Back

A Trypanosome Trifecta: an independently tunable triple inducer system for genetic studies in Trypanosoma brucei.

Romprey, M. J.; Armstrong, R.; McGill, P.; Klingbeil, M. M.

2025-09-01 microbiology
10.1101/2025.08.31.673413 bioRxiv
Show abstract

Trypanosoma brucei is a model unicellular parasite for cellular and molecular genetic studies, but tools for more multiplexed experiments are limited. Tetracycline (Tet)-inducible gene regulation has been a long-standing and effective approach for overexpression, RNAi and genome wide screens. The single inducer Tet-On system has been foundational for studying essential genes that are required for biological processes and identifying potential drug targets. To achieve greater flexibility in experimental design, we capitalized upon previously described dual inducer systems that combined vanillic acid (Van) or cumate (Cym) with Tet as inducers. Here we report the development of a triple inducer system combining Cym, Van and Tet repressor elements to selectively regulate the expression of three genetic elements within a single cell line called PHITER. As proof of principle to demonstrate independent control, we adapted the previously characterized Van/Tet dual inducer RNAi complementation cell line, IBCompVaT for additional Cym inducible expression of an eGFP reporter. We provide evidence that each of the inducer elements operate independently with no evidence of leaky expression. In this system, Cym induction is specific and tunable. Additionally, simultaneous triple induction for POLIB RNAi complementation with wildtype POLIB and eGFP expression phenocopied the near complete rescue of POLIB RNAi previously reported. This system enables the study of complex and pleiotropic phenotypes through more sophisticated experimental design.

Published in mSphere (predicted rank #2) · training set

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.