The soil microbiome modulates the sorghum root metabolome and cellular traits with a concomitant reduction of Striga infection.

Sorghum bicolor is among the most important cereals globally and a staple crop for smallholder farmers in sub-Saharan Africa. Approximately 20% of sorghum yield is lost annually in Africa due to infestation with the root parasitic weed Striga hermonthica. Existing Striga management strategies are not singularly effective and integrated approaches are needed. Here, we demonstrate the functional potential of the soil microbiome to suppress Striga infection in sorghum. We associate this suppression with microbiome-mediated induction of root endodermal suberization and aerenchyma formation and with depletion of haustorium-inducing factors, compounds required for the initial stages of Striga infection. We further identify specific bacterial taxa that trigger the observed Striga-suppressive traits. Collectively, our study describes the importance of the soil microbiome in the early stages of root infection by Striga and pinpoints mechanisms of Striga suppression. These findings open avenues to broaden the effectiveness of integrated Striga management practices.

Back to the roots: defining the core microbiome of Sorghum bicolor in agricultural field soils from the centre of origin.

Sorghum is a major staple crop in sub-Saharan Africa with yields severely impacted by biotic and abiotic factors. Here, we analysed the taxonomic diversity and biogeographical distribution of bacterial taxa of 48 agricultural fields along a transect of approximately 2000 km across the Ethiopian sorghum belt, the centre of origin of sorghum. The ultimate goal is to identify-yet-unexplored-beneficial plant-microbe associations. Based on bulk soil bacterial communities and DArT-SNP analyses of 59 sorghum accessions, we selected three microbiologically distinct field soils and 12 sorghum genotypes, including commercial varieties, wild relatives, and farmer-preferred landraces. The results showed a core rhizosphere microbiome of 2125 amplicon sequence variants (ASVs), belonging to eight bacterial families consistently found across the three soil types and the 12 sorghum genotypes. Integration of the rhizosphere bacterial community analysis with DArT-SNP sorghum genotyping revealed the association of differentially abundant ASVs with sorghum genotypic traits, including the distinct recruitment of Pseudomonadaceae by the stay-green, drought-tolerant, and wild sorghum genotypes. Collectively, these results provide new insights into the core and accessory bacterial taxa in the sorghum rhizosphere in the centre of origin, setting a baseline for targeted isolation and functional characterization of putative beneficial rhizobacteria.

Molecular detection and quantification of the Striga seedbank in agricultural soils.

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.