2023
DOI: 10.1186/s13071-022-05616-w
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“Mi Casa, Tu Casa”: the coati nest as a hub of Trypanosoma cruzi transmission in the southern Pantanal biome revealed by molecular blood meal source identification in triatomines

Abstract: Background The study of the ecology of Trypanosoma cruzi is challenging due to its extreme adaptive plasticity, resulting in the parasitism of hundreds of mammal species and dozens of triatomine species. The genetic analysis of blood meal sources (BMS) from the triatomine vector is an accurate and practical approach for gathering information on which wild mammal species participate in a local transmission network. South American coatis, Nasua nasua, act as important reservoir host species of T.… Show more

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Cited by 6 publications
(3 citation statements)
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“…This update also describes triatomine biting reports by MDC inhabitants and uses cytochrome b (cytb) metabarcoding to provide a snapshot of triatomine blood feeding patterns in the parishes with highest triatomine collection yield. Bloodmeal metabarcoding (most often targeting cytb and 12S) is widely used in the study of Chagas disease vector ecology [22][23][24][25][26][27][28][29][30]. For example, this tool has helped infer triatomine dispersal rates between wild and human-inhabited ecotopes (e.g., cases of high connectivity and re-infestation potential shown for T. infestans in Bolivia [22] and T. dimidiata in Ecuador [23]), or gauge triatomine diet specialization and overlap (e.g., within [24] and across triatomine species in Colombia [25]).…”
Section: Introductionmentioning
confidence: 99%
“…This update also describes triatomine biting reports by MDC inhabitants and uses cytochrome b (cytb) metabarcoding to provide a snapshot of triatomine blood feeding patterns in the parishes with highest triatomine collection yield. Bloodmeal metabarcoding (most often targeting cytb and 12S) is widely used in the study of Chagas disease vector ecology [22][23][24][25][26][27][28][29][30]. For example, this tool has helped infer triatomine dispersal rates between wild and human-inhabited ecotopes (e.g., cases of high connectivity and re-infestation potential shown for T. infestans in Bolivia [22] and T. dimidiata in Ecuador [23]), or gauge triatomine diet specialization and overlap (e.g., within [24] and across triatomine species in Colombia [25]).…”
Section: Introductionmentioning
confidence: 99%
“…Spatial overlap in the previous month drove the probability of current hantavirus infection, indicating a link between shared use of the environment and pathogen exposure. Most knowledge of how spatial overlap shapes environmental pathogen transmission has focused on key spatial locations where wildlife congregate, such as dens, nests, or refuges (Corner et al, 2003; Godfrey et al, 2009; Pessanha et al, 2023) or point sources of food or water (Garnett et al, 2002; Titcomb et al, 2021). Studies exploring the role of spatial overlap more broadly have found mixed effects on transmission-related outcomes: spatial overlap predicted contact rates in racoons (Robert et al, 2012) and predicted tick loads and tick-borne blood parasite infection in a territorial lizard (Godfrey et al, 2010), but did not predict faecal-oral bacterial infection in studies of lizards and giraffes (Bull et al, 2012; VanderWaal et al, 2014).…”
Section: Discussionmentioning
confidence: 99%
“…We also found weak support for previous overlap with reproductive females decreasing the likelihood of current infection in non-reproductive voles (see Supplement: Fed-Control Treatment; Figure S3). key spatial locations where wildlife congregate, such as dens, nests, or refuges (35,44,77) or point sources of food or water (78,79). Studies exploring the role of spatial overlap more broadly have found mixed effects on transmission-related outcomes: spatial overlap predicted contact rates in racoons (80) and predicted tick loads and tick-borne blood parasite infection in a territorial lizard (45), but did not predict faecal-oral bacterial infection in other studies of lizards and giraffes (39,42).…”
Section: Models Of Infection Likelihood By Treatment 1 Rmentioning
confidence: 99%