A Deep Dive into False Positive Gene Essentiality Predictions with Modeling and Multi-Omics Analysis

Essentiality assays are commonly practiced as important tools for the discovery of gene functions. Growth/no growth screens of single gene knockout strain collections are often utilized to test the predictive power of COBRA models. False positive predictions occur when computational analysis predicts a gene to be non-essential, however experimental screens deem the gene to be essential. In this study, we explored the definition of conditional essentiality from a phenotypic and genomic perspective. Gene-deletion strains associated with false positive predictions of gene essentiality on defined minimal medium for E. coli were targeted for extended growth tests followed by population sequencing. Of the twenty false positive strains available and confirmed from the Keio knock-out collection, 11 strains were shown to grow with longer incubation periods making these actual true positives. These strains grew reproducibly with a diverse range of growth phenotypes. It was found that 9 out of 11 of the false positive strains that grew acquired mutations in at least one replicate experiment and the types of mutations ranged from SNPs and small indels associated with regulatory or metabolic elements to large regions of genome duplication. Comparison of the detected adaptive mutations and modeling predictions of alternate pathways and isozymes suggested agreement for the observed growth phenotype for 6 out of the 9 cases where mutations were observed, which was further validated by analysis of the transcriptomes of selected strains. In conclusion, longer-term growth experiments followed by whole genome sequencing and transcriptome analysis can provide a better understanding of conditional gene essentiality and mechanisms of adaptation to such perturbations. Compensatory mutations are largely reproducible mechanisms and are in agreement with genome-scale modeling predictions to loss of function gene deletion events.