Pub Date : 2024-08-20DOI: 10.1016/j.copbio.2024.103179
Despite success in treating hematologic malignancies, chimeric antigen receptor-T cell (CAR-T) therapy still faces multiple challenges that have halted progress, especially against solid tumors. Recent advances in nanoscale engineeirng provide several avenues for overcoming these challenges, including more efficienct programming of CAR-Ts ex vivo, promoting immune responsiveness in the tumor microenvironment (TME) in vivo, and boosting CAR-T function in situ. Here, we summarize recent innovations that leverage nanotechnology to directly address the major obstacles that impede CAR-T therapy from reaching its full potential across various cancer types. We conclude with a commentary on the state of the field and how nanotechnology can shape the future of CAR-T and adoptive cell therapy in immuno-oncology.
{"title":"Jump-starting chimeric antigen receptor-T cells to go the extra mile with nanotechnology","authors":"","doi":"10.1016/j.copbio.2024.103179","DOIUrl":"10.1016/j.copbio.2024.103179","url":null,"abstract":"<div><p>Despite success in treating hematologic malignancies, chimeric antigen receptor-T cell (CAR-T) therapy still faces multiple challenges that have halted progress, especially against solid tumors. Recent advances in nanoscale engineeirng provide several avenues for overcoming these challenges, including more efficienct programming of CAR-Ts <em>ex vivo</em>, promoting immune responsiveness in the tumor microenvironment (TME) <em>in vivo</em>, and boosting CAR-T function <em>in situ</em>. Here, we summarize recent innovations that leverage nanotechnology to directly address the major obstacles that impede CAR-T therapy from reaching its full potential across various cancer types. We conclude with a commentary on the state of the field and how nanotechnology can shape the future of CAR-T and adoptive cell therapy in immuno-oncology.</p></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0958166924001150/pdfft?md5=c73b9469a2059f2e314a0128c61443fe&pid=1-s2.0-S0958166924001150-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142012207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-15DOI: 10.1016/j.copbio.2024.103181
Phosphorus (P) enrichment of water impairs its quality by stimulating algal growth and eutrophication, affecting an estimated 1.7 billion people. Remediation costs are substantial, estimated at $1 billion annually in Europe and $2.4 billion in the USA. Agricultural intensification over the past 50 years has increased P use brought into the system from mined fertiliser sources. This has enriched soil P concentrations and loss to surface waters via pathways such as surface runoff and subsurface flow, which are influenced by precipitation, slope, and farming practices. Effective mitigation of losses involves managing P sources, mobilisation, and transport/delivery mechanisms. The cost-effectiveness of mitigation actions can be improved if they are targeted to critical source areas (CSAs), which are small zones that disproportionately contribute to P loss. While targeting CSAs works well in areas with variable topography, flatter landscapes require managing legacy sources, such as enriched soil P to prevent P losses.
磷 (P) 富集会刺激藻类生长和水体富营养化,从而损害水质,估计影响到 17 亿人。修复成本巨大,估计欧洲每年需要 10 亿美元,美国每年需要 24 亿美元。过去 50 年的农业集约化增加了从采矿肥料来源进入系统的钾用量。这增加了土壤中的钾浓度,并通过地表径流和地下流动等途径流失到地表水中,而地表径流和地下流动又受到降水、坡度和耕作方式的影响。要有效减少损失,就必须对 P 的来源、动员和运输/输送机制进行管理。如果针对关键源区(CSA)采取减缓行动,则可提高成本效益,关键源区是指造成过多 P 损失的小区域。在地形多变的地区,以关键源区为目标效果很好,而在地势较平坦的地区,则需要管理遗留源,如富集土壤中的钾,以防止钾流失。
{"title":"Reducing phosphorus losses from agricultural land to surface water","authors":"","doi":"10.1016/j.copbio.2024.103181","DOIUrl":"10.1016/j.copbio.2024.103181","url":null,"abstract":"<div><p>Phosphorus (P) enrichment of water impairs its quality by stimulating algal growth and eutrophication, affecting an estimated 1.7 billion people. Remediation costs are substantial, estimated at $1 billion annually in Europe and $2.4 billion in the USA. Agricultural intensification over the past 50 years has increased P use brought into the system from mined fertiliser sources. This has enriched soil P concentrations and loss to surface waters via pathways such as surface runoff and subsurface flow, which are influenced by precipitation, slope, and farming practices. Effective mitigation of losses involves managing P sources, mobilisation, and transport/delivery mechanisms. The cost-effectiveness of mitigation actions can be improved if they are targeted to critical source areas (CSAs), which are small zones that disproportionately contribute to P loss. While targeting CSAs works well in areas with variable topography, flatter landscapes require managing legacy sources, such as enriched soil P to prevent P losses.</p></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0958166924001174/pdfft?md5=aa0d8f95b7dd6423868a4643ffb7db69&pid=1-s2.0-S0958166924001174-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141991270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-09DOI: 10.1016/j.copbio.2024.103174
Single-cell multi-omics and spatial technology have been widely applied to biomedical studies and recently to environmental studies. The cell size detected by single-cell omics ranges from ∼2 µm (e.g., Bacillus subtilis) to ∼120 µm (e.g., human oocytes). Simultaneous detection of single-cell multi-omics is available to human and plant tissues while limited to microbial samples. Spatial technology enables mapping the detected biomolecules in situ. The recent advances in Matrix-Assisted Laser Desorption/Ionization-Mass Spectrometry Imaging and Micro/Nanodroplet Processing in One Pot for Trace Samples for the first time allow the application of spatial multi-omics in highly heterogeneous environmental samples composed of plants, fungi, and bacteria. We envision that these technologies will continue to advance our understanding of unique cell types, their developmental trajectory, and the intercellular signaling and interaction within biological samples.
{"title":"From single cell to spatial multi-omics: unveiling molecular mechanisms in dynamic and heterogeneous systems","authors":"","doi":"10.1016/j.copbio.2024.103174","DOIUrl":"10.1016/j.copbio.2024.103174","url":null,"abstract":"<div><p>Single-cell multi-omics and spatial technology have been widely applied to biomedical studies and recently to environmental studies. The cell size detected by single-cell omics ranges from ∼2 µm (e.g., <em>Bacillus subtilis</em>) to ∼120 µm (e.g., human oocytes). Simultaneous detection of single-cell multi-omics is available to human and plant tissues while limited to microbial samples. Spatial technology enables mapping the detected biomolecules <em>in situ</em>. The recent advances in Matrix-Assisted Laser Desorption/Ionization-Mass Spectrometry Imaging and Micro/Nanodroplet Processing in One Pot for Trace Samples for the first time allow the application of spatial multi-omics in highly heterogeneous environmental samples composed of plants, fungi, and bacteria. We envision that these technologies will continue to advance our understanding of unique cell types, their developmental trajectory, and the intercellular signaling and interaction within biological samples.</p></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141912104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-08-05DOI: 10.1016/j.copbio.2024.103175
In recent years, the rapid advancement of generative artificial intelligence (GenAI) has revolutionized the landscape of drug design, offering innovative solutions to potentially expedite the discovery of novel therapeutics. GenAI encompasses algorithms and models that autonomously create new data, including text, images, and molecules, often mirroring characteristics of existing datasets. This comprehensive review delves into the realm of GenAI for drug design, emphasizing recent advancements and methodologies that have propelled the field forward. Specifically, we focus on three prominent paradigms: transformers, diffusion models, and reinforcement learning algorithms, which have been exceptionally impactful in the last few years. By synthesizing insights from a myriad of studies and developments, we elucidate the potential of these approaches in accelerating the drug discovery process. Through a detailed analysis, we explore the current state and future directions of GenAI in the context of drug design, highlighting its transformative impact on pharmaceutical research and development.
{"title":"Generative artificial intelligence for small molecule drug design","authors":"","doi":"10.1016/j.copbio.2024.103175","DOIUrl":"10.1016/j.copbio.2024.103175","url":null,"abstract":"<div><p>In recent years, the rapid advancement of generative artificial intelligence (GenAI) has revolutionized the landscape of drug design, offering innovative solutions to potentially expedite the discovery of novel therapeutics. GenAI encompasses algorithms and models that autonomously create new data, including text, images, and molecules, often mirroring characteristics of existing datasets. This comprehensive review delves into the realm of GenAI for drug design, emphasizing recent advancements and methodologies that have propelled the field forward. Specifically, we focus on three prominent paradigms: transformers, diffusion models, and reinforcement learning algorithms, which have been exceptionally impactful in the last few years. By synthesizing insights from a myriad of studies and developments, we elucidate the potential of these approaches in accelerating the drug discovery process. Through a detailed analysis, we explore the current state and future directions of GenAI in the context of drug design, highlighting its transformative impact on pharmaceutical research and development.</p></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141897023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-08-05DOI: 10.1016/j.copbio.2024.103177
The advent of highly efficient genome editing (GE) tools, coupled with high-throughput genome sequencing, has paved the way for the accelerated domestication of crop wild relatives. New crops could thus be rapidly created that are well adapted to cope with drought, flooding, soil salinity, or insect damage. De novo domestication avoids the complexity of transferring polygenic stress resistance from wild species to crops. Instead, new crops can be created by manipulating major genes in stress-resistant wild species. However, the genetic basis of certain relevant domestication-related traits often involve epistasis and pleiotropy. Furthermore, pan-genome analyses show that structural variation driving gene expression changes has been selected during domestication. A growing body of work suggests that the Solanaceae family, which includes crop species such as tomatoes, potatoes, eggplants, peppers, and tobacco, is a suitable model group to dissect these phenomena and operate changes in wild relatives to improve agronomic traits rapidly with GE. We briefly discuss the prospects of this exciting novel field in the interface between fundamental and applied plant biology and its potential impact in the coming years.
{"title":"De novo domestication in the Solanaceae: advances and challenges","authors":"","doi":"10.1016/j.copbio.2024.103177","DOIUrl":"10.1016/j.copbio.2024.103177","url":null,"abstract":"<div><p>The advent of highly efficient genome editing (GE) tools, coupled with high-throughput genome sequencing, has paved the way for the accelerated domestication of crop wild relatives. New crops could thus be rapidly created that are well adapted to cope with drought, flooding, soil salinity, or insect damage. <em>De novo</em> domestication avoids the complexity of transferring polygenic stress resistance from wild species to crops. Instead, new crops can be created by manipulating major genes in stress-resistant wild species. However, the genetic basis of certain relevant domestication-related traits often involve epistasis and pleiotropy. Furthermore, pan-genome analyses show that structural variation driving gene expression changes has been selected during domestication. A growing body of work suggests that the Solanaceae family, which includes crop species such as tomatoes, potatoes, eggplants, peppers, and tobacco, is a suitable model group to dissect these phenomena and operate changes in wild relatives to improve agronomic traits rapidly with GE. We briefly discuss the prospects of this exciting novel field in the interface between fundamental and applied plant biology and its potential impact in the coming years.</p></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141897022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-08-03DOI: 10.1016/j.copbio.2024.103178
Lignin valorization faces persistent biomanufacturing challenges due to the heterogeneous and toxic carbon substrates derived from lignin depolymerization. To address the heterogeneous nature of aromatic feedstocks, plant cell wall engineering and ‘lignin first’ pretreatment methods have recently emerged. Next, to convert the resulting aromatic substrates into value-added chemicals, diverse microbial host systems also continue to be developed. This includes microbes that (1) lack aromatic metabolism, (2) metabolize aromatics but not sugars, and (3) co-metabolize both aromatics and sugars, each system presenting unique pros and cons. Considering the intrinsic complexity of lignin-derived substrate mixtures, emerging and non-model microbes with native metabolism for aromatics appear poised to provide the greatest impacts on lignin valorization via biomanufacturing.
{"title":"Biomanufacturing of value-added chemicals from lignin","authors":"","doi":"10.1016/j.copbio.2024.103178","DOIUrl":"10.1016/j.copbio.2024.103178","url":null,"abstract":"<div><p>Lignin valorization faces persistent biomanufacturing challenges due to the heterogeneous and toxic carbon substrates derived from lignin depolymerization. To address the heterogeneous nature of aromatic feedstocks, plant cell wall engineering and ‘lignin first’ pretreatment methods have recently emerged. Next, to convert the resulting aromatic substrates into value-added chemicals, diverse microbial host systems also continue to be developed. This includes microbes that (1) lack aromatic metabolism, (2) metabolize aromatics but not sugars, and (3) co-metabolize both aromatics and sugars, each system presenting unique pros and cons. Considering the intrinsic complexity of lignin-derived substrate mixtures, emerging and non-model microbes with native metabolism for aromatics appear poised to provide the greatest impacts on lignin valorization via biomanufacturing.</p></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141888732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-08-01DOI: 10.1016/j.copbio.2024.103176
G protein–coupled receptors (GPCRs) are the largest family of transmembrane receptors in humans. Over 800 GPCRs regulate the (patho)biology of every organ, tissue, and cell type. Consequently, GPCRs are the most prominent therapeutic targets in medicine. Although over 30% of current U.S. Food and Drug Administration-approved drugs target GPCR signaling, most receptors remain understudied and therapeutically underutilized. Challenges include an incomplete understanding of GPCR signaling, pharmacology, structural biology, and the multiplicity of endogenous GPCR ligands, in addition to a scarcity of biological and pharmacological tools for elucidating GPCR-mediated cellular processes beyond initial signaling events. Various mammalian, insect, and yeast cell models currently address some of these needs. Here, we review recent advances in yeast synthetic biology that are helping to catalyze new and unexpected conceptual and technical breakthroughs in GPCR-based medicine and biotechnology.
{"title":"Advances in yeast synthetic biology for human G protein–coupled receptor biology and pharmacology","authors":"","doi":"10.1016/j.copbio.2024.103176","DOIUrl":"10.1016/j.copbio.2024.103176","url":null,"abstract":"<div><p>G protein–coupled receptors (GPCRs) are the largest family of transmembrane receptors in humans. Over 800 GPCRs regulate the (patho)biology of every organ, tissue, and cell type. Consequently, GPCRs are the most prominent therapeutic targets in medicine. Although over 30% of current U.S. Food and Drug Administration-approved drugs target GPCR signaling, most receptors remain understudied and therapeutically underutilized. Challenges include an incomplete understanding of GPCR signaling, pharmacology, structural biology, and the multiplicity of endogenous GPCR ligands, in addition to a scarcity of biological and pharmacological tools for elucidating GPCR-mediated cellular processes beyond initial signaling events. Various mammalian, insect, and yeast cell models currently address some of these needs. Here, we review recent advances in yeast synthetic biology that are helping to catalyze new and unexpected conceptual and technical breakthroughs in GPCR-based medicine and biotechnology.</p></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141855065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-07-20DOI: 10.1016/j.copbio.2024.103165
Emerging biotechnologies that solve pressing environmental and climate emergencies will require harnessing the vast functional diversity of the underlying microbiomes driving such engineered processes. Modeling is a critical aspect of process engineering that informs system design as well as aids diagnostic optimization of performance. ‘Conventional’ bioprocess models assume homogenous biomass within functional guilds and thus fail to predict emergent properties of diverse microbial physiologies, such as product specificity and community interactions. Yet, recent advances in functional ‘omics-based approaches can provide a ‘lens’ through which we can probe and measure in situ ecophysiologies of environmental microbiomes. Here, we overview microbial community modeling approaches that incorporate functional ‘omics data, which we posit can advance our ability to design and control new environmental biotechnologies going forward.
{"title":"Advancing environmental biotechnology with microbial community modeling rooted in functional ‘omics","authors":"","doi":"10.1016/j.copbio.2024.103165","DOIUrl":"10.1016/j.copbio.2024.103165","url":null,"abstract":"<div><p>Emerging biotechnologies that solve pressing environmental and climate emergencies will require harnessing the vast functional diversity of the underlying microbiomes driving such engineered processes. Modeling is a critical aspect of process engineering that informs system design as well as aids diagnostic optimization of performance. ‘Conventional’ bioprocess models assume homogenous biomass within functional guilds and thus fail to predict emergent properties of diverse microbial physiologies, such as product specificity and community interactions. Yet, recent advances in functional ‘omics-based approaches can provide a ‘lens’ through which we can probe and measure <em>in situ</em> ecophysiologies of environmental microbiomes. Here, we overview microbial community modeling approaches that incorporate functional ‘omics data, which we posit can advance our ability to design and control new environmental biotechnologies going forward.</p></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141731864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-07-20DOI: 10.1016/j.copbio.2024.103173
Bioelectrochemical sensor (BES) technologies have been developed to measure soluble carbon concentrations in wastewater. However, architectures and analytical methods developed in controlled laboratory environments fail to predict BES behavior during field deployments at water resource recovery facilities (WRRFs). Here, we examine the possibilities and obstacles associated with integrating BESs into environmental sensing networks and machine learning algorithms to monitor the biodegradable carbon dynamics and microbial metabolism at WRRFs. This approach highlights the potential of BESs to provide real-time insights into full-scale biodegradable carbon consumption across WRRFs.
{"title":"Mechanistic and data-driven modeling of carbon respiration with bio-electrochemical sensors","authors":"","doi":"10.1016/j.copbio.2024.103173","DOIUrl":"10.1016/j.copbio.2024.103173","url":null,"abstract":"<div><p>Bioelectrochemical sensor (BES) technologies have been developed to measure soluble carbon concentrations in wastewater. However, architectures and analytical methods developed in controlled laboratory environments fail to predict BES behavior during field deployments at water resource recovery facilities (WRRFs). Here, we examine the possibilities and obstacles associated with integrating BESs into environmental sensing networks and machine learning algorithms to monitor the biodegradable carbon dynamics and microbial metabolism at WRRFs. This approach highlights the potential of BESs to provide real-time insights into full-scale biodegradable carbon consumption across WRRFs.</p></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0958166924001095/pdfft?md5=cc786865a004b1011d3266ae775abc7a&pid=1-s2.0-S0958166924001095-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141731865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-18DOI: 10.1016/j.copbio.2024.103172
Microbes orchestrate nearly all major biogeochemical processes. The ability to program their influence on plant growth and development is attractive for sustainable agriculture. However, the complexity of microbial ecosystems and our limited understanding of the mechanisms by which plants and microbes interact with each other and the environment make it challenging to use microbiomes to influence plant growth. Novel technologies at the intersection of microbial ecology, systems biology, and bioengineering provide new tools to probe the role of plant microbiomes across environments. Here, we summarize recent studies on plant and microbe responses to abiotic stresses, showcasing key molecules and micro-organisms that are important for plant health. We highlight opportunities to use synthetic microbial communities to understand the complexity of plant–microbial interactions and discuss future avenues of programming ecology to improve plant and ecosystem health.
{"title":"Roots of synthetic ecology: microbes that foster plant resilience in the changing climate","authors":"","doi":"10.1016/j.copbio.2024.103172","DOIUrl":"10.1016/j.copbio.2024.103172","url":null,"abstract":"<div><p>Microbes orchestrate nearly all major biogeochemical processes. The ability to program their influence on plant growth and development is attractive for sustainable agriculture. However, the complexity of microbial ecosystems and our limited understanding of the mechanisms by which plants and microbes interact with each other and the environment make it challenging to use microbiomes to influence plant growth. Novel technologies at the intersection of microbial ecology, systems biology, and bioengineering provide new tools to probe the role of plant microbiomes across environments. Here, we summarize recent studies on plant and microbe responses to abiotic stresses, showcasing key molecules and micro-organisms that are important for plant health. We highlight opportunities to use synthetic microbial communities to understand the complexity of plant–microbial interactions and discuss future avenues of programming ecology to improve plant and ecosystem health.</p></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141637879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}