Optimal control of gene expression for fast proteome adaptation to environmental change

Carbon upshift in Lactococcus cremoris elicits immediate initiation of proteome-wide adaptation, coinciding with growth acceleration and pyruvate dissipation switching

Fitness optimization in a dynamic environment requires bacteria to adapt their proteome in a tightly regulated manner by altering protein production and/or degradation. Here, we investigate proteome adaptation in Lactococcus cremoris following a sudden nutrient upshift (e.g., nutrients that allow faster growth) and focus especially on the fate of redundant proteins after the shift. Protein turnover analysis demonstrated that L. cremoris cultures shifted from galactose to glucose, immediately accelerate growth and initiate proteome-wide adjustment toward glucose-optimized composition. Redundant proteins were predominantly adjusted by lowering (or stopping) protein production combined with dilution by growth. However, pyruvate formate lyase activator (PflA) was actively degraded, which appears correlated to reduced 4Fe-4S cofactor availability. Active PflA removal induces the shutdown of galactose-associated mixed acid fermentation to accelerate the switch toward glucose-associated homolactic fermentation. Our work deciphers molecular adjustments upon environmental change that drive physiological adaptation, including growth rate and central energy metabolism.IMPORTANCEBacteria adapt to their environment by adjusting their molecular makeup, in particular their proteome, which ensures fitness optimization under the newly encountered environmental condition. We present a detailed analysis of proteome adaptation kinetics in Lactococcus cremoris following its acute transition from galactose to glucose media, as an example of a sudden nutrient quality upshift. Analysis of the replacement times of individual proteins after the nutrient upshift established that the entire proteome is instantly adjusting to the new condition, which coincides with immediate growth rate acceleration and metabolic adaptation. The latter is driven by the active removal of the pyruvate formate lyase activator protein that is pivotal in controlling pyruvate dissipation in L. cremoris. Our work exemplifies the amazing rate of molecular adaptation in bacteria that underlies physiological adjustments, including growth rate and carbon metabolism. This mechanistic study contributes to our understanding of adaptation in L. cremoris during the dynamic conditions it encounters during (industrial) fermentation, even though environmental transitions in these processes are mostly more gradual than the acute shift studied here.

Optimal resource allocation in micro-organisms under periodic nutrient fluctuations

Although microorganisms often live in dynamic environments, most studies, both experimental and theoretical, are carried out under static conditions. In this work, we investigate the issue of optimal resource allocation in bacteria growing in periodic environments. We consider a dynamic model describing the microbial metabolism under varying conditions, involving a control variable quantifying the protein precursors allocation. Our objective is to determine the optimal strategies maximizing the long-term growth of cells under a piecewise-constant periodic environment. Firstly, we perform a theoretical analysis of the resulting optimal control problem (OCP), based on the application the Pontryagin's Maximum Principle (PMP). We determine that the structure of the optimal control must be bang-bang, with possibly some singular arcs corresponding to optimal equilibria of the system. If the control presents singular arcs, then these can only be reached and left through chattering arcs. We also use a direct optimization method, implemented in the BOCOP software, to solve the studied OCP. Our study reveals that the optimal solution over a large time horizon is related to the one over a single period of the varying environment with periodic constraints. Moreover, we observe that the maximal average growth rate attainable under periodic conditions can be higher than the one under a constant environment. We further extend our analysis to conduct a qualitative comparison between the predictions from our model and some recent biological experiments on E. coli. This analysis particularly highlights the mechanisms of action of the ppGpp signaling molecule, thus providing relevant explanations of the experimental observations. In conclusion, our study corroborates previous research indicating that this molecule plays a crucial role in the regulation of resource allocation of protein precursors in E. coli.

Antibacterial Effects of Thermosonication Technology on Salmonella typhimurium Strains Identified from Swine Food Chain: An In Vitro Study

Among innovative food technologies, ultrasounds have demonstrated physical damages (provided by frequency and intensity factors) on bacterial structures while determining the microbiological stabilization of many foodstuffs. This study tested the efficacy of the thermosonication process on 16 Salmonella typhimurium strains belonging to the academic biobank (isolated from swine slaughterhouses). All strains were exposed to focused ultrasounds, generated by the Waveco® system (Milan, Italy), with the following settings: 40 KHz coupled with 80 W at different 5 min intervals starting from 5 to 15 ones, and focusing on two different temperatures: 40 °C and 50 °C. After each treatment, all strains were directly plated onto count agars immediately (t0) and after 24 h (t24) of storage at refrigerated temperature. The results showed bacterial reductions by prolonging the sonication treatments until 15 min (i.e., 50 °C for 15 min reduced of 2.16 log CFU/gr the initial loads). In the present in vitro study, the most considerable decrease was observed after 24 h. It meant that Salmonella strains were lethally damaged at the wall level, confirming the ultrasound bactericidal effect on loads. The present in vitro scientific investigation demonstrates the practical bactericidal effects of thermosonication, highlighting promising applications at the industry level for food microbial stabilization and shelf-life prolongation.

How Does Escherichia coli Allocate Proteome?

Microorganisms are shown to actively partition their intracellular resources, such as proteins, for growth optimization. Recent experiments have begun to reveal molecular components unpinning the partition; however, quantitatively, it remains unclear how individual parts orchestrate to yield precise resource allocation that is both robust and dynamic. Here, we developed a coarse-grained mathematical framework that centers on guanosine pentaphosphate (ppGpp)-mediated regulation and used it to systematically uncover the design principles of proteome allocation in Escherichia coli. Our results showed that the cellular ability of resource partition lies in an ultrasensitive, negative feedback-controlling topology with the ultrasensitivity arising from zero-order amino acid kinetics and the negative feedback from ppGpp-controlled ribosome synthesis. In addition, together with the time-scale separation between slow ribosome kinetics and fast turnovers of ppGpp and amino acids, the network topology confers the organism an optimization mechanism that mimics sliding mode control, a nonlinear optimization strategy that is widely used in man-made systems. We further showed that such a controlling mechanism is robust against parameter variations and molecular fluctuations and is also efficient for biomass production over time. This work elucidates the fundamental controlling mechanism of E. coli proteome allocation, thereby providing insights into quantitative microbial physiology as well as the design of synthetic gene networks.

Hysteretic and asynchronous regime shifts of bacterial and micro-eukaryotic communities driven by nutrient loading

Nutrient pollution is pervasive in many urban rivers, while restoration measures that reduce nutrient loading but fail to improve biological communities often lack effectiveness due to the indispensable role of biota, especially multi-taxa, in enhancing ecosystem stability and function. The investigation of the response patterns of multi-taxa to the nutrient loading in urban rivers is important for the recovery of biota structure and thus ecosystem function. However, little is known about the response patterns of multi-taxa and their impact on ecosystem structure and function in urban rivers. Here, the study, from the perspective of alternative stable states theory, showed the hysteretic response of both bacterial and micro-eukaryotic communities to nutrient loading based on the field investigation and environmental DNA metabarcoding. Bistability was shown to exist in both bacterial and micro-eukaryotic communities, demonstrating that the response of microbiota to nutrient loading was a regime shifts with hysteresis. Potential analysis then indicated that the increased nutrient loading drove regime shifts in the bacterial community and the micro-eukaryotic community towards a state dominated by anaerobic bacteria and benthic Bacillariophyta, respectively. High nutrient loading was found to reduce the relative abundance of metazoan, but increase that of eukaryotic algae, which made the trophic pyramid top-lighter and bottom-heavier, probably exacerbating the degradation of ecosystem function. It should be noted that, in response to the reduced nutrient loading, the recovery threshold of micro-eukaryotic communities (nutrient loading = ∼0.5) was lower than that of bacterial communities (nutrient loading = ∼1.2), demonstrating longer hysteresis of micro-eukaryotic communities. In addition, the markedly positive correlation between the status of microbial communities and N-related enzyme activities suggested the recovery of microbial communities probably will benefit the improvement of N-cycling functionality. The obtained results provide a deep insight into the collapse and recovery trajectories of multi-trophic microbiota to the nutrient loading gradient and their impact on the N transformation potential, therefore benefiting the restoration and management of urban rivers.

Optimal control of ribosome population for gene expression under periodic nutrient intake

Translation of proteins is a fundamental part of gene expression that is mediated by ribosomes. As ribosomes significantly contribute to both cellular mass and energy consumption, achieving efficient management of the ribosome population is also crucial to metabolism and growth. Inspired by biological evidence for nutrient-dependent mechanisms that control both ribosome-active degradation and genesis, we introduce a dynamical model of protein production, that includes the dynamics of resources and control over the ribosome population. Under the hypothesis that active degradation and biogenesis are optimal for maximizing and maintaining protein production, we aim to qualitatively reproduce empirical observations of the ribosome population dynamics. Upon formulating the associated optimization problem, we first analytically study the stability and global behaviour of solutions under constant resource input, and characterize the extent of oscillations and convergence rate to a global equilibrium. We further use these results to simplify and solve the problem under a quasi-static approximation. Using biophysical parameter values, we find that optimal control solutions lead to both control mechanisms and the ribosome population switching between periods of feeding and fasting, suggesting that the intense regulation of ribosome population observed in experiments allows to maximize and maintain protein production. Finally, we find some range for the control values over which such a regime can be observed, depending on the intensity of fasting.

Emergent Lag Phase in Flux-Regulation Models of Bacterial Growth

Lag phase is observed in bacterial growth during a sudden change in conditions: growth is inhibited whilst cells adapt to the environment. Bi-phasic, or diauxic growth is commonly exhibited by many species. In the presence of two sugars, cells initially grow by consuming the preferred sugar then undergo a lag phase before resuming growth on the second. Biomass increase is characterised by a diauxic growth curve: exponential growth followed by a period of no growth before a second exponential growth. Recent literature lacks a complete dynamic description, artificially modelling lag phase and employing non-physical representations of precursor pools. Here, we formulate a rational mechanistic model based on flux-regulation/proteome partitioning with a finite precursor pool that reveals core mechanisms in a compact form. Unlike earlier systems, the characteristic dynamics emerge as part of the solution, including the lag phase. Focussing on growth of Escherichia coli on a glucose-lactose mixture we show results accurately reproduce experiments. We show that for a single strain of E. coli, diauxic growth leads to optimised biomass yields. However, intriguingly, for two competing strains diauxic growth is not always the best strategy. Our description can be generalised to model multiple different microorganisms and investigate competition between species/strains.

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.

Computational Analysis of the Ligand-Binding Sites of the Molecular Chaperone OppA from Yersinia pseudotuberculosis

The function of chaperones is to correct or degrade misfolded proteins inside the cell. Classic molecular chaperones such as GroEL and DnaK have not been found in the periplasm of Yersinia pseudotuberculosis. Some periplasmic substrate-binding proteins could be bifunctional, such as OppA. Using bioinformatic tools, we try to elucidate the nature of the interactions between OppA and ligands from four proteins with different oligomeric states. Using the crystal structure of the proteins Mal12 alpha-glucosidase from Saccharomyces cerevisiae S288C, LDH rabbit muscle lactate dehydrogenase, EcoRI endonuclease from Escherichia coli and THG Geotrichum candidum lipase, a hundred models were obtained in total, including five different ligands from each enzyme with five conformations of each ligand. The best values for Mal12 stem from ligands 4 and 5, with conformation 5 for both; for LDH, ligands 1 and 4, with conformations 2 and 4, respectively; for EcoRI, ligands 3 and 5, with conformation 1 for both; and for THG, ligands 2 and 3, with conformation 1 for both. The interactions were analyzed with LigProt, and the length of the hydrogen bridges has an average of 2.8 to 3.0 Å. The interaction within the OppA pocket is energetically favored due to the formation of hydrogen bonds both of OppA and of the selected enzymes. The Asp 419 residue is important in these junctions.

Enzyme expression kinetics by Escherichia coli during transition from rich to minimal media depends on proteome reserves

Bacterial fitness depends on adaptability to changing environments. In rich growth medium, which is replete with amino acids, Escherichia coli primarily expresses protein synthesis machineries, which comprise ~40% of cellular proteins and are required for rapid growth. Upon transition to minimal medium, which lacks amino acids, biosynthetic enzymes are synthesized, eventually reaching ~15% of cellular proteins when growth fully resumes. We applied quantitative proteomics to analyse the timing of enzyme expression during such transitions, and established a simple positive relation between the onset time of enzyme synthesis and the fractional enzyme 'reserve' maintained by E. coli while growing in rich media. We devised and validated a coarse-grained kinetic model that quantitatively captures the enzyme recovery kinetics in different pathways, solely on the basis of proteomes immediately preceding the transition and well after its completion. Our model enables us to infer regulatory strategies underlying the 'as-needed' gene expression programme adopted by E. coli.

Optimal transcriptional regulation of dynamic bacterial responses to sudden drug exposures

Cellular responses to the presence of toxic compounds in their environment require prompt expression of the correct levels of the appropriate enzymes, which are typically regulated by transcription factors that control gene expression for the duration of the response. The characteristics of each response dictate the choice of regulatory parameters such as the affinity of the transcription factor to its binding sites and the strength of the promoters it regulates. Although much is known about the dynamics of cellular responses, we still lack a framework to understand how different regulatory strategies evolved in natural systems relate to the selective pressures acting in each particular case. Here, we analyze a dynamical model of a typical antibiotic response in bacteria, where a transcriptionally repressed enzyme is induced by a sudden exposure to the drug that it processes. We identify strategies of gene regulation that optimize this response for different types of selective pressures, which we define as a set of costs associated with the drug, enzyme, and repressor concentrations during the response. We find that regulation happens in a limited region of the regulatory parameter space. While responses to more costly (toxic) drugs favor the usage of strongly self-regulated repressors, responses where expression of enzyme is more costly favor the usage of constitutively expressed repressors. Only a very narrow range of selective pressures favor weakly self-regulated repressors. We use this framework to determine which costs and benefits are most critical for the evolution of a variety of natural cellular responses that satisfy the approximations in our model and to analyze how regulation is optimized in new environments with different demands.

Comparative sub-cellular proteome analyses reveals metabolic differentiation and production of effector-like molecules in the dieback phytopathogen Phytophthora cinnamomi

Phytopathogenic oomycetes pose a significant threat to global biodiversity and food security. The proteomes of these oomycetes likely contain important factors that contribute to their pathogenic success, making their discovery crucial for elucidating pathogenicity. Phytophthora cinnamomi is a root pathogen that causes dieback in a wide variety of crops and native vegetation world-wide. Virulence proteins produced by P. cinnamomi are not well defined and a large-scale approach to understand the biochemistry of this pathogen has not been documented. Soluble mycelial, zoospore and secreted proteomes were obtained and label-free quantitative proteomics was used to compare the composition of the three sub-proteomes. A total of 4635 proteins were identified, validating 17.7% of the predicted gene set. The mycelia were abundant in transporters for nutrient acquisition, metabolism and cellular proliferation. The zoospores had less metabolic related ontologies but were abundant in energy generating, motility and signalling associated proteins. Virulence-associated proteins were identified in the secretome such as candidate effector and effector-like proteins, which interfere with the host immune system. These include hydrolases, cell wall degrading enzymes, putative necrosis-inducing proteins and elicitins. The secretome elicited a hypersensitive response on the roots of a model host and thus suggests evidence of effector activity. SIGNIFICANCE: Phytophthora cinnamomi is a phytopathogenic oomycete that causes dieback disease in native vegetation and several horticultural crops such as avocado, pineapple and macadamia. Whilst this pathogen has significance world-wide, its pathogenicity and virulence have not been described in depth. We carried out comparative label-free proteomics of the mycelia, zoospores and secretome of P. cinnamomi. This study highlights the differential metabolism and cellular processes between the sub-proteomes. Proteins associated with metabolism, nutrient transport and cellular proliferation were over represented in the mycelia. The zoospores have a specialised proteome showing increased energy generation geared towards motility. Candidate effectors and effector-like secreted proteins were also identified, which can be exploited for genetic resistance. This demonstrates a better understanding of the biology and pathogenicity of P. cinnamomi infection that can subsequently be used to develop effective methods of disease management.

Time-Optimal Adaptation in Metabolic Network Models

Analysis of metabolic models using constraint-based optimization has emerged as an important computational technique to elucidate and eventually predict cellular metabolism and growth. In this work, we introduce time-optimal adaptation (TOA), a new constraint-based modeling approach that allows us to evaluate the fastest possible adaptation to a pre-defined cellular state while fulfilling a given set of dynamic and static constraints. TOA falls into the mathematical problem class of time-optimal control problems, and, in its general form, can be broadly applied and thereby extends most existing constraint-based modeling frameworks. Specifically, we introduce a general mathematical framework that captures many existing constraint-based methods and define TOA within this framework. We then exemplify TOA using a coarse-grained self-replicator model and demonstrate that TOA allows us to explain several well-known experimental phenomena that are difficult to explore using existing constraint-based analysis methods. We show that TOA predicts accumulation of storage compounds in constant environments, as well as overshoot uptake metabolism after periods of nutrient scarcity. TOA shows that organisms with internal temporal degrees of freedom, such as storage, can in most environments outperform organisms with a static intracellular composition. Furthermore, TOA reveals that organisms adapted to better growth conditions than present in the environment (“optimists”) typically outperform organisms adapted to poorer growth conditions (“pessimists”).

Shifts in benthic bacterial communities associated with farming stages and a microbiological proxy for assessing sulfidic sediment conditions at fish farms

To assess the aquaculture-induced sediment conditions associated with sulfur cycles, shifts in bacterial communities across farming stages were investigated. The sulfate reduction rate (SRR), and concentrations of acid volatile sulfide (AVS) and H₂S were significantly higher at the mid- and post-farming stages than at the early stage, indicating that the aquaculture effects persist even after harvest. Incomplete organic carbon-oxidizing sulfate-reducing bacteria (IO-SRB) affiliated with Desulfobulbaceae, and gammaproteobacterial sulfur oxidizing bacteria (SOB) (Thiohalobacter, Thioprofundum, and Thiohalomonas) were dominant during the early stage, whereas fermenting bacteria (Bacteroidetes and Firmicutes) and complete oxidizing SRB (CO-SRB) belonging to Desulfobacteraceae, and epsilonproteobacterial SOB (Sulfurovum) dominated during the mid- and post-stages. The shift in SRB and SOB communities well reflected the anoxic and sulfidic conditions of farm sediment. Especially, the Sulfurovum-like SOB correlated highly and positively with H₂S, AVS, and SRR, suggesting that they could be relevant microbiological proxies to assess sulfidic conditions in farm sediment.

High-Pressure-Induced Sublethal Injuries of Food Pathogens-Microscopic Assessment

High Hydrostatic Pressure (HHP) technology is considered an alternative method of food preservation. Nevertheless, the current dogma is that HHP might be insufficient to preserve food lastingly against some pathogens. Incompletely damaged cells can resuscitate under favorable conditions, and they may proliferate in food during storage. This study was undertaken to characterize the extent of sublethal injuries induced by HHP (300-500 MPa) on Escherichia coli and Listeria inncua strains. The morphological changes were evaluated using microscopy methods such as Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), and Epifluorescence Microscopy (EFM). The overall assessment of the physiological state of tested bacteria through TEM and SEM showed that the action of pressure on the structure of the bacterial membrane was almost minor or unnoticeable, beyond the L. innocua wild-type strain. However, alterations were observed in subcellular structures such as the cytoplasm and nucleoid for both L. innocua and E. coli strains. More significant changes after the HHP of internal structures were reported in the case of wild-type strains isolated from raw juice. Extreme condensation of the cytoplasm was observed, while the outline of cells was intact. The percentage ratio between alive and injured cells in the population was assessed by fluorescent microscopy. The results of HHP-treated samples showed a heterogeneous population, and red cell aggregates were observed. The percentage ratio of live and dead cells (L/D) in the L. innocua collection strain population was higher than in the case of the wild-type strain (69%/31% and 55%/45%, respectively). In turn, E. coli populations were characterized with a similar L/D ratio. Half of the cells in the populations were distinguished as visibly fluorescing red. The results obtained in this study confirmed sublethal HHP reaction on pathogens cells.

A simple regulatory architecture allows learning the statistical structure of a changing environment

Bacteria live in environments that are continuously fluctuating and changing. Exploiting any predictability of such fluctuations can lead to an increased fitness. On longer timescales, bacteria can 'learn' the structure of these fluctuations through evolution. However, on shorter timescales, inferring the statistics of the environment and acting upon this information would need to be accomplished by physiological mechanisms. Here, we use a model of metabolism to show that a simple generalization of a common regulatory motif (end-product inhibition) is sufficient both for learning continuous-valued features of the statistical structure of the environment and for translating this information into predictive behavior; moreover, it accomplishes these tasks near-optimally. We discuss plausible genetic circuits that could instantiate the mechanism we describe, including one similar to the architecture of two-component signaling, and argue that the key ingredients required for such predictive behavior are readily accessible to bacteria.

LED control of gene expression in a nanobiosystem composed of metallic nanoparticles and a genetically modified E. coli strain

BACKGROUND

Within the last decade, genetic engineering and synthetic biology have revolutionized society´s ability to mass-produce complex biological products within genetically-modified microorganisms containing elegantly designed genetic circuitry. However, many challenges still exist in developing bioproduction processes involving genetically modified microorganisms with complex or multiple gene circuits. These challenges include the development of external gene expression regulation methods with the following characteristics: spatial-temporal control and scalability, while inducing minimal permanent or irreversible system-wide conditions. Different stimuli have been used to control gene expression and mitigate these challenges, and they can be characterized by the effect they produce in the culture media conditions. Invasive stimuli that cause permanent, irreversible changes (pH and chemical inducers), non-invasive stimuli that cause partially reversible changes (temperature), and non-invasive stimuli that cause reversible changes in the media conditions (ultrasound, magnetic fields, and light).

METHODS

Opto-control of gene expression is a non-invasive external trigger that complies with most of the desired characteristics of an external control system. However, the disadvantage relies on the design of the biological photoreceptors and the necessity to design them to respond to a different wavelength for every bioprocess needed to be controlled or regulated in the microorganism. Therefore, this work proposes using biocompatible metallic nanoparticles as external controllers of gene expression, based on their ability to convert light into heat and the capacity of nanotechnology to easily design a wide array of nanostructures capable of absorbing light at different wavelengths and inducing plasmonic photothermal heating.

RESULTS

Here, we designed a nanobiosystem that can be opto-thermally triggered using LED light. The nanobiosystem is composed of biocompatible gold nanoparticles and a genetically modified E. coli with a plasmid that allows mCherry fluorescent protein production at 37 °C in response to an RNA thermometer.

CONCLUSIONS

The LED-triggered photothermal protein production system here designed offers a new, cheaper, scalable switchable method, non-destructive for living organisms, and contribute toward the evolution of bioprocess production systems.

Coupled microbiome analyses highlights relative functional roles of bacteria in a bivalve hatchery

BACKGROUND

Microbial communities are ubiquitous throughout ecosystems and are commensal with hosts across taxonomic boundaries. Environmental and species-specific microbiomes are instrumental in maintaining ecosystem and host health, respectively. The introduction of pathogenic microbes that shift microbiome community structure can lead to illness and death. Understanding the dynamics of microbiomes across a diversity of environments and hosts will help us to better understand which taxa forecast survival and which forecast mortality events.

RESULTS

We characterized the bacterial community microbiome in the water of a commercial shellfish hatchery in Washington state, USA, where the hatchery has been plagued by recurring and unexplained larval mortality events. By applying the complementary methods of metagenomics and metaproteomics we were able to more fully characterize the bacterial taxa in the hatchery at high (pH 8.2) and low (pH 7.1) pH that were metabolically active versus present but not contributing metabolically. There were shifts in the taxonomy and functional profile of the microbiome between pH and over time. Based on detected metagenomic reads and metaproteomic peptide spectral matches, some taxa were more metabolically active than expected based on presence alone (Deltaproteobacteria, Alphaproteobacteria) and some were less metabolically active than expected (e.g., Betaproteobacteria, Cytophagia). There was little correlation between potential and realized metabolic function based on Gene Ontology analysis of detected genes and peptides.

CONCLUSION

The complementary methods of metagenomics and metaproteomics contribute to a more full characterization of bacterial taxa that are potentially active versus truly metabolically active and thus impact water quality and inter-trophic relationships.

The promise of dawn: Microalgae photoacclimation as an optimal control problem of resource allocation

Photosynthetic microorganisms are known to adjust their photosynthetic capacity according to light intensity. This so-called photoacclimation process is thought to maximize growth at equilibrium, but its dynamics under varying conditions remains less understood. To tackle this problem, microalgae growth and photoacclimation are represented by a (coarse-grained) resource allocation model. Using optimal control theory (the Pontryagin maximum principle) and numerical simulations, we determine the optimal strategy of resource allocation to maximize microalgal growth rate over a time horizon. We show that, after a transient, the optimal trajectory approaches the optimal steady state, a behavior known as the turnpike property. Then, a bi-level optimization problem is solved numerically to estimate model parameters from experimental data. The fitted trajectory represents well a Dunaliella tertiolecta culture facing a light down-shift. Finally, we study photoacclimation dynamics under day/night cycle. In the optimal trajectory, the synthesis of the photosynthetic apparatus surprisingly starts a few hours before dawn. This anticipatory behavior has actually been observed both in the laboratory and in the field. This shows the algal predictive capacity and the interest of our method which predicts this phenomenon.

Morphological and physiological changes in Lentilactobacillus hilgardii cells after cold plasma treatment

Atmospheric cold plasma (ACP) inactivation of Lentilactobacillus hilgardii was investigated. Bacteria were exposed to ACP dielectric barrier discharge with helium and oxygen as working gases for 5, 10, and 15 min. The innovative approach in our work for evaluation of bacterial survival was the use in addition to the classical plate culture method also flow cytometry which allowed the cells to be sorted and revealed different physiological states after the plasma treatment. Results showed total inhibition of bacterial growth after 10-min of ACP exposure. However, the analysis of flow cytometry demonstrated the presence of 14.4% of active cells 77.5% of cells in the mid-active state and 8.1% of dead cells after 10 min. In addition, some of the cells in the mid-active state showed the ability to grow again on culture medium, thus confirming the hypothesis of induction of VBNC state in L .hilgardii cells by cold plasma. In turn, atomic force microscopy (AFM) which was used to study morphological changes in L. hilgardii after plasma treatment at particular physiological states (active, mid-active, dead), showed that the surface roughness of the mid-active cell (2.70 ± 0.75 nm) was similar to that of the control sample (2.04 ± 0.55 nm). The lack of considerable changes on the cell surface additionally explains the effective cell resuscitation. To the best of our knowledge, AFM was used for the first time in this work to analyze cells which have been sorted into subpopulations after cold plasma treatment and this is the first work indicating the induction of VBNC state in L. hilgardii cells after exposure to cold plasma.

Using optimal control to understand complex metabolic pathways

BACKGROUND

Optimality principles have been used to explain the structure and behavior of living matter at different levels of organization, from basic phenomena at the molecular level, up to complex dynamics in whole populations. Most of these studies have assumed a single-criteria approach. Such optimality principles have been justified from an evolutionary perspective. In the context of the cell, previous studies have shown how dynamics of gene expression in small metabolic models can be explained assuming that cells have developed optimal adaptation strategies. Most of these works have considered rather simplified representations, such as small linear pathways, or reduced networks with a single branching point, and a single objective for the optimality criteria.

RESULTS

Here we consider the extension of this approach to more realistic scenarios, i.e. biochemical pathways of arbitrary size and structure. We first show that exploiting optimality principles for these networks poses great challenges due to the complexity of the associated optimal control problems. Second, in order to surmount such challenges, we present a computational framework which has been designed with scalability and efficiency in mind, including mechanisms to avoid the most common pitfalls. Third, we illustrate its performance with several case studies considering the central carbon metabolism of S. cerevisiae and B. subtilis. In particular, we consider metabolic dynamics during nutrient shift experiments.

CONCLUSIONS

We show how multi-objective optimal control can be used to predict temporal profiles of enzyme activation and metabolite concentrations in complex metabolic pathways. Further, we also show how to consider general cost/benefit trade-offs. In this study we have considered metabolic pathways, but this computational framework can also be applied to analyze the dynamics of other complex pathways, such as signal transduction or gene regulatory networks.

Abiotic and past climatic conditions drive protein abundance variation among natural populations of the caddisfly Crunoecia irrorata

Deducing impacts of environmental change on species and the populations they form in nature is an important goal in contemporary ecology. Achieving this goal is hampered by our limited understanding of the influence of naturally occurring environmental variation on the molecular systems of ecologically relevant species, as the pathways underlying fitness-affecting plastic responses have primarily been studied in model organisms and under controlled laboratory conditions. Here, to test the hypothesis that proteome variation systematically relates to variation in abiotic conditions, we establish such relationships by profiling the proteomes of 24 natural populations of the spring-dwelling caddisfly Crunoecia irrorata. We identified protein networks whose abundances correlated with environmental (abiotic) gradients such as in situ pH, oxygen- and nitrate concentrations but also climatic data such as past thermal minima and temperature seasonality. Our analyses suggest that variations in abiotic conditions induce discrete proteome responses such as the differential abundance of proteins associated with cytoskeletal function, heat-shock proteins and proteins related to post-translational modification. Identifying these drivers of proteome divergence characterizes molecular "noise", and positions it as a background against which molecular signatures of species' adaptive responses to stressful conditions can be identified.

3-Hydroxybutyrate Derived from Poly-3-Hydroxybutyrate Mobilization Alleviates Protein Aggregation in Heat-Stressed Herbaspirillum seropedicae SmR1

Under conditions of carbon starvation or thermal, osmotic, or oxidative shock, mutants affected in the synthesis or mobilization of poly-3-hydroxybutyrate (PHB) are known to survive less well. It is still unclear if the synthesis and accumulation of PHB are sufficient to protect bacteria against stress conditions or if the stored PHB has to be mobilized. Here, we demonstrated that mobilization of PHB in Herbaspirillum seropedicae SmR1 was heat-shock activated at 45°C. In situ proton (¹H) nuclear magnetic resonance spectroscopy (i.e., ¹H-nuclear magnetic resonance) showed that heat shock increased amounts of 3-hydroxybutyrate (3HB) only in H. seropedicae strains able to synthesize and mobilize PHB. H. seropedicae SmR1 mutants unable to synthesize or mobilize PHB were more susceptible to heat shock and survived less well than the parental strain. When 100 mM 3-hydroxybutyrate was added to the medium, the ΔphaC1 strain (an H. seropedicae mutant unable to synthesize PHB) and the double mutant with deletion of both phaZ1 and phaZ2 (i.e., ΔphaZ1.2) (unable to mobilize PHB) showed partial rescue of heat adaptability (from 0% survival without 3HB to 40% of the initial viable population). Addition of 200 mM 3HB before the imposition of heat shock reduced protein aggregation to 15% in the ΔphaC1 mutant and 12% in the ΔphaZ1.2 mutant. We conclude that H. seropedicae SmR1 is naturally protected by 3HB released by PHB mobilization, while mutants unable to generate large amounts of 3HB under heat shock conditions are less able to cope with heat damage.IMPORTANCE Bacteria are subject to abrupt changes in environmental conditions affecting their growth, requiring rapid adaptation. Increasing the concentration of some metabolites can protect bacteria from hostile conditions that lead to protein denaturation and precipitation, as well as damage to plasma membranes. In this work, we demonstrated that under thermal shock, the bacterium Herbaspirillum seropedicae depolymerized its intracellular stock polymer known as poly-3-hydroxybutyrate (PHB), rapidly increasing the concentration of 3-hydroxybutyrate (3HB) and decreasing protein precipitation by thermal denaturation. Mutant H. seropedicae strains unable to produce or depolymerize PHB suffered irreparable damage during thermal shock, resulting in fast death when incubated at 45°C. Our results will contribute to the development of bacteria better adapted to high temperatures found either in natural conditions or in industrial processes. In the case of H. seropedicae and other bacteria that interact beneficially with plants, the understanding of PHB metabolism can be decisive for the development of more-competitive strains and their application as biofertilizers in agriculture.

Robot Cookies - Plant Cell Packs as an Automated High-Throughput Screening Platform Based on Transient Expression

The high-throughput screening of recombinant protein expression is advantageous during early process development because it allows the identification of optimal expression constructs and process conditions. Simple screening platforms based on microtiter plates are available for microbes and animal cells, but this was not possible for plants until the development of plant cell packs (PCPs), also known as “cookies,” which provide a versatile and scalable screening tool for recombinant protein production. PCPs are prepared from plant cell suspension cultures by removing the medium and molding the biomass. PCPs can be cast into 96-well plates for high-throughput screening, but the manual handling effort currently limits the throughput to ∼500 samples per day. We have therefore integrated the PCP method with a fully automated laboratory liquid-handling station. The “robot cookies” can be prepared and infiltrated with Agrobacterium tumefaciens by centrifugation, minimizing operator handling and reducing the likelihood of errors during repeated runs, such as those required in a design of experiments approach. The accumulation of fluorescent protein in the cytosol, apoplast, endoplasmic reticulum or plastids is easily detected using an integrated plate reader, reducing the inter-experimental variation to <5%. We also developed a detergent-based chemical lysis method for protein extraction in a 96-well format, which was adapted for automated downstream processing using miniaturized columns allowing subsequent protein analysis. The new automated method reduces the costs of the platform to <0.5 € per PCP infiltration (a saving of >50%) and facilitates a five-fold increase in throughput to >2500 samples per day.

A Bacterial Growth Law out of Steady State

Bacterial growth follows simple laws in constant conditions. However, bacteria in nature often face fluctuating environments. We therefore ask whether there are growth laws that apply to changing environments. We derive a law for upshifts using an optimal resource-allocation model: the post-shift growth rate equals the geometrical mean of the pre-shift growth rate and the growth rate on saturating carbon. We test this using chemostat and batch culture experiments, as well as previous data from several species. The increase in growth rate after an upshift indicates that ribosomes have spare capacity (SC). We demonstrate theoretically that SC has the cost of slow steady-state growth but is beneficial after an upshift because it prevents large overshoots in intracellular metabolites and allows rapid response to change. We also provide predictions for downshifts. The present study quantifies the optimal degree of SC, which rises the slower the growth rate, and suggests that SC can be precisely regulated.

DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression

BACKGROUND

Genome-scale models of metabolism and macromolecular expression (ME models) enable systems-level computation of proteome allocation coupled to metabolic phenotype.

RESULTS

We develop DynamicME, an algorithm enabling time-course simulation of cell metabolism and protein expression. DynamicME correctly predicted the substrate utilization hierarchy on a mixed carbon substrate medium. We also found good agreement between predicted and measured time-course expression profiles. ME models involve considerably more parameters than metabolic models (M models). We thus generate an ensemble of models (each model having its rate constants perturbed), and then analyze the models by identifying archetypal time-course metabolite concentration profiles. Furthermore, we use a metaheuristic optimization method to calibrate ME model parameters using time-course measurements such as from a (fed-) batch culture. Finally, we show that constraints on protein concentration dynamics ("inertia") alter the metabolic response to environmental fluctuations, including increased substrate-level phosphorylation and lowered oxidative phosphorylation.

CONCLUSIONS

Overall, DynamicME provides a novel method for understanding proteome allocation and metabolism under complex and transient environments, and to utilize time-course cell culture data for model-based interpretation or model refinement.

Optimal control of bacterial growth for the maximization of metabolite production

Microorganisms have evolved complex strategies for controlling the distribution of available resources over cellular functions. Biotechnology aims at interfering with these strategies, so as to optimize the production of metabolites and other compounds of interest, by (re)engineering the underlying regulatory networks of the cell. The resulting reallocation of resources can be described by simple so-called self-replicator models and the maximization of the synthesis of a product of interest formulated as a dynamic optimal control problem. Motivated by recent experimental work, we are specifically interested in the maximization of metabolite production in cases where growth can be switched off through an external control signal. We study various optimal control problems for the corresponding self-replicator models by means of a combination of analytical and computational techniques. We show that the optimal solutions for biomass maximization and product maximization are very similar in the case of unlimited nutrient supply, but diverge when nutrients are limited. Moreover, external growth control overrides natural feedback growth control and leads to an optimal scheme consisting of a first phase of growth maximization followed by a second phase of product maximization. This two-phase scheme agrees with strategies that have been proposed in metabolic engineering. More generally, our work shows the potential of optimal control theory for better understanding and improving biotechnological production processes.

Modeling the multi-scale mechanisms of macromolecular resource allocation

As microbes face changing environments, they dynamically allocate macromolecular resources to produce a particular phenotypic state. Broad 'omics' data sets have revealed several interesting phenomena regarding how the proteome is allocated under differing conditions, but the functional consequences of these states and how they are achieved remain open questions. Various types of multi-scale mathematical models have been used to elucidate the genetic basis for systems-level adaptations. In this review, we outline several different strategies by which microbes accomplish resource allocation and detail how mathematical models have aided in our understanding of these processes. Ultimately, such modeling efforts have helped elucidate the principles of proteome allocation and hold promise for further discovery.

Maintaining maximal metabolic flux by gene expression control

One of the marvels of biology is the phenotypic plasticity of microorganisms. It allows them to maintain high growth rates across conditions. Studies suggest that cells can express metabolic enzymes at tuned concentrations through adjustment of gene expression. The associated transcription factors are often regulated by intracellular metabolites. Here we study metabolite-mediated regulation of metabolic-gene expression that maximises metabolic fluxes across conditions. We developed an adaptive control theory, qORAC (for ‘Specific Flux (q) Optimization by Robust Adaptive Control’), and illustrate it with several examples of metabolic pathways. The key feature of the theory is that it does not require knowledge of the regulatory network, only of the metabolic part. We derive that maximal metabolic flux can be maintained in the face of varying N environmental parameters only if the number of transcription-factor binding metabolites is at least equal to N. The controlling circuits appear to require simple biochemical kinetics. We conclude that microorganisms likely can achieve maximal rates in metabolic pathways, in the face of environmental changes.

To attain high growth rates, microorganisms need to sustain high activities of metabolic reactions. Since the catalysing enzymes are in finite supply, cells need to carefully tune their concentrations. When conditions change, cells need to adjust those concentrations. How cells maintain high metabolism rates across conditions by way of gene regulatory mechanisms and whether they can maximise metabolic activity is far from clear. Here we present a general theory that solves this metabolic control problem, which we have called qORAC for specific flux (q) Optimisation by Robust Adaptive Control. It considers that external changes are sensed by internal “sensor” metabolites that bind to transcription factors in order to regulate enzyme-synthesis rates. We show that such a combined system of metabolism and its gene network can self-optimise its metabolic activity across conditions. We present the mathematical conditions for the required adaptive control for robust system-steering to optimal states across conditions. We provide explicit examples of such self-optimising coupled metabolism and gene network systems. We prove that a cell can be robust to changes in K parameters, e.g. external conditions, if at least K internal metabolite concentrations act transcription-factor binding sensors. We find that the optimal relation of the enzyme synthesis rates of self-optimising systems and the concentration of the sensor metabolites can generally be implemented by basic biochemistry. Our results indicate how cells are able to maintain maximal reaction rates, even in changing conditions.

Escherichia coli translation strategies differ across carbon, nitrogen and phosphorus limitation conditions

For cells to grow faster they must increase their protein production rate. Microorganisms have traditionally been thought to accomplish this increase by producing more ribosomes to enhance protein synthesis capacity, leading to the linear relationship between ribosome level and growth rate observed under most growth conditions previously examined. Past studies have suggested that this linear relationship represents an optimal resource allocation strategy for each growth rate, independent of any specific nutrient state. Here we investigate protein production strategies in continuous cultures limited for carbon, nitrogen and phosphorus, which differentially impact substrate supply for protein versus nucleic acid metabolism. Unexpectedly, we find that at slow growth rates, Escherichia coli achieves the same protein production rate using three different strategies under the three different nutrient limitations. Under phosphorus (P) limitation, translation is slow due to a particularly low abundance of ribosomes, which are RNA-rich and thus particularly costly for phosphorous-limited cells. Under nitrogen (N) limitation, translation elongation is slowed by processes including ribosome stalling at glutamine codons. Under carbon (C) limitation, translation is slowed by accumulation of inactive ribosomes not bound to messenger RNA. These extra ribosomes enable rapid growth acceleration during nutrient upshift. Thus, bacteria tune ribosome usage across different limiting nutrients to enable balanced nutrient-limited growth while also preparing for future nutrient upshifts.

A Dynamic Model of Resource Allocation in Response to the Presence of a Synthetic Construct

Introducing synthetic constructs into bacteria often carries a burden that leads to reduced fitness and selective pressure for organisms to mutate their constructs and hence to a reduced functional lifetime. Understanding burden requires suitable methods for accurate measurement and quantification. We develop a dynamic growth model from physiologically relevant first-principles that allows parameters relevant to burden to be extracted from standard growth curves. We test several possibilities for the response of a bacterium to a new environment in terms of resource allocation. We find that burden manifests in the time taken to respond to new conditions as well as the rate of growth in exponential phase. Furthermore, we see that the presence of a synthetic construct hastens the reduction of ribosomes when approaching stationary phase, altering memory effects from previous periods of growth.

A global resource allocation strategy governs growth transition kinetics of Escherichia coli

A grand challenge of systems biology is to predict the kinetic responses of living systems to perturbations starting from the underlying molecular interactions. Changes in the nutrient environment have long been used to study regulation and adaptation phenomena in microorganisms and they remain a topic of active investigation. Although much is known about the molecular interactions that govern the regulation of key metabolic processes in response to applied perturbations, they are insufficiently quantified for predictive bottom-up modelling. Here we develop a top-down approach, expanding the recently established coarse-grained proteome allocation models from steady-state growth into the kinetic regime. Using only qualitative knowledge of the underlying regulatory processes and imposing the condition of flux balance, we derive a quantitative model of bacterial growth transitions that is independent of inaccessible kinetic parameters. The resulting flux-controlled regulation model accurately predicts the time course of gene expression and biomass accumulation in response to carbon upshifts and downshifts (for example, diauxic shifts) without adjustable parameters. As predicted by the model and validated by quantitative proteomics, cells exhibit suboptimal recovery kinetics in response to nutrient shifts owing to a rigid strategy of protein synthesis allocation, which is not directed towards alleviating specific metabolic bottlenecks. Our approach does not rely on kinetic parameters, and therefore points to a theoretical framework for describing a broad range of such kinetic processes without detailed knowledge of the underlying biochemical reactions.

A Pathway Proteomic Profile of Ischemic Stroke Survivors Reveals Innate Immune Dysfunction in Association with Mild Symptoms of Depression - A Pilot Study

Depression after stroke is a common occurrence, raising questions as to whether depression could be a long-term biological and immunological sequela of stroke. Early explanations for post-stroke depression (PSD) focused on the neuropsychological/psychosocial effects of stroke on mobility and quality of life. However, recent investigations have revealed imbalances of inflammatory cytokine levels in association with PSD, though to date, there is only one published proteomic pathway analysis testing this hypothesis. Thus, we examined the serum proteome of stroke patients (n = 44, mean age = 63.62 years) and correlated these with the Montgomery–Åsberg Depression Rating Scale (MADRS) scores at 3 months post-stroke. Overall, the patients presented with mild depression symptoms on the MADRS, M = 6.40 (SD = 7.42). A discovery approach utilizing label-free relative quantification was employed utilizing an LC-ESI–MS/MS coupled to a LTQ-Orbitrap Elite (Thermo-Scientific). Identified peptides were analyzed using the gene set enrichment approach on several different genomic databases that all indicated significant downregulation of the complement and coagulation systems with increasing MADRS scores. Complement and coagulation systems are traditionally thought to play a key role in the innate immune system and are established precursors to the adaptive immune system through pro-inflammatory cytokine signaling. Both systems are known to be globally affected after ischemic or hemorrhagic stroke. Thus, our results suggest that lowered complement expression in the periphery in conjunction with depressive symptoms post-stroke may be a biomarker for incomplete recovery of brain metabolic needs, homeostasis, and inflammation following ischemic stroke damage. Further proteomic investigations are now required to construct the temporal profile, leading from acute lesion damage to manifestation of depressive symptoms. Overall, the findings provide support for the involvement of inflammatory and immune mechanisms in PSD symptoms and further demonstrate the value and feasibility of the proteomic approach in stroke research.

Synchronized mitochondrial and cytosolic translation programs

Oxidative phosphorylation (OXPHOS) is fundamental for life. OXPHOS complexes pose a unique challenge for the cell, because their subunits are encoded on two different genomes, the nuclear genome and the mitochondrial genome. Genomic approaches designed to study nuclear/cytosolic and bacterial gene expression have not been broadly applied to the mitochondrial system; thus the co-regulation of OXPHOS genes remains largely unexplored. Here we globally monitored mitochondrial and nuclear gene expression processes in Saccharomyces cerevisiae during mitochondrial biogenesis, when OXPHOS complexes are synthesized. Nuclear- and mitochondrial-encoded OXPHOS transcript levels do not increase concordantly. Instead, we observe that mitochondrial and cytosolic translation are rapidly and dynamically regulated in a strikingly synchronous fashion. Furthermore, the coordinated translation programs are controlled unidirectionally through the intricate and dynamic control of cytosolic translation. Thus the nuclear genome carefully directs the coordination of mitochondrial and cytosolic translation to orchestrate the timely synthesis of each OXPHOS complex, representing an unappreciated regulatory layer shaping the mitochondrial proteome. Our whole-cell genomic profiling approach establishes a foundation for global gene regulatory studies of mitochondrial biology.

Dynamical Allocation of Cellular Resources as an Optimal Control Problem: Novel Insights into Microbial Growth Strategies

Microbial physiology exhibits growth laws that relate the macromolecular composition of the cell to the growth rate. Recent work has shown that these empirical regularities can be derived from coarse-grained models of resource allocation. While these studies focus on steady-state growth, such conditions are rarely found in natural habitats, where microorganisms are continually challenged by environmental fluctuations. The aim of this paper is to extend the study of microbial growth strategies to dynamical environments, using a self-replicator model. We formulate dynamical growth maximization as an optimal control problem that can be solved using Pontryagin’s Maximum Principle. We compare this theoretical gold standard with different possible implementations of growth control in bacterial cells. We find that simple control strategies enabling growth-rate maximization at steady state are suboptimal for transitions from one growth regime to another, for example when shifting bacterial cells to a medium supporting a higher growth rate. A near-optimal control strategy in dynamical conditions is shown to require information on several, rather than a single physiological variable. Interestingly, this strategy has structural analogies with the regulation of ribosomal protein synthesis by ppGpp in the enterobacterium Escherichia coli. It involves sensing a mismatch between precursor and ribosome concentrations, as well as the adjustment of ribosome synthesis in a switch-like manner. Our results show how the capability of regulatory systems to integrate information about several physiological variables is critical for optimizing growth in a changing environment.

Microbial growth is the process by which cells sustain and reproduce themselves from available matter and energy. Strategies enabling microorganisms to optimize their growth rate have been extensively studied, but mostly in stable environments. Here, we build a coarse-grained model of microbial growth and use methods from optimal control theory to determine a resource allocation scheme that would lead to maximal biomass accumulation when the cells are dynamically shifted from one growth medium to another. We compare this optimal solution with several cellular implementations of growth control, based on the capacity of the cell to sense different physiological variables. We find that strategies maximizing growth in steady-state conditions perform quite differently in dynamical conditions. Moreover, the control strategy with performance close to the theoretical maximum exploits information of more than one physiological variable, suggesting that optimization of microbial growth in dynamical rather than steady environments requires broader sensory capacities. Interestingly, the ppGpp alarmone system in the enterobacterium Escherichia coli, known to play an important role in growth control, has structural similarities with the control strategy approaching the theoretical maximum. It senses a discrepancy between the concentrations of precursors and ribosomes, and adjusts ribosome synthesis in an on-off fashion. This suggests that E. coli is adapted for environments with intermittent, rapid changes in nutrient availability.

Mechanistic links between cellular trade-offs, gene expression, and growth

Intracellular processes rarely work in isolation but continually interact with the rest of the cell. In microbes, for example, we now know that gene expression across the whole genome typically changes with growth rate. The mechanisms driving such global regulation, however, are not well understood. Here we consider three trade-offs that, because of limitations in levels of cellular energy, free ribosomes, and proteins, are faced by all living cells and we construct a mechanistic model that comprises these trade-offs. Our model couples gene expression with growth rate and growth rate with a growing population of cells. We show that the model recovers Monod's law for the growth of microbes and two other empirical relationships connecting growth rate to the mass fraction of ribosomes. Further, we can explain growth-related effects in dosage compensation by paralogs and predict host-circuit interactions in synthetic biology. Simulating competitions between strains, we find that the regulation of metabolic pathways may have evolved not to match expression of enzymes to levels of extracellular substrates in changing environments but rather to balance a trade-off between exploiting one type of nutrient over another. Although coarse-grained, the trade-offs that the model embodies are fundamental, and, as such, our modeling framework has potentially wide application, including in both biotechnology and medicine.

Computing the functional proteome: recent progress and future prospects for genome-scale models

Constraint-based models enable the computation of feasible, optimal, and realized biological phenotypes from reaction network reconstructions and constraints on their operation. To date, stoichiometric reconstructions have largely focused on metabolism, resulting in genome-scale metabolic models (M-Models). Recent expansions in network content to encompass proteome synthesis have resulted in models of metabolism and protein expression (ME-Models). ME-Models advance the predictions possible with constraint-based models from network flux states to the spatially resolved molecular composition of a cell. Specifically, ME-Models enable the prediction of transcriptome and proteome allocation and limitations, and basal expression states and regulatory needs. Continued expansion in reconstruction content and constraints will result in an increasingly refined representation of cellular composition and behavior.

Emergence of robust growth laws from optimal regulation of ribosome synthesis

Bacteria must constantly adapt their growth to changes in nutrient availability; yet despite large-scale changes in protein expression associated with sensing, adaptation, and processing different environmental nutrients, simple growth laws connect the ribosome abundance and the growth rate. Here, we investigate the origin of these growth laws by analyzing the features of ribosomal regulation that coordinate proteome-wide expression changes with cell growth in a variety of nutrient conditions in the model organism Escherichia coli. We identify supply-driven feedforward activation of ribosomal protein synthesis as the key regulatory motif maximizing amino acid flux, and autonomously guiding a cell to achieve optimal growth in different environments. The growth laws emerge naturally from the robust regulatory strategy underlying growth rate control, irrespective of the details of the molecular implementation. The study highlights the interplay between phenomenological modeling and molecular mechanisms in uncovering fundamental operating constraints, with implications for endogenous and synthetic design of microorganisms.

Measuring bacterial adaptation dynamics at the single-cell level using a microfluidic chemostat and time-lapse fluorescence microscopy

We monitored the dynamics of cell dimensions and reporter GFP expression in individual E. coli cells growing in a microfluidic chemostat using time-lapse fluorescence microscopy. This combination of techniques allows us to study the dynamical responses of single bacterial cells to nutritional shift-down or shift-up for longer times and with more precision over the chemical environment than similar experiments performed on conventional agar pads. We observed two E. coli strains containing different promoter-reporter gene constructs and measured how both their cell dimensions and the GFP expression change after nutritional upshift and downshift. As expected, both strains have similar adaptation dynamics for cell size rearrangement. However, the strain with a ribosomal RNA promoter dependent reporter has a faster GFP production rate than the strain with a constitutive promoter reporter. As a result, the mean GFP concentration in the former strain changes rapidly with the nutritional shift, while that in the latter strain remains relatively stable. These findings characterize the present microfluidic chemostat as a versatile platform for measuring single-cell bacterial dynamics and physiological transitions.