Comparative central nervous system transcriptomic analysis during trypanosomiasis with a comparative...

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Transcript of Comparative central nervous system transcriptomic analysis during trypanosomiasis with a comparative...

Page 1: Comparative central nervous system transcriptomic analysis during trypanosomiasis with a comparative proteomic analyses of cerebrospinal fluid in a murine model to detect biomarkers

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Page 3: Comparative central nervous system transcriptomic analysis during trypanosomiasis with a comparative proteomic analyses of cerebrospinal fluid in a murine model to detect biomarkers

Comparative CNS transcriptomic analysis during trypanosomiasis and comparative proteomic analyses of CSF in a murine model to detect biomarkers of CNS infection.

Specialist summary

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Trypanosoma brucei spp. is the protozoan causative agent of human African trypanosomiasis. HAT is endemic to sub Saharan Africa and the subspecies T. b. gambiense and T. b. rhodesiense have distinct geographical regions (fig 1). Transmitted by tsetse flies (Glossina spp.); HAT has two life stages, the hemolymphatic (S1) stage and the meningoencephalitic (S2) stage where the parasite crosses the BBB to the CNS. S2 HAT causes disturbed sleeping patterns, coma and untreated results in death. Diagnosis requires a lumbar puncture, and treatment for late stage HAT requires toxic drugs.

Figure 1 Geographical distribution of human African trypanosomiasis (Brun et al. 2010)

Trypanosoma brucei gambiense is distributed across West Africa and T. b. rhodesiense across east Africa. The black line indicates their border (Brun et al. 2010).

Since 2000 the World Health Organisation (WHO) has partnered with the pharmaceutical industry, leading a surveillance programme and supplying treatments free to countries endemic with HAT. Within 10 years the number of reported cases dropped below 10,000 for the first time in 50 years, and the disease is now targeted for eradication by 2020.

This project will investigate S2 HAT using omics technology. Identifying biomarkers of S2 infection with the outcomes of: identifying phenotypic changes between S1 and S2 HAT,

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detection of CNS biomarkers for HAT and their presence in blood. Then a comparison of infected human samples against the normal human proteome database

Figure 2 Reported cases of HAT and population screened

Reported cases of human African trypanosomiasis fell to only a few thousand in the 1960s but neglect and civil unrest seen the disease resurge, peaking in the late 1990s. Fewer than 8,000 cases were been reported in 2012 (Brun et al. 2010).

Summary for non-scientists

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Sleeping sickness or Human African trypanosomiasis (HAT) is an extremely debilitating disease caused by a small single celled parasite called Trypanosoma infecting humans when bitten by infected tsetse flies (fig. 3A The tsetse flies become infected when taking a blood meal from other infected humans or mammals, such as livestock. ). The disease mostly affects people in rural and farming regions of Sub-Saharan Africa (fig. 3C).

HAT displays two distinct stages, first the parasite lives and reproduces in the blood causing fever, headaches, joint pain and itching. In the second stage the parasite has moved into the brain and spinal cord or the central nervous system (CNS), this effects behaviour, disrupting sensory perception, causing confusion and extreme fatigue which gives the disease its name. If left untreated HAT is fatal, though fatalities are dropping (fig. 3B).

Biomarkers occur naturally in the body in response to an infection or disease, and are useful for identifying the unknown cause of an illness. Currently painful lumbar puncture is required to confirm CNS HAT. However, this project will use molecular biology and analytical techniques to identify biomarkers to identify in human blood unique to Trypanosoma infection.

Figure 3 Life cycle, rates of reported HAT and geographical distribution of Trypanosoma spp.

Figure 3. (A) Trypanosome’s lifecycles passes from infected tsetse to humans and back, but livestock can also become infected and act as a source to infect feeding tsetse. (B) Reported cases of HAT. (C) Geographic distribution of T. b. gambiense and T. b. rhodesiense, the black line is a general boundary with some overlap. (Adapted from (Kristensson et al. 2010).

Aims

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This project will identify whether trypanosomes alter their phenotype in order to transverse the BBB, or indeed if significant shifts in gene regulation occur at any stage during the infectious cycle. If present, identifying dramatic shifts in gene expression may indicate new drug targets.

Building on previous work, we will observe whether the upregulated proteins in mouse CSF are also detectable in peripheral blood. This is an important first step in the non-invasive detection of late stage HAT.

We will create a normal mouse CSF proteome database that will function as a resource for any researchers working with particularly CD-1 mice, in a CNS field. By comparing the infected mouse CSF proteome to confirm (or identify) previous works observation of three upregulated CSF proteins. Here we can begin looking for these biomarkers within less invasive samples e.g. blood.

Upregulated CSF proteins in mice may translate to humans. Create an infected human CSF proteome dataset and compare it with the normal human CSF proteome database.

Background

Mapping transcriptomes of HAT at periodic intervals to identify varying stage specific gene expression

In changing environments organisms must adapt to their surroundings to survive. A common example is a change in gene expression, which in the case of multistage organisms like protozoa may indicate specific stages of development (Geiger et al. 2011). When trypanosomes are transferred from mammalian hosts to Tsetse flies, a shift in gene regulation occurs altering both surface coat presentation and the parasites mode of energy metabolism, Trypanosomes transferred to humans from Tsetse initiate a VSG coat and have the ability to pre-adapt to infect a Tsetse vector (Queiroz et al. 2009).

Siegel (2010) found a nearly 6% variation between these stages (90% genome coverage) and though there is a lack of regulation of gene expression at a transcription initiation level, regulation of transcript abundance at different life stages is wide (Jensen et al. 2009). To date, several studies on Trypanosoma spp. report shifts in expression levels between parasite species (Simo et al. 2010) and between different life stages(Kabani et al. 2009, Jensen et al. 2009) (Siegel et al. 2010). The latter studies compare the hemolymphatic human stage with the procyclic Tsetse fly stage, it is unknown if any phenotypic variation occurs between hemolymphatic and encephalitic HAT specifically. As Queiroz (2009) described, Trypanosomes can pre-emptively alter phenotypes in preparation for different life stages.

At this point it is unclear whether any such alteration of gene expression is expressed before transit across the BBB and CNS infection is initiated. Identifying stage specific shifts in gene expression may provide new and less toxic drug targets. Further, by creating a timeline of transcriptome variation across the infection period it will be possible to identify any significant phenotypic changes at significant periods in disease progression.

CNS biomarkers from CSF indicative of stage 2 HAT

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Hemolymphatic stage HAT has few specific early symptoms and is diagnosed by thick and thin films slides from peripheral blood in T. b. rhodesiense (Kennedy 2004). Lymph node aspirates and bone marrow tissues can also be used (Organization 1998). T. b. gambiense are difficult to detect in blood and the card agglutination trypanosomiasis test (CATT) is a fast serologic test used for population screening in endemic areas. The CATT lacks specificity but is a useful indicator for suspect cases (WHO, 2010). More sensitive techniques are available such as the mini-anion exchange centrifugation technique. However, Matashi (2015) showed no accredited HAT diagnosis centres visited in Democratic Republic of Congo (n=9) had facilities (often electricity) to run the test and though all the centres carried out the CATT only two centres had facilities to run the test appropriately, likely compromising the result and substantially delaying the patients diagnosis and treatment (Hasker et al. 2011).

All CATT positive patients and suspected late stage patients require painful and invasive lumbar puncture to positively confirm HAT. Identification is made by observing trypanosomes in the CSF (chance observation) or WBC count >5/μl (WHO,) or increase increased CSF protein (>37 mg/100 mL), though Lejon (2003) advocates different criteria and Jamonneau (2003) states the latter two diagnostic techniques may indicate a plethora CNS infections and WBC count is nonspecific of stage specificity. Sensitive and accurate staging of HAT is important as early stage drugs are less toxic but don’t cross the BBB. Identifying the specific stage of infection can reduce exposure to ineffectual toxic drug treatments with side effects (fig. 4) (Amin et al. 2010, Kennedy 2004).

Amin (2010) has showed in a murine model that lipocalin 2, secretory leukocyte peptidase inhibitor and the chemokine CXCL10 were each increased in late stage HAT infected mice. Though murine models don’t necessarily translate to humans, evidence of these biomarkers suggests there may be an equivalent human response. Biomarkers of CNS HAT have yet to be identified, and it is currently not possible to identify when S2 infections began.

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Figure 4 History and current drug treatments for HAT

CNS biomarkers from blood samples in a murine model

Utilising CSF as a source of biomarkers indicative of CNS infection is preferable to peripheral system biomarkers (i.e. blood), simply because of the proximity of CSF to the CNS. However, lumbar puncture is necessary to obtain CSF and confirm diagnosis of stage 2 HAT (with subsequent CSF samples taken periodically over two years for follow up). As collecting blood samples is less invasive, painful and with fewer potential negative outcomes, it’s pragmatic to search for the presence of stage 2 HAT biomarkers in blood, though finding biomarkers in blood presents some obstacles.

Studies regarding traumatic brain injuries have assessed CSF and blood (fig. 1) to identify biomarkers of neurological dysfunction and showed proteins expressed in the CSF can be detected in peripheral blood, albeit at low concentrations (Rissin et al. 2010, Rissin et al. 2011, Randall et al. 2013). These concentrations are too low for standard immunoassay (Zetterberg, Smith, and Blennow 2013); which typically measure protein concentrations above 10 -12M6 however, digital ELISA can detect subfemtomolar concentrations (10-16M6- 10-12M6) of proteins while using standard ELISA reagents (Rissin et al. 2010).

Several proteins of interest for late stage trypanosomiasis have previously been identified by Amin (2010), showing in a murine model that late stage trypanosomiasis causes the upregulation of lipocalin 2 (roll in apoptosis by iron sequestration), secretory leukocyte peptidase inhibitor (SLPI) and CXCL10 (chemokine) with T. b. gambiense and T. b. rhodesiense. To date no work has been carried out to identify if these proteins are observable in mouse blood.

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Experimental design

Animal husbandry and experimental design

The murine model will provide all samples necessary for these projects, human samples will be collected in Africa. Pathogen free adult female and male CD-1 mice will be infected with I. b. brucei strain GVR35. Nine groups of five replicate mice within three treatment groups (n=150). Five mice from each treatment group will have CSF extracted on days; 2, 4, 6, 8, 12, 16, 20 and day 22, providing comparable samples running over all stages of infection under each treatment (table 1).

On sampling days, parasites will be observed for stage development (long slender or stumpy form) and parasitemia will be monitored with a haemocytometer. Three treatments will be studied (table 1); infected mice, treated mice (after CNS infection established) and control mice. Mouse strain and infection rate as Myburgh (2013). CSF will be collected by lumbar puncture as Li (2008) and checked for blood contamination, blood samples will be taken. Subsamples will be analysed for microbiology and cell counts, and processed. Each mouse on its designated sampling day will have all samples drawn and infected mice will be euthanized.

Trypanosomes for transcriptome analysis can be enriched and obtained from blood by centrifugation and pH gradient chromatography to separate white blood cells. If low counts, Trypansosmes can be enriched by electrokinetic technique (Menachery et al. 2012) and centrifuged and separated by chromatography.

HAT infected CSF samples will be collected and stored at suitable hospitals in the Democratic Republic of Congo until 20 male and 20 female (age 24 – 55) are collected, then transported to the Glasgow Polyomics Centre.

Table 1 Infected mice (n = 45): CSF, blood and urine will be sampled per sample day. Infected and S2 drug treated mice (n = 45): CSF, blood and urine will be sampled per sample day. Control mice (n = 45): CSF, blood and urine will be sampled per sample day. Pre and post CNS infection days are of particular interest, to identify CNS infection specific changes in the gene expression. Pre and post 21 days after infection are also significant as this is the period where the hemolymphatic stage drug Melarsoprol is no longer effective in killing encephalitic parasites and may be also indicative of a change in gene expression.

Simultaneous measurement of Trypanosome transcriptomes at periodic intervals under

different conditions, to identify stage specific variation of gene expression of HAT.

Table 1 Mouse experimental seup, treatments and replicates

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Hypotheses: There is no significant difference in the transcriptome of T. b. brucei ranging early hemolymphatic stage (excluding long slender to stumpy shifts) to established meningoencephalitic HAT.

The transcriptome will be analysed at different stages during development of S2 trypanosomiasis and under different treatments (table 1); normal transcriptomes, infected transcriptomes and infected but drug treated transcriptomes. The normal transcriptome will be the basis for comparison and the treated transcriptome will identify whether early CNS infection is indicative of later gene expression in the CNS, regardless of presence of Trypanosoma. The infected transcriptome will provide data regarding changes in gene expression due to persistent infection.

Three cDNA libraries (fig. 5) for each replicate will be generated for Illumina RNA-Seq; 5 -SL enriched,′ 5 -triphosphate- end enriched, and 3 -poly(A) enriched. ′ ′

Figure 5 Creating three Trypanosoma Illumina RNA-Seq Libraries

Fig. 5 Protocol of the steps required for generating the three RNA-Seq libraries. Providing “unprecedented heterogeneity of pre-mRNA processing sites, improved identification of novel coding and noncoding transcripts from unannotated genes, and quantification of cellular abundance of RNA.” (Kolev, Ullu, and Tschudi 2015)

RNA and protein preparations: Purified hemolymphatic and encephalitic parasites will be transferred to TRIzol until RNA extraction. Total RNA will be treated with turbo-DNase and absence of DNA, confirmation by PCR GAPDH endogenous control. RNA quality will be verified by rRNA electrophoresis under denaturing conditions before the creation of the cDNA library, outlined below as per Kolev et al. (2015).

Sequencing, the libraries will be pair-end read (2x100bp cycles) with 200 million reads per sample, for transcriptome assembly by Illumina HiSeq2000. Data delivery and

Figure 6 Flowchart for RNA-Seq experiment

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formatting will be informed by bioinformaticians at Glasgow Polyomics. Data quality will be assessed (quality scores, quality plots, data will be trimmed or filtered if necessity then reassessed for quality).

As there is no reference transcriptome (fig. 5), the control mouse data will be sequenced de novo (transcriptome assembly with reference genome) (fig. 5a), to act as a reference transcriptome dataset. Sequence reads will be aligned to chromosomes in Trypanosoma genome sequence with bowtie acting as a reference transcriptome. The infected and infection treated data analysis can continue (fig. 5b), to differential expression analysis.

Alignment considerations and transcript-end analyses will be performed as per Kolev (2015). Processed RNA-Seq reads will run on the Generic Genome Browser (Sterin, 2002) providing interactive annotation and analysis of transcript ends and novel transcriptome features(Kolev, Ullu, and Tschudi 2015). Transcript abundances will be measured by the number of reads aligning within a given nucleotide window as Kolov (2015).

CNS protein biomarkers from CSF, indicative of meningoencephalitic trypanosomiasis?

Hypothesis: There is no difference between proteomes of individuals infected with Trypanosomiasis over the course of infection to late stage of meningoencephalitic HAT.

Generating CSF proteomes: Technical details as per Zhang (2015a); this study requires the creation of four CSF proteome databases, a normal mouse proteome, an infected mouse proteome, an infected & treated mouse proteome and a HAT infected proteome.

Protein in the samples will be quantitated by the Bradford method. Equal volume from each sample will be pooled (4 replicates/treatment/day) for the proteome analysis to reduce technical variation across 5 mouse samples (per 3 treatments) per sampling day.

Analysis: For each pooled treatment sample (Fig. 7) two subsamples will be depleted of high-abundance proteins with a multiple affinity removal column/HPLC, one will not, resulting in 3 subsamples; a flow-through protein sample, a bound protein sample and a non-depleted sample. Which will be digested with filter-aided sample preparation as per Wisniewski (2009) and subjected to high-pH RPLC column separation. The fractions produced will be analysed by LC-MS/MS.

Data analysis: These MS/MS spectra data for mice will be compared and searched against the Mouse Genome Informatics Uniprot FASTA database. Identified proteins will be individually evaluated by manual inspection followed by a target-decoy cross analysis to identify false positive rates (Elias and Gygi 2007, Leary Swan et al. 2009). Protein abundances in the samples will be quantified by peak intensity-based absolute quantification (iBAQ algorithm) (Schwanhäusser et al. 2011, Zhang, Guo, Zou, Yang, Zhang, Ji, Shao, Wang, et al. 2015) (Schwanhäusser, 2011, Zhang 2015). Proteome variation will then be compared across time of infection within treatments against their controls and compared across treatments (MANOVA); variations in protein abundance will indicate potential biomarkers for infection. To confirm any differences between the proteomes, samples depleted of high abundance proteins (from above) will be compared by two-dimensional gel electrophoresis.

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Comparing proteomes: Data will be log transformed, MANOVA will be used to identify whether there are statistically significant differences between the treatment groups (on a given sample day) and their counterpart control. Proteins varying significantly between the infected and control samples will be highlighted as potential biomarkers for CNS S2 infection. Further by comparing the proteomes from initial infection to day 24, one can observe any substantial changes in protein expression over the infection period.

Figure 7 Workflow used to determine normal CSF proteomes

Figure 6 “CSF will be pooled and depleted of high-abundance proteins with an immunoaffinity column. The flow-through proteins, the bound proteins, and the original proteins (extracted directly from the CSF samples without immunoaffinity depletion) will be collected separately, digested according, and separated into 30 fractions each by high-pH RPLC, each fraction will be subjected to proteomic analysis by nano-RPLC-MS/MS” (Zhang 2015). A total of 90 LC–MS/MS analyses will be combined to produce the comprehensive CSF proteome map of a normal mouse” (Zhang 2015, Wiśniewski, 2009).

CNS biomarkers in blood samples from a murine model indicating meningoencephalitic trypanosomiasis.

Digital ELISA (fig. 8) will be used to identify and quantify lipocalin 2, SLPI and CXCL10 in mouse peripheral blood, if present. Serum will be collected from whole blood samples. Blood will be let clot and centrifuged, the resulting supernatant (serum) will be divided into 0.5ml aliquots and analysed immediately. To detect the biomarkers in serum; magnetic beads (2.7 μm diameter) are covered with capture antibody and single proteins are captured as in standard ELSIA. A fluorescent enzyme reporter attached (fig. 4a). The beads are isolated into individual chambers (fig. 4b) and fluorescence imaging can then be used to detect a single protein molecule (Rissin et al. 2010). The samples are serially diluted and as the fluorescent markers are confined into individual wells only one marker is required to raise signal above background and accurate concentrations can be calculated. Variation

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occurs in this method due to the distribution of fluorescent tags (Poisson distribution). The samples will be analysed in triplicate to account for this.

Figure 8 Digital ELISA based on arrays of femtoliter-sized wells.

Figure 8 (a,b) “Single protein molecules are captured and labelled on beads using standard ELISA reagents (a), and beads with or without a labelled immunoconjugate are loaded into femtoliter-volume well arrays for isolation and detection of single molecules by fluorescence imaging (b). (c) Scanning electron micrograph of a small section of a femtoliter-volume well array after bead loading. Beads (2.7 μm diameter) were loaded into an array of wells with diameters of 4.5 μm and depths of 3.25 μm. (d) Fluorescence image of a small section of the femtoliter-volume well array after signals from single enzymes are generated. Whereas the majority of femtoliter-volume chambers contain a bead from the assay, only a fraction of those beads possess catalytic enzyme activity, indicating a single, bound protein molecule. The concentration of protein in bulk solution is correlated to the percentage of beads that carry a protein molecule” (Rissin et al. 2010).

Impact of your work

Identifying a change in phenotype in CNS HAT will open the door to drug discovery and further research. If a change in phenotype is required to transit the BBB the upregulated genes can be intensively studies e.g. gene knockouts to identify precise mechanisms by which Trypanosoma cross the BBB. This would benefit researchers, intensifying drug discovery on these specific genetic sites.

Identifying, as others have, upregulated proteins in the CSF and importantly in the blood, researchers can being investigating sensitive blood tests to negate the need for painful lumbar puncture on patients. This is an important first step in the non-invasive detection of late stage HAT.

Create a normal mouse CSF proteome database that will function as a resource for any researchers working with particularly CD-1 mice, in a CNS field. Reducing the number of animals required for experiment, part of the 3 R’s.

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Identifying CSF biomarkers couple result in biomarkers found in less invasive samples e.g. blood and urine in the future.

The upregulated CSF proteins in mice are proof of concept and we will identify if this translates to humans by creating an infected human CSF proteome dataset and comparing it with the normal human CSF proteome database.

Data sharing

The three transcriptomes generated will be uploaded to The Welcome Trust Sanger Institute Trypanosoma brucei resource. RNA-Seq cDNA libraries: Sequence reads will be archived NCBI Sequence Read Archive. The mice proteomes will also be made freely available on in these databases. As will the human proteome and subsequent blood analysis data. The data analyses from respective experiments will provide important information regarding fighting HAT and so will be published in high impact journals. Poster presentations will be held at the Kinetoplastid Molecular Cell Biology Meeting and the General Conference of the International Scientific Council for Trypanosomiasis Research and Control. Seminars will he held as part of on-going science communication at University of Glasgow and the works will be incorporated there.

The assessment for this course will take the form of a grant proposal. You should design, describe and justify a program of experiments that will employ -omic approaches to address a research topic that will be suggested by your tutor for this work. The proposal should describe work that could be completed by one experienced scientist working for 3 years in a suitably equipped and resourced laboratory.

You should present your ideas, in the form of a 1 slide/5 minute overview, to your tutor. This presentation will form 20% of the marks for the assessment. Feedback will be provided to help you formulate the complete grant proposal, assessment of which will comprise the remaining 80% of the marks.

Your proposal should include:

Appropriate application of omic technologies. Experiments to validate the results from the above.

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The proposal should be written using the following headings and length limits:

Title (200 characters max) Specialist summary (200 words max)

o To be comprehensible by scientists in this research area Summary for non-scientists (200 words max)

o To be comprehensible to non-scientists Aims (200 words max)

o What specific advances you aim to make? Background (800 words max)

o What are the issues you will address?o What has been done previously?o What are the gaps in knowledge?

Experimental design (1200 words max)o Focus on the approaches you will take, not on technical detailso Describe the samples you will analyseo Include any data analysis stepso Highlight challenges you foresee and steps you will take to ameliorate them.

Impact of your work (200 words max)o What will the output be?o Who will benefit from the output?o How will they benefit?

Data sharing (200 words max)o How will you share your data?

Guidelines:

Don’t worry about costs – assume you will work in a suitably equipped lab. Keep your plans focussed on your specified question State a hypothesis that you will test Keep biology background to a minimum (but you will need some!) Avoid technical detail of methods. Describe experimental design and controls Include relevant references (not included in word limits)

Amin, Daniel Ndem, Dieudonné Mumba Ngoyi, Gondwe-Mphepo Nhkwachi, Maria Palomba, Martin Rottenberg, Philippe Büscher, Krister Kristensson, and Willias Masocha. 2010. "Identification of stage biomarkers for human African trypanosomiasis." The American journal of tropical medicine and hygiene 82 (6):983-990.

Brun, Reto, Johannes Blum, Francois Chappuis, and Christian Burri. 2010. "Human african trypanosomiasis." The Lancet 375 (9709):148-159.

Elias, Joshua E, and Steven P Gygi. 2007. "Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry." Nature methods 4 (3):207-214.

Geiger, Anne, Gustave Simo, Pascal Grébaut, Jean-Benoît Peltier, Gérard Cuny, and Philippe Holzmuller. 2011. "Transcriptomics and proteomics in human African trypanosomiasis: current status and perspectives." Journal of proteomics 74 (9):1625-1643.

Hasker, E, C Lumbala, F Mbo, A Mpanya, V Kande, P Lutumba, and M Boelaert. 2011. "Health care‐seeking behaviour and diagnostic delays for Human African Trypanosomiasis in the Democratic Republic of the Congo." Tropical Medicine & International Health 16 (7):869-874.

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Jamonneau, Vincent, Philippe Solano, André Garcia, V Lejon, N Dje, TW Miezan, P N'Guessan, Gérard Cuny, and P Büscher. 2003. "Stage determination and therapeutic decision in human African trypanosomiasis: value of polymerase chain reaction and immunoglobulin M quantification on the cerebrospinal fluid of sleeping sickness patients in Cote d'Ivoire." Tropical Medicine & International Health 8 (7):589-594.

Jensen, Bryan C, Dhileep Sivam, Charles T Kifer, Peter J Myler, and Marilyn Parsons. 2009. "Widespread variation in transcript abundance within and across developmental stages of Trypanosoma brucei." BMC genomics 10 (1):482.

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