Integrable deformations of the Bogoyavlenskij-Itoh Lotka-Volterra systems

We construct a family of integrable deformations of the Bogoyavlenskij-Itoh systems and construct a Lax operator with spectral parameter for it. Our approach is based on the construction of a family of compatible Poisson structures for the undeformed system, whose Casimirs are shown to yield a generating function for the integrals in involution of the deformed systems. We show how these deformations are related to the Veselov-Shabat systems.Comment: 23 pages, 14 reference

A Bayesian Framework for Functional Mapping through Joint Modeling of Longitudinal and Time-to-Event Data

The most powerful and comprehensive approach of study in modern biology is to understand the whole process of development and all events of importance to development which occur in the process. As a consequence, joint modeling of developmental processes and events has become one of the most demanding tasks in statistical research. Here, we propose a joint modeling framework for functional mapping of specific quantitative trait loci (QTLs) which controls developmental processes and the timing of development and their causal correlation over time. The joint model contains two submodels, one for a developmental process, known as a longitudinal trait, and the other for a developmental event, known as the time to event, which are connected through a QTL mapping framework. A nonparametric approach is used to model the mean and covariance function of the longitudinal trait while the traditional Cox proportional hazard (PH) model is used to model the event time. The joint model is applied to map QTLs that control whole-plant vegetative biomass growth and time to first flower in soybeans. Results show that this model should be broadly useful for detecting genes controlling physiological and pathological processes and other events of interest in biomedicine

Systems mapping: how to improve the genetic mapping of complex traits through design principles of biological systems

<p>Abstract</p> <p>Background</p> <p>Every phenotypic trait can be viewed as a "system" in which a group of interconnected components function synergistically to yield a unified whole. Once a system's components and their interactions have been delineated according to biological principles, we can manipulate and engineer functionally relevant components to produce a desirable system phenotype.</p> <p>Results</p> <p>We describe a conceptual framework for mapping quantitative trait loci (QTLs) that control complex traits by treating trait formation as a dynamic system. This framework, called systems mapping, incorporates a system of differential equations that quantifies how alterations of different components lead to the global change of trait development and function through genes, and provides a quantitative and testable platform for assessing the interplay between gene action and development. We applied systems mapping to analyze biomass growth data in a mapping population of soybeans and identified specific loci that are responsible for the dynamics of biomass partitioning to leaves, stem, and roots.</p> <p>Conclusions</p> <p>We show that systems mapping implemented by design principles of biological systems is quite versatile for deciphering the genetic machineries for size-shape, structural-functional, sink-source and pleiotropic relationships underlying plant physiology and development. Systems mapping should enable geneticists to shed light on the genetic complexity of any biological system in plants and other organisms and predict its physiological and pathological states.</p

A Conceptual Framework for Mapping Quantitative Trait Loci Regulating Ontogenetic Allometry

Although ontogenetic changes in body shape and its associated allometry has been studied for over a century, essentially nothing is known about their underlying genetic and developmental mechanisms. One of the reasons for this ignorance is the unavailability of a conceptual framework to formulate the experimental design for data collection and statistical models for data analyses. We developed a framework model for unraveling the genetic machinery for ontogenetic changes of allometry. The model incorporates the mathematical aspects of ontogenetic growth and allometry into a maximum likelihood framework for quantitative trait locus (QTL) mapping. As a quantitative platform, the model allows for the testing of a number of biologically meaningful hypotheses to explore the pleiotropic basis of the QTL that regulate ontogeny and allometry. Simulation studies and real data analysis of a live example in soybean have been performed to investigate the statistical behavior of the model and validate its practical utilization. The statistical model proposed will help to study the genetic architecture of complex phenotypes and, therefore, gain better insights into the mechanistic regulation for developmental patterns and processes in organisms

Identification of miRNAs and their targets by high-throughput sequencing and degradome analysis in cytoplasmic male-sterile line NJCMS1A and its maintainer NJCMS1B of soybean

Table S1. Summary of small RNA annotations from NJCMS1A and NJCMS1B. Table S2. Known miRNAs identified in NJCMS1A and NJCMS1B. Table S3. Family member distribution in conserved miRNA families. Table S4. Summary of miRNA families found in NJCMS1A and NJCMS1B. Table S5. Novel miRNAs on the other arm of known pre-miRNAs. Table S6. Novel miRNAs identified in NJCMS1A and NJCMS1B. Table S7-1. High-confidence known miRNAs identified in NJCMS1A and NJCMS1B. Table S7-2. High-confidence novel miRNAs identified in NJCMS1A and NJCMS1B. Table S8-1. The up-regulated miRNAs identified in NJCMS1A and NJCMS1B. Table S8-2. The down-regulated miRNAs identified in NJCMS1A and NJCMS1B. Table S9. The targets of miRNAs identified in NJCMS1A and NJCMS1B. Table S10. Targets of novel miRNAs in NJCMS1A and NJCMS1B. Table S11. Primers used in this study. (ZIP 637 kb

Functional mapping of genotype-environment interactions for soybean growth by a semiparametric approach

<p>Abstract</p> <p>Background</p> <p>Functional mapping is a powerful approach for mapping quantitative trait loci (QTLs) that control biological processes. Functional mapping incorporates mathematical aspects of growth and development into a general QTL mapping framework and has been recently integrated with composite interval mapping to build up a so-called composite functional mapping model, aimed to separate multiple linked QTLs on the same chromosomal region.</p> <p>Results</p> <p>This article reports the principle of using composite functional mapping to estimate the effects of QTL-environment interactions on growth trajectories by parametrically modeling the tested QTL in a marker interval and nonparametrically modeling the markers outside the interval as co-factors. With this new model, we can characterize the dynamic patterns of the genetic effects of QTLs governing growth trajectories, estimate the global effects of the underlying QTLs during the course of growth and development, and test the differentiation in the shapes of QTL genotype-specific growth curves between different environments. By analyzing a real example from a soybean genome project, our model detects several QTLs that cause significant genotype-environment interactions for plant height growth processes.</p> <p>Conclusions</p> <p>The model provides a basis for deciphering the genetic architecture of trait expression adjusted to different biotic and abiotic environments for any organism.</p

Proteomics study of changes in soybean lines resistant and sensitive to Phytophthora sojae

<p>Abstract</p> <p>Background</p> <p><it>Phytophthora sojae </it>causes soybean root and stem rot, resulting in an annual loss of 1-2 billion US dollars in soybean production worldwide. A proteomic technique was used to determine the effects on soybean hypocotyls of infection with <it>P. sojae</it>.</p> <p>Results</p> <p>In the present study, 46 differentially expressed proteins were identified in soybean hypocotyls infected with <it>P. sojae</it>, using two-dimensional electrophoresis and matrix-assisted laser desorption/ionization tandem time of flight (MALDI-TOF/TOF). The expression levels of 26 proteins were significantly affected at various time points in the tolerant soybean line, Yudou25, (12 up-regulated and 14 down-regulated). In contrast, in the sensitive soybean line, NG6255, only 20 proteins were significantly affected (11 up-regulated and 9 down-regulated). Among these proteins, 26% were related to energy regulation, 15% to protein destination and storage, 11% to defense against disease, 11% to metabolism, 9% to protein synthesis, 4% to secondary metabolism, and 24% were of unknown function.</p> <p>Conclusion</p> <p>Our study provides important information on the use of proteomic methods for studying protein regulation during plant-oomycete interactions.</p

Detecting the QTL-Allele System of Seed Oil Traits Using Multi-Locus Genome-Wide Association Analysis for Population Characterization and Optimal Cross Prediction in Soybean

Soybean is one of the world's major vegetative oil sources, while oleic acid and linolenic acid content are the major quality traits of soybean oil. The restricted two-stage multi-locus genome-wide association analysis (RTM-GWAS), characterized with error and false-positive control, has provided a potential approach for a relatively thorough detection of whole-genome QTL-alleles. The Chinese soybean landrace population (CSLRP) composed of 366 accessions was tested under four environments to identify the QTL-allele constitution of seed oil, oleic acid and linolenic acid content (SOC, OAC, and LAC). Using RTM-GWAS with 29,119 SNPLDBs (SNP linkage disequilibrium blocks) as genomic markers, 50, 98, and 50 QTLs with 136, 283, and 154 alleles (2–9 per locus) were detected, with their contribution 82.52, 90.31, and 83.86% to phenotypic variance, corresponding to their heritability 91.29, 90.97, and 90.24% for SOC, OAC, and LAC, respectively. The RTM-GWAS was shown to be more powerful and efficient than previous single-locus model GWAS procedures. For each trait, the detected QTL-alleles were organized into a QTL-allele matrix as the population genetic constitution. From which the genetic differentiation among 6 eco-populations was characterized as significant allele frequency differentiation on 28, 56, and 30 loci for the three traits, respectively. The QTL-allele matrices were also used for genomic selection for optimal crosses, which predicted transgressive potential up to 24.76, 40.30, and 2.37% for the respective traits, respectively. From the detected major QTLs, 38, 27, and 25 candidate genes were annotated for the respective traits, and two common QTL covering eight genes were identified for further study

Molecular Cloning, Characterization and Expression Analysis of Two Members of the Pht1 Family of Phosphate Transporters in Glycine max

BACKGROUND: Phosphorus is one of the macronutrients essential for plant growth and development. The acquisition and translocation of phosphate are pivotal processes of plant growth. In a large number of plants, phosphate uptake by roots and translocation within the plant are presumed to occur via a phosphate/proton cotransport mechanism. PRINCIPAL FINDINGS: We cloned two cDNAs from soybean (Glycine max), GmPT1 and GmPT2, which show homology to the phosphate/proton cotransporter PHO84 from the budding yeast Saccharomyces cerevisiae. The amino acid sequence of the products predicted from GmPT1 and GmPT2 share 61% and 63% identity, respectively, with the PHO84 in amino acid sequence. The deduced structure of the encoded proteins revealed 12 membrane-spanning domains with a central hydrophilic region. The molecular mass values are ∼58.7 kDa for GmPT1 and ∼58.6 kDa for GmPT2. Transiently expressed GFP-protein fusions provide direct evidence that the two Pi transporters are located in the plasma membrane. Uptake of radioactive orthophosphate by the yeast mutant MB192 showed that GmPT1 and GmPT2 are dependent on pH and uptake is reduced by the addition of uncouplers of oxidative phosphorylation. The K(m) for phosphate uptake by GmPT1 and GmPT2 is 6.65 mM and 6.63 mM, respectively. A quantitative real time RT-PCR assay indicated that these two genes are expressed in the roots and shoots of seedlings whether they are phosphate-deficient or not. Deficiency of phosphorus caused a slight change of the expression levels of GmPT1 and GmPT2. CONCLUSIONS: The results of our experiments show that the two phosphate transporters have low affinity and the corresponding genes are constitutively expressed. Thereby, the two phosphate transporters can perform translocation of phosphate within the plant