FIG 2.

Screening of 755 P. pastoris transformants indicates that plasmid design, vector linearization, and the type of integration event (specific/nonspecific) mostly influences the expression range of outliers but not the population distribution. (A) Vectors providing GUT1 integration sequences and a standard vector design were linearized with SwaI and/or SacI targeting the integration events depicted in Fig. 1. Cells were pregrown on glucose for 60 h and subsequently induced with methanol for 48 h. eGFP reporter fluorescence normalized to cell growth (OD₆₀₀) is shown. Results of landscapes typical for work with P. pastoris (40–43) are shown. Each bar represents a transformant (n = 252, 252, and 251 sample points). (B) The data from panel A are shown as a boxplot (59). Center lines show the medians, and box limits indicate the 25th and 75th percentiles as determined by R software. Whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles, and outliers are represented by dots. n = 252, 252, and 251 sample points. (C) Expression landscapes of the vector providing GUT1 integration sequences linearized with SwaI sorted by specific/nonspecific integration. Hence, the first third of the data from panel A is shown in a rearranged fashion. Transformants were replica plated in glycerol-containing medium after growth on glucose for 60 h to test for specific/nonspecific integration. Note that the GUT1-SacI and STD-SacI integrating vectors cannot be tested for correct integration in this way. (D) The same data from panel C are shown as a boxplot (59). Center lines show the medians, and box limits indicate the 25th and 75th percentiles as determined by R software. Whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles, and outliers are represented by dots; the width of the boxes is proportional to the square root of the sample size. n = 158 and 94 sample points.