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. 2022 Feb 26;11(3):316. doi: 10.3390/antibiotics11030316

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

Xia Xiao 1,2, Yi Li 1, Liang Li 1,3, Yan Q Xiong 1,4,*
Editor: Ágnes Pál-Sonnevend
PMCID: PMC8944730  PMID: 35326779

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.

Keywords: MRSA, human endothelial cell damage, virulence factors

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.

Figure 1.

Figure 1

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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.

Table 1.

Mutants significantly decrease HMEC-1 damage vs. JE2 WT strain (EC damage rate ≤ 30%).

Locus Gene Name Description % EC Damage
(Mean ± SD)
SAUSA300_0261 hypothetical conserved hypothetical protein 29.83 ± 8.34
SAUSA300_1172 hypothetical M16 family peptidase 29.74 ± 4.80
SAUSA300_0083 hypothetical hypothetical protein 29.70 ± 10.14
SAUSA300_1386 hypothetical phiETA ORF59-like protein 29.57 ± 1.07
SAUSA300_0076 hypothetical ABC transporter ATP-binding protein 29.57 ± 4.10
SAUSA300_1712 ribH 6,7-dimethyl-8-ribityllumazine synthase 29.49 ± 9.83
SAUSA300_1457 malR maltose operon transcriptional repressor 29.46 ± 2.79
SAUSA300_1309 hypothetical IS200 family transposase 29.41 ± 8.13
SAUSA300_1253 glcT transcription antiterminator 29.37 ± 4.04
SAUSA300_1797 hypothetical conserved hypothetical protein 29.37 ± 4.79
SAUSA300_1759 hypothetical hypothetical protein 29.25 ± 2.85
SAUSA300_2386 hypothetical beta-lactamase 29.13 ± 1.62
SAUSA300_2434 hypothetical transporter protein 29.13 ± 5.28
SAUSA300_2037 hypothetical ATP-dependent RNA helicase 28.67 ± 8.90
SAUSA300_1654 hypothetical proline dipeptidase 28.46 ± 4.20
SAUSA300_0615 hypothetical putative monovalent cation/H+ antiporter subunit F 28.45 ± 4.24
SAUSA300_1659 tpx thiol peroxidase 28.41 ± 7.42
SAUSA300_1478 hypothetical putative lipoprotein 28.28 ± 4.37
SAUSA300_2455 hypothetical putative fructose-1,6-bisphosphatase 28.27 ± 5.83
SAUSA300_1297 acyP acylphosphatase 28.23 ± 4.50
SAUSA300_2606 hisF imidazole glycerol phosphate synthase subunit HisF 27.62 ± 4.01
SAUSA300_0795 hypothetical hypothetical protein 27.38 ± 6.00
SAUSA300_1683 hypothetical bifunctional 3-deoxy-7-phosphoheptulonate synthase/chorismate mutase 27.26 ± 6.86
SAUSA300_2618 hypothetical hypothetical protein 27.23 ± 7.65
SAUSA300_1398 hypothetical phiSLT ORF123-like protein 27.16 ± 11.43
SAUSA300_0059 hypothetical conserved hypothetical protein 27.07 ± 7.67
SAUSA300_1764 epiD lantibiotic epidermin biosynthesis protein EpiD 26.84 ± 3.46
SAUSA300_2332 hypothetical heat shock protein 26.78 ± 8.46
SAUSA300_1040 hypothetical hypothetical protein 26.74 ± 8.21
SAUSA300_2280 fosB fosfomycin resistance protein FosB 26.67 ± 8.68
SAUSA300_1750 hypothetical conserved hypothetical protein 26.62 ± 9.44
SAUSA300_0883 hypothetical putative surface protein 26.40 ± 12.90
SAUSA300_1964 hypothetical hypothetical protein 26.38 ± 7.19
SAUSA300_0290 hypothetical putative lipoprotein 26.29 ± 8.56
SAUSA300_1672 nagE phosphotransferase system, N-acetylglucosamine-specific IIBC component 26.21 ± 5.46
SAUSA300_2023 rsbW anti-sigma-B factor, serine-protein kinase 26.01 ± 0.14
SAUSA300_0190 ipdC indole-3-pyruvate decarboxylase 25.81 ± 7.93
SAUSA300_2413 hypothetical hypothetical protein 25.79 ± 4.70
SAUSA300_0798 hypothetical ABC transporter substrate-binding protein 25.59 ± 3.93
SAUSA300_0489 ftsH putative cell division protein FtsH 25.55 ± 5.76
SAUSA300_1093 pyrB aspartate carbamoyltransferase catalytic subunit 25.49 ± 1.23
SAUSA300_0517 hypothetical RNA methyltransferase 25.39 ± 8.18
SAUSA300_1740 hypothetical hypothetical protein 25.37 ± 9.05
SAUSA300_0540 hypothetical HAD family hydrolase 25.26 ± 9.24
SAUSA300_2272 hypothetical hypothetical protein 25.25 ± 4.80
SAUSA300_1968 hypothetical putative phage transcriptional regulator 25.23 ± 9.97
SAUSA300_0642 hypothetical hypothetical protein 25.21 ± 4.58
SAUSA300_2358 hypothetical ABC transporter permease 25.11 ± 6.08
SAUSA300_1984 mroQ hypothetical protein 25.07 ± 9.15
SAUSA300_1266 trpF N-(5′-phosphoribosyl)anthranilate isomerase 25.05 ± 7.12
SAUSA300_2251 hypothetical dehydrogenase family protein 25.00 ± 3.65
SAUSA300_0706 hypothetical putative osmoprotectant ABC transporter ATP-binding protein 24.95 ± 11.00
SAUSA300_0941 hypothetical putative ferrichrome ABC transporter 24.69 ± 6.43
SAUSA300_0951 sspA V8 protease 24.55 ± 8.41
SAUSA300_1875 hypothetical exonuclease 24.52 ± 10.68
SAUSA300_0566 hypothetical amino acid permease 24.49 ± 5.06
SAUSA300_0871 hypothetical hypothetical protein 24.49 ± 12.19
SAUSA300_0565 hypothetical conserved hypothetical protein 24.43 ± 5.34
SAUSA300_0391 hypothetical hypothetical protein 24.38 ± 0.45
SAUSA300_1328 hypothetical putative drug transporter 24.10 ± 7.38
SAUSA300_2279 hypothetical LysR family regulatory protein 23.92 ± 10.37
SAUSA300_0505 hypothetical glutamine amidotransferase subunit PdxT 23.61 ± 3.46
SAUSA300_0470 ksgA dimethyladenosine transferase 23.56 ± 7.13
SAUSA300_1106 hypothetical putative lipoprotein 23.45 ± 8.92
SAUSA300_1991 agrC accessory gene regulator protein C 23.44 ± 9.71
SAUSA300_0108 hypothetical antigen, 67 kDa 23.33 ± 6.80
SAUSA300_2326 araC transcription regulatory protein 23.30 ± 5.35
SAUSA300_1399 hypothetical phiSLT ORF110-like protein 23.29 ± 0.65
SAUSA300_1942 hypothetical hypothetical protein 23.29 ± 11.27
SAUSA300_0079 hypothetical putative lipoprotein 23.27 ± 6.02
SAUSA300_1384 hypothetical phiSLT ORF100b-like protein, holin 23.25 ± 6.98
SAUSA300_1950 hypothetical hypothetical protein 23.24 ± 9.64
SAUSA300_0320 gehB triacylglycerol lipase 23.13 ± 9.02
SAUSA300_0370 hypothetical putative enterotoxin 23.06 ± 9.01
SAUSA300_1224 hypothetical conserved hypothetical protein 22.85 ± 4.12
SAUSA300_1925 hypothetical phiPVL ORF17-like protein 22.72 ± 9.85
SAUSA300_1271 hypothetical hydrolase-like protein 22.57 ± 5.67
SAUSA300_0547 sdrD sdrD protein 22.52 ± 1.23
SAUSA300_0561 hypothetical hypothetical protein 22.37 ± 6.87
SAUSA300_2367 hlgB gamma-hemolysin component B 22.27 ± 7.70
SAUSA300_1671 hypothetical hypothetical protein 22.15 ± 10.08
SAUSA300_2341 narJ respiratory nitrate reductase, subunit delta 22.11 ± 4.50
SAUSA300_0420 hypothetical hypothetical protein 22.10 ± 8.19
SAUSA300_2281 hutG formimidoylglutamase 22.05 ± 12.63
SAUSA300_1427 hypothetical phiSLT ORF86-like protein 21.94 ± 2.49
SAUSA300_0691 saeR DNA-binding response regulator SaeR 21.93 ± 10.56
SAUSA300_1519 hypothetical hypothetical protein 21.86 ± 0.84
SAUSA300_0253 scdA cell wall biosynthesis protein ScdA 21.83 ± 12.24
SAUSA300_2459 hypothetical MarR family transcriptional regulator 21.58 ± 6.37
SAUSA300_2505 hypothetical acetyltransferase 21.48 ± 5.28
SAUSA300_0652 hypothetical hypothetical protein 21.46 ± 9.86
SAUSA300_1213 hypothetical hypothetical protein 21.42 ± 8.18
SAUSA300_1216 hypothetical cardiolipin synthetase 21.40 ± 13.46
SAUSA300_0395 hypothetical superantigen-like protein 21.39 ± 9.28
SAUSA300_1016 cyoE protoheme IX farnesyltransferase 21.38 ± 6.70
SAUSA300_1126 rnc ribonuclease III 21.34 ± 5.04
SAUSA300_1437 hypothetical phiSLT ORF204-like protein 21.26 ± 3.02
SAUSA300_2145 hypothetical glycine betaine transporter 21.18 ± 9.85
SAUSA300_2288 hypothetical ABC transporter ATP-binding protein 21.10 ± 15.49
SAUSA300_0698 pabA para-aminobenzoate synthase, glutamine
amidotransferase, component II		 21.05 ± 4.75
SAUSA300_0519 hypothetical hypothetical protein 20.86 ± 6.93
SAUSA300_2330 hypothetical hypothetical protein 20.82 ± 4.02
SAUSA300_0141 deoB phosphopentomutase 20.69 ± 9.71
SAUSA300_1684 hypothetical hypothetical protein 20.53 ± 11.18
SAUSA300_1595 tgt queuine tRNA-ribosyltransferase 20.53 ± 9.07
SAUSA300_0442 hypothetical hypothetical protein 20.45 ± 3.70
SAUSA300_0744 lgt prolipoprotein diacylglyceryl transferase 20.44 ± 5.61
SAUSA300_1576 recD2 helicase, RecD/TraA family 20.41 ± 6.63
SAUSA300_2088 luxS S-ribosylhomocysteinase 20.40 ± 2.33
SAUSA300_0131 hypothetical putative Bacterial sugar transferase 20.28 ± 13.49
SAUSA300_0649 hypothetical hypothetical protein 20.23 ± 0.89
SAUSA300_2550 nrdG anaerobic ribonucleotide reductase, small subunit 20.22 ± 10.12
SAUSA300_2168 hypothetical hypothetical protein 20.16 ± 4.12
SAUSA300_2587 hypothetical accessory secretory protein Asp1 20.06 ± 9.42
SAUSA300_2548 hypothetical hypothetical protein 19.98 ± 7.37
SAUSA300_1021 hypothetical hypothetical protein 19.92 ± 15.09
SAUSA300_0456 rrlA 23S ribosomal RNA 19.91 ± 0.15
SAUSA300_0431 hypothetical hypothetical protein 19.86 ± 4.23
SAUSA300_1247 hypothetical conserved hypothetical protein 19.79 ± 10.23
SAUSA300_2108 mtlD mannitol-1-phosphate 5-dehydrogenase 19.74 ± 9.18
SAUSA300_2516 hypothetical short chain dehydrogenase/reductase family oxidoreductase 19.65 ± 10.14
SAUSA300_0450 treR trehalose operon repressor 19.59 ± 13.38
SAUSA300_0422 hypothetical hypothetical protein 19.54 ± 2.66
SAUSA300_1739 hypothetical hypothetical protein 19.47 ± 8.56
SAUSA300_0257 lrgB antiholin-like protein LrgB 19.47 ± 17.61
SAUSA300_0056 hypothetical hypothetical protein 19.05 ± 4.22
SAUSA300_2352 hypothetical addiction module antitoxin 18.95 ± 11.82
SAUSA300_2236 hypothetical hypothetical protein 18.82 ± 4.26
SAUSA300_1409 hypothetical hypothetical protein 18.77 ± 11.78
SAUSA300_1304 hypothetical hypothetical protein 18.73 ± 5.92
SAUSA300_1934 hypothetical phi77 ORF020-like protein, phage major tail protein 18.68 ± 3.51
SAUSA300_1279 phoU phosphate transport system regulatory protein PhoU 18.68 ± 7.74
SAUSA300_1217 hypothetical ABC transporter ATP-binding protein 18.66 ± 8.42
SAUSA300_0468 hypothetical TatD family hydrolase 18.62 ± 0.90
SAUSA300_2132 hypothetical hypothetical protein 18.54 ± 17.28
SAUSA300_0288 essD/esaD hypothetical protein 18.50 ± 12.03
SAUSA300_2461 hypothetical glyoxalase family protein 18.38 ± 6.48
SAUSA300_1349 bshA glycosyl transferase, group 1 family protein 18.26 ± 11.03
SAUSA300_1009 typA GTP-binding protein 18.22 ± 6.42
SAUSA300_1755 splD serine protease SplD 18.20 ± 6.01
SAUSA300_1966 hypothetical phi77 ORF014-like protein, phage anti-repressor protein 18.04 ± 5.61
SAUSA300_1307 arlS sensor histidine kinase protein 18.01 ± 7.14
SAUSA300_1918 hlb truncated beta-hemolysin 17.91 ± 11.34
SAUSA300_1569 hypothetical U32 family peptidase 17.90 ± 6.37
SAUSA300_1397 hypothetical phiSLT ORF213-like protein, major tail protein 17.88 ± 16.40
SAUSA300_1032 hypothetical putative iron compound ABC transporter iron compound-binding protein 17.87 ± 9.01
SAUSA300_0259 hypothetical PTS system, IIA component 17.72 ± 4.08
SAUSA300_1070 hypothetical hypothetical protein 17.66 ± 6.61
SAUSA300_1474 hypothetical hypothetical protein 17.57 ± 3.84
SAUSA300_1451 hypothetical short chain dehydrogenase/reductase family oxidoreductase 17.47 ± 4.46
SAUSA300_0769 hypothetical hypothetical protein 17.42 ± 7.43
SAUSA300_2098 arsR ArsR family transcriptional regulator 17.36 ± 8.42
SAUSA300_0094 hypothetical hypothetical protein 17.32 ± 9.77
SAUSA300_1470 ispA geranyltranstransferase 17.29 ± 13.19
SAUSA300_1403 hypothetical phiSLT ORF412-like protein, portal protein 17.28 ± 10.80
SAUSA300_2432 hypothetical MutT/NUDIX family hydrolase 17.26 ± 15.82
SAUSA300_0631 hypothetical putative nucleoside transporter 17.25 ± 11.20
SAUSA300_1000 potB spermidine/putrescine ABC transporter permease 17.14 ± 5.86
SAUSA300_2559 hypothetical DNA-binding response regulator 17.10 ± 8.85
SAUSA300_2467 srtA sortase 17.01 ± 6.72
SAUSA300_2300 hypothetical transcriptional regulator, TetR family 16.92 ± 5.04
SAUSA300_0916 hypothetical hypothetical protein 16.89 ± 2.85
SAUSA300_1444 scpB segregation and condensation protein B 16.85 ± 6.40
SAUSA300_0995 hypothetical branched-chain alpha-keto acid dehydrogenase subunit E2 16.83 ± 18.68
SAUSA300_0419 hypothetical tandem lipoprotein 16.78 ± 3.58
SAUSA300_1563 accC acetyl-CoA carboxylase, biotin carboxylase 16.73 ± 11.04
SAUSA300_2027 alr alanine racemase 16.70 ± 16.05
SAUSA300_2607 hisA phoribosyl)-5-((5-phosphoribosylamino)methylideneamino) imidazole-4-carboxamide 16.70 ± 11.46
SAUSA300_0023 hypothetical hypothetical protein 16.69 ± 16.09
SAUSA300_1622 tig trigger factor 16.44 ± 5.67
SAUSA300_0011 hypothetical hypothetical protein 16.37 ± 4.02
SAUSA300_1097 pyrF orotidine 5′-phosphate decarboxylase 16.34 ± 8.94
SAUSA300_1339 hypothetical hypothetical protein 16.25 ± 5.49
SAUSA300_0585 hypothetical hypothetical protein 16.24 ± 13.38
SAUSA300_0839 nfu hypothetical protein 16.23 ± 12.30
SAUSA300_0071 hypothetical ISSep1-like transposase 16.19 ± 3.17
SAUSA300_0651 hypothetical CHAP domain-contain protein 16.09 ± 6.91
SAUSA300_1599 hypothetical hypothetical protein 16.02 ± 7.75
SAUSA300_1607 hypothetical hypothetical protein 16.02 ± 8.76
SAUSA300_0588 hypothetical hypothetical protein 15.86 ± 15.72
SAUSA300_2276 hypothetical peptidase, M20/M25/M40 family 15.84 ± 1.33
SAUSA300_2055 murA UDP-N-acetylglucosamine 1-carboxyvinyltransferase 15.79 ± 10.49
SAUSA300_0808 hypothetical hypothetical protein 15.69 ± 12.88
SAUSA300_0759 gpmI phosphoglyceromutase 15.68 ± 9.84
SAUSA300_0857 ppiB hypothetical protein 15.66 ± 4.76
SAUSA300_1051 hypothetical hypothetical protein 15.51 ± 14.05
SAUSA300_1383 hypothetical phiSLT ORF484-like protein, lysin 15.46 ± 15.13
SAUSA300_1566 hypothetical hypothetical protein 15.42 ± 14.25
SAUSA300_2040 hypothetical hypothetical protein 15.42 ± 12.63
SAUSA300_1145 xerC tyrosine recombinase xerC 15.33 ± 4.57
SAUSA300_0687 hypothetical putative hemolysin 15.14 ± 12.23
SAUSA300_0630 hypothetical ABC transporter ATP-binding protein 15.07 ± 10.45
SAUSA300_1577 hypothetical TPR domain-containing protein 14.93 ± 1.75
SAUSA300_1288 dapA dihydrodipicolinate synthase 14.75 ± 7.53
SAUSA300_1937 hypothetical phi77 ORF045-like protein 14.69 ± 8.83
SAUSA300_1419 hypothetical phiSLT ORF80-like protein 14.65 ± 9.06
SAUSA300_2345 nirD nitrite reductase (NAD(P)H), small subunit 14.54 ± 4.64
SAUSA300_1365 rpsA 30S ribosomal protein S1 14.53 ± 3.46
SAUSA300_0029 hypothetical hypothetical protein 14.39 ± 3.30
SAUSA300_2575 hypothetical BglG family transcriptional antiterminator 14.12 ± 4.67
SAUSA300_1497 hypothetical glycine dehydrogenase subunit 1 14.08 ± 4.09
SAUSA300_1682 ccpA catabolite control protein A 14.04 ± 8.43
SAUSA300_0657 hypothetical hypothetical protein 14.02 ± 7.45
SAUSA300_1955 hypothetical putative endodeoxyribonuclease RusA 13.92 ± 10.12
SAUSA300_0924 ktrD sodium transport family protein 13.85 ± 14.78
SAUSA300_0077 hypothetical ABC transporter ATP-binding protein 13.80 ± 6.67
SAUSA300_0504 pdxS pyridoxal biosynthesis lyase PdxS 13.58 ± 7.70
SAUSA300_0195 hypothetical transcriptional regulator 13.06 ± 13.37
SAUSA300_1308 arlR DNA-binding response regulator 13.05 ± 5.02
SAUSA300_0859 hypothetical NADH-dependent flavin oxidoreductase 12.99 ± 7.37
SAUSA300_1721 hypothetical hypothetical protein 12.97 ± 3.93
SAUSA300_0186 argC N-acetyl-gamma-glutamyl-phosphate reductase 12.92 ± 16.00
SAUSA300_2641 hypothetical hypothetical protein 12.90 ± 8.36
SAUSA300_0987 hypothetical cytochrome D ubiquinol oxidase, subunit II 12.85 ± 10.22
SAUSA300_1696 dat D-alanine aminotransferase 12.74 ± 5.48
SAUSA300_1283 hypothetical phosphate ABC transporter, phosphate-binding protein PstS 12.73 ± 9.23
SAUSA300_1185 miaB (dimethylallyl)adenosine tRNA methylthiotransferase 12.62 ± 10.40
SAUSA300_2365 hlgA gamma-hemolysin component A 12.56 ± 10.54
SAUSA300_1394 hypothetical hypothetical protein 12.34 ± 12.26
SAUSA300_0115 sirC iron compound ABC transporter permease SirC 12.30 ± 6.17
SAUSA300_2284 hypothetical hypothetical protein 12.20 ± 10.36
SAUSA300_2225 moaC molybdenum cofactor biosynthesis protein MoaC 12.08 ± 9.05
SAUSA300_0244 hypothetical zinc-binding dehydrogenase family oxidoreductase 12.05 ± 9.79
SAUSA300_2022 rpoF RNA polymerase sigma factor SigB 12.05 ± 6.83
SAUSA300_1089 lspA lipoprotein signal peptidase 11.97 ± 6.81
SAUSA300_1618 hemX hemA concentration negative effector hemX 11.88 ± 1.05
SAUSA300_0117 sirA iron compound ABC transporter iron compound-binding protein SirA 11.83 ± 7.84
SAUSA300_0899 mecA adaptor protein 11.58 ± 10.37
SAUSA300_2492 hypothetical acetyltransferase family protein 11.55 ± 7.80
SAUSA300_1433 hypothetical putative phage regulatory protein 11.41 ± 8.17
SAUSA300_1244 mscL large conductance mechanosensitive channel protein 11.32 ± 7.21
SAUSA300_0049 hypothetical hypothetical protein 11.30 ± 0.62
SAUSA300_1667 hypothetical putative glycerophosphoryl diester phosphodiesterase 11.30 ± 7.51
SAUSA300_0994 pdhB pyruvate dehydrogenase E1 component, beta subunit 11.20 ± 8.12
SAUSA300_0974 purN phosphoribosylglycinamide formyltransferase 11.07 ± 8.08
SAUSA300_0067 hypothetical universal stress protein 11.02 ± 9.02
SAUSA300_1590 rsh (relA) GTP pyrophosphokinase 10.95 ± 7.18
SAUSA300_0526 hypothetical methyltransferase small subunit 10.80 ± 10.78
SAUSA300_0952 hypothetical aminotransferase, class I 10.57 ± 6.79
SAUSA300_1694 trmB tRNA (guanine-N(7)-)-methyltransferase 10.55 ± 16.08
SAUSA300_0041 hypothetical hypothetical protein 10.41 ± 2.09
SAUSA300_1449 hypothetical MutT/nudix family protein 10.11 ± 13.24
SAUSA300_0724 hypothetical hypothetical protein 10.06 ± 2.60
SAUSA300_1757 splB serine protease SplB 9.41 ± 4.17
SAUSA300_0476 hypothetical hypothetical protein 9.18 ± 8.05
SAUSA300_2052 hypothetical single-stranded DNA- binding protein family 9.11 ± 18.19
SAUSA300_2176 cbiO cobalt transporter ATP-binding subunit 9.03 ± 9.11
SAUSA300_1112 stp1 protein phosphatase 2C domain-containing protein 8.98 ± 14.19
SAUSA300_0789 hypothetical putative thioredoxin 8.89 ± 18.33
SAUSA300_0379 ahpF alkyl hydroperoxide reductase subunit F 8.46 ± 4.49
SAUSA300_0348 tatA twin arginine-targeting protein translocase 8.36 ± 5.53
SAUSA300_0469 rnmV hypothetical protein 8.35 ± 0.35
SAUSA300_1792 hypothetical hypothetical protein 8.20 ± 4.58
SAUSA300_2061 atpH F0F1 ATP synthase subunit delta 7.98 ± 1.29
SAUSA300_1092 pyrP uracil permease 7.85 ± 2.60
SAUSA300_0905 hypothetical hypothetical protein 7.61 ± 3.76
SAUSA300_0444 gltC LysR family regulatory protein 7.59 ± 2.70
SAUSA300_2646 trmE tRNA modification GTPase TrmE 7.41 ± 8.81
SAUSA300_2105 mtlF PTS system, mannitol specific IIBC component 6.95 ± 0.84
SAUSA300_2486 clpL putative ATP-dependent Clp proteinase 6.73 ± 0.02
SAUSA300_1887 pcrB geranylgeranylglyceryl phosphate synthase-like protein 6.58 ± 3.46
SAUSA300_1653 hypothetical metal-dependent hydrolase 6.25 ± 8.63
SAUSA300_2393 opuCa glycine betaine/carnitine/choline ABC transporter ATP-binding protein 6.25 ± 7.87
SAUSA300_1183 hypothetical 2-oxoglutarate ferredoxin oxidoreductase subunit beta 6.19 ± 1.88
SAUSA300_0393 hypothetical hypothetical protein 6.18 ± 2.30
SAUSA300_0174 hypothetical hypothetical protein 6.15 ± 1.39
SAUSA300_0841 hypothetical hypothetical protein 5.97 ± 2.99
SAUSA300_1096 carB carbamoyl phosphate synthase large subunit 5.89 ± 2.89
SAUSA300_2593 hypothetical hypothetical protein 5.84 ± 3.04
SAUSA300_0221 pflA pyruvate formate-lyase activating enzyme 5.68 ± 18.96
SAUSA300_0996 lpdA dihydrolipoamide dehydrogenase 5.49 ± 2.87
SAUSA300_1992 agrA accessory gene regulator protein A 5.34 ± 14.81
SAUSA300_1147 hslU ATP-dependent protease ATP-binding subunit HslU 4.99 ± 6.72
SAUSA300_1120 recG ATP-dependent DNA helicase RecG 4.60 ± 0.15
SAUSA300_2078 murA UDP-N-acetylglucosamine 1-carboxyvinyltransferase 3.18 ± 3.15
SAUSA300_1583 cymR hypothetical protein 2.48 ± 0.46
SAUSA300_0992 hypothetical hypothetical protein 2.24 ± 20.30
SAUSA300_0634 fhuB ferrichrome transport permease fhuB 2.22 ± 4.57
SAUSA300_0750 whiA hypothetical protein 1.88 ± 4.32
SAUSA300_2485 hypothetical methylated DNA-protein cysteine methyltransferase 1.78 ± 9.18
SAUSA300_0426 hypothetical hypothetical protein 0.95 ± 5.09
SAUSA300_2598 capA capsular polysaccharide biosynthesis protein Cap1A 0.85 ± 1.11
SAUSA300_2246 hypothetical hypothetical protein 0.51 ± 16.59
SAUSA300_2518 hypothetical hydrolase family protein 0.42 ± 7.59
SAUSA300_0355 hypothetical acetyl-CoA acetyltransferase −0.68 a ± 4.44
SAUSA300_0398 hypothetical superantigen-like protein −0.83 a ± 2.42
SAUSA300_2226 moaB molybdenum cofactor biosynthesis protein B −1.15 a ± 5.09
SAUSA300_0945 hypothetical isochorismate synthase family protein −1.17 a ± 14.05
SAUSA300_0904 yjbI hypothetical protein −1.32 a ± 9.61
SAUSA300_0423 hypothetical hypothetical protein −2.20 a ± 9.08
SAUSA300_1422 hypothetical phiSLT ORF65-like protein −2.77 a ± 6.35
SAUSA300_0068 hypothetical cadmium-exporting ATPase, truncation −2.79 a ± 8.85
SAUSA300_1870 hypothetical hypothetical protein −2.92 a ± 15.58
SAUSA300_1139 sucD succinyl-CoA synthetase subunit alpha −2.94 a ± 8.32
SAUSA300_0918 ugtP diacylglycerol glucosyltransferase −3.09 a ± 8.63
SAUSA300_0597 hypothetical putative endonuclease III −3.15 a ± 14.78
SAUSA300_0326 hypothetical hypothetical protein −3.64 a ± 2.40
SAUSA300_0690 saeS sensor histidine kinase SaeS −4.88 a ± 14.01
SAUSA300_0560 vraB acetyl-CoA c-acetyltransferase −5.06 a ± 6.53
SAUSA300_2334 hypothetical hypothetical protein -5.12 a ± 7.55
SAUSA300_2025 rsbU sigma-B regulation protein −5.19 a ± 6.08
SAUSA300_2152 lacD tagatose 1,6-diphosphate aldolase −5.59 a ± 11.59
SAUSA300_1680 acuA acetoin utilization protein AcuA −5.94 a ± 10.87
SAUSA300_2024 rsbV anti-sigma-B factor, antagonist −6.77 a ± 14.71
SAUSA300_0618 mntC ABC transporter substrate-binding protein −6.85 a ± 4.61
SAUSA300_1876 hypothetical DNA polymerase IV −6.91 a ± 9.59
SAUSA300_1465 hypothetical 2-oxoisovalerate dehydrogenase, E1 component, beta subunit −7.15 a ± 6.73
SAUSA300_1573 hypothetical Holliday junction resolvase-like protein −10.10 a ± 6.88
SAUSA300_1473 nusB transcription antitermination protein NusB −10.84 a ± 10.00
SAUSA300_1357 aroC chorismate synthase −11.88 a ± 0.89
SAUSA300_1095 carA carbamoyl phosphate synthase small subunit −14.12 a ± 10.52
SAUSA300_1469 argR arginine repressor −14.16 a ± 8.61
SAUSA300_1615 hemB delta-aminolevulinic acid dehydratase −14.95 a ± 14.12
SAUSA300_1467 lpdA dihydrolipoamide dehydrogenase −15.68 a ± 14.07
SAUSA300_0993 pdhA pyruvate dehydrogenase E1 component, alpha subunit −17.05 a ± 10.66
SAUSA300_0752 clpP ATP-dependent Clp protease proteolytic subunit −17.66 a ± 11.34
SAUSA300_1715 ribD riboflavin biosynthesis protein −23.78 a ± 4.28
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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%).

Locus Gene Name Description % EC Damage (Mean ± SD)
SAUSA300_1197 ND a glutathione peroxidase 62.86 ± 5.67
SAUSA300_1333 hypothetical conserved hypothetical protein 62.17 ± 3.05
SAUSA300_1485 hypothetical conserved hypothetical protein 61.86 ± 6.12
SAUSA300_2221 moaD molybdopterin converting factor, subunit 1 61.64 ± 3.61
SAUSA300_0206 azoR flavodoxin family protein 60.82 ± 6.24
SAUSA300_0335 mepA MATE efflux family protein 60.15 ± 8.13
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a ND: not determined.

Table 3.

Verification of EC damage of JE WT strain and selected mutants using 24-well plates assay.

Locus Group Gene Name % EC Damage (Mean ± SD)
384-Well Plates 24-Well Plates
JE2 Wildtype 46.19 ± 2.97 42.43 ± 6.44
SAUSA300_1197 EC damage ≥ 60%
in 384-well plates		 hypothetical 62.86 ± 5.67 59.40 ± 1.50
SAUSA300_1333 hypothetical 62.17 ± 3.05 66.92 ± 0.84
SAUSA300_1485 hypothetical 61.86 ± 6.12 61.75 a
SAUSA300_2221 moaD 61.64 ± 3.61 59.90 ± 1.08
SAUSA300_0206 hypothetical 60.82 ± 6.24 69.33 ± 0.48
SAUSA300_0335 hypothetical 60.15 ± 8.31 63.35 ± 2.06
SAUSA300_1040 EC damage ≤ 30%
in 384-well plates		 hypothetical 26.74 ± 8.21 30.92 a
SAUSA300_1875 hypothetical 24.52 ± 10.68 30.51 a
SAUSA300_0871 hypothetical 24.49 ± 12.19 28.60 a
SAUSA300_1950 hypothetical 23.24 ± 9.64 25.87 a
SAUSA300_0253 scdA 21.83 ± 12.24 22.52 a
SAUSA300_0649 hypothetical 20.24 ± 0.89 22.65 a
SAUSA300_2587 hypothetical 20.06 ± 9.42 26.45 a
SAUSA300_0631 hypothetical 17.25 ± 11.20 23.00 a
SAUSA300_2027 alr 16.70 ± 16.05 3.28 ± 1.38
SAUSA300_2055 murA 15.79 ± 10.49 7.62 ± 0.59
SAUSA300_1682 ccpA 14.04 ± 8.43 13.43 a
SAUSA300_1696 dat 12.74 ± 5.48 14.99 ± 1.34
SAUSA300_0974 purN 11.07 ± 8.08 20.58 a
SAUSA300_1563 accC 16.73 ± 11.04 11.82 ± 0.72
SAUSA300_0041 hypothetical 10.41 ± 2.09 3.30 a
SAUSA300_0994 pdhB 11.20 ± 8.12 19.36 a
SAUSA300_0186 argC 12.92 ± 16.00 15.20 ± 2.13
SAUSA300_1992 agrA 5.34 ± 14.81 −3.82 ± 1.77
SAUSA300_0355 hypothetical −0.68 ± 4.44 −1.20 a
SAUSA300_0690 saeS −4.89 ± 14.01 −12.80 ± 1.77
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a Verification of these mutants was performed once using the 24-well plates assay.

Table 4.

Numbers of genes from different KEGG pathway categories.

Categories Sub-Groups No. of Mutants with
Decreased HMEC-1
Damage		 No. of Mutants with
Increased HMEC-1
Damage
Metabolism Carbohydrate metabolism 53
Amino acid metabolism 33
Metabolism of cofactors and vitamins 11
Lipid metabolism 8 1
Nucleotide metabolism 8
Biosynthesis of other secondary metabolites 7
Energy metabolism 7
Metabolism of other amino acids 3 1
Metabolism of terpenoids and polyketides 3
Glycan biosynthesis and metabolism 2
Xenobiotics biodegradation and metabolism 1
Genetic information processing Homologous recombination 4
DNA replication 2
Mismatch repair 2
Protein export 2
Ribosome 2
Sulfur relay system 2 1
RNA degradation 1
Environmental information processing Two-component system 13
ABC transporters 9
Other 3
Cellular processes Quorum sensing 9
Total 185 3
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Figure 2.

Figure 2

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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.

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.

Figure 3.

Figure 3

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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.

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/2079-6382/11/3/316/s1, Table S1: HMEC-1 damage caused by all the mutant strains, except mutants presented in Table 1 and Table 2 in the NTML.

Click here for additional data file. (63.8KB, zip)

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.

Footnotes

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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