Quantitative metabolic fluxes regulated by trans-omic networks

Protein constraints in genome-scale metabolic models: Data integration, parameter estimation, and prediction of metabolic phenotypes

Genome-scale metabolic models provide a valuable resource to study metabolism and cell physiology. These models are employed with approaches from the constraint-based modeling framework to predict metabolic and physiological phenotypes. The prediction performance of genome-scale metabolic models can be improved by including protein constraints. The resulting protein-constrained models consider data on turnover numbers (kcat ) and facilitate the integration of protein abundances. In this systematic review, we present and discuss the current state-of-the-art regarding the estimation of kinetic parameters used in protein-constrained models. We also highlight how data-driven and constraint-based approaches can aid the estimation of turnover numbers and their usage in improving predictions of cellular phenotypes. Finally, we identify standing challenges in protein-constrained metabolic models and provide a perspective regarding future approaches to improve the predictive performance.

A Markov constraint to uniquely identify elementary flux mode weights in unimolecular metabolic networks

Elementary flux modes (EFMs) are minimal, steady state pathways characterizing a flux network. Fundamentally, all steady state fluxes in a network are decomposable into a linear combination of EFMs. While there is typically no unique set of EFM weights that reconstructs these fluxes, several optimization-based methods have been proposed to constrain the solution space by enforcing some notion of parsimony. However, it has long been recognized that optimization-based approaches may fail to uniquely identify EFM weights and return different feasible solutions across objective functions and solvers. Here we show that, for flux networks only involving single molecule transformations, these problems can be avoided by imposing a Markovian constraint on EFM weights. Our Markovian constraint guarantees a unique solution to the flux decomposition problem, and that solution is arguably more biophysically plausible than other solutions. We describe an algorithm for computing Markovian EFM weights via steady state analysis of a certain discrete-time Markov chain, based on the flux network, which we call the cycle-history Markov chain. We demonstrate our method with a differential analysis of EFM activity in a lipid metabolic network comparing healthy and Alzheimer's disease patients. Our method is the first to uniquely decompose steady state fluxes into EFM weights for any unimolecular metabolic network.

Functional decomposition of metabolism allows a system-level quantification of fluxes and protein allocation towards specific metabolic functions

Quantifying the contribution of individual molecular components to complex cellular processes is a grand challenge in systems biology. Here we establish a general theoretical framework (Functional Decomposition of Metabolism, FDM) to quantify the contribution of every metabolic reaction to metabolic functions, e.g. the synthesis of biomass building blocks. FDM allowed for a detailed quantification of the energy and biosynthesis budget for growing Escherichia coli cells. Surprisingly, the ATP generated during the biosynthesis of building blocks from glucose almost balances the demand from protein synthesis, the largest energy expenditure known for growing cells. This leaves the bulk of the energy generated by fermentation and respiration unaccounted for, thus challenging the common notion that energy is a key growth-limiting resource. Moreover, FDM together with proteomics enables the quantification of enzymes contributing towards each metabolic function, allowing for a first-principle formulation of a coarse-grained model of global protein allocation based on the structure of the metabolic network.

Integrated omic techniques and their genomic features for invasive weeds

Many emerging invasive weeds display rapid adaptation against different stressful environments compared to their natives. Rapid adaptation and dispersal habits helped invasive populations have strong diversity within the population compared to their natives. Advances in molecular marker techniques may lead to an in-depth understanding of the genetic diversity of invasive weeds. The use of molecular techniques is rapidly growing, and their implications in invasive weed studies are considered powerful tools for genome purposes. Here, we review different approach used multi-omics by invasive weed studies to understand the functional structural and genomic changes in these species under different environmental fluctuations, particularly, to check the accessibility of advance-sequencing techniques used by researchers in genome sequence projects. In this review-based study, we also examine the importance and efficiency of different molecular techniques in identifying and characterizing different genes, associated markers, proteins, metabolites, and key metabolic pathways in invasive and native weeds. Use of these techniques could help weed scientists to further reduce the knowledge gaps in understanding invasive weeds traits. Although these techniques can provide robust insights about the molecular functioning, employing a single omics platform can rarely elucidate the gene-level regulation and the associated real-time expression of weedy traits due to the complex and overlapping nature of biological interactions. We conclude that different multi-omic techniques will provide long-term benefits in launching new genome projects to enhance the understanding of invasive weeds' invasion process.

Graphene quantum dots disturbed the energy homeostasis by influencing lipid metabolism of macrophages

To investigate the potential factors of graphene quantum dots (GQDs), the assessment impact on the innate immune system is one of the most important. As the innate immune cell, macrophages possess phagocytosis activity and affect immunomodulation. Higher oxygen consumption rates (OCR) are used to gain insight into GQDs' effects on macrophages. Metabolomics profiling also revealed that GQDs exposure provoked an increase in phosphoglycerides, sphingolipids, and oxidized lipids in macrophages. The molecular pathways disrupted by GQDs were associated with lipid and energy metabolisms. Metabolite flux analysis was used to evaluate changes in the lipid metabolism of macrophages exposed to 100 µg mL^-1 GQDs for 24 and 48 h. A combination of ¹³C-flux analysis and metabolomics revealed the regulation of lipid biosynthesis influenced the balance of energy metabolism. Integrated proteomics and metabolomics analyses showed that nicotinic acid adenine dinucleotide and coenzyme Q10 were significantly increased under GQDs treatment, alongside upregulated protein activity (e.g., Cox5b and Cd36). The experimental evidences were expected to be provided in this study to reveal the potential harmful effect from exposure to GQDs.