Bayesian Regression Facilitates Quantitative Modeling of Cell Metabolism.

This paper presents Maud, a command-line application that implements Bayesian statistical inference for kinetic models of biochemical metabolic reaction networks. Maud takes into account quantitative information from omics experiments and background knowledge as well as structural information about kinetic mechanisms, regulatory interactions, and enzyme knockouts. Our paper reviews the existing options in this area, presents a case study illustrating how Maud can be used to analyze a metabolic network, and explains the biological, statistical, and computational design decisions underpinning Maud.

Adaptive laboratory evolution of Clostridium autoethanogenum to metabolize CO2 and H2 enhances growth rates in chemostat and unravels proteome and metabolome alterations.

Gas fermentation of CO2 and H2 is an attractive means to sustainably produce fuels and chemicals. Clostridium autoethanogenum is a model organism for industrial CO to ethanol and presents an opportunity for CO2-to-ethanol processes. As we have previously characterized its CO2/H2 chemostat growth, here we use adaptive laboratory evolution (ALE) with the aim of improving growth with CO2/H2. Seven ALE lineages were generated, all with improved specific growth rates. ALE conducted in the presence of 2% CO along with CO2/H2 generated Evolved lineage D, which showed the highest ethanol titres amongst all the ALE lineages during the fermentation of CO2/H2. Chemostat comparison against the parental strain shows no change in acetate or ethanol production, while Evolved D could achieve a higher maximum dilution rate. Multi-omics analyses at steady state revealed that Evolved D has widespread proteome and intracellular metabolome changes. However, the uptake and production rates and titres remain unaltered until investigating their maximum dilution rate. Yet, we provide numerous insights into CO2/H2 metabolism via these multi-omics data and link these results to mutations, suggesting novel targets for metabolic engineering in this bacterium.

Shu: visualization of high-dimensional biological pathways.

SUMMARY: Shu is a visualization tool that integrates diverse data types into a metabolic map, with a focus on supporting multiple conditions and visualizing distributions. The goal is to provide a unified platform for handling the growing volume of multi-omics data, leveraging the metabolic maps developed by the metabolic modeling community. In addition, shu offers a streamlined python API, based on the Grammar of Graphics, for easy integration with data pipelines. AVAILABILITY AND IMPLEMENTATION: Freely available at https://github.com/biosustain/shu under MIT/Apache 2.0 license. Binaries are available in the release page of the repository and the web application is deployed at https://biosustain.github.io/shu.

Synergistic investigation of natural and synthetic C1-trophic microorganisms to foster a circular carbon economy.

A true circular carbon economy must upgrade waste greenhouse gases. C1-based biomanufacturing is an attractive solution, in which one carbon (C1) molecules (e.g. CO2, formate, methanol, etc.) are converted by microbial cell factories into value-added goods (i.e. food, feed, and chemicals). To render C1-based biomanufacturing cost-competitive, we must adapt microbial metabolism to perform chemical conversions at high rates and yields. To this end, the biotechnology community has undertaken two (seemingly opposing) paths: optimizing natural C1-trophic microorganisms versus engineering synthetic C1-assimilation de novo in model microorganisms. Here, we pose how these approaches can instead create synergies for strengthening the competitiveness of C1-based biomanufacturing as a whole.

Comparative constraint-based modelling of fruit development across species highlights nitrogen metabolism in the growth-defence trade-off.

Although primary metabolism is well conserved across species, it is useful to explore the specificity of its network to assess the extent to which some pathways may contribute to particular outcomes. Constraint-based metabolic modelling is an established framework for predicting metabolic fluxes and phenotypes and helps to explore how the plant metabolic network delivers specific outcomes from temporal series. After describing the main physiological traits during fruit development, we confirmed the correlations between fruit relative growth rate (RGR), protein content and time to maturity. Then a constraint-based method is applied to a panel of eight fruit species with a knowledge-based metabolic model of heterotrophic cells describing a generic metabolic network of primary metabolism. The metabolic fluxes are estimated by constraining the model using a large set of metabolites and compounds quantified throughout fruit development. Multivariate analyses showed a clear common pattern of flux distribution during fruit development with differences between fast- and slow-growing fruits. Only the latter fruits mobilise the tricarboxylic acid cycle in addition to glycolysis, leading to a higher rate of respiration. More surprisingly, to balance nitrogen, the model suggests, on the one hand, nitrogen uptake by nitrate reductase to support a high RGR at early stages of cucumber and, on the other hand, the accumulation of alkaloids during ripening of pepper and eggplant. Finally, building virtual fruits by combining 12 biomass compounds shows that the growth-defence trade-off is supported mainly by cell wall synthesis for fast-growing fruits and by total polyphenols accumulation for slow-growing fruits.

An automated workflow for multi-omics screening of microbial model organisms.

Multi-omics datasets are becoming of key importance to drive discovery in fundamental research as much as generating knowledge for applied biotechnology. However, the construction of such large datasets is usually time-consuming and expensive. Automation might enable to overcome these issues by streamlining workflows from sample generation to data analysis. Here, we describe the construction of a complex workflow for the generation of high-throughput microbial multi-omics datasets. The workflow comprises a custom-built platform for automated cultivation and sampling of microbes, sample preparation protocols, analytical methods for sample analysis and automated scripts for raw data processing. We demonstrate possibilities and limitations of such workflow in generating data for three biotechnologically relevant model organisms, namely Escherichia coli, Saccharomyces cerevisiae, and Pseudomonas putida.

Omics data for sampling thermodynamically feasible kinetic models.

Kinetic models are key to understanding and predicting the dynamic behaviour of metabolic systems. Traditional models require kinetic parameters which are not always available and are often estimated in vitro. Ensemble models overcome this challenge by sampling thermodynamically feasible models around a measured reference point. However, it is unclear if the convenient distributions used to generate the ensemble produce a natural distribution of model parameters and hence if the model predictions are reasonable. In this paper, we produced a detailed kinetic model for the central carbon metabolism of Escherichia coli. The model consists of 82 reactions (including 13 reactions with allosteric regulation) and 79 metabolites. To sample the model, we used metabolomic and fluxomic data from a single steady-state time point for E. coli K-12 MG1655 growing on glucose minimal M9 medium (average sampling time for 1000 models: 11.21 ± 0.14 min). Afterwards, in order to examine whether our sampled models are biologically sound, we calculated Km, Vmax and kcat for the reactions and compared them to previously published values. Finally, we used metabolic control analysis to identify enzymes with high control over the fluxes in the central carbon metabolism. Our analyses demonstrate that our platform samples thermodynamically feasible kinetic models, which are in agreement with previously published experimental results and can be used to investigate metabolic control patterns within cells. This renders it a valuable tool for the study of cellular metabolism and the design of metabolic pathways.

Protocol for absolute quantification of proteins in Gram-negative bacteria based on QconCAT-based labeled peptides.

Mass-spectrometry-based absolute protein quantification uses labeled quantification concatamer (QconCAT) as internal standards (ISs). To calculate the amount of protein(s), the ion intensity ratio between the analyte and its cognate IS is compared in each biological sample. The present protocol describes a systematic workflow to design, produce, and purify QconCATs and to quantify soluble proteins in Pseudomonas putida KT2440. Our methodology enables the quantification of detectable peptide and serves as a versatile platform to produce ISs for different biological systems.

RNA-seq Sample Preparation Kits Strongly Affect Transcriptome Profiles of a Gas-Fermenting Bacterium.

Transcriptome analysis via RNA sequencing (RNA-seq) has become a standard technique employed across various biological fields of study. The rapid adoption of the RNA-seq approach has been mediated, in part, by the development of different commercial RNA-seq library preparation kits compatible with standard next-generation sequencing (NGS) platforms. Generally, the essential steps of library preparation, such as rRNA depletion and first-strand cDNA synthesis, are tailored to a specific group of organisms (e.g., eukaryotes versus prokaryotes) or genomic GC content. Therefore, the selection of appropriate commercial products is of crucial importance to capture the transcriptome of interest as closely to the native state as possible without introduction of technical bias. However, researchers rarely have the resources and time to test various commercial RNA-seq kits for their samples. This work reports a side-by-side comparison of RNA-seq data from Clostridium autoethanogenum obtained using three commercial rRNA removal and strand-specific library construction products of NuGEN Technologies, Qiagen, and Zymo Research and assesses their performance relative to published data. While all three vendors advertise their products as suitable for prokaryotes, we found significant differences in their performance regarding rRNA removal, strand specificity, and most importantly, transcript abundance distribution profiles. Notably, RNA-seq data obtained with Qiagen products were most similar to published data and delivered the best results in terms of library strandedness and transcript abundance distribution range. Our results highlight the importance of finding appropriate organism-specific workflows and library preparation products for RNA-seq studies. IMPORTANCE RNA-seq is a powerful technique for transcriptome profiling while involving elaborate sample processing before library sequencing. We show that RNA-seq library preparation kits can strongly affect the outcome of an RNA-seq experiment. Although library preparation benefits from the availability of various commercial kits, choosing appropriate products for the specific samples can be challenging for new users or for users working with unconventional organisms. Evaluating the performance of different commercial products requires significant financial and time investments infeasible for most researchers. Therefore, users are often guided in their choice of kits by published data involving similar input samples. We conclude that important consideration should be given to selecting sample processing workflows for any given organism.

Absolute Proteome Quantification in the Gas-Fermenting Acetogen Clostridium autoethanogenum.

Microbes that can recycle one-carbon (C1) greenhouse gases into fuels and chemicals are vital for the biosustainability of future industries. Acetogens are the most efficient known microbes for fixing carbon oxides CO2 and CO. Understanding proteome allocation is important for metabolic engineering as it dictates metabolic fitness. Here, we use absolute proteomics to quantify intracellular concentrations for >1,000 proteins in the model acetogen Clostridium autoethanogenum grown autotrophically on three gas mixtures (CO, CO+H2, or CO+CO2+H2). We detect the prioritization of proteome allocation for C1 fixation and the significant expression of proteins involved in the production of acetate and ethanol as well as proteins with unclear functions. The data also revealed which isoenzymes are likely relevant in vivo for CO oxidation, H2 metabolism, and ethanol production. The integration of proteomic and metabolic flux data demonstrated that enzymes catalyze high fluxes with high concentrations and high in vivo catalytic rates. We show that flux adjustments were dominantly accompanied by changing enzyme catalytic rates rather than concentrations. IMPORTANCE Acetogen bacteria are important for maintaining biosustainability as they can recycle gaseous C1 waste feedstocks (e.g., industrial waste gases and syngas from gasified biomass or municipal solid waste) into fuels and chemicals. Notably, the acetogen Clostridium autoethanogenum is being used as a cell factory in industrial-scale gas fermentation. Here, we perform reliable absolute proteome quantification for the first time in an acetogen. This is important as our work advances both rational metabolic engineering of acetogen cell factories and accurate in silico reconstruction of their phenotypes. Furthermore, this absolute proteomics data set serves as a reference toward a better systems-level understanding of the ancient metabolism of acetogens.

Perfusion culture of Chinese Hamster Ovary cells for bioprocessing applications.

Much of the biopharmaceutical industry's success over the past 30 years has relied on products derived from Chinese Hamster Ovary (CHO) cell lines. During this time, improvements in mammalian cell cultures have come from cell line development and process optimization suited for large-scale fed-batch processes. Originally developed for high cell densities and sensitive products, perfusion processes have a long history. Driven by high volumetric titers and a small footprint, perfusion-based bioprocess research has regained an interest from academia and industry. The recent pandemic has further highlighted the need for such intensified biomanufacturing options. In this review, we outline the technical history of research in this field as it applies to biologics production in CHO cells. We demonstrate a number of emerging trends in the literature and corroborate these with underlying drivers in the commercial space. From these trends, we speculate that the future of perfusion bioprocesses is bright and that the fields of media optimization, continuous processing, and cell line engineering hold the greatest potential. Aligning in its continuous setup with the demands for Industry 4.0, perfusion biomanufacturing is likely to be a hot topic in the years to come.

multiTFA: a Python package for multi-variate thermodynamics-based flux analysis.

MOTIVATION: We achieve a significant improvement in thermodynamic-based flux analysis (TFA) by introducing multivariate treatment of thermodynamic variables and leveraging component contribution, the state-of-the-art implementation of the group contribution methodology. Overall, the method greatly reduces the uncertainty of thermodynamic variables. RESULTS: We present multiTFA, a Python implementation of our framework. We evaluated our application using the core Escherichia coli model and achieved a median reduction of 6.8 kJ/mol in reaction Gibbs free energy ranges, while three out of 12 reactions in glycolysis changed from reversible to irreversible. AVAILABILITY AND IMPLEMENTATION: Our framework along with documentation is available on https://github.com/biosustain/multitfa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multicopy Targeted Integration for Accelerated Development of High-Producing Chinese Hamster Ovary Cells.

The ever-growing biopharmaceutical industry relies on the production of recombinant therapeutic proteins in Chinese hamster ovary (CHO) cells. The traditional timelines of CHO cell line development can be significantly shortened by the use of targeted gene integration (TI). However, broad use of TI has been limited due to the low specific productivity (qP) of TI-generated clones. Here, we show a 10-fold increase in the qP of therapeutic glycoproteins in CHO cells through the development and optimization of a multicopy TI method. We used a recombinase-mediated cassette exchange (RMCE) platform to investigate the effect of gene copy number, 5' and 3' gene regulatory elements, and landing pad features on qP. We evaluated the limitations of multicopy expression from a single genomic site as well as multiple genomic sites and found that a transcriptional bottleneck can appear with an increase in gene dosage. We created a dual-RMCE system for simultaneous multicopy TI in two genomic sites and generated isogenic high-producing clones with qP of 12-14 pg/cell/day and product titer close to 1 g/L in fed-batch. Our study provides an extensive characterization of the multicopy TI method and elucidates the relationship between gene copy number and protein expression in mammalian cells. Moreover, it demonstrates that TI-generated CHO cells are capable of producing therapeutic proteins at levels that can support their industrial manufacture.

Redox controls metabolic robustness in the gas-fermenting acetogen Clostridium autoethanogenum.

Living biological systems display a fascinating ability to self-organize their metabolism. This ability ultimately determines the metabolic robustness that is fundamental to controlling cellular behavior. However, fluctuations in metabolism can affect cellular homeostasis through transient oscillations. For example, yeast cultures exhibit rhythmic oscillatory behavior in high cell-density continuous cultures. Oscillatory behavior provides a unique opportunity for quantitating the robustness of metabolism, as cells respond to changes by inherently compromising metabolic efficiency. Here, we quantify the limits of metabolic robustness in self-oscillating autotrophic continuous cultures of the gas-fermenting acetogen Clostridium autoethanogenum Online gas analysis and high-resolution temporal metabolomics showed oscillations in gas uptake rates and extracellular byproducts synchronized with biomass levels. The data show initial growth on CO, followed by growth on CO and H2 Growth on CO and H2 results in an accelerated growth phase, after which a downcycle is observed in synchrony with a loss in H2 uptake. Intriguingly, oscillations are not linked to translational control, as no differences were observed in protein expression during oscillations. Intracellular metabolomics analysis revealed decreasing levels of redox ratios in synchrony with the cycles. We then developed a thermodynamic metabolic flux analysis model to investigate whether regulation in acetogens is controlled at the thermodynamic level. We used endo- and exo-metabolomics data to show that the thermodynamic driving force of critical reactions collapsed as H2 uptake is lost. The oscillations are coordinated with redox. The data indicate that metabolic oscillations in acetogen gas fermentation are controlled at the thermodynamic level.

Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A TetR-Family Protein (CAETHG_0459) Activates Transcription From a New Promoter Motif Associated With Essential Genes for Autotrophic Growth in Acetogens.

Acetogens can fix carbon (CO or CO2) into acetyl-CoA via the Wood-Ljungdahl pathway (WLP) that also makes them attractive cell factories for the production of fuels and chemicals from waste feedstocks. Although most biochemical details of the WLP are well understood and systems-level characterization of acetogen metabolism has recently improved, key transcriptional features such as promoter motifs and transcriptional regulators are still unknown in acetogens. Here, we use differential RNA-sequencing to identify a previously undescribed promoter motif associated with essential genes for autotrophic growth of the model-acetogen Clostridium autoethanogenum. RNA polymerase was shown to bind to the new promoter motif using a DNA-binding protein assay and proteomics enabled the discovery of four candidates to potentially function directly in control of transcription of the WLP and other key genes of C1 fixation metabolism. Next, in vivo experiments showed that a TetR-family transcriptional regulator (CAETHG_0459) and the housekeeping sigma factor (σA) activate expression of a reporter protein (GFP) in-frame with the new promoter motif from a fusion vector in Escherichia coli. Lastly, a protein-protein interaction assay with the RNA polymerase (RNAP) shows that CAETHG_0459 directly binds to the RNAP. Together, the data presented here advance the fundamental understanding of transcriptional regulation of C1 fixation in acetogens and provide a strategy for improving the performance of gas-fermenting bacteria by genetic engineering.

Minimizing Clonal Variation during Mammalian Cell Line Engineering for Improved Systems Biology Data Generation.

Mammalian cells are widely used to express genes for basic biology studies and biopharmaceuticals. Current methods for generation of engineered cell lines introduce high genomic and phenotypic diversity, which hamper studies of gene functions and discovery of novel cellular mechanisms. Here, we minimized clonal variation by integrating a landing pad for recombinase-mediated cassette exchange site-specifically into the genome of CHO cells using CRISPR and generated subclones expressing four different recombinant proteins. The subclones showed low clonal variation with high consistency in growth, transgene transcript levels and global transcriptional response to recombinant protein expression, enabling improved studies of the impact of transgenes on the host transcriptome. Little variation over time in subclone phenotypes and transcriptomes was observed when controlling environmental culture conditions. The platform enables robust comparative studies of genome engineered CHO cell lines and can be applied to other mammalian cells for diverse biological, biomedical and biotechnological applications.

From reconstruction to C4 metabolic engineering: A case study for overproduction of polyhydroxybutyrate in bioenergy grasses.

The compartmentalization of C4 plants increases photosynthetic efficiency, while constraining how material and energy must flow in leaf tissues. To capture this metabolic phenomenon, a generic plant metabolic reconstruction was replicated into four connected spatiotemporal compartments, namely bundle sheath (B) and mesophyll (M) across the day and night cycle. The C4 leaf model was used to explore how amenable polyhydroxybutyrate (PHB) production is with these four compartments working cooperatively. A strategic pattern of metabolite conversion and exchange emerged from a systems-level network that has very few constraints imposed; mainly the sequential two-step carbon capture in mesophyll, then bundle sheath and photosynthesis during the day only. The building of starch reserves during the day and their mobilization during the night connects day and night metabolism. Flux simulations revealed that PHB production did not require rerouting of metabolic pathways beyond what is already utilised for growth. PHB yield was sensitive to photoassimilation capacity, availability of carbon reserves, ATP maintenance, relative photosynthetic activity of B and M, and type of metabolites exchanged in the plasmodesmata, but not sensitive towards compartmentalization. Hence, the compartmentalization issues currently encountered are likely to be kinetic or thermodynamic limitations rather than stoichiometric.

H2 drives metabolic rearrangements in gas-fermenting Clostridium autoethanogenum.

BACKGROUND: The global demand for affordable carbon has never been stronger, and there is an imperative in many industrial processes to use waste streams to make products. Gas-fermenting acetogens offer a potential solution and several commercial gas fermentation plants are currently under construction. As energy limits acetogen metabolism, supply of H2 should diminish substrate loss to CO2 and facilitate production of reduced and energy-intensive products. However, the effects of H2 supply on CO-grown acetogens have yet to be experimentally quantified under controlled growth conditions. RESULTS: Here, we quantify the effects of H2 supplementation by comparing growth on CO, syngas, and a high-H2 CO gas mix using chemostat cultures of Clostridium autoethanogenum. Cultures were characterised at the molecular level using metabolomics, proteomics, gas analysis, and a genome-scale metabolic model. CO-limited chemostats operated at two steady-state biomass concentrations facilitated co-utilisation of CO and H2. We show that H2 supply strongly impacts carbon distribution with a fourfold reduction in substrate loss as CO2 (61% vs. 17%) and a proportional increase of flux to ethanol (15% vs. 61%). Notably, H2 supplementation lowers the molar acetate/ethanol ratio by fivefold. At the molecular level, quantitative proteome analysis showed no obvious changes leading to these metabolic rearrangements suggesting the involvement of post-translational regulation. Metabolic modelling showed that H2 availability provided reducing power via H2 oxidation and saved redox as cells reduced all the CO2 to formate directly using H2 in the Wood-Ljungdahl pathway. Modelling further indicated that the methylene-THF reductase reaction was ferredoxin reducing under all conditions. In combination with proteomics, modelling also showed that ethanol was synthesised through the acetaldehyde:ferredoxin oxidoreductase (AOR) activity. CONCLUSIONS: Our quantitative molecular analysis revealed that H2 drives rearrangements at several layers of metabolism and provides novel links between carbon, energy, and redox metabolism advancing our understanding of energy conservation in acetogens. We conclude that H2 supply can substantially increase the efficiency of gas fermentation and thus the feed gas composition can be considered an important factor in developing gas fermentation-based bioprocesses.