Wet Lab
Algae Cultivation
To determine the optimal cultivation conditions (which will be beneficial for the Proof of Concept experiments, as we need to differentiate between environmental factors and protein expression effects on growth rates), we conducted experiments to cultivate our Zooxanthellae under various light and temperature conditions and plotted its growth curve.
You can refer to our Algae Cultivation page in the Engineering Cycle section for more information.
Initial Trial to Cultivate the Algae
After purchasing Zooxanthellae from a company, we began to expand the culture according to the protocol provided. We incubated the algae for almost a month, plotted the data, and used a logistic curve to fit the growth. After this one-month period, the algae were still in the exponential growth phase. This result suggests that using the K-value as an indicator for growth state is not feasible within the timeline of our experiments. Therefore, we decided to abandon the K-value as a metric in subsequent experiments. The slow growth rate also implies that we may need to optimize the cultivation conditions further.
Figure 1. Algae cultured with the same container and same start of culture data was used to obtain cell number data and the mean values of their cell concentration were used to fit a logistic curve. This experiment aimed to validate our algae culture method and find an optimal sampling approach. instead of trying to get some conclusions. Due to the high error rate of our cell counter, each point represents the average value obtained from multiple measurements. The logistic fit had an R² of 0.9790.
Phylogenetic Identification
Following consultation with experts, we decided to use sequence alignment to determine the phylogenetic classification of the algae and then adopt appropriate cultivation methods. Despite this, we found that the growth rate was still low. Communication with researchers from the South China Sea Institute of Oceanology revealed that our cultivation conditions were suboptimal, and we were advised to identify the phylogenetic position of the purchased algae. Through sequence alignment, we determined that the strain belongs to the E clade and halted agitation. The alignment results are shown below, with Alexandrium kutnerae as the outgroup and GY-H50 (our strain) marked in red.
Figure 2. Through sequence alignment, we determined that the strain belongs to the E clade and halted agitation. The alignment results are shown below, with _Alexandrium kutnerae_ as the outgroup and GY-H50 (our strain) marked in red.
Preliminary Experiments to Determine Optimal Light Conditions
We aimed to explore the optimal light intensity for cultivating Zooxanthellae, but the appropriate experimental range was unclear. Additionally, since we had only one artificial climate chamber (which meant only one light intensity could be tested at a time), we needed a simpler method to control light conditions. Therefore, we conducted preliminary experiments to determine the optimal light intensity range and test shading methods.
The experimental results below indicate that 7500 lux may fall within the optimal range. Notably, these data were collected after one day of separate cultivation, during which the biomass of the algae changed drastically. This suggests that light intensity may not be the sole factor influencing growth. Based on these observations, we decided to abandon the shading method (see Engineering Cycle, Algae Cultivation).
Figure 3. Tukey's multiple comparisons test showing significant differences between various light intensities (lux). Comparisons between 0 lux and 7500 lux, as well as 2000 lux and 7500 lux, revealed highly significant differences (P < 0.0001). Similarly, comparisons between 5000 lux and 7500 lux showed a highly significant difference (P < 0.0001). Additionally, comparisons between 7500 lux and both 12000 lux and 20000 lux were also highly significant (P < 0.0001). All other comparisons did not show significant differences (P > 0.05).
Optimal Cultivation under Different Temperature and Light Conditions
To determine the optimal and stress conditions for Zooxanthellae, we cultivated the algae under varying temperature and light intensities and periodically measured the biomass to assess its viability. The results are shown below.
For light intensity, the optimal biomass was observed at 6000 lux, with reduced growth at both higher and lower intensities. For temperature, the optimal biomass occurred at 26°C, with growth decreasing at both higher and lower temperatures. These results suggest that 6000 lux and 26°C are the optimal conditions for Zooxanthellae cultivation, and deviations from these values could compromise the algae's survival.
Figure 4. The three images in the top row represent results from different light intensities, and the three images in the bottom row represent results from varying temperatures. The left column shows raw data, the middle column shows normalized results using the initial values, and the right column displays one-way ANOVA test results from the final day of cultivation. The rightmost two images reveal that biomass follows a unimodal distribution centered around 6000 lux and 26°C. Due to imprecise temperature control in the water bath used for cultivation, variance within groups was high, and statistical significance was not achieved in some cases. Additionally, the 25°C group exhibited abnormally low biomass due to an experimental anomaly. For light intensity, 3000 vs. 4500 lux yielded an adjusted P-value of 0.0238, indicating significant differences (_), while 4500 vs. 6000 lux had a highly significant difference (\*\*\*\*) with an adjusted P-value of <0.0001. 6000 vs. 7500 lux also showed significant differences (\*\*) with a P-value of 0.0048. However, 7500 vs. 9000 lux showed no significant differences (P = 0.5965). For temperature, 24°C vs. 25°C and 25°C vs. 26°C indicated significant differences (P = 0.0006, _), while other temperature comparisons were not significant (P > 0.05).
Proof of Concept
By conducting this experiment, what we want to know is whether our apporach, protecting corals by blocking excessive light, works for zooxanthellae. We co-cultured the algae with different concentration of E. coli in semi-solid medium which constitutively expresses aeBlue, and found that under both concentration gradients, the average biomass of Zooxanthellae with aeBlue protection is indeed higher than that of the control group, suggesting that aeBlue may indeed provide some photoprotection to Zooxanthellae. However, it is unfortunate that these differences are not statistically significant, despite the low p-values. From the second result figure, it is observable that the average cell density of Zooxanthellae is marginally reduced when co-cultured with E. coli expressing aeBlue at a relative concentration of 100%. This shows that there exists a possibility that the growth of Zooxanthellae may be inhibited when there is an excess of aeBlue protein. We then made a model to predict the behaviour of our plasmid, and confirmed our guess. Therefore, it is imperative that the expression of aeBlue is regulated under specific conditions. Specifically, our goal is to achieve a regulatory mechanism where the expression of aeBlue by E. coli is modulated in response to variations in light intensity, ensuring that the protein's expression aligns with the light conditions to prevent its overexpression and potential negative impacts on Zooxanthellae.
Figure 5. This figure illustrates the final cell density of Zooxanthellae (10^5/mL) co-cultured with Escherichia coli expressing aeBlue protein at 100% (OD700=0.8) and 50%(OD700=0.4) concentration (experimental group) and E. coli transformed with the corresponding empty pBS vector (control group). The statistical significance of the difference in cell density between the experimental and control groups was assessed using an unpaired t-test, with a calculated t-value of 0.0587 and 0.2661 respectively, indicating no significant difference between the two groups.
Figure 6. This figure illustrates the final cell density of Zooxanthellae ($$10^5$$/mL) following co-culture with Escherichia coli expressing aeBlue protein at two different relative concentrations: 100% and 50%. The unpaired t-test conducted to assess the statistical significance of the observed difference in cell density between the groups yielded a p-value of 0.7736, indicating that the difference in cell density is not statistically significant.
Light Sensor Characterization
In our project, we utilized a light sensor called LexRO, which was submitted to the Registry (BBa_K5280129). Before submission, we conducted several characterization experiments.
Cell Toxicity
We wanted to investigate whether the LexRO protein induces cell toxicity. A series of experiments focused on the characteristics of LexRO. Initially, we transformed E. coli with the LexRO expression vector and tested whether it would cause cell death or interfere with future fluorescence detection. The results showed minimal impact on cell viability. However, due to LexRO's light-absorbing properties, it may interfere with reporter detection, which requires further experimental validation.
Figure 7. In the experiments, _E. coli_ transformed with an empty vector were used as negative controls. Left panel: _E. coli_ transformed with different plasmids were cultivated under optimal conditions, and OD600 was measured every 20 minutes. Central panel: Fluorescence levels were detected under fluorescence conditions with Ex 485 nm and Em 535 nm, where fluorescence from LexRO was visible. Right panel: Fluorescence intensity was normalized to OD600, with values from early-stage cultivation excluded.
Next, we compared the toxicity of LexRO with a previously reported light sensor, EL222, known for inducing cytotoxicity. We introduced the expression vectors for these proteins into E. coli. The results further confirmed that LexRO did not induce cytotoxicity, and EL222 also exhibited minimal toxicity, suggesting that EL222's toxicity may only occur under specific cultivation conditions. This requires further validation.
Figure 8. Cultivated cells transformed with different plasmids under optimal conditions were synchronized, and samples were taken every 10 minutes to measure the OD600.
Switch Ratio
To verify whether our light sensor could effectively regulate downstream gene expression, we conducted preliminary experiments to observe LexRO's ability to modulate gene expression under light and dark conditions. We tested two dilution ratios, 1:10 and 1:100, and found that the dilution ratio had little impact on the final results. Given that a 1:100 ratio more closely resembles the initial state of cells being introduced into a new growth environment, it was deemed the better choice. Based on these findings and supporting literature, we established 1:100 as the standard dilution ratio for future characterization experiments.
Figure 9. The top two panels display OD600 values for two dilution ratios, while the bottom two panels present normalized mCherry fluorescence values. Fluorescence was measured at Ex = 585 nm and Em = 610 nm.
Aim: The experiment aimed to evaluate the efficacy of LexRO as a photosensor for regulating gene expression.
Method: An expression vector containing mCherry as the reporter gene was constructed, and bacteria harboring this vector were cultivated under both inducing and non-inducing conditions. Gene expression levels were analyzed and compared.
Results: Statistical analysis showed that LexRO achieved a switch ratio of approximately 6, indicating its efficacy as a regulatory protein. This surpasses commonly used optogenetic elements such as EL222, which has a switch ratio of less than 5, as reported by Li et al. (2020).
Figure 10. Transfected cells with the expression vector were cultured under light/dark conditions, and after 25 hours, flow cytometry was used to measure fluorescence intensity and distribution. The three left panels show the gating methods, while the far-right panel represents the downstream expression intensity using the median fluorescence intensity of the cell population, with three controls for each light and dark group. An unpaired t-test yielded a P-value of 0.0292, which is below 0.05.
Time-Course Characterization
Aim: This experiment aimed to characterize the time-dependent regulatory performance of LexRO, particularly its ability to repress gene expression under induced (light) and non-induced (dark) conditions.
Method: The experiment measured the time-course relationship of LexRO by comparing fluorescence intensity in induced and non-induced conditions. Tin foil was used for shading, and blue light lamps were used for induction. However, challenges such as suboptimal shading and heat generation from the lamps affected the experimental conditions.
Results: LexRO exhibited strong repression of gene expression in the early stages of growth under dark conditions. Under light conditions (reversibility group), the fluorescence intensity responded, albeit with a delay. Sampling-induced expression in the dark group, insufficient shading, and temperature fluctuations introduced variability in the data. The experiment lacked a positive control due to the incomplete construction of a constant mCherry expression plasmid.
Figure 11. The upper panel shows time-course characterization of LexRO, where bacteria transformed with the reporter expression vector were continuously cultured under inducing and non-inducing conditions for 12 hours, with samples taken every hour to characterize growth metrics. The lower panel illustrates the reversibility of LexRO regulation. Bacteria were alternated between inducing and non-inducing conditions every 3 hours, with hourly measurements. A fluorescence spike was observed in the dark group at the 10th hour, indicating a potential procedural issue.
Thermosensor Characterisation
To investigate whether our thermosensor could regulate gene expression in response to temperature and compare the performance of different thermosensors, we transformed bacteria with various thermosensor expression vectors and cultured them under different temperature conditions. Recognition sites for BBa_K5280427-BBa_K5280431 were derived from thermosensors represented in the figure to their left. BBa_K5280427-BBa_K5280430 contain two recognition sites, but no significant difference was found compared to those with one recognition site. While two recognition sites theoretically reduce leakage, they also result in lower "on" activity, which is worth noting.
Figure 8. The values of Fluorescence/OD700 of mCherry with BBa_K5280412, BBa_K5280415, BBa_K5280422, BBa_K5280424, BBa_K5280427, BBa_K5280428, BBa_K5280429, BBa_K5280430, BBa_K5280431, and BBa_K2541203 incubated at 25°C, 29°C, 33°C, and 37°C for 14 hours. Fluorescence of mCherry was measured at Ex = 587 nm and Em = 610 nm.
Dry Lab
Large Language Model
Ultimately, I named our large language model CoralGenie. While LLMs have been trained on vast amounts of data, they do not specifically accommodate your private data. To address this limitation, we integrated LlamaIndex into CoralGenie's framework, which connects to LLMs' data sources and incorporates our data into the existing datasets. This approach is commonly referred to as Retrieval-Augmented Generation (RAG). RAG empowers you to query your data using LLMs, transforming it and generating new insights. You can ask questions about your data, create chatbots, and build semi-autonomous agents.
This capability ensures that CoralGenie not only possesses certain functionalities of ChatGPT—such as answering what iGEM is—but, more importantly, when you configure your data, CoralGenie will assist you in navigating the literature you wish to explore. It serves as a conversational agent that retrieves information from books to answer your inquiries.
Figure 13. Illustration of the Retrieval-Augmented Generation (RAG) framework, depicting the integration of external data sources with large language models to enhance response accuracy and contextual relevance.
Figure 14. Comparative analysis of CoralGenie and ChatGPT, highlighting key differences in functionality, data integration, and response capabilities related to coral knowledge queries.
Circuit Modelling
As for the circuit model, we developed numerous ordinary differential equations to simulate the dynamic behavior of the gene circuit and plotted time-dependent graphs of various components, including mRNA, protein levels, and light intensity in the vicinity of the coral microenvironment. This demonstrates the success of our gene circuit construction. Ultimately, we also illustrated the synergistic effects of temperature and light intensity—two critical factors contributing to coral bleaching—by generating heat maps. 
[Figure 18] This heatmap illustrates the effect of two different environmental factors—temperature and light intensity—on the downstream aeBlue expression in our circuit. As seen, aeBlue expression increases as both temperature and light intensity rise. 
[Figure 19] Time-dependent expression profiles of circuit components, illustrating the dynamic behavior of mRNA, protein levels, and light intensity in the coral microenvironment.
