Next Article in Journal
Discrepancies in Antimicrobial Susceptibility between the JP2 and the Non-JP2 Genotype of Aggregatibacter actinomycetemcomitans
Previous Article in Journal
Changing Epidemiology of Respiratory Tract Infection during COVID-19 Pandemic
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Identification of Methicillin-Resistant Staphylococcus aureus (MRSA) Genetic Factors Involved in Human Endothelial Cells Damage, an Important Phenotype Correlated with Persistent Endovascular Infection

1
The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
2
College of Veterinary Medicine, Yangzhou University, Yangzhou 225009, China
3
Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD 20993, USA
4
David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
*
Author to whom correspondence should be addressed.
Antibiotics 2022, 11(3), 316; https://doi.org/10.3390/antibiotics11030316
Submission received: 30 January 2022 / Revised: 18 February 2022 / Accepted: 24 February 2022 / Published: 26 February 2022

Abstract

:
Methicillin-resistant Staphylococcus aureus (MRSA) is a leading cause of life-threatening endovascular infections. Endothelial cell (EC) damage is a key factor in the pathogenesis of these syndromes. However, genetic factors related to the EC damage have not been well studied. This study aims to identify genetic determinants that impact human EC damage by screening the genome-wide Nebraska Transposon Mutant Library (NTML). A well-established MTT assay was used to test the in vitro damage of human EC cell line (HMEC-1) caused by each mutant strain in the NTML. We first confirmed some global regulators and genes positively impact the EC damage, which is consistent with published results. These data support the utility of the high-throughput approach. Importantly, we demonstrated 317 mutants significantly decreased the EC damage, while only 6 mutants enhanced the EC damage vs. parental JE2 strain. The majority of these genes have not been previously defined to affect human EC damage. Interestingly, many of these newly identified genes are involved in metabolism, genetic and environmental information processing, and cellular processes. These results advance our knowledge of staphylococcal genetic factors related to human EC damage which may provide novel targets for the development of effective agents against MRSA endovascular infection.

1. Introduction

Staphylococcus aureus is the most common cause of endovascular infection, including infective endocarditis (IE). Despite the use of gold-standard antibiotics, morbidity and mortality associated with these syndromes remain unacceptably high [1]. In addition, the emergence of methicillin-resistant S. aureus (MRSA) further complicates the management of patients with these infections and emphasizes this public health threat [1]. Therefore, there is an urgent need to understand specific genetic factors involved in the pathogenesis and antibiotic treatment outcome of MRSA endovascular infection.
It is generally recognized that the pathogenesis of S. aureus is complex and probably involves the coordinate expression of multiple gene products, including a variety of surface adhesive proteins and exoproteins [2]. Once S. aureus enters into the bloodstream, it must avoid host innate defense killing to survive. When the organism has persisted in the bloodstream, it must then colonize and invade the endothelial cells (ECs) lining of the blood vessels, and, subsequently, damage the ECs to infect deeper tissues to cause organ dissemination [3]. It has been well demonstrated that EC damage plays a crucial role in the pathogenesis of many human diseases, including endovascular infections [4]. In addition, we have recently demonstrated a positive correlation between in vitro human EC damage and virulence, as well as vancomycin treatment persistent outcome in an experimental endocarditis model caused by clinical MRSA isolates [5]. However, little is known about the genetic factors involved in the EC damage in S. aureus.
The Nebraska Transposon Mutant Library (NTML) consists of 1920 sequence-defined transposon insertion mutants of non-essential genes in a community-associated (CA) MRSA USA300 strain, JE2 [6]. This library has been used for screening several biological phenotypes, including hemolysis, proteolysis, carotenoid pigment formation, antibiotic susceptibility, and biofilm formation [6,7,8]. These investigations demonstrate that the NTML may serve as a valuable genetic tool to study host-pathogen interaction.
Numerous investigations have used human umbilical vein EC (HUVECs) to study microbial–EC interactions. However, the use of HUVECs requires a constant supply of umbilical cords, and there are significant donor-to-donor variations in these ECs. To overcome these difficulties, immortalized ECs, including human microvascular EC (HMEC-1), have been developed. These cell lines have better availability and less variability [9]. In addition, we previously compared S. aureus EC damage with HMEC-1 cell line and HUVECs, and found HMEC-1 cells were more susceptible to damage caused by S. aureus vs. HUVECs [10]. In addition, the HMEC-1 cell line has been used to study the EC interactions with multiple microorganisms, including S. aureus [10,11,12]. Thus, in the current investigation, the HMEC-1 cell line was employed to test the impact of all the mutant strains in the NTML on its damage.
In the current study, we aimed to identify staphylococcal genes associated with the EC damage by performing an unbiased genome-wide screening of all mutations in the NTML. This study will remarkably advance our understanding of staphylococcal genetic factors related to human EC damage which may provide novel targets for the development of effective compounds against MRSA endovascular infections.

2. Results

2.1. The MTT Assay Is Applicable to the High Throughput Screening of Genes Involved in HMEC-1 Damage

We confirmed some S. aureus genetic factors which have previously been reported to affect EC damage. For instance, global regulator (e.g., agr, saeSR, and arlSR) and structural genes related to gamma-hemolysin (e.g., hlg) and serine-like protease (e.g., spl) positively impact EC damage. In addition, the control arlR mutant strain caused significantly less EC damage (<30%) vs. JE2 parental strain, which is in accordance with the previously reported results. These results proved the feasibility and reliability of this high throughput screening assay.

2.2. Identified Staphylococcal Genes Impacting HMEC-1 Damages

The mean HMEC-1 damage rate caused by the JE2 parental strain is 46.19 ± 2.97%. To focus on the genes which highly affect the EC damage, we set up the EC damage rates of ≤30% or ≥60% with p values less than 0.05 as cutoffs for data analysis. Screening of the whole NTML displayed that 317 individual gene mutations led to significantly decreased HMEC-1 damage rates (≤30%; p <0.05; Figure 1, Table 1), suggesting these genes positively impact the EC damage. Only six mutant strains demonstrated significantly increased HMEC-1 damage (≥60%, p < 0.05; Figure 1, Table 2), including four genes with known functions (e.g., mepA, azoR, and moaD, and SAUSA300_1197) and two hypothetical genes with unknown function. EC damage rates of the rest mutants from the NTML were presented in Supplementary Table S1. JE2 parental strain and randomly selected mutants showed similar EC damage rates between 24-well and 384-well plates assay (Table 3). Some of the mutants that caused significant changes to EC damage were successfully classified into KEGG categories, including metabolism, genetic information processing, environmental information processing, and cellular processes (Table 4). For the KEGG categories, ~65% of genes functioned in metabolism pathways, ~24% involved in environmental information processing, ~11% acted in genetic information processes, and ~9% associated with cellular processes (Figure 2). In addition, some of these genes had multiple functions in the different KEGG pathways.

3. Discussion

It is well recognized that EC damage plays a crucial role in the pathogenesis of S. aureus endovascular infection [5,13,14]. For instance, we have demonstrated a positive correlation between in vitro EC damage and virulence, as well as antibiotic treatment persistent outcome in an experimental endocarditis model caused by clinical MRSA isolates [5]. In addition, we also noticed that clinical MRSA strains collected from patients with persistent bacteremia cause significantly greater EC damage compared to clinical resolving MRSA isolates [15]. Moreover, the inactivation of agr, saeR, and arlSR has been proved significantly reduce EC damage as compared to their respective parental strains [13,16]. However, these studies only focused on a few virulence factors in S. aureus. Thus, the current study was designed to broadly define genetic determiners in S. aureus which involve in human EC damage using a high-throughput approach to screen a transposon mutant library containing 1920 non-essential gene mutants in MRSA USA300 JE2 background.
In the current study, we first verified the reliability of our high-throughput screening system. Consistent with previous reports [13,16], we demonstrated that the inactivation of global regulators such as agr, arlRS, or saeRS significantly decreases EC damage. In addition, consistent results were obtained between 384-well and 24-well plates assays, which validated the improvement of testing significantly more samples each time.
Several interesting and important observations emerged from the present investigations. Overall, over 320 mutants had a significant impact on the EC damage. The majority of these mutants significantly reduced EC damage vs. JE2 parental strain. Using KEGG pathway analysis, mutant strains were classified into four categories, including metabolism, genetic information processing, environmental information processing, and cellular processes (Figure 3). Only six mutants were found with significantly increased EC damage vs. JE2 parental strain. Importantly, many of these genes are not previously defined to impact human EC damage in S. aureus.
Many staphylococcal genetic factors related to metabolism were shown to intimately impact the EC damage. For instance, several gene mutants related to carbohydrate metabolism including tricarboxylic acid (TCA) cycle (e.g., pdhA, and lpdA) showed significantly decreased EC damage. Inactivation of pdhA or lpdA was reported to be associated with slower growth [17,18]. Since the TCA cycle processes produce the main energy resources for cellular activities [19], inactivation of corresponding TCA genes may result in lack of energy which may subsequently cause slower growth and decrease EC damage. In addition, mutants with genes related to energy metabolism (e.g., cyoE, and atpH) also displayed lower EC damage rates vs. parental strain JE2. It has been reported that cyoE encoding a protoheme IX farnesyltransferase is essential for processing heme into the electron transport chain and plays a critical role in cytolytic toxins production in S. aureus. Deletion of cyoE in S. aureus significantly decreases the expression of cytolytic toxins [20]. Turner et al. reported that mutation of aptH (associated with ATP synthase) had attenuated virulence and less invasiveness in vivo [21]. These results suggest that genetic factors associated with energy metabolism have activities on EC damage that may link to virulence. Lipid metabolism genes (e.g., gehB, and ugtP) were reported to promote biofilm formation and host cell invasion [22]. We found that the mutation of these genes had significantly decreased EC damage vs. JE2 parental strain. These results may indicate a connection between lipid metabolism and EC damage. Genetic factors associated with nucleotides metabolism (e.g., purN) were also found to positively impact the EC damage. purN encodes the enzyme in de novo purine biosynthesis pathway which generates ATP and GTP that can be processed to stringent response alarmone, guanosine 3′-diphosphate-5-di(tri)phosphate ((p)ppGpp) [15]. Increased GTP and subsequent (p)ppGpp levels lead to enhanced persistent bacteremia (PB) phenotypes including a higher EC damage rate [15]. It is worthwhile to mention, genes related to staphylococcal cell-wall peptidoglycan biosynthesis (e.g., murA) and cell division (e.g., scdA) showed significant positive effects on EC damage. Cell-wall synthesis has long been considered an important target for novel anti-S. aureus agents [23,24], and our findings have implications for the approach.
In the genetic information processing pathways, genes involved in homologous recombination (e.g., recD, and recG), ribosome (e.g., rrlA, and rpsA), and protein export (e.g., lspA, and tatA) were identified to affect EC damage. For example, the signal peptidase encoded by lspA is required for biogenesis of bacterial lipoproteins, and failure to produce mature lipoproteins has previously been shown to impair pathogenicity and immune-modulating [25]. The results suggested that some genes related to genetic information processing also play a role in human EC damage.
The inactivation of genes involved in environmental information processing pathways such as ABC transporter (e.g., fhuB, and mntC) and two-component system (e.g., saeSR, and arlSR) also decreased EC damage. These findings were in accordance with previous studies showing the presence of these gene products was associated with higher in vivo virulence potential vs. their respective WT strains [13,26,27,28].
Genes involved in cellular process, specifically quorum sensing (e.g., agr, and luxS), were identified to contribute to the EC damage. It is well known that quorum sensing via agr plays a central role in the pathogenesis of S. aureus. Under high cell density, agr is responsible for the increased expression of many toxins which may impact the EC damage [16], while the function of luxS in S. aureus has not been well investigated.
Genes unidentified in the KEGG pathways also showed a positive impact on the HMEC-1 damage in the current study. Some of these genes have been previously demonstrated to correlate with biofilm formation (e.g., xerC), oxidative killing (e.g., nfu, and yjbI), hemolysis (e.g., hlb), and heat shock (e.g., hslU) [29,30,31,32]. In addition, few phage genes (SAUSA300_1433, SAUSA300_1934, SAUSA300_1936, SAUSA300_1968) were also shown impacts on the HMEC-1 damage.
Mutants of six genes had elevated EC damage indicating their negative impact on the EC damage. Among these genes, mepA encodes a multidrug efflux pump protein [33], azoR encodes quinone reductase [34], moaD encodes one of the subunits of molydopterin synthase involved in sulfur relay system pathway [35], gene SAUSA300_1197 encodes glutathione peroxidase. Further investigations related to the relationship between these genes and EC damage are needed.

4. Materials and Methods

4.1. Bacteria and Growth Conditions

The strains used in the current study include MRSA JE2 (a plasmid-cured derivative of LAC USA300) and 1920 transposon non-essential gene mutants within the NTML [6]. The NTML was kindly provided by the Network on Antimicrobial Resistance in Staphylococcus aureus (NARSA). The library was supplied in five 384-well microtiter plates. The plates containing MRSA mutant strains were duplicated and cultured in tryptic soy broth (TSB; Becton, Dickinson and Company, Franklin Lakes, NJ, USA). On the experiment day, bacterial strains were freshly inoculated in TSB media and cultured at 37 °C for 3 h to obtain logarithmic phase cells [36], and adjusted to an OD600nm of 0.500 (~108 CFU/mL) and diluted accordingly. S. aureus inocula were confirmed by quantitative culture.

4.2. Endothelial Cell (HMEC-1) Culture

The HMEC-1 cell line was obtained from Kathryn Kellar, of the Centers for Disease Control (CDC), in the U.S., and maintained as recommended [10]. Primary cells were established from human dermal microvascular endothelial cells and immortalized by transfection with a Pbr322-based plasmid containing the coding region for the simian virus 40 large T-antigen [10].

4.3. HMEC-1 Damage Assay

The effect of MRSA strains on EC damage was determined using a well-established 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay as described previously [13,37,38]. Briefly, logarithmic phase MRSA cells (1 × 105 CFU/well) were added to HMEC-1 cells in 384-well plates with a density of ~5 × 103 EC/well in MCDB131 medium to reach a multiplicity of infection (MOI) of 20, which JE2 parental caused ~50% HMEC-1 damage as established in our pilot experiments. After 3 hr invasion, extracellular MRSA cells were killed by adding lysostaphin (10 μg/mL) in full medium MCDB131 (Sigma-Aldrich, St. Louis, MO, USA) supplemented with 20% bovine calf serum, 2 mM glutamine, 100 IU/mL penicillin, and 100 mg/mL streptomycin [13,37]. At 18 hr incubation at 37 °C, MTT (5 mg/mL; Sigma-Aldrich, St. Louis, MO, USA) in Hank’s Balanced Salt Solution (HBSS, Thermo Fisher Scientific, Waltham, MA, USA) was added and incubated for 2 h, then the medium was replaced with 0.04 M HCl in absolute isopropanol (Thermo Fisher Scientific, Waltham, MA, USA) to stop the reaction and lyse the cells. Absorbance was measured at 560 nm (A560nm) using a microplate reader Synergy 2 (BioTek, Winooski, VT, USA). Uninfected HMEC-1 served as a negative control, and wells containing medium alone were used for background correction in each round. In addition, EC infected with ΔarlR in JE2 was selected as an additional control group as it was reported that arlSR inactivation leads to >70% reduction in human EC damage vs. JE2 parental strain [13]. EC damage was calculated using the following formula: 1 − (A560nm of test well/A560nm of 0% − damage control well) as previously described [37]. Each experiment was performed three times in triplicate.

4.4. Verification of the HMEC-1 Damage Screening Results

After the screening of the whole library, JE2 WT strain and 20 randomly selected mutant strains with significantly decreased EC damage were confirmed again with the same MTT method using 24-well plates. In addition, the mutant strains with significantly increased EC damage were also tested in 24-well plates to confirm the damage results with the same method.

4.5. Statistical Analysis

Statistical analysis was performed using GraphPad Prism 9 (GraphPad Software, Inc., San Diego, CA, USA). p-values were determined using the paired rank-sum test between mutant and JE2 wild-type strains. p < 0.05 was considered statistically significant.

4.6. KEGG Enrichment Analysis

The genes that caused a significant change in EC damage were classified using the Kyoto Encyclopedia of Genes and Genomes (KEGG) mapper tool with the mode of Staphylococcus aureus subsp. aureus USA300-FPR3757 (saa) [39]. The genes from different KEGG pathway categories were further analyzed.

5. Conclusions

To our knowledge, the present study provides the first whole-genome screen to identify genetic factors that impact human EC damage in S. aureus. Importantly, we defined a set of staphylococcal genes, which are not previously known to be associated with EC damage, significantly contribute to this phenotype. Although these findings need to be further verified using mutation strains generated by gene deletion and complementation techniques, our results provide new insights into the relationship between genetic factors and EC damage in S. aureus. These genetic factors may be ideal targets for the development of effective therapeutic strategies to treat invasive MRSA endovascular infection.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/antibiotics11030316/s1, Table S1: HMEC-1 damage caused by all the mutant strains, except mutants presented in Table 1 and Table 2 in the NTML.

Author Contributions

Y.Q.X. designed the study. X.X. and L.L. performed the experiments. Y.L., X.X. and Y.Q.X. performed data analysis and wrote the paper. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Institutes of Health/National Institute of Allergy and Infectious Diseases grant R01AI139244 to Y.Q.X.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Fowler, V.G., Jr.; Miro, J.M.; Hoen, B.; Cabell, C.H.; Abrutyn, E.; Rubinstein, E.; Corey, G.R.; Spelman, D.; Bradley, S.F.; Barsic, B.; et al. Staphylococcus aureus endocarditis: A consequence of medical progress. JAMA 2005, 293, 3012–3021. [Google Scholar] [CrossRef]
  2. Cheung, A.L.; Bayer, A.S.; Zhang, G.; Gresham, H.; Xiong, Y.Q. Regulation of virulence determinants in vitro and in vivo in Staphylococcus aureus. FEMS Immunol. Med. Microbiol. 2004, 40, 1–9. [Google Scholar] [CrossRef]
  3. Ondusko, D.S.; Nolt, D. Staphylococcus aureus. Pediatr. Rev. 2018, 39, 287–298. [Google Scholar] [CrossRef]
  4. Chorianopoulos, E.; Bea, F.; Katus, H.A.; Frey, N. The role of endothelial cell biology in endocarditis. Cell Tissue Res. 2009, 335, 153–163. [Google Scholar] [CrossRef] [PubMed]
  5. Seidl, K.; Bayer, A.S.; McKinnell, J.A.; Ellison, S.; Filler, S.G.; Xiong, Y.Q. In vitro endothelial cell damage is positively correlated with enhanced virulence and poor vancomycin responsiveness in experimental endocarditis due to methicillin-resistant Staphylococcus aureus. Cell Microbiol. 2011, 13, 1530–1541. [Google Scholar] [CrossRef] [PubMed]
  6. Fey, P.D.; Endres, J.L.; Yajjala, V.K.; Widhelm, T.J.; Boissy, R.J.; Bose, J.L.; Bayles, K.W. A genetic resource for rapid and comprehensive phenotype screening of nonessential Staphylococcus aureus genes. mBio 2013, 4, e00537-12. [Google Scholar] [CrossRef] [PubMed]
  7. Paudel, A.; Hamamoto, H.; Panthee, S.; Matsumoto, Y.; Sekimizu, K. Large-Scale Screening and Identification of Novel Pathogenic Staphylococcus aureus Genes Using a Silkworm Infection Model. J. Infect. Dis. 2020, 221, 1795–1804. [Google Scholar] [CrossRef] [PubMed]
  8. Bose, J.L.; Fey, P.D.; Bayles, K.W. Genetic tools to enhance the study of gene function and regulation in Staphylococcus aureus. Appl. Environ. Microbiol. 2013, 79, 2218–2224. [Google Scholar] [CrossRef]
  9. Ades, E.W.; Candal, F.J.; Swerlick, R.A.; George, V.G.; Summers, S.; Bosse, D.C.; Lawley, T.J. HMEC-1: Establishment of an immortalized human microvascular endothelial cell line. J. Investig. Dermatol. 1992, 99, 683–690. [Google Scholar] [CrossRef]
  10. Seidl, K.; Solis, N.V.; Bayer, A.S.; Hady, W.A.; Ellison, S.; Klashman, M.C.; Xiong, Y.Q.; Filler, S.G. Divergent responses of different endothelial cell types to infection with Candida albicans and Staphylococcus aureus. PLoS ONE 2012, 7, e39633. [Google Scholar] [CrossRef]
  11. Luo, X.; Zhang, X.; Zhao, T.; Zeng, T.; Liu, W.; Deng, M.; Zhao, F. A preliminary study on the proinflammatory mechanisms of Treponema pallidum outer membrane protein Tp92 in human macrophages and HMEC-1 cells. Microb. Pathog. 2017, 110, 176–183. [Google Scholar] [CrossRef]
  12. Ferrero, M.C.; Bregante, J.; Delpino, M.V.; Barrionuevo, P.; Fossati, C.A.; Giambartolomei, G.H.; Baldi, P.C. Proinflammatory response of human endothelial cells to Brucella infection. Microbes Infect. 2011, 13, 852–861. [Google Scholar] [CrossRef]
  13. Seidl, K.; Leemann, M.; Zinkernagel, A.S. The ArlRS two-component system is a regulator of Staphylococcus aureus-induced endothelial cell damage. Eur. J. Clin. Microbiol. Infect. Dis. 2018, 37, 289–292. [Google Scholar] [CrossRef] [PubMed]
  14. Seidl, K.; Bayer, A.S.; Fowler, V.G., Jr.; McKinnell, J.A.; Abdel Hady, W.; Sakoulas, G.; Yeaman, M.R.; Xiong, Y.Q. Combinatorial phenotypic signatures distinguish persistent from resolving methicillin-resistant Staphylococcus aureus bacteremia isolates. Antimicrob. Agents Chemother. 2011, 55, 575–582. [Google Scholar] [CrossRef] [PubMed]
  15. Li, L.; Bayer, A.S.; Cheung, A.; Lu, L.; Abdelhady, W.; Donegan, N.P.; Hong, J.I.; Yeaman, M.R.; Xiong, Y.Q. The Stringent Response Contributes to Persistent Methicillin-Resistant Staphylococcus aureus Endovascular Infection Through the Purine Biosynthetic Pathway. J. Infect. Dis. 2020, 222, 1188–1198. [Google Scholar] [CrossRef] [PubMed]
  16. Haslinger-Loffler, B.; Kahl, B.C.; Grundmeier, M.; Strangfeld, K.; Wagner, B.; Fischer, U.; Cheung, A.L.; Peters, G.; Schulze-Osthoff, K.; Sinha, B. Multiple virulence factors are required for Staphylococcus aureus-induced apoptosis in endothelial cells. Cell Microbiol. 2005, 7, 1087–1097. [Google Scholar] [CrossRef]
  17. Braungardt, H.; Singh, V.K. Impact of Deficiencies in Branched-Chain Fatty Acids and Staphyloxanthin in Staphylococcus aureus. Biomed. Res. Int. 2019, 2019, 2603435. [Google Scholar] [CrossRef]
  18. Singh, V.K.; Sirobhushanam, S.; Ring, R.P.; Singh, S.; Gatto, C.; Wilkinson, B.J. Roles of pyruvate dehydrogenase and branched-chain alpha-keto acid dehydrogenase in branched-chain membrane fatty acid levels and associated functions in Staphylococcus aureus. J. Med. Microbiol. 2018, 67, 570–578. [Google Scholar] [CrossRef]
  19. Campbell, C.; Fingleton, C.; Zeden, M.S.; Bueno, E.; Gallagher, L.A.; Shinde, D.; Ahn, J.; Olson, H.M.; Fillmore, T.L.; Adkins, J.N.; et al. Accumulation of Succinyl Coenzyme A Perturbs the Methicillin-Resistant Staphylococcus aureus (MRSA) Succinylome and Is Associated with Increased Susceptibility to Beta-Lactam Antibiotics. mBio 2021, 12, e0053021. [Google Scholar] [CrossRef]
  20. Stevens, E.; Laabei, M.; Gardner, S.; Somerville, G.A.; Massey, R.C. Cytolytic toxin production by Staphylococcus aureus is dependent upon the activity of the protoheme IX farnesyltransferase. Sci. Rep. 2017, 7, 13744. [Google Scholar] [CrossRef]
  21. Turner, A.K.; Barber, L.Z.; Wigley, P.; Muhammad, S.; Jones, M.A.; Lovell, M.A.; Hulme, S.; Barrow, P.A. Contribution of proton-translocating proteins to the virulence of Salmonella enterica serovars Typhimurium, Gallinarum, and Dublin in chickens and mice. Infect. Immun. 2003, 71, 3392–3401. [Google Scholar] [CrossRef] [PubMed]
  22. Nguyen, M.T.; Luqman, A.; Bitschar, K.; Hertlein, T.; Dick, J.; Ohlsen, K.; Broker, B.; Schittek, B.; Gotz, F. Staphylococcal (phospho)lipases promote biofilm formation and host cell invasion. Int. J. Med. Microbiol. 2018, 308, 653–663. [Google Scholar] [CrossRef] [PubMed]
  23. Schneider, T.; Sahl, H.G. An oldie but a goodie—Cell wall biosynthesis as antibiotic target pathway. Int. J. Med. Microbiol. 2010, 300, 161–169. [Google Scholar] [CrossRef] [PubMed]
  24. Kimura, K.; Bugg, T.D. Recent advances in antimicrobial nucleoside antibiotics targeting cell wall biosynthesis. Nat. Prod. Rep. 2003, 20, 252–273. [Google Scholar] [CrossRef] [PubMed]
  25. Yang, D.; Ho, Y.X.; Cowell, L.M.; Jilani, I.; Foster, S.J.; Prince, L.R. A Genome-Wide Screen Identifies Factors Involved in S. aureus-Induced Human Neutrophil Cell Death and Pathogenesis. Front. Immunol. 2019, 10, 45. [Google Scholar] [CrossRef]
  26. Cabrera, G.; Xiong, A.; Uebel, M.; Singh, V.K.; Jayaswal, R.K. Molecular characterization of the iron-hydroxamate uptake system in Staphylococcus aureus. Appl. Environ. Microbiol. 2001, 67, 1001–1003. [Google Scholar] [CrossRef]
  27. Handke, L.D.; Gribenko, A.V.; Timofeyeva, Y.; Scully, I.L.; Anderson, A.S. MntC-Dependent Manganese Transport Is Essential for Staphylococcus aureus Oxidative Stress Resistance and Virulence. mSphere 2018, 3, e00336-18. [Google Scholar] [CrossRef]
  28. Munzenmayer, L.; Geiger, T.; Daiber, E.; Schulte, B.; Autenrieth, S.E.; Fraunholz, M.; Wolz, C. Influence of Sae-regulated and Agr-regulated factors on the escape of Staphylococcus aureus from human macrophages. Cell Microbiol. 2016, 18, 1172–1183. [Google Scholar] [CrossRef]
  29. Atwood, D.N.; Beenken, K.E.; Loughran, A.J.; Meeker, D.G.; Lantz, T.L.; Graham, J.W.; Spencer, H.J.; Smeltzer, M.S. XerC Contributes to Diverse Forms of Staphylococcus aureus Infection via agr-Dependent and agr-Independent Pathways. Infect. Immun. 2016, 84, 1214–1225. [Google Scholar] [CrossRef]
  30. Mashruwala, A.A.; Pang, Y.Y.; Rosario-Cruz, Z.; Chahal, H.K.; Benson, M.A.; Mike, L.A.; Skaar, E.P.; Torres, V.J.; Nauseef, W.M.; Boyd, J.M. Nfu facilitates the maturation of iron-sulfur proteins and participates in virulence in Staphylococcus aureus. Mol. Microbiol. 2015, 95, 383–409. [Google Scholar] [CrossRef]
  31. Austin, C.M.; Garabaglu, S.; Krute, C.N.; Ridder, M.J.; Seawell, N.A.; Markiewicz, M.A.; Boyd, J.M.; Bose, J.L. Contribution of YjbIH to Virulence Factor Expression and Host Colonization in Staphylococcus aureus. Infect. Immun. 2019, 87. [Google Scholar] [CrossRef] [PubMed]
  32. Katayama, Y.; Baba, T.; Sekine, M.; Fukuda, M.; Hiramatsu, K. Beta-hemolysin promotes skin colonization by Staphylococcus aureus. J. Bacteriol. 2013, 195, 1194–1203. [Google Scholar] [CrossRef] [PubMed]
  33. Kaatz, G.W.; McAleese, F.; Seo, S.M. Multidrug resistance in Staphylococcus aureus due to overexpression of a novel multidrug and toxin extrusion (MATE) transport protein. Antimicrob. Agents Chemother. 2005, 49, 1857–1864. [Google Scholar] [CrossRef]
  34. Liu, G.; Zhou, J.; Fu, Q.S.; Wang, J. The Escherichia coli azoreductase AzoR Is involved in resistance to thiol-specific stress caused by electrophilic quinones. J. Bacteriol. 2009, 191, 6394–6400. [Google Scholar] [CrossRef]
  35. Daniels, J.N.; Wuebbens, M.M.; Rajagopalan, K.V.; Schindelin, H. Crystal structure of a molybdopterin synthase-precursor Z complex: Insight into its sulfur transfer mechanism and its role in molybdenum cofactor deficiency. Biochemistry 2008, 47, 615–626. [Google Scholar] [CrossRef] [PubMed]
  36. Vriesema, A.J.; Beekhuizen, H.; Hamdi, M.; Soufan, A.; Lammers, A.; Willekens, B.; Bakker, O.; Welten, A.G.; Veltrop, M.H.; van De Gevel, J.S.; et al. Altered gene expression in Staphylococcus aureus upon interaction with human endothelial cells. Infect. Immun. 2000, 68, 1765–1772. [Google Scholar] [CrossRef]
  37. Seidl, K.; Zinkernagel, A.S. The MTT assay is a rapid and reliable quantitative method to assess Staphylococcus aureus induced endothelial cell damage. J. Microbiol. Methods 2013, 92, 307–309. [Google Scholar] [CrossRef]
  38. Quiblier, C.; Seidl, K.; Roschitzki, B.; Zinkernagel, A.S.; Berger-Bachi, B.; Senn, M.M. Secretome analysis defines the major role of SecDF in Staphylococcus aureus virulence. PLoS ONE 2013, 8, e63513. [Google Scholar] [CrossRef]
  39. Kanehisa, M.; Sato, Y.; Kawashima, M. KEGG mapping tools for uncovering hidden features in biological data. Protein Sci. 2022, 31, 47–53. [Google Scholar] [CrossRef]
Figure 1. The global map of in vitro HMEC-1 damage rate caused by the mutant strains in the NTML. The vertical dashed line represents the mean of HMEC-1 damage rate of parental strain USA300 JE2 (46.19%); and the horizontal dashed line represents the p value of 0.05. The bright red dots represent ≤30% EC damage caused, while the bright blue dots represent ≥60% EC damage due to the study mutant strains in the NTML and p < 0.05 vs. JE2 WT strain. Damage rate below zero means the A560nm of the test well is higher than the A560nm of the negative damage control, which indicates that the mutant causes no damage to the EC.
Figure 1. The global map of in vitro HMEC-1 damage rate caused by the mutant strains in the NTML. The vertical dashed line represents the mean of HMEC-1 damage rate of parental strain USA300 JE2 (46.19%); and the horizontal dashed line represents the p value of 0.05. The bright red dots represent ≤30% EC damage caused, while the bright blue dots represent ≥60% EC damage due to the study mutant strains in the NTML and p < 0.05 vs. JE2 WT strain. Damage rate below zero means the A560nm of the test well is higher than the A560nm of the negative damage control, which indicates that the mutant causes no damage to the EC.
Antibiotics 11 00316 g001
Figure 2. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the mutant strains significantly decreasing HMEC-1 damage rate: (A) genes were identified in the KEGG database and belonged to four major KEGG pathways; (B) the sub-pathway enrichment analysis of the genes in the metabolism pathway; and (C) the sub-pathway enrichment analysis of the genes in the other three pathways.
Figure 2. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the mutant strains significantly decreasing HMEC-1 damage rate: (A) genes were identified in the KEGG database and belonged to four major KEGG pathways; (B) the sub-pathway enrichment analysis of the genes in the metabolism pathway; and (C) the sub-pathway enrichment analysis of the genes in the other three pathways.
Antibiotics 11 00316 g002
Figure 3. Genetic factors in MRSA JE2 strain contribute to the HMEC-1 damage by KEGG analysis. These factors may ultimately impact the pathogenesis and treatment outcome in MRSA endovascular infection.
Figure 3. Genetic factors in MRSA JE2 strain contribute to the HMEC-1 damage by KEGG analysis. These factors may ultimately impact the pathogenesis and treatment outcome in MRSA endovascular infection.
Antibiotics 11 00316 g003
Table 1. Mutants significantly decrease HMEC-1 damage vs. JE2 WT strain (EC damage rate ≤ 30%).
Table 1. Mutants significantly decrease HMEC-1 damage vs. JE2 WT strain (EC damage rate ≤ 30%).
LocusGene NameDescription% EC Damage
(Mean ± SD)
SAUSA300_0261hypotheticalconserved hypothetical protein29.83 ± 8.34
SAUSA300_1172hypotheticalM16 family peptidase29.74 ± 4.80
SAUSA300_0083hypotheticalhypothetical protein29.70 ± 10.14
SAUSA300_1386hypotheticalphiETA ORF59-like protein29.57 ± 1.07
SAUSA300_0076hypotheticalABC transporter ATP-binding protein29.57 ± 4.10
SAUSA300_1712ribH6,7-dimethyl-8-ribityllumazine synthase29.49 ± 9.83
SAUSA300_1457malRmaltose operon transcriptional repressor29.46 ± 2.79
SAUSA300_1309hypotheticalIS200 family transposase29.41 ± 8.13
SAUSA300_1253glcTtranscription antiterminator29.37 ± 4.04
SAUSA300_1797hypotheticalconserved hypothetical protein29.37 ± 4.79
SAUSA300_1759hypotheticalhypothetical protein29.25 ± 2.85
SAUSA300_2386hypotheticalbeta-lactamase29.13 ± 1.62
SAUSA300_2434hypotheticaltransporter protein29.13 ± 5.28
SAUSA300_2037hypotheticalATP-dependent RNA helicase28.67 ± 8.90
SAUSA300_1654hypotheticalproline dipeptidase28.46 ± 4.20
SAUSA300_0615hypotheticalputative monovalent cation/H+ antiporter subunit F28.45 ± 4.24
SAUSA300_1659tpxthiol peroxidase28.41 ± 7.42
SAUSA300_1478hypotheticalputative lipoprotein28.28 ± 4.37
SAUSA300_2455hypotheticalputative fructose-1,6-bisphosphatase28.27 ± 5.83
SAUSA300_1297acyPacylphosphatase28.23 ± 4.50
SAUSA300_2606hisFimidazole glycerol phosphate synthase subunit HisF27.62 ± 4.01
SAUSA300_0795hypotheticalhypothetical protein27.38 ± 6.00
SAUSA300_1683hypotheticalbifunctional 3-deoxy-7-phosphoheptulonate synthase/chorismate mutase27.26 ± 6.86
SAUSA300_2618hypotheticalhypothetical protein27.23 ± 7.65
SAUSA300_1398hypotheticalphiSLT ORF123-like protein27.16 ± 11.43
SAUSA300_0059hypotheticalconserved hypothetical protein27.07 ± 7.67
SAUSA300_1764epiDlantibiotic epidermin biosynthesis protein EpiD26.84 ± 3.46
SAUSA300_2332hypotheticalheat shock protein26.78 ± 8.46
SAUSA300_1040hypotheticalhypothetical protein26.74 ± 8.21
SAUSA300_2280fosBfosfomycin resistance protein FosB26.67 ± 8.68
SAUSA300_1750hypotheticalconserved hypothetical protein26.62 ± 9.44
SAUSA300_0883hypotheticalputative surface protein26.40 ± 12.90
SAUSA300_1964hypotheticalhypothetical protein26.38 ± 7.19
SAUSA300_0290hypotheticalputative lipoprotein26.29 ± 8.56
SAUSA300_1672nagEphosphotransferase system, N-acetylglucosamine-specific IIBC component26.21 ± 5.46
SAUSA300_2023rsbWanti-sigma-B factor, serine-protein kinase26.01 ± 0.14
SAUSA300_0190ipdCindole-3-pyruvate decarboxylase25.81 ± 7.93
SAUSA300_2413hypotheticalhypothetical protein25.79 ± 4.70
SAUSA300_0798hypotheticalABC transporter substrate-binding protein25.59 ± 3.93
SAUSA300_0489ftsHputative cell division protein FtsH25.55 ± 5.76
SAUSA300_1093pyrBaspartate carbamoyltransferase catalytic subunit25.49 ± 1.23
SAUSA300_0517hypotheticalRNA methyltransferase25.39 ± 8.18
SAUSA300_1740hypotheticalhypothetical protein25.37 ± 9.05
SAUSA300_0540hypotheticalHAD family hydrolase25.26 ± 9.24
SAUSA300_2272hypotheticalhypothetical protein25.25 ± 4.80
SAUSA300_1968hypotheticalputative phage transcriptional regulator25.23 ± 9.97
SAUSA300_0642hypotheticalhypothetical protein25.21 ± 4.58
SAUSA300_2358hypotheticalABC transporter permease25.11 ± 6.08
SAUSA300_1984mroQhypothetical protein25.07 ± 9.15
SAUSA300_1266trpFN-(5′-phosphoribosyl)anthranilate isomerase25.05 ± 7.12
SAUSA300_2251hypotheticaldehydrogenase family protein25.00 ± 3.65
SAUSA300_0706hypotheticalputative osmoprotectant ABC transporter ATP-binding protein24.95 ± 11.00
SAUSA300_0941hypotheticalputative ferrichrome ABC transporter24.69 ± 6.43
SAUSA300_0951sspAV8 protease24.55 ± 8.41
SAUSA300_1875hypotheticalexonuclease24.52 ± 10.68
SAUSA300_0566hypotheticalamino acid permease24.49 ± 5.06
SAUSA300_0871hypotheticalhypothetical protein24.49 ± 12.19
SAUSA300_0565hypotheticalconserved hypothetical protein24.43 ± 5.34
SAUSA300_0391hypotheticalhypothetical protein24.38 ± 0.45
SAUSA300_1328hypotheticalputative drug transporter24.10 ± 7.38
SAUSA300_2279hypotheticalLysR family regulatory protein23.92 ± 10.37
SAUSA300_0505hypotheticalglutamine amidotransferase subunit PdxT23.61 ± 3.46
SAUSA300_0470ksgAdimethyladenosine transferase23.56 ± 7.13
SAUSA300_1106hypotheticalputative lipoprotein23.45 ± 8.92
SAUSA300_1991agrCaccessory gene regulator protein C23.44 ± 9.71
SAUSA300_0108hypotheticalantigen, 67 kDa23.33 ± 6.80
SAUSA300_2326araCtranscription regulatory protein23.30 ± 5.35
SAUSA300_1399hypotheticalphiSLT ORF110-like protein23.29 ± 0.65
SAUSA300_1942hypotheticalhypothetical protein23.29 ± 11.27
SAUSA300_0079hypotheticalputative lipoprotein23.27 ± 6.02
SAUSA300_1384hypotheticalphiSLT ORF100b-like protein, holin23.25 ± 6.98
SAUSA300_1950hypotheticalhypothetical protein23.24 ± 9.64
SAUSA300_0320gehBtriacylglycerol lipase23.13 ± 9.02
SAUSA300_0370hypotheticalputative enterotoxin23.06 ± 9.01
SAUSA300_1224hypotheticalconserved hypothetical protein22.85 ± 4.12
SAUSA300_1925hypotheticalphiPVL ORF17-like protein22.72 ± 9.85
SAUSA300_1271hypotheticalhydrolase-like protein22.57 ± 5.67
SAUSA300_0547sdrDsdrD protein22.52 ± 1.23
SAUSA300_0561hypotheticalhypothetical protein22.37 ± 6.87
SAUSA300_2367hlgBgamma-hemolysin component B22.27 ± 7.70
SAUSA300_1671hypotheticalhypothetical protein22.15 ± 10.08
SAUSA300_2341narJrespiratory nitrate reductase, subunit delta22.11 ± 4.50
SAUSA300_0420hypotheticalhypothetical protein22.10 ± 8.19
SAUSA300_2281hutGformimidoylglutamase22.05 ± 12.63
SAUSA300_1427hypotheticalphiSLT ORF86-like protein21.94 ± 2.49
SAUSA300_0691saeRDNA-binding response regulator SaeR21.93 ± 10.56
SAUSA300_1519hypotheticalhypothetical protein21.86 ± 0.84
SAUSA300_0253scdAcell wall biosynthesis protein ScdA21.83 ± 12.24
SAUSA300_2459hypotheticalMarR family transcriptional regulator21.58 ± 6.37
SAUSA300_2505hypotheticalacetyltransferase21.48 ± 5.28
SAUSA300_0652hypotheticalhypothetical protein21.46 ± 9.86
SAUSA300_1213hypotheticalhypothetical protein21.42 ± 8.18
SAUSA300_1216hypotheticalcardiolipin synthetase21.40 ± 13.46
SAUSA300_0395hypotheticalsuperantigen-like protein21.39 ± 9.28
SAUSA300_1016cyoEprotoheme IX farnesyltransferase21.38 ± 6.70
SAUSA300_1126rncribonuclease III21.34 ± 5.04
SAUSA300_1437hypotheticalphiSLT ORF204-like protein21.26 ± 3.02
SAUSA300_2145hypotheticalglycine betaine transporter21.18 ± 9.85
SAUSA300_2288hypotheticalABC transporter ATP-binding protein21.10 ± 15.49
SAUSA300_0698pabApara-aminobenzoate synthase, glutamine
amidotransferase, component II
21.05 ± 4.75
SAUSA300_0519hypotheticalhypothetical protein20.86 ± 6.93
SAUSA300_2330hypotheticalhypothetical protein20.82 ± 4.02
SAUSA300_0141deoBphosphopentomutase20.69 ± 9.71
SAUSA300_1684hypotheticalhypothetical protein20.53 ± 11.18
SAUSA300_1595tgtqueuine tRNA-ribosyltransferase20.53 ± 9.07
SAUSA300_0442hypotheticalhypothetical protein20.45 ± 3.70
SAUSA300_0744lgtprolipoprotein diacylglyceryl transferase20.44 ± 5.61
SAUSA300_1576recD2helicase, RecD/TraA family20.41 ± 6.63
SAUSA300_2088luxSS-ribosylhomocysteinase20.40 ± 2.33
SAUSA300_0131hypotheticalputative Bacterial sugar transferase20.28 ± 13.49
SAUSA300_0649hypotheticalhypothetical protein20.23 ± 0.89
SAUSA300_2550nrdGanaerobic ribonucleotide reductase, small subunit20.22 ± 10.12
SAUSA300_2168hypotheticalhypothetical protein20.16 ± 4.12
SAUSA300_2587hypotheticalaccessory secretory protein Asp120.06 ± 9.42
SAUSA300_2548hypotheticalhypothetical protein19.98 ± 7.37
SAUSA300_1021hypotheticalhypothetical protein19.92 ± 15.09
SAUSA300_0456rrlA23S ribosomal RNA19.91 ± 0.15
SAUSA300_0431hypotheticalhypothetical protein19.86 ± 4.23
SAUSA300_1247hypotheticalconserved hypothetical protein19.79 ± 10.23
SAUSA300_2108mtlDmannitol-1-phosphate 5-dehydrogenase19.74 ± 9.18
SAUSA300_2516hypotheticalshort chain dehydrogenase/reductase family oxidoreductase19.65 ± 10.14
SAUSA300_0450treRtrehalose operon repressor19.59 ± 13.38
SAUSA300_0422hypotheticalhypothetical protein19.54 ± 2.66
SAUSA300_1739hypotheticalhypothetical protein19.47 ± 8.56
SAUSA300_0257lrgBantiholin-like protein LrgB19.47 ± 17.61
SAUSA300_0056hypotheticalhypothetical protein19.05 ± 4.22
SAUSA300_2352hypotheticaladdiction module antitoxin18.95 ± 11.82
SAUSA300_2236hypotheticalhypothetical protein18.82 ± 4.26
SAUSA300_1409hypotheticalhypothetical protein18.77 ± 11.78
SAUSA300_1304hypotheticalhypothetical protein18.73 ± 5.92
SAUSA300_1934hypotheticalphi77 ORF020-like protein, phage major tail protein18.68 ± 3.51
SAUSA300_1279phoUphosphate transport system regulatory protein PhoU18.68 ± 7.74
SAUSA300_1217hypotheticalABC transporter ATP-binding protein18.66 ± 8.42
SAUSA300_0468hypotheticalTatD family hydrolase18.62 ± 0.90
SAUSA300_2132hypotheticalhypothetical protein18.54 ± 17.28
SAUSA300_0288essD/esaDhypothetical protein18.50 ± 12.03
SAUSA300_2461hypotheticalglyoxalase family protein18.38 ± 6.48
SAUSA300_1349bshAglycosyl transferase, group 1 family protein18.26 ± 11.03
SAUSA300_1009typAGTP-binding protein18.22 ± 6.42
SAUSA300_1755splDserine protease SplD18.20 ± 6.01
SAUSA300_1966hypotheticalphi77 ORF014-like protein, phage anti-repressor protein18.04 ± 5.61
SAUSA300_1307arlSsensor histidine kinase protein18.01 ± 7.14
SAUSA300_1918hlbtruncated beta-hemolysin17.91 ± 11.34
SAUSA300_1569hypotheticalU32 family peptidase17.90 ± 6.37
SAUSA300_1397hypotheticalphiSLT ORF213-like protein, major tail protein17.88 ± 16.40
SAUSA300_1032hypotheticalputative iron compound ABC transporter iron compound-binding protein17.87 ± 9.01
SAUSA300_0259hypotheticalPTS system, IIA component17.72 ± 4.08
SAUSA300_1070hypotheticalhypothetical protein17.66 ± 6.61
SAUSA300_1474hypotheticalhypothetical protein17.57 ± 3.84
SAUSA300_1451hypotheticalshort chain dehydrogenase/reductase family oxidoreductase17.47 ± 4.46
SAUSA300_0769hypotheticalhypothetical protein17.42 ± 7.43
SAUSA300_2098arsRArsR family transcriptional regulator17.36 ± 8.42
SAUSA300_0094hypotheticalhypothetical protein17.32 ± 9.77
SAUSA300_1470ispAgeranyltranstransferase17.29 ± 13.19
SAUSA300_1403hypotheticalphiSLT ORF412-like protein, portal protein17.28 ± 10.80
SAUSA300_2432hypotheticalMutT/NUDIX family hydrolase17.26 ± 15.82
SAUSA300_0631hypotheticalputative nucleoside transporter17.25 ± 11.20
SAUSA300_1000potBspermidine/putrescine ABC transporter permease17.14 ± 5.86
SAUSA300_2559hypotheticalDNA-binding response regulator17.10 ± 8.85
SAUSA300_2467srtAsortase17.01 ± 6.72
SAUSA300_2300hypotheticaltranscriptional regulator, TetR family16.92 ± 5.04
SAUSA300_0916hypotheticalhypothetical protein16.89 ± 2.85
SAUSA300_1444scpBsegregation and condensation protein B16.85 ± 6.40
SAUSA300_0995hypotheticalbranched-chain alpha-keto acid dehydrogenase subunit E216.83 ± 18.68
SAUSA300_0419hypotheticaltandem lipoprotein16.78 ± 3.58
SAUSA300_1563accCacetyl-CoA carboxylase, biotin carboxylase16.73 ± 11.04
SAUSA300_2027alralanine racemase16.70 ± 16.05
SAUSA300_2607hisAphoribosyl)-5-((5-phosphoribosylamino)methylideneamino) imidazole-4-carboxamide16.70 ± 11.46
SAUSA300_0023hypotheticalhypothetical protein16.69 ± 16.09
SAUSA300_1622tigtrigger factor16.44 ± 5.67
SAUSA300_0011hypotheticalhypothetical protein16.37 ± 4.02
SAUSA300_1097pyrForotidine 5′-phosphate decarboxylase16.34 ± 8.94
SAUSA300_1339hypotheticalhypothetical protein16.25 ± 5.49
SAUSA300_0585hypotheticalhypothetical protein16.24 ± 13.38
SAUSA300_0839nfuhypothetical protein16.23 ± 12.30
SAUSA300_0071hypotheticalISSep1-like transposase16.19 ± 3.17
SAUSA300_0651hypotheticalCHAP domain-contain protein16.09 ± 6.91
SAUSA300_1599hypotheticalhypothetical protein16.02 ± 7.75
SAUSA300_1607hypotheticalhypothetical protein16.02 ± 8.76
SAUSA300_0588hypotheticalhypothetical protein15.86 ± 15.72
SAUSA300_2276hypotheticalpeptidase, M20/M25/M40 family15.84 ± 1.33
SAUSA300_2055murAUDP-N-acetylglucosamine 1-carboxyvinyltransferase15.79 ± 10.49
SAUSA300_0808hypotheticalhypothetical protein15.69 ± 12.88
SAUSA300_0759gpmIphosphoglyceromutase15.68 ± 9.84
SAUSA300_0857ppiBhypothetical protein15.66 ± 4.76
SAUSA300_1051hypotheticalhypothetical protein15.51 ± 14.05
SAUSA300_1383hypotheticalphiSLT ORF484-like protein, lysin15.46 ± 15.13
SAUSA300_1566hypotheticalhypothetical protein15.42 ± 14.25
SAUSA300_2040hypotheticalhypothetical protein15.42 ± 12.63
SAUSA300_1145xerCtyrosine recombinase xerC15.33 ± 4.57
SAUSA300_0687hypotheticalputative hemolysin15.14 ± 12.23
SAUSA300_0630hypotheticalABC transporter ATP-binding protein15.07 ± 10.45
SAUSA300_1577hypotheticalTPR domain-containing protein14.93 ± 1.75
SAUSA300_1288dapAdihydrodipicolinate synthase14.75 ± 7.53
SAUSA300_1937hypotheticalphi77 ORF045-like protein14.69 ± 8.83
SAUSA300_1419hypotheticalphiSLT ORF80-like protein14.65 ± 9.06
SAUSA300_2345nirDnitrite reductase (NAD(P)H), small subunit14.54 ± 4.64
SAUSA300_1365rpsA30S ribosomal protein S114.53 ± 3.46
SAUSA300_0029hypotheticalhypothetical protein14.39 ± 3.30
SAUSA300_2575hypotheticalBglG family transcriptional antiterminator14.12 ± 4.67
SAUSA300_1497hypotheticalglycine dehydrogenase subunit 114.08 ± 4.09
SAUSA300_1682ccpAcatabolite control protein A14.04 ± 8.43
SAUSA300_0657hypotheticalhypothetical protein14.02 ± 7.45
SAUSA300_1955hypotheticalputative endodeoxyribonuclease RusA13.92 ± 10.12
SAUSA300_0924ktrDsodium transport family protein13.85 ± 14.78
SAUSA300_0077hypotheticalABC transporter ATP-binding protein13.80 ± 6.67
SAUSA300_0504pdxSpyridoxal biosynthesis lyase PdxS13.58 ± 7.70
SAUSA300_0195hypotheticaltranscriptional regulator13.06 ± 13.37
SAUSA300_1308arlRDNA-binding response regulator13.05 ± 5.02
SAUSA300_0859hypotheticalNADH-dependent flavin oxidoreductase12.99 ± 7.37
SAUSA300_1721hypotheticalhypothetical protein12.97 ± 3.93
SAUSA300_0186argCN-acetyl-gamma-glutamyl-phosphate reductase12.92 ± 16.00
SAUSA300_2641hypotheticalhypothetical protein12.90 ± 8.36
SAUSA300_0987hypotheticalcytochrome D ubiquinol oxidase, subunit II12.85 ± 10.22
SAUSA300_1696datD-alanine aminotransferase12.74 ± 5.48
SAUSA300_1283hypotheticalphosphate ABC transporter, phosphate-binding protein PstS12.73 ± 9.23
SAUSA300_1185miaB(dimethylallyl)adenosine tRNA methylthiotransferase12.62 ± 10.40
SAUSA300_2365hlgAgamma-hemolysin component A12.56 ± 10.54
SAUSA300_1394hypotheticalhypothetical protein12.34 ± 12.26
SAUSA300_0115sirCiron compound ABC transporter permease SirC12.30 ± 6.17
SAUSA300_2284hypotheticalhypothetical protein12.20 ± 10.36
SAUSA300_2225moaCmolybdenum cofactor biosynthesis protein MoaC12.08 ± 9.05
SAUSA300_0244hypotheticalzinc-binding dehydrogenase family oxidoreductase12.05 ± 9.79
SAUSA300_2022rpoFRNA polymerase sigma factor SigB12.05 ± 6.83
SAUSA300_1089lspAlipoprotein signal peptidase11.97 ± 6.81
SAUSA300_1618hemXhemA concentration negative effector hemX11.88 ± 1.05
SAUSA300_0117sirAiron compound ABC transporter iron compound-binding protein SirA11.83 ± 7.84
SAUSA300_0899mecAadaptor protein11.58 ± 10.37
SAUSA300_2492hypotheticalacetyltransferase family protein11.55 ± 7.80
SAUSA300_1433hypotheticalputative phage regulatory protein11.41 ± 8.17
SAUSA300_1244mscLlarge conductance mechanosensitive channel protein11.32 ± 7.21
SAUSA300_0049hypotheticalhypothetical protein11.30 ± 0.62
SAUSA300_1667hypotheticalputative glycerophosphoryl diester phosphodiesterase11.30 ± 7.51
SAUSA300_0994pdhBpyruvate dehydrogenase E1 component, beta subunit11.20 ± 8.12
SAUSA300_0974purNphosphoribosylglycinamide formyltransferase11.07 ± 8.08
SAUSA300_0067hypotheticaluniversal stress protein11.02 ± 9.02
SAUSA300_1590rsh (relA)GTP pyrophosphokinase10.95 ± 7.18
SAUSA300_0526hypotheticalmethyltransferase small subunit10.80 ± 10.78
SAUSA300_0952hypotheticalaminotransferase, class I10.57 ± 6.79
SAUSA300_1694trmBtRNA (guanine-N(7)-)-methyltransferase10.55 ± 16.08
SAUSA300_0041hypotheticalhypothetical protein10.41 ± 2.09
SAUSA300_1449hypotheticalMutT/nudix family protein10.11 ± 13.24
SAUSA300_0724hypotheticalhypothetical protein10.06 ± 2.60
SAUSA300_1757splBserine protease SplB9.41 ± 4.17
SAUSA300_0476hypotheticalhypothetical protein9.18 ± 8.05
SAUSA300_2052hypotheticalsingle-stranded DNA- binding protein family9.11 ± 18.19
SAUSA300_2176cbiOcobalt transporter ATP-binding subunit9.03 ± 9.11
SAUSA300_1112stp1protein phosphatase 2C domain-containing protein8.98 ± 14.19
SAUSA300_0789hypotheticalputative thioredoxin8.89 ± 18.33
SAUSA300_0379ahpFalkyl hydroperoxide reductase subunit F8.46 ± 4.49
SAUSA300_0348tatAtwin arginine-targeting protein translocase8.36 ± 5.53
SAUSA300_0469rnmVhypothetical protein8.35 ± 0.35
SAUSA300_1792hypotheticalhypothetical protein8.20 ± 4.58
SAUSA300_2061atpHF0F1 ATP synthase subunit delta7.98 ± 1.29
SAUSA300_1092pyrPuracil permease7.85 ± 2.60
SAUSA300_0905hypotheticalhypothetical protein7.61 ± 3.76
SAUSA300_0444gltCLysR family regulatory protein7.59 ± 2.70
SAUSA300_2646trmEtRNA modification GTPase TrmE7.41 ± 8.81
SAUSA300_2105mtlFPTS system, mannitol specific IIBC component6.95 ± 0.84
SAUSA300_2486clpLputative ATP-dependent Clp proteinase6.73 ± 0.02
SAUSA300_1887pcrBgeranylgeranylglyceryl phosphate synthase-like protein6.58 ± 3.46
SAUSA300_1653hypotheticalmetal-dependent hydrolase6.25 ± 8.63
SAUSA300_2393opuCaglycine betaine/carnitine/choline ABC transporter ATP-binding protein6.25 ± 7.87
SAUSA300_1183hypothetical2-oxoglutarate ferredoxin oxidoreductase subunit beta6.19 ± 1.88
SAUSA300_0393hypotheticalhypothetical protein6.18 ± 2.30
SAUSA300_0174hypotheticalhypothetical protein6.15 ± 1.39
SAUSA300_0841hypotheticalhypothetical protein5.97 ± 2.99
SAUSA300_1096carBcarbamoyl phosphate synthase large subunit5.89 ± 2.89
SAUSA300_2593hypotheticalhypothetical protein5.84 ± 3.04
SAUSA300_0221pflApyruvate formate-lyase activating enzyme5.68 ± 18.96
SAUSA300_0996lpdAdihydrolipoamide dehydrogenase5.49 ± 2.87
SAUSA300_1992agrAaccessory gene regulator protein A5.34 ± 14.81
SAUSA300_1147hslUATP-dependent protease ATP-binding subunit HslU4.99 ± 6.72
SAUSA300_1120recGATP-dependent DNA helicase RecG4.60 ± 0.15
SAUSA300_2078murAUDP-N-acetylglucosamine 1-carboxyvinyltransferase3.18 ± 3.15
SAUSA300_1583cymRhypothetical protein2.48 ± 0.46
SAUSA300_0992hypotheticalhypothetical protein2.24 ± 20.30
SAUSA300_0634fhuBferrichrome transport permease fhuB2.22 ± 4.57
SAUSA300_0750whiAhypothetical protein1.88 ± 4.32
SAUSA300_2485hypotheticalmethylated DNA-protein cysteine methyltransferase1.78 ± 9.18
SAUSA300_0426hypotheticalhypothetical protein0.95 ± 5.09
SAUSA300_2598capAcapsular polysaccharide biosynthesis protein Cap1A0.85 ± 1.11
SAUSA300_2246hypotheticalhypothetical protein0.51 ± 16.59
SAUSA300_2518hypotheticalhydrolase family protein0.42 ± 7.59
SAUSA300_0355hypotheticalacetyl-CoA acetyltransferase−0.68 a ± 4.44
SAUSA300_0398hypotheticalsuperantigen-like protein−0.83 a ± 2.42
SAUSA300_2226moaBmolybdenum cofactor biosynthesis protein B−1.15 a ± 5.09
SAUSA300_0945hypotheticalisochorismate synthase family protein−1.17 a ± 14.05
SAUSA300_0904yjbIhypothetical protein−1.32 a ± 9.61
SAUSA300_0423hypotheticalhypothetical protein−2.20 a ± 9.08
SAUSA300_1422hypotheticalphiSLT ORF65-like protein−2.77 a ± 6.35
SAUSA300_0068hypotheticalcadmium-exporting ATPase, truncation−2.79 a ± 8.85
SAUSA300_1870hypotheticalhypothetical protein−2.92 a ± 15.58
SAUSA300_1139sucDsuccinyl-CoA synthetase subunit alpha−2.94 a ± 8.32
SAUSA300_0918ugtPdiacylglycerol glucosyltransferase−3.09 a ± 8.63
SAUSA300_0597hypotheticalputative endonuclease III−3.15 a ± 14.78
SAUSA300_0326hypotheticalhypothetical protein−3.64 a ± 2.40
SAUSA300_0690saeSsensor histidine kinase SaeS−4.88 a ± 14.01
SAUSA300_0560vraBacetyl-CoA c-acetyltransferase−5.06 a ± 6.53
SAUSA300_2334hypotheticalhypothetical protein-5.12 a ± 7.55
SAUSA300_2025rsbUsigma-B regulation protein−5.19 a ± 6.08
SAUSA300_2152lacDtagatose 1,6-diphosphate aldolase−5.59 a ± 11.59
SAUSA300_1680acuAacetoin utilization protein AcuA−5.94 a ± 10.87
SAUSA300_2024rsbVanti-sigma-B factor, antagonist−6.77 a ± 14.71
SAUSA300_0618mntCABC transporter substrate-binding protein−6.85 a ± 4.61
SAUSA300_1876hypotheticalDNA polymerase IV−6.91 a ± 9.59
SAUSA300_1465hypothetical2-oxoisovalerate dehydrogenase, E1 component, beta subunit−7.15 a ± 6.73
SAUSA300_1573hypotheticalHolliday junction resolvase-like protein−10.10 a ± 6.88
SAUSA300_1473nusBtranscription antitermination protein NusB−10.84 a ± 10.00
SAUSA300_1357aroCchorismate synthase−11.88 a ± 0.89
SAUSA300_1095carAcarbamoyl phosphate synthase small subunit−14.12 a ± 10.52
SAUSA300_1469argRarginine repressor−14.16 a ± 8.61
SAUSA300_1615hemBdelta-aminolevulinic acid dehydratase−14.95 a ± 14.12
SAUSA300_1467lpdAdihydrolipoamide dehydrogenase−15.68 a ± 14.07
SAUSA300_0993pdhApyruvate dehydrogenase E1 component, alpha subunit−17.05 a ± 10.66
SAUSA300_0752clpPATP-dependent Clp protease proteolytic subunit−17.66 a ± 11.34
SAUSA300_1715ribDriboflavin biosynthesis protein−23.78 a ± 4.28
a EC damage below zero is due to the A560nm value of the mutant was higher than the A560nm of the negative control.
Table 2. Mutants significantly increase HMEC-1 damage vs. JE2 WT strain (EC damage rate ≥ 60%).
Table 2. Mutants significantly increase HMEC-1 damage vs. JE2 WT strain (EC damage rate ≥ 60%).
LocusGene NameDescription% EC Damage (Mean ± SD)
SAUSA300_1197ND aglutathione peroxidase62.86 ± 5.67
SAUSA300_1333hypotheticalconserved hypothetical protein62.17 ± 3.05
SAUSA300_1485hypotheticalconserved hypothetical protein61.86 ± 6.12
SAUSA300_2221moaDmolybdopterin converting factor, subunit 161.64 ± 3.61
SAUSA300_0206azoRflavodoxin family protein60.82 ± 6.24
SAUSA300_0335mepAMATE efflux family protein60.15 ± 8.13
a ND: not determined.
Table 3. Verification of EC damage of JE WT strain and selected mutants using 24-well plates assay.
Table 3. Verification of EC damage of JE WT strain and selected mutants using 24-well plates assay.
LocusGroupGene Name% EC Damage (Mean ± SD)
384-Well Plates24-Well Plates
JE2Wildtype 46.19 ± 2.97 42.43 ± 6.44
SAUSA300_1197EC damage ≥ 60%
in 384-well plates
hypothetical62.86 ± 5.6759.40 ± 1.50
SAUSA300_1333hypothetical62.17 ± 3.0566.92 ± 0.84
SAUSA300_1485hypothetical61.86 ± 6.1261.75 a
SAUSA300_2221moaD61.64 ± 3.61 59.90 ± 1.08
SAUSA300_0206hypothetical60.82 ± 6.2469.33 ± 0.48
SAUSA300_0335hypothetical60.15 ± 8.3163.35 ± 2.06
SAUSA300_1040EC damage ≤ 30%
in 384-well plates
hypothetical26.74 ± 8.2130.92 a
SAUSA300_1875hypothetical24.52 ± 10.6830.51 a
SAUSA300_0871hypothetical24.49 ± 12.1928.60 a
SAUSA300_1950hypothetical23.24 ± 9.6425.87 a
SAUSA300_0253scdA21.83 ± 12.2422.52 a
SAUSA300_0649hypothetical20.24 ± 0.8922.65 a
SAUSA300_2587hypothetical20.06 ± 9.4226.45 a
SAUSA300_0631hypothetical17.25 ± 11.2023.00 a
SAUSA300_2027alr16.70 ± 16.053.28 ± 1.38
SAUSA300_2055murA15.79 ± 10.497.62 ± 0.59
SAUSA300_1682ccpA14.04 ± 8.4313.43 a
SAUSA300_1696dat12.74 ± 5.4814.99 ± 1.34
SAUSA300_0974purN11.07 ± 8.0820.58 a
SAUSA300_1563accC16.73 ± 11.0411.82 ± 0.72
SAUSA300_0041hypothetical10.41 ± 2.093.30 a
SAUSA300_0994pdhB11.20 ± 8.1219.36 a
SAUSA300_0186argC12.92 ± 16.0015.20 ± 2.13
SAUSA300_1992agrA5.34 ± 14.81−3.82 ± 1.77
SAUSA300_0355hypothetical−0.68 ± 4.44−1.20 a
SAUSA300_0690saeS−4.89 ± 14.01−12.80 ± 1.77
a Verification of these mutants was performed once using the 24-well plates assay.
Table 4. Numbers of genes from different KEGG pathway categories.
Table 4. Numbers of genes from different KEGG pathway categories.
CategoriesSub-GroupsNo. of Mutants with
Decreased HMEC-1
Damage
No. of Mutants with
Increased HMEC-1
Damage
MetabolismCarbohydrate metabolism53
Amino acid metabolism33
Metabolism of cofactors and vitamins11
Lipid metabolism81
Nucleotide metabolism8
Biosynthesis of other secondary metabolites7
Energy metabolism7
Metabolism of other amino acids31
Metabolism of terpenoids and polyketides3
Glycan biosynthesis and metabolism2
Xenobiotics biodegradation and metabolism1
Genetic information processingHomologous recombination4
DNA replication2
Mismatch repair2
Protein export2
Ribosome2
Sulfur relay system21
RNA degradation1
Environmental information processingTwo-component system13
ABC transporters9
Other3
Cellular processesQuorum sensing9
Total 1853
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Xiao, X.; Li, Y.; Li, L.; Xiong, Y.Q. Identification of Methicillin-Resistant Staphylococcus aureus (MRSA) Genetic Factors Involved in Human Endothelial Cells Damage, an Important Phenotype Correlated with Persistent Endovascular Infection. Antibiotics 2022, 11, 316. https://doi.org/10.3390/antibiotics11030316

AMA Style

Xiao X, Li Y, Li L, Xiong YQ. Identification of Methicillin-Resistant Staphylococcus aureus (MRSA) Genetic Factors Involved in Human Endothelial Cells Damage, an Important Phenotype Correlated with Persistent Endovascular Infection. Antibiotics. 2022; 11(3):316. https://doi.org/10.3390/antibiotics11030316

Chicago/Turabian Style

Xiao, Xia, Yi Li, Liang Li, and Yan Q. Xiong. 2022. "Identification of Methicillin-Resistant Staphylococcus aureus (MRSA) Genetic Factors Involved in Human Endothelial Cells Damage, an Important Phenotype Correlated with Persistent Endovascular Infection" Antibiotics 11, no. 3: 316. https://doi.org/10.3390/antibiotics11030316

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop