Eric J Stevenson & Susan C Kandarian
Boston University, Boston, MA, USA

https://doi.org/10.36866/pn.54.24

Our understanding of the mechanisms regulating skeletal muscle plasticity is still in its infancy. With the explosion of genomic information in the past decade, new tools have become available making it possible to obtain a global view of the changes in gene expression that underlie muscle adaptation. Transcriptional profiling has led to major advances in the understanding of biology in simple cellular systems. However, this degree of success has proven more elusive in complex tissues. It has become apparent, however, that with the proper focus and use of bioinformatics tools it is possible to design experiments that yield meaningful results. Several studies in recent years have successfully employed such techniques to develop a better understanding of the molecular processes that regulate muscle mass and plasticity.

Muscle atrophy is commonly thought of in terms of inactivity and sedentary life styles. However, profound muscle wasting also occurs with several pathological conditions. In these situations, muscle protein loss can become chronic and lead to severe complications and death. On the other hand, disuse atrophy is unique in that protein loss seems to reach an endpoint and actually involves an extensive process of remodelling. The mystery here is whether common or distinct pathways control atrophy in both situations. In an attempt to address this question, two groups have recently used expression profiling to identify common markers of atrophy in a variety of experimental models.

The majority of proteolysis during atrophy is mediated by the ubiquitin-proteasome system. Ubiquitin ligases (E3s) are involved in targeting specific proteins for degradation by the proteasome. Gomes and associates used microarrays to identify a previously uncharacterized gene that was highly induced in skeletal muscle during fasting (Gomes et al. 2001). Subsequent analysis showed that this gene was a novel muscle-specific E3 they named Atrogin-1 that is also induced with diabetes, cancer and renal failure. This same gene was concurrently cloned and characterized by another group and named muscle atrophy F-box (MAFbx) (Bodine et al. 2001). Differential display methods showed this gene was also induced in several models involving muscle disuse. In addition, they discovered another muscle-specific E3 activated in all disuse models, which they named muscle ring finger 1 (MuRF1). The finding that knockout mice lacking these genes are partially resistant to muscle atrophy following denervation is a testament to their functionality as universal mediators of atrophy.

By analyzing gene expression at multiple time points, clustering algorithms can be used to identify sets of coordinately regulated genes. Early work with yeast demonstrated the power of this approach in identifying regulatory networks. Since muscle adaptations likely involve the coordinated actions of many genes working in parallel, the potential use for such methods to study muscle biology becomes obvious. Several studies have emerged that have shown that these methods can also be adapted to studying regulatory mechanisms even in a tissue as complex as skeletal muscle.

Our laboratory has used this approach to analyze how gene expression patterns change at multiple time points during the first 14 days of disuse atrophy in rats (Stevenson et al. 2003).

This analysis provided us with an abundance of new genes to study, including another E3, Nedd4, that is activated during atrophy. However, it was the temporal aspect to the study that provided us with even more information. Clustering was used to segregate differentially expressed genes into sets sharing similar activation or deactivation patterns during atrophy. This allowed the development of a timeline with respect to behaviour of genes in a broad array of functional categories. One of the most interesting elements involved the fact that regulatory genes, while often upregulated early, were also in clusters that represented genes with distinct peaks during the later stages of atrophy. This supports the idea that the atrophy process is marked by several sequential phases. For example, genes that are activated in early clusters may activate the genes involved in protein degradation, but the maintenance of this phenotype may be regulated by genes that fall into clusters representing later activation.

Other groups have taken this approach a step further by using temporal expression patterns to identify downstream targets of MyoD. It is well established that MyoD is an arbiter of myogenic lineage, but it has also been shown to modulate the transcriptional response of muscle genes to different activity paradigms. Indeed, MyoD expression is activated during muscle regeneration, and with increased or decreased mechanical loading. In each of these situations, MyoD activates or deactivates a small subset of the genes that contain MyoD binding sites in their promoters. Therefore, the question arises as to how such specificity is accomplished in each of these situations.

Bergstrom et al. (2002) have used temporal expression profiling to investigate how MyoD orchestrates the process of differentiation. MyoD overexpression was used to induce differentiation in MyoD-/-/Myf5-/-fibroblasts, and expression was measured at several time points using microarrays. By using cyclohexamide to inhibit synthesis of other activated regulatory factors the authors were able to strictly identify targets of MyoD. Using clustering they were able to show that MyoD can initiate several distinct subprograms of gene expression through promoter-specific recognition rather than global activation of all MyoD regulated genes.

Another group was able to use similar techniques in vivo to discover novel targets of MyoD activated during muscle regeneration (Zhao et al. 2002). As expected, MyoD and several of its known downstream targets were activated at a time point consistent with the formation of new fibres. Clustering was used to identify genes with an activation pattern similar to that of Ulip, one of these known targets.

Promoter databases and sequence analysis tools were then used to show that a small subset of these genes actually had potential MyoD binding sites in their promoters. Gel-shift assays and chromatin immunoprecipitation were used to discover a functional binding site in the promoter of Slug, a member of the snail/slug family of transcriptional repressors. The functionality of this relationship was demonstrated in vivo using slug-dependent reporter constructs and through the demonstration that Slug-null mice show impaired ability to regenerate after injury.

As the studies described herein show, a good deal of progress has been made in the development of effective microarray studies in muscle. With the development of increasingly sophisticated annotation and analysis tools and the introduction of whole-genome chips, this area of research promises to be as much as a boon to the field of muscle biology as it has to other less complex systems cellular systems.

References

Bergstrom DA, Penn BH, Strand A, Perry RL, Rudnicki MA & TapscottSJ (2002). Promoter-specific regulation of MyoD binding and signal transduction cooperate to pattern gene expression. Mol Cell 9, 587-600.

Bodine SC, Latres E, Baumhueter S, Lai VK, Nunez L,Clarke BA, Poueymirou WT, Panaro FJ, Na E, Dharmarajan K, Pan ZQ, Valenzuela DM, DeChiara TM, Stitt TN, Yancopoulos GD & Glass DJ (2001). Identification of ubiquitin ligases required for skeletal muscle atrophy. Science 294, 1704-1708.

Gomes MD, Lecker SH, Jagoe RT, Navon A & Goldberg AL (2001). Atrogin-1, a muscle-specific F-box protein highly expressed during muscle atrophy. Proc Natl Acad Sci U S A 98, 14440-14445.

Stevenson EJ, Giresi PG, Koncarevic A & Kandarian SC (2003). Global analysis of gene expression patterns during disuse atrophy in rat skeletal muscle. J Physiol 551, 33-48.

Zhao P, Iezzi S, Carver E, Dressman D, Gridley T, Sartorelli V & Hoffman EP (2002). Slug is a novel downstream target of MyoD. Temporal profiling in muscle regeneration. J Biol Chem 277, 30091-30101.