bastkowski, s. | bioRxiv Science

bioRxiv2022

LoRTIS Software Suite: Transposon mutant analysis using long-read sequencing

To date transposon insertion sequencing (TIS) methodologies have used short-read nucleotide sequencing technology. However, short-read sequences are unlikely to be matched correctly within repeated genomic regions which are longer than the sequence read. This drawback may be overcome using long-read sequencing technology. We have developed a suite of new analysis tools, the "LoRTIS software suite" (LoRTIS-SS), that produce transposon insertion site mapping data for a reference genome using long-read nucleotide sequence data. Long-read nucleotide sequence data can be applied to TIS, this enables the unique mapping of transposon insertion sites within long genomic repeated sequences. Here we present long-read TIS analysis software, LoRTIS-SS, which uses the Snakemake framework to manage the workflow. A docker image is provided, complete with dependencies and ten scripts are included for experiment specific data processing before or after use of the main workflow. The workflow uses long-read nucleotide sequence data such as those generated by the MinION sequencer (Oxford Nanopore Technologies). The unique mapping properties of long-read sequence data were exemplified by reference to the ribosomal RNA genes of Escherichia coli strain BW25113, of which there are 7 copies of [~]4.9 kbases in length that are at least 99% similar. Of reads that matched within rRNA genes, approximately half matched uniquely. The software workflow outputs data compatible with the established Bio-TraDIS analysis toolkit allowing for existing workflows to be easily upgraded to support long-read sequencing.

bioinformatics↗

bioRxiv2019

A new massively-parallel transposon mutagenesis approach comparing multiple datasets identifies novel mechanisms of action and resistance to triclosan

The mechanisms by which antimicrobials exert inhibitory effects against bacterial cells and by which bacteria display resistance vary under different conditions. Our understanding of the full complement of genes which can influence sensitivity to many antimicrobials is limited and often informed by experiments completed in a small set of exposure conditions. Capturing a broader suite of genes which contribute to survival under antimicrobial stress will improve our understanding of how antimicrobials work and how resistance can evolve. Here, we apply a new version of TraDIS (Transposon Directed Insert Sequencing); a massively parallel transposon mutagenesis approach to identify different responses to the common biocide triclosan across a 125-fold range of concentrations. We have developed a new bioinformatic tool AlbaTraDIS allowing both predictions of the impacts of individual transposon inserts on gene function to be made and comparisons across multiple TraDIS data sets. This new TraDIS approach allows essential genes as well as non-essential genes to be assayed for their contribution to bacterial survival and growth by modulating their expression. Our results demonstrate that different sets of genes are involved in survival following exposure to triclosan under a wide range of concentrations spanning bacteriostatic to bactericidal. The identified genes include those previously reported to have a role in triclosan resistance as well as a new set of genes not previously implicated in triclosan sensitivity. Amongst these novel genes are those involved in barrier function, small molecule uptake and integrity of transcription and translation. These data provide new insights into potential routes of triclosan entry and bactericidal mechanisms of action. Our data also helps to put recent work which has demonstrated the ubiquitous nature of triclosan in people and the built environment into context in terms of how different triclosan exposures may influence evolution of bacteria. We anticipate the approach we show here that allows comparisons across multiple experimental conditions of TraDIS data will be a starting point for future work examining how different drug conditions impact bacterial survival mechanisms.

microbiology↗