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Aims: Progressive chronic kidney disease represents a dreadful complication of type 2 diabetes mellitus (T2DM). We tested the pattern of use and the renal effects of old glucose-lowering drugs in T2DM patients cared for by Italian general practitioners (GPs). Methods: Data of 2606 T2DM patients were extracted from the databases of GPs, who do not have access to the most recent glucose-lowering drugs in Italy. The rate of kidney function decline was calculated by CKD-EPIcr, based on two consecutive creatinine values. Results: Metformin was used in 55% of cases, either alone or with sulfonylureas/repaglinide, across the whole spectrum of CKD (from 66% in stage G1 to only 8% in G4). Sulfonylurea use peaked at 21–22% in stage G2–G3a, whereas repaglinide use significantly increased from 8% in G1 to 22% in G4. The median rate of CKD decline was − 1.64mL/min/1.73m2 per year; it was higher in G1 (− 3.22 per year) and progressively lower with CKD severity. 826 cases (31.7%) were classified as fast progressors (eGFR decline more negative than − 5mL/min/1.73m2 per year). The risk of fast progressing CKD was associated with increasing BMI, albuminuria, and sulfonylurea use, alone (OR, 1.47; 95% confidence interval, 1.16–1.85), or in association with metformin (OR, 1.40; 95% CI 1.04–1.88). No associations were demonstrated for metformin, cardiovascular and lipid lowering drug use. Conclusion: In the setting of Italian family practice, sulfonylurea use is associated with progressive CKD in patients with T2DM. Metformin, at doses progressively reduced according to CKD stages, as recommended by guidelines, is not associated with fast progression.

Black holes are regions of space-time where gravity becomes so strong to confine everything. Their classical general relativistic description however shows critical aspects when faced with the established quantum nature of matter. Alternative approaches and descriptions, like the horizon quantum mechanics and corpuscular models, have therefore been proposed in order to investigate their quantum structure, and search for new phenomenological signatures.

We review the basic elements of the Minimal Geometric Deformation approach in detail. This method has been successfully used to generate brane-world configurations from general relativistic perfect fluid solutions.

Recent studies have shown that copy number variants (CNVs) are important sources of variability of mammalian genomes. We applied a cross species array comparative genome hybridization (aCGH) experiment using as reference the cattle genome to investigate, for the first time, variability in the sheep and goat genomes derived from copy number variation and identified 431 and 358 CNVs, respectively. A comparison of these results to those obtained in other mammals for similar experiments is reported. The identified CNVs could be important in determining phenotypic and production differences between and within breeds. Further studies will be carried out to evaluate the identified CNVs from both functional and evolutionary points of view.

Signal peptides are short, cleavable, N-terminal peptides (from 15 up to 50 residue long) that are present in most proteins targeted towards the secretory pathway, including proteins that localize in intermediate steps such as the endoplasmic reticulum and the Golgi apparatus. The annotation of signal peptides in proteins is then an important step for characterizing the protein function and localization and for determining the sequence of the mature protein. In the lack of experimental data, prediction methods are useful tools that allow large-scale proteome annotation. We developed SPpred a new predictor of signal peptides based on Grammatical Restrained Hidden Conditional Random Fields, a recently introduced machine-learning tool well suited to solve labeling problems (Fariselli et al., 2009) SPpred is trained on a non-redundant dataset of proteins where the presence of a signal peptide was experimentally validated, and comprising 1495 sequences from Eukaryotes, 417 from Gram- bacteria and 104 from Gram+ bacteria. The prediction performances were evaluated in cross-validation considering the 2016 positive examples and 15,714 non redundant negative examples from Eukaryotes, 1741 from Gram- bacteria and 922 from Gram+ bacteria. For all the three classes SPpred predicts the presence of a signal peptide with a Matthews correlation coefficient equal to 0.87 and an accuracy ranging between 97% and 98 %. The accuracy in predicting the cleavage site ranges from 95% (in Gram+ bacteria) to 97% (in Eukaryotes). Due to the hydrophobic composition of signal peptides, a known problem of most available predictive methods is the fact that they tend to predict as signal peptides many N-terminal transmembrane alpha-helices. In the case of Tppred, the rate of false predictions is limited to the 4.3% of proteins endowed with a transmembrane alpha-helix in the first 50 residues of their sequence.

Laccases are biotechnologically interesting enzymes belonging to the polyphenol oxidases family. They are widely distributed throughout the phylogenetic scale from bacteria to mammals. In fungi the analysis of three-dimensional crystal structures of laccases indicate that ascomycete laccases are processed at their C-termini, at a conserved cleavage site, resulting in the proteolytic removal of C-terminal residues. We have isolated and cloned in expression vectors the cDNAs encoding two laccase isoforms (Ery3 and Ery4) from the basidiomycete Pleurotus eryngii. The Ery3 enzyme expressed in Saccharomyces cerevisiae, is functional, whereas the recombinant Ery4 protein does not show enzymatic activity. In order to explain this evidence, we investigated the relationship between the structure of the C-terminal extension and laccase enzymatic activity. The tasks of the present study were to determine the biological role of laccase C-terminal, and to validate a "molecular engineering" approach for the production of recombinant laccases with novel biochemical properties. Genetically engineered mutant genes were produced from Ery4 by: i) progressive 3'-terminal deletions, ii) point mutations, iii) Ery3/Ery4 chimeras. The mutant genes were expressed in S. cerevisiae and active recombinant laccase isoforms were produced, exhibiting each a different biological behaviour. The correlations between the structural information deriving from both biochemical and bioinformatic analyses shed light on the role of Cterminal region in determining laccase functions. The obtained data also indicated that our approach could represent an efficient method for laccase genetic engineering. To our knowledge, this study has produced the first evidences obtained by biotechnological approach of the involvement of the C-terminal tail in the inactivation/activation process of a basidiomycete laccase.

Motivation: Alternative splicing has been suggested as a key mechanism for increasing the the functional landscape of the human genome. In order to detect structural and functional features of alternative protein variants originated from the same gene, a pipeline for annotating all the alternative splicing variants included in the ASPicDB database has been implemented, by integrating different state of the art tools for similarity search and for the prediction of structural and functional features of a protein starting from its residue sequence. Methods: For each one of the 254,195 protein variants coming from 17,142 human genes a first layer of annotation consists in searching with BLAST for similar sequences annotated in the SwissProt data base (rel. 53.0) or endowed with a resolved three dimensional structure in the PDB data base (rel. Apr 09). Moreover, remote homology searches are performed by mapping on the sequence the structural and functional domains collected in the PFAM database (rel. 23.0). To this aim we adopted the pfam_scan.pl program, downloaded from the PFAM ftp site (ftp://ftp.sanger.ac.uk/pub/databases/Pfam/Tools/). The second layer of annotation results form a decision tree integrating several predictive tools developed by the Bologna Biocomputing Group. As a first step, N-terminal signal peptides and C-terminal GPI-anchor propeptides are predicted with Spep (Fariselli et al., 2003) and PredGPI (Pierleoni et al., 2008), respectively. When present, they are cleaved from the protein sequence and the presence and localization of alpha-helical transmembrane domains is predicted with ENSEMBLE (Martelli et al., 2003). Secondary structure and cysteine bonding state are predicted with SecPred (Jacoboni et al., 2000) and CysPres (Martelli et al., 2004), respectively. The subcellular localization of globular proteins is predicted with BaCelLo (Pierleoni et al., 2006). Results: As a result of the first annotation layer, 228,737 protein variants share similarity with a SwissProt sequence with an E-value lower than 10-5. Out of these proteins, 129,828 share also similarity with a sequence included in the PDB database. In these cases, the transfer of the functional and structural annotations by similarity is feasible, at least for the aligned regions. 350,870 PFAM domains are also mapped onto 159,538 protein variants with an E-value lower than 10-5. The second layer of annotation discriminates 28,991 and 1,679 sequences endowed with signal peptides and GPI-anchor propeptides, respectively. 41,594 variants are predicted as transmembrane and, among globular proteins, BaCelLo classifies 69,251 sequences as nuclear, 90,267 as cytoplasmic, 19,514 as mitochondrial and 31,996 as secreted. The structural and functional annotations of the proteins encoded by the transcript variants were added in the ASPicDB database (Castrignanò et al. 2008) and can be browsed by means of a graphical search interface, that also allows to retrieve all the genes whose splicing variants encode proteins with specific structural or functional properties (e.g. PFAM or TM domain, protein type, etc.) (Fig. 1A) or showing differences in specific features (Fig. 1B) . Availability: The ASPicDB, supplemented with the annotations for the protein coding transcripts, is available at http://www.caspur.it/ASPicDB/ Availability: http://www.caspur.it/ASPicDB/

In this paper we evaluate the performance of machine learning methods in the task of predicting the bonding state of cysteines starting from protein sequences. This task is very important and is the first step for the identification of disulfide bonds in proteins. We score the performance of three different approaches, such as: 1) Hidden Support Vector Machines (HSVM) which integrates the SVM predictions with a Hidden Markov Model; 2) SVM-HMM which discriminatively trains models that are isomorphic to a kth-order hidden Markov model; 3) Grammatical-Restrained Hidden Conditional Random Fields (GRHCRF) that we recently introduced. To evaluate the present (and future) methods we built a new non-redundant dataset. We report the performance using indices based on per-cysteine and per-protein scores. Furthermore, we evaluate two different encoding schemes based on sequence profile and position specific scoring matrix (PSSM) as computed with the PSI-BLAST program. The sequence profile consists of a matrix that reports the different residue frequencies for each sequence position as internally scored by PSI-BLAST program, while the PSSM modulates such frequencies with respect to the reference substitution score matrix (BLOSUM62). The evaluation is carried out with different dimensions of the local cysteine environment and using different Markov models. Our results show that when the evolutionary information is encoded with PSSM all the methods perform better than with sequence profile. Finally, among the different methods it appears that GRHCRFs performs slightly better than the others achieving a per protein accuracy of 87% with a correlation coefficient of 0.73. Considering that our dataset does not contain trivial protein cases (only one cysteine per protein) the accuracy achieved is among the best performing reported so far.