Proteome-wide alterations in Escherichia coli translation rates upon anaerobiosis

The catabolic nature of fermentative substrates influences proteomic rewiring in Escherichia coli under anoxic growth

BACKGROUND

During anaerobic batch fermentation of substrates by Escherichia coli, there is a decline in cell proliferation rates and a huge demand is placed on cellular proteome to cater to its catabolic and anabolic needs under anoxic growth. Considering cell growth rates as a physiological parameter, previous studies have established a direct relationship between E. coli growth rate and cellular ribosomal content for fast-proliferating cells. In this study, we integrated experimental findings with a systemic coarse-grained proteome allocation model, to characterize the physiological outcomes at slow growth rate during anaerobic fermentative catabolism of different glycolytic and non-glycolytic substrates.

RESULTS

The anaerobic catabolism of substrates favored high ribosomal abundances at lower growth rates. Interestingly, a modification of the previously discussed "growth law", the ratio of active to inactive ribosomal proteome was found to be linearly related to the growth rate for cells proliferating at slow to moderate regime (growth rate < 0.8 h- 1). Also, under nutrient- and oxygen-limiting growth conditions, the proteome proportion allocated for ribosomal activity was reduced, and the resources were channelized towards metabolic activities to overcome the limitations imposed during uptake and metabolizing substrate. The energy-intensive uptake mechanism or lower substrate affinity, expended more catabolic proteome, which reduced its availability to other cellular functions.

CONCLUSIONS

Thus, the nature of catabolic substrates imposed either uptake limitation or metabolic limitation coupled with ribosomal limitation (arising due to anoxic and nutritional stress), which resulted in higher proteome expenditure leading to sub-optimal growth phenotype. This study can form the basis for analyzing E. coli's ability to optimize metabolic efficiency under different environmental conditions- including stress responses. It can be further extended to optimizing the industrial anaerobic conversions for improving productivity and yield.

Design and thermodynamic analysis of a pathway enabling anaerobic production of poly-3-hydroxybutyrate in Escherichia coli

Utilizing anaerobic metabolisms for the production of biotechnologically relevant products presents potential advantages, such as increased yields and reduced energy dissipation. However, lower energy dissipation may indicate that certain reactions are operating closer to their thermodynamic equilibrium. While stoichiometric analyses and genetic modifications are frequently employed in metabolic engineering, the use of thermodynamic tools to evaluate the feasibility of planned interventions is less documented. In this study, we propose a novel metabolic engineering strategy to achieve an efficient anaerobic production of poly-(R)-3-hydroxybutyrate (PHB) in the model organism Escherichia coli. Our approach involves re-routing of two-thirds of the glycolytic flux through non-oxidative glycolysis and coupling PHB synthesis with NADH re-oxidation. We complemented our stoichiometric analysis with various thermodynamic approaches to assess the feasibility and the bottlenecks in the proposed engineered pathway. According to our calculations, the main thermodynamic bottleneck are the reactions catalyzed by the acetoacetyl-CoA β-ketothiolase (EC 2.3.1.9) and the acetoacetyl-CoA reductase (EC 1.1.1.36). Furthermore, we calculated thermodynamically consistent sets of kinetic parameters to determine the enzyme amounts required for sustaining the conversion fluxes. In the case of the engineered conversion route, the protein pool necessary to sustain the desired fluxes could account for 20% of the whole cell dry weight.

Escherichia coli Small Proteome

Escherichia coli was one of the first species to have its genome sequenced and remains one of the best-characterized model organisms. Thus, it is perhaps surprising that recent studies have shown that a substantial number of genes have been overlooked. Genes encoding more than 140 small proteins, defined as those containing 50 or fewer amino acids, have been identified in E. coli in the past 10 years, and there is substantial evidence indicating that many more remain to be discovered. This review covers the methods that have been successful in identifying small proteins and the short open reading frames that encode them. The small proteins that have been functionally characterized to date in this model organism are also discussed. It is hoped that the review, along with the associated databases of known as well as predicted but undetected small proteins, will aid in and provide a roadmap for the continued identification and characterization of these proteins in E. coli as well as other bacteria.

Prediction of Novel Bacterial Small RNAs From RIL-Seq RNA-RNA Interaction Data

The genomic revolution and subsequent advances in large-scale genomic and transcriptomic technologies highlighted hidden genomic treasures. Among them stand out non-coding small RNAs (sRNAs), shown to play important roles in post-transcriptional regulation of gene expression in both pro- and eukaryotes. Bacterial sRNA-encoding genes were initially identified in intergenic regions, but recent evidence suggest that they can be encoded within other, well-defined, genomic elements. This notion was strongly supported by data generated by RIL-seq, a RNA-seq-based methodology we recently developed for deciphering chaperon-dependent sRNA-target networks in bacteria. Applying RIL-seq to Hfq-bound RNAs in Escherichia coli, we found that ∼64% of the detected RNA pairs involved known sRNAs, suggesting that yet unknown sRNAs may be included in the ∼36% remaining pairs. To determine the latter, we first tested and refined a set of quantitative features derived from RIL-seq data, which distinguish between Hfq-dependent sRNAs and “other RNAs”. We then incorporated these features in a machine learning-based algorithm that predicts novel sRNAs from RIL-seq data, and identified high-scoring candidates encoded in various genomic regions, mostly intergenic regions and 3′ untranslated regions, but also 5′ untranslated regions and coding sequences. Several candidates were further tested and verified by northern blot analysis as Hfq-dependent sRNAs. Our study reinforces the emerging concept that sRNAs are encoded within various genomic elements, and provides a computational framework for the detection of additional sRNAs in Hfq RIL-seq data of E. coli grown under different conditions and of other bacteria manifesting Hfq-mediated sRNA-target interactions.

Redirecting Metabolic Flux via Combinatorial Multiplex CRISPRi-Mediated Repression for Isopentenol Production in Escherichia coli

CRISPR interference (CRISPRi) via target guide RNA (gRNA) arrays and a deactivated Cas9 (dCas9) protein has been shown to simultaneously repress expression of multiple genomic DNA loci. By knocking down endogenous genes in competing pathways, CRISPRi technology can be utilized to redirect metabolic flux toward target metabolite. In this study, we constructed a CRISPRi-mediated multiplex repression system to silence transcription of several endogenous genes to increase precursor availability in a heterologous isopentenol biosynthesis pathway. To identify genomic knockdown targets in competing pathways, we first designed a single-gRNA library with 15 individual targets, where 3 gRNA cassettes targeting gene asnA, prpE, and gldA increased isopentenol titer by 18-24%. We then combined the 3 single-gRNA cassettes into a two- or three-gRNA array and observed up to 98% enhancement in production by fine-tuning the repression level through titrating dCas9 expression. Our strategy shows that multiplex combinatorial knockdown of competing genes using CRISPRi can increase production of the target metabolite, while the repression level needs to be adjusted to balance the metabolic network and achieve the maximum titer improvement.

Mathematical models of microbial growth and metabolism: a whole-organism perspective

We review the principles underpinning the development of mathematical models of the metabolic activities of micro-organisms. Such models are important to understand and chart the substantial contributions made by micro-organisms to geochemical cycles, and also to optimise the performance of bioreactors that exploit the biochemical capabilities of these organisms. We advocate an approach based on the principle of dynamic allocation. We survey the biological background that motivates this approach, including nutrient assimilation, the regulation of gene expression, and the principles of microbial growth. In addition, we discuss the classic models of microbial growth as well as contemporary approaches. The dynamic allocation theory generalises these classic models in a natural manner and is readily amenable to the additional information provided by transcriptomics and proteomics approaches. Finally, we touch upon these organising principles in the context of the transition from the free-living unicellular mode of life to multicellularity.

Optimal management of nutrient reserves in microorganisms under time-varying environmental conditions

Intracellular reserves are a conspicuous feature of many bacteria; such internal stores are often present in the form of inclusions in which polymeric storage compounds are accumulated. Such reserves tend to increase in times of plenty and be used up in times of scarcity. Mathematical models that describe the dynamical nature of reserve build-up and use are known as "cell quota," "dynamic energy/nutrient budget," or "variable-internal-stores" models. Here we present a stoichiometrically consistent macro-chemical model that accounts for variable stores as well as adaptive allocation of building blocks to various types of catalytic machinery. The model posits feedback loops linking expression of assimilatory machinery to reserve density. The precise form of the "regulatory law" at the heart of such a loop expresses how the cell manages internal stores. We demonstrate how this "regulatory law" can be recovered from experimental data using several empirical data sets. We find that stores should be expected to be negligibly small in stable growth-sustaining environments, but prominent in environments characterised by marked fluctuations on time scales commensurate with the inherent dynamic time scale of the organismal system.

Variable-Internal-Stores models of microbial growth and metabolism with dynamic allocation of cellular resources

Variable-Internal-Stores models of microbial metabolism and growth have proven to be invaluable in accounting for changes in cellular composition as microbial cells adapt to varying conditions of nutrient availability. Here, such a model is extended with explicit allocation of molecular building blocks among various types of catalytic machinery. Such an extension allows a reconstruction of the regulatory rules employed by the cell as it adapts its physiology to changing environmental conditions. Moreover, the extension proposed here creates a link between classic models of microbial growth and analyses based on detailed transcriptomics and proteomics data sets. We ascertain the compatibility between the extended Variable-Internal-Stores model and the classic models, demonstrate its behaviour by means of simulations, and provide a detailed treatment of the uniqueness and the stability of its equilibrium point as a function of the availabilities of the various nutrients.

Isocost Lines Describe the Cellular Economy of Genetic Circuits

Genetic circuits in living cells share transcriptional and translational resources that are available in limited amounts. This leads to unexpected couplings among seemingly unconnected modules, which result in poorly predictable circuit behavior. In this study, we determine these interdependencies between products of different genes by characterizing the economy of how transcriptional and translational resources are allocated to the production of proteins in genetic circuits. We discover that, when expressed from the same plasmid, the combinations of attainable protein concentrations are constrained by a linear relationship, which can be interpreted as an isocost line, a concept used in microeconomics. We created a library of circuits with two reporter genes, one constitutive and the other inducible in the same plasmid, without a regulatory path between them. In agreement with the model predictions, experiments reveal that the isocost line rotates when changing the ribosome binding site strength of the inducible gene and shifts when modifying the plasmid copy number. These results demonstrate that isocost lines can be employed to predict how genetic circuits become coupled when sharing resources and provide design guidelines for minimizing the effects of such couplings.

Time-series analysis of the transcriptome and proteome of Escherichia coli upon glucose repression

Time-series transcript- and protein-profiles were measured upon initiation of carbon catabolite repression in Escherichia coli, in order to investigate the extent of post-transcriptional control in this prototypical response. A glucose-limited chemostat culture was used as the CCR-free reference condition. Stopping the pump and simultaneously adding a pulse of glucose, that saturated the cells for at least 1h, was used to initiate the glucose response. Samples were collected and subjected to quantitative time-series analysis of both the transcriptome (using microarray analysis) and the proteome (through a combination of 15N-metabolic labeling and mass spectrometry). Changes in the transcriptome and corresponding proteome were analyzed using statistical procedures designed specifically for time-series data. By comparison of the two sets of data, a total of 96 genes were identified that are post-transcriptionally regulated. This gene list provides candidates for future in-depth investigation of the molecular mechanisms involved in post-transcriptional regulation during carbon catabolite repression in E. coli, like the involvement of small RNAs.

Rapid temporal dynamics of transcription, protein synthesis, and secretion during macrophage activation

Macrophages provide the first line of host defense with their capacity to react to an array of cytokines and bacterial components requiring tight regulation of protein expression and secretion to invoke a properly tuned innate immune response. To capture the dynamics of this system, we introduce a novel method combining pulsed stable isotope labeling with amino acids in cell culture (SILAC) with pulse labeling using the methionine analog azidohomoalanine that allows the enrichment of newly synthesized proteins via click-chemistry followed by their identification and quantification by mass spectrometry. We show that this permits the analysis of proteome changes on a rapid time scale, as evidenced by the detection of 4852 newly synthesized proteins after only a 20-min SILAC pulse. We have applied this methodology to study proteome response during macrophage activation in a time-course manner. We have combined this with full proteome, transcriptome, and secretome analyses, producing an integrative analysis of the first 3 h of lipopolysaccharide-induced macrophage activation. We observed the rapid induction of multiple processes well known to TLR4 signaling, as well as anti-inflammatory proteins and proteins not previously associated with immune response. By correlating transcriptional, translational, and secretory events, we derived novel mechanistic principles of processes specifically induced by lipopolysaccharides, including ectodomain shedding and proteolytic processing of transmembrane and extracellular proteins and protein secretion independent of transcription. In conclusion, we demonstrate that the combination of pulsed azidohomoalanine and pulsed SILAC permits the detailed characterization of proteomic events on a rapid time scale. We anticipate that this approach will be very useful in probing the immediate effects of cellular stimuli and will provide mechanistic insight into cellular perturbation in multiple biological systems. The data have been deposited in ProteomeXchange with the identifier PXD000600.

Chemical tools for temporally and spatially resolved mass spectrometry-based proteomics

Accurate measurements of the abundances, synthesis rates and degradation rates of cellular proteins are critical for understanding how cells and organisms respond to changes in their environments. Over the past two decades, there has been increasing interest in the use of mass spectrometry for proteomic analysis. In many systems, however, protein diversity as well as cell and tissue heterogeneity limit the usefulness of mass spectrometry-based proteomics. As a result, researchers have had difficulty in systematically identifying proteins expressed within specified time intervals, or low abundance proteins expressed in specific tissues or in a few cells in complex microbial systems. In this review, we present recently-developed tools and strategies that probe these two subsets of the proteome: proteins synthesized during well-defined time intervals--temporally resolved proteomics--and proteins expressed in predetermined cell types, cells or cellular compartments--spatially resolved proteomics--with a focus on chemical and biological mass spectrometry-based methodologies.

Transcriptome and proteome dynamics of a light-dark synchronized bacterial cell cycle

BACKGROUND

Growth of the ocean's most abundant primary producer, the cyanobacterium Prochlorococcus, is tightly synchronized to the natural 24-hour light-dark cycle. We sought to quantify the relationship between transcriptome and proteome dynamics that underlie this obligate photoautotroph's highly choreographed response to the daily oscillation in energy supply.

METHODOLOGY/PRINCIPAL FINDINGS

Using RNA-sequencing transcriptomics and mass spectrometry-based quantitative proteomics, we measured timecourses of paired mRNA-protein abundances for 312 genes every 2 hours over a light-dark cycle. These temporal expression patterns reveal strong oscillations in transcript abundance that are broadly damped at the protein level, with mRNA levels varying on average 2.3 times more than the corresponding protein. The single strongest observed protein-level oscillation is in a ribonucleotide reductase, which may reflect a defense strategy against phage infection. The peak in abundance of most proteins also lags that of their transcript by 2-8 hours, and the two are completely antiphase for some genes. While abundant antisense RNA was detected, it apparently does not account for the observed divergences between expression levels. The redirection of flux through central carbon metabolism from daytime carbon fixation to nighttime respiration is associated with quite small changes in relative enzyme abundances.

CONCLUSIONS/SIGNIFICANCE

Our results indicate that expression responses to periodic stimuli that are common in natural ecosystems (such as the diel cycle) can diverge significantly between the mRNA and protein levels. Protein expression patterns that are distinct from those of cognate mRNA have implications for the interpretation of transcriptome and metatranscriptome data in terms of cellular metabolism and its biogeochemical impact.

Proteomic analysis of HIV-T cell interaction: an update

This mini-review summarizes techniques applied in, and results obtained with, proteomic studies of human immunodeficiency virus type 1 (HIV-1)–T cell interaction. Our group previously reported on the use of two-dimensional differential gel electrophoresis (2D-DIGE) coupled to matrix assisted laser-desorption time of flight peptide mass fingerprint analysis, to study T cell responses upon HIV-1 infection. Only one in three differentially expressed proteins could be identified using this experimental setup. Here we report on our latest efforts to test models generated by this data set and extend its analysis by using novel bioinformatic algorithms. The 2D-DIGE results are compared with other studies including a pilot study using one-dimensional peptide separation coupled to MS^E, a novel mass spectrometric approach. It can be concluded that although the latter method detects fewer proteins, it is much faster and less labor intensive. Last but not least, recent developments and remaining challenges in the field of proteomic studies of HIV-1 infection and proteomics in general are discussed.

The aerobic respiratory chain of Escherichia coli: from genes to supercomplexes

In spite of the large number of reports on the aerobic respiratory chain of Escherichia coli, from gene transcription regulation to enzyme kinetics and structural studies, an integrative perspective of this pathway is yet to be produced. Here, a multi-level analysis of the aerobic respiratory chain of E. coli was performed to find correlations between gene transcription, enzyme activity, growth dynamics, and supercomplex formation and composition. The transcription level of all genes encoding the aerobic respiratory chain of E. coli varied significantly in response to bacterial growth. Coordinated expression patterns were observed between the genes encoding NADH : quinone oxidoreductase and complex I (NDH-1), alternative NADH : quinone oxidoreductase (NDH-2) and cytochrome bdI, and also between sdhA and appC, encoding succinate dehydrogenase and cytochrome bdII, respectively. In general, the rates of the respiratory chain activities increased from mid-exponential to late-stationary phase, with no significant further variation occurring until the mid-stationary phase. Multi-level correlations between gene transcription, enzyme activity and growth dynamics were also found in this study. The previously reported NADH dehydrogenase and formate : oxygen oxidoreductase supercomplexes of E. coli were already assembled at mid-exponential phase and remained throughout growth. A new succinate oxidase supercomplex composed of succinate dehydrogenase and cytochrome bdII was identified, in agreement with the suggestion provided by the coordinated transcription of sdhA and appC.

Global relative and absolute quantitation in microbial proteomics

Proteomic studies are designed to yield either qualitative information on proteins (identification, distribution, posttranslational modifications, interactions, structure and function) or quantitative information (abundance, distribution within different localizations, temporal changes in abundance due to synthesis and degradation or both). To this end these studies can draw upon a wide range of qualitative and quantitative gel-based and gel-free techniques. This review summarizes current proteomic workflows for global relative or absolute protein quantitation and their application in microbial physiology.

Restricting fermentative potential by proteome remodeling: an adaptive strategy evidenced in Bacillus cereus

Pathogenesis hinges on successful colonization of the gastrointestinal (GI) tract by pathogenic facultative anaerobes. The GI tract is a carbohydrate-limited environment with varying oxygen availability and oxidoreduction potential (ORP). How pathogenic bacteria are able to adapt and grow in these varying conditions remains a key fundamental question. Here, we designed a system biology-inspired approach to pinpoint the key regulators allowing Bacillus cereus to survive and grow efficiently under low ORP anoxic conditions mimicking those encountered in the intestinal lumen. We assessed the proteome components using high throughput nanoLC-MS/MS techniques, reconstituted the main metabolic circuits, constructed ΔohrA and ΔohrR mutants, and analyzed the impacts of ohrA and ohrR disruptions by a novel round of shotgun proteomics. Our study revealed that OhrR and OhrA are crucial to the successful adaptation of B. cereus to the GI tract environment. Specifically, we showed that B. cereus restricts its fermentative growth under low ORP anaerobiosis and sustains efficient aerobic respiratory metabolism, motility, and stress response via OhrRA-dependent proteome remodeling. Finally, our results introduced a new adaptive strategy where facultative anaerobes prefer to restrict their fermentative potential for a long term benefit.

Growth rate-dependent control in Enterococcus faecalis: effects on the transcriptome and proteome, and strong regulation of lactate dehydrogenase

Enterococcus faecalis V583 was grown in a glucose-limited chemostat at three different growth rates (0.05, 0.15, and 0.4 h⁻¹). The fermentation pattern changed with growth rate, from a mostly homolactic profile at a high growth rate to a fermentation dominated by formate, acetate, and ethanol production at a low growth rate. A number of amino acids were consumed at the lower growth rates but not by fast-growing cells. The change in metabolic profile was caused mainly by decreased flux through lactate dehydrogenase. The transcription of ldh-1, encoding the principal lactate dehydrogenase, showed very strong growth rate dependence and differed by three orders of magnitude between the highest and the lowest growth rates. Despite the increase in ldh-1 transcript, the content of the Ldh-1 protein was the same under all conditions. Using microarrays and quantitative PCR, the levels of 227 gene transcripts were found to be affected by the growth rate, and 56 differentially expressed proteins were found by proteomic analyses. Few genes or proteins showed a growth rate-dependent increase or decrease in expression across the whole range of conditions, and many showed a maximum or minimum at the middle growth rate (i.e., 0.15 h⁻¹). For many gene products, a discrepancy between transcriptomic and proteomic data were seen, indicating posttranscriptional regulation of expression.