Pub Date : 2026-05-01Epub Date: 2026-01-21DOI: 10.1016/j.biotechadv.2026.108809
Adam A. Aboalroub
Nuclear Magnetic Resonance (NMR) spectroscopy is a crucial tool in structural biology, uniquely capable of revealing protein structure, dynamics, and interactions at atomic resolution in environments that closely resemble native conditions. The combination of key methodological breakthroughs—including strategic isotopic labeling, stronger magnetic fields, cryogenic probes, and advanced pulse sequences—has established NMR as the definitive method for gaining atomic-level insights into complex biomolecules, especially pathogenic proteins involved in disease. These advances enable various NMR techniques, from high-resolution solution and solid-state NMR (ssNMR) for insoluble assemblies to in-cell NMR. Beyond structural analysis, NMR provides robust quantitative performance, high reproducibility, and rich structural information, making it a valuable platform for biomolecular analysis and metabolomics. This review aims to provide a comprehensive overview of these critical roles, with a particular emphasis on the transformative influence of integrating Artificial Intelligence (AI) into NMR techniques to accelerate metabolomics-based biomarker discovery for various diseases and conditions.
{"title":"Advances in NMR Spectroscopy for biological systems: Principles, techniques, and their growing scope","authors":"Adam A. Aboalroub","doi":"10.1016/j.biotechadv.2026.108809","DOIUrl":"10.1016/j.biotechadv.2026.108809","url":null,"abstract":"<div><div>Nuclear Magnetic Resonance (NMR) spectroscopy is a crucial tool in structural biology, uniquely capable of revealing protein structure, dynamics, and interactions at atomic resolution in environments that closely resemble native conditions. The combination of key methodological breakthroughs—including strategic isotopic labeling, stronger magnetic fields, cryogenic probes, and advanced pulse sequences—has established NMR as the definitive method for gaining atomic-level insights into complex biomolecules, especially pathogenic proteins involved in disease. These advances enable various NMR techniques, from high-resolution solution and solid-state NMR (ssNMR) for insoluble assemblies to in-cell NMR. Beyond structural analysis, NMR provides robust quantitative performance, high reproducibility, and rich structural information, making it a valuable platform for biomolecular analysis and metabolomics. This review aims to provide a comprehensive overview of these critical roles, with a particular emphasis on the transformative influence of integrating Artificial Intelligence (AI) into NMR techniques to accelerate metabolomics-based biomarker discovery for various diseases and conditions.</div></div>","PeriodicalId":8946,"journal":{"name":"Biotechnology advances","volume":"88 ","pages":"Article 108809"},"PeriodicalIF":12.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-05-01Epub Date: 2026-01-23DOI: 10.1016/j.biotechadv.2026.108810
Shu-Tong Wu , Xiao-Chuan Zheng , Chuan Chen , Zhong-Fang Sun , Kai-Kai Wu , De-Feng Xing , Shan-Shan Yang , Ai-Jie Wang , Nan-Qi Ren , Lei Zhao
The bioconversion of carbon dioxide (CO2) into polyhydroxyalkanoates (PHAs) represents a transformative paradigm at the nexus of climate mitigation and sustainable manufacturing, offering a route to valorize a greenhouse gas (GHG) liability into high-value, biodegradable polymers. This critical review provides a systematic analysis of the technological landscape for CO2-to-PHA bioconversion, comparing the two dominant strategies: direct, single-organism autotrophic routes and modular, two-step hybrid systems that couple abiotic CO2 reduction with microbial fermentation. While direct autotrophic processes offer conceptual simplicity, they exhibit a wide performance gap: photoautotrophs are typically constrained by low volumetric productivities (<10 mg L−1 h−1) due to light limitation, whereas optimized chemoautotrophic systems (e.g., Cupriavidus necator) can achieve significantly higher rates of up to 1.55 g L−1 h−1. In contrast, two-step hybrid systems show promise for modularity by decoupling CO2 activation from biosynthesis. However, current integrated platforms generally demonstrate productivities in the milligram range (e.g., <25 mg L−1 h−1). Critical bottlenecks, specifically inefficient gas-liquid mass transfer (low kLa), catalyst instability (<100 h lifetime), and the high energy penalty of downstream separation, persist across all platforms. Currently keeping production costs ($3–8/kg) well above the economic threshold. The path forward requires a strategic roadmap focused on three pillars: dynamic metabolic control via synthetic biology, process intensification using advanced reactor engineering, and holistic system integration. The successful convergence of these disciplines, supported by robust techno-economic frameworks and life-cycle assessments, is critical to transforming CO2-to-PHA bioconversion from a promising concept into a cornerstone technology for the circular bioeconomy.
{"title":"Biomanufacturing polyhydroxyalkanoates from CO2: A critical review of advances, challenges, and solutions for autotrophic and hybrid systems","authors":"Shu-Tong Wu , Xiao-Chuan Zheng , Chuan Chen , Zhong-Fang Sun , Kai-Kai Wu , De-Feng Xing , Shan-Shan Yang , Ai-Jie Wang , Nan-Qi Ren , Lei Zhao","doi":"10.1016/j.biotechadv.2026.108810","DOIUrl":"10.1016/j.biotechadv.2026.108810","url":null,"abstract":"<div><div>The bioconversion of carbon dioxide (CO<sub>2</sub>) into polyhydroxyalkanoates (PHAs) represents a transformative paradigm at the nexus of climate mitigation and sustainable manufacturing, offering a route to valorize a greenhouse gas (GHG) liability into high-value, biodegradable polymers. This critical review provides a systematic analysis of the technological landscape for CO<sub>2</sub>-to-PHA bioconversion, comparing the two dominant strategies: direct, single-organism autotrophic routes and modular, two-step hybrid systems that couple abiotic CO<sub>2</sub> reduction with microbial fermentation. While direct autotrophic processes offer conceptual simplicity, they exhibit a wide performance gap: photoautotrophs are typically constrained by low volumetric productivities (<10 mg L<sup>−1</sup> h<sup>−1</sup>) due to light limitation, whereas optimized chemoautotrophic systems (e.g., <em>Cupriavidus necator</em>) can achieve significantly higher rates of up to 1.55 g L<sup>−1</sup> h<sup>−1</sup>. In contrast, two-step hybrid systems show promise for modularity by decoupling CO<sub>2</sub> activation from biosynthesis. However, current integrated platforms generally demonstrate productivities in the milligram range (e.g., <25 mg L<sup>−1</sup> h<sup>−1</sup>). Critical bottlenecks, specifically inefficient gas-liquid mass transfer (low <em>k</em><sub>L</sub><em>a</em>), catalyst instability (<100 h lifetime), and the high energy penalty of downstream separation, persist across all platforms. Currently keeping production costs ($3–8/kg) well above the economic threshold. The path forward requires a strategic roadmap focused on three pillars: dynamic metabolic control via synthetic biology, process intensification using advanced reactor engineering, and holistic system integration. The successful convergence of these disciplines, supported by robust techno-economic frameworks and life-cycle assessments, is critical to transforming CO<sub>2</sub>-to-PHA bioconversion from a promising concept into a cornerstone technology for the circular bioeconomy.</div></div>","PeriodicalId":8946,"journal":{"name":"Biotechnology advances","volume":"88 ","pages":"Article 108810"},"PeriodicalIF":12.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-05-01Epub Date: 2026-02-03DOI: 10.1016/j.biotechadv.2026.108814
Charandatta Muddana, Binbin Wang, Pei-Ti Sun, Yinjie J. Tang
Large language models (LLMs) are transforming how biotechnology review articles are conceived and written. This study evaluates LLMs in generating scientific review articles across three case studies with the focus on biomanufacturing and microbiology. Structured prompts were used across models under “Deep Research” modes, and both statistical and manual analysis (e.g., writing structure, citation metrics, critical depth, citation validity, and hallucination rate) were conducted. Results revealed that while LLMs can summarize large volumes of literature and generate structured outputs with coherent flow and illustrative tables, they fell short in critical analysis, quantitative reasoning, and citation reliability. “Pro” versions of LLMs produced more accurate and extensive citations than base versions, though issues of redundancy, bias toward specific Open Access publishers (e.g., MDPI, Frontiers), and occasional fabrication persisted. Among models, GPT-5 Pro was deeper in its analysis but had fewer citations. Gemini 2.5 Pro and the Perplexity Pro search engine retrieved broader literature with limited critique. Qwen 3 Max and DeepSeek R1 offered moderate balance, with the latter having higher hallucination rates in the application programming interface (API) version. Overall, “Pro” versions of LLMs generate decent summaries of reviewing topics but lack scholarly standards of novelty, rigor, and citation accuracy. Strategic integration and cross-validation of LLMs improve the quality of review papers. The findings underscore the need for new opinions, quantitative analysis or simulation, and interdisciplinary knowledge synthesis to publish valuable biotechnology reviews.
{"title":"Comparative evaluation of large language models for biotechnology review writing","authors":"Charandatta Muddana, Binbin Wang, Pei-Ti Sun, Yinjie J. Tang","doi":"10.1016/j.biotechadv.2026.108814","DOIUrl":"10.1016/j.biotechadv.2026.108814","url":null,"abstract":"<div><div>Large language models (LLMs) are transforming how biotechnology review articles are conceived and written. This study evaluates LLMs in generating scientific review articles across three case studies with the focus on biomanufacturing and microbiology. Structured prompts were used across models under “Deep Research” modes, and both statistical and manual analysis (e.g., writing structure, citation metrics, critical depth, citation validity, and hallucination rate) were conducted. Results revealed that while LLMs can summarize large volumes of literature and generate structured outputs with coherent flow and illustrative tables, they fell short in critical analysis, quantitative reasoning, and citation reliability. “Pro” versions of LLMs produced more accurate and extensive citations than base versions, though issues of redundancy, bias toward specific Open Access publishers (e.g., MDPI, Frontiers), and occasional fabrication persisted. Among models, GPT-5 Pro was deeper in its analysis but had fewer citations. Gemini 2.5 Pro and the Perplexity Pro search engine retrieved broader literature with limited critique. Qwen 3 Max and DeepSeek R1 offered moderate balance, with the latter having higher hallucination rates in the application programming interface (API) version. Overall, “Pro” versions of LLMs generate decent summaries of reviewing topics but lack scholarly standards of novelty, rigor, and citation accuracy. Strategic integration and cross-validation of LLMs improve the quality of review papers. The findings underscore the need for new opinions, quantitative analysis or simulation, and interdisciplinary knowledge synthesis to publish valuable biotechnology reviews.</div></div>","PeriodicalId":8946,"journal":{"name":"Biotechnology advances","volume":"88 ","pages":"Article 108814"},"PeriodicalIF":12.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-05-01Epub Date: 2026-01-31DOI: 10.1016/j.biotechadv.2026.108819
Qianmao Wen , Xinyu Li , Jiaxing Song , Junlin Xu , Yajie Meng , Leyi Wei , Zilong Zhang , Quan Zou , Feifei Cui
Signal peptides are short amino acid sequences located at the N-terminus of proteins. They guide newly synthesized proteins to their correct cellular destinations, playing a crucial role in protein localization and transport. Traditional experimental methods for identifying signal peptides are typically time-consuming, costly, and labor-intensive, driving rapid development of computational alternatives. Over the past two decades, researchers have proposed various computational approaches, with prediction accuracy continuously improving through evolution from early statistical and rule-based algorithms to deep learning. In this review, we systematically summarize these computational approaches, emphasizing methodological evolution and framework design. We compile representative computational methods, comparing their prediction outcomes and identifying existing limitations. Finally, we discuss current challenges and emerging opportunities, aiming to advance the development of computational frameworks characterized by unified evaluation, biologically grounded interpretation, and generative modeling.
{"title":"Computational methods for signal peptide prediction: From statistical models to deep learning","authors":"Qianmao Wen , Xinyu Li , Jiaxing Song , Junlin Xu , Yajie Meng , Leyi Wei , Zilong Zhang , Quan Zou , Feifei Cui","doi":"10.1016/j.biotechadv.2026.108819","DOIUrl":"10.1016/j.biotechadv.2026.108819","url":null,"abstract":"<div><div>Signal peptides are short amino acid sequences located at the N-terminus of proteins. They guide newly synthesized proteins to their correct cellular destinations, playing a crucial role in protein localization and transport. Traditional experimental methods for identifying signal peptides are typically time-consuming, costly, and labor-intensive, driving rapid development of computational alternatives. Over the past two decades, researchers have proposed various computational approaches, with prediction accuracy continuously improving through evolution from early statistical and rule-based algorithms to deep learning. In this review, we systematically summarize these computational approaches, emphasizing methodological evolution and framework design. We compile representative computational methods, comparing their prediction outcomes and identifying existing limitations. Finally, we discuss current challenges and emerging opportunities, aiming to advance the development of computational frameworks characterized by unified evaluation, biologically grounded interpretation, and generative modeling.</div></div>","PeriodicalId":8946,"journal":{"name":"Biotechnology advances","volume":"88 ","pages":"Article 108819"},"PeriodicalIF":12.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-05-01Epub Date: 2026-02-18DOI: 10.1016/j.biotechadv.2026.108846
Boting Li, Weifeng Liu
Synthetic microbial consortia (SMCs) represent a paradigm shift from monocultures to multi-strain systems that leverage ecological interactions for enhanced environmental adaptation and bioproduction. This review systematically sorts out engineering strategies for constructing stable SMCs, focusing on three core principles regarding host selection based on obligate mutualism (e.g., auxotrophs), pathway modularization to resolve metabolic conflicts, and dynamic regulation using tools like quorum sensing and optogenetics. We demonstrate the efficacy of SMCs in diverse applications including high-value compound synthesis and lignocellulosic biomass conversion through consolidated bioprocessing and inhibitor mitigation. SMCs enabling advanced functions in engineered living materials, environmental remediation, and biomedical innovation via division of labor are also described. Despite such progress, challenges in scalability and real-time control of SMCs under industrial conditions remain. We conclude that SMCs serve to bridge evolutionary ecology and biotechnology, offering robust solutions for sustainable biomanufacturing and beyond.
{"title":"Engineering microbial consortia for biosynthesis: Construction, regulation, and applications","authors":"Boting Li, Weifeng Liu","doi":"10.1016/j.biotechadv.2026.108846","DOIUrl":"10.1016/j.biotechadv.2026.108846","url":null,"abstract":"<div><div>Synthetic microbial consortia (SMCs) represent a paradigm shift from monocultures to multi-strain systems that leverage ecological interactions for enhanced environmental adaptation and bioproduction. This review systematically sorts out engineering strategies for constructing stable SMCs, focusing on three core principles regarding host selection based on obligate mutualism (e.g., auxotrophs), pathway modularization to resolve metabolic conflicts, and dynamic regulation using tools like quorum sensing and optogenetics. We demonstrate the efficacy of SMCs in diverse applications including high-value compound synthesis and lignocellulosic biomass conversion through consolidated bioprocessing and inhibitor mitigation. SMCs enabling advanced functions in engineered living materials, environmental remediation, and biomedical innovation via division of labor are also described. Despite such progress, challenges in scalability and real-time control of SMCs under industrial conditions remain. We conclude that SMCs serve to bridge evolutionary ecology and biotechnology, offering robust solutions for sustainable biomanufacturing and beyond.</div></div>","PeriodicalId":8946,"journal":{"name":"Biotechnology advances","volume":"88 ","pages":"Article 108846"},"PeriodicalIF":12.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146257221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-05-01Epub Date: 2026-02-03DOI: 10.1016/j.biotechadv.2026.108833
Qian Liu , Qiu-Jun Liu , Shaohua Kang , Jiawei Li , Haotian Xia , Hao Qi
DNA has emerged as a highly promising next-generation data storage medium, offering unprecedented density, long-term durability, and low energy consumption compared to conventional storage technologies. Despite rapid advances, significant challenges remain across the entire spectrum of data storage scenarios—ranging from deep cold archival storage to hot, real-time data processing. For deep cold data, where data may not be accessed for decades, the primary requirements are extremely long-term preservation and maximized physical information density. Strategies such as silica encapsulation, porous photonic microspheres, and robust microbial chassis have demonstrated theoretical storage lifespans of centuries to millennia. In cold data scenarios, where files are accessed periodically, the ability to perform random access and repeated retrievals without data loss is critical. Advances in molecular indexing, microfluidic partitioning, and low-bias isothermal amplification offer promising solutions, while cellular systems provide a low-cost platform for repetitive readouts via high-fidelity replication. Warm and hot data storage presents the greatest technical barriers, including insufficient read/write throughput, high latency, and the lack of native support for dynamic data operations. Bridging this gap requires integration with automated, scalable molecular systems and further improvements in speed and reusability. Therefore, enabling the practical application of DNA storage across deep cold, cold, warm, and hot data scenarios will require sustained interdisciplinary efforts in molecular design, storage media engineering, and data retrieval strategies, along with system-level integration to meet the diverse demands of long-term durability, random access, and real-time responsiveness.
{"title":"From deep archival to real-time applications: Challenges and opportunities in DNA data storage","authors":"Qian Liu , Qiu-Jun Liu , Shaohua Kang , Jiawei Li , Haotian Xia , Hao Qi","doi":"10.1016/j.biotechadv.2026.108833","DOIUrl":"10.1016/j.biotechadv.2026.108833","url":null,"abstract":"<div><div>DNA has emerged as a highly promising next-generation data storage medium, offering unprecedented density, long-term durability, and low energy consumption compared to conventional storage technologies. Despite rapid advances, significant challenges remain across the entire spectrum of data storage scenarios—ranging from deep cold archival storage to hot, real-time data processing. For deep cold data, where data may not be accessed for decades, the primary requirements are extremely long-term preservation and maximized physical information density. Strategies such as silica encapsulation, porous photonic microspheres, and robust microbial chassis have demonstrated theoretical storage lifespans of centuries to millennia. In cold data scenarios, where files are accessed periodically, the ability to perform random access and repeated retrievals without data loss is critical. Advances in molecular indexing, microfluidic partitioning, and low-bias isothermal amplification offer promising solutions, while cellular systems provide a low-cost platform for repetitive readouts via high-fidelity replication. Warm and hot data storage presents the greatest technical barriers, including insufficient read/write throughput, high latency, and the lack of native support for dynamic data operations. Bridging this gap requires integration with automated, scalable molecular systems and further improvements in speed and reusability. Therefore, enabling the practical application of DNA storage across deep cold, cold, warm, and hot data scenarios will require sustained interdisciplinary efforts in molecular design, storage media engineering, and data retrieval strategies, along with system-level integration to meet the diverse demands of long-term durability, random access, and real-time responsiveness.</div></div>","PeriodicalId":8946,"journal":{"name":"Biotechnology advances","volume":"88 ","pages":"Article 108833"},"PeriodicalIF":12.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-05-01Epub Date: 2026-01-19DOI: 10.1016/j.biotechadv.2026.108806
Yi Shi , Lefei Wang , Yao Chen , Ling Jiang
Covalent bond–forming peptide tagging systems have emerged as powerful and versatile tools across a broad spectrum of biological and biotechnological applications. This review systematically summarizes the origins, molecular mechanisms of intramolecular covalent bond formation, major classes, and design strategies of peptide tagging systems. Based on their underlying chemistry, current systems are primarily categorized into isopeptide-bond-based and ester-bond-based platforms, both of which have demonstrated prominent utility in protein cyclization as well as in vivo and in vitro multi-enzyme assembly. Beyond these applications, isopeptide-bond-forming systems have been widely adopted as robust purification tags, whereas ester-bond-based systems offer unique opportunities for pH-responsive modulation of enzyme activity. Collectively, peptide tagging systems based on either isopeptide or ester bond formation represent an expanding and highly efficient toolkit for biotechnology. Continued advances in their design and application are expected to further broaden their functional scope and provide innovative solutions for future developments in protein engineering and related fields.
{"title":"Recent advances in the covalent-bond-based peptide tagging systems and their applications","authors":"Yi Shi , Lefei Wang , Yao Chen , Ling Jiang","doi":"10.1016/j.biotechadv.2026.108806","DOIUrl":"10.1016/j.biotechadv.2026.108806","url":null,"abstract":"<div><div>Covalent bond–forming peptide tagging systems have emerged as powerful and versatile tools across a broad spectrum of biological and biotechnological applications. This review systematically summarizes the origins, molecular mechanisms of intramolecular covalent bond formation, major classes, and design strategies of peptide tagging systems. Based on their underlying chemistry, current systems are primarily categorized into isopeptide-bond-based and ester-bond-based platforms, both of which have demonstrated prominent utility in protein cyclization as well as <em>in vivo</em> and <em>in vitro</em> multi-enzyme assembly. Beyond these applications, isopeptide-bond-forming systems have been widely adopted as robust purification tags, whereas ester-bond-based systems offer unique opportunities for pH-responsive modulation of enzyme activity. Collectively, peptide tagging systems based on either isopeptide or ester bond formation represent an expanding and highly efficient toolkit for biotechnology. Continued advances in their design and application are expected to further broaden their functional scope and provide innovative solutions for future developments in protein engineering and related fields.</div></div>","PeriodicalId":8946,"journal":{"name":"Biotechnology advances","volume":"88 ","pages":"Article 108806"},"PeriodicalIF":12.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146000541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-05-01Epub Date: 2026-01-26DOI: 10.1016/j.biotechadv.2026.108812
Xuenan Shui , Chen Deng , Xiaoman He , Daolun Liang , Dekui Shen , Wangbiao Guo , Wenlei Zhu , Xue Ning , Richen Lin
Semi-artificial photosynthesis, integrating biocatalysts with photosensitive materials to enable self-photosensitization in non-photosynthetic microorganisms, is a rapidly evolving interdisciplinary field for solar-driven energy and chemical production using air, water, and sunlight. However, the efficiency of such constructed biocatalysts is often impeded by the limited biocompatibility, prevalent biotoxicity, and narrow spectral response associated with photosensitive materials. Quantum dots (QDs), zero-dimensional crystals, exhibit favorable photoexcitation properties and enhanced biocompatibility, providing essential reducing equivalents for microbial metabolisms. This review examines recent advances in semi-artificial photosynthesis, focusing on the self-assembly of microorganisms in conjunction with QDs. It highlights the biocompatible, directional design of QDs and explores the underlying mechanisms of electron and energy transfer within the microbe-QDs complexes. By leveraging the synergies of solar absorption and biocatalytic activity, this review discusses the future trajectory and potential improvements in semi-artificial photosynthesis, offering a paradigm-shifting approach to sustainable solar energy utilization. The solar-powered QDs-biocatalyst biohybrids for semi-artificial photosynthesis are projected to emerge as a transformative technology in advanced energy production.
{"title":"Solar-powered quantum dot-biocatalyst biohybrids for semi-artificial photosynthesis: Advances in interfacial design and energy-mass transfer optimisation","authors":"Xuenan Shui , Chen Deng , Xiaoman He , Daolun Liang , Dekui Shen , Wangbiao Guo , Wenlei Zhu , Xue Ning , Richen Lin","doi":"10.1016/j.biotechadv.2026.108812","DOIUrl":"10.1016/j.biotechadv.2026.108812","url":null,"abstract":"<div><div>Semi-artificial photosynthesis, integrating biocatalysts with photosensitive materials to enable self-photosensitization in non-photosynthetic microorganisms, is a rapidly evolving interdisciplinary field for solar-driven energy and chemical production using air, water, and sunlight. However, the efficiency of such constructed biocatalysts is often impeded by the limited biocompatibility, prevalent biotoxicity, and narrow spectral response associated with photosensitive materials. Quantum dots (QDs), zero-dimensional crystals, exhibit favorable photoexcitation properties and enhanced biocompatibility, providing essential reducing equivalents for microbial metabolisms. This review examines recent advances in semi-artificial photosynthesis, focusing on the self-assembly of microorganisms in conjunction with QDs. It highlights the biocompatible, directional design of QDs and explores the underlying mechanisms of electron and energy transfer within the microbe-QDs complexes. By leveraging the synergies of solar absorption and biocatalytic activity, this review discusses the future trajectory and potential improvements in semi-artificial photosynthesis, offering a paradigm-shifting approach to sustainable solar energy utilization. The solar-powered QDs-biocatalyst biohybrids for semi-artificial photosynthesis are projected to emerge as a transformative technology in advanced energy production.</div></div>","PeriodicalId":8946,"journal":{"name":"Biotechnology advances","volume":"88 ","pages":"Article 108812"},"PeriodicalIF":12.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-05-01Epub Date: 2026-02-12DOI: 10.1016/j.biotechadv.2026.108844
Yupeng Nie , Jiayuan Liang , Ruiguo Li , Mingjing Yao , Xuebing Ren , Zhiqiang Xiong , Guangqiang Wang , Lianzhong Ai , Yanjun Tian
Erythritol is a four‑carbon sugar alcohol that is naturally synthesized by various microorganisms as an osmotic pressure protectant. Given its health attributes, such as natural origin and zero calories, the demand for erythritol as a sweetener in the food industry has rapidly increased. Microbial fermentation is currently the primary method for producing erythritol but faces technical bottlenecks, such as high raw material costs, low fermentation efficiency, and byproduct accumulation. This paper systematically reviews the research progress and cutting-edge strategies for enhancing the efficiency of erythritol synthesis from multiple perspectives, including the selection and reconstruction of chassis cells, exploration and modification of key enzyme elements, refined design and modification of metabolic modules, system-level metabolic network analyses and intelligent breeding, and circular biomanufacturing systems. It provides an in-depth analysis of key technologies and innovative approaches at each level, offering a forward-looking perspective on future research directions. This paper aims to provide a theoretical foundation for constructing efficient microbial cell factories and promoting the green and low-carbon manufacturing of erythritol.
{"title":"Advances in the microbial production of erythritol: From synthetic biological foundations to circular biomanufacturing","authors":"Yupeng Nie , Jiayuan Liang , Ruiguo Li , Mingjing Yao , Xuebing Ren , Zhiqiang Xiong , Guangqiang Wang , Lianzhong Ai , Yanjun Tian","doi":"10.1016/j.biotechadv.2026.108844","DOIUrl":"10.1016/j.biotechadv.2026.108844","url":null,"abstract":"<div><div>Erythritol is a four‑carbon sugar alcohol that is naturally synthesized by various microorganisms as an osmotic pressure protectant. Given its health attributes, such as natural origin and zero calories, the demand for erythritol as a sweetener in the food industry has rapidly increased. Microbial fermentation is currently the primary method for producing erythritol but faces technical bottlenecks, such as high raw material costs, low fermentation efficiency, and byproduct accumulation. This paper systematically reviews the research progress and cutting-edge strategies for enhancing the efficiency of erythritol synthesis from multiple perspectives, including the selection and reconstruction of chassis cells, exploration and modification of key enzyme elements, refined design and modification of metabolic modules, system-level metabolic network analyses and intelligent breeding, and circular biomanufacturing systems. It provides an in-depth analysis of key technologies and innovative approaches at each level, offering a forward-looking perspective on future research directions. This paper aims to provide a theoretical foundation for constructing efficient microbial cell factories and promoting the green and low-carbon manufacturing of erythritol.</div></div>","PeriodicalId":8946,"journal":{"name":"Biotechnology advances","volume":"88 ","pages":"Article 108844"},"PeriodicalIF":12.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146161044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-05-01Epub Date: 2026-02-04DOI: 10.1016/j.biotechadv.2026.108837
Xiaoxiao Jiang , Yujie Wang , Zhanyu Wang , Xu Yang , Yuguang Mu , Rui Zhai , Tao Wei , Mingjie Jin
The recalcitrance of lignocellulosic biomass, stemming from its complex cellulose-hemicellulose-lignin matrix, remains the primary techno-economic bottleneck in sugar-platform biorefineries. Surfactants have emerged as versatile process-intensifying agents capable of overcoming these interfacial and chemical barriers. While previous reviews have largely focused on macroscopic yield improvements, a critical synthesis elucidating the molecular-level surfactant-biomass-enzyme interplay is lacking. This review provides a comprehensive analysis of surfactant-mediated mechanisms across both pretreatment and enzymatic hydrolysis. Uniquely, we highlight the role of surfactants beyond physical dominance, detailing their capacity to induce in-situ chemical modifications of lignin during pretreatment. Mechanisms such as surfactant grafting via α-etherification, phenolic hydroxyl blocking, and C5 position stabilization are critically examined for their roles in preventing lignin condensation and mitigating downstream enzyme inhibition. Furthermore, we elucidate how surfactants modulate interfacial phenomena during hydrolysis, from shielding non-productive lignin adsorption sites to stabilizing enzyme conformation against shear and thermal stresses. Finally, the review outlines a roadmap for transitioning from empirical screening to the rational design of sustainable, multi-functional surfactants, emphasizing their integration into closed-loop biorefinery processes.
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