Coordination of microbial metabolism

Enhancing β-Carotene Production in Escherichia coli by Perturbing Central Carbon Metabolism and Improving the NADPH Supply

Beta (β)-carotene (C40H56; a provitamin) is a particularly important carotenoid for human health. Many studies have focused on engineering Escherichia coli as an efficient heterologous producer of β-carotene. Moreover, several strains with potential for use in the industrial production of this provitamin have already been constructed via different metabolic engineering strategies. In this study, we aimed to improve the β-carotene-producing capacity of our previously engineered E. coli strain ZF43ΔgdhA through further gene deletion and metabolic pathway manipulations. Deletion of the zwf gene increased the resultant strain's β-carotene production and content by 5.1 and 32.5%, respectively, relative to the values of strain ZF43ΔgdhA, but decreased the biomass by 26.2%. Deletion of the ptsHIcrr operon further increased the β-carotene production titer from 122.0 to 197.4 mg/L, but the provitamin content was decreased. Subsequently, comparative transcriptomic analysis was used to explore the dynamic transcriptional responses of the strains to the blockade of the pentose phosphate pathway and inactivation of the phosphotransferase system. Lastly, based on the analyses of comparative transcriptome and reduction cofactor, several strategies to increase the NADPH supply were evaluated for enhancement of the β-carotene content. The combination of yjgB gene deletion and nadK overexpression led to increased β-carotene production and content. The best strain, ECW4/p5C-nadK, produced 266.4 mg/L of β-carotene in flask culture and 2,579.1 mg/L in a 5-L bioreactor. The latter value is the highest reported from production via the methylerythritol phosphate pathway in E. coli. Although the strategies applied is routine in this study, the combinations reported were first implemented, are simple but efficient and will be helpful for the production of many other natural products, especially isoprenoids. Importantly, we demonstrated that the use of the methylerythritol phosphate pathway alone for efficient β-carotene biosynthesis could be achieved via appropriate modifications of the cell metabolic functions.

Untargeted Defining Protein-Metabolites Interaction Based on Label-Free Kinetic Size Exclusion Chromatography-Mass Spectrometry

The specific interactions between protein and metabolites (PMIs) are closely related to many cellular processes and play a vital role in signal transduction and regulating material and energy metabolism. However, most of the available analytical strategies for PMIs involve chemical modification of metabolites or immobilization of protein, which has restricted current PMIs study mainly to lipid-protein and hydrophobic metabolites. In this work, a label-free online kinetic size exclusion chromatography-mass spectrometry (KSEC-MS) method combined with untargeted metabolomics was developed to define PMIs in a complex system. The metabolite mixture and target protein were injected into the SEC column sequentially without preincubation, and the separation results of KSEC were monitored by global metabolite profiling with mass spectrometry. The potential ligands in the metabolite mixture can be discovered if their migration patterns were affected by the target protein and the variation was positively correlated with the concentration of target protein. To verify this approach, carbonic anhydrase was first selected as a test protein, and acetazolamide as its known inhibitor was successfully defined. Furthermore, human serum albumin (HSA) as the common transport carrier of metabolites was selected as a target protein to demonstrate the usefulness of this approach. Multiple endogenous ligands of HSA were simultaneously defined from the extracted metabolites of human serum; most of them are polar metabolites rather than nonpolar lipids. This approach can provide a novel way for mapping and identifying unknown PMIs in a complex system, especially for polar metabolites-protein interactions.

Dynamic resource allocation drives growth under nitrogen starvation in eukaryotes

Cells can sense changes in their extracellular environment and subsequently adapt their biomass composition. Nutrient abundance defines the capability of the cell to produce biomass components. Under nutrient-limited conditions, resource allocation dramatically shifts to carbon-rich molecules. Here, we used dynamic biomass composition data to predict changes in growth and reaction flux distributions using the available genome-scale metabolic models of five eukaryotic organisms (three heterotrophs and two phototrophs). We identified temporal profiles of metabolic fluxes that indicate long-term trends in pathway and organelle function in response to nitrogen depletion. Surprisingly, our calculations of model sensitivity and biosynthetic cost showed that free energy of biomass metabolites is the main driver of biosynthetic cost and not molecular weight, thus explaining the high costs of arginine and histidine. We demonstrated how metabolic models can accurately predict the complexity of interwoven mechanisms in response to stress over the course of growth.

The allosteric control mechanism of bacterial glycogen biosynthesis disclosed by cryoEM

Glycogen and starch are the major carbon and energy reserve polysaccharides in nature, providing living organisms with a survival advantage. The evolution of the enzymatic machinery responsible for the biosynthesis and degradation of such polysaccharides, led the development of mechanisms to control the assembly and disassembly rate, to store and recover glucose according to cell energy demands. The tetrameric enzyme ADP-glucose pyrophosphorylase (AGPase) catalyzes and regulates the initial step in the biosynthesis of both α-polyglucans. AGPase displays cooperativity and allosteric regulation by sensing metabolites from the cell energy flux. The understanding of the allosteric signal transduction mechanisms in AGPase arises as a long-standing challenge. In this work, we disclose the cryoEM structures of the paradigmatic homotetrameric AGPase from Escherichia coli (EcAGPase), in complex with either positive or negative physiological allosteric regulators, fructose-1,6-bisphosphate (FBP) and AMP respectively, both at 3.0 Å resolution. Strikingly, the structures reveal that FBP binds deeply into the allosteric cleft and overlaps the AMP site. As a consequence, FBP promotes a concerted conformational switch of a regulatory loop, RL2, from a "locked" to a "free" state, modulating ATP binding and activating the enzyme. This notion is strongly supported by our complementary biophysical and bioinformatics evidence, and a careful analysis of vast enzyme kinetics data on single-point mutants of EcAGPase. The cryoEM structures uncover the residue interaction networks (RIN) between the allosteric and the catalytic components of the enzyme, providing unique details on how the signaling information is transmitted across the tetramer, from which cooperativity emerges. Altogether, the conformational states visualized by cryoEM reveal the regulatory mechanism of EcAGPase, laying the foundations to understand the allosteric control of bacterial glycogen biosynthesis at the molecular level of detail.

Modeling the formation of microorganism-derived dissolved organic nitrogen (mDON) in the activated sludge system

Microorganism-derived dissolved organic nitrogen (mDON) represents a significant and inevitable portion of dissolved organic nitrogen (DON) in the wastewater biotreatment processes. In the existing method, mDON concentrations are indirectly measured by the values of DON concentrations from the reactors with DON-free influent. However, this becomes problematic when influent contains DON. Especially when the real wastewater is involved, the paucity of the direct methods to quantitatively measure mDON is a major barrier to further research. This limitation is due to the difficulty of segregating mDON from the other nitrogenous organics, e.g., influent DON. In this study, we propose the ASM-mDON model based on ASM #1, which incorporates the production and consumption of mDON in the activated sludge processes to predict the mDON concentrations. In four independent lab-scale tests, our model was established and calibrated to obtain the accurate values of mDON (R² = 0.929, p < 0.05), and the validity and applicability of the model were successfully examined by comparing the simulated and measured data. Moreover, the universality of the ASM-mDON model was further confirmed by simulating mDON production in a full-scale wastewater treatment plant. A reasonable prediction of mDON formation was shown in a full-scale test (1.98 ± 0.71 mg/L in June and 1.51 ± 0.54 mg/L in July) and is indirectly supported by an algal bioassay (p < 0.05, t-test). This study provides a useful approach to the efficient and accurate evaluation of mDON formation, which will improve current strategies designed to minimize the effluent mDON in wastewater bioprocesses.

Quorum sensing for population-level control of bacteria and potential therapeutic applications

Quorum sensing (QS), a microbial cell-to-cell communication process, dynamically regulates a variety of metabolism and physiological activities. In this review, we provide an update on QS applications based on autoinducer molecules including acyl-homoserine lactones (AHLs), auto-inducing peptides (AIPs), autoinducer 2 (AI-2) and indole in population-level control of bacteria, and highlight the potential in developing novel clinical therapies. We summarize the development in the combination of various genetic circuits such as genetic oscillators, toggle switches and logic gates with AHL-based QS devices in Gram-negative bacteria. An overview is then offered to the state-of-the-art of much less researched applications of AIP-based QS devices with Gram-positive bacteria, followed by a review of the applications of AI-2 and indole based QS for interspecies communication among microbial communities. Building on these general-purpose QS applications, we highlight the disruptions and manipulations of QS devices as potential clinical therapies for diseases caused by biofilm formation, antibiotic resistance and the phage invasion. The last part of reviewed literature is dedicated to mathematical modelling for QS applications. Finally, the key challenges and future perspectives of QS applications in monoclonal synthetic biology and synthetic ecology are discussed.

Deciphering the Metabolic Pathway Difference Between Saccharopolyspora pogona and Saccharopolyspora spinosa by Comparative Proteomics and Metabonomics

Butenyl-spinosyn, a secondary metabolite produced by Saccharopolyspora pogona, exhibits strong insecticidal activity than spinosyn. However, the low synthesis capacity and unknown metabolic characteristics of butenyl-spinosyn in wild-type S. pogona limit its broad application and metabolic engineering. Here, we showed that S. pogona exhibited increased glucose consumption ability and growth rate compared with S. spinosa, but the production of butenyl-spinosyn was much lower than that of spinosyn. To further elucidate the metabolic mechanism of these different phenotypes, we performed a comparative proteomic and metabolomic study on S. pogona and S. spinosa to identify the change in the abundance levels of proteins and metabolites. We found that the abundance of most proteins and metabolites associated with glucose transport, fatty acid metabolism, tricarboxylic acid cycle, amino acid metabolism, energy metabolism, purine and pyrimidine metabolism, and target product biosynthesis in S. pogona was higher than that in S. spinosa. However, the overall abundance of proteins involved in butenyl-spinosyn biosynthesis was much lower than that of the high-abundance protein chaperonin GroEL, such as the enzymes related to rhamnose synthesis. We speculated that these protein and metabolite abundance changes may be directly responsible for the above phenotypic changes in S. pogona and S. spinosa, especially affecting butenyl-spinosyn biosynthesis. Further studies revealed that the over-expression of the rhamnose synthetic genes and methionine adenosyltransferase gene could effectively improve the production of butenyl-spinosyn by 2.69- and 3.03-fold, respectively, confirming the reliability of this conjecture. This work presents the first comparative proteomics and metabolomics study of S. pogona and S. spinosa, providing new insights into the novel links of phenotypic change and metabolic difference between two strains. The result will be valuable in designing strategies to promote the biosynthesis of butenyl-spinosyn by metabolic engineering.

Metabolite Sequestration Enables Rapid Recovery from Fatty Acid Depletion in Escherichia coli

Microbes adapt their metabolism to take advantage of nutrients in their environment. Such adaptations control specific metabolic pathways to match energetic demands with nutrient availability. Upon depletion of nutrients, rapid pathway recovery is key to release cellular resources required for survival under the new nutritional conditions. Yet, little is known about the regulatory strategies that microbes employ to accelerate pathway recovery in response to nutrient depletion. Using the fatty acid catabolic pathway in Escherichia coli, here, we show that fast recovery can be achieved by rapid release of a transcriptional regulator from a metabolite-sequestered complex. With a combination of mathematical modeling and experiments, we show that recovery dynamics depend critically on the rate of metabolite consumption and the exposure time to nutrients. We constructed strains with rewired transcriptional regulatory architectures that highlight the metabolic benefits of negative autoregulation over constitutive and positive autoregulation. Our results have wide-ranging implications for our understanding of metabolic adaptations, as well as for guiding the design of gene circuitry for synthetic biology and metabolic engineering.IMPORTANCE Rapid metabolic recovery during nutrient shift is critical to microbial survival, cell fitness, and competition among microbiota, yet little is known about the regulatory mechanisms of rapid metabolic recovery. This work demonstrates a previously unknown mechanism where rapid release of a transcriptional regulator from a metabolite-sequestered complex enables fast recovery to nutrient depletion. The work identified key regulatory architectures and parameters that control the speed of recovery, with wide-ranging implications for the understanding of metabolic adaptations as well as synthetic biology and metabolic engineering.

Small RNA-binding protein RapZ mediates cell envelope precursor sensing and signaling in Escherichia coli

The RNA-binding protein RapZ cooperates with small RNAs (sRNAs) GlmY and GlmZ to regulate the glmS mRNA in Escherichia coli. Enzyme GlmS synthesizes glucosamine-6-phosphate (GlcN6P), initiating cell envelope biosynthesis. GlmZ activates glmS expression by base-pairing. When GlcN6P is ample, GlmZ is bound by RapZ and degraded through ribonuclease recruitment. Upon GlcN6P depletion, the decoy sRNA GlmY accumulates through a previously unknown mechanism and sequesters RapZ, suppressing GlmZ decay. This circuit ensures GlcN6P homeostasis and thereby envelope integrity. In this work, we identify RapZ as GlcN6P receptor. GlcN6P-free RapZ stimulates phosphorylation of the two-component system QseE/QseF by interaction, which in turn activates glmY expression. Elevated GlmY levels sequester RapZ into stable complexes, which prevents GlmZ decay, promoting glmS expression. Binding of GlmY also prevents RapZ from activating QseE/QseF, generating a negative feedback loop limiting the response. When GlcN6P is replenished, GlmY is released from RapZ and rapidly degraded. We reveal a multifunctional sRNA-binding protein that dynamically engages into higher-order complexes for metabolite signaling.

Development of a RP-HPLC method for determination of glucose in Shewanella oneidensis cultures utilizing 1-phenyl-3-methyl-5-pyrazolone derivatization

A method was developed and validated for low-level detection of glucose. The method involves quantitation of glucose though derivitization with 1-phenyl-3-methyl-5-pyrazolone (PMP) and HPLC-DAD analysis. The developed method was found to be accurate and robust achieving detection limits as low as 0.09 nM. The applicability of the method was tested against microbial samples with glucose acting as a carbon fuel source. The method was shown to be able to accurately discriminate and quantify PMP-glucose derivatives within Shewanella oneidensis MR-1 samples. The method proved capable at examining glucose usage during the early hours of microbial growth, with detectable usage occurring as early as two hours. S. oneidensis cultures were found to grow more effectively in the presence of oxygen which coincided with more efficient glucose usage. Glucose usage further increased in the presence of competing electron acceptors. The rate at which S. oneidensis reached exponential growth was affected by the presence of ferric iron under microaerobic conditions. Such samples reached exponential growth approximately two hours sooner than aerobic samples.

Computational data mining method for isotopomer analysis in the quantitative assessment of metabolic reprogramming

Measurement of metabolic flux levels using stable isotope labeling has been successfully used to investigate metabolic redirection and reprogramming in living cells or tissues. The metabolic flux ratio between two reactions can be estimated from the ¹³C-labeling patterns of a few metabolites combined with the knowledge of atom mapping in the complicated metabolic network. However, it remains unclear whether an observed change in the labeling pattern of the metabolites is sufficient evidence of a shift in flux ratio between two metabolic states. In this study, a data analysis method was developed for the quantitative assessment of metabolic reprogramming. The Metropolis-Hastings algorithm was used with an in silico metabolic model to generate a probability distribution of metabolic flux levels under a condition in which the ¹³C-labeling pattern was observed. Reanalysis of literature data demonstrated that the developed method enables analysis of metabolic redirection using whole ¹³C-labeling pattern data. Quantitative assessment by Cohen’s effect size (d) enables a more detailed read-out of metabolic reprogramming information. The developed method will enable future applications of the metabolic isotopomer analysis to various targets, including cultured cells, whole tissues, and organs.

Phylogenetic debugging of a complete human biosynthetic pathway transplanted into yeast

Cross-species pathway transplantation enables insight into a biological process not possible through traditional approaches. We replaced the enzymes catalyzing the entire Saccharomyces cerevisiae adenine de novo biosynthesis pathway with the human pathway. While the 'humanized' yeast grew in the absence of adenine, it did so poorly. Dissection of the phenotype revealed that PPAT, the human ortholog of ADE4, showed only partial function whereas all other genes complemented fully. Suppressor analysis revealed other pathways that play a role in adenine de-novo pathway regulation. Phylogenetic analysis pointed to adaptations of enzyme regulation to endogenous metabolite level 'setpoints' in diverse organisms. Using DNA shuffling, we isolated specific amino acids combinations that stabilize the human protein in yeast. Thus, using adenine de novo biosynthesis as a proof of concept, we suggest that the engineering methods used in this study as well as the debugging strategies can be utilized to transplant metabolic pathway from any origin into yeast.

Limits to a classic paradigm: most transcription factors in E. coli regulate genes involved in multiple biological processes

Transcription factors (TFs) are important drivers of cellular decision-making. When bacteria encounter a change in the environment, TFs alter the expression of a defined set of genes in order to adequately respond. It is commonly assumed that genes regulated by the same TF are involved in the same biological process. Examples of this are methods that rely on coregulation to infer function of not-yet-annotated genes. We have previously shown that only 21% of TFs involved in metabolism regulate functionally homogeneous genes, based on the proximity of the gene products’ catalyzed reactions in the metabolic network. Here, we provide more evidence to support the claim that a 1-TF/1-process relationship is not a general property. We show that the observed functional heterogeneity of regulons is not a result of the quality of the annotation of regulatory interactions, nor the absence of protein–metabolite interactions, and that it is also present when function is defined by Gene Ontology terms. Furthermore, the observed functional heterogeneity is different from the one expected by chance, supporting the notion that it is a biological property. To further explore the relationship between transcriptional regulation and metabolism, we analyzed five other types of regulatory groups and identified complex regulons (i.e. genes regulated by the same combination of TFs) as the most functionally homogeneous, and this is supported by coexpression data. Whether higher levels of related functions exist beyond metabolism and current functional annotations remains an open question.

Microbial energy management-A product of three broad tradeoffs

Wherever thermodynamics allows, microbial life has evolved to transform and harness energy. Microbial life thus abounds in the most unexpected places, enabled by profound metabolic diversity. Within this diversity, energy is transformed primarily through variations on a few core mechanisms. Energy is further managed by the physiological processes of cell growth and maintenance that use energy. Some aspects of microbial physiology are streamlined for energetic efficiency while other aspects seem suboptimal or even wasteful. We propose that the energy that a microbe harnesses and devotes to growth and maintenance is a product of three broad tradeoffs: (i) economic, trading enzyme synthesis or operational cost for functional benefit, (ii) environmental, trading optimization for a single environment for adaptability to multiple environments, and (iii) thermodynamic, trading energetic yield for forward metabolic flux. Consideration of these tradeoffs allows one to reconcile features of microbial physiology that seem to opposingly promote either energetic efficiency or waste.

Coupled metabolic-hydrodynamic modeling enabling rational scale-up of industrial bioprocesses

Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind.

Chemoproteomic profiling of protein-metabolite interactions

Small molecule metabolites play important roles in regulating protein functions, which are acted through either covalent non-enzymatic post-translational modifications or non-covalent binding interactions. Chemical proteomic strategies can help delineate global landscapes of cellular protein-metabolite interactions and provide molecular insights about their mechanisms of action. In this review, we summarized the recent progress in developments and applications of chemoproteomic strategies to profile protein-metabolite interactions.

Emergent microscale gradients give rise to metabolic cross-feeding and antibiotic tolerance in clonal bacterial populations

Bacteria often live in spatially structured groups such as biofilms. In these groups, cells can collectively generate gradients through the uptake and release of compounds. In turn, individual cells adapt their activities to the environment shaped by the whole group. Here, we studied how these processes can generate phenotypic variation in clonal populations and how this variation contributes to the resilience of the population to antibiotics. We grew two-dimensional populations of Escherichia coli in microfluidic chambers where limiting amounts of glucose were supplied from one side. We found that the collective metabolic activity of cells created microscale gradients where nutrient concentration varied over a few cell lengths. As a result, growth rates and gene expression levels varied strongly between neighbouring cells. Furthermore, we found evidence for a metabolic cross-feeding interaction between glucose-fermenting and acetate-respiring subpopulations. Finally, we found that subpopulations of cells were able to survive an antibiotic pulse that was lethal in well-mixed conditions, likely due to the presence of a slow-growing subpopulation. Our work shows that emergent metabolic gradients can have important consequences for the functionality of bacterial populations as they create opportunities for metabolic interactions and increase the populations' tolerance to environmental stressors. This article is part of a discussion meeting issue 'Single cell ecology'.

Phenotypic heterogeneity of microbial populations under nutrient limitation

Phenotypic heterogeneity is a phenomenon in which genetically identical individuals have different characteristics. This behavior can also be found in bacteria, even if they grow as monospecies in well-mixed environments such as bioreactors. Here it is discussed how phenotypic heterogeneity is generated by internal factors and how it is promoted under nutrient-limited growth conditions. A better understanding of the molecular levels that control phenotypic heterogeneity could improve biotechnological production processes.

Identifying Metabolic Perturbations and Toxic Effects of Rac-Metalaxyl and Metalaxyl-M in Mice Using Integrative NMR and UPLC-MS/MS Based Metabolomics

Although metabolic perturbations are sensitive indicators for low-dose toxic effects, the metabolic mechanisms affected by rac-metalaxyl and metalaxyl-M in mammals from a metabolic profiling perspective remain unclear. In this study, the metabolic perturbations and toxic effects of rac-metalaxyl and metalaxyl-M in mice were carefully investigated using integrative nuclear magnetic resonance (NMR) and ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) based metabolomics. Histopathology, NMR-based untargeted urine profile, multivariate pattern recognition, metabolite identification, pathway analysis, UPLC-MS/MS based targeted serum amino acids, and tryptophan pathway analysis were determined after rac-metalaxyl and metalaxyl-M exposure, individually. Histopathology indicated that metalaxyl-M induced greater hepatocellular inflammatory, necrosis, and vacuolation in mice than rac-metalaxyl at the same exposure dosage. The metabolic perturbations induced by rac-metalaxyl and metalaxyl-M were directly separated using partial least-squares discriminant analysis (PLS-DA). Furthermore, metabolite identification and pathway analysis indicated that rac-metalaxyl mainly induced ten urine metabolite changes and four pathway fluctuations. However, metalaxyl-M induced 19 urine metabolite changes and six pathway fluctuations. Serum amino acids and tryptophan pathway metabolite changes induced by rac-metalaxyl and metalaxyl-M were also different even at the same exposure level. Such results may provide specific insight into the metabolic perturbations and toxic effects of rac-metalaxyl and metalaxyl-M, and contribute to providing available data for health risk assessments of rac-metalaxyl and metalaxyl-M at a metabolomics level.

Influence of the Crc global regulator on substrate uptake rates and the distribution of metabolic fluxes in Pseudomonas putida KT2440 growing in a complete medium

When the soil bacterium Pseudomonas putida grows in a complete medium, it prioritizes the assimilation of preferred carbon sources, optimizing its metabolism and growth. This regulatory process is orchestrated by the Crc and Hfq proteins. The present work examines the changes that occur in metabolic fluxes when the crc gene is inactivated and cells grow exponentially in LB complete medium. Analyses were performed at three different moments during exponential growth, examining the assimilation rates for the compounds present in LB, changes in the proteome, and the changes in metabolic fluxes predicted by the iJN1411 metabolic model for P. putida KT2440. During the early exponential phase, consumption rates for sugars, many organic acids and most amino acids were higher in a Crc-null strain than in the wild type, leading to an overflow of the metabolic pathways and the leakage of pyruvate and acetate. These accelerated consumption rates decreased during the mid-exponential phase, when cells mostly used sugars and alanine. At later times, pyruvate was recovered from the medium and utilized. The higher consumption rates of the Crc-null strain reduced the growth rate. The lack of the Crc/Hfq regulatory system thus led to unbalanced metabolism with poorly optimized metabolic fluxes.

Reverse diauxie phenotype in Pseudomonas aeruginosa biofilm revealed by exometabolomics and label-free proteomics

Microorganisms enhance fitness by prioritizing catabolism of available carbon sources using a process known as carbon catabolite repression (CCR). Planktonically grown Pseudomonas aeruginosa is known to prioritize the consumption of organic acids including lactic acid over catabolism of glucose using a CCR strategy termed "reverse diauxie." P. aeruginosa is an opportunistic pathogen with well-documented biofilm phenotypes that are distinct from its planktonic phenotypes. Reverse diauxie has been described in planktonic cultures, but it has not been documented explicitly in P. aeruginosa biofilms. Here a combination of exometabolomics and label-free proteomics was used to analyze planktonic and biofilm phenotypes for reverse diauxie. P. aeruginosa biofilm cultures preferentially consumed lactic acid over glucose, and in addition, the cultures catabolized the substrates completely and did not exhibit the acetate secreting "overflow" metabolism that is typical of many model microorganisms. The biofilm phenotype was enabled by changes in protein abundances, including lactate dehydrogenase, fumarate hydratase, GTP cyclohydrolase, L-ornithine N(5)-monooxygenase, and superoxide dismutase. These results are noteworthy because reverse diauxie-mediated catabolism of organic acids necessitates a terminal electron acceptor like O2, which is typically in low supply in biofilms due to diffusion limitation. Label-free proteomics identified dozens of proteins associated with biofilm formation including 16 that have not been previously reported, highlighting both the advantages of the methodology utilized here and the complexity of the proteomic adaptation for P. aeruginosa biofilms. Documenting the reverse diauxic phenotype in P. aeruginosa biofilms is foundational for understanding cellular nutrient and energy fluxes, which ultimately control growth and virulence.

Adapting to the Airways: Metabolic Requirements of Pseudomonas aeruginosa during the Infection of Cystic Fibrosis Patients

Pseudomonas aeruginosa is one of the major causes of morbidity and mortality of cystic fibrosis patients. During the infection, the bacteria colonize the nutritional rich lung mucus, which is present in the airway secretions in the patients, and they adapt their phenotype accordingly to the lung environment. In the airways, P. aeruginosa undergoes a broad metabolic rewiring as a consequence of the nutritional and stressful complexity of the lungs. However, the role of such metabolic rewiring on the infection outcome is poorly understood. Here, we review the metabolic evolution of clinical strains of P. aeruginosa during a cystic fibrosis lung infection and the metabolic functions operating in vivo under patho-physiological conditions. Finally, we discuss the perspective of modeling the cystic fibrosis environment using genome scale metabolic models of P. aeruginosa. Understanding the physiological changes occurring during the infection may pave the way to a more effective treatment for P. aeruginosa lung infections.

Systematic identification of metabolites controlling gene expression in E. coli

Metabolism controls gene expression through allosteric interactions between metabolites and transcription factors. These interactions are usually measured with in vitro assays, but there are no methods to identify them at a genome-scale in vivo. Here we show that dynamic transcriptome and metabolome data identify metabolites that control transcription factors in E. coli. By switching an E. coli culture between starvation and growth, we induce strong metabolite concentration changes and gene expression changes. Using Network Component Analysis we calculate the activities of 209 transcriptional regulators and correlate them with metabolites. This approach captures, for instance, the in vivo kinetics of CRP regulation by cyclic-AMP. By testing correlations between all pairs of transcription factors and metabolites, we predict putative effectors of 71 transcription factors, and validate five interactions in vitro. These results show that combining transcriptomics and metabolomics generates hypotheses about metabolism-transcription interactions that drive transitions between physiological states.

Interactions between metabolites and transcription factors are known to control gene expression but analyzing these events at genome-scale is challenging. Here, the authors integrate dynamic metabolome and transcriptome data from E.coli to predict regulatory metabolite-transcription factor interactions.

Evolution of (p)ppGpp-HPRT regulation through diversification of an allosteric oligomeric interaction

The alarmone (p)ppGpp regulates diverse targets, yet its target specificity and evolution remain poorly understood. Here, we elucidate the mechanism by which basal (p)ppGpp inhibits the purine salvage enzyme HPRT by sharing a conserved motif with its substrate PRPP. Intriguingly, HPRT regulation by (p)ppGpp varies across organisms and correlates with HPRT oligomeric forms. (p)ppGpp-sensitive HPRT exists as a PRPP-bound dimer or an apo- and (p)ppGpp-bound tetramer, where a dimer-dimer interface triggers allosteric structural rearrangements to enhance (p)ppGpp inhibition. Loss of this oligomeric interface results in weakened (p)ppGpp regulation. Our results reveal an evolutionary principle whereby protein oligomerization allows evolutionary change to accumulate away from a conserved binding pocket to allosterically alter specificity of ligand interaction. This principle also explains how another (p)ppGpp target GMK is variably regulated across species. Since most ligands bind near protein interfaces, we propose that this principle extends to many other protein-ligand interactions.

Gene networks that compensate for crosstalk with crosstalk

Crosstalk is a major challenge to engineering sophisticated synthetic gene networks. A common approach is to insulate signal-transduction pathways by minimizing molecular-level crosstalk between endogenous and synthetic genetic components, but this strategy can be difficult to apply in the context of complex, natural gene networks and unknown interactions. Here, we show that synthetic gene networks can be engineered to compensate for crosstalk by integrating pathway signals, rather than by pathway insulation. We demonstrate this principle using reactive oxygen species (ROS)-responsive gene circuits in Escherichia coli that exhibit concentration-dependent crosstalk with non-cognate ROS. We quantitatively map the degree of crosstalk and design gene circuits that introduce compensatory crosstalk at the gene network level. The resulting gene network exhibits reduced crosstalk in the sensing of the two different ROS. Our results suggest that simple network motifs that compensate for pathway crosstalk can be used by biological networks to accurately interpret environmental signals.

Crosstalk between genetic circuits is a major challenge for engineering sophisticated networks. Here the authors design networks that compensate for crosstalk by integrating, not insulating, pathways.

Host-Microbe-Drug-Nutrient Screen Identifies Bacterial Effectors of Metformin Therapy

Metformin is the first-line therapy for treating type 2 diabetes and a promising anti-aging drug. We set out to address the fundamental question of how gut microbes and nutrition, key regulators of host physiology, affect the effects of metformin. Combining two tractable genetic models, the bacterium E. coli and the nematode C. elegans, we developed a high-throughput four-way screen to define the underlying host-microbe-drug-nutrient interactions. We show that microbes integrate cues from metformin and the diet through the phosphotransferase signaling pathway that converges on the transcriptional regulator Crp. A detailed experimental characterization of metformin effects downstream of Crp in combination with metabolic modeling of the microbiota in metformin-treated type 2 diabetic patients predicts the production of microbial agmatine, a regulator of metformin effects on host lipid metabolism and lifespan. Our high-throughput screening platform paves the way for identifying exploitable drug-nutrient-microbiome interactions to improve host health and longevity through targeted microbiome therapies. VIDEO ABSTRACT.

Redox rebalance against genetic perturbations and modulation of central carbon metabolism by the oxidative stress regulation

The micro-aerophilic organisms and aerobes as well as yeast and higher organisms have evolved to gain energy through respiration (via oxidative phosphorylation), thereby enabling them to grow much faster than anaerobes. However, during respiration, reactive oxygen species (ROSs) are inherently (inevitably) generated, and threaten the cell's survival. Therefore, living organisms (or cells) must furnish the potent defense systems to keep such ROSs at harmless level, where the cofactor balance plays crucial roles. Namely, NADH is the source of energy generation (catabolism) in the respiratory chain reactions, through which ROSs are generated, while NADPH plays important roles not only for the cell synthesis (anabolism) but also for detoxifying ROSs. Therefore, the cell must rebalance the redox ratio by modulating the fluxes of the central carbon metabolism (CCM) by regulating the multi-level regulation machinery upon genetic perturbations and the change in the growth conditions. Here, we discuss about how aerobes accomplish such cofactor homeostasis against redox perturbations. In particular, we consider how single-gene mutants (including pgi, pfk, zwf, gnd and pyk mutants) modulate their metabolisms in relation to cofactor rebalance (and also by adaptive laboratory evolution). We also discuss about how the overproduction of NADPH (by the pathway gene mutation) can be utilized for the efficient production of useful value-added chemicals such as medicinal compounds, polyhydroxyalkanoates, and amino acids, all of which require NADPH in their synthetic pathways. We then discuss about the metabolic responses against oxidative stress, where αketoacids play important roles not only for the coordination between catabolism and anabolism, but also for detoxifying ROSs by non-enzymatic reactions, as well as for reducing the production of ROSs by repressing the activities of the TCA cycle and respiration (via carbon catabolite repression). Thus, we discuss about the mechanisms (basic strategies) that modulate the metabolism from respiration to respiro-fermentative metabolism causing overflow, based on the role of Pyk activity, affecting the NADPH production at the oxidative pentose phosphate (PP) pathway, and the roles of αketoacids for the change in the source of energy generation from the oxidative phosphorylation to the substrate level phosphorylation.

Characterization and mechanism of the effects of Mg-Fe layered double hydroxide nanoparticles on a marine bacterium: new insights from genomic and transcriptional analyses

BACKGROUND

Layered double hydroxides (LDHs) have received widespread attention for their potential applications in catalysis, polymer nanocomposites, pharmaceuticals, and sensors. Here, the mechanism underlying the physiological effects of Mg-Fe layered double hydroxide nanoparticles on the marine bacterial species Arthrobacter oxidans KQ11 was investigated.

RESULTS

Increased yields of marine dextranase (Aodex) were obtained by exposing A. oxidans KQ11 to Mg-Fe layered double hydroxide nanoparticles (Mg-Fe-LDH NPs). Furthermore, the potential effects of Mg-Fe-LDH NPs on bacterial growth and Aodex production were preliminarily investigated. A. oxidans KQ11 growth was not affected by exposure to the Mg-Fe-LDH NPs. In contrast, a U-shaped trend of Aodex production was observed after exposure to NPs at a concentration of 10 μg/L-100 mg/L, which was due to competition between Mg-Fe-LDH NP adsorption on Aodex and the promotion of Aodex expression by the NPs. The mechanism underling the effects of Mg-Fe-LDH NPs on A. oxidans KQ11 was investigated using a combination of physiological characterization, genomics, and transcriptomics. Exposure to 100 mg/L of Mg-Fe-LDH NPs led to NP adsorption onto Aodex, increased expression of Aodex, and generation of a new Shine-Dalgarno sequence (GGGAG) and sRNAs that both influenced the expression of Aodex. Moreover, the expressions of transcripts related to ferric iron metabolic functions were significantly influenced by treatment.

CONCLUSIONS

These results provide valuable information for further investigation of the A. oxidans KQ11 response to Mg-Fe-LDH NPs and will aid in achieving improved marine dextranase production, and even improve such activities in other marine microorganisms.

Systematic mapping of protein-metabolite interactions in central metabolism of Escherichia coli

Metabolite binding to proteins regulates nearly all cellular processes, but our knowledge of these interactions originates primarily from empirical in vitro studies. Here, we report the first systematic study of interactions between water-soluble proteins and polar metabolites in an entire biological subnetwork. To test the depth of our current knowledge, we chose to investigate the well-characterized Escherichia coli central metabolism. Using ligand-detected NMR, we assayed 29 enzymes towards binding events with 55 intracellular metabolites. Focusing on high-confidence interactions at a false-positive rate of 5%, we detected 98 interactions, among which purine nucleotides accounted for one-third, while 50% of all metabolites did not interact with any enzyme. In contrast, only five enzymes did not exhibit any metabolite binding and some interacted with up to 11 metabolites. About 40% of the interacting metabolites were predicted to be allosteric effectors based on low chemical similarity to their target's reactants. For five of the eight tested interactions, in vitro assays confirmed novel regulatory functions, including ATP and GTP inhibition of the first pentose phosphate pathway enzyme. With 76 new candidate regulatory interactions that have not been reported previously, we essentially doubled the number of known interactions, indicating that the presently available information about protein-metabolite interactions may only be the tip of the iceberg.

Effect of microbial activity and microbial community structure on the formation of dissolved organic nitrogen (DON) and bioavailable DON driven by low temperatures

Dissolved organic nitrogen (DON) formed by microbial metabolism in wastewater treatment processes adversely impacts wastewater reuse and receiving waters quality, and microbial metabolism is greatly influenced by temperatures. However, little is known about the effect of microorganisms on DON and bioavailable DON (ABDON) formation under low temperatures. In this study, six reactors were operated at low (8 °C and 15 °C) and room (25 °C) temperatures to evaluate the relationship between microbial activity, microbial communities, and DON and ABDON. Results showed that DON and ABDON concentrations significantly increased at low temperatures (p < 0.05, t-test). DON formation was significantly correlated to microbial activity only, with adenosine triphosphate (negative, r = -0.64) and polysaccharide (positive, r = 0.61) as key indicators; however, ABDON formation was influenced by both microbial activity (polysaccharide > triphenyltetrazolium chloride-dehydrogenases > adenosine triphosphate) and microbial community structure. Short-term tests using the biomass from six reactors were performed at room temperature to further validate the relationship. The distinct differences between these results provide a basis for different strategies on reducing effluent DON and ABDON under low temperatures.

Metabolic activity affects the response of single cells to a nutrient switch in structured populations

Microbes live in ever-changing environments where they need to adapt their metabolism to different nutrient conditions. Many studies have characterized the response of genetically identical cells to nutrient switches in homogeneous cultures; however, in nature, microbes often live in spatially structured groups such as biofilms where cells can create metabolic gradients by consuming and releasing nutrients. Consequently, cells experience different local microenvironments and vary in their phenotype. How does this phenotypic variation affect the ability of cells to cope with nutrient switches? Here, we address this question by growing dense populations of Escherichia coli in microfluidic chambers and studying a switch from glucose to acetate at the single-cell level. Before the switch, cells vary in their metabolic activity: some grow on glucose, while others cross-feed on acetate. After the switch, only few cells can resume growth after a period of lag. The probability to resume growth depends on a cells' phenotype prior to the switch: it is highest for cells cross-feeding on acetate, while it depends in a non-monotonic way on the growth rate for cells growing on glucose. Our results suggest that the strong phenotypic variation in spatially structured populations might enhance their ability to cope with fluctuating environments.

Pseudomonas putida KT2440 metabolism undergoes sequential modifications during exponential growth in a complete medium as compounds are gradually consumed

Pseudomonas putida is a soil bacterium with a versatile and robust metabolism. When confronted with mixtures of carbon sources, it prioritizes the utilization of the preferred compounds, optimizing metabolism and growth. This response is particularly strong when growing in a complex medium such as LB. This work examines the changes occurring in P. putida KT2440 metabolic fluxes, while it grows exponentially in LB medium and sequentially consumes the compounds available. Integrating the uptake rates for each compound at three different moments during the exponential growth with the changes observed in the proteome, and with the metabolic fluxes predicted by the iJN1411 metabolic model for this strain, allowed the metabolic rearrangements that occurred to be determined. The results indicate that the bacterium changes significantly the configuration of its metabolism during the early, mid and late exponential phases of growth. Sugars served as an energy source during the early phase and later as energy and carbon source. The configuration of the tricarboxylic acids cycle varied during growth, providing no energy in the early phase, and turning to a reductive mode in the mid phase and to an oxidative mode later on. This work highlights the dynamism and flexibility of P. putida metabolism.

Mining traits for the enrichment and isolation of not-yet-cultured populations

BACKGROUND

The lack of pure cultures limits our understanding into 99% of bacteria. Proper interpretation of the genetic and the transcriptional datasets can reveal clues for the enrichment and even isolation of the not-yet-cultured populations. Unraveling such information requires a proper mining method.

RESULTS

Here, we present a method to infer the hidden traits for the enrichment of not-yet-cultured populations. We demonstrate this method using Candidatus Accumulibacter. Our method constructs a whole picture of the carbon, electron, and energy flows in the not-yet-cultured populations from the genomic datasets. Then, it decodes the coordination across three flows from the transcriptional datasets. Based on it, our method diagnoses the status of the not-yet-cultured populations and provides strategy to optimize the enrichment systems.

CONCLUSION

Our method could shed light to the exploration into the bacterial dark matter in the environments.

GalK limits type I-F CRISPR-Cas expression in a CRP-dependent manner

CRISPR-Cas adaptive immune systems protect bacteria from phage predation, and other foreign genetic elements such as plasmids. Significant advances have been made regarding how CRISPR-Cas systems elicit immunity; however, comparatively little is known about their regulation. To study CRISPR-Cas regulation, we describe the construction of suicide lacZ-reporter plasmids with different antibiotic resistance cassettes. Through recombination into the host chromosome, single-copy expression can be achieved, thus preserving natural gene expression and maintaining a reporter expression output that reflects regulation within a normal genomic context. Previous work determined that the galactose metabolism gene galM, decreased the expression of the cas operon in Pectobacterium atrosepticum. We used the new integrative reporters to investigate galK, a gene that is located elsewhere in the genome and is responsible for the conversion of α-D-Galactose to Galactose-1-P during galactose metabolism. Deletion of galK led to elevated cas expression in a CRP-dependent manner but had no effect on CRISPR array expression. These results highlight that the metabolic status of the host cell is linked to the induction of CRISPR-Cas immunity.

Metabolic Remodeling during Biofilm Development of Bacillus subtilis

Bacterial biofilms are ubiquitous in natural environments and play an important role in many clinical, industrial, and ecological settings. Although much is known about the transcriptional regulatory networks that control biofilm formation in model bacteria such as Bacillus subtilis, very little is known about the role of metabolism in this complex developmental process. To address this important knowledge gap, we performed a time-resolved analysis of the metabolic changes associated with bacterial biofilm development in B. subtilis by combining metabolomic, transcriptomic, and proteomic analyses. Here, we report a widespread and dynamic remodeling of metabolism affecting central carbon metabolism, primary biosynthetic pathways, fermentation pathways, and secondary metabolism. This report serves as a unique hypothesis-generating resource for future studies on bacterial biofilm physiology. Outside the biofilm research area, this work should also prove relevant to any investigators interested in microbial physiology and metabolism.

Biofilms are structured communities of tightly associated cells that constitute the predominant state of bacterial growth in natural and human-made environments. Although the core genetic circuitry that controls biofilm formation in model bacteria such as Bacillus subtilis has been well characterized, little is known about the role that metabolism plays in this complex developmental process. Here, we performed a time-resolved analysis of the metabolic changes associated with pellicle biofilm formation and development in B. subtilis by combining metabolomic, transcriptomic, and proteomic analyses. We report surprisingly widespread and dynamic remodeling of metabolism affecting central carbon metabolism, primary biosynthetic pathways, fermentation pathways, and secondary metabolism. Most of these metabolic alterations were hitherto unrecognized as biofilm associated. For example, we observed increased activity of the tricarboxylic acid (TCA) cycle during early biofilm growth, a shift from fatty acid biosynthesis to fatty acid degradation, reorganization of iron metabolism and transport, and a switch from acetate to acetoin fermentation. Close agreement between metabolomic, transcriptomic, and proteomic measurements indicated that remodeling of metabolism during biofilm development was largely controlled at the transcriptional level. Our results also provide insights into the transcription factors and regulatory networks involved in this complex metabolic remodeling. Following upon these results, we demonstrated that acetoin production via acetolactate synthase is essential for robust biofilm growth and has the dual role of conserving redox balance and maintaining extracellular pH. This report represents a comprehensive systems-level investigation of the metabolic remodeling occurring during B. subtilis biofilm development that will serve as a useful road map for future studies on biofilm physiology.

Reliable imaging of ATP in living budding and fission yeast

Adenosine triphosphate (ATP) is a main metabolite essential for all living organisms. However, our understanding of ATP dynamics within a single living cell is very limited. Here, we optimized the ATP-biosensor QUEEN and monitored the dynamics of ATP with good spatial and temporal resolution in living yeasts. We found stable maintenance of ATP concentration in wild-type yeasts, regardless of carbon sources or cell cycle stages, suggesting that mechanism exists to maintain ATP at a specific concentration. We further found that ATP concentration is not necessarily an indicator of metabolic activity, as there is no clear correlation between ATP level and growth rates. During fission yeast meiosis, we found a reduction in ATP levels, suggesting that ATP homeostasis is controlled by differentiation. The use of QUEEN in yeasts offers an easy and reliable assay for ATP dynamicity and will answer several unaddressed questions about cellular metabolism in eukaryotes.

Chemical synthesis rewriting of a bacterial genome to achieve design flexibility and biological functionality

The fundamental biological functions of a living cell are stored within the DNA sequence of its genome. Classical genetic approaches dissect the functioning of biological systems by analyzing individual genes, yet uncovering the essential gene set of an organism has remained very challenging. It is argued that the rewriting of entire genomes through the process of chemical synthesis provides a powerful and complementary research concept to understand how essential functions are programed into genomes.

Understanding how to program biological functions into artificial DNA sequences remains a key challenge in synthetic genomics. Here, we report the chemical synthesis and testing of Caulobacter ethensis-2.0 (C. eth-2.0), a rewritten bacterial genome composed of the most fundamental functions of a bacterial cell. We rebuilt the essential genome of Caulobacter crescentus through the process of chemical synthesis rewriting and studied the genetic information content at the level of its essential genes. Within the 785,701-bp genome, we used sequence rewriting to reduce the number of encoded genetic features from 6,290 to 799. Overall, we introduced 133,313 base substitutions, resulting in the rewriting of 123,562 codons. We tested the biological functionality of the genome design in C. crescentus by transposon mutagenesis. Our analysis revealed that 432 essential genes of C. eth-2.0, corresponding to 81.5% of the design, are equal in functionality to natural genes. These findings suggest that neither changing mRNA structure nor changing the codon context have significant influence on biological functionality of synthetic genomes. Discovery of 98 genes that lost their function identified essential genes with incorrect annotation, including a limited set of 27 genes where we uncovered noncoding control features embedded within protein-coding sequences. In sum, our results highlight the promise of chemical synthesis rewriting to decode fundamental genome functions and its utility toward the design of improved organisms for industrial purposes and health benefits.

Increasing consensus of context-specific metabolic models by integrating data-inferred cell functions

Genome-scale metabolic models provide a valuable context for analyzing data from diverse high-throughput experimental techniques. Models can quantify the activities of diverse pathways and cellular functions. Since some metabolic reactions are only catalyzed in specific environments, several algorithms exist that build context-specific models. However, these methods make differing assumptions that influence the content and associated predictive capacity of resulting models, such that model content varies more due to methods used than cell types. Here we overcome this problem with a novel framework for inferring the metabolic functions of a cell before model construction. For this, we curated a list of metabolic tasks and developed a framework to infer the activity of these functionalities from transcriptomic data. We protected the data-inferred tasks during the implementation of diverse context-specific model extraction algorithms for 44 cancer cell lines. We show that the protection of data-inferred metabolic tasks decreases the variability of models across extraction methods. Furthermore, resulting models better capture the actual biological variability across cell lines. This study highlights the potential of using biological knowledge, inferred from omics data, to obtain a better consensus between existing extraction algorithms. It further provides guidelines for the development of the next-generation of data contextualization methods.

Disease Development Is Accompanied by Changes in Bacterial Protein Abundance and Functions in a Refined Model of Dextran Sulfate Sodium (DSS)-Induced Colitis

Using the acute dextran sulfate sodium (DSS)-induced colitis model, studies have demonstrated that intestinal inflammation is accompanied by major changes in the composition of the intestinal microbiota. Only little is known about the microbial changes and more importantly their functional impact in the chronic DSS colitis model. We used a refined model of chronic DSS-induced colitis that reflects typical symptoms of the human disease without detrimental weight loss usually observed in DSS models. We sampled cecum and colon content as well as colon mucus from healthy and diseased mouse cohorts ( n = 12) and applied 16S rRNA gene sequencing and metaproteomics. An increase of Prevotella sp. in both colon content and mucus was observed. Functional differences were observed between sample types demonstrating the importance of separately sampling lumen content and mucus. The abundance of Desulfovibrio, a sulfate-reducing bacterium, was positively associated with the carbon metabolism. Lachnoclostridium was positively correlated to both vitamin B6 and tryptophan metabolism. In summary, functional changes in the distal colon caused by DSS treatment were more pronounced in the mucus-associated microbiota than in the microbiota present in the distal colon content.

Proteome-wide solubility and thermal stability profiling reveals distinct regulatory roles for ATP

Adenosine triphosphate (ATP) plays fundamental roles in cellular biochemistry and was recently discovered to function as a biological hydrotrope. Here, we use mass spectrometry to interrogate ATP-mediated regulation of protein thermal stability and protein solubility on a proteome-wide scale. Thermal proteome profiling reveals high affinity interactions of ATP as a substrate and as an allosteric modulator that has widespread influence on protein complexes and their stability. Further, we develop a strategy for proteome-wide solubility profiling, and discover ATP-dependent solubilization of at least 25% of the insoluble proteome. ATP increases the solubility of positively charged, intrinsically disordered proteins, and their susceptibility for solubilization varies depending on their localization to different membrane-less organelles. Moreover, a few proteins, exhibit an ATP-dependent decrease in solubility, likely reflecting polymer formation. Our data provides a proteome-wide, quantitative insight into how ATP influences protein structure and solubility across the spectrum of physiologically relevant concentrations.

ATP can function as a biological hydrotrope, but its global effects on protein solubility have not yet been characterized. Here, the authors quantify the effect of ATP on the thermal stability and solubility of the cellular proteome, providing insights into protein solubility regulation by ATP.

Multiple links connect central carbon metabolism to DNA replication initiation and elongation in Bacillus subtilis

DNA replication is coupled to growth by an unknown mechanism. Here, we investigated this coupling by analyzing growth and replication in 15 mutants of central carbon metabolism (CCM) cultivated in three rich media. In about one-fourth of the condition tested, defects in replication resulting from changes in initiation or elongation were detected. This uncovered 11 CCM genes important for replication and showed that some of these genes have an effect in one, two or three media. Additional results presented here and elsewhere (Jannière, L., Canceill, D., Suski, C., et al. (2007), PLoS One, 2, e447.) showed that, in the LB medium, the CCM genes important for DNA elongation (gapA and ackA) are genetically linked to the lagging strand polymerase DnaE while those important for initiation (pgk and pykA) are genetically linked to the replication enzymes DnaC (helicase), DnaG (primase) and DnaE. Our work thus shows that the coupling between growth and replication involves multiple, medium-dependent links between CCM and replication. They also suggest that changes in CCM may affect initiation by altering the functional recruitment of DnaC, DnaG and DnaE at the chromosomal origin, and may affect elongation by altering the activity of DnaE at the replication fork. The underlying mechanism is discussed.

Impact of primary carbon sources on microbiome shaping and biotransformation of pharmaceuticals and personal care products

Knowledge of the conditions that promote the growth and activity of pharmaceutical and personal care product (PPCP)-degrading microorganisms within mixed microbial systems are needed to shape microbiomes in biotreatment reactors and manage process performance. Available carbon sources influence microbial community structure, and specific carbon sources could potentially be added to end-of-treatment train biotreatment systems (e.g., soil aquifer treatment [SAT]) to select for the growth and activity of a range of microbial phylotypes that collectively degrade target PPCPs. Herein, the impacts of primary carbon sources on PPCP biodegradation and microbial community structure were explored to identify promising carbon sources for PPCP biotreatment application. Six types of primary carbon sources were investigated: casamino acids, two humic acid and peptone mixtures (high and low amounts of humic acid), molasses, an organic acids mixture, and phenol. Biodegradation was tracked for five PPCPs (diclofenac, 5-fluorouracil, gemfibrozil, ibuprofen, and triclosan). Primary carbon sources were found to differentially impact microbial community structures and rates and efficiencies of PPCP biotransformation. Of the primary carbon sources tested, casamino acids, organic acids, and phenol showed the fastest biotransformation; however, on a biomass-normalized basis, both humic acid-peptone mixtures showed comparable or superior biotransformation. By comparing microbial communities for the different primary carbon sources, abundances of unclassified Beijerinckiaceae, Beijerinckia, Sphingomonas, unclassified Sphingomonadaceae, Flavobacterium, unclassified Rhizobiales, and Nevskia were statistically linked with biotransformation of specific PPCPs.

Extreme pathway analysis reveals the organizing rules of metabolic regulation

Cellular systems shift metabolic states by adjusting gene expression and enzyme activities to adapt to physiological and environmental changes. Biochemical and genetic studies are identifying how metabolic regulation affects the selection of metabolic phenotypes. However, how metabolism influences its regulatory architecture still remains unexplored. We present a new method of extreme pathway analysis (the minimal set of conically independent metabolic pathways) to deduce regulatory structures from pure pathway information. Applying our method to metabolic networks of human red blood cells and Escherichia coli, we shed light on how metabolic regulation are organized by showing which reactions within metabolic networks are more prone to transcriptional or allosteric regulation. Applied to a human genome-scale metabolic system, our method detects disease-associated reactions. Thus, our study deepens the understanding of the organizing principle of cellular metabolic regulation and may contribute to metabolic engineering, synthetic biology, and disease treatment.

Transcriptomic responses of the marine cyanobacterium Prochlorococcus to viral lysis products

Viral infection of marine phytoplankton releases a variety of dissolved organic matter (DOM). The impact of viral DOM (vDOM) on the uninfected co-occurring phytoplankton remains largely unknown. Here, we conducted transcriptomic analyses to study the effects of vDOM on the cyanobacterium Prochlorococcus, which is the most abundant photosynthetic organism on Earth. Using Prochlorococcus MIT9313, we showed that its growth was not affected by vDOM, but many tRNAs increased in abundance. We tested tRNA-gly and found that its abundance increased upon addition of glycine. The decreased transcript abundances of N metabolism genes also suggested that Prochlorococcus responded to organic N compounds in vDOM. Addition of vDOM to Prochlorococcus reduced the maximum photochemical efficiency of photosystem II and CO₂ fixation while increasing its respiration rate, consistent with differentially abundant transcripts related to photosynthesis and respiration. One of the highest positive fold-changes was observed for the 6S RNA, a noncoding RNA functioning as a global transcriptional regulator in bacteria. The high level of 6S RNA might be responsible for some of the observed transcriptional responses. Taken together, our results revealed the transcriptional regulation of Prochlorococcus in response to viral lysis products and suggested its metabolic potential to utilize organic N compounds.

Allosteric Feedback Inhibition Enables Robust Amino Acid Biosynthesis in E. coli by Enforcing Enzyme Overabundance

Microbes must ensure robust amino acid metabolism in the face of external and internal perturbations. This robustness is thought to emerge from regulatory interactions in metabolic and genetic networks. Here, we explored the consequences of removing allosteric feedback inhibition in seven amino acid biosynthesis pathways in Escherichia coli (arginine, histidine, tryptophan, leucine, isoleucine, threonine, and proline). Proteome data revealed that enzyme levels decreased in five of the seven dysregulated pathways. Despite that, flux through the dysregulated pathways was not limited, indicating that enzyme levels are higher than absolutely needed in wild-type cells. We showed that such enzyme overabundance renders the arginine, histidine, and tryptophan pathways robust against perturbations of gene expression, using a metabolic model and CRISPR interference experiments. The results suggested a sensitive interaction between allosteric feedback inhibition and enzyme-level regulation that ensures robust yet efficient biosynthesis of histidine, arginine, and tryptophan in E. coli.

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Amino acid biosynthesis enzymes do not normally operate at maximum capacity

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Allosteric feedback inhibition ensures that enzymes are overabundant

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Enzyme overabundance provides robustness against decreases in gene expression

Yeast Systems Biology: The Continuing Challenge of Eukaryotic Complexity

Research on yeast has produced a plethora of tools and resources that have been central to the progress of systems biology. This chapter reviews these resources, explains the innovations that have been made since the first edition of this book, and introduces the constituent chapters of the current edition. The value of these resources not only in building and testing models of the functional networks of the yeast cell, but also in providing a foundation for network studies on the molecular basis of complex human diseases is considered. The gaps in this vast compendium of data, including enzyme kinetic characteristics, biomass composition, transport processes, and cell-cell interactions are discussed, as are the interactions between yeast cells and those of other species. The relevance of these studies to both traditional and advanced biotechnologies and to human medicine is considered, and the opportunities and challenges in using unicellular yeasts to model the systems of multicellular organisms are presented.

Metabolomics: A Microbial Physiology and Metabolism Perspective

Metabolomics is valuable for studying microbial metabolism, which is often used to elucidate biological functions. Effective application of metabolomics is enhanced by fundamental understanding of microbial physiology and metabolism. This review briefly highlights important aspects of metabolism that are essential for designing and executing effective metabolic and metabolomics studies. The influence of microbial physiology and metabolism on growth, energy metabolism and regulation is briefly reviewed. The chapter also evaluates factors affecting metabolic prediction.