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

Abstract

Maize feeding value is strongly linked to plant digestibility. Cell wall composition and structure can partly explain cell wall digestibility variations, and we recently showed that tissue lignification and lignin spatial distribution also contribute to cell wall digestibility variations. Although the genetic determinism of digestibility and cell wall composition has been studied for more than 20 years, little is available concerning that of tissue lignification. Moreover, maize yield is negatively impacted by water deficit, and we newly highlighted the impact of water deficit on cell wall digestibility and composition together with tissue lignification. Consequently, the aim of this study was to explore the genetic mechanisms of lignin distribution in link with cell wall composition and digestibility under contrasted water regimes. Maize internodes from a recombinant inbred line (RIL) population grown in field trials with contrasting irrigation scenarios were biochemically and histologically quantified. Results obtained showed that biochemical and histological traits have different response thresholds to water deficit. Histological profiles were therefore only modified under pronounced water deficit, while most of the biochemical traits responded whatever the strength of the water deficit. Three main clusters of quantitative trait locus (QTL) for histological traits were detected. Interestingly, overlap between the biochemical and histological clusters is rare, and one noted especially colocalizations between histological QTL/clusters and QTL for p-coumaric acid content. These findings reinforce the suspected role of tissue p-coumaroylation for both the agronomic properties of plants as well as their digestibility.

Precise control of water stress in the field reveals different response thresholds for forage yield and digestibility of maize hybrids

“…These results are consistent with previous work on maize inbred lines where water stress was found to have similar effects on lignin content and digestibility ( El Hage et al., 2018 ; Virlouvet et al., 2019 ; El Hage et al., 2021 ), as well as, results observed in other grasses ( Emerson et al., 2014 ; Sanaullah et al., 2014 ; Perrier et al., 2017 ). What is striking about the results of maize hybrids compared to those of maize inbred lines is the low variation in lignin content independent of water conditions.…”

“…New targets or combinations of targets need to be found. These could be new biochemical traits such as p -coumaric acids or histological traits ( Méchin et al., 2005 ; Zhang et al., 2011 ; El Hage et al., 2021 ; Zhang, 2021 ; Lopez-Marnet et al., 2022 ) to study the localization of lignified tissue by FASGA staining of internode cross section.…”

A robust and efficient automatic method to segment maize FASGA stained stem cross section images to accurately quantify histological profile

“…Overall and again, the presented workflow allowed to faithfully and automatically segmented FASGA stained maize internode cross section, allowing the identification of variation (even subtle) between internodes from different genotypes. This underlies the fact that this analytical tool is therefore efficient and ideal for its use in selection and/or genetics studies [19,27,48]. Indeed, using this workflow allowed us to pinpoint obvious variation between histological profiles of F4 and F7 previously reported (Barrière et al, 2017) but it also allowed us to highlight more subtle variations such as the presence of more schlerenchymatous tissues of F7 bundles compared to the bundles of F4, corroborating also the fact that F4 is more easily degradable than F7.…”

“…The histology of maize stems was also largely revisited recently taking advantages of the development of microscopy, combined with automated image analysis workflows. To resolve histological traits, many strategies have been developed, for example, using a dark background with specific fluorescence filters [5,13], fluorescence from safranin stained stem sections [14], mass spectrometry imaging [15], X-ray microcomputed tomography [16][17][18][19][20], light background with flatbed document scanner [21], Maüle staining [12,22], phloroglucinol staining [23,24], or FASGA (Fucsina, Alcian blue, Safranina, Glicerina and Aqua) staining [12,[25][26][27]. These methods allowed the exploration of large sets of samples.…”