Mari Cleide Sogayar

Evaluating different methods of microarray data normalization

BackgroundWith the development of DNA hybridization microarray technologies, nowadays it is possible to simultaneously assess the expression levels of thousands to tens of thousands of genes. Quantitative comparison of microarrays uncovers distinct patterns of gene expression, which define different cellular phenotypes or cellular responses to drugs. Due to technical biases, normalization of the intensity levels is a pre-requisite to performing further statistical analyses. Therefore, choosing a suitable approach for normalization can be critical, deserving judicious consideration.ResultsHere, we considered three commonly used normalization approaches, namely: Loess, Splines and Wavelets, and two non-parametric regression methods, which have yet to be used for normalization, namely, the Kernel smoothing and Support Vector Regression. The results obtained were compared using artificial microarray data and benchmark studies. The results indicate that the Support Vector Regression is the most robust to outliers and that Kernel is the worst normalization technique, while no practical differences were observed between Loess, Splines and Wavelets.ConclusionIn face of our results, the Support Vector Regression is favored for microarray normalization due to its superiority when compared to the other methods for its robustness in estimating the normalization curve.

Bone Morphogenetic Proteins Facts, Challenges, and Future Perspectives

Bone morphogenetic proteins (BMPs) are members of the TGF-β superfamily, acting as potent regulators during embryogenesis and bone and cartilage formation and repair. Cell and molecular biology approaches have unveiled the great complexity of BMP action, later confirmed by transgenic animal studies. Genetic engineering allows for the production of large amounts of BMPs for clinical use, but they have systematically been associated with a delivery system, such as type I collagen and calcium phosphate ceramics, to ensure controlled release and to maximize their biological activity at the surgical site, avoiding systemic diffusion. Clinical orthopedic studies have shown the benefits of FDA-approved recombinant human BMPs (rhBMPs) 2 and 7, but side effects, such as swelling, seroma, and increased cancer risk, have been reported, probably due to high BMP dosage. Several studies have supported the use of BMPs in periodontal regeneration, sinus lift bone-grafting, and non-unions in oral surgery. However, the clinical use of BMPs is growing mainly in off-label applications, with robust evidence to ascertain rhBMPs’ safety and efficacy through well-designed, randomized, and double-blind clinical trials. Here we review and discuss the critical data on BMP structure, mechanisms of action, and possible clinical applications.

Antisense intronic non-coding RNA levels correlate to the degree of tumor differentiation in prostate cancer

A large fraction of transcripts are expressed antisense to introns of known genes in the human genome. Here we show the construction and use of a cDNA microarray platform enriched in intronic transcripts to assess their biological relevance in pathological conditions. To validate the approach, prostate cancer was used as a model, and 27 patient tumor samples with Gleason scores ranging from 5 to 10 were analyzed. We find that a considerably higher fraction (6.6%, [23/346]) of intronic transcripts are significantly correlated (P⩽0.001) to the degree of prostate tumor differentiation (Gleason score) when compared to transcripts from unannotated genomic regions (1%, [6/539]) or from exons of known genes (2%, [27/1369]). Among the top twelve transcripts most correlated to tumor differentiation, six are antisense intronic messages as shown by orientation-specific RT-PCR or Northern blot analysis with strand-specific riboprobe. Orientation-specific real-time RT–PCR with six tumor samples, confirmed the correlation (P=0.024) between the low/high degrees of tumor differentiation and antisense intronic RASSF1 transcript levels. The need to use intron arrays to reveal the transcriptome profile of antisense intronic RNA in cancer has clearly emerged.

The contribution of 700,000 ORF sequence tags to the definition of the human transcriptome

Open reading frame expressed sequences tags (ORESTES) differ from conventional ESTs by providing sequence data from the central protein coding portion of transcripts. We generated a total of 696,745 ORESTES sequences from 24 human tissues and used a subset of the data that correspond to a set of 15,095 full-length mRNAs as a means of assessing the efficiency of the strategy and its potential contribution to the definition of the human transcriptome. We estimate that ORESTES sampled over 80% of all highly and moderately expressed, and between 40% and 50% of rarely expressed, human genes. In our most thoroughly sequenced tissue, the breast, the 130,000 ORESTES generated are derived from transcripts from an estimated 70% of all genes expressed in that tissue, with an equally efficient representation of both highly and poorly expressed genes. In this respect, we find that the capacity of the ORESTES strategy both for gene discovery and shotgun transcript sequence generation significantly exceeds that of conventional ESTs. The distribution of ORESTES is such that many human transcripts are now represented by a scaffold of partial sequences distributed along the length of each gene product. The experimental joining of the scaffold components, by reverse transcription–PCR, represents a direct route to transcript finishing that may represent a useful alternative to full-length cDNA cloning.

Bone morphogenetic proteins: from structure to clinical use

Bone morphogenetic proteins (BMPs) are multi-functional growth factors belonging to the transforming growth factor ss superfamily. Family members are expressed during limb development, endochondral ossification, early fracture, and cartilage repair. The activity of BMPs was first identified in the 1960s but the proteins responsible for bone induction were unknown until the purification and cloning of human BMPs in the 1980s. To date, about 15 BMP family members have been identified and characterized. The signal triggered by BMPs is transduced through serine/threonine kinase receptors, type I and II subtypes. Three type I receptors have been shown to bind BMP ligands, namely: type IA and IB BMP receptors and type IA activin receptors. BMPs seem to be involved in the regulation of cell proliferation, survival, differentiation and apoptosis, but their hallmark is their ability to induce bone, cartilage, ligament, and tendon formation at both heterotopic and orthotopic sites. This suggests that, in the future, they may play a major role in the treatment of bone diseases. Several animal studies have illustrated the potential of BMPs to enhance spinal fusion, repair critical-size defects, accelerate union, and heal articular cartilage lesions. Difficulties in producing and purifying BMPs from bone tissue have prompted the attempts made by several laboratories, including ours, to express these proteins in the recombinant form in heterologous systems. This review focuses on BMP structure, molecular mechanisms of action and significance and potential applications in medical, dental and veterinary practice for the treatment of cartilage and bone-related diseases.

Correlation between MMPs and their inhibitors in breast cancer tumor tissue specimens and in cell lines with different metastatic potential

BackgroundThe metastatic disease rather than the primary tumor itself is responsible for death in most solid tumors, including breast cancer. The role of matrix metalloproteinases (MMPs), tissue inhibitors of MMPs (TIMPs) and Reversion-inducing cysteine-rich protein with Kazal motifs (RECK) in the metastatic process has previously been established. However, in all published studies only a limited number of MMPs/MMP inhibitors was analyzed in a limited number of cell lines. Here, we propose a more comprehensive approach by analyzing the expression levels of several MMPs (MMP-2, MMP-9 and MMP-14) and MMP inhibitors (TIMP-1, TIMP-2 and RECK) in different models (five human breast cancer cell lines, 72 primary breast tumors and 30 adjacent normal tissues).MethodsWe analyzed the expression levels of MMP-2, MMP-9 and MMP-14 and their inhibitors (TIMP-1, TIMP-2 and RECK) by quantitative RT-PCR (qRT-PCR) in five human breast cancer cell lines presenting increased invasiveness and metastatic potential, 72 primary breast tumors and 30 adjacent normal tissues. Moreover, the role of cell-extracellular matrix elements interactions in the regulation of expression and activity of MMPs and their inhibitors was analyzed by culturing these cell lines on plastic or on artificial ECM (Matrigel).ResultsThe results demonstrated that MMPs mRNA expression levels displayed a positive and statistically significant correlation with the transcriptional expression levels of their inhibitors both in the cell line models and in the tumor tissue samples. Furthermore, the expression of all MMP inhibitors was modulated by cell-Matrigel contact only in highly invasive and metastatic cell lines. The enzyme/inhibitor balance at the transcriptional level significantly favors the enzyme which is more evident in tumor than in adjacent non-tumor tissue samples.ConclusionOur results suggest that the expression of MMPs and their inhibitors, at least at the transcriptional level, might be regulated by common factors and signaling pathways. Therefore, the multi-factorial analysis of these molecules could provide new and independent prognostic information contributing to the determination of more adequate therapy strategies for each patient.

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.

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

BackgroundMetastasis 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.MethodsThe 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.ResultsIn 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.ConclusionAltogether, 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.

Bone Morphogenetic Proteins: Structure, biological function and therapeutic applications

Bone Morphogenetic Proteins (BMPs) are multifunctional secreted cytokines, which belong to the TGF-β superfamily. These glycoproteins act as a disulfide-linked homo- or heterodimers, being potent regulators of bone and cartilage formation and repair, cell proliferation during embryonic development and bone homeostasis in the adult. BMPs are promising molecules for tissue engineering and bone therapy. The present review discusses this family of proteins, their structure and biological function, their therapeutic applications and drawbacks, their effects on mesenchymal stem cells differentiation, and the cell signaling pathways involved in this process.

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/.