Reconstruction and modeling protein translocation and compartmentalization in Escherichia coli at the genome-scale

BackgroundMembranes play a crucial role in cellular functions. Membranes provide a physical barrier, control the trafficking of substances entering and leaving the cell, and are a major determinant of cellular ultra-structure. In addition, components embedded within the membrane participate in cell signaling, energy transduction, and other critical cellular functions. All these processes must share the limited space in the membrane; thus it represents a notable constraint on cellular functions. Membrane- and location-based processes have not yet been reconstructed and explicitly integrated into genome-scale models.ResultsThe recent genome-scale model of metabolism and protein expression in Escherichia coli (called a ME-model) computes the complete composition of the proteome required to perform whole cell functions. Here we expand the ME-model to include (1) a reconstruction of protein translocation pathways, (2) assignment of all cellular proteins to one of four compartments (cytoplasm, inner membrane, periplasm, and outer membrane) and a translocation pathway, (3) experimentally determined translocase catalytic and porin diffusion rates, and (4) a novel membrane constraint that reflects cell morphology. Comparison of computations performed with this expanded ME-model, named iJL1678-ME, against available experimental data reveals that the model accurately describes translocation pathway expression and the functional proteome by compartmentalized mass.ConclusioniJL1678-ME enables the computation of cellular phenotypes through an integrated computation of proteome composition, abundance, and activity in four cellular compartments (cytoplasm, periplasm, inner and outer membrane). Reconstruction and validation of the model has demonstrated that the iJL1678-ME is capable of capturing the functional content of membranes, cellular compartment-specific composition, and that it can be utilized to examine the effect of perturbing an expanded set of network components. iJL1678-ME takes a notable step towards the inclusion of cellular ultra-structure in genome-scale models

solveME: fast and reliable solution of nonlinear ME models.

BackgroundGenome-scale models of metabolism and macromolecular expression (ME) significantly expand the scope and predictive capabilities of constraint-based modeling. ME models present considerable computational challenges: they are much (>30 times) larger than corresponding metabolic reconstructions (M models), are multiscale, and growth maximization is a nonlinear programming (NLP) problem, mainly due to macromolecule dilution constraints.ResultsHere, we address these computational challenges. We develop a fast and numerically reliable solution method for growth maximization in ME models using a quad-precision NLP solver (Quad MINOS). Our method was up to 45 % faster than binary search for six significant digits in growth rate. We also develop a fast, quad-precision flux variability analysis that is accelerated (up to 60× speedup) via solver warm-starts. Finally, we employ the tools developed to investigate growth-coupled succinate overproduction, accounting for proteome constraints.ConclusionsJust as genome-scale metabolic reconstructions have become an invaluable tool for computational and systems biologists, we anticipate that these fast and numerically reliable ME solution methods will accelerate the wide-spread adoption of ME models for researchers in these fields

Principles of proteome allocation are revealed using proteomic data and genome-scale models

Integrating omics data to refine or make context-specific models is an active field of constraint-based modeling. Proteomics now cover over 95% of the Escherichia coli proteome by mass. Genome-scale models of Metabolism and macromolecular Expression (ME) compute proteome allocation linked to metabolism and fitness. Using proteomics data, we formulated allocation constraints for key proteome sectors in the ME model. The resulting calibrated model effectively computed the "generalist" (wild-type) E. coli proteome and phenotype across diverse growth environments. Across 15 growth conditions, prediction errors for growth rate and metabolic fluxes were 69% and 14% lower, respectively. The sector-constrained ME model thus represents a generalist ME model reflecting both growth rate maximization and "hedging" against uncertain environments and stresses, as indicated by significant enrichment of these sectors for the general stress response sigma factor σS. Finally, the sector constraints represent a general formalism for integrating omics data from any experimental condition into constraint-based ME models. The constraints can be fine-grained (individual proteins) or coarse-grained (functionally-related protein groups) as demonstrated here. This flexible formalism provides an accessible approach for narrowing the gap between the complexity captured by omics data and governing principles of proteome allocation described by systems-level models

Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways

Escher is a web application for visualizing data on biological pathways. Three key features make Escher a uniquely effective tool for pathway visualization. First, users can rapidly design new pathway maps. Escher provides pathway suggestions based on user data and genome-scale models, so users can draw pathways in a semi-automated way. Second, users can visualize data related to genes or proteins on the associated reactions and pathways, using rules that define which enzymes catalyze each reaction. Thus, users can identify trends in common genomic data types (e.g. RNA-Seq, proteomics, ChIP)--in conjunction with metabolite- and reaction-oriented data types (e.g. metabolomics, fluxomics). Third, Escher harnesses the strengths of web technologies (SVG, D3, developer tools) so that visualizations can be rapidly adapted, extended, shared, and embedded. This paper provides examples of each of these features and explains how the development approach used for Escher can be used to guide the development of future visualization tools

Adaptations of Escherichia coli strains to oxidative stress are reflected in properties of their structural proteomes.

BACKGROUND:The reconstruction of metabolic networks and the three-dimensional coverage of protein structures have reached the genome-scale in the widely studied Escherichia coli K-12 MG1655 strain. The combination of the two leads to the formation of a structural systems biology framework, which we have used to analyze differences between the reactive oxygen species (ROS) sensitivity of the proteomes of sequenced strains of E. coli. As proteins are one of the main targets of oxidative damage, understanding how the genetic changes of different strains of a species relates to its oxidative environment can reveal hypotheses as to why these variations arise and suggest directions of future experimental work. RESULTS:Creating a reference structural proteome for E. coli allows us to comprehensively map genetic changes in 1764 different strains to their locations on 4118 3D protein structures. We use metabolic modeling to predict basal ROS production levels (ROStype) for 695 of these strains, finding that strains with both higher and lower basal levels tend to enrich their proteomes with antioxidative properties, and speculate as to why that is. We computationally assess a strain's sensitivity to an oxidative environment, based on known chemical mechanisms of oxidative damage to protein groups, defined by their localization and functionality. Two general groups - metalloproteins and periplasmic proteins - show enrichment of their antioxidative properties between the 695 strains with a predicted ROStype as well as 116 strains with an assigned pathotype. Specifically, proteins that a) utilize a molybdenum ion as a cofactor and b) are involved in the biogenesis of fimbriae show intriguing protective properties to resist oxidative damage. Overall, these findings indicate that a strain's sensitivity to oxidative damage can be elucidated from the structural proteome, though future experimental work is needed to validate our model assumptions and findings. CONCLUSION:We thus demonstrate that structural systems biology enables a proteome-wide, computational assessment of changes to atomic-level physicochemical properties and of oxidative damage mechanisms for multiple strains in a species. This integrative approach opens new avenues to study adaptation to a particular environment based on physiological properties predicted from sequence alone

Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion

In mammalian cells, >25% of synthesized proteins are exported through the secretory pathway. The pathway complexity, however, obfuscates its impact on the secretion of different proteins. Unraveling its impact on diverse proteins is particularly important for biopharmaceutical production. Here we delineate the core secretory pathway functions and integrate them with genome-scale metabolic reconstructions of human, mouse, and Chinese hamster ovary\ua0cells. The resulting reconstructions enable the computation of energetic costs and machinery demands of each secreted protein. By integrating additional omics data, we find that highly secretory cells have adapted to reduce expression and secretion of other expensive host cell proteins. Furthermore, we predict metabolic costs and maximum productivities of biotherapeutic proteins and identify protein features that most significantly impact protein secretion. Finally, the model successfully predicts the increase in secretion of a monoclonal antibody after silencing a highly expressed selection marker. This work represents a knowledgebase of the mammalian secretory pathway that serves as a novel tool for systems biotechnology

The y-ome defines the 35% of Escherichia coli genes that lack experimental evidence of function

Experimental studies of Escherichia coli K-12 MG1655 often implicate poorly annotated genes in cellular phenotypes. However, we lack a systematic understanding of these genes. How many are there? What information is available for them? And what features do they share that could explain the gap in our understanding? Efforts to build predictive, whole-cell models of E. coli inevitably face this knowledge gap. We approached these questions systematically by assembling annotations from the knowledge bases EcoCyc, EcoGene, UniProt and RegulonDB. We identified the genes that lack experimental evidence of function (the 'y-ome') which include 1600 of 4623 unique genes (34.6%), of which 111 have absolutely no evidence of function. An additional 220 genes (4.7%) are pseudogenes or phantom genes. y-ome genes tend to have lower expression levels and are enriched in the termination region of the E. coli chromosome. Where evidence is available for y-ome genes, it most often points to them being membrane proteins and transporters. We resolve the misconception that a gene in E. coli whose primary name starts with 'y' is unannotated, and we discuss the value of the y-ome for systematic improvement of E. coli knowledge bases and its extension to other organisms

Fast growth phenotype of E. coli K-12 from adaptive laboratory evolution does not require intracellular flux rewiring

Adaptive laboratory evolution (ALE) is a widely-used method for improving the fitness of microorganisms in selected environmental conditions. It has been applied previously to Escherichia coli K-12 MG1655 during aerobic exponential growth on glucose minimal media, a frequently used model organism and growth condition, to probe the limits of E. coli growth rate and gain insights into fast growth phenotypes. Previous studies have described up to 1.6-fold increases in growth rate following ALE, and have identified key causal genetic mutations and changes in transcriptional patterns. Here, we report for the first time intracellular metabolic fluxes for six such adaptively evolved strains, as determined by high-resolution 13C-metabolic flux analysis. Interestingly, we found that intracellular metabolic pathway usage changed very little following adaptive evolution. Instead, at the level of central carbon metabolism the faster growth was facilitated by proportional increases in glucose uptake and all intracellular rates. Of the six evolved strains studied here, only one strain showed a small degree of flux rewiring, and this was also the strain with unique genetic mutations. A comparison of fluxes with two other wild-type (unevolved) E. coli strains, BW25113 and BL21, showed that inter-strain differences are greater than differences between the parental and evolved strains. Principal component analysis highlighted that nearly all flux differences (95%) between the nine strains were captured by only two principal components. The distance between measured and flux balance analysis predicted fluxes was also investigated. It suggested a relatively wide range of similar stoichiometric optima, which opens new questions about the path-dependency of adaptive evolution