Decoding post-transcriptional gene expression controls in trypanosomatids using machine learning

We recently described a pervasive cis-regulatory role for sequences in Trypanosoma brucei mRNA untranslated regions (UTRs). Specifically, increased translation efficiency (TE) was associated with the dosage and density of A-rich tracts. This finding raised three related questions: (1) What relative contributions do UTRs and codon usage bias make to TE in T. brucei? (2) What relative contributions do these sequences make to mRNA steady-state levels in T. brucei? (3) Do these sequences make substantial contributions to TE and/or mRNA steady-state levels in the related parasitic trypanosomatids, T. cruzi and Leishmania?

To address these questions, we applied machine learning to analyze existing transcriptome, TE, and proteomics data.

Our predictions indicate that both UTRs and codon usage bias impact gene expression in all three trypanosomatids, but with substantial differences. In T. brucei, TE is primarily correlated with longer A-rich and C-poor UTRs. The situation is similar in T. cruzi, but codon usage bias makes a greater contribution to TE. In Leishmania, median TE is higher and is more strongly correlated with longer (A)U-rich UTRs and with codon usage bias. Codon usage bias has a major impact on mRNA abundance in all three trypanosomatids, while analysis of T. brucei proteomics data yielded results consistent with the view that this is due to differential translation elongation rates.

Taken together, our findings indicate that gene expression control in trypanosomatids operates primarily at the point of translation, which is impacted by both UTRs and codon usage. We suggest a model whereby UTRs control the rate of translation initiation, while favoured codons increase the rate of translation elongation, thereby reducing mRNA turnover.