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

TL;DR

This study uses machine learning to show how gene expression is controlled during translation in three parasitic trypanosomatids by analyzing UTRs and codon usage.

Contribution

The study reveals distinct roles of UTRs and codon usage in translation efficiency and mRNA stability across three trypanosomatid species.

Findings

In T. brucei, translation efficiency is primarily linked to A-rich and C-poor UTRs.

Codon usage bias has a major impact on mRNA abundance in all three trypanosomatids.

Leishmania shows higher median translation efficiency, strongly influenced by (A)U-rich UTRs and codon usage.

Abstract

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.

We study how three parasites (
Trypanosoma brucei,
Trypanosoma cruzi, and
Leishmania) control gene expression. Using computer analyses, we looked at two key factors: alternative codons, which are translated to incorporate the same amino acid in a protein, and UnTranslated Regions (UTRs); both of which can impact messenger RNA stability or the rate at which messenger RNA is translated to produce protein. We found that the impact of codons and UTRs is primarily at the point of translation. Codon usage bias likely impacts mRNA stability by increasing the rate of translation. Understanding these regulatory processes will reveal how these parasites and related cells function, in terms of expressing thousands of different proteins at appropriate levels.

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