**4. Unraveling** *T. cruzi* **transmission cycles in the Yucatan peninsula (Mexico): an example of the metabarcoding approach use**

As a proof of concept, we recently performed a pilot study of the metabarcoding approach presented above using Chagas disease in the Yucatan peninsula (Mexico) [27]. In this region, *T. dimidiata* is the main vector, and different genetic subgroups of this species [30–32] live in sympatry [33]. The different molecular markers we selected for our metabarcoding approach are described below: (i) to classify *T. dimidiata* in its different genetics subgroups, we used primers targeting the Internal Transcribed Spacer ITS-2 as previously described [34]; (ii) for blood-feeding source identification, we used vertebrate universal primers targeting the 12S rRNA gene [35]; (iii) for *T. cruzi*, we used primers targeting the mini-exon gene, allowing further classification of the parasite in its different DTUs [36]; and (iv) finally, we used universal primers targeting the bacterial 16S rRNA gene to identify bacterial microbiome composition [37]. This way, we aimed to determine if there were detectable interaction patterns between the genetic subgroups of *T. dimidiata*, their blood-feeding hosts, the infection with *T. cruzi*, the parasite DTUs, and the microbiome composition, allowing elucidating at finer scales the *T. cruzi* transmission cycles in the study area.

This study, which was based on 14 *T. dimidiata* bugs collected in wild as well as in domestic ecotopes, evidences the feasibility and high sensibility of the proposed approach [27]. For example, we identified an average number of blood-feeding species per bug of 4.9 ± 0.7 and up to 7 blood-feeding species and 11 blood-feeding individuals in a single bug. Contrastingly, current techniques based on direct sequencing of PCR products can only identify the dominant sequence/host in each sample [38], while the addition of a cloning step prior to sequencing generally

allows detecting up to three to five host species in some bugs [14, 39–41]. In the same way, we easily identified different DTUs infecting single bugs, while to date, most studies have relied on conventional Sanger sequencing approaches that are only capable of detecting the dominant genotype in biological samples, which almost precludes the possibility of detecting multiclonality. Based on this observation, NGS approaches capable of inventorying multiclonal infections are now being progressively adopted [42–46]. Regarding midgut microbiome, we were able to detect 23 bacterial orders and observed that its composition differed according to blood-feeding sources (**Figure 3**). Finally, all the 14 bugs belonged all to the same genetic subgroup.

To further assess potential transmission cycles of *T. cruzi* parasites by *T. dimidiata* among the identified blood source species, a feeding and parasite transmission network was constructed (**Figure 4**). Nodes of the network represent the species identified as blood meal sources, while the size of the corresponding node indicates feeding frequency on each species. Edges link species which are found together in multiple blood meals within individual bugs. Since birds cannot carry *T. cruzi* parasites, they only play a role as blood sources for triatomines, which is indicated by dotted edge connections between hosts. The solid lines between mammals indicate potential parasite transmission pathways. This network nicely highlights the mammals which would play the main role in *T. cruzi* transmission to human in the study area. Humans (*Homo sapiens* in **Figure 4**) may thus become infected by *T. cruzi* parasites originating from dogs (*Canis lupus*), cows (*Bos taurus*), and mice (*Mus musculus*), as well as from sylvatic hosts such as porcupines (*Coendou* spp.), squirrels (*Sciurus* spp.), and fruit bats (*Artibeus* spp.). Particularly, dogs appear as key actors which may favor parasite transmission to humans. This kind of networks is very informative, as it allows evidencing the animals that would play the main roles in the transmission of any pathogen to human (complementary studies focused directly on these animals may nevertheless be necessary) and that should be targeted as part of integrated control strategies aimed at disrupting parasite transmission. For example, management of the dogs and other peridomestic animals can be part of EcoHealth/One Health approaches [47]. The network presented is the result of a pilot study based on a limited sample and is only used here to illustrate the potential of the proposed metabarcoding approach. Increasing the sample size in a wide variety of ecotopes and integrating vector, microbiome and coinfection data will undoubtedly allow identifying atrisk situations and disentangling transmission cycles. It may also help to identify bacteria which are part of the normal microbiota of triatomine bugs, bacteria

#### **Figure 3.**

*Gut microbiome composition of* Triatoma dimidiata*. The average composition of the microbiome from 14 individuals is shown to the level of bacterial order (A). There are significant differences between male and female microbiomes, with females presenting a greater diversity of orders. (B) Microbiome composition is also significantly different depending on the dominant blood meal present in triatomine gut, which was identified by the analysis of 12S rRNA vertebrate sequences. Figure taken from [27].*

**91**

**Figure 4.**

*Metabarcoding: A Powerful Yet Still Underestimated Approach for the Comprehensive Study…*

associated with the presence/absence of infection of the bugs with *T. cruzi*, or bacteria of vital importance to the bugs. This knowledge can have important applications for the development of innovative control strategies [48–50].

*Feeding and possible parasite transmission network of* Triatoma dimidiata*. Blood source nodes correspond to domestic (green symbols) and sylvatic (orange symbols) host species, as well as humans (blue), with the size proportional to the feeding frequency on each host. Diamond-shaped nodes represent birds, which do not carry*  Trypanosoma cruzi *parasites, and circles represent mammals, which can be infected by* T. cruzi*. Edges link species which are found together in multiple blood meals within individual bugs, and the width of the lines is proportional to the frequency of the association between species. Solid dark gray lines link mammalian species, among which* T. cruzi *may circulate, while dotted light gray lines involve bird species, which only serve as blood sources for the bugs. Humans may thus become infected by* T. cruzi *parasites originating from dogs, cows, and mice, as well as from sylvatic hosts such as porcupines, squirrels, and fruit bats. Dogs can play a key role as domestic host/reservoir favoring parasite transmission to humans. On the other hand, cats, rats, and pigs play a* 

cost-effectiveness and likelihood of success [53].

*secondary role in parasite transmission. Figure taken from [27].*

actors involved in transmission.

The information provided by the approach can also be used to feed models including the hosts involved in the transmission to help assessing the effects of different host community managements on *T. cruzi* transmission to human and understand transmission dynamics over time [51, 52]. Transmission models are becoming increasingly important in vector-borne disease control programs. They allow evaluating different control strategies or combinations of them and assessing their

Consequently, the approach presented here provides very high-value information that can be used in multiple ways for further design and implementation of sustainable, effective, and locally adapted control strategies and deserves to be extended to other eco-epidemiological contexts and to any vector-borne pathogen. To date, metabarcoding approaches for the study of human vector-borne diseases using natural populations of vectors are being progressively adopted, but they are still timidly used [54, 55]. Moreover, they are still generally focused only on one of the components of transmission cycles, such as blood-feeding hosts [56–59], plant-feeding hosts [28], microbiome composition [60, 61], or vector diversity [62] (**Table 1**), thus providing limited information, while the approach can be easily more integrative, as we illustrated here, to simultaneously identify the different

*DOI: http://dx.doi.org/10.5772/intechopen.89839*

*Metabarcoding: A Powerful Yet Still Underestimated Approach for the Comprehensive Study… DOI: http://dx.doi.org/10.5772/intechopen.89839*

#### **Figure 4.**

*Vector-Borne Diseases - Recent Developments in Epidemiology and Control*

genetic subgroup.

allows detecting up to three to five host species in some bugs [14, 39–41]. In the same way, we easily identified different DTUs infecting single bugs, while to date, most studies have relied on conventional Sanger sequencing approaches that are only capable of detecting the dominant genotype in biological samples, which almost precludes the possibility of detecting multiclonality. Based on this observation, NGS approaches capable of inventorying multiclonal infections are now being progressively adopted [42–46]. Regarding midgut microbiome, we were able to detect 23 bacterial orders and observed that its composition differed according to blood-feeding sources (**Figure 3**). Finally, all the 14 bugs belonged all to the same

To further assess potential transmission cycles of *T. cruzi* parasites by *T. dimidiata* among the identified blood source species, a feeding and parasite transmission network was constructed (**Figure 4**). Nodes of the network represent the species identified as blood meal sources, while the size of the corresponding node indicates feeding frequency on each species. Edges link species which are found together in multiple blood meals within individual bugs. Since birds cannot carry *T. cruzi* parasites, they only play a role as blood sources for triatomines, which is indicated by dotted edge connections between hosts. The solid lines between mammals indicate potential parasite transmission pathways. This network nicely highlights the mammals which would play the main role in *T. cruzi* transmission to human in the study area. Humans (*Homo sapiens* in **Figure 4**) may thus become infected by *T. cruzi* parasites originating from dogs (*Canis lupus*), cows (*Bos taurus*), and mice (*Mus musculus*), as well as from sylvatic hosts such as porcupines (*Coendou* spp.), squirrels (*Sciurus* spp.), and fruit bats (*Artibeus* spp.). Particularly, dogs appear as key actors which may favor parasite transmission to humans. This kind of networks is very informative, as it allows evidencing the animals that would play the main roles in the transmission of any pathogen to human (complementary studies focused directly on these animals may nevertheless be necessary) and that should be targeted as part of integrated control strategies aimed at disrupting parasite transmission. For example, management of the dogs and other peridomestic animals can be part of EcoHealth/One Health approaches [47]. The network presented is the result of a pilot study based on a limited sample and is only used here to illustrate the potential of the proposed metabarcoding approach. Increasing the sample size in a wide variety of ecotopes and integrating vector, microbiome and coinfection data will undoubtedly allow identifying atrisk situations and disentangling transmission cycles. It may also help to identify bacteria which are part of the normal microbiota of triatomine bugs, bacteria

*Gut microbiome composition of* Triatoma dimidiata*. The average composition of the microbiome from 14 individuals is shown to the level of bacterial order (A). There are significant differences between male and female microbiomes, with females presenting a greater diversity of orders. (B) Microbiome composition is also significantly different depending on the dominant blood meal present in triatomine gut, which was identified* 

*by the analysis of 12S rRNA vertebrate sequences. Figure taken from [27].*

**90**

**Figure 3.**

*Feeding and possible parasite transmission network of* Triatoma dimidiata*. Blood source nodes correspond to domestic (green symbols) and sylvatic (orange symbols) host species, as well as humans (blue), with the size proportional to the feeding frequency on each host. Diamond-shaped nodes represent birds, which do not carry*  Trypanosoma cruzi *parasites, and circles represent mammals, which can be infected by* T. cruzi*. Edges link species which are found together in multiple blood meals within individual bugs, and the width of the lines is proportional to the frequency of the association between species. Solid dark gray lines link mammalian species, among which* T. cruzi *may circulate, while dotted light gray lines involve bird species, which only serve as blood sources for the bugs. Humans may thus become infected by* T. cruzi *parasites originating from dogs, cows, and mice, as well as from sylvatic hosts such as porcupines, squirrels, and fruit bats. Dogs can play a key role as domestic host/reservoir favoring parasite transmission to humans. On the other hand, cats, rats, and pigs play a secondary role in parasite transmission. Figure taken from [27].*

associated with the presence/absence of infection of the bugs with *T. cruzi*, or bacteria of vital importance to the bugs. This knowledge can have important applications for the development of innovative control strategies [48–50]. The information provided by the approach can also be used to feed models including the hosts involved in the transmission to help assessing the effects of different host community managements on *T. cruzi* transmission to human and understand transmission dynamics over time [51, 52]. Transmission models are becoming increasingly important in vector-borne disease control programs. They allow evaluating different control strategies or combinations of them and assessing their cost-effectiveness and likelihood of success [53].

Consequently, the approach presented here provides very high-value information that can be used in multiple ways for further design and implementation of sustainable, effective, and locally adapted control strategies and deserves to be extended to other eco-epidemiological contexts and to any vector-borne pathogen. To date, metabarcoding approaches for the study of human vector-borne diseases using natural populations of vectors are being progressively adopted, but they are still timidly used [54, 55]. Moreover, they are still generally focused only on one of the components of transmission cycles, such as blood-feeding hosts [56–59], plant-feeding hosts [28], microbiome composition [60, 61], or vector diversity [62] (**Table 1**), thus providing limited information, while the approach can be easily more integrative, as we illustrated here, to simultaneously identify the different actors involved in transmission.


**93**

**5. Conclusions**

*biological samples.*

**Table 1.**

**Acknowledgements**

School of Public Health and Tropical Medicine.

*Metabarcoding: A Powerful Yet Still Underestimated Approach for the Comprehensive Study…*

Vectors and vertebrate bloodfeeding hosts

Vertebrate bloodfeeding hosts, *Trypanosoma cruzi* parasite, midgut bacterial microbiome, triatomine bug

**Target DNA Main findings Reference**

Contrasting ecological features and feeding behavior among dipteran species, which allowed unveiling arboreal and terrestrial mammals, as well as birds, lizards, and amphibians. Lower vertebrate diversity was found in sites undergoing higher levels of human-induced perturbation

Ecological associations of triatomines which shape *T. cruzi* transmission cycles. Different DTUs infecting single bugs. Identification of 14 blood-feeding species. Up to 7 blood-feeding species and 11 blood-feeding individuals identified in a single bug. Human, dog, cow, and mice were the most common host-feeding sources. Dog was highlighted as the main host involved in the pathway of *T. cruzi* transmission to human. Dynamic midgut microbiome, including 23 bacterial orders, which differed according to blood sources

[54]

[27]

In this chapter, we presented a metabarcoding approach to study vector-borne pathogen transmission cycles and their dynamics and illustrated the feasibility and high sensitivity of the proposed approach with a recent study performed using Chagas disease in the Yucatan peninsula (Mexico), as a study model. Currently, NGS technologies are quickly becoming more affordable and cost-effective. Moreover, many bioinformatics tools have allowed to greatly simplify analyses in the last years. Consequently, this powerful approach deserves to be generalized to other eco-epidemiological contexts to unravel the transmission cycles of any vectorborne pathogen and their dynamics, which in turn will help the implementation of sustainable, effective, and locally adapted control strategies of their transmission.

*Metabarcoding approaches for the study of human vector-borne diseases using natural populations of vectors as* 

This work received financial support from CONACYT (National Council of Science and Technology, Mexico) Basic Science (Project ID: CB2015-258752) and National Problems (Project ID: PN2015-893) Programs. This work was also funded by the Louisiana Board of Regents through the Board of Regents Support Fund [# LESASF (2018-2021)-RD-A-19] and grant #632083 from Tulane University

*DOI: http://dx.doi.org/10.5772/intechopen.89839*

**origin**

3 sites along a gradient of anthropogenic pressure in French Guayana, area of Saint-Georges de l'Oyapock

Different habitats in rural Yucatan (Mexico)

**Vector Geographic** 

Mosquitoes and sand flies (various species)

Triatomine bugs (*Triatoma dimidiata*)

*Brumptomyia* spp.)


*Metabarcoding: A Powerful Yet Still Underestimated Approach for the Comprehensive Study… DOI: http://dx.doi.org/10.5772/intechopen.89839*

#### **Table 1.**

*Vector-Borne Diseases - Recent Developments in Epidemiology and Control*

Mammalian bloodfeeding hosts

Vertebrate bloodfeeding hosts

Mammal bloodfeeding hosts

Vertebrate bloodfeeding hosts

Plantfeeding hosts

Bacterial and eukaryotic microbiome

Bacterial microbiome

**Target DNA Main findings Reference**

Unbiased characterization of mammalian blood-feeding hosts, including unsuspected hosts and mixed blood meals. Human, dog, and pig were the most common host-feeding sources. The approach can also be adapted to evaluate interindividual variations among human blood meals

The four most common mosquito species had similar host-feeding patterns. The most commonly detected hosts in these species were humans,

Accuracy of the short 12S marker proposed for the identification of Amazonian mammals. The accuracy of taxonomic assignations highly depends on the comprehensiveness of the

Reliability of the metabarcoding approach proposed for the identification of vertebrate blood-feeding host

Sand flies preferentially feed on *Cannabis sativa* plants. Potential utility for sand fly control

Endosymbiont *Wigglesworthia* was highly prominent. Potential role for *Salmonella* and *Serratia* in fly refractoriness to trypanosome infection. V4 region of the small subunit of the 16S ribosomal RNA gene was more efficient than the V3V4 region at describing the totality of the bacterial diversity

based on the mitochondrial 16S rRNA for identification of sand fly diversity in bulk samples

Sand flies Efficiency of metabarcoding

Patterns of microbial composition and diversity that affect pathogen prevalence appeared to differ by both vector species and habitat for a given species. Microbial composition was less diverse in urban areas

cattle, and ducks

reference library

[56]

[57]

[58]

[59]

[28]

[60]

[61]

[62]

**Vector Geographic** 

Mosquitoes *(Anopheles punctulatus)*

Mosquitoes (*Culex* and *Anopheles* spp.)

Mosquitoes and sand flies

Triatomine bugs (*Rhodnius pallescens*)

Phlebotomine sandflies (*Phlebotomus* and *Lutzomya* spp.)

Mosquitoes (*Aedes* and *Culex* spp.)

Tse-tse flies (*Glossina palpalis palpalis*)

Phlebotomine sand flies (*Lutzomya* and *Brumptomyia* spp.)

**origin**

Different villages in Papua New Guinea

Different sites in the coast of the Caspian Sea in northern Iran

Forest sites in French Guiana

Two sampling sites in in Panama

Different sampling sites in Brazil, Israel, and Ethiopia

Different habitats across central Thailand

Two

Various locations in French Guiana

trypanosomiasis foci in Cameroon

**92**

*Metabarcoding approaches for the study of human vector-borne diseases using natural populations of vectors as biological samples.*
