The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies present topological network alterations relative to norms seen in the general population. Therefore, methods to discriminate the processes that generate the different classes of networks (e.g., normal and disease) might be crucial for the diagnosis, prognosis, and treatment of the disease. It is known that several topological properties of a network (graph) can be described by the distribution of the spectrum of its adjacency matrix. Moreover, large networks generated by the same random process have the same spectrum distribution, allowing us to use it as a “fingerprint”. Based on this relationship, we introduce and propose the entropy of a graph spectrum to measure the “uncertainty” of a random graph and the Kullback-Leibler and Jensen-Shannon divergences between graph spectra to compare networks. We also introduce general methods for model selection and network model parameter estimation, as well as a statistical procedure to test the nullity of divergence between two classes of complex networks. Finally, we demonstrate the usefulness of the proposed methods by applying them to (1) protein-protein interaction networks of different species and (2) on networks derived from children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and typically developing children. We conclude that scale-free networks best describe all the protein-protein interactions. Also, we show that our proposed measures succeeded in the identification of topological changes in the network while other commonly used measures (number of edges, clustering coefficient, average path length) failed.
The numerous genome sequencing projects produced unprecedented amount of data providing significant information to the discovery of novel non-coding RNA (ncRNA). Several ncRNAs have been described to control gene expression and display important role during cell differentiation and homeostasis. In the last decade, high throughput methods in conjunction with approaches in bioinformatics have been used to identify, classify, and evaluate the expression of hundreds of ncRNA in normal and pathological states, such as cancer. Patient outcomes have been already associated with differential expression of ncRNAs in normal and tumoral tissues, providing new insights in the development of innovative therapeutic strategies in oncology. In this review, we present and discuss bioinformatics advances in the development of computational approaches to analyze and discover ncRNA data in oncology using high throughput sequencing technologies.
bioinformatics; high throughput sequencing; cancer; non-coding RNA; gene expression
Picture Archiving and Communication Systems (PACS) have been widely deployed in healthcare institutions, and they now constitute a normal commodity for practitioners. However, its installation, maintenance, and utilization are still a burden due to their heavy structures, typically supported by centralized computational solutions. In this paper, we present Dicoogle, a PACS archive supported by a document-based indexing system and by peer-to-peer (P2P) protocols. Replacing the traditional database storage (RDBMS) by a documental organization permits gathering and indexing data from file-based repositories, which allows searching the archive through free text queries. As a direct result of this strategy, more information can be extracted from medical imaging repositories, which clearly increases flexibility when compared with current query and retrieval DICOM services. The inclusion of P2P features allows PACS internetworking without the need for a central management framework. Moreover, Dicoogle is easy to install, manage, and use, and it maintains full interoperability with standard DICOM services.
PACS; Digital Imaging and Communications in Medicine (DICOM); Medical imaging; Peer-to-peer; Computer communication networks; Open source; PACS implementation; Information storage and retrieval
In human systemic lupus erythematosus (SLE), diverse autoantibodies accumulate over years before disease manifestation. Unaffected relatives of SLE patients frequently share a sustained production of autoantibodies with indiscriminable specificity, usually without ever acquiring the disease. We studied relations of IgG autoantibody profiles and peripheral blood activated regulatory T-cells (aTregs), represented by CD4+CD25bright T-cells that were regularly 70–90% Foxp3+. We found consistent positive correlations of broad-range as well as specific SLE-associated IgG with aTreg frequencies within unaffected relatives, but not patients or unrelated controls. Our interpretation: unaffected relatives with shared genetic factors compensated pathogenic effects by aTregs engaged in parallel with the individual autoantibody production. To study this further, we applied a novel analytic approach named coreferentiality that tests the indirect relatedness of parameters in respect to multivariate phenotype data. Results show that independently of their direct correlation, aTreg frequencies and specific SLE-associated IgG were likely functionally related in unaffected relatives: they significantly parallelled each other in their relations to broad-range immunoblot autoantibody profiles. In unaffected relatives, we also found coreferential effects of genetic variation in the loci encoding IL-2 and CD25. A model of CD25 functional genetic effects constructed by coreferentiality maximization suggests that IL-2-CD25 interaction, likely stimulating aTregs in unaffected relatives, had an opposed effect in SLE patients, presumably triggering primarily T-effector cells in this group. Coreferentiality modeling as we do it here could also be useful in other contexts, particularly to explore combined functional genetic effects.
Clinical studies have shown antineoplastic effectiveness of monoclonal antibodies (MAbs) against EGFR for different indications. Several MAbs directed to EGFR were developed recently, such as matuzumab, but there is still lack of information on preclinical data on its combination with chemo-radiation. Thus, the present study intended to examine the molecular pathways triggered by matuzumab alone or associated to chemo-radiotherapy in gynecological cell lines and its impact on cell growth and signaling.
Combination of matuzumab with radiation and cisplatin did not enhance its cytostatic effects on A431, Caski and C33A cells (high, intermediate and low EGFR expression, respectively) in clonogenic assays, when compared to controls. The lack of effect was mediated by persistent signaling through EGFR due to its impaired degradation. In spite of the fact that matuzumab inhibited phosphorylation of EGFR, it had no effect upon cell viability. To analyze which downstream molecules would be involved in the EGFR signaling in the presence of matuzumab, we have tested it in combination with either PD98059 (MAPK inhibitor), or LY294002 (PI3K inhibitor). Matuzumab exhibited a synergic effect with LY294002, leading to a reduction of Akt phosphorylation that was followed by a decrease in A431 and Caski cells survival. The combination of PD98059 and matuzumab did not show the same effect suggesting that PI3K is an important effector of EGFR signaling in matuzumab-treated cells. Nonetheless, matuzumab induced ADCC in Caski cells, but not in the C33A cell line, suggesting that its potential therapeutic effects in vitro are indeed dependent on EGFR expression.
Matuzumab combined with chemoradiation did not induce cytotoxic effects on gynecological cancer cell lines in vitro, most likely due to impaired EGFR degradation. However, a combination of matuzumab and PI3K inhibitor synergistically inhibited pAkt and cell survival, suggesting that the use of PI3K/Akt inhibitors could overcome intrinsic resistance to matuzumab in vitro. Altogether, data presented here can pave the way to a rational design of clinical strategies in patients with resistant profile to anti-EGFR inhibitors based on combination therapy.
Matuzumab; PI3K/Akt pathway; EGFR; gynecological cancer; cervical cancer; Cetuximab
The effectiveness of screening colonoscopy in decreasing the incidence of colorectal cancer (CRC) is largely dependent on the detection of polyps and the quality of the procedure. Several key quality measures have been proposed to improve the effectiveness of screening colonoscopies.
To evaluate quality indicators of screening colonoscopy in a tertiary hospital.
All CRC screening colonoscopies performed between 2005 and 2009 in a single tertiary center were reviewed for internationally accepted quality measures.
Of the 1545 individuals who underwent first-time screening colonoscopy 38% were male and 62% were female. The mean age of the patients was 60.4 years and the mean difference in ages was ± 10.3 years. Cecal intubation rate was 91% (1336), however ileocecal valve photo documentation was performed in only 81% (1248) colonoscopies. The quality of bowel preparation was classified as: good 76% (1171), reasonable 11% (174), and poor 13% (200). Polyp detection rate (PDR) was 33% (503). The prevalence of polyps ≥1 cm in size was 5% (82). PDR was significantly higher in men than in women (44%  vs 25% , P = 0.0001). Other factors significantly influencing PDR were quality of bowel preparation (odds ratio [OR]: 1.28, 95% confidence interval [CI]: 0.9–1.6) and age over 50 (OR: 1.9, 95% CI: 1.3–2.9). Left colonic polyps were associated with a risk ratio of 2.3 (95% CI: 1.8–2.9) of lesions in the other colonic segments compared to no polyps in the left colon. None of the colonoscopists reported withdrawal time.
Cecal intubation rate and quality of bowel preparation were suboptimal. The polyp detection rate compares favorably to accepted standards and its main determinants are male sex, age >50 years, quality of bowel preparation, and the presence of left colonic polyps.
colorectal cancer; screening colonoscopy; quality indicators
Various research projects often involve determining the relative position of genomic coordinates, intervals, single nucleotide variations (SNVs), insertions, deletions and translocations with respect to genes and their potential impact on protein translation. Due to the tremendous increase in throughput brought by the use of next-generation sequencing, investigators are routinely faced with the need to annotate very large datasets. We present Segtor, a tool to annotate large sets of genomic coordinates, intervals, SNVs, indels and translocations. Our tool uses segment trees built using the start and end coordinates of the genomic features the user wishes to use instead of storing them in a database management system. The software also produces annotation statistics to allow users to visualize how many coordinates were found within various portions of genes. Our system currently can be made to work with any species available on the UCSC Genome Browser. Segtor is a suitable tool for groups, especially those with limited access to programmers or with interest to analyze large amounts of individual genomes, who wish to determine the relative position of very large sets of mapped reads and subsequently annotate observed mutations between the reads and the reference. Segtor (http://lbbc.inca.gov.br/segtor/) is an open-source tool that can be freely downloaded for non-profit use. We also provide a web interface for testing purposes.
The correct identification of Candida species is of great importance, as it presents prognostic and therapeutical significance, allowing an early and appropriate antifungical therapy. The purpose of this study was to identify isolates of Candida spp. from oral mucosa of 38 patients with oral candidosis evaluated in 2004 by phenotypic methods and PCR, discriminating C. albicans from the other Candida species. The tests used for phenotypic analysis were germ-tube and chlamydoconidia production, culture in CHROMAgar™ Candida, carbohydrate assimilation test, growth at 45ºC and culture in Tween 80 agar. Genotypic confirmation was performed by PCR. Phenotypic tests showed that 63.2% strains formed germ-tubes, 73.7% produced chlamydoconidia, and 63.2% showed green colonies in chromogenic medium, presumptively indicating C. albicans or C. dubliniensis. The carbohydrate assimilation test confirmed these results. A total of 21% strains were identified as C. krusei and 13.2% were indicative of C. tropicalis. Of these later strains, three produced chlamydoconidia. The association of other phenotypic tests with culture in Tween 80 agar identified 95.8% of strains as C. albicans and 4.2% as C. dubliniensis. All 24 strains indicative of C. albicans and C. dubliniensis were confirmed by PCR as C. albicans.
Candida spp.; – identification – PCR – phenotypic tests
CD4+CD25+ regulatory T cells play an essential role in maintaining immune homeostasis and preventing autoimmunity. Therefore, defects in Treg development, maintenance or function have been associated with several human autoimmune diseases including Systemic Lupus Erythematosus (SLE), a systemic autoimmune disease characterized by loss of tolerance to nuclear components and significantly more frequent in females.
To investigate the involvement of Treg in SLE pathogenesis, we determined the frequency of CD4+CD25+CD45RO+ T cells, which encompass the majority of Treg activity, in the PBMC of 148 SLE patients (76 patients were part of 54 families), 166 relatives and 117 controls. SLE patients and their relatives were recruited in several Portuguese hospitals and through the Portuguese Lupus Association. Control individuals were blood donors recruited from several regional blood donor centers. Treg frequency was significantly lower in SLE patients than healthy controls (z = -6.161, P < 0.00001) and intermediate in the relatives' group. Remarkably, this T cell subset was also lower in females, most strikingly in the control population (z = 4.121, P < 0.001). We further ascertained that the decreased frequency of Treg in SLE patients resulted from the specific reduction of bona fide FOXP3+CD4+CD25+ Treg. Treg frequency was negatively correlated with SLE activity index (SLEDAI) and titers of serum anti-dsDNA antibodies. Both Treg frequency and disease activity were modulated by IVIg treatment in a documented SLE case. The segregation of Treg frequency within the SLE families was indicative of a genetic trait. Candidate gene analysis revealed that specific variants of CTLA4 and TGFβ were associated with the decreased frequency of Treg in PBMC, while FOXP3 gene variants were associated with affection status, but not with Treg frequency.
SLE patients have impaired Treg production or maintenance, a trait strongly associated with SLE disease activity and autoantibody titers, and possibly resulting from the inability to convert FOXP3+CD25- into FOXP3+CD25+ T cells. Treg frequency is highly heritable within SLE families, with specific variants of the CTLA4 and TGFβ genes contributing to this trait, while FOXP3 contributes to SLE through mechanisms not involving a modulation of Treg frequency. These findings establish that the genetic components in SLE pathogenesis include genes related to Treg generation or maintenance.
Germ-cell tumors are a high-proliferative type of cancer that may evolve to significant bulky disease. Tumor lysis syndrome is rarely reported in this setting. The reports of three patients with germ-cell tumors who developed severe acute tumor lysis syndrome following the start of their anticancer therapy are presented. All patients developed renal dysfunction and multiorgan failure. Patients with extensive germ-cell tumors should be kept on close clinical and laboratory monitoring. Physicians should be aware of this uncommon but severe complication and consider early admission to the intensive care unit for the institution of measures to prevent acute renal failure.
Acute renal failure; germ-cell tumors; tumor lysis syndrome
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.
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.
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.
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.
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.
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 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.
With 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.
Here, 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.
In 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.
Activation of caspase-9 in response to treatment with cytotoxic drugs is inhibited in NSCLC cells, which may contribute to the clinical resistance to chemotherapy shown in this type of tumor. The aim of the present study was to investigate the mechanism of caspase-9 inhibition, with a focus on a possible role of TUCAN as caspase-9 inhibitor and a determinant of chemosensitivity in NSCLC cells.
Caspase-9 processing and activation were investigated by Western blot and by measuring the cleavage of the fluorogenic substrate LEHD-AFC. Proteins interaction assays, and RNA interference in combination with cell viability and apoptosis assays were used to investigate the involvement of TUCAN in inhibition of caspase-9 and chemosensitivity NSCLC.
Analysis of the components of the caspase-9 activation pathway in a panel of NSCLC and SCLC cells revealed no intrinsic defects. In fact, exogenously added cytochrome c and dATP triggered procaspase-9 cleavage and activation in lung cancer cell lysates, suggesting the presence of an inhibitor. The reported inhibitor of caspase-9, TUCAN, was exclusively expressed in NSCLC cells. However, interactions between TUCAN and procaspase-9 could not be demonstrated by any of the assays used. Furthermore, RNA interference-mediated down-regulation of TUCAN did not restore cisplatin-induced caspase-9 activation or affect cisplatin sensitivity in NSCLC cells.
These results indicate that procaspase-9 is functional and can undergo activation and full processing in lung cancer cell extracts in the presence of additional cytochrome c/dATP. However, the inhibitory protein TUCAN does not play a role in inhibition of procaspase-9 and in determining the sensitivity to cisplatin in NSCLC.