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Three macrophage migration inhibitory factor (MIF)-related sequences were identified from a Mytilus galloprovincialis EST library. The consensus sequence included a 5'-UTR of 32 nucleotides, the complete ORF of 345 nucleotides, and a 3'-UTR of 349 nucleotides. As for other MIFs, M. galloprovincialis ORF does not include any signal or C-terminus extensions. The translated sequence of 115 amino acids possesses a molecular mass of 12,681.4, a pI of 6.27 and a stability index of 21.48. Its 3D structure resembles human MIF except for one shorter α-helix. Although evolutionary separated from ticks and vertebrates, Mg-MIF appeared to be closely related to Pinctada fucata and Haliotis, but not to Chlamys farreri and Biomphalaria glabrata. Numerous mutation points were observed within the Mg-MIF ORF, defining 11 amino acid variants within the mussels from Palavas-France and 14 amino acid variants within the mussels from Palermo-Italy. The 2 major variants from Palavas were identical to 2 of the 4 major variants from Palermo. In all the 18 Mg-MIF variants, residues involved in tautomerase and in oxidoreductase activities were conserved. Generally, one mussel expressed 2 Mg-MIF amino acid sequences but with different frequencies of occurrence. Mg-MIF is constitutively expressed principally in hemocytes and in the mantle. In contrast to other animal models, Mg-MIF expression was always down regulated following challenge by bacteria and fungi, confirming previous data obtained with microarray. Down regulation started as soon as 1 h and Mg-MIF expression returned to background 9-48 h after the challenge. Exception was regarding the yeast, Candidaalbicans, down-regulation between 9 and 72 h, suggesting yeast and bacteria-filamentous fungi trigger different mechanisms of elimination.
Pubmed ID: 22085783
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Web application for prediction of the presence and location of signal peptide cleavage sites in amino acid sequences from different organisms. The method incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of several artificial neural networks.
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