Plasmatic membrane toll-like receptor expressions in human astrocytomas

<div><p>Toll-like receptors (TLRs) are the first to identify disturbances in the immune system, recognizing pathogens such as bacteria, fungi, and viruses. Since the inflammation process plays an important role in several diseases, TLRs have been considered potential therapeutic targets, including treatment for cancer. However, TLRs’ role in cancer remains ambiguous. This study aims to analyze the expression levels of plasmatic cell membrane TLRs (TLR1, TLR2, TLR4, TLR5, and TLR6) in human astrocytomas the most prevalent tumors of CNS different grades (II-IV). We demonstrated that TLR expressions were higher in astrocytoma samples compared to non-neoplastic brain tissue. The gene and protein expressions were observed in GBM cell lines U87MG and A172, proving their presence in the tumor cells. Associated expressions between the known heterodimers TLR1-TLR2 were found in all astrocytoma grades. In GBMs, the mesenchymal subtype showed higher levels of TLR expressions in relation to classical and proneural subtypes. A strong association of TLRs with the activation of cell cycle process and signaling through canonical, inflammasome and ripoptosome pathways was observed by <i>in silico</i> analysis, further highlighting TLRs as interesting targets for cancer treatment.</p></div

Immunofluorescence of TLR1, TLR2, TLR4, TLR5, and TLR6 in GBM cell lines.

<p>A172 (A) and U87MG (B). TLR1, TLR4, and TLR6 are stained in red, TLR2 and TLR5 in green, and nuclei in blue by DAPI. The presence of all five TLRs was detected in both cell lines. Expression of TLR5 was more intense in A172 compared to U87MG. TLR4 and TLR5 positivity were detected in both tumor lineage cells nuclei. Magnification of 400x.</p

Imunohistochemistry for TLR1, TLR2, TLR4, TLR5, and TLR6 in GBM cases.

<p>Positive immunolabelling of GBM tumor cells for TLRs are demonstrated at 600x magnification of tumor tissues, and at 800x magnification in a multinucleated GBM tumor cell. The distribution of the Immunolabeling score (ILS) for these TLRs in five GBM cases was presented as a dispersion graph, where the black dots represent the mean ILS obtained by the two independent investigators for each individual tumor case, and the horizontal bar represent the mean ILS for each receptor. TLR4 and TLR5 positive staining were detected in tumor nuclei.</p

Heatmap with major genes of the TLR signaling pathways from the TCGA dataset.

<p>RPKM gene expression levels are normalized by z-scores, and comparatively up-regulated RNA expression values are presented in red and down-regulated values in blue. Mean values are in white. TLRs downstream signaling pathways: canonical, ripoptosome, and inflammasome pathways are activated in mesenchymal GBM subtype. Genes of unrelated pathways were added to show their randomic expression levels, including a microglia marker.</p

<i>TLR1</i>, <i>TLR2</i>, <i>TLR4</i>, <i>TLR</i>5, and <i>TLR</i>6 expression levels in astrocytomas of different malignant grades.

<p>(A) The analyzed samples consisted of 22 non-neoplastic (NN) cases, 26 astrocytoma grade II (AGII) cases, 18 astrocytoma grade III (AGIII) cases, and 96 glioblastoma (GBM) cases. Data are represented by box and whisker plots, with the median represented by the line in the middle of the boxes, and top and bottom boxes represent the first and third quartiles. qRT-PCR values are normalized by three housekeeping genes (<i>HPRT</i>, <i>GUSB</i>, <i>TBP</i>). For statistical analysis, Kruskal-Wallis and Dunn’s tests were applied, wherein (*) <i>p</i> < 0.05 when compared to NN cases and (†) <i>p</i> < 0.05 when compared to AGII (Dunn test), all the genes present <i>p</i><0.01 (Kruskal-Wallis). (B) Correlation between <i>TLR2</i>-<i>TLR1</i>, <i>TLR2</i>-<i>TLR6</i>, <i>TLR2</i>-<i>TLR4</i>, <i>TLR2</i>-<i>TLR5</i>, <i>TLR4</i>-<i>TLR1</i>, <i>TLR4</i>-<i>TLR5</i>, and <i>TLR4</i>-<i>TLR6</i> are demonstrated in GBM cases. Statistical analysis was made by the Spearman-rho correlation, and <i>p</i><0.05 were considered significative.</p

Used primer sequences.

<p>Used primer sequences.</p

<i>TLR1</i>, <i>TLR2</i>, <i>TLR4</i>, <i>TLR</i>5, and <i>TLR</i>6 expression levels in GBM molecular subtypes from TCGA dataset.

<p>Data are represented by box and whisker plots, with the median represented by the line in the middle of the boxes, and top and botton boxes represent the first and third quartiles. The dataset was divided into 37 proneural (PN) cases, 40 classical (CS) subtype cases, and 55 mesenchymal (MES) subtype cases, in which (*) and (†) are <i>p</i> < 0.05 when mesenchymal group was compared to proneural cases and to classical cases, respectively (Dunn’s test) and <i>p</i><0.01 (Kruskal-Wallis).</p

Schematic proposition of the TLR canonical signaling pathway through TIRAP by PTEN regulation.

<p>PTEN alterations may lead to upregulation of TLR signaling pathway and may increase tumor cell proliferation. Loss of PTEN repressor role by its deletion or phosphorylation, as may occur more frequently in classical subtype, may activate downstream TLR canonical pathway through the decreased inhibition over TIRAP. This pathway through TIRAP-PTEN may not be the major mechanism in mesenchymal subtype, and the integrity of PTEN may inhibit TIRAP and then not activate this pathway in proneural subtype.</p

CoGA: An R Package to Identify Differentially Co-Expressed Gene Sets by Analyzing the Graph Spectra

<div><p>Gene set analysis aims to identify predefined sets of functionally related genes that are differentially expressed between two conditions. Although gene set analysis has been very successful, by incorporating biological knowledge about the gene sets and enhancing statistical power over gene-by-gene analyses, it does not take into account the correlation (association) structure among the genes. In this work, we present CoGA (Co-expression Graph Analyzer), an R package for the identification of groups of differentially <i>associated</i> genes between two phenotypes. The analysis is based on concepts of Information Theory applied to the spectral distributions of the gene co-expression graphs, such as the spectral entropy to measure the randomness of a graph structure and the Jensen-Shannon divergence to discriminate classes of graphs. The package also includes common measures to compare gene co-expression networks in terms of their structural properties, such as centrality, degree distribution, shortest path length, and clustering coefficient. Besides the structural analyses, CoGA also includes graphical interfaces for visual inspection of the networks, ranking of genes according to their “importance” in the network, and the standard differential expression analysis. We show by both simulation experiments and analyses of real data that the statistical tests performed by CoGA indeed control the rate of false positives and is able to identify differentially co-expressed genes that other methods failed.</p></div

REACTOME ACTIVATED NOTCH1 TRANSMITS SIGNAL TO THE NUCLEUS (RANTSN) gene expression heatmap.

<p>Heatmap showing the expression levels of genes belonging to the REACTOME ACTIVATED NOTCH1 TRANSMITS SIGNAL TO THE NUCLEUS pathway in astrocytoma grade II (green label) and oligodendroglioma grade II (blue label) microarrays. The red, black, and green colors on the expression matrix represent, respectively, the highest, intermediate, and lowest expression levels.</p