Deciphering hallmark processes of aging from interaction networks.

BACKGROUND: Aging is broadly considered to be a dynamic process that accumulates unfavourable structural and functional changes in a time dependent fashion, leading to a progressive loss of physiological integrity of an organism, which eventually leads to age-related diseases and finally to death. SCOPE OF REVIEW: The majority of aging-related studies are based on reductionist approaches, focusing on single genes/proteins or on individual pathways without considering possible interactions between them. Over the last few decades, several such genes/proteins were independently analysed and linked to a role that is affecting the longevity of an organism. However, an isolated analysis on genes and proteins largely fails to explain the mechanistic insight of a complex phenotype due to the involvement and integration of multiple factors. MAJOR CONCLUSIONS: Technological advance makes it possible to generate high-throughput temporal and spatial data that provide an opportunity to use computer-based methods. These techniques allow us to go beyond reductionist approaches to analyse large-scale networks that provide deeper understanding of the processes that drive aging. GENERAL SIGNIFICANCE: In this review, we focus on systems biology approaches, based on network inference methods to understand the dynamics of hallmark processes leading to aging phenotypes. We also describe computational methods for the interpretation and identification of important molecular hubs involved in the mechanistic linkage between aging related processes. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

Network analyses reveal new insights into the effect of multicomponent Tr14 compared to single-component diclofenac in an acute inflammation model.

BACKGROUND: Modifying the acute inflammatory response has wide clinical benefits. Current options include non-steroidal anti-inflammatory drugs (NSAIDs) and therapies that may resolve inflammation. Acute inflammation involves multiple cell types and various processes. We, therefore, investigated whether an immunomodulatory drug that acts simultaneously at multiple sites shows greater potential to resolve acute inflammation more effectively and with fewer side effects than a common anti-inflammatory drug developed as a small molecule for a single target. In this work, we used time-series gene expression profiles from a wound healing mouse model to compare the effects of Traumeel (Tr14), a multicomponent natural product, to diclofenac, a single component NSAID on inflammation resolution. RESULTS: We advance previous studies by mapping the data onto the "Atlas of Inflammation Resolution", followed by in silico simulations and network analysis. We found that Tr14 acts primarily on the late phase of acute inflammation (during resolution) compared to diclofenac, which suppresses acute inflammation immediately after injury. CONCLUSIONS: Our results provide new insights how network pharmacology of multicomponent drugs may support inflammation resolution in inflammatory conditions.

Nanoparticles in the environment: assessment using the causal diagram approach.

Nanoparticles (NPs) cause concern for health and safety as their impact on the environment and humans is not known. Relatively few studies have investigated the toxicological and environmental effects of exposure to naturally occurring NPs (NNPs) and man-made or engineered NPs (ENPs) that are known to have a wide variety of effects once taken up into an organism. A review of recent knowledge (between 2000-2010) on NP sources, and their behaviour, exposure and effects on the environment and humans was performed. An integrated approach was used to comprise available scientific information within an interdisciplinary logical framework, to identify knowledge gaps and to describe environment and health linkages for NNPs and ENPs. The causal diagram has been developed as a method to handle the complexity of issues on NP safety, from their exposure to the effects on the environment and health. It gives an overview of available scientific information starting with common sources of NPs and their interactions with various environmental processes that may pose threats to both human health and the environment. Effects of NNPs on dust cloud formation and decrease in sunlight intensity were found to be important environmental changes with direct and indirect implication in various human health problems. NNPs and ENPs exposure and their accumulation in biological matrices such as microbiota, plants and humans may result in various adverse effects. The impact of some NPs on human health by ROS generation was found to be one of the major causes to develop various diseases. A proposed cause-effects diagram for NPs is designed considering both NNPs and ENPs. It represents a valuable information package and user-friendly tool for various stakeholders including students, researchers and policy makers, to better understand and communicate on issues related to NPs.

Network- and enrichment-based inference of phenotypes and targets from large-scale disease maps.

Complex diseases are inherently multifaceted, and the associated data are often heterogeneous, making linking interactions across genes, metabolites, RNA, proteins, cellular functions, and clinically relevant phenotypes a high-priority challenge. Disease maps have emerged as knowledge bases that capture molecular interactions, disease-related processes, and disease phenotypes with standardized representations in large-scale molecular interaction maps. Various tools are available for disease map analysis, but an intuitive solution to perform in silico experiments on the maps in a wide range of contexts and analyze high-dimensional data is currently missing. To this end, we introduce a two-dimensional enrichment analysis (2DEA) approach to infer downstream and upstream elements through the statistical association of network topology parameters and fold changes from molecular perturbations. We implemented our approach in a plugin suite for the MINERVA platform, providing an environment where experimental data can be mapped onto a disease map and predict potential regulatory interactions through an intuitive graphical user interface. We show several workflows using this approach and analyze two RNA-seq datasets in the Atlas of Inflammation Resolution (AIR) to identify enriched downstream processes and upstream transcription factors. Our work improves the usability of disease maps and increases their functionality by facilitating multi-omics data integration and exploration.

Sequence-based approach for rapid identification of cross-clade CD8+ T-cell vaccine candidates from all high-risk HPV strains.

Human papilloma virus (HPV) is the primary etiological agent responsible for cervical cancer in women. Although in total 16 high-risk HPV strains have been identified so far. Currently available commercial vaccines are designed by targeting mainly HPV16 and HPV18 viral strains as these are the most common strains associated with cervical cancer. Because of the high level of antigenic specificity of HPV capsid antigens, the currently available vaccines are not suitable to provide cross-protection from all other high-risk HPV strains. Due to increasing reports of cervical cancer cases from other HPV high-risk strains other than HPV16 and 18, it is crucial to design vaccine that generate reasonable CD8+ T-cell responses for possibly all the high-risk strains. With this aim, we have developed a computational workflow to identify conserved cross-clade CD8+ T-cell HPV vaccine candidates by considering E1, E2, E6 and E7 proteins from all the high-risk HPV strains. We have identified a set of 14 immunogenic conserved peptide fragments that are supposed to provide protection against infection from any of the high-risk HPV strains across globe.

Single wall carbon nanotubes block ion passage in mechano-sensitive ion channels by interacting with extracellular domain.

Mechanosensitive ion channel transduce physical stresses to cells by the response of electro-chemicals exchange. The purpose of this study was to improve understanding of ion channel inhibition process by SWCNTs. PatchDock and FireDock results suggest that the SWCNTs interact with the extracellular domain of the ion channels. SWCNTs can be considered as the new class of ion channel inhibitors.

Identification of immunogenic consensus T-cell epitopes in globally distributed influenza-A H1N1 neuraminidase.

Antigenic drift is the ability of the swine influenza virus to undergo continuous and progressive changes in response to the host immune system. These changes dictate influenza vaccine updates annually to ensure inclusion of antigens of the most current strains. The identification of those peptides that stimulate T-cell responses, termed T-cell epitopes, is essential for the development of successful vaccines. In this study, the highly conserved and specific epitopes from neuraminidase of globally distributed H1N1 strains were predicted so that these potential vaccine candidates may escape with antigenic drift. A total of nine novel CD8(+) T-cell epitopes for MHC class-I and eight novel CD4(+) T-cell epitopes for MHC class-II alleles were proposed as novel epitope based vaccine candidates. Additionally, the epitope FSYKYGNGV was identified as a highly conserved, immunogenic and potential vaccine candidate, capable for generating both CD8(+) and CD4(+) responses.

Computational analysis and modeling the effectiveness of 'Zanamivir' targeting neuraminidase protein in pandemic H1N1 strains.

Antigenic drift causes number of mutations in neuraminidase protein of H1N1 swine influenza virus. We analyzed neuraminidase mutations in H1N1 strains distributed over six continents, at both the sequence and structural level. Mutations in the nearby residues of the drug binding site play crucial role in the binding affinity of the drug with the protein. For this purpose, mutant models were generated for the neuraminidase protein from 34 pandemic H1N1 isolates and docking were performed with zanamivir drug. Multiple sequence alignment (MSA) and variations in docking score suggest that there are considerable changes in the binding affinity of neuraminidase with zanamivir, which leads to probable ineffectiveness of zanamivir in the isolated samples of pandemic H1N1 collected from quite a few countries. To further evaluate the effectiveness of the antiviral drugs, we derived, calibrated and analyzed an ordinary differential equations based mathematical model for H1N1 infection dynamics and drug mediated virus deactivation.

In silico CD4+ T-cell epitope prediction and HLA distribution analysis for the potential proteins of Neisseria meningitidis Serogroup B--a clue for vaccine development.

Neisseria meningitidis, an exclusive human pathogen, is a major cause of mortality due to meningococcal meningitis and sepsis in many developing countries. Three meningococcal serogroup B proteins, i.e. T-cell stimulating protein A (TspA), autotransporter A (AutA), and IgA-specific serine endopeptidase (IGA1) elicits CD4+ T-cell response and may enhance the effectiveness of meningococcal vaccines by acting as protective immunogens. A very limited data on T-helper cell epitopes in MenB proteins is available. Hence, in silico prediction of peptide sequences which may act as helper T lymphocyte epitopes in MenB proteins was carried out by NetMHCIIpan web server. HLA distribution analysis was done by using the population coverage tool of Immune Epitope Database to determine the fraction of individuals in various populations expected to respond to a given set of predicted T-cell epitopes based on HLA genotype frequencies. Six epitopic core sequences, two from each MenB proteins, i.e. AutA, TspA and IgA1 protease were predicted to associate with a large number of HLA-DR alleles. These six peptides may act as T-cell epitope in more than 95% of populations in 8 out of 12 populations considered. The T-cell stimulation potential of these predicted peptides containing the core epitopic sequences is to be validated by using laboratory experiments for their efficient use as peptide vaccine candidates against N. meningitidis serogroup B.