The main vector for visceral leishmaniasis (VL) in Brazil is Lutzomyia longipalpis. However, the absence of L. longipalpis in a region of autochthonous VL demonstrates the participation of other species in the transmission of the parasite. Studies conducted in La Banda, Argentina, and São Vicente Férrer, Pernambuco State, Brazil, have correlated the absence of L. longipalpis and the presence of L. migonei with autochthonous cases of VL. In São Vicente Férrer, Pernambuco, there was evidence for the natural infection of L. migonei with Leishmania infantum chagasi. Thus, the objective of this work was to assess the ecology of the sand flies L. longipalpis and L. migonei in Fortaleza, an endemic area for VL. Insect capture was conducted at 22 sampling points distributed across four regions of Fortaleza. In total, 32,403 sand flies were captured; of these, 18,166 (56%) were identified as L. longipalpis and 14,237 (44%) as L. migonei. There were significant density differences found between the vectors at each sampling site (indoors and outdoors) (p <0.0001). These findings confirm that L. migonei and L. longipalpis are distributed throughout Fortaleza, where they have adapted to an indoor environment, and suggest that L. migonei may share the role as a vector with L. longipalpis in the transmission of VL in Fortaleza.Keywords: Lutzomyia longipalpis, Lutzomyia migonei, visceral leishmaniasis, Fortaleza. Resumo
The objective of this study was to perform an epidemiological survey to determine the areas at risk of visceral leishmaniasis through the detection and quantification of natural infection by Leishmania infantum in Lutzomyia longipalpis. The sandflies were captured between February 2009 and January 2010, at 21 sites in four regions of the Fortaleza municipality. Samples were screened for the presence of Leishmania DNA by Real Time PCR (qPCR), amplification of kDNA minicircle sequence. Out of the 123 pools of analyzed sandflies, 45 were positive for L.infantum, and the minimum infection rate was 3.7%. In the north, south, east and west regions, the pool screen assay predicted sand-fly infection prevalence of 3.4%, 4.7%, 4.9% and 8.4%, respectively. The parasite load ranged from 2.45 ± 0.96 to 2,820,246 ± 106,072. No statistical differences were found with respect to the frequency of sand-fly infection between the regions (P=0.3014), seasons (P = 0.3906) or trap locations (P = 0.8486). Statistical differences were found with respect to the frequency of sand-fly infection between the two seasons only in the west region (P=0.0152). The qPCR was able to detect and quantify L. infantum in L. longipalpis, therefore succeeding in identifying the areas of greatest risk of VL transmission.Keywords: Leishmania infantum, Lutzomyia longipalpis, minimum infection rate, qPCR. ResumoO objetivo foi realizar um estudo epidemiológico para determinar as áreas de risco de transmissão de leishmaniose visceral pela detecção e quantificação de infecções naturais por Leishmania infantum em Lutzomyia longipalpis. As coletas foram realizadas entre fevereiro de 2009 e janeiro de 2010 em 21 locais, distribuídos em quatro regiões do município de Fortaleza. As amostras foram testadas quanto à presença de DNA de Leishmania por PCR em tempo real (qPCR). Dos 123 pools de flebotomíneos investigados, 45 foram positivos para L. infantum, e a taxa de infecção mínima foi de 3,7%. Nas regiões Norte, Sul, Leste e Oeste, a prevalência de flebotomíneos infectados foi de 3,4%, 4,7%, 4,9% e 8,4%, respectivamente. A carga de parasitas nos pools variou de 2,45 ± 0,96 a 2.820.246 ± 106.072. Não foram observadas diferenças significativas na frequência de flebotomíneos infectados entre as regiões (P = 0,3014), estação do ano (P = 0,3906) ou localização da armadilha (P = 0,8486). Foram observadas diferenças significativas na frequência de flebotomíneos somente na região oeste durante a estação chuvosa (P = 0,0152). A qPCR foi capaz de detectar e quantificar L. infantum em L. longipalpis, identificando as áreas de maior risco de transmissão de leishmaniose visceral.Palavras-chave: Leishmania infantum, Lutzomyia longipalpis, taxa de infecção mínima, qPCR.
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 limiting pathogen spread as well as accurately predicting 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 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 global change on local transmission cycles.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.