Engineered systems of inducible anti-repressors for the next generation of biological programming

Traditionally engineered genetic circuits have almost exclusively used naturally occurring transcriptional repressors. Recently, non-natural transcription factors (repressors) have been engineered and employed in synthetic biology with great success. However, transcriptional anti-repressors have largely been absent with regard to the regulation of genes in engineered genetic circuits. Here, we present a workflow for engineering systems of non-natural anti-repressors. In this study, we create 41 inducible anti-repressors. This collection of transcription factors respond to two distinct ligands, fructose (anti-FruR) or D-ribose (anti-RbsR); and were complemented by 14 additional engineered anti-repressors that respond to the ligand isopropyl β-d-1-thiogalactopyranoside (anti-LacI). In turn, we use this collection of anti-repressors and complementary genetic architectures to confer logical control over gene expression. Here, we achieved all NOT oriented logical controls (i.e., NOT, NOR, NAND, and XNOR). The engineered transcription factors and corresponding series, parallel, and series-parallel genetic architectures represent a nascent anti-repressor based transcriptional programming structure.


Statistics
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Data
Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability The authors declare that all data supporting the findings of this study are available within the paper and its supplementary information. The analyzed data and source data will be made available in Supplementary  Field-specific reporting Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection. We have demonstrated internally our ability to replicate results in this publication and previous ones with n = 6 biological replicates, including between different days. We chose to increase sample size to n = 12 for genetic logic gates to increase our power in statistical hypothesis testing. Post-hoc power analyses have also been performed to verify a desirable power for phenotyping purposes.
No data has been excluded from analyses. All data collected is shown on plots and is included in the Source Data file.
All attempts at replication were successful. Data for anti-repression matrices were gathered over 2 (or more) days (for each condition) and, internally, testing for phenotype and performance shows repeatability from day-to-day (within +/-1 standard deviation). Logic gate assaying shows similar repeatability with assaying occurring over 2 (or more) days (for each condition) and several colony forming units (cfus) were selected to verify phenotype when assaying (but note, only data for one cfu is reported in publication). In all cases, all cfus exhibited the same phenotype. All experiments were independently repeated over 2 (or more) days. Additionally, all phenotyped single TF interactions (e.g., NOT experiments) exhibited the same phenotype when paired with other TFs to construct logical operations. Additionally, cell cytometry analysis, where appropriate, was measured (for each condition) over 2 days.
For site-saturation mutagenesis, resultant colonies following PCR and transformation were chosen/picked at random (sampling 3x for a 95% probability of obtaining all amino acids in the library). Similarly, resultant colonies following FACS were chosen at random for plate assay to verify phenotype. All transformants were included in FACS sorting (streaked from selection plates). In all cloning experiments (to construct genetic logic, reporter, and modified TFs), resultant colonies following PCR and transformation were chosen at random for sequencing, to verify correct construction. For phenotyping (single TF) and logic gate testing (multiple TF), colonies were chosen at random for testing at varying conditions. Colonies were likewise chosen at random from transformations from isolated DNA from site-saturation libraries (following initial screening) to verify phenotype. Phenotypes/performance was verified by testing multiple cfus (over 2 or more days). The same colony reported for microplate assay was chosen for cell cytometry analysis, where appropriate. Transformations were performed only with sequence-verified DNA (not chosen at random), which was essential to ensure proper BUO performance/behavior.
Blinding is not applicable in this study. For a given biological unit operation, it was essential for the authors to know the bacteria genotype (bearing which plasmid(s)) for proper testing. Namely, following randomization (per above) of plated colonies of a specific transformation (with specific plasmids), colonies had to then be cultured and assayed in conditions particular to that BUO: antibiotic resistance, ligand(s) to be added, and number of biological replicates to perform. It was required that the authors know which reagents to add to the media to evaluate each BUO (and to then report this). Additionally, all groups were subjected to all conditions evaluated -i.e., a colony of a given genotype was subjected to the control condition (without ligand), as well as with any ligand(s) that were relevant.