Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria

A central aim of cell biology was to understand the strategy of gene expression in response to the environment. Here, we study gene expression response to metabolic challenges in exponentially growing Escherichia coli using mass spectrometry. Despite enormous complexity in the details of the underlying regulatory network, we find that the proteome partitions into several coarse‐grained sectors, with each sector's total mass abundance exhibiting positive or negative linear relations with the growth rate. The growth rate‐dependent components of the proteome fractions comprise about half of the proteome by mass, and their mutual dependencies can be characterized by a simple flux model involving only two effective parameters. The success and apparent generality of this model arises from tight coordination between proteome partition and metabolism, suggesting a principle for resource allocation in proteome economy of the cell. This strategy of global gene regulation should serve as a basis for future studies on gene expression and constructing synthetic biological circuits. Coarse graining may be an effective approach to derive predictive phenomenological models for other ‘omics’ studies.     

CRISPR interference–mediated metabolic engineering of Corynebacterium glutamicum for homo‐butyrate production

Combinatorial metabolic engineering enabled the development of efficient microbial cell factories for modulating gene expression to produce desired products. Here, we report the combinatorial metabolic engineering of Corynebacterium glutamicum to produce butyrate by introducing a synthetic butyrate pathway including phosphotransferase and butyrate kinase reactions and repressing the essential acn gene‐encoding aconitase, which has been targeted for downregulation in a genome‐scale model. An all‐in‐one clustered regularly interspaced short palindromic repeats interference system for C. glutamicum was used for tunable downregulation of acn in an engineered strain, where by‐product‐forming reactions were deleted and the synthetic butyrate pathway was inserted, resulting in butyrate production (0.52 ± 0.02 g/L). Subsequently, biotin limitation enabled the engineered strain to produce butyrate (0.58 ± 0.01 g/L) without acetate formation for the entire duration of the culture. These results demonstrate the potential homo‐production of butyrate using engineered C. glutamicum. This method can also be applied to other industrial microorganisms.     

Genome‐scale metabolic reconstruction and constraint‐based modelling of the Antarctic bacterium Pseudoalteromonas haloplanktis TAC125

The Antarctic strain Pseudoalteromonas haloplanktis TAC125 is one of the model organisms of cold‐adapted bacteria and is currently exploited as a new alternative expression host for numerous biotechnological applications. Here, we investigated several metabolic features of this strain through in silico modelling and functional integration of –omics data. A genome‐scale metabolic model of P. haloplanktis TAC125 was reconstructed, encompassing information on 721 genes, 1133 metabolites and 1322 reactions. The predictive potential of this model was validated against a set of experimentally determined growth rates and a large dataset of growth phenotypic data. Furthermore, evidence synthesis from proteomics, phenomics, physiology and metabolic modelling data revealed possible drawbacks of cold‐dependent changes in gene expression on the overall metabolic network of P. haloplanktis TAC125. These included, for example, variations in its central metabolism, amino acid degradation and fatty acid biosynthesis. The genome‐scale metabolic model described here is the first one reconstructed so far for an Antarctic microbial strain. It allowed a system‐level investigation of variations in cellular metabolic fluxes following a temperature downshift. It represents a valuable platform for further investigations on P. haloplanktis TAC125 cellular functional states and for the design of more focused strategies for its possible biotechnological exploitation.     

Metabolic modeling to identify engineering targets for Komagataella phaffii: The effect of biomass composition on gene target identification

Genome‐scale metabolic models are valuable tools for the design of novel strains of industrial microorganisms, such as Komagataella phaffii (syn. Pichia pastoris). However, as is the case for many industrial microbes, there is no executable metabolic model for K. phaffiii that confirms to current standards by providing the metabolite and reactions IDs, to facilitate model extension and reuse, and gene‐reaction associations to enable identification of targets for genetic manipulation. In order to remedy this deficiency, we decided to reconstruct the genome‐scale metabolic model of K. phaffii by reconciling the extant models and performing extensive manual curation in order to construct an executable model (Kp.1.0) that conforms to current standards. We then used this model to study the effect of biomass composition on the predictive success of the model. Twelve different biomass compositions obtained from published empirical data obtained under a range of growth conditions were employed in this investigation. We found that the success of Kp1.0 in predicting both gene essentiality and growth characteristics was relatively unaffected by biomass composition. However, we found that biomass composition had a profound effect on the distribution of the fluxes involved in lipid, DNA, and steroid biosynthetic processes, cellular alcohol metabolic process, and oxidation‐reduction process. Furthermore, we investigated the effect of biomass composition on the identification of suitable target genes for strain development. The analyses revealed that around 40% of the predictions of the effect of gene overexpression or deletion changed depending on the representation of biomass composition in the model. Considering the robustness of the in silico flux distributions to the changing biomass representations enables better interpretation of experimental results, reduces the risk of wrong target identification, and so both speeds and improves the process of directed strain development.     

Transient expression assays in grapevine: a step towards genetic improvement

In the past few years, the usefulness of transient expression assays has continuously increased for the characterization of unknown gene function and metabolic pathways. In grapevine (Vitis vinifera L.), one of the most economically important fruit crops in the world, recent systematic sequencing projects produced many gene data sets that require detailed analysis. Due to their rapid nature, transient expression assays are well suited for large‐scale genetic studies. Although genes and metabolic pathways of any species can be analysed by transient expression in model plants, a need for homologous systems has emerged to avoid the misinterpretation of results due to a foreign genetic background. Over the last 10 years, various protocols have thus been developed to apply this powerful technology to grapevine. Using cell suspension cultures, somatic embryos, leaves or whole plantlets, transient expression assays enabled the study of the function, regulation and subcellular localization of genes involved in specific metabolic pathways such as the biosynthesis of phenylpropanoids. Disease resistance genes that could be used for marker‐assisted selection in conventional breeding or for stable transformation of elite cultivars have also been characterized. Additionally, transient expression assays have proved useful for shaping new tools for grapevine genetic improvement: synthetic promoters, silencing constructs, minimal linear cassettes or viral vectors. This review provides an update on the different tools (DNA constructs, reporter genes, vectors) and methods (Agrobacterium‐mediated and direct gene transfer methods) available for transient gene expression in grapevine. The most representative results published thus far are then described.     

Segmented linear modeling of CHO fed‐batch culture and its application to large scale production

We describe a systematic approach to model CHO metabolism during biopharmaceutical production across a wide range of cell culture conditions. To this end, we applied the metabolic steady state concept. We analyzed and modeled the production rates of metabolites as a function of the specific growth rate. First, the total number of metabolic steady state phases and the location of the breakpoints were determined by recursive partitioning. For this, the smoothed derivative of the metabolic rates with respect to the growth rate were used followed by hierarchical clustering of the obtained partition. We then applied a piecewise regression to the metabolic rates with the previously determined number of phases. This allowed identifying the growth rates at which the cells underwent a metabolic shift. The resulting model with piecewise linear relationships between metabolic rates and the growth rate did well describe cellular metabolism in the fed‐batch cultures. Using the model structure and parameter values from a small‐scale cell culture (2 L) training dataset, it was possible to predict metabolic rates of new fed‐batch cultures just using the experimental specific growth rates. Such prediction was successful both at the laboratory scale with 2 L bioreactors but also at the production scale of 2000 L. This type of modeling provides a flexible framework to set a solid foundation for metabolic flux analysis and mechanistic type of modeling. Biotechnol. Bioeng. 2017;114: 785–797. © 2016 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc.     

Genome‐scale gene/reaction essentiality and synthetic lethality analysis

Synthetic lethals are to pairs of non‐essential genes whose simultaneous deletion prohibits growth. One can extend the concept of synthetic lethality by considering gene groups of increasing size where only the simultaneous elimination of all genes is lethal, whereas individual gene deletions are not. We developed optimization‐based procedures for the exhaustive and targeted enumeration of multi‐gene (and by extension multi‐reaction) lethals for genome‐scale metabolic models. Specifically, these approaches are applied to iAF1260, the latest model of Escherichia coli, leading to the complete identification of all double and triple gene and reaction synthetic lethals as well as the targeted identification of quadruples and some higher‐order ones. Graph representations of these synthetic lethals reveal a variety of motifs ranging from hub‐like to highly connected subgraphs providing a birds‐eye view of the avenues available for redirecting metabolism and uncovering complex patterns of gene utilization and interdependence. The procedure also enables the use of falsely predicted synthetic lethals for metabolic model curation. By analyzing the functional classifications of the genes involved in synthetic lethals, we reveal surprising connections within and across clusters of orthologous group functional classifications.     

Recombinant tagging system using ribosomal frameshifting to monitor protein expression

For rapid and accurate quantitation of recombinant proteins during expression and after purification, we introduce a new tagging strategy that expresses both target proteins and limitedly tagged target proteins together in a single cell at a constant ratio by utilizing cis‐elements of programmed ‐1 ribosomal frameshifting (‐1RFS) as an embedded device. ‐1RFS is an alternative reading mechanism that effectively controls protein expression by many viruses. When a target gene is fused to the enhanced green fluorescent protein (EGFP) gene with a ‐1RFS element implanted between them, the unfused target and the target‐GFP fusion proteins are expressed at a fixed ratio. The expression ratio between these two protein products is adjustable simply by changing ‐1RFS signals. This limited‐tagging system would be valuable for the real‐time monitoring of protein expression when optimizing expression condition for a new protein, and in monitoring large‐scale bioprocesses without a large metabolic burden on host cells. Furthermore, this strategy allows for the direct measurement of the quantity of a protein on a chip surface and easy application to proteomewide study of gene products. Biotechnol. Bioeng. 2013; 110: 898–904. © 2012 Wiley Periodicals, Inc.     

Effect of amino acid supplementation on titer and glycosylation distribution in hybridoma cell cultures—Systems biology‐based interpretation using genome‐scale metabolic flux balance model and multivariate data analysis

Genome‐scale flux balance analysis (FBA) is a powerful systems biology tool to characterize intracellular reaction fluxes during cell cultures. FBA estimates intracellular reaction rates by optimizing an objective function, subject to the constraints of a metabolic model and media uptake/excretion rates. A dynamic extension to FBA, dynamic flux balance analysis (DFBA), can calculate intracellular reaction fluxes as they change during cell cultures. In a previous study by Read et al. (2013), a series of informed amino acid supplementation experiments were performed on twelve parallel murine hybridoma cell cultures, and this data was leveraged for further analysis (Read et al., Biotechnol Prog. 2013;29:745–753). In order to understand the effects of media changes on the model murine hybridoma cell line, a systems biology approach is applied in the current study. Dynamic flux balance analysis was performed using a genome‐scale mouse metabolic model, and multivariate data analysis was used for interpretation. The calculated reaction fluxes were examined using partial least squares and partial least squares discriminant analysis. The results indicate media supplementation increases product yield because it raises nutrient levels extending the growth phase, and the increased cell density allows for greater culture performance. At the same time, the directed supplementation does not change the overall metabolism of the cells. This supports the conclusion that product quality, as measured by glycoform assays, remains unchanged because the metabolism remains in a similar state. Additionally, the DFBA shows that metabolic state varies more at the beginning of the culture but less by the middle of the growth phase, possibly due to stress on the cells during inoculation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1163–1173, 2016     

BAK and BAX deletion using zinc‐finger nucleases yields apoptosis‐resistant CHO cells

Anoxic and metabolic stresses in large‐scale cell culture during biopharmaceutical production can induce apoptosis. Strategies designed to ameliorate the problem of apoptosis in cell culture have focused on mRNA knockdown of pro‐apoptotic proteins and over‐expression of anti‐apoptotic ones. Apoptosis in cell culture involves mitochondrial permeabilization by the pro‐apoptotic Bak and Bax proteins; activity of either protein is sufficient to permit apoptosis. We demonstrate here the complete and permanent elimination of both the Bak and Bax proteins in combination in Chinese hamster ovary (CHO) cells using zinc‐finger nuclease‐mediated gene disruption. Zinc‐finger nuclease cleavage of BAX and BAK followed by inaccurate DNA repair resulted in knockout of both genes. Cells lacking Bax and Bak grow normally but fail to activate caspases in response to apoptotic stimuli. When grown using scale‐down systems under conditions that mimic growth in large‐scale bioreactors they are significantly more resistant to apoptosis induced by starvation, staurosporine, and sodium butyrate. When grown under starvation conditions, BAX‐ and BAK‐deleted cells produce two‐ to fivefold more IgG than wild‐type CHO cells. Under normal growth conditions in suspension culture in shake flasks, double‐knockout cultures achieve equal or higher cell densities than unmodified wild‐type cultures and reach viable cell densities relevant for large‐scale industrial protein production. Biotechnol. Bioeng. 2010; 105: 330–340. © 2009 Wiley Periodicals, Inc.     

The effects of G418 on the growth and metabolism of recombinant mammalian cell lines

It is widely reported that the growth of recombinant bacteria and yeast is adversely affected by increased metabolic load caused by the maintenance of plasmid copy number and recombinant protein expression. Reports suggest that recombinant mammalian systems are similarly affected by increased metabolic load. However, in comparison to bacterial systems relatively little information exists. It was the aim of this study to test the effects of recombinant gene expression on the growth and metabolism of two industrially important cell lines. A BHK and CHO cell line were stably transfected with the human gastric inhibitory peptide (h-GIP)and glucagon receptor respectively. Selection was by way of the neomycin resistance (neo ^r) gene using G418.The growth and metabolism of both cell lines was affected by the presence of G418 in a manner indicative of increased metabolic load and which appeared to be caused by over-expression of the neomycin resistance protein. The two cell lines differed in their metabolic response to G418, which suggested that some cell lines or clones may be better able to tolerate a metabolic load than others. Growth under increased metabolic load was affected by medium composition with serum, insulin and glutamine concentration as influencing factors. Implications for the use of G418 are discussed. This revised version was published online in July 2006 with corrections to the Cover Date.     

Metabolic modeling of endosymbiont genome reduction on a temporal scale

A fundamental challenge in Systems Biology is whether a cell‐scale metabolic model can predict patterns of genome evolution by realistically accounting for associated biochemical constraints. Here, we study the order in which genes are lost in an in silico evolutionary process, leading from the metabolic network of Eschericia coli to that of the endosymbiont Buchnera aphidicola. We examine how this order correlates with the order by which the genes were actually lost, as estimated from a phylogenetic reconstruction. By optimizing this correlation across the space of potential growth and biomass conditions, we compute an upper bound estimate on the model's prediction accuracy (R=0.54). The model's network‐based predictive ability outperforms predictions obtained using genomic features of individual genes, reflecting the effect of selection imposed by metabolic stoichiometric constraints. Thus, while the timing of gene loss might be expected to be a completely stochastic evolutionary process, remarkably, we find that metabolic considerations, on their own, make a marked 40% contribution to determining when such losses occur.     

A negative genetic interaction map in isogenic cancer cell lines reveals cancer cell vulnerabilities

Improved efforts are necessary to define the functional product of cancer mutations currently being revealed through large‐scale sequencing efforts. Using genome‐scale pooled shRNA screening technology, we mapped negative genetic interactions across a set of isogenic cancer cell lines and confirmed hundreds of these interactions in orthogonal co‐culture competition assays to generate a high‐confidence genetic interaction network of differentially essential or differential essentiality (DiE) genes. The network uncovered examples of conserved genetic interactions, densely connected functional modules derived from comparative genomics with model systems data, functions for uncharacterized genes in the human genome and targetable vulnerabilities. Finally, we demonstrate a general applicability of DiE gene signatures in determining genetic dependencies of other non‐isogenic cancer cell lines. For example, the PTEN^?/? DiE genes reveal a signature that can preferentially classify PTEN‐dependent genotypes across a series of non‐isogenic cell lines derived from the breast, pancreas and ovarian cancers. Our reference network suggests that many cancer vulnerabilities remain to be discovered through systematic derivation of a network of differentially essential genes in an isogenic cancer cell model.