Mechanisms of gap gene expression canalization in the Drosophila blastoderm

<p>Abstract</p> <p>Background</p> <p>Extensive variation in early gap gene expression in the <it>Drosophila </it>blastoderm is reduced over time because of gap gene cross regulation. This phenomenon is a manifestation of canalization, the ability of an organism to produce a consistent phenotype despite variations in genotype or environment. The canalization of gap gene expression can be understood as arising from the actions of attractors in the gap gene dynamical system.</p> <p>Results</p> <p>In order to better understand the processes of developmental robustness and canalization in the early <it>Drosophila </it>embryo, we investigated the dynamical effects of varying spatial profiles of Bicoid protein concentration on the formation of the expression border of the gap gene <it>hunchback</it>. At several positions on the anterior-posterior axis of the embryo, we analyzed attractors and their basins of attraction in a dynamical model describing expression of four gap genes with the Bicoid concentration profile accounted as a given input in the model equations. This model was tested against a family of Bicoid gradients obtained from individual embryos. These gradients were normalized by two independent methods, which are based on distinct biological hypotheses and provide different magnitudes for Bicoid spatial variability. We showed how the border formation is dictated by the biological initial conditions (the concentration gradient of maternal Hunchback protein) being attracted to specific attracting sets in a local vicinity of the border. Different types of these attracting sets (point attractors or one dimensional attracting manifolds) define several possible mechanisms of border formation. The <it>hunchback </it>border formation is associated with intersection of the spatial gradient of the maternal Hunchback protein and a boundary between the attraction basins of two different point attractors. We demonstrated how the positional variability for <it>hunchback </it>is related to the corresponding variability of the basin boundaries. The observed reduction in variability of the <it>hunchback </it>gene expression can be accounted for by specific geometrical properties of the basin boundaries.</p> <p>Conclusion</p> <p>We clarified the mechanisms of gap gene expression canalization in early <it>Drosophila </it>embryos. These mechanisms were specified in the case of <it>hunchback </it>in well defined terms of the dynamical system theory.</p

Canalization of Gene Expression and Domain Shifts in the Drosophila Blastoderm by Dynamical Attractors

The variation in the expression patterns of the gap genes in the blastoderm of the fruit fly Drosophila melanogaster reduces over time as a result of cross regulation between these genes, a fact that we have demonstrated in an accompanying article in PLoS Biology (see Manu et al., doi:10.1371/journal.pbio.1000049). This biologically essential process is an example of the phenomenon known as canalization. It has been suggested that the developmental trajectory of a wild-type organism is inherently stable, and that canalization is a manifestation of this property. Although the role of gap genes in the canalization process was established by correctly predicting the response of the system to particular perturbations, the stability of the developmental trajectory remains to be investigated. For many years, it has been speculated that stability against perturbations during development can be described by dynamical systems having attracting sets that drive reductions of volume in phase space. In this paper, we show that both the reduction in variability of gap gene expression as well as shifts in the position of posterior gap gene domains are the result of the actions of attractors in the gap gene dynamical system. Two biologically distinct dynamical regions exist in the early embryo, separated by a bifurcation at 53% egg length. In the anterior region, reduction in variation occurs because of stability induced by point attractors, while in the posterior, the stability of the developmental trajectory arises from a one-dimensional attracting manifold. This manifold also controls a previously characterized anterior shift of posterior region gap domains. Our analysis shows that the complex phenomena of canalization and pattern formation in the Drosophila blastoderm can be understood in terms of the qualitative features of the dynamical system. The result confirms the idea that attractors are important for developmental stability and shows a richer variety of dynamical attractors in developmental systems than has been previously recognized

Stochastic Effects in Retrotransposon Dynamics Revealed by Modeling under Competition for Cellular Resources

Transposons are genomic elements that can relocate within a host genome using a ‘cut’- or ‘copy-and-paste’ mechanism. They make up a significant part of many genomes, serve as a driving force for genome evolution, and are linked with Mendelian diseases and cancers. Interactions between two specific retrotransposon types, autonomous (e.g., LINE1/L1) and nonautonomous (e.g., Alu), may lead to fluctuations in the number of these transposons in the genome over multiple cell generations. We developed and examined a simple model of retrotransposon dynamics under conditions where transposon replication machinery competed for cellular resources: namely, free ribosomes and available energy (i.e., ATP molecules). Such competition is likely to occur in stress conditions that a malfunctioning cell may experience as a result of a malignant transformation. The modeling revealed that the number of actively replicating LINE1 and Alu elements in a cell decreases with the increasing competition for resources; however, stochastic effects interfere with this simple trend. We stochastically simulated the transposon dynamics in a cell population and showed that the population splits into pools with drastically different transposon behaviors. The early extinction of active Alu elements resulted in a larger number of LINE1 copies occurring in the first pool, as there was no competition between the two types of transposons in this pool. In the other pool, the competition process remained and the number of L1 copies was kept small. As the level of available resources reached a critical value, both types of dynamics demonstrated an increase in noise levels, and both the period and the amplitude of predator–prey oscillations rose in one of the cell pools. We hypothesized that the presented dynamical effects associated with the impact of the competition for cellular resources inflicted on the dynamics of retrotransposable elements could be used as a characteristic feature to assess a cell state, or to control the transposon activity

Prognostic Analysis of Human Pluripotent Stem Cells Based on Their Morphological Portrait and Expression of Pluripotent Markers

The ability of human pluripotent stem cells for unlimited proliferation and self-renewal promotes their application in the fields of regenerative medicine. The morphological assessment of growing colonies and cells, as a non-invasive method, allows the best clones for further clinical applications to be safely selected. For this purpose, we analyzed seven morphological parameters of both colonies and cells extracted from the phase-contrast images of human embryonic stem cell line H9, control human induced pluripotent stem cell (hiPSC) line AD3, and hiPSC line HPCASRi002-A (CaSR) in various passages during their growth for 120 h. The morphological phenotype of each colony was classified using a visual analysis and associated with its potential for pluripotency and clonality maintenance, thus defining the colony phenotype as the control parameter. Using the analysis of variance for the morphological parameters of each line, we showed that selected parameters carried information about different cell lines and different phenotypes within each line. We demonstrated that a model of classification of colonies and cells by phenotype, built on the selected parameters as predictors, recognized the phenotype with an accuracy of 70&ndash;75%. In addition, we performed a qRT-PCR analysis of eleven pluripotency markers genes. By analyzing the variance of their expression in samples from different lines and with different phenotypes, we identified group-specific sets of genes that could be used as the most informative ones for the separation of the best clones. Our results indicated the fundamental possibility of constructing a morphological portrait of a colony informative for the automatic identification of the phenotype and for linking this portrait to the expression of pluripotency markers

Modeling of Flowering Time in <i>Vigna radiata</i> with Artificial Image Objects, Convolutional Neural Network and Random Forest

Flowering time is an important target for breeders in developing new varieties adapted to changing conditions. In this work, a new approach is proposed in which the SNP markers influencing time to flowering in mung bean are selected as important features in a random forest model. The genotypic and weather data are encoded in artificial image objects, and a model for flowering time prediction is constructed as a convolutional neural network. The model uses weather data for only a limited time period of 5 days before and 20 days after planting and is capable of predicting the time to flowering with high accuracy. The most important factors for model solution were identified using saliency maps and a Score-CAM method. Our approach can help breeding programs harness genotypic and phenotypic diversity to more effectively produce varieties with a desired flowering time

Combination of SNP influences for the 213 individual genotypes.

<p>(A) The scatterplot for the genotype score vs. the sum of the SNP scores over all SNPs from the genotype, for all genotypes and values of <i>k</i> corresponding to contribution from two or more SNPs from the same regulatory region. The blue color labels points with a prevailing influence from only one SNP within a genotype, and the red color corresponds to the competition from multiple SNPs, as described in the text. The threshold value (70% of the largest SNP score) chosen to discriminate the blue and red points was manually tuned to make the red points comprise all points outside the additivity line (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184657#pone.0184657.s010" target="_blank">S5 Fig</a> shows the deviation from this line as a function of this threshold). The panel shows the magnified graph for better visualisation, and the inset displays the full range. (B) Measure of SNP influence additivity for the population (green line) in comparison with the same measure for families of genotypes randomly simulated under the neutral SFS (red) and the population-derived SFS (blue). The measure is the mean distance from the points to the line of pure additivity <i>y</i> = <i>x</i>, as explained in the text. The distances were calculated for all <i>k</i> values related to a genotype (1 ≤ <i>k</i> ≤ 3400) and for 213 polymorphic genotypes from the population or from a family of randomly simulated genotypes, and an average value was obtained for the population and for each family. For each SFS type, the probability density function (PDF) was estimated from the mean distance distribution across 100 families of randomly simulated genotypes.</p

Positive and negative influence of SNPs from the study population.

<p>(A) Number of activating (‘A’) and repressing (‘R’) TFBSs containing SNPs with purely positive influence on gene expression (red), purely negative influence (blue), and with alternating sign (green). (B) Distribution of Δ<i>P</i> values for activating and repressing TFBSs and for different signs of SNP influence on expression. The labels and colors have the same meaning as in A. The dots show outliers.</p