Advancing the central role of non-model biorepositories in predictive modeling of emerging pathogens

Choclo virus (CHOV) recovered from deep metatranscriptomics of archived frozen tissues in natural history biorepositories

BACKGROUND

Hantaviruses are negative-stranded RNA viruses that can sometimes cause severe disease in humans; however, they are maintained in mammalian host populations without causing harm. In Panama, sigmodontine rodents serve as hosts to transmissible hantaviruses. Due to natural and anthropogenic forces, these rodent populations are having increased contact with humans.

METHODS

We extracted RNA and performed Illumina deep metatranscriptomic sequencing on Orthohantavirus seropositive museum tissues from rodents. We acquired sequence reads mapping to Choclo virus (CHOV, Orthohantavirus chocloense) from heart and kidney tissue of a two-decade old frozen museum sample from a Costa Rican pygmy rice rat (Oligoryzomys costaricensis) collected in Panama. Reads mapped to the CHOV reference were assembled and then validated by visualization of the mapped reads against the assembly.

RESULTS

We recovered a 91% complete consensus sequence from a reference-guided assembly to CHOV with an average of 16X coverage. The S and M segments used in our phylogenetic analyses were nearly complete (98% and 99%, respectively). There were 1,199 ambiguous base calls of which 93% were present in the L segment. Our assembled genome varied 1.1% from the CHOV reference sequence resulting in eight nonsynonymous mutations. Further analysis of all publicly available partial S segment sequences support a clear relationship between CHOV clinical cases and O. costaricensis acquired strains.

CONCLUSIONS

Viruses occurring at extremely low abundances can be recovered from deep metatranscriptomics of archival tissues housed in research natural history museum biorepositories. Our efforts resulted in the second CHOV genome publicly available. This genomic data is important for future surveillance and diagnostic tools as well as understanding the evolution and pathogenicity of CHOV.

Integrating databases for spatial analysis of parasite-host associations and the novel Brazilian dataset

Incomplete information on parasites, their associated hosts, and their precise geographical location hampers the ability to predict disease emergence in Brazil, a continental-sized country characterised by significant regional disparities. Here, we demonstrate how the NCBI Nucleotide and GBIF databases can be used as complementary databases to study spatially georeferenced parasite-host associations. We also provide a comprehensive dataset of parasites associated with mammal species that occur in Brazil, the Brazilian Mammal Parasite Occurrence Data (BMPO). This dataset integrates wild mammal species' morphological and life-history traits, zoonotic parasite status, and zoonotic microparasite transmission modes. Through meta-networks, comprising interconnected host species linked by shared zoonotic microparasites, we elucidate patterns of zoonotic microparasite dissemination. This approach contributes to wild animal and zoonoses surveillance, identifying and targeting host species accountable for disproportionate levels of parasite sharing within distinct biomes. Moreover, our novel dataset contributes to the refinement of models concerning disease emergence and parasite distribution among host species.