Presentation_1_Analysis of Gene Expression Variance in Schizophrenia Using Structural Equation Modeling.PDF

<p>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.</p

Table_1_Analysis of Gene Expression Variance in Schizophrenia Using Structural Equation Modeling.CSV

<p>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.</p

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

Translating natural genetic variation to gene expression in a computational model of the <i>Drosophila</i> gap gene regulatory network

<div><p>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 <i>Drosophila</i> to analyze single nucleotide polymorphisms (SNPs) in a panel of natural sequenced <i>D. melanogaster</i> 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.</p></div

Summary of the approach.

<p>(A) Extraction of 90 SNPs within 889 model TFBSs of four gap genes from 213 polymorphic individual <i>D. mel</i> genotypes. The polymorphic TFBSs are marked by red. The SNP table is represented graphically, with a black box indicating that a given SNP is present in a given genotype. (B) Simulation of random mutations in the model TFBSs includes generating each possible SNV, or sets of SNVs randomly generated under either the neutral or population-derived site frequency spectrum (SFS). (C) The SNP frequency distributions derived from the study population (blue; the population-derived spectrum) and from the short intron sequences of the DGRP data (red; the neutral spectrum). (D) The distributions of the number of SNVs per genotype resulted from SNPs in the study population (blue) and in one family of randomly mutated genotypes. The family contains a total of 90 SNPs within the model TFBSs simulated under the neutral frequency spectrum (from C). The mean number of SNVs per genotype across 100 families is 17 ± 5. (E) The study population and randomly mutated genotypes were evaluated in the model of gap gene expression. The deviation of their expression patterns from the patterns for the reference genotype, estimated by the three scores, provides the measure of SNP influence on gene expression. (F) Three main directions for the analysis of SNP influence on gene expression.</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

Variation of expression in the study population.

<p>The left column of panels shows expression patterns (model solutions) at the end of cleavage cycle 14A (time class T8; see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184657#sec002" target="_blank">Methods</a>) for 213 individual genotypes and for the reference genotype, together with the observed expression patterns. Many gray curves group together in various spatial domains because the variation is small relative to the maximal expression level, as a consequence the curves coalesce in these parts of the figure. The same figures for all nine time points are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184657#pone.0184657.s006" target="_blank">S1 Fig</a>. The right column of panels represents the heat map for the population average of the expression difference <b>Δv</b>, for the same genes as for the panels on the left. The spatial coordinate represents the percent of the embryo length, and the time is in minutes from the start of the cleavage cycle 13.</p

Variation of SNP influence on the levels of transcriptional activation and gene products.

<p>Coefficient of variation (the standard deviation to the mean ratio) for different subsets of the set of absolute values (blue) and (red) for all genotypes from the study population and all <i>k</i> values. The coefficient was calculated for values of or exceeding the percentile values shown on the horizontal axis.</p

Mechanisms for SNP influence sign alterations.

<p>(A) Examples of local interactions of a polymorphic TFBS (red box) with other TFBSs (blue boxes) leading to alternating sign of SNP influence on expression. The horizontal lines represent segments of the regulatory region. Activating (repressing) TFBSs are shown above (below) these lines and labeled with ‘A’ (‘R’). The SNP position is marked via a short vertical line. In 3), the SNP appears in the overlap region of two TFBSs, so both sites are polymorphic in this picture. They are shown at the center of the DNA line to express that both sites can be either activating or repressing (in any combination) in this situation. In 4) and 5), <i>d</i>1 and <i>d</i>2 indicate the range of repression from sites R1 and R2, respectively. The activating site is repressed by both repressors in 4) and only by R2 in 5). (B) Graphs of the difference as a function of the spatial position <i>i</i> on the A–P axis of the embryo in the mid-cleavage cycle 14A (time class T4) and for the gene <i>Kr</i>. The graphs are shown for one genotype from the study population and its three SNPs from the regulatory region of <i>Kr</i>. The indices of these SNPs (#73, 81, and 88) are according to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184657#pone.0184657.s005" target="_blank">S2 Table</a>. SNP #73 has the genomic position 21106823 and corresponds to the change of nucleotide G to A; SNP #81, 21113108, T/G; SNP #88, 21113686, C/T. Each SNP appears in multiple overlapping TFBSs of multiple TFs (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184657#pone.0184657.s005" target="_blank">S2 Table</a>). (C) Dynamics of for the same genotype, gene, and SNPs as in (B), shown for time classes T1–T8 in the cleavage cycle 14A and for nucleus <i>i</i> = 54. The blue curve in (B) and (C) demonstrates the alternating sign of SNP influence.</p

A New Stochastic Model for Subgenomic Hepatitis C Virus Replication Considers Drug Resistant Mutants

<div><p>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.</p></div