Brain energy metabolism is optimized to minimize the cost of enzyme synthesis and transport.

The energy metabolism of the brain is poorly understood partly due to the complex morphology of neurons and fluctuations in ATP demand over time. To investigate this, we used metabolic models that estimate enzyme usage per pathway, enzyme utilization over time, and enzyme transportation to evaluate how these parameters and processes affect ATP costs for enzyme synthesis and transportation. Our models show that the total enzyme maintenance energy expenditure of the human body depends on how glycolysis and mitochondrial respiration are distributed both across and within cell types in the brain. We suggest that brain metabolism is optimized to minimize the ATP maintenance cost by distributing the different ATP generation pathways in an advantageous way across cell types and potentially also across synapses within the same cell. Our models support this hypothesis by predicting export of lactate from both neurons and astrocytes during peak ATP demand, reproducing results from experimental measurements reported in the literature. Furthermore, our models provide potential explanation for parts of the astrocyte-neuron lactate shuttle theory, which is recapitulated under some conditions in the brain, while contradicting other aspects of the theory. We conclude that enzyme usage per pathway, enzyme utilization over time, and enzyme transportation are important factors for defining the optimal distribution of ATP production pathways, opening a broad avenue to explore in brain metabolism.

Genomic insights into fish pathogenic bacteria: A systems biology perspective for sustainable aquaculture.

Fish diseases significantly challenge global aquaculture, causing substantial financial losses and impacting sustainability, trade, and socioeconomic conditions. Understanding microbial pathogenesis and virulence at the molecular level is crucial for disease prevention in commercial fish. This review provides genomic insights into fish pathogenic bacteria from a systems biology perspective, aiming to promote sustainable aquaculture. It covers the genomic characteristics of various fish pathogens and their industry impact. The review also explores the systems biology of zebrafish, fish bacterial pathogens, and probiotic bacteria, offering insights into fish production, potential vaccines, and therapeutic drugs. Genome-scale metabolic models aid in studying pathogenic bacteria, contributing to disease management and antimicrobial development. Researchers have also investigated probiotic strains to improve aquaculture health. Additionally, the review highlights bioinformatics resources for fish and fish pathogens, which are essential for researchers. Systems biology approaches enhance understanding of bacterial fish pathogens by revealing virulence factors and host interactions. Despite challenges from the adaptability and pathogenicity of bacterial infections, sustainable alternatives are necessary to meet seafood demand. This review underscores the potential of systems biology in understanding fish pathogen biology, improving production, and promoting sustainable aquaculture.

Genome-scale metabolic models reveal determinants of phenotypic differences in non-Saccharomyces yeasts.

BACKGROUND: Use of alternative non-Saccharomyces yeasts in wine and beer brewing has gained more attention the recent years. This is both due to the desire to obtain a wider variety of flavours in the product and to reduce the final alcohol content. Given the metabolic differences between the yeast species, we wanted to account for some of the differences by using in silico models. RESULTS: We created and studied genome-scale metabolic models of five different non-Saccharomyces species using an automated processes. These were: Metschnikowia pulcherrima, Lachancea thermotolerans, Hanseniaspora osmophila, Torulaspora delbrueckii and Kluyveromyces lactis. Using the models, we predicted that M. pulcherrima, when compared to the other species, conducts more respiration and thus produces less fermentation products, a finding which agrees with experimental data. Complex I of the electron transport chain was to be present in M. pulcherrima, but absent in the others. The predicted importance of Complex I was diminished when we incorporated constraints on the amount of enzymatic protein, as this shifts the metabolism towards fermentation. CONCLUSIONS: Our results suggest that Complex I in the electron transport chain is a key differentiator between Metschnikowia pulcherrima and the other yeasts considered. Yet, more annotations and experimental data have the potential to improve model quality in order to increase fidelity and confidence in these results. Further experiments should be conducted to confirm the in vivo effect of Complex I in M. pulcherrima and its respiratory metabolism.

Genome-scale metabolic modeling reveals key features of a minimal gene set.

Mesoplasma florum, a fast-growing near-minimal organism, is a compelling model to explore rational genome designs. Using sequence and structural homology, the set of metabolic functions its genome encodes was identified, allowing the reconstruction of a metabolic network representing ˜ 30% of its protein-coding genes. Growth medium simplification enabled substrate uptake and product secretion rate quantification which, along with experimental biomass composition, were integrated as species-specific constraints to produce the functional iJL208 genome-scale model (GEM) of metabolism. Genome-wide expression and essentiality datasets as well as growth data on various carbohydrates were used to validate and refine iJL208. Discrepancies between model predictions and observations were mechanistically explained using protein structures and network analysis. iJL208 was also used to propose an in silico reduced genome. Comparing this prediction to the minimal cell JCVI-syn3.0 and its parent JCVI-syn1.0 revealed key features of a minimal gene set. iJL208 is a stepping-stone toward model-driven whole-genome engineering.

Computational systems biology of cellular processes in Arabidopsis thaliana: an overview.

Systems biology strives for gaining an understanding of biological phenomena by studying the interactions of different parts of a system and integrating the knowledge obtained into the current view of the underlying processes. This is achieved by a tight combination of quantitative experimentation and computational modeling. While there is already a large quantity of systems biology studies describing human, animal and especially microbial cell biological systems, plant biology has been lagging behind for many years. However, in the case of the model plant Arabidopsis thaliana, the steadily increasing amount of information on the levels of its genome, proteome and on a variety of its metabolic and signalling pathways is progressively enabling more researchers to construct models for cellular processes for the plant, which in turn encourages more experimental data to be generated, showing also for plant sciences how fruitful systems biology research can be. In this review, we provide an overview over some of these recent studies which use different systems biological approaches to get a better understanding of the cell biology of A. thaliana. The approaches used in these are genome-scale metabolic modeling, as well as kinetic modeling of metabolic and signalling pathways. Furthermore, we selected several cases to exemplify how the modeling approaches have led to significant advances or new perspectives in the field.

Genome-scale evaluation of core one-carbon metabolism in gammaproteobacterial methanotrophs grown on methane and methanol.

Methylotuvimicrobium alcaliphilum 20Z is a promising platform strain for bioconversion of one-carbon (C1) substrates into value-added products. To carry out robust metabolic engineering with methylotrophic bacteria and to implement C1 conversion machinery in non-native hosts, systems-level evaluation and understanding of central C1 metabolism in methanotrophs under various conditions is pivotal but yet elusive. In this study, a genome-scale integrated approach was used to provide in-depth knowledge on the metabolic pathways of M. alcaliphilum 20Z grown on methane and methanol. Systems assessment of core carbon metabolism indicated the methanol assimilation pathway is mostly coupled with the efficient Embden-Meyerhof-Parnas (EMP) pathway along with the serine cycle. In addition, an incomplete TCA cycle operated in M. alcaliphilum 20Z on methanol, which might only supply precursors for de novo synthesis but not reducing powers. Instead, it appears that the direct formaldehyde oxidation pathway supply energy for the whole metabolic system. Additionally, a comparative transcriptomic analysis in multiple gammaproteobacterial methanotrophs also revealed the transcriptional responses of central metabolism on carbon substrate change. These findings provided a systems-level understanding of carbon metabolism and new opportunities for strain design to produce relevant products from different C1-feedstocks.

Genome scale metabolic modeling reveals the metabolic potential of three Type II methanotrophs of the genus Methylocystis.

Genome Scale Metabolic Models (GSMMs) of the recently sequenced Methylocystis hirsuta and two other methanotrophs from the genus Methylocystis have been reconstructed. These organisms are Type II methanotrophs with the ability of accumulating Polyhydroxyalkanoates under nutrient limiting conditions. For the first time, GSMMs have been reconstructed for Type II methanotrophs. These models, combined with experimental biomass and PHB yields of Methylocystis hirsuta, allowed elucidating the methane oxidation mechanism by the enzyme pMMO (particulate methane monooxygenase) in these organisms. In contrast to Type I methanotrophs, which use the "direct coupling mechanism", Type II methanotrophs appear to use the so called "redox arm mechanism". The utilization of the "redox arm mechanism", which involves the coupling between methane oxidation and complex I of the respiratory chain, was confirmed by inhibition of complex I with catechol. Utilization of the "redox arm" mechanism leads to lower biomass yields on methane compared to Type I methanotrophs. However, the ability of Type II methanotrophs to redirect high metabolic carbon fluxes towards acetoacetyl-CoA under nitrogen limiting conditions makes these organisms promising platforms for metabolic engineering.

Systematic metabolic engineering of Methylomicrobium alcaliphilum 20Z for 2,3-butanediol production from methane.

Methane is considered a next-generation feedstock, and methanotrophic cell-based biorefinery is attractive for production of a variety of high-value compounds from methane. In this work, we have metabolically engineered Methylomicrobium alcaliphilum 20Z for 2,3-butanediol (2,3-BDO) production from methane. The engineered strain 20Z/pBudK.p, harboring the 2,3-BDO synthesis gene cluster (budABC) from Klebsiella pneumoniae, accumulated 2,3-BDO in methane-fed shake flask cultures with a titer of 35.66 mg/L. Expression of the most efficient gene cluster was optimized using selection of promoters, translation initiation rates (TIR), and the combination of 2,3-BDO synthesis genes from different sources. A higher 2,3-BDO titer of 57.7 mg/L was measured in the 20Z/pNBM-Re strain with budA of K. pneumoniae and budB of Bacillus subtilis under the control of the Tac promoter. The genome-scale metabolic network reconstruction of M. alcaliphilum 20Z enabled in silico gene knockout predictions using an evolutionary programming method to couple growth and 2,3-BDO production. The ldh, ack, and mdh genes in M. alcaliphilum 20Z were identified as potential knockout targets. Pursuing these targets, a triple-mutant strain ∆ldh ∆ack ∆mdh was constructed, resulting in a further increase of the 2,3-BDO titer to 68.8 mg/L. The productivity of this optimized strain was then tested in a fed-batch stirred tank bioreactor, where final product concentrations of up to 86.2 mg/L with a yield of 0.0318 g-(2,3-BDO) /g-CH4 were obtained under O2-limited conditions. This study first demonstrates the strategy of in silico simulation-guided metabolic engineering and represents a proof-of-concept for the production of value-added compounds using systematic approaches from engineered methanotrophs.

Integration of Biomass Formulations of Genome-Scale Metabolic Models with Experimental Data Reveals Universally Essential Cofactors in Prokaryotes.

The composition of a cell in terms of macromolecular building blocks and other organic molecules underlies the metabolic needs and capabilities of a species. Although some core biomass components such as nucleic acids and proteins are evident for most species, the essentiality of the pool of other organic molecules, especially cofactors and prosthetic groups, is yet unclear. Here we integrate biomass compositions from 71 manually curated genome-scale models, 33 large-scale gene essentiality datasets, enzyme-cofactor association data and a vast array of publications, revealing universally essential cofactors for prokaryotic metabolism and also others that are specific for phylogenetic branches or metabolic modes. Our results revise predictions of essential genes in Klebsiella pneumoniae and identify missing biosynthetic pathways in models of Mycobacterium tuberculosis. This work provides fundamental insights into the essentiality of organic cofactors and has implications for minimal cell studies as well as for modeling genotype-phenotype relations in prokaryotic metabolic networks.

Leveraging metabolic modeling and machine learning to uncover modulators of quiescence depth.

Quiescence, a temporary withdrawal from the cell cycle, plays a key role in tissue homeostasis and regeneration. Quiescence is increasingly viewed as a continuum between shallow and deep quiescence, reflecting different potentials to proliferate. The depth of quiescence is altered in a range of diseases and during aging. Here, we leveraged genome-scale metabolic modeling (GEM) to define the metabolic and epigenetic changes that take place with quiescence deepening. We discovered contrasting changes in lipid catabolism and anabolism and diverging trends in histone methylation and acetylation. We then built a multi-cell type machine learning model that accurately predicts quiescence depth in diverse biological contexts. Using both machine learning and genome-scale flux simulations, we performed high-throughput screening of chemical and genetic modulators of quiescence and identified novel small molecule and genetic modulators with relevance to cancer and aging.

Modeling Microbial Communities: Perspective and Challenges.

Microbial communities are immensely important due to their widespread presence and profound impact on various facets of life. Understanding these complex systems necessitates mathematical modeling, a powerful tool for simulating and predicting microbial community behavior. This review offers a critical analysis of metabolic modeling and highlights key areas that would greatly benefit from broader discussion and collaboration. Moreover, we explore the challenges and opportunities linked to the intricate nature of these communities, spanning data generation, modeling, and validation. We are confident that ongoing advancements in modeling techniques, such as machine learning, coupled with interdisciplinary collaborations, will unlock the full potential of microbial communities across diverse applications.

Predicting metabolic fluxes from omics data via machine learning: Moving from knowledge-driven towards data-driven approaches.

The accurate prediction of phenotypes in microorganisms is a main challenge for systems biology. Genome-scale models (GEMs) are a widely used mathematical formalism for predicting metabolic fluxes using constraint-based modeling methods such as flux balance analysis (FBA). However, they require prior knowledge of the metabolic network of an organism and appropriate objective functions, often hampering the prediction of metabolic fluxes under different conditions. Moreover, the integration of omics data to improve the accuracy of phenotype predictions in different physiological states is still in its infancy. Here, we present a novel approach for predicting fluxes under various conditions. We explore the use of supervised machine learning (ML) models using transcriptomics and/or proteomics data and compare their performance against the standard parsimonious FBA (pFBA) approach using case studies of Escherichia coli organism as an example. Our results show that the proposed omics-based ML approach is promising to predict both internal and external metabolic fluxes with smaller prediction errors in comparison to the pFBA approach. The code, data, and detailed results are available at the project's repository[1].

Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism.

With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.

A High-Quality Genome-Scale Model for Rhodococcus opacus Metabolism.

Rhodococcus opacus is a bacterium that has a high tolerance to aromatic compounds and can produce significant amounts of triacylglycerol (TAG). Here, we present iGR1773, the first genome-scale model (GSM) of R. opacus PD630 metabolism based on its genomic sequence and associated data. The model includes 1773 genes, 3025 reactions, and 1956 metabolites, was developed in a reproducible manner using CarveMe, and was evaluated through Metabolic Model tests (MEMOTE). We combine the model with two Constraint-Based Reconstruction and Analysis (COBRA) methods that use transcriptomics data to predict growth rates and fluxes: E-Flux2 and SPOT (Simplified Pearson Correlation with Transcriptomic data). Growth rates are best predicted by E-Flux2. Flux profiles are more accurately predicted by E-Flux2 than flux balance analysis (FBA) and parsimonious FBA (pFBA), when compared to 44 central carbon fluxes measured by 13C-Metabolic Flux Analysis (13C-MFA). Under glucose-fed conditions, E-Flux2 presents an R2 value of 0.54, while predictions based on pFBA had an inferior R2 of 0.28. We attribute this improved performance to the extra activity information provided by the transcriptomics data. For phenol-fed metabolism, in which the substrate first enters the TCA cycle, E-Flux2's flux predictions display a high R2 of 0.96 while pFBA showed an R2 of 0.93. We also show that glucose metabolism and phenol metabolism function with similar relative ATP maintenance costs. These findings demonstrate that iGR1773 can help the metabolic engineering community predict aromatic substrate utilization patterns and perform computational strain design.

Curating COBRA Models of Microbial Metabolism.

Constraint-based reconstruction and analysis (COBRA) methods have been used for over 20 years to generate genome-scale models of metabolism in biological systems. The COBRA models have been utilized to gain new insights into the biochemical conversions that occur within organisms and allow their survival and proliferation. Using these models, computational biologists can conduct a variety of different analyses such as examining network structures, predicting metabolic capabilities, resolving unexplained experimental observations, generating and testing new hypotheses, assessing the nutritional requirements of a biosystem and approximating its environmental niche, identifying missing enzymatic functions in the annotated genomes, and engineering desired metabolic capabilities in model organisms. This chapter details the protocol for developing curated system-level COBRA models of metabolism in microbes.

A Beginner's Guide to the COBRA Toolbox.

COBRA toolbox is one of the most popular tools for systems biology analyses using genome-scale metabolic reconstructions. The toolbox permits the use of many constraint-based analytical methods for examining characteristics of metabolism in the biosystems ranging in complexity from single cells to microbial communities and ultimately multicellular organisms. The toolbox has a number of different variants that can be used depending on a user's choice of programming language. Here, I provide a basic tutorial for beginners that plan to use the original MATLAB version of the toolbox.

Combination of Genome-Scale Models and Bioreactor Dynamics to Optimize the Production of Commodity Chemicals.

The current production of a number of commodity chemicals relies on the exploitation of fossil fuels and hence has an irreversible impact on the environment. Biotechnological processes offer an attractive alternative by enabling the manufacturing of chemicals by genetically modified microorganisms. However, this alternative approach poses some important technical challenges that must be tackled to make it competitive. On the one hand, the design of biotechnological processes is based on trial-and-error approaches, which are not only costly in terms of time and money, but also result in suboptimal designs. On the other hand, the manufacturing of chemicals by biological processes is almost exclusively carried out by batch or fed-batch cultures. Given that batch cultures are expensive and not easy to scale, technical means must be developed to make continuous cultures feasible and efficient. In order to address these challenges, we have developed a mathematical model able to integrate in a single model both the genome-scale metabolic model for the organism synthesizing the chemical of interest and the dynamics of the bioreactor in which the organism is cultured. Such a model is based on the use of Flexible Nets, a modeling formalism for dynamical systems. The integration of a microscopic (organism) and a macroscopic (bioreactor) model in a single net provides an overall view of the whole system and opens the door to global optimizations. As a case study, the production of citramalate with respect to the substrate consumed by E. coli is modeled, simulated and optimized in order to find the maximum productivity in a steady-state continuous culture. The predicted computational results were consistent with the wet lab experiments.

High-throughput and reliable acquisition of in vivo turnover number fuels precise metabolic engineering.

As synthetic biology enters the era of quantitative biology, mathematical information such as kinetic parameters of enzymes can offer us an accurate knowledge of metabolism and growth of cells, and further guidance on precision metabolic engineering. k cat , termed the turnover number, is a basic parameter of enzymes that describes the maximum number of substrates converted to products each active site per unit time. It reflects enzyme activity and is essential for quantitative understanding of biosystems. Usually, the k cat values are measured in vitro, thus may not be able to reflect the enzyme activity in vivo. In this case, Davidi et al. defined a surrogate k m a x v i v o (k app ) for k cat and developed a high throughput method to acquire k m a x v i v o from omics data. Heckmann et al. and Chen et al. proved that the surrogate parameter can be a good embodiment of the physiological state of enzymes and exhibit superior performance for enzyme-constrained metabolic model to the default one. These breakthroughs will fuel the development of system and synthetic biology.

Elucidating the metabolic characteristics of pancreatic ß-cells from patients with type 2 diabetes (T2D) using a genome-scale metabolic modeling.

Diabetes is a global health problem caused primarily by the inability of pancreatic ß-cells to secrete adequate insulin. Despite extensive research, the identity of factors contributing to the dysregulated metabolism-secretion coupling in the ß-cells remains elusive. The present study attempts to capture some of these factors responsible for the impaired ß-cell metabolism-secretion coupling that contributes to diabetes pathogenesis. The metabolic-flux profiles of pancreatic ß-cells were predicted using genome-scale metabolic modeling for ten diabetic patients and ten control subjects. Analysis of these flux states shows reduction in the mitochondrial fatty acid oxidation and mitochondrial oxidative phosphorylation pathways, that leads to decreased insulin secretion in diabetes. We also observed elevated reactive oxygen species (ROS) generation through peroxisomal fatty acid ß-oxidation. In addition, cellular antioxidant defense systems were found to be attenuated in diabetes. Our analysis also uncovered the possible changes in the plasma metabolites in diabetes due to the ß-cells failure. These efforts subsequently led to the identification of seven metabolites associated with cardiovascular disease (CVD) pathogenesis, thus establishing its link as a secondary complication of diabetes.

Protein cost allocation explains metabolic strategies in Escherichia coli.

In-depth understanding of microbial growth is crucial for the development of new advances in biotechnology and for combating microbial pathogens. Condition-specific proteome expression is central to microbial physiology and growth. A multitude of processes are dependent on the protein expression, thus, whole-cell analysis of microbial metabolism using genome-scale metabolic models is an attractive toolset to investigate the behaviour of microorganisms and their communities. However, genome-scale models that incorporate macromolecular expression are still inhibitory complex: the conceptual and computational complexity of these models severely limits their potential applications. In the need for alternatives, here we revisit some of the previous attempts to create genome-scale models of metabolism and macromolecular expression to develop a novel framework for integrating protein abundance and turnover costs to conventional genome-scale models. We show that such a model of Escherichia coli successfully reproduces experimentally determined adaptations of metabolism in a growth condition-dependent manner. Moreover, the model can be used as means of investigating underutilization of the protein machinery among different growth settings. Notably, we obtained strongly improved predictions of flux distributions, considering the costs of protein translation explicitly. This finding in turn suggests protein translation being the main regulation hub for cellular growth.