Ординатура / Офтальмология / Английские материалы / Eye, Retina, and Visual System of the Mouse_Chalupa, Williams_2008

protein expression levels or alterations in functional interactions of the protein. In most cases, the ultimate predictor of phenotype is the protein, not the transcript.

Unlike the simple Mendelian trait of albinism, where a single base mutation can cause a complete lack of protein function, most physical traits are regulated by complex genetic interactions that result in modest variation in different transcripts. The genomic loci regulating these complex traits can be difficult to extract from a genetically diverse outbred population. The genetic variability in inbred strains and recombinant inbred strains (such as the BXD strain set) offers unique opportunities to define genomic loci regulating complex traits. The study of these complex traits is facilitated by the availability of many different inbred strains of mice and specific Web-based genomic tools, many of which can be found at GeneNetwork. In this highly interactive database, numerous phenotypes are presented from sets of inbred panels of mice, including CNS phenotypes for cerebellum (Neumann et al., 1993), hippocampus (Lu et al., 2001; Kempermann et al., 2006), striatum (Rosen and Williams, 2001), and amygdala (Mozhui et al., 2007). Of specific interest to the vision community are data sets related to the eye, the retina, and the central visual system.

At the University of Tennessee, the Williams group has used these genetically variable populations of mouse strains to define regulatory loci controlling specific phenotypes. These phenotypic characterizations include variability in retinal ganglion cell (RGC) number, eye weight, and cell number in the lateral geniculate nucleus (Williams, Strom, and Goldowitz, 1998; Seecharan et al., 2003). For example, the population of RGCs in the mouse has a bimodal distribution, from a low of approximately 45,000–50,000 in CAST/Ei and BXD27 to a high of 75,000 in BXD5 and BXD32 (Williams et al., 1996; Williams, Strom, and Goldowitz, 1998; Seecharan et al., 2003). A major QTL on chromosome 11 that modulates RGC population size, Nnc1, has been mapped to a 4 Mb interval (peak at 98 Mb, 1.5 LOD confidence interval 97–101 Mb). Within this locus are genomic elements (potential candidate genes) that are involved in regulation of the number of RGCs in the mouse retina (Williams et al., 1996). In addition, Williams, Strom, and Goldowitz (1998) mapped two other contributing loci on chromosome 2 and chromosome 8 by simply quantifying the number of RGCs in 38 recombinant inbred strains and using composite interval-mapping techniques. This type of quantitative method was also used to map genomic loci controlling eye weight (Zhou and Williams, 1999) and cell number in the lateral geniculate nucleus (Seecharan et al., 2003). The traditional QTL mapping strategy defines loci that regulate a physical trait. This approach allows a systematic approach to defining a major genomic loci regulating a complex trait, such as RGC number in the mouse. Furthermore, we can use the QTL mapping methods as the basis

for defining genetic networks controlling gene expression and phenotypes.

The power of inbred strains extends beyond their use as a tool to map genomic loci controlling physical traits. These reference panels of mice can also be used to unravel genetic networks controlling the structure of the eye, its development, and its response to diseases. By treating the changes in mRNA levels as a physical phenotype, differences in transcriptional control can be evaluated using traditional QTL mapping methods to define groups of transcripts with similar patterns of expression. These similar patterns of expression reveal commonality in regulatory networks that are correlated to specific genomic loci. By combining gene expression profiling with genetic linkage analysis, the location of genetic elements that modulate expression levels of specific groups of genes can be identified (Darvasi, 1998; Broman, 2005). This type of expression genetics has been used to identify genes underlying complex traits, diseases, and behavioral phenotypes (Morley et al., 2004; Bystrykh et al., 2005). Examples of this type of integrated gene expression profiling and linkage analysis are studies that have identified genes predisposing individuals to hypertension (Cd36) and atherosclerosis (ABCG5 and ABCG8) (Aitman et al., 1999; Berge et al., 2000). These studies illustrate the power of combining gene expression profiling with linkage analysis to study gene expression.

Quantitative trait locus mapping in the Hamilton Eye Institute Mouse Eye Database

The QTL mapping functions in GeneNetwork can be used as a tool to understand the genetic networks controlling changes observed in traditional micoarray studies. As stated earlier, the genetic variability in inbred strains and mouse diversity (BXD) strain sets can be used to define groups of transcripts with similar patterns of expression. These similar patterns reveal commonality in regulatory networks that are correlated with specific genomic loci. By combining gene expression profiling with genetic linkage analysis, the location of genetic elements that modulate expression levels of specific groups of genes can be identified (Darvasi, 1998; Broman, 2005). The high levels of heritable variation in gene expression allow researchers to correlate the expression variability with one or more regions of the genome. Some of these loci modulate genes within the interval where they are located (cis-acting), although most loci modulate genes from a distance, at a different genomic location (trans-acting). HEIMED allows exploration of the genetic variability in the BXD RI strains and selected inbred strains. To examine regulatory networks, the BXD RI strain set plays a preeminent role in identifying QTLs controlling the level of gene expression. This analysis can be used to define cis-acting and trans-acting QTLs.

Three transcripts (Tyrp1, Gpnmb, and Tyr) were selected. The Tyrp1 gene and the Gpnmb gene show strong cis-QTLs (figure 54.3A and C ); Tyr demonstrates a strong trans-QTL. The Tyr gene is quite variable across mouse strains; there is a twofold difference in gene expression across the BXD RI strains and other inbred strains. Running a genome-wide scan by selecting the interval mapping function reveals a linkage of expression variability of Tyr with a locus on chromosome 4 (see figure 54.3A). Note that the peak linkage on chromosome 4 likelihood ratio statistic (LRS) is more than 40 megabases and is at the same locus as the Tyrp1 gene itself. The strong cis-QTL of Tyrp1 at the same genomic locus as the trans-QTL for Tyr makes Tyrp1 a potential candidate gene controlling the gene expression variability of Tyr.

Defining genetic networks in traditional microarray experiments

Like many groups, we have used microarray methods to profile global changes following specific experimental manipulations. In this process, changes in gene expression are identified and analyzed to define functional patterns of expression. For most microarray studies, this is the end of the process. However, with the bioinformatics tools offered on GeneNetwork, the analysis of microarray gene clusters can be extended to define genetic networks regulating these changes in gene expression. To illustrate this process, we take an example from our research group.

We have used microarray techniques to obtain a picture of the global changes occurring in the retina following injury (Vazquez-Chona et al., 2004). The transcriptome-wide analysis was designed to look at the temporal regulation of transcripts controlling retinal wound healing. This gene expression profiling identified clusters of genes that are regulated in specific temporal patterns (Vazquez-Chona et al., 2004). From all these data, the response of the retina to injury could be subdivided into three temporal phases of gene expression: early acute, delayed subacute, and late chronic. Transcripts in each phase appear to be functionally related and reflect known cellular changes. The global changes occurring after injury are similar across different injury models, including mechanical trauma, ischemia, and increased intraocular pressure. The similarities in these profiles suggest that the changes in gene expression are part of common biochemical and cellular processes involved in the response of the retina to injury. As with many microarray experiments, this description of changes in the transcriptome leaves one with clusters of genes and little understanding of the basic mechanisms involved in regulating gene expression. To take the analysis to the next level, we used the databases and bioinformatics tools on GeneNetwork to examine the common regulatory elements controlling the clusters of genes in the temporal response to injury.

The basic approach to extract the underlying genetic networks involves, in sequence, identifying robust changes in gene expression following injury; defining regulatory loci (trans-QTLs), using genetic analysis of transcript data at GeneNetwork; and predicting candidate regulators, using bioinformatic resources that are available online. We defined a set of acute phase genes that are commonly expressed in the retina, brain, and spinal cord after traumatic injury (Vazquez-Chona et al., 2005). We then used the expression QTL analysis combined transcriptome profiling with linkage analysis to reveal variability in the chromosomal loci modulating gene expression. The analysis was made using the BXD RI strain sets and the bioinformatics tools available at genenetwork.org. Expression of acute phase genes in BXD RI mouse forebrains is modulated by a major QTL on chromosome 12 between 15 and 32 Mb. With the loci identified, the next step was to identify the candidate genes.

The identification of candidate genes within specific intervals of the BXD strain set is simplified by the nature of the strain set and the expression data set. For a gene to have a QTL that can be mapped, there must be significant variability in gene expression across the BXD strain set. In HEIMED, 10,000 probe sets have significant variability and have a significant LRS score. The second feature that identifies a potential candidate gene is that it must either be within the locus or have a cis-QTL. Within HEIMED, 5,000 probe sets have significant cis-QTLs. Within the chromosome 12 interval, there are three good candidate genes, Id2, Lpin1, and Sox11. All these transcripts have strong cis-QTLs and are known either to be transcription factors (Yokota and Mori, 2002; Jankowski et al., 2006) or to have the potential to localize to the cell’s nucleus (Peterfy et al., 2005). Thus, these three genes are ideal candidates to be upstream regulators of the injury network.

The novel combination of microarray analysis, expression genetics, and bioinformatics provides a new and powerful approach to defining regulatory elements in the genome. Using this approach, we were able to generate specific, testable hypotheses to define the pathways that regulate proliferative and reactive responses in the retina and elsewhere in the CNS. As more diverse gene expression data sets become available, a comparison of gene expression and regulation in different biological contexts should help identify the regulatory elements controlling the reactive response in the retina.

Conclusion

The preceding analysis provides a glimpse into the revolution in genomics and genetics that is allowing us to examine and compare gene expression in different tissues. By defining the genetic variability among strains, we can now account

Figure 54.3 Interval mapping computes linkage maps for the entire genome. The numbers across the top of the three panels indicate mouse chromosomes, from Chr 1 to Chr X. We used the genetic analysis of transcript expression at GeneNetwork to define genomic loci that control transcript abundance variability in mouse eye. The variability in transcript abundance in the segregating population of BXD RI strains makes it possible to map transcript abundance to a specific chromosomal locus. Genetic linkage maps (A–C ) show genes with significant LRS scores (the significance level is indicated by the upper line). A, Individual genome-wide maps for Tyrp1 demonstrating a strong cis-QTL (the location of the gene

is indicated by the black triangle). B, Genome-wide scan for Tyr, a gene with a strong trans-QTL. C, Genome-wide scan for Gpnmb, an additional gene with a strong cis-QTL. Maps were generated by linking transcript variability against 8,222. The linkage between transcript variation and genetic differences at a particular genetic locus is measured in terms of likelihood ratio statistic (LRS, solid line). Dashed horizontal lines mark transcript-specific significance thresholds for genome-wide P < 0.05 (upper band) and genomewide P < 0.63 (lower band). The triangle indicates gene location. Linkage maps were generated using the interval mapping tool at GeneNetwork.

for many phenotypic differences in the mouse eye. The mouse eye is genetically unique in that findings in the mouse can directly apply to understanding gene expression in human conditions leading to the loss of sight. Defining the genetic variability and differences in gene expression across strains of mice that are fully mapped and sequenced allows a direct correlation between eye phenotypes and disease states with genomic loci and candidate genes. Tran- scriptome-wide analysis of the eye allows us to define genetic variability between strains not only to specific cellular phenotypes, but also to the response of cells to changes in the state of the eye. Using HEIMED allows the vision research community to define molecular signatures within the tissues of the eye, identify candidate genes for human disease, begin to understand genetic networks regulating tissue-specific gene expression, and identify the complex interactions of genomic loci that underlie the complex structures of the eye. As technology progresses, it should be possible to use cell type–specific analysis of mRNA and protein expression across large genetically defined and manipulated panels of mice. The use of these methods in large genetic reference panels of mice and the multiscale integration of the resultant data should allow for the definition of genes responsible for complex genetic diseases. We are currently using this approach to define genomic loci and candidate genes associated with susceptibility and resistance retinal ganglion to cell death in human diseases.

acknowledgments Support for the acquisition of microarray data sets was generously provided by Dr. Barrrett Haik, chair of the Department of Ophthalmology and director of the Hamilton Eye Institute. This work is supported by the National Eye Institute (RO1EY17841), the Integrative Neuroscience Initiative on Alcoholism (U01AA13499, U24AA13513), the Human Brain Project (P20-DA21131), NCI (U01CA105417), and the Biomedical Informatics Research Network, NCRR (U24RR021760). All arrays were processed at the VA Medical Center, Memphis, by Dr. Yan Jiao and Dr. Weikuan Gu. We thank Dr. Lu Lu for his contributions to every aspect of the HEIMED project, Bill Orr for his assistance, and Dr. Mohamed Nassr for his assistance in data analysis.

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Retinal degenerative diseases (RDs) are a major cause of untreatable blindness in the Western world (Bressler et al., 2003; Hartong et al., 2006). A vast majority of RDs are categorized as orphan diseases, affecting fewer than 200,000 individuals in the population; nevertheless, these diseases collectively represent an important health problem (Weleber, 2005). Of almost 200 mapped RD loci, as many as 130 genes have been identified (www.sph.uth.tmc.edu/Retnet/sumdis.htm). These studies have assisted in better clinical diagnosis and management (Kalloniatis and Fletcher, 2004; Hartong et al., 2006). However, the precise molecular events and cellular pathways of disease pathogenesis are relatively less well understood, and effective treatment paradigms are still not available for most retinopathies. A major challenge has been the heterogeneity of mutations resulting in photoreceptor degeneration, making it economically impractical for pharmaceutical companies to invest in therapies that may alleviate the pathology in small subsets of populations. Mutational heterogeneity is illustrated most noticeably in the case of rhodopsin, where at least 88 mutations can cause the autosomal dominant type of retinitis pigmentosa (RP) yet two alleles lead to RP only in a homozygous state (Gal et al., 1997). Another impediment is that the affected individuals exhibit extensive variations in disease phenotype even though the same gene may be involved. Thus, the design of therapeutic strategies must take into account the complexity of retinal phenotypes and the heterogeneity of gene defects.

Gene therapy, a paradigm for individualized treatment, has been used extensively to deliver a functional gene to counter the effect of a nonfunctional gene or to eliminate a mutant allele in animal models of RD (Acland et al., 2001; Hauswirth and Lewin, 2000; Mori et al., 2002; Dinculescu et al., 2005). A number of generalized approaches are also being developed to surmount the complex diversity of RDs; these include the surgical implantation of optoelectronic retinal prostheses (Zrenner, 2002; Humayun et al., 2003;

Rizzo et al., 2004), transplantation of cells (Gouras et al., 1994; Lund et al., 2001; MacLaren et al., 2006), and treatment with growth or survival factors (Faktorovich et al., 1990; Frasson et al., 1999; Delyfer et al., 2004). All of these therapeutic strategies suffer from certain limitations. Gene therapy protocols need to address extensive heterogeneity in gene mutations, apart from safety and efficacy problems associated with viral vectors. For electronic chips, limited field of vision and resolution are among some of the difficulties. Transplantation of cells faces different kinds of hurdles, including functional integration into the retina, biocompatibility, long-term survival of cell transplants, and ethical issues. In the case of treatments involving growth and survival factors, the delivery of optimal amounts and their usefulness or side effects over the long term constitute primary concerns. We therefore need to incorporate additional paradigms for effective design of therapies for RDs.

Comprehensive studies have established that dysfunction or death of rod and cone photoreceptors are the primary cause of blindness in the vast majority of RDs (Rattner et al., 1999; Pacione et al., 2003). A better understanding of photoreceptor development, function, and survival would greatly assist in effective design of treatments for blinding retinal diseases. This chapter first discusses gene profiling using microarrays and the construction of regulatory networks underlying photoreceptor differentiation. We then describe a systematic approach to delineating molecular pathways of photoreceptor cell death caused by inherited gene defects, with a goal of developing novel treatment paradigms.

Expression profiling using microarrays

Microarrays of oligonucleotides or DNA fragments derived from expressed sequence regions allow simultaneous measurement of expression changes in thousands of genes under a given condition (Duggan et al., 1999; Zareparsi et al.,

2004). Temporal gene profiles of a specific tissue or cell type can provide valuable information for constructing networks or pathways involved in a complex biological process, such as differentiation or disease pathogenesis. The choice of a specific microarray platform (e.g., cDNA array, short vs. long oligonucleotide arrays) for expression profiling depends on the purpose of the experiments. Because our goal is to compare gene profiles from multiple different experiments (i.e., profiles from the retina or photoreceptors of multiple mutant mice at different stages of disease progression), it is highly desirable to employ stringent quality control and minimize intersample experimental variations. Therefore, we have optimized conditions for microarray analysis using mouse GeneChips MOE430.2.0 (www.affymetrix.com); these microarrays include 45,101 probe sets corresponding to about 39,000 transcripts of 34,000 annotated genes. Discussions of microarrays and associated methods can be found in several recent publications (Duggan et al., 1999; Livesey et al., 2000; Mu et al., 2001; Farjo et al., 2002; Yoshida et al., 2002; Diaz et al. 2003; Hackam et al., 2004; Zareparsi et al., 2004).

A major challenge in gene profiling experiments is the extraction of useful information from enormous data sets. The mammalian retina consists of six major types of neurons and one type of glia; however, many distinct subtypes can be identified based on morphology and function (Masland, 2001). Global profiling of the retina would reveal average changes in expression of genes in many different cell types; several of these would reflect manifestations secondary to RD. Notably, a number of photoreceptor-specific genes are mutated in RDs, and even when the mutant gene is widely expressed, photoreceptors are considered the primary site of disease in a majority of patients with RD. In patients with RP and in most rd mouse mutants, rods appear to be the first retinal cell type that is affected. Even in age-related macular degeneration (AMD), increased loss of rods is observed in the central visual field during early stages (Curcio et al., 1996). It is therefore likely that gene profiling of purified rod (and cone) photoreceptors rather than the whole retina will yield more useful data for network or pathway construction to fully comprehend differentiation or degenerative disease.

NRL and gene profiling of purified rod (and cone) photoreceptors

The basic motif-leucine zipper transcription factor (TF), NRL (Swaroop et al., 1992), is essential for rod photoreceptor differentiation; loss of NRL in mice (Nrl−/−) results in transformation of postmitotic rod precursors to cones (Mears et al., 2001; Daniele et al., 2005; Akimoto et al., 2006), whereas ectopic expression of NRL converts cones to rods (Oh et al., 2007). NRL is expressed preferentially in rods and

the pineal gland (Swain et al., 2001; Akimoto et al., 2006). We recently used Nrl promoter to direct the expression of GFP (Nrlp-EGFP transgene) in mice (wt-Gfp) and successfully marked the rod photoreceptors with GFP at the time of birth (Akimoto et al., 2006). GFP tagging allowed us to identify and purify rod photoreceptors at different stages of development using flow cytometry (FACS analysis) (figure 55.1). Evaluation of retinas from the Nrl−/−-Gfp mice (generated by breeding wt-Gfp mice with the Nrl−/− mice) provided a direct demonstration of the transformation of rods to cones in the absence of NRL, and this also led to efficient enrichment of cone photoreceptors by FACS (Akimoto et al., 2006). To produce gene signatures of developing and mature rods or cones, we used total RNA from 1–5 × 105 FACSpurified cells for linear amplification. Biotin-labeled fragmented cDNAs derived from the amplification were then hybridized to mouse GeneChips (Affymetrix). Profiling of rods (from wt-Gfp retina) and cones (from Nrl−/−-Gfp retina) at five distinct stages of development revealed comprehensive sets of differentially expressed genes that distinguish photoreceptors from other cell types (Akimoto et al., 2006). This expression analysis also uncovered the advantages of profiling purified cells, as many changes were missed when profiles of the entire retina were studied. Differential gene expression patterns at specific developmental stages provide basic data sets for higher-order analysis, such as clustering, to identify genes that behave similarly and those unique to a particular cell. However, construction of regulatory networks or pathways is a major challenge, requiring sophisticated statistical tools to bring out features such as key regulators (nodes or hubs), the hierarchies they sustain, and the genes they regulate.

Characteristics of biological networks and relevance to photoreceptors

The rationale for generating regulatory networks is to explain the spatial and temporal execution of specific cellular functions (Davidson et al., 2003). Networks consist of nodes representing molecules (such as protein, DNA, RNA and metabolites) and edges that explain the association between nodes. Networks offer a quantifiable description based on factors such as degree or connectivity between hubs, degree distribution, path length, and clustering coefficient between the various elements (Barabasi and Oltvai, 2004; Blais and Dynlacht, 2005). A majority of biological networks are scalefree and, unlike random networks, are nonuniform, having few highly connected hubs and many nodes that have only a few links (Barabasi and Albert, 1999). Many networks also display the “small world” property with high clustering but small characteristic path lengths between two nodes (Watts and Strogatz, 1998). Transcriptional and posttranscription regulatory networks can describe many of the cellular

Figure 55.1 A, A single rod photoreceptor isolated from adult wt-Gfp mice shows the expression of GFP. B, The same cell shows staining for rhodpsin (red) in the outer segment (OS) and inner segment (IS) regions and bis-benzimide staining (blue) for the

activities and are subdivided into physical and functional networks. Although physical networks are represented by interactions such as protein-protein/DNA/RNA or RNARNA, the functional networks represent the outcome of these interactions, such as regulation of gene expression or signaling pathways (Walhout, 2006). Analysis of highthroughput yeast two-hybrid data has revealed about 2,800 interactions among 8,100 human proteins tested, discovering more than 300 new connections to more than 100 disease-associated proteins (Rual et al., 2005). Alterations in transcriptome and proteomic signatures in metastatic and clinically localized prostate cancer, along with the concordance between the protein and transcript levels, were able to predict clinical outcomes in prostate cancer and other solid tumors (Varambally et al., 2005). Though biological network assemblies are still in their infancy, numerous recent studies represent important steps toward understanding complex physiological processes.

At this stage, it is not conceivable to decipher the single master network that would describe all the transcribed genes, expressed proteins, and protein-protein interactions in the entire retina (or even in rod and cone photorecep-

nucleus. C, Samples of dissociated cells are viewed under the microscope before (shown) and after flow sorting. D, Flow cytometry allows the GFP-positive population of cells to be gated (R3) and sorted separately. See color plate 63.

tors) at a single specific developmental stage. Hence, our goal is to establish gene regulatory networks in rod photoreceptors under normal and disease conditions. This information can then be integrated into protein expression and interaction networks. These subnetworks are not independent; instead, they link with each other to form more complex patterns. Hence, the biological process can be visualized as the highly regulated and progressive generation of subnetworks that combine together to describe the unique properties of a specific cell type during differentiation or disease (Blais and Dynlacht, 2005; Fraser and Marcotte, 2004).

Hierarchy in regulatory networks underlying photoreceptor development

A key question pertaining to photoreceptor development is how NRL expression in postmitotic photoreceptor precursors initiates the cascade of molecular events that suppress the cone-specific differentiation pathway and establish rod lineage and function. In many cases TFs form key hubs of a regulatory network and display a hierarchical organization