Quero Gastón, Bonnecarrère Victoria, Simondi Sebastián, Santos Jorge, Fernández Sebastián, Gutierrez Lucía, Garaycochea Silvia, Borsani Omar
Departamento de Biología Vegetal, Facultad de Agronomía, Universidad de la República, Garzón 809, Montevideo, Uruguay.
Unidad de Biotecnología, Estación Experimental Wilson Ferreira Aldunate, Instituto Nacional de Investigación Agropecuaria (INIA), Ruta 48, Km 10, Rincón del Colorado, 90200, Canelones, Uruguay.
Photosynth Res. 2021 Dec;150(1-3):97-115. doi: 10.1007/s11120-020-00721-2. Epub 2020 Feb 18.
The photosynthesis process is determined by the intensity level and spectral quality of the light; therefore, leaves need to adapt to a changing environment. The incident energy absorbed can exceed the sink capability of the photosystems, and, in this context, photoinhibition may occur in both photosystem II (PSII) and photosystem I (PSI). Quantum yield parameters analyses reveal how the energy is managed. These parameters are genotype-dependent, and this genotypic variability is a good opportunity to apply mapping association strategies to identify genomic regions associated with photosynthesis energy partitioning. An experimental and mathematical approach is proposed for the determination of an index which estimates the energy per photon flux for each spectral bandwidth (Δ) of the light incident (QI index). Based on the QI, the spectral quality of the plant growth, environmental lighting, and the actinic light of PAM were quantitatively very similar which allowed an accurate phenotyping strategy of a rice population. A total of 143 genomic single regions associated with at least one trait of chlorophyll fluorescence were identified. Moreover, chromosome 5 gathers most of these regions indicating the importance of this chromosome in the genetic regulation of the photochemistry process. Through a GWAS strategy, 32 genes of rice genome associated with the main parameters of the photochemistry process of photosynthesis in rice were identified. Association between light-harvesting complexes and the potential quantum yield of PSII, as well as the relationship between coding regions for PSI-linked proteins in energy distribution during the photochemical process of photosynthesis is analyzed.
光合作用过程由光的强度水平和光谱质量决定；因此，叶片需要适应不断变化的环境。吸收的入射能量可能超过光系统的汇能力，在这种情况下，光系统II（PSII）和光系统I（PSI）都可能发生光抑制。量子产率参数分析揭示了能量是如何管理的。这些参数依赖于基因型，这种基因型变异性是应用图谱关联策略来识别与光合作用能量分配相关的基因组区域的好机会。本文提出了一种实验和数学方法来确定一个指数，该指数估计入射光每个光谱带宽（Δ）的每光子通量能量（QI指数）。基于QI，植物生长的光谱质量、环境光照和PAM的光化光在数量上非常相似，这使得对水稻群体进行准确的表型分析策略成为可能。总共鉴定出143个与至少一种叶绿素荧光性状相关的基因组单区域。此外，第5号染色体聚集了这些区域中的大部分，表明该染色体在光化学过程的遗传调控中的重要性。通过全基因组关联研究（GWAS）策略，鉴定出32个与水稻光合作用光化学过程主要参数相关的水稻基因组基因。分析了捕光复合体与PSII潜在量子产率之间的关联，以及光合作用光化学过程中PSI连接蛋白编码区在能量分布中的关系。
