Random insertional mutagenesis screens are important tools in microbial genetics studies. Investigators in fungal systems have used the plant pathogen Agrobacterium tumefaciens to create tagged, random mutations for genetic screens in their fungal species of interest through a unique process of trans-kingdom cellular transconjugation. However, identifying the locations of insertion has traditionally required tedious PCR-based methods, limiting the effective throughput of this system. We have developed an efficient genomic sequencing and analysis method (AIM-Seq) to facilitate identification of randomly generated genomic insertions in microorganisms. AIM-Seq combines batch sampling, whole genome sequencing, and a novel bioinformatics pipeline, AIM-HII, to rapidly identify sites of genomic insertion. We have specifically applied this technique to Agrobacterium-mediated transconjugation in the human fungal pathogen Cryptococcus neoformans. With this approach, we have screened a library of C. neoformans cell wall mutants, selecting twenty-seven mutants of interest for analysis by AIM-Seq. We identified thirty-five putative genomic insertions in known and previously unknown regulators of cell wall processes in this pathogenic fungus. We confirmed the relevance of a subset of these by creating independent mutant strains and analyzing resulting cell wall phenotypes. Through our sequence-based analysis of these mutations, we observed "typical" insertions of the Agrobacterium transfer DNA as well as atypical insertion events, including large deletions and chromosomal rearrangements. Initially applied to C. neoformans, this mutant analysis tool can be applied to a wide range of experimental systems and methods of mutagenesis, facilitating future microbial genetic screens.
Keywords: AIM-Seq; Agrobacterium-mediated transconjugation; Cryptococcus neoformans; Fungal cell wall; Fungal pathogen; Insertional mutagenesis; T-DNA.
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