Best-bet integrated strategies for containing drug-resistant trypanosomes in cattle

Background African animal trypanosomosis is a major constraint to the rearing of productive livestock in the sub-humid Sudan-Sahel zone of West Africa where cotton is grown. Trypanosomosis is mainly controlled using trypanocidal drugs, but the effective use of drugs is threatened by the development of widespread resistance. This study tested integrated best-bet strategies for containment and/ or reversal of trypanocide resistance in villages in south-east Mali where resistance has been reported. Methods Four sentinel villages each from an intervention area (along the road from Mali to Burkina Faso) and a control area (along the road from Mali to Côte d’Ivoire) were selected for the study. Tsetse control was based on deltamethrin-treated stationary attractive devices and targeted cattle spraying between March 2008 and November 2009. Trypanosome-positive cattle were selectively treated with 3.5 mg/kg diminazene aceturate. Strategic helminth control using 10 mg/kg albendazole was also undertaken. During the intervention, tsetse densities along drainage lines, trypanosome infections and faecal egg counts in risk cattle (3 to 12 months of age) were monitored. Results Catch reductions of 66.5 % in Glossina palpalis gambiensis and 90 % in G. tachinoides were observed in the intervention area. Trypanosome prevalence was significantly (p < 0.05) lower in the intervention area (2.3 %; 1.3-3.6 %) compared to the control area (17.3 %; 14.8-20.1 %). Albendazole treatment resulted in a faecal egg count reduction of 55.6 % and reduced trypanosome infection risk (2.9 times lower than in the placebo group) although not significantly (p > 0.05). Further studies are required before confirming the existence of albendazole resistant strongyles in the study area. Conclusion Integration of best-bet strategies in areas of multiple drug- resistance is expected to reduce trypanosome infection risk thus contributing to containment of trypanocidal drug resistance. Integrated best-bet strategies could therefore be considered a viable trypanosomosis control option especially in areas where multiple drug-resistance has been reported

Spatial distribution of Glossina sp. and Trypanosoma sp. in south-western Ethiopia

Background Accurate information on the distribution of the tsetse fly is of paramount importance to better control animal trypanosomosis. Entomological and parasitological surveys were conducted in the tsetse belt of south-western Ethiopia to describe the prevalence of trypanosomosis (PoT), the abundance of tsetse flies (AT) and to evaluate the association with potential risk factors. Methods The study was conducted between 2009 and 2012. The parasitological survey data were analysed by a random effects logistic regression model, whereas the entomological survey data were analysed by a Poisson regression model. The percentage of animals with trypanosomosis was regressed on the tsetse fly count using a random effects logistic regression model. Results The following six risk factors were evaluated for PoT (i) altitude: significant and inverse correlation with trypanosomosis, (ii) annual variation of PoT: no significant difference between years, (iii) regional state: compared to Benishangul-Gumuz (18.0 %), the three remaining regional states showed significantly lower PoT, (iv) river system: the PoT differed significantly between the river systems, (iv) sex: male animals (11.0 %) were more affected than females (9.0 %), and finally (vi) age at sampling: no difference between the considered classes. Observed trypanosome species were T. congolense (76.0 %), T. vivax (18.1 %), T. b. brucei (3.6 %), and mixed T. congolense/vivax (2.4 %). The first four risk factors listed above were also evaluated for AT, and all have a significant effect on AT. In the multivariable model only altitude was retained with AT decreasing with increasing altitude. Four different Glossina species were identified i.e. G. tachinoides (52.0 %), G. pallidipes (26.0 %), G.morsitans submorsitans (15.0 %) and G. fuscipes fuscipes (7.0 %). Significant differences in catches/trap/day between districts were observed for each species. No association could be found between the tsetse fly counts and trypanosomosis prevalence. Conclusions Trypanosomosis remains a constraint to livestock production in south-western Ethiopia. Four Glossina and three Trypanosoma species were observed. Altitude had a significant impact on AT and PoT. PoT is not associated with AT, which could be explained by the importance of mechanical transmission. This needs to be investigated further as it might jeopardize control strategies that target the tsetse fly population

Identification of Tsetse (Glossina spp.) using matrix-assisted laser desorption/ionisation time of flight mass spectrometry

Glossina (G.) spp. (Diptera: Glossinidae), known as tsetse flies, are vectors of African trypanosomes that cause sleeping sickness in humans and nagana in domestic livestock. Knowledge on tsetse distribution and accurate species identification help identify potential vector intervention sites. Morphological species identification of tsetse is challenging and sometimes not accurate. The matrix-assisted laser desorption/ionisation time of flight mass spectrometry (MALDI TOF MS) technique, already standardised for microbial identification, could become a standard method for tsetse fly diagnostics. Therefore, a unique spectra reference database was created for five lab-reared species of riverine-, savannah- and forest- type tsetse flies and incorporated with the commercial Biotyper 3.0 database. The standard formic acid/acetonitrile extraction of male and female whole insects and their body parts (head, thorax, abdomen, wings and legs) was used to obtain the flies' proteins. The computed composite correlation index and cluster analysis revealed the suitability of any tsetse body part for a rapid taxonomical identification. Phyloproteomic analysis revealed that the peak patterns of G. brevipalpis differed greatly from the other tsetse. This outcome was comparable to previous theories that they might be considered as a sister group to other tsetse spp. Freshly extracted samples were found to be matched at the species level. However, sex differentiation proved to be less reliable. Similarly processed samples of the common house fly Musca domestica (Diptera: Muscidae; strain: Lei) did not yield any match with the tsetse reference database. The inclusion of additional strains of morphologically defined wild caught flies of known origin and the availability of large-scale mass spectrometry data could facilitate rapid tsetse species identification in the futur

Blutmahlzeitanalyse von Tsetsefliegen (Glossina spp.) mittels PCR und Speziesdifferenzierung mit MALDI TOF MS als Beiträge zu rationaler Vektorbekämpfung

Tsetse flies inhabit 10 million km2 of subsaharan Africa, transmitting Human African Trypanosomoses (HAT) and African Animal Trypanosomoses (AAT). Public health services of most African countries are not able to reach the affected rural communities. Besides, trypanocides often are inefficient and vaccinations are unavailable. Thus, various means of vector control remain for disease management. In order to avoid unreasonable interventions against tsetse, decision support tools help defining the most efficient control strategies: trypanosomosis risk assessment and profound knowledge on local tsetse populations and their behaviour. Large-scale risk surveys and tedious serological laboratory analyses are too expensive at the community-level. That is why the objective of this work was rationalizing trypanosomosis risk assessment and improving current tsetse analysis methods. Chapter 1 provides a literature review on trypanosomosis epidemiology, tsetse biology, physiology, control means and methods for risk assessment and bloodmeal analysis. Chapter 2 deals with the application of a tsetse challenge formula that simplified relative AAT risk estimation in 2 villages of the Sikasso region in southeast Mali. During 6 months tsetse were trapped at animal watering sites, followed by microscopic examination of the flies for trypanosome infection rates and by PCR analysis of tsetse bloodmeals. Bloodmeals were identified by species- specific cytochrome b primers that amplified vertebrate mitochondrial DNA and by sequencing unidentifiable samples. The outcome of the field study revealed that Glossina morsitans submorsitans had vanished, while Glossina palpalis gambiensis (Gpg) and Glossina tachinoides (Gt) were still present in this area with 369 and 105 caught tsetse, respectively. Further, it became obvious that the tsetse were unevenly distributed with catches of 2-152 flies per trap with the majority in direct proximity of watering places while being absent from distances of 20 metres and onwards from a river. Trypanosome infection rates of the flies varied between 0% and 33.3% depending on the trapping location. The analysis of 120 bloodmeals revealed cattle and humans as main hosts while 2 samples showed crocodile DNA. The tsetse challenge of the 2 villages differed significantly with 6 days vs. 77 days that had to be spent by cattle at the watering site in order to contract AAT. The obtained value could in both cases be linked to the trypanosome prevalence of nearby cattle herds. Further analysis of tsetse deriving from 20 traps in 4 villages revealed unexpected differences between the 279 analysed Gt and Gpg. Gt demonstrated no host preference whatsoever because their feeding pattern comprised in equal shares humans, cattle and surprisingly mixed meals of both. Multiple host feeding, yet rarely been described in tsetse research, did occur significantly less often in Gpg (p<0.05). Gpg showed a preference for humans over cattle (66.5% and 10.3%, respectively). The infection rate also differed with Gt being 3-fold more likely to be infected with trypanosomes (18.5%) than Gpg (5.5%). Therefore, chapter 3 contains a logistic regression analysis of the factor mixed bloodmeal towards the factors species, infection, hunger stage and sex. The statistics demonstrated that multiple host feeding was not linked to high infection rates or age but that it positively correlated with female sex in Gt and fully engorged Gpg. It is then discussed how multiple feeding possibly impacts trypanosomosis transmission mechanisms, assuming a higher vectorial competence of Gt compared to Gpg. Although PCR has proven more sensitive than serological methods, the development of MALDI TOF MS (matrix- assisted laser desorption/ionisation time-of-flight mass spectrometry) has become a more rapid tool for routine microbial diagnostics. Insects have rarely been specified by proteomic means, so chapter 4 consists of a proteomic database construction for the tsetse species G. morsitans morsitans, G. pallidipes, G. austeni, G. palpalis gambiensis and G. brevipalpis based by MALDI TOF MS. Lab-reared flies were analysed as entire insects and dissected, obtaining their head, wings, legs, thorax and abdomen. After a simple protein extraction, 60 mass spectrum peak patterns were created as reference spectra. The following principle component and cluster analysis confirmed that each body part was suitable for exact speciation. Evaluation of the database by crosschecking with newly extracted isolates resulted in a composite correlation index that demonstrated reliable tsetse speciation. Dendrograms drawing on peak similarity showed that G. brevipalpis stood consistently apart from the other species, confirming genomic findings that suggested their sister group status. As expected, tsetse of the morsitans group tended to cluster, with the exception of G. austeni that did not show consistent affinities to any of the 3 groups reflecting uncertainties about their group status in recent tsetse taxonomy literature. So, the constructed database apparently displayed genomic findings at the protein level and it proved to be a rapid and accurate tool for tsetse species determination. The results are discussed in chapter 5. It could be demonstrated that a simplified risk assessment formula is able to provide AAT risk trends. This will be useful for planning future vector interventions more rationally, making it available for community-based projects. Thereby, species-specific PCR proved more efficient for bloodmeal analysis than serological methods. Still, obtaining the host preference remains the most laborious tsetse parameter, making it the limiting factor to a more time-efficient risk evaluation. Since rapid MALDI-based diagnostics at the species-level could be established, extending the database is warranted for high-throughput proteomic tsetse identification at the population-level, trypanosome diagnostics and bloodmeal analysis.Tsetsefliegen sind in über 10 Millionen km2 Land in Afrika südlich der Sahara verbreitet und übertragen die durch Trypanosomen verursachte menschliche Schlafkrankheit (HAT) und die Viehseuche Nagana (AAT). Den Gesundheitsbehörden ist es oft unmöglich, die betroffenen Kommunen zu erreichen. Außerdem sind viele Trypanozide unwirksam und Impfungen nicht verfügbar, weswegen die verschiedenen Methoden der Vektorbekämpfung oft effektiver sind. Um die verfügbaren Mittel sinnvoll einzusetzen und Fehlentscheidungen zu vermeiden, werden komplizierte Transmissions-Risiko-Modelle eingesetzt. Dazu ist fundiertes Wissen über regionale Tsetsepopulationen und deren Verhalten nötig. Da groß angelegte Studien und aufwändige Laboranalysen für Projekte auf kommunaler Ebene unbezahlbar sind, hatte diese Arbeit das Ziel, die Risikoanalyse zu vereinfachen und konventionelle Labormethoden zu verbessern. Kapitel 1 beinhaltet eine Literaturübersicht der HAT- und AAT-Epidemiologie, Tsetsebiologie, ihrer Physiologie sowie Bekämpfungsmethoden, Transmissionsmodelle und zu Methoden der Blutmahlzeitanalyse. Kapitel 2 beschreibt die Anwendung der „tsetse challenge“-Formel, um das relative AAT- Risiko für Rinderherden in zwei Dörfern Südostmalis einzuschätzen. Während sechs Monaten wurden Tsetse an Wasserstellen gefangen, mikroskopisch auf Trypanosomen untersucht und anschließend Blutmahlzeiten (BM) mit spezies- spezifischen Cytochrom-b-Primern und Sequenzierung auf deren Herkunft untersucht. Es stellte sich heraus, dass Glossina morsitans submorsitans nicht mehr in der Region vorkommt, dafür wurden 369 Glossina palpalis gambiensis (Gpg) und 105 Glossina tachinoides (Gt) gefangen. Dabei wurde deutlich, dass die scheinbare Abundanz mit Fängen von zwei bis 152 Fliegen pro Falle und Tag stark schwankte und dass sie nur in direkter Nähe zu den Flussläufen vorkamen. Die Infektionsraten der Fliegen variierten zwischen 0% und 33.3% und die Analyse von 120 BM ergab Hausrinder und Menschen als Hauptwirte, während nur zwei BM Krokodil-DNS enthielten. Das relative AAT-Risiko (tsetse challenge) der beiden Dörfer unterschied sich signifikant mit sechs und 77 Tagen, die ein Rind an einer Wasserstelle verbringen müsste, um mit AAT infiziert zu werden. Das Ergebnis spiegelte sich in beiden Fällen in der AAT-Prävalenz umliegender Rinderherden wider. Als die Studie auf vier Dörfer ausgeweitet wurde, stellten sich signifikante Unterschiede zwischen den 279 analysierten Gt und Gpg heraus. Gt-BM bestanden in gleichen Anteilen aus Rindern, Menschen und aus gemischten Anteilen beider Wirte. Frakturierte (F) BM in Tsetse sind bisher kaum beschrieben worden und sie kamen signifikant häufiger in Gt als in Gpg vor (p<0.05). Gpg zeigten dabei eine deutliche Präferenz für Menschen (66.5%). Weil die Infektionsrate von Gt (18.5%) deutlich höher war als die von Gpg (5.5%), wurde eine logistische Regressionsanalyse durchgeführt: Kapitel 3 stellt den Einfluss des Faktors FBM auf Spezies, Alter, Infektionsrate, Hungerzustand und Geschlecht dar. Dabei wurde demonstriert, dass FBM unabhängig vom Infektionsstatus waren, aber sie korrelierten positiv mit dem Merkmal „weiblich“ bei Gt und „voll gesogen“ bei Gpg. Es wird diskutiert, inwiefern FBM Infektionsmechanismen beeinflussen, wobei von einer höheren Vektorkapazität von Gt gegenüber Gpg ausgegangen wird. Die angewandte PCR ist zwar sensitiver als etablierte serologische Methoden, aber MALDI TOF MS (matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry) bietet schnellere Ergebnisse und ist in der mikrobiellen Diagnostik bereits Routine. Kapitel 4 beschäftigt sich deswegen mit dem Erstellen einer proteomischen Datenbank für die Tsetsespezies G. morsitans morsitans, G. pallidipes, G. austeni, G. palpalis gambiensis und G. brevipalpis mittels MALDI TOF MS. Laborgezüchtete Fliegen wurden als ganze Individuen und seziert in Kopf, Flügel, Beine, Thorax und Abdomen analysiert. Nach einer einfachen Proteinextraktion, wurden 60 MSP’s (main spectra) als Referenzspektren geschaffen und eine Komponenten- und Cluster-Analyse durchgeführt, wobei sich jedes Körperteil als nutzbar für eine exakte Spezifizierung erwies. Die Zuverlässigkeit der Datenbank wurde erfolgreich mit neu extrahierten Tsetse- Isolaten getestet, dargestellt in dem farblich abgestuften CCI (composite correlation index). Dendrogramme, die Ähnlichkeiten zwischen den 70 meistreproduzierten Peaks darstellen, zeigten eine große Distanz von G. brevipalpis zu den anderen Spezies. Dies bestätigte Ergebnisse einer Studie des Genoms, in der ein Schwesterstatus von G. brevipalpis zu anderen Tsetse postuliert wird. Auch G. austeni spiegelte Kontroversen aus Taxonomiestudien über deren Gruppenzugehörigkeit wider, da sie entweder mit der Savannen- oder der Flussgruppe Cluster bildete, abhängig vom analysierten Körperteil. Insgesamt bot die MALDI-Datenbank eine schnelle und exakte Speziesbestimmung von Tsetse und lieferte nebenbei nützliche taxonomische Informationen. Die Ergebnisse werden in Kapitel 5 diskutiert. Auch eine vereinfachte Formel der Risiko- Einschätzung bietet wertvolle Informationen über AAT, was eine rationale Planung von Vektorbekämpfungsprojekten auf kommunaler Ebene möglich macht. Dabei erwies sich spezies-spezifische PCR der BM als effizient, auch wenn das Ermitteln der Wirtspräferenz aufwändig bleibt. Seitdem sich eine MALDI-basierte Tsetse-Spezifizierung als möglich erwiesen hat, könnte eine Ausweitung der proteomischen Analyse von Tsetsefliegen auf BM, Infektionsstatus und Populationszugehörigkeit zu einer Routine-Methode in der Tsetsediagnostik werden

Identification of Tsetse (<i>Glossina</i> spp.) Using Matrix-Assisted Laser Desorption/Ionisation Time of Flight Mass Spectrometry

<div><p><i>Glossina (G.)</i> spp. (Diptera: Glossinidae), known as tsetse flies, are vectors of African trypanosomes that cause sleeping sickness in humans and nagana in domestic livestock. Knowledge on tsetse distribution and accurate species identification help identify potential vector intervention sites. Morphological species identification of tsetse is challenging and sometimes not accurate. The matrix-assisted laser desorption/ionisation time of flight mass spectrometry (MALDI TOF MS) technique, already standardised for microbial identification, could become a standard method for tsetse fly diagnostics. Therefore, a unique spectra reference database was created for five lab-reared species of riverine-, savannah- and forest- type tsetse flies and incorporated with the commercial Biotyper 3.0 database. The standard formic acid/acetonitrile extraction of male and female whole insects and their body parts (head, thorax, abdomen, wings and legs) was used to obtain the flies' proteins. The computed composite correlation index and cluster analysis revealed the suitability of any tsetse body part for a rapid taxonomical identification. Phyloproteomic analysis revealed that the peak patterns of <i>G. brevipalpis</i> differed greatly from the other tsetse. This outcome was comparable to previous theories that they might be considered as a sister group to other tsetse spp. Freshly extracted samples were found to be matched at the species level. However, sex differentiation proved to be less reliable. Similarly processed samples of the common house fly <i>Musca dome</i>stica (Diptera: Muscidae; strain: Lei) did not yield any match with the tsetse reference database. The inclusion of additional strains of morphologically defined wild caught flies of known origin and the availability of large-scale mass spectrometry data could facilitate rapid tsetse species identification in the future.</p></div

Spectra reproducibility among the biological and technical replicates.

<p>Overlay view of 27 spectra obtained from biological and technical replicates of <i>Glossina austeni</i> female whole insect. The masses (in Da) of the ions are shown on the <i>x</i>-axis and the <i>m/z</i> value stands for mass to charge ratio. On the y-axis, the relative intensity of the ions (a.u., arbitrary units) is shown. In the insert, zoomed m/z 5000 to 5200 displays the uniformity among the measured spectra and the stacked view m/z 9000 to 12500 provides a direct comparison of all 27 measured spectra.</p

Score-oriented main spectra dendrogram of whole <i>Glossina spp</i>. extracts.

<p>The dendrogram was calculated by Biotyper 3.0 software with distance measure set at correlation and linkage set at complete.</p

Representative spectra from the whole insect and different body parts of female <i>Glossina austeni</i>.

<p>Peak pattern of whole and body parts extractions of <i>Glossina austeni</i> female. The x-axis <i>m/z</i> values represent the mass to charge ratio and on the y-axis the relative intensity of the ions (a.u., arbitrary units) is shown. A) Whole insect, B) abdomen, C) head, D) legs, E) thorax and F) wings.</p

Laboratory-reared <i>Glossina (G.)</i> spp. selected for the compilation of spectra database.

1<p>Tsetse & Trypanosomiasis Research Institute, Tanga, Tanzania;</p>2<p>International Atomic Energy Agency, Seibersdorf, Austria.</p

Composite correlation index of tsetse spectra sets.

<p>Evaluation of uniqueness among the spectra sets of 60 tsetse spectra measurements of male (M) and female (F) individuals and their body parts. Composite correlation index matrix was calculated with Biotyper 3.0 software in the mass range of 3000–12000 Da, resolution 4, 4 intervals and auto-correction off. Red indicates relatedness between the spectra sets and dark green indicates incongruence.</p