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When is a bacterial “virulence factor” really virulent?

Bacterial traits that contribute to disease are termed ‘virulence factors’ and there is much interest in therapeutic approaches that disrupt such traits. However, ecological theory predicts disease severity to be multifactorial and context dependent, which might complicate our efforts to identify the most generally important virulence factors. Here, we use meta-analysis to quantify disease outcomes associated with one well-studied virulence factor – pyoverdine, an iron-scavenging compound secreted by the opportunistic pathogen Pseudomonas aeruginosa. Consistent with ecological theory, we found that the effect of pyoverdine, albeit frequently contributing to disease, varied considerably across infection models. In many cases its effect was relatively minor, suggesting that pyoverdine is rarely essential for infections. Our work demonstrates the utility of meta-analysis as a tool to quantify variation and overall effects of purported virulence factors across different infection models. This standardised approach will help us to evaluate promising targets for anti-virulence approaches.

theory predicts disease severity to be multifactorial and context dependent, which 18 might complicate our efforts to identify the most generally important virulence factors. 19 Here, we use meta-analysis to quantify disease outcomes associated with one well-20 studied virulence factorpyoverdine, an iron-scavenging compound secreted by the 21 opportunistic pathogen Pseudomonas aeruginosa. Consistent with ecological theory, 22 we found that the effect of pyoverdine, albeit frequently contributing to disease, 23 varied considerably across infection models. In many cases its effect was relatively 24 minor, suggesting that pyoverdine is rarely essential for infections. Our work 25 demonstrates the utility of meta-analysis as a tool to quantify variation and overall 26 effects of purported virulence factors across different infection models. This 27 standardised approach will help us to evaluate promising targets for anti-virulence 28 approaches. 29 INTRODUCTION 30 Understanding which bacterial characteristics contribute most to disease is a major 31 area of research in microbiology and infection biology (1-3). Bacterial characteristics 32 that reduce host health and/or survival are considered 'virulence factors'. Such 33 factors include structural features like flagella and pili that facilitate attachment to 34 host cells (4, 5), as well as secreted products like toxins and enzymes that degrade 35 host tissue (6, 7), or siderophores that scavenge iron from the host (8). Research on 36 virulence factors has not only increased our fundamental understanding of the 37 mechanisms underlying virulence, but has also identified potential novel targets for 38 antibacterial therapy. There is indeed much current interest in developing 'anti- 39 virulence' drugs to disrupt virulence factor productionthe idea being that by simply 40 disarming pathogens rather than killing them outright, we could ostensibly elicit 41 weaker selection for drug resistance (9-11).
in a specific host context will similarly enhance virulence in another bacterial strain, 48 or in a different host (12). Yet, ecological theory predicts that the effects of a given 49 trait will frequently vary in response to the environment (78). In the context of 50 infections, this may be particularly true for opportunistic pathogens, which face very 51 heterogeneous environments: they can live in environmental reservoirs (e.g. soil, 52 household surfaces), as commensals of healthy hosts, or, when circumstances allow, 53 as pathogens, causing serious infections in a range of different hosts and host 54 tissues (13-15). Opportunistic pathogens underlie many hospital-acquired infections, 55 especially in immune-compromised patients (16)(17)(18), and the treatment of such 56 infections is often challenging because, as generalists, such pathogens are pre-57 selected to be tenacious and highly adaptable. Thus, in designing new anti-virulence 58 drugs against opportunistic pathogens, we need to know not only whether the 59 targeted trait is indeed associated with pathogenicity, but also the generality of this Here we show how a meta-analysis approach can be used to quantify variation and 64 overall effects of virulence factors across host environments. As a test case, we 65 focus on pyoverdine, a siderophore secreted by the opportunistic pathogen 66 Pseudomonas aeruginosa to scavenge iron from the host environment (19). Table 1 67 provides an overview of the workflow of our meta-analysis, where we combined the 68 outcomes of 76 individual virulence experiments from 23 studies (12, 20-41, see also 69 Tables S1 and S2 in the supplemental material). Using a weighted meta-analysis 70 approach, we were able to investigate the evidence for pyoverdine's contribution to 71 virulence across eight host species, including vertebrates, invertebrates and plants, 72 five tissue infection models and various P. aeruginosa genotypes. We chose 73 pyoverdine production as the model trait for our analysis because: (i) it has been 74 extensively studied across a range of Pseudomonas strains (42); (ii) its virulence 75 effects have been examined in a large number of host species; (iii) P. aeruginosa is 76 one of the most troublesome opportunistic human pathogens, responsible for many 77 multi-drug resistant nosocomial infections (43, 44); and (iv) multiple anti-virulence 78 drugs have been proposed to target pyoverdine production and uptake (22,23,45). 79 Relationship between effect sizes and moderator variables. We combined data 112 from the set of experiments described above in a meta-analysis to determine the 113 extent to which pyoverdine's effect on virulence varied across four moderator 114 variables: (i) host taxa, (ii) tissue types, (iii) pathogen wildtype background, and (iv) 115 pyoverdine-mutation type. To obtain a comparable measure of virulence across 116 experiments, we extracted in each instance the number of cases where a given 117 infection type did or did not have a virulent outcome (i.e. dead vs. alive, or with vs. 118 without symptoms) for both the mutant (m) and the wildtype (w) strain for each 119 experiment (see materials and methods for details). We then took as our effect size 120 the log-odds-ratio, i.e. ln ((mvirulent / mnon-virulent) / (wvirulent / wnon-virulent)) (see Table S2 121 in the supplemental material), a commonly-used measure especially suitable for 122 binary response variables like survival (48). 123 Consistent with the theoretical prediction that host-pathogen interactions and host 124 ecology are important modulators of virulence, we found considerable variation in 125 the effect sizes across experiments and subgroups of all moderators (Fig. 1). 126 Pyoverdine-deficient mutants showed substantially reduced virulence in invertebrate 127 and mammalian hosts, whereas there was little evidence for such an effect in plants 128 (Fig. 1A). Overall, evidence for pyoverdine being an important virulence factor was 129 backgrounds, we see that pyoverdine deficiency reduced virulence in experiments 134 featuring the well-characterized PA14 and PAO1 strains (Fig. 1C) whereas the 135 reduction was less pronounced in experiments with less well-characterized wildtype 136 strains. This could be due to sampling error (only a few experiments used these 137 strains) or it may be that these strains really behave differently from PA14 and PAO1. 138 Finally, we observed that the nature of the pyoverdine-deficiency mutation matters 139 (Fig. 1D). Infections with strains carrying well-defined mutations known to exclusively 140 (or at least primarily) affect pyoverdine production showed a relatively consistent 141 reduction in virulence. Conversely, where mutants were poorly-defined, or carried 142 mutations likely to affect other traits beyond pyoverdine, here the virulence pattern 143 was much more variable, with both reduced and increased virulence relative to 144 wildtype infections (Fig. 1D). We posit that at least some of the differences in 145 observed virulence between these mutants and their wildtype counterparts was likely 146 due to pleiotropic differences in phenotypes unrelated to pyoverdine.

148
Assessing the relative importance of moderator variables. Fig. 1  Using this restricted dataset, we performed a series of meta-regression models to 167 test for significant differences between subgroups of our moderator factors, and we 168 also estimated the share of total variance in effect sizes that is explained by each 169 moderator variable (Fig. 2). These models revealed that infection type is the  reports is associated with a high uncertainty due to a comparatively low sample size. 241 Moreover, the observed effect cannot unambiguously be attributed to pyoverdine 242 because an undefined UV-mutagenized mutant was used. We highlight this example 243 not to criticise it, but rather because it serves to demonstrate why drawing inferences 244 from (appropriately weighted) aggregations of all available evidence is preferable to 245 focusing solely on the results of a single study.

334
Literature search. We conducted an extensive literature search, using a 335 combination of two online databases: Web of Science and Google Scholar. The 336 following terms were used to search abstracts and full texts: "aeruginosa" in 337 combination with "pyoverdin" or "pyoverdine" and in combination with "virulence" or 338 "infection" or "pathogen" or "disease" or "mortality" or "lethality". This search was first  Table S1 in the supplemental material). This information was used to 385 categorize the experiments and identify potentially important moderator variables 386 (see below). For some experiments conducted in mammals, data on in vivo growth of a wildtype 417 and a pyoverdine deficient strain were available in addition to virulence measures (see 418   Table S4). We also calculated effect sizes (standardized mean differences) for this set 419 of studies (Fig. S4). This limited dataset shows a similar pattern to the main dataset 420 shown in Fig. 1, but was not included in the main analysis because we were primarily 421 interested in quantifying virulence effects (i.e. host damage and/or mortality) and not 422 pathogen growth.  functions from packages 'meta' (72) and 'metafor' (73). We used the 'metabin' 485 function to transform the count data into the (log-) odds ratio described above. We 486 then weighted these values by the inverse of their respective squared standard 487 errors, and pooled them to obtain a single distribution of effect sizes. We reasoned 488 that the variability of the effect sizes in our dataset probably reflects more than simple 489 sampling error around a single true mean. Rather, we assume that our effect sizes 490 represent a random sample from a larger distribution comprising all possible true 491 effect size estimates. As such, we inferred that a random effects meta-analysis would 492 be more appropriate for our dataset than a fixed effects model (for further discussion, 493 see (62, 63). In a random effects meta-analysis, we partition the total heterogeneity 494 observed in our dataset (described by the statistic Q) into two constituent parts - predictable ways, to the observed heterogeneity of our assembled effect sizes. We 518 investigated four potential moderators, namely the host taxon, the type of infection, 519 the wildtype background of the infecting strains, and the type of mutant involved (i.e. 520 whether more or less pleiotropy was expected). In a first approach, we split the 521 dataset into subgroups representing different levels of these moderators, and 522 estimated pooled means within these different subgroups. For this, we again used 523 random-effect meta-analysis (as above), but we set the level of between-experiment 524 heterogeneity (τ 2 ) to be common across all subgroups.     Table S1 in the supplemental material). The numbers in brackets on the Y-axis correspond to the citation number of the corresponding publication.

FIG. 2 Test for differences between subgroups of moderator variables with regard to the effect sizes for pyoverdine as a virulence factor in P. aeruginosa.
Our baseline condition for all comparisons is the following: gut infections in invertebrate hosts, using the P. aeruginosa wildtype strain PA14 vs a pyoverdinedeficient PA14 mutant with a low expected level of pleiotropy. The effect size for this baseline scenario is set to zero. All other scenarios had more extreme (negative) effect sizes, and are therefore scaled relative to this baseline condition. Comparisons reveal that virulence in pyoverdine-deficient strains was significantly more reduced in systemic compared to gut infections, and that most effect size variation is explained by the infection type. There were no significant effect size differences between any of the other subgroups. Bars show the difference in log odds-ratio (± 95% confidence interval) between the baseline and any of the alternate conditions. Values given in brackets indicate percentage of effect size heterogeneity explained by a specific moderator.

FIG. 3 Association between effect sizes and their standard errors across 76
experiments examining the role of pyoverdine production for virulence in P. aeruginosa. In the absence of bias, we should see an inverted funnel-shaped cloud of points, more or less symmetrically distributed around the mean effect size (vertical dotted line). Instead, we see an over-representation of low-certainty experiments associated with strong (negative) effect sizes. This suggests a significant publication bias: experiments with low-certainty and weak or contrary effects presumably do exist, but are under-represented here (note the absence of data points in the cross-hatched triangle). Effect sizes are given as log-odds-ratio. Each symbol represents a single experiment. Symbol colours and shapes stand for different host organisms (red circles = invertebrates; blue squares = mammals; green diamonds = plants). Large symbols denote the experiments included in the core dataset. The solid shaded area represents the 95% confidence interval for the weighted linear regression using the complete dataset. Note that due to the stronger weights accorded to high certainty experiments (i.e. the points towards the top of the plot), many of the lower-weighted (higher-uncertainty) points towards the bottom of the plot lie quite far from the regression line and also outside the confidence interval. We searched (see details in main text) for any reports of experiments featuring monoclonal infections of whole live host organisms with P. aeruginosa strains known to vary in pyoverdine phenotype, where virulence was quantified in terms of host mortality.

Extract and standardize effect sizes and their standard errors
For each case reporting host survival, we calculated the (log) ratio of mortality odds from pyoverdine-mutant infections vs. wildtype infections -i.e. the (log) odds-ratio.

4a. Check heterogeneity across studies
Our assembled effect sizes were more heterogeneous than expected from chance -even when we allowed that some of this variation could be due to random noise.
4b. Consider putative moderator variables (optional) We tested for evidence of distinct sub-groups in our dataset, within which the effect sizes might be more homogeneous. We identified four putative moderators and codified each study for the following: (i) host taxon; (ii) infection type; (iii) strain background; and (iv) level of pleiotropy expected, given the particular mutation(s) involved.
4c. Check for publication bias (optional) We found that smaller / lower-powered studies were more likely to report large effect sizes in support of the hypothesis, whereas larger / higher-powered studies tended to report smaller effect sizes.

Derive mean effect size(s); quantify influence of moderator variables (if applicable)
Despite the steps taken (see above), our dataset still showed substantial heterogeneity.
Estimates of mean effect sizes (in / across subgroups) and moderator coefficients should therefore be viewed as best approximations.  Table S4 in the supplemental material).