two-tailed Student’s t-test. They also showed greater up-regulation fold changes
than non-transposon-associated antibiotic resistance genes, with significant p-values in Wilcoxon’s rank-sum test
SCieNTiFiC ReporTs | (2018) 8:3515 | DOI:10.1038/s41598-018-21841-9 5www.nature.com/scientificreports/
a Monocistronic Other genes
57
f ** * **
Transcripts
439 (11.0 %) Biological Processes:
(32.7 %) Response to antibiotic;
Proteins Response to stress;
2
Polycistronic Proteins without GOs
Transcripts with GOs Amino acids metabolic process;
126 274 Acyl-carrier-protein biosynthetic process;
905
(67.3 %) (24.4 %) (53.0 %) Folic acid biosynthetic process;
Cell division;
Transposition, DNA-mediated;
Log(Fold change)
Other known resistance genes
60
GO enrichment 0
b Low High
(11.6 %) (q-value < 0.05)
G4338
G4241
G2324
G3924
G3868
G4336
G2818
G2790
G3005
G4344
G2781
G4342
G2792
G1418
G3795
G2021
G3796
G3797
G1386
G1387
G2992
G3891
G2816
G2771
G3902
G3585
G1273
G4285
G2819
G3584
G2910
G3688
G2960
G2958
G2763
G2858
G2859
G2721
G2722
G2508
G2585
S3 -2
S2 Sensitive strains
S1
R9
R8
R7
R6 -4
R5 Resistant strains
R4
R3 Transposon Non-transposon Transposon Non-transposon Transposon Non-transposon
R2 associated associated associated associated associated associated
R1
Group I Group II
R3 R4 R5
Li
Ph
Response to antibiotic Transposon, DNA mediated
po
os
po
ph
ly
or
Amikacin treated
sa
el
cc
ay
G eb
G nal
*: p-value < 0.05
ha
sig
ro io
Imipenem treated
ro tr
rid
up sy
up ans
III nth
**: p-value < 0.01
I V duc
Meropenem treated
e
tio
tic
n
pr
sy
oc
ste
es
m
s
c d e
100 100 100
p-value = 0.097 p-value < 0.01 p-value < 0.01
90 90 90
80 80 80
70 70 70
Percentile
Percentile
60 60
Percentile
60
50 50 50
40 40 40
30 30 30
20 20 20
10 10 10 Transposon-associated
0 0 0 Non-transposon-associated
2 4 6 8 10 12 14 4 6 8 10 12 14 4 6 8 10 12 14
Log Expression Log Expression Log Expression
Figure 4. Transposons were up-regulated in multidrug-resistant strains and their associated genes maintained
relatively higher expression in antibiotic-treated environments. (a) Statistics of genes co-transcribed with
known antibiotic resistance genes. (b) Heat map of differentially expressed genes between multidrug-resistant
strains and -sensitive strains, classified by functional groups. (c–e) Comparison of percentile-of-expression
values between transposon-associated genes and non-transposon-associated genes at antibiotic-free mid-log
phase (c), antibiotic-treated mid-log phase (d), and antibiotic-treated stationary phase (e). Blue line represents
transposon-associated genes; red line represents non-transposon-associated genes. p-values were calculated by
Wilcoxon’s rank-sum test. (f) Boxplots of fold changes of known antibiotic resistance genes under the antibiotic
treatments. Each point represents a log(fold change) value of a known antibiotic resistance gene under a certain
antibiotic treatment. p-values were calculated by Wilcoxon’s rank-sum test.
(p < 0.05) (Fig. 4f). However, non-transposon-associated antibiotic resistance genes were not all up-regulated
under antibiotic treatment. Their expression patterns were found to be strain-specific.
Based on the drug resistance profiles and transcriptome results of the 12 strains collected in this study, and
combined with the 15 published strains with full genome sequences, several candidate resistance genes were
identified (Fig. 5). Among these antibiotic-specific resistance genes, beta-lactamase OXA-23 was discov-
ered to be transposon-associated, which was previously reported to confer carbapenem resistance in many
carbapenem-resistant strains3,4,15–18. In addition, we identified that two transposon-associated genes, namely,
mph, which codes for the macrolide 2′-phosphotransferase homolog, and mel, which codes for the macrolide
efflux protein homolog, were assembled onto the same transcript downstream of the transposon gene tnpD in
all amikacin-resistant strains (R4, R5, R6, R7, and R8), and were also identified in the genomes of A. baumannii
strains MDR-TJ and TYTH-1 (Fig. 6a). This combination of genes can be identified by BLAST on the plasmids in
other amikacin-resistant A. baumannii strains, such as BJAB0714 and BJAB0868, and also in other gram-negative
bacteria, such as Providencia rettgeri, Providencia stuatii, Klebsiella pneumoniae, Klebsiella oxytoca, Citrobacter
freundii, Salmonella enterica, and Escherichia coli, with nearly full-length coverage at >90% identities. It should
be noticed that mph and mel may not be the determinants for amikacin resistance as other antibiotic resistance
genes may have an cooperative effect.
Transposon-associated mph and mel conferred amikacin resistance in E. coli. Reverse-transcription
PCR (RT-PCR) confirmed that mph and mel were frequently co-transcribed (Fig. 6b), while tnpD and mel were also
traceably co-transcribed (Fig. S7a). It is noticed that amplicon 1 could not be amplified with RT-PCR, which may
indicate an assembly error in this contig. This is the disadvantage of transcriptome assembly using short reads19.
Quantitative PCR further confirmed that mel and mph had similar expression levels (Fig. 6c). To study whether the
two candidates conferred amikacin resistance, mel, mph and the positive control aacBI, which is known to confer
amikacin resistance20,21, were independently transformed into the amikacin-sensitive E. coli BL21 (Table S3). Their
SCieNTiFiC ReporTs | (2018) 8:3515 | DOI:10.1038/s41598-018-21841-9 6www.nature.com/scientificreports/
e
ras
sfe
23 t r anein
A- o t
ph ro
OX hos ux p
Hypothetical proteins a se ' -p effl
2
(on the same transcript in R6) tam lide lide
lac ro ro
e ta- ac ac m
ne m in
B M M pe ne ac
2 26 221 218 211 210 209208 207 204 991 966 8 28 826 825 823 785 e ro ipe mik
G3 G3 G3 G3 G3 G3 G3 G3 G3 G2 G2 G2 G2 G2 G2 G2 M Im A
Published genomes Strains in this study
R1
R2
R3
R4
Color Keys
R5
R6 Not detectable or not exist
R7 Exist (on genomic data)
R8
R9 Lowly expressed Candidate genes
S1 Expressed
S2 Highly expressed
S3
BJAB07104
BJAB0715
BJAB0868
MDR−TJ
MDR−ZJ06 Data not available
TCDC−AB0715
TYTH−1 Sensitive Antibiotic resistance
1656−2
ATCC17978 Resistant
AYE
AB0057
AB307−0294
D1279779
ATCC17978
SDF
Carbapenem resistant Amikacin resistant Antibiotic
related candidates related candidates resistance
Figure 5. Identification of novel antibiotic-specific resistance genes. Each square represents the expression level
or the presence of a gene (down the column) in a given strain (across the row). The resistance of each strain to
the three antibiotics is shown on the right panel.
protein expressions were confirmed by western blotting (Fig. S7b). Both mel and mph expression increased the
resistance of BL21 under amikacin treatment at different concentrations (Fig. 6d). The MIC of the negative control
was 6 μg/ml, while the aacBI, mel and mph transformed strains had MICs around 16 μg/ml.
Discussion
Comparative genomics studies have been widely conducted to understand the evolution of pathogens toward
multidrug resistance. However, knowledge of the global regulation of antibiotic resistance genes is still inade-
quate, due to the lack of RNA-seq data and suitable methodologies for comparative transcriptomics. In this study,
we collected nine multidrug-resistant and three multidrug-sensitive A. baumannii strains, cultured them with
or without antibiotic treatment, and developed a de novo assembly-based method, by which polycistronic tran-
scripts, the major form of transcription in bacteria, could be reconstructed, to analyze the comparative transcrip-
tomics. This novel method of analysis revealed, for the first time, that the regulation of antibiotic resistance genes
was performed at the level of operons. Antibiotic resistance genes were systematically analyzed to determine, first,
with which genes they were co-transcribed, and second, which genes and pathways were significantly modulated
under treatment with antibiotics.
First, our data suggested that under treatment with both classes of antibiotics, genes in several operons that are
involved in ATP, RNA, and protein synthesis were simultaneously up-regulated, suggesting that the rates of energy
and protein production were generally elevated among multidrug-resistant A. baumannii. Many genes involved
in these pathways were also reported to be up-regulated in Wolbachia under doxycycline treatment22. Moreover,
the strains also responded differently to the two classes of antibiotics. Amikacin, an aminoglycoside that targets
the translation machinery, induced up-regulation of over 149 genes, among which were genes involved in protein
folding and lysis, while only 4 genes were down-regulated. In contrast, the two carbapenem-related antibiotics
(imipenem and meropenem) led to a general down-regulation of gene expression, including of a large number of
transcription factors. In addition, most of the imipenem- and meropenem-specific genes encoded either hypo-
thetical proteins or proteins without functional annotation. Thus, there remains a large knowledge gap concern-
ing our understanding of the transcriptional response under carbapenem treatment.
Second, the presence of a single antibiotic resistance gene may not necessarily confer antibiotic resistance in
A. baumannii. A wide spectrum of antibiotic resistance genes have been identified in all strains, including the
resistant and sensitive ones (Fig. 3). Some of these genes, e.g. AdeB (G933) (Table S7), a member belonging to the
AdeABC efflux pump system, have been reported inadequate in development of aminoglycoside resistance23. We
have shown in this study that expression of mel and mph could confer amikacin resistance, though the MIC of E.
coli strains expressing AacBI, Mel and Mph under amikacin were around 16 μg/ml, which was lower than the MIC
of the multidrug-resistant A. baumannii strains (Table S1). Our data suggest that multiple antibiotic resistance
genes are necessary to confer antibiotic resistance in clinically isolated multidrug-resistant strains.
Third, our data showed that antibiotic resistance genes that were co-transcribed with transposons gener-
ally had higher fold-changes than other resistance genes, which were not associated with transposons, under
antibiotic treatment and were more likely to confer antibiotic-specific resistance. Both multidrug-resistant
and -sensitive strains possessed a wealth of antibiotic resistance genes. Most antibiotic resistance genes were
involved in important operons and were transcribed with other genes. Based on the GO annotations of their
co-transcribed genes, antibiotic resistance genes can be classified into two categories: transposon-associated
SCieNTiFiC ReporTs | (2018) 8:3515 | DOI:10.1038/s41598-018-21841-9 7www.nature.com/scientificreports/
a c RNA-seq qPCR RNA-seq qPCR
3.5
(RPKM) (Relative expression) (RPKM) (Relative expression)
(Reads number per base)
400 1 1000 0.09
3.0
0.9 900 0.08
Log10 Expression
350
0.8 800 0.07
300 700
0.7
2.5
0.06
250 0.6 600 0.05
200 0.5 500
400 0.04
0.4
2.0
150 0.03
0.3 300
100 200 0.02
0.2
50 0.01
1.5
0.1 100
0 0 0 0
R5 R6 R7 R8 S2 S3 R5 R6 R7 R8 S2 S3
RNA-seq qPCR
1.0
RNA-seq qPCR
G2819 G2826
Amplicon 1 Transposase TnpD Macrolide efflux protein
RNA-seq qPCR
(Mel)
Amplicon 2 Amplicon 3
(RPKM) (Relative expression)
G2819 G2826 G2828 450 0.1
Transposase TnpD Macrolide efflux protein Macrolide 2’-phosphotransferase 400 0.09
350 0.08
(Mel) (Mph) 0.07
300
b Genomic DNA
(20 cycles)
RNA
(30 cycles)
250
200
0.06
0.05
0.04
R5 R6 R7 R8 S2 S3 R5 R6 R7 R8 S2 S3 150 0.03
100 0.02
Amplicon 1 50 0.01
0 0
Amplicon 2 R5 R6 R7 R8 S2 S3
RNA-seq qPCR
Amplicon 3
G2828
Macrolide 2’-phosphotransferase
(Mph)
d
1.6
1.4
1.6
2 ug/ml amikacin 1.4
4 ug/ml amikacin 8 ug/ml amikacin
1.4 1.2
1.2
1.2 1
O.D 600
1
O.D 600
1 Negative control
O.D 600
Negative control 0.8
0.8 Negative control
0.8 AacBI (positive control) AacBI (positive control)
AacBI (positive control) 0.6
0.6 Mel Mel
0.6 Mel
0.4 Mph 0.4 Mph
0.4 Mph
0.2 0.2
0.2
0 1 2 3 4 5 6 7
0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7
Time (hours) Time (hours) Time (hours)
Figure 6. Transposon-associated mel and mph were co-transcribed and both contributed to amikacin
resistance. (a) Transcript structure of the transposon gene with the two candidates. (b) Reverse-transcription
PCR result of connection regions among three genes (mel, mph, and tnpD). (c) Quantitative PCR results for
those three genes. (d) Growth of E. coli transformed with the two candidate genes (mel and mph) in different
amikacin concentrations.
genes, which are co-transcribed with at least one transposon gene, and non-transposon-associated genes.
Transposons are among the major players in horizontal gene transfer, which contributes to the evolution of resist-
ance islands in A. baumannii genomes24. It has been reported that the insertion of transposable elements may cre-
ate active promoters and induce high-level expressions of downstream genes25,26. Moreover, our results revealed
that transposable elements could also shape gene expression patterns under antibiotic treatment. Not all trans-
posable elements and their associated genes were up-regulated under antibiotic treatment (Fig. S8a,b), but they in
general already have higher fold-changes when compared with non-transposon-associated genes. We found that
transposon-associated resistance genes had a higher preference to be associated with drug inactivation enzymes,
which was also reported recently in a comparative genomic study2. Therefore, transposable elements could serve
as an efficient marker for identifying co-transcribed candidate antibiotic resistance genes, through which two
novel amikacin resistance genes, namely mel and mph, were reported and experimentally validated in this study.
Although the relationships between antibiotic treatment and transcription of resistance genes-associated trans-
posons remains unclear, four proteins have been reported as potential regulators involved in transposons expres-
sion27,28. In this study, three of these regulators were identified, including FIS (G2401), H-NS (G3933) and IFH
(G4261) (Table S8). It would be of interest to investigate relationships between transposons expression and these
regulators as it could serve as an effective way of reversing drug resistance by inhibiting the transcriptional activ-
ities of transposons.
Materials and Methods
Selection of strains. All strains were isolated from patients at hospitals in Hong Kong (Tables S1 and S2).
PFGE was performed to identify the strain lineages according to Seifeit et al.29. The PFGE patterns were clus-
tered using the Unweighted Pair Group Method with Arithmetic Mean. Different lineages were determined by a
similarity cutoff of 80%, with 1.5% position tolerance and 1% band optimization. The minimum inhibitory con-
centrations (MICs) of the antibiotics were determined according to Wiegand et al.30. Multilocus sequence typing
was performed as described at PubMLST (http://pubmlst.org/abaumannii/). Strains with different lineages or
antibiotic resistance properties were selected for further study.
Determination of minimum inhibitory concentration (MIC). The MIC of the strains was determined
and interpreted according to the criteria of Clinical and Laboratory Standards Institute (CLSI)31. Briefly, the
SCieNTiFiC ReporTs | (2018) 8:3515 | DOI:10.1038/s41598-018-21841-9 8www.nature.com/scientificreports/
bacteria were grown in 5% blood agar overnight. Bacterial suspension were prepared to a turbidity equivalent to
0.5 McFarland. Bacteria were inoculated to the antibiotic-containing Mueller-Hinton agar plates and incubated
at 37 °C for 16 hours and the MICs were read.
Bacteria culture. A. baumannii was first cultured in BHI broth overnight. 1 ml overnight culture was inoc-
ulated to 100 ml fresh BHI broth. The mid-log (OD600 ~ 1.00) and stationary (OD600 ~ 2.1) samples were har-
vested. The bacteria cultures were incubated at 37 °C with 200 rpm shaking.
To determine the optimal antibiotic concentrations for the antibiotic-treated conditions in LB, 2-fold serial
dilutions were performed, starting with the MICs as previously determined, to discover the maximum antibiotic
concentrations at which bacterial growth was not significantly inhibited compared with that in an antibiotic-free
medium. The final antibiotic concentrations used, in a range from 1/8 to 1/64 of the MICs, are listed in Table S4.
Library preparation and strand-specific RNA-sequencing. Ten to 15 ml of bacteria culture was pel-
leted by centrifugation at 3,500 g for 10 minutes at room temperature. Two to 4 ml of RNAprotect (Qiagen) was
added to the pellet and mixed by vortexing. The pellet was re-precipitated by centrifugation at 3,500 g for 10 min-
utes at room temperature, and the supernatant was discarded. Standard TRIzol (Life Technologies) protocol was
then applied to extract total RNAs from the cell pellets. To ensure that the DNA was completely removed, DNase
digestion was performed and the total RNA samples were further purified by acidic phenol–chloroform. mirVana
(Life Technologies) was used to deplete tRNAs and other small RNA portions, and the large RNA molecules
were retained. Ribosomal RNAs were then removed by Ribo-Zero rRNA removal kits for gram-negative bacteria
(Epicentre). External RNA Controls Consortium (ERCC) RNA Spike-in Control Mixes (Ambion) were added
to each rRNA-depleted RNA sample according to the user guide. The sequencing libraries were prepared using
a ScriptSeq v2 RNA-seq Library Preparation kit (Epicentre) with rRNA-depleted samples, and all of the libraries
were sequenced by Illumina HiSeq 2000 following the strand-specific sequencing protocol for 100 cycles. All
RNA-seq data have been deposited to SRA with the accession SRP075337.
Reads processing and expression calculation. The first six bases of reads and the adaptors were first
removed by in-house-developed pipelines. Then, low-quality sequences were trimmed by Trimmomatic 0.3032
with the following parameters: SLIDINGWINDOW:4:20 and MINLEN:50.
The transcripts were assembled using Trinity r2013-08-1411 with default parameters and specifying–SS_lib_
type as FR. All transcripts were annotated using BLAST based on the 15 full A. baumannii genomes retrieved
from the NCBI (Table S5), with both coverage and identity larger than or equal to 0.8. Overlapped annotations
on transcripts were further combined if they overlapped each other by at least 70% of their lengths. Based on
the gene annotations of the transcripts, the RPKM (reads per kilobase per million mapped reads) values33 were
calculated to determine the gene expression levels. The expression values were normalized by methods previously
described34, which can also be found in the supporting documents, under headings Angular based linear regres-
sion, Finding the best regression model and Normalizing samples in different conditions. The expression levels
of all genes are listed in Table S8.
Differential expression determination. Differentially expressed genes between two phenotypical groups
of strains (fast-growing vs slow-growing, resistant vs sensitive) were determined by a spatial angular method (refer
to the Supplementary methods for a detailed description). Differentially expressed genes between antibiotic-free
conditions and antibiotic-treated conditions were determined by the following set of criteria: (1) to be consid-
ered as an up-regulation under antibiotic treatment, the normalized expression value of the gene in the treated
sample at mid-log phase must be larger than or equal to 50 RPKM; and to be considered as a down-regulation,
the normalized expression value of the gene in the untreated sample at mid-log phase must be larger than or
equal to 50 RPKM; (2) for up-regulation under antibiotic treatment, the normalized expression value of the
gene in the treated sample at mid-log phase must be at least 2-fold larger than that in the untreated sample; for
down-regulation under antibiotic treatment, the normalized expression value of the gene in the untreated sample
at mid-log phase must be at least 2-fold larger than that in the treated sample; (3) finally, for up-regulation under
antibiotic treatment, according to (2), the normalized expression value of the gene in the treated sample at sta-
tionary phase must be larger than that in the untreated sample at mid-log phase; and for down-regulation, accord-
ing to (2), the normalized expression value of the gene in the untreated sample at mid-log phase must be larger
than that that in the treated sample at stationary phase. Any gene that was up-regulated in at least two out of three
strains and at the same time did not show any down-regulation was considered a common up-regulated gene (and
conversely for the common down-regulated genes). However, because the amikacin-treated R4 sample at mid-log
phase had failed to be sequenced, only genes up-regulated or down-regulated in both the R3 and R5 strains
were used to determine common differentially expressed genes for amikacin. For Fig. S5A, the criteria used for
the amikacin-treated R4 samples were loosened. Genes whose normalized expression values in the untreated
sample at mid-log phase were smaller than those in the treated sample at stationary phase were considered to be
up-regulated (likewise, down-regulated genes were considered to be those whose normalized expression values in
the treated sample at stationary phase were smaller than those in the untreated sample at mid-log phase).
Other bioinformatic analysis. Details of the expression normalization are described in the supporting
documents, under headings Angular based linear regression, Finding the best regression model and Normalizing
samples in different conditions. For phylogenetic analysis, genes that were expressed in the antibiotic-free sam-
ples of all strains and could be identified in all 15 of the complete A. baumannii genomes were selected, whereas
transposon-related genes and common drug resistance genes were excluded. The sequences were aligned sepa-
rately using MUSCLE35 3.8.31 and only the aligned regions were then extracted and concatenated. The phyloge-
netic trees were constructed using the neighbor-joining method in MEGA 636. To annotate GO terms to each
SCieNTiFiC ReporTs | (2018) 8:3515 | DOI:10.1038/s41598-018-21841-9 9www.nature.com/scientificreports/
gene, BLASTX searches for all of the genes were performed against the nr protein database with default param-
eters, and only the top 50 hits were retained, which were then annotated by Blast2GO37 with default parameters.
GO enrichment was measured in terms of q-values, calculated based on a hypergeometric distribution and the
Benjamini–Hochberg procedure. The q-values of the functional correlation of the GOs were determined using a
chi-square distribution in a contingency table and with the Benjamini–Hochberg procedure.
Validation of differentially expressed genes by quantitative PCR (qPCR). Power SYBR Green PCR
master mix (Life Technologies) was used according to the standard protocol. A master mix solution was made for
each sample. Each 20 μl technical replicate contained 5 ng templates, 375 nM primers (Table S6), and 1× power
SYBR Green PCR master Mix. qPCR was performed using an ABI 7500 fast qPCR machine with 40 thermal
cycles. Each thermal cycle began with a holding stage at 50 °C for 2 minutes, following by another holding stage at
95 °C for 10 minutes. Then, the cycling stage started with a denaturing step at 95 °C for 15 seconds, followed by a
1-minute annealing and elongation step at 60 °C. The top 3 most stably expressed genes at mid-log phase growing
in both the antibiotic-free and antibiotic-treated media were used separately as reference genes. ATP-dependent
protease (G707) had the smallest variations between biological replications and fitted the trend of RNA-seq. It was
therefore chosen as the reference gene in the qPCR validation step.
First-strand cDNA synthesis. The SuperScript III first-strand synthesis SuperMix for qRT-PCR (Life
Technologies) was used. The reaction mixture was prepared according to the standard protocol. The mixture was
first incubated at 25 °C for 10 minutes, followed by incubation at 50 °C for 30 minutes. The reaction was termi-
nated at 85 °C for 5 minutes and cooled over ice. 2 U of E. coli RNase H was added to the mixture and incubated
at 37 °C for 20 minutes.
PCR and Sanger sequencing. A 50-μl reaction mixture was prepared for each PCR reaction, including
45 μl Platinum PCR SuperMix (Life Technologies) solution, 5 μM primers (Table S6) and 5 ng templates. The
reaction started with a 2-minute incubation at 94 °C. Twenty cycles were performed with 30 seconds of denatur-
ing at 94 °C, 30 seconds of annealing at 57 °C, and 1 minute/kb extension at 72 °C. The PCR products were purified
by PureLink Quick Gel Extraction and a PCR purification Combo Kit (Life Technologies) based on the standard
protocol. The correct sequences of the PCR products were confirmed by Sanger sequencing.
Bacterial transformation. All plasmids were constructed either using a Champion pET101 Directional ™
®
TOPO Expression Kit following the standard protocol, or by a gene synthesis service from GenScript (Nanjing,
China) on pET15b, which is the closest plasmid to pET101 in the sequence (Table S3). 1 to 10 ng plasmids were
transformed to BL21 competent cells (Life Technologies) following the standard protocol. Single colonies were
selected from the LB agar plate with 50 ug/ml ampicillin. The DNA of the plasmids was extracted and the correct
sequences were confirmed by Sanger sequencing.
Western blot analysis. Bacterial cells were harvested after 4 hours induction in 1 mM IPTG. The OD600
values were adjusted to 1.0. All samples were lyzed in bacterial lysis buffer (1% SDS in PBS) and loaded onto 12%
SDS-PAGE for western blot analysis. 1:3000 anti-HIS antibodies (GE 27-4710-01) and 1:2000 anti-FLAG antibod-
ies (Sigma F3165) in 5% fat-free milk were used as primary antibodies to detect the expression of the candidate
resistance genes, engineered with either an HIS-tag (for AacBI and Mel) or a FLAG-tag (for Mph).
Testing of candidate antibiotic resistance genes. Bacteria transformed with the expression vector
were incubated at 37 °C in LB at 150 rpm until OD600 was equal to 0.4. 1 mM IPTG was added and the bacte-
ria were incubated for 4 hours to induce protein expression. The culture was diluted to a final OD600 of 0.01.
Amikacin at the desired concentrations along with 1 mM IPTG were added, and the bacteria were incubated at
37 °C and 150 rpm. The OD600 values were taken every one hour.
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Acknowledgements
This study was supported by a Health and Medical Research Fund – Infectious Disease (HMRF) 11100512 from
the Food and Health Bureau of the Hong Kong Government, a CUHK Direct Grant 4053041 from the university
to T.F.C, and in part supported by an Innovation Technology Fund of the Innovation Technology Commission,
the Lo Kwee-Seong Biomedical Research Fund and Lee Hysan Foundation.
Author Contributions
Conceived and designed the experiments: M.I., S.K.W.T., and T.F.C. Performed the experiments: H.Q., N.W.L.,
and J.F.L. Analyzed the data and interpreted the results: H.Q., X.L., A.K.Y., and T.F.C. Contributed reagents/
materials/analysis tools: M.I., J.F.L., T.C.K.L., and S.K.W.T. Wrote the paper: H.Q. and T.F.C.
Additional Information
Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-018-21841-9.
Competing Interests: The authors declare no competing interests.
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