This project aims to identify biomarkers for detecting central nervous system infection in human African trypanosomiasis (HAT), also known as sleeping sickness. The project will use omics technologies to compare gene expression and protein profiles between the early hemolymphatic stage and late meningoencephalitic stage of HAT, when the parasite has crossed the blood-brain barrier. Proteomic analyses will be conducted on cerebrospinal fluid from a murine HAT model to identify upregulated proteins and determine if they are also present in peripheral blood, representing a potential non-invasive detection method. Proteome databases of normal mouse and human cerebrospinal fluid will be generated for comparison to infected samples. The goals are

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

4. Specialist summary
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 bruceigambienseis distributed across West Africa and T. b. rhodesiense
across east Africa. The blackline 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.

5. 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,
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. Few er than 8,000 cases w erebeen reported in 2012 (Brun et al. 2010).
Summary for non-scientists

6. 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 generalboundary w ith some overlap. (Adapted from(Kristensson et al. 2010).
Aims

7. 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,identifyingdramaticshiftsingene expressionmayindicate new drugtargets.
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 beginlookingfor these biomarkerswithinlessinvasive samplese.g.blood.
Upregulated CSF proteins in mice may translate to humans. Create an infected human CSF proteome
datasetand compare itwiththe normal human CSFproteome 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
differentlife 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
phenotypicchangesatsignificantperiodsindisease progression.
CNS biomarkersfrom CSF indicative of stage 2 HAT

8. 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(Haskeretal.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
toxicdrug treatmentswithside effects(fig. 4) (Aminetal.2010, Kennedy2004).
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
currentlynotpossible toidentifywhenS2infectionsbegan.

9. Figure 4 History and current drug treatments for HAT
CNS biomarkersfrom blood samplesin 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 inblood,though findingbiomarkersinbloodpresentssome 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 10-12
M6
however, digital ELISA can
detect subfemtomolar concentrations (10-16
M6
- 10-12
M6
) of proteins while using standard ELISA
reagents(Rissinetal.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 identifyif these proteinsare observable inmouseblood.

10. Experimental design
Animal husbandryandexperimental 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,
providingcomparable samplesrunningoverall stagesof infection undereachtreatment(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
samplesdrawnand infectedmice willbe euthanized.
Trypanosomes fortranscriptome analysiscanbe enrichedandobtainedfrombloodby
centrifugationandpHgradientchromatographyto separate white bloodcells.If low counts,
Trypansosmes canbe enriched by electrokinetictechnique (Menacheryetal.2012) and centrifuged
and separatedbychromatography.
HAT infectedCSFsamples will be collectedandstoredatsuitable hospitalsinthe Democratic
Republicof Congountil 20 male and 20 female (age 24– 55) are collected,thentransportedtothe
GlasgowPolyomicsCentre.
Table 1 Infected mice (n = 45): CSF, blood and urine w ill be sampled per sample day. Infected and S2 drug treated mice (n =
45): CSF, blood and urine w ill be
sampled per sample day. Control
mice (n = 45): CSF, blood and
urine w ill 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
expressionofHAT.
Table 1 Mouse experimental seup, treatments and replicates

11. 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
meningoencephaliticHAT.
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 dataregardingchangesingene expressiondue topersistentinfection.
Three cDNA libraries (fig. 5) for each replicate will be generated for Illumina RNA-Seq; 5′-SL enriched,
5′-triphosphate- endenriched,and3′-poly(A)enriched.
Figure 5 Creating three Trypanosoma Illumina RNA-SeqLibraries
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
fromunannotated genes, and quantification of cellular abundance of RNA.” (Kolev, Ullu, and Tschudi2015)
RNA and protein preparations: Purified hemolymphatic and encephalitic parasiteswill 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 etal. (2015).

12. 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
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 necessitythenreassessedforquality).
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
expressionanalysis.
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
readsaligningwithinagivennucleotide window asKolov(2015).
CNS proteinbiomarkers from CSF,indicative of meningoencephalitictrypanosomiasis?
Hypothesis: There is no difference between proteomes of individuals infected with
Trypanosomiasisoverthecourseof infection to late stageof meningoencephaliticHAT.
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 &treatedmouse proteome andaHAT 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
variationacross 5 mouse samples (per3treatments) persamplingday.
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-pHRPLCcolumn separation.The fractionsproducedwill be analysedbyLC-MS/MS.
Figure 6 Flowchart for RNA-Seq experiment

13. 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.
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
inproteinexpressionoverthe infectionperiod.
Figure 7 Workflow used to determine normal CSF proteomes
Figure 6 “CSF w ill be pooled and depleted of high-abundance proteins w ith an immunoaffinity column. The flow-through
proteins, the bound proteins, and the original proteins (extracted directly from the CSF samples w ithout immunoaffinity
depletion) w ill be collected separately, digested according, and separated into 30 fractions each by high-pH RPLC, each
fraction w ill 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, Wiśniew ski, 2009).

14. 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
occurs in this method due to the distribution of fluorescent tags (Poisson distribution). The samples
will be analysedintriplicate toaccountfor 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 w ithout a labelled immunoconjugate are loaded into femtoliter-volume w ell arrays for isolation and detection of single
molecules by fluorescence imaging (b). (c) Scanning electron micrograph of a small section of a femtoliter-volume w ell 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 w ell 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

15. 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. Thiswouldbenefitresearchers,intensifyingdrugdiscoveryon these specificgeneticsites.
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 onpatients. Thisisan importantfirststepinthe non-invasive detectionof 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
requiredforexperiment,partof the 3 R’s.
Identifying CSF biomarkers couple result in biomarkers found in less invasive samples e.g.
bloodandurine inthe 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 humanCSF 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
communicationatUniversityof Glasgowandthe workswill be incorporatedthere.
The assessmentforthiscourse will take the formof a grant proposal.Youshoulddesign,describe
and justify aprogramof experiments thatwill employ -omicapproachestoaddressaresearchtopic
that will be suggestedbyyourtutorforthiswork. The proposal shoulddescribeworkthatcouldbe

16. completedbyone experiencedscientistworkingfor3 yearsin a suitablyequippedandresourced
laboratory.
You shouldpresentyourideas,inthe formof a 1 slide/5minute overview,toyourtutor.This
presentationwill form20%of the marksfor the assessment.Feedbackwill be providedtohelpyou
formulate the complete grantproposal,assessmentof whichwill comprisethe remaining80% of the
marks.
Your proposal shouldinclude:
 Appropriate applicationof omictechnologies.
 Experimentsto validatethe resultsfromthe above.
The proposal should be writtenusingthe followingheadings andlengthlimits:
 Title (200 characters max)
 Specialistsummary(200wordsmax)
o To be comprehensible byscientistsinthisresearcharea
 Summaryfor non-scientists(200wordsmax)
o To be comprehensible tonon-scientists
 Aims(200 wordsmax)
o What specificadvancesyouaimtomake?
 Background(800 wordsmax)
o What are the issuesyouwill address?
o What has beendone previously?
o What are the gaps inknowledge?
 Experimentaldesign(1200 wordsmax)
o Focuson the approachesyouwill take, notontechnical details
o Describe the samplesyouwill analyse
o Include anydata analysissteps
o Highlightchallengesyouforeseeandstepsyouwill take toameliorate them.
 Impact of your work(200 wordsmax)
o What will the outputbe?
o Who will benefitfromthe output?
o How will theybenefit?
 Data sharing(200 wordsmax)
o How will youshare yourdata?
Guidelines:
 Don’tworry aboutcosts – assume youwill workina suitablyequippedlab.
 Keepyourplansfocussedonyourspecifiedquestion
 State a hypothesisthatyouwill test
 Keepbiologybackgroundtoaminimum(butyouwill needsome!)
 Avoidtechnical detailof methods.
 Describe experimental designandcontrols
 Include relevantreferences(notincludedinwordlimits)
Amin, Daniel Ndem, Dieudonné Mumba Ngoyi, Gondwe-Mphepo Nhkwachi, Maria Palomba, Martin
Rottenberg, Philippe Büscher, Krister Kristensson, and Willias Masocha. 2010. "Identification

17. of stage biomarkers for human African trypanosomiasis." The American journal of tropical
medicine and hygiene82 (6):983-990.
Brun, Reto, Johannes Blum, Francois Chappuis, and Christian Burri. 2010. "Human african
trypanosomiasis." TheLancet375 (9709):148-159.
Elias, Joshua E, and Steven P Gygi. 2007. "Target-decoy search strategy for increased confidence in
large-scale proteinidentificationsbymassspectrometry." Naturemethods 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:
currentstatus and perspectives." Journalof proteomics 74 (9):1625-1643.
Hasker, E, C Lumbala, F Mbo, A Mpanya, V Kande, P Lutumba, and MBoelaert. 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.
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
& InternationalHealth 8 (7):589-594.
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