Dissecting the phenotypic components and genetic architecture of maize stem vascular bundles using high‐throughput phenotypic analysis

Abstract

High-throughput phenotyping is increasingly becoming an important tool for rapid advancement of genetic gain in breeding programmes. Manual phenotyping of vascular bundles is tedious and time-consuming, which lags behind the rapid development of functional genomics in maize. More robust and automated techniques of phenotyping vascular bundles traits at highthroughput are urgently needed for large crop populations. In this study, we developed a standard process for stem micro-CT data acquisition and an automatic CT image process pipeline to obtain vascular bundle traits of stems including geometry-related, morphology-related and distribution-related traits. Next, we analysed the phenotypic variation of stem vascular bundles between natural population subgroup (480 inbred lines) based on 48 comprehensively phenotypic information. Also, the first database for stem micro-phenotypes, MaizeSPD, was established, storing 554 pieces of basic information of maize inbred lines, 523 pieces of experimental information, 1008 pieces of CT scanning images and processed images, and 24 192 pieces of phenotypic data. Combined with genome-wide association studies (GWASs), a total of 1562 significant single nucleotide polymorphism (SNPs) were identified for 30 stem micro-phenotypic traits, and 84 unique genes of 20 traits such as VBNum, VBAvArea and PZVBDensity were detected. Candidate genes identified by GWAS mainly encode enzymes involved in cell wall metabolism, transcription factors, protein kinase and protein related to plant signal transduction and stress response. The results presented here will advance our knowledge about phenotypic trait components of stem vascular bundles and provide useful information for understanding the genetic controls of vascular bundle formation and development.

Responses of Maize Internode to Water Deficit Are Different at the Biochemical and Histological Levels

“…This maintenance of the value for heritability for most of the chemical traits goes along with the fact that biochemical traits responded similarly each year. Under I scenario, heritability of histological trait is in accordance with what is reported (rind thickness h 2 = 58% in Mazaheri et al, 2019; bundle-related traits h 2 from 12.8 to 83.6% in Zhang et al, 2020). However, histological traits were only impacted by NI scenario in 2014 but not in 2015 (see Figure 1 and section "Discussion").…”

“…These authors also detected QTL for PCAest at the top of chromosome 2 and on chromosome 4, positions that colocalize with QTL for PCAest mapped in our study. The 1-Hist cluster did not colocalize with any association found by Zhang et al (2020) using a panel of 482 lines and computed tomography (Du et al, 2016). Nevertheless, the QTL for Bundle_number on chromosome 1 at 61.4 Mb colocalized with some of their associations.…”

“…A number of QTL for vascular bundle system or stem aerenchymas have been mapped in plants of agronomical interest such as in tomato (Coaker et al, 2002), wheat (Sang et al, 2010), rice (Sasahara et al, 1999;Zhang et al, 2002;Cui et al, 2003;Bai et al, 2012), and in sorghum (Casto et al, 2018). In maize, the genetic determinism of histological traits has also recently been investigated (Huang et al, 2016;Mazaheri et al, 2019;Zhang et al, 2020). Here, the use of FASGA-stained cross section allowed us to delimit the rind and the pith fractions and to differentiate level of lignification in the different fractions of maize internode.…”

Darkfield and Fluorescence Macrovision of a Series of Large Images to Assess Anatomical and Chemical Tissue Variability in Whole Cross-Sections of Maize Stems

“…A significant inverse correlation was observed between the vascular bundle size and density. Heckwolf et al (2015) and Zhang et al (2021) also found variations in stem diameter as well as in the area of the rind and pith of the inbred lines they analysed. Zhang et al (2021) further analysed the variation in vascular bundle traits and reported wide phenotypic variations in vascular bundle size, number, and distribution density.…”

“…Once tissues are segmented, it is a straightforward process to measure the rind thickness, pith area, vascular bundle area or vascular bundle size or shape. These descriptors were related to stem lodging ( Zhang et al, 2018 ), developmental stages ( Zhang et al, 2020 ), and water stress ( Legland et al, 2017 ; El Hage et al, 2018 ) or used to analyse the phenotypic variation between lines ( El Hage et al, 2018 ; Zhang et al, 2021 ). In addition to tissue segmentation, Devaux and Legland (2014) proposed applying grey-level granulometry using morphological closings to directly extract cell size distributions from grey-level images.…”