Deaminases, ubiquitous enzymes found in all living organisms from bacteria to humans, serve diverse and crucial functions. Notably, purine and pyrimidine deaminases, while biologically essential for regulating nucleotide pools, exhibit exceptional versatility in biotechnology. This review systematically consolidates current knowledge on deaminases, showcasing their potential uses and relevance in the field of biotechnology. Thus, their transformative impact on pharmaceutical manufacturing is highlighted as catalysts for the synthesis of nucleic acid derivatives. Additionally, the role of deaminases in food bioprocessing and production is also explored, particularly in purine content reduction and caffeine production, showcasing their versatility in this field. The review also delves into most promising biomedical applications including deaminase-based GDEPT and genome and transcriptome editing by deaminase-based systems. All in all, illustrated with practical examples, we underscore the role of purine and pyrimidine deaminases in advancing sustainable and efficient biotechnological practices. Finally, the review highlights future challenges and prospects in deaminase-based biotechnological processes, encompassing both industrial and medical perspectives.

Increasing attention is being focused on using lignocellulose for valuable products. Microbial decomposition can convert lignocellulose into renewable biofuels and other high-value bioproducts, contributing to sustainable development. However, the presence of inhibitors in lignocellulosic hydrolysates can negatively affect microorganisms during fermentation. Improving microbial tolerance to these hydrolysates is a major focus in metabolic engineering. Traditional detoxification methods increase costs, so there is a need for cheap and efficient cell-based detoxification strategies. Synthetic biology approaches offer several strategies for improving microbial tolerance, including redox balancing, membrane engineering, omics-guided technologies, expression of protectants and transcription factors, irrational engineering, cell flocculation, and other novel technologies. Advances in molecular biology, high-throughput sequencing, and artificial intelligence (AI) allow for precise strain modification and efficient industrial production. Developing AI-based computational models to guide synthetic biology efforts and creating large-scale heterologous libraries with automation and high-throughput technologies will be important for future research.

Corynebacterium glutamicum, a well-studied industrial model microorganism, has garnered widespread attention due to its ability for producing amino acids with a long history. In recent years, research efforts have been increasingly focused on exploring its potential for producing various organic acids beyond amino acids. Organic acids, which are characterized by their acidic functional groups, have diverse applications across industries such as food, agriculture, pharmaceuticals, and biobased materials. Leveraging advancements in metabolic engineering and synthetic biology, the metabolic pathways of C. glutamicum have been broadened to facilitate the production of numerous high-value organic acids. This review summarizes the recent progress in metabolic engineering for the production of both amino acids and other organic acids by C. glutamicum. Notably, these acids include, amino acids (lysine, isoleucine, and phenylalanine), TCA cycle-derived organic acids (succinic acid, α-ketoglutaric acid), aromatic organic acids (protocatechuate, 4-amino-3-hydroxybenzoic acid, anthranilate, and para-coumaric acid), and other organic acids (itaconic acid and cis, cis-muconic acid).

Currently, global annual CO emissions from fossil fuel consumption are extremely high, surpassing tens of billions of tons, yet our capacity to capture and utilize CO remains below a small fraction of the amount generated. Microbial electrosynthesis (MES) systems, an integration of microbial metabolism with electrochemistry, have emerged as a highly efficient and promising bio-based carbon-capture-and-utilization technology over other conventional techniques. MES is a unique technology for lowering the atmospheric CO as well as CO in the biogas, and also simultaneously convert them to renewable bioenergy, such as biomethane. As such, MES techniques could be applied for biogas upgrading to generate high purity biomethane, which has the potential to meet natural gas standards. This article offers a detailed overview and assessment of the latest advancements in MES for biomethane production and biogas upgrading, in terms of selecting optimal methane production pathways and associated electron transfer processes, different electrode materials and types, inoculum sources and microbial communities, ion-exchange membrane, externally applied energy level, operating temperature and pH, mode of operation, CO delivery method, selection of inorganic carbon source and its concentration, start-up time, and system pressure. It also highlights the current MES challenges associated with upscaling, design and configuration, long-term stability, energy demand, techno-economics, achieving net negative carbon emission, and other operational issues. Moreover, we provide a summary of current and future opportunities to integrate MES with other unique biosystems, such as methanotrophic bioreactors, and incorporate quorum sensing, 3D printing, and machine learning to further develop MES as a better biomethane-producer and biogas upgrading technique.

Fusarium is genetically diverse and widely distributed geographically. It is one of the genera with more endophytes (which cause no damage to the host plants). This review highlights the capability of Fusarium species to degrade environmental pollutants and describes the biodegradation pathways of some of the emerging environmental contaminants. Some Fusarium species use metabolic strategies enabling them to efficiently mineralize high concentrations of toxic environmental pollutants. These fungi can degrade hydrocarbons, pesticides, herbicides, dyes, pharmaceutical compounds, explosives, plastics, and plastic additives, among other pollutants, and possess high metal biosorption capabilities. According to data from consulted reports, Fusarium strains showed a percentage of biodegradation of a variety of contaminants ranging between 30 % and 100 % for different tested concentrations (from 1 mg to 10 g/L) in a time range between 10 h and 90 d. Enzymes such as esterase, cutinase, laccase, lignin peroxidase, manganese peroxidase, dehydrogenase, lipase, dioxygenase, and phosphoesterase were detected during the pollutant biodegradation process. Fusarium oxysporum, Fusarium solani, and Fusarium culmorum are the most studied species of this genus. Owing to their metabolic versatility, these fungal species and their enzymes represent promising tools for bioremediation applications to mitigate the adverse effects of environmental pollution.

In bacteria, where gene transcription and translation occur concurrently, post-transcriptional regulation is acknowledged to be effective and precise. The 5' untranslated regions (5' UTRs) typically harbor diverse post-transcriptional regulatory elements, like riboswitches, RNA thermometers, small RNAs, and upstream open reading frames, that serve to modulate transcription termination, translation initiation, and mRNA stability. Consequently, exploring 5' UTR-derived regulatory elements is vital for synthetic biology and metabolic engineering. Over the past few years, the investigation of successive mechanisms has facilitated the development of various genetic tools from bacterial 5' UTRs. This review consolidates current understanding of 5' UTR regulatory functions, presents recent progress in 5' UTR -element design and screening, updates the tools and regulatory strategies developed, and highlights the challenges and necessity of establishing reliable bioinformatic analysis methods and non-model bacterial chassis in the future.

Cytokines are important regulators of immune responses, making them attractive targets for autoimmune diseases and cancer therapeutics. Yet, the significance of cytokine glycosylation remains underestimated. Many cytokines carry N- and O-glycans and some even undergo C-mannosylation. Recombinant cytokines produced in heterologous host cells may lack glycans or exhibit a different glycosylation pattern such as varying levels of galactosylation, sialylation, fucosylation or xylose addition compared to their human counterparts, potentially impacting critical immune interactions. We focused on cytokines that are currently utilized or designed in advanced therapeutic formats, including immunocytokines, fusokines, engager cytokines, and genetically engineered 'supercytokines.' Despite the innovative designs of these cytokine derivatives, their glycosylation patterns have not been extensively studied. By examining the glycosylation of the human native cytokines, G-CSF and GM-CSF, interferons β and γ, TNF-α and interleukins-2, -3 -4, -6, -7, -9, -12, -13, -15, -17A, -21, and - 22, we aim to assess its potential impact on their therapeutic derivatives. Understanding the glycosylation of the native cytokines could provide critical insights into the safety, efficacy, and functionality of these next-generation cytokine therapies, affecting factors such as stability, bioactivity, antigenicity, and half-life. This knowledge can guide the choice of optimal expression hosts for production and advance the development of effective cytokine-based therapeutics and synthetic immunology drugs.

Cell therapy manufacturing requires precise monitoring of critical parameters to ensure product quality, consistency and to facilitate the implementation of cost-effective processes. While conventional analytical methods offer limited real-time insights, integration of process analytical technology tools such as Raman spectroscopy in bioprocessing has the potential to drive efficiency and reliability during the manufacture of cell-based therapies while meeting stringent regulatory requirements. The non-destructive nature of Raman spectroscopy, combined with its ability to be integrated on-line with scalable platforms, allows for continuous data acquisition, enabling real-time correlations between process parameters and critical quality attributes. Herein, we review the role of Raman spectroscopy in cell therapy bioprocessing and discuss how simultaneous measurement of distinct parameters and attributes, such as cell density, viability, metabolites and cell identity biomarkers can streamline on-line monitoring and facilitate adaptive process control. This, in turn, enhances productivity and mitigates process-related risks. We focus on recent advances integrating Raman spectroscopy across various manufacturing stages, from optimizing culture media feeds to monitoring bioprocess dynamics, covering downstream applications such as detection of co-isolated contaminating cells, cryopreservation, and quality control of the drug product. Finally, we discuss the potential of Raman spectroscopy to revolutionize current practices and accelerate the development of advanced therapy medicinal products.

The persistent challenge of water pollution, exacerbated by slow progress in ecofriendly technologies and accumulating pollutants, underscores the need for innovative solutions. Constructed Wetland Microbial Fuel Cell (CW-MFC) emerges as an intriguing environmental technology capable of adressing this issue by eliminating contaminants from wastewater while simultaneously producing green energy as an additional bonus. In recent years, CW-MFC technology has gained attention due to its sustainability and promising prospects for a circular waste-free industry. However, due to various technological and biological challenges, it has not yet achieved wide-scale application. This review examines the current state of CW-MFC technology and identifies both biotic and abiotic strategies for optimization through operational and structural improvements affecting biocomponents. Our review highlights several key findings: (1) Plants play an important role in reducing the system's inner resistance through mechanisms such as radial oxygen loss, evapotranspiration, and high photosynthetic flow, which facilitate electroactive bacteria and affect redox potential. (2) Plant characteristics such as root porosity, phloem and aerenchyma development, chlorophyll content, and plant biomass are key indicators of CW-MFC performance and significantly impact both pollutant removal and energy harvesting. (3) We expand the criteria for selecting suitable plants to include mesophytes and C3 pollutant-tolerant species, in addition to traditional aquatic and C4 plants. Additionally, the review presents several technical approaches that enhance CW-MFC efficiency: (1) design optimization, (2) use of novel materials, and (3) application of external electrical fields, aeration, light, and temperature adjustments. CW-MFCs are capable of nearly complete elimination of a wide range of contaminants, including organic matter (84 % ± 10), total nitrogen (80 % ± 7) and phosphorus (79 % ± 18) compounds, metals (86 % ± 10), pharmaceuticals (87 % ± 7), dyes (90 % ± 8), and other complex pollutants, while generating green energy. We hope our findings will be useful in optimizing CW-MFC design and providing insights for researchers aiming to advance the technology and facilitate its future scaling.

Enzymes offer a more environmentally friendly and low-impact solution to conventional chemistry, but they often require additional engineering for their application in industrial settings, an endeavour that is challenging and laborious. To address this issue, the power of machine learning can be harnessed to produce predictive models that enable the in silico study and engineering of improved enzymatic properties. Such machine learning models, however, require the conversion of the complex biological information to a numerical input, also called protein representations. These inputs demand special attention to ensure the training of accurate and precise models, and, in this review, we therefore examine the critical step of encoding protein information to numeric representations for use in machine learning. We selected the most important approaches for encoding the three distinct biological protein representations - primary sequence, 3D structure, and dynamics - to explore their requirements for employment and inductive biases. Combined representations of proteins and substrates are also introduced as emergent tools in biocatalysis. We propose the division of fixed representations, a collection of rule-based encoding strategies, and learned representations extracted from the latent spaces of large neural networks. To select the most suitable protein representation, we propose two main factors to consider. The first one is the model setup, which is influenced by the size of the training dataset and the choice of architecture. The second factor is the model objectives such as consideration about the assayed property, the difference between wild-type models and mutant predictors, and requirements for explainability. This review is aimed at serving as a source of information and guidance for properly representing enzymes in future machine learning models for biocatalysis.

Aptamers are excellent recognition molecules obtained from systematic evolution of ligands by exponential enrichment (SELEX) that have been extensively researched for constructing aptasensors. However, in the process from SELEX to the construction of aptasensors, there are many disadvantages, such as tedious and repetitive operations, interference from external factors, and low efficiency, which seriously limits their application scope and development. Introducing the microfluidic technology can realize the integration and intelligence of SELEX and aptasensing, improve the efficiency of SELEX, and enhance the detection performance and convenience of aptasensing. Hence, in this review, the characteristics of various chips based on different driving forces are described firstly. And then summarizing the design of microfluidic devices based on different SELEX methods and showing the strategies of microfluidic aptasensors based on different detection modes. Finally, discussing the difficulties and challenges encountered when microfluidic is integrated with the SELEX and the aptasensors.

With the increased demand for bio-based products and the rapid development of biomanufacturing technologies, biocatalytic reactions including microorganisms and enzyme based, have become promising approaches. Prior to the scale-up of production process, environmental and economic feasibility analysis are essential for the development of a sustainable and intelligent bioeconomy in the context of industry 4.0. To achieve these goals, process simulation supports system optimization, improves energy and resource utilization efficiencies, and supports digital bioprocessing. However, due to the insufficient understanding of cellular metabolism and interaction mechanisms, there is still a lack of rational and transparent simulation tools to efficiently simulate, control, and optimize microbial/enzymatic reaction processes. Therefore, there is an urgent need to develop frameworks that integrate kinetic modeling, process simulation, and sustainability analysis for bioreaction simulations and their optimization. This review summarizes and compares the advantages and disadvantages of different process simulation software and models in simulating biocatalytic processes, identifies the limitations of traditional reaction kinetics models, and proposes the requirement of simulations close to real reactions. In addition, we explore the current state of kinetic modeling at the microscopic scale and how process simulation can be linked to kinetic models of cellular metabolism and computational fluid dynamics modeling. Finally, this review discusses the requirement of sensitivity analysis and how machine learning can assist with optimization of simulations to improve energy efficiency and product yields from techno-economic and life cycle assessment perspectives.

As global temperatures rise and arid climates intensify, the reserves of Earth's resources and the future development of humankind are under unprecedented pressure. Traditional methods of food production are increasingly inadequate in meeting the demands of human life while remaining environmentally sustainable and resource-efficient. Consequently, the sustainable supply of lipids is expected to become a pivotal area for future food development. Lignocellulose biomass (LB), as the most abundant and cost-effective renewable resource, has garnered significant attention from researchers worldwide. Thus, bioprocessing based on LB is appearing as a sustainable model for mitigating the depletion of energy reserves and reducing carbon footprints. Currently, the transformation of LB primarily focuses on producing biofuels, such as bioethanol, biobutanol, and biodiesel, to address the energy crisis. However, there are limited reports on the production of single cell oil (SCO) from LB. This review, therefore, provides a comprehensive summary of the research progress in lignocellulosic pretreatment. Subsequently, it describes how the capability for lignocellulosic use can be conferred to cells through genetic engineering. Additionally, the current status of saccharification and fermentation of LB is outlined. The article also highlights the advances in synthetic biology aimed at driving the development of oil-producing microorganism (OPM), including genetic transformation, chassis modification, and metabolic pathway optimization. Finally, the limitations currently faced in SCO production from straw are discussed, and future directions for achieving high SCO yields from various perspectives are proposed. This review aims to provide a valuable reference for the industrial application of green SCO production.

Engineered transcription factors (eTFs) binding diversed functional nucleic acids (dFNAs), as innovative biorecognition systems, have gradually become indispensable core elements for building synthetic biosensors. They not only circumvent the limitations of the original TF-based biosensing technologies, but also inject new vitality into the field of synthetic biosensing. This review aims to provide the first comprehensive and systematic dissection of the eTF-dFNA synthetic biosensor concept. Firstly, the core principles and interaction mechanisms of eTF-dFNA biosensors are clarified. Next, we elaborate on the construction strategies of eTF-dFNA synthetic biosensors, detailing methods for the personalized customization of eTFs (irrational design, rational design, and semi-rational design) and dFNAs (SELEX, modifying and predicting), along with the exploration of strategies for the flexible selection of signal amplification and output modes. Furthermore, we discuss the exceptional performance and substantial advantages of eTF-dFNA synthetic biosensors, analyzing them from four perspectives: recognition domain, detection speed, sensitivity, and construction methodology. Building upon this analysis, we present their outstanding applications in point-of-care diagnostics, food-safety detection, environmental monitoring, and production control. Finally, we address the current limitations of eTF-dFNA synthetic biosensors candidly and envision the future direction of this technology, aiming to provide valuable insights for further research and applications in this burgeoning field.

Each year, millions of tons of plastics are produced for use in such applications as packaging, construction, and textiles. While plastic is undeniably useful and convenient, its environmental fate and transport have raised growing concerns about waste and pollution. However, the ease and low cost of producing virgin plastic have so far made conventional plastic recycling economically unattractive. Common contaminants in plastic waste and shortcomings of the recycling processes themselves typically mean that recycled plastic products are of relatively low quality in some cases. The high cost and high energy requirements of typical recycling operations also reduce their economic benefits. In recent years, the bio-upcycling of chemically treated plastic waste has emerged as a promising alternative to conventional plastic recycling. Unlike recycling, bio-upcycling uses relatively mild process conditions to economically transform pretreated plastic waste into value-added products. In this review, we first provide a précis of the general methodology and limits of conventional plastic recycling. Then, we review recent advances in hybrid chemical/biological upcycling methods for different plastics, including polyethylene terephthalate, polyurethane, polyamide, polycarbonate, polyethylene, polypropylene, polystyrene, and polyvinyl chloride. For each kind of plastic, we summarize both the pretreatment methods for making the plastic bio-available and the microbial chassis for degrading or converting the treated plastic waste to value-added products. We also discuss both the limitations of upcycling processes for major plastics and their potential for bio-upcycling.

Biotechnology has been built on the foundation of a small handful of well characterized and well-engineered organisms. Recent years have seen a breakout performer gain attention as a new entrant into the bioengineering toolbox: Vibrio natriegens. This review covers recent research efforts into making V. natriegens a biotechnology platform, using a large language model (LLM) and knowledge graph to expedite the literature survey process. Scientists have made advancements in research pertaining to the fundamental metabolic characteristics of V. natriegens, development and characterization of synthetic biology tools, systems biology analysis and metabolic modeling, bioproduction and metabolic engineering, and microbial ecology. Each of these subcategories has relevance to the future of V. natriegens for bioengineering applications. In this review, we cover these recent advancements and offer context for the impact they may have on the field, highlighting benefits and drawbacks of using this organism. From examining the recent bioengineering research, it appears that V. natriegens is on the precipice of becoming a platform bacterium for the future of biotechnology.

Energy produced from renewable sources such as sun or wind are intermittent, depending on circumstantial factors. This fact explains why energy supply and demand do not match. In this context, the interest in biomethanation has increased as an interesting contribution to the Power-to-gas concept (P2G), transforming the extra amount of produced electricity into methane (CH). The reaction between green hydrogen (H) (produced by electrolysis) and CO (pollutant present in biogas) can be catalysed by different microorganisms to produce biomethane, that can be injected into existing natural gas grid if reaching the standards. Thus, energy storage for both hydrogen and electricity, as well as transportation problems would be solved. However, H diffusion to the liquid phase for its further biological conversion is the main bottleneck due to the low solubility of H. This review includes the state-of-the-art in biological hydrogenotrophic methanation (BHM) and membrane-based technologies. Specifically, the use of hollow-fiber membrane bioreactors as a technology to overcome H diffusion limitations is reviewed. Furthermore, the influence of operating conditions, microbiology, H diffusion and H injection methods are critically discussed before setting the main recommendations about BHM.

Probing biological processes in living organisms that could provide one-of-a-kind insights into real-time alterations of significant physiological parameters is a formidable task that calls for specialized analytic devices. Classical biochemical methods have significantly aided our understanding of the mechanisms that regulate essential biological processes. These methods, however, are typically insufficient for investigating transient molecular events since they focus primarily on the end outcome. Fluorescence resonance energy transfer (FRET) microscopy is a potent tool used for exploring non-invasively real-time dynamic interactions between proteins and a variety of biochemical signaling events using sensors that have been meticulously constructed. Due to their versatility, FRET-based sensors have enabled the rapid and standardized assessment of a large array of biological variables, facilitating both high-throughput research and precise subcellular measurements with exceptional temporal and spatial resolution. This review commences with a brief introduction to FRET theory and a discussion of the fluorescent molecules that can serve as tags in different sensing modalities for studies in chemical biology, followed by an outlining of the imaging techniques currently utilized to quantify FRET highlighting their strengths and shortcomings. The article also discusses the various donor-acceptor combinations that can be utilized to construct FRET scaffolds. Specifically, the review provides insights into the latest real-time bioimaging applications of FRET-based sensors and discusses the common architectures of such devices. There has also been discussion of FRET systems with multiplexing capabilities and multi-step FRET protocols for use in dual/multi-analyte detections. Future research directions in this exciting field are also mentioned, along with the obstacles and opportunities that lie ahead.

Bilirubin (BR) is among the most potent endogenous antioxidants that originates from the heme catabolic pathway. Despite being considered as a dangerous and cytotoxic waste product at high concentrations, BR has potent antioxidant effects leading to the reduction of oxidative stress and inflammation, which play an important role in the development and progression of cancer. The purpose of this study is to introduce PEGylated BR nanoparticles (NPs), themselves or in combination with other anti-cancer agents. BR is effective when loaded into various nanoparticles and used in cancer therapy. Interestingly, BRNPs can be manipulated to create stimuli-responsive carriers providing a sustained and controlled, as well as on-demand, release of drug in response to internal or external factors such as reactive oxygen species, glutathione, light, enzymes, and acidic pH. This review suggests that BRNPs have the potential as tumor microenvironment-responsive delivery systems for effective targeting of various types of cancers.

Interest in red microalgae of the Porphyridium genus has surged due to their richness in phycobiliproteins, polyunsaturated fatty acids, and sulfated polysaccharides. These biomasses and their derivatives find applications across food, feed, nutraceutical, pharmaceutical, and cosmetic industries. A deeper understanding of their properties and extraction methods is essential to optimize downstream processing. This paper comprehensively reviews Porphyridium sp., focusing on cultivation techniques, bioproduct extraction, purification, and characterization. It delves into protein, lipid, and polysaccharide extraction, considering the influence of culture conditions on biomass yield. Various methods like chromatography, electrophoresis, and membrane-based techniques for cell lysis and bioproduct recovery are explored, highlighting their pros and cons. By offering diverse insights, this review aims to inspire innovative research and industry progress in red microalgae biotechnology, contributing to sustainable solutions across sectors.

With the growth of the chemical industry over the last decade, the need for cheaper (and more environmentally friendly) alternatives to petrochemicals of ever-increasing cost has grown steadily. Oleochemicals and biodiesel (OC/BD) are considered as green alternatives to petroleum derivatives, because they come from renewable oils and fats. OC/BD are currently produced by the traditional energy intensive chemical catalyzed methods, which have several economic and environmental drawbacks. For these reasons, the enzymatic production of OC/BD has attracted a growing attention for their greener pathway with respect to the chemically catalyzed processes. Lipase-catalyzed processes have a low energy requirement, since reactions are performed under atmospheric pressure and mild temperature and without the creation of side reactions. Furthermore, utilization of enzyme catalysts offers many advantages such as reducing the initial capital investment due to simplified downstream processing steps. Despite all the previous advantages, however, the high cost of lipases restricted their large-scale utilization. In the past decade, efforts have been made to reduce the cost of the enzymatic-catalyzed synthesis of OC/BD. However, most previous studies have studied only the technical feasibility of the lipase-catalyzed reactions and overlocked the economic viability. This review critically discusses the factors affecting the promotion of the economic feasibility of the enzymatic processes from the lab to large scale. These include reactor configuration, type of feedstock, conditions optimization, immobilization, lipase-producing microorganisms, and substrate diversification. In addition, this review reports the recent advances in lipase-catalyzed production of fatty acids, fatty esters, monoglycerides, and biodiesel in the lab as well as in the large-scales. To the best of authors' knowledge, this is the first review article reports the recent global progress achieved in both lab- and large-scale for the enzymatic production of OC/BD.