A review of methods for the reconstruction and analysis of integrated genome-scale models of metabolism and regulation

The current survey aims to describe the main methodologies for extending the reconstruction and analysis of genome-scale metabolic models and phenotype simulation with Flux Balance Analysis mathematical frameworks, via the integration of Transcriptional Regulatory Networks and/or gene expression data. Although the surveyed methods are aimed at improving phenotype simulations obtained from these models, the perspective of reconstructing integrated genome-scale models of metabolism and gene expression for diverse prokaryotes is still an open challenge.This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UIDB/04 469/2020 unit and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020 -Programa Operacional Regional do Norte. Fernando Cruz holds a doctoral fellowship (SFRH/BD/139198/2018) funded by the FCT. This study was supported by the European Commission through project SHIKIFACTORY100 -Modular cell factories for the production of 100 compounds from the shikimate pathway (Reference 814408). The submitted manuscript has been created by UChicago Argonne, LLC as Operator of Argonne National Laboratory (`Argonne') under Contract No. DE-AC02-06CH11357 with the U.S. Department of Energy. The U.S. Government retains for itself, and others acting on its behalf, a paid-up, nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan.info:eu-repo/semantics/publishedVersio

Metabolic engineering of microorganisms for the overproduction of fatty acids

Fatty acids naturally synthesized in many organisms are promising starting points for the catalytic production of industrial chemicals and diesel-like biofuels. However, bio-production of fatty acids in microbial hosts relies heavily on manipulating tightly regulated fatty acid biosynthetic pathways, thus complicating the engineering for higher yields. With the advent of systems metabolic engineering, we demonstrated an iterative metabolic engineering effort that integrates computationally driven predictions and metabolic flux analysis (MFA) was demonstrated to meet this challenge. With wild type E. coli fluxomic data, the OptForce procedure was employed to suggest genetic manipulations for fatty acid overproduction. In accordance with the OptForce prioritization of interventions, fabZ and acyl-ACP thioesterase were upregulated and fadD was deleted to arrive at a strain that produces 1.70 g/L and 0.14 g fatty acid/g glucose of C14-16 fatty acid in minimal medium. However, OptForce does not infer gene regulation, enzyme inhibition and metabolic toxicity. Along with transcriptomics and metabolomics analysis, we re-deployed OptForce simulation using the redefined flux distribution as constraints to generate predictions for the second generation fatty acid-overproducing strain. MFA identified the up-regulation of the TCA cycle and down-regulation of pentose phosphate pathway under fatty acid overproduction to replenish the need of energy and reducing molecules. The elevation of intracellular metabolite levels in the TCA cycle complemented the flux findings. With re-defined flux boundary of the first generation strain, OptForce suggested the interruption of TCA cycle such as removal of succinate dehydrogenase as the most prioritized genetic intervention to further improve fatty acid production. Meanwhilem, the whole genome transcriptional analysis revealed acid stress response, membrane disruption, colanic acid and biofilm formation during fatty acid production, thus pinpointing the targets for future metabolic engineering effort. These results highlight the benefit of using computational strain design and system metabolic engineering tools in systematically guiding the strain design to produce free fatty acids. Nonetheless, Saccharomyces cerevisiae is another attractive host organism for the production of biochemicals and biofuels. However, S. cerevisiae is very susceptible to octanoic acid toxicity. Transcriptomics analysis revealed membrane stress and intracellular acidification during octanoic acid stress. MFA illustrated the increase of flux in the TCA cycle possibly to facilitate the ATP-binding-cassette transporter activities. Further efforts can focus on improving membrane integrity or explore oleaginious yeasts to enhance the tolerance against fatty acids

Development of cell factories for the efficient production of mannosylglycerate, a thermolyte with great potential in biotechnology

Mannosylglycerate (MG) is a compatible solute implicated in the response to osmotic or heat stresses in many marine microorganisms adapted to hot environments. MG shows a remarkable ability to protect model proteins, especially against heat denaturation; however, high production costs prevented the industrial exploitation of these features. This thesis has two main objectives: i) to assess the efficacy of MG as protein stabilizer in the intracellular milieu; and ii) to develop a bio-based process for production of MG at competitive cost. The first goal was achieved by using a yeast model of Parkinson’s disease in which an aggregation-prone protein, eGFP-tagged α-synuclein, was expressed along with the biosynthetic activities that catalyze the formation of MG from GDP-mannose and 3-phosphoglycerate. There was a reduction of 3.3-fold in the number of cells containing fluorescent foci of α-synuclein, in comparison with a control strain without MG. It was also proven that inhibition of aggregation was due to direct MG-protein effects, i.e., MG acted in vivo as a chemical chaperone. This opened a way for drug development against diseases related with protein misfolding. Towards the second objective, genes PMI40 and PSA1 of the GDP-mannose pathway were over-expressed in the industrial microorganism, Saccharomyces cerevisiae, to redirect metabolic flux towards that MG precursor. This strategy led to 2.2-fold increase in MG production (15.86 mgMG.gDW-1) for cells cultivated in controlled batch mode. Further improvement was achieved by cultivation in chemostat mode at a dilution rate of 0.15 h-1; a constant productivity of 1.79 mgMG.gDW-1h-1 was reached. Next, a holist approach was undertaken by using in silico tools to identify engineering strategies that would lead to efficient channeling of substrates to MG production. The proposed strains were constructed and characterized in batch fermentation and continuous mode and led to an improved MG production of 25.3 mgMG.gDW-1 and 3.4 mgMG.L-1h-1, respectively

Analysis of metabolic flux using dynamic labeling and metabolic modeling

Metabolic fluxes and the capacity to modulate them are a crucial component of the ability of the plant cell to react to environmental perturbations. Our ability to quantify them and to attain information concerning the regulatory mechanisms which control them is therefore essential to understand and influence metabolic networks. For all but the simplest of flux measurements labelling methods have proven to be the most informative. Both steady-state and dynamic labelling approaches having been adopted in the study of plant metabolism. Here the conceptual basis of these complementary approaches, as well as their historical application in microbial, mammalian and plant sciences are reviewed and an update on technical developments in label distribution analyses is provided. This is supported by illustrative cases studies involving the kinetic modelling of secondary metabolism. One issue that is particularly complex in the analysis of plant fluxes is the extensive compartmentation of the plant cell. This problem is discussed from both theoretical and experimental perspectives and the current approaches used to address it are assessed. Finally, current limitations and future perspectives of kinetic modelling of plant metabolism are discussed

Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models.

Understanding the adaptive responses of individual bacterial strains is crucial for microbiome engineering approaches that introduce new functionalities into complex microbiomes, such as xenobiotic compound metabolism for soil bioremediation. Adaptation requires metabolic reprogramming of the cell, which can be captured by multi-omics, but this data remains formidably challenging to interpret and predict. Here we present a new approach that combines genome-scale metabolic modeling with transcriptomics and exometabolomics, both of which are common tools for studying dynamic population behavior. As a realistic demonstration, we developed a genome-scale model of Pseudomonas veronii 1YdBTEX2, a candidate bioaugmentation agent for accelerated metabolism of mono-aromatic compounds in soil microbiomes, while simultaneously collecting experimental data of P. veronii metabolism during growth phase transitions. Predictions of the P. veronii growth rates and specific metabolic processes from the integrated model closely matched experimental observations. We conclude that integrative and network-based analysis can help build predictive models that accurately capture bacterial adaptation responses. Further development and testing of such models may considerably improve the successful establishment of bacterial inoculants in more complex systems

Mapping interactions between metabolites and transcriptional regulators at a genome-scale

The control and regulation of cellular metabolism is required to maintain the biosynthesis of building blocks and energy, but also to prevent the loss of energy and to be able to quickly adjust to changing conditions. Hence, the metabolic network and the flow of genetic information has multiple layers of regulation and information is transmitted between gene expression and metabolism. For this purpose, metabolites serve as key signals of the regulatory network to balance metabolism via the adjustment of protein levels and the activity of enzymes. Understanding these regulations and interplays of bacterial metabolism will enable us to improve the modelling and engineering of metabolic networks and ultimately to develop new antibiotics and production strains. The aim of this thesis is to investigate which regulatory mechanisms are used by the cell to respond to genetic perturbations. Moreover, we develop new methods to map protein-metabolite interactions and to prove their functionality in the cell. After introducing the fundamentals of metabolic network regulation, we investigate in chapter 1 how Escherichia coli (E. coli) reacts to genetic perturbations. We use a library of 7177 CRISPRi strains to perform a pooled fitness growth assay, demonstrating the buffering effects of metabolism. Additionally, measuring the metabolome and proteome of 30 arrayed CRISPRi strains enables us to elucidate three gene-specific buffering mechanisms. In chapter 2, we use our new insights about genetic perturbations of chapter 1 to develop a method for systematically mapping interactions between metabolites and transcriptional regulators. CRISPRi leads to a knockdown of a gene and therefore induces specific changes in the metabolome and proteome of the cell. We therefore combine the pooled CRISPRi library with a fluorescent reporter for transcription factor activity and extract cells, which show a response of the reporter to the changing conditions, via FACS from the pooled library. By analyzing proteome and metabolome data, we confirm previously reported and discover new interactions. With chapter 3, we provide a detailed protocol of how to work with CRISPRi libraries. We explain the design and construction of sgRNAs of arrayed as well as pooled CRISPRi strains and how to perform growth assays. Furthermore, we explain the execution and analysis of Illumina Next-generation sequencing of pooled libraries. We also explain the sorting of cells from pooled libraries via FACS. In chapter 4, we show how to find new interactions between metabolites and transcription factors by external perturbations. By switching a growing E. coli culture between growth and glucose limitation, we provoke strong changes of metabolite levels and transcript levels. Calculating the transcription factor activity from gene expression levels and correlating them with metabolite levels, enables us to recover known interactions but also to discover new interactions, of which we prove five in in vitro binding assays. In chapter 5, we investigate the function of allosteric regulation of metabolic enzymes in amino acid pathways of E. coli. We constructed 7 mutants of allosteric enzymes to remove the allosteric feedback regulation. By metabolomics, proteomics and flux profiling analysis we show how allostery helps to adjust enzyme levels of the cell. Furthermore, using a metabolic model and the application of CRISPRi we show how well-adjusted enzyme levels make the cell more stable towards genetic perturbations

A systems biology approach reveals major metabolic changes in the thermoacidophilic archaeon Sulfolobus solfataricus in response to the carbon source L-fucose versus D-glucose

Archaea are characterised by a complex metabolism with many unique enzymes that differ from their bacterial and eukaryotic counterparts. The thermoacidophilic archaeon Sulfolobus solfataricus is known for its metabolic versatility and is able to utilize a great variety of different carbon sources. However, the underlying degradation pathways and their regulation are often unknown. In this work, we analyse growth on different carbon sources using an integrated systems biology approach. The comparison of growth on L-fucose and D-glucose allows first insights into the genome-wide changes in response to the two carbon sources and revealed a new pathway for L-fucose degradation in S. solfataricus. During growth on L-fucose we observed major changes in the central carbon metabolic network, as well as an increased activity of the glyoxylate bypass and the 3-hydroxypropionate/4-hydroxybutyrate cycle. Within the newly discovered pathway for L-fucose degradation the following key reactions were identified: (i) L-fucose oxidation to L-fuconate via a dehydrogenase, (ii) dehydration to 2-keto-3-deoxy-L-fuconate via dehydratase, (iii) 2-keto-3-deoxy-L-fuconate cleavage to pyruvate and L-lactaldehyde via aldolase and (iv) L-lactaldehyde conversion to L-lactate via aldehyde dehydrogenase. This pathway as well as L-fucose transport shows interesting overlaps to the D-arabinose pathway, representing another example for pathway promiscuity in Sulfolobus species

Toward a systems-level understanding of gene regulatory, protein interaction, and metabolic networks in cyanobacteria.

Cyanobacteria are essential primary producers in marine ecosystems, playing an important role in both carbon and nitrogen cycles. In the last decade, various genome sequencing and metagenomic projects have generated large amounts of genetic data for cyanobacteria. This wealth of data provides researchers with a new basis for the study of molecular adaptation, ecology and evolution of cyanobacteria, as well as for developing biotechnological applications. It also facilitates the use of multiplex techniques, i.e., expression profiling by high-throughput technologies such as microarrays, RNA-seq, and proteomics. However, exploration and analysis of these data is challenging, and often requires advanced computational methods. Also, they need to be integrated into our existing framework of knowledge to use them to draw reliable biological conclusions. Here, systems biology provides important tools. Especially, the construction and analysis of molecular networks has emerged as a powerful systems-level framework, with which to integrate such data, and to better understand biological relevant processes in these organisms. In this review, we provide an overview of the advances and experimental approaches undertaken using multiplex data from genomic, transcriptomic, proteomic, and metabolomic studies in cyanobacteria. Furthermore, we summarize currently available web-based tools dedicated to cyanobacteria, i.e., CyanoBase, CyanoEXpress, ProPortal, Cyanorak, CyanoBIKE, and CINPER. Finally, we present a case study for the freshwater model cyanobacteria, Synechocystis sp. PCC6803, to show the power of meta-analysis, and the potential to extrapolate acquired knowledge to the ecologically important marine cyanobacteria genus, Prochlorococcus

A systems biology approach sheds new light on the regulation of acid adaptation in Escherichia coli BW25113 and MG1655 strains

The ability of Escherichia coli to survive in extreme acid conditions is an important component of its physiology. In my study I have profiled the Escherichia coli K-12 BW25113 strain using microarray technology and I have analysed a multi-omics dataset representing the transcriptional and metabolic responses of the MG1655 Escherichia coli strain. An initial high-level model in the BW25113 strain representing the interaction between two component systems regulators and effectors functions was built using the ARACNE methodology. My model supported the view that acid resistance involves a mechanism based on the transcriptional switch between the expression of genes encoding aerobic and anaerobic enzymes and controlled by the two-component system regulator OmpR. Experimental validation of the model confirmed this hypothesis. This model allowed me to predict that the MG1655 strain would be more sensitive to acid than the related BW25113 strain. Acid exposure induced an opposite response in this strain by repressing most of the anaerobic enzymes in favour of the aerobic metabolism. A dynamical model, developed by using State Space Models, revealed three potential regulators of acid adaptation in the MG1655 strain: OmpR, YehT and DcuR. I concluded that OmpR has a key role in acid adaptation in both strains and that the ability to reassess the balance in the expression of bioenergetics genes is more important for survival than proton detoxification

Microbial lifestyle engineering

Using Pseudomonas putida KT2440 as a proof-of-concept organism, this thesis was aimed at microbial lifestyle engineering for industrial applications. In this thesis, a structured approach was applied by first determining what microbial improvements industry is looking for by conducting a series of interviews with both industry and academia. Besides pinpointing the fields of interest from an industrial perspective, the interviews also clarified the limitations of the actual implementation of novel or (synthetically) adapted strains developed. Strain safety being at the top of their list, we first checked the claimed GRAS safety level of P. putida KT2440. A major obstacle for the breakthrough of P. putida KT2440 to be widely used as a biotechnological host is its obligate aerobic metabolism. In silico-directed strain improvement were initiated by the adaptation of strict aerobic P. putida KT2440 to micro-oxic and anoxic conditions. Adaptation to micro-oxic levels was done by first creating a design for a recombinant strain capable of anaerobic fermentation. The bottlenecks uncovered were resolved by insertion of three genes, and the recombinant strains were monitored through an adaptive laboratory evolution method with oxygen gradients set up specifically for this purpose. Recombinant strains were able to grow under micro-oxic conditions. Strain performance did not improve compared to the negative control under anoxic conditions. A more elaborate in-silico analysis was performed, combining protein domain analysis, transcriptomic analysis and genome-scale metabolic models to design a recombinant P. putida KT2440 strain capable of anaerobic respiration. Another general limitation in strains is their limited thermo-tolerance. We discovered a strong universal connection between NAD+ availability and thermo-tolerance. By replacing one single gene for a thermophilic heterolog in mesophilic prokaryotes, both P. putida and E. coli showed instant improved thermo-tolerance. Insertion of the aspartate NAD+ biogeneration pathway in eukaryotic yeast S. cerevisiae resulted in a similar effect. To determine the value of this thermo-tolerance in industry, a down-scaled microfluidics system was developed to mimic temperature fluctuations occurring in large scale  bioreactors. The novel discovery between thermo-tolerance and NAD+ availabilty was patented