Embedding machine-readable proteins interactions data in scientific articles for easy access and retrieval

Extraction of protein-protein interactions data from scientific literature remains a hard, time- and resource-consuming task. This task would be greatly simplified by embedding in the source, i.e. research articles, a standardized, synthetic, machine-readable codification for protein-protein interactions data description, to make the identification and the retrieval of such very valuable information easier, faster, and more reliable than now.
We shortly discuss how this information can be easily encoded and embedded in research papers with the collaboration of authors and scientific publishers, and propose an online demonstrative tool that shows how to help and allow authors for the easy and fast conversion of such valuable biological data into an embeddable, accessible, computer-readable codification

Charting the NF-kB pathway interactome map

One of the phenomena observed in human aging is the progressive increase of a systemic inflammatory state, a condition referred to as “inflammaging”, negatively correlated with longevity. The five components of the Nuclear Factor kB (NF-kB) family are prominent mediators of inflammation. Several different signaling pathways activated by very diverse stimuli converge on NF-kB, resulting in a regulatory system characterized by high complexity. It is increasingly recognized that the number of components that impinges upon phenotypic outcomes of signal transduction pathways may be higher than those taken into consideration from canonical pathway representations. Scope of this analysis is to provide a wider, systemic picture of such intricate signaling system

Encoding the states of interacting proteins to facilitate biological pathways reconstruction

<p>Abstract</p> <p>Background</p> <p>In a systems biology perspective, protein-protein interactions (PPI) are encoded in machine-readable formats to avoid issues encountered in their retrieval for the reconstruction of comprehensive interaction maps and biological pathways. However, the information stored in electronic formats currently used doesn't allow a valid automatic reconstruction of biological pathways.</p> <p>Results</p> <p>We propose a logical model of PPI that takes into account the "state" of proteins before and after the interaction. This information is necessary for proper reconstruction of the pathway.</p> <p>Conclusions</p> <p>The adoption of the proposed model, which can be easily integrated into existing machine-readable formats used to store the PPI data, would facilitate the automatic or semi-automated reconstruction of biological pathways.</p> <p>Reviewers</p> <p>This article was reviewed by Dr. Wen-Yu Chung (nominated by Kateryna Makova), Dr. Carl Herrmann (nominated by Dr. Purificación López-García) and Dr. Arcady Mushegian.</p

Quantifying the relevance of different mediators in the human immune cell network

Immune cells coordinate their efforts for the correct and efficient functioning of the immune system (IS). Each cell type plays a distinct role and communicates with other cell types through mediators such as cytokines, chemokines and hormones, among others, that are crucial for the functioning of the IS and its fine tuning. Nevertheless, a quantitative analysis of the topological properties of an immunological network involving this complex interchange of mediators among immune cells is still lacking. Here we present a method for quantifying the relevance of different mediators in the immune network, which exploits a definition of centrality based on the concept of efficient communication. The analysis, applied to the human immune system, indicates that its mediators significantly differ in their network relevance. We found that cytokines involved in innate immunity and inflammation and some hormones rank highest in the network, revealing that the most prominent mediators of the IS are molecules involved in these ancestral types of defence mechanisms highly integrated with the adaptive immune response, and at the interplay among the nervous, the endocrine and the immune systems.Comment: 10 pages, 3 figure

Gene Regulatory Network Modeling of Macrophage Differentiation Corroborates the Continuum Hypothesis of Polarization States

Macrophages derived from monocyte precursors undergo specific polarization processes which are influenced by the local tissue environment: classically activated (M1) macrophages, with a pro-inflammatory activity and a role of effector cells in Th1 cellular immune responses, and alternatively activated (M2) macrophages, with anti-inflammatory functions and involved in immunosuppression and tissue repair. At least three different subsets of M2 macrophages, namely, M2a, M2b, and M2c, are characterized in the literature based on their eliciting signals. The activation and polarization of macrophages is achieved through many, often intertwined, signaling pathways. To describe the logical relationships among the genes involved in macrophage polarization, we used a computational modeling methodology, namely, logical (Boolean) modeling of gene regulation. We integrated experimental data and knowledge available in the literature to construct a logical network model for the gene regulation driving macrophage polarization to the M1, M2a, M2b, and M2c phenotypes. Using the software GINsim and BoolNet, we analyzed the network dynamics under different conditions and perturbations to understand how they affect cell polarization. Dynamic simulations of the network model, enacting the most relevant biological conditions, showed coherence with the observed behavior of in vivo macrophages. The model could correctly reproduce the polarization toward the four main phenotypes as well as to several hybrid phenotypes, which are known to be experimentally associated to physiological and pathological conditions. We surmise that shifts among different phenotypes in the model mimic the hypothetical continuum of macrophage polarization, with M1 and M2 being the extremes of an uninterrupted sequence of states. Furthermore, model simulations suggest that anti-inflammatory macrophages are resilient to shift back to the pro-inflammatory phenotype

EpiGEN: an epistasis simulation pipeline

Abstract Summary Simulated data are crucial for evaluating epistasis detection tools in genome-wide association studies. Existing simulators are limited, as they do not account for linkage disequilibrium (LD), support limited interaction models of single nucleotide polymorphisms (SNPs) and only dichotomous phenotypes or depend on proprietary software. In contrast, EpiGEN supports SNP interactions of arbitrary order, produces realistic LD patterns and generates both categorical and quantitative phenotypes. Availability and implementation EpiGEN is implemented in Python 3 and is freely available at https://github.com/baumbachlab/epigen. Supplementary information Supplementary data are available at Bioinformatics online

Feeling of ownership over an embodied avatar's hand brings about fast changes of fronto-parietal cortical dynamics

When we look at our body parts, we are immediately aware that they belong to us and we rarely doubt about the integrity, continuity, and sense of ownership of our body. Despite this certainty, immersive virtual reality (IVR) may lead to a strong feeling of embodiment over an artificial body part seen from a first-person perspective (1PP). Although such feeling of ownership (FO) has been described in different situations, it is not yet understood how this phenomenon is generated at neural level. To track the real-time brain dynamics associated with FO, we delivered transcranial magnetic stimuli over the hand region in the primary motor cortex (M1) and simultaneously recorded electroencephalography (EEG) in 19 healthy volunteers (11 male/8 female) watching IVR renderings of anatomically plausible (full-limb) versus implausible (hand disconnected from the forearm) virtual limbs. Our data show that embodying a virtual hand is temporally associated with a rapid drop of cortical activity of the onlookers' hand region in the M1 contralateral to the observed hand. Spatiotemporal analysis shows that embodying the avatar's hand is also associated with fast changes of activity within an interconnected fronto-parietal circuit ipsilateral to the brain stimulation. Specifically, an immediate reduction of connectivity with the premotor area is paralleled by an enhancement in the connectivity with the posterior parietal cortex (PPC) which is related to the strength of ownership illusion ratings and thus likely reflects conscious feelings of embodiment. Our results suggest that changes of bodily representations are underpinned by a dynamic cross talk within a highly-plastic, fronto-parietal network

Network and systems medicine: Position paper of the European Collaboration on Science and Technology action on Open Multiscale Systems Medicine

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management