Humberto Miguel Garay-Malpartida

Modeling gene expression regulatory networks with the sparse vector autoregressive model.

BackgroundTo 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.ResultsWe 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.ConclusionThe 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 a priori 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.

CaSPredictor: a new computer-based tool for caspase substrate prediction

MOTIVATION In vitro studies have shown that the most remarkable catalytic features of caspases, a family of cysteineproteases, are their stringent specificity to Asp (D) in the S1 subsite and at least four amino acids to the left of scissile bound. However, there is little information about the substrate recognition patterns in vivo. The prediction and characterization of proteolytic cleavage sites in natural substrates could be useful for uncovering these structural relationships. RESULTS PEST-like sequences rich in the amino acids Ser (S), Thr (T), Pro (P), Glu or Asp (E/D), including Asn (N) and Gln (Q) are adjacent structural/sequential elements in the majority of cleavage site regions of the natural caspase substrates described in the literature, supporting its possible implication in the substrate selection by caspases. We developed CaSPredictor, a software which incorporated a PEST-like index and the position-dependent amino acid matrices for prediction of caspase cleavage sites in individual proteins and protein datasets. The program predicted successfully 81% (111/137) of the cleavage sites in experimentally verified caspase substrates not annotated in its internal data file. Its accuracy and confidence was estimated as 80% using ROC methodology. The program was much more efficient in predicting caspase substrates when compared with PeptideCutter and PEPS software. Finally, the program detected potential cleavage sites in the primary sequences of 1644 proteins in a dataset containing 9986 protein entries. AVAILABILITY Requests for software should be made to Dr José E. Belizário SUPPLEMENTARY INFORMATION Supplementary information is available for academic users at site http://icb.usp.br/~farmaco/Jose/CaSpredictorfiles.

Time-varying modeling of gene expression regulatory networks using the wavelet dynamic vector autoregressive method

MOTIVATION A variety of biological cellular processes are achieved through a variety of extracellular regulators, signal transduction, protein-protein interactions and differential gene expression. Understanding of the mechanisms underlying these processes requires detailed molecular description of the protein and gene networks involved. To better understand these molecular networks, we propose a statistical method to estimate time-varying gene regulatory networks from time series microarray data. One well known problem when inferring connectivity in gene regulatory networks is the fact that the relationships found constitute correlations that do not allow inferring causation, for which, a priori biological knowledge is required. Moreover, it is also necessary to know the time period at which this causation occurs. Here, we present the Dynamic Vector Autoregressive model as a solution to these problems. RESULTS We have applied the Dynamic Vector Autoregressive model to estimate time-varying gene regulatory networks based on gene expression profiles obtained from microarray experiments. The network is determined entirely based on gene expression profiles data, without any prior biological knowledge. Through construction of three gene regulatory networks (of p53, NF-kappaB and c-myc) for HeLa cells, we were able to predict the connectivity, Granger-causality and dynamics of the information flow in these networks. SUPPLEMENTARY INFORMATION Additional figures may be found at http://mariwork.iq.usp.br/dvar/.

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

BackgroundToll-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.ResultsCD11b 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.ConclusionsTaken together, our data indicate a novel function for TLR4 as a molecule capable of altering homeostasis of pancreatic β-cells.

MODELING NONLINEAR GENE REGULATORY NETWORKS FROM TIME SERIES GENE EXPRESSION DATA

In cells, molecular networks such as gene regulatory networks are the basis of biological complexity. Therefore, gene regulatory networks have become the core of research in systems biology. Understanding the processes underlying the several extracellular regulators, signal transduction, protein-protein interactions, and differential gene expression processes requires detailed molecular description of the protein and gene networks involved. To understand better these complex molecular networks and to infer new regulatory associations, we propose a statistical method based on vector autoregressive models and Granger causality to estimate nonlinear gene regulatory networks from time series microarray data. Most of the models available in the literature assume linearity in the inference of gene connections; moreover, these models do not infer directionality in these connections. Thus, a priori biological knowledge is required. However, in pathological cases, no a priori biological information is available. To overcome these problems, we present the nonlinear vector autoregressive (NVAR) model. We have applied the NVAR model to estimate nonlinear gene regulatory networks based entirely on gene expression profiles obtained from DNA microarray experiments. We show the results obtained by NVAR through several simulations and by the construction of three actual gene regulatory networks (p53, NF-kappaB, and c-Myc) for HeLa cells.

Glypican-3 reexpression regulates apoptosis in murine adenocarcinoma mammary cells modulating PI3K/Akt and p38MAPK signaling pathways

Glypican-3 (GPC3) is a proteoglycan involved in proliferation and cell survival. Several reports demonstrated that GPC3 is downregulated in some tumors, such as breast cancer. Previously, we determined that GPC3 reexpression in the murine mammary adenocarcinoma LM3 cells induced an impairment of their invasive and metastatic capacities, associated with a decrease of their motility and an increase of their cell death. We demonstrated that GPC3 inhibits canonical Wnt signaling, as well as it activates non canonical pathway. Now, we identified signaling pathways responsible for the pro-apoptotic role of GPC3 in LM3 cells. We found for the first time that GPC3 inhibits the PI3K/Akt anti-apoptotic pathway while it stimulates the p38MAPK stress-activated one. We report a concomitant modulation of CDK inhibitors as well as of pro- and anti-apoptotic molecules. Our results provide new clues regarding the mechanism involved in the modulation induced by GPC3 of mammary tumor cell growth and survival.

Aminoacetone, a putative endogenous source of methylglyoxal, causes oxidative stress and death to insulin-producing RINm5f cells.

Aminoacetone (AA), triose phosphates, and acetone are putative endogenous sources of potentially cytotoxic and genotoxic methylglyoxal (MG), which has been reported to be augmented in the plasma of diabetic patients. In these patients, accumulation of MG derived from aminoacetone, a threonine and glycine catabolite, is inferred from the observed concomitant endothelial overexpression of circulating semicarbazide-sensitive amine oxidases. These copper-dependent enzymes catalyze the oxidation of primary amines, such as AA and methylamine, by molecular oxygen, to the corresponding aldehydes, NH4(+) ion and H2O2. We recently reported that AA aerobic oxidation to MG also takes place immediately upon addition of catalytic amounts of copper and iron ions. Taking into account that (i) MG and H2O2 are reportedly cytotoxic to insulin-producing cell lineages such as RINm5f and that (ii) the metal-catalyzed oxidation of AA is propagated by O2(*-) radical anion, we decided to investigate the possible pro-oxidant action of AA on these cells taken here as a reliable model system for pancreatic beta-cells. Indeed, we show that AA (0.10-5.0 mM) administration to RINm5f cultures induces cell death. Ferrous (50-300 microM) and Fe(3+) ion (100 microM) addition to the cell cultures had no effect, whereas Cu(2+) (5.0-100 microM) significantly increased cell death. Supplementation of the AA- and Cu(2+)-containing culture medium with antioxidants, such as catalase (5.0 microM), superoxide dismutase (SOD, 50 U/mL), and N-acetylcysteine (NAC, 5.0 mM) led to partial protection. mRNA expression of MnSOD, CuZnSOD, glutathione peroxidase, and glutathione reductase, but not of catalase, is higher in cells treated with AA (0.50-1.0 mM) plus Cu(2+) ions (10-50 microM) relative to control cultures. This may imply higher activity of antioxidant enzymes in RINm5f AA-treated cells. In addition, we have found that AA (0.50-1.0 mM) plus Cu(2+) (100 microM) (i) increase RINm5f cytosolic calcium; (ii) promote DNA fragmentation; and (iii) increase the pro-apoptotic (Bax)/antiapoptotic (Bcl-2) ratio at the level of mRNA expression. In conclusion, although both normal and pathological concentrations of AA are probably much lower than those used here, it is tempting to propose that excess AA in diabetic patients may drive oxidative damage and eventually the death of pancreatic beta-cells.

Expression of NADPH oxidase in human pancreatic islets

AIMS NADPH oxidase (NOX) is a known source of superoxide anions in phagocytic and non-phagocytic cells. In this study, the presence of this enzyme in human pancreatic islets and the importance of NADPH oxidase in human β-cell function were investigated. MAIN METHODS AND KEY FINDINGS In isolated human pancreatic islets, the expression of NADPH oxidase components was evidenced by real-time PCR (p22(PHOX), p47(PHOX) and p67(PHOX)), Western blotting (p47(PHOX) and p67(PHOX)) and immunohistochemistry (p47(PHOX), p67(PHOX) and gp91(PHOX)). Immunohistochemistry experiments showed co-localization of p47(PHOX), p67(PHOX) and gp91(PHOX) (isoform 2 of NADPH oxidase-NOX2) with insulin secreting cells. Inhibition of NADPH oxidase activity impaired glucose metabolism and glucose-stimulated insulin secretion. SIGNIFICANCE These findings demonstrate the presence of the main intrinsic components of NADPH oxidase comprising the NOX2 isoform in human pancreatic islets, whose activity also contributes to human β-cell function.

Chronic nicotine treatment decreases LPS signaling through NF-κB and TLR-4 modulation in the hippocampus

The hippocampus is a brain region that is rich in nicotinic acetylcholine receptors (nAChRs), especially the α7 subtype. The hippocampus is severely affected in disorders that have a neuroinflammatory component, such as Alzheimers disease, Parkinsons disease, and schizophrenia. Previous studies demonstrated both in vivo and in vitro that nicotine inhibits immunological responses, including those that are triggered by the inflammatory agent lipopolysaccharide (LPS), the endotoxin of Gram-negative bacteria. The present study investigated whether chronically administered nicotine interferes with the nuclear binding of nuclear factor-κB (NF-κB) and the expression of LPS-induced inflammatory response genes. The results indicated that chronic nicotine administration (0.1mg/kg, s.c., 14days) inhibited the LPS-induced nuclear binding of NF-κB and mRNA expression levels of Tnf, Il1b, Nos2, and Tlr4. The presence of both the selective α7 nAChR antagonist methyllycaconitine (MLA; 5.0mg/kg i.p., 14days) and the nonselective nAChR antagonist mecamylamine (Meca; 1.0mg/kg, s.c., 14days) reversed the inhibitory effects of nicotine. These results suggest that the chronic activation of α7- and αxβy-containing nAChRs reduces acute inflammatory responses in the brain.

NUCEL (Cell and Molecular Therapy Center) : A Multidisciplinary Center for Translational Research in Brazil

Social and economical development is closely associated with technological innovation and a well-developed biotechnological industry. In the last few years, Brazil’s scientific production has been steadily increasing; however, the number of patents is lagging behind, with technological and translational research requiring governmental incentive and reinforcement. The Cell and Molecular Therapy Center (NUCEL) was created to develop activities in the translational research field, addressing concrete problems found in biomedical and veterinary areas and actively searching for solutions by employing a genetic engineering approach to generate cell lines over-expressing recombinant proteins to be transferred to local biotech companies, aiming at furthering the development of a national competence for local production of biopharmaceuticals of widespread use and of life-saving importance. To this end, mammalian cell engineering technologies were used to generate cell lines over-expressing several different recombinant proteins of biomedical and biotechnological interest, namely, recombinant human Amylin/IAPP for diabetes treatment, human FVIII and FIX clotting factors for hemophilia, human and bovine FSH for fertility and reproduction, and human bone repair proteins (BMPs). Expression of some of these proteins is also being sought with the baculovirus/insect cell system (BEVS) which, in many cases, is able to deliver high-yield production of recombinant proteins with biological activity comparable to that of mammalian systems, but in a much more cost-effective manner. Transfer of some of these recombinant products to local Biotech companies has been pursued by taking advantage of the São Paulo State Foundation (FAPESP) and Federal Government (FINEP, CNPq) incentives for joint Research Development and Innovation partnership projects.