Identifying and prioritizing potential human-infecting viruses from their genome sequences.
Determining which animal viruses may be capable of infecting humans is currently intractable at the time of their discovery, precluding prioritization of high-risk viruses for early investigation and outbreak preparedness. Given the increasing use of genomics in virus discovery and the otherwise spa...
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| Language: | English |
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Public Library of Science (PLoS)
2021-09-01
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| Series: | PLoS Biology |
| Online Access: | https://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.3001390&type=printable |
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| author | Nardus Mollentze Simon A Babayan Daniel G Streicker |
| author_facet | Nardus Mollentze Simon A Babayan Daniel G Streicker |
| author_sort | Nardus Mollentze |
| collection | DOAJ |
| description | Determining which animal viruses may be capable of infecting humans is currently intractable at the time of their discovery, precluding prioritization of high-risk viruses for early investigation and outbreak preparedness. Given the increasing use of genomics in virus discovery and the otherwise sparse knowledge of the biology of newly discovered viruses, we developed machine learning models that identify candidate zoonoses solely using signatures of host range encoded in viral genomes. Within a dataset of 861 viral species with known zoonotic status, our approach outperformed models based on the phylogenetic relatedness of viruses to known human-infecting viruses (area under the receiver operating characteristic curve [AUC] = 0.773), distinguishing high-risk viruses within families that contain a minority of human-infecting species and identifying putatively undetected or so far unrealized zoonoses. Analyses of the underpinnings of model predictions suggested the existence of generalizable features of viral genomes that are independent of virus taxonomic relationships and that may preadapt viruses to infect humans. Our model reduced a second set of 645 animal-associated viruses that were excluded from training to 272 high and 41 very high-risk candidate zoonoses and showed significantly elevated predicted zoonotic risk in viruses from nonhuman primates, but not other mammalian or avian host groups. A second application showed that our models could have identified Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) as a relatively high-risk coronavirus strain and that this prediction required no prior knowledge of zoonotic Severe Acute Respiratory Syndrome (SARS)-related coronaviruses. Genome-based zoonotic risk assessment provides a rapid, low-cost approach to enable evidence-driven virus surveillance and increases the feasibility of downstream biological and ecological characterization of viruses. |
| format | Article |
| id | doaj-art-7b610add7e994491b0b4d404eb5c74b2 |
| institution | Kabale University |
| issn | 1544-9173 1545-7885 |
| language | English |
| publishDate | 2021-09-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Biology |
| spelling | doaj-art-7b610add7e994491b0b4d404eb5c74b22025-08-20T03:44:40ZengPublic Library of Science (PLoS)PLoS Biology1544-91731545-78852021-09-01199e300139010.1371/journal.pbio.3001390Identifying and prioritizing potential human-infecting viruses from their genome sequences.Nardus MollentzeSimon A BabayanDaniel G StreickerDetermining which animal viruses may be capable of infecting humans is currently intractable at the time of their discovery, precluding prioritization of high-risk viruses for early investigation and outbreak preparedness. Given the increasing use of genomics in virus discovery and the otherwise sparse knowledge of the biology of newly discovered viruses, we developed machine learning models that identify candidate zoonoses solely using signatures of host range encoded in viral genomes. Within a dataset of 861 viral species with known zoonotic status, our approach outperformed models based on the phylogenetic relatedness of viruses to known human-infecting viruses (area under the receiver operating characteristic curve [AUC] = 0.773), distinguishing high-risk viruses within families that contain a minority of human-infecting species and identifying putatively undetected or so far unrealized zoonoses. Analyses of the underpinnings of model predictions suggested the existence of generalizable features of viral genomes that are independent of virus taxonomic relationships and that may preadapt viruses to infect humans. Our model reduced a second set of 645 animal-associated viruses that were excluded from training to 272 high and 41 very high-risk candidate zoonoses and showed significantly elevated predicted zoonotic risk in viruses from nonhuman primates, but not other mammalian or avian host groups. A second application showed that our models could have identified Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) as a relatively high-risk coronavirus strain and that this prediction required no prior knowledge of zoonotic Severe Acute Respiratory Syndrome (SARS)-related coronaviruses. Genome-based zoonotic risk assessment provides a rapid, low-cost approach to enable evidence-driven virus surveillance and increases the feasibility of downstream biological and ecological characterization of viruses.https://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.3001390&type=printable |
| spellingShingle | Nardus Mollentze Simon A Babayan Daniel G Streicker Identifying and prioritizing potential human-infecting viruses from their genome sequences. PLoS Biology |
| title | Identifying and prioritizing potential human-infecting viruses from their genome sequences. |
| title_full | Identifying and prioritizing potential human-infecting viruses from their genome sequences. |
| title_fullStr | Identifying and prioritizing potential human-infecting viruses from their genome sequences. |
| title_full_unstemmed | Identifying and prioritizing potential human-infecting viruses from their genome sequences. |
| title_short | Identifying and prioritizing potential human-infecting viruses from their genome sequences. |
| title_sort | identifying and prioritizing potential human infecting viruses from their genome sequences |
| url | https://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.3001390&type=printable |
| work_keys_str_mv | AT nardusmollentze identifyingandprioritizingpotentialhumaninfectingvirusesfromtheirgenomesequences AT simonababayan identifyingandprioritizingpotentialhumaninfectingvirusesfromtheirgenomesequences AT danielgstreicker identifyingandprioritizingpotentialhumaninfectingvirusesfromtheirgenomesequences |