Phenotype forecasting with SNPs data through gene-based Bayesian networks

<p>Abstract</p> <p>Background</p> <p>Bayesian networks are powerful instruments to learn genetic models from association studies data. They are able to derive the existing correlation between genetic markers and phenotypic traits and, at the same time, to find the relationships between the markers themselves. However, learning Bayesian networks is often non-trivial due to the high number of variables to be taken into account in the model with respect to the instances of the dataset. Therefore, it becomes very interesting to use an abstraction of the variable space that suitably reduces its dimensionality without losing information. In this paper we present a new strategy to achieve this goal by mapping the SNPs related to the same gene to one meta-variable. In order to assign states to the meta-variables we employ an approach based on classification trees.</p> <p>Results</p> <p>We applied our approach to data coming from a genome-wide scan on 288 individuals affected by arterial hypertension and 271 nonagenarians without history of hypertension. After pre-processing, we focused on a subset of 24 SNPs. We compared the performance of the proposed approach with the Bayesian network learned with SNPs as variables and with the network learned with haplotypes as meta-variables. The results were obtained by running a hold-out experiment five times. The mean accuracy of the new method was 64.28%, while the mean accuracy of the SNPs network was 58.99% and the mean accuracy of the haplotype network was 54.57%.</p> <p>Conclusion</p> <p>The new approach presented in this paper is able to derive a gene-based predictive model based on SNPs data. Such model is more parsimonious than the one based on single SNPs, while preserving the capability of highlighting predictive SNPs configurations. The prediction performance of this approach was consistently superior to the SNP-based and the haplotype-based one in all the test sets of the evaluation procedure. The method can be then considered as an alternative way to analyze the data coming from association studies.</p

Phenotype forecasting with SNPs data through gene-based Bayesian networks

<p>Abstract</p> <p>Background</p> <p>Bayesian networks are powerful instruments to learn genetic models from association studies data. They are able to derive the existing correlation between genetic markers and phenotypic traits and, at the same time, to find the relationships between the markers themselves. However, learning Bayesian networks is often non-trivial due to the high number of variables to be taken into account in the model with respect to the instances of the dataset. Therefore, it becomes very interesting to use an abstraction of the variable space that suitably reduces its dimensionality without losing information. In this paper we present a new strategy to achieve this goal by mapping the SNPs related to the same gene to one meta-variable. In order to assign states to the meta-variables we employ an approach based on classification trees.</p> <p>Results</p> <p>We applied our approach to data coming from a genome-wide scan on 288 individuals affected by arterial hypertension and 271 nonagenarians without history of hypertension. After pre-processing, we focused on a subset of 24 SNPs. We compared the performance of the proposed approach with the Bayesian network learned with SNPs as variables and with the network learned with haplotypes as meta-variables. The results were obtained by running a hold-out experiment five times. The mean accuracy of the new method was 64.28%, while the mean accuracy of the SNPs network was 58.99% and the mean accuracy of the haplotype network was 54.57%.</p> <p>Conclusion</p> <p>The new approach presented in this paper is able to derive a gene-based predictive model based on SNPs data. Such model is more parsimonious than the one based on single SNPs, while preserving the capability of highlighting predictive SNPs configurations. The prediction performance of this approach was consistently superior to the SNP-based and the haplotype-based one in all the test sets of the evaluation procedure. The method can be then considered as an alternative way to analyze the data coming from association studies.</p

Linear and nonlinear post-processing of numerically forecasted surface temperature

International audienceIn this paper we test different approaches to the statistical post-processing of gridded numerical surface air temperatures (provided by the European Centre for Medium-Range Weather Forecasts) onto the temperature measured at surface weather stations located in the Italian region of Puglia. We consider simple post-processing techniques, like correction for altitude, linear regression from different input parameters and Kalman filtering, as well as a neural network training procedure, stabilised (i.e. driven into the absolute minimum of the error function over the learning set) by means of a Simulated Annealing method. A comparative analysis of the results shows that the performance with neural networks is the best. It is encouraging for systematic use in meteorological forecast-analysis service operations

Reinitiation of protein synthesis in Escherichia coli can be induced by mRNA cis-elements unrelated to canonical translation initiation signals

AbstractIn Eubacteria, de novo translation of some internal cistrons may be inefficient or impossible unless the 5′ neighboring cistron is also translated (translational coupling). Translation reinitiation is an extreme case of translational coupling in which translation of a message depends entirely on the presence of a nearby terminating ribosome. In this work, the characteristics of mRNA cis-elements inducing the reinitiation process in Escherichia coli have been investigated using a combinatorial approach. A number of novel translational reinitiation sequences (TRSs) were thus identified, which show a wide range of reinitiation activities fully dependent on a translational coupling event and unrelated to the presence/absence of secondary structure or mRNA stability. Moreover, some of the isolated TRSs are similar to intercistronic sequences present in the E. coli genome

Genomic organization and single-nucleotide polymorphism map of desmuslin, a novel intermediate filament protein on chromosome 15q26.3

BACKGROUND: Desmuslin is an α-dystrobrevin-interacting protein expressed primarily in heart and skeletal muscle. The desmuslin protein interacts with and is closely related to desmin, a protein encoded by a locus mutated in some forms of hereditary distal myopathy. As a muscle-specific intermediate filament protein, desmuslin is also a candidate for myopathies of unknown etiology. RESULTS: The desmuslin gene was localized to chromosome 15q26.3 by electronic screening of the human DNA sequence database. Primer pairs were designed to amplify the 5 exons of the desmuslin gene in 11 overlapping DNA segments. The desmuslin gene was screened for mutations in 71 patients with various forms of myopathy for which there was no known cause. In this analysis, 10 common and 2 rare amino acid altering single-nucleotide polymorphisms were identified, all of which were seen in a control population of individuals thus making these unlikely causes of the phenotype. Interestingly, one of the single-nucleotide polymorphisms found in a patient resulted in a premature stop codon in the first exon. The nonsense mutation was also detected in the patient's unaffected father and one unaffected control; it was detected in 0.44% (2/454) of unrelated chromosomes and is therefore predicted to have a homozygous frequency of 0.002%. CONCLUSION: No causative mutations were found in the desmuslin gene. However, the single-nucleotide polymorphisms mapped in this study represent a well-mapped group that can be used for disequilibrium studies of this region of chromosome 15q26.3

Interaction of rat liver glucocorticoid receptor with heparin.

When rat liver cytosol containing [3H]dexamethasone-glucocorticoid receptor complex is exposed to immobilized heparin (Sepharose-heparin; Seph-hep) the steroid receptor complex binds to the substituted Sepharose avidly [Kd = 3.5 (+/- 1.7) X 10(-10) M], and 80-90% of the receptor present is adsorbed to the solid phase after 40 min at 0 degree C. The binding is enhanced by Mn2+ (10 mM) and Mg2+, whereas Ca2+ and Sr2+ are ineffective. Sodium molybdate (10 mM) does not influence the reaction but enhances receptor stability. Moreover, binding of the receptor to Seph-hep is dependent on the ionic strength of the medium, because binding is totally reversed by 300 mM KCl. The bound [3H]dexamethasone-receptor complex can be recovered from Seph-hep with solutions (4 mg/mL) of heparin (95% release), dextran sulfate (88%), and chondroitin sulfate (63%); total calf liver RNA is less effective (9%), whereas dextran, D-glucosamine, N-acetyl-D-glucosamine, D-glucuronic acid, and sheared calf thymus DNA are totally ineffective (less than 3%). Both "native" and temperature "transformed" forms of the glucocorticoid receptor interact with immobilized heparin. These results strongly suggest that the receptor site that binds heparin is distinct from that binding DNA. An immediate application of this newly found ability of the glucocorticoid receptor to interact with heparin is the use of Seph-hep for affinity chromatography purification of the glucocorticoid receptor. A purification of 10-fold, with a recovery of 55-65%, can be achieved by using either 4 mg/mL heparin or 300 mM KCl to elute [3H]dexamethasone-receptor bound to the resin

Antimicrobial and antibiofilm activities of new synthesized Silver Ultra-NanoClusters (SUNCs) against Helicobacter pylori

Helicobacter pylori colonizes approximately 50% of the world\u2019s population and it is the cause of chronic gastritis, peptic ulcer disease and gastric cancer. The increase of antibiotic resistance is one of the biggest challenges of our century due to its constant increase. In order to identify an alternative or adjuvant strategy to the standard antibiotic therapy, the in vitro activity of newly synthesized Silver Ultra-NanoClusters (SUNCs), characterized by an average size inferior to 5 nm, against clinical strains of Helicobacter pylori, with different antibiotic susceptibilities, was evaluated in this study. MICs and MBCs were determined by the broth microdilution method, whereas the effect of drug combinations by the checkerboard assay. The Minimum Biofilm Eradication Concentration (MBEC) was measured using AlamarBlue (AB) assay and Colony Forming Unit (CFU) counts. The cytotoxicity was evaluated by performing the MTT assay on AGS cell line. The inhibitory activity was expressed in terms of bacteriostatic and bactericidal potential, with MIC50, MIC90, and MBC50 of 0.33 mg/L against planktonic Helicobacter pylori strains. Using the fractional inhibitory concentration index, SUNCs showed synergism with metronidazole in one clinical strain, and very close to synergistic effect on the reference strain; the combination with clarythromicin evidenced an effect very close to synergism on both strains considered. The biofilm eradication was obtained after treatment with 2X, 3X and 4X MIC value. Moreover, SUNCs showed low toxicity on human cells and was effective in eradicating a mature biofilm produced by H. pylori. The data presented in this study demonstrate that SUNCs could represent a novel strategy for the treatment of H. pylori infections either alone or in combination with metronidazole