Optimization of carbon and energy utilization through differential translational efficiency

Model of metabolism and gene expression predicts proteome allocation in Pseudomonas putida

The genome-scale model of metabolism and gene expression (ME-model) for Pseudomonas putida KT2440, iPpu1676-ME, provides a comprehensive representation of biosynthetic costs and proteome allocation. Compared to a metabolic-only model, iPpu1676-ME significantly expands on gene expression, macromolecular assembly, and cofactor utilization, enabling accurate growth predictions without additional constraints. Multi-omics analysis using RNA sequencing and ribosomal profiling data revealed translational prioritization in P. putida, with core pathways, such as nicotinamide biosynthesis and queuosine metabolism, exhibiting higher translational efficiency, while secondary pathways displayed lower priority. Notably, the ME-model significantly outperformed the M-model in alignment with multi-omics data, thereby validating its predictive capacity. Thus, iPpu1676-ME offers valuable insights into P. putida's proteome allocation and presents a powerful tool for understanding resource allocation in this industrially relevant microorganism.

Harnessing acetogenic bacteria for one-carbon valorization toward sustainable chemical production

The pressing climate change issues have intensified the need for a rapid transition towards a bio-based circular carbon economy. Harnessing acetogenic bacteria as biocatalysts to convert C1 compounds such as CO2, CO, formate, or methanol into value-added multicarbon chemicals is a promising solution for both carbon capture and utilization, enabling sustainable and green chemical production. Recent advances in the metabolic engineering of acetogens have expanded the range of commodity chemicals and biofuels produced from C1 compounds. However, producing energy-demanding high-value chemicals on an industrial scale from C1 substrates remains challenging because of the inherent energetic limitations of acetogenic bacteria. Therefore, overcoming this hurdle is necessary to scale up the acetogenic C1 conversion process and realize a circular carbon economy. This review overviews the acetogenic bacteria and their potential as sustainable and green chemical production platforms. Recent efforts to address these challenges have focused on enhancing the ATP and redox availability of acetogens to improve their energetics and conversion performances. Furthermore, promising technologies that leverage low-cost, sustainable energy sources such as electricity and light are discussed to improve the sustainability of the overall process. Finally, we review emerging technologies that accelerate the development of high-performance acetogenic bacteria suitable for industrial-scale production and address the economic sustainability of acetogenic C1 conversion. Overall, harnessing acetogenic bacteria for C1 valorization offers a promising route toward sustainable and green chemical production, aligning with the circular economy concept.

SkinCom, a synthetic skin microbial community, enables reproducible investigations of the human skin microbiome

Existing models of the human skin have aided our understanding of skin health and disease. However, they currently lack a microbial component, despite microbes' demonstrated connections to various skin diseases. Here, we present a robust, standardized model of the skin microbial community (SkinCom) to support in vitro and in vivo investigations. Our methods lead to the formation of an accurate, reproducible, and diverse community of aerobic and anaerobic bacteria. Subsequent testing of SkinCom on the dorsal skin of mice allowed for DNA and RNA recovery from both the applied SkinCom and the dorsal skin, highlighting its practicality for in vivo studies and -omics analyses. Furthermore, 66% of the responses to common cosmetic chemicals in vitro were in agreement with a human trial. Therefore, SkinCom represents a valuable, standardized tool for investigating microbe-metabolite interactions and facilitates the experimental design of in vivo studies targeting host-microbe relationships.

The Calvin Benson cycle in bacteria: New insights from systems biology

The Calvin Benson cycle in phototrophic and chemolithoautotrophic bacteria has ecological and biotechnological importance, which has motivated study of its regulation. I review recent advances in our understanding of how the Calvin Benson cycle is regulated in bacteria and the technologies used to elucidate regulation and modify it, and highlight differences between and photoautotrophic and chemolithoautotrophic models. Systems biology studies have shown that in oxygenic phototrophic bacteria, Calvin Benson cycle enzymes are extensively regulated at post-transcriptional and post-translational levels, with multiple enzyme activities connected to cellular redox status through thioredoxin. In chemolithoautotrophic bacteria, regulation is primarily at the transcriptional level, with effector metabolites transducing cell status, though new methods should now allow facile, proteome-wide exploration of biochemical regulation in these models. A biotechnological objective is to enhance CO2 fixation in the cycle and partition that carbon to a product of interest. Flux control of CO2 fixation is distributed over multiple enzymes, and attempts to modulate gene Calvin cycle gene expression show a robust homeostatic regulation of growth rate, though the synthesis rates of products can be significantly increased. Therefore, de-regulation of cycle enzymes through protein engineering may be necessary to increase fluxes. Non-canonical Calvin Benson cycles, if implemented with synthetic biology, could have reduced energy demand and enzyme loading, thus increasing the attractiveness of these bacteria for industrial applications.

Resource allocation in mammalian systems

Cells execute biological functions to support phenotypes such as growth, migration, and secretion. Complementarily, each function of a cell has resource costs that constrain phenotype. Resource allocation by a cell allows it to manage these costs and optimize their phenotypes. In fact, the management of resource constraints (e.g., nutrient availability, bioenergetic capacity, and macromolecular machinery production) shape activity and ultimately impact phenotype. In mammalian systems, quantification of resource allocation provides important insights into higher-order multicellular functions; it shapes intercellular interactions and relays environmental cues for tissues to coordinate individual cells to overcome resource constraints and achieve population-level behavior. Furthermore, these constraints, objectives, and phenotypes are context-dependent, with cells adapting their behavior according to their microenvironment, resulting in distinct steady-states. This review will highlight the biological insights gained from probing resource allocation in mammalian cells and tissues.

Simultaneous biogas upgrading and medium-chain fatty acids production using a dual membrane biofilm reactor

Utilizing H2-assisted ex-situ biogas upgrading and acetate recovery holds great promise for achieving high value utilization of biogas. However, it faces a significant challenge due to acetate's high solubility and limited economic value. To address this challenge, we propose an innovative strategy for simultaneous upgrading of biogas and the production of medium-chain fatty acids (MCFAs). A series of batch tests evaluated the strategy's efficiency under varying initial gas ratios (v/v) of H2, CH4, CO2, along with varying ethanol concentrations. The results identified the optimal conditions as initial gas ratios of 3H2:3CH4:2CO2 and an ethanol concentration of 241.2 mmol L-1, leading to maximum CH4 purity (97.2 %), MCFAs yield (54.2 ± 2.1 mmol L-1), and MCFAs carbon-flow distribution (62.3 %). Additionally, an analysis of the microbial community's response to varying conditions highlighted the crucial roles played by microorganisms such as Clostridium, Proteiniphilum, Sporanaerobacter, and Bacteroides in synergistically assimilating H2 and CO2 for MCFAs production. Furthermore, a 160-day continuous operation using a dual-membrane aerated biofilm reactor (dMBfR) was conducted. Remarkable achievements were made at a hydraulic retention time of 2 days, including an upgraded CH4 content of 96.4 ± 0.3 %, ethanol utilization ratio (URethanol) of 95.7 %, MCFAs production rate of 28.8 ± 0.3 mmol L-1 d-1, and MCFAs carbon-flow distribution of 70 ± 0.8 %. This enhancement is proved to be an efficient in biogas upgrading and MCFAs production. These results lay the foundation for maximizing the value of biogas, reducing CO2 emissions, and providing valuable insights into resource recovery.

Translational efficiency in gas-fermenting bacteria: Adding a new layer of regulation to gene expression in acetogens

Major advances in mastering metabolism of single carbon (C1) gaseous feedstocks in acetogenic microorganisms are primed to fuel the transition toward environmentally sustainable and cost-efficient production schemes of biofuels and value-added biochemicals. Since acetogens grow under autotrophic energy-limited conditions, protein synthesis is expected to be controlled. This survey integrated publicly available RNA sequencing and ribosome profiling studies of several acetogens, providing data on genome-scale transcriptional and translational responses of A. woodii, E. limosum, C. drakei, and C. ljungdahlii to autotrophic and heterotrophic growth conditions. The extent of translational efficiency turned out to vary across key functional modules in acetogens' metabolism. Translational control was confirmed to support stoichiometric protein production in multimeric complexes. Comparing the autotrophic to the heterotrophic growth condition revealed growth-dependent regulation of translational efficiency, pointing at translational buffering as a widespread phenomenon shared by acetogens.

A diversified, widespread microbial gene cluster encodes homologs of methyltransferases involved in methanogenesis

Analyses of microbial genomes have revealed unexpectedly wide distributions of enzymes from specialized metabolism, including methanogenesis, providing exciting opportunities for discovery. Here, we identify a family of gene clusters (the type 1 mlp gene clusters (MGCs)) that encodes homologs of the soluble coenzyme M methyltransferases (SCMTs) involved in methylotrophic methanogenesis and is widespread in bacteria and archaea. Type 1 MGCs are expressed and regulated in medically, environmentally, and industrially important organisms, making them likely to be physiologically relevant. Enzyme annotation, analysis of genomic context, and biochemical experiments suggests these gene clusters play a role in methyl-sulfur and/or methyl-selenide metabolism in numerous anoxic environments, including the human gut microbiome, potentially impacting sulfur and selenium cycling in diverse, anoxic environments.

Guild and Niche Determination Enable Targeted Alteration of the Microbiome

Microbiome science has greatly contributed to our understanding of microbial life and its essential roles for the environment and human health1-5. However, the nature of microbial interactions and how microbial communities respond to perturbations remains poorly understood, resulting in an often descriptive and correlation-based approach to microbiome research6-8. Achieving causal and predictive microbiome science would require direct functional measurements in complex communities to better understand the metabolic role of each member and its interactions with others. In this study we present a new approach that integrates transcription and translation measurements to predict competition and substrate preferences within microbial communities, consequently enabling the selective manipulation of the microbiome. By performing metatranscriptomic (metaRNA-Seq) and metatranslatomic (metaRibo-Seq) analysis in complex samples, we classified microbes into functional groups (i.e. guilds) and demonstrated that members of the same guild are competitors. Furthermore, we predicted preferred substrates based on importer proteins, which specifically benefited selected microbes in the community (i.e. their niche) and simultaneously impaired their competitors. We demonstrated the scalability of microbial guild and niche determination to natural samples and its ability to successfully manipulate microorganisms in complex microbiomes. Thus, the approach enhances the design of pre- and probiotic interventions to selectively alter members within microbial communities, advances our understanding of microbial interactions, and paves the way for establishing causality in microbiome science.

Differences in the regulatory strategies of marine oligotrophs and copiotrophs reflect differences in motility

Aquatic bacteria frequently are divided into lifestyle categories oligotroph or copiotroph. Oligotrophs have proportionately fewer transcriptional regulatory genes than copiotrophs and are generally non-motile/chemotactic. We hypothesized that the absence of chemotaxis/motility in oligotrophs prevents them from occupying nutrient patches long enough to benefit from transcriptional regulation. We first confirmed that marine oligotrophs are generally reduced in genes for transcriptional regulation and motility/chemotaxis. Next, using a non-motile oligotroph (Ca. Pelagibacter st. HTCC7211), a motile copiotroph (Alteromonas macleodii st. HOT1A3), and [¹⁴ C]l-alanine, we confirmed that l-alanine catabolism is not transcriptionally regulated in HTCC7211 but is in HOT1A3. We then found that HOT1A3 took 2.5-4 min to initiate l-alanine oxidation at patch l-alanine concentrations, compared to <30 s for HTCC7211. By modelling cell trajectories, we predicted that, in most scenarios, non-motile cells spend <2 min in patches, compared to >4 min for chemotactic/motile cells. Thus, the time necessary for transcriptional regulation to initiate prevents transcriptional regulation from being beneficial for non-motile oligotrophs. This is supported by a mechanistic model we developed, which predicted that HTCC7211 cells with transcriptional regulation of l-alanine metabolism would produce 12% of their standing ATP stock upon encountering an l-alanine patch, compared to 880% in HTCC7211 cells without transcriptional regulation.

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.

Engineering Acetogenic Bacteria for Efficient One-Carbon Utilization

C1 gases, including carbon dioxide (CO₂) and carbon monoxide (CO), are major contributors to climate crisis. Numerous studies have been conducted to fix and recycle C1 gases in order to solve this problem. Among them, the use of microorganisms as biocatalysts to convert C1 gases to value-added chemicals is a promising solution. Acetogenic bacteria (acetogens) have received attention as high-potential biocatalysts owing to their conserved Wood–Ljungdahl (WL) pathway, which fixes not only CO₂ but also CO. Although some metabolites have been produced via C1 gas fermentation on an industrial scale, the conversion of C1 gases to produce various biochemicals by engineering acetogens has been limited. The energy limitation of acetogens is one of the challenges to overcome, as their metabolism operates at a thermodynamic limit, and the low solubility of gaseous substrates results in a limited supply of cellular energy. This review provides strategies for developing efficient platform strains for C1 gas conversion, focusing on engineering the WL pathway. Supplying liquid C1 substrates, which can be obtained from CO₂, or electricity is introduced as a strategy to overcome the energy limitation. Future prospective approaches on engineering acetogens based on systems and synthetic biology approaches are also discussed.

Genetic Evidence Reveals the Indispensable Role of the rseC Gene for Autotrophy and the Importance of a Functional Electron Balance for Nitrate Reduction in Clostridium ljungdahlii

For Clostridium ljungdahlii, the RNF complex plays a key role for energy conversion from gaseous substrates such as hydrogen and carbon dioxide. In a previous study, a disruption of RNF-complex genes led to the loss of autotrophy, while heterotrophy was still possible via glycolysis. Furthermore, it was shown that the energy limitation during autotrophy could be lifted by nitrate supplementation, which resulted in an elevated cellular growth and ATP yield. Here, we used CRISPR-Cas12a to delete: (1) the RNF complex-encoding gene cluster rnfCDGEAB; (2) the putative RNF regulator gene rseC; and (3) a gene cluster that encodes for a putative nitrate reductase. The deletion of either rnfCDGEAB or rseC resulted in a complete loss of autotrophy, which could be restored by plasmid-based complementation of the deleted genes. We observed a transcriptional repression of the RNF-gene cluster in the rseC-deletion strain during autotrophy and investigated the distribution of the rseC gene among acetogenic bacteria. To examine nitrate reduction and its connection to the RNF complex, we compared autotrophic and heterotrophic growth of our three deletion strains with either ammonium or nitrate. The rnfCDGEAB- and rseC-deletion strains failed to reduce nitrate as a metabolic activity in non-growing cultures during autotrophy but not during heterotrophy. In contrast, the nitrate reductase deletion strain was able to grow in all tested conditions but lost the ability to reduce nitrate. Our findings highlight the important role of the rseC gene for autotrophy, and in addition, contribute to understand the connection of nitrate reduction to energy metabolism.

Faster Growth Enhances Low Carbon Fuel and Chemical Production Through Gas Fermentation

Gas fermentation offers both fossil carbon-free sustainable production of fuels and chemicals and recycling of gaseous and solid waste using gas-fermenting microbes. Bioprocess development, systems-level analysis of biocatalyst metabolism, and engineering of cell factories are advancing the widespread deployment of the commercialised technology. Acetogens are particularly attractive biocatalysts but effects of the key physiological parameter–specific growth rate (μ)—on acetogen metabolism and the gas fermentation bioprocess have not been established yet. Here, we investigate the μ-dependent bioprocess performance of the model-acetogen Clostridium autoethanogenum in CO and syngas (CO + CO₂+H₂) grown chemostat cultures and assess systems-level metabolic responses using gas analysis, metabolomics, transcriptomics, and metabolic modelling. We were able to obtain steady-states up to μ ∼2.8 day⁻¹ (∼0.12 h⁻¹) and show that faster growth supports both higher yields and productivities for reduced by-products ethanol and 2,3-butanediol. Transcriptomics data revealed differential expression of 1,337 genes with increasing μ and suggest that C. autoethanogenum uses transcriptional regulation to a large extent for facilitating faster growth. Metabolic modelling showed significantly increased fluxes for faster growing cells that were, however, not accompanied by gene expression changes in key catabolic pathways for CO and H₂ metabolism. Cells thus seem to maintain sufficient “baseline” gene expression to rapidly respond to CO and H₂ availability without delays to kick-start metabolism. Our work advances understanding of transcriptional regulation in acetogens and shows that faster growth of the biocatalyst improves the gas fermentation bioprocess.

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.

Analytical tools for unravelling the metabolism of gas-fermenting Clostridia

Acetogens harness the Wood-Ljungdahl Pathway, a unique metabolic pathway for C1 capture close to the thermodynamic limit. Gas fermentation using acetogens is already used for CO-to-ethanol conversion at industrial-scale and has the potential to valorise a range of C1 and waste substrates to short-chain and medium-chain carboxylic acids and alcohols. Advances in analytical quantification and metabolic modelling have helped guide industrial gas fermentation designs. Further advances in the measurements of difficult to measure metabolites are required to improve kinetic modelling and understand the regulation of acetogen metabolism. This will help guide future synthetic biology designs needed to realise the full potential of gas fermentation in stimulating a circular bioeconomy.

Genome-Scale Analysis of Acetobacterium woodii Identifies Translational Regulation of Acetogenesis

Acetogens synthesize acetyl-CoA via the CO₂-fixing Wood-Ljungdahl pathway. Despite their ecological and biotechnological importance, their translational regulation of carbon and energy metabolisms remains unclear. Here, we report how carbon and energy metabolisms in the model acetogen Acetobacterium woodii are translationally controlled under different growth conditions. Data integration of genome-scale transcriptomic and translatomic analyses revealed that the acetogenesis genes, including those of the Wood-Ljungdahl pathway and energy metabolism, showed changes in translational efficiency under autotrophic growth conditions. In particular, genes encoding the Wood-Ljungdahl pathway are translated at similar levels to achieve efficient acetogenesis activity under autotrophic growth conditions, whereas genes encoding the carbonyl branch present increased translation levels in comparison to those for the methyl branch under heterotrophic growth conditions. The translation efficiency of genes in the pathways is differentially regulated by 5′ untranslated regions and ribosome-binding sequences under different growth conditions. Our findings provide potential strategies to optimize the metabolism of syngas-fermenting acetogenic bacteria for better productivity.

IMPORTANCE Acetogens are capable of reducing CO₂ to multicarbon compounds (e.g., ethanol or 2,3-butanediol) via the Wood-Ljungdahl pathway. Given that protein synthesis in bacteria is highly energy consuming, acetogens living at the thermodynamic limit of life are inevitably under translation control. Here, we dissect the translational regulation of carbon and energy metabolisms in the model acetogen Acetobacterium woodii under heterotrophic and autotrophic growth conditions. The latter may be experienced when acetogen is used as a cell factory that synthesizes products from CO₂ during the gas fermentation process. We found that the methyl and carbonyl branches of the Wood-Ljungdahl pathway are activated at similar translation levels during autotrophic growth. Translation is mainly regulated by the 5′-untranslated-region structure and ribosome-binding-site sequence. This work reveals novel translational regulation for coping with autotrophic growth conditions and provides the systematic data set, including the transcriptome, translatome, and promoter/5′-untranslated-region bioparts.

Signal metrics analysis of oscillatory patterns in bacterial multi-omic networks

MOTIVATION

One of the branches of Systems Biology is focused on a deep understanding of underlying regulatory networks through the analysis of the biomolecules oscillations and their interplay. Synthetic Biology exploits gene or/and protein regulatory networks towards the design of oscillatory networks for producing useful compounds. Therefore, at different levels of application and for different purposes, the study of biomolecular oscillations can lead to different clues about the mechanisms underlying living cells. It is known that network-level interactions involve more than one type of biomolecule as well as biological processes operating at multiple omic levels. Combining network/pathway-level information with genetic information it is possible to describe well-understood or unknown bacterial mechanisms and organism-specific dynamics.

RESULTS

Following the methodologies used in signal processing and communication engineering, a methodology is introduced to identify and quantify the extent of multi-omic oscillations. These are due to the process of multi-omic integration and depend on the gene positions on the chromosome. Ad hoc signal metrics are designed to allow further biotechnological explanations and provide important clues about the oscillatory nature of the pathways and their regulatory circuits. Our algorithms designed for the analysis of multi-omic signals are tested and validated on 11 different bacteria for thousands of multi-omic signals perturbed at the network level by different experimental conditions. Information on the order of genes, codon usage, gene expression and protein molecular weight is integrated at three different functional levels. Oscillations show interesting evidence that network-level multi-omic signals present a synchronized response to perturbations and evolutionary relations along taxa.

AVAILABILITY AND IMPLEMENTATION

The algorithms, the code (in language R), the tool, the pipeline and the whole dataset of multi-omic signal metrics are available at: https://github.com/lodeguns/Multi-omicSignals.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Biotechnology for secure biocontainment designs in an emerging bioeconomy

Genetically modified organisms (GMOs) have emerged as an integral component of a sustainable bioeconomy, with an array of applications in agriculture, bioenergy, and biomedicine. However, the rapid development of GMOs and associated synthetic biology approaches raises a number of biosecurity concerns related to environmental escape of GMOs, detection thereof, and impact upon native ecosystems. A myriad of genetic safeguards have been deployed in diverse microbial hosts, ranging from classical auxotrophies to global genome recoding. However, to realize the full potential of microbes as biocatalytic platforms in the bioeconomy, a deeper understanding of the fundamental principles governing microbial responsiveness to biocontainment constraints, and interactivity of GMOs with the environment, is required. Herein, we review recent analytical biotechnological advances and strategies to assess biocontainment and microbial bioproductivity, as well as opportunities for predictive systems biodesigns towards securing a viable bioeconomy.

Translational remodeling by RNA-binding proteins and noncoding RNAs

Responsible for generating the proteome that controls phenotype, translation is the ultimate convergence point for myriad upstream signals that influence gene expression. System-wide adaptive translational reprogramming has recently emerged as a pillar of cellular adaptation. As classic regulators of mRNA stability and translation efficiency, foundational studies established the concept of collaboration and competition between RNA-binding proteins (RBPs) and noncoding RNAs (ncRNAs) on individual mRNAs. Fresh conceptual innovations now highlight stress-activated, evolutionarily conserved RBP networks and ncRNAs that increase the translation efficiency of populations of transcripts encoding proteins that participate in a common cellular process. The discovery of post-transcriptional functions for long noncoding RNAs (lncRNAs) was particularly intriguing given their cell-type-specificity and historical definition as nuclear-functioning epigenetic regulators. The convergence of RBPs, lncRNAs, and microRNAs on functionally related mRNAs to enable adaptive protein synthesis is a newer biological paradigm that highlights their role as "translatome (protein output) remodelers" and reinvigorates the paradigm of "RNA operons." Together, these concepts modernize our understanding of cellular stress adaptation and strategies for therapeutic development. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications Translation > Translation Regulation Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs.

Synthetic Biology on Acetogenic Bacteria for Highly Efficient Conversion of C1 Gases to Biochemicals

Synthesis gas, which is mainly produced from fossil fuels or biomass gasification, consists of C1 gases such as carbon monoxide, carbon dioxide, and methane as well as hydrogen. Acetogenic bacteria (acetogens) have emerged as an alternative solution to recycle C1 gases by converting them into value-added biochemicals using the Wood-Ljungdahl pathway. Despite the advantage of utilizing acetogens as biocatalysts, it is difficult to develop industrial-scale bioprocesses because of their slow growth rates and low productivities. To solve these problems, conventional approaches to metabolic engineering have been applied; however, there are several limitations owing to the lack of required genetic bioparts for regulating their metabolic pathways. Recently, synthetic biology based on genetic parts, modules, and circuit design has been actively exploited to overcome the limitations in acetogen engineering. This review covers synthetic biology applications to design and build industrial platform acetogens.

Linking metabolic phenotypes to pathogenic traits among "Candidatus Liberibacter asiaticus" and its hosts

Candidatus Liberibacter asiaticus (CLas) has been associated with Huanglongbing, a lethal vector-borne disease affecting citrus crops worldwide. While comparative genomics has provided preliminary insights into the metabolic capabilities of this uncultured microorganism, a comprehensive functional characterization is currently lacking. Here, we reconstructed and manually curated genome-scale metabolic models for the six CLas strains A4, FL17, gxpsy, Ishi-1, psy62, and YCPsy, in addition to a model of the closest related culturable microorganism, L. crescens BT-1. Predictions about nutrient requirements and changes in growth phenotypes of CLas were confirmed using in vitro hairy root-based assays, while the L. crescens BT-1 model was validated using cultivation assays. Host-dependent metabolic phenotypes were revealed using expression data obtained from CLas-infected citrus trees and from the CLas-harboring psyllid Diaphorina citri Kuwayama. These results identified conserved and unique metabolic traits, as well as strain-specific interactions between CLas and its hosts, laying the foundation for the development of model-driven Huanglongbing management strategies.

A network of RNA-binding proteins controls translation efficiency to activate anaerobic metabolism

Protein expression evolves under greater evolutionary constraint than mRNA levels, and translation efficiency represents a primary determinant of protein levels during stimuli adaptation. This raises the question as to the translatome remodelers that titrate protein output from mRNA populations. Here, we uncover a network of RNA-binding proteins (RBPs) that enhances the translation efficiency of glycolytic proteins in cells responding to oxygen deprivation. A system-wide proteomic survey of translational engagement identifies a family of oxygen-regulated RBPs that functions as a switch of glycolytic intensity. Tandem mass tag-pulse SILAC (TMT-pSILAC) and RNA sequencing reveals that each RBP controls a unique but overlapping portfolio of hypoxic responsive proteins. These RBPs collaborate with the hypoxic protein synthesis apparatus, operating as a translation efficiency checkpoint that integrates upstream mRNA signals to activate anaerobic metabolism. This system allows anoxia-resistant animals and mammalian cells to initiate anaerobic glycolysis and survive hypoxia. We suggest that an oxygen-sensitive RBP cluster controls anaerobic metabolism to confer hypoxia tolerance.

mRNA translation efficiency is regulated in response to stimuli. Here the authors employ mass spectrometry analysis of ribosome fractions and show that under hypoxia, oxygen-sensitive RNA binding proteins enhance the translation efficiency of glycolysis pathway transcripts.

Predicting proteome allocation, overflow metabolism, and metal requirements in a model acetogen

The unique capability of acetogens to ferment a broad range of substrates renders them ideal candidates for the biotechnological production of commodity chemicals. In particular the ability to grow with H2:CO2 or syngas (a mixture of H2/CO/CO2) makes these microorganisms ideal chassis for sustainable bioproduction. However, advanced design strategies for acetogens are currently hampered by incomplete knowledge about their physiology and our inability to accurately predict phenotypes. Here we describe the reconstruction of a novel genome-scale model of metabolism and macromolecular synthesis (ME-model) to gain new insights into the biology of the model acetogen Clostridium ljungdahlii. The model represents the first ME-model of a Gram-positive bacterium and captures all major central metabolic, amino acid, nucleotide, lipid, major cofactors, and vitamin synthesis pathways as well as pathways to synthesis RNA and protein molecules necessary to catalyze these reactions, thus significantly broadens the scope and predictability. Use of the model revealed how protein allocation and media composition influence metabolic pathways and energy conservation in acetogens and accurately predicted secretion of multiple fermentation products. Predicting overflow metabolism is of particular interest since it enables new design strategies, e.g. the formation of glycerol, a novel product for C. ljungdahlii, thus broadening the metabolic capability for this model microbe. Furthermore, prediction and experimental validation of changing secretion rates based on different metal availability opens the window into fermentation optimization and provides new knowledge about the proteome utilization and carbon flux in acetogens.