Volatile compound diversity and conserved alarm behaviour in Triatoma dimidiata

Background: Triatoma dimidiata (Latreille) is a key vector complex of Trypanosoma cruzi, etiologic agent of Chagas disease, as it spans North, Central, and South America. Although morphological and genetic studies clearly indicate existence of at least five clades within the species, there has been no robust or systematic revision, or appropriate nomenclature change for species within the complex. Three of the clades (haplogroups) are distributed in Mexico, and recent evidence attests to dispersal of clades across previously "presumed"monotypic geographic regions. Evidence of niche conservatism among sister species of this complex suggests that geographic dispersal is possible for non-sympatric populations, although no information is available on the behavioural aspects of potential interclade interactions, for instance whether differentiation of chemical signaling or response to these signals could impede communication among the haplogroups. Methods: Volatiles emitted by disturbed bugs, Brindley's (BGs), and metasternal (MGs) glands were identified using solid-phase micro-extraction (SPME) and gas chromatography coupled mass spectrometry (GC-MS). Volatile compounds emitted by BGs and MGs, and those secreted by disturbed nymphs and adults, of the three Mexican T. dimidiata haplogroups were tested for avoidance behaviour by conspecific nymphs and adults using an olfactometer. Results: Triatoma dimidiata haplogroups all have three age-related alarm responses: absence of response by early stage nymphs, stage-specific response by 4-5th stage nymphs, and a shared 4-5th nymph and adult response to adult compounds. Disturbed bugs released 15 to 24 compounds depending on the haplogroup, among which were three pyrazines, the first report of these organoleptics in Triatominae. Isobutyric acid from BGs was the most abundant molecule in the response in all haplogroups, in addition to 15 (h1) to 21 (h2 and h3) MG compounds. Avoidance behaviour of disturbed bugs and volatiles emitted by BGs were haplogroup specific, while those from the MG were not. Conclusions: Discriminant and cluster analysis of BG +MG compounds indicate significant separation among the three haplogroups, while alarm response compounds were similar between h2 and h3, both distinct from h1. This latter haplogroup is ancestral phylogenetically to the other two. Our results suggest that alarm responses are a conserved behaviour in the Triatoma dimidiata complex.Fil: May Concha, Irving Jesus. Provincia de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Universidad Autónoma de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción; Argentina. Instituto Nacional de Salud Pública; MéxicoFil: Rojas, Julio C.. El Colegio de la Frontera Sur; MéxicoFil: Cruz López, Leopoldo. El Colegio de la Frontera Sur; MéxicoFil: Ibarra-Cerdeña, Carlos N.. Instituto Politécnico Nacional. Centro de Investigación y de Estudios Avanzados; MéxicoFil: Ramsey, Janine. Instituto Nacional de Salud Pública; Méxic

Using Biotic Interaction Networks for Prediction in Biodiversity and Emerging Diseases

Networks offer a powerful tool for understanding and visualizing inter-species interactions within an ecology. Previously considered examples, such as trophic networks, are just representations of experimentally observed direct interactions. However, species interactions are so rich and complex it is not feasible to directly observe more than a small fraction. In this paper, using data mining techniques, we show how potential interactions can be inferred from geographic data, rather than by direct observation. An important application area for such a methodology is that of emerging diseases, where, often, little is known about inter-species interactions, such as between vectors and reservoirs. Here, we show how using geographic data, biotic interaction networks that model statistical dependencies between species distributions can be used to infer and understand inter-species interactions. Furthermore, we show how such networks can be used to build prediction models. For example, for predicting the most important reservoirs of a disease, or the degree of disease risk associated with a geographical area. We illustrate the general methodology by considering an important emerging disease - Leishmaniasis. This data mining approach allows for the use of geographic data to construct inferential biotic interaction networks which can then be used to build prediction models with a wide range of applications in ecology, biodiversity and emerging diseases

Phylogeny and Niche Conservatism in North and Central American Triatomine Bugs (Hemiptera: Reduviidae: Triatominae), Vectors of Chagas' Disease

The niche conservatism hypothesis states that related species diverge in niche characteristics at lower rates than expected, given their lineage divergence. Here we analyze whether niche conservatism is a common pattern among vector species (Hemiptera: Reduviidae: Triatominae) of Trypanosoma cruzi that inhabit North and Central America, a highly heterogeneous landmass in terms of environmental gradients. Mitochondrial and nuclear loci were used in a multi-locus phylogenetic framework to reconstruct phylogenetic relationships among species and estimate time of divergence of selected clades to draw biogeographic inferences. Then, we estimated similarity between the ecological niche of sister species and tested the niche conservatism hypothesis using our best estimate of phylogeny. Triatoma is not monophyletic. A primary clade with all North and Central American (NCA) triatomine species from the genera Triatoma, Dipetalogaster, and Panstrongylus, was consistently recovered. Nearctic species within the NCA clade (T. p. protracta, T. r. rubida) diverged during the Pliocene, whereas the Neotropical species (T. phyllosoma, T. longipennis, T. dimidiata complex) are estimated to have diverged more recently, during the Pleistocene. The hypothesis of niche conservatism could not be rejected for any of six sister species pairs. Niche similarity between sister species best fits a retention model. While this framework is used here to infer niche evolution, it has a direct impact on spatial vector dynamics driven by human population movements, expansion of transportation networks and climate change scenarios.CNIC was funded with a graduate scholarship from CONACYT (Consejo Nacional de Ciencia y Tecnologia) for his PhD studies in the Biomedical Sciences Program of the UNAM (Universidad Nacional Autonoma de Mexico), fulfilled in part by this study. Studies on vector bionomics and ecology were funded by CONACYT Fomix Morelos MOR-2004-C02-012 and CONACYT FONSEC 69997 and 161405 to JMR. This work was partially supported by DGAPA-UNAM (PAPIIT 487 IN225408, IN202711) and the CONACYT-CB-2009/132811 to VSC, and PAPIIT 2013488 and CONACYT-511 to AZR. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Ecological Connectivity of Trypanosoma cruzi Reservoirs and Triatoma pallidipennis Hosts in an Anthropogenic Landscape with Endemic Chagas Disease

Traditional methods for Chagas disease prevention are targeted at domestic vector reduction, as well as control of transfusion and maternal-fetal transmission. Population connectivity of Trypanosoma cruzi-infected vectors and hosts, among sylvatic, ecotone and domestic habitats could jeopardize targeted efforts to reduce human exposure. This connectivity was evaluated in a Mexican community with reports of high vector infestation, human infection, and Chagas disease, surrounded by agricultural and natural areas. We surveyed bats, rodents, and triatomines in dry and rainy seasons in three adjacent habitats (domestic, ecotone, sylvatic), and measured T. cruzi prevalence, and host feeding sources of triatomines. Of 12 bat and 7 rodent species, no bat tested positive for T. cruzi, but all rodent species tested positive in at least one season or habitat. Highest T. cruzi infection prevalence was found in the rodents, Baiomys musculus and Neotoma mexicana. In general, parasite prevalence was not related to habitat or season, although the sylvatic habitat had higher infection prevalence than by chance, during the dry season. Wild and domestic mammals were identified as bloodmeals of T. pallidipennis, with 9% of individuals having mixed human (4.8% single human) and other mammal species in bloodmeals, especially in the dry season; these vectors tested >50% positive for T. cruzi. Overall, ecological connectivity is broad across this matrix, based on high rodent community similarity, vector and T. cruzi presence. Cost-effective T. cruzi, vector control strategies and Chagas disease transmission prevention will need to consider continuous potential for parasite movement over the entire landscape. This study provides clear evidence that these strategies will need to include reservoir/host species in at least ecotones, in addition to domestic habitats.This study was funded by the Secretaria de Salud and Consejo Nacional de Ciencia y Tecnología (CONACyt) project MOR-2004-CO2-012 to JMR, and by the Universidad Nacional Autónoma de México (PAPIIT project 225408 to VS-C, and the Sistema de Informática para la Biodiversidad y el Ambiente [SIBA], and Tecnologías para la Universidad de la Información y la Computación. AEGC was funded with a scholarship from CONACyT for a M. Sc. degree in vector-borne diseases at the Instituto Nacional de Salud Publica. CNIC is funded with a scholarship from CONACyT for studies at the graduate program in Biomedical Sciences of the Universidad Nacional Autonoma de Mexico

Using Biotic Interaction Networks for Prediction in Biodiversity and Emerging Diseases

Abstract: Networks offer a powerful tool for understanding and visualizing inter-species ecological and evolutionary interactions. Previously considered examples, such as trophic networks, are just representations of experimentally observed direct interactions. However, species interactions are so rich and complex it is not feasible to directly observe more than a small fraction. In this paper, using data mining techniques, we show how potential interactions can be inferred from geographic data, rather than by direct observation. An important application area for this methodology is that of emerging diseases, where, often, little is known about inter-species interactions, such as between vectors and reservoirs. Here, we show how using geographic data, biotic interaction networks that model statistical dependencies between species distributions can be used to infer and understand inter-species interactions. Furthermore, we show how such networks can be used to build prediction models. For example, for predicting the most important reservoirs of a disease, or the degree of disease risk associated with a geographical area. We illustrate the general methodology by considering an important emerging disease -Leishmaniasis. This data mining methodology allows for the use of geographic data to construct inferential biotic interaction networks which can then be used to build prediction models with a wide range of applications in ecology, biodiversity and emerging diseases

Metabarcoding: A Powerful Yet Still Underestimated Approach for the Comprehensive Study of Vector-Borne Pathogen Transmission Cycles and Their Dynamics

The implementation of sustainable control strategies aimed at disrupting the transmission of vector-borne pathogens requires a comprehensive knowledge of the vector ecology in the different eco-epidemiological contexts, as well as the local pathogen transmission cycles and their dynamics. However, even when focusing only on one specific vector-borne disease, achieving this knowledge is highly challenging, as the pathogen may exhibit a high genetic diversity and multiple vector species or subspecies and host species may be involved. In addition, the development of the pathogen and the vectorial capacity of the vectors may be affected by their midgut and/or salivary gland microbiome. The recent advent of Next-Generation Sequencing (NGS) technologies has brought powerful tools that can allow for the simultaneous identification of all these essential components, although their potential is only just starting to be realized. We present a metabarcoding approach that can facilitate the description of comprehensive host-pathogen networks, integrate important microbiome and coinfection data, identify at-risk situations, and disentangle the transmission cycles of vector-borne pathogens. This powerful approach should be generalized to unravel the transmission cycles of any pathogen and their dynamics, which in turn will help the design and implementation of sustainable, effective, and locally adapted control strategies

Leishmania (L.) mexicana infected bats in Mexico: novel potential reservoirs

Leishmania (Leishmania) mexicana causes cutaneous leishmaniasis, an endemic zoonosis affecting a growing number of patients in the southeastern states of Mexico. Some foci are found in shade-grown cocoa and coffee plantations, or near perennial forests that provide rich breeding grounds for the sand fly vectors, but also harbor a variety of bat species that live off the abundant fruits provided by these shade-giving trees. The close proximity between sand flies and bats makes their interaction feasible, yet bats infected with Leishmania (L.) mexicana have not been reported. Here we analyzed 420 bats from six states of Mexico that had reported patients with leishmaniasis. Tissues of bats, including skin, heart, liver and/or spleen were screened by PCR for Leishmania (L.) mexicana DNA. We found that 41 bats (9.77%), belonging to 13 species, showed positive PCR results in various tissues. The infected tissues showed no evidence of macroscopic lesions. Of the infected bats, 12 species were frugivorous, insectivorous or nectarivorous, and only one species was sanguivorous (Desmodus rotundus), and most of them belonged to the family Phyllostomidae. The eco-region where most of the infected bats were caught is the Gulf Coastal Plain of Chiapas and Tabasco. Through experimental infections of two Tadarida brasiliensis bats in captivity, we show that this species can harbor viable, infective Leishmania (L.) mexicana parasites that are capable of infecting BALB/c mice. We conclude that various species of bats belonging to the family Phyllostomidae are possible reservoir hosts for Leishmania (L.) mexicana, if it can be shown that such bats are infective for the sand fly vector. Further studies are needed to determine how these bats become infected, how long the parasite remains viable inside these potential hosts and whether they are infective to sand flies to fully evaluate their impact on disease epidemiology

Using Biotic Interaction Networks for Prediction in Biodiversity and Emerging Diseases

Networks offer a powerful tool for understanding and visualizing inter-species ecological and evolutionary interactions. Previously considered examples, such as trophic networks, are just representations of experimentally observed direct interactions. However, species interactions are so rich and complex it is not feasible to directly observe more than a small fraction. In this paper, using data mining techniques, we show how potential interactions can be inferred from geographic data, rather than by direct observation. An important application area for this methodology is that of emerging diseases, where, often, little is known about inter-species interactions, such as between vectors and reservoirs. Here, we show how using geographic data, biotic interaction networks that model statistical dependencies between species distributions can be used to infer and understand inter-species interactions. Furthermore, we show how such networks can be used to build prediction models. For example, for predicting the most important reservoirs of a disease, or the degree of disease risk associated with a geographical area. We illustrate the general methodology by considering an important emerging disease - Leishmaniasis. This data mining methodology allows for the use of geographic data to construct inferential biotic interaction networks which can then be used to build prediction models with a wide range of applications in ecology, biodiversity and emerging diseases

Current and Future Niche of North and Central American Sand Flies (Diptera: Psychodidae) in Climate Change Scenarios

Ecological niche models are useful tools to infer potential spatial and temporal distributions in vector species and to measure epidemiological risk for infectious diseases such as the Leishmaniases. The ecological niche of 28 North and Central American sand fly species, including those with epidemiological relevance, can be used to analyze the vector’s ecology and its association with transmission risk, and plan integrated regional vector surveillance and control programs. In this study, we model the environmental requirements of the principal North and Central American phlebotomine species and analyze three niche characteristics over future climate change scenarios: i) potential change in niche breadth, ii) direction and magnitude of niche centroid shifts, iii) shifts in elevation range. Niche identity between confirmed or incriminated Leishmania vector sand flies in Mexico, and human cases were analyzed. Niche models were constructed using sand fly occurrence datapoints from Canada, USA, Mexico, Guatemala and Belize. Nine non-correlated bioclimatic and four topographic data layers were used as niche components using GARP in OpenModeller. Both B2 and A2 climate change scenarios were used with two general circulation models for each scenario (CSIRO and HadCM3), for 2020, 2050 and 2080. There was an increase in niche breadth to 2080 in both scenarios for all species with the exception of Lutzomyia vexator. The principal direction of niche centroid displacement was to the northwest (64%), while the elevation range decreased greatest for tropical, and least for broad-range species. Lutzomyia cruciata is the only epidemiologically important species with high niche identity with that of Leishmania spp. in Mexico. Continued landscape modification in future climate change will provide an increased opportunity for the geographic expansion of NCA sand flys’ ENM and human exposure to vectors of Leishmaniases