The in vivo Tetrahymena thermophila extracellular glucose drop assay for characterization of mammalian insulin activity

Use of E-64 cysteine protease inhibitor for the recombinant protein production in Tetrahymena thermophila

Tetrahymena thermophila is an alternative organism for recombinant protein production. However, the production efficiency in T. thermophila is quite low mainly due to the rich cysteine proteases. In this study, we studied whether supplementation of the E-64 inhibitor to T. thermophila cultures increases the recombinant protein production efficiency without any toxic side effects. Our study showed that supplementation of E-64 had no lethal effects on T. thermophila cells in flask culture at 30 °C and 38 °C. In vitro protease activity analysis using secretome as protease enzyme source from E-64-supplemented cell cultures showed a reduced protein substrate degradation using bovine serum albumin, rituximab, and milk lactoglobulin proteins. E-64 also prevented proteolysis of the recombinantly produced and secreted TtmCherry2-sfGFP fusion protein at some level. This reduced inhibitory effect of E-64 could be due to genetic compensation of the inhibited proteases. As a result, the 5 µM concentration of E-64 was found to be a non-toxic protease inhibitory supplement to improve extracellular recombinant protein production efficiency in T. thermophila. This study suggests that the use of E-64 may increase the efficiency of extracellular recombinant protein production by continuously reducing extracellular cysteine protease activity during cultivation.

Recombinant production of hormonally active human insulin from pre-proinsulin by Tetrahymena thermophila

Alternative cell factories, such as the unicellular ciliate eukaryotic Tetrahymena thermophila, may be required for the production of protein therapeutics that are challenging to produce in conventional expression systems. T. thermophila (Tt) can secrete proteins with the post-translational modifications necessary for their function in humans. In this study, we tested if T. thermophila could process the human pre-proinsulin to produce hormonally active human insulin (hINS) with correct modifications. Flask and bioreactor culture of T. thermophila were used to produce the recombinant Tt-hINS either with or without an affinity tag from a codon-adapted pre-proinsulin sequence. Our results indicate that T. thermophila can produce a 6 kDa Tt-hINS monomer with the appropriate disulfide bonds after removal of the human insulin signal sequence or endogenous phospholipase A signal sequence, and the C-peptide of the human insulin. Additionally, Tt-hINS can form 12 kDa dimeric, 24 kDa tetrameric, and 36 kDa hexameric complexes. Tt-hINS-sfGFP fusion protein was localized to the vesicles within the cytoplasm and was secreted extracellularly. Assessing the affinity-purified Tt-hINS activity using the in vivo T. thermophila extracellular glucose drop assay, we observed that Tt-hINS induced a significant reduction (approximately 21 %) in extracellular glucose levels, indicative of its functional insulin activity. Our results demonstrate that T. thermophila is a promising candidate for the pharmaceutical and biotechnology industries as a host organism for the production of human protein drugs.

Performance Comparison of Five Methods for Tetrahymena Number Counting on the ImageJ Platform: Assessing the Built-in Tool and Machine-Learning-Based Extension

Previous methods to measure protozoan numbers mostly rely on manual counting, which suffers from high variation and poor efficiency. Although advanced counting devices are available, the specialized and usually expensive machinery precludes their prevalent utilization in the regular laboratory routine. In this study, we established the ImageJ-based workflow to quantify ciliate numbers in a high-throughput manner. We conducted Tetrahymena number measurement using five different methods: particle analyzer method (PAM), find maxima method (FMM), trainable WEKA segmentation method (TWS), watershed segmentation method (WSM) and StarDist method (SDM), and compared their results with the data obtained from the manual counting. Among the five methods tested, all of them could yield decent results, but the deep-learning-based SDM displayed the best performance for Tetrahymena cell counting. The optimized methods reported in this paper provide scientists with a convenient tool to perform cell counting for Tetrahymena ecotoxicity assessment.