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(Investigative Ophthalmology and Visual Science. 2003;44:3842-3855.)
© 2003 by The Association for Research in Vision and Ophthalmology, Inc.
DOI:  10.1167/iovs.03-0170
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Comprehensive Evaluation of the Extraocular Muscle Critical Period by Expression Profiling in the Dark-Reared Rat and Monocularly Deprived Monkey

Georgiana Cheng,1,2 Michael J. Mustari,3,4 Sangeeta Khanna,1,2 and John D. Porter1,2,5,6

1From the Departments of Ophthalmology, 5Neurology, and 6Neurosciences and the 2Visual Sciences Research Center, Case Western Reserve University and University Hospitals of Cleveland, Cleveland, Ohio; and 3Yerkes National Primate Research Center and the 4Department of Neurology, Emory University, Atlanta, Georgia.


   Abstract
Top
 Abstract
Materials and Methods
 Results
 Discussion
 Conclusions
 References
 
PURPOSE. To address the consequences of visual deprivation paradigms in rat (dark rearing) and monkey (monocular deprivation) on extraocular muscle (EOM) development using genome-wide expression profiling.

METHODS. Serial analysis of gene expression (SAGE) was used to determine alterations in the EOM transcriptome induced by dark rearing of rats from birth to postnatal day 45. Data were compared with previously published normal EOM SAGE library. DNA microarray similarly assessed changes in gene expression patterns of EOMs of monkeys reared from birth to 4 months of age with monocular deprivation.

RESULTS. Dark rearing produced changes in expression of 280 transcripts in rat EOM. Of these, 71 were known genes representing functional categories that included energy metabolism/mitochondrial-related (21%), protein synthesis and modification (14%), lipid metabolism (13%), and muscle-related (6%) transcripts. Together, the predominant pattern reflected an energetic shift toward fatty acid ß-oxidation and integrated alterations in both myofibers and supportive tissues. The response of monkey rectus muscles to monocular deprivation was considerably less severe.

CONCLUSIONS. The visual deprivation paradigms used in this study mimic alterations that are associated with the common disorders of strabismus, congenital cataract, and amblyopia. These data show that postnatal EOM maturation is broadly susceptible to changes in activity patterns that are a consequence of visuomotor maldevelopment. The data extend the concept of an EOM-critical period and establish that activity patterns in developing eye movement systems play vital determinant roles in the novel EOM phenotype.

The critical period is a postnatal window during which time patterned retinal activity functions to shape structural and functional properties of the sensory visual system.1 2 Inappropriate patterns of visual experience during this critical period can perturb the formation and/or pruning of neural connections in visual relay and processing centers and irreversibly alter the ability to produce an accurate representation of the world around us. A key correlate of the critical period is its tight temporal constraints, because identically altered sensory experience later in life does not produce a similar degradation of visual acuity. The critical-period concept has influenced strategies for management of congenital or infantile disorders of vision and eye alignment and is not restricted to the afferent visual system. Visual malexperience also produces less well-understood age-dependent alterations in the oculomotor system.3 4 5 6 7 8 9 10 Because skeletal muscle is highly responsive to changes in activation patterns, maldevelopment of visuomotor control systems may, in turn, have deleterious consequences for the extraocular muscles (EOMs).

Evidence for critical periods in any skeletal muscle is limited. Perturbation of postnatal steroid hormone levels or muscle-activation patterns have consequences for maturation of specialized muscle fiber types in laryngeal or jaw musculature.11 12 13 14 EOM is fundamentally distinct from other skeletal musculature15 16 17 18 19 and there is evidence that both early and late influences shape EOM myofiber phenotypes. The survival of EOM primordia in an organotypic nerve–muscle coculture system is dependent on trophic support that can be provided by oculomotor, but not spinal, motoneurons.20 In addition, several EOM traits emerge in parallel with visual and motor system maturation,21 22 23 24 so that the complex requirements of the five oculomotor control systems (vestibulo-ocular, optokinetic, pursuit, saccadic, and vergence) may shape novel EOM properties by critical period-dependent mechanisms. We previously extended early findings that visual deprivation paradigms alter oculomotor motor unit properties25 26 27 28 to show that both dark rearing and perinatal compromise of the vestibulo-ocular reflex suppress the postnatal expression of an EOM-specific trait, the EOM myosin heavy chain isoform (Myh13).29 30

Here, we used two experimental paradigms to address the EOM critical-period concept: expression profiling with serial analysis of gene expression (SAGE) in dark-reared rats and DNA microarray in monocularly deprived (MD) monkeys. Dark rearing blocks the development of the columnar organization of primary visual cortex. Subsequent analysis of alterations in rat EOMs by SAGE offers the distinct advantage of an unbiased genomic screen, potentially detecting changes in any transcript, that also allows valid comparisons among data sets generated in independent experiments or laboratories.15 31 MD perturbs ocular dominance column development, putting one eye at a competitive advantage in a way that mimics the visual consequences of human strabismus and amblyopia. Analysis of MD monkey EOMs using DNA microarrays (Affymetrix, Santa Clara, CA) provides a broad-based and unbiased assessment of activity-induced changes. Collectively, data from these approaches identify a broad pattern of EOM phenotype determination by visuomotor activity and lend support to the concept of an EOM critical period.


   Materials and Methods
Top
 Abstract
 Materials and Methods
Results
 Discussion
 Conclusions
 References
 
Animals
Forty-five-day-old male Sprague-Dawley rats (Harlan, Indianapolis, IN) were reared in darkness (DR) from birth. All animal maintenance (cage changing and feeding) was conducted making brief use of a low-intensity lamp with a red filter (1A Safelight Filter; Eastman Kodak, Rochester, NY). Rat rod photoreceptors are barely sensitive to the extreme end of the spectrum, and so a dark-adapted eye may be exposed to fairly high luminance levels of deep red light without loss of adaptation.32

Two infant macaque monkeys (Macaca mulatta) were reared from birth to 4 months of age with MD produced by tarsorrhaphy. Two additional normally reared, age-matched monkeys served as the control. The closed eyelids were examined on a daily basis to ensure that opposition remained intact and that no signs of irritation occurred. Monkeys were subjected to visual acuity testing with the forced-choice preferential looking (FPL) method.33 Assessments of monocular visual acuity were made with Teller acuity cards using prescribed procedures (Vistech Consultants, Inc., Dayton, OH). Each card has black-and-white stripes of a particular width on one side of the card, surrounded by an isoluminant gray background. The monkey was positioned in such a way that its preferential looking toward stripes could be assessed. Infant monkeys naturally look toward stripes at one end of the card rather then the homogenous gray field on the other end. By varying the number of stripes (cycles) per degree of visual angle, acuity can be estimated reliably. In 4-month-old monkeys, we found no measurable acuity in the MD eye, even at the lowest spatial frequency stimulus (0.32 cyc/deg) tested. This finding is consistent with the profound amblyopia associated with MD. In contrast, the visually experienced eye of MD monkeys evinced acuity development similar to that of the age-matched normal control monkeys (i.e., >9.0 cyc/deg).

All animal procedures were approved by the Institutional Animal Care and Use Committee at Case Western Reserve University (rat and monkey) and Emory University (monkey) and were in accordance with the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research.

MicroSAGE Protocol and Data Analysis: Rat EOM
At the end of the DR period, all rectus and oblique EOMs were rapidly isolated and removed from both orbits of each of five rats after asphyxiation with carbon dioxide. These are the same muscles that were used in our prior normative study. Tissues were pooled, flash frozen in liquid nitrogen, and stored at -80°C. A SAGE library then was generated using the microSAGE protocol (version 1.0e; http://www.sagenet.org/ provided in the public domain by the Johns Hopkins Oncology Center, Molecular Genetics Laboratory, Baltimore, MD),34 with modifications as described previously.15 Briefly, muscle tissue poly (A)+ RNA was reverse transcribed and 14-bp oligonucleotide sequences (tags) were then cut from cDNA with the anchoring enzyme NlaIII (New England Biolabs, Beverly, MA) and released from the 3' end by using the tagging enzyme BsmFI (New England Biolabs). After SAGE tag isolation and ligation to form ditags, ditags were amplified by PCR, digested by NlaIII, and ligated to form concatemers. Concatenated ditags were purified and cloned into the SphI site of a vector (pZErO-1; Invitrogen, Carlsbad, CA).

Two thirds of the SAGE tags reported herein were sequenced from plasmids that were randomly selected from bacterial colonies, and one third were from PCR products amplified with M13 forward and reverse primers from colonies that contained inserts more than 616 bp in length. SAGE tag sequencing was performed by Genome Therapeutics Corp. (Waltham, MA; using BigDye terminator chemistry [Applied Biosystems, Inc., Foster City, CA], on a MegaBace automated sequencer [Amersham Biosciences, Piscataway, NJ]). Tag sequences were extracted with eSAGE 1.2a software.35 SAGE tag sequences containing base calls with Phred (CodonCode Corp, Dedham, MA) quality values less than 20 were not included in the database.

After sequencing, sequence and location data for each tag were used to identify specific transcripts based on National Center for Biotechnology Information UniGene build number 115 for rat (http://www.ncbi.nlm.nih.gov/UniGene/ provided in the public domain by NCBI). The DR EOM SAGE data then were compared with our existing light-reared (LR; 45-day old rats reared under a normal 12-hour light–dark cycle) EOM SAGE library (available at http://www.ncbi.nlm.nih.gov/geo/ under accession number GSM581; NCBI),15 after reanalysis of the previous library using eSAGE 1.2a software (http://genome.nhgri.nih.gov/eSAGE/ provided in the public domain by NCBI) and the current UniGene build to ensure equivalent decoding of SAGE tags. eSAGE and the statistical method of Audic and Claverie (http://igs-server.cnrs-mrs.fr/~audic/significance.html/)36 were used to identify genes that were differentially expressed in EOMs of the DR versus LR animals.

DNA Microarray Protocol and Data Analysis: Monkey EOM
Lateral rectus and medial rectus muscles of the MD and control monkeys were independently isolated and compared by using DNA microarray. EOMs from the deprived eye were isolated and processed for DNA microarray, as described previously,17 37 except that human U133 (A and B) array sets (Affymetrix) were used to evaluate monkey EOMs. These arrays interrogate >33,000 well-documented human transcripts. The probe sets on these human arrays have been shown to have sufficient sequence homology for use in monkeys.38

Microarray data were scaled to the same target intensity and analyzed by computer (Microarray Suite; MAS ver. 5.0; Affymetrix). Pair-wise comparisons were made between MD and control monkey EOM samples. Transcripts defined as differentially regulated met the criteria of (1) a consistent increased–decreased call versus control in both replicates, based on Wilcoxon’s signed rank test (the algorithm assesses microarray probe pair saturation, calculates a probability [P] and determines increased, decreased, or no-change calls) and (2) absolute value of the average difference in the multiple of change of 1.8 or more.


   Results
Top
 Abstract
 Materials and Methods
 Results
Discussion
 Conclusions
 References
 
Distribution of DR Rat EOM SAGE Tags
SAGE was used to obtain a quantitative gene expression profile from DR EOM that was then compared with our existing LR EOM SAGE library15 using eSAGE. Two independent quality-control measurements validate the SAGE library presented herein. First, the average GC content of tags in our DR SAGE library was nearly identical with that of the LR library (DR: 46.4%, LR: 48.0%), indicating the absence of any GC content bias in both (GC bias results from loss of AT-rich regions during tag processing and is defined as GC content >=55%). Second, linker contamination of our library was very low. The finding of only 0.05% linker sequences was substantially below reports of 0.65% or more in other SAGE libraries.39

In SAGE terminology, total SAGE tag number indicates the combined number of copies of all sequenced tags (transcripts). The number of unique tags is equivalent to the total number of distinct transcripts expressed in the library at 1 copy/transcript or more and is the sum of the matched tags (those represented as a known gene or EST in UniGene) and the novel tags (those with identities not recognized by genomics databases). Of the 31,776 total SAGE tags that we accumulated from DR EOM, nearly one-third (10,105) were unique tags and approximately 55% of these were matched tags. Unique tag sequences and counts for the complete DR EOM SAGE library have been deposited to the NCBI Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/) under accession number GSM3893. Figure 1A summarizes the distribution of matched and novel tags in our DR SAGE library, as a function of copy number. As in most SAGE libraries, the proportion of SAGE tags matching known genes was skewed toward the more abundant tags. Ninety-seven percent of the unique tags in the highest abundance class (>=100 copies/transcript) matched recognized genes or ESTs, whereas only 44.4% of the single-copy tags matched either genes or ESTs in UniGene. Figure 1B shows the percentage of matched genes and ESTs as a function of copy number.



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  		FIGURE 1. Frequency distribution histograms of SAGE tags derived from DR rat EOM. (A) Semilog plot of the frequency of SAGE tags detected at specific abundance levels. Matched tags are those that are represented in genomics databases as known genes or ESTs. Novel tags represent mRNA sequences not found in these databases. Novel tags were most abundant among the single-copy group and declined as a percentage of all tags with increasing copy number. (B) Plot showing the frequency of matched tags among tag abundance groups.

 
Comparison of DR and LR Rat EOM SAGE Libraries
The frequency of each tag in our SAGE libraries reflects the relative abundance of the corresponding mRNA in each EOM tissue sample. This feature of SAGE allows digital comparisons between independently generated libraries. The most abundant SAGE tags in DR EOM (detected at a frequency of >=0.1% of all tags or >=32 copies/unique tag) also usually were matched tags. Only 5 of the top 96 unique transcripts were novel tags. Similar findings were obtained in the reanalysis of the LR library. Several housekeeping genes (glyceraldehyde-3-phosphate dehydrogenase, ß-actin, and lens epithelial protein) were among the most abundant transcripts in both DR and LR libraries, but none was differentially expressed in the two libraries by the conservative criteria used in our study (P <= 0.05 and multiple of change >=3).

Figure 2 illustrates similarities and differences in unique tag identities detected in the DR and LR EOM SAGE libraries. When single-copy tags were considered, the degree of correspondence in transcripts present in both libraries was low, approximately 20% of unique tags in the combined libraries were shared in DR and LR data. Tags in the intersection of the two libraries were, however, more likely to represent known genes or ESTs (75% matched tags). However, when a threshold of 3 copies per tag or more was applied, 78% of unique tags in the combined data were shared between the two libraries. At this threshold, only 3% (105) of all transcripts represented in both libraries were observed in the DR library only. All 105 of these DR library-specific tags met criteria for significance in comparison with the LR library (P <= 0.05). By contrast, 18.6% (564) of unique tags were found only in the larger LR library (only 68 of these differed in expression from the DR library).



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  		FIGURE 2. Venn diagrams representing SAGE tag distribution patterns in the dark-reared (DR) and light-reared (LR) rat EOM libraries. (A) Distribution between the two libraries when all SAGE tag copy number categories were considered. Note overlap represents only 19.7% of the total number of unique tags. (B) Distribution between the two libraries when SAGE tag copy number was restricted to three copies per tag or more. Overlap between the two libraries was 78%.

 
Identity of Differentially Expressed SAGE Tags in DR Versus LR Rat Libraries
Most SAGE tags detected in DR and LR EOMs were represented as single copies; these comprised 71.5% and 72.9% of total tag counts in DR and LR libraries, respectively. Because we used a stringent Phred base-calling threshold, sequencing accuracy was high and most single-copy tags probably represent "real" transcripts. However, at low tag abundance levels there is less confidence in the discrimination capability of the SAGE technique. In identifying differentially regulated transcripts in EOM that result from the dark rearing paradigm, we required a 3-fold change or more and significance level of P <= 0.05. By these criteria, 280 unique tags were differentially expressed in DR versus LR EOM, including 172 upregulated and 108 downregulated tags in the DR sample. All 280 unique tags were expressed at three copies or more in at least one of the two libraries.

Transcripts meeting selection criteria for differential expression in DR EOM were functionally characterized by using gene ontology and other information available from the LocusLink (http://www.ncbi.nlm.nih.gov/LocusLink/ NCBI) and GeneCards (http://bioinfo.weizmann.ac.il/cards/ Weizmann Institute of Science, Rehovot, Israel) databases (Fig. 3 and Table 1 ). Individual transcripts were assigned to as many as two functional categories. Most of the 280 differentially regulated unique tags represented either ESTs (49%) or uncharacterized novel tags (25%). Of the 71 unique tags representing known genes, the energy metabolism/mitochondrial-related transcript category was the most abundant (21.3% of known genes), followed by the other (15%) protein synthesis/modification (13.8%) and lipid metabolism (12.5%) categories. Muscle-related transcripts constituted only 6.3% of the unique tags representing known genes. Table 1 lists all unique tags identified as differentially regulated in DR EOM, with corresponding gene identifications and functions.



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  		FIGURE 3. Functional category distribution of SAGE tags detected as differentially regulated in DR rat EOM. Top: Functional distribution of all 280 unique tags. Bottom: Functional distribution of the 71 matched tags that have been linked to known genes. Mus, muscle-related; Cyto, cytoskeletal; Met, metabolism/mitochondrial related; Lip, lipid metabolism; CS/ECM, cell surface/extracellular matrix; Sig, signaling/cell–cell communication; C/D, cell proliferation/death; Trans, transcription; Pro, protein synthesis/modification; Rec, receptor/transporter/ion channel; Imm, immune response; Oth, other/unknown.

 

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  		TABLE 1. Differentially Regulated Transcripts in Dark-Reared Rat EOM

 

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  		TABLE 1A. (continued). Differentially Regulated Transcripts in Dark-Reared Rat EOM

 

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  		TABLE 1B. (continued). Differentially Regulated Transcripts in Dark-Reared Rat EOM

 

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  		TABLE 1C. (continued). Differentially Regulated Transcripts in Dark-Reared Rat EOM

 

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  		TABLE 1D. (continued). Differentially Regulated Transcripts in Dark-Reared Rat EOM

 

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  		TABLE 1E. (continued). Differentially Regulated Transcripts in Dark-Reared Rat EOM

 
Differential Expression in EOMs of MD Monkeys by DNA Microarray
Lateral rectus and medial rectus muscles from control and MD monkeys were independently evaluated (n = 2/experimental group). Because the extent to which MD alters the monocular versus binocular components of conjugate gaze mechanisms is unclear, deprived eye EOMs were compared with those from untreated, age-matched control monkeys. Using DNA microarray, more than 33,000 transcripts were analyzed and data subjected to a strict filter (consistent increase or decrease call across replicates and fold change cutoff of >=1.8; using Affymetrix MAS 5.0 software). Lateral rectus muscles showed differential regulation of 20 transcripts, with 5 increased and 15 decreased in expression in the muscles of the deprived eye. Twenty transcripts also were differentially regulated in the deprived eye medial rectus, with an equal number up- and downregulated. There was little overlap among the expression patterns in lateral and medial recti, as only glutamine synthase and an uncharacterized mRNA sequence (GenBank BF514079) were differentially regulated in both muscles. Identities and functions of transcripts differentially regulated by MD are shown in Tables 2 and 3 .


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  		TABLE 2. Differentially Regulated Transcripts in Monocularly Deprived Monkey Lateral Rectus

 

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  		TABLE 3. Differentially Regulated Transcripts in Monocularly Deprived Monkey Medial Rectus

 

   Discussion
Top
 Abstract
 Materials and Methods
 Results
 Discussion
Conclusions
 References
 
Using genome-wide expression profiling with SAGE, we have demonstrated a broad pattern of differential gene expression in rat and monkey EOMs subsequent to experimental perturbations of visual system development. Data show that the altered visuomotor system output associated with dark rearing produced substantial changes in 280 transcripts encompassing a wide range of muscle tissue functions. MD produced alterations in fewer transcripts in monkey EOM. Patterned changes in gene expression in the current study are attributed to the activity dependence of maturing EOM and its associated tissues. These data provide support for the EOM critical-period concept, suggesting that EOM maturation is regulated, at least in part, by the activity patterns of developing visuomotor systems.

The visual deprivation models and gene profiling techniques used were selected to take full advantage of the strengths of each model. Because the rat has a limited binocular field, we chose dark rearing to globally suppress the development of primary visual cortex. SAGE provides excellent in-depth expression analysis and our previously published, normative rat EOM SAGE library served as a control in our study. By contrast, the substantial binocular field of the macaque monkey allowed us to use the more specific, clinically relevant strategy of MD. A SAGE database is not available for the monkey, and the human database could not be used, because species sequence differences become problematic when gene identity is based on a very short SAGE tag sequence. Thus, human microarrays (Affymetrix), which survey approximately 33,000 genes and ESTs and have been demonstrated to have sufficient sequence homology for use in monkey,38 were used. Collectively, the visual deprivation paradigms and expression profiling tools used in the study are well accepted and provide considerable insight into transcriptional changes in EOM that result from visual system maldevelopment.

SAGE and DNA microarray are formidable tools for assessing the entire transcriptome of cell or tissue types. In an in vivo assessment of the EOM expression profile, we acknowledge that multiple cell types (e.g., muscle, neural, vascular, connective tissue, resident inflammatory cells) contributed to the SAGE and microarray data generated herein, and it is likely that visual deprivation has produced transcriptional changes in supportive tissues as well as myofibers. However, muscle is an integration of a wide array of tissue types and the complex systems biology question of how activity-dependent regulation of the novel EOM phenotype is achieved cannot be addressed only through study of isolated myofibers. In our study, we used stringent controls and data acceptance criteria to provide a conservative estimate of genes that may participate in an EOM critical period.

In serving as the effector organ for the wide dynamic range of eye movement control systems ranging from pursuit and vergence movements of less than 1 deg/sec to saccades that can exceed 800 deg/sec, while maintaining precise interocular alignment and foveation of visual targets, the EOMs arguably face the most extreme demands of any skeletal muscle. Thus, the baseline morphology and gene expression profile of EOM is fundamentally different from that of other striated muscles, with this muscle group encompassing traits from both cardiac and skeletal musculature in adapting to the eye movement role.15 16 17 18 19 A key gap in EOM biology is the lack of understanding of developmental mechanisms that modulate assembly of the atypical EOM fiber types and their associated supporting tissues. The correlation between the complexity and diversity of eye movement control systems and the novel properties of the EOMs suggested to us that the two were mechanistically linked, with the functional demands placed on EOM acting as direct determinants of the muscle phenotype.

In prior studies, we demonstrated activity dependence in the postnatal appearance of a key EOM trait, the EOM-specific myosin (Myh13).29 30 That Myh13 is tightly regulated is seen in both its late expression24 and spatial restriction to only the perijunctional regions of specific EOM fiber types.24 40 41 Interference in maturation of either the visual afferent system29 or the vestibulo-ocular reflex30 led to suppression of Myh13 mRNA, presumably as a result of decreased oculomotor motoneuron activity in both paradigms. Neither manipulation was effective in downregulation of Myh13 when applied in adult rats. Collectively, these data established that Myh13 expression by EOM myofibers requires specific signaling, most likely neural activity patterns, during a postnatal temporal window and thereby suggests the existence of an EOM critical period. We did not, however, detect changes in Myh13 here. This may be the result of the very restricted distribution of Myh13, the relatively low level of Myh13 suppression, and/or differences in the sensitivity of the techniques used. In particular, we have taken a very stringent approach toward analysis of the expression profiling data presented herein that may lead to false negatives.

In this study, we extended the breadth of EOM transcripts that are influenced by the alterations in visuomotor activity that accompany dark rearing. Each differentially regulated transcript is a candidate for the EOM critical period. The 280 distinct transcripts that met selection criteria for differential regulation in DR rat EOM were not restricted to myofiber-specific genes, but included genes shared by myofibers and other cell types and genes usually associated with other tissue types. Several muscle-specific genes (upregulated: sarcosin, triadin 1, calpain 3, and cardiac calsequestrin; downregulated calponin 3) were differentially regulated in DR EOM. EOM is known to express cardiac muscle transcript isoforms,17 19 42 and dark rearing increased the expression of two of these, triadin 1 and cardiac calsequestrin. Several transcripts that function as cytoskeletal elements also were differentially expressed (e.g., upregulated: dynein-associated protein RKM23 and afadin; downregulated: calponin 3). Changes in Myh13 did not reach significance.

The most substantial change in DR EOM involved transcripts related to intermediary and energy metabolism (>21% of the known genes differentially expressed in DR). A key rate-limiting enzyme of glycolysis (phosphofructokinase-muscle isoform) was downregulated, whereas several transcripts related to lipid metabolism were upregulated (dodecenoyl-CoA delta isomerase, transaldolase 1, malic enzyme 1, enoyl-CoA hydratase short chain 1, and acyl-CoA oxidase) or downregulated (acetoacetyl-CoA synthetase, thyroid hormone responsive protein, and fatty acid-CoA ligase long chain 5). Upregulation of acyl-CoA oxidase and enoyl-CoA hydratase short chain 1 is particularly compelling, because these represent the first two steps of the pathway for ß-oxidation of fatty acids. Three ESTs with homology to ß-oxidation pathway transcripts (acyl-CoA dehydrogenase, acyl-CoA thioester hydrolase, and carnitine/acylcarnitine translocase) also were upregulated in DR EOM. These data suggest a shift in DR EOM toward an energy mode that is normally predominant in cardiac, but not skeletal, muscle. Consistent with the energetics theme, a total of seven mitochondria-related transcripts with a broad range of functions were induced in DR EOM. Taken together, the shifts in several muscle-specific and energy metabolism protein transcripts are consistent with the alteration of usage patterns of the EOMs.

DR-induced changes in transcripts that function in protein translation and posttranslational modification were prominent (13.8% of known genes identified in DR EOM), but due to the broad expression of these genes the involved cell types cannot yet be established. Among nonmuscle transcripts differentially expressed in DR EOM were two downregulated collagen genes (collagen type 1 alpha 1 and procollagen C-proteinase enhancer protein). The high percentage of ESTs and novel tags in our library (collectively 74% of all detected transcripts) makes it difficult to assess the full scope of the EOM critical period at this time. Several of the ESTs have sequence homology to genes that fit the altered cytoskeletal and mitochondrial transcript themes. As genomic databases are completed, the EST and novel tag data presented in this report can be reassessed from a broader knowledge of the identities of differentially regulated transcripts.

The patterned changes in gene expression of MD monkey EOMs were considerably less severe than in DR rats. Although control monkey lateral and medial rectus muscles do not differ in baseline gene expression patterns (unpublished data), they exhibited differential expression responses to MD. Differences in the lateral and medial rectus response are not unexpected, because the loss of binocular vision in MD disrupts disjunctive fusional vergence movements, whereas conjugate eye movements driven by the nondeprived eye appear to be normal.7 Moreover, both visual pursuit and optokinetic eye movements exhibit a temporal–nasal asymmetry that is accentuated in visual maldevelopment. The different affect on horizontal rectus muscles may be a consequence of such differences in conjugate or disjunctive eye movements. As in DR rats, few muscle-specific transcripts were altered in monkey EOMs; increased expression of MEF2C in the MD medial rectus is consistent with myogenesis. Extracellular matrix components were downregulated in both medial and lateral recti, as they were in DR rat EOMs. Two transcripts related to posttranslational protein modification (HSPB1 and PPIF) were upregulated in MD medial rectus, consistent with differential regulation of multiple genes in this class in DR rat EOM. Few transcripts linked to mitochondrial function or energy metabolism were differentially regulated in MD EOMs (PDK4, a glycolysis regulator, was repressed in lateral rectus; TKT, a pentose phosphate pathway enzyme, and OGDH, a tricarboxylic acid cycle enzyme, were induced in medial rectus). Thus, the shift in energy metabolism that was seen in DR rats was not observed in the MD monkey. Genes shared between the medial and lateral recti of MD monkeys and between the rat DR and monkey MD models are indicated in Tables 2 and 3 . Although only two transcripts were shared between MD lateral and medial recti and only one was shared between MD monkey and DR rat, there was sharing of functional categories of differentially regulated transcripts.

We suggest that the nature and severity of the gene expression changes in the MD monkey versus the DR rat are consequences of inherent differences in the visual deprivation paradigms. Rather than the prevention of primary visual cortex (V1) maturation that occurs in DR, MD alters ocular dominance column maturation in V1, while preserving at least a monocular drive to eye movement control systems from the non-deprived eye. Lennerstrand and Hansen26 and Lennerstrand27 28 observed decreases in the contraction speed and fatigue resistance in EOMs of MD cats, but saw no accompanying shift in muscle fiber type composition. Similarly, DR rat EOMs show a slowing of contraction speed,43 but neither DR nor MD altered the expression of many muscle fiber–specific transcripts. Moreover, congenitally strabismic monkeys, which share visual deficits with MD, exhibit modest morphologic changes in specific EOM fiber types.44 Taken together, the differences in expression signatures obtained from DR and MD paradigms then suggest that EOM requires visuomotor-driven activity for proper development, but that any MD-induced functional adaptations in EOM are associated with transcriptional changes that are either modest or largely below the threshold of DNA microarray.


   Conclusions
Top
 Abstract
 Materials and Methods
 Results
 Discussion
 Conclusions
References
 
The DR and MD paradigms used in the current study mimic alterations that are associated with the common disorders of strabismus, congenital cataract, and amblyopia. Our data show that postnatal EOM maturation is broadly susceptible to visuomotor maldevelopment, suggesting that postnatal eye movement system activity plays a vital determinant role in the maturation of the novel EOM phenotype. Differentially regulated transcripts identified herein represent new gene candidates for an EOM critical period. Yet, the full nature of this critical period is only partially understood, because many of the transcripts detected in DR rat EOM represent ESTs or novel SAGE tags. Collectively, our findings then extend the concept of an EOM critical period and suggest that the nature of visual deprivation is an important factor in this critical period.


   Acknowledgements
 
The authors thank Xiaohua Zhou, Sriram Kasturi, Anita Merriam, Patrick Leahy, Francisco Andrade, and Bendi Gong for technical support.


   Footnotes
 
Supported by National Eye Institute Grants R01 EY09834 (JDP), R01 EY12779 (JDP), and P30 EY11371, and a Departmental Award and a Walt and Lilly Disney Award for Amblyopia Research (JDP) from Research to Prevent Blindness. JDP holds the Carl F. Asseff, MD, Professorship in Ophthalmology. The DNA microarray core facility is supported by National Institutes of Health Grant P30 CA43703.

Submitted for publication February 18, 2003; revised April 2, 2003; accepted April 24, 2003.

Disclosure: G. Cheng, None; M.J. Mustari, None; S. Khanna, None; J.D. Porter, None

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: Georgiana Cheng, Department of Ophthalmology, Case Western Reserve University, 11100 Euclid Avenue, Cleveland, OH 44106-5068; gxc18{at}po.cwru.edu.


   References
Top
 Abstract
 Materials and Methods
 Results
 Discussion
 Conclusions
 References
 

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