Dear PhotosynQ User,
Welcome to the PhotosynQ community newsletter!
We have the following Open Access Article by Kanazawa et al. (2021) as our featured project this month. This project explains certain limitations to photosynthesis by observing plants under real field conditions. We hope you enjoy reading our selected study and please stay safe and healthy! Best, PhotosynQ Team
Atsuko Kanazawa, Abhijnan Chattopadhyay, Sebastian Kuhlgert, Hainite Tuitupou, Tapabrata Maiti, and David M. Kramer
Light potentials of photosynthetic energy storage in the field: what limits the ability to use or dissipate rapidly increased light energy?
Royal Society Open Science https://doi.org/10.1098/rsos.211102
The ability of plants to manage the rapid fluctuations of environmental conditions such as light is important for the efficient continuation of photosynthesis. This study defines a new term, 'light potential' (LP), to express the plant's response to sudden increases in light intensity through productive phytochemistry or accelerating photoprotective mechanisms. Three mechanistic models are proposed to describe the processes that can limit LP of photosynthetic and photoprotective mechanisms (Scheme 1).
Model 1: Linear electron flux limited by downstream PS1 metabolism.
Model 1 predicts ‘over-reduction’ of the ETC (electron transfer chain) due to limited PS1 electron acceptors resulting in a reduction of QA and P700+. This can lead to decreases in photoprotective measures.
Model 2: Linear electron flux limited by photoprotective quenching (NPQ).
Model 2 predicts that with high NPQ, ETC will become more oxidized through QA & P700+. Depending on the form of NPQ utilized, this could negatively affect photosynthetic efficiency.
Model 3: Linear electron flux limited by pH control of PQH2 oxidation.
Model 3 predicts that under a more acidic lumen plastoquinol oxidation at the cytochrome b6f complex will slow, and if qE is not activated, QA will be reduced and P+ oxidized.
Gaussian mixture model clustering analysis was applied to the LP data sets to determine distinct interactions between photosynthetic and environmental parameters. The data was shown to break down into 4 clusters: leaf temperature (3, red), PARamb (1, blue) or both (2 and 4) which can be mechanistically explained by Model 2 or Model 3 or both 2 and 3 (Figure 8 above).
Highlights the importance of research in dissecting the photosynthetic limitation of plants under field conditions with a combination of multifaceted measurements of photosynthesis and environment.
Successfully employs an unsupervised clustering approach (i.e., automatic discovery of the natural grouping of data recorded) to cluster the data under different subsets of conditions showing the path forward for making data-driven plant research.
This research and the open source data that accompanies it will enable researchers to further discover the photosynthetic behaviors of crop plants under diverse conditions.