Molecular characterization of short-term primary cultures and comparison with corresponding tumor tissue of Brazilian glioblastoma patients

Background: Glioblastoma, the most frequent and malignant adult brain tumor, has been extensively studied. However, there is no effective treatment, and to overcome this challenging scenario, it is essential to improve preclinical biological models. This study aimed to molecularly characterize short-term glioblastoma primary cultures and to compare them with patient tumor profiles. Methods: Glioblastoma cell lines were established from Barretos Cancer Hospital patients diagnosed with glioblastoma. The cells were cultured with DMEM (+)10% FBS (+)1% PS and were molecularly characterized using array CGH (aCGH), next-generation and Sanger sequencing. Results: We established four short-term glioblastoma cultures and we found that the primary cells exhibited a diversity of chromosomal aberrations, with gain of chromosome 7 and loss of chromosomes 10, 13 and 17p being the most frequent alterations. Mutation profiling showed that hotspot TERT promoter mutations were present in 3/4 cases, followed by mutations in TP53 (2/4) and in the RB1, BRAF and PTEN (1/4) genes. A similar chromosomal and mutation pattern was observed in all short-term cultures and matched frozen tumors. Conclusions: Herein, short-term glioblastoma primary cultures were successfully characterized and had genetic make-ups that were similar to those of patient tumors, suggesting that short-term primary cultures are suitable in vitro models for studies of glioblastoma biology.Universal/CNPq (475358/2011-2-Reis RM), FAPESP (2012/19590-0-Reis RM) and the MCTI/CNPq No. 73/2013 (Reis RM) grants. Bidinotto LT was a recipient of the FAPESP fellowship (2011/08523-7 and 2012/08287-4)info:eu-repo/semantics/publishedVersio

Copy number profiling of Brazilian astrocytomas

Copy number alterations (CNA) are one of the driving mechanisms of glioma tumorigenesis, and are currently used as important biomarkers in the routine setting. Therefore, we performed CNA profiling of 65 astrocytomas of distinct malignant grades (WHO grade I-IV) of Brazilian origin, using array-CGH and microsatellite instability analysis (MSI), and investigated their correlation with TERT and IDH1 mutational status and clinico-pathological features. Furthermore, in silico analysis using the Oncomine database was performed to validate our findings and extend the findings to gene expression level. We found that the number of genomic alterations increases in accordance with glioma grade. In glioblastomas (GBM), the most common alterations were gene amplifications (PDGFRA, KIT, KDR, EGFR, and MET) and deletions (CDKN2A and PTEN). Log-rank analysis correlated EGFR amplification and/or chr7 gain with better survival of the patients. MSI was observed in 11% of GBMs. A total of 69% of GBMs presented TERT mutation, whereas IDH1 mutation was most frequent in diffuse (85.7%) and anaplastic (100%) astrocytomas. The combination of 1p19q deletion and TERT and IDH1 mutational status separated tumor groups that showed distinct age of diagnosis and outcome. In silico validation pointed to less explored genes that may be worthy of future investigation, such as CDK2, DMRTA1, and MTAP. Herein, using an extensive integrated analysis, we indicated potentially important genes, not extensively studied in gliomas, that could be further explored to assess their biological and clinical impact in astrocytomas.This study was partially supported by the Universal/National Counsel of Technological and Scientific Development (CNPq) (475358/2011-2 – R.M.R.), São Paulo Research Foundation (FAPESP) (2012/19590-0 and 2016/09105-8 – R.M.R.) and the Fundação para a Ciência e a Tecnologia (FCT) (PTDC/SAU-ONC/115513/2009-FCMO-01-0124FEDER-015949). L.T.B. was recipient of FAPESP fellowships (2011/ 08523-7 and 2012/08287-4), N.C.C.was recipient of a FAPESP fellowship (2013/25787-3), M.L.S. was recipient of a CNPq/Programa Institucional de Bolsas de Iniciação Científica (PIBIC) fellowship (100707/ 2014-9), W.M. was recipient of FAPESP (2013/15515-6) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)/ Programa de Suporte à Pós-Graduação de Instituições de Ensino Particulares (Prosup) fellowships, and M.V.P. was a Postdoctoral research fellow under the FCT project PTDC/SAU-ONC/115513/2009. R.M.R. has a CNPq scholarship. C.J. and A.M. acknowledge National Health Service funding to the National Institute for Health Research Biomedical Research Centre at The Royal Marsden and the Institute of Cancer Research.info:eu-repo/semantics/publishedVersio

Novel Primate-Specific Genes, RMEL 1, 2 and 3, with Highly Restricted Expression in Melanoma, Assessed by New Data Mining Tool

Melanoma is a highly aggressive and therapy resistant tumor for which the identification of specific markers and therapeutic targets is highly desirable. We describe here the development and use of a bioinformatic pipeline tool, made publicly available under the name of EST2TSE, for the in silico detection of candidate genes with tissue-specific expression. Using this tool we mined the human EST (Expressed Sequence Tag) database for sequences derived exclusively from melanoma. We found 29 UniGene clusters of multiple ESTs with the potential to predict novel genes with melanoma-specific expression. Using a diverse panel of human tissues and cell lines, we validated the expression of a subset of three previously uncharacterized genes (clusters Hs.295012, Hs.518391, and Hs.559350) to be highly restricted to melanoma/melanocytes and named them RMEL1, 2 and 3, respectively. Expression analysis in nevi, primary melanomas, and metastatic melanomas revealed RMEL1 as a novel melanocytic lineage-specific gene up-regulated during melanoma development. RMEL2 expression was restricted to melanoma tissues and glioblastoma. RMEL3 showed strong up-regulation in nevi and was lost in metastatic tumors. Interestingly, we found correlations of RMEL2 and RMEL3 expression with improved patient outcome, suggesting tumor and/or metastasis suppressor functions for these genes. The three genes are composed of multiple exons and map to 2q12.2, 1q25.3, and 5q11.2, respectively. They are well conserved throughout primates, but not other genomes, and were predicted as having no coding potential, although primate-conserved and human-specific short ORFs could be found. Hairpin RNA secondary structures were also predicted. Concluding, this work offers new melanoma-specific genes for future validation as prognostic markers or as targets for the development of therapeutic strategies to treat melanoma

Molecular profiling of a rare rosette-forming glioneuronal tumor arising in the spinal cord

Rosette-forming glioneuronal tumor (RGNT) of the IV ventricle is a rare and recently recognized brain tumor entity. It is histologically composed by two distinct features: a glial component, resembling pilocytic astrocytoma, and a component forming neurocytic rosettes and/or perivascular rosettes. Herein, we describe a 33-year-old man with RGNT arising in the spinal cord. Following an immunohistochemistry validation, we further performed an extensive genomic analysis, using array-CGH (aCGH), whole exome and cancer-related hotspot sequencing, in order to better understand its underlying biology. We observed the loss of 1p and gain of 1q, as well as gain of the whole chromosomes 7, 9 and 16. Local amplifications in 9q34.2 and 19p13.3 (encompassing the gene SBNO2) were identified. Moreover, we observed focal gains/losses in several chromosomes. Additionally, on chromosome 7, we identified the presence of the KIAA1549:BRAF gene fusion, which was further validated by RT-PCR and FISH. Across all mutational analyses, we detected and validated the somatic mutations of the genes MLL2, CNNM3, PCDHGC4 and SCN1A. Our comprehensive molecular profiling of this RGNT suggests that MAPK pathway and methylome changes, driven by KIAA1549:BRAF fusion and MLL2 mutation, respectively, could be associated with the development of this rare tumor entity.Conselho Nacional de Desenvolvimento Científico e Tecnológico [475358/2011-2] to RMR (www.cnpq.br); Fundação de Amparo a Pesquisa do Estado de São Paulo [2012/19590-0] to RMR and [2011/08523-7 and 2012/08287-4] to LTB (www.fapesp.br); the Foundation for Science and Technology (FCT) [PTDC/SAU-ONC/115513/2009] to RMR; and the National Cancer Institute [P30CA046934] to MG

Caracterization and identification proteins of computational of the type IUP in predicted proteoma of the Schistosoma mansoni

Submitted by Repositório Arca ([email protected]) on 2019-05-07T13:26:33Z No. of bitstreams: 2 license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) raul.pdf: 2198774 bytes, checksum: d39ccdbe5e7b0f2da30374cde4961751 (MD5)Approved for entry into archive by Nuzia Santos ([email protected]) on 2019-07-31T13:54:38Z (GMT) No. of bitstreams: 2 raul.pdf: 2198774 bytes, checksum: d39ccdbe5e7b0f2da30374cde4961751 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5)Made available in DSpace on 2019-07-31T13:54:38Z (GMT). No. of bitstreams: 2 raul.pdf: 2198774 bytes, checksum: d39ccdbe5e7b0f2da30374cde4961751 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2010Fundação Oswaldo Cruz. Centro de Pesquisa Rene Rachou. Belo Horizonte, MG, Brasil.A relação entre estrutura e função protéica é um dos conceitos mais bem estabelecidos da biologia molecular. O acúmulo de evidências experimentais, cujos primeiros trabalhos datam de 1890, suportam essa hipótese com grande embasamento científico. Apesar da existência de evidências de mais de um século de estudos, somente no inicio da década de 90 começaram a surgir trabalhos mostrando de forma conclusiva a existência de proteínas funcionalmente ativas, mas incapazes de manter uma conformação estável em condições fisiológicas. Tais proteínas, hoje conhecidas como IUPs (do inglês Intrinsically Unstructured Proteins) estão envolvidas em importantes processos de saúde e doenças, tais como o câncer e diversos processos de interação parasito/hospedeiro. A presente dissertação tem como proposta o estabelecimento de um pipeline computacional visando à avaliação dos diferentes algoritmos de predição de desordem estrutural, seu desempenho e a posterior aplicação dessa ferramenta no estudo in silico do conteúdo de IUPs presentes no proteoma predito de Schistosoma mansoni. Complementarmente, foi desenhado um banco de dados MySQL capaz de albergar toda a informação de desordem estrutural juntamente com diferentes dados de caracterização das IUPs para S. mansoni. Foram analisados um total de 10.417 proteínas, 7.373 predições de desordem estrutural, mais de 24.600 predições de características estruturais e funcionais, desenvolvidos 21 scripts, e todas essas predições e scripts desenvolvidos foram integrados em um pipeline totalmente automático e inédito para análise de desordem estrutural. Nossas análises de sensibilidade e especificidade implementadas pela análise de gráficos ROC e pela integração de resultados utilizando bancos de dados relacionais indicam que a predição integrativa (consenso de quatro diferentes metodologias de predição) de desordem estrutural apresenta um ganho de 40% na correta identificação de regiões desordenadas se comparada às predições de cada metodologia individualmente. Aproximadamente 5,5% das regiões desordenadas identificadas tiveram suas coordenadas limítrofes ajustadas após comparação com as coordenadas de domínios conservados. Nossos resultados indicam que aproximadamente 33,6% do proteoma predito de S. mansoni apresenta desordem estrutural. Destas, 2% apresentam domínios transmembrana e 7% apresentam peptídeo sinal. A comparação do perfil funcional das IUPs com as proteínas globulares de S. mansoni demonstra uma maior proporção de IUPs envolvidas em processos de regulação celular e componentes extracelulares.The relationship between protein structure and function is one of the more well-established concepts of molecular biology. The accumulation of experimental evidence, dating from 1890, put this hypothesis on a strong scientific base. Despite the evidence of more than half a century of studies, only in the early 90's began to surface studies showing conclusively the existence of functionally active proteins, but unable to maintain a stable conformation under physiological conditions. These proteins, today known as IUPs (Intrinsically Unstructured Proteins) are involved in important processes in health and diseases such as cancer and various processes of host and parasite interaction. This work is a proposal to establish a computational pipeline in order to evaluate different algorithms for prediction of structural disorder, their performance and the posterior application of this tool in the in silico study of the content of IUPs present in the S. mansoni predicted proteome. In addition, a MySQL database was developed to store all the information of structural disorder together with different data of IUPs characterization for S. mansoni. We analyzed a total of 10,417 proteins, 7,373 predictions of structural disorder, more than 24,600 predictions of structural and functional characteristics, developed 21 scripts, and all these predictions and scripts were integrated in an original and totally automatic pipeline for analysis of structural disorder. Our analysis of sensitivity and specificity implemented by the analysis of ROC graphics and by the integration of results using relational databases, indicate that the integrative prediction (consensus of four different methods of prediction) of structural disorder shows an increase of 50% in correctly identifying disordered regions compared to the predictions of each single method. Approximately 5.5% of identified disordered regions had their boundaries coordinates adjusted after comparison with conserved domain coordinates. Our results indicate that approximately 33.6% of the predicted proteome of S. mansoni presents structural disorder. 2% of these, have at least one transmembrane domain and 7% had signal peptide. The comparison of functional profile of IUPs with globular proteins of S. mansoni shows the biggest proportion of IUPs are involved in process of cellular regulation and extracellular components

Automatic Assignment of Prokaryotic Genes to Functional Categories Using Literature Profiling

<div><p>In the last years, there was an exponential increase in the number of publicly available genomes. Once finished, most genome projects lack financial support to review annotations. A few of these gene annotations are based on a combination of bioinformatics evidence, however, in most cases, annotations are based solely on sequence similarity to a previously known gene, which was most probably annotated in the same way. As a result, a large number of predicted genes remain unassigned to any functional category despite the fact that there is enough evidence in the literature to predict their function. We developed a classifier trained with term-frequency vectors automatically disclosed from text <em>corpora</em> of an ensemble of genes representative of each functional category of the J. Craig Venter Institute Comprehensive Microbial Resource (JCVI-CMR) ontology. The classifier achieved up to 84% precision with 68% recall (for confidence≥0.4), F-measure 0.76 (recall and precision equally weighted) in an independent set of 2,220 genes, from 13 bacterial species, previously classified by JCVI-CMR into unambiguous categories of its ontology. Finally, the classifier assigned (confidence≥0.7) to functional categories a total of 5,235 out of the ∼24 thousand genes previously in categories “Unknown function” or “Unclassified” for which there is literature in MEDLINE. Two biologists reviewed the literature of 100 of these genes, randomly picket, and assigned them to the same functional categories predicted by the automatic classifier. Our results confirmed the hypothesis that it is possible to confidently assign genes of a real world repository to functional categories, based exclusively on the automatic profiling of its associated literature. The LitProf - Gene Classifier web server is accessible at: <a href="http://www.cebio.org/litprofGC">www.cebio.org/litprofGC</a>.</p> </div

Examples of genes classified by LitProf- Gene Classifier and further validated by manually reviewing their literature.

*<p>The GO terms from Biological Process, Molecular Function and Cellular Component ontologies associated with each gene in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047436#pone-0047436-t004" target="_blank">table 4</a> (or, in most cases, their prokaryotic orthologs) were retrieved from AmiGO (<a href="http://amigo.geneontology.org" target="_blank">http://amigo.geneontology.org</a>) by querying the database with their canonical gene names. In most cases the GO terms retrieved supported the functional categorization predicted by LitProf – Gene Classifier, although there is not an exact correspondence between GO and JCVI-CMR ontologies. Six gene names out 16 tested had no match in AmiGO.</p

Recall vs. precision of the classifier.

<p>The red line represents the average performance of the initial classifier trained with the original categories of the JCVI-CMR ontology. The blue line, represents the average performance of the final classifier trained with a rearranged version of the ontology where noisy subcategories were merged together to create the Mix Category. For red and blue lines, the average was calculated from 100 replicates of 10-fold cross validation. The green line represents the performance of the final classifier in an independent gene set. Horizontal bars represent the standard deviations of recall. The dashed lines represent the standard deviation of precision for the blue curve.</p

Taxonomy distribution of genes in the training dataset.

<p>Taxonomy distribution of genes in the training dataset.</p

Gene distribution in the functional categories of the JCVI-CMR ontology.

<p>Only categories used to train the classifier are shown. Mix category regroups the noisy subcategories. The original column refers to the complete J. Craig Venter Institute Comprehensive Microbial Resource (JCVI-CMR). The training column refers to the dataset used to train the classifier. The classified column refers to the “Unknown function” and “Unclassified” genes that were classified by LitProf- Gene Classifier with confidence≥0.7. There is no significant difference between the original and training datasets (p>0.05 in paired t-test; confidence level of 95%).</p