Biomedical subjects

Renée X de Menezes

Publications and source records attributed to Renée X de Menezes.

8 recordsLinked to original sources

PubMed2006

Gene expression profiling highlights defective myogenesis in DMD patients and a possible role for bone morphogenetic protein 4.

Duchenne Muscular Dystrophy (DMD) is characterized by progressive muscle weakness and wasting. Despite the sustained presence of satellite cells in their skeletal muscles, muscle regeneration in DMD patients seems inefficient and unable to compensate for the continuous muscle fiber loss. To find a molecular explanation, we compared the gene expression profiles of myoblasts from healthy individuals and DMD patients during activation and differentiation in culture. DMD cultures showed significant gene expression changes, even before dystrophin is expressed. We found a higher expression level of bone morphogenetic protein 4 (BMP4) in DMD cultures, which we demonstrate to inhibit differentiation into myotubes. In the later stages of differentiation, we observed a significant decline in expression of sarcomeric genes in the absence of dystrophin, probably contributing to sarcomeric instability. These results support the hypothesis that inefficient muscle regeneration is caused by impaired myoblast differentiation and impaired maintenance of the myotubes.

Adolescent↗

PubMed2005

The expression of 70 apoptosis genes in relation to lineage, genetic subtype, cellular drug resistance, and outcome in childhood acute lymphoblastic leukemia.

Childhood acute lymphoblastic leukemia (ALL) consists of various subtypes that respond differently to cytotoxic drugs and therefore have a markedly different clinical outcome. We used microarrays to investigate, in 190 children with ALL at initial diagnosis, whether 70 key apoptosis genes were differentially expressed between leukemic subgroups defined by lineage, genetic subtype, in vitro drug resistance, and clinical outcome. The expression of 44 of 70 genes was significantly different in T-versus B-lineage ALL, 22 genes differed in hyperdiploid versus nonhyperdiploid, 16 in TEL-AML1-positive versus-negative, and 13 in E2A-rearranged versus germ-line B-lineage ALL. Expression of MCL1 and DAPK1 was significantly associated with prednisolone sensitivity, whereas BCL2L13, HRK, and TNF were related to L-asparaginase resistance. BCL2L13 overexpression was also associated with unfavorable clinical outcome (P < .001). Multivariate analysis including known risk factors revealed that BCL2L13 expression was an independent prognostic factor (P = .011). The same trend was observed in a validation group of 92 children with ALL treated on a different protocol at St Jude (P = .051). In conclusion, ALL subtypes have a unique expression pattern of apoptosis genes and our data suggest that selective genes are linked to cellular drug resistance and prognosis in childhood B-lineage ALL.

Apoptosis↗

PubMed2005

Dissecting systems-wide data using mixture models: application to identify affected cellular processes.

BACKGROUND: Functional analysis of data from genome-scale experiments, such as microarrays, requires an extensive selection of differentially expressed genes. Under many conditions, the proportion of differentially expressed genes is considerable, making the selection criteria a balance between the inclusion of false positives and the exclusion of false negatives. RESULTS: We developed an analytical method to determine a p-value threshold from a microarray experiment that is dependent on the quality and design of the data set. To this aim, populations of p-values are modeled as mathematical functions in which the parameters to describe these functions are estimated in an unsupervised manner. The strength of the method is exemplified by its application to a published gene expression data set of sporadic and familial breast tumors with BRCA1 or BRCA2 mutations. CONCLUSION: We present an objective and unsupervised way to set thresholds adapted to the quality and design of the experiment. The resulting mathematical description of the data sets of genome-scale experiments enables a probabilistic approach in systems biology.

Breast Neoplasms↗

PubMed2004

Quantile smoothing of array CGH data.

MOTIVATION: Plots of array Comparative Genomic Hybridization (CGH) data often show special patterns: stretches of constant level (copy number) with sharp jumps between them. There can also be much noise. Classic smoothing algorithms do not work well, because they introduce too much rounding. To remedy this, we introduce a fast and effective smoothing algorithm based on penalized quantile regression. It can compute arbitrary quantile curves, but we concentrate on the median to show the trend and the lower and upper quartile curves showing the spread of the data. Two-fold cross-validation is used for optimizing the weight of the penalties. RESULTS: Simulated data and a published dataset are used to show the capabilities of the method to detect the segments of changed copy numbers in array CGH data.

Algorithms↗

PubMed2004

Genomic profiling by DNA amplification of laser capture microdissected tissues and array CGH.

Comparative genomic hybridization by means of BAC microarrays (array CGH) allows high-resolution profiling of copy-number aberrations in tumor DNA. However, specific genetic lesions associated with small but clinically relevant tumor areas may pass undetected due to intra-tumor heterogeneity and/or the presence of contaminating normal cells. Here, we show that the combination of laser capture microdissection, phi29 DNA polymerase-mediated isothermal genomic DNA amplification, and array CGH allows genomic profiling of very limited numbers of cells. Moreover, by means of simple statistical models, we were able to bypass the exclusion of amplification distortions and variability prone areas, and to detect tumor-specific chromosomal gains and losses. We applied this new combined experimental and analytical approach to the genomic profiling of colorectal adenomatous polyps and demonstrated our ability to accurately detect single copy gains and losses affecting either whole chromosomes or small genomic regions from as little as 2 ng of DNA or 1000 microdissected cells.

Adenomatous Polyps↗

PubMed2004

Gene expression variation between mouse inbred strains.

BACKGROUND: In this study, we investigated the effect of genetic background on expression profiles. We analysed the transcriptome of mouse hindlimb muscle of five frequently used mouse inbred strains using spotted oligonucleotide microarrays. RESULTS: Through ANOVA analysis with a false discovery rate of 10%, we show that 1.4% of the analysed genes is significantly differentially expressed between these mouse strains. Differential expression of several of these genes has been confirmed by quantitative RT-PCR. The number of genes affected by genetic background is approximately ten-fold lower than the number of differentially expressed genes caused by a dystrophic genetic defect. CONCLUSIONS: We conclude that evaluation of the effect of background on gene expression profiles in the tissue under study is an effective and sensible approach when comparing expression patterns in animal models with heterogeneous genetic backgrounds. Genes affected by the genetic background can be excluded in subsequent analyses of the disease-related changes in expression profiles. This is often a more effective strategy than backcrossing and inbreeding to obtain isogenic backgrounds.

Animals↗

PubMed2004

Intensity-based analysis of two-colour microarrays enables efficient and flexible hybridization designs.

In two-colour microarrays, the ratio of signal intensities of two co-hybridized samples is used as a relative measure of gene expression. Ratio-based analysis becomes complicated and inefficient in multi-class comparisons. We therefore investigated the validity of an intensity-based analysis procedure. To this end, two different cRNA targets were hybridized together, separately, with a common reference and in a self-self fashion on spotted 65mer oligonucleotide microarrays. We found that the signal intensity of the cRNA targets was not influenced by the presence of a target labelled in the opposite colour. This indicates that targets do not compete for binding sites on the array, which is essential for intensity-based analysis. It is demonstrated that, for good-quality arrays, the correlation of signal intensity measurements between the different hybridization designs is high (R > 0.9). Furthermore, ratio calculations from ratio- and intensity-based analyses correlated well (R > 0.8). Based on these results, we advocate the use of separate intensities rather than ratios in the analysis of two-colour long-oligonucleotide microarrays. Intensity-based analysis makes microarray experiments more efficient and more flexible: It allows for direct comparisons between all hybridized samples, while circumventing the need for a reference sample that occupies half of the hybridization capacity.

Animals↗

PubMed2004

Microarray data analysis: a hierarchical T-test to handle heteroscedasticity.

The analysis of differential gene expression in microarray experiments requires the development of adequate statistical tools. This article describes a simple statistical method for detecting differential expression between two conditions with a low number of replicates. When comparing two group means using a traditional t-test, gene-specific variance estimates are unstable and can lead to wrong conclusions. We construct a likelihood ratio test while modelling these variances hierarchically across all genes, and express it as a t-test statistic. By borrowing information across genes we can take advantage of their large numbers, and still yield a gene-specific test statistic. We show that this hierarchical t-test is more powerful than its traditional version and generates less false positives in a simulation study, especially with small sample sizes. This approach can be extended to cases where there are more than two groups.

Algorithms↗