Predicting community dynamics of antibiotic-sensitive and -resistant species in fluctuating environments (article)

This is the author accepted manuscript. The final version is available from the Royal Society via the DOI in this recordThe dataset associated with this article is available in ORE: https://doi.org/10.24378/exe.2323Microbes occupy almost every niche within and on their human hosts. Whether colonizing the gut, mouth or bloodstream, microorganisms face temporal fluctuations in resources and stressors within their niche but we still know little of how environmental fluctuations mediate certain microbial phenotypes, notably antimicrobial-resistant ones. For instance, do rapid or slow fluctuations in nutrient and antimicrobial concentrations select for, or against, resistance? We tackle this question using an ecological approach by studying the dynamics of a synthetic and pathogenic microbial community containing two species, one sensitive and the other resistant to an antibiotic drug where the community is exposed to different rates of environmental fluctuation. We provide mathematical models, supported by experimental data, to demonstrate that simple community outcomes, such as competitive exclusion, can shift to coexistence and ecosystem bistability as fluctuation rates vary. Theory gives mechanistic insight into how these dynamical regimes are related. Importantly, our approach highlights a fundamental difference between resistance in single-species populations, the context in which it is usually assayed, and that in communities. While fast environmental changes are known to select against resistance in single-species populations, here we show that they can promote the resistant species in mixed-species communities. Our theoretical observations are verified empirically using a two-species Candida community.European Research Council (ERC)Engineering and Physical Sciences Research Council (EPSRC

Evolution of drug-resistant and virulent small colonies in phenotypically diverse populations of the human fungal pathogen Candida glabrata

This is the author accepted manuscript. the final version is available from the Royal Society via the DOI in this recordData accessibility: Additional description of methods, results and the supplementary figures are provided in the electronic supplementary material file ‘Duxbury_Methods_Figures1-11EMS.pdf.’ The datasets supporting this article have been uploaded as part of the supplementary material in file ‘Duxbury_RawDataEMS.xlsx’.Antimicrobial resistance frequently carries a fitness cost to a pathogen, measured as a reduction in growth rate compared to the sensitive wild-type, in the absence of antibiotics. Existing empirical evidence points to the following relationship between cost of resistance and virulence. If a resistant pathogen suffers a fitness cost in terms of reduced growth rate it commonly has lower virulence compared to the sensitive wild-type. If this cost is absent so is the reduction in virulence. Here we show, using experimental evolution of drug resistance in the fungal human pathogen Candida glabrata, that reduced growth rate of resistant strains need not result in reduced virulence. Phenotypically heterogeneous populations were evolved in parallel containing highly resistant sub-population small colony variants (SCVs) alongside sensitive sub-populations. Despite their low growth rate in the absence of an antifungal drug, the SCVs did not suffer a marked alteration in virulence compared with the wild-type ancestral strain, or their co-isolated sensitive strains. This contrasts with classical theory that assumes growth rate to positively correlate with virulence. Our work thus highlights the complexity of the relationship between resistance, basic life-history traits and virulence.Biotechnology and Biological Sciences Research Council (BBSRC)European Research Council (ERC)Engineering and Physical Sciences Research Council (EPSRC

Antibiotic Cycling and Antibiotic Mixing: which one best mitigates antibiotic resistance?

Published onlineJournal ArticleThis is the final version of the article. Available from Oxford University Press via the DOI in this record.Can we exploit our burgeoning understanding of molecular evolution to slow the progress of drug resistance? One role of an infection clinician is exactly that: to foresee trajectories to resistance during antibiotic treatment and to hinder that evolutionary course. But can this be done at a hospital-wide scale? Clinicians and theoreticians tried to when they proposed two conicting behavioural strategies that are expected to curb resistance evolution in the clinic, these are known as 'antibiotic cycling' and 'antibiotic mixing'. However, the accumulated data from clinical trials, now approaching 4 million patient days of treatment, is too variable for cycling or mixing to be deemed successful. The former implements the restriction and prioritisation of di_erent antibiotics at di_erent times in hospitals in a manner said to 'cycle' between them. In antibiotic mixing, appropriate antibiotics are allocated to patients but randomly.Mixing results in no correlation, in time or across patients, in the drugs used for treatment which is why theorists saw this as an optimal behavioural strategy. So while cycling and mixing were proposed as ways of controlling evolution, we show there is good reason why clinical datasets cannot choose between them: by re-examining the theoretical literature we show prior support for the theoretical optimality of mixing was misplaced. Our analysis is consistent with a pattern emerging in data: neither cycling or mixing is a priori better than the other at mitigating selection for antibiotic resistance in the clinic.REB was funded during this work by an MRC Discipline Hopping Fellowship G0802611, RPM was funded by a Conacyt PhD award, all authors were supported by EPSRC grant EP/I00503X/1 (grant holder REB)

Kinase inhibition leads to hormesis in a dual phosphorylation-dephosphorylation cycle

This is the final version of the article. Available from the publisher via the DOI in this record.Many antimicrobial and anti-tumour drugs elicit hormetic responses characterised by low-dose stimulation and high-dose inhibition. While this can have profound consequences for human health, with low drug concentrations actually stimulating pathogen or tumour growth, the mechanistic understanding behind such responses is still lacking. We propose a novel, simple but general mechanism that could give rise to hormesis in systems where an inhibitor acts on an enzyme. At its core is one of the basic building blocks in intracellular signalling, the dual phosphorylation-dephosphorylation motif, found in diverse regulatory processes including control of cell proliferation and programmed cell death. Our analytically-derived conditions for observing hormesis provide clues as to why this mechanism has not been previously identified. Current mathematical models regularly make simplifying assumptions that lack empirical support but inadvertently preclude the observation of hormesis. In addition, due to the inherent population heterogeneities, the presence of hormesis is likely to be masked in empirical population-level studies. Therefore, examining hormetic responses at single-cell level coupled with improved mathematical models could substantially enhance detection and mechanistic understanding of hormesis.Funding bodies: BBSRC (BB/J010340/1); EPSRC (EP/I00503X/1); Wellcome Trust (ISSF to University of Exeter)

Seeking patterns of antibiotic resistance in ATLAS, an open, raw MIC database with patient metadata

This is the final version. Available on open access from Nature Research via the DOI in this recordData availability: ATLAS is available following website registration*. Data and further information can be downloaded from the following links: Project overview: https://amr.theodi.org/project-overview Project description: https://wellcome.ac.uk/sites/default/files/antimicrobial-resistance-surveillance-sharing-industry-data.pdf Data download*: https://www.synapse.org/#!Synapse:syn17009517/wiki/585653 The same dataset is available from this link: https://s3-eu-west-1.amazonaws.com/amr-prototype-data/Open+Atlas_Reuse_Data.xlsx Data was extracted from the English Surveillance Programme for Antimicrobial Utilisation and Resistance (ESPAUR) report from years 2013-2018. These were downloaded from the following UK government website: https://www.gov.uk/government/publications/english-surveillance-programme-antimicrobial-utilisation-and-resistance-espaur-report ResistanceMap data is published by the Centre for Disease, Dynamics Economics and Policy28, it can be downloaded from https://github.com/gwenknight/empiricprescribing/tree/master/data, Data for the European Centre for Disease Prevention and Control (ECDC) can be downloaded from https://atlas.ecdc.europa.eu/public/index.aspx?Dataset=27#x00026;HealthTopic=4. The file we used in this paper can be downloaded from https://github.com/PabloCatalan/atlas/tree/master/data/europe_resistance_data.csv EUCAST data can only be obtained by contacting individuals named on their website https://www.eucast.org/mic_distributions_and_ecoffs/ and requesting access to MIC histograms, which we were granted.Code availability: Analysis codes66 written in Python 3.0 using pandas can be downloaded here: https://github.com/PabloCatalan/atlas or https://doi.org/10.5281/zenodo.6390565. Codes have been written to provide straightforward access to data so that figures from this manuscript can be reproduced and to help facilitate the development of new analyses. Interested readers are encouraged to seek assistance from corresponding authors in case it is not clear how those codes are used.Antibiotic resistance represents a growing medical concern where raw, clinical datasets are under-exploited as a means to track the scale of the problem. We therefore sought patterns of antibiotic resistance in the Antimicrobial Testing Leadership and Surveillance (ATLAS) database. ATLAS holds 6.5M minimal inhibitory concentrations (MICs) for 3,919 pathogen-antibiotic pairs isolated from 633k patients in 70 countries between 2004 and 2017. We show most pairs form coherent, although not stationary, timeseries whose frequencies of resistance are higher than other databases, although we identified no systematic bias towards including more resistant strains in ATLAS. We sought data anomalies whereby MICs could shift for methodological and not clinical or microbiological reasons and found artefacts in over 100 pathogen-antibiotic pairs. Using an information-optimal clustering methodology to classify pathogens into low and high antibiotic susceptibilities, we used ATLAS to predict changes in resistance. Dynamics of the latter exhibit complex patterns with MIC increases, and some decreases, whereby subpopulations' MICs can diverge. We also identify pathogens at risk of developing clinical resistance in the near future.Engineering and Physical Sciences Research Council (EPSRC)Ramón Areces Postdoctoral FellowshipMinisterio de Ciencia, Innovación y Universidades/FEDEREuropean Research Council (ERC)Biotechnology and Biological Sciences Research Council (BBSRC)David Phillips FellowshipNational Health and Medical Research Counci

Informed Switching Strongly Decreases the Prevalence of Antibiotic Resistance in Hospital Wards

Antibiotic resistant nosocomial infections are an important cause of mortality and morbidity in hospitals. Antibiotic cycling has been proposed to contain this spread by a coordinated use of different antibiotics. Theoretical work, however, suggests that often the random deployment of drugs (“mixing”) might be the better strategy. We use an epidemiological model for a single hospital ward in order to assess the performance of cycling strategies which take into account the frequency of antibiotic resistance in the hospital ward. We assume that information on resistance frequencies stems from microbiological tests, which are performed in order to optimize individual therapy. Thus the strategy proposed here represents an optimization at population-level, which comes as a free byproduct of optimizing treatment at the individual level. We find that in most cases such an informed switching strategy outperforms both periodic cycling and mixing, despite the fact that information on the frequency of resistance is derived only from a small sub-population of patients. Furthermore we show that the success of this strategy is essentially a stochastic phenomenon taking advantage of the small population sizes in hospital wards. We find that the performance of an informed switching strategy can be improved substantially if information on resistance tests is integrated over a period of one to two weeks. Finally we argue that our findings are robust against a (moderate) preexistence of doubly resistant strains and against transmission via environmental reservoirs. Overall, our results suggest that switching between different antibiotics might be a valuable strategy in small patient populations, if the switching strategies take the frequencies of resistance alleles into account

Multiwavelength observations of nova SMCN 2016-10a --- one of the brightest novae ever observed

We report on multiwavelength observations of nova SMCN 2016-10a. The present observational set is one of the most comprehensive for any nova in the Small Magellanic Cloud, including: low, medium, and high resolution optical spectroscopy and spectropolarimetry from SALT, FLOYDS, and SOAR; long-term OGLE $V$- and $I$- bands photometry dating back to six years before eruption; SMARTS optical and near-IR photometry from $\sim$ 11 days until over 280 days post-eruption; $Swift$ satellite X-ray and ultraviolet observations from $\sim$ 6 days until 319 days post-eruption. The progenitor system contains a bright disk and a main sequence or a sub-giant secondary. The nova is very fast with $t_2 \simeq$ 4.0 $\pm$ 1.0 d and $t_3 \simeq$ 7.8 $\pm$ 2.0 d in the $V$-band. If the nova is in the SMC, at a distance of $\sim$ 61 $\pm$ 10 kpc, we derive $M_{V,\mathrm{max}} \simeq - 10.5$ $\pm$ 0.5, making it the brightest nova ever discovered in the SMC and one of the brightest on record. At day 5 post-eruption the spectral lines show a He/N spectroscopic class and a FWHM of $\sim$ 3500 kms$^{-1}$ indicating moderately high ejection velocities. The nova entered the nebular phase $\sim$ 20 days post-eruption, predicting the imminent super-soft source turn-on in the X-rays, which started $\sim$ 28 days post-eruption. The super-soft source properties indicate a white dwarf mass between 1.2 M$_{\odot}$ and 1.3 M$_{\odot}$ in good agreement with the optical conclusions

Understanding the limits to generalizability of experimental evolutionary models.

Post print version of article deposited in accordance with SHERPA RoMEO guidelines. The final definitive version is available online at: http://www.nature.com/nature/journal/v455/n7210/abs/nature07152.htmlGiven the difficulty of testing evolutionary and ecological theory in situ, in vitro model systems are attractive alternatives; however, can we appraise whether an experimental result is particular to the in vitro model, and, if so, characterize the systems likely to behave differently and understand why? Here we examine these issues using the relationship between phenotypic diversity and resource input in the T7-Escherichia coli co-evolving system as a case history. We establish a mathematical model of this interaction, framed as one instance of a super-class of host-parasite co-evolutionary models, and show that it captures experimental results. By tuning this model, we then ask how diversity as a function of resource input could behave for alternative co-evolving partners (for example, E. coli with lambda bacteriophages). In contrast to populations lacking bacteriophages, variation in diversity with differences in resources is always found for co-evolving populations, supporting the geographic mosaic theory of co-evolution. The form of this variation is not, however, universal. Details of infectivity are pivotal: in T7-E. coli with a modified gene-for-gene interaction, diversity is low at high resource input, whereas, for matching-allele interactions, maximal diversity is found at high resource input. A combination of in vitro systems and appropriately configured mathematical models is an effective means to isolate results particular to the in vitro system, to characterize systems likely to behave differently and to understand the biology underpinning those alternatives

Socio-economic drivers of specialist anglers targeting the non-native European catfish (Silurus glanis) in the UK.

Information about the socioeconomic drivers of Silurus glanis anglers in the UK were collected using questionnaires from a cross section of mixed cyprinid fisheries to elucidate human dimensions in angling and non-native fisheries management. Respondents were predominantly male (95%), 30-40 years of age with £500 per annum. The proportion of time spent angling for S. glanis was significantly related to angler motivations; fish size, challenge in catch, tranquil natural surroundings, escape from daily stress and to be alone were considered important drivers of increased time spent angling. Overall, poor awareness of: the risks and adverse ecological impacts associated with introduced S. glanis, non-native fisheries legislation, problems in use of unlimited ground bait and high fish stocking rates in angling lakes were evident, possibly related to inadequate training and information provided by angling organisations to anglers, as many stated that they were insufficiently informed

Swift follow-up observations of candidate gravitational-wave transient events

We present the first multi-wavelength follow-up observations of two candidate gravitational-wave (GW) transient events recorded by LIGO and Virgo in their 2009-2010 science run. The events were selected with low latency by the network of GW detectors and their candidate sky locations were observed by the Swift observatory. Image transient detection was used to analyze the collected electromagnetic data, which were found to be consistent with background. Off-line analysis of the GW data alone has also established that the selected GW events show no evidence of an astrophysical origin; one of them is consistent with background and the other one was a test, part of a "blind injection challenge". With this work we demonstrate the feasibility of rapid follow-ups of GW transients and establish the sensitivity improvement joint electromagnetic and GW observations could bring. This is a first step toward an electromagnetic follow-up program in the regime of routine detections with the advanced GW instruments expected within this decade. In that regime multi-wavelength observations will play a significant role in completing the astrophysical identification of GW sources. We present the methods and results from this first combined analysis and discuss its implications in terms of sensitivity for the present and future instruments.Comment: Submitted for publication 2012 May 25, accepted 2012 October 25, published 2012 November 21, in ApJS, 203, 28 ( http://stacks.iop.org/0067-0049/203/28 ); 14 pages, 3 figures, 6 tables; LIGO-P1100038; Science summary at http://www.ligo.org/science/Publication-S6LVSwift/index.php ; Public access area to figures, tables at https://dcc.ligo.org/cgi-bin/DocDB/ShowDocument?docid=p110003