Components of small-GTPases signaling pathways are direct PR targets.

<p>(A) T47D cells, treated with R5020 or EtOH for 3 hrs, were used for RNA extraction. RT-qPCR was performed using specific primers for two small GTPases (<i>ARF6, RERG</i>) and several GEFs (<i>AKAP13</i>, <i>FGD4, NET1, PSD4, VAV3</i>) and GAPs (<i>ASAP2</i>, <i>ARHGAP17, ARHGAP42, RAP1GAP2</i>). Expression levels were normalized to GAPDH. Error bars indicate the SEM. (p-value<0.005, single asterisk indicates p-value<0.5 and double asterisk p-value<0.05) (B) Visualization of the ChIP-seq data on the UCSC browser. PR binding sites in an intron of the <i>NET1</i> gene and in distal intragenic regions of <i>FGD4</i> and <i>AKAP13</i> are depicted by the black blocks. (C) ChIP-qPCR experiments show increased PR binding in the three sites shown in (B) after 1 hr of progestin treatment (single asterisk indicates p-value<0.05, double asterisk p-value<0.01 and triple asterisk p-value<0.001). The promoter of GAPDH is used as a negative control. (D) ChIP-qPCR experiments for methylated H3K4. The PR binding sites shown in (B) are also enriched for this mark (p-value<0.05) of active enhancers and promoters. An intergenic region is used as a negative control. Error bars represent SE.</p

PR regulation of specific transcript variants.

<p>T47D cells were treated for 0 to 12-qPCR using transcript-specific primers. Expression levels were normalized to GAPDH. (A) Time course experiments for the <i>NET1</i> transcripts show strong induction of the NET1.2 variant after progestin treatment, while the NET1.1 variant retains a stable expression pattern. To confirm the NET1.1 levels of expression, two different set of transcript-specific primers were used (in green and red) giving identical results. (B) Time course experiments for the two <i>KANK1</i> transcripts confirm upregulation of the KANK1.2 after progestin treatment. (C) Time course experiments for the two TSC22D3 transcript variants show that only the TSC22D3.2 variant is strongly upregulated after progestin treatment. (D) ChIP-qPCR experiments show significant increase in polII binding on the promoters of NET1.2, KANK1.2 and TSC22D3.2 after 1 hr of progestin treatment (ethanol-treated control is set to 1) (p-value<0.05). Error bars represent SE except in (A), where they represent the SEM.</p

Progestin-regulated small-GTPases, GAPs and GEFs.

<p>Progestin-regulated small-GTPases, GAPs and GEFs.</p

Identification of PR-regulated genes by RNA-sequencing.

<p>(A) Scatter plot of global gene expression in the EtOH- and R5020- treated T47D cells. In total, 10,997 and 10,930 genes (FPKM>1) are expressed respectively. The high Pearson correlation coefficient (r = 0.966) indicates similar expression profiles between the two samples as expected. (B) Volcano plot (p-value vs. fold change of expression) for all differentially expressed genes (DEGs) between vehicle- and progestin- treated cells. To determine DEGs, a threshold of 1.5-fold change was set and p-value cut-offs were 0.05 and 0.15 for the high (dark blue) and low (light blue) stringency groups respectively. (C) A total of 711 up-regulated and 576 down-regulated genes were identified and classified to two stringency groups, as described above.</p

Gene ontology analysis for PR target genes.

<p>(A) Highly enriched biological processes were determined by using FatiGO. All GO annotations related to the same biological process are shown in the same color. Grey bars represent other processes. (B) Highly enriched molecular functions were determined by using FatiGO and the top fifteen are presented here. Results are clustered by function and the percentage of DEGs that were associated with each GO annotation is shown. (C) Eight of the top ten most highly enriched GO annotations for cellular component (as determined by FatiGO) fall into 3 broad categories: plasma membrane, cell junction and Golgi apparatus. Percentage of DEGs that were identified in each category is shown.</p

Differentially expressed genes detected by RNA-seq are validated by RT-qPCR.

<p>T47D cells were treated for 3-qPCR using specific primers for the genes shown. Expression levels were normalized to GAPDH. A side-by-side comparison of the RT-qPCR results with the RNA-seq data is shown. (A) Expression levels of high stringency genes as measured by RT-qPCR and RNA-seq. <i>FOXA1</i> was the only selected down-regulated gene and its expression pattern was confirmed. (B) Expression levels of low stringency genes as measured by RT-qPCR and RNA-seq. Five downregulated (<i>ABCC3, GLIS2, HEXIM2, KLF3</i> and <i>PYCARD</i>) and five upregulated genes were selected and confirmed by RT-qPCR. Error bars represent the SEM.</p

Expression levels (in FPKM) of transcript variants according to the RNA-seq data.

<p>Expression levels (in FPKM) of transcript variants according to the RNA-seq data.</p

Inclusion of Quercetin in Gold Nanoparticles Decorated with Supramolecular Hosts Amplifies Its Tumor Targeting Properties

Despite the anticancer potential of natural products (NPs), their limited bioavailability necessitates laborious derivatization or covalent conjugation to delivery vehicles. To unleash their potential, we developed a nanohybrid delivery platform with a noncovalently tunable surface. Initially, the active compound was encapsulated in a macrocycle, p-sulfonatocalix­[4]­arene, enabling a 62 000-fold aqueous solubility amplification as also a 2.9-fold enhancement in its cytotoxicity with respect to the parent compound in SW-620 colon cancer cells. A pH stimuli responsive behavior was recorded for this formulate, where a programmable release of quercetin from the macrocycle was monitored in an acidic environment. Then, a nanoparticle gold core was decorated with calixarene hosts to accommodate noncovalently NPs. The loaded nanocarrier with the NP quercetin dramatically enhanced the cytotoxicity (>50-fold) of the parent NP in colon cancer and altered its cell membrane transport mode. In vivo experiments in a mouse 4T1 tumor model showed a reduction of tumor volume in mice treated with quercetin-loaded nanoparticles without apparent toxic effects. Further analysis of the tumor-derived RNA highlighted that treatment with quercetin-loaded nanoparticles altered the expression of 27 genes related to apoptosis