Back

The pho1;2a'-m1.1 allele of Phosphate1 conditions mis-regulation of the phosphorus starvation response in maize (Zea mays ssp. mays L.)

Alonso-Nieves, A. L.; Salazar-Vidal, M. N.; Torres-Rodriguez, J. V.; Perez-Vazquez, L. M.; Massange-Sanchez, J. A.; Gillmor, C. S.; Sawers, R. J. H.

2022-03-12 plant biology
10.1101/2022.03.10.483828 bioRxiv
Show abstract

PHO1 proteins play a central role in plant inorganic phosphorus translocation and sensing. The maize (Zea mays ssp. mays) genome encodes two co-orthologs of the Arabidopsis PHO1 gene, designated ZmPho1;2a and ZmPho1;2b. Here, we report the characterization of the transposon-footprint allele Zmpho1;2a-m1.1, which we refer to hereafter as pho1;2a. The pho1;2a allele is a stable derivative formed by excision of an Activator element from the ZmPho1;2a gene. The pho1;2a allele contains an 8 bp insertion at the point of excision that disrupts the reading frame and is predicted to generate a premature translational stop. We show that the pho1;2a allele is linked to a dosage-dependent reduction in transcript accumulation and a mild reduction in seedling growth that is enhanced under nutrient deficient conditions. Characterization of the shoot and root transcriptomes of seedlings segregating the pho1;2a mutation under different nutrient conditions revealed pho1;2a to have a dominant effect on patterns of transcript accumulation. Gene set enrichment analysis of the transcripts mis-regulated in pho1;2a mutants suggests that Pho1;2a functions in the fine-tuning of the transcriptional phosphate starvation response. We discuss our results with reference to possible genetic redundancy among maize Pho1 genes and in the context of reports linking functional variation in Pho1;2a to agronomically important traits.

Matching journals

The top 4 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.