Metris et al. BMC Genomics 2011, 12:535 http://www.biomedcentral.com/1471-2164/12/535 used: returning to our glycolate to glyoxylate intercon- version hypothesis, Cj1213c is a putative glycolate oxi- dase subunit D (EC 1.1.3.15) [23,55], or an alkylglycerone-phosphate synthase (EC 2.5.1.26) [52], or a D-lactate dehydrogenase (EC 1.1.2.4) [53] making the degradation of glycolate into glyoxylate an uncertain assumption. An inherent limitation of the FBA method is the suit- ability of the objective function [56]. For instance, by optimising the biomass, FBA does not take into account the microaerophilic and capnophilic properties of C. jejuni. These may indeed constitute additional con- straints like a maximum concentration of oxygen-sensi- tive enzymes neglected in these simulations except as modelled by the limiting uptake rate of oxygen. Alterna- tively, trade-off functions may be more appropriate objective function than the optimisation of the biomass with these kind of micro-organisms [57]. The FBA method has the potential of being condition specific to determine the essential genes. In this study, they were determined in laboratory conditions. However as more data become available on the conditions in the chicken gut, the model has the potential of being used in situations relevant to the food industry. Transposon methods Flux balance analysis methods have a good track record of predicting essential genes [16], however, they only focus on metabolism-related genes. In this study, the FBA model only contained reactions linked to 388 genes, 24% of the total genome. In vitro transposition has the advantage of targeting the whole genome. We describe the construction of two transposon mutant libraries in C. jejuni NCTC11168 and the mapping of a total of 9550 inserts in the genome, this represents a coverage of 5.94× using the method of Stahl and Stintzi [20]. Data from the combined transposon mutagenesis libraries predicted 233 essential genes (14% of the gen- ome total). While FBA only uses a subset of genes from the genome, the number of predicted essential genes was similar at 175 (11% of the genome). These numbers are similar to the published C. jejuni study (194 genes, 12% of the genome) although the overlap between the published study and the data presented in this study is comparatively small (only 8 genes predicted by all three methods). Compared to other published essential gene prediction studies, the number of predicted essential genes is between the lower quartile and median with respect to total number of essential genes and percen- tage of the genome predicted to be essential. However, reviewing all published microbial essential gene predic- tions, we noted there was no correlation between num- ber of predicted essential genes and genome size (data not shown). Clearly the relationship between genome Page 10 of 14 size, complexity of niche, and indispensable genes is complex, plus a number of caveats should always be considered when interpreting this sort of data: any gene containing at least one insert can be said to be non- essential under the growth conditions described. How- ever, the inverse logic is not true. The absence of an insert in a gene does not necessarily mean that the gene is required for growth and hence essential. Transposon insertion may not have occurred for a variety of reasons: chance, sequence bias of the transposase or transposon depletion during the reaction. Although no detailed stu- dies of the sequence preference of either transposases used here have been carried out and it is generally assumed they are essentially random, we used two dif- ferent transposases in an attempt to minimise any effect of sequence bias. An over-representation of small hypothetical proteins (and accordingly, small genes lack- ing an insert) may have resulted from the random nat- ure of the transposon insertion: i.e. the smaller the gene, the smaller the chance of transposon insertion; however, no gene size bias was observed when comparing genes with insert with genes encompassing the entire genome. The genome of C. jejuni NCTC 11168 totals 1,641,481 bases and thus our libraries represent inser- tions in only ~0.006% of the possible positions. It is possible that the number of inserts identified is an under representation of the actual total since inserts close together would generate very similar sized PCR products that may fail to be discriminated on the agar- ose gels. Additionally, the abundance of individual mutants in the isolated pooled genomic DNA may also affect whether a band is visible. Since the genomic DNA used was isolated from pooled colonies, it is pos- sible that any mutant resulting in reduced growth and hence colony size would be under represented in the pooled material and as a consequence, would not be detected. The whole genome in vitro transposition pre- sented in this study should be seen as a high-through- put method, as opposed to a high precision method. A number of caveats should not be ignored: the library unlikely represents all possible insertion points and some regions may be naturally more resistant to accept- ing an insert. Detection is constrained by the primer library, which in this case was optimized for microarray probe generation. The PCR and agarose gel-based approach also suffers from more common technical drawbacks such as smaller PCR products are more favourably amplified that longer ones and accuracy of sizing gel fragments is not infallible. Additionally, polar effects due to operon structure, may result in the recovery of some mutants, if the transcription and translation of upstream genes is perturbed by a transpo- son insert, as was shown for the C. jejuni fur gene which in itself is not essential [58]