Biotechnology advances最新文献

Two-component systems (TCSs) are prevalent signaling pathways in bacteria. These systems mediate phosphotransfer between histidine kinase and a response regulator, facilitating responses to diverse physical, chemical, and biological stimuli. Advancements in synthetic and structural biology have repurposed TCSs for applications in monitoring heavy metals, disease-associated biomarkers, and the production of bioproducts. However, the utility of many TCS biosensors is hindered by undesired performance due to the lack of effective engineering methods. Here, we briefly discuss the architectures and regulatory mechanisms of TCSs. We also summarize the recent advancements in TCS engineering by experimental or computational-based methods to fine-tune the biosensor functional parameters, such as response curve and specificity. Engineered TCSs have great potential in the medical, environmental, and biorefinery fields, demonstrating a crucial role in a wide area of biotechnology.

Chimeric antigen receptor (CAR)-T cells are emerging as a generation-defining therapeutic however their manufacture remains a major barrier to meeting increased market demand. Monitoring critical quality attributes (CQAs) and critical process parameters (CPPs) during manufacture would vastly enrich acquired information related to the process and product, providing feedback to enable real-time decision making. Here we identify specific CAR-T cytokines as value-adding analytes and discuss their roles as plausible CPPs and CQAs. High sensitivity sensing technologies which can be easily integrated into manufacture workflows are essential to implement real-time monitoring of these cytokines. We therefore present biosensors as enabling technologies and evaluate recent advancements in cytokine detection in cell cultures, offering promising translatability to CAR-T biomanufacture. Finally, we outline emerging sensing technologies with future promise, and provide an overall outlook on existing gaps to implementation and the optimal sensing platform to enable cytokine monitoring in CAR-T biomanufacture.

Plant molecular farming (PMF) has been promoted as a fast, efficient and cost-effective alternative to bacteria and animal cells for the production of biopharmaceutical proteins. Numerous plant species have been tested to produce a wide range of drug candidates. However, PMF generally lacks a systematic, streamlined and seamless workflow to continuously fill the product pipeline. Therefore, it is currently unable to compete with established platforms in terms of routine, throughput and horizontal integration (the rapid translation of product candidates to preclinical and clinical development). Individual management decisions, limited funding and a lack of qualified production capacity can hinder the execution of such projects, but we also lack suitable technologies for sample handling and data management. This perspectives article will highlight current bottlenecks in PMF and offer potential solutions that combine PMF with existing technologies to build an integrated facility of the future for product development, testing, manufacturing and clinical translation. Ten major bottlenecks have been identified and are discussed in turn: automated cloning and simplified transformation options, reproducibility of bacterial cultivation, bioreactor integration with automated cell handling, options for rapid mid-scale candidate and product manufacturing, interconnection with (group-specific or personalized) clinical trials, diversity of (post-)infiltration conditions, development of downstream processing platforms, continuous process operation, compliance of manufacturing conditions with biosafety regulations, scaling requirements for cascading biomass.

Cell line development represents a crucial step in the development process of a therapeutic glycoprotein. Chinese hamster ovary (CHO) cells are the most frequently employed mammalian host cell system for the industrial manufacturing of biologics. The predominant application of CHO cells for heterologous recombinant protein expression lies in the relative simplicity of stably introducing ectopic DNA into the CHO host cell genome. Since CHO cells were first used as expression host for the industrial production of biologics in the late 1980s, stable genomic transgene integration has been achieved almost exclusively by random integration. Since then, random transgene integration had become the gold standard for generating stable CHO production cell lines due to a lack of viable alternatives. However, it was eventually demonstrated that this approach poses significant challenges on the cell line development process such as an increased risk of inducing cell line instability. In recent years, significant discoveries of new and highly potent (semi)-targeted transgene integration systems have paved the way for a technological revolution in the cell line development sector. These advanced methodologies comprise the application of transposase-, recombinase- or Cas9 nuclease-mediated site-specific genomic integration techniques, which enable a scarless transfer of the transgene expression cassette into transcriptionally active loci within the host cell genome. This review summarizes recent advancements in the field of transgene integration technologies for CHO cell line development and compare them to the established random integration approach. Moreover, advantages and limitations of (semi)-targeted integration techniques are discussed, and benefits and opportunities for the biopharmaceutical industry are outlined.

Metabolic burden is defined by the influence of genetic manipulation and environmental perturbations on the distribution of cellular resources. The rewiring of microbial metabolism for bio-based chemical production often leads to a metabolic burden, followed by adverse physiological effects, such as impaired cell growth and low product yields. Alleviating the burden imposed by undesirable metabolic changes has become an increasingly attractive approach for constructing robust microbial cell factories. In this review, we provide a brief overview of metabolic burden engineering, focusing specifically on recent developments and strategies for diminishing the burden while improving robustness and yield. A variety of examples are presented to showcase the promise of metabolic burden engineering in facilitating the design and construction of robust microbial cell factories. Finally, challenges and limitations encountered in metabolic burden engineering are discussed.

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.

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.

Anaerobic digestion (AD) has been proven to be an effective green technology for producing biomethane while reducing environmental pollution. The interspecies electron transfer (IET) processes in AD are critical for acetogenesis and methanogenesis, and these IET processes are carried out via mediated interspecies electron transfer (MIET) and direct interspecies electron transfer (DIET). The latter has recently become a topic of significant interest, considering its potential to allow diffusion-free electron transfer during the AD process steps. To date, different multi-heme c-type cytochromes, electrically conductive pili (e-pili), and other relevant accessories during DIET between microorganisms of different natures have been reported. Additionally, several studies have been carried out on metagenomics and metatranscriptomics for better detection of DIET, the role of DIET's stimulation in alleviating stressed conditions, such as high organic loading rates (OLR) and low pH, and the stimulation mechanisms of DIET in mixed cultures and co-cultures by various conductive materials. Keeping in view this significant research progress, this study provides in-depth insights into the DIET-active microbial community, DIET mechanisms of different species, utilization of various approaches for stimulating DIET, characterization approaches for effectively detecting DIET, and potential future research directions. This study can help accelerate the field's research progress, enable a better understanding of DIET in complex microbial communities, and allow its utilization to alleviate various inhibitions in complex AD processes.

In order to improve the flavor profiles, food security, probiotic effects and shorten the fermentation period of traditional fermented foods, lactic acid bacteria (LAB) were often considered as the ideal candidate to participate in the fermentation process. In general, LAB strains possessed the ability to develop flavor compounds via carbohydrate metabolism, protein hydrolysis and amino acid metabolism, lipid hydrolysis and fatty acid metabolism. Based on the functional properties to inhibit spoilage microbes, foodborne pathogens and fungi, those species could improve the safety properties and prolong the shelf life of fermented products. Meanwhile, influence of LAB on texture and functionality of fermented food were also involved in this review. As for the adverse effect carried by environmental challenges during fermentation process, engineering strategies based on exogenous addition, cross protection, and metabolic engineering to improve the robustness and of LAB were also discussed in this review. Besides, this review also summarized the potential strategies including microbial co-culture and metabolic engineering for improvement of fermentation performance in LAB strains. The authors hope this review could contribute to provide an understanding and insight into improving the industrial functionalities of LAB.

Cordyceps militaris, widely recognized as a medicinal and edible mushroom in East Asia, contains a variety of bioactive compounds, including cordycepin (COR), pentostatin (PTN) and other high-value compounds. This review explores the potential of developing C. militaris as a cell factory for the production of high-value chemicals and nutrients. This review comprehensively summarizes the fermentation advantages, metabolic networks, expression elements, and genome editing tools specific to C. militaris and discusses the challenges and barriers to further research on C. militaris across various fields, including computational biology, existing DNA elements, and genome editing approaches. This review aims to describe specific and promising opportunities for the in-depth study and development of C. militaris as a new chassis cell. Additionally, to increase the practicability of this review, examples of the construction of cell factories are provided, and promising strategies for synthetic biology development are illustrated.