Genome accessibility improves prediction of ChIP-seq profiles in comparison to a model that only considers motif score.

<p>Motif score alone explains only 35% of the observed variance <b>(A)</b>, while the improved biophysically motivated model that incorporates genome accessibility explains 63% of the variance <b>(B)</b> (p<10⁻¹⁶, likelihood ratio test). The predicted coverage is estimated from parameters fitted for <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004891#pcbi.1004891.e001" target="_blank">Eq 1</a>. Coverage is represented in terms of log(<i>p</i>_<i>ij</i>). The panels display a subset of 10000 points that was randomly selected to reduce the density of points and improve visualization.</p

Genome accessibility improves binding peak prediction in ChIP-seq profiles.

<p>Reference ChIP-seq peaks are defined according to method previously described [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004891#pcbi.1004891.ref015" target="_blank">15</a>]. A receiver and operator characteristic curve is shown in panel <b>(A)</b>. Three models are presented for <i>de novo</i> peak prediction (see main text for details). The accessibility parameter (blue and orange lines) increases peak prediction from 0.69 to 0.82 in comparison to a model that only accounts for motif score (violet-red line). <b>(B)</b> Accuracy of genome accessibility estimation as a function of number of ChIP-seq experiments. The accuracy of accessibility values is defined as the Pearson correlation between the estimated values for a subset of ChIP-seq experiments and the one estimated for entire dataset (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004891#pcbi.1004891.s002" target="_blank">S2 Fig</a>). The expected accuracy of accessibility values is defined as the mean value of 100 samples. Error bars represent one standard error.</p

Genome accessibility correlates with genomic features.

<p><b>(A)</b> Intergenic regions are more accessible than protein coding genic regions (p<10⁻¹⁶). <b>(B)</b> Regions associated with amino acid and carbohydrate metabolism and transport (COGs E and G) show statistically reduced accessibility. Genes associated with transcription and translation (COGs K and J) show statistically higher accessibility (p<0.05, Bonferroni correction). <b>(C)</b> Gene expression is positively correlated with accessibility. The correlation of DNA accessibility with gene expression after controlling for values of motif affinity is 0.278 (p<3.98 10^−56-; function pcor and pcor.test, R package <i>ggm</i>). <b>(D)</b> Expected gene expression is highest at an intermediate level of accessibility. Accessibility bins with less than 10 data points are clustered with the neighboring bin with fewer data points. Error bars represent one standard error from the mean.</p

The role of genome accessibility in TF-binding <i>in vivo</i>.

<p>The genome accessibility model differentiates genomic regions as accessible <b>(A)</b> or not accessible <b>(B)</b>. ChIP-seq data show that coverage cannot be explained by binding affinity alone. Example data is shown for an accessible region <b>(A)</b> that has a weak binding site (small purple box, p-value ~ 5x10^-4) and high ChIP-seq coverage. The gray dashed line indicates the location of the TF-binding site motif. Example data is shown for an inaccessible region <b>(B)</b> with a strong binding site (big purple box, p-value ~ 5x10^-6) but low coverage. Example data shown are for <i>M</i>. <i>tuberculosis</i> DosR ChIP-seq experiments [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004891#pcbi.1004891.ref015" target="_blank">15</a>].</p

Schematic representation of a host population models that includes the possibility of resistance loss.

<p>A modified implementation of a previous host population model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080775#pone.0080775-Bonhoeffer1" target="_blank">[40]</a> under a combination of two drugs <i>a</i> and <i>b</i> (Equation S6 and S7 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080775#pone.0080775.s005" target="_blank">File S1</a>) takes into account the possibility of resistance loss. Hosts can be infected by pathogens of four different types: wild type, a-resistant, b-resistant and a,b-resistant. The numbers of individuals infected are correspondingly represented by variables <i>y_w</i>, <i>y_a</i>, <i>y_b</i>, and <i>y_a,b</i>. The original model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080775#pone.0080775-Bonhoeffer1" target="_blank">[40]</a> considered only the possibility of acquiring resistance (black arrows). In our modified host population model, motivated by our findings in the single host model, we assume that a nonzero resistance-decaying rate can cause loss of resistance (red arrows).</p

Illustration of the infection dynamics model and of a novel strategy to fight resistance.

<p>(<i>A</i>) Schematic representation of the main dynamical transitions based on the model from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080775#pone.0080775-DAgata1" target="_blank">[42]</a>. The arrows represent the possible fates of the populations of sensitive and resistant pathogen strains. Horizontal gene transfer (rate <i>τ</i>) and plasmid loss (rate <i>ρ</i>) are the mechanisms responsible for interconverting between sensitive and resistant strains. The use of an antibiotic can reduce the sensitive population, but is not effective against the resistant one. Conversely, the cost of carrying a plasmid causes a reduction of the resistant population in the absence of antibiotic use. Also, both strains are susceptible to immune system killing. This model of infection dynamics can be used to search for optimal treatments. <i>(B)</i> Schematic representation of the current state of an infection and its treatment. Regular antibiotic is effective against an infection caused by the sensitive strain, but is not effective against an infection with high abundance of resistant pathogens (<i>B-top</i>). Here we show that an effective control of the infection can be obtained by initially treating against the resistant strain (antiR condition) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080775#pone.0080775-Chait1" target="_blank">[33]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080775#pone.0080775-Palmer1" target="_blank">[34]</a> and subsequently applying antibiotic treatment (<i>B-bottom</i>).</p

Resistance attenuation occurs in the in the absence of antibiotic treatment when the abundance of sensitive pathogen is saturated.

<p>The resistant and sensitive strains have to compete for resources when the bacterial population approaches carrying capacity. This competition reduces the abundance of resistant strains due to the cost of resistance. Under this saturated conditions, both the probability of plasmid loss <i>(A)</i> and the growth rate <i>(B)</i> affect resistance attenuation. (<i>A</i>) The intensity of resistance attenuation increases with the probability of plasmid loss (<i>ρ</i>). (<i>B</i>) The intensity of resistance attenuation increases with the difference in growth rate between both strains. In this analysis, we set up the probability of resistance loss to be equal to zero to highlight only the effects of growth rate. The left panel shows a case in which both sensitive and resistant strains have the same growth rate. In this case, both strains can coexist with high population abundance. In the right panel, we assume that a plasmid cost reduces resistance growth rate from 2.77 to 2 day⁻¹. The abundance of the resistant pathogen decreases over time when the abundance of the sensitive pathogen is saturated. The intensity of resistance attenuation is proportional to the difference in growth rate. Unless otherwise mentioned, all parameters used in this analysis correspond to the default values described in Table S1 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080775#pone.0080775.s005" target="_blank">File S1</a> for no treatment condition. Initial abundances of sensitive and resistant pathogens are 10⁸ and 10⁹ cells respectively.</p

Resistance attenuation is influenced by the nature of antiR treatment and by the plasmid loss rate.

<p>The nature of the antiR treatment (whether bactericidal or bacteriostatic, see Text S1 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080775#pone.0080775.s005" target="_blank">File S1</a>) and the rate of plasmid loss influence the dynamics of resistance attenuation. We illustrate the resistance decaying rate (<i>A</i>) and <i>t_clear</i> (<i>B</i>) as a function of the rate of plasmid loss and the nature of treatment. At low rates of plasmid loss (<i>ρ≈0</i>), antiR treatment increases the resistance attenuation by a factor ∼15, independently of the nature of antiR treatment. Values are estimated according to data published in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080775#pone.0080775-Chait1" target="_blank">[33]</a>.</p

Resistance attenuation is boosted when the population of sensitive pathogens approaches carrying capacity.

<p>This figure shows the infection dynamics of both resistant (dashed red line) and sensitive (solid blue line) pathogens under antiR treatment (purple shade). The decrease in the abundance of resistant pathogen is relatively small when the sensitive strain is far from carrying capacity (time t<8 days), but is strengthened when the sensitive population reaches carrying capacity. The initial abundances of sensitive and resistant pathogens are 10⁸ and 10⁹ cells respectively.</p

AntiR treatment boosts resistance attenuation and leads to total healing.

<p>Both antibiotic suspension (no treatment) and antiR treatment can reduce the abundance of resistant pathogens. However, this reduction is greater under antiR treatment. This figures illustrates the potential advantage of an antiR treatment in fighting a resistant infection. When no treatment is applied, the fraction of resistant population decreases slowly (<i>A</i> and <i>B</i>, time window between 16 and 36 hours) and it is followed by an ineffective antibiotic treatment. In (<i>B</i>), the resistance attenuation is faster due to treatment against resistance (antiR, purple-shaded area), and leads to an effective antibiotic treatment (t>36h). The black dashed horizontal line marks a single cell, i.e. the level below which the infection is healed. The initial abundance of both sensitive and resistant pathogens is 10⁹ cells. Note that the period of antibiotic suspension preceding an antiR treatment is not necessary for an optimal therapy and is shown in this figure only for highlighting the different slopes.</p