Elucidating the Mechanism of mRNA Therapy
Jordan Ontiveros, PhD, RNA Scientist, Sanofi
Jordan Ontiveros, PhD, RNA Scientist, Sanofi started his presentation at TIDES USA in San Diego by sharing Sanofi mRNA Center of Excellence’s aim of developing next-generation mRNA for applications in vaccines and beyond by integrating learnings from pandemic and bridging knowledge gaps.
Ontiveros underlined that designing attributes of both mRNA and lipid nanoparticles (LNP) in an mRNA therapeutic is essential in terms of potency. Types and chemistries of LNP, parameters regarding functional regions of mRNA such as 5’ cap, 5’ untranslated region (UTR), coding region, 3’ UTR and poly(A) tail effect stability, translation efficiency, and immune-stimulatory profile.
Ontiveros later discussed LNP formulation optimization using cell-based assays. He said that lipid structure affects the efficacy and different lipid components such as ionizable lipids, cationic lipids sterols, PEG, and helper lipids might change the overall effect. Cell-based potency assays enable one to compare different lipid families and find out the most useful LNPs for further studies.
They also showed that optimized formulations not only enhance LNP uptake but also improve RNA delivery to cells. He also touched upon the mechanistic steps underlying mRNA-LNP potency as follows:
Cell uptake pathway
Endosomal trafficking and escape
mRNA degradation
Translation efficiency
Protein trafficking
mRNA Sequence Factors
In the next part of his talk, he focused on characterizing the translation activity of different mRNA sequences. He said that there are different mRNA sequence features that dictate the potency such as 5’ cap, 5’ UTR, coding region, 3’ UTR, and poly(A) tail. He underscored the modifications in the coding region regarding GC content, and codon content will impact structure and stability. A given amino acid sequence can be encoded by different sequences thanks to the fact that one amino acid can be encoded by more than one nucleotide triplet. At Sanofi, using a digital machine learning approach, they can construct vast libraries of mRNAs encoding the same protein. He summarized the advantages of codon optimization as follows:
Increase protein expression
Increase stability
Improve purity
Lower reactogenicity and so on
Following that, Ontiveros explained polysome profiling as the way to assess the effect of sequence features on mRNA translation. The results quantify an mRNA’s association with actively translating ribosomes. He explained the technique in a few steps:
Cell lysate is separated across a sucrose gradient by weight and size.
Global ribosome state is captured by absorbance and fractionation. In the middle, there is an 80S peak corresponding to the single monosome bound to a single mRNA. Small wavy peaks correspond to multiple ribosomes bound to a single mRNA.
Translational activity of specific mRNAs is quantified by RT-qPCR across fractions.
He said by using this technique, it is possible to quantify a free mRNA, monosomal RNA, and RNA bound by many ribosomes.
Then he shared data to that showed the effect of codon optimization on translation efficiency. The data suggested that codon optimization shows greater protein expression than native mRNA because codon optimized RNA displayed greater association with multimer units of ribosomes.