Plan
Comptes Rendus

Molecular biology and genetics
In vivo filtering of in vitro expression data reveals MyoD targets
[Le filtrage in vivo de données d'expression in vitro révèle des cibles de MyoD]
Comptes Rendus. Biologies, Volume 326 (2003) no. 10-11, pp. 1049-1065.

Résumés

A published set of downstream targets of MyoD defined in a well-controlled in vitro experiment was filtered for relevance to muscle regeneration using a 27-time-point in vivo murine regeneration series. Using interactive hierarchical and Bayes soft clustering, only a minority of the targets defined in vitro can be confirmed in vivo (∼50% of induced transcripts, and none of repressed transcripts). This approach provided strong support that 18 targets including of MyoD are biologically relevant during myoblast differentiation.

. Un ensemble publié de cibles en aval de MyoD, défini dans une expérience bien contrôlée in vivo a été filtrée par sa relation avec la régénération musculaire en utilisant une série temporelle de 27 points de régénération chez la souris. En utilisant des regroupements hiérarchiques ou bayésiens, seule une minorité des cibles définies in vitro peuvent être confirmées in vivo (∼50 % des transcrits induits et aucun des transcrits réprimés). Cette approche a fourni de fortes indications selon lesquelles 18 cibles (dont 13 nouvelles) de MyoD sont biologiquement importantes pendant la différenciation des myoblastes.

Métadonnées
Reçu le :
Accepté le :
Publié le :
DOI : 10.1016/j.crvi.2003.09.035
Keywords: clustering, expression profiling, microarray, muscle regeneration, MyoD
Mot clés : microréseaux, MyoD, profil d'expression, régénération musculaire, regroupement

Po Zhao 1 ; Jinwook Seo 2 ; Zuyi Wang 1, 3 ; Yue Wang 4 ; Ben Shneiderman 2 ; Eric P. Hoffman 1

1 Research Center for Genetic Medicine, Children's National Medical Center, Washington, DC 20010, USA
2 Department of Computer Science and Human-Computer Interaction Laboratory, Institute for Advanced Computer Studies and Institute for Systems Research, University of Maryland College Park, MD 20742, USA
3 Department of Electrical Engineering and Computer Science, The Catholic University of America, Washington, DC 20064, USA
4 Department of Electrical and Computer Engineering and Alexandria Research Institute, Virginia Polytechnic Institute and State University, Alexandria, VA 22314, USA
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     author = {Po Zhao and Jinwook Seo and Zuyi Wang and Yue Wang and Ben Shneiderman and Eric P. Hoffman},
     title = {In vivo filtering of in vitro expression data reveals {MyoD} targets},
     journal = {Comptes Rendus. Biologies},
     pages = {1049--1065},
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     doi = {10.1016/j.crvi.2003.09.035},
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Po Zhao; Jinwook Seo; Zuyi Wang; Yue Wang; Ben Shneiderman; Eric P. Hoffman. In vivo filtering of in vitro expression data reveals MyoD targets. Comptes Rendus. Biologies, Volume 326 (2003) no. 10-11, pp. 1049-1065. doi : 10.1016/j.crvi.2003.09.035. https://comptes-rendus.academie-sciences.fr/biologies/articles/10.1016/j.crvi.2003.09.035/

Version originale du texte intégral

1 Introduction

Muscle differentiation has been widely used as a model system for both embryonic development, and post-natal regeneration of tissues. Both processes require the expression of myogenic regulatory factors (MRFs), transcription factors that control the staged induction of genes important for interpretation of positional cues, proliferation, and differentiation of myogenic cells. To date, most MRFs are transcriptional factors of the basic helix-loop-helix family, with MyoD being the prototypical member, and the most extensively studied. All MRFs recognize a downstream target promoter motif, called the ‘E-box’, that share the consensus sequence of CANNTG. Most basic helix-loop-helix transcription factors function as heterodimers, and transcription regulation by MyoD has been extensively studied with regards to its binding partners (E-proteins, MEF2, Id proteins, Twist), binding sites (E-boxes), protein modulation (acetylation, phosphorylation), and upstream signaling molecules during embryonic myogenesis [1–12]. Although MyoD is important in embyonic myogenesis and postnatal muscle regeneration, there are very few known downstream gene targets of MyoD.

Experimental in vitro systems using inducible transcription factor or signaling molecule constructs in cultured cells are a mainstay of molecular biology. The emergence of expression profiling with microarrays has led to a powerful experimental design where expression profiling is done immediately after induction of the transcription factor to identify potential downstream targets across much of the genome [13–15]. The candidate targets derived from expression profiling can be compared to existing knowledge of previously characterized downstream targets (positive control), with follow up studies using gel shifts, chromatin immunoprecipitation assays, and promoter construct assays to confirm the novel downstream targets.

It is clear that in vitro experimental systems have been critical to our knowledge of transcriptional cascades, and to our knowledge of biology in general. However, the relative simplicity and tightly controlled nature of in vitro experimental systems comes with the liability of unknown relevance to the in vivo state. Particularly worrying is the fact that the majority of in vitro systems utilize over-expression constructs, leading to non-physiological levels of specific proteins in the cultured cell under study. Many transcription factors function as heterodimers, as does MyoD, and non-physiological levels of the transcription factor is likely to alter affinity and binding to specific promoters. Thus, it is expected that supra-physiological levels of MyoD would activate transcription of target genes that are not similarly regulated by MyoD in the in vivo state (e.g., promiscuous, non-physiologically-relevant binding targets). There is data in yeast, however, that suggests that overexpression may not induce as much pathway artifact as one might expect [16].

A recent study identified a large series of putative downstream targets of MyoD using embryonic fibroblast cultures transfected with an inducible MyoD expressing construct [13]. The study was very nicely designed, using cell cultures knocked-out for endogenous MyoD and Myf5 expression, and also used cycloheximide to block translation to limit the expression changes to those genes directly downstream of MyoD. Both spotted cDNA arrays (5103 elements), and Affymetrix murine mu6800 arrays (4144 probe sets) were used, and 83 genes found induced by MyoD, and 28 genes apparently repressed by MyoD (number was counted based on Ref. [13] and its supplemental material). Confirmation of induced expression by northern blot was done for a few of the 111 genes identified (M-cadherin, Rb1, Myogenin, Prdc, and Fstl). As with any such study, it becomes important to determine which of the potential downstream targets have relevance to either embryonic myogenesis, or post-natal muscle regeneration. Indeed, the authors found nearly as many genes down-regulated by MyoD as they did up-regulated genes. This is surprising given that MyoD is considered a transcriptional activator, and not a repressor, and suggests that there are likely confounding variables in in vitro experimental systems that are not currently understood, or able to be controlled.

We have previously reported an in vivo model for identification of downstream targets of MyoD by induction of staged muscle regeneration using cardiotoxin (CTX) injection [14]. We conducted a 6 time point expression profiling series, and found in vivo induction of MyoD expression at 12 hrs and 3 days after CTX injection. The 12 hr peak of MyoD expression was coordinately expressed with key repressor binding partners (Id), while the 3 day peak of MyoD expression correlated with expression of known downstream gene targets of MyoD binding and transcriptional activation (acetylcholine receptor, desmin, Myogenin). We studied coordinately expressed genes at the 3-day-time point as candidates for downstream gene targets of MyoD, found potential E box binding sites in a subset of the temporal gene cluster, and showed that both calpain 6 and Slug had E boxes in their promoters that gel shifted consistent with MyoD binding. We then went on to prove that Slug was a novel downstream target of MyoD by chromatin immunoprecipitation assays, and in vitro assays of Slug promoter constructs [14]. There were two major drawbacks to using our 6 time point series to systematically uncover large numbers of downstream targets of MyoD. First, the temporal resolution of our 6 time point series was relatively crude, and was unable to temporally distinguish induction of MyoD vs. induction of its downstream gene targets. Second, the in vivo experimental system is fundamentally ‘noisy’. Specifically, MyoD is known to carry out its function in myogenic cell lineages, however there are multitudes of other, additional cell types in regenerating muscle (vasculature and vascular remodeling, inflammatory cells, connective tissue remodeling, etc.). Thus, any temporal cluster of transcripts could derive from many different types of cells, other than the desired myoblasts.

Here, we overcame these two drawbacks using a novel experimental design. First, we increased the temporal resolution of our regeneration series to include 27 time points following CTX injection. This enabled temporal clustering with much higher resolution. To our knowledge, this is the most dense time series in any expression profiling study to date. Second, we took advantage of a recently published in vitro study, where an inducible MyoD construct was used to identify potential MyoD binding gene targets [13]. This nicely designed study included the use of MyoD/Myf5 knockout cell lines, inhibition of translation using cyclohexamide, and used both cDNA and Affymetrix arrays to identify up- and down-regulated genes. We filtered the in vitro candidate gene list against our 27 time point series in vivo temporal series to identify those genes that were likely bona fide, biologically relevant downstream targets of MyoD.

2 Materials and methods

2.1 Induction of staged muscle degeneration/regeneration

Staged skeletal muscle degeneration/regeneration was induced by injection of cardiotoxin (CTX) as previously described [14]. Two mice were injected in gastrocnemius muscles of both sides, and then sacrificed at each of the following time points: 0, 12h, 1d, 2d, 3d, 3.5d, 4d, 4.5d, 5d, 5.5d, 6d, 6.5d, 7d, 7.5d, 8d, 8.5d, 9d, 9.5d, 10d, 11d, 12d, 13d, 14d, 16d, 20d, 30d, and 40d.

At each time point, two of the four muscles showing the most extensive and consistent histological changes were used for expression profiling on individual GeneChips.

2.2 Expression profiling

Expression profiling using Affymetrix U74Av2 (12,488 probe sets) was done as previously described [14]. Quality control measures included >4-fold cRNA amplification (from total RNA/cDNA), scaling factors <2 to reach a whole-chip normalization of 800, and visual observation of hybridization patterns for chip defects (see http://microarray.cnmcresearch.org for further descriptions).

Gene lists at each time point are available on our web site (http://microarray.cnmcresearch.org, Programs in Genomic Applications). More detailed descriptions of methods for generating gene lists are also on the website (http://microarray.cnmcresearch.org, Programs in Genomic Application). All image files (.dat) and absolute analysis files (.chip) corresponding to each expression profile are also available on our database.

2.3 Data analysis

Probe set analysis and intra-chip normalization to a shared target intensity were derived from Microarray Suite version 5.0. For temporal clustering, the signal intensity values (absolute analyses) of the probe sets for all 54 profiles were loaded into GeneSpring, and each probe set was normalized to the median of its expression levels in all 54 profiles. Further hierarchical clustering analysis was limited to probe sets with at least ten present calls (18.5%) in all 54 profiles and p-value <0.05 for Kruskal–Wallis test, corrected with Benjamini and Hochberg False Discovery Rate. This was to eliminate the signal values of probe sets with 80% of ‘absent calls’, inclusion of which leads to considerable artefactual noise in temporal clustering, with random fluctuations at or below background hybridization levels showing ‘statistically significant’ clustering with user- or candidate gene-defined patterns. A hierarchical clustering algorithm using Pearson correlation was used to temporally group those probe sets based on their expression pattern across the 27 time points.

Clustering analysis of the expression of MyoD downstream targets used Hierarchical Clustering Explorer (HCE) and VISual Statistical Data Analyzer (VISDA) [17,18]. HCE input dataset was in vitro MyoD downstream target genes identified by Bergstrom et al. [13] that were also present on Affymetrix U74Av2 Genechips. Each probe set was normalized to the median of its expression levels in all 54 profiles. Probe sets used for VISDA analysis were filtered by requiring more than 2 present calls in any of the 54 profiles. The expression levels of each probe set across all time points were normalized to zero mean and one standard deviation to eliminate scale effect in clustering. Replicates were averaged for both HCE and VISDA analysis.

3 Results

3.1 Expression profiling of muscle degeneration/regeneration

Gastrocnemii of 54 mice were injected with CTX using a custom 10-needle manifold covering 1 cm2 [14], with four separate gastrocnemii harvested at each of 27 time points from two CTX-injected mice (54 mice, 108 gastrocnemii). Two histologically-matched muscles from each time point were expression profiled (Affymetrix U74Av2). We have previously shown that the use of inbred mice eliminates most inter-individual variability in expression profiles [14]. Thus, the replicates used for expression profiling were sometimes from the same mouse, and sometimes from different mice, but we have shown that this does not introduce a confounding variable in the temporal profiles [14].

We filtered data for probe sets performing well and with significantly sufficient hybridization patterns using filters. We required >18.5% ‘present calls’ for each gene (see Ref. [14]) (e.g. ⩾10 present calls in 54 U74Av2 profiles) and a statistically significant difference between any two time points (p value <0.05 using Kruskal–Wallis test, adjusted with Benjamini and Hochberg False Discovery Rate). About 37.5% of genes on the U74Av2 microarray survived the present call filter (4687 of 12488). Note that this filtering method reduces the sensitivity of the analysis for genes expressed at very low levels, but also increases the specificity of the analysis such that poorly performing probe sets with poor signal-to-noise ratios (SNRs) are not included in further analysis. Signal intensity probe set values for all 54 profiles for the 4687 genes surviving the present call filter were then subjected to temporal clustering using GeneSpring software package.

3.2 Temporal clustering of a 27-time-point muscle regeneration series

We first used unsupervised hierarchical clustering to determine if the time series variable was the major variable in the 4687 genes 54 profiles, as opposed to technical variables and sources of noise (inter-animal, inter-muscle, etc.) (Appendix A). This analysis showed good grouping of adjacent time points, indicating that the time variable was the major variable in the study. There was some mixing of profiles in the later time points of regeneration, however this was to be expected due to the fact that most regeneration is known to be complete by 2 wks post-CTX injection, and we therefore expected relatively little difference between the later profiles.

We then used supervised hierarchical clustering based on the time series to define temporal clusters (coordinately regulated groups) of transcripts (Appendix B). This data agreed with our previous 6 time point report [14], but showed a larger number of temporal clusters due to the greater temporal resolution. General groups of genes involved in inflammation, myogenesis, and myofiber maturation (contractile proteins) are shown (Appendix B). There were also novel clusters induced very late in regeneration that were correlated with either slow twitch myofiber maturation, and likely tendon remodeling.

All further analysis of this data set was focused on the early time points coinciding with the two peaks of endogenous MyoD induction at 12 hrs and 3 days (Fig. 1A).

Fig. 1

Temporal profile of MyoD, and known and potential downstream target genes. A. Shown is the temporal profile of MyoD over the entire 27 time point series. MyoD shows biphasic induction only at very early time points following induction of regeneration by CTX. Error bars are derived from independent replicates at each time point. B. Shown is an expansion of the 0–8 day time point data, showing more clearly the expression pattern of MyoD with error bars. C. Shown is a well-characterized downstream target of MyoD, Slug, as we have previously described [14]. The temporal pattern of Slug expression is consistent with being downstream of MyoD after the second phase (3 day) of MyoD expression. D. Shown is an example of one of the potential novel downstream targets of MyoD (Hes6) determined by an in vitro experimental system [13]. This potential candidate shows a pattern consistent with activation by MyoD at the second phase of induction (3 day time point). E. Shown is an example of a potential novel downstream target of MyoD (Bin1) identified by in vitro profiling [13] that does not fit with our in vivo temporal data. This gene was induced by MyoD in vitro, yet shows a pattern that is not consistent with activation by MyoD in vivo. This could be due to an embryonic-specific induction, or due to non-relevance to the in vivo state.

3.3 In vivo expression of in vitro MyoD downstream targets

Bergstrom et al. [13] studied direct downstream targets of MyoD in vitro by inducing exogenous MyoD expression while blocking new protein synthesis in MyoD-/-/Myf5-/- mouse embryo fibroblasts. By using cDNA arrays and Affymetrix Genechips, Bergstrom et al. identified 83 genes up-regulated by MyoD and 28 genes down-regulated by MyoD; each of these 111 was deemed a candidate for direct downstream targets of MyoD binding to each promoter.

We sought to define the subset of the 111 potential in vitro targets of MyoD that were biologically relevant for muscle regeneration. This was done by filtering the 111 putative targets against the 27-time-point in vivo muscle regeneration expression profile series. Three different approaches were used: (1) hierarchical clustering using correlation coefficients; (2) hierarchical clustering using the interactive HCE program to visualize temporal groupings of the 111 genes [17]; and (3). Bayes soft clustering using the VISDA program [18]. The latter is a statistically-principled approach of the entire 27-time-point data series, with subsequent fitting of the 111 genes into statistically-defined gene clusters.

Of the 83 up-regulated and 28 down-regulated genes, 73/83 and 25/28 were represented by probe sets on the U74Av2 chip, respectively. We then used hierarchical clustering algorithm (Pearson correlation) to cluster increased and decreased genes, respectively (Appendices C, D). We tested the up-regulated in vitro data set against the 27 time point series, and ran both the spotted cDNA and Affymetrix data of Bergstrom et al. separately (Appendix C). The data is seen to resolve into temporal clusters, two of which map well with the induction of MyoD during muscle regeneration (Fig. 1).

Specifically, both the spotted cDNA candidates, and the Affymetrix candidates show a temporal cluster of approximately 25% of genes at the 12 hr time point (corresponding to the 12 hr peak of MyoD expression in vivo), and another 25% map to a second temporal cluster at 3 days (corresponding to the 3 day peak of MyoD expression in vivo). The remaining 50% of genes show expression patterns that do not correlate with expression of MyoD during muscle regeneration in vivo. This analysis suggests that about 50% of the genes identified as potential downstream targets of MyoD in vitro are biologically relevant for muscle regeneration in vivo. In Fig. 1, we show expansion of the time point graph to the 0–8 day time frame for MyoD (Fig. 1B), and for examples of mRNAs showing biological relevance in regeneration (Slug, Fig. 1C; Hes6 Fig. 1D), and one mRNA not showing relevance (Bin1, Fig. 1E).

We conducted a similar analysis of genes that were found to be down-regulated in the Bergstrom et al. data set. Temporal clustering showed that very few of the down-regulated in vitro-defined MyoD targets corresponded to the temporal expression pattern of MyoD in vivo (Appendix D). There was no visually apparent down-regulated gene clusters at either the 12 hr or 3 day time point (Appendix D). This suggests that few, if any, of the genes down-regulated by MyoD in vitro are biologically relevant to muscle regeneration in vivo. This is perhaps expected, as MyoD has been shown to act as a transcriptional activator, and not a repressor. Thus, down-regulated genes may be an artifact of the in vitro assay system.

3.4 Increasing the confidence of temporal clustering: application of HCE to cluster discovery

There are many complementary methods emerging for cluster discovery in microarray data. One method to increase the resolution and sensitivity of temporal cluster discovery is through visual interaction with the program. In this manner, we can ‘supervise’ the clustering based on our expectation of clusters conforming to the two peaks of MyoD expression (12 h, 3 days; Fig. 1). The Hierarchical Clustering Explorer program (HCE) permits rapid user manipulation of data analysis, so that different computational algorithms and stringencies of analysis can be employed in an interactive manner, optimizing the sought-after clusters using human visualization [17].

We applied HCE to the up-regulated and down-regulated in vitro targets, and searched for the most visually satisfying 12 h and 3 day clusters (Fig. 2A, Table 1, Appendix E). Complete linkage and average linkage methods were used to cluster up- and down- regulated genes respectively to achieve a better visualization of clusters. HCE was unable to identify visually satisfying clustering of the down-regulated cluster, as was the case with the GeneSpring analyses (Fig. 2B; Appendix F). However, HCE-filtered data showed the same 12-h and 3-day temporal clusters, with considerable membership in the clusters shared with the default GeneSpring analyses (Table 1, Appendix E).

Fig. 2

MyoD downstream targets in vitro have different expression patterns in muscle regeneration as shown by HCE. Shown is the hierarchical clustering of the mean of two replicates each for 27 time points, showing downstream targets of MyoD in vitro, based on their expression in muscle regeneration in vivo. For up-regulated genes in vitro (panel A, hierarchical clustering using the complete linkage), three major expression patterns were identified: up-regulation with a peak between 12 h and day 3, up-regulation with a peak at day 3, and no up-regulation. The first two patterns correspond to 12 h peak and day 3 peak of MyoD expression respectively. Ambiguous branches do not show a clear 12 h or 3d upregulation pattern. For down-regulated genes in vitro (panel B, hierarchical clustering using the average linkage), expression of eleven genes was found increased, six genes were found down-regulated, five genes were not differentially regulated.

Table 1

Expression of in vitro upregulated MyoD targets in muscle regeneration and in HCE and VISDA cluster

GenBank1 UniGene Symbol Gene name FC2 TP3 HCE4 VISDA5
cluster cluster
Genes showing 3 day peak6
AJ245402 cdh15 Cadherin 15 gene +3.7 3d 3d 1
X13945 Lmyc-1 L-myc gene +5.4 3d 3d 1
AW048812 Mm.29549 Hes6 Hairy and enhancer of split 6 +8.3 3.5d 3d 1
X15784 Mm.16528 Myog Myogenin +14.4 3.5d 3d 1
M17640 Mm.4583 Chrna1 cholinergic receptor, nicotinic, alpha polypeptide 1 +9.6 3.5d 3d 1
AV248455 Mm.2810 Chrng cholinergic receptor, nicotinic, gamma polypeptide +45 3.5d 3d 1
L23423 Mm.179747 Itga7 integrin alpha 7 +2.1 3.5d 3d 1
U43900 Mm.3965 Stam signal transducing adaptor molecule 1 +2.8 3.5d 3d 1
L47600 Mm.632 Tnnt2 troponin T2, cardiac +30 3.5d 3d 1
M18779 Mm.1526 Myod1 myogenic differentiation 1 +3.6 3d Bi-ph 1
M27266 Mm.4848 Fyn Fyn proto-oncogene +3 3.5d Bi-ph 2
X15788 Mm.1272 Rapsn receptor-associated protein of the synapse, 43 kDa +1.3 3.5d Other 1
AW061260 Mm.23742 Nes Nestin +5 3.5d Other 1
U23921 Mm.4150 Osp94 osmotic stress protein 94 kDa +2.7 3.5d Other 1
U51014 Mm.69751 Pep4 peptidase 4 +1.7 3d 3d 10
U94423 Mm.87279 Mef2a myocyte enhancer factor 2A +2.1 3.5d 3d 10
AI842874 Mm.182692 Sh3glb1 SH3-domain GRB2-like B1 (endophilin) +1.6 3.5d 3d 10
M26391 Mm.304 Rb1 retinoblastoma 1 +1.8 3.5d 3d 10
Genes showing 12h-2d peak
M18779 Mm.1526 Myod1 myogenic differentiation 1 +2.1 12h Bi-ph 1
X54098 Mm.7362 Lmnb2 lamin B2 +2 12h 12h/amb 1
U54803 Lice cysteine protease +2.4 1d 12h/amb 1
M27266 Mm.4848 Fyn Fyn proto-oncogene +2 12h Bi-ph 2
M86183 Mm.7417 Ccnd3 cyclin D3 +2 1d Other 2
D14883 Mm.4261 KAI1 Kangai 1 +2.5 12h 12h/amb 4
AF096875 Mm.21389 Dio2 deiodinase, iodothyronine, type II +4.7 12h 12h/amb 5
M15832 Mm.738 Col4a1 procollagen, type IV, alpha 1 +2.3 12h Other 9
AB012611 Mm.45048 Stam2 signal transducing adaptor molecule 2 +2.4 12h 12h/amb 10
M31690 Mm.3217 Ass1 arginosuccinate synthetase 1 +2 2d 12h/amb 14
X98475 VASP vasodilator-stimulated phosphoprotein +2.8 12h Other 15
X14432 Mm.24096 Thbd thrombomodulin +12 12h 12h/amb 15
AF077861 Idb2 inhibitor of DNA binding 2 +6.4 12h 12h/amb 15
X61940 Mm.2404 Dusp1 Dual specificity phosphatase 1 +2.2 12h 12h/amb 16
Z19521 Mm.3213 Ldlr low density lipoprotein receptor +2.4 12h 12h/amb 16
AJ250723 Mm.38450 Sept9 Septin 9 +2.6 12h 12h/amb 18
X61172 Mm.2433 Man2a1 Mannosidase 2, alpha 1 +3 12h 12h/amb 18
U03434 Mm.14926 Atp7a ATPase, Cu++ transporting, alpha polypeptide +2.7 2d 12h/amb 18
D26532 Mm.4081 Runx1 Runt related transcription factor 1 +13.3 12h 12h/amb 18
AA791012 Mm.29766 Gmfg Glia maturation factor, gamma +6.4 12h 12h/amb 18
AW215736 Mm.181860 2310057H16Rik Ricken cDNA 2310057H16 gene +7.3 12h 12h/amb 18

1 GeneBank accession number used by Affymetrix probe sets.

2 FC-fold change.

3 TP-time point.

4 HCE cluster is determined by visualization of HCE output (Fig. 6A). 3d – 3-day cluster; 12h – 12-h cluster; amb – ambiguous branches; Bi-ph – Bi-phasic.

5 VISDA clusters are as indicated in Fig. 7.

6 Genes are grouped by visual examination of the expression pattern in GeneSpring.

Taking both the GeneSpring and HCE output together, three major gene expression patterns were identified in muscle regeneration: up-regulation between 12 h and 3 day post-injection (20/73 genes, corresponding the 12 h peak of MyoD), up-regulation around 3 day post injection (17/73 genes, corresponding to the 3 day peak of MyoD), and no up-regulation (36/73 genes). Thus, a total 37 (50.7%) genes showed increased in vivo expression corresponding to upregulation of MyoD expression in vitro.

Of the 25 MyoD direct down-regulated targets in Bergstrom's study, only six of them showed decreased expression in the muscle regeneration temporal series. Five of the genes did not show differential expression. In contrast, eleven of the genes showed up-regulation at various time points (Appendix F). Three of the genes were called ‘absent’ in all profiles, and thus not informative.

3.5 Clustering analysis of in vitro MyoD downstream targets using VISDA

A major disadvantage of the hierarchical clustering methods described above is that there is little statistical support for the data output. Thus, identification of clusters by supervised temporal hierarchical clustering can be considered a relatively non-robust method of data analysis. We therefore applied an alternative, more robust statistical analysis of the data using a recently described program, VISDA [18]. VISDA interrogates large data sets by first reducing the dimensionality of the temporal series by discriminatory data project and subsequent cluster decomposition by soft data clustering. This method of fitting a statistical model to the data set allows the selection of the number of clusters and definition of clusters statistically (cluster discovery). The data is then partitioned into the supported number of clusters, and further partitioned if desired.

Application of VISDA to the 54 profiles (probe sets were filtered by requiring more than 2 present calls in any of the profiles) suggested support for a minimum of 18 temporal clusters (Fig. 3), with membership in each cluster ranging from 167 to 664 genes. VISDA detected the 3 day temporal cluster, and at 18 clusters split this into two clusters (clusters 1 and 10; Fig. 3). Mapping of the Bergstrom et al. upregulated genes to the 18 VISDA clusters showed many to map to clusters 1 and 10, and these same genes showed visual segregation to the 3 day time point peak of MyoD by hierarchical clustering (Table 1, Appendix G). The concordance of the GeneSpring, HCE, and VISDA analysis for 18 potential downstream targets at the 3 day peak of MyoD expression (VISDA clusters 1 and 10) provides strong support for these 18 genes representing biologically relevant downstream targets of MyoD involved in myoblast differentiation during muscle regeneration. These 18 genes were Cadherin 15 (cdh15), L-myc, Hairy and enhancer of split 6 (Hes6), Myogenin, cholinergic receptor α1 (Chrna1), cholinergic receptor gamma (Chrng), integrin α7 (Itga7), signal transducing adaptor molecule 1 (Stam), cardiac troponin T2 (Tnnt2), MyoD, Fyn, receptor-associated protein of the synapse (Rapsn), Nestin, osmotic stress protein 94 kDa (Osp94), peptidase 4 (Pep4), myocyte enhancer factor 2A (Mef2a), SH3-domain GRB2-like B1 (Sh3glb1), and retinoblastoma 1(Rb1). Only myogenin, MyoD, Chrna1, Chrng, and Itga 8 have been previously characterized.

Fig. 3

Clustering analysis using VISDA. Shown is the 18 temporal clusters derived from VISDA analysis. Number of genes in each cluster was listed in the table.

The down-regulated genes did not show good mapping to any specific VISDA cluster (Appendix F). Thus, VISDA was unable to provide any independent support for a down-regulated MyoD cluster corresponding to the peaks of MyoD expression. This data is consistent with the GeneSpring, and HCE data presented above, and also current knowledge of MyoD as a transcriptional activator, rather than a repressor.

4 Discussion

In vitro systems (e.g., cell culture) avoid many of the problems intrinsic to in vivo experimental systems. For example, cell culture systems used to dissect myogenic pathways typically employ pure cell populations, with synchronized response to the transcription factor under study (e.g., [13]). While the in vitro experimental systems can be elegantly controlled, there are many issues which question relevance to the in vivo embryonic development or muscle regeneration. For example, during embryogenesis, cells in the dermatomyotome become committed to the muscle lineage via positional cues provided by Wnt and Shh expression in neighboring cells. These positional cues are clearly lacking from the in vitro system. Likely, in muscle regeneration, myoblasts only proliferate and differentiate in the context of a pre-existing myofiber basal lamina. Indeed, the presence of a defective myofiber basal lamina precludes muscle regeneration, as evidenced by the failure of regeneration in both mice and humans lacking the basal lamina merosin protein (laminin alpha2) [19,20]. Thus, downstream targets of MyoD defined in vitro, may, or may not, have any relevance to muscle development or regeneration in vivo.

In this report, we combine the advantages of the in vitro experimental system, with an in vivo filter for biological relevance. Specifically, we used the excellent in vitro data set of potential MyoD downstream targets recently published [13], and filtered this against a 27-time-point muscle regeneration series described here. The identification of the subset of in vitro downstream targets of MyoD that were biologically relevant for muscle regeneration was done by a series of data visualization and statistical methods, including hierarchical clustering (both supervised and unsupervised), HCE, and VISDA Bayes soft clustering. Through these analyses, we provide support for approximately half of the candidates for transcriptional activation by MyoD reported by Bergstrom et al. (Fig. 2, Table 1, Appendices C–E). We provide the strongest support for 18 downstream candidates, that we show by all methods to map to the second peak of MyoD expression at 3 days after CTX injection. These 18 genes are: cdh15, L-myc, Hes, Myogenin, Chrna1, Chrng, Itga7, Stam, cardiac troponin T2, MyoD, Fyn, Rapsn, Nestin, Osp94, Pep4, Mef2a, Sh3glb1, and Rb1. Of these 18, Cdh15, L-myc, Hes6, Stam, Tnnt2, Fyn, Rapsn, Nestin, Osp94, Pep4, Mef2a, Sh3glb1 and Rb1 are ‘novel’ downstream targets of MyoD that have not been previously characterized outside of the expression profiling papers discussed here [13,14], this paper. It is out side the scope of this current manuscript to describe the potential functional roles of each of these novel downstream MyoD targets in muscle regeneration. This will also require further functional and molecular studies.

Importantly, we were unable to provide support for any of the potential down-regulated MyoD targets described by Bergstrom et al. [13]. MyoD is well characterized as a transcriptional activator, where binding to E box sequences in target promoters leads to transcriptional induction of the downstream target gene. Thus, it was unexpected that a series of down-regulated transcripts were identified after in vitro induction of MyoD expression [13]. As translation was blocked by cycloheximide, these down-regulated transcripts suggested that MyoD might also act as a transcriptional repressor, perhaps by binding of specific partner proteins in a heterodimer [13]. If the down-regulated transcripts were biologically relevant, then we would have seen these transcripts temporally cluster into groups that were down-regulated at one of the two peaks of MyoD expression in vivo (12 h, or 3 days). None of our methods (hierarchical clustering, HCE, VISDA) identified any such cluster.

This data suggests that the negatively regulated candidate genes defined by in vitro studies are likely experimental artifacts, and that MyoD does indeed function solely as a transcriptional activator. Alternatively, the downstream targets could be so selective that they do not cluster effectively. Also, downregulated genes at the 12-h time point correspond to most myofibrillar proteins, as myofibers are being actively destroyed by macrophages at this time point. Thus, these few potential down-regulated targets may be poorly resolved by our in vivo data.

In conclusion, we have presented a novel experimental approach that combines the advantages of both the in vivo and in vitro experimental systems of studying muscle transcriptional cascades and regeneration. We propose that a similar combined data filtering approach can be applied to any tissue or cell type where similar stimuli can be provided both in vitro and in vivo.

Acknowledgements

Supported by grants from NIH (3RO1 NS29525-09; UO1 HL66614-01; NO1-NS-1-2339) and the Muscular Dystrophy Association USA.

Appendix A

Shown in Fig. 4 is unsupervised hierarchical clustering of 54 profiles (GeneSpring). Expression profiles are clustered based on similarity. Four clusters can be distinguished, each of which correlates with different general stages of muscle regeneration. Cluster 1 corresponds to the stage of normal muscle and nearly completely regenerated muscle. Cluster 2 corresponds to the early stage with inflammation as the major event. Cluster 3 corresponds to the early muscle regenerating stage. Cluster 4 corresponds to the late muscle regenerating stage. While most profiles are clustered with appropriate periods of time, some discordance is seen between 5.5 d and 14 d. This may be due to non-synchronized satellite cell activation and differentiation, or more closely related profiles over this time period.

Fig. 4

Unsupervised hierarchical clustering shows that the time series is the dominant variable relative to technical variables in the 27-time-point expression series.

Appendix B

Shown in Fig. 5 is the dendrogram derived from the temporal hierarchical clustering algorithm (GeneSpring). Each row represents a time point of the time series averaged over two replicates at each time point (54 U74A profiles). Each vertical colored bar (lower part of figure) represents a single probe set (gene) in the profile (4687 total). Vertical bars in red color indicate over-expression relative to the reference value, which is the median of the expression levels of the corresponding gene in all 54 profiles. Blue color represents under-expression relative to the median. The intensity of the color represents the confidence of the data, which generally correlates with the fold changes relative to the reference value. This algorithm clusters genes with similar expression patterns based on correlation coefficients. The distance between two genes on the dendrogram reflects the temporal expression profile similarity.

Fig. 5

Supervised (time series) hierarchical clustering of genes over a 27-time-point temporal series of muscle degeneration/regeneration expression profiles.

Specific functional clusters are indicated (early inflammation cluster containing macrophage marker genes, myogenesis cluster, and myofibrillogenesis cluster).

Appendix C

Shown in Fig. 6 are potential downstream targets of MyoD defined by an in vitro cell-based assay system, defined by expression profiling with spotted cDNA arrays (Panel A), or Affymetrix arrays (Panel B), with expression of each gene studied as a function of muscle regeneration in vivo. Using Pearson's correlation coefficients for nearest neighbor analysis, the data from both the cDNA and Affymetrix gene sets resolves into at least three visually apparent temporal clusters. One cluster shows little expression at time 0, but with rapid increase in transcription at 12 hrs (12-h cluster). A second cluster shows induction of transcription at 3 days. These two clusters correspond to the two peaks of MyoD expression in vivo during muscle regeneration (see Fig. 3). This suggests that these two up-regulated gene clusters are likely biologically relevant to muscle regeneration, as their induction by MyoD in vitro correlates with temporally correct induction by MyoD in vivo. This also suggests that the majority of genes that are not contained in these two clusters are not biologically relevant to muscle regeneration.

Fig. 6

Filtering of genes up-regulated by MyoD in in vitro, against a 27-time-point in vivo regeneration series.

Appendix D

Shown in Fig. 7 is the visualization of genes showing downregulation by in vitro induction of MyoD, detected using both spotted cDNA arrays (Panel A), and Affymetrix arrays (Panel B) [13]. Temporal hierarchical clustering of these 22 genes/probe sets using Pearson's correlation coefficient shows poor resolution of the data into temporal clusters (compare to Fig. 4). Very few of the down-regulated genes show similar down-regulation at either the 12-h or 3-day peak of MyoD expression. This analysis suggests that few of the down-regulated gene targets for MyoD are biologically relevant to muscle regeneration in vivo.

Fig. 7

Genes showing down-regulation by MyoD in vitro do not cluster well with in vivo data.

Appendix E

See Table 2.

Table 2

Expression of in vitro upregulated MyoD targets in muscle regeneration and in HCE and VISDA cluster1

GenBank UniGene Symbol Gene name FC TP HCE VISDA
cluster cluster
Genes not showing upregulation at 12h to 3d
D76432 Mm.3929 Zfx1ha zinc finger homeobox 1a Other 3
X04647 Mm.181021 Col4a2 procollagen, type IV, alpha 2 Other 3
AB026432 Mm.29623 Ddb1 damage specific DNA binding protein 1 (127 kDa) Other 3
X58196 H19 H19 Other 3
U69176 Mm.2399 Lama4 laminin, alpha 4 3
M14537 Mm.86425 Chrnb1 cholinergic receptor, nicotinic, beta polypeptide 1 (muscle) Other 3
Y17138 Mm.686 Actc1 actin, alpha, cardiac Other 4
X80903 Mm.4875 Dll1 delta-like 1 homolog (Drosophila) 12h/amb 4
M12347 Mm.89137 Acta1 actin, alpha 1, skeletal muscle 12h/amb 5
X59382 Mm.2766 Pva parvalbumin Other 5
L02526 Mm.1059 Map2k1 mitogen activated protein kinase kinase 1 12h/amb 6
U07617 Mm.6900 Grb2 growth factor receptor bound protein 2 12h/amb 7
AA023625 Mm.9213 Esr1 estrogen receptor 1 (alpha) 12h/amb 7
U62021 Mm.5142 Nptx1 neuronal pentraxin 1 Other 7
U68267 Mm.125614 Mybph myosin binding protein H Other 9
AA688834 Mm.17307 Rnpc2 ESTs, Weakly similar to SIG41 [M.musculus] 12h/amb 10
U64828 Mm.2028 Ncoa1 nuclear receptor coactivator 1 10
D14340 Mm.4342 Tjp1 tight junction protein 1 Other 10
AI835917 Mm.181309 2010003F24Rik RIKEN cDNA 2010003F24 gene Other 11
L12447 Mm.578 Igfbp5 insulin-like growth factor binding protein 5 Other 12
U13174 Mm.4168 Slc12a2 solute carrier family 12, member 2 Other 12
U43512 Mm.7524 Dag1 dystroglycan 1 Other 12
U60884 Mm.4383 Bin1 myc box dependent interacting protein 1 Other 13
AI852672 Mm.26550 Pfkm phosphofructokinase, muscle Other 13
AJ131021 Mm.32033 Rps6ka2 ribosomal protein S6 kinase, 90kD, polypeptide 2 13
J04992 Mm.39469 Tnni2 troponin I, skeletal, fast 2 Other 13
U15541 Mm.3841 Cox8b cytochrome c oxidase, subunit VIIIb Other 13
M74570 Mm.4514 Aldh1a1 alcohol dehydrogenase family 1, subfamily A1 Other 13
D38216 Mm.4519 Ryr1 ryanodine receptor 1, skeletal muscle Other 13
L04966 Mm.22660 Rab18 RAB18, member RAS oncogene family Other 13
X99963 Mm.687 Arhb aplysia ras-related homolog B (RhoB) 12h/amb 14
AW122933 Mm.28107 Enpp2 ectonucleotide pyrophosphatase/phosphodiesterase 2 Other 17
D29016 Mm.3204 Fdft1 farnesyl diphosphate farnesyl transferase 1 Other 17
Z38015 Mm.6529 Dm15 dystrophia myotonica kinase, B15 Other 17
Genes absent
AF026799 Mm.17911 Lgals6 lectin, galactose binding, soluble 6
M17031 Mm.22845 Src Rous sarcoma oncogene
Genes not on U74Av2
AI430906 Mm.179671 Tns tensin
AI326148 Mm.725 Rpl7a ribosomal protein L7a
AI323868 Mm.55 Rfp ret finger protein
AI413515 Mm.14097 Tapbp TAP binding protein
AI451071 Mm.25594 Prkar2b protein kinase, cAMP dependent regulatory, type II beta
AI448706 Mm.105023 Spnb2 beta-spectrin 2, non-erythrocytic
W51433
W97775 Mm.39046 1110055E19Rik RIKEN cDNA 1110055E19 gene
AA059763 Similar to tubulin beta 2
W12919 Mm.141443 Ldh1 lactate dehydrogenase 1, A chain

1 See Table 1 for interpretation of symbol and grouping.

Appendix F

See Table 3.

Table 3

Expression of in vitro down-regulated MyoD targets in muscle regeneration and in HCE and VISDA cluster1

GenBank UniGene Symbol Gene name FC TP VISDA
cluster
Genes showing downregulatation
U03419 Mm.22621 Col1a1 procollagen, type I, alpha 1 −2.7 1d 2
X98055 Mm.2746 Gstt1 glutathione S-transferase, theta 1 −5 12h 6
L47480 BMP4 bone morphogenic protein 4 −2 3d 8
Y00516 Mm.16763 Aldo1 aldolase 1, A isoform −3 2d 8
AI852765 Mm.24193 Gpc1 glypican 1 −5 2d 12
D29639 Mm.2491 Hadhsc L-3-hydroxyacyl-Coenzyme A dehydrogenase, short chain −9 2d 13
Genes not showing downregulation
M92380 Mm.22763 Fstl Follistatin-like +3 3d 3
AA763466 Mm.22621 Col1a1 procollagen, type I, alpha 1 +2.5 5.5d 3
D13664 Mm.10681 Osf-2 osteoblast specific factor 2 (fasciclin I-like) +33 5.5d 3
AF053943 Mm.4665 Aebp1 AE binding protein 1 +2.8 6.5d 3
AI848798 Mm.21549 Cryac crystallin, alpha C +2.2 12h 5
AA638816 Mm.368 Psmb4 proteasome (prosome, macropain) subunit, beta type 4 NC 7
AB011030 Mm.25760 Prdc protein related to DAN and cerberus NC 7
U62021 NPTX1 neuronal pentraxin 1 NC 7
U65636 Mm.368 Psmb4 proteasome (prosome, macropain) subunit, beta type 4 NC 11
X57024 Mm.10600 Glud glutamate dehydrogenase NC 14
L19932 Mm.14455 Tgfbi transforming growth factor, beta induced, 68 kDa +26 2d 15
X14678 Mm.192224 Zfp36 zinc finger protein 36 +4.7 12h 15
X83601 Mm.4663 Ptx3 pentaxin related gene +7.6 12h 15
U49513 Mm.2271 Ccl9 chemokine (C-C motif) ligand 9 +45 1d 15
U27267 Mm.4660 Cxcl5 chemokine (C-X-C motif) ligand 5 +200 12h 15
L15429 Mm.856 Tm4sf1 transmembrane 4 superfamily member 1 +2 12h 16
Genes absent
X51468 Mm.2453 Smst somatostatin
AV145762 Mm.29564 Mmp2 matrix metalloproteinase 2
D30782 Mm.4791 Ereg epiregulin
Genes not on U74Av2
AI448727 Mm.33892 Copeb core promoter element binding protein
AI429718 Mm.9944 Semcap3 semaphorin cytoplasmic domain-associated protein 3A
Z25524 integrin associated protein

1 See Table 1 for interpretation of symbol and grouping.

Appendix G

The 18 VISDA clusters are shown in Fig. 8, with the mapping of each of the in vitro MyoD candidate target genes to the clusters. Panel A shows the Bergstrom et al. up-regulated genes, and Panel B shows the down-regulated genes.

Fig. 8

Mapping of potential in vitro down-stream targets of MyoD to VISDA clusters.

The clusters corresponding to the biphasic MyoD induction pattern in muscle regeneration at 12 h and 3 days are shown by color (see also Fig. 6). This shows that about 25% of the in vitro defined target candidates map to two clusters corresponding to induction at 3 days (see Table 1). This provides additional strong support for a 3-day cluster, and for these genes mapping to this cluster being likely bona fide downstream targets of MyoD at the 3-day time point. There was less support for genes showing temporal clustering to the 12-h time point. This cluster did not resolve as well in the entire data set, and is likely merged with inflammatory gene clusters.

There was no support for a gene cluster corresponding to down-regulation by MyoD at either the 12-h or 3-day-time points. This is expected given the small number of candidates in this group, and the action of MyoD as a transcriptional activator.


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