Back

Differences in stomatal sensitivity to COC2 and light influences variation in water use efficiency and leaf carbon isotope composition in two genotypes of the C4 plant Zea mays.

Crawford, J. D.; Twohey, R. J.; Pathare, V. S.; Studer, A. J.; Cousins, A. B.

2023-12-04 plant biology
10.1101/2023.12.01.569655 bioRxiv
Show abstract

The ratio of net CO2 uptake (Anet) and stomatal conductance (gs) is an intrinsic measurement of leaf water use efficiency (WUEi) however its measurement can be challenging for large phenotypic screens. Measurements of leaf carbon isotope composition ({delta}13Cleaf) may be a scalable tool to approximate WUEi for screening because it in part reflects the competing influences of Anet and gs on the CO2 partial pressure (pCO2) inside the leaf over time. However, in C4 photosynthesis the CO2 concentrating mechanism complicates the relationship between {delta}13Cleaf and WUEi. Despite this complicated relationship, several studies have shown genetic variation in {delta}13Cleaf across C4 plants. Yet there has not been a clear demonstration of whether Anet or gs are the causal mechanisms controlling WUEi and {delta}13Cleaf. Our approach was to characterize leaf photosynthetic traits of two Zea mays recombinant inbred lines (Z007E0067 and Z007E0150) which consistently differ for {delta}13Cleaf even though they have minimal confounding genetic differences. We demonstrate that these two genotypes contrasted in WUEi driven by differences in the speed of stomatal responses to changes in pCO2 and light that lead to unproductive leaf water loss. These findings provide support that differences in {delta}13Cleaf in closely related genotypes do reflect greater WUEi and further suggests that differences in stomatal kinetic response to changing environmental conditions is a key target to improve WUEi.

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.