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Transcriptional Profile of Rat Extraocular Muscle by Serial Analysis of Gene Expression

¹ From the Departments of Ophthalmology, ² Neurology, and ³ Neurosciences, Case Western Reserve University; and The Research Institute of University Hospitals of Cleveland, Cleveland, Ohio.

	Abstract

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PURPOSE. Although extraocular muscle (EOM) is skeletal muscle, aspects of its biology are unlike other striated muscle. In this study, the broad molecular genetics profile underlying the novel EOM phenotype was examined.

METHODS. Serial analysis of gene expression (SAGE) was used to quantify adult rat EOM gene transcripts. SAGE isolates and sequences 10-bp tags from defined locations in mRNA-derived cDNA. Tag sequence-location was used to extract transcript identity from a curated SAGE database, and detection frequencies reflected abundance of corresponding mRNAs.

RESULTS. The 54,764 expressed sequence tags generated and sequenced from EOM included 17,602 unique tags. Of the unique tags, 7.8% were detected at high to intermediate levels (5 copies), 19.3% at lower levels (2–4 copies), and 72.9% as single copies; 40% of the tags matched known expressed sequence tags (ESTs), most of which (85.7%) represented a unique EST. Tags without matches in the SAGE database and those expressed as single copies only were not considered further. SAGE tags expressed at more than 0.1% of total transcripts reflected several aspects of muscle biology, including sarcomeric structure, energy metabolism, and ribosomal protein expression. Genes highly expressed in EOM were compared with other existing muscle expression databases to identify conserved and novel patterns in EOM.

CONCLUSIONS. The data provide a normative gene expression database and a novel molecular signature that will facilitate study of EOM development and function and of the mechanisms behind its preferential targeting or sparing in neuromuscular disease.

	Introduction

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The oculomotor system exhibits precision and diversity unlike that of any other skeletomotor system. As the effector organ for ocular motility, the extraocular muscles (EOMs) are adapted for novel functional demands. Thus, there are fundamental phenotypic differences between EOMs and other skeletal muscles.^{1 2 3} In skeletal muscle biology, the basic structural and functional unit is the multinucleated myofiber. Patterned covariations in myofiber traits that determine contraction speed and energy metabolism (aerobic, anaerobic, or both) identify distinctive muscle fiber types. Consensus classification schemes have three to four distinct fiber types, each differing in speed and fatigue resistance, that are conserved in virtually all mammalian skeletal muscles.^{4 5} Functional role-specific skeletal muscles thus contain the requisite proportions of these few myofiber types. EOM is a clear exception to this rule, because the six EOM-specific fiber types do not fit any of the traditional skeletal muscle fiber classification schemes.^{2 3}

Gene-profiling technologies have demonstrated considerable power in generation of cell and tissue molecular signatures and identification of disease-associated gene expression changes and represent a rapid and efficient means to elucidate alterations in cell signaling or metabolic pathways. Despite this potential, there has been only limited use of genome-wide screening techniques in vision science. We recently gained new insight into the underlying molecular mechanisms that differentiate EOMs from other skeletal muscles through pair-wise muscle group comparisons, using high-density oligonucleotide microarray technology.⁶ These data established striking similarities in gene expression patterns between mouse hindlimb and some craniofacial (masticatory) muscles, but substantial divergence of EOMs from both muscle groups. Although the scope of our prior DNA microarray study was broad—approximately 10,000 genes and ESTs evaluated—this sample represents, at best, no more than 25% to 35% of the mouse genome. Thus, there indeed are other as yet unrecognized patterned differences in gene expression between EOMs and prototypical skeletal muscles. Collectively, knowledge of the molecular signature of EOMs is vital for both modeling of its novel structural and functional properties and for understanding its differential response in a variety of neuromuscular and autoimmune diseases.^{3 7 8 9 10 11 12 13 14}

For this study, we used serial analysis of gene expression (SAGE) to take advantage of recently available genome project databases and high-throughput DNA sequencing technology to obtain a quantitative transcriptional profile of EOM. In brief, SAGE relies on the isolation of a short oligonucleotide sequence from a defined location within cDNA derived from tissue poly(A)⁺ RNA to quantify the specific transcript.¹⁵ The restriction enzyme, NlaIII, termed the "anchoring" enzyme in SAGE, cuts at the 3'-most occurrence of an NlaIII restriction site. After product ligation to a linker, a tagging enzyme is used to release a 10-bp SAGE tag. The location and sequence of the tag identifies a specific transcription product. Often SAGE tags map to only one expressed gene, but a caveat to the technique is that tag-to-gene assignments are sometimes ambiguous and a single tag may map to two or more genes. Accumulation of thousands of SAGE tags specific for genes or ESTs (termed matched tags) allows the establishment of a global gene expression signature for a tissue. SAGE is differentiated from the other contemporary gene-profiling technology, DNA microarray, in that it is not restricted to a preselected set of probes, but has the potential to detect any expressed transcript. Any 10-bp SAGE tag sequences that do not match to known genes or ESTs (novel tags), but are present at high copy numbers, represent logical targets for gene discovery.

Using a modification of SAGE adapted for small amounts of starting mRNA (microSAGE¹⁶ ) we profiled more than 50,000 transcripts from adult rat EOM, to generate a tissue-specific expression library, and compared these data with previously published skeletal muscle profiles. The data established that EOM has a unique molecular signature.

	Materials and Methods

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Animals
These studies used 45-day-old Sprague-Dawley rats (Harlan, Indianapolis, IN). All animal procedures were approved by the Institutional Animal Care and Use Committee at Case Western Reserve University and were in accordance with the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research. EOMs were rapidly isolated and removed from five male rats after asphyxiation with carbon dioxide. Tissues were flash-frozen in liquid nitrogen and stored at -80°C.

SAGE Protocol
In this study, we used the published microSAGE protocol (version 1.0e; available at http://www.sagenet.org/),¹⁶ with modifications that will be described later. EOMs from five rats were combined, 50 mg of tissue was homogenized, and mRNA was isolated using an extraction kit (Dynabeads mRNA Direct; Dynal, Oslo, Norway), according to the manufacturer’s instructions. Poly(A)⁺ mRNA was bound to oligo (dT) 25 and converted to cDNA with a synthesis kit (Gibco BRL, Rockville, MD), according to the manufacturer’s instructions. The cDNA was evaluated by PCR for integrity and then cleaved with the anchoring enzyme, NlaIII (New England BioLabs, Beverly, MA). The identity of original mRNA copies is derived from the sequence of SAGE tags lying immediately 3' to the 3'-most NlaIII cDNA-cleavage site. The 3' terminal cDNA fragments were isolated with magnetic beads and ligated to one of two annealed linker pairs. Ligated linker contained recognition sites for BsmFI, allowing cDNA- tags to be released from beads by the tagging enzyme BsmFI (New England BioLabs). Tags then were ligated to one another to form ditags, after blunt ending with Klenow (Pharmacia, Peapack, NJ).

The ditags were amplified by PCR and products analyzed by polyacrylamide gel electrophoresis (PAGE) and then digested with NlaIII. The products containing the ditags were then ligated to form concatamers. To prevent overrepresentation of clones with short insertions (which ligate to the vector more efficiently than larger ones), the concatamers were size fractionated by PAGE. Three separate fractions, approximately corresponding to 0.5 to 0.9 kb, 0.9 to 1.5 kb, and 1.5 to 2.5 kb, were cut from gels. The concatamer-containing cDNA from each fraction was purified and cloned into the SphI site of a vector (pZErO-1; Invitrogen, Carlsbad, CA). Insert-containing colonies were screened by PCR with M13 forward and reverse primers. PCR products containing inserts of more than 616 bp in length, which should contain at least 15 ditags each, were submitted for DNA sequencing.

SAGE Tag Sequencing and Analysis
SAGE tag sequencing was performed by San Ming Wang (Department of Hematology and Oncology, University of Chicago Medical Center, Chicago, IL) with a sequencing kit and sequencer (Big-Dye Sequencing Kit and Prism 377 DNA Sequencer; (PE-Applied Biosystems, Foster City, CA). Tag sequences were extracted from clone sequence data with SAGE 300 software. Conversion of tag sequence data to represented genes was performed using software at the Rn.seq.all.z/SAGE map site, available at the National Center for Biotechnology Information (NCBI; http://www.ncbi.nlm.nih.gov). All sequences were arranged in a FASTA file, and the NCBI Unigene database (available at http://www.ncbi.nlm.nih.gov/Unigene) was searched to yield identities of the original mRNA. This tag-to-gene screen separates the 10-bp tags with matches to known genes and ESTs (matched tags) from those with no matches (novel tags) and quantifies tag frequency.

EOM SAGE data were compared with human leg muscle gene expression frequency data in existing SAGE (http://www.urmc.rochester.edu/smd/CRC/Swindex.html) and EST (http://telethon.bio.unipd.it/GETProfiles/) databases. The statistical test of Audic and Claverie,¹⁷ available as an executable program via a Web interface (http://igs-server.cnrs-mrs.fr/audic/significance.html), was used to identify genes that were differentially expressed in EOM versus leg muscle. This digital gene expression profiling method compares databases by computing probabilities based on individual tag copy numbers and overall SAGE library size.

	Results

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Distribution of SAGE Tags
SAGE was used to generate a quantitative gene expression profile from EOM. Two quality control measurements indicate that the SAGE data presented herein are of high quality. First, linker contamination of our library was very low; our data contained only 0.086% linker sequences versus 0.65% or more in other recent studies.^{18 19} Second, analysis of the average GC content²⁰ of tags in our SAGE library (48.04% GC) indicated no GC content bias (bias in a library can arise from denaturation and experimental loss of AT-rich regions and is defined as GC content 55%). Because quantitative differential gene expression methodologies are highly sensitive to the quality of both the original mRNA preparation and the cDNA library, quality control measures are an important consideration.

SAGE generated two data sets from rat EOM: 10-bp tags for qualitative identification of expressed transcripts when matched against the NCBI SAGE database and relative transcript abundance data that provide for both a global expression profile and the ability to make quantitative same-gene and cross-gene expression frequency comparisons. A total of 17,602 unique SAGE tag sequences were extracted from the 54,764 expressed sequences we accumulated from adult EOM. The finding of approximately 32% unique tags in EOM was higher than reported in a prior skeletal muscle SAGE study (23%).²¹ Individual EOM-derived tag abundance ranged from 1 to 2109 copies per gene. Among the 17,602 identified unique tags, 72.9% were present as single copies; 19.3% had 2 to 4 copies; 4.3%, 5 to 9; 3.2%, 10 to 99; and 0.3% 100 or more (Fig. 1) . Tags expressed only as single copies were not considered further.

View larger version (19K): [in this window] [in a new window]   Figure 1. Frequency distribution of EOM-derived SAGE tags. Matched tags denote SAGE tag sequences that map into the NCBI database, and novel tags are those that did not have a sequence match in the database. (A) Semilog plot showing the frequency distribution of SAGE tags detected at specific abundance levels. Novel tags were most frequent among tags expressed only as single copies, and their level declined relative to matched-tag frequency, with increasing copy number. (B) Plot illustrating the frequency of matched SAGE tags. When expressed as a percentage of total tags in each bin, matched-tag frequency increased as a function of copy number.

Table 1 shows the identities of the most abundant SAGE tag species in rat EOM (n = 93), representing all genes and ESTs expressed at a level 0.1% or more of total tags accumulated (i.e., 55 copies). This group is identified throughout as the most frequently expressed genes in EOM. All but 8 of the 93 tags in this group were matched tags, with 10-bp sequences identical with at least one tag in the NCBI SAGE database (Fig. 1) . Comparison of our SAGE data with an existing DNA microarray database from rat EOM (Khanna S, Merriam A, Leahy P, Andrade F, Porter J, unpublished data, 2001) showed that many transcripts detected by SAGE have not been previously identified in EOM. Of the 93 most frequently expressed transcripts, DNA microchip data were available for 55 of them; 48 of the SAGE transcripts had been previously identified as present, and 7 were judged as absent in rat EOM by the microarrays (13% noncorrespondence; Table 1 ; see the Discussion section). The two most abundant mRNA species in EOM code for the Glu-Pro dipeptide repeat protein (present as four different SAGE tags accounting for 3.9%, 1.0%, 0.2%, and 0.2% of total transcripts) and N^G,N^G-dimethylarginine dimethylaminohydrolase (Ddah1; three tags: 3.3%, 2.8%, and 0.7% of transcripts). The presence of multiple SAGE tag species for these two genes may represent alternatively spliced isoforms.

	Discussion

Top Abstract Introduction Materials and Methods Results Discussion Conclusions References

 
Skeletal muscle is an elegant structure–function model. Individual myofibers exhibit patterned variations in traits that collectively determine whether contractions are slow or fast and whether the most efficient mode of energy metabolism is matched to the fiber usage pattern. Although factors directing their development are only poorly understood,^{26 27 28 29} the fundamentally distinct properties of EOM are likely a consequence of the diverse demands placed on them by oculomotor control systems with dynamic ranges from slow vergence movements to rapid saccades. Although there is not an absolute one-to-one correspondence of transcript level to protein content, the biological potential of EOM, like any tissue, is determined at the gene-expression level. In this study, we used a powerful gene profiling tool, SAGE, to define a molecular signature for EOM that is unbiased by any predetermined gene selection criteria. Strict quality control measures indicate low to absent contamination or selective loss of transcripts, thereby providing confidence in these results. Data establish the molecular expression profile of EOM and, when compared with the expression pattern of other skeletal muscles, strongly support the notion that EOM is a fundamentally different type of skeletal muscle.⁶

SAGE as a Gene Expression–Profiling Tool
There are two contemporary tools for determination of broad gene expression patterns, DNA microarray and SAGE, each with its own particular advantages. Combined use of both gene-profiling technologies has allowed important advances in understanding retinal cell lineage. DNA microarray relies on either cDNA or oligonucleotide probes arrayed on a microchip and the differential binding of labeled target tissue transcripts to the probe arrays. Microarray analysis has high sensitivity and is equally efficient at detection of both high and low copy number transcripts, but its use is restricted to only those gene-specific probes that have been designed into an array. Although high-density commercial arrays can evaluate expression changes using more than 10,000 probes, this is still a restricted sample of the entire mammalian genome, and data obtained from low-density or specialized arrays are inherently biased by probe quality and selection. DNA microarray is excellent, however, at quantification of same-gene changes in expression level but does not provide a portable measure of the level of gene expression and does not allow reliable cross-gene comparisons due to differences in transcript-to-probe binding efficiency.

By contrast, SAGE can extract and identify the transcript of any nuclear-transcribed gene, without preexisting bias by investigator or microarray chip designer and allows equally precise quantification of both same-gene and cross-gene expression levels. Moreover, SAGE data are portable in that a database is universally acceptable for comparisons in other experiments, whereas microarray data sets may be too dependent on factors such as microarray type, probe design, transcript isolation, purification, labeling and hybridization conditions, and scanner photomultiplier tube sensitivity to be useful in direct quantitative comparisons with data obtained by other investigators. However, low-copy-number mRNAs are poorly detected by SAGE, in part because high-copy-number transcripts overwhelm rare transcripts during SAGE tag isolation. Signal transduction and transcription factor transcripts, in particular, can be specifically detected in microarray experiments, but are generally expressed at levels too low to be consistently found in a library of 50,000 SAGE tags. In sum, SAGE quantifies the more highly expressed gene transcripts, allowing them to be placed in a relative rank order and compared with other SAGE databases, without the bias of investigator or commercially selected probes.

EOM and Tissue Expression Analysis
EOM exhibits a complex architecture, containing six diverse muscle fiber types plus cellular elements associated with an extensive extracellular matrix and vascular supply (for review, see Spencer and Porter² ). As potential transcript sources, the predominant cell types in skeletal muscle include myofibers, muscle stem (satellite) cells, fibroblasts, Schwann cells, resident cellular defense elements, endothelial cells, circulating cells trapped in the microvasculature, and arteriole-associated smooth muscle cells. Yet, myonuclei comprise an estimated 75% of all nuclei in skeletal muscle tissue.³⁰ Thus, it is a reasonable assumption that our SAGE data predominantly represent EOM transcripts. The substantial level of myofibrillar proteins exclusively expressed in muscle and electron transport proteins heavily concentrated in muscle is consistent with muscle transcripts as the predominant species of mRNA in the EOM. An exception is the presence of high copy numbers of ß- and -globin (collectively, 0.8% of total SAGE tags in EOM), suggesting that blood elements contribute to the transcript pool.

EOM myofibers are specialized for low force production but high fatigue resistance. Consistent with observations that myofilaments occupy less of the EOM fiber cross-sectional area than in other skeletal muscles,³¹ myofibrillar protein transcripts (e.g., myosin, -actin, troponin, and tropomyosin) comprised only 10.6% of EOM gene transcripts detected at copy numbers higher than 100, versus the 27.8% myofibrillar transcripts in the SAGE human leg muscle library.²¹ Likewise, EOM has among the highest mitochondrial content of any skeletal muscle (among the six fiber types, 5%–24% of fiber volume).³¹ The abundance of mitochondria is reflected in the relative abundance of nuclear-encoded transcripts for oxidative phosphorylation proteins in EOM (mitochondrial transcripts are not isolated by the methodology used herein and were not detected in our library). Taken together, the global gene expression profile obtained by SAGE, to a great extent, specifies the transcriptional properties of the muscle fiber component of EOM and faithfully reflects myofiber subcellular organelle content.

The EOM Transcriptome Versus That of Other Skeletal Muscles
Although the rat genome project is not complete (and thus the NlaIII restriction sites necessary for SAGE tag identification have not been sequenced for all genes), very few (8.6%) of the most frequently expressed genes in EOM did not have a corresponding tag identifier in the NCBI rat SAGE database (Table 1) . Thus, the highly expressed genes detected and quantified by SAGE can be confidently compared with other existing skeletal muscle data without the limitation of excessive numbers of unknown transcripts. Of the top 0.1% or more of EOM transcripts, we had previously detected 52% of them as present in rat EOM by DNA microarray (Khanna S, Merriam A, Leahy P, Andrade F, Porter J, unpublished data, 2001). The remainder of the genes either were not represented on the microarray chips or the array probes do not appear to adequately detect the intended genes (e.g., muscle creatine kinase and fructose bisphosphate aldolase are established muscle genes but were not detected by the specific probe sets on Affymetrix rat microarrays obtained from Affymetrix (Santa Clara, CA); Khanna S, Merriam A, Leahy P, Andrade F, Porter J, unpublished data, 2001). Our comparisons of the EOM transcription profile with that of the human vastus lateralis muscle, a prototypical skeletal muscle often used in diagnostic muscle biopsies, showed that their concordance in expression pattern was only 33% to 47% (Table 3) .^{21 25} A caveat of this analysis is the possibility of species differences that may distort observed muscle class differences. Normally, functionally distinct skeletal muscles differ only in their percentage content of the three to four highly conserved myofiber types. This translates into relatively small gene expression differences between predominantly fast- and slow-twitch skeletal muscles.³² In that study, detected differences were largely restricted to genes that could be easily associated with the known fiber type differences. By contrast, EOM fiber types are unlike the traditional skeletal muscle types, and the expression differences between EOM and other skeletal muscles seen here extend well beyond genes that specify fast- or slow-twitch fiber phenotypes.

The two most frequent transcripts in EOM, Glu-Pro dipeptide repeat protein and Ddah1, were not among the most frequent transcripts in either of the existing vastus lateralis muscle databases. Because the role of the dipeptide repeat proteins is unknown, it is difficult to speculate about the functional significance of this difference in gene expression. Proteins with glutamic acid-proline are particularly abundant in cardiac and skeletal muscle³³ and have been previously detected as present in EOM (Table 1 and unpublished data, 2001). Ddah1 codes for an enzyme that metabolizes neuronal nitric oxide synthase (nNOS) inhibitors. High Ddah1 levels in EOMs may provide for selective regulation of NOS inhibitor concentration and, in turn, positive regulation of NOS activity in vitro.³⁴ The muscle group–specific role that specialized control of nNOS might play in EOM is unknown, but the high expression level of Ddah1 suggests that it is vital to EOM function, perhaps by modulating EOM contractile function in ways suggested by Richmonds and Kaminski.³⁵

Some of the differences between our EOM SAGE data and the human skeletal muscle EST library likely are related to known functional differences between the two muscles. Expression mismatches can be attributed, in part, to the fiber type composition of the two muscles (type IIA and I predominance in the vastus lateralis versus novel fiber types, with no type I [slow-twitch] component, in EOM). Although the two muscle groups shared high expression of at least five key sarcomeric genes (-1 actin; myosin heavy chain 1 [type IIX/D]); troponin I, fast; tropomyosin 1; and myosin light chain 1), seven of the most frequently expressed skeletal muscle sarcomeric proteins not detected among the most highly expressed group in EOM were slow-twitch muscle fiber–specific transcripts (ß-cardiac [slow] myosin heavy chain; troponin I, slow; myosin light chain 3, slow; myosin-binding protein C, slow-type; myosin light chain, regulatory, slow; troponin C, slow; sarcoplasmic reticulum calcium ATPase 2 [slow-twitch]). The fast-twitch type IIA muscle fiber transcript (myosin heavy chain 2) does not have a rat SAGE tag identifier and thus its levels could not be evaluated. The relative low number of slow-twitch transcripts relates to the total absence of slow-twitch fiber types in EOM; some slow-twitch transcripts may be represented in the novel slow-tonic EOM fiber types that comprise a small percentage of EOM fibers. By contrast, the EOM-specific myosin heavy chain (Myh13; 0.2% of transcripts) is not expressed in limb musculature. By SAGE, myosin heavy chain expression levels in EOM were in the order EOM > IIX > IIB > embryonic, with three other isoforms known to be present in EOM (neonatal, I, and IIA) not detected at more than 10 copies. A quantitative PCR study ranked the frequency of myosin transcripts in rat EOM in the order IIB > IIA > IIX > EOM > I > neonatal > embryonic.³⁶ Some, but not all, interexperimental differences can be attributed to the lack of SAGE tag identifiers for, and therefore an inability to assess, the rat neonatal and IIA myosin heavy chain isoforms.

Skeletal muscle fiber types differ in mechanisms for energy metabolism, with the predominant modes being oxidative, oxidative-glycolytic, and glycolytic. Most skeletal muscles are dependent on substantial glycogen stores for energy metabolism. By contrast, EOM exhibits exceptionally low glycogen content for a skeletal muscle.² Our prior gene profiling study showed that expression of many enzymes related to glycogen metabolism and gluconeogenesis were at lower levels than in leg musculature.⁶ Our observation that glycogen phosphorylase (<0.02% of EOM transcripts) and phosphoglycerate mutase (not detected in EOM at >0.009% of transcripts) are not very abundant in EOM (Table 2) is consistent with these data. Mitochondrial content represents a distinguishing feature of several EOM fiber types, with few other skeletal muscles exhibiting such a high mitochondrial volume.³¹ Consistent with a substantial reliance on oxidative phosphorylation, EOM exhibited high expression levels of several nuclear-encoded isoforms of cytochrome c when compared with the vastus lateralis muscle (Table 2) . Of nine cytochrome c oxidase isoforms detected at five or more copies in EOM, four were expressed at higher levels, three at lower levels, one was not different, and one was not detected in the vastus lateralis. Also related to energy metabolism in fast-twitch muscle fiber types, enzymes concerned with phosphocreatine metabolism (muscle creatine kinase and sarcomeric creatine kinase) did not follow the established fast-twitch pattern, but were differentially expressed in EOM compared with the vastus lateralis (Table 2) . Myoglobin expression in EOM was approximately half that of the vastus lateralis. Collectively, data suggest that EOM may use novel substrates and pathways for energy metabolism that differ from those that typify other skeletal muscles.

Another major difference between EOM and human vastus lateralis transcriptomes was in the expression of ribosomal proteins, with 11 conserved between the most highly expressed genes in the two muscle groups, 3 specific to EOM, and 58 specific to leg muscle. Although the core of the ribosome translational unit is RNA, the ribosomal proteins are integral components, decorating the rRNA subunit cores and contributing to transfer (t)RNA binding sites, among other roles. It is difficult to speculate about the significance of these substantial differences in ribosomal protein expression, because the extent to which these muscle group differences may reflect simple species differences or may be determinants of tissue-specific translation is unclear. Moreover, some ribosomal proteins have secondary functions independent of their involvement in protein biosynthesis. Until such roles are clarified, it is difficult to interpret the substantial muscle group differences in expression of this protein group.

Finally, we detected significant differences between EOM and other skeletal muscles in the expression of several cytoskeletal structural proteins (desmin, titin, vimentin, and nebulin; Table 2 ). Desmin, as the main intermediate filament protein in skeletal muscle, is of great importance as a part of the cytoskeleton that supports transfer of generated force to the muscle tendon.³⁷ That levels of desmin and other noncontractile cytoskeletal proteins are either low or possibly absent in EOM strongly suggests that cytoskeletal organization in EOM may be very different from other skeletal muscles. Such internal cytoskeletal differences may directly relate to the differential sensitivity of EOM in a variety of neuromuscular disorders, including muscular dystrophy and nemaline myopathies.

	Conclusions

Top Abstract Introduction Materials and Methods Results Discussion Conclusions References

 
SAGE has been used previously to study skeletal muscle only in very limited situations (normal human profile, aging changes, and a disuse model) but has the potential to identify the molecular determinants of tissue phenotype and disease-associated changes in those determinants. Our data provide a molecular identity for an important but little understood skeletal muscle, EOM, and support the concept that EOM is dramatically different from other skeletal muscles. A broad understanding of the biology of the final common pathway for eye movements, achievable only through gene profiling, will aid global understanding of the functions and diversity of oculomotor control mechanisms.

	Acknowledgements

	Footnotes

 
Supported by Grants R01-EY09834, R01-EY12779, and P30-EY11370 from the National Eye Institute and a Departmental Grant and a Senior Scientific Investigator Award from Research to Prevent Blindness. JDP received support as the Carl F. Asseff, MD, Professor in Ophthalmology.

Submitted for publication August 29, 2001; revised November 15, 2001; accepted December 5, 2001.

Commercial relationships policy: N.

The publication costs of this article were defrayed in part by page charge payment. This article must therefore be marked "advertisement" in accordance with 18 U.S.C. 1734 solely to indicate this fact.

Corresponding author: John D. Porter, Department of Ophthalmology, University Hospitals of Cleveland, Case Western Reserve University, 11100 Euclid Avenue, Cleveland, OH 44106-5068; jdp7{at}po.cwru.edu

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Top Abstract Introduction Materials and Methods Results Discussion Conclusions References

 

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