Back

Comparative Analysis of Tylosema esculentum Mitochondrial DNA Revealed Two Distinct Genome Structures

Li, J.; Cullis, C. A.

2023-03-29 plant biology
10.1101/2023.03.27.534440 bioRxiv
Show abstract

Tylosema esculentum (marama bean), an underutilized legume with edible and nutritious seeds, has the potential to improve local food security in southern Africa. This study investigated the diversity of marama mitogenomes by mapping sequencing data from 84 individuals to the previously published reference mitogenome. Two distinct germplasms were found, and a new mitogenome structure containing three circular molecules and one long linear chromosome was identified, with a unique 2,108 bp fragment and primers were designed on that for marama mitogenome typing. This structural variation increases copy number of certain genes, including nad9, rrns and rrn5. The two mitogenomes also differed at 230 loci, with only one nonsynonymous substitution in matR. The evolutionary analysis suggested that the divergence of marama mitogenomes may be related to soil moisture level. Heteroplasmy in the marama mitogenome was concentrated at specific loci, including 127,684 bp to 127,686 bp on chromosome LS1 (OK638188), and may be crucial in the evolution. Additionally, the mitogenomes of marama contained a cpDNA insertion of over 9 kb with numerous polymorphisms, resulting in the loss of function of the genes on that segment. This comprehensive analysis of marama mitogenome diversity may provide valuable insight for future improvement of the bean. HighlightThe analysis of 84 marama mitogenomes revealed two germplasms and the structural variation affects certain gene copy numbers. Soil moisture levels may have played important roles in the mitogenome divergence.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.