Transcriptional and Non-Transcriptional Functions of PPARβ/δ in Non-Small Cell Lung Cancer

<div><p>Peroxisome proliferator-activated receptor β/δ (PPARβ/δ) is a nuclear receptor involved in regulation of lipid and glucose metabolism, wound healing and inflammation. PPARβ/δ has been associated also with cancer. Here we investigated the expression of PPARβ/δ and components of the prostaglandin biosynthetic pathway in non-small cell lung cancer (NSCLC). We found increased expression of PPARβ/δ, Cox-2, cPLA₂, PGES and VEGF in human NSCLC compared to normal lung. In NSCLC cell lines PPARβ/δ activation increased proliferation and survival, while PPARβ/δ knock-down reduced viability and increased apoptosis. PPARβ/δ agonists induced Cox-2 and VEGF transcription, suggesting the existence of feed-forward loops promoting cell survival, inflammation and angiogenesis. These effects were seen only in high PPARβ/δ expressing cells, while low expressing cells were less or not affected. The effects were also abolished by PPARβ/δ knock-down or incubation with a PPARβ/δ antagonist. Induction of VEGF was due to both binding of PPARβ/δ to the VEGF promoter and PI3K activation through a non-genomic mechanism. We found that PPARβ/δ interacted with the PI3K regulatory subunit p85α leading to PI3K activation and Akt phosphorylation. Collectively, these data indicate that PPARβ/δ might be a central element in lung carcinogenesis controlling multiple pathways and representing a potential target for NSCLC treatment.</p> </div

Expression of PPARβ/δ, PPARγ, cPLA₂, Cox-2, PGES, PGIS, and VEGF in non-small cell lung cancers.

<p>RNA was extracted from tumors and adjacent normal lung tissue from patients with non-small cell lung cancer and examined by RT-PCR. Data represent the ratio of gene expression in tumors relative to the paired normal tissue based on densitometric analysis and normalized to β-actin used as reference gene. Black bars mark the tumors with highest expression of PPARβ/δ (T/N ratio ≥4).</p

PPARβ/δ activation affects growth and survival of non-small cell lung cancer cells.

<p>(<b>A</b>) H441, H358 and A549 cells were incubated with cPGI₂ in serum-free medium and cell viability was assessed after 72 h with MTT assay. *P<0.01 relative to control cells. (<b>B</b>) Cells were incubated with L165041 as above. *P<0.01 relative to control cells. (<b>C</b>) Cells were incubated with cPGI₂ (10 µM) for 24 h and analyzed by flow cytometry. <i>Top panel</i>, representative flow cytometric profile of H358 cells incubated with and without cPGI₂. <i>Bottom panel</i>, cell cycle distribution in cells after 24 h incubation with and without cPGI₂. The increase in S phase cells in H441 and H358 cells determined in triplicate experiments was statistically significant (P<0.01). (<b>D</b>) H441 cells were transfected with siRNA for PPARβ/δ and GL3 and cell viability was determined after 72 h with MTT. *P<0.01 relative to control cells. (<b>E</b>) H358 cells transfected with PPARβ/δ siRNA and GL3 were stained with annexin V and propidium iodide and analyzed by flow cytometry. The percentage of annexin V positive cells (apoptotic cells) is indicated in each panel. P<0.05. (<b>F</b>) H358 cells were transfected with siRNA for PPARβ/δ and GL3, lysed and analyzed by immunoblotting with a caspase-3 antibody. *P<0.01.</p

Expression of PPARβ/δ in non-small cell lung cancer cell lines and tumor samples.

<p>(<b>A</b>) RNA isolated from the indicated cell lines was amplified by RT-PCR to assess the level of PPARβ/δ, PPARγ, cPLA₂, Cox-2, PGIS, and PGES RNA. GAPDH was used as a reference gene. (<b>B</b>) H441 and A549 cells were transfected with a PPARβ/δ responsive luciferase reporter (DRE) or basic pGL3 luciferase reporter (Basic). Luciferase activity was assessed after 24 h. * P<0.01. (<b>C</b>) RNA isolated from lung tumors and adjacent normal lung tissue was analyzed by RT-PCR with primers specific for PPARβ/δ and β-actin.</p

Natural antisense transcripts drive a regulatory cascade controlling c-MYC transcription

<p><i>Cis</i>-natural antisense transcripts (<i>cis-</i>NATs) are long noncoding RNAs transcribed from the opposite strand and overlapping coding and noncoding genes on the sense strand. <i>cis-</i>NATs are widely present in the human genome and can be involved in multiple mechanisms of gene regulation. Here, we describe the presence of <i>cis-</i>NATs in the 3′ distal region of the c-MYC locus and investigate their impact on transcriptional regulation of this key oncogene in human cancers. We found that <i>cis-</i>NATs are produced as consequence of the activation of cryptic transcription initiation sites in the 3′ distal region downstream of the c-MYC 3′UTR. The process is tightly regulated and leads to the formation of two main transcripts, NAT6531 and NAT6558, which differ in their ability to fold into stem-loop secondary structures. NAT6531 acts as a substrate for DICER and as a source of small RNAs capable of modulating c-MYC transcription. This complex system, based on the interplay between <i>cis</i>-NATs and NAT-derived small RNAs, may represent an important layer of epigenetic regulation of the expression of c-MYC and other genes in human cells.</p

Molecular Determinants for Unphosphorylated STAT3 Dimerization Determined by Integrative Modeling

Signal transducer and activator of transcription factors (STATs) are proteins that can translocate into the nucleus, bind DNA, and activate gene transcription. STAT proteins play a crucial role in cell proliferation, apoptosis, and differentiation. The prevalent view is that STAT proteins are able to form dimers and bind DNA only upon phosphorylation of specific tyrosine residues in the transactivation domain. However, this paradigm has been questioned recently by the observation of dimers of unphosphorylated STATs (USTATs) by X-ray, Förster resonance energy transfer, and site-directed mutagenesis. A more complex picture of the dimerization process and of the role of the dimers is, thus, emerging. Here we present an integrated modeling study of STAT3, a member of the STAT family of utmost importance in cancer development and therapy, in which we combine available experimental data with several computational methodologies such as homology modeling, protein–protein docking, and molecular dynamics to build reliable atomistic models of USTAT3 dimers. The models generated with the integrative approach presented here were then validated by performing computational alanine scanning for all the residues in the protein–protein interface. These results confirmed the experimental observation of the importance of some of these residues (in particular Leu78 and Asp19) in the USTAT3 dimerization process. Given the growing importance of USTAT3 dimers in several cellular pathways, our models provide an important tool for studying the effects of pathological mutations at the molecular and/or atomistic level, and in the rational design of new inhibitors of dimerization

Inhibition of Sp regulated genes by MTM-SDK and MTM-SK in prostate cancer cells <i>in vitro</i>.

<p>PC3 cells were treated with 100 nM of MTM-SDK, MTM-SK or vehicle (DMSO) for 24 h. A) Gene expression was measured by qRT-PCR. Data were normalized to <i>B2M</i> RNA level and are presented as percentage of expression compared to vehicle-treated cells (control). Data represent the mean ± SD from 3 independent experiments. B) Binding of Sp1 to the promoters of <i>C-MYC</i> and <i>VEGF</i> in control and drug treated cells was determined by ChIP using an anti-Sp1 specific antibody. DNA in input and immunoprecipitated fractions was quantified by qPCR with primers encompassing the Sp binding site in the gene promoters. Data (mean ± SD) from 3 independent experiments are expressed as percentage of input DNA in immunoprecipated fractions. *, P<0.01; **, P<0.001. C) Level of Sp1, Sp3 and Sp4 mRNA was determined by RT-PCR in PC3 cells incubated with 100 nM of MTM-SDK, MTM-SK or vehicle for 24 and 48 h. GAPDH was used as control. D) Protein level of Sp1, c-Myc, XIAP, and cyclin D1 was determined by immunoblotting in PC3 cells incubated with 100 nM of MTM-SDK, MTM-SK or vehicle for 24 and 48 h.</p

Pharmacokinetics profile of MTM-SDK and MTM-SK in mice.

<p>Mice (n = 3/group) received a single IV injection of MTM-SK (A) or MTM-SDK (B) and plasma levels were determined by LC-MS. Doses of MTM-SK and MTM-SDK were 18 mg/kg and 2 mg/kg, respectively.</p

Antiproliferative effects of MTM-SDK and MTM-SK in prostate cancer cells <i>in vitro</i>.

<p>Prostate cancer cells (DU145, 22Rv1, PC3 and LNCaP) and primary cultures of normal human fibroblasts (NHF) were incubated with the compounds for 72 h. Cell viability was measured by the colorimetric MTT assay. Data are presented as mean ± SD of triplicate samples of 3 independent experiments. IC₅₀ for each cell type are reported in the bottom panel.</p

Antitumor activity of MTM-SDK and MTM-SK in subcutaneous prostate tumor xenografts.

<p>Mice with subcutaneous tumors were treated with the indicated doses of MTM-SDK, MTM-SK or sterile saline solution (control) given by IP injections twice a week for five weeks (q3d×10). Tumor volume (A) and body weight (B) were measured twice a week. Results are expressed as mean ± SD of the tumor volume in each group. Arrows indicate start and end of the treatment. **, P<0.001.</p