Dataset Information

Mapping biological process relationships and disease perturbations within a pathway network.

ABSTRACT: Molecular interaction networks are routinely used to map the organization of cellular function. Edges represent interactions between genes, proteins, or metabolites. However, in living cells, molecular interactions are dynamic, necessitating context-dependent models. Contextual information can be integrated into molecular interaction networks through the inclusion of additional molecular data, but there are concerns about completeness and relevance of this data. We developed an approach for representing the organization of human cellular processes using pathways as the nodes in a network. Pathways represent spatial and temporal sets of context-dependent interactions, generating a high-level network when linked together, which incorporates contextual information without the need for molecular interaction data. Analysis of the pathway network revealed linked communities representing functional relationships, comparable to those found in molecular networks, including metabolism, signaling, immunity, and the cell cycle. We mapped a range of diseases onto this network and find that pathways associated with diseases tend to be functionally connected, highlighting the perturbed functions that result in disease phenotypes. We demonstrated that disease pathways cluster within the network. We then examined the distribution of cancer pathways and showed that cancer pathways tend to localize within the signaling, DNA processes and immune modules, although some cancer-associated nodes are found in other network regions. Altogether, we generated a high-confidence functional network, which avoids some of the shortcomings faced by conventional molecular models. Our representation provides an intuitive functional interpretation of cellular organization, which relies only on high-quality pathway and Gene Ontology data. The network is available at https://data.mendeley.com/datasets/3pbwkxjxg9/1.

SUBMITTER: Stoney R

PROVIDER: S-EPMC5995814 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Mapping biological process relationships and disease perturbations within a pathway network.

Stoney Ruth R Robertson David L DL Nenadic Goran G Schwartz Jean-Marc JM

NPJ systems biology and applications 20180611

Molecular interaction networks are routinely used to map the organization of cellular function. Edges represent interactions between genes, proteins, or metabolites. However, in living cells, molecular interactions are dynamic, necessitating context-dependent models. Contextual information can be integrated into molecular interaction networks through the inclusion of additional molecular data, but there are concerns about completeness and relevance of this data. We developed an approach for repr ...[more]

PMID: 29900005

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Project description:A goal of genomics is to understand the relationships between biological processes. Pathways contribute to functional interplay within biological processes through complex but poorly understood interactions. However, limited functional references for global pathway relationships exist. Pathways from databases such as KEGG and Reactome provide discrete annotations of biological processes. Their relationships are currently either inferred from gene set enrichment within specific experiments, or by simple overlap, linking pathway annotations that have genes in common. Here, we provide a unifying interpretation of functional interaction between pathways by systematically quantifying coexpression between 1,330 canonical pathways from the Molecular Signatures Database (MSigDB) to establish the Pathway Coexpression Network (PCxN). We estimated the correlation between canonical pathways valid in a broad context using a curated collection of 3,207 microarrays from 72 normal human tissues. PCxN accounts for shared genes between annotations to estimate significant correlations between pathways with related functions rather than with similar annotations. We demonstrate that PCxN provides novel insight into mechanisms of complex diseases using an Alzheimer's Disease (AD) case study. PCxN retrieved pathways significantly correlated with an expert curated AD gene list. These pathways have known associations with AD and were significantly enriched for genes independently associated with AD. As a further step, we show how PCxN complements the results of gene set enrichment methods by revealing relationships between enriched pathways, and by identifying additional highly correlated pathways. PCxN revealed that correlated pathways from an AD expression profiling study include functional clusters involved in cell adhesion and oxidative stress. PCxN provides expanded connections to pathways from the extracellular matrix. PCxN provides a powerful new framework for interrogation of global pathway relationships. Comprehensive exploration of PCxN can be performed at http://pcxn.org/.

Project description:BackgroundEfforts have been made by health research granting agencies to bring research closer to patients' concerns. In Canada, such efforts were formalized in 2011 with the funding of the Strategy for Patient-Oriented Research (SPOR)'s research networks to address research priorities identified by patients and accelerate the translation of research findings into patient care and health care policy. Among these networks, SPOR Diabetes Action Canada (DAC) has created patient-partner circles to facilitate their integration within the network. The nature of the relationships within this atypical patient-oriented research network is systematically explored in this paper.MethodsA cross-sectional social network study was conducted among the SPOR DAC's network members to examine inter-individual interactions, and the topics discussed the most between members. Descriptive data analyses were conducted to explore which discussion topics were discussed most among members whose primary roles were research, administration, governance, and patient representation.ResultsThe response rate was 51.9%, providing data on 76.5% of the maximum number of connections in the network. The survey captured 2763 inter-individual relationships. Responses to a sub-question inserted in the survey show that 482 of these relationships (17,4%) existed before joining the network in collaboration on a research project. Most ties captured in the survey were yearly or quarterly, while few relationships were monthly, weekly, or daily. In measured relationships, members discussed several topics, the most frequent being scientific research, patient engagement, network coordination and governance, and operations and management. The topics associated with the most significant proportion of relationships captured in the survey were scientific research (45.4%) and patient engagement (40.7%). Management & operations and governance & coordination follow, corresponding to 24.3 and 23.9% of the captured relationships. All discussion topic subnetworks were either somewhat or highly centralized, meaning that relationships were not equally distributed among members involved in these discussions. Of the 1256 relationships involving exchanges about scientific research, 647 (51.5%) involved a researcher, 419 (33.3%) an administrator, 182 (14.5%) a patient partner, and 82 (6.5%) a member whose primary role is network governance.ConclusionsScientific research and patient engagement were the most common topics discussed, consistent with the patient-centered research at the heart of the SPOR Diabetes Action Canada network. The study identified several relationships where a patient partner has discussed scientific research with a researcher. However, relationships involving research discussions were three times more common between a researcher and an administrator than between a researcher and a patient partner, although twice as many patient partners as administrators participated in the survey. The institutionalization of patient-partner involvement in large research networks is an evolving practice for which optimal engagement methods are still being explored.

Dataset Information

Mapping biological process relationships and disease perturbations within a pathway network.

Publications

Mapping biological process relationships and disease perturbations within a pathway network.

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