Histone acetylation and GAF occupancy are important covariates in predicting HSF binding intensity.

<p>Plotted are the relative values of the sums of the coefficients associated with all rules that reference each covariate in the rules ensemble <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002610#pgen.1002610-Friedman1" target="_blank">[20]</a>. Results are shown for (A) the histone variant and modification model and (B) the non-Histone factor model.</p

Pentamers within the HSEs are dependent upon their consensus match and also their position relative to the other pentamers.

<p>A) The mixture model defines each pentamer within the HSE as strict or relaxed depending upon how well it conforms to the canonical HSE. Note that the position of relaxed pentamers strongly influences their composition. B) A probabilistic sequence model reveals that the presence of two strict (red) and one relaxed (blue) pentamer provides the best explanation of the data.</p

Genomic chromatin and PB–seq data accurately predict in vivo HSF binding intensity.

<p>A) The intensity of in vivo ChIP-seq peaks is not recapitulated by in vitro PB–seq data; however, genomic DNase I hypersensitivity data and histone modification ChIP-chip data can be used to accurately predict HSF binding intensity. B) The experimentally determined ratio between in vivo ChIP-seq HSF intensity and in vitro PB–seq intensity is plotted against the predicted in vivo/actual PB–seq ratio. The Pearson correlation for each model is shown.</p

In vitro binding reveals potential HSF binding sites.

<p>The blue box highlights strong differences in the usage of potential binding sites in vivo at the Cpr67B locus, while the green boxes highlight differences in the magnitude of binding to major heat shock genes promoters, despite comparable in vitro binding affinities.</p

DNase I hypersensitivity can be inferred using histone marks and MNase data.

<p>A) The intensity of DNase I hypersensitivity landscape is inferred by models (colors) that use histone modification profiles, non-histone factor profiles, DNase I data and MNase-seq data. B) The experimentally determined DNase I hypersensitivity data is plotted against inferred intensity for the various models. The Pearson correlation for each model is shown.</p

Recombinant HSF binds HSEs with picomolar affinity in vitro.

<p>A and B) The mobility of the constant 200 attomole HSE probe shifts into a trimeric-HSF:HSE complex as increasing HSF is added. There is no HSF in the left-most lane, the right-most lane contains 3 nM HSF (1 nM trimeric HSF), and the intervening lanes contain two-fold serial dilutions of HSF. C) A hyperbolic curve based on the Kd equation (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002610#s4" target="_blank">Methods</a>) was modeled using the band shift data, indicating a Kd of 42.6 pM (95% confidence interval of 36.8–49.4 pM). D) A hyperbolic curve based on the Kd equation (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002610#s4" target="_blank">Methods</a>) was modeled using the band shift data, indicating a Kd of 224 pM (95% confidence interval of 181–276 pM). E) The intensity of each isolated HSE in the <i>Drosophila</i> genome is transformed to an absolute Kd using the absolute Kds calculated from band shift data in panels A and B. The Kd values range from 40–400 pM.</p

In vitro and in vivo binding of HSF to genomic HSEs do not correlate.

<p>A) A scatter plot comparing the observed in vivo HSF binding intensity and in vitro binding intensity for each isolated HSE indicates that the vast majority of in vivo binding is suppressed (green) or abolished (blue), if we assume that the top seven most DNase I hypersensitive isolated HSE clusters provide the best estimates for sites that are minimally influenced by chromatin. After scaling, red points have similar in vivo and in vitro intensity, black points may be enhanced in vivo, while green and blue points are suppressed and abolished, respectively. B) The points from panel A were categorized, and the resulting bar chart shows the relative frequencies of each category.</p

Specific binding of the aptamer to human HSF1 <i>in vitro</i>.

<p>(<b>A</b>) Electrophoretic motility shift assay (EMSA) using radiolabeled iaRNA^HSF1 (1 nM) and increasing amounts of human HSF1 protein shows that the aptamer binds to its target avidly. (<b>B</b>) Quantification of independent EMSA reveals the apparent affinity of the iaRNA^HSF1 for HSF1 as Kd∼25 nM (n = 5).</p

Inhibiting Heat Shock Factor 1 in Human Cancer Cells with a Potent RNA Aptamer

<div><p>Heat shock factor 1 (HSF1) is a master regulator that coordinates chaperone protein expression to enhance cellular survival in the face of heat stress. In cancer cells, HSF1 drives a transcriptional program distinct from heat shock to promote metastasis and cell survival. Its strong association with the malignant phenotype implies that HSF1 antagonists may have general and effective utilities in cancer therapy. For this purpose, we had identified an avid RNA aptamer for HSF1 that is portable among different model organisms. Extending our previous work in yeast and Drosophila, here we report the activity of this aptamer in human cancer cell lines. When delivered into cells using a synthetic gene and strong promoter, this aptamer was able to prevent HSF1 from binding to its DNA regulation elements. At the cellular level, expression of this aptamer induced apoptosis and abolished the colony-forming capability of cancer cells. At the molecular level, it reduced chaperones and attenuated the activation of the MAPK signaling pathway. Collectively, these data demonstrate the advantage of aptamers in drug target validation and support the hypothesis that HSF1 DNA binding activity is a potential target for controlling oncogenic transformation and neoplastic growth.</p></div

iaRNA^HSF1 expression attenuates transformed growth.

<p>HSF1 inhibition by iaRNA^HSF1 inhibits transformed growth in soft agar. Soft agar analysis of non-transfected HeLa cells (top left) or control RNA over-expressing HeLa (bottom left), shows that iaRNA^HSF1 over-expression (bottom right) inhibits cellular transformation (colony formation) in a similar manner as treatment of HeLa cells with 150 nM 17-AAG (top right) (Day 14).</p