Back

Thermo-sensing and argonaute-dependent transcriptome remodelling in trypanosomes

Bravo Ruiz, G.; Tinti, M.; Horn, D.

2026-01-21 microbiology
10.64898/2026.01.20.700697 bioRxiv
Show abstract

Vector-borne parasites, such as African trypanosomes, experience temperature fluctuation due to fever symptomatic of infection, during developmental life cycle transitions, or due to diurnal shift. Mechanisms underpinning RNA-based thermo-sensing remain largely uncharacterised in these and other eukaryotic cells, however. Notably, trypanosomes exhibit almost exclusive polycistronic transcription, such that gene expression controls are dominated by post-transcriptional mechanisms mediated by mRNA-binding proteins and thousands of mRNA 3-untranslated regions (3-UTRs). Here, we quantify the transcriptomes and proteomes of bloodstream form Trypanosoma brucei following growth at 34{degrees}C, 37{degrees}C or 40{degrees}C for six hours. Approximately fifty genes encoding classical (co-)chaperones, with (UUA)n-rich 3-UTRs, display the expected heat-shock response. The expression of approximately 1,000 additional transcripts is also correlated with temperature, and these transcripts have relatively long 3-UTRs enriched in potentially complementary poly-purine tracts, poly-pyrimidine tracts, and palindromic sequences (P5-UTRs). To assess the potential impacts of mRNA secondary structure transcriptome-wide, we quantify mRNAs in cells lacking the central RNA interference nuclease argonaute (AGO1). Strains lacking AGO1 display increased retroposon expression, as expected, and strikingly abrogated thermo-regulation of transcripts with P5-UTRs. Thus, thermo-sensing involves argonaute-dependent transcriptome-remodelling in trypanosomes. We propose a post-transcriptional zipper hypothesis whereby access to regulatory motifs is controlled by temperature-sensitive mRNA secondary structure.

Matching journals

The top 3 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.