bioRxivMicrobiologyPreprint
Investigating unexplained genetic variation and its expression in the arbuscular mycorrhizal fungus Rhizophagus irregularis
F. G. G. Masclaux, T. Wyss, M. Pagni, P. Rosikiewicz, I. R. Sanders
Abstract
Arbuscular mycorrhizal fungi (AMF) are important symbionts of plants. Recently, studies of the AMF Rhizophagus irregularis recorded within-isolate genetic variation that does not completely match the proposed homokaryon or heterokaryon state (where heterokaryons comprise a population of two distinct nucleus genotypes). We re-analysed published data showing that bi-allelic sites (and their frequencies), detected in proposed homo- and heterokaryote R. irregularis isolates, were similar across independent studies using different techniques. This indicated that observed within-fungus genetic variation was not an artefact of sequencing and that such within-fungus genetic variation possibly exists. We looked to see if bi-allelic transcripts from three R. irregularis isolates matched those observed in the genome as this would give a strong indication of whether bi-allelic sites recorded in the genome were reliable variants. In putative homokaryon isolates, very few bi-allelic transcripts matched those in the genome. In a putative heterokaryon, a large number of bi-allelic transcripts matched those in the genome. Bi-allelic transcripts also occurred in the same frequency in the putative heterokaryon as predicted from allele frequency in the genome. Our results indicate that while within-fungus genome variation in putative homokaryon and heterokaryon AMF was highly similar in 2 independent studies, there was little support that this variation is transcribed in homokaryons. In contrast, within-fungus variation thought to be segregated among two nucleus genotypes in a heterokaryon isolate was indeed transcribed in a way that is proportional to that seen in the genome.
- organism: fungi (other)
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: chromatin & epigenetic regulation
- research focus: gene regulatory network inference
- approach: statistical model
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