DOI: 10.5061/dryad.qv9s4mwk2
title: Data from: Glutaredoxin regulation of primary root growth confers early drought stress tolerance in pearl millet

description: Seedling root traits impact plant establishment under challenging
 environments. Pearl millet is one of the most heat- and drought-tolerant
 cereal crops that provides a vital food source across the sub-Saharan
 Sahel region. Pearl millet’s early root system features a single
 fast-growing primary root which we hypothesize is an adaptation to the
 Sahelian climate. Using crop modelling, we demonstrate that early drought
 stress is an important constraint in agrosystems in the Sahel where pearl
 millet was domesticated. Furthermore, we show that increased pearl millet
 primary root growth is correlated with increased early water stress
 tolerance in field conditions. Genetics including GWAS and QTL approaches
 identify genomic regions controlling this key root trait. Combining gene
 expression data, re-sequencing and re-annotation of one of these genomic
 regions identified a glutaredoxin-encoding gene PgGRXC9 as the
 candidate stress resilience root growth regulator. Functional
 characterization of its closest Arabidopsis
 homolog AtROXY19 revealed a novel role for this
 glutaredoxin (GRX) gene clade in regulating cell elongation. In summary,
 our study suggests a conserved function for GRX genes in conferring root
 cell elongation and enhancing resilience of pearl millet to its Sahelian
 environment. 
description: Field experiments were
 performed at the CNRA station (Centre National de Recherche Agronomique)
 of the Institut Sénégalais des Recherches Agricoles (ISRA) in Bambey,
 Senegal (14.42°N, 16.28°W), during the dry season of 2018 and 2020 to
 fully control irrigation. Fields are composed of deep sandy soil with low
 levels of clay and silt (12%) and organic matter (0.4%). Clay and silt
 content increase with soil depth from 10.2% in the 0 to 0.2 m layer to
 13.3% in the 0.8 to 1.2 m layer. Experiments were set up using a complete
 randomized block design with 4 plots per variety, each composed of 6 rows
 6.3 m-long with 0.9 m between plants and 0.9 m between rows (42
 plants/plot). Irrigation was provided after sowing (30 mm of water) to
 allow seeds to germinate and was followed by a period of 42 days of water
 withholding to impose seedling drought stress. Thinning was performed 15
 days after sowing to conserve a single plant per planting hole.
 Fertilization (NPK) following standard recommendation of 150 kg
 ha<sup>-1</sup> NPK (15-15-15) was applied to the entire trial
 after sowing and before irrigation. Fields were maintained free of weeds.
 Plant height was measured at 42 days after sowing. Stay-green trait
 expressed as the percentage of green leaves compared to the total number
 of leaves was estimated on 3 plants per plot at 42 days after sowing.
 Survival rate was measured as the percentage of surviving plants at 42
 days after sowing in each plot compared to the initial number of plants
 that had emerged. Photosynthesis parameters (FvFm: maximum quantum
 efficiency of photosystem II and PI: performance index of photosynthesis)
 were measured on three plants per plot at 32 days after sowing in both
 2018 and 2020 using a Handy Pea chlorophyll fluorometer (Hansatech
 Instruments Ltd.). Plants were
 phenotyped for primary root length with a paper-based hydroponic system as
 previously described (Passot et al., 2016). Seeds were surface-sterilized and
 pre-germinated in Petri dishes, transferred into pouches 24 hours after
 germination at a density of 3 seeds per paper and then maintained in a
 growth room with a 14-hour photoperiod (28°C during day and 24°C during
 night). Pictures of the root systems were taken 6 days after germination
 with a D5100 DSLR camera (Nikon) at a resolution of 16 M pixels. The
 camera was fixed on a holder to maintain the same distance between the
 lens and each root system. Primary root lengths were measured using
 RootNav (Pound et al.,
 2013). 
 Statistical analyses were
 performed using R version 4.0.2 5 (R core team, 2018.). Principal component analyses were performed
 using the prcomp() function. Pearson’s correlation analyses were performed
 using the corr() function within the ggcorrplot package. The variance of
 each variable was partitioned into components attributable to the
 genotypic (line) and year in interaction with block as additional factor
 using an analysis of variance (aov() function in the agricolae package).
 Adjusted means of the variables for the different lines across the two
 experiments were further calculated using the least-squares means
 lsmeans() function (lsmeans package).
description: Any software able to open a tab file (Microsoft Excel,
 Numbers).