A novel network-based approach for discovering dynamic metabolic biomarkers in cardiovascular disease

Abstract

Metabolic biomarkers may play an important role in the diagnosis, prognostication and assessment of response to pharmacological therapy in complex diseases. The process of discovering new metabolic biomarkers is a non-trivial task which involves a number of bioanalytical processing steps coupled with a computational approach for the search, prioritization and verification of new biomarker candidates. Kinetic analysis provides an additional dimension of complexity in time-series data, allowing for a more precise interpretation of biomarker dynamics in terms of molecular interaction and pathway modulation. A novel network-based computational strategy for the discovery of putative dynamic biomarker candidates is presented, enabling the identification and verification of unexpected metabolic signatures in complex diseases such as myocardial infarction. The novelty of the proposed method lies in combining metabolic time-series data into a superimposed graph representation, highlighting the strength of the underlying kinetic interaction of preselected analytes. Using this approach, we were able to confirm known metabolic signatures and also identify new candidates such as carnosine and glycocholic acid, and pathways that have been previously associated with cardiovascular or related diseases. This computational strategy may serve as a complementary tool for the discovery of dynamic metabolic or proteomic biomarkers in the field of clinical medicine.

Graphlet Laplacians for topology-function and topology-disease relationships

“…Genomic, proteomic, interactomic, metabolomic and other data, are typically modeled as networks (also called graphs). This abundance of networked data started the fields of network biology, allowing us to uncover molecular mechanisms of a broad range of diseases, such as rare Mendelian disorders (Smedley et al, 2014), cancer (Leiserson et al, 2015), and metabolic diseases (Baumgartner et al, 2018). In personalized medicine, network analysis is applied to the tasks of bio-marker discovery (Li et al, 2015), patient stratification (Gligorijević et al, 2016) and drug repurposing (Durán et al, 2017).…”

Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases

“…Furthermore, metabolic network analysis can predict disease progression. By analyzing dynamic changes in metabolic network models, researchers can simulate disease progression and predict the progression rate and possible outcomes [102]. This elucidates disease occurrence mechanisms and provides important guidance for disease treatment and intervention.…”

Network medicine in Cardiovascular Research

“…Notable recent examples include a dynamic network analysis platform integrating patient plasma metabolite levels from multiple timepoints following alcohol septal ablation ('planned myocardial injury') that led to the identification of carnosine and glycocholic acid as novel metabolites associated with acute myocardial injury. 118 NEDD9 in PAH is another important example of a novel biomarker discovery facilitated by the integrative network analysis of multiple perturbed biological pathways. 7,95 At the same time, a paradigm shift that network medicine bringsapproaching disease as a consequence of perturbed interactions among multiple biological entities rather than a single altered molecular pathway-highlights the current limitation of relying on a single molecule as a biomarker for diagnosing, prognosticating, or assessing therapeutic efficacy for a complex disease process (e.g., NT-proBNP in heart failure).…”