Genetic Analysis of High Protein Content in ‘AC Proteus’ Related Soybean Populations Using SSR, SNP, DArT and DArTseq Markers

Key message: Several AC Proteus derived genomic regions (QTLs, SNPs) have been identified which may prove useful for further development of high yielding high protein cultivars and allele-specific marker developments. High seed protein content is a trait which is typically difficult to introgress into soybean without an accompanying reduction in seed yield. In a previous study, ‘AC Proteus’ was used as a high protein source and was found to produce populations that did not exhibit the typical association between high protein and low yield. Five high x low protein RIL populations and a high x high protein RIL population were evaluated by either quantitative trait locus (QTL) analysis or bulk segregant analyses (BSA) following phenotyping in the field. QTL analysis in one population using SSR, DArT and DArTseq markers found two QTLs for seed protein content on chromosomes 15 and 20. The BSA analyses suggested multiple genomic regions are involved with high protein content across the five populations, including the two previously mentioned QTLs. In an alternative approach to identify high protein genes, pedigree analysis identified SNPs for which the allele associated with high protein was retained in seven high protein descendants of AC Proteus on chromosomes 2, 17 and 18. Aside from the two identified QTLs (five genomic regions in total considering the two with highly elevated test statistic, but below the statistical threshold and the one with epistatic interactions) which were some distance from Meta-QTL regions and which were also supported by our BSA analysis within five populations. These high protein regions may prove useful for further development of high yielding high protein cultivars

The α’ subunit of β-conglycinin and various glycinin subunits of soy are not required to modulate hepatic lipid metabolism in rats

Purpose: This study examined the effect of soy proteins with depletion of different subunits of the two major storage proteins, β-conglycinin and glycinin, on hepatic lipids and proteins involved in lipid metabolism in rats, since the bioactive component of soy responsible for lipid-lowering is unclear. Methods: Weanling Sprague Dawley rats were fed diets containing either 20% casein protein in the absence (casein) or presence (casein + ISF) of isoflavones or 20% alcohol-washed soy protein isolate (SPI) or 20% soy protein concentrates derived from a conventional (Haro) or 2 soybean lines lacking the α’ subunit of β-conglycinin and the A1-3 (1TF) or A1-5 (1a) subunits of glycinin. After 8 weeks, the rats were necropsied and liver proteins and lipids were extracted and analysed. Results: The results showed that soy protein diets reduced lipid droplet accumulation and content in the liver compared to casein diets. The soy protein diets also decreased the level of hepatic mature SREBP-1 and FAS in males, with significant decreases in diets 1TF and 1a compared to the casein diets. The effect of the soy protein diets on female hepatic mature SREBP-1, FAS, and HMGCR was confounded since casei

Mapping and identification of a potential candidate gene for a novel maturity locus, E10, in soybean

Key message: E10 is a new maturity locus in soybean and FT4 is the predicted/potential functional gene underlying the locus.Abstract: Flowering and maturity time traits play crucial roles in economic soybean production. Early maturity is critical for north and west expansion of soybean in Canada. To date, 11 genes/loci have been identified which control time to flowering and maturity; however, the molecular bases of almost half of them are not yet clear. We have identified a new maturity locus called “E10” located at the end of chromosome Gm08. The gene symbol E10e10 has been approved by the Soybean Genetics Committee. The e10e10 genotype results in 5–10 days earlier maturity than E10E10. A set of presumed E10E10 and e10e10 genotypes was used to identify contrasting SSR and SNP haplotypes. These haplotypes, and their association with maturity, were maintained through five backcross generations. A functional genomics approach using a predicted protein–protein interaction (PPI) approach (Protein–protein Interaction Prediction Engine, PIPE) was used to investigate approximately 75 genes located in the genomic region that SSR and SNP analyses identified as the location of the E10 locus. The PPI analysis identified FT4 as the most likely candidate gene underlying the E10 locus. Sequence analysis of the two FT4 alleles identified three SNPs, in the 5′UTR, 3′UTR and fourth exon in the coding region, which result in differential mRNA structures. Allele-specific markers were developed for this locus and are available for soybean breeders to efficiently develop earlier maturing cultivars using molecular marker assisted breeding

PIPE4: Fast PPI Predictor for Comprehensive Inter- and Cross-Species Interactomes

The need for larger-scale and increasingly complex protein-protein interaction (PPI) prediction tasks demands that state-of-the-art predictors be highly efficient and adapted to inter- and cross-species predictions. Furthermore, the ability to generate comprehensive interactomes has enabled the appraisal of each PPI in the context of all predictions leading to further improvements in classification performance in the face of extreme class imbalance using the Reciprocal Perspective (RP) framework. We here describe the PIPE4 algorithm. Adaptation of the PIPE3/MP-PIPE sequence preprocessing step led to upwards of 50x speedup and the new Similarity Weighted Score appropriately normalizes for window frequency when applied to any inter- and cross-species prediction schemas. Comprehensive interactomes for three prediction schemas are generated: (1) cross-species predictions, where Arabidopsis thaliana is used as a proxy to predict the comprehensive Glycine max interactome, (2) inter-species predictions between Homo sapiens-HIV1, and (3) a combined schema involving both cross- and inter-species predictions, where both Arabidopsis thaliana and Caenorhabditis elegans are used as proxy species to predict the interactome between Glycine max (the soybean legume) and Heterodera glycines (the soybean cyst nematode). Comparing PIPE4 with the state-of-the-art resulted in improved performance, indicative that it should be the method of choice for complex PPI prediction schemas