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2012; Moore et al., 2009 ). The gene expression and screening datasets
suggest that this general pattern (i.e., a loss of regenerative capacity due to
shift in the balance of growth-promoting vs. growth-inhibiting genes) is
widespread. As CNS neurons age and lose regenerative capacity, many
hundreds of genes are both up- and downregulated, and the same is true
as PNS neurons or zebrafish neurons respond to axotomy and initiate
regenerative growth. The HCS discussed, whether based on
overexpression or knockdown, here have generally detected both growth
promotion and growth suppression. Clearly in the search for molecular
controls of low regenerative competence, both missing growth promoters
and emergent growth inhibitors are likely to play important roles.
In the face of this complexity, gene profiling and HCS technologies have
emerged as important tools, and have proven to be effective at identifying mo-
lecular targets for promoting CNS axon regeneration in vivo ( Blackmore,
Moore, et al., 2010; Blackmore, Wang, et al., 2012; Moore et al., 2009 ).
Much remains to be learned, however. In considering the gene profiling
datasets, for instance, the available information compares neurons across age
only in a na¨ve, uninjured state. Missing from these datasets is information
about how the response to injury differs in immature versus mature CNS
neurons, which is likely to be more informative. Thus, experiments that
profile both injured and uninjured neurons across age are warranted.
Additionally, neurons within a single CNS population display a range of
regenerative potentials. For instance, minorities of brainstem or RGC
neurons extend axons into peripheral nerve grafts ( Cui & Harvey, 2000;
Richardson, Issa, & Aguayo, 1984 ). Purifying and comparing these
“responders” to their nonregenerative neighbors would likely provide
important insights and help identify new candidate genes for testing. Finally,
from a technical perspective, gene profiling datasets are poised to improve
dramatically with the adoption of RNAseq, which compared to microarrays
is more sensitive, can detect novel transcripts, and perhaps most importantly
will allow analysis of isoform diversity ( Lerch et al., 2012; Metzker, 2010 ).
It is clear that alternative splicing and alternative start sites can produce
isoforms that differ in their function, abundance, or localization, but the
question of how isoform diversity relates to regenerative capacity remains
unexplored at the genome-wide level. In summary, even given the dramatic
progress in recent years, the field is poised for accelerated growth in the
amount and kind of data available to compare neurons of different
regenerative abilities. These datasets are clearly more powerful in aggregate
than in isolation; thus creating mechanisms to share, analyze, and synthesize
the divergent datasets is strongly needed in the field.
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