Dynamic Axonal Translation in Developing and Mature Visual Circuits

Summary Local mRNA translation mediates the adaptive responses of axons to extrinsic signals, but direct evidence that it occurs in mammalian CNS axons in vivo is scant. We developed an axon-TRAP-RiboTag approach in mouse that allows deep-sequencing analysis of ribosome-bound mRNAs in the retinal ganglion cell axons of the developing and adult retinotectal projection in vivo. The embryonic-to-postnatal axonal translatome comprises an evolving subset of enriched genes with axon-specific roles, suggesting distinct steps in axon wiring, such as elongation, pruning, and synaptogenesis. Adult axons, remarkably, have a complex translatome with strong links to axon survival, neurotransmission, and neurodegenerative disease. Translationally co-regulated mRNA subsets share common upstream regulators, and sequence elements generated by alternative splicing promote axonal mRNA translation. Our results indicate that intricate regulation of compartment-specific mRNA translation in mammalian CNS axons supports the formation and maintenance of neural circuits in vivo.


Histological analysis
Mouse embryos were fixed by immersion in 4% paraformaldehyde in PBS. Adult mice were transcardially perfused with the same fixative, and entire eyes and brains were dissected out and post-fixed. For X-gal staining, brains and retinae were dissected out, washed in ice-cold PBS and fixed for 1 hour in 1% formaldehyde, 0.2% glutaraldehyde, 2mM MgCl 2 , 5mM EGTA, 0.02% NP-40 in PBS [pH7.5]) at 4°C on a shaker. The dissected tissues were rinsed three times for 20 min each in PBS and stained with X-gal staining solution (1mg/ml X-gal diluted in DMF, 0.01% Sodium deoxycholate, 0.02% NP-40, 5 mM potassium ferricyanide, 2 mM MgCl 2 in PBS) between 3 to 48 hours at 37°C in the dark. After washing with PBS, the tissues were post-fixed in 4% paraformaldehyde in PBS for 30 min at 4°C. For immunohistochemistry, tissues were saturated in 30% sucrose, embedded in OCT, frozen on dry ice, sectioned at 12 µm by using a cryostat (Leica CM3050S), and then visualized using an anti-HA antibody (Abcam ab9110) and a secondary antibody conjugated to Alexa Fluor 488 (Life technologies). For immuno-gold electron microscopy, tissues were fixed in 4% paraformaldehyde in 0.1M HEPES (pH 7.4), and the HA-tagged ribosomes were visualized by the same anti-HA antibody and IgG conjugated with gold (10nm-15nm) at the Cambridge Advanced Imaging Centre (University of Cambridge, UK). Specific labeling was evident as ultrastructurally identifiable ribosomes were labeled by multiple gold particles (Fig. 1F, purple arrows). We did, however, occasionally observe scattered gold particles in the Crenegative tissue (Fig. 1G, white arrow), but these were never clustered as seen in the Cre-positive group. Therefore, we considered only two or more gold particles in close proximity (within 50 nm between particles) as specific labeling of HA-positive ribosomes.

Axon-Translating Ribosome Affinity Purification (TRAP)
Tissue samples were dissected and snap-frozen in liquid nitrogen and genotyped.
Whole eyes were used as the cell body group and the superior colliculus, where retinal axons terminate, were used as the axon terminal group. Tissues from crepositive and negative were pooled for TRAP (three eyes and superior colliculi for one group). Tissues were homogenized in lysis buffer (20mM HEPES-KOH, 5mM MgCl 2 , 150mM KCl, 1mM DTT, SUPERase In, and Complete EDTA-free Protease Inhibitor Cocktail) in the presence of cycloheximide (to stop translational elongation and to lock translating ribosomes on the mRNA) and rapamycin (to prevent new translational initiation during immunoprecipitation), and post-mitochondrial fractions were collected. We optimized the TRAP protocol before performing axon-TRAP.
First, we found the polyclonal HA antibody (9110, Abcam) is superior to the one used in the original RiboTag study (HA11, Covance) (Sanz et al., 2009). Although the two antibodies were similarly effective in precipitating HA-rpL22, the polyclonal antibody (Abcam ab9110) co-purified much more 80S ribosomes ( Fig. 2A). We estimated that approximately 40% of HA-tagged translating ribosomes could be purified this way, as the amount of TRAPed 80S ribosomes (i.e. co-immunoprecipitated rpS3a, a 40S ribosomal protein) was approximately 10% of the input (i.e. rpS3a in total input) ( Fig.   2A). This estimation is based on the findings that (1) roughly 50% of retinal cells express HA-tagged rpL22 (i.e. 50% cells do not express tagged ribosomes), and that  Fig. 2A). We found that the indirect immunoprecipitation protocol, which allows the antibody to bind the antigen before the purification of the antigen-antibody complexes, was more specific than the direct protocol, which utilizes the antibody pre-conjugated to Protein G-magnetic beads. We think that nonspecific binding of mRNAs to Protein G-magnetic beads was reduced in the indirect protocol, as the tissue lysate was pre-cleared with unconjugated Protein Gmagnetic beads. Pre-cleared ribosome-mRNA complexes were immunoprecipitated by an anti-HA antibody and Dynabeads Protein G (Life Technologies 10004D). Total RNA was extracted from the ribosome-mRNA complexes using an RNeasy mini kit (Qiagen) followed by in-column DNase treatment to remove genomic DNA contamination. The RNA samples were examined for quantity and quality using the Agilent Bioanalyzer 2100 (Agilent Technologies).

Amplification of cDNAs
The amount of axon-TRAPed mRNA was minute and had to be amplified (Fig. S2A).
We used a method developed by Tang and colleagues for single cell transcriptomics (Tang et al., 2009) with slight modification (Fig. S2A). First, TRAPed RNAs were treated with DNase I to eliminate genomic DNA contamination and then mRNAs were reverse-transcribed with oligo(dT) primer with a linker. After second strand synthesis with another linker, the double-strand cDNAs were amplified by ten rounds of PCR and visualized by agarose gel electrophoresis (Fig. 2B). The specificity of TRAP was evident because it was dependent on Cre. As expected, the amount of TRAPed mRNAs was lower in the SC (mRNAs from RGC axons) than in the eye (mRNAs from the cell body and proximal neurites), and therefore was further amplified by five additional rounds of PCR. Although axon-TRAP was clearly dependent on Cre and therefore specific, increasing PCR cycles led to an increased background (i.e. amplified cDNAs from the Cre-negative SC). We reasoned that the amplified DNAs in the Cre-negative SC would reflect the relative abundance of mRNAs in the SC. We sequenced these mRNAs and used the data as a negative control for bioinformatics analysis of axon-TRAPed mRNAs (Fig. 2B). Amplified cDNAs were subjected to paired-end 100 or 90 bp sequencing on the Illumina HiSeq2000.
For the in vitro ribosome run-off experiment, eyes or superior colliculi were homogenized by the 400 µl lysis buffer without cycloheximide, and then 200 µl of rabbit reticulocyte lysate (Promega) and 8 µl of Harringtonine (100 ug /ml) and 4 µl of 4E1RCat (5 mM) were added to the 188 µl lysate, followed by 37 °C incubation for 30 min. To stop the in vitro translation elongation, 800 µl of ice-cold lysis buffer with cycloheximide was added, followed by immunoprecipitation with the anti-HA antibody. The purified RNAs were subjected to RNA sequencing on the Illumina NextSeq 500. To reflect the difference in amount of amplified cDNAs between the samples with and without run-off translation on the sequencing depth, we first measured the amount of cDNA by both Tapestation DNA and by Qubit. We then adjusted the ratio between the amounts of input libraries for multiplex sequencing using the ratio of cDNA amount between the two samples. All RNA-seq data are deposited in Gene Expression Omnibus (GEO) datasets under accession number GSE79352.

Mapping of sequence reads and normalization of read counts
The sequence reads were mapped to the mouse genome (mm10) using TopHat 2 version 2.0.12 (Kim et al., 2013) with default settings, except for the "--read-realignedit-dist 0" option. This option was chosen to reduce mapping to pseudogenes.
Although we detected a significant number of sequencing reads of the primer dimers in several samples, which are probably caused by the matching problem between our primers and Illumina primers, because there is no biological reason that these dimers bias the relative abundance of sequence reads of endogenous mRNAs, we analyzed all sequence reads that can be mapped to the mouse genome sequence by TopHat 2. Transcript assembly and estimation of FPKM (Fragments Per Kilobase of transcript per Million fragments sequenced) values were performed using Cufflinks version 2.2.1 (Trapnell et al., 2010). Read counts for each gene were determined using HTSeq version 0.6.1p1 (http://www-huber.embl.de/users/anders/HTSeq/). For the analysis of run-off samples, we used the read counts for all DEGs (adult) detected either in the run-off positive or negative sample.

Differential gene expression analysis and Gene Ontology based enrichment analysis
For the identification of translated mRNAs in RGC axons, we applied the differential gene expression analysis on read count data between two biological replicates of Cre-positive and Cre-negative samples using NOISeq (Tarazona et al., 2011) in default conditions with probability threshold 0.7. Although this approach has a risk of filtering out actively translating mRNAs if the same mRNAs exist in high abundance in SC-resident cells and are proportionately represented in the negative control, we thought that it would be appropriate to take a conservative approach when analyzing mRNAs identified by highly sensitive RNA-seq from samples with potentially low signal-to-noise ratio. DEG analysis identified a subset of Cre-dependent mRNAs ( Fig. 2D, right panel, red dots) (Table S1). In some cases, genes that passed our filtering criteria and were identified as DEGs in one stage failed the same test in other stages (Fig. 2D, orange), because their abundance in the negative control varied depending on the stage. Most of these mRNAs, however, were found in the axon-TRAPed mRNAs at all stages tested, suggesting that these genes became false negatives in other stages. Therefore, when we analyzed the developmental change in translation levels, we used the FPKM (fragments per kilobase of transcript per million mapped reads) values of all four stages for genes that have passed the DEG test in at least one stage (Union of DEGs) (Fig. 2D, orange and peach).
To perform a GO-based analysis for neuronal functions (Figs. 4A and S4A), we selected 455 neuron-related GO terms (Table S2) using the following criteria: all offspring GO terms (249 terms) of 'Neuron development (GO:0048666)', which contain GO terms related to neuron/axon development, and all offspring GO terms (206 terms) of "Synaptic transmission (GO:0007268)", which contain GO terms related to neurotransmission. The offspring GO terms were identified using the GOBPOFFSPRING function of GO.db, an R package (Table S2). The enrichment analysis for gene ontology was carried out with DAVID and topGO version 2.18.

Ingenuity Pathway Analysis
Ingenuity Pathway Analysis (Qiagen) for canonical pathways (Fig. 5A) was performed according to the manufacturer's instructions. For quantitative analysis of stage-dependent axonal translation of each mRNA, we calculated the ratio in read counts of each mRNA from one stage to the next. Then we performed Ingenuity Pathway Analysis (IPA) for upstream regulators, which mainly utilizes published results of gene knockdown or knockout studies. For example, if the protein product of a gene decreases when a specific translational regulator is knocked out, the gene would be described as "positively regulated" by the translational regulator (and vice versa) (Fig. 5B).

Analysis of target mRNAs in the mTOR pathway and RNA binding proteins
We analyzed the known binding targets of RBPs using the results of previous studies on FMRP, TDP-43, FUS and APC. In addition, we carried out the same analysis on the targets of mTORC1 to confirm the result of IPA analysis. If any of these molecules regulate stage-specific translation in the axon, their target mRNAs would show coordinate changes in translation. We used the gene sets that were identified in previous studies (Colombrita et al., 2012;Darnell et al., 2011;Preitner et al., 2014;Thoreen et al., 2012). We analyzed mTOR targets that are described in the previous report (Thoreen et al., 2012) with a threshold of log2 (Torin1/Vehicle) < -1, FMRP targets (Darnell et al., 2011) with a threshold of rank (based upon chi-square score) < 100 and all of the APC targets (Preitner et al., 2014), TDP-43 targets and FUS targets identified (Colombrita et al., 2012). In the FPKM ratio analysis for consecutive developmental stages, we used all genes that were detected in RNA-seq in order to avoid any bias caused by differential gene expression analysis. Principle component analysis (PCA) was performed on normalized read counts of all samples to compare gene expression in 12 different conditions by using the "prcomp" function in the R Software package (version 2.13.0). Data were plotted using the first two PCs which explained up to 73.2% of the total variance: 66.1% explained by PC1; and 7.1% by PC2.

Analysis of mRNA isoforms
Alternative isoforms were analyzed on mapped reads from the P0.5 sample using MISO. Before this analysis, the mean and the standard deviation of the insert length and the total number of mapped read pairs were computed using the "pe_utils" utilities (Retina: mean=177.8, sdev=9.7, dispersion=0.7, num_pairs=3262858, Axon: mean=178.3, sdev=13.5, dispersion=1.0, num_pairs=2535309, Cre_negative axon: mean=179.6, sdev=9.2, dispersion=0.7, num_pairs=3235484). We used Mouse genome (mm10) alternative events version 1.0 (Wang et al., 2008) to perform "exoncentric" analyses. We counted and analyzed only the events in which both splicing variants are detected both in axon and in cell body (the events of "0 < Ψ <1" both in axon and in cell body). For the discovery of novel splice variants, we performed reference-independent transcript reconstruction using Cufflinks version 2.2.1 (Trapnell et al., 2010) and compared the sequences of reconstructed transcripts with the UCSC Genes transcript annotations by using the Cuffcompare program from the Cufflinks package. For the identification of circular RNAs, we first extracted the fusion transcripts from the unmapped sequence reads by using TopHat-fusion (Kim and Salzberg, 2011) (TopHat 2.0.12, parameters:--fusion-search --keep-fasta-order --bowtie1 --no-coverage-search), and then identified junction reads from back spliced exons using CIRCexplorer-1.1.1 (Zhang et al., 2014). Mapped sequence reads are visualized using the Integrative Genomics Viewer (IGV) version 2.3.46 (Robinson et al., 2011).

Identification of regulatory motifs in RNA
De novo motif analysis was performed using HOMER version 3.0 (Heinz et al., 2010) with custom FASTA files. For the finding of motifs in UTRs, we used the UTR sequences of genes whose levels were higher in the axon than in the retina (axon / retina > 100, Fig. 3A) in P0.5 translatomes. For finding of motifs in axon-enriched alternative exons, detection of differential exon usage was performed using DEXseq version 1.10.8 (Anders et al., 2012) on R version 3.1.0, and exons were selected with a Benjamini-Hochberg adjusted p-value cut-off of < 0.1 (Benjamini and Hochberg, 1995). To validate these motifs, we extracted the genes that contain these motifs allowing 0-1 mismatch and then compared the translation levels between axon and retina. Sequences of UTRs were retrieved from Ensemble BioMart (Kinsella et al., 2011). Motif containing genes were identified using the Biostrings package on R version 3.1.2.
Motif sequences are in Table S3. The reporter plasmids were expressed in the retina by targeted electroporation into Xenopus laevis embryos, and fluorescence was imaged in cultured retinal ganglion cell axons as previously described (Leung et al., 2006), except that 1% fetal bovine serum was added to the medium. In experiments to confirm translation-dependency of FRAP, the translation inhibitor anisomycin (40 µM) was added 30 min prior to imaging. FRAP experiments were performed on an Olympus IX81 inverted microscope equipped with a PerkinElmer Spinning Disk UltraVIEW VoX and a 60x (NA, 1.30) Olympus silicone oil immersion objective. Images were acquired with an ORCA-Flash4.0 V2 CMOS camera (Hamamatsu) using Volocity software (PerkinElmer).
Photobleaching was performed using an UltraVIEW PhotoKinesis device (PerkinElmer). Regions of interest were manually defined so that growth cones and >20µm of the axon shaft were bleached (thus reducing likelihood of fluorescence recovery resulting from myr-d2EGFP diffusion from unbleached areas of the axon shaft). Photobleaching was performed at 50-85% laser power (488 nm laser line) with 20-30 bleach cycles. Time-lapse images were captured at 1 min intervals using a 488 nm laser line at 25% laser power for myr-d2EGFP and a 561 nm laser line (31 % laser power) for visualization of axons using a membrane-targeted mCherry-CAAX reporter, in addition to phase contrast. Exposure time for the 488 channel was adjusted to avoid pixel saturation and was typically between 50-200ms.
Quantification of fluorescence intensity was performed using Volocity software (PerkinElmer). At each time point, the outline of the growth cone (ROI) was traced using the mCherry-CAAX reporter (561 channel) and phase contrast images. Mean gray values from the 488 channel were subsequently calculated as mean pixel intensity per unit area within the ROI. Subtracting the mean background pixel intensity per unit area from an equivalently sized ROI immediately adjacent to the growth cone normalised intensity values in the growth cone. Unhealthy axons exhibiting signs of photo-toxicity after FRAP (characterised by blebbing, growth cone collapse and/or retraction) were excluded from analysis. In addition, we only quantified growth cones of axons extending more than 100 µm from the eye explant to reduce effects of somal diffusion. Relative fluorescent recovery (R) at each time point was calculated by the formula: R x = (I x -I post ) / (I pre -I post ). Where, I x = normalised fluorescent intensity of the growth cone ROI at time point 'x', I pre = normalised fluorescent intensity before photobleaching and I post = normalised fluorescent intensity immediately after photobleaching (t=0 mins).
Data were analysed using PRISM software (Graphpad). Significance was tested using a two-way ANOVA.