Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network

Network Modeling and Control of Dynamic Disease Pathways, Review and Perspectives

Dynamic disease pathways are a combination of complex dynamical processes among bio-molecules in a cell that leads to diseases. Network modeling of disease pathways considers disease-related bio-molecules (e.g. DNA, RNA, transcription factors, enzymes, proteins, and metabolites) and their interaction (e.g. DNA methylation, histone modification, alternative splicing, and protein modification) to study disease progression and predict therapeutic responses. These bio-molecules and their interactions are the basic elements in the study of the misregulation in the disease-related gene expression that lead to abnormal cellular responses. Gene regulatory networks, cell signaling networks, and metabolic networks are the three major types of intracellular networks for the study of the cellular responses elicited from extracellular signals. The disease-related cellular responses can be prevented or regulated by designing control strategies to manipulate these extracellular or other intracellular signals. The paper reviews the regulatory mechanisms, the dynamic models, and the control strategies for each intracellular network. The applications, limitations and the prospective for modeling and control are also discussed.

Proteomic analysis for the effects of non-saponin fraction with rich polysaccharide from Korean Red Ginseng on Alzheimer's disease in a mouse model

Background

The most common type of dementia, Alzheimer's disease (AD), is marked by the formation of extracellular amyloid beta (Aβ) plaques. The impairments of axons and synapses appear in the process of Aβ plaques formation, and this damage could cause neurodegeneration. We previously reported that non-saponin fraction with rich polysaccharide (NFP) from Korean Red Ginseng (KRG) showed neuroprotective effects in AD. However, precise molecular mechanism of the therapeutic effects of NFP from KRG in AD still remains elusive.

Methods

To investigate the therapeutic mechanisms of NFP from KRG on AD, we conducted proteomic analysis for frontal cortex from vehicle-treated wild-type, vehicle-treated 5XFAD mice, and NFP-treated 5XFAD mice by using nano-LC-ESI-MS/MS. Metabolic network analysis was additionally performed as the effects of NFP appeared to be associated with metabolism according to the proteome analysis.

Results

Starting from 5,470 proteins, 2,636 proteins were selected for hierarchical clustering analysis, and finally 111 proteins were further selected for protein-protein interaction network analysis. A series of these analyses revealed that proteins associated with synapse and mitochondria might be linked to the therapeutic mechanism of NFP. Subsequent metabolic network analysis via genome-scale metabolic models that represent the three mouse groups showed that there were significant changes in metabolic fluxes of mitochondrial carnitine shuttle pathway and mitochondrial beta-oxidation of polyunsaturated fatty acids.

Conclusion

Our results suggested that the therapeutic effects of NFP on AD were associated with synaptic- and mitochondrial-related pathways, and they provided targets for further rigorous studies on precise understanding of the molecular mechanism of NFP.

Machine learning-guided evaluation of extraction and simulation methods for cancer patient-specific metabolic models

Genome-scale metabolic model (GEM) has been established as an important tool to study cellular metabolism at a systems level by predicting intracellular fluxes. With the advent of generic human GEMs, they have been increasingly applied to a range of diseases, often for the objective of predicting effective metabolic drug targets. Cancer is a representative disease where the use of GEMs has proved to be effective, partly due to the massive availability of patient-specific RNA-seq data. When using a human GEM, so-called context-specific GEM needs to be developed first by using cell-specific RNA-seq data. Biological validity of a context-specific GEM highly depends on both model extraction method (MEM) and model simulation method (MSM). However, while MEMs have been thoroughly examined, MSMs have not been systematically examined, especially, when studying cancer metabolism. In this study, the effects of pairwise combinations of three MEMs and five MSMs were evaluated by examining biological features of the resulting cancer patient-specific GEMs. For this, a total of 1,562 patient-specific GEMs were reconstructed, and subjected to machine learning-guided and biological evaluations to draw robust conclusions. Noteworthy observations were made from the evaluation, including the high performance of two MEMs, namely rank-based ‘task-driven Integrative Network Inference for Tissues’ (tINIT) or ‘Gene Inactivity Moderated by Metabolism and Expression’ (GIMME), paired with least absolute deviation (LAD) as a MSM, and relatively poorer performance of flux balance analysis (FBA) and parsimonious FBA (pFBA). Insights from this study can be considered as a reference when studying cancer metabolism using patient-specific GEMs.

Metabolomic Analysis of Defense-Related Reprogramming in Sorghum bicolor in Response to Colletotrichum sublineolum Infection Reveals a Functional Metabolic Web of Phenylpropanoid and Flavonoid Pathways

The metabolome of a biological system provides a functional readout of the cellular state, thus serving as direct signatures of biochemical events that define the dynamic equilibrium of metabolism and the correlated phenotype. Hence, to elucidate biochemical processes involved in sorghum responses to fungal infection, a liquid chromatography-mass spectrometry-based untargeted metabolomic study was designed. Metabolic alterations of three sorghum cultivars responding to Colletotrichum sublineolum, were investigated. At the 4-leaf growth stage, the plants were inoculated with fungal spore suspensions and the infection monitored over time: 0, 3, 5, 7, and 9 days post inoculation. Non-infected plants were used as negative controls. The metabolite composition of aqueous-methanol extracts were analyzed on an ultra-high performance liquid chromatography system coupled to high-definition mass spectrometry. The acquired multidimensional data were processed to create data matrices for multivariate statistical analysis and chemometric modeling. The computed chemometric models indicated time- and cultivar-related metabolic changes that reflect sorghum responses to the fungal infection. Metabolic pathway and correlation-based network analyses revealed that this multi-component defense response is characterized by a functional metabolic web, containing defense-related molecular cues to counterattack the pathogen invasion. Components of this network are metabolites from a range of interconnected metabolic pathways with the phenylpropanoid and flavonoid pathways being the central hub of the web. One of the key features of this altered metabolism was the accumulation of an array of phenolic compounds, particularly de novo biosynthesis of the antifungal 3-deoxyanthocynidin phytoalexins, apigeninidin, luteolinidin, and related conjugates. The metabolic results were complemented by qRT-PCR gene expression analyses that showed upregulation of defense-related marker genes. Unraveling key characteristics of the biochemical mechanism underlying sorghum-C. sublineolum interactions, provided valuable insights with potential applications in breeding crop plants with enhanced disease resistance. Furthermore, the study contributes to ongoing efforts toward a comprehensive understanding of the regulation and reprogramming of plant metabolism under biotic stress.

The best models of metabolism

Biochemical systems are among of the oldest application areas of mathematical modeling. Spanning a time period of over one hundred years, the repertoire of options for structuring a model and for formulating reactions has been constantly growing, and yet, it is still unclear whether or to what degree some models are better than others and how the modeler is to choose among them. In fact, the variety of options has become overwhelming and difficult to maneuver for novices and experts alike. This review outlines the metabolic model design process and discusses the numerous choices for modeling frameworks and mathematical representations. It tries to be inclusive, even though it cannot be complete, and introduces the various modeling options in a manner that is as unbiased as that is feasible. However, the review does end with personal recommendations for the choices of default models. WIREs Syst Biol Med 2017, 9:e1391. doi: 10.1002/wsbm.1391 For further resources related to this article, please visit the WIREs website.

Framework and resource for more than 11,000 gene-transcript-protein-reaction associations in human metabolism

Alternative splicing plays important roles in generating different transcripts from one gene, and consequently various protein isoforms. However, there has been no systematic approach that facilitates characterizing functional roles of protein isoforms in the context of the entire human metabolism. Here, we present a systematic framework for the generation of gene-transcript-protein-reaction associations (GeTPRA) in the human metabolism. The framework in this study generated 11,415 GeTPRA corresponding to 1,106 metabolic genes for both principal and nonprincipal transcripts (PTs and NPTs) of metabolic genes. The framework further evaluates GeTPRA, using a human genome-scale metabolic model (GEM) that is biochemically consistent and transcript-level data compatible, and subsequently updates the human GEM. A generic human GEM, Recon 2M.1, was developed for this purpose, and subsequently updated to Recon 2M.2 through the framework. Both PTs and NPTs of metabolic genes were considered in the framework based on prior analyses of 446 personal RNA-Seq data and 1,784 personal GEMs reconstructed using Recon 2M.1. The framework and the GeTPRA will contribute to better understanding human metabolism at the systems level and enable further medical applications.

A Hybrid of Metabolic Flux Analysis and Bayesian Factor Modeling for Multiomic Temporal Pathway Activation

The growing availability of multiomic data provides a highly comprehensive view of cellular processes at the levels of mRNA, proteins, metabolites, and reaction fluxes. However, due to probabilistic interactions between components depending on the environment and on the time course, casual, sometimes rare interactions may cause important effects in the cellular physiology. To date, interactions at the pathway level cannot be measured directly, and methodologies to predict pathway cross-correlations from reaction fluxes are still missing. Here, we develop a multiomic approach of flux-balance analysis combined with Bayesian factor modeling with the aim of detecting pathway cross-correlations and predicting metabolic pathway activation profiles. Starting from gene expression profiles measured in various environmental conditions, we associate a flux rate profile with each condition. We then infer pathway cross-correlations and identify the degrees of pathway activation with respect to the conditions and time course using Bayesian factor modeling. We test our framework on the most recent metabolic reconstruction of Escherichia coli in both static and dynamic environments, thus predicting the functionality of particular groups of reactions and how it varies over time. In a dynamic environment, our method can be readily used to characterize the temporal progression of pathway activation in response to given stimuli.

Metabolic flux analysis of Escherichia coli knockouts: lessons from the Keio collection and future outlook

Cellular metabolic and regulatory systems are of fundamental interest to biologists and engineers. Incomplete understanding of these complex systems remains an obstacle to progress in biotechnology and metabolic engineering. An established method for obtaining new information on network structure, regulation and dynamics is to study the cellular system following a perturbation such as a genetic knockout. The Keio collection of all viable Escherichia coli single-gene knockouts is facilitating a systematic investigation of the regulation and metabolism of E. coli. Of all omics measurements available, the metabolic flux profile (the fluxome) provides the most direct and relevant representation of the cellular phenotype. Recent advances in (13)C-metabolic flux analysis are now permitting highly precise and accurate flux measurements for investigating cellular systems and guiding metabolic engineering efforts.

Flux-coupled genes and their use in metabolic flux analysis

As large volumes of omics data have become available, systems biology is playing increasingly important roles in elucidating new biological phenomena, especially through genome-scale metabolic network modeling and simulation. Much effort has been exerted on integrating omics data with metabolic flux simulation, but further development is necessary for more accurate flux estimation. To move one step forward, we adopted the concept of flux-coupled genes (FCGs), which show that their expression transition patterns upon perturbations are correlated with their corresponding flux values, as additional constraints in metabolic flux analysis. It was found that gnd, pfkB, rpe, sdhB, sdhD, sucA, and zwf genes, mostly associated with pentose phosphate pathway and TCA cycle, were the most consistent FCGs in Escherichia coli based on its transcriptome and (13) C-flux data obtained from the chemostat cultivation at five different dilution rates. Consequently, constraints-based flux analyses with FCGs as additional constraints were conducted for the seven single-gene knockout mutants, compared with those obtained without using FCGs. This strategy of constraining the metabolic flux analysis with FCGs is expected to be useful due to the relative ease in obtaining transcriptional information in the functional genomics era.

Network analysis: tackling complex data to study plant metabolism

Incomplete knowledge of biochemical pathways makes the holistic description of plant metabolism a non-trivial undertaking. Sensitive analytical platforms, which are capable of accurately quantifying the levels of the various molecular entities of the cell, can assist in tackling this task. However, the ever-increasing amount of high-throughput data, often from multiple technologies, requires significant computational efforts for integrative analysis. Here we introduce the application of network analysis to study plant metabolism and describe the construction and analysis of correlation-based networks from (time-resolved) metabolomics data. By investigating the interactions between metabolites, network analysis can help to interpret complex datasets through the identification of key network components. The relationship between structural and biological roles of network components can be evaluated and employed to aid metabolic engineering.