Orthopaedic Research Society

Mapping the human and mouse genomes was an enormous undertaking and is providing an incredible wealth of knowledge for basic and applied research. However, knowing the code is like taking a census of each individual in a country. Although this is an important first step, the functionality of a country cannot be known simply by knowing the names and locations of its citizens. This functionality also comes from knowing how the individuals work together for a common cause. Likewise, the functionality of complex metabolic systems or complex organs like bone, heart, muscle, and tendon depends on how individual genes and sets of genes work together to facilitate the cellular activities responsible for the development and maintenance of the physical traits and physiological processes. Complex, heritable conditions such as obesity, osteoporosis, and heart disease cannot be easily examined using a “one gene-one disease” paradigm. Certainly, single gene mutations can have deleterious consequences. However, chronic conditions are incredibly complex because many gene variants contribute to phenotypic variation. New approaches are now available to understand how these complex systems work, and to obtain new perspectives into biological mechanisms. Inbred mouse strains are typically thought to be useful for identifying the function of individual genes or for identifying novel quantitative trait loci (QTLs) that contribute to variability in a trait of interest. However, recent work using inbred mouse strains has shown that there is more complexity to the functioning of the genome than originally thought, and that there are higher level biological controls that need to be considered in genetic analyses. This workshop will explore how inbred mouse strains and systems biology provide a venue to understand basic biological principles that contribute to phenotypic variation and how these concepts can be translated to the human skeleton and other traits. SPEAKERS Modifier Genes: Simple Traits and Complex Systems, in Sickness and in Health Joseph H. Nadeau, PhD Department of Genetics, Case Western Reserve University, Cleveland, OH USA Bone Mechanics, Muscle Mass, and Activity-Related Behavior: Structural Equation Modeling of their Relationships and Genetic Influence Dean H. Lang, PhD Dept. of Kinesiology, Pennsylvania State University, University Park, PA USA Genetic Randomization Reveals Co-adapted Traits that Contribute to the Functionality and Fragility of Long Bone Karl J. Jepsen, PhD Department of Orthopaedics, Mount Sinai School of Medicine, New York, NY USA Modifier Genes: Simple Traits and Complex Systems, in Sickness and in Health Joseph H. Nadeau, PhD Department of Genetics Case Western Reserve University, Cleveland, OH USA A meaningful understanding of the genetic architecture of complex traits in most organisms remains elusive because we do not have good estimates of the number of genes that underlie these traits, the magnitude of their effects, the extent to which they interact, or their impact on systems properties. Chromosome substitution strains (CSSs) enable statistically powerful tests based on characterizing inbred strains that have a single, unique and non-overlapping genetic difference. A survey of 90 blood, bone and metabolic traits in a mouse panel of CSSs and 54 traits in rat CSSs revealed a remarkable number of quantitative trait loci (QTLs) that have large and highly nonadditive effects. Many of these act as genetic modifiers of multigenic targets. Similar effects were also found for several traits in congenic and sub-congenic strains derived from selected CSSs. In addition, single chromosome substitutions shifted phenotypes in the host strain strongly in the direction of the donor strain, and phenotypes in CSSs were usually constrained within the range of phenotypic variation displayed by the parental strains. By examining correlations between traits within and among CSSs, variation in functional relations between traits was discovered. These studies suggest that networks of interacting genes buffer organisms from genetic and environmental perturbations and that targeting convergence points in downstream pathways may yield disproportionately large and general switches between distinct phenotypes. Finally, the deep genetic and functional complexities in these strains provide a glimpse into the genetic architecture and systems properties in individual genotypes, which is a prerequisite for personalizing medicine. Bone Mechanics, Muscle Mass, and Activity-Related Behavior: Structural Equation Modeling of their Relationships and Genetic Influence An Integrative Approach to the Study of Bone Quality Dean H. Lang, PhD Department of Kinesiology Pennsylvania State University, University Park, PA USA Biological processes are known to be part of highly integrated systems. Experiments employing a reductionist approach often identify significant physiological pathways that appear much less profound when examined at the systems level. As with most biological systems, skeletal integrity is the end result of a dynamic system of regulation with many degrees of freedom. Redundant pathways can produce conflicting results when attempts are made to isolate single genes that influence skeletal phenotypes without using a polygenic frame of reference or a complex systems approach. The complex system responsible for bone adaptation, and ultimately responsible for bone quality, extends beyond the skeletal system to encompass other physiologic systems and processes. Principal among these may be components related to muscle mass and force generation as well as components dictating locomotion and activityrelated behaviors, as these contribute substantially to the loads borne by the skeleton. Previous work has conclusively shown that variations in peak bone mass and rates of bone loss are influenced by variations in muscle mass or muscle strength (Arden and Spector, 1997; Li et al., 2001; de Jong et al., 2004) as well as the frequency and duration of activity (Kaye and Kusy, 1995; Gordon et al., 1989). Twin studies (Dequeker et al., 1987) as well as inbred mouse studies (Lang et al., 2005) have confirmed that bone properties are heritable. Quantitative Trait Loci (QTL) analysis is a tool that allows one to examine a phenotype within the context of the system functioning as a whole. A major and fair criticism of QTL analysis is that it infrequently leads to discovery at the gene level. Nevertheless, significant insight can be gained from QTL results without ever identifying the underlying genes. Genetic influence can be identified and responses to treatment or varying environments can be investigated. While the potential for extracting useful information about how a complex phenotype responds given the identity of a QTL is valuable in itself, the limiting factor of QTL analysis is the inability to know where in the complex pathway of the system the QTL is exerting its influence. We have attempted to further explore complex pathways involved in bone homeostasis by incorporating multiple skeletal, muscle, and activity phenotypes, along with genetic markers representing QTL, into structural equation models (SEM). Models were used to investigate inter-relationships among measures of muscle strength (implied by muscle mass), skeletal integrity, physical activity-related behaviors, and genetic loci with potential effects on these traits in F2 cohorts of B6 and D2 inbred mice at 200 and 500 days of age. Muscle mass, several skeletal phenotypes, and activity measures were used in QTL analyses. Chromosomal regions that were identified with potential pleiotropic effects across at least two of the three domains (bone, muscle, or activity) were selected for follow-up analyses using structural equation modeling. Thus, an integrative approach was employed in our attempts to unravel and better define some of the many mechanisms responsible for the maintenance of bone health over the life span, and in so doing provide clues to better enable individual gene and gene product identification and function. Central to our investigations are gene-environment interactions leading to differences in bone’s ability to resist fracture, which itself is a consequence of the actions of many phenotypes working in concert. The F2 animals used in our work were derived from inbred mouse strains with known differences in activity level. B6 mice have been shown to have a higher activity level compared to D2 mice (Mayeda and Hofstetter, 1999; and Lerman et al., 2002). In addition, Lerman et al., (2002) reported that B6 mice had the greatest voluntary wheel distance, duration, and speed compared to several inbred mouse strains including D2 mice. The genetic influence on skeletal differences observed in B6 and D2 mouse strains could therefore result from genetically induced differences in natural activity level, as well as differential skeletal responses to such activities. Results from our structural equation models indicate that muscle is a significant predictor of bone’s mechanical performance. Although not as strong an indicator as muscle, loading and locomotion are also significant predictors of skeletal characteristics. The relationships among loading, locomotion, and bone indicate that eliminating the effects of loading during locomotion results in locomotion having a negative association with bone in both young and older males. This could be the result of increased energy expenditure required for locomotion which reduces non-lean mass (fat), thereby reducing skeletal loads. When eliminating the confounding affects of locomotion in older females, loading by other means significantly predicts the mechanical behavior of lower extremity long bones. Several sex by age interactions were also observed. For example, in the female tibia, muscle was a better predictor of bone in young females while loading was a better predictor of bone in older females. These results confirm that the effects of frequency and magnitude of loading on the skeleton are both sex and age dependant. Further exploration of these relationships could prove important from an interventional perspective, by enhancing our understanding of the magnitude and frequency of resistance exercise needed to maintain skeletal health overt the life cycle. The results from our study also suggest that a gene (or gene cluster) on chromosome 4 has pleiotropic effects on muscle, activity, and bone, and another on chromosomes 7 and 9 influences both muscle and bone. A very interesting finding from one of our SEM models suggests that a gene or genes near marker D4Mit255 may modulate loading throughout young and middle aged female mice, to produce a significant association between the marker and bone in older females that was not yet apparent in young females. The modeling results for male femurs (with marker D7mit69), and female tibias (with marker D9mit212), indicate that the markers predicted both muscle and bone in young animals, but only bone in older animals. It could be the case that both of these QTL exert their influence on bone through muscle during growth and development, and that the bone strength in young animals is maintained in older animals despite changes in muscle mass. Clearly, the regions on chromosomes 4, 7, and 9 contribute to musculoskeletal health. Structural equation modeling has detected interesting relationships between genetic action, sex, activity, and aging. Results illustrate that genes influence skeletal health through a complex causal field and emphasize the need for careful consideration of diverse mechanistic pathways in the search for new interventions and treatments for age-related bone loss. To further delineate potential in this regard, future investigations might employ genotypic selection for loci of interest, expose the resulting animals to environmental and/or pharmacological interventions and evaluate responses.