Visual account of protein investment in cellular functions

Pareto Optimality Explanation of the Glycolytic Alternatives in Nature

The Entner-Doudoroff (ED) and Embden-Meyerhof-Parnas (EMP) glycolytic pathways are largely conserved across glycolytic species in nature. Is this a coincidence, convergent evolution or there exists a driving force towards either of the two pathway designs? We addressed this question by first employing a variant of the optStoic algorithm to exhaustively identify over 11,916 possible routes between glucose and pyruvate at different pre-determined stoichiometric yields of ATP. Subsequently, we analyzed the thermodynamic feasibility of all the pathways at physiological metabolite concentrations and quantified the protein cost of the feasible solutions. Pareto optimality analysis between energy efficiency and protein cost reveals that the naturally evolved ED and EMP pathways are indeed among the most protein cost-efficient pathways in their respective ATP yield categories and remain thermodynamically feasible across a wide range of ATP/ADP ratios and pathway intermediate metabolite concentration ranges. In contrast, pathways with higher ATP yield (>2) while feasible, are bound within stringent and often extreme operability ranges of cofactor and intermediate metabolite concentrations. The preponderance of EMP and ED is thus consistent with not only optimally balancing energy yield vs. enzyme cost but also with ensuring operability for wide metabolite concentration ranges and ATP/ADP ratios.

Growth-dependent recombinant product formation kinetics can be reproduced through engineering of glucose transport and is prone to phenotypic heterogeneity

Background

Escherichia coli W3110 and a group of six isogenic derivatives, each displaying distinct specific rates of glucose consumption were characterized to determine levels of GFP production and population heterogeneity. These strains have single or combinatory deletions in genes encoding phosphoenolpyruvate:sugar phosphotransferase system (PTS) permeases as PtsG and ManX, as well as common components EI, Hpr protein and EIIA, also the non-PTS Mgl galactose/glucose ABC transporter. They have been transformed for expressing GFP based on a lac-based expression vector, which is subject to bistability.

Results

These strains displayed specific glucose consumption and growth rates ranging from 1.75 to 0.45 g/g h and 0.54 to 0.16 h⁻¹, respectively. The rate of acetate production was strongly reduced in all mutant strains when compared with W3110/pV21. In bioreactor cultures, wild type W3110/pV21 produced 50.51 mg/L GFP, whereas strains WG/pV21 with inactive PTS IICBGlc and WGM/pV21 with the additional inactivation of PTS IIABMan showed the highest titers of GFP, corresponding to 342 and 438 mg/L, respectively. Moreover, we showed experimentally that bistable expression systems, as lac-based ones, induce strong phenotypic segregation among microbial populations.

Conclusions

We have demonstrated that reduction on glucose consumption rate in E. coli leads to an improvement of GFP production. Furthermore, from the perspective of phenotypic heterogeneity, we observed in this case that heterogeneous systems are also the ones leading to the highest performance. This observation suggests reconsidering the generally accepted proposition stating that phenotypic heterogeneity is generally unwanted in bioprocess applications.

Electronic supplementary material

The online version of this article (10.1186/s12934-019-1073-5) contains supplementary material, which is available to authorized users.

The human allicin-proteome: S-thioallylation of proteins by the garlic defence substance allicin and its biological effects

A single clove of edible garlic (Allium sativum L.) of about 10 g produces up to 5 mg of allicin (diallylthiosulfinate), a thiol-reactive sulfur-containing defence substance that gives injured garlic tissue its characteristic smell. Allicin induces apoptosis or necrosis in a dose-dependent manner but biocompatible doses influence cellular metabolism and signalling cascades. Oxidation of protein thiols and depletion of the glutathione pool are thought to be responsible for allicin's physiological effects. Here, we studied the effect of allicin on post-translational thiol-modification in human Jurkat T-cells using shotgun LC-MS/MS analyses. We identified 332 proteins that were modified by S-thioallylation in the Jurkat cell proteome which causes a mass shift of 72 Da on cysteines. Many S-thioallylated proteins are highly abundant proteins, including cytoskeletal proteins tubulin, actin, cofilin, filamin and plastin-2, the heat shock chaperones HSP90 and HSPA4, the glycolytic enzymes GAPDH, ALDOA, PKM as well the protein translation factor EEF2. Allicin disrupted the actin cytoskeleton in murine L929 fibroblasts. Allicin stimulated the immune response by causing Zn2+ release from proteins and increasing the Zn2+-dependent IL-1-triggered production of IL-2 in murine EL-4 T-cells. Furthermore, allicin caused inhibition of enolase activity, an enzyme considered a cancer therapy target. In conclusion, our study revealed the widespread extent of S-thioallylation in the human Jurkat cell proteome and showed effects of allicin exposure on essential cellular functions of selected targets, many of which are targets for cancer therapy.

Essential metabolism for a minimal cell

JCVI-syn3A, a robust minimal cell with a 543 kbp genome and 493 genes, provides a versatile platform to study the basics of life. Using the vast amount of experimental information available on its precursor, Mycoplasma mycoides capri, we assembled a near-complete metabolic network with 98% of enzymatic reactions supported by annotation or experiment. The model agrees well with genome-scale in vivo transposon mutagenesis experiments, showing a Matthews correlation coefficient of 0.59. The genes in the reconstruction have a high in vivo essentiality or quasi-essentiality of 92% (68% essential), compared to 79% in silico essentiality. This coherent model of the minimal metabolism in JCVI-syn3A at the same time also points toward specific open questions regarding the minimal genome of JCVI-syn3A, which still contains many genes of generic or completely unclear function. In particular, the model, its comparison to in vivo essentiality and proteomics data yield specific hypotheses on gene functions and metabolic capabilities; and provide suggestions for several further gene removals. In this way, the model and its accompanying data guide future investigations of the minimal cell. Finally, the identification of 30 essential genes with unclear function will motivate the search for new biological mechanisms beyond metabolism.

One way that researchers can test whether they understand a biological system is to see if they can accurately recreate it as a computer model. The more they learn about living things, the more the researchers can improve their models and the closer the models become to simulating the original. In this approach, it is best to start by trying to model a simple system.

Biologists have previously succeeded in creating ‘minimal bacterial cells’. These synthetic cells contain fewer genes than almost all other living things and they are believed to be among the simplest possible forms of life that can grow on their own. The minimal cells can produce all the chemicals that they need to survive – in other words, they have a metabolism. Accurately recreating one of these cells in a computer is a key first step towards simulating a complete living system.

Breuer et al. have developed a computer model to simulate the network of the biochemical reactions going on inside a minimal cell with just 493 genes. By altering the parameters of their model and comparing the results to experimental data, Breuer et al. explored the accuracy of their model. Overall, the model reproduces experimental results, but it is not yet perfect. The differences between the model and the experiments suggest new questions and tests that could advance our understanding of biology. In particular, Breuer et al. identified 30 genes that are essential for life in these cells but that currently have no known purpose.

Continuing to develop and expand models like these to reproduce more complex living systems provides a tool to test current knowledge of biology. These models may become so advanced that they could predict how living things will respond to changing situations. This would allow scientists to test ideas sooner and make much faster progress in understanding life on Earth. Ultimately, these models could one day help to accelerate medical and industrial processes to save lives and enhance productivity.

Analysis of the Bacterial and Host Proteins along and across the Porcine Gastrointestinal Tract

Pigs are among the most important farm animals worldwide and research to optimize their feed efficiency and improve their welfare is still in progress. The porcine intestinal microbiome is so far mainly known from sequencing-based studies. Digesta and mucosa samples from five different porcine gastrointestinal tract sections were analyzed by metaproteomics to obtain a deeper insight into the functions of bacterial groups with concomitant analyses of host proteins. Firmicutes (Prevotellaceae) dominated mucosa and digesta samples, followed by Bacteroidetes. Actinobacteria and Proteobacteria were much higher in abundance in mucosa compared to digesta samples. Functional profiling reveals the presence of core functions shared between digesta and mucosa samples. Protein abundances of energy production and conversion were higher in mucosa samples, whereas in digesta samples more proteins were involved in lipid transport and metabolism; short-chain fatty acids production were detected. Differences were also highlighted between sections, with the small intestine appearing more involved in carbohydrate transport and metabolism than the large intestine. Thus, this study produced the first functional analyses of the porcine GIT biology, discussing the findings in relation to expected bacterial and host functions.

Subcellular transcriptomes and proteomes of developing axon projections in the cerebral cortex

The development of neural circuits relies on axon projections establishing diverse, yet well-defined, connections between areas of the nervous system. Each projection is formed by growth cones-subcellular specializations at the tips of growing axons, encompassing sets of molecules that control projection-specific growth, guidance, and target selection¹. To investigate the set of molecules within native growth cones that form specific connections, here we developed growth cone sorting and subcellular RNA-proteome mapping, an approach that identifies and quantifies local transcriptomes and proteomes from labelled growth cones of single projections in vivo. Using this approach on the developing callosal projection of the mouse cerebral cortex, we mapped molecular enrichments in trans-hemispheric growth cones relative to their parent cell bodies, producing paired subcellular proteomes and transcriptomes from single neuron subtypes directly from the brain. These data provide generalizable proof-of-principle for this approach, and reveal molecular specializations of the growth cone, including accumulations of the growth-regulating kinase mTOR², together with mRNAs that contain mTOR-dependent motifs^3,4. These findings illuminate the relationships between subcellular distributions of RNA and protein in developing projection neurons, and provide a systems-level approach for the discovery of subtype- and stage-specific molecular substrates of circuit wiring, miswiring, and the potential for regeneration.

Quantitative insights into the cyanobacterial cell economy

Phototrophic microorganisms are promising resources for green biotechnology. Compared to heterotrophic microorganisms, however, the cellular economy of phototrophic growth is still insufficiently understood. We provide a quantitative analysis of light-limited, light-saturated, and light-inhibited growth of the cyanobacterium Synechocystis sp. PCC 6803 using a reproducible cultivation setup. We report key physiological parameters, including growth rate, cell size, and photosynthetic activity over a wide range of light intensities. Intracellular proteins were quantified to monitor proteome allocation as a function of growth rate. Among other physiological acclimations, we identify an upregulation of the translational machinery and downregulation of light harvesting components with increasing light intensity and growth rate. The resulting growth laws are discussed in the context of a coarse-grained model of phototrophic growth and available data obtained by a comprehensive literature search. Our insights into quantitative aspects of cyanobacterial acclimations to different growth rates have implications to understand and optimize photosynthetic productivity.

Specific MHC-I Peptides Are Induced Using PROTACs

Peptides presented by the class-I major histocompatibility complex (MHC-I) are important targets for immunotherapy. The identification of these peptide targets greatly facilitates the generation of T-cell-based therapeutics. Herein, we report the capability of proteolysis targeting chimera (PROTAC) compounds to induce the presentation of specific MHC class-I peptides derived from endogenous cellular proteins. Using LC-MS/MS, we identified several BET-derived MHC-I peptides induced by treatment with three BET-directed PROTAC compounds. To understand our ability to tune this process, we measured the relative rate of presentation of these peptides under varying treatment conditions using label-free mass spectrometry quantification. We found that the rate of peptide presentation reflected the rate of protein degradation, indicating a direct relationship between PROTAC treatment and peptide presentation. We additionally analyzed the effect of PROTAC treatment on the entire immunopeptidome and found many new peptides that were displayed in a PROTAC-specific fashion: we determined that these identifications map to the BET pathway, as well as, potential off-target or unique-to-PROTAC pathways. This work represents the first evidence of the use of PROTAC compounds to induce the presentation of MHC-I peptides from endogenous cellular proteins, highlighting the capability of PROTAC compounds for the discovery and generation of new targets for immunotherapy.

A linked organ-on-chip model of the human neurovascular unit reveals the metabolic coupling of endothelial and neuronal cells

The neurovascular unit (NVU) regulates metabolic homeostasis as well as drug pharmacokinetics and pharmacodynamics in the central nervous system. Metabolic fluxes and conversions over the NVU rely on interactions between brain microvascular endothelium, perivascular pericytes, astrocytes and neurons, making it difficult to identify the contributions of each cell type. Here we model the human NVU using microfluidic organ chips, allowing analysis of the roles of individual cell types in NVU functions. Three coupled chips model influx across the blood-brain barrier (BBB), the brain parenchymal compartment and efflux across the BBB. We used this linked system to mimic the effect of intravascular administration of the psychoactive drug methamphetamine and to identify previously unknown metabolic coupling between the BBB and neurons. Thus, the NVU system offers an in vitro approach for probing transport, efficacy, mechanism of action and toxicity of neuroactive drugs.

Clinical Proteomics of Breast Cancer Reveals a Novel Layer of Breast Cancer Classification

Breast cancer classification has been the focus of numerous worldwide efforts, analyzing the molecular basis of breast cancer subtypes and aiming to associate them with clinical outcome and to improve the current diagnostic routine. Genomic and transcriptomic profiles of breast cancer have been well established, however the proteomic contribution to these profiles has yet to be elucidated. In this work, we utilized mass spectrometry-based proteomic analysis on more than 130 clinical breast samples to demonstrate intertumor heterogeneity across three breast cancer subtypes and healthy tissue. Unsupervised analysis identified four proteomic clusters, among them, one that represents a novel luminal subtype characterized by increased PI3K signaling. This subtype was further validated using an independent protein-based dataset, but not in two independent transcriptome cohorts. These results demonstrate the importance of deep proteomic analysis, which may affect cancer treatment decision making.Significance: These findings utilize extensive proteomics to identify a novel luminal breast cancer subtype, highlighting the added value of clinical proteomics in breast cancer to identify unique features not observable by genomic approaches. Cancer Res; 78(20); 6001-10. ©2018 AACR.

Staphylococcus aureus Biofilm Growth on Cystic Fibrosis Airway Epithelial Cells Is Enhanced during Respiratory Syncytial Virus Coinfection

The airways of individuals with cystic fibrosis (CF) are commonly chronically infected, and Staphylococcus aureus is the dominant bacterial respiratory pathogen in CF children. CF patients also experience frequent respiratory virus infections, and it has been hypothesized that virus coinfection increases the severity of S. aureus lung infections in CF. We investigated the relationship between S. aureus and the CF airway epithelium and observed that coinfection with respiratory syncytial virus (RSV) enhances S. aureus biofilm growth. However, iron, which was previously found to be a significant factor influencing Pseudomonas aeruginosa biofilms during virus coinfection, plays a minor role in S. aureus coinfections. Transcriptomic analyses provided new insight into how bacterial and viral pathogens alter host defense and suggest potential pathways by which dampening of host responses to one pathogen may favor persistence of another in the CF airways, highlighting complex interactions occurring between bacteria, viruses, and the host during polymicrobial infections.

Staphylococcus aureus is a major cause of chronic respiratory infection in patients with cystic fibrosis (CF). We recently showed that Pseudomonas aeruginosa exhibits enhanced biofilm formation during respiratory syncytial virus (RSV) coinfection on human CF airway epithelial cells (AECs). The impact of respiratory viruses on other bacterial pathogens during polymicrobial infections in CF remains largely unknown. To investigate if S. aureus biofilm growth in the CF airways is impacted by virus coinfection, we evaluated S. aureus growth on CF AECs. Initial studies showed an increase in S. aureus growth over 24 h, and microscopy revealed biofilm-like clusters of bacteria on CF AECs. Biofilm growth was enhanced when CF AECs were coinfected with RSV, and this observation was confirmed with S. aureus CF clinical isolates. Apical conditioned medium from RSV-infected cells promoted S. aureus biofilms in the absence of the host epithelium, suggesting that a secreted factor produced during virus infection benefits S. aureus biofilms. Exogenous iron addition did not significantly alter biofilm formation, suggesting that it is not likely the secreted factor. We further characterized S. aureus-RSV coinfection in our model using dual host-pathogen RNA sequencing, allowing us to observe specific contributions of S. aureus and RSV to the host response during coinfection. Using the dual host-pathogen RNA sequencing approach, we observed increased availability of nutrients from the host and upregulation of S. aureus genes involved in growth, protein translation and export, and amino acid metabolism during RSV coinfection.

IMPORTANCE The airways of individuals with cystic fibrosis (CF) are commonly chronically infected, and Staphylococcus aureus is the dominant bacterial respiratory pathogen in CF children. CF patients also experience frequent respiratory virus infections, and it has been hypothesized that virus coinfection increases the severity of S. aureus lung infections in CF. We investigated the relationship between S. aureus and the CF airway epithelium and observed that coinfection with respiratory syncytial virus (RSV) enhances S. aureus biofilm growth. However, iron, which was previously found to be a significant factor influencing Pseudomonas aeruginosa biofilms during virus coinfection, plays a minor role in S. aureus coinfections. Transcriptomic analyses provided new insight into how bacterial and viral pathogens alter host defense and suggest potential pathways by which dampening of host responses to one pathogen may favor persistence of another in the CF airways, highlighting complex interactions occurring between bacteria, viruses, and the host during polymicrobial infections.

Optimal Allocation of Bacterial Protein Resources under Nonlethal Protein Maturation Stress

Under different environmental stresses, bacteria optimize the allocation of cellular resources through a variety of mechanisms. Recently, researchers have used phenomenological models to quantitatively characterize the allocation of bacterial protein resources under metabolic and translational limitations. Some stresses interfere with protein maturation, thereby enhancing the expression of chaperones and proteases. However, the reallocation of protein resources caused by such environmental stresses has not been modeled quantitatively. Here, we developed a dynamic model of coarse-grained protein resource fluxes based on a self-replicator that includes protein maturation and degradation. Through flux balance analysis, it produces a constrained optimization problem that can be solved analytically. Accordingly, we predicted protein allocation fractions as functions of growth rate under different limitations, which are basically in line with empirical data. We cultured Escherichia coli in media containing different concentrations of chloramphenicol, acetic acid, and paraquat and measured the functional relationship between the expression level of β-galactosidase driven by a constitutive promoter and the bacterial growth rate, respectively. Taking into account the possible mode of stress limitation on the fluxes, our model reproduces this experimentally measured relationship. In addition, our model is in good agreement with the experimental relationship between growth rate and proteome fraction of unnecessary protein in E. coli, considering the unoptimized upregulation of chaperones with useless protein overexpression. The results provide a more systematic view of bacterial stress adaptation that may help in designing for bioengineering or medical interventions.

Mapping the HLA Ligandome of Colorectal Cancer Reveals an Imprint of Malignant Cell Transformation

Immune cell infiltrates have proven highly relevant for colorectal carcinoma prognosis, making colorectal cancer a promising candidate for immunotherapy. Because tumors interact with the immune system via HLA-presented peptide ligands, exact knowledge of the peptidome constitution is fundamental for understanding this relationship. Here, we comprehensively describe the naturally presented HLA ligandome of colorectal carcinoma and corresponding nonmalignant colon (NMC) tissue. Mass spectrometry identified 35,367 and 28,132 HLA class I ligands on colorectal carcinoma and NMC, attributable to 7,684 and 6,312 distinct source proteins, respectively. Cancer-exclusive peptides were assessed on source protein level using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein analysis through evolutionary relationships (PANTHER), revealing pathognomonic colorectal carcinoma-associated pathways, including Wnt, TGFβ, PI3K, p53, and RTK-RAS. Relative quantitation of peptide presentation on paired colorectal carcinoma and NMC tissue further identified source proteins from cancer- and infection-associated pathways to be overrepresented merely within the colorectal carcinoma ligandome. From the pool of tumor-exclusive peptides, a selected HLA-ligand subset was assessed for immunogenicity, with the majority exhibiting an existing T-cell repertoire. Overall, these data show that the HLA ligandome reflects cancer-associated pathways implicated in colorectal carcinoma oncogenesis, suggesting that alterations in tumor cell metabolism could result in cancer-specific, albeit not mutation-derived, tumor antigens. Hence, a defined pool of unique tumor peptides, attributable to complex cellular alterations that are exclusive to malignant cells, might comprise promising candidates for immunotherapeutic applications.Significance: Cancer-associated pathways are reflected in the antigenic landscape of colorectal cancer, suggesting that tumor-specific antigens do not necessarily have to be mutation-derived but may also originate from other alterations in cancer cells. Cancer Res; 78(16); 4627-41. ©2018 AACR.

In vitro gentamicin exposure alters caveolae protein profile in cochlear spiral ligament pericytes

Background

The aminoglycoside antibiotic gentamicin is an ototoxic drug and has been used experimentally to investigate cochlear damage induced by noise.We have investigated the changes in the protein profile associated with caveolae in gentamicin treated and untreated spiral ligament (SL) pericytes, specialized cells in the blood labyrinth barrier of the inner ear microvasculature. Pericytes from various microvascular beds express caveolae, protein and cholesterol rich microdomains, which can undergo endocytosis and transcytosis to transport small molecules in and out the cells. A different protein profile in transport-specialized caveolae may induce pathological changes affecting the integrity of the blood labyrinth barrier and ultimately contributing to hearing loss.

Method

Caveolae isolation from treated and untreated cells is achieved through ultracentrifugation of the lysates in discontinuous gradients. Mass spectrometry (LC-MS/MS) analysis identifies the proteins in the two groups. Proteins segregating with caveolae isolated from untreated SL pericytes are then compared to caveolae isolated from SL pericytes treated with the gentamicin for 24 h. Data are analyzed using bioinformatic tools.

Results

The caveolae proteome in gentamicin treated cells shows that 40% of total proteins are uniquely associated with caveolae during the treatment, and 15% of the proteins normally associated with caveolae in untreated cell are suppressed. Bioinformatic analysis of the data shows a decreased expression of proteins involved in genetic information processing, and an increase in proteins involved in metabolism, vesicular transport and signal transduction in gentamicin treated cells. Several Rab GTPases proteins, ubiquitous transporters, uniquely segregate with caveolae and are significantly enriched in gentamicin treated cells.

Conclusion

We report that gentamicin exposure modifies protein profile of caveolae from SL pericytes. We identified a pool of proteins which are uniquely segregating with caveolae during the treatment, mainly participating in metabolic and biosynthetic pathways, in transport pathways and in genetic information processing. Finally, we show for the first time proteins associated with caveolae SL pericytes linked to nonsyndromic hearing loss.

Simulating biological processes: stochastic physics from whole cells to colonies

The last few decades have revealed the living cell to be a crowded spatially heterogeneous space teeming with biomolecules whose concentrations and activities are governed by intrinsically random forces. It is from this randomness, however, that a vast array of precisely timed and intricately coordinated biological functions emerge that give rise to the complex forms and behaviors we see in the biosphere around us. This seemingly paradoxical nature of life has drawn the interest of an increasing number of physicists, and recent years have seen stochastic modeling grow into a major subdiscipline within biological physics. Here we review some of the major advances that have shaped our understanding of stochasticity in biology. We begin with some historical context, outlining a string of important experimental results that motivated the development of stochastic modeling. We then embark upon a fairly rigorous treatment of the simulation methods that are currently available for the treatment of stochastic biological models, with an eye toward comparing and contrasting their realms of applicability, and the care that must be taken when parameterizing them. Following that, we describe how stochasticity impacts several key biological functions, including transcription, translation, ribosome biogenesis, chromosome replication, and metabolism, before considering how the functions may be coupled into a comprehensive model of a 'minimal cell'. Finally, we close with our expectation for the future of the field, focusing on how mesoscopic stochastic methods may be augmented with atomic-scale molecular modeling approaches in order to understand life across a range of length and time scales.

Competitive resource allocation to metabolic pathways contributes to overflow metabolisms and emergent properties in cross-feeding microbial consortia

Resource scarcity is a common stress in nature and has a major impact on microbial physiology. This review highlights microbial acclimations to resource scarcity, focusing on resource investment strategies for chemoheterotrophs from the molecular level to the pathway level. Competitive resource allocation strategies often lead to a phenotype known as overflow metabolism; the resulting overflow byproducts can stabilize cooperative interactions in microbial communities and can lead to cross-feeding consortia. These consortia can exhibit emergent properties such as enhanced resource usage and biomass productivity. The literature distilled here draws parallels between in silico and laboratory studies and ties them together with ecological theories to better understand microbial stress responses and mutualistic consortia functioning.

Proteomic Analysis of Combined Gemcitabine and Birinapant in Pancreatic Cancer Cells

Pancreatic cancer is characterized by mutated signaling pathways and a high incidence of drug resistance. Comprehensive, large-scale proteomic analysis can provide a system-wide view of signaling networks, assist in understanding drug mechanisms of action and interactions, and serve as a useful tool for pancreatic cancer research. In this study, liquid chromatography-mass spectrometry-based proteomic analysis was applied to characterize the combination of gemcitabine and birinapant in pancreatic cancer cells, which was shown previously to be synergistic. A total of 4069 drug-responsive proteins were identified and quantified in a time-series proteome analysis. This rich dataset provides broad views and accurate quantification of signaling pathways. Pathways relating to DNA damage response regulations, DNA repair, anti-apoptosis, pro-migration/invasion were implicated as underlying mechanisms for gemcitabine resistance and for the beneficial effects of the drug combination. Promising drug targets were identified for future investigation. This study also provides a database for systems mathematical modeling to relate drug effects and interactions in various signaling pathways in pancreatic cancer cells.

An essential regulatory function of the DnaK chaperone dictates the decision between proliferation and maintenance in Caulobacter crescentus

Hsp70 chaperones are well known for their important functions in maintaining protein homeostasis during thermal stress conditions. In many bacteria the Hsp70 homolog DnaK is also required for growth in the absence of stress. The molecular reasons underlying Hsp70 essentiality remain in most cases unclear. Here, we demonstrate that DnaK is essential in the α-proteobacterium Caulobacter crescentus due to its regulatory function in gene expression. Using a suppressor screen we identified mutations that allow growth in the absence of DnaK. All mutations reduced the activity of the heat shock sigma factor σ³², demonstrating that the DnaK-dependent inactivation of σ³² is a growth requirement. While most mutations occurred in the rpoH gene encoding σ³², we also identified mutations affecting σ³² activity or stability in trans, providing important new insight into the regulatory mechanisms controlling σ³² activity. Most notably, we describe a mutation in the ATP dependent protease HslUV that induces rapid degradation of σ³², and a mutation leading to increased levels of the house keeping σ⁷⁰ that outcompete σ³² for binding to the RNA polymerase. We demonstrate that σ³² inhibits growth and that its unrestrained activity leads to an extensive reprogramming of global gene expression, resulting in upregulation of repair and maintenance functions and downregulation of the growth-promoting functions of protein translation, DNA replication and certain metabolic processes. While this re-allocation from proliferative to maintenance functions could provide an advantage during heat stress, it leads to growth defects under favorable conditions. We conclude that Caulobacter has co-opted the DnaK chaperone system as an essential regulator of gene expression under conditions when its folding activity is dispensable.

Molecular chaperones of the Hsp70 family belong to the most conserved cellular machineries throughout the tree of life. These proteins play key roles in maintaining protein homeostasis, especially under heat stress conditions. In diverse bacteria the Hsp70 homolog DnaK is essential for growth even in the absence of stress. However, the molecular mechanisms underlying the essential nature of DnaK have in most cases not been studied. We found in the α-proteobacterium Caulobacter crescentus that the function of DnaK as a folding catalyst is dispensable in the absence of stress. Instead, its sole essential function under such conditions is to inhibit the activity of the heat shock sigma factor σ³². Our findings highlight that some bacteria have co-opted chaperones as essential regulators of gene expression under conditions when their folding activity is not required. Furthermore, our work illustrates that essential genes can perform different essential functions in discrete growth conditions.

Ribosomes are optimized for autocatalytic production

Many fine-scale features of ribosomes have been explained in terms of function, revealing a molecular machine that is optimized for error-correction, speed and control. Here we demonstrate mathematically that many less well understood, larger-scale features of ribosomes-such as why a few ribosomal RNA molecules dominate the mass and why the ribosomal protein content is divided into 55-80 small, similarly sized segments-speed up their autocatalytic production.

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.

Dietary changes in nutritional studies shape the structural and functional composition of the pigs' fecal microbiome-from days to weeks

BACKGROUND

The possible impact of changes in diet composition on the intestinal microbiome is mostly studied after some days of adaptation to the diet of interest. The question arises if a few days are enough to reflect the microbial response to the diet by changing the community composition and function. The present study investigated the fecal microbiome of pigs during a time span of 4 weeks after a dietary change to obtain insights regarding the time required for adaptation. Four different diets were used differing in either protein source (field peas meal vs. soybean meal) or the concentration of calcium and phosphorus (CaP).

RESULTS

Twelve pigs were sampled at seven time points within 4 weeks after the dietary change. Fecal samples were used to sequence the 16S rRNA gene amplicons to analyse microbial proteins via LC-MS/MS and to determine the SCFA production. The analysis of OTU abundances and quantification values of proteins showed a significant separation of three periods of time (p = 0.001). Samples from the first day are used to define the 'zero period'; samples of weeks 1 and 2 are combined as 'metabolic period' and an 'equilibrium period was defined based on samples from weeks 3 and 4. Only in this last period, a separation according to the supplementation of CaP was significantly detectable (p = 0.001). No changes were found based on the corn-soybean meal or corn-field peas administration. The analysis of possible factors causing this significant separation showed only an overall change of bacterial members and functional properties. The metaproteomic approach yielded a total of about 9700 proteins, which were used to deduce possible metabolic functions of the bacterial community.

CONCLUSIONS

A gradual taxonomic and functional rearrangement of the bacterial community has been depicted after a change of diet composition. The adaptation lasts several weeks despite the usually assumed time span of several days. The obtained knowledge is of a great importance for the design of future nutritional studies. Moreover, considering the high similarities between the porcine and human gastrointestinal tract anatomy and physiology, the findings of the current study might imply in the design of human-related nutritional studies.

'Hotspots' of Antigen Presentation Revealed by Human Leukocyte Antigen Ligandomics for Neoantigen Prioritization

The remarkable clinical efficacy of the immune checkpoint blockade therapies has motivated researchers to discover immunogenic epitopes and exploit them for personalized vaccines. Human leukocyte antigen (HLA)-binding peptides derived from processing and presentation of mutated proteins are one of the leading targets for T-cell recognition of cancer cells. Currently, most studies attempt to identify neoantigens based on predicted affinity to HLA molecules, but the performance of such prediction algorithms is rather poor for rare HLA class I alleles and for HLA class II. Direct identification of neoantigens by mass spectrometry (MS) is becoming feasible; however, it is not yet applicable to most patients and lacks sensitivity. In an attempt to capitalize on existing immunopeptidomics data and extract information that could complement HLA-binding prediction, we first compiled a large HLA class I and class II immunopeptidomics database across dozens of cell types and HLA allotypes and detected hotspots that are subsequences of proteins frequently presented. About 3% of the peptidome was detected in both class I and class II. Based on the gene ontology of their source proteins and the peptide's length, we propose that their processing may partake by the cellular class II presentation machinery. Our database captures the global nature of the in vivo peptidome averaged over many HLA alleles, and therefore, reflects the propensity of peptides to be presented on HLA complexes, which is complementary to the existing neoantigen prediction features such as binding affinity and stability or RNA abundance. We further introduce two immunopeptidomics MS-based features to guide prioritization of neoantigens: the number of peptides matching a protein in our database and the overlap of the predicted wild-type peptide with other peptides in our database. We show as a proof of concept that our immunopeptidomics MS-based features improved neoantigen prioritization by up to 50%. Overall, our work shows that, in addition to providing huge training data to improve the HLA binding prediction, immunopeptidomics also captures other aspects of the natural in vivo presentation that significantly improve prediction of clinically relevant neoantigens.

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.

A Protocol for Generating and Exchanging (Genome-Scale) Metabolic Resource Allocation Models

In this article, we present a protocol for generating a complete (genome-scale) metabolic resource allocation model, as well as a proposal for how to represent such models in the systems biology markup language (SBML). Such models are used to investigate enzyme levels and achievable growth rates in large-scale metabolic networks. Although the idea of metabolic resource allocation studies has been present in the field of systems biology for some years, no guidelines for generating such a model have been published up to now. This paper presents step-by-step instructions for building a (dynamic) resource allocation model, starting with prerequisites such as a genome-scale metabolic reconstruction, through building protein and noncatalytic biomass synthesis reactions and assigning turnover rates for each reaction. In addition, we explain how one can use SBML level 3 in combination with the flux balance constraints and our resource allocation modeling annotation to represent such models.

Bacterial Cellulose Shifts Transcriptome and Proteome of Cultured Endothelial Cells Towards Native Differentiation

Preserving the native phenotype of primary cells in vitro is a complex challenge. Recently, hydrogel-based cellular matrices have evolved as alternatives to conventional cell culture techniques. We developed a bacterial cellulose-based aqueous gel-like biomaterial, dubbed Xellulin, which mimics a cellular microenvironment and seems to maintain the native phenotype of cultured and primary cells. When applied to human umbilical vein endothelial cells (HUVEC), it allowed the continuous cultivation of cell monolayers for more than one year without degradation or dedifferentiation. To investigate the impact of Xellulin on the endothelial cell phenotype in detail, we applied quantitative transcriptomics and proteomics and compared the molecular makeup of native HUVEC, HUVEC on collagen-coated Xellulin and collagen-coated cell culture plastic (polystyrene).Statistical analysis of 12,475 transcripts and 7831 proteins unveiled massive quantitative differences of the compared transcriptomes and proteomes. K-means clustering followed by network analysis showed that HUVEC on plastic upregulate transcripts and proteins controlling proliferation, cell cycle and protein biosynthesis. In contrast, HUVEC on Xellulin maintained, by and large, the expression levels of genes supporting their native biological functions and signaling networks such as integrin, receptor tyrosine kinase MAP/ERK and PI3K signaling pathways, while decreasing the expression of proliferation associated proteins. Moreover, CD34-an endothelial cell differentiation marker usually lost early during cell culture - was re-expressed within 2 weeks on Xellulin but not on plastic. And HUVEC on Xellulin showed a significantly stronger functional responsiveness to a prototypic pro-inflammatory stimulus than HUVEC on plastic.Taken together, this is one of the most comprehensive transcriptomic and proteomic studies of native and propagated HUVEC, which underscores the importance of the morphology of the cellular microenvironment to regulate cellular differentiation, and demonstrates, for the first time, the potential of Xellulin as versatile tool promoting an in vivo-like phenotype in primary and propagated cell culture.

Taking control over microbial populations: Current approaches for exploiting biological noise in bioprocesses

Phenotypic plasticity of microbial cells has attracted much attention and several research efforts have been dedicated to the description of methods aiming at characterizing phenotypic heterogeneity and its impact on microbial populations. However, different approaches have also been suggested in order to take benefit from noise in a bioprocess perspective, e.g. by increasing the robustness or productivity of a microbial population. This review is dedicated to outline these controlling methods. A common issue, that has still to be addressed, is the experimental identification and the mathematical expression of noise. Indeed, the effective interfacing of microbial physiology with external parameters that can be used for controlling physiology depends on the acquisition of reliable signals. Latest technologies, like single cell microfluidics and advanced flow cytometric approaches, enable linking physiology, noise, heterogeneity in productive microbes with environmental cues and hence allow correctly mapping and predicting biological behavior via mathematical representations. However, like in the field of electronics, signals are perpetually subjected to noise. If appropriately interpreted, this noise can give an additional insight into the behavior of the individual cells within a microbial population of interest. This review focuses on recent progress made at describing, treating and exploiting biological noise in the context of microbial populations used in various bioprocess applications.

Label-free deep shotgun proteomics reveals protein dynamics during tomato fruit tissues development

Current innovations in mass-spectrometry-based technologies allow deep coverage of protein expression. Despite its immense value and in contrast to transcriptomics, only a handful of studies in crop plants engaged with global proteome assays. Here, we present large-scale shotgun proteomics profiling of tomato fruit across two key tissues and five developmental stages. A total of 7738 individual protein groups were identified and reliably measured at least in one of the analyzed tissues or stages. The depth of our assay enabled identification of 61 differentially expressed transcription factors, including renowned ripening-related regulators and elements of ethylene signaling. Significantly, we measured proteins involved in 83% of all predicted enzymatic reactions in the tomato metabolic network. Hence, proteins representing almost the complete set of reactions in major metabolic pathways were identified, including the cytosolic and plastidic isoprenoid and the phenylpropanoid pathways. Furthermore, the data allowed us to discern between protein isoforms according to expression patterns, which is most significant in light of the weak transcript-protein expression correspondence. Finally, visualization of changes in protein abundance associated with a particular process provided us with a unique view of skin and flesh tissues in developing fruit. This study adds a new dimension to the existing genomic, transcriptomic and metabolomic resources. It is therefore likely to promote translational and post-translational research in tomato and additional species, which is presently focused on transcription.

Systems Biology of Metabolism: A Driver for Developing Personalized and Precision Medicine

Systems biology uses mathematical models to analyze large datasets and simulate system behavior. It enables integrative analysis of different types of data and can thereby provide new insight into complex biological systems. Here will be discussed the challenges of using systems medicine for advancing the development of personalized and precision medicine to treat metabolic diseases like insulin resistance, obesity, NAFLD, NASH, and cancer. It will be illustrated how the concept of genome-scale metabolic models can be used for integrative analysis of big data with the objective of identifying novel biomarkers that are foundational for personalized and precision medicine.

Neurons derived from different brain regions are inherently different in vitro: a novel multiregional brain-on-a-chip

Brain in vitro models are critically important to developing our understanding of basic nervous system cellular physiology, potential neurotoxic effects of chemicals, and specific cellular mechanisms of many disease states. In this study, we sought to address key shortcomings of current brain in vitro models: the scarcity of comparative data for cells originating from distinct brain regions and the lack of multiregional brain in vitro models. We demonstrated that rat neurons from different brain regions exhibit unique profiles regarding their cell composition, protein expression, metabolism, and electrical activity in vitro. In vivo, the brain is unique in its structural and functional organization, and the interactions and communication between different brain areas are essential components of proper brain function. This fact and the observation that neurons from different areas of the brain exhibit unique behaviors in vitro underline the importance of establishing multiregional brain in vitro models. Therefore, we here developed a multiregional brain-on-a-chip and observed a reduction of overall firing activity, as well as altered amounts of astrocytes and specific neuronal cell types compared with separately cultured neurons. Furthermore, this multiregional model was used to study the effects of phencyclidine, a drug known to induce schizophrenia-like symptoms in vivo, on individual brain areas separately while monitoring downstream effects on interconnected regions. Overall, this work provides a comparison of cells from different brain regions in vitro and introduces a multiregional brain-on-a-chip that enables the development of unique disease models incorporating essential in vivo features.NEW & NOTEWORTHY Due to the scarcity of comparative data for cells from different brain regions in vitro, we demonstrated that neurons isolated from distinct brain areas exhibit unique behaviors in vitro. Moreover, in vivo proper brain function is dependent on the connection and communication of several brain regions, underlining the importance of developing multiregional brain in vitro models. We introduced a novel brain-on-a-chip model, implementing essential in vivo features, such as different brain areas and their functional connections.

Targeted proteome analysis of single-gene deletion strains of Saccharomyces cerevisiae lacking enzymes in the central carbon metabolism

Central carbon metabolism is controlled by modulating the protein abundance profiles of enzymes that maintain the essential systems in living organisms. In this study, metabolic adaptation mechanisms in the model organism Saccharomyces cerevisiae were investigated by direct determination of enzyme abundance levels in 30 wild type and mutant strains. We performed a targeted proteome analysis using S. cerevisiae strains that lack genes encoding the enzymes responsible for central carbon metabolism. Our analysis revealed that at least 30% of the observed variations in enzyme abundance levels could be explained by global regulatory mechanisms. A enzyme-enzyme co-abundance analysis revealed that the abundances of enzyme proteins involved in the trehalose metabolism and glycolysis changed in a coordinated manner under the control of the transcription factors for global regulation. The remaining variations were derived from local mechanisms such as a mutant-specific increase in the abundances of remote enzymes. The proteome data also suggested that, although the functional compensation of the deficient enzyme was attained by using more resources for protein biosynthesis, available resources for the biosynthesis of the enzymes responsible for central metabolism were not abundant in S. cerevisiae cells. These results showed that global and local regulation of enzyme abundance levels shape central carbon metabolism in S. cerevisiae by using a limited resource for protein biosynthesis.

Ribosome profiling-guided depletion of an mRNA increases cell growth rate and protein secretion

Recombinant protein production coopts the host cell machinery to provide high protein yields of industrial enzymes or biotherapeutics. However, since protein translation is energetically expensive and tightly controlled, it is unclear if highly expressed recombinant genes are translated as efficiently as host genes. Furthermore, it is unclear how the high expression impacts global translation. Here, we present the first genome-wide view of protein translation in an IgG-producing CHO cell line, measured with ribosome profiling. Through this we found that our recombinant mRNAs were translated as efficiently as the host cell transcriptome, and sequestered up to 15% of the total ribosome occupancy. During cell culture, changes in recombinant mRNA translation were consistent with changes in transcription, demonstrating that transcript levels influence specific productivity. Using this information, we identified the unnecessary resistance marker NeoR to be a highly transcribed and translated gene. Through siRNA knock-down of NeoR, we improved the production- and growth capacity of the host cell. Thus, ribosomal profiling provides valuable insights into translation in CHO cells and can guide efforts to enhance protein production.

Unbalanced Growth, Senescence and Aging

Usually, cells balance their growth with their division. Coordinating growth inputs with cell division ensures the proper timing of division when sufficient cell material is available and affects the overall rate of cell proliferation. At a very fundamental level, cellular replicative lifespan-defined as the number of times a cell can divide, is a manifestation of cell cycle control. Hence, control of mitotic cell divisions, especially when the commitment is made to a new round of cell division, is intimately linked to replicative aging of cells. In this chapter, we review our current understanding, and its shortcomings, of how unbalanced growth and division, can dramatically influence the proliferative potential of cells, often leading to cellular and organismal aging phenotypes. The interplay between growth and division also underpins cellular senescence (i.e., inability to divide) and quiescence, when cells exit the cell cycle but still retain their ability to divide.

The Protein Cost of Metabolic Fluxes: Prediction from Enzymatic Rate Laws and Cost Minimization

Bacterial growth depends crucially on metabolic fluxes, which are limited by the cell’s capacity to maintain metabolic enzymes. The necessary enzyme amount per unit flux is a major determinant of metabolic strategies both in evolution and bioengineering. It depends on enzyme parameters (such as k_cat and K_M constants), but also on metabolite concentrations. Moreover, similar amounts of different enzymes might incur different costs for the cell, depending on enzyme-specific properties such as protein size and half-life. Here, we developed enzyme cost minimization (ECM), a scalable method for computing enzyme amounts that support a given metabolic flux at a minimal protein cost. The complex interplay of enzyme and metabolite concentrations, e.g. through thermodynamic driving forces and enzyme saturation, would make it hard to solve this optimization problem directly. By treating enzyme cost as a function of metabolite levels, we formulated ECM as a numerically tractable, convex optimization problem. Its tiered approach allows for building models at different levels of detail, depending on the amount of available data. Validating our method with measured metabolite and protein levels in E. coli central metabolism, we found typical prediction fold errors of 4.1 and 2.6, respectively, for the two kinds of data. This result from the cost-optimized metabolic state is significantly better than randomly sampled metabolite profiles, supporting the hypothesis that enzyme cost is important for the fitness of E. coli. ECM can be used to predict enzyme levels and protein cost in natural and engineered pathways, and could be a valuable computational tool to assist metabolic engineering projects. Furthermore, it establishes a direct connection between protein cost and thermodynamics, and provides a physically plausible and computationally tractable way to include enzyme kinetics into constraint-based metabolic models, where kinetics have usually been ignored or oversimplified.

“Enzyme cost”, the amount of protein needed for a given metabolic flux, is crucial for the metabolic choices cells have to make. However, due to the technical limitations of linear optimization methods, this cost has traditionally been ignored by constraint-based metabolic models such as Flux Balance Analysis. On the other hand, more detailed kinetic models which use ordinary differential equations to simulate fluxes for different choices of enzyme allocation, are computationally demanding and not scalable enough. In this work, we developed a method which utilizes the full kinetic model to predict steady-state enzyme costs, using a scalable and robust algorithm based on convex optimization. We show that the minimization of enzyme cost is a meaningful optimality principle by comparing our predictions to measured enzyme and metabolite levels in exponentially growing E. coli. This method could be used to quantify the enzyme cost of many other pathways and explain why evolution has selected some low-yield metabolic strategies, including aerobic fermentation in yeast and cancer cells. Furthermore, future metabolic engineering projects could benefit from our method by choosing pathways that reduce the total amount of enzyme required for the synthesis of a value-added product.

Quantitative nature of overexpression experiments

Overexpression experiments are sometimes considered as qualitative experiments designed to identify novel proteins and study their function. However, in order to draw conclusions regarding protein overexpression through association analyses using large-scale biological data sets, we need to recognize the quantitative nature of overexpression experiments. Here I discuss the quantitative features of two different types of overexpression experiment: absolute and relative. I also introduce the four primary mechanisms involved in growth defects caused by protein overexpression: resource overload, stoichiometric imbalance, promiscuous interactions, and pathway modulation associated with the degree of overexpression.

A Cell-type-resolved Liver Proteome

Parenchymatous organs consist of multiple cell types, primarily defined as parenchymal cells (PCs) and nonparenchymal cells (NPCs). The cellular characteristics of these organs are not well understood. Proteomic studies facilitate the resolution of the molecular details of different cell types in organs. These studies have significantly extended our knowledge about organogenesis and organ cellular composition. Here, we present an atlas of the cell-type-resolved liver proteome. In-depth proteomics identified 6000 to 8000 gene products (GPs) for each cell type and a total of 10,075 GPs for four cell types. This data set revealed features of the cellular composition of the liver: (1) hepatocytes (PCs) express the least GPs, have a unique but highly homogenous proteome pattern, and execute fundamental liver functions; (2) the division of labor among PCs and NPCs follows a model in which PCs make the main components of pathways, but NPCs trigger the pathways; and (3) crosstalk among NPCs and PCs maintains the PC phenotype. This study presents the liver proteome at cell resolution, serving as a research model for dissecting the cell type constitution and organ features at the molecular level.

Role of Proteome Physical Chemistry in Cell Behavior

We review how major cell behaviors, such as bacterial growth laws, are derived from the physical chemistry of the cell’s proteins. On one hand, cell actions depend on the individual biological functionalities of their many genes and proteins. On the other hand, the common physics among proteins can be as important as the unique biology that distinguishes them. For example, bacterial growth rates depend strongly on temperature. This dependence can be explained by the folding stabilities across a cell’s proteome. Such modeling explains how thermophilic and mesophilic organisms differ, and how oxidative damage of highly charged proteins can lead to unfolding and aggregation in aging cells. Cells have characteristic time scales. For example, E. coli can duplicate as fast as 2–3 times per hour. These time scales can be explained by protein dynamics (the rates of synthesis and degradation, folding, and diffusional transport). It rationalizes how bacterial growth is slowed down by added salt. In the same way that the behaviors of inanimate materials can be expressed in terms of the statistical distributions of atoms and molecules, some cell behaviors can be expressed in terms of distributions of protein properties, giving insights into the microscopic basis of growth laws in simple cells.

Overloaded and stressed: whole-cell considerations for bacterial synthetic biology

The predictability and robustness of engineered bacteria depend on the many interactions between synthetic constructs and their host cells. Expression from synthetic constructs is an unnatural load for the host that typically reduces growth, triggers stresses and leads to decrease in performance or failure of engineered cells. Work in systems and synthetic biology has now begun to address this through new tools, methods and strategies that characterise and exploit host-construct interactions in bacteria. Focusing on work in E. coli, we review here a selection of the recent developments in this area, highlighting the emerging issues and describing the new solutions that are now making the synthetic biology community consider the cell just as much as they consider the construct.

Integrated In Silico Analysis of Pathway Designs for Synthetic Photo-Electro-Autotrophy

The strong advances in synthetic biology enable the engineering of novel functions and complex biological features in unprecedented ways, such as implementing synthetic autotrophic metabolism into heterotrophic hosts. A key challenge for the sustainable production of fuels and chemicals entails the engineering of synthetic autotrophic organisms that can effectively and efficiently fix carbon dioxide by using sustainable energy sources. This challenge involves the integration of carbon fixation and energy uptake systems. A variety of carbon fixation pathways and several types of photosystems and other energy uptake systems can be chosen and, potentially, modularly combined to design synthetic autotrophic metabolism. Prior to implementation, these designs can be evaluated by the combination of several computational pathway analysis techniques. Here we present a systematic, integrated in silico analysis of photo-electro-autotrophic pathway designs, consisting of natural and synthetic carbon fixation pathways, a proton-pumping rhodopsin photosystem for ATP regeneration and an electron uptake pathway. We integrated Flux Balance Analysis of the heterotrophic chassis Escherichia coli with kinetic pathway analysis and thermodynamic pathway analysis (Max-min Driving Force). The photo-electro-autotrophic designs are predicted to have a limited potential for anaerobic, autotrophic growth of E. coli_, given the relatively low ATP regenerating capacity of the proton pumping rhodopsin photosystems and the high ATP maintenance of E. coli. If these factors can be tackled, our analysis indicates the highest growth potential for the natural reductive tricarboxylic acid cycle and the synthetic pyruvate synthase–pyruvate carboxylate -glyoxylate bicycle. Both carbon fixation cycles are very ATP efficient, while maintaining fast kinetics, which also results in relatively low estimated protein costs for these pathways. Furthermore, the synthetic bicycles are highly thermodynamic favorable under conditions analysed. However, the most important challenge identified for improving photo-electro-autotrophic growth is increasing the proton-pumping rate of the rhodopsin photosystems, allowing for higher ATP regeneration. Alternatively, other designs of autotrophy may be considered, therefore the herein presented integrated modeling approach allows synthetic biologists to evaluate and compare complex pathway designs before experimental implementation.

Metabolite concentrations, fluxes and free energies imply efficient enzyme usage

In metabolism, available free energy is limited and must be divided across pathway steps to maintain ΔG negative throughout. For each reaction, ΔG is log-proportional both to a concentration ratio (reaction quotient-to-equilibrium constant) and to a flux ratio (backward-to-forward flux). Here we use isotope labeling to measure absolute metabolite concentrations and fluxes in Escherichia coli, yeast, and a mammalian cell line. We then integrate this information to obtain a unified set of concentrations and ΔG for each organism. In glycolysis, we find that free energy is partitioned so as to mitigate unproductive backward fluxes associated with ΔG near zero. Across metabolism, we observe that absolute metabolite concentrations and ΔG are substantially conserved, and that most substrate (but not inhibitor) concentrations exceed the associated enzyme binding site affinity. The observed conservation of metabolite concentrations is consistent with an evolutionary drive to utilize enzymes efficiently given thermodynamic and osmotic constraints.

Global characterization of in vivo enzyme catalytic rates and their correspondence to in vitro kcat measurements

Turnover numbers, also known as kcat values, are fundamental properties of enzymes. However, kcat data are scarce and measured in vitro, thus may not faithfully represent the in vivo situation. A basic question that awaits elucidation is: how representative are kcat values for the maximal catalytic rates of enzymes in vivo? Here, we harness omics data to calculate kmax(vivo), the observed maximal catalytic rate of an enzyme inside cells. Comparison with kcat values from Escherichia coli, yields a correlation ofr(2)= 0.62 in log scale (p < 10(-10)), with a root mean square difference of 0.54 (3.5-fold in linear scale), indicating that in vivo and in vitro maximal rates generally concur. By accounting for the degree of saturation of enzymes and the backward flux dictated by thermodynamics, we further refine the correspondence between kmax(vivo) and kcat values. The approach we present here characterizes the quantitative relationship between enzymatic catalysis in vitro and in vivo and offers a high-throughput method for extracting enzyme kinetic constants from omics data.

Allergy-Protective Arabinogalactan Modulates Human Dendritic Cells via C-Type Lectins and Inhibition of NF-κB

Arabinogalactan (AG) isolated from dust of a traditional farm prevents disease in murine models of allergy. However, it is unclear whether this polysaccharide has immune regulatory properties in humans. The aim of this study was to test the influence of AG on the immune-stimulating properties of human dendritic cells (DCs). Moreover, we sought to identify the receptor to which AG binds. AG was produced from plant callus tissue under sterile conditions to avoid the influence of pathogen-associated molecular patterns in subsequent experiments. The influence of AG on the human immune system was investigated by analyzing its impact on monocyte-derived DCs. To analyze whether the T cell stimulatory capacity of AG-stimulated DCs is altered, an MLR with naive Th cells was performed. We revealed that AG reduced T cell proliferation in a human MLR. In the search for a molecular mechanism, we found that AG binds to the immune modulatory receptors DC-specific ICAM-3 -: grabbing non integrin (DC-SIGN) and macrophage mannose receptor 1 (MMR-1). Stimulation of these receptors with AG simultaneously with TLR4 stimulation with LPS increased the expression of the E3 ubiquitin-protein ligase tripartite motif -: containing protein 21 and decreased the phosphorylation of NF-κB p65 in DCs. This led to a reduced activation profile with reduced costimulatory molecules and proinflammatory cytokine production. Blocking of MMR-1 or DC-SIGN with neutralizing Abs partially inhibits this effect. We conclude that AG dampens the activation of human DCs by LPS via binding to DC-SIGN and MMR-1, leading to attenuated TLR signaling. This results in a reduced T cell activation capacity of DCs.

Proteome reallocation in Escherichia coli with increasing specific growth rate

Cells usually respond to changing growth conditions with a change in the specific growth rate (μ) and adjustment of their proteome to adapt and maintain metabolic efficiency. Description of the principles behind proteome resource allocation is important for understanding metabolic regulation in response to changing μ. Thus, we analysed the proteome resource allocation dynamics of Escherichia coli into different metabolic processes in response to changing μ. E. coli was grown on minimal and defined rich media in steady state continuous cultures at different μ and characterised combining two LC-MS/MS-based proteomics methods: stable isotope labelling by amino acids in cell culture (SILAC) and intensity based label-free absolute quantification. We detected slowly growing cells investing more proteome resources in energy generation and carbohydrate transport and metabolism whereas for achieving faster growth cells needed to devote most resources to translation and processes closely related to the protein synthesis pipeline. Furthermore, down-regulation of energy generation and carbohydrate metabolism proteins with faster growth displayed very similar expression dynamics with the global transcriptional regulator CRP (cyclic AMP receptor protein), pointing to a dominant protein resource allocating role of this protein. Our data also suggest that acetate overflow may be the result of global proteome resource optimisation as cells saved proteome resources by switching from fully respiratory to respiro-fermentative growth. The presented results give a quantitative overview of how E. coli adjusts its proteome to achieve faster growth and in future could contribute to the design of more efficient cell factories through proteome optimisation.

Toward a Whole-Cell Model of Ribosome Biogenesis: Kinetic Modeling of SSU Assembly

Central to all life is the assembly of the ribosome: a coordinated process involving the hierarchical association of ribosomal proteins to the RNAs forming the small and large ribosomal subunits. The process is further complicated by effects arising from the intracellular heterogeneous environment and the location of ribosomal operons within the cell. We provide a simplified model of ribosome biogenesis in slow-growing Escherichia coli. Kinetic models of in vitro small-subunit reconstitution at the level of individual protein/ribosomal RNA interactions are developed for two temperature regimes. The model at low temperatures predicts the existence of a novel 5'→3'→central assembly pathway, which we investigate further using molecular dynamics. The high-temperature assembly network is incorporated into a model of in vivo ribosome biogenesis in slow-growing E. coli. The model, described in terms of reaction-diffusion master equations, contains 1336 reactions and 251 species that dynamically couple transcription and translation to ribosome assembly. We use the Lattice Microbes software package to simulate the stochastic production of mRNA, proteins, and ribosome intermediates over a full cell cycle of 120 min. The whole-cell model captures the correct growth rate of ribosomes, predicts the localization of early assembly intermediates to the nucleoid region, and reproduces the known assembly timescales for the small subunit with no modifications made to the embedded in vitro assembly network.

CTP:phosphocholine cytidylyltransferase: Function, regulation, and structure of an amphitropic enzyme required for membrane biogenesis

CTP:phosphocholine cytidylyltransferase (CCT) catalyzes a rate-limiting and regulated step in the CDP-choline pathway for the synthesis of phosphatidylcholine (PC) and PC-derived lipids. Control of CCT activity is multi-layered, and includes direct regulation by reversible membrane binding involving a built-in lipid compositional sensor. Thus CCT contributes to phospholipid compositional homeostasis. CCT also modifies the curvature of its target membrane. Knowledge of CCT structure and regulation of its catalytic function are relatively advanced compared to many lipid metabolic enzymes, and are reviewed in detail. Recently the genetic origins of two human developmental and lipogenesis disorders have been traced to mutations in the gene for CCTα.

How fast-growing bacteria robustly tune their ribosome concentration to approximate growth-rate maximization

Maximization of growth rate is an important fitness strategy for bacteria. Bacteria can achieve this by expressing proteins at optimal concentrations, such that resources are not wasted. This is exemplified for Escherichia coli by the increase of its ribosomal protein-fraction with growth rate, which precisely matches the increased protein synthesis demand. These findings and others have led to the hypothesis that E. coli aims to maximize its growth rate in environments that support growth. However, what kind of regulatory strategy is required for a robust, optimal adjustment of the ribosome concentration to the prevailing condition is still an open question. In the present study, we analyze the ppGpp-controlled mechanism of ribosome expression used by E. coli and show that this mechanism maintains the ribosomes saturated with its substrates. In this manner, overexpression of the highly abundant ribosomal proteins is prevented, and limited resources can be redirected to the synthesis of other growth-promoting enzymes. It turns out that the kinetic conditions for robust, optimal protein-partitioning, which are required for growth rate maximization across conditions, can be achieved with basic biochemical interactions. We show that inactive ribosomes are the most suitable ‘signal’ for tracking the intracellular nutritional state and for adjusting gene expression accordingly, as small deviations from optimal ribosome concentration cause a huge fractional change in ribosome inactivity. We expect to find this control logic implemented across fast-growing microbial species because growth rate maximization is a common selective pressure, ribosomes are typically highly abundant and thus costly, and the required control can be implemented by a small, simple network.

Translate to divide: сontrol of the cell cycle by protein synthesis

Protein synthesis underpins much of cell growth and, consequently, cell multiplication. Understanding how proliferating cells commit and progress into the cell cycle requires knowing not only which proteins need to be synthesized, but also what determines their rate of synthesis during cell division.

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

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