Investigating the deregulation of metabolic tasks via Minimum Network Enrichment Analysis (MiNEA) as applied to nonalcoholic fatty liver disease using mouse and human omics data

Abstract

Nonalcoholic fatty liver disease (NAFLD) is associated with metabolic syndromes 15 spanning a wide spectrum of diseases, from simple steatosis to the more complex nonalcoholic steatohepatitis. To identify the deregulation that occurs in metabolic processes at the molecular level that give rise to these various NAFLD phenotypes, algorithms such as pathway enrichment analysis (PEA) can be used. These analyses Author SummaryThis work aims to introduce a network-based enrichment analysis using metabolic networks and transcriptomics data. Previous pathways/subsystems enrichment methods use predefined gene annotations of metabolic processes and gene annotations can differ based on different resources and can produce bias in pathways definitions. 45Thus, we introduce a framework, Minimum Network Enrichment Analysis (MiNEA), which first finds all possible minimal-size networks for a given metabolic process/task and then identifies deregulated minimal networks using deregulated genes between two conditions. MiNEA also identifies the deregulation in key reactions that are overlapped across all possible minimal-size networks. We applied 50MiNEA to identify deregulated metabolic tasks and their synthesis networks in the steatosis and nonalcoholic steatohepatitis (NASH) disease using a metabolic network and transcriptomics data of mouse and human liver samples. We identified key regulators of NASH form the synthesis networks of hydrogen peroxide and ceramide in both humans and mice. We also identified opposite deregulation in NASH for the 55 phosphatidylserine synthesis network between humans and mice. MiNEA finds synthesis networks for a given target metabolite and due to this it is flexible to study deregulation in different phenotypes. MiNEA can be widely applicable for studying context-specific metabolism for any organism because the metabolic networks and context-specific gene expression data are now available for many organisms. 60

The ETFL formulation allows multi-omics integration in thermodynamics-compliant metabolism and expression models

“…An additional gauge constraint will be needed for the relative data. Previous transcriptomic integration methods, such as REMI 23 , iMAT 24 , GIMME 25 , or MINEA 26 , can also be adequately reformulated for ETFL. Metabolomics can still be integrated using TFA 2,3 .…”

Path to improving the life cycle and quality of genome-scale models of metabolism

“…Sometimes, multiple BOFs are added to represent different cellular states, or to model the effect of stressors such as drugs and antibiotics (Heavner et al, 2012;Sahoo et al, 2015;Seif et al, 2019aSeif et al, , 2019b. BOFs are increasingly being replaced with ''metabolic tasks,'' especially across GEM specialization efforts (Agren et al, 2014;Pandey and Hatzimanikatis, 2019;Richelle et al, 2019). ''Metabolic tasks'' are defined by Thiele et al as ''non-zero flux(es) through a reaction or through a pathway leading to the production of a metabolite B from a metabolite A'' (Thiele et al, 2013a(Thiele et al, , 2013b.…”

ETFL: A formulation for flux balance models accounting for expression, thermodynamics, and resource allocation constraints

“…An additional gauge constraint will be 487 needed for relative data. Previous transcriptomic integration methods, such as 488 REMI [22], iMAT [23], GIMME [24], or MINEA [25], can also be adequately 489 reformulated for ETFL. Metabolomics can still be integrated using TFA [1,2].…”