Today I have made a new publication foray and submitted a manuscript to bioRxiv. This is the main paper to have come out of work on our BBSRC Lux grant. We are yet to find a peer-review home – but one of our co-authors has already had a conversation with someone who wants to use the method – so it was time to put the manuscript out there while we continue with the peer-review process. R code and Biomodels submission will follow. The manuscript details are:
This weeks sees the launch of our BBSRC Lux grant.
We are delighted to have recruited two experienced and talented PostDocs to work on the project.
Neil Doherty will work with Phil Hill and Dave Scott on the experimental elements of the work. Neil has a BSc in Biochemistry from the University of Warwick and a PhD in molecular microbiology also from the University of Warwick. He has since been carrying out postdoctoral research at the University of Nottingham in a number of molecular microbiology research groups, where he has carried out a wide range of experimental work in Staphylococcus aureus, Helicobacter pylori, Streptococcus pneumoniae and Escherichia coli.
Mudassar Iqbal will work with me on the modelling and inference elements of the work. Mudassar has a MSc in physics from the University of the Punjab, an MRes in modelling and simulation of complex realities from the ICTP/SISSA, Trieste, and a PhD in bioinformatics at the University of Kent. He was since carrying out postdoctoral research at the Warwick Systems Biology Centre. Mudassar’s experience includes development of algorithms for analysis of codon usage bias, protein-protein interactions and inference in transcriptomics.
We welcome both Neil and Mudassar to Nottingham and look forward to several years of interdisciplinary research.
It’s official! We’ve received the offer letter from the BBSRC for our grant! The total award is £607K + FEC and the grant will fund two postdocs for three years (one experimental and one theoretical) from 4th April 2011. Here are the lay and technical summaries of our plans:
Bacteria are important in human, animal and plant health and disease. They are responsible for healthy functioning of our gut and healthy soil; they are also responsible for many food-borne infections such as E. coli and Salmonella, animal infections including mastitis in cows and sheep, and hospital infections such as C. difficile and the “superbug” MRSA.
Bacteria can switch different genes on and off in different environmental conditions. For example, in the presence of host cells, they can switch on virulence genes that establish infection, or in the presence of antibiotics they can switch on genes to counter them, such as by pumping them out of their cells. Because such changes in gene activity are so important, a great deal of experimental work aimed at understanding bacteria and preventing harm to humans and animals involves measuring theses changes. The aim of this work is to develop better methods for measuring changes in gene activity.
There are many experimental ways of determining gene activity. The method we intend to improve is based around a special set of genes that make some bacteria glow in the dark. We can take the glow-in-the-dark genes and hook them up in other bacteria in a way that can allow us to measure the response of any gene in the cells: when the gene under study would be switched on, the bacteria will glow.
This technology has many advantages over other technologies. Firstly, it is very sensitive, allowing us to measure very small and fast changes easily. This is an advantage over one major alternative technology, which is using the fluorescent proteins made by jellyfish (whose inventors won the Nobel Prize in 2008), which is slower and suffers from greater background noise. Secondly, because we are measuring light, we can take very many measurements in quick succession. This means that we can capture detailed time series of responses easily and cheaply; other technologies are more expensive and complicated to use, making such detailed measurements either impractical or impossible. Thirdly, because we are measuring light, it is possible to take repeated measurements in live animals without having to slaughter them. Other technologies require experimenters to kill an animal for every measurement taken. Animal experiments are crucial for developing and testing antibiotics; this technology, if applied properly, will allow researchers to greatly reduce the number of animals needed in such work.
Glow-in-the-dark technology is not without its draw-backs. In order to work, the bacteria make a special set of proteins, and these proteins control a complex set of chemical reactions that result in light. Thus the measured light is only an indirect measurement of gene activity. We want to be able to know what the gene activity is from the light measurement. To do this, we need to know how long it takes the cells to make these special proteins, how quickly each step of the chemical reactions that produce light take place, and how long each of the key chemicals persist in the cells. These numbers then need to be fed into a detailed mathematical model that describes all these events, and sophisticated computer algorithms can then be used to work out the gene activity.
In this work, we will focus on the bacterium Staphylococcus aureus, which is important in many infections in animals and humans, including skin infections, pneumonia and mastitis, as well as having antibiotic-resistant forms such as MRSA. However, the approach we develop is intended to be general and applicable to other bacteria. The outcomes of this work will be glow-in-the-dark technology specially optimized for S. aureus; all the measurements necessary for working out gene activity from light measurements; and the mathematical models and computer software needed for the calculations. Thus this work will help researchers to understand and combat this and other bacteria, including the development of new antibiotics to target MRSA.
The aim of this work is to create improved, robust bio-luminescent reporter systems complemented by computer software embedding predictive mathematical models to enable reliable estimation of promoter activity from bioluminescent measurements from in vitro or in vivo use. These aims will be achieved using Systems and Synthetic Biology principles building on our existing Systems Biology and Bioluminescence-Gene Engineering programmes.
The Lux luminescent system uses genes from bacterial bioluminescence hooked up to a promoter of interest so that light is emitted when the promoter is activated. The Lux proteins catalyze a series of chemical reactions for the production of light and the recycling of the aldehyde and reduced flavin substrates necessary for light production. This system has the advantages of being fast and sensitive, with very low background and allows for capture of high density time courses, making the data highly suited for mathematical modelling and systems biology. Moreover, the Lux system is ideal for in vivo work, so that models of infection can be studied with a reduction of animal use, as kinetic data can be captured in live animals.
However, because of the complexity of the light production, the luminescent read-out is only an indirect measure of promoter activity. We propose to develop a system for inferring promoter activity for bioluminescent read-out. This will entail (i) extending our existing mathematical model of the Lux system to include transcription, translation and turn-over of the Lux mRNA and proteins. (ii) accurately measuring the molecular and biochemical kinetic parameters in the model. (iii) designing and building synthetic Lux operons optimized for S. aureus. (iv) Developing statistical software (MCMC) that embed mathematical model and measured parameters to infer promoter activity from light read-out. (v) Applying these methods to in vivo data using S. aureus. (vi) Releasing plasmids and software for general use.