A novel approach for determining environment-specific protein costs: the case of Arabidopsis thaliana

Motivation: Comprehensive understanding of cellular processes requires development of approaches which consider the energetic balances in the cell. The existing approaches that address this problem are based on defining energy-equivalent costs which do not include the effects of a changing environment. By incorporating these effects, one could provide a framework for integrating ‘omics’ data from various levels of the system in order to provide interpretations with respect to the energy state and to elicit conclusions about putative global energy-related response mechanisms in the cell

Reduced mitochondrial respiration in T cells of patients with major depressive disorder

Converging evidence indicates that major depressive disorder (MDD) and metabolic disorders might be mediated by shared (patho)biological pathways. However, the converging cellular and molecular signatures remain unknown. Here, we investigated metabolic dysfunction on a systemic, cellular, and molecular level in unmedicated patients with MDD compared with matched healthy controls (HC). Despite comparable BMI scores and absence of cardiometabolic disease, patients with MDD presented with significant dyslipidemia. On a cellular level, T cells obtained from patients with MDD exhibited reduced respiratory and glycolytic capacity. Gene expression analysis revealed increased carnitine palmitoyltransferase IA (CPT1a) levels in T cells, the rate-limiting enzyme for mitochondrial long-chain fatty acid oxidation. Together, our results indicate metabolic dysfunction in unmedicated, non-overweight patients with MDD on a systemic, cellular, and molecular level. This evidence for reduced mitochondrial respiration in T cells of patients with MDD provides translation of previous animal studies regarding a putative role of altered immunometabolism in depression pathobiology

A hybrid flux balance analysis and machine learning pipeline elucidates the metabolic response of cyanobacteria to different growth conditions

Machine learning has recently emerged as a promising tool for inferring multi-omic relationships in biological systems. At the same time, genome-scale metabolic models (GSMMs) can be integrated with such multi-omic data to refine phenotypic predictions. In this work, we use a multi-omic machine learning pipeline to analyze a GSMM of Synechococcus sp. PCC 7002, a cyanobacterium with large potential to produce renewable biofuels. We use regularized flux balance analysis to observe flux response between conditions across photosynthesis and energy metabolism. We then incorporate principal-component analysis, k-means clustering, and LASSO regularization to reduce dimensionality and extract key cross-omic features. Our results suggest that combining metabolic modeling with machine learning elucidates mechanisms used by cyanobacteria to cope with fluctuations in light intensity and salinity that cannot be detected using transcriptomics alone. Furthermore, GSMMs introduce critical mechanistic details that improve the performance of omic-based machine learning methods

Experimental strategies to assess the biological ramifications of multiple drivers of global ocean change-A review

Marine life is controlled by multiple physical and chemical drivers and by diverse ecological processes. Many of these oceanic properties are being altered by climate change and other anthropogenic pressures. Hence, identifying the influences of multifaceted ocean change, from local to global scales, is a complex task. To guide policy-making and make projections of the future of the marine biosphere, it is essential to understand biological responses at physiological, evolutionary and ecological levels. Here, we contrast and compare different approaches to multiple driver experiments that aim to elucidate biological responses to a complex matrix of ocean global change. We present the benefits and the challenges of each approach with a focus on marine research, and guidelines to navigate through these different categories to help identify strategies that might best address research questions in fundamental physiology, experimental evolutionary biology and community ecology. Our review reveals that the field of multiple driver research is being pulled in complementary directions: the need for reductionist approaches to obtain process-oriented, mechanistic understanding and a requirement to quantify responses to projected future scenarios of ocean change. We conclude the review with recommendations on how best to align different experimental approaches to contribute fundamental information needed for science-based policy formulation

Functional centrality as a predictor of shifts in metabolic flux states.

BACKGROUND: The flux phenotype describes the entirety of biochemical conversions in a cell, which renders it a key characteristic of metabolic function. To quantify the functional relevance of individual biochemical reactions, functional centrality has been introduced based on cooperative game theory and structural modeling. It was shown to be capable to determine metabolic control properties utilizing only structural information. Here, we demonstrate the capability of functional centrality to predict changes in the flux phenotype. RESULTS: We use functional centrality to successfully predict changes of metabolic flux triggered by switches in the environment. The predictions via functional centrality improve upon predictions using control-effective fluxes, another measure aiming at capturing metabolic control using structural information. CONCLUSIONS: The predictions of flux changes via functional centrality corroborate the capability of the measure to gain a mechanistic understanding of metabolic control from the structure of metabolic networks

Multi-objective shadow prices point at principles of metabolic regulation

AbstractPerturbations in environmental and intracellular conditions often lead to changes across all cellular layers, from transcription to metabolism. Regulatory mechanisms are key to mediating these changes to maintain homeostasis and to ensure viability. Since changes in metabolic reaction rates are partly due to perturbations in metabolite concentrations, it is expected that metabolites with large effect on those reaction rates which govern metabolic functionality are tightly regulated. The extent of metabolic regulation has been quantified by the sensitivity of an individual metabolic function to changes in metabolite concentrations, in particular by shadow prices in the constraint-based modeling framework. However, the system-wide characterization of the extent to which metabolite concentrations are regulated in the more realistic scenario of multiple contending tasks remains elusive. Here we examine multi-objective shadow prices for the central carbon metabolism of Escherichia coli whose reaction rates are shaped by several contending metabolic functions. We determine shadow prices for sampled solutions of the Pareto front, which characterizes the space of multi-objective optima, for three contending metabolic functions that provide the best agreement with 13C-labeling experiments. By analyzing the parts of the Pareto front closest to the experimentally determined flux phenotypes, we show that E. coli operates in the vicinity of an area of the Pareto front which facilitates robust and efficient regulation. In addition, we find significant associations between features of the transcriptional regulatory network and the sensitivity of E. coli's metabolic functionality to changes in metabolite concentrations. We demonstrate that the structural constraints of the metabolic network together with data on condition-specific flux phenotypes can be effectively used to dissect metabolic regulation on a system-wide level

Structural control of metabolic flux

Organisms have to continuously adapt to changing environmental conditions or undergo developmental transitions. To meet the accompanying change in metabolic demands, the molecular mechanisms of adaptation involve concerted interactions which ultimately induce a modification of the metabolic state, which is characterized by reaction fluxes and metabolite concentrations. These state transitions are the effect of simultaneously manipulating fluxes through several reactions. While metabolic control analysis has provided a powerful framework for elucidating the principles governing this orchestrated action to understand metabolic control, its applications are restricted by the limited availability of kinetic information. Here, we introduce structural metabolic control as a framework to examine individual reactions' potential to control metabolic functions, such as biomass production, based on structural modeling. The capability to carry out a metabolic function is determined using flux balance analysis (FBA). We examine structural metabolic control on the example of the central carbon metabolism of Escherichia coli by the recently introduced framework of functional centrality (FC). This framework is based on the Shapley value from cooperative game theory and FBA, and we demonstrate its superior ability to assign "share of control" to individual reactions with respect to metabolic functions and environmental conditions. A comparative analysis of various scenarios illustrates the usefulness of FC and its relations to other structural approaches pertaining to metabolic control. We propose a Monte Carlo algorithm to estimate FCs for large networks, based on the enumeration of elementary flux modes. We further give detailed biological interpretation of FCs for production of lactate and ATP under various respiratory conditions

Reactions with highest ranking according to functional centrality.

<p>Rankings are shown for the metabolic functions of lactate production (LAC) and ATP production (ATP) under conditions of aerobic respiration (), nitrate respiration () and fermentation (). Reactions with highest FCs obtain lowest numbering.</p