Genome-Scale Metabolic Network Validation of Shewanella oneidensis Using Transposon Insertion Frequency Analysis

Transposon mutagenesis, in combination with parallel sequencing, is becoming a powerful tool for en-masse mutant analysis. A probability generating function was used to explain observed miniHimar transposon insertion patterns, and gene essentiality calls were made by transposon insertion frequency analysis (TIFA). TIFA incorporated the observed genome and sequence motif bias of the miniHimar transposon. The gene essentiality calls were compared to: 1) previous genome-wide direct gene-essentiality assignments; and, 2) flux balance analysis (FBA) predictions from an existing genome-scale metabolic model of Shewanella oneidensis MR-1. A three-way comparison between FBA, TIFA, and the direct essentiality calls was made to validate the TIFA approach. The refinement in the interpretation of observed transposon insertions demonstrated that genes without insertions are not necessarily essential, and that genes that contain insertions are not always nonessential. The TIFA calls were in reasonable agreement with direct essentiality calls for S. oneidensis, but agreed more closely with E. coli essentiality calls for orthologs. The TIFA gene essentiality calls were in good agreement with the MR-1 FBA essentiality predictions, and the agreement between TIFA and FBA predictions was substantially better than between the FBA and the direct gene essentiality predictions.Author Summary: Metabolic modeling techniques play a central role in rational design of industrial strains, personalized medicine, and automated network reconstruction. However, due to the large size of models, very few have been comprehensively tested using single gene knockout mutants for every gene in the model. Such a genetic test could evaluate whether genes that for a given condition are predicted to be essential by a model, are indeed essential in reality (and vice versa). We developed a new probability-based technology that identifies the essentiality of genes from observed transposon insertion data. This data was acquired by pooling tens of thousands of transposon mutants, and localizing the insertion locations all at once by using massive parallel sequencing. We utilized this gene essentiality data for the genome-scale genetic validation of a metabolic model. For instance: our work identified nonessential genes that were predicted to be essential for growth by an existing metabolic model of Shewanella oneidensis, highlighting incomplete areas within this metabolic model.