Acioli-Santos, B. | Search bioRxiv

bioRxiv2024

Genomic and phenotypic characterization of an Oropouche virus strain implicated in the 2023-24 large-scale outbreak in Brazil

The Orthobunyavirus oropoucheense in a arthropod borne zoonotic pathogen known to infect sylvatic animals and humans by means of biting midges transmission. Several large-scale human outbreaks of Oropouche virus (OROV), primarily confined to the Amazon region, were documented over the decades. However, since 2022, more widespread OROV outbreaks have been unfolding in Brazil and across South America, with cases exported to Cuba, Italy, and Germany. I In Brazil, the virus has reached and established communitary transmission in states from all major regions of the country. Here we isolated, characterized the cytopathic effect and the full genomic sequence of two OROV isolates from the current outbreak. Our data shows that OROV can readily infect and replicate in non-human primate cells, supporting the role of non-human primates as important reservoirs. Phylogenetic data supports a direct introduction of the same lineages causing the 2022-24 outbreak in Brazil from the Amazonas state, the epicenter of the epidemics in Brazil. Lastly, as case counts accumulate in the state and the Northeast region, clear evidence supports established and sustained transmission. Continued studies are critical to understand the transmission cycle in this region, the most important vectors and reservoirs, to appropriately deploy control measures.

microbiology↗

bioRxiv2020

Hypothetical Human Immune Genome Complex Gradient May Help To Explain The Congenital Zika Symdrome Catastrophe In Brazil: A New Theory

There are few data considering human genetics as an important risk factor for birth abnormalities related to ZIKV infection during pregnancy, even though sub-Saharan African populations are apparently more resistant to CZS as compared to populations in the Americas. We hypothesized that single nucleotide variants (SNVs), especially in innate immune genes, could make some populations more susceptible to Zika congenital complications than others. Differences in the SNV frequencies among continental populations provide great potential for Machine Learning techniques. We explored a key immune genomic gradient between individuals from Africa, Asia and Latin America, working with complex signatures, using 297 SNVs. We employed a two-step approach. In the first step, decision trees (DTs) were used to extract the most discriminating SNVs among populations. In the second step, machine learning algorithms were used to evaluate the quality of the SNV pool identified in step one for discriminating between individuals from sub-Saharan African and Latin-American populations. Our results suggest that 10 SNVs from 10 genes (CLEC4M, CD58, OAS2, CD80, VEPH1, CTLA4, CD274, CD209, PLAAT4, CREB3L1) were able to discriminate sub-Saharan Africans from Latin American populations using only immune genome data, with an accuracy close to 100%. Moreover, we found that these SNVs form a genome gradient across the three main continental populations. These SNVs are important elements of the innate immune system and in the response against viruses. Our data support the Human Immune Genome Complex Gradient hypothesis as a new theory that may help to explain the CZS catastrophe in Brazil.

genetics↗