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A dynamic network approach for the study of human phenotypes.

ABSTRACT: The use of networks to integrate different genetic, proteomic, and metabolic datasets has been proposed as a viable path toward elucidating the origins of specific diseases. Here we introduce a new phenotypic database summarizing correlations obtained from the disease history of more than 30 million patients in a Phenotypic Disease Network (PDN). We present evidence that the structure of the PDN is relevant to the understanding of illness progression by showing that (1) patients develop diseases close in the network to those they already have; (2) the progression of disease along the links of the network is different for patients of different genders and ethnicities; (3) patients diagnosed with diseases which are more highly connected in the PDN tend to die sooner than those affected by less connected diseases; and (4) diseases that tend to be preceded by others in the PDN tend to be more connected than diseases that precede other illnesses, and are associated with higher degrees of mortality. Our findings show that disease progression can be represented and studied using network methods, offering the potential to enhance our understanding of the origin and evolution of human diseases. The dataset introduced here, released concurrently with this publication, represents the largest relational phenotypic resource publicly available to the research community.

SUBMITTER: Hidalgo CA

PROVIDER: S-EPMC2661364 | biostudies-literature | 2009 Apr

REPOSITORIES: biostudies-literature

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A dynamic network approach for the study of human phenotypes.

Hidalgo César A CA Hidalgo César A CA Blumm Nicholas N Barabási Albert-László AL Christakis Nicholas A NA

PLoS computational biology 20090410 4

The use of networks to integrate different genetic, proteomic, and metabolic datasets has been proposed as a viable path toward elucidating the origins of specific diseases. Here we introduce a new phenotypic database summarizing correlations obtained from the disease history of more than 30 million patients in a Phenotypic Disease Network (PDN). We present evidence that the structure of the PDN is relevant to the understanding of illness progression by showing that (1) patients develop diseases ...[more]

PMID: 19360091

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Project description:BackgroundThere is an association between repetitive head injury (RHI) and a pathologic diagnosis of chronic traumatic encephalopathy (CTE) characterized by the aggregation of proteins including tau. The underlying molecular events that cause these abnormal protein accumulations remain unclear. Here, we hypothesized that identifying the human brain proteome from serial CTE stages (CTE I-IV) would provide critical new insights into CTE pathogenesis. Brain samples from frontotemporal lobar degeneration due to microtubule associated protein tau (FTLD-MAPT) mutations were also included as a distinct tauopathy phenotype for comparison.MethodsIsobaric tandem mass tagged labeling and mass spectrometry (TMT-MS) followed by integrated differential and co-expression analysis (i.e., weighted gene co-expression network analysis (WGCNA)) was used to define modules of highly correlated proteins associated with clinical and pathological phenotypes in control (n = 23), CTE (n = 43), and FTLD-MAPT (n = 12) post-mortem cortical tissues. We also compared these findings to network analysis of AD brain.ResultsWe identified over 6000 unique proteins across all four CTE stages which sorted into 28 WGCNA modules. Consistent with Alzheimer's disease, specific modules demonstrated reduced neuronal protein levels, suggesting a neurodegeneration phenotype, while other modules were increased, including proteins associated with inflammation and glial cell proliferation. Notably, unique CTE-specific modules demonstrated prominent enrichment of immunoglobulins, including IGHM and IGLL5, and extracellular matrix (ECM) proteins as well as progressive protein changes with increasing CTE pathologic stage. Finally, aggregate cell subtype (i.e., neurons, microglia, astrocytes) protein abundance levels in CTE cases were similar in expression to AD, but at intermediate levels between controls and the more exaggerated phenotype of FTLD-MAPT, especially in astrocytes.ConclusionsOverall, we identified thousands of protein changes in CTE postmortem brain and demonstrated that CTE has a pattern of neurodegeneration in neuronal-synaptic and inflammation modules similar to AD. We also identified unique CTE progressive changes, including the enrichment of immunoglobulins and ECM proteins even in early CTE stages. Early and sustained changes in astrocyte modules were also observed. Overall, the prominent overlap with FTLD-MAPT cases confirmed that CTE is on the tauopathy continuum and identified CTE stage specific molecular phenotypes that provide novel insights into disease pathogenesis.

Project description:Gene expression data generated systematically in a given system over multiple time points provides a source of perturbation that can be leveraged to infer causal relationships among genes explaining network changes. Previously, we showed that food intake has a large impact on blood gene expression patterns and that these responses, either in terms of gene expression level or gene-gene connectivity, are strongly associated with metabolic diseases. In this study, we explored which genes drive the changes of gene expression patterns in response to time and food intake. We applied the Granger causality test and the dynamic Bayesian network to gene expression data generated from blood samples collected at multiple time points during the course of a day. The simulation result shows that combining many short time series together is as powerful to infer Granger causality as using a single long time series. Using the Granger causality test, we identified genes that were supported as the most likely causal candidates for the coordinated temporal changes in the network. These results show that PER1 is a key regulator of the blood transcriptional network, in which multiple biological processes are under circadian rhythm regulation. The fasted and fed dynamic Bayesian networks showed that over 72% of dynamic connections are self links. Finally, we show that different processes such as inflammation and lipid metabolism, which are disconnected in the static network, become dynamically linked in response to food intake, which would suggest that increasing nutritional load leads to coordinate regulation of these biological processes. In conclusion, our results suggest that food intake has a profound impact on the dynamic co-regulation of multiple biological processes, such as metabolism, immune response, apoptosis and circadian rhythm. The results could have broader implications for the design of studies of disease association and drug response in clinical trials.

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A dynamic network approach for the study of human phenotypes.

Publications

A dynamic network approach for the study of human phenotypes.

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