Background: Typical microorganism studies link genetic markers to physiological observations, like growth and survival. Experiments are carefully designed, comparing wildtype strains with knockout strains, and replications are conducted to capture biological variation. To maintain monoclonal strains, strain preservation systems are used to keep the number of generations between the primary stock and the experimental measurement low, to decrease the influence of spontaneous mutations on the experimental outcome. The impact of spontaneous mutations during the minimal number of growth cycles for the experimental design is, however, poorly studied. Results: We set out to characterize the mutation landscape using a transcriptomic dataset of Schizophyllum commune, a laboratory model for mushroom formation. We designed a methodology to detect SNPs from the RNA-seq data, and found a mutation rate of 1.923 10-8 per haploid genome per base per generation, highly similar to the previously described mutation rate of S. commune in the wild. Our results imply that approximately 300 mutations are generated during growth of a colony on an agar plate, of which 5 would introduce stop codons. Knock-outs did not incur an increase of mutations and chromosomal recombination occurring at mating type loci was frequent. We found that missense and nonsense SNPs were selected against throughout the experiment. Also, most mutations show a low variant allele frequency and appear only in a small part of the population. Yet, we found 40 genes that gained a nonsense mutation affecting one of its annotated protein domains, and more than 400 genes having a missense mutation inside an annotated protein domain. Further, we found transcription factors, metabolic genes and cazymes having gained a mutation. Hence, the mutation landscape is wide-spread and has many functional annotations. Conclusions: We have shown that spontaneous mutations accumulate in typical microorganism experiments, where one usually assumes that these do not happen. As these mutations possibly confound experiments they should be minimized as much as possible, or, at least, be trackable. Therefore, we recommend labs to ensure that biological replicates originate from different parental plates, as much as possible.
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