Scaling photosynthesis from gene to leaf

Scientists combine models to create a powerful multiscale model that can identify key transcription factors regulating leaf photosynthesis.

Worldwide food production must increase by 70% to meet the needs of an additional 2.3 billion people by 2050. While future increases in elevated carbon dioxide will boost the yield of many crops, resource availability for enzymes important for photosynthesis limits their productivity under elevated carbon dioxide. In a paper recently published by in silico Plants, Dr. Marshall-Colon and colleagues used a multiscale model to pinpoint which enzyme-controlling transcription factors will increase productivity under future carbon dioxide levels.

a multiscale model of soybean leaf photosynthesis

The authors first created a multiscale model of soybean leaf photosynthesis by integrating three models across molecular and organ-level scales using the yggdrasil framework. This multiscale model was then used to scale processes from gene expression through photosynthetic metabolism to predict leaf physiology in response to rising carbon dioxide. Flux control analysis was used to identify enzymes requiring the greatest change to adapt optimally to elevated carbon dioxide. Linking the GRN to protein concentrations, which serve as the input to the metabolic model, made it possible to identify key transcription factors that could be up- or down-regulated to improve photosynthesis. Specific enzymes that had high control coefficients in ambient and elevated carbon dioxide were then identified.

The multiscale model of soybean photosynthesis successfully predicted the acclimatory changes in the photosynthetic apparatus of plants grown under high carbon dioxide levels in the field. The model predicted that ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) was less limiting under elevated carbon dioxide that it is under ambient carbon dioxide and should be down-regulated allowing re-allocation of resource to enzymes controlling the rate of regeneration of ribulose-1:5 bisphosphate (RuBP).

By linking the gene regulatory network through protein concentration to the metabolic model, the authors were able to identify transcription factors that matched the up- and down-regulation of genes needed to improve photosynthesis. The analysis specifically identified the transcription factor GmGATA2, which down-regulated genes for RuBisCO synthesis while up-regulating genes controlling RuBP generation and starch synthesis.

“Existing models of photosynthesis do not provide a means to link observed transcriptional changes with metabolism and photosynthetic capacity at the leaf level. Our integrated model overcomes this, resulting in a model able to predict field-observed photosynthetic acclimation under elevated CO2 concentrations. The model also yielded predictions about specific regulatory mechanisms that we can now target with engineering to improve the photosynthetic efficiency of soybean under future CO2 concentrations,” says Amy Marshall-Colon, Assistant Professor of plant biology at the University of Illinois.

The authors plan to use the predictions to guide genetic improvement in soybean plants growing under high carbon dioxide with the goal of future-proofing crops.

Source code for the models in this study can be found in the repository located at https://github.com/cropsinsilico/yggdrasil.

Further reading

Ainsworth, E. A., & Long, S. P. (2004). What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. New Phytologist, 165(2), 351–372. https://doi.org/10.1111/j.1469-8137.2004.01224.x

Kannan, K., Wang, Y., Lang, M., Challa, G. S., Long, S. P., & Marshall-Colon, A. (2019). Combining gene network, metabolic, and leaf-level models show means to future-proof soybean photosynthesis under rising CO2. In Silico Plants. https://doi.org/10.1093/insilicoplants/diz008

Lang, M. (2019). yggdrasil: a Python package for integrating computational models across languages and scales. In Silico Plants, 1(1). https://doi.org/10.1093/insilicoplants/diz001

Long, S. P., Ainsworth, E. A., Rogers, A., & Ort, D. R. (2004). RISING ATMOSPHERIC CARBON DIOXIDE: Plants FACE the Future. Annual Review of Plant Biology, 55(1), 591–628. https://doi.org/10.1146/annurev.arplant.55.031903.141610