Genomic gene expression changes resulting from Trypanosomiasis: a horizontal study Examining expression changes elucidated by micro arrays in seminal tissues associated with the pathophysiology of Trypanosomiasis during disease progression

1. Genome responses of trypanosome infected cattle
The encounter between cattle and trypanosomes elicits changes in the activities of both genomes - that of the host and
that of the parasite. These changes determine the fate of the host and parasite and the outcome of the encounter.
Although the outcome in most cattle is a slow death following progressive anaemia and loss of body condition, in other
cattle the outcome is more favourable; these cattle regain the initiative, suppress parasite growth, recover from the
initial clinical signs, gain weight, and reproduce normally. Cattle exhibiting this latter outcome are said to be
trypanotolerant. Below is a synopsis of the genome responses that set apart the trypanotolerant cattle from the
susceptible cattle during experimental trypanosome infection
MetaCore GeneGo was used to identify networks
amongst the genes that were differentially expressed. The
figure (right) shows the largest network of connected
genes that were differently expressed by trypanosome
infected resistant (N’Dama) and susceptible (Boran)
cattle.
STAT3 and c-Fos have the most connectivity. STAT3 is a
transcription factor which is activated in response to the
IL-6 family of cytokines and is involved in the acute
phase response in the liver
Interestingly, STAT3 is modulated by RAC1 which is in
turn controlled by VAV1 and ARHGAP15 which are both
located in the QTLs controlling trypanotolerance.
 Trypanosome infection induces profound changes in the genome function manifested by changes in the steady state level of
many genes.
 The differences in the genome responses of resistant and susceptible cattle correspond to some of the phenotypic attributes
that correlate with susceptibility.
In this study, twenty N’Dama (tolerant) cattle and
20 Boran (susceptible) cattle were experimentally
infected with a lethal dose of T. congolense
IL1180. Liver biopsy samples were taken from
each individual in specified days prior to and post
infection such that at each time point there were
samples from at least 5 Boran and 5 N’Dama (A).
The mRNA profiles in the biopsy material were
assayed using the Affymetrix system (B). The gene
data were fed into an analysis workflow (C) that
integrates the expression measures, gene ontology,
QTL information, and gene pathways data.
Rennie C1
Hulme H2
Fisher P2
Hall L3
Agaba M4
Noyes HA 1
Kemp SJ1,4
Brass A2,5
Acknowledgements: This work was wholly supported by The Wellcome Trust. The authors would also like to thank Dr Park based in Dr McHugh Õs group at University College Dublin for
sharing bovine gene symbol information for Affymetrix probes.
Abstract
High throughput technologies inevitably produce vast quantities of data. This presents challenges in terms of developing effective analysis methods, particularly where the analysis involves
combining data derived from different experimental technologies.
In this investigation, we applied a systematic approach to combine microarray gene expression data, QTL data and pathway analysis resources in order to identify functional candidate genes
underlying tolerance of Trypanosoma congolense infection in cattle (see Agaba et al poster at this conference). We automated much of the analysis using Taverna workflows previously
developed for the study of trypanotolerance in the mouse model.
We identified pathways represented by genes within the QTL regions, and subsequently ranked this list according to which pathways were over-represented in the set of genes that were
differentially expressed (over time or between tolerant NÕdama and susceptible Boran breeds) at various timepoints after T. congolense infection. The genes within the QTL that played a role
in the highest-ranked pathways were flagged as strong candidates for experimental confirmation.
1 School of
Biological
Sciences,
BioSciences
Building,
University of
Liverpool, Crown
Street, Liverpool,
L69 7ZB, UK
2 School of
Computer
Science, Kilburn
Building,
University of
Manchester,
Oxford Road,
Manchester, M13
9PL, UK
3 Roslin Institute,
Roslin, Midlothian,
EH25 9PS,
Scotland, UK
4 ILRI, PO Box
30709, Nairobi,
00100, Kenya
5 Faculty of Life
Sciences,
University of
Manchester,
Smith Building,
Oxford Road,
Manchester, M13
9PT, UK
A systematic, data-driven approach to the
combined analysis of microarray and QTL data
AnaemiaBTA27
AnaemiaBTA16
Anaemia and parasitaemiaBTA7
ParasitaemiaBTA4
AnaemiaBTA2
PhenotypeQTL
location
Background
African bovine trypanosomiasis is one of the most important diseases affecting African livestock production. West African
taurine cattle, such as the N'dama, are more resistant to the pathological consequences of trypanosomiasis ( trypanotolerant )
than East African zebu cattle, such as the Boran.
A microarray timecourse experiment was carried out to investigate gene expression in N'dama and Boran cattle infected with
Trypanosoma congolense, in order to identify the genes underlying trypanotolerance (see Agaba et al poster at this
conference for more details).
Trypanotolerance
Trypanotolerance is a complex phenotype involving several distinct components, likely to involve separate genetic control
mechanisms. Key features include the ability to control anaemia, control parasitaemia and maintain bodyweight. Data on
trypanotolerance QTL suggests that phenotypic traits involved in trypanotolerance may be influenced by multiple genetic loci
and possibly complex epistatic or environmental effects ( Proc Natl Acad Sci USA 2003;100(13);7443-7448 ).
Microarray data
Microarray data for liver samples extracted from Boran and N'dama cattle at 0, 12, 15, 18, 21, 26, 29, 32 and 35 days post-
infection were analysed . Outliers were identified using dChip and removed before the remaining hybridisations were
normalised using the Robust Multi-Array (RMA) method. Principal Components Analysis (PCA) was used to check that the
hybridisations clustered as expected.
T-tests were used to identify genes that were differentially expressed (p<=0.01) between the two breeds at each timepoint
and paired T-tests (using data for the same individual animals at different timepoints ) were used to identify genes that were
differentially expressed (p<=0.01) within breed at any timepoint compared to day 0.
QTL data
16 trypanotolerance QTL had been identified in a previous mapping
study (Proc Natl Acad Sci USA 2003;100(13);7443-7448 ). 5 of
these QTL were selected based on the phenotypic trait involved,
the mapping resolution and the strength of the effect (see table on
the left for a summary of the QTL and associated phenotypes).
The base-pair positions of these QTL relative to the EnsEMBL
bovine genome preliminary build Btau2.0 were determined
manually
Combined analysis approach
The combined analysis approach is described in Figure 1 (right). In brief, it involves mapping QTL genes and Affymetrix
microarray probes to genes in the EnsEMBL bovine preliminary build Btau2.0 then identifying KEGG pathways that include
the EnsEMBL genes. The two resulting pathway lists are compared to generate a list of KEGG pathways that include at least
one differentially expressed gene and at least one gene in the QTL. The pathway list is then ranked according to the results
of a Fisher exact test performed on the microarray data using DAVID, and annotated using literature searches and various
public databases of gene and pathway information.
Large sections of the analysis were automated (shown in blue in Figure 1) by adapting Taverna workflows previously
developed for the study of trypanosomiasis responses in mice ( Nucl Acids Res 2007;35(16);5625-5633). The adaptations
required involved mapping genes to human homologues and using bovine IDs and human IDs in the analysis, rather than
murine IDs.
Results
The analysis procedure itself could be reused or adapted for studying another species or another phenotypic trait for which
QTL data are available.
In the case of the bovine trypanotolerance study, the result can be quantified in terms of the reduction of an enormous set of
potential targets for investigation to a manageable shortlist of the most likely targets. Out of 24128 probesets on the array,
12591 were significantly differentially expressed (p <= 0.01 in one or more T-tests comparing expression between breeds or
over time). 8342 of these probesets could be mapped to a known gene. In total they represented 7071 unique gene symbols.
In contrast, there were 127 genes in the QTL that were involved in pathways identified by the combined analysis protocol. If
we only include pathways with a significant (p<=0.05) score on the DAVID Fisher exact test, the list of targets is reduced to
only 51 genes (shown in the table below. Note that these results are based on an analysis with EnsEMBL bovine genome
preliminary build Btau2.0. A more recent preliminary build is available, and the analysis will be repeated, and key findings
discussed in a future publication).
Discussion
Automated approaches are becoming increasingly necessary to enable researchers to handle the output from modern high-throughput technologies. Data-driven methods are useful in
studying complex phenotypes where an analysis based solely on biological processes already known to be involved may be insufficient. Pathway-based approaches provide a means to link
microarray data to QTL data in a biologically meaningful way.
Pathway-based, data-driven, systematic, semi-automated analysis approaches provide an excellent means to triage data from high-throughput technologies providing a shortlist of viable
targets for thorough manual investigation and experimental confirmation
Figure 1. Summary of the combined analysis procedute.
Stages of the analysis that were automated using
Taverna workflows are in blue
B C
A
The Affy chips contained 24K probe sets. Of these between 600 and 750 probes were differently
expressed between infected and uninfected cattle. Principle component analysis of the expression data
clearly shows genome-wide differences between the transcriptomes of tolerant () and susceptible ( )
cattle (Top Right, PCA component 3) and some of these differences are associated with the presence
and progression of trypanosome infection (Top left, PCA component 1).
Agaba M1
Hulme H2
Rennie C3
Mwakaya J1
Ogugo M1
BrassA2
Kemp SJ1,3
L Hall4
Addresses
1International Livestock
Research Institute,
Box 30709 - 00100, Nairobi
Kenya
2The University of Manchester
LF8 Kilburn Bldg, Oxford Rd
Manchester M13 9PL
UK
3School of Biological
Sciences,
University of Liverpool,
Liverpool, L69 7ZB, UK
4Roslin Institute, Roslin,
Midlothian, EH25 9PS,
Scotland, UK
Acknowledgements:
We thank all the staff at the
ILRI large animal facility and
all colleagues in the Welcome
Trust Consortium
This work was supported by
the WellcomeTrust.
Data from an experiment showing the expression of
thousands of genes on a single GeneChip® probe
array. Image courtesy of Affymetrix.