Helicobacter pylori and Epstein–Barr Virus Co-Infection in Gastric Disease: What Is the Correlation with p53 Mutation, Genes Methylation and Microsatellite Instability in a Cohort of Sicilian Population?

Genetic predisposition, environmental factors, and infectious agents interact in the development of gastric diseases. Helicobacter pylori (Hp) and Epstein–Barr virus (EBV) infection has recently been shown to be correlated with these diseases. A cross-sectional study was performed on 100 hospitalized Italian patients with and without gastric diseases. The patients were stratified into four groups. Significant methylation status differences among CDH1, DAPK, COX2, hMLH1 and CDKN2A were observed for coinfected (Hp-EBV group) patients; particularly, a significant presence of COX2 (p = 0.0179) was observed. For microsatellite instability, minor stability was described in the Hp-HBV group (69.23%, p = 0.0456). Finally, for p53 mutation in the EBV group, exon 6 was, significantly, most frequent in comparison to others (p = 0.0124), and in the Hp-EBV group exon 8 was, significantly, most frequent in comparison to others (p < 0.0001). A significant positive relationship was found between patients with infection (Hp, EBV or both) and p53 mutation (rho = 0.383, p = 0.0001), methylation status (rho = 0.432, p < 0.0001) and microsatellite instability (rho = 0.285, p = 0.004). Finally, we observed among infection and methylation status, microsatellite instability, and p53 mutation a significant positive relationship only between infection and methylation status (OR = 3.78, p = 0.0075) and infection and p53 mutation (OR = 6.21, p = 0.0082). According to our analysis, gastric disease in the Sicilian population has different pathways depending on the presence of various factors, including infectious agents such as Hp and EBV and genetic factors of the subject

Prediction of protein long-range contacts using an ensemble of genetic algorithm classifiers with sequence profile centers

Background. Prediction of long-range inter-residue contacts is an important topic in bioinformatics research. It is helpful for determining protein structures, understanding protein foldings, and therefore advancing the annotation of protein functions. Results. In this paper, we propose a novel ensemble of genetic algorithm classifiers (GaCs) to address the long-range contact prediction problem. Our method is based on the key idea called sequence profile centers (SPCs). Each SPC is the average sequence profiles of residue pairs belonging to the same contact class or non-contact class. GaCs train on multiple but different pairs of long-range contact data (positive data) and long-range non-contact data (negative data). The negative data sets, having roughly the same sizes as the positive ones, are constructed by random sampling over the original imbalanced negative data. As a result, about 21.5% long-range contacts are correctly predicted. We also found that the ensemble of GaCs indeed makes an accuracy improvement by around 5.6% over the single GaC. Conclusions. Classifiers with the use of sequence profile centers may advance the long-range contact prediction. In line with this approach, key structural features in proteins would be determined with high efficiency and accuracy. © 2010 Li and Chen; licensee BioMed Central Ltd

Genomic diversity of Oenococcus oeni from different winemaking regions of Portugal

Oenococcus oeni is an alcohol-tolerant, acidophilic lactic acid bacterium that plays an important role in the elaboration of wine. It is often added as a starter culture to carry out malolactic conversion. Given the economic importance of this reaction, the taxonomic structure of this species has been studied in detail. In the present work, phenotypic and molecular approaches were used to identify 121 lactic acid bacteria strains isolated from the wines of three winemaking regions of Portugal. The strains were differentiated at the genomic level by M13-PCR fingerprinting. Twenty-seven genomic clusters represented by two or more isolates and 21 single-member clusters, based on an 85% similarity level, were recognized by hierarchic numerical analysis. M13-PCR fingerprinting patterns revealed a high level of intraspecific genomic diversity in O. oeni. Moreover, this diversity could be partitioned according to the geographical origin of the isolates. Thus, M13-PCR fingerprint analysis may be an appropriate methodology to study the O. oeni ecology of wine during malolactic fermentation as well as to trace new malolactic starter cultures and evaluate their dominance over the native microbiota

Baubles, Bangles, and Biotypes: A Critical Review of the use and Abuse of the Biotype Concept

Pest species of insects are notoriously prone to escape the weapons deployed in management efforts against them. This is particularly true in herbivorous insects. When a previously successful tactic fails the insect population has apparently adapted to it and is often considered to be a new or distinct entity, and given the non-formal category ‘biotype’. The entities falling under the umbrella term ‘biotype’ are not consistent either within or between biotypes, and their underlying genetic composition and origins, while generally unknown, are likely heterogeneous within and variable between biotypes. In some cases race or species may be more appropriate referents. Some examples of applications of the concept in the context of host plant resistance are discussed. It is argued here that the term ‘biotype’ and its applications are overly simplistic, confused, have not proved useful in current pest management, and lack predictive power for future management

Tamoxifen in early-stage estrogen receptor-positive breast cancer: overview of clinical use and molecular biomarkers for patient selection

Tamoxifen was the first targeted anticancer agent for breast cancer patients and its effects on reduction of breast cancer events and improvement in overall survival are undisputed. Hence, it has long been considered an essential part of patient care. Recent results of several large adjuvant hormonal trials evaluating the use of aromatase inhibitors in comparison with the previous standard of five years of tamoxifen has led to a paradigm shift, ensuring the inclusion of an aromatase inhibitor as part of standard endocrine therapy for most postmenopausal women diagnosed today with estrogen receptor-positive breast cancer. However, one could argue that despite statistically significant improvements in breast cancer events, an overall survival advantage has not been clear. In this review, we discuss recent genomic and molecular data pertaining to estrogen receptor-positive breast cancer and how this knowledge may aid clinicians to prescribe adjuvant hormonal treatment in the future. A combination of gene expression and genetic aberration markers may be most useful in discerning a population that is still appropriate for adjuvant tamoxifen treatment

Modeling restricted enrollment and optimal cost-efficient design in multicenter clinical trials

Design and forecasting of patient enrollment is among the greatest challenges that the clinical research enterprize faces today, as inefficient enrollment can be a major cause of drug development delays. Therefore, the development of the innovative statistical and artificial intelligence technologies for improving the efficiency of clinical trials operation are of the imperative need. This paper is describing further developments in the innovative statistical methodology for modeling and forecasting patient enrollment. The underlying technique uses a Poisson-gamma enrollment model developed by Anisimov & Fedorov in the previous publications and is extended here to analytic modeling of the enrollment on country/region level. A new analytic technique based on the approximation of the enrollment process in country/region by a Poisson-gamma process with aggregated parameters is developed. Another innovative direction is the development of the analytic technique for modeling the enrollment under some restrictions (enrollment caps in countries). Some discussion on using historic trials for better prediction of the enrollment in the new trials is provided. These results are used for solving the problem of optimal trial cost-efficient enrollment design: find an optimal allocation of sites/countries that minimizes the global trial cost given that the probability to reach an enrollment target in time is no less than some prescribed probability. Different techniques to find an optimal solution for high dimensional optimization problem for the cases of unrestricted and restricted enrollment and for a small and large number of countries are discussed.Comment: 22 pages, 3 figure

Fibromyalgia: When Distress Becomes (Un)sympathetic Pain

Fibromyalgia is a painful stress-related disorder. A key issue in fibromyalgia research is to investigate how distress could be converted into pain. The sympathetic nervous system is the main element of the stress response system. In animal models, physical trauma, infection, or distressing noise can induce abnormal connections between the sympathetic nervous system and the nociceptive system. Dorsal root ganglia sodium channels facilitate this type of sympathetic pain. Similar mechanisms may operate in fibromyalgia. Signs of sympathetic hyperactivity have been described in this condition. Genetic factors and/or distressful lifestyle may lead to this state of sympathetic hyperactivity. Trauma and infection are recognized fibromyalgia triggers. Women who suffer from fibromyalgia have catecholamine-evoked pain. Sympathetic dysfunction may also explain nonpain-related fibromyalgia symptoms. In conclusion, in fibromyalgia, distress could be converted into pain through forced hyperactivity of the sympathetic component of the stress response system

The Columnar Lined Esophagus: aspects on the assessment of dysplasia and on the relationship with the esophageal submucosal glands

Columnar metaplasia, where columnar epithelium replaces the normal squamous epithelium in esophagus, is considered to be a precancerous condition in which the development of adenocarcinoma can be followed through various grades of dysplasia. The interpretation of these histological changes is subjective and suffers from considerable inter-observer variation among pathologists. In study I, we devised and tested two clinically applicable methods for immunohistochemical assessment of p53 and Ki67 as surrogate dysplasia markers. Using these methods, the inter-observer agreement improved substantially from mean k value 0.24 for H&E evaluation to 0.71 and 0.52 for p53 and Ki67 evaluations, respectively. There was a correlation between severity of dysplasia, p53 over-expression and shift of the proliferation zone towards the mucosal surface. We conclude that our methods are reproducible and associated with less inter-observer variation than morphologic dysplasia grading, and that p53 and Ki67 are useful supplementary prognostic markers. The origin of columnar metaplasia in esophagus is debated. The submucosal glands have been proposed as a stem cell source, but studies of the human esophageal glands are rare. In studies II – IV, we conducted comparative and descriptive analyses of the distribution and morphology of the submucosal glands in patients with columnar metaplasia in esophagus. We have shown that there is an accumulation of submucosal glands beneath the transformation-zones between squamous and columnar mucosa, and that the submucosal glands in the columnar lined part of esophagus are hyperplastic. There are overlapping immunophenotypes between the submucosal gland unit, the columnar metaplasia and the transformation-zones for the markers CK17, CK4 and lysozyme. We propose that the submucosal glands are the esophageal counterparts of skin adnexa as a source of re-epithelialization, and conclude that in esophagus both neosquamous islands and columnar metaplasia originate in the submucosal gland unit

Connectivity independent protein-structure alignment: a hierarchical approach

BACKGROUND: Protein-structure alignment is a fundamental tool to study protein function, evolution and model building. In the last decade several methods for structure alignment were introduced, but most of them ignore that structurally similar proteins can share the same spatial arrangement of secondary structure elements (SSE) but differ in the underlying polypeptide chain connectivity (non-sequential SSE connectivity). RESULTS: We perform protein-structure alignment using a two-level hierarchical approach implemented in the program GANGSTA. On the first level, pair contacts and relative orientations between SSEs (i.e. α-helices and β-strands) are maximized with a genetic algorithm (GA). On the second level residue pair contacts from the best SSE alignments are optimized. We have tested the method on visually optimized structure alignments of protein pairs (pairwise mode) and for database scans. For a given protein structure, our method is able to detect significant structural similarity of functionally important folds with non-sequential SSE connectivity. The performance for structure alignments with strictly sequential SSE connectivity is comparable to that of other structure alignment methods. CONCLUSION: As demonstrated for several applications, GANGSTA finds meaningful protein-structure alignments independent of the SSE connectivity. GANGSTA is able to detect structural similarity of protein folds that are assigned to different superfamilies but nevertheless possess similar structures and perform related functions, even if these proteins differ in SSE connectivity

Classification of 5-HT1A Receptor Ligands on the Basis of Their Binding Affinities by Using PSO-Adaboost-SVM

In the present work, the support vector machine (SVM) and Adaboost-SVM have been used to develop a classification model as a potential screening mechanism for a novel series of 5-HT1A selective ligands. Each compound is represented by calculated structural descriptors that encode topological features. The particle swarm optimization (PSO) and the stepwise multiple linear regression (Stepwise-MLR) methods have been used to search descriptor space and select the descriptors which are responsible for the inhibitory activity of these compounds. The model containing seven descriptors found by Adaboost-SVM, has showed better predictive capability than the other models. The total accuracy in prediction for the training and test set is 100.0% and 95.0% for PSO-Adaboost-SVM, 99.1% and 92.5% for PSO-SVM, 99.1% and 82.5% for Stepwise-MLR-Adaboost-SVM, 99.1% and 77.5% for Stepwise-MLR-SVM, respectively. The results indicate that Adaboost-SVM can be used as a useful modeling tool for QSAR studies