Wheat Varietal Differences in Below Ground Biomass Revealed by a Semi-Quantitative Estimation of Wheat Root DNA in Soil Samples

Abstract

Root research on field grown crops is hindered by the difficulty of estimating root biomass in soil. Root washing, the current standard method is laborious and expensive. Biochemical methods to quantify root biomass in soil, targeting species-specific DNA, have potential as a more efficient assay. We combined an efficient DNA extraction method, designed specifically to extract DNA from soil, with well-established quantitative PCR methods to estimate the root biomass of twenty-two wheat varieties grown in field trials over two seasons. We also developed an assay for estimating root biomass for black-grass, a common weed of wheat cultivation. Two robust qPCR assays were developed to estimate the quantity of plant root DNA in soil samples, one specific to wheat and barley, and a second specific to black-grass. The DNA qPCR method was comparable, with high correlations, with the results of root washing from soil cores taken from winter wheat field trials. The DNA qPCR assay showed both variety and depth as significant factors in the distribution of root biomass in replicated field trials. The results suggest that these DNA qPCR assays are a useful, high throughput tool for investigating the genetic basis of wheat root biomass distribution in field grown crops, and the impact of black-grass root systems on crop production.

Full text

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Wheat varietal differences in below ground biomass revealed by a
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semi-quantitative estimation of wheat root DNA in soil samples
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Huw Jones1, Steven Bentley1, Lydia Smith1, Alison Karley2, Tracy Valentine2, Charlotte
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White3, Rhys Ashton4, Lesley Boyd1
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1NIAB, Huntingdon Road, Cambridge, CB3 0LE, UK
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2James Hutton Institute, Invergowrie, Dundee, DD2 5DA, Scotland, UK
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3ADAS Gleadthorpe, Meden Vale, Mansfield, Nottinghamshire, NG20 9PD, UK
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4 Rothamsted Research, Harpenden, AL5 2JQ, UK
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Corresponding author: Huw Jones (huw.jones@niab.com)
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Abstract
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Root research on field grown crops is hindered by the difficulty of estimating root biomass in
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soil. Root washing, the current standard method is laborious and expensive. Biochemical
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methods to quantify root biomass in soil, targeting species-specific DNA, have potential as a
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more efficient assay. We combined an efficient DNA extraction method, designed
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specifically to extract DNA from soil, with well-established quantitative PCR methods to
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estimate the root biomass of twenty-two wheat varieties grown in field trials over two
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seasons. We also developed an assay for estimating root biomass for black-grass, a common
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weed of wheat cultivation.
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Two robust qPCR assays were developed to estimate the quantity of plant root DNA in soil
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samples, one specific to wheat and barley, and a second specific to black-grass. The DNA
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qPCR method was comparable, with high correlations, with the results of root washing from
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soil cores taken from winter wheat field trials. The DNA qPCR assay showed both variety
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and depth as significant factors in the distribution of root biomass in replicated field trials.
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The results suggest that these DNA qPCR assays are a useful, high throughput tool for
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investigating the genetic basis of wheat root biomass distribution in field grown crops, and
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the impact of black-grass root systems on crop production.
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Keywords: Root biomass, wheat, field crops, black-grass, high-throughput
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© 2019 by the author(s). Distributed under a Creative Commons CC BY license.

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1. Introduction
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In the UK wheat is the single largest cereal crop, nationally accounting for 65% of total
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cereal production (DEFRA, 2017). The increasing global demand for wheat means that plant
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breeders are faced with the challenge of improving grain production against a scenario of
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changing environmental conditions, rising costs and dwindling resources. These
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considerations drive global agriculture towards reduced input regimes, particularly a
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decreased dependence upon nitrogen-phosphorus-potassium (NPK) fertilisers. While,
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historically, wheat breeding has focussed on the impact of above ground plant characteristics
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on yield, these challenges now increase the need to understand how root growth and root
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interactions with the soil environment; biological, chemical and physical, work together to
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influence yield (den Herder et al., 2010). Plant architecture genes such as Rht (reduced
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height) increase grain yield through a repartitioning of biomass to the grain, increasing the
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harvest index, but their effect on partitioning of biomass between roots and shoots is less well
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understood. In addition to genetic differences, many agronomic practices are known to
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influence root establishment and biomass development, e.g. position in a crop rotation,
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nitrogen application and timing, cultivation type, seed rate, sowing date and plant growth
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regulator applications (Hoad, 2001; Bayles et al., 2002).
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Root phenotyping is a rapidly developing field (George et al., 2014) with particular attention
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paid to traits that may influence drought resistance (Wasaya et al., 2018) and nitrogen use
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efficiency (Rosolem et al., 2017). Field observations in trench walls are laborious (Carter et
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al., 2019) and observations through buried tubes (mini rhizotrons) require site preparation
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and complex calibrations (Postic et al., 2019). The current standard method of quantify root
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biomass is to wash roots free from the soil and quantify as root length per unit volume of soil
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(White et al., 2015). Image analysis methods aid data capture (Zhu et al., 2011; Bauhus &
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Messier, 1999), but the washing process is laborious, time consuming and hence expensive.
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The results obtained by these methods are informative with regards the proportions of fine to
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coarse roots, but results may not be transferable between different soil types (Kücke et al.,
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1995). Field root phenotyping of wheat, using a ‘core break – root count’ method, showed
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considerable variation for deep root traits (Wasson et al., 2014). Non-invasive geophysical
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methods, such as ground penetrating radar and electrical resistivity tomography, have been
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successful in measuring large tree roots (Butnor et al., 2001, Paglis, 2013). However, these
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procedures are currently less informative for plants with fine root structures, where the root
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dimensions are similar to those of soil aggregates and pores (Amato et al., 2009). However
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root electrical capacitance has been shown to correlate with root mass for barley in
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glasshouse experiments (Dietrich et al., 2013) and electrical resistance tomography has been
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used to measure soil profile drying which is, in turn, a proxy for root activity (Whalley et al.,
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2017).
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The use of rhizotron-based systems for root characterisation are well established (James et
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al., 1985), and being amenable to automation allow for repeated measurements during plant
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development (Lobet & Draye, 2013). However rhizotrons, being artificial environments, are
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somewhat removed from the field environment. Root biomass correlations between rhizotron
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and field were found to be high during the vegetative growth phases, but low during the
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reproductive growth phases (Watt et al., 2013). Allied to rhizotrons are X-ray computed
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tomography (CT) systems capable of visualising detailed root structures in soil. Industrial
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micro-CT systems with resolutions of 500 nm or less (Mooney et al., 2012; Xu et al.,2018),
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coupled with automated systems for sample presentation and data processing (Mairhofer et
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al., 2012), are also a valuable tool for root phenotyping in rhizotrons.
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Quantitative, species-specific DNA detection methods, coupled with robust soil extraction
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techniques, have been deployed to identify and quantify roots in soil. Real-time PCR has
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been used to differentiate between grassland species in mixtures of roots washed from soil
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(Mommer et al., 2008), to quantify root ratios (Zhang et al., 2014) and to measure roots from
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a mixed population of meadow grasses (Riley et al., 2010, Haling et al., 2011; Haling et al.,
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2012). Detecting roots by DNA-based methods is however not straightforward (Mommer et
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al., 2011): soil contains humic acids that are known to inhibit PCR by binding MgCl2, so
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appropriate modification of DNA extraction methods is required. The concentration of plant
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DNA in soil has been shown to decline rapidly after plant death (Riley et al., 2010; Bithell et
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al., 2015; Pierre et al., 2018), therefore the plant DNA in soil samples is largely derived from
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live roots. As roots comprise a small part of the total soil volume the most suitable PCR
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targets are those present at high copy number in the plant genome, e.g. ribosomal DNA
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internal transcribed spacer (rDNA ITS) regions. DNA-based assays targeting rDNA ITS were
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successfully used to assess root development under drought-conditions in Australian wheat
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varieties (Huang et al., 2013), assessing the root health of sugar-cane ratoons (Pierre et al.,
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2018) and investigating root responses to phosphorous fertility status (McDonald et al., 2017)
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Black-grass (Alopecurus myosuroides. Huds) is an annual weed which presents a major
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problem to European cereal growers. Relatively low populations of 8-12 plants m-2 have been
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shown to have a significant impact on wheat grain yields (Naylor, 2008). An efficient method
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by which to measure root development of the crop and the weed is required to understand
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competition for water and nutrients in the field. While partitioning of total root biomass
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between weed and crop species in washed roots can be carried out using a variety of
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techniques (Mommer et al., 2011), including visual observation (Zhang et al., 2018), infra-
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red spectroscopy (Meinen & Rauber, 2015) and biochemical analysis of plant waxes (Dawson
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et al., 2000), species can only be reliably distinguished by sequencing the rDNA ITS region
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(Linder et al, 2000). Species-specific quantitative PCR has been used to quantify root
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biomass of a single species in perennial grass swards (Haling et al, 2012) and to determine
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the ratio of different species within mixed sward samples (Haling et al., 2011).
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In this study we have developed semi-quantitative DNA-based assays able to estimate root
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biomass of field grown wheat varieties and black-grass using root DNA extracted from soil
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core samples. We compared this qPCR assay to the results obtained with standard root
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washing procedures for estimating root biomass from soil cores. The qPCR-assay was then
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used to compare differences in root biomass between wheat varieties, at different depths in
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field trials grown over two seasons. We discuss the power and limitations of this method, and
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outline the potential of this technology as a tool for plant breeders, agronomists and root
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biologists.
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2. Results
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2.1 Development of wheat and black-grass specific qPCR assays for soil extracted DNA
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The soils for which DNA extraction methods were developed had textures described as sandy
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loam, sandy silt loam, silt loam, silty-clay loam, clay loam and fine loam over clay. We found
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the PowerSoil DNA extraction kit yielded DNA of sufficient quantity and quality to carry out
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qPCR, however, the DNA yield was not sufficient to assess DNA concentration or quality on
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an agarose gel. Single copy gene targets did not give reliable PCR results using genomic
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DNA (data not shown), however when PCR was carried out using primers targeting the
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ribosomal internally transcribed spacer (ITS) region amplification products were obtained for
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the majority of soil samples tested. The calibration of the qPCR system showed the expected
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log – linear response between concentration and Ct (cycle threshold). Amplification
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efficiencies were between 0.982 - 1.135 across all plates, with correlation coefficients in the
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range 0.981 – 0.995 over a five decade range of 1000 µg/µl to 0.1 µg/µl. An analysis of
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variance of RBD values obtained from the technical, DNA replications showed no significant
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difference between RBD values (F= 0.13, p = 0.722).
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Wheat primers were tested for specificity against a range of field crops grown in the UK.
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Amplicons were obtained for wheat and barley DNA, but there was no reaction with maize,
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oilseed rape or faba bean DNA. The wheat primers were also tested against black-grass and
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found to produce no amplification. With the black-grass primers amplicons were obtained
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only with black-grass DNA, there was no amplification with wheat and barley DNA. Soil
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extracts for cores taken from an area of bare soil within the 2012 trial site gave no PCR
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amplification with wheat ITS primers.
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2.2 Comparison between the DNA-based and root washing assays
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Root biomass, as measured by the DNA-based PCR assay (RBD; µg dry roots / g air dried
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soil) was compared to root length density (RLD: cm/cm3) at the 4 depths taken through the
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soil profile in the 2012 and 2014 trials (Figure 1; Table 1). High Pearson correlations were
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found in both the 2012 (r = 0.7947; df = 10; p = 0.002) and the 2014 (r = 0.674; df = 22; p <
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0.001) trials, while combining the data from the two seasons gave a value of r = 0.702 (df =
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34, p < 0.001). Examining the wheat varieties independently also showed good correlations
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between RBD and RLD measurements; Alchemy r = 0.918 (df = 2, p = 0.082), Glasgow r =
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0.762 (df =10, p = 0.004), Oakley r = 0.735 (df = 14, p < 0.001) and Viscount r = 0.992 (df =
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2, p = 0.007). The DNA qPCR method therefore provided a good estimate of root biomass,
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even at the lower depths where lower RLDs were found.
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2.3 Comparison of root biomass between wheat varieties and soil depth in the 2012 pilot trial
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A one-way ANOVA of the 2012 RLD data indicated that differences in root content by depth
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were highly significant (F = 182.9; p < 0.001), with RLD values decreasing with soil depth,
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but that differences between varieties were not significant (F = 0.17; p = 0.846). A one-way
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ANOVA of the 2012 RBD data also highlighted significant differences in root biomass by
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depth (F = 6.83; p < 0.003), but not between varieties (F = 1.03; p = 0.375). However, the
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low p value for RBD (p = 0.360) compared to RLD (p = 0.839) suggests that RBD
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measurements on a series of larger field trials might offer a better prospect of discriminating
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among wheat varieties than RLD.
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2.4 Comparison of root biomass between wheat varieties and soil depth in the 2014 and 2015
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trials
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For soil cores sampled from the 2014 and 2015 trials a linear mixed model analysis of RBD
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showed highly significant differences between varieties, depths and the interactions between
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varieties x depth, but no significant difference between years (Table 2). However, a variety x
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year effect was seen, indicating that the root biomass produced by each wheat variety differed
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between the 2014 and 2015 field trials.
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In general, the highest RBD values were found in the upper soil profiles and the lowest
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values at depth, with all 22 wheat varieties tested (Figure 2, Supplementary Table 2). At each
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depth RBD varied between 0.7-721 µg /g (0-250 mm), 0.9 - 394 µg /g (250-500 mm), 0.0 -
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119 µg /g (500-750 mm) and 0.0 - 42.3 µg /g (750-1000 mm). More than 50% of the
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measured RBD was in the upper 500 mm of the soil profile in all, but two of the plots
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sampled in each field trial (data not shown). The proportion of RBD in the upper 500 mm of
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the soil profile averaged 79% in 2014 and 88% in 2015. Regression analysis showed that a
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quadratic fit best described the variation in RBD with depth, for all varieties. The regression
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equations were integrated and used to calculate D50 and D95 by the method of Schenk and
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Jackson, (2005). The values for D50 had a range of 274-620 mm below the soil surface, with a
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mean of 459 mm. The values for D95 had a range of 695-976 mm below the soil surface, with
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a mean of 876 mm. The mean results over two years are shown in Table 3 and the full results
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are given in Supplementary Table 3. The values for D50 and D95 allow rapid identification of
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shallow rooting and deep rooting varieties, and indicate that varieties Norman and SHW Xi19
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/ (Xi19 // SHW-218) >18 are shallow rooting, while varieties Cadenza and Xi 19 are deep
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rooting.
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In the 2014 trial RLD data was only obtained for two of the 22 wheat varieties. Therefore an
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analysis of variation between wheat varieties using the RLD data was not undertaken.
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2.5 Influence of key genetic traits on RDB values
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The varieties under test varied in their seasonal growth habit, in their status at the semi-
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dwarfing, Rht loci, the photoperiod response, Ppd loci and the presence/ absence of the rye
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translocation (1B/1R) (Supplementary Table 1). Highly significant differences (F= 18.67, p <
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0.001) were found in RBD values between varieties with different seasonal growth habits,
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with spring types having the greater average RBD within the soil profile, followed by
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alternative and winter types. Variation at the Rht loci was also associated with variation in the
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RBD phenotype (F= 2.71, p < 0.050), with Rht showing a significant interaction with trial
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year (F = 3.61, p = 0.013). No significant variation in the RBD values was accounted for by
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the presence or absence of the rye translocation (F = 0.47, p = 0.506), or variation at the Ppd
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loci (F = 1.73, p=0.096).
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The variation in RBD values associated with the Rht loci was significant in 2014 (p < 0.001),
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but not in 2015 (p =0.128). In 2014, wheat varieties harbouring wild type alleles and Rht2
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had greater average RBD throughout the soil profile than those harbouring Rht1 and Rht8;
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this trend was not observed in the 2015 data. These observations may be linked to differences
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in the weather conditions at the 2014 and 2015 test sites. In 2014 the winter and spring
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temperatures were uncharacteristically high (anomaly 1.8ºC and 1.6 ºC) relative to the thirty
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year average (1981-2010), while conditions in 2015 were closer to the thirty year average
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(anomaly 0.3ºC and 0.2ºC) (http://www.metoffice.gov.uk/climate/uk/summaries/)
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(Supplementary Table 4).
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2.6 Heritability of the RBD phenotype
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Broad sense heritability for total RBD in the soil profile was calculated as 0.16, while the
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heritability of RBD was 0.11 in the upper 250 mm of the soil profile, 0.21 in the profile at
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250-500 mm depth, 0.00 in the profile at 500-750 mm depth and 0.43 in the profile at 750-
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1000 mm depth. These results suggest that RBD, particularly RBD at depth should be
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amenable to selection by plant breeders.
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2.7 Black-grass observations
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In 2015 soil cores were taken within the wheat trial from areas with ‘low’, ‘moderate’ and
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‘high’ black-grass. Black-grass RBD values were 0.0 µg/ g dry soil in ‘low’ black-grass areas
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(no discernible black-grass foliage observed), between 0.0 and 2.5 µg/ g dry soil in
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‘moderate’ black-grass areas (50 black-grass heads m-2) and between 1.9 and 18.2 µg/ g dry
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soil in ‘high’ black-grass (300 black-grass heads m-2) areas (Table 4). The RBD of wheat
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roots in these soil cores was also measured, being similar to the RBD values obtained in the
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full 2015 wheat variety trial. In the soil cores taken from the ‘high’ density black-grass area,
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over 70% of the black-grass root RBD was in the top 250 mm of the soil profile, while in the
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‘moderate’ density black-grass area, over 90% of the root biomass was in this upper profile.
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Three observations do not allow any conclusions to be drawn on whether ‘high’ black-grass
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densities inhibit wheat root development, but our results show that this technique could be of
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value in larger, crop-weed competition studies.
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3. Discussion
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Traditional root washing methods used to assess root development in field experiments are
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both time consuming and costly. In this study we have developed a robust, semi-quantitative
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PCR method to reliably measure root biomass of wheat and the major weed of cereal crops,
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black-grass, down to soil depths of 1 metre. We show that the qPCR assay can distinguish
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wheat from among most other major agricultural crops, and from black-grass. The ability to
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exclude weed roots from the total root density represents an advance over conventional root
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washing methods, while the ability to quantify black-grass root biomass relative to wheat root
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biomass will be useful in competition experiments to determine the impact of weeds on wheat
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production.
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Despite the inherent variation present within the PCR technology (Karlen et al., 2014), the
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estimate of root biomass as determined by RBD correlated extremely well with classical root
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washing RLD measurements in both the 2012 and 2014 field trials. In general root density
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decreased with soil depth. However the RBD assay did identify distinct differences between
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wheat varieties in root distribution through the soil profile, some varieties from the 22 tested
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being better at producing roots at depth, with a significant interaction between varieties and
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depth being observed in both the 2014 and 2015 field trials. A variety x year effect was also
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observed, indicating that root production was significantly influenced by the different
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climatic and environmental growing conditions prevalent in the 2014 (Burkees Field, silty
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clay loam) and 2015 (Willow Tree Field, silt loam / sandy silt loam) trials.
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Compared with current methods we can see that the RBD assay has both strengths and
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weaknesses. Cores can be taken at any point in the growing season, allowing root biomass
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accumulation in the field to be assessed through-out the growing season. The soils assayed in
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this study had textures described as sandy loam, sandy silt loam, silt loam, silty clay loam,
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clay loam and fine loam over clay, with RBD working equally as well in all these soil types.
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Basically the method can be implemented in any soil that can pass through a mill. Removal
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of roots by washing from heavy soils requires prolonged sample pre-treatment with sodium
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hexametaphosphate solution and use of a hydropneumatic elutriation system (Thivierge et al.,
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2015).
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The RBD assay makes the assumption that the ratio of ribosomal DNA to genomic DNA
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does not differ between wheat varieties or the developmental stage of the plant (Huang et al.,
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2013). For example, in older roots the cortex dies leaving only the stele, thus older, larger
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roots may be under-represented by the RBD assay. Conversely, very fine roots, which are
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difficult to wash from soil samples, may be under-represented in the RLD assay (Sierra et al.,
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2003). Clearly the RBD method does not allow a detailed dissection of root architecture; for
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example rooting angles or the ratio of fine to coarse roots. However, the DNA based method
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does allow root development to be studied in field situations through-out the growing season,
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with processing time being less than that required for soil washing assays.
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Despite the limitations, the RBD assay would allow cost effective (Supplementary Material
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part 5) estimation of root biomass within the soil profile, supporting studies of rooting
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behaviour between different wheat genotypes and an exploration of the effects of differing
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agricultural practices on root development. In developing this RBD assay as a standard
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method to be adopted by the research community we would seek to develop standardised
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calibration materials and agreement on the basis by which results are declared, that would
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allow comparable results to be shared by the root research community.
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4. Materials and methods
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4.1 Wheat variety trial root-soil core sampling
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Soil samples were collected from wheat variety field trials over three growing seasons, soil
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cores being taken from within each variety plot (Table 5). In 2012 a three variety trial was
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grown with one plot per variety. In 2014 and 2015, eighteen wheat varieties and four
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breeders’ lines were grown, with three replicate plots per genotype. The varieties grown in
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each trial are given in Supplementary Table 1. The wheat varieties were planted in a
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randomised complete block field trials design (Supplementary Materials Part 3). In 2012 soil
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cores were also taken from adjacent, uncultivated areas of the site. Soil data for each site was
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taken from the LANDIS Land information system (Landis, 2014; Supplementary Materials
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Part 2).
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Ten soil cores, measuring 1 m depth x 30 mm diameter, were sampled from each 10 x 2 m
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plot in accordance with standardised methods (White et al. 2015). The soil cores were
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sampled when the wheat crop had reached growth stage (GS) 51-65 (Zadoks et al., 1974).
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Five cores were sampled within the rows and five were taken between the rows, in
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accordance with the spatial sampling as proposed by Bengough et al. (2000). The cores were
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divided into four portions, representing 250 mm depth intervals in the soil profile. The four
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sections from the ten plot cores were bulked into a single sample representing a depth
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interval, giving one sample at each of four depths per plot.
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Soil cores were taken in the 2012 pilot trial and a subset of the 2014 trial for both root
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washing estimates of root length density (RLD) and for root biomass DNA (RBD)
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estimations using a semi-quantitative PCR assay developed in this study. Soil cores were
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taken in the 2014 and 2015 trial for RBD analyses. In the 2012 trial, cores were taken for
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RBD and RLD analysis from one replicate plot of each of three varieties; Alchemy, Oakley
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and Viscount, while in the 2014 trials cores were taken from three replicate plots of two
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varieties; Glasgow and Oakley. To assess black-grass root biomass additional cores were
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taken in the 2015 trial from three areas in the ‘discard’ crop surrounding the trial (variety
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Crusoe). These areas were judged by visual inspection as having high, moderate and low
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density black-grass populations: the black-grass population was estimated by counting the
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number of individuals within four quarter m2 quadrats.
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4.2 Wheat varieties assessed for root biomass
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The wheat varieties grown in the 2014 and 2015 trials were selected based on genotypic
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diversity and phenotypic information from rhizotube experiments undertaken on a collection
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of 100 wheat varieties and breeders lines (Greenland et al., 2017). In addition, the two
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breeders lines SHW Xi19 / (Xi19 // SHW-218)>18 and SHW Xi19 / (Xi19 // SHW-218)>19
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were included. These backcross-derived lines from the cross (Xi19 / (Xi19 // SHW-218))
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were each descended from different BC1 plants (plants XS-218>18 and XS218>19,
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respectively). SHW-218 is a synthetic hexaploid wheat supplied by CIMMYT, with the
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published pedigree Ceta / Ae squarrosa (895) (Gosman et al., 2014). Two near-isogenic lines
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(NIL) that harboured variation at the Rht (reduced height) locus in the background of variety
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Mercia were supplied by the Genetic Resources Unit, Norwich, UK. Additional data
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(including seasonality, Rht, presence or absence of the rye translocation 1B/1R and the
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predicted photoperiod response) on these varieties is provided by Alison Bentley (pers.
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Comm; Supplementary Table 1).
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4.3 Extraction of roots from soil samples by root washing
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RLD were carried out at ADAS, Gleadthorpe on the cores sampled in the 2012 field trial, and
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at Rothamsted Research (RRes) on a subset of cores sampled in the 2014 field trial. RLD was
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not measured on the 2015 soil cores. The roots were extracted from the soil cores using a
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standard root washing system (Delta-T Devices Ltd, Burwell, Cambridge) and collected on a
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550 μm wire mesh filter (ADAS) or 500 μm sieve (RRes). Root length was assessed using
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WinRHIZO software (Regent Instruments Inc. Sainte Foy, Qc, Canada) (White et al., 2015).
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Root biomass determined by soil washing were expressed as root length density (RLD),
324
expressed as the length of roots recovered per volume of soil (cm/cm3).
325
4.4 Extraction of DNA from soil samples
326
Soil samples were frozen within three hours of collection and stored at -18°C. Samples were
327
dried at 30°C in a re-circulating oven for a minimum of 72 hours. The dried soil was milled
328
using a Humboldt H4199.5F soil mill fitted with a 2 mm screen. The milled soil was sub-
329
sampled by quartering to yield a laboratory sample. DNA was extracted from two 0.25 g
330
portions of soil using a PowerSoil DNA extraction kit (MO BIO Laboratories, Inc., Carlsbad,
331
USA.) in accordance with the manufacturer’s protocols; thus technical, DNA duplicates were
332
obtained for each milled soil sample. The PowerSoil DNA extraction kit has been reliably
333
reported to achieve DNA yields from soil equivalent to methods used in a commercial testing
334
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laboratory (Haling et al., 2011). While weighing the 0.25g portions of soil we noted the
335
presence of a small number of visible, but not necessarily evenly distributed, root fibres of up
336
to 5mm within the milled soil.
337
4.5 Preparation of root DNA calibration materials
338
We calibrated our RBD assay using DNA taken from lypholised roots of wheat variety Xi19
339
grown in horticultural sand and harvested at growth stage 20-23 (Zadoks et al., 1974). Root
340
material was washed free of sand, rapidly frozen on ‘dry ice’, freeze dried, milled to a
341
powder in a domestic coffee mill and stored at -18oC. DNA was extracted from 100 mg of
342
dried root using the modified Tanksely method (Fulton et al., 1995) and re-suspended in 100
343
µl Tris – EDTA, pH8.0 at 1mg/µl. DNA standards were prepared from this reference DNA as
344
a series of ten-fold dilutions, allowing calibration in a five decade range of 1000 µg/µl to 0.1
345
µg/µl. Black-grass calibration standards were prepared in the same way.
346
4.6 PCR quantification of root DNA in soil samples
347
Primers and fluorescent reporter probes were designed that targeted the wheat internal
348
transcribed spacer region within the 5.8S ribosomal RNA gene (Table 6). The target sequence
349
was acquired from NCBI Genbank AF438186.1 Triticum aestivum (Sharma et al., 2002), and
350
the primers and fluorescent reporter probes were designed using Primer3 (Untergrasser et al.,
351
2012). The primers were tested for specificity by PCR using DNA extracted from wheat,
352
barley, faba bean, maize, oilseed rape and black-grass. The PCR products were visualised on
353
a 1% agarose gel containing ethidium bromide (0.1µg ethidium bromide/ml of gel solution).
354
A black-grass target sequence was acquired from NCBI Genbank KM523760.1 (Soreng et
355
al., 2015), and primers and fluorescent reporter probes designed using Primer3 (Table 6). The
356
black-grass primers and fluorescent reporter probes were tested for specificity using DNA
357
extracted from black-grass, wheat and barley.
358
Wheat root DNA from soil extracts was quantified by real time PCR using an ABI 7900,
359
running triplicate 6 µl reactions comprising 1.0 µl template DNA, 0.5 µl primers-probe
360
solution, with primers and fluorescent reporter probes at 5 mM, 2.5 µl Thermo Fisher
361
Scientific ABsolute Blue qPCR ROX Mix and 2.0 µl water (Thermo Fisher Scientific, 2014).
362
Amplification was carried out using 10 min activation at 95°C , followed by 40 cycles of
363
15sec at 95°C and 60sec at 60°C , monitoring fluorescence at each cycle. The soil DNA
364
extracts were quantified in a series of 15 PCR batches (384 well). The quantity of wheat root
365
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in each extract was calculated using SDS software (version 2.2, Applied Biosystems) with
366
reference to serial dilutions of the reference DNA standard included with every batch. Soil
367
DNA extracts were allocated to plates in plot number order, such that all technical
368
replications of all soil depth samples from a plot were allocated before including extracts
369
from the next plot. The quantity of root DNA (Root Biomass DNA – RBD) in each sample
370
was expressed as wheat root dry weight (μg) per weight of air dried soil (g), rather than
371
describing roots by reference to a quantity of DNA per unit mass of soil.
372
4.7 Data analysis
373
All qPCR data were processed using Applied Biosystems SDS 2.2, and the results collated
374
and analysed in Microsoft Excel. Analysis of variance (ANOVA) was carried out using
375
Genstat 12.1.0.3338, and correlations and regressions using R-stat (version 3.0.1). All
376
statistical analyses were carried out on original data, without prior averaging of technical
377
duplicates. As part of the data quality control process we inspected the technical DNA
378
duplicates for gross errors likely to have arisen from sampling large root fibres in one of the
379
two technical duplicates. Three measurements (out of 1056) were removed that had RBD
380
values greater than 500 µg/g, being at least ten-fold higher than their paired DNA technical
381
replicate sample.
382
Where comparisons were made between estimates of RLD and RBD, correlations were
383
calculated in R-stat. Data from the 2014 and 2015 wheat variety trials were subject to
384
analysis by REML linear mixed model implemented in Genstat using a model:
385
       
Where
 is The RBD of the ith variety in the jth year in the kth field replication in the lth
technical replication; variety, depth and year were treated as fixed effects while
field and technical replication and plate allocation were treated as random effects
When the model was amended to include additional data (a) (e.g. seasonality, Rht etc) the
386
variety term was nested within additional data.
387
      
   
Where
 is The RBD of the ith variety in the jth year in the kth field replication in the lth
technical replication; additional data, variety, depth and year were treated as fixed
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14
effects while field and technical replication and PCR batch were treated as random
effects
388
The RBD data was regressed against the root depth for each variety profile and modelled for
389
the best fit using the ‘poly’ function in R, applying linear, quadratic or cubic models, and
390
selecting the model yielding the lowest residual as the best fit. The coefficients calculated
391
from the results of these regressions were used to generate equations to predict RBD at depth.
392
Integration of these equations allowed calculation of the proportion of RBD within a defined
393
range of soil depths, which in turn allowed prediction of the soil depth containing 50% and
394
95% of all roots (D50 and D95) (Schenk and Jackson, 2005) using ‘solver’ in Microsoft Excel.
395
Estimates of variance were obtained by fitting a linear mixed model in R using the lme4
396
package (Bates et al, 2015) and the model:
397
    
Where
 is The RBD of the ith variety in the jth year in the kth field replication in the
lth technical replication
398
All effects, apart from the mean (µ) were treated as random effects. Variance components
399
associated with the random effects (variety, v; year, y; field replicate, r; technical replicate, t
400
and the error term, e) were estimated using REML as implemented in the lmer function.
401
Broad sense heritabilities were calculated using equations 1 and 2 from Piepho and Möhring
402
(2007):
403


󰇛󰇜
 

404
405
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15
Acknowledgements:
406
We are grateful for funding from the Biotechnology and Biological Sciences Research
407
Council under grant BB/H014381/1, Agriculture and Horticulture Development Board under
408
reference AHDB RD-2008-3575: “New wheat root ideotypes for improved resource use
409
efficiency and yield performance in reduced input agriculture” and funding through the
410
strategic research programme funded by the Scottish Government’s Rural and Environment
411
Science and Analytical Services Division.
412
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Table 1: DNA-based (RBD; µg dry roots / g air dried soil) and root washing assays (RLD:
567
cm/cm3) for wheat varieties in 2012 and 2014 field trials. (Pearson’s correlation between
568
RBD and RLD for all varieties is 0.702 (df = 34, p-value = <0.001))
569
Trial
Rep
Depth
Variety
RBD
RLD
Terrington 2012
Pilot experiment
A
0-250
Alchemy
41.2
3.2
A
250-500
Alchemy
8.4
2.1
A
500-750
Alchemy
5.3
1.4
A
750-1000
Alchemy
0.2
0.6
A
0-250
Oakley
9.1
3.0
A
250-500
Oakley
6.1
1.7
A
500-750
Oakley
1.2
0.9
A
750-1000
Oakley
9.1
0.6
A
0-250
Viscount
40.8
3.2
A
250-500
Viscount
13.2
1.8
A
500-750
Viscount
6.1
1.1
A
750-1000
Viscount
0.9
1.0
Terrington 2014
A
0-250
Glasgow
62.8
6.5
A
250-500
Glasgow
27.4
3.4
A
500-750
Glasgow
7.1
1.1
A
750-1000
Glasgow
2.1
1.1
B
0-250
Glasgow
7.7
4.5
B
250-500
Glasgow
4.6
2.6
B
500-750
Glasgow
3.1
1.3
B
750-1000
Glasgow
5.8
0.8
C
0-250
Glasgow
12.0
4.9
C
250-500
Glasgow
10.4
3.1
C
500-750
Glasgow
2.6
1.6
C
750-1000
Glasgow
2.7
0.8
A
0-250
Oakley
49.4
5.5
A
250-500
Oakley
12.4
2.1
A
500-750
Oakley
26.8
0.9
A
750-1000
Oakley
34.5
0.4
B
0-250
Oakley
54.5
5.9
B
250-500
Oakley
5.6
2.6
B
500-750
Oakley
2.7
1.5
B
750-1000
Oakley
9.4
0.7
C
0-250
Oakley
79.0
5.4
C
250-500
Oakley
52.5
2.5
C
500-750
Oakley
13.2
1.2
C
750-1000
Oakley
1.4
0.5
570
Table 2: Effects of experimental terms on RBD (µg dry roots / g air dried soil) among 22 wheat varieties over two trial
571
years, shown by linear mixed model analysis implemented in REML (d.f. = degress of freedom, F pr, F-statistic
572
probability)
573
Fixed term
Wald statistic
d.f.
F statistic
F pr
Year
0.09
1
0.09
0.759
Depth
300.84
3
100.28
<0.001
Variety
137.58
21
6.55
<0.001
Year.Depth
6.54
3
2.18
0.088
Year.Variety
98.26
21
4.68
<0.001
Depth.Variety
134.80
63
2.14
<0.001
Year.Depth.Variety
90.84
63
1.44
0.012
574
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Table 3: Estimates of the soil depths (mm) containing 50% and 95% of all roots (D50 and D95) for each variety. (A table of
575
D50 and D95 for each year is given in Supplementary Table 4)
576
Depth (mm)
D50
D95
Wheat Variety
Alchemy
-492
-936
Avalon
-597
-952
Beaver
-482
-872
SHW Xi19 / (Xi19 // SHW-218) >18
-274
-738
SHW Xi19 / (Xi19 // SHW-218) >19
-613
-976
Buster
-398
-961
Cadenza
-573
-953
Cappelle Desprez
-451
-827
Glasgow
-305
-907
Hereward
-392
-904
Mercia
-458
-759
Mercia Rht8
-359
-887
Mercia Rht8 D1
-567
-915
Norman
-380
-782
Oakley
-442
-844
Paragon
-514
-812
Rialto
-312
-903
Robigus
-448
-841
Savannah
-453
-695
Soissons
-486
-893
Spark
-488
-966
Xi19
-620
-948
Overall mean
-459
-876
577
578
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21
Table 4: The biomass of wheat and black-grass roots measured at four different depths in the soil profile using the DNA-
579
based assay (RDB), sampled from three black-grass population densities
580
Black-grass population
Depth
Black-grass
Wheat
(mm)
µg/ g soil
µg/ g soil
Low black-grass infestation
0 heads per m2
0-250
0.0
98.9
250-500
0.0
77.9
500-750
0.0
69.3
750-1000
0.0
21.8
Medium black-grass infestation
50 heads per m2
0-250
2.5
32.5
250-500
0.1
16.5
500-750
0.0
49.9
750-1000
0.0
21.5
High black-grass infestation
300 heads per m2
0-250
18.2
42.7
250-500
1.9
8.9
500-750
2.1
3.8
750-1000
2.4
4.8
581
582
Table 5: Wheat field trials sampled for root quantification
583
Site
Year
Trial design
Grid reference
Soil series
Soil texture
Terrington St Clement,
Norfolk (Pilot expt.)
2012
Three varieties in one field
replication
TF 496 226
Wisbech
Coarse Silt
Burkees Field, Eastland Bank,
Walpole St Andrew, Norfolk
2014
Eighteen varieties and four
breeders’ lines in three field
replications
TF 500 184
Blacktoft
Silty Clay loam
Willow Tree Field, Burman
Farm, Terrington St Clement,
Norfolk
2015
Eighteen varieties and four
breeders’ lines in three field
replications
TF 537 239
Wisbech
Silt loam /
sandy silt loam
584
Table 6: Primers and fluorescent reporter probe designed for wheat and black-grass semi-quantitative PCR assay
585
Target
Primer Code
Sequence
Wheat detection
Forward
TritITS2_F
CAACCACCCTCATCGGGAAT
Reverse
TritITS2_R
TCGGATGCACTGCGTTGATA
Internal oligo
TritITS2_Probe
[JOE]GACCGAAGATCGGGCTGCCG[TAM]
Black-grass detection
Forward
AlopITS2_F
CAAATACGCTCCCACGTCCT
Reverse
AlopITS2_R
GCACTGCGGTAAGTAAAGCG
Internal oligo
AlopITS2_Probe
[6FAM]GTCACGAAAGGGGCGGTGGG[TAM]
586
587
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22
588
Figure 1: Correlations between root washing (RLD) and DNA based assay (RBD) showing
589
varieties as letters and depths by colours. Depth represented in order dark red (0 – 250) >
590
blue (250 – 500) > green (500 – 750) >orange (750 – 1000).
591
592
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593
594
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595
596
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597
598
Figure 2: The distribution of root biomass (μg /g roots in dry soil) for varieties by depth in
599
each of the two growing seasons. Δ = 2014, □ = 2015 ‘Synthetic 18’ = SHW Xi19 / (Xi19 //
600
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26
SHW-218) >18, ‘Synthetic 19’ = SHW Xi19 / (Xi19 // SHW-218) >19 (A table of means for
601
each year is given in Supplementary Table 3).
602
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