1. Ecology
  2. Epidemiology and Global Health
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Tracking zoonotic pathogens using blood-sucking flies as 'flying syringes'

  1. Paul-Yannick Bitome-Essono  Is a corresponding author
  2. Benjamin Ollomo
  3. Céline Arnathau
  4. Patrick Durand
  5. Nancy Diamella Mokoudoum
  6. Lauriane Yacka-Mouele
  7. Alain-Prince Okouga
  8. Larson Boundenga
  9. Bertrand Mve-Ondo
  10. Judicaël Obame-Nkoghe
  11. Philippe Mbehang-Nguema
  12. Flobert Njiokou
  13. Boris Makanga
  14. Rémi Wattier
  15. Diego Ayala
  16. Francisco J Ayala
  17. Francois Renaud
  18. Virginie Rougeron
  19. Francois Bretagnolle
  20. Franck Prugnolle  Is a corresponding author
  21. Christophe Paupy  Is a corresponding author
  1. UMR 6282 CNRS-université de Bourgogne-Franche Comté-EPHE-AgroSup, France
  2. Centre International de Recherches Médicales de Franceville, Gabon
  3. Institut de Recherche en Écologie Tropicale, Gabon
  4. UMR 224-5290 IRD-CNRS-UM, Centre IRD de Montpellier, France
  5. Laboratoire de Parasitologie et Écologie, Faculté des Sciences de l'Université de Yaoundé 1, Cameroun
  6. University of California, United States
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Cite as: eLife 2017;6:e22069 doi: 10.7554/eLife.22069

Abstract

About 60% of emerging infectious diseases in humans are of zoonotic origin. Their increasing number requires the development of new methods for early detection and monitoring of infectious agents in wildlife. Here, we investigated whether blood meals from hematophagous flies could be used to identify the infectious agents circulating in wild vertebrates. To this aim, 1230 blood-engorged flies were caught in the forests of Gabon. Identified blood meals (30%) were from 20 vertebrate species including mammals, birds and reptiles. Among them, 9% were infected by different extant malaria parasites among which some belonged to known parasite species, others to new parasite species or to parasite lineages for which only the vector was known. This study demonstrates that using hematophagous flies as ‘flying syringes’ constitutes an interesting approach to investigate blood-borne pathogen diversity in wild vertebrates and could be used as an early detection tool of zoonotic pathogens.

https://doi.org/10.7554/eLife.22069.001

eLife digest

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.

https://doi.org/10.7554/eLife.22069.002

Introduction

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).

Results

Host identification from blood meals

A total of 4099 hematophagous flies were caught in four national parks of Gabon during dry and rainy seasons over a cumulated sampling period of 16 weeks (Figure 1a). Among them, six tsetse fly species, six stomoxid species and six tabanid species were identified (Table 1).

Monitoring vertebrate haemosporidian diversity using haematophagous flies.

(a) Localization of the sampling sites (red dots) in Gabon (Central Africa). (b) Number of blood meals originating from the different vertebrate species. (c) Position within the Cytb phylogeny of the haemosporidian Cytb sequences PCR-amplified from the blood meals of engorged flies with identified hosts (red isolates) and unidentified hosts (green isolates). Black isolates: references (Table 4). Bootstrap values at important nodes are shown.

https://doi.org/10.7554/eLife.22069.003
Table 1

Number and proportion of specimens captured per fly species. The number of engorged specimens and blood meals identified in each fly species are also indicated.

https://doi.org/10.7554/eLife.22069.004
Fly speciesNumber of collected specimensProportion (%)Number of engorged specimensNumber of identified blood meals
Glossinidae225254.941218423
Glossina caliginea1443.518733
G. fusca congolensis2105.1210442
G. fuscipes fuscipes2907.0721493
G. pallicera newsteadi1573.839737
G. palpalis palpalis137233.47662218
G. tabaniformis791.93540
Muscidae136233.2394
Stomoxys calcitrans2455.9852
S. inornatus3348.1400
S. niger niger2536.1742
S. niger bilineatus2245.4600
S. omega omega1974.8100
S. transvittatus1092.6600
Tabanidae48511.8331
Ancala sp41100
Atylotus sp1042.5300
Chrysops sp1563.8131
Haematopota sp130.3100
Tabanus par521.2700
Tabanus taeniola1202.9300
Total40991001230428

Among the 4099 caught flies, 1230 (30%) were engorged with blood. These were mostly tsetse flies (n = 1218; 99%), particularly Glossina palpalis palpalis (n = 662; 54%) and G. fuscipes fuscipes (n = 214; 18%) specimens. The blood meal origin was successfully identified in 33% and 43% of these flies, respectively (Table 1).

Overall, the blood meal origin was successfully identified in 428 fly samples (35%) using a PCR system amplifying long fragments of Cytb (450 bp) or COI genes (330 bp or 660 bp). Specifically, blood meals were from 20 vertebrate species, including 12 families and 8 orders (Figure 1b and Tables 2 and 3).

Table 2

Number and origin of blood meals according to the fly species (Fsp), park and climatic season.

https://doi.org/10.7554/eLife.22069.005
Number of identified blood meals by fly species (Fsp)
Moukalaba-DoudouLopé
Rainy seasonDry seasonRainy seasonDry season
Taxonomic group/Order/FamilyHost speciesN° IdentifiedFsp1Fsp2Fsp3Fsp4Fsp5Fsp1Fsp2Fsp3Fsp4Fsp5Fsp8Fsp1Fsp2Fsp3Fsp4Fsp5Fsp1Fsp2Fsp3Fsp4Fsp5Fsp6Fsp7
Mammals
  Artiodactyla295143811711421233413
   BovidaeCephalophus
silvicultor
65
kobus
ellipsiprymnus
431
Syncerus caffer12635711329211057128161
Tragelaphus spekii9516415137694121
   HippopotamidaeHippopotamus amphibius211
   SuidaePotamochoerus
porcus
312
  Carnivora1
   HerpestidaeHerpestinae sp11
  Primates67
   HominidaeGorilla gorilla321
Homo sapiens6411132221312141
  Proboscidae10
   ElephantidaeLoxodonta cyclotis10712
Reptiles
  Crocodilia233
   CrocodylidaeCrocodylus niloticus3
Mecistops
cataphractus
19116
Osteolaemus tetraspis11
  Squamata12
   PythonidaePython sebae82
   VaranidaeVaranus sp4211
  Testudines16
   TestunidaeKinixys erosa11
   PelomedusidaePelusios castaneus3111
Pelusios chapini11
Pelusios marani1138
Birds
  Ciconiformes4
   CiconiidaeCiconia sp412
8 orders/12 families20 species428311144122164891671871611112262512
  1. Fsp1 = Glossina caliginea; Fsp2 = G. fusca congolensis; Fsp3 = G. fuscipes fuscipes; Fsp4 = G. pallicera newsteadi; Fsp5 = G. palpalis palpalis; Fsp6 = Stomoxys calcitrans; Fsp7 = S. niger niger; Fsp8 = Chrysops sp.

Table 3

Number and origin of blood meals according to the fly species (Fsp), park and climatic season.

https://doi.org/10.7554/eLife.22069.006
Number of identified blood meals by fly species (Fsp)
La LékédiIvindo
Rainy seasonDry seasonDry season
Taxonomic group/Order/FamilyHost speciesN° IdentifiedFsp1Fsp3Fsp4Fsp5Fsp6Fsp1Fsp2Fsp3Fsp4Fsp5Fsp1Fsp2Fsp3Fsp5
Mammals
  Artiodactyla295213
   BovidaeCephalophus
silvicultor
65
kobus
ellipsiprymnus
4
Syncerus caffer126211610881311
Tragelaphus spekii95131285591
   HippopotamidaeHippopotamus amphibius2
   SuidaePotamochoerus
porcus
3
  Carnivora1
   HerpestidaeHerpestinae sp1
  Primates67
   HominidaeGorilla gorilla3
Homo sapiens6441511
  Proboscidae10
   ElephantidaeLoxodonta cyclotis10
Reptiles
  Crocodilia23
   CrocodylidaeCrocodylus niloticus3
Mecistops
cataphractus
192423
Osteolaemus
tetraspis
1
  Squamata12
   PythonidaePython sebae8222
   VaranidaeVaranus sp4
  Testudines16
   TestunidaeKinixys erosa1
   PelomedusidaePelusios
castaneus
3
Pelusios chapini1
Pelusios marani11
Birds
  Ciconiformes4
   CiconiidaeCiconia sp41
8 orders/12 families20 species4282411318202015321122
  1. Fsp1 = Glossina caliginea; Fsp2 = G. fusca congolensis; Fsp3 = G. fuscipes fuscipes; Fsp4 = G. pallicera newsteadi; Fsp5 = G. palpalis palpalis; Fsp6 = Stomoxys calcitrans; Fsp7 = S. niger niger; Fsp8 = Chrysops sp.

A trial study using a PCR system amplifying a shorter fragment (150 bp of the gene 16S) to deal with potential DNA degradation in the blood meal showed a high gain of sensitivity in the determination of the origin of the blood meal. Thus, out of 89 previously unidentified blood meals, the host was identified for 76% (n = 68) of them. The list of newly identified hosts is given in Figure 2. This shows a high gain of sensitivity with the new PCR system.

Number of blood meals identified using the shorter PCR system of Boessenkool et al. (2012) out of the previously unidentified 89 blood meals.
https://doi.org/10.7554/eLife.22069.007

Pathogen identification from blood meals

Extant malaria parasites were detected in 37 (8.7%) of the 428 identified blood meals (Figure 1c, red isolates). Phylogenetic analyses revealed that 29.7% of these parasites belonged to Plasmodium falciparum (n = 11, Figure 1c; group 1), 8.1% to Plasmodium adleri (n = 3, Figure 1c; group 2), and 8.1% to a recently described lineage of parasites infecting wild ungulates (n = 4, Figure 1c; group 3) (Boundenga et al., 2016). For all blood meals, the identified host represented the known natural host (or one of the hosts) of such parasites. Sequences of unknown parasite lineages or of parasites for which the hosts were not known were also obtained. For instance, one sequence (Figure 1c; group 4) detected in a blood meal originating from an ungulate was related to parasites previously isolated only from Anopheles mosquitoes (Boundenga et al., 2016). One sequence detected in a blood meal originating from a bird was related to bat Haemosporida (Nycteria), (Figure 1c; group 5). Finally, 18 sequences (Figure 1c; group 6) that were amplified from blood meals originating from ungulates formed an independent and never described lineage related to groups 3 and 4.

In addition, 100 additional samples for which identification of the blood meal failed were randomly chosen for malarial parasite screening. This analysis showed that 7% were infected with P. falciparum (n = 4, group 1), P. praefalciparum (n = 1, group 7), malaria parasites of antelopes from group 6 (n = 1) and parasites of tortoises (group 8, n = 1) (Figure 1c, green isolates).

For the parasite, the use of a shorter PCR system led to less conclusive results than those obtained for the host identification. Out of the 91 blood meals that were negative to Plasmodium with a PCR system amplifying a long Cytb fragment, only one was found positive with the new system. The positive individual corresponded to a Tragelaphus spekii and was infected with a parasite belonging to group 3 (Figure 1c).

Discussion

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.

Conclusion

Despite the significant scientific advances in the medical field, humans are still unable to predict where, when and how epidemics arise. Around 60% of emerging diseases in humans are of zoonotic origin. The progressive reduction of wild habitats will increase the contacts between humans and species that are potential reservoirs of diseases. We propose here a new non-invasive tool that can help identifying pathogens that circulate in wildlife before they spread in humans.

Materials and methods

Study sites

The fly sampling was carried out in four wildlife reserves in Gabon (Figure 1a): Moukalaba-Doudou National Park (MDNP; S: 2° 26' 08"/E: 10° 25' 18''), La Lopé National Park (LNP; S: 0° 31' 31"/E: 11° 32' 34"), La Lékédi Park (LP; S: 1° 45' 32"/E: 13° 03' 16") and Ivindo National Park (INP; N: 0° 30' 82"/E: 12° 48' 20"). Both MDNP and LNP are dominated by mature forests and mosaic forest-savannah. The INP is largely dominated by mature forest with some open biotopes that characterize the secondary forest. The LP is a private park dominated by large savannahs and some secondary forest and primary forest patches.

Sampling strategy

Hematophagous flies were sampled during the rainy and dry seasons between 2012 and 2014. In INP and MDNP, sampling was done during two years following a gradient of human activity from primary forest to villages. In the other parks, flies were sampled during a single year. Flies were collected by using Vavoua and Nzi traps (Laveissiere and Grebaut, 1990; Acapovi et al., 2001; Mihok, 2002; Gilles et al., 2007). The Vavoua trap, initially developed for the capture of tsetse flies was also successfully used for the capture of stomoxids at La Réunion Island (Laveissiere and Grebaut, 1990; Gilles et al., 2007). The Nzi trap was more adapted to the capture of Glossina pallidipes and tabanids in Africa (Acapovi et al., 2001; Mihok, 2002). In each park, we placed 24 traps (12 Vavoua and 12 Nzi) during 2 weeks per climatic season. Each trap was activated from 7:00 AM to 5:00 PM.

Identification and dissection of hematophagous flies

Freshly collected hematophagous flies were identified using a stereo-microscope and taxonomic procedure. The fly species (tsetse, stomoxids and tabanids) was determined following the determination keys of Pollock (1982), Brunhes et al., 1998, Zumpt, 1973, Garros et al. (2004) and Oldroyd (1973), on the basis of their morphological characteristics, such as size, color, wing venation structure and proboscis.

After species identification, engorged flies were dissected individually in a drop of Dulbecco's phosphate buffered saline solution (1x DPBS) to isolate blood meals from midgut. Each hematophagous fly was dissected on a slide using one forceps and one scalpel that were changed each time to avoid contaminations. Each blood meal was transferred in a 1.5-ml microtube containing 50 µl of RNAlater stabilization solution (Qiagen: Store at RT Tissue Collection) to stabilize and protect nucleic acids of vertebrate hosts and pathogens contained in the blood meals. Samples were kept at ambient temperature during field session and then frozen at −80°C until DNA extraction.

DNA extraction

Samples were centrifuged at 15,000 rpm at 4°C for 10 min to remove the RNAlater solution. Pellets were used to extract DNA using the DNeasy Blood and Tissue Kit (Qiagen) according to the manufacturer’s instructions. Extracted DNA was eluted in 100 µl of buffer AE and stored at −20°C.

Blood meal identification

The origin of blood meals was determined using the extracted DNA to amplify a 450 bp fragment of the cytochrome b (Cytb) gene using previously published primers (Townzen et al., 2008). PCR amplifications were performed using a GeneAmp 9700 thermal cycler (Applied Biosystems, USA) with 50 µL reaction mixtures containing 4 µL template DNA, 10 mM Tris-HCl (pH = 9), 50 mM KCl, 3 mM MgCl2, 20 pmol each primer (5’CCCCTCAGAATGATATTTGTCCTCA3’ and 5’CCATCCAACATCTCAGCATGATGAAA3’), 200 mM dNTP and 1 U Taq polymerase. The thermal cycling conditions consisted of 3.5 min at 95°C, 40 cycles of 30s at 95°C, 50s at 58°C, and 40s at 72°C, followed by 5 min at 72°C. When Cytb amplification failed, a 330 bp and/or a 660 bp fragment of the cytochrome oxydase subunit I (COI) gene was amplified using previously described primers and protocols (Townzen et al., 2008). All PCR-amplified products (10 µl) were run on 1.5% agarose gels in TBE buffer, and positive samples were sent to Beckman Coulter Genomics (France) for sequencing in both directions (forward and reverse) after purification. Consensus sequences were compared with existent sequences using the NCBI nucleotide Blast search (Altschul et al., 1990) to determine the host species. Hosts were identified when the amplified and reference sequences showed at least 98% similarity.

Haemosporidia detection and identification

Haemosporidian parasite detection was performed in samples with identified blood meal origin and also in 100 randomly chosen samples for which blood meal origin could not be identified.

Haemosporidian parasites were detected by PCR amplification of a portion of the Cytb gene (~790 bp) using a nested PCR protocol, as previously published (Ollomo et al., 2009). PCR products were checked on 1.5% agarose gels before shipment to EUROFINS MWG (Germany) for sequencing in both directions (reverse and forward) after purification. Multiple alignments of haemosporidian sequences were done using Muscle (Edgar, 2004). A phylogenetic tree with the haemosporidian sequences obtained in our study and a set of reference sequences was built using Maximum likelihood (ML) methods and phylogeny.fr (Dereeper et al., 2008) (see Table 4 for accession numbers). The ML model used for construction of the tree was GTR (General Time Reversible)+Γ (Gamma distribution)+I (Invariable site distribution).

Table 4

Cytb sequences of parasites recovered in this study and of those used as references for phylogenetic analyses and their Genbank accession numbers.

https://doi.org/10.7554/eLife.22069.008
IsolateAccession number
Anopheles coustaniKT367855
An. gabonensis2KT367852
An. gabonensis279KT367853
An. gabonensis3KT367861
An. marshalliiKT367857
An. mouchetiKT367864
An. obscurus2KT367846
An. obscurus78KT367849
Cephalophus_silvicultor_1336KY631949
Cephalophus_silvicultor_1368KY631947
Cephalophus_silvicultor_484KY631963
Ciconia_sp_445KY631985
E15_Podocnemis_expansa_PeruKF049492
E24_Podocnemis_expansa_PeruKF049495
Gorilla_gorilla_34KY631983
Gorilla_gorilla_756KY631982
Gorilla_gorilla_761KY631981
P_sp._JA7_J725GU252027
Haemoproteus_majorisAY099045
Haemoproteus_sp.HM222472
Haemoproteus_sp._GA02CI1HM222486
Haemoproteus_sp._NA16K65HM222487
Hepatocystis_sp. AA201_blikeJQ070951
Hepatocystis_sp.JQ070884
Hepatocystis_sp._AA2012JQ070956
HO11_Cephalophus_nigrofonsKT367819
HO13_Cephalophus_monticolaKT367833
HO613_Cephalophus_monticolaKT367836
HO9_Kinixys_erosaKT367843
Homo_sapiens_476KY631978
Homo_sapiens_481KY631977
Homo_sapiens_57KY631979
Homo_sapiens_574KY631976
Homo_sapiens_635KY631975
Homo_sapiens_636KY631974
Homo_sapiens_638KY631973
Homo_sapiens_639KY631972
Homo_sapiens_668KY631969
Homo_sapiens_806KY631968
Homo_sapiens_832KY631967
Leucocytozoon_caulleryiAB302215
Leucocytozoon_dubreuliAY099063
Leucocytozoon_majorisFJ168563
Leucocytozoon_sabrazesiAB299369
M0278_Cephalophus_monticolaKT367834
NG238_Kinixys_erosaKT367844
NG277_Ceratogymna_atrataKT367825
NY195_Cephalophus_dorsalisKT367838
Nycteria_sp._R_alc_C9_1KF159720
Nycteria_sp._R_lan_G3_1KF159690
OI52_PangolinKT367818
OL123_Cephalophus_monticolaKT367822
OL131_Cephalophus_callipygusKT367830
P_adleriHM235081
P_azurophilumAY099055
P_billcollinsiKP875474
P_blacklockiHM235065
P_cynomolgiAB444126
P_falciparum_3D7AF069605
P_gaboniJF895307
P_gallinaceumAF069612
P_gonderiJF923751
P_knowlesiJQ345504
P_malariaeHM000110
P_ovaleGU723548
P_praefalciparum_MOEBJF923761
P_reichenowiKP875479
P_relictumAY733090
P_sp._DAJJF923753
P_vivaxKF591834
P_atheruriAY099054
P_giganteumAY099053
P_vinckei_isolate_1KJ700853
P_vinckei_isolate_2KJ700854
P_yoelii_killickiDQ414658
P. atheruriHQ712051
P. cyclopsi_Hip_cy_L4_1_SchaerKF159674
P. voltaicum_M_ang_G1_1_1KF159671
Parahaemoproteus_sp._bird_sp.17GQ141581
Parahaemoproteus_sp. _bird_sp.19GQ141585
Parahaemoproteus_vireonisFJ168561
Plasmodium_sp._birdGQ141574
Plasmodium_sp._bird_sp._12HM222485
Plasmodium_sp._GD2_GD201GU252012
Plasmodium_sp._lineage_JA01KM598212
Polychromophilus_melanipherus_haplotype_VIIIKJ131277
Polychromophilus_murinus_haplotype_3HM055585
Polychromophilus_sp._Min_vil_G3_2KF159699
Polychromophilus_sp._Pip_gran_G3_1KF159714
Polychromophilus_sp._Neo_cap_G3KF159700
Syncerus_caffer_1138KY631953
Syncerus_caffer_1417KY631942
Tragelaphus_eurycerus_1324KY631950
Tragelaphus_spekii_1051KY631961
Tragelaphus_spekii_1155KY631959
Tragelaphus_spekii_1175KY631958
Tragelaphus_spekii_1228KY631957
Tragelaphus_spekii_1245KY631956
Tragelaphus_spekii_1291KY631955
Tragelaphus_spekii_1299KY631954
Tragelaphus_spekii_1300KY631952
Tragelaphus_spekii_1306KY631951
Tragelaphus_spekii_1348KY631948
Tragelaphus_spekii_1386KY631946
Tragelaphus_spekii_1394KY631945
Tragelaphus_spekii_1399KY631944
Tragelaphus_spekii_1413KY631943
Tragelaphus_spekii_385KY631964
Tragelaphus_spekii_56KY631965
U65_Podocnemis_unifilis_PeruKF049506
Unknown_host_1036KY631960
Unknown_host_110KY631966
Unknown_host_512KY631962
Unknown_host_520KY631980
Unknown_host_649KY631971
Unknown_host_665KY631970
Unknown_host_819KY631984

Anti-contamination procedures

Several measures were taken to avoid contaminations during our manipulations. Extraction of DNA was performed at the CIRMF (Gabon) in a laboratory working on mosquitoes. The room in which extraction was performed was away from the rooms in which DNA was amplified in this lab.

DNA extracts were then sent to France at the IRD (Montpellier). There, blood meal and Plasmodium identification was performed. This lab had never worked before on Plasmodium from ungulates or reptiles. Amplification of host DNA was never or very rarely performed in this lab. When the work was performed, no work on Plasmodium has been performed in this lab for almost 4 years. In addition, the laboratory is designed to avoid contaminations. Clearly defined and separated areas are devoted for each step of the PCR process: one area is devoted to the preparation of reagents (mix PCR). Another room is dedicated to the pre-PCR manipulation (loading of native DNA). This step is done under a cabinet to avoid contamination of the sample with DNA from the operator. Finally, an area is devoted to PCR-amplified DNA. In this area, cabinets are used to deposit the first PCR product into the reagents of the second PCR (for nested PCRs). All cabinets are equipped with UV lamps and are always decontaminated with DNA-free solutions before and after manipulations. Gloves and coats are changed when moving between the areas and plugged tips are used at all steps. Blank controls were always incorporated at all steps of the experimental procedure and were always negative.

Several observations confirm the authenticity of our results: (1) >80% of the hosts that were found have never been manipulated in our lab (hosts that are not humans or non-human primates); (2) the parasite always corresponded to the expected host (antelope parasites were always found in antelopes, human parasites in humans and gorilla parasites in gorillas). Contaminations by external DNA would have lead to random association of hosts and parasites; (3) A new lineage of parasites was discovered.

Trial study to amplify shorter PCR fragments

Since DNA is likely to be degraded in many of our samples, the use of PCR systems targeting fragments of shorter size might improve performance. To determine if this could be the case with our study system, we performed supplementary analyses using (1) a PCR system targeting a shorter fragment of the vertebrate mitochondrial DNA to identify the blood meal origin and (2) a PCR system targeting a shorter fragment of the Cytb DNA to identify the parasite. For the identification of the host, the PCR system used was the one amplifying a fragment of 150 bp of 16S as described in Boessenkool et al. (2012) and using the primers 16Smam1 and 16Smam2. This PCR system was used on blood meals that failed to be identified using our original PCR system (see the paragraph ‘Blood meal identification’). A total of 89 blood meals were tested for this trial study. For the parasite, we designed new primer sets to amplify a shorter fragment of the Cytb gene of the parasite (~177 bp). This new PCR system was applied to blood meals for which the host was identified but that were negative to Plasmodium with our long PCR system (~790 bp, see Material and methods above). A total of 91 blood samples were tested. For the first round of amplifications, we used 6 μL of DNA template in a 25 μL reaction volume, containing: 12.5 μL of Mix PCR (Qiagen), 2.5 μL solution Q (Qiagen), and 4 pmol of each primer (cytb1F CTCTATTAATTTAGTTAAAGCACACTT and 454R CCWGTWGCYTGCATYTATCT). Cycling conditions were 15 min at 95°C, 30 s at 94°C, 90 s at 57°C, 90 s at 72°C (40 cycles), and 10 min at 72°C. For the second round of amplification, we used 1.5 μL of the first PCR template in a 25 μL reaction volume, containing 2.5 μL of 10× buffer, 1.25 mM MgCl2, 250 μM of each dNTP, 10 pmol of each primer (454F2 WAATTAYCCATGYCCATTRAA and Plas1rc CACCATCCACTCCATAATTCTC), and 0.1 unit Taq Platinum (Invitrogen). Cycling conditions for the second round were 5 min at 95°C, 30 s at 94°C, 30 s at 50°C, 90 s at 72°C (35 cycles),and 10 min at 72°C. The amplified products (5 μL) were run on 1.5% agarose gels in TAE buffer. The PCR-amplified products (177 bp) were used as templates for sequencing. DNA sequencing was performed by Eurofins MWG.

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Decision letter

  1. Ben Cooper
    Reviewing Editor; Mahidol Oxford Tropical Medicine Research Unit, Thailand

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Tracking zoonotic pathogens using blood-sucking flies as "flying syringes"" for consideration by eLife. Your article has been favorably evaluated by Prabhat Jha (Senior Editor) and three reviewers, one of whom, Ben Cooper (Reviewer #1), is a member of our Board of Reviewing Editors. The following individual involved in review of your submission has agreed to reveal their identity: Sébastien Calvignac-Spencer (Reviewer #3).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

The study demonstrates the potential utility of molecular analysis of blood collected from blood sucking flies for monitoring infectious agents in wild animal sources using as an example an analysis of malaria parasites identified from a collection of 4100 flies (mostly tsetse) captured in pristine habitats in Gabon. From this collection, 1230 flies were engorged with blood and the origin of the blood meal was identified from >400 flies and malaria parasite genetic material from 37 individual flies. A number of know malaria parasite species were identified and a couple of potentially new parasites were found or linked to a host for the first time.

All reviewers thought that this was interesting work and that the kind of approach described in this paper is very promising.

All agreed, however, that a number of essential revisions are required. In particular, it was felt that there was much room for technical improvements and clarifications. For example, the relatively low rate of recovery of vertebrate sequences probably finds an explanation in the excessive length of the PCR system used by the authors – most groups in the eDNA/iDNA field have converged to using systems targeting fragments <200bp (experimental demonstrations of fragment length dependent loss of sensitivity beyond this size can be found in Schnell et al. Curr Biol 2012, Calvignac-Spencer et al. Mol Ecol 2013 for iDNA and a host of eDNA papers).

The reviewers also agreed that individual-based analyses are unlikely to be applied on the massive scales that would be needed to find these needles in this haystack (37 malaria positive out of 4,000 individuals – knowing malaria parasites are hemoparasites!). To demonstrate the potential utility of the approach the authors should ideally show experimentally that technical solutions more in line with high-throughput analyses would work, e.g. no gut dissection + multi-individual pooling + amplicon NGS (and/or combination thereof).

Essential revisions:

1) More analyses should be provided together with additional comparative data from the literature. The authors gave raw data information in their supplementary files, with which pathogen in which blood meal from flies in the different localities. With these, the authors may better investigate the limitations of the method by comparing the relative abundance and diversity of vertebrates with other estimates of wildlife abundance (i.e. census for national parks). They should certainly be able to compare their pathogen results using flies with other studies that have investigated directly the prevalence and diversity of microbes / parasites among the wildlife.

2) Additional lab experiments should be performed to address concerns outlined above:

i) use an alternative, shorter mammal-targeting system on the negatives (e.g. 16S by Taylor 1996, made very popular by Boessenkool et al. 2012);

ii) a shorter Plasmodium targeting system on the mammal positives (unless the system already targeted a fragment in the range of 100-200bp);

iii) a couple of DNA extract pooling experiments coupled with bulk PCR and amplicon NGS (e.g. with 10 pools of 10 DNA extracts or so).

i) and ii) should increase sensitivity (one can expect that given malaria positives in apparently mammal negative flies). iii) will open the way to a more realistic implementation of the tool.

3) The authors should explain very clearly what anti-contamination measures were taken.

The finding of P. falciparum and P. adleri in a lab famous for their work on great ape malaria parasites will necessarily raise many eyebrows. More generally, the fact that most of the malaria parasite sequences belong to a handful species/lineages also raises the question of potential contaminations, including during the experiments, i.e. PCR products generated during this study that may have contaminated the next experiments. The authors should add a section describing their anti-contamination measures in the Materials and methods.

4) Currently the description and reporting of the statistical analysis is inadequate, and this needs to be addressed:

i) Twice in the subsection “Host identification from blood meals”, p-values are quoted without any accompanying point estimates or confidence intervals. These p-values, on their own, are just about meaningless (an arbitrary difference, of no biological significance, can give a p-value as small as you like with enough data). The authors need to report the effect size and confidence intervals.

ii) Subsection “Data analysis”. This section is too vague. If mixed effects models are used, what is the clustering variable? If a model with binomial distribution was used, what was the link function? How was season modelled? etc. Full details of the models should be given and full results can be reported in the supplementary material.

5) While the reviewers acknowledge that such work has only rarely been performed, it is untrue to state that "the proof of concept has never been obtained". Actually, the concept was even given a name (xenosurveillance) in a paper in which Plasmodium sp. sequences were detected in blood-fed mosquito pools (although the authors rather insisted on their detection of EBV and CDV; Grubaugh et al. PLoS NTD 2015). There are many other papers on this idea that could be cited. Among others:

Calvignac-Spencer S et al. 2013. Carrion fly-derived DNA as a tool for comprehensive and cost-effective assessment of mammalian biodiversity. Molecular Ecology doi: 10.1111/mec.12183

Kent RJ 2009. Molecular methods for arthropod blood meal identification and applications to ecological and vector-borne disease. Molecular Ecology Resources doi: 10.1111/j.1755-0998.2008.02469.x

Lee P-S et al. 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(4):e0123871. doi:10.1371/journal.pone.0123871

Schubert G et al. 2014. Targeted detection of mammalian species using carrion fly-derived DNA. Molecular Ecology Resources doi: 10.1111/1755-0998.12306and even from blood meals of leeches:

Schnell ID et al. 2015. DNA from terrestrial haematophagous leeches as a wildlife surveying and monitorings tool – prospects, pitfalls and avenues to be developed. Frontiers in Zoology (2015) 12:24

DOI 10.1186/s12983-015-0115-z

Within the last month a closely related paper has also appeared in Scientific Reports "Assessing the feasibility of fly based surveillance of wildlife infectious diseases" doi:10.1038/srep37952. This paper should now be referenced in your revised submission.

https://doi.org/10.7554/eLife.22069.009

Author response

Essential revisions:

1) More analyses should be provided together with additional comparative data from the literature. The authors gave raw data information in their supplementary files, with which pathogen in which blood meal from flies in the different localities. With these, the authors may better investigate the limitations of the method by comparing the relative abundance and diversity of vertebrates with other estimates of wildlife abundance (i.e. census for national parks). They should certainly be able to compare their pathogen results using flies with other studies that have investigated directly the prevalence and diversity of microbes / parasites among the wildlife.

We agree with reviewers that it would be nice if we could compare our results of vertebrate diversity or pathogen prevalence with data from the literature. Unfortunately, these data are lacking for the parks where we worked, preventing a good comparative analysis. The only information we could obtain is, for the best, an incomplete list of mammal species without information on their abundance for the park of La Lopé (White, 1994, Journal of Animal Ecology). What we can notice from the comparison of the list of species found in the blood meals of the flies and the list of large vertebrates present in the park is that there is an overrepresentation of terrestrial vertebrate species and a lack of species found in canopy. There is also a lack of small mammals, like rodents or bats.

For the pathogens, a direct comparison of what has been found with the literature is not possible. Most malaria agents discovered in our study are from antelopes. The only study that was published on it was from bushmeat samples collected all over Gabon, which clearly precludes a comparison with our sites of study as there is a chance that prevalence may greatly vary from one site to another, as previously noted on Plasmodium/ apes studies.

2) Additional lab experiments should be performed to address concerns outlined above:

i) use an alternative, shorter mammal-targeting system on the negatives (e.g. 16S by Taylor 1996, made very popular by Boessenkool et al. 2012);

ii) a shorter Plasmodium targeting system on the mammal positives (unless the system already targeted a fragment in the range of 100-200bp);

iii) a couple of DNA extract pooling experiments coupled with bulk PCR and amplicon NGS (e.g. with 10 pools of 10 DNA extracts or so).

i) and ii) should increase sensitivity (one can expect that given malaria positives in apparently mammal negative flies). iii) will open the way to a more realistic implementation of the tool.

Following reviewers’ recommendations, shorter PCR systems were used for 1) the identification of the blood meal and 2) the identification of the infections with haemosporidian parasites.

For the blood meal, we used the primers designed by Taylor et al. 1996 targeting a short fragment of the mitochondrial DNA of mammals. We tested this new PCR on 89 blood meals for which we were not able to identify the host with our previous PCR system. Out of the 89, we were able to identify the host in 76% of the cases after amplification and sequencing. This clearly demonstrates a far better sensitivity of this system based on the amplification of a shorter fragment.

For the parasites, we designed new primers sets (based on previously published primers) to amplify a shorter fragment of the Cyt-b gene of the parasite. The newly amplified fragment was shorter than 200 bp. To determine if this would lead to a gain of sensitivity, we tested 91 blood meals for which the host was determined but were negative with previous Plasmodium PCR system. We re-did the Plasmodium PCR with the new system. We were able to identify one additional positive individual, which do not indicate a large gain in sensitivity for the detection of the parasite. The sensitivity of the two PCRs should however be tested with precision and need more development.

We did not perform any experiment using NGS or high throughput technologies due to a lack of time. However, as highlighted in the Discussion, the tool needs now to be developed and optimized and NGS or the use of high throughput diagnostic method is a clear direction of improvement.

These pilot tests have now been added to the new version of the manuscript.

3) The authors should explain very clearly what anti-contamination measures were taken.

The finding of P. falciparum and P. adleri in a lab famous for their work on great ape malaria parasites will necessarily raise many eyebrows. More generally, the fact that most of the malaria parasite sequences belong to a handful species/lineages also raises the question of potential contaminations, including during the experiments, i.e. PCR products generated during this study that may have contaminated the next experiments. The authors should add a section describing their anti-contamination measures in the Materials and methods.

Several measures were taken to avoid contaminations during our manipulations. Extraction of DNA was performed at the CIRMF (Gabon) in a laboratory working on mosquitoes and not manipulating Plasmodium or mammal DNA. The room in which extraction was performed was away from the rooms in which DNA was amplified in this lab.

DNA extracts were then sent in France at the IRD (Montpellier). There, blood meal and Plasmodium identification was performed. This lab had never worked before on Plasmodium from wild animals, only human and non-human primate Plasmodium. Amplification of host DNA was never or very rarely performed in this lab. When the work was performed, no work on Plasmodium has been performed in this lab for almost four years. In addition, the laboratory is designed to avoid contaminations. Clearly defined and separated areas are devoted for each step of the PCR process: one area is devoted to the preparation of reagents (mix PCR). Another room is dedicated to the pre-PCR manipulation (loading of native DNA). This step is done under a cabinet to avoid contamination of the sample with DNA from the operator. Finally, an area is devoted to PCR-amplified DNA. In this area, cabinets are used to deposit PCR1 into the reagents of PCR2 (for nested PCRs). All cabinets are equipped with UV lamps and are always decontaminated with DNA free solutions after and before manipulations. Gloves and coats are changed when moving between the areas and plugged tips are used at all steps. Blank controls were always incorporated at all steps of the experimental procedure and were always negative.

Several observations confirm the authenticity of our results: 1) 85% of the hosts that were found were never manipulated in our labs (hosts that are not humans or apes). 2) the parasite always corresponded to the expected host (antelope parasite were always found in antelopes, human parasite in humans and gorilla parasites in gorillas). Contaminations by external DNA would have lead to random association of hosts and parasites. 3) A new lineage of parasites was discovered.

The experimental procedure is entirely detailed in the Materials and methods section of the new version of the manuscript.

4) Currently the description and reporting of the statistical analysis is inadequate, and this needs to be addressed:

i) Twice in the subsection “Host identification from blood meals”, p-values are quoted without any accompanying point estimates or confidence intervals. These p-values, on their own, are just about meaningless (an arbitrary difference, of no biological significance, can give a p-value as small as you like with enough data). The authors need to report the effect size and confidence intervals.

ii) Subsection “Data analysis”. This section is too vague. If mixed effects models are used, what is the clustering variable? If a model with binomial distribution was used, what was the link function? How was season modelled? etc. Full details of the models should be given and full results can be reported in the supplementary material.

We thank the reviewer for pointing this out and agree. However, we have decided to remove these descriptive statistical analyses because we felt it did not add a lot to the main message of the paper and would bring too much confusion. We therefore deleted Supporting Table S1 from the previous version of the manuscript.

5) While the reviewers acknowledge that such work has only rarely been performed, it is untrue to state that "the proof of concept has never been obtained". Actually, the concept was even given a name (xenosurveillance) in a paper in which Plasmodium sp. sequences were detected in blood-fed mosquito pools (although the authors rather insisted on their detection of EBV and CDV; Grubaugh et al. PLoS NTD 2015). There are many other papers on this idea that could be cited. Among others:

Calvignac-Spencer S et al. 2013. Carrion fly-derived DNA as a tool for comprehensive and cost-effective assessment of mammalian biodiversity. Molecular Ecology doi: 10.1111/mec.12183

Kent RJ 2009. Molecular methods for arthropod blood meal identification and applications to ecological and vector-borne disease. Molecular Ecology Resources doi: 10.1111/j.1755-0998.2008.02469.x

Lee P-S et al. 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(4):e0123871. doi:10.1371/journal.pone.0123871

Schubert G et al. 2014. Targeted detection of mammalian species using carrion fly-derived DNA. Molecular Ecology Resources doi: 10.1111/1755-0998.12306and even from blood meals of leeches:

Schnell ID et al. 2015. DNA from terrestrial haematophagous leeches as a wildlife surveying and monitorings tool – prospects, pitfalls and avenues to be developed. Frontiers in Zoology (2015) 12:24

DOI 10.1186/s12983-015-0115-z

Within the last month a closely related paper has also appeared in Scientific Reports "Assessing the feasibility of fly based surveillance of wildlife infectious diseases" doi:10.1038/srep37952. This paper should now be referenced in your revised submission.

We thank reviewers for these references that for the most we indeed knew. There are now cited in the paper. However, most of them concern mammal biodiversity and not pathogen diversity. The aim of our paper is clearly to estimate pathogen diversity which goes a step further to most of these studies cited above. The only two references that are close in principle to what we propose are those of Grubaugh et al. PLos NTD 2015 and the very recent one from Hoffman et al. 2016. Note however that the second reference, although close in principle, uses carrion-flies to detect pathogens from carcasses and not hematophagous flies. There are now cited in the new version of the manuscript and the term “xenosurveillance” is now used.

https://doi.org/10.7554/eLife.22069.010

Article and author information

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.
    ORCID icon 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.
    ORCID icon 0000-0002-7122-2079

Funding

Agence Universitaire de la Francophonie

  • Paul-Yannick Bitome-Essono
  • Flobert Njiokou
  • Francois Bretagnolle
  • Franck Prugnolle
  • Christophe Paupy

Service de Coopération et d'Action Culturelle de l'ambassade de France au Gabon

  • Paul-Yannick Bitome-Essono
  • Francois Bretagnolle

Laboratoires Mixtes Internationaux (LMI ZOFAC IRD)

  • Benjamin Ollomo
  • Franck Prugnolle
  • Christophe Paupy

Centre International de Recherches Médicales de Franceville

  • Paul-Yannick Bitome-Essono
  • Benjamin Ollomo
  • Diego Ayala
  • Virginie Rougeron
  • Franck Prugnolle
  • Christophe Paupy

Agence Nationale de la Recherche (ANR JCJC 07-2012-ORIGIN)

  • Virginie Rougeron
  • Franck Prugnolle
  • Christophe Paupy

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

Authors thank all the reviewers for their constructive and helpfull comments. This study was carried out with a financial support of: ‘Agence Universitaire de la Francophonie’ (AUF), the ‘Service de Cooperation et d'Action Culturelle’ (SCAC) of French Embassy in Gabon, the ‘Institut Français’ of Libreville (IF), the ‘Conseil Régional de Bourgogne’ and the ‘Bonus Qualité Recherche’ (BQR) of Université de Bourgogne. This work was also funded by Institut de Recherche pour le Développement (Laboratoire Mixte International ZOFAC), Centre International de Recherches Médicales de Franceville (CIRMF), as well as the Agence Nationale de la Recherche (ANR) programme Jeunes Chercheuses Jeunes Chercheurs (JCJC) Sciences de la Vie, de la Santé et des Ecosystèmes 7–2012 project ORIGIN (ANR JCJC SVSE 7–2012 ORIGIN). We thank the Agence Nationale des Parcs Nationaux (ANPN) and the Centre National de la Recherche Scientifique et Technologique (CENAREST) of Gabon who authorized this study and facilitated the access to national Parks. Authors also thank Eric Willaume from the park of La Lékédi for his help.

Reviewing Editor

  1. Ben Cooper, Reviewing Editor, Mahidol Oxford Tropical Medicine Research Unit, Thailand

Publication history

  1. Received: October 4, 2016
  2. Accepted: March 14, 2017
  3. Accepted Manuscript published: March 28, 2017 (version 1)
  4. Version of Record published: May 11, 2017 (version 2)

Copyright

© 2017, Bitome-Essono et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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