Mining metabolic pathways through gene expression

Abstract

Motivation: An observed metabolic response is the result of the coordinated activation and interaction between multiple genetic pathways. However, the complex structure of metabolism has meant that a compete understanding of which pathways are required to produce an observed metabolic response is not fully understood. In this article, we propose an approach that can identify the genetic pathways which dictate the response of metabolic network to specific experimental conditions.Results: Our approach is a combination of probabilistic models for pathway ranking, clustering and classification. First, we use a non-parametric pathway extraction method to identify the most highly correlated paths through the metabolic network. We then extract the defining structure within these top-ranked pathways using both Markov clustering and classification algorithms. Furthermore, we define detailed node and edge annotations, which enable us to track each pathway, not only with respect to its genetic dependencies, but also allow for an analysis of the interacting reactions, compounds and KEGG sub-networks. We show that our approach identifies biologically meaningful pathways within two microarray expression datasets using entire KEGG metabolic networks.Availability and implementation: An R package containing a full implementation of our proposed method is currently available from http://www.bic.kyoto-u.ac.jp/pathway/timhancockContact: timhancock@kuicr.kyoto-u.ac.jpSupplementary information: Supplementary data are available at Bioinformatics online.

What mRNA Abundances Can Tell us about Metabolism

“…(ii) Differential expression profiles can be used to differentiate between states (changed conditions, time series)—normally logarithmic expression values are subtracted, which is called relative , e.g., to quantify changed metabolic activities [ 63 ]. (iii) A third alternative is to analyze the correlation of expression changes for each pair of genes, called co-expression , e.g., to assert which metabolic paths are controlled concertedly [ 64 ].…”

“…Expression correlations are used to determine which genes are commonly regulated, for instance to predict transcription factors. Metabolic pathways with a high correlation of genes coding the necessary enzymes can be considered as a functional mode of operation in a particular cell type [ 64 , 76 , 77 ]. Ihmels et al .…”

“…Often a stoichiometric model is used to compute flux distributions in the flux-balance framework [ 62 , 65 , 69 , 74 ]. A different approach is to use metabolic paths (small linear chains of reactions) which do not necessarily form a complete network [ 64 , 84 ]. An alternative way to represent the metabolic system is to compute the set of elementary flux modes first [ 40 ] and perform the analysis using these flux distributions [ 41 ].…”

“…Its basic idea is to assign a probability value to gene states. Expression profiles have been used to rank reaction paths [ 64 ] or similarly “metabolic modules” [ 91 ]. Based on the textbook pathway definition (e.g., implemented in KEGG [ 73 ]), expression values have been used to score pathways in a framework called differential rank conservation [ 75 , 92 , 93 ].…”

Emerging Role of Metabolic Pathways in Abiotic Stress Tolerance

“…The complexity of metabolism/metabolic pathways poses a challenge in identifying the key regulatory components of metabolic pathways involved in abiotic stress responses. A few databases of known metabolic pathways in different organisms are available and few models have been proposed [25][26][27][28], which can help investigate the key metabolic pathways involved in a particular biological response. Although better and user-friendly databases and tools are required, the existing databases and models can also be used to elucidate the key metabolic pathways and functional components responsible for abiotic stress responses.…”