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Comprehensive expression profiling by muscle tissue class and identification of the molecular niche of extraocular muscle¹

SANGEETA KHANNA^*,2, ANITA P. MERRIAM^*, BENDI GONG^*, PATRICK LEAHY and JOHN D. PORTER^*,,

Department of
^* Ophthalmology,
The Comprehensive Cancer Center, and the Departments of
Neurology, and
Neurosciences, Case Western Reserve University and The Research Institute of University Hospitals of Cleveland, Cleveland, Ohio, USA

²Correspondence: Department of Ophthalmology, University Hospitals of Cleveland, 11100 Euclid Ave., Cleveland, OH 44106-5068, USA. E-mail: sxk128{at}po.cwru.edu

SPECIFIC AIMS

The goal was to use DNA microarray to develop a catalog of preferentially expressed genes by muscle class including smooth, cardiac, and skeletal muscle. Our strategy was to identify those transcripts that were preferentially expressed in a specific muscle class in comparison to all other classes. Many of the identified transcripts have known muscle class-specific functions or have been linked to a class-specific disease. By virtue of their expression patterns, other genes among the preferentially expressed lists may identify muscle class-specific processes and/or represent candidate muscle disease genes. We also sought to determine the niche occupied by extraocular muscle (EOM) in the wider context of the traditional muscle classes, since it has been characterized as fundamentally distinct from other skeletal muscles.

PRINCIPAL FINDINGS

1. Global muscle tissue class gene expression signatures
To identify global patterns of gene expression in the three established muscle tissue classes, we first used DNA microarray to compare relative expression levels in rodent skeletal (gastrocnemius/soleus), cardiac, and smooth muscles. Of the 8799 gene probes analyzed by rat Affymetrix U34A microarrays, using replicate measures and stringent acceptance criteria, 602 genes were identified with muscle tissue class-specific expression patterns as shown in Fig. 1 . Stomach samples showed the highest number of preferentially expressed genes (392), followed by skeletal (115), then cardiac (95) muscle. Functional classification of the muscle class-specific expression profiles were not restricted to contraction- or fatigue-related transcripts, but represented a broad spectrum of functional categories, including transcription factors, signal transduction molecules, extracellular matrix and cytoskeleton transcripts, and energy metabolism genes. We saw many genes with known muscle-specific roles in each of these muscle class-specific lists.

View larger version (51K): [in this window] [in a new window]   Figure 1. Hierarchical clustering of the gene probes identified as differentially expressed in the three muscle classes (skeletal, cardiac, and smooth) using Affymetrix MAS version 5. Genes identified as class specific have distinctly higher expression levels in the class they represent. The independent replicates of each tissue type, leg (n=3), heart (n=4), and stomach (n=3) are represented here. Note the high degree of concordance of expression levels between replicates for the individual gene probes. The scale at the top denotes normalized expression levels (red=high expression, blue=low expression).

2. Candidate muscle class-specific functional and disease genes
We rank ordered the differentially expressed genes obtained from the pairwise muscle group comparisons by summing the log₂ values of the average fold differences between the muscle class of interest and the other two muscle groups (genetic dosage index). Most of these genes, by virtue of being most highly specific of each muscle class, were noted to have known cellular roles in the specific muscle class and many play established roles in muscle diseases.

3. Relationship of EOM among the established muscle tissue classes
Hierarchical cluster analysis using 5294 probes expressed (present or marginal call) in any one of the four muscle types indicated the magnitude of muscle class differences in our global gene expression analysis. Overall, smooth and striated (heart, leg, and EOM) muscle class samples formed the two major branches of the resulting dendrogram. However, whereas cardiac and skeletal muscle samples segregated into separate nodes of the striated muscle branch, the independent leg and EOM samples did not completely segregate. A phylogenetic tree based on the pairwise fold differences values for each differentially regulated transcript clearly demonstrated global similarities and differences in expression levels among the four muscle groups studied here (see Fig. 2 ). Three major branches were evident in trees: cardiac, skeletal, and smooth muscle. Although there was variation among the replicates of the same tissue, this was small relative to the intermuscle class differences. EOM and leg were on separate branches of the skeletal muscle node and resided much closer to each other than to the mutually exclusive groups formed by heart and stomach.

View larger version (26K): [in this window] [in a new window]   Figure 2. Schematic of experimental design and results.

4. Diversity in gene and protein expression profiles of EOM and hindlimb muscle
Specific differences between EOM and hindlimb musculature were examined further by transcript and protein measures. Head-to-head comparisons of EOM and leg samples identified 136 genes with higher expression in EOM and 89 with higher expression in leg muscle. Known structural and functional differences between EOM and the skeletal muscle prototype were accompanied by a spectrum of gene expression differences (Fig. 3 A). Using hierarchical cluster analysis, two major observations could be drawn from the patterns of preferential gene expression in EOM vs. hindlimb muscle. First, some transcripts that were enriched in EOM were expressed at very low levels or were absent from hindlimb (note the large areas of blue, indicating low expression, among the leg data in Fig. 3B ). Some of these same genes appeared to be shared with either cardiac (** in Fig. 3B ) or smooth (* in Fig. 3B ) muscle, reinforcing the theme that EOM uses the full range of muscle tissue potential to meet its functional roles. Traits conserved between EOM and cardiac muscle included cytoskeletal and mitochondrial transcripts. A pattern of conservation of smooth muscle traits in EOM might relate in part to the high content of blood vessel-associated smooth muscle in the EOM, which is more vascular than other skeletal musculature.

View larger version (49K): [in this window] [in a new window]   Figure 3. Gene expression distinctions between EOM and hindlimb muscle. A) Functional classification of differentially expressed genes in EOM vs. leg muscle. 136 probes were consistently expressed at a higher level in EOM compared with leg, with an average fold difference of ≥ twofold (MAS 5.0). 89 probes were expressed at higher levels in leg. B) Hierarchical clustering of expression levels for this subset of 225 genes across all four muscle groups. Note EOM sharing of cardiac (**) and smooth muscle (*) traits that are not found in hindlimb.

Using proteomic screening of EOM vs. leg muscle, by Western analysis based on a large library of signaling protein antibodies we identified further key differences between the two muscle groups, as 12% (96 of >800) of the proteins surveyed met strict analytic criteria for differential expression. Several proteins identified as differentially regulated in immunoblots are known to play functional roles in skeletal muscle fiber size or hypertrophy. Of these, EOM exhibited higher levels of paxillin and lower levels of calcineurin/protein phosphatase 3, Cdc42, JNK1/Mapk8, and protein kinase B kinase than hindlimb muscle. We speculate that the small myofiber size in adult EOM may directly relate to the aggregate low expression of several skeletal muscle positive growth regulatory proteins that we identified here.

CONCLUSIONS

Our approach used broad gene expression profiling to identify characteristic patterns of differentially expressed transcripts by muscle class. Although many identified transcripts were the established muscle class-specific genes (e.g., for skeletal muscle: Myf6, Eno3, and Myh4; for cardiac muscle: Csx, Plm, and Nppa), we also noted transcripts that had not been previously linked to muscle functions. Among the transcripts with the highest genetic dosage index, many known muscle class-specific disease-related genes were identified.

Both hierarchical clustering and genetic distance tree analysis clearly delineated the genetic distance between the major striated and smooth muscle groups and further resolved cardiac and skeletal muscle as two distinct nodes. However, at the global expression level assessed by these tools, EOM failed to emerge as genetically distinct from the skeletal muscle group. Aggregate gene expression differences between EOM and hindlimb muscle then are not robust enough to distinguish EOM as a separate muscle tissue class. Although EOM shares more traits with skeletal muscle than with any other class, phylogenetic analysis tools also suggest that EOM may have more in common with cardiac muscle than hindlimb does (see Fig. 3B ). We speculate that EOM may achieve its unique character due to the combination of tissue-specific traits, expression of some genes not typically encountered in skeletal muscle, and novel expression levels of many transcripts that are conserved in skeletal muscle. Taken together, the comprehensive genomic approach used here advances understanding of the phenotypic range of muscle as a tissue and identifies putative muscle disease genes. Even though our data do not represent a catalog of all possible differentially expressed genes, findings take an important step toward identifying the transcriptional profiles underlying muscle class specialization and disease susceptibility. Subsequent studies that integrate the muscle class molecular signatures and their inter-relationships may lead to a better model of muscle biology in health and disease.

FOOTNOTES

¹ To read the full text of this article, go to http://www.fasebj.org/cgi/doi/10.1096/fj.02-1108fje; doi: 10.1096/fj.02-1108fje

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