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. 2017 Jul 21;12(7):e0180670.
doi: 10.1371/journal.pone.0180670. eCollection 2017.

Proteomics analysis reveals a dynamic diurnal pattern of photosynthesis-related pathways in maize leaves

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Proteomics analysis reveals a dynamic diurnal pattern of photosynthesis-related pathways in maize leaves

Dan Feng et al. PLoS One. .

Abstract

Plant leaves exhibit differentiated patterns of photosynthesis rates under diurnal light regulation. Maize leaves show a single-peak pattern without photoinhibition at midday when the light intensity is maximized. This mechanism contributes to highly efficient photosynthesis in maize leaves. To understand the molecular basis of this process, an isobaric tag for relative and absolute quantitation (iTRAQ)-based proteomics analysis was performed to reveal the dynamic pattern of proteins related to photosynthetic reactions. Steady, single-peak and double-peak protein expression patterns were discovered in maize leaves, and antenna proteins in these leaves displayed a steady pattern. In contrast, the photosystem, carbon fixation and citrate pathways were highly controlled by diurnal light intensity. Most enzymes in the limiting steps of these pathways were major sites of regulation. Thus, maize leaves optimize photosynthesis and carbon fixation outside of light harvesting to adapt to the changes in diurnal light intensity at the protein level.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Generation of iTRAQ proteomics data.
(A) Venn diagram of identified proteins. The numbers indicate the counts of identified maize leaf proteins with differential and steady levels. (B) The distribution of the fold-changes of the identified proteins.
Fig 2
Fig 2. KEGG pathway analysis of the identified proteins.
(A) Identified proteins with steady levels are enriched in diverse KEGG pathways. (B) The most significantly enriched pathways corresponding to the identified proteins exhibiting differential levels.
Fig 3
Fig 3. Dendrogram heat map of differential protein levels.
(A) Clustering of proteomics data based on time points. Note that the data corresponding to 9 o’clock contribute major differences. (B) Clustering of proteomics data based on differential proteins.
Fig 4
Fig 4. The k-means clustering of differentially expressed proteins.
Blue lines indicate the centroid of the k-means in each group. Red lines indicate the dynamic patterns level of proteins in each group. x-axis is the sampling time points used for proteomics analysis.
Fig 5
Fig 5. Expression levels of 15 selected genes that are related to photosynthesis and carbon metabolism.
The expression levels of each gene are presented relative to the first time point (i.e., 6 AM). Values are the means ±standard error (SE) of three independent experiments with at least three replicates each.

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