Foot ulcers and associated infections significantly contribute to morbidity and mortality in diabetes. While diverse pathogens are found in the diabetes-related infected ulcers, Staphylococcus aureus remains one of the most virulent and widely prevalent pathogens. The high prevalence of S. aureus in chronic wound infections, especially in clinical settings, is attributed to its ability to evolve and acquire resistance against common antibiotics and to elicit an array of virulence factors. In this study, whole genome comparison of four strains of S. aureus (MUF168, MUF256, MUM270, and MUM475) isolated from diabetic foot ulcer (DFU) infections showing varying resistance patterns was carried out to study the genomic similarity, antibiotic resistance profiling, associated virulence factors, and sequence variations in drug targets. The comparative genome analysis showed strains MUM475 and MUM270 to be highly resistant, MUF256 with moderate levels of resistance, and MUF168 to be the least resistant. Strain MUF256 and MUM475 harbored more virulence factors compared with other two strains. Deleterious sequence variants were observed suggesting potential role in altering drug targets and drug efficacy. This comparative whole genome study offers new molecular insights that may potentially inform evidence-based diagnosis and treatment of DFUs in the clinic.
Get full access to this article
View all access options for this article.
References
1.
AlcockBP, RaphenyaAR, LauTTY, et al.CARD 2020: Antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Res, 2020; 48(D1):D517–D525; doi: 10.1093/nar/gkz935
2.
AltschulSF, GishW, MillerW, et al.Basic local alignment search tool. J Mol Biol, 1990; 215(3):403–410; doi: 10.1016/S0022-2836(05)80360-2
3.
AzizRK, BartelsD, BestA, et al.The RAST server: Rapid annotations using subsystems technology. BMC Genomics, 2008; 9:75; doi: 10.1186/1471-2164-9-75
4.
BowlingFL, JudeEB, BoultonAJM. MRSA and diabetic foot wounds: Contaminating or infecting organisms?. Curr Diab Rep, 2009; 9(6):440–444; doi: 10.1007/s11892-009-0072-z
5.
BrennanMB, HessTM, BartleB, et al.Diabetic foot ulcer severity predicts mortality among veterans with type 2 diabetes. J Diabetes Complications, 2017; 31(3):556–561; doi: 10.1016/j.jdiacomp.2016.11.020
6.
CaoM, BernatBA, WangZ, et al.FosB, a cysteine-dependent fosfomycin resistance protein under the control of σw, an extracytoplasmic-function σ factor in Bacillus subtilis. J Bacteriol, 2001; 183(7):2380–2383; doi: 10.1128/JB.183.7.2380-2383.2001
7.
ChenL, ZhengD, LiuB, et al.VFDB 2016: Hierarchical and refined dataset for big data analysis—10 years on. Nucleic Acids Res, 2016; 44(D1):D694–D697; doi: 10.1093/nar/gkv1239
8.
ChoiY, ChanAP. PROVEAN web server: A tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics, 2015; 31(16):2745–2747; doi: 10.1093/bioinformatics/btv195
9.
CLSI. Clinical and Laboratory Standards Institute. Performance Standards for Antimicrobial Susceptibility Testing: Twenty-First Informational Supplement M100-S21. CLSI: Wayne, PA, USA; 2011.
10.
CollF, McNerneyR, PrestonMD, et al.Rapid determination of anti-tuberculosis drug resistance from whole-genome sequences. Genome Med, 2015; 7(1):51; doi: 10.1186/s13073-015-0164-0
11.
DookieN, RambaranS, PadayatchiN, et al.Evolution of drug resistance in Mycobacterium tuberculosis: A review on the molecular determinants of resistance and implications for personalized care. J Antimicrob Chemother, 2018; 73(5):1138–1151; doi: 10.1093/jac/dkx506
12.
DosterE, LakinSM, DeanCJ, et al.MEGARes 2.0: A database for classification of antimicrobial drug, biocide and metal resistance determinants in metagenomic sequence data. Nucleic Acids Res, 2020; 48(D1):D561–D569; doi: 10.1093/nar/gkz1010
13.
Dunyach-RemyC, EssebeCN, SottoA, et al.Staphylococcus aureus toxins and diabetic foot ulcers: Role in pathogenesis and interest in diagnosis. Toxins (Basel), 2016; 8(7):1–20; doi: 10.3390/toxins8070209
14.
FeldgardenM, BroverV, HaftDH, et al.Validating the AMRFINder tool and resistance gene database by using antimicrobial resistance genotype-phenotype correlations in a collection of isolates. Antimicrob Agents Chemother, 2019; 63(11):1–19; doi: 10.1128/AAC.00483-19
15.
GreenMR, SambrookJ. Isolation of high-molecular-weight DNA using organic solvents. Cold Spring Harb Protoc, 2017;2017(4):pdb.prot093450; doi: 10.1101/pdb.prot093450
16.
GurevichA, SavelievV, VyahhiN, et al.QUAST: Quality assessment tool for genome assemblies. Bioinformatics, 2013; 29(8):1072–1075; doi: 10.1093/bioinformatics/btt086
17.
HackbarthCJ, ChambersHF. BlaI and blar1 regulate β-lactamase and pbp 2a production in methicillin-resistant Staphylococcus aureus. Antimicrob Agents Chemother, 1993; 37(5):1144–1149; doi: 10.1128/AAC.37.5.1144
18.
HameedHMA, IslamMM, ChhotarayC, et al.Molecular targets related drug resistance mechanisms in mdr-, xdr-, and tdr- Mycobacterium tuberculosis strains. Front Cell Infect Microbiol, 2018; 8:114; doi: 10.3389/fcimb.2018.00114
19.
HeberleH, Meirelles GV, SilvaFR, et al.InteractiVenn: A web-based tool for the analysis of sets through venn diagrams. BMC Bioinformatics, 2015; 16:169; doi: 10.1186/s12859-015-0611-3
20.
HoldenMTG, FeilEJ, LindsayJA, et al.Complete genomes of two clinical Staphylococcus aureus strains: Evidence for the rapid evolution of virulence and drug resistance. Proc Natl Acad Sci U S A, 2004; 101(26):9786–9791; doi: 10.1073/pnas.0402521101
21.
Huerta-CepasJ, SzklarczykD, HellerD, et al.EggNOG 5.0: A hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res, 2019; 47(D1):D309–D314; doi: 10.1093/nar/gky1085
LaveryLA, ArmstrongDG, WunderlichRP, et al.Risk factors for foot infections in individuals with diabetes. Diabetes Care, 2006; 29(6):1288–1293; doi: 10.2337/dc05-2425
LuongTT, DunmanPM, MurphyE, et al.Transcription profiling of the mgra regulon in Staphylococcus aureus. J Bacteriol, 2006; 188(5):1899–1910; doi: 10.1128/JB.188.5.1899-1910.2006
27.
MadeiraF, ParkYM, LeeJ, et al.The EMBL-EBI search and sequence analysis tools apis in 2019. Nucleic Acids Res, 2019; 47(W1):W636–W641; doi: 10.1093/nar/gkz268
28.
ManniM, BerkeleyMR, SeppeyM, et al.BUSCO update: Novel and streamlined workflows along with broader and deeper phylogenetic coverage for scoring of eukaryotic, prokaryotic, and viral genomes. Mol Biol Evol, 2021; 38(10):4647–4654; doi: 10.1093/molbev/msab199
29.
McKennaA, HannaM, BanksE, et al.The genome analysis toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res, 2010; 20(9):1297–1303; doi: 10.1101/GR.107524.110
30.
Meier-KolthoffJP, AuchAF, KlenkHP, et al.Genome sequence-based species delimitation with confidence intervals and improved distance functions. BMC Bioinformatics, 2013; 14:60; doi: 10.1186/1471-2105-14-60
31.
MuraliTS, KavithaS, SpoorthiJ, et al.Characteristics of microbial drug resistance and its correlates in chronic diabetic foot ulcer infections. J Med Microbiol, 2014a;63:1377–1385; doi: 10.1099/jmm.0.076034-0
32.
MuraliTS, PaulB, ParikhH, et al.Genome sequences of four clinical Staphylococcus aureus strains with diverse drug resistance profiles isolated from diabetic foot ulcers. Genome Announc, 2014b;2(2):e00204-14; doi: 10.1128/genomea.00204-14
33.
NurjadiD, OlalekanAO, LayerF, et al.Emergence of trimethoprim resistance gene dfrG in Staphylococcus aureus causing human infection and colonization in sub-saharan africa and its import to Europe. J Antimicrob Chemother, 2014; 69(9):2361–2368; doi: 10.1093/jac/dku174
34.
OtarighoB, FaladeMO. Analysis of antibiotics resistant genes in different strains of Staphylococcus aureus. Bioinformation, 2018; 14(03):113–122; doi: 10.6026/97320630014113
35.
PageAJ, CumminsCA, HuntM, et al.Roary: Rapid large-scale prokaryote pan genome analysis. Bioinformatics, 2015; 31(22):3691–3693; doi: 10.1093/bioinformatics/btv421
36.
PercivalSL, MaloneM, MayerD, et al.Role of anaerobes in polymicrobial communities and biofilms complicating diabetic foot ulcers. Int Wound J, 2018; 15(5):776–782; doi: 10.1111/iwj.12926
37.
PetkauA, Stuart-EdwardsM, StothardP, et al.Interactive microbial genome visualization with GView. Bioinformatics, 2010; 26(24):3125–3126; doi: 10.1093/bioinformatics/btq588
38.
PougetC, Dunyach-RemyC, PantelA, et al.New adapted in vitro technology to evaluate biofilm formation and antibiotic activity using live imaging under flow conditions. Diagnostics, 2021; 11(10):1746; doi: 10.3390/diagnostics11101746
39.
PrasadASB, ShrupthaP, PrabhuV, et al.Pseudomonas aeruginosa virulence proteins pseudolysin and protease iv impede cutaneous wound healing. Lab Investig, 2020; 100(12):1532–1550; doi: 10.1038/s41374-020-00478-1
40.
PriceJ, GordonNC, CrookD, et al.The usefulness of whole genome sequencing in the management of Staphylococcus aureus infections. Clin Microbiol Infect, 2013; 19(9):784–789; doi: 10.1111/1469-0691.12109
RedgraveLS, SuttonSB, WebberMA, et al.Fluoroquinolone resistance: mechanisms, impact on bacteria, and role in evolutionary success. Trends Microbiol, 2014; 22(8):438–445; doi: 10.1016/j.tim.2014.04.007
43.
SchärfeCPI, TremmelR, SchwabM, et al.Genetic variation in human drug-related genes. Genome Med, 2017; 9(1):117; doi: 10.1186/s13073-017-0502-5
44.
ScholarE. Bleomycin. In: EnnaSJ, BylundDB (eds): XPharm: The Comprehensive Pharmacology Reference. Elsevier, Inc. 2007; pp. 1–6.
SeemannT. Abricate: Mass screening of contigs for antimicrobial and virulence genes; 2021. Available from: https://github.com/tseemann/abricate [Last accessed: October 26, 2021].
47.
ShettigarK, JainS, BhatDV, et al.Virulence determinants in clinical Staphylococcus aureus from monomicrobial and polymicrobial infections of diabetic foot ulcers. J Med Microbiol, 2016; 65(12):1392–1404; doi: 10.1099/jmm.0.000370
48.
SieversF, WilmA, DineenD, et al.Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol, 2011; 7:539; doi: 10.1038/msb.2011.75
49.
SinghN, ArmstrongDG, LipskyBA. Preventing foot ulcers in patients with diabetes. JAMA, 2005; 293(2):217–228; doi: 10.1001/jama.293.2.217
50.
StothardP, WishartDS. Circular genome visualization and exploration using CGView. Bioinformatics, 2005; 21(4):537–539; doi: 10.1093/bioinformatics/bti054
VermaSS, JosyulaN, VermaA, et al.Rare variants in drug target genes contributing to complex diseases, Phenome-Wide. Sci Rep, 2018; 8(1):4; doi: 10.1038/s41598-018-22834-4
53.
WishartDS, FeunangYD, GuoAC, et al.DrugBank 5.0: A major update to the drugbank database for 2018. Nucleic Acids Res, 2018; 46(D1):D1074–D1082; doi: 10.1093/nar/gkx1037
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
