Screening and modular design for metabolic pathway optimization

Advancing microbial production through artificial intelligence-aided biology

Microbial cell factories (MCFs) have been leveraged to construct sustainable platforms for value-added compound production. To optimize metabolism and reach optimal productivity, synthetic biology has developed various genetic devices to engineer microbial systems by gene editing, high-throughput protein engineering, and dynamic regulation. However, current synthetic biology methodologies still rely heavily on manual design, laborious testing, and exhaustive analysis. The emerging interdisciplinary field of artificial intelligence (AI) and biology has become pivotal in addressing the remaining challenges. AI-aided microbial production harnesses the power of processing, learning, and predicting vast amounts of biological data within seconds, providing outputs with high probability. With well-trained AI models, the conventional Design-Build-Test (DBT) cycle has been transformed into a multidimensional Design-Build-Test-Learn-Predict (DBTLP) workflow, leading to significantly improved operational efficiency and reduced labor consumption. Here, we comprehensively review the main components and recent advances in AI-aided microbial production, focusing on genome annotation, AI-aided protein engineering, artificial functional protein design, and AI-enabled pathway prediction. Finally, we discuss the challenges of integrating novel AI techniques into biology and propose the potential of large language models (LLMs) in advancing microbial production.

Two-Tiered Selection and Screening Strategy to Increase Functional Enzyme Production in E. coli

Development of recombinant enzymes as industrial biocatalysts or metabolic pathway elements requires soluble expression of active protein. Here we present a two-step strategy, combining a directed evolution selection with an enzyme activity screen, to increase the soluble production of enzymes in the cytoplasm of E. coli. The directed evolution component relies on the innate quality control of the twin-arginine translocation pathway coupled with antibiotic selection to isolate point mutations that promote intracellular solubility. A secondary screen is applied to ensure the solubility enhancement has not compromised enzyme activity. This strategy has been successfully applied to increase the soluble production of a fungal endocellulase by 30-fold in E. coli without change in enzyme specific activity through two rounds of directed evolution.

Research progress in bioremediation of petroleum pollution

With the enhancement of environmental protection awareness, research on the bioremediation of petroleum hydrocarbon environmental pollution has intensified. Bioremediation has received more attention due to its high efficiency, environmentally friendly by-products, and low cost compared with the commonly used physical and chemical restoration methods. In recent years, bacterium engineered by systems biology strategies have achieved biodegrading of many types of petroleum pollutants. Those successful cases show that systems biology has great potential in strengthening petroleum pollutant degradation bacterium and accelerating bioremediation. Systems biology represented by metabolic engineering, enzyme engineering, omics technology, etc., developed rapidly in the twentieth century. Optimizing the metabolic network of petroleum hydrocarbon degrading bacterium could achieve more concise and precise bioremediation by metabolic engineering strategies; biocatalysts with more stable and excellent catalytic activity could accelerate the process of biodegradation by enzyme engineering; omics technology not only could provide more optional components for constructions of engineered bacterium, but also could obtain the structure and composition of the microbial community in polluted environments. Comprehensive microbial community information lays a certain theoretical foundation for the construction of artificial mixed microbial communities for bioremediation of petroleum pollution. This article reviews the application of systems biology in the enforce of petroleum hydrocarbon degradation bacteria and the construction of a hybrid-microbial degradation system. Then the challenges encountered in the process and the application prospects of bioremediation are discussed. Finally, we provide certain guidance for the bioremediation of petroleum hydrocarbon-polluted environment.

Machine learning for metabolic engineering: A review

Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.

Microbial biosurfactants: A broad analysis of properties, applications, biosynthesis, and techno-economical assessment of rhamnolipid production

Biosurfactants are surface-active molecules originated from renewable resources, which are produced by microbial fermentation or chemical/enzymatic catalysis. These molecules present important advantages as compared to petrochemical surfactants, given their resistance to extreme conditions, biodegradability, specificity, and environmental compatibility. Besides that, the high production costs hinder its commercialization. In this way, this article aimed to analyze microbial biosurfactants production, focusing on the optimization of metabolic pathways and production processes, to identify key aspects and provide alternatives to allow a cost-effective production at industrial scale. This was achieved by a broad analysis of biosurfactants properties, applications, and biosynthetic pathways (in terms of yield, cofactors, and energy), in addition to an assessment of production-associated costs. As a result of the present extensive data survey and analysis, key production aspects are disclosed. The metabolic pathway yield analysis demonstrated that production of biosurfactants can be significantly improved (highest theoretical yield was 0.47 g_{biosurfactant} /g_substrate ) by the use of biomolecular engineering techniques to generate optimized synthetic pathways. With an alternative proposed pathway for surfactin, yield was improved and imbalance in cofactors and ATP was reduced. Analysis of productive costs indicated that to make rhamnolipids commercial production feasible, the main efforts should focus on lowering substrate costs as well as the identification of energy-efficient unit operations to lower electricity cost, since these parameters accounted for 19.36 and 78.22%, respectively, of the production costs. The data generated by this analysis highlight the need for multidisciplinary collaboration to make rhamnolipids economically feasible, including biomolecular engineering and process intensification.

Microbial production of vitamin K2: current status and future prospects

Vitamin K2, also called menaquinone, is an essential lipid-soluble vitamin that plays a critical role in blood clotting and prevention of osteoporosis. It has become a focus of research in recent years and has been widely used in the food and pharmaceutical industries. This review will briefly introduce the functions and applications of vitamin K2 first, after which the biosynthesis pathways and enzymes will be analyzed in-depth to highlight the bottlenecks facing the microbial vitamin K2 production on the industrial scale. Then, various strategies, including strain mutagenesis and genetic modification, different cultivation modes, fermentation and separation processes, will be summarized and discussed. The future prospects and perspectives of microbial menaquinone production will also be discussed finally.

Systems Metabolic Engineering Meets Machine Learning: A New Era for Data-Driven Metabolic Engineering

The recent increase in high-throughput capacity of 'omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data-driven modeling methods have become increasingly valuable to metabolic strain design. In this review, the nature of 'omics is discussed and a broad introduction to the ML algorithms combining these datasets into predictive models of metabolism and metabolic rewiring is provided. Next, this review highlights recent work in the literature that utilizes such data-driven methods to inform various metabolic engineering efforts for different classes of application including product maximization, understanding and profiling phenotypes, de novo metabolic pathway design, and creation of robust system-scale models for biotechnology. Overall, this review aims to highlight the potential and promise of using ML algorithms with metabolic engineering and systems biology related datasets.

Discovering novel hydrolases from hot environments

Novel hydrolases from hot and other extreme environments showing appropriate performance and/or novel functionalities and new approaches for their systematic screening are of great interest for developing new processes, for improving safety, health and environment issues. Existing processes could benefit as well from their properties. The workflow, based on the HotZyme project, describes a multitude of technologies and their integration from discovery to application, providing new tools for discovering, identifying and characterizing more novel thermostable hydrolases with desired functions from hot terrestrial and marine environments. To this end, hot springs worldwide were mined, resulting in hundreds of environmental samples and thousands of enrichment cultures growing on polymeric substrates of industrial interest. Using high-throughput sequencing and bioinformatics, 15 hot spring metagenomes, as well as several sequenced isolate genomes and transcriptomes were obtained. To facilitate the discovery of novel hydrolases, the annotation platform Anastasia and a whole-cell bioreporter-based functional screening method were developed. Sequence-based screening and functional screening together resulted in about 100 potentially new hydrolases of which more than a dozen have been characterized comprehensively from a biochemical and structural perspective. The characterized hydrolases include thermostable carboxylesterases, enol lactonases, quorum sensing lactonases, gluconolactonases, epoxide hydrolases, and cellulases. Apart from these novel thermostable hydrolases, the project generated an enormous amount of samples and data, thereby allowing the future discovery of even more novel enzymes.

Modular Pathway Rewiring of Yeast for Amino Acid Production

Amino acids find various applications in biotechnology in view of their importance in the food, feed, pharmaceutical, and personal care industries as nutrients, additives, and drugs, respectively. For the large-scale production of amino acids, microbial cell factories are widely used and the development of amino acid-producing strains has mainly focused on prokaryotes Corynebacterium glutamicum and Escherichia coli. However, the eukaryote Saccharomyces cerevisiae is becoming an even more appealing microbial host for production of amino acids and derivatives because of its superior molecular and physiological features, such as amenable to genetic engineering and high tolerance to harsh conditions. To transform S. cerevisiae into an industrial amino acid production platform, the highly coordinated and multiple layers regulation in its amino acid metabolism should be relieved and reconstituted to optimize the metabolic flux toward synthesis of target products. This chapter describes principles, strategies, and applications of modular pathway rewiring in yeast using the engineering of l-ornithine metabolism as a paradigm. Additionally, detailed protocols for in vitro module construction and CRISPR/Cas-mediated pathway assembly are provided.

Gas fermentation: cellular engineering possibilities and scale up

Low carbon fuels and chemicals can be sourced from renewable materials such as biomass or from industrial and municipal waste streams. Gasification of these materials allows all of the carbon to become available for product generation, a clear advantage over partial biomass conversion into fermentable sugars. Gasification results into a synthesis stream (syngas) containing carbon monoxide (CO), carbon dioxide (CO2), hydrogen (H2) and nitrogen (N2). Autotrophy-the ability to fix carbon such as CO2 is present in all domains of life but photosynthesis alone is not keeping up with anthropogenic CO2 output. One strategy is to curtail the gaseous atmospheric release by developing waste and syngas conversion technologies. Historically microorganisms have contributed to major, albeit slow, atmospheric composition changes. The current status and future potential of anaerobic gas-fermenting bacteria with special focus on acetogens are the focus of this review.

Improving Metabolic Pathway Efficiency by Statistical Model-Based Multivariate Regulatory Metabolic Engineering

Metabolic engineering entails target modification of cell metabolism to maximize the production of a specific compound. For empowering combinatorial optimization in strain engineering, tools and algorithms are needed to efficiently sample the multidimensional gene expression space and locate the desirable overproduction phenotype. We addressed this challenge by employing design of experiment (DoE) models to quantitatively correlate gene expression with strain performance. By fractionally sampling the gene expression landscape, we statistically screened the dominant enzyme targets that determine metabolic pathway efficiency. An empirical quadratic regression model was subsequently used to identify the optimal gene expression patterns of the investigated pathway. As a proof of concept, our approach yielded the natural product violacein at 525.4 mg/L in shake flasks, a 3.2-fold increase from the baseline strain. Violacein production was further increased to 1.31 g/L in a controlled benchtop bioreactor. We found that formulating discretized gene expression levels into logarithmic variables (Linlog transformation) was essential for implementing this DoE-based optimization procedure. The reported methodology can aid multivariate combinatorial pathway engineering and may be generalized as a standard procedure for accelerating strain engineering and improving metabolic pathway efficiency.

SensiPath: computer-aided design of sensing-enabling metabolic pathways

Genetically-encoded biosensors offer a wide range of opportunities to develop advanced synthetic biology applications. Circuits with the ability of detecting and quantifying intracellular amounts of a compound of interest are central to whole-cell biosensors design for medical and environmental applications, and they also constitute essential parts for the selection and regulation of high-producer strains in metabolic engineering. However, the number of compounds that can be detected through natural mechanisms, like allosteric transcription factors, is limited; expanding the set of detectable compounds is therefore highly desirable. Here, we present the SensiPath web server, accessible at http://sensipath.micalis.fr SensiPath implements a strategy to enlarge the set of detectable compounds by screening for multi-step enzymatic transformations converting non-detectable compounds into detectable ones. The SensiPath approach is based on the encoding of reactions through signature descriptors to explore sensing-enabling metabolic pathways, which are putative biochemical transformations of the target compound leading to known effectors of transcription factors. In that way, SensiPath enlarges the design space by broadening the potential use of biosensors in synthetic biology applications.

Experimental and computational optimization of an Escherichia coli co-culture for the efficient production of flavonoids

Metabolic engineering and synthetic biology have enabled the use of microbial production platforms for the renewable production of many high-value natural products. Titers and yields, however, are often too low to result in commercially viable processes. Microbial co-cultures have the ability to distribute metabolic burden and allow for modular specific optimization in a way that is not possible through traditional monoculture fermentation methods. Here, we present an Escherichia coli co-culture for the efficient production of flavonoids in vivo, resulting in a 970-fold improvement in titer of flavan-3-ols over previously published monoculture production. To accomplish this improvement in titer, factors such as strain compatibility, carbon source, temperature, induction point, and inoculation ratio were initially optimized. The development of an empirical scaled-Gaussian model based on the initial optimization data was then implemented to predict the optimum point for the system. Experimental verification of the model predictions resulted in a 65% improvement in titer, to 40.7±0.1mg/L flavan-3-ols, over the previous optimum. Overall, this study demonstrates the first application of the co-culture production of flavonoids, the most in-depth co-culture optimization to date, and the first application of empirical systems modeling for improvement of titers from a co-culture system.