Metris et al. BMC Genomics 2011, 12:535 http://www.biomedcentral.com/1471-2164/12/535 either through poor food preparation hygiene or under- cooking [9]. While the symptoms associated with C. jejuni infection (diarrhoea, vomiting, and stomach pains) often only last between 2 to 5 days, sequelae of C. jejuni infection include more serious autoimmune diseases like Guillain-Barré syndrome, Miller-Fisher syndrome [9], and reactive arthritis [10]. While human infection often does not require antibiotic intervention, the organism is endemic in poultry and farm animals, and it would be advantageous to have treatment options before entry in the food chain. One approach for the identification of new antibiotic targets for a particular bacterial pathogen is to identify non-redundant cellular functions or metabolic pathways that are indispensible for growth and/or survival of that organism; for example, key metabolic enzymes or cell wall synthesis proteins. In the post-genomic era, genome analysis makes both bioinformatic predictions and tar- geted mutagenesis strategies feasible, due to the avail- ability of large, curated datasets. However, genome annotation is often incomplete and incorrect, and meta- bolic redundancy (alternative pathways or catalytic activ- ities) can confound such rational approaches. For instance, a comprehensive study in Salmonella of essen- tial genes required during infection showed that many enzymes are not essential, partly due to metabolic redundancy [11]. An alternative (experimental, high- throughput) strategy is the use of random approaches such as transposon mutagenesis to identify essential genes that would be needed to infect an animal model or to grow and proliferate [12-14]. In silico, essential genes of well characterised micro- organisms, such as E. coli, are predicted with high accu- racy by Flux Balance Analysis (FBA) of the metabolic network [15]. FBA consists of the computation of the fluxes going through the metabolic reactions when the cells are in a homeostatic state [15]. The reactions are linked to the genes that encode the corresponding enzymes with Boolean relationships, and, a gene is pre- dicted to be essential if in silico deletion results in negli- gible biomass [15]. According to Feist et al., [16], about 90% of essential genes of E. coli can be predicted in a given environment. For micro-organisms other than the established model systems, the accuracy is lower (60- 80%) [17,18]. Nevertheless, it provides insights into cel- lular metabolism, which can be useful to identify poten- tial new drug targets [19]. In this study we have constructed a genome scale metabolic model of the food-borne pathogen Campylo- bacter jejuni and discuss this organism’s metabolism. Additionally, we have combined both in silico and in vivo approaches to make predictions about essential genes. A published FBA model of the closely related organism Helicobacter pylori [18] provided the basis for Page 2 of 14 a Campylobacter FBA model. The essential genes pre- dicted from FBA of the reconstructed genome scale model of C. jejuni were compared to new experimen- tally generated transposon mutagenesis data, and a recently published independent whole genome transpo- son mutagenesis study [20]. Whilst the overlap between the three methods is comparatively small, the combina- tion of these methods has drawn focus to the shikimate pathway, a known target pathway for new antimicrobial interventions in other bacterial pathogens [21]. Results Metabolic network reconstruction In a first step, we have reconstructed the metabolic net- work of C. jejuni based on the annotation of the NCTC 11168 genome sequence [22,23], a published model of the related bacterium H. pylori [18] and extensive litera- ture mining. The resulting network is made of 536 reac- tions in total accounting for 388 genes and 467 metabolites, that is, it is of similar size as the model of H. pylori (see Table 1 for the number of reactions in this model as compared with the H. pylori model and the Additional file 1: model for a table with the model itself). In this section we will discuss specific areas of C. jejuni metabolism as present in, or predicted by, our model. Central metabolism The reactions of the central metabolism have been mainly drawn from the literature since contrarily to H. pylori, C. jejuni is predicted to contain a complete TCA cycle with some enzymes characteristic for anaerobes [24]. C. jejuni does not metabolise glucose [25] and the genome annotation suggests that the Embden-Meyerhof pathway only functions in gluconeogenesis [24], so over- all the space of solution of fluxes is different from H. pylori. The respiratory chain of C. jejuni is more com- plex than that of H. pylori [26], and C. jejuni can use sulphite as an electron donor [27]. The biosynthesis of folate results in the production of glycolaldehyde, which can cause cell damage by electro- philic attack of negatively-charged molecules [28]. In the H. pylori and E. coli models, glycolaldehyde is converted to glycolate by a glycolaldehyde dehydrogenase and Table 1 Number of reactions in the C. jejuni model (this paper) and in the H. pylori model [18]. Type of reaction C. jejuni model H. pylori model Metabolic Transport Sink/demands Exchange Total 410 (367) 64 (32) 3 59 536 399 (317) 77 (38) 3 74 553 The number of reactions associated to genes is given in brackets.