Understanding bacteria and challenges in microbiology
In 2020 we celebrate 75 years of the anniversary of our founding with a year of activities dedicated to demonstrating the impact of microbiologists’ past, present and future – bringing together and empowering communities that help shape the future of microbiology. We are launching new collections of digital content throughout the anniversary year. The second digital hub is 'Understanding bacteria and the challenges in microbiology', which will explore novel antimicrobial strategies, the world of biofilms and bacteria in industry.
Collection Contents
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Cinnamaldehyde disrupts biofilm formation and swarming motility of Pseudomonas aeruginosa
Bacterial biofilms can cause serious health care complications associated with increased morbidity and mortality. There is an urge to discover and develop new biofilm inhibitors from natural products or by modifying natural compounds or understanding the modes of action of existing compounds. Cinnamaldehyde (CAD), one of the major components of cinnamon oil, has been demonstrated to act as an antimicrobial agent against a number of Gram-negative and Gram-positive pathogens, including Pseudomonas aeruginosa, Helicobacter pylori and Listeria monocytogenes. Despite the mechanism of action of CAD against the model organism P. aeruginosa being undefined, based on its antimicrobial properties, we hypothesized that it may disrupt preformed biofilms of P. aeruginosa. The minimum inhibitory concentration (MIC) of CAD for planktonic P. aeruginosa was determined to be 11.8 mM. Membrane depolarization assays demonstrated disruption of the transmembrane potential of P. aeruginosa. CAD at 5.9 mM (0.5 MIC) disrupted preformed biofilms by 75.6 % and 3 mM CAD (0.25 MIC) reduced the intracellular concentrations of the secondary messenger, bis-(3′–5′)-cyclic dimeric guanosine monophosphate (c-di-GMP), which controls P. aeruginosa biofilm formation. The swarming motility of P. aeruginosa was also reduced by CAD in a concentration-dependent manner. Collectively, these findings show that sub-MICs of CAD can disrupt biofilms and other surface colonization phenotypes through the modulation of intracellular signalling processes.
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Colworth prize lecture 2016: exploiting new biological targets from a whole-cell phenotypic screening campaign for TB drug discovery
More LessMycobacterium tuberculosis is the aetiological agent of tuberculosis (TB) and is the leading bacterial cause of mortality and morbidity in the world. One third of the world’s population is infected with TB, and in conjunction with HIV represents a serious problem that urgently needs addressing. TB is a disease of poverty and mostly affects young adults in their productive years, primarily in the developing world. The most recent report from the World Health Organisation states that 8 million new cases of TB were reported and that ~1.5 million people died from TB. The efficacy of treatment is threatened by the emergence of multi-drug and extensively drug-resistant strains of M. tuberculosis. It can be argued that, globally, M. tuberculosis is the single most important infectious agent affecting mankind. Our research aims to establish an academic-industrial partnership with the goal of discovering new drug targets and hit-to-lead new chemical entities for TB drug discovery.
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CamOptimus: a tool for exploiting complex adaptive evolution to optimize experiments and processes in biotechnology
Multiple interacting factors affect the performance of engineered biological systems in synthetic biology projects. The complexity of these biological systems means that experimental design should often be treated as a multiparametric optimization problem. However, the available methodologies are either impractical, due to a combinatorial explosion in the number of experiments to be performed, or are inaccessible to most experimentalists due to the lack of publicly available, user-friendly software. Although evolutionary algorithms may be employed as alternative approaches to optimize experimental design, the lack of simple-to-use software again restricts their use to specialist practitioners. In addition, the lack of subsidiary approaches to further investigate critical factors and their interactions prevents the full analysis and exploitation of the biotechnological system. We have addressed these problems and, here, provide a simple‐to‐use and freely available graphical user interface to empower a broad range of experimental biologists to employ complex evolutionary algorithms to optimize their experimental designs. Our approach exploits a Genetic Algorithm to discover the subspace containing the optimal combination of parameters, and Symbolic Regression to construct a model to evaluate the sensitivity of the experiment to each parameter under investigation. We demonstrate the utility of this method using an example in which the culture conditions for the microbial production of a bioactive human protein are optimized. CamOptimus is available through: (https://doi.org/10.17863/CAM.10257).
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