Narrowing down the targets for yield improvement in rice under normal and abiotic stress conditions via expression profiling of yield-related genes

Abstract

Background: Crop improvement targeting high yield and tolerance to environmental stresses has become the need of the hour. Yield improvement via breeding or gene pyramiding aiming comprehensive incorporation of the agronomically favored traits requires an in-depth understanding of the molecular basis of these traits. The present study describes expression profiling of yield-related genes in rice with respect to different developmental stages and various abiotic stress conditions.

Results: Our analysis indicates developmental regulation of the yield-related genes pertaining to the genetic reprogramming involved at the corresponding developmental stage. The gene expression data can be utilized to specifically select particular genes which can potentially function synergistically for enhancing the yield while maintaining the source-sink balance. Furthermore, to gain some insights into the molecular basis of yield penalty during various abiotic stresses, the expression of selected yield-related genes has also been analyzed by qRT-PCR under such stress conditions. Our analysis clearly showed a tight transcriptional regulation of a few of these yield-related genes by abiotic stresses. The stress-responsive expression patterns of these genes could explain some of the most important stress-related physiological manifestations such as reduced tillering, smaller panicles and early completion of the life cycle owing to reduced duration of vegetative and reproductive phases.

Conclusions: Development of high yielding rice varieties which maintain their yield even under stress conditions may be achieved by simultaneous genetic manipulation of certain combination of genes such as LRK1 and LOG, based on their function and expression profile obtained in the present study. Our study would aid in investigating in future, whether over-expressing or knocking down such yield-related genes can improve the grain yield potential in rice.

Figure 1

Classification of genes regulating yield-traits. Pie-chart showing distribution of the yield-related genes in various classes on the basis of cellular functions predicted for the encoded proteins as per RGAP7 – Rice genome database. As is evident from the chart, more than half of the genes belong either to the class of transcription factors or signaling proteins.

Figure 2

Developmental expression profile of various functional classes of yield-related genes. Heat maps show microarray-based developmental expression profile based on hierarchical clustering of different classes of yield-related genes viz. genes controlling, (A) panicle development, (B) rate of spikelet formation, (C) duration of panicle differentiation, (D) Tillering, (E) grain weight, and (F) plant height. The nine physiologically distinct developmental stages in which expression of these genes was analyzed are – germination, seedling, tillering, stem elongation, booting, heading, flowering, milk, and dough; as shown at the top of the heat map. The heat maps were generated using values for log₂-transformed mean signal intensities on Affymetrix 51 K array for each of the genes in respective developmental stages. Clustering in the heat maps is based on average linkage method and Pearson correlation distance metric. Color bar at the bottom represents scale for log₂ expression (signal intensity).

Figure 3

Microarray-based expression profile of yield-related genes under different abiotic stress conditions. Heat map shows expression profile based on hierarchical clustering of various yield-related genes under different stress-conditions viz. cold, drought, salt, and heat. Color bar at the bottom represents scale for log₂ fold change in expression. For hierarchical clustering in the heat map, weighted average linkage method using Pearson correlation as the distance metric (scale shown at the top left of the heat map) was used. Eight of the genes viz. D3, LRK1, OsEATB, RCN1, LOG, DEP1, SP1, and OsSPL14 were found to be significantly regulated in one or more abiotic stresses.

Figure 4

qRT-PCR confirms altered expression of few yield-related genes under abiotic stress conditions. Histograms (A-H) depict fold change (log₂ scale) in expression of stress-regulated yield-related genes viz. (A) D3, (B) LRK1, (C) OsEATB, (D) RCN1, (E) OsSPL14, (F) LOG, (G) DEP1, (H) SP1; under different abiotic stress conditions – cold, drought, salinity, and heat as obtained via qRT-PCR. For expression analysis by qRT-PCR, 10 day old seedlings of IR64 variety (a moderately sensitive cultivar) of rice were subjected to stress treatment for 6 hours followed by RNA isolation, first strand cDNA synthesis and real-time PCR. Error bars show standard deviation. (I) Heat map generated on the basis of above changes in gene expression using average linkage hierarchical clustering with Pearson correlation as the distance metric. Color bar represents scale for log₂ fold change in expression.

Figure 5

In silico analysis of the putative promoter region of stress-regulated ‘yield-related’ genes of rice. Diagram shows the approximate positions of putative stress-related cis-regulatory elements present in the ~1 kb upstream region of various stress-regulated ‘yield’ genes of rice as predicted by PlantCARE database. Various stress-related elements viz. Myb-binding site (MBS), Anoxia response element (ARE), Salicylic acid response element (SARE), Heat shock element (HSE), and ABA-response elements (ABRE) are represented by different shapes as depicted above.