Predicting Zoonotic Risk of Influenza A Viruses from Host Tropism Protein Signature Using Random Forest

Abstract

Influenza A viruses remain a significant health problem, especially when a novel subtype emerges from the avian population to cause severe outbreaks in humans. Zoonotic viruses arise from the animal population as a result of mutations and reassortments, giving rise to novel strains with the capability to evade the host species barrier and cause human infections. Despite progress in understanding interspecies transmission of influenza viruses, we are no closer to predicting zoonotic strains that can lead to an outbreak. We have previously discovered distinct host tropism protein signatures of avian, human and zoonotic influenza strains obtained from host tropism predictions on individual protein sequences. Here, we apply machine learning approaches on the signatures to build a computational model capable of predicting zoonotic strains. The zoonotic strain prediction model can classify avian, human or zoonotic strains with high accuracy, as well as providing an estimated zoonotic risk. This would therefore allow us to quickly determine if an influenza virus strain has the potential to be zoonotic using only protein sequences. The swift identification of potential zoonotic strains in the animal population using the zoonotic strain prediction model could provide us with an early indication of an imminent influenza outbreak.

Natural Reassortants of Potentially Zoonotic Avian Influenza Viruses H5N1 and H9N2 from Egypt Display Distinct Pathogenic Phenotypes in Experimentally Infected Chickens and Ferrets

“…The Egyptian HPAIV H5N1 and LPAIV H9N2 parent strains and putative reassortants were first analyzed for their zoonotic potential using the FluLeap influenza zoonotic prediction system, a machine learning approach based on the protein sequences (23). Both the H5N1 and H9N2 parent strains were predicted to be of low zoonotic risk.…”

“…The two parent strains of H5N1 and H9N2 were analyzed for their zoonotic risks and reassortant possibilities using the FluLeap influenza zoonotic prediction system (http://fluleap.bic.nus.edu.sg), based on machine learning approaches (23). The sequences of 11 viral proteins translated from the genomic sequences of the two strains (HA, M1, M2, NA, NP, NS1, NEP, PA, PB1, PB1-F2, and PB2) were first subjected to individual host tropism predictions.…”

“…Using the host tropism prediction results for the 11 proteins as features, the random forest strain prediction model classifies the strain as avian, human, or zoonotic (23), with the accompanying zoonotic probability estimate denoting the zoonotic propensity of the strain.…”

IdentifyingStreptococcus pneumoniaegenes associated with invasive disease using pangenome-based whole genome sequence typing

“…Thus, we can identify which genes are associated with differing phenotypes, but subtler methods will be necessary to discern exactly which alleles are responsible for which phenotypic changes. A feasible future extension of our method could be adding variables encoding more information about the alleles, such as structural properties of their resulting proteins [54]. However, such an extension will necessitate an efficient way of combining the genetic and protein information as the interactions between genes and their translated protein characteristics will likely have a substantial effect on the results.…”

HopPER: an adaptive model for probability estimation of influenza reassortment through host prediction

“…Villa and Lässig determined rate and average selective effect of reassortment process in human influenza H3N2 using a new method to map reassortment events from joint genealogies of multiple genome segments [18]. Eng et al developed an influenza reassortment simulation tool through host tropism protein signatures [19]. This program computationally simulates reassortment between the eight viral segments and then generates a list of all possible reassortant progeny based on the signatures.…”