Arabidopsis Seed Content QTL Mapping Using High-Throughput Phenotyping: The Assets of Near Infrared Spectroscopy

Abstract

Seed storage compounds are of crucial importance for human diet, feed and industrial uses. In oleo-proteaginous species like rapeseed, seed oil and protein are the qualitative determinants that conferred economic value to the harvested seed. To date, although the biosynthesis pathways of oil and storage protein are rather well-known, the factors that determine how these types of reserves are partitioned in seeds have to be identified. With the aim of implementing a quantitative genetics approach, requiring phenotyping of 100s of plants, our first objective was to establish near-infrared reflectance spectroscopic (NIRS) predictive equations in order to estimate oil, protein, carbon, and nitrogen content in Arabidopsis seed with high-throughput level. Our results demonstrated that NIRS is a powerful non-destructive, high-throughput method to assess the content of these four major components studied in Arabidopsis seed. With this tool in hand, we analyzed Arabidopsis natural variation for these four components and illustrated that they all displayed a wide range of variation. Finally, NIRS was used in order to map QTL for these four traits using seeds from the Arabidopsis thaliana Ct-1 × Col-0 recombinant inbred line population. Some QTL co-localized with QTL previously identified, but others mapped to chromosomal regions never identified so far for such traits. This paper illustrates the usefulness of NIRS predictive equations to perform accurate high-throughput phenotyping of Arabidopsis seed content, opening new perspectives in gene identification following QTL mapping and genome wide association studies.

Improving seed oil and protein content in Brassicaceae: some new genetic insights from Arabidopsis thaliana

“…4), suggesting that seed filling in these species is highly constrained and that manipulating both components independently may be difficult. This was confirmed by QTL/GWAS studies in these species, in which oil and protein QTL often co-localise but display inverse effect, as expected due to balance between the two main compounds of the seed (Chung et al, 2003;Nichols et al, 2006;Bouchet et al, 2014;Hwang et al, 2014;Jasinski et al, 2016). In addition, some attempts to separate oil and protein QTL in soybean were unsuccessful (Chung et al, 2003;Nichols et al, 2006), reinforcing the hypothesis that the same genes are controlling both traits.…”

“…In parallel, a fast, accurate, and high throughput method, based on near infrared spectrometry (NIRS), to measure Arabidopsis seed oil, protein, carbon and nitrogen contents was developed at IJPB (Jasinski et al, 2016). The development of a NIRS model consists in correlating the NIRS spectra of 100-150 samples with their actual compound content obtained by a reference method, in general labour intensive and timeconsuming.…”

“…However, some results are not in complete agreement with this assertion. Indeed, a SNP at which one allele was associated with both higher protein and oil content was identified in soybean (Hwang et al, 2014) and QTL specific of oil content or protein content were identified in rapeseed as well as in Arabidopsis (Bouchet et al, 2014;Jasinski et al, 2016). These QTL are of great interest for improving both traits independently.…”

ATR-FTIR Microspectroscopy Brings a Novel Insight Into the Study of Cell Wall Chemistry at the Cellular Level

“…Cell wall composition may be determined by prediction equations based on near-infrared absorbance spectra of ground plant materials. This technique has been successfully used to predict cell wall composition in Arabidopsis thaliana (Jasinski et al, 2016), forage crops (Fairbrother and Brink, 1990;Molano et al, 2016;Baldy et al, 2017;Li et al, 2017), rice (Huang et al, 2017) and poplar (Gebreselassie et al, 2017). Mid-infrared (MIR) spectroscopy is complementary to predictive near-infrared (NIR) spectroscopy.…”

Prediction Model of the Key Components for Lodging Resistance in Rapeseed Stalk Using Near-Infrared Reflectance Spectroscopy (NIRS)

“…e PLSR approach was used on the full spectra to model NIR spectroscopy data [12]. NIRS predictive equations were used to provide accurate high-throughput phenotyping of seed content, opening new perspectives in gene identification following QTL mapping and genome-wide association studies [13].…”