The Avian Transcription Factor c-Rel is Expressed in Lymphocyte Precursor Cells and Antigen-Presenting Cells During Thymus Development

Transcription factors of the Rel/NF-κB family are widely involved in the immune system. In this study, we investigate the in vivo expression of the avian protein c-Rel in the T-cell lineage during thymus development. The majority of thymocytes do not express the c-Rel protein. However, lymphocyte precursor cells that colonize the thymus express the c-Rel protein shortly after their homing in the organ and before they begin to differentiate, c-Rel is also detected in different subsets of,antigen-presenting cells such as epithelial cells, dendritic cells, and macrophages. In vitro studies have shown that Rel/NF-κB proteins are sequestered in an inactive form in the cytoplasm by interaction with the IκBα inhibitory protein. By immunocytochemistry, we show that in vivo c-Rel is localized in the cytoplasm of antigen-presenting cells but in both the cytoplasm and nucleus of lymphocyte precursor cells. The cytoplasmic localization of c-Rel in antigen-presenting cells correlates with a high expression of IκBα, whereas the nuclear localization of c-Rel in lymphocyte precursor cells correlates with a much lower expression of IκBα. These results suggest that c-Rel might be constitutively activated in lymphocyte precursor cells

Computational cancer biology: education is a natural key to many locks

BACKGROUND: Oncology is a field that profits tremendously from the genomic data generated by high-throughput technologies, including next-generation sequencing. However, in order to exploit, integrate, visualize and interpret such high-dimensional data efficiently, non-trivial computational and statistical analysis methods are required that need to be developed in a problem-directed manner. DISCUSSION: For this reason, computational cancer biology aims to fill this gap. Unfortunately, computational cancer biology is not yet fully recognized as a coequal field in oncology, leading to a delay in its maturation and, as an immediate consequence, an under-exploration of high-throughput data for translational research. SUMMARY: Here we argue that this imbalance, favoring ’wet lab-based activities’, will be naturally rectified over time, if the next generation of scientists receives an academic education that provides a fair and competent introduction to computational biology and its manifold capabilities. Furthermore, we discuss a number of local educational provisions that can be implemented on university level to help in facilitating the process of harmonization

Activation of Src Mediates PDGF-Induced Smad1 Phosphorylation and Contributes to the Progression of Glomerulosclerosis in Glomerulonephritis

Platelet-derived growth factor (PDGF) plays critical roles in mesangial cell (MC) proliferation in mesangial proliferative glomerulonephritis. We showed previously that Smad1 contributes to PDGF-dependent proliferation of MCs, but the mechanism by which Smad1 is activated by PDGF is not precisely known. Here we examined the role of c-Src tyrosine kinase in the proliferative change of MCs. Experimental mesangial proliferative glomerulonephritis (Thy1 GN) was induced by a single intravenous injection of anti-rat Thy-1.1 monoclonal antibody. In Thy1 GN, MC proliferation and type IV collagen (Col4) expression peaked on day 6. Immunohistochemical staining for the expression of phospho-Src (pSrc), phospho-Smad1 (pSmad1), Col4, and smooth muscle α-actin (SMA) revealed that the activation of c-Src and Smad1 signals in glomeruli peaked on day 6, consistent with the peak of mesangial proliferation. When treated with PP2, a Src inhibitor, both mesangial proliferation and sclerosis were significantly reduced. PP2 administration also significantly reduced pSmad1, Col4, and SMA expression. PDGF induced Col4 synthesis in association with increased expression of pSrc and pSmad1 in cultured MCs. In addition, PP2 reduced Col4 synthesis along with decreased pSrc and pSmad1 protein expression in vitro. Moreover, the addition of siRNA against c-Src significantly reduced the phosphorylation of Smad1 and the overproduction of Col4. These results provide new evidence that the activation of Src/Smad1 signaling pathway plays a key role in the development of glomerulosclerosis in experimental glomerulonephritis

Breast cancer patients' clinical outcome measures are associated with Src kinase family member expression

<p>BACKGROUND: This study determined mRNA expression levels for Src kinase family (SFK) members in breast tissue specimens and assessed protein expression levels of prominent SFK members in invasive breast cancer to establish associations with clinical outcome. Ki67 was investigated to determine association between SFK members and proliferation.</p> <p>METHODS: The mRNA expression levels were assessed for eight SFK members by quantitative real-time PCR. Immunohistochemistry was performed for c-Src, Lyn, Lck and Ki67.</p> <p>RESULTS: mRNA expression was quantified in all tissue samples. SRC and LYN were the most highly expressed in malignant tissue. LCK was more highly expressed in oestrogen receptor (ER)-negative, compared with ER-positive tumours. High cytoplasmic Src kinase protein expression was significantly associated with decreased disease-specific survival. Lyn was not associated with survival at any cellular location. High membrane Lck expression was significantly associated with improved survival. Ki67 expression correlated with tumour grade and nuclear c-Src, but was not associated with survival.</p> <p>CONCLUSIONS: All eight SFK members were expressed in different breast tissues. Src kinase was highest expressed in breast cancer and had a negative impact on disease-specific survival. Membrane expression of Lck was associated with improved clinical outcome. High expression of Src kinase correlated with high proliferation.</p&gt

The proportion of cancer-related entries in PubMed has increased considerably; is cancer truly "The Emperor of All Maladies"?

In this work, the public database of biomedical literature PubMed was mined using queries with combinations of keywords and year restrictions. It was found that the proportion of Cancer-related entries per year in PubMed has risen from around 6% in 1950 to more than 16% in 2016. This increase is not shared by other conditions such as AIDS, Malaria, Tuberculosis, Diabetes, Cardiovascular, Stroke and Infection some of which have, on the contrary, decreased as a proportion of the total entries per year. Organ-related queries were performed to analyse the variation of some specific cancers. A series of queries related to incidence, funding, and relationship with DNA, Computing and Mathematics, were performed to test correlation between the keywords, with the hope of elucidating the cause behind the rise of Cancer in PubMed. Interestingly, the proportion of Cancer-related entries that contain "DNA", "Computational" or "Mathematical" have increased, which suggests that the impact of these scientific advances on Cancer has been stronger than in other conditions. It is important to highlight that the results obtained with the data mining approach here presented are limited to the presence or absence of the keywords on a single, yet extensive, database. Therefore, results should be observed with caution. All the data used for this work is publicly available through PubMed and the UK's Office for National Statistics. All queries and figures were generated with the software platform Matlab and the files are available as supplementary material

Ancestry and diversity of the HMG box superfamily.

The HMG box is a novel type of DNA-binding domain found in a diverse group of proteins. The HMG box superfamily comprises a.o. the High Mobility Group proteins HMG1 and HMG2, the nucleolar transcription factor UBF, the lymphoid transcription factors TCF-1 and LEF-1, the fungal mating-type genes mat-Mc and MATA1, and the mammalian sex-determining gene SRY. The superfamily dates back to at least 1,000 million years ago, as its members appear in animals, plants and yeast. Alignment of all known HMG boxes defined an unusually loose consensus sequence. We constructed phylogenetic trees connecting the members of the HMG box superfamily in order to understand their evolution. This analysis led us to distinguish two subfamilies: one comprising proteins with a single sequence-specific HMG box, the other encompassing relatively non sequence-specific DNA-binding proteins with multiple HMG boxes. By studying the extent of diversification of the superfamily, we found that the speed of evolution was very different within the various groups of HMG-box containing factors. Comparison of the evolution of the two boxes of ABF2 and of mtTF1 implied different diversification models for these two proteins. Finally, we provide a tree for the highly complex group of SRY-like ('Sox' genes), clustering at least 40 different loci that rapidly diverged in various animal lineages