Search

HCV is an important cause of hepatocellular carcinoma (HCC). HCV NS5A domain-1 interacts with cellular proteins inducing pro-oncogenic pathways. Thus, we explore genetic variations in NS5A domain-1 and their association with HCC, by analyzing 188 NS5A sequences from HCV genotype-1b infected DAA-naïve cirrhotic patients: 34 with HCC and 154 without HCC. Specific NS5A mutations significantly correlate with HCC: S3T (8.8% vs. 1.3%, p = 0.01), T122M (8.8% vs. 0.0%, p &lt, 0.001), M133I (20.6% vs. 3.9%, p &lt, 0.001), and Q181E (11.8% vs. 0.6%, p &lt, 0.001). By multivariable analysis, the presence of &gt, 1 of them independently correlates with HCC (OR (95%CI): 21.8 (5.7–82.3), p &lt, 0.001). Focusing on HCC-group, the presence of these mutations correlates with higher viremia (median (IQR): 5.7 (5.4–6.2) log IU/mL vs. 5.3 (4.4–5.6) log IU/mL, p = 0.02) and lower ALT (35 (30–71) vs. 83 (48–108) U/L, p = 0.004), suggesting a role in enhancing viral fitness without affecting necroinflammation. Notably, these mutations reside in NS5A regions known to interact with cellular proteins crucial for cell-cycle regulation (p53, p85-PIK3, and β-catenin), and introduce additional phosphorylation sites, a phenomenon known to ameliorate NS5A interaction with cellular proteins. Overall, these results provide a focus for further investigations on molecular bases of HCV-mediated oncogenesis. The role of theseNS5A domain-1 mutations in triggering pro-oncogenic stimuli that can persist also despite achievement of sustained virological response deserves further investigation.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Introduction: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by communication impairments, limited social interaction, restricted interests, repetitive behaviors and stereotypies. It manifests within the first 3 years of age and lasts for a lifetime with dramatic personal, familial and social consequences. ASD affects much more males than females (male:female=5:1) and its prevalence, that is continuously increasing, interests 2.24% of children and 1% of the general population [1,3]. Although genetics play a key role in ASD, its etiology is complex. Since most of individuals with ASD suffer from additional comorbidities including gastrointestinal disorders, intestinal permeability, inflammation and allergies, a gene-environment interaction has been proposed as ASD triggering [3]. Dysbiosis has been frequently associated with many neurological disorders including ASD. Thanks to the last advances in metagenomics, much progress has been made in the knowledge of gut microbiota profile and role, especially the prokaryotic one, but literature lacks of studies about eukaryotic colonizer of human intestine and their role in human health. Aims Here, employing different bioinformatics tools, we propose a metagenomics pilot study to define the prokaryotic and eukaryotic gut microbiota of children with ASD and neurotypical controls. The aims are to test different metagenomics pipelines and set up the more performing bioinformatics conditions to identify ASD microbial biomarkers useful for patient stratification and personalized treatments. Materials and Methods: We isolated DNA from stools collected from 6 children with ASD (5 males and 1 female) and 6 neurotypical controls matching for age and sex. Both 16S and 18S were amplified for each DNA and Illumina libraries prepared. NGS was performed by Illunima MiSeq platform coupled with Flowcell V3 2X300 and forward and reverse reading, reaching about 22Milion of sequences. As for bioinformatics analysis, three different software with several pipelines were applied: the automatic pipeline of SILVAngs analysis platform (https://ngs.arb-silva.de/silvangs/) [4], the MiSeq SOP pipeline of Mothur (https://mothur.org/) [5], and two pipelines of Qiime2 (https://qiime2.org/) [6]. The latter includes the Dada2 pipeline and the Deblur one. All the analyses were performed against SILVAv132 database [7], the only one that includes both 16s and 18S reference database. Results: The results obtained from the four tools are superimposable, also at different level of taxonomy detail. Restricting to Bacteria results, the identified genera are comparable with literature data [8]. As for Fungi, this first round of analysis doesn't return relevant differences between patients and controls. Further studies are needed to set up a pipeline specifically for the 18S datasets. Acknowledgements: EU project GEMMA (grant agreement No 825033), EPTRI and CNRBiOmics. Istituto San Vincenzo Erba and Albese, Italy

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition, prevalence is 1:68. Although genetics plays a key role in ASD etiology, only 30% of patients exhibit ASD associated genetic variants that are detectable by CGH, GWAS and/or exome NGS approach and hundreds of genes have been involved. Here, we tested the hypothesis that variants in non-coding and/or NGS unmappable regions could be involved in ASD and detected by long-reads whole-genome sequencing (LR-WGS). This approach allows to probe previously unreadable genomic tracts and discover disease-associated phased loci across very long haplotype blocks. Thus, we performed LR-WGS, by 10XGenomics technology, on genomic DNA isolated from 10 children with ASD (7 males and 3 females, including 2 couples of male siblings and 1 couple of male-female siblings) followed by bioinformatics analysis performed by 10X-Long Ranger pipeline. All the sequences passed the QC test, and the longest phase block reached 9M bp in length. We first looked at large structural variants (SVs) detectable only by long-read approach. We found a total of 204 hetero- or homo-zygous large SVs (deletions, duplications, inversions and breakends), up to 30 per subject and affecting 52 distinct genomic regions of 10^5 bp in average within almost all chromosomes. Interestingly, each SV affected from 1 up to 10 subjects and 51 out of 53 SVs involved genes that have never been associated with autism (not listed in SFARI database). All genes were submitted to DAVID 6.8 and METASCAPE gene enrichment tools evidencing some pathways including that of immune response and olfactory transduction. These SVs will be validated on an independent set of DNA samples previously collected from children with ASD and healthy controls. Resulting data will be integrated with clinical data to find genotype-phenotype associations. Small variants will be analyzed and validated, too. In conclusion, LR-WGS successfully discovered 51 new gene variants in 10 patients with ASD that, if confirmed, could explain some pathogenetic mechanisms of the disorder and represent possible diagnostic biomarkers for patient stratification and personalized medicine approaches.

CXCR3 is a G-protein coupled receptor expressed principally on leukocytes, monocytes and epithelial cells; it is involved in leukocyte traffic, integrin activation, cytoskeletal changes and chemotactic migration, by binding to its classical ligands, CXCL-9/10/11 (1). Three splicing variants of CXCR3 are known: CXCR3a, the most common isoform, consisting of 368 amino acid residues; CXCR3b resulting from an alternative splicing of the CXCR3 mRNA with a 52 aa extended N-terminal domain when compared to the isoform a, while CXCR3-alt is a significantly truncated variant not involved in classical ligand binding (1). CXCL10, the interferon-?-inducible protein (IP-10) belongs to the CXC family of chemokines and acts as an immunoinflammatory mediator, inhibits angiogenesis and displays antitumor properties (2). Several studies indicated that the CXCR3 N-terminal domain plays a key role in determining binding affinity, receptor selectivity, and also in regulating allosteric signalling through the receptor (3). Moreover, tyrosine sulfation in chemokine receptors is emerging as a post-translational modification that contributes substantially to ligand binding (2). Tyr 27 and 29, two of the N- terminal tyrosine can be sulfonated. Finally, Kleist and co-workers hypothesize a "two-step" model, where receptor binding and activation can be dissociated (4). In the first step, the chemokine binds to the N-term domain of the receptor. In the second, residues on IP10 N-terminal bind to the binding cavity on the TM domain of the receptor and induce the allosteric communication to the cytosol. In this work, we analyse by a supervised molecular dynamics (SuMD) (5) simulation, the binding mechanism of IP10 on CXCR3. We explored the binding site in order to evaluate the residue- residue interactions between the chemockine and its receptor. Starting from the same equilibrated system, 3 runs of supervised MD were performed using the same conditions. A residue selection from the CXCR3 N-term domain and all the Extra-Cellular Loop (ECL) were considered for the binding interface, instead the entire chemokine was treated as ligand in order to perform the supervision on the distance between the receptor (CXCR3) and the ligand (CXCL10). They resulted with consistent similarity in both ligand-receptor contacts and interaction energies. Simulations didn't had identical durations (30.8 ns for simulation 1, 31.2 ns for simulation 2 and 36.6 ns for simulation 3), but all of them reached the ligand correctly positioned in the binding site in 15ns. Simulations 2 and 3 presented overlapping situations of decreasing energies related to minor R-L distances and of contacts, involving both residues from the CXCR3-N- term and the Extra-cellular ?-sheet, which has been described as an important player for the receptor activation. The next step will be the evaluation of the effect of tyrosine sulfation (Y27 and Y29) on binding mode and affinity.

Cystic fibrosis (CF) is mainly caused by the mutation F508del of the cystic fibrosis transmembrane conductance regulator (CFTR) that is thus retained in the endoplasmic reticulum and degraded. New drugs able to rescue F508del-CFTR trafficking and activity are eagerly awaited, a goal that requires the availability of computational and experimental models closely resembling the F508del-CFTR structure and environment in vivo. Here we describe the development of a biosensor based on F508del-CFTR in a lipid environment that proved to be endowed with a wider analytical potential in respect to the previous CFTR-based biosensors. Integrated with an appropriate computational model of the whole human F508del-CFTR in lipid environment and CFTR stability and functional assays, the new biosensor allowed the identification and characterization at the molecular level of the binding modes of some known F508del-CFTR-rescuing drugs and of a new aminoarylthiazole-Lumacaftor/Tezacaftor hybrid derivative endowed with promising F508del-CFTR-binding and rescuing activity.

OBJECTIVES: This study aims to evaluate the reliability and clinical utility of NS3 sequencing in hepatitis C virus (HCV) 1-infected patients who were candidates to start a PI-containing regimen. METHODS: NS3 protease sequencing was performed by in-house-developed HCV-1 subtype-specific protocols. Phylogenetic analysis was used to test sequencing reliability and concordance with previous genotype/subtype assignment by commercial genotyping assays. RESULTS: Five hundred and sixty-seven HCV plasma samples with quantifiable HCV-RNA from 326 HCV-infected patients were collected between 2011 and 2014. Overall, the success rate of NS3 sequencing was 88.9%. The success rate between the two subtype protocols (HCV-1a/HCV-1b) was similarly high for samples with HCV-RNA >3 log IU/mL (>92% success rate), while it was slightly lower for HCV-1a samples with HCV-RNA ≤3 log IU/mL compared with HCV-1b samples. Phylogenetic analysis confirmed the genotype/subtype given by commercial genotyping assays in 92.9% (303/326) of cases analysed. In the remaining 23 cases (7.1%), 1 was HCV-1g (previously defined as subtype 1a), 1 was HCV-4d (previously defined as genotype 1b) and 1 was HCV-1b (previously defined as genotype 2a/2c). In the other cases, NS3 sequencing precisely resolved the either previous undetermined/discordant subtype 1 or double genotype/subtype assignment by commercial genotyping assays. Resistance-associated variants (RAVs) to PI were detected in 31.0% of samples. This prevalence changed according to PI experience (17.1% in PI-naive patients versus 79.2% in boceprevir/telaprevir/simeprevir-failing patients). Among 96 patients with available virological outcome following boceprevir/telaprevir treatment, a trend of association between baseline NS3 RAVs and virological failure was observed (particularly for HCV-1a-infected patients: 3/21 failing patients versus 0/22 achieving sustained virological response; P = 0.11). CONCLUSIONS: HCV-NS3 sequencing provides reliable results and at the same time gives two clinically relevant pieces of information: a correct subtype/genotype assignment and the detection of variants that may interfere with the efficacy of PI.

Background & Aims: Despite the excellent efficacy of direct-acting antivirals (DAA) reported in clinical trials, virological failures can occur, often associated with the development of resistance-associated substitutions (RASs). This study aimed to characterize the presence of clinically relevant RASs to all classes in real-life DAA failures. Methods: Of the 200 virological failures that were analyzed in 197 DAA-treated patients, 89 with pegylated-interferon+ribavirin (PegIFN+RBV) and 111 without (HCV-1a/1b/1g/2/3/4=58/83/1/6/24/25; 56.8% treatment experienced; 65.5% cirrhotic) were observed. Sanger sequencing of NS3/NS5A/NS5B was performed by home-made protocols, at failure (N= 200) and whenever possible at baseline (N= 70). Results: The majority of the virological failures were relapsers (57.0%), 22.5% breakthroughs, 20.5% non-responders. RAS prevalence varied according to IFN/RBV use, DAA class, failure type and HCV genotype/subtype. It was 73.0% in IFN group vs 49.5% in IFN free, with the highest prevalence of NS5A-RASs (96.1%), compared to NS3-RASs (75.9% with IFN, 70.5% without) and NS5B-RASs (66.6% with IFN, 20.4% without, in sofosbuvir failures). In the IFN-free group, RASs were higher in breakthrough/non-responders than in relapsers (90.5% vs 40.0%, P= 2 DAA classes showed multiclass resistance, including 11/11 NS3+NS5A failures. Furthermore, 20.0% of patients had baseline-RASs, which were always confirmed at failure. Conclusions: In our failure setting, RAS prevalence was remarkably high in all genes, with a partial exception for NS5B, whose limited resistance is still higher than previously reported. This multiclass resistance advocates for HCV resistance testing at failure, in all three genes for the best second-line therapeutic tailoring.

Gene-environment interaction, through abnormal intestinal adsorption, has been proposed as possible mechanism for autism pathogenesis in those patients lacking of causative genetic variants. Haptoglobin (HP) is a haemoglobin binding and acute-phase plasma protein, encoded by two co-dominant alleles, HP-1 and HP-2, producing pre-HP-1 and pre-HP-2 proteins that mature in HP-1 and HP-2, respectively. Due to a 1.7Kb copy number variation in the HP gene, the HP-2 allele has two extra exons with respect to HP-1 (Fig. 1). Thus the HP protein is a dimer in homozygous subjects for HP-1 allele and is multimer in homozygous HP-2. Endogenous pre-HP-2 protein deregulates intestinal tight-junctions through EGFR and PAR2 activation, increases intestinal permeability and has been associated with autoimmune and inflammatory diseases as well as with psychiatric conditions (Fasano, 2011; Sturgeon and Fasano, 2016). Since the association between HP alleles and autism has just been investigated in a very small sample size of patients and controls (Rose et al., 2018), we genotyped, by PCR analysis, HP in a cohort of Italian patients with autism (n=406) and in controls (n=367). The aim was to evaluate the possible association of HP-2 and autism spectrum disorder (ASD). Contrary to what we expected, HP-1 allele distribution was different between patients and controls (36.3% and 29.4%, respectively) and significantly associated with autism (P=0.0041). Since a subgroup of patients and controls have already been genotyped by Illumina Human Omni-15-8 v.1.0 and Affy-6.0 chips, respectively, we are trying to impute HP alleles from flanking SNP haplotypes. HP alleles will therefore be predicted in publicly available large cohorts of patients with autism.

Gluten is a complex molecule made of gliadin and glutenins. During digestion, gliadin is reduced into small peptides of about 20 amino acids enriched in glutamine and prolines. In the intestinal epithelium the CXCR3 (CX-Chemokine Receptor Type 3, a G-Protein Coupled Receptor - GPCR) binds two of the gliadin peptides (pep 111-130 QQQQQQQQQQQQILQQILQQ and pep 151-170 QVLQQSTYQLLQELCCQHLW). Moreover, it is involved in celiac disease (CD) development inducing an increase of intestinal permeability, resulting in zonulin release, which is induced and activated through the MyD88-pathway. Two of the three splicing variants of CXCR3 are involved in CD: CXCR3-A and CXCR3-B. Furthermore, it is known that gliadin peptides are modified by tissue transglutaminase (tTG) converting specific glutamine (Q) residues into glutamic acid (E): pep 111-130E QQQQQQQQQQQQILQQILQE and pep 151-170E QVLQESTYQLLQELCCQHLW. Evaluate differences between deamidated and non-modified gliadin peptides bound to both CXCR3 a and b isoforms The selected conformations resulting from the Autodock CG simulations (Table 1) correspond to the complexes with the lowest binding energy if considering peptide 111-130; instead, for peptide 151-170, the chosen conformations result the second in terms of binding energy and the first in terms of cluster numerosity, since the lowest binding energy conformation displays a backward orientation of the peptide. HADDOCK run (Table 2) returned up to 14 clusters for each protein-peptide docking simulation, displaying different score ranges between deamidated and normal peptides. Instead, regarding energy values they display overlapping results except for Cxcr3b-pep111 complexes. The model with the lowest score and/or the lowest binding energy (VdW, Electrostatic and Desolvation) from each run was selected for MD simulations. MD trajectories analysis suggested either a change in CXCR3 TM-helices or in C- ter flexibility when comparing non-modified peptides to deamidated ones. Therefore, what we expect to understand is the effect of the gliadin deamidation, first on binding to CXCR3, secondly on the signal transmission to the cytoplasmic domain. Accordingly, an H-bond analysis was performed on MD trajectories. Results displayed an uninterrupted H-bond network from the binding cavity to the C-ter helix in the CXCR3a/111-130 (Fig. 2a) and CXCR3b/111-130E (Fig. 2b) complexes. But, the two complexes differ for the receptor residues involved in peptide binding (in blue in Fig.2), since the deamidated peptide binds charged residues, while the non-modified interacts with non-charged residues. Likewise, also the resulting Hbond networks involve different residues. The other CXCR3 complexes display several H-bond network stops before the C-ter. Haddock docking simulations did not allow a discrimination between non-modified and deamidated peptides while Autodock CG application displays a preference for CXCR3 a or b isoforms by deamidated peptides 111-130 and 151-170, respectively. H-bond analysis let us evaluate the effects of deamidation on the binding mode and on the affinity with CXCR3 isoforms, concluding that deamidation actively influences the binding to the receptor. Furthermore, we did not observe any substantial differences in signal transduction for peptide 151-170 (Fig. 2c), while peptide 111-130 displayed two distinct networks for each isoform (Fig. 2a and 2b). Given that CXCR3 is a GPCR, so the C-ter is involved in the activation of the G-coupled protein, these results suggest pep 111-130 as favoured ligand than pep 151-170, both for CXCR3 a and b.

Spinal cord injury (SCI) counts about 17,000 new cases each year in the United States. Since axons lose the competence to regenerate in adult mammals, SCI can lead to permanent neurological damages with dramatic personal, social and economic impacts. The long-term deficit of SCI results first from the type of insult and then from the secondary phase that includes many pathophysiological events. Among these, inflammation and epigenetic factors play a crucial role in the recovery of neuron connections. Variations in epigenetic and immune contribution, in turn, depend on age and health status as well as to microbiota profile of individuals at the time of, or consequent to SCI. Indeed, gut microorganisms, highly influence the immune system, but also produce bioactive substances such as folates, butyrate and acetate that participate to the epigenetic processes. Interestingly, variations in the profile of microbiota and bioactive substances have been described in patients with neurologic intestine because of SCI. Recently, different approaches, including stem cell therapy, use of biomaterial and laser therapy, have been proposed for neuronal regeneration after SCI but they are still far from resolutive interventions. As the complexity of pathophysiological processes of the secondary phase can deeply condition the success of regeneration, we provide a landscape of microbiota-epigenetic-immune modulation of neurological recovery predisposition or prevention. We discuss most of data about epigenetics (microRNAs, circulating microRNAs and chromatin remodelling) after SCI in animal models as well as microbiome profile of patients with SCI. We also propose a bioinformatics approach to compare "comics" data (gut microbiome, circulating microRNAs and inflammatory profiles) of patiens with SCI before and after laser therapy to evaluate and improve laser induced neuroregeneration. Acknowledgements: Flagship InterOmics (PB05).

Motivation NGS has revolutionized the genomic research. Technological advances have reduced the sequencing costs while have notably increased the sequence throughput. Typically, artifacts of different nature that arise during the sequencing process affect NGS data. As these artifacts may influence the downstream analyses, data quality control (QC) becomes mandatory. Different QC tools have been proposed. Without claiming to be exhaustive, let us cite some of the most popular tools, i.e., FASTQC [1], FASTX-Toolkit [2], NGS QC Toolkit [3], PRINSEQ [4]. FASTQC is a widely adopted tool for quality assessment. It provides a set of analyses to assess the raw data according to multiple aspects. It also provides a GUI to report all analyses highlighting problems in the data. FASTX-Toolkit is a collection of command line tools for FASTA/FASTQ file processing including some quality statistics. It also provides tools for quality filtering/trimming. NGS QC Toolkit is a standalone tool for QC that includes tools for sequence trimming and statistics calculation. It is also equipped with modules to generate statistics in graphical format. PRINSEQ is a web-based and standalone tool that can be used to generate summary statistics of sequence and quality data and to filter and trim sequences. It should be pointed out that the massive amounts of generated sequences make QC computationally intensive. Despite that, only some tools implement a multicore processing strategy to deal with that computational challenge. In our opinion, even though these tools implement very useful features, their implementation does not permit to efficiently analyze large amounts of NGS data. We deem that QC tasks can be efficiently parallelized on manycore architectures as GPUs. GPUs are devices equipped with hundreds of cores able to handle thousands of threads simultaneously, so that a very high level of parallelism can be reached. In this work, we present G-FastQC (GPU Fast Quality Control) a GPU-based tool for quality assessment, filtering, and trimming of NGS data. Methods G-FastQC has been devised to be massively parallelized on NVIDIA GPUs. It supports single- and paired-end libraries generated with Illumina platforms. G-FastQC implements a set of analyses to perform QC checks on raw data. These analyses allow to assess the data according to aspects related to the quality scores and content of the sequences. In particular, G-FastQC allows to calculate the: average quality values across all bases at each position; quality score distribution over all sequences; GC content across the whole length of each sequence; GC content across all bases; sequence content across all bases at each position.To help users to analyze the data quality, G-FastQC has been integrated with an interactive web-based interface built using Shiny[5] that allows to plot graphs of the performed analyses.G-FastQC also supports quality filtering and trimming. As for quality filtering, G-FastQC allows to filter sequences based on the amount of both low quality and N nucleotides as well as to filter sequences based on the GC content. It can also be used to mask the nucleotides with a quality score lower than a given threshold. As for trimming, G-FastQC implements an operator based on a sliding window approach. It analyzes the amount of low quality nucleotides in a sliding window. When the amount of these nucleotides is higher than a given threshold, G-FastQC trims all nucleotides from the start of the window to the 3'. Trimmed sequences with length lower than a given threshold can be automatically discarded. The same sliding window approach has been used to implement an operator aimed at masking these nucleotides rather than trimming them. It should be pointed out, that unlike the above-mentioned tools, G-FastQC has been designed to perform analysis of several datasets in a single run. Results Experiments carried out on a 12 cores Intel Xeon CPU E5-2667 2.90 GHz and an NVIDIA Tesla k20c show that G-FastQC outperforms notably the other tools in terms of computing time. For instance, in the task of filtering low-quality sequences, it has been 21.4x/28.3x faster than FASTX-Toolkit/NGS QC Toolkit analyzing a dataset consisting of 50M of 100 bp reads. Similar results have been obtained comparing the performance of G-FastQC with those of the other tools to generate the quality reports, and in the tasks of filtering/trimming the raw data. References 1. http://www.bioinformatics.abraham.ac.uk/projects/fastqc/ 2. http://hannonlab.cshl.edu/fastx_toolkit 3. Patel RK, Jain M (2012) NGS QC Toolkit: a toolkit for quality control of next generation sequencing data, PloS one, 7(2), e30619. 4. Schmieder R, Robert E (2011) Quality control and preprocessing of metagenomic datasets. Bioinformatics, 27(6), 863-864. 5. https://shiny.rstudio.com

Tissues and organs are generated during development and repaired over the lifetime by stem cells with extensive self-renewal and differentiation potential. The characteristics and the identity of the stem and differentiated cells are determined by specific transcription factors that act together with chromatin regulators to stabilize expression patterns that maintain the cell identities. Disruption of the chromatin state or changes in the expression levels of chromatin regulators is associated with cellular reprogramming, disease and oncogenesis. While a large number of chromatin regulators have been identified, the epigenomic processes by which stem cells differentiate remains largely unknown for some somatic stem cell types. Three dimensional organoid cultures generated from patient derived single cells with stem cells properties, allow for investigating regulators of the chromatin state and gene expression patterns in mammary gland normal or tumor development. In this work by using cell lineage tracing and cell tracking, I characterized homogenous populations of cells derived from single mammary cells with organoid formation capacity. My findings suggest that chromatin changes in the histone state of mammary cells both initiate and stabilize gene expression patterns before the establishment of lineage specifying transcription factors.

Motivation BBMRI-ERIC mission is to construct a pan-European biobanking infrastructure, building on existing infrastructure, resources and technologies, specifically complemented with innovative components and properly embedded into European ethical, legal and societal frameworks. BBMRI.it is the national node member of the BBMRI-ERIC infrastructures involving the majority biobanks and biological resource centers located in Italy. The Italian node is actually composed by more than 18 universities, 23 IRCCS, 40 hospitals, associations of patients. The activities of BBMRI.it are aimed at harmonizing the standard operating procedures (SOPs) of biobanks, implementing the quality management system and encouraging public/private partnerships. For this purpose and in collaboration with the BBMRI.it partners we have developed a dedicated web portal (http://www.bbmri.it) to construct and operate a sustainable infrastructure for biological information in Italy to support biomedical research and its translation to medicine in Italy. Methods The BBMRI.it web portal [1] has been developed in order to collect the data and to ensure the easiest way to interact with the central node of BBMRI-ERIC infrastructures. For the development of this portal we have using the Liferay community Web Portal [2]. Based on the Liferay services, we have organized a public area of the portal for publishing all the information regarding the activities of the BBMRI.it node in Italy and Europe. The portal provide also a private area, only available via Login for the registered user in order to perform these specific services: A) To collect the initial information from the Biobank's we have developed the Survey for Biobank's accreditation using the Java Vaadin Framework [3] (Version 7.1.15). By means of this service users can add all Biobanks information online and submit to the evaluation committee the formal request to be accredited in the BBMRI.it network. All the information are stored into a specific database managed by a dedicate middleware developed with the Hibernate Framework [3]. B) The BBMRI.it Directory Service has been developed in order to allow the users to search on-line the public information regarding all the Biobanks in Italy and in Europe by the Directory 3.1 based on the MIABIS standard. This Service is realized using the Java PrimeFaces Framework [4] (Version 5.2). Thought this application is it possible to store and retrieve the information, based on the biobanks accepted in the Italian Network. This system is able to collect the data and to share the information with the BBMRI-ERIC Directory 3.1. This application is divided in 4 categories: Biobanks Networks, Biobanks, Collections and Contacts. A specific middleware for management of data has been implemented in order to manage the data with a RDBMS interconnection locally and with the BBMRI-ERIC Directory; this allow the utilization of multiple data connectors for sharing information with an unique user interface. C) The BBMRI.it Biobank Manager service, is available for the users administration in order to validate the submitted data; this operation consist in different steps: Step 1) The registered Biobank after the admission in BBMRI.it Network is granted with the permission for access to the BBMRI.it Directory Service in order to complete the information requested. Step 2) The data already present in the survey are automatically added into the BBMRI.it Directory Structure in order to simplify the process of share the information with the BBMRI-ERIC Directory. Step 3) Finally the biobanks owner is informed via email about the final acceptance in BBMRI.it. All the services described have been integrated into the portal as a portlets and can be utilized by the users in according with their role in the portal. Results We have developed the BBMRI.it Portal to facilitate the collection of data and the collaboration among the biobanks. This portal represent the centralize resource for the storage and the analysis of the information related the biobanks. By using the Directory application is possible to interact and share the information with the central European directory server. Actually the BBMRI.it Directory stores total information about more than 70 biobanks. Finally for the bioinformatics analysis of the Omics data generated from the Biobanks samples we have also developed a platform hosted on a computer cluster at the Institute of Biomedical Technologies (ITB/CNR) in Milan able to combines several Bioinformatics and Systems Biology tools. References [1] BBMRI.it Project web Site: http://www.bbmri.it [2] Liferay Portal Site: http://www.liferay.com [3] Vaadin Site: https://vaadin.com/home [3] Hibernate Site: http://www.hibernate.org [4] Primefaces: http://www.primefaces.org

Objectives: spinal cord injury (SCI) triggers a cascade of intrinsic pathophysiological events that influence the SCI long-term deficit and that should be therapeutically modulated. The aim is to identify biomarkers to favour neuronal recovery after SCI. Material and Methods: we reviewed the literature indexed in PubMed about SCI, SCI epigenetics, SCI microbiota and novel therapeutic strategies for SCI repair. Results: inflammation-epigenetics-microbiota vicious cycle affects recovery after SCI. The dysregulation of specific microRNAs and circulating microRNAs was found in spinal cord and in serum, respectively, after SCI thus circulating microRNAs are promising biomarkers for evaluating the severity of SCI, as demonstrated in animal models. SCI patients often display neurogenic intestine dysfunction including changes in gut microbiome composition with a significant reduction in butyrate producing bacteria. Butyrate is a potent anti-inflammatory agent, a histone deacetylase inhibitor and suppresses inflammation in the CNS probably reducing microglia-mediated neurotoxicity. In mice, induced gut dysbiosis exacerbates neurologic damage impairing recovery after SCI. To date, there is not a cure for SCI, however new regenerative approaches are recently suggested. In rats, valproic acid administration after SCI protects motoneurons through modulation of apoptotic pathways. The microRNA-based therapy, manipulating the expression of specific microRNAs can activate or block target genes involved in neuro-regeneration; advances in CNS microRNA delivery technologies able to cross the spinal cord blood barrier have been reached. In SC injured rats, passive cycling exercises modulated microRNAs and gene transcription favouring biochemical and cellular restore, and potentially damage recovery. Then again, regarding microbiota, probiotic-induced eubiosis improves loco-motor recovery in mice. New nutraceuticals have also been demonstrated to help in neuro-regeneration and SCI recovery. This is the case of Naringin, curcumin epigallocathechin-3-gallate, omega-3 polyunsaturated fatty acid docosahexaenoic acid (DHA) that play anti-oxidant, anti-inflammatory and anti-apoptotic rules.

CXCR3 is a G-protein coupled receptor expressed principally on leukocytes, monocytes and epithelial cells; it is involved in leukocyte traffic, integrin activation, cytoskeletal changes and chemotactic migration by binding to its classical ligands, CXCL-9/10/11. Moreover, it is involved in celiac disease by binding of 2 peptides produced from gliadin digestion (111-130 and 151-170). This interaction induces an increase of intestinal permeability in a zonulin dependent way: the cytosolic adapter protein MyD88, crucial for the maintenance of gut homeostasis, is recruited and activates zonulin release. The latter, in turn, transactivates epidermal growth factor receptor (EGFr) through proteinase activated receptor 2 (PAR2) leading to tight junctions disassembly by the combination of TJ protein phosphorylation and actin polymerization. This causes the rearrangement of the filaments of actin and the subsequent displacement of proteins from the junctional complex. Three splicing variants of CXCR3 are known: CXCR3-A, the most common isoform, consists of 368 amino acid residues and is the most frequently expressed on immune cells; it couples to a G-protein which mediates pro-migratory and proliferative signalling and increases intracellular calcium levels; CXCR3-B, which can bind CXCL4 in addition to classical CXCL, results from an alternative splicing of the CXCR3 mRNA with a 52 aa extended NH2-terminal domain when compared to the isoform A, while CXCR3-alt is a significantly truncated variant activated only by CXCL11 and not involved in celiac disease. Recently it has been demonstrated that isoform A is more abundantly expressed in the intestinal mucosa of celiac patients, while the B isoform in gluten sensitive, non-celiac patients suggesting that both isoforms are involved in gliadin binding. Our goal is to evaluate the differential binding of the natural ligand, CXCL10 (or IP-10), and of the two gliadin peptides, on the two CXCR3 isoforms (-A and -B) involved in intestinal permeability. The 3D model of both isoforms, CXCR3-A (UNIPROT-id: P49682-1) and CXCR3-B (UNIPROT-id: P49682-2) were obtained from GPCR-I-TASSER [1]. Models were included in a membrane system with CHARMM-GUI server [2]. The obtained complexes were refined by MD simulation in agreement with CHARMM-GUI suggested protocol for Gromacs. X-ray structure of IP-10 was obtained from PDB (PDB ID: 1LV9), while gliadin peptides were predicted using different de novo peptide structure modeling servers: PEP-FOLD 3 [3] , PEPstrMOD [4] and QUARK [5]. Overall, 4 conformers were obtained for peptide 111-130 and 5 for 151-170. Protein-protein docking of CXCR3(A/B)-IP10 was performed with Haddock [6] and ZDOCK [7] servers, as well as protein-peptide docking of CXCR3(A/B)-gliadin(111-130/151-170) was performed with Z-DOCK, Haddock, and a coarse grained (CG) application of AutoDock. CXCR3(A/B)-IP10 docking outputs from ZDOCK have been discarded as the ligand did not interact with the binding cavity of the receptor, while Haddock results have been evaluated through structural analysis (FCC, i-RMSD and l-RMSD) and the HADDOCK model with the lowest score has been selected for further simulations. Docking complexes of CXCR3(A/B)-gliadin peptides obtained from ZDOCK have been clustered by chimera clustering tool and 1 conformer for each complex has been achieved; Moreover, HADDOCK run returned 1 or 2 clusters for each peptide-protein docking simulation. The AutoDock CG simulation on CXCR3(A/B)-gliadin peptides is ongoing. Outputs will be processed in order to compare the different docking algorithms employed. Given that our aim is to identify the molecular determinants of peptide binding instead of the natural ligand and the different binding mode and affinity between CXCR3 isoforms, complexes will undergo MD simulations. Moreover an MMPBSA analysis on the trajectories will be performed in order to determine the binding affinity.

Gluten, the protein of wheat, is a complex molecule made of gliadin and glutenins; it is considered as "the environmental component" triggering Celiac disease (CD) since its components are both toxic for CD patients. During digestion, gliadin is reduced into small peptides of about 20 amino acids enriched in glutamine and prolines. These peptides can be deamidated by transglutaminase, converting specific glutamine (Q) residues into glutamic acid (E). In the intestinal epithelium the isoforms A and B of CX-Chemokine Receptor Type 3 (CXCR3), a G-Protein Coupled Receptor, specifically binds two of the gliadin peptides (111-130 and 151-170). Both gliadin and CXCR3 involved in CD onset. Evaluate the differential binding of the two-gliadin peptides. Evaluate differences between deamidated peptides and normal while binding the two CXCR3 isoforms (A and B). HADDOCK run returned up to 14 clusters for each peptide- protein docking simulation. The model with the lowest score and/or the lowest binding energy (VdW, Electrostatic and Desolvation) from each run was selected for MD simulation. They display different scores between deamidated and normal peptides. Among the resulting conformations from the Autodock CG simulations, the selected one correspond to the complexes with the lowest binding energy if considering peptide 111-130; instead, for peptide 151-170, the chosen conformation results the second in terms of binding energy but the first in terms of cluster numerosity, since the lowest binding energy conformation displays a backward orientation of the peptide. Haddock docking simulations suggested a higher affinity for normal peptides than for deamidated ones. Autodock CG application display a preference for A or B isoforms by deamidated peptides 111-130 and 151-170, respectively; on the contrary, normal peptides show an inverted preference for Cxcr3 isoforms. Given that our aim is to evaluate the effects of deamidation on the binding mode and affinity between CXCR3 isoforms, the MD simulations of CXCR3-gliadin are on-going.

Motivation Nowadays, advances in technology has arisen in a huge amount of data in both biomedical research and healthcare systems. This growing amount of data gives rise to the need for new research methods and analysis techniques. Analysis of these data offers new opportunities to define novel diagnostic processes. Therefore, a greater integration between healthcare and biomedical data is essential to devise novel predictive models in the field of biomedical diagnosis. In this context, the digitalization of clinical exams and medical records is becoming essential to collect heterogeneous information. Analysis of these data by means of big data technologies will allow a more in depth understanding of the mechanisms leading to diseases, and contextually it will facilitate the development of novel diagnostics and personalized therapeutics. The recent application of big data technologies in the medical fields will offer new opportunities to integrate enormous amount of medical and clinical information from population studies. Therefore, it is essential to devise new strategies aimed at storing and accessing the data in a standardized way. Moreover, it is important to provide suitable methods to manage these heterogeneous data. Methods In this work, we present a new information technology infrastructure devised to efficiently manage huge amounts of heterogeneous data for disease prevention and precision medicine. A test set based on data produced by a clinical and diagnostic laboratory has been built to set up the infrastructure. When working with clinical data is essential to ensure the confidentiality of sensitive patient data. Therefore, the set up phase has been carried out using "anonymous data". To this end, specific techniques have been adopted with the aim to ensure a high level of privacy in the correlation of the medical records with important secondary information (e.g., date of birth, place of residence). It should be noted that the rigidity of relational databases does not lend to the nature of these data. In our opinion, better results can be obtained using non-relational (NoSQL) databases. Starting from these considerations, the infrastructure has been developed on a NoSQL database with the aim to combine scalability and flexibility performances. In particular, MongoDB [1] has been used as it fits better to manage different types of data on large scale. In doing so, the infrastructure is able to provide an optimized management of huge amounts of heterogeneous data, while ensuring high speed of analysis. Results The presented infrastructure exploits big data technologies in order to overcome the limitations of relational databases when working with large and heterogeneous data. The infrastructure implements a set of interface procedures aimed at preparing the metadata for importing data in a NOSQL DB. Moreover, data can also be represented as a graph using Neo4j [2]; The Neo4J DB allows you to emphasize and enhance the connections between the data and facilitate the retrieve and navigation of data (Fig 1). Experimental tests on huge amount of data show that our infrastructure exhibits performances in terms of speed and scalability unachievable with relational databases. These performances are mainly related to ability of the infrastructure to index any type of field as well as to customize the queries. In particular, the high flexibility to customize the queries increases the search performance and specificity of the results. As for future work, we planned to implement new functions and operators to perform specialized statistics analysis on big data. References [1] http://www.mongodb.org [2] http://neo4j.com/

Introduction Advances in next-generation sequencing technologies is facilitating the sequencing and de-novo assembly of genomes from different eukaryotic and prokaryotic species. The growing number of fully assembled sequences is providing new opportunities to study the genome of different species. In addition, the availability of genome sequences of related species provides valuable opportunities to compare them with the aim to study their evolution. In this context, researchers are increasingly relying on comparative genomics to explore the genomic signals that control gene function across many species with the aim to better understand the structure and function of genes. This information helps researchers to develop new approaches for treating of diseases. To perform these analyses researchers need to use a variety of tools to annotate, analyze, explore, and compare genome sequences. Specialized tools and with different features have been proposed to the scientific community. However, the use of these tools may be difficult for biologists. In general, to facilitate the work of researchers, these tools should adopt common standards to ensure interoperability among them [1]. However, tools and analytical methods do not always use common standards making difficult the interoperability. Moreover, researchers may need to replicate scientific analyses presented in a paper, as well as to apply modifications, and to update data according to their specific needs. This may involve the analysis of huge amount of data, posing new computational challenges. Another problem is related to the sharing of data. In fact, typically, each tool generates and stores data using formats that are incompatible with the other ones. To this end, researchers involved in these analyses should have access to a single platform designed with the aim to ensure direct interoperability among the tools while supporting automated computation. According to these considerations, we implemented a standard-based HPC platform aimed at providing support at researchers involved in the tasks of annotating, analyzing, and visualizing eukaryotic and prokaryotic genomes. Methods The platform has been conceived with the aim to provide support for storage, annotation and analysis of both eukaryotic and prokaryotic genomes. To this end, it has been built on a hardware infrastructure intended to enable big-data classes of applications which consists of a massive storage platform of 1.6 PB, 2040 CPU cores, 16 NVIDIA K20 GPUs, and 2 big memory nodes (i.e., 1 node equipped with 1TB and 1 node equipped with 512GB of memory). Most of the tools integrated in the platform are components of the Generic Model Organism Database (GMOD) project [2]. GMOD is a collection of interconnected tools and databases widely used by the scientific community that includes several components for managing, annotating and visualizing genomic data. Many of the GMOD components are mature tools with several years of development and testing driven by diverse groups of developers, scientists, and laboratories that use and/or improve these components every day. In particular, the platform supports the following GMOD components: Maker [3][4], Chado[5], WebApollo [6], Gbrowse [7][8], SynView [9], and Galaxy [10]. Moreover, the platform also provides support to the (not GMOD component) GA4GH API [11] released by the Global Alliance for Genomics and Health [12] to share genomic data. In the following of this section, the above tools are briefly described. ? Maker is an annotation pipeline that can be used for de-novo annotation of genomes as well as for updating of existing annotations with the aim to reflect new evidence. It should be pointed out that Maker is MPI-capable for rapid parallelization across computer clusters. This feature makes it also suitable to annotate large genomes. Its annotation pipeline identifies repeats, aligns ESTs and proteins to a genome, produces ab-initio gene predictions and automatically synthesizes these data into gene annotations with evidencebased quality values. Maker outputs annotations in the standard GFF3 format that can be directly loaded into relational databases as Chado and genome browsers that adhere to the GMOD standards. ? Chado is a relational database schema that underlies many GMOD installations. It is used to represent different type of biological data. It has been designed to handle biological knowledge related to sequence, sequence comparisons, phenotypes, genotypes, ontologies, publications, and phylogeny. ? WebApollo is a web-based manual annotation environment for distributed community. It allows multiple users to annotate parts of the same sequence concurrently. To this end, any change made by other users is notified in the user's browser window. WebApollo keeps tracks of all changes made that can be approved or rejected by an administrator. ? GBrowse is a powerful and mature genome browser. It supports most of genome browser features, including qualitative and quantitative tracks, track uploading, track sharing, track downloading, and interactive track configuration. ? SynView is a interactive and customizable comparative genomic visualization tool based on GBrowse. It is able to display both the genomes comparison and the associated functional annotations in the same working environment. ? Galaxy is an open web-based scientific workflow system for data intensive biomedical research easily accessible to researchers that do not have programming experience. It provides an environment that helps researchers to run bioinformatics analysis tools as well as ad-hoc defined workflows. ? GA4GH API have been defined with the aim to allow a interoperable exchange of genomic information among multiple organizations and on multiple platforms. This is a freely available open standard for interoperability, that uses common web protocols to support serving and sharing of data on DNA sequences, genomic annotations and genomic variations. The API allow to create a data source that can be easily integrated in genomic analysis pipeline as well as integrated into specialized genomics platforms. Results The above tools have been properly installed and configured to work in the platform. The platform provides different access points. Authorized users can access to Maker to annotate their sequences as well as for updating of existing ones. Annotation data obtained with Maker are loaded into a Chado database and integrated into both WebApollo and GBrowse. Users can access to WebApollo for manual annotation, and to GBrowse to explore the annotated sequences as well as to compare genome sequences with SynView. GBrowse has been configured to show different tracks to explore alignments, protein-coding genes, CDS, mRNA, and other regions, as well as the GC content of the analyzed sequences. Users can customize the tracks as well as download them or upload other tracks for visualization. Moreover, GBrowse has also been configured to send data to Galaxy. Using this feature, users can visualize selected tracks on a given part of a sequence, and with a single click can send these data to Galaxy for analysis. As for Galaxy, it has been configured to run jobs on the previously described hardware infrastructure and to provide support for both CPU- and GPU-based tools [13]. It should be pointed out that bioinformatics is exploring new computational approaches based on the use of hardware accelerators such as the GPUs. Use of GPUs over the last years has resulted in significant increases in the performance of certain applications. Despite GPUs are increasingly used most laboratories do not have access to a GPU cluster or server. In this context, it is very important to provide useful services to use these tools with the aim to ensure reproducibility of specific analyses. It should be observed that Galaxy supports different distributed resource managers with the aim to enable different clusters. In our opinion, SLURM [14] represents the most suitable workload manager to manage and control jobs. SLURM is a highly configurable workload and resource manager and it is currently used on six of the ten most powerful computers in the world including the Piz Daint, utilizing over 5000 NVIDIA Tesla K20 GPUs. Users can interactively perform and refine their analyses using Galaxy. In particular, users can send or upload data into Galaxy from the other access points of the platform, analyze them, and store the results ignoring the implementation details of the underlying computing infrastructure. A GA4GH server has also been installed with the aim to share and make easy to integrate annotation data into external genomic analysis pipelines. It should be pointed out that the GA4GH API allows to retrieve genomic data according to different criteria that can be combined to meet the needs of the researcher. Then, to facilitate the work of researchers, specialized tools have been added to Galaxy to retrieve annotation data from the GA4GH server. Currently, the platform is used to support some initiatives of the InterOmics Project [15] aimed at studying human cancer, microbiome, and some plant genomes. Conclusions We presented a dedicated platform for genome annotation and analysis. The platform has been implemented with the aim to help researchers to store, annotate, explore, and analyze genomic data. The main goal that has driven the design of the platform has been to facilitate analysis and computational reproducibility. To this end, the platform integrates interconnected tools that use common standards while ensuring automated computation on a robust hardware infrastructure that support both CPU- and GPU-based computation. The GA4Gh open standard has been used to share genomic data. The platform is intended to be dynamic, therefore it will be integrated with other specialized software solutions that will be useful for the genome analysis. Currently, we are working to integrate other specialized comparative genomics tools of the GMOD project as CMAP [16] and SynBrowse [17].

Motivation Recent advances in genome sequencing and biological data analysis technologies used in bioinformatics have led to a fast and continuous increase in biological data. The difficulty of managing the huge amounts of data currently available to researchers and the need to have results within a reasonable time have led to the use of distributed and parallel computing infrastructures for their analysis. Recently, bioinformatics is exploring new approaches based on the use of hardware accelerators as GPUs. From an architectural perspective, GPUs are very different from traditional CPUs. Indeed, the latter are devices composed of few cores with lots of cache memory able to handle a few software threads at a time. Conversely, the former are devices equipped with hundreds of cores able to handle thousands of threads simultaneously, so that a very high level of parallelism can be reached. Use of GPUs over the last years has resulted in significant increases in the performance of certain applications. Despite GPUs are increasingly used in bioinformatics most laboratories do not have access to a GPU cluster or server. In this context, it is very important to provide useful services to use these tools. Methods A web-based platform has been implemented with the aim to enable researchers to perform their analysis through dedicated GPU-based computing resources. To this end, a GPU cluster equipped with 16 NVIDIA Tesla k20c cards has been configured. The infrastructure has been built upon the Galaxy technology [1]. Galaxy is an open web-based scientific workflow system for data intensive biomedical research accessible to researchers that do not have programming experience. Let us recall that Galaxy provides a public server, but it does not provide support to GPU-computing. By default, Galaxy is designed to run jobs on local systems. However, it can also be configured to run jobs on a cluster. The front-end Galaxy application runs on a single server, but tools are run on cluster nodes instead. To this end, Galaxy supports different distributed resource managers with the aim to enable different clusters. For the specific case, in our opinion SLURM [2] represents the most suitable workload manager to manage and control jobs. SLURM is a highly configurable workload and resource manager and it is currently used on six of the ten most powerful computers in the world including the Piz Daint, utilizing over 5000 NVIDIA Tesla K20 GPUs. Results GPU-based tools [3] devised by our group for quality control of NGS data have been used to test the infrastructure. Initially, this activity required to make changes to the tools with the aim to optimize the parallelization on the cluster according to the adopted workload manager. Successively, the tools have been converted into web-based services accessible through the Galaxy portal. Currently, we are working to optimize the workload manager configuration. As for future work, we planned to share through Galaxy other GPU-based tools for NGS analyses released by our group [4][5] as well as specialized workflows created using these and other validated tools imported from the Galaxy ToolShed repository. These activities will be carried-out through the European ELIXIR project. 1. Goecks, J, Nekrutenko, A, Taylor, J and The Galaxy Team. "Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences". Genome Biol. 2010 Aug 25;11(8):R86. 2. http://slurm.schedmd.com/ 3. Manconi, A et al. "G-CNV: a GPU-based tool for preparing data to detect CNVs with readdepth methods". Frontiers in Bioengineering and Biotechnology, 2015, 3. 4. Manconi, A et al. "GPU-BSM: A GPU-based tool to map bisulfite-treated reads". PLoS ONE 9(5): e97277. doi:10.1371/journal.pone.0097277, 2014. 5. Manconi, A et al. "A tool for mapping single nucleotide polymorphisms using graphics processing units". BMC Bioinformatics, 15(Suppl 1), S10, 2014

Precision medicine is an emerging and novel approach for both disease treatment and prevention. Precision medicine allows to classify individuals into subpopulations that differ in their susceptibility to a particular disease with the aim to tailor the medical treatment to the individual characteristics of each patient. To provide precision medicine to patients researchers needs to analyze huge amounts of heterogeneous data from both biomedical research and healthcare systems. The growing amount of these data gives rise to the need for new research methods and analysis techniques. In this paper we present a infrastructure that exploits new strategies aimed at storing, accessing, and analyzing efficiently these heterogeneous data.

Summary The identification of genes and SNPs involved in human diseases remains a challenge. Many public resources, databases and applications, collect biological data and perform annotations, increasing the global biological knowledge. The need of SNPs prioritization is emerging with the development of new high-throughput genotyping technologies, which allow to develop customized disease-oriented chips. Therefore, given a list of genes related to a specific biological process or disease as input, a crucial issue is finding the most relevant SNPs to analyse. The selection of these SNPs may rely on the relevant a-priori knowledge of biomolecular features characterising all the annotated SNPs and genes of the provided list. The bioinformatics approach described here allows to retrieve a ranked list of significant SNPs from a set of input genes, such as candidate genes associated with a specific disease. The system enriches the genes set by including other genes, associated to the original ones by ontological similarity evaluation. The proposed method relies on the integration of data from public resources in a vertical perspective (from genomics to systems biology data), the evaluation of features from biomolecular knowledge, the computation of partial scores for SNPs and finally their ranking, relying on their global score. Our approach has been implemented into a web based tool called SNPRanker, which is accessible through at the URL http://www.itb.cnr.it/snpranker. An interesting application of the presented system is the prioritisation of SNPs related to genes involved in specific pathologies, in order to produce custom arrays.

BACKGROUND: CaSR gene is a candidate for calcium nephrolithiasis. Single-nucleotide polymorphisms (SNPs) encompassing its regulatory region were associated with calcium nephrolithiasis. AIMS: We tested SNPs in the CaSR gene regulatory region associated with calcium nephrolithiasis and their effects in kidney. SUBJECTS AND METHODS: One hundred sixty-seven idiopathic calcium stone formers and 214 healthy controls were genotyped for four CaSR gene SNPs identified by bioinformatics analysis as modifying transcription factor binding sites. Strontium excretion after an oral load was tested in 55 stone formers. Transcriptional activity induced by variant alleles at CaSR gene promoters was compared by luciferase reporter gene assay in HEK-293 and HKC-8 cells. CaSR and claudin-14 mRNA levels were measured by real-time PCR in 107 normal kidney medulla samples and compared in patients with different CaSR genotype. RESULTS: Only rs6776158 (A>G), located in the promoter 1, was associated with nephrolithiasis. Its minor G allele was more frequent in stone formers than controls (37.8% vs 26.4%, P = .001). A reduced strontium excretion was observed in GG homozygous stone formers. Luciferase fluorescent activity was lower in cells transfected with the promoter 1 including G allele at rs6776158 than cells transfected with the A allele. CaSR mRNA levels were lower in kidney medulla samples from homozygous carriers for the G allele at rs6776158 than carriers for the A allele. Claudin-14 mRNA levels were also lower in GG homozygous subjects. CONCLUSIONS: Minor allele at rs6776158 may predispose to calcium stones by decreasing transcriptional activity of the CaSR gene promoter 1 and CaSR expression in kidney tubules.

The HIRMA project (Hepatocarcinoma Innovative Research Markers) aims at identifying and characterizing innovative markers related to the biological role of hepatotropic viruses (HBC, HBV and HDV) in the development of hepatocellular carcinoma, which is the third leading cause of cancer death worldwide. To achieve this result 30 research and hospital centres were involved to create an Italian collaborative network able to share and manage the information regarding a population of patients and their biological samples stored in biobanks. In order to allow the collection, the management and the search of the clinical data a dedicated centralized web portal (http://www.hirma.it) has been developed

Phenotypic variation results from the interaction between genetic variation and environment but it is even more evident that epigenetics acts as an additional component contributing to phenotypic variation. In fact, despite the complexity and sophistication of maize breeding, in some cases the large degree of the phenotypic variation unexplained by allelic variation has been unravelled by epigenetic variation, even if its contribution is still enigmatic due to the relatively few characterized natural epigenetic alleles (epialleles). In plants, one of the epigenetic marks is cytosine methylation (mC) and it occurs in symmetric (CG and CHG, where H is A, C, or T) as well as asymmetric (CHH) contexts. Changes in mC can occur in a spontaneous or induced manner, producing new epialleles that might lead to aberrant gene expression and phenotypic variation. Increasing evidence suggests that epigenetic variation may also arise in a genetic-independent manner, i.e. pure epigenetic variation, providing a mechanism for phenotypic variation in the absence of DNA mutations. Within this context, maize represents a good model to study epigenetic variation because it exhibits different phenomena like transposon silencing, gene imprinting and paramutation. To estimate the extent of pure epigenetic variation in maize and to identify the DNA regions targeted, we developed two maize lines derived from the highly inbred line Mo17. These lines had been separately propagated in a natural environment by single-seed descent for 6 generations. For each line, we assayed gene expression and methylation genome-wide by using RNA-seq and Reduced Representation Bisulfite Sequencing (RRBS) analysis, respectively. The latter method combines restriction enzymes and bisulfite sequencing avoiding the analysis of the entire genome, thus, reducing the costs and computational needs for analyzing the 2 Gb maize genome. Differentially expressed genes and differentially methylated regions were identified between the two groups as well as between single individual plants.

BACKGROUND: Calcium-sensing receptor gene (CaSR) is a candidate to explain susceptibility to calcium kidney stones. Thus, we studied CaSR gene single-nucleotide polymorphisms (SNPs) and haplotypes associated with stones. METHODS: Four hundred and sixty-three calcium stone formers and 213 healthy controls were genotyped for 21 SNPs mapping the whole CaSR gene. CaSR gene structure was studied. SNPs and haplotypes were analysed for association with stones. RESULTS: Three haplotype blocks were identified in the CaSR gene. The first block was characterized by six SNPs and included gene promoters. The rs7652589 and rs1501899 SNPs and the CATTCA haplotype of the first block were significantly more frequent in normocitraturic calcium kidney stone formers than controls. The risk of stones was increased in normocitraturic homozygous patients and heterozygotes for the CATTCA haplotype. The rate of stones was higher in stone formers with the CATTCA haplotype. In a three-generation family, calcium stones were associated with the CATTCA haplotype. The bioinformatic analysis identified a new site for the octamer-binding transcription factor 1 in the presence of the variant alleles at the rs7652589 and rs1501899 SNPs. This transcription factor may downregulate the transcription of vitamin D-dependent genes and the CaSR expression. Conclusion. SNPs and CATTCA haplotype of the CaSR gene first block is associated with kidney stones in normocitraturic patients.

In the last decade, different computing paradigms and modelling frameworks for the description and simulation of biochemical systems have been proposed. Here, we consider membrane systems, in particular, tissue P systems and τ-DPP, for the development of a novel variant of membrane systems with sizes associated to the volumes involved in the structure and to the molecular species occurring inside the system. Moreover, this variant allows the communication of objects among non adjacent membranes arranged in a hybrid structure, that is, organised in a tissue-like fashion where nodes can have a complex internal structure. The features presented in the new variant of P systems can be used to describe, among others, reaction-diffusion systems, where molecules are involved both in chemical reactions and diffusive processes, and their movements depend on the free space of the volumes; or systems where exist privileged pathways between membranes, which are inspired by the role of microtubule in protein transport within the intracellular space.

Review article on complex systems in BioMedicine

The brain is, without any doubt, the most, complex system of the human body. Its complexity is also due to the extremely high number of neurons, as well as the huge number of synapses connecting them. Each neuron is capable to perforin complex tasks, like learning and memorizing a large class of patterns. The simulation of large neuronal systems is challenging for both technological and computational reasons, and can open new perspectives for the comprehension of brain functioning. A well-known and widely accepted model of bidirectional synaptic plasticity, the BCM model, is stated by a differential equation approach based on bistability and selectivity properties. We have modified the BCM model extending it from a single-neuron to a whole-network model. This new model is capable to generate interesting network topologies starting from a small number of local parameters, describing the interaction between incoming and outgoing links from each neuron. We have characterized this model in terms of complex network theory, showing how this, learning rule can be a support For network generation.

Summary The Human Genome Project has deeply transformed biology and the field has since then expanded to the management, processing, analysis and visualization of large quantities of data from genomics, proteomics, medicinal chemistry and drug screening. This huge amount of data and the heterogeneity of software tools that are used implies the adoption on a very large scale of new, flexible tools that can enable researchers to integrate data and analysis on the network. ICT technology standards and tools, like Web Services and related languages, and workflow management systems, can support the creation and deployment of such systems. While a number of Web Services are appearing and personal workflow management systems are also being more and more offered to researchers, a reference portal enabling the vast majority of unskilled researchers to take profit from these new technologies is still lacking. In this paper, we introduce the rationale for the creation of such a portal and present the architecture and some preliminary results for the development of a portal for the enactment of workflows of interest in oncology.