About 60% of new infectious diseases in humans come from animals. Their increasing number and rapid spread are linked to increasing levels of contact between humans and wildlife, as recently highlighted by the epidemics of Zika in Brazil or Ebola in West Africa. To anticipate and prevent similar outbreaks in the future, it would be ideal to develop new methods for the early detection and monitoring of infectious diseases in wild animals.

Currently, three methods are mainly used to screen wild animals for infectious disease, but these all have limitations. Analyses of bushmeat and game meat only investigate those animals that are eaten by humans. Testing the organs and tissues of trapped animals can be difficult and harmful for both the humans and animals involved. Collecting and examining samples of feces, urine or saliva cannot detect all diseases and can be difficult to do for some species.

Bitome-Essono et al. now demonstrate a new method for assessing the diseases carried by wild animals: using blood-sucking flies as 'flying syringes' to collect their blood. During several weeks of sampling in Gabon, Central Africa, Bitome-Essono et al. trapped thousands of these flies, about a third of which were engorged with blood. Analyses of these blood samples revealed that they had come from 20 different species, including birds, mammals and reptiles. Different malaria parasites could also be detected in the blood.

Although the study performed by Bitome-Essono et al. only focused on malaria parasites, in the future the technique could be extended to analyze a number of disease-causing microbes – including viruses, bacteria, protozoa and macroparasites – that are found in the blood of wild animals.

Emerging and re-emerging human infectious diseases have increased in recent years. Around one-fourth of the 1415 pathogens known to infect humans appeared between 1940 and 2004 and their appearance has gradually increased since 1980 (Taylor et al., 2001; Woolhouse and Gaunt, 2007; Jones et al., 2008; Daszak et al., 2004). Today, seven new pathogens appear every year and this number should reach 15–20 by 2020 (Woolhouse et al., 2008), mostly due to the growth of human activities that increase contact with novel sources of pathogens and favor their spread worldwide (Murray et al., 2015). Emerging threats mainly concern viruses, such as HIV (Sharp and Hahn, 2011), SARS-CoV and MERS-CoV (de Wit et al., 2016), avian flu (Alexander, 2007) and more recently Ebola (Baize et al., 2014), chikungunya (Burt et al., 2012) and Zika (Wikan and Smith, 2016). However, disease emergence and re-emergence also concern bacteria (e.g. Helicobacter pylori, Salmonella sp., etc.) and parasites (e.g. Plasmodium knowlesi in South-East Asia). Sixty per cent of diseases emerging in humans are zoonoses and wildlife plays a key role by providing a zoonotic pool from which previously unknown pathogens may emerge (Taylor et al., 2001; Woolhouse and Gaunt, 2007; Jones et al., 2008; Daszak et al., 2004). The case of P. knowlesi in South-East Asia is a good example. This parasite emerged in the human population after a transfer from Asian macaques. It is now considered as the fifth human malaria agent after Plasmodium falciparum, Plasmodium vivax, Plasmodium malariae and Plasmodium ovale (Singh and Daneshvar, 2013). Such emerging diseases constitute a massive public health issue that requires active monitoring for signs of outbreaks and rapid diagnosis of the involved pathogen. Therefore, it is crucial to anticipate and prevent potential epidemic and pandemic outbreaks by developing new methods for the early detection and monitoring of infectious agents in wild animal sources (Kuiken et al., 2005; Wolfe et al., 2005). However, in many cases, monitoring is limited or impossible due to our poor knowledge about the ecology of these pathogens (i.e. where, when and how these agents circulate in the wildlife). The case of the Ebola virus is quite exemplary. Indeed, the exact nature of its reservoir(s) remains uncertain, although thousands of animals have been screened during the last 40 years (e.g. [Marí Saéz et al., 2015]).

Nowadays, pathogen circulation in wild animals is screened using mainly two methods: bushmeat analysis or direct trapping of animals for organ and tissue collection. These methods are pertinent in many cases, but present some weaknesses. Bushmeat represents only a fraction of the fauna (the one consumed by humans), whereas animal trapping can be difficult or dangerous. Moreover, such manipulation may be harmful for threatened and protected species. As a consequence, several methods were developed in the last years to study pathogen diversity from wild fauna without the need of direct contacts with animals, for example, by using fecal, urine or saliva samples (e.g. [Santiago et al., 2002; Prugnolle et al., 2010; Pesapane et al., 2013; Taberlet et al., 2012]). However, the value of these non-invasive methods remains limited because not all pathogens can be detected and not all reservoirs can be explored by these methods (for instance, it is difficult to collect feces or saliva of reptiles without trapping them). Therefore, new non-invasive methods are crucially needed to provide new opportunities for screening a larger range of hosts and pathogens.

The use of hematophagous flies as ‘flying syringes’ may constitute a new approach to track and survey blood-borne pathogens in the wild (Calvignac-Spencer et al., 2013). Nucleic acids (DNA or RNA) of vertebrate hosts or of pathogens in arthropod blood meals are preserved and detectable for several days (Calvignac-Spencer et al., 2013; Kent, 2009; Muturi et al., 2011; Grubaugh et al., 2015; Lee et al., 2015). For example, HIV was detected 8 days and 10 to 14 days after blood ingestion by bugs and by ticks, respectively (Webb et al., 1989; Humphery-Smith et al., 1993). Recently, the H5N1 flu virus was found viable in mosquitoes (Barbazan et al., 2008), although its transmission by these insects is unproven (Sawabe et al., 2006). Grubaugh and colleagues (Grubaugh et al., 2015) applied such an idea (that they called ‘xenosurveillance’) using Anohpeles mosquitoes to estimate the diversity of viruses infecting human populations in remote areas. Nevertheless, blood-engorged mosquitoes are very difficult to collect in forest and often show strong host preferences (in particular for mammals). Arthropods with more generalist blood feeding patterns would be more useful to survey pathogens from a large range of vertebrates (including mammals, birds and reptiles) in these highly complex ecosystems.

Hematophagous flies (tsetse flies, stomoxids and tabanids) could be good candidates for this purpose since they are usually large Diptera (length comprised between 3 and 25 mm) and hematophagous in both sexes, with the exception of male tabanids (Mullens, 2002). They are easy to trap and some studies performed on tsetse flies and stomoxids showed that 20 to 40% of trapped flies are engorged with blood (Mavoungou et al., 2008; Simo et al., 2012). These flies feed on a large spectrum of vertebrate hosts, including birds, reptiles and mammals (Muturi et al., 2011; Clausen et al., 1998; Muzari et al., 2010). The omnipresence of hematophagous flies in certain habitats and their opportunistic blood-feeding behaviour (Muturi et al., 2011; Muzari et al., 2010; Späth, 2000) make of them compelling candidates to obtain blood meals from different vertebrate hosts for pathogen detection.

In the present study, we investigated the possibility of using hematophagous flies as ‘flying syringes’ to explore the diversity of extant malaria parasites (Haemosporida) infecting wild vertebrates living in the forests of Gabon (Central Africa).

In this study, we tested whether hematophagous flies could be used as ‘flying syringes’ to identify blood-borne pathogens circulating in the wild vertebrate fauna of Gabon. Our results show that the blood meals of the captured engorged flies can be successfully used to analyze the diversity of extant malaria parasites. Despite a limited sampling effort (a total of 4 weeks of sampling for each park), we could screen the diversity of haemosporidian parasites from a large range of vertebrate hosts, including mammals, birds and reptiles. Parasites were detected in more than 8% of the analyzed samples. These malaria parasites belonged to already known, but also to never previously described lineages. In addition, the method allowed identifying the natural hosts of parasites for which only the vectors were known.

Concerning the method efficiency, 30% of blood meals were obtained from 4099 hematophagous flies. This result is consistent with previous studies (Mavoungou et al., 2008; Simo et al., 2012) showing that most hematophagous flies caught using traps are often seeking hosts for a blood meal. Other methods using a dip net seem to have a better capture efficiency with more than 40% of engorged flies caught on their resting places (Gouteux et al., 1984). However, this method requires spending a lot of time in the field because of difficulties in finding their resting sites and catching the flies.

Tsetse flies provided 99% of the collected blood meals (54% by Glossina palpalis palpalis) and they are an interesting candidate as ‘flying syringes’. Indeed, differently from stomoxids and tabanids, both sexes are exclusively hematophagous in tsetse flies. In addition, G. p. palpalis is considered to be an opportunistic species concerning its feeding behaviour, thus explaining the large diversity of blood meals (Clausen et al., 1998; Simo et al., 2008; Weitz, 1963). Conversely, stomoxids and tabanids show sex-specific differences in feeding behaviour and this may partly explain the smaller number of blood meals collected in these two families. In stomoxids, both sexes are hematophagous, but males sometimes feed on nectar (Wall and Shearer, 1997). Moreover, the digestion of stomoxids starts more rapidly than in the other hematophagous flies (Moffatt et al., 1995). Male and female tabanids feed on nectar just after their emergence as adults. Only after having been fertilized, females start sucking blood (Mullens, 2002). Therefore, engorged stomoxid and tabanid flies are more difficult to capture. Additionally, the lack of engorged stomoxids and tabanids could be explained by the fact that we sampled flies only at floor level. Indeed, some stomoxid species readily feed on arboreal monkeys that are mostly found higher in the tree layer (Mavoungou et al., 2008).

The low rate (35%) of blood meal identifications could be explained by the degradation of host DNA during digestion in the fly midgut or by a too small blood quantity in the midgut. The stage of digestion might influence DNA degradation and the host identification efficiency. Nevertheless, the diversity of hosts we successfully identified, mainly in tsetse fly blood meals, was large, including big terrestrial (elephants) and semi-aquatic mammals (hippopotamus) and also reptiles and birds. As previously noted, the diversity of blood meals can be due to the fly high mobility, their opportunistic feeding behaviour and their frequent feeding. In our study, most blood meals were from terrestrial animals (i.e. that live primarily on the ground) and very few from arboreal species. As mentioned above, this result is potentially biased by the trophic preferences of tsetse flies and by the capture method that excluded canopy levels. Previous studies have shown that hematophagous flies sampled in canopies mainly feed on arboreal species (Mavoungou et al., 2008). Therefore, changes in trap position could broaden the range of host species analysed. We can also notice the absence of small mammals (e.g., rodents or bats) within the diversity of host vertebrates we identified. This may be explained by the trophic preferences of the flies we sampled which could have a preferential taste for large vertebrates as previously documented for tsetse flies (e.g. [Muturi et al., 2011; Späth, 2000]).

Concerning pathogen detection, we detected extant haemosporidian parasites in 8.65% of the 428 blood meals for which the host origin was successfully identified. Moreover, we also detected parasites in blood meals of unknown origin, thus increasing the number of detected parasites. Together, these results show that blood meals collected from hematophagous flies are suitable for tracking blood-borne pathogens from wild animals. Haemosporidian pathogens ingested by hematophagous flies during their blood meal can remain detectable in the fly digestive tract even after partial digestion of the blood meal. We observed congruence between the identified hosts and the detected pathogens. As expected, P. falciparum was detected in human blood and P. adleri in gorilla blood. Haemosporidian lineages are often host-specific or restricted to certain classes of vertebrate hosts. Therefore, the unknown host could be inferred from the detected haemosporidian species (Figure 1c). For example, the blood meal from unknown host N°110 could have originated from a Kinixys turtle (Kinixys sp.). Similarly, the blood meals from the unknown hosts N°649, 520, 665, 512 and 819 could have originated from humans (Homo sapiens).

The present study demonstrates the possibility to use hematophagous flies as ‘flying syringes’ to analyze the diversity of pathogens circulating in wildlife. We think that there is now room for improvement of the tool; for instance, by improving the methods used to identify the blood meals and the pathogens. Since DNA is likely to be degraded in many blood meals (Calvignac-Spencer et al., 2013; Schnell et al., 2012), the use of PCR systems targeting fragments of shorter size could potentially improve the performance of detection. A trial study based on 89 previously unidentified blood meals using a PCR system amplifying a shorter fragment (<150 bp) (Boessenkool et al., 2012) than the one used in the present study allowed the identification of 76% (n = 68) of the hosts (Figure 2). This represents an important gain of sensitivity. However, these primers are still not ideal for our purpose as they were designed for optimal amplification of mammal DNA and often fail to properly amplify the DNA of other classes of vertebrates. A similar PCR system targeting the entire range of vertebrates still remains to be developed. For Plasmodium, our trial for amplifying a shorter fragment of Cytb (<200 bp) using a combination of previously published primers did not increase the sensitivity. Indeed, out of 91 samples for which the blood meal was successfully identified but in which no haemosporidian infection was detected with our long Cytb PCR system, only one was shown to be positive with the short PCR system. However, it is possible that other PCR systems, more optimized, could indeed improve the sensitivity of Plasmodium detection. Another direction of improvement could be the use of high-throughput sequencing technologies on pools of blood-engorged flies or amplicons to ease the identification of both hosts and parasites (especially in the case of mixed blood meals or mixed infections). Finally, another way to improve the tool could be to use high-throughput multiplexed pathogen detection methods for the simultaneous testing of many samples in rapid succession. With such improvements, this approach of ‘xenorsurveillance’ could usefully complete recently developed methods based on the analysis of other invertebrates (carrion flies (Hoffmann et al., 2016), mosquitoes [Grubaugh et al., 2015]) and become an innovative way for the concomitant surveillance of many enzootic blood-borne pathogens, such as viruses (chikungunya, Zika), bacteria, protozoa and macro-parasites. The use of hematophagous flies as ‘flying syringes’ could indeed improve public health management by allowing the surveillance and early detection of zoonotic pathogens and thus prevent they spread to humans before they cause massive infections. This tool could also help to better understand the circulation in wildlife of other enzootic viruses, such as chikungunya or Zika, especially at the interface between natural/sylvan environments and, consequently, improving our knowledge of their natural history. From a broader perspective, this method could also be useful for people interested in wildlife biodiversity and conservation. Indeed, it could help monitoring the wildlife diversity within a specific region as demonstrated with other invertebrate systems (Calvignac-Spencer et al., 2013; Lee et al., 2015; Schnell et al., 2012; Schubert et al., 2015). More importantly, it could also allow detecting the emergence of new diseases in wild animals that may threaten their long-term survival.

1. 1. Acapovi G
   2. Yao Y
   3. N’Goran E
   4. Dia ML
   5. Desquesnes M

   (2001)

   Abondance relative des tabanidés dans la région des savanes de côte d’Ivoire

   Revue d'élevage Et De Médecine Vétérinaire Des Pays Tropicaux 54:974–980.

   • Google Scholar
2. 1. Alexander DJ

   (2007) An overview of the epidemiology of avian influenza

   Vaccine 25:5637–5644.

   https://doi.org/10.1016/j.vaccine.2006.10.051

   • PubMed
   • Google Scholar
3. 1. Altschul SF
   2. Gish W
   3. Miller W
   4. Myers EW
   5. Lipman DJ

   (1990) Basic local alignment search tool

   Journal of Molecular Biology 215:403–410.

   https://doi.org/10.1016/S0022-2836(05)80360-2

   • PubMed
   • Google Scholar
4. 1. Baize S
   2. Pannetier D
   3. Oestereich L
   4. Rieger T
   5. Koivogui L
   6. Magassouba N
   7. Soropogui B
   8. Sow MS
   9. Keïta S
   10. De Clerck H
   11. Tiffany A
   12. Dominguez G
   13. Loua M
   14. Traoré A
   15. Kolié M
   16. Malano ER
   17. Heleze E
   18. Bocquin A
   19. Mély S
   20. Raoul H
   21. Caro V
   22. Cadar D
   23. Gabriel M
   24. Pahlmann M
   25. Tappe D
   26. Schmidt-Chanasit J
   27. Impouma B
   28. Diallo AK
   29. Formenty P
   30. Van Herp M
   31. Günther S

   (2014) Emergence of Zaire ebola virus disease in guinea

   The New England Journal of Medicine 371:1418–1425.

   https://doi.org/10.1056/NEJMoa1404505

   • PubMed
   • Google Scholar
5. 1. Barbazan P
   2. Thitithanyanont A
   3. Missé D
   4. Dubot A
   5. Bosc P
   6. Luangsri N
   7. Gonzalez JP
   8. Kittayapong P

   (2008) Detection of H5N1 avian influenza virus from mosquitoes collected in an infected poultry farm in Thailand

   Vector Borne and Zoonotic Diseases 8:105–110.

   https://doi.org/10.1089/vbz.2007.0142

   • PubMed
   • Google Scholar
6. 1. Boessenkool S
   2. Epp LS
   3. Haile J
   4. Bellemain E
   5. Edwards M
   6. Coissac E
   7. Willerslev E
   8. Brochmann C

   (2012) Blocking human contaminant DNA during PCR allows amplification of rare mammal species from sedimentary ancient DNA

   Molecular Ecology 21:1806–1815.

   https://doi.org/10.1111/j.1365-294X.2011.05306.x

   • PubMed
   • Google Scholar
7. 1. Boundenga L
   2. Makanga B
   3. Ollomo B
   4. Gilabert A
   5. Rougeron V
   6. Mve-Ondo B
   7. Arnathau C
   8. Durand P
   9. Moukodoum ND
   10. Okouga AP
   11. Delicat-Loembet L
   12. Yacka-Mouele L
   13. Rahola N
   14. Leroy E
   15. Ba CT
   16. Renaud F
   17. Prugnolle F
   18. Paupy C

   (2016) Haemosporidian parasites of antelopes and other vertebrates from Gabon, central africa

   PLoS One 11:e0148958.

   https://doi.org/10.1371/journal.pone.0148958

   • PubMed
   • Google Scholar
8. 1. Brunhes J
   2. Cuisance D
   3. Geoffroy B
   4. Hervy J

   (1998)

   Eds ORSTOM

   Les glossines ou mouches Tsé-tsé. Logiciel d’identification et d’enseignement, Eds ORSTOM, France, Montpellier.

   • Google Scholar
9. 1. Burt FJ
   2. Rolph MS
   3. Rulli NE
   4. Mahalingam S
   5. Heise MT

   (2012) Chikungunya: a re-emerging virus

   The Lancet 379:662–671.

   https://doi.org/10.1016/S0140-6736(11)60281-X

   • PubMed
   • Google Scholar
10. 1. Calvignac-Spencer S
    2. Leendertz FH
    3. Gilbert MT
    4. Schubert G

    (2013) An invertebrate stomach's view on vertebrate ecology: certain invertebrates could be used as "vertebrate samplers" and deliver DNA-based information on many aspects of vertebrate ecology

    BioEssays : News and Reviews in Molecular, Cellular and Developmental Biology 35:1004–1013.

    https://doi.org/10.1002/bies.201300060

    • PubMed
    • Google Scholar
11. 1. Clausen PH
    2. Adeyemi I
    3. Bauer B
    4. Breloeer M
    5. Salchow F
    6. Staak C

    (1998) Host preferences of tsetse (Diptera: glossinidae) based on bloodmeal identifications

    Medical and Veterinary Entomology 12:169–180.

    https://doi.org/10.1046/j.1365-2915.1998.00097.x

    • PubMed
    • Google Scholar
12. 1. Daszak P
    2. Tabor GM
    3. Kilpatrick AM
    4. Epstein J
    5. Plowright R

    (2004) Conservation medicine and a new agenda for emerging diseases

    Annals of the New York Academy of Sciences 1026:1–11.

    https://doi.org/10.1196/annals.1307.001

    • PubMed
    • Google Scholar
13. 1. de Wit E
    2. van Doremalen N
    3. Falzarano D
    4. Munster VJ

    (2016) SARS and MERS: recent insights into emerging coronaviruses

    Nature Reviews. Microbiology 14:523–534.

    https://doi.org/10.1038/nrmicro.2016.81

    • PubMed
    • Google Scholar
14. 1. Dereeper A
    2. Guignon V
    3. Blanc G
    4. Audic S
    5. Buffet S
    6. Chevenet F
    7. Dufayard JF
    8. Guindon S
    9. Lefort V
    10. Lescot M
    11. Claverie JM
    12. Gascuel O

    (2008) Phylogeny.fr: robust phylogenetic analysis for the non-specialist

    Nucleic Acids Research 36:W465–W469.

    https://doi.org/10.1093/nar/gkn180

    • PubMed
    • Google Scholar
15. 1. Edgar RC

    (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput

    Nucleic Acids Research 32:1792–1797.

    https://doi.org/10.1093/nar/gkh340

    • PubMed
    • Google Scholar
16. 1. Garros C
    2. Gilles J
    3. Duvallet G

    (2004)

    Un nouveau caractère morphologique pour distinguer stomoxys calcitrans et S. niger (Diptera: muscidae) : Comparaison de populations de l’Ile de la Réunion

    Parasite : Journal De La Société Française De Parasitologie 11:329–332.

    • Google Scholar
17. 1. Gilles J
    2. David JF
    3. Duvallet G
    4. De La Rocque S
    5. Tillard E

    (2007) Efficiency of traps for stomoxys calcitrans and stomoxys niger niger on reunion island

    Medical and Veterinary Entomology 21:65–69.

    https://doi.org/10.1111/j.1365-2915.2006.00658.x

    • PubMed
    • Google Scholar
18. 1. Gouteux J
    2. Bois J
    3. Laveissiere C
    4. Couret D
    5. Mustapha A

    (1984)

    Ecologie des glossines en secteur pré-forestier de côte d’Ivoire. 9. Les lieux de repos

    Cahiers ORSTOM 22:159–174.

    • Google Scholar
19. 1. Grubaugh ND
    2. Sharma S
    3. Krajacich BJ
    4. Fakoli LS
    5. Bolay FK
    6. Diclaro JW
    7. Johnson WE
    8. Ebel GD
    9. Foy BD
    10. Brackney DE

    (2015) Xenosurveillance: a novel mosquito-based approach for examining the human-pathogen landscape

    PLoS Neglected Tropical Diseases 9:e0003628.

    https://doi.org/10.1371/journal.pntd.0003628

    • PubMed
    • Google Scholar
20. 1. Hoffmann C
    2. Stockhausen M
    3. Merkel K
    4. Calvignac-Spencer S
    5. Leendertz FH

    (2016) Assessing the feasibility of fly based surveillance of wildlife infectious diseases

    Scientific Reports 6:37952.

    https://doi.org/10.1038/srep37952

    • PubMed
    • Google Scholar
21. 1. Humphery-Smith I
    2. Donker G
    3. Turzo A
    4. Chastel C
    5. Schmidt-Mayerova H

    (1993) Evaluation of mechanical transmission of HIV by the african soft tick, ornithodoros moubata

    Aids 7:341–348.

    https://doi.org/10.1097/00002030-199303000-00006

    • PubMed
    • Google Scholar
22. 1. Jones KE
    2. Patel NG
    3. Levy MA
    4. Storeygard A
    5. Balk D
    6. Gittleman JL
    7. Daszak P

    (2008) Global trends in emerging infectious diseases

    Nature 451:990–993.

    https://doi.org/10.1038/nature06536

    • PubMed
    • Google Scholar
23. 1. Kent RJ

    (2009) Molecular methods for arthropod bloodmeal identification and applications to ecological and vector-borne disease studies

    Molecular Ecology Resources 9:4–18.

    https://doi.org/10.1111/j.1755-0998.2008.02469.x

    • PubMed
    • Google Scholar
24. 1. Kuiken T
    2. Leighton FA
    3. Fouchier RA
    4. LeDuc JW
    5. Peiris JS
    6. Schudel A
    7. Stöhr K
    8. Osterhaus AD

    (2005) Public health. Pathogen surveillance in animals

    Science 309:1680–1681.

    https://doi.org/10.1126/science.1113310

    • PubMed
    • Google Scholar
25. 1. Laveissiere C
    2. Grebaut P

    (1990)

    Recherches sur les pièges à glossines (Diptera : glossinidae). Mise au point d’un modèle économique: le piège 'Vavoua'

    Tropical Medicine and Parasitology: Official Organ of Deutsche Tropenmedizinische Gesellschaft and of Deutsche Gesellschaft Für Technische Zusammenarbeit 41:185–192.

    • Google Scholar
26. 1. Lee PS
    2. Sing KW
    3. Wilson JJ

    (2015) Reading mammal diversity from flies: the persistence period of amplifiable mammal mtDNA in blowfly guts (Chrysomya megacephala) and a new DNA Mini-Barcode target

    PLoS One 10:e0123871.

    https://doi.org/10.1371/journal.pone.0123871

    • PubMed
    • Google Scholar
27. 1. Mavoungou JF
    2. Simo G
    3. Gilles J
    4. De Stordeur E
    5. Duvallet G

    (2008) [Ecology of stomoxyine fulies (Diptera: muscidae) in Gabon. II. blood meals analysis a nd epidemiologic consequences]

    Parasite 15:611–615.

    https://doi.org/10.1051/parasite/2008154611

    • PubMed
    • Google Scholar
28. 1. Mihok S

    (2002) The development of a multipurpose trap (the nzi) for tsetse and other biting flies

    Bulletin of Entomological Research 92:385–403.

    https://doi.org/10.1079/BER2002186

    • PubMed
    • Google Scholar
29. 1. Moffatt MR
    2. Blakemore D
    3. Lehane MJ

    (1995) Studies on the synthesis and secretion of trypsin in the midgut of stomoxys calcitrans

    Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology 110:291–300.

    https://doi.org/10.1016/0305-0491(94)00155-N

    • Google Scholar
30. 1. Mullens BA

    (2002)

    Medical and Veterinary Entomology

    263–277, Horse Flies and Deer Flies (Tabanidae), Medical and Veterinary Entomology, 10.1016/B978-012510451-7/50015-3.

    • Google Scholar
31. 1. Murray KA
    2. Preston N
    3. Allen T
    4. Zambrana-Torrelio C
    5. Hosseini PR
    6. Daszak P

    (2015) Global biogeography of human infectious diseases

    PNAS 112:12746–12751.

    https://doi.org/10.1073/pnas.1507442112

    • PubMed
    • Google Scholar
32. 1. Muturi CN
    2. Ouma JO
    3. Malele II
    4. Ngure RM
    5. Rutto JJ
    6. Mithöfer KM
    7. Enyaru J
    8. Masiga DK

    (2011) Tracking the feeding patterns of tsetse flies (Glossina genus) by analysis of bloodmeals using mitochondrial cytochromes genes

    PLoS One 6:e17284.

    https://doi.org/10.1371/journal.pone.0017284

    • PubMed
    • Google Scholar
33. 1. Muzari MO
    2. Burgess GW
    3. Skerratt LF
    4. Jones RE
    5. Duran TL

    (2010) Host preferences of tabanid flies based on identification of blood meals by ELISA

    Veterinary Parasitology 174:191–198.

    https://doi.org/10.1016/j.vetpar.2010.08.040

    • PubMed
    • Google Scholar
34. 1. Oldroyd H

    (1973)

    Insects and Other Arthropods of Medical Importance

    195–202, Tabanidae, Insects and Other Arthropods of Medical Importance.

    • Google Scholar
35. 1. Ollomo B
    2. Durand P
    3. Prugnolle F
    4. Douzery E
    5. Arnathau C
    6. Nkoghe D
    7. Leroy E
    8. Renaud F

    (2009) A new malaria agent in african hominids

    PLoS Pathogens 5:e1000446.

    https://doi.org/10.1371/journal.ppat.1000446

    • PubMed
    • Google Scholar
36. 1. Pesapane R
    2. Ponder M
    3. Alexander KA

    (2013) Tracking pathogen transmission at the human-wildlife interface: banded mongoose and Escherichia coli

    EcoHealth 10:115–128.

    https://doi.org/10.1007/s10393-013-0838-2

    • PubMed
    • Google Scholar
37. 1. Pollock JN

    (1982)

    Training Manual for Tsetse Control Personnel

    1–280, Tsetse biology, systematics and distribution, techniques, Training Manual for Tsetse Control Personnel, Vol. 1, Fao.

    • Google Scholar
38. 1. Prugnolle F
    2. Durand P
    3. Neel C
    4. Ollomo B
    5. Ayala FJ
    6. Arnathau C
    7. Etienne L
    8. Mpoudi-Ngole E
    9. Nkoghe D
    10. Leroy E
    11. Delaporte E
    12. Peeters M
    13. Renaud F

    (2010) African great apes are natural hosts of multiple related malaria species, including plasmodium falciparum

    PNAS 107:1458–1463.

    https://doi.org/10.1073/pnas.0914440107

    • PubMed
    • Google Scholar
39. 1. Marí Saéz A
    2. Weiss S
    3. Nowak K
    4. Lapeyre V
    5. Zimmermann F
    6. Düx A
    7. Kühl HS
    8. Kaba M
    9. Regnaut S
    10. Merkel K
    11. Sachse A
    12. Thiesen U
    13. Villányi L
    14. Boesch C
    15. Dabrowski PW
    16. Radonić A
    17. Nitsche A
    18. Leendertz SA
    19. Petterson S
    20. Becker S
    21. Krähling V
    22. Couacy-Hymann E
    23. Akoua-Koffi C
    24. Weber N
    25. Schaade L
    26. Fahr J
    27. Borchert M
    28. Gogarten JF
    29. Calvignac-Spencer S
    30. Leendertz FH

    (2015) Investigating the zoonotic origin of the West African Ebola epidemic

    EMBO Molecular Medicine 7:17–23.

    https://doi.org/10.15252/emmm.201404792

    • PubMed
    • Google Scholar
40. 1. Santiago ML
    2. Rodenburg CM
    3. Kamenya S
    4. Bibollet-Ruche F
    5. Gao F
    6. Bailes E
    7. Meleth S
    8. Soong SJ
    9. Kilby JM
    10. Moldoveanu Z
    11. Fahey B
    12. Muller MN
    13. Ayouba A
    14. Nerrienet E
    15. McClure HM
    16. Heeney JL
    17. Pusey AE
    18. Collins DA
    19. Boesch C
    20. Wrangham RW
    21. Goodall J
    22. Sharp PM
    23. Shaw GM
    24. Hahn BH

    (2002) SIVcpz in wild chimpanzees

    Science 295:465.

    https://doi.org/10.1126/science.295.5554.465

    • PubMed
    • Google Scholar
41. 1. Sawabe K
    2. Hoshino K
    3. Isawa H
    4. Sasaki T
    5. Hayashi T
    6. Tsuda Y
    7. Kurahashi H
    8. Tanabayashi K
    9. Hotta A
    10. Saito T
    11. Yamada A
    12. Kobayashi M

    (2006)

    Detection and isolation of highly pathogenic H5N1 avian influenza A viruses from blow flies collected in the vicinity of an infected poultry farm in Kyoto, Japan, 2004

    The American Journal of Tropical Medicine and Hygiene 75:327–332.

    • PubMed
    • Google Scholar
42. 1. Schnell IB
    2. Thomsen PF
    3. Wilkinson N
    4. Rasmussen M
    5. Jensen LRD
    6. Willerslev E
    7. Bertelsen MF
    8. Gilbert MTP

    (2012) Screening mammal biodiversity using DNA from leeches

    Current Biology 22:R262–R263.

    https://doi.org/10.1016/j.cub.2012.02.058

    • PubMed
    • Google Scholar
43. 1. Schubert G
    2. Stockhausen M
    3. Hoffmann C
    4. Merkel K
    5. Vigilant L
    6. Leendertz FH
    7. Calvignac-Spencer S

    (2015) Targeted detection of mammalian species using carrion fly-derived DNA

    Molecular Ecology Resources 15:285–294.

    https://doi.org/10.1111/1755-0998.12306

    • PubMed
    • Google Scholar
44. 1. Sharp PM
    2. Hahn BH

    (2011) Origins of HIV and the AIDS pandemic

    Cold Spring Harbor Perspectives in Medicine 1:a006841.

    https://doi.org/10.1101/cshperspect.a006841

    • PubMed
    • Google Scholar
45. 1. Simo G
    2. Njiokou F
    3. Mbida Mbida JA
    4. Njitchouang GR
    5. Herder S
    6. Asonganyi T
    7. Cuny G

    (2008) Tsetse fly host preference from sleeping sickness foci in Cameroon: epidemiological implications

    Infection, Genetics and Evolution : Journal of Molecular Epidemiology and Evolutionary Genetics in Infectious Diseases 8:34–39.

    https://doi.org/10.1016/j.meegid.2007.09.005

    • PubMed
    • Google Scholar
46. 1. Simo G
    2. Silatsa B
    3. Flobert N
    4. Lutumba P
    5. Mansinsa P
    6. Madinga J
    7. Manzambi E
    8. De Deken R
    9. Asonganyi T

    (2012) Identification of different trypanosome species in the mid-guts of tsetse flies of the malanga (Kimpese) sleeping sickness focus of the democratic republic of congo

    Parasites & Vectors 5:201.

    https://doi.org/10.1186/1756-3305-5-201

    • PubMed
    • Google Scholar
47. 1. Singh B
    2. Daneshvar C

    (2013) Human infections and detection of plasmodium knowlesi

    Clinical Microbiology Reviews 26:165–184.

    https://doi.org/10.1128/CMR.00079-12

    • PubMed
    • Google Scholar
48. 1. Späth J

    (2000) Feeding patterns of three sympatric tsetse species (Glossina spp.) (Diptera: glossinidae) in the preforest zone of côte d'ivoire

    Acta Tropica 75:109–118.

    https://doi.org/10.1016/S0001-706X(99)00096-0

    • PubMed
    • Google Scholar
49. 1. Taberlet P
    2. Coissac E
    3. Hajibabaei M
    4. Rieseberg LH

    (2012) Environmental DNA

    Molecular Ecology 21:1789–1793.

    https://doi.org/10.1111/j.1365-294X.2012.05542.x

    • PubMed
    • Google Scholar
50. 1. Taylor LH
    2. Latham SM
    3. Woolhouse ME

    (2001) Risk factors for human disease emergence

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 356:983–989.

    https://doi.org/10.1098/rstb.2001.0888

    • PubMed
    • Google Scholar
51. 1. Townzen JS
    2. Brower AV
    3. Judd DD

    (2008) Identification of mosquito bloodmeals using mitochondrial cytochrome oxidase subunit I and cytochrome b gene sequences

    Medical and Veterinary Entomology 22:386–393.

    https://doi.org/10.1111/j.1365-2915.2008.00760.x

    • PubMed
    • Google Scholar
52. Book

    1. Wall R
    2. Shearer D

    (1997)

    Veterinary Entomology

    Chapman & Hall.

    • Google Scholar
53. 1. Webb PA
    2. Happ CM
    3. Maupin GO
    4. Johnson BJ
    5. Ou CY
    6. Monath TP

    (1989) Potential for insect transmission of HIV: experimental exposure of cimex hemipterus and toxorhynchites amboinensis to human immunodeficiency virus

    The Journal of Infectious Diseases 160:970–977.

    https://doi.org/10.1093/infdis/160.6.970

    • PubMed
    • Google Scholar
54. 1. Weitz B

    (1963)

    The feeding habits of glossina

    Bulletin of the World Health Organization 28:711–729.

    • PubMed
    • Google Scholar
55. 1. Wikan N
    2. Smith DR

    (2016) Zika Virus: history of a newly emerging arbovirus

    The Lancet. Infectious Diseases 16:e119–e126.

    https://doi.org/10.1016/S1473-3099(16)30010-X

    • PubMed
    • Google Scholar
56. 1. Wolfe ND
    2. Daszak P
    3. Kilpatrick AM
    4. Burke DS

    (2005) Bushmeat hunting, deforestation, and prediction of zoonoses emergence

    Emerging Infectious Diseases 11:1822–1827.

    https://doi.org/10.3201/eid1112.040789

    • PubMed
    • Google Scholar
57. 1. Woolhouse M
    2. Gaunt E

    (2007) Ecological origins of novel human pathogens

    Critical Reviews in Microbiology 33:231–242.

    https://doi.org/10.1080/10408410701647560

    • PubMed
    • Google Scholar
58. 1. Woolhouse ME
    2. Howey R
    3. Gaunt E
    4. Reilly L
    5. Chase-Topping M
    6. Savill N

    (2008) Temporal trends in the discovery of human viruses

    Proceedings. Biological Sciences 275:2111–2115.

    https://doi.org/10.1098/rspb.2008.0294

    • PubMed
    • Google Scholar
59. 1. Zumpt F

    (1973)

    Taxonomy, Biology, Economic Importance and Control Measures

    The Stomoxynae biting flies of the world, Taxonomy, Biology, Economic Importance and Control Measures.

    • Google Scholar

Author details

1. Paul-Yannick Bitome-Essono

   1. Biogéosciences Unit, Équipe Écologie-Évolutive, UMR 6282 CNRS-université de Bourgogne-Franche Comté-EPHE-AgroSup, Dijon, France
   2. Équipes UBEEP-ESV, Centre International de Recherches Médicales de Franceville, Franceville, Gabon
   3. Département de Biologie et Écologie Animale, Institut de Recherche en Écologie Tropicale, Libreville, Gabon

   Contribution

   P-YB-E, Conceptualization, Methodology, Writing—original draft, Writing—review and editing

   For correspondence

   bitomessono@yahoo.fr

   Competing interests

   The authors declare that no competing interests exist.

2. Benjamin Ollomo

   Équipes UBEEP-ESV, Centre International de Recherches Médicales de Franceville, Franceville, Gabon

   Contribution

   BO, Conceptualization, Methodology

   Contributed equally with

   Francois Bretagnolle, Franck Prugnolle and Christophe Paupy

   Competing interests

   The authors declare that no competing interests exist.

3. Céline Arnathau

   MIVEGEC Unit, UMR 224-5290 IRD-CNRS-UM, Centre IRD de Montpellier, Montpellier, France

   Contribution

   CA, Investigation, Methodology

   Competing interests

   The authors declare that no competing interests exist.

4. Patrick Durand

   MIVEGEC Unit, UMR 224-5290 IRD-CNRS-UM, Centre IRD de Montpellier, Montpellier, France

   Contribution

   PD, Investigation, Writing—review and editing

   Competing interests

   The authors declare that no competing interests exist.

5. Nancy Diamella Mokoudoum

   Équipes UBEEP-ESV, Centre International de Recherches Médicales de Franceville, Franceville, Gabon

   Contribution

   NDM, Investigation, Methodology

   Competing interests

   The authors declare that no competing interests exist.

6. Lauriane Yacka-Mouele

   Équipes UBEEP-ESV, Centre International de Recherches Médicales de Franceville, Franceville, Gabon

   Contribution

   LY-M, Investigation, Methodology

   Competing interests

   The authors declare that no competing interests exist.

7. Alain-Prince Okouga

   Équipes UBEEP-ESV, Centre International de Recherches Médicales de Franceville, Franceville, Gabon

   Contribution

   A-PO, Investigation, Methodology

   Competing interests

   The authors declare that no competing interests exist.

8. Larson Boundenga

   Équipes UBEEP-ESV, Centre International de Recherches Médicales de Franceville, Franceville, Gabon

   Contribution

   LB, Investigation, Methodology

   Competing interests

   The authors declare that no competing interests exist.

9. Bertrand Mve-Ondo

   Équipes UBEEP-ESV, Centre International de Recherches Médicales de Franceville, Franceville, Gabon

   Contribution

   BM-O, Investigation, Methodology

   Competing interests

   The authors declare that no competing interests exist.

10. Judicaël Obame-Nkoghe

    Équipes UBEEP-ESV, Centre International de Recherches Médicales de Franceville, Franceville, Gabon

    Contribution

    JO-N, Investigation, Methodology

    Competing interests

    The authors declare that no competing interests exist.

11. Philippe Mbehang-Nguema

    1. Équipes UBEEP-ESV, Centre International de Recherches Médicales de Franceville, Franceville, Gabon
    2. Département de Biologie et Écologie Animale, Institut de Recherche en Écologie Tropicale, Libreville, Gabon

    Contribution

    PM-N, Investigation, Methodology

    Competing interests

    The authors declare that no competing interests exist.

12. Flobert Njiokou

    Département de Biologie Animale et Physiologie, Laboratoire de Parasitologie et Écologie, Faculté des Sciences de l'Université de Yaoundé 1, Yaoundé, Cameroun

    Contribution

    FN, Investigation, Methodology

    Competing interests

    The authors declare that no competing interests exist.

13. Boris Makanga

    1. Équipes UBEEP-ESV, Centre International de Recherches Médicales de Franceville, Franceville, Gabon
    2. Département de Biologie et Écologie Animale, Institut de Recherche en Écologie Tropicale, Libreville, Gabon

    Contribution

    BM, Investigation, Methodology

    Competing interests

    The authors declare that no competing interests exist.

14. Rémi Wattier

    Biogéosciences Unit, Équipe Écologie-Évolutive, UMR 6282 CNRS-université de Bourgogne-Franche Comté-EPHE-AgroSup, Dijon, France

    Contribution

    RW, Formal analysis, Investigation, Methodology

    Competing interests

    The authors declare that no competing interests exist.

15. Diego Ayala

    1. Équipes UBEEP-ESV, Centre International de Recherches Médicales de Franceville, Franceville, Gabon
    2. MIVEGEC Unit, UMR 224-5290 IRD-CNRS-UM, Centre IRD de Montpellier, Montpellier, France

    Contribution

    DA, Investigation, Methodology

    Competing interests

    The authors declare that no competing interests exist.

16. Francisco J Ayala

    Department of Ecology and Evolutionary Biology, University of California, Irvine, United States

    Contribution

    FJA, Investigation, Methodology, Writing—review and editing

    Competing interests

    The authors declare that no competing interests exist.

17. Francois Renaud

    MIVEGEC Unit, UMR 224-5290 IRD-CNRS-UM, Centre IRD de Montpellier, Montpellier, France

    Contribution

    FR, Conceptualization, Writing—review and editing

    Competing interests

    The authors declare that no competing interests exist.

18. Virginie Rougeron

    1. Équipes UBEEP-ESV, Centre International de Recherches Médicales de Franceville, Franceville, Gabon
    2. MIVEGEC Unit, UMR 224-5290 IRD-CNRS-UM, Centre IRD de Montpellier, Montpellier, France

    Contribution

    VR, Conceptualization, Investigation, Methodology, Writing—review and editing

    Competing interests

    The authors declare that no competing interests exist.

19. Francois Bretagnolle

    Biogéosciences Unit, Équipe Écologie-Évolutive, UMR 6282 CNRS-université de Bourgogne-Franche Comté-EPHE-AgroSup, Dijon, France

    Contribution

    FB, Conceptualization, Formal analysis, Supervision, Investigation, Methodology, Writing—original draft, Writing—review and editing

    Contributed equally with

    Benjamin Ollomo, Franck Prugnolle and Christophe Paupy

    Competing interests

    The authors declare that no competing interests exist.

20. Franck Prugnolle

    1. Équipes UBEEP-ESV, Centre International de Recherches Médicales de Franceville, Franceville, Gabon
    2. MIVEGEC Unit, UMR 224-5290 IRD-CNRS-UM, Centre IRD de Montpellier, Montpellier, France

    Contribution

    FP, Conceptualization, Formal analysis, Supervision, Investigation, Methodology, Writing—original draft, Writing—review and editing

    Contributed equally with

    Benjamin Ollomo, Francois Bretagnolle and Christophe Paupy

    For correspondence

    franck.prugnolle@ird.fr

    Competing interests

    The authors declare that no competing interests exist.

    "This ORCID iD identifies the author of this article:" 0000-0001-8519-1253

21. Christophe Paupy

    1. Équipes UBEEP-ESV, Centre International de Recherches Médicales de Franceville, Franceville, Gabon
    2. MIVEGEC Unit, UMR 224-5290 IRD-CNRS-UM, Centre IRD de Montpellier, Montpellier, France

    Contribution

    CP, Conceptualization, Formal analysis, Supervision, Investigation, Methodology, Writing—original draft, Writing—review and editing

    Contributed equally with

    Benjamin Ollomo, Francois Bretagnolle and Franck Prugnolle

    For correspondence

    christophe.paupy@ird.fr

    Competing interests

    The authors declare that no competing interests exist.

    "This ORCID iD identifies the author of this article:" 0000-0002-7122-2079

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