Giosuè Lo Bosco

Genome-wide characterization of chromatin binding and nucleosome spacing activity of the nucleosome remodelling ATPase ISWI

The evolutionarily conserved ATP‐dependent nucleosome remodelling factor ISWI can space nucleosomes affecting a variety of nuclear processes. In Drosophila, loss of ISWI leads to global transcriptional defects and to dramatic alterations in higher‐order chromatin structure, especially on the male X chromosome. In order to understand if chromatin condensation and gene expression defects, observed in ISWI mutants, are directly correlated with ISWI nucleosome spacing activity, we conducted a genome‐wide survey of ISWI binding and nucleosome positioning in wild‐type and ISWI mutant chromatin. Our analysis revealed that ISWI binds both genic and intergenic regions. Remarkably, we found that ISWI binds genes near their promoters causing specific alterations in nucleosome positioning at the level of the Transcription Start Site, providing an important insights in understanding ISWI role in higher eukaryote transcriptional regulation. Interestingly, differences in nucleosome spacing, between wild‐type and ISWI mutant chromatin, tend to accumulate on the X chromosome for all ISWI‐bound genes analysed. Our study shows how in higher eukaryotes the activity of the evolutionarily conserved nucleosome remodelling factor ISWI regulates gene expression and chromosome organization genome‐wide.

GenClust: A genetic algorithm for clustering gene expression data

BackgroundClustering is a key step in the analysis of gene expression data, and in fact, many classical clustering algorithms are used, or more innovative ones have been designed and validated for the task. Despite the widespread use of artificial intelligence techniques in bioinformatics and, more generally, data analysis, there are very few clustering algorithms based on the genetic paradigm, yet that paradigm has great potential in finding good heuristic solutions to a difficult optimization problem such as clustering.ResultsGenClust is a new genetic algorithm for clustering gene expression data. It has two key features: (a) a novel coding of the search space that is simple, compact and easy to update; (b) it can be used naturally in conjunction with data driven internal validation methods. We have experimented with the FOM methodology, specifically conceived for validating clusters of gene expression data. The validity of GenClust has been assessed experimentally on real data sets, both with the use of validation measures and in comparison with other algorithms, i.e., Average Link, Cast, Click and K-means.ConclusionExperiments show that none of the algorithms we have used is markedly superior to the others across data sets and validation measures; i.e., in many cases the observed differences between the worst and best performing algorithm may be statistically insignificant and they could be considered equivalent. However, there are cases in which an algorithm may be better than others and therefore worthwhile. In particular, experiments for GenClust show that, although simple in its data representation, it converges very rapidly to a local optimum and that its ability to identify meaningful clusters is comparable, and sometimes superior, to that of more sophisticated algorithms. In addition, it is well suited for use in conjunction with data driven internal validation measures and, in particular, the FOM methodology.

Stimulation of ATP synthesis via oxidative phosphorylation in wheat mitochondria irradiated with Helium-Neon laser

In order to ascertain whether oxidative phosphorylation in plant mitochondria is sensitive to light, coupled durum wheat (Triticum durum Desf.) mitochondria were irradiated with a low power continuous wave Helium‐Neon laser (fluence: 2 Joules/cm2), with measurements made of certain processes related to ATP production. As a result of irradiation, an increase in the rate of ATP synthesis was found, as continuously monitored via luciferine/luciferase, moreover the mitochondrial ATP and ADP endogenous contents were found to increase and decrease, respectively with a 1:1 stoichiometry, as revealed by HPLC measurements. Consistently, an increase in mitochondrial rate of ΔΨ generation was found as measured by using the fluorescent probe safranine. Thus, this paper gives a first evidence of a novel property of plant mitochondria: the direct light sensitivity of ATP synthesis via oxidative phosphorylation.

A memetic algorithm for binary image reconstruction

This paper deals with a memetic algorithm for the reconstruction of binary images, by using their projections along four directions. The algorithm generates by network flows a set of initial images according to two of the input projections and lets them evolve toward a solution that can be optimal or close to the optimum. Switch and compactness operators improve the quality of the reconstructed images which belong to a given generation, while the selection of the best image addresses the evolution to an optimal output.

Associations between Notch-2, Akt-1 and HER2/neu expression in invasive human breast cancer: a tissue microarray immunophenotypic analysis on 98 patients.

Objective: We aimed to investigate the existence of associations between well-established and newly recognized biological and phenotypic features of breast cancer involved in tumor progression and prognosis. Methods: Ninety-eight cases of invasive breast cancer were assessed for the immunohistochemical expression of estrogen and progesterone receptors, Ki-67, HER2, Akt-1, and Notch-2, using the tissue microarray technique. Data regarding tumor histotype, histological grade, tumor size and lymph node status were collected for each patient and included in the analysis. Results: Several significant associations between histological and/or immunophenotypic features came from the analysis of our data. Positive associations were observed between estrogen and progesterone receptors, tumor grade and proliferation index, tumor grade and HER2, Akt-1 and estrogen receptors, and Notch-2 and HER2. Inverse associations were noted between hormone receptors and tumor grade, hormone receptors and HER2, Akt-1 and tumor grade, and Akt-1 and nodal invasion. Conclusions: Our results, showing the existence of a number of estrogen receptor-positive tumors with Akt-1 expression, better degree of differentiation, and no lymph node involvement, along with the presence of HER2-positive tumors with strong Notch-2 expression, support the role of Notch and Akt in breast cancer progression and suggest that they may also represent new appealing therapeutic targets.

Applications of alignment-free methods in epigenomics

Epigenetic mechanisms play an important role in the regulation of cell type-specific gene activities, yet how epigenetic patterns are established and maintained remains poorly understood. Recent studies have supported a role of DNA sequences in recruitment of epigenetic regulators. Alignment-free methods have been applied to identify distinct sequence features that are associated with epigenetic patterns and to predict epigenomic profiles. Here, we review recent advances in such applications, including the methods to map DNA sequence to feature space, sequence comparison and prediction models. Computational studies using these methods have provided important insights into the epigenetic regulatory mechanisms.

A Multi-Layer Method to Study Genome-Scale Positions of Nucleosomes

The basic unit of eukaryotic chromatin is the nucleosome, consisting of about 150 bp of DNA wrapped around a protein core made of histone proteins. Nucleosomes position is modulated in vivo to regulate fundamental nuclear processes. To measure nucleosome positions on a genomic scale both theoretical and experimental approaches have been recently reported. We have developed a new method, Multi-Layer Model (MLM), for the analysis of nucleosome position data obtained with microarray-based approach. The MLM is a feature extraction method in which the input data is processed by a classifier to distinguish between several kinds of patterns. We applied our method to simulated-synthetic and experimental nucleosome position data and found that besides a high nucleosome recognition and a strong agreement with standard statistical methods, the MLM can identify distinct classes of nucleosomes, making it an important tool for the genome wide analysis of nucleosome position and function. In conclusion, the MLM allows a better representation of nucleosome position data and a significant reduction in computational time.

Shape-based features for cat ganglion retinal cells classification

This article presents a quantitative and objective approach to cat ganglion cell characterization and classification. The combination of several biologically relevant features such as diameter, eccentricity, fractal dimension, influence histogram, influence area, convex hull area, and convex hull diameter are derived from geometrical transforms and then processed by three different clustering methods (Wards hierarchical scheme, K-means and genetic algorithm), whose results are then combined by a voting strategy. These experiments indicate the superiority of some features and also suggest some possible biological implications.

Combining One Class Fuzzy KNN's

This paper introduces a parallel combination of N> 2 one class fuzzy KNN(FKNN) classifiers. The classifier combination consists of a new optimization procedure based on a genetic algorithm applied to FKNNs, that differ in the kind of similarity used. We tested the integration techniques in the case of N= 5 similarities that have been recently introduced to face with categorical data sets. The assessment of the method has been carried out on two public data set, the Masquerading User Data (www.schonlau.net) and the badges database on the UCI Machine Learning Repository (http://www.ics.uci.edu/~mlearn/). Preliminary results show the better performance obtained by the fuzzy integration respect to the crisp one.

A memetic approach to discrete tomography from noisy projections

Discrete tomography deals with the reconstruction of images from very few projections, which is, in the general case, an NP-hard problem. This paper describes a new memetic reconstruction algorithm. It generates a set of initial images by network flows, related to two of the input projections, and lets them evolve towards a possible solution, by using crossover and mutation. Switch and compactness operators improve the quality of the reconstructed images during each generation, while the selection of the best images addresses the evolution to an optimal result. One of the most important issues in discrete tomography is known as the stability problem and it is tackled here, in the case of noisy projections, along four directions. Extensive experiments have been carried out to evaluate the robustness of the new methodology. A comparison with the output of two other evolutionary algorithms and a generalized version of a deterministic method shows the effectiveness of our new algorithm.