Searching for signatures of cold adaptations in modern and archaic humans: hints from the brown adipose tissue genes.

Adaptation to low temperatures has been reasonably developed in the human species during the colonization of the Eurasian landmass subsequent to Out of Africa migrations of anatomically modern humans. In addition to morphological and cultural changes, also metabolic ones are supposed to have favored human isolation from cold and body heat production and this can be hypothesized also for most Neandertal and at least for some Denisovan populations, which lived in geographical areas that strongly experienced the last glacial period. Modulation of non-shivering thermogenesis, for which adipocytes belonging to the brown adipose tissue are the most specialized cells, might have driven these metabolic adaptations. To perform an exploratory analysis aimed at looking into this hypothesis, variation at 28 genes involved in such functional pathway was investigated in modern populations from different climate zones, as well as in Neandertal and Denisovan genomes. Patterns of variation at the LEPR gene, strongly related to increased heat dissipation by mitochondria, appeared to have been shaped by positive selection in modern East Asians, but not in Europeans. Moreover, a single potentially cold-adapted LEPR allele, different from the supposed adaptive one identified in Homo sapiens, was found also in Neandertal and Denisovan genomes. These findings suggest that independent mechanisms for cold adaptations might have been developed in different non-African human groups, as well as that the evolution of possible enhanced thermal efficiency in Neandertals and in some Denisovan populations has plausibly entailed significant changes also in other functional pathways than in the examined one.

High-throughput SNP discovery in the rabbit (Oryctolagus cuniculus) genome by next-generation semiconductor-based sequencing.

The European rabbit (Oryctolagus cuniculus) is a domesticated species with one of the broadest ranges of economic and scientific applications and fields of investigation. Rabbit genome information and assembly are available (oryCun2.0), but so far few studies have investigated its variability, and massive discovery of polymorphisms has not been published yet for this species. Here, we sequenced two reduced representation libraries (RRLs) to identify single nucleotide polymorphisms (SNPs) in the rabbit genome. Genomic DNA of 10 rabbits belonging to different breeds was pooled and digested with two restriction enzymes (HaeIII and RsaI) to create two RRLs which were sequenced using the Ion Torrent Personal Genome Machine. The two RRLs produced 2 917 879 and 4 046 871 reads, for a total of 280.51 Mb (248.49 Mb with quality >20) and 417.28 Mb (360.89 Mb with quality >20) respectively of sequenced DNA. About 90% and 91% respectively of the obtained reads were mapped on the rabbit genome, covering a total of 15.82% of the oryCun2.0 genome version. The mapping and ad hoc filtering procedures allowed to reliably call 62 491 SNPs. SNPs in a few genomic regions were validated by Sanger sequencing. The Variant Effect Predictor Web tool was used to map SNPs on the current version of the rabbit genome. The obtained results will be useful for many applied and basic research programs for this species and will contribute to the development of cost-effective solutions for high-throughput SNP genotyping in the rabbit.

Exploring copy number variation in the rabbit (Oryctolagus cuniculus) genome by array comparative genome hybridization.

The European rabbit (Oryctolagus cuniculus) is relevant in a large spectrum of fields: it is a livestock, a pet, a biomedical model and a biotechnology tool, a wild resource and a pest. The sequencing of the rabbit genome has opened new perspectives to study this lagomorph at the genome level. We herein investigated for the first time the O. cuniculus genome by array comparative genome hybridization (aCGH) and established a first copy number variation (CNV) genome map in this species comprising 155 copy number variation regions (CNVRs; 95 gains, 59 losses, 1 with both gain and loss) covering ~0.3% of the OryCun2.0 version. About 50% of the 155 CNVRs identified spanned 139 different protein coding genes, 110 genes of which were annotated or partially annotated (including Major Histocompatibility Complex genes) with 277 different gene ontology terms. Many rabbit CNVRs might have a functional relevance that should be further investigated.

Identification and association analysis of several hundred single nucleotide polymorphisms within candidate genes for back fat thickness in Italian Large White pigs using a selective genotyping approach.

Combining different approaches (resequencing of portions of 54 obesity candidate genes, literature mining for pig markers associated with fat deposition or related traits in 77 genes, and in silico mining of porcine expressed sequence tags and other sequences available in databases), we identified and analyzed 736 SNP within candidate genes to identify markers associated with back fat thickness (BFT) in Italian Large White sows. Animals were chosen using a selective genotyping approach according to their EBV for BFT (276 with most negative and 279 with most positive EBV) within a population of ≈ 12,000 pigs. Association analysis between the SNP and BFT has been carried out using the MAX test proposed for case-control studies. The designed assays were successful for 656 SNP: 370 were excluded (low call rate or minor allele frequency <5%), whereas the remaining 286 in 212 genes were taken for subsequent analyses, among which 64 showed a P(nominal) value <0.1. To deal with the multiple testing problem in a candidate gene approach, we applied the proportion of false positives (PFP) method. Thirty-eight SNP were significant (P(PFP) < 0.20). The most significant SNP was the IGF2 intron3-g.3072G>A polymorphism (P(nominal) < 1.0E-50). The second most significant SNP was the MC4R c.1426A>G polymorphism (P(nominal) = 8.0E-05). The third top SNP (P(nominal) = 6.2E-04) was the intronic TBC1D1 g.219G>A polymorphic site, in agreement with our previous results obtained in an independent study. The list of significant markers also included SNP in additional genes (ABHD16A, ABHD5, ACP2, ALMS1, APOA2, ATP1A2, CALR, COL14A1, CTSF, DARS, DECR1, ENPP1, ESR1, GH1, GHRL, GNMT, IKBKB, JAK3, MTTP, NFKBIA, NT5E, PLAT, PPARG, PPP2R5D, PRLR, RRAGD, RFC2, SDHD, SERPINF1, UBE2H, VCAM1, and WAT). Functional relationships between genes were obtained using the Ingenuity Pathway Analysis (IPA) Knowledge Base. The top scoring pathway included 19 genes with a P(nominal) < 0.1, 2 of which (IKBKB and NFKBIA) are involved in the hypothalamic IKKß/NFκB program that could represent a key axis to affect fat deposition traits in pigs. These results represent a starting point to plan marker-assisted selection in Italian Large White nuclei for BFT. Because of similarities between humans and pigs, this study might also provide useful clues to investigate genetic factors affecting human obesity.

A first comparative map of copy number variations in the sheep genome.

We carried out a cross species cattle-sheep array comparative genome hybridization experiment to identify copy number variations (CNVs) in the sheep genome analysing ewes of Italian dairy or dual-purpose breeds (Bagnolese, Comisana, Laticauda, Massese, Sarda, and Valle del Belice) using a tiling oligonucleotide array with ~385,000 probes designed on the bovine genome. We identified 135 CNV regions (CNVRs; 24 reported in more than one animal) covering ~10.5 Mb of the virtual sheep genome referred to the bovine genome (0.398%) with a mean and a median equal to 77.6 and 55.9 kb, respectively. A comparative analysis between the identified sheep CNVRs and those reported in cattle and goat genomes indicated that overlaps between sheep and both other species CNVRs are highly significant (P<0.0001), suggesting that several chromosome regions might contain recurrent interspecies CNVRs. Many sheep CNVRs include genes with important biological functions. Further studies are needed to evaluate their functional relevance.

The prediction of protein-protein interacting sites in genome-wide protein interaction networks: the test case of the human cell cycle.

In this paper we aim at investigating possible correlations between the number of putative interaction patches of a given protein, as inferred by an algorithm that we have developed, and its degree (number of edges of the protein node in a protein interaction network). We focus on the human cell cycle that, as compared with other biological processes, comprises the largest number of proteins whose structure is known at atomic resolution both as monomers and as interacting complexes. For predicting interaction patches we specifically develop a HM-SVM based method reaching 71% overall accuracy with a correlation coefficient value equal to 0.43 on a non redundant set of protein complexes. To test the biological meaning of our predictions, we also explore whether interacting patches contain energetically important residues and/or disease related mutations and find that predicted patches are endowed with both features. Based on this, we propose that mapping the protein with all the predicted interaction patches bridges the molecule to the interactome at the cell level. To test our hypothesis we downloaded interaction data from interaction data bases and find that the number of predicted interaction patches significantly correlates (Pearson correlation value >0.3) with the number of the known interactions (edges) per protein in the human interactome, as contained in MINT and IntAct. We also show that the correlation increases (Pearson correlation value >0.5) when the subcellular co-localization and the co-expression levels of the interacting partners are taken into account.

The effect of backbone on the small-world properties of protein contact maps.

In the last years, small-world behavior has been extensively described for proteins, when they are represented by the undirected graph defined by the inter-residue protein contacts. By adopting this representation it was possible to compute the average clustering coefficient (C) and characteristic path length (L) of protein structures, and their values were found to be similar to those of graphs characterized by small-world topology. In this comment, we analyze a large set of non-redundant protein structures (1753) and show that by randomly mimicking the protein collapse, the covalent structure of the protein chain significantly contributes to the small-world behavior of the inter-residue contact graphs. When protein graphs are generated, imposing constraints similar to those induced by the backbone connectivity, their characteristic path lengths and clustering coefficients are indistinguishable from those computed using the real contact maps showing that L and C values cannot be used for 'protein fingerprinting'. Moreover we verified that these results are independent of the selected protein representations, residue composition and protein secondary structures.

Terlipressin infusion induces ischemia of breast skin in a cirrothic patient with hepatorenal syndrome.

We report a case of a 65-year-old woman with hepatitis C virus-related decompensated cirrhosis with hepatorenal syndrome, treated by high dose of terlipressin. Few hours after the highest dose was started, the patient complained burning pain in breasts, followed by the development of extensive bilateral cyanosis of breast's skin. When terlipressin was immediately stopped, pain and skin cyanosis rapidly disappeared. The peculiarity of our case is that cyanosis did not develop in common peripheral sites (e.g. fingers, toes, etc.) but in an atypical area, as skin of the breasts. Probably, this particular behaviour could be explained by the anatomical position of her large size breasts, that resulting as an extremely sloping and stretching region thus filling the maximum effect of gravity.

Molecular mechanism of thioredoxin regulation in photosynthetic A2B2-glyceraldehyde-3-phosphate dehydrogenase.

Chloroplast glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is a light-regulated, NAD(P)H-dependent enzyme involved in plant photosynthetic carbon reduction. Unlike lower photosynthetic organisms, which only contain A(4)-GAPDH, the major GAPDH isoform of land plants is made up of A and B subunits, the latter containing a C-terminal extension (CTE) with fundamental regulatory functions. Light-activation of AB-GAPDH depends on the redox state of a pair of cysteines of the CTE, which can form a disulfide bond under control of thioredoxin f, leading to specific inhibition of the NADPH-dependent activity. The tridimensional structure of A(2)B(2)-GAPDH from spinach chloroplasts, crystallized in the oxidized state, shows that each disulfide-containing CTE is docked into a deep cleft between a pair of A and B subunits. The structure of the CTE was derived from crystallographic data and computational modeling and confirmed by site-specific mutagenesis. Structural analysis of oxidized A(2)B(2)-GAPDH and chimeric mutant [A+CTE](4)-GAPDH revealed that Arg-77, which is essential for coenzyme specificity and high NADPH-dependent activity, fails to interact with NADP in these kinetically inhibited GAPDH tetramers and is attracted instead by negative residues of oxidized CTE. Other subtle changes in catalytic domains and overall conformation of the tetramers were noticed in oxidized A(2)B(2)-GAPDH and [A+CTE](4)-GAPDH, compared with fully active A(4)-GAPDH. The CTE is envisioned as a redox-sensitive regulatory domain that can force AB-GAPDH into a kinetically inhibited conformation under oxidizing conditions, which also occur during dark inactivation of the enzyme in vivo.

K-Fold: a tool for the prediction of the protein folding kinetic order and rate.

UNLABELLED: K-Fold is a tool for the automatic prediction of the protein folding kinetic order and rate. The tool is based on a support vector machine (SVM) that was trained on a data set of 63 proteins, whose 3D structure and folding mechanism are known from experiments already described in the literature. The method predicts whether a protein of known atomic structure folds according to a two-state or a multi-state kinetics and correctly classifies 81% of the folding mechanisms when tested over the training set of the 63 proteins. It also predicts as a further option the logarithm of the folding rate. To the best of our knowledge, the tool discriminates for the first time whether a protein is characterized by a two state or a multiple state kinetics, during the folding process, and concomitantly estimates also the value of the constant rate of the process. When used to predict the logarithm of the folding rate, K-Fold scores with a correlation value to the experimental data of 0.74 (with a SE of 1.2). AVAILABILITY: http://gpcr.biocomp.unibo.it/cgi/predictors/K-Fold/K-Fold.cgi. SUPPLEMENTARY INFORMATION: http://gpcr.biocomp.unibo.it/~emidio/K-Fold/K-Fold_help.html.

Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information.

MOTIVATION: Human single nucleotide polymorphisms (SNPs) are the most frequent type of genetic variation in human population. One of the most important goals of SNP projects is to understand which human genotype variations are related to Mendelian and complex diseases. Great interest is focused on non-synonymous coding SNPs (nsSNPs) that are responsible of protein single point mutation. nsSNPs can be neutral or disease associated. It is known that the mutation of only one residue in a protein sequence can be related to a number of pathological conditions of dramatic social impact such as Alzheimer's, Parkinson's and Creutzfeldt-Jakob's diseases. The quality and completeness of presently available SNPs databases allows the application of machine learning techniques to predict the insurgence of human diseases due to single point protein mutation starting from the protein sequence. RESULTS: In this paper, we develop a method based on support vector machines (SVMs) that starting from the protein sequence information can predict whether a new phenotype derived from a nsSNP can be related to a genetic disease in humans. Using a dataset of 21 185 single point mutations, 61% of which are disease-related, out of 3587 proteins, we show that our predictor can reach more than 74% accuracy in the specific task of predicting whether a single point mutation can be disease related or not. Our method, although based on less information, outperforms other web-available predictors implementing different approaches. AVAILABILITY: A beta version of the web tool is available at http://gpcr.biocomp.unibo.it/cgi/predictors/PhD-SNP/PhD-SNP.cgi

A novel RGDS-analog inhibits angiogenesis in vitro and in vivo.

In this study the anti-angiogenic action of a novel non-peptide RGDS-analog named RAM was tested in vitro and in vivo. RAM inhibited FGF-2-induced chemotaxis by 80% in an adhesion-independent way. Further, it induced HUVEC-apoptosis in collagen-seeded HUVEC, indicating that such pro-apoptotic effect was adhesion-independent. In vivo studies revealed that RAM inhibited FGF-2 induced angiogenesis by 60% in the mouse Matrigel-assay and in the chicken-egg chorion-allantoic membrane assay. Finally, RAM was markedly more stable in serum as compared to the template RGDS and after 24 h incubation in 100% serum was significantly more active than RGDS. Taken together these results show that RAM exerts anti-chemotactic and pro-apoptotic effects, by an unexpected adhesion-independent mechanism, as we have recently shown for the template RGDS molecule [Blood 103 (2004) 4180], and has in vivo relevant anti-angiogenic properties, with marked stability in serum; therefore, RAM represents a novel promising anti-angiogenic molecule.

Dynamics of the minimally frustrated helices determine the hierarchical folding of small helical proteins.

In this paper we aim at determining the key residues of small helical proteins in order to build up reduced models of the folding dynamics. We start by arguing that the folding process can be dissected into concurrent fast and slow dynamics. The fast events are the quasiautonomous coil-to-helix transitions occurring in the minimally frustrated initiation sites of folding in the early stages of the process. The slow processes consist in the docking of the fluctuating helices formed in these critical sites. We show that a neural network devised to predict native secondary structures from sequence can be used to estimate the probabilities of formation of these helical traits as they are embedded in the protein. The resulting probabilities are shown to correlate well with the experimental helicities measured in the same isolated peptides. The relevance of this finding to the hierarchical character of folding is confirmed within the framework of a diffusion-collision-like mechanism. We demonstrate that thermodynamic and topological features of these critical helices allow accurate estimation of the folding times of five proteins that have been kinetically studied. This suggests that these critical helices determine the fundamental events of the whole folding process. A remarkable feature of our model is that not all of the native helices are eligible as critical helices, whereas the whole set of the native helices has been used so far in other reconstructions of the folding mechanism. This stresses that the minimally frustrated helices of these helical proteins comprise the minimal set of determinants of the folding process.

A neural network approach to evaluate fold recognition results.

Fold recognition techniques assist the exploration of protein structures, and web-based servers are part of the standard set of tools used in the analysis of biochemical problems. Despite their success, current methods are only able to predict the correct fold in a relatively small number of cases. We propose an approach that improves the selection of correct folds from among the results of two methods implemented as web servers (SAMT99 and 3DPSSM). Our approach is based on the training of a system of neural networks with models generated by the servers and a set of associated characteristics such as the quality of the sequence-structure alignment, distribution of sequence features (sequence-conserved positions and apolar residues), and compactness of the resulting models. Our results show that it is possible to detect adequate folds to model 80% of the sequences with a high level of confidence. The improvements achieved by taking into account sequence characteristics open the door to future improvements by directly including such factors in the step of model generation. This approach has been implemented as an automatic system LIBELLULA, available as a public web server at http://www.pdg.cnb.uam.es/servers/libellula.html.

Characterisation of immunodominant protein encoded by the F1L gene of orf virus strains isolated in Italy.

We analysed the molecular properties of the immunodominant protein of different orf virus strains isolated in Italy. The F1L encoding genes and the deduced amino acid sequences of all strains were determined and compared, and they showed several mutations. Structural analysis was carried out in order to assess the influence of amino acid variations on protein structure demonstrating a conservation of the secondary structure. Western blot analysis and immunogold electron microscopy showed that all orf virus strains were antigenically identical. The results of our study confirmed the immunogenicity of the F1L protein; furthermore, our data suggest a possible involvement of the protein in the virus cycle.

Protein structure prediction and biomolecular recognition: from protein sequence to peptidomimetic design with the human beta3 integrin.

Computational tools can bridge the gap between sequence and protein 3D structure based on the notion that information is to be retrieved from the databases and that knowledge-based methods can help in approaching a solution of the protein-folding problem. To this aim our group has implemented neural network-based predictors capable of performing with some success in different tasks, including predictions of the secondary structure of globular and membrane proteins, the topology of membrane proteins and porins and stable alpha-helical segments suited for protein design. Moreover we have developed methods for predicting contact maps in proteins and the probability of finding a cysteine in a disulfide bridge, tools which can contribute to the goal of predicting the 3D structure starting from the sequence (the so called ab initio prediction). All our predictors take advantage of evolution information derived from the structural alignments of homologous (evolutionary related) proteins and taken from the sequence and structure databases. When it is necessary to build models for proteins of unknown spatial structure, which have very little homology with other proteins of known structure, non-standard techniques need to be developed and the tools for protein structure predictions may help in protein modeling. The results of a recent simulation performed in our lab highlights the role of high performing computing technology and of tools of computational biology in protein modeling and peptidomimetic design.

Prediction of contact maps with neural networks and correlated mutations.

Contact maps of proteins are predicted with neural network-based methods, using as input codings of increasing complexity including evolutionary information, sequence conservation, correlated mutations and predicted secondary structures. Neural networks are trained on a data set comprising the contact maps of 173 non-homologous proteins as computed from their well resolved three-dimensional structures. Proteins are selected from the Protein Data Bank database provided that they align with at least 15 similar sequences in the corresponding families. The predictors are trained to learn the association rules between the covalent structure of each protein and its contact map with a standard back propagation algorithm and tested on the same protein set with a cross-validation procedure. Our results indicate that the method can assign protein contacts with an average accuracy of 0.21 and with an improvement over a random predictor of a factor >6, which is higher than that previously obtained with methods only based either on neural networks or on correlated mutations. Furthermore, filtering the network outputs with a procedure based on the residue coordination numbers, the accuracy of predictions increases up to 0.25 for all the proteins, with an 8-fold deviation from a random predictor. These scores are the highest reported so far for predicting protein contact maps.

Prediction of disulfide connectivity in proteins.

MOTIVATION: A major problem in protein structure prediction is the correct location of disulfide bridges in cysteine-rich proteins. In protein-folding prediction, the location of disulfide bridges can strongly reduce the search in the conformational space. Therefore the correct prediction of the disulfide connectivity starting from the protein residue sequence may also help in predicting its 3D structure. RESULTS: In this paper we equate the problem of predicting the disulfide connectivity in proteins to a problem of finding the graph matching with the maximum weight. The graph vertices are the residues of cysteine-forming disulfide bridges, and the weight edges are contact potentials. In order to solve this problem we develop and test different residue contact potentials. The best performing one, based on the Edmonds-Gabow algorithm and Monte-Carlo simulated annealing reaches an accuracy significantly higher than that obtained with a general mean force contact potential. Significantly, in the case of proteins with four disulfide bonds in the structure, the accuracy is 17 times higher than that of a random predictor. The method presented here can be used to locate putative disulfide bridges in protein-folding. AVAILABILITY: The program is available upon request from the authors. CONTACT: Casadio@alma.unibo.it; Piero@biocomp.unibo.it.

Improved prediction of the number of residue contacts in proteins by recurrent neural networks.

Knowing the number of residue contacts in a protein is crucial for deriving constraints useful in modeling protein folding, protein structure, and/or scoring remote homology searches. Here we use an ensemble of bi-directional recurrent neural network architectures and evolutionary information to improve the state-of-the-art in contact prediction using a large corpus of curated data. The ensemble is used to discriminate between two different states of residue contacts, characterized by a contact number higher or lower than the average value of the residue distribution. The ensemble achieves performances ranging from 70.1% to 73.1% depending on the radius adopted to discriminate contacts (6Ato 12A). These performances represent gains of 15% to 20% over the base line statistical predictors always assigning an aminoacid to the most numerous state, 3% to 7% better than any previous method. Combination of different radius predictors further improves the performance. SERVER: http://promoter.ics.uci.edu/BRNN-PRED/.

Model of interaction of the IL-1 receptor accessory protein IL-1RAcP with the IL-1beta/IL-1R(I) complex.

A preliminary model has been calculated for the activating interaction of the interleukin 1 receptor (IL-1R) accessory protein IL-1RAcP with the ligand/receptor complex IL-1beta/IL-1R(I). First, IL-1RAcP was modeled on the crystal structure of IL-1R(I) bound to IL-1beta. Then, the IL-1RAcP model was docked using specific programs to the crystal structure of the IL-1beta/IL-1R(I) complex. Two types of models were predicted, with comparable probability. Experimental data obtained with the use of IL-1beta peptides and antibodies, and with mutated IL-1beta proteins, support the BACK model, in which IL-1RAcP establishes contacts with the back of IL-1R(I) wrapped around IL-1beta.