No abstract

Motivation: Single nucleotide polymorphisms are the most common form of variation in human DNA, and are involved in many research fields, from molecular biology to medical therapy. The technological opportunity to deal with long DNA sequences using shotgun sequencing has raised the problem of fragment recombination. In this regard, Single Individual Haplotyping (SIH) problem has received considerable attention over the past few years.Results: In this article, we survey seven recent approaches to the SIH problem and evaluate them extensively using real human haplotype data from the HapMap project. We also implemented a data generator tailored to the current shotgun sequencing technology that uses haplotypes from the HapMap project.Availability: The data we used to compare the algorithms are available on demand, since we think they represent an important benchmark that can be used to easily compare novel algorithmic ideas with the state of the art. Moreover, we had to re-implement six of the algorithms surveyed because the original code was not available to us. Five of these algorithms and the data generator used in this article endowed with a Web interface are available at http://bioalgo.iit.cnr.it/rehapContact: filippo.geraci@iit.cnr.it

The World Wide Web (WWW) is rapidly becoming important for society as a medium for sharing data, information and services, and there is a growing interest in tools for understanding collective behaviors and emerging phenomena in the WWW. In this paper we focus on the problem of searching and classifying communities in the web. Loosely speaking a community is a group of pages related to a common interest. More formally communities have been associated in the computer science literature with the existence of a locally dense sub-graph of the web-graph (where web pages are nodes and hyper-links are arcs of the web-graph). The core of our contribution is a new scalable algorithm for finding relatively dense subgraphs in massive graphs. We apply our algorithm on web-graphs built on three publicly available large crawls of the web (with raw sizes up to 120M nodes and 1G arcs). The effectiveness of our algorithm in finding dense subgraphs is demonstrated experimentally by embedding artificial communities in the web-graph and counting how many of these are blindly found. Effectiveness increases with the size and density of the communities: it is close to 100% for communities of a thirty nodes or more (even at low density). It is still about 80% even for communities of twenty nodes with density over 50% of the arcs present. At the lower extremes the algorithm catches 35% of dense communities made of ten nodes. We complete our Community Watch system by clustering the communities found in the web-graph into homogeneous groups by topic and labelling each group by representative keywords.

The dopamine D2 and D3 receptors are implicated in schizophrenia and its pharmacological treatments. These receptors undergo intracellular trafficking processes that are modulated by dysbindin-1 (Dys). Indeed, Dys variants alter cognitive responses to antipsychotic drugs through D2-mediated mechanisms. However, the mechanism by which Dys might selectively interfere with the D3 receptor subtype is unknown. Here, we revealed an interaction between functional genetic variants altering Dys and D3. Specifically, both in patients with schizophrenia and in genetically modified mice, concomitant reduction in D3 and Dys functionality was associated with improved executive and working memory abilities. This D3/Dys interaction produced a D2/D3 imbalance favoring increased D2 signaling in the prefrontal cortex (PFC) but not in the striatum. No epistatic effects on the clinical positive and negative syndrome scale (PANSS) scores were evident, while only marginal effects on sensorimotor gating, locomotor functions, and social behavior were observed in mice. This genetic interaction between D3 and Dys suggests the D2/D3 imbalance in the PFC as a target for patient stratification and procognitive treatments in schizophrenia.

Depression is a risk factor for the development of Alzheimer’s disease (AD), and the presence of depressive symptoms significantly increases the conversion of mild cognitive impairment (MCI) into AD. A long-term treatment with antidepressants reduces the risk to develop AD, and different second-generation antidepressants such as selective serotonin reuptake inhibitors (SSRIs) are currently being studied for their neuroprotective properties in AD. In the present work, the SSRI fluoxetine and the new multimodal antidepressant vortioxetine were tested for their ability to prevent memory deficits and depressive-like phenotype induced by intracerebroventricular injection of amyloid-β (1-42) (Aβ 1-42 ) oligomers in 2-month-old C57BL/6 mice. Starting from 7 days before Aβ injection, fluoxetine (10 mg/kg) and vortioxetine (5 and 10 mg/kg) were intraperitoneally injected daily for 24 days. Chronic treatment with fluoxetine and vortioxetine (both at the dose of 10 mg/kg) was able to rescue the loss of memory assessed 14 days after Aβ injection by the passive avoidance task and the object recognition test. Both antidepressants reversed the increase in immobility time detected 19 days after Aβ injection by forced swim test. Vortioxetine exerted significant antidepressant effects also at the dose of 5 mg/kg. A significant deficit of transforming growth factor-β1 (TGF-β1), paralleling memory deficits and depressive-like phenotype, was found in the hippocampus of Aβ-injected mice in combination with a significant reduction of the synaptic proteins synaptophysin and PSD-95. Fluoxetine and vortioxetine completely rescued hippocampal TGF-β1 levels in Aβ-injected mice as well as synaptophysin and PSD-95 levels. This is the first evidence that a chronic treatment with fluoxetine or vortioxetine can prevent both cognitive deficits and depressive-like phenotype in a non-transgenic animal model of AD with a key contribution of TGF-β1.

Social networks have demonstrated in the last few years to be a powerful and flexible concept useful to represent and analyze data. They borrow some basic concepts from sociology in order to model how people (or data items) establish relationships with each other. The study of these relationships can provide a deeper understanding of many emergent global phenomena. The amount of data available in the form of social networks data is growing by the day, and this poses many computational challenging problems for their analysis. In fact many analysis tools suitable to analyze small to medium sized networks are inefficient for large social networks. In this paper we present a novel approach for the computation of the betweenness centrality, which speeds up considerably Brandes' algorithm, in the context of social networking. Our algorithm exploits the natural sparsity of the data to algebraically (and efficiently) determine the betweenness of those nodes organized as trees embedded in the social network. Moreover, for the residual network, which is often of much smaller size we modify the Brandes' algorithm so that we can remove the nodes already processed and perform the computation of the shortest paths only for the remaining nodes. We tested our algorithm using a set of 18 real sparse large social networks provided by Sistemi Territoriali which is an Italian ICT company specialized in Business Intelligence. Our tests show that our algorithm consistently runs more than an order of magnitude faster than the Brandes' procedure on such sparse networks.

AbstractmiRandola (http://mirandola.iit.cnr.it/) is a database of extracellular non-coding RNAs (ncRNAs) that was initially published in 2012, foreseeing the relevance of ncRNAs as non-invasive biomarkers. An increasing amount of experimental evidence shows that ncRNAs are frequently dysregulated in diseases. Further, ncRNAs have been discovered in different extracellular forms, such as exosomes, which circulate in human body fluids. Thus, miRandola 2017 is an effort to update and collect the accumulating information on extracellular ncRNAs that is spread across scientific publications and different databases. Data are manually curated from 314 articles that describe miRNAs, long non-coding RNAs and circular RNAs. Fourteen organisms are now included in the database, and associations of ncRNAs with 25 drugs, 47 sample types and 197 diseases. miRandola also classifies extracellular RNAs based on their extracellular form: Argonaute2 protein, exosome, microvesicle, microparticle, membrane vesicle, high density lipoprotein and circulating. We also implemented a new web interface to improve the user experience.

Translational animal models for studying post-traumatic stress disorder (PTSD) are valuable for elucidating the poorly understood neurobiology of this neuropsychiatric disorder. These models should encompass crucial features, including persistence of PTSD-like phenotypes triggered after exposure to a single traumatic event, trauma susceptibility/resilience and predictive validity. Here we propose a novel arousal-based individual screening (AIS) model that recapitulates all these features. The AIS model was designed by coupling the traumatization (24 h restraint) of C57BL/6 J mice with a novel individual screening. This screening consists of z-normalization of post-trauma changes in startle reactivity, which is a measure of arousal depending on neural circuits conserved across mammals. Through the AIS model, we identified susceptible mice showing long-lasting hyperarousal (up to 56 days post-trauma), and resilient mice showing normal arousal. Susceptible mice further showed persistent PTSD-like phenotypes including exaggerated fear reactivity and avoidance of trauma-related cue (up to 75 days post-trauma), increased avoidance-like behavior and social/cognitive impairment. Conversely, resilient mice adopted active coping strategies, behaving like control mice. We further uncovered novel transcriptional signatures driven by PTSD-related genes as well as dysfunction of hypothalamic–pituitary–adrenal axis, which corroborated the segregation in susceptible/resilient subpopulations obtained through the AIS model and correlated with trauma susceptibility/resilience. Impaired hippocampal synaptic plasticity was also observed in susceptible mice. Finally, chronic treatment with paroxetine ameliorated the PTSD-like phenotypes of susceptible mice. These findings indicate that the AIS model might be a new translational animal model for the study of crucial features of PTSD. It might shed light on the unclear PTSD neurobiology and identify new pharmacological targets for this difficult-to-treat disorder.