Biology Reference
In-Depth Information
different targets. Global transcriptional analysis has been applied
to the study of human adenoviral vectors, of HIV-1-derived vec-
tors, and also of AAV vectors [
3
-
9
]. We used gene chips to dissect
the response of hepatic cells to helper dependent (HD) and
E1-deleted human adenovirus vectors, fi nding that they induce a
response of equal magnitude, in contrast to that observed at the
organism level, but with different specifi c properties [
10
]. We
compared the effect of HD human (HD-HAd) and canine
(HD-CAV-2) adenoviral vectors on human brain cells, and
assessed that the toxicity-versus-effi cacy ratio would suggest that
the canine adenoviral vector is more suited for neuron gene trans-
fer than the human one [
10
]. We also observed that, in brain cells,
HIV-1-derived vectors activate a strong interferon response, dif-
ferently from human and canine HD adenovectors [
10
]. Di
Pasquale et al. assayed AAV-5-based vector transduction in tumor
lines and established a correlation between transduction and tran-
scriptional phenotypes, and with this study the PDGF receptor
was identifi ed as a functional ligand of AAV-5 [
3
].
In sum, chip studies have been proven a useful tool for the
understanding of the biology of viral vectors and of their relative
toxicity, and have also been helpful to make new fi ndings on
the biology of wild type counterpart of viral vectors. However, the
analysis of the literature also points on one caveat emerging from
chip experiments, directly related to the extreme sensitivity of the
transcriptome to external insults, that is the interpretation of chip
results can be biased by the specifi c experimental conditions of
each data set. Indeed, if we compare independent studies, not only
the nature of the vector but also the experimental conditions, the
cell type, the timing of the analysis, and the statistical criteria
applied, can infl uence the transcriptome response, and therefore it
can be sometimes diffi cult to drive general conclusions. We believe
that one way to better extrapolate the biology from high-
throughput studies is to switch from an approach based on the
single gene analysis, to one that evaluates the data sets in terms of
pathway modulation and applies meta-analysis criteria, which can
better meet the needs for correctly understand genomic data. In
this chapter then, we detail not only the experimental aspects of
transcriptome analysis but also the in silico approaches for data sets
interpretation.
2
Materials
2.1 Cell Culture
1. Complete medium: Dulbecco's Modifi ed Eagle's Medium
(D-MEM) supplemented with 10 % fetal bovine serum.
2. Trypsin/EDTA solution: 0.05 % trypsin, 0.48 mM EDTA.
3. F-12 Ham nutrient mixture (SIGMA).
