Wenhao Bo

Genome-wide analysis of salt-responsive and novel microRNAs in Populus euphratica by deep sequencing

BackgroundPopulus euphratica is a representative model woody plant species for studying resistance to abiotic stresses such as drought and salt. Salt stress is one of the most common environmental factors that affect plant growth and development. MicroRNAs (miRNAs) are small, noncoding RNAs that have important regulatory functions in plant growth, development, and response to abiotic stress.ResultsTo investigate the miRNAs involved in the salt-stress response, we constructed four small cDNA libraries from P. euphratica plantlets treated with or without salt (300 mM NaCl, 3 days) in either the root or leaf. Using high-throughput sequencing to identify miRNAs, we found 164 conserved miRNAs belonging to 44 families. Of these, 136 novel miRNAs were from the leaf, and 128 novel miRNAs were from the root. In response to salt stress, 95 miRNAs belonging to 46 conserved miRNAs families changed significantly, with 56 miRNAs upregulated and 39 miRNAs downregulated in the leaf. A comparison of the leaf and root tissues revealed 155 miRNAs belonging to 63 families with significantly altered expression, including 84 upregulated and 71 downregulated miRNAs. Furthermore, 479 target genes in the root and 541 targets of novel miRNAs in the leaf were predicted, and functional information was annotated using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases.ConclusionsThis study provides a novel visual field for understanding the regulatory roles of miRNAs in response to salt stress in Populus.

An open-pollinated design for mapping imprinting genes in natural populations

With the increasing recognition of its role in trait and disease development, it is crucial to account for genetic imprinting to illustrate the genetic architecture of complex traits. Genetic mapping can be innovated to test and estimate effects of genetic imprinting in a segregating population derived from experimental crosses. Here, we describe and assess a design for imprinting detection in natural plant populations. This design is to sample maternal plants at random from a natural population and collect open-pollinated (OP) seeds randomly from each maternal plant and germinate them into seedlings. A two-stage hierarchical platform is constructed to jointly analyze maternal and OP progeny markers. Through tracing the segregation and transmission of alleles from the parental to progeny generation, this platform allows parent-of-origin-dependent gene expression to be discerned, providing an avenue to estimate the effect of imprinting genes on a quantitative trait. The design is derived to estimate imprinting effects expressed at the haplotype level. Its usefulness and utilization were validated through computer simulation. This OP-based design provides a tool to detect the genomic distribution and pattern of imprinting genes as an important component of heritable variation that is neglected in traditional genetic studies of complex traits.

A synthetic framework for modeling the genetic basis of phenotypic plasticity and its costs

The phenotype of an individual is controlled not only by its genes, but also by the environment in which it grows. A growing body of evidence shows that the extent to which phenotypic changes are driven by the environment, known as phenotypic plasticity, is also under genetic control, but an overall picture of genetic variation for phenotypic plasticity remains elusive. Here, we develop a model for mapping quantitative trait loci (QTLs) that regulate environment-induced plastic response. This model enables geneticists to test whether there exist actual QTLs that determine phenotypic plasticity and, if there are, further test how plasticity QTLs control the costs of plastic response by dissecting the genetic correlation of phenotypic plasticity and trait value. The model was used to analyze real data for grain yield of winter wheat (Triticum aestivum), leading to the detection of pleiotropic QTLs and epistatic QTLs that affect phenotypic plasticity and its cost in this crop.

Genetic diversity and population structure of the Tibetan poplar (Populus szechuanica var. tibetica) along an altitude gradient

BackgroundThe Tibetan poplar (Populus szechuanica var. tibetica Schneid), which is distributed at altitudes of 2,000-4,500 m above sea level, is an ecologically important species of the Qinghai-Tibet Plateau and adjacent areas. However, the genetic adaptations responsible for its ability to cope with the harsh environment remain unknown.ResultsIn this study, a total of 24 expressed sequence tag microsatellite (EST-SSR) markers were used to evaluate the genetic diversity and population structure of Tibetan poplars along an altitude gradient. The 172 individuals were of genotypes from low-, medium- and high-altitude populations, and 126 alleles were identified. The expected heterozygosity (HE) value ranged from 0.475 to 0.488 with the highest value found in low-altitude populations and the lowest in high-altitude populations. Genetic variation was low among populations, indicating a limited influence of altitude on microsatellite variation. Low genetic differentiation and high levels of gene flow were detected both between and within the populations along the altitude gradient. An analysis of molecular variance (AMOVA) showed that 6.38% of the total molecular variance was attributed to diversity between populations, while 93.62% variance was associated with differences within populations. There was no clear correlation between genetic variation and altitude, and a Mantel test between genetic distance and altitude resulted in a coefficient of association of r = 0.001, indicating virtually no correlation.ConclusionMicrosatellite genotyping results showing genetic diversity and low differentiation suggest that extensive gene flow may have counteracted local adaptations imposed by differences in altitude. The genetic analyses carried out in this study provide new insight for conservation and optimization of future arboriculture.

A statistical procedure to map high-order epistasis for complex traits

Genetic interactions or epistasis have been thought to play a pivotal role in shaping the formation, development and evolution of life. Previous work focused on lower-order interactions between a pair of genes, but it is obviously inadequate to explain a complex network of genetic interactions and pathways. We review and assess a statistical model for characterizing high-order epistasis among more than two genes or quantitative trait loci (QTLs) that control a complex trait. The model includes a series of start-of-the-art standard procedures for estimating and testing the nature and magnitude of QTL interactions. Results from simulation studies and real data analysis warrant the statistical properties of the model and its usefulness in practice. High-order epistatic mapping will provide a routine procedure for charting a detailed picture of the genetic regulation mechanisms underlying the phenotypic variation of complex traits.

Cloning of the Cryptochrome-Encoding PeCRY1 Gene from Populus euphratica and Functional Analysis in Arabidopsis

Cryptochromes are photolyase-like blue/UV-A light receptors that evolved from photolyases. In plants, cryptochromes regulate various aspects of plant growth and development. Despite of their involvement in the control of important plant traits, however, most studies on cryptochromes have focused on lower plants and herbaceous crops, and no data on cryptochrome function are available for forest trees. In this study, we isolated a cryptochrome gene, PeCRY1, from Euphrates poplar (Populus euphratica), and analyzed its structure and function in detail. The deduced PeCRY1 amino acid sequence contained a conserved N-terminal photolyase-homologous region (PHR) domain as well as a C-terminal DQXVP-acidic-STAES (DAS) domain. Secondary and tertiary structure analysis showed that PeCRY1 shares high similarity with AtCRY1 from Arabidopsis thaliana. PeCRY1 expression was upregulated at the mRNA level by light. Using heterologous expression in Arabidopsis, we showed that PeCRY1 overexpression rescued the cry1 mutant phenotype. In addition, PeCRY1 overexpression inhibited hypocotyl elongation, promoted root growth, and enhanced anthocyanin accumulation in wild-type background seedlings grown under blue light. Furthermore, we examined the interaction between PeCRY1 and AtCOP1 using a bimolecular fluorescence complementation (BiFc) assay. Our data provide evidence for the involvement of PeCRY1 in the control of photomorphogenesis in poplar.

Allotetraploid and autotetraploid models of linkage analysis

As a group of important plant species in agriculture and biology, polyploids have been increasingly studied in terms of their genome structure and organization. There are two types of polyploids, allopolyploids and autopolyploids, each resulting from a different genetic origin, which undergo meiotic divisions of a distinct complexity. A set of statistical models has been developed for linkage analysis, respectively for each type, by taking into account their unique meiotic behavior, i.e. preferential pairing for allopolyploids and double reduction for autopolyploids. We synthesized these models and modified them to accommodate the linkage analysis of less informative dominant markers. By reanalysing a published data set of varying ploidy in Arabidopsis, we corrected the estimates of the meiotic recombination frequency aimed to study the significance of polyploidization.

Inferring the evolutionary history of outcrossing populations through computing a multiallelic linkage–linkage disequilibrium map

Summary Linkage disequilibrium (LD), the non-random association of alleles at different loci, has been used as an important parameter to study the genetic diversity and evolutionary history of natural populations. A joint analysis of LD with the linkage of the same marker pair has proven to gain more insight into the genetic signature of population diversification than LD analysis alone. We develop a unifying framework for simultaneously estimating the linkage and LD across pairs of multiallelic markers. The framework has particular power to construct the LD map from any markers with an arbitrary number of alleles per locus. We provide an efficient strategy to manipulate disequilibrium parameters whose number increases exponentially with the number of alleles. The model was tested through extensive simulation studies and validated by analysing a real marker data set from a population genetic research project of euphrates poplar, a desert tree, distributed in the north-western China. For widespread undomesticated natural populations, compared with biallelic markers, multiallelic markers with a high level of polymorphism are more powerful to study their genetic structure and organization of an outcrossing population. The model developed will potentially have an immediate implication for population and evolutionary genetic studies.

A QTL model to map the common genetic basis for correlative phenotypic plasticity

As an important mechanism for adaptation to heterogeneous environment, plastic responses of correlated traits to environmental alteration may also be genetically correlated, but less is known about the underlying genetic basis. We describe a statistical model for mapping specific quantitative trait loci (QTLs) that control the interrelationship of phenotypic plasticity between different traits. The model is constructed by a bivariate mixture setting, implemented with the EM algorithm to estimate the genetic effects of QTLs on correlative plastic response. We provide a series of procedure that test (1) how a QTL controls the phenotypic plasticity of a single trait; and (2) how the QTL determines the correlation of environment-induced changes of different traits. The model is readily extended to test how epistatic interactions among QTLs play a part in the correlations of different plastic traits. The model was validated through computer simulation and used to analyse multi-environment data of genetic mapping in winter wheat, showing its utilization in practice.

Systems mapping: how to map genes for biomass allocation toward an ideotype

The recent availability of high-throughput genetic and genomic data allows the genetic architecture of complex traits to be systematically mapped. The application of these genetic results to design and breed new crop types can be made possible through systems mapping. Systems mapping is a computational model that dissects a complex phenotype into its underlying components, coordinates different components in terms of biological laws through mathematical equations and maps specific genes that mediate each component and its connection with other components. Here, we present a new direction of systems mapping by integrating this tool with carbon economy. With an optimal spatial distribution of carbon fluxes between sources and sinks, plants tend to maximize whole-plant growth and competitive ability under limited availability of resources. We argue that such an economical strategy for plant growth and development, once integrated with systems mapping, will not only provide mechanistic insights into plant biology, but also help to spark a renaissance of interest in ideotype breeding in crops and trees.