STSM REPORT
Title: Analysis of microarray data using Biolayout
Reference: ECOST-STSM-FA0902-230111-004898
Background
Our group is focused in the development of efficient vaccines against porcine
reproductive and respiratory virus (PRRSV), using transmissible gastroenteritis
coronavirus (TGEV) derived vectors. For the design of biosafe vectors, the
knowledge of virulence factors is essential. TGEV contains several genus specific
genes, whose function is unknown. The role of one of these genes, gene 7, was
analyzed by the generation of a recombinant mutant TGEV lacking gene 7 expression
(rTGEV-7). The behavior of this virus was analyzed in vitro and in vivo. It was
found that protein 7 counteracts host antiviral defenses and modulates TGEV
virulence. For a deeper study, microarray analyses were performed. RNAs were
extracted from mock, wt or mutant infected cells, at two times post infection, and
analyzed using Affymetrix porcine gene expression array. Pairwise analyses were
performed and visualized using Fiesta viewer (Oliveros J.C., 2007). Hierarchical
clustering of gene expression patterns, in wt and mutant viruses infected cells
compared with non infected ones, was performed using Multi experiment Viewer
(MeV) (Saeed AI, et al, 2006; Saeed AI, et al, 2003). Functional relationships between
candidate genes were explored using DAVID (Huang DW, et al, 2009; Dennis G Jr, et
al, 2003) and Ingenuity tools. Nevertheless, the analysis of porcine microarray data is
difficult using pre-existing tools. Therefore, for a global overview of porcine gene
expression results and functional relationships between genes, Biolayout
(Theocharidis A., et al, 2009; Freeman T.C., et al, 2007; Goldovsky L., et al, 2005)
training was requested in the context of a STSM at Roslin Institute.
Objectives


Training on Biolayout software use
Analysis of our set of data using Biolayout
Results
After a brief introduction on Biolayout interface and use with Roslin datasets, the
project was started with our gene expression analyses datasets. There were 15
datasets, including mock infected cells, wt and mutant viruses infected cells at 6 and
12 hpi, with three biological replicates each.
After data normalization, porcine probes were re-annotated using North Carolina
University annotation of the porcine Affymetrix chip (Tsai S., et al., 2006). A table
with the right format (TGEV.expression) was obtained. TGEV datasets were then
loaded on Biolayout. A correlation value of 0.95 was selected, leading to 9773 nodes
and 351789 edges, and clustering was performed. After clustering, more than 250
clusters (classes) were obtained. As usual, the first classes contained more genes and
were of better quality.
The preliminary analysis of the clusters obtained perfectly fits with previous analyses
using MeV and DAVID. For instance, a clear difference in innate immune response
was found between mutant and wt infected cells, as expected based on previous
results. Additional differences were observed between wt and mutant viruses. As an
example, genes belonging to cluster 5 were upregulated at 12 hpi in wt infected cells
but not in mutant infected cells; genes in cluster 18 were only upregulated in wt
infected cells, both at 6 and 12 hpi (Fig. 1).
Figure 1. Biolayout results for clusters 5 (left) and 18 (right).
Specific genes were also searched, based on previous knowledge. For instance,
caspases expression was analyzed, as mutant virus induced a faster and stronger
apoptosis than the wt virus. Biolayout results indicated that some caspase genes were
upregulated in mutant virus compared with the wt one, and also that the expression
pattern of different caspase genes was different, suggesting a differential involvement
of each caspase in the virus induced apoptosis (Fig. 2).
Figure 2. Biolayout results for caspase probes. Each color represents a cluster, in which each caspase
was found. Dark blue, caspase 3; brown, caspase 4; light green caspase 6; emerald green, caspase 7.
Conclusions
Biolayout software was very useful for a global view of our microarray datasets. The
obtained results fit with the previous preliminary results resulting form other
microarray analysis softwares, such as MeV or DAVID. The Biolayout results also
are in agreement with expected results based on experimental infections data.
Therefore, Biolayout is a useful tool for all the people in our lab, and its use will be
promoted.
References
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