ϕ24 B integration increases cell proliferation

Growth rates of bacteria can differ due to a range of environmental parameters. To investigate the impact of ϕ24 B on E. coli, viable cell counts were determined during growth comparing E. coli B strain MC1061 to single and double lysogens, the latter integrated into separate locations in the MC1061 chromosome43. Under standard growth conditions, the single and double lysogens showed significantly higher cell numbers compared to the naïve MC1061 (>200%, Fig. 1). This alongside a statistically significant increase in doubling time of 18 minutes for the single lysogen compared to 20 minutes for the naïve MC1061 (p < 0.006), calculated from each growth curve (data not shown, n = 9). As the cultures reached mid to late exponential growth, the differences in growth rates diminished (Fig. 1). The greatest difference in growth was identified in early growth (Fig. 1). This was supported by a shorter lag time in the single and double lysogen compared to MC1061 with a 0.5 and 1.8-fold increase respectively in cell number after the first hour of growth. Stationary phase in the double lysogen is achieved earlier compared to MC1061 and the single lysogen as nutrients are utilised rapidly alongside the accumulation of inhibitory components of growth.

Figure 1: Clustered column graph representing percentage increase in cell proliferation of single (ϕ24 B ::ΔKan, dark grey) and double (ϕ24 B ::ΔKan, ϕ24 B ::ΔCat, light grey) MC1061 lysogens. Cultures were grown at 37 °C (CFU.ml) and samples taken over a 7 hour period including experimental and technical replicates (n = 9). Percentage increases or decreases show differences in growth of the lysogens compared to the uninfected MC1061 represented here as 0 on the x axis. Significance threshold P values ***<0.001, **<0.01, *<0.05, significance below the x axis demonstrates greater growth from the Naïve host. Full size image

ϕ24 B integration alters utilisation of different mono-phosphates and inability to respire using β-D-Allose

To explore the single lysogen related differences in cell respiration during growth we used the Biolog Phenotype MicroArray. This determined functional changes in respiration and metabolism resulting from phage conversion over a 48 h period with recordings taken every 15 min. The lysogen acquired the ability to respire and grow utilising uridine-2-monophosphate (U-2-P) when compared to the naïve MC1061 (SI Fig. 1, panel A). Phage mediated subversion of pyrimidine and purine synthesis by lytic phages has previously been reported and will be discussed later. Conversely, integration of the phage inhibited the lysogens ability to use D-Allose for respiration.

ϕ24 B integration alters resistance to osmotic stress or antimicrobials

Again using the Biolog phenotypic array the single lysogen is able to tolerate a range of antimicrobial agents that have both extracellular and intracellular targets (Fig. 2). The respiration curves derived for this experiment are provided in the supplementary information (SI - Figs 1 and 2). Tests showing differences in respiration profile were determined in the presence of 22 antimicrobials and 7 increases in salt concentration (SI Table 2). Of these 29 different tests, the lysogen showed a level of tolerance to 17 antimicrobials (SI Table 2). Data presented in Fig. 2 (n = 3) are comparisons of the area under the respiration curve illustrating those that were altered significantly. ϕ24 B infection promotes tolerance to 8-hydroxyquinoline (P < 0.000), chloroxylenol (P < 0.0037), and cefmetazole (P < 0.0026), cefoxitin, (P < 0.015) cefemendole (P < 0.0239) and amoxicillin (P < 0.057). Integration of ϕ24 B into the primary site 250 bp upstream of IntS inhibits respiration utilising B-D-allose. Lysogeny also limits cell respiration in the presence of oxolinic acid although this is linked to phage induction as the cellular target is DNA gyrase. Inhibition of DNA gyrase has been previously shown to stimulate temperate phages to the lytic life cycle as cellular stress stimulates RecA, LexA and proteolytic cleavage of the repressor protein promoting phage induction44.

Figure 2: A comparison of Area Under the Respiration Curve (AURC) data from the Biolog bacterial phenotypic microarray. Data plotted shows the addition of supplemented nutrients or chemical challenge showed statistically significant difference in rates of respiration between the lysogen and naïve MC1061 host (for P values see SI Table 2). Arbitrary Omnilog fluorescence values (y-axis) show differences between the naïve MC1061 (light grey) host and the lysogen (dark grey) over a 47.5 h time period (n = 3). Error bars represent SEM. Graphs A-F show significantly higher amount of respiration of the lysogen compared to the naïve host under the following conditions; (A) U-2-monophosphate, (B) 8-hydroxyquinoline, (C) chloroxylenol, (D) cefoxitin, (E) cefomendole and (F) amoxacillin. Graphs (G–I) show mean AURC values where growth on different carbon sources or chemical challenge that has a detrimental effect on the respiration of MC1061 when converted by ϕ24 B , these inculde; (G) β_D-Allose, (H) ofloxacin and (I) oxolinic acid. Full size image

ϕ24B integration increases MC1061 tolerance to sub-inhibitory concentrations of chloroxylenol and 8-hydroxyquinoline

To better understand the level of antimicrobial tolerance of the single lysogen, we first determined sub-inhibitory concentrations (SIC) against both MC1061 and the lysogen that reduce cell growth by ~60%. The antimicrobials chloroxylenol, oxolinic acid and 8-hydroxyquinoline were selected to validate the Biolog data. Prior to comparison, an approximate SIC range was determined for MC1061 utilising each of the 3 test drugs. Figure 3 illustrates increased tolerance by the lysogen in the presence of chloroxylenol and 8-hydroxyquinoline. Conversely, the naïve host shows increased tolerance compared to the lysogen in the presence of oxolinic acid. This also offers a positive control for the assay as oxolinic acid targets DNA gyrase and therefore stimulates phage induction26. Phage induction was confirmed by the presence of free phage compared to the un- induced control (data not shown).

Figure 3 Response in growth of both MC1061 (light grey) and the ϕ24 B lysogen (Dark grey) to an increasing concentration of (A) 8-hydroxyquinoline, (B) chloroxylenol, and (C) oxolinic acid. Bacterial growth was measured by increase in optical density at 600 nm after 18 hours growth at 37 °C, as per original Biolog assay. Error bars represent the standard error of the mean (SEM) (n = 12). Significance represented by (P) thresholds; ***<0.001, **<0.01, *<0.05. Full size image

Metabolic profiles comparing naïve MC1061 to ϕ24 B Lysogen

We used an untargeted metabolite profiling approach using high resolution LC-MS (≤1 ppm mass accuracy in full scan) to determine metabolic differences between bacterial host and lysogen during growth and when challenged with a sub-inhibitory concentration of test antibiotic. To broadly compare findings, significant metabolic differences (p < 0.05) were observed between both growth phase and antimicrobial challenge. In total, >11 K ion features or possible metabolites were determined across all of the different tests performed. Of these 81 showed discrimination between the naïve MC1061 and the ϕ24 B lysogen that had clean chromatogram peaks and <5% coefficient variable (CV) (SI Table 3). These 81 metabolites that show differences can be further stratified to each test.

The metabolite data was analysed using supervised and non-supervised methods of multivariate analysis. Principal Component Analysis was first employed to visualise trends in the dataset and identify potential outliers. To further interrogate the data, Partial-Least Squared Discriminant Analysis models (PLS-DA) were generated and score plots are shown in (Fig. 4A–C). The PLS-DA models for both hydroxyquinoline and chloroxylenol conditions score plots had good discriminating ability, establishing the metabolic differences between the lysogen and naïve host. During standard growth conditions component 1 failed to discriminate: Q2 −0.556, R2Y 0.262, as R2Y and Q2 < 0.5, although certain metabolites showed significant differences between the lysogen and MC1061. The 8-hydroxyquinoline component 1: Q2 0.74, R2Y 0.89 and the chloroxylenol component 1: Q2 0.802, R2Y 0.923 were both discriminatory with an R2Y and Q2 > 0.5. Further model statistics can be found in the supplementary information SI Table 5. Stx-phage ϕ24 B has been previously shown to undergo spontaneous induction27 and may impact the metabolite profile through sequestration of host function and movement to lysis. We therefore compared the metabolite profiles of both the lysogen and MC1061 with a phage inducing agent, oxolinic acid (DNA gyrase inhibitor). No correlation was seen between metabolite profiles of the lysogen or MC1061 when compared to that of the lysogen undergoing induction with oxolinic acid (data not shown).

Figure 4 (A–C) The metabolite profiles of MC1061 versus lysogen and multivariate analysis using partial least discriminant analysis (PLS-DA). The panels represent score plots from PLS-DA models of: (A) Standard growth conditions, and supplementation with (B) 8-hydroxyquinoline and (C) chloroxylenol, between the naïve host (light grey spot) and lysogen (dark grey spot), the model discriminatory parameters for the PLS-DA analysis are described in the results section and in SI Table 5. Full size image

ϕ24 B integration alters the metabolite profile of MC1061 in standard growth conditions

Out of the 81 discriminatory metabolites determined in this study, only 16 were shown to discriminate between the naïve host and single lysogen under standard culture conditions. Of these 16 metabolites 4 were found in higher levels in the lysogen. This suggests that the lysogen down regulates certain metabolic functions or is directing metabolism along a different pathway, or both. It is likely to support the change in biology we report in this work and increased rates of early growth by the lysogen.

Early growth in the lysogen demonstrates an observable difference in metabolic profile compared to the naïve MC1061. Under standard growth conditions during early growth, 5 metabolites in total were shown to discriminate between the naïve MC1061 and lysogen. Of these, 1 was higher compared to the naïve control (Fig. 3 SI). During stationary phase, in standard growth conditions, only 9 metabolites in total showed significant difference and all were found in lower levels in the lysogen.

As phage-mediated metabolic differences are present during standard culture, the differences in metabolite profiles under challenge with sub-inhibitory concentrations of 8-hydroxyquinoline and chloroxylenol were tested (Fig. 2B,C). The previous Biolog results showed that the lysogen displays a tolerance to these 2 antibiotics.

ϕ24 B integration alters the metabolite profile of MC1061 during growth under sub-inhibitory concentrations of 8-hydroxyquinoline

Upon treatment with 8-hydroxyquinoline, there were 29 metabolites that showed significant difference between the naïve MC1061 and single lysogen. Of these 29 metabolites, 22 were found in higher levels in the lysogen. Early growth phase in the lysogen demonstrates an observable difference in metabolite profile compared to naïve MC1061. Under 8-hydroxyquinoline stress during early growth, 6 metabolites in total were shown to discriminate between the naïve MC1061 and lysogen. Of these, 5 were higher compared to the naïve control (Fig. 3 SI).

ϕ24 B integration alters the metabolite profile of MC1061 during growth under sub-inhibitory concentrations of chloroxylenol

Under chloroxylenol treatment, 41 metabolites showed significant differences between the naïve MC1061 and lysogen. Of these 41, the lysogen had 22 metabolites with significantly higher levels compared to the naïve host. Early growth phase in the lysogen demonstrates an observable difference in metabolic change compared to the naïve MC1061. Under chloroxylenol stress during early growth, 13 metabolites in total were shown to discriminate between the naïve MC1061 and lysogen. Of these, 9 were higher compared to the naïve control (Fig. 3 SI).

Alteration in metabolomics profile and antimicrobial tolerance is not linked to kanamycin resistance selective marker used to detoxify ϕ24 B

The kanamycin gene (aph3) used to detoxify the ϕ24 B phage18 is used as a selective marker only prior to experimentation. As there is no consistency in the metabolic profiles when the lysogen cultures are treated with the 2 different antimicrobials this cannot be driven by ϕ24 B encoding aph3. The metabolite profiles are also discriminatory to each of the 2 antimicrobials tested.

Characterising the metabolites that discriminate between the naïve MC1061 and ϕ24 B lysogen

The discriminatory metabolites determined from each test were compared with metabolite databases and were putatively identified based on exact mass and empirical formula (LCMS methods section). The identity of each metabolite was confirmed using fragmentation analysis using a secondary MS/MS stage. Identities with fragment similarity were found for 58 of the 81 metabolites discriminating between the naïve and lysogen. We focus here on 6 particular metabolites as they have robust identities from fragmentation patterns, retention times, and low accurate mass error (PPM), relating to known curated bacterial metabolites (Table 4 SI). The 6 metabolites are: hexadecanoic acid (a fatty acid that is utilised in the construction of lipid A), sphinganine (putative kinase), 5-Methyluridine (nucleotide synthesis, specifically pyrimidine), ophthalmic acid (glutathione analogue), pimelic acid and FAPy-Adenine, with PPM error margins of 0 ± 1 (0.17, −0.64, 0.45, 1.31, 0.56 and −1.00, respectively).

The lysogen has significantly higher intensity levels of pimelic acid under all tests, specifically during early growth (Fig. 5). FAPy-Adenine, a bacterial stress marker45, is only seen in stressed conditions in these analyses, with the lysogen expressing significantly lower intensity during early growth and higher intensity at stationary phase growth (Fig. 5). Hexadecanoic acid is identified in significantly higher abundance under cellular stress of chloroxylenol, and is further increased in the lysogen during early growth (P = 0.04). Metabolite sphinganine is present under standard conditions in higher intensity in the naïve MC1061. When challenged with chloroxylenol, intensity levels of sphinganine were undetectable in both naïve and lysogen during early growth. During mid-exponential and stationary phase growth under choloroxylenol test there is >100 fold increase in intensity of sphinganine in both the naïve and lysogen. 5-Methyluridine is present at stationary phase in all conditions, and is also identified in higher intensity when challenged with both antibiotics. Ophthalmic acid was present at all stages of growth under standard conditions where the lysogen shows lower intensity at early and mid-growth, and higher levels at stationary phase. When treated with either antimicrobial agent, ophthalmic acid was only present at stationary growth, with significantly higher intensity found in the lysogen (P = 0.001). During standard culture, there are 16 metabolites responsible for the differences seen between the core metabolic profiles of naïve host and lysogen during the 3 growth phases. Importantly 10 of these, including pimelic acid, are also present when the lysogen is challenged with chloroxylenol and 8-hydroxyquinoline.

Figure 5: Biotin concentration, FAPy-Adenine and pimelic acid intensity showing significant biological differences between naïve host and lysogen during growth and antimicrobial challenge. (A1): Changes in cellular stress marker FAPy-Adenine abundances under the challenge of chloroxylenol at early, mid and stationary growth between the lysogen (dark grey) and naive Host (light grey). (B1): Average pimelic acid abundance under chloroxylenol at early, mid and stationary growth between the lysogen and naive Host. (A2): Average FAPy-Adenine abundances under selective pressure of 8-hydroxyquinoline at early, mid and stationary growth between the lysogen and naive MC1061. (B2): Average pimelic acid abundances under challenge with 8-hydroxyquinoline at early, mid and stationary growth between the lysogen and naive Host. (A3): Average pimelic acid abundances under standard conditions at early, mid and stationary growth between the lysogen and naive Host. (B3): Variance in the amounts of Biotin present in samples of Ф24 B lysogen and MC1061 naïve Host. Error bars derived from standard error of the mean (n = 3). Biotin Quantitation test performed using BioVision® quantitation kit (7.5) using a modified protocol. Two tailed significance represented by ***<0.001, **<0.01, *<0.05, key: *Inc. = Increase, *expo = exponential growth. Full size image

In the absence of antibiotics, the metabolite profile shows less discrimination between the lysogen and host at the 3 stages of growth by PLS-DA (Fig. 4A). Changes in individual metabolite abundances were measured as before (Figs 4 and 6), and >100 were deemed possible biologically relevant metabolites. From the confirmed compounds, a total of 16 metabolites (SI Table 3) were shown to discriminate between MC1061 and the ϕ24 B lysogen.

Figure 6: Heatmap generated by metabolic levels of 81 metabolites using HCA and DM Culture conditions and presence or absence of phage can be found alongside each profile (H = 8-hydroxyquinoline, C = chloroxylenol). Each individual tile represents a metabolite. The colour of a given tile denotes higher or lower intensity of the metabolite. The colour scale key is: dark blue: lowest levels; white: mid-point; dark red: highest level. The gradient between these colours represents variation in the levels of the metabolite across the colour scale (putative ID’s can be found in SI Table 3). Pimelic acid is highlighted across all profiles with a hatched box. Full size image

We further analysed these data using Hierarchical cluster analysis (HCA) and Euclidean dissimilarity matrix (DM) to create a heatmap that discriminates between 81 metabolites across all tests in this study (Fig. 6). The unsupervised heatmap shows that the metabolic profiles have separated by condition.