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Eduardo M Reis

Publications and source records attributed to Eduardo M Reis.

9 recordsLinked to original sources

Evaluation of reference-based two-color methods for measurement of gene expression ratios using spotted cDNA microarrays.

BACKGROUND: Spotted cDNA microarrays generally employ co-hybridization of fluorescently-labeled RNA targets to produce gene expression ratios for subsequent analysis. Direct comparison of two RNA samples in the same microarray provides the highest level of accuracy; however, due to the number of combinatorial pair-wise comparisons, the direct method is impractical for studies including large number of individual samples (e.g., tumor classification studies). For such studies, indirect comparisons using a common reference standard have been the preferred method. Here we evaluated the precision and accuracy of reconstructed ratios from three indirect methods relative to ratios obtained from direct hybridizations, herein considered as the gold-standard. RESULTS: We performed hybridizations using a fixed amount of Cy3-labeled reference oligonucleotide (RefOligo) against distinct Cy5-labeled targets from prostate, breast and kidney tumor samples. Reconstructed ratios between all tissue pairs were derived from ratios between each tissue sample and RefOligo. Reconstructed ratios were compared to (i) ratios obtained in parallel from direct pair-wise hybridizations of tissue samples, and to (ii) reconstructed ratios derived from hybridization of each tissue against a reference RNA pool (RefPool). To evaluate the effect of the external references, reconstructed ratios were also calculated directly from intensity values of single-channel (One-Color) measurements derived from tissue sample data collected in the RefOligo experiments. We show that the average coefficient of variation of ratios between intra- and inter-slide replicates derived from RefOligo, RefPool and One-Color were similar and 2 to 4-fold higher than ratios obtained in direct hybridizations. Correlation coefficients calculated for all three tissue comparisons were also similar. In addition, the performance of all indirect methods in terms of their robustness to identify genes deemed as differentially expressed based on direct hybridizations, as well as false-positive and false-negative rates, were found to be comparable. CONCLUSION: RefOligo produces ratios as precise and accurate as ratios reconstructed from a RNA pool, thus representing a reliable alternative in reference-based hybridization experiments. In addition, One-Color measurements alone can reconstruct expression ratios without loss in precision or accuracy. We conclude that both methods are adequate options in large-scale projects where the amount of a common reference RNA pool is usually restrictive.

Adenocarcinoma↗

Large-scale transcriptome analyses reveal new genetic marker candidates of head, neck, and thyroid cancer.

A detailed genome mapping analysis of 213,636 expressed sequence tags (EST) derived from nontumor and tumor tissues of the oral cavity, larynx, pharynx, and thyroid was done. Transcripts matching known human genes were identified; potential new splice variants were flagged and subjected to manual curation, pointing to 788 putatively new alternative splicing isoforms, the majority (75%) being insertion events. A subset of 34 new splicing isoforms (5% of 788 events) was selected and 23 (68%) were confirmed by reverse transcription-PCR and DNA sequencing. Putative new genes were revealed, including six transcripts mapped to well-studied chromosomes such as 22, as well as transcripts that mapped to 253 intergenic regions. In addition, 2,251 noncoding intronic RNAs, eventually involved in transcriptional regulation, were found. A set of 250 candidate markers for loss of heterozygosis or gene amplification was selected by identifying transcripts that mapped to genomic regions previously known to be frequently amplified or deleted in head, neck, and thyroid tumors. Three of these markers were evaluated by quantitative reverse transcription-PCR in an independent set of individual samples. Along with detailed clinical data about tumor origin, the information reported here is now publicly available on a dedicated Web site as a resource for further biological investigation. This first in silico reconstruction of the head, neck, and thyroid transcriptomes points to a wealth of new candidate markers that can be used for future studies on the molecular basis of these tumors. Similar analysis is warranted for a number of other tumors for which large EST data sets are available.

Alternative Splicing↗

Gene expression arrays in cancer research: methods and applications.

During the last 5 years, the number of papers describing data obtained by microarray technology increased exponentially with about 3000 papers in 2003. Undoubtedly, cancer is by far the disease that received most of the attention as far as the amount of data generated. As array technology is rather new and highly dependent on bioinformatics, mathematics and statistics, a clear understanding of the knowledge and information derived from array-based experiments is not widely appreciated. We shall review herein some of the issues related to the construction of DNA arrays, quantities and heterogeneity of probes and targets, the consequences of the physical characteristics of the probes, data extraction and data analysis as well as the applications of array technology. Our goal is to bring to the general audience, some of the basics of array technology and its possible application in oncology. By discussing some of the basic aspects of the methodology, we hope to stimulate criticism concerning the conclusions proposed by authors, especially in the light of the very low degree of reproducibility already proven when commercially available platforms were compared . Regardless of its pitfalls, it is unquestionable that array technology will have a great impact in the management of cancer and its applications will range from the discovery of new drug targets, new molecular tools for diagnosis and prognosis as well as for a tailored treatment that will take into account the molecular determinants of a given tumor. Hence, we shall also highlight some of the already available and promising applications of array technology on the day-to-day practice of oncology.

Cluster Analysis↗

As antisense RNA gets intronic.

Recent work describing the transcriptional output of the human genome points to the existence of a significant number of non-coding RNA transcripts coming from intronic regions, with a fraction of these being oriented antisense relative to the protein-coding mRNA of the known gene. In this article, we survey the main findings of the large-scale expression analysis projects that led to the identification of antisense intronic messages and which demonstrate their ubiquitous expression in the human genome. We review the current knowledge on long, unspliced, intronic antisense transcripts, a new class of non-coding RNAs, recently described by our group to be correlated with the degree of tumor differentiation in prostate cancer, which we postulate is involved in the fine tuning of gene expression in eukaryotes. Possible mechanisms of antisense intronic transcript biogenesis and function in gene expression regulation are discussed, as is their involvement in human diseases. While there is still no conclusive evidence demonstrating a functional role for these long, intronic antisense messages, the far-reaching implications of their existence for the mechanisms regulating gene expression certainly warrant further experimentation.

Alternative Splicing↗

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< or =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.

Cell Differentiation↗

RASL11A, member of a novel small monomeric GTPase gene family, is down-regulated in prostate tumors.

We performed a genome-wide search for novel loci encoding for Ras-related proteins based on the genome mapping coordinates of the cancer-derived EST dataset at GenBank. Partial sequences from two novel human genes were identified and subsequently used for full length transcript cloning. RASL11A and ARL9 belong to two novel subfamilies coding for small GTPases that we found to be highly conserved among eukaryotes. The Arl9/Arl10 subfamily displays a conserved interswitch toggle that places it evolutionarily closer to the Arf family. Rasl11 proteins are more closely related to the Ras branch of GTPases. All orthologues newly identified here exhibit an Asn residue in place of the highly conserved Thr35 of the G domain, suggesting that the universal switch mechanism of small GTPases may be structurally different in this subfamily. We determined by Northern blot that RASL11A is transcribed in several human tissues and that it is down-regulated in prostate tumors as measured by quantitative real-time PCR. These results highlight a previously uncharacterized subfamily of Ras-related genes that may have a tumor suppressor role in prostate cancer.

ADP-Ribosylation Factors↗

Transcriptome analysis of the acoelomate human parasite Schistosoma mansoni.

Schistosoma mansoni is the primary causative agent of schistosomiasis, which affects 200 million individuals in 74 countries. We generated 163,000 expressed-sequence tags (ESTs) from normalized cDNA libraries from six selected developmental stages of the parasite, resulting in 31,000 assembled sequences and 92% sampling of an estimated 14,000 gene complement. By analyzing automated Gene Ontology assignments, we provide a detailed view of important S. mansoni biological systems, including characterization of metazoa-specific and eukarya-conserved genes. Phylogenetic analysis suggests an early divergence from other metazoa. The data set provides insights into the molecular mechanisms of tissue organization, development, signaling, sexual dimorphism, host interactions and immune evasion and identifies novel proteins to be investigated as vaccine candidates and potential drug targets.

Animals↗

ESTWeb: bioinformatics services for EST sequencing projects.

ESTWeb is an internet based software package designed for uniform data processing and storage for large-scale EST sequencing projects. The package provides for: (a) reception of sequencing chromatograms; (b) sequence processing such as base-calling, vector screening, comparison with public databases; (c) storage of data and analysis in a relational database, (d) generation of a graphical report of individual sequence quality; and (e) issuing of reports with statistics of productivity and redundancy. The software facilitates real-time monitoring and evaluation of EST sequence acquisition progress along an EST sequencing project.

Computational Biology↗

Zerg: a very fast BLAST parser library.

SUMMARY: Zerg is a library of sub-routines that parses the output from all NCBI BLAST programs (Blastn, Blastp, Blastx, Tblastn and Tblastx) and returns the attributes of a BLAST report to the user. It is optimized for speed, being especially useful for large-scale genomic analysis. Benchmark tests show that Zerg is over two orders of magnitude faster than some widely used BLAST parsers. AVAILABILITY: http://bioinfo.iq.usp.br/zerg

Database Management Systems↗