About the Project
My group recently published an article describing the allelic variation in the Neisseria meningitidis for the intergenic region upstream of a gene encoding a vaccine antigen (1). This antigen, fHbp, is a component of Bexsero (4C-MenB), a new recombinant vaccine for preventing meningitis and septicaemia by serogroup B strains of N. meningitidis. Analysis of ~1,000 genomes determined that nine sequences of this region were found in ~90% of N. meningitidis isolates. Experimental quantification of gene expression combined with mathematical treatments allowed association of specific variant nucleotides and genetic elements with differences in gene expression.
We now want to apply a combination of bioinformatic analyses of genome sequences, mathematical treatments of datasets and experimental tests of predictions to a global analysis of gene expression in three bacterial pathogens – Neisseria meningitidis, Neisseria gonorrhoeae and Campylobacter jejuni. Multiple genome sequences (>10,000) are available for these species and all these species are readily amenable to genetic manipulation.
The over-arching aim will be to determine the extent of genetic variation in the intergenic regions for multiple genes with a range of functions. Genetic variation will be assessed by bioinformatic analyses and correlated with experimentally determined gene expression levels (determined by RNA-Seq2 and/or qPCR1). Mathematical testing will be utilised to determine the specific sequences controlling expression with confirmation by site-directed mutagenesis of key nucleotides. Further work may involve mechanistic testing of the trans-acting factors (e.g. RNA polymerase binding) or extrapolation to other promoters and species through bioinformatic and mathematical integrative testing.
Supervision will be provided by Prof Chris Bayliss, a specialist in the genomics of bacterial pathogens, Prof Dave Grainger, a specialist in functional analyses of transcription and gene expression, and Prof Alexander Gorban, a specialist in mathematical treatments of biological problems.
We are looking for individuals who are interested in combining mathematical approaches with bioinformatics or individuals who want to combine mathematical approaches and experimentation. Training will be provided to bridge gaps in each of these areas. We anticipate that this programme will produced a researcher who has the perfect combination of theoretical and experimental expertise for tackling the vast under-utilised genome datasets now available to infectious disease researchers.
Techniques that will be undertaken during the project:
Experimental skills will include multiplex PCR, qPCR, RNA-Seq, GeneScan, cloning, and site-directed modification of DNA sequences, and bacterial growth. Bioinformatic approaches will accessing of databases, manipulation of Illumina and RNA-Seq sequence data, phylogenetics, multi-genome alignments, running programmes and pipelines in Python or R. Mathematical approaches will include linear regression and agglomerative clustering.
(1) Cayrou, C., Akinduko, A. A., Mirkes, E. M., Lucidarme, J., Clark, S. A., Green, L. R., . . . Bayliss, C. D. (2018). Clustered intergenic region sequences as predictors of factor H Binding Protein expression patterns and for assessing Neisseria meningitidis strain coverage by meningococcal vaccines. PLOS ONE, 13(5), 17 pages. doi:1371/journal.pone.0197186
(2) Anjum, A., Brathwaite, K. J., Aidley, J., Connerton, P. L., Cummings, N. J., Parkhill, J., Connection, I. & Bayliss, C. D. (2016). Phase variation of a Type IIG restriction-modification enzyme alters site-specific methylation patterns and gene expression in Campylobacter jejuni strain NCTC11168. NUCLEIC ACIDS RESEARCH, 44(10), 4581-4594. doi:1093/nar/gkw019
PhasomeIt: an 'omics' approach to cataloguing the potential breadth of phase variation in the genus Campylobacter (2018). Aidley J, Wanford JJ, Green LR, Sheppard SK, Bayliss CD. Microb Genom. PMID: 30351264.
Genome-wide association of functional traits linked with Campylobacter jejuni survival from farm to fork (2017). Yahara K, Méric G, et al., Bayliss CD, Grant A, Maskell D, Didelot X, Kelly DJ, Sheppard SK. Environ Microbiol. 19:361-380. PMID: 27883255.
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