Modeling challenges in the synthetic biology of secondary metabolism

Learning metabolic dynamics from irregular observations by Bidirectional Time-Series State Transfer Network

Modeling microbial metabolic dynamics is important for the rational optimization of both biosynthetic systems and industrial processes to facilitate green and efficient biomanufacturing. Classical approaches utilize explicit equation systems to represent metabolic networks, enabling the quantification of pathway fluxes to identify metabolic bottlenecks. However, these white-box models, despite their diverse applications, have limitations in simulating metabolic dynamics and are intrinsically inaccurate for industrial strains that lack information on network structures and kinetic parameters. On the other hand, black-box models do not rely on prior mechanistic knowledge of strains but are built upon observed time-series trajectories of biosynthetic systems in action. In practice, these observations are typically irregular, with discontinuously observed time points across multiple independent batches, each time point potentially containing missing measurements. Learning from such irregular data remains challenging for existing approaches. To address this issue, we present the Bidirectional Time-Series State Transfer Network (BTSTN) for modeling metabolic dynamics directly from irregular observations. Using evaluation data sets derived from both ideal dynamic systems and a real-world fermentation process, we demonstrate that BTSTN accurately reconstructs dynamic behaviors and predicts future trajectories. This approach exhibits enhanced robustness against missing measurements and noise, as compared to the state-of-the-art methods.IMPORTANCEIndustrial biosynthetic systems often involve strains with unclear genetic backgrounds, posing challenges in modeling their distinct metabolic dynamics. In such scenarios, white-box models, which commonly rely on inferred networks, are thereby of limited applicability and accuracy. In contrast, black-box models, such as statistical models and neural networks, are directly fitted or learned from observed time-series trajectories of biosynthetic systems in action. These methods typically assume regular observations without missing time points or measurements. If the observations are irregular, a pre-processing step becomes necessary to obtain a fully filled data set for subsequent model training, which, at the same time, inevitably introduces errors into the resulting models. BTSTN is a novel approach that natively learns from irregular observations. This distinctive feature makes it a unique addition to the current arsenal of technologies modeling metabolic dynamics.

Flux balance analysis-based metabolic modeling of microbial secondary metabolism: Current status and outlook

In microorganisms, different from primary metabolism for cellular growth, secondary metabolism is for ecological interactions and stress responses and an important source of natural products widely used in various areas such as pharmaceutics and food additives. With advancements of sequencing technologies and bioinformatics tools, a large number of biosynthetic gene clusters of secondary metabolites have been discovered from microbial genomes. However, due to challenges from the difficulty of genome-scale pathway reconstruction and the limitation of conventional flux balance analysis (FBA) on secondary metabolism, the quantitative modeling of secondary metabolism is poorly established, in contrast to that of primary metabolism. This review first discusses current efforts on the reconstruction of secondary metabolic pathways in genome-scale metabolic models (GSMMs), as well as related FBA-based modeling techniques. Additionally, potential extensions of FBA are suggested to improve the prediction accuracy of secondary metabolite production. As this review posits, biosynthetic pathway reconstruction for various secondary metabolites will become automated and a modeling framework capturing secondary metabolism onset will enhance the predictive power. Expectedly, an improved FBA-based modeling workflow will facilitate quantitative study of secondary metabolism and in silico design of engineering strategies for natural product production.

Metabolomics in the Context of Plant Natural Products Research: From Sample Preparation to Metabolite Analysis

Plant-derived natural products have long been considered a valuable source of lead compounds for drug development. Natural extracts are usually composed of hundreds to thousands of metabolites, whereby the bioactivity of natural extracts can be represented by synergism between several metabolites. However, isolating every single compound from a natural extract is not always possible due to the complex chemistry and presence of most secondary metabolites at very low levels. Metabolomics has emerged in recent years as an indispensable tool for the analysis of thousands of metabolites from crude natural extracts, leading to a paradigm shift in natural products drug research. Analytical methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) are used to comprehensively annotate the constituents of plant natural products for screening, drug discovery as well as for quality control purposes such as those required for phytomedicine. In this review, the current advancements in plant sample preparation, sample measurements, and data analysis are presented alongside a few case studies of the successful applications of these processes in plant natural product drug discovery.

Genetic Transformation System for Woody Plant Tripterygium wilfordii and Its Application to Product Natural Celastrol

Tripterygium wilfordii is a perennial woody liana medicinal plant with several crucial biological activities. Although studies on tissue culture have previously been conducted, research on genetic transformation is much more challenging and therefore results in slower progress. In the present study, a highly efficient transformation system involving the particle bombardment of T. wilfordii with the reporter egfp gene using the PDS-1000/He system was established. A total of seven parameters affecting the genetic transformation were investigated using an L18 (6 × 36)-type orthogonal array. The result indicated that DNA delivery conditions of 3-cm target distance, 1100 psi helium pressure, 28 mmHg chamber vacuum pressure, three times number of bombardment, CaCl2 as precipitation agent, 2 μg plasmid DNA concentration and 48 h post-bombardment incubation time were optimal for T. wilfordii cell suspensions transformation. The average transformation efficiency was 19.17%. Based on this transformation system, the overexpression of two T. wilfordii farnesyl pyrophosphate synthase genes (TwFPSs) was performed in cell suspensions. Integration of the TwFPSs in the genome was verified by PCR analysis and also by Southern blotting using hygromycin gene as a probe. Real-time quantitative PCR analysis showed that the expression of TwFPS1&2 was highly up regulated in transgenic cell suspensions compared with control cells. The detection of metabolites showed that TwFPS1&2 could highly increase the celastrol content (973.60 μg/g) in transgenic cells. These results indicated that this transformation system is an effective protocol for characterizing the function of genes in the terpenoid biosynthetic pathway.

Respectful Modeling: Addressing Uncertainty in Dynamic System Models for Molecular Biology

Although there is still some skepticism in the biological community regarding the value and significance of quantitative computational modeling, important steps are continually being taken to enhance its accessibility and predictive power. We view these developments as essential components of an emerging 'respectful modeling' framework which has two key aims: (i) respecting the models themselves and facilitating the reproduction and update of modeling results by other scientists, and (ii) respecting the predictions of the models and rigorously quantifying the confidence associated with the modeling results. This respectful attitude will guide the design of higher-quality models and facilitate the use of models in modern applications such as engineering and manipulating microbial metabolism by synthetic biology.

Natural Product Biosynthesis in Escherichia coli: Mentha Monoterpenoids

The era of synthetic biology heralds in a new, more "green" approach to fine chemical and pharmaceutical drug production. It takes the knowledge of natural metabolic pathways and builds new routes to chemicals, enables nonnatural chemical production, and/or allows the rapid production of chemicals in alternative, highly performing organisms. This route is particularly useful in the production of monoterpenoids in microorganisms, which are naturally sourced from plant essential oils. Successful pathways are constructed by taking into consideration factors such as gene selection, regulatory elements, host selection and optimization, and metabolic considerations of the host organism. Seamless pathway construction techniques enable a "plug-and-play" switching of genes and regulatory parts to optimize the metabolic functioning in vivo. Ultimately, synthetic biology approaches to microbial monoterpenoid production may revolutionize "natural" compound formation.

Metabolic engineering with systems biology tools to optimize production of prokaryotic secondary metabolites

This Highlight examines current status of metabolic engineering and systems biology tools deployed for the optimal production of prokaryotic secondary metabolites.

Butyrolactone signalling circuits for synthetic biology

Signalling circuits based on quorum sensing mechanisms have been popular tools for synthetic biology. Recent advances in our understanding of the analogous systems regulating antibiotics production in soil bacteria suggest that these might provide useful complementary tools to increase the complexity of possible circuit designs. Here we discuss the diversity of these natural circuits, which use γ-butyrolactones (GBLs) as their main inter-cellular signal, highlighting the range of new building blocks they could provide, as well as a number of exciting recent applications of GBL-based circuits in heterologous systems. We conclude by presenting examples of the novel circuit complexity that could become accessible through the use of GBL-based designs.

Synthetic biology advances for pharmaceutical production

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Synthetic biology is quickly moving from proof of concept to industrial application.

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Pharmaceuticals are a promising target for advanced genetic engineering.

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Genome sequence data indicate vast underexploited biosynthetic capacity.

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Synthetic biology can create libraries of novel chemicals enriched for bioactivity.

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Synthetic biology expands the range of available chassis organisms for industry.

Synthetic biology enables a new generation of microbial engineering for the biotechnological production of pharmaceuticals and other high-value chemicals. This review presents an overview of recent advances in the field, describing new computational and experimental tools for the discovery, optimization and production of bioactive molecules, and outlining progress towards the application of these tools to pharmaceutical production systems.

The silicon trypanosome: a test case of iterative model extension in systems biology

The African trypanosome, Trypanosoma brucei, is a unicellular parasite causing African Trypanosomiasis (sleeping sickness in humans and nagana in animals). Due to some of its unique properties, it has emerged as a popular model organism in systems biology. A predictive quantitative model of glycolysis in the bloodstream form of the parasite has been constructed and updated several times. The Silicon Trypanosome is a project that brings together modellers and experimentalists to improve and extend this core model with new pathways and additional levels of regulation. These new extensions and analyses use computational methods that explicitly take different levels of uncertainty into account. During this project, numerous tools and techniques have been developed for this purpose, which can now be used for a wide range of different studies in systems biology.

Nitrogen regulation of fungal secondary metabolism in fungi

Fungi occupy diverse environments where they are constantly challenged by stressors such as extreme pH, temperature, UV exposure, and nutrient deprivation. Nitrogen is an essential requirement for growth, and the ability to metabolize a wide variety of nitrogen sources enables fungi to colonize different environmental niches and survive nutrient limitations. Favored nitrogen sources, particularly ammonium and glutamine, are used preferentially, while the expression of genes required for the use of various secondary nitrogen sources is subject to a regulatory mechanism called nitrogen metabolite repression. Studies on gene regulation in response to nitrogen availability were carried out first in Saccharomyces cerevisiae, Aspergillus nidulans, and Neurospora crassa. These studies revealed that fungi respond to changes in nitrogen availability with physiological and morphological alterations and activation of differentiation processes. In all fungal species studied, the major GATA transcription factor AreA and its co-repressor Nmr are central players of the nitrogen regulatory network. In addition to growth and development, the quality and quantity of nitrogen also affects the formation of a broad range of secondary metabolites (SMs). Recent studies, mainly on species of the genus Fusarium, revealed that AreA does not only regulate a large set of nitrogen catabolic genes, but can also be involved in regulating production of SMs. Furthermore, several other regulators, e.g., a second GATA transcription factor, AreB, that was proposed to negatively control nitrogen catabolic genes by competing with AreA for binding to GATA elements, was shown to act as activator of some nitrogen-repressed as well as nitrogen-induced SM gene clusters. This review highlights our latest understanding of canonical (AreA-dependent) and non-canonical nitrogen regulation mechanisms by which fungi may regulate biosynthesis of certain SMs in response to nitrogen availability.

A kinetic-based approach to understanding heterologous mevalonate pathway function in E. coli

To aid in debugging efforts to increase yield, titer, and productivity of engineered metabolic pathways, computational models are increasingly needed to predict how changes in experimentally manipulable variables such as enzyme expression map to changes in pathway flux. Here, an ordinary differential equation model is developed for a heterologous mevalonate pathway in E. coli using kinetic parameters culled from literature and enzyme concentrations derived from Selective Reaction Monitoring Mass Spectrometry (SRM-MS). To identify parameters most important to further experimental investigation, a global sensitivity analysis was performed, which pointed to amorphadiene synthase activity as the main limiting factor for amorphadiene production. Furthermore, the model predicted that in this local enzyme expression regime, the overall pathway flux is insensitive to farnesyl pyrophosphate (FPP)-mediated inhibition of mevalonate kinase, not supporting a hypothesis that had previously been posited to be limiting amorphadiene production. To test these predictions experimentally, two strains were constructed: (1) a strain containing a homologous mevalonate kinase with weaker feedback inhibition, and (2) a strain with greater amorphadiene synthase expression. The experimental results validate the qualitative model hypotheses and accurately match the predicted productivities for the two strains, particularly when an in vivo-derived kcat for amorphadiene synthase was substituted for the literature value. These results demonstrate the utility of using kinetic representations of engineered metabolic pathways parameterized with experimentally derived protein concentrations and enzyme kinetic constants to predict productivities and test hypotheses about engineering strategies.

ProbMetab: an R package for Bayesian probabilistic annotation of LC-MS-based metabolomics

Summary: We present ProbMetab, an R package that promotes substantial improvement in automatic probabilistic liquid chromatography–mass spectrometry-based metabolome annotation. The inference engine core is based on a Bayesian model implemented to (i) allow diverse source of experimental data and metadata to be systematically incorporated into the model with alternative ways to calculate the likelihood function and (ii) allow sensitive selection of biologically meaningful biochemical reaction databases as Dirichlet-categorical prior distribution. Additionally, to ensure result interpretation by system biologists, we display the annotation in a network where observed mass peaks are connected if their candidate metabolites are substrate/product of known biochemical reactions. This graph can be overlaid with other graph-based analysis, such as partial correlation networks, in a visualization scheme exported to Cytoscape, with web and stand-alone versions.

Availability and implementation: ProbMetab was implemented in a modular manner to fit together with established upstream (xcms, CAMERA, AStream, mzMatch.R, etc) and downstream R package tools (GeneNet, RCytoscape, DiffCorr, etc). ProbMetab, along with extensive documentation and case studies, is freely available under GNU license at: http://labpib.fmrp.usp.br/methods/probmetab/.

Contact:rvencio@usp.br

Supplementary information:Supplementary data are available at Bioinformatics online.

Metabolomics for secondary metabolite research

Metabolomics, the global characterization of metabolite profiles, is becoming an increasingly powerful tool for research on secondary metabolite discovery and production. In this review we discuss examples of recent technological advances and biological applications of metabolomics in the search for chemical novelty and the engineered production of bioactive secondary metabolites.

Bacterial competition reveals differential regulation of the pks genes by Bacillus subtilis

Bacillus subtilis is adaptable to many environments in part due to its ability to produce a broad range of bioactive compounds. One such compound, bacillaene, is a linear polyketide/nonribosomal peptide. The pks genes encode the enzymatic megacomplex that synthesizes bacillaene. The majority of pks genes appear to be organized as a giant operon (>74 kb from pksC-pksR). In previous work (P. D. Straight, M. A. Fischbach, C. T. Walsh, D. Z. Rudner, and R. Kolter, Proc. Natl. Acad. Sci. U. S. A. 104:305-310, 2007, doi:10.1073/pnas.0609073103), a deletion of the pks operon in B. subtilis was found to induce prodiginine production by Streptomyces coelicolor. Here, colonies of wild-type B. subtilis formed a spreading population that induced prodiginine production from Streptomyces lividans, suggesting differential regulation of pks genes and, as a result, bacillaene. While the parent colony showed widespread induction of pks expression among cells in the population, we found the spreading cells uniformly and transiently repressed the expression of the pks genes. To identify regulators that control pks genes, we first determined the pattern of pks gene expression in liquid culture. We next identified mutations in regulatory genes that disrupted the wild-type pattern of pks gene expression. We found that expression of the pks genes requires the master regulator of development, Spo0A, through its repression of AbrB and the stationary-phase regulator, CodY. Deletions of degU, comA, and scoC had moderate effects, disrupting the timing and level of pks gene expression. The observed patterns of expression suggest that complex regulation of bacillaene and other antibiotics optimizes competitive fitness for B. subtilis.