Additional file 4 of A benchmark study on error-correction by read-pairing and tag-clustering in amplicon-based deep sequencing

Figure S4. Tag distribution in different error-correction schemes. The histogram of tags. Tags are random nucleotides for readout consensus, comprising 8 nucleotides from each direction of reads. Every bar represents the number of tags that appeared certain times. Scheme 1 means the tag distribution in the original dataset. (EPS 89 kb

Adaptive treatment strategy improves treatment outcome substantially—a case when the risk of <i>de novo</i> resistance is low (with wild-type genotype-1b HCV at baseline).

<p><b>(A)</b> Theoretical prediction of the treatment duration needed to eliminate each partially resistant mutant under perfect adherence (gray bar), and the maximum number of additional doses needed to compensate for missing doses, <i>N</i>_{<i>m</i>,<i>max</i>} (colored bars). Green, blue and red denote results when 1, 2 and 3 consecutive daily doses are missed, respectively. The longer the colored bars, the greater the impact of missing doses. The symbols next to the bars for <i>N</i>_{<i>m</i>,<i>max</i>} show the type of mutant investigated in panels (B,D,F). <b>(B)</b> Theoretical prediction of the risk of <i>de novo</i> resistance, Φ_<i>m</i>, over time (as shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004040#pcbi.1004040.g003" target="_blank">Fig 3D</a>), for the three mutants with highest risks of generating fully resistant mutants. The dashed black line shows Φ_<i>m</i> = 0.01. <b>(C)</b> Treatment outcomes 1–3 days of doses are missed randomly. Colored areas denote the fractions of simulations with outcomes of viral relapse without full resistance (light blue) and viral clearance (gray). <b>(D)</b> Comparison between theory predictions and simulations of the number of cells infected by different mutants after 24 weeks of treatment. <b>(E)</b> Treatment outcomes if adaptive treatment strategy is followed. The area above the black dashed line denotes the fraction of patients where virus is not cleared after 24 weeks’ treatment. After 24 weeks, patients take the prescribed number of make-up doses without missing further doses. White areas denote adherence levels that are not allowed by the adaptive treatment strategy. <b>(F)</b> Same comparison as in panel (D) for the guided dosing simulation.</p

Enhancing School Leadership; evaluating the use of virtual learning communities

Sample Information. A total of 20 male and 20 female mice were included in this experiment. Each animal was treated with either corn oil or a single dose of TCDD (125, 250, 500 or 1000 μg/kg) dissolved in corn oil. Liver tissue was collected 4 days after treatment. (XLS 31 kb

Adaptive treatment strategy prevents <i>de novo</i> resistance and improves treatment outcome substantially—a case when the risk of <i>de novo</i> resistance is high.

<p>Theoretical prediction and simulation for patients with the Y93H mutant virus (genotype-1b) at baseline under combination therapy of daclatasvir and asunaprevir. Thus, the mutants considered here all have the Y93H mutation. The theoretical predictions and simulation results are plotted in the same way as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004040#pcbi.1004040.g004" target="_blank">Fig 4</a>. Dark red areas in panel (C,E) denote the fraction of patients with <i>de novo</i> full resistance to the combination therapy. Note that the fraction of patients with <i>de novo</i> resistance in the guided dosing scenario is very small (<0.1%). When doses are guided, so that mutant viral load does not rebound to the pre-treatment level, the theoretical prediction agrees well with simulation as shown in panel (F).</p

There is a high-risk window early in treatment when missing doses is more likely to cause <i>de novo</i> resistance.

<p><b>(A)</b> The changes in the risk of <i>de novo</i> resistance, Φ_<i>m</i>, generated by a partially resistant mutant over time. The two sets of trajectories, A and B, differ in that the value of <i>μ</i>_<i>eff</i> for trajectory B is smaller by a factor of 10^–5 (representing one additional nucleotide mutation) than the value set for trajectory A. Each set of trajectories shows the risk when the number of doses missed (<i>m</i>) is 1,2 or 3. <b>(B)</b> Dynamics of the two time-varying quantities in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004040#pcbi.1004040.e004" target="_blank">Eq 4</a>, i.e. the number of cells infected by the partially resistant mutant relative to the initial number before treatment (I(t)/I(0); blue dashed line), and the value of Θ(<i>t</i>), green dotted lines, as shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004040#pcbi.1004040.e004" target="_blank">Eq 4</a>. Under effective treatment, the number of infected cells <i>I</i>(<i>t</i>) decreases exponentially, while the number of target cells rebounds to the infection-free level quickly, causing an increase in R_{<i>ave</i>,<i>m</i>} and thus Θ(<i>t</i>). Together these changes cause Φ_<i>m</i> to increase initially and then to decrease exponentially at longer times (as seen in panel A).</p

The impacts of suboptimal adherence on viral fitness.

<p><b>(A)</b> A schematic illustrating how a non-preexisting mutant, A’B’, fully resistant to a combination therapy involving two drugs, A and B, can be generated when adherence is suboptimal. Each black circle represents a mutant on the parameter space of resistance levels to A and B. AB, A’B and AB’ are preexisting mutants that are non-resistant, resistant to A only and resistant to B only, respectively. Colored areas denote parameter regimes where mutants are fully resistant to the therapy (red), can grow if doses are missed (pink), and do not grow (blue). Note that the pink area can grow or shrink on the parameter space depending on the number of consecutively missed doses and drug PK/PD, and mutants lying in the pink area are ‘partially resistant mutants’. <b>(B)</b> The dynamics of viral strains under treatment are determined by several factors: drug concentration, [D], which decreases with an increasing number of missed doses, <i>m</i> (upper panel); how viral replication is affected by drug (1-ε; middle panel); and the relative number of target cells, <i>h</i>(t) (lower panel). Upon effective treatment, <i>h</i>(<i>t</i>) increases to the infection-free level. <b>(C)</b> We integrate all these factors into a single fitness parameter, <i>R</i>_<i>eff</i>(<i>t</i>). Viral fitness increases as drug concentration drops (indicated by shades of green) and as target cell abundance rises (the blue arrow). Values of <i>R</i>_<i>eff</i>(<i>t</i>) can exceed 1, i.e. positive growth, if doses are missed after a period of effective treatment.</p

Primer sequences for target genes.

<p>Primer sequences for target genes.</p

siRNA sequences.

<p>siRNA sequences.</p

Overexpression of SOCS1 decreases the antiviral effect of IFN-γ and tyrosine phosphorylation of STAT1 during MHV-68 infection in NIH3T3 fibroblasts.

<p>NIH3T3 cells were transfected with an expression plasmid encoding SOCS1 (pSOCS1) or control (pcDNA3.1). (A) At 48 hours after transfection, the cells were pre-treated with IFN-γ (10 U/mL) for 12 hours, and were then infected with MHV-68 at MOI = 10. At the indicated times post infection, the viral titer was measured by plaque assay. *<i>p</i> < 0.05, **<i>p</i> < 0.01, ***<i>p</i><0.001, pSOCS1+IFN-γ <i>vs</i>. pcDNA3.1+IFN-γ. In parallel, western blotting was performed to assess the SOCS1 expression levels in cells without IFN-γ treatment. (B) RT-qPCR was used to detect the viral ORF50 mRNA expression at 13 hpi. Data are expressed as values relative to the MHV-68 infected, pcDNA3.1 transfected cells without IFN-γ treatment. *<i>p</i> < 0.05, ***<i>p</i> < 0.001. (C) Cells were infected with MHV-68 for the indicated times, with the last hour of infection being in the presence of IFN-γ (10 U/mL) or not. Proteins were extracted, and western blotting was performed to evaluate the tyrosine phosphorylation level of STAT1. Results are representative of three independent experiments and are expressed as mean ± S.E.M.</p

Inhibition of murine herpesvirus-68 replication by IFN-gamma in macrophages is counteracted by the induction of SOCS1 expression

<div><p>Gamma interferon (IFN-γ) is known to negatively regulate murine gammaherpesvirus-68 (MHV-68 or γHV-68) replication. This process involves the suppression of the viral gene replication and transcription activator (RTA) promoter, as well as activation of signal transducers and activators of transcription (STAT1). Notably, this effect is gradually attenuated during MHV-68 infection of bone marrow-derived macrophages (BMMs), which raised the possibility that the virus may utilize a mechanism that counteracts the antiviral effect of IFN-γ. By identifying the cellular factors that negatively regulate JAK-STAT1 signaling, we revealed that the infection of BMMs by MHV-68 induces the expression of suppressor of cytokine signaling 1 (SOCS1) and that depletion of SOCS1 restores the inhibitory effect of IFN-γ on virus replication. Moreover, we demonstrated that the expression of SOCS1 was induced as a result of the Toll-like receptor 3 (TLR3) mediated activation of the NF-κB signaling cascade. In conclusion, we report that TLR3-TRAF-NF-κB signaling pathway play a role in the induction of SOCS1 that counteracts the antiviral effect of IFN-γ during MHV-68 infection. This process is cell type-specific: it is functional in macrophages, but not in epithelial cells or fibroblasts. Our study reveals a mechanism that balances the immune responses and the escape of a gamma-herpesvirus in some antigen-presenting cells.</p></div