Ribosomal RNA: turning a problem into a tool

Five members of the Pasteur Network— the Institut Pasteur du Cambodge, the Institut Pasteur de Bangui, the Institut Pasteur de Madagascar, the Institut Pasteur de la Guyane and the Institut Pasteur—have collaboratively published the results of a study on mosquito ribosomal RNA. In addition to the release of 234 complete ribosomal RNA sequences from 33 mosquito species to public databases, the study presents the bioinformatics methodology used to assemble these sequences. The eLife article also assesses the use of ribosomal RNA as a molecular marker for taxonomic and phylogenetic studies on mosquitoes. These findings will facilitate the discovery and monitoring of viruses in all the mosquito species investigated as well as others.

Monitoring virus circulation with RNA sequencing (RNA-seq)

Mosquitoes are known to transmit many pathogenic viruses among humans and animals. Most of these viruses carry genetic information in the form of ribonucleic acid or RNA. By sequencing the RNA found in mosquitoes, a technique known as RNA-seq,

we can identify what viral pathogens are circulating in certain mosquito populations by detecting their RNA genomes. This also allows us to discover potential emerging viral pathogens.

However, the mosquito itself contain plenty of RNA, specifically the RNAs that make up protein-producing machines called ribosomes. This type of RNA is aptly called ribosomal RNA. The hyperabundant presence of ribosomal RNA constitute “background noise”, which can reduce the sensitivity of pathogen detection by masking the sequences of interest, and they need to be removed from the sample. To successfully remove or deplete ribosomal RNA, we need to know its reference sequence.

However, the lack of reference ribosomal RNA sequences for a large majority of mosquito species makes it difficult to perform RNA-seq in these species. Only a few vector species were listed on public databases—a collection of all known genetic sequences for all living things. This gap leads to a neglect of the transmission cycles perpetuated by other mosquito species endemic to more remote environments, which are responsible for the infection of reservoir animals. To allow for virus discovery and monitoring in a wider range of mosquito species, the team expanded the current collection of reference ribosomal RNA sequences.

Employing the collective expertise of the Pasteur Network members

By bringing together their expertise and resources, scientists from the Institut Pasteur du Cambodge, the Institut Pasteur de Bangui, the Institut Pasteur de Madagascar, the Institut Pasteur de la Guyane and the Institut Pasteur, all members of the Pasteur Network, have released a large assemblage of ribosomal RNA sequences to public databases. Using a unique bioinformatics method described in the study, the team was able to assemble the complete ribosomal RNA sequences for all their specimens, even in the presence of contaminating biological material. This genomic resource constitutes a set of 234 complete ribosomal RNA sequences of 33 mosquito species.

The implications of this genomic resource

These novel sequences allow for the physical and computational elimination of interfering ribosomal RNA sequence reads, the aforementioned “background noise”, leading to the detection of target viral genomic RNA at increased sensitivity. In addition, ribosomal RNAs can be used for the molecular identification of the mosquito species under study. The accuracy of molecular identification of mosquito species using ribosomal RNA sequences is comparable to that of the mitochondrial cytochrome c oxidase I gene sequence—the gold standard and current reference in molecular taxonomy. The bioinformatics methodology and sequences resulting from this collaboration will thus help to discover and monitor known and potential new pathogens in a large number of insect species by RNA-seq metagenomics.

For more information:
Ribosomal RNA (rRNA) sequences from 33 globally distributed mosquito species for improved metagenomics and species identification
eLife, janvier 2023.
Cassandra Koh, Lionel Frangeul, Hervé Blanc, Carine Ngoagouni,Sébastien Boyer, Philippe Dussart, Nina Grau, Romain Girod, Jean-Bernard Duchemin and Maria-Carla Saleh.
DOI: 10.7554/eLife.82762 / https://elifesciences.org/articles/82762