Skip to main content
NCBI home page
FEMS Microbiology Letters logoLink to FEMS Microbiology Letters
. 2025 Jun 12;372:fnaf059. doi: 10.1093/femsle/fnaf059

The genome-wide de novo mutations and icaA gene expression levels in Staphylococcus aureus under long-term subinhibitory and semisubinhibitory nisin exposure

Hüseyin Özgür Özdemirel 1,✉, Sibel Kucukyildirim 2
PMCID: PMC12199728  PMID: 40504520

Abstract

The emergence and rapid spread of multidrug-resistant pathogens have caused a need for alternative antimicrobials, and bacteriocins are considered promising alternatives due to their lower risk of resistance development. Regarding this, we aimed to investigate the long-term subinhibitory and semisubinhibitory concentrations of a commonly used bacteriocin (nisin) in Staphylococcus aureus using an experimental evolution approach followed by genome sequencing. We then performed RT-qPCR to examine changes in the expression level of the biofilm-related icaA gene in evolved lines. We found that while nisin treatment did not significantly elevate the base-substitution rates, there was a significant decrease in the insertion/deletion rate in the lines exposed to the subinhibitory concentration of nisin. We also revealed an increase in nonsynonymous mutations in specific genes (e.g. sarS and cap8) associated with resistance and virulence mechanisms. Importantly, we observed a transition bias in the nisin-treated lines for the first time, and it may be related to the resistance development to nisin. RT-qPCR analysis of the icaA gene showed a reduced expression levels in nisin-treated groups, although the results were not statistically significant. These findings show the potential outcomes of nisin exposure in S. aureus and emphasize the need for careful consideration of bacteriocins in clinical practice.

Keywords: bacteriocin, antimicrobial resistance, pathogens, biofilm, food additive

This study demonstrates that long-term exposure to the bacteriocin nisin in Staphylococcus aureus leads to distinct genomic alterations, including reduced insertion/deletion rates, increased nonsynonymous mutations in resistance- and resistance-related genes, and a novel transition bias, underscoring the need for cautious use of bacteriocins in clinical settings.

Introduction

The rapid emergence and spread of multidrug-resistant pathogens are a global health problem, emphasizing the growing need for alternative strategies to treat these infections. Bacteriocins are considered a promising substitute for conventional antibiotics because of their distinct action mechanisms and narrow-spectrum may reduce the risk of resistance development (Gradisteanu Pircalabioru et al. 2021). Bacteriocins can inhibit the bacterial growth by leading membrane alterations or by acting as pore-forming agents. Previous studies have demonstrated the inhibitory or lethal effects of bacteriocins against clinically important pathogens, including methicillin-resistant Staphylococcus aureus, vancomycin-resistant Enterococcus, multidrug-resistant Pseudomonas aeruginosa, Klebsiella pneumoniae, and Acinetobacter spp. (Falagas et al. 2008, Oman and van der Donk 2009, Piper et al. 2009). It has been recently suggested that certain bacteriocins, particularly nisin, can modulate biofilm-related icaA gene expression and effect established polysaccharide intracellular adhesin-rich biofilms, thereby potentiating the activity of conventional antibiotics against pathogenic bacteria (Jesus et al. 2021, Shivaee et al. 2021, Sharafi et al. 2024). Many bacteriocins are used as food preservatives, and their effects on pathogens in foods—such as meat, dairy products, fruits, vegetables, and grains—have been widely studied (Delvesbroughton 1990, Arques et al. 2015, Gharsallaoui et al. 2016). Nisin A is the only bacteriocin that was approved as a safe food additive by the Food and Agriculture Organization/World Health Organization (FAO/WHO) in 1969 and by the US Food and Drug Administration (FDA) in 1988 and is on the Generally Recognized as Safe (GRAS) list (de Arauz et al. 2009, Gradisteanu Pircalabioru et al. 2021). It has been commonly used since then.

Mechanisms that underlying resistance to conventional antibiotics are extensively studied (Darby et al. 2023), but there is a growing need to extend these efforts to bacteriocin resistance. Bacteria can acquire resistance to bacteriocins through different mechanisms—including enzymatic inactivation by peptidases, modification of the target, cellular filamentation, secretion of molecules that neutralize and bind them, and decreased cell surface permeability—paralleling responses observed against conventional antibiotics (Gradisteanu Pircalabioru et al. 2021). Furthermore, the frequency of spontaneous bacteriocin resistance can vary depending on the microorganism, the type of bacteriocin, the strain tested, and the experimental conditions used (Gravesen et al. 2002, Blake et al. 2011). Studies have shown that S. aureus may develop resistance to nisin through mutations in the nsaS gene. (De Martinis et al. 1997, Mantovani and Russell 2001, Kramer et al. 2008, Sun et al. 2009, Randall et al. 2018). Therefore it is important to understand these mechanisms before evaluating the use of bacteriocins clinically (Barbosa et al. 2021). In this study, we aimed to reveal the effects of long-term nisin A exposure on genome-wide de novo mutations and icaA gene expression in S. aureus by using an experimental evolution (EE) strategy combined with high-throughput whole genome resequencing and RT-qPCR (Quantitative reverse transcription polymerase chain reaction). Our work may help develop strategies to prevent resistance problems in the future.

Materials and methods

Bacterial strain and growth conditions

Throughout this study, S. aureus (ATCC25923) was used and cultivated in tryptic soy broth (TSB) or tryptic soy agar (TSA) plates at 37°C. Nisin A was ordered from a local supplier for food additives, and the stock solution (10 g/l) was prepared by dissolving in TSB medium.

The genome of the S. aureus ATCC strain 25 923 that used in our work has been sequenced by Treangen et al. (2014).

Minimum inhibitory concentration determination

To find out the minimum inhibitory concentration (MIC) of nisin A, a broth microdilution protocol modified from Andrews (2001) was used. The bacteria were cultured in TSB at 37°C until the visible turbidity was equal to the 0.5 McFarland standard. Then, the culture diluted 1:10 in Mueller–Hinton Broth (MHB) to obtain 500.000 CFU/ml, and were inoculated into 0.1 ml cation-adjusted MHB containing different concentrations of nisin A (0, 2, 4, 8, 16, 32, 64, 128, 256, 512, and 1024 μg/ml). After incubating at 37°C for 24 h, OD values were evaluated with EnSightTM Multimode Plate Reader (PerkinElmer) at 570 nm, and the MIC value was determined as 128 μg/ml. This protocol was conducted in three independent assays include three biological replicates.

EE lines

To examine the effect of long-term nisin A treatment on genome-wide de novo mutations, S. aureus was cultured overnight in TSB at 37°C and transferred to a TSA plate using the quadrant streaking (Pelczar and Reid 1958). After overnight incubation, a randomly selected colony was transferred to a fresh TSA plate using the same technique. Three groups of EE lines were initiated from the same ancestral colony, and each group consisting of forty EE lines. The first group was cultured in TSB without nisin A as the control group. The second group of lines was cultured in TSB with 64 μg/ml of subinhibitory nisin A (SI) concentration, while the third group was cultured in TSB with 32 μg/ml of semisubinhibitory nisin A (SSI) concentration. The lines were transferred daily to fresh TSB at a dilution of 1:100 and incubated at 37°C. All EE lines were maintained for 190 days, and at the end of experiment, all lines were stored at −80°C in 25% glycerol stock.

Whole genome resequencing and data analysis

DNA extractions were performed from all EE lines using the Monarch® HMW DNA Extraction Kit (NEB) following the manufacturer’s instructions. The DNA concentrations were measured using a Qubit 3.0 fluorometer (Life Technologies). Then, a total of 27 randomly chosen samples (7 EE lines from the control group, 10 EE lines from the SI group, and 10 EE lines from the SSI group) were prepared for WGS. The Illumina DNA Prep kit (Illumina) was used to construct WGS libraries. Library preparation and paired-end 150-nt read sequencing were done by the AgriGx Hub at Ankara University, Turkiye.

The adaptor sequences of each paired-end read were trimmed using Trimmomatic-0.39 with settings LEADING:3 TRAILING:3 SLIDINGWINDOW:4:20 MINLEN:40 (Bolger et al. 2014). After trimming, the genome sequences were mapped using BWA-0.7.17 mem to the reference genome [National Center for Biotechnology Information (NCBI) accession number: NZ_CP009361.1] (Li and Durbin 2009). SAM alignment files were created using samtools (Li et al. 2009) and converted to pileup files using the same tool. A combination of Picard-tools-1.141 and HaplotypeCaller in GATK-3.6 (Poplin et al. 2018) was used to remove duplicate reads and identify insertion/deletion (indel) events and base-pair substitutions (BPS). The functional effects of mutations were identified using SNPeff (Cingolani et al. 2012). Mutation calls were confirmed with breseq-0.37.1 (Deatherage and Barrick 2014). Then, mutations were visually controlled by IGV (Robinson et al. 2011). The formula used to estimate the mutation rate (μ; per nucleotide site per generation) was μ = m/(n T), where m represents the total number of mutations, n represents the number of sites analyzed in the line, and T is the number of generations the EE line passed (Long et al. 2016). The confidence intervals of mutation rate estimates were calculated using the Poisson cumulative distribution function approximated by the χ2 distribution. All statistical analyses and plots were performed using R Studio version 4.3.3.

Quantitative PCR

Five randomly selected EE lines from each group were incubated for 48 and 72 h for biofilm formation at 37°C, and then RNAs were isolated using the Monarch Total RNA Miniprep Kit (NEB) following the manufacturer’s instructions. cDNA was synthesized using AMV Reverse Transcriptase (NEB). RNA and cDNA amounts were measured with a Qubit 3.0 fluorometer. The qPCR reaction contained 1X WizPure qPCR Master (SYBR), 1 µM/1 µM primers, and 200 ng cDNA in a 20 µl volume. To determine the effect of long-term nisin A treatment on biofilm formation, we used primers specific to the biofilm-associated icaA gene (Eleaume and Jabbouri 2004). 16S rRNA primers (Nadkarni et al. 2002) were used to normalize the expression levels and the relative fold changes were calculated using the delta–delta Ct method (Rao et al. 2013). The primer sequences are shown in Table S1.

Results and discussion

Mutation rates

A total of 27 randomly chosen samples (7 EE lines from the control group, 10 EE lines from the SI group, and 10 EE lines from the SSI group) were sequenced. After calling mutations in those EE lines, we found that three EE lines were cross-contaminated and were removed from further analysis (two EE lines from the control group and one EE line from the SI group).

In the control group, 32 base-substitution changes were found in five EE lines. In the SSI group, 90 mutations were revealed in 10 EE lines, while in the SI group, 65 mutations were found in 9 EE lines (Table 1 and Tables S2–S4). The estimated per site per generation base-substitution rate in the control lines {5.07 × 10–10 [95% Poisson confidence intervals (CI), 3.47 × 10–10–7.16 × 10–10]} was consistent with the previously reported genome-wide mutation rate in wild-type S. aureus (4.38 × 10–10) (Long et al. 2018). Although an increase in the base-substitution rate was observed in the SSI group (7.07 × 10–10; 95% CI, 5.68 × 10–10–8.68 × 10–10) and the SI group (5.69 × 10–10; 95% CI, 4.39 × 10–10–7.26 × 10–10) compared to the control group however, the difference was not statistically significant (Kruskal–Wallis test, P > .05) (Fig. 1). Therefore, our results showed that long-term nisin A treatment at SI and SSI concentrations was not significantly affect the base-substitution rate in S. aureus under the experimental conditions applied in this study.

Table 1.

Mutation patterns.

Control lines SSI lines* SI lines**
Number of base-substitutions 32 90 65
Number of indels 10 18 5
Transitions/transversions 0.85 2.58 2.64
Number of synonymous mutations 4 12 7
Number of missense mutations 24 58 45
Number of nonsense mutations 1 5 9
Base-substitution mutation rate (×10−10) 5.08 7.07 5.69
Insertion–deletion rate (×10−10) 1.59 1.41 0.43
Mutation rate in the AT direction (×10−10) 8.26 14.43 10.2
Mutation rate in the GC direction (×10−10) 0.94 1.74 1.82
Open in a new tab
*

SSI: EE lines that were treated with semisubinhibitory nisin (SSI) concentration.

**

SI: EE lines that were treated with subinhibitory nisin (SSI) concentration.

Figure 1.

Figure 1.

Open in a new tab

Mutation spectrum. The spectrum of base-pair substitution mutation (bpsm) and indel rates observed for the S. aureus EE lines is represented by circles (control group), triangles (subinhibitory group, SI), and squares (semisubinhibitory group, SSI). These represent the mean mutation rates for each mutation type, normalized by the nucleotide base composition of the S. aureus genome. The bars indicate the 95% confidence intervals of the mean.

There were three insertions and seven deletions in the control group, seven insertions and 11 deletions in the SSI group, and two insertions and three deletions in the SI group (Table 1 and Tables S2–S5). The corresponding per site per generation indel rates were determined as follows: 1.58 × 10–10 (95% CI, 0.76 × 10–10–2.92 × 10–10) for the control group, 1.41 × 10–10 (95% CI, 0.84 × 10–10–2.23 × 10–10) for the SSI group, and 0.44 × 10–10 (95% CI, 0.14 × 10–10–1.02 × 10–10) for the SI group (Fig. 1), the difference between the control and the SI groups was statistically significant (Mann–Whitney (MW) U test, P = .02).

Mutation spectrum

In all EE lines, the mutation rate in the AT direction (GC → AT transitions and GC →TA transversions) was higher than the mutation rate in the GC direction (AT → GC transitions and AT → CG transversions). The observed mutation rates in the AT direction for the control, SSI, and SI groups were 8.26, 14.43, and 10.2 (×10−10), respectively, while the mutation rates in the GC direction were 0.94, 1.74, and 1.82 (×10−10) (Table 1 and Tables S2–S4). While a significant difference was observed between the control and SSI groups for GC → AT transitions (MW U test, P = .07), no significant difference was found for AT direction mutations (MW U test, P > .05). The observed transition/transversion ratios in the control, SSI, and SI groups were 0.85, 2.58, and 2.64, respectively (Table 1). While the transversion rate was relatively high in the control group (0.85), the transition rate was visibly high in the SSI and SI groups (2.58 and 2.64, respectively). The difference among groups was statistically significant (the control and SSI groups: MW U test, P = .04; the control and SI groups: MW U test, P = .04). Consistently, previous studies have shown that during the adaptive evolution of bacterial pathogens, especially in the development of antibiotic resistance, the transition bias may play a crucial role (Payne et al. 2019, Cano et al. 2022). And, according to our knowledge, this work is the first report that showed a transition bias related to nisin A treatment in S. aureus.

Functional effects of mutations

The ratio of coding and noncoding mutations in the control, SSI, and SI groups was 29/3, 75/15, and 61/4, respectively. The number of synonymous, missense, and nonsense mutations were 4/24/1, 12/58/5, and 7/45/9, respectively, in the control, SSI, and SI groups (Table 1 and Table S6). To investigate whether selection may have biased the mutation rate and spectrum, we assessed the functional effects of each mutation located within a coding region. The ratio of nonsynonymous to synonymous substitutions was not different from the neutral expectation in control lines (χ2 test, P = .36). In addition, we did not observe a significant difference in the nonsynonymous/synonymous BPS ratio between the nisin A-treated groups and the control group (χ2 test, P > .05 in all cases). This finding indicates that nonsynonymous BPSs were not selectively favored by nisin A treatment. However, we observed particular genes were enriched with nonsynonymous mutations in SI and SSI lines, and also some of those mutations were common (Table 2). One of those genes was graS, and that gene encodes a member of two-component regulatory system (TCS) involved in resistance against cationic antimicrobial peptides. It has also previously shown that TCS (BraRS) is associated with nisin A resistance in addition to bacitracin resistance (Hiron et al. 2011, Kawada-Matsuo et al. 2013).

Table 2.

Base-substitution mutations that may be associated with nisin resistance.

EE line (#) Chromosome Position Reference Mutation Gene Protein Functional effects
SSI-1 c2c883777a574734_1 1 648 639 G A graS_2 A105T MISSENSE
SSI-3 c2c883777a574734_1 1 648 640 C T graS_2 A105V MISSENSE
SSI-10 c2c883777a574734_1 1 648 678 G A graS_2 V118M MISSENSE
SI-8 c2c883777a574734_1 2 293 738 C T ytrA P12S MISSENSE
SI-9 c2c883777a574734_1 2 293 768 A T ytrA K22* NONSENSE
SI-3 c2c883777a574734_1 2 293 925 C T ytrA T74I MISSENSE
SSI-2 SSI-5 c2c883777a574734_1 1 424 390 C A cap8A_1 V151L MISSENSE
SSI-7 c2c883777a574734_1 1 424 390 C T cap8A_1 V151M MISSENSE
SSI-10 c2c883777a574734_1 1 424 668 T A cap8A_1 D58V MISSENSE
SI-1 SI-2 c2c883777a574734_1 1 424 669 C A cap8A_1 D58Y MISSENSE
SSI2 c2c883777a574734_1 1 466 978 A G sarS D11G MISSENSE
SI-8 c2c883777a574734_1 1 467 140 C A sarS S65* NONSENSE
SI-6 c2c883777a574734_1 1 467 197 G A sarS R84Q MISSENSE
SSI-1 c2c883777a574734_1 1 467 208 G T sarS D88Y MISSENSE
SI-9 c2c883777a574734_1 1 467 215 G T sarS R90L MISSENSE
SI-1 SI-2 c2c883777a574734_1 1 467 225 C G sarS Y93* NONSENSE
SI-5 c2c883777a574734_1 1 467 295 C T sarS Q117* NONSENSE
Open in a new tab

* Stop codon

(#)

SI: EE lines that were treated with subinhibitory nisin (SI) concentration. SSI: EE lines that were treated with semisubinhibitory nisin (SSI) concentration.

In addition, four different nonsynonymous mutations were detected in the cap8 gene in 6 out of 19 SI and SSI lines (Table 2), that gene encodes a capsular polysaccharide (CP) in most S. aureus strains. Since nisin A forms stable complexes with CP precursors, our results may suggest that the mutational enrichment on cap8 gene may be related to nisin A resistance.

icaA expression levels

The expression level of the biofilm-related icaA gene in five randomly selected EE lines from the control group was compared with that in five randomly selected EE lines from the SSI and SI groups. After 48-h incubation, the levels decreased 0.92-fold (SE = 0.36) in the SSI group and 0.64-fold (SE = 0.20) in the SI group compared to the control group (Fig. 2), however, the difference was not significant between the control and nisin A-treated groups (MW U test, P > .05 in all cases). We also tested 72-h incubation and observed that biofilm levels decreased 0.76-fold (SE = 0.09) in the SSI group and 0.73-fold (SE = 0.14) in the SI group compared to the control group (Fig. 2), but the difference was not statistically significant (MW U test, P > .05 in all cases). Although similar works are available in the current literature, our study differs from them in the way that if applies EE approach. In consistent with our results, Ghapanvari et al. (2022) examined the effects of nisin A on biofilm levels in S. aureus using crystal violet assay, and found that nisin A inhibited biofilm formation at 24 and 48 h and its effect was dose-dependent. Jesus et al. (2021) also showed that nisin-biogel slightly increased the expression of icaA gene at 1/4 and 1/8 MIC, while slightly decreased icaA expression at 1/2 MIC. However, a study by (Shivaee et al. 2021) determined the biofilm formation levels based on the expression levels of biofilm-related icaA, fn b, ebpS, and eno genes at different time points (2, 8, and 24 h), and showed that nisin A may have an inducing effect on biofilm formation and its effect may change in time.

Figure 2.

Figure 2.

Open in a new tab

icaA gene expression fold change. Quantitative PCR analysis of biofilm-associated icaA gene expression at 48 and 72 h. Data were normalized 16S rRNA and relative fold changes were calculated using the delta–delta Ct method. Values are fold changes +/– standard error. N = 5 for each EE line.

In conclusion, the growing resistance of pathogens to conventional antimicrobials is a major threat to human health, leading search for natural antimicrobials such as nisin A as promising alternatives. Studies have demonstrated that nisin A has antimicrobial activity against important pathogens such as Enterococcus faecalis, Listeria monocytogenes, and S. aureus, yet research on its long-term effect is insufficient. Thus, it is important to examine the long-term consequences of nisin A (or alternative antimicrobials) on resistance development before considering their clinical use. With this study, we aimed to test the long-term effects of nisin A on the target bacteria at very low concentrations and found that nisin A treatment did not significantly increase the overall mutation rates, but did result in an elevated rate of nonsynonymous mutations in particular genes, suggesting a potential resistance development. We also observed a transition bias that appeared to be influenced by nisin A treatment. Therefore, our work highlights the importance of investigating the long-term effects of nisin A, particularly its role in selecting resistant strains and its relationship with time and dose on biofilm formation, which may contribute to evaluating the effectiveness of nisin A as an antimicrobial agent. Further studies will improve our understanding of these dynamics and help us to develop strategies to minimize resistance while using nisin A in food preservation and medical applications.

Supplementary Material

fnaf059_Supplemental_File
fnaf059_supplemental_file.xlsx (45.9KB, xlsx)

Acknowledgment

We would like to thank the Turkish National Science e-Infrastructure (TRUBA) for providing computing and data storage resources for the bioinformatic analyses.

Contributor Information

Hüseyin Özgür Özdemirel, Department of Biology, Hacettepe University, Ankara 06800, Turkiye.

Sibel Kucukyildirim, Department of Biology, Hacettepe University, Ankara 06800, Turkiye.

Conflict of interest

The authors declare no conflicts of interest.

Funding

The author, H. Özgür Özdemirel, was supported by The Council of Higher Education 100/2000 PhD scholarship. This work was supported by a grant from Hacettepe University Research Fund (FHD-2021-19584).

Data availability

Whole genome sequencing data were deposited in the NCBI SRA under Bio Project ID of PRJNA1182695.

References

  1. Andrews  JM.  Determination of minimum inhibitory concentrations. J Antimicrob Chemother. 2001;48:5–16. 10.1093/jac/48.suppl_1.5. [DOI] [PubMed] [Google Scholar]
  2. Arques  JL, Rodriguez  E, Langa  S.  et al.  Antimicrobial activity of lactic acid bacteria in dairy products and gut: effect on pathogens. Biomed Res Int. 2015;2015:1. 10.1155/2015/584183. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Barbosa  AAT, de Melo  MR, da Silva  CMR.  et al.  Nisin resistance in Gram-positive bacteria and approaches to circumvent resistance for successful therapeutic use. Crit Rev Microbiol. 2021;47:376–85. 10.1080/1040841X.2021.1893264. [DOI] [PubMed] [Google Scholar]
  4. Blake  KL, Randall  CP, O’Neill  AJ.  In vitro studies indicate a high resistance potential for the lantibiotic nisin in Staphylococcus aureus and define a genetic basis for nisin resistance. Antimicrob Agents Chemother. 2011;55:2362–8. 10.1128/AAC.01077-10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bolger  AM, Lohse  M, Usadel  B.  Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20. 10.1093/bioinformatics/btu170. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Cano  AV, Rozhonova  H, Stoltzfus  A.  et al.  Mutation bias shapes the spectrum of adaptive substitutions. Proc Natl Acad Sci USA. 2022;119. 10.1073/pnas.2119720119. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Cingolani  P, Platts  A, Wang le  L.  et al.  A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: sNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly. 2012;6:80–92. 10.4161/fly.19695. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Darby  EM, Trampari  E, Siasat  P.  et al.  Molecular mechanisms of antibiotic resistance revisited. Nat Rev Microbiol. 2023;21:280–95. 10.1038/s41579-022-00820-y. [DOI] [PubMed] [Google Scholar]
  9. de Arauz  LJ, Jozala  AF, Mazzola  PG.  et al.  Nisin biotechnological production and application: a review. Trends Food Sci Technol. 2009;20:146–54. 10.1016/j.tifs.2009.01.056. [DOI] [Google Scholar]
  10. Deatherage  DE, Barrick  JE.  Identification of mutations in laboratory-evolved microbes from next-generation sequencing data using breseq. Methods Mol Biol. 2014;1151:165–88. 10.1007/978-1-4939-0554-6_12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Delvesbroughton  J.  Nisin and its application as a food preservative. Int J Dairy Tech. 1990;43:73–76. 10.1111/j.1471-0307.1990.tb02449.x. [DOI] [Google Scholar]
  12. De Martinis  ECP, Crandall  AD, Mazzotta  AS.  et al.  Influence of pH, salt, and temperature on nisin resistance in Listeria monocytogenes (dagger). J Food Prot. 1997;60:420–3. 10.4315/0362-028X-60.4.420. [DOI] [PubMed] [Google Scholar]
  13. Eleaume  H, Jabbouri  S.  Comparison of two standardisation methods in real-time quantitative RT-PCR to follow Staphylococcus aureus genes expression during in vitro growth. J Microbiol Methods. 2004;59:363–70. 10.1016/j.mimet.2004.07.015. [DOI] [PubMed] [Google Scholar]
  14. Falagas  ME, Grammatikos  AP, Michalopoulos  A.  Potential of old-generation antibiotics to address current need for new antibiotics. Expert Rev Anti Infect Ther. 2008;6:593–600. 10.1586/14787210.6.5.593. [DOI] [PubMed] [Google Scholar]
  15. Ghapanvari  P, Taheri  M, Jalilian  FA.  et al.  The effect of nisin on the biofilm production, antimicrobial susceptibility and biofilm formation of Staphylococcus aureus and Pseudomonas aeruginosa. Eur J Med Res. 2022;27:173. 10.1186/s40001-022-00804-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Gharsallaoui  A, Oulahal  N, Joly  C.  et al.  Nisin as a food preservative: part 1: physicochemical properties, antimicrobial activity, and main uses. Crit Rev Food Sci Nutr. 2016;56:1262–74. 10.1080/10408398.2013.763765. [DOI] [PubMed] [Google Scholar]
  17. Gradisteanu Pircalabioru  G, Popa  LI, Marutescu  L.  et al.  Bacteriocins in the era of antibiotic resistance: rising to the challenge. Pharmaceutics. 2021;13:196. 10.3390/pharmaceutics13020196. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Gravesen  A, Jydegaard Axelsen  AM, Mendes da Silva  J.  et al.  Frequency of bacteriocin resistance development and associated fitness costs in Listeria monocytogenes. Appl Environ Microbiol. 2002;68:756–64. 10.1128/AEM.68.2.756-764.2002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Hiron  A, Falord  M, Valle  J.  et al.  Bacitracin and nisin resistance in Staphylococcus aureus: a novel pathway involving the BraS/BraR two-component system (SA2417/SA2418) and both the BraD/BraE and VraD/VraE ABC transporters. Mol Microbiol. 2011;81:602–22. 10.1111/j.1365-2958.2011.07735.x. [DOI] [PubMed] [Google Scholar]
  20. Jesus  C, Soares  R, Cunha  E.  et al.  Influence of nisin-biogel at subinhibitory concentrations on virulence expression in Staphylococcus aureus isolates from diabetic foot infections. Antibiotics. 2021;10:1501. 10.3390/antibiotics10121501. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Kawada-Matsuo  M, Yoshida  Y, Zendo  T.  et al.  Three distinct two-component systems are involved in resistance to the class I bacteriocins, Nukacin ISK-1 and nisin A, in Staphylococcus aureus. PLoS One. 2013;8:e69455. 10.1371/journal.pone.0069455. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Kramer  NE, Hasper  HE, van den Bogaard  PTC.  et al.  Increased D-alanylation of lipoteichoic acid and a thickened septum are main determinants in the nisin resistance mechanism of Lactococcus lactis. Microbiology. 2008;154:1755–62. 10.1099/mic.0.2007/015412-0. [DOI] [PubMed] [Google Scholar]
  23. Li  H, Durbin  R.  Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60. 10.1093/bioinformatics/btp324. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Li  H, Handsaker  B, Wysoker  A.  et al.  The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9. 10.1093/bioinformatics/btp352. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Long  H, Sung  W, Kucukyildirim  S.  et al.  Evolutionary determinants of genome-wide nucleotide composition. Nat Ecol Evol. 2018;2:237–40. 10.1038/s41559-017-0425-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Long  HA, Miller  SF, Strauss  C.  et al.  Antibiotic treatment enhances the genome-wide mutation rate of target cells. Proc Natl Acad Sci USA. 2016;113:E2498–505. 10.1073/pnas.1601208113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Mantovani  HC, Russell  JB.  Nisin resistance of Streptococcus bovis. Appl Environ Microbiol. 2001;67:808–13. 10.1128/AEM.67.2.808-813.2001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Nadkarni  MA, Martin  FE, Jacques  NA.  et al.  Determination of bacterial load by real-time PCR using a broad-range (universal) probe and primers set. Microbiology. 2002;148:257–66. 10.1099/00221287-148-1-257. [DOI] [PubMed] [Google Scholar]
  29. Oman  TJ, van der Donk  WA.  Insights into the mode of action of the two-peptide lantibiotic haloduracin. ACS Chem Biol. 2009;4:865–74. 10.1021/cb900194x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Payne  JL, Menardo  F, Trauner  A.  et al.  Transition bias influences the evolution of antibiotic resistance in Mycobacterium tuberculosis. PLoS Biol. 2019;17:e3000265. 10.1371/journal.pbio.3000265. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Pelczar  MJ, Reid  RD.  1958. Laboratory Exercises in Microbiology. New York, NY: McGraw-Hill. [Google Scholar]
  32. Piper  C, Draper  LA, Cotter  PD.  et al.  A comparison of the activities of lacticin 3147 and nisin against drug-resistant Staphylococcus aureus and Enterococcus species. J Antimicrob Chemother. 2009;64:546–51. 10.1093/jac/dkp221. [DOI] [PubMed] [Google Scholar]
  33. Poplin  R, Ruano-Rubio  V, DePristo  MA.  et al.  Scaling accurate genetic variant discovery to tens of thousands of samples. bioRxiv. 2018;201178. 10.1101/201178 [DOI] [Google Scholar]
  34. Randall  CP, Gupta  A, Utley-Drew  B.  et al.  Acquired nisin resistance in Staphylococcus aureus involves constitutive activation of an intrinsic peptide antibiotic detoxification module. mSphere. 2018;3. 10.1128/mSphereDirect.00633-18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Rao  X, Huang  X, Zhou  Z.  et al.  An improvement of the 2^(-delta delta CT) method for quantitative real-time polymerase chain reaction data analysis. Biostat Bioinforma Biomath. 2013;3:71–85. https://www.ncbi.nlm.nih.gov/pubmed/25558171. (Accessed date:25 Nov 2024) [PMC free article] [PubMed] [Google Scholar]
  36. Robinson  JT, Thorvaldsdottir  H, Winckler  W.  et al.  Integrative genomics viewer. Nat Biotechnol. 2011;29:24–26. 10.1038/nbt.1754. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Sharafi  T, Ghaemi  EA, Rafiee  M.  et al.  Combination antimicrobial therapy: in vitro synergistic effect of anti-staphylococcal drug oxacillin with antimicrobial peptide nisin against Staphylococcus epidermidis clinical isolates and Staphylococcus aureus biofilms. Ann Clin Microbiol Antimicrob. 2024;23:7. 10.1186/s12941-024-00667-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Shivaee  A, Rajabi  S, Farahani  HE.  et al.  Effect of sub-lethal doses of nisin on Staphylococcus aureus toxin production and biofilm formation. Toxicon. 2021;197:1–5. 10.1016/j.toxicon.2021.03.018. [DOI] [PubMed] [Google Scholar]
  39. Sun  Z, Zhong  J, Liang  X.  et al.  Novel mechanism for nisin resistance via proteolytic degradation of nisin by the nisin resistance protein NSR. Antimicrob Agents Chemother. 2009;53:1964–73. 10.1128/AAC.01382-08. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Treangen  TJ, Maybank  RA, Enke  S.  et al.  Complete genome sequence of the quality control strain Staphylococcus aureus subsp. aureus ATCC 25923. Genome Announc. 2014;2. 10.1128/genomeA.01110-14. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

fnaf059_Supplemental_File
fnaf059_supplemental_file.xlsx (45.9KB, xlsx)

Data Availability Statement

Whole genome sequencing data were deposited in the NCBI SRA under Bio Project ID of PRJNA1182695.

Articles from FEMS Microbiology Letters are provided here courtesy of Oxford University Press

RESOURCES