The genetic architecture of soybean photothermal adaptation to high latitudes.

Soybean is a major plant protein source for both human food and animal feed, but to meet global demands as well as a trend towards regional production, soybean cultivation needs to be expanded to higher latitudes. In this study, we developed a large diversity panel consisting of 1503 early-maturing soybean lines and used genome-wide association mapping to dissect the genetic architecture underlying two crucial adaptation traits, flowering time and maturity. This revealed several known maturity loci, E1, E2, E3, and E4, and the growth habit locus Dt2 as causal candidate loci, and also a novel putative causal locus, GmFRL1, encoding a homolog of the vernalization pathway gene FRIGIDA-like 1. In addition, the scan for quantitative trait locus (QTL)-by-environment interactions identified GmAPETALA1d as a candidate gene for a QTL with environment-dependent reversed allelic effects. The polymorphisms of these candidate genes were identified using whole-genome resequencing data of 338 soybeans, which also revealed a novel E4 variant, e4-par, carried by 11 lines, with nine of them originating from Central Europe. Collectively, our results illustrate how combinations of QTL and their interactions with the environment facilitate the photothermal adaptation of soybean to regions far beyond its center of origin.

Maximization through optimization? On the relationship between hybrid performance and parental genetic distance.

Heterosis is the improved performance of hybrids compared with their parental components and is widely exploited in agriculture. According to quantitative genetic theory, genetic distance between parents at heterotic quantitative trait loci is required for heterosis, but how heterosis varies with genetic distance has remained elusive, despite intensive research on the topic. Experimental studies have often found a positive association between heterosis and genetic distance that, however, varied in strength. Most importantly, it has remained unclear whether heterosis increases continuously with genetic distance or whether there is an optimum genetic distance after which heterosis declines again. Here, we revisit the relationship between heterosis and genetic distance and provide perspectives on how to maximize heterosis and hybrid performance in breeding, as well as the consequences for the design of heterotic groups and the utilization of more exotic material and genetic resources.

Characteristics of plant circular RNAs.

Circular RNA (circRNA) is a kind of covalently closed single-stranded RNA molecules that have been proved to play important roles in transcriptional regulation of genes in diverse species. With the rapid development of bioinformatics tools, a huge number (95143) of circRNAs have been identified from different plant species, providing an opportunity for uncovering the overall characteristics of plant circRNAs. Here, based on publicly available circRNAs, we comprehensively analyzed characteristics of plant circRNAs with the help of various bioinformatics tools as well as in-house scripts and workflows, including the percentage of coding genes generating circRNAs, the frequency of alternative splicing events of circRNAs, the non-canonical splicing signals of circRNAs and the networks involving circRNAs, miRNAs and mRNAs. All this information has been integrated into an upgraded online database, PlantcircBase 3.0 (http://ibi.zju.edu.cn/plantcircbase/). In this database, we provided browse, search and visualization tools as well as a web-based blast tool, BLASTcirc, for prediction of circRNAs from query sequences based on searching against plant genomes and transcriptomes.

The performance of phenomic selection depends on the genetic architecture of the target trait.

KEY MESSAGE: The phenomic predictive ability depends on the genetic architecture of the target trait, being high for complex traits and low for traits with major QTL. Genomic selection is a powerful tool to assist breeding of complex traits, but a limitation is the costs required for genotyping. Recently, phenomic selection has been suggested, which uses spectral data instead of molecular markers as predictors. It was shown to be competitive with genomic prediction, as it achieved predictive abilities as high or even higher than its genomic counterpart. The objective of this study was to evaluate the performance of phenomic prediction for triticale and the dependency of the predictive ability on the genetic architecture of the target trait. We found that for traits with a complex genetic architecture, like grain yield, phenomic prediction with NIRS data as predictors achieved high predictive abilities and performed better than genomic prediction. By contrast, for mono- or oligogenic traits, for example, yellow rust, marker-based approaches achieved high predictive abilities, while those of phenomic prediction were very low. Compared with molecular markers, the predictive ability obtained using NIRS data was more robust to varying degrees of genetic relatedness between the training and prediction set. Moreover, for grain yield, smaller training sets were required to achieve a similar predictive ability for phenomic prediction than for genomic prediction. In addition, our results illustrate the potential of using field-based spectral data for phenomic prediction. Overall, our result confirmed phenomic prediction as an efficient approach to improve the selection gain for complex traits in plant breeding.

Genetic Dissection of Phosphorus Use Efficiency and Genotype-by-Environment Interaction in Maize.

Genotype-by-environment interaction (G-by-E) is a common but potentially problematic phenomenon in plant breeding. In this study, we investigated the genotypic performance and two measures of plasticity on a phenotypic and genetic level by assessing 234 maize doubled haploid lines from six populations for 15 traits in seven macro-environments with a focus on varying soil phosphorus levels. It was found intergenic regions contributed the most to the variation of phenotypic linear plasticity. For 15 traits, 124 and 31 quantitative trait loci (QTL) were identified for genotypic performance and phenotypic plasticity, respectively. Further, some genes associated with phosphorus use efficiency, such as Zm00001eb117170, Zm00001eb258520, and Zm00001eb265410, encode small ubiquitin-like modifier E3 ligase were identified. By significantly testing the main effect and G-by-E effect, 38 main QTL and 17 interaction QTL were identified, respectively, in which MQTL38 contained the gene Zm00001eb374120, and its effect was related to phosphorus concentration in the soil, the lower the concentration, the greater the effect. Differences in the size and sign of the QTL effect in multiple environments could account for G-by-E. At last, the superiority of G-by-E in genomic selection was observed. In summary, our findings will provide theoretical guidance for breeding P-efficient and broadly adaptable varieties.

Expansion of MIR482/2118 by a class-II transposable element in cotton.

Some plant microRNA (miRNA) families contain multiple members generating identical or highly similar mature miRNA variants. Mechanisms underlying the expansion of miRNA families remain elusive, although tandem and/or segmental duplications have been proposed. In this study of two tetraploid cottons, Gossypium hirsutum and Gossypium barbadense, and their extant diploid progenitors, Gossypium arboreum and Gossypium raimondii, we investigated the gain and loss of members of the miR482/2118 superfamily, which modulates the expression of nucleotide-binding site leucine-rich repeat (NBS-LRR) disease resistance genes. We found significant expansion of MIR482/2118d in G. barbadense, G. hirsutum and G. raimondii, but not in G. arboreum. Several newly expanded MIR482/2118d loci have mutated to produce different miR482/2118 variants with altered target-gene specificity. Based on detailed analysis of sequences flanking these MIR482/2118 loci, we found that this expansion of MIR482/2118d and its derivatives resulted from an initial capture of an MIR482/2118d by a class-II DNA transposable element (TE) in G. raimondii prior to the tetraploidization event, followed by transposition to new genomic locations in G. barbadense, G. hirsutum and G. raimondii. The 'GosTE' involved in the capture and proliferation of MIR482/2118d and its derivatives belongs to the PIF/Harbinger superfamily, generating a 3-bp target site duplication upon insertion at new locations. All orthologous MIR482/2118 loci in the two diploids were retained in the two tetraploids, but mutation(s) in miR482/2118 were observed across all four species as well as in different cultivars of both G. barbadense and G. hirsutum, suggesting a dynamic co-evolution of miR482/2118 and its NBS-LRR targets. Our results provide fresh insights into the mechanisms contributing to MIRNA proliferation and enrich our knowledge on TEs.

Genome-wide identification of oil biosynthesis-related long non-coding RNAs in allopolyploid Brassica napus.

BACKGROUND: Long noncoding RNAs (lncRNAs) are transcripts longer than 200 bp that do not encode proteins but nonetheless have been shown to play important roles in various biological processes in plants. Brassica napus is an important seed oil crop worldwide and the target of many genetic improvement activities. To understand better the function of lncRNAs in regulating plant metabolic activities, we carried out a genome-wide lncRNA identification of lncRNAs in Brassica napus with a focus on lncRNAs involved in lipid metabolism. Twenty ribosomal RNA depleted strand specific RNA-seq (ssRNA-seq) datasets were generatred using RNAs isolated from B. napus seeds at four developmental stages. For comparison we also included 30 publically available RNA-seq datasets generated from poly(A) enriched mRNAs isolated from from various Brassica napus tissues in our analysis. RESULTS: A total of 8905 lncRNA loci were identified, including 7100 long intergenic noncoding RNA (lincRNA) loci and 1805 loci generating long noncoding natural antisense transcript (lncNAT). Many lncRNAs were identified only in the ssRNA-seq and poly(A) RNA-seq dataset, suggesting that B. napus has a large lncRNA repertoire and it is necessary to use libraries prepared from different tissues and developmental stages as well as different library preparation approaches to capture the whole spectrum of lncRNAs. Analysis of coexpression networks revealed that among the regulatory modules are networks containing lncRNAs and protein-coding genes related to oil biosynthesis indicating a possible role of lncRNAs in the control of lipid metabolism. One such example is that several lncRNAs are potential regulators of BnaC08g11970D that encodes oleosin1, a protein found in oil bodies and involved in seed lipid accumulation. We also observed that the expression levels of B. napus lncRNAs is positively correlated with their conservation levels. CONCLUSIONS: We demonstrated that the B. napus genome has a large number of lncRNA and that these lncRNAs are expressed broadly across many developmental times and in different tissue types. We also provide evidence indicating that specific lncRNAs appear to be important regulators of lipid biosynthesis forming regulatory networks with transcripts involved in lipid biosynthesis. We also provide evidence that these lncRNAs are conserved in other species of the Brassicaceae family.

Unraveling the potential of phenomic selection within and among diverse breeding material of maize (Zea mays L.).

Genomic selection is a well-investigated approach that facilitates and supports selection decisions for complex traits and has meanwhile become a standard tool in modern plant breeding. Phenomic selection has only recently been suggested and uses the same statistical procedures to predict the targeted traits but replaces marker data with near-infrared spectroscopy data. It may represent an attractive low-cost, high-throughput alternative but has not been sufficiently studied until now. Here, we used 400 genotypes of maize (Zea mays L.) comprising elite lines of the Flint and Dent heterotic pools as well as 6 Flint landraces, which were phenotyped in multienvironment trials for anthesis-silking-interval, early vigor, final plant height, grain dry matter content, grain yield, and phosphorus concentration in the maize kernels, to compare the predictive abilities of genomic as well as phenomic prediction under different scenarios. We found that both approaches generally achieved comparable predictive abilities within material groups. However, phenomic prediction was less affected by population structure and performed better than its genomic counterpart for predictions among diverse groups of breeding material. We therefore conclude that phenomic prediction is a promising tool for practical breeding, for instance when working with unknown and rather diverse germplasm. Moreover, it may make the highly monopolized sector of plant breeding more accessible also for low-tech institutions by combining well established, widely available, and cost-efficient spectral phenotyping with the statistical procedures elaborated for genomic prediction - while achieving similar or even better results than with marker data.

Training set design in genomic prediction with multiple biparental families.

Genomic selection is a powerful tool to reduce the cycle length and enhance the genetic gain of complex traits in plant breeding. However, questions remain about the optimum design and composition of the training set. In this study, we used 944 soybean [Glycine max (L.) Merr.] recombinant inbred lines from eight families derived through a partial-diallel mating design among five parental lines. The cross-validated prediction accuracies for the six traits seed yield, 1,000-seed weight, protein yield, plant height, protein content, and oil content were high, ranging from 0.79 to 0.87. We investigated among-family predictions, making use of the special mating design with different degrees of relatedness among families. Generally, the prediction accuracy decreased from full-sibs to half-sib families to unrelated families. However, half-sib and unrelated families also showed substantial variation in their prediction accuracy for a given family, which appeared to be caused at least in part by the shared segregation of quantitative trait loci in both the training and prediction sets. Combining several half-sib families in composite training sets generally led to an increase in the prediction accuracy compared with the best family alone. The prediction accuracy increased with the size of the training set, but for comparable prediction accuracy, substantially more half-sibs were required than full-sibs. Collectively, our results highlight the potential of genomic selection for soybean breeding and, in a broader context, illustrate the importance of the targeted design of the training set.

Hesperetin, a SIRT1 activator, inhibits hepatic inflammation via AMPK/CREB pathway.

Silent mating type information regulation 2 homolog 1 (SIRT1) is an important inflammatory regulator, which epigenetically reprograms inflammation by altering the acetylation of NF-κB. Hesperetin, as a common flavonoid, has been proven to have a significant effect on acute inflammatory diseases. However, the detailed molecular mechanism by which hesperetin alleviates inflammatory response and accompanied tissue injury is poorly understood. Our results show that SIRT1 is required for the inhibitory effect of hesperetin on inflammation. Hesperetin suppresses the acetylation of RelA/p65 to reduce NF-κB activity by inducing SIRT1 expression. Mechanistically, hesperetin increases SIRT1 expression through AMPK/CREB pathway. Additionally, the protective effect of hesperetin against LPS/D-GalN-induced hepatitis in mice is also dependent on SIRT1. Our study suggests that hesperetin is an SIRT1 activator and could be potential candidates for the treatments of inflammatory conditions.

The flavonoid-rich Quzhou Fructus Aurantii extract modulates gut microbiota and prevents obesity in high-fat diet-fed mice.

BACKGROUND: Flavonoids are reported to modulate the composition of gut microbiota, which play an important role in preventing obesity and associated metabolic diseases. In this study, we investigated the effect of Total Flavonoids of Quzhou Fructus Aurantii Extract (TFQ) on gut microbial community in mice fed with a high-fat diet (HFD). METHODS: C57BL/6J mice were fed with either a chow diet or HFD with or without oral gavage of TFQ (300 mg/kg/day) for 12 weeks. RESULTS: Our data indicate TFQ significantly reduced obesity, inflammatio,n and liver steatosis. TFQ elevates the expression of tight junction proteins and reduces metabolic endotoxemia. In addition, TFQ treatment reverses HFD-induced gut dysbiosis, as indicated by the reduction of Firmicutes to Bacteroidetes ratio, the increase of genera Akkermansia and Alistipes, and the decrease of genera Dubosiella, Faecalibaculum, and Lactobacillus. CONCLUSION: These findings support a prebiotic role of TFQ as a dietary supplement for the intervention of gut dysbiosis and obesity-related metabolic disorders.