Extension of the yeast metabolic model to include iron metabolism and its use to estimate global levels of iron-recruiting enzyme abundance from cofactor requirements.

Metabolic networks adapt to changes in their environment by modulating the activity of their enzymes and transporters; often by changing their abundance. Understanding such quantitative changes can shed light onto how metabolic adaptation works, or how it can fail and lead to a metabolically dysfunctional state. We propose a strategy to quantify metabolic protein requirements for cofactor-utilising enzymes and transporters through constraint-based modelling. The first eukaryotic genome-scale metabolic model to comprehensively represent iron metabolism was constructed, extending the most recent community model of the Saccharomyces cerevisiae metabolic network. Partial functional impairment of the genes involved in the maturation of iron-sulphur (Fe-S) proteins was investigated employing the model and the in silico analysis revealed extensive rewiring of the fluxes in response to this functional impairment, despite its marginal phenotypic effect. The optimal turnover rate of enzymes bearing ion cofactors can be determined via this novel approach; yeast metabolism, at steady state, was determined to employ a constant turnover of its iron-recruiting enzyme at a rate of 3.02 × 10 -11  mmol·(g biomass) -1 ·h  -1 .the Leverhulme Trust (ECF-2016-681 to DD) EC 7th FP (BIOLEDGE Contract no: 289126 to SGO), BBSRC (BRIC2.2 to SGO)

Recombinant expression of insoluble enzymes in Escherichia coli: a systematic review of experimental design and its manufacturing implications.

Recombinant enzyme expression in Escherichia coli is one of the most popular methods to produce bulk concentrations of protein product. However, this method is often limited by the inadvertent formation of inclusion bodies. Our analysis systematically reviews literature from 2010 to 2021 and details the methods and strategies researchers have utilized for expression of difficult to express (DtE), industrially relevant recombinant enzymes in E. coli expression strains. Our review identifies an absence of a coherent strategy with disparate practices being used to promote solubility. We discuss the potential to approach recombinant expression systematically, with the aid of modern bioinformatics, modelling, and 'omics' based systems-level analysis techniques to provide a structured, holistic approach. Our analysis also identifies potential gaps in the methods used to report metadata in publications and the impact on the reproducibility and growth of the research in this field.Non

Biomass composition: the "elephant in the room" of metabolic modelling.

Genome-scale stoichiometric models, constrained to optimise biomass production are often used to predict mutant phenotypes. However, for Saccharomyces cerevisiae, the representation of biomass in its metabolic model has hardly changed in over a decade, despite major advances in analytical technologies. Here, we use the stoichiometric model of the yeast metabolic network to show that its ability to predict mutant phenotypes is particularly poor for genes encoding enzymes involved in energy generation. We then identify apparently inefficient energy-generating pathways in the model and demonstrate that the network suffers from the high energy burden associated with the generation of biomass. This is tightly connected to the availability of phosphate since this macronutrient links energy generation and structural biomass components. Variations in yeast's biomass composition, within experimentally-determined bounds, demonstrated that flux distributions are very sensitive to such changes and to the identity of the growth-limiting nutrient. The predictive accuracy of the yeast metabolic model is, therefore, compromised by its failure to represent biomass composition in an accurate and context-dependent manner.The authors gratefully acknowledge the financial support from the Turkish State Planning Organization (DPT09K120520 to BK), TUBITAK (106M444 to BK), BBSRC (BRIC2.2 to SGO), EU 7th Framework Programme (BIOLEDGE Contract No: 289126 to SGO).This is the final version. It was first published by Springer at http://dx.doi.org/10.1007/s11306-015-0819-

Computer-Aided Strategies for Determining the Amino Acid Composition of Medium for Chinese Hamster Ovary Cell-Based Biomanufacturing Platforms.

Chinese hamster ovary (CHO) cells are used for the production of the majority of biopharmaceutical drugs, and thus have remained the standard industry host for the past three decades. The amino acid composition of the medium plays a key role in commercial scale biologics manufacturing, as amino acids constitute the building blocks of both endogenous and heterologous proteins, are involved in metabolic and non-metabolic pathways, and can act as main sources of nitrogen and carbon under certain conditions. As biomanufactured proteins become increasingly complex, the adoption of model-based approaches become ever more popular in complementing the challenging task of medium development. The extensively studied amino acid metabolism is exceptionally suitable for such model-driven analyses, and although still limited in practice, the development of these strategies is gaining attention, particularly in this domain. This paper provides a review of recent efforts. We first provide an overview of the widely adopted practice, and move on to describe the model-driven approaches employed for the improvement and optimization of the external amino acid supply in light of cellular amino acid demand. We conclude by proposing the likely prevalent direction the field is heading towards, providing a critical evaluation of the current state and the future challenges and considerations

Handling variability and incompleteness of biological data by flexible nets: a case study for Wilson disease.

Mathematical models that combine predictive accuracy with explanatory power are central to the progress of systems and synthetic biology, but the heterogeneity and incompleteness of biological data impede our ability to construct such models. Furthermore, the robustness displayed by many biological systems means that they have the flexibility to operate under a range of physiological conditions and this is difficult for many modeling formalisms to handle. Flexible nets (FNs) address these challenges and represent a paradigm shift in model-based analysis of biological systems. FNs can: (i) handle uncertainties, ranges and missing information in concentrations, stoichiometry, network topology, and transition rates without having to resort to statistical approaches; (ii) accommodate different types of data in a unified model that integrates various cellular mechanisms; and (iii) be employed for system optimization and model predictive control. We present FNs and illustrate their capabilities by modeling a well-established system, the dynamics of glucose consumption by a microbial population. We further demonstrate the ability of FNs to take control actions in response to genetic or metabolic perturbations. Having bench-marked the system, we then construct the first quantitative model for Wilson disease-a rare genetic disorder that impairs copper utilization in the liver. We used this model to investigate the feasibility of using vitamin E supplementation therapy for symptomatic improvement. Our results indicate that hepatocytic inflammation caused by copper accumulation was not aggravated by limitations on endogenous antioxidant supplies, which means that treating patients with antioxidants is unlikely to be effective

Automated liquid-handling operations for robust, resilient, and efficient bio-based laboratory practices

Increase in the adoption of liquid handling devices (LHD) can facilitate experimental activities. Initially adopted by businesses and industry-based laboratories, the practice has also moved to academic environments, where a wide range of non-standard/non-typical experiments can be performed. Current protocols or laboratory analyses require researchers to transfer liquids for the purpose of dilution, mixing, or inoculation, among other operations. LHD can render laboratories more efficient by performing more experiments per unit of time, by making operations robust and resilient against external factors and unforeseen events such as the COVID-19 pandemic, and by remote operation. The present work reviews literature that reported the adoption and utilisation of LHD available in the market and presents examples of their practical use. Applications demonstrate the critical role of automation in research development and its ability to reduce human intervention in the experimental workflow. Ultimately, this work will provide guidance to academic researchers to determine which LHD can fulfil their needs and how to exploit their use in both conventional and non-conventional applications. Furthermore, the breadth of applications and the scarcity of academic institutions involved in research and development that utilise these devices highlights an important area of opportunity for shift in technology to maximize research outcomes

Online Data Condensation for Digitalised Biopharmaceutical Processes

Efficient control of a bioprocess relies on the ability to systematically capture and represent the process dynamics of critical process parameters. Multivariate monitoring techniques in biopharmaceuticals has resulted in the generation of large amounts of data comprising real-time measurements of critical quality and performance attributes. If exploited efficiently, these can provide an opportunity for developing better control action. For this, it is important to have a comprehensive view of the critical process parameter landscape, which can only be achieved by integrating both online and offline data into a single data matrix that can then be subjected to standard data analysis protocols. However, owing to the difference in the number of readings available for variables recorded online and offline, there is a need for new methods to achieve condensation capability. This paper introduces a novel methodology for condensing online data into an offline data matrix, which performed better when compared to traditionally employed averaging and helped increase the number of variables available for representing the design space of the process. The method was also used to understand how error propagates through online data, so as to identify an interval of tolerance in online monitoring of bioprocesses

How yeast re-programmes its transcriptional profile in response to different nutrient impulses.

BACKGROUND: A microorganism is able to adapt to changes in its physicochemical or nutritional environment and this is crucial for its survival. The yeast, Saccharomyces cerevisiae, has developed mechanisms to respond to such environmental changes in a rapid and effective manner; such responses may demand a widespread re-programming of gene activity. The dynamics of the re-organization of the cellular activities of S. cerevisiae in response to the sudden and transient removal of either carbon or nitrogen limitation has been studied by following both the short- and long-term changes in yeast's transcriptomic profiles. RESULTS: The study, which spans timescales from seconds to hours, has revealed the hierarchy of metabolic and genetic regulatory switches that allow yeast to adapt to, and recover from, a pulse of a previously limiting nutrient. At the transcriptome level, a glucose impulse evoked significant changes in the expression of genes concerned with glycolysis, carboxylic acid metabolism, oxidative phosphorylation, and nucleic acid and sulphur metabolism. In ammonium-limited cultures, an ammonium impulse resulted in the significant changes in the expression of genes involved in nitrogen metabolism and ion transport. Although both perturbations evoked significant changes in the expression of genes involved in the machinery and process of protein synthesis, the transcriptomic response was delayed and less complex in the case of an ammonium impulse. Analysis of the regulatory events by two different system-level, network-based approaches provided further information about dynamic organization of yeast cells as a response to a nutritional change. CONCLUSIONS: The study provided important information on the temporal organization of transcriptomic organization and underlying regulatory events as a response to both carbon and nitrogen impulse. It has also revealed the importance of a long-term dynamic analysis of the response to the relaxation of a nutritional limitation to understand the molecular basis of the cells' dynamic behaviour.The authors greatly acknowledge the financial support for the research from the BBSRC (Grant BB/C505140/1 to SGO), and the travel grants for DD kindly provided by the Research Council of Turkey (TUBITAK) through the BDP programme and the Turkish State Planning Organization DPT09K120520. The research was also financially supported by Bogazici University Research Fund through Project No 631 and TUBITAK through Project No 106M444. Further support came from European Commission though the Coordination Action Project YSBN (Contract No.018942 to both BK and SGO) and UNICELLSYS Collaborative Project (No. 201142 to SGO)

Transcriptional regulation of the genes involved in protein metabolism and processing in Saccharomyces cerevisiae.

Topological analysis of large networks, which focus on a specific biological process or on related biological processes, where functional coherence exists among the interacting members, may provide a wealth of insight into cellular functionality. This work presents an unbiased systems approach to analyze genetic, transcriptional regulatory and physical interaction networks of yeast genes possessing such functional coherence to gain novel biological insight. The present analysis identified only a few transcriptional regulators amongst a large gene cohort associated with the protein metabolism and processing in yeast. These transcription factors are not functionally required for the maintenance of these tasks in growing cells. Rather, they are involved in rewiring gene transcription in response to such major challenges as starvation, hypoxia, DNA damage, heat shock or the accumulation of unfolded proteins. Indeed, only a subset of these proteins were captured empirically in the nuclear-enriched fraction of non-stressed yeast cells, suggesting that the transcriptional regulation of protein metabolism and processing in yeast is primarily concerned with maintaining cellular robustness in the face of threat by either internal or external stressors

A Novel Strategy for Selection and Validation of Reference Genes in Dynamic Multidimensional Experimental Design in Yeast

<div><h3>Background</h3><p>Understanding the dynamic mechanism behind the transcriptional organization of genes in response to varying environmental conditions requires time-dependent data. The dynamic transcriptional response obtained by real-time RT-qPCR experiments could only be correctly interpreted if suitable reference genes are used in the analysis. The lack of available studies on the identification of candidate reference genes in dynamic gene expression studies necessitates the identification and the verification of a suitable gene set for the analysis of transient gene expression response.</p> <h3>Principal Findings</h3><p>In this study, a candidate reference gene set for RT-qPCR analysis of dynamic transcriptional changes in <em>Saccharomyces cerevisiae</em> was determined using 31 different publicly available time series transcriptome datasets. Ten of the twelve candidates (<em>TPI1</em>, <em>FBA1</em>, <em>CCW12</em>, <em>CDC19</em>, <em>ADH1</em>, <em>PGK1</em>, <em>GCN4</em>, <em>PDC1</em>, <em>RPS26A</em> and <em>ARF1</em>) we identified were not previously reported as potential reference genes. Our method also identified the commonly used reference genes <em>ACT1</em> and <em>TDH3</em>. The most stable reference genes from this pool were determined as <em>TPI1</em>, <em>FBA1</em>, <em>CDC19</em> and <em>ACT1</em> in response to a perturbation in the amount of available glucose and as <em>FBA1</em>, <em>TDH3</em>, <em>CCW12</em> and <em>ACT1</em> in response to a perturbation in the amount of available ammonium. The use of these newly proposed gene sets outperformed the use of common reference genes in the determination of dynamic transcriptional response of the target genes, <em>HAP4</em> and <em>MEP2</em>, in response to relaxation from glucose and ammonium limitations, respectively.</p> <h3>Conclusions</h3><p>A candidate reference gene set to be used in dynamic real-time RT-qPCR expression profiling in yeast was proposed for the first time in the present study. Suitable pools of stable reference genes to be used under different experimental conditions could be selected from this candidate set in order to successfully determine the expression profiles for the genes of interest.</p> </div