Tn-Core: A Toolbox for Integrating Tn-seq Gene Essentiality Data and Constraint-Based Metabolic Modeling

Evaluating E. coli genome-scale metabolic model accuracy with high-throughput mutant fitness data

The Escherichia coli genome-scale metabolic model (GEM) is an exemplar systems biology model for the simulation of cellular metabolism. Experimental validation of model predictions is essential to pinpoint uncertainty and ensure continued development of accurate models. Here, we quantified the accuracy of four subsequent E. coli GEMs using published mutant fitness data across thousands of genes and 25 different carbon sources. This evaluation demonstrated the utility of the area under a precision-recall curve relative to alternative accuracy metrics. An analysis of errors in the latest (iML1515) model identified several vitamins/cofactors that are likely available to mutants despite being absent from the experimental growth medium and highlighted isoenzyme gene-protein-reaction mapping as a key source of inaccurate predictions. A machine learning approach further identified metabolic fluxes through hydrogen ion exchange and specific central metabolism branch points as important determinants of model accuracy. This work outlines improved practices for the assessment of GEM accuracy with high-throughput mutant fitness data and highlights promising areas for future model refinement in E. coli and beyond.

A genome-scale metabolic reconstruction of soybean and Bradyrhizobium diazoefficiens reveals the cost-benefit of nitrogen fixation

Nitrogen-fixing symbioses allow legumes to thrive in nitrogen-poor soils at the cost of diverting some photoassimilate to their microsymbionts. Effort is being made to bioengineer nitrogen fixation into nonleguminous crops. This requires a quantitative understanding of its energetic costs and the links between metabolic variations and symbiotic efficiency. A whole-plant metabolic model for soybean (Glycine max) with its associated microsymbiont Bradyrhizobium diazoefficiens was developed and applied to predict the cost-benefit of nitrogen fixation with varying soil nitrogen availability. The model predicted a nitrogen-fixation cost of c. 4.13 g C g-1 N, which when implemented into a crop scale model, translated to a grain yield reduction of 27% compared with a non-nodulating plant receiving its nitrogen from the soil. Considering the lower nitrogen content of cereals, the yield cost to a hypothetical N-fixing cereal is predicted to be less than half that of soybean. Soybean growth was predicted to be c. 5% greater when the nodule nitrogen export products were amides versus ureides. This is the first metabolic reconstruction in a tropical crop species that simulates the entire plant and nodule metabolism. Going forward, this model will serve as a tool to investigate carbon use efficiency and key mechanisms within N-fixing symbiosis in a tropical species forming determinate nodules.

Unlocking the magic in mycelium: Using synthetic biology to optimize filamentous fungi for biomanufacturing and sustainability

Filamentous fungi drive carbon and nutrient cycling across our global ecosystems, through its interactions with growing and decaying flora and their constituent microbiomes. The remarkable metabolic diversity, secretion ability, and fiber-like mycelial structure that have evolved in filamentous fungi have been increasingly exploited in commercial operations. The industrial potential of mycelial fermentation ranges from the discovery and bioproduction of enzymes and bioactive compounds, the decarbonization of food and material production, to environmental remediation and enhanced agricultural production. Despite its fundamental impact in ecology and biotechnology, molds and mushrooms have not, to-date, significantly intersected with synthetic biology in ways comparable to other industrial cell factories (e.g. Escherichia coli,Saccharomyces cerevisiae, and Komagataella phaffii). In this review, we summarize a suite of synthetic biology and computational tools for the mining, engineering and optimization of filamentous fungi as a bioproduction chassis. A combination of methods across genetic engineering, mutagenesis, experimental evolution, and computational modeling can be used to address strain development bottlenecks in established and emerging industries. These include slow mycelium growth rate, low production yields, non-optimal growth in alternative feedstocks, and difficulties in downstream purification. In the scope of biomanufacturing, we then detail previous efforts in improving key bottlenecks by targeting protein processing and secretion pathways, hyphae morphogenesis, and transcriptional control. Bringing synthetic biology practices into the hidden world of molds and mushrooms will serve to expand the limited panel of host organisms that allow for commercially-feasible and environmentally-sustainable bioproduction of enzymes, chemicals, therapeutics, foods, and materials of the future.

Genomic landscapes of bacterial transposons and their applications in strain improvement

Transposons are mobile genetic elements that can give rise to gene mutation and genome rearrangement. Due to their mobility, transposons have been exploited as genetic tools for modification of plants, animals, and microbes. Although a plethora of reviews have summarized families of transposons, the transposons from fermentation bacteria have not been systematically documented, which thereby constrain the exploitation for metabolic engineering and synthetic biology purposes. In this review, we summarize the transposons from the most used fermentation bacteria including Escherichia coli, Bacillus subtilis, Lactococcus lactis, Corynebacterium glutamicum, Klebsiella pneumoniae, and Zymomonas mobilis by literature retrieval and data mining from GenBank and KEGG. We also outline the state-of-the-art advances in basic research and industrial applications especially when allied with other genetic tools. Overall, this review aims to provide valuable insights for transposon-mediated strain improvement. KEY POINTS: • The transposons from the most-used fermentation bacteria are systematically summarized. • The applications of transposons in strain improvement are comprehensively reviewed.

Gradients in gene essentiality reshape antibacterial research

Essential genes encode the processes that are necessary for life. Until recently, commonly applied binary classifications left no space between essential and non-essential genes. In this review, we frame bacterial gene essentiality in the context of genetic networks. We explore how the quantitative properties of gene essentiality are influenced by the nature of the encoded process, environmental conditions and genetic background, including a strain's distinct evolutionary history. The covered topics have important consequences for antibacterials, which inhibit essential processes. We argue that the quantitative properties of essentiality can thus be used to prioritize antibacterial cellular targets and desired spectrum of activity in specific infection settings. We summarize our points with a case study on the core essential genome of the cystic fibrosis pathobiome and highlight avenues for targeted antibacterial development.

Genome-scale metabolic reconstruction of the symbiosis between a leguminous plant and a nitrogen-fixing bacterium

The mutualistic association between leguminous plants and endosymbiotic rhizobial bacteria is a paradigmatic example of a symbiosis driven by metabolic exchanges. Here, we report the reconstruction and modelling of a genome-scale metabolic network of Medicago truncatula (plant) nodulated by Sinorhizobium meliloti (bacterium). The reconstructed nodule tissue contains five spatially distinct developmental zones and encompasses the metabolism of both the plant and the bacterium. Flux balance analysis (FBA) suggests that the metabolic costs associated with symbiotic nitrogen fixation are primarily related to supporting nitrogenase activity, and increasing N2-fixation efficiency is associated with diminishing returns in terms of plant growth. Our analyses support that differentiating bacteroids have access to sugars as major carbon sources, ammonium is the main nitrogen export product of N2-fixing bacteria, and N2 fixation depends on proton transfer from the plant cytoplasm to the bacteria through acidification of the peribacteroid space. We expect that our model, called 'Virtual Nodule Environment' (ViNE), will contribute to a better understanding of the functioning of legume nodules, and may guide experimental studies and engineering of symbiotic nitrogen fixation.

Application of combinatorial optimization strategies in synthetic biology

In the first wave of synthetic biology, genetic elements, combined into simple circuits, are used to control individual cellular functions. In the second wave of synthetic biology, the simple circuits, combined into complex circuits, form systems-level functions. However, efforts to construct complex circuits are often impeded by our limited knowledge of the optimal combination of individual circuits. For example, a fundamental question in most metabolic engineering projects is the optimal level of enzymes for maximizing the output. To address this point, combinatorial optimization approaches have been established, allowing automatic optimization without prior knowledge of the best combination of expression levels of individual genes. This review focuses on current combinatorial optimization methods and emerging technologies facilitating their applications.

Systems and synthetic biology tools for advanced bioproduction hosts

The genomic revolution ushered in an era of discovery and characterization of enzymes from novel organisms that fueled engineering of microbes to produce commodity and high-value compounds. Over the past decade advances in synthetic biology tools in recent years contributed to significant progress in metabolic engineering efforts to produce both biofuels and bioproducts resulting in several such related items being brought to market. These successes represent a burgeoning bio-economy; however, significant resources and time are still necessary to progress a system from proof-of-concept to market. In order to fully realize this potential, methods that examine biological systems in a comprehensive, systematic and high-throughput manner are essential. Recent success in synthetic biology has coincided with the development of systems biology and analytical approaches that kept pace and scaled with technology development. Here, we review a selection of systems biology methods and their use in synthetic biology approaches for microbial biotechnology platforms.