Isolation of Trypanosoma brucei gambiense from Cured and Relapsed Sleeping Sickness Patients and Adaptation to Laboratory Mice

Human African trypanosomiasis, or sleeping sickness, is still a major public health problem in central Africa. Melarsoprol is widely used for treatment of patients where the parasite has already reached the brain. In some regions in Angola, Sudan, Uganda and Democratic Republic of the Congo, up to half of the patients cannot be cured with melarsoprol. From previous investigations it is not yet clear what causes these high relapse rates. Therefore we aimed to establish a parasite collection isolated from cured as well as relapsed patients for downstream comparative drug sensitivity profiling. From 360 sleeping sickness patients, blood and cerebrospinal fluid (CSF) was collected before treatment and along the prescribed 24 months follow-up. Blood and CSF were inoculated in thicket rats (Grammomys surdaster), Natal multimammate mice (Mastomys natalensis) and immunodeficient laboratory mice (Mus musculus). Thus, we established a unique collection of Trypanosoma brucei gambiense type I parasites, isolated in the same disease focus and within a limited period, including 12 matched strains isolated from the same patient before treatment and after relapse. This collection is now available for genotypic and phenotypic characterisation to investigate the mechanism behind abnormally high treatment failure rates in Mbuji-Mayi, Democratic Republic of the Congo

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

Schematic view of the <i>AQP2/3</i> variants identified in this study (adapted from Graf et al [20]).

<p>Sequence of <i>AQP3</i> was not verified. Positions of primer: black box = AQP2/3_F, green box = AQP2_R, red box = AQP2/3_R. A) Reference locus of <i>AQP2</i> and <i>AQP3</i>, with wild-type <i>AQP2</i> found in the melarsoprol and pentamidine sensitive strain <i>T.b. gambiense</i> LiTat 1.3 and in all strains from Masi-Manimba. B) Chimera of <i>AQP2</i> and <i>AQP3</i> occurring in a melarsoprol and pentamidine resistant <i>T.b. brucei</i> strain as described by Baker <i>et al.</i><a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003212#pntd.0003212-Baker1" target="_blank">[19]</a>. C) Chimera of <i>AQP2</i> and <i>AQP3</i> plus loss of <i>AQP3</i> in all <i>T.b. gambiense</i> strains from Mbuji-Mayi as described in this article and by Graf et al. <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003212#pntd.0003212-Graf1" target="_blank">[20]</a>. D) New chimera of <i>AQP2</i> and <i>AQP3</i>, possibly outside the known locus, found in all <i>T.b. gambiense</i> strains from Mbuji-Mayi. E) New chimera of <i>AQP2</i> and <i>AQP3</i> plus loss of <i>AQP3</i> found in two old Congolese <i>T.b. gambiense</i> strains, MBA and KEMLO. F) New chimera of <i>AQP2</i> and <i>AQP3</i>, without loss of <i>AQP3</i>, found in all four <i>T.b. gambiense</i> strains isolated in Masi-Manimba.</p

Genotype, strains and accession numbers for <i>TbAQP2</i> and <i>TbAQP2/3</i>.

<p>*direct sequencing results suggested 18 heterozygous single nucleotide polymorphisms in the <i>AQP2</i> coding sequence of <i>T.b. gambiense</i> MM01, MM03, MM05, and MM06.</p><p>Genotype, strains and accession numbers for <i>TbAQP2</i> and <i>TbAQP2/3</i>.</p

Phenotype of melarsoprol resistant strains.

<p>Number of relapsing mice (out of 6 infected) and day post-infection that relapses were observed after treatment with melarsoprol at different dosages and repetitions. DPI: days post-infection, BW: body weight, rep: repetition, na: not applicable,</p><p>*: relapsing population used for AQP2/3 RFLP analysis.</p><p>Phenotype of melarsoprol resistant strains.</p

Restriction digest profile generated with SfaNI PCR-RFLP on DNA of the <i>T.b. gambiense</i> strains as listed in Table 1 and of the two <i>T.b. brucei</i> control strains.

<p>Lanes 1, 26, 27 and 52 = GeneRuler 100 bp Plus DNA Ladder (Fermentas), lanes 2 to 44 = <i>T.b. gambiense</i> strains isolated from Mbuji-Mayi, lanes 45–48 = <i>T.b. gambiense</i> strains isolated from Masi-Manimba, lane 49 = <i>T.b. brucei</i> 427 WT, lane 50 = <i>T.b. brucei</i> 427 AT1/P2 KO, lane 51 = negative PCR control.</p

Melarsoprol Sensitivity Profile of <i>Trypanosoma brucei gambiense</i> Isolates from Cured and Relapsed Sleeping Sickness Patients from the Democratic Republic of the Congo

<div><p>Background</p><p>Sleeping sickness caused by <i>Trypanosoma brucei</i> (<i>T.b</i>.) <i>gambiense</i> constitutes a serious health problem in sub-Sahara Africa. In some foci, alarmingly high relapse rates were observed in patients treated with melarsoprol, which used to be the first line treatment for patients in the neurological disease stage. Particularly problematic was the situation in Mbuji-Mayi, East Kasai Province in the Democratic Republic of the Congo with a 57% relapse rate compared to a 5% relapse rate in Masi-Manimba, Bandundu Province. The present study aimed at investigating the mechanisms underlying the high relapse rate in Mbuji-Mayi using an extended collection of recently isolated <i>T.b. gambiense</i> strains from Mbuji-Mayi and from Masi-Manimba.</p><p>Methodology/Principal Findings</p><p>Forty five <i>T.b. gambiense</i> strains were used. Forty one were isolated from patients that were cured or relapsed after melarsoprol treatment in Mbuji-Mayi. <i>In vivo</i> drug sensitivity tests provide evidence of reduced melarsoprol sensitivity in these strains. This reduced melarsoprol sensitivity was not attributable to mutations in <i>TbAT1</i>. However, in all these strains, irrespective of the patient treatment outcome, the two aquaglyceroporin (<i>AQP</i>) 2 and 3 genes are replaced by chimeric <i>AQP2/3</i> genes that may be associated with resistance to pentamidine and melarsoprol. The 4 <i>T.b. gambiense</i> strains isolated in Masi-Manimba contain both wild-type <i>AQP2</i> and a different chimeric <i>AQP2</i>/3. These findings suggest that the reduced <i>in vivo</i> melarsoprol sensitivity of the Mbuji-Mayi strains and the high relapse rates in that sleeping sickness focus are caused by mutations in the <i>AQP2/AQP3</i> locus and not by mutations in <i>TbAT1</i>.</p><p>Conclusions/Significance</p><p>We conclude that mutations in the <i>TbAQP2/3</i> locus of the local <i>T.b. gambiense</i> strains may explain the high melarsoprol relapse rates in the Mbuji-Mayi focus but other factors must also be involved in the treatment outcome of individual patients.</p></div

Restriction digest profile generated with AvaI PCR-RFLP on DNA of the <i>T.b. gambiense</i> strains as listed in Table 1, including the four strains isolated from relapsed mice.

<p>Lanes 1, 26, 27 and 52 = GeneRuler 100 bp Plus DNA Ladder (Fermentas), lanes 2 to 44 = <i>T.b. gambiense</i> strains isolated from Mbuji-Mayi, lane 45 = <i>T.b. gambiense</i> 15BT relapse 10 mg/kg BW, lane 46 = <i>T.b. gambiense</i> 163AT relapse 10 mg/kg BW, lane 47 = <i>T.b. gambiense</i> 346AT relapse 10 mg/kg BW, lane 48 = <i>T.b. gambiense</i> 346AT relapse 12 mg/kg BW, lane 49 = <i>T.b. gambiense</i> MBA, lane 50 = <i>T.b. gambiense</i> MM01, lane 51 = negative PCR control.</p

List of <i>T.b. gambiense</i> strains used in this study.

<p>In alias name: AT = after treatment, BT = before treatment. Treatment outcome: outcome of patient treated with melarsoprol (in Mbuji-Mayi) or with nifurtimox-eflornithine combination therapy (Masi-Manimba). Couple = number of the couple of two strains isolated from the same patient.</p><p>List of <i>T.b. gambiense</i> strains used in this study.</p