Model-based identification of conditionally-essential genes from transposon-insertion sequencing data

Abstract

The understanding of bacterial gene function has been greatly enhanced by recent advancements in the deep sequencing of microbial genomes. Transposon insertion sequencing methods combines next-generation sequencing techniques with transposon mutagenesis for the exploration of the essentiality of genes under different environmental conditions. We propose a model-based method that uses regularized negative binomial regression to estimate the change in transposon insertions attributable to gene-environment changes in this genetic interaction study without transformations or uniform normalization. An empirical Bayes model for estimating the local false discovery rate combines unique and total count information to test for genes that show a statistically significant change in transposon counts. When applied to RB-TnSeq (randomized barcode transposon sequencing) and Tn-seq (transposon sequencing) libraries made in strains of Caulobacter crescentus using both total and unique count data the model was able to identify a set of conditionally beneficial or conditionally detrimental genes for each target condition that shed light on their functions and roles during various stress conditions.

Discovering Genetic Modulators of the Protein Homeostasis System through Multilevel Analysis

“…As illustrated in Figures 4(C-E), the wild-type strain tolerates low heat and moderate oxidative stress well on its own, but combining low heat with high oxidative stress results in a substantial fitness defect compared to these stresses in isolation. Similarly, the ∆clpA strain shows synergistic declines in growth with low heat and high oxidative stress, although, for this strain, it is even more striking as individual low heat stress treatment improves growth, as we reported previously (21). By contrast, the ∆clpB strain consistently demonstrates a lack of synergistic growth defects when combined with heat and oxidative stress, supporting the predictions drawn from the analysis of the single-perturbation TIS data.…”

“…Based on the unique insertion counts, the genes are classified as essential, conditionally essential, conditionally beneficial, conditionally detrimental, or conditionally neutral as described previously (21) except median counts were used to increase robustness to outlying values.…”

“…We fit a regularized negative binomial regression model to unique counts to estimate the environmental and genetic fitness effects as done previously (21). We define a regression model for each gene or locus tag in the Caulobacter crescentus NA1000 genome.…”

Computational analysis of genes with lethal knockout phenotype and prediction of essential genes in archaea

“…And indeed, the number of experimentally identified essential genes, that is, those genes for which knockout is lethal, in free-living bacteria and archaea varies from 270 to 640, and this number depends on conditions of experiment and gene content of tested strains [( 4 – 9 ) and references therein]. Furthermore, computational analysis of some essential genes showed that they belong to integrated mobile genetic elements (MGE), and some are antitoxins of known toxin-antitoxin (TA) systems suggesting that the lethal effect of their disruption is due to induction of cellular toxicity rather than due to the inactivation of an essential function ( 10 – 12 ). Conversely, the lack of a lethal knockout effect for some genes can be caused by the presence of a paralog or a functional analog such that none of the two genes is essential individually, but one of the two has to be present for survival ( 13 ).…”