GFP Experimentation

Before conducting CRISPRi experiments with the selected critical gene inhA from M. tuberculosis, Lambert iGEM first tested the competency of the TXTL system and dCas9 protein by testing the CRISPRi mechanism on deGFP with previously validated sgRNA sequences (Marshall et al., 2020). Following the protocol outlined by Marshall et al. (2020), we ordered sgRNA6 (BBa_K5096620) (targeting the promoter region), sgRNA9 (BBa_K5096074) (targeting the deGFP coding region), and sgRNA-NT (non-targeting sgRNA), and utilized dSpyCas9 along with the myTXTL Sigma 70 kit (see Fig. 1). The emitted fluorescence of deGFP was quantified using a Synergy HTX Gen5 plate reader, with measurements taken every 10 minutes for 16 hours at 29° Celsius. The plate was slightly shaken each time a measurement was taken.

Figure 1. Diagram of the target locations of sgRNAs and graphs of the deGFP expression from different sgRNAs tested in the paper Rapid and Scalable Characterization of CRISPR Technologies Using an E. coli Cell-Free Transcription-Translation System.

Results

Sigma 70 TXTL Master Mix

To test the TXTL system, we measured deGFP fluorescence using the commercial pTXTL-P70a(2)-deGFP plasmid from Arbor Biosciences in the Sigma 70 TXTL Master Mix (Marshall et al., 2018) (see Table 1 and Fig. 2).

Figure 2. Graph verifying the functionality of the Sigma 70 Master Mix using pTXTL-P70a(2)-deGFP HP to express fluorescence compared to a negative control.

Concentrations

After verifying our TXTL kits, we finalized the concentrations of each reagent for the CRISPRi system with guidance from Esther Lee, an undergraduate from Georgia Institute of Technology with experience in CRISPRi experimentation (see Table 2).

Controls

We proceeded with additional reactions involving CRISPRi reagents and GFP to test controls and isolate the individual reagents for effectiveness. We received plates of P70a-deGFP and PCD017 (dSpyCas9) plasmids transformed into E. coli from Dr. Vincent Noireaux, and we isolated the plasmid DNA via miniprep for use in the Sigma 70 TXTL Master Mix as positive and negative controls (see Fig. 3).

Figure 3. Graphs of deGFP expression from our positive control and negative control assays.

GFP CRISPRi Experimentation

After confirming successful expression from our positive control P70a-deGFP plasmid, we decided to run a CRISPRi reaction using sgRNA6 and sgRNA9 to downregulate P70a-deGFP expression. We ran our positive control, negative control, and two experimental groups with sgRNA6 and sgRNA9 (see Fig. 4) (Marshall et al., 2018).

Figure 4. Graphs of deGFP expression from our positive control (deGFP and Arbor Bioscience’s deGFP), negative control, sgRNA6 and sgRNA9 assays.

After analyzing the relative fluorescence of our CRISPRi reactions compared to our positive control at the end of the reaction period, we observed that sgRNA6 and sgRNA9 achieved a 79.4% and 87.3% decrease in relative fluorescence units, respectively, confirming successful gene repression.

Troubleshooting Low Fluorescence

Although our CRISPRi reactions with sgRNA6 and sgRNA9 were determined to be successful due to the significant decrease in fluorescence, Esther Lee, an undergraduate from the Georgia Institute of Technology, suggested that we should expect a fluorescence output of 20,000 RFU. This indicated that our overall fluorescence levels were notably lower than anticipated. To address this, we reprepared P70a-deGFP and dCas9 proteins via multiple minipreps to achieve higher yield in the TXTL system.To ensure maximum GFP expression, we initially used a significantly greater volume of GFP (3uL), yielding over 10,000 RFU. Subsequently, we ran positive controls with 0.6uL of GFP, aligned with our experimental groups, which achieved significantly higher GFP levels than our previous positive controls (see Table 4 and Fig. 5). This adjustment successfully addressed the issue of low fluorescence in our CRISPRi system.

Figure 5. Graphs of deGFP expression from different volumes of positive controls (reprepared deGFP and Arbor Bioscience’s GFP and negative control, which resulted in a significantly higher level of fluorescence than previous trials.

GFP CRISPRi Experimentation

In the final CRISPRi trial, we used our newly prepared reagents alongside all of the sgRNAs from the previous full CRISPRi reaction. The trial results were successful, with our positive controls exhibiting high fluorescence levels and the sgRNAs effectively repressing deGFP production as anticipated. We quantified the repression, with sgRNA6 achieving 54.8% downregulation and sgRNA9 achieving 59.8% downregulation (see Fig. 6). This final run concluded our GFP testing and confirmed the efficacy of our Sigma70 TXTL kits and the dCas9 protein we utilized.

Figure 6. Graphs of deGFP expression from our positive control (deGFP and Arbor Bioscience’s deGFP), negative control, sgRNA6 and sgRNA9 assays. The assays with sgRNA6 and sgRNA9 resulted in fluorescence levels higher than the negative control but lower than the positive control.

sgRNA9 Characterization

After successfully running our CRISPRi reactions, we characterized sgRNA9 to determine the most optimal concentration for adding to the TXTL lysates. We conducted a concentration curve by varying the final concentration of sgRNA9 from 3nM to 10nM (see Table 5 and Table 6).

a)

b)

Results indicated that sgRNA9 at a 5nM final concentration achieved approximately 44% repression, despite low GFP repression, suggesting that the CRISPRi reaction was effective. However, final concentrations of 3nM, 8nM, and 10nM did not exhibit fluorescence, as the RFU values were similar to the negative control (see Fig. 7). At last, the 5nM concentration proved to be the most optimal among the 3nM-10nM range when the reaction is run with the 1nM final concentration of deGFP, suggesting that a 1:5 ratio of deGFP to sgRNA is most effective in the TXTL lysates with this particular sgRNA.

Figure 7. Graphs of sgRNA9 concentration curve ranging from 3nM-10n. Results indicate that 5nM is the most effective concentration within the tested range.

CRISPR vs CRISPRi

After completing all of our experimentation to successfully show that CRISPRi can be used to repress GFP, Lambert iGEM decided to run a CRISPR reaction using pCas9 (BBa_K5096400) (see Fig. 8) protein to compare the rates of repression between gene knockdown and knockout.

Figure 8. Full Sequence Map of pCas9 from Benchling (Benchling, 2024).

We tested our CRISPR vs. CRISPRi reactions by following the same protocol as our CRISPRi reaction and utilized pCas9 with sgRNA9 to repress and cut the P70a-deGFP (see Fig. 9).

Figure 9. Graphs of deGFP expression in our positive control, negative control, and CRISPR and CRISPRi reactions.

Although the CRISPR reaction resulted in lower fluorescence, indicating greater repression, both CRISPR and CRISPRi yielded successful results. The CRISPR reaction achieved 80.0% repression of deGFP, while CRISPRi achieved 59.8% downregulation. These values demonstrate that despite CRISPR achieving higher repression than CRISPRi, both mechanisms surpassed the threshold for effective repression of GFP. Industry professionals we consulted, such as Dr. Scot Ouellette, a postdoctoral researcher at the Department of Pathology, Microbiology, and Immunology at the University of Nebraska Medical Center, and Ms. Amy Enright, a microbiology PhD student at the University of Wisconsin-Madison with extensive CRISPRi research experience, agreed that >50% repression is a baseline for determining the success of the CRISPRi system. However, since the degree of necessary repression can vary based on the specific target gene and organism, we plan to design and conduct a functional study in the future to more accurately assess loss of function.

Modeling

Our modeling committee also utilized MATLAB, a platform that enables wetlab committees to simulate various parameters such as target genes and binding coefficients, predicting experimental success. This approach allows the wetlab committees to focus on the most optimal concentrations and configurations, streamlining our experiments and enhancing efficiency. The reciprocal relationship between the modeling and wetlab results facilitates refinement of both the mathematical predictions and experimental design, ultimately improving the accuracy of our GFP CRISPRi application (see Model CRISPRi).

References