Network proteomics of human dermal wound healing.

OBJECTIVE: The healing of wounds is critical in protecting the human body against environmental factors. The mechanisms involving protein expression during this complex physiological process have not been fully elucidated. APPROACH: Here, we use reverse-phase protein microarrays (RPPA) involving 94 phosphoproteins to study tissue samples from tubes implanted in healing dermal wounds in seven human subjects tracked over two weeks. We compare the proteomic profiles to proteomes of controls obtained from skin biopsies from the same subjects. MAIN RESULTS: Compared to previous proteomic studies of wound healing, our approach focuses on wound tissue instead of wound fluid, and has the sensitivity to go beyond measuring only highly abundant proteins. To study the temporal dynamics of networks involved in wound healing, we applied two network analysis methods that integrate the experimental results with prior knowledge about protein-protein physical and regulatory interactions, as well as higher-level biological processes and associated pathways. SIGNIFICANCE: We uncovered densely connected networks of proteins that are up- or down-regulated during human wound healing, as well as their relationships to microRNAs and to proteins outside of our set of targets that we measured with proteomic microarrays.

Network analysis of human post-mortem microarrays reveals novel genes, microRNAs, and mechanistic scenarios of potential importance in fighting huntington's disease.

Huntington's disease is a progressive neurodegenerative disorder characterized by motor disturbances, cognitive decline, and neuropsychiatric symptoms. In this study, we utilized network-based analysis in an attempt to explore and understand the underlying molecular mechanism and to identify critical molecular players of this disease condition. Using human post-mortem microarrays from three brain regions (cerebellum, frontal cortex and caudate nucleus) we selected in a four-step procedure a seed set of highly modulated genes. Several protein-protein interaction networks, as well as microRNA-mRNA networks were constructed for these gene sets with the Elsevier Pathway Studio software and its associated ResNet database. We applied a gene prioritizing procedure based on vital network topological measures, such as high node connectivity and centrality. Adding to these criteria the guilt-by-association rule and exploring their innate biomolecular functions, we propose 19 novel genes from the analyzed microarrays, from which CEBPA, CDK1, CX3CL1, EGR1, E2F1, ERBB2, LRP1, HSP90AA1 and ZNF148 might be of particular interest for experimental validation. A possibility is discussed for dual-level gene regulation by both transcription factors and microRNAs in Huntington's disease mechanism. We propose several possible scenarios for experimental studies initiated via the extra-cellular ligands TGFB1, FGF2 and TNF aiming at restoring the cellular homeostasis in Huntington's disease.

Network Topology Analysis of Post-Mortem Brain Microarrays Identifies More Alzheimer's Related Genes and MicroRNAs and Points to Novel Routes for Fighting with the Disease.

Network-based approaches are powerful and beneficial tools to study complex systems in their entirety, elucidating the essential factors that turn the multitude of individual elements into a functional system. In this study we used critical network topology descriptors and guilt-by-association rule to explore and understand the significant molecular players, drug targets and underlying biological mechanisms of Alzheimer's disease. Analyzing two post-mortem brain gene microarrays (GSE4757 and GSE28146) with Pathway Studio software package we constructed and analyzed a set of protein-protein interaction, as well as miRNA-target networks. In a 4-step procedure the expression datasets were normalized using Robust Multi-array Average approach, while the modulation of gene expression by the disease was statistically evaluated by the empirical Bayes method from the limma Bioconductor package. Representative set of 214 seed-genes (p<0.01) common for the three brain sections of the two microarrays was thus created. The Pathway Studio analysis of the networks built identified 15 new potential AD-related genes and 17 novel AD-involved microRNAs. Using KEGG pathways relevant in Alzheimer's disease we built an integrated mechanistic network from the interactions between the overlapping genes in these pathways. Routes of possible disease initiation process were thus revealed through the CD4, DCN, and IL8 extracellular ligands. DAVID and IPA enrichment analysis uncovered a number of deregulated biological processes and pathways including neuron projection/differentiation, aging, oxidative stress, chemokine/ neurotrophin signaling, long-term potentiation and others. The findings in this study offer information of interest for subsequent experimental studies.

Trends in information theory-based chemical structure codification.

This report offers a chronological review of the most relevant applications of information theory in the codification of chemical structure information, through the so-called information indices. Basically, these are derived from the analysis of the statistical patterns of molecular structure representations, which include primitive global chemical formulae, chemical graphs, or matrix representations. Finally, new approaches that attempt to go "back to the roots" of information theory, in order to integrate other information-theoretic measures in chemical structure coding are discussed.

A Network Approach to Wound Healing.

OBJECTIVE: The wound healing process is well-understood on the cellular and tissue level; however, its complex molecular mechanisms are not yet uncovered in their entirety. Viewing wounds as perturbed molecular networks provides the tools for analyzing and optimizing the healing process. It helps to answer specific questions that lead to better understanding of the complexity of the process. What are the molecular pathways involved in wound healing? How do these pathways interact with each other during the different stages of wound healing? Is it possible to grasp the entire mechanism of regulatory interactions in the healing of a wound? APPROACH: Networks are structures composed of nodes connected by links. A network describing the state of a cell taking part in the healing process may contain nodes representing genes, proteins, microRNAs, metabolites, and drug molecules. The links connecting nodes represent interactions such as binding, regulation, co-expression, chemical reaction, and others. Both nodes and links can be weighted by numbers related to molecular concentration and the intensity of intermolecular interactions. Proceeding from data and from molecular profiling experiments, different types of networks are built to characterize the stages of the healing process. Network nodes having a higher degree of connectivity and centrality usually play more important roles for the functioning of the system they describe. RESULTS: We describe here the algorithms and software packages for building, manipulating and analyzing networks proceeding from information available from a literature or database search or directly extracted from experimental gene expression, metabolic, and proteomic data. Network analysis identifies genes/proteins most differentiated during the healing process, and their organization in functional pathways or modules, and their distribution into gene ontology categories of biological processes, molecular functions, and cellular localization. We provide an example of how network analysis can be used to reach better understanding of regulation of key wound healing mediators and microRNAs that regulate them. INNOVATION: Univariate statistical tests widely used in clinical studies are not enough to improve understanding and optimize the processes of wound healing. Network methods of analysis of patients "omics" data, such as transcriptoms, proteomes, and others can provide a better insight into the healing processes and help in development of better treatment practices. We review several articles that are examples of this emergent approach to the study of wound healing. CONCLUSION: Network analysis has the potential to considerably contribute to the better understanding of the molecular mechanisms of wound healing and to the discovery of means to control and optimize that process.

A network view on Parkinson's disease.

Network-based systems biology tools including Pathway Studio 9.0 were used to identify Parkinson's disease (PD) critical molecular players, drug targets, and underlying biological processes. Utilizing several microarray gene expression datasets, biomolecular networks such as direct interaction, shortest path, and microRNA regulatory networks were constructed and analyzed for the disease conditions. Network topology analysis of node connectivity and centrality revealed in combination with the guilt-by-association rule 17 novel genes of PD-potential interest. Seven new microRNAs (miR-132, miR-133a1, miR-181-1, miR-182, miR-218-1, miR-29a, and miR-330) related to Parkinson's disease were identified, along with more microRNA targeted genes of interest like RIMS3, SEMA6D and SYNJ1. David and IPA enrichment analysis of KEGG and canonical pathways provided valuable mechanistic information emphasizing among others the role of chemokine signaling, adherence junction, and regulation of actin cytoskeleton pathways. Several routes for possible disease initiation and neuro protection mechanisms triggered via the extra-cellular ligands such as CX3CL1, SEMA6D and IL12B were thus uncovered, and a dual regulatory system of integrated transcription factors and microRNAs mechanisms was detected.

Genome-wide essential gene identification in Streptococcus sanguinis.

A clear perception of gene essentiality in bacterial pathogens is pivotal for identifying drug targets to combat emergence of new pathogens and antibiotic-resistant bacteria, for synthetic biology, and for understanding the origins of life. We have constructed a comprehensive set of deletion mutants and systematically identified a clearly defined set of essential genes for Streptococcus sanguinis. Our results were confirmed by growing S. sanguinis in minimal medium and by double-knockout of paralogous or isozyme genes. Careful examination revealed that these essential genes were associated with only three basic categories of biological functions: maintenance of the cell envelope, energy production, and processing of genetic information. Our finding was subsequently validated in two other pathogenic streptococcal species, Streptococcus pneumoniae and Streptococcus mutans and in two other gram-positive pathogens, Bacillus subtilis and Staphylococcus aureus. Our analysis has thus led to a simplified model that permits reliable prediction of gene essentiality.

A survey of current software for network analysis in molecular biology.

Software for network motifs and modules is briefly reviewed, along with programs for network comparison. The three major software packages for network analysis, CYTOSCAPE, INGENUITY and PATHWAY STUDIO, and their associated databases, are compared in detail. A comparative test evaluated how these software packages perform the search for key terms and the creation of network from those terms and from experimental expression data.

Evolution of metabolic network organization.

BACKGROUND: Comparison of metabolic networks across species is a key to understanding how evolutionary pressures shape these networks. By selecting taxa representative of different lineages or lifestyles and using a comprehensive set of descriptors of the structure and complexity of their metabolic networks, one can highlight both qualitative and quantitative differences in the metabolic organization of species subject to distinct evolutionary paths or environmental constraints. RESULTS: We used a novel representation of metabolic networks, termed network of interacting pathways or NIP, to focus on the modular, high-level organization of the metabolic capabilities of the cell. Using machine learning techniques we identified the most relevant aspects of cellular organization that change under evolutionary pressures. We considered the transitions from prokarya to eukarya (with a focus on the transitions among the archaea, bacteria and eukarya), from unicellular to multicellular eukarya, from free living to host-associated bacteria, from anaerobic to aerobic, as well as the acquisition of cell motility or growth in an environment of various levels of salinity or temperature. Intuitively, we expect organisms with more complex lifestyles to have more complex and robust metabolic networks. Here we demonstrate for the first time that such organisms are not only characterized by larger, denser networks of metabolic pathways but also have more efficiently organized cross communications, as revealed by subtle changes in network topology. These changes are unevenly distributed among metabolic pathways, with specific categories of pathways being promoted to more central locations as an answer to environmental constraints. CONCLUSIONS: Combining methods from graph theory and machine learning, we have shown here that evolutionary pressures not only affects gene and protein sequences, but also specific details of the complex wiring of functional modules in the cell. This approach allows the identification and quantification of those changes, and provides an overview of the evolution of intracellular systems.

Cellular automata modeling of FASL-initiated apoptosis.

Two strategies for fighting cancer by modulating FASL-induced apoptosis were modeled by 2D-cellular automata. Our models predict that cancer cells can be killed by maximizing the apoptosis via joint suppression of FLIP and IAP inhibitors by siRNA and SMAC proteins, respectively. It was also predicted that the presumed feedback loop CASP3-->CASP9-->|IAP in the intrinsic pathway accelerates the apoptosis, but does not change significantly the concentration of DFF40, the protein that decomposes DNA. The alternative strategy of preventing the killing of the immune system's T-cells, via minimizing their tumor-induced FAS-L apoptosis by overexpression of FLIP and IAP, was also shown to be promising with a predicted considerable synergy action of the two inhibitors. Dual suppression or overexpression of apoptosis inhibitors emerges thus as promising approach in the fight against cancer. Our modeling has also brought some light on the process of turning type-I cells into type-II ones, which emerges as compensatory mechanism in case of damaged or silenced FASL pathway by preserving about the same self-death level at only 10-12% lower performance rate.

Toward a classification of isodynamic feed-forward motifs.

A preceding study analysed how the topology of network motifs affects the overall rate of the underlying biochemical processes. Surprisingly, it was shown that topologically non-isomorphic motifs can still be isodynamic in the sense that they exhibit the exact same performance rate. Because of the high prevalence of feed-forward functional modules in biological networks, one may hypothesize that evolution tends to favour motifs with faster dynamics. As a step towards ranking the efficiency of feed-forward network motifs, we use a linear flow model to prove theorems establishing that certain classes of motifs are isodynamic. In partitioning the class of all motifs on n nodes into equivalence classes based upon their dynamics, we establish a basis for comparing the efficiency/performance rates of different motifs. The potential biological importance of the theorems is briefly discussed and is the subject of an ongoing large-scale project.

An in silico approach to the analysis of acute wound healing.

The complex interactions that characterize acute wound healing have stymied the development of effective therapeutic modalities. The use of computational models holds the promise to improve our basic approach to understanding the process. By modifying an existing ordinary differential equation model of systemic inflammation to simulate local wound healing, we expect to improve the understanding of the underlying complexities of wound healing and thus allow for the development of novel, targeted therapeutic strategies. The modifications in this local acute wound healing model include: evolution from a systemic model to a local model, the incorporation of fibroblast activity, and the effects of tissue oxygenation. Using these modifications we are able to simulate impaired wound healing in hypoxic wounds with varying levels of contamination. Possible therapeutic targets, such as fibroblast death rate and rate of fibroblast recruitment, have been identified by computational analysis. This model is a step toward constructing an integrative systems biology model of human wound healing.

Shortest-path network analysis is a useful approach toward identifying genetic determinants of longevity.

BACKGROUND: Identification of genes that modulate longevity is a major focus of aging-related research and an area of intense public interest. In addition to facilitating an improved understanding of the basic mechanisms of aging, such genes represent potential targets for therapeutic intervention in multiple age-associated diseases, including cancer, heart disease, diabetes, and neurodegenerative disorders. To date, however, targeted efforts at identifying longevity-associated genes have been limited by a lack of predictive power, and useful algorithms for candidate gene-identification have also been lacking. METHODOLOGY/PRINCIPAL FINDINGS: We have utilized a shortest-path network analysis to identify novel genes that modulate longevity in Saccharomyces cerevisiae. Based on a set of previously reported genes associated with increased life span, we applied a shortest-path network algorithm to a pre-existing protein-protein interaction dataset in order to construct a shortest-path longevity network. To validate this network, the replicative aging potential of 88 single-gene deletion strains corresponding to predicted components of the shortest-path longevity network was determined. Here we report that the single-gene deletion strains identified by our shortest-path longevity analysis are significantly enriched for mutations conferring either increased or decreased replicative life span, relative to a randomly selected set of 564 single-gene deletion strains or to the current data set available for the entire haploid deletion collection. Further, we report the identification of previously unknown longevity genes, several of which function in a conserved longevity pathway believed to mediate life span extension in response to dietary restriction. CONCLUSIONS/SIGNIFICANCE: This work demonstrates that shortest-path network analysis is a useful approach toward identifying genetic determinants of longevity and represents the first application of network analysis of aging to be extensively validated in a biological system. The novel longevity genes identified in this study are likely to yield further insight into the molecular mechanisms of aging and age-associated disease.

Phylogenetic distances are encoded in networks of interacting pathways.

MOTIVATION: Although metabolic reactions are unquestionably shaped by evolutionary processes, the degree to which the overall structure and complexity of their interconnections are linked to the phylogeny of species has not been evaluated in depth. Here, we apply an original metabolome representation, termed Network of Interacting Pathways or NIP, with a combination of graph theoretical and machine learning strategies, to address this question. NIPs compress the information of the metabolic network exhibited by a species into much smaller networks of overlapping metabolic pathways, where nodes are pathways and links are the metabolites they exchange. RESULTS: Our analysis shows that a small set of descriptors of the structure and complexity of the NIPs combined into regression models reproduce very accurately reference phylogenetic distances derived from 16S rRNA sequences (10-fold cross-validation correlation coefficient higher than 0.9). Our method also showed better scores than previous work on metabolism-based phylogenetic reconstructions, as assessed by branch distances score, topological similarity and second cousins score. Thus, our metabolome representation as network of overlapping metabolic pathways captures sufficient information about the underlying evolutionary events leading to the formation of metabolic networks and species phylogeny. It is important to note that precise knowledge of all of the reactions in these pathways is not required for these reconstructions. These observations underscore the potential for the use of abstract, modular representations of metabolic reactions as tools in studying the evolution of species. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cellular automata simulation of topological effects on the dynamics of feed-forward motifs.

BACKGROUND: Feed-forward motifs are important functional modules in biological and other complex networks. The functionality of feed-forward motifs and other network motifs is largely dictated by the connectivity of the individual network components. While studies on the dynamics of motifs and networks are usually devoted to the temporal or spatial description of processes, this study focuses on the relationship between the specific architecture and the overall rate of the processes of the feed-forward family of motifs, including double and triple feed-forward loops. The search for the most efficient network architecture could be of particular interest for regulatory or signaling pathways in biology, as well as in computational and communication systems. RESULTS: Feed-forward motif dynamics were studied using cellular automata and compared with differential equation modeling. The number of cellular automata iterations needed for a 100% conversion of a substrate into a target product was used as an inverse measure of the transformation rate. Several basic topological patterns were identified that order the specific feed-forward constructions according to the rate of dynamics they enable. At the same number of network nodes and constant other parameters, the bi-parallel and tri-parallel motifs provide higher network efficacy than single feed-forward motifs. Additionally, a topological property of isodynamicity was identified for feed-forward motifs where different network architectures resulted in the same overall rate of the target production. CONCLUSION: It was shown for classes of structural motifs with feed-forward architecture that network topology affects the overall rate of a process in a quantitatively predictable manner. These fundamental results can be used as a basis for simulating larger networks as combinations of smaller network modules with implications on studying synthetic gene circuits, small regulatory systems, and eventually dynamic whole-cell models.

Predicting aging/longevity-related genes in the nematode Caenorhabditis elegans.

We present a novel mathematical/computational strategy for predicting genes/proteins associated with aging/longevity. The novelty of our method arises from the topological analysis of an organismal longevity gene/protein network (LGPN), which extends the existing cellular networks. The LGPN nodes represent both genes and corresponding proteins. Links stand for all known interactions between the nodes. The LGPN of C. elegans incorporated 362 genes/proteins, 160 connecting and 202 age-related ones, from a list of 321 with known impact on aging/longevity. A 'longevity core' of 129 directly interacting genes or proteins was identified. This core may shed light on the large-scale mechanisms of aging. Predictions were made, based upon the finding that LGPN hubs and centrally located nodes have higher likelihoods of being associated with aging/longevity than do randomly selected nodes. Analysis singled-out 15 potential aging/longevity-related genes for further examination: mpk-1, gei-4, csp-1, pal-1, mkk-4, 4O210, sem-5, gei-16, 1O814, 5M722, ife-3, ced-10, cdc-42, 1O776Co, and 1O690.

From molecular to biological structure and back.

A comparative analysis of the topological structure of molecules and molecular biology networks revealed both similarity and differences in the methods used, as well as in the essential features of the two types of systems. Molecular graphs are static and, due to the limitations in atomic valence, show neither power distribution of vertex degrees nor "small-world" properties, which are typical for dynamic evolutionary networks. Areas of mutual benefits from an exchange of methods and ideas are outlined for the two fields. More specifically, chemical graph theory might make use of some new descriptors of network structure. Of interest for quantitative structure-property relationship/quantitative structure-activity relationship and drug design might be the conclusion that descriptors based on distributions of vertex degrees, distances, and subgraphs seem to be more relevant to biological information than the single-number descriptors. The network concepts of centrality, clustering, and cliques provide a basis for similar studies in theoretical chemistry. The need of dynamic theory of molecular topology is advocated.

Impaired wound healing.

Nonhealing wounds represent a significant cause of morbidity and mortality for a large portion of the population. One of the underlying mechanisms responsible for the failure of chronic wounds to heal is an out-of-control inflammatory response that is self-sustaining. Underappreciation of the inherent complexity of the healing wound has led to the failure of monotherapies, with no significant reduction in wound healing times. A model of the inflammatory profile of a nonhealing wound is one in which the equilibrium between synthesis and degradation has been shifted toward degradation. This review summarizes the current information regarding acute wound healing responses as contrasted to the delayed response characteristic of chronic wounds. In addition, some initial complexity theoretical models are proposed to define and explain the underlying pathophysiology.