Networks and Graphs Discovery in Metabolomics Data Analysis and Interpretation

Abstract

Both targeted and untargeted mass spectrometry-based metabolomics approaches are used to understand the metabolic processes taking place in various organisms, from prokaryotes, plants, fungi to animals and humans. Untargeted approaches allow to detect as many metabolites as possible at once, identify unexpected metabolic changes, and characterize novel metabolites in biological samples. However, the identification of metabolites and the biological interpretation of such large and complex datasets remain challenging. One approach to address these challenges is considering that metabolites are connected through informative relationships. Such relationships can be formalized as networks, where the nodes correspond to the metabolites or features (when there is no or only partial identification), and edges connect nodes if the corresponding metabolites are related. Several networks can be built from a single dataset (or a list of metabolites), where each network represents different relationships, such as statistical (correlated metabolites), biochemical (known or putative substrates and products of reactions), or chemical (structural similarities, ontological relations). Once these networks are built, they can subsequently be mined using algorithms from network (or graph) theory to gain insights into metabolism. For instance, we can connect metabolites based on prior knowledge on enzymatic reactions, then provide suggestions for potential metabolite identifications, or detect clusters of co-regulated metabolites. In this review, we first aim at settling a nomenclature and formalism to avoid confusion when referring to different networks used in the field of metabolomics. Then, we present the state of the art of network-based methods for mass spectrometry-based metabolomics data analysis, as well as future developments expected in this area. We cover the use of networks applications using biochemical reactions, mass spectrometry features, chemical structural similarities, and correlations between metabolites. We also describe the application of knowledge networks such as metabolic reaction networks. Finally, we discuss the possibility of combining different networks to analyze and interpret them simultaneously.

Graph Properties of Mass-Difference Networks for Profiling and Discrimination in Untargeted Metabolomics

“…The potential of this methodology has been highlighted in the characterization of different biological systems (Moritz et al, 2015(Moritz et al, , 2017Clancy et al, 2018;Kaling et al, 2018;Laber et al, 2021). An in-depth review of MDiNs and all types of networks used in metabolomics has recently been published (Amara et al, 2022).…”

“…MDiNs are one of the major types of experimental networks used for metabolomics data analysis (Amara et al, 2022) and have been primarily used for molecular formula assignment and annotation (Tziotis et al, 2011;Moritz et al, 2017) or the depiction of a system's chemical diversity (Tziotis et al, 2011).…”

“…Because each edge has a corresponding absolute change of metabolite formulas (addition or subtraction), the formula assignment of an entire network component can be made from a single reliably annotated node through formula propagation (Breitling et al, 2006;Tziotis et al, 2011;Moritz et al, 2017;Amara et al, 2022). As a few examples, MDiNs have been used in this way in studies on mouse cecal samples (Walker et al, 2014), yeast (Liu et al, 2016), and mouse adipose tissue and human blood plasma (Laber et al, 2021), among others (Moritz et al, 2015;Willkommen et al, 2018).…”

Signed Distance Correlation (SiDCo): A network analysis application of distance correlation for identifying metabolic networks disrupted in Dementia with Lewy Bodies

“…This approach requires analyses of biological networks as a parallel investigation to individual feature characteristics and establishing the interconnection of these features within a biological system while comparing different disease conditions (Ma'ayan, 2011). Methods for such biological network development are broadly divided into knowledge-driven and data-driven approaches where knowledge-based networks primarily aim to contribute to data interpretation while data-based networks derive novel interactions from the data (Amara, et al, 2022). Combining these two approaches can help identify biologically active processes that associate with disease progression.…”

“…Several methods for data-driven network determination have been used in metabolomics. Pearson or Spearman correlation-based methods are arguably the most prevalent (Amara, et al, 2022). While providing critical information about the direction of dependencies, both methods measure linear or monotonic correlations and cannot detect non-linear metabolite interactions (Rosato, et al, 2018).…”