Back

Dynamic growth re-orientation orchestrates flatness in the Arabidopsis leaf

Harline, K.; Fruleux, A.; Lane, B.; Mosca, G.; Strauss, S.; Tavakolian, N.; Satterlee, J. W.; Li, C.-B.; Singh, A.; Smith, R. S.; Boudaoud, A. S.; Roeder, A. H. K.

2022-11-02 plant biology
10.1101/2022.11.01.514736 bioRxiv
Show abstract

The growth and division of cells in plant leaves is highly dynamic in time and space, even though cells cannot move relative to their neighbors. Thus, organ shape must emerge from carefully coordinated growth, especially in leaves that remain relatively flat as they grow. Here we explored the phenotype of the jagged and wavy (jaw-D) mutant in Arabidopsis thaliana, in which the leaves do not remain flat. It has previously been shown that the jaw-D mutant phenotype is caused by the overexpression of miR319, which represses TCP transcription factors, thus delaying maturation of the leaf. We analyzed cell dynamics in wild type and jaw-D by performing time lapse live imaging of developing leaves. We found that the progression of maturation from the tip of the leaf downward was delayed in jaw-D relative to wild type based on several markers of maturation, in agreement with the role of TCP transcription factors in promoting maturation. We further found that these changes in maturation were accompanied by differences in the coordination of growth across the leaf, particularly across the medial-lateral axis, causing growth conflicts that prevent the leaf from remaining flat. Although leaf flatness is often framed as a problem that requires the local synchronization of growth on the abaxial vs adaxial sides of the leaf, our results based on the jaw-D phenotype suggest that wild-type plants also need to coordinate growth more globally across the leaf blade to maintain flatness.

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.