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

Biosensor characterization: formal methods from the perspective of proteome fractions

Many studies characterize transcription factors and other regulatory elements to control gene expression in recombinant systems. However, most lack a formal approach to analyse the inherent and context-specific variations of these regulatory components. This study addresses this gap by establishing a formal framework from which convenient methods are inferred to characterize regulatory circuits. We modelled the bacterial cell as a collection of proteome fractions. Deriving the time-dependent proteome fraction, we obtained a general theorem that describes its change as a function of its expression fraction, a specific portion of the total biosynthesis flux of the cell. Formal deduction reveals that when the proteome fraction reaches a maximum, it becomes equivalent to its expression fraction. This equation enables the reliable measurement of the expression fraction through direct protein quantification. In addition, the experimental data demonstrate a linear correlation between protein production rate and specific growth rate over a significant time period. This suggests a constant expression fraction within this window. For an Isopropyl β- d-1-thiogalactopyranoside (IPTG) biosensor, in five cellular contexts, expression fractions determined by the maximum method and the slope method produced strikingly similar dose-response parameters when independently fit to a Hill function. Furthermore, by analysing two more biosensors, for mercury and cumate detection, we demonstrate that the slope method can be applied effectively to various systems. Therefore, the concepts presented here provide convenient methods for obtaining dose-response parameters, clearly defining the time interval of their validity and offering a framework for interpreting typical biosensor outputs in terms of bacterial physiology. Graphical Abstract Nutrients, transformed by the action of the Nutrient Fixators (purple arrow), are used at a rate of ρ for Protein biosynthesis. The total rate ρ is multiplied by expression fractions fR, fC, fH, and fQ to obtain the biosynthesis rate (black arrows) of each proteome fraction ΦR, ΦC, ΦH, ΦQ, respectively. In a graph of Growth rate versus Proteome Fraction Production Rate, a linear function (green lines) can be observed, and its slope is equal to the expression fraction at each condition.

Understanding the triacylglycerol-based carbon anabolic differentiation in Cyperus esculentus and Cyperus rotundus developing tubers via transcriptomic and metabolomic approaches

BACKGROUND

Yellow nutsedge (Cyperus esculentus, known as 'YouShaDou' in China, YSD) and purple nutsedge (Cyperus rotundus, known as 'XiangFuZi' in China, XFZ), closely related Cyperaceae species, exhibit significant differences in triacylglycerol (TAG) accumulation within their tubers, a key factor in carbon flux repartitioning that highly impact the total lipid, carbohydrate and protein metabolisms. Previous studies have attempted to elucidate the carbon anabolic discrepancies between these two species, however, a lack of comprehensive genome-wide annotation has hindered a detailed understanding of the underlying molecular mechanisms.

RESULTS

This study utilizes transcriptomic analyses, supported by a comprehensive YSD reference genome, and metabolomic profiling to uncover the mechanisms underlying the major carbon perturbations between the developing tubers of YSD and XFZ germplasms harvested in Yunnan province, China, where the plant biodiveristy is renowned worldwide and may contain more genetic variations relative to their counterparts in other places. Our findings indicate distinct expression patterns of key regulatory genes involved in TAG biosynthesis and lipid droplet formation, including transcriptional factors and structural genes such as ABI3 transcriptional factor, rate-limiting enzymes GPAT3/6/9 and DGAT2/3, and oleosin and caleosin homologs. Furthermore, our omics data suggest that these differences in gene expression are not the sole contributors to the diverse tuber compositions. Instead, complex interactions among highly regulated catalytic reactions, governing carbohydrate, protein, and species-specific metabolite metabolisms, such as starch and sucrose metabolic pathways, flavonoid and amino acids biosynthetic pathways, collectively contribute to the pronounced carbon anabolic differentiation primarily evident in TAG accumulation, as well as the starch properties in mature tubers.

CONCLUSION

This study offers new metabolic insights into the high-value underground non-photosynthetic tissues of Cyperaceae species, which harbors not only high biomass productivity but also abundant nutrients as favorable food or industrial sources in the modern agriculture. The detailed omics analyses aim to deepen our understanding of the Cyperaceae species, which may potentially broaden their application values and facilitate the molecular breeding of better varieties to ameliorate the food safety problem.

A framework for understanding collective microbiome metabolism

Microbiome metabolism underlies numerous vital ecosystem functions. Individual microbiome members often perform partial catabolism of substrates or do not express all of the metabolic functions required for growth. Microbiome members can complement each other by exchanging metabolic intermediates and cellular building blocks to achieve a collective metabolism. We currently lack a mechanistic framework to explain why microbiome members adopt partial metabolism and how metabolic functions are distributed among them. Here we argue that natural selection for proteome efficiency-that is, performing essential metabolic fluxes at a minimal protein investment-explains partial metabolism of microbiome members, which underpins the collective metabolism of microbiomes. Using the carbon cycle as an example, we discuss motifs of collective metabolism, the conditions under which these motifs increase the proteome efficiency of individuals and the metabolic interactions they result in. In summary, we propose a mechanistic framework for how collective metabolic functions emerge from selection on individuals.

Unveiling Metabolic Engineering Strategies by Quantitative Heterologous Pathway Design

Constructing efficient cell factories requires the rational design of metabolic pathways, yet quantitatively predicting the potential pathway for breaking stoichiometric yield limit in hosts remains challenging. This leaves it uncertain whether the pathway yield of various products can be enhanced to surpass the stoichiometric yield limit and whether common strategies exist. Here, a high-quality cross-species metabolic network model (CSMN) and a quantitative heterologous pathway design algorithm (QHEPath) are developed to address this challenge. Through systematic calculations using CSMN and QHEPath, 12,000 biosynthetic scenarios are evaluated across 300 products and 4 substrates in 5 industrial organisms, revealing that over 70% of product pathway yields can be improved by introducing appropriate heterologous reactions. Thirteen engineering strategies, categorized as carbon-conserving and energy-conserving, are identified, with 5 strategies effective for over 100 products. A user-friendly web server is developed to quantitatively calculate and visualize the product yields and pathways, which successfully predicts biologically plausible strategies validated in literature for multiple products.

Flexibility and sensitivity in gene regulation out of equilibrium

Cells adapt to environments and tune gene expression by controlling the concentrations of proteins and their kinetics in regulatory networks. In both eukaryotes and prokaryotes, experiments and theory increasingly attest that these networks can and do consume biochemical energy. How does this dissipation enable cellular behaviors forbidden in equilibrium? This open question demands quantitative models that transcend thermodynamic equilibrium. Here, we study the control of simple, ubiquitous gene regulatory networks to explore the consequences of departing equilibrium in transcription. Employing graph theory to model a set of especially common regulatory motifs, we find that dissipation unlocks nonmonotonicity and enhanced sensitivity of gene expression with respect to a transcription factor's concentration. These features allow a single transcription factor to act as both a repressor and activator at different concentrations or achieve outputs with multiple concentration regimes of locally enhanced sensitivity. We systematically dissect how energetically driving individual transitions within regulatory networks, or pairs of transitions, generates a wide range of more adjustable and sensitive phenotypic responses than in equilibrium. These results generalize to more complex regulatory scenarios, including combinatorial control by multiple transcription factors, which we relate and often find collapse to simple mathematical behaviors. Our findings quantify necessary conditions and detectable consequences of energy expenditure. These richer mathematical behaviors-feasibly accessed using biological energy budgets and rates-may empower cells to accomplish sophisticated regulation with simpler architectures than those required at equilibrium.

Free-energy transduction mechanisms shape the flux space of metabolic networks

The transduction of free energy in metabolic networks represents a thermodynamic mechanism by which the free energy derived from nutrients is converted to drive nonspontaneous, energy-requiring metabolic reactions. This transduction is typically observed in processes that generate energy-rich molecules such as ATP and NAD(P)H, which, in turn, power specific reactions, particularly biosynthetic reactions. This property establishes a pivotal connection between the intricate topology of metabolic networks and their ability to redirect energy for functional purposes. The present study proposes a dedicated framework aimed at exploring the relationship between free-energy dissipation, network topology, and metabolic objectives. The starting point is that, regardless of the network topology, nonequilibrium chemostatting conditions impose stringent thermodynamic constraints on the feasible flux steady states to satisfy energy and entropy balances. An analysis of randomly sampled reaction networks shows that the network topology imposes additional constraints that restrict the accessible flux solution space, depending on key structural features such as the reaction's molecularity, reaction cycles, and conservation laws. Notably, topologies featuring multimolecular reactions that implement free-energy transduction mechanisms tend to extend the accessible flux domains, facilitating the achievement of metabolic objectives such as anabolic flux maximization or flux rerouting capacity. This approach is applied to a coarse-grained model of carbohydrate metabolism, highlighting the structural requirements for optimal biomass yield.

Salmonella Typhimurium screen identifies shifts in mixed-acid fermentation during gut colonization

How enteric pathogens adapt their metabolism to a dynamic gut environment is not yet fully understood. To investigate how Salmonella enterica Typhimurium (S.Tm) colonizes the gut, we conducted an in vivo transposon mutagenesis screen in a gnotobiotic mouse model. Our data implicate mixed-acid fermentation in efficient gut-luminal growth and energy conservation throughout infection. During initial growth, the pathogen utilizes acetate fermentation and fumarate respiration. After the onset of gut inflammation, hexoses appear to become limiting, as indicated by carbohydrate analytics and the increased need for gluconeogenesis. In response, S.Tm adapts by ramping up ethanol fermentation for redox balancing and supplying the TCA cycle with α-ketoglutarate for additional energy. Our findings illustrate how S.Tm flexibly adapts mixed fermentation and its use of the TCA cycle to thrive in the changing gut environment. Similar metabolic wiring in other pathogenic Enterobacteriaceae may suggest a broadly conserved mechanism for gut colonization.

Cell Geometry and Membrane Protein Crowding Constrain Growth Rate, Overflow Metabolism, Respiration, and Maintenance Energy

A metabolic theory is presented for predicting maximum growth rate, overflow metabolism, respiration efficiency, and maintenance energy flux based on the intersection of cell geometry, membrane protein crowding, and metabolism. The importance of cytosolic macromolecular crowding on phenotype has been established in the literature but the importance of surface area has been largely overlooked due to incomplete knowledge of membrane properties. We demonstrate that the capacity of the membrane to host proteins increases with growth rate offsetting decreases in surface area-to-volume ratios (SA:V). This increase in membrane protein is hypothesized to be essential to competitive Escherichia coli phenotypes. The presented membrane-centric theory uses biophysical properties and metabolic systems analysis to successfully predict the phenotypes of E. coli K-12 strains, MG1655 and NCM3722, which are genetically similar but have SA:V ratios that differ up to 30%, maximum growth rates on glucose media that differ by 40%, and overflow phenotypes that start at growth rates that differ by 80%. These analyses did not consider cytosolic macromolecular crowding, highlighting the distinct properties of the presented theory. Cell geometry and membrane protein crowding are significant biophysical constraints on phenotype and provide a theoretical framework for improved understanding and control of cell biology.

A quantitative description of light-limited cyanobacterial growth using flux balance analysis

The metabolism of phototrophic cyanobacteria is an integral part of global biogeochemical cycles, and the capability of cyanobacteria to assimilate atmospheric CO2 into organic carbon has manifold potential applications for a sustainable biotechnology. To elucidate the properties of cyanobacterial metabolism and growth, computational reconstructions of genome-scale metabolic networks play an increasingly important role. Here, we present an updated reconstruction of the metabolic network of the cyanobacterium Synechocystis sp. PCC 6803 and its quantitative evaluation using flux balance analysis (FBA). To overcome limitations of conventional FBA, and to allow for the integration of experimental analyses, we develop a novel approach to describe light absorption and light utilization within the framework of FBA. Our approach incorporates photoinhibition and a variable quantum yield into the constraint-based description of light-limited phototrophic growth. We show that the resulting model is capable of predicting quantitative properties of cyanobacterial growth, including photosynthetic oxygen evolution and the ATP/NADPH ratio required for growth and cellular maintenance. Our approach retains the computational and conceptual simplicity of FBA and is readily applicable to other phototrophic microorganisms.

Multilayered regulation of amino acid metabolism in Escherichia coli

Amino acid metabolism in Escherichia coli has long been studied and has established the basis for regulatory mechanisms at the transcriptional, posttranscriptional, and posttranslational levels. In addition to the classical signal transduction cascade involving posttranslational modifications (PTMs), novel PTMs in the two primary nitrogen assimilation pathways have recently been uncovered. The regulon of the master transcriptional regulator NtrC is further expanded by a small RNA derived from the 3´UTR of glutamine synthetase mRNA, which coordinates central carbon and nitrogen metabolism. Furthermore, recent advances in sequencing technologies have revealed the global regulatory networks of transcriptional and posttranscriptional regulators, Lrp and GcvB. This review provides an update of the multilayered and interconnected regulatory networks governing amino acid metabolism in E. coli.