Biological network analysis: from topological indexes to biological applications towards personalised medicine.

Systems Biology encompasses different research areas, sharing graph theory as a common conceptual framework. Its main focus is the modelling and investigation of molecular interactions as complex networks. Notably, although experimental datasets allow the construction of context-specific molecular networks, the effect of quantitative variations of molecular states, i.e. the biochemical status, is not in- corporated into the current network topologies. This fact poses great limitations in terms of predictive power. To overcome these limitations we have developed a novel methodology that allows incorporating experimental quantitative data into the graph topology, thus leading to a potentiated network representation. It is now possible to model, at graph level, the outcome of a specific experimental analysis. The mathematical approach, based on a demonstrated theorem, was validated in four different pathological contexts, including B-Cell Lymphocytic Leukaemia, Amyloidosis, Pancreatic Endocrine Tumours and Myocardial Infarc- tion. Reconstructing disease-specific, potentiated networks coupled to topolog- ical analysis and machine learning techniques allowed the automatic discrimina- tion of healthy versus unhealthy subjects in every context. Our methodology takes advantage of the topological information extracted from protein-protein in- teractions networks integrating experimental data into their topology. Incorpo- rating quantitative data of molecular state into graphs permits to obtain enriched representations that are tailored to a specific experimental condition, or to a sub- ject, leading to an effective personalised approach. Moreover, in order to validate the biological results, we have developed an app, for the Cytoscape platform, that allows the creation of randomised networks and the randomisation of exist- ing, real networks. Since there is a lack of tools for generating and randomising networks, our app helps researchers to exploit different, well known random net- work models that could be used as a benchmark for validating the outcomes from real datasets. We also proposed three possibile approaches for creating randomly weighted networks starting from the experimental, quantitative data. Finally, some of the functionalities of our app, plus some other functions, were devel- oped, in R, to allow exploiting the potential of this language and to perform network analysis using our multiplication model. In summary, we developed a workflow that starts from the creation of a set of personalised networks that are able to integrate numerical information. We gave some directions that guide the researchers in performing the network analysis. Finally, we developed a Java App and some R functions that permit to validate all the findings using a random network based approach

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

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 MOG(35-55). 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 \u3b14 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-beta (A beta) 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 A beta and tau pathologies. The counter-ligand of VCAM-1-alpha 4 beta 1 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 alpha 4 integrins control leukocyte-endothelial interactions in AD mice. Therapeutic targeting of VLA-4 using antibodies that specifically block alpha 4 integrins improved the memory of 3xTg-AD mice compared to an isotype control. These antibodies also reduced neuropathological hallmarks of AD, including microgliosis, A beta load and tau hyperphosphorylation. Our results demonstrate that alpha 4 integrin-dependent leukocyte trafficking promotes cognitive impairment and AD neuropathology, suggesting that the blockade of alpha 4 integrins may offer a new therapeutic strategy in AD

Biological network analysis with CentiScaPe: centralities and experimental dataset integration [v2; ref status: indexed, http://f1000r.es/55u]

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

Node6 and Node9 interference values of the network in figure 2, expressed as percentage.

<p>Node6 and Node9 interference values of the network in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088938#pone-0088938-g002" target="_blank">figure 2</a>, expressed as percentage.</p

Node Interference and Robustness: Performing Virtual Knock-Out Experiments on Biological Networks: The Case of Leukocyte Integrin Activation Network

<div><p>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 and 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.</p></div

The robustness analysis of node3 and node4.

<p>The robustness analysis of node3 and node4.</p

Interference values of the network in figure 1, expressed as percentage.

<p>Interference values of the network in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088938#pone-0088938-g001" target="_blank">figure 1</a>, expressed as percentage.</p