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The concentration of genes that control domestication traits in so-called “agroislands” complicates breeding. Therefore, information about where such islands are located in the genome and with what traits of domestication they are associated is of great importance for further research aimed at improving modern varieties. In the search for such information in the genomes of chickpea landraces, the BayPass program was used to assess the association of single nucleotide polymorphisms with population-specific covariates chosen as complexes of domestication syndrome traits in cultivars of the same breeding purpose. The regions of the genome with which these covariates are associated are located on chromosomes 1, 5, 6, 7, and 8. These regions are potential agroislands, as indirectly confirmed by earlier data.

Background There is a plethora of methods for genome-wide association studies. However, only a few of them may be classified as multi-trait and multi-locus, i.e. consider the influence of multiple genetic variants to several correlated phenotypes. Results We propose a multi-trait multi-locus model which employs structural equation modeling (SEM) to describe complex associations between SNPs and traits - multi-trait multi-locus SEM (mtmlSEM). The structure of our model makes it possible to discriminate pleiotropic and single-trait SNPs of direct and indirect effect. We also propose an automatic procedure to construct the model using factor analysis and the maximum likelihood method. For estimating a large number of parameters in the model, we performed Bayesian inference and implemented Gibbs sampling. An important feature of the model is that it correctly copes with non-normally distributed variables, such as some traits and variants. Conclusions We applied the model to Vavilov’s collection of 404 chickpea (Cicer arietinum L.) accessions with 20-fold cross-validation. We analyzed 16 phenotypic traits which we organized into five groups and found around 230 SNPs associated with traits, 60 of which were of pleiotropic effect. The model demonstrated high accuracy in predicting trait values.

—We have analyzed the gene expression dynamics in floral transition in the CDC Frontier chickpea cultivar. We provide a model, in several versions, to predict the expression dynamics of five flowering genes, taking the expression of their regulators as an input. The models were trained using the random forest method on the previously published expression data for ten flowering genes under the short- and long-day growing conditions. The resulting models correctly predict the dynamics of the average expression levels under long days. We show that the models for CDC Frontier mainly reproduce the regulatory interactions between the key genes described for the Arabidopsis thaliana model plant. Based on the analysis, we hypothesize that the short-day data and the long-day data contain qualitatively different information, which may be due to different regulatory modules that function in different conditions. For the regulators of the flower meristem identity genes AP1 and LFY, our models predict FTa3 as the main activator and TFL1c as the main repressor under long days.

Modern flax cultivars are susceptible to many diseases, arguably, the most economically damaging of these is the Fusarium wilt fungal disease. Over the past decades international flax breeding initiatives resulted in the development of resistant cultivars. However, much remains to be learned about the mechanisms of resistance to Fusarium infection in flax. As a first step to uncover the genetic factors associated with resistance to Fusarium wilt disease, we performed a genome-wide association study (GWAS) using 297 accessions from the collection of the Federal Research Centre of the Bast Fiber Crops, Torzhok, Russia. These genotypes were infected with a highly pathogenic Fusarium oxysporum f.sp. lini MI39 strain, the wilt symptoms were documented in the course of three successive years. Six different single-locus models implemented in GAPIT3 R package were applied to a selected subset of 72,526 SNPs. A total of 15 QTNs (Quantitative Trait Nucleotides) were detected during at least two years of observation, while eight QTNs were found during all three years of the experiment. Of these, ten QTNs occupied a region of 640 Kb at the start of chromosome 1, while the remaining QTNs mapped to chromosomes 8, 11 and 13. All stable QTNs demonstrate a statistically significant allelic effect across 3 years of the experiment. Importantly, several QTNs spanned regions that harbored genes involved in the pathogen recognition and plant immunity response, including the KIP1-like protein (Lus10025717) and NBS-LRR protein (Lus10025852). Our results provide novel insights into the genetic architecture of flax resistance to Fusarium wilt and pinpoint potential candidate genes for further in-depth studies.

Regression models for time to flowering had been developed for VIR chickpea landraces collected in Turkey and Ethiopia. Predicted flowering time coincides closely with experimental data. The difference between models is statistically significant. The impact of the temperature to the model for Ethiopia landraces is weaker than that for Turkey landraces, 48 and 60% respectively. The impact of precipitation on phenotype is more than 80%. The obtained results are in agreement with climate characteristics at collection sites.

Mungbean (Vigna radiata) (L.) R.Wilczek and urdbean (V. mungo) (L.) Hepper are two warm-season legumes that are nutritionally dense and contribute to food security. Despite their value, they have historically been underutilized. The N. I. Vavilov’s All-Russian Institute of Plant Genetic Resources Vigna germplasm collection is particularly critical for these crops because it includes accessions acquired over a century, from before 1920 into the twenty-first century. Here we evaluate 986 accessions of V. radiata (822) and V. mungo (164) from the VIR collection to assess historical shifts before and after the Green Revolution in three of the most important traits for breeding: the maturity period, seed productivity per plant, and the weight of 1000 seeds. In addition, for the subset of 200 accessions collected in 1910–1926, another 23 morphological and agronomic traits have been evaluated. This examination allows us to assess differences among these two closely related species, reveals the differences in germplasm from different regions, and trace changes in phenotypic characters in accessions acquired at different times. Indian landraces, in the region of primary domestication and diversification of both crops, are the most phenotypically diverse. It is evident that phenotypic diversity has decayed over time, with shifts towards medium time to maturation and large-seeded types in more recently acquired accessions bred after the start of the Green Revolution. The substantial phenotypic variation in the collection indicates that a number of breeder-desired traits are present in the VIR collection, particularly in the older material pre-dating the advent of Green Revolution breeding.

Fusarium wilt of flax is an aggressive disease caused by the soil-borne fungal pathogen Fusarium oxysporum f. sp. lini. It is a challenging pathogen presenting a constant threat to flax production industry worldwide. Previously, we reported chromosome-level assemblies of 5 highly pathogenic F. oxysporum f. sp. lini strains. We sought to characterize the genomic architecture of the fungus and outline evolutionary mechanisms shaping the pathogen genome. Here, we reveal the complex multi-compartmentalized genome organization and uncover its diverse evolutionary dynamics, which boosts genetic diversity and facilitates host adaptation. In addition, our results suggest that host of functions implicated in the life cycle of mobile genetic elements are main contributors to dissimilarity between proteomes of different Fusaria. Finally, our experiments demonstrate that mobile genetics elements are expressed in planta upon infection, alluding to their role in pathogenicity. On the whole, these results pave the way for further in-depth studies of evolutionary forces shaping the host–pathogen interaction.

Transition to flowering is an important stage of plant development. Many regulatory modules that control floral transition are conservative across plants. This process is best studied for the model plant Arabidopsis thaliana. The homologues of Arabidopsis genes responsible for the flowering initiation in legumes have been identified, and available data on their expression provide a good basis for gene network modeling. In this study, we developed several dynamical models of a gene network controlling transition to flowering in pea (Pisum sativum) using two different approaches. We used differential equations for modeling a previously proposed gene regulation scheme of floral initiation in pea and tested possible alternative hypothesis about some regulations. As the second approach, we applied neural networks to infer interactions between genes in the network directly from gene expression data. All models were verified on previously published experimental data on the dynamic expression of the main genes in the wild type and in three mutant genotypes. Based on modeling results, we made conclusions about the functionality of the previously proposed interactions in the gene network and about the influence of different growing conditions on the network architecture. It was shown that regulation of the PIM, FTa1, and FTc genes in pea does not correspond to the previously proposed hypotheses. The modeling suggests that short- and long-day growing conditions are characterized by different gene network architectures. Overall, the results obtained can be used to plan new experiments and create more accurate models to study the flowering initiation in pea and, in a broader context, in legumes.

A defining challenge of the 21st century is meeting the nutritional demands of the growing human population, under a scenario of limited land and water resources and under the specter of climate change. The Vavilov seed bank contains numerous landraces collected nearly a hundred years ago, and thus may contain &lsquo, genetic gems&rsquo, with the potential to enhance modern breeding efforts. Here, we analyze 407 landraces, sampled from major historic centers of chickpea cultivation and secondary diversification. Genome-Wide Association Studies (GWAS) conducted on both phenotypic traits and bioclimatic variables at landraces sampling sites as extended phenotypes resulted in 84 GWAS hits associated to various regions. The novel haploblock-based test identified haploblocks enriched for single nucleotide polymorphisms (SNPs) associated with phenotypes and bioclimatic variables. Subsequent bi-clustering of traits sharing enriched haploblocks underscored both non-random distribution of SNPs among several haploblocks and their association with multiple traits. We hypothesize that these clusters of pleiotropic SNPs represent co-adapted genetic complexes to a range of environmental conditions that chickpea experienced during domestication and subsequent geographic radiation. Linking genetic variation to phenotypic data and a wealth of historic information preserved in historic seed banks are the keys for genome-based and environment-informed breeding intensification.

Diversifcation of crop production in the Russian Federation could be partly achieved by the introduction and production of minor and underutilized crops. Green gram or mung bean (Vigna radiata(L.) R. Wilczek) and black gram or urd (V. mungo(L.) Hepper) are grain legume crops cultivated in limited areas in the Russian Federation. Meanwhile, green gram occupies about 8.5 % of the world production area under pulses (without soybean). It is cultivated mainly in countries of Southeast Asia, but production is expanding to the entire subtropical belt of the globe. In our country these crops can be successfully grown on irrigation in a number of regions in the southern area of the European part and the Russian Far East, where the temperatures during their vegetation are about 28–30 °C and always above 15 °C. The purpose of this paper is to summarize the world’s experience in breeding improvement of mung bean and urd as crops with promise for cultivation in certain soil and climatic zones of the Russian Federation. The world production, use of these high-protein crops, history and peculiarities of breeding, including in the USSR, are covered. To expand the production of both crops in the Russian Federation, their popularization and development of breeding are required. Basic requirements for modern varieties include resistance to biotic and abiotic stressors which can be introgressed from wild relatives. The great importance of both crops in the Asian countries led to the rapid development of molecular researches there. The genome of black gram has been fully sequenced, the genome of green gram has been partly sequenced. Some genes and QTL of adaptability traits have been marked and mapped in a number of wild species of the genusVigna. The role of wild relatives in the breeding of crops concerned is discussed. In the world genebanks, signifcant genetic resources of mung bean and urd have been accumulated. All this creates prerequisites for the development of marker-assistant and genomic breeding.

The genomes of mammals contain thousands of deleterious mutations. It is important to be able to recognize them with high precision. In conservation biology, the small size of fragmented populations results in accumulation of damaging variants. Preserving animals with less damaged genomes could optimize conservation efforts. In breeding of farm animals, trade‐offs between farm performance versus general fitness might be better avoided if deleterious mutations are well classified. In humans, the problem of such a precise classification has been successfully solved, in large part due to large databases of disease‐causing mutations. However, this kind of information is very limited for other mammals. Here, we propose to better use information available on human mutations to enable classification of damaging mutations in other mammalian species. Specifically, we apply transfer learning—machine learning methods—improving small dataset for solving a focal problem (recognizing damaging mutations in our companion and farm animals) due to the use of much large datasets available for solving a related problem (recognizing damaging mutations in humans). We validate our tools using mouse and dog annotated datasets and obtain significantly better results in companion to the SIFT classifier. Then, we apply them to predict deleterious mutations in cattle genomewide dataset.

The Vavilov Institute of Plant Genetic Resources (VIR), in St. Petersburg, Russia, houses a unique genebank, with historical collections of landraces. When they were collected, the geographical distribution and genetic diversity of most crops closely reflected their historical patterns of cultivation established over the preceding millennia. We employed a combination of genomics, computational biology and phenotyping to characterize VIR’s 147 chickpea accessions from Turkey and Ethiopia, representing chickpea’s center of origin and a major location of secondary diversity. Genotyping by sequencing identified 14,059 segregating polymorphisms and genome-wide association studies revealed 28 GWAS hits in potential candidate genes likely to affect traits of agricultural importance. The proportion of polymorphisms shared among accessions is a strong predictor of phenotypic resemblance, and of environmental similarity between historical sampling sites. We found that 20 out of 28 polymorphisms, associated with multiple traits, including days to maturity, plant phenology, and yield-related traits such as pod number, localized to chromosome 4. We hypothesize that selection and introgression via inadvertent hybridization between more and less advanced morphotypes might have resulted in agricultural improvement genes being aggregated to genomic ‘agro islands’, and in genotype-to-phenotype relationships resembling widespread pleiotropy.

This review deals with the recent studies expanding the idea of positional information in the early embryogenesis of Drosophila melanogaster. Previous studies showed that, in the course of segment determination in Drosophila, information created by gradients of products of maternal coordinate genes is not “read” statically, being interpreted by their zygotic target genes via regulatory interactions. This leads to spatial shifts in the expression of target genes relative to the original positions as well as to dynamic reduction in the zygotic expression variability. However, according to recent data, interpretation of positional information includes the interaction between not only zygotic target genes but also the maternal coordinate genes themselves. Different systems of maternal coordinate genes (maternal systems)—the posterior-anterior, terminal, and dorsoventral—can interact with each other. This is usually expressed in the regulation of zygotic target genes of one maternal system by other maternal systems. The concept of a “morphogenetic network” was introduced to define the interaction of maternal systems during determination of spatial gene expression in the early Drosophila embryo.

Domestication and subsequent breeding have eroded genetic diversity in the modern chickpea crop by ˜100-fold. Corresponding reductions to trait variation create the need, and an opportunity, to identify and harness the genetic capacity of wild species for crop improvement. Here we analyze trait segregation in a series of wild x cultivated hybrid populations to delineate the genetic underpinnings of domestication traits. Two species of wild chickpea, C. reticulatum and C. echinospermum, were crossed with the elite, early flowering C. arietinum cultivar ICCV96029. KASP genotyping of F2 parents with an FT-linked molecular marker enabled selection of 284 F3 families with reduced phenological variation: 255 F3 families of C. arietinum x reticulatum (AR) derived from 17 diverse wild parents and 29 F3 families of C. arietinum x echinospermum (AE) from 3 wild parents. The combined 284 lineages were genotyped using a genotyping-by-sequencing strategy and phenotyped for agronomic traits. 50 QTLs in 11 traits were detected from AR and 35 QTLs in 10 traits from the combined data. Using hierarchical clustering to assign traits to six correlated groups and mixed model based multi-trait mapping, four pleiotropic loci were identified. Bayesian analysis further identified four inter-trait relationships controlling the duration of vegetative growth and seed maturation, for which the underlying pleiotropic genes were mapped. A random forest approach was used to explore the most extreme trait differences between AR and AE progenies, identifying traits most characteristic of wild species origin. Knowledge of the genomic basis of traits that segregate in wild-cultivated hybrid populations will facilitate chickpea improvement by linking genetic and phenotypic variation in a quantitative genetic framework.

Schizophrenia (SCZ) is a psychiatric disorder of unknown etiology. There is evidence suggesting that aberrations in neurodevelopment are a significant attribute of schizophrenia pathogenesis and progression. To identify biologically relevant molecular abnormalities affecting neurodevelopment in SCZ we used cultured neural progenitor cells derived from olfactory neuroepithelium (CNON cells). Here, we tested the hypothesis that variance in gene expression differs between individuals from SCZ and control groups. In CNON cells, variance in gene expression was significantly higher in SCZ samples in comparison with control samples. Variance in gene expression was enriched in five molecular pathways: serine biosynthesis, PI3K-Akt, MAPK, neurotrophin and focal adhesion. More than 14% of variance in disease status was explained within the logistic regression model (C-value = 0.70) by predictors accounting for gene expression in 69 genes from these five pathways. Structural equation modeling (SEM) was applied to explore how the structure of these five pathways was altered between SCZ patients and controls. Four out of five pathways showed differences in the estimated relationships among genes: between KRAS and NF1, and KRAS and SOS1 in the MAPK pathway; between PSPH and SHMT2 in serine biosynthesis; between AKT3 and TSC2 in the PI3K-Akt signaling pathway; and between CRK and RAPGEF1 in the focal adhesion pathway. Our analysis provides evidence that variance in gene expression is an important characteristic of SCZ, and SEM is a promising method for uncovering altered relationships between specific genes thus suggesting affected gene regulation associated with the disease. We identified altered gene-gene interactions in pathways enriched for genes with increased variance in expression in SCZ. These pathways and loci were previously implicated in SCZ, providing further support for the hypothesis that gene expression variance plays important role in the etiology of SCZ.

Various molecular configurations can be formed by combinations of free and occupied transcription-factor binding sites at an enhancer site that regulates the transcriptional activity of a target gene. Thermodynamic models of gene expression enable the calculation of the probability of transcriptional activation of a target gene using the probabilities that correspond to specific molecular configurations of an enhancer. A simple stochastic model for the formation of such configurations is presented in this study. The processes of the non-specific binding of transcription factors to DNA, the sliding of proteins along DNA, and energetically favorable binding to specific sites are taken into account in the model, which has been used to investigate the properties of molecular configurations of the regulatory site of the knirps gene, which is expressed during early embryonic development of Drosophila in a manner that is dependent on the activity of the Hunchback and Bicoid transcription factors. The assumptions that are included in the model allowed for discrimination between three successive dynamic stages that are characterized by different binding rates. Transcription-factor binding sites could be divided into three groups according to the degree of occupancy. The probability of occupation that was calculated for the sites in a quasi-equilibrium state was substantially different from the distribution that was predicted using the assumption of thermodynamic equilibrium that is employed in thermodynamic models of expression. These results can serve as the first step towards the refinement of expression models based on more detailed information on the regulatory regions of target genes.

Expression patterns of segmentation genes form under the influence of gradients of maternal transcription factors, which initiate spatially local expression in the segmentation gene cascade. Bcd acts as one of these activators. A model of the regulation in the gap-gene network was studied by varying the Bcd concentration. The topology that is known for the gap-gene network was found to be insufficient for explaining the experimental finding that the hb anterior expression domain shifts when the Bcd concentration changes in the embryo. Modeling that was performed to comply with this experimental finding yielded a new topology, which determined the proper shifts of the hb expression domain. The result indicates that interactions of hb, Kr, and gt act as key regulatory factors to ensure the correct behavior of the hb expression pattern upon changes in Bcd concentration. This study made it possible to specify the limits of the validity of phenomenological models of gene networks.

ICCV 96029 is the earliest flowering chickpea cultivar. We analyzed the model of the gene network controlling floral transition and hypothesized that this chickpea cultivar is significantly different from other legumes in organization of mobile flowering signals related to the expression of the FT (FLOWERING LOCUS T) gene paralogs. Unlike pea and other legumes, that according to previously obtained data could demonstrate cross-regulation between FT-like genes and different nature of the associated flowering signals, the simulation results for chickpea suggest that the FT paralogs play additive roles in the promotion of flowering.

Here we characterize the response of the Drosophila segmentation system to mutations in two gap genes, Kr and kni, in the form of single or double homozygotes and single heterozygotes. Segmentation gene expression in these genotypes was quantitatively monitored with cellular resolution in space and 6.5 to 13 min resolution in time. As is the case with wild type, we found that gene expression domains in the posterior portion of the embryo shift to the anterior over time. In certain cases, such as the gt posterior domain in Kr mutants, the shifts are significantly larger than is seen in wild type embryos. We also investigated the effects of Kr and kni on the variability of gene expression. Mutations often produce variable phenotypes, and it is well known that the cuticular phenotype of Kr mutants is variable. We sought to understand the molecular basis of this effect. We find that throughout cycle 14A the relative levels of eve and ftz expression in stripes 2 and 3 are variable among individual embryos. Moreover, in Kr and kni mutants, unlike wild type, the variability in positioning of the posterior Hb domain and eve stripe 7 is not decreased or filtered with time. The posterior Gt domain in Kr mutants is highly variable at early times, but this variability decreases when this domain shifts in the anterior direction to the position of the neighboring Kni domain. In contrast to these findings, positional variability throughout the embryo does not decrease over time in double Kr;kni mutants. In heterozygotes the early expression patterns of segmentation genes resemble patterns seen in homozygous mutants but by the onset of gastrulation they become similar to the wild type patterns. Finally, we note that gene expression levels are reduced in Kr and kni mutant embryos and have a tendency to decrease over time. This is a surprising result in view of the role that mutual repression is thought to play in the gap gene system.

Living organisms well adapt themselves to changes in the environment and are robust to potential damage such as mutations. The epigenetic mechanism whereby the suppression of phenotypic variation is achieved has been called canalization. This paper summarizes results of research that employed experimental and theoretical approaches to uncover the mechanisms of canalization of variation in expression of segmentation genes.

Analysis of the quantitative data obtained by processing the confocal images showed that the initial variability of the expression pattern of Drosophila zygotic segmentation genes was strongly reduced by the onset of gastrulation. The following variability components were studied: the range of gene expression intensity in different embryos, the time and succession of the formation of expression domain, types of formation, and domain positioning. At the level of zygotic genes, the positioning error proved to be dynamically filtered with time.

Here we characterize the expression of the full system of genes which control the segmentation morphogenetic field of Drosophila at the protein level in one dimension. The data used for this characterization are quantitative with cellular resolution in space and about 6 min in time. We present the full quantitative profiles of all 14 segmentation genes which act before the onset of gastrulation. The expression patterns of these genes are first characterized in terms of their average or typical behavior. At this level, the expression of all of the genes has been integrated into a single atlas of gene expression in which the expression levels of all genes in each cell are specified. We show that expression domains do not arise synchronously, but rather each domain has its own specific dynamics of formation. Moreover, we show that the expression domains shift position in the direction of the cephalic furrow, such that domains in the anlage of the segmented germ band shift anteriorly while those in the presumptive head shift posteriorly. The expression atlas of integrated data is very close to the expression profiles of individual embryos during the latter part of the blastoderm stage. At earlier times gap gene domains show considerable variation in amplitude, and significant positional variability. Nevertheless, an average early gap domain is close to that of a median individual. In contrast, we show that there is a diversity of developmental trajectories among pair-rule genes at a variety of levels, including the order of domain formation and positional accuracy. We further show that this variation is dynamically reduced, or canalized, over time. As the first quantitatively characterized morphogenetic field, this system and its behavior constitute an extraordinarily rich set of materials for the study of canalization and embryonic regulation at the molecular level.

We describe an automated high-throughput method to measure protein levels in single nuclei in blastoderm embryos of Drosophila melanogaster by means of immunofluorescence. The method consists of a chain of specific algorithms assembled into an image processing pipeline. This pipeline transforms a confocal scan of an embryo stained with fluorescently tagged antibodies into a text file. This text file contains a numerical identifier for each nucleus, the coordinates of its centroid, and the average concentrations of three proteins in that nucleus. The central algorithmic component of the method is the automatic identification of nuclei by edge detection with the use of watersheds as an error-correction step. This method provides high-throughput quantification at cellular resolution.

Here we present a method for the removal of nonspecific background signal from fluorescently localized expression patterns of Drosophila segmentation genes. Our algorithm for removal of background signal brings the data to a common standard form with zero background and removes systematic error in gene expression levels caused by the presence of background. The method is based on the discovery, reported here, that background is well fit by a very broad two-dimensional paraboloid. The paraboloid is determined from the area of the embryo in which a given gene is not expressed and the whole pattern is then normalized by this paraboloid to remove background from the entire embryo. The software implementing this algorithm is available from the authors.

Morphogen gradients contribute to pattern formation by determining positional information in morphogenetic fields. Interpretation of positional information is thought to rely on direct, concentration-threshold-dependent mechanisms for establishing multiple differential domains of target gene expression. In Drosophila, maternal gradients establish the initial position of boundaries for zygotic gap gene expression, which in turn convey positional information to pair-rule and segment-polarity genes, the latter forming a segmental pre-pattern by the onset of gastrulation. Here we report, on the basis of quantitative gene expression data, substantial anterior shifts in the position of gap domains after their initial establishment. Using a data-driven mathematical modelling approach, we show that these shifts are based on a regulatory mechanism that relies on asymmetric gap-gap cross-repression and does not require the diffusion of gap proteins. Our analysis implies that the threshold-dependent interpretation of maternal morphogen concentration is not sufficient to determine shifting gap domain boundary positions, and suggests that establishing and interpreting positional information are not independent processes in the Drosophila blastoderm.

Background We perform the theoretical analysis of a gene network sub-system, composed of a feed-forward loop, in which the upstream transcription factor regulates the target gene via two parallel pathways: directly, and via interaction with miRNA. Results As the molecular mechanisms of miRNA action are not clear so far, we elaborate three mathematical models, in which miRNA either represses translation of its target or promotes target mRNA degradation, or is not re-used, but degrades along with target mRNA. We examine the feed-forward loop dynamics quantitatively at the whole time interval of cell cycle. We rigorously proof the uniqueness of solutions to the models and obtain the exact solutions in one of them analytically. Conclusions We have shown that different mechanisms of miRNA action lead to a variety of types of dynamical behavior of feed-forward loops. In particular, we found that the ability of feed-forward loop to dampen fluctuations introduced by transcription factor is the model and parameter dependent feature. We also discuss how our results could help a biologist to infer the mechanism of miRNA action.

Annotating the genotype-phenotype relationship, and developing a proper quantitative description of the relationship, requires understanding the impact of natural genomic variation on gene expression. We apply a sequence-level model of gap gene expression in the early development of Drosophila to analyze single nucleotide polymorphisms (SNPs) in a panel of natural sequenced D. melanogaster lines. Using a thermodynamic modeling framework, we provide both analytical and computational descriptions of how single-nucleotide variants affect gene expression. The analysis reveals that the sequence variants increase (decrease) gene expression if located within binding sites of repressors (activators). We show that the sign of SNP influence (activation or repression) may change in time and space and elucidate the origin of this change in specific examples. The thermodynamic modeling approach predicts non-local and non-linear effects arising from SNPs, and combinations of SNPs, in individual fly genotypes. Simulation of individual fly genotypes using our model reveals that this non-linearity reduces to almost additive inputs from multiple SNPs. Further, we see signatures of the action of purifying selection in the gap gene regulatory regions. To infer the specific targets of purifying selection, we analyze the patterns of polymorphism in the data at two phenotypic levels: the strengths of binding and expression. We find that combinations of SNPs show evidence of being under selective pressure, while individual SNPs do not. The model predicts that SNPs appear to accumulate in the genotypes of the natural population in a way biased towards small increases in activating action on the expression pattern. Taken together, these results provide a systems-level view of how genetic variation translates to the level of gene regulatory networks via combinatorial SNP effects.

As an RNA virus, hepatitis C virus (HCV) is able to rapidly acquire drug resistance, and for this reason the design of effective anti-HCV drugs is a real challenge. The HCV subgenomic replicon-containing cells are widely used for experimental studies of the HCV genome replication mechanisms, for drug testing in vitro and in studies of HCV drug resistance. The NS3/4A protease is essential for virus replication and, therefore, it is one of the most attractive targets for developing specific antiviral agents against HCV. We have developed a stochastic model of subgenomic HCV replicon replication, in which the emergence and selection of drug resistant mutant viral RNAs in replicon cells is taken into account. Incorporation into the model of key NS3 protease mutations leading to resistance to BILN-2061 (A156T, D168V, R155Q), VX-950 (A156S, A156T, T54A) and SCH 503034 (A156T, A156S, T54A) inhibitors allows us to describe the long term dynamics of the viral RNA suppression for various inhibitor concentrations. We theoretically showed that the observable difference between the viral RNA kinetics for different inhibitor concentrations can be explained by differences in the replication rate and inhibitor sensitivity of the mutant RNAs. The pre-existing mutants of the NS3 protease contribute more significantly to appearance of new resistant mutants during treatment with inhibitors than wild-type replicon. The model can be used to interpret the results of anti-HCV drug testing on replicon systems, as well as to estimate the efficacy of potential drugs and predict optimal schemes of their usage.

Developmental processes are robust, or canalised: dynamic patterns of gene expression across space and time are regulated reliably and precisely in the presence of genetic and environmental perturbations. It remains unclear whether canalisation relies on specific regulatory factors (such as heat-shock proteins), or whether it is based on more general redundancy and distributed robustness at the network level. The latter explanation implies that mutations in many regulatory factors should exhibit loss of canalisation. Here, we present a quantitative characterisation of segmentation gene expression patterns in mutants of the terminal gap gene tailless (tll) in Drosophila melanogaster. Our analysis provides new insights into the dynamic mechanisms underlying gap gene regulation, and reveals significantly increased variability of gene expression in the mutant compared to the wild-type background. We show that both position and timing of posterior segmentation gene expression domains vary strongly from embryo-to-embryo in tll mutants. This variability must be caused by a vulnerability in the regulatory system which is hidden or buffered in the wild-type, but becomes uncovered by the deletion of tll. Our analysis provides evidence that loss of canalisation in mutants could be more widespread than previously thought., Highlights ► We present a quantitative analysis of spatial gene expression in Drosophila mutants. ► Dynamic gap domain shifts do not depend on expression of terminal gap genes or hb. ► Expression variability is greatly increased in a tll mutant background. ► This indicates de-canalisation (loss of developmental robustness) in the mutant. ► Such de-canalisation is a common phenomenon in mutants of developmental regulators.

We present a review of noise buffering mechanisms responsible for developmental robustness. We focus on functions of chaperone Hsp90, miRNA, and cross-regulation of gap genes in Drosophila. The noise buffering mechanisms associated with these functions represent specific examples of the developmental canalization, reducing the phenotypical variability in presence of either genetic or environmental perturbations. We demonstrate that robustness often appears as a function of a network of interacting elements and that the system level approach is needed to understand the mechanisms of noise filtering.

Background Extensive variation in early gap gene expression in the Drosophila 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. Results In order to better understand the processes of developmental robustness and canalization in the early Drosophila 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 hunchback. 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 hunchback 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 hunchback is related to the corresponding variability of the basin boundaries. The observed reduction in variability of the hunchback gene expression can be accounted for by specific geometrical properties of the basin boundaries. Conclusion We clarified the mechanisms of gap gene expression canalization in early Drosophila embryos. These mechanisms were specified in the case of hunchback in well defined terms of the dynamical system theory.

Background The statistical thermodynamics based approach provides a promising framework for construction of the genotype-phenotype map in many biological systems. Among important aspects of a good model connecting the DNA sequence information with that of a molecular phenotype (gene expression) is the selection of regulatory interactions and relevant transcription factor bindings sites. As the model may predict different levels of the functional importance of specific binding sites in different genomic and regulatory contexts, it is essential to formulate and study such models under different modeling assumptions. Results We elaborate a two-layer model for the Drosophila gap gene network and include in the model a combined set of transcription factor binding sites and concentration dependent regulatory interaction between gap genes hunchback and Kruppel. We show that the new variants of the model are more consistent in terms of gene expression predictions for various genetic constructs in comparison to previous work. We quantify the functional importance of binding sites by calculating their impact on gene expression in the model and calculate how these impacts correlate across all sites under different modeling assumptions. Conclusions The assumption about the dual interaction between hb and Kr leads to the most consistent modeling results, but, on the other hand, may obscure existence of indirect interactions between binding sites in regulatory regions of distinct genes. The analysis confirms the previously formulated regulation concept of many weak binding sites working in concert. The model predicts a more or less uniform distribution of functionally important binding sites over the sets of experimentally characterized regulatory modules and other open chromatin domains.

In the paper, we address the problem of the temporal characterization of Drosophila embryos. We have developed a method for automated staging of an embryo on the basis of a confocal image of its gene expression pattern. Phases of spectral Fourier coefficients were used as the features characterizing temporal changes in expression patterns. The age detection is implemented by applying support vector regression, which is a machine learning method for creating regression functions of arbitrary type from a set of training data. The training set is composed of embryos for which the precise developmental age was determined by measuring the degree of membrane invagination. Testing the quality of regression on the training set showed a good prediction accuracy.

Genetic studies have revealed that segment determination in Drosophila melanogaster is based on hierarchical regulatory interactions among maternal coordinate and zygotic segmentation genes. The gap gene system constitutes the most upstream zygotic layer of this regulatory hierarchy, responsible for the initial interpretation of positional information encoded by maternal gradients. We present a detailed analysis of regulatory interactions involved in gap gene regulation based on gap gene circuits, which are mathematical gene network models used to infer regulatory interactions from quantitative gene expression data. Our models reproduce gap gene expression at high accuracy and temporal resolution. Regulatory interactions found in gap gene circuits provide consistent and sufficient mechanisms for gap gene expression, which largely agree with mechanisms previously inferred from qualitative studies of mutant gene expression patterns. Our models predict activation of Kr by Cad and clarify several other regulatory interactions. Our analysis suggests a central role for repressive feedback loops between complementary gap genes. We observe that repressive interactions among overlapping gap genes show anteroposterior asymmetry with posterior dominance. Finally, our models suggest a correlation between timing of gap domain boundary formation and regulatory contributions from the terminal maternal system.

The segmentation gene network in Drosophila embryo solves the fundamental problem of embryonic patterning: how to establish a periodic pattern of gene expression, which determines both the positions and the identities of body segments. The gap gene network constitutes the first zygotic regulatory tier in this process. Here we have applied the systems-level approach to investigate the regulatory effect of gap gene Kruppel (Kr) on segmentation gene expression. We acquired a large dataset on the expression of gap genes in Kr null mutants and demonstrated that the expression levels of these genes are significantly reduced in the second half of cycle 14A. To explain this novel biological result we applied the gene circuit method which extracts regulatory information from spatial gene expression data. Previous attempts to use this formalism to correctly and quantitatively reproduce gap gene expression in mutants for a trunk gap gene failed, therefore here we constructed a revised model and showed that it correctly reproduces the expression patterns of gap genes in Kr null mutants. We found that the remarkable alteration of gap gene expression patterns in Kr mutants can be explained by the dynamic decrease of activating effect of Cad on a target gene and exclusion of Kr gene from the complex network of gap gene interactions, that makes it possible for other interactions, in particular, between hb and gt, to come into effect. The successful modeling of the quantitative aspects of gap gene expression in mutant for the trunk gap gene Kr is a significant achievement of this work. This result also clearly indicates that the oversimplified representation of transcriptional regulation in the previous models is one of the reasons for unsuccessful attempts of mutant simulations., Author Summary Systems biology is aimed to develop an understanding of biological function or process as a system of interacting components. Here we apply the systems-level approach to understand how the blueprints for segments in the fruit fly Drosophila embryo arise. We obtain gene expression data and use the gene circuits method which allow us to reconstruct the segment determination process in the computer. To understand the system we need not only to describe it in detail, but also to comprehend what happens when certain stimuli or disruptions occur. Previous attempts to model segmentation gene expression patterns in a mutant for a trunk gap gene were unsuccessful. Here we describe the extension of the model that allows us to solve this problem in the context of Kruppel (Kr) gene. We show that remarkable alteration of gap gene expression patterns in Kr mutants can be explained by dynamic decrease of the activating effect of Cad on a target gene and exclusion of Kr from the complex network of gap gene interactions, that makes it possible for other interactions, in particular between hb and gt, to come into effect.

Developing embryos exhibit a robust capability to reduce phenotypic variations that occur naturally or as a result of experimental manipulation. This reduction in variation occurs by an epigenetic mechanism called canalization, a phenomenon which has resisted understanding because of a lack of necessary molecular data and of appropriate gene regulation models. In recent years, quantitative gene expression data have become available for the segment determination process in the Drosophila blastoderm, revealing a specific instance of canalization. These data show that the variation of the zygotic segmentation gene expression patterns is markedly reduced compared to earlier levels by the time gastrulation begins, and this variation is significantly lower than the variation of the maternal protein gradient Bicoid. We used a predictive dynamical model of gene regulation to study the effect of Bicoid variation on the downstream gap genes. The model correctly predicts the reduced variation of the gap gene expression patterns and allows the characterization of the canalizing mechanism. We show that the canalization is the result of specific regulatory interactions among the zygotic gap genes. We demonstrate the validity of this explanation by showing that variation is increased in embryos mutant for two gap genes, Krüppel and knirps, disproving competing proposals that canalization is due to an undiscovered morphogen, or that it does not take place at all. In an accompanying article in PLoS Computational Biology (doi:10.1371/journal.pcbi.1000303), we show that cross regulation between the gap genes causes their expression to approach dynamical attractors, reducing initial variation and providing a robust output. These results demonstrate that the Bicoid gradient is not sufficient to produce gap gene borders having the low variance observed, and instead this low variance is generated by gap gene cross regulation. More generally, we show that the complex multigenic phenomenon of canalization can be understood at a quantitative and predictive level by the application of a precise dynamical model., Author Summary Animals have an astonishing ability to develop reliably in spite of variable conditions during embryogenesis. More than 60 years ago, it was proposed that this property of development, called canalization, results from genetic interactions that adjust biochemical reactions so as to bring about reliable outcomes. Since then, a great deal of progress has been made in understanding the buffering of genotypic and environmental variation, and individual mutations that reveal variation have been identified. However, the mechanisms by which genetic interactions produce canalization are not yet well understood, because this requires molecular data on multiple developmental determinants and models that correctly predict complex interactions. We make use of gene expression data at both high spatial and temporal resolution for the gap genes involved in the segmentation of Drosophila. We also apply a mathematical model to show that cross regulation among the gap genes is responsible for canalization in this system. Furthermore, the model predicted specific interactions that cause canalization, and the prediction was validated experimentally. Our results show that groups of genes can act on one another to reduce variation and highlights the importance of genetic networks in generating robust development., DuringDrosophila development, the expression patterns of gap genes are much less variable than the Bicoid morphogen gradient. Modeling and experiments show that this specific instance of canalization or developmental robustness occurs by gap gene cross regulation.

Motivation: In this paper we address the problem of the determination of developmental age of an embryo from its segmentation gene expression patterns in Drosophila. Results: By applying support vector regression we have developed a fast method for automated staging of an embryo on the basis of its gene expression pattern. Support vector regression is a statistical method for creating regression functions of arbitrary type from a set of training data. The training set is composed of embryos for which the precise developmental age was determined by measuring the degree of membrane invagination. Testing the quality of regression on the training set showed good prediction accuracy. The optimal regression function was then used for the prediction of the gene expression based age of embryos in which the precise age has not been measured by membrane morphology. Moreover, we show that the same accuracy of prediction can be achieved when the dimensionality of the feature vector was reduced by applying factor analysis. The data reduction allowed us to avoid over-fitting and to increase the efficiency of the algorithm. Availability: This software may be obtained from the authors. Contact: samson@fn.csa.ru Keywords: gene expression patterns; development; embryo staging; support vector regression; segmentation genes; Drosophila. *To whom correspondence should be addressed.

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., Author Summary C. H. Waddington predicted in 1942 that networks of chemical reactions in embryos can counteract the effects of variable developmental conditions to produce reliable outcomes. The experimental signature of this process, called “canalization,” is the reduction of the variation of the concentrations of molecular determinants between individuals over time. Recently, Waddington's prediction was confirmed in embryos of the fruit fly Drosophila by observing the expression of a network of genes involved in generating the basic segmented body plan of this animal. Nevertheless, the details of how interactions within this genetic network reduced variation were still not understood. We use an accurate mathematical model of a part of this genetic network to demonstrate how canalization comes about. Our results show that coupled chemical reactions having multiple steady states, or attractors, can account for the reduction of variation in development. The variation reduction process can be driven not only by chemical steady states, but also by special pathways of motion through chemical concentration space to which neighboring pathways converge. These results constitute a precise mathematical characterization of a healing process in the fruit fly embryo.

Background: The detailed analysis of transcriptional regulation is crucially important for understanding biological processes. The gap gene network in Drosophila attracts large interest among researches studying mechanisms of transcriptional regulation. It implements the most upstream regulatory layer of the segmentation gene network. The knowledge of molecular mechanisms involved in gap gene regulation is far less complete than that of genetics of the system. Mathematical modeling goes beyond insights gained by genetics and molecular approaches. It allows us to reconstruct wild-type gene expression patterns in silico, infer underlying regulatory mechanism and prove its sufficiency. Results: We developed a new model that provides a dynamical description of gap gene regulatory systems, using detailed DNA-based information, as well as spatial transcription factor concentration data at varying time points. We showed that this model correctly reproduces gap gene expression patterns in wild type embryos and is able to predict gap expression patterns in Kr mutants and four reporter constructs. We used four-fold cross validation test and fitting to random dataset to validate the model and proof its sufficiency in data description. The identifiability analysis showed that most model parameters are well identifiable. We reconstructed the gap gene network topology and studied the impact of individual transcription factor binding sites on the model output. We measured this impact by calculating the site regulatory weight as a normalized difference between the residual sum of squares error for the set of all annotated sites and the set, from which the site of interest was left out. Conclusions: The reconstructed topology of the gap gene network is in agreement with previous modeling results and data from literature. We showed that 1) the regulatory weights of transcription factor binding sites show very weak correlation with their PWM score; 2) sites with low regulatory weight are important for the model output; 3) functional important sites are not exclusively located in cis-regulatory elements, but are rather dispersed through regulatory region. It is of importance that some of the sites with high functional impact in hb, Kr and kni regulatory regions coincide with strong sites annotated and verified in Dnase I footprint assays. Keywords: transcription; thermodynamics; reaction-diffusion; drosophila