Professional Education


  • Doctor of Philosophy, University Of Exeter (2016)
  • Bachelor of Science, Universidad De Malaga (2010)
  • PhD, University of Exeter (UK), Quantitative Biology (2016)
  • BSc, Universidad de M├ílaga (Spain), Biology (2010)

Stanford Advisors


All Publications


  • The unconstrained evolution of fast and efficient antibiotic-resistant bacterial genomes NATURE ECOLOGY & EVOLUTION Reding-Roman, C., Hewlett, M., Duxbury, S., Gori, F., Gudelj, I., Beardmore, R. 2017; 1 (3): 50

    Abstract

    Evolutionary trajectories are constrained by trade-offs when mutations that benefit one life history trait incur fitness costs in other traits. As resistance to tetracycline antibiotics by increased efflux can be associated with an increase in length of the Escherichia coli chromosome of 10% or more, we sought costs of resistance associated with doxycycline. However, it was difficult to identify any because the growth rate (r), carrying capacity (K) and drug efflux rate of E. coli increased during evolutionary experiments where the species was exposed to doxycycline. Moreover, these improvements remained following drug withdrawal. We sought mechanisms for this seemingly unconstrained adaptation, particularly as these traits ought to trade-off according to rK selection theory. Using prokaryote and eukaryote microorganisms, including clinical pathogens, we show that r and K can trade-off, but need not, because of 'rK trade-ups'. r and K trade-off only in sufficiently carbon-rich environments where growth is inefficient. We then used E. coli ribosomal RNA (rRNA) knockouts to determine specific mutations, namely changes in rRNA operon (rrn) copy number, than can simultaneously maximize r and K. The optimal genome has fewer operons, and therefore fewer functional ribosomes, than the ancestral strain. It is, therefore, unsurprising for r-adaptation in the presence of a ribosome-inhibiting antibiotic, doxycycline, to also increase population size. We found two costs for this improvement: an elongated lag phase and the loss of stress protection genes.

    View details for DOI 10.1038/s41559-016-0050

    View details for Web of Science ID 000417170400006

    View details for PubMedID 28812723

  • Using a Sequential Regimen to Eliminate Bacteria at Sublethal Antibiotic Dosages PLOS BIOLOGY Fuentes-Hernandez, A., Plucain, J., Gori, F., Pena-Miller, R., Reding, C., Jansen, G., Schulenburg, H., Gudelj, I., Beardmore, R. 2015; 13 (4): e1002104

    Abstract

    We need to find ways of enhancing the potency of existing antibiotics, and, with this in mind, we begin with an unusual question: how low can antibiotic dosages be and yet bacterial clearance still be observed? Seeking to optimise the simultaneous use of two antibiotics, we use the minimal dose at which clearance is observed in an in vitro experimental model of antibiotic treatment as a criterion to distinguish the best and worst treatments of a bacterium, Escherichia coli. Our aim is to compare a combination treatment consisting of two synergistic antibiotics to so-called sequential treatments in which the choice of antibiotic to administer can change with each round of treatment. Using mathematical predictions validated by the E. coli treatment model, we show that clearance of the bacterium can be achieved using sequential treatments at antibiotic dosages so low that the equivalent two-drug combination treatments are ineffective. Seeking to treat the bacterium in testing circumstances, we purposefully study an E. coli strain that has a multidrug pump encoded in its chromosome that effluxes both antibiotics. Genomic amplifications that increase the number of pumps expressed per cell can cause the failure of high-dose combination treatments, yet, as we show, sequentially treated populations can still collapse. However, dual resistance due to the pump means that the antibiotics must be carefully deployed and not all sublethal sequential treatments succeed. A screen of 136 96-h-long sequential treatments determined five of these that could clear the bacterium at sublethal dosages in all replicate populations, even though none had done so by 24 h. These successes can be attributed to a collateral sensitivity whereby cross-resistance due to the duplicated pump proves insufficient to stop a reduction in E. coli growth rate following drug exchanges, a reduction that proves large enough for appropriately chosen drug switches to clear the bacterium.

    View details for DOI 10.1371/journal.pbio.1002104

    View details for Web of Science ID 000354824500004

    View details for PubMedID 25853342

    View details for PubMedCentralID PMC4390231

  • Testing the optimality properties of a dual antibiotic treatment in a two-locus, two-allele model JOURNAL OF THE ROYAL SOCIETY INTERFACE Pena-Miller, R., Fuentes-Hernandez, A., Reding, C., Gudelj, I., Beardmore, R. 2014; 11 (96): 20131035

    Abstract

    Mathematically speaking, it is self-evident that the optimal control of complex, dynamical systems with many interacting components cannot be achieved with 'non-responsive' control strategies that are constant through time. Although there are notable exceptions, this is usually how we design treatments with antimicrobial drugs when we give the same dose and the same antibiotic combination each day. Here, we use a frequency- and density-dependent pharmacogenetics mathematical model based on a standard, two-locus, two-allele representation of how bacteria resist antibiotics to probe the question of whether optimal antibiotic treatments might, in fact, be constant through time. The model describes the ecological and evolutionary dynamics of different sub-populations of the bacterium Escherichia coli that compete for a single limiting resource in a two-drug environment. We use in vitro evolutionary experiments to calibrate and test the model and show that antibiotic environments can support dynamically changing and heterogeneous population structures. We then demonstrate, theoretically and empirically, that the best treatment strategies should adapt through time and constant strategies are not optimal.

    View details for DOI 10.1098/rsif.2013.1035

    View details for Web of Science ID 000336159200002

    View details for PubMedID 24812050

    View details for PubMedCentralID PMC4032525