Emre Özdemir

Predictable expression of heterologous genes remains a key challenge in biotechnology, largely due to cellular stresses imposed on the production host. Here, we systematically dissect stress responses in Escherichia coli MG1655 expressing diverse proteins under varying promoters and translation efficiencies. Using independent component analysis on new and existing large transcriptomic datasets, we identify distinct responses to transcriptional and translational stresses: excessive heterologous messenger RNA (mRNA) triggers a cold shock response that controls mRNA stability (cspA–I, deaD), while protein production activates a heat shock response involving proteolysis and chaperone functions. We further identify a broad adaptation response, consistently co-activated with the heat shock response during protein production, that provides stationary phase regulation (rspAB) and osmoregulation (betABIT). Targeting these latter functions through strain and media modifications significantly increases eGFP production. Other host stress responses depend on the protein being expressed; e.g. we find production of cysteine-rich proteins to uniquely activate functions regulating iron- and redox homeostasis and oxidative stress responses. This work demonstrates a holistic, systems-level view of cellular stresses to heterologous gene expression by considering transcriptional, translational, and product-specific contributions, paving the way toward predictable and optimized expression strategies.

Replacing petrochemicals with compounds from bio-based manufacturing processes remains an important part of the global effort to move towards a sustainable future. However, achieving economic viability requires both optimized cell factories and innovative processes. Here, we address this challenge by developing a fermentation platform, which enables two concurrent fermentations in one bioreactor. We first construct a xylitol producing Escherichia coli strain in which CRISPRi-mediated gene silencing is used to switch the metabolism from aerobic to anaerobic, even when the bacteria are under oxic conditions. The switch also decouples growth from production, which further increases the yield. The strain produces acetate as a byproduct, which is subsequently metabolized under oxic conditions by a secondary E. coli strain. Through constraint-based metabolic modelling this strain is designed to co-valorize glucose and the excreted acetate to a secondary product. This unique syntrophic consortium concept facilitates the implementation of “two fermentations in one go”, where the concurrent fermentation displays similar titers and productivities as compared to two separate single strain fermentations.

Predictable expression of heterologous genes in a production host is a fundamental challenge in biotechnology. While traditional methods focus on manipulating expression and the property of the heterologous gene, a systems biology approach can complement with designs to improve the host itself. Previously, Independent Component Analysis (ICA) of the RNAseq data helped reveal independently modulated gene sets (iModulons) in bacteria. This was later applied to identify common stress responses related to heterologous gene expression for Escherichia coli . In this study, we expand this analysis with additional non-enzymatic proteins and apply our findings to design novel protein production optimization. By leveraging the Precise-1K transcriptomics knowledge base, we identify three iModulons as novel transcriptional responses to protein production stress; Cold Shock, gcvB sRNA, and the uncharacterized UC-9 iModulons. By studying the gene membership in the UC-9 iModulon, we discover effective novel design targets for improving protein production. This study demonstrates the value of big data analytics and systems understanding of host responses for designing novel strategies to optimize protein production.

Background: Lactobacillus reuteri is a heterofermentative Lactic Acid Bacterium (LAB) that is commonly used for food fermentations and probiotic purposes. Due to its robust properties, it is also increasingly considered for use as a cell factory. It produces several industrially important compounds such as 1,3-propanediol and reuterin natively, but for cell factory purposes, developing improved strategies for engineering and fermentation optimization is crucial. Genome-scale metabolic models can be highly beneficial in guiding rational metabolic engineering. Reconstructing a reliable and a quantitatively accurate metabolic model requires extensive manual curation and incorporation of experimental data. Results: A genome-scale metabolic model of L. reuteri JCM 1112^T was reconstructed and the resulting model, Lreuteri_530, was validated and tested with experimental data. Several knowledge gaps in the metabolism were identified and resolved during this process, including presence/absence of glycolytic genes. Flux distribution between the two glycolytic pathways, the phosphoketolase and Embden–Meyerhof–Parnas pathways, varies considerably between LAB species and strains. As these pathways result in different energy yields, it is important to include strain-specific utilization of these pathways in the model. We determined experimentally that the Embden–Meyerhof–Parnas pathway carried at most 7% of the total glycolytic flux. Predicted growth rates from Lreuteri_530 were in good agreement with experimentally determined values. To further validate the prediction accuracy of Lreuteri_530, the predicted effects of glycerol addition and adhE gene knock-out, which results in impaired ethanol production, were compared to in vivo data. Examination of both growth rates and uptake- and secretion rates of the main metabolites in central metabolism demonstrated that the model was able to accurately predict the experimentally observed effects. Lastly, the potential of L. reuteri as a cell factory was investigated, resulting in a number of general metabolic engineering strategies. Conclusion: We have constructed a manually curated genome-scale metabolic model of L. reuteri JCM 1112^T that has been experimentally parameterized and validated and can accurately predict metabolic behavior of this important platform cell factory.

Promoters are key components of cell factory design, allowing precise expression of genes in a heterologous pathway. Several commonly-used promoters in yeast cell factories belong to glycolytic genes, highly expressed in actively-growing yeast when glucose is used as a carbon source. However, their expression can be suboptimal when alternate carbon sources are used, or if there is a need to decouple growth from production. Hence, there is a need for alternate promoters for different carbon sources and production schemes. In this work, we demonstrate a reversal of regulatory function in two glycolytic yeast promoters by replacing glycolytic regulatory elements with ones induced by the diauxic shift. We observe a shift in induction from glucose-rich to glucose-poor medium without loss of regulatory activity, and strong ethanol induction. Applications of these promoters were validated for expression of the vanillin biosynthetic pathway, reaching production of vanillin comparable to pathway designs using strong constitutive promoters.

We present a selection design that couples S-adenosylmethionine–dependent methylation to growth. We demonstrate its use in improving the enzyme activities of not only N-type and O-type methyltransferases by 2-fold but also an acetyltransferase of another enzyme category when linked to a methylation pathway in Escherichia coli using adaptive laboratory evolution. We also demonstrate its application for drug discovery using a catechol O-methyltransferase and its inhibitors entacapone and tolcapone. Implementation of this design in Saccharomyces cerevisiae is also demonstrated. Many important biological processes require methylation, e.g., DNA methylation and synthesis of flavoring compounds, neurotransmitters, and antibiotics. Most methylation reactions in cells are catalyzed by S-adenosylmethionine (SAM)–dependent methyltransferases (Mtases) using SAM as a methyl donor. Thus, SAM-dependent Mtases have become an important enzyme category of biotechnological interests and as healthcare targets. However, functional implementation and engineering of SAM-dependent Mtases remains difficult and is neither cost effective nor high throughput. Here, we are able to address these challenges by establishing a synthetic biology approach, which links Mtase activity to cell growth such that higher Mtase activity ultimately leads to faster cell growth. We show that better-performing variants of the examined Mtases can be readily obtained by growth selection after repetitive cell passages. We also demonstrate the usefulness of our approach for discovery of Mtase-specific drug candidates. We further show our approach is not only applicable in bacteria, exemplified by Escherichia coli, but also in eurkaryotic organisms such as budding yeast Saccharomyces cerevisiae.

Computational systems biology methods enable rational design of cell factories on a genome-scale and thus accelerate the engineering of cells for the production of valuable chemicals and proteins. Unfortunately, the majority of these methods’ implementations are either not published, rely on proprietary software, or do not provide documented interfaces, which has precluded their mainstream adoption in the field. In this work we present cameo, a platform-independent software that enables in silico design of cell factories and targets both experienced modelers as well as users new to the field. It is written in Python and implements state-of-the-art methods for enumerating and prioritizing knockout, knock-in, overexpression, and down-regulation strategies and combinations thereof. Cameo is an open source software project and is freely available under the Apache License 2.0. A dedicated Web site including documentation, examples, and installation instructions can be found at http://cameo.bio. Users can also give cameo a try at http://try.cameo.bio.