Time Course Analysis of Gene Expression Patterns in Zebrafish Eye During Optic Nerve Regeneration

Abstract

It is well-established that neurons in the adult mammalian central nervous system (CNS) are terminally differentiated and, if injured, will be unable to regenerate their connections. In contrast to mammals, zebrafish and other teleosts display a robust neuroregenerative response. Following optic nerve crush (ONX), retinal ganglion cells (RGC) regrow their axons to synapse with topographically correct targets in the optic tectum, such that vision is restored in approximately 21 days. What accounts for these differences between teleostean and mammalian responses to neural injury is not fully understood. A time course analysis of global gene expression patterns in the zebrafish eye after ONX can help to elucidate cellular and molecular mechanisms that contribute to a successful neuroregeneration. To define different phases of regeneration after ONX, alpha tubulin 1 (tuba1) and growth-associated protein 43 (gap43), markers previously shown to correspond to morphophological events, were measured by real time quantitative PCR (qPCR). Microarray analysis was then performed at defined intervals (6 hours, 1, 4, 12, and 21 days) post-ONX and compared to SHAM. Results show that optic nerve damage induces multiple, phase-related transcriptional programs, with the maximum number of genes changed and highest fold-change occurring at 4 days. Several functional groups affected by optic nerve regeneration, including cell adhesion, apoptosis, cell cycle, energy metabolism, ion channel activity, and calcium signaling, were identified. Utilizing the whole eye allowed us to identify signaling contributions from the vitreous, immune and glial cells as well as the neural cells of the retina. Comparisons between our dataset and transcriptional profiles from other models of regeneration in zebrafish retina, heart and fin revealed a subset of commonly regulated transcripts, indicating shared mechanisms in different regenerating tissues. Knowledge of gene expression patterns in all components of the eye in a model of successful regeneration provides an entry point for functional analyses, and will help in devising hypotheses for testing normal and toxic regulatory factors.

Figure 1. Expression of established gene markers of optic nerve regeneration as a function of time after ONX, as determined by qPCR analysis

Each data point represents the mean fold change (ONX/SHAM) +/− SEM of 3 independent biological replicates. Both genes showed significant differences across regeneration stages by one-way ANOVA P < 0.05.

Figure 2. Transcript abundance at each time point following ONX

Plot shows individual probe sets from each time point up- or down-regulated at least 2-fold. Note the breaks in the scale on the positive y-axis.

Figure 3. Comparison of gene expression profiles by microarray and qPCR analysis

The time course of expression changes of selected genes as determined by the microarray analysis (A) and qPCR (B). in (A) two clusters represent genes down-regulated (top panel) or up-regulated (lower panel) during the early phase of regeneration. (B) The qPCR expression profiles of the selected target genes are very similar to the microarray-based profiles. Microarray results are plotted using the standard red/green (up/down) coding and mean (n = 3) fold change of SHAM/ONX is plotted for the qPCR analysis.

Figure 4. Gene ontological analysis of changes in gene expression after ONX

Selected gene ontology biological process or molecular function categories. Percentages represent the number of genes regulated in one gene ontology group compared to all genes differentially regulated at that time point (1, 4, or 21 days). Ontological groups were chosen according to whether each gene class was statistically over-represented using Onto-Express (with a hypergeometric distribution and an FDR correction (P < 0.05)) in up-regulated or down-regulated genes in at least one time point.

Figure 5. Cluster analysis and target gene expression

Cluster analysis was performed on microarray data using cluster and heat maps were constructed using TreeView. Two genes from each gene ontology class were selected for qPCR analysis at nine time-points (0 hrs, 6 hrs and 1, 2, 4, 6, 12, 16 and 21 days). The results are represented as mean fold-change of ONX over SHAM. Error bars show standard error of mean (S.E.M) of biological triplicates. For expanded heat maps, see Fig. S1.