When more is less: Emergent suppressive interactions in three-drug combinations

Understanding how stressors combine to affect population abundances and trajectories is a fundamental ecological problem with increasingly important implications worldwide. Generalisations about interactions among stressors are challenging due to different categorisation methods and how stressors vary across species and systems. Here, we propose using a newly introduced framework to analyse data from the last 25 years on ecological stressor interactions, for example combined effects of temperature, salinity and nutrients on population survival and growth. We contrast our results with the most commonly used existing methodanalysis of variance (ANOVA)and show that ANOVA assumptions are often violated and have inherent limitations for detecting interactions. Moreover, we argue that rescalingexamining relative rather than absolute responsesis critical for ensuring that any interaction measure is independent of the strength of single-stressor effects. In contrast, non-rescaled measureslike ANOVAfind fewer interactions when single-stressor effects are weak. After reexamining 840 two-stressor combinations, we conclude that antagonism and additivity are the most frequent interaction types, in strong contrast to previous reports that synergy dominates yet supportive of more recent studies that find more antagonism. Consequently, measuring and reassessing the frequency of stressor interaction types is imperative for a better understanding of how stressors affect populations.

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“…Finally, we note that we identified interactions as ‘inconclusive’ when the normalisation factor

(||, {\overset{false~}{w}}_{XY} - w_{X} w_{Y})

was equal to zero, consistent with previous studies (e.g. Beppler et al 2017).…”

Section: Methodssupporting

confidence: 91%

Using a newly introduced framework to measure ecological stressor interactions

et al. 2020

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“…After conducting careful dose-dependence tests in vitro , the next key step in translating suppressive combinations to the clinic would be testing suppressive combinations in animal models to establish better understanding of risks as well as necessary concentrations, pharmacodynamics, and pharmacokinetics. 107 These tests would also help us understand how quickly resistance profiles of bacterial populations change in response to suppressive drugs in vivo. This would in turn inform the timing of switches from suppressive to synergistic or classic antibiotic treatment once most of the resistant bacteria have been killed.…”

Section: Future Directionsmentioning

confidence: 99%

Suppressive drug combinations and their potential to combat antibiotic resistance

Singh

Yeh

2017

J Antibiot

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Antibiotic effectiveness often changes when two or more such drugs are administered simultaneously, and unearthing antibiotic combinations with enhanced efficacy (synergy) has been a longstanding clinical goal. However, antibiotic resistance, which undermines individual drugs, threatens such combined treatments. Remarkably, it has emerged that antibiotic combinations whose combined effect is lower than that of at least one of the individual drugs can slow or even reverse the evolution of resistance. We synthesize and review studies of such so-called “suppressive interactions” in the literature. We examine why these interactions have been largely disregarded in the past, the strategies used to identify them, their mechanistic basis, demonstrations of their potential to reverse the evolution of resistance, and arguments for and against using them in clinical treatment. We suggest future directions for research on these interactions, aiming to expand the basic body of knowledge on suppression and to determine their applicability in the clinic.

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“…The consequences of the accumulation of these short chain fatty acids and other small molecules on microenvironments, as well as their effect on bacterial physiology and antibiotic treatment efficacy in vivo , have yet to be systematically explored. Our results emphasize the need to probe the action of antibiotics – as well as other drugs that are thought not to target microbial growth 45 – in complex and varied conditions 46 . Furthermore, our findings highlight the utility of studying growth physiology in co-cultures in the absence of antibiotics for uncovering novel mechanisms of community-encoded protection against antibiotics.…”

Section: Discussionmentioning

confidence: 76%

Bacterial interspecies interactions modulate pH-mediated antibiotic tolerance in a model gut microbiota

Aranda-Díaz

Obadia²,

Thomsen

et al. 2019

Preprint

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20Despite decades of investigation into how antibiotics affect isolated bacteria, it 21 remains highly challenging to predict consequences for communities in complex 22 55 or (iii) tolerance, whereby the entire population enters an altered physiological 56 state that is not susceptible to the antibiotic 5 . Members of multispecies 57 communities, such as biofilms and models of urinary tract infections, can display 58 altered sensitivity to antibiotics 6-9 . A few studies have delved into the molecular 59 mechanisms behind cross-species antibiotic protection and sensitization. For 60 5 example, the exoproducts of Pseudomonas aeruginosa affect the survival of 61 Staphylococcus aureus through changes in antibiotic uptake, cell-wall integrity, 62 and intracellular ATP pools 10 . In synthetic communities, intracellular antibiotic 63 degradation affords cross-species protection against chloramphenicol 11 . 64 Additionally, metabolic dependencies within synthetic communities can lower 65 the viability of bacteria when antibiotics eliminate providers of essential 66 metabolites, leading to an apparent change in the minimum inhibitory 67 concentration (MIC) of the dependent species 9 . However, we still lack 68 understanding of how contextual metabolic interactions between bacteria affect 69 the physiological processes targeted by antibiotics and the resulting balance 70 between growth inhibition (bacteriostatic activity) and death (bactericidal 71 activity). 72 73 Characterizing the impact of metabolic interactions on antibiotic susceptibility 74 requires functional understanding of how bacterial species interact during 75 normal growth. Interspecies interactions can occur through specific mechanisms 76 within members of a community (e.g. cross-feeding or competition for specific 77 resources), or through global environmental variables modified by bacterial 78 activity. An example of the latter is pH, which has recently been shown to drive 79 6 community dynamics in a highly defined laboratory system of decomposition 80 bacteria 12 . 81 82 While synthetic communities afford the opportunity to design and to tune 83 bacterial interactions, it is unclear whether findings are relevant to natural 84 communities. The stably associated gut microbiota of Drosophila melanogaster fruit 85 flies constitutes a naturally simple model community for determining how 86 metabolic interactions between species affect growth, physiology, and the action 87 of antibiotics 13 . This community consists of ~5 species predominantly from the 88Lactobacillus and Acetobacter genera 14 (Fig. 1a). Lactobacilli produce lactic acid 15 , 89 while acetobacters are acetic acid bacteria that are distinguished by their ability 90 to oxidize lactate to carbon dioxide and water 16 . Short chain fatty acids, such as 91 lactate, decrease the pH of natural fermentations and may constitute a 92 mechanism through which pH plays a prominent role in community dynamics. 93 The naturally low number of species in Drosophila gut microbiota and its 94 comp...

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When more is less: Emergent suppressive interactions in three-drug combinations

Cited by 31 publications

References 47 publications

Using a newly introduced framework to measure ecological stressor interactions

Using a newly introduced framework to measure ecological stressor interactions

Suppressive drug combinations and their potential to combat antibiotic resistance

Bacterial interspecies interactions modulate pH-mediated antibiotic tolerance in a model gut microbiota

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