Molecular detection and quantification of the Striga seedbank in agricultural soils

Abstract

Striga hermonthica (Del.) Benth is a devastating parasitic weed in Sub-Saharan Africa (SSA) and its soil seedbank is the major factor contributing to its prevalence and persistence. To date, there is a little information on the Striga seedbank density in agricultural fields in SSA due to the lack of reliable detection and quantification methods. We developed a high-throughput method that combines density- and size-based separation techniques with quantitative polymerase chain reaction (qPCR)-based detection of Striga seeds in soil. The method was optimised and validated by introducing increasing numbers of Striga seeds in two physicochemically different Striga-free agricultural soils. The results showed that as little as one seed of S. hermonthica per 150 g of soil could be detected. This technique was subsequently tested on soil samples of 48 sorghum fields from different agro-ecological zones in Ethiopia to map the geospatial distribution of the Striga seedbank along a trajectory of more than 1500 km. Considerable variation in Striga seed densities was observed. Striga seeds were detectable in 75% of the field soils with densities up to 86 seeds per 150 g of soil. The Striga seed density in soil and the number of emerged Striga plants in the field showed a non-linear relationship. In conclusion, the method developed allows for accurate mapping of the Striga seedbank in physicochemically diverse SSA field soils and can be used to assess the impact of management strategies on Striga seedbank dynamics.

Keywords: Striga seed density; mapping; qPCR; sorghum field; sub‐Saharan Africa; weed seed.

FIGURE 1

Molecular detection of Striga seeds in soil. (A) Gel electrophoresis of the PCR amplification products of five Striga hermonthica marker genes. A total of 14 primer sets were tested with 3 sets for gene 1 (StHe0GB1_1), 2 sets for gene 2 (StHe0GB1_9), 2 sets for gene 3 (StHe0GB1_2), 4 sets for gene 4 (StHe0GB1_76) and 3 sets for gene 5 (StHe0GB1_93)). The genomic DNA was extracted from (I) 50 mg Striga seeds, (II) 50 mg Striga seeds mixed in 100 mg of Dutch agricultural soil, and (III) 100 mg of Dutch agricultural soil (no Striga seed added; control). For the 14 primer sets, the sizes of the predicted PCR products are 145, 161, 170, 115, 157, 111, 200, 112, 154, 101, 100, 276, 70 and 520 base pairs (see Table S1). (B) Mean Cq values of the qPCR analysis with the 14 primer sets using DNA extracted from Dutch agricultural soil mixed with S. hermonthica seeds. The qPCR analysis was tested at two different annealing temperatures (56°C, 60°C). Mean Cq values (± SE) of three biological replicates (with two technical replicates per biological replicate) are shown. Different letters above each of the bars represent statistically significant differences (p < 0.05) between the Cq values of each of the 14 primer sets

FIGURE 2

Influence of soil physicochemical properties on Striga seed detection. (A) qPCR detection of 65 Striga hermonthica seeds mixed into seven physicochemically different Dutch agricultural soils (D08, D10, D11, D13, D20, D21, D17). After mixing the seeds into these soils, total DNA was extracted and subjected to qPCR with primer set 14 (see Figure 1B). Different letters above the bars indicate a statistically significant difference (p < 0.05) between the Cq values of the seven soils; (B) qPCR detection of different Striga seed densities introduced into two physicochemically distinct Dutch agricultural soils (D08, D17). In contrast to the procedure used in panel a, soils amended with the Striga seeds were first treated with K₂CO₃ for size‐dependent separation of the Striga seeds from the soil matrix prior to DNA extraction. For both experiments, the mean Cq values (± SE) are shown for three biological replications and two technical replications per biological replication

FIGURE 3

Standard curves to quantify Striga seeds in soil. (A) Relationship between the Cq values obtained in qPCR analysis of plasmid DNA containing the Striga marker gene StHe0GB1_93 (gene 5, Figure 1A) and the logarithm of the gene copy number. For each log gene copy number, three replicates were used in qPCR; (B) relationship between different Striga hermonthica seed densities mixed into agricultural soil and the estimated gene copy number. For each Striga seed density, three biological replications and two technical replications per biological replication were used. For each of the panels, a linear regression analysis was performed as shown in the equation including R² values

FIGURE 4

Geospatial mapping of Striga seedbank in sorghum belt of Ethiopia. (A) Map of Ethiopia showing the different sorghum growing agroecological zones and the agricultural field sites where the 48 soil samples (E numbers) were collected. If Striga seed is detected and quantified in 150 g of soil sample, the site is depicted with black dots otherwise with white dots. (B) Number of Striga seeds quantified by qPCR in 150 g of soil collected from each of these naturally infested field sites. Mean values (± SE) of three biological replicates (with two technical replicates per biological replicate) are shown

FIGURE 5

Relationship between Striga seedbank density and emerged Striga plants for 48 sorghum fields in Ethiopia (map shown in Figure 4A). (A) Striga emergence and Striga seed densities of 48 naturally infested sorghum fields in Ethiopia. (B) Non‐linear relationship between the number of emerged Striga plants per m² and the number of Striga seeds detected per 150 g of soil sample. Striga emergence on the Y‐axis is shown on a log2 scale. For each sorghum field, the Striga seed density was quantified per 150 g of soil for three biological replicates as depicted in Figure 4B. Striga emergence was counted from four randomly chosen spots per field site and the number of sorghum plants counted per m² was used to normalise the number of emerged Striga plants