Pancreatic cancer-derived S-100A8 N-terminal peptide: a diabetes cause?

BACKGROUND: Our aim was to identify the pancreatic cancer diabetogenic peptide. METHODS: Pancreatic tumor samples from patients with (n=15) or without (n=7) diabetes were compared with 6 non-neoplastic pancreas samples using SDS-PAGE. RESULTS: A band measuring approximately 1500 Da was detected in tumors from diabetics, but not in neoplastic samples from non-diabetics or samples from non-neoplastic subjects. Sequence analysis revealed a 14 amino acid peptide (1589.88 Da), corresponding to the N-terminal of the S100A8. At 50 nmol/L and 2 mmol/L, this peptide significantly reduced glucose consumption and lactate production by cultured C(2)C(12) myoblasts. The 14 amino acid peptide caused a lack of myotubular differentiation, the presence of polynucleated cells and caspase-3 activation. CONCLUSIONS: The 14 amino acid peptide from S100A8 impairs the catabolism of glucose by myoblasts in vitro and may cause hyperglycemia in vivo. Its identification in biological fluids might be helpful in diagnosing pancreatic cancer in patients with recent onset diabetes mellitus

Bioinformatics in Italy: BITS2011, the Eighth Annual Meeting of the Italian Society of Bioinformatics

The BITS2011 meeting, held in Pisa on June 20-22, 2011, brought together more than 120 Italian researchers working in the field of Bioinformatics, as well as students in Bioinformatics, Computational Biology, Biology, Computer Sciences, and Engineering, representing a landscape of Italian bioinformatics research

Allogeneic hematopoietic cell transplantation from unrelated donors in multiple myeloma: study from the italian bone marrow donor registry.

AbstractTo evaluate trends in allografting from unrelated donors, we conducted a study on 196 consecutive myeloma patients transplanted between 2000 and 2009 in Italy. Twenty-eight percent, 37%, and 35%, respectively, received myeloablative, reduced-intensity, and nonmyeloablative conditioning. In these 3 cohorts, 1-year and 5-year transplantation-related mortalities were 28.8% and 37.0%, 20.3% and 31.3%, and 25.0% and 30.3%, respectively (P = .745). Median overall survival (OS) and event-free survival from transplantation for the 3 cohorts were 29 and 10 months, 11 and 6 months, and 32 and 13 months, respectively (P = .039 and P = .049). Overall cumulative incidences of acute and chronic graft-versus-host-disease (GVHD) were 46.1% and 51.1%. By Cox multivariate analyses, chronic GVHD was significantly associated with longer OS (hazard ratio [HR], .51; P = .009), whereas the use of peripheral blood stem cells was borderline significant (HR, .55; P = .051). Better response posttransplantation was associated with longer event-free survival (HR, 2.13 to 4.25; P < .001). Acute GVHD was associated with poorer OS (HR, 2.53; P = .001). This analysis showed a strong association of acute and chronic GVHD and depth of response posttransplantation with clinical outcomes. Long-term disease control remains challenging regardless of the conditioning. In the light of these results, prospective trials may be designed to better define the role of allografting from unrelated donors in myeloma

Argot2: a large scale function prediction tool relying on semantic similarity of weighted Gene Ontology terms

Background: Predicting protein function has become increasingly demanding in the era of next generation sequencing technology. The task to assign a curator-reviewed function to every single sequence is impracticable. Bioinformatics tools, easy to use and able to provide automatic and reliable annotations at a genomic scale, are necessary and urgent. In this scenario, the Gene Ontology has provided the means to standardize the annotation classification with a structured vocabulary which can be easily exploited by computational methods.Results: Argot2 is a web-based function prediction tool able to annotate nucleic or protein sequences from small datasets up to entire genomes. It accepts as input a list of sequences in FASTA format, which are processed using BLAST and HMMER searches vs UniProKB and Pfam databases respectively; these sequences are then annotated with GO terms retrieved from the UniProtKB-GOA database and the terms are weighted using the e-values from BLAST and HMMER. The weighted GO terms are processed according to both their semantic similarity relations described by the Gene Ontology and their associated score. The algorithm is based on the original idea developed in a previous tool called Argot. The entire engine has been completely rewritten to improve both accuracy and computational efficiency, thus allowing for the annotation of complete genomes.Conclusions: The revised algorithm has been already employed and successfully tested during in-house genome projects of grape and apple, and has proven to have a high precision and recall in all our benchmark conditions. It has also been successfully compared with Blast2GO, one of the methods most commonly employed for sequence annotation. The server is freely accessible at http://www.medcomp.medicina.unipd.it/Argot2Journal Articleinfo:eu-repo/semantics/publishe