Editor's evaluation: A high-throughput cytotoxicity screening platform reveals agr-independent mutations in bacteraemia-associated Staphylococcus aureus that promote intracellular persistence (2023)

Full text Figures and data Side by side Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Staphylococcus aureus infections are associated with high mortality rates. Often considered an extracellular pathogen, S. aureus can persist and replicate within host cells, evading immune responses, and causing host cell death. Classical methods for assessing S. aureus cytotoxicity are limited by testing culture supernatants and endpoint measurements that do not capture the phenotypic diversity of intracellular bacteria. Using a well-established epithelial cell line model, we have developed a platform called InToxSa (intracellular toxicity of S. aureus) to quantify intracellular cytotoxic S. aureus phenotypes. Studying a panel of 387 S. aureus bacteraemia isolates, and combined with comparative, statistical, and functional genomics, our platform identified mutations in S. aureus clinical isolates that reduced bacterial cytotoxicity and promoted intracellular persistence. In addition to numerous convergent mutations in the Agr quorum sensing system, our approach detected mutations in other loci that also impacted cytotoxicity and intracellular persistence. We discovered that clinical mutations in ausA, encoding the aureusimine non-ribosomal peptide synthetase, reduced S. aureus cytotoxicity, and increased intracellular persistence. InToxSa is a versatile, high-throughput cell-based phenomics platform and we showcase its utility by identifying clinically relevant S. aureus pathoadaptive mutations that promote intracellular residency. Editor's evaluation This paper describes a new method to investigate Staphylococcus aureus intracellular virulence that has produced important insights into the mechanisms of staphylococcal pathogenesis. The results are convincing and the methodology is state-of-the-art. The authors have responded to the reviewer comments and resolved the issues identified during the review. This paper will be of interest to scientists studying microbial intracellular pathogenesis and cell biology. https://doi.org/10.7554/eLife.84778.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Staphylococcus aureus is a leading cause of hospital-acquired infections, a problem exacerbated by increasing resistance to last-line antibiotics (Murray et al., 2022; Tong et al., 2015). S. aureus is traditionally considered an extracellular pathogen as it produces many secreted virulence factors, including superantigens, degradative enzymes, and cytolytic toxins (Tam and Torres, 2019). The potent pore-forming toxins (PFTs), including alpha-hemolysin and leukocidins, are among the major virulence determinants of S. aureus (Cheung et al., 2021). These toxins induce rapid host cell death, including death of the leukocytes and neutrophils recruited to remove bacteria from infected tissues (Surewaard et al., 2016; Thammavongsa et al., 2015). Long-term asymptomatic S. aureus carriage is common but invasive infection is rare. Thus, understanding the changes enabling S. aureus to switch from a common coloniser of the nasal cavity to an invasive pathogen is a major research focus. A powerful discovery approach has been used to compare the cytolytic activities of secreted S. aureus virulence factors between colonising and bacteraemia isolates, followed by genomics to uncover the bacterial genetic loci linked with high/low cytolytic activity (Collins et al., 2008; Giulieri et al., 2018; Laabei et al., 2014; Laabei et al., 2021; McConville et al., 2022). These toxicity analyses use methods that monitor eukaryotic cell viability upon exposure to S. aureus culture supernatants (Dankoff et al., 2020; Das et al., 2016; Giulieri et al., 2018) and integrate these phenotypes within genome-wide association studies (GWAS) and other comparative bacterial population genomics techniques (Giulieri et al., 2018; Recker et al., 2017). However, such toxicity assays are limited in that they focus on exogenous virulence factors that have accumulated in the culture media during bacterial growth. Thus, such methods can be limiting as they report phenotypes that are temporally skewed and ignore the host cell-bacterium context under which the production of these factors is controlled during infection. When these endpoint toxicity assays are conducted at scale with many hundreds of bacterial isolates, additional issues caused by filter-sterilisation, freeze-storage, and other manipulations during the preparation of bacterial supernatants prior to incubation with host cells may increase assay variability (Giulieri et al., 2018; McConville et al., 2022). Another restriction of toxicity assays is that they treat S. aureus as an obligate extracellular pathogen, whilst the literature has reported its capacity to adopt an intracellular behaviour, readily adhering to and invading various eukaryotic cells, including non-professional phagocytes (Flannagan et al., 2016; Foster et al., 2014; Soe et al., 2021). Upon internalisation, intracellular S. aureus initially reside in phagolysosomes, where low pH is a cue for bacterial replication (Flannagan et al., 2018; Lâm et al., 2010). S. aureus uses its arsenal of PFTs to escape from this degradative compartment, into the cytosol and cause the death of host cells (Moldovan and Fraunholz, 2019; Siegmund et al., 2021). This intracellular niche confers protection to S. aureus from antibiotics and immune responses (Peyrusson et al., 2020; Strobel et al., 2016). While guarding S. aureus from host attack, intracellular residency also enables the creation of a reservoir for the pathogen to persist in an infected host and could lead to bacterial transmigration into distal host tissues, from where the bacteria can cause protracted infections and more serious disease (Jorch et al., 2019; Surewaard et al., 2016). Toxicity and persistence of S. aureus in an intracellular context are critical pathogenesis traits. However, understanding these traits and their microevolution across diverse collections of clinical S. aureus strains, and correlating them with specific bacterial genetic variations has been hampered by the lack of a high-throughput platform for trait characterisation. To address these issues, we took advantage of the capacity of S. aureus to invade cultured epithelial cell lines and established a 96-well assay format to accurately monitor over time the bacterial toxicity exerted from within host cells. HeLa cells are adherent epithelial cells and represent an amenable model to study the pathogenesis of most intracellular bacteria causing human disease, including S. aureus (Das et al., 2016; Stelzner et al., 2020a; Stelzner et al., 2020b). We modified an antibiotic/enzyme protection assay, using a combination of gentamicin and lysostaphin, to kill extracellular bacteria while preserving the viability of intracellular bacteria (Kim et al., 2019). Using propidium iodide (PI) as a marker of host cell death, the assay measured changes in fluorescence of HeLa cells over time caused by the presence of intracellular S. aureus. Combined with the power of bacterial genomics and evolutionary convergence analysis, we used the assay to screen a large collection of S. aureus bacteraemia isolates. Our large-scale pheno-genomics approach revealed known and previously undescribed loss-of-function mutations that were significantly associated with reduced intracellular cytotoxicity and increased intracellular persistence. Results InToxSa assay development We set out to develop a high-throughput and continuous cell death assay that could measure the intracellular toxicity of S. aureus in a format we named InToxSa. We used adherent HeLa-CCL2 epithelial cells as an infection model in a 96-well format and infected them with S. aureus at a standardised multiplicity of infection (MOI). Following an infection period of 2 hr, infected cells were treated with an antibiotic/enzyme combination to specifically kill extracellular bacteria and prevent further reinfection by bacteria released by cells dying during the assay. HeLa cell viability was continuously monitored by measuring PI fluorescence (see methods). Reduced HeLa cell viability was indicated by increasing fluorescence over time (Figure 1A). We used regression to fit standardised curves to the PI uptake data and calculated seven kinetic parameters including the area under the curve (AUC) representing the total of PI uptake over time, peak PI uptake [μmax], the time to μmax [t(μmax)], the maximum rate in PI uptake [rmax], the time to rmax [t(rmax)], trough, and time to trough (Figure 1—figure supplement 1). Figure 1 with 1 supplement see all Download asset Open asset Establishing the intracellular toxicity of S. aureus (InToxSa) assay. (A) Overview of InToxSa assay. (B) Flow chart of the analytical pathway. (C) Plot of propidium iodide (PI) fluorescence uptake over 20 hr as a measure of S. aureus intracellular cytotoxicity in HeLa cells. Depicted curves are wild-type S. aureus JE2 (blue), the isogenic S. aureus JE2 agrA transposon mutant (red), and uninfected cells (yellow). The PI uptake curve for JE2 is annotated with five kinetic parameters. For each curve, the thick line represents the mean and the shading, the standard deviation. Curves are fitted with cubic smooth splines (see methods). To minimise batch effect, all kinetics data have been transformed using proportion of maximum scoring (POMS) using JE2 controls as reference minimum and maximum values (Little, 2013). x-axis is time and y-axis is PI uptake, represented as a proportion of maximal fluorescence in JE2-infected cells, where for every measured plate, a PI uptake value of 1 represents the maximum of JE2 PI uptake and zero its minimum. (D) Summary of five independent InToxSa experiments to assess assay and parameter variation. Violin plots represent the density distribution of all five replicates (based on Gaussian kernel density estimation) and the nested box plots (boxes represent median, first and third quartiles, whiskers represent are based on ×1.5 interquartile range) show the distribution of within plate replicates (3–5 technical replicates per plate replicate) for the three most discriminatory of seven parameters inferred from the PI uptake data (Figure 1—figure supplement 1). We then mapped out a series of experiments to validate InToxSa and explored bacterial genetic factors linked to intracellular cytotoxicity (Figure 1B). To demonstrate method performance, we measured the intracellular toxicity of the wild-type S. aureus USA300 strain JE2 against an isogenic agrA mutant (Nebraska Transposon Library mutant NE1532; Fey et al., 2013), using non-infected cells as a baseline (Figure 1C). S. aureus JE2 caused a rapid and substantial increase in PI fluorescence over time, reflective of the known high cytotoxicity of this strain (Das et al., 2016; Grosz et al., 2014; McConville et al., 2022). Cells infected with the agrA mutant yielded significantly lower PI uptake (AUC) and slower (rmax), indicating HeLa cell viability during the infection course and is consistent with the reported low cytotoxicity of S. aureus agr mutants (Figure 1C; Laabei et al., 2021; McConville et al., 2022). We then assessed the reproducibility and repeatability of InToxSa across five experimental replicates, each time using both biologically independent HeLa cell culture passages and independent S. aureus cultures with the same two comparator strains (JE2 wild type and the isogenic agrA transposon mutant) (Figure 1C). We measured seven PI uptake curve kinetic parameters (Figure 1C, Figure 1—figure supplement 1). We observed that the PI uptake AUC, peak PI uptake [μmax], and maximum PI uptake rate [rmax] for S. aureus JE2 compared to the agrA mutant and non-infected cells were significantly different, had very low intra-assay variation, and were among the most discriminatory and reproducible PI uptake curve parameters (Figure 1D, Table 1, Figure 1—figure supplement 1). At experimental endpoints, the acidity of the culture media had not changed, and no bacterial growth was observed after plating the media from infected 96-well plates, indicating that InToxSa assessed the cytotoxicity caused only by intracellular S. aureus (data not shown). Table 1 Summary of intracellular toxicity of S. aureus (InToxSa) assay performance. StrainNo. biological replicatesArea under the curve [AUC]Peak uptakeMax uptake[μmax][rmax]MeanStd. dev.CoVMeanStd. dev.CoVMeanStd. dev.CoVS. aureus JE2 wild type2516214.60.0910.090.090.0030.00040.13S. aureus JE2 agrA mutant1130.9140.450.170.060.350.00040.00020.52Non-infected155.318.33.460.070.071.020.00010.000050.32 Note: Std. dev. = standard deviation; CoV = coefficient of variation. Confirmation of S. aureus internalisation in HeLa cells We used confocal microscopy to confirm the presence of intracellular S. aureus (Figure 2A). HeLa cells grown on coverslips were infected with the same conditions used for InToxSa and analysed at 3, 8, and 24 hr post-infection. These timepoints were selected to reflect the key InToxSa timepoints highlighted during JE2 infection (start of PI uptake measurement, peak PI uptake [μmax], and experimental endpoint, respectively). We observed that at each timepoint, both JE2 and the agrA mutant co-localised with the lysosomal marker LAMP-1. However, the agrA mutant was detected in higher numbers within cells compared to wild type (Figure 2A, B). At later timepoints (8 and 24 hr) the number of JE2-infected cells decreased, with fewer detectable intracellular bacteria, suggesting that JE2-infected cells had died, consistent with bacterial cytotoxicity. In contrast, the number of cells infected with the agrA mutant did not vary significantly, indicating cell survival during the infection time course (Figure 2B). It also appeared that the number of intracellular bacteria increased for cells infected with the agrA mutant, suggesting bacterial replication over time. This latter observation was explored in more depth using high-content/high-throughput imaging (see later section). The microscopy results support the InToxSa assay outputs and indicate that non-cytotoxic S. aureus, such as the agrA mutant, can persist within cells without affecting cell viability. Figure 2 Download asset Open asset Fluorescence confocal microscopy of intracellular S. aureus. (A) HeLa cells were infected with S. aureus (wild-type JE2 or isogenic agrA mutant) and imaged at 3, 8, and 24 hr post-infection. Fixed cells were labelled with LAMP-1, S. aureus antibodies and 4′,6-diamidino-2-phenylindole (DAPI). (B) Manual quantification of confocal microscopy. Graph shows the percentage of cells infected with S. aureus at each of the three timepoints. At least 50 cells (n cells = 51–112) were counted in 3–5 fields of view, with at least 12 cells counted per field (n field = 12–40). Shown are all data points, mean, and standard deviation. Significance was assessed using two-way analysis of variance (ANOVA). Null hypothesis (no difference between means) rejected for adj p < 0.05. *p = 0.04, ***p = 0.007, ****p = <0.0001. InToxSa benchmarking against trypan blue exclusion assay In a previous study, we used a trypan blue exclusion assay with THP1 human macrophages exposed to culture supernatants to test within-host cytotoxicity variations from 51 clinical S. aureus isolated from 20 patients with bacteraemia (Giulieri et al., 2018). These 51 S. aureus isolates were originally selected because they were associated with phenotypic changes occurring during bacteraemia, such as infection relapse, antibiotic treatment failure, longer duration of bacteraemia, and augmented vancomycin MIC; phenotypes likely resulting from within-host evolution (Giulieri et al., 2018). Thus, the isolates were categorised by patient and by their sequential isolation, with the first isolate considered as 'baseline' and the subsequent isolates designated as 'evolved'. All 51 isolates were screened with InToxSa and compared with the original trypan blue assay data (Figure 3). Using trypan blue exclusion, only the evolved isolate from patient 50 (P_50) showed a significant phenotypic difference in cytotoxicity (Figure 3A). This difference was attributed to a loss-of-function mutation in agrA (T88M). InToxSa also identified a significant cytotoxicity difference for the P_50 isolate pair, validating the previous observation, despite the methodological and host cell type differences. However, InToxSa detected significantly reduced cytotoxicity for the evolved isolates from five more patient pairs compared to the original trypan blue screen (Figure 3B). These results support the higher sensitivity of InToxSa in uncovering S. aureus cytotoxicity variations resulting from the evolution of the bacterial population during bloodstream infection. Figure 3 Download asset Open asset Performance of intracellular toxicity of S. aureus (InToxSa) against trypan blue exclusion assay. Comparative evaluation of S. aureus intracellular cytotoxicity with bacterial supernatants for 51 paired isolates from 20 patients with S. aureus bacteraemia. (A) Supernatant-based cytotoxicity on THP1 cells. Episode with significant difference in THP1 survival between baseline and evolved isolates is boxed in blue (p < 0.05). Assay performed in biological and technical duplicates. Bars represent mean percentage of dead cells; error bars show range between duplicates. Toxicity within isolate groups was compared using analysis of variance (ANOVA) with Bonferroni correction (B) propidium iodide (PI) uptake of infected HeLa cells. Values are mean area under the curve (AUC) and standard deviation. Episodes exhibiting significant phenotypic differences between baseline and evolved isolates are boxed in red (p < 0.05). Assay performed in biological and technical triplicates. PI uptake AUC within isolate groups was compared using ANOVA with Bonferroni correction. Screening a large collection of clinical S. aureus to evaluate intracellular cytotoxicity A major motivation for developing the InToxSa assay was to develop a pheno-genomics platform to efficiently measure the intracellular cytotoxicity profiles of a large collection of clinical S. aureus isolates and then use the power of comparative and statistical genomics to find bacterial genetic loci associated with intracellular cytotoxicity. To this end, we analysed 387 clinical S. aureus isolates, obtained from 298 episodes of bacteraemia and for which genome sequences were available (Giulieri et al., 2018; Holmes et al., 2011; Holmes et al., 2014; Holmes et al., 2018). A 164,449 single nucleotide polymorphism (SNP) core-genome phylogeny was inferred for this collection. The 387 isolates spanned 32 sequence types (STs) and were dominated by ST239, accounting for 30% of isolates (n:117), followed by ST22 (n:32, 8%), ST5 (n:34, 8%), ST45 (n:28, 7%), and ST1 (n:18, 5%). Fifty-three percent of the isolates were mecA positive (Figure 4A; Supplementary file 3). Figure 4 with 4 supplements see all Download asset Open asset Intracellular cytotoxicity assessment of 387 bloodstream-associated clinical S. aureus isolates. (A) Maximum likelihood phylogeny based on 164,449 core-genome SNPs for 387 S. aureus, showing sequence type (ST) and MRSA distribution. The heatmap depicts the mean area under the curve (AUC) of cytotoxicity based on intracellular toxicity of S. aureus (InToxSa) propidium iodide (PI) uptake assay. AUC values range from non-cytotoxic (score: 0, dark blue) to highly cytotoxic (score: 200, yellow). Adjacent to the heatmap (closed and open circles) are 28 pairs of genetically related, but phenotypically discordant isolates (see Figure 5). (B) 'Variable importance plot' showing different PI uptake metrics (features) in an unsupervised random forest (RF) machine learning model. The higher the value of 'accuracy decrease' or 'Gini index decrease', the higher the importance of the feature in the model. (C) Scatter plot of the two most discriminatory PI uptake kinetic metrics (AUC and maximum PI uptake rate). Dots are coloured based on the clustering obtained from the proximity matrix of the RF model. (D) Scatter plot showing the two principal components with the strongest association with PI uptake (lineage effect as measured using pyseer). Dots and ellipses are coloured based on the clustering obtained from the proximity matrix of the unsupervised machine learning model. (E) Manhattan plot of gene-burden genome-wide association study (GWAS) of cytotoxicity (PI uptake AUC) of 387 clinical isolates. We assessed each of the 387 isolates by InToxSa, with the cytotoxicity phenotype for each isolate represented by mean PI uptake (AUC) and displayed as a heatmap, aligned with the core-genome phylogeny (Figure 4A). Several patterns were readily seen in the data. There was a wide range of cytotoxicity profiles across the 387 isolates, with notable variations within each ST suggesting frequent adaptation events affecting intracellular cytotoxicity levels. Two STs (ST239 and ST22) associated with lower cytotoxicity. These variations are consistent with previous et al., 2021). To the most InToxSa parameters for our statistical genomics we assessed their importance by the data using unsupervised random forest (RF) machine learning and 2021). In this model, were each of the PI uptake values for the 387 isolates and controls and were the seven PI uptake curve parameters file We then RF feature importance and showed with importance plots that the PI uptake AUC, and rmax were the most for the model (Figure consistent with our InToxSa assessment using JE2 and the agrA mutant (Figure 1C, Using the proximity matrix from the unsupervised RF model, we three PI uptake with and high intracellular cytotoxicity We labelled each of the 387 PI uptake data with these three and cytotoxicity and the AUC and rmax values against each other (Figure these parameters were suggesting that the AUC is to capture intracellular cytotoxicity differences between S. aureus isolates. We used principal analysis of the PI uptake data as an unsupervised learning approach (Figure supplement 1). When the first two components of the variance we observed a where the same toxicity groups could be within a cytotoxicity among clinical isolates (Figure supplement 1). analysis using InToxSa outputs to S. aureus linked to intracellular cytotoxicity We used to genetic of cytotoxicity, as mean PI uptake The of cytotoxicity by genetic was a lower the obtained for other phenotypes such as vancomycin resistance (Giulieri et al., A lower could be resulting from the InToxSa assay variability or caused by differences in or to To assess the of to we using of a genetic matrix by a that genome to a set of et al., 2016). with the most for was by and (Figure supplement 3). We then the association between the first of the genetic variance and the PI uptake phenotype in et al., 2016; Giulieri et al., et al., 2018). In with the based on the phylogeny and cytotoxicity heatmap (Figure we observed significant represented by and (Figure Figure supplement of the this is consistent with differences in cytotoxicity between (Figure supplement 3B). Using the three cytotoxicity by RF as (Figure we the 387 these two While intracellular cytotoxicity was associated with S. aureus this analysis showed that not the as indicated by the significant between the three cytotoxicity across and (Figure This is consistent with other phenotypes et al., 2016; Giulieri et al., et al., and that from specific events cytotoxicity, the use of and convergent evolution to these for the observed population we then used gene-burden to and S. aureus loci significantly associated with intracellular cytotoxicity (PI uptake AUC) as a continuous for only agrA the p < the important of this to cytotoxicity (Figure Supplementary file 5). We also considered the loci that did not genome-wide statistical The most significant (p = the to the and is for the of a exposed and staphylococcal which is associated with and of epithelial cells and to human et al., et al., Another was (p = encoding an linked with and previously associated with S. aureus cytotoxicity et al., 2018). of convergent mutations in genetic pairs with InToxSa cytotoxicity profiles the for this of analysis, the gene-burden detected the agrA with a high but it did not have power to mutations other occurring in agr This was with our as by our power (Figure supplement Our power have also showed that the number of could increase the of the This can be by limiting the analysis to isolates, with the advantage of the to the population we to mutations that the intracellular cytotoxicity using comparative genomics a to microbial and 2021; Giulieri et al., and We used evolutionary convergence analysis to additional loci associated with intracellular cytotoxicity among the 387 S. aureus isolates. Our approach was to genetically pairs of isolates with PI uptake AUC values from independent across the phylogeny and then for mutations between the We calculated genetic between all 387 and calculated a uptake AUC value for each We selected 28 independent S. aureus pairs with a genetic core-genome SNPs and a significant in PI uptake AUC between reference and (Figure within each in but not in were identified and annotated using a that we have developed for S. aureus within-host evolution analysis (Giulieri et al., We have previously that an approach using of genome in a as a reference the most of the genetic et al., 2022). There were between and mutations within the 28 pairs (Figure the in which these mutations were to a core-genome phylogeny from the paired S. aureus showed convergent mutations in the In our analysis identified convergent mutations in other with three independent mutations and with two independent (Figure Supplementary file However, because of the effect and the of for S. aureus and of these mutations were only in ST239, a in our collection. In addition to loci to the of virulence factors such as the agr or in to host extracellular matrix such as and and of the convergent mutations were in in and associated with the cell to the of the cell