Genetic dissection of the gene coexpression network underlying photosynthesis in Populus

Photosynthesis is a key reaction that ultimately generates the carbohydrates needed to form woody tissues in trees. However, the genetic regulatory network of protein‐encoding genes (PEGs) and regulatory noncoding RNAs (ncRNAs), including microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), underlying the photosynthetic pathway is unknown. Here, we integrated data from coexpression analysis, association studies (additive, dominance and epistasis), and expression quantitative trait nucleotide (eQTN) mapping to dissect the causal variants and genetic interaction network underlying photosynthesis in Populus. We initially used 30 PEGs, 6 miRNAs and 12 lncRNAs to construct a coexpression network based on the tissue‐specific gene expression profiles of 15 Populus samples. Then, we performed association studies using a natural population of 435 unrelated Populus tomentosa individuals, and identified 72 significant associations (P ≤ 0.001, q ≤ 0.05) with diverse additive and dominance patterns underlying photosynthesis‐related traits. Analysis of epistasis and eQTNs revealed that the complex genetic interactions in the coexpression network contribute to phenotypes at various levels. Finally, we demonstrated that heterologously expressing the most highly linked gene (PtoPsbX1) in this network significantly improved photosynthesis in Arabidopsis thaliana, pointing to the functional role of PtoPsbX1 in the photosynthetic pathway. This study provides an integrated strategy for dissecting a complex genetic interaction network, which should accelerate marker‐assisted breeding efforts to genetically improve woody plants.     

A genome‐wide association study of 23 agronomic traits in Chinese wheat landraces

Uncovering the genetic basis of agronomic traits in wheat landraces is important for ensuring global food security via the development of improved varieties. Here, 723 wheat landraces from 10 Chinese agro‐ecological zones were evaluated for 23 agronomic traits in six environments. All accessions could be clustered into five subgroups based on phenotypic data via discriminant function analysis, which was highly consistent with genotypic classification. A genome‐wide association study was conducted for these traits using 52 303 DArT‐seq markers to identify marker‐trait associations and candidate genes. Using both the general linear model and the mixed linear model, 149 significant markers were identified for 21 agronomic traits based on best linear unbiased prediction values. Considering the linkage disequilibrium decay distance in this study, significant markers within 10 cM were combined as a quantitative trait locus (QTL), with a total of 29 QTL identified for 15 traits. Of these, five QTL for heading date, flag leaf width, peduncle length, and thousand kernel weight had been reported previously. Twenty‐five candidate genes associated with significant markers were identified. These included the known vernalization genes VRN‐B1 and vrn‐B3 and the photoperiod response genes Ppd and PRR. Overall, this study should be helpful in elucidating the underlying genetic mechanisms of complex agronomic traits and performing marker‐assisted selection in wheat.     

Detection of major loci associated with the variation of 18 important agronomic traits between Solanum pimpinellifolium and cultivated tomatoes

Wild species can be used to improve various agronomic traits in cultivars; however, a limited understanding of the genetic basis underlying the morphological differences between wild and cultivated species hinders the integration of beneficial traits from wild species. In the present study, we generated and sequenced recombinant inbred lines (RILs, 201 F₁₀ lines) derived from a cross between Solanum pimpinellifolium and Solanum lycopersicum tomatoes. Based on a high‐resolution recombination bin map to uncover major loci determining the phenotypic variance between wild and cultivated tomatoes, 104 significantly associated loci were identified for 18 agronomic traits. On average, these loci explained ~39% of the phenotypic variance of the RILs. We further generated near‐isogenic lines (NILs) for four identified loci, and the lines exhibited significant differences for the associated traits. We found that two loci could improve the flower number and inflorescence architecture in the cultivar following introgression of the wild‐species alleles. These findings allowed us to construct a trait–locus network to help explain the correlations among different traits based on the pleiotropic or linked loci. Our results provide insights into the morphological changes between wild and cultivated tomatoes, and will help to identify key genes governing important agronomic traits for the molecular selection of elite tomato varieties.     

OsPIN5b modulates rice (Oryza sativa) plant architecture and yield by changing auxin homeostasis,transport and distribution

Plant architecture attributes such as tillering, plant height and panicle size are important agronomic traits that determine rice (Oryza sativa) productivity. Here, we report that altered auxin content, transport and distribution affect these traits, and hence rice yield. Overexpression of the auxin efflux carrier‐like gene OsPIN5b causes pleiotropic effects, mainly reducing plant height, leaf and tiller number, shoot and root biomass, seed‐setting rate, panicle length and yield parameters. Conversely, reduced expression of OsPIN5b results in higher tiller number, more vigorous root system, longer panicles and increased yield. We show that OsPIN5b is an endoplasmic reticulum (ER) ‐localized protein that participates in auxin homeostasis, transport and distribution in vivo. This work describes an example of an auxin‐related gene where modulating its expression can simultaneously improve plant architecture and yield potential in rice, and reveals an important effect of hormonal signaling on these traits.     

Searching new signals for production traits through gene‐based association analysis in three Italian cattle breeds

Genome‐wide association studies (GWAS) have been widely applied to disentangle the genetic basis of complex traits. In cattle breeds, classical GWAS approaches with medium‐density marker panels are far from conclusive, especially for complex traits. This is due to the intrinsic limitations of GWAS and the assumptions that are made to step from the association signals to the functional variations. Here, we applied a gene‐based strategy to prioritize genotype–phenotype associations found for milk production and quality traits with classical approaches in three Italian dairy cattle breeds with different sample sizes (Italian Brown n = 745; Italian Holstein n = 2058; Italian Simmental n = 477). Although classical regression on single markers revealed only a single genome‐wide significant genotype–phenotype association, for Italian Holstein, the gene‐based approach identified specific genes in each breed that are associated with milk physiology and mammary gland development. As no standard method has yet been established to step from variation to functional units (i.e., genes), the strategy proposed here may contribute to revealing new genes that play significant roles in complex traits, such as those investigated here, amplifying low association signals using a gene‐centric approach.     

PAY1 improves plant architecture and enhances grain yield in rice

Plant architecture, a complex of the important agronomic traits that determine grain yield, is a primary target of artificial selection of rice domestication and improvement. Some important genes affecting plant architecture and grain yield have been isolated and characterized in recent decades; however, their underlying mechanism remains to be elucidated. Here, we report genetic identification and functional analysis of the PLANT ARCHITECTURE AND YIELD 1 (PAY1) gene in rice, which affects plant architecture and grain yield in rice. Transgenic plants over‐expressing PAY1 had twice the number of grains per panicle and consequently produced nearly 38% more grain yield per plant than control plants. Mechanistically, PAY1 could improve plant architecture via affecting polar auxin transport activity and altering endogenous indole‐3‐acetic acid distribution. Furthermore, introgression of PAY1 into elite rice cultivars, using marker‐assisted background selection, dramatically increased grain yield compared with the recipient parents. Overall, these results demonstrated that PAY1 could be a new beneficial genetic resource for shaping ideal plant architecture and breeding high‐yielding rice varieties.     

Genome-wide association study-based identification genes influencing agronomic traits in rice (Oryza sativa L.)

Rice is one of the most important cereal crops, providing the daily dietary intake for approximately 50% of the global human population. Here, we re-sequenced 259 rice accessions, generating 1371.65 Gb of raw data. Furthermore, we performed genome-wide association studies (GWAS) on 13 agronomic traits using 2.8 million single nucleotide polymorphisms (SNPs) characterized in 259 rice accessions. Phenotypic data and best linear unbiased prediction (BLUP) values of each of the 13 traits over two years of each trait were used for the GWAS. The results showed that 816 SNP signals were significantly associated with the 13 agronomic traits. Then we detected candidate genes related to target traits within 200 kb upstream and downstream of the associated SNP loci, based on linkage disequilibrium (LD) blocks in the whole rice genome. These candidate genes were further identified through haplotype block constructions. This comprehensive study provides a timely and important genomic resource for breeding high yielding rice cultivars.     

Genomewide landscape of gene–metabolome associations in Escherichia coli

Metabolism is one of the best‐understood cellular processes whose network topology of enzymatic reactions is determined by an organism's genome. The influence of genes on metabolite levels, however, remains largely unknown, particularly for the many genes encoding non‐enzymatic proteins. Serendipitously, genomewide association studies explore the relationship between genetic variants and metabolite levels, but a comprehensive interaction network has remained elusive even for the simplest single‐celled organisms. Here, we systematically mapped the association between > 3,800 single‐gene deletions in the bacterium Escherichia coli and relative concentrations of > 7,000 intracellular metabolite ions. Beyond expected metabolic changes in the proximity to abolished enzyme activities, the association map reveals a largely unknown landscape of gene–metabolite interactions that are not represented in metabolic models. Therefore, the map provides a unique resource for assessing the genetic basis of metabolic changes and conversely hypothesizing metabolic consequences of genetic alterations. We illustrate this by predicting metabolism‐related functions of 72 so far not annotated genes and by identifying key genes mediating the cellular response to environmental perturbations.     

High quality genome of Erigeron breviscapus provides a reference for herbal plants in Asteraceae

Erigeron breviscapus is an important medicinal plant in Compositae and the first species to realize the whole process from the decoding of the draft genome sequence to scutellarin biosynthesis in yeast. However, the previous low‐quality genome assembly has hindered the optimization of candidate genes involved in scutellarin synthesis and the development of molecular‐assisted breeding based on the genome. Here, the E. breviscapus genome was updated using PacBio RSII sequencing data and Hi‐C data, and increased in size from 1.2 Gb to 1.43 Gb, with a scaffold N50 of 156.82 Mb and contig N50 of 140.95 kb, and a total of 43,514 protein‐coding genes were obtained and oriented onto nine pseudo‐chromosomes, thus becoming the third plant species assembled to chromosome level after sunflower and lettuce in Compositae. Fourteen genes with evidence for positive selection were identified and found to be related to leaf morphology, flowering and secondary metabolism. The number of genes in some gene families involved in flavonoid biosynthesis in E. breviscapus have been significantly expanded. In particular, additional candidate genes involved in scutellarin biosynthesis, such as flavonoid‐7‐O‐glucuronosyltransferase genes (F7GATs) were identified using updated genome. In addition, three candidate genes encoding indole‐3‐pyruvate monooxygenase YUCCA2 (YUC2), serine carboxypeptidase‐like 18 (SCPL18), and F‐box protein (FBP), respectively, were identified to be probably related to leaf development and flowering by resequencing 99 individuals. These results provided a substantial genetic basis for improving agronomic and quality traits of E. breviscapus, and provided a platform for improving other draft genome assemblies to chromosome‐level.     

Utilization of UPLC/Q‐TOF‐MS‐Based Metabolomics and AFLP‐Based Marker‐Assisted Selection to Facilitate/Assist Conventional Breeding of Polygala tenuifolia

As one of the most important traditional Chinese medicine, the quality of Polygala tenuifolia is difficult to control and a new method must be established to facilitate/assist the breeding of P. tenuifolia. In this study, UPLC/Q‐TOF‐MS‐based metabolomics analysis was performed to determine the chemical composition and screen metabolite biomarkers according to agronomic traits. A total of 29 compounds and 18 metabolite biomarkers were found. AFLP‐based marker‐assisted selection (MAS) was used to identify molecular marker bands and screen characteristic bands associated with specific agronomic traits. 184 bands and 76 characteristic AFLP bands were found. The correlation network between compounds and characteristic AFLP bands was built, so we may directly breed certain P. tenuifolia herbs with special agronomic traits (or characteristic AFLP bands), which exhibit specific pharmacological functions depending on the content of the active compounds. The proposed method of metabolomics coupled with MAS could facilitate/assist the breeding of P. tenuifolia.     

Predicting kinetic constants of protein-protein interactions based on structural properties

Elucidating kinetic processes of protein–protein interactions (PPI) helps to understand how basic building blocks affect overall behavior of living systems. In this study, we used structure‐based properties to build predictive models for kinetic constants of PPI. A highly diverse PPI dataset, protein–protein kinetic interaction data and structures (PPKIDS), was built. PPKIDS contains 62 PPI with complex structures and kinetic constants measured experimentally. The influence of structural properties on kinetics of PPI was studied using 35 structure‐based features, describing different aspects of complex structures. Linear models for the prediction of kinetic constants were built by fitting with selected subsets of structure‐based features. The models gave correlation coefficients of 0.801, 0.732, and 0.770 for k_off, k_on, and K_d, respectively, in leave‐one‐out cross validations. The predictive models reported here use only protein complex structures as input and can be generally applied in PPI studies as well as systems biology modeling. Our study confirmed that different properties play different roles in the kinetic process of PPI. For example, k_on was affected by overall structural features of complexes, such as the composition of secondary structures, the change of translational and rotational entropy, and the electrostatic interaction; while k_off was determined by interfacial properties, such as number of contacted atom pairs per 100 Ų. This information provides useful hints for PPI design. Proteins 2010;79:720–734. © 2010 Wiley‐Liss, Inc.     

Genome sequence of M6, a diploid inbred clone of the high‐glycoalkaloid‐producing tuber‐bearing potato species Solanum chacoense,reveals residual heterozygosity

Cultivated potato (Solanum tuberosum L.) is a highly heterozygous autotetraploid that presents challenges in genome analyses and breeding. Wild potato species serve as a resource for the introgression of important agronomic traits into cultivated potato. One key species is Solanum chacoense and the diploid, inbred clone M6, which is self‐compatible and has desirable tuber market quality and disease resistance traits. Sequencing and assembly of the genome of the M6 clone of S. chacoense generated an assembly of 825 767 562 bp in 8260 scaffolds with an N50 scaffold size of 713 602 bp. Pseudomolecule construction anchored 508 Mb of the genome assembly into 12 chromosomes. Genome annotation yielded 49 124 high‐confidence gene models representing 37 740 genes. Comparative analyses of the M6 genome with six other Solanaceae species revealed a core set of 158 367 Solanaceae genes and 1897 genes unique to three potato species. Analysis of single nucleotide polymorphisms across the M6 genome revealed enhanced residual heterozygosity on chromosomes 4, 8 and 9 relative to the other chromosomes. Access to the M6 genome provides a resource for identification of key genes for important agronomic traits and aids in genome‐enabled development of inbred diploid potatoes with the potential to accelerate potato breeding.     

Assessment of sorghum genetic resources of Ethiopia for anthracnose (Colletotrichum sublineolum Henn.) resistance and agronomic traits

Sorghum anthracnose caused by Colletotrichum sublineolum Henn. is one of the key diseases limiting sorghum production and productivity. Development of anthracnose‐resistant sorghum genotypes possessing yield‐promoting agronomic traits is an important breeding goal in sorghum improvement programs. The objective of this study was to determine the responses of diverse sorghum genetic resources for anthracnose resistance and agronomic traits to identify desirable lines for breeding. A total of 366 sorghum collections and three standard checks were field evaluated during the 2016 and 2017 cropping seasons. Lines were artificially inoculated with a virulent pure isolate of the pathogen. Anthracnose disease severity was assessed to calculate the area under disease progress curve (AUDPC). Agronomic traits such as panicle length (PL), panicle width (PW), head weight (HW) and thousand grain weight (TGW) were measured. Lines showed highly significant differences (p < .001) for anthracnose severity, AUDPC and agronomic traits. Among the collections 32 lines developed levels of disease severity between 15% and 30% in both seasons. The following sorghum landraces were selected: 71708, 210903, 74222, 73955, 74685, 74670, 74656, 74183, 234112, 69412, 226057, 214852, 71420, 71484, 200126, 71557, 75120, 71547, 220014, 228179, 16212, 16173, 16133, 69088, 238388, 16168 and 71570. These landraces had a relatively low anthracnose severity possessing farmer‐preferred agronomic traits. The selected genotypes are useful genetic resources to develop anthracnose‐resistant sorghum cultivars.     

Genome‐wide detection of genetic loci and candidate genes for teat number and body conformation traits at birth in Chinese Sushan pigs

Body conformation at birth and teat number are economically important traits in the pig industry, as these traits are usually explored to evaluate the growth and reproductive potential of piglets. To detect genetic loci and candidate genes for these traits, we performed a GWAS on 269 pigs from a recently developed Chinese breed (Sushan) using 38  128 informative SNPs on the Affymetrix Porcine SNP 55K Array. In total, we detected one genome‐wide significant (P = 1.31e‐6) SNP for teat number on chromosome X and 15 chromosome‐wide significant SNPs for teat number, body weight, body length, chest circumference and cannon circumference at birth on chromosomes 1, 3, 4, 6, 7, 9, 10, 13, 14, 15, 17 and 18. The most significant SNP had an additive effect of 0.74 × total teat number, explaining 20% of phenotypic variance. Five significant SNPs resided in the previously reported quantitative trait loci for these traits and seven significant SNPs had a pleiotropic effect on multiple traits. Intriguingly, 12 of the genes nearest to the significant SNPs are functionally related to body conformation and teat number traits, including SPRED2, MKX, TMSB4X and ESR1. GO analysis revealed that candidate genes proximal to the significant SNPs were enriched in the G‐protein coupled receptor and steroid hormone‐mediated signaling pathway. Our findings shed light on the genetic basis of the measured traits and provide molecular markers especially for the genetic improvement of teat number in Sushan and related pigs.     

‘Real time’ genetic manipulation: a new tool for ecological field studies

Field experiments with transgenic plants often reveal the functional significance of genetic traits that are important for the performance of the plants in their natural environments. Until now, only constitutive overexpression, ectopic expression and gene silencing methods have been used to analyze gene‐related phenotypes in natural habitats. These methods do not allow sufficient control over gene expression for the study of ecological interactions in real time, of genetic traits that play essential roles in development, or of dose‐dependent effects. We applied the sensitive dexamethasone (DEX)‐inducible pOp6/LhGR expression system to the ecological model plant Nicotiana attenuata and established a lanolin‐based DEX application method to facilitate ectopic gene expression and RNA interference‐mediated gene silencing in the field and under challenging conditions (e.g. high temperature, wind and UV radiation). Fully established field‐grown plants were used to silence phytoene desaturase and thereby cause photobleaching only in specific plant sectors, and to activate expression of the cytokinin (CK) biosynthesis gene isopentenyl transferase (ipt). We used ipt expression to analyze the role of CKs in both the glasshouse and the field to understand resistance to the native herbivore Tupiocoris notatus, which attacks plants at small spatial scales. By spatially restricting ipt expression and elevating CK levels in single leaves, damage by T. notatus increased, demonstrating the role of CKs in this plant–herbivore interaction at a small scale. As the arena of most ecological interactions is highly constrained in time and space, these tools will advance the genetic analysis of dynamic traits that matter for plant performance in nature.