iSoybean: A database for the mutational fingerprints of soybean

Soybean (Glycine max L. Merrill) is one of the most important commercial crops worldwide. However, soybean has undergone severe genetic bottlenecks during domestication (Hyten et al., 2006). It is essential to exploit novel sources of genetic diversity and to expand gene pools for soybean improvement. Plant mutation breeding has been widely used by plant breeders to create novel genetic diversity. Ethyl methanesulfonate (EMS) is a chemical mutagen believed to mainly induce point mutations, which is commonly used to develop mutant populations in soybean (Li et al., 2017; Tsuda et al., 2015). However, lack of genome-wide characterization of mutations restricts the utilization of these mutant populations in the soybean community. To provide novel genetic diversity for soybean breeding, we developed an EMS-induced mutant population and performed whole-genome sequencing (WGS) of 1044 mutant lines for the characterization of induced mutations (Figure 1a). About 21.5% of plants showed visual phenotypic variation in the M2 population, including leaf morphology, plant architecture and seed shape (Figure S1). On average, 76 million reads (11.4 Gb) were generated for each mutant line, resulting in an average sequencing depth of 11.2x (Table S1). In total, 6 774 731 mutations including 3 141 030 homozygous and 3 633 701 heterozygous mutations were pinpointed in 1044 mutant lines, giving an average mutation density of ~1 mutation per 150 kb for each mutant line (~6.7 mutations per kb for 1044 mutant lines) (Figure 1b, Table S1). EMS primarily induces GC > AT transitions. Totally, 4 801 170 GC > AT mutations (71% of total mutations) were detected in EMS-treated mutant population (Table S1). To examine the error rate for mutation identification, we randomly selected 105 GC > AT and 45 non-GC > AT mutations for validation using Sanger sequencing (Table S2). Among them, 104 GC > AT (99%) and 43 non-GC > AT (96%) mutations were confirmed to be positive, suggesting low error rate for identification of both GC > AT and non-GC > AT mutations in this study. In addition to point mutations, we identified 22 373 small Indels (<50 bp), representing an average of 21.2 small Indels per mutant line (Figure S2a). A total of 1018 genes were found to be affected by 1034 small Indels. Compared with point mutations, small Indels were relatively rare in the mutant population (Figure S2b). Previous studies confirmed EMS mutagenesis could induce large structural variations in rice and wheat (Henry et al., 2014). Through the calculation of coverage variation along chromosomes, we detected 37 large deletions (>20 kb) in 33 mutant lines (Figure 1c). Totally, 401 genes were knocked out by these large deletions (Table S3). To further analyse the effect of mutations on gene functions, we classified the mutations in gene models into truncation mutations (stop gained, start loss and mis-splicing), missense mutations and synonymous mutations (Figure 1d). We identified 34 178 truncation mutations, affecting 22 092 protein-coding genes which account for 41.8% of all soybean genes in reference genome (Figure 1d). In addition, there were 87% (48 613 genes) of soybean genes affected by 385 142 missense mutations. In total, 92.9% of soybean genes were affected by truncation or missense mutation, of which 85% of soybean genes contained two or more non-synonymous mutations (Figure S3). For example, we observed larger seeds by knockout of GmKIX8-1 in mutant NJAU1840 and early flowering due to knockout of GmE1 in mutant NJAU0143 as reported in previous studies (Figure 1e, Figure S4) (Nguyen et al., 2021; Xia et al., 2012). The high density of mutations in the gene regions could facilitate functional genomics through forward and reverse genetic approaches. As an example, we examined DNA methylation changes by mutations in genes involved in DNA methylation (Figure S5). In plants, DNA methylation is catalysed in CG, CHG and CHH contexts through maintenance and de novo pathways (Figure S5). All homologous genes contained at least one truncation or missense mutation in our mutant population (Figure S5). To examine the effects of these mutations on DNA methylation, we analysed genome-wide DNA methylation changes by truncation mutations of GmDCL3 (Glyma.04G057400), GmMET1a (Glyma.04G187600) and GmCMT2a (Glyma.16G103500), compared with wild type (WT) (Figure S6). No obvious DNA methylation changes in gene region were observed in gmmet1a and gmcmt2a mutants compared with WT (Figure 1f), which may be due to gene redundancy of GmMET1 and GmCMT2 in soybean genome (Figure S5). There is only one homologue of Arabidopsis DCL3 gene in soybean. Expectedly, gmdcl3 mutant showed much lower CHH methylation levels in the gene region than WT (Figure 1f). Consistent with the function of DCL3 in the generation of 24-nt small RNAs (smRNAs), small RNA-seq analysis revealed a substantial decrease of 24-nt smRNAs in gmdcl3 mutant compared with WT (Figure 1g). These results demonstrate the feasibility of this mutant population to elucidate gene function through reverse genetics.

Soybean (Glycine max L. Merrill) is one of the most important commercial crops worldwide. However, soybean has undergone severe genetic bottlenecks during domestication (Hyten et al., 2006). It is essential to exploit novel sources of genetic diversity and to expand gene pools for soybean improvement. Plant mutation breeding has been widely used by plant breeders to create novel genetic diversity. Ethyl methanesulfonate (EMS) is a chemical mutagen believed to mainly induce point mutations, which is commonly used to develop mutant populations in soybean (Li et al., 2017;Tsuda et al., 2015). However, lack of genome-wide characterization of mutations restricts the utilization of these mutant populations in the soybean community.
To provide novel genetic diversity for soybean breeding, we developed an EMS-induced mutant population and performed whole-genome sequencing (WGS) of 1044 mutant lines for the characterization of induced mutations ( Figure 1a). About 21.5% of plants showed visual phenotypic variation in the M2 population, including leaf morphology, plant architecture and seed shape ( Figure S1). On average, 76 million reads (11.4 Gb) were generated for each mutant line, resulting in an average sequencing depth of 11.2x (Table S1). In total, 6 774 731 mutations including 3 141 030 homozygous and 3 633 701 heterozygous mutations were pinpointed in 1044 mutant lines, giving an average mutation density of~1 mutation per 150 kb for each mutant line (~6.7 mutations per kb for 1044 mutant lines) ( Figure 1b, Table S1). EMS primarily induces GC > AT transitions. Totally, 4 801 170 GC > AT mutations (71% of total mutations) were detected in EMS-treated mutant population (Table S1). To examine the error rate for mutation identification, we randomly selected 105 GC > AT and 45 non-GC > AT mutations for validation using Sanger sequencing (Table S2). Among them, 104 GC > AT (99%) and 43 non-GC > AT (96%) mutations were confirmed to be positive, suggesting low error rate for identification of both GC > AT and non-GC > AT mutations in this study.
In addition to point mutations, we identified 22 373 small Indels (<50 bp), representing an average of 21.2 small Indels per mutant line ( Figure S2a). A total of 1018 genes were found to be affected by 1034 small Indels. Compared with point mutations, small Indels were relatively rare in the mutant population ( Figure S2b). Previous studies confirmed EMS mutagenesis could induce large structural variations in rice and wheat (Henry et al., 2014). Through the calculation of coverage variation along chromosomes, we detected 37 large deletions (>20 kb) in 33 mutant lines ( Figure 1c). Totally, 401 genes were knocked out by these large deletions (Table S3).
To further analyse the effect of mutations on gene functions, we classified the mutations in gene models into truncation mutations (stop gained, start loss and mis-splicing), missense mutations and synonymous mutations (Figure 1d). We identified 34 178 truncation mutations, affecting 22 092 protein-coding genes which account for 41.8% of all soybean genes in reference genome (Figure 1d). In addition, there were 87% (48 613 genes) of soybean genes affected by 385 142 missense mutations. In total, 92.9% of soybean genes were affected by truncation or missense mutation, of which 85% of soybean genes contained two or more non-synonymous mutations ( Figure S3). For example, we observed larger seeds by knockout of GmKIX8-1 in mutant NJAU1840 and early flowering due to knockout of GmE1 in mutant NJAU0143 as reported in previous studies (Figure 1e, Figure S4) (Nguyen et al., 2021;Xia et al., 2012).
The high density of mutations in the gene regions could facilitate functional genomics through forward and reverse genetic approaches. As an example, we examined DNA methylation changes by mutations in genes involved in DNA methylation ( Figure S5). In plants, DNA methylation is catalysed in CG, CHG and CHH contexts through maintenance and de novo pathways ( Figure S5). All homologous genes contained at least one truncation or missense mutation in our mutant population ( Figure S5). To examine the effects of these mutations on DNA methylation, we analysed genome-wide DNA methylation changes by truncation mutations of GmDCL3 (Glyma.04G057400), GmMET1a (Glyma.04G187600) and GmCMT2a (Glyma.16G103500), compared with wild type (WT) ( Figure S6). No obvious DNA methylation changes in gene region were observed in gmmet1a and gmcmt2a mutants compared with WT (Figure 1f), which may be due to gene redundancy of GmMET1 and GmCMT2 in soybean genome ( Figure S5). There is only one homologue of Arabidopsis DCL3 gene in soybean. Expectedly, gmdcl3 mutant showed much lower CHH methylation levels in the gene region than WT (Figure 1f). Consistent with the function of DCL3 in the generation of 24-nt small RNAs (smRNAs), small RNA-seq analysis revealed a substantial decrease of 24-nt smRNAs in gmdcl3 mutant compared with WT ( Figure 1g). These results demonstrate the feasibility of this mutant population to elucidate gene function through reverse genetics. To make the mutant population available to soybean researchers, we established a website named iSoybean (www.isoybean. org) (Figure 1h). Users can search for mutations for a specific gene or browse all mutations in genomes using a JBrowse graphic interface. The desired mutant seeds can be freely requested from the Nanjing Agricultural University using iSoybean website. In conclusion, our sequenced mutant population provides valuable open-access resource for mutation discovery and will facilitate functional genomic studies to promote genetic breeding in soybean.     Figure S5 Mutations identified in genes involved in DNA methylation and demethylation. Genes in red indicate genes carrying truncation mutations, including stop gained, mis-splicing and start lost. Genes in brown indicate genes carrying missense mutations. Figure S6 Validation of truncation mutations in DNA methylation-related genes. (a-c) Sanger sequencing validation of truncation mutations in GmMET1a (a), GmCMT2a (b) and GmDCL3 (c) respectively. Black triangles indicate positions of mutations on genes. The retained intron in mature mRNA in gmdcl3 mutant is indicated in blue. Appendix S1 Supplementary methods.