Synthetic biology (Oxford, England)最新文献

{"title":"Building Biofoundry India: challenges and path forward.","authors":"Binay Panda,&nbsp;Pawan K Dhar","doi":"10.1093/synbio/ysab015","DOIUrl":"https://doi.org/10.1093/synbio/ysab015","url":null,"abstract":"<p><p>Biofoundry is a place where biomanufacturing meets automation. The highly modular structure of a biofoundry helps accelerate the design-build-test-learn workflow to deliver products fast and in a streamlined fashion. In this perspective, we describe our efforts to build Biofoundry India, where we see the facility add a substantial value in supporting research, innovation and entrepreneurship. We describe three key areas of our focus, harnessing the potential of non-expressing parts of the sequenced genomes, using deep learning in pathway reconstruction and synthesising enzymes and metabolites. Toward the end, we describe specific challenges in building such facility in India and the path to mitigate some of those working with the other biofoundries worldwide.</p>","PeriodicalId":74902,"journal":{"name":"Synthetic biology (Oxford, England)","volume":" ","pages":"ysab015"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/ff/dc/ysab015.PMC8546612.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39825390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pathway engineering for high-yield production of lutein in Escherichia coli.","authors":"Miho Takemura,&nbsp;Akiko Kubo,&nbsp;Asuka Watanabe,&nbsp;Hanayo Sakuno,&nbsp;Yuka Minobe,&nbsp;Takehiko Sahara,&nbsp;Masahiro Murata,&nbsp;Michihiro Araki,&nbsp;Hisashi Harada,&nbsp;Yoshinobu Terada,&nbsp;Katsuro Yaoi,&nbsp;Kohji Ohdan,&nbsp;Norihiko Misawa","doi":"10.1093/synbio/ysab012","DOIUrl":"https://doi.org/10.1093/synbio/ysab012","url":null,"abstract":"<p><p>Lutein is an industrially important carotenoid pigment, which is essential for photoprotection and photosynthesis in plants. Lutein is crucial for maintaining human health due to its protective ability from ocular diseases. However, its pathway engineering research has scarcely been performed for microbial production using heterologous hosts, such as <i>Escherichia coli</i>, since the engineering of multiple genes is required. These genes, which include tricky key carotenoid biosynthesis genes typically derived from plants, encode two sorts of cyclases (lycopene ε- and β-cyclase) and cytochrome P450 CYP97C. In this study, upstream genes effective for the increase in carotenoid amounts, such as isopentenyl diphosphate isomerase (<i>IDI</i>) gene, were integrated into the <i>E. coli</i> JM101 (DE3) genome. The most efficient set of the key genes (<i>MpLCYe, MpLCYb</i> and <i>MpCYP97C</i>) was selected from among the corresponding genes derived from various plant (or bacterial) species using <i>E. coli</i> that had accumulated carotenoid substrates. Furthermore, to optimize the production of lutein in <i>E. coli</i>, we introduced several sorts of plasmids that contained some of the multiple genes into the genome-inserted strain and compared lutein productivity. Finally, we achieved 11 mg/l as lutein yield using a mini jar. Here, the high-yield production of lutein was successfully performed using <i>E. coli</i> through approaches of pathway engineering. The findings obtained here should be a base reference for substantial lutein production with microorganisms in the future.</p>","PeriodicalId":74902,"journal":{"name":"Synthetic biology (Oxford, England)","volume":" ","pages":"ysab012"},"PeriodicalIF":0.0,"publicationDate":"2021-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/synbio/ysab012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39573081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A computational walk to the hidden peaks of protein performance.","authors":"Sonja Billerbeck","doi":"10.1093/synbio/ysab011","DOIUrl":"https://doi.org/10.1093/synbio/ysab011","url":null,"abstract":"Spiders use them to catch their prey, plants rely on them to fix carbon and mammals need them for eye vision—proteins. Proteins play critical roles in nature, and not surprisingly, synthetic biologists heavily rely on their functional diversity to build new therapeutics (1), catalysts (2) and materials (3). But natural proteins are rarely optimal for their envisioned human uses. They rather need to be engineered to enhance their performance. Recently, researchers introduced a machine-learning guided paradigm that can predict which mutations in a protein will enhance function with only 24 functional data sets as input (4). This paradigm could significantly accelerate the engineering of improved proteins for medicine, food, agriculture and industrial applications. The desire to optimize a protein’s function has always been a centerpiece of synthetic biology, and for decades, protein engineers have innovated the capacities of directed evolution (2) and rational protein engineering. One prominent bottleneck for the engineering of proteins is the difficulty in understanding a protein’s so-called fitness landscape. That means to know, which mutationwillmake a protein better, while in fact, mostmutations render a protein dysfunctional. The function of a protein is dictated by its amino acid sequence, and protein scientists picture the relationship between sequence and function of a protein as if it was a rugged landscape with shallow hills and high peaks, separated by valleys (5). Valleys represent sequence variants that are not functional, while the highest peaks represent the most functional mutations. Protein engineers now seek to walk through this landscape—each step being one mutation away from the wild-type sequence—in order to explore if they can find higher peaks of performance in sequence space. As the shape of the landscape is mostly unknown, the walk is random and requires the generation of many sequences and the evaluation of their function. Generating this data is often experimentally difficult or expensive. Most importantly, very distant regions of the landscape, where functional peak performance might hide, are not accessible by this search. Recently, researchers have started to perform this walk through a protein’s sequence space computationally, using deep learning (6). Although several success stories have been reported, each case still relies on a large number of experimental input data. The Church group at Harvard Medical School and the Wyss Institute for Biologically Inspired Engineering now developed a way to mitigate the notorious shortage in experimental data that constrains the engineering of many proteins, by making use of the vast number of publicly available protein sequence data (4, 7). Instead of learning the fitness landscape of an individual protein from experimental data, they first built a deep learning algorithm that extracts the fundamental features of all functional proteins from the &gt;20 million available unlabeled a","PeriodicalId":74902,"journal":{"name":"Synthetic biology (Oxford, England)","volume":" ","pages":"ysab011"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/synbio/ysab011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39573080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Three overlooked key functional classes for building up minimal synthetic cells.","authors":"Antoine Danchin","doi":"10.1093/synbio/ysab010","DOIUrl":"https://doi.org/10.1093/synbio/ysab010","url":null,"abstract":"<p><p>Assembly of minimal genomes revealed many genes encoding unknown functions. Three overlooked functional categories account for some of them. Cells are prone to make errors and age. As a first key function, discrimination between proper and changed entities is indispensable. Discrimination requires management of information, an authentic, yet abstract, currency of reality. For example proteins age, sometimes very fast. The cell must identify, then get rid of old proteins without destroying young ones. Implementing discrimination in cells leads to the second set of functions, usually ignored. Being abstract, information must nevertheless be embodied into material entities, with unavoidable idiosyncratic properties. This brings about novel unmet needs. Hence, the buildup of cells elicits specific but awkward material implementations, 'kludges' that become essential under particular settings, while difficult to identify. Finally, a third functional category characterizes the need for growth, with metabolic implementations allowing the cell to put together the growth of its cytoplasm, membranes, and genome, spanning different spatial dimensions. Solving this metabolic quandary, critical for engineering novel synthetic biology chassis, uncovered an unexpected role for CTP synthetase as the coordinator of nonhomothetic growth. Because a significant number of SynBio constructs aim at creating cell factories we expect that they will be attacked by viruses (it is not by chance that the function of the CRISPR system was identified in industrial settings). Substantiating the role of CTP, natural selection has dealt with this hurdle <i>via</i> synthesis of the antimetabolite 3'-deoxy-3',4'-didehydro-CTP, recruited for antiviral immunity in all domains of life.</p>","PeriodicalId":74902,"journal":{"name":"Synthetic biology (Oxford, England)","volume":" ","pages":"ysab010"},"PeriodicalIF":0.0,"publicationDate":"2021-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/synbio/ysab010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39930099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Moving towards chemical-free agriculture, 37 kb at a time.","authors":"Sonja Billerbeck","doi":"10.1093/synbio/ysab009","DOIUrl":"https://doi.org/10.1093/synbio/ysab009","url":null,"abstract":"Domestic crop plants are modern marvels of extensive breeding; however, many of their natural defenses against pests and pathogens have been lost. Wild relatives still harbor disease resistance genes, but transferring these large sequences into complex, polyploid plant genomes calls for advanced genomic engineering technologies. Recently, government researchers in Australia, successfully transferred a 37kb resistance stack into the genome of a domesticated wheat species such that it is pro-tected against the rapidly evolving wheat leaf rust pathogen Puccinia graminis f. sp. tritici ( Pgt ) without losing any agronomic features. 1 Plant diseases caused by pathogenic fungi can devastate crop yield and pose a threat to food security. 2,3 About 30% of our most important crops are lost every year to fungal diseases. 3 Over decades, agricultural crops have been bred towards maxi-mum productivity under high fungicide treatment, meanwhile breeding out the plants’ own defense genes. 3 The genetic ar-mory still intact in wild crop relatives (so-called R genes) could provide an effective means towards a chemical-free disease control. 4 Introducing those genes into domestic crops is a multi-factorial challenge yet underappreciated by much of the synthetic biology community.","PeriodicalId":74902,"journal":{"name":"Synthetic biology (Oxford, England)","volume":" ","pages":"ysab009"},"PeriodicalIF":0.0,"publicationDate":"2021-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7937910/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25488216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transcriptional control of <i>Clostridium autoethanogenum</i> using CRISPRi.","authors":"Nicholas Fackler,&nbsp;James Heffernan,&nbsp;Alex Juminaga,&nbsp;Damien Doser,&nbsp;Shilpa Nagaraju,&nbsp;R Axayacatl Gonzalez-Garcia,&nbsp;Séan D Simpson,&nbsp;Esteban Marcellin,&nbsp;Michael Köpke","doi":"10.1093/synbio/ysab008","DOIUrl":"https://doi.org/10.1093/synbio/ysab008","url":null,"abstract":"<p><p>Gas fermentation by <i>Clostridium autoethanogenum</i> is a commercial process for the sustainable biomanufacturing of fuels and valuable chemicals using abundant, low-cost C1 feedstocks (CO and CO₂) from sources such as inedible biomass, unsorted and nonrecyclable municipal solid waste, and industrial emissions. Efforts toward pathway engineering and elucidation of gene function in this microbe have been limited by a lack of genetic tools to control gene expression and arduous genome engineering methods. To increase the pace of progress, here we developed an inducible CRISPR interference (CRISPRi) system for <i>C. autoethanogenum</i> and applied that system toward transcriptional repression of genes with ostensibly crucial functions in metabolism.</p>","PeriodicalId":74902,"journal":{"name":"Synthetic biology (Oxford, England)","volume":" ","pages":"ysab008"},"PeriodicalIF":0.0,"publicationDate":"2021-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/synbio/ysab008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38853817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint universal modular plasmids (JUMP): a flexible vector platform for synthetic biology.","authors":"Marcos Valenzuela-Ortega,&nbsp;Christopher French","doi":"10.1093/synbio/ysab003","DOIUrl":"https://doi.org/10.1093/synbio/ysab003","url":null,"abstract":"<p><p>Generation of new DNA constructs is an essential process in modern life science and biotechnology. Modular cloning systems based on Golden Gate cloning, using Type IIS restriction endonucleases, allow assembly of complex multipart constructs from reusable basic DNA parts in a rapid, reliable and automation-friendly way. Many such toolkits are available, with varying degrees of compatibility, most of which are aimed at specific host organisms. Here, we present a vector design which allows simple vector modification by using modular cloning to assemble and add new functions in secondary sites flanking the main insertion site (used for conventional modular cloning). Assembly in all sites is compatible with the PhytoBricks standard, and vectors are compatible with the Standard European Vector Architecture (SEVA) as well as BioBricks. We demonstrate that this facilitates the construction of vectors with tailored functions and simplifies the workflow for generating libraries of constructs with common elements. We have made available a collection of vectors with 10 different microbial replication origins, varying in copy number and host range, and allowing chromosomal integration, as well as a selection of commonly used basic parts. This design expands the range of hosts which can be easily modified by modular cloning and acts as a toolkit which can be used to facilitate the generation of new toolkits with specific functions required for targeting further hosts.</p>","PeriodicalId":74902,"journal":{"name":"Synthetic biology (Oxford, England)","volume":" ","pages":"ysab003"},"PeriodicalIF":0.0,"publicationDate":"2021-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/synbio/ysab003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25404686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Starting from scratch: a workflow for building truly novel proteins.","authors":"Pablo Cárdenas","doi":"10.1093/synbio/ysab005","DOIUrl":"https://doi.org/10.1093/synbio/ysab005","url":null,"abstract":"In its early days, synthetic biology was often defined as repurposing existing biological parts for new applications. More recently, we have seen work that pushes the boundaries of the field past repurposing and into the design of truly novel biological parts. To date, most attempts at designing complex, non-structural protein functions have hinged on grafting protein motifs with known functions onto synthetic protein scaffolds. This ‘top-down’ approach to synthetic protein design unfortunately depends on the structural compatibility of the functional sites with rigid scaffolds used. A study recently published in Nature Chemical Biology describes an alternative ‘bottom-up’ approach in which structural elements are created de novo to support the functional elements in whatever conformation they are specified in (1). The multi-institutional team led by Bruno Correira’s lab at École Polytechnique Fédérale de Lausanne demonstrated the efficacy of their novel approach by building tunable biosensors for epitope-specific antibodies and dual-specific ligands for synthetic cell receptors. To achieve this ‘bottom-up’ design, the authors used TopoBuilder (2), a previously published computational tool, to generate three-dimensional ‘sketches’ of a given protein of interest with specific functional motifs in their idealized conformations. With the help of another software tool, Rosetta FunFoldDes (3), the authors created and simulated tens of thousands of possible designs fulfilling the design criteria, which were filtered by favorable thermodynamic predictions for stability and folding. A combinatorial library of up to 10 million variants was built from elements of the best designs. The libraries were then screened by binding affinity to the desired target(s) and protease digestion using yeast surface display, and the best protein variants were selected by sequencing the output. Finally, the structure and behavior of the final products were determined using a variety of different physical and chemical assays. The authors used their workflow to design a biosensor based on bioluminescence resonant energy transfer (4) to sense antibodies with affinities for a single, specific epitope found in respiratory syncytial virus and metapneumovirus, two respiratory pathogens. The novel design pipeline made it possible to present the single epitope in scaffolds with different binding affinities to the target antibody. This, in turn, allowed for tuning the biosensor’s response. Furthermore, the workflow was used to create a synthetic ligand capable of binding to two different, previously created synthetic mammalian signal receptors, which trigger expression of a reporter gene (5). The authors demonstrated the ligand simultaneously bound its two distinct targets by showing it only triggered output signal when both types of receptor were present in a cell. The work presented by Yang et al. is exciting for its practically universal applicability in biological research. Appli","PeriodicalId":74902,"journal":{"name":"Synthetic biology (Oxford, England)","volume":" ","pages":"ysab005"},"PeriodicalIF":0.0,"publicationDate":"2021-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7966779/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25510053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aquarium: open-source laboratory software for design, execution and data management.","authors":"Justin Vrana,&nbsp;Orlando de Lange,&nbsp;Yaoyu Yang,&nbsp;Garrett Newman,&nbsp;Ayesha Saleem,&nbsp;Abraham Miller,&nbsp;Cameron Cordray,&nbsp;Samer Halabiya,&nbsp;Michelle Parks,&nbsp;Eriberto Lopez,&nbsp;Sarah Goldberg,&nbsp;Benjamin Keller,&nbsp;Devin Strickland,&nbsp;Eric Klavins","doi":"10.1093/synbio/ysab006","DOIUrl":"https://doi.org/10.1093/synbio/ysab006","url":null,"abstract":"<p><p>Automation has been shown to improve the replicability and scalability of biomedical and bioindustrial research. Although the work performed in many labs is repetitive and can be standardized, few academic labs can afford the time and money required to automate their workflows with robotics. We propose that human-in-the-loop automation can fill this critical gap. To this end, we present Aquarium, an open-source, web-based software application that integrates experimental design, inventory management, protocol execution and data capture. We provide a high-level view of how researchers can install Aquarium and use it in their own labs. We discuss the impacts of the Aquarium on working practices, use in biofoundries and opportunities it affords for collaboration and education in life science laboratory research and manufacture.</p>","PeriodicalId":74902,"journal":{"name":"Synthetic biology (Oxford, England)","volume":" ","pages":"ysab006"},"PeriodicalIF":0.0,"publicationDate":"2021-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/synbio/ysab006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39250784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harnessing synthetic biology to expand chemical diversity of antibiotics.","authors":"Fatima Enam","doi":"10.1093/synbio/ysaa029","DOIUrl":"https://doi.org/10.1093/synbio/ysaa029","url":null,"abstract":"Antibiotic resistance is one of the greatest challenges facing public health. Historically, glycopeptide antibiotics (GPA) have been used in the treatment of infections caused by Grampositive pathogens, including multiresistant Staphylococcus aureus (MRSA) infections and enterococcal infections, which are resistant to beta-lactams and other antibiotics. Rising GPA resistance has led to the discovery and clinical development of synthetic second-generation glycopeptides but more needs to be done to stand up against the evolution of resistance mechanisms. In a recent publication in Nature Communications, a team of scientists led by Gerard Wright at McMaster University in Canada, developed a clever synthetic biology platform for the production and discovery of novel GPAs. GPAs are heptapeptides that are naturally synthesized in actinobacteria, orchestrated by co-localized genes in the genome, called biosynthetic gene clusters (BGCs), and a host of other genes for modification, regulation and transport. Although next-generation sequencing technologies and machine-learning tools have enabled the identification of a wealth of BGCs in bacterial genomes, low or no yields of GPAs from parental actinomycetes, lack of heterologous hosts for expressing BGCs and difficulty in cloning these large constructs (&gt;70 kb) has kept these GPAs elusive. Xu et al. engineered Streptomyces coelicolor, a genetically tractable microorganism, that contains the biosynthetic machinery and precursors required for GPA biosynthesis, including pathways for nonproteinogenic amino acid components to create the GPAHex chassis. To clone the large BGCs and increase selection efficiency, they developed an optimized transformation associated recombination (TAR) system that allows isolation and manipulation of large DNA constructs. The TAR system relies on a copy number control replicon that can be conditionally induced for high copy numbers. The system utilizes a ura3 counter-selection marker, which was also optimized by introducing additional transcriptional initiation sites between the TATA box and the start codon to maximize ura3 transcription. To demonstrate the synthesis of a GPA, they targeted corbomycin, a Type V GPA. Corbomycin was discovered by phylogeny guided genome mining, but further development was hindered by low titers in the producer Streptomyces strain. Xu et al. cloned a 76 kb region, constituting the peptide scaffold and six TISs, into the S. coelicolor chassis strain via E. coli-yeast triparental mating. Growth inhibition was observed against Bacillus subtilis and the production titer was 65.4 mg/l, 19-fold higher compared to the parental Streptomyces strain. The authors also used the GPAHex platform for the discovery of a GPA in an Amycolatopsis strain that shares homology with the teicoplanin class of GPAs but is transcriptionally inactive in the parental strain. They cloned the scaffold including four TISs into the chassis strain to express GP1416 that displayed an","PeriodicalId":74902,"journal":{"name":"Synthetic biology (Oxford, England)","volume":" ","pages":"ysaa029"},"PeriodicalIF":0.0,"publicationDate":"2021-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8056980/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38853816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}