Identification of groundnut (Arachis hypogaea) SSR markers suitable for multiple resistance traits QTL mapping in African germplasm

AbstractBackgroundThis study aimed to identify and select informative Simple Sequence Repeat (SSR) markers that may be linked to resistance to important groundnut diseases such as Early Leaf Spot, Groundnut Rosette Disease, rust and aflatoxin contamination. To this end, 799 markers were screened across 16 farmer preferred and other cultivated African groundnut varieties that are routinely used in groundnut improvement, some with known resistance traits.ResultsThe SSR markers amplified 817 loci and were graded on a scale of 1 to 4 according to successful amplification and ease of scoring of amplified alleles. Of these, 376 markers exhibited Polymorphic Information Content (PIC) values ranging from 0.06 to 0.86, with 1476 alleles detected at an average of 3.7 alleles per locus. The remaining 423 markers were either monomorphic or did not work well. The best performing polymorphic markers were subsequently used to construct a dissimilarity matrix that indicated the relatedness of the varieties in order to aid selection of appropriately diverse parents for groundnut improvement. The closest related varieties were MGV5 and ICGV-SM 90704 and most distant were Chalimbana and 47–10. The mean dissimilarity value was 0.51, ranging from 0.34 to 0.66.DiscussionOf the 376 informative markers identified in this study, 139 (37%) have previously been mapped to the Arachis genome and can now be employed in Quantitative Trait Loci (QTL) mapping and the additional 237 markers identified can be used to improve the efficiency of introgression of resistance to multiple important biotic constraints into farmer-preferred varieties of Sub-Saharan Africa

Widely Targeted Metabolomics Based on Large-Scale MS/MS Data for Elucidating Metabolite Accumulation Patterns in Plants

Metabolomics is an ‘omics’ approach that aims to analyze all metabolites in a biological sample comprehensively. The detailed metabolite profiling of thousands of plant samples has great potential for directly elucidating plant metabolic processes. However, both a comprehensive analysis and a high throughput are difficult to achieve at the same time due to the wide diversity of metabolites in plants. Here, we have established a novel and practical metabolomics methodology for quantifying hundreds of targeted metabolites in a high-throughput manner. Multiple reaction monitoring (MRM) using tandem quadrupole mass spectrometry (TQMS), which monitors both the specific precursor ions and product ions of each metabolite, is a standard technique in targeted metabolomics, as it enables high sensitivity, reproducibility and a broad dynamic range. In this study, we optimized the MRM conditions for specific compounds by performing automated flow injection analyses with TQMS. Based on a total of 61,920 spectra for 860 authentic compounds, the MRM conditions of 497 compounds were successfully optimized. These were applied to high-throughput automated analysis of biological samples using TQMS coupled with ultra performance liquid chromatography (UPLC). By this analysis, approximately 100 metabolites were quantified in each of 14 plant accessions from Brassicaceae, Gramineae and Fabaceae. A hierarchical cluster analysis based on the metabolite accumulation patterns clearly showed differences among the plant families, and family-specific metabolites could be predicted using a batch-learning self-organizing map analysis. Thus, the automated widely targeted metabolomics approach established here should pave the way for large-scale metabolite profiling and comparative metabolomics

Biogeographical survey of soil microbiomes across sub-Saharan Africa:structure, drivers, and predicted climate-driven changes

BACKGROUND: Top-soil microbiomes make a vital contribution to the Earth’s ecology and harbor an extraordinarily high biodiversity. They are also key players in many ecosystem services, particularly in arid regions of the globe such as the African continent. While several recent studies have documented patterns in global soil microbial ecology, these are largely biased towards widely studied regions and rely on models to interpolate the microbial diversity of other regions where there is low data coverage. This is the case for sub-Saharan Africa, where the number of regional microbial studies is very low in comparison to other continents. RESULTS: The aim of this study was to conduct an extensive biogeographical survey of sub-Saharan Africa’s top-soil microbiomes, with a specific focus on investigating the environmental drivers of microbial ecology across the region. In this study, we sampled 810 sample sites across 9 sub-Saharan African countries and used taxonomic barcoding to profile the microbial ecology of these regions. Our results showed that the sub-Saharan nations included in the study harbor qualitatively distinguishable soil microbiomes. In addition, using soil chemistry and climatic data extracted from the same sites, we demonstrated that the top-soil microbiome is shaped by a broad range of environmental factors, most notably pH, precipitation, and temperature. Through the use of structural equation modeling, we also developed a model to predict how soil microbial biodiversity in sub-Saharan Africa might be affected by future climate change scenarios. This model predicted that the soil microbial biodiversity of countries such as Kenya will be negatively affected by increased temperatures and decreased precipitation, while the fungal biodiversity of Benin will benefit from the increase in annual precipitation. CONCLUSION: This study represents the most extensive biogeographical survey of sub-Saharan top-soil microbiomes to date. Importantly, this study has allowed us to identify countries in sub-Saharan Africa that might be particularly vulnerable to losses in soil microbial ecology and productivity due to climate change. Considering the reliance of many economies in the region on rain-fed agriculture, this study provides crucial information to support conservation efforts in the countries that will be most heavily impacted by climate change. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40168-022-01297-w

Study of the (+)-Pisatin Biosynthetic Pathway by RNAi and Development of a Novel Method to Elicit the Production of Plant Secondary Metabolites

(+)-Pisatin, ([+]-[6aR,11aR]-6a-hydroxy-3-methoxy-8,9-methylenedioxypterocarpan) is the major phytoalexin of the garden pea (Pisum sativum L.). Despite being the first phytoalexin to be chemically characterized, its biosynthesis remains to be fully elucidated. RNA-mediated genetic interference (RNAi) was used to gain further insights into the (+)-pisatin biosynthetic pathway. The expression of three genes, isoflavone reductase (IFR) catalyzing the reduction of 7,2'-dihydroxy-4',5'-methylenedioxyisoflavone to (-)-sophorol, sophorol reductase (SOR) involved in reducing (-)-sophorol to (-)-7,2'-dihydroxy-4',5'-methylenedioxyisoflavanol and hydroxymaackiain-3-O methyltransferase (HMM) involved in methylation of (+)-6a-hydroxymaackiain to (+)-pisatin was silenced. The genes are transcriptionally co-regulated during (+)-pisatin biosynthesis, with the IFR and SOR proposed to function upstream of the HMM gene.Hairy roots expressing the HMM RNAi construct, deficient in (+)-pisatin biosynthesis were identified. However, these did not accumulate (+)-6a-hydroxymaackiain, precursor to (+)-pisatin. Instead they accumulated 2,7,4'-trihydroxyisoflavanone, daidzein, liquiritigenin and isoformononetin. The amino acid sequence of HMM is very similar to that of another methyltransferase, hydroxyisoflavanone-4'-O-methyltransferase (HI4MOT), found in most legumes. HI4?MOT catalyzes the methylation of 2,7,4'-trihydroxyisoflavanone (THI) to 2,7-dihydroxy-4'-methoxyisoflavanone, one of the earliest enzymatic steps in isoflavonoid biosynthesis. In pea, HI4OMT may be the same enzyme as "HMM" catalyzing the methylation of both THI and (+)-6a-hydroxymaackiain. Preventing the methylation of THI could divert pea intermediates to the production of daidzein and isoformononetin instead of (+)-pisatin.None of the transgenic hairy roots expressing the IFR RNAi construct were totally deficient in (+)-pisatin biosynthesis. However, all produced reduced amounts of (+)-pisatin, with one culture accumulating 7,2'-dihydroxy-4',5'-methylenedioxyisoflavone, the substrate for IFR. Hairy roots expressing the SOR RNAi construct deficient in (+)-pisatin biosynthesis were identified. These accumulated (-)-sophorol, the substrate for SOR. These data provide evidence for the involvement of these genes and the intermediates with (-)-optical activity in (+)-pisatin biosynthesis.The elicitation of the biosynthesis of secondary metabolites in plant cell and tissue cultures by electric current was explored. Electric current was demonstrated to elicit the biosynthesis of secondary metabolites in pea hairy and intact roots, seedling, root and cell suspension cultures of various plant species. Electric current has potential for use as an elicitor of secondary metabolites in basic and commercial research ventures

Methyl Salicylate Is a Critical Mobile Signal for Plant Systemic Acquired Resistance

In plants, the mobile signal for systemic acquired resistance (SAR), an organism-wide state of enhanced defense to subsequent infections, has been elusive. By stimulating immune responses in mosaic tobacco plants created by grafting different genetic backgrounds, we showed that the methyl salicylate (MeSA) esterase activity of salicylic acid - binding protein 2 (SABP2), which converts MeSA into salicylic acid (SA), is required for SAR signal perception in systemic tissue, the tissue that does not receive the primary (initial) infection. Moreover, in plants expressing mutant SABP2 with unregulated MeSA esterase activity in SAR signal - generating, primary infected leaves, SAR was compromised and the associated increase in MeSA levels was suppressed in primary infected leaves, their phloem exudates, and systemic leaves. SAR was also blocked when SA methyl transferase (which converts SA to MeSA) was silenced in primary infected leaves, and MeSA treatment of lower leaves induced SAR in upper untreated leaves. Therefore, we conclude that MeSA is a SAR signal in tobacco

Additional file 7 of Biogeographical survey of soil microbiomes across sub-Saharan Africa: structure, drivers, and predicted climate-driven changes

Additional file 7. Figure S7. MIROC6 model predictions for mean annual temperature (oC) (A) and mean annual precipitation (mm) (B) under too different GH emission scenarios (SSP126 and SSP585), predicted for 2040-2060 and 2080-2100 temporal windows. The predicted datasets are grouped according to country, as indicated by the vertical dashed lines

Additional file 7 of Biogeographical survey of soil microbiomes across sub-Saharan Africa: structure, drivers, and predicted climate-driven changes

Additional file 7. Figure S7. MIROC6 model predictions for mean annual temperature (oC) (A) and mean annual precipitation (mm) (B) under too different GH emission scenarios (SSP126 and SSP585), predicted for 2040-2060 and 2080-2100 temporal windows. The predicted datasets are grouped according to country, as indicated by the vertical dashed lines

Additional file 1 of Biogeographical survey of soil microbiomes across sub-Saharan Africa: structure, drivers, and predicted climate-driven changes

Additional file 1. Figure S1. Distribution of samples across the 9 African countries according to their land cover (LC) classification. Land cover codes used were the following: LC_1 - Rainfed croplands; LC_2 - Mosaic Cropland (50-70%) / Vegetation (grassland, shrubland, forest) (20-50%); LC_3 - Mosaic Vegetation (grassland, shrubland, forest) (50-70%) / Cropland (20-50%); LC_4 - Closed to open (\u3e15%) broadleaved evergreen and/or semi-deciduous forest (\u3e5m); LC_5 - Closed (\u3e40%) broadleaved deciduous forest (\u3e5m); LC_6 - Open (15-40%) broadleaved deciduous forest (\u3e5m); LC_10 - Mosaic Forest/Shrubland (50-70%) / Grassland (20-50%); LC_11 - Mosaic Grassland (50-70%) / Forest/Shrubland (20-50%); LC_12 - Closed to open (\u3e15%) shrubland (\u3c5m); LC_13 - Closed to open (\u3e15%) grassland; LC_14 - Sparse (\u3e15%) vegetation (woody vegetation, shrubs, grassland); LC_17 - Closed to open (\u3e15%) vegetation (grassland, shrubland, woody vegetation) on regularly flooded or waterlogged soil; LC_18 - Artificial surfaces and associated areas (urban areas \u3e50%); LC_19 - Bare areas

Additional file 8 of Biogeographical survey of soil microbiomes across sub-Saharan Africa: structure, drivers, and predicted climate-driven changes

Additional file 8. Figure S8-A. Predicted prokaryotic Shannon biodiversity index values (expressed as natural log scale) in soils of the 9 sub-Saharan Africa countries used in this study, for 2040-2060 and 2080-2100 under two distinct GH emission scenarios (SSP126 and SSP585), and comparison with current predicted Shannon biodiversity as estimated by SEM. Pairwise significance values of differences in biodiversity means between the different years and scenarios are represented by the brackets with the following nomenclature: * - p-value \u3c 0.05; ** - p-value \u3c 0.01; *** - p-value \u3c 0.001