Toll-like receptor 4 (TLR4) expression in human and murine pancreatic beta-cells affects cell viability and insulin homeostasis

<p>Abstract</p> <p>Background</p> <p>Toll-like receptor 4 (TLR4) is widely recognized as an essential element in the triggering of innate immunity, binding pathogen-associated molecules such as Lipopolysaccharide (LPS), and in initiating a cascade of pro-inflammatory events. Evidence for TLR4 expression in non-immune cells, including pancreatic β-cells, has been shown, but, the functional role of TLR4 in the physiology of human pancreatic β-cells is still to be clearly established. We investigated whether TLR4 is present in β-cells purified from freshly isolated human islets and confirmed the results using MIN6 mouse insulinoma cells, by analyzing the effects of TLR4 expression on cell viability and insulin homeostasis.</p> <p>Results</p> <p>CD11b positive macrophages were practically absent from isolated human islets obtained from non-diabetic brain-dead donors, and TLR4 mRNA and cell surface expression were restricted to β-cells. A significant loss of cell viability was observed in these β-cells indicating a possible relationship with TLR4 expression. Monitoring gene expression in β-cells exposed for 48h to the prototypical TLR4 ligand LPS showed a concentration-dependent increase in TLR4 and CD14 transcripts and decreased insulin content and secretion. TLR4-positive MIN6 cells were also LPS-responsive, increasing TLR4 and CD14 mRNA levels and decreasing cell viability and insulin content.</p> <p>Conclusions</p> <p>Taken together, our data indicate a novel function for TLR4 as a molecule capable of altering homeostasis of pancreatic β-cells.</p

GEDI: a user-friendly toolbox for analysis of large-scale gene expression data

Abstract\ud \ud \ud \ud Background\ud \ud Several mathematical and statistical methods have been proposed in the last few years to analyze microarray data. Most of those methods involve complicated formulas, and software implementations that require advanced computer programming skills. Researchers from other areas may experience difficulties when they attempting to use those methods in their research. Here we present an user-friendly toolbox which allows large-scale gene expression analysis to be carried out by biomedical researchers with limited programming skills.\ud \ud \ud \ud Results\ud \ud Here, we introduce an user-friendly toolbox called GEDI (Gene Expression Data Interpreter), an extensible, open-source, and freely-available tool that we believe will be useful to a wide range of laboratories, and to researchers with no background in Mathematics and Computer Science, allowing them to analyze their own data by applying both classical and advanced approaches developed and recently published by Fujita et al.\ud \ud \ud \ud Conclusion\ud \ud GEDI is an integrated user-friendly viewer that combines the state of the art SVR, DVAR and SVAR algorithms, previously developed by us. It facilitates the application of SVR, DVAR and SVAR, further than the mathematical formulas present in the corresponding publications, and allows one to better understand the results by means of available visualizations. Both running the statistical methods and visualizing the results are carried out within the graphical user interface, rendering these algorithms accessible to the broad community of researchers in Molecular Biology.This research was supported by FAPESP, CAPES, CNPq, FINEP and PRP-USP.This research was supported by FAPESP, CAPES, CNPq, FINEP and PRPUSP

Modeling gene expression regulatory networks with the sparse vector autoregressive model

<p>Abstract</p> <p>Background</p> <p>To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems.</p> <p>Results</p> <p>We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets.</p> <p>Conclusion</p> <p>The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any <it>a priori </it>information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.</p

Androgen responsive intronic non-coding RNAs

BACKGROUND: Transcription of large numbers of non-coding RNAs originating from intronic regions of human genes has been recently reported, but mechanisms governing their biosynthesis and biological functions are largely unknown. In this work, we evaluated the existence of a common mechanism of transcription regulation shared by protein-coding mRNAs and intronic RNAs by measuring the effect of androgen on the transcriptional profile of a prostate cancer cell line. RESULTS: Using a custom-built cDNA microarray enriched in intronic transcribed sequences, we found 39 intronic non-coding RNAs for which levels were significantly regulated by androgen exposure. Orientation-specific reverse transcription-PCR indicated that 10 of the 13 were transcribed in the antisense direction. These transcripts are long (0.5–5 kb), unspliced and apparently do not code for proteins. Interestingly, we found that the relative levels of androgen-regulated intronic transcripts could be correlated with the levels of the corresponding protein-coding gene (asGAS6 and asDNAJC3) or with the alternative usage of exons (asKDELR2 and asITGA6) in the corresponding protein-coding transcripts. Binding of the androgen receptor to a putative regulatory region upstream from asMYO5A, an androgen-regulated antisense intronic transcript, was confirmed by chromatin immunoprecipitation. CONCLUSION: Altogether, these results indicate that at least a fraction of naturally transcribed intronic non-coding RNAs may be regulated by common physiological signals such as hormones, and further corroborate the notion that the intronic complement of the transcriptome play functional roles in the human gene-expression program

Cloning and characterization of a novel alternatively spliced transcript of the human CHD7 putative helicase

<p>Abstract</p> <p>Background</p> <p>The <it>CHD7 </it>(Chromodomain Helicase DNA binding protein 7) gene encodes a member of the chromodomain family of ATP-dependent chromatin remodeling enzymes. Mutations in the <it>CHD7 </it>gene are found in individuals with CHARGE, a syndrome characterized by multiple birth malformations in several tissues. CHD7 was identified as a binding partner of PBAF complex (Polybromo and BRG Associated Factor containing complex) playing a central role in the transcriptional reprogramming process associated to the formation of multipotent migratory neural crest, a transient cell population associated with the genesis of various tissues. <it>CHD7 </it>is a large gene containing 38 annotated exons and spanning 200 kb of genomic sequence. Although genes containing such number of exons are expected to have several alternative transcripts, there are very few evidences of alternative transcripts associated to <it>CHD7 </it>to date indicating that alternative splicing associated to this gene is poorly characterized.</p> <p>Findings</p> <p>Here, we report the cloning and characterization by experimental and computational studies of a novel alternative transcript of the human <it>CHD7 </it>(named CHD7 CRA_e), which lacks most of its coding exons. We confirmed by overexpression of CHD7 CRA_e alternative transcript that it is translated into a protein isoform lacking most of the domains displayed by the canonical isoform. Expression of the CHD7 CRA_e transcript was detected in normal liver, in addition to the DU145 human prostate carcinoma cell line from which it was originally isolated.</p> <p>Conclusions</p> <p>Our findings indicate that the splicing event associated to the CHD7 CRA_e alternative transcript is functional. The characterization of the CHD7 CRA_e novel isoform presented here not only sets the basis for more detailed functional studies of this isoform, but, also, contributes to the alternative splicing annotation of the <it>CHD7 </it>gene and the design of future functional studies aimed at the elucidation of the molecular functions of its gene products.</p

TGF-β1 modulates the homeostasis between MMPs and MMP inhibitors through p38 MAPK and ERK1/2 in highly invasive breast cancer cells

<p>Abstract</p> <p>Background</p> <p>Metastasis is the main factor responsible for death in breast cancer patients. Matrix metalloproteinases (MMPs) and their inhibitors, known as tissue inhibitors of MMPs (TIMPs), and the membrane-associated MMP inhibitor (RECK), are essential for the metastatic process. We have previously shown a positive correlation between MMPs and their inhibitors expression during breast cancer progression; however, the molecular mechanisms underlying this coordinate regulation remain unknown. In this report, we investigated whether TGF-β1 could be a common regulator for MMPs, TIMPs and RECK in human breast cancer cell models.</p> <p>Methods</p> <p>The mRNA expression levels of TGF-β isoforms and their receptors were analyzed by qRT-PCR in a panel of five human breast cancer cell lines displaying different degrees of invasiveness and metastatic potential. The highly invasive MDA-MB-231 cell line was treated with different concentrations of recombinant TGF-β1 and also with pharmacological inhibitors of p38 MAPK and ERK1/2. The migratory and invasive potential of these treated cells were examined in vitro by transwell assays.</p> <p>Results</p> <p>In general, TGF-β2, TβRI and TβRII are over-expressed in more aggressive cells, except for TβRI, which was also highly expressed in ZR-75-1 cells. In addition, TGF-β1-treated MDA-MB-231 cells presented significantly increased mRNA expression of MMP-2, MMP-9, MMP-14, TIMP-2 and RECK. TGF-β1 also increased TIMP-2, MMP-2 and MMP-9 protein levels but downregulated RECK expression. Furthermore, we analyzed the involvement of p38 MAPK and ERK1/2, representing two well established Smad-independent pathways, in the proposed mechanism. Inhibition of p38MAPK blocked TGF-β1-increased mRNA expression of all MMPs and MMP inhibitors analyzed, and prevented TGF-β1 upregulation of TIMP-2 and MMP-2 proteins. Moreover, ERK1/2 inhibition increased RECK and prevented the TGF-β1 induction of pro-MMP-9 and TIMP-2 proteins. TGF-β1-enhanced migration and invasion capacities were blocked by p38MAPK, ERK1/2 and MMP inhibitors.</p> <p>Conclusion</p> <p>Altogether, our results support that TGF-β1 modulates the mRNA and protein levels of MMPs (MMP-2 and MMP-9) as much as their inhibitors (TIMP-2 and RECK). Therefore, this cytokine plays a crucial role in breast cancer progression by modulating key elements of ECM homeostasis control. Thus, although the complexity of this signaling network, TGF-β1 still remains a promising target for breast cancer treatment.</p

Unveiling novel genes upregulated by both rhBMP2 and rhBMP7 during early osteoblastic transdifferentiation of C2C12 cells

<p>Abstract</p> <p>Findings</p> <p>We set out to analyse the gene expression profile of pre-osteoblastic C2C12 cells during osteodifferentiation induced by both rhBMP2 and rhBMP7 using DNA microarrays. Induced and repressed genes were intercepted, resulting in 1,318 induced genes and 704 repressed genes by both rhBMP2 and rhBMP7. We selected and validated, by RT-qPCR, 24 genes which were upregulated by rhBMP2 and rhBMP7; of these, 13 are related to transcription (<it>Runx2, Dlx1, Dlx2, Dlx5, Id1, Id2, Id3, Fkhr1, Osx, Hoxc8, Glis1, Glis3 </it>and <it>Cfdp1</it>), four are associated with cell signalling pathways (<it>Lrp6, Dvl1, Ecsit </it>and <it>PKCδ</it>) and seven are associated with the extracellular matrix (<it>Ltbp2, Grn, Postn, Plod1, BMP1, Htra1 </it>and <it>IGFBP-rP10</it>). The novel identified genes include: <it>Hoxc8, Glis1, Glis3, Ecsit, PKCδ, LrP6, Dvl1, Grn, BMP1, Ltbp2, Plod1, Htra1 </it>and <it>IGFBP-rP10</it>.</p> <p>Background</p> <p>BMPs (bone morphogenetic proteins) are members of the TGFβ (transforming growth factor-β) super-family of proteins, which regulate growth and differentiation of different cell types in various tissues, and play a critical role in the differentiation of mesenchymal cells into osteoblasts. In particular, rhBMP2 and rhBMP7 promote osteoinduction <it>in vitro </it>and <it>in vivo</it>, and both proteins are therapeutically applied in orthopaedics and dentistry.</p> <p>Conclusion</p> <p>Using DNA microarrays and RT-qPCR, we identified both previously known and novel genes which are upregulated by rhBMP2 and rhBMP7 during the onset of osteoblastic transdifferentiation of pre-myoblastic C2C12 cells. Subsequent studies of these genes in C2C12 and mesenchymal or pre-osteoblastic cells should reveal more details about their role during this type of cellular differentiation induced by BMP2 or BMP7. These studies are relevant to better understanding the molecular mechanisms underlying osteoblastic differentiation and bone repair.</p

Proteome profiling of triple negative breast cancer cells overexpressing NOD1 and NOD2 receptors unveils molecular signatures of malignant cell proliferation

Abstract Background Triple negative breast cancer (TNBC) is a malignancy with very poor prognosis, due to its aggressive clinical characteristics and lack of response to receptor-targeted drug therapy. In TNBC, immune-related pathways are typically upregulated and may be associated with a better prognosis of the disease, encouraging the pursuit for immunotherapeutic options. A number of immune-related molecules have already been associated to the onset and progression of breast cancer, including NOD1 and NOD2, innate immune receptors of bacterial-derived components which activate pro-inflammatory and survival pathways. In the context of TNBC, overexpression of either NOD1or NOD2 is shown to reduce cell proliferation and increase clonogenic potential in vitro. To further investigate the pathways linking NOD1 and NOD2 signaling to tumorigenesis in TNBC, we undertook a global proteome profiling of TNBC-derived cells ectopically expressing each one of these NOD receptors. Results We have identified a total of 95 and 58 differentially regulated proteins in NOD1- and NOD2-overexpressing cells, respectively. We used bioinformatics analyses to identify enriched molecular signatures aiming to integrate the differentially regulated proteins into functional networks. These analyses suggest that overexpression of both NOD1 and NOD2 may disrupt immune-related pathways, particularly NF-κB and MAPK signaling cascades. Moreover, overexpression of either of these receptors may affect several stress response and protein degradation systems, such as autophagy and the ubiquitin-proteasome complex. Interestingly, the levels of several proteins associated to cellular adhesion and migration were also affected in these NOD-overexpressing cells. Conclusions Our proteomic analyses shed new light on the molecular pathways that may be modulating tumorigenesis via NOD1 and NOD2 signaling in TNBC. Up- and downregulation of several proteins associated to inflammation and stress response pathways may promote activation of protein degradation systems, as well as modulate cell-cycle and cellular adhesion proteins. Altogether, these signals seem to be modulating cellular proliferation and migration via NF-κB, PI3K/Akt/mTOR and MAPK signaling pathways. Further investigation of altered proteins in these pathways may provide more insights on relevant targets, possibly enabling the immunomodulation of tumorigenesis in the aggressive TNBC phenotype