Introduction

Crop aluminum (Al) toxicity on acidic soils (soil pH ≤ 5), which comprise approximately 50% of the world’s arable lands1, severely reduces crop yields on a global scale. Because acid soils are prevalent in tropical and sub-tropical regions such as in Sub-Saharan Africa2, where sorghum is an important staple food crop for millions of poor rural people, Al tolerance is critical for food security. Aluminum ions are solubilized from clay minerals at acidic soil pH values as the rhizotoxic ionic form, Al3+, which damages root systems, thereby reducing root growth. As a consequence, water and nutrient uptake are constrained, leading to severe yield losses3,4,5,6.

Plant species exhibit extensive genetic variation for Al tolerance that is associated both with internal and external mechanisms of tolerance5,7. Some plant species can detoxify Al internally, via formation of non-toxic Al complexes with organic acids or other chelators, with subsequent sequestration of these complexes into the vacuoles8,9. The most widespread and studied Al tolerance mechanism takes place in the root tip apoplast and rhizosphere, where low molecular weight organic acids such as citrate and malate are released from the root apices via plasma membrane transporters. These organic acids chelate Al3+, forming non-toxic, Al-organic acid complexes, thereby protecting the root apex, which is particularly sensitive to Al3+ 7,10. Al-activated citrate and malate transporters belonging to the Aluminum-Activated Malate Transporter (ALMT) and the Multidrug and Toxic Compound Extrusion (MATE) families have been shown to mediate organic acid-based Al exclusion from the root apices, thus conferring Al tolerance in plant species. The first major Al tolerance genes belonging to these gene families were cloned in wheat (TaALMT111), sorghum (SbMATE12) and barley (HvAACT113). A number of homologs of these genes have been subsequently identified in other cultivated plant species including maize14, soybean15, rice16, rice bean17, wheat18, barley19, rye20, rapeseed21 and also in Arabidopsis22,23.

In sorghum, the AltSB locus on chromosome 3 controls a large fraction of the phenotypic variation for Al tolerance24. Positional cloning showed that SbMATE (GenBank accession EF611342), an Al-activated citrate transporter that is more highly expressed in root apices of Al tolerant accessions, underlies the AltSB locus. SbMATE is transcriptionally induced by Al3+ and mediates citrate release from root apices, thereby conferring sorghum Al tolerance12. Introgression of a single tolerant SbMATE allele has been recently shown to increase sorghum grain yield by ~0.6 ton ha−1 on an acid, Al toxic soil, both in inbred lines and in hybrids25. Based on the Carvalho et al. study25, in homozygosity, SbMATE increased grain yield by a striking 50% over the population mean with no yield penalty in the absence of Al toxicity. This makes SbMATE a major asset for sorghum production on acid soils.

Highly Al tolerant accessions occur at a low frequency in sorghum (0.05) and are not randomly distributed across the sorghum diversity continuum, being prevalent primarily in guinea and to a lesser extent in caudatum sorghums26. Therefore, provided the target Al tolerant alleles are present in a given sorghum germplasm, typically extensive Al tolerance phenotyping is necessary for identifying Al tolerance sources. Association mapping applied to the AltSB locus was used to identify SNP and indel loci either within or in close vicinity to SbMATE that were associated with Al tolerance27. In a sorghum association panel, 79% of the accessions harboring the A SbMATE allele at the intronic SNP locus, 6083, were either highly or intermediately Al tolerant. This contrasts with the phenotypic proportions for Al tolerance and sensitivity, where 79% of the accessions in the association panel were Al sensitive. These results indicate that allele mining strategies, which seek to identify novel, superior and beneficial alleles in potentially large germplasm or natural populations28,29, may be used to facilitate the identification of Al tolerance donors in sorghum.

Here we describe the development of an easy-to-use marker system based on Amplification Refractory Mutation System (ARMS) markers30 to tag SbMATE SNP loci previously shown to be associated with Al tolerance in a sorghum association panel. We validated those markers by allele mining in three different sorghum germplasm sets, including a panel comprising sorghum accessions adapted to West Africa. Finally, we characterized the physiological and molecular nature of the superior AltSB alleles that were identified, which are now available for breeders throughout the world to increase sorghum yields on acidic, Al toxic soils.

Results

Alt SB marker development

We selected five SNPs and one indel loci that we have shown to have strong association with Al tolerance27 to use for marker development. Those are the SNP loci, 5985, 6083 and 6094, which are located within the second intron of SbMATE, and SNPs 8364 and 8423, which are found approximately 1 kb downstream of the SbMATE stop codon (Fig. 1). The indel locus, 12487, is located approximately 4 Kb downstream of 8364/8423.

Figure 1
figure 1

Position and association signals with Al tolerance of the loci used for marker development. The top bar depicts base pair positions in the context of the AltSB locus. The MITE transposon inserted in the promoter region of SbMATE and the SbMATE gene model are depicted (gray boxes indicate exons). Root apices of Al tolerant and Al sensitive lines, with the Al sensitive lines exhibiting typical high Al accumulation as revealed by hematoxylin staining24, are shown along with the respective AltSB alleles (alleles linked in coupling with Al tolerance are in bold type). Association analysis with Al tolerance was reported by Caniato et al.27 with a population structure (Q) + kinship (K) model and a heat map depicting the resulting association probabilities is shown for the selected loci (modified from Fig. 3 in Caniato et al.27).

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Dominant (based on three primers) and four-primer, codominant ARMS-PCR30 marker systems were developed (Supplementary Table S1) for the SNP loci 5985 and 6094 (Fig. 2a,c) and for SNPs 6083, 8364, 8423 (Fig. 2b,d,e), respectively. For 6083 and 8423, confirmatory genotyping with the three-primer system was occasionally necessary. Amplification with all ARMS primers except for 8423 yielded a monomorphic band, which can thus be used as a positive PCR control as reported previously30. Insertion-specific primers were developed for the indel locus 12487 (Fig. 2f). The amplification profile for a dominant marker system developed for locus 5519 is shown in Supplementary Fig. S1. This marker tags a rare allele (A) specifically present in the highly Al tolerant line, SC56631, which was not used for allele mining in the present study due to its low frequency but can be used for a marker assisted backcross approach using SC566 as the Al tolerance donor.

Figure 2
figure 2

ARMS-PCR and Indel markers for AltSB. Amplifications profiles are shown for two sorghum lines with different AltSB haplotypes, SC283 (Al tolerant) and BR007 (Al sensitive) as well as for a heterozygous individual. A 1 Kb molecular-weight size marker was loaded in the first well. The PCR products were resolved in 1.5% agarose gel. The amplification profiles of dominant (a,c), co-dominant (b,d,e) and the dominant indel (f) marker systems are shown. We are showing cropped gel images, which are presented either individually for each co-dominant marker (b,d,e) and for the indel marker (f) or separated by blank spaces in the case of each of the two alleles revealed by each dominant marker system (a,c).

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Pooling strategy

DNA pooling is an efficient approach for identifying low-frequency alleles, such as AltSB alleles that confer Al tolerance27, which minimizes the number of PCR reactions needed when large germplasm collections are to be screened. For efficient pooling, favorable alleles need to be detected even when they are present in much lower proportions compared to the alternative alleles. Optimum pool sizes were calculated based on the method described by Amos et al.32 and revised by Gastwirth33, using the allele frequencies reported in Caniato et al.27. Simulations based on population sizes ranging from 100 to 400 individuals indicated the optimum pool size to be four individuals per pool (Supplementary Table S2). Using this pool size for genotyping with marker 6083, pooling efficiency varied from 43.2% to 85.8% for a population size of 100 and 400 individuals, respectively (Supplementary Table S2).

Next, a sensitivity test was performed for all marker loci by preparing mixtures with different proportions of each marker allele. The objective of this test was to confirm if the favorable allele of each marker would be detectable even under strong under-representation within a pool. Figure 3 shows a representative amplification profile for marker 6083. This marker was tested in samples obtained by pooling different proportions of genomic DNA of BR007 (Al sensitive) and SC283 (Al tolerant) in ratios varying from 1(BR007):0(SC283) to 1:20. The favorable, SC283 allele at 6083 was clearly detected even when the alternate BR007 allele was present at a 20-fold excess (20:1, Fig. 3), which indicates that the optimal pool size of four individuals determined for this marker (Supplementary Table S2) is adequate for proper detection of the target allele even when a single accession harboring the SC283 is present in the DNA pool. In contrast, amplification of the Al sensitive allele was strongest when only this allele was present in the pool and showed reduced efficiency even at the lowest degree of under-representation (1:3), although it was clearly detectable even at the 1:10 proportion. Because it is the Al tolerant allele that is rare compared to the Al sensitive allele, higher amplification efficiency of the former with the ARMS system is advantageous for allele mining strategies based on the 6083 marker to identify Al tolerant accessions. Reduced detection sensitivity for the favorable allele was observed only for marker 8364, which was therefore not deemed appropriate for use in DNA pooling strategies.

Figure 3
figure 3

Detection sensitivity upon DNA pooling for the four-primer 6083 ARMS. DNA from the sorghum lines, BR007 (Al sensitive) and SC283 (Al tolerant) were mixed in equal (1:1) proportions (BR007:SC283) or so that the BR007 (1:3 to 1:20) or the SC283 (3:1 to 20:1) alleles were under-represented in the DNA pool at different proportions. Unmixed DNA from BR007 (1:0) and SC283 (0:1), which are homozygous for the unfavorable and favorable alleles, respectively, were included as controls. L = 1Kb ladder.

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Allele mining for aluminum tolerance based on Alt SB

The AltSB markers were used to identify accessions harboring Al tolerant alleles of AltSB in three different sorghum panels, designated based on their origins as members of the SAP (Sorghum Association Panel, with 377 accessions34), INRAN (Institut National de la Recherche Agronomique du Niger panel, with 164 accessions) and ICRISAT (International Crops Research Institute for the Semi-Arid Tropics panel, with 187 accessions). Upon genotyping, accessions harboring at least one favorable allele at the marker loci were phenotyped for Al tolerance based on Al inhibition of root growth in nutrient solution.

Of the 377 accessions from the SAP panel, 30 possessed at least one favorable AltSB allele (Fig. 4a). Phenotypic assessment of Al tolerance showed that 60% (18) of those accessions were sensitive to Al (RNRG < 30%), while the remaining 40% were either intermediately tolerant to Al (30% < RNRG < 80%, 4 accessions) or Al tolerant (RNRG > 80%, 8 accessions). Fourteen INRAN accessions harbored favorable AltSB alleles (Fig. 4a) and those were equally split into intermediately tolerant and Al sensitive, with no Al tolerant accession present in this panel. The frequency of accessions with favorable AltSB alleles was similar in the SAP and INRAN panels (0.08) but much lower compared to the ICRISAT panel (0.22). Although 41 accessions with favorable alleles were identified in the ICRISAT panel, the majority of those were intermediately Al tolerant (13 accessions), whereas 3 highly Al tolerant accessions were found in that panel. These data indicate that the expression of the Al tolerance phenotype in accessions harboring favorable AltSB alleles is highly variable across panels in a manner that cannot be fully explained by differences in population size.

Figure 4
figure 4

Allele mining in the SAP, INRAN and ICRISAT panels. (a) Diagram depicting the allele mining procedure in the SAP, INRAN and ICRISAT panels, which were used to identify accessions harboring at least one favorable AltSB allele at marker loci, 5985, 6083, 6094, 8364, 8423 and 12487. The distribution of those accessions in classes of Al tolerance is shown. (b) Haplotypes of Al tolerant accessions and the Al sensitive line, BR007 (H2), included as reference. Alleles in bold are linked in coupling with Al tolerance27. The reference Al tolerant and Al sensitive haplotypes from SC283 (H5) and BR007 (H2), which is possibly an ancestral haplotype27, are marked with single and double asterisks, respectively. Al tolerance was assessed via Al inhibition of root growth (relative net root growth, RNRG) in nutrient solution containing {27} µM Al3+ at pH 4.

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The only marker locus for which all highly Al tolerant accessions identified in the SAP and INRAN panels harbored only the favorable allele was the SNP locus, 6083 (Fig. 4b). Previously, 6083 was found to show the strongest statistical association with Al tolerance, increasing Al tolerance by a striking ~60% RNRG, and was the most effective for the identification of Al tolerant accessions in a 254-member sorghum association panel (Fig. 3 and Table 2 in27).

We compared the power of selection based solely on the favorable allele of the 6083 marker to selection based on the presence of at least one favorable AltSB allele considering all loci combined in identifying Al tolerant and intermediate accessions (Table 1). Selection with marker 6083 alone allowed us to recover thirty-four out of a total of thirty-five Al tolerant and intermediate accessions identified when the three panels were genotyped with all markers used for allele mining, at the same time reducing the number of Al sensitive accessions (Table 1). No other marker yielded the same efficiency in selecting Al tolerant and intermediate accessions compared to 6083 (Supplementary Table S3).

Table 1 Number of Al tolerant and intermediate (T + I), and Al sensitive (S) accessions with at least one favorable AltSB allele considering joint selection with all markers or harboring the favorable (A) allele at marker 6083.
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Population structure and allele mining

Previously, Caniato et al.26 identified six sorghum subpopulations featuring distinct racial and geographic origins using a 254-member sorghum association panel selected for maximum diversity. Therefore, we genotyped the 85 accessions harboring favorable AltSB alleles identified here by allele mining with the same markers used by Caniato et al.26 and assigned those accessions to the different subpopulations defined in that study based on their membership coefficients. Most of these accessions (76%) were assigned to subpopulations enriched for members in the guinea race (Fig. 5 and Supplementary Fig. S2). Within those, 62 accessions clustered with guinea margaritferum and guineas from Western Africa (subpopulation Q1) and 2 accessions were assigned to guineas from Southern Africa and Asia (Q6) (Fig. 5). Among those 64 accessions, six were Al tolerant (four in Q1 and two in Q6) whereas the other five Al tolerant accessions found across all panels (Supplementary Fig. S2) were assigned to subpopulation Q3 (one accession), which includes breeding lines used in Brazil and in the US, kafir accessions from southern Africa (Q4, 2 accessions), and durra, bicolor and caudatum accessions from eastern Africa and Asia (Q5, 2 accessions). The two Al tolerant accessions classified as kafir from southern Africa are the Al tolerance standard, SC283 and SC184. SC283, which is in fact a guinea type24, could not be clearly attributed to any single subpopulation, as it showed relationship (membership coefficients between 0.2 and 0.4) to Q4 (kafir accessions from southern Africa), Q3 (Brazil and US derivatives) and Q6 (guineas from southern Africa and Asia) as shown in Supplementary Table S4, suggesting that inter-racial hybridizations took place in the lineage that originated SC283. SC184, in turn, is classified in the Caffira subseries35, supporting its higher membership coefficient to subpopulation Q4. Except for that, considering all panels, the majority of highly Al tolerant accessions clustered with guinea types.

Figure 5
figure 5

Subpopulation assignments for sorghum accessions with favorable AltSB alleles. The genotypic data for accessions with favorable AltSB alleles was integrated into the Caniato et al.26 dataset (Fig. S3 and Table S6 in26) by genotyping with the same set of SSR markers used in that study. The subpopulations are: Q1, guinea accessions from western Africa and guinea margaritiferum accessions; Q2, caudatum accessions from Africa and group of transplanted caudatum and durra accessions from Lake Chad region; Q3, lines from the Embrapa collection and US; Q4, kafir accessions from southern Africa; Q5, durra accessions from central eastern Africa and from Asia; bicolor and caudatum accessions from Asia; and Q6, guinea accessions from southern Africa and Asia. Double and single asterisks depict Al tolerant and intermediate individuals, respectively.

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Among the intermediate accessions, there was again prevalence of membership to guineas from western Africa and guinea margaritiferum (87.5%, Q1, Supplementary Fig. S2). All four intermediate accessions from the SAP belong to Q1 as well as the majority of intermediate accessions in the INRAN and ICRISAT panels (5 and 8, respectively). The other intermediate accessions clustered in Q2, which includes caudatum types and durra, and in Q5, which, in addition to those races, also includes bicolor accessions. Eighty percent of the Al sensitive accessions harboring favorable AltSB alleles (40 accessions) belonged to subpopulation Q1. The subpopulation membership coefficients for the 85 accessions selected by allele mining are shown in Supplementary Table S4.

Physiological and molecular characterization of the accessions identified by allele mining with Alt SB markers

SNP and indel loci associated with Al tolerance were previously used to identify eight different AltSB haplotypes, H1–H8, in a sorghum association panel (Fig. 1 in27). Here we undertook a physiological and molecular characterization of Al tolerant sorghum accessions harboring different AltSB haplotypes. This analysis included haplotypes that were most frequently found in the Al tolerant accessions identified in the present study by allele mining in our largest germplasm set, the SAP (H5, H6/H8; Table S4).

A high and positive correlation coefficient was observed between SbMATE expression and citrate exudation (r = 0.76 p = 0.001, Fig. 6a), and SbMATE expression was also highly but negatively correlated with Al accumulation in root apices (r = −0.69 p = 0.003, Fig. 6b). These results, along with the positive correlation between SbMATE expression and Al tolerance (measured as relative net root growth, r = 0.66 p = 0.005, Fig. 6c) indicate that the accessions identified by allele mining effectively use SbMATE-dependent Al-activated citrate release to exclude Al from sensitive sites in the root apex as their main Al tolerance mechanism. Citrate release was highly and negatively correlated with Al accumulation (r = −0.66 p = 0.0005, Fig. 5d), indicating that SbMATE is indeed the major player in the Al exclusion mechanism taking place in those accessions. However, a slightly lower correlation coefficient of 0.55 between Al tolerance and citrate release/Al accumulation (Fig. 6e,f) suggest that some of these lines may also rely on auxiliary Al tolerance mechanism acting in concert with SbMATE to tolerate high levels of Al toxicity.

Figure 6
figure 6

Correlation analysis between SbMATE expression and Al tolerance for sorghum accessions harboring different AltSB haplotypes. The Pearson correlation coefficient (r) and its associated p-value are shown. Standard error bars are shown. Correlation analysis was determined between the following physiological and molecular determinants of Al tolerance: SbMATE expression and citrate exudation (a), SbMATE expression and Al accumulation in root apices (b), SbMATE expression and Al tolerance measured by Relative Net Root Growth (RNRG) (c), citrate exudation and aluminum accumulation in root apices (d), Al tolerance and citrate exudation (e) and Al tolerance and Al accumulation in root apices (f). SbMATE expression was assessed26 by exposing sorghum accessions to {27} µM Al3+ for five days in nutrient solution at pH 4.0. For assessing citrate exudation, root exudates were collected for 6 hours in the presence of {27} µM Al3+. The first centimeter of the root was collected for assessing Al accumulation. Relative SbMATE expression was assessed by quantitative RT-PCR using the ΔΔCt method50.

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Discussion

The prevalence of low-pH acidic soils in tropical and sub-tropical regions of the world1, coupled with the strong deleterious effect of Al toxicity on root development and crop production on these acid soils36, requires the efficient identification of Al tolerant genetic resources to ensure food security worldwide. The Al-activated root citrate transporter, SbMATE, which underlies the sorghum Al tolerance AltSB locus, has been shown to significantly increase grain yield under Al toxicity and, as such, is essential for sorghum production on acidic, Al toxic soils25. Efficient utilization of important genes for crop improvement while avoiding adoption of one or a few donors, which leads to an undesirable narrowing of the genetic basis, is a significant challenge in modern agriculture37. Hence, the need for developing a high throughput molecular breeding approach that could be flexibly adopted by plant breeders for sorghum production on acid soils motivated us to develop and validate AltSB-specific markers. The resulting easy-to-use, low cost gene-specific markers can be used both for germplasm characterization via allele mining and for marker assisted introgression to improve sorghum Al tolerance.

The SNP and indel loci that were used to develop the AltSB markers were shown to be highly associated with Al tolerance in a sorghum association panel and some of them are possibly causative12,27. Marker loci based on functional polymorphisms or in tight linkage disequilibrium with causative variants, as is the case for the AltSB markers, offer greater flexibility for germplasm characterization in comparison to the traditional flanking marker approach. The latter requires the artificial creation of a high linkage disequilibrium context for detecting marker-trait associations, which is impractical when the final goal is to characterize large germplasm collections. If superior AltSB alleles can be identified directly in locally adapted accessions, the need for marker-assisted introgression of superior alleles from exotic donors is precluded, avoiding the time and cost associated with backcross breeding to recover adaptive traits.

Al tolerance has been shown to be a rare trait in sorghum, where only about 5% and 15% of the accessions show high and intermediate Al tolerance, respectively26. In addition, a population structure analysis showed that highly Al tolerant accessions are mostly found in subpopulations enriched for guinea and to a lesser extent caudatum sorghums. An analysis with AltSB haplotypes reconstructed from loci associated with Al tolerance showed that haplotypes with only Al sensitive alleles of AltSB are found at a frequency exceeding 0.827. Conversely, the H5 haplotype, which harbors only alleles linked in coupling with Al tolerance, is likely a derived haplotype stemming from its lower frequency of 0.05, being prevalent in subpopulations enriched mostly in guinea types, which overlaps with the occurrence of the Al tolerance trait.

Our physiological and molecular analysis of different AltSB haplotypes was undertaken with sixteen accessions within which eleven were highly Al tolerant, one was intermediately tolerant, and four accessions were Al sensitive (based on relative net root growth at 5 days as shown in Supplementary Table S4 in27). The majority of the Al tolerant lines harbor haplotypes H5, H6 and H827, which are the haplotypes found in all eight highly Al tolerant accessions identified by allele mining in the SAP panel (Supplementary Table S4). This analysis confirmed that high Al tolerance in these haplotypes results from allelic variation ultimately affecting SbMATE expression, with high SbMATE expression leading to higher organic acid release that acts to exclude Al3+ from sensitive sites in the root apex. The other three highly Al tolerant lines found in the ICRISAT panel possess the novel haplotypes, H9, H10 and H21, all of which, nonetheless, harbor the favorable, A allele, at the 6083 marker locus. We observed both here and previously in another germplasm set27 that maximum recovery of Al tolerant accessions can be obtained by selecting for the favorable allele of 6083 alone. Thus, it is likely that the allelic differences between H5, which harbor only Al tolerant alleles, and the H9, H10 and H21 haplotypes does not significantly change SbMATE expression and its function in providing Al tolerance via Al-activated citrate release into the rhizosphere.

Rare variants controlling important traits such as β-carotene content in maize grains have been reported and, as is the case for sorghum Al tolerance, are not randomly distributed in the species germplasm38. This indicates the need for designing specific and integrative strategies exploring complementarities between low and high LD contexts to maximize the chances of detecting rare variants, which is particularly challenging when based solely on genome wide scans with typically sub-optimal population sizes.

The majority (~73%) of the more Al tolerant accessions recovered in the present study by allele mining were clustered mostly in subpopulation Q1, which contains guinea types from Western Africa, supporting a Western-African origin of AltSB as previously hypothesized26,27. Within eighty-five accessions showing favorable AltSB alleles, we have been able to identify eight and three highly Al tolerant accessions in the SAP and ICRISAT panels, respectively. In the largest germplasm set, the SAP, among 30 accessions showing favorable AltSB alleles, eight (~27%) were highly tolerant to Al toxicity. Therefore, assuming the phenotypic frequency of high Al tolerance of 0.05 as previously reported26,39, AltSB-based selection led to a 5.3-fold enrichment of Al tolerance in the SAP.

The usefulness of allele mining using AltSB markers compared to phenotypic selection alone to identify highly Al tolerance donors can be clearly perceived using our largest germplasm set, the SAP. In order to assess that we asked how many accessions of the 377-member SAP one would need to phenotype without marker information to rescue at least the same number of Al tolerant accessions, eight, that were identified within the thirty accessions selected by allele mining. This outcome can be modeled with a Poisson distribution with \(P=1-[(P(X=0)+\)\(P(X=1)+\ldots +P(X=m-1))|\lambda ]\), where \(P\) is the probability of success in rescuing at least a given number \((m)\) of Al tolerant accessions (\(X)\), found at a frequency of 0.05. Based on sampling with \(\lambda =15\,(300\,\times \,0.05),\,\)in the absence of marker-based selection, 300 accessions, almost the entire panel, would need to be phenotyped to be 98% sure of identifying at least eight highly Al tolerant accessions. This contrasts with marker-based selection, whereby the same number of highly Al tolerant accessions were identified by screening only 30 accessions, which translates into a 10-fold reduction in the number of accessions needed to be phenotyped. Additionally, the DNA pooling strategy we employed significantly minimized the number of PCR reactions necessary for the detection of the rare mutations underlying the AltSB locus, which also improved the genotyping efficiency. This is demonstrated when we carried out allele mining in the SAP using the 6083 AltSB marker, where DNA pooling resulted in a 55% reduction in the number of samples to be genotyped. Because eight highly Al tolerant accessions were in fact identified in the SAP, allele mining with 6083 led to the efficient identification of Al tolerant donors with a great reduction in cost and time, which makes in-house genotyping with AltSB markers highly amenable to high throughput genotyping in large germplasm collections or breeding germplasm, with minimal infrastructure required.

Selection of favorable Al tolerant alleles using AltSB markers was not always associated with high Al tolerance, which we also observed previously27. In Melo et al.31 we had previously reported on an incomplete transfer of Al tolerance from parents to near isogenic lines, which was paralleled by a reduction in SbMATE expression31,40. Incomplete transfer of Al tolerance has been previously observed also in wheat41 indicating the importance of genetic background effects in the expression of the Al tolerance phenotype (reviewed by42). Therefore, the identification of Al sensitive lines showing Al tolerant haplotypes is likely due to the action of auxiliary loci influencing SbMATE expression in trans, the impact of which on Al tolerance is variable, depending on the magnitude of the cis effects controlled by elements located within the AltSB locus31.

The largest germplasm set used for allele mining, the SAP, is a diverse panel assembled to maximize geographic and racial diversity, including many guinea and caudatum types43. Thirty-four percent of the accessions in the SAP were assigned to guinea and/or caudatum subpopulations, 22% were assigned to subpopulations including guineas, caudatums and other races, and 44% clustered in subpopulations including other morphological races such as kafir and durra (Supplementary Table 1 in43). Therefore, it is not surprising that the most effective recovery of Al tolerant accessions was obtained using the SAP, since it includes the sorghum gene pool where Al tolerance is most prevalent. More than half of the accessions in the ICRISAT panel are also guinea types (Supplementary Table S4 and44), which are commonly grown in West Africa. Comparing the ICRISAT and the SAP panel, we were successful in identifying Al tolerant accessions in the ICRISAT panel but at a lower efficiency compared to the SAP (8 and 3 Al tolerant accessions were identified in the SAP and ICRISAT panels, respectively). However, the number of usable, Al intermediate donors was even higher in the ICRISAT panel. It is interesting that despite the smaller size of the ICRISAT panel (about half the number of accessions in the SAP), more accessions with favorable AltSB alleles were identified in the ICRISAT panel (41 vs. 30, Fig. 4), which possibly is due to a higher prevalence of guinea types in the ICRISAT panel compared to the SAP. It is thus possible that the highest population size and broader overall diversity of the SAP acted to minimize, at least to some extent, genetic background effects that appear to have a more prominent role in reducing Al tolerance in the ICRISAT panel.

We have only identified accessions with intermediate Al tolerance in the INRAN panel, which contained accessions collected only in Niger, encompassing different morphological races. Only 14 accessions with favorable AltSB alleles were found in the INRAN panel. With such a small space for Al tolerance to express itself over a context where genetic background effects are important, high Al tolerance in this germplasm or others where non-guinea/caudatum sorghums prevail would need to be introgressed from exotic sources.

To face the challenge of feeding the estimated world population of seven billion people by the year 2050, it is absolutely essential to sustainably improve crop yields on more marginal, stress-prone lands, such as in Sub-Saharan Africa. This study addresses this topic via exploiting and linking the cloning of SbMATE, which underlies the AltSB locus and has a strong beneficial impact on grain yield on acidic, Al toxic soils, with efficient germplasm characterization and marker-assisted selection strategies. Allele mining and marker assisted selection with gene-specific AltSB markers has great potential for improving food security on acid soils that comprise up to 40% of the world’s potentially arable lands1, while at the same time broadening the genetic basis of sorghum breeding programs targeting acid soil regions throughout the world.

Materials and Methods

Primer design and PCR analyses

Six SNP and one 19 bp indel loci located within the AltSB locus and previously shown to be associated with Al tolerance in a 254-member association panel27 were used for marker development for allele mining purposes. We designed a dominant marker system for the 19 bp indel polymorphism, where one of the primers anneals to the indel region so that the presence of a 685 bp PCR product tags the insertion allele. Marker conversion for the SNP loci was undertaken based on the ARMS-PCR strategy30. Briefly, this method employs primers flanking the target polymorphism either simultaneously, with two internal, allele-specific primers (codominant, four-primer system) or with each of the allele-specific primers being assayed in separate PCR reactions (dominant, three-primer ARMS). The last base in the 3′ end of the allele-specific primers is complementary to each SNP allele and, to increase specificity, a deliberate mismatch was introduced at position −2 or −3 from the 3′ terminus. Primers were designed using the primer design computer program, BatchPrimer345, available at http://wheat.pw.usda.gov/demos/BatchPrimer3, by limiting the size ratio of the allelic bands to 1.1–1.5 fold, CG content between 20 to 80% and primer melting temperature (Tm) differing by at most 5 °C. Default settings were used for the other parameters.

The sorghum lines, BR007 (Al sensitive) and SC283 (Al tolerant), polymorphic for the selected SNP and indel loci (Table S4 in27), were used for marker validation. PCR reactions for the indel polymorphism were performed in a total volume of 20 µL with 2.0 pmol of each primer, 0.2 mM dNTP, 1 U of Taq polymerase, 20 mM Tris–HCl (pH 8.4), 50 mM KCl; 2 mM MgCl2 and 30 ng of genomic DNA. PCR reactions for the SNP markers were carried out in a 13 µL total volume with 2.0 pmol of each outer primer, 10 pmol of each inner (allele-specific) primer, 1X Go Taq® Green Master Mix (Promega, Foster City, CA) and 30 ng of genomic DNA. PCR reactions for SNP 8364 contained 1.2 pmol of the inner primers. Amplifications proceeded with an initial denaturation step of 94 °C for 4 min followed by 35 cycles at 94 °C for 1 min, 1 min of annealing at specific temperatures for each assay as detailed in Supplementary Table S1, 72 °C for 1 min followed by a final extension step at 72 °C for 7 min. The amplification products were separated by electrophoresis in a 1.5% agarose gel. Primer sequences are shown in Supplementary Table S1.

Pooling strategy

We estimated the optimal pool size that would ensure the identification of low frequency Al tolerance alleles with minimum genotyping using the method described by Amos et al.32 and revised in33. First, we simulated population sizes ranging from 100 to 400 individuals and calculated the pool sizes and associated expected pooling efficiency (1 − F) as follows.

Taking the favorable allele frequency as π and n as the number of individuals, for s sized pools, the probability γ that at least one accession within a pool has the target allele is given by γ = 1 − (1 − π)s. The expected number of PCR reactions needed to identify all mutations in a given population is E(Y) = (n/s) + nγ. If the ratio E(Y):n, F, is smaller than 1, the pooling strategy leads to a reduction in the number of PCR reactions33. The pooling efficiency is given by 1 − F, where F is the ratio between γ and n.

To determine the sensitivity of the different marker assays to identify the desired allele with a given pool size, we mixed in different proportions genomic DNA of two contrasting lines for AltSB, BR007 and SC283, and verified if the allele of interest could be visually detected.

Allele mining based on Alt SB

Three sorghum panels were used for allele mining based on the AltSB locus: 1) The Sorghum Association Panel (SAP) panel is composed of 377 sorghum accessions including both tropical converted and breeding accessions43 and is available at the USDA-ARS – Germplasm Resources Information (ARS – GRIN, https://www.ars-grin.gov/); 2) the INRAN panel was provided by the Institut National de la Recherche Agronomique du Niger (INRAN, BP 429 Niamey, Niger) and has 164 accessions, of which 142 are landraces and 22 are breeding lines and 3) the ICRISAT panel was provided by the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT, BP 320 Bamako, Mali) and has 187 accessions. Genomic DNA was isolated from 500 mg of leaf tissue from a total of 728 accessions using the protocol described by46 and PCR reactions for the AltSB markers were performed as described in the section primer design and PCR analyses. Within each panel, the total number of accessions was divided into pools, which contained DNA of four accessions per pool. Then, 100 ng of pooled genomic DNA was used for genotyping with the AltSB markers. Accessions within each “positive” pool, that is, pools containing favorable AltSB alleles, were then genotyped individually.

Assessment of Al tolerance in nutrient solution

The sorghum accessions with favorable AltSB alleles were analyzed for Al tolerance in nutrient solutions containing either 0 or 148 µM Al3+, which correspond to free Al3+ activities of {0} and {27} µM (values inside brackets indicate Al3+ activity estimated with the speciation software, GEOCHEM-EZ47). The experiments consisted of a completely randomized design with two replications and seven plants per replication.

Hydroponic assessment of Al tolerance was undertaken as described in40. Briefly, seeds of each genotype were germinated for four days and seedlings were transferred to containers with nutrient solution lacking Al at pH 4.0, which were placed in a growth chamber with 27 °C day and 20 °C night temperatures, light intensity of 330 mmol photons m−2 s−1, under a 12 h photoperiod. After 24 h of acclimation, the initial length of each seedling’s root growing in control solution (ilc) was measured. The solution was then replaced by nutrient solution of identical composition but containing either no Al or {27} µM Al3+ supplied as AlK(SO4)2.12H2O. Final root lengths under Al treatment (flAl) or control solution (flc) were obtained after five days of exposure to Al. For each line, mean values of relative net root growth (RNRG) were estimated by dividing net root growth under Al treatment (flAl - ilc) by net root growth without Al (flc - ilc) and expressed in percentage. We classified each sorghum accession for Al tolerance as described previously by Caniato et al.26: Al sensitive (RNRG < 30%), intermediately tolerant (30% < RNRG < 80%, designated intermediate) and Al tolerant (RNRG > 80%).

Population structure of sorghum accessions carrying superior Alt SB alleles

Accessions with favorable AltSB alleles were assessed for population structure. Caniato et al.26 previously assessed population structure of 254 sorghum accessions representative of the diversity present in cultivated sorghum. The same 38 simple sequence repeat (SSR) markers used in26 were used for genotyping the accessions identified in the present study by AltSB-based allele mining. These SSR markers belong to an SSR kit (http://sorghum.cirad.fr/SSR_kit/) developed within the Generation Challenge Programme (GCP) and are evenly distributed across the sorghum genome. Fragment sizes were determined based on migration relative to an internal size standard using the GeneMapper 3.5 software. Allele sizes obtained for 10 control lines were compared to the expected allele sizes posted on http://sorghum.cirad.fr/SSR_kit/alleles.html and a correction factor for each marker was imposed to normalize allele sizes so that accessions containing favorable AltSB alleles identified in the present study could be integrated into the26 database. A Bayesian cluster analysis implemented in the software STRUCTURE48 was used to estimate population structure. This analysis was undertaken based on previous information on population structure in sorghum described in26, which revealed six subpopulations featuring distinct racial and geographic origins. The admixture model with correlated allele frequencies was adopted, with burn-in length 100,000 and 100,000 run length, for k equal 6.

Physiological and Molecular Analyses

We selected 16 sorghum accessions representing different AltSB haplotypes for further physiological and molecular analyses related to SbMATE-mediated Al exclusion from root apices. The 16 sorghum accessions included the most Al tolerant accessions in27 and the Al sensitive lines, BR007 and BR012. The final set encompassed six of the eight AltSB haplotypes described in27 and included H5 and H6/H8, which are the haplotypes most frequently found in the Al tolerant accessions identified by allele mining in our largest germplasm set, the SAP (H5, H6/H8; Supplementary Table S4). Accession designations with the respective haplotypes (H) are: BR007 (H2), BR012 (H1), SC112 (H2), IS29691 (H2), IS21519 (H5), IS25077 (H4), IS10801 (H5), SC283 (H5), IS14351 (H5), IS23142 (H1), IS26457 (H6), IS26554 (H6), SC549 (H6), SC566 (H8), 9929030 (H8) and SC175 (H2), which were characterized for Al tolerance in nutrient solution as described previously.

Root Organic Acid Exudation

Seeds were germinated as described above. Roots of seven uniform seedlings of each accession were inserted through holes (2 mm) drilled on the bottom of polyethylene cups, which were transferred to containers filled with 8 L of nutrient solution lacking Al (pH 4.0). These containers were then placed in a growth chamber with 27 °C day and 20 °C night temperatures, light intensity of 330 mmol photons m−2 s−1, and a 12 h photoperiod. After 24 h of acclimation, the solution was replaced by nutrient solution of identical composition but containing either no Al or {27} µM Al3+ supplied as AlK(SO4)2.12H2O. After five days of exposure to Al, the plastics cups were transferred to falcon tubes filled with 45 mL of a simple salt solution containing 4.3 mM CaCl2 pH 4.5 with or without {27} µM of Al3+. Exudate collection was allowed to proceed for 6 h for each experimental unit consisting of seven plants per accession, with three replications. After 6 hours, the exudate solutions were collected and passed through an OnGuard II Ag anionic silver chromatograph column (Dionex, http://www.dionex.com.br/) and then treated with a Dowex® 50WX8 cationic resin (Sigma Aldrich, http://www.dionex.com.br/). Subsequently, 1 mL sub-samples were lyophilized and resuspended in 0.1 mL ultrapure water. Organic acid analysis was performed using a capillary electrophoresis system as described in49. Root apices were collected for assessing root tip Al accumulation as described below.

Al Accumulation in Root Apices

Seed germination and cultivation in hydroponics were as described above for quantitation of Al tolerance. For assessing root tip Al accumulation, each experimental unit consisted of seven plants per accession, with 3 replications. After five days of exposure to nutrient solution containing {27} µM Al3 at pH 4.0, seedling roots were washed with 8 L of ultra-pure water for 15 min, under aeration. A second washing period of 25 min was allowed to proceed with fresh ultra-pure water. The first centimeter of the primary roots was collected and oven-dried at 55 °C overnight. Dry weights were determined using a microgram balance (MT2; Mettler, Greifensee, Switzerland). Dry samples were digested with 100 µL of 70% (w/v) perchloric acid, resuspended in 2 mL of 0.5% (w/v) nitric acid and analyzed using an inductively coupled argon plasma emission spectrometer (Model 51000, Perkin-Elmer/Sciex, Norwalk, CT).

SbMATE expression profile

Sorghum seedlings were grown following the same procedures used for assessing Al tolerance in nutrient solution containing {27} µM Al3+. Each experimental unit (accession) consisted of the first centimeter of root apices collected from 28 intact plants, five days after Al3+ exposure. These 28 plants per genotype were divided into four sets (seven plants per set) and each set was randomized inside a growth chamber. Total RNA was isolated from tissue samples using the RNeasy Plant Mini Kit (Qiagen, Valencia, CA) and 10 U of DNase I (RNase free) from the same manufacturer was added to each sample following incubation at room temperature for 15 min. First-strand cDNA was synthesized using 2 mg of total RNA with the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA). SbMATE transcripts were quantified with using the TaqMan Gene Expression kit on the ABI Prism 7500 Real Time PCR System (Applied Biosystems, Foster City, CA). A series of cDNA dilutions were used for making standard curves both for SbMATE transcripts and for 18S RNA which was used as the internal reference. Then, the selected dilution for specific cDNA samples (10 ng for SbMATE transcripts and 0.01 ng for 18S RNA) were used as real-time PCR templates to quantify relative transcript levels following the conditions recommended by the manufacturer. The forward (F) and reverse (R) primers, as well as the probe sequences are F: 59-CAG CCATTGCCCATGTTCTTT-39, R: 59-ACCAGCTTGCTCAGCATTATCA-39 and Probe: 6FAM- CCCAGTACCTGATAACGC-TAMRA. Levels of expression for endogenous 18S RNA were determined using TaqMan Ribosomal RNA Control Reagents (Applied Biosystems, Foster City, CA). Distilled water or products of room temperature reactions without reverse transcriptase were used as negative controls. SbMATE transcript levels were normalized to endogenous 18S RNA and SbMATE expression relative to that in the Al sensitive accession, BR012, was calculated. Three technical reps were used. The experiment was repeated twice with similar results.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.