Nuclear Expression of KLF6 Tumor Suppressor Factor Is Highly Associated with Overexpression of ERBB2 Oncoprotein in Ductal Breast Carcinomas

Background Krüppel-like factor 6 (KLF6) is an evolutionarily conserved and ubiquitously expressed protein that belongs to the mammalian Sp1/KLF family of transcriptional regulators. Though KLF6 is a transcription factor and harbors a nuclear localization signal it is not systematically located in the nucleus but it was detected in the cytoplasm of several tissues and cell lines. Hence, it is still not fully settled whether the tumor suppressor function of KLF6 is directly associated with its ability to regulate target genes. Methodology/Principal Findings In this study we analyzed KLF6 expression and sub-cellular distribution by immunohistochemistry in several normal and tumor tissues in a microarray format representing fifteen human organs. Results indicate that while both nuclear and cytoplasmic distribution of KLF6 is detected in normal breast tissues, breast carcinomas express KLF6 mainly detected in the cytoplasm. Expression of KLF6 was further analyzed in breast cancer tissues overexpressing ERBB2 oncoprotein, which is associated with poor disease prognosis and patient\u27s survival. The analysis of 48 ductal carcinomas revealed a significant population expressing KLF6 predominantly in the nuclear compartment (X2 p = 0.005; Fisher p = 0.003). Moreover, this expression pattern correlates directly with early stage and small ductal breast tumors and linked to metastatic events in lymph nodes. Conclusions/Significance Data are consistent with a preferential localization of KLF6 in the nuclear compartment of early stage and small HER2-ERBB2 overexpressing ductal breast tumor cells, also presenting lymph node metastatic events. Thus, KLF6 tumor suppressor could represent a new molecular marker candidate for tumor prognosis and/or a potential target for therapy strategies

BAFF Mediates Splenic B Cell Response and Antibody Production in Experimental Chagas Disease

Chagas disease, caused by the protozoan Trypanosoma cruzi, is endemic in Central and South America. It affects 20 million people and about 100 million people are at risk of infection in endemic areas. Some cases have been identified in non-endemic countries as a consequence of blood transfusion and organ transplantation. Chagas disease presents three stages of infection. The acute phase appears one to two weeks after infection and includes fever, swelling around the bite site, enlarged lymph glands and spleen, and fatigue. This stage is characterized by circulating parasites and many immunological disturbances including a massive B cell response. In general, the acute episode self-resolves in about 2 months and is followed by a clinically silent indeterminate phase characterized by absence of circulating parasites. In about one-third of the cases, the indeterminate phase evolves into a chronic phase with clinically defined cardiac or digestive disturbances. Current knowledge suggests that the persistence of parasites coupled with an unbalanced immune response sustain inflammatory response in the chronic stage. We believe that an effective treatment for chronic Chagas disease should combine antiparasitic drugs with immunomodulators aimed at reducing inflammation and autoreactive response. Our findings enlighten a new role of BAFF-BAFF-R signaling in parasite infection that partially controls polyclonal B cell response but not parasitespecific class-switched primary effectors B cells

Identification of HCV-cirrhosis with and without HCC by qPCR validated genes.

<p>Canonical plot derived from applying quadratic discriminate analysis to nine validated genes from the best L1-penalized regression model in an independent set of HCV-cirrhotic samples. The 95% confidence ellipses of HCV-cirrhosis with (wHCC) and without HCC (woHCC) are illustrated in red and blue ovals, respectively. Individual samples are indicated by red (wHCC) and blue (woHCC) squares.</p

Best fitting prediction model for HCC identification in HCV-cirrhotic patients.

<p>L1-penalized regression model identified 17 differentially expressed Psets as listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040275#pone-0040275-t002" target="_blank"><b>Table 2</b></a>. <b>A)</b> Three-dimensional plot derived from applying classical multidimensional scaling (MDS) to the gene expression dataset for those genes identified by the L1-penalized regression model. Individual samples are represented by a colored square for wHCC (green), woHCC (blue), and iHCC (red). <b>B)</b> Two-way supervised hierarchical clustering and heatmap using ward’s method including all training set samples and Psets identified by the best fitting model.</p

Best fitting prediction model for HCC detection in HCV-cirrhotic patients.

<p>Best fitting prediction model for HCC detection in HCV-cirrhotic patients.</p

Independent validation study of the genes included in the prediction model.

<p>Independent validation study of the genes included in the prediction model.</p

Cell cycle associated genes scheme.

<p>The sketch represents the cell cycle phases and interphases. Phases are named inside the main circle. Black bars indicate interphases. The G2/M interphase is indicated by a red bar. Cell cycle progression is illustrated by a round green arrow and potential progression by dashed blue arrow. Cell cycle blockade and progression are indicated by a red T and a black arrow head, respectively, whereas the size and thickness is proportional to the cell cycle regulation. Genes involved in each phase are listed in tables depending on the cell cycle phase. Red: up-regulation, green: down-regulation; genes involved in positive cell cycle progression are underlined.</p

Molecular and cellular functions deregulated in HCV-cirrhosis with HCC.

<p>A <i>p</i>-value expressed as –log range of 10.2 to 30.0 was applied to identify the most significant cellular functions. Present functions and sub-groups are indicated in purple. Color intensity indicates the statistical significance for differential expressed function as the dark color the most significant. Involved cellular functions were identified using IPA tool.</p

Apoptosis canonical pathway.

<p>The differentially expressed molecules are represented by color as up-regulated (red) and down-regulated (green). Color intensity indicates fold change values estimation for each molecule.</p