Sex-Specific Bioaccumulation, Maternal Transfer, and Tissue Distribution of Legacy and Emerging Per- and Polyfluoroalkyl Substances in Snakes (Enhydris chinensis) and the Impact of Pregnancy.

The effects of sex and pregnancy on the bioaccumulation and tissue distribution of legacy and emerging per- and polyfluoroalkyl substances (PFASs) in Chinese water snakes were investigated. The bioaccumulation factor of PFASs showed a positive correlation with their protein-water partition coefficients (log KPW), and steric hindrance effects were observed when the molecular volume was > 357 Å3. PFAS levels in females were significantly lower than those in males. The chemical composition of pregnant females was significantly different from that of non-pregnant females and males. The maternal transfer efficiencies of perfluorooctane sulfonic acid were higher than those of other PFASs, and a positive correlation between the maternal transfer potential and log KPW was observed for other PFASs. Tissues with high phospholipid content exhibited higher concentrations of ∑PFASs. Numerous physiological changes occurred in maternal organ systems during pregnancy, leading to the re-distribution of chemicals among different tissues. The change in tissue distribution of PFASs that are easily and not-so-easily maternally transferred was in the opposite direction. The extent of compound transfer from the liver to the egg determined tissue re-distribution during pregnancy.

np2 QTL: networking phenotypic plasticity quantitative trait loci across heterogeneous environments.

Despite its critical importance to our understanding of plant growth and adaptation, the question of how environment-induced plastic response is affected genetically remains elusive. Previous studies have shown that the reaction norm of an organism across environmental index obeys the allometrical scaling law of part-whole relationships. The implementation of this phenomenon into functional mapping can characterize how quantitative trait loci (QTLs) modulate the phenotypic plasticity of complex traits to heterogeneous environments. Here, we assemble functional mapping and allometry theory through Lokta-Volterra ordinary differential equations (LVODE) into an R-based computing platform, np2 QTL, aimed to map and visualize phenotypic plasticity QTLs. Based on LVODE parameters, np2 QTL constructs a bidirectional, signed and weighted network of QTL-QTL epistasis, whose emergent properties reflect the ecological mechanisms for genotype-environment interactions over any range of environmental change. The utility of np2 QTL was validated by comprehending the genetic architecture of phenotypic plasticity via the reanalysis of published plant height data involving 3502 recombinant inbred lines of maize planted in multiple discrete environments. np2 QTL also provides a tool for constructing a predictive model of phenotypic responses in extreme environments relative to the median environment.

A dissection model for mapping complex traits.

Many quantitative traits are composites of other traits that contribute differentially to genetic variation. Quantitative trait locus (QTL) mapping of these composite traits can benefit by incorporating the mechanistic process of how their formation is mediated by the underlying components. We propose a dissection model by which to map these interconnected components traits under a joint likelihood setting. The model can test how a composite trait is determined by pleiotropic QTLs for its component traits or jointly by different sets of QTLs each responsible for a different component. The model can visualize the pattern of time-varying genetic effects for individual components and their impacts on composite traits. The dissection model was used to map two composite traits, stemwood volume growth decomposed into its stem height, stem diameter and stem form components for Euramerican poplar adult trees, and total lateral root length constituted by its average lateral root length and lateral root number components for Euphrates poplar seedlings. We found the pattern of how QTLs for different components contribute to phenotypic variation in composite traits. The detailed understanding of the genetic machineries of composite traits will not only help in the design of molecular breeding in plants and animals, but also shed light on the evolutionary processes of quantitative traits under natural selection.

Functional mapping of N deficiency-induced response in wheat yield-component traits by implementing high-throughput phenotyping.

As overfertilization leads to environmental concerns and the cost of N fertilizer increases, the issue of how to select crop cultivars that can produce high yields on N-deficient soils has become crucially important. However, little information is known about the genetic mechanisms by which crops respond to environmental changes induced by N signaling. Here, we dissected the genetic architecture of N-induced phenotypic plasticity in bread wheat (Triticum aestivum L.) by integrating functional mapping and semiautomatic high-throughput phenotyping data of yield-related canopy architecture. We identified a set of quantitative trait loci (QTLs) that determined the pattern and magnitude of how wheat cultivars responded to low N stress from normal N supply throughout the wheat life cycle. This analysis highlighted the phenological landscape of genetic effects exerted by individual QTLs, as well as their interactions with N-induced signals and with canopy measurement angles. This information may shed light on our mechanistic understanding of plant adaptation and provide valuable information for the breeding of N-deficiency tolerant wheat varieties.

How trees allocate carbon for optimal growth: insight from a game-theoretic model.

How trees allocate photosynthetic products to primary height growth and secondary radial growth reflects their capacity to best use environmental resources. Despite substantial efforts to explore tree height-diameter relationship empirically and through theoretical modeling, our understanding of the biological mechanisms that govern this phenomenon is still limited. By thinking of stem woody biomass production as an ecological system of apical and lateral growth components, we implement game theory to model and discern how these two components cooperate symbiotically with each other or compete for resources to determine the size of a tree stem. This resulting allometry game theory is further embedded within a genetic mapping and association paradigm, allowing the genetic loci mediating the carbon allocation of stemwood growth to be characterized and mapped throughout the genome. Allometry game theory was validated by analyzing a mapping data of stem height and diameter growth over perennial seasons in a poplar tree. Several key quantitative trait loci were found to interpret the process and pattern of stemwood growth through regulating the ecological interactions of stem apical and lateral growth. The application of allometry game theory enables the prediction of the situations in which the cooperation, competition or altruism is an optimal decision of a tree to fully use the environmental resources it owns.

The genetic control of leaf allometry in the common bean, Phaseolus vulgaris.

BACKGROUND: To maximize photosynthetic efficiency, plants have evolved a capacity by which leaf area scales allometrically with leaf mass through interactions with the environment. However, our understanding of genetic control of this allometric relationship remains limited. RESULTS: We integrated allometric scaling laws expressed at static and ontogenetic levels into genetic mapping to identify the quantitative trait loci (QTLs) that mediate how leaf area scales with leaf mass and how such leaf allometry, under the control of these QTLs, varies as a response to environment change. A major QTL detected by the static model constantly affects the allometric growth of leaf area vs. leaf mass for the common bean (Phaseolus vulgaris) in two different environments. The ontogenetic model identified this QTL plus a few other QTLs that determine developmental trajectories of leaf allometry, whose expression is contingent heavily upon the environment. CONCLUSIONS: Our results gain new insight into the genetic mechanisms of how plants program their leaf morphogenesis to adapt to environmental perturbations.

Bivariate genome-wide association study of the growth plasticity of Staphylococcus aureus in coculture with Escherichia coli.

Phenotypic plasticity is the capacity to change the phenotype in response to different environments without alteration of the genotype. Despite sufficient evidence that microorganisms have a major role in the fitness and sickness of eukaryotes, there has been little research regarding microbial phenotypic plasticity. In this study, 45 strains of Staphylococcus aureus were grown for 12 days in both monoculture and in coculture with the same strain of Escherichia coli to create a competitive environment. Cell abundance was determined by quantitative PCR every 24 h, and growth curves of each S. aureus strain under the two sets of conditions were generated. Combined with whole-genome resequencing data, bivariate genome-wide association study (GWAS) was performed to analyze the growth plasticity of S. aureus in coculture. Finally, 20 significant single-nucleotide polymorphisms (eight annotated, seven unannotated, and five non-coding regions) were obtained, which may affect the competitive growth of S. aureus. This study advances genome-wide bacterial growth plasticity research and demonstrates the potential of bivariate GWAS for bacterial phenotypic plasticity research. KEY POINTS: • Growth plasticity of S. aureus was analyzed by bivariate GWAS. • Twenty significant SNPs may affect the growth plasticity of S. aureus.

Two-stage identification of SNP effects on dynamic poplar growth.

This project proposes an approach to identify significant single nucleotide polymorphism (SNP) effects, both additive and dominant, on the dynamic growth of poplar in diameter and height. The annual changes in yearly phenotypes based on regular observation periods are considered to represent multiple responses. In total 156,362 candidate SNPs are studied, and the phenotypes of 64 poplar trees are recorded. To address this ultrahigh dimensionality issue, this paper adopts a two-stage approach. First, the conventional genome-wide association studies (GWAS) and the distance correlation sure independence screening (DC-SIS) methods (Li et al., 2012) were combined to reduce the model dimensions at the sample size; second, a grouped penalized regression was applied to further refine the model and choose the final sparse SNPs. The multiple response issue was also carefully addressed. The SNP effects on the dynamic diameter and height growth patterns of poplar were systematically analyzed. In addition, a series of intensive simulation studies was performed to validate the proposed approach.

Integrating Evolutionary Game Theory into Mechanistic Genotype-Phenotype Mapping.

Natural selection has shaped the evolution of organisms toward optimizing their structural and functional design. However, how this universal principle can enhance genotype-phenotype mapping of quantitative traits has remained unexplored. Here we show that the integration of this principle and functional mapping through evolutionary game theory gains new insight into the genetic architecture of complex traits. By viewing phenotype formation as an evolutionary system, we formulate mathematical equations to model the ecological mechanisms that drive the interaction and coordination of its constituent components toward population dynamics and stability. Functional mapping provides a procedure for estimating the genetic parameters that specify the dynamic relationship of competition and cooperation and predicting how genes mediate the evolution of this relationship during trait formation.

Evo-Devo-EpiR: a genome-wide search platform for epistatic control on the evolution of development.

Evo-devo is a theory proposed to study how phenotypes evolve by comparing the developmental processes of different organisms or the same organism experiencing changing environments. It has been recognized that nonallelic interactions at different genes or quantitative trait loci, known as epistasis, may play a pivotal role in the evolution of development, but it has proven difficult to quantify and elucidate this role into a coherent picture. We implement a high-dimensional genome-wide association study model into the evo-devo paradigm and pack it into the R-based Evo-Devo-EpiR, aimed at facilitating the genome-wide landscaping of epistasis for the diversification of phenotypic development. By analyzing a high-throughput assay of DNA markers and their pairs simultaneously, Evo-Devo-EpiR is equipped with a capacity to systematically characterize various epistatic interactions that impact on the pattern and timing of development and its evolution. Enabling a global search for all possible genetic interactions for developmental processes throughout the whole genome, Evo-Devo-EpiR provides a computational tool to illustrate a precise genotype-phenotype map at interface between epistasis, development and evolution.

AlloMap6: an R package for genetic linkage analysis in allohexaploids.

Allopolyploids are a group of polyploids with more than two sets of chromosomes derived from different species. Previous linkage analysis of allopolyploids is based on the assumption that different chromosomes pair randomly during meiosis. A more sophisticated model to relax this assumption has been developed for allotetraploids by incorporating the preferential pairing behavior of homologous over homoeologous chromosomes. Here, we show that the basic principle of this model can be extended to perform linkage analysis of higher-ploidy allohexaploids, where multiple preferential pairing factors are used to characterize chromosomal-pairing meiotic features between different constituent species. We implemented the extended model into an R package, called AlloMap6, allowing the recombination fractions and preferential pairing factors to be estimated simultaneously. Allomap6 has two major functionalities, computer simulation and real-data analysis. By analyzing a real data from a full-sib family of allohexaploid persimmon, we tested and validated the usefulness and utility of this package. AlloMap6 lays a foundation for allohexaploid genetic mapping and provides a new horizon to explore the chromosomal kinship of allohexaploids.

A computing platform to map ecological metabolism by integrating functional mapping and the metabolic theory of ecology.

Whole-organism metabolic rate co-varies allometrically with body mass, and is also affected by temperature through different biochemical mechanisms. Here we implement a computational platform to map specific quantitative trait loci (QTLs) that govern the dependence of metabolic rate on size and temperature. The model was formulated within settings of genetic mapping or genome-wide association studies through a mapping population genotyped by a set of molecular markers throughout the genome and phenotyped for metabolic parameters over a range of temperature. The model, estimated by a maximum-likelihood approach, allows a genome-wide search for the underlying metabolic QTLs and the estimation of genotype-specific parameters that specify the metabolism of an organism. Our model provides a tool to detect pleiotropy and epistasis that cause the size- and temperature-dependent change of metabolic rate.

A high-dimensional linkage analysis model for characterizing crossover interference.

Linkage analysis has played an important role in understanding genome structure and evolution. However, two-point linkage analysis widely used for genetic map construction can rarely chart a detailed picture of genome organization because it fails to identify the dependence of crossovers distributed along the length of a chromosome, a phenomenon known as crossover interference. Multi-point analysis, proven to be more advantageous in gene ordering and genetic distance estimation for dominant markers than two-point analysis, is equipped with a capacity to discern and quantify crossover interference. Here, we review a statistical model for four-point analysis, which, beyond three-point analysis, can characterize crossover interference that takes place not only between two adjacent chromosomal intervals, but also over multiple successive intervals. This procedure provides an analytical tool to elucidate the detailed landscape of crossover interference over the genome and further infer the evolution of genome structure and organization.

A Regulatory Network for miR156-SPL Module in Arabidopsis thaliana.

Vegetative phase changes in plants describes the transition between juvenile and adult phases of vegetative growth before flowering. It is one of the most fundamental mechanisms for plants to sense developmental signals, presenting a complex process involving many still-unknown determinants. Several studies in annual and perennial plants have identified the conservative roles of miR156 and its targets, SBP/SPL genes, in guiding the switch of plant growth from juvenile to adult phases. Here, we review recent progress in understanding the regulation of miR156 expression and how miR156-SPLs mediated plant age affect other processes in Arabidopsis. Powerful high-throughput sequencing techniques have provided rich data to systematically study the regulatory mechanisms of miR156 regulation network. From this data, we draw an expanded miR156-regulated network that links plant developmental transition and other fundamental biological processes, gaining novel and broad insight into the molecular mechanisms of plant-age-related processes in Arabidopsis.

The genetic architecture of shoot-root covariation during seedling emergence of a desert tree, Populus euphratica.

The coordination of shoots and roots is critical for plants to adapt to changing environments by fine-tuning energy production in leaves and the availability of water and nutrients from roots. To understand the genetic architecture of how these two organs covary during developmental ontogeny, we conducted a mapping experiment using Euphrates poplar (Populus euphratica), a so-called hero tree able to grow in the desert. We geminated intraspecific F1 seeds of Euphrates Poplar individually in a tube to obtain a total of 370 seedlings, whose shoot and taproot lengths were measured repeatedly during the early stage of growth. By fitting a growth equation, we estimated asymptotic growth, relative growth rate, the timing of inflection point and duration of linear growth for both shoot and taproot growth. Treating these heterochronic parameters as phenotypes, a univariate mapping model detected 19 heterochronic quantitative trait loci (hQTLs), of which 15 mediate the forms of shoot growth and four mediate taproot growth. A bivariate mapping model identified 11 pleiotropic hQTLs that determine the covariation of shoot and taproot growth. Most QTLs detected reside within the region of candidate genes with various functions, thus confirming their roles in the biochemical processes underlying plant growth.

A multi-Poisson dynamic mixture model to cluster developmental patterns of gene expression by RNA-seq.

Dynamic changes of gene expression reflect an intrinsic mechanism of how an organism responds to developmental and environmental signals. With the increasing availability of expression data across a time-space scale by RNA-seq, the classification of genes as per their biological function using RNA-seq data has become one of the most significant challenges in contemporary biology. Here we develop a clustering mixture model to discover distinct groups of genes expressed during a period of organ development. By integrating the density function of multivariate Poisson distribution, the model accommodates the discrete property of read counts characteristic of RNA-seq data. The temporal dependence of gene expression is modeled by the first-order autoregressive process. The model is implemented with the Expectation-Maximization algorithm and model selection to determine the optimal number of gene clusters and obtain the estimates of Poisson parameters that describe the pattern of time-dependent expression of genes from each cluster. The model has been demonstrated by analyzing a real data from an experiment aimed to link the pattern of gene expression to catkin development in white poplar. The usefulness of the model has been validated through computer simulation. The model provides a valuable tool for clustering RNA-seq data, facilitating our global view of expression dynamics and understanding of gene regulation mechanisms.

2HiGWAS: a unifying high-dimensional platform to infer the global genetic architecture of trait development.

Whole-genome search of genes is an essential approach to dissecting complex traits, but a marginal one-single-nucleotide polymorphism (SNP)/one-phenotype regression analysis widely used in current genome-wide association studies fails to estimate the net and cumulative effects of SNPs and reveal the developmental pattern of interplay between genes and traits. Here we describe a computational framework, which we refer to as two-side high-dimensional genome-wide association studies (2HiGWAS), to associate an ultrahigh dimension of SNPs with a high dimension of developmental trajectories measured across time and space. The model is implemented with a dual dimension-reduction procedure for both predictors and responses to select a sparse but full set of significant loci from an extremely large pool of SNPs and estimate their net time-varying effects on trait development. The model can not only help geneticists to precisely identify an entire set of genes underlying complex traits but also allow them to elucidate a global picture of how genes control developmental and dynamic processes of trait formation. We investigated the statistical properties of the model via extensive simulation studies. With the increasing availability of GWAS in various organisms, 2HiGWAS will have important implications for genetic studies of developmental compelx traits.

Functional mapping of seasonal transition in perennial plants.

Unlike annuals, all perennial plants undergo seasonal transitions during ontogeny. As an adaptive response to seasonal changes in climate, the seasonal pattern of growth is likely to be under genetic control, although its underlying genetic basis remains unknown. Here, we develop a computational model that can map specific quantitative trait loci (QTLs) responsible for seasonal transitions of growth in perennials. The model is founded on functional mapping, a statistical framework to map developmental dynamics, which is reformed to integrate a seasonally adjusted growth function. The new model is equipped with a capacity to characterize the genetic effects of QTLs on seasonal alternation at different ages and then to better elucidate the genetic architecture of development. The model is implemented with a series of testing procedures, including (i) how a QTL controls an overall ontogenetic growth curve, (ii) how the QTL determines seasonal trajectories of growth within years and (iii) how it determines the dynamic nature of age-specific season response. The model was validated through computer simulation. The extension of season adjustment to other types of biological curves is statistically straightforward, facilitating a wider variety of genetic studies into ontogenetic growth and development in perennial plants.

[Interactions between Escherichia coli and Staphylococcus aureus determined by genome-wide association analysis].

Objective: We studied the interactions in a co-culture of two bacteria. Methods: By pairwise co-culturing of 36 Escherichia coli and 36 Staphylococcus aureus strains, we monitored the growth of each species in an interaction environment. We identified numerous Single Nucleotide Polymorphisms (SNPs) by whole-genome sequencing used as genetic markers to predict variations in phenotypic traits. Genome-wide association study (GWAS) was applied to identify loci that controlled competition between the two species. Results: In E. coli, 162 significant SNPs affected the change of maximum growth rate by comparing initials strains with those grown in co-culture, and 36 significant SNPs affected the change of maximum growth rate comparing monoculture and co-culture strains. Five of the significant E. coli genes we identified after annotation this time were also reported in other evolutionary studies. We also identified 85 significant SNPs in S. aureus that affected the change of maximum growth rate by comparing initial strains with those grown in monoculture. About the change of bacterial numbers, we found that 706 significant SNPs were associated in E. coli and 129 in S. aureus. Thirteen of the E. coli significant genes in this study were also verified in previous evolutionary reports Conclusion: We found several significant genes both in monoculture and co-culture affecting the interaction of E. coli and S. aureus. GWAS has the potential to study interspecific interactions of bacteria.

Computational identification of genes modulating stem height-diameter allometry.

The developmental variation in stem height with respect to stem diameter is related to a broad range of ecological and evolutionary phenomena in trees, but the underlying genetic basis of this variation remains elusive. We implement a dynamic statistical model, functional mapping, to formulate a general procedure for the computational identification of quantitative trait loci (QTLs) that control stem height-diameter allometry during development. Functional mapping integrates the biological principles underlying trait formation and development into the association analysis of DNA genotype and endpoint phenotype, thus providing an incentive for understanding the mechanistic interplay between genes and development. Built on the basic tenet of functional mapping, we explore two core ecological scenarios of how stem height and stem diameter covary in response to environmental stimuli: (i) trees pioneer sunlit space by allocating more growth to stem height than diameter and (ii) trees maintain their competitive advantage through an inverse pattern. The model is equipped to characterize 'pioneering' QTLs (piQTLs) and 'maintaining' QTLs (miQTLs) which modulate these two ecological scenarios, respectively. In a practical application to a mapping population of full-sib hybrids derived from two Populus species, the model has well proven its versatility by identifying several piQTLs that promote height growth at a cost of diameter growth and several miQTLs that benefit radial growth at a cost of height growth. Judicious application of functional mapping may lead to improved strategies for studying the genetic control of the formation mechanisms underlying trade-offs among quantities of assimilates allocated to different growth parts.