A Boolean Approach to Linear Prediction for Signaling Network Modeling

The task of the DREAM4 (Dialogue for Reverse Engineering Assessments and Methods) “Predictive signaling network modeling” challenge was to develop a method that, from single-stimulus/inhibitor data, reconstructs a cause-effect network to be used to predict the protein activity level in multi-stimulus/inhibitor experimental conditions. The method presented in this paper, one of the best performing in this challenge, consists of 3 steps: 1. Boolean tables are inferred from single-stimulus/inhibitor data to classify whether a particular combination of stimulus and inhibitor is affecting the protein. 2. A cause-effect network is reconstructed starting from these tables. 3. Training data are linearly combined according to rules inferred from the reconstructed network. This method, although simple, permits one to achieve a good performance providing reasonable predictions based on a reconstructed network compatible with knowledge from the literature. It can be potentially used to predict how signaling pathways are affected by different ligands and how this response is altered by diseases

Deep Convolutional Neural Network for Survival Estimation of Amyotrophic Lateral Sclerosis patients

We propose a convolutional neural network (CNN) coupled with a fully connected top layer for survival estimation. We design an objective function to directly estimate the probability of survival at discrete time intervals, conditional to the patient not having incurred any adverse event at previous time points. We test our CNN and objective function on a large dataset of longitudinal data of patients with Amyotrophic Lateral Sclerosis (ALS). We compare our CNN and the objective function against other neural networks designed for survival analysis, and against the optimization of Cox-partial-likelihood or a simple logistic classifier. The use of our objective function outperforms both Cox-partial-likelihood and logistic classifier, independently of the network architecture, and our deep CNN provides the best results in terms of AU-ROC, accuracy and mean absolute erro

Biodiversity of Prokaryotic Communities Associated with the Ectoderm of Ectopleura crocea (Cnidaria, Hydrozoa)

The surface of many marine organisms is colonized by complex communities of microbes, yet our understanding of the diversity and role of host-associated microbes is still limited. We investigated the association between Ectopleura crocea (a colonial hydroid distributed worldwide in temperate waters) and prokaryotic assemblages colonizing the hydranth surface. We used, for the first time on a marine hydroid, a combination of electron and epifluorescence microscopy and 16S rDNA tag pyrosequencing to investigate the associated prokaryotic diversity. Dense assemblages of prokaryotes were associated with the hydrant surface. Two microbial morphotypes were observed: one horseshoe-shaped and one fusiform, worm-like. These prokaryotes were observed on the hydrozoan epidermis, but not in the portions covered by the perisarcal exoskeleton, and their abundance was higher in March while decreased in late spring. Molecular analyses showed that assemblages were dominated by Bacteria rather than Archaea. Bacterial assemblages were highly diversified, with up to 113 genera and 570 Operational Taxonomic Units (OTUs), many of which were rare and contributed to <0.4%. The two most abundant OTUs, likely corresponding to the two morphotypes present on the epidermis, were distantly related to Comamonadaceae (genus Delftia) and to Flavobacteriaceae (genus Polaribacter). Epibiontic bacteria were found on E. crocea from different geographic areas but not in other hydroid species in the same areas, suggesting that the host-microbe association is species-specific. This is the first detailed report of bacteria living on the hydrozoan epidermis, and indeed the first study reporting bacteria associated with the epithelium of E. crocea. Our results provide a starting point for future studies aiming at clarifying the role of this peculiar hydrozoan-bacterial association

Helicobacter pylori Up-regulates Cyclooxygenase-2 mRNA Expression and Prostaglandin E2 Synthesis in MKN 28 Gastric Mucosal Cells in Vitro

Helicobacter pylori has been suggested to play a role in the development of gastric carcinoma in humans. Also, mounting evidence indicates that cyclooxygenase-2 overexpression is associated with gastrointestinal carcinogenesis. We studied the effect of H. pylori on the expression and activity of cyclooxygenase-1 and cyclooxygenase-2 in MKN 28 gastric mucosal cells. H. pylori did not affect cyclooxygenase-1 expression, whereas cyclooxygenase-2 mRNA levels increased by 5-fold at 24 h after incubation of MKN 28 cells with broth culture filtrates or bacterial suspensions from wild-type H. pylori strain. Also, H. pylori caused a 3-fold increase in the release of prostaglandin E2, the main product of cyclooxygenase activity. This effect was specifically related to H. pylori because it was not observed with Escherichia coli and was independent of VacA, CagA, or ammonia. H. pylori isogenic mutants specifically lacking picA or picB, which are responsible for cytokine production by gastric cells, were less effective in the up-regulation of cyclooxygenase-2 mRNA expression and in the stimulation of prostaglandin E2 release compared with the parental wild-type strain. This study suggests that development of gastric carcinoma associated with H. pylori infection may depend on the activation of cyclooxygenase-2-related events