Kinase/phosphatase overexpression reveals pathways regulating hippocampal neuron morphology

A large‐scale gain‐of‐function analysis in primary mammalian neurons

Electroporation‐mediated transfection was used to overexpress kinases and phosphatases in embryonic rat hippocampal neurons. These neurons quickly adhere to laminin‐coated plates, initiating neurite growth within hours (Esch et al, 1999). By 48 h, neurons typically possess several minor neurites and one major neurite (likely to develop into the axon) (Dotti et al, 1988). We marked transfected neurons by cotransfection with mCherry, a red fluorescent protein (RFP) (Shaner et al, 2004); transfection efficiency averaged 17.3% (95% confidence interval (95 CI), 16.6–18%) of the βIII‐tubulin‐positive neurons. Only transfected neurons were analyzed; neurons were defined as transfected (RFP+; Figure 1B and D, arrowheads) if their RFP intensities were greater than 2 s.d. above the mean of non‐transfected controls (Figure 1E and F). Control experiments demonstrated that >80% of RFP+ neurons were cotransfected with the gene of interest (data not shown). Except when measuring the percent of neurons with neurites (%Neurite+), we considered neurons for further analysis only if they had at least one neurite >10 μm (Neurite+; Figure 1A and B) to avoid measuring potentially non‐viable neurons (Figure 1C and D).

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Figure 1.

Hippocampal neurons assayed for neurite growth after transfection. (A–D) Hippocampal neurons growing on laminin, divided along two axes, producing four categories: Neurite+ (A, B), neurons that have neurites, and Neurite− (C, D), neurons without. (A, C) RFP−, neurons that are not expressing red fluorescent protein (RFP) reporter. (B, D) RFP+ neurons are expressing reporter, and thus are likely to be expressing the plasmid of interest. (B, D) RFP+ neurons are identified by red cell bodies and arrowheads. (E, F) Scatter plots of RFP intensity from over 60 000 neurons in one experiment, plotted against nuclear (Hoechst) intensity. Each marker indicates one neuron. (E) RFP was added to the transfection as a reporter gene, and in (F) no reporter was added. Black horizontal line is transfection criterion. Scale bar=100 μm.

We obtained quantitative data for many cellular and neuronal morphological parameters from each neuron imaged. These included nuclear morphology (nuclear area and Hoechst dye intensity), soma morphology (tubulin intensity, area, and shape), and numerous parameters of neurite morphology (e.g. tubulin intensity along the neurites, number of primary neurites, neurite length, number of branches, distance from the cell body to the branches, number of crossing points, width and area of the neurites, and longest neurite; Supplementary Figure 1). Other parameters were reported on a ‘per well’ basis, including the percentage of transfected neurons in a condition (%RFP+), as well as the percentage of neurons initiating neurite growth (%Neurite+). Data for each treatment were normalized to the control (pSport CAT) within the same experiment, then aggregated across replicate experiments.

Validation of normalization and use of transfected neurons

We constructed a linear model incorporating experimental and treatment terms to test the validity of our normalization and our method for selecting transfected neurons (Figure 2C). The main sources of variance were the treatment (overexpression of kinases and phosphatases) and various aspects of experimental technique (animals, cells, time of prep, transfection, etc).

where C=constant, E=experiment effects, T=treatment effects, and ε=error.

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Figure 2.

Morphological parameters reduced and normalization validated. (A) Clusters of correlated parameters were established by correlation between pairs. Braces indicate groups of closely related parameters. (B) Bar chart of correlation coefficients with primary neurite count. Bar chart depicts the correlation coefficient with 95% confidence intervals. (C) Generalized linear model developed to validate the population of cells to analyze. Top, Equation representing the explained variance from combinations of the experimental (E) effect and treatment (T) effect. Parameters were fit either as unnormalized raw values (Raw) or using relative change/normalized (Norm). We also considered the difference between untransfected (RFP−) and transfected (RFP+) neurons. By fitting the model to the equation NTLij for explained variance, very high fits were obtained for each group. The contribution from the treatment=signal (black bars) increased with transfected/normalized data, whereas the contribution from experimental variations=noise (white bars) decreased to 6% when using normalized, transfected data. Source data are available for this figure at http://www.nature.com/msb.

Source data for Figure 2A‐B [msb201052-sup-0002-SourceData-S2.xls]

Source data for Figure 2C [msb201052-sup-0003-SourceData-S3.xls]

The linear models fit the data appropriately, with coefficients of RFP− 0.62, 0.59 and RFP+ 0.87, 0.84 (for raw and normalized data models, respectively). The models were analyzed to determine the respective contributions of the treatment term (representing changes specifically because of overexpression), and the experimental term (representing noise). When analyzing cells defined as non‐transfected, the experimental (noise) contribution predominated, both for raw and normalized data (Figure 2C). However, for cells defined as transfected (RFP+), the treatment effect increased (to 34%) and the noise contribution decreased (to 32%), even for raw data. For normalized data from transfected cells, the treatment contribution increased to 51%, whereas the experimental contribution became negligible (6%). We concluded that it was appropriate to analyze the RFP+ neurons, and that normalization significantly reduced the noise in the screen, revealing the effect of cDNA overexpression. Notably, the interaction term maintained a sizable contribution in our model (not shown), suggesting that a non‐linear model would be able to explain more of the variance than our simple linear one.

Neurite number is regulated separately from neurite elongation

Correlations among the 19 normalized parameters were analyzed for neurons transfected with all kinase and phosphatase clones (Figure 2A). Primarily, this approach was taken to simplify the number of variables. Five groups of parameters as well as four single parameters emerged from the correlation analysis. Parameter groups comprised clusters of red intensity measures, soma geometry measures, neurite crossing, branching and neurite area, and neurite length measures (defined in Supplementary Figure 1). On the basis of this analysis, the primary variables that define the neurite morphology are primary neurite count, neurite average length, and average branches. Interestingly, primary neurite count was not well correlated with neurite length or branching (Figure 2A). The Pearson correlation coefficient (r²) between the number of primary neurites and the average length of the neurites was 0.332 (95 CI, 0.247–0.412), and between the number of primary neurites and average branching was 0.227 (95 CI, 0.146–0.302). In contrast, the correlation coefficient of average branching with neurite average length was 0.670 (95 CI, 0.626–0.712) (Figure 2B). The primary neurite count was well correlated with neurite total length (NTL). This is expected as NTL is defined as (average length × primary neurite count) (Supplementary Figure 1).

There are at least two possible explanations for the low correspondence between primary neurite number and either neurite length or branch number. First, the number of primary neurites in embryonic hippocampal neurons could be very stable and imperturbable. Indeed, primary neurite number did not vary a great deal across the entire data set (2.13±1.14 s.d.). However, we identified a group of genes (see below) that significantly perturbed primary neurite count, rendering this explanation unlikely. Therefore, a more likely possibility is that signaling mechanisms underlying the neurite number determination are different than those controlling length/branching of the neurites. A mechanism for this phenomenon has recently been examined (Shelly et al, 2010).

Phenotypic perturbation after overexpression of kinases and phosphatases

Neurons were transfected with each of 724 cDNA plasmids, from the NIH Mammalian Genome Collection (MGC) (Gerhard et al, 2004), mapping onto 622 different human and mouse genes. As 130 of these genes were orthologs, 492 unique genes were screened. After image/tracing validation, a number of clones were removed from the analysis (because of poor tracing or low cell number), leaving 449 unique genes (plus five control genes) in the final analysis.

To identify relevant functional classes of neuronal phenotypes, three key parameters were considered—primary neurite count, average length, and branching. Changes in these parameters resulting from gene overexpression were examined using hierarchical clustering (Figure 3). Of the 454 unique genes (including controls), 59 had statistically significant positive effects in neurite count and length, and are grouped on the left side of the heat map in green. Only nine genes increased both the primary neurite count and the average neurite length, whereas 23 genes increased neurite average length. An additional 32 genes increased the primary neurite count, but with little or no increase in length. Only a few of the genes assayed were significant determinants of both neurite length and neurite count. It is likely that these genes control general neurite initiation, whereas the other phenotypic classes regulate more specific cellular programs. A majority of genes with significant effects (92) were inhibitory to neurite growth.

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Figure 3.

Phenotypic perturbation after kinase and phosphatase overexpression in hippocampal neurons. Overexpression of genes coding for particular kinases and phosphatases significantly perturbed neuronal morphology. (A) Dendrogram produced from hierarchical clustering sorted the genes by phenotype. (B) Blocks of color indicate significance and direction of effect for neurite count and neurite average length, where green indicates values significantly above control, red significantly below, and black not significant. (C–E) Heat map of genes that are significantly different from control, with three different parameters, neurite count (C), average length (D), and average branches (E), where green indicates a relative (normalized) increase, and red a decrease in value. Values for red and green are as follows, neurite count −0.25 to 0.3, neurite length −0.47 to 0.51, and branch points −0.51 to 1.66. Genes not significantly different than control were originally clustered above (I), indicated by ‘ns.’ (F–K) Six representative images of neurite growth with tubulin immunofluorescence (grayscale) and nuclear (blue), cell body (white), and neurite tracing (yellow/green) overlays. The Entrez Gene symbol is listed below the image (left to right: choline kinase, origin recognition complex like, TGF‐β receptor, control (pSport‐CAT), inositol polyphosphate‐5‐phosphatase, PKC iota). Black triangles indicate a gene's location in the cluster and heat map. (L) Cartoons of stereotyped neurite growth phenotype for each example image. Scale bar=100 μm. Source data are available for this figure at www.nature.com/msb.

Source data for Figure 3 [msb201052-sup-0005-SourceData-S5.xls]

We identified several genes that significantly altered neurite growth. For example, choline kinase α (CHKA) promoted positive growth enhancement for all parameters after overexpression in hippocampal neurons (Figure 3F). Origin recognition complex subunit 4‐like and the TGF‐β receptor 2, increased the length of hippocampal neuron processes (Figure 3G and H). Origin recognition complex was previously reported to regulate dendrite development (Huang et al, 2005). Inositol polyphosphate phosphatase (INPP5B) overexpression increased the number of primary neurites and led to decreased neurite length (Figure 3J). Although these genes had enhancing effects on laminin, they had little effect on neurite growth on the artificial substrate polylysine (Supplementary Figure 2). The strongest inhibitory genes produced very short neurites with only a few neurons initiating growth (e.g. atypical PKC iota, PRKCI; Figure 3K). These inhibitory genes did not generally affect survival (Supplementary Figure 3). Overall, we identified several neurite growth activators, and many neurite growth inhibitors in hippocampal neurons. The full table of results is provided in Supplementary Table 1.

Effects of known neurite‐regulatory genes on neuronal morphology

We selected a group of genes that were tested in our experiments and had been previously implicated in neuron development, neuritogenesis, axon elongation, or neuronal polarity. Table I reports the results of overexpression of these genes in hippocampal neurons for four parameters: nuclear intensity (increases reflect nuclear condensation; Marcus et al, 1979), primary neurite count, neurite average length, and average branching. The latter three parameters were chosen because they defined the three distinct groups of neurite variables. Genes that had significant effects (after correction for multiple comparisons) are indicated. As expected, the protein kinase A catalytic subunit (PRKACA) and the atypical PKCs (PRKCI, PRKCH) strongly perturbed the neuron's phenotype. Overexpression of these kinases was sufficient to inhibit neurite growth. PKA activity has previously been suggested to affect growth on laminin negatively (Bixby, 1989), but also has been linked to positive regulation of neurite growth (Kao et al, 2002). It is possible that our (inhibitory) results with PKA reflect mislocalization because of expression of the catalytic subunit in the absence of the regulatory subunit. Note that although overexpression of PKA inhibited neurite length, it did not increase the nuclear intensity, suggesting cell death was not the cause (Table I). To determine whether PKA's effect was due to its kinase activity, we compared expression of wild‐type PRKACA to that of an inactive mutant. Transfection of PRKACA led to strong inhibition of neurite growth (Figure 4B), whereas transfection of a kinase‐dead mutant (K73A; Figure 4C) did not alter neuronal morphology (compare Figure 4A). Therefore, PRKACA's kinase activity was required for its inhibition of neurite growth.

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Figure 4.

Overexpression of PKA catalytic subunit α inhibits neurite growth. Its kinase activity is responsible for the phenotype change. (A–C) Hippocampal neurons transfected with control (mCherry, A), PKA catalytic subunit α (PRKACA, B), and kinase‐dead mutant of PKA (PRKACA K73A, C). Arrowheads indicate PRKACA‐transfected neurons; arrows indicate neurons expressing the kinase‐dead mutant of PKA. The neurons’ processes are visualized by βIII‐tubulin (green), and mCherry coexpression (red). Scale bar=100 μm.

Neuronal morphology is altered by expression of members of kinase and phosphatase families

Related proteins are often involved in similar neuronal functions. For example, families of receptor protein tyrosine phosphatases are involved in motor axon extension and guidance in both Drosophila and in vertebrates (Desai et al, 1997; Stepanek et al, 2005), and a large family of Eph receptor tyrosine kinases regulates guidance of retinotectal projections, motor axons, and axons in the corpus callosum (Brennan et al, 1997; Klein, 2001; Mendes et al, 2006). We therefore asked whether families of related genes produced similar phenotypes when overexpressed in hippocampal neurons. Our set of genes covered 40% of the known protein kinases (Supplementary Table 2), and many of the non‐protein kinases and phosphatases.

Genes were sorted into one of five categories before the sequence alignment to produce distinct trees: protein kinases (Figure 5A), non‐protein kinases (Figure 5B), protein phosphatases (Figure 5C), other phosphatases (Figure 5D), and other genes (Figure 5E). Phylogenetic trees were used to determine amino‐acid similarity and infer evolutionary distance. Related families were thus clustered into branches and subbranches. Genes identified as protein kinases (Manning et al, 2002; Caenepeel et al, 2004) were aligned using the amino‐acid sequences of the kinase domains only, whereas other genes were aligned by their entire sequences. The alignment successfully clustered the genes into known families. For example, the protein kinase tree (Figure 5A) matched the organization of previously identified protein kinase groups (Manning et al, 2002; Caenepeel et al, 2004). Also, 81% of the genes with obvious siblings (i.e. PPP2CA, PPP2CB) were linked to their sibling in the trees.

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Figure 5.

Families of kinases and phosphatases modify neurite outgrowth. Screened genes were aligned by sequence then displayed as phylogenetic trees, representing evolutionarily related proteins. Genes were separated into five primary categories, protein kinases (A), non‐protein kinases (B), protein phosphatases (C), non‐protein phosphatases (D), and others (E). Protein kinases were further subdivided into seven families (based on kinase domain sequences). Branch lengths on the trees represent the amino‐acid change from parent to sibling and therefore the evolutionary ‘distance’ between genes. Labels beside the branches are the official Entrez Gene symbol. Circular markers were overlaid atop the trees, with the color of the marker indicating either no change in neurite length from control (white), an increase (green) or decrease (red). KLF4 and KLF7 are transcription factors that are regulators of neurite growth (Moore et al, 2009; Blackmore et al, 2010), and were included in the screen as additional controls. Source data are available for this figure at www.nature.com/msb.

Source data for Figure 5 [msb201052-sup-0006-SourceData-S6.xls]

To establish the neuronal phenotype for individual genes within families, we plotted the functional data for NTL on markers overlying the trees (Figure 5). The neuronal response observed after the overexpression of individual kinases and phosphatases was diverse across gene families. Expression of most kinase and phosphatase genes reduced the length of the neurites compared with controls (Figure 5, red markers). Some families were particularly inhibitory. For example, BMP/activin receptors, PKAs/PKCs (PRKACx, PRKCx), and most protein serine/threonine phosphatases (PPP branch) inhibited neurite extension.

Gene families commonly exhibit redundant function (Urrutia et al, 1997; Xian and Zhou, 2004). Redundant gene function has often been identified when two or more knockouts are required to produce a phenotype (Heber et al, 2000). Our technique allowed us to measure whether different members of gene families had similar (potentially redundant) or distinct effects on neuronal phenotype.

To determine whether groups of related genes affect neuronal morphology in similar ways, we used sequence alignment information to construct gene clusters (Figure 6A). Genes were clustered at nine different thresholds of similarity (called ‘tiers’). The functional effect for a particular parameter was then averaged within each cluster of a given tier, and non‐parametric statistics were performed to determine the significance of the effect. Results from the neurite initiation parameter are shown in Figure 6B. Here, the giant cluster containing every gene has an average near control level and is colored white (Figure 6B, bottom tier, labeled 1 on the left). As the threshold is increased, clusters of fewer but more closely related genes are constructed, and their mean effect on the phenotype is displayed by the color of the rectangles in the heat map (red=decrease, green=increase, diamonds and asterisks indicate significance).

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Figure 6.

Bioinformatics identify putative families of active genes. (A) Genes overexpressed in hippocampal neurons, arranged by phylogenetic relationships (as in Figure 5, but ‘unrolled’) where the most closely related genes are next to each other. The five trees are preserved in the five sections (columns) of the graph. (B) Cluster heat map for the frequency of neurite initiation. At the bottom (labeled 1), all of the genes are combined into one large cluster, where there was no net effect on neurite growth (white). Going up from the bottom, genes are grouped in smaller, more focused families, with 3, 9, 21, 45, 88, 199, and 347 families per tier (row of the heat map). Each rectangle represents a family of genes (corresponding to the labels in A) that has a mean effect that reduced (red), or increased (green) neurite initiation. Non‐parametric statistics were performed on each family, where diamonds indicate values for spiked bootstrap (^P⩽0.05, ^^P⩽0.01). Asterisks indicate significance corrected by Benjamini–Hochberg (^*P⩽0.05, ^**P⩽0.01, ^***P⩽0.001). (C) Compilation of significant families for four parameters: primary neurite count, frequency of neurite initiation, neurite average length, and average branching. Colored rectangles demarcate the extent of the family (for instance CDKN3, PTEN, PTP4A1, CDC14A&B), the color the direction and magnitude of the effect, with labels giving the common names for the genes. Note that the large family of non‐protein kinases (from FUK to PFKL) has smaller nested families with even greater effects. The calcineurins (PPP3CA&B), and the kinases IRF6, CHKA, and CDK5R1, had the greatest ability to perturb neurite growth. Source data are available for this figure at www.nature.com/msb.

Source data for Figure 6 [msb201052-sup-0004-SourceData-S4.xls]

We analyzed results for this cluster analysis with the three key neurite parameters (average neurite length, primary neurite count, and average branching) in addition to the frequency of neurite initiation (from the percent of neurons with axons or dendrites). Genes that perturbed each of these phenotypes are grouped in Figure 6C. Eight families, most with only a few genes, produced significant changes for one or two parameters. A diverse family of non‐protein kinases had a positive effect on neurite outgrowth in three of the four parameters analyzed. This family of kinases consisted of a variety of enzymes, mostly sugar and lipid kinases, with the strongest effects coming from IRF6 and CHKA.

Molecular pathways revealed by neuronal phenotypes

Over the past 10–20 years, literally hundreds of neuronal growth and regeneration‐associated genes have been identified (e.g. Table I). However, determining which of these genes are primary regulators of process development has been challenging. It is possible to refine the understanding of these genes with existing pathway information. One hurdle is that most genes with pathway annotations are members of multiple pathways. Of the 550 genes we successfully screened, 46% had pathway annotations, and of those, 66% were members of more than one pathway. Of the latter, half were members of four or more pathways. We performed pathway analysis for 79 different pathways in 24 categories using Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa et al, 2004) (http://www.genome.jp/kegg/pathway.html) (Figure 7A and B). This analysis was displayed in the form of a heat map (Figure 7C). KEGG provided the classification of main categories (Figure 7A), subcategories (Figure 7E), and the pathways (Figure 7B). Genes were then placed in these pathway ‘bins.’ The color of the bin indicates the strength and the direction of the effect observed (Figure 7C and D). Pathways involved in proliferation of specific cancers, dorso‐ventral axis formation, cytokine signaling, and ErbB signaling each had genes that were significant in their ability to modify neuronal morphology (Figure 7C). In addition, groups of pathways (as determined by KEGG pathways) such as the cancer pathways and amino‐acid metabolism pathways had significant effects (Figure 7D). Summary data for the most significant results are shown for neurite length, branching, red intensity (from RFP cotransfection, does not equate with the level of expression of cotransfected gene), and soma area. The pathways most involved in producing the observed phenotypes included cancer, apoptosis, axis formation, adherens junctions, neuroactive ligand–receptor interactions, and ErbB signaling (Figure 7G). Complete results of pathway analysis are shown in Supplementary Figure 4. Pathway cluster analysis showed that individual genes that were members of groups (from known pathways) had similar activities when overexpressed in hippocampal neurons. In particular, pathways involved in cancer cell proliferation potentiated neurite extension and branching. Manipulations of genes in these pathways may be important to enhance axon or dendrite growth after injury.

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Figure 7.

Neural process development is affected by known pathways, including cancer pathways. Pathway annotations from the KEGG grouped genes into 79 pathways. (A) KEGG categories. (B) KEGG pathway names. (C) Heat map for pathway's effect on neurite average length as indicated by color (red, decrease; white, no change from control; green, increase). (D) The 24 pathway subcategories, with heat map representing average effects of all genes in the category. (E) Names of pathway subcategories. (F) Legend for heat map. Symbols atop the heat map indicated significance from non‐parametric statistics, where diamond is P⩽0.05 by spiked bootstrap, and square is P⩽0.05 by bootstrap. (G) After the analysis was repeated for four of the other parameters, the most significant pathways were compiled. They included three cancer pathways, the closely related ErbB signaling pathway, and dorsal‐ventral axis formation pathway. Source data are available for this figure at www.nature.com/msb.

Source data for Figure 7 [msb201052-sup-0007-SourceData-S7.zip]

Novel growth‐associated genes

Our study uncovered a number of genes, including developmentally active genes, not previously linked to the regulation of axon/dendrite growth. For example, activin receptor 1 (ACVR1) is involved in Xenopus axis formation (Hemmati‐Brivanlou and Melton, 1992) and downregulated during peripheral nerve regeneration (Morita et al, 1996). Both ACVR1 and ACVRL1 (and their isoforms) reduced neurite growth in our assays. IRF6 is also developmentally regulated during early development (Hatada et al, 1997) and its expression potentiated neurite growth in hippocampal neurons. CHKA, which strongly increased hippocampal neurite growth and neurite initiation, is an essential gene (Wu et al, 2008) that has been observed in nerve endings (synaptosomes) (Spanner and Ansell, 1979). Elucidating the mechanism through which CHKA influences neurite growth will be a fruitful future direction of study. SBK1 (SH3‐binding kinase 1) is a novel and mostly uncharacterized serine/threonine kinase that is highly expressed in the brain (Nara et al, 2001). Two mouse clones of this gene with different UTRs inhibited neurite growth in our assays. Another serine/threonine kinase, microtubule‐associated serine/threonine kinase‐like (MASTL), which inhibited neurite outgrowth, is known to interact with microtubules. A non‐protein kinase, neural leucine‐rich repeat 1 (LRRN1), is highly expressed in early somitic myoblasts (Haines et al, 2005) and inhibited neurite growth when overexpressed.

Neurite growth activators were very rare in the AGC arm of the protein kinase tree, with the exception of protein kinase N2 (PKN2), known to be activated by Rho GTP, and regulate cell cycle proteins such as Cdc25B (Schmidt et al, 2007). Another AGC kinase, ribosomal protein S6 kinase/mitogen and stress activated kinase/p90rsk (RPS6KA4) inhibited neurite growth. RPS6KA4 has been shown to be required for CREB and ATF phosphorylation in fibroblasts (Wiggin et al, 2002), as well as being involved in cAMP and insulin‐related proliferation (Coulonval et al, 2000). It has previously been implicated in regulating CAM‐mediated neurite growth (Wong et al, 1996).

Two protein phosphatases, both members of the myotubularin family, significantly decreased neurite growth. Myotubularin related 2 (MTMR2) is known to be involved in myelin neuropathies, but is currently thought to act in Schwann cells, not neurons (Bolis et al, 2005). SET‐binding factor 1 (SBF1) a pseudo‐protein phosphatase, has been seen to decrease proliferation after its forced expression in fibroblasts (Firestein and Cleary, 2001). We observed SBF1 to decrease neurite growth after overexpression in hippocampal neurons.

Other genes with strong effects have either less known about them, or no obvious connection to neural function or pathways. The serine threonine kinase protein serine kinase H1 is involved in Golgi maintenance (Brede et al, 2003) and its overexpression enhanced growth. CMPK1 (cytidine monophosphate UMP‐CMP kinase 1) a kinase involved in pyrimidine synthesis, inhibited neurite growth when overexpressed. The protein phosphatase protein phosphatase 1K was inhibitory, whereas protein phosphatase EF hand calcium‐binding domain 2 (PPEF2) and sphingomyelin phosphodiesterase 1 acid lysosomal (SMPD1) were strong growth promoters in our screen, but the underlying mechanisms are unclear.

Candidates for future study from genetic and pathway analyses

Our phylogenetic analysis identified several families, such as the chemokine ligands, protein tyrosine phosphatases, and a large group of non‐protein kinases, with members that strongly perturbed neurite outgrowth. Several of these were expected, especially the Calcineurins, Cdc14s, PI3K (Da Silva et al, 2005), and atypical PKCs. Other families were not expected to affect neurite growth, but did. These include a group of sugar kinases, NADK, and others. Not every member of this family had the same effect, but these enzymes generally potentiated neurite growth over control (surprising considering the neurons were growing on laminin, which strongly promotes neurite growth in the control state (Baronvanevercooren et al, 1982)). These non‐protein kinases will be interesting to study to determine their functions in neuronal process development.

Pathway analysis revealed that genes in several cancer pathways were highly active. This is not unexpected, as growth of neurons is tied to the same cascades that lead to proliferation of cancer cells (Nakagawara, 2001). These pathways dominate the analysis, either because many of their genes were selected for study, and have been previously studied, or that these signals (as activated by overexpression) are quite strong compared with other pathways. Finally, the signal transduction pathways ‘neuroactive ligand–receptor interaction’ and ‘cytokine–cytokine receptor interaction’ have members that produced strong effects in opposite directions (Supplementary Figure 4, purple blocks). The study of these complete pathways will be an important undertaking for future analysis.

Comparison with other screens

Recently, three groups have used RNAi to test the necessity of various genes on neurite development or cell migration, which is likely to involve overlapping mechanisms (Enomoto et al, 2001; Maness and Schachner, 2007). Loh et al (2008) used the SH‐SY5Y cell line in an siRNA screen of 750 kinases, many of which were also assayed in our experiments. Over half of the ‘hit’ siRNAs targeted genes in the tyrosine kinase or tyrosine kinase‐like families. The intersection of hit genes from Loh et al, and genes from our study is listed in Supplementary Table 3. ERBB2, RYK, PRKAB1 enhanced neurite growth after overexpression in our assays and reduced neurite length after knockdown in the Loh screen. Conversely, PRKCI, PKN1, PDPK1 NEK3, MASTL inhibited neurite growth after overexpression in our assays and enhanced axon growth after knockdown in the Loh screen. Overexpression/knockdown of other genes (CDK9, FES, STK38L) led to the same phenotype in both screens. An siRNA screen by Sepp et al (2008) used Drosophila primary neurons to study the disruption of neural phenotypes in a genome‐wide screen. They found that siRNAs for over 100 genes significantly perturbed neuronal phenotypes, leading to reduced elongation, excessive branching, loss of fasciculation, or blebbing (Sepp et al, 2008). The orthologs of four of the active genes from Sepp et al were also screened in our experiments. Two of these, ZAP70 and HTATIP, were weakly, but not significantly, inhibitory to neurite growth. Our analyses saw stronger effects (shorter neurites and more condensed chromatin) with the overexpression of LIMK2 and LRRN1 (see also Supplementary Table 3). Finally, Simpson et al (2008) performed an RNAi screen of kinase and phosphatases genes, assaying the ability of a monolayer of cells from a breast cancer cell line to heal after wounding. They found siRNAs targeting several genes that either potentiated or inhibited the migratory ability of the cells.

Comparing the results of these siRNA screens with each other, it is interesting to note that not a single gene appeared in all three. In fact, only three genes were hits in two of the three screens: BCAR3, LIMK1, and the JNK kinase MAP2K7. The lack of overlap in these RNAi screens supports the notion that such large‐scale knockdown experiments require special analysis of the raw data for proper interpretation (Sacher et al, 2008). In our own unpublished experiments using siRNA and shRNA approaches, we have found it much more difficult to knock down protein expression in primary neurons than in cell lines. This is consistent with reports using transgenic mice expressing shRNAs; knock down in the CNS is less effective than in other tissues (Sasaguri et al, 2009). The slow and variable loss of protein expression using RNAi in neurons makes it a problematic strategy in a screening context. A recent high‐content analysis of a genome‐wide RNAi screen found that the majority of siRNAs have off‐target effects (Collinet et al, 2010). Consequently, using RNAi in primary neurons as a screening approach will require much better validated libraries and assays that use novel approaches to allow protein expression to be knocked down before neurite growth starts (Davare et al, 2009). It is therefore critical to use alternative methods, such as overexpression.

Plasmid cDNA library

A glycerol stock cDNA library from the NIH MGC (Gerhard et al, 2004) in 96‐well format was purchased from Open Biosystems (ThermoFisher, Huntsville, AL) that included IRAT (human) and IRAV (mouse) clones. The library was replicated and the original and daughter plates stored in −80 degree freezers, sealed with foil tape (Corning Costar 6570, Lowell, MA). The Gene Ontology (http://amigo.geneontology.org/) was consulted to determine an up‐to‐date list of kinases and phosphatases. Gene ontologies for ‘Kinase Activity,’ and ‘Phosphoric Ester Hydrolase activity’ were used to generate a list of kinases, phosphatases, and interacting genes. A custom program written on the Qiagen BioRobot‐3000 (Germantown, MD) picked clones from the glycerol stock plates into the kinase‐phosphatase sublibrary, which totaled nine 96‐well plates. During the cherry‐picking process, a set of 10–12 plates were thawed, wiped, uncapped, and the foil cover carefully removed. Disposable tips were used to inoculate media in deep‐well 96‐well blocks. During the process, glycerol stocks for mCherry (Shu et al, 2006) were also picked and inoculated into specified control wells on the plate. Other wells were left empty, to facilitate non‐transfected controls and to allow other clones to be tested.

Plasmid preparation

QIAprep 96 Turbo BioRobot Kit (Qiagen 962141) was used to produce transfection quality plasmid. Briefly, two deep‐well blocks from the kit were filled with 1.4 ml Terrific Broth (Invitrogen 22711, Carlsbad, CA) with 150 μg/ml ampicillin (Invitrogen 11593) in each well. A 96‐pin replicator tool (Nalge Nunc 250520, Rochester, NY) was used to inoculate the thawed glycerol stock plate into fresh media. Plates were incubated for 20–24 h at 37°C, shaking at 300 r.p.m. The plates were spun down serially such that the pellets were overlaid and concentrated. The pellets were resuspended in Qiagen's ‘P1’ buffer, and the manufacturer's instructions were followed. Elution was performed at room temperature, with 120 μl of endotoxin‐free water.

Plasmid concentration was analyzed with NanoDrop spectrophotometer (Thermo Fisher, Wilmington, DE). Plasmid was generally purified at 300±100 ng/μl with an average 260/280 ratio of 2.8. If plasmid concentrations were below 300, plasmids were concentrated by isopropanol precipitation.

Kinase‐dead PRKACA mutant

The kinase‐dead mutant of human PRKACA was obtained by substituting lysine at position 73 for alanine (G → A substitution in AAG triplet coding for lysine). This was achieved by PCR using the QuickChangeII site‐directed Mutagenesis Kit (Strategene) with the following primers: K73A sense 5′‐GGAACCACTATGCCATGGAGATCCTCGACAAACA‐3′; K73A anti‐sense 5′‐TGTTTGTCGAGGATCTCCATGGCATAGTGGTTCC‐3′. The resulting construct was verified by restriction analysis using the newly created NcoI site (underlined in the sense primer) and sequencing.

Neuronal cell culture

Embryonic hippocampal culture has been described earlier (Goslin et al, 1998; Oliva et al, 2006). Briefly, adult pregnant Sprague‐Daley rats were euthanized by CO₂ and the E18 embryos were dissected in fresh Hibernate media (BrainBits, HE‐Ca 500, Springfield, IL) supplemented with B27 (Invitrogen 17504). Isolated hippocampi were transferred to Hibernate media without B27 and incubated for 15 min at 37°C with 0.25% Trypsin (Invitrogen 25300), in the presence of DNaseI at final concentration of 0.5 mg/ml (Sigma D5025). The tissue was then washed five times with the same medium supplemented with B27 and triturated until no clumps were visible (about 5–10 times). Dissociated neurons were counted and used for transfection during the next 2 h.

Transfected and non‐transfected neurons were grown in 96‐well plates (Perkin Elmer, 6005182, Waltham, MA) coated with 10 μg/ml Poly‐d lysine (Sigma P7886), and 10 μg/ml laminin (Sigma L2020). When cells were plated on PLL (Sigma P2636) alone as a substrate, the concentration was 1 mg/ml. Enriched neurobasal medium (ENB), modified from Meyer‐Franke et al (1995) included Neurobasal (Invitrogen 12348), penicillin/streptomycin, insulin (Sigma I6634 5 mg/ml), sodium pyruvate (1 mM), transferrin (Sigma T1147 100 mg/ml), BSA (Sigma A4161 100 mg/ml), progesterone (Sigma #P8783 60 ng/ml), putrescine (Sigma P7505 16 mg/ml), sodium selenite (Sigma S5261 40 ng/ml), triiodo‐thyronine (Sigma T6397, 1 × ), l‐glutamine (1 mM), N‐acetyl cysteine (Sigma A8199NAC, 5 mg/ml), and B27. Media was also supplemented with CNTF (10 ng/ml).

Transfection

Transfection of embryonic hippocampal neurons was accomplished using the Amaxa 96‐well ‘Shuttle’ nucleoporation system (Lonza, Walkserville, MD) following the manufacturer's instructions. Briefly, the Amaxa 96‐well nucleoporation plate was loaded with the mixture of 75 000 neurons in 20 μl of Amaxa transfection solution, and 400 ng of total DNA (including mCherry reporter with ratio plasmid to reporter 6:1) in a volume of 2 μl. The rat neuron transfection, ‘high‐efficiency’ program was used, and the neurons were recovered with 80 μl of ENB+HEPES (20 mM, Invitrogen 15630). Several control plasmids were used including pSport mCherry (reporter alone) and pSport CAT, a plasmid with no CMV promoter (chloramphenicol acetyltransferase gene). Cells were then plated at two different densities—8000 and 12 000 cells per well. Transfection efficiency was validated by cotransfecting mCherry with plasmids from the library containing myelin basic protein, vimentin, GFAP, and NCAM cDNAs and subsequent identification by antibody staining (data not shown).

Fix/stain

Neurons were incubated in 37°C 5% CO₂ incubators for 48 h. Plates were removed and immediately fixed with room temperature 4% PFA, 4% Sucrose in PBS by removing 50 μl of medium and underlaying 120 μl of fixative for 30 min. Plates were rinsed with PBS and stained with anti‐βIII‐tubulin (Aves Labs, TUJ) and Hoechst dye (Invitrogen 33342).

Imaging/tracing

Cellomics KineticScan Reader (Thermo Scientific Cellomics, Pittsburgh, PA) was used to automatically image nine fields in each well of the plates at × 10 magnification in three different channels for nuclear staining (Hoechst), neurite staining (βIII‐tubulin), and the reporter gene (mCherry). Images were traced automatically using the Neuronal Profiling Bioapplication version 2.x. For most of the downstream analysis, only the Transfected+ and Neurite+ populations were analyzed. Reported neurite measures are listed in Supplementary Figure 1. Other parameters were not analyzed because of high variability in the controls. Supplementary tables include complete results. Images and other data sets are available on request.

Overview

Neurons were transfected and plated in six replicate wells for each cDNA plasmid, at two different densities. Two independent experimental replicates were performed for each of the clones. The clones that changed the morphology of the neurites reproducibly were transfected a third time. Over 10 000 wells were imaged and analyzed, each with nine fields, and one field from each well was manually annotated to check the quality of the automated tracing. The annotation identified acute errors that led to the images being removed from the data set (mostly because of poor focus). The resulting data were normalized (see below) so that the resulting value indicated difference from control for each parameter.

Analysis

Bioinformatics