# GnT Motifs Can Increase T:A→G:C Mutation Rates Over 1000-fold in Bacteria

## TL;DR

This study identifies specific DNA sequences in bacteria that can dramatically increase mutation rates, which could help explain how bacteria evolve rapidly.

## Contribution

The paper discovers modular nucleotide motifs (GnT) that can increase T:A→G:C mutation rates over 1000-fold in bacteria.

## Key findings

- Homopolymeric G tracts with a 3′ T frequently mutate to extend the G tract.
- Flanking nucleotides significantly influence the mutation rate of GnT motifs.
- GnT motifs are composed of modular components affecting mutation rates quantifiably.

## Abstract

Nucleotides across a genome do not mutate at equal frequencies. Instead, specific nucleotide positions can exhibit much higher mutation rates than the genomic average due to their immediate nucleotide neighbors. These “mutational hotspots” can play a prominent role in adaptive evolution, yet we lack knowledge of which short nucleotide sequences drive hotspots. In this work, we employ a combination of experimental evolution with Pseudomonas fluorescens and bioinformatic analysis of various Salmonella species to characterize a short nucleotide motif (≥8 bp) that can drive T:A→G:C mutation rates >1000-fold higher than the baseline T→G rate in bacteria. First, we experimentally confirm previous analysis showing that homopolymeric tracts (≥3) of G with a 3′ T frequently mutate so that the T is replaced with a G, resulting in an extension of the guanine tract, i.e. GGGT → GGGG. We then demonstrate that the potency of this T:A→G:C hotspot is dependent on the nucleotides immediately flanking the GnT sequence. We find that the dinucleotide immediately 5′ to a G4 tract and the dinucleotide immediately 3′ to the T strongly affect the T:A→G:C mutation rate, which ranges from ∼5-fold higher than the typical rate to over 1000-fold higher depending on the flanking elements. GnT motifs are therefore comprised of several modular nucleotide components which each exert a significant, quantifiable effect on the mutation rate. This work advances our ability to accurately identify the position and quantify the mutagenicity of hotspot motifs predicated on short nucleotide sequences.