A quantitative method for proteome reallocation using minimal regulatory interventions

Enhancing Escherichia coli abiotic stress resistance through ornithine lipid formation

Escherichia coli is a common host for biotechnology and synthetic biology applications. During growth and fermentation, the microbes are often exposed to stress conditions, such as variations in pH or solvent concentrations. Bacterial membranes play a key role in response to abiotic stresses. Ornithine lipids (OLs) are a group of membrane lipids whose presence and synthesis have been related to stress resistance in bacteria. We wondered if this stress resistance could be transferred to bacteria not encoding the capacity to form OLs in their genome, such as E. coli. In this study, we engineered different E. coli strains to produce unmodified OLs and hydroxylated OLs by expressing the synthetic operon olsFC. Our results showed that OL formation improved pH resistance and increased biomass under phosphate limitation. Transcriptome analysis revealed that OL-forming strains differentially expressed stress- and membrane-related genes. OL-producing strains also showed better growth in the presence of the ionophore carbonyl cyanide 3-chlorophenylhydrazone (CCCP), suggesting reduced proton leakiness in OL-producing strains. Furthermore, our engineered strains showed improved heterologous violacein production at phosphate limitation and also at low pH. Overall, this study demonstrates the potential of engineering the E. coli membrane composition for constructing robust hosts with an increased abiotic stress resistance for biotechnology and synthetic biology applications. KEY POINTS: • Ornithine lipid production in E. coli increases biomass yield under phosphate limitation. • Engineered strains show an enhanced production phenotype under low pH stress. • Transcriptome analysis and CCCP experiments revealed reduced proton leakage.

Recombinant protein expression in proteome-reduced cells under aerobic and oxygen-limited regimes

Industrial cultures are hindered by the physiological complexity of the host and the limited mass transfer capacity of conventional bioreactors. In this study, a minimal cell approach was combined with genetic devices to overcome such issues. A flavin mononucleotide-based fluorescent protein (FbFP) was expressed in a proteome-reduced Escherichia coli (PR). When FbFP was expressed from a constitutive protein generator (CPG), the PR strain produced 47% and 35% more FbFP than its wild type (WT), in aerobic or oxygen-limited regimes, respectively. Metabolic and expression models predicted more efficient biomass formation at higher fluxes to FbFP, in agreement with these results. A microaerobic protein generator (MPG) and a microaerobic transcriptional cascade (MTC) were designed to induce FbFP expression upon oxygen depletion. The FbFP fluorescence using the MTC in the PR strain was 9% higher than that of the WT bearing the CPG under oxygen limitation. To further improve the PR strain, the pyruvate dehydrogenase complex regulator gene was deleted, and the Vitreoscilla hemoglobin was expressed. Compared to oxygen-limited cultures of the WT, the engineered strains increased the FbFP expression more than 50% using the MTC. Therefore, the designed expression systems can be a valuable alternative for industrial cultivations.

Milligrams to kilograms: making microbes work at scale

If biomanufacturing can become a sustainable route for producing chemicals, it will provide a critical step in reducing greenhouse gas emissions to fight climate change. However, efforts to industrialize microbial synthesis of chemicals have met with varied success, due, in part, to challenges in translating laboratory successes to industrial scale. With a particular focus on Escherichia coli, this review examines the lessons learned when studying microbial physiology and metabolism under conditions that simulate large-scale bioreactors and methods to minimize cellular waste through reduction of maintenance energy, optimizing the stress response and minimizing culture heterogeneity. With general strategies to overcome these challenges, biomanufacturing process scale-up could be de-risked and the time and cost of bringing promising syntheses to market could be reduced.

Resource-aware construct design in mammalian cells

Resource competition can be the cause of unintended coupling between co-expressed genetic constructs. Here we report the quantification of the resource load imposed by different mammalian genetic components and identify construct designs with increased performance and reduced resource footprint. We use these to generate improved synthetic circuits and optimise the co-expression of transfected cassettes, shedding light on how this can be useful for bioproduction and biotherapeutic applications. This work provides the scientific community with a framework to consider resource demand when designing mammalian constructs to achieve robust and optimised gene expression.

Engineering resource allocation in artificially minimized cells: Is genome reduction the best strategy?

The elimination of the expression of cellular functions that are not needed in a certain well-defined artificial environment, such as those used in industrial production facilities, has been the goal of many cellular minimization projects. The generation of a minimal cell with reduced burden and less host-function interactions has been pursued as a tool to improve microbial production strains. In this work, we analysed two cellular complexity reduction strategies: genome and proteome reduction. With the aid of an absolute proteomics data set and a genome-scale model of metabolism and protein expression (ME-model), we quantitatively assessed the difference of reducing genome to the correspondence of reducing proteome. We compare the approaches in terms of energy consumption, defined in ATP equivalents. We aim to show what is the best strategy for improving resource allocation in minimized cells. Our results show that genome reduction by length is not proportional to reducing resource use. When we normalize calculated energy savings, we show that strains with the larger calculated proteome reduction show the largest resource use reduction. Furthermore, we propose that reducing highly expressed proteins should be the target as the translation of a gene uses most of the energy. The strategies proposed here should guide cell design when the aim of a project is to reduce the maximum amount or cellular resources.

Stability, robustness, and containment: preparing synthetic biology for real-world deployment

As engineered microbes are used in increasingly diverse applications across human health and bioproduction, the field of synthetic biology will need to focus on strategies that stabilize and contain the function of these populations within target environments. To this end, recent advancements have created layered sensing circuits that can compute cell survival, genetic contexts that are less susceptible to mutation, burden, and resource control circuits, and methods for population variability reduction. These tools expand the potential for real-world deployment of complex microbial systems by enhancing their environmental robustness and functional stability in the face of unpredictable host response and evolutionary pressure.

Glucose consumption rate-dependent transcriptome profiling of Escherichia coli provides insight on performance as microbial factories

Background

The modification of glucose import capacity is an engineering strategy that has been shown to improve the characteristics of Escherichia coli as a microbial factory. A reduction in glucose import capacity can have a positive effect on production strain performance, however, this is not always the case. In this study, E. coli W3110 and a group of four isogenic derivative strains, harboring single or multiple deletions of genes encoding phosphoenolpyruvate:sugar phosphotransferase system (PTS)-dependent transporters as well as non-PTS transporters were characterized by determining their transcriptomic response to reduced glucose import capacity.

Results

These strains were grown in bioreactors with M9 mineral salts medium containing 20 g/L of glucose, where they displayed specific growth rates ranging from 0.67 to 0.27 h⁻¹, and specific glucose consumption rates (qs) ranging from 1.78 to 0.37 g/g h. RNA-seq analysis revealed a transcriptional response consistent with carbon source limitation among all the mutant strains, involving functions related to transport and metabolism of alternate carbon sources and characterized by a decrease in genes encoding glycolytic enzymes and an increase in gluconeogenic functions. A total of 107 and 185 genes displayed positive and negative correlations with qs, respectively. Functions displaying positive correlation included energy generation, amino acid biosynthesis, and sugar import.

Conclusion

Changes in gene expression of E. coli strains with impaired glucose import capacity could be correlated with qs values and this allowed an inference of the physiological state of each mutant. In strains with lower qs values, a gene expression pattern is consistent with energy limitation and entry into the stationary phase. This physiological state could explain why these strains display a lower capacity to produce recombinant protein, even when they show very low rates of acetate production. The comparison of the transcriptomes of the engineered strains employed as microbial factories is an effective approach for identifying favorable phenotypes with the potential to improve the synthesis of biotechnological products.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01909-y.

System Principles Governing the Organization, Architecture, Dynamics, and Evolution of Gene Regulatory Networks

Synthetic biology aims to apply engineering principles for the rational, systematical design and construction of biological systems displaying functions that do not exist in nature or even building a cell from scratch. Understanding how molecular entities interconnect, work, and evolve in an organism is pivotal to this aim. Here, we summarize and discuss some historical organizing principles identified in bacterial gene regulatory networks. We propose a new layer, the concilion, which is the group of structural genes and their local regulators responsible for a single function that, organized hierarchically, coordinate a response in a way reminiscent of the deliberation and negotiation that take place in a council. We then highlight the importance that the network structure has, and discuss that the natural decomposition approach has unveiled the system-level elements shaping a common functional architecture governing bacterial regulatory networks. We discuss the incompleteness of gene regulatory networks and the need for network inference and benchmarking standardization. We point out the importance that using the network structural properties showed to improve network inference. We discuss the advances and controversies regarding the consistency between reconstructions of regulatory networks and expression data. We then discuss some perspectives on the necessity of studying regulatory networks, considering the interactions’ strength distribution, the challenges to studying these interactions’ strength, and the corresponding effects on network structure and dynamics. Finally, we explore the ability of evolutionary systems biology studies to provide insights into how evolution shapes functional architecture despite the high evolutionary plasticity of regulatory networks.

Regulatory perturbations of ribosome allocation in bacteria reshape the growth proteome with a trade-off in adaptation capacity

Bacteria regulate their cellular resource allocation to enable fast growth-adaptation to a variety of environmental niches. We studied the ribosomal allocation, growth, and expression profiles of two sets of fast-growing mutants of Escherichia coli K-12 MG1655. Mutants with only three of the seven copies of ribosomal RNA operons grew faster than the wild-type strain in minimal media and show similar phenotype to previously studied fast-growing rpoB mutants. Comparing these two different regulatory perturbations (rRNA promoters or rpoB mutations), we show how they reshape the proteome for growth with a concomitant fitness cost. The fast-growing mutants shared downregulation of hedging functions and upregulated growth functions. They showed longer diauxic shifts and reduced activity of gluconeogenic promoters during glucose-acetate shifts, suggesting reduced availability of the RNA polymerase for expressing hedging proteome. These results show that the regulation of ribosomal allocation underlies the growth/hedging phenotypes obtained from laboratory evolution experiments.

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Mutants with only three ribosomal operons grow faster than wild-type in minimal medium

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Faster growth of mutants is achieved by increased ribosome content

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Fast-growing mutants display reduced hedging expression and adaptation trade-offs

Global pleiotropic effects in adaptively evolved Escherichia coli lacking CRP reveal molecular mechanisms that define the growth physiology

Evolution facilitates emergence of fitter phenotypes by efficient allocation of cellular resources in conjunction with beneficial mutations. However, system-wide pleiotropic effects that redress the perturbations to the apex node of the transcriptional regulatory networks remain unclear. Here, we elucidate that absence of global transcriptional regulator CRP in Escherichia coli results in alterations in key metabolic pathways under glucose respiratory conditions, favouring stress- or hedging-related functions over growth-enhancing functions. Further, we disentangle the growth-mediated effects from the CRP regulation-specific effects on these metabolic pathways. We quantitatively illustrate that the loss of CRP perturbs proteome efficiency, as evident from metabolic as well as ribosomal proteome fractions, that corroborated with intracellular metabolite profiles. To address how E. coli copes with such systemic defect, we evolved Δcrp mutant in the presence of glucose. Besides acquiring mutations in the promoter of glucose transporter ptsG, the evolved populations recovered the metabolic pathways to their pre-perturbed state coupled with metabolite re-adjustments, which altogether enabled increased growth. By contrast to Δcrp mutant, the evolved strains remodelled their proteome efficiency towards biomass synthesis, albeit at the expense of carbon efficiency. Overall, we comprehensively illustrate the genetic and metabolic basis of pleiotropic effects, fundamental for understanding the growth physiology.

Vitreoscilla Haemoglobin: A Tool to Reduce Overflow Metabolism

Overflow metabolism is a phenomenon extended in nature, ranging from microbial to cancer cells. Accumulation of overflow metabolites pose a challenge for large-scale bioprocesses. Yet, the causes of overflow metabolism are not fully clarified. In this work, the underlying mechanisms, reasons and consequences of overflow metabolism in different organisms have been summarized. The reported effect of aerobic expression of Vitreoscilla haemoglobin (VHb) in different organisms are revised. The use of VHb to reduce overflow metabolism is proposed and studied through flux balance analysis in E. coli at a fixed maximum substrate and oxygen uptake rates. Simulations showed that the presence of VHb increases the growth rate, while decreasing acetate production, in line with the experimental measurements. Therefore, aerobic VHb expression is considered a potential tool to reduce overflow metabolism in cells.

Reconstructing the transcription regulatory network to optimize resource allocation for robust biosynthesis

An ideal microbial cell factory (MCF) should deliver maximal resources to production, which conflicts with the microbe's native growth-oriented resource allocation strategy and can therefore lead to early termination of the high-yield period. Reallocating resources from growth to production has become a critical factor in constructing robust MCFs. Instead of strengthening specific biosynthetic pathways, emerging endeavors are focused on rearranging the gene regulatory network to fundamentally reprogram the resource allocation pattern. Combining this idea with transcriptional regulation within the hierarchical regulatory network, this review discusses recent engineering strategies targeting the transcription machinery, module networks, regulatory edges, and bottom network layer. This global view will help to construct a production-oriented phenotype that fully harnesses the potential of MCFs.

Intelligent host engineering for metabolic flux optimisation in biotechnology

Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

Understanding and mathematical modelling of cellular resource allocation in microorganisms: a comparative synthesis

BACKGROUND

The rising consensus that the cell can dynamically allocate its resources provides an interesting angle for discovering the governing principles of cell growth and metabolism. Extensive efforts have been made in the past decade to elucidate the relationship between resource allocation and phenotypic patterns of microorganisms. Despite these exciting developments, there is still a lack of explicit comparison between potentially competing propositions and a lack of synthesis of inter-related proposals and findings.

RESULTS

In this work, we have reviewed resource allocation-derived principles, hypotheses and mathematical models to recapitulate important achievements in this area. In particular, the emergence of resource allocation phenomena is deciphered by the putative tug of war between the cellular objectives, demands and the supply capability. Competing hypotheses for explaining the most-studied phenomenon arising from resource allocation, i.e. the overflow metabolism, have been re-examined towards uncovering the potential physiological root cause. The possible link between proteome fractions and the partition of the ribosomal machinery has been analysed through mathematical derivations. Finally, open questions are highlighted and an outlook on the practical applications is provided. It is the authors' intention that this review contributes to a clearer understanding of the role of resource allocation in resolving bacterial growth strategies, one of the central questions in microbiology.

CONCLUSIONS

We have shown the importance of resource allocation in understanding various aspects of cellular systems. Several important questions such as the physiological root cause of overflow metabolism and the correct interpretation of 'protein costs' are shown to remain open. As the understanding of the mechanisms and utility of resource application in cellular systems further develops, we anticipate that mathematical modelling tools incorporating resource allocation will facilitate the circuit-host design in synthetic biology.

Picking the right metaphors for addressing microbial systems: economic theory helps understanding biological complexity

Any descriptive language is necessarily metaphoric and interpretative. Two somewhat overlapping-but not identical-languages have been thoroughly employed in the last decade to address the issue of regulatory complexity in biological systems: the terminology of network theory and the jargon of electric circuitry. These approaches have found many formal equivalences between the layout of extant genetic circuits and the architecture of man-made counterparts. However, these languages still fail to describe accurately key features of biological objects, in particular the diversity of signal-transfer molecules and the diffusion that is inherent to any biochemical system. Furthermore, current formalisms associated with networks and circuits can hardly face the problem of multi-scale regulatory complexity-from single molecules to entire ecosystems. We argue that the language of economic theory might be instrumental not only to portray accurately many features of regulatory networks, but also to unveil aspects of the biological complexity problem that remain opaque to other types of analyses. The main perspective opened by the economic metaphor when applied to control of microbiological activities is a focus on metabolism, not gene selfishness, as the necessary background to make sense of regulatory phenomena. As an example, we analyse and reinterpret the widespread phenomenon of catabolite repression with the formal frame of the consumer's choice theory.

Corynebacterium glutamicum Regulation beyond Transcription: Organizing Principles and Reconstruction of an Extended Regulatory Network Incorporating Regulations Mediated by Small RNA and Protein-Protein Interactions

Corynebacterium glutamicum is a Gram-positive bacterium found in soil where the condition changes demand plasticity of the regulatory machinery. The study of such machinery at the global scale has been challenged by the lack of data integration. Here, we report three regulatory network models for C. glutamicum: strong (3040 interactions) constructed solely with regulations previously supported by directed experiments; all evidence (4665 interactions) containing the strong network, regulations previously supported by nondirected experiments, and protein-protein interactions with a direct effect on gene transcription; sRNA (5222 interactions) containing the all evidence network and sRNA-mediated regulations. Compared to the previous version (2018), the strong and all evidence networks increased by 75 and 1225 interactions, respectively. We analyzed the system-level components of the three networks to identify how they differ and compared their structures against those for the networks of more than 40 species. The inclusion of the sRNA-mediated regulations changed the proportions of the system-level components and increased the number of modules but decreased their size. The C. glutamicum regulatory structure contrasted with other bacterial regulatory networks. Finally, we used the strong networks of three model organisms to provide insights and future directions of the C.glutamicum regulatory network characterization.

Quantitative proteomics characterization of cancer biomarkers and treatment

Cancer accounted for 16% of all death worldwide in 2018. Significant progress has been made in understanding tumor occurrence, progression, diagnosis, treatment, and prognosis at the molecular level. However, genomics changes cannot truly reflect the state of protein activity in the body due to the poor correlation between genes and proteins. Quantitative proteomics, capable of quantifying the relatively different protein abundance in cancer patients, has been increasingly adopted in cancer research. Quantitative proteomics has great application potentials, including cancer diagnosis, personalized therapeutic drug selection, real-time therapeutic effects and toxicity evaluation, prognosis and drug resistance evaluation, and new therapeutic target discovery. In this review, the development, testing samples, and detection methods of quantitative proteomics are introduced. The biomarkers identified by quantitative proteomics for clinical diagnosis, prognosis, and drug resistance are reviewed. The challenges and prospects of quantitative proteomics for personalized medicine are also discussed.

Plasmid DNA Production in Proteome-Reduced Escherichia coli

The design of optimal cell factories requires engineering resource allocation for maximizing product synthesis. A recently developed method to maximize the saving in cell resources released 0.5% of the proteome of Escherichia coli by deleting only three transcription factors. We assessed the capacity for plasmid DNA (pDNA) production in the proteome-reduced strain in a mineral medium, lysogeny, and terrific broths. In all three cases, the pDNA yield from biomass was between 33 and 53% higher in the proteome-reduced than in its wild type strain. When cultured in fed-batch mode in shake-flask, the proteome-reduced strain produced 74.8 mg L^-1 pDNA, which was four times greater than its wild-type strain. Nevertheless, the pDNA supercoiled fraction was less than 60% in all cases. Deletion of recA increased the pDNA yields in the wild type, but not in the proteome-reduced strain. Furthermore, recA mutants produced a higher fraction of supercoiled pDNA, compared to their parents. These results show that the novel proteome reduction approach is a promising starting point for the design of improved pDNA production hosts.