Using Pathway Signatures as Means of Identifying Similarities among Microarray Experiments

Widespread use of microarrays has generated large amounts of data, the interrogation of the public microarray repositories, identifying similarities between microarray experiments is now one of the major challenges. Approaches using defined group of genes, such as pathways and cellular networks (pathway analysis), have been proposed to improve the interpretation of microarray experiments. We propose a novel method to compare microarray experiments at the pathway level, this method consists of two steps: first, generate pathway signatures, a set of descriptors recapitulating the biologically meaningful pathways related to some clinical/biological variable of interest, second, use these signatures to interrogate microarray databases. We demonstrate that our approach provides more reliable results than with gene-based approaches. While gene-based approaches tend to suffer from bias generated by the analytical procedures employed, our pathway based method successfully groups together similar samples, independently of the experimental design. The results presented are potentially of great interest to improve the ability to query and compare experiments in public repositories of microarray data. As a matter of fact, this method can be used to retrieve data from public microarray databases and perform comparisons at the pathway level

Body mass index rather than the phenotype impacts precocious ultrasound cardiovascular risk markers in polycystic ovary syndrome

Objective Research into cardiovascular disease (CV) prevention has demonstrated a variety of ultrasound (US) markers predicting risk in the general population but which have been scarcely used for polycystic ovary syndrome (PCOS). Obesity is a major factor contributing to CV disease in the general population, and it is highly prevalent in PCOS. However, it is still unclear how much risk is attributable to hyperandrogenism. This study evaluates the most promising US CV risk markers in PCOS and compares them between different PCOS phenotypes and BMI values. Design Women fulfilling the Rotterdam criteria for PCOS were recruited from our outpatient clinic for this cross-sectional study. Methods Participants (n\u2009=\u2009102) aged 38.9 \ub1 7.4 years were stratified into the four PCOS phenotypes and the three BMI classes (normal-weight, overweight, obese). They were assessed for clinical and biochemical parameters together with the following US markers: coronary intima-media thickness (cIMT), flow-mediated vascular dilation (FMD), nitroglycerine-induced dilation (NTG), and epicardial fat thickness (EFT). Results There was no statistical difference among the four phenotypes in terms of cIMT, FMD, NTG or EFT, however all the US parameters except NTG showed significant differences among the three BMI classes. Adjusting for confounding factors in multiple regression analyses, EFT retained the greatest direct correlation with BMI and cIMT remained directly correlated but to a lesser degree. Conclusions This study showed that obesity rather than the hyperandrogenic phenotype negatively impacts precocious US CV risk markers in PCOS. In addition, EFT showed the strongest association with BMI, highlighting its potential for estimating CV risk in PCOS

T Regulatory Cells Are Markers of Disease Activity in Multiple Sclerosis Patients

FoxP3+ Treg cells are believed to play a role in the occurrence of autoimmunity and in the determination of clinical recurrences. Contradictory reports are, however, available describing frequency and function of Treg cells during autoimmune diseases. We examined, by both polychromatic flow cytometry, and real-time RT-PCR, several Treg markers in peripheral blood mononuclear cells from patients with multiple sclerosis (MS), an autoimmune disease affecting the central nervous system. We found that Tregs, as defined by CD25, CD39, FoxP3, CTLA4, and GITR expression, were significantly decreased in stable MS patients as compared to healthy donors, but, surprisingly, restored to normal levels during an acute clinical attack. We conclude that Treg cells are not involved in causing clinical relapses, but rather react to inflammation in the attempt to restore homeostasis

Information technology controlled greenhouse: A system architecture

The technological level of greenhouse cultivation, especially in the Mediterranean countries such as Italy, Turkey, Greece and Spain, is low, despite protected crops are of considerable importance both for extension and for the production of fresh foodstuffs and for exported ornamental plants. The project 'HouseGarden High Tech' intends to increase the technologies of greenhouse cultures by the creation of an integrated network of sensors and automation technologies, controlled by an ICT (Information and Communication Technologies) approach, for the agronomic development of horticultural crops. In the High Tech greenhouse, innovative technologies will be tested, in order to stimulate the growth and development of plants with optimized use of chemical products. In particular, High Tech greenhouse is designed to manage, in a controlled and efficient way, different types of crops with different cultivation needs. The High Tech greenhouse is a versatile and multifunctional environment, equipped with sensors and monitoring systems that allow the acquisition of data and information processed by a specially designed 'computer brain', to perform effective active controls (e.g. in retro-action), thus optimizing crop management and providing a useful decision support tool. In this High-Tech greenhouse, an innovative technology is used, the Non-Thermal Plasma technology (NTP), to generate ionized gas streams, to treat plants and irrigation water with sanitizing and stimulating properties of growth and vegetative development. The ICT based control and management of data automates the cultivation of crops and nursery products in a short and controlled time, using specific mathematical yield models that are developed throughout the project

Effects of Nonthermal Plasma (NTP) on the Growth and Quality of Baby Leaf Lettuce (<i>Lactuca sativa</i> var. <i>acephala</i> Alef.) Cultivated in an Indoor Hydroponic Growing System

The aim of this research was to develop an effective protocol for the application of nonthermal plasma (NTP) technology to the hydroponic nutrient solution, and to investigate its effects on the growth and quality of baby leaf lettuce (Lactuca sativa var. acephala Alef.) grown in a hydroponic growing system (HGS) specifically designed for indoor home cultivation. Four HGSs were placed in separate growth chambers with temperature of 24 ± 1 °C and relative humidity of 70 ± 5%). Lettuce plants were grown for nine days in nutrient solutions treated with NTP for 0 (control) to 120 s every hour. Results of the first experiments showed that the optimal operating time of NTP was 120 s h−1. Fresh leaf biomass was increased by the 60 and 120 s NTP treatments compared to the control. Treating the nutrient solution with NTP also resulted in greater leaf content of total chlorophylls, carotenoids, total phenols, and total antioxidant capacity. NTP also positively influenced chlorophyll a fluorescence in Photosystem I (PSI) and photosynthetic electron transport. These results revealed that the NTP treatment of the nutrient solution could improve the production and quality of hydroponically grown baby leaf lettuce

Eu.Gene Analyzer a tool for integrating gene expression data with pathway databases

MOTIVATION: Eu.Gene Analyzer is an easy-to-use, stand-alone application that allows rapid and powerful microarray data analysis in the context of biological pathways. Its intuitive graphical user interface makes it an easy and flexible tool, even for the first-time user. Eu.Gene supports a variety of array platforms, organisms and pathway ontologies, transparently deals with multiple nomenclature systems and seamlessly integrates data from different sources. Two different statistical methods, the Fisher Exact Test and the Gene Set Enrichment Analysis (GSEA), are implemented to identify biological pathways transcriptionally affected under experimental conditions. A suite of tools is offered to define, visualize and share custom non-redundant pathway sets. In conclusion, Eu.Gene Analyzer is a new software application that takes advantage of information from multiple pathway databases to build a comprehensive interpretation of experimental results in a simple, intuitive environment