Systems-level analysis of mechanisms regulating yeast metabolic flux

13C-metabolic flux analysis of Saccharomyces cerevisiae in complex media

Saccharomyces cerevisiae is often cultivated in complex media for applications in food and other biochemical production. However, 13C-metabolic flux analysis (13C-MFA) has been conducted for S. cerevisiae cultivated in synthetic media, resulting in a limited understanding of the metabolic flux distributions under the complex media. In this study, 13C-MFA was applied to S. cerevisiae cultivated in complex media to quantify the metabolic fluxes in the central metabolic network. S. cerevisiae was cultivated in a synthetic dextrose (SD) medium supplemented with 20 amino acids (SD + AA) and yeast extract peptone dextrose (YPD) medium. The results revealed that glutamic acid, glutamine, aspartic acid, and asparagine are incorporated into the TCA cycle as carbon sources in parallel with glucose consumption. Based on these findings, we successfully conducted 13C-MFA of S. cerevisiae cultivated in SD + AA and YPD media using parallel labeling and measured amino acid uptake rates. Furthermore, we applied the developed approach to 13C-MFA of yeast cultivated in malt extract medium. The analysis revealed that the metabolic flux through the anaplerotic and oxidative pentose phosphate pathways was lower in complex media than in synthetic media. Owing to the reduced carbon loss by the branching pathways, carbon flow toward ethanol production via glycolysis could be elevated. 13C-MFA of S. cerevisiae cultured in complex media provides valuable insights for metabolic engineering and process optimization in industrial yeast fermentation.

Coarse-Grained Modeling Elucidates Differential Metabolism of Saccharomyces cerevisiae under Varied Nutrient Limitations

Microorganisms such as Saccharomyces cerevisiae have a native ability to adapt their metabolism to varying nutrient conditions. Understanding their responses to nutrient limitations is critical for decoding cellular physiology and designing strategies for metabolic engineering. While the influence of carbon availability on yeast metabolism has been extensively studied, the role of nitrogen availability remains relatively underexplored. In this study, we utilized a coarse-grained kinetic model to systematically analyze and compare the effects of carbon and nitrogen limitations on yeast metabolism. Our model successfully revealed the differential metabolic characteristics of S. cerevisiae under carbon- and nitrogen-limited chemostat conditions. It also highlighted the significance of protein activity regulation at varying carbon-to-nitrogen ratios, and elucidated distinct strategies employed to maintain ATP homeostasis. This study provides a computational tool for investigating yeast physiology under nutrient limitations and offers quantitative and mechanistic insights into yeast metabolism.

Cytosolic fructose - an underestimated player in the regulation of sucrose biosynthesis

BACKGROUND

Plants must continuously adapt to environmental fluctuations, which significantly influence their photosynthetic performance and overall metabolism. The sucrose cycling system within plant cells plays a critical regulatory role during stress conditions. This study employed a systems biology approach to analyze system stabilities mathematically under various regulatory conditions impacting sucrose cycling dynamics. We investigated the effects of mutations within this cycle, specifically HEXOKINASE1 (Arabidopsis thaliana gin2-1), alongside high-light exposure. Finally, we confirmed the modeling output in vitro by enzyme assays.

RESULTS

The implementation of experimental subcellular metabolite data into a Structural Kinetic Model (SKM) enabled exploration of regulatory responses and system stabilities within a three-compartment model. Within system instabilities, gin2-1 was more instable than its wild type. The gin2-1 mutation particularly was destabilized when fructokinase function was impaired by phosphorylated sugars. Additionally, we confirmed that phosphorylated sugars serve as stronger activators of sucrose-phosphate synthase (SPS) than glucose. Interestingly, models with fructose SPS activation exhibited a similar stability pattern. Consequently, we proposed and confirmed in silico a triple activation of SPS by highly activating phosphorylated sugars and lower activating non-phosphorylated hexoses. Additionally, we biochemically confirmed the previously unknown, but now predicted, activation of SPS by fructose in vitro.

CONCLUSION

In summary, our study highlights the essential role of sucrose cycling in plant cells under stress conditions. The in silico findings reveal that phosphorylated sugars are stronger activators of SPS than glucose and introduce a previously unknown activation mechanism by fructose. These potential activation capacities were confirmed in vitro through SPS enzyme activity assays, underscoring the efficiency of our systems biology approach. Overall, this research provides valuable insights into carbohydrate metabolism regulation and paves the way for future investigations to deepen our understanding of the complexities involved in sucrose cycling and biosynthesis in plants.

Lactate activates trained immunity by fueling the tricarboxylic acid cycle and regulating histone lactylation

Trained immunity refers to the long-term memory of the innate immune cells. However, little is known about how environmental nutrient availability influences trained immunity. This study finds that physiologic carbon sources impact glucose contribution to the tricarboxylic acid (TCA) cycle and enhance cytokine production of trained monocytes. Our experiments demonstrate that trained monocytes preferentially employe lactate over glucose as a TCA cycle substrate, and lactate metabolism is required for trained immune cell responses to bacterial and fungal infection. Except for the contribution to the TCA cycle, endogenous lactate or exogenous lactate also supports trained immunity by regulating histone lactylation. Further transcriptome analysis, ATAC-seq, and CUT&Tag-seq demonstrate that lactate enhance chromatin accessibility in a manner dependent histone lactylation. Inhibiting lactate-dependent metabolism by silencing lactate dehydrogenase A (LDHA) impairs both lactate fueled the TCA cycle and histone lactylation. These findings suggest that lactate is the hub of immunometabolic and epigenetic programs in trained immunity.

Allosteric Effectors Outcompete Transcript Levels and Substrate Concentration in Regulating Central Carbon Flux During the Crabtree Effect Transition

While the metabolic shift in Saccharomyces cerevisiae across the Crabtree effect is well-documented, the role of allosteric regulation in this transition remains unclear. Here, we investigated allosteric regulation by inducing a growth rate shift from 0.2 to 0.35 h. Our results revealed 35 regulatory interactions across 23 central carbon metabolism reactions, with allosteric effectors explaining 29% of flux changes-surpassing the contributions of transcript abundance and substrate concentration. Additionally, key Crabtree-responsive reactions' flux changes were co-regulated by allosteric effectors, transcript abundance, and enzyme turnover numbers. These underscore the significance of allosteric regulation in metabolic adaptation during growth rate transitions in Saccharomyces cerevisiae.

A systems-level, semi-quantitative landscape of metabolic flux in C. elegans

Metabolic flux, or the rate of metabolic reactions, is one of the most fundamental metrics describing the status of metabolism in living organisms. However, measuring fluxes across the entire metabolic network remains nearly impossible, especially in multicellular organisms. Computational methods based on flux balance analysis have been used with genome-scale metabolic network models to predict network-level flux wiring1-6. However, such approaches have limited power because of the lack of experimental constraints. Here, we introduce a strategy that infers whole-animal metabolic flux wiring from transcriptional phenotypes in the nematode Caenorhabditis elegans. Using a large-scale Worm Perturb-Seq (WPS) dataset for roughly 900 metabolic genes7, we show that the transcriptional response to metabolic gene perturbations can be integrated with the metabolic network model to infer a highly constrained, semi-quantitative flux distribution. We discover several features of adult C. elegans metabolism, including cyclic flux through the pentose phosphate pathway, lack of de novo purine synthesis flux and the primary use of amino acids and bacterial RNA as a tricarboxylic acid cycle carbon source, all of which we validate by stable isotope tracing. Our strategy for inferring metabolic wiring based on transcriptional phenotypes should be applicable to a variety of systems, including human cells.

Enhanced flux potential analysis links changes in enzyme expression to metabolic flux

Algorithms that constrain metabolic network models with enzyme levels to predict metabolic activity assume that changes in enzyme levels are indicative of flux variations. However, metabolic flux can also be regulated by other mechanisms such as allostery and mass action. To systematically explore the relationship between fluctuations in enzyme expression and flux, we combine available yeast proteomic and fluxomic data to reveal that flux changes can be best predicted from changes in enzyme levels of pathways, rather than the whole network or only cognate reactions. We implement this principle in an 'enhanced flux potential analysis' (eFPA) algorithm that integrates enzyme expression data with metabolic network architecture to predict relative flux levels of reactions including those regulated by other mechanisms. Applied to human data, eFPA consistently predicts tissue metabolic function using either proteomic or transcriptomic data. Additionally, eFPA efficiently handles data sparsity and noisiness, generating robust flux predictions with single-cell gene expression data. Our approach outperforms alternatives by striking an optimal balance, evaluating enzyme expression at pathway level, rather than either single-reaction or whole-network levels.

Immobilization of Saccharomyces cerevisiae on polyhydroxyalkanoate/konjac glucan nanofiber membranes: Characterization, immobilization efficiency and cellular activity

Yeast immobilization systems can recoup yeast losses in continuous batch fermentation and relieve substrate or product inhibition. We report the use of solution blow spinning process to efficiently prepare polyhydroxyalkanoate (PHB) /konjac glucomannan (KGM) nanofiber membranes as immobilization carriers for Saccharomyces cerevisiae. The prepared PHB/KGM nanofiber membranes had fiber diameters similar to the scale of yeast cells. Incorporating KGM significantly enhanced the porosity (from 87.21 % to 91.74 %), crystallinity, and hydrophilicity (reducing water contact angle from 135.8° to 110.1°), while increasing the specific surface area (from 10.24 to 17.79 m2/g) of pure PHB nanofiber membranes. Thermal stability was maintained (degradation temperatures above 250 °C). These changes enhanced the force between the nanofiber membranes and the cells and facilitated their autoimmobilization on the nanofiber membranes. The highest yeast immobilization efficiency of 87.93 % could be achieved at a KGM addition ratio of 400:2. Yeast showed no loss of cellular activity on the immobilized carriers of natural materials and maintained or even improved fermentation kinetics during at least three consecutive alcoholic fermentations These findings indicate that PHB/KGM nanofiber membranes can serve as effective carriers for yeast immobilization, promoting the sustainable production of fermented foods.

Regulation of gene expression through protein-metabolite interactions

Organisms have to adapt to changes in their environment. Cellular adaptation requires sensing, signalling and ultimately the activation of cellular programs. Metabolites are environmental signals that are sensed by proteins, such as metabolic enzymes, protein kinases and nuclear receptors. Recent studies have discovered novel metabolite sensors that function as gene regulatory proteins such as chromatin associated factors or RNA binding proteins. Due to their function in regulating gene expression, metabolite-induced allosteric control of these proteins facilitates a crosstalk between metabolism and gene expression. Here we discuss the direct control of gene regulatory processes by metabolites and recent progresses that expand our abilities to systematically characterize metabolite-protein interaction networks. Obtaining a profound map of such networks is of great interest for aiding metabolic disease treatment and drug target identification.

The type of carbon source not the growth rate it supports can determine diauxie in Saccharomyces cerevisiae

How cells choose between carbon sources is a classic example of cellular decision-making. Microbes often prioritise glucose, but there has been little investigation of whether other sugars are also preferred. Here we study budding yeast growing on mixtures of sugars with palatinose, a sucrose isomer that cells catabolise with the MAL regulon. We find that the decision-making involves more than carbon flux-sensing: yeast prioritise galactose over palatinose, but sucrose and fructose weakly if at all despite each allowing faster growth than palatinose. With genetic perturbations and transcriptomics, we show that the regulation is active with repression of the MAL genes via Gal4, the GAL regulon's master regulator. We argue, using mathematical modelling, that cells enforce their preference for galactose through weakening the MAL regulon's positive feedback. They do so through decreasing intracellular palatinose by repressing MAL11, the palatinose transporter, and expressing the isomaltases IMA1 and IMA5. Supporting these predictions, we show that deleting IMA1 abolishes diauxie. Our results demonstrate that budding yeast actively prioritises carbon sources other than glucose and that such priorities need not reflect differences in growth rates. They imply that carbon-sensing strategies even in model organisms are more complex than previously thought.

Decoding Sugars: Mass Spectrometric Advances in the Analysis of the Sugar Alphabet

Monosaccharides play a central role in metabolic networks and in the biosynthesis of glycomolecules, which perform essential functions across all domains of life. Thus, identifying and quantifying these building blocks is crucial in both research and industry. Routine methods have been established to facilitate the analysis of common monosaccharides. However, despite the presence of common metabolites, most organisms utilize distinct sets of monosaccharides and derivatives. These molecules therefore display a large diversity, potentially numbering in the hundreds or thousands, with many still unknown. This complexity presents significant challenges in the study of glycomolecules, particularly in microbes, including pathogens and those with the potential to serve as novel model organisms. This review discusses mass spectrometric techniques for the isomer-sensitive analysis of monosaccharides, their derivatives, and activated forms. Although mass spectrometry allows for untargeted analysis and sensitive detection in complex matrices, the presence of stereoisomers and extensive modifications necessitates the integration of advanced chromatographic, electrophoretic, ion mobility, or ion spectroscopic methods. Furthermore, stable-isotope incorporation studies are critical in elucidating biosynthetic routes in novel organisms.

Understanding metabolic plasticity at single cell resolution

It is increasingly clear that cellular metabolic function varies not just between cells of different tissues, but also within tissues and cell types. In this essay, we envision how differences in central carbon metabolism arise from multiple sources, including the cell cycle, circadian rhythms, intrinsic metabolic cycles, and others. We also discuss and compare methods that enable such variation to be detected, including single-cell metabolomics and RNA-sequencing. We pay particular attention to biosensors for AMPK and central carbon metabolites, which when used in combination with metabolic perturbations, provide clear evidence of cellular variance in metabolic function.

Harnessing the optimization of enzyme catalytic rates in engineering of metabolic phenotypes

The increasing availability of enzyme turnover number measurements from experiments and of turnover number predictions from deep learning models prompts the use of these enzyme parameters in precise metabolic engineering. Yet, there is no computational approach that allows the prediction of metabolic engineering strategies that rely on the modification of turnover numbers. It is also unclear if modifications of turnover numbers without alterations in the host's transcriptional regulatory machinery suffice to increase the production of chemicals of interest. Here, we present a constraint-based modeling approach, termed Overcoming Kinetic rate Obstacles (OKO), that uses enzyme-constrained metabolic models to predict in silico strategies to increase the production of a given chemical, while ensuring specified cell growth. We demonstrate that the application of OKO to enzyme-constrained metabolic models of Escherichia coli and Saccharomyces cerevisiae results in strategies that can at least double the production of over 40 compounds with little penalty to growth. Interestingly, we show that the overproduction of compounds of interest does not entail only an increase in the values of turnover numbers. Lastly, we demonstrate that a refinement of OKO, allowing also for manipulation of enzyme abundance, facilitates the usage of the available compendia and deep learning models of turnover numbers in the design of precise metabolic engineering strategies. Our results expand the usage of genome-scale metabolic models toward the identification of targets for protein engineering, allowing their direct usage in the generation of innovative metabolic engineering designs for various biotechnological applications.

Identifying effective evolutionary strategies-based protocol for uncovering reaction kinetic parameters under the effect of measurement noises

BACKGROUND

The transition from explanative modeling of fitted data to the predictive modeling of unseen data for systems biology endeavors necessitates the effective recovery of reaction parameters. Yet, the relative efficacy of optimization algorithms in doing so remains under-studied, as to the specific reaction kinetics and the effect of measurement noises. To this end, we simulate the reactions of an artificial pathway using 4 kinetic formulations: generalized mass action (GMA), Michaelis-Menten, linear-logarithmic, and convenience kinetics. We then compare the effectiveness of 5 evolutionary algorithms (CMAES, DE, SRES, ISRES, G3PCX) for objective function optimization in kinetic parameter hyperspace to determine the corresponding estimated parameters.

RESULTS

We quickly dropped the DE algorithm due to its poor performance. Baring measurement noise, we find the CMAES algorithm to only require a fraction of the computational cost incurred by other EAs for both GMA and linear-logarithmic kinetics yet performing as well by other criteria. However, with increasing noise, SRES and ISRES perform more reliably for GMA kinetics, but at considerably higher computational cost. Conversely, G3PCX is among the most efficacious for estimating Michaelis-Menten parameters regardless of noise, while achieving numerous folds saving in computational cost. Cost aside, we find SRES to be versatilely applicable across GMA, Michaelis-Menten, and linear-logarithmic kinetics, with good resilience to noise. Nonetheless, we could not identify the parameters of convenience kinetics using any algorithm.

CONCLUSIONS

Altogether, we identify a protocol for predicting reaction parameters under marked measurement noise, as a step towards predictive modeling for systems biology endeavors.

Integration of proteomic data with genome-scale metabolic models: A methodological overview

The integration of proteomics data with constraint-based reconstruction and analysis (COBRA) models plays a pivotal role in understanding the relationship between genotype and phenotype and bridges the gap between genome-level phenomena and functional adaptations. Integrating a generic genome-scale model with information on proteins enables generation of a context-specific metabolic model which improves the accuracy of model prediction. This review explores methodologies for incorporating proteomics data into genome-scale models. Available methods are grouped into four distinct categories based on their approach to integrate proteomics data and their depth of modeling. Within each category section various methods are introduced in chronological order of publication demonstrating the progress of this field. Furthermore, challenges and potential solutions to further progress are outlined, including the limited availability of appropriate in vitro data, experimental enzyme turnover rates, and the trade-off between model accuracy, computational tractability, and data scarcity. In conclusion, methods employing simpler approaches demand fewer kinetic and omics data, consequently leading to a less complex mathematical problem and reduced computational expenses. On the other hand, approaches that delve deeper into cellular mechanisms and aim to create detailed mathematical models necessitate more extensive kinetic and omics data, resulting in a more complex and computationally demanding problem. However, in some cases, this increased cost can be justified by the potential for more precise predictions.

Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community

Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM of Saccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This has increased the quality and scope of the model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or reference conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference for nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 mutant strains. Well able to predict the strains' growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine learning models. Based on those findings we anticipate that Yeast9 will continue to empower systems biology studies of yeast metabolism.

Energy Metabolism Enhance Perylenequinone Biosynthesis in Shiraia sp. Slf14 through Promoting Mitochondrial ROS Accumulation

Perylenequinones (PQs) are important natural compounds that have been extensively utilized in recent years as agents for antimicrobial, anticancer, and antiviral photodynamic therapies. In this study, we investigated the molecular mechanisms regulating PQ biosynthesis by comparing Shiraia sp. Slf14 with its low PQ titer mutant, Slf14(w). The results indicated that the strain Slf14 exhibited a higher PQ yield, a more vigorous energy metabolism, and a more pronounced oxidation state compared to Slf14(w). Transcriptome analysis consistently revealed that the differences in gene expression between Slf14 and Slf14(w) are primarily associated with genes involved in redox processes and energy metabolism. Additionally, reactive oxygen species (ROS) were shown to play a crucial role in promoting PQ synthesis, as evidenced by the application of ROS-related inhibitors and promoters. Further results demonstrated that mitochondria are significant sources of ROS, which effectively regulate PQ biosynthesis in Shiraia sp. Slf14. In summary, this research revealed a noteworthy finding: the higher energy metabolism of the strain Slf14 is associated with increased intracellular ROS accumulation, which in turn triggers the activation and expression of gene clusters responsible for PQ synthesis.

Deciphering sphingolipid biosynthesis dynamics in Arabidopsis thaliana cell cultures: Quantitative analysis amid data variability

Sphingolipids are pivotal for plant development and stress responses. Growing interest has been directed toward fully comprehending the regulatory mechanisms of the sphingolipid pathway. We explore its de novo biosynthesis and homeostasis in Arabidopsis thaliana cell cultures, shedding light on fundamental metabolic mechanisms. Employing 15N isotope labeling and quantitative dynamic modeling approach, we obtained data with notable variations and developed a regularized and constraint-based dynamic metabolic flux analysis (r-DMFA) framework to predict metabolic shifts due to enzymatic changes. Our analysis revealed key enzymes such as sphingoid-base hydroxylase (SBH) and long-chain-base kinase (LCBK) to be critical for maintaining sphingolipid homeostasis. Disruptions in these enzymes were found to affect cellular viability and increase the potential for programmed cell death (PCD). Despite challenges posed by data variability, this work enhances our understanding of sphingolipid metabolism and demonstrates the utility of dynamic modeling in analyzing complex metabolic pathways.

Machine learning of metabolite-protein interactions from model-derived metabolic phenotypes

Unraveling metabolite-protein interactions is key to identifying the mechanisms by which metabolism affects the function of other cellular layers. Despite extensive experimental and computational efforts to identify the regulatory roles of metabolites in interaction with proteins, it remains challenging to achieve a genome-scale coverage of these interactions. Here, we leverage established gold standards for metabolite-protein interactions to train supervised classifiers using features derived from genome-scale metabolic models and matched data on protein abundance and reaction fluxes to distinguish interacting from non-interacting pairs. Through a comprehensive comparative study, we explore the impact of different features and assess the effect of gold standards for non-interacting pairs on the performance of the classifiers. Using data sets from Escherichia coli and Saccharomyces cerevisiae, we demonstrate that the features constructed by integrating fluxomic and proteomic data with metabolic phenotypes predicted from genome-scale metabolic models can be effectively used to train classifiers, accurately predicting metabolite-protein interactions in the context of metabolism. Our results reveal that the high performance of classifiers trained on these features is unaffected by the method used to generate gold standards for non-interacting pairs. Overall, our study introduces valuable features that improve the performance of identifying metabolite-protein interactions in the context of metabolism.

Machine learning for the advancement of genome-scale metabolic modeling

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

Assessing structural uncertainty of biochemical regulatory networks in metabolic pathways under varying data quality

Ordinary differential equation (ODE) models are powerful tools for studying the dynamics of metabolic pathways. However, key challenges lie in constructing ODE models for metabolic pathways, specifically in our limited knowledge about which metabolite levels control which reaction rates. Identification of these regulatory networks is further complicated by the limited availability of relevant data. Here, we assess the conditions under which it is feasible to accurately identify regulatory networks in metabolic pathways by computationally fitting candidate network models with biochemical systems theory (BST) kinetics to data of varying quality. We use network motifs commonly found in metabolic pathways as a simplified testbed. Key features correlated with the level of difficulty in identifying the correct regulatory network were identified, highlighting the impact of sampling rate, data noise, and data incompleteness on structural uncertainty. We found that for a simple branched network motif with an equal number of metabolites and fluxes, identification of the correct regulatory network can be largely achieved and is robust to missing one of the metabolite profiles. However, with a bi-substrate bi-product reaction or more fluxes than metabolites in the network motif, the identification becomes more challenging. Stronger regulatory interactions and higher metabolite concentrations were found to be correlated with less structural uncertainty. These results could aid efforts to predict whether the true metabolic regulatory network can be computationally identified for a given stoichiometric network topology and dataset quality, thus helping to identify optimal measures to mitigate such identifiability issues in kinetic model development.

Microbial reaction rate estimation using proteins and proteomes

Microbes transform their environments using diverse enzymatic reactions. However, it remains challenging to measure microbial reaction rates in natural environments. Despite advances in global quantification of enzyme abundances, the individual relationships between enzyme abundances and their reaction rates have not been systematically examined. Using matched proteomic and reaction rate data from microbial cultures, we show that enzyme abundance is often insufficient to predict its corresponding reaction rate. However, we discovered that global proteomic measurements can be used to make accurate rate predictions of individual reaction rates (median R 2 = 0.78). Accurate rate predictions required only a small number of proteins and they did not need explicit prior mechanistic knowledge or environmental context. These results indicate that proteomes are encoders of cellular reaction rates, potentially enabling proteomic measurements in situ to estimate the rates of microbially mediated reactions in natural systems.

Smart Molecular Imaging and Theranostic Probes by Enzymatic Molecular In Situ Self-Assembly

Enzymatic molecular in situ self-assembly (E-MISA) that enables the synthesis of high-order nanostructures from synthetic small molecules inside a living subject has emerged as a promising strategy for molecular imaging and theranostics. This strategy leverages the catalytic activity of an enzyme to trigger probe substrate conversion and assembly in situ, permitting prolonging retention and congregating many molecules of probes in the targeted cells or tissues. Enhanced imaging signals or therapeutic functions can be achieved by responding to a specific enzyme. This E-MISA strategy has been successfully applied for the development of enzyme-activated smart molecular imaging or theranostic probes for in vivo applications. In this Perspective, we discuss the general principle of controlling in situ self-assembly of synthetic small molecules by an enzyme and then discuss the applications for the construction of "smart" imaging and theranostic probes against cancers and bacteria. Finally, we discuss the current challenges and perspectives in utilizing the E-MISA strategy for disease diagnoses and therapies, particularly for clinical translation.

Robustness of mitochondrial biogenesis and respiration explain aerobic glycolysis

A long-standing observation is that in fast-growing cells, respiration rate declines with increasing growth rate and is compensated by an increase in fermentation, despite respiration being more efficient than fermentation. This apparent preference for fermentation even in the presence of oxygen is known as aerobic glycolysis, and occurs in bacteria, yeast, and cancer cells. Considerable work has focused on understanding the potential benefits that might justify this seemingly wasteful metabolic strategy, but its mechanistic basis remains unclear. Here we show that aerobic glycolysis results from the saturation of mitochondrial respiration and the decoupling of mitochondrial biogenesis from the production of other cellular components. Respiration rate is insensitive to acute perturbations of cellular energetic demands or nutrient supplies, and is explained simply by the amount of mitochondria per cell. Mitochondria accumulate at a nearly constant rate across different growth conditions, resulting in mitochondrial amount being largely determined by cell division time. In contrast, glucose uptake rate is not saturated, and is accurately predicted by the abundances and affinities of glucose transporters. Combining these models of glucose uptake and respiration provides a quantitative, mechanistic explanation for aerobic glycolysis. The robustness of specific respiration rate and mitochondrial biogenesis, paired with the flexibility of other bioenergetic and biosynthetic fluxes, may play a broad role in shaping eukaryotic cell metabolism.

Metabolomics and Microbial Metabolism: Toward a Systematic Understanding

Over the past decades, our understanding of microbial metabolism has increased dramatically. Metabolomics, a family of techniques that are used to measure the quantities of small molecules in biological samples, has been central to these efforts. Advances in analytical chemistry have made it possible to measure the relative and absolute concentrations of more and more compounds with increasing levels of certainty. In this review, we highlight how metabolomics has contributed to understanding microbial metabolism and in what ways it can still be deployed to expand our systematic understanding of metabolism. To that end, we explain how metabolomics was used to (a) characterize network topologies of metabolism and its regulation networks, (b) elucidate the control of metabolic function, and (c) understand the molecular basis of higher-order phenomena. We also discuss areas of inquiry where technological advances should continue to increase the impact of metabolomics, as well as areas where our understanding is bottlenecked by other factors such as the availability of statistical and modeling frameworks that can extract biological meaning from metabolomics data.

Emergence of Orchestrated and Dynamic Metabolism of Saccharomyces cerevisiae

Microbial metabolism is a fundamental cellular process that involves many biochemical events and is distinguished by its emergent properties. While the molecular details of individual reactions have been increasingly elucidated, it is not well understood how these reactions are quantitatively orchestrated to produce collective cellular behaviors. Here we developed a coarse-grained, systems, and dynamic mathematical framework, which integrates metabolic reactions with signal transduction and gene regulation to dissect the emergent metabolic traits of Saccharomyces cerevisiae. Our framework mechanistically captures a set of characteristic cellular behaviors, including the Crabtree effect, diauxic shift, diauxic lag time, and differential growth under nutrient-altered environments. It also allows modular expansion for zooming in on specific pathways for detailed metabolic profiles. This study provides a systems mathematical framework for yeast metabolic behaviors, providing insights into yeast physiology and metabolic engineering.

The metabolic domestication syndrome of budding yeast

Cellular metabolism evolves through changes in the structure and quantitative states of metabolic networks. Here, we explore the evolutionary dynamics of metabolic states by focusing on the collection of metabolite levels, the metabolome, which captures key aspects of cellular physiology. Using a phylogenetic framework, we profiled metabolites in 27 populations of nine budding yeast species, providing a graduated view of metabolic variation across multiple evolutionary time scales. Metabolite levels evolve more rapidly and independently of changes in the metabolic network's structure, providing complementary information to enzyme repertoire. Although metabolome variation accumulates mainly gradually over time, it is profoundly affected by domestication. We found pervasive signatures of convergent evolution in the metabolomes of independently domesticated clades of Saccharomyces cerevisiae. Such recurring metabolite differences between wild and domesticated populations affect a substantial part of the metabolome, including rewiring of the TCA cycle and several amino acids that influence aroma production, likely reflecting adaptation to human niches. Overall, our work reveals previously unrecognized diversity in central metabolism and the pervasive influence of human-driven selection on metabolite levels in yeasts.

AutonoMS: Automated Ion Mobility Metabolomic Fingerprinting

Automation is dramatically changing the nature of laboratory life science. Robotic lab hardware that can perform manual operations with greater speed, endurance, and reproducibility opens an avenue for faster scientific discovery with less time spent on laborious repetitive tasks. A major bottleneck remains in integrating cutting-edge laboratory equipment into automated workflows, notably specialized analytical equipment, which is designed for human usage. Here we present AutonoMS, a platform for automatically running, processing, and analyzing high-throughput mass spectrometry experiments. AutonoMS is currently written around an ion mobility mass spectrometry (IM-MS) platform and can be adapted to additional analytical instruments and data processing flows. AutonoMS enables automated software agent-controlled end-to-end measurement and analysis runs from experimental specification files that can be produced by human users or upstream software processes. We demonstrate the use and abilities of AutonoMS in a high-throughput flow-injection ion mobility configuration with 5 s sample analysis time, processing robotically prepared chemical standards and cultured yeast samples in targeted and untargeted metabolomics applications. The platform exhibited consistency, reliability, and ease of use while eliminating the need for human intervention in the process of sample injection, data processing, and analysis. The platform paves the way toward a more fully automated mass spectrometry analysis and ultimately closed-loop laboratory workflows involving automated experimentation and analysis coupled to AI-driven experimentation utilizing cutting-edge analytical instrumentation. AutonoMS documentation is available at https://autonoms.readthedocs.io.

Prediction and integration of metabolite-protein interactions with genome-scale metabolic models

Metabolites, as small molecules, can act not only as substrates to enzymes, but also as effectors of activity of proteins with different functions, thereby affecting various cellular processes. While several experimental techniques have started to catalogue the metabolite-protein interactions (MPIs) present in different cellular contexts, characterizing the functional relevance of MPIs remains a challenging problem. Computational approaches from the constrained-based modeling framework allow for predicting MPIs and integrating their effects in the in silico analysis of metabolic and physiological phenotypes, like cell growth. Here, we provide a classification of all existing constraint-based approaches that predict and integrate MPIs using genome-scale metabolic networks as input. In addition, we benchmark the performance of the approaches to predict MPIs in a comparative study using different features extracted from the model structure and predicted metabolic phenotypes with the state-of-the-art metabolic networks of Escherichia coli and Saccharomyces cerevisiae. Lastly, we provide an outlook for future, feasible directions to expand the consideration of MPIs in constraint-based modeling approaches with wide biotechnological applications.

The PP2A-like phosphatase Ppg1 mediates assembly of the Far complex to balance gluconeogenic outputs and enables adaptation to glucose depletion

To sustain growth in changing nutrient conditions, cells reorganize outputs of metabolic networks and appropriately reallocate resources. Signaling by reversible protein phosphorylation can control such metabolic adaptations. In contrast to kinases, the functions of phosphatases that enable metabolic adaptation as glucose depletes are poorly studied. Using a Saccharomyces cerevisiae deletion screen, we identified the PP2A-like phosphatase Ppg1 as required for appropriate carbon allocations towards gluconeogenic outputs-trehalose, glycogen, UDP-glucose, UDP-GlcNAc-after glucose depletion. This Ppg1 function is mediated via regulation of the assembly of the Far complex-a multi-subunit complex that tethers to the ER and mitochondrial outer membranes forming localized signaling hubs. The Far complex assembly is Ppg1 catalytic activity-dependent. Ppg1 regulates the phosphorylation status of multiple ser/thr residues on Far11 to enable the proper assembly of the Far complex. The assembled Far complex is required to maintain gluconeogenic outputs after glucose depletion. Glucose in turn regulates Far complex amounts. This Ppg1-mediated Far complex assembly, and Ppg1-Far complex dependent control of gluconeogenic outputs enables adaptive growth under glucose depletion. Our study illustrates how protein dephosphorylation is required for the assembly of a multi-protein scaffold present in localized cytosolic pools, to thereby alter gluconeogenic flux and enable cells to metabolically adapt to nutrient fluctuations.

KETCHUP: Parameterizing of large-scale kinetic models using multiple datasets with different reference states

Large-scale kinetic models provide the computational means to dynamically link metabolic reaction fluxes to metabolite concentrations and enzyme levels while also conforming to substrate level regulation. However, the development of broadly applicable frameworks for efficiently and robustly parameterizing models remains a challenge. Challenges arise due to both the heterogeneity, paucity, and difficulty in obtaining flux and/or concentration data but also due to the computational difficulties of the underlying parameter identification problem. Both the computational demands for parameterization, degeneracy of obtained parameter solutions and interpretability of results has so far limited widespread adoption of large-scale kinetic models despite their potential. Herein, we introduce the Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the (non)steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP is benchmarked against previously parameterized large-scale kinetic models demonstrating an at least an order of magnitude faster convergence than the tool K-FIT while at the same time attaining better data fits. This versatile toolbox accepts different kinetic descriptions, metabolic fluxes, enzyme levels and metabolite concentrations, under either steady-state or instationary conditions to enable robust kinetic model construction and parameterization. KETCHUP supports the SBML format and can be accessed at https://github.com/maranasgroup/KETCHUP.

Characterising the metabolic rewiring of extremely slow growing Komagataella phaffii

Retentostat cultivations have enabled investigations into substrate-limited near-zero growth for a number of microbes. Quantitative physiology at these near-zero growth conditions has been widely discussed, yet characterisation of the fluxome is relatively under-reported. We investigated the rewiring of metabolism in the transition of a recombinant protein-producing strain of Komagataella phaffii to glucose-limited near-zero growth rates. We used cultivation data from a 200-fold range of growth rates and comprehensive biomass composition data to integrate growth rate dependent biomass equations, generated using a number of different approaches, into a K. phaffii genome-scale metabolic model. Here, we show that a non-growth-associated maintenance value of 0.65 mmol ATP g CDW - 1 h - 1 and a growth-associated maintenance value of 108 mmol ATP g CDW - 1 lead to accurate growth rate predictions. In line with its role as energy source, metabolism is rewired to increase the yield of ATP per glucose. This includes a reduction of flux through the pentose phosphate pathway, and a greater utilisation of glycolysis and the TCA cycle. Interestingly, we observed activity of an external, non-proton translocating NADH dehydrogenase in addition to the malate-aspartate shuttle. Regardless of the method used for the generation of biomass equations, a similar, yet different, growth rate dependent rewiring was predicted. As expected, these differences between the different methods were clearer at higher growth rates, where the biomass equation provides a much greater constraint than at slower growth rates. When placed on an increasingly limited glucose diet, the metabolism of K. phaffii adapts, enabling it to continue to drive critical processes sustaining its high viability at near-zero growth rates.

Determination of Metabolic Fluxes by Deep Learning of Isotope Labeling Patterns

Fluxomics offers a direct readout of metabolic state but relies on indirect measurement. Stable isotope tracers imprint flux-dependent isotope labeling patterns on metabolites we measure; however, the relationship between labeling patterns and fluxes remains elusive. Here we innovate a two-stage machine learning framework termed ML-Flux that streamlines metabolic flux quantitation from isotope tracing. We train machine learning models by simulating atom transitions across five universal metabolic models starting from 26 13C-glucose, 2H-glucose, and 13C-glutamine tracers within feasible flux space. ML-Flux employs deep-learning-based imputation to take variable measurements of labeling patterns as input and successive neural networks to convert the ensuing comprehensive labeling information into metabolic fluxes. Using ML-Flux with multi-isotope tracing, we obtain fluxes through central carbon metabolism that are comparable to those from a least-squares method but orders-of-magnitude faster. ML-Flux is deployed as a webtool to expand the accessibility of metabolic flux quantitation and afford actionable information on metabolism.

Controlling circuitry underlies the growth optimization of Saccharomyces cerevisiae

Microbial growth emerges from coordinated synthesis of various cellular components from limited resources. In Saccharomyces cerevisiae, cyclic AMP (cAMP)-mediated signaling is shown to orchestrate cellular metabolism; however, it remains unclear quantitatively how the controlling circuit drives resource partition and subsequently shapes biomass growth. Here we combined experiment with mathematical modeling to dissect the signaling-mediated growth optimization of S. cerevisiae. We showed that, through cAMP-mediated control, the organism achieves maximal or nearly maximal steady-state growth during the utilization of multiple tested substrates as well as under perturbations impairing glucose uptake. However, the optimal cAMP concentration varies across cases, suggesting that different modes of resource allocation are adopted for varied conditions. Under settings with nutrient alterations, S. cerevisiae tunes its cAMP level to dynamically reprogram itself to realize rapid adaptation. Moreover, to achieve growth maximization, cells employ additional regulatory systems such as the GCN2-mediated amino acid control. This study establishes a systematic understanding of global resource allocation in S. cerevisiae, providing insights into quantitative yeast physiology as well as metabolic strain engineering for biotechnological applications.

Bicyclo[3.2.0]carbocyclic Molecules and Redox Biotransformations: The Evolution of Closed-Loop Artificial Linear Biocatalytic Cascades and Related Redox-Neutral Systems

The role of cofactor recycling in determining the efficiency of artificial biocatalytic cascades has become paramount in recent years. Closed-loop cofactor recycling, which initially emerged in the 1990s, has made a valuable contribution to the development of this aspect of biotechnology. However, the evolution of redox-neutral closed-loop cofactor recycling has a longer history that has been integrally linked to the enzymology of oxy-functionalised bicyclo[3.2.0]carbocyclic molecule metabolism throughout. This review traces that relevant history from the mid-1960s to current times.

Metabolite interactions in the bacterial Calvin cycle and implications for flux regulation

Metabolite-level regulation of enzyme activity is important for microbes to cope with environmental shifts. Knowledge of such regulations can also guide strain engineering for biotechnology. Here we apply limited proteolysis-small molecule mapping (LiP-SMap) to identify and compare metabolite-protein interactions in the proteomes of two cyanobacteria and two lithoautotrophic bacteria that fix CO2 using the Calvin cycle. Clustering analysis of the hundreds of detected interactions shows that some metabolites interact in a species-specific manner. We estimate that approximately 35% of interacting metabolites affect enzyme activity in vitro, and the effect is often minor. Using LiP-SMap data as a guide, we find that the Calvin cycle intermediate glyceraldehyde-3-phosphate enhances activity of fructose-1,6/sedoheptulose-1,7-bisphosphatase (F/SBPase) from Synechocystis sp. PCC 6803 and Cupriavidus necator in reducing conditions, suggesting a convergent feed-forward activation of the cycle. In oxidizing conditions, glyceraldehyde-3-phosphate inhibits Synechocystis F/SBPase by promoting enzyme aggregation. In contrast, the glycolytic intermediate glucose-6-phosphate activates F/SBPase from Cupriavidus necator but not F/SBPase from Synechocystis. Thus, metabolite-level regulation of the Calvin cycle is more prevalent than previously appreciated.

Simulated Design-Build-Test-Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering

Combinatorial pathway optimization is an important tool in metabolic flux optimization. Simultaneous optimization of a large number of pathway genes often leads to combinatorial explosions. Strain optimization is therefore often performed using iterative design-build-test-learn (DBTL) cycles. The aim of these cycles is to develop a product strain iteratively, every time incorporating learning from the previous cycle. Machine learning methods provide a potentially powerful tool to learn from data and propose new designs for the next DBTL cycle. However, due to the lack of a framework for consistently testing the performance of machine learning methods over multiple DBTL cycles, evaluating the effectiveness of these methods remains a challenge. In this work, we propose a mechanistic kinetic model-based framework to test and optimize machine learning for iterative combinatorial pathway optimization. Using this framework, we show that gradient boosting and random forest models outperform the other tested methods in the low-data regime. We demonstrate that these methods are robust for training set biases and experimental noise. Finally, we introduce an algorithm for recommending new designs using machine learning model predictions. We show that when the number of strains to be built is limited, starting with a large initial DBTL cycle is favorable over building the same number of strains for every cycle.

Local flux coordination and global gene expression regulation in metabolic modeling

Genome-scale metabolic networks (GSMs) are fundamental systems biology representations of a cell's entire set of stoichiometrically balanced reactions. However, such static GSMs do not incorporate the functional organization of metabolic genes and their dynamic regulation (e.g., operons and regulons). Specifically, there are numerous topologically coupled local reactions through which fluxes are coordinated; the global growth state often dynamically regulates many gene expression of metabolic reactions via global transcription factor regulators. Here, we develop a GSM reconstruction method, Decrem, by integrating locally coupled reactions and global transcriptional regulation of metabolism by cell state. Decrem produces predictions of flux and growth rates, which are highly correlated with those experimentally measured in both wild-type and mutants of three model microorganisms Escherichia coli, Saccharomyces cerevisiae, and Bacillus subtilis under various conditions. More importantly, Decrem can also explain the observed growth rates by capturing the experimentally measured flux changes between wild-types and mutants. Overall, by identifying and incorporating locally organized and regulated functional modules into GSMs, Decrem achieves accurate predictions of phenotypes and has broad applications in bioengineering, synthetic biology, and microbial pathology.

Recent Review on Selected Xenobiotics and Their Impacts on Gut Microbiome and Metabolome

As it is well known, the gut is one of the primary sites in any host for xenobiotics, and the many microbial metabolites responsible for the interactions between the gut microbiome and the host. However, there is a growing concern about the negative impacts on human health induced by toxic xenobiotics. Metabolomics, broadly including lipidomics, is an emerging approach to studying thousands of metabolites in parallel. In this review, we summarized recent advancements in mass spectrometry (MS) technologies in metabolomics. In addition, we reviewed recent applications of MS-based metabolomics for the investigation of toxic effects of xenobiotics on microbial and host metabolism. It was demonstrated that metabolomics, gut microbiome profiling, and their combination have a high potential to identify metabolic and microbial markers of xenobiotic exposure and determine its mechanism. Further, there is increasing evidence supporting that reprogramming the gut microbiome could be a promising approach to the intervention of xenobiotic toxicity.

Maximizing multi-reaction dependencies provides more accurate and precise predictions of intracellular fluxes than the principle of parsimony

Intracellular fluxes represent a joint outcome of cellular transcription and translation and reflect the availability and usage of nutrients from the environment. While approaches from the constraint-based metabolic framework can accurately predict cellular phenotypes, such as growth and exchange rates with the environment, accurate prediction of intracellular fluxes remains a pressing problem. Parsimonious flux balance analysis (pFBA) has become an approach of choice to predict intracellular fluxes by employing the principle of efficient usage of protein resources. Nevertheless, comparative analyses of intracellular flux predictions from pFBA against fluxes estimated from labeling experiments remain scarce. Here, we posited that steady-state flux distributions derived from the principle of maximizing multi-reaction dependencies are of improved accuracy and precision than those resulting from pFBA. To this end, we designed a constraint-based approach, termed complex-balanced FBA (cbFBA), to predict steady-state flux distributions that support the given specific growth rate and exchange fluxes. We showed that the steady-state flux distributions resulting from cbFBA in comparison to pFBA show better agreement with experimentally measured fluxes from 17 Escherichia coli strains and are more precise, due to the smaller space of alternative solutions. We also showed that the same principle holds in eukaryotes by comparing the predictions of pFBA and cbFBA against experimentally derived steady-state flux distributions from 26 knock-out mutants of Saccharomyces cerevisiae. Furthermore, our results showed that intracellular fluxes predicted by cbFBA provide better support for the principle of minimizing metabolic adjustment between mutants and wild types. Together, our findings point that other principles that consider the dynamics and coordination of steady states may govern the distribution of intracellular fluxes.

Biallelic NFATC1 mutations cause an inborn error of immunity with impaired CD8+ T-cell function and perturbed glycolysis

The nuclear factor of activated T cells (NFAT) family of transcription factors plays central roles in adaptive immunity in murine models; however, their contribution to human immune homeostasis remains poorly defined. In a multigenerational pedigree, we identified 3 patients who carry germ line biallelic missense variants in NFATC1, presenting with recurrent infections, hypogammaglobulinemia, and decreased antibody responses. The compound heterozygous NFATC1 variants identified in these patients caused decreased stability and reduced the binding of DNA and interacting proteins. We observed defects in early activation and proliferation of T and B cells from these patients, amenable to rescue upon genetic reconstitution. Stimulation induced early T-cell activation and proliferation responses were delayed but not lost, reaching that of healthy controls at day 7, indicative of an adaptive capacity of the cells. Assessment of the metabolic capacity of patient T cells revealed that NFATc1 dysfunction rendered T cells unable to engage in glycolysis after stimulation, although oxidative metabolic processes were intact. We hypothesized that NFATc1-mutant T cells could compensate for the energy deficit due to defective glycolysis by using enhanced lipid metabolism as an adaptation, leading to a delayed, but not lost, activation responses. Indeed, we observed increased 13C-labeled palmitate incorporation into citrate, indicating higher fatty acid oxidation, and we demonstrated that metformin and rosiglitazone improved patient T-cell effector functions. Collectively, enabled by our molecular dissection of the consequences of loss-of-function NFATC1 mutations and extending the role of NFATc1 in human immunity beyond receptor signaling, we provide evidence of metabolic plasticity in the context of impaired glycolysis observed in patient T cells, alleviating delayed effector responses.

Principles of metabolome conservation in animals

Metabolite levels shape cellular physiology and disease susceptibility, yet the general principles governing metabolome evolution are largely unknown. Here, we introduce a measure of conservation of individual metabolite levels among related species. By analyzing multispecies tissue metabolome datasets in phylogenetically diverse mammals and fruit flies, we show that conservation varies extensively across metabolites. Three major functional properties, metabolite abundance, essentiality, and association with human diseases predict conservation, highlighting a striking parallel between the evolutionary forces driving metabolome and protein sequence conservation. Metabolic network simulations recapitulated these general patterns and revealed that abundant metabolites are highly conserved due to their strong coupling to key metabolic fluxes in the network. Finally, we show that biomarkers of metabolic diseases can be distinguished from other metabolites simply based on evolutionary conservation, without requiring any prior clinical knowledge. Overall, this study uncovers simple rules that govern metabolic evolution in animals and implies that most tissue metabolome differences between species are permitted, rather than favored by natural selection. More broadly, our work paves the way toward using evolutionary information to identify biomarkers, as well as to detect pathogenic metabolome alterations in individual patients.

Prediction of metabolite-protein interactions based on integration of machine learning and constraint-based modeling

Motivation

Metabolite-protein interactions play an important role in regulating protein functions and metabolism. Yet, predictions of metabolite-protein interactions using genome-scale metabolic networks are lacking. Here, we fill this gap by presenting a computational framework, termed SARTRE, that employs features corresponding to shadow prices determined in the context of flux variability analysis to predict metabolite-protein interactions using supervised machine learning.

Results

By using gold standards for metabolite-protein interactomes and well-curated genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae, we found that the implementation of SARTRE with random forest classifiers accurately predicts metabolite-protein interactions, supported by an average area under the receiver operating curve of 0.86 and 0.85, respectively. Ranking of features based on their importance for classification demonstrated the key role of shadow prices in predicting metabolite-protein interactions. The quality of predictions is further supported by the excellent agreement of the organism-specific classifiers on unseen interactions shared between the two model organisms. Further, predictions from SARTRE are highly competitive against those obtained from a recent deep-learning approach relying on a variety of protein and metabolite features. Together, these findings show that features extracted from constraint-based analyses of metabolic networks pave the way for understanding the functional roles of the interactions between proteins and small molecules.

Availability and implementation

https://github.com/fayazsoleymani/SARTRE.

Convergent Approaches to Delineate the Metabolic Regulation of Tumor Invasion by Hyaluronic Acid Biosynthesis

Metastasis is the leading cause of breast cancer-related deaths and is often driven by invasion and cancer-stem like cells (CSCs). Both the CSC phenotype and invasion are associated with increased hyaluronic acid (HA) production. How these independent observations are connected, and which role metabolism plays in this process, remains unclear due to the lack of convergent approaches integrating engineered model systems, computational tools, and cancer biology. Using microfluidic invasion models, metabolomics, computational flux balance analysis, and bioinformatic analysis of patient data, the functional links between the stem-like, invasive, and metabolic phenotype of breast cancer cells as a function of HA biosynthesis are investigated. These results suggest that CSCs are more invasive than non-CSCs and that broad metabolic changes caused by overproduction of HA play a role in this process. Accordingly, overexpression of hyaluronic acid synthases (HAS) 2 or 3 induces a metabolic phenotype that promotes cancer cell stemness and invasion in vitro and upregulates a transcriptomic signature predictive of increased invasion and worse patient survival. This study suggests that HA overproduction leads to metabolic adaptations to satisfy the energy demands for 3D invasion of breast CSCs highlighting the importance of engineered model systems and multidisciplinary approaches in cancer research.

Causes and consequences of endogenous hypoxia on growth and metabolism of developing maize kernels

Maize (Zea mays) kernels are the largest cereal grains, and their endosperm is severely oxygen deficient during grain fill. The causes, dynamics, and mechanisms of acclimation to hypoxia are minimally understood. Here, we demonstrate that hypoxia develops in the small, growing endosperm, but not the nucellus, and becomes the standard state, regardless of diverse structural and genetic perturbations in modern maize (B73, popcorn, sweet corn), mutants (sweet4c, glossy6, waxy), and non-domesticated wild relatives (teosintes and Tripsacum species). We also uncovered an interconnected void space at the chalazal pericarp, providing superior oxygen supply to the placental tissues and basal endosperm transfer layer. Modeling indicated a very high diffusion resistance inside the endosperm, which, together with internal oxygen consumption, could generate steep oxygen gradients at the endosperm surface. Manipulation of oxygen supply induced reciprocal shifts in gene expression implicated in controlling mitochondrial functions (23.6 kDa Heat-Shock Protein, Voltage-Dependent Anion Channel 2) and multiple signaling pathways (core hypoxia genes, cyclic nucleotide metabolism, ethylene synthesis). Metabolite profiling revealed oxygen-dependent shifts in mitochondrial pathways, ascorbate metabolism, starch synthesis, and auxin degradation. Long-term elevated oxygen supply enhanced the rate of kernel development. Altogether, evidence here supports a mechanistic framework for the establishment of and acclimation to hypoxia in the maize endosperm.

Metabolic Engineering of Model Microorganisms for the Production of Xanthophyll

Xanthophyll is an oxidated version of carotenoid. It presents significant value to the pharmaceutical, food, and cosmetic industries due to its specific antioxidant activity and variety of colors. Chemical processing and conventional extraction from natural organisms are still the main sources of xanthophyll. However, the current industrial production model can no longer meet the demand for human health care, reducing petrochemical energy consumption and green sustainable development. With the swift development of genetic metabolic engineering, xanthophyll synthesis by the metabolic engineering of model microorganisms shows great application potential. At present, compared to carotenes such as lycopene and β-carotene, xanthophyll has a relatively low production in engineering microorganisms due to its stronger inherent antioxidation, relatively high polarity, and longer metabolic pathway. This review comprehensively summarized the progress in xanthophyll synthesis by the metabolic engineering of model microorganisms, described strategies to improve xanthophyll production in detail, and proposed the current challenges and future efforts needed to build commercialized xanthophyll-producing microorganisms.

Complex regulation in a Comamonas platform for diverse aromatic carbon metabolism

Critical to a sustainable energy future are microbial platforms that can process aromatic carbons from the largely untapped reservoir of lignin and plastic feedstocks. Comamonas species present promising bacterial candidates for such platforms because they can use a range of natural and xenobiotic aromatic compounds and often possess innate genetic constraints that avoid competition with sugars. However, the metabolic reactions of these species are underexplored, and the regulatory mechanisms are unknown. Here we identify multilevel regulation in the conversion of lignin-related natural aromatic compounds, 4-hydroxybenzoate and vanillate, and the plastics-related xenobiotic aromatic compound, terephthalate, in Comamonas testosteroni KF-1. Transcription-level regulation controls initial catabolism and cleavage, but metabolite-level thermodynamic regulation governs fluxes in central carbon metabolism. Quantitative 13C mapping of tricarboxylic acid cycle and cataplerotic reactions elucidates key carbon routing not evident from enzyme abundance changes. This scheme of transcriptional activation coupled with metabolic fine-tuning challenges outcome predictions during metabolic manipulations.

Shaping bacterial gene expression by physiological and proteome allocation constraints

Networks of molecular regulators are often the primary objects of focus in the study of gene regulation, with the machinery of protein synthesis tacitly relegated to the background. Shifting focus to the constraints imposed by the allocation of protein synthesis flux reveals surprising ways in which the actions of molecular regulators are shaped by physiological demands. Using carbon catabolite repression as a case study, we describe how physiological constraints are sensed through metabolic fluxes and how flux-controlled regulation gives rise to simple empirical relations between protein levels and the rate of cell growth.

In-Cell NMR Approach for Real-Time Exploration of Pathway Versatility: Substrate Mixtures in Nonengineered Yeast

The central carbon metabolism of microbes will likely be used in future sustainable bioproduction. A sufficiently deep understanding of central metabolism would advance the control of activity and selectivity in whole-cell catalysis. Opposite to the more obvious effects of adding catalysts through genetic engineering, the modulation of cellular chemistry through effectors and substrate mixtures remains less clear. NMR spectroscopy is uniquely suited for in-cell tracking to advance mechanistic insight and to optimize pathway usage. Using a comprehensive and self-consistent library of chemical shifts, hyperpolarized NMR, and conventional NMR, we probe the versatility of cellular pathways to changes in substrate composition. Conditions for glucose influx into a minor pathway to an industrial precursor (2,3-butanediol) can thus be designed. Changes to intracellular pH can be followed concurrently, while mechanistic details for the minor pathway can be derived using an intermediate-trapping strategy. Overflow at the pyruvate level can be induced in nonengineered yeast with suitably mixed carbon sources (here glucose with auxiliary pyruvate), thus increasing glucose conversion to 2,3-butanediol by more than 600-fold. Such versatility suggests that a reassessment of canonical metabolism may be warranted using in-cell spectroscopy.

Crosstalk between Metabolite Production and Signaling Activity in Breast Cancer

The reprogramming of metabolism is a recognized cancer hallmark. It is well known that different signaling pathways regulate and orchestrate this reprogramming that contributes to cancer initiation and development. However, recent evidence is accumulating, suggesting that several metabolites could play a relevant role in regulating signaling pathways. To assess the potential role of metabolites in the regulation of signaling pathways, both metabolic and signaling pathway activities of Breast invasive Carcinoma (BRCA) have been modeled using mechanistic models. Gaussian Processes, powerful machine learning methods, were used in combination with SHapley Additive exPlanations (SHAP), a recent methodology that conveys causality, to obtain potential causal relationships between the production of metabolites and the regulation of signaling pathways. A total of 317 metabolites were found to have a strong impact on signaling circuits. The results presented here point to the existence of a complex crosstalk between signaling and metabolic pathways more complex than previously was thought.