Possible title: Predicting the outcomes of genetic exchange in polyclonal infections
The problem: Genetic exchange between closely related pathogens can increase virulence and is responsible for many failed attempts at control (spread of antibiotic resistance, escape from immune surveillance and vaccine immunity). At present we have no ways to anticipate or prevent this, largely because we are ignorant of the constraints and biases of the underlying steps. Previous (sparse?) attempts to understand genetic exchange have been based on inferences from (i) laboratory studies of the mechanism and regulation of DNA-transfer processes and recombination and (ii) detection of past recombination events in natural populations. The former bear little relation to events in real populations, and the latter are confounded by later time, genetic drift and natural selection.
Hypothesis: Identifying the constraints on transformational genetic exchange will allow the outcomes of natural recombination events to be predicted.
Significance: The ability to predict the most likely genetic exchange events will help researchers prepare for new variant strains of bacteria. Identifying the causes of the constraints may also permit interventions that block genetic exchange in polyclonal infections such as the cystic fibrosis lung.
A. Identify the DNA sequence effects that constrain DNA uptake.
- Clarify the sequence biases of DNA uptake by H. influenzae.
- Identify the proteins that interact with the preferred sequences.
- Identify sequence biases of DNA uptake by other bacteria, especially those not known to exhibit bias.
- Characterize the effects of these biases in natural and simulated communities.
B. Identify the constraints on homologous recombination between H. influenzae strains.
- Identify the effects of DNA sequences and sequence heterologies on the extents and endpoints of recombination tracts.
- Identify the effects of DNA sequences and sequence heterologies on recombination frequencies across the H. influenzae genome
C. Use the results of the above studies to develop a probabilistic model of recombination, and test these predictions using datasets of recombination events from natural and simulated bacterial communities.
- A dataset of past recombination events inferred from genome sequence data of H. influenzae strains.
- A metagenomic dataset derived from a short-term evolution of a polyclonal H. influenzaelaboratory culture.
- A metagenomic dataset from cystic fibrosis sputum.
The figure shows the structure of H. influenzae recombination tracts (data from Mell et al. Transformation of natural genetic variation into Haemophilus influenzae. PLoS Pathog 7(7): e1002151. doi:10.1371/journal.ppat.1002151)
On another topic, I've been getting emails about a new paper in the Journal of Biological Chemistry, which claims to explain the growth of GFAJ-1 by use of phosphate from degraded ribosomes. I don't think this idea is sound, but I'm going to leave it for Carmen Drahl and Ed Yong to deal with.