Biomedical subjects

Alessandro Cestaro

Publications and source records attributed to Alessandro Cestaro.

4 recordsLinked to original sources

PubMed2006

Computational reconstruction of the human skeletal muscle secretome.

In multicellular organisms, secreted proteins play pivotal regulatory roles in intercellular communication. Proteins secreted by skeletal muscle can act locally on muscle cells through autocrine/paracrine loops and on surrounding tissues such as muscle blood vessels, or they can be released into the blood stream, thus producing systemic effects. By a computational approach, we have screened 6255 products of genes expressed in normal human skeletal muscle. Putatively secreted proteins were identified by sequential steps of sieving, through prediction of signal peptide, recognition of transmembrane regions, and analysis of protein annotation. The resulting putative skeletal muscle secretome consists of 319 proteins, including 78 still uncharacterized proteins. This is the first human skeletal muscle secretome produced by computational analysis. Knowledge of proteins secreted by skeletal muscle could stimulate development of novel treatments for different diseases, including muscle atrophy and dystrophy. In addition, better knowledge of the secretion process in skeletal muscle can be useful for future gene therapy approaches.

Animals↗

PubMed2005

Structural insights into the function of human caveolin 1.

Caveolin-1 (Cav-1) is emerging as the central protein controlling caveolae formation, caveolae trafficking, and cellular signalling. In particular, it is known that Cav-1 interacts and modulates the activity of several signalling proteins through the so-called caveolin scaffolding domain. In this paper, we used a bioinformatics approach to assess the validity of some long-standing structural features of Cav-1. We could confirm the existence of a membrane spanning region of Cav-1 and highlight an interesting pattern of palmitoylated cysteine residues explaining the structural features of the Cav-1 C-terminal region. Moreover, the scaffolding domain is predicted to have a different structure than previously reported.

Amino Acid Sequence↗

PubMed2005

Laterally transferred elements and high pressure adaptation in Photobacterium profundum strains.

BACKGROUND: Oceans cover approximately 70% of the Earth's surface with an average depth of 3800 m and a pressure of 38 MPa, thus a large part of the biosphere is occupied by high pressure environments. Piezophilic (pressure-loving) organisms are adapted to deep-sea life and grow optimally at pressures higher than 0.1 MPa. To better understand high pressure adaptation from a genomic point of view three different Photobacterium profundum strains were compared. Using the sequenced piezophile P. profundum strain SS9 as a reference, microarray technology was used to identify the genomic regions missing in two other strains: a pressure adapted strain (named DSJ4) and a pressure-sensitive strain (named 3TCK). Finally, the transcriptome of SS9 grown under different pressure (28 MPa; 45 MPa) and temperature (4 degrees C; 16 degrees C) conditions was analyzed taking into consideration the differentially expressed genes belonging to the flexible gene pool. RESULTS: These studies indicated the presence of a large flexible gene pool in SS9 characterized by various horizontally acquired elements. This was verified by extensive analysis of GC content, codon usage and genomic signature of the SS9 genome. 171 open reading frames (ORFs) were found to be specifically absent or highly divergent in the piezosensitive strain, but present in the two piezophilic strains. Among these genes, six were found to also be up-regulated by high pressure. CONCLUSION: These data provide information on horizontal gene flow in the deep sea, provide additional details of P. profundum genome expression patterns and suggest genes which could perform critical functions for abyssal survival, including perhaps high pressure growth.

Atmospheric Pressure↗

PubMed2003

MANIFOLD: protein fold recognition based on secondary structure, sequence similarity and enzyme classification.

We present a protein fold recognition method, MANIFOLD, which uses the similarity between target and template proteins in predicted secondary structure, sequence and enzyme code to predict the fold of the target protein. We developed a non-linear ranking scheme in order to combine the scores of the three different similarity measures used. For a difficult test set of proteins with very little sequence similarity, the program predicts the fold class correctly in 34% of cases. This is an over twofold increase in accuracy compared with sequence-based methods such as PSI-BLAST or GenTHREADER, which score 13-14% correct first hits for the same test set. The functional similarity term increases the prediction accuracy by up to 3% compared with using the combination of secondary structure similarity and PSI-BLAST alone. We argue that using functional and secondary structure information can increase the fold recognition beyond sequence similarity.

Algorithms↗