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Comparative proteomic analysis of sensitive and multi-drug resistant Aeromonas hydrophila isolated from diseased fish
Journal Pre-proof Comparative proteomic analysis of sensitive and multi-drug resistant Aeromonas hydrophila isolated from diseased fish Wei Zhu, Shuxin Zhou, Weihua Chu PII:
S0882-4010(19)31667-5
DOI:
https://doi.org/10.1016/j.micpath.2019.103930
Reference:
YMPAT 103930
To appear in:
Microbial Pathogenesis
Received Date: 18 September 2019 Revised Date:
13 December 2019
Accepted Date: 13 December 2019
Please cite this article as: Zhu W, Zhou S, Chu W, Comparative proteomic analysis of sensitive and multi-drug resistant Aeromonas hydrophila isolated from diseased fish, Microbial Pathogenesis (2020), doi: https://doi.org/10.1016/j.micpath.2019.103930. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
Comparative proteomic analysis of sensitive and multi-drug resistant Aeromonas hydrophila isolated from diseased fish Wei Zhu, Shuxin Zhou, Weihua Chu* Department of Pharmaceutical Microbiology, School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China * Correspondence: [email protected]
Highlights Sensitive and multi-drug resistant Aeromonas hydrophila strains were isolated from diseased fish. Altered proteins between sensitive and multi-drug resistant A. hydrophila isolates were compared. A total of 61 and 17 differently expressed proteins were identified in multi-drug resistant and susceptible A. hydrophila respectively. Bioinformatics analysis showed biological processes related proteins were down-regulated in MDR strain.
Abstract: Bacterial hemorrhagic septicemia caused by multi-drug resistant (MDR) Aeromonas hydrophila has exponentially increased in the past decade, and reached an alarming rate making it a major concern in the aquaculture industry in China. The aim of this study was to investigate the difference in the regulation of proteins expression in multi-drug resistance and susceptible A. hydrophila strains isolated from diseased fish using two-dimensional electrophoresis (2-DE) combined with mass spectrometry. 28 isolates of A. hydrophila were successfully identified by biochemical tests. Antibiotic susceptibility test results showed that all the isolates have different drug resistant patterns. A total of 61 and 17 differently expressed proteins were identified in MDR and susceptible A. hydrophila, respectively, evidencing that biological processes related to carbon
metabolism, biosynthesis of secondary metabolites, microbial metabolism in diverse environments, cationic
antimicrobial
peptide
(CAMP)
resistance
and
propanoate
metabolism
were
down-regulated in MDR strain, while proteins involved in biosynthesis of antibiotics, glycolysis/gluconeogenesis were highly expressed in the sensitive strain. The analysis of differentially expressed proteins from multi-drug resistance and susceptible strains suggests that a number of proteins are involved in several metabolic metabolism pathways plays an important role in A. hydrophila drug resistance. Our findings provide new insights about mechanisms involved in drug resistance and propose possible novel targets for developing alternative antibacterial drugs.
Keywords: Aeromonas hydrophila; Multi-drug resistance; susceptible; Proteomics; 2DE; mass spectrometry
1. Introduction
Aeromonas hydrophila is a potential zoonotic pathogen with a worldwide distribution in freshwater and marine environments and has different pathogenic potential to humans and other animals [1, 2]. In humans, it can cause gastroenteritis, hemolytic uremic syndrome, peritonitis, skin infections, bacteremia, meningitis, and necrotizing fasciitis[3, 4]. It can cause different diseases in aquatic animals, such as motile aeromonad septicemia, red sore disease [5, 6]. In China, A.
hydrophila
has
caused
high
mortality
and
significant
economic
losses
in
the aquaculture industry every year. Traditionally, multi-antimicrobial agents have been used to control this bacterial pathogen. However, the abuse or misuse of antibiotics coupled with the limited new development of antimicrobials has led to the problem of multidrug-resistant bacteria.
Antimicrobial susceptibility profiles and the corresponding resistance determinants of A. hydrophila have been extensively reported by many researchers [7, 8]. Proteomics has evolved as an important tool for the investigation of microbial resistance mechanisms [9, 10]. Few proteomic investigations on molecular mechanisms related to multi-drug resistance of A. hydrophila have been reported. In this present study, an integrated approach based on two-dimensional electrophoreses (2DE) coupled with mass spectrometry (MS) techniques were used to resolve A. hydrophila multi-resistant proteome, investigate protein networks and also identify new putative candidates involved in resistance mechanisms.
2. Materials and methods 2.1. Bacteria strains isolation and identification
Sampling of diseased fish was carried out from different commercial fishery farms located in Jiangsu Province from May 2015 to August 2018. The sampled fish weighed from 50 to 200 g each with a typical symptom of bacterial septicemia. Bacterial isolation was carried out using Ryan's aeromonas medium base (RAM, Oxoid, CM0833) containing 5 mg/L Ampicillin as a selective medium. The fluids of the internal organs of diseased fish were swabbed on the selective media agar plates. The inoculated plates underwent incubation at 28°C for 24 - 48 h. All the suspected bacteria colonies were purified, and standard biochemical classification was performed using the VITEK2 system (bioMerieux) and an API 20E test kit (bioMerieux) at 28°C for 48 h, following the manufacturer’s instructions [11, 12]. The cultures were stored at − 80 °C in Luria Bretani (LB) broth supplemented with 15% glycerol.
2.2. Antibiotic susceptibility testing
24 different antibacterial agents were used to detect the antibiotic susceptibility of clinical isolates according to the criteria described by CLSI (2016) [13]. All A. hydrophila strains were grown in LB broth for 12-16 hrs, and then the turbidity of the culture was adjusted to an equivalence of a 0.5 McFarland standard for antibiotic susceptibility detection. 2.3. Whole-cell protein preparation, two-dimensional gel electrophoresis, In-gel protein digestion, protein identification and database searches
The preparation of whole-cell bacterial proteins was determined using the method as described by Hu et al., Du et al. and Piras et al. [14, 15, 16] with some minor modifications. The concentration of whole-cell protein was measured by the Bradford protein assay (BioRad, CA, USA) using bovine serum albumin (BSA) as a standard [17]. The bacterial cell protein samples were stored at -20°C until use. 2-D gel electrophoresis, in-gel protein digestion, protein identification were performed as described by the method of Du et al., Piras et al. and Sui et al. [15, 16, 18]. IEF was performed using the IPG-phor IEF system (BioRad, CA, USA) and Immobiline 24-cm DryStrip IPG strips (pH 4–7). The 2-D electrophoresis was then performed on a 12% SDS-PAGE. Gel electrophoresis was conducted at 16°C with 1.0 W gel-1 for 1 h and was later alternated with 10 W gel-1 until the dye formed was approximately 1 cm above the bottom of the gel. The protein signals were visualized by staining the gel with Coomassie Brilliant Blue (CBB) G-250. The gel was imaged with a BioRad Fluor-S system and then analyzed using PDQuest (Version 7.2.0; BioRad) software. Protein spots were first identified and later coordinated automatically in view of the total densities of the gels. For each of the identified spots, the mean relative volume was thought to be equivalent to its expression level at each stage. Identified spots demonstrating more than 2-fold-change difference (P < 0.05) were changed according to the mean relative volume and
treated as differentially expressed protein spots. The differentially expressed proteins were
extracted from gels after washing the gels with double-distilled water and destaining twice with 50 mM NH4HCO3 in 50% (v/v) acetonitrile (ACN) for CBB G-250-stained spots. The proteins were then mixed with 10 mM DTT in 50 mM NH4HCO3 and then alkylated with 40 mM iodoacetamide in 50 mM NH4HCO3 for 1 h at room temperature. The gel was first dried with 100% (v/v) ACN and then digested overnight at 37°C with the addition of 15 µL of trypsin (Promega, USA; 1:50, enzyme to protein) in 50 mM NH4HCO3. The resulting peptides were extracted twice with 0.1% TFA (v/v) in 50% (v/v) ACN. The samples were air-dried and analyzed with the Bruker Daltonics UltrafleXtreme MALDI TOF/TOF Proteomics Analyzer (Bremen, Germany). All protein spectra were searched for using the MASCOT search engine (http://www.matrixscience.com) in the UniProt database. The mass tolerance for peptides was set to 100 ppm, and the mass tolerance of TOF/TOF fragments was set to 0.5 Da using cysteine carbamidomethylation as a fixed modification. Methionine oxidation was used as a variable modification. The confidence in the peptide mass fingerprinting matches (P<0.05) was determined based on the MASCOT score and was verified if an accurate overlapping of the matched peptides with the major peaks of the mass spectrum was observed. Ions score is -10*Log(P), where P is the probability that the observed match is a random event. Individual ions scores > 33 indicate identity or extensive homology (p<0.05). Protein scores are derived from ions scores as a non-probabilistic basis for ranking protein hits. Only significant hits (P<0.05) based on the MASCOT probability analysis were accepted. For GO
enrichment analysis UniProt IDs of identified proteins were retrieved from UniProt knowledgebase (UniProtKB) (http://www.uniprot.org/). OmicsBean (http://www.omicsbean.cn), which integrated Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, was employed to analyze the obtained differential abundance proteins (http://www.genome.ad.jp/kegg/) [19]. Each experiment was performed in triplicate and identified in three technical replicate experiments. 2.4. Statistical analysis Student’s t-test was used to confirm the p-value between different groups, p-values < 0.05 were considered statistically significant. 3. Results
3.1. Antibiotic susceptibility characteristics of clinical A. hydrophila isolates
An antibiotic susceptibility test was performed on all 28 A. hydrophila isolates. Results were interpreted according to the guidelines of the CLSI (2016). All 28 clinical isolates expressed some level of resistant to the class of antibiotics as depicted in Table 1 and Figure 1. All isolates were found to be completely resistant to Penicillin (100%) and Ampicillin (100%) and a high prevalence of resistance to Amoxicillin (96.4%), Piperacillin (92.9%), Cefalexin (78.6%), Doxitard (75%) and Teicoplanin (67.9%). Isolates were highly sensitive to compounds like Streptomycin (100%), Gentamicin (100%), Kanamycin (96.4%), and Fosfomycin (92.9%). Of all 28 isolates, TZ-16 was resistant to 16 antibiotics, while 150488 was only resistant to 6 antibiotics. 3.2. Proteome analysis of multi-drug resistant and sensitive A. hydrophila strains A comparative proteomic analysis was performed. Considering the number of antibiotics, most bacteria showed resistance to isolate TZ-16 and 150488 was chosen as multi-drug resistant strain
and sensitive strain for proteome analysis. To investigate the different proteins expression of multi-drug resistant and sensitive A. hydrophila strains, 2-D electrophoresis maps using IEF on 24 cm, pH 4-7, nonlinear IPG gels were utilized and compared to whole-cell protein profiles. The results indicated more than 800 protein spots fall within the pH range of 4-7 on the Coomassie G-250-stained gels (Fig. 2). The cellular component by GO analysis of the peptides is presented in Fig. 3A. Using GO analyses, the proteins/peptides can be categorized into several biological processes (Fig. 3B). The major molecular functions of peptides obtained by GO analysis were organic cyclic compound binding (30%) and ligase activity (10%), structural molecule activity (7%), ion binding (6%) and others (Fig. 3 C). After quantitative analysis, 80 differentially expressed protein spots with more than 2-fold-change difference (P < 0.05) compared to each other were selected, comprising 61 proteins in multi-drug resistant strain and 17 insensitive strain. All the differentially expressed protein spots were marked with numbers as shown in Fig. 2. The differentially expressed protein spots were excised from electrophoresis gels, digested and then analyzed by MALDI-TOF/TOF MS. The results showed that only 71 proteins were identified, among all 80 proteins, 2 protein spots were not identified (spots 0620 and 7520), other proteins corresponded to one ID (13 spots represent 6 protein IDs: spots 2125 and 2126; 2135 and 2333; 2525 and 4335; 4727 and 7639; 4813 and 4816; 7424, 7425 and 7426). The identified proteins as shown in Table 2, were sub-divided into 7 categories based on KEGG pathways. These pathways included biosynthesis of antibiotics, carbon metabolism, microbial metabolism in diverse environments, glycolysis/gluconeogenesis, biosynthesis of secondary metabolites, cationic antimicrobial peptide (CAMP) resistance and propanoate
metabolism. Proteins involved in the biosynthesis of secondary metabolites, cationic antimicrobial peptide (CAMP) resistance, carbon metabolism, microbial metabolism in diverse environments, and propanoate metabolism were found to be significantly expressed in the MDR, while proteins involved in the biosynthesis of antibiotics, glycolysis/gluconeogenesis were higher expressed in the sensitive strain. 4. Discussion Antibiotic resistance of A. hydrophila to multiple antibacterial agents has become a serious public health concern due to it’s transmission from diseased fish to humans and subsequently causing human diseases [20]. In this study, A. hydrophila isolates from diseased fish revealed a high resistance to β-lactam antibiotics such as Penicillin, Ampicillin, and Amoxicillin. The high rates of resistance to these classes of antibiotics have been reported in Aeromonas sp. by other researchers [21, 22, 23, 24, 25]. β-lactam antibiotics resistance in Aeromonas sp. is as a result of their intrinsic resistance and chromosomal mediated enzyme that can be transferred to successive progeny during cell division. Aeromonas spp. harbours different chromosomal β‐lactamase genes, which can produce β‐lactamase and degrade β-lactam antibiotics [26, 27, 28]. Multi-drug resistance in bacteria have previously been documented, nonetheless, information regarding proteomic profile changes is available only for bacteria resistant with respect to a single antibiotic. Outer membrane proteins (OMPs) play essential roles in oxytetracycline (OXY) induced resistant strain compared to its original stain [29, 30]. Bactericidal metabolic processes are involved in bacterial adaptive resistance to OXY in A. hydrophila [31]. The down-regulation of metabolism
pathways
such
as
carbon
metabolism,
pyruvate
metabolism,
and
glycolysis/gluconeogenesis has been reported by Li, however, β-Lactam resistance, RNA
degradation, and amino acids biosynthesis processes are more likely to increase when exposed to chlortetracycline (CTC) [32]. Our results provide an insight into the mechanism of antibiotics as strategic in clinical A. hydrophila and can provide potential bactericidal strategies for drug development based on metabolic pathways.
5. Conclusion In summary, a high incidence of multiple drug resistance amongst A. hydrophila was observed. In this study, our results obtained from the comparative proteomics studies gives an overview of metabolism changes and the importance of proteins related to carbon metabolism, biosynthesis of secondary metabolites, microbial metabolism in diverse environments, cationic antimicrobial peptide (CAMP) resistance and propanoate metabolism expressed in multi-resistant bacteria. Metabolic processes could be suggested as a possible target for the development of new antibiotic drugs. Conflicts of interest All authors declare that they have no conflict of interest.
Acknowledgements
This work was financially supported by the National Natural Science Foundation of China (grant no. 31672676) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) for data interpretation and design of the study. The authors are grateful to Emmanuel Konadu Sarkodie for English editing of the manuscript. Authors’ contributions WC conceived and supervised the project. WZ and SZ performed experimental research work. SZ
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Figure Legends Figure 1. Percentage antibiotics susceptibility levels of 28 isolated strains of Aeromonas hydrophila. to 24 different antibiotics Figure 2. Comparative 2-DE maps of proteins extracted from whole cells of A. hydrophila. A: Multi-drug resistant strain TZ-16, B: Sensitive strain 150488. Proteins were separated by 2-D electrophoresis using pH 4-7 nonlinear IPG strips and 12% SDS-PAGE. The numbers with arrows indicate the identified protein, and the differentially expressed protein IDs are provided in Table 2. Figure 3. Gene ontology (GO) analysis of differentially expressed proteins of A. hydrophila in multi-drug resistant and sensitive strains based on cellular component (A), biological process (B) and molecular function (C).
Table 1 Antibiotic resistance profile of all 28 clinical isolates to 24 different antimicrobial agents Strains
Resistance to drugs
TZ-16
PG, AMX, SAM, PIP, CEL, RAD, CEC, CFM, MOX, ATM, KAN, TET, TEC, SXT, CIP, FOS PG, AMX, SAM, PIP, CEL, RAD, ATM, TET, DOX, NFL, SXT, CIP PG, AMX, SAM, CEL, RAD, ATM, TET, SXT, NFL, CIP PG, AMX, SAM, PIP, CEL, RAD, ATM, TET, SXT, NFL PG, AMX, SAM, PIP, CEL, RAD, TET, DOX, AZM, SXT PG, AMX, SAM, PIP, CEL, RAD, CEC, TET, DOX, AZM, TEC, SXT, NFL PG, AMX, SAM, PIP, CEL, TET, DOX, AZM PG, AMX, SAM, PIP, TET, SXT, NFL, CIP PG, AMX, SAM, PIP, CEL, TET, DOX, AZM PG, AMX, SAM, PIP, CEL, TET, DOX，NFL PG, AMX, SAM, PIP, CEL, RAD, CEC, CFM, IPM, TET, DOX, TEC PG, AMX, SAM, PIP, CEL, TET, DOX, AZM, TEC, SXT PG, AMX, SAM, PIP, CEL, CEC, MOX, AZM, TEC, FOS PG, AMX, SAM, PIP, CEL, CEC, CFM, DOX, TEC PG, AMX, SAM, PIP, CEL, TET PG, AMX, SAM, PIP, CEL, RAD, CEC, DOX, SXT, TEC PG, AMX, SAM, PIP, CEL, RAD, CEC, DOX, TEC，IPM PG, AMX, SAM, PIP, CEL, RAD, CEC, DOX, TEC PG, AMX, SAM, PIP, IPM, TET, DOX, TEC PG, AMX, SAM, PIP, CEL, RAD, CEC, DOX, TEC PG, AMX, SAM, PIP, CEL, RAD, CEC, DOX, TEC PG, AMX, SAM, PIP, CEL, RAD, CEC, DOX, TEC PG, AMX, SAM, PIP, CEL, CEC, DOX, TEC PG, AMX, SAM, PIP, CEL, DOX, TEC PG, AMX, SAM, PIP, CEL, CEC, DOX, TEC PG, AMX, SAM, PIP, CEL, DOX, TEC，TET PG, TET, TEC, SXT, SAM，PIP, CEL, IPM PG, AMX, SAM, PIP, DOX, TEC
JH-37 JH-38 JH-39 JH-36 DH-41 JH-33 JY-25 JH-31 JH-34 150226-2 DH-29 XH-16 JH-25 JH-26 161014-2 161014-3 161014-1 150226-1 150078 XX-55 150333-1 NJ-24 BSK-10 CS-2 XX-62 JY-24 150488
Penicillin G (PG), Amoxicillin (AMX) , Sulbactam/ampicillin (SAM) , Piperacillin (PIP) , Cefalexin (CEL) , Cephradine (RAD) , Cefaclor (CEC) , Cefixime (CFM) , Imipenem (IMP) , Meropenem (MEM) , Moxalactam (MOX) , Aztreonam (ATM) , Streptomycin (STR) , Kanamycin (KAN) , Gentamicin (GEN) , Tetracycline (TET) , Doxitard (DOX) , Azithromycin (AZM) , Teicoplanin (TEC) , Sulphamethox-azole/Trimethoprim (SXT) , Norfloxacin (NFL) , Ciprofloxacin (CIP) , Fosfomycin (FOS) , Polymyxin B (POL)
Table 2 Functional classes of selected proteins of A. hydrophila that were differentially expressed in multi-drug resistant and sensitive strains. Sport
Fold Protein ID
Gene name
Sequence
Protein Description
Number
Change coverage (%)
Carbon metabolism 3909
Q9I2V5
acnB
Aconitate hydratase 2
0.023
7
6724
A0A160EYA9
AHA_3059
NAD-dependent malic enzyme
0.071
14
4613
B0KI05
pckA
Phosphoenolpyruvate carboxykinase
0.077
6
7424
A0A160F052
gcvT
Aminomethyltransferase
0.1
16
5631
A0A142E709
pyk-3
Pyruvate kinase
0.103
6
5421
A0A081UZ91
gap-2
0.15
5
Cysteine synthase
5.409
32
Glyceraldehyde-3-phosphate dehydrogenase 8420
A0A160EU11
cysK
Biosynthesis of antibiotics 2523
A0A167RUH3 ispE
Long-chain fatty acid transporter
0.103
12
1019
A0KGL0
dxs
10 kDa chaperonin
42.255
42
1139
Q88PK1
ndk
Nucleoside diphosphate kinase
22.487
38
9516
A0KEL0
fadA
3-ketoacyl-CoA thiolase
8.068
11
0.023
36
0.148
22
Biosynthesis of secondary metabolites 6,7-dimethyl-8-ribityllumazine 5136
Q88QH6
ribE-2 synthase
4729
A5VZB6
leuA-2
2-isopropylmalate synthase
Microbial metabolism in diverse environments 3721
A0A081UXX6 glnA
Glutamine synthetase
0.138
19
1229
A0A0T6TL53
maiA
Stringent starvation protein A
0.152
31
crr
PTS glucose transporter subunit IIA
27.213
42
Glycolysis / Gluconeogenesis 1018
A0A0T6U862
Cationic antimicrobial peptide (CAMP) resistance
6723
V9ZUT5
sapA
Tyrosinase
0.036
5
6118
A0A081UP59
AHA_1132
Peptidyl-prolyl cis-trans isomerase
0.038
6
AHA_2415
Glycerol dehydrogenase
0.104
19
30S ribosomal protein S6
0.027
20
0.046
9
Propanoate metabolism 5521
A0A165SYX4
Other KEGG Pathways 1013
A0KG67
rpsF
2515
A0KF45
rpoA
DNA-directed
RNA
polymerase
subunit alpha 4731
B0KRL5
lysS
Lysine--tRNA ligase
0.062
20
5630
B2FHY8
atpD
ATP synthase subunit beta
0.084
11
2122
P18782
cyaA
Tellurium resistance protein TerE
0.102
7
6722
A0A0T6QWZ6
groL
60 kDa chaperonin
0.131
13
6820
A0A0T6PNK7
pstB
PrkA family serine protein kinase
0.142
5
7116
B4SKV4
rplJ
50S ribosomal protein L10
0.184
37
8116
B2FKJ7
rplI
50S ribosomal protein L9
0.197
9
2135
A0A160EZ82
carB
Elongation factor P
61.505
26
1027
A0KQA6
rplL
50S ribosomal protein L7/L12
22.588
48
8419
A0A165TCM0
kbl
8.445
12
2-amino-3-ketobutyrate coenzyme A ligase 5330
A0A165T0B6
ligA
Uncharacterized protein
6.098
7
grpE
Protein grpE
0.024
22
Unknown 2117
Q88DU1
1412
A0A0A5QEY7 SH16_02770 Fructose-bisphosphate aldolase class 2 0.04
26
2129
A0A0T6TVH0 AHA_1733
0.049
28
3118
A0A0A5NLP6 SH16_00579 Superoxide dismutase
0.055
7
3717
A0A167SEX3
hgpB
Ligand-gated channel protein
0.061
19
1223
A0A142E0I5
AHA_3652
Uncharacterized protein
0.081
22
8313
A0A081UMM6 AHA_3314
RNA-binding protein
0.082
9
Acetoin utilization protein AcuB
0521
A0A142DY02
lamB
Maltoporin (Fragment)
0.106
6
5123
Q88PD5
AHA_1403
Superoxide dismutase
0.114
10
3117
A0A0T6NPB4
AHA_4025
Regulator
0.123
31
7214
A0A0A5NG60 tpiA
Triosephosphate isomerase
0.127
17
3424
A0A0A5NHQ9 SH16_02269 Uncharacterized protein
0.132
7
2125
A0A0T6T965
Ferritin
0.135
34
8219
A0A0T6RM44 AHA_1514
Peroxidase
0.135
29
5422
A0A0J1JV94
0.157
12
AHA_0054
Isocitrate
dehydrogenase,
SH16_02387 NAD-dependent
6422
A0A165S362
AHA_1969
Iron transporte
0.165
11
2121
A0A0A5NJ83
SH16_02903 Bacterioferritin
0.176
10
4514
A0A0A5NL54
ribB
0.184
12
0.194
19
3,4-dihydroxy-2-butanone 4-phosphate synthase D-ribose 4219
ABC
transporter
A0A0F6KCU8 AHA_0114 substrate-binding protein
2416
P24016
AHA_3793
Outer membrane protein A
0.195
10
8121
A0A0S3BNI0
hmp
Flavohemoprotein
0.197
4
1607
A0KNK6
prfB
Peptide chain release factor 2
44.024
19
7824
A0A0A5NPA2 SH16_01084 Formate acetyltransferase 1
17.016
10
8123
A0A160EXV3
AHA_0967
14.447
20
7639
A0A0J1K1U0
SH16_01926 Malate synthase A
11.181
9
4821
A0A081UXX7 typA
8.562
17
1134
A0A0A5NHL1 SH16_02169
7.43
17
6.924
38
HuvX protein
GTP-binding protein TypA Glucose-specific
phosphotransferase
enzyme IIA component Nucleoside 3124
A0A165S4U5
rnk regulator
diphosphate
kinase
Dear editors, It is our pleasure to submit our manuscript titled “Comparative proteomic analysis of sensitive and multi-drug resistant Aeromonas hydrophila isolated from diseased fish” for publication in Microbial Pathogenesis as an original research article. This work compared the whole cell proteins of drug resistant and sensitive A. hydrophila strains and provided new perspectives on drug resistance in bacteria. Results form our study giving new insights about mechanisms involved in drug resistance and suggesting possible novel targets for developing alternative antibacterial drugs. The manuscript is an original work and has not been submitted or is under consideration for publication in another journal. We also confirm that all the listed authors have participated actively in the study, and have seen and approved the submitted manuscript. The authors do not have any possible conflicts of interest. We trust that the manuscript meets the high standard of the Microbial Pathogenesis. We are looking forward to your response.
Best Regards!
Weihua Chu
Corresponding author: Dr. Weihua Chu NO.24, Tongjiaxiang Department of Pharmaceutical Microbiology School of Life Science & Technology China Pharmaceutical University Nanjing 210009 P. R. China E-mail: [email protected] Tel: 086-25-83271398 Fax: 086-25-83271398
S0882-4010(19)31667-5
DOI:
https://doi.org/10.1016/j.micpath.2019.103930
Reference:
YMPAT 103930
To appear in:
Microbial Pathogenesis
Received Date: 18 September 2019 Revised Date:
13 December 2019
Accepted Date: 13 December 2019
Please cite this article as: Zhu W, Zhou S, Chu W, Comparative proteomic analysis of sensitive and multi-drug resistant Aeromonas hydrophila isolated from diseased fish, Microbial Pathogenesis (2020), doi: https://doi.org/10.1016/j.micpath.2019.103930. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
Comparative proteomic analysis of sensitive and multi-drug resistant Aeromonas hydrophila isolated from diseased fish Wei Zhu, Shuxin Zhou, Weihua Chu* Department of Pharmaceutical Microbiology, School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China * Correspondence: [email protected]
Highlights Sensitive and multi-drug resistant Aeromonas hydrophila strains were isolated from diseased fish. Altered proteins between sensitive and multi-drug resistant A. hydrophila isolates were compared. A total of 61 and 17 differently expressed proteins were identified in multi-drug resistant and susceptible A. hydrophila respectively. Bioinformatics analysis showed biological processes related proteins were down-regulated in MDR strain.
Abstract: Bacterial hemorrhagic septicemia caused by multi-drug resistant (MDR) Aeromonas hydrophila has exponentially increased in the past decade, and reached an alarming rate making it a major concern in the aquaculture industry in China. The aim of this study was to investigate the difference in the regulation of proteins expression in multi-drug resistance and susceptible A. hydrophila strains isolated from diseased fish using two-dimensional electrophoresis (2-DE) combined with mass spectrometry. 28 isolates of A. hydrophila were successfully identified by biochemical tests. Antibiotic susceptibility test results showed that all the isolates have different drug resistant patterns. A total of 61 and 17 differently expressed proteins were identified in MDR and susceptible A. hydrophila, respectively, evidencing that biological processes related to carbon
metabolism, biosynthesis of secondary metabolites, microbial metabolism in diverse environments, cationic
antimicrobial
peptide
(CAMP)
resistance
and
propanoate
metabolism
were
down-regulated in MDR strain, while proteins involved in biosynthesis of antibiotics, glycolysis/gluconeogenesis were highly expressed in the sensitive strain. The analysis of differentially expressed proteins from multi-drug resistance and susceptible strains suggests that a number of proteins are involved in several metabolic metabolism pathways plays an important role in A. hydrophila drug resistance. Our findings provide new insights about mechanisms involved in drug resistance and propose possible novel targets for developing alternative antibacterial drugs.
Keywords: Aeromonas hydrophila; Multi-drug resistance; susceptible; Proteomics; 2DE; mass spectrometry
1. Introduction
Aeromonas hydrophila is a potential zoonotic pathogen with a worldwide distribution in freshwater and marine environments and has different pathogenic potential to humans and other animals [1, 2]. In humans, it can cause gastroenteritis, hemolytic uremic syndrome, peritonitis, skin infections, bacteremia, meningitis, and necrotizing fasciitis[3, 4]. It can cause different diseases in aquatic animals, such as motile aeromonad septicemia, red sore disease [5, 6]. In China, A.
hydrophila
has
caused
high
mortality
and
significant
economic
losses
in
the aquaculture industry every year. Traditionally, multi-antimicrobial agents have been used to control this bacterial pathogen. However, the abuse or misuse of antibiotics coupled with the limited new development of antimicrobials has led to the problem of multidrug-resistant bacteria.
Antimicrobial susceptibility profiles and the corresponding resistance determinants of A. hydrophila have been extensively reported by many researchers [7, 8]. Proteomics has evolved as an important tool for the investigation of microbial resistance mechanisms [9, 10]. Few proteomic investigations on molecular mechanisms related to multi-drug resistance of A. hydrophila have been reported. In this present study, an integrated approach based on two-dimensional electrophoreses (2DE) coupled with mass spectrometry (MS) techniques were used to resolve A. hydrophila multi-resistant proteome, investigate protein networks and also identify new putative candidates involved in resistance mechanisms.
2. Materials and methods 2.1. Bacteria strains isolation and identification
Sampling of diseased fish was carried out from different commercial fishery farms located in Jiangsu Province from May 2015 to August 2018. The sampled fish weighed from 50 to 200 g each with a typical symptom of bacterial septicemia. Bacterial isolation was carried out using Ryan's aeromonas medium base (RAM, Oxoid, CM0833) containing 5 mg/L Ampicillin as a selective medium. The fluids of the internal organs of diseased fish were swabbed on the selective media agar plates. The inoculated plates underwent incubation at 28°C for 24 - 48 h. All the suspected bacteria colonies were purified, and standard biochemical classification was performed using the VITEK2 system (bioMerieux) and an API 20E test kit (bioMerieux) at 28°C for 48 h, following the manufacturer’s instructions [11, 12]. The cultures were stored at − 80 °C in Luria Bretani (LB) broth supplemented with 15% glycerol.
2.2. Antibiotic susceptibility testing
24 different antibacterial agents were used to detect the antibiotic susceptibility of clinical isolates according to the criteria described by CLSI (2016) [13]. All A. hydrophila strains were grown in LB broth for 12-16 hrs, and then the turbidity of the culture was adjusted to an equivalence of a 0.5 McFarland standard for antibiotic susceptibility detection. 2.3. Whole-cell protein preparation, two-dimensional gel electrophoresis, In-gel protein digestion, protein identification and database searches
The preparation of whole-cell bacterial proteins was determined using the method as described by Hu et al., Du et al. and Piras et al. [14, 15, 16] with some minor modifications. The concentration of whole-cell protein was measured by the Bradford protein assay (BioRad, CA, USA) using bovine serum albumin (BSA) as a standard [17]. The bacterial cell protein samples were stored at -20°C until use. 2-D gel electrophoresis, in-gel protein digestion, protein identification were performed as described by the method of Du et al., Piras et al. and Sui et al. [15, 16, 18]. IEF was performed using the IPG-phor IEF system (BioRad, CA, USA) and Immobiline 24-cm DryStrip IPG strips (pH 4–7). The 2-D electrophoresis was then performed on a 12% SDS-PAGE. Gel electrophoresis was conducted at 16°C with 1.0 W gel-1 for 1 h and was later alternated with 10 W gel-1 until the dye formed was approximately 1 cm above the bottom of the gel. The protein signals were visualized by staining the gel with Coomassie Brilliant Blue (CBB) G-250. The gel was imaged with a BioRad Fluor-S system and then analyzed using PDQuest (Version 7.2.0; BioRad) software. Protein spots were first identified and later coordinated automatically in view of the total densities of the gels. For each of the identified spots, the mean relative volume was thought to be equivalent to its expression level at each stage. Identified spots demonstrating more than 2-fold-change difference (P < 0.05) were changed according to the mean relative volume and
treated as differentially expressed protein spots. The differentially expressed proteins were
extracted from gels after washing the gels with double-distilled water and destaining twice with 50 mM NH4HCO3 in 50% (v/v) acetonitrile (ACN) for CBB G-250-stained spots. The proteins were then mixed with 10 mM DTT in 50 mM NH4HCO3 and then alkylated with 40 mM iodoacetamide in 50 mM NH4HCO3 for 1 h at room temperature. The gel was first dried with 100% (v/v) ACN and then digested overnight at 37°C with the addition of 15 µL of trypsin (Promega, USA; 1:50, enzyme to protein) in 50 mM NH4HCO3. The resulting peptides were extracted twice with 0.1% TFA (v/v) in 50% (v/v) ACN. The samples were air-dried and analyzed with the Bruker Daltonics UltrafleXtreme MALDI TOF/TOF Proteomics Analyzer (Bremen, Germany). All protein spectra were searched for using the MASCOT search engine (http://www.matrixscience.com) in the UniProt database. The mass tolerance for peptides was set to 100 ppm, and the mass tolerance of TOF/TOF fragments was set to 0.5 Da using cysteine carbamidomethylation as a fixed modification. Methionine oxidation was used as a variable modification. The confidence in the peptide mass fingerprinting matches (P<0.05) was determined based on the MASCOT score and was verified if an accurate overlapping of the matched peptides with the major peaks of the mass spectrum was observed. Ions score is -10*Log(P), where P is the probability that the observed match is a random event. Individual ions scores > 33 indicate identity or extensive homology (p<0.05). Protein scores are derived from ions scores as a non-probabilistic basis for ranking protein hits. Only significant hits (P<0.05) based on the MASCOT probability analysis were accepted. For GO
enrichment analysis UniProt IDs of identified proteins were retrieved from UniProt knowledgebase (UniProtKB) (http://www.uniprot.org/). OmicsBean (http://www.omicsbean.cn), which integrated Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, was employed to analyze the obtained differential abundance proteins (http://www.genome.ad.jp/kegg/) [19]. Each experiment was performed in triplicate and identified in three technical replicate experiments. 2.4. Statistical analysis Student’s t-test was used to confirm the p-value between different groups, p-values < 0.05 were considered statistically significant. 3. Results
3.1. Antibiotic susceptibility characteristics of clinical A. hydrophila isolates
An antibiotic susceptibility test was performed on all 28 A. hydrophila isolates. Results were interpreted according to the guidelines of the CLSI (2016). All 28 clinical isolates expressed some level of resistant to the class of antibiotics as depicted in Table 1 and Figure 1. All isolates were found to be completely resistant to Penicillin (100%) and Ampicillin (100%) and a high prevalence of resistance to Amoxicillin (96.4%), Piperacillin (92.9%), Cefalexin (78.6%), Doxitard (75%) and Teicoplanin (67.9%). Isolates were highly sensitive to compounds like Streptomycin (100%), Gentamicin (100%), Kanamycin (96.4%), and Fosfomycin (92.9%). Of all 28 isolates, TZ-16 was resistant to 16 antibiotics, while 150488 was only resistant to 6 antibiotics. 3.2. Proteome analysis of multi-drug resistant and sensitive A. hydrophila strains A comparative proteomic analysis was performed. Considering the number of antibiotics, most bacteria showed resistance to isolate TZ-16 and 150488 was chosen as multi-drug resistant strain
and sensitive strain for proteome analysis. To investigate the different proteins expression of multi-drug resistant and sensitive A. hydrophila strains, 2-D electrophoresis maps using IEF on 24 cm, pH 4-7, nonlinear IPG gels were utilized and compared to whole-cell protein profiles. The results indicated more than 800 protein spots fall within the pH range of 4-7 on the Coomassie G-250-stained gels (Fig. 2). The cellular component by GO analysis of the peptides is presented in Fig. 3A. Using GO analyses, the proteins/peptides can be categorized into several biological processes (Fig. 3B). The major molecular functions of peptides obtained by GO analysis were organic cyclic compound binding (30%) and ligase activity (10%), structural molecule activity (7%), ion binding (6%) and others (Fig. 3 C). After quantitative analysis, 80 differentially expressed protein spots with more than 2-fold-change difference (P < 0.05) compared to each other were selected, comprising 61 proteins in multi-drug resistant strain and 17 insensitive strain. All the differentially expressed protein spots were marked with numbers as shown in Fig. 2. The differentially expressed protein spots were excised from electrophoresis gels, digested and then analyzed by MALDI-TOF/TOF MS. The results showed that only 71 proteins were identified, among all 80 proteins, 2 protein spots were not identified (spots 0620 and 7520), other proteins corresponded to one ID (13 spots represent 6 protein IDs: spots 2125 and 2126; 2135 and 2333; 2525 and 4335; 4727 and 7639; 4813 and 4816; 7424, 7425 and 7426). The identified proteins as shown in Table 2, were sub-divided into 7 categories based on KEGG pathways. These pathways included biosynthesis of antibiotics, carbon metabolism, microbial metabolism in diverse environments, glycolysis/gluconeogenesis, biosynthesis of secondary metabolites, cationic antimicrobial peptide (CAMP) resistance and propanoate
metabolism. Proteins involved in the biosynthesis of secondary metabolites, cationic antimicrobial peptide (CAMP) resistance, carbon metabolism, microbial metabolism in diverse environments, and propanoate metabolism were found to be significantly expressed in the MDR, while proteins involved in the biosynthesis of antibiotics, glycolysis/gluconeogenesis were higher expressed in the sensitive strain. 4. Discussion Antibiotic resistance of A. hydrophila to multiple antibacterial agents has become a serious public health concern due to it’s transmission from diseased fish to humans and subsequently causing human diseases [20]. In this study, A. hydrophila isolates from diseased fish revealed a high resistance to β-lactam antibiotics such as Penicillin, Ampicillin, and Amoxicillin. The high rates of resistance to these classes of antibiotics have been reported in Aeromonas sp. by other researchers [21, 22, 23, 24, 25]. β-lactam antibiotics resistance in Aeromonas sp. is as a result of their intrinsic resistance and chromosomal mediated enzyme that can be transferred to successive progeny during cell division. Aeromonas spp. harbours different chromosomal β‐lactamase genes, which can produce β‐lactamase and degrade β-lactam antibiotics [26, 27, 28]. Multi-drug resistance in bacteria have previously been documented, nonetheless, information regarding proteomic profile changes is available only for bacteria resistant with respect to a single antibiotic. Outer membrane proteins (OMPs) play essential roles in oxytetracycline (OXY) induced resistant strain compared to its original stain [29, 30]. Bactericidal metabolic processes are involved in bacterial adaptive resistance to OXY in A. hydrophila [31]. The down-regulation of metabolism
pathways
such
as
carbon
metabolism,
pyruvate
metabolism,
and
glycolysis/gluconeogenesis has been reported by Li, however, β-Lactam resistance, RNA
degradation, and amino acids biosynthesis processes are more likely to increase when exposed to chlortetracycline (CTC) [32]. Our results provide an insight into the mechanism of antibiotics as strategic in clinical A. hydrophila and can provide potential bactericidal strategies for drug development based on metabolic pathways.
5. Conclusion In summary, a high incidence of multiple drug resistance amongst A. hydrophila was observed. In this study, our results obtained from the comparative proteomics studies gives an overview of metabolism changes and the importance of proteins related to carbon metabolism, biosynthesis of secondary metabolites, microbial metabolism in diverse environments, cationic antimicrobial peptide (CAMP) resistance and propanoate metabolism expressed in multi-resistant bacteria. Metabolic processes could be suggested as a possible target for the development of new antibiotic drugs. Conflicts of interest All authors declare that they have no conflict of interest.
Acknowledgements
This work was financially supported by the National Natural Science Foundation of China (grant no. 31672676) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) for data interpretation and design of the study. The authors are grateful to Emmanuel Konadu Sarkodie for English editing of the manuscript. Authors’ contributions WC conceived and supervised the project. WZ and SZ performed experimental research work. SZ
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Figure Legends Figure 1. Percentage antibiotics susceptibility levels of 28 isolated strains of Aeromonas hydrophila. to 24 different antibiotics Figure 2. Comparative 2-DE maps of proteins extracted from whole cells of A. hydrophila. A: Multi-drug resistant strain TZ-16, B: Sensitive strain 150488. Proteins were separated by 2-D electrophoresis using pH 4-7 nonlinear IPG strips and 12% SDS-PAGE. The numbers with arrows indicate the identified protein, and the differentially expressed protein IDs are provided in Table 2. Figure 3. Gene ontology (GO) analysis of differentially expressed proteins of A. hydrophila in multi-drug resistant and sensitive strains based on cellular component (A), biological process (B) and molecular function (C).
Table 1 Antibiotic resistance profile of all 28 clinical isolates to 24 different antimicrobial agents Strains
Resistance to drugs
TZ-16
PG, AMX, SAM, PIP, CEL, RAD, CEC, CFM, MOX, ATM, KAN, TET, TEC, SXT, CIP, FOS PG, AMX, SAM, PIP, CEL, RAD, ATM, TET, DOX, NFL, SXT, CIP PG, AMX, SAM, CEL, RAD, ATM, TET, SXT, NFL, CIP PG, AMX, SAM, PIP, CEL, RAD, ATM, TET, SXT, NFL PG, AMX, SAM, PIP, CEL, RAD, TET, DOX, AZM, SXT PG, AMX, SAM, PIP, CEL, RAD, CEC, TET, DOX, AZM, TEC, SXT, NFL PG, AMX, SAM, PIP, CEL, TET, DOX, AZM PG, AMX, SAM, PIP, TET, SXT, NFL, CIP PG, AMX, SAM, PIP, CEL, TET, DOX, AZM PG, AMX, SAM, PIP, CEL, TET, DOX，NFL PG, AMX, SAM, PIP, CEL, RAD, CEC, CFM, IPM, TET, DOX, TEC PG, AMX, SAM, PIP, CEL, TET, DOX, AZM, TEC, SXT PG, AMX, SAM, PIP, CEL, CEC, MOX, AZM, TEC, FOS PG, AMX, SAM, PIP, CEL, CEC, CFM, DOX, TEC PG, AMX, SAM, PIP, CEL, TET PG, AMX, SAM, PIP, CEL, RAD, CEC, DOX, SXT, TEC PG, AMX, SAM, PIP, CEL, RAD, CEC, DOX, TEC，IPM PG, AMX, SAM, PIP, CEL, RAD, CEC, DOX, TEC PG, AMX, SAM, PIP, IPM, TET, DOX, TEC PG, AMX, SAM, PIP, CEL, RAD, CEC, DOX, TEC PG, AMX, SAM, PIP, CEL, RAD, CEC, DOX, TEC PG, AMX, SAM, PIP, CEL, RAD, CEC, DOX, TEC PG, AMX, SAM, PIP, CEL, CEC, DOX, TEC PG, AMX, SAM, PIP, CEL, DOX, TEC PG, AMX, SAM, PIP, CEL, CEC, DOX, TEC PG, AMX, SAM, PIP, CEL, DOX, TEC，TET PG, TET, TEC, SXT, SAM，PIP, CEL, IPM PG, AMX, SAM, PIP, DOX, TEC
JH-37 JH-38 JH-39 JH-36 DH-41 JH-33 JY-25 JH-31 JH-34 150226-2 DH-29 XH-16 JH-25 JH-26 161014-2 161014-3 161014-1 150226-1 150078 XX-55 150333-1 NJ-24 BSK-10 CS-2 XX-62 JY-24 150488
Penicillin G (PG), Amoxicillin (AMX) , Sulbactam/ampicillin (SAM) , Piperacillin (PIP) , Cefalexin (CEL) , Cephradine (RAD) , Cefaclor (CEC) , Cefixime (CFM) , Imipenem (IMP) , Meropenem (MEM) , Moxalactam (MOX) , Aztreonam (ATM) , Streptomycin (STR) , Kanamycin (KAN) , Gentamicin (GEN) , Tetracycline (TET) , Doxitard (DOX) , Azithromycin (AZM) , Teicoplanin (TEC) , Sulphamethox-azole/Trimethoprim (SXT) , Norfloxacin (NFL) , Ciprofloxacin (CIP) , Fosfomycin (FOS) , Polymyxin B (POL)
Table 2 Functional classes of selected proteins of A. hydrophila that were differentially expressed in multi-drug resistant and sensitive strains. Sport
Fold Protein ID
Gene name
Sequence
Protein Description
Number
Change coverage (%)
Carbon metabolism 3909
Q9I2V5
acnB
Aconitate hydratase 2
0.023
7
6724
A0A160EYA9
AHA_3059
NAD-dependent malic enzyme
0.071
14
4613
B0KI05
pckA
Phosphoenolpyruvate carboxykinase
0.077
6
7424
A0A160F052
gcvT
Aminomethyltransferase
0.1
16
5631
A0A142E709
pyk-3
Pyruvate kinase
0.103
6
5421
A0A081UZ91
gap-2
0.15
5
Cysteine synthase
5.409
32
Glyceraldehyde-3-phosphate dehydrogenase 8420
A0A160EU11
cysK
Biosynthesis of antibiotics 2523
A0A167RUH3 ispE
Long-chain fatty acid transporter
0.103
12
1019
A0KGL0
dxs
10 kDa chaperonin
42.255
42
1139
Q88PK1
ndk
Nucleoside diphosphate kinase
22.487
38
9516
A0KEL0
fadA
3-ketoacyl-CoA thiolase
8.068
11
0.023
36
0.148
22
Biosynthesis of secondary metabolites 6,7-dimethyl-8-ribityllumazine 5136
Q88QH6
ribE-2 synthase
4729
A5VZB6
leuA-2
2-isopropylmalate synthase
Microbial metabolism in diverse environments 3721
A0A081UXX6 glnA
Glutamine synthetase
0.138
19
1229
A0A0T6TL53
maiA
Stringent starvation protein A
0.152
31
crr
PTS glucose transporter subunit IIA
27.213
42
Glycolysis / Gluconeogenesis 1018
A0A0T6U862
Cationic antimicrobial peptide (CAMP) resistance
6723
V9ZUT5
sapA
Tyrosinase
0.036
5
6118
A0A081UP59
AHA_1132
Peptidyl-prolyl cis-trans isomerase
0.038
6
AHA_2415
Glycerol dehydrogenase
0.104
19
30S ribosomal protein S6
0.027
20
0.046
9
Propanoate metabolism 5521
A0A165SYX4
Other KEGG Pathways 1013
A0KG67
rpsF
2515
A0KF45
rpoA
DNA-directed
RNA
polymerase
subunit alpha 4731
B0KRL5
lysS
Lysine--tRNA ligase
0.062
20
5630
B2FHY8
atpD
ATP synthase subunit beta
0.084
11
2122
P18782
cyaA
Tellurium resistance protein TerE
0.102
7
6722
A0A0T6QWZ6
groL
60 kDa chaperonin
0.131
13
6820
A0A0T6PNK7
pstB
PrkA family serine protein kinase
0.142
5
7116
B4SKV4
rplJ
50S ribosomal protein L10
0.184
37
8116
B2FKJ7
rplI
50S ribosomal protein L9
0.197
9
2135
A0A160EZ82
carB
Elongation factor P
61.505
26
1027
A0KQA6
rplL
50S ribosomal protein L7/L12
22.588
48
8419
A0A165TCM0
kbl
8.445
12
2-amino-3-ketobutyrate coenzyme A ligase 5330
A0A165T0B6
ligA
Uncharacterized protein
6.098
7
grpE
Protein grpE
0.024
22
Unknown 2117
Q88DU1
1412
A0A0A5QEY7 SH16_02770 Fructose-bisphosphate aldolase class 2 0.04
26
2129
A0A0T6TVH0 AHA_1733
0.049
28
3118
A0A0A5NLP6 SH16_00579 Superoxide dismutase
0.055
7
3717
A0A167SEX3
hgpB
Ligand-gated channel protein
0.061
19
1223
A0A142E0I5
AHA_3652
Uncharacterized protein
0.081
22
8313
A0A081UMM6 AHA_3314
RNA-binding protein
0.082
9
Acetoin utilization protein AcuB
0521
A0A142DY02
lamB
Maltoporin (Fragment)
0.106
6
5123
Q88PD5
AHA_1403
Superoxide dismutase
0.114
10
3117
A0A0T6NPB4
AHA_4025
Regulator
0.123
31
7214
A0A0A5NG60 tpiA
Triosephosphate isomerase
0.127
17
3424
A0A0A5NHQ9 SH16_02269 Uncharacterized protein
0.132
7
2125
A0A0T6T965
Ferritin
0.135
34
8219
A0A0T6RM44 AHA_1514
Peroxidase
0.135
29
5422
A0A0J1JV94
0.157
12
AHA_0054
Isocitrate
dehydrogenase,
SH16_02387 NAD-dependent
6422
A0A165S362
AHA_1969
Iron transporte
0.165
11
2121
A0A0A5NJ83
SH16_02903 Bacterioferritin
0.176
10
4514
A0A0A5NL54
ribB
0.184
12
0.194
19
3,4-dihydroxy-2-butanone 4-phosphate synthase D-ribose 4219
ABC
transporter
A0A0F6KCU8 AHA_0114 substrate-binding protein
2416
P24016
AHA_3793
Outer membrane protein A
0.195
10
8121
A0A0S3BNI0
hmp
Flavohemoprotein
0.197
4
1607
A0KNK6
prfB
Peptide chain release factor 2
44.024
19
7824
A0A0A5NPA2 SH16_01084 Formate acetyltransferase 1
17.016
10
8123
A0A160EXV3
AHA_0967
14.447
20
7639
A0A0J1K1U0
SH16_01926 Malate synthase A
11.181
9
4821
A0A081UXX7 typA
8.562
17
1134
A0A0A5NHL1 SH16_02169
7.43
17
6.924
38
HuvX protein
GTP-binding protein TypA Glucose-specific
phosphotransferase
enzyme IIA component Nucleoside 3124
A0A165S4U5
rnk regulator
diphosphate
kinase
Dear editors, It is our pleasure to submit our manuscript titled “Comparative proteomic analysis of sensitive and multi-drug resistant Aeromonas hydrophila isolated from diseased fish” for publication in Microbial Pathogenesis as an original research article. This work compared the whole cell proteins of drug resistant and sensitive A. hydrophila strains and provided new perspectives on drug resistance in bacteria. Results form our study giving new insights about mechanisms involved in drug resistance and suggesting possible novel targets for developing alternative antibacterial drugs. The manuscript is an original work and has not been submitted or is under consideration for publication in another journal. We also confirm that all the listed authors have participated actively in the study, and have seen and approved the submitted manuscript. The authors do not have any possible conflicts of interest. We trust that the manuscript meets the high standard of the Microbial Pathogenesis. We are looking forward to your response.
Best Regards!
Weihua Chu
Corresponding author: Dr. Weihua Chu NO.24, Tongjiaxiang Department of Pharmaceutical Microbiology School of Life Science & Technology China Pharmaceutical University Nanjing 210009 P. R. China E-mail: [email protected] Tel: 086-25-83271398 Fax: 086-25-83271398