Engineering bionanoreactor in bacteria for efficient hydrogen production.

Hydrogen production through water splitting is a vital strategy for renewable and sustainable clean energy. In this study, we developed an approach integrating nanomaterial engineering and synthetic biology to establish a bionanoreactor system for efficient hydrogen production. The periplasmic space (20 to 30 nm) of an electroactive bacterium, Shewanella oneidensis MR-1, was engineered to serve as a bionanoreactor to enhance the interaction between electrons and protons, catalyzed by hydrogenases for hydrogen generation. To optimize electron transfer, we used the microbially reduced graphene oxide (rGO) to coat the electrode, which improved the electron transfer from the electrode to the cells. Native MtrCAB protein complex on S. oneidensis and self-assembled iron sulfide (FeS) nanoparticles acted in tandem to facilitate electron transfer from an electrode to the periplasm. To enhance proton transport, S. oneidensis MR-1 was engineered to express Gloeobacter rhodopsin (GR) and the light-harvesting antenna canthaxanthin. This led to efficient proton pumping when exposed to light, resulting in a 35.6% increase in the rate of hydrogen production. The overexpression of native [FeFe]-hydrogenase further improved the hydrogen production rate by 56.8%. The bionanoreactor engineered in S. oneidensis MR-1 achieved a hydrogen yield of 80.4 µmol/mg protein/day with a Faraday efficiency of 80% at a potential of -0.75 V. This periplasmic bionanoreactor combines the strengths of both nanomaterial and biological components, providing an efficient approach for microbial electrosynthesis.

Regulatory framework for genetically modified organisms in the Kingdom of Eswatini.

The Kingdom of Eswatini is a Party to the Convention on Biological Diversity and to the Cartagena Protocol on Biosafety. As Party, Eswatini has domesticated these agreements by passing the Biosafety Act, of 2012 to provide for the safe handling, transfer, and use of living modified organisms (LMOs) in the country. The Act regulates living modified organisms to be used for confined field trials, commercial release, import, export, and transit, and for food, feed, and processing. Guidance is provided for prospective applicants before any application is made to the Competent Authority. This framework also provides for the regulation of emerging technologies such as synthetic biology and genome editing. The regulatory framework for living modified organisms aims to provide an enabling environment for the precautionary use of modern biotechnology and its products in the country in order to safeguard biological diversity and human health.

Efficient and markerless gene integration with SlugCas9-HF in Kluyveromyces marxianus.

The nonconventional yeast Kluyveromyces marxianus has potential for industrial production, but the lack of advanced synthetic biology tools for precise engineering hinders its rapid development. Here, we introduce a CRISPR-Cas9-mediated multilocus integration method for assembling multiple exogenous genes. Using SlugCas9-HF, a high-fidelity Cas9 nuclease, we enhance gene editing precision. Specific genomic loci predisposed to efficient integration and expression of heterologous genes are identified and combined with a set of paired CRISPR-Cas9 expression plasmids and donor plasmids to establish a CRISPR-based biosynthesis toolkit. This toolkit enables genome integration of large gene modules over 12 kb and achieves simultaneous quadruple-locus integration in a single step with 20% efficiency. As a proof-of-concept, we apply the toolkit to screen for gene combinations that promote heme production, revealing the importance of HEM4Km and HEM12Sc. This CRISPR-based toolkit simplifies the reconstruction of complex pathways in K. marxianus, broadening its application in synthetic biology.

Microbial Nanotechnology for Precision Nanobiosynthesis: Innovations, Current Opportunities and Future Perspectives for Industrial Sustainability.

A new area of biotechnology is nanotechnology. Nanotechnology is an emerging field that aims to develope various substances with nano-dimensions that have utilization in the various sectors of pharmaceuticals, bio prospecting, human activities and biomedical applications. An essential stage in the development of nanotechnology is the creation of nanoparticles. To increase their biological uses, eco-friendly material synthesis processes are becoming increasingly important. Recent years have shown a lot of interest in nanostructured materials due to their beneficial and unique characteristics compared to their polycrystalline counterparts. The fascinating performance of nanomaterials in electronics, optics, and photonics has generated a lot of interest. An eco-friendly approach of creating nanoparticles has emerged in order to get around the drawbacks of conventional techniques. Today, a wide range of nanoparticles have been created by employing various microbes, and their potential in numerous cutting-edge technological fields have been investigated. These particles have well-defined chemical compositions, sizes, and morphologies. The green production of nanoparticles mostly uses plants and microbes. Hence, the use of microbial nanotechnology in agriculture and plant science is the main emphasis of this review. The present review highlights the methods of biological synthesis of nanoparticles available with a major focus on microbially synthesized nanoparticles, parameters and biochemistry involved. Further, it takes into account the genetic engineering and synthetic biology involved in microbial nanobiosynthesis to the construction of microbial nanofactories.

Rhodopsin-based light-harvesting system for sustainable synthetic biology.

Rhodopsins, a diverse class of light-sensitive proteins found in various life domains, have attracted considerable interest for their potential applications in sustainable synthetic biology. These proteins exhibit remarkable photochemical properties, undergoing conformational changes upon light absorption that drive a variety of biological processes. Exploiting rhodopsin's natural properties could pave the way for creating sustainable and energy-efficient technologies. Rhodopsin-based light-harvesting systems offer innovative solutions to a few key challenges in sustainable engineering, from bioproduction to renewable energy conversion. In this opinion article, we explore the recent advancements and future possibilities of employing rhodopsins for sustainable engineering, underscoring the transformative potential of these biomolecules.

Construction of lignan glycosides biosynthetic network in Escherichia coli using mutltienzyme modules.

BACKGROUND: Due to the complexity of the metabolic pathway network of active ingredients, precise targeted synthesis of any active ingredient on a synthetic network is a huge challenge. Based on a complete analysis of the active ingredient pathway in a species, this goal can be achieved by elucidating the functional differences of each enzyme in the pathway and achieving this goal through different combinations. Lignans are a class of phytoestrogens that are present abundantly in plants and play a role in various physiological activities of plants due to their structural diversity. In addition, lignans offer various medicinal benefits to humans. Despite their value, the low concentration of lignans in plants limits their extraction and utilization. Recently, synthetic biology approaches have been explored for lignan production, but achieving the synthesis of most lignans, especially the more valuable lignan glycosides, across the entire synthetic network remains incomplete. RESULTS: By evaluating various gene construction methods and sequences, we determined that the pCDF-Duet-Prx02-PsVAO gene construction was the most effective for the production of (+)-pinoresinol, yielding up to 698.9 mg/L after shake-flask fermentation. Based on the stable production of (+)-pinoresinol, we synthesized downstream metabolites in vivo. By comparing different fermentation methods, including "one-cell, one-pot" and "multicellular one-pot", we determined that the "multicellular one-pot" method was more effective for producing (+)-lariciresinol, (-)-secoisolariciresinol, (-)-matairesinol, and their glycoside products. The "multicellular one-pot" fermentation yielded 434.08 mg/L of (+)-lariciresinol, 96.81 mg/L of (-)-secoisolariciresinol, and 45.14 mg/L of (-)-matairesinol. Subsequently, ultilizing the strict substrate recognition pecificities of UDP-glycosyltransferase (UGT) incorporating the native uridine diphosphate glucose (UDPG) Module for in vivo synthesis of glycoside products resulted in the following yields: (+)-pinoresinol glucoside: 1.71 mg/L, (+)-lariciresinol-4-O-D-glucopyranoside: 1.3 mg/L, (+)-lariciresinol-4'-O-D-glucopyranoside: 836 µg/L, (-)-secoisolariciresinol monoglucoside: 103.77 µg/L, (-)-matairesinol-4-O-D-glucopyranoside: 86.79 µg/L, and (-)-matairesinol-4'-O-D-glucopyranoside: 74.5 µg/L. CONCLUSIONS: By using various construction and fermentation methods, we successfully synthesized 10 products of the lignan pathway in Isatis indigotica Fort in Escherichia coli, with eugenol as substrate. Additionally, we obtained a diverse range of lignan products by combining different modules, setting a foundation for future high-yield lignan production.

Mechanism-based and data-driven modeling in cell-free synthetic biology.

Cell-free systems have emerged as a versatile platform in synthetic biology, finding applications in various areas such as prototyping synthetic circuits, biosensor development, and biomanufacturing. To streamline the prototyping process, cell-free systems often incorporate a modeling step that predicts the outcomes of various experimental scenarios, providing a deeper insight into the underlying mechanisms and functions. There are two recognized approaches for modeling these systems: mechanism-based modeling, which models the underlying reaction mechanisms; and data-driven modeling, which makes predictions based on data without preconceived interactions between system components. In this highlight, we focus on the latest advancements in both modeling approaches for cell-free systems, exploring their potential for the design and optimization of synthetic genetic circuits.

Deconstructing synthetic biology across scales: a conceptual approach for training synthetic biologists.

Synthetic biology allows us to reuse, repurpose, and reconfigure biological systems to address society's most pressing challenges. Developing biotechnologies in this way requires integrating concepts across disciplines, posing challenges to educating students with diverse expertise. We created a framework for synthetic biology training that deconstructs biotechnologies across scales-molecular, circuit/network, cell/cell-free systems, biological communities, and societal-giving students a holistic toolkit to integrate cross-disciplinary concepts towards responsible innovation of successful biotechnologies. We present this framework, lessons learned, and inclusive teaching materials to allow its adaption to train the next generation of synthetic biologists.

Toward biomanufacturing of next-generation bacterial nanocellulose (BNC)-based materials with tailored properties: A review on genetic engineering approaches.

Bacterial nanocellulose (BNC) is a biopolymer that is drawing significant attention for a wide range of applications thanks to its unique structure and excellent properties, such as high purity, mechanical strength, high water holding capacity and biocompatibility. Nevertheless, the biomanufacturing of BNC is hindered due to its low yield, the instability of microbial strains and cost limitations that prevent it from being mass-produced on a large scale. Various approaches have been developed to address these problems by genetically modifying strains and to produce BNC-based biomaterials with added value. These works are summarized and discussed in the present article, which include the overexpression and knockout of genes related and not related with the nanocellulose biosynthetic operon, the application of synthetic biology approaches and CRISPR/Cas techniques to modulate BNC biosynthesis. Further discussion is provided on functionalized BNC-based biomaterials with tailored properties that are incorporated in-vivo during its biosynthesis using genetically modified strains either in single or co-culture systems (in-vivo manufacturing). This novel strategy holds potential to open the road toward cost-effective production processes and to find novel applications in a variety of technology and industrial fields.

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.

Machine learning for the advancement of genome-scale metabolic modeling.

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

Broadening the application of Yarrowia lipolytica synthetic biology tools to explore the potential of Yarrowia clade diversity.

Yeasts have established themselves as prominent microbial cell factories, and the availability of synthetic biology tools has led to breakthroughs in the rapid development of industrial chassis strains. The selection of a suitable microbial host is critical in metabolic engineering applications, but it has been largely limited to a few well-defined strains. However, there is growing consideration for evaluating strain diversity, as a wide range of specific traits and phenotypes have been reported even within a specific yeast genus or species. Moreover, with the advent of synthetic biology tools, non-type strains can now be easily and swiftly reshaped. The yeast Yarrowia lipolytica has been extensively studied for various applications such as fuels, chemicals, and food. Additionally, other members of the Yarrowia clade are currently being evaluated for their industrial potential. In this study, we demonstrate the versatility of synthetic biology tools originally developed for Y. lipolytica by repurposing them for engineering other yeasts belonging to the Yarrowia clade. Leveraging the Golden Gate Y. lipolytica tool kit, we successfully expressed fluorescent proteins as well as the carotenoid pathway in at least five members of the clade, serving as proof of concept. This research lays the foundation for conducting more comprehensive investigations into the uncharacterized strains within the Yarrowia clade and exploring their potential applications in biotechnology.

Designing a synthetic moss genome using GenoDesigner.

The de novo synthesis of genomes has made unprecedented progress and achieved milestones, particularly in bacteria and yeast. However, the process of synthesizing a multicellular plant genome has not progressed at the same pace, due to the complexity of multicellular plant genomes, technical difficulties associated with large genome size and structure, and the intricacies of gene regulation and expression in plants. Here we outline the bottom-up design principles for the de novo synthesis of the Physcomitrium patens (that is, earthmoss) genome. To facilitate international collaboration and accessibility, we have developed and launched a public online design platform called GenoDesigner. This platform offers an intuitive graphical interface enabling users to efficiently manipulate extensive genome sequences, even up to the gigabase level. This tool is poised to greatly expedite the synthesis of the P. patens genome, offering an essential reference and roadmap for the synthesis of plant genomes.

Nanomaterials-assisted gene editing and synthetic biology for optimizing the treatment of pulmonary diseases.

The use of nanomaterials in gene editing and synthetic biology has emerged as a pivotal strategy in the pursuit of refined treatment methodologies for pulmonary disorders. This review discusses the utilization of nanomaterial-assisted gene editing tools and synthetic biology techniques to promote the development of more precise and efficient treatments for pulmonary diseases. First, we briefly outline the characterization of the respiratory system and succinctly describe the principal applications of diverse nanomaterials in lung ailment treatment. Second, we elaborate on gene-editing tools, their configurations, and assorted delivery methods, while delving into the present state of nanomaterial-facilitated gene-editing interventions for a spectrum of pulmonary diseases. Subsequently, we briefly expound on synthetic biology and its deployment in biomedicine, focusing on research advances in the diagnosis and treatment of pulmonary conditions against the backdrop of the coronavirus disease 2019 pandemic. Finally, we summarize the extant lacunae in current research and delineate prospects for advancement in this domain. This holistic approach augments the development of pioneering solutions in lung disease treatment, thereby endowing patients with more efficacious and personalized therapeutic alternatives.

Synthetic microbial ecology: engineering habitats for modular consortia.

Microbiomes, the complex networks of micro-organisms and the molecules through which they interact, play a crucial role in health and ecology. Over at least the past two decades, engineering biology has made significant progress, impacting the bio-based industry, health, and environmental sectors; but has only recently begun to explore the engineering of microbial ecosystems. The creation of synthetic microbial communities presents opportunities to help us understand the dynamics of wild ecosystems, learn how to manipulate and interact with existing microbiomes for therapeutic and other purposes, and to create entirely new microbial communities capable of undertaking tasks for industrial biology. Here, we describe how synthetic ecosystems can be constructed and controlled, focusing on how the available methods and interaction mechanisms facilitate the regulation of community composition and output. While experimental decisions are dictated by intended applications, the vast number of tools available suggests great opportunity for researchers to develop a diverse array of novel microbial ecosystems.

Controlling the expression of heterologous genes in Bdellovibrio bacteriovorus using synthetic biology strategies.

Bdellovibrio bacteriovorus HD100 is an obligate predatory bacterium that preys upon Gram-negative bacteria. It has been proposed to be applied as a "living antibiotic" in several fields such as agriculture or even medicine, since it is able to prey upon bacterial pathogens. Its interesting lifestyle makes this bacterium very attractive as a microbial chassis for co-culture systems including two partners. A limitation to this goal is the scarcity of suitable synthetic biology tools for predator domestication. To fill this gap, we have firstly adapted the hierarchical assembly cloning technique Golden Standard (GS) to make it compatible with B. bacteriovorus HD100. The chromosomal integration of the Tn7 transposon's mobile element, in conjunction with the application of the GS technique, has allowed the systematic characterization of a repertoire of constitutive and inducible promoters, facilitating the control of the expression of heterologous genes in this bacterium. PJExD/EliR proved to be an exceptional promoter/regulator system in B. bacteriovorus HD100 when precise regulation is essential, while the synthetic promoter PBG37 showed a constitutive high expression. These genetic tools represent a step forward in the conversion of B. bacteriovorus into an amenable strain for microbial biotechnology approaches.

A high-throughput synthetic biology approach for studying combinatorial chromatin-based transcriptional regulation.

The construction of synthetic gene circuits requires the rational combination of multiple regulatory components, but predicting their behavior can be challenging due to poorly understood component interactions and unexpected emergent behaviors. In eukaryotes, chromatin regulators (CRs) are essential regulatory components that orchestrate gene expression. Here, we develop a screening platform to investigate the impact of CR pairs on transcriptional activity in yeast. We construct a combinatorial library consisting of over 1,900 CR pairs and use a high-throughput workflow to characterize the impact of CR co-recruitment on gene expression. We recapitulate known interactions and discover several instances of CR pairs with emergent behaviors. We also demonstrate that supervised machine learning models trained with low-dimensional amino acid embeddings accurately predict the impact of CR co-recruitment on transcriptional activity. This work introduces a scalable platform and machine learning approach that can be used to study how networks of regulatory components impact gene expression.

Research progress on biosynthesis of erythritol and multi-dimensional optimization of production strategies.

Erythritol, as a new type of natural sweetener, has been widely used in food, medical, cosmetics, pharmaceutical and other fields due to its unique physical and chemical properties and physiological functions. In recent years, with the continuous development of strategies such as synthetic biology, metabolic engineering, omics-based systems biology and high-throughput screening technology, people's understanding of the erythritol biosynthesis pathway has gradually deepened, and microbial cell factories with independent modification capabilities have been successfully constructed. In this review, the cheap feedstocks for erythritol synthesis are introduced in detail, the environmental factors affecting the synthesis of erythritol and its regulatory mechanism are described, and the tools and strategies of metabolic engineering involved in erythritol synthesis are summarized. In addition, the study of erythritol derivatives is helpful in expanding its application field. Finally, the challenges that hinder the effective production of erythritol are discussed, which lay a foundation for the green, efficient and sustainable production of erythritol in the future and breaking through the bottleneck of production.

Partial RNA design.

MOTIVATION: RNA design is a key technique to achieve new functionality in fields like synthetic biology or biotechnology. Computational tools could help to find such RNA sequences but they are often limited in their formulation of the search space. RESULTS: In this work, we propose partial RNA design, a novel RNA design paradigm that addresses the limitations of current RNA design formulations. Partial RNA design describes the problem of designing RNAs from arbitrary RNA sequences and structure motifs with multiple design goals. By separating the design space from the objectives, our formulation enables the design of RNAs with variable lengths and desired properties, while still allowing precise control over sequence and structure constraints at individual positions. Based on this formulation, we introduce a new algorithm, libLEARNA, capable of efficiently solving different constraint RNA design tasks. A comprehensive analysis of various problems, including a realistic riboswitch design task, reveals the outstanding performance of libLEARNA and its robustness. AVAILABILITY AND IMPLEMENTATION: libLEARNA is open-source and publicly available at: https://github.com/automl/learna_tools.