Biomedical subjects

Barbara Celegato

Publications and source records attributed to Barbara Celegato.

4 recordsLinked to original sources

PubMed2006

Parallel protein and transcript profiles of FSHD patient muscles correlate to the D4Z4 arrangement and reveal a common impairment of slow to fast fibre differentiation and a general deregulation of MyoD-dependent genes.

Here, we present the first study of a human neuromuscular disorder at transcriptional and proteomic level. Autosomal dominant facio-scapulo-humeral muscular dystrophy (FSHD) is caused by a deletion of an integral number of 3.3-kb KpnI repeats inside the telomeric region D4Z4 at the 4q35 locus. We combined a muscle-specific cDNA microarray platform with a proteomic investigation to analyse muscle biopsies of patients carrying a variable number of KpnI repeats. Unsupervised cluster analysis divides patients into three classes, according to their KpnI repeat number. Expression data reveal a transition from fast-glycolytic to slow-oxidative phenotype in FSHD muscle, which is accompanied by a deficit of proteins involved in response to oxidative stress. Besides, FSHD individuals show a disruption in the MyoD-dependent gene network suggesting a coregulation at transcriptional level during myogenesis. We also discuss the hypothesis that D4Z4 contraction may affect in trans the expression of a set of genes involved in myogenesis, as well as in the regeneration pathway of satellite cells in adult tissue. Muscular wasting could result from the inability of satellite cells to successfully differentiate into mature fibres and from the accumulation of structural damages caused by a reactive oxygen species (ROS) imbalance induced by an increased oxidative metabolism in fibres.

Adolescent↗

PubMed2003

Improved detection of differentially expressed genes in microarray experiments through multiple scanning and image integration.

The variability of results in microarray technology is in part due to the fact that independent scans of a single hybridised microarray give spot images that are not quite the same. To solve this problem and turn it to our advantage, we introduced the approach of multiple scanning and of image integration of microarrays. To this end, we have developed specific software that creates a virtual image that statistically summarises a series of consecutive scans of a microarray. We provide evidence that the use of multiple imaging (i) enhances the detection of differentially expressed genes; (ii) increases the image homogeneity; and (iii) reveals false-positive results such as differentially expressed genes that are detected by a single scan but not confirmed by successive scanning replicates. The increase in the final number of differentially expressed genes detected in a microarray experiment with this approach is remarkable; 50% more for microarrays hybridised with targets labelled by reverse transcriptase, and 200% more for microarrays developed with the tyramide signal amplification (TSA) technique. The results have been confirmed by semi-quantitative RT-PCR tests.

False Negative Reactions↗

PubMed2003

Pattern recognition in gene expression profiling using DNA array: a comparative study of different statistical methods applied to cancer classification.

Large-scale parallel measurements of the expression of many thousands genes are now available with high-density array made with collections of cDNA fragments, or oligonucleotide corresponding to different transcripts. These technologies have been applied to cancer investigations since the availability of such a large number of markers makes DNA array a powerful diagnostic tool for tumour and patient classification. Over the last two years, a series of computational tools have been developed for the analysis of different aspects of gene profiling. Our work tries to compare a series of supervised statistical techniques on the basis of their ability to correctly classify different types of tumours. A simulation approach was initially used to control the huge source of variation among and between patients, and to evaluate the ability of algorithms to classify tumours in relation to different types of experimental variables. Different techniques for reduction of data dimension were then added to the discriminant analysis and compared according to their ability to capture the main genetic information. The simulation results have been tested by applying the selected classification algorithms to two experimental microarray datasets of human cancers, and by measuring the correspondent rates of misclassification. Our analyses identify in these datasets a series of genes principally involved in tumour characterization. The functional role of these discriminant transcripts is discussed.

Algorithms↗

PubMed2002

Gene expression profiling in dysferlinopathies using a dedicated muscle microarray.

We have performed expression profiling to define the molecular changes in dysferlinopathy using a novel dedicated microarray platform made with 3'-end skeletal muscle cDNAs. Eight dysferlinopathy patients, defined by western blot, immunohistochemistry and mutation analysis, were investigated with this technology. In a first experiment RNAs from different limb-girdle muscular dystrophy type 2B patients were pooled and compared with normal muscle RNA to characterize the general transcription pattern of this muscular disorder. Then the expression profiles of patients with different clinical traits were independently obtained and hierarchical clustering was applied to discover patient-specific gene variations. MHC class I genes and genes involved in protein biosynthesis were up-regulated in relation to muscle histopathological features. Conversely, the expression of genes codifying the sarcomeric proteins titin, nebulin and telethonin was down-regulated. Neither calpain-3 nor caveolin, a sarcolemmal protein interacting with dysferlin, was consistently reduced. There was a major up-regulation of proteins interacting with calcium, namely S100 calcium-binding proteins and sarcolipin, a sarcoplasmic calcium regulator.

Adolescent↗