Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

4.2 Target Prediction and Validation Introduction
species, etc. As a result, prediction methods differ widely in their relative de-
grees of accuracy and coverage and the results produced often disagree.
With predictions in the range of 300 evolutionarily conserved targets per
mammalian microRNA family, it appears that microRNAs have the poten-
tial to modulate the expression of nearly all the mammalian mRNAs [43]. The
strong evolutionary pressure enacting the maintenance of the conserved tar-
get sites within most of the 3'UTRs [150] is avoided by some housekeeping
genes through the acquisition of exceptionally short 3'UTRs that are depleted
of target sites [71]. A fundamental step in the microRNA target prediction
pipeline is the experimental target validation of the predicted targets in order
to confirm the validity of an approach over another. Several validation meth-
ods have been employed, ranging from traditional genetic studies, rescue as-
says [70], reporter-gene constructs [237,273] and mutation studies [71,124,237].
In addition to confirming or discharging hypotheses built on networks of pre-
dicted targets, these validation approaches represent the real bottleneck of
the whole process since they are most time-consuming and expensive. High-
throughput approaches have also been developed, involving over-expression of
microRNAs in cell lines followed by microarray profiling to detect downregu-
lated targets [283] and the reverse approach of depleting microRNAs to identify
up-regulated targets [425].
Assaying only relative changes in target mRNA levels without measuring the
corresponding protein abundance is not sufficient to characterise all functional
targets [19]. Thus, it is necessary to demonstrate that in addition to medi-
ating the repression of gene expression through transcriptional degradation,
microRNAs also directly repress translation [446]. Since proteins have dif-
ferent turnover rates, a microRNA may require more or less time to change
their steady-state levels. With the new version of the Stable Isotope Label-
ing by Amino acids in Cell culture (SILAC) protocol developed by Rajwesky
et al [446], called pulse-SILAC (pSILAC), only differences in newly synthe-
sized proteins are detected by assaying the changes in their steady-state levels.
SILAC is a technique based on mass spectrometry that uses isotopic rather
than radioactive labelling of amino acids, to assay protein abundance in a cell.
In standard and pSILAC, two cell populations are grown in identical culture
media except for the presence, in one of the two cultures, of an isotope-labeled
amino acid. In standard SILAC the labeled amino acid is fed to the cell cul-
ture with the growth medium, so that it can be slowly incorporated into all
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