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In vivo genome editing with clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 generates powerful tools to study gene regulation and function. We revised the homology-assisted CRISPR knock-in method to convert Drosophila GAL4 lines to LexA lines using a new universal knock-in donor strain. A balancer chromosome–linked donor strain with both body color (yellow) and eye red fluorescent protein (RFP) expression markers simplified the identification of LexA knock-in using light or fluorescence microscopy. A second balancer chromosome–linked donor strain readily converted the second chromosome–linked GAL4 lines regardless of target location in the cis-chromosome but showed limited success for the third chromosome–linked GAL4 lines. We observed a consistent and robust expression of the yellow transgene in progeny harboring a LexA knock-in at diverse genomic locations. Unexpectedly, the expression of the 3xP3-RFP transgene in the “dual transgene” cassette was significantly increased compared with that of the original single 3xP3-RFP transgene cassette in all tested genomic locations. Using this improved screening approach, we generated 16 novel LexA lines; tissue expression by the derived LexA and originating GAL4 lines was similar or indistinguishable. In collaboration with 2 secondary school classes, we also established a systematic workflow to generate a collection of LexA lines from frequently used GAL4 lines.

Genome editing in vivo with CRISPR/Cas9 generates powerful tools to study gene regulation and function. We developed CRISPR-based methods that permitted secondary school student scientists to convert Drosophila GAL4 lines to LexA lines. Our novel curricula implement a new donor strain optimizing Homology-assisted CRISPR knock-in (HACK) that simplifies screening using light microscopy. Successful curricula adoption by a consortium of schools led to the generation and characterization of 16 novel LexA lines. This includes extensive comparative tissue expression analysis between the parental Gal4 and derived LexA lines. From this collaboration, we established a workflow to systematically generate LexA lines from frequently-used GAL4 lines. Modular courses developed from this effort can be tailored to specific secondary school scheduling needs, and serve as a template for science educators to innovate courses and instructional goals. Our unique collaborations highlight that resources and expertise harnessed by university-based research laboratories can transform experiential science instruction in secondary schools while addressing research needs for the community of science.

Can a bacterial cell model vet large datasets from disparate sources? Macklin et al. explored whether a comprehensive mathematical model can be used to verify or find conflicts in massive amounts of data that have been reported for the bacterium Escherichia coli , produced in thousands of papers from hundreds of labs. Although most data were consistent, there were data that could not accommodate known biological results, such as insufficient output of RNA polymerases and ribosomes to produce measured cell-doubling times. Other analyses showed that for some essential proteins, no RNA may be transcribed or translated in a cell's lifetime, but viability can be maintained without certain enzymes through a pool of stable metabolites produced earlier. Science , this issue p. eaav3751 Construction of a large-scale mechanistic model of Escherichia coli brings models and large datasets together to enhance knowledge in biology. The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of Escherichia coli enabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle—and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.

The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model ofEscherichia colienabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle—and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.