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Cheadle, J. B.

Publications and source records attributed to Cheadle, J. B..

2 recordsLinked to original sources

Engineering the Maize Root Microbiome: A Rapid MoClo Toolkit and Identification of Potential Bacterial Chassis for studying Plant-Microbe Interactions

Sustainably enhancing crop production is a necessity given the increasing demands for staple crops and their associated carbon/nitrogen inputs. Plant-associated microbiomes offer one avenue for addressing this demand; however, studying these communities and engineering them has remained a challenge due to limited genetic tools and methods. In this work, we detail the development of the Maize Root ToolKit (MRTK); a rapid Modular Cloning (MoClo) toolkit that only takes 2.5 hours to generate desired constructs (5400 potential plasmids) that replicate and express heterologous genes in Enterobacter ludwigii strain AA4 (Elu), Pseudomonas putida AA7 (Ppu), Herbaspirillum robiniae strain AA6 (Hro), Stenotrophomonas maltophilia strain AA1 (Sma) and Brucella pituitosa strain AA2 (Bpi) which comprise a model maize root synthetic community (SynCom). In addition to these genetic tools, we describe a highly efficient transformation protocol (10^7-10^9 transformants/{micro}g of DNA) for each of these strains. Utilizing this highly efficient transformation protocol, we identified endogenous expression sequences for each strain (ES; promoter and ribosomal binding sites) via genomic promoter trapping. Overall, the MRTK is a scalable platform that expands the genetic engineering toolbox while providing a standardized, high efficiency transformation method that can be implemented across a diverse group of root commensals. These results unlock the ability to elucidate and engineer plant-microbe interactions promoting plant growth for each of the 5 bacterial strains in this study.

synthetic biology↗

Dug: A Semantic Search Engine Leveraging Peer-Reviewed Literature to Span Biomedical Data Repositories

MotivationAs the number of public data resources continues to proliferate, identifying relevant datasets across heterogenous repositories is becoming critical to answering scientific questions. To help researchers navigate this data landscape, we developed Dug: a semantic search tool for biomedical datasets utilizing evidence-based relationships from curated knowledge graphs to find relevant datasets and explain why those results are returned. ResultsDeveloped through the National Heart, Lung, and Blood Institutes (NHLBI) BioData Catalyst ecosystem, Dug has indexed more than 15,911 study variables from public datasets. On a manually curated search dataset, Dugs total recall (total relevant results/total results) of 0.79 outperformed default Elasticsearchs total recall of 0.76. When using synonyms or related concepts as search queries, Dug (0.36) far outperformed Elasticsearch (0.14) in terms of total recall with no significant loss in the precision of its top results. Availability and ImplementationDug is freely available at https://github.com/helxplatform/dug. An example Dug deployment is also available for use at https://search.biodatacatalyst.renci.org/. Contactawaldrop@rti.org or scox@renci.org

bioinformatics↗