WO2013003897A1

WO2013003897A1 - Endorepellin peptides and fragments thereof as biomarkers for physical activity

Info

Publication number: WO2013003897A1
Application number: PCT/AU2012/000797
Authority: WO
Inventors: Anthony James PARKER; Anthony Wilfred PARKER; Zee Upton; David Ian LEAVESLEY
Original assignee: Queensland University Of Technology
Priority date: 2011-07-01
Filing date: 2012-07-02
Publication date: 2013-01-10

Abstract

Abstract The present invention discloses peptide biomarkers of physical activity derived from endorepellin, such as the LG3 peptide of endorepellin and fragments thereof. In particular, the invention discloses reagents and methods of detecting, monitoring, determining or assessing physical activity level, health, fitness and or tissue breakdown based on using an endorepellin peptide biomarker.

Description

Endorepellin peptides and fragments thereof as biomarkers for Physical Activity

Technical Field

The present invention relates to peptide biomarkers of physical activity derived from endorepellin, such as the LG3 peptide of endorepellin and fragments thereof. In particular, the invention relates to reagents and methods of detecting, monitoring, determining or assessing physical activity level, health, fitness and or tissue breakdown based on using an endorepellin peptide biomarker.

Background

Work related injury and illness currently costs the Australian economy in excess of $35 billion annually. A large proportion of this cost is attributable directly to musculoskeletal injury. The current methods of risk assessment in the workplace are limited and often inadequate in providing quantitative data on the biological impact of exposure to the performance of specific tasks by individual workers. This is especially true in relation to risk factors associated with musculoskeletal injury, such as prolonged exposure to high intensity physical activity. Despite increased automation of many procedures, particularly in the mining sector, workers often perform tasks that require intense levels of physical activity.

Repetition of specific movement patterns, repetitive action involving higher forces than normal and regular or repetitive exposure to high loads can damage both muscle and connective tissues. Muscle damage can be associated with the breakdown of structural proteins, stress responses and inflammation, while connective tissue damage is associated with the effects of physical load on the extracellular matrix of musculoskeletal structures. Muscle damage after exercise has been documented directly by myofibrilar disruption, inflammation and infiltration of leukocytes and indirectly by the perception of soreness and prolonged loss of strength and range of motion.

The relationship between physical demands in the workplace and musculoskeletal injuries is acknowledged, although direct evidence is lacking due to the lack of quantitative measures of individual exposure to variable work patterns and evaluation of the impact of exposures on an increasingly diverse workforce. Importantly, the development of musculoskeletal disorders often occurs as a function of exposure to high frequency low physical loads that causes gradual breakdown of tissue, which under ideal circumstances would be repaired. Unfortunately in many situations the rate of damage exceeds the repair capacity of the tissue. The rate of cumulative damage relative to differing levels of exposure cannot currently be determined, thus reducing the potential for monitoring and preventing the condition. This invention has the potential to provide a monitoring / diagnostic tool for tissue breakdown relative to exposure to physical activity.

Biomarker analysis is also used in athletes to monitor exertion and fatigue to allow for subsequent adjustment of training loads. Monitoring of these biomarkers can be used to assist in the design and implementation of an athlete's training to induce an Over-reached' state, rather than Over-trained' state. An overtrained state is thought to be a stress response to excessive training load or frequency with insufficient recovery. An overtrained state may also be described as stateness, overwork, burnout and chronic fatigue. Accordingly, biomarker analysis provides a means for examining exertion and fatigue in workers induced by excess physical activity, although few candidate biomarkers currently exist to monitor these parameters. A range of biomarkers has been considered for the monitoring of training and fatigue in athletes with many candidates associated with stress response, inflammation and altered metabolic states. Cortisol and urea are commonly selected as representative biomarkers of these processes. However, Cortisol is often presented as a 'stress' hormone but functions as an anti-inflammatory and catabolic enhancer in response to exercise. In addition, Cortisol has previously been examined as an indicator of increased catabolic metabolism in construction workers exposed to 12 hr. workdays and extended work weeks. Further, elevated blood urea levels are considered a measure of increased muscle protein breakdown.

Thus, there exists a need for methods for detecting, monitoring, determining or assessing the physical activity level, health or fitness of, or the status of tissue breakdown in, a subject and biomarkers for use in those methods.

Summary

The invention is predicated in part on the surprising finding that levels of a C-terminal fragment of the anti-angiogenic protein endorepellin, known as the LG3 peptide, are higher in the urine of physically active workers compared to less physically active workers. Thus, in a first aspect there is provided an endorepellin peptide biomarker for detecting, monitoring, determining or assessing physical activity level, health, fitness or tissue breakdown. In a second aspect there is provided a sample comprising an endorepellin peptide biomarker or fragment thereof and a capture reagent wherein the capture reagent binds the biomarker or fragment thereof.

The biological sample may be, or be derived from, a bodily excretion, secretion, biopsy, skin cells or hair follicles. For example the sample may be, or be derived from urine, sweat, saliva, lacrimal fluid, blood, blood plasma, blood serum or cerumen.

The capture reagent may be an antibody, aptamer, affibody, diabody, minibody or fragments thereof. The capture reagent may be bound to an insoluble support.

In a third aspect there is provided the use of an endorepellin peptide or a fragment thereof as a biomarker for detecting, monitoring, determining or assessing physical activity level, health, fitness or tissue breakdown. In a fourth aspect there is provided a method for determining or assessing physical activity level, health fitness and/or tissue breakdown status of a subject comprising detecting at least one endorepellin peptide biomarker in a biological sample from the subject, wherein the presence or level of the biomarker is indicative of physical activity level, health and/or fitness of, and/or tissue breakdown in, the subject.

The method may further comprise qualitative and/or quantitative detection of the biomarker. These include mass spectrometry, immunoassay, gel electrophoresis, ELISA, radioimmunoassay, western blotting proteomic fingerprinting or LC-MS/MS (liquid chromatography tandem mass spectrometry). The sample may be, or may be derived from, a bodily excretion, secretion, biopsy, skin cells or hair follicles. The sample may be, or be derived from urine, sweat, saliva, lacrimal fluid, blood, blood plasma, blood serum or cerumen. ln one embodiment the level of the biomarker is compared to a baseline level of the biomarker wherein a difference between the level of the biomarker and the baseline level indicates physical activity level, health, fitness, and/or tissue breakdown status of the subject. In a fifth aspect there is provided a method for monitoring physical activity, health, fitness and/or tissue breakdown status of a subject comprising :

(a) measuring at least one endorepellin peptide biomarker in a biological sample from the subject obtained at a first time;

(b) measuring at least one endorepellin peptide biomarker in at least one further biological sample from the subject obtained at a second time; and

(c) comparing the first measurement and the second measurement; wherein a difference between the measurements indicates the physical activity, health, fitness and/or tissue breakdown status of the subject. In some embodiments, the method further comprises exposing the subject to physical exercise or activity.

In some embodiments, the method further comprises determining whether the subject has recovered from the physical exercise or activity based on the comparison.

In a sixth aspect there is provided a method of monitoring the efficacy of exercise or physical activity in a subject comprising:

(c) comparing the first measurement and the second measurement; wherein difference between the measurements indicates efficacy of exercise or physical activity. In a seventh aspect there is provided a method for managing the physical activity, health, fitness and/or tissue breakdown status of a subject comprising: (a) measuring at least one endorepellin peptide biomarker in a biological sample from the subject obtained at a first time;

(c) comparing the first measurement and the second measurement; wherein the difference between the measurements indicates the physical activity, health, fitness and/or tissue breakdown status of the subject; and

(d) maintaining, adjusting or ceasing the level of physical activity of the subject based on the difference between the measurements.

In one embodiment the method may further comprise measuring at least one endorepellin peptide biomarker in at least one further biological sample from the subject obtained at one or more further times. The measuring may comprise qualitative or quantitative detection of the biomarker. The biomarker may be measured by mass spectrometry, immunoassay, gel electrophoresis, ELISA, radioimmunoassay, western blotting or proteomic fingerprinting. The sample may be, or may be derived from, a bodily excretion, secretion, biopsy, skin cells or hair follicles. The sample may be, or be derived from urine, sweat, saliva, lacrimal fluid, blood, blood plasma, blood serum or cerumen.

In one embodiment the level of the biomarker may be compared to a baseline level of the biomarker wherein a difference between the level of the biomarker and the baseline level indicates physical activity, health, fitness or efficacy of exercise or physical activity

In an eighth aspect there is provided a kit for detecting, monitoring, determining or assessing physical activity level, health, fitness or tissue breakdown status of a subject, the kit comprising at least one capture reagent for an endorepellin peptide biomarker and/or at least one reference endorepellin peptide biomarker. The kit may further comprise reagents for detection of the biomarker. The capture reagent may be bound to an insoluble support.

In a ninth aspect, the present invention provides a method for treating tissue breakdown in a subject, the method comprising: (a) detecting at least one endorepellin peptide biomarker in a biological sample from the subject, wherein the presence or level of the biomarker is indicative of tissue breakdown in the subject; and

(b) exposing the subject to an agent or therapy that ameliorates, reduces or inhibits tissue breakdown in the subject.

In a tenth aspect, the present invention provides a method for assessing the efficacy of an agent or therapy for ameliorating, reducing, or inhibiting tissue breakdown, the method comprising:

(a) providing a subject having tissue breakdown;

(b) exposing the subject to the agent or therapy; and

(c) detecting at least one endorepellin peptide biomarker in a biological sample from the subject, wherein the absence or level of the biomarker is indicative of reduced tissue breakdown or a reduced rate of tissue breakdown in the subject, which indicates that the agent or therapy is efficacious for ameliorating, reducing or inhibiting tissue breakdown.

In some embodiments, this method comprises

(a) measuring at least one endorepellin peptide biomarker in a biological sample from the subject before exposing the subject to the agent or therapy;

(b) measuring at least one endorepellin peptide biomarker in at least one further biological sample from the subject obtained after exposing the subject to the agent or therapy; and

(c) comparing the first measurement and the second measurement; wherein a difference between the measurements indicates reduced tissue breakdown or a reduced rate of tissue breakdown in the subject, which indicates that the agent or therapy is efficacious for ameliorating, reducing or inhibiting tissue breakdown.

In any one of the preceding aspects and embodiments the biomarker may comprise endorepellin, the LG3 peptide or fragments thereof. In one embodiment the biomarker consists essentially of the LG3 peptide or a fragment thereof. In another embodiment the biomarker consists of the LG3 peptide or a fragment thereof. In a further embodiment the biomarker is selected from at least one of SEQ ID NO: 1 to SEQ ID NO: 17. In a specific embodiment, the biomarker consists or consists essentially of the amino acid sequence set forth in SEQ ID NO: 15. Brief Description of the Drawings

Embodiments of the invention are described and exemplified herein, by way of non-limiting example only, with reference to the following figures.

Figure 1 shows a spectral feature at m/z 16,881 is associated with physically active workers. (A) Cluster analysis of Surface Enhanced Laser Desorption / Ionization - Time of Flight Mass Spectrometry (SELDI-TOF MS) data from urinary protein of mining workers. The 3 values centered on m/z 16881 constitute a major feature differentiating between crew (Red, Pre, Post & 24 hr.) and operators (Blue, Pre, Post & 24 hr.). Red squares indicate peak intensities above the average and green squares indicate peak intensities below the average intensity for the specified m/z value. Note block of m/z values centered on m/z 16881. (B) Closer examination of the spectral feature m/z 16881 revealed that although the mean peak intensity appeared different between the cohorts at each time point the difference was not significant. Further examination of individual worker spectra indicated that 1 operator (W5) had a relatively high peak intensity at m/z 16,881 in all three of the worker's samples and according to information provided in the health and physical activity questionnaires W5 was the only participant to have engaged in a gym workout (resistance training) within the 24 hr period prior to sampling. Thus W5 and the crew were re-classified as the physically active (PA) cohort (n=5) and the remaining operators were reclassified as the non-physically active (NPA) cohort (n=5). The peak intensity of m/z 16881 was significantly higher in the physically active workers compared to the non- physically active workers. Data is the mean peak intensity +/- SEM. Significance is given as * p < 0.05 or # p < 0.01 (Mann Whiney - U Test). (C) The spectral feature centered on m/z 16881 (shaded box) generally displays a higher intensity in the physically active workers compared to workers who were less physically active. Spectral profiles for each worker are stacked replicates (n=3 per profile). Figure 2 shows the spectral feature at m/z 16,881 which is a broad tri-phasic peak and is visible by SDSPAGE. (A) The hypothesized pattern of intensity of m/z 16,881 in stacked replicate spectra, expected to be observed in an SDS-PAGE gel. (B) A band, which matched the expected pattern of intensity for the feature at m/z 16,881 , was detected at approximately 20 kDa by SDS-PAGE (arrow) suggesting that the bands at approximately 20 kDa in the gel were the proteins that constituted m/z 16,881 in the spectra. (C) The band at approximately 20 kDa was extracted from excised bands from a non-stained replicate SDS-PAGE gel. Examination of the extracted protein by SELDI-TOF MS on CM 10 ProteinChip™ arrays confirmed that the approximately 20 kDa band was the feature originally detected by SELDI-TOF MS at m/z 16,881.

Figure 3 shows the peptides identified by LC- MS/MS map to the LG3 peptide of endorepellin, the C- terminal bioactive fragment of Perlecan. (A) Perlecan (bold lower case and underlined), the C terminal of Perlecan containing Endorepellin (lowercase text) and the LG3 Peptide of endorepellin (BOLD CAPITALS). Individual peptides identified by MS/MS of tryptic in-gel digest in light grey and dark grey highlights. Sequence coverage includes the LG3 peptide; however, the first 25 residues of the LG3 peptide were not detected. Western blot analysis confirmed that the approximately 20 kDa protein observed by SDS-PAGE and the spectral feature at ink m/z 16881 are derived from endorepellin. (B) Western Blot of worker urine samples using goat anti-human endorepellin polyclonal antibody (1 :10,000) showing an immuno-reactive band at approximately 20 kDa confirming the presence of an endorepellin fragment. Figure 4 is a photographic representation of a western blot showing the expression window of the LG3 peptide in the hours following a) a 12 km stationary bike ride or b) a 2 km treadmill run. Both exercise programs a) and b) incorporated lower limb loading and a cardiovascular component.

Figure 5 is a photographic representation of a western blot and a graphical representation of normalized densitometry data relating to that western blot, showing 1) An initial decline in LG3 levels post run possibly due to the large quantity of water consumed (therefore forcing the 2 hr. sample and rapidly producing a dilute urine); a return to the expected peak within the expression window and return to baseline as expected within 24 hrs. Figure 6 show a) Homo sapiens, P98160(3687- 391], full length endorepellin sequence! Blue text represents the LG3 sequence, blue-underlined text is the N-terminal tryptic peptide of LG3. Red underlined is the C-terminal tryptic peptide of Endorepellin after BMP-1 cleavage; b) tryptic sequence covering the BMP-1 and metalloprotease cleavage site (specific for the presence of endorepellin and PGBM) pre- and post-cleavage.

Figure 7 is a graphical representation showing a) a survey scan of LG3[1 :25] inidcating the dominant precursor m/z state of this peptide are [M+3]³⁺ (z3) and [M+4]⁴* (z4). b) an Extracted Ion Chromatogram (XIC) for z3 and z4 showing that the signal from the z3 charge state is more sensitive than the z4 state across the LC gradient for LG3[i:25j. The isotopic distrobutions for z4 and z3 are illustrated in c and d. Figure 8 is a graphical representation showing fragmentation spectra for precursor m/z 924.99 (a) and the ions chosen for SRM analysis, (b, red lines)

Some figures and text contain color representations or entities. Color illustrations are available from the Applicant upon request or from an appropriate Patent Office. A fee may be imposed if obtained from a Patent Office.

A listing of nucleotide sequences corresponding to the sequence identifiers referred to in the specification is provided. The nucleotide sequence of human endorepellin is set forth in SEQ ID NO: 1 and the sequence of human LG3 peptide is set forth in SEQ ID NO: 2. The peptide fragments of LG3 detected by mass spectrometry are set forth in SEQ ID NOs: 3 to 17.

Definitions

Certain terms are used herein which shall have the meanings set forth as follows. As used in this application, the singular form "a", "an" and "the" include plural references unless the context clearly dictates otherwise. For example, the term a "biomarker" or an "endorepellin peptide" also includes a plurality of biomarkers or endorepellin peptides respectively. Unless the context requires otherwise or specifically stated to the contrary, integers, steps, or elements of the invention recited herein as singular integers, steps or elements clearly encompass both singular and plural forms of the recited integers, steps or elements.

By "about" is meant a measurement, quantity, level, activity, value, number, frequency, percentage, dimension, size, amount, weight or length that varies by as much as 10, 9, 8, 7, 6, 5, 4, 3, 2 or 1 % to a reference measurement, quantity, level, activity, value, number, frequency, percentage, dimension, size, amount, weight or length. T e term "biological sample" as used herein refers to a sample that may be extracted, untreated, treated, diluted or concentrated from a subject. The biological sample may include a biological fluid such as whole blood, serum, plasma, saliva, lacrimal fluid, urine, sweat, ascitic fluid, peritoneal fluid, synovial fluid, amniotic fluid, cerebrospinal fluid, and the like. In some embodiments the biological sample comprises cerumen (ear wax). In certain embodiments the biological sample comprises a tissue biopsy. In certain embodiments, the biological sample comprises urine.

Throughout this specification and the claims which follow, unless the context requires otherwise, the word "comprise", and variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps. Thus, use of the term "comprising" and the like indicates that the listed elements are required or mandatory, but that other elements are optional and may or may not be present. By "consisting of is meant including, and limited to, whatever follows the phrase "consisting of. Thus, the phrase "consisting of indicates that the listed elements are required or mandatory, and that no other elements may be present. By "consisting essentially of is meant including any elements listed after the phrase, and limited to other elements that do not interfere with or contribute to the activity or action specified in the disclosure for the listed elements. Thus, the phrase "consisting essentially of indicates that the listed elements are required or mandatory, but that other elements are optional and may or may not be present depending upon whether or not they affect the activity or action of the listed elements.

The term "differentially present", as used herein to describe the amount or activity of an endorepellin peptide biomarker, refers to an increase or decrease in the amount or activity of the endorepellin peptide biomarker relative to the amount or activity of a corresponding endorepellin peptide biomarker in a control subject or control population, and encompasses a higher or lower amount or activity of a endorepellin peptide biomarker in a tissue sample or body fluid relative to a reference sample. In certain embodiments, an endorepellin peptide biomarker is differentially present if its amount or activity in a biological sample obtained from a test subject is at least 110%, 120%, 130%, 140%, 150%, 160%, 170%, 180%, 190%, 200%, 300%, 400%, 500%, 600%, 700%, 800%, 900% or 1000%, or no more than about 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, 0.01%, 0.001% or 0.0001% of the amount or activity of a corresponding endorepellin peptide biomarker in a reference sample obtained from a control subject or control population.

By "obtained" is meant to come into possession. Biological or reference samples so obtained include, for example, nucleic acid extracts or polypeptide extracts isolated or derived from a particular source. For instance, the extract may be isolated directly from a biological fluid or tissue of a subject.

The term "sequence identity" or "percentage of sequence identity" may be determined by comparing two optimally aligned sequences or subsequences over a comparison window or span, wherein the portion of the polynucleotide sequence in the comparison window may optionally comprise additions or deletions (i.e., gaps) as compared to the reference sequence (which does not comprise additions or deletions) for optimal alignment of the two sequences. Thus, a "percentage of sequence identity" is calculated by comparing two optimally aligned sequences over the window of comparison, determining the number of positions at which the identical nucleic acid base (e.g., A, T, C, G, I) or the identical amino acid residue (e.g., Ala, Pro, Ser, Thr, Gly, Val, Leu, He, Phe, Tyr, Trp, Lys, Arg, His, Asp, Glu, Asn, Gin, Cys and Met) occurs in both sequences to yield the number of matched positions, dividing the number of matched positions by the total number of positions in the window of comparison [i.e., the window size), and multiplying the result by 100 to yield the percentage of sequence identity. For the purposes of the present invention, "sequence identity" will be understood to mean the "match percentage" calculated by the DNASIS computer program (Version 2.5 for windows; available from Hitachi Software engineering Co., Ltd., South San Francisco, California, USA) using standard defaults as used in the reference manual accompanying the software. "Similarity" refers to the percentage number of amino acids that are identical or constitute conservative substitutions as described in the following illustrative table.

Sub-classes Amino acids

Acidic Aspartic acid, Glutamic acid

Basic Noncyclic: Arginine, Lysine; Cyclic: Histidine

Charged Aspartic acid, Glutamic acid, Arginine, Lysine, Histidine

Small Glycine, Serine, Alanine, Threonine, Proline Sutxtosset Amino acids

Polar/neutral Asparagine, Histidine, Glutamine, Cysteine, Serine, Threonine

Polar/large Asparagine, Glutamine

Hydrophobic Tyrosine, Valine, Isoleucine, Leucine, Methionine, Phenylalanine,

Tryptophan

Aromatic Tryptophan, Tyrosine, Phenylalanine

Residues that influence Glycine and Proline

chain orientation

Similarity may be determined using sequence comparison programs such as GAP (Deveraux ef al. 1984, Nucleic Acids Research 12, 387-395). In this way, sequences of a similar or substantially different length to those cited herein might be compared by insertion of gaps into the alignment, such gaps being determined, for example, by the comparison algorithm used by GAP.

The term "subject" as used herein refers to any subject, particularly a vertebrate subject, and even more particularly a mammalian subject, for whom therapy or prophylaxis is desired. Suitable vertebrate animals that fall within the scope of the invention include, but are not restricted to, any member of the subphylum Chordata including primates (e.g., humans, monkeys and apes, and includes species of monkeys such from the genus Macaca (e.g., cynomologus monkeys such as Macaca fascicularis, and/or rhesus monkeys [Macaca mulatta)) and baboon (Papio ursinus), as well as marmosets (species from the genus Callithrix), squirrel monkeys (species from the genus Saimiri) and tamarins (species from the genus Saguinus), as well as species of apes such as chimpanzees (Pan troglodytes)), rodents (e.g., mice rats, guinea pigs), lagomorphs (e.g., rabbits, hares), bovines (e.g., cattle), ovines (e.g., sheep), caprines (e.g., goats), porcines (e.g., pigs), equines (e.g., horses), canines (e.g., dogs), felines (e.g., cats), avians (e.g., chickens, turkeys, ducks, geese, companion birds such as canaries, budgerigars efc.), marine mammals (e.g., dolphins, whales), reptiles (snakes, frogs, lizards efc.), and fish. In specific embodiments, the subject is a mammal illustrative examples of which includes human, primates, livestock animals (e.g. sheep, pigs, cattle, horses, donkeys), laboratory test animals (e.g. mice, rabbits, rats, guinea pigs), performance and show animals (e.g. horses, livestock, dogs, cats), companion animals (e.g. dogs, cats) and captive wild animals. Preferably, the mammal is human or a laboratory test animal. Suitably, the mammal is a human. Detailed Description

It is to be understood at the outset, that the figures and examples provided herein are to exemplify and not to limit the invention and its various embodiments. An endorepellin peptide biomarker is provided for detecting, monitoring, determining and/or assessing physical activity level, health, fitness or tissue breakdown status of a subject. Compositions, methods and kits are also provided for the detection, monitoring, determining and/or assessing physical activity level, health, fitness or tissue breakdown status of a subject. The methods generally comprise qualitative and/or quantitative detection of the endorepellin peptide biomarker. The methods may also comprise the use of the compositions that typically comprise a sample comprising a biomarker of physical activity or tissue breakdown, such as an endorepellin peptide and at least one agent for the detection of that biomarker.

Biomarkers

The biomarkers of the invention are differentially present in active subjects and are therefore useful in detecting, monitoring, determining or assessing physical activity level, health or fitness as well as tissue breakdown in active subjects. A biomarker is a molecule differentially present in a subject of one group (e.g., being physically active) compared to another group (e.g., not physically active). Typically, a biomarker is differentially present between different groups if the mean or median expression level of the biomarker in the different groups is statistically significant. A biomarker or combination of biomarkers provides an indication that a subject belongs to a particular group or has responded to a stimulus, for example physical activity. The level of a biomarker may also indicate the extent to which a subject has responded to a stimulus. Accordingly, biomarkers are useful as indicators of responses to stimuli (e.g., physical activity).

As disclosed herein biomarkers for physical activity are derived from the protein perlecan, particularly the endorepellin portion of perlecan. Perlecan is a constitutively expressed five domain heparan sulfate proteoglycan found in nearly all basement membranes and in the interstitial matrix of certain tissues. It is involved in the stabilization of other basement membrane molecules and functions to regulate cell adhesion and vessel permeability. The biomarkers disclosed herein may be derived from a plurality of forms of perlecan or endorepellin. These forms can result from either or both of pre- and post-translational modification of the protein. Pre-translational modified forms include allelic variants, splice variants and RNA editing forms. Post-translationally modified forms include forms resulting from proteolytic cleavage (e.g., fragments of a parent protein), glycosylation, phosphorylation, lipidation, oxidation, methylation, cysteinylation, sulphonation, acetylation, chlorination, hydroxylation, formylation, farnesylation, myristoylation, palmitoylation, steroylation, geranylgeranylation, glutathionylation or combinations thereof. In vivo degradation of perlecan produces bioactive polypeptides including domain five, known as endorepellin (SEQ ID NO: 1). These polypeptides have activities distinct from perlecan, such as modulation of angiogenesis. For example, perlecan is pro-angiogenic while endorepellin is anti- angiogenic. Endorepellin is a 705 amino acid polypeptide comprising three laminin G (LamG) domains LamG1 (LG1), LamG2 (LG2), and LamG3 (LG3), which are separated by two EGF-like domains within each pair of LamG modules. Physiological endorepellin is cleaved proteolytically by either bone morphogenetic protein-1 (BMP-1) / tolloid like metalloprotease or caspase-3 mediated Cathepsin-L mechanisms to release either the three-LamG domain cassette or the last LamG domain, LamG3, also known as the LG3 peptide (SEQ ID NO: 2). Thus an endorepellin peptide biomarker is typically endorepellin or any fragment thereof generated by in vivo or ex vivo cleavage of endorepellin. Ex vivo cleavage of endorepellin may include cleavage of the polypeptide during sample collection, preparation for detection or during detection for example in a mass spectrometry method for example SEQ ID NOs 3-17. Accordingly in some embodiments an endorepellin peptide biomarker may comprise, consists or consists essentially of at least one of SEQ ID NOs 3-17. In specific embodiments, an endorepellin peptide biomarker may comprise, consists or consists essentially of the amino acid sequence set forth in SEQ ID NO: 15.

In some embodiments the biomarker may be endorepellin or a fragment or derivative thereof. The fragment may be the LG3 peptide. The fragments may be derived from proteolysis by endogenous proteases. The term "fragment" as it relates to endorepellin refers to an amino acid sequence that comprises a subset of the full length endorepellin amino acid sequence. A fragment of endorepellin can be a polypeptide in which amino acid residues are deleted as compared to endorepellin itself, but where the remaining amino acid sequence is typically identical to the corresponding positions in endorepellin. Such deletions can occur at the amino-terminus or carboxyl-terminus of endorepellin, or alternatively at both termini. Fragments are typically at least 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,18, 19, 20, 21 , 22, 23, 24 or 25 amino acids long, at least 30, 35, 40, 45, 50, 55, 60, 65, 70 or 75 amino acids long or at least 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650 or 700 amino acids long.

A fragment of endorepellin can comprise additional amino acids on one or both sides of the sequence corresponding to endorepellin, for example any of SEQ ID NOs: 2 to 17 wherein the additional amino acids can number from 5, 10, 15, 20, 30, 40, 50, or up to 100 or more residues.

The term "derivative" as it relates to endorepellin, refers to endorepellin or a fragment thereof having one or more amino acid substitutions, deletions and/or additions wherein the derivative will display at least about 70, 75, 80, 85, 90, 91 , 92, 93, 94, 95, 96, 97, 98, 99 % similarity or identity to a reference endorepellin polypeptide sequence, or the LG3 peptide as, for example, set forth in any one of SEQ ID NOs: 2-15. The term "derivative" as it relates to endorepellin also refers to endorepellin or a fragment thereof having one or more amino acid residues chemically modified, e.g., by alkylation, acylation esterification or amidation and/or one or more amino acid residues biologically modified, e.g. by lipidoylation such as myristoylation, glycation, glycosylate, phosphorylation, acetylation, acylation, methylation, hydroxylation, biotinylation or ubiquitinylation, chlorination, hydroxylation, formylation, farnesylation, myristoylation, palmitoylation, steroylation, geranylgeranylation, glutathionylation.

The biomarker may be a polypeptide or peptide having a substantially similar amino acid sequence to endorepellin, the LG3 peptide or fragments thereof. Silent substitutions of amino acids, wherein the replacement of an amino acid with a structurally or chemically similar amino acid does not significantly alter the structure, conformation or activity of the peptide, are well known in the art. For example, one polar amino acid(s), such as threonine, may be substituted for another polar amino acid(s), such as serine. It will be appreciated that the term endorepellin includes shortened proteins or peptides wherein one or more amino acids is removed from either or both ends of endorepellin, LG3 or fragments thereof, or from an internal region of the peptides. In general, an endorepellin biomarker with a substantially similar amino acid sequence to endorepellin, the LG3 peptide or a fragment thereof will display at least about 70, 75, 80, 85, 90, 91 , 92, 93, 94, 95, 96, 97, 98, 99 % similarity or identity to a reference endorepellin polypeptide sequence, or the LG3 peptide or a ^• fragment thereof as, for example, set forth in any one of SEQ ID NOs: 2-15.

When reference is made herein to detecting a biomarker or to measuring the amount of a biomarker, it means detecting and measuring the biomarker with or without resolving various forms of the biomarker. For example, the step of detecting a biomarker includes measuring biomarker by means that do not differentiate between various forms of the biomarker (e.g., certain immunoassays) as well as by means that differentiate some forms from other forms or that measure a specific form of the biomarker (e.g., endorepellin or LG3). In contrast when it is desired to measure a particular form or forms of a biomarker, such as a particular form of endorepellin, the particular form (or forms) is specified. For example, "measuring LG3" means measuring LG3 in a way that distinguishes it from for example endorepellin.

While individual biomarkers are useful diagnostic biomarkers, a combination of biomarkers can provide greater sensitivity or specificity for a particular activity level than a single biomarker alone. That is, the detection of a plurality of biomarkers in a sample can increase the sensitivity and/or specificity of the test. A combination or panel of at least two biomarkers is sometimes referred to as a "biomarker profile" or "biomarker fingerprint." Accordingly, the biomarkers of the invention can be combined with other biomarkers of activity level, health or fitness (hereafter referred to as ancillary biomarkers) to improve the sensitivity and/or specificity of the methods.

Examples of ancillary biomarkers useful for detecting, monitoring, determining or assessing physical activity level, health or fitness or tissue breakdown status include urea, ammonia, Cortisol, testosterone, catecholamines, glutamine, immunoglobulins, endostatin, angiostatin, angiogenin, tumstatin, arresten, mitostatin, myostatin, bone morphogenetic protein 1 / tolloid - like . metalloprotease, cathepsin L, nitric oxide (NO), nitric oxide synthase (NOS), creatine kinase (CK), either M or B isoform, myoglobin, vascular endothelial growth factor (VEGF), insulin like growth factor 1 (IGF-I), growth hormone (GH), transforming growth factor - beta 1 , 2 or 3 (TGF-βι, β2 or β), platelet derived growth factor (PDGF), interleukins of any type, troponin-l, troponin-T, matrix protein , degradation product(s) such as pyridinolines and deoxypyridinolines from collagen turnover, tissue specific intracellular proteins, titin, nebulin, myosin, actin, etc., intracellular proteins derived from inflammatory cell degranulation, inflammatory cytokines etc.

The power of a method to accurately detect, monitor, determine or assess physical activity level, health, fitness or tissue breakdown status may be measured as the sensitivity or specificity of the method. Sensitivity is defined as the probability that the detection of a biomarker in a sample from a subject is indicative of that subject having a certain status (e.g., physical activity, tissue breakdown etc.) when in fact they do have that status. That is, sensitivity is a measure of the proportion of positives that are correctly identified as such (e.g., the percentage of active subjects correctly identified as active). Specificity is defined as the probability that detection of a biomarker is indicative of a subject who does not have a particular status (e.g. physically active, tissue breakdown etc.) when in fact they do not have that particular status. That is, specificity is a measure of the proportion of negatives that are correctly identified (e.g., the percentage of inactive subjects correctly identified as inactive). ^{* ■}

Ideally, detection of a biomarker should be highly sensitive and highly specific. In some embodiments the detection of a biomarker may be highly sensitive, but not necessarily specific. In some embodiments the levels of biomarkers of the invention show a statistical difference in subjects with different physical activity levels. Methods using these biomarkers alone or in combination with other biomarkers, such as ancillary biomarkers preferably show a sensitivity and specificity of at least 75%, or at least 80%, or at least 85%, or at least 90%, or at least 95%, or at least 98%, or about 100%.

In some embodiments, the methods comprise comparing the amount (level) or activity of a biomarker of the invention to one or more preselected or threshold amounts or activities. Thresholds may be selected that provide an acceptable ability to predict diagnosis, prognostic risk, treatment success, etc. In illustrative examples, receiver operating characteristic (ROC) curves are calculated by plotting the value of a variable versus its relative frequency in two populations (called arbitrarily, for example, "high physical activity" and "low physical activity," "healthy" and "unhealthy," "fit" and "unfit," "tissue breakdown positive" and "tissue breakdown negative" or "low risk" and "high risk").

For any particular biomarker, a distribution of biomarker levels or activities for subjects with a first condition as compared to subjects with a second condition (e.g., physically active vs. physically inactive subjects, or healthy vs. unhealthy subjects, or tissue breakdown positive vs. tissue breakdown negative, or fit vs. unfit subjects) will likely overlap. Under such conditions, a test does not absolutely distinguish the first condition and the second condition with 100% accuracy, and the area of overlap indicates where the test cannot distinguish between these conditions. A threshold is selected, above which (or below which, depending on how a biomarker changes between the first and second conditions) the test is considered to be "positive" and below which the test is considered to be "negative." The area under the ROC curve is a measure of the probability that the perceived measurement will allow correct identification of a condition (see, e.g., Hanley et al., Radiology 143: 29-36 (1982). Alternatively, or in addition, thresholds may be established by obtaining an earlier biomarker result from the same patient, to which later results may be compared. In these embodiments, the individual in effect acts as their own "control group." In markers that increase with condition severity or prognostic risk, an increase over time in the same patient can indicate a worsening of the condition or a failure of a treatment regimen, while a decrease over time can indicate remission or amelioration of the condition or success of a treatment regimen. Conversely, in some embodiments marker levels may decrease with condition severity or prognostic risk and thus a decrease in the marker level over time can indicate a worsening of the condition or failure of a treatment regime, while an increase over time may indicate success of a treatment regimen.

In certain embodiments, a panel of biomarkers is selected to distinguish any pair of groups selected from "high physical activity" and "low physical activity," "healthy" and "non-healthy," "fit" and "unfit," "tissue breakdown positive" and "tissue breakdown negative" or "low risk" and "high risk" with at least about 70%, 80%, 85%, 90% or 95% sensitivity, suitably in combination with at least about 70% 80%, 85%, 90% or 95% specificity. In some embodiments, both the sensitivity and specificity are at least about 75%, 80%, 85%, 90% or 95%. ln some embodiments, a positive likelihood ratio, negative likelihood ratio, odds ratio, or hazard ratio is used as a measure of the ability of the methods of the present invention to predict a condition, prognostic risk, or treatment outcome. In the case of a positive likelihood ratio, a value of 1 indicates that a positive result is equally likely among subjects in both a first group (e.g., physically inactive, unhealthy or unfit; or tissue breakdown negative) and a second (control) group (e.g., physically active, healthy, fit, or normal; or tissue breakdown positive); a value greater than 1 indicates that a positive result is more likely in the first group; and a value less than 1 indicates that a positive result is more likely in the second group. In the case of a negative likelihood ratio, a value of 1 indicates that a negative result is equally likely among subjects in both groups; a value greater than 1 indicates that a negative result is more likely in the first group; and a value less than 1 indicates that a negative result is more likely in the second group. In certain embodiments, biomarker panels are selected to exhibit a positive or negative likelihood ratio of at least about 1.5 or more or about 0.67 or less, at least about 2 or more or about 0.5 or less, at least about 5 or more or about 0.2 or less, at least about 10 or more or about 0.1 or less, or at least about 20 or more or about 0.05 or less.

In the case of an odds ratio, a value of 1 indicates that a positive result is equally likely among subjects in both the first and second groups; a value greater than 1 indicates that a positive result is more likely in the first group; and a value less than 1 indicates that a positive result is more likely in the second group. In certain embodiments, biomarker panels are selected to exhibit an odds ratio of at least about 2 or more or about 0.5 or less, at least about 3 or more or about 0.33 or less, at least about 4 or more or about 0.25 or less, at least about 5 or more or about 0.2 or less, or at least about 10 or more or about 0.1 or less.

In the case of a hazard ratio, a value of 1 indicates that the relative risk is equal in both the first and second groups; a value greater than 1 indicates that the risk is greater in the first group; and a value less than 1 indicates that the risk is greater in the second group. In certain embodiments, biomarker panels are selected to exhibit a hazard ratio of at least about 1.1 or more or about 0.91 or less, at least about 1.25 or more or about 0.8 or less, at least about 1.5 or more or about 0.67 or less, at least about 2 or more or about 0.5 or less, or at least about 2.5 or more or about 0.4 or less. ln some cases, multiple thresholds may be determined in so-called "tertile," "quartile," or "quintile" analyses. In these methods, the first and second groups (or "high risk" and "low risk") groups are considered together as a single population, and are divided into 3, 4, or 5 (or more) "bins" having equal numbers of individuals. The boundary between two of these "bins" may be considered "thresholds." A risk (of a particular diagnosis or prognosis for example) can be assigned based on which "bin'' a test subject falls into.

In other embodiments, particular thresholds for the biomarker(s) measured are not relied upon to determine if the biomarker level(s) obtained from a subject are correlated to a particular diagnosis or prognosis. For example, a temporal change in the biomarker(s) can be used to rule in or out one or more particular diagnoses and/or prognoses. Alternatively, biomarker(s) are correlated to a condition, prognosis, etc., by the presence or absence of the biomarker(s) in a particular assay format. In the case of biomarker panels, the present invention may utilize an evaluation of the entire profile of biomarkers to provide a single result value (e.g., a "panel response" value expressed either as a numeric score or as a percentage risk). In such embodiments, an increase, decrease, or other change (e.g., slope over time) in a certain subset of biomarkers may be sufficient to indicate a particular condition or future outcome in one patient, while an increase, decrease, or other change in a different subset of biomarkers may be sufficient to indicate the same or a different condition or outcome in another patient. One example of a change in a certain subset of biomarkers may be a change in the levels of two or more biomarkers in a panel such that the ratios between the biomarkers are altered. In some embodiments this relationship between biomarkers may be used to indicate a particular condition or future outcome in a patient.

Methods

The biomarkers of the invention can be used to detect, monitor, determine or assess physical activity level or the status of tissue breakdown in a subject. Physical activity is associated with improved health and is known to reduce the risk of developing diseases such as cardiovascular disease, type 2 diabetes and osteoporosis, have a stroke and certain types of cancers, such as colon and breast cancer. Accordingly the biomarkers of the invention can be used to detect, monitor or assess health or well-being. Physical activity is also a determinant of cardiovascular fitness. Accordingly the biomarkers of the invention can be used to detect, monitor, determine or assess fitness. Physical activity also results in micro-damage of tissue resulting in tissue breakdown. Accordingly the biomarkers of the invention can be used to detect, monitor, determine or assess tissue breakdown (e.g., tissue breakdown resulting from: cardiovascular damage including damage to vascular, lymphatic and epidermal basement membranes; musculoskeletal damage including damage to muscle, tendon, ligament, menisci, bone, connective tissue, endomysium, perimysium, epimysium, endoneuryum, perineurium, epineurium, facia, and cartilage; and damage to neuromuscular junctions and neurqendothelial junctions, which may occur for example from exercise or other physical insult). This, in one aspect, methods for determining the physical activity level, health, fitness and/or tissue breakdown status of a subject are provided. The physical activity level, health, fitness and/or tissue breakdown status of the subject may be associated with a characteristic amount of a biomarker or relative amounts of a set of biomarkers (a profile or fingerprint). The physical activity level, health, fitness and/or tissue breakdown status of a subject is typically determined by measuring the relevant biomarker or biomarkers and then comparing those measurements with a reference amount and/or pattern of biomarkers associated with an physical activity level, health, fitness and/or tissue breakdown status. In some embodiments the physical activity level, health, fitness and/or tissue breakdown status of a subject is determined by comparing the level of the biomarker to a baseline level of the biomarker or to a reference level of the biomarker in the subject.

A "baseline level" is a control level, and in some embodiments, a normal level, of biomarker expression or activity against which a test level of biomarker can be compared. Therefore, it can be determined, based on the control or baseline level of biomarker whether a sample has a measurable increase, decrease, or substantially no change in biomarker level, as compared to the baseline level. In one aspect, the baseline level of a biomarker can be indicative of an inactive subject. Therefore, in some embodiments, the terms "physical activity level", "health," "fitness" or "tissue breakdown status" used in reference to a baseline level of biomarker typically refers to a baseline level of a biomarker established in a sample from a subject or a population of subjects, which is believed to be at one status (e.g., an inactive subject, tissue breakdown negative subject etc.). Conversely, in other embodiments, the baseline level of a biomarker can be indicative of another status (e.g., an active, healthy or fit subject, tissue breakdown positive subject etc.). In another embodiment, the baseline level can be established from a previous sample from a subject, so that the physical activity, physical inactivity, health or fitness of a subject can be monitored over time and/or so that the efficacy of a given exercise or physical activity can be evaluated over time.

The method for establishing a baseline level of a biomarker is preferably the same method that will be used to evaluate the sample from the subject. In a preferred embodiment, the baseline level is established using the same sample type as the sample to be evaluated.

In one embodiment, the baseline level of a biomarker is established in an autologous control sample obtained from the subject. That is, the sample is obtained from the same subject from which the sample to be evaluated is obtained. The control sample is preferably the same sample type as the sample to be evaluated.

The methods may involve detecting the biomarker in a subject or a sample from the subject sample using any method known in the art such as for example capture on a SELDI protein chip array followed by detection by mass spectrometry. However, it is contemplated that any method known in the art may be used to detect the biomarker. The presence or level of the biomarker may be compared to a predetermined level or reference level of the biomarker to distinguish an active subject from an inactive subject. The predetermined level represents a measured amount of a biomarker above which or below which a subject is classified as having a particular physical activity state, such as active or inactive. As is well understood in the art, by defining the particular predetermined level of the biomarker used in a method, the sensitivity or specificity of the method can be increased. The particular level can be determined, for example, by measuring the amount of the biomarker in a statistically significant number of samples from subjects with different physical activity states and determining the reference level to suit the desired level of specificity and sensitivity.

In some embodiments, the methods comprise comparing the amount (level) or activity of a biomarker of the invention to one or more preselected or threshold amounts or activities. Thresholds may be selected that provide an acceptable ability to detect, monitor, determine, or assess physical activity level, health, fitness and/or tissue breakdown status. For example, receiver operating characteristic (ROC) curves are calculated by plotting the value of a variable such as the level of a biomarker versus its relative frequency in two populations (called arbitrarily, for example, "high physical activity" and "low physical activity", "healthy" and "unhealthy", "fit" and "unfit", "tissue breakdown positive" and "tissue breakdown negative" or "low risk" and "high risk").

For any particular biomarker such as the LG3 peptide or other endorepellin peptide, a distribution of biomarker levels or activities for subjects with a first condition as compared to subjects with a second condition (e.g., physically active vs. physically inactive subjects, or healthy vs. unhealthy subjects, or fit vs. unfit subjects, or tissue breakdown positive vs. tissue breakdown negative subjects) will likely overlap. Under such conditions, a test does not absolutely distinguish the first condition and the second condition with 100% accuracy, and the area of overlap indicates where the test cannot distinguish between these conditions. A threshold is selected, above which (or below which, depending on how a biomarker changes between the first and second conditions) the test is considered to be "positive" and below which the test is considered to be "negative". The area under the ROC curve is a measure of the probability that the perceived measurement will allow correct identification of a condition (see, e.g., Hanley et at.. Radiology 143: 29-36 (1982). Alternatively, or in addition, thresholds may be established by obtaining an earlier biomarker result from the same subject, to which later results may be compared. In these embodiments, the subject in effect acts as their own "control group." In markers that increase with physical activity level, an increase over time in the same subject can indicate an increased physical activity level, health and/or fitness, while a decrease over time can indicate a decreased physical activity level, health and/or fitness. Accordingly, in markers that increase with physical activity level no change or no significant change over time can indicate physical inactivity.

In certain embodiments, a panel of biomarkers is selected to distinguish any pair of groups selected from "high physical activity" and "low physical activity", "healthy" and "non-healthy", "fit" and "unfit", "tissue breakdown positive" and "tissue breakdown negative" or "low risk" and "high risk" with at least about 70%, 80%, 85%, 90% or 95% sensitivity, suitably in combination with at least about 70% 80%, 85%, 90% or 95% specificity. In some embodiments, both the sensitivity and specificity are at least about 75%, 80%, 85%, 90% or 95%. In some embodiments, a positive likelihood ratio, negative likelihood ratio, odds ratio, or hazard ratio is used as a measure of the ability of the methods of the present invention to predict a condition, prognostic risk, or treatment outcome. In the case of a positive likelihood ratio, a value of 1 indicates that a positive result is equally likely among subjects in both a first group (e.g., physically inactive, unhealthy or unfit; tissue breakdown negative) and a second (control) group (e.g., physically active, ^• healthy, fit, or normal; tissue breakdown positive); a value greater than 1 indicates that a positive result is more likely in the first group; and a value less than 1 indicates that a positive result is more likely in the second group. In the case of a negative likelihood ratio, a value of 1 indicates that a negative result is equally likely among subjects in both groups; a value greater than 1 indicates that a negative result is more likely in the first group; and a value less than 1 indicates that a negative result is more likely in the second group. In certain embodiments, biomarker panels are selected to exhibit a positive or negative likelihood ratio of at least about 1.5 or more or about 0.67 or less, at least about 2 or more or about 0.5 or less, at least about 5 or more or about 0.2 or less, at least about 10 or more or about 0.1 or less, or at least about 20 or more or about 0,05 or less.

In the case of a hazard ratio, a value of 1 indicates that the relative risk is equal in both the first and second groups; a value greater than 1 indicates that the risk is greater in the first group; and a value less than 1 indicates that the risk is greater in the second group. In certain embodiments, biomarker panels are selected to exhibit a hazard ratio of at least about 1.1 or more or about 0.91 or less, at least about 1.25 or more or about 0.8 or less, at least about 1.5 or more or about 0.67 or less, at least about 2 or more or about 0.5 or less, or at least about 2.5 or more or about 0.4 or less.

In some cases, multiple thresholds may be determined in so-called "fertile", "quartile", or "quintile" analyses. In these methods, the first and second groups (or "active" and "inactive" or "fit" and "unfit") groups are considered together as a single population and are divided into 3, 4, or 5 (or more) "bins" having equal numbers of subjects. The boundary between two of these "bins" may be considered "thresholds." The likelihood (of a particular physical activity level for example) can be assigned based on which "bin" a test subject falls into. In other embodiments, particular thresholds for the biomarker(s) measured are not relied upon to determine if the biomarker level(s) obtained from a subject are correlated to a particular physical activity level, level of health, level of physical fitness or level of tissue breakdown. For example, a temporal change in the biomarker(s) can be used to rule in or out one or more particular diagnoses and/or prognoses. Alternatively, biomarker(s) are correlated to a condition, prognosis, etc., by the presence or absence of the biomarker(s) in a particular assay format. In the case of biomarker panels, the present invention may utilize an evaluation of the entire profile of biomarkers to provide a single result value (e.g., a "panel response" value expressed either as a numeric score or as a percentage risk). In such embodiments, an increase, decrease, or other change (e.g., slope over time) in a certain subset of biomarkers may be sufficient to indicate a particular physical activity level, health, fitness and/or tissue breakdown status in one subject, while an increase, decrease, or other change in a different subset of biomarkers may be sufficient to indicate the same or a different physical activity level, health, fitness and/or tissue breakdown status in another subject.

Detection of Biomarkers

Biomarkers of the invention can be detected by any suitable method. Such methods include for example, mass spectrometry, immunological methods such as immunoassays and ELISAs, optical methods, protein microarrays ligand binding assays, electrophoresis and chromatography. Radioimmunoassays, spectrometry, microarrays, western blotting and labeled or enzyme amplified technologies may also be employed. Proteomic fingerprinting may be used. In preferred embodiments the detection methods include immunological methods, mass spectrometry methods, or liquid chromatography tandem mass spectrometry (LC-MS/ S) methods.

Detection by Mass Spectrometry

In a preferred embodiment, the biomarkers of this invention are detected by mass spectrometry. Examples of mass spectrometers are time-of-flight, magnetic sector, quadrupole filter, ion trap, ion cyclotron resonance, electrostatic sector analyzer and hybrids of these. In another method, the biomarkers are detected by LC-MS or LC-LC-MS. This involves resolving the proteins and/or peptides in a sample by one or two passes through liquid chromatography, followed by mass spectrometry analysis, typically electrospray ionization. In a preferred embodiment, the biomarkers are detected by LC-MS/MS (liquid chromatography tandem mass spectrometry) which involves resolving the proteins and/or peptides in a sample by one pass through liquid chromatography followed by two mass spectrometry analyses. One example of a method to detect and/or quantify a biomarker is the LC-MS/MS approach using selected or multiple reaction monitoring such as that described in Lange et al. Mol Syst Biol 2008, 4, 222. In one embodiment the LC-MS/MS approach may utilize biomarker standards. An example of such a standard is a 'QconCAT' or 'Quantification conCATamer" which is a heavy isotope labeled synthetic protein concatamer comprising biomarker(s) of interest (for example endorepellin, LG3 and optionally at least one ancillary biomarker). The QconCAT is typically added to the sample in a known quantity. Subsequent mass spectroscopic analysis produces pairs of biomarker peaks such that for each biomarker, a corresponding peak of the heavier, isotope-labeled chemically identical reference biomarker is produced. Since the amount of QconCAT in each sample is known the amount of each of the reference biomarkers is also known. The amount of the original biomarker in the sample can then be determined from the relative heights ^"of peak pairs. This method enables quantitation of each biomarker in a sample and determination of the stoichiometry between them. The method is described in more detail in Pratt ef al Nat Protoc 2006, 1 , (2), 1029-43 and Beynon ef al . Nat Methods 2005, 2, (8), 587-9.

One advantage of this approach is that it is expected to be more reliable than a SELDI (Surface Enhanced Laser Desorption and Ionization) approach such as that described below and is expected to be at least as sensitive as immunological methods of detecting biomarkers. Further the LC- MS/MS approach may enable high throughput screening or processing of samples.

In another method, the mass spectrometer is a laser desorption/ionization (LDI) mass spectrometer. In laser desorption/ionization mass spectrometry, a sample putatively containing a biomarker of the invention is placed on the surface of a mass spectrometry probe, a device comprising a support, the device adapted for introduction into a mass spectrometer and for presentation of a biomarker to ionizing energy. A laser desorption mass spectrometer employs laser energy, typically from an ultraviolet laser or infrared laser, to desorb biomarkers from a surface or support, to volatilize and ionize them and make them available to the ion optics of the mass spectrometer. The analysis of proteins by LDI can take the form of MALDI "Matrix-assisted laser desorption/ionization" or of SELDI "Surface Enhanced Laser Desorption and Ionization".

A mass spectrometric technique for use in the invention is "Surface Enhanced Laser Desorption and Ionization" or "SELDI". This refers to a method of desorption/ionization gas phase ion spectrometry in which a biomarker is captured on the surface of a SELDI mass spectrometry probe. SELDI also is called "affinity capture mass spectrometry" or "Surface-Enhanced Affinity Capture" ("SEAC"). This version involves the use of an insoluble support having a material or reagent on the support that captures the biomarker by a covalent or more typically a non-covalent affinity interaction (i.e. adsorption) between the material or reagent and the biomarker. The material is variously called an "adsorbent", a "capture reagent", an "affinity reagent" or an "affinity capture reagent." The capture reagent can be any material capable of binding a biomarker. The capture reagent is attached to the support by physisorption or chemisorption. In certain embodiments a support has the capture reagent already attached to the support. In other embodiments, the support is pre-activated and includes a reactive moiety capable of binding the capture reagent, e.g., via a reaction forming a covalent or coordinate covalent bond. Epoxide and acyl-imidizole are useful reactive moieties to coyalently bind polypeptide capture reagents such as antibodies or cellular receptors. Nitrilotriacetic acid and iminodiacetic acid are useful due to their function as chelating agents of metal ions that interact non-covalently with histidine containing peptides. Capture reagents are typically classified as chromatographic adsorbents and biospecific capture reagents. "Chromatographic capture reagents" refers to a material typically used in chromatography and may include for example, ion exchange materials, metal chelators (e.g., nitrilotriacetic acid or iminodiacetic acid), immobilized metal chelates, hydrophobic interaction adsorbents, hydrophilic interaction adsorbents, dyes, simple biomolecules (e.g., nucleotides, amino acids, simple sugars and fatty acids) and mixed mode adsorbents (e.g., hydrophobic attraction/electrostatic repulsion adsorbents). "Biospecific capture reagents" refers to a capture reagent comprising a biomolecule, e.g., a nucleic acid molecule (e.g., an aptamer), a polypeptide, a peptide, a polysaccharide, a lipid, a steroid or a conjugate of these (e.g., a glycoprotein, a lipoprotein, a glycolipid, a nucleic acid (e.g:, DNA)-protein conjugate),. In certain instances, the biospecific capture reagent can be a macromolecular structure such as a multiprotein complex, a biological membrane or a virus. Examples of biospecific capture reagents are antibodies, affibodies, microbodies, receptor proteins and nucleic acids. Biospecific adsorbents typically have higher specificity for a biomarker than chromatographic adsorbents. In some embodiments bioselective capture reagents refers to an adsorbent that binds to a biomarker with an affinity of at least about 10 ^_5 M, or at least about 10 ^"6 M, or at least about 10 ^~7 , or at least about 10 ^"8 M, or at least about 10 ^"9M, or at least about 10 ^"10 M.

In general, a support with a capture reagent bound to its surface is contacted with a sample putatively containing a biomarker for a period of time sufficient to allow the biomarker to bind to the capture reagent. After an incubation period, the support is washed to remove unbound material. Any suitable washing solutions can be used; preferably, aqueous solutions are employed. The extent to which molecules remain bound can be manipulated by adjusting the stringency of the wash. The elution characteristics of a wash solution can depend, for example, on pH, ionic strength, hydrophobicity, degree of chaotropism, detergent strength, and temperature. Unless the support has both SEAC and SEND properties (as described below), an energy absorbing molecule then is applied to the substrate with the bound biomarkers.

In one embodiment the capture reagent is an antibody, fragment or derivative thereof that binds the biomarker. After washing the support to remove unbound material, the biomarkers are eluted from the solid phase and detected by applying to a SELDI chip that binds the biomarkers and analyzing by SELDI.

The biomarker bound to the support may be detected in a gas phase ion spectrometer such as a time-of-flight mass spectrometer. The biomarkers are ionized by an ionization source such as a laser, the generated ions are collected by an ion optic assembly and a mass analyzer then disperses and analyzes the passing ions. The detector then translates information of the detected ions into mass-to-charge ratios. Detection of a biomarker typically will involve detection of signal intensity. Thus, both the quantity and mass of the biomarkers can be determined. Another method of laser desorption mass spectrometry is called Surface-Enhanced Neat Desorption ("SEND"). SEND involves the use of supports comprising energy absorbing molecules that are chemically bound to the support surface ("SEND probe"). The phrase "energy absorbing molecules" (EAM) denotes molecules that are capable of absorbing energy from a laser desorption/ionization source and, thereafter, contribute to desorption and ionization of biomarker molecules in contact therewith. The EAM category includes molecules used in MALDI, frequently referred to as "matrix", and is exemplified by cinnamic acid derivatives, sinapinic acid (SPA), cyano-hydroxy-cinnamic acid (CHCA) and dihydroxybenzoic acid, ferulic acid, and hydroxyaceto-phenone derivatives. In certain embodiments, the energy absorbing molecule is incorporated into a linear or cross-linked polymer, e.g., a polymethacrylate. For example, the composition can be a co-polymer of a-cyano-4- methacryloyloxycinnamic acid and acrylate. In another embodiment, the composition is a co-polymer of a-cyano-4-methacryloyloxycinnamic acid, acrylate and 3-(tri-ethoxy)silyl propyl methacrylate. In another embodiment, the composition is a co-polymer of a-cyano-4-methacryloyloxycinnamic acid and octadecylmethacrylate.

SEAC/SEND is a version of laser desorption mass spectrometry in which both a capture reagent and an energy absorbing molecule are attached to the sample presenting surface. SEAC/SEND probes therefore allow the capture of biomarkers through affinity capture and ionization/desorption without the need to apply external matrix. For example, a C18 SEND biochip is a version of SEAC/SEND, comprising a C18 moiety which functions as a capture reagent, and a CHCA moiety which functions as an energy absorbing moiety.

MALDI is a method of laser desorption/ionization used to analyze biomolecules such as peptide biomarkers. In one MALDI method, a sample putatively containing a biomarker is mixed with matrix and deposited directly on a MALDI chip. In some embodiments biomarkers are preferably first captured with biospecific (e.g., an antibody) or chromatographic materials coupled to an insoluble support such as a resin (e.g., in a spin column). Specific affinity materials that bind the biomarkers of this invention are described above. After purification on the affinity material, the biomarkers are eluted and then detected by MALDI. Analysis of biomarkers by time-of-flight mass spectrometry generates a time-of-flight spectrum. The time-of-flight spectrum ultimately analyzed typically does not represent the signal from a single pulse of ionizing energy against a sample, but rather the sum of signals from a number of pulses. This reduces noise and increases dynamic range. This time-of-flight data is then subject to data processing. In Ciphergen's ProteinChip™ software, data processing typically includes TOF-to-M/Z transformation to generate a mass spectrum, baseline subtraction to eliminate instrument offsets and high frequency noise filtering to reduce high frequency noise.

Data generated by desorption and detection of biomarkers can be analyzed with the use of a programmable computer. The computer program analyzes the data to indicate the number of biomarkers detected, and optionally the strength of the signal and the determined molecular mass for each biomarker detected. Data analysis can include steps of determining signal strength of a biomarker and removing data deviating from a predetermined statistical distribution. For example, the observed peaks can be normalized, by calculating the height of each peak relative to a reference.

The computer can transform the resulting data into various formats for display. The standard spectrum can be displayed, but in one useful format only the peak height and mass information are retained from the spectrum view, yielding a cleaner image and enabling biomarkers with nearly identical molecular weights to be more easily seen. In another useful format, two or more spectra are compared, conveniently highlighting biomarkers present at different levels between samples. Using any of these formats, it can be readily determined whether a particular biomarker is present in a sample or the level of the biomarker can be qualitatively and/or quantitatively assessed. Analysis generally involves the identification of peaks in the spectrum that represent signal from a biomarker. Peak selection can be done visually although software is also available, for example the Cluster wizard™ software (Bio-Rad) exemplified herein can automate the detection of peaks. In general, this software functions by identifying signals having a signal-to-noise ratio above a selected threshold and labeling the mass of the peak at the centroid of the peak signal. In one useful application, many spectra are compared to identify identical peaks present in some selected percentage of the mass spectra. One version of this software clusters all peaks appearing in the various spectra within a defined mass range, and assigns a mass (M/Z) to all the peaks that are near the mid-point of the mass (M/Z) cluster.

Software used to analyze the data can include code that applies an algorithm to the analysis of the signal to determine whether the signal represents a peak in a signal that corresponds to a biomarker according to the present invention. The software may also subject the data from biomarker peaks to classification tree or other analyses to determine whether a biomarker's peak or combination of biomarker peaks indicates the status of the subject. For example, the data may be compared to a variety of parameters obtained, either directly or indirectly, from the mass spectrometric analysis of the sample in order to determine the status of the subject. These parameters include, but are not limited to, the presence or absence of one or more peaks, the shape of a peak or group of peaks, the height of one or more peaks, the log of the height of one or more peaks and other arithmetic manipulations of peak height data. Detection by Immunoassay

This invention contemplates qualitative and/or quantitative detection of the biomarkers of the invention by traditional immunoassays including, for example, sandwich immunoassays including ELISA or fluorescence-based immunoassays, as well as other enzyme immunoassays. In one embodiment, the biomarkers of the invention are measured by a method other than mass spectrometry or other than methods that rely on a measurement of the mass of the biomarker. In one such embodiment, the biomarkers of this invention are measured by immunoassay. Immunoassay requires biospecific capture reagents, such as antibodies, to capture the biomarkers. Antibodies can be produced by methods well known in the art, e.g., by immunizing animals with the biomarkers. Biomarkers can be isolated from samples based on their binding characteristics. Alternatively, if the amino acid sequence of a polypeptide biomarker is known, the polypeptide can be synthesized and used to generate antibodies by methods well known in the art.

• Another embodiment provides a method for detecting endorepellin or fragments thereof in a sample of body fluid, such as sweat, saliva, lacrimal fluid, blood, blood plasma, serum, or urine. A sample is obtained and contacted with one or more primary antibodies that specifically bind to an antigen comprising an epitope of endorepellin, the LG3 peptide or fragments thereof under conditions allowing the formation of an antibody-antigen complex. Typically the antibody is bound to an insoluble support such that the support can be washed to remove the sample. Antibody-antigen complexes are then detected by any method known in the art. These may include binding the complex with a second antibody or other molecule that is conjugated to a detectable label or adding a detectable label directly to the primary antibody. The amount of label detected may be quantified relative to a standard curve of known amounts of the biomarker.

In the SELDI-based immunoassay, a biospecific capture reagent for the biomarker is attached to the surface of an MS probe, such as a pre-activated ProteinC ip™ array. The biomarker is then specifically captured on the biochip through this reagent, and the captured biomarker is detected by mass spectrometry.

In one embodiment the biomarker may be detected using mass spectrometry with an immunoassay. First, a biospecific capture reagent (e.g., an antibody of fragment thereof, minibody, aptamer or Affibody that recognizes the biomarker and other forms of it) is used to capture the biomarker of interest. Preferably, the biospecific capture reagent is bound to an insoluble phase, such as a bead, a plate, a membrane or chip. After unbound materials are washed away, the captured biomarkers are detected and/or measured by mass spectrometry. This method will also result in the capture of protein interactors that are bound to the proteins or that are otherwise recognized by antibodies and that, themselves, can be biomarkers. Various forms of mass spectrometry are useful for detecting the protein forms, including laser desorption approaches, such as traditional MALDI or SELDI, and electrospray ionization.

In one embodiment, a sample may be analyzed by means of a biochip. A biochip generally comprises an insoluble substrate having a substantially planar surface, to which a capture reagent (also called an adsorbent or affinity reagent) is attached. Frequently, the surface of a biochip comprises a plurality of addressable locations, each of which has the capture reagent bound there. Protein biochips are biochips adapted for the capture of polypeptides. Many protein biochips are described in the art and are commercially available.

In some embodiments a sample, particularly a sweat sample may be collected using a dermal patch. The patch may be constructed by any means known in the art and preferably comprises a material into which the sample can migrate and/or accumulate. The material may bind the biomarker. A biomarker may be detected or measured in the patch either while in situ on the subject or after removal from the subject. This may be accomplished by incorporation of a detection system in the patch, for example the patch may contain an antibody specific or selective for the biomarker together with one or more detectable labels. The detectable label may be directly conjugated to the antibody. Alternatively the detectable label can be used to label the antibody indirectly, such as by conjugation to a secondary antibody for example via a biotin-avidin linkage or other method known in the art. Detectable labels for use in dermal patches labels include, but are not limited to an enzyme label, a fluorescent label, a chemiluminescent label or a bioluminescent label. Examples of enzyme labels include horseradish peroxidase, β-galactosidase and alkaline phosphatase.

In some embodiments the dermal patch may be disassembled for detection of the biomarker in the sample. For example a dermal patch may be used to continuously collect a sample over a period of physical activity exposure (e.g. over the course of a work shift, roster or exercise workout). At the end of the period the patch may be collected and the biomarker detected according to the methods described herein.

Compositions

There are also provided herein compositions comprising a sample containing a biomarker together with one or more agents for the detection of that biomarker. In one aspect there is provided a composition comprising a biospecific capture reagent, such as an antibody and a sample comprising at least one biomarker. The capture reagent typically binds the biomarker. The composition may be purified. Such compositions are useful for purifying the biomarkers or in assays for detecting the biomarkers. The sample may be, or be derived from or be derived from a bodily fluid excretion, secretion, biopsy, skin cells or hair follicles. The sample may be, or be derived from urine, sweat, lacrimal fluid, blood, blood plasma, serum, saliva or cerumen.

A sample may be derived from a bodily excretion or secretion for example urine, sweat, lacrimal fluid, blood, blood plasma, serum, saliva or cerumen by any method known in the art. For example samples may be derived from a bodily fluid by fractionation of peptides of proteins in the fluid for example by differential precipitation (e.g. using ammonium sulfate or trichloroacetic acid), size exclusion chromatography, ion-exchange chromatography or dialysis. In some embodiments the sample may be a biopsy, for example a skin or muscle biopsy or biopsy comprising cartilage, ligament and/or tendon. In other embodiments the sample may be skin cells for example collected by swabbing the skin, particularly by way of a buccal swab. Skin cells may also be obtained from hair follicles. In embodiments where the sample is a biopsy or skin cells further processing of the sample is typically required before biomarker detection.

In another aspect there is provided a composition comprising a biomarker of the invention and a biospecific capture reagent, such as an antibody. The capture reagent typically binds the biomarker of the invention. The composition may be purified. Such compositions are useful for detecting the biomarker.

In another embodiment, this invention provides an article comprising an insoluble substrate to which is attached an adsorbent, e.g., a chromatographic adsorbent or a biospecific capture reagent, to which is further bound a biomarker of the invention. In one embodiment, the article is a biochip or a support for mass spectrometry, e.g., SELDI. Such articles are useful detecting the biomarkers.

The biomarker may comprise endorepellin, the LG3 peptide or fragments thereof. In one embodiment the biomarker may consist essentially of the LG3 peptide or a fragment thereof. In another embodiment the biomarker may be the LG3 peptide or a fragment thereof. In a further embodiment the biomarker may be selected from at least one of SEQ ID NO: 1 to SEQ ID NO: 15. The capture reagent may be an antibody, aptamer, affibody, diabody, minibody or fragments thereof.

Monitoring

There is provided herein methods for monitoring the efficacy of exercise or physical activity in a subject, particularly in the context of modulating health or fitness. The methods typically involve assessing the level of a biomarker prior to or at the time of beginning a particular exercise or physical activity and subsequently assessing the level of the biomarker periodically throughout the exercise or physical activity or on completion of the exercise or physical activity.

In one embodiment, the method comprises determining the efficacy of, exercise or physical activity. The method is useful in assessing the effectiveness of exercise or physical activity, as well as monitoring the progress of a subject engaged in exercise or physical activity. The exercise or physical activity may involve a particular regimen. The regimen may involve a single bout of exercise or physical activity or multiple bouts over time. If the exercise or physical activity has an impact on the health or fitness of the subject, the amounts or relative amounts (e.g., the pattern or profile) of the biomarker changes toward a level of profile associated with health. Therefore, the changes in the amounts of the biomarker in the subject can be monitored during the course of exercise or physical activity. Accordingly, this method involves measuring one or more biomarkers in a subject engaged in exercise or physical activity, and correlating the amounts of the biomarker with the health or fitness of the subject. One embodiment of this method involves determining the levels of the biomarker at two or more time points during a course of exercise or physical activity, e.g., a first time for example at the start of an exercise program and a second time, for example at the end of an exercise program or after the exercise program, and comparing the change in amounts of the biomarker, if any. For example, the biomarkers can be measured before and after a workers shift, or working week. The effect of exercise or physical activity is determined based on these comparisons. In some embodiments in which exercise is desirable, an exercise or physical activity is effective when the biomarkers trend away from a baseline, while an exercise or physical activity is ineffective when the biomarkers do not trend away from a baseline. In other embodiments in which exercise is not desirable, a treatment (e.g., no exercise) is effective when the biomarkers do not trend away from a baseline, while a treatment (e.g., no exercise or physical activity) is ineffective when the biomarkers do not trend away from a baseline.

Subject Management

Subject treatment and/or counseling options can be selected or devised based on the qualitative and/or quantitative detection of a biomarker.

In some embodiments the presence or amount of a biomarker is indicative of the physical activity level, health or fitness of the subject. Accordingly in subjects engaged in physical activity, such as athletes or workers, the presence or amount of a biomarker above a predetermined level may be used to manage the subject based on the presence or amount of the biomarker. Such management includes for example prescribing increased or decreased levels of physical activity, a reduction of workload or cessation of an physical activity. For example, if a subject has an elevated level of a biomarker, the subject may be at risk of injury due to an unsustainable level of physical activity thus the management of that subject may include the prescription of reduced physical activity levels until such time as the biomarker level returns to an acceptable level. Conversely, if a subject has a low level of a biomarker, the subject may not be adversely affected by their level of physical activity, thus the management of that subject may include a recommendation that the subject can engage in more physical activity.

Alternatively, where a subject is engaged in for example an exercise program or physically demanding work the presence or level of a biomarker may be monitored over time to assess if the level of physical activity is appropriate or sustainable. For example if the level of biomarker increases substantially over time this may be an indication that level of physical activity is inappropriate or not sustainable. Alternatively, if the level of the biomarker is substantially unchanged over time the level of physical activity may be appropriate and sustainable. In other embodiments the presence of a biomarker may be an indication that level of physical activity is inappropriate or not sustainable.

Alternatively, where a biomarker is initially present in an inactive subject who subsequently engages in an exercise program or physically demanding work an absence or decreasing level of the biomarker over time after beginning that exercise program or work may be an indication that the level of physical activity is appropriate or sustainable. In other embodiments a sustained absence of a biomarker or sustained level of a biomarker below a threshold level may indicate pathological levels of physical inactivity such that increased levels of activity may produce a health benefit.

Kits

In an aspect there is provided a kit for detecting a biomarker. In another aspect there is provided a kit for detecting, monitoring, determining or assessing physical activity level, health, fitness or tissue breakdown status, wherein the kits are used to detect biomarkers of the invention. Typically the kit comprises one or more antibodies, affibodies, minibodies or fragments thereof that detect one or more epitopes of endorepellin, and a detectable label. The detectable label may be directly conjugated to the antibody. Alternatively the detectable label can be used to label the antibody indirectly, such as by conjugation to a secondary antibody for example via a biotin-avidin linkage or other method known in the art. Detectable labels for use in immunoassays and methods for detecting the labels are known in the art. Suitable labels include, but are not limited to an enzyme label, a radiolabel, a fluorescent label, a chemiluminescent label, a bioluminescent label, or a particulate label. Examples of enzyme labels include horseradish peroxidase, β-galactosidase, and alkaline phosphatase. Examples of radiolabels include ³²P, ³H, ¹⁴C, ^S, ¹²⁵l, or ¹³¹l. Particulate labels may include latex labels and colloidal metal labels such as colloidal gold, silver, tin, and other metals.

The kit may include a support on which the antibody is bound, a washing solution, and a vessel for reacting the sample with the antibody. Supports include, but are not limited to, glass, plastic, and polymeric substrata. The support may be a dipstick or strip.

In some embodiments, the kits comprise the biomarker of the invention as reference biomarkers or suitable controls. Jhus, the kits may comprise as suitable reference biomarkers or controls, endorepellin, the LG3 peptide or fragments thereof. In illustrative examples of this type, the reference or control biomarker is selected from at least one of SEQ ID NO: 1 to SEQ ID NO: 15.

In some embodiments, the kit comprises an insoluble support, such as a chip, a microtiter plate or a bead or resin having a capture reagent attached, thereto. The capture reagent binds a biomarker of the invention. Thus, for example, the kits of the present invention may comprise mass spectrometry supports for SELDI, such as ProteinChip™ arrays. In the case of biospecific capture reagents, the kit may comprise an insoluble support with a reactive surface and a container comprising the biospecific capture reagent such as an antibody, aptamer, affibody, diabody, minibody or fragments thereof.

In an embodiment, the kit comprises a washing solution or instructions for making a washing solution, in which the combination of the capture reagent and the washing solution allows capture of the biomarkers or biomarkers on the solid support for subsequent detection by, e.g., mass spectrometry. The kit may include more than type of capture reagent, each may be present on a different solid support. In another embodiment, the kit comprises one or more containers with biomarker samples, to be used as standard for calibration.

The kit can also feature printed instructions for using the kit to qualitatively or quantitatively determine one or more biomarkers of the present invention. The present invention will now be further described in greater detail by reference to the following specific examples, which should not be construed as in any way limiting the scope of the invention.

Examples Example 1: Experimental Procedures

Subjects

Ten healthy male mining industry employees. were recruited on-site at a remote open-cut mine in Queensland, Australia. Participants completed an informed consent form and a questionnaire concerning general health information, exercise habits and previous musculoskeletal injuries. All sampling was performed with the approval of Queensland University of Technology ethics committee. /

Sampling

Urine samples were collected from mine site employees at 12 hour intervals. The pre-shift sample was collected at 18:00 prior to an overnight 12 hour shift (PRE). Post-shift samples were collected on-site at the end of the same shift (POST). The following sample was collected after a 12 hour rest period and prior to commencement of the next shift (24 hr.). Samples were kept overnight at 4°C and then transported to the Institute of Health and Biomedical Innovation, Brisbane where they were stored at -20*C until required.

Urea and Cortisol assays

All samples were thawed at room temperature and a single aliquot of each sample was sent to Queensland Medical Laboratories (Murarrie, QLD, Australia) for urinary urea and Cortisol testing. Urinary, urea was measured by an automated kinetic assay, using a Roche Cobas Integra 800 (Roche Diagnostics, Basel, Switzerland; analytic coefficient of variation being < 4%). Urinary Cortisol levels were measured by competitive immunoassay, using a Bayer Centaur Immunoassay System (Bayer Diagnostics, Tarrytown, NY, USA; analytic coefficient of variation being < 4%). Creatinine levels were analyzed by the Jaffe method using a Roche Cobas Integra 800 automated analyzer (Roche Diagnostics, Basel, Switzerland; analytic coefficient of variation being < 3%) to standardize for diuresis. Sample preparation for SELDI-TOF MS analysis

Each thawed sample was clarified by centrifugation at 1500 x g for 10 minutes, aliquoted and stored at -20°C until required for ultra-filtration. Individual aliquots of each sample were then thawed, pre- filtered through a 0.2 m syringe filter prior to the transfer of 4 mL of each filtered sample to separate Amicon Ultra-4 3000 NMWL centrifugal ultra-filtration devices (Millipore, Billerica, MA, USA) which had been pre-rinsed with dd-HbO as per manufacturer's instructions. The loaded ultrafiltration devices were then centrifuged at 4000 x g for 40 min in a Beckman Coulter swing bucket centrifuge (Beckman Coulter, Gladesville, NSW, Australia). The urinary protein (retentate) was washed with 3.5 mL of ddhbO and centrifuged as above to desalt prior to the transfer of individual aliquots to low bind Eppendorf™ tubes and storage at -80°C until required for analysis. An unrelated quality control (QC) urine sample was prepared in an identical fashion.

The protein concentration of each sample was determined by bicinchoninic acid (BCA) protein assay (Pierce, Rockford, IL, USA) and adjusted to 0.42 mg/mL in binding buffer (10 mM sodium acetate (NaAc), pH 4.5) (SIGMA® Life Sciences, Castle Hill, NSW, Australia). CM10 (weak cation exchange) ProteinChip® arrays (Bio-Rad, Hercules, CA, USA) were then pre- equilibrated with 50 μί of binding buffer using a bioprocessor (BioRad) and placed on a plate shaker for 5 min (x2). Triplicate 35 L sub-samples were applied to the surface of the CM 10 arrays in a semi-randomized fashion to ensure that no replicate samples occupied spots on the same chip and that each chip received a single QC replicate sample. The samples were allowed to bind for 1 h on a plate shaker prior to washing each spot for 5 min (x 3) with binding buffer, followed by 2 x 1 min washes with ddH20. The arrays were allowed to air dry prior to, between and following 2 x 1 μί applications of 50% Sinapinic acid (SPA) in 50% ACN:0.5% TFA. Each array was then analyzed on a PCS 4000 personal edition SELDI-TOF Mass Spectrometer (Bio-Rad).

Data acquisition, inspection and pre-processing

The QC sample served as a quality assurance measure with the purpose of determining any batch or chip variability downstream of the data acquisition. Spectra were generated within the range of 0 to 20000 Da, while matrix attenuation was set to 500 Da, focus mass was set to 5000 Da, sampling rate was set at 800 MHz, 2x warming shots at 1600 nJ and 15x data shots at 1500 nJ were performed with a partition of 1 of 4. A total of 530 shots per spot were acquired for analysis. Data generated from the warming shots were excluded from the averaged spectra.

Using ProteinChip® Data Manager Software 3.0.7 (Bio-Rad), all spectra were normalised by total ion current between 2000 Da and 20000 Da. First round spectral analysis incorporated exclusion based on Normalisation factor (Nf). Any spectra with a Nf greater than 2.5 standard deviations from the mean were excluded. A calibration peptide chip was prepared using pure porcine Dynorphin (2147.5 Da), bovine insulin (5733.5 Da), bovine ubiquitin (8564.8 Da) (Bio-Rad) according to manufacturer's instructions and run on the same day as the worker samples. An external calibration equation was generated from the calibrant spectra and applied to each worker spectrum A smoothing window of 25 points was used before fitting the baseline and filtering parameters were set to 'on' with an average width of 0.2 times expected peak width. The spectra were collected and the peaks were clustered using Cluster wizard™ (Bio-Rad,) with the following parameters unless otherwise stated; S/N = 3.0, valley depth = 3.0, centroid fraction = 0.1 %, second pass option off. Individual peak clusters were accepted with m/z coefficients of variation (CV) of 0.5 % or less to ensure accurate peak alignment.

Chip to chip variation was assessed by Pearson's product-moment correlations of the control spectra in open source 'R' statistical computing and graphics program, version 2.10.1 (www.r- proiect.org). A regression coefficient of less than r = 0.84 was set as criteria for further analysis of chip to chip variability as described previously [22]. CVs were calculated for select peaks of interest in each set of experimental replicates. These CVs were then assessed in order to confirm reproducibility.

Gel electrophoresis and protein isolation

For non-reducing SDS-PAGE, equivalent amounts of concentrated urinary protein were prepared in 4X NuPAGE® LDS sample buffer (Invitrogen, Carlsbad, CA, USA), loaded into NuPAGE® 4%-12% bis-tris gradient gels (Invitrogen) electrophoresed in 1X NuPAGE® MES SDS running buffer (Invitrogen) at 200V for 20 min at 4°C. Silver stains were performed using SilverSNAP® Stain Kit II (Pierce) as per manufacturer's instructions.

In order to isolate specific protein bands, samples were loaded in duplicate onto a single NuPAGE® gel (Invitrogen) such that vertical bisection of the gel resulted in replicate gels. One of the gels was silver stained and the other was overlaid precisely on top of the silver stained gel with a sheet of clear overhead projector film in between to keep the gels separate. A portion of the unstained gel corresponding to the approximately 20kDa band of interest was excised, placed into a microcentrifuge tube containing SELDI-TOF MS binding buffer and finely diced with the razor blade. The diced gel pieces were incubated at RT for 3 days in 50% ACN after which the supernatant was transferred to a separate tube and the gel pieces were then incubated in 100% ACN with shaking for a further two days. The supernatant was added to the previously collected supernatant and the protein containing solution was dried under vacuum in a centrifuge. The protein pellet was resuspended in 50 mM ammonium bicarbonate, pH 7 and a portion of the buffer was then applied to a CM 10 protein chip array and subjected to SELDI-TOF MS as described above.

Protein identification

Concentrated urinary protein (W4 and W5) were run on fresh SDS-PAGE gels and the approximately 20 kDa bands of interest were excised as described above, The protein contained in the gel pieces was then reduced with dithiothreitol, alkylated with iodoacetamide and digested with Trypsin as per a standard in gel digestion protocol routinely used in the inventors' laboratory. The digested protein was then subjected to Liquid Chromatography Tandem Mass Spectrometry (LC MS/MS) (1100 Series HPLC, Agilent Technologies, Boblingen, Germany coupled with a QStar Elite Quadrupole - Time Of Flight Mass Spectrometer, Applied Biosystems, Foster City, CA.USA) and the resulting peptide mass spectra were analysed using the MASCOT search algorithm against the Ludwig Non- Redundant database and subsequently validated using the Trans Proteomic Pipeline (TPP) and the Peptide Prophet tool.

Western blot analysis

Concentrated urinary proteins were transferred to a NT nitrocellulose membrane (Pall Corporation, Pensacola, FL, USA) in 25 mM Tris base, 40 mM Glycine, 10% v/v Methanol following SDS-PAGE as described above. The membrane was incubated overnight in 5% w/v skim milk powder (SMP) in TBST (100 mM Tris, 150 mM NaCI and 0.1% v/v Tween 20) at 4°C, washed for 6 x 5 min in TBST and then incubated for 1 hr at RT with goat anti-human endorepellin antibody (R&D Systems Minneapolis, MN, USA) (1:10000) in 5% SMPfl^"BST. Membranes were washed for 6 x 5 min then incubated for 1 hr at RT with HRP-conjugated rabbit anti-goat secondary antibody (1 :10,000) (R&D Systems Minneapolis, MN, USA) prior to a further 6 x 5 min washes with TBST and detection using an ECL Plus western blot detection kit (GE Healthcare, Little Chalfont, Buckinghamshire, UK) as per manufacturer's instructions.

Statistical Analysis

Data are presented as mean ± standard error (SE) or standard deviation (SD) as indicated. Students t-test (worker physical activity characteristics) or Mann Whitney-U test (Cortisol, urea and m/z 16,881 data) were performed to test for group differences with significance accepted at p < 0.05 and p < 0.01. Pearson correlations were used to determine the strength of association between replicate control data to assess potential chip to chip variation.

Example 2: Subject demographics

General health and physical activity information was collected from 10 male mining workers (maintenance crew (crew) n=4; plant operators (operators) n=6) who performed a 12-hour overnight shift at an open cut coal mining operation in central Queensland, Australia. The age range was between 26 and 61 with an average of 38.5 years. The years of service in the mining industry ranged between 2 and 30 years with an average of 6.9 years. The average height, weight and BMI ± SD were 173 cm ± 6 cm, 83.70 kg ± 17.75kg and 27.97 kg/m² ± 4.79 kg/m2 respectively. Of the 10 workers, 6 declared that they had previously had a musculoskeletal injury, although the most recent was 3-months prior to the study date. Importantly, only one worker, an operator (W5), reported performing physical exercise outside of work hours in the 24 hr prior to the beginning of the study. In terms of general physical activity, the crew spent significantly less time seated and significantly more time standing and walking during the shift compared to the operators (p < 0.05) (Table 1).

Table 1. Summary of physical activity. * = p<0.05

% of shift spent Crew Operators

seated 16.5%± 7.8% (n=4) * 85.9% ± 8.0% (n=6)

standing 67.2%± 30.1% (n=4)* 9.1% ± 5.9% (n=5)

walking 56.1%± 32.1% (n=3)* 8.4% ± 6.6% (n=5) Example 3: Urinary urea and Cortisol assays

It was established that sampling procedures for mining employees needed to be non-invasive in order to conform to workplace requirements. Given that workers were already involved in occupational urine testing, the ideal source of sampling was considered to be urine. Average urinary urea levels increased significantly in both post-shift (post) (p < 0.01) and 12 hr post-shift (24 hr) (p < 0.01) samples compared to pre-shift samples (pre). In a similar fashion, urinary Cortisol levels significantly increased post-shift when compared to pre-shift levels (p < 0.05) and remained elevated prior to the next shift (p < 0.05) (Table 2). Interestingly, while there was no significant difference in the level of Cortisol between crew and operators the trend in the latter demonstrated recovery or a similar response to that expected for a circadian rhythm, whereas the crew did not display recovery in Cortisol levels potentially suggesting exposure to increased stress (Fig 4).

Values are means ± SE. Indicates values are significantly greater than PRE value (p<0.01).

"Indicates value significantly greater than PRE value (p<0.05).

Example 4: Urinary protein analysis by SELDI-TOF MS.

SELDI-TOF MS profiles of mining worker urinary proteins were generated to detect biomarkers of musculoskeletal injury, fatigue and/or physical exertion. Cluster analysis of the spectra was performed to reveal those spectral peaks which were associated with either the maintenance crew cohort who were engaged in more physically active work or the operator cohort who were less physically active during the shift. This resulted in a cluster of 59 spectral peaks, of which a block of 3 peaks at m/z 16741.63, 16,881.59 and 17038.85 were of particular interest since these appeared to be up-regulated in the maintenance crew (Fig 1 A). Analysis of the peak intensities of the central spectral feature at m/z 16,881 indicated that there was not a significant difference between crew and operators (Fig 1B). However, inspection of the raw spectra revealed that worker 5 (W5), an operator, had peak intensities for the central m/z 16,881 peak which were more similar to those of the crew cohort than to the other operators. Examination of the worker responses to the health and activity questionnaires revealed that only W5 had engaged in a gym workout in the 24 hr prior to urine sampling, therefore we then re-grouped the cohorts into physically active (crew + W5) vs non-physically active (operators w/o W5). The difference in peak intensity is significant between the workers exposed to higher levels of physical activity and those not exposed to higher level physical activity at the pre (p < 0.05), post (p < 0.01) and 24 hr (p < 0.05) time points (Fig 1B). Furthermore, inspection of the raw stacked spectra for each worker clearly demonstrated the difference in peak intensity of the feature surrounding m/z 16881 between the cohorts (Fig 1C).

Example 5: Protein isolation and identification.

To detect the broad tri-phasic peak by silver stain following SDS-PAGE samples from a crew member (W4 Pre & W4 24hr) and 2 samples from an operator (W6 Pre & W6 24hr) were selected based on maximum (W4) and minimum (W6) peak intensities for m/z 16,881 observed from the spectral data (Fig 2A). The W5 Pre sample was also included as it was hypothesised that the peak of interest would appear as a more intensely staining band in the W4 and W5 samples compared to the W6 samples (Fig 2B). The samples were electrophoresed and the gel silver-stained. An intensely staining band corresponding to an approximate molecular weight of approximately 20 kDa in the W4 and W5 samples which did not appear as intense in the W6 samples was observed (Fig 2B).

In order to confirm that the intensely staining band was indeed the tri-phasic peak detected in the SELDI spectra at m/z 16,881 the SDS-PAGE was again performed with the same samples in replicate gels. The approximately 20 kDa band was excised, isolated and the extracted protein was subjected to SELDI-TOF MS. A tri-phasic peak at the corresponding m/z to the original analysis was detected although the intensity was lower than in the original spectra (Fig 2C). This was not unexpected as only a portion of the diffused protein from the excised gel piece was used. This data indicates the protein in the gel piece and corresponding intensely staining band are in fact the same protein responsible for the tri-phasic peak detected in the SELDI-TOF mass spectra at m/z 16,881.

To identify the protein or proteins contained in the approximately 20kDa band and therefore the spectral feature surrounding m/z 16,881 triplicate gel pieces from 2 individual workers (W4 and W5) were excised from freshly prepared gels. The protein contained in the gel pieces was subjected to in- gel digestion and the tryptic peptides were analysed by LC-MS/MS. The resulting peptide mass spectra were analysed using the MASCOT search algorithm. A specific fragment of the Basement Membrane Specific Heparan Sulphate Proteoglycan Core Protein 2 (HSPG2) or Pelecan, Non- secretory ribonuclease and Putative uncharacterised protein STX5 were identified with high probability scores (all > 54) (Table 3). Table 3. Periecan is identified as the 20 kDa band excised from the SDS-PAGE gel

Protein identification was performed using the MASCOT search algorithm within the LudwigNR database. Results are displayed for proteins identified from peptides within gel fragments excised at a molecular weight of approximately 20 kDa in triplicate from 2 individuals. The basement membrane-specific heparan sulfate proteoglycan core protein (Periecan), in addition to its fragmented form, was identified as the protein band excised from the silver-stained gel. This is based on the high probability values, for correct identification of the protein band, attributed to the protein from the peptide data.

* Ions score is -10*log(F'), where P is the probability that the observed match is a random event. Individual ions scores

>54 indicate identity of extensive homology (p<0.05) Protein scores are derived from ions scores as a non-probabilistic basis for ranking protein hits

The MS/MS data were then validated using the Trans Proteomic Pipeline (TPP) and the Peptide Prophet tool which resulted in a return of 30 spectra with probability scores above the calculated Minimum Probability Threshold (MPT) of 0.76, corresponding to p = 0.05. Twelve of these 30 peptides (with a MPT of > 0.91, p = 0,025) corresponded to peptides which directly identified the Perlecan protein LG3 fragment (Table 4). Table 4. Peptide prophet probability scores for 4 highest confidence peptides of HSPG2.

Peptides from all samples validated through the TPP - these 4 peptide sequences, out of the 10 identified from MS/MS spectral data, were validated with greater than 95% confidence. The remaining 6 peptides either had a MASCOT ion score below 57 or were lower than the 95% confidence interval as calculated by PeptideProphet.

PeptideProphet Ions Peptide Neutral Charge m/z Pi Ion Protein Acc No Probability Peptide Score

Mass

9/20 membrane-specific heparan

sulfate proteoglycan core P98160

1 . R.SPGPNVAVNAK.G 1052.5614 2 527.288 8.47 73.87

protein Taxid=9606 [Homo sapiens]

9/20 membrane-specific heparan P98160 sulfate proteoglycan core P98160

R.SPGPNVAVNAK.G 1052.5614 2 527.288 8.47 61.05

protein Taxid=9606 [Homo sapiens]

9/20 membrane-specific heparan P98160 sulfate proteoglycan core P98160

1 R.SPGPNVAVNAK.G 1052.5614 2 527.288 8.47 67.48

protein Taxid=9606 [Homo sapiens]

9/34 membrane-specific heparan

sulfate proteoglycan core

0.9999 K.GSVYIGGAPDVATLTGGR.F 1689.8686 2 B45.9416 5.84 60.93 P98160 protein Taxid=9606 [Homo sapiens] 9/34 membrane-specific heparan

sulfate proteoglycan core

0.9999 K.GSVYIGGAPDVATLTGGR.F 1689.8686 B45.9416 5.84 69.98 P98160 protein Taxid=9606 [Homo sapiens]

9/26 R.GSIQVDGEELVSGR.S membrane-specific heparan

sulfate proteoglycan core . p98160

0.9869 1444.7158 2 723.3652 4.14 85.58*

protein Taxid=9606 [Homo sapiens]

8/26 R.GSIQVDGEELVSGR.S membrane-specific heparan

sulfate proteoglycan core

0.9868 1444.7158 2 723.3652 4.14 71.63* P98160 protein Taxid=9606 [Homo sapiens]

7/26 R.GSIQVDGEELVSGR.S membrane-specific heparan

sulfate proteoglycan core

0,9836 1444.7158 2 723.3652 4.14 63.28* P98160 protein Taxid=9606 [Homo sapiens]

8/26 R.GSIQVDGEELVSGR.S membrane-specific heparan P98160 sulfate proteoglycan core P98160

0.9701 1444.7158 2 723.3652 4.14 68.07*

protein Taxid=9606 [Homo sapiens]

7/26 R.GSIQVDGEELVSGR.S membrane-specific heparan

sulfate proteoglycan core

0,9651 1444.7158 2 723.3652 4.14 57.80* P98160 protein Taxid=9606 [Homo sapiens]

7/26 R.GSIQVDGEELVSGR.S membrane-specific heparan

sulfate proteoglycan core

0.9194 1444.7158 2 723:3652 4.14 54.02* P98160 protein Taxid=9606 [Homo sapiens]

membrane-specific heparan sulfate proteoglycan core

0.9999 13/34 K.GNVYIGGAPDVATLTGGR.F 1716.8795 2 859.447 5.84 63.29 Q59EGO protein Taxid=9606 [Homo sapiens]

Analysis through Protein Prophet gave a confidence value of 1.000 for both the Perlecan protein (fragment) and the Perlecan protein with the probability for these as 1.0000 and 0.9899, respectively (Table 5).

Table 5. Analysis through ProteinProphet gave a confidence value of 1.000 for both the Perlecan protein (fragment) and the Perlecan protein with the probability for these as 1.0000 and 0.9899, respectively.

membrane-specific

heparan sulfate 0.9899 1 1.5 1 4 7.02 proteoglycan core protein

Further analysis of the Perlecan sequence indicated that the peptides identified by MS/MS mapped exclusively to domain 5 of the parent protein and specifically to the C terminal LG3 peptide. The LG3 peptide molecule spans 195 amino acids (residues 4197-4391 of the parent protein) and has a theoretical mass of 20549 Da which corresponds with the protein band observed on the gel (Fig 2b). However, the theoretical mass did not correspond to the original m/z 16881 feature detected in the original SELDI-TOF MS data. In order to further characterize the sequence coverage of the MS/MS data and to account for this apparent discrepancy, we performed an in s///co trypsin digest of the LG3 peptide using the Peptide Mass tool on ExPasy Proteomics Server. The peptides identified by LC- MS/MS matched the in silico generated tryptic fragments and collectively resulted in 95.9% sequence coverage of the putative LG3 peptide (Fig 3A). Interestingly, the MS/MS data did not identify any sequence from the first 25 residues of the LG3 peptide. The sequence coverage of these experimental data spanned 158 amino acids incorporating residues 26 to 183 of the parent LG3 peptide or 4222 - 4379 of the perlecan parent sequence. The putative peptide covered by the sequence data has a theoretical mass of 16641 Da which more closely corresponds to the central m/z 16881 peak from the original experimental data.

In order to further confirm that the protein band observed at approximately 20kDa was indeed derived from perlecan / endorepellin we subjected 4 worker urine samples which exhibited high (W4 Pre and W5 Pre) and low (W6 Pre and W10 Pre) intensity SELDI TOF MS peaks at m/z 16881 to western blot analysis using a polyclonal antibody raised against full length endorepellin. A strong immunoreactive band was observed which corresponded to the approximately 20kDa band observed in the silver-stain of worker urinary protein and indicates that this band is derived from endorepellin (Fig 3B). Moreover, the intensity of the bands is consistent with the difference in intensity of the peaks at m/z 16881 observed in the original SELDI TOF MS data.

Discussion of the above Examples

The analysis of worker urinary protein by SELDI-TOF MS was used to show that workers engaged in strenuous physical activity have higher urinary levels of the LG3 peptide of endorepellin. Initial cluster analysis of the spectral data using cluster wizard software (BioRad) revealed that a number of m/z peaks were associated with either the maintenance crew or operator cohorts in the study. The most striking of these was a triphasic peak with a central m/z of 16,881 (Fig. 1) which could be subsequently visualised by SDS-PAGE (Fig. 2) and identified as the LG3 fragment of endorepellin using LS-MS/MS (Fig. 3 and Tables 3-5).

As described herein there is an association between urinary LG3 peptide levels and physical activity in a cohort of mining workers. Thus, endorepellin / LG3 peptide and fragments thereof are biomarkers of physical activity. Example 6: Different types of exercise induce L63 expression

The inventors have shown that the LG3 peptide increases in urine between around 4 hr. - 9 hr. following either a 2 km treadmill run or a 12 km stationary bike ride (Fig. 4). The increase was observed in exercises which involve a high cardiovascular component (up to about 80% maximal HR) or lower limb loading such as would be experienced in cycling or running (Fig. 4 a,b). These exercises were carried out in a gym environment with the bike setting in interval mode and with the intensity increasing the subject heart rate (HR) up to 80% of predicted maximal, while the treadmill was set to level (no incline) with a speed of 10 km/hr. Importantly, the temporal expression profile demonstrated in the 2 km run data indicates a level of recovery at 24 hr. post intervention. It is also important to note that each of these exercises was quite transient, lasting less than 30 min. This is important because situations where the exercise or activity is extended then- it may be possible to evaluate the recovery of an individual by their LG3 levels following a recovery period. It should also be noted that the window within which LG3 peptide appears in urine may vary according to the types of exercise performed (e.g., concentric vs. eccentric, cardiovascular vs. resistance exercises etc.) and such variations may be determined using routine techniques, as for example described herein.

To test if the expression profile was different in a road running situation, the same subject performed a 2 km road run. The exercise included up and down hill running and the intensity of the run was more sustained. While the water intake following the exercise was not specifically measured, more water was consumed after this trial compared to the 2 km treadmill run (Fig. 5). The base line intensities were higher than expected in this trial but this could be due to the ambulatory activity during the "pre" period in this trial compared to the "pre" period before the treadmill run (Fig. 5), where the subject was sedentary throughout the day before the exercise trial. Taken together these data demonstrate that different types of exercise induce LG3 expression, and that LG3 is an indicator of physical activity. Of note, knowledge of the urinary LG3 expression window following exercise allowed us "to guide the design of an exercise program (in this case a road run) such that implementation of the exercise program (run) maintains or increases endorepellin or LG3 levels." That is to say it is possible to use the knowledge of urinary LG3 peptide expression to guide the design of an exercise which would specifically lead to an increase in LG3 levels. These data provide evidence of the utility of the urinary LG3 peptide for demonstrating a moderate intensity of physical activity above base level ambulatory or sedentary activity levels.

Methods

Exercise protocols

The exercise protocols were designed to determine the "expression window" or the period of time in which the LG3 appeared and then disappeared from the urine following exercise. The exercise exemplars documented herein are as described essentially with each result.

SDS-PAGE. Western Blot and Densitometry

The present inventors attempted to develop a system for evaluation of loading by performing a dot blot of the samples evaluated by western analysis, using the dot blot densitometry to normalize loading and the western blot results. This was applied to the later data where appropriate. The earlier results were not normalized in this way but appear to be more or less consistent with the later results. SDS-PAGE

1. Samples were taken out of the -80°C and placed on ice.

2. 1.16 pg of total protein was prepared in separate tubes to a total volume of 28 for each sample.

3. The content in each tube was mixed and pulse centrifuged.

4. 4 μί of each sample was transferred to clean Eppendorf™ tubes for dot blot analysis (evaluation of loading). 5. 8 pL of loading buffer was added to the remaining 24 pL of samples.

6. The content in each tube was mixed and pulse centrifuged.

7. The samples were loaded into a 15 well NuPAGE Tris HCL 4-12% gradient gels.

8. The samples were electrophoresed in NuPAGE MES buffer at 200 V constant for 35 minutes at 4°C.

9. Following electrophoresis the protein was transferred to a nitrocellulose membrane using Semi-dry Transfer for 90 minutes at 45 mAmps / gel

Western Blotting

1. Following protein transfer the nitrocellulose membranes were blocked in 5% skim milk powder in Tris buffered saline / 0.1% Tween 20 (TBST) for 1 hour at room temperature (RT) on a shaker and protected from contaminants with aluminium foil.

2. The blocked membrane was incubated with a goat anti-human Endorepellin polyclonal antibody as primary (1 : 10,000 dilution) for 1 hour at RT on a shaker.

3. The membrane was washed in TBST for 6 x 5 minutes at RT on a shaker.

. 4. The membrane was incubated in a either a 1:10,000 dilution of rabbit anti-goat IgG HRP- conjugated secondary antibody in TBST for 30 minutes at RT on a shaker.

5. The secondary antibody was removed and the membrane was washed in TBST for 6 x 5 minutes at RT on a shaker.

6. Each membrane and strip was transferred to a clean white weighing tray and incubated in Amersham™ ECL™ Prime western blot detection reagent (GE Healthcare Ltd, Little Chalfont, Buckinghamshire, UK) as per manufacturer's instructions.

7. Chemiluminescence was captured by exposure of the membrane to X-Ray film (Fujifilm, Tokyo, Japan) and subsequently developed using an Agfa automated film developer CP-

^■ 1000 (Mortsel, Belgium).

Dot Blot

1. 2 pL of each sample (prepared as above) was transferred to a nitrocellulose membrane strip and allowed to air dry. 2. To visualize the protein spots the membrane was reversibly stained using MemCode™ Reversible Protein Stain Kit (Peirce, Rockford, IL, USA) as per manufacturer's instructions. Briefly,

A. Stain

1. The nitrocellulose membrane containing the proteins was rinsed with milliQ water and quickly decanted.

. 2. 1 mL of MemCode™ Reversible Protein Stain was added to the nitrocellulose membrane.

3. The membrane was placed on a shaker at room temperature for 30 seconds (Stained proteins appear as turquoise-blue bands).

B. Destain (remove background)

1. 1 mL of MemCode™ Destain Reagent was added to the membrane and removed after a few seconds. This step was repeated two additional times.

2. 1 mL of the Destain Reagent was added to the membrane and agitated for 5 minutes on a shaker.

3. The membrane was rinsed four times by adding milliQ water to the tray and decanting after a few seconds.

4. The membrane was washed with milliQ water for 5 minutes on a shaker with agitation. Densitometry

1. A digital image of the dot blot was obtained using gel doc or of western blot films using a flatbed scanner.

2. Densitometry was performed using Image J (NIH).

Example 6: SR assay for high throughput-targeted analysis of the LG3 peptide

Due to a lack of specific antibody availability for full length LG3, the aim of this study was to develop a sensitive and specific assay for LG3 that would be applicable to human serum or urine samples, all the while avoiding confounding or cross reactivity with Endorepellin or Basement membrane-specific heparan sulfate proteoglycan core protein (Perlecan). The full-length Endorepellin sequence is provided in Fig. 6a. This sequence contains the LG3 fragment (blue) which is known to be proteolytically liberated from Perlecan or Endorepellin by both bone morphogenetic protein-1 (BMP- 1) and Tolloid-like Metalloprotease (Gonzalez ef al. 2005, J Biol Chem 280(8): 7080-7087). The underlined regions of sequence in Fig. 6a illustrate the most C-terminal and N-terminal tryptic peptides after endogenous cleavage between the Asparagine (N) and Aspartic Acid (D) residues (red-blue interface) Fig. 6b. Note that both these underlined regions are specific for LG3 liberation and as such may both be used depending on the sample. For example, LG3 is in urine thus the N- terminal LG3 (underlined blue) peptide would be a good candidate to assay. Alternatively, the C- Terminal Endorepellin peptide (underlined red) might be a better target for assessing if LG3 has been liberated within a sample that readily depletes LG3 but not Endorepellin from the environment. Either way, both are specific for LG3 liberation from Endorepellin or Perlecan.

The top Blast-p protein identities for LG3[i:25i are all derivatives of Perlecan. Table 5 shows the top 4 identifications based on the N-Terminal LG3 sequence. The top hit for this sequence aligns with full length LG3 itself with an expect-value (E-Value) of 2e-²¹. Thus the number of hits on this sequence that can be expected to be observed by chance (using this database) is extremely small (2e ²¹). This indicates that this sequence containing 25 amino acids, while not full length LG3, is highly specific for LG3.

Table 5: Blast-p results for LGtyss]

Query

Protein Accession E-Value

Coverage

-Chain A, Laminin G Like Domain 3 From Human

3SH4 A 100% 2e-21 Perlecan

-Basement membrane-specific heparan sulfate

BAD93088.1 100% 3e-21 proteoglycan core protein precursor variant

-heparan sulfate proteoglycan 2 (perlecan), isoform

EAW94994.1 100% 3e-21 CRA_a

-heparan sulfate proteoglycan 2 (perlecan), isoform

EAW94995.1 100% 3e-21 CRA_b

The goal of LC- S/MS analysis of LG3[i:25] was to determine the dominant precursor charge state in an LC-ESI instrument, to do this the QSTAR-Elite was used. LC-MS/MS analysis of LG3[i:25] revealed two primary precursor mass-to-charge (m/z) states coming from this 2771.98 Da peptide which were m/z 925 and 694 (average m/z) which correspond to [M+3]³⁺ and [M+4]⁴⁺ charge states, Fig. 7a. The Extracted Ion Chromatogram (XIC) for both parent ion m/z states is produced in Fig. 7b and demonstrates that the [M+3]³⁺ state (z3) has an increased signal that is consistent across the elution of LG3|i:25]. The isotopic distribution for both precursor m/z states is provided in Fig. 7c and d, which shows the isotopic envelope for each, not that the theoretical m/z of 924.99 and 693.99 correspond to the measured average m/z of 925 and 694 for z3 and z4. Based on these results further development of the assay was based on the precursor m/z of 924.99 which corresponds to a [M+3]³⁺ m/z state.

The subsequent fragmentation spectra of m/z 925 is illustrated in Fig. 8a. The transitions highlighted in red (Fig. 8b) correspond to Q3 ions of m/z 799.9 (y7⁺), 283.3 (b3⁺), 1018.2 (y9⁺) and 1152.8 (y202⁺) in Table 6.

Table 6: LG3 precursor (Q1) and product ion transitions (Q3)

SRM Transition Ion Q1 Q3

3+

LG3[i-24] y7⁺ 924.99 799.90

3+

LG3[i-24] y20²⁺ 924.99 1152.76

3+

LG3[i-24i y9⁺ 924.99 1018.16

3+

LG3[i-24] b3⁺ 924.99 283.30

LG3 is highly specific for the BMP-1 cleavage. Since urine may also contain the larger Perlecan, potentially due to sloughing of the lower urinary tract epithelium, detection of the BMP-1 cut site would be highly specific for LG3 and not the parent Perlecan indicating a specific source of the LG3. The N-terminal BMP-1-tryptic peptide of LG3 is 25 amino acids in length beginning with an Aspartic acid and ending at an Arginine. This length and sequence contributes to its specificity, in fact a BLAST® search reports a 4e-¹⁸ E-value for Heparan sulphate proteoglycan core protein with 100 % sequence specificity for this protein. The E-value gives an indication of the number of hits on this sequence one can expect to see by pure chance when searching a database of a particular size. The lower this value the more significant the match, as can be seen by the E-value above, this sequence is incredibly specific for LG3. In addition, detection and quantification of the BMP-1 liberated LG3 allows inferences to be made regarding biological processes at the tissue source of the LG3.

Methods

Tryptic digest

A 20 g amount of total urinary protein was dried down before resuspending in 50 μΙ_ of reducing buffer consisting of 20% TFE, 50 mM (NH₄)HC0₃ (pH 7.8) 20 mM DTT and incubating at 55° C for 1 h. Samples were allowed to cool before adding 2.625 μΙ_ of 1.1 M IAA and incubating away from light at RT (-24° C). This volume is then reduced to dryness before resuspending in 50 μί of digest buffer consisting of 50 mM (NH₄)HC03 (pH 7.8). Trypsin was added at a 1 :20 concentration of enzyme : protein and incubated overnight (-18 h) at 37°C in an oven to avoid temperature gradients. The following day, the solution was acidified to remove the activity of the enzyme by addition of 1.5it of FA prior to lyophilizing the sample to dryness. The final digest was resuspended buffer A which consisted of 5% ACN, 1% FA. In another experiment, samples were first cleaned by passing through a 10 kDa ultra-filtration device and SDS-PAGE gel electrophoresis

For SDS-PAGE, 1 ^g of concentrated urinary protein or 1 ^g protein digest from above was

prepared in 4 X NuPAGE® LDS sample buffer. The protein samples were loaded into NuPAGE® 4% - 12% bis-tris gradient gels and electrophoresed in 1 X NuPAGE® MES SDS running buffer at 200 V for 40 min at 4° C. Western blot

Concentrated urinary proteins or tryptic digest samples were transferred to a NT nitrocellulose membrane (Pall Corporation, Pensacola, FL, USA) in 25 mM Tris base, 192 mM Glycine, 20% v/v Methanol, following SDS-PAGE as described above. The transfer occurred over 1 h with continual buffer agitation at 200 mamps at 4° C. The membrane was blocked in 5% w/v BSA/TBST (100 mM Tris, 150 mM NaCI and 0.1% v/v Tween 20) for 15 min at RT (~24°C) and incubated for 1 hr at RT with a goat anti-human endorepellin primary antibody (R&D Systems Minneapolis, MN, USA) (1 :10,000) in 5% BSA/TBST. The transfer was then washed for 5 min (x 2) in 0.5% BSA/TBST before incubating for 1 hr at RT with HRP-conjugated rabbit anti-goat secondary antibody (1 :10,000) (R&D Systems) prior to a further 6 x 5 min washes with TBST and detection using an ECL Plus western blot detection kit (GE Healthcare, Little Chalfont, Buckinghamshire, UK) as per the manufacturer's instructions.

Standard peptide

The N-terminal tryptic peptide of LG3 (LG3[i-25i) was purchased from Mimotopes (Clayton, VIC, Australia). Stock solutions were made by dissolving 1 mg LG3[i-25] in 2% ACN, 1% FA producing a 1 . mg.ml ¹ suspension which was stored at -80° C. Calibration standards were prepared by taking a 1 :1000 dilution of working suspension resulting in 1 Mg.ml-¹ standard which was further serially diluted to 16 ng.mM in 2% ACN, 1% FA.

Sequence analysis

Prior to assay development the sequence specificity of LG3[i:25] for determination of full length LG3 was assessed using Blast-p. The search was performed against the Non-redundant protein sequences database containing 18581586 sequences, organism was set to human (taxid:9606) and the protein-protein BLAST algorithm (BLASTP 2.2.26+) was selected for sequence alignments.

Chromatography optimization

LG3[125] was applied to the resolving column (C18 reverse phase column; 150 x 2.10 mm 2.6 pm 100 A, Phenomenex; Torrance, CA, USA) in 23 % buffer B (1 % FA, 90% acetonitrile, v/v) for 3.5 min to remove non-specific analytes and reduce ion suppression from the sample matrix.

Mass Spectrometry

LC-QqTOF

Dominant precursor masses were determined by injecting 5 pL of 1 pg/nnl LG3[1 25] standard onto a C18 reverse phase column (150 χ 0.3 mm, 5 pm 300 A; Grace; Deerfield, IL, USA) attached to a uHPLC system (Shimadzu) containing 1% FA [v/v, buffer A) and 1% FA in acetonitrile {vAr, buffer B). Peptide was resolved over 24 min at 3 ί / min starting with 1 % B from 0 to 3.5 min before applying a single gradient from 1 % B to 80 % B from 3.5 to 12 min followed by washing at 80 % B till 15 min before re-equilibrating the column with 1 % B till 24 min. For information dependent acquisition (IDA) MS/MS the uHPLC system was directly coupled to a nanospray ion source on a QSTAR-Elite Qq- TOF MS/MS system (Applied Biosystems). The instrument was run in positive mode where the top two ions from the precursor scan were targeted for fragmentation and MS/MS analysis. A Dynamic exclusion was set to exclude ions for 10 s following MS/MS analysis. LC-QqQ

Selected Reaction Monitoring (SRM) was performed by resolving peptides through a C18 reverse phase column (150 x 2.10 mmm 2.6 μπη 100 A, Phenomenex; Torrance, CA, USA) using a range of gradients (see chromatography section). The uHPLC system (Shimadzu) was directly coupled to a TurboV source on a 4000 QTRAP MS/MS system (Applied Biosystems). Controlling the instrument was Analyst software (version 1.5.2, Applied Biosystems) in positive ion mode. The ion spray voltage was set to 5300 V with a source temperature to 450° C or 500° C as indicated; UHP nitrogen (99.99%) was used for the curtain, nebulizer and turbo gasses which were set to 20, 35 and 35 unless otherwise stated.

Acquisition of analyte was performed using Selected Reaction Monitoring (SRM) without scheduling. The LG3[i;25] peptide was detected under [M + 3H]³⁺ precursor masses in Q1. The transitions used are outlined in Table 6. Peaks were quantified by integration using Skyline (version 1.2.0.3510) (MacLean ef a/. 2010)

Collision energy (CE), Dwell time (DT), Declustering potential (DP) and gas temperatures were optimized to increase sensitivity. The effects of each parameter on the signal of LG3[i:25] were assessed using the integrated peak area.

Claims

CLAI S

1. Use of an endorepellin peptide biomarker for detecting, monitoring, determining or assessing physical activity level or tissue breakdown.

2. The use of claim 1 wherein the biomarker comprises, consists or consists essentially of endorepellin, the LG3 peptide or fragments thereof.

3. The use of claim 2 wherein the biomarker is selected from at least one of SEQ ID NO: 1 to SEQ ID NO: 15.

4. The use of any one of claims 1 to 3 wherein the physical activity level or tissue breakdown is detected monitored or assessed by qualitatively or quantitatively detecting the presence or level of the biomarker in a biological sample, wherein the biological sample is or is derived from a bodily excretion, secretion, biopsy, skin cells or hair follicles.

5. The use of claim 4 wherein the biological sample is or is derived from urine, sweat, saliva, lacrimal fluid, blood, blood plasma, blood serum or cerumen.

6. The use of claim 4 wherein the qualitative or quantitative detection is performed by at least one of mass spectrometry, immunoassay, gel electrophoresis, ELISA, radioimmunoassay, western blotting, proteomic fingerprinting or liquid chromatography tandem mass spectrometry (LC MS/MS).

7. The use of any one of claims 4 to 6 wherein the level of the biomarker is compared to a baseline level of the biomarker wherein a difference between the level of the biomarker and the baseline level indicates physical activity level or tissue breakdown status of the subject.

8. A method for monitoring physical activity level or tissue breakdown status of a subject comprising:

(a) measuring at least one endorepellin peptide biomarker in a biological sample from the subject obtained at a first time; (b) measuring at least one endorepellin peptide biomarker in at least one further biological sample from the subject obtained at a second time; and

(c) comparing the first measurement and the second measurement; wherein a difference between the measurements indicates the physical activity or tissue breakdown status of the subject.

9. The method of claim 8, further comprising exposing the subject to physical exercise or activity. >

10. The method of claim 8 or claim 9, further comprising determining whether the subject has recovered from the physical exercise or activity based on the comparison.

11. A method of monitoring the efficacy of exercise or physical activity in a subject comprising:

(c) comparing the first measurement and the second measurement; wherein difference between the measurements indicates efficacy of exercise or physical activity.

12. A method for managing the physical activity, health and/or fitness of a subject comprising:

(c) comparing the first measurement and the second measurement; wherein the difference between the measurements indicates the physical activity, health and/or fitness of the subject; and

13. The method of any one of claims 8 to 12 further comprisirig measuring at least one endorepellin peptide biomarker in at least one further biological sample from the subject obtained at one or more further times.

14. The method of any one of claims 8 to 13 wherein the biomarker is measured by at least one of mass spectrometry, immunoassay, gel electrophoresis, ELISA, radioimmunoassay, western blotting, proteomic fingerprinting or liquid chromatography tandem mass spectrometry (LC MS/MS).

15. The method of any one of claims 8 to 14 wherein the biological sample is, or is derived from a bodily excretion, secretion, biopsy, skin cells or hair follicles.

16. The method of claim 15 wherein the biological sample is, or is derived from urine, sweat, saliva, lacrimal fluid, blood, blood plasma, blood serum or cerumen.

17. The method of any one of claims 8 to 16 wherein a level of the biomarker is determined and compared to a baseline level of the biomarker wherein a difference between the level of the biomarker and the baseline level indicates physical activity or tissue breakdown status of the subject.

18. The method of any one of claims 8 to 17 wherein the endorepellin peptide biomarker comprises, consists or consists essentially of endorepellin, the LG3 peptide or fragments thereof.

19. The method of any one of claims 8 to 18 wherein the endorepellin peptide biomarker of is selected from at least one of SEQ ID NO: 1 to SEQ ID NO: 15.

20. A kit for detecting, monitoring, determining or assessing physical activity level or tissue breakdown status of a subject, the kit comprising at least one capture reagent for an endorepellin peptide biomarker and/or at least one reference endorepellin peptide biomarker.