Analysing omics data sets with weighted nodes networks (WNNets).

Current trends in biomedical research indicate data integration as a fundamental step towards precision medicine. In this context, network models allow representing and analysing complex biological processes. However, although effective in unveiling network properties, these models fail in considering the individual, biochemical variations occurring at molecular level. As a consequence, the analysis of these models partially loses its predictive power. To overcome these limitations, Weighted Nodes Networks (WNNets) were developed. WNNets allow to easily and effectively weigh nodes using experimental information from multiple conditions. In this study, the characteristics of WNNets were described and a proteomics data set was modelled and analysed. Results suggested that degree, an established centrality index, may offer a novel perspective about the functional role of nodes in WNNets. Indeed, degree allowed retrieving significant differences between experimental conditions, highlighting relevant proteins, and provided a novel interpretation for degree itself, opening new perspectives in experimental data modelling and analysis. Overall, WNNets may be used to model any high-throughput experimental data set requiring weighted nodes. Finally, improving the power of the analysis by using centralities such as betweenness may provide further biological insights and unveil novel, interesting characteristics of WNNets.

LFA-1 Controls Th1 and Th17 Motility Behavior in the Inflamed Central Nervous System.

Leukocyte trafficking is a key event during autoimmune and inflammatory responses. The subarachnoid space (SAS) and cerebrospinal fluid are major routes for the migration of encephalitogenic T cells into the central nervous system (CNS) during experimental autoimmune encephalomyelitis (EAE), the animal model of multiple sclerosis, and are sites of T cell activation before the invasion of CNS parenchyma. In particular, autoreactive Th1 and Th17 cell trafficking and reactivation in the CNS are required for the pathogenesis of EAE. However, the molecular mechanisms controlling T cell dynamics during EAE are unclear. We used two-photon laser microscopy to show that autoreactive Th1 and Th17 cells display distinct motility behavior within the SAS in the spinal cords of mice immunized with the myelin oligodendrocyte glycoprotein peptide MOG35-55. Th1 cells showed a strong directional bias at the disease peak, moving in a straight line and covering long distances, whereas Th17 cells exhibited more constrained motility. The dynamics of both Th1 and Th17 cells were strongly affected by blocking the integrin LFA-1, which interfered with the deformability and biomechanics of Th1 but not Th17 cells. The intrathecal injection of a blocking anti-LFA-1 antibody at the onset of disease significantly inhibited EAE progression and also strongly reduced neuro-inflammation in the immunized mice. Our results show that LFA-1 plays a pivotal role in T cell motility during EAE and suggest that interfering with the molecular mechanisms controlling T cell motility can help to reduce the pathogenic potential of autoreactive lymphocytes.

Blockade of α4 integrins reduces leukocyte-endothelial interactions in cerebral vessels and improves memory in a mouse model of Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by cognitive decline associated with the deposition of amyloid-ß (Aß) plaques, hyperphosphorylation of tau protein, and neuronal loss. Vascular inflammation and leukocyte trafficking may contribute to AD pathogenesis, and a better understanding of these inflammation mechanisms could therefore facilitate the development of new AD therapies. Here we show that T cells extravasate in the proximity of cerebral VCAM-1+ vessels in 3xTg-AD transgenic mice, which develop both Aß and tau pathologies. The counter-ligand of VCAM-1 - α4ß1 integrin, also known as very late antigen-4 (VLA-4) - was more abundant on circulating CD4+ T cells and was also expressed by a significant proportion of blood CD8+ T cells and neutrophils in AD mice. Intravital microscopy of the brain microcirculation revealed that α4 integrins control leukocyte-endothelial interactions in AD mice. Therapeutic targeting of VLA-4 using antibodies that specifically block α4 integrins improved the memory of 3xTg-AD mice compared to an isotype control. These antibodies also reduced neuropathological hallmarks of AD, including microgliosis, Aß load and tau hyperphosphorylation. Our results demonstrate that α4 integrin-dependent leukocyte trafficking promotes cognitive impairment and AD neuropathology, suggesting that the blockade of α4 integrins may offer a new therapeutic strategy in AD.

MYC-related microRNAs signatures in non-Hodgkin B-cell lymphomas and their relationships with core cellular pathways.

In order to investigate the role of microRNAs in the pathogenesis of different B-cell lymhoma subtypes, we have applied an array-based assay to a series of 76 mixed non-Hodgkin B-cell lymphomas, including Burkitt's lymphoma (BL), diffuse large B-cell lymphoma, primary mediastinal B-cell lymphoma, mantle cell lymphoma (MCL) and follicular lymphoma. Lymphomas clustered according to histological subtypes, driven by two miRNA clusters (the miR-29 family and the miR-17-92 cluster). Since the two miRNA clusters are known to be MYC-regulated, we investigated whether this would be supported in MYC-driven experimental models, and found that this signature separated BL cell lines and a MYC-translocated MCL cell lines from normal germinal center B-cells and other B-cell populations. Similar results were also reproduced in tissue samples comparing BL and reactive lymph node samples. The same series was then quantitatively analyzed for MYC expression by immunohistochemistry and MYC protein levels were compared with corresponding miRNA signatures. A specific metric was developed to summarize the levels of MYC-related microRNAs and the corresponding protein levels. We found that MYC-related signatures are directly related to MYC protein expression across the whole spectrum of B-cells and B-cell lymphoma, suggesting that the MYC-responsive machinery shows predominantly quantitative, rather than qualitative, modifications in B-cell lymphoma. Novel MYC-related miRNAs were also discovered by this approach. Finally, network analysis found that in BL MYC-related differentially expressed miRNAs could control, either positively or negatively, a limited number of hub proteins, including BCL2, CDK6, MYB, ZEB1, CTNNB1, BAX and XBP1.

Identification of microRNAs implicated in the late differentiation stages of normal B cells suggests a central role for miRNA targets ZEB1 and TP53.

In the late B cell differentiation stages, miRNAs expression modifications promoting or inhibiting key pathways are only partially defined. We isolated 29 CD19+ human B cell samples at different stages of differentiation: B cells from peripheral blood; naïve, germinal center (GC) and subepithelial (SE) B cells from tonsils. SE cells were further split in activated and resting B cell. The miRNA expression profile of these B cells was assessed by microarray analysis and selected miRNAs were validated by quantitative RT-PCR and in situ hybridization on normal tonsils. The comparison of all samples showed changes in 107 miRNAs in total. Among 48 miRNAs differentially expressed in naïve, GC and SE cells, we identified 8 miRNAs: mir-323, mir-138, mir-9*, mir-211, mir-149, mir-373, mir-135a and mir-184, strictly specific to follicular cells that had never been implicated before in late stages of B cell development. Moreover, we unveiled 34 miRNAs able to discriminate between CD5- activated B cells and resting B cells. The miRNAs profile of CD5- resting B cells showed a higher similarity to naïve CD5+ than CD5- activated B cells. Finally, network analysis on shortest paths connecting gene targets suggested ZEB1 and TP53 as key miRNA targets during the follicular differentiation pathway. These data confirm and extend our knowledge on the miRNAs-related regulatory pathways involved in the late B cell maturation.

Creating, generating and comparing random network models with NetworkRandomizer.

Biological networks are becoming a fundamental tool for the investigation of high-throughput data in several fields of biology and biotechnology. With the increasing amount of information, network-based models are gaining more and more interest and new techniques are required in order to mine the information and to validate the results. We have developed an app for the Cytoscape platform which allows the creation of randomized networks and the randomization of existing, real networks. Since there is a lack of tools for generating and randomizing networks, our app helps researchers to exploit different, well known random network models which could be used as a benchmark for validating real datasets. We also propose a novel methodology for creating random weighted networks starting from experimental data. Finally the app provides a statistical tool which compares real versus random attributes, in order to validate all the numerical findings. In summary, our app aims at creating a standardised methodology for the validation of the results in the context of the Cytoscape platform.

Finding the shortest path with PesCa: a tool for network reconstruction.

Network analysis is of growing interest in several fields ranging from economics to biology. Several methods have been developed to investigate different properties of physical networks abstracted as graphs, including quantification of specific topological properties, contextual data enrichment, simulation of pathway dynamics and visual representation. In this context, the PesCa app for the Cytoscape network analysis environment is specifically designed to help researchers infer and manipulate networks based on the shortest path principle. PesCa offers different algorithms allowing network reconstruction and analysis starting from a list of genes, proteins and in general a set of interconnected nodes. The app is useful in the early stage of network analysis, i.e. to create networks or generate clusters based on shortest path computation, but can also help further investigations and, in general, it is suitable for every situation requiring the connection of a set of nodes that apparently do not share links, such as isolated nodes in sub-networks. Overall, the plugin enhances the ability of discovering interesting and not obvious relations between high dimensional sets of interacting objects.

Neutrophils promote Alzheimer's disease-like pathology and cognitive decline via LFA-1 integrin.

Inflammation is a pathological hallmark of Alzheimer's disease, and innate immune cells have been shown to contribute to disease pathogenesis. In two transgenic models of Alzheimer's disease (5xFAD and 3xTg-AD mice), neutrophils extravasated and were present in areas with amyloid-ß (Aß) deposits, where they released neutrophil extracellular traps (NETs) and IL-17. Aß42 peptide triggered the LFA-1 integrin high-affinity state and rapid neutrophil adhesion to integrin ligands. In vivo, LFA-1 integrin controlled neutrophil extravasation into the CNS and intraparenchymal motility. In transgenic Alzheimer's disease models, neutrophil depletion or inhibition of neutrophil trafficking via LFA-1 blockade reduced Alzheimer's disease-like neuropathology and improved memory in mice already showing cognitive dysfunction. Temporary depletion of neutrophils for 1 month at early stages of disease led to sustained improvements in memory. Transgenic Alzheimer's disease model mice lacking LFA-1 were protected from cognitive decline and had reduced gliosis. In humans with Alzheimer's disease, neutrophils adhered to and spread inside brain venules and were present in the parenchyma, along with NETs. Our results demonstrate that neutrophils contribute to Alzheimer's disease pathogenesis and cognitive impairment and suggest that the inhibition of neutrophil trafficking may be beneficial in Alzheimer's disease.

Node interference and robustness: performing virtual knock-out experiments on biological networks: the case of leukocyte integrin activation network.

The increasing availability of large network datasets derived from high-throughput experiments requires the development of tools to extract relevant information from biological networks, and the development of computational methods capable of detecting qualitative and quantitative changes in the topological properties of biological networks is of critical relevance. We introduce the notions of node interference and robustness as measures of the reciprocal influence between nodes within a network. We examine the theoretical significance of these new, centrality-based, measures by characterizing the topological relationships between nodes and groups of nodes. Node interference analysis allows topologically determining the context of functional influence of single nodes. Conversely, the node robustness analysis allows topologically identifying the nodes having the highest functional influence on a specific node. A new Cytoscape plug-in calculating these measures was developed and applied to a protein-protein interaction network specifically regulating integrin activation in human primary leukocytes. Notably, the functional effects of compounds inhibiting important protein kinases, such as SRC, HCK, FGR and JAK2, are predicted by the interference and robustness analysis, are in agreement with previous studies and are confirmed by laboratory experiments. The interference and robustness notions can be applied to a variety of different contexts, including, for instance, the identification of potential side effects of drugs or the characterization of the consequences of genes deletion, duplication or of proteins degradation, opening new perspectives in biological network analysis.

Biological network analysis with CentiScaPe: centralities and experimental dataset integration.

The growing dimension and complexity of the available experimental data generating biological networks have increased the need for tools that help in categorizing nodes by their topological relevance. Here we present CentiScaPe, a Cytoscape app specifically designed to calculate centrality indexes used for the identification of the most important nodes in a network. CentiScaPe is a comprehensive suite of algorithms dedicated to network nodes centrality analysis, computing several centralities for undirected, directed and weighted networks. The results of the topological analysis can be integrated with data set from lab experiments, like expression or phosphorylation levels for each protein represented in the network. Our app opens new perspectives in the analysis of biological networks, since the integration of topological analysis with lab experimental data enhance the predictive power of the bioinformatics analysis.