for RNA precipitation. After 2 h of incubation at −20◦C, the tubes were centrifuged at 12,000 × g at 4◦C for 15 min and the supernatant discarded. An equal volume of cold absolute ethanol (Merck, Gernsheim Germany) was then added to the tube, which was centrifuged at 12,000 × g at 4◦C for 5 min to obtain the RNA pellet. The pellet was resuspended in 100 µl of DNase-free and RNase-free water. Additionally, the sample was treated with 2 U of DNase I (Promega, Fitchburg, WI, United States) followed by a 20-min incubation at 37◦C. The RNA quality and quantity were determine using a Nanodrop 1000 (Life Technologies, Camarillo, CA, United States), and the sample was then stored in 20-µl aliquots at −80◦C.

Total RNA was extracted with Trizol (Chomczynski and Sacchi, 2006) for three independent experiments. Briefly, 200 ng of total RNA for each sample was subjected to cDNA synthesis using a TURBO DNA-free Kit (Thermo Fisher, MA, United States) according to the manufacturer's protocol. The DNase inactivation reagent was removed by centrifugation at 10,000 × g for 1.5 min and the supernatant was aliquoted into fresh tubes for the reverse transcription step using SuperScript III Reverse Transcriptase (Invitrogen, CA, United States) according to the manufacturer's protocol. Realtime PCR was performed using SYBR Green PCR Master Mix (Invitrogen, CA, United States) based on the manufacturer's instructions, with an ABi 7500 Real-Time PCR Detection System (Applied Biosystems, MA, United States). Each sample was run in triplicate with 300 nM of each primer (**Supplementary Table S2**) with the following conditions: 95◦C for 10 min, 40 cycles of 95◦C for 30 s, and then 60◦C for 1 min. Melting curves were generated by a cycle of 95◦C for 1 min and 60◦C for 1 min. The relative quantitation of mRNA expression was normalized to the constitutive expression of the 16S rRNA housekeeping gene and calculated by the comparative 11CT method (Livak and Schmittgen, 2001; Wang et al., 2014).

#### Mitogenicity and Cytokine Release in Human Lymphocytes

Bacteria were grown in THB (Oxoid) with 0.2% yeast extract overnight at 37◦C. The overnight cultures were then diluted 1:100 in fresh THB, grown to mid-log phase, harvested by centrifugation at 3000 × g for 10 min, and then washed three times with phosphate-buffered saline (PBS). Pelleted cells were resuspended in PBS, heat-killed (100◦C, 30 min), and subjected to centrifugation at 11,000 × g for 20 min at 4◦C to remove cell debris. The supernatant (GBS cell extract) was aliquoted and stored at −80◦C until required. Protein concentrations were determined using protein assay dye reagent concentrate (Bio-Rad) with bovine serum albumin (Sigma) as a standard.

Peripheral blood mononuclear cells (PBMCs) were isolated from the whole blood of healthy individuals (obtained from the Hong Kong Red Cross Blood Transfusion Service) by density gradient centrifugation using Ficoll-Paque (GE Healthcare). The human mononuclear cells were washed with PBS, resuspended in medium (RPMI 1640 with 10% FBS), and seeded at 2 × 105 per ml in a 96-well View Plate (Perkin Elmer). Twenty-four hours later, GBS cell extract (prepared as described in the bacterial strains and growth conditions sections) was added at a final concentration of 25 µg/ml. Phytohemagglutinin (PHA, 10 µg/ml) and culture medium alone were included as controls. After incubation for 24 h, the proliferation of lymphocytes was detected using alamarBlue (Life Technologies) according to the manufacturer's protocol. Fluorescence emission was measured using an EnSpire Multimode Plate Reader (Perkin Elmer) at 585 nm with an excitation wavelength of 570 nm. Experiments were performed in triplicate.

#### Cytokine Measurements

After stimulating PBMCs, the supernatant from cell cultures was collected after incubation for 3, 6, 12, and 24 h to measure cytokine release. Interleukin (IL)-1β, IL-6, IL-8, IL-10, IL-12, and tumor necrosis factor alpha (TNF-α) were evaluated by ELISA according to the manufacturer's instructions (BD Biosciences). Measurements were performed at an OD of 450 nm (EnSpire Multimode Plate Readers, PerkinElmer).

## Mouse Infection Model

Animal experiments were performed with permission of the Animal Experimentation Ethics Committee (AEEC) of the Chinese University of Hong Kong.

The virulence of 1bceR GBS III-4 mutant strains was compared to that of the wild-type strain CU\_GBS\_08, the CU\_GBS\_12 strain with a natural truncation of bceR, and the ATCC 12403 Type strain as a control using a mouse model. The ATCC strain belongs to serogroup III and originated from a case of fatal septicemia3 . The GBS inoculum was prepared by diluting overnight cultures 1:100 into THB. Cultures were incubated at 35◦C, and then bacteria were harvested by centrifugation at 1200 × g for 10 min at 4◦C. The pellet was then washed twice and resuspended in 5 ml of PBS. GBS was then prepared by diluting the PBS suspension to 107 CFU/ml. Dilutions were confirmed by colony counts on blood agar. Six-week-old CD1 mice were purchased from The Laboratory Animal Services Centre (The Chinese University of Hong Kong, Hong Kong) and infected via intraperitoneal injection with 0.1 ml of the GBS inoculum at 107 CFU/ml. The control group was injected with an equivalent volume of sterile PBS. Each group contained 30 mice. The mice were monitored for 10 days and those surviving at this time were sacrificed under anesthesia. The health condition of the mice was monitored daily and animals showing signs of excess weight loss, severe pain, and distress were euthanized before the end of study. The LD50 was calculated, and the Kaplan–Meier survival curve for infection and control groups with an endpoint of 10 days was prepared. The study was approved by the University Animal Experimentation Ethics Committee (AEEC; Reference no.:13- 063-MIS) and conducted at The Laboratory Animal Services Centre in compliance with International Guiding Principles for Biomedical Research Involving Animals and The Hong Kong Code of Practice for Care and Use of Animals for Experimental Purposes.

3https://www.atcc.org/products/all/12403.aspx

## H2O2 Stress Assay

fmicb-10-00010 January 22, 2019 Time: 17:43 # 5

The GBS strains were plated on blood agar plates and incubated at 35◦C in 5% CO2. Bacterial cells were suspended in pre-warmed THB with shaking at 200 rpm overnight. The overnight cultured bacterial cells were then diluted 1:100 in THB and incubated at 37◦C with shaking at 200 rpm to achieve an OD600 of 0.8– 1.0. The bacteria were resuspended in THB at a concentration of 4 × 107 CFU/ml, and then 40 mM H2O2 was added at RT for 15 min. After treatment, fresh THY broth was added to stop the reaction and the bacteria were harvested by centrifugation at 4000 × g for 15 min. Bacterial viability after H2O2 treatment was then examined through the culture and enumeration of bacterial colonies. Serial dilutions of medium were used for CFU counting. Each experiment was conducted in triplicate.

#### Determination of Biofilm Biomass by Crystal Violet Staining and CFU Counting

The GBS strains were plated on blood agar plates and incubated at 35◦C in 5% CO2. Overnight bacterial cultures were then suspended in pre-warmed THB overnight and 24-well flat bottom plates (Costar, Boston, MA, United States) were used to support biofilm growth. Then, the overnight bacterial cultures were diluted 1:100 in THB and incubated at 37◦C with shaking at 200 rpm to achieve an OD600 of 0.8–1.0. The bacteria were harvested by centrifugation at 4000 × g for 15 min. After washing with PBS, the cells were diluted 1:10 with pre-warmed THB, and 500 µl of cells was added to each well of a 24-well plate and incubated at 37◦C with 5% CO2 overnight without shaking. All samples were run in triplicate.

Biofilm biomass was quantified by measuring the absorbance of crystal violet (Olson et al., 2002). After removing the culture medium, the plates were gently washed with PBS twice to remove the floating cells. Biofilms were stained with 300 µl of 0.5% crystal violet (Sigma, MO, United States) (prepared in 10% ethanol) for 15 min at RT. After staining, the plates were gently washed with PBS three times and dried at RT. Then, 500 µl of 95% ethanol was added to each well and incubated for 15 min to dissolve the biofilms. OD595 values were measured using a DTX 880 plate reader (Molecular Devices, San Jose, CA, United States).

Bacterial viability in biofilms was also examined by enumerating bacterial colonies. After removing the culture medium, the plates were gently washed with PBS twice to remove floating cells, which was followed by the addition of 500 µl of fresh THB to each well. The cells were collected by scraping the bottom of each well with a sterile cell scraper. Serial dilutions of the medium were used for CFU enumeration, and each experiment was performed in triplicate.

### Two-Dimensional Gel Electrophoresis (2DE) and Mass Spectrometry

The GBS strains were plated on blood agar plates and incubated at 35◦C in 5% CO2. Bacterial cells were suspended in pre-warmed THB with shaking at 200 rpm overnight. Then, the overnight bacterial cultures were diluted1:100 in THB and incubated at 35◦C with shaking at 200 rpm to mid-log phase, after which, the bacterial cells were harvested by centrifuging at 4000 × g for 20 min at 4◦C. For whole protein extraction, the instructions of the total protein extraction kit (Bio-Rad, United States) were followed, and protein quantitation was performed using RC DC Protein Assay reagent (Bio-Rad, United States). Then, 2DE was conducted following the protocol of a previous study (Jones et al., 2004).

The gel photos were normalized and compared using software PDQuest (Version8.0.1, Bio-Rad, United States). The Boolean method was chosen to compare the intensity of the protein spots to determine both fold-changes and statistically significantly differences between GBS III-4 wild-type and 1bceR strains. From the results, we found that the expression of three proteins was significantly decreased in the 1bceR strain (>2-fold reduction in expression), and these three protein spots were cut from the original 2-DE gel and sent to the proteomic core laboratory of The University of Hong Kong for mass spectrometry-based identification.

#### Statistical Analysis

Data are expressed as the mean ± SD. Statistical comparisons between different treatment groups were performed using a one-way analysis of variance (ANOVA), followed by a post hoc Dunnett's test using GraphPad Prism 6.05 for Windows (GraphPad Software, San Diego CA, United States). Differences were considered as significant at p < 0.05, and were denoted as ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, and ∗∗∗∗p < 0.0001.

## RESULTS

#### Whole Genome Sequencing and Comparative Genomics Analysis of GBS Serotype III-4 Strains

The genomes of three invasive and two non-invasive GBS serotype III-4 strains were sequenced using a Roche 454 and Illumina Solexa Genome Analyzer, according to the manufacturer's instructions, and have been submitted to GenBank as either draft or complete genomes (**Table 1**). The genomes of the meningitis/septicemia strains were compared to those of the non-invasive strains. All single nucleotide polymorphisms (SNPs) from the ORFs were called using Mauve (version 2.3.1) software (Darling et al., 2010). Sequence alignment was performed to compare gene sequence variations among these strains. Genes that encode hypothetical proteins and those related to bacteriophages were not analyzed further. From this, we narrowed down our list to four truncated genes of interest as indicated in **Supplementary Table S3**. These genes showed 100% nucleotide identity to those of other GBS strains in GenBank. SNPs were confirmed by PCR-based Sanger sequencing to filter out false positive SNPs, which can occur with next generation sequencing.

Comparative genome analysis revealed a non-synonymous substitution (truncation) of a DNA binding regulator (Accession no: CU\_GBS08\_01010) in the non-invasive GBS strain, and the truncation of bceR at c.288delG was determined to generate a

#### TABLE 1 | List of strains in this study.

fmicb-10-00010 January 22, 2019 Time: 17:43 # 6

stop codon, abrogating expression of a region of the mRNA encoding the last 20 aa of the receiver domain and the DNAbinding domain. BLAST analyses revealed that this regulator was most closely related to the TCS response regulator protein BceR of S. gallolyticus, with 69% protein sequence homology (GenBank no: CDO17747.1). Although this gene was present in all GBS strains examined, the sequences harbored ∼30% differences compared to the bceR genes of other bacteria, suggesting that this gene might have specific functions in GBS. Based on the location of the truncation of the response gene, we predicted that the bceR-like response would be aborted in the non-invasive strain. The present study therefore focused on the role of this response regulator gene in this TCS of GBS. We thus knocked down this gene in the wild-type invasive strain CU\_GBS\_08 to elucidate its role in antimicrobial peptide resistance, stress response, and virulence in this invasive GBS strain. Our working model is depicted in **Figure 1**. Together with evidence that the transporter-encoding bceAB gene is activated by bacitracin, we have re-named this regulator bceR of the two-component system bceRS in this complete genome (GenBank genome: CP010874).

### The 1bceR Strain Is More Sensitive to Bacitracin and Antimicrobial Peptides

It is known that bceR-like systems comprise components of antimicrobial peptide detoxification modules, such as the graRS system of S. aureus, as the MIC values of some AMPs were decreased in strains with mutations in this system (Cui et al., 2005; Meehl et al., 2007). Here, the MICs of selected AMPs and antibiotics were measured for the 1bceR, complementation, and wild-type strains (**Table 2**). MICs for the mutant strain were 256- and 4-fold lower for bacitracin and LL-37, respectively, compared to those for the wild-type strain. However, 1bceR complementation with the pDL289-bceR plasmid restored resistance to both bacitracin and LL-37 (**Table 2**). No difference in resistance was observed between the wild-type strain and the isogenic 1bceR strain for other antibiotics.

#### TABLE 2 | Minimal inhibitory concentrations (MIC) of antimicrobial peptides and other antibiotics in GBS strains.

aMinimum inhibitory concentration, obtained according to CLSI protocol (CLSI, 2011).

## Expression of bceA, bceB, and dltA Is Reduced in the 1bceR GBS Strain

graRS, a bceRS-like system of S. aureus, was reported to induce AMP resistance not only by pumping AMPs out via an ABC transporter, but also by lowering the overall negative net charge of the cell envelope by upregulating expression of the dlt operon and mprF (Li et al., 2007; Meehl et al., 2007). Thus, the expression of bceA, bceB, dltA, and mprF was evaluated in the presence of a sub-inhibitory concentration of bacitracin in wild-type and 1bceR strains and normalized to 16s rRNA expression. Expression levels in GBS strains grown in THB only were used as controls and adjusted to 1. As shown in **Figure 2**, levels of bceA, bceB, and dltA were higher when respective strains were grown in THB containing bacitracin at 1/8 the MIC value for CU\_GBS\_08 (bacitracin: MIC, 64 µg/ml) compared to those when bacteria were grown in the presence of bacitracin at 1/8 the MIC value for CU\_GBS\_1bceR (bacitracin: MIC, 0.25 µg/ml; p < 0.0001) and for CU\_GBS\_12 (bacitracin: MIC, 0.25 µg/ml; p < 0.0001; **Figures 2A–C**). However, no significant difference of mprF expression was found between the wild-type strain and 1bceR strain (**Figure 2D**).

## Mitogenicity and Pro-inflammatory Response Induced by GBS in Human PBMCs

The proliferation of PBMCs was evaluated after 24 h of stimulation with GBS or 10 µg/ml PHA to evaluate mitogenicity and the ability of GBS to induce the proliferation of these

cells. As shown in **Figure 3**, although all bacteria induced the proliferation of PBMCs, the 1bceR strain demonstrated a significantly reduced immunogenicity (p < 0.0001). Similarly, levels of the cytokines TNF-α, IL-6, IL-8, IL-1β, IL-10, and IL-12 were determined, as shown in **Figures 4A–F**. The isogenic mutant

strain 1bceR induced a significant decrease in the expression of pro-inflammatory cytokines when compared to that with the wild-type strain. The decreased release of TNF-α was the most obvious (p < 0.0001) and was approximately fourfold decreased compared to that with the wild-type strain. This was followed by IL-6, IL-1β, and IL-10, which were decreased by approximately twofold with the 1bceR strain (p < 0.001 for IL-6 and IL-10 and p < 0.01 for IL-1β). Peak IL-6 expression was delayed to 24 h with the 1bceR strain, and the release of IL-8 was approximately 1.4 fold lower for this strain (p < 0.0001). The release of IL-12 could not be detected in the presence of both wild-type and mutant strains. Further, the complementation of 1bceR using pDL289 reversed the change in cytokine release.

## The Deletion of bceR Attenuates Virulence in a Mouse Infection Model

The virulence of the wild-type and 1bceR strains was studied using a mouse infection model via intraperitoneal inoculation. The lethal concentration (LD50) at which 50% of the mice died in the tested group at the specified time point was then calculated. The LD50 values of the 1bceR and wild-type strains were 1 × 107 and 3 × 106 CFU, respectively (**Supplementary Table S4**). Moreover, the survival rates of mice infected intraperitoneally with GBS at 107 CFU after 10 days of inoculation are shown in **Figure 5**. As observed, the virulence of the 1bceR strain was attenuated compared to that of the wild-type strain, as revealed by the increased survival rate of 23.3% versus 0% with the wild-type strain (p < 0.01).

### Bacterial Survival in Response to H2O2 Stress Is Decreased in the 1bceR Strain

Next, the response of the 1bceR, wild-type, and complementation strains to H2O2 stress was assessed (**Figure 6**).

The mutant strain was significantly more susceptible to H2O2 than the wild-type strain. Specifically, the survival rate of the mutant strain was reduced by 20% compared to that of the wild-type strain (p < 0.001); however, no significant difference in susceptibility was observed between wild-type and non-invasive CU\_GBS\_12 strains.

## Biofilm Formation Is Impaired in the 1bceR Strain

The ability of the wild-type, 1bceR, 1bceR complementation, and CU\_GBS\_12 (non-invasive) strains to form biofilms was assessed by crystal violet staining and CFU enumeration (**Figures 7A,B**). One-way ANOVA analysis showed that biofilm formation was impaired significantly in the 1bceR strain when compared to that in the wild-type strain (p < 0.05 and p < 0.0001, for crystal violet staining and CFU numbers, respectively), which was reversed by complementation. The biofilms were also evaluated by confocal microscopy (CLSM), wherein the cell density (xy images) and thickness (xz images) of biofilms were assessed. As shown in **Supplementary Figures S1A–C**, most cells in the biofilms were stained green, indicating that more live cells were present. However, a decreased signal was detected, based on the xy and xz images, for the bceR strain when compared to that with the wild-type strain, which indicated that fewer living or dead cells were present with the 1bceR strain. Thus, CLSM images revealed that loss of the bceR-like regulator inhibited

stained with crystal violet were measured at OD595. Significance was determined by one-way ANOVA (∗p < 0.05, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001).

TABLE 3 | Results of proteins identification by mass spectrometry.

biofilm formation, resulting in a lower cell density and reduced thickness.

## Deletion of bceR Alters Protein Expression in the GBS Strain

Proteomic analysis of bacteria harvested at mid-log phase was performed using 2-DE and mass spectrometry. This revealed three proteins that were reduced by greater than twofold in the 1bceR strain; the Boolean operation of the PDQuest software (version 8.0.1, Bio-Rad, United States) was then used to compare the intensities of the protein spots (**Table 3** and **Figure 8**). This analysis indicated that alkyl hydroperoxide reductase (AhpC), the Gls24 family stress protein (Gls24), and alcohol dehydrogenase (Adh) were decreased by 2.72-, 2.79-, and 2.59-fold, respectively. Real-time PCR was conducted to confirm the results of 2DE-mass spectrometry at the RNA level, and these three markers were reduced by 6.73-, 3.56-, and 6.7-fold, respectively, in the 1bceR strain (**Figure 9**).

## DISCUSSION

In this study, the bceR-like gene, belonging to the bceRS-like TCS family was described in GBS, and was found to mediate AMP and environmental stress resistance. The bceR-like system is associated with resistance to cell wall-targeting antimicrobial peptides in B. subtilis (Bernard et al., 2007; Dintner et al., 2014). Moreover, the bceR-like system (graR) of S. aureus was previously found to respond to vancomycin and polymyxin B, and the homologous proteins encoded by these genes were determined to mediate resistance to bacitracin and nisin in S. mutans and Lactococcus lactis, respectively (Tsuda et al., 2002; Kramer et al., 2006). In GBS, we found that the deletion of bceR resulted in an increased sensitivity to bacitracin and human cathelicidin LL-37. The regulatory effect of bceR on the ABC transporter bceAB, which encodes a protein that can pump out AMPs from the bacterial cells, is possibly the major mechanism of AMP resistance conferred by the bceR-like system of GBS. However, the loss of bceR in GBS did not alter sensitivity to erythromycin and beta-lactam antibiotics; these results demonstrate that the structurally homologous bceRS system might play a specific role in GBS, which highlights the importance of determining the individual roles of bceR-like systems in the pathogenesis of different Gram-positive species.

In S. epidermidis, the TA alanylation system, dltAB, and mprF, which encodes a lipid modification enzyme, were also found to be controlled by the bceR-like system (Li et al., 2007b; Sass et al., 2008). In GBS, the D-alanylation of TA was found to confer resistance to cationic peptides, and the lack of DltA was related to increased sensitivity to phagocytic cells and attenuated bacterial virulence (Poyart et al., 2003; Saar et al., 2012). DltA is a cytoplasmic carrier protein ligase that catalyzes the D-alanylation of the D-alanyl carrier protein DltC. DltB is a transmembrane protein that was reported to be involved in the efflux of activated

D-alanine to the site of acylation (Joseph et al., 2004; Mandin et al., 2005). In GBS, we found that the expression of dltA was downregulated in the 1bceR strain in the presence of bacitracin, suggesting that it might be regulated by the bceR-like system. Suppressing the D-alanylation of lipoteichoic acids through the repression of dltA would increase the negative charge of the GBS envelope, resulting in susceptibility of the 1bceR strain to AMPs. In addition to the negative charge of bacteria, the density of the cell wall was shown to be altered in dltA mutants of Streptococcus pyogenes and the deletion of this gene was found to suppress the production of virulence-related proteins (Cox et al., 2009; Grubaugh et al., 2018; Luo et al., 2018). Moreover, the bceRlike system (virRS) was identified to regulate bacterial adhesion and entry into eukaryotic cells in Listeria monocytogenes, and the dlt operon, mprF, and bceAB were all found to be controlled by the regulator virR (Abachin et al., 2002; Camejo et al., 2009), suggesting that dltA might contribute to virulence in GBS, which requires further investigation. mprF was not differentially expressed in the presence or absence of bacitracin, indicating that this gene might respond to other inducers.

In addition to resistance to AMPs, GBS bceR was found to mediate environmental stress resistance and biofilm formation. Accordingly, the 1bceR strain displayed increased sensitivity to H2O2 stress when compared to the invasive CU\_GBS\_08 strain, which was similar to results reported for the TCS graRS of S. aureus, which was found to be involved in resistance to superoxide radicals (Falord et al., 2011). The underlying mechanism is still unclear, but we found that the 1bceR strain exhibited reduced expression of the alkyl hydroperoxide reductase AhpC, the zinc-dependent alcohol dehydrogenase Adh, and a Gls24 family protein. These proteins have been reported to be involved in oxidative stress resistance and biofilm formation

deviation of the mean values from at least three replicate.

(Becker et al., 2001; Teng et al., 2005; Cosgrove et al., 2007), implying the contribution of the bceR-like regulator to these processes in GBS. Experiments demonstrating the effects of other environmental factors on the survival of the wild-type/mutant strains, such as different pH, temperature, and osmotic pressure, were also consistent with results from previous studies on GBS (Yang et al., 2012). However, significant differences in pH tolerance, temperature tolerance, and osmotic stress resistance between wild-type and 1bceR strains were not detected (data not shown). Bacterial cells within biofilms are difficult to eradicate, as they are highly resistant to antibiotics and the host immune system. The difference in biofilm-forming ability between GBS isolates from asymptomatic pregnant women (carriers) and those isolated from clinical infections was previously found to be statistically significant (Olson et al., 2002). The protein Adh was previously reported to catalyze the reversible conversion of acetaldehyde to ethanol, which is known to enhance the production of Staphylococcus biofilms; moreover, Adh expression was found to be upregulated in Staphylococcus biofilms (Becker et al., 2001; Finelli et al., 2003). In our study, all strains were able to form biofilms, but the biofilm biomass of the wild-type strain was significantly greater than that of the 1bceR strain. This is consistent with a previous report suggesting that the TCS graRS is involved in biofilm formation in S. aureus (Shanks et al., 2008).

The invasive CU\_GBS\_08 strain used in this study was isolated from a non-pregnant adult with toxic shock syndrome, indicating the virulence of this invasive clinical strain. Therefore, the role of the bceR-like system in virulence was assessed by using both in vitro cytokine release assays and an in vivo mouse infection model. Our results demonstrated the mitogenic nature of this regulator and its ability to induce a significant pro-inflammatory cytokine response, which is a characteristic of the development of sepsis and septic shock. Cytokines are soluble proteins that play a significant role in inflammation and the regulation of immune responses (von Hunolstein et al., 1997). Significantly increased production of TNF-α, IL-6, and IL-1β was detected after infection with the wild-type strain compared to that with the 1bceR strain. These three cytokines were reported to be positively related to disease severity (De Bont et al., 1993; Cusumano et al., 1996; von Hunolstein et al., 1997). It was previously reported that S. epidermidis and S. aureus mutant strains devoid of the bceR-like system are more susceptible to neutrophil-mediated killing (Cheung et al., 2010). Moreover, the expression of IL-8, a major activator of neutrophils and lymphocytes (Cusumano et al., 1996; Vallejo et al., 1996; von Hunolstein et al., 1997) was found to be reduced in 1bceR strains. However, the deletion of bceR did not completely abrogate the proliferation of mononuclear cells and cytokine release, suggesting that other factors are also involved in the virulence and pathogenicity of this strain.

In our mouse infection model, ATCC12403, which originated from a case of fatal septicemia, was used as a control. Our wildtype invasive strain resulted in lethality that was decreased by two orders of magnitude compared to that with the ATCC strain, thus indicating its hyper-virulence. Further the attenuation of virulence in the 1bceR strain was demonstrated; moreover, the Gls24 family protein was previously found to be related to bacterial virulence (Teng et al., 2005). The bceR-like system was previously found regulate numerous virulence factors in S. aureus and L. monocytogenes (Joseph et al., 2004; Falord et al., 2011), which in turn indicates that bceR might be involved in cross-talk with other regulator(s) in GBS. The non-invasive GBS strain was the least virulent among the stains tested, and harbors mutations in addition to the bceR truncation; this indicates that other gene(s) involved in bacterial virulence need

to be characterized. TCSs are widely used as signal transduction systems by bacteria to respond to changing growth conditions. The ability of GBS to efficiently adapt to different host niches during the infectious cycle is important for the pathogenicity of these strains. bceRS-like TCSs are widespread in Grampositive bacteria and are associated with a range of bacterial activities. Further, their contributions to these activities in GBS have not been sufficiently recognized. Our results indicated that bceR is involved in environmental stress resistance, antimicrobial peptide resistance, and virulence, processes that are crucial for the survival of GBS in response to different microenvironments that are encountered during infection. Thus, bceR could be a potential target to modulate and attenuate virulence.

#### AUTHOR CONTRIBUTIONS

YY, ML, HZ, CL, and AL performed the experimental work. YY analyzed the data with supervision of MI and prepared first draft of the manuscript. MI and KF contributed to the GBS strains collection and design of the project. MI and GZ contributed essential ideas and discussion. All authors contributed to the drafts of

#### REFERENCES

the manuscript, revision and approved the manuscript submission.

### FUNDING

This work was supported by the Health and Medical Research Fund of the Food and Health Bureau, HKSAR Government (reference number: 12110612, PI to MI).

## ACKNOWLEDGMENTS

We thank Professor Craig E. Rubens for kindly providing the plasmid pJRS233 used for bceR knock out. We are grateful to Professor Marc Ouellette for generously providing the plasmid pDL289 used for bceR complementation.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb. 2019.00010/full#supplementary-material

analysis of responses to defensin challenge. Int. J. Med. Microbiol. 298, 619–633. doi: 10.1016/j.ijmm.2008.01.011

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Yang, Luo, Zhou, Li, Luk, Zhao, Fung and Ip. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# In vitro Effects of Antimicrobial Agents on Planktonic and Biofilm Forms of Staphylococcus saprophyticus Isolated From Patients With Urinary Tract Infections

#### Edited by:

José Luis Capelo, Universidade Nova de Lisboa, Portugal

#### Reviewed by:

Mariusz Stanislaw Grinholc, Intercollegiate Faculty of Biotechnology of University of Gdansk and Medical University ´ of Gdansk, Poland ´ Mire Zloh, University of Hertfordshire, United Kingdom Anca Butiuc-Keul, Babe ¸s-Bolyai University, Romania

#### \*Correspondence:

Maria de Lourdes Ribeiro de Souza da Cunha<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Antimicrobials, Resistance and Chemotherapy, a section of the journal Frontiers in Microbiology

Received: 29 May 2018 Accepted: 11 January 2019 Published: 28 January 2019

#### Citation:

Martins KB, Ferreira AM, Pereira VC, Pinheiro L, Oliveira A and Cunha MLRS (2019) In vitro Effects of Antimicrobial Agents on Planktonic and Biofilm Forms of Staphylococcus saprophyticus Isolated From Patients With Urinary Tract Infections. Front. Microbiol. 10:40. doi: 10.3389/fmicb.2019.00040 Katheryne Benini Martins1 , Adriano Martison Ferreira1,2, Valéria Cataneli Pereira1 , Luiza Pinheiro1 , Adilson de Oliveira1 and Maria de Lourdes Ribeiro de Souza da Cunha1 \*

1 Department of Microbiology and Immunology, Institute of Biosciences, UNESP – Universidade Estadual Paulista, Botucatu, Brazil, 2 Department of Tropical Diseases, Botucatu School of Medicine University Hospital, UNESP – Universidade Estadual Paulista, Botucatu, Brazil

Bacterial biofilms play an important role in urinary tract infections (UTIs), being responsible for persistent infections that lead to recurrences and relapses. Staphylococcus saprophyticus is one of the main etiological agents of UTIs, however, little is known about biofilm production in this species and especially about its response to the antimicrobial agents used to treat UTIs when a biofilm is present. For this reason, the aim of this work was to evaluate the response of S. saprophyticus biofilms to five antimicrobial agents. Staphylococcus saprophyticus was evaluated for antimicrobial susceptibility in its planktonic form by means of minimum inhibitory concentration (MIC) and in biofilms by means of minimum inhibitory concentration in biofilm (MICB) against the following antimicrobial agents by the microdilution technique: vancomycin, oxacillin, trimethoprim/sulfamethoxazole, ciprofloxacin, and norfloxacin. Of the 169 S. saprophyticus studied, 119 produced a biofilm as demonstrated by the polystyrene plate adherence method. Biofilm cells of S. saprophyticus exhibited a considerable increase in MICB when compared to the planktonic forms, with an increase of more than 32 times in the MICB of some drugs. Some isolates switched from the category of susceptible in the planktonic condition to resistant in the biofilm state. Statistical analysis of the results showed a significant increase in MICB (p < 0.0001) for all five drugs tested in the biofilm state compared to the planktonic form. Regarding determination of the minimum bactericidal concentration in biofilm (MBCB), there were isolates for which the minimum bactericidal concentration of all drugs was equal to or higher than the highest concentration tested.

Keywords: Staphylococcus saprophyticus, biofilm, antimicrobial resistance, MICB, MBCB

## INTRODUCTION

fmicb-10-00040 January 24, 2019 Time: 16:14 # 2

In order to survive in hostile environments such as in host tissues (antibodies, phagocytes, etc.,) or on an inert surface where they are exposed to inhospitable conditions (UV light, desiccation, heat, cold), bacteria adapt by forming adherent populations (sessile bacteria) organized in a structure called biofilm (Mah and O'Toole, 2001).

Li et al. (2005) demonstrated that biofilm formation in Staphylococcus spp. depends on Polysaccharide Intercellular Adhesin (PIA), whose biosynthesis is mediated by the ica operon. This operon contains the icaADBC genes and the regulatory icaR gene, which is transcribed in the direction opposite to the ica operon. In the case of the icaR gene, some studies have suggested that its product is a transcription repressor that plays an adaptive role in the regulation of the expression of the ica operon according to environmental conditions. Some factors such as anaerobic growth, the presence of antibiotics at subinhibitory concentrations, and environmental stress such as high osmolarity may increase expression of the ica operon. In addition to PIA, the existence of ica-independent mechanisms for biofilm formation in Staphylococcus spp., such as proteins and DNA, has been highlighted (Mendoza-Olazarán et al., 2015).

Once formed, these biofilms render the cells less accessible to the defense system of the organism, impairing the action of antibiotics. Biofilms thus represent basic survival strategies of these microorganisms, a fact that explains why biofilms are considered to be of major public health importance. Furthermore, the proximity of cells inside microcolonies or between microcolonies provides an excellent environment for the exchange of genetic material. The mechanism of conjugation, i.e., the transfer of plasmids between bacteria, occurs at a higher proportion between bacterial cells in biofilms than between planktonic cells (Águila-Arcos et al., 2017).

In the laboratory, the effectiveness of an antibiotic is evaluated with the microorganism in its planktonic form (free cells). However, these assays only reveal the concentration of the chemotherapeutic agent that is necessary to inhibit growth or kill planktonic bacteria (Jorgensen and Ferraro, 2009). Maximum resistance to antibiotics is achieved once microorganisms complete the formation of the mature biofilm (Høiby et al., 2010).  Therefore, in some circumstances, the use of planktonic bacteria for the selection of chemotherapeutic agents may be inappropriate.

Biofilm formation can be considered a virulence determinant that is responsible for the long-term persistence of bacteria in the genitourinary tract (Costerton et al., 1999). Urinary catheters and other prosthetic devices predispose to urinary tract infections (UTIs) by destroying natural barriers (urethral sphincter) and providing a nidus for infection that serves as a substrate for biofilm formation. Bacterial biofilms play an important role in UTIs, being responsible for persistent infections that lead to recurrences and relapses (Delcaru et al., 2016).

The most commonly prescribed antibiotics for the treatment of UTIs are trimethoprim/sulfamethoxazole, fluoroquinolones, first- and second-generation cephalosporins, amoxicillin + clavulanate, and nitrofurantoin (Lee et al., 2008). According to the CLSI M100-S26 document (2016), routine susceptibility testing of urinary S. saprophyticus isolates is not recommended since this microorganism is normally susceptible to the antimicrobial agents used to treat acute uncomplicated UTIs (nitrofurantoin, sulfamethoxazole/trimethoprim, or a fluoroquinolone). However, 17.6% of the S. saprophyticus isolated from UTIs tested by Ferreira et al. (2012) were resistant to sulfamethoxazole/trimethoprim, a fact that may lead to therapeutic failure when UTIs are treated empirically. Antibiotic resistance seems to have emerged also among S. saprophyticus strains and antimicrobial susceptibility testing of these strains is therefore necessary.

Staphylococcus saprophyticus is one of the main etiological agents of UTIs, however, little is known about biofilm production in this species and especially about its response to the antimicrobial agents used to treat UTIs when a biofilm is present. For this reason, the aim of this work was to evaluate the response of S. saprophyticus biofilms to five antimicrobial agents.

#### MATERIALS AND METHODS

#### Samples

Staphylococcus saprophyticus isolated from the urine of different patients were used in the study. The strains were obtained in a prospective study through isolation in the Laboratory of Microbiology, University Hospital of the Botucatu School of Medicine (HC-FMB), SP, Brazil, in 2013 and 2014 or were obtained from a culture collection established in 2008. The samples were collected from patients originating from wards, outpatient clinics, emergency rooms, and basic health units of Botucatu and region. The present study was approved by the institutional Ethics Committee (Protocol 16269813.1.0000.5411) and was exempt from the requirement of free informed consent of the participants in this study since we did not use clinical data of the patients and had no contact with the patients. Bacteria had previously been isolated from the patients and were stored at the Laboratory of Microbiology (HC-FMB).

Individuals of both genders and all ages with S. saprophyticuspositive urine cultures compatible with UTI, with a colony count equal to or greater than 100,000 colony forming units per milliliter of urine ( ≥ 105 CFU/mL) according to the criteria of Kass (1956), were included. Samples were collected according to the urine collection protocol of the service.

The isolates were seeded on blood agar with 5% sheep blood (secondary isolation) and stained by the Gram staining method for the assessment of purity and observation of their specific morphology and staining. After confirmation of these characteristics, the strains were submitted to the catalase, DNAse, and tube coagulase (gold standard) tests to distinguish Staphylococcus aureus and coagulase-negative staphylococci (CoNS) as recommended by Koneman et al. (1997).

## DNA Extraction and Identification of S. saprophyticus

DNA was extracted from isolates identified as CoNS with the Illustra R Kit (GE Healthcare) according to manufacturer's instructions.

Isolates identified as CoNS were genotyped using primers targeting conserved sequences adjacent to the 16S and 23S genes by the internal transcribed spacer-PCR (ITS-PCR) technique described by Couto et al. (2001). The G1 "GAAGTCGTAACAAGG" 16S and L1 "CAAGGCATCCA CCGT" 23S primers were used. The efficiency of the amplifications was monitored by electrophoresis on 3% MetaPhor agarose prepared in 1X TBE buffer and stained with SYBR Safe. The following international reference strains were used as controls: S. epidermidis (ATCC 12228), S. epidermidis (ATCC 35983), S. haemolyticus (ATCC 29970), S. hominis (ATCC 27844), S. hominis subsp. novobiosepticus (ATCC 700237), S. lugdunensis (ATCC 700328), S. saprophyticus (ATCC 15305), and S. warneri (ATCC 10209).

#### Detection of mecA Gene for Oxacillin Resistance

For detection of the mecA gene, PCR was performed using the mecA1 (AAA ATC GAT GGT AAA GGT TGG) and mecA2 (AGT TCT GCA GTA CCG GAT TTG) – 533 (bp) primers according to the parameters described by Murakami et al. (1991). International reference strains were included in all reactions: S. aureus ATCC 33591 (positive) and S. aureus ATCC 25923 (negative).

Agarose gels were prepared at a concentration of 2% in 1X TBE, stained with SYBR Safe DNA Gel Stain R (Invitrogen), and visualized under a UV transilluminator.

#### Detection of Biofilm Production by the Polystyrene Plate Adherence Method (Christensen et al., 1985) Modified by Oliveira and Cunha (2010)

The method of detecting biofilm production in culture plates proposed by Christensen et al. (1985) was used, with modifications proposed by Oliveira and Cunha (2010). This method is based on the spectrophotometric reading of optical density of the adherent material produced by the bacteria. International reference strains used as positive (S. aureus ATCC 29213, S. epidermidis ATCC 35983) and negative controls (S. aureus ATCC 33591, S. epidermidis ATCC12228) and sterile TSB were included in all tests. Optical density reading was carried out in an ELISA reader (Labsystems, model Multiskan EX) using a 540-nm filter. Samples were classified as negative when the cut-off value corresponded to the classification of non-adherent ( ≤ 0.111) and as positive when the cut-off value corresponded to the classification of weakly adherent (>0.111 or ≤0.222) or strongly adherent (>0.222). These cut-offs values were established by Oliveira and Cunha (2010).

## Evaluation of Biofilm Formation With Visualization by Scanning Electron Microscopy (SEM) in an Isolate of Biofilm-Producing S. saprophyticus

A biofilm-producing S. saprophyticus isolate in the polystyrene plate adherence test was selected for confirmation of biofilm production by SEM. The biofilm-producing strain was first isolated in BHI broth and 108 CFU of bacteria were transferred to a conical tube (Falcon-CORNING) containing 2 mL TSB culture medium prepared with 2% glucose and a 0.5-cm segment of VYGON umbilical catheter (reference 1270.04, 0.8 mm × 1.5 mm diameter). The tube was incubated under constant stirring for 48 h at 100 rpm/37◦C for bacterial growth and biofilm formation. After this period, the catheter segment was removed, washed with PBS, immersed in 2.5% glutaraldehyde solution, fixed in an increasing alcohol series (15, 30, 50, 70, 90, and 100%) for 15 min each, dried in a vacuum centrifuge for 5 min, metallized with gold, and visualized under a scanning electron microscope to evidence biofilm production.

#### Determination of MIC of Vancomycin, Oxacillin, Norfloxacin, Ciprofloxacin, and Trimethoprim/Sulfamethoxazole for Planktonic Cells of S. saprophyticus by the Broth Microdilution Method

The broth microdilution method was used for determination of the (MIC) for planktonic cells of S. saprophyticus. Sterile microtiterplates with Müller-Hinton broth adjusted with cations (Oxoid, United Kingdom) as recommended by the CLSI (2016) were used. A stock solution of each drug was prepared in 3,200 µg/mL distilled water. Serial dilutions were made in a microtiter plate containing Müller-Hinton broth at concentrations on a logarithmic scale of two, comprising the breakpoints (CLSI, 2016), in a final volume of 100 µL. For preparation of the inoculum, the isolates were first seeded on blood agar. After incubation for 24 h, isolated colonies were seeded in BHI broth and the bacterial suspensions were adjusted to a turbidity of 0.5 McFarland standard (1 × 108 CFU/mL), diluted at 1:1000, and added to the wells in a final volume of 200 µL and final bacterial concentration of 5× 104 CFU/well. The plates were incubated in an oven at 35◦C and the MIC was read after 24 and 48 h of incubation. A positive control containing the broth and bacterial suspension and a negative control containing only the Müller-Hinton broth were used. In addition, Enterococcus faecalis ATCC 29212 and S. aureus ATCC 29213 (susceptible to vancomycin) were used as negative controls, and E. faecalis ATCC 51299 (resistant to vancomycin) and S. aureus ATCC 33591 (resistant to oxacillin) were used as positive controls. The MIC was defined as the lowest concentration of antimicrobial that completely inhibited the growth of the microorganism as detected by the naked eye. Wells with turbidity and/or the presence of bacteria at the bottom of the well were classified as positive growth. The susceptibility and resistance cut-offs recommended by the CLSI (2016) were

used to determine the MIC for planktonic cells. The same cutoffs were used to evaluate the biofilm antimicrobial susceptibility of the isolates since no standards exist for biofilm tests.

#### Determination of (MICB) and (MBCB) of Vancomycin, Oxacillin, Norfloxacin, Ciprofloxacin, and Trimethoprim/Sulfamethoxazole for S. saprophyticus Biofilm by the Broth Microdilution Method

Bactericidal concentrations for biofilms (MBCB) were determined by adapting the test method described by Frank et al. (2007). The isolates cultured for 22 h in TSB with 2% glucose were adjusted to a turbidity of 1.0 McFarland standard (corresponding to 1 × 108 to 2 × 108 CFU/mL) and diluted at 1:50 in TSB with 2% glucose. Aliquots (200 µL) were plated in 96-well flat bottom plates (Nuclon Delta, Nunc, Denmark), covered with a 96-pin cap (Nunc-TSP; Nunc), and incubated for 24 h to allow biofilm formation on the pins. To remove non-adherent cells, the biofilms formed on the pins were washed by immersion in a series of three 96-well plates filled with 200 µL of sterile saline phosphate-buffered saline (PBS). The cap with the pins was placed on a flat bottom plate prepared for broth microdilution susceptibility testing. The wells contained 200 µL of antimicrobial agent diluted in CAMHB (Müller-Hinton broth supplemented with cations, 100 mg/mL calcium, and 50 mg/mL magnesium) or 200 µL of CAMHB without drugs as positive growth control. The biofilms were exposed to the antimicrobials for 24 h. The cap with the pins was removed, washed three times in PBS as described above, and transferred to 96-well plates containing 200 µL TSB plus 2% glucose. On that occasion, prior to discarding the plate with the antibiotics, a "naked eye" reading was performed to determine the MIC of the antibiotics for biofilm cells (MICB). Subsequently, the biofilm cells formed on the cap pins were dislodged by sonication for 5 min at 40 kHz (Hielscher, Ultrasonic Technology, UIP250MTP) in 96-well plates containing fresh culture medium for cell recovery. The cap with the pins was discarded and replaced with a normal cap and optical density was measured in a plate reader equipped with a 600-nm filter. Wells containing TSB plus 2% pure glucose (without inoculation) were used as spectrophotometric sterility controls. The plate was incubated for 24 h and a second optical density measurement at 600 nm was taken. The MBCB was defined as the lowest concentration of the drug that exhibited a change in optical density at 600 nm of 10% of the reading obtained for the positive growth control between the readings performed before incubation and after 24 h. For better control of the efficacy of the test, we used the biofilm-producing strain S. epidermidis ATCC 35983 and the non-producing strain S. epidermidis ATCC 12228 as controls.

#### Statistical Analysis

Correlation analysis between antimicrobial susceptibility and the inhibitory concentration of the drugs for planktonic and biofilm bacteria was performed using the Chi-squared test or Fisher's exact test (SPSS R 13.0 software), adopting a level of significance <0.05.

#### RESULTS

#### Detection of Biofilm Production by the Polystyrene Plate Adherence Method

A total of 169 samples of S. saprophyticus isolated from patients with UTI were used. Of these, 119 (70.4%) produced a biofilm and 88 (52.1%) were classified as strongly adherent and 31 (18.3%) as weakly adherent.

#### Evaluation of Biofilm Formation With Visualization by SEM

An S. saprophyticus isolate classified as strongly adherent in the evaluation of biofilm production on polystyrene plates was selected for SEM analysis of biofilm production. **Figure 1** shows the biofilm structure produced by S. saprophyticus isolated from a case of UTI.

#### Evaluation of Antimicrobial Susceptibility of Planktonic and Biofilm Cells of S. saprophyticus

Biofilm antimicrobial susceptibility was evaluated in the 119 isolates producing biofilms on polystyrene plates. The same drugs as those employed to evaluate antimicrobial susceptibility in planktonic isolates for determination of MIC were used to test the biofilm antimicrobial susceptibility by establishing the (MICB; **Table 1**).

The determination of MIC in planktonic cells against the five antimicrobials revealed that 117 (98.3%) isolates were resistant to oxacillin, with MIC50 of 1 µg/mL and MIC90 of 2 µg/mL, but only three isolates (2.5%) were positive for the mecA gene. These three isolates exhibited the highest MIC (256 µg/mL), while the other 116 showed MIC ranging from ≤ 0.25 to 2 µg/mL. In addition, 21 (17.7%) isolates were resistant to trimethoprim/sulfamethoxazole, with MIC50 of 0.25/2.38 µg/mL and MIC90 of 4/76 µg/mL. All isolates were susceptible to vancomycin with MIC50 of 1 µg/mL and MIC90 of 2 µg/mL, to norfloxacin with MIC50 of 2 µg/mL and MIC90 of 4 µg/mL, and to ciprofloxacin with MIC50 and MIC90 of 0.25 µg/mL (**Figure 2**).

Using the criteria for interpretation of susceptibility tests recommended by the CLSI (2016) for determination of MIC in planktonic CoNS as a guideline to evaluate the antimicrobial susceptibility of the biofilm isolates, none of the drugs was found to be totally effective against the biofilm isolates. Statistical analysis of the results showed a significant increase in MICB (p < 0.0001) for all five drugs tested in the biofilm state compared to the planktonic forms (**Figure 2**).

There was a considerable increase in susceptible planktonic isolates that became resistant in the biofilm state (**Table 1**). Of the 119 biofilm isolates analyzed, 28 (23.5%) exhibited intermediate resistance or resistance to vancomycin (MICB 1 to 64 µg/mL). All isolates were resistant to oxacillin (MICB 0.5 to 2048),

41 (34.4%) exhibited intermediate resistance or resistance to norfloxacin (MICB 2 to 64 µg/mL), 30 (25.2%) demonstrated intermediate resistance or resistance to ciprofloxacin (MICB 0.125 to 64 µg/mL), and 58 (48.7%) were resistant to trimethoprim/sulfamethoxazole (MICB 0.06/1.18 µg/mL to 64/1,216 µg/mL), considering the CLSI (2016) cut-off point for resistance in planktonic cells (**Table 1** and **Figure 2**). Regarding resistance to trimethoprim/sulfamethoxazole, it is important to note that 21 (17.7%) of the 58 (48.7%) isolates resistant to MICB were already resistant in the MIC evaluation of this drug; thus, 37 (31.1%) of the isolates changed from susceptible to resistant in the biofilm state.

The biofilm isolates exhibited a considerable increase in MICB when compared to the planktonic forms, with an increase of more than 32 times in the values of some drugs. Some isolates switched from the category of susceptible in the planktonic condition to resistant in the biofilm state (**Figure 2** and **Table 2**).

Regarding determination of the MBCB, there were isolates for which the minimum bactericidal concentration of all drugs was equal to or higher than the highest concentration tested (**Figure 3**), with emphasis on norfloxacin with 33 (27.7%) samples with MBCB > 128 µg/mL and

TABLE 1 | Comparison of drug resistance profile between planktonic and biofilm cells of Staphylococcus saprophyticus.

•, Drugs without intermediate resistance MIC; R, resistant; IR, intermediate resistance; Trim/Sut, trimethoprim/sulfamethoxazole.

trimethoprim/sulfamethoxazole with 36 (30.2%) samples with MBCB > 128/2,432 µg/mL.

#### DISCUSSION

The formation of bacterial biofilms is the basis of many persistent infectious diseases. This persistence is attributed mainly to the increased antibiotic resistance of biofilm cells (Mah, 2012).

The MIC has been used as a gold standard to determine the antimicrobial susceptibility of pathogenic bacteria (Costerton et al., 1995). When MIC determination reveals that the drug is not effective in inhibiting the growth of a given organism, the drug in question will not be used for the treatment of infection because it will be clinically ineffective (Potera, 1999). However, if a microorganism is considered susceptible in vitro, it does not necessarily mean that the drug will have the same effect in vivo (Pratt and Kolter, 1999; Mendoza-Olazarán et al., 2015; Algburi et al., 2017). In routine clinical laboratories, antimicrobial susceptibility testing for antibiotic selection continues to be performed using planktonic cells, a fact that impairs evaluation of the efficacy of the antimicrobial tested since these bacteria are protected by the biofilm in the patient and the response will not be the same as obtained in the tests.

The determination of MIC to evaluate the susceptibility of planktonic S. saprophyticus cells revealed that most samples were susceptible to the antibiotics tested. Regarding oxacillin resistance, 98.3% of the planktonic cells were resistant in the microdilution test, but only three isolates were positive for the mecA gene. The three samples that were positive for the mecA gene showed the highest MIC (256 µg/mL) and the remaining 116 had MIC ranging from ≤ 0.25 to 2 µg/mL. Similar results have been reported in other studies and might be due to the fact that the breakpoint recommended by the CLSI overestimates resistance in this species (Ferreira et al., 2012).

In general, the antibiotics tested proved to be ineffective in S. saprophyticus biofilms as resistant isolates were found for all

drugs tested. This is a matter of concern because high doses of antibiotics would be necessary to eliminate these microorganisms organized in biofilms, which is clinically impractical. Biofilm cells may be more resistant to antibiotics because the bacteria are protected against the action of the drugs, with the biofilm impairing the entry of molecules by acting as a physical barrier for diffusion. In addition, biofilm cells have reduced metabolic and growth rates and the biofilm matrix can adsorb or react with the antibiotics, thereby reducing the amount of antibiotics available to interact with cells in the biofilm. Another possibility is that the biofilm cells are tolerant to antibiotics. Hence, treatment may lead to the eradication of most part of the biofilm population, but a fraction of persistent cells is not affected and thus acts as a nucleus for reinfection after therapy discontinuation (Lewis, 2012).

The microorganisms inside a biofilm express different phenotypic characteristics when compared to their free-living homologs. In a study investigating whether the antibiotic resistance genes aac6-aph2a, ermC, and tetK, which confer resistance to gentamicin, erythromycin and tetracycline, are likely to be disseminated via conjugative transfer, Águila-Arcos et al. (2017) searched for horizontal transfer genes from two common staphylococcal plasmids, (i) conjugative pSK41 and (ii) mobilizable pT181, in 25 staphylococcal biofilm-forming clinical isolates belonging to the species S. aureus, S. epidermidis, S. hominis, and S. capitis. Both horizontal transfer and antibiotic resistance genes were detected in these staphylococcal isolates. Therefore, biofilms represent a hot spot for horizontal gene transfer by bacterial conjugation. This horizontal gene transfer is important for the genetic diversity of microbial communities and favors the exchange of genes that can contribute to the chronic nature of infections (Vuotto et al., 2014). The detection of horizontal transfer and antibiotic resistance genes in these clinical staphylococcal strains isolated from biofilms points to the potential risk of the development and dissemination of multidrug-resistant bacteria.

The most commonly prescribed antibiotics for the treatment of UTIs are trimethoprim/sulfamethoxazole, fluoroquinolones (ciprofloxacin or norfloxacin), first and second generations of cephalosporins, amoxicillin + clavulanate, and nitrofurantoin (Lee et al., 2008). In the present study, 17.7% of the samples were already resistant to trimethoprim/sulfamethoxazole in the evaluation of planktonic MIC, while 48.7% of the biofilm samples were resistant. In addition, 31.1% of the samples changed from susceptible to resistant in the biofilm state, an alarming finding considering that the trimethoprim/sulfamethoxazole combination is considered the first-line drug for the treatment of uncomplicated UTIs (Drekonja and Johnson, 2008). Thus, the frequent use of the drug in empirical therapy is associated with an increase in the clinical failure rate, especially if the microorganism grows in biofilms, as observed in the present study.

The administration of fluoroquinolones is recommended for uncomplicated UTIs in areas where the incidence of trimethoprim/sulfamethoxazole resistance is higher than 10%, as well as for the treatment of complicated UTIs and acute pyelonephritis (Blondeau, 2004). Fluoroquinolones have been successfully used to treat a wide range of community-acquired and hospital-acquired infections, and rates of resistance to fluoroquinolones remain low (Oliveira et al., 2016). In fact, in the present study, all planktonic samples were susceptible

•, Drugs without intermediate resistance MIC; X, Number of times MIC increased in biofilm samples; S-I, susceptible-intermediate: percentage of isolates with intermediate resistance only in the presence of the biofilm; S-R, susceptible-resistant: percentage of isolates that were resistant only in the presence of the biofilm; Trim/Sut, trimethoprim/sulfamethoxazole.

to norfloxacin and ciprofloxacin, however, the same was not observed for the biofilm samples, with 34.4% of the isolates exhibiting intermediate resistance or resistance to norfloxacin and 25.2% exhibiting intermediate resistance or resistance to ciprofloxacin. The presence of the biofilm increased the MIC by two, four, eight and up to 32 times the values obtained for some drugs, with some samples switching from the category of susceptible in the planktonic condition to resistant in the biofilm state. This phenomenon was more frequently observed for norfloxacin, ciprofloxacin, and trimethoprim/sulfamethoxazole.

Oliveira et al. (2016) evaluated the MIC for planktonic and biofilm cells of Staphylococcus spp. comparing six drugs, and observed a two-, four-, eight-, and up to 16-fold increase of MIC in the presence of the biofilm compared to planktonic cells, mainly for the drugs vancomycin and erythromycin. In that study, among the 20 S. saprophyticus isolates studied, no planktonic samples were resistant to vancomycin and linezolid. However, regarding the MICB, the percentage of samples that moved from susceptible to resistant or intermediate resistant was 53.8% for vancomycin and 30.8% for erythromycin. The authors also observed that S. haemolyticus, S. hominis, S. warneri, and S. lugdunensis isolates did not exhibit much variation of MIC in the presence of the biofilm, probably because these species are poor biofilm producers.

Regarding determination of MBCB in the present study, there were isolates for which the MBCM of all drugs was equal to or higher than the highest concentration tested. The results corroborate the observation that microorganisms susceptible to certain antimicrobials in conventional laboratory tests may be highly resistant to the same antimicrobials when grown in biofilms. Consequently, infectious diseases involving biofilms are generally difficult to treat. Bacterial biofilms play an important role in UTIs, being responsible for persistent infections that lead to recurrences and relapses (Delcaru et al., 2016).

Studies have demonstrated the importance of bacterial biofilm formation in UTIs, particularly chronic cystitis and catheterassociated infections (Hancock et al., 2007). Urinary catheters and other prosthetic devices predispose to UTIs by serving as a substrate for biofilm formation, carrying a higher bacterial burden and increasing the risk of epithelial adhesion.

The finding that S. saprophyticus isolates can produce biofilms, in addition to the observation of resistance to the antimicrobial

agents when these microorganisms were grown in biofilms, suggests that biofilm formation is a very important virulence factor for S. saprophyticus, which permits this species to establish persistent UTIs. This study demonstrated that the severity of UTIs depends not only on the susceptibility of the microorganism to the antibiotics commonly used for treatment, but also on the virulence of the bacteria. Biofilm production by S. saprophyticus and its role in UTIs remain poorly studied. Treatment of this infection is usually simple and rapid, however, if not treated correctly with efficient antimicrobials, progression to much more severe infection of the kidneys (pyelonephritis) may occur that can lead to generalized infection, renal abscesses, and loss of kidney function. No data are available correlating the inefficacy of antibiotics in the treatment of UTIs with the biofilm formation by S. saprophyticus or any other species. However, the results of the present study show that more attention should be given to this virulence factor in S. saprophyticus and to the antimicrobial treatments used since in vitro biofilm formation decreases the susceptibility of the microorganisms to the antibiotics tested. The results of conventional antimicrobial susceptibility tests (MIC) cannot be applied to microorganisms grown in biofilms as the antimicrobials tested were unable to eradicate biofilm-bound bacteria. This was clearly demonstrated in the present study.

#### CONCLUSION

The present study shows that biofilm production is a successful strategy for the microbial survival of S. saprophyticus and

#### REFERENCES

should be taken into account in the treatment of UTIs that do not consistently respond to therapeutic concentrations, as the response to antimicrobials may be impaired in bacterial biofilms. This virulence factor may increase the survival capacity of the pathogen during the treatment of infection with antimicrobial agents.

#### AUTHOR CONTRIBUTIONS

KM participated in the conception and design of the study, carried out the microbiological tests, and wrote the paper. AF provided the clinical material and helped with the conception and design of the study. VP, LP, and AO helped with the microbiological tests. MC was responsible for the conception and design of the study, coordination of laboratory work, data analysis, and manuscript writing.

### FUNDING

This work was funded by the Coordination for the Improvement of Higher Education Personnel (CAPES) through a Master/Doctorate fellowship program and the National Council for Scientific and Technological Development (CNPq, grant 304051/2017-9) and by the state funding agency São Paulo Research Foundation (FAPESP – grant 2017/21396-0).

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Martins, Ferreira, Pereira, Pinheiro, Oliveira and Cunha. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Signal Transduction Proteins in *Acinetobacter baumannii*: Role in Antibiotic Resistance, Virulence, and Potential as Drug Targets

#### P. Malaka De Silva1 and Ayush Kumar 1,2 \*

1 Department of Microbiology, University of Manitoba, Winnipeg, MB, Canada, 2 Manitoba Chemosensory Biology Group, University of Manitoba, Winnipeg, MB, Canada

#### *Edited by:*

José Luis Capelo, Universidade Nova de Lisboa, Portugal

#### *Reviewed by:*

Maria Soledad Ramirez, California State University, Fullerton, United States M. Teresa Machini, University of São Paulo, Brazil

> *\*Correspondence:* Ayush Kumar<EMAIL_ADDRESS>
#### *Specialty section:*

This article was submitted to Antimicrobials, Resistance and Chemotherapy, a section of the journal Frontiers in Microbiology

*Received:* 22 August 2018 *Accepted:* 14 January 2019 *Published:* 30 January 2019

#### *Citation:*

De Silva PM and Kumar A (2019) Signal Transduction Proteins in Acinetobacter baumannii: Role in Antibiotic Resistance, Virulence, and Potential as Drug Targets. Front. Microbiol. 10:49. doi: 10.3389/fmicb.2019.00049 Acinetobacter baumannii is a notorious pathogen in health care settings around the world, primarily due to high resistance to antibiotics. A. baumannii also shows an impressive capability to adapt to harsh conditions in clinical settings, which contributes to its persistence in such conditions. Following their traditional role, the Two Component Systems (TCSs) present in A. baumannii play a crucial role in sensing and adapting to the changing environmental conditions. This provides A. baumannii with a greater chance of survival even in unfavorable conditions. Since all the TCSs characterized to date in A. baumannii play a role in its antibiotic resistance and virulence, understanding the underlying molecular mechanisms behind TCSs can help with a better understanding of the pathways that regulate these phenotypes. This can also guide efforts to target TCSs as novel drug targets. In this review, we discuss the roles of TCSs in A. baumannii, their molecular mechanisms, and most importantly, the potential of using small molecule inhibitors of TCSs as potential novel drug targets.

#### Keywords: two-component systems, PmrAB, AdeRS, BfmRS, stress

## INTRODUCTION

Acinetobacter baumannii is a Gram-negative coccobacillus, which is an important opportunistic human pathogen that causes hospital-acquired infections (Peleg et al., 2008a, 2012; Visca et al., 2011; Wong et al., 2017). Clinical importance of A. baumannii is emphasized by the fact that it is listed by the WHO as the "top priority" pathogen that urgently need novel and effective therapeutic options (http://www.who.int/medicines/publications/WHO-PPL-Short\_Summary\_25Feb-ET\_NM\_WHO.pdf). The success of A. baumannii in hospital environments can be mainly attributed to its ability to display multi-drug resistant phenotypes due to the rather robust acquisition of antibiotic resistance mechanisms (Dijkshoorn et al., 2007; Antunes et al., 2014). These include antibiotic modifying enzymes, decreased permeability to antibiotic molecules, and efflux pumps that extrude the antibiotic molecules out to the periplasm and beyond (Gordon and Wareham, 2010; Lee et al., 2017).

Multi- and pan-drug resistance in A. baumannii is an alarming development for healthcare facilities around the world (Rodriguez-Bano et al., 2004; Agodi et al., 2010; Sievert et al., 2013; Labarca et al., 2016). As a result, some infections caused by multi-drug resistant A. baumannii have become virtually untreatable with our current arsenal of antibiotics (Maragakis and Perl, 2008). Further, without any new antibiotics for Gramnegative bacteria, such as A. baumannii in the developmental pipeline, we are on the verge of a post-antibiotic era where even a minor infection could have lethal consequences for the patient (Xie et al., 2018).

Apart from its multidrug resistance, the success of A. baumannii can also be attributed to its ability to survive and persist in the harsh conditions found within hospital environmental niches (Jawad et al., 1998; Rajamohan et al., 2010). Constant and prolonged exposure to antiseptics and desiccating agents, endurance of less than optimal temperatures, and sudden changes of the environmental and nutritional conditions when transferred into the human body from an abiotic surface are some of the challenges that A. baumannii faces in its role as an opportunistic human pathogen. Therefore, in order to be a successful pathogen, A. baumannii needs to sense and adapt to these changes in an efficient and timely manner.

Signal transduction mechanisms in bacteria play a crucial role in adapting to environmental changes. TCSs are one of the most ubiquitous signal transduction systems present in bacteria that help them sense and adapt to the environmental conditions (Alm et al., 2006; Wood et al., 2018). TCSs therefore play a role in bacterial adaptive responses which can lead to the modulation of their antibiotic susceptibility and virulence. Consequently, these systems are vital to study in order to understand the mechanisms of antibiotic resistance and virulence in bacteria (Poole, 2012; Kroger et al., 2016; Schaefers et al., 2017; Kenney, 2018; Lingzhi et al., 2018). Further, TCSs can also serve as an attractive target when developing anti-virulence therapeutics (Gotoh et al., 2010b). In this review, we describe the roles of TCSs in the resistance and virulence of A. baumannii and their potential to be used as novel therapeutic targets.

#### TWO COMPONENT SYSTEMS (TCSs)

TCSs are the most widespread signal transduction system present in bacteria and archaea (Stock et al., 2000). Typically, a TCS consists of two components, a histidine kinase (HK) and a response regulator (RR) (**Figure 1**). A high level of specificity with the HK and the RR is observed within the TCSs of a bacterial cell (Szurmant et al., 2007). However, there are instances where a single HK protein can have multiple cognate RR proteins (Lopez-Redondo et al., 2010) or when a single RR protein can be activated by multiple HK proteins (Laub and Goulian, 2007). Since their first description in 1986 (Nixon et al., 1986), an enormous amount of both HK and RR proteins have been discovered and characterized in a wide variety of bacteria (Whitworth and Cock, 2009). It is estimated that an average bacterial genome can contain up to 50–60 TCS-encoding genes (Whitworth, 2008; Whitworth and Cock, 2008; Wuichet et al., 2010). Given the advancement in bioinformatics and next generation sequencing techniques, specific databases dedicated to TCSs have become available that provide valuable information about these proteins (Ulrich and Zhulin, 2007; Barakat et al., 2011).

The TCSs in bacterial systems have implications for a wide variety of regulatory functions relating to sensing and adapting to their environment. In pathogenic bacteria, these functions often include but are not limited to antibiotic susceptibility modulation and virulence-related phenotypes, such as biofilm formation and motility (Tiwari et al., 2017).

## TCSs IN *Acinetobacter baumannii*

An overview of various genomes of well-characterized A. baumannii clinical isolates show the presence of close to 20 different genes/operons that encode for TCSs (**Table 1**). Most of these genes and operons have a high degree of conservation at nucleotide level, indicating that they may be involved in the important functions. However, as mentioned above, the effector domains of A. baumannii RR proteins can be quite diverse which is shown in **Figure 2**. Below we describe the TCSs in A. baumannii that have been characterized to date.

#### AdeRS

AdeRS is the first characterized and also the most studied TCS in A. baumannii. It was first described in a clinical strain A. baumannii BM4454, when the inactivation of adeS resulted in an increased susceptibility to aminoglycosides due to the downregulation of the RND efflux pump AdeABC (Marchand et al., 2004) (**Figure 3**). Since it was first identified, a number of mutations in either adeR, adeS, or both have been shown to be directly responsible for the overexpression of the AdeABC pump (Ruzin et al., 2007; Yoon et al., 2013; Sun et al., 2016). Considering AdeRS system's role in the expression of AdeABC, it can be said that it plays a role in the susceptibility of A. baumannii to antibiotics that are substrates of the AdeABC pump. Further, the overexpression of AdeABC efflux pump has been associated with the decreased susceptibility to tigecycline observed in some clinical isolates of A. baumannii (Sun et al., 2014; Yuhan et al., 2016) thus implicating an indirect role of AdeRS in the susceptibility toward tigecycline. This is important since tigecycline is one of the last resort antibiotics for the treatment of multidrug resistant A. baumannii infections (Ni et al., 2016). However, there needs to be further investigations into this due to the possibility of involvement of other factors for the observed tigecycline susceptibility (Yoon et al., 2013).

Recent transcriptomics data suggest that the role of AdeRS extends well-beyond the expression of AdeABC efflux pump. A study in A. baumannii AYE showed that AdeRS controls the expression of almost 600 different genes (Richmond et al., 2016). Products of a number of these genes are believed to play a role in virulence, biofilm formation and multi drug efflux activity. However, deletion of adeB in the same strain resulted in similar phenotypes as deletion of adeRS. This suggests that at least some phenotypic changes observed upon the adeRS deletion may be a result of the decreased expression of the AdeABC efflux pump (Richmond et al., 2016).

FIGURE 1 | Schematic diagram showing the cellular architecture of a typical two-component regulatory system as well the mechanism of phosphotransfer between two components (modified with permission from Springer Nature Du et al., 2018. A prototypical TCS, comprised of a membrane-bound sensory histidine kinase (HK) and a cytosolic response regulator (RR) protein, is shown. The basic mechanism of a TCS involves the HK sensing the environmental changes and relaying the message to the RR effectively through phosphorelays to initiate the necessary response. HK proteins, usually dimers, possess several conserved domains that are essential for their function, such as dimerization and histidine phosphotransfer (DHp) domain and catalytic ATP binding (CA) domain which make up the catalytic core of the HK (Bhate et al., 2015). The H-box containing the conserved histidine residue, that gets phosphorylated, is located in the DHp domain (Casino et al., 2009, 2010). The CA domain binds ATP and phosphorylates the histidine residue, thus initiating the HK autophosphorylation (Zschiedrich et al., 2016). The DHp and CA domains are conserved among all HK proteins and the sensory domains are variable conferring specificity of signal recognition. The phosphoryl group from the H-box of the HK is ultimately transferred to a conserved aspartate residue of the receiver (REC) domain of the cognate RR thus activating the RR (Yamamoto et al., 2005). While the REC domain is highly conserved, the effector domains of RR display variability conferring specificity to the protein (Zschiedrich et al., 2016). Following its activation, dephosphorylation of the RR is critical to maintain the efficient regulatory capacity of the TCSs (Kenney, 2010). This is achieved through the phosphatase activity of the HK (Hsing and Silhavy, 1997).

The multifaceted regulon of AdeRS remains to be explored further, especially in clinically relevant phenotypes of A. baumannii. Further, environmental signals that activate the sensor kinase, AdeS, remain mostly unknown. However, we recently uncovered evidence that AdeRS system maybe responding to the NaCl concentrations in the growth medium (De Silva and Kumar, 2017). This work links adaption to environmental conditions, such as NaCl concentration to antibiotic susceptibility (as a result of expression of the AdeABC pump) as well as virulence factors, such as biofilm formation and surface-associated motility. It is therefore obvious that AdeRS plays a role in the antibiotic susceptibility of A. baumannii but also possibly in its virulence. However, it's role in antibiotic susceptibility and virulence is likely to be more strain-specific, as it is not uncommon to find disrupted copies of adeRS genes in clinical isolates of A. baumannii, such as LAC-4 and AB031 (**Table 1**).

#### BaeSR

BaeSR, named such because of its homology with an E. coli TCS (Leblanc et al., 2011), mediates a possible "cross–talk" with other TCSs. It has been shown to regulate overlapping regulons with other TCSs in A. baumannii. BaeSR was initially thought to be associated with the regulation of AdeABC RND efflux pump expression (Lin et al., 2014) (**Figure 3**). This is indicative of a possible cross–talk between BaeSR and AdeRS. Further investigations into the BaeSR revealed that it may also modulate the expression of AdeIJK and MacAB-TolC efflux pumps (Henry et al., 2012). However, efforts to determine the DNA binding sites in the promoters corresponding to the observed target genes remain unsuccessful, leaving room for further explorations (Lin et al., 2015). A phenotypic microarray screen revealed that the deletion of baeR resulted in reduced tolerance of A. baumannii to tannic acid (Lin et al., 2015), a diverse group of natural antibacterial compound (Henis et al., 1964). Tannic acids

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has been used as a topical agent in burn patients (Hupkens et al., 1995) as they display effective antibacterial activity against various bacteria, including E. coli, Staphylococcus aureus, Staphylococcus epidermidis, Salmonella spp. etc (Kim et al., 2016). Tannic acid has also been shown to inhibit biofilm formation in Staphylococcus aureus (Payne et al., 2013). In A. baumannii, Tannic acids are being explored as adjuvants for antimicrobial therapy. They were shown to synergize the activity of novobiocin, rifampicin, and fusidic acid against MDR A. baumannii (Chusri et al., 2009). However, the role of BaeSR TCS in tannic acid as well as the expression of efflux pumps controlled by BaeSR will have to be considered to in order to explore the clinical usage of tannic acid as an adjuvant therapy options against A. baumannii .

Studies on the environmental signals that BaeSR responds to remain limited. However, expression of baeR and baeS in A. baumannii is induced by sucrose (20% w/v) (Lin et al., 2014), suggesting that BaeSR may be involved in A. baumannii's response to osmotic stress.

#### PmrAB

A. baumannii's resistance to commonly used antibiotics has led to an increased use of "last resort" antibiotics, such as colistin (Karaiskos et al., 2017; Jiménez-Guerra et al., 2018). As a result, emergence of colistin resistance is becoming more common in A. baumannii (Cai et al., 2012; Lean et al., 2015). Investigations into the mechanisms of resistance to colistin in A. baumannii have revealed the involvement of PmrAB resistance (Park et al., 2011; Rolain et al., 2013), named so for its role in polymixin (**Figure 3**). PmrAB has been described in various Gram-negative pathogens including E. coli (Quesada et al., 2015), Salmonella enterica (Gunn, 2008 ), Klebsiella pneumoniae (Cheng et al., 2010), and Pseudomonas aeruginosa (Lee and Ko, 2014) and has been shown to have a similar function colistin resistance. Observations of mutations in both pmrA (RR) and pmrB (HK) leading to decreased susceptibility to colistin presented preliminary evidence of the connection between PmrAB and colistin susceptibility in A. baumannii (Adams et al., 2009). Further, both colistin-resistant clinical isolates as well as laboratory generated spontaneous mutants showed phosphoethanolamine modification of lipid A of lipopolysaccharide (LPS) within the outer membrane (Arroyo et al., 2011; Beceiro et al., 2011). The modification of lipid A is mediated by PmrC which is generally part of the same operon as pmrAB (Raetz et al., 2007). PmrC can add phosphoethanolamine to either 4 ′ or 1 ′ phosphate of lipid A (Da Silva and Domingues, 2017). This modification of LPS results in a positively charged phosphate groups and prevents the binding of the cationic colistin (Tamayo et al., 2005a,b; Arroyo et al., 2011). Mutations in both pmrA and pmrB cause the overexpression of the pmrCAB operon.

Observations that low pH or supplementation of Fe 3 + in the growth medium (Adams et al., 2009) lead to colistin resistance may suggest that PmrB could be responding to those signals (Gunn, 2008). However, growth of A. baumannii under low pH or in iron supplemented growth media failed to alter the expression of pmrA (Adams et al., 2009). Therefore, the environmental signals to which PmrAB responds to in A. baumannii remain elusive.

FIGURE 2 | Schematic diagram of the conserved domains of all the response regulators of A. baumannii ATCC17978 as determined by a ScanProsite (de Castro et al., 2006). The figure depicts the receiver domain (orange) and the different effector domains identified by ScanProsite (Lux\_R type HTH domain in green, LytTR type HTH domain in red, OmpR\_PhoB type DNA binding domain in violet, Sigma-54 interaction domain in purple, and ANTAR domain in yellow). Most abundant effector domain was the OmpR\_PhoB type DNA binding domain which was present in seven response regulators followed by the Lux\_R type HTH domain which was present in three response regulators. The other three types of effector domains were exclusive to single response regulators. The numbers in parenthesis refer to the PROSITE accession numbers of the respective domains. The hybrid sensor kinase A1S\_2811 was not included in the figure due to the lack of a distinct response regulator protein.

## GacSA

GacSA is a TCS that is well-characterized in Pseudomonas sp. (Gooderham and Hancock, 2009). GacSA in A. baumannii ATCC19606 was identified when the transposon insertions in the gacS sensor kinase gene rendered the mutants incapable of utilizing citrate as the sole carbon source (Dorsey et al., 2002). This suggests that GacSA is involved in citrate metabolism. Since the initial characterization of GacSA in A. baumannii ATCC19606, a number of subsequent studies have carried out the functional characterization of GacSA TCS in A. baumannii ATCC17978. Interestingly, in A. baumannii ATCC17978, the gacS gene is not linked to the response regulator-encoding gene. Rather, it has both a HisKA domain and a REC domain suggesting that it could function as a hybrid sensor kinase.

Although, there is also a possibility that in A. baumannii ATCC17978, the response regulator for GacS is encoded elsewhere in the genome. This indicates that the organization of the gacSA genes may vary from strain to strain in A. baumannii.

BfmRS, and A1S\_2811) as well as their known stimuli are depicted.

In addition to the initially observed metabolic role of gacS, a transposon mutant with a disrupted gacS gene displayed significantly reduced A. baumannii's ability to inhibit Candida albicans (Peleg et al., 2008b). gacS deletion mutant also displayed attenuated virulence in a mouse infection model (Cerqueira et al., 2014). Deletion of gacS also led to the revelation of its involvement in a number of other virulence related functions. These include control of pili synthesis, motility, and biofilm formation, resistance against human serum, and metabolism of aromatic compounds (Cerqueira et al., 2014) (**Figure 3**). GacSA is involved in the regulation of the aromatic compound catabolism through the paa operon that encodes the components of the phenylacetic acid catabolic pathway. The paa gene cluster is significantly downregulated in the gacSA deletion mutants which may explain their attenuated virulence in a mouse septicaemia model (Cerqueira et al., 2014). The attenuated virulence of gacSA deletion mutants was observed in a later study involving a zebra fish virulence model as well (Bhuiyan et al., 2016) adding to the repertoire of studies that suggest that GacSA may function as a global virulence regulator in A. baumannii.

#### BfmRS

Biofilm formation is an important virulence factor of pathogens, such as A. baumannii that helps them survive harsh conditions present in hospital environments. The ability of A. baumannii to form biofilms starts with its attachment to surfaces that is mediated by the expression of pili. The expression of pili is mediated by the csu operon in A. baumannii and is under the regulatory control of BfmRS (Tomaras et al., 2008). Deletion of the response regulator bfmR in A. baumannii ATCC19606 resulted in the complete abolishment of biofilm formation (Tomaras et al., 2008) (**Figure 3**). While of the role of csu operon in the attachment of A. baumannii on abiotic surfaces is wellestablished (Tomaras et al., 2003; Moon et al., 2017; Pakharukova et al., 2018), its role in the adherence of A. baumannii to human epithelial cells remains ambiguous. It was observed that A. baumannii ATCC19606 strain lacking csuE in fact adhered to bronchial epithelial cells better than the wild-type parent making the role pili in adherence to epithelial cells unclear (de Breij et al., 2009). It is possible that this was a strain specific outcome and further investigations are required to draw definitive conclusions.

In addition to regulating biofilm formation, BfmRS also plays a role in regulating the exopolysaccharide production (Geisinger et al., 2018). Exopolysaccharides play an important role in virulence of A. baumannii as they are a component of the capsule, which protects A. baumannii against serum killing and increasing the virulence in animal models. Further, antibiotic exposure leads to an increase in capsule production in A. baumannii mediated by increased expression of genes in K-locus, which in turn is regulated by the BfmRS system (Geisinger and Isberg, 2015).

Crystal structure of BfmR shows that it binds to its own promoter with higher affinity in an inactive (dephosphorylated) state compared to the active (phosphorylated) state (Draughn et al., 2018). This is unusual behavior highlights a unique selfregulation strategy of BfmRS system Therefore, BfmRS system is an excellent candidate to study not only the mechanisms that regulate virulence factors in A. baumannii but also the functioning of the TCSs systems in general.

#### A1S\_2811

A1S\_2811 is a recently characterized hybrid sensor kinase possessing four histidine–containing phosphotransfer domains as well as a regulatory CheA-like domain and a CheYlike receiver domain (Chen et al., 2017). CheA and CheY homologs in E. coli and P. aeruginosa are associated with regulatory roles in controlling motility via regulating either pili or flagella (Li et al., 1995; Alon et al., 1998; Bertrand et al., 2010). Interestingly, in A. baumannii, this hybrid sensor kinase is expressed in an operon composed of five genes where the four other genes upstream are pilJ, pilI, pilH, and pilG. Phenotypic analysis of the deletion mutant of A1S\_2811 revealed a significant reduction in surface motility and biofilm formation at the gas-liquid interface. More intriguingly, abaI, which encodes a N-acylhomoserine lactone involved in quorum sensing, was also significantly downregulated. Supplementation with synthetic homoserine lactone complemented the biofilm and motility phenotypes (**Figure 3**). This suggests that A1S\_2811 regulates biofilm formation and surface motility through an AbaI-associated quorum sensing pathway rather than the conventional pili associated pathway (Chen et al., 2017). This is in contrast to the BfmRS mediated regulon controlling biofilm formation in A. baumannii. Association of both BfmRS and A1S\_2811 with biofilm formation is also an example of one phenotype being under the control of multiple regulatory networks formed by different TCSs.

#### TCSs AS POTENTIAL NOVEL DRUG TARGETS IN BACTERIAL PATHOGENS

Given the important role that TCSs play in regulating the clinically-relevant phenotypes (virulence and/or antibiotic resistance) of bacterial pathogens, it has been proposed that targeting them therapeutically can offer an alternate treatment strategy against multidrug resistant pathogens (Barrett and Hoch, 1998; Stephenson and Hoch, 2002a,b, 2004; Gotoh et al., 2010b; Cardona et al., 2018). TCSs in A. baumannii as well as other organisms offer promise as novel drug targets because of a number of reasons; (i) their conserved nature among bacteria, (ii) their involvement in modulating antibiotic resistance and virulence phenotypes, (iii) their absence in mammalian cells thus reducing off–target toxicity, (iv) lesser potential of resistance development, as the focus of the approach is to supress virulence and/or antibiotic susceptibility rather than killing the cells. It is therefore not all that surprising that TCSs from different organisms have been studied as potential therapeutic targets. **Table 2** summarizes a few examples of the use of TCSs inhibitors used in bacterial pathogens other than A. baumannii.

#### POTENTIAL OF TCSs AS NOVEL DRUG TARGETS IN *A. baumannii*

In A. baumannii, small molecule inhibitors, such as 2 aminoimidazole compounds have shown great promise in inhibiting the action of both PmrA and BfmR. The 2 aminoimidazole-based adjuvants used in combination with colistin were able to reverse colistin resistance in A. baumannii clinical isolates through inhibiting PmrAB and thereby abolishing the lipid A modification (Brackett et al., 2016). A promising feature of this strategy was that no resistance toward the PmrAB inhibitor was observed during the testing period of 7-days (Harris et al., 2014). Yet another example is the use of small molecule 2-aminoimidazole derivatives to inhibit the functions of BfmR (Thompson et al., 2012), such as biofilm formation (Milton et al., 2018). However, as with any other small molecule inhibitor, the cytotoxicity of the compounds used against PmrAB and BfmRS remains to be determined before the inhibitors could be deployed in a clinical setting.

Preliminary findings on the inhibition of BfmRS and PmrAB system are encouraging. In addition, AdeRS, A1S\_2811, or GacSA can potentially be explored as therapeutic targets because of the important role they have been shown to play in the antibiotic resistance and virulence of A. baumannii.

#### CHALLENGES IN TARGETING TCSs FOR THERAPUTICS

Despite the fact that the investigations of the TCSs show an increasing amount of information being uncovered during the

recent years, the majority of these efforts have focused on the cellular functions carried out by TCSs. This has left a void of information about the environmental signals that act as a trigger for the histidine kinase stimulation. The proposed stimuli for the already characterized TCSs are limited to osmotic stress for BaeSR (Lin et al., 2014), monovalent cations for AdeRS (De Silva and Kumar, 2017); and possibly low pH and Fe3+ for PmrAB (Gunn, 2008; Adams et al., 2009). Uncovering the environmental stimuli that activate a TCS response is critical in understanding the molecular pathways that are used for gene regulation by a particular TCS. These pathways can then be better exploited to render A. baumannii non-virulent and/or antibiotic susceptible. However, it is often difficult to determine these signals due to an array of practical reasons including, but not limited to, the potential ability of sensor kinases to detect multiple stimuli and difficulty in expressing, purifying, and experimenting with histidine kinase proteins in vitro in their natural conformations.

#### CONCLUSIONS AND FUTURE PERSPECTIVES

It is evident that the characterized TCSs present in A. baumannii are responsible for controlling a number of antibiotic resistance and virulence associated phenotypes, which contribute to the success of this organism as a human pathogen. Research on

#### REFERENCES

TCSs in A. baumannii has extended our knowledge on virulence and resistance mechanisms in this organism over the last few years. However, there is still a considerable knowledge gap in comprehensive understanding of the complete TCS regulatory networks. Nonetheless, TCSs present themselves as potential targets for drug design and the use of 2-aminoimidazole compounds is are encouraging. A better characterization of these systems both genetically and functionally is key for the potential use of TCS as therapeutic targets.

#### AUTHOR CONTRIBUTIONS

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

#### ACKNOWLEDGMENTS

AK's laboratory is supported by grants from the Natural Science and Engineering Council of Canada (NSERC, RGPIN-2015- 05550), the Canadian Institutes of Health Research (CIHR, PJT-152945), and Cystic Fibrosis Foundation (CFF). PD is supported by Graduate fellowships from the University of Manitoba. Authors thank Raelene Engelberg and Kaleigh Ducas-Mowchun for help with the editing of this manuscript. Authors dedicate this work to Asha, who died from XDR-A. baumannii infection.

pmrAB two-component regulatory system. Antimicrob. Agents Chemother. 55, 3370–3379. doi: 10.1128/AAC.00079-11

Acinetobacter baumannii. Eur. J. Clin. Microbiol. Infect. Dis. 33, 2141–2147. doi: 10.1007/s10096-014-2179-7

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 De Silva and Kumar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Variation in Mutant Prevention Concentrations

Crystal Gianvecchio1† , Natalie Ann Lozano1† , Claire Henderson1 , Pooneh Kalhori1 , Austin Bullivant1 , Alondra Valencia1 , Lauren Su1 , Gladys Bello1 , Michele Wong1 , Emoni Cook1 , Lakhia Fuller1 , Jerome B. Neal III1 and Pamela J. Yeh1,2 \*

1 Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, United States, 2 Santa Fe Institute, Santa Fe, NM, United States

Objectives: Understanding how phenotypic traits vary has been a longstanding goal of evolutionary biologists. When examining antibiotic-resistance in bacteria, it is generally understood that the minimum inhibitory concentration (MIC) has minimal variation specific to each bacterial strain-antibiotic combination. However, there is a less studied resistance trait, the mutant prevention concentration (MPC), which measures the MIC of the most resistant sub-population. Whether and how MPC varies has been poorly understood. Here, we ask a simple, yet important question: How much does the MPC vary, within a single strain-antibiotic association? Using a Staphylococcus species and five antibiotics from five different antibiotic classes—ciprofloxacin, doxycycline, gentamicin, nitrofurantoin, and oxacillin—we examined the frequency of resistance for a wide range of concentrations per antibiotic, and measured the repeatability of the MPC, the lowest amount of antibiotic that would ensure no surviving cells in a 1010 population of bacteria.

Results: We found a wide variation within the MPC and distributions that were rarely normal. When antibiotic resistance evolved, the distribution of the MPC changed, with all distributions becoming wider and some multi-modal.

Conclusion: Unlike the MIC, there is high variability in the MPC for a given bacterial strain-antibiotic combination.

Keywords: antibiotic resistance, selection, Staphylococcus epidermidis, repeatability, replication

## INTRODUCTION

The increase in antibiotic-resistant bacteria is globally an urgent public health issue (Dijkshoorn et al., 2007; Nordmann et al., 2007; Davies and Davies, 2010; Brusselaers et al., 2011; Bush et al., 2011; Morehead and Scarbrough, 2018). The minimum inhibitory concentration (MIC), defined as the lowest concentration of an antimicrobial agent that inhibits growth of the wild type population, assuming no mutations, by 99% (Haight and Finland, 1952; Sanders et al., 1984; Sanders, 2001; Obolski et al., 2015) has been used extensively to classify bacteria as resistant to an antibiotic (Dong et al., 1999; Drlica, 2003; Epstein et al., 2004). Yet the MIC is a single measurement of resistance; it captures one parameter of resistance, but not all.

As antibiotic concentrations increase, the first steep decline in colony numbers, representing an ∼1% recovery, corresponds to the MIC. After exposing cells to antibiotics at MIC levels, there

#### Edited by:

José Luis Capelo, Universidade Nova de Lisboa, Portugal

#### Reviewed by:

Jozsef Soki, University of Szeged, Hungary Min Yue, Zhejiang University, China

#### \*Correspondence:

Pamela J. Yeh<EMAIL_ADDRESS>†These authors have contributed equally to this work

#### Specialty section:

This article was submitted to Antimicrobials, Resistance and Chemotherapy, a section of the journal Frontiers in Microbiology

Received: 31 October 2018 Accepted: 11 January 2019 Published: 31 January 2019

#### Citation:

Gianvecchio C, Lozano NA, Henderson C, Kalhori P, Bullivant A, Valencia A, Su L, Bello G, Wong M, Cook E, Fuller L, Neal JB III and Yeh PJ (2019) Variation in Mutant Prevention Concentrations. Front. Microbiol. 10:42. doi: 10.3389/fmicb.2019.00042

**414**

will often still exist a population of resistant mutants due to spontaneous mutations, considered to be single-step resistant mutants. As concentrations increase beyond the MIC, these single step mutants will remain until a concentration that reduces colony recovery to 0% is achieved. Above this concentration, no single-step mutants can exist. This concentration is the second metric of resistance, the mutant prevention concentration (MPC). The MPC is defined as the MIC of the least-susceptible, single-step mutant (Dong et al., 1999; Firsov et al., 2003; Allen et al., 2004; Drlica et al., 2006; Hansen et al., 2006; Drlica and Zhao, 2007; Firsov et al., 2008). This is experimentally measured by determining the lowest antibiotic concentration that can kill all single-step resistant mutants within a population size of 1010 cells (Feldman, 1976; König et al., 1998; Zhao and Drlica, 2001; Gould and MacKenzie, 2002). This concentration of cells is similar to the numbers of cells found in some infectious cases in clinical situations (Zhao and Drlica, 2001; Gould and MacKenzie, 2002). The concentrations between MIC and MPC, defined as the mutant selection window (MSW), signify the antibiotic concentration range for which evolution of resistance can occur by selecting for the non-susceptible portion of the population (**Figure 1**; Drlica, 2003; Drlica and Zhao, 2007).

While the MIC for each bacterial antibiotic-strain pair is typically considered a single value with high repeatability (Dong et al., 1999; Zhao and Drlica, 2001; Li et al., 2002; Firsov et al., 2003; Zinner et al., 2003; Allen et al., 2004; Li et al., 2004; Metzler et al., 2004a,b; Marcusson et al., 2005; Hansen et al., 2006; Olofsson et al., 2006; Drlica and Zhao, 2007; Firsov et al., 2008; Liu et al., 2013; Oshima et al., 2017; Zhang et al., 2017), it is unclear if this is true for the MPC. Because the MPC is dependent on the probability and timing of mutations that confer resistance, it seems likely that the MPC would have a greater variance than MICs, but the variation in the MPC has not been well studied.

Previous work typically has examined MPCs using fluoroquinolone antibiotics. Studies using Staphylococcus aureus (Dong et al., 1999; Drlica, 2003; Firsov et al., 2003; Allen et al., 2004; Metzler et al., 2004a; Firsov et al., 2008), Mycobacterium tuberculosis (Rodriguez et al., 2004; Drlica and Zhao, 2007), and the poultry pathogen Mycoplasma gallisepcticum (Zhang et al., 2017) have obtained values for the MPC, and the MSW, by examining the presence of resistant mutants at sub-MPC and MPC antibiotic concentrations in vitro. Their results confirm that resistant mutants are enriched when bacteria were exposed to concentrations that fall within the MSW. While the MPC and MSW have been widely described in M. tuberculosis in adults as defined values (Rodriguez et al., 2004), in one review of the antibiotic dosing used in child tuberculosis, it was found that the heterogeneity of MICs could result in a range of MPCs (Jaganath et al., 2017). Multiple studies using Streptococcus pneumonia (Li et al., 2002; Drlica, 2003; Zinner et al., 2003) and Haemophilus influenzae (Li et al., 2004; Metzler et al., 2004b) emphasize the variability in mutation accumulation and observe increasing MSWs with successive mutations. Many studies on the MPC also consider the pharmacokinetics/pharmacodynamics of the antibiotics (Drlica, 2003; Marcusson et al., 2005; Olofsson et al., 2006). Interestingly, one such study found the MIC to be weakly correlated to the MPC using E. coli (Marcusson et al., 2005), also suggesting that the MPC may be a more unpredictable resistance parameter. In all of the studies mentioned, it is important to note that there were less than five replicates of the MPC obtained.

Our study focuses on a strain of Staphylococcus epidermidis, a gram positive bacterium that colonizes the skin and mucus membranes of the human body, and represents a large part of the normal microflora (Widerström et al., 2012). An opportunistic pathogen, S. epidermidis is also the leading cause of infections due to intravenous medical devices, resulting in significant healthcare costs (Uckay et al., 2009). There has been little work done to determine MPC variation using S. epidermidis, with one study showing stability in MPC values using two replicate experiments (Liu et al., 2013). Our study uses 20 replicate experiments per bacteria-antibiotic strain to investigate the variability of MPCs. Specifically, we address the following questions: Are the MPCs replicable in highly controlled laboratory conditions? What is the variation in MPCs? Does the variation differ between antibiotics and/or strains? Here we show that the MPC can vary significantly, and the ranges differ between antibiotics and through the evolution of resistance. Our results indicate a large role for stochasticity in determining the MPC of a bacterial strain with a specific antibiotic.

### MATERIALS AND METHODS

#### Culture Conditions

A master tube of S. epidermidis (ATCC 14990), was our ancestral strain and grown overnight in Luria Broth (LB) media (10 g tryptone, 5 g yeast extract, and 10 g NaCl), and then frozen with 25% glycerol at −80◦C. Several hundred aliquots were made from the master tube and also kept frozen with 25% glycerol at −80◦C. S. epidermidis (ATCC 14990) was evolved to each of five antibiotics: ciprofloxacin, doxycycline, gentamicin, nitrofurantoin, and oxacillin. We obtained and purified one independent spontaneously resistant mutant for each antibiotic, resulting in five resistant strains. For all resistant strains collected, we confirmed resistance by streak-purifying colonies onto agar plates containing antibiotic concentrations above the known MIC. For all experiments described here, we used freshly thawed aliquots of the ancestral strain and the resistant strains. Each replicate experiment required one aliquot. Strains were grown (aerated) in LB media for approximately 8 h at 37◦C to a density of roughly 109 cells per ml and serially diluted to approximately 105 cells per mL for MIC determination on agar plates.

#### Antibiotics

We used five antibiotics: Ciprofloxacin hydrochloride (CPR) (MP Biochemicals 199020), Doxycycline hyclate (DOX) (Sigma-Aldrich D9891), Gentamycin sulfate salt (GEN) (Sigma-Aldrich G1264), Nitrofurantoin (NTR) (Sigma-Aldrich N7878), and Oxacillin sodium salt (OX) (Sigma-Aldrich 28221). Ciprofloxacin, a synthetic second-generation fluoroquinolone, inhibits DNA synthesis by inhibiting bacterial enzymes DNA gyrase and topoisomerase, which are involved in the unwinding

and supercoiling of DNA during DNA replication (Hooper et al., 1987). Doxycycline, a broad-spectrum tetracycline, inhibits bacterial protein synthesis by binding to the 30S ribosomal subunit and preventing aminoacyl tRNA from binding (Roberts, 1996; Chopra and Roberts, 2001). Gentamicin, an aminoglycoside, inhibits bacterial protein synthesis by targeting the ribosomal A site (Hahn and Sarre, 1969; Yoshizawa et al., 1998). Nitrofurantoin, a multiple-mechanism nitrofuran, inhibits a variety of bacterial enzymes, including those involved in DNA and RNA synthesis as well as carbohydrate synthesis (Shah and Wade, 1989; McOsker and Fitzpatrick, 1994). Oxacillin, a beta-lactam penicillin, inhibits bacterial cell wall synthesis (Park and Strominger, 1957). These antibiotics were chosen because of their clinical importance, widespread use, and different mechanisms of action.

## Determination of Liquid Minimum Inhibitory Concentration (MIC) Estimates

MIC estimates in liquid culture were determined using microtiter plates with serial and equidistant dilutions of antibiotics. Approximately 103–104 cells were inoculated in each well with 100 µl LB and allowed to grow for 22 h, shaken at 220 revolutions per minute (rpm) and incubated at 37◦C (Tecan Infinite M200 PRO Multimode Microplate Reader). The liquid MIC estimate was determined by the lowest antibiotic concentration observed to inhibit growth by at least 95%, compared to the positive control. We also included negative controls on each 96 well-plate to validate no contamination of media.

## Determination of Agar MIC

Liquid MIC levels were used as a starting point to determine agar MIC levels. Agar tests tend to yield very similar MIC levels, but on occasion there may be minor differences. We plated two 100 mm agar plates for antibiotic concentrations ranging from 0.2 × liquid MIC and ending at 1.7 × liquid MIC estimate in increments of 0.1 × liquid MIC. Viable cells were quantified as colony forming units (CFUs). We inoculated each plate using 105 cells, resulting in a CFU population that has a limited probability of spontaneous mutation (Martinez and Baquero, 2000; O'neill et al., 2001). These cells were spread via the Copacabana method (Worthington et al., 2001; Mills et al., 2005), which involves the equal distribution of bacteria via sterile glass beads. We conducted the agar MIC assays in duplicate and recorded the median and range for each MIC for each bacterial strain. We prepared agar plates using 1000 mL of MilliQ water, 15 g agar powder, and one 25 g LB tablet (10 g tryptone, 5 g yeast extract, 10 g NaCl, and 1.5 g/L Tris/Tris HCl).

## Determination of Mutant Prevention Concentration (MPC)

MPC was determined as the antibiotic concentration that prevents the growth of any resistant mutants following an inoculum of 1010 cells on LB plates containing dilutions of antibiotic (Dong et al., 1999; Drlica, 2003). A population of 1010, allows for the consideration of single-step mutants, which is imperative in defining the MPC (Martinez and Baquero, 2000; O'neill et al., 2001). From a frozen aliquot, we grew a

bacterial culture overnight for 18 h at 37◦C and then inoculated this culture in LB until the inoculum reached an OD600 between 0.45 and 0.7. We then centrifuged the bacterial culture (4000 rpm × 4 min, 4◦C). We resuspended and combined all bacterial pellets in 7.5 mL of the original supernatant to give 1010 cells. We used liquid MIC estimates to plan the incremental concentrations used in MPC experiments. We performed two preliminary MPC experiments with concentrations ranging from 1 × liquid MIC estimate to 64 × liquid MIC estimate, increasing by a factor of two. We repeated MPC experiments 20 times, with three replicates per antibiotic concentration. To measure MPC, we plated at least 1010 bacterial cells on agar plates and spread the inoculum via the Copacabana method (Worthington et al., 2001; Mills et al., 2005). Plates were then incubated at 37◦C for 72 h. We determined MPC to be the lowest concentration of antibiotic where all three agar plates for a single concentration showed zero colonies. We prepared agar plates using 1000 mL of MilliQ water, 15 g agar powder, and one 25 g LB tablet (10 g tryptone, 5 g yeast extract, 10 g NaCl, and 1.5 g/L Tris/Tris HCl).

#### Mutant Selection Window (MSW)

Using MICs and MPCs, we determined the MSWs of ancestral and resistant strains in terms of the MIC of the ancestral strain. Using the MIC of the ancestral strain allowed us to directly compare the MSWs between the two strains.

#### RESULTS

We found that MPC estimates varied widely within a single antibiotic, indicating low repeatability of MPC. This was true of most antibiotics tested (**Table 1** and **Figure 2**). The inter-quartile range (IQR) varied among the antibiotics used and whether the strain was the resistant or ancestral strain. The ancestral strain had a more robust signal for a single MPC value where the resistant strain was much more variable (**Figure 2**).

The distribution of most MPCs do not appear normal (**Figure 2**). All of the resistant strains did not meet the requirements of a normal distribution (Shapiro-Wilk test (p < 0.05) and Kolmogorov-Smirnov test (p < 0.001)). The

TABLE 1 | Mean, standard deviation, median, and IQR of MPCs for both strains of Staphylococcus epidermidis (ancestral and resistant) for all antibiotics tested. All values reported in micrograms per milliliter.

ancestral strains did have a mix of distributions; doxycycline and nitrofurantoin both failed to reject the null hypothesis of a Shapiro-Wilk test (p > 0.05). We also demonstrate using a twosample Kolmogorov–Smirnov test, that the MPC distributions change as resistance evolves. In all direct comparisons of ancestral and resistant strains (with the same antibiotic) the distributions of the MPC values are different (p < 0.001).

We also found that the MSW changed when resistance is evolved (**Figure 3**). There is less variation in the MIC values than there is in the MPC values. The MSW not only shifts but also widens as resistance evolves.

## DISCUSSION

Our results show a range of MPCs in replicate experiments, indicating a large role for stochasticity and limited repeatability for this trait. In this study, the MPC trait is not easily predictable. This variation in MPCs is in contrast to MICs, which are generally predictable for each bacterial strain-antibiotic combination within a particular laboratory setting. For example, although variation in the MIC among different labs has been shown as a result of variations in strains as well as assay variations, individual studies within labs show consistency in the determination of the MIC (Mouton et al., 2017). Thus, while one trait (MIC) is more predictable and repeatable given a certain selection pressure, another (MPC) varies greatly due to stochastic processes. While previous studies indicate that MPCs can be fairly stable (Blondeau et al., 2001; Li et al., 2004; Marcusson et al., 2005; Olofsson et al., 2006), the number of replicates in these studies (two or three), would be insufficient to examine effects of stochasticity on the appearance of mutants.

The change in the MPC is large enough to account for the change of distribution and variation within the resistant strain as there is little to no overlap in the inter-quartile range (IQR). This supports the idea that although the MPC distribution is large and somewhat unpredictable, we can be confident that the MPC of a resistant strain is higher than an ancestral strain.

Our results here suggest two potentially relevant clinical notes. First, it has been proposed that if clinicians target MPCs, there can be no resistant bacteria left in a population within an individual patient (Dong et al., 1999). While this has not proven practical in most cases given the high concentrations of antibiotics needed, there has been work towards determining antibiotic combinations that lower the MPC (Michel et al., 2008). If used clinically (which is entirely hypothetical, since it is not currently used in the clinic), there should be care to understand that MPCs can vary with each bacteria and antibiotic combination and that failure to recognize variation in the MPC could result in inaccurate dosing. Therefore, this study suggests that MPCs should be understood as a range with confidence intervals, rather than as a single number. This study also reveals a significant change in the distribution of the MPC between ancestral and resistant strains, emphasizing the unpredictability of this trait when a bacterial strain acquires a spontaneous mutation conferring antibiotic resistance. Not only do distributions of the MPC in resistant strains increase, but

or NTR fail to reject the null hypothesis of a Shapiro-Wilk test (p > 0.05). Furthermore, when comparing the MPC distributions between the ancestral strain and the

resistant strain for each antibiotic the distributions are not the same (2-sample Kolmogorov-Smirnov test, p < 0.001).

the shapes of the distributions also change considerably. With nitrofurantoin and oxacillin, the distribution of the MPC changes from unimodal distributions in the ancestral strains to bimodal distributions in the resistant strains (See **Figure 2**). In either case, any intermediate steps taken to move a population off its trajectory towards maximal resistance—for example, using a different antibiotic against a population of bacteria—needs to consider the fact that there may not be a deterministic response of the pathogen population to the new stressor.

There has been some contention as to the utility of MPCs when the resistance mechanisms evaluated in vitro do not match the resistance mechanisms that would be found in a clinical setting (Smith et al., 2003). In this study, the acquisition of spontaneous chromosomal mutations was the primary mechanism of resistance when isolating and purifying resistant strains. However, horizontal transfer is typically required for resistance to aminoglycosides like oxacillin, β-lactams like gentamicin, and tetracyclines like doxycycline (Roberts, 1996; Smith et al., 2003). The distributions found in this study offer a first look at the unpredictability of MPC variation in resistant strains. Moreover, ciprofloxacin is a fluoroquinolone in which the mechanism of resistance is largely spontaneous chromosomal mutations (Pantosti et al., 2007).

It is known that the MIC fluctuates with inoculum size, with smaller inocula leading to lower MIC estimates (Granier et al., 2002; Egervärn et al., 2007; Wiegand et al., 2008). Even when testing the MIC values between liquid and agar media, slight differences are found. It would be worthwhile to investigate whether similar fluctuations exist for MPC testing. To elucidate evolutionary potentials in variation, this study used 1010 cells, an inoculum size similar to the number of bacterial cells found in naturally-occurring bacterial infections (Feldman, 1976; König et al., 1998; Zhao and Drlica, 2001; Gould and MacKenzie, 2002). Testing a range of large inoculum concentrations may provide further information about how MPCs depend upon cell concentrations present at the time of antibiotic administration. Our findings are particularly relevant to understanding variation in bacterial responses to antibiotics at high cell densities.

strain.

fmicb-10-00042 January 31, 2019 Time: 14:57 # 6

Toprak et al. (2012) showed that resistance to different antibiotics involved different types of pathways: some antibiotics had a very stereotyped pathway with similar mutations evolved in the same order, whereas other antibiotics had much more variation in timing and type of mutation (Toprak et al., 2012). With regards to the MPC, it could be illuminating to quantify and examine the specific genetic mutations underlying resistant strains of bacteria at similar and dissimilar MPCs. This would give more information regarding which specific mutations are needed, and how many unique mutations or combinations of mutations exist, to yield high antibiotic resistance. A better understanding of the amount of variation by bacteria and antibiotic could provide a more complete story regarding the variation underlying MPCs. This current study provides a first step, which shows high variability in this important resistance trait.

Luria-Delbruck fluctuations, defined as fluctuations in the frequency of spontaneous mutations in microbial populations (Luria and Delbrück, 1943), may affect the evolutionary trajectory of populations. If a mutation occurs early on in the growth of the population there would be more cells with mutations because of the exponential characteristic of cell division in bacteria (Sarkar, 1991). Conversely, if a mutation arises later, there will be fewer cells exhibiting that mutation. Thus, a low probability event, which occurs early on, may have drastic and amplified results (Skipper, 1983; Rosche and Foster, 2000). Luria-Delbruck fluctuations can, but do not necessarily, have a large impact on the number of resistant mutants in a given population of bacteria (Ford et al., 2013). If a spontaneous mutant arises early in the population growth phase and happens to confer resistance to a given antibiotic, then in the presence of the antibiotic, the ending population will be comprised largely of this resistant mutant and daughter cells. Depending on the exact timing of the appearance of the mutation, a population may exhibit many resistant cells, or very few. Understanding, therefore, the mutations and patterns below the MPC would also be a very useful future study in elucidating fluctuations in the MPC and MSW.

In summary, we find that even in highly controlled laboratory environments, MPCs vary widely, not only from differences in strain and antibiotic, but from replicates with the same strain and same antibiotic. Several other factors may also affect MPC variation, such as CFU concentrations, mutation type, and

inocula size and in the future, these factors should be investigated. Understanding how and why the MPC varies can allow us to lay the foundations for more comprehensive dosing strategies that take into consideration the presence and elimination of single-step resistant mutants. From a clinical perspective, caution should be taken when determining how reliable certain therapeutic treatments will be in terms of completely eliminating resistant mutants. From an evolutionary perspective, we show the significant role of stochasticity in bacteria evolving antibiotic resistance.

#### DATA AVAILABILITY STATEMENT

The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.

### AUTHOR CONTRIBUTIONS

PY conceived of the presented idea and planned the experiments. CG, NL, CH, PK, AB, AV, LS, GB, MW, EC, LF, and JN carried out the experiments. NL analyzed the data. CG, NL, and PY discussed

#### REFERENCES

and contributed to the interpretation of results. CG, NL, CH, MW, and PY contributed to the final version of the manuscript. PY supervised the project.

## FUNDING

We are grateful for funding from the UCLA Department of Ecology and Evolutionary Biology, and the UCLA David Geffen School of Medicine. We also thank the Hellman Foundation (PY) and a KL2 Fellowship (PY) through the NIH/National Center for Advancing Translational Science (NCATS) UCLA CTSI grant number UL1TR001881. This investigation was supported in part by National Institutes of Health, under Ruth L. Kirschstein National Research Service Award (T32-GM008185) (NL). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

#### ACKNOWLEDGMENTS

We are grateful to Tina Kang, Maral Sakayan, and Ian Boucher for their assistance in the lab, and Portia Mira for comments on the manuscript.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Gianvecchio, Lozano, Henderson, Kalhori, Bullivant, Valencia, Su, Bello, Wong, Cook, Fuller, Neal and Yeh. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Multidrug-Resistant Enterobacter cloacae Complex Emerging as a Global, Diversifying Threat

Medini K. Annavajhala, Angela Gomez-Simmonds and Anne-Catrin Uhlemann\*

Division of Infectious Diseases, Department of Medicine, Columbia University, New York, NY, United States

The Enterobacter cloacae complex (ECC) includes common nosocomial pathogens capable of producing a wide variety of infections. Broad-spectrum antibiotic resistance, including the recent emergence of resistance to last-resort carbapenems, has led to increased interest in this group of organisms and carbapenem-resistant E. cloacae complex (CREC) in particular. Molecular typing methods based on heat-shock protein sequence, pulsed-field gel electrophoresis, comparative genomic hybridization, and, most recently, multilocus sequence typing have led to the identification of over 1069 ECC sequence types in 18 phylogenetic clusters across the globe. Whole-genome sequencing and comparative genomics, moreover, have facilitated global analyses of clonal composition of ECC and specifically of CREC. Epidemiological and genomic studies have revealed diverse multidrug-resistant ECC clones including several potential epidemic lineages. Together with intrinsic β-lactam resistance, members of the ECC exhibit a unique ability to acquire genes encoding resistance to multiple classes of antibiotics, including a variety of carbapenemase genes. In this review, we address recent advances in the molecular epidemiology of multidrug-resistant E. cloacae complex, focusing on the global expansion of CREC.

Keywords: carbapenem-resistant Enterobacteriaceae, carbapenem-resistant Enterobacter cloacae complex, carbapenemase, multidrug-resistance, bacterial genomics

## INTRODUCTION

Enterobacter spp., the second most common carbapenem-resistant Enterobacteriaceae (CRE) in the United States, increasingly contribute to the spread of carbapenem-resistant infections (Wilson et al., 2017). In particular, Enterobacter cloacae complex (ECC) are common nosocomial pathogens capable of producing a wide variety of infections, such as pneumonia, urinary tract infections, and septicemia (Sanders et al., 1997; Wisplinghoff et al., 2004). The emergence of multidrug resistance (MDR), including resistance to the last-resort carbapenems meropenem, imipenem, and ertapenem, has led to an increased interest in these organisms.

Molecular analyses based on multilocus sequence typing (MLST) and heat-shock protein (hsp) typing have led to the re-definition of members within this complex (Hoffmann and Roggenkamp, 2003; Paauw et al., 2008; Miyoshi-Akiyama et al., 2013). Whole-genome sequencing (WGS), moreover, has allowed for reproducible population-level analyses to determine clonal structure and diversity in ECC and CREC collections ranging from localized, regional outbreaks to global studies

#### Edited by:

José Luis Capelo, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Portugal

#### Reviewed by:

Jason Sahl, Northern Arizona University, United States Miklos Fuzi, Semmelweis University, Hungary

\*Correspondence:

Anne-Catrin Uhlemann<EMAIL_ADDRESS>
#### Specialty section: