Ranking insertion, deletion and nonsense mutations based on their effect on genetic information

<p>Abstract</p> <p>Background</p> <p>Genetic variations contribute to normal phenotypic differences as well as diseases, and new sequencing technologies are greatly increasing the capacity to identify these variations. Given the large number of variations now being discovered, computational methods to prioritize the functional importance of genetic variations are of growing interest. Thus far, the focus of computational tools has been mainly on the prediction of the effects of amino acid changing single nucleotide polymorphisms (SNPs) and little attention has been paid to indels or nonsense SNPs that result in premature stop codons.</p> <p>Results</p> <p>We propose computational methods to rank insertion-deletion mutations in the coding as well as non-coding regions and nonsense mutations. We rank these variations by measuring the extent of their effect on biological function, based on the assumption that evolutionary conservation reflects function. Using sequence data from budding yeast and human, we show that variations which that we predict to have larger effects segregate at significantly lower allele frequencies, and occur less frequently than expected by chance, indicating stronger purifying selection. Furthermore, we find that insertions, deletions and premature stop codons associated with disease in the human have significantly larger predicted effects than those not associated with disease. Interestingly, the large-effect mutations associated with disease show a similar distribution of predicted effects to that expected for completely random mutations.</p> <p>Conclusions</p> <p>This demonstrates that the evolutionary conservation context of the sequences that harbour insertions, deletions and nonsense mutations can be used to predict and rank the effects of the mutations.</p

Domain shuffling in a sensor protein contributed to the evolution of insect pathogenicity in plant-beneficial Pseudomonas protegens.

Pseudomonas protegens is a biocontrol rhizobacterium with a plant-beneficial and an insect pathogenic lifestyle, but it is not understood how the organism switches between the two states. Here, we focus on understanding the function and possible evolution of a molecular sensor that enables P. protegens to detect the insect environment and produce a potent insecticidal toxin specifically during insect infection but not on roots. By using quantitative single cell microscopy and mutant analysis, we provide evidence that the sensor histidine kinase FitF is a key regulator of insecticidal toxin production. Our experimental data and bioinformatic analyses indicate that FitF shares a sensing domain with DctB, a histidine kinase regulating carbon uptake in Proteobacteria. This suggested that FitF has acquired its specificity through domain shuffling from a common ancestor. We constructed a chimeric DctB-FitF protein and showed that it is indeed functional in regulating toxin expression in P. protegens. The shuffling event and subsequent adaptive modifications of the recruited sensor domain were critical for the microorganism to express its potent insect toxin in the observed host-specific manner. Inhibition of the FitF sensor during root colonization could explain the mechanism by which P. protegens differentiates between the plant and insect host. Our study establishes FitF of P. protegens as a prime model for molecular evolution of sensor proteins and bacterial pathogenicity