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Abstract The aim of this study was to investigate the relationship between source-specific ambient particulate air pollution concentrations and the incidence of dementia. The study encompassed 70,057 participants from the Västerbotten intervention program cohort in Northern Sweden with a median age of 40 years at baseline. High-resolution dispersion models were employed to estimate source-specific particulate matter (PM) concentrations, such as PM10 and PM2.5 from traffic, exhaust, and biomass (mainly wood) burning, at the residential addresses of each participant. Cox regression models, adjusted for potential confounding factors, were used for the assessment. Over 884,847 person-years of follow-up, 409 incident dementia cases, identified through national registers, were observed. The study population’s average exposure to annual mean total PM10 and PM2.5 lag 1–5 years was 9.50 µg/m3 and 5.61 µg/m3, respectively. Increased risks were identified for PM10-Traffic (35% [95% CI 0–82%]) and PM2.5-Exhaust (33% [95% CI − 2 to 79%]) in the second exposure tertile for lag 1–5 years, although no such risks were observed in the third tertile. Interestingly, a negative association was observed between PM2.5-Wood burning and the risk of dementia. In summary, this register-based study did not conclusively establish a strong association between air pollution exposure and the incidence of dementia. While some evidence indicated elevated risks for PM10-Traffic and PM2.5-Exhaust, and conversely, a negative association for PM2.5-Wood burning, no clear exposure–response relationships were evident.

Constituents of air pollution, the ultrafine particles (UFP) with a diameter of ≤0.1 μm, are considerably related to traffic emissions. Several studies link air pollution to Alzheimer's disease (AD), yet the exact relationship between the two remains poorly understood. Mitochondria are known targets of environmental toxicants, and their dysfunction is associated with neurodegenerative diseases. The olfactory mucosa (OM), located at the rooftop of the nasal cavity, is directly exposed to the environment and in contact with the brain. Mounting evidence suggests that the UFPs can impact the brain directly through the olfactory tract. By using primary human OM cultures established from nasal biopsies of cognitively healthy controls and individuals diagnosed with AD, we aimed to decipher the effects of traffic-related UFPs on mitochondria. The UFP samples were collected from the exhausts of a modern heavy-duty diesel engine (HDE) without aftertreatment systems, run with renewable diesel (A0) and petroleum diesel (A20), and from an engine of a 2019 model diesel passenger car (DI-E6d) equipped with state-of-the-art aftertreatment devices and run with renewable diesel (Euro6). OM cells were exposed to three different UFPs for 24-h and 72-h, after which cellular processes were assessed on the functional and transcriptomic levels. Our results show that UFPs impair mitochondrial functions in primary human OM cells by hampering oxidative phosphorylation (OXPHOS) and redox balance, and the responses of AD cells differ from cognitively healthy controls. RNA-Seq and IPA® revealed inhibition of OXPHOS and mitochondrial dysfunction in response to UFPs A0 and A20. Functional validation confirmed that A0 and A20 impair cellular respiration, decrease ATP levels, and disturb redox balance by altering NAD and glutathione metabolism, leading to increased ROS and oxidative stress. RNA-Seq and functional assessment revealed the presence of AD-related alterations in human OM cells and that different fuels and engine technologies elicit differential effects.

Abstract Mantle cell lymphoma (MCL) is an incurable B-cell malignancy characterized by a high clinical variability. Therefore, there is a critical need to define parameters that identify high-risk patients for aggressive disease and therapy resistance. B-cell receptor (BCR) signaling is crucial for MCL initiation and progression and is a target for therapeutic intervention. We interrogated BCR signaling proteins (SYK, LCK, BTK, PLCγ2, p38, AKT, NF-κB p65, and STAT5) in 30 primary MCL samples using phospho-specific flow cytometry. Anti-IgM modulation induced heterogeneous BCR signaling responses among samples allowing the identification of two clusters with differential responses. The cluster with higher response was associated with shorter progression free survival (PFS) and overall survival (OS). Moreover, higher constitutive AKT activity was predictive of inferior response to the Bruton's tyrosine kinase inhibitor (BTKi) ibrutinib. Time-to-event analyses showed that MCL international prognostic index (MIPI) high-risk category and higher STAT5 response were predictors of shorter PFS and OS whilst MIPI high-risk category and high SYK response predicted shorter OS. In conclusion, we identified BCR signaling properties associated with poor clinical outcome and resistance to ibrutinib, thus highlighting the prognostic and predictive significance of BCR activity and advancing our understanding of signaling heterogeneity underlying clinical behavior of MCL.

Abstract Background Microglia are the endogenous immune cells of the brain and act as sensors of pathology to maintain brain homeostasis and eliminate potential threats. In Alzheimer's disease (AD), toxic amyloid beta (Aβ) accumulates in the brain and forms stiff plaques. In late-onset AD accounting for 95% of all cases, this is thought to be due to reduced clearance of Aβ. Human genome-wide association studies and animal models suggest that reduced clearance results from aberrant function of microglia. While the impact of neurochemical pathways on microglia had been broadly studied, mechanical receptors regulating microglial functions remain largely unexplored. Methods Here we showed that a mechanotransduction ion channel, PIEZO1, is expressed and functional in human and mouse microglia. We used a small molecule agonist, Yoda1, to study how activation of PIEZO1 affects AD-related functions in human induced pluripotent stem cell (iPSC)-derived microglia-like cells (iMGL) under controlled laboratory experiments. Cell survival, metabolism, phagocytosis and lysosomal activity were assessed using real-time functional assays. To evaluate the effect of activation of PIEZO1 in vivo, 5-month-old 5xFAD male mice were infused daily with Yoda1 for two weeks through intracranial cannulas. Microglial Iba1 expression and Aβ pathology were quantified with immunohistochemistry and confocal microscopy. Published human and mouse AD datasets were used for in-depth analysis of PIEZO1 gene expression and related pathways in microglial subpopulations. Results We show that PIEZO1 orchestrates Aβ clearance by enhancing microglial survival, phagocytosis, and lysosomal activity. Aβ inhibited PIEZO1-mediated calcium transients, whereas activation of PIEZO1 with a selective agonist, Yoda1, improved microglial phagocytosis resulting in Aβ clearance both in human and mouse models of AD. Moreover, PIEZO1 expression was associated with a unique microglial transcriptional phenotype in AD as indicated by assessment of cellular metabolism, and human and mouse single-cell datasets. Conclusion These results indicate that the compromised function of microglia in AD could be improved by controlled activation of PIEZO1 channels resulting in alleviated Aβ burden. Pharmacological regulation of these mechanoreceptors in microglia could represent a novel therapeutic paradigm for AD.

Abstract Hypoxia induces changes in the secretion of extracellular vesicles (EVs) in several non‐neuronal cells and pathological conditions. EVs are packed with biomolecules, such as microRNA(miR)‐21‐5p, which respond to hypoxia. However, the true EV association of miR‐21‐5p, and its functional or biomarker relevance, are inadequately characterised. Neurons are extremely sensitive cells, and it is not known whether the secretion of neuronal EVs and miR‐21‐5p are altered upon hypoxia. Here, we characterised the temporal EV secretion profile and cell viability of neurons under hypoxia. Hypoxia induced a rapid increase of miR‐21a‐5p secretion in the EVs, which preceded the elevation of hypoxia‐induced tissue or cellular miR‐21a‐5p. Prolonged hypoxia induced cell death and the release of morphologically distinct EVs. The EVs protected miR‐21a‐5p from enzymatic degradation but a remarkable fraction of miR‐21a‐5p remained fragile and non‐EV associated. The increase in miR‐21a‐5p secretion may have biomarker potential, as high blood levels of miR‐21‐5p in stroke patients were associated with significant disability at hospital discharge. Our data provides an understanding of the dynamic regulation of EV secretion from neurons under hypoxia and provides a candidate for the prediction of recovery from ischemic stroke.

Abstract Background Graphs are mathematical structures widely used for expressing relationships among elements when representing biomedical and biological information. On top of these representations, several analyses are performed. A common task is the search of one substructure within one graph, called target. The problem is referred to as one-to-one subgraph search, and it is known to be NP-complete. Heuristics and indexing techniques can be applied to facilitate the search. Indexing techniques are also exploited in the context of searching in a collection of target graphs, referred to as one-to-many subgraph problem. Filter-and-verification methods that use indexing approaches provide a fast pruning of target graphs or parts of them that do not contain the query. The expensive verification phase is then performed only on the subset of promising targets. Indexing strategies extract graph features at a sufficient granularity level for performing a powerful filtering step. Features are memorized in data structures allowing an efficient access. Indexing size, querying time and filtering power are key points for the development of efficient subgraph searching solutions. Results An existing approach, GRAPES, has been shown to have good performance in terms of speed-up for both one-to-one and one-to-many cases. However, it suffers in the size of the built index. For this reason, we propose GRAPES-DD, a modified version of GRAPES in which the indexing structure has been replaced with a Decision Diagram. Decision Diagrams are a broad class of data structures widely used to encode and manipulate functions efficiently. Experiments on biomedical structures and synthetic graphs have confirmed our expectation showing that GRAPES-DD has substantially reduced the memory utilization compared to GRAPES without worsening the searching time. Conclusion The use of Decision Diagrams for searching in biochemical and biological graphs is completely new and potentially promising thanks to their ability to encode compactly sets by exploiting their structure and regularity, and to manipulate entire sets of elements at once, instead of exploring each single element explicitly. Search strategies based on Decision Diagram makes the indexing for biochemical graphs, and not only, more affordable allowing us to potentially deal with huge and ever growing collections of biochemical and biological structures.

Abstract Background Ischemic stroke is a devastating disease without a cure. The available treatments for ischemic stroke, thrombolysis by tissue plasminogen activator, and thrombectomy are suitable only to a fraction of patients and thus novel therapeutic approaches are urgently needed. The neuroinflammatory responses elicited secondary to the ischemic attack further aggravate the stroke-induced neuronal damage. It has been demonstrated that these responses are regulated at the level of non-coding RNAs, especially miRNAs. Methods We utilized lentiviral vectors to overexpress miR-669c in BV2 microglial cells in order to modulate their polarization. To detect whether the modulation of microglial activation by miR-669c provides protection in a mouse model of transient focal ischemic stroke, miR-669c overexpression was driven by a lentiviral vector injected into the striatum prior to induction of ischemic stroke. Results Here, we demonstrate that miR-669c-3p, a member of chromosome 2 miRNA cluster (C2MC), is induced upon hypoxic and excitotoxic conditions in vitro and in two different in vivo models of stroke. Rather than directly regulating the neuronal survival in vitro, miR-669c is capable of attenuating the microglial proinflammatory activation in vitro and inducing the expression of microglial alternative activation markers arginase 1 (Arg1), chitinase-like 3 (Ym1), and peroxisome proliferator-activated receptor gamma (PPAR-γ). Intracerebral overexpression of miR-669c significantly decreased the ischemia-induced cell death and ameliorated the stroke-induced neurological deficits both at 1 and 3 days post injury (dpi). Albeit miR-669c overexpression failed to alter the overall Iba1 protein immunoreactivity, it significantly elevated Arg1 levels in the ischemic brain and increased colocalization of Arg1 and Iba1. Moreover, miR-669c overexpression under cerebral ischemia influenced several morphological characteristics of Iba1 positive cells. We further demonstrate the myeloid differentiation primary response gene 88 (MyD88) transcript as a direct target for miR-669c-3p in vitro and show reduced levels of MyD88 in miR-669c overexpressing ischemic brains in vivo. Conclusions Collectively, our data provide the evidence that miR-669c-3p is protective in a mouse model of ischemic stroke through enhancement of the alternative microglial/macrophage activation and inhibition of MyD88 signaling. Our results accentuate the importance of controlling miRNA-regulated responses for the therapeutic benefit in conditions of stroke and neuroinflammation.

Transcription factors (TFs) are proteins that promote or reduce the expression of genes by binding short genomic DNA sequences known as transcription factor binding sites (TFBS). While several tools have been developed to scan for potential occurrences of TFBS in linear DNA sequences or reference genomes, no tool exists to find them in pangenome variation graphs (VGs). VGs are sequence-labelled graphs that can efficiently encode collections of genomes and their variants in a single, compact data structure. Because VGs can losslessly compress large pangenomes, TFBS scanning in VGs can efficiently capture how genomic variation affects the potential binding landscape of TFs in a population of individuals. Here we present GRAFIMO (GRAph-based Finding of Individual Motif Occurrences), a command-line tool for the scanning of known TF DNA motifs represented as Position Weight Matrices (PWMs) in VGs. GRAFIMO extends the standard PWM scanning procedure by considering variations and alternative haplotypes encoded in a VG. Using GRAFIMO on a VG based on individuals from the 1000 Genomes project we recover several potential binding sites that are enhanced, weakened or missed when scanning only the reference genome, and which could constitute individual-specific binding events. GRAFIMO is available as an open-source tool, under the MIT license, at https://github.com/pinellolab/GRAFIMO and https://github.com/InfOmics/GRAFIMO.

Abstract Background Pancreatic ductal adenocarcinoma (PDAC) is a highly devastating disease with an overall 5-year survival rate of less than 8%. New evidence indicates that PDAC cells release pro-inflammatory metabolites that induce a marked alteration of normal hematopoiesis, favoring the expansion and accumulation of myeloid-derived suppressor cells (MDSCs). We report here that PDAC patients show increased levels of both circulating and tumor-infiltrating MDSC-like cells. Methods The frequency of MDSC subsets in the peripheral blood was determined by flow cytometry in three independent cohorts of PDAC patients (total analyzed patients, n = 117). Frequency of circulating MDSCs was correlated with overall survival of PDAC patients. We also analyzed the frequency of tumor-infiltrating MDSC and the immune landscape in fresh biopsies. Purified myeloid cell subsets were tested in vitro for their T-cell suppressive capacity. Results Correlation with clinical data revealed that MDSC frequency was significantly associated with a shorter patients’ overall survival and metastatic disease. However, the immunosuppressive activity of purified MDSCs was detectable only in some patients and mainly limited to the monocytic subset. A transcriptome analysis of the immunosuppressive M-MDSCs highlighted a distinct gene signature in which STAT3 was crucial for monocyte re-programming. Suppressive M-MDSCs can be characterized as circulating STAT3/arginase1-expressing CD14+ cells. Conclusion MDSC analysis aids in defining the immune landscape of PDAC patients for a more appropriate diagnosis, stratification and treatment.

Abstract The 17th International NETTAB workshop was held in Palermo, Italy, on October 16-18, 2017. The special topic for the meeting was “Methods, tools and platforms for Personalised Medicine in the Big Data Era”, but the traditional topics of the meeting series were also included in the event. About 40 scientific contributions were presented, including four keynote lectures, five guest lectures, and many oral communications and posters. Also, three tutorials were organised before and after the workshop. Full papers from some of the best works presented in Palermo were submitted for this Supplement of BMC Bioinformatics. Here, we provide an overview of meeting aims and scope. We also shortly introduce selected papers that have been accepted for publication in this Supplement, for a complete presentation of the outcomes of the meeting.

Patient classification based on clinical and genomic data will further the goal of precision medicine. Interpretability is of particular relevance for models based on genomic data, where sample sizes are relatively small (in the hundreds), increasing overfitting risk netDx is a machine learning method to integrate multi-modal patient data and build a patient classifier. Patient data are converted into networks of patient similarity, which is intuitive to clinicians who also use patient similarity for medical diagnosis. Features passing selection are integrated, and new patients are assigned to the class with the greatest profile similarity. netDx has excellent performance, outperforming most machine-learning methods in binary cancer survival prediction. It handles missing data – a common problem in real-world data – without requiring imputation. netDx also has excellent interpretability, with native support to group genes into pathways for mechanistic insight into predictive features. The netDx Bioconductor package provides multiple workflows for users to build custom patient classifiers. It provides turnkey functions for one-step predictor generation from multi-modal data, including feature selection over multiple train/test data splits. Workflows offer versatility with custom feature design, choice of similarity metric; speed is improved by parallel execution. Built-in functions and examples allow users to compute model performance metrics such as AUROC, AUPR, and accuracy. netDx uses RCy3 to visualize top-scoring pathways and the final integrated patient network in Cytoscape. Advanced users can build more complex predictor designs with functional building blocks used in the default design. Finally, the netDx Bioconductor package provides a novel workflow for pathway-based patient classification from sparse genetic data.

Olfactory function, orchestrated by the cells of the olfactory mucosa at the rooftop of the nasal cavity, is disturbed early in the pathogenesis of Alzheimer’s disease (AD). Biometals including zinc and calcium are known to be important for sense of smell and to be altered in the brains of AD patients. Little is known about elemental homeostasis in the AD patient olfactory mucosa. Here we aimed to assess whether the disease-related alterations to biometal homeostasis observed in the brain are also reflected in the olfactory mucosa. We applied RNA sequencing to discover gene expression changes related to metals in olfactory mucosal cells of cognitively healthy controls, individuals with mild cognitive impairment and AD patients, and performed analysis of the elemental content to determine metal levels. Results demonstrate that the levels of zinc, calcium and sodium are increased in the AD olfactory mucosa concomitantly with alterations to 17 genes related to metal-ion binding or metal-related function of the protein product. A significant elevation in alpha-2-macroglobulin, a known metal-binding biomarker correlated with brain disease burden, was observed on the gene and protein levels in the olfactory mucosa cells of AD patients. These data demonstrate that the olfactory mucosa cells derived from AD patients recapitulate certain impairments of biometal homeostasis observed in the brains of patients.

The current COVID-19 pandemic presents unprecedented new challenges to public health and medical care delivery. To control viral transmission, social distancing measures have been implemented all over the world, interrupting the access to routine medical care for many individuals with neurological diseases. Cognitive disorders are common in many neurological conditions, e.g., stroke, traumatic brain injury, Alzheimer's disease, and other types of dementia, Parkinson's disease and parkinsonian syndromes, and multiple sclerosis, and should be addressed by cognitive rehabilitation interventions. To be effective, cognitive rehabilitation programs must be intensive and prolonged over time; however, the current virus containment measures are hampering their implementation. Moreover, the reduced access to cognitive rehabilitation might worsen the relationship between the patient and the healthcare professional. Urgent measures to address issues connected to COVID-19 pandemic are, therefore, needed. Remote communication technologies are increasingly regarded as potential effective options to support health care interventions, including neurorehabilitation and cognitive rehabilitation. Among them, telemedicine, virtual reality, augmented reality, and serious games could be in the forefront of these efforts. We will briefly review current evidence-based recommendations on the efficacy of cognitive rehabilitation and offer a perspective on the role of tele- and virtual rehabilitation to achieve adequate cognitive stimulation in the era of social distancing related to COVID-19 pandemic. In particular, we will discuss issues related to their diffusion and propose a roadmap to address them. Methodological and technological improvements might lead to a paradigm shift to promote the delivery of cognitive rehabilitation to people with reduced mobility and in remote regions.

Olfaction is orchestrated by olfactory mucosal cells located in the upper nasal cavity. Olfactory dysfunction manifests early in several neurodegenerative disorders including Alzheimer’s disease, however, disease-related alterations to the olfactory mucosal cells remain poorly described. The aim of this study was to evaluate the olfactory mucosa differences between cognitively healthy individuals and Alzheimer’s disease patients. We report increased amyloid-beta secretion in Alzheimer’s disease olfactory mucosal cells and detail cell-type-specific gene expression patterns, unveiling 240 differentially expressed disease-associated genes compared to the cognitively healthy controls, and five distinct cell populations. Overall, alterations of RNA and protein metabolism, inflammatory processes, and signal transduction were observed in multiple cell populations, suggesting their role in Alzheimer’s disease-related olfactory mucosa pathophysiology. Furthermore, the single-cell RNA-sequencing proposed alterations in gene expression of mitochondrially located genes in AD OM cells, which were verified by functional assays, demonstrating altered mitochondrial respiration and a reduction of ATP production. Our results reveal disease-related changes of olfactory mucosal cells in Alzheimer’s disease and demonstrate the utility of single-cell RNA sequencing data for investigating molecular and cellular mechanisms associated with the disease.

Abstract Background Pan-genome approaches afford the discovery of homology relations in a set of genomes, by determining how some gene families are distributed among a given set of genomes. The retrieval of a complete gene distribution among a class of genomes is an NP-hard problem because computational costs increase with the number of analyzed genomes, in fact, all-against-all gene comparisons are required to completely solve the problem. In presence of phylogenetically distant genomes, due to the variability introduced in gene duplication and transmission, the task of recognizing homologous genes becomes even more difficult. A challenge on this field is that of designing fast and adaptive similarity measures in order to find a suitable pan-genome structure of homology relations. Results We present PanDelos, a stand alone tool for the discovery of pan-genome contents among phylogenetic distant genomes. The methodology is based on information theory and network analysis. It is parameter-free because thresholds are automatically deduced from the context. PanDelos avoids sequence alignment by introducing a measure based on k-mer multiplicity. The k-mer length is defined according to general arguments rather than empirical considerations. Homology candidate relations are integrated into a global network and groups of homologous genes are extracted by applying a community detection algorithm. Conclusions PanDelos outperforms existing approaches, Roary and EDGAR, in terms of running times and quality content discovery. Tests were run on collections of real genomes, previously used in analogous studies, and in synthetic benchmarks that represent fully trusted golden truth. The software is available at https://github.com/GiugnoLab/PanDelos.

Abstract Background R has become the de-facto reference analysis environment in Bioinformatics. Plenty of tools are available as packages that extend the R functionality, and many of them target the analysis of biological networks. Several algorithms for graphs, which are the most adopted mathematical representation of networks, are well-known examples of applications that require high-performance computing, and for which classic sequential implementations are becoming inappropriate. In this context, parallel approaches targeting GPU architectures are becoming pervasive to deal with the execution time constraints. Although R packages for parallel execution on GPUs are already available, none of them provides graph algorithms. Results This work presents cuRnet, a R package that provides a parallel implementation for GPUs of the breath-first search (BFS), the single-source shortest paths (SSSP), and the strongly connected components (SCC) algorithms. The package allows offloading computing intensive applications to GPU devices for massively parallel computation and to speed up the runtime up to one order of magnitude with respect to the standard sequential computations on CPU. We have tested cuRnet on a benchmark of large protein interaction networks and for the interpretation of high-throughput omics data thought network analysis. Conclusions cuRnet is a R package to speed up graph traversal and analysis through parallel computation on GPUs. We show the efficiency of cuRnet applied both to biological network analysis, which requires basic graph algorithms, and to complex existing procedures built upon such algorithms.

Abstract Background High throughput technologies have provided the scientific community an unprecedented opportunity for large-scale analysis of genomes. Non-coding RNAs (ncRNAs), for a long time believed to be non-functional, are emerging as one of the most important and large family of gene regulators and key elements for genome maintenance. Functional studies have been able to assign to ncRNAs a wide spectrum of functions in primary biological processes, and for this reason they are assuming a growing importance as a potential new family of cancer therapeutic targets. Nevertheless, the number of functionally characterized ncRNAs is still too poor if compared to the number of new discovered ncRNAs. Thus platforms able to merge information from available resources addressing data integration issues are necessary and still insufficient to elucidate ncRNAs biological roles. Results In this paper, we describe a platform called Arena-Idb for the retrieval of comprehensive and non-redundant annotated ncRNAs interactions. Arena-Idb provides a framework for network reconstruction of ncRNA heterogeneous interactions (i.e., with other type of molecules) and relationships with human diseases which guide the integration of data, extracted from different sources, via mapping of entities and minimization of ambiguity. Conclusions Arena-Idb provides a schema and a visualization system to integrate ncRNA interactions that assists in discovering ncRNA functions through the extraction of heterogeneous interaction networks. The Arena-Idb is available at http://arenaidb.ba.itb.cnr.it

Abstract Background The analysis of tissue-specific protein interaction networks and their functional enrichment in pathological and normal tissues provides insights on the etiology of diseases. The Pan-cancer proteomic project, in The Cancer Genome Atlas, collects protein expressions in human cancers and it is a reference resource for the functional study of cancers. However, established protocols to infer interaction networks from protein expressions are still missing. Results We have developed a methodology called Inference Network Based on iRefIndex Analysis (INBIA) to accurately correlate proteomic inferred relations to protein-protein interaction (PPI) networks. INBIA makes use of 14 network inference methods on protein expressions related to 16 cancer types. It uses as reference model the iRefIndex human PPI network. Predictions are validated through non-interacting and tissue specific PPI networks resources. The first, Negatome, takes into account likely non-interacting proteins by combining both structure properties and literature mining. The latter, TissueNet and GIANT, report experimentally verified PPIs in more than 50 human tissues. The reliability of the proposed methodology is assessed by comparing INBIA with PERA, a tool which infers protein interaction networks from Pathway Commons, by both functional and topological analysis. Conclusion Results show that INBIA is a valuable approach to predict proteomic interactions in pathological conditions starting from the current knowledge of human protein interactions.

Mesenchymal stromal cells (MSCs) are adult, multipotent cells of mesodermal origin representing the progenitors of all stromal tissues. MSCs possess significant and broad immunomodulatory functions affecting both adaptive and innate immune responses once MSCs are primed by the inflammatory microenvironment. Recently, the role of extracellular vesicles (EVs) in mediating the therapeutic effects of MSCs has been recognized. Nevertheless, the molecular mechanisms responsible for the immunomodulatory properties of MSC-derived EVs (MSC-EVs) are still poorly characterized. Therefore, we carried out a molecular characterization of MSC-EV content by high-throughput approaches. We analyzed miRNA and protein expression profile in cellular and vesicular compartments both in normal and inflammatory conditions. We found several proteins and miRNAs involved in immunological processes, such as MOES, LG3BP, PTX3, and S10A6 proteins, miR-155-5p, and miR-497-5p. Different in silico approaches were also performed to correlate miRNA and protein expression profile and then to evaluate the putative molecules or pathways involved in immunoregulatory properties mediated by MSC-EVs. PI3K-AKT signaling pathway and the regulation of actin cytoskeleton were identified and functionally validated in vitro as key mediators of MSC/B cell communication mediated by MSC-EVs. In conclusion, we identified different molecules and pathways responsible for immunoregulatory properties mediated by MSC-EVs, thus identifying novel therapeutic targets as safer and more useful alternatives to cell or EV-based therapeutic approaches.

Stressful conditions induce the cell to save energy and activate a rescue program modulated by mammalian target of rapamycin (mTOR). Along with transcriptional and translational regulation, the cell relies also on post-transcriptional modulation to quickly adapt the translation of essential proteins. MicroRNAs play an important role in the regulation of protein translation, and their availability is tightly regulated by RNA competing mechanisms often mediated by long noncoding RNAs (lncRNAs). In our paper, we simulated the response to growth adverse condition by bimiralisib, a dual PI3K/mTOR inhibitor, in diffuse large B cell lymphoma cell lines, and we studied post-transcriptional regulation by the differential analysis of exonic and intronic RNA expression. In particular, we observed the upregulation of a lncRNA, lncTNK2-2:1, which correlated with the stabilization of transcripts involved in the regulation of translation and DNA damage after bimiralisib treatment. We identified miR-21-3p as miRNA likely sponged by lncTNK2-2:1, with consequent stabilization of the mRNA of p53, which is a master regulator of cell growth in response to DNA damage.

Growing grapevine (Vitis vinifera) is a key contribution to the economy of many countries. Tools provided by genomics and bioinformatics did help researchers in obtaining biological knowledge about the different cultivars. Several genetic markers for common diseases were identified. Recently, the impact of microbiome has been proved to be of fundamental importance both in humans and in plants for its ability to confer protection or induce diseases. In this review we report current knowledge about grapevine microbiome, together with a description of the available computational methodologies for meta-omics analysis.

Background: Diffuse large B-cell lymphoma (DLBCL), marginal zone lymphoma (MZL) and follicular lymphoma (FL) are the most common B-cell lymphomas (BCL) in dogs. Recent investigations have demonstrated overlaps of these histotypes with the human counterparts, including clinical presentation, biologic behavior, tumor genetics, and treatment response. The molecular mechanisms that underlie canine BCL are still unknown and new studies to improve diagnosis, therapy, and the utilization of canine species as spontaneous animal tumor models are undeniably needed. Recent work using human DLBCL transcriptomes has suggested that long non-coding RNAs (lncRNAs) play a key role in lymphoma pathogenesis and pinpointed a restricted number of lncRNAs as potential targets for further studies. Results: To expand the knowledge of non-coding molecules involved in canine BCL, we used transcriptomes obtained from a cohort of 62 dogs with newly-diagnosed multicentric DLBCL, MZL and FL that had undergone complete staging work-up and were treated with chemotherapy or chemo-immunotherapy. We developed a customized R pipeline performing a transcriptome assembly by multiple algorithms to uncover novel lncRNAs, and delineate genome-wide expression of unannotated and annotated lncRNAs. Our pipeline also included a new package for high performance system biology analysis, which detects high-scoring network biological neighborhoods to identify functional modules. Moreover, our customized pipeline quantified the expression of novel and annotated lncRNAs, allowing us to subtype DLBCLs into two main groups. The DLBCL subtypes showed statistically different survivals, indicating the potential use of lncRNAs as prognostic biomarkers in future studies. Conclusions: In this manuscript, we describe the methodology used to identify lncRNAs that differentiate B-cell lymphoma subtypes and we interpreted the biological and clinical values of the results. We inferred the potential functions of lncRNAs to obtain a comprehensive and integrative insight that highlights their impact in this neoplasm.

After publication of this supplement article [1], it was brought to our attention that reference 10 and reference 12 in the article are incorrect.

Over the years, computation has become a fundamental part of the scientific practice in several research fields that goes far beyond the boundaries of natural sciences. Data mining, machine learning, simulations and other computational methods lie today at the hearth of the scientific endeavour in a growing number of social research areas from anthropology to economics. In this scenario, an increasingly important role is played by analytical platforms: integrated environments allowing researchers to experiment cutting-edge data-driven and computation-intensive analyses. The paper discusses the appearance of such tools in the emerging field of computational legal science. After a general introduction to the impact of computational methods on both natural and social sciences, we describe the concept and the features of an analytical platform exploring innovative cross-methodological approaches to the academic and investigative study of crime. Stemming from an ongoing project involving researchers from law, computer science and bioinformatics, the initiative is presented and discussed as an opportunity to raise a debate about the future of legal scholarship and, inside of it, about the challenges of computational legal science.

We present NetMatchStar, a Cytoscape app to find all the occurrences of a query graph in a network and check for its significance as a motif with respect to seven different random models. The query can be uploaded or built from scratch using Cytoscape facilities. The app significantly enhances the previous NetMatch in style, performance and functionality. Notably NetMatchStar allows queries with wildcards.

We present NetMatchStar, a Cytoscape app to find all the occurrences of a query graph in a network and check for its significance as a motif with respect to seven different random models. The query can be uploaded or built from scratch using Cytoscape facilities. The app significantly enhances the previous NetMatch in style, performance and functionality. Notably NetMatchStar allows queries with wildcards.

Comparing protein interaction networks can reveal interesting patterns of interactions for a specific function or process in distantly related species. In this paper we present GASOLINE, a Cytoscape app for multiple local alignments of PPI (protein-protein interaction) networks. The app is based on the homonymous greedy and stochastic algorithm. GASOLINE starts with the identification of sets of similar nodes, called seeds of the alignment. Alignments are then extended in a greedy manner and finally refined. Both the identification of seeds and the extension of alignments are performed through an iterative Gibbs sampling strategy. GASOLINE is a Cytoscape app for computing and visualizing local alignments, without requiring any post-processing operations. GO terms can be easily attached to the aligned proteins for further functional analysis of alignments. GASOLINE can perform the alignment task in few minutes, even for a large number of input networks.

The analysis of structure and dynamics of biological networks plays a central role in understanding the intrinsic complexity of biological systems. Biological networks have been considered a suitable formalism to extend evolutionary and comparative biology. In this paper we present GASOLINE, an algorithm for multiple local network alignment based on statistical iterative sampling in connection to a greedy strategy. GASOLINE overcomes the limits of current approaches by producing biologically significant alignments within a feasible running time, even for very large input instances. The method has been extensively tested on a database of real and synthetic biological networks. A comprehensive comparison with state-of-the art algorithms clearly shows that GASOLINE yields the best results in terms of both reliability of alignments and running time on real biological networks and results comparable in terms of quality of alignments on synthetic networks. GASOLINE has been developed in Java, and is available, along with all the computed alignments, at the following URL: http://ferrolab.dmi.unict.it/gasoline/gasoline.html.

We present a new classification method for expression profiling data, called MIDClass (Microarray Interval Discriminant CLASSifier), based on association rules. It classifies expressions profiles exploiting the idea that the transcript expression intervals better discriminate subtypes in the same class. A wide experimental analysis shows the effectiveness of MIDClass compared to the most prominent classification approaches.

Biological applications, from genomics to ecology, deal with graphs that represents the structure of interactions. Analyzing such data requires searching for subgraphs in collections of graphs. This task is computationally expensive. Even though multicore architectures, from commodity computers to more advanced symmetric multiprocessing (SMP), offer scalable computing power, currently published software implementations for indexing and graph matching are fundamentally sequential. As a consequence, such software implementations (i) do not fully exploit available parallel computing power and (ii) they do not scale with respect to the size of graphs in the database. We present GRAPES, software for parallel searching on databases of large biological graphs. GRAPES implements a parallel version of well-established graph searching algorithms, and introduces new strategies which naturally lead to a faster parallel searching system especially for large graphs. GRAPES decomposes graphs into subcomponents that can be efficiently searched in parallel. We show the performance of GRAPES on representative biological datasets containing antiviral chemical compounds, DNA, RNA, proteins, protein contact maps and protein interactions networks.

MicroRNAs are small noncoding RNAs that play an important role in the regulation of various biological processes through their interaction with cellular messenger RNAs. They are frequently dysregulated in cancer and have shown great potential as tissue-based markers for cancer classification and prognostication. microRNAs are also present in extracellular human body fluids such as serum, plasma, saliva, and urine. Most of circulating microRNAs are present in human plasma and serum cofractionate with the Argonaute2 (Ago2) protein. However, circulating microRNAs have been also found in membrane-bound vesicles such as exosomes. Since microRNAs circulate in the bloodstream in a highly stable, extracellular form, they may be used as blood-based biomarkers for cancer and other diseases. A knowledge base of extracellular circulating miRNAs is a fundamental tool for biomedical research. In this work, we present miRandola, a comprehensive manually curated classification of extracellular circulating miRNAs. miRandola is connected to miRò, the miRNA knowledge base, allowing users to infer the potential biological functions of circulating miRNAs and their connections with phenotypes. The miRandola database contains 2132 entries, with 581 unique mature miRNAs and 21 types of samples. miRNAs are classified into four categories, based on their extracellular form: miRNA-Ago2 (173 entries), miRNA-exosome (856 entries), miRNA-HDL (20 entries) and miRNA-circulating (1083 entries). miRandola is available online at: http://atlas.dmi.unict.it/mirandola/index.html.

BACKGROUND: Chromosomal fragile sites are heritable specific loci especially prone to breakage. Some of them are associated with human genetic disorders and several studies have demonstrated their importance in genome instability in cancer. MicroRNAs (miRNAs) are small non-coding RNAs responsible of post-transcriptional gene regulation and their involvement in several diseases such as cancer has been widely demonstrated. The altered expression of miRNAs is sometimes due to chromosomal rearrangements and epigenetic events, thus it is essential to study miRNAs in the context of their genomic locations, in order to find significant correlations between their aberrant expression and the phenotype. PRINCIPAL FINDINGS: Here we use statistical models to study the incidence of human miRNA genes on fragile sites and their association with cancer-specific translocation breakpoints, repetitive elements, and CpG islands. Our results show that, on average, fragile sites are denser in miRNAs and also in protein coding genes. However, the distribution of miRNAs and protein coding genes in fragile versus non-fragile sites depends on chromosome. We find also a positive correlation between fragility and repeats, and between miRNAs and CpG islands. CONCLUSION: Our results show that the relationship between site fragility and miRNA density is far more complex than previously thought. For example, we find that protein coding genes seem to be following similar patterns as miRNAs, if considered their overall distribution. However, once we allow for differences at the chromosome level in our statistical analysis, we find that distribution of miRNA and protein coding genes in fragile sites is very different from that of miRNA. This is a novel result that we believe may help discover new potential correlations between the localization of miRNAs and their crucial role in biological processes and in the development of diseases.