Accurate prediction of gene expression by integration of DNA sequence statistics with detailed modeling of transcription regulation

Gene regulation involves a hierarchy of events that extend from specific protein-DNA interactions to the combinatorial assembly of nucleoprotein complexes. The effects of DNA sequence on these processes have typically been studied based either on its quantitative connection with single-domain binding free energies or on empirical rules that combine different DNA motifs to predict gene expression trends on a genomic scale. The middle-point approach that quantitatively bridges these two extremes, however, remains largely unexplored. Here, we provide an integrated approach to accurately predict gene expression from statistical sequence information in combination with detailed biophysical modeling of transcription regulation by multidomain binding on multiple DNA sites. For the regulation of the prototypical lac operon, this approach predicts within 0.3-fold accuracy transcriptional activity over a 10,000-fold range from DNA sequence statistics for different intracellular conditions.Comment: 15 pages, 5 figure

Multilevel Deconstruction of the In Vivo Behavior of Looped DNA-Protein Complexes

Protein-DNA complexes with loops play a fundamental role in a wide variety of cellular processes, ranging from the regulation of DNA transcription to telomere maintenance. As ubiquitous as they are, their precise in vivo properties and their integration into the cellular function still remain largely unexplored. Here, we present a multilevel approach that efficiently connects in both directions molecular properties with cell physiology and use it to characterize the molecular properties of the looped DNA-lac repressor complex while functioning in vivo. The properties we uncover include the presence of two representative conformations of the complex, the stabilization of one conformation by DNA architectural proteins, and precise values of the underlying twisting elastic constants and bending free energies. Incorporation of all this molecular information into gene-regulation models reveals an unprecedented versatility of looped DNA-protein complexes at shaping the properties of gene expression.Comment: Open Access article available at http://www.plosone.org/article/fetchArticle.action?articleURI=info%3Adoi%2F10.1371%2Fjournal.pone.000035

Trafficking Coordinate Description of Intracellular Transport Control of Signaling Networks

Many cellular networks rely on the regulated transport of their components to transduce extracellular information into precise intracellular signals. The dynamics of these networks is typically described in terms of compartmentalized chemical reactions. There are many important situations, however, in which the properties of the compartments change continuously in a way that cannot naturally be described by chemical reactions. Here, we develop an approach based on transport along a trafficking coordinate to precisely describe these processes and we apply it explicitly to the TGF-{\beta} signal transduction network, which plays a fundamental role in many diseases and cellular processes. The results of this newly introduced approach accurately capture for the first time the distinct TGF-{\beta} signaling dynamics of cells with and without cancerous backgrounds and provide an avenue to predict the effects of chemical perturbations in a way that closely recapitulates the observed cellular behavior.Comment: 17 pages, 5 figure

The psychological science accelerator’s COVID-19 rapid-response dataset

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data

Figure 2

<p>Two-conformation analysis of the <i>in vivo</i> free energy of DNA looping. The <i>in vivo</i> free energy of looping DNA by the <i>lac</i> repressor (blue symbols) was obtained as described in Saiz et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000355#pone.0000355-Saiz1" target="_blank">[10]</a> (see also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000355#s4" target="_blank">Methods</a>) from the measured repression levels of Muller et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000355#pone.0000355-Muller1" target="_blank">[11]</a> for wild type (WT1) and of Becker et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000355#pone.0000355-Becker1" target="_blank">[22]</a> for wild type (WT2) and a mutant that does not express the architectural HU protein (ΔHU). As repression levels in the absence of looping (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000355#s4" target="_blank">Methods</a> and Saiz et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000355#pone.0000355-Saiz1" target="_blank">[10]</a>) we have used 135 (WT1), 2.3 (WT2), and 1.7 (ΔHU). The thick black continuous lines correspond in each case to the best fit to the free energy Δ<i>G_l</i> given by Equations 1 and 2, which considers the contributions of two looped conformations. The contributions of each conformation are shown separately as red () and black () dashed lines. The values of the parameters for the best fit are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000355#pone-0000355-t001" target="_blank">Table 1</a>.</p

<i>In vivo</i> values of the molecular parameters of the looped DNA-<i>lac</i> repressor complex.

<p>The data shows the best fit values of the parameters of the model with two distinct looped DNA-<i>lac</i> repressor conformations (Equations 1 and 2) to the <i>in vivo</i> free energies obtained from Muller et al. experiments <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000355#pone.0000355-Muller1" target="_blank">[11]</a> for wild type (WT1) and from Becker et al. experiments <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000355#pone.0000355-Becker1" target="_blank">[22]</a> for wild type (WT2) and a mutant that does not express the architectural HU protein (ΔHU).</p

Figure 3

<p>Free energy of looping for a two-conformation elastic DNA model. Different types of behavior are obtained by changing two key parameters: the difference in optimal free energies (Δ<i>G</i>_0,1–Δ<i>G</i>_0,2) and optimal phases (<i>L_opt</i>_,1–<i>L_opt</i>_,2). (A) The difference in optimal free energies between the two configurations increases from 0 kcal/mol (blue) to 1.5 kcal/mol (red) in increments of 0.5 kcal/mol whereas the difference in optimal phases is kept fixed at 4.2 bp. (B, C) The difference in optimal phases between the two conformations increases from −5.5 bp (blue) to 0 bp (red) in increments of 5.5/3 bp whereas the difference in optimal free energies is kept fixed at 1 kcal/mol (B) and at 0 kcal/mol (C).</p

Figure 1

<p>Two plausible alternative loop conformations of the <i>lac</i> repressor-DNA complex. The bidentate repressor, with the two dimers that form the functional tetramer shown in red, simultaneously binds DNA, colored orange, at two sites. The two structures represent two plausible trajectories of the DNA loop and two plausible conformations of the <i>lac</i> repressor (V-shaped and extended).</p