Identifying new members of virulence signalling networks: three new sensor kinases in the GacS network of Pseudomonas aeruginosa

Antibiotic resistance is becoming a major problem in the treatment of bacterial infections. New strategies for tackling infection are needed and a promising way is to target the sensory networks that bacteria use to choreograph their behaviour during infection. Sensory networks allow bacteria to monitor the state of their host and to decide upon the appropriate responses during the infection. These networks often have multiple different sensors each detecting a different stimulus, allowing bacteria to weigh-up different signals and to make the important decisions regarding the progress of the infection. Interfering with these networks has great potential to reduce bacterial virulence. However, since some bacteria have over a hundred different sensors, unravelling which sensors work together is challenging and a major rate limiting step for devising ways of targeting these networks to reduce virulence.

In this proposal, we are trialling an approach for predicting which sensors work together in networks. The approach involves analysing the protein sequences of the sensors and looking at a set of residues known as the specificity residues. We have found that sensors which work together often have very similar specificity residues, and therefore we reason that we can predict which sensors work together by looking for groups of sensors with similar specificity residues. We have chosen to trial the approach with the GacS network which plays a crucial role in regulating the virulence of the opportunistic pathogen Pseudomonas aeruginosa, which is the third most common healthcare acquired bacterial infection and also a particular problem for patients with respiratory conditions such as cystic fibrosis. Prior to this study, the GacS network was known to employ five different sensors and using our approach of looking for other sensors with similar specificity residues, we have identified a further three sensors that we predict form part of the GacS network. Excitingly, these three new sensors have all been previously implicated in infection. Moreover, we have obtained preliminary data implicating these sensors in controlling infection relevant behaviours that are known to be regulated by the GacS network.

This proposal aims to validate our approach for predicting which sensors work together by showing that the three new sensors, that we have predicted, signal via the GacS network and play an important role in infection. We will determine the role of the sensors in infection by comparing the behaviour of mutants lacking the sensors to the wild-type strain in a selection of assays that either directly monitor virulence using a respiratory infection model or monitor behaviours that are required for virulence. Next, we will determine the list of genes (regulon) whose expression is controlled by the new sensors and by identifying overlaps with those controlled by the GacS network, determine the extent to which the new sensors signal via the GacS network. Finally, to determine how the new sensors fit into the GacS network, we will look for interactions between the new sensor proteins and the other proteins in the GacS network, and then determine the consequences of those interactions for decision making in the network.

In addition to revealing novel potential drug targets in P. aeruginosa, an important pathogen in its own right, these studies will also serve as a proof-of-principle for using similar specificity residues to predict which sensors work together in other pathogens. Consistent with this vision, many other important pathogens have groups of sensors with very similar specificity residues including Bacillus anthracis, Burkholderia cenocepacia, Burkholderia pseudomallei, Clostridium difficile, Klebsiella pneumoniae, Mycobacterium tuberculosis, Streptococcus pneumoniae, Vibrio cholerae and Vibrio vulnificus, and therefore this work has the potential to reveal promising new drug targets in these species.

Pathogens rely on sensory networks to make decisions that are critical for survival during infection. With antibiotic resistance levels rising, interfering with these networks is a promising new way of treating infection. Many of these networks employ multiple sensor kinases that work together to detect and integrate different signals. However, determining which kinases collaborate to form these multikinase-networks (MKNs) is challenging and is a major rate limiting step for devising ways of targeting these networks to reduce virulence.

In this proposal, we will trial an approach for predicting which kinases work together in MKNs, based on the premise that these kinases should share their specificity determining residues to enable them to interact with the same receiver domains. Using this approach, we have predicted three new kinases in the GacS network controlling virulence in Pseudomonas aeruginosa. Our aim is to validate this predictive approach by showing that these new kinases signal via the GacS network and play an important role in infection. We will determine the role of the kinases by investigating the behaviour of deletion mutants in virulence assays. Next, the regulon controlled by each kinase will be determined using RNA-seq, and compared with the regulon of the GacS network to determine the extent to which the new kinases signal via the GacS network. Finally, we will test for interactions between the new kinases and the other proteins in the GacS network to find how the new kinases fit into this network.

In addition to determining the role of the new kinases in the GacS network, this study will, more widely, serve as proof-of-principle for using shared specificity residues to predict new members of virulence signalling networks in other pathogens. Indeed, similar to P. aeruginosa, many other pathogens have groups of kinases that share their specificity residues and therefore this work will help to reveal these potential drug targets.

Sensory networks employing multiple sensor kinases are particularly well suited to regulating virulence because they allow bacteria to detect multiple different stimuli during an infection and to make a balanced decision about the progress of the infection. With the rise of antibiotic resistance, targeting these networks has great potential to offer an alternative strategy for tackling infection that reduces virulence. However, progress in this area is impeded because discovering which sensor kinases work together in these networks is a time consuming process. We have developed a rapid way for predicting many of these networks and in this proposal we will trial it on the GacS network of Pseudomonas aeruginosa. This network plays a major role in controlling the virulence of this important pathogen, which is the third most commonly acquired hospital infection (accounting for around 6 % of cases), and is a particular problem for cystic fibrosis (CF) patients (around 80% become chronically infected) and sufferers of chronic obstructive pulmonary disease (COPD). Using this approach, we have predicted three new sensor kinases in the GacS network and excitingly all three of these have previously been implicated in infection. In this proposal, we will determine the extent to which the new kinases signal via the GacS network, and will assess their potential promise as drug targets by investigating how loss of each of them affects virulence.

This proposal has the immediate potential to uncover new drug targets in the GacS network, but more widely, the approach we are trialling here can be applied to any other pathogen to discover new therapeutic targets. Indeed our approach predicts the use of multikinase-networks in a diverse range of pathogens including Bacillus anthracis, Burkholderia cenocepacia, Burkholderia pseudomallei, Clostridium difficile, Klebsiella pneumoniae, Mycobacterium tuberculosis, Streptococcus pneumoniae, Vibrio cholerae and Vibrio vulnificus. This work therefore has the potential to ultimately benefit patients suffering from these bacterial infections. Although clearly there is long road from discovering drug targets to developing drugs. As explained in the pathways to impact section, we will take forward promising drug targets that we identify in this work by engaging will the DiscoverAssist venture (run by the Stevenage Bioscience Catalyst) to begin the process of drug development.

Throughout the project, we will engage with clinicians, clinical microbiologists and drug companies (e.g. Forest Laboratories) through contributing to the annual CF microbiology meeting, which brings together a diverse range of expertise with the overall aim of establishing collaborations that could lead to new therapeutic strategies. In addition, we will also publicise our research to school children and enthuse them about research careers via the annual schools visits that we have planned and via our participation in the "Men in White" event at Exeter Medical School.

The project also provides the opportunity for the employed PDRAs to not only develop their research skills but also to acquire a broad range of transferable skills within in a multidisciplinary research environment. These will include project planning, communication, record keeping, presentation skills to a variety of different audiences and report writing. This will contribute to the career progression of the PDRAs and help to prepare them for future careers either in academia, industry or beyond.