Embryonic development of Eucorydia yasumatsui Asahina, with special reference to external morphology (Insecta: Blattodea, Corydiidae)

As the first step in the comparative embryological study of Blattodea, with the aim of reconstructing the groundplan and phylogeny of Dictyoptera and Polyneoptera, the embryonic development of a corydiid was examined and described in detail using Eucorydia yasumatsui. Ten to fifteen micropyles are localized on the ventral side of the egg, and aggregated symbiont bacterial “mycetomes” are found in the egg. The embryo is formed by the fusion of paired blastodermal regions, with higher cellular density on the ventral side of the egg. This type of embryo formation, regarded as one of the embryological autapomorphies of Polyneoptera, was first demonstrated for “Blattaria” in the present study. The embryo undergoes embryogenesis of the short germ band type, and elongates to its full length on the ventral side of the egg. The embryo undergoes katatrepsis and dorsal closure, and then finally, it acquires its definitive form, keeping its original position on the ventral side of the egg, with its anteroposterior axis never reversed throughout development. The information obtained was compared with that of previous studies on other insects. “Micropyles grouped on the ventral side of the egg” is thought to be a part of the groundplan of Dictyoptera, and “possession of bacteria in the form of mycetomes” to be an apomorphic groundplan of Blattodea. Corydiid embryos were revealed to perform blastokinesis of the “non-reversion type (N)”, as reported in blaberoid cockroaches other than Corydiidae (“Ectobiidae,” Blaberidae, etc.) and in Mantodea; the embryos of blattoid cockroaches (Blattidae and Cryptocercidae) and Isoptera undergo blastokinesis of the “reversion type (R),” in which the anteroposterior axis of the embryo is reversed during blastokinesis. Dictyopteran blastokinesis types can be summarized as “Mantodea (N) + Blattodea [= Blaberoidea (N) + Blattoidea (R) + Isoptera (R)]”

Evaluating different methods of microarray data normalization

BACKGROUND: 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. RESULTS: 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. CONCLUSION: 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

For more than money : willingness of health professionals to stay in remote Senegal

The study was funded through a Research Grant for International Health, H25-11, from the Ministry of Health, Welfare and Labour, Japan (http://www.ncgm.go.jp/kaihatsu/), and undertaken as part of the project Réseau Vision Tokyo 2010, funded by the Japan International Cooperation Agency. Acknowledgement The authors would like to express their profound gratitude to the fieldwork team and to the health professionals who responded to the survey questionnaire. Thanks also to four reviewers whose comments have improved the paper. The datasets used and/or analysed in the study are available from the corresponding author on reasonable request.Peer reviewedPublisher PD

Multivariate gene expression analysis reveals functional connectivity changes between normal/tumoral prostates

<p>Abstract</p> <p>Background</p> <p>Prostate cancer is a leading cause of death in the male population, therefore, a comprehensive study about the genes and the molecular networks involved in the tumoral prostate process becomes necessary. In order to understand the biological process behind potential biomarkers, we have analyzed a set of 57 cDNA microarrays containing ~25,000 genes.</p> <p>Results</p> <p>Principal Component Analysis (PCA) combined with the Maximum-entropy Linear Discriminant Analysis (MLDA) were applied in order to identify genes with the most discriminative information between normal and tumoral prostatic tissues. Data analysis was carried out using three different approaches, namely: (i) differences in gene expression levels between normal and tumoral conditions from an univariate point of view; (ii) in a multivariate fashion using MLDA; and (iii) with a dependence network approach. Our results show that malignant transformation in the prostatic tissue is more related to functional connectivity changes in their dependence networks than to differential gene expression. The MYLK, KLK2, KLK3, HAN11, LTF, CSRP1 and TGM4 genes presented significant changes in their functional connectivity between normal and tumoral conditions and were also classified as the top seven most informative genes for the prostate cancer genesis process by our discriminant analysis. Moreover, among the identified genes we found classically known biomarkers and genes which are closely related to tumoral prostate, such as KLK3 and KLK2 and several other potential ones.</p> <p>Conclusion</p> <p>We have demonstrated that changes in functional connectivity may be implicit in the biological process which renders some genes more informative to discriminate between normal and tumoral conditions. Using the proposed method, namely, MLDA, in order to analyze the multivariate characteristic of genes, it was possible to capture the changes in dependence networks which are related to cell transformation.</p

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

Identification of a novel SEREX antigen family, ECSA, in esophageal squamous cell carcinoma

<p>Abstract</p> <p>Background</p> <p>Diagnosis of esophageal squamous cell carcinoma (SCC) may improve with early diagnosis. Currently it is difficult to diagnose SCC in the early stage because there is a limited number of tumor markers available.</p> <p>Results</p> <p>Fifty-two esophageal SCC SEREX antigens were identified by SEREX (serological identification of antigens by recombinant cDNA expression cloning) using a cDNA phage library and sera of patients with esophageal SCC. Sequence analysis revealed that three of these antigens were similar in amino acid sequences, and they were designated as ECSA (esophageal carcinoma SEREX antigen)-1, -2 and -3. The ECSA family was also similar to an EST clone, hepatocellular carcinoma-associated antigen 25a (HCA25a). Serum antibody levels to ECSA-1, -2 and -3 were significantly higher in patients with esophageal SCC than in healthy donors. Based on the conserved amino acid sequences, three peptides were synthesized and used for enzyme-linked immunosorbent assays (ELISA). The serum antibody levels against one of these peptides were significantly higher in patients with esophageal SCC. This peptide sequence was also conserved in FAM119A, GOSR1 and BBS5, suggesting that these are also ECSA family members. Reverse transcription followed by quantitative PCR analysis showed that the mRNA expression levels of ECSA-1, -2 and -3 and FAM119A but not of HCA25a, GOSR1 and BBS5 were frequently elevated in esophageal SCC tissues.</p> <p>Conclusions</p> <p>We have identified a new gene family designated ECSA. Serum antibodies against the conserved domain of the ECSA family may be a promising tumor marker for esophageal SCC.</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