Relevant Results

While awaiting the arrival of the genetic constructs, various DNA extraction and purification protocols were standardized in order to establish a reliable methodology for confirming the correct transformation of Pichia pastoris GS115 cells with the pPICZAα vector.

Four different protocols were tested: Wizard ® Genomic DNA Purification Kit Promega from Bacteria, Wizard® Genomic DNA Purification Kit Promega from Yeast, Bioline DNA Extraction Kit, and a protocol called Extraction of Genomic DNA from Yeast, which was taken from the master's thesis of Mónica Amezcua Castillo, who had previously worked with P. pastoris strains, allowing her methodology to be adapted and applied to the experimental conditions of this project. In the case of the Wizard® protocols, the same basic procedure was used, differing only in the use of EDTA and sodium citrate (Na₃C₆H₅O₇) as chelating agents.These compounds are essential for inhibiting nucleases and protecting the integrity of the genetic material during the extraction process (Amezcua, 2010).

To evaluate the efficiency of the protocols, gel electrophoresis and DNA concentration readings were performed using NanoDrop equipment. In the case of the Wizard® protocols, electrophoresis did not show visible bands, as seen in Figure 1, suggesting a low amount or possible fragmentation of the extracted DNA. However, the NanoDrop results (Table 1) showed significant variations in the concentrations obtained. Sample GS115 W_E1 had a concentration of 24.3 ng/μL, indicating partial extraction. In contrast, sample GS115 W_E2 reached a concentration of 1235 ng/μL, reflecting a highly efficient extraction using EDTA. Sample GS115 W_C1, with 533.3 ng/μL, also showed good results with sodium citrate, although with a lower yield compared to EDTA.

For its part, the protocol with the Bioline DNA Extraction Kit, designed for quick and simple extraction without the need for aggressive enzymatic treatments, proved ineffective for yeasts due to their resistant cell walls. Electrophoresis again revealed no bands (Figure 2), and measurements with the NanoDrop (Table 2) yielded very low or even negative values indicating an almost total absence of recovered DNA, suggesting that this kit does not effectively break down the cell wall of P. pastoris.

Finally, the Extraction of Genomic DNA from Yeast protocol was evaluated. This method is based on the use of detergents, heat, and chelating agents to break down the cell and release the DNA. In this case, electrophoresis was not performed, but quantification was carried out using NanoDrop (Table 3). Although the results obtained are low, they demonstrate that the protocol allows DNA to be obtained.

Table 3. Results obtained from quantification of Extraction of Genomic DNA from Yeast

Considering that a minimum concentration of 25–50 ng/μL is required to obtain at least 2.5 μg of DNA, the results allow us to conclude that the Wizard® Promega protocol using EDTA was the most efficient. The GS115 W_E2 sample far exceeded this threshold, while the use of citrate also showed good results. In contrast, the Bioline protocol was ineffective in yeast.

In conclusion, the most recommended protocol for genomic DNA extraction in P. pastoris GS115, with the aim of obtaining concentrations useful for further analysis, is the Wizard® Genomic DNA Purification Kit Promega (from Yeast) using EDTA, as it provides the highest concentration and quality of DNA compared to the other methods evaluated.

During the experimental preparation phase, growth rates were determined for available strains including GS115 Pichia pastoris with vector construct, E.Coli with vector construct, and a strain of modified Pichia with specific deletions.

The methodology involved calibrating equipment by warming the biophotometer for 15 minutes at 600 nm wavelength, preparing blank solutions with appropriate sterile media (LB for E. coli, YPD for yeast), and performing blank calibration with clean cuvettes. Samples were collected from incubator flasks using sterile technique, and optical density measurements were taken at OD₆₀₀ with serial dilutions performed when readings exceeded 1.0. Measurements were scheduled at inoculation, every 2 hours for the first 12 hours, then every 4-6 hours until 48 hours, with final readings at 24 and 48 hours, and all data including time points, strain identification, readings, dilution factors, and incubation conditions were recorded.

Growth curves were constructed plotting OD₆₀₀ versus time to identify growth phases. As shown in Figure 3, growth of the three strains was monitored over a six-hour period, with the OpenPichia strain displaying marked increase compared to the other two strains (GS115 and E. coli) whose OD₆₀₀ values remained relatively low with little variation throughout the experimental period. Linear trendlines were applied in Figure 3 to highlight growth trajectories. To determine specific growth rates, data were transformed by plotting natural logarithm of OD₆₀₀ values as a function of time, allowing data to fit an exponential growth model.

Figure 3. Growth of strains over time

As illustrated in Figure 4, the slopes of linear regressions indicated specific growth rates, with the highest corresponding to the OpenPichia strain (y = 0.4604x – 0.1485, R² = 0.9382), followed by the GS115 strain (y = 0.1463x + 0.2218, R² = 0.9866) and E. coli strain (y = 0.1192x + 0.1982, R² = 0.9957). Linear regression analysis revealed substantial differences in specific growth rates among the three strains, with the OpenPichia strain exhibiting the highest growth rate at 0.78 h⁻¹ and remarkably short doubling time of approximately 0.89 hours, while the other two strains (GS115 and E. coli) displayed much lower and similar growth rates of approximately 0.13 h⁻¹ with doubling times of 5.32 h and 5.15 h respectively, as shown in Table 4. The high R² values for all three strains, as displayed in Figure 4, indicated the exponential growth phase during this period, validating the exponential growth model applicability.

Figure 4. Representation of the natural logarithmic transformation of OD600 values as a function of time

Examining values between consecutive time points further highlighted differences in growth dynamics (table 4), with the OpenPichia strain accelerating throughout the measurement period from 0.62 to 0.94 h⁻¹, while the GS115 strain maintained steady growth ranging from 0.11 to 0.15 h⁻¹, and the E. coli strain showed slight acceleration from 0.09 to 0.18 h⁻¹. The linear regression method provided more robust growth rate estimates by considering all data points simultaneously rather than relying on consecutive measurements, thereby minimizing measurement variability impact.

Table 4. Growth rates of the three strains, with the

In conclusion, the OpenPichia strain showed exponential growth during the measured period with growth rate approximately 6 times higher than the other two strains, which had similar, more modest growth rates suggesting comparable metabolic efficiency under experimental conditions. These findings, as visualized in Figures 3 and 4, have important implications for bioprocess optimization as substantially faster growth may offer advantages for industrial applications requiring rapid biomass accumulation, with the observed growth difference possibly attributed to transformation stress or evidence that vector integration limits organism growth in the GS115 and E. coli strains.