Phylogenetic and biogeographical traits predict unrecognized hosts of zoonotic leishmaniasis
The spatio-temporal distribution of leishmaniasis, a parasitic vector-borne zoonotic disease, is significantly impacted by land-use change and climate warming in the Americas. However, predicting and containing outbreaks is challenging as the zoonotic Leishmania system is highly complex: leishmanias...
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ftdoajarticles:oai:doaj.org/article:568a6d0abd894430acf7e8ceeca5a279 2023-07-02T03:31:35+02:00 Phylogenetic and biogeographical traits predict unrecognized hosts of zoonotic leishmaniasis Caroline K. Glidden Aisling Roya Murran Rafaella Albuquerque Silva Adrian A. Castellanos Barbara A. Han Erin A. Mordecai 2023-05-01T00:00:00Z https://doaj.org/article/568a6d0abd894430acf7e8ceeca5a279 EN eng Public Library of Science (PLoS) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10231829/?tool=EBI https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 https://doaj.org/article/568a6d0abd894430acf7e8ceeca5a279 PLoS Neglected Tropical Diseases, Vol 17, Iss 5 (2023) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2023 ftdoajarticles 2023-06-11T00:37:56Z The spatio-temporal distribution of leishmaniasis, a parasitic vector-borne zoonotic disease, is significantly impacted by land-use change and climate warming in the Americas. However, predicting and containing outbreaks is challenging as the zoonotic Leishmania system is highly complex: leishmaniasis (visceral, cutaneous and muco-cutaneous) in humans is caused by up to 14 different Leishmania species, and the parasite is transmitted by dozens of sandfly species and is known to infect almost twice as many wildlife species. Despite the already broad known host range, new hosts are discovered almost annually and Leishmania transmission to humans occurs in absence of a known host. As such, the full range of Leishmania hosts is undetermined, inhibiting the use of ecological interventions to limit pathogen spread and the ability to accurately predict the impact of global change on disease risk. Here, we employed a machine learning approach to generate trait profiles of known zoonotic Leishmania wildlife hosts (mammals that are naturally exposed and susceptible to infection) and used trait-profiles of known hosts to identify potentially unrecognized hosts. We found that biogeography, phylogenetic distance, and study effort best predicted Leishmania host status. Traits associated with global change, such as agricultural land-cover, urban land-cover, and climate, were among the top predictors of host status. Most notably, our analysis suggested that zoonotic Leishmania hosts are significantly undersampled, as our model predicted just as many unrecognized hosts as unknown hosts. Overall, our analysis facilitates targeted surveillance strategies and improved understanding of the impact of environmental change on local transmission cycles. Author summary Leishmaniasis is a zoonotic, vector borne disease of poverty with a high burden throughout the Americas: within Latin America there are an estimated 58,500 new cases per year and 54,050 years of life lost due to disability. Although the World Health Organization has ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic |
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Directory of Open Access Journals: DOAJ Articles |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Caroline K. Glidden Aisling Roya Murran Rafaella Albuquerque Silva Adrian A. Castellanos Barbara A. Han Erin A. Mordecai Phylogenetic and biogeographical traits predict unrecognized hosts of zoonotic leishmaniasis |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
The spatio-temporal distribution of leishmaniasis, a parasitic vector-borne zoonotic disease, is significantly impacted by land-use change and climate warming in the Americas. However, predicting and containing outbreaks is challenging as the zoonotic Leishmania system is highly complex: leishmaniasis (visceral, cutaneous and muco-cutaneous) in humans is caused by up to 14 different Leishmania species, and the parasite is transmitted by dozens of sandfly species and is known to infect almost twice as many wildlife species. Despite the already broad known host range, new hosts are discovered almost annually and Leishmania transmission to humans occurs in absence of a known host. As such, the full range of Leishmania hosts is undetermined, inhibiting the use of ecological interventions to limit pathogen spread and the ability to accurately predict the impact of global change on disease risk. Here, we employed a machine learning approach to generate trait profiles of known zoonotic Leishmania wildlife hosts (mammals that are naturally exposed and susceptible to infection) and used trait-profiles of known hosts to identify potentially unrecognized hosts. We found that biogeography, phylogenetic distance, and study effort best predicted Leishmania host status. Traits associated with global change, such as agricultural land-cover, urban land-cover, and climate, were among the top predictors of host status. Most notably, our analysis suggested that zoonotic Leishmania hosts are significantly undersampled, as our model predicted just as many unrecognized hosts as unknown hosts. Overall, our analysis facilitates targeted surveillance strategies and improved understanding of the impact of environmental change on local transmission cycles. Author summary Leishmaniasis is a zoonotic, vector borne disease of poverty with a high burden throughout the Americas: within Latin America there are an estimated 58,500 new cases per year and 54,050 years of life lost due to disability. Although the World Health Organization has ... |
format |
Article in Journal/Newspaper |
author |
Caroline K. Glidden Aisling Roya Murran Rafaella Albuquerque Silva Adrian A. Castellanos Barbara A. Han Erin A. Mordecai |
author_facet |
Caroline K. Glidden Aisling Roya Murran Rafaella Albuquerque Silva Adrian A. Castellanos Barbara A. Han Erin A. Mordecai |
author_sort |
Caroline K. Glidden |
title |
Phylogenetic and biogeographical traits predict unrecognized hosts of zoonotic leishmaniasis |
title_short |
Phylogenetic and biogeographical traits predict unrecognized hosts of zoonotic leishmaniasis |
title_full |
Phylogenetic and biogeographical traits predict unrecognized hosts of zoonotic leishmaniasis |
title_fullStr |
Phylogenetic and biogeographical traits predict unrecognized hosts of zoonotic leishmaniasis |
title_full_unstemmed |
Phylogenetic and biogeographical traits predict unrecognized hosts of zoonotic leishmaniasis |
title_sort |
phylogenetic and biogeographical traits predict unrecognized hosts of zoonotic leishmaniasis |
publisher |
Public Library of Science (PLoS) |
publishDate |
2023 |
url |
https://doaj.org/article/568a6d0abd894430acf7e8ceeca5a279 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 17, Iss 5 (2023) |
op_relation |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10231829/?tool=EBI https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 https://doaj.org/article/568a6d0abd894430acf7e8ceeca5a279 |
_version_ |
1770270944858734592 |