Escherichia coli robustly expresses ATP synthase at growth rate‐maximizing concentrations

A histidine-rich extension of the mitochondrial F0 subunit ATP6 from the ice worm Mesenchytraeus solifugus increases ATP synthase activity in bacteria

Bioenergetic profiles of psychrophiles across domains of life are unusual in that intracellular ATP levels increase with declining temperature. Whole-transcriptome sequencing of the glacier ice worm Mesenchytraeus solifugus revealed a unique C-terminal extension on the ATP6 protein, which forms part of the proton pore of mitochondrial ATP synthase (Complex V). This extension, positioned near the proton exit pore, comprises alternating histidine residues thought to increase proton flux through Complex V leading to elevated ATP synthesis. To test this hypothesis, we fused the M. solifugus C-terminal extension to Escherichia coli AtpB (the ATP6 orthologue) and observed a ~ 5-fold increase in ATP synthesis. This enhancement was unidirectional as we observed no change to ATP hydrolysis rates. These findings offer an avenue for identifying critical factors associated with ice worm adaptation.

Sensitivities in protein allocation models reveal distribution of metabolic capacity and flux control

MOTIVATION

Expanding on constraint-based metabolic models, protein allocation models (PAMs) enhance flux predictions by accounting for protein resource allocation in cellular metabolism. Yet, to this date, there are no dedicated methods for analyzing and understanding the growth-limiting factors in simulated phenotypes in PAMs.

RESULTS

Here, we introduce a systematic framework for identifying the most sensitive enzyme concentrations (sEnz) in PAMs. The framework exploits the primal and dual formulations of these models to derive sensitivity coefficients based on relations between variables, constraints, and the objective function. This approach enhances our understanding of the growth-limiting factors of metabolic phenotypes under specific environmental or genetic conditions. Compared to other traditional methods for calculating sensitivities, sEnz requires substantially less computation time and facilitates more intuitive comparison and analysis of sensitivities. The sensitivities calculated by sEnz cover enzymes, reactions and protein sectors, enabling a holistic overview of the factors influencing metabolism. When applied to an Escherichia coli PAM, sEnz revealed major pathways and enzymes driving overflow metabolism. Overall, sEnz offers a computational efficient framework for understanding PAM predictions and unraveling the factors governing a particular metabolic phenotype.

AVAILABILITY AND IMPLEMENTATION

sEnz is implemented in the modular toolbox for the generation and analysis of PAMs in Python (PAModelpy; v.0.0.3.3), available on Pypi (https://pypi.org/project/PAModelpy/). The source code together with all other python scripts and notebooks are available on GitHub (https://github.com/iAMB-RWTH-Aachen/PAModelpy).

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.

Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality

Genome-scale metabolic models are powerful tools for understanding cellular physiology. Flux balance analysis (FBA), in particular, is an optimization-based approach widely employed for predicting metabolic phenotypes. In model microbes such as Escherichia coli, FBA has been successful at predicting essential genes, i.e. those genes that impair survival when deleted. A central assumption in this approach is that both wild type and deletion strains optimize the same fitness objective. Although the optimality assumption may hold for the wild type metabolic network, deletion strains are not subject to the same evolutionary pressures and knock-out mutants may steer their metabolism to meet other objectives for survival. Here, we present FlowGAT, a hybrid FBA-machine learning strategy for predicting essentiality directly from wild type metabolic phenotypes. The approach is based on graph-structured representation of metabolic fluxes predicted by FBA, where nodes correspond to enzymatic reactions and edges quantify the propagation of metabolite mass flow between a reaction and its neighbours. We integrate this information into a graph neural network that can be trained on knock-out fitness assay data. Comparisons across different model architectures reveal that FlowGAT predictions for E. coli are close to those of FBA for several growth conditions. This suggests that essentiality of enzymatic genes can be predicted by exploiting the inherent network structure of metabolism. Our approach demonstrates the benefits of combining the mechanistic insights afforded by genome-scale models with the ability of deep learning to infer patterns from complex datasets.

Whole-cell metabolic control analysis

Since its conception some fifty years ago, metabolic control analysis (MCA) aims to understand how cells control their metabolism by adjusting the activity of their enzymes. Here we extend its scope to a whole-cell context. We consider metabolism in the evolutionary context of growth-rate maximisation by optimisation of protein concentrations. This framework allows for the prediction of flux control coefficients from proteomics data or stoichiometric modelling. Since genes compete for finite biosynthetic resources, we treat all protein concentrations as interdependent. We show that elementary flux modes (EFMs) emerge naturally as the optimal metabolic networks in the whole-cell context and we derive their control properties. In the evolutionary optimum, the number of expressed EFMs is determined by the number of protein-concentration constraints that limit growth rate. We use published glucose-limited chemostat data of S. cerevisiae to illustrate that it uses only two EFMs prior to the onset of fermentation and that it uses four EFMs during fermentation. We discuss published enzyme-titration data to show that S. cerevisiae and E. coli indeed can express proteins at growth-rate maximising concentrations. Accordingly, we extend MCA to elementary flux modes operating at an optimal state. We find that the expression of growth-unassociated proteins changes results from classical metabolic control analysis. Finally, we show how flux control coefficients can be estimated from proteomics and ribosome-profiling data. We analyse published proteomics data of E. coli to provide a whole-cell perspective of the control of metabolic enzymes on growth rate. We hope that this paper stimulates a renewed interest in metabolic control analysis, so that it can serve again the purpose it once had: to identify general principles that emerge from the biochemistry of the cell and are conserved across biological species.

Coping with stress: How bacteria fine-tune protein synthesis and protein transport

Maintaining a functional proteome under different environmental conditions is challenging for every organism, in particular for unicellular organisms, such as bacteria. In order to cope with changing environments and stress conditions, bacteria depend on strictly coordinated proteostasis networks that control protein production, folding, trafficking, and degradation. Regulation of ribosome biogenesis and protein synthesis are cornerstones of this cellular adaptation in all domains of life, which is rationalized by the high energy demand of both processes and the increased resistance of translationally silent cells against internal or external poisons. Reduced protein synthesis ultimately also reduces the substrate load for protein transport systems, which are required for maintaining the periplasmic, inner, and outer membrane subproteomes. Consequences of impaired protein transport have been analyzed in several studies and generally induce a multifaceted response that includes the upregulation of chaperones and proteases and the simultaneous downregulation of protein synthesis. In contrast, generally less is known on how bacteria adjust the protein targeting and transport machineries to reduced protein synthesis, e.g., when cells encounter stress conditions or face nutrient deprivation. In the current review, which is mainly focused on studies using Escherichia coli as a model organism, we summarize basic concepts on how ribosome biogenesis and activity are regulated under stress conditions. In addition, we highlight some recent developments on how stress conditions directly impair protein targeting to the bacterial membrane. Finally, we describe mechanisms that allow bacteria to maintain the transport of stress-responsive proteins under conditions when the canonical protein targeting pathways are impaired.