Transcriptional control in embryonic Drosophila midline guidance assessed through a whole genome approach
© Magalhães et al; licensee BioMed Central Ltd. 2007
Received: 22 December 2006
Accepted: 31 July 2007
Published: 31 July 2007
During the development of the Drosophila central nervous system the process of midline crossing is orchestrated by a number of guidance receptors and ligands. Many key axon guidance molecules have been identified in both invertebrates and vertebrates, but the transcriptional regulation of growth cone guidance remains largely unknown. It is established that translational regulation plays a role in midline crossing, and there are indications that transcriptional regulation is also involved. To investigate this issue, we conducted a genome-wide study of transcription in Drosophila embryos using wild type and a number of well-characterized Drosophila guidance mutants and transgenics. We also analyzed a previously published microarray time course of Drosophila embryonic development with an axon guidance focus.
Using hopach, a novel clustering method which is well suited to microarray data analysis, we identified groups of genes with similar expression patterns across guidance mutants and transgenics. We then systematically characterized the resulting clusters with respect to their relevance to axon guidance using two complementary controlled vocabularies: the Gene Ontology (GO) and anatomical annotations of the Atlas of Pattern of Gene Expression (APoGE) in situ hybridization database. The analysis indicates that regulation of gene expression does play a role in the process of axon guidance in Drosophila. We also find a strong link between axon guidance and hemocyte migration, a result that agrees with mounting evidence that axon guidance molecules are co-opted in vertebrate vascularization. Cell cyclin activity in the context of axon guidance is also suggested from our array data. RNA and protein expression patterns of cell cyclins in axon guidance mutants and transgenics support this possible link.
This study provides important insights into the regulation of axon guidance in vivo.
The process by which axons cross the midline during development of the Central Nervous System (CNS) in Drosophila is of great interest and has been the focus of much scientific research [1, 2]. After neuroblast delamination and cell fate decisions, axons undergo a journey that will eventually wire them to the appropriate targets. This journey includes a series of steps, one of which is the decision to cross (or not cross) the midline. Four key regulators of midline crossing are known: slit, robo, robo2 and comm. Growth cones in which the robo and robo2 receptors are present at the membrane sense the presence of the slit ligand and do not cross the midline [3, 4]. Conversely, growth cones without robo and robo2 at the membrane are not able to respond to the repulsive cue of slit and do cross the midline. robo levels at the growth cone membrane are regulated by comm.
A major question in midline crossing is how regulation occurs through gene expression, i.e., to what extent is transcription relevant in the control of growth cone pathways. It is well established that rapid local translation does occur [5, 6]. Some literature also indicates that transcription is relevant [7–9]. We designed this study of transcription during CNS development to i) find relevant genes in the axon guidance process, ii) elucidate the role of transcription regulation during midline crossing in Drosophila, and iii) determine whether any upstream changes are visible in axon guidance mutants. Our analysis uses two whole-genome microarray data sets: (1) axon guidance – Drosophila embryos, stage 15, of axon guidance Gain Of Function (GOF) and Loss Of Function (LOF) mutants, and (2) time course – a developmental wild type time course, consisting of 12 non-overlapping 1 hour collection points (previously published ). The axon guidance (AG) data was collected as part of this study and is published here for the first time. We have thoroughly reanalyzed the previously published time course (TC) data.
To identify clusters of co-expressed genes in each data set, we employ the hopach clustering methodology . Clustering algorithms have been successfully applied to microarray studies, but the systematic analysis of clustering output remains a major challenge. A significant obstacle is the lack of reproducibility, or even assessment of variability, of clustering results. Clustering output can vary dramatically from one repetition of an experiment to another, particularly with the small sample sizes typical in microarray experiments. The R statistical package hopach  uses a bootstrap resampling method to assess cluster variability, producing a measure of membership of each gene in every cluster. For this study, we adapted hopach to use these cluster membership values to assign genes to clusters, while allowing genes to belong to more than one cluster – a type of fuzzy clustering. This modification improves reproducibility and results in cluster output that is suitable for functional characterization. hopach is freely available and is easily used by biologists, providing a statistically sound framework for analysis of microarray data.
We characterize gene clusters with two complementary sources of annotation, the Gene Ontology (GO) , and the Atlas of Pattern of Gene Expression (APoGE), a component of the Berkeley Drosophila Genome Project . APoGE provides pictures of the expression pattern of each gene in the Drosophila embryo and a description of the anatomical structures in which the gene is expressed. A controlled vocabulary is used by individual curators (Amy Beaton and Volker Hartenstein), experts who assure consistency in each call. The goals and scope of APoGE are described in . Its use for characterization of microarray data has never been explored. Together, GO and APoGE provide a rich, multi-dimensional vocabulary for annotating hopach clusters. In this study, we analyze clusters in which genes with terms related to axon guidance are significantly over represented. Querying the APoGE in situ database for genes expressed with specific spatial and temporal patterns provides a means to further explore the axon guidance clusters.
Applying this approach, we identify striking changes in gene expression across GOF/LOF, as well as groups of axon guidance genes co-expressed during wild type development. These findings suggest that regulation of gene expression does in fact play a role in midline crossing. In addition, our analysis indicates that axon guidance genes may be involved in the process of hemocyte migration, and that cell cyclins are active during midline crossing. These observations are confirmed through independent RNA and protein expression studies.
Results and discussion
Transcriptional Control in Axon Guidance Mutants
Significant Clusters for Relevant Controlled Vocabulary Terms in Axon Guidance Data
Significant Clusters for Each Term in AG Data
Querying APoGE for Cluster AG164 reveals genes with hemocyte expression, including Dgk, pyd, 103E12, CG8780, CG11100, CG18549 and CG32354. Hemocytes are Drosophila blood cells, which migrate during embryonic development , with the direct involvement of the Vascular Endothelial Growth Factor (VEGF) pathway . Hemocytes are relevant in CNS development , and it is known that mutants for Pvr, a key molecule in the VEGF pathway, show a longitudinal phenotype and CNS condensation defects .
Transcriptional Control in Time Course
Applying hopach to the developmental data set first published in  results in 253 clusters. One of these (cluster TC139) contains 6626 non-differentially expressed genes (with a similar level of expression in all developmental stages). This result illustrates the ability of the hopach algorithm to place low-variability genes in a single large cluster, while forming distinct (much smaller) clusters for other patterns.
Significant Clusters for Relevant Controlled Vocabulary Terms in Time Course Data
Significant Clusters for Each Term in TC Data
A query of APoGE for genes in Clusters TC90 to TC97 with midline expression similar to comm, comm2 and robo2, yielded argos. The gene argos has been shown to be relevant in axon guidance in the visual system , which further strengthens the possibility of a role for argos in midline axon guidance. Sulfated (Sulf1) is expressed in cells that appear to be adjacent to comm2 and robo2 expressing cells. Sulf1 has not been previously implicated in axon guidance. It is our future project to investigate whether either argos or Sulf1 has a genetic interaction with comm, comm2 and robo2.
Time course clusters TC140, TC141 and TC142 are the most relevant clusters for the APoGE terms ("embryonic brain", "ventral nerve cord", "ventral midline"). They do not contain axon guidance genes, but they have many cyclins, including CycA, polo, cdc2, CycB3 CycE and ago. Cell cyclins are involved in cell division and in CNS development, playing an important role in neuroblast division. However, a role for cyclins has not been reported in post-mitotic neurons, the type of neurons involved in axon guidance. It is known that CycA and CycE mutants have severe defects in the commissures and longitudinal axon tracts of the CNS [21, 22], and Cdk5 controls some aspects of axon patterning in vivo . The cyclins CycA, cdc2 and CycB3 genetically interact with each other [24, 25] and are important in the earlier stages of intense cell division . Agreeing with this role, the TC data shows high expression early in development, but suggests other functions at later stages because i) there is sustained expression well past the intense initial cell divisions, and ii) the APoGE images show a similar pattern of expression for all cyclins for later stages.
Co-expression of Cell Cyclins in Axon Guidance and Time Course Data
Cell Cyclin Expression in Axon Guidance and Time Course
Cell Cyclin Expression in Axon Guidance and Time Course
Axon Guidance AG1-AG93 (%)
Time Course TC140-TC142 (%)
cell cyclin like
not cell cyclin like
n = 102
Cell Cyclin Protein and RNA Expression in Axon Guidance Mutants and Transgenics
The microarray data does not indicate a significant change in cell cyclin expression in axon guidance mutants. But in view of the robo GOF results, we decided to analyze the cyclin expression in the axon guidance mutants slit, robo, and robo12. Consistent with the microarray data, there are no visible quantitative cyclin expression changes in these mutants. Yet several major anatomical rearrangements in the cyclin expressing cells are evident. In wild type embryos, cyclin staining is visible from the dorsal side up to and at the midline. In the mutants for slit (5j) and robo12 (5k), there is a distinct gap in cyclin staining around the midline, and no dorsal cyclin-expressing cells are visible. At the midline, slit and robo12 mutants show WT-like cyclin staining. In robo mutants (5l) no apparent change is observed.
Midline crossing in Drosophila is a model of choice for axon guidance, and yet we are only beginning to understand the role of transcription in the control of axon guidance. Control does occur through protein translation. In such a spatially and temporally fine-tuned process, it is intuitive that transcription would also be involved. Previous work in flies and mice points in this direction; the role of transcription factors is also emerging as very relevant, as reviewed in . To investigate this topic we have undertaken a whole-genome expression study of mutants and transgenics of the key axon guidance genes. If control of midline crossing proceeds through transcription, the genetic perturbations induced in the mutants and transgenics should reveal clusters enriched for genes known to be involved in axon guidance. We also investigated the expression patterns of known axon guidance genes during normal development in a wild type microarray time course, using a previously published microarray data set.
We have found that the hopach package provides a robust, yet flexible, approach to clustering gene expression data. hopach is one of several hybrid algorithms  which are gaining popularity in the microarray community. The extension of hopach proposed here has the advantage that all genes can be assigned to a cluster, even when all pair-wise distances cannot be stored in computer memory. Also, the assignment of genes to clusters are based on bootstrap membership values, accounting directly for variability and making results reproducible .
To characterize clusters, we recommend integrating gene annotations from multiple complementary controlled vocabularies. In this study we used GO and APoGE, which combine the bulk of literature knowledge with nascent efforts towards a complete genetic expression characterization in Drosophila. When co-expression in microarray data is coupled with a shared RNA expression pattern and/or shared terms in the GO vocabulary, gene annotation via the "guilt by association" paradigm utilized so often in functional genomics becomes a much less tenuous method. For a specific cluster space, we used two independent data sets – a mutant/transgenic data set and a time course data set – and focused on genes which are co-expressed on both data sets. This approach is conceptually similar to techniques where two different axes are used, such as 2-D electrophoresis protein separation.
Our analysis revealed clear co-expression of axon guidance genes in both the GOF/LOF data and the developmental time course. This finding indicates that transcription does in fact play a role in control of midline crossing. We identified several clusters containing many genes known to be relevant in midline crossing, but also numerous less well characterized genes. Cluster AG132 is one of the most interesting clusters; genes such as dock, plexB, Cam, CadN, caps, spen, Fs(2)Ket, Con, RhoGAP19D, Rac2, and shot belong to this cluster. By querying APoGE for spatial/temporal expression patterns of interest, we were able to immediately identify several dozen candidates for further studies. We have selected a pool of candidates meeting both of the following criteria: 1) membership in a selected axon guidance cluster (as discussed in this paper), and 2) a known pattern of expression in the wild-type CNS, according to APoGE. In short, these genes are present in the right time and place to be involved in axon guidance, and further, their expression patterns are perturbed when the guidance process is disrupted. This candidate pool is thus a very attractive target for genetic analysis, the more so because many candidate genes are already represented by mutant stocks in publically available collections. Sensitized genetic backgrounds, such as the slit and robo heterozygotes, have been successfully used in the past to identify additional genes involved in pathfinding . We suggest using trans-heterozygote analysis to search for interactions between (1) candidate genes within the same cluster (such as AG132, AG164, and TC91), and (2) candidate genes and key axon guidance genes (slit, robo, etc.)
Our analysis suggests that known axon guidance genes may also be involved in hemocyte migration. Cluster AG164 contains a significant number of genes expressed in hemocytes, the Drosophila blood cells. Hemocytes migrate during development, with the VEGF pathway involved in the guidance process. We observed through RNA expression analysis that hemocyte migration does not proceed normally in slit and slit.robo – hemocytes are absent in the central segments of the embryo. In robo12, a double mutant for genes robo and robo2 (the known receptors for slit), hemocyte migration is normal, even though the CNS phenotype is the same as in slit and slit.robo. Hence, we speculate that slit may function independently of its robo receptors in this context. Blood vessel migration has been linked in mammals to the same molecular processes as axon guidance during recent studies . Both slit and robo have been implicated in several ways in the vascularization system of vertebrates [31, 32]. Since vascularization is a more recent evolutionary development than axon guidance [33, 30], it appears that some of the molecules involved in axon guidance may have been co-opted by vascularization mechanisms fairly early during evolution. Prompted by the observation that numerous cell cyclin genes are present in a few clusters in our microarray data, we analyzed RNA and protein patterns of cyclins in axon guidance mutants and transgenics. We examined several robo GOF (UR.mef, UR.yf, FRM), because they show the strongest overexpression in the cyclin cluster of the axon guidance microarray data. We also studied the LOF mutants of each of the key axon guidance genes slit, robo and robo12. The overexpression of robo is accompanied with an increase in RNA and protein levels of several cyclins. We also observe that cyclin expressing cells are dislocated in slit and in the double robo12 mutants. The cyclin expressing cells are visible away from the midline, exactly the opposite direction of the axons that are stalled at the midline in these two mutants. Our results suggest that coordinated expression of cyclins may play a role in timing the midline crossing process. The role of cell cyclins might be to adequately time the developmental state of the neurons and to assure that all the multiple signaling pathways for axon guidance are working in the proper time and location, similar to the role cyclins play in the checkpoints in cell division .
In conclusion, this study has shown that axon guidance is under transcriptional control. We have also observed that genes transcriptionally regulated in axon guidance mutants and transgenics are expressed in hemocytes, indicating that blood vessel migration may employ a mechanism involving axon guidance molecules. Furthermore, our results suggest that coordinated expression of cell cyclins may play a role in timing the midline crossing process. Together, these results provide new insights into the roles of axon guidance genes in vivo and demonstrate the validity of our whole genome approach as a method to study transcriptional networks in other biological systems.
We used 17 mutants and transgenic animals in this experiment. Flies were placed in cages at room temperature and plates were changed every hour. We collected embryos at late stage 15, and visually assessed before RNA extraction. For lethal mutations, the CyO-Kruppel GFP balancer was used  and the homozygous mutants were selected against fluorescence under a UV microscope. All mutants are available from Bloomington.
WT flies are Canton-S flies. comm embryos result from crossing CommΔe39 with commP . commΔe39 is a null allele. commP is a 900 bp deletion of the 5' UTR and the transcription start site. comm/WT was obtained by crossing commΔe39 with WT flies. The robo LOF is robo4 and is a point mutation . robo.WT was obtained by crossing robo4with WT flies. robo2x123 results from an excision of EP 2582 . robo12 is the robo, robo2 double mutant resulting from the recombination of robo4 with robo2x123 . slit2 comes from an EMS screen and is a point mutation . slit.WT results from crossing slit2 with WT flies. slit.robo was obtained by recombination of slit1 with robo4. UC is the comm GOF, and corresponds to a single copy of the UAS-comm construct crossed with the driver elavGal4 . UC2x is a stronger comm GOF and was obtained using two copies of UAS-comm crossed with the driver elavGal4 (this work). UR is a robo GOF that results from crossing UAS-robo flies with the postmitotic neural driver elavGal4 flies . UR.yf is a phenotypically stronger robo GOF obtained from the cross of UAS-robo-yf flies with the postmitotic driver elavGal4 . UR.mef designates embryos with robo overexpression in the muscles, produced by crossing UAS-robo-yf flies with the mesodermal driver mef2Gal4 . UR2 is a robo2 GOF obtained by crossing the construct UAS-robo2 with the elavGal4 driver . FRM is a robo GOF, obtained by crossing the driver elavGal4 with a UAS chimeric construct, which combines the robo cytoplasmic domain with the frazzled extracellular domain . UNC5 is the unc-5 GOF, obtained by crossing, a GS-element insert located upstream of unc-5, with the elavGal4 (GSunc5 kindly provided by John Thomas).
Developmental Time Course Stocks
The time course data set was published and details reported in . There are 12 samples of 3 replicates each of non-overlapping 1 hour collection, starting from 30 to 90 minutes and ending at 11.5 to 12.5 hours post egg laying.
DNA Microarrays and Target Preparation
RNA was extracted from the embryos using QIAGEN columns according to manufacturer recommendations. Embryonic RNA was hybridized to Affymetrix DrosGenome1 microarrays according to the standard Affymetrix protocols.
Expression measures were calculated using the RMA algorithm in the package rma, applied as indicated in , with the recommended quantile-quantile normalization procedure. The siggenes package was used to rank all genes based on differential expression (log2 ratio) relative to a control, which was RNA from WT flies. The top ranked most differentially expressed genes in each condition (mutant or transgenic) were selected until their number totaled 2000 for each experiment. These provided the initial seed data set for hierarchical clustering.
Clustering with hopach
hopach is a hybrid hierarchical clustering method specifically designed for analysis of microarray data. The algorithm builds a non-binary, hierarchical tree, but also assigns genes to clusters . The final level of the tree provides a meaningful ordered list in which nearby genes are similarly expressed. The first level of the tree with maximal average cluster homogeneity is identified and cluster labels are assigned based on the partitioning of genes in this level. Then, each gene's membership in every cluster is assessed using a non-parametric bootstrap method that fixes the cluster profiles, and for each of many resampled data sets reassigns every gene to the cluster whose profile is closest. The proportion in which a gene appears in each cluster is a measure of its cluster membership. Thus, the variability of the estimated cluster labels can be directly assessed from a single data set through this resampling approach.
We adapted the hopach methodology as follows:
1. Initial clusters
Apply the standard hopach algorithm with an appropriate choice of distance to a set of several thousand pre-selected genes. This produces initial clusters, each represented by a medoid gene.
2. Bootstrap extended clusters
Run the non-parametric bootstrap with the fixed medoids (from the previous step) as described in  to obtain a percentage membership value (between 0 and 1, with 1 meaning that a gene only belongs to that cluster) for each gene in every cluster. Reassign genes to clusters based on an appropriate threshold for bootstrap membership. Possible values include > 0.8 (genes belong to only one cluster, very homogenous clusters), > 0.5 (genes belong to only one cluster, less homogeneous) and > 0.3 (genes can be present in more than one cluster – fuzzy clustering). Note that this step is performed using all genes in the data set (not just selected genes), so that every gene is now assigned to a cluster.
3. Final clusters
Optionally, apply hopach again to every extended cluster to produce a new set of sub-clusters. In general, large clusters will be further divided at this step, whereas smaller ones will not. When the membership threshold in Step 2 is large enough that genes belong to only one cluster, this second application of hopach can be used to create a full hierarchical tree and a unique, final ordering of all genes. If this step is skipped, the bootstrap extended clusters are the final clusters.
4. Order within clusters
Reorder the genes in each cluster based on distance to the medoid – the medoid for each cluster is thus ranked one, genes with low ranks form the core of the cluster, and genes with high ranks are more peripheral.
This modified hopach method was applied to each data set separately, beginning with 2000 selected genes and including all genes on the Affymetrix DrosGenome1 microarray in Step 2. We used cosine-angle (uncentered correlation) distance (Step 1) and a membership threshold of 0.3 (Step 2).
The vocabularies for GO were downloaded from . APoGE vocabulary can be obtained upon request. We selected terms relevant to axon guidance: "axon guidance" (GO:0007411) in GO; "ventral nerve cord", "embryonic brain" and "ventral midline" in APoGE. Over representation in each cluster was evaluated using the hypergeometric distribution. Clusters of size 10 or larger were ordered based on hypergeometric p-value, which gives the probability of finding by chance alone the observed number of genes or more in that cluster annotated with the term (conditional on the total number of genes, the size of the cluster, and the frequency of the annotation over all genes). For each term, this produces one p-value for each cluster. For the most interesting clusters, we queried all the available pictures in APoGE, searching for patterns consistent with relevance to axon guidance mechanisms.
In Situ Hybridization and Antibody Staining
In situ hybridization were done to assess RNA expression of several specific genes. The experiment was conducted in 96 well plates and followed the protocol published in . Antibody staining of whole mount embryos followed the standard procedure outlined in [46, 29]. BP102 (a gift from Corey Goodman) is a specific antibody for the Drosophila embryo central nervous system. B102 was used at concentrations of 1:10. The antibodies against the cyclins CycA, CycB, CycB3 and Cdc2 were kindly provided by Patrick O'Farrel and were used at concentrations of 1:5, 1:5, 1:5 and 1:4. The HRP-conjugated secondary antibodies [Jackson Labs] were used at a 1:250 dilution.
We thank Corey S. Goodman, Beth Blankemeier, Eileen Li, António Coutinho, and Carlos Duarte. Tiago R. Magalhães was supported by a fellowship from the Fundação para a Ciência e a Tecnologia.
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