Could medieval medicine help the fight against antimicrobial resistance?

The emergence of antibiotic-resistant bacteria, combined with a severely stalled discovery pipeline for new antibiotics being developed, has the potential to undo the advances in infection control achieved in the last century. One way around this impasse might be to re-explore the medicinal practices of the medieval world. Why? This is because although the medieval world was ignorant of so much of modern theory, it seems that centuries of practice by medieval doctors could have produced some treatments for infections that were effective. These could contain antimicrobial compounds suitable for development into antibiotics. Our interdisciplinary team, initially based at the University of Nottingham, tested an eyesalve described in the tenth century Anglo-Saxon ‘Bald’s Leechbook’ with startling results. By following the recipe as closely as possible, we created a cocktail that can kill one of the most common causes of eye infections, the bacterium Staphylococcus aureus. More significantly, Bald’s eyesalve can kill a range of antibiotic-resistant bacteria. This chapter details our team’s initial findings and places them in the context of an interdisciplinary analysis of how medieval doctors used the materia medica available to them. We present novel results confirming the reliability of Bald’s eyesalve as an anti-Staphylococcal agent. Further, we demonstrate the potential of ‘big data’ approaches to turn medical texts into predictive databases for selecting natural materials for antibiotic testing. Finally, we present our work as an example of how interdisciplinary dialogue can significantly advance scholarship

Data Mining a Medieval Medical Text Reveals Patterns in Ingredient Choice That Reflect Biological Activity against Infectious Agents

We used established methodologies from network science to identify patterns in medicinal ingredient combinations in a key medieval text, the 15th-century Lylye of Medicynes, focusing on recipes for topical treatments for symptoms of microbial infection. We conducted experiments screening the antimicrobial activity of selected ingredients. These experiments revealed interesting examples of ingredients that potentiated or interfered with each other’s activity and that would be useful bases for future, more detailed experiments. Our results highlight (i) the potential to use methodologies from network science to analyze medieval data sets and detect patterns of ingredient combination, (ii) the potential of interdisciplinary collaboration to reveal different aspects of the ethnopharmacology of historical medical texts, and (iii) the potential development of novel therapeutics inspired by premodern remedies in a time of increased need for new antibiotics.The pharmacopeia used by physicians and laypeople in medieval Europe has largely been dismissed as placebo or superstition. While we now recognize that some of the materia medica used by medieval physicians could have had useful biological properties, research in this area is limited by the labor-intensive process of searching and interpreting historical medical texts. Here, we demonstrate the potential power of turning medieval medical texts into contextualized electronic databases amenable to exploration by the use of an algorithm. We used established methodologies from network science to reveal patterns in ingredient selection and usage in a key text, the 15th-century Lylye of Medicynes, focusing on remedies to treat symptoms of microbial infection. In providing a worked example of data-driven textual analysis, we demonstrate the potential of this approach to encourage interdisciplinary collaboration and to shine a new light on the ethnopharmacology of historical medical texts

An ex vivo lung model to study bronchioles infected with Pseudomonas aeruginosa biofilms

A key aim in microbiology is to determine the genetic and phenotypic bases of bacterial virulence, persistence and antimicrobial resistance in chronic biofilm infections. This requires tractable, high-throughput models that reflect the physical and chemical environment encountered in specific infection contexts. Such models will increase the predictive power of microbiological experiments and provide platforms for enhanced testing of novel antibacterial or antivirulence therapies. We present an optimised ex vivo model of cystic fibrosis lung infection: ex vivo culture of pig bronchiolar tissue in artificial cystic fibrosis mucus. We focus on the formation of biofilms by Pseudomonas aeruginosa. We show highly repeatable and specific formation of biofilms that resemble clinical biofilms by a commonly-studied lab strain and ten cystic fibrosis isolates of this key opportunistic pathogen

Building a better biofilm - formation of in vivo-like biofilm structures by Pseudomonas aeruginosa in a porcine model of cystic fibrosis lung infection

Pseudomonas aeruginosa biofilm infections in the cystic fibrosis (CF) lung are highly resistant to current antimicrobial treatments and are associated with increased mortality rates. The existing models for such infections are not able to reliably mimic the clinical biofilms observed. We aimed to further optimise an ex vivo pig lung (EVPL) model for P. aeruginosa CF lung infection that can be used to increase understanding of chronic CF biofilm infection. The EVPL model will facilitate discovery of novel infection prevention methods and treatments, and enhanced exploration of biofilm architecture. We investigated purine metabolism and biofilm formation in the model using transposon insertion mutants in P. aeruginosa PA14 for key genes: purD, gacA and pelA. Our results demonstrate that EVPL recapitulates a key aspect of in vivo P. aeruginosa infection metabolism, and that the pathogen forms a biofilm with a clinically realistic structure not seen in other in vitro studies. Two pathways known to be required for in vivo biofilm infection - the Gac regulatory pathway and production of the Pel exopolysaccharide - are essential to the formation of this mature, structured biofilm on EVPL tissue. We propose the high-throughput EVPL model as a validated biofilm platform to bridge the gap between in vitro work and CF lung infection

Exploring the Effects of Working for Endowments on Behaviour in Standard Economic Games

In recent years, significant advances have been made in understanding the adaptive (ultimate) and mechanistic (proximate) explanations for the evolution and maintenance of cooperation. Studies of cooperative behaviour in humans invariably use economic games. These games have provided important insights into the mechanisms that maintain economic and social cooperation in our species. However, they usually rely on the division of monetary tokens which are given to participants by the investigator. The extent to which behaviour in such games may reflect behaviour in the real world of biological markets – where money must be earned and behavioural strategies incur real costs and benefits – is unclear. To provide new data on the potential scale of this problem, we investigated whether people behaved differently in two standard economic games (public goods game and dictator game) when they had to earn their monetary endowments through the completion of dull or physically demanding tasks, as compared with simply being given the endowment. The requirement for endowments to be ‘earned’ through labour did not affect behaviour in the dictator game. However, the requirement to complete a dull task reduced cooperation in the public goods game among the subset of participants who were not familiar with game theory. There has been some effort to test whether the conclusions drawn from standard, token-based cooperation games adequately reflect cooperative behaviour ‘in the wild.’ However, given the almost total reliance on such games to study cooperation, more exploration of this issue would be welcome. Our data are not unduly worrying, but they do suggest that further exploration is needed if we are to make general inferences about human behaviour from the results of structured economic games

Predicting antibiotic-associated virulence of pseudomonas aeruginosa using an ex vivo lung biofilm model

Background: Bacterial biofilms are known to have high antibiotic tolerance which directly affects clearance of bacterial infections in people with cystic fibrosis (CF). Current antibiotic susceptibility testing methods are either based on planktonic cells or do not reflect the complexity of biofilms in vivo. Consequently, inaccurate diagnostics affect treatment choice, preventing bacterial clearance and potentially selecting for antibiotic resistance. This leads to prolonged, ineffective treatment. Methods: In this study, we use an ex vivo lung biofilm model to study antibiotic tolerance and virulence of Pseudomonas aeruginosa. Sections of pig bronchiole were dissected, prepared and infected with clinical isolates of P. aeruginosa and incubated in artificial sputum media to form biofilms, as previously described. Then, lung-associated biofilms were challenged with antibiotics, at therapeutically relevant concentrations, before their bacterial load and virulence were quantified and detected, respectively. Results: The results demonstrated minimal effect on the bacterial load with therapeutically relevant concentrations of ciprofloxacin and meropenem, with the latter causing an increased production of proteases and pyocyanin. A combination of meropenem and tobramycin did not show any additional decrease in bacterial load but demonstrated a slight decrease in total proteases and pyocyanin production. Conclusion: In this initial study of six clinical isolates of P. aeruginosa showed high levels of antibiotic tolerance, with minimal effect on bacterial load and increased proteases production, which could negatively affect lung function. Thus, the ex vivo lung model has the potential to be effectively used in larger studies of antibiotic tolerance in in vivo-like biofilms, and show how sub optimal antibiotic treatment of biofilms may potentially contribute to exacerbations and eventual lung failure. We demonstrate a realistic model for understanding antibiotic resistance and tolerance in biofilms clinically and for molecules screening in anti-biofilm drug development

The fitness of pseudomonas aeruginosa quorum sensing signal cheats is influenced by the diffusivity of the environment

Experiments examining the social dynamics of bacterial quorum sensing (QS) have focused on mutants which do not respond to signals and the role of QS-regulated exoproducts as public goods. The potential for QS signal molecules to themselves be social public goods has received much less attention. Here, we analyze how signal-deficient (lasI) mutants of the opportunistic pathogen Pseudomonas aeruginosa interact with wild-type cells in an environment where QS is required for growth. We show that when growth requires a “private” intracellular metabolic mechanism activated by the presence of QS signal, lasI mutants act as social cheats and outcompete signal-producing wild-type bacteria in mixed cultures, because they can exploit the signals produced by wild-type cells. However, reducing the ability of signal molecules to diffuse through the growth medium results in signal molecules becoming less accessible to mutants, leading to reduced cheating. Our results indicate that QS signal molecules can be considered social public goods in a way that has been previously described for other exoproducts but that spatial structuring of populations reduces exploitation by noncooperative signal cheats

Do bacterial “virulence factors” always increase virulence? A meta-analysis of pyoverdine production in pseudomonas aeruginosa as a test case

Bacterial traits that contribute to disease are termed “virulence factors” and there is much interest in therapeutic approaches that disrupt such traits. What remains less clear is whether a virulence factor identified as such in a particular context is also important in infections involving different host and pathogen types. Here, we address this question using a meta-analytic approach. We statistically analyzed the infection outcomes of 81 experiments associated with one well-studied virulence factor—pyoverdine, an iron-scavenging compound secreted by the opportunistic pathogen Pseudomonas aeruginosa. We found that this factor is consistently involved with virulence across different infection contexts. However, the magnitude of the effect of pyoverdine on virulence varied considerably. Moreover, its effect on virulence was relatively minor in many cases, suggesting that pyoverdine is not indispensable in infections. Our works supports theoretical models from ecology predicting that disease severity is multifactorial and context dependent, a fact that might complicate our efforts to identify the most important virulence factors. More generally, our study highlights how comparative approaches can be used to quantify the magnitude and general importance of virulence factors, key knowledge informing future anti-virulence treatment strategies