Upload
others
View
4
Download
0
Embed Size (px)
Citation preview
1
Current Knowledge on Equine Musculoskeletal Biomarkers and Future Needs: A White
Paper
Wayne McIlwraith, Christopher Little, Peter Clegg, Sheila Laverty, David Frisbie, Mark Vaudin,
Stina Ekman, Eva Skiöldebrand, Joanna Price, Christopher Riley, Christopher Kawcak, Roger
Smith
Introduction
This paper is in follow-up to the 2009 Dorothy Russell Havemeyer Foundation workshop on
biomarkers. The end goal of this effort is to develop a validated biomarker platform that could be
used practically for diagnosis and prognosis of disease, and response to therapy, as well as
disease prediction and study of disease of the joint organ, bone, tendon and ligamentous
structures. This paper reviews current knowledge and future needs as well as gaps in knowledge.
State of knowledge in equine musculoskeletal biomarkers was reviewed by McIlwraith in 2005.
Since that time specific reviews have been made by Garvican et al (2010a, 2010b) and
Mobasheri et al (2010) as part of a collection of papers on biomarkers in veterinary medicine
published in the Veterinary Journal. More recently the use of serum biomarker levels for
predicting severe musculoskeletal injury has been reported (Frisbie et al 2010).
The need and potential benefit of biomarkers of disease in horses is quite comparable to that of
humans and workshops and working groups have similarly been used to identify the needs and
strategies for biomarker development (Kraus et al 2010, Kraus et al 2011). It has been said that a
disease starts when detected by the best biomarker available to define it (Kraus et al 2011).
Those authors proposed that this usually requires the presence of a clinical symptom, which
often occurs well into the progression of an illness or disease. There is significant evidence that
there are often early, pre-symptomatic biomarkers of illness and disease which if detected may
allow for earlier treatment. This forms the basis for the power and importance of implying
biomarkers to osteoarthritis (OA), a disease often characterized by a prolonged, asymptomatic
molecular phase, a pre-radiographic phase and a recalcitrant later radiographic stage with evident
structural joint changes, frequent pain and loss of function (Kraus et al 2011).
The equine athlete may also present somewhat unique applications for biomarkers of
musculoskeletal health that may not be as relevant in humans. One of the issues in people is for
example who gets monitored for OA biomarkers or in which groups will it be of most cost
benefit? The “low hanging fruit” in human medicine is likely to be in predicting patients that
will have rapid progression of existing joint disease and thus may benefit most from available
therapies, monitoring response to therapy for existing disease, and identifying which patients will
and which will not progress to OA following injury such as ACL tear. These applications would
also apply to the horse with existing disease or following injury, and thus biomarker research can
inform in both directions between the species. However in horses, a greater need may be in
monitoring currently healthy athletes to predict and thereby hopefully prevent both catastrophic
injuries such as long-bone fractures or tendon/ligament tears, as well as impending joint disease.
This use of biomarkers will often fall outside the various classifications described in the BIPED
system. Advances in this area would be extremely useful in equine medicine, not only from the
perspective of preventing disease and maximizing performance, but also in managing the
2
increasing ethical issue of racing-associated injuries. There is little research in biomarker
monitoring of human athletes, and in this area the equine community could lead the way and
provide novel insights for translation to humans.
Participants in the 2009 Havemeyer workshop were Wayne McIlwraith, David Frisbie, Chris
Little, Troy Trumble, Peter Clegg, Roger Smith, Jack Quinn, Chris Kawcak, Jo Price, Elwyn
Firth, Mark Vaudin, Sheila Laverty, Eva Skioldebrand, Stina Ekman, Chris Riley and John
Kisiday as well as prominent experts from the human biomarker research field Robin Poole,
Dick Heinegård, Bruce Caterson and Virginia Byers Kraus. The help of the last four participants
has been critical in guidance and, in particular, the recent papers of Kraus et al (2010, 2011) may
be used as templates for how we have moved forward in parallel fashion.
This paper represents the currently available biomarker applications and a plan for the way
forward by the authors.
Definitions
A biomarker is a characteristic that is objectively measured and evaluated as an indicator of
normal biologic processes, pathogenic processes or pharmacologic responses to a therapeutic
intervention (Biomarkers Definitions Working Group 2001). This is in contrast to a clinical
endpoint that is a marker or variable that measures how a patient feels, functions or survives. A
biomarker becomes a surrogate endpoint when it is appropriately qualified to substitute for a
clinical endpoint. The evolution in molecular biology has led to the expansion of the notion of
what constitutes a potential biomarker to include, not only proteins and protein fragments, but
also metabolites, carbohydrate biomarkers, genomic biomarkers (RNA and DNA), cellular
biomarkers (that may be captured for instance in a cell pellet extracted from body fluids), and
imaging biomarkers (Kraus et al 2011). Biomarkers have also been classified, based upon their
characteristics, into two major groups: the so called soluble or “wet” biomarkers (fluid
biomarkers) and “dry biomarkers” which consist of visual analog scales (VAS), questionnaires,
performed tasks or imaging (Kraus et al 2011).
In a recent review on biomarkers in OA, it was noted that the OA disease process is increasingly
being considered a continuum, beginning with an inciting event, such as genetic variation or
injury, progressing through molecular, pre-radiographic and radiographic stages culminating in
end-stage disease (Kraus 2010). Based on this reclassification of the disease as a continuum of a
series of stages, it is proposed that biomarkers could play a pivotal role in disease detection and
monitoring, particularly during the critical, early molecular stages when other tools are not
readily able to identify nascent OA (Kraus 2011). In human medicine such efforts are occurring
in parallel with efforts to harness biomarkers for applications in a variety of diseases and to
standardize the regulatory process for biomarker qualification in validating new therapies.
The terms „qualification‟ and „validation‟ have also been used in the context of biomarkers.
Qualification is a process applied to a particular biomarker to support its use as a surrogate
endpoint in drug discovery, development or post-approval and, where appropriate, in regulatory
decision making (Biomarkers Definitions Working Group 2001). In contrast, validation of a
biomarker is much broader and can relate to verification of analytical performance characteristics
3
(such as precision, accuracy, or stability) as well as clinical correlation of a biomarker with a
biological process or clinical outcome.
Further information can be gathered from the summary of the 2009 OA Biomarkers Workshop
(the first workshop as part of the Osteoarthritis Research Society International [OARSI] OA
biomarkers global initiative) (Kraus et al 2010) and the Osteoarthritis Research Society
International Federal Drug Administration (OARSI-FDA) Biomarkers Working Group (Kraus et
al 2011). An earlier 2006 meeting produced the BIPED classification system (Bauer et al 2006)
which organized biomarkers into five categories depending on their ability (BIPEDS – burden of
disease, investigative, prognostic, efficacy of intervention, diagnosis of a disease and safety of
interventions). A potential model for tabulating available biomarker tests in horses is the method
of van Spil et al (2010) (the detailed tables have been supplied by Virginia Kraus).
Development of a biomarker sample bank (Dr.’s Sheila Laverty, Christopher Riley and
Peter Clegg)
It was agreed by the working group that this should be a major priority. While Biobanking is
emerging as an important research tool in the human field, it is now also gaining momentum in
veterinary medicine (Schrohl et al 2008; Castelhano et al 2009). A Biobank is a repository of
biological material that has been collected and stored in a standardized fashion and whose
phenotype, origin, date of collection and location can be easily determined (Schrohl et al 2008).
These specimens can then be distributed to the Biobank users based on preset guidelines.
Specimens may be stored at one site or several sites. However, the overarching feature is a
powerful informatics program that permits efficient and reliable management of all the
Biobank‟s specimens (Amin et al 2010). A key element of a Biobank is that all necessary legal
and ethical permissions are in place to allow appropriate use of materials for research purposes.
This is obviously complicated in the case of an international Biobank where different legislative
frameworks and cultural issues may have an impact (Blow et al 2009). An additional important
objective is to harmonize not only sample collection and storage methods but also data recording
(quality, completeness, consistency) relating to samples at different storage sites (Founti et al
2009; Amin et al 2010).
The reasons to create an Equine Joint Disease Biobank are various and include: maximization of
use of fluids and tissues; reduction in the number of horses used in clinical research; fostering
global collaborative studies by facilitating access to samples; the development of standard
operating procedures for archiving, collection, storage, and treatment to generate reliable results;
avoidance of a wealth of tissue being underutilized or discarded; provision of a larger database
and enhances the number of cases included in studies (improved statistical power) as equine
research is expensive. In the case of naturally occurring disease (OA and osteochondrosis) some
sites may have access to abattoirs, slaughter houses and clinical necropsy facilities where
specimens may be harvested and banked to facilitate collaboration with others. Furthermore,
because investigative technologies are becoming increasingly expensive and specialized it will
facilitate multicenter studies and synergize equine research in the field.
4
Summaries of equine biomarker technologies currently used and available
It was agreed at the time of the 2009 meeting that a table would be prepared of biomarkers to
include their state of qualification and validation as well as sensitivity, specificity and other
specific details. Based on evaluation of these techniques it has been chosen to summarize
biomarkers technologies below and a workshop to place all techniques into tabulated form with
specifics of qualification, validation, sensitivity and specificity is planned in 2013.
Equine inflammatory biomarkers in synovial fluid and serum (Dr. Stina Ekman and Dr. Eva
Skiöldebrand)
Osteoarthritis (OA) of synovial joints always involves a low grade inflammation with
proinflammatory cytokines; such as IL-1, IL-6 (also modulatory) and TNF and anti-
inflammatory factors; IL-1receptor antagonist (ra), IL-10, IL-4 (Goldring and Otero, 2011;
Kapoor et al 2011) and interferon-gamma (IFN-γ) (Kelchtermans et al 2008). Other
inflammatory factors are; HMGB-1 (Hou et al 2011), prostaglandins (DeGrauw et al 2009),
acute phase protein as serum amyloid A (SAA) (Hulten et al 1999) , complement components
(Osborne et al. 1995) and metalloproteinases( MMP:s) (for review see: Abigail et al 2012;
Heinegård and Saxne, 2011).
It is proposed that more than 90% homology of amino acid sequences between equine and other
species is a minimal requirement for the antibody to cross react. To prove the specificity of a
non-equine antibody on specific equine epitopes, a gel electrophoresis and a Western Blot has to
be performed, and a single band of the appropriate molecular weight should be identified. Also
the protein (usually fractionated on a gel) should ideally be analyzed with mass spectrometry to
fully identify the protein. Descriptions of the antibodies based on review of publications as well
as experience of the authors are summarized here. In many publications the specificity of the
non-equine antibody is not clearly stated; hence the validation of the assays used could not be
described.
Interleukin-1 (IL-1) is a general name for two distinct proteins, IL-1 alpha and IL-1 beta. They
bind to the same receptors and exert identical biological effects. Due to the low homology of IL-
1 amino acid sequences between equine and other species (65-75%) (Kato et al 1997), equine
specific antibodies/assays are needed. Non-specific immune assays were initially used in equine
research (Bertone et al 2001) and a bioassay in synovial fluid measured by phytohaemagglutinin
induced proliferation of C3H/HeJ mouse thymocytes has been reported (Alwan et al 1991). The
use of an equine specific ELISA from R&D has been described by Hraha et al (2011) and an IL-
1 beta monoclonal antibody has been used in synovial fluid and is available from R&D (Kamm
et al 2010). This ELISA did not detect concentrations above the lower limit of sensitivity. A
commercial ELISA (Kingfisher Biotech NQSA) has been tested on synovial fluid, serum and cell
culture medium by Ekman and Skiöldebrand 2011 (unpublished data). The concentration range
measured was 2.34 ng/ml – 150 ng/ml and up to 430 ng/ml in synovial fluid from OA joints was
found.
Equine IL-1 receptor antagonist (eq IL-1ra): this assay is now available through R&D Systems
and is a sandwich ELISA. Due to the low homology of IL-1ra between equine and other species,
equine specific assays must be used. Validation of the ELISA is not described in the commercial
kit. Use of this ELISA has been reported by Hraha et al (2011). Previously a human IL-1ra
5
ELISA was useful in looking at gene therapy with an adenoviral vector (Frisbie et al 2002) but
cross reactivity was not possible in another study with autologous conditioned serum (Frisbie et
al 2007). In the latter study a mouse ELISA resulted in some degree of cross reactivity.
Interleukin-6 (IL-6): a sandwich ELISA using polyclonal goat anti-equine IL-6 antibody for
encoding, with recombinant equine IL-6 as a standard in biotinylated goat anti-equine IL-6 has
been developed and used in serum (Burton et al 2009, Ley et al 2007).
Tumor necrosis factor (TNF) alpha and beta bind to the same receptors and they have an amino
acid identity of 28%. TNF-L is the proteolytic ligand of the TNF super family. Equine TNF-L
shares 69%-89% amino acid sequence identity with bovine, canine, rat, feline, human (87%),
mouse, porcine and rat (Su et al 1991). Equine specific ELISA from Thermo-Scientific has been
used by Hraha et al 2011.
IL-10 equine specific ELISA (R&D Systems) has been used by Hraha et al 2011. Validation data
for the ELISA is not provided in the commercial kit. Other equine inflammatory markers that
have been successfully assayed include High-ability group box 1 protein (HMGB-1) (Brown et
al 2009, Ley C doctoral thesis 2010) and prostaglandins E2 (PGE2) (Kawcak et al 1997). Other
ELISA assays available include equine IL-4 and IL-2 with antibodies available from R&D
Systems as well as interferon-gamma (IFN-gamma) also from R&D, substance P (DeGrauw et al
2009), nitric oxide (Simmons et al 1999) and acute phase protein serum amyloid A (SAA)
(Hulten et al 1999) and metalloproteinases (van den Boom et al 2004).
Markers of collagen degradation and synthesis in cartilage (Dr.’s David Frisbie and Peter
Clegg)
Type II collagen is a major component of articular cartilage and its breakdown is a key feature of
osteoarthritis. The products of cartilage collagen metabolism can be detected in the, synovial
fluid, blood and urine. Several biomarker assays are available that can be used to measure the
synthesis and degradation of collagen and therefore provide information regarding cartilage
turnover (Garvican et al 2010a).
PIICP: The cleaved C-propeptide of type II collagen is detected by the procollagen type II C-
propeptide (PIICP) assay (also referred to variously as CPII) and is released as part of the
secretion of newly synthesized type II collagen from the cell. Immunoassays for PIICP have
been developed. The ELISA detects the presence of three kDa C-propeptides, connected by
disulphate links which are released into the circulation following cleavage by C-propeptide. The
half life of the cleaved propeptide is relatively short (in cartilage 16 hours) (Nelson et al 1998)
(in serum 18 hours) (Poole 2000) and is therefore a useful indicator of recent synthesis. Samples
of PIICP were increased in both synovial fluid and serum of clinical cases of osteochondral
fragmentation in the carpus (Frisbie et al 1999), and showed increases with both exercise and
experimentally induced OA in a controlled equine study (Frisbie et al 2008). In another study
synovial fluid PIICP levels were not predictive of lameness located in the metacarpophalangeal
joint (deGrauw et al 2006). CPII was also increased in synovial fluid and serum of young horses
with OCD (Laverty 2000; Billinghurst et al 2004; Billinghurst et al 2003) Baseline
concentrations of PIICP are reportedly higher in the serum of dogs and horses than humans; as a
6
result the greater systemic PIICP production may mask any upregulation which occurs from a
single damaged joint (Robion et al 2001). However, work with a single joint equine OA model
has caused consistent change in equine serum analysis (Frisbie et al 2008). Caution should be
exercised when comparing CPII results across studies: the earliest CPII studies, prior to the
development of commercial ELISAs, were performed using radioimmunoassays (Frisbie et al
1999; Laverty 2000; Robion 2011; Celeste 2005) and absolute differences in serum levels
detected could be attributed to differences in techniques.
PIIAN/PIINP: The N-propeptide of type II collagen includes a 69 amino acid, cysteine-rich
domain (this is termed type IIA collagen or PIIAN for the first of two isoforms seen in immature
chondrocytes). The other isoform (type IIB or PIINP) excluding this globular domain is
produced by mature adult chondrocytes (Sandell 1991). The N-propeptides are cleaved and
released as part of collagen secretion and therefore may be indicative of collagen synthesis. Use
of PIIAN/PIINP has provided some information in human OA but appear to show most promise
when combined with other biomarker assays (Garvican et al 2010a).
CTX-II is a type II collagen C-telopeptide fragment (CTX-II) ELISA that uses a monoclonal
antibody specific for a six amino acid sequence present exclusively in the C-terminal telopeptide
of type II collagen. Release of this telopeptide occurs through proteolysis of collagen within
cartilage extracellular matrix by both MMPs (Karsdal et al 2008) and cathepsin K (McDougall JJ
et al 2010). Increase in CTX-II fragment levels has been associated with OA outcome parameters
in humans. In human OA patients concentrations of urinary CTX-II (uCTX-II) were found to be
1.53 fold higher than the healthy controls and a trend towards an association between joint space
narrowing and uCTX-II levels were noted (Christgau et al 2001). Increased amounts of urine and
synovial fluid CTX-II have been detected soon after knee injury with higher concentrations
correlated with more rapid progression of destruction (Christgau et al 2001, Lohmander et al
2003, Garnero 2000). Increases in synovial fluid CTX-II have also been associated with
osteochondral fragmentation in adult horses when compared to a control population (Nicholson
et al 2010). While a significant decrease in the serum levels of CTX-II were seen in a similar
comparison. It should be noted that normal yearlings also have an increased synovial fluid
concentration compared to normal adults and differences based on the anatomic location of the
joint was also demonstrated with this biomarker.
A C2C/Col CEQ assay exists for the detection of the neoepitope generated at the C-terminal end
of the three-quarter length fragment by the action of collagenolytic MMP at a specific site in the
collagen II triple helix. Thus the C2C antibody recognizes the majority of collagenase cleaved
type II collagen. In most studies the C2C assay has shown increased levels associated with OA.
In the horse C2C concentrations were elevated in synovial fluid following experimental joint
inflammation (de Grauw et al 2009) and in both synovial fluid and serum in early experimental
OA (Frisbie et al 2008).
The Col CEQ assay was developed to deal with species nuances and identifies the C-terminus
neoepitope produced by collagenase digestion specifically of equine type II collagen. This
neoepitope has been shown to increase in equine OA (Billinghurst et al 2001). Concentrations of
synovial fluid and serum Col CEQ have also been shown to significantly increase in joints with
experimental OA and also it arises as a result of exercise (Frisbie et al 2008).
7
C1,2C antibody (Col II-3/4Cshort) detects collagenase-cleaved equine type II collagen but also
reacts with fragments of type I collagen in human, equine and bovine synovial fluid samples
(Billinghurst et al 1997, 2000). It is possible that type I collagen from sources such as bone,
tendon, ligament and skin have distorted results from previous studies utilizing the C1,2C assay
(Garvican et al 2010a). This biomarker has also been shown to increase with exercise and early
experimental OA (Frisbie et al 2008).
Helix-II (urinary type II collagen helical peptide) is a newer biochemical marker of cartilage
degradation in human OA (Charni et al 2005; Eyre and Weis 2009) but has not been tested in the
horse at this stage.
Cathepsin K (cat-k) cleavage of type II collagen. There is a neoepitope antibody marker for cat-
K cleavage of type II collagen in articular cartilage (Dejica et al 2008) and also evidence to
suggest that based on using this antibody that cat-K degrades articular cartilage in naturally
occurring equine osteoarthritis (Vinardell et al 2009).
Bone biomarkers (Dr. Joanna Price)
Changes in bone mass and structure associated with disease can be assessed using quantitative
ultrasound (QUS), dual energy x-ray absorptiometry (DEXA) or quantitative computerized
tomography (pQCT) (Drum et al 2007). However, it may take months for changes in bone mass
and architecture to be of sufficient magnitude that they can be detected using these methods.
Furthermore, QCT and DEXA are not straightforward to use in horses in the field and thus their
use is currently predominantly restricted to research studies. In contrast, bone biomarkers
measure dynamic changes in bone cell activity and can be measured in body fluids using fairly
straightforward methods. They can also be repeated at frequent intervals and so are convenient to
use in clinical case-based studies. Although bone biomarkers remain predominantly research
tools, a considerable amount of work has been undertaken in recent years on their potential
clinical applications including identification of horses at risk of fracture and other injuries
(Frisbie et al 2011).
Bone biomarkers are generally classified as markers of bone formation or markers of bone
resorption/degradation, although some reflect changes in both processes. In general they are
enzymes expressed by osteoblasts or osteoclasts or are organic components released during the
synthesis and resorption of bone matrix (Price 2011). Equine bone biomarker research has been
informed by the extensive use of bone turnover markers for evaluating human metabolic bone
disease processes, osteoporosis in particular. A small number of equine-specific assays have
been developed and many equine studies have utilized markers that were originally developed
for use in man. Formation biomarkers include the following:
1. Bone alkaline phosphatase (ALP) human immunoassays have now been validated for use
in the horse (Price et al 1995).
2. Osteocalcin (OC or BGP) which is otherwise known as „bone gla-protein‟ (BGP) is the
most abundant noncollagenous protein in bone matrix and a small fraction is released into
the circulation following synthesis by osteoblasts. Equine specific assays have been
8
developed in recent years and a competitive human immunoassay has been validated for
equine use (Hoyt and Siciliano 1999).
3. The carboxyterminal propeptide of type I collagen (PICP) type I collagen is the most
abundant collagen in bone and the procollagen molecule contains both amino (PINP) and
carboxyterminal (PICP) extension domains that are enzymatically cleaved off before
fibril formation and are released into the circulation. These propeptides provide
quantitative measures of newly synthesized type I collagen and in humans serum levels
reflect the rate of bone formation. PICP is measured in horses by a human RIA (Price et
al 1995). However, because type I collagen is not „bone specific‟ synthesis in other
collagen type I containing soft tissues (e.g. skin, tendon) may contribute to PICP
concentrations in serum. Human PINP assays that have been tested to date do not appear
to show equine cross reactivity.
Biomarkers that measure changes in bone resorption are usually a reflection of osteoclastic
resorption of bone matrix with degradation of products of type I collagen released into the
circulation. The most recently developed resorption biomarkers can be measured in serum and
this has increased the repertoire that can be used in horses.
1. Cross-linked collagen telopeptides, cross-links are associated amino (N-) and carboxy
(C-) termini in the type I collagen molecule and a number of peptide assays have been
developed for measuring telopeptides generated during osteoclastic resorption. The first
of these was an RIA for the carboxy-terminal telopeptide of type I collagen (ICTP) and
this assay works well in horses (Price et al 1995). The C-terminal telopeptide recognized
by the ICTP assay is specifically generated by the action of MMPs rather than cathepsin
K which releases a separately identified fragment (CTX-1, see below) (Garnero et al
2003). The ICTP assay is therefore often abbreviated CTX-MMP. The ICTP assay was
used quite extensively in early bone biomarker studies in the horse (Price 2011). As noted
above for formation markers, degradation products recognized by ICTP can arise from
any tissue containing this collagen, and elevated levels have been reported in humans
with myocardial infarction (Manhenke et al 2011).
2. More recently an immunoassay (CTX-1) was developed that recognized the C-terminal
telopeptide of type I collagen released by cathepsin K, and this has been used in the horse
(Frisbie et al 2008). Although not exclusively expressed by osteoclasts, cathepsin K
appears to be the predominant enzyme responsible for proteolysis of collagen in bone
associated with the activity of these cells (Fuller et al 2007) so this biomarker may be
more specific for turnover of bone that other collagen I catabolites. (JO COMMENT?)
3. Other markers of bone resorption include hydroxyproline (HYP), hydroxylysine
glycosides and pyridinium cross links of collagen. Pyridinoline (PYR) or hydroxylysol
Pyridinoline (HP) is found in cartilage, bone ligaments and vessels whereas
dioxypyridinoline (DPD) or lysolpyridinoline (LP) is found most exclusively in bone and
dentine. In osteoclastic bone resorption the cross-links are cleaved and the components
released into the circulation. High performance liquid chromatography (HPLC) provides
a reliable method for measuring total urinary DPD and PYD concentrations in the horse
but the method is cumbersome and although it is possible to measure DPD and PYD in
horse serum, this assay at the moment can only be used in horses less than two years of
age since serum concentrations in mature horses are below the limit of detection.
9
Because various factors can influence biomarkers in horses including time of day, diet, season,
age, gender, pregnancy, lactation, stage of estrus, breed, and inter-current disease, these should
therefore be controlled for in any study (e.g. samples should be collected at the same time of
day).
There is accumulating evidence that bone biomarkers particularly, if measured serially in the
same animal, have potential for identifying horses at risk of injury and as objective measures for
monitoring treatment. They also have been used for monitoring the effects of exercise on the
equine skeleton (Price et al 2003). Although one study measuring bone biomarkers at the start of
the training season (ICTP, PICP, CTX-1 and osteocalcin) to predict fracture showed them not to
be of value, another study showed that longitudinal (monthly) sampling was useful (osteocalcin
and CTX-1) in predicting injury (Frisbie et al 2011). Osteocalcin and ICTP were significantly
higher in horses that subsequently developed dorsal metacarpal disease.
Protein Biomarkers (Dr. Roger Smith)
The development of a specific assay for a protein biomarker of tendon injury relies on
identifying a protein which either is specific for tendon tissue or with at least a restricted
distribution, and/or whose fragmentation with injury is specific for injury process. In addition,
the biomarker must be released from tendon with damage and reach the general circulation with
no or limited removal on route to blood. It must be detectable in easily accessed body fluids with
have low natural levels to enable easy differentiation between normal and injury. Our studies
into the matrix composition of tendon have provided several candidates for molecular markers of
tendon disease, two of which are Cartilage Oligomeric Matrix Protein (COMP) and fibromodulin
(FBM).
COMP is a non-collagenous extracellular matrix protein found predominantly in tissues whose
function is mainly to resist load – cartilage, tendon, ligament, and meniscus. Studies in man
have demonstrated that it may be used as a prognostic marker in both rheumatoid arthritis and
osteoarthritis (Saxne and Heinegard 1992; Lohmander et al 1994). COMP is especially enriched
in mid-metacarpal region of superficial digital flexor tendon in young adult horse (Smith et al
1997), which is the most common site of injury, and has been found to be released after injury
with limited removal by the lymphatics prior to entry into the blood.
FBM is a member of the small leucine-rich repeat protein family (SLRPs), predominantly
associated with the collagen fibrillar network in tissues. It is especially abundant in tendon,
where „knock-out‟ experiments in mice have demonstrated it to have a functional role in tendon
organization and strength (Svensson et al 1999, 2000).
We have therefore investigated whether tissue, serum and/or tendon sheath synovial fluid levels
of these markers can be used as a biomarker for tendon disease in horses. COMP concentrations
in serum were found to vary with age.
Total COMP concentrations in an age-matched group with superficial digital flexor tendinopathy
(1.45µg/ml±0.35) were not significantly different from the control group (1.42µg/ml ± 0.31)
with no significant effect of severity or duration. Furthermore in a large cross-sectional study of
10
a population of racehorses, serum COMP levels were not predictive of the presence of superficial
digital flexor tendon injury determined ultrasonographically. Exercise, however, appears to
influence serum COMP levels. A study on a small group of international event horses
demonstrated a significant rise in the mean population serum values between pre-season and
after 4 months of training suggesting that COMP levels did reflect training level.
Synovial fluid COMP levels have been shown to be a predictor of intra-thecal tendon pathology
(Smith et al 2011). Synovial fluid samples were collected from 77 digital tendon sheaths which
were examined either tenoscopically (those with clinically significant pathologic changes; n=37)
or at post mortem (to ensure an absence of pathologic change i.e., controls; n=40). Significantly
raised levels of COMP occurred in cases of intra-thecal tendon disease compared to controls so
this assay may be useful for the prediction of the presence of tendon damage pre-surgery. Further
work has been done with a neo-epitope ELISA that only recognizes a specific sequence exposed
after enzymatic cleavage of COMP. This COMP fragment is not present in normal digital sheath
fluid. An antiserum has been generated to the new N-terminal created by (?? MMP) cleavage in
this antiserum labeled a 100kDa fragment on Western Blots. Normal digital sheath synovial fluid
contained 5.3 ± 1.5 µg/ml of COMP fragment protein compared to tendon injuries which had
63.2±25.3 µg/ml of the fragment.
A fibromodulin neo-epitope antiserum was evaluated against equine tendon extracts from young
and aged tendon and from six injured tendon extracts. This neo-epitope was first identified in
cartilage explants stimulated with IL-1 (Heathfield et al 2004). An antiserum, raised against the
epitope, recognizes the new N-terminus after MMP-13 cleavage of fibromodulin in bovine
cartilage explants and was found to label only extracts from tendons which had been recently
injured and not extracts from chronic injury or aged tendons, thereby indicating a highly specific
marker of acute tendon injury.
The development of a serological assay for tendon injury is still the Holy Grail for the tendon
clinician and this goal is yet to be realized. However, information above can potentially help
understand the disease process as well as potentially be a clinical tool for diagnosis. One
essential research tool that is still absent from our armory is the ability to detect the early stages
of tendon degeneration (rather than clinical disease) and these tests can potentially enable us to
do this.
Current status of imaging biomarkers in horses (Dr. Chris Kawcak)
Diagnostic imaging has been of great importance in equine medicine to characterize a diseased
joint. However, diagnostic imaging particularly radiography and ultrasonography often
underestimate the amount of damage in the area. In recent years computed tomography (CT) and
magnetic resonance imaging (MRI) have allowed for three dimensional characterization of
joints. Although this has improved diagnostic capabilities, pathologic tissues must still be present
in order for these imaging modalities to be useful. In the past the pathologic changes must have
been structural in nature to be detected, although now they may be physiologic. For example,
bone marrow lesions on MRI and uptake of contrast agent into diseased tissues on CT
examinations are indicative of methods that show the physiologic change when structural
changes may not be present (Powell 2011, Vallance et al 2011a, 2011b). Therefore, with the
11
introduction of three-dimensional imaging techniques, higher resolution and the ability to image
physiologic changes has improved early diagnosis of disease. Nuclear scintigraphy is also used
and has proven to be a sensitive (but rather non-specific) indicator of early disease. However,
one must be cautious that because of higher resolution the potential incidence of over
interpretation (and false-positive identification) of images increases.
A correlative study between the various available imaging techniques and the development of
experimental OA in the horse has been reported (Kawcak et al 2008). The paper serves as an
example of the potential to have multiple imaging biomarkers for clinical OA. At the moment a
true imaging biomarker that could be used to predict onset and/or progression of joint disease
does not exist in any species including man. Recently researchers have investigated the influence
of structural change on disease incidence and there is an emerging field of study in which shape
differences in joints are being correlated to disease incidence. Initially these started as simple
two-dimensional shape measurements but are now emerging into complex three-dimensional
shaped models. The only such published study in horses is by Kawcak et al (2010) where horses
with condylar fracture had a narrower lateral condyle in the palmar aspect of the joint compared
to those horses without. In addition the contralateral limb in fractured horses showed a narrower
lateral condyle in the dorsal aspect of the joint compared to those horses that did not fracture.
The measurements in this study were two-dimensional in nature although it lays the groundwork
for three-dimensional studies in the future. In addition, some researchers have investigated the
influence of subchondral bone density on incidence of osteoarthritis with fracture (Shi et al
2011). The downside of these studies is that nobody has ever been able to predict or determine a
density level at which pathologic changes occur. This is compounded in the horse since all
athletic horses will adapt to loading by increasing subchondral bone density. The point at which
subchondral bone density switches from being adaptive to pathologic is unknown.
Genetic biomarkers (Dr. Mark Vaudin and Joanna Price)
Genetic sequence variants hold particular promise for predicting disease risk and guiding
appropriate decisions and treatments. Genetic biomarkers are defined as genetic variations
(mutations or polymorphisms) that can predict disease susceptibility, disease outcome, or
treatment response. The success of the human genome project has facilitated the development of
resources to detect altered risks for diseases like cancer, cardiovascular disease and diabetes, as
well as chronic episodic conditions like asthma and less common diseases such as inflammatory
bowel disease. With a detailed understanding of a patient‟s genetic profile and current
physiological status, the potential exists for future risk to be accurately predicted and the optimal
treatment chosen.
The use of genetic biomarkers to estimate risk is potentially more straightforward than using
non-genetic ones because genetic biomarkers can be detected almost without error and do not
vary in an individual over time. Also, unlike non-genetic markers, they only need to be
determined once, and this can be early in life allowing appropriate decisions on treatments or
adjustments to begin earlier, potentially increasing their effects. A possible scenario could be
foals routinely having samples collected in the first few weeks of life for genetic screening
purposes. Recent developments in next-generation sequencing technologies have resulted in an
explosion in the availability of genome-wide polymorphisms in a large number of different
12
animal and plant species. High throughput genotyping systems using high-density chips
containing tens (or hundreds) of thousands of genome-wide single nucleotide polymorphism
(SNP) markers, have therefore become widely available to identify genotypes of individuals for a
large number of SNPs at relatively low cost. Interpreting this wealth of data and understanding
the relationship between genetic variation, environmental influences and epigenetic regulation in
determining disease susceptibility is the next great challenge.
This revolution in genomic resources has led to a dramatic impact on the application of genetics
and genomics in the horse which can be attributed to the publication of a high quality draft
sequence of equine genome (Wade et al 2009). There are approximately 20,000 protein-coding
equine genes with a high degree of orthology to the human, mouse and dog gene sets. A SNP
map consisting of more than one million markers has been generated by the addition of 100,000
whole genome shotgun reads from each of seven different horse breeds (REFERENCE?). The
term haplotype refers to a set of genetic markers that are inherited together as a consequence of
their chromosomal co-localization. Haplotype may refer to as few as two genetic variants or to
an entire chromosome depending on the number of recombination events that have occurred
between a given set of variants. Linkage disequilibrium is the non-random association of alleles
at adjacent loci. When a particular allele at one locus is found together on the same chromosome
with a specific allele at a second locus-more often than expected if the loci were segregating
independently in a population the loci are in disequilibrium.
The development of an equine SNP genotyping chip (the Illumina EquineSNP50 BeadChip), and
the more recent second generation equine SNP chip (EquineSNP70 BeadChip) with a greater
number (approx 74,000) of SNP markers has facilitated a range of genotyping applications
including whole genome case-control association studies, commonly used in the study of human
disease for many years. These studies include finding a marker associated with foal
immunodeficiency syndrome (FIS) at the Animal Health Trust in the UK (Fox-Clipsham et al
2011), as well as a company Equinome Ltd. developing a test for detecting which of three
variants (TT, CC, CT) of the myostatin gene (MSTN) a Thoroughbred horse has inherited (Hill
et al 2010). The test can apparently reveal whether the horse is best suited to racing over short,
middle or middle-to-long distances. The Equinome website
(http://www.equinome.com/index.html) claims that “C:C horses are best suited to short distance
races, C:T horses are best suited to middle-distance races and T:T horses are best suited to
middle- to long-distance races” respectively. There is also preliminary evidence that devastating
diseases such as fractures and tendon injury may have some genetic basis.
Post-genomic technologies (Dr. Peter Clegg) The availability of the equine genome opens up a number of post-genomic technologies to
equine researchers. The potential is for the development of novel tests that can be both
prognostic and predictive of disease processes in the horse (Ramery et al 2009). For instance, the
use of microarray technology may assist us in determining the effects (both positive and
negative) of a pharmacological agent such as intraarticular corticosteroids (McIlwraith 2010).
Genomics – While PCR has revolutionized many aspects of biological investigation its major
limitation is that each reaction can only identify the expression of a single gene. In order to fully
understand the complex process of gene expression in a population of cells, or in tissues,
13
techniques of global determination of gene expression are required, where it‟s possible to
determine quantitatively the expression levels of many different genes, in a multiplexed reaction
(Clegg unpublished data 2012). However, it is also to be recognized that gene expression data is
one dimensional and specific regulation of a particular gene does not always relate to a cellular
response of protein transcription (Rousseau et al 2008, Clegg 2011). DNA microarrays used to
quantify gene expression in cells or tissues consist of thousands of unique DNA oligonucleotides
or probes that are spotted microscopically onto the array. Each spot (which may be present in
duplicate or more) on the array consists of a short unique section of a specific gene to which
cDNA synthesized from the mRNA of experimental samples is able to hybridize. Successful
hybridization is normally detected by fluorophore or chemiluminescence labeled targets to
determine relative abundance of the experimental sample (Duggan et al 1999).
Due to the lack of complete equine genome sequences until recently, specific microarray
technology for the horse has been relatively slow to develop. The first equine microarray
developed contained 3103 specific equine probe sets (Gu and Bertone 2004). Whilst more
recently a 12,300 gene whole transcript oligonucleotide microarray (Glaser et al 2009), a 9,367
gene cDNA microarray (Coleman et al 2007, Mienaltowski et al 2008) and a 21,351 gene
microarray (Bright et al 2009) have been described but the authors of this review (DFF and
CWM) (Eastman 2005) have identified upregulation of certain genes using a 3100 genechip
microarray developed in Australia. Microarrays have been used to investigate aspects of cartilage
development and repair (Mienaltowski et al 2008, 2009). Commerical equine microarrays are
now available from companies such as Affymetrix.
Sequencing the first genomes, for instance the human genome, took many years, but advances in
genomic technology have allowed acceleration of sequencing technology. Using technology such
as SOLiD and 454 sequencing, which can allow 5 x10
9 base pairs per day to be sequenced, such
techniques are beginning to revolutionise the fields of transcriptomics and genomics. Such
technology is beginning to be used to define mRNA transcription and has huge potential for use
in animals such as the horse where sequence data is currently poorly annotated (Cloonan et al.
2008; Tang et al. 2009)
Proteomics; The study of large-scale protein definition is more widely accessible through the
discipline of proteomics and the concept was first proposed by Wilkins (Bayles et al 1996) and is
analogous to genomics, the study of the gene. Another challenge has been studying the proteome
as the number of proteins that need to be identified as there are a myriad of post-translational
modifications that occur following gene expression which results in many times more proteins
than the coding potential of the organism. Proteomic techniques rely on techniques to separate
proteins, characterization of separated proteins by mass spectrometry (MS) and information
mining using bioinformatics tools. Proteomic analysis has been used in muscle biopsies take
from horses under different training regimens to identify biomarkers for muscle development in
the horse (Bouwman 2010), a pathogenesis of equine recurrent uveitis (Deeg 2009), immunity
against R.Equi (Roncada et al 2005), identification of mucins in equine respiratory disease
(Rousseau et al 2007) and identification of biological changes in proteins due to anabolic steroid
administration in horses (Barton et al 2009).
14
Metabolomics – in the post-genomic era it has become clear that solely mapping the genes,
mRNA and proteins of the living system does not reveal its phenotype. Consequently researchers
have turned their interest to the metabolome (or the metabolic compliment to functional
genomics) and thus metabolomics is a rapidly expanding post-genomic science that utilizes
analytical techniques to measure low molecular weight metabolites in biological samples (Dunn
et al 2005, Griffin 2003, Wilson et al 2005). The principal analytical techniques used in
metabolomics are mass spectrometry (MS) and nuclear magnetic resonance (NMR),
spectroscopy. Techniques generate huge amounts of data in complex spectral profiles which
must be then analyzed using bioinformatics and statistical methods. Currently there is no
published data on metabolomic profiling in the horse and its potential use as a biomarker in
musculoskeletal disease, but recent publications in human and animal models OA suggests it
may have potential (Blanco et al 2012; Adams et al 2012; Maher et al 2012).
Summary
There are exciting prospects and considerable advances made with biomarkers for equine
musculoskeletal disease and injury but there is still considerable work to do before having a
clinically available biomarker panel. We will continue to work with colleagues in human
medicine to learn from their research but can hopefully contribute back to them with data from
the horse which provides a clinically relevant study group with some unique applications of
biomarkers in prediction of disease susceptibility, changes with exercise (or over training) and
possibly athletic ability.
15
References
Adams SB Jr, Setton LA, Kensicki E, Bolognesi MP, Toth AP, Nettles DL. Global metabolic
profiling of human osteoarthritic synovium. Osteoarthritis Cartilage 2012;20:64-67.
Amin W, Singh H, Pople AK, Winters S, Dhir R, Parwani AV, Becich MJ.
A decade of experience in the development and implementation of tissue banking informatics
tools for intra and inter-institutional translational research. J Pathol Inform 2010;1:12.
Barton C, Beck P, Kay R, Teale P, Roberts J. Multiplexed LC-MS/MS analysis of horse plasma
proteins to study doping in sport. Proteomics 2009;9:3058-3065.
Bauer DC, Hunter DJ, Abramson SB, Attur M, Corr M, Felson D, Heinegård D, Jordan JM,
Kepler TB, Lane NE, Saxne T, Tyree B, Kraus VB. Classification of osteoarthritis biomarkers: a
proposed approach. Osteoarthritis Cartilage 2006;14:723-727.
Bayles DO, Annous BA, Wilkinson BJ. Cold stress proteins induced in Listeria monocytogenes
in response to temperature downshock and growth at low temperatures. Appl Environ Microbiol
1996;62:1116-1119.
Billinghurst RC, Buxton EM, Edwards MC, McGraw MS, McIlwraith CW. Use of an anti-
neoepitope antibody for identification of type-II collagen degradation in equine articular
cartilage. Am J Vet Res 2001;62:1031-1039.
Billinghurst RC, Brama PAJ, van Weeren PR, McIlwraith CW. Significant exercise-related
changes in the serum levels of two biomarkers of collagen metabolism in young horses.
Osteoarthritis Cartilage 2003;11:760-769.
Billinghurst RC, Brama PAJ, van Weeren PR, McIlwraith CW. Evaluation of serum
concentrations of biomarkers of skeletal metabolism and results of radiography as indicators of
severity of osteochondrosis in foals. Am J Vet Res 2004;65:143-150.
Billinghurst RC, Dahlberg L, Ionescu M, Reiner A, Bourne R, Rorabeck C, Mitchell P, Hambor
J, Diekmann O, Tschesche H, Chen J, Van Wart H, Poole AR. Enhanced cleavage of type II
collagen by collagenases in osteoarthritic cartilage. J Clin Invest 1997;99:1534-1545.
Billinghurst RC et al 2000.
Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred
definitions and conceptual frameworks. Clin Pharmacol Ther 2001;69:89-95.
Blanco FJ, Ruiz-Romero C. Osteoarthritis: Metabolomic characterization of metabolic
phenotypes in OA. Nat Rev Rheumatol 2012;8:130-132.
Blow N. Biobanking: freezer burn. Nature Methods 2009;6:173-178.
16
Bouwman FG, van Ginneken MME, Noben J-P, Royackers E, de Graaf-Roelfsema E, Wijnberg
ID, van der Kolk JH, Mariman ECM, van Breda E. Differential expression of equine muscle
biopsy proteins during normal training and intensified training in young Standardbred horses
using proteomics technology. Comp. Biochem. Physiol. D. 2010;5:55-64.
Brama PAJ, Te Koppele JM, Beekman BR, van Weeren PR, Barneveld A. Matrix
metalloproteinase activity in equine synovial fluid: influence of age, osteoarthritis and
osteochondrosis. Ann Rheum Dis 1998;57:697-699.
Bright LA, Burgess SC, Chowdhary B, Swiderski CE, McCarthy FM. Structural and functional-
annotation of an equine whole genome oligoarray. BMC Bioinformatics 2009;10 Suppl 11:S8.
Brown MP, Trumble TN, Merritt KA. High-mobility group box chromosomal protein 1 as
potential inflammatory biomarker of joint injury in Thoroughbreds. Am J Vet Res 2009;70:1230-
1235.
Burton AB, Wagner B, Erb HN, Ainsworth TM. Serum interleukin-6 (IL-6) and IL-10
concentrations in normal and septic foals. Vet Immunolpathol 2009;132:122-128.
Caplan AI. All MSCs are pericytes? Stem Cell Stem 2008;3:229-230.
Catterall JB, Stabler TV, Flannery CR, Kraus VB. Changes in serum and synovial fluid
biomarkers after acute injury. Arthritis Res Ther 2010;12:R229.
Charni N, Juillet F, Garnero P. Urinary type II collagen helical peptide (Helix-II) as a new
biochemical marker of cartilage degradation in patients with osteoarthritis and rheumatoid
arthritis. Arthritis Rheum 2005;52:1081-1090.
Christgau S, Garnero P, Fledelius C, Moniz, C, Ensig M, Gineyts E, Rosenquist C, Qvist P.
Collagen type II c-telopeptide fragments as an index of cartilage degradation. Bone 2001;29:209-
215.
Cibere J, Zhang H, Garnero P, Poole AR, Lobanok T, Saxne T, Kraus VB, Way A, Thorne A,
Wong H, Singer J, Kopec J, Guermazi A, Peterfy C, Nicolaou S, Munk PL, Esdaile JM.
Association of biomarkers with pre-radiographically defined knee osteoarthritis in a population
based cohort. Arthritis Rheum 2009;60:1372-1380.
Cleary OB, Trumble TN, Merritt KA, Brown MP. Effect of exercise and osteochondral injury on
synovial fluid and serum concentrations of carboxy-terminal telopeptide fragments of type II
collagen in racehorses. Am J Vet Res 2010;71:33-40.
Clegg PD, Barr ED, Peffers MJ. How can post-genomic science help equine clinical practice?
Submitted? (PETE?).
Clutterback AL, Harris P, Allaway D, Mobasheri A. Matrix metalloproteinases in inflammatory
pathologies of the horse. Vet J 2012;183:27-38.
17
Coleman SJ, Gong G, Gaile DP, Chowdhary BP, Bailey E, Liu L, MacLeod JN. Evaluation of
Compass as a comparative mapping tool for ESTs using horse radiation hybrid maps. Anim
Genet 2007;38:294-302.
Conrozier T, Poole AR, Ferrand F, Mathieu P, Vincent F, Piperno M, Verret C, Ionescu M,
Vignon E. Serum concentrations of type II collagen biomarkers (C2C, C1, 2C and CPII) suggest
different pathophysiologies in patients with hip osteoarthritis. Clin Exp Rheumatol 2008;26:430-
435.
de Grauw JC. Molecular monitoring of equine joint homeostatsis. Review article. Vet Q
2011;31:77-86.
de Grauw JC, van de Lest CH, Brama PAJ, Rambags BPB, van Weeren PR. In vivo effects of
meloxicam on inflammatory mediators, MMP activity in cartilage biomarkers in equine joints
with acute synovitis. Equine Vet J 2009a;41:693-699.
de Grauw JC, van de Lest CH, van Weeren PR. Inflammatory mediators in cartilage biomarkers
in synovial fluid after a single inflammatory insult: a longitudinal experimental study. Arthritis
Res Ther 2009b;11:R35.
Deeg CA. A proteomic approach for studying the pathogenesis of spontaneous equine recurrent
uveitis (ERU). Vet Immunol Immunopathol 2009;128:132-136.
Dejica VM, Mort JS, Laverty S, Percival MD, Antoniou J, Zukor DJ, Poole AR. Cleavage of
type II collagen by cathepsin K in human osteoarthritic cartilage. Am J Pathol 2008;173:161-
169.
Doube M, Firth EC, Boyde A. Variations in articular calcified cartilage by site and exercise in
the 18-month-old equine distal metacarpal condyle. Osteoarthritis Cartilage 2007;15:1283-1292.
Dowthwaite GP, Bishop JC, Redman SN, Khan IM, Rooney P, Evans DJ, Haughton L, Bayram
Z, Boyer S, Thomson B, Wolfe MS, Archer CW. The surface of articular cartilage contains a
progenitor cell population. J Cell Sci 2004;117:889-897.
Drum MG, Kawcak CE, Norrdin RW, Park RD, Les CM, McIlwraith CW. Comparison of Gross
and Histopathologic Findings with Quantitative Computed Tomographic Bone Density in the
Distal Third Metacarpal Bone of Racehorses. Vet Radiol Ultrasound 2007;48:518-527.
Duggan DJ, Bittner M, Chen Y, Meltzer P, Trent JM. Expression profiling using cDNA
microarrays. Nat Genet 1999;21:10-14.
Dunn WB, Bailey NJC Johnson HE. Measuring the metabolome: current analytical technologies.
The Analyst 2005;130:606-625.
18
Dykgraaf S, Firth EC, Rogers CW, Kawcak CE. Effects of exercise on chondrocyte viability and
subchondral bone sclerosis in the distal third metacarpal and metatarsal bones of young horses.
Vet J 2008;178:53-61.
Eastman E. Molecular Markers in the Diagnosis of Equine Disease. Havemeyer Foundation
Monograph Series: Proceedings of a workshop on equine musculoskeletal biomarkers Eds:
McIlwraith and Wade 2005;22:40-41.
Eyre DR, Weis MA. The Helix-II epitope: a cautionary tale from a cartilage biomarker based on
an invalid collagen sequence. Osteoarthritis Cartilage 2009;17:423-6.
Founti P, Topouzis F, van Koolwijk L, Traverso CE, Pfeiffer N, Viswanathan AC. Biobanks and
the importance of detailed phenotyping: a case study – the European Glaucoma Society
GlaucoGENE project. Br J Ophtlhalmol 2009;93:577-581.
Fox-Clipsham LY, Carter SD, Goodhead I, Hall N, Knottenbelt DC, May PDF, Ollier WE,
Swinburne JE. 2011;PLoS Genet/journal.pgen.1002133.
Frisbie DD, Al-Sobayil F, Billinghurst RC, Kawcak CE, McIlwraith CW. Changes in synovial
fluid and serum biomarkers with exercise and early osteoarthritis in horses. Osteoarthritis
Cartilage 2008;16:1196-1204.
Frisbie DD, Ghivizzani SC, Robbins PD, Evans CH, McIlwraith CW. Treatment of experimental
equine osteoarthritis by in vivo delivery of the equine interleukin-1 receptor antagonist gene.
Gene Therapy 2002;9:12-20.
Frisbie DD, Kawcak CE, Werpy NM, Park RD, McIlwraith CW. Clinical, biochemical and
histologic effects of intra-articular administration of autologous conditioned serum in horses with
experimentally induced osteoarthritis. Am J Vet Res 2007;68:290-296.
Frisbie DD, McIlwraith CW, Arthur RM, Blea J, Baker VA, Billinghurst RC. Serum biomarker
levels for musculoskeletal disease in two- and three-year-old racing Thoroughbred horses: A
prospective study of 130 horses. Equine Vet J 2010;42:643-651.
Frisbie DD, Ray CS, Ionescu M, Poole AR, Chapman PL, McIlwraith CW. Measurement of
synovial fluid and serum concentrations of the 846 epitope of chondroitin sulfate and of carboxy
propeptides of type II procollagen for diagnosis of osteochondral fragmentation in horses. Am J
Vet Res 1999;60:306-309.
Fuller K, Kirstein B, Chambers TJ. Regulation and enzymatic basis of bone resorption by human
osteoclasts. Clin Sci (Lond) 2007;112:567-575.
Garnero P. Markers of bone turnover for the prediction of fracture risk. Osteoporosis Int
2000;11(S6):55-65.
19
Garnero P, Ferreras M, Karsdal MA, Nicamhlaoibh R, Risteli J, Borel O, Qvist P, Delmas PD,
Foged NT, Delaissé JM. The type I collagen fragments ICTP and CTX reveal distinct enzymatic
pathways of bone collagen degradation. J Bone Miner Res 2003;18:859-867.
Garvican ER, Vaughan-Thomas A, Innes JF, Clegg PD. Biomarkers of cartilage turnover. Part I:
Markers of collagen degradation and synthesis. Vet J 2010a;185:36-42.
Garvican ER, Vaughan-Thomas A, Clegg PD, Innes JF. Biomarkers of cartilage turnover. Part II:
Non-collagenous markers. Vet J 2010b;185:43-49.
Glaser KF, Sun Q, Wells MT, Nixon AJ. Development of a novel equine whole transcript
oligonucleotide GeneChip microarray and its use in gene expression profiling of normal
articular-epiphyseal cartilage. Equine Vet J 2009;41:663-670.
Griffin JL. Metabonomics: NMR spectroscopy and pattern recognition analysis of body fluids
and tissues for charachterisation of xenobiotic toxicity and disease diagnosis. Current Opin
Chem Bio 2003;7:648-654.
Gu WS, Bertone AL. Generation of performance of an equine-specific large-scale gene
expression microarray. Am J Vet Res 2004;65:1664-1673.
Hayes AJ, Tudor D, Nowell MA, Caterson B, Hughes CE. Chondroitin sulfate sulphation motifs
as putative biomarkers for isolation of articular cartilage progenitor cells. J Histochem Cytochem
2008;56:125-138.
Heathfield TF, Onnerfjord P, Dahlberg L, Heinegard D. Cleavage of fibromodulin in cartilage
explants involves removal of the N-terminal tyrosine sulfate-rich region by proteolysis at a site
that is sensitive to matrix metalloproteinase-13. J Biol Chem 2004;279:6286-6295.
Heinegård D, Saxne T. The role of the cartilage matrix in osteoarthritis. Nat Rev Rheumatol
2011; 7;50-56.
Hill EW, McGivney BA, Gu, j, Whiston R, MacHugh DE. 2010;BMC Genomics, 11:552.
Hoyt S, Siciliano PD. Comparison of ELISA and RIA techniques for the detection of serum
osteocalcin in horses. In, Proceedings of the 16th
Equine Nutritional Physiology Society
Symposium 1999:351.
Hraha TH, Doremus C, McIlwraith CW, Frisbie DD. Autologous conditioned serum: The
comparative cytokine profile of two commercial methods (IRAP and IRAP II) using equine
blood. Equine Vet J 2011;43:516-521.
Hulten C, Tulamo R-M, Suominen MM, Burvall K, Marhaug G, Forsberg M. A non-competitive
chemiluminescence enzyme immunoassay for the equine acute phase protein serum amaloyde A
(SAA) – a clinically useful inflammatory marker in the horse. Vet Immunol Immunopathol
1999;68:267-281.
20
Jackson BF, Goodship AE, Eastell R, Price JS. Evaluation of serum concentrations of
biochemical markers of bone metabolism and insulin-like growth factor I associated with
treadmill exercise in young horses. Am J Vet Res 2003;64:1549-1556.
Johnstone B, Hering TM, Caplan AI, Goldberg VM, Yoo JU. In vitro chondrogenesis of bone
marrow-derived mesenchymal progenitor cells. Exp Cell Res 1998;238:265-272.
Kamm JL, Nixon AJ, Witte TH. Cytokine and catabolic enzyme expression in synovium,
synovial fluid and articular cartilage of naturally osteoarthritic equine carpi. Equine Vet J
2010;42:693-699.
Karsdal MA, Madsen SH, Christiansen C, Henriksen K, Fosang AJ, Sondergaard BC. Arthritis
Res Ther 2008;10:R63.
Kato H, Ohashi T, Nakamura N, Nishimura Y, Watari T, Goitsuka R, Tsujimoto H, Hasegawa A.
Molecular cloning of equine interleukin-1 alpha and –beta cDNAs. Vet Imunopathol
1995;48:221-231.
Kawcak CE, Frisbie DD, McIlwraith CW, Trotter GW, Gillette SM, Powers BE, Walton RM.
Effects of intravenous administration of sodium hyaluronate on carpal joints in exercising horses
after arthroscopic surgery and osteochondral fragmentation. Am J Vet Res 1997;58:1132-1140.
Kawcak CE, Frisbie DD, Werpy NM, Park RD, McIlwraith CW. Effects of exercise vs
experimental osteoarthritis on imaging outcomes. Osteoarthritis Cartilage 2008;16:1519-25.
Kawcak CE, McIlwraith CW, Firth EC. Effects of early exercise on metacarpophalangeal joint in
horses. Am J Vet Res 2009.
Kawcak CE, Zimmermann CZ, Easton KL, et al. Effects of third metacarpal geometry on the
incidence of condylar fractures in Thoroughbred racehorses. Am Assoc Equine Pract 2010;197.
Kelchtermans H, Billiau A, Matthys P. How interferon-gamma keeps autoimmune diseases in
check. Trends Immunol. 2008;29: 479-86.
Kisiday JD, Kopesky PW, Evans CH, Grodzinsky AJ, McIlwraith CW, Frisbie DD. Evaluation
of adult equine bone marrow- and adipose-derived progenitor cell chondrogenesis in hydrogel
cultures. J Orthop Res 2008;26:322-331.
Kolf CM, Cho E, Tuan RS. Mesenchymal stromal cells. Biology of adult mesenchymal stem
cells: regulation of niche, self-renewal and differentiation. Arthritis Res Ther 2007;9:204.
Kraus VB. Waiting for action on the osteoarthritis front. Curr Drug Targets 2010;11:1-2.
Kraus VB. Osteoarthritis year 2010 in review: biochemical markers. Osteoarthritis Cartilage
2011;19:346-353.
21
Kraus VB, Burnett B, Coindreau J, Cottrell S, Eyre D, Gendreau M, Gardiner J, Garnero P,
Hardin J, Henrotin Y, Heinegård D, Ko A, Lohmander LS, Mathews G, Menetski J, Moskowitz
R, Persiani S, Poole AR, Rousseau J-C, Todman M, OARSI FDA Osteoarthritis Biomarkers
Working Group. Application of biomarkers in the development of drugs intended for the
treatment of osteoarthritis. Osteoarthritis Cartilage 2011;19:515-542.
Kraus VB, Kepler TB, Stabler T, Renner J, Jordan JM. First qualification study of serum
biomarkers as indicators of total body burden of osteoarthritis. PLoS ONE 2010;5:e9739.
Kraus VB, Nevitt M, Sandell LJ. Summary of the OA biomarkers workshop 2009 – biochemical
biomarkers: biology, validation and clinical studies. Osteoarthritis Cartilage 2010;18:742-745.
Kraus VB, Huebner JL, Fink C, King JB, Brown S, Vail TP, Guilak F. Urea is a passive
transport marker for arthritis biomarker studies. Arthritis Rheum 2002;46:420-427.
Laverty S, Ionescu M, Marcoux M, Boure L, Doize B, Poole AR. Alterations in cartilage type-II
procollagen and aggrecan contents in synovial fluid in equine osteochondrosis. J Orthop Res
2000;18:399-405.
Ley C. Doctoral thesis 2010;16, SLU Sweden. Inflammatory response in equine joints.
Unpublished data.
Ley C, Ekman S, Elmén A, Nilsson G, Eloranta M-L. Interleukin-6 and tumour necrosis factor in
synovial fluid from horses with carpal joint pathology. J Vet Med A 2007;54:346-351.
Ley C, Ekman S, Ronéus B, Eloranta M-L. Interleukin-6 and high mobility group box protein-1
in synovial membranes and osteochondral fragments in equine osteoarthritis. Res Vet Sci
2009;86:490-497.
Lohmander LS, Saxne T, Heinegard DK. Release of cartilage oligomeric matrix protein (COMP)
into joint fluid after knee injury and in osteoarthritis. Ann Rheum Dis 1994;53:8-13.
MacCoss MJ, et al. Shotgun identification of protein modifications from protein complexes and
lens tissue. Proc Natl Acad Sci USA 2002;99:7900-7905.
Maher AD, Coles C, White J, Bateman JF, Fuller ES, Burkhardt D, Little CB, Cake M, Read R,
Rochfort S. 1H NMR Spectroscopy of Serum Reveals Unique Metabolic Fingerprints Associated
with Subtypes of Surgically-induced Osteoarthritis in Sheep. J Proteome Res, Accepted July 12,
2012.
Manhenke C, Orn S, Squire I, Radauceanu A, Alla F, Zannad F, Dickstein K. The prognostic
value of circulating markers of collagen turnover after acute myocardial infarction. Int J Cardiol
2011;150:277-282.
McDougall JJ, Schuelert N, Bowyer J. Osteoarthritis Cartilage. 2010;18:1355-1357.
22
McIlwraith CW. The use of intra-articular corticosteroids in the horse – What do we know on a
scientific basis? Equine Vet J 2010;42:563-571.
McIlwraith CW. Use of synovial and serum biomarkers in equine bone and joint disease. A
review. Equine Vet J 2005;37:473-482.
Mienaltowski MJ, Huang L, Stromberg AJ, MacLeod JN. Differential gene expression
associated with postnatal equine articular cartilage maturation. BMC Musculoskelet Disord
2008;9:149.
Mienaltowski MJ, Huang L, Frisbie DD, McIlwraith CW, Stromberg AJ, Bathke AC, MacLeod
JN. Transcriptional profiling differences for articular cartilage and repair tissue in equine joint
surface lesions. BMC Medical Genomics 2009;2:60.
Mobasheri A, Cassidy JP. Biomarkers in veterinary medicine: Towards targeted, individualized
therapies for companion animals. Vet J 2010;185:1-3.
Moffat PA, Firth EC, Rogers CW, Smith RKW, Barneveld A, Goodship AE, Kawcak CE,
McIlwraith CW, van Weeren PR. The influence of exercise during growth on ultrasonographic
parameters of the superficial digital flexor tendon of young Thoroughbred horses. Equine Vet J
2008;40:136-140.
Nelson F, Dahlberg L, Laverty S, Reiner A, Pidoux I, Ionescu M, et al. Evidence for altered
synthesis of type II collagen in patients with osteoarthritis. J Clin Invest 1998;102:2115-2125.
Nicholson AM, Trumble TN, Merritt KA, Brown MP. Associations of horse age, joint type, and
osteochondral injury with serum and synovial fluid concentrations of type II collagen biomarkers
in Thoroughbreds. Am J Vet Res 2010;71:741-749.
Nugent GE, Law AW, Wong EG, Temple MM, Bae WC, Chen AC, Kawcak CE, Sah RL. Site-
and exercise-related variation in structure and function of cartilage from equine distal metacarpal
condyle. Osteoarthritis Cartilage 2004;12:826-833.
Osborne AC, Carter SD, May SA, Bennett D. Anti-collagen antibodies and immune complexes
in equine joint disease. Vet Immunol Immunopathol 1995;45:19-30.
Poole AR. NIH white paper: Biomarkers, the osteoarthritis initiative. NIH, NIAMS News and
Events. 2000. www.nih.gov/niams/news/oisg, www.oarsi.org/pdfs/White_Paper_R_Poole_2-3-
2000.pdf
Poole AR. What do measurements of molecular biomarkers in different body fluids really tell
us? Arthritis Res Ther 2011;13:110.
Poole R, Blake S, Buschmann M, Goldring S, Laverty S, Lockwood S, Matyas J, McDougall J,
Pritzker K, Rudolphi K, van den Berg W, Yaksh T. Recommendations for the use of preclinical
models in the study and treatment of osteoarthritis. Osteoarthritis Cartilage 2010;18:S10-S16.
23
Powell SE. Low-field standing magnetic resonance imaging findings of the
metacarpo/metatarsophalangeal joint of racing Thoroughbreds with lameness localised to the
region: A retrospective study of 131 horses. Equine Vet J 2011;44:169-177.
Pratt JM, Simpson DM, Dohert MK, Rivers J, Gaskell SJ, Baynon RJ. Multiplexed absolute
quantification for proteomics using concatenated signature peptides encoded by Qconcat genes.
Nature Protocols 2006;1:1029-1043.
Price J. Bone Biomarkers. In, Ross MW and Dyson SJ (eds), Diagnosis and Management of
Lameness in the Horse, 2nd
edition, Elsevier Saunders, St. Louis 2011;947-952.
Price JS, Jackson B, et al. Age related changes in biochemical markers of bone metabolism in
horses. Equine Vet J 1995;27:201-207.
Ramery E, Closset R, Art T, Bureau F, Lekeux P. Expression microarrays in equine sciences.
Veterinary Immunology and Immunopathology 2009;127:197-202.
Ramery E, Closset R, Bureau F, Art T, Lekeux P. Relevance of using a human microarray to
study gene expression in heaves-effected horses. Vet J 2008;177:216-221.
Riley CB, McClure JT, Low-Ying S, Shaw RA. Use of Fourier-transform infrared spectroscopy
for the diagnosis of failure of transfer of passive immunity and measurement of immunoglobulin
concentrations in horses. J Vet Intern Med 2007;21:828-834.
Robion FC, Doizé B, Bouré L, Marcoux M, Ionescu M, Riner A, Poole AR, Laverty S. Use of
synovial fluid markers of cartilage synthesis and turnover to study effects of repeated intra-
articular administration of methylprednisolone acetate on articular cartilage in vivo. J Orthop Res
2001;19:250-258.
Rogers CW, Firth EC, McIlwraith CW, Barneveld A, Goodship AE, Kawcak CE, Smith RKW,
van Weeren PR . Evaluation of a new strategy to modulate skeletal development in
Thoroughbred performance horses by imposing track-based exercise during growth. Equine Vet
J 2008a;40:111-118.
Rogers CW, Firth EC, McIlwraith CW, Barneveld A, Goodship AE, Kawcak CE, Smith RKW,
van Weeren PR. Evaluation of a new strategy to modulate skeletal development in racehorses by
imposing track-based exercise during growth: The effects on 2- and 3-year-old racing careers.
Equine Vet J 2008b;40:119-127.
Roncada, P., Bonizzi, L., Fortin, R., Menandro, M.L. and Greppi, G.F. A proteomic approach to
investigate immunity against R. Equi in foals. Vet Res Commun 2005;29 Suppl 2:215-219.
Rousseau K, Kirkham S, Johnson L, Fitzpatrick B, Howard M, Adams EJ, Rogers DF, Knight D,
Clegg P, Thornton DJ. Proteomic analysis of polymeric salivary mucins: no evidence for
MUC19 in human saliva. Biochem J 2008;413:545-552.
24
Rousseau K, Kirkham S, McKane S, Newton R, Clegg P, Thornton DJ. Muc5b and Muc5ac are
the major oligomeric mucins in equine airway mucus. Am J Physiol Lung Cell Mol Physiol
2007;292:L1396-1404.
Sandell LJ. Alternatively spliced type II procollagen mRNAs define distinct populations of cells
during vertebral development: differential expression of the amino-propeptide. J Cell Bio
1991;114:1307-1319.
Sandell LJ, Hering TM. Cell biology, biochemistry, and molecular biology of articular cartilage
in osteoarthritis, in Osteoarthritis, Saunders, Philadelphia. 2007 pp73-106.
Saxne T, Heinegard D. Cartilage oligomeric matrix protein: a novel marker of cartilage turnover
detectable in synovial fluid and blood. Br J Rheumatol 1992;31:583-591.
Schrohl AS, Würtz S, Kohn E, Banks RE, Nielsen HJ, Sweep FC, Brünner N.
Banking of biological fluids for studies of disease-associated protein biomarkers. Mol Cell
Proteomics 2008;7:2061-6.
Shi L, Wang D, Riggs CM, et al. Statistical analysis of bone mineral density using voxel-based
morphometry-an application on proximal sesamoid bones in racehorses. J Orthop Res
2011;29:1230-1236.
Smith MM, Sakuari G, Smith SM, Young AA, Melrose J, Stewart CM, Appleyard RC, Peterson
JL, Gillies RM, Dart AJ, Sonnabend DH, Little CB. Modulation of aggrecan and ADAMTS
expression in ovine tendinopathy induced by altered strain. Arthritis Rheum 2008;58:1055-1066.
Smith MR, Wright IM, Minshall GJ, Dudhia J, Verheyen K, Heinegard D, Smith RK. Increased
cartilage oligomeric matrix protein concentrations in equine digital flexor tendon sheath synovial
fluid predicts intrathecal tendon damage. Vet Surg 2011;40:54-58.
Smith RK, Heinegard D. Cartilage oligomeric matrix protein (COMP) levels in digital sheath
synovial fluid and serum with tendon injury. Equine Vet J 2000;32:52-8.
Smith RK, Zunino L, Webbon PM, Heinegard D. The distribution of cartilage oligomeric matrix
protein (COMP) in tendon and its variation with tendon site, age and load. Matrix Biol
1997;16:255-271.
Su XZ, Morris DD, McGraw RA. Cloning and characterization of gene TNF alpha encoding
equine tumor necrosis factor alpha. Gene 1991;107:319-321.
Svensson L, Aszodi A, Reinholt FP, Fassler R, Heinegard D, Oldberg A. Fibromodulin-null mice
have abnormal collagen fibrils, tissue organization, and altered lumican deposition in tendon. J
Biol Chem 1999;274:9636-9647.
Svensson L, Narlid I, Oldberg A. Fibromodulin and lumican bind to the same region on collagen
type I fibrils. FEBS Lett 2000;470:178-182.
25
Swingler TE, Waters JG, Davidson RK, Pennington CJ, Puente XS, Darrah C, Cooper A, Donell
ST, Guile GR, Wang W, Clark IM. Degradome expression profiling in human articular cartilage.
Arthritis Res Ther 2009;11:R96.
Trumble TN, Brown MP, Merritt KA. Decreased aggrecan (G1/G2) to CTX II ratios in synovial
fluid of horses with osteochondral injury. Osteoarthritis Cartilage 2008;16:S62-S63.
Trumble TN, Nicholson AM, Merritt KA, Brown MP. Degradation to synthesis rations of type II
collagen biomarkers in synovial fluid and serum in Thoroughbred racehorses. In Proc OARSI
World Congress 2009.
Trumble TN, Scarbrough AB, Brown MP. Osteochondral injury increases type II collagen
degradation products (C2C) in synovial fluid of Thoroughbred racehorses. Osteoarthritis
Cartilage 2009;17:371-374.
Vallance SA, Bell RJW, Spriet M, Kass PH, Puchalski SM. Comparisons of computed
tomography, contrast enhanced computed tomography and standing low-field magnetic
resonance imaging in horses with lameness localised to the foot. Part 1: Anatomic visualisation
scores. Equine Vet J 2011a;44:51-56.
Vallance SA, Bell RJW, Spriet M, Kass PH, Puchalski SM. Comparisons of computed
tomography, contrast-enhanced computed tomography and standing low-field magnetic
resonance imaging in horses with lameness localised to the foot. Part 2: Lesion identification.
Equine Vet J 2011b;44:149-156.
Van den Boom R, Brama PAJ, Kiers GH, De Groot J, van Weeren PR. The influence of repeated
arthrocentesis in exercise in general activity in cytokine levels in equine joints. Equine Vet J
2004;36:155-159.
Van den Boom R, Brama PAJ, Kiers GH, De Groot J, van Weeren PR. Assessment of the effects
of age and joint disease on hydroxyproline and glycosaminoglycan concentration in synovial
fluid from the metacarpophalangeal joint of horses. Am J Vet Res 2004a;65:296-302.
van Spil WE, DeGroot J, Lems WF, Oostveen JCM, Lafeber FPJG. Serum and urinary
biochemical markers for knee and hip osteoarthritis: a systematic review applying the consensus
BIPED criteria. Osteoarthritis Cartilage 2010;18:605-612.
van Weeren PR, Firth EC, Brommer H, Hyttinen MM, Helminen HJ, Rogers CW, DeGroot J,
Brama PAJ. Early exercise advances the maturation of glycosaminoglycans and collagen in the
extracellular matrix of articular cartilage in the horse. Equine Vet J 2008;40:128-135.
Vijarnsorn M, Riley CB, Ryan DAJ, Rose PL, Shaw RA. Indentification of infrared absorption
spectral characteristics of synovial fluid of horses with osteochondrosis of the tarsocrural joint.
Am J Vet Res 2007;68:517-523.
26
Vijarnsorn M, Riley CB, Shaw RA, McIlwraith CW, Ryan DAJ, Rose PL, Spangler E. Use of
infrared spectroscopy for diagnosis of traumatic arthritis in horses. Am J Vet Res 2006;67:1286-
1292.
Vinardell T, Dejica V, Poole AR, Mort JS, Richard H, Laverty S. Evidence to suggest that
cathepsin K degrades articular cartilage in naturally occurring equine osteoarthritis.
Osteoarthritis Cartilage 2009;17:375-383.
Wade CM, Giulotto E, Sigurdsson S, Zoli M, Gnerre S, Imsland F, Lear TL, Adelson DL, Bailey
E, Bellone RR, Blöcker H, Distl O, Edgar RC, Garber M, Leeb T, Mauceli E, MacLeod JN,
Penedo MC, Raison JM, Sharpe T, Vogel J, Andersson L, Antczak DF, Biagi T, Binns MM,
Chowdhary BP, Coleman SJ, Della Valle G, Fryc S, Guérin G, Hasegawa T, Hill EW, Jurka J,
Kiialainen A, Lindgren G, Liu J, Magnani E, Mickelson JR, Murray J, Nergadze SG, Onofrio R,
Pedroni S, Piras MF, Raudsepp T, Rocchi M, Røed KH, Ryder OA, Searle S, Skow L,
Swinburne JE, Syvänen AC, Tozaki T, Valberg SJ, Vaudin M, White JR, Zody MC; Broad
Institute Genome Sequencing Platform; Broad Institute Whole Genome Assembly Team, Lander
ES, Lindblad-Toh K. 2009. Science;326(5954):865-7.
Whitfield PD, German AJ, Noble PJ. Metabolomics: an emerging post-genomic tool for
nutrition. Br J Nutrition 2004;92:549-555.
Wilke MM, Nydam DV, Nixon AJ. Enhanced early chondrogenesis in articular defects following
arthroscopic mesenchymal stem cell implantation in an equine model. J Orthop Res
2007;25:913-925.
Wilson ID, Plumb R, Granger J, Major H, Williams R, Lenz EM. HPLC-MS-based methods for
the study of metabonomics. J Chromatography B 2005;817:67-76.