Transposon mutagenesis in a hyper-invasive clinical isolate of Campylobacter jejuni reveals a number of genes with potential roles in invasion.

Transposon mutagenesis has been applied to a hyper-invasive clinical isolate of Campylobacter jejuni, 01/51. A random transposon mutant library was screened in an in vitro assay of invasion and 26 mutants with a significant reduction in invasion were identified. Given that the invasion potential of C. jejuni is relatively poor compared to other enteric pathogens, the use of a hyper-invasive strain was advantageous as it greatly facilitated the identification of mutants with reduced invasion. The location of the transposon insertion in 23 of these mutants has been determined; all but three of the insertions are in genes also present in the genome-sequenced strain NCTC 11168. Eight of the mutants contain transposon insertions in one region of the genome (approximately 14 kb), which when compared with the genome of NCTC 11168 overlaps with one of the previously reported plasticity regions and is likely to be involved in genomic variation between strains. Further characterization of one of the mutants within this region has identified a gene that might be involved in adhesion to host cells.

Parameter Estimation for Hidden Markov Models with Intractable Likelihoods

Approximate Bayesian computation (ABC) is a popular technique for analysing data for complex models where the likelihood function is intractable. It involves using simulation from the model to approximate the likelihood, with this approximate likelihood then being used to construct an approximate posterior. In this paper, we consider methods that estimate the parameters by maximizing the approximate likelihood used in ABC. We give a theoretical analysis of the asymptotic properties of the resulting estimator. In particular, we derive results analogous to those of consistency and asymptotic normality for standard maximum likelihood estimation. We also discuss how sequential Monte Carlo methods provide a natural method for implementing our likelihood-based ABC procedures.