Comparative Analysis of Phenolic Compound Characterization and Their Biosynthesis Genes between Two Diverse Bread Wheat (Triticum aestivum) Varieties Differing for Chapatti (Unleavened Flat Bread) Quality.

Monica Sharma,Ankita Mishra,Anuradha Singh,Joy Roy,Sanjay Jachak,Jagdeep Singh,Sukhvinder P Singh,Rajat Sandhir,Pankaj Kumar

doi:10.3389/fpls.2016.01870

Abstract

Phenolic compounds (PCs) affect the bread quality and can also affect the other types of end-use food products such as chapatti (unleavened flat bread), now globally recognized wheat-based food product. The detailed analysis of PCs and their biosynthesis genes in diverse bread wheat (Triticum aestivum) varieties differing for chapatti quality have not been studied. In this study, the identification and quantification of PCs using UPLC-QTOF-MS and/or MS/MS and functional genomics techniques such as microarrays and qRT-PCR of their biosynthesis genes have been studied in a good chapatti variety, “C 306” and a poor chapatti variety, “Sonalika.” About 80% (69/87) of plant phenolic compounds were tentatively identified in these varieties. Nine PCs (hinokinin, coutaric acid, fertaric acid, p-coumaroylqunic acid, kaempferide, isorhamnetin, epigallocatechin gallate, methyl isoorientin-2′-O-rhamnoside, and cyanidin-3-rutinoside) were identified only in the good chapatti variety and four PCs (tricin, apigenindin, quercetin-3-O-glucuronide, and myricetin-3-glucoside) in the poor chapatti variety. Therefore, about 20% of the identified PCs are unique to each other and may be “variety or genotype” specific PCs. Fourteen PCs used for quantification showed high variation between the varieties. The microarray data of 44 phenolic compound biosynthesis genes and 17 of them on qRT-PCR showed variation in expression level during seed development and majority of them showed low expression in the good chapatti variety. The expression pattern in the good chapatti variety was largely in agreement with that of phenolic compounds. The level of variation of 12 genes was high between the good and poor chapatti quality varieties and has potential in development of markers. The information generated in this study can be extended onto a larger germplasm set for development of molecular markers using QTL and/or association mapping approaches for their application in wheat breeding.

Highlights

Bread wheat (Triticum aestivum L.) is an important cereal crop whose flour is processed into several end-use food products such as bread, chapatti, pasta, etc (Rao et al, 1986; Shewry, 2009)
Tentative Identification of Phenolic Compounds on UPLC-QTOF-molecular ions (MS) in Good and Poor Chapatti (Unleavened Flat Bread) Quality Bread Wheat Varieties. Both free and bound forms of Phenolic compounds (PCs) from six samples of the two diverse wheat varieties for chapatti quality were fractionated, and the bound PCs were further fractionated into alkali-labile and acid-labile components by successive alkaline and acid treatments
The characterization of PCs using modern analytical tools such as UPLC-QTOF-MS or MS/MS and functional genomics analysis of their biosynthesis genes in diverse bread wheat varieties differing for chapatti quality have not been studied

Summary

Introduction

Bread wheat (Triticum aestivum L.) is an important cereal crop whose flour is processed into several end-use food products such as bread, chapatti (unleavened flat bread), pasta, etc (Rao et al, 1986; Shewry, 2009). It is presumed that they can affect the other types of end-use food products such as chapatti (unleavened flat bread), which is globally recognized wheat-based food product. The detailed analysis of PCs and their biosynthesis genes in diverse bread wheat (T. aestivum) varieties differing for chapatti quality have not been studied. The integrated approaches of analytical tools such as UPLC-QTOF-MS and -MS/MS and functional genomics techniques such as microarrays and qRTPCR were used for the identification and quantification of PCs and quantitative expression analysis of their biosynthesis genes in two diverse bread wheat varieties differing for chapatti quality. The information generated in this study can be extended onto a larger wheat germplasm set for development of molecular markers using QTL and/or association mapping approaches for their application in wheat breeding

Methods

Results

Discussion

Conclusion