DOI: 10.25338/b8h04n
title: Root and shoot variation in relation to potential intermittent drought adaptation of Mesoamerican wild common bean (Phaseolus vulgaris L.)

description: • Background Wild crop relatives have been potentially subjected to
 stresses on an evolutionary time scale prior to domestication. Among these
 stresses, drought is one of the main factors limiting crop productivity
 and its impact is likely to increase under current scenarios of global
 climate change. We sought to determine to what extent wild common bean
 (Phaseolus vulgaris) exhibited adaptation to drought stress, whether this
 potential adaptation is dependent on the climatic conditions of the
 location of origin of individual populations, and to what extent
 domesticated common bean reflects potential drought adaptation. • Methods
 An extensive and diverse set of wild beans from across Mesoamerica, along
 with a set of reference Mesoamerican domesticated cultivars, were
 evaluated for root and shoot traits related to drought adaptation.
 A water deficit experiment was conducted by growing each genotype
 in a long transparent tube in greenhouse conditions so that root growth,
 in addition to shoot growth, could be monitored. • Results Phenotypic and
 landscape genomic analyses, based on single-nucleotide polymorphisms,
 suggested that beans originating from central and north-west Mexico and
 Oaxaca, in the driest parts of their distribution, produced more biomass
 and were deeper-rooted. Nevertheless, deeper rooting was correlated with
 less root biomass production relative to total biomass. Compared with wild
 types, domesticated types showed a stronger reduction and delay in growth
 and development in response to drought stress. Specific genomic regions
 were associated with root depth, biomass productivity and drought
 response, some of which showed signals of selection and were previously
 related to productivity and drought tolerance. • Conclusions The drought
 tolerance of wild beans consists in its stronger ability, compared with
 domesticated types, to continue growth in spite of water-limited
 conditions. This study is the first to relate bean response to drought to
 environment of origin for a diverse selection of wild beans. It provides
 information that needs to be corroborated in crosses between wild and
 domesticated beans to make it applicable to breeding programmes.
description: <em>Experimental setup</em> To observe root growth
 during the experiment, a screening method (Polanía et al., 2009) was used
 with some modifications in a climate-controlled greenhouse (19–30  °C
 diurnal range, ~1000 µmol photons m−2 s−1 PAR at midday) in the Orchard
 Park Greenhouse facilities at the University of California-Davis
 (Fig. 3A). We used transparent tubes of 7.6 cm diameter and 1.2 m length
 made from polyethylene terephthalate (Fig.  3B;
 www.cleartecpackaging.com). The tubes were filled with a 2:1 sand:topsoil
 mix (bulk density of 1.70 g cm−3) up to 1 m high, with a similar amount of
 soil volume and compaction. A  1-cm layer of perlite was placed on the
 surface to reduce water loss through evaporation (Fig. 3B). The tubes were
 arranged in three blocks, each consisting of a wire grid structure 9.6 m
 long by 1.2 m wide, for a plant density of 24.3 plants m−2. The structure
 was covered in plastic (white exterior, black interior; Fig. 3A, B) to
 avoid root exposure to light and overheating. A 1.2-m long bamboo culm was
 placed at the centre of each tube for plant support. 
 The experimental design was a randomized complete block design,
 with two treatments and three replicates per treatment, and one plant per
 block–treatment combination. Blocks were planted on consecutive days. Two
 or three mechanically scarified seeds per genotype were planted in the
 tubes, one or two being later discarded to leave one plant after
 germination. The treatments consisted of full irrigation and irrigation
 withdrawal. For irrigation, the tubes were watered to field capacity at
 the time of planting and initial weight was recorded. Further irrigation
 to field capacity was given to both treatments until the expanded first
 trifoliate leaf stage (between V2 and V3), ~16 d after planting. The
 irrigation was continued in the irrigated treatment until the end of the
 experiment, while the drought treatment received no further irrigation.
 Plants were submitted to water stress for ~18 d. The drought treatment was
 applied to the vegetative stage only, immediately before reproductive
 initiation. This was performed to account for the non-synchronism in
 flowering initiation, especially among wild genotypes. Moreover, this
 vegetative stress represents a common intermittent water deficit in
 tropical and subtropical regions of Mexico and central and northern South
 America. Plant traits After seeding, all tubes were
 monitored on a daily basis for time for emergence (the shoot was visible
 over the perlite layer; stage V1). The number of days to reach V3 stage
 was also recorded when the first trifoliate leaf was completely expanded.
 The number of days to reach each developmental stage was counted based on
 the day that the seed was placed in each block. Visual root depth and
 plant height were measured at an interval of 3–5 d (following the sequence
 used to plant each block). The relative amount of chlorophyll was measured
 28 d after planting using a SPAD-502Plus Chlorophyll Meter
 (Konica-Minolta). At the end of the experiment, the above-ground biomass
 was collected, separating the leaves and stems. Leaf area was determined
 with Easy Leaf Area (Easlon and Bloom, 2014). Specific leaf area was
 calculated using six leaf punches with a known area (2.83 cm2 ) of the
 most recently expanded mature leaves (Fig. 3C), dried in a 50 °C oven for
 a week (Cornelissen et al., 2003). At the end of the experiment, the roots
 were washed (Fig. 3E). Nodules and root whorl number were then counted and
 the roots were measured for their final length and dried. The
 environmental variables [Priestley–Taylor α coefficient (PTAC), annual
 temperature and soil bulk density] were extracted from the geographical
 coordinates of the origin of each population using the package raster
 (Hijmans and van Etten, 2016). The PTAC is the ratio of actual to
 potential evapotranspiration, and integrates soil water availability
 assuming similar soil characteristics (Priestley and Taylor, 1972). The
 PTAC was obtained from the CGIAR-CSI database (Trabucco and Zomer 2010)
 (www.cgiar-csi.org), annual mean temperature from WorldClim (Hijmans et 
 al., 2005) (www.worldclim.org) and soil bulk density from SoilGrids (Hengl
 et al., 2014) (www. soilgrids1km.isric.org). All variables were at 1-km
 resolution. Statistical analyses The data were analysed
 using linear mixed models in R (R Development Core Team, 2017). Genotype,
 treatment (irrigated, drought) and their interaction were fixed effects.
 Blocks and their interaction with genotype and drought treatment were
 random effects. Statistical analyses were performed with the lme4 package
 (Bates et al., 2015) and lmerTest to determine the significance of effects
 and to calculate least-squares means, using type-III hypothesis testing
 with Satterthwaite approximation for degrees of freedom. We determined R2
 with the piecewiseSEM package (Nakagawa and Schielzeth, 2013). Marginal R2
 is the variance explained by the fixed factors, while conditional R2
 includes fixed and random factors. The coefficient of variation was
 calculated with sjstats (Lüdecke, 2016). Broad-sense heritability was
 estimated with REML (Holland et al., 2003) as h2 =σG 2 /(σG 2 +σGT 2 +σE 2
 ), where σG 2 is the genetic variance, σGT 2 is the variance of the
 genotype × treatment interaction and σE 2 is the variance of the
 experimental error. The correlation between traits was calculated only
 among wild samples and plotted with the corrplot package (Wei and Simko,
 2016). Environmental associations. A Bayesian network analysis was
 performed with bnlearn (Scutari, 2012) to jointly analyse the phenotypic
 and environmental variables and the treatment effect. All continuous
 variables were jointly discretized to preserve the dependence structure
 while bypassing normality assumptions (Nagarajan et al., 2013; Scutari and
 Denis, 2014). The continuous variables were discretized as multinomials
 with three levels using the hartemik method, and the treatment was left as
 a binomial. Structure learning was performed with a score base structure
 using the TABU greedy search. Average bootstrapping (10 000 iterations)
 with the Bayesian Dirichlet equivalent (bde) was used to obtain a
 consensus network. The nodes from phenotype towards environmental
 variables and treatment were blacklisted to improve stability. The trait
 ‘root whorl number’ was excluded due to the small phenotypic range, which
 did not allow proper discretization of the rest of the traits. To evaluate
 the variation between genetic groups, the phenotypic and ecological data
 were analysed using linear mixed models in R (R Development Core Team,
 2017). Genetic group (n  =  3), treatment (irrigated, drought) and their
 interaction were considered fixed effects. The accessions were considered
 to belong to the group with the highest ancestry coefficient among the
 three groups. Statistical analyses were performed in the lme4 package
 (Bates et al., 2015). The package lmerTest (Kuznetsova et al., 2015) was
 used to estimate the effect significance using type-III hypothesis testing
 with Satterthwaite approximation for degrees of freedom and least-squares
 mean calculation. Genotyping. DNA was extracted from a random individual
 of each accession using a modified ammonium acetate-based protocol
 (Pallotta et  al., 2003). The 112 selected accessions were genotyped with
 5398 single-nucleotide polymorphism (SNP) markers from the BARCBean6K_3
 BeadChip SNP chip platform (Song et al., 2015) at the USDA-ARS Soybean
 Genomics Improvement Laboratory, Beltsville, MD, USA. After filtering in
 GenomeStudio Module v1.8.4 (Illumina, San Diego, CA, USA), the SNP calling
 was performed automatically and with subsequent manual adjustments; after
 filtering for quality control and a 0.15 Gencall score cutoff, 5186 SNPs
 remained. The same accessions were also subjected to genotyping by
 sequencing (GBS) based on the CviAII enzyme (Ariani et al., 2016). The
 reads were aligned to the G19833 reference genome version 2.1
 [https://phytozome.jgi.doe.gov/pz/portal. html#!info?alias=Org_Pvulgaris
 (S. Jackson, P.  McClean, J. Schmutz, unpubl. res.)] with BWA (Li and
 Durbin, 2009) and filtered with SAMtools (Li et al., 2009) for minimum
 mapping quality of 10 and mean and maximum read depths of 5 and 1000,
 respectively. The joint dataset of the GBS and SNP chips consisted of 11
 447 SNP markers.