WO2023073392A1 - Biomarqueurs et leurs utilisations - Google Patents

Biomarqueurs et leurs utilisations Download PDF

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Publication number
WO2023073392A1
WO2023073392A1 PCT/IB2021/000750 IB2021000750W WO2023073392A1 WO 2023073392 A1 WO2023073392 A1 WO 2023073392A1 IB 2021000750 W IB2021000750 W IB 2021000750W WO 2023073392 A1 WO2023073392 A1 WO 2023073392A1
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Prior art keywords
biomarker
biomarkers
inflammation
value
clic4
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PCT/IB2021/000750
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English (en)
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Antony RAPISARDA
Brian Andrew FOX
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Genodx
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Priority to PCT/IB2021/000750 priority Critical patent/WO2023073392A1/fr
Priority to AU2022375208A priority patent/AU2022375208B2/en
Priority to EP22884808.1A priority patent/EP4423300A1/fr
Priority to PCT/AU2022/051312 priority patent/WO2023070173A1/fr
Publication of WO2023073392A1 publication Critical patent/WO2023073392A1/fr
Priority to AU2024219424A priority patent/AU2024219424A1/en

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P31/00—Antiinfectives, i.e. antibiotics, antiseptics, chemotherapeutics
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/112—Disease subtyping, staging or classification
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/26—Infectious diseases, e.g. generalised sepsis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/60—Complex ways of combining multiple protein biomarkers for diagnosis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/70—Mechanisms involved in disease identification
    • G01N2800/7095—Inflammation

Definitions

  • This disclosure relates generally to biomarkers of inflammatory disease. More particularly, the present disclosure relates to biomarkers and their use in methods, compositions, apparatuses, devices and kits for determining an indicator that is useful for assessing a likelihood that a type of inflammation is present or absent in a joint of a subject.
  • the present disclosure arises from the determination that certain host response biomarkers from synovial fluid, including RNA transcripts, have strong discrimination performance for specifically differentiating between subjects with infectious inflammation and those with non- infectious or 'sterile' inflammation.
  • these expression products have high negative predictive value (NPV) and as such, are useful in excluding infection as the cause of the presenting clinical signs of joint inflammation and/or joint pain.
  • methods, apparatuses, compositions, devices and kits are disclosed, which take advantage of these biomarkers to determine a likelihood that a type of inflammation is present or absent in joints of subjects presenting with joint pain and/or at least one clinical sign of inflammation in or proximal to the joint.
  • the disclosed methods, apparatuses, compositions, devices and kits are used for exclude or 'rule out' the presence of infectious joint inflammation.
  • determining an indicator used in assessing a likelihood that a type of inflammation is present or absent in a joint of a subject wherein the type of inflammation is selected from infectious inflammation and non- infectious inflammation.
  • These methods general comprise, consist or consist essentially of:
  • a biomarker value for at least one biomarker e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or more biomarkers
  • a respective biomarker value is indicative of a level of a corresponding biomarker in the sample
  • the at least one biomarker is selected from a first panel of biomarkers comprising, consisting or consisting essentially of ACO2, AP3M1, ATG4B, C5orfl5, CANX, CDKN1A, CSNK1D, CWC27, CXCL8, DTNBP1, DUSP1, EIF2S1, EMP1, ERP44, FCGR3B, FFAR2, FPR1, FYB1, GBP1, H3-3B, HNRNPAB, IARS2, IPO8, IRF2, KCTD2, KCTD3, KLF13, KLHL12, LARP4, LMNA, MCL1, MLLT6, MOCS3, MRPL20
  • the subject has joint pain and/or at least one clinical sign of inflammation (e.g., acute inflammation) in, or proximal to, the joint.
  • the inflammation may comprise one or more of redness, increased heat, swelling, pain and loss of function in, or proximal to, the joint.
  • biomarker values are obtained for a plurality of biomarkers, wherein the plurality of biomarkers is selected from the first panel of biomarkers and optionally from a second panel of biomarkers comprising API5, AQP9, ATIC, CISH, CLIC4, CSF2RB, CSF3R, DUSP5, ETV6, GADD45B, GRINA, HCK, HLA-E, IER3, IL1B, IL1RN, IMMT, LILRB3, LRPPRC, LYN, NFKBIA, OSM, PDE4B, PI3, PLAUR, PLEK, PPIF, SEMA4D, STARD7, TNFAIP2, TNFAIP3 and ZFP36.
  • biomarker values are determined for at least two, three, four, five, six, seven or eight biomarkers.
  • biomarker values are determined for a first biomarker and a second biomarker, wherein the first biomarker is selected from a first set of biomarkers that are expressed at a higher level in infectious inflammation than in non-infectious inflammation, and wherein the second biomarker is selected from a second set of biomarkers that are expressed at a lower level in infectious inflammation than in non-infectious inflammation, and/or from a third set of biomarkers that improve the discrimination performance of the first biomarker, wherein the first set of biomarkers comprises, consists or consists essentially of AQP9, C5orfl5, CANX, CDKN1A, CISH, CLIC4, CSF2RB, CSF3R, CXCL8, DTNBP1, DUSP1, DUSP5, ERP44, ETV6, FCGR3B, FFAR2, FPR1, FYB1, GADD45B, GBP1, GRINA, H3-3B, HCK, HLA-E, IRF2, LIL
  • biomarker values are determined for a first biomarker, a second biomarker, a third biomarker and optionally a fourth biomarker, wherein the first and second biomarkers are selected from a first set of biomarkers that are expressed at a higher level in infectious inflammation than in non-infectious inflammation, and wherein the third and optional fourth biomarkers are selected from a second set of biomarkers that are expressed at a lower level in infectious inflammation than in non-infectious inflammation, and/or a third set of biomarkers that improve the discrimination performance of the first and/or second biomarkers, wherein the first set of biomarkers comprises, consists or consists essentially of AQP9, C5orfl5, CANX, CDKN 1A, CISH, CLIC4, CSF2RB, CSF3R, CXCL8, DTNBP1, DUSP1, DUSP5, ERP44, ETV6, FCGR3B, FFAR2, FPR1, FYB1, GADD45B,
  • biomarker values are determined for a first biomarker, a second biomarker, a third biomarker, optionally a fourth biomarker, a fifth biomarker, a sixth biomarker and optionally one or both of a seventh biomarker and an eighth biomarker, wherein the first biomarker, second biomarker, third biomarker and optional fourth biomarker are selected from a first set of biomarkers that are expressed at a higher level in infectious inflammation than in non-infectious inflammation, and wherein the fifth biomarker, sixth biomarker and optional seventh and eighth biomarkers are selected from a second set of biomarkers that are expressed at a lower level in infectious inflammation than in non-infectious inflammation, and/or from a third set of biomarkers that improve the discrimination performance of the first biomarker, second biomarker, third biomarker and optional fourth biomarker, wherein the first set of biomarkers comprises, consists or consists essentially of AQP9, C5orf
  • the methods further comprise applying a function to biomarker values to yield at least one functionalized biomarker value and determining the indicator using the at least one functionalized biomarker value.
  • the function includes at least one of: (a) multiplying biomarker values; (b) dividing biomarker values; (c) adding biomarker values; (d) subtracting biomarker values; (e) a weighted sum of biomarker values; (f) a log sum of biomarker values; (g) a geometric mean of biomarker values; and (h) a sigmoidal function of biomarker values.
  • the methods further comprise combining the biomarker values to provide a composite score and determining the indicator using the composite score.
  • the biomarker values are combined by adding, multiplying, subtracting, and/or dividing biomarker values.
  • Individual biomarker values may represent a measured amount or concentration of a corresponding biomarker in the sample.
  • individual biomarker values may be a logarithmic representation of a measured amount or concentration of a corresponding biomarker in the sample.
  • the methods comprise analyzing the biomarker value(s) or composite score with reference to corresponding reference biomarker value ranges or cut-off values, or composite score ranges or cut-off values, to determine the indicator.
  • the indicator indicates a likelihood of a presence of infectious inflammation if the biomarker value(s) or composite score is indicative of the level of the biomarker(s) in the sample that correlates with an increased likelihood of a presence of infectious inflammation relative to a predetermined reference biomarker value range or cut-off value, and wherein the indicator indicates a likelihood of the presence of non-infectious inflammation if the biomarker value(s) or composite score is indicative of the level of the biomarker(s) in the sample that correlates with an increased likelihood of the presence of non-infectious inflammation relative to a predetermined reference biomarker value range or cut-off value.
  • the methods may comprise ruling out the likelihood of infectious joint inflammation in the subject or not, based on the indicator.
  • the joint may be a synovial joint, a fibrous joint or a cartilaginous joint.
  • the synovial joint is a knee joint, wrist joint, shoulder joint, hip joint, elbow joint or ankle joint.
  • the synovial joint is a knee joint.
  • the sample may comprise synovial fluid, lymph fluid, joint exudate, joint transudate, or combination thereof.
  • the sample comprises leukocytes.
  • apparatuses for determining an indicator used in assessing a likelihood that a type of inflammation is present or absent in a joint of a subject, wherein the type of inflammation is selected from infectious inflammation and non- infectious inflammation are disclosed herein in another aspect.
  • These apparatuses general comprise, consist or consist essentially of at least one electronic processing device that:
  • a biomarker value for at least one biomarker e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or more biomarkers
  • a respective biomarker value is indicative of a level of a corresponding biomarker in the sample
  • the at least one biomarker is selected from a first panel of biomarkers comprising, consisting or consisting essentially of ACO2, AP3M1, ATG4B, C5orfl5, CANX, CDKN1A, CSNK1D, CWC27, CXCL8, DTNBP1, DUSP1, EIF2S1, EMP1, ERP44, FCGR3B, FFAR2, FPR1, FYB1, GBP1, H3-3B, HNRNPAB, IARS2, IPO8, IRF2, KCTD2, KCTD3, KLF13, KLHL12, LARP4, LMNA, MCL1, MLLT6, MOCS3, MRPL20
  • the at least one electronic processing device :
  • biomarker values for a plurality of biomarkers wherein the plurality of biomarkers is selected from the first panel of biomarkers and optionally from a second panel of biomarkers comprising API5, AQP9, ATIC, CISH, CLIC4, CSF2RB, CSF3R, DUSP5, ETV6, GADD45B, GRINA, HCK, HLA-E, IER3, IL1B, IL1RN, IMMT, LILRB3, LRPPRC, LYN, NFKBIA, OSM, PDE4B, PI3, PLAUR, PLEK, PPIF, SEMA4D, STARD7, TNFAIP2, TNFAIP3 and ZFP36.
  • compositions for determining an indicator used in assessing a likelihood that a type of inflammation is present or absent in a joint of a subject wherein the type of inflammation is selected from infectious inflammation and non- infectious inflammation.
  • These compositions generally comprise, consist or consist essentially of a mixture of a DNA polymerase (e.g. , a thermostable DNA polymerase), synovial fluid leukocyte cDNA from a subject with joint pain and/or at least one clinical sign of inflammation (e.g. , acute inflammation) in, or proximal to, the joint, wherein the synovial fluid leukocyte cDNA comprises at least one cDNA (e.g.
  • cDNA biomarkers comprising, consisting or consisting essentially of ACO2, AP3M1, ATG4B, C5orfl5, CANX, CDKN1A, CSNK1D, CWC27, CXCL8, DTNBP1, DUSP1, EIF2S1, EMP1, ERP44, FCGR3B, FFAR2, FPR1, FYB1, GBP1, H3-3B, HNRNPAB, IARS2, IPO8, IRF2, KCTD2, KCTD3, KLF13, KLHL12, LARP4, LMNA, MCL1, MLLT6, MOCS3, MRPL20, MRPL37, MXD1, MYO1F, NAGA, NAMPT, NINJ1, NUP58, PARP14, PIK3AP1, PIK3R5, PIP4K2B, PKN1, PLEC, PLXDC2, P0LG
  • the synovial fluid leukocyte cDNA comprises at least one cDNA (e.g. , 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or more cDNA) selected from a second panel of cDNA biomarkers comprising API5, AQP9, ATIC, CISH, CLIC4, CSF2RB, CSF3R, DUSP5, ETV6, GADD45B, GRINA, HCK, HLA-E, IER3, IL1B, IL1RN, IMMT, LILRB3, LRPPRC, LYN, NFKBIA, OSM, PDE4B, PI3, PLAUR, PLEK, PPIF, SEMA4D, STARD7, TNFAIP2, TNFAIP3 and ZFP36, and wherein the composition further comprises for the at least one cDNA of the second panel of cDNA biomarkers at least one oligonucleotide primer or probe that hybridizes to the cDNA.
  • the composition further comprises for the at least
  • the compositions comprise for respective cDNA two oligonucleotide primers that hybridize to opposite complementary strands of the cDNA.
  • the compositions comprise for a respective cDNA an oligonucleotide probe that hybridizes to the cDNA or a polynucleotide corresponding thereto (e.g. , a polynucleotide product resulting nucleic acid amplification of the cDNA).
  • the oligonucleotide probe may comprise a heterologous label (e.g. , a fluorescent label).
  • the labeled oligonucleotide probe may comprise a fluorophore.
  • the labeled oligonucleotide probe further comprises a quencher.
  • different labeled oligonucleotide probes are included in the composition for hybridizing to different cDNAs, wherein individual oligonucleotide probes comprise detectably distinct labels (e.g. different fluorophores), or at least a subset of oligonucleotide probes comprises the same label (e.g. same fluorophore).
  • compositions comprise for each of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16 of the cDNAs at least one oligonucleotide primer and/or probe that hybridizes to the cDNA. In other embodiments, the compositions comprise for each of up to 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
  • 15 or 16 of the cDNAs at least one oligonucleotide primer and/or probe that hybridizes to the cDNA.
  • Individual cDNAs and their corresponding oligonucleotide primer(s) and/or probe(s) may be present in separate reaction vessels or in the same reaction vessel.
  • devices for nucleic acid amplification of synovial fluid leukocyte cDNA. These devices comprise a plurality of reaction vessels, wherein individual reaction vessels comprise a composition as broadly described above and elsewhere herein. Devices disclosed herein may consist of 2 to 100, 2 to 50, 2 to 40, 2 to 30, 2 to 20, 2 to
  • reaction vessels 15, 2 to 12, 2 to 10 or 2 to 8 reaction vessels (and all integer vessels in between).
  • the devices consist of 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16 reaction vessels.
  • one or more reaction vessels are used for single-plex amplification of cDNA, and/or one or more reaction vessels are used for multiplex amplification of cDNA (e.g., 2-plex, 3-plex, 4-plex, 5-plex or 6-plex amplifications).
  • methods for inhibiting the development or progression of infectious inflammation or non-infectious inflammation in a subject with joint pain and/or at least one clinical sign of inflammation (e.g., acute inflammation) in, or proximal to, the joint.
  • These methods generally comprise, consist or consist essentially of:
  • the methods further comprise: taking a sample from the subject and determining an indicator indicative of a likelihood of a presence of infectious inflammation or indicative of a likelihood of a presence of non-infectious inflammation using the indicator-determining method.
  • the methods further comprise: sending a sample obtained from the subject to a laboratory at which the indicator is determined according to the indicator-determining method, and optionally receiving the indicator from the laboratory.
  • kits for determining an indicator used in assessing a likelihood that a type of inflammation is present or absent in a joint of a subject wherein the type of inflammation is selected from infectious inflammation and non-infectious inflammation
  • the kit comprising: (1) for each of at least one nucleic acid biomarker (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or more biomarkers) at least one oligonucleotide primer and/or at least one oligonucleotide probe that hybridizes to the nucleic acid biomarker, wherein the at least one biomarker is selected from a first panel of biomarkers comprising, consisting or consisting essentially of ACO2, AP3M1, ATG4B, C5orfl5, CANX, CDKN1A, CSNK1D, CWC27, CXCL8, DTNBP1, DUSP1, EIF2S1, EMP1, ERP44, FCGR3B, FFAR2,
  • at least one nucleic acid biomarker
  • kits comprise at least one oligonucleotide primer and/or at least one oligonucleotide probe for each of a plurality of biomarkers (e.g. , at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16 biomarkers, or up to 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16 biomarkers), wherein the plurality of biomarkers is selected from the first panel of biomarkers and optionally from a second panel of biomarkers comprising API5, AQP9, ATIC, CISH, CLIC4, CSF2RB, CSF3R, DUSP5, ETV6, GADD45B, GRINA, HCK, HLA-E, IER3, IL1B, IL1RN, IMMT, LILRB3, LRPPRC, LYN, NFKBIA, OSM, PDE4B, PI3, PLAUR, PLEK, PPIF, SEMA4D, STARD7, TNFA
  • kits may further comprise any one or more of: a DNA polymerase (e.g. , a thermostable DNA polymerase); for each nucleic acid biomarker a pair of forward and reverse oligonucleotide primers that permit nucleic acid amplification of at least a portion of the nucleic acid biomarker to produce an amplicon; for each nucleic acid biomarker an oligonucleotide probe that comprises a heterologous label and hybridizes to the nucleic acid biomarker or an amplicon of the nucleic acid biomarker; one or more reagents for preparing mRNA from a cell or cell population from a sample obtained from a site of inflammation associated with the joint of the subject; one or more reagents for preparing cDNA from the mRNA; one or more reagents for amplifying cDNA; and one or more of deoxynucleotides, buffer(s), positive and negative controls, and reaction vessel(s).
  • Figure 1 is a graphical representation showing expression levels of ADCY7, CSRNP2, DNAJC4, FBXO28, GNAI2, HNRNPU, NEK8, PBLD, PTMA, RABL2B, RHOT2, RNF25, SRRM2, TMBIM6, TMED4, TPM3, UCK1, ZNF787 in subjects with infectious joint inflammation and in subjects with non-infectious joint inflammation.
  • Figure 2 is a diagrammatic representation showing a network diagram where each square represents one of the gene clusters, and is labeled by the representative gene. Clusters with fewer than 25 members are not included, for clarity.
  • the nodes (squares) are connected by a line if they have a Pearson correlation of at least 0.6.
  • the color of each node is based on the AUC of the ROC curve and ranges from blue to red. Values near 0.5 (grey) do not have any predictive value on their own, values near 1.0 (red) are where the overall gene expression is higher in the cases (infected), and values near 0.0 (blue) are where the gene expression is lower in the infected samples (and higher in sterile). Boxes with a black border have an absolute AUC greater than expected by chance.
  • Figure 3 is a graphical representation showing the number of gene members belonging to each of 269 gene clusters.
  • Figure 4 is a graphical representation showing a histogram of the AUC values for the 269 genes which are the representative members of the 269 clusters.
  • Figure 5 is a graphical representation showing a box plot and dot plot of the expression values of the gene member with the highest AUC among the 269 clusters, where the samples are split into the two main final retrospective physician diagnosis (RPD) groups of infected and sterile.
  • RPD retrospective physician diagnosis
  • Figure 6 is a graphical representation showing a smoothed histogram of the AUC values for the 72,092 pairs of genes (pink), compared to the distribution of AUC values (blue) determined from randomly permuted RPD labels of the samples.
  • Figure 7 is a graphical representation showing a boxplot and dot plot of the expression values of the gene pair with the highest AUC among all pairs of the 269 clusters, where the samples are split into the two main final RPD groups of infected and sterile.
  • Figure 8 is a graphical representation showing the AUC of the 300,000 3- to 4- gene signatures that were searched (left panel), and the AUC of the best 1,000 3- to 4-gene signatures from the same data set (right panel).
  • Figure 9 is a graphical representation showing a boxplot and dot plot of the expression values of the 4-gene signature with the highest AUC among an optimized subset of 3- and 4-gene signatures from the 269 clusters, where the samples are split into the two main final RPD groups of infected and sterile.
  • Figure 10 is a graphical representation showing a boxplot and dot plot of the expression values of the 6-gene signature with the highest AUC among an optimized subset of 6- to 8-gene signatures from the 269 clusters, where the samples are split into the two main final RPD groups of infected and sterile.
  • Figure 11 is an illustration of an example output depicting an indicator that is useful for assessing the likelihood of infectious joint inflammation or non-infectious joint inflammation in a patient.
  • a method of aiding diagnosis of a disease or condition can comprise measuring certain biomarkers (e.g., the joint inflammation biomarkers disclosed herein) in a biological sample of an individual.
  • the "amount” or “level” of a biomarker is a detectable level or amount in a sample. These can be measured by methods known to one skilled in the art and also disclosed herein. These terms encompass a quantitative amount or level (e.g., weight or moles), a semi- quantitative amount or level, a relative amount or level (e.g., weight % or mole % within class), a concentration, and the like. Thus, these terms encompass absolute or relative amounts or levels or concentrations of a biomarker in a sample. The expression level or amount of biomarker assessed can be used to determine the response to treatment.
  • Amplification generally refers to the process of producing multiple copies of a desired sequence.
  • Multiple copies mean at least two copies.
  • a “copy” does not necessarily mean perfect sequence complementarity or identity to the template sequence.
  • copies can include nucleotide analogs such as deoxyinosine, intentional sequence alterations (such as sequence alterations introduced through a primer comprising a sequence that is hybridizable, but not complementary, to the template), and/or sequence errors that occur during amplification.
  • amplicon refers to a nucleic acid that is the product of amplification.
  • an amplicon may be homologous to a reference sequence, a target sequence, or any sequence of nucleic acid that has been subjected to amplification.
  • concentration of amplicon sequence will be significantly greater than the concentration of original (template) nucleic acid sequence.
  • biomarker refers to an indicator, e.g., predictive, diagnostic, and/or prognostic, which can be detected in a sample.
  • the biomarker may serve as an indicator of a particular subtype of a disease or disorder (e.g., infectious inflammation or non- infectious inflammation, etc.), characterized by certain, molecular, pathological, histological, and/or clinical features, and/or may serve as an indicator of a particular cell type or state and/or or response to therapy.
  • Biomarkers include, but are not limited to, polynucleotides (e.g., DNA, and/or RNA), polynucleotide copy number alterations (e.g., DNA copy numbers), polypeptides, polypeptide and polynucleotide modifications (e.g., posttranslational modifications), carbohydrates, and/or glycolipid-based molecular markers.
  • a biomarker may be present in a sample obtained from a subject before the onset of a physiological or pathophysiological state (e.g., infectious inflammation or non-infectious inflammation, etc.), including a symptom, thereof (e.g., joint pain, joint inflammation, etc.).
  • the presence of the biomarker in a sample obtained from the subject can be indicative of an increased risk that the subject will develop the physiological or pathophysiological state or symptom thereof.
  • the biomarker may be normally expressed in an individual, but its expression may change (/.e., it is increased (upregulated; over-expressed) or decreased (downregulated; under-expressed) before the onset of a physiological or pathophysiological state, including a symptom thereof.
  • a change in the level of the biomarker may be indicative of an increased risk that the subject will develop the physiological or pathophysiological state or symptom thereof.
  • a change in the level of a biomarker may reflect a change in a particular physiological or pathophysiological state, or symptom thereof, in a subject, thereby allowing the nature (e.g., severity) of the physiological or pathophysiological state, or symptom thereof, to be tracked over a period of time.
  • This approach may be useful in, for example, monitoring a treatment regimen for the purpose of assessing its effectiveness (or otherwise) in a subject.
  • reference to the level of a biomarker includes the concentration of a biomarker, or the level of expression of a biomarker, or the activity of the biomarker.
  • biomarker value refers to a value measured or functionalized for at least one corresponding biomarker of a subject and which is typically indicative of an abundance or concentration of a biomarker in a sample obtained from the subject.
  • the biomarker values could be measured biomarker values, which are values of biomarkers measured for the subject. These values may be quantitative or qualitative.
  • a measured biomarker value may refer to the presence or absence of a biomarker or may refer to a level of a biomarker, in a sample.
  • the measured biomarker values can be values relating to raw or normalized biomarker levels (e.g., a raw, non-normalized biomarker level, or a normalized biomarker levels that is determined relative to an internal or external control biomarker level) and to mathematically transformed biomarker levels (e.g., a logarithmic representation of a biomarker level such as amplification amount, cycle time, etc.).
  • the biomarker values could be functionalized biomarker values, which are values that have been functionalized from one or more measured biomarker values, for example by applying a function to the one or more measured biomarker values.
  • Biomarker values can be of any appropriate form depending on the manner in which the values are determined.
  • the biomarker values could be determined using high-throughput technologies such as mass spectrometry, sequencing platforms, array and hybridization platforms, immunoassays, flow cytometry, or any combination of such technologies and in representative examples, the biomarker values relate to a level of activity or abundance of an expression product or other measurable molecule, quantified using a nucleic acid assay such as real-time polymerase chain reaction (RT-PCR), sequencing or the like.
  • RT-PCR real-time polymerase chain reaction
  • the biomarker values can be in the form of amplification amounts, or cycle times, which are a logarithmic representation of the levels of the biomarker within a sample and which thus correspond to mathematical transformations of raw or normalized biomarker levels, as will be appreciated by persons skilled in the art.
  • the expression "functionalized biomarker value" in the context, for example, of a ratio of levels of a pair of biomarkers in a sample obtained from a subject does not necessarily mean that the functionalized biomarker value is one that results from a division of one measured biomarker value by another measured biomarker value.
  • the measured biomarker values can be combined using any suitable function, whereby the resulting functionalized biomarker value is one that corresponds to or reflects a ratio of non-normalized (e.g., raw) or normalized biomarker levels.
  • biomarker signature refers to one or a combination of biomarkers whose expression is an indicator, e.g., predictive, diagnostic, and/or prognostic.
  • the biomarker signature may serve as an indicator of a particular subtype of a disease or disorder (e.g., infectious inflammation or non-infectious inflammation, etc.) or symptom thereof (e.g., response to therapy, drug resistance, and/or disease burden) characterized by certain molecular, pathological, histological, and/or clinical features.
  • the biomarker signature is a "gene signature”.
  • gene signature is used interchangeably with “gene expression signature” and refers to one or a combination of polynucleotides whose expression is an indicator, e.g., predictive, diagnostic, and/or prognostic.
  • the biomarker signature is a "protein signature.”
  • protein signature is used interchangeably with “protein expression signature” and refers to one or a combination of polypeptides whose expression is an indicator, e.g., predictive, diagnostic, and/or prognostic.
  • a biomarker signature may comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 or more biomarkers.
  • a biomarker signature comprises hundreds, or even thousands, of biomarkers or indications thereof.
  • a biomarker signature can further comprise one or more controls or internal standards.
  • a biomarker signature comprises at least one biomarker, or indication thereof, that serves as an internal standard.
  • a biomarker signature comprises an indication of one or more types of biomarkers.
  • biomarker signature is also used herein to refer to a biomarker value or combination of at least two biomarker values, wherein individual biomarker values correspond to values of biomarkers that can be measured or functionalized from one or more subjects, which combination is characteristic of a discrete condition, stage of condition, subtype of condition or a prognosis for a discrete condition, stage of condition, subtype of condition.
  • signature biomarkers is used to refer to a subset of the biomarkers that have been identified for use in a biomarker signature that can be used in performing a clinical assessment, such as to rule in or rule out a specific condition, different stages or severity of conditions, subtypes of different conditions or different prognoses.
  • the number of signature biomarkers will vary, but is typically of the order of 16 or less (e.g., 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2 or 1).
  • clinical parameter refers any clinical measure of a disease state (e.g., joint inflammation) of a patient; for example, joint pain, joint stiffness, tenderness, swelling, warmth, patient global health assessment, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) levels, etc.
  • ESR erythrocyte sedimentation rate
  • CRP C-reactive protein
  • complementarity refers to polynucleotides (/.e., a sequence of nucleotides) related by the base-pairing rules.
  • sequence "A- G-T” is complementary to the sequence "T-C-A.”
  • Complementarity may be “partial,” in which only some of the nucleic acids' bases are matched according to the base pairing rules. Or, there may be “complete” or “total” complementarity between the nucleic acids. The degree of complementarity between nucleic acid strands has significant effects on the efficiency and strength of hybridization between nucleic acid strands.
  • composite score refers to an aggregation of the obtained values for biomarkers measured in a sample from a subject optionally in combination with one or more patient clinical parameters.
  • the obtained biomarker values are normalized to provide a composite score for each subject tested.
  • the "biomarker composite score” is used, at least in part, by the machine learning system to determine the "risk score” for each subject tested wherein the numerical value (e.g., a multiplier, a percentage, etc.) indicating increased likelihood of having joint inflammation for the stratified grouping becomes the "risk score”.
  • the numerical value e.g., a multiplier, a percentage, etc.
  • the term "correlates” or “correlates with” and like terms refers to a statistical association between two or more things, such as events, characteristics, outcomes, numbers, data sets, etc., which may be referred to as "variables”. It will be understood that the things may be of different types. Often the variables are expressed as numbers (e.g., measurements, values, likelihood, risk), wherein a positive correlation means that as one variable increases, the other also increases, and a negative correlation (also called anti-correlation) means that as one variable increases, the other variable decreases.
  • numbers e.g., measurements, values, likelihood, risk
  • correlating a biomarker signature with the presence or absence of a condition comprises determining the presence, absence, level or amount of at least one biomarker in a subject that has that condition; or in persons known to be free of that condition.
  • a profile of IRS biomarker levels, absences or presences is correlated to a global probability or a particular outcome, using receiver operating characteristic (ROC) curves.
  • cut-off value is a level (or concentration) which may be an absolute level or a relative level, which is indicative of whether a subject has a particular disease or condition (e.g., infectious joint inflammation or non-infectious joint inflammation), or is at risk of having a particular disease or condition (e.g., infectious joint inflammation or non- infectious joint inflammation).
  • a subject is regarded as having the disease or condition or being at risk of having the disease or condition if either the level of the biomarker(s) detected and determined, respectively, is lower than the cut-off value, or the level of the biomarker(s) detected and determined, respectively, is higher than the cut-off value.
  • the terms “detectably distinct” and “detectably different” are used interchangeably herein to refer to a signal that is distinguishable or separable by a physical property either by observation or by instrumentation.
  • a fluorophore is readily distinguishable either by spectral characteristics or by fluorescence intensity, lifetime, polarization or photo-bleaching rate from another fluorophore in a sample, as well as from additional materials that are optionally present.
  • the terms “detectably distinct” and “detectably different” refer to a set of labels (such as dyes, suitably organic dyes) that can be detected and distinguished simultaneously.
  • the phrase "developing a classifier” refers to using input variables to generate an algorithm or classifier capable of distinguishing between two or more states (e.g., infectious joint inflammation and non-infectious joint inflammation).
  • diagnosis As used herein, the terms “diagnosis”, “diagnosing” and the like are used interchangeably herein to encompass determining the likelihood that a subject will develop a condition, or the existence or nature of a condition in a subject. These terms also encompass determining the severity of disease or episode of disease, as well as in the context of rational therapy, in which the diagnosis guides therapy, including initial selection of therapy, modification of therapy (e.g., adjustment of dose or dosage regimen), and the like.
  • likelihood is meant a measure of whether a subject with particular measured or derived biomarker values actually has a condition (or not) based on a given mathematical model. An increased likelihood for example may be relative or absolute and may be expressed qualitatively or quantitatively.
  • an increased likelihood may be determined simply by determining the subject's measured or derived biomarker values for at least two biomarkers and placing the subject in an "increased likelihood” category, based upon previous population studies.
  • the term “likelihood” is also used interchangeably herein with the term “probability”.
  • the term “risk” relates to the possibility or probability of a particular event occurring at some point in the future.
  • “Risk stratification” refers to an arraying of known clinical risk factors to allow physicians to classify patients into a low, moderate, high or highest risk of developing a particular disease or condition.
  • a "diagnostic amount" of a biomarker refers to an amount of a biomarker in a subject's sample that is consistent with a diagnosis of infectious joint inflammation or non-infectious joint inflammation.
  • a diagnostic amount can be either an absolute amount (e.g., pg/mL) or a relative amount (e.g., relative intensity of signals).
  • a biomarker can be a polynucleotide or polypeptide which is present at an elevated level or at a decreased level in samples of patients with infectious joint inflammation compared to samples of subjects with non-infectious joint inflammation.
  • a biomarker can be a polynucleotide or polypeptide which is detected at a higher frequency or at a lower frequency in samples of patients with infectious joint inflammation compared to samples of subjects with non- infectious joint inflammation.
  • a biomarker can be differentially present in terms of quantity, frequency or both.
  • discrimination performance refers to numeric representation of the index including, for example, sensitivity, specificity, positive predictability, negative predictability or accuracy.
  • discrimination performance may also refer to a value computed by the functions of the indexes. For example, sensitivity, specificity, positive predictive value, negative predictive value and accuracy may each be used as the discrimination performance, or alternatively, the sum of two or more indexes, e.g., the sum of sensitivity and specificity, the sum of sensitivity and positive predictive value, or the sum of negative predictive value and accuracy, may be used as the discrimination performance.
  • expression product refers to any product produced during the process of gene expression including polypeptide products and polynucleotide products.
  • Fluorophore as used herein to refer to a moiety that absorbs light energy at a defined excitation wavelength and emits light energy at a different defined wavelength.
  • fluorescence labels include, but are not limited to: Alexa Fluor dyes (Alexa Fluor 350, Alexa Fluor 488, Alexa Fluor 532, Alexa Fluor 546, Alexa Fluor 568, Alexa Fluor 594, Alexa Fluor 633, Alexa Fluor 660 and Alexa Fluor 680), AMCA, AMCA-S, BODIPY dyes (BODIPY FL, BODIPY R6G, BODIPY TMR, BODIPY TR, BODIPY 530/550, BODIPY 558/568, BODIPY 564/570, BODIPY 576/589, BODIPY 581/591, BODIPY 630/650, BODIPY 650/665), Carboxyrhodamine 6G, carboxy-X-rho
  • gene refers to a stretch of nucleic acid that codes for a polypeptide or for an RNA chain that has a function. While it is the exon region of a gene that is transcribed to form mRNA, the term “gene” also includes regulatory regions such as promoters and enhancers that govern expression of the exon region.
  • the term "higher" with reference to a biomarker measurement refers to a statistically significant and measurable difference in the level of a biomarker compared to the level of another biomarker or to a control level where the biomarker measurement is greater than the level of the other biomarker or the control level.
  • the difference is suitably at least about 10%, or at least about 20%, or of at least about 30%, or of at least about 40%, or at least about 50%.
  • homologous sequences or sequences with homology refer to nucleic acid sequences that exhibit at least 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% sequence identity to one another.
  • homologs, homologous sequences or sequences with homology refer to nucleic acid sequences that hybridize under high stringency conditions to one another.
  • High stringency conditions include and encompass from at least about 31% v/v to at least about 50% v/v formamide and from at least about 0.01 M to at least about 0.15 M salt for hybridization at 42° C, and at least about 0.01 M to at least about 0.15 M salt for washing at 42° C.
  • High stringency conditions also may include 1% BSA, 1 mM EDTA, 0.5 M NaHP04 (pH 7.2), 7% SDS for hybridization at 65° C, and (i) 0.2 x SSC, 0.1% SDS; or (ii) 0.5% BSA, ImM EDTA, 40 mM NaHPC (pH 7.2), 1% SDS for washing at a temperature in excess of 65° C.
  • immobilized means that a molecular species of interest is fixed to a solid support, suitably by covalent linkage. This covalent linkage can be achieved by different means depending on the molecular nature of the molecular species. Moreover, the molecular species may be also fixed on the solid support by electrostatic forces, hydrophobic or hydrophilic interactions or Van-der-Waals forces. The above described physicochemical interactions typically occur in interactions between molecules.
  • the molecules remain immobilized or attached to a support under conditions in which it is intended to use the support, for example in applications requiring nucleic acid amplification and/or sequencing or in in antibody-binding assays.
  • oligonucleotides or primers are immobilized such that a 3' end is available for enzymatic extension and/or at least a portion of the sequence is capable of hybridizing to a complementary sequence.
  • immobilization can occur via hybridization to a surface attached primer, in which case the immobilized primer or oligonucleotide may be in the 3'-5' orientation.
  • immobilization can occur by means other than base-pairing hybridization, such as the covalent attachment.
  • the term "increase” or “increased' with reference to a biomarker level refers to a statistically significant and measurable increase in the biomarker level compared to the level of another biomarker or to a control level.
  • the increase is suitably an increase of at least about 10%, or an increase of at least about 20%, or an increase of at least about 30%, or an increase of at least about 40%, or an increase of at least about 50%.
  • the term "indicator” as used herein refers to a result or representation of a result, including any information, number (e.g., biomarker value including functionalized biomarker value and composite score), ratio, signal, sign, mark, or note by which a skilled artisan can estimate and/or determine a likelihood or risk of whether or not a subject is suffering from a given disease or condition.
  • the "indicator” may optionally be used together with other clinical characteristics, to arrive at a diagnosis (that is, the occurrence or nonoccurrence) of infectious inflammation or non-infectious inflammation or a prognosis for infectious inflammation or non-infectious inflammation in a subject. That such an indicator is "determined” is not meant to imply that the indicator is 100% accurate.
  • the skilled clinician may use the indicator together with other clinical indicia to arrive at a diagnosis.
  • inflammation generally refers to a response in vasculated tissues to cellular or tissue injury usually caused by physical, chemical and/or biological agents, that is marked in the acute form by the classical sequences of pain, heat, redness, swelling, and loss of function (e.g., limb movement, weight bearing, etc.) and usually serves as a mechanism initiating the elimination, dilution or walling-off of noxious agents and/or of damaged tissue.
  • Inflammation histologically involves a complex series of events, including dilation of the arterioles, capillaries, and venules with increased permeability and blood flow, exudation of fluids including plasma proteins, and leukocyte migration into the inflammatory focus.
  • Inflammation may be caused by extraneous physical or chemical injury or by biological agents, e.g., viruses, bacteria, fungi, protozoan or metazoan parasite infections, as well as inflammation which is seemingly unprovoked, e.g., which occurs in the absence of demonstrable injury or infection, inflammation responses to self-antigens (auto-immune inflammation), inflammation responses to engrafted xenogeneic or allogeneic cells, tissues or organs, inflammation responses to allergens, etc.
  • the term covers both acute inflammation and chronic inflammation.
  • the term includes both local or localized inflammation, as well as systemic inflammation, i.e., where one or more inflammatory processes are not confined to a particular tissue but occur generally in the endothelium and/or other organ systems.
  • the inflammation is acute inflammation, which is usually of sudden onset, marked by the classical signs of heat, redness, swelling, pain, and loss of function (e.g., limb movement, weight bearing, etc.), and in which vascular and exudative processes predominate; catarrhal inflammation, which is a form affecting mainly a mucous surface, marked by a copious discharge of mucus and epithelial debris; chronic inflammation, which is prolonged and persistent inflammation marked chiefly by new connective tissue formation; it may be a continuation of an acute form or a prolonged low-grade form; interstitial inflammation, which is inflammation affecting chiefly the stroma of an organ; traumatic inflammation, which is one that follows a wound or injury; ulcerative inflammation, in which necrosis on or near the surface leads
  • biomarkers are provided that are useful for stratifying inflammation into infectious inflammation and non-infectious inflammation.
  • infectious inflammation refers to inflammation that is associated with and/or is caused by the invasion and multiplication of microorganisms such as bacteria, viruses, fungi and parasites that are not normally present within the body.
  • non-infectious inflammation also referred to herein as “sterile inflammation” refers to inflammation that is not associated with and/or is not caused by the invasion and multiplication of microorganisms such as bacteria, viruses, fungi and parasites that are not normally present within the body.
  • joint pain refers to a joint disorder or condition that involves inflammation and/or pain of one or more joints, suitably synovial joints.
  • joint pain encompasses a variety of types and subtypes of arthritis of various etiologies and causes, either known or unknown, including, but not limited to, infectious arthritis and non- infectious arthritis.
  • Non-limiting examples of non-infectious arthritis include, arthritis resulting from joint surgery (e.g., joint repair or joint replacement), autoimmune arthropathies including for example rheumatoid arthritis, psoriatic arthritis and lupus-related arthritis, gouty arthritis, osteoarthritis, seronegative arthritis, reactive arthritis, Reiter's disease, calcium pyrophosphate disease, carcinomatous polyarthritis and chondrocalcinosis, or painful local tissues affected by bursitis, tenosynovitis, epicondylitis, synovitis and/or other disorders.
  • joint surgery e.g., joint repair or joint replacement
  • autoimmune arthropathies including for example rheumatoid arthritis, psoriatic arthritis and lupus-related arthritis, gouty arthritis, osteoarthritis, seronegative arthritis, reactive arthritis, Reiter's disease, calcium pyrophosphate disease, carcinomatous polyarthritis and chondrocalcinosis,
  • label is used herein in a broad sense to refer to an agent that is capable of providing a detectable signal, either directly or through interaction with one or more additional members of a signal producing system and that has been artificially added, linked or attached via chemical manipulation to a molecule.
  • Labels can be visual, optical, photonic, electronic, acoustic, optoacoustic, by mass, electro-chemical, electro-optical, spectrometry, enzymatic, or otherwise chemically, biochemically hydrodynamically, electrically or physically detectable.
  • Labels can be, for example tailed reporter, marker or adapter molecules.
  • a molecule such as a nucleic acid molecule is labeled with a detectable molecule selected form the group consisting of radioisotopes, fluorescent compounds, bioluminescent compounds, chemiluminescent compounds, metal chelators or enzymes.
  • labels include, but are not limited to, the following radioisotopes (e.g., 3 H, 14 C, 35 S, 125 I, 131 I), fluorescent labels (e.g., FITC, rhodamine, lanthanide phosphors), luminescent labels such as luminol; enzymatic labels (e.g., horseradish peroxidase, beta-galactosidase, luciferase, alkaline phosphatase, acetylcholinesterase), biotinyl groups (which can be detected by marked avidin, e.g., streptavidin containing a fluorescent marker or enzymatic activity that can be detected by optical or calorimetric methods), predetermined polypeptide epitopes recognized by a secondary reporter (e.g., leucine zipper pair sequences, binding sites for secondary antibodies, metal binding domains, epitope tags).
  • radioisotopes e.g., 3 H, 14 C, 35 S,
  • the term "lower" with reference to a biomarker measurement refers to a statistically significant and measurable difference in the level of a biomarker compared to the level of another biomarker or to a control level where the biomarker measurement is less than the level of the other biomarker or the control level.
  • the difference is suitably at least about 10%, or at least about 20%, or of at least about 30%, or of at least about 40%, or at least about 50%.
  • microarray refers to an arrangement of array elements, e.g., probes (including primers), ligands, biomarker nucleic acid sequence or protein sequences on a substrate.
  • array elements e.g., probes (including primers), ligands, biomarker nucleic acid sequence or protein sequences on a substrate.
  • microarray includes within its scope “high-density arrays” and “low-density arrays”.
  • the microarray refers to a substrate or collection of substrates or surfaces bearing a plurality of array elements (e.g., discrete regions having particular moieties, e.g., proteins (e.g., antibodies), nucleic acids (e.g., oligonucleotide probes), etc., immobilized thereto), where the array elements are present at a density of about 100 elements/ cm 2 or more, about 1,000 elements/ cm 2 or more, about 10,000 elements/ cm 2 or more, or about 100,000 elements/ cm 2 or more.
  • array elements e.g., discrete regions having particular moieties, e.g., proteins (e.g., antibodies), nucleic acids (e.g., oligonucleotide probes), etc., immobilized thereto
  • the array elements are present at a density of about 100 elements/ cm 2 or more, about 1,000 elements/ cm 2 or more, about 10,000 elements/ cm 2 or more, or about 100,000 elements/ cm 2 or more.
  • a "high-density array” is one that comprises a plurality of array elements for detecting about 100 or more different biomarkers, about 1,000 or more different biomarkers, about 10,000 or more different biomarkers, or about 100,000 or more different biomarkers.
  • a "high-density array” is one that comprises a plurality of array elements for detecting biomarkers of about 100 or more different genes, of about 1,000 or more different genes, of about 10,000 or more different genes, or of about 100,000 or more different genes.
  • the elements of a high-density array are not labeled.
  • low-density array refers to a substrate or collection of substrates or surfaces bearing a plurality of array elements (e.g., discrete regions having particular moieties, e.g., proteins (e.g., antibodies), nucleic acids (e.g., oligonucleotide probes), etc., immobilized thereto), where the array elements are present at a density of about 100 elements/ cm 2 or less, about 50 elements/ cm 2 or less, about 20 elements/ cm 2 or less, or about 10 elements/ cm 2 or less.
  • array elements e.g., discrete regions having particular moieties, e.g., proteins (e.g., antibodies), nucleic acids (e.g., oligonucleotide probes), etc., immobilized thereto
  • the array elements are present at a density of about 100 elements/ cm 2 or less, about 50 elements/ cm 2 or less, about 20 elements/ cm 2 or less, or about 10 elements/ cm 2 or less.
  • a "low-density array” is one that comprises a plurality of array elements for detecting about 100 or less different biomarkers, about 50 or less different biomarkers, about 20 or less different biomarkers (e.g., 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2 or 1 distinct biomarker(s)), or about 10 or less different biomarkers (e.g., 10, 9, 8, 7, 6, 5, 4, 3, 2 or 1 distinct biomarker(s)).
  • a "low-density array” is one that comprises a plurality of array elements for detecting biomarkers of about 100 or less different genes, of about 50 or less different genes, of about 20 or less different genes (e.g., 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2 or 1 distinct gene(s)), or of about 10 or less different genes (e.g., 10, 9, 8, 7, 6, 5, 4, 3, 2 or 1 distinct gene(s)).
  • the elements of a low-density array are not labeled.
  • microbial refers to a microscopic organism comprising either a single cell or a plurality of cells and encompasses, but is not limited to, prokaryotes such as bacteria, viruses and archaea; and forms of eukaryotes such as protozoan, yeast, fungi and algae.
  • normalization when used in conjunction with measurement of biomarkers across samples and time, refer to mathematical methods, including but not limited to multiple of the median (MoM), standard deviation normalization, sigmoidal normalization, etc., where the intention is that these normalized values allow the comparison of corresponding normalized values from different datasets in a way that eliminates or minimizes differences and gross influences.
  • MoM median
  • standard deviation normalization standard deviation normalization
  • sigmoidal normalization sigmoidal normalization
  • nucleic acid or “polynucleotide” as used herein includes RNA, mRNA, miRNA, cRNA, cDNA, mtDNA, or DNA.
  • the term typically refers to a polymeric form of nucleotides of at least 10 bases in length, either ribonucleotides or deoxynucleotides or a modified form of either type of nucleotide.
  • the term includes single and double stranded forms of DNA or RNA.
  • samples so obtained include, for example, nucleic acid extracts or polypeptide extracts isolated or derived from a particular source.
  • the extract may be isolated directly from a biological fluid or tissue of a subject.
  • the term "panel” refers to specific combination of biomarkers used to determine an indicator for assessing a likelihood that a type of inflammation is present or absent in a joint of a subject.
  • the term “panel” may also refer to an assay comprising a set of biomarkers used for such a determination. This term can also refer to a profile or index of expression patterns of one or more biomarkers described herein. The number of biomarkers useful for a biomarker panel is based on the sensitivity and specificity value for the particular combination of biomarker values.
  • the term "positive response” means that the result of a treatment regimen includes some clinically significant benefit, such as the prevention, or reduction of severity, of symptoms, or a slowing of the progression of the condition.
  • the term “negative response” means that a treatment regimen provides no clinically significant benefit, such as the prevention, or reduction of severity, of symptoms, or increases the rate of progression of the condition.
  • Protein Polypeptide and “peptide” are used interchangeably herein to refer to a polymer of amino acid residues and to variants and synthetic analogues of the same.
  • primer an oligonucleotide which, when paired with a strand of DNA, is capable of initiating the synthesis of a primer extension product in the presence of a suitable polymerizing agent.
  • the primer is preferably single-stranded for maximum efficiency in amplification but can alternatively be double-stranded.
  • a primer must be sufficiently long to prime the synthesis of extension products in the presence of the polymerization agent. The length of the primer depends on many factors, including application, temperature to be employed, template reaction conditions, other reagents, and source of primers.
  • the primer may be at least about 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 50, 75, 100, 150, 200, 300, 400, 500, to one base shorter in length than the template sequence at the 3' end of the primer to allow extension of a nucleic acid chain, though the 5' end of the primer may extend in length beyond the 3' end of the template sequence.
  • primers can be large polynucleotides, such as from about 35 nucleotides to several kilobases or more.
  • Primers can be selected to be “substantially complementary” to the sequence on the template to which it is designed to hybridize and serve as a site for the initiation of synthesis.
  • substantially complementary it is meant that the primer is sufficiently complementary to hybridize with a target polynucleotide.
  • the primer contains no mismatches with the template to which it is designed to hybridize but this is not essential.
  • non-complementary nucleotide residues can be attached to the 5' end of the primer, with the remainder of the primer sequence being complementary to the template.
  • non-complementary nucleotide residues or a stretch of non-complementary nucleotide residues can be interspersed into a primer, provided that the primer sequence has sufficient complementarity with the sequence of the template to hybridize therewith and thereby form a template for synthesis of the extension product of the primer.
  • probe refers to a molecule that binds to a specific sequence or sub-sequence or other moiety of another molecule. Unless otherwise indicated, the term “probe” typically refers to a nucleic acid probe that binds to another nucleic acid, also referred to herein as a "target polynucleotide", through complementary base pairing. Probes can bind target polynucleotides lacking complete sequence complementarity with the probe, depending on the stringency of the hybridization conditions. Probes can be labeled directly or indirectly and include primers within their scope.
  • prognosis refers to a prediction of the probable course and outcome of a clinical condition or disease.
  • a prognosis is usually made by evaluating factors or symptoms of a disease that are indicative of a favorable or unfavorable course or outcome of the disease.
  • prognosis refers to an increased probability that a certain course or outcome will occur; that is, that a course or outcome is more likely to occur in a subject exhibiting a given condition, when compared to those individuals not exhibiting the condition.
  • proximal to is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to the spatial relationship between various elements in comparison to a particular point of reference. In general, the term indicates an element is located relatively near to the reference point than another element.
  • quencher includes any moiety that in close proximity to a donor fluorophore, takes up emission energy generated by the donor fluorophore and either dissipates the energy as heat or emits light of a longer wavelength than the emission wavelength of the donor fluorophore. In the latter case, the quencher is considered to be an acceptor fluorophore.
  • the quenching moiety can act via proximal (/.e., collisional) quenching or by Forster or fluorescence resonance energy transfer (“FRET"). Quenching by FRET is generally used in TaqManTM probes while proximal quenching is used in molecular beacon and ScorpionTM type probes.
  • Suitable quenchers are selected based on the fluorescence spectrum of the particular fluorophore.
  • Useful quenchers include, for example, the Black HoleTM quenchers BHQ-1, BHQ-2, and BHQ-3 (Biosearch Technologies, Inc.), and the ATTO-series of quenchers (ATTO 540Q, ATTO 580Q, and ATTO 612Q; Atto-Tec GmbH).
  • a reaction vessel refers to any container, chamber, device, or assembly, in which a reaction can occur in accordance with the present disclosure.
  • a reaction vessel may be a microtube, for example, but not limited to, a 0.2 mL or a 0.5 mL reaction tube such as a MicroAmpTM Optical tube (Applied BiosystemsTM, Thermo Fisher Scientific) or a micro-centrifuge tube, or other containers of the sort in common practice in molecular biology laboratories.
  • a reaction vessel may be a well in a microtiter plate (e.g., 96-well plate, 384-well plate) such as a TaqManTM Array plate (Applied BiosystemsTM; Thermo Fisher Scientific), a spot on a glass slide, a well in an Applied BiosystemsTM TaqManTM Array Card or Plate (Thermo Fisher Scientific) or a through-hole of an Applied BiosystemsTM TaqManTM OpenArrayTM plate (Thermo Fisher Scientific).
  • a plurality of reaction vessels may reside on the same support.
  • lab-on-a-chip-like devices available for example from Caliper, Fluidigm and Life Technologies Corp., including the Ion 316TM and Ion 318TM Chip, may serve as reaction vessels in the disclosed methods and devices.
  • various microfluidic approaches may be employed. It will be recognized that a variety of reaction vessels are available in the art and fall within the scope of the present disclosure.
  • the term “reduce” or “reduced” with reference to a biomarker level refers to a statistically significant and measurable reduction in the biomarker level compared to the level of another biomarker or to a control level.
  • the reduction is suitably a reduction of at least about 10%, or a reduction of at least about 20%, or a reduction of at least about 30%, or a reduction of at least about 40%, or a reduction of at least about 50%.
  • rule-out and its grammatical equivalents refer to a diagnostic test with high sensitivity that optionally coupled with a clinical assessment indicates a lower likelihood of a particular condition (e.g., infectious inflammation or non-infectious inflammation). Accordingly, the term “ruling out” as used herein is meant that the subject is selected not to receive a treatment protocol or regimen for treating a specified condition (e.g., infectious inflammation or non-infectious inflammation).
  • sample includes any biological specimen that may be extracted, untreated, treated, diluted or concentrated from a subject.
  • biological samples may include, without limitation, biological fluids such as whole blood, serum, red blood cells, white blood cells, plasma, joint exudate, synovial fluid, cell lysates, cellular secretion products and inflammation fluid.
  • Samples may include tissue samples (e.g., synovial tissue samples) and biopsies, tissue homogenates and the like.
  • Exemplary samples for use in accordance with the present disclosure include fluid samples, particularly fluid samples from, or adjacent to, a synovial joint.
  • Advantageous samples may include ones comprising any one or more biomarkers as taught herein in detectable quantities.
  • the sample is readily obtainable by minimally invasive methods, allowing the removal or isolation of the sample from the subject.
  • the sample may contain blood such as peripheral blood, or a fraction or extract thereof.
  • the sample may comprise blood cells such as mature, immature or developing leukocytes, including lymphocytes, polymorphonuclear leukocytes, neutrophils, monocytes, reticulocytes, basophils, coelomocytes, hemocytes, eosinophils, megakaryocytes, macrophages, dendritic cells natural killer cells, or fraction of such cells (e.g., a nucleic acid or protein fraction).
  • the sample comprises synovial fluid.
  • solid support refers to a solid inert surface or body to which a molecular species, such as a nucleic acid and polypeptides can be immobilized.
  • solid supports include glass surfaces, plastic surfaces, latex, dextran, polystyrene surfaces, polypropylene surfaces, polyacrylamide gels, gold surfaces, and silicon wafers.
  • the solid supports are in the form of membranes, chips or particles.
  • the solid support may be a glass surface (e.g., a planar surface of a flow cell channel).
  • the solid support may comprise an inert substrate or matrix which has been "functionalized", such as by applying a layer or coating of an intermediate material comprising reactive groups which permit covalent attachment to molecules such as polynucleotides.
  • such supports can include polyacrylamide hydrogels supported on an inert substrate such as glass.
  • the molecules e.g., polynucleotides
  • the intermediate material e.g., a hydrogel
  • the intermediate material can itself be non-covalently attached to the substrate or matrix (e.g., a glass substrate).
  • the support can include a plurality of particles or beads each having a different attached molecular species.
  • vertebrate animals that fall within the scope of the invention include, but are not restricted to, any member of the phylum Chordata, subphylum vertebrata including primates, 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 etc.), marine mammals (e.g.,
  • synovial fluid refers to the liquid produced by the synovial membranes of a joint. Synovial fluid lubricates and facilitates movement of the joint.
  • the term “synovium” refers to the thin layer of connective tissue with a free smooth surface that lines the capsule of a joint.
  • the "synovial membrane” refers to the connective-tissue membrane that lines the cavity of a synovial joint and produces the synovial fluid.
  • Synovial fluid typically comprises nucleated cells such as leukocytes, non-limiting examples of which include neutrophils, lymphocytes (e.g., T and/or B lymphocytes), monocytes, and macrophages.
  • synovial joint refers to a joint between two bones that includes an articular capsule forming a synovial cavity typically containing synovial fluid (although it is contemplated that a joint having an articular capsule absent synovial fluid (e.g., where the fluid may have been removed surgically) is still considered a synovial joint).
  • intraarticular or “intra-articular space” refers to the space (whether or not containing synovial fluid) confined by the articular capsule.
  • synovial joint encompasses joints lined with articular cartilage or joint that were previously lined with articular cartilage, wherein the cartilage has been degraded through pathological processes conditions (e.g., rheumatoid arthritis) or artificially removed (e.g., by surgery).
  • synovial tissue and “synovium” refer to the thin, loose vascular connective tissue that makes up, more specifically lines the interior of all joints and also the sheaths surrounding tendons such as in the hands and feet.
  • Synovial tissue contains synovial cells, which secrete a viscous liquid called synovial fluid; this liquid contains proteins and hyaluronic acid and serves as a lubricant and nutrient for the joint cartilage surfaces.
  • treatment regimen refers to prophylactic and/or therapeutic (/.e., after onset of a specified condition) treatments, unless the context specifically indicates otherwise.
  • treatment regimen encompasses natural substances and pharmaceutical agents (/.e., "drugs”) as well as any other treatment regimen including but not limited to dietary treatments, physical therapy or exercise regimens, surgical interventions, and combinations thereof.
  • joint inflammation e.g., joint pain
  • biomarkers are commonly, specifically and differentially expressed in samples obtained from sites of joint inflammation.
  • results presented herein provide clear evidence that specific biomarkers can be used, optionally in combination with clinical parameters, to differentiate between infectious joint inflammation and non-infectious joint inflammation with a remarkable degree of accuracy. Additionally, it has been determined that the disclosed biomarkers can exclude joint inflammation with a NPV greater than 95% at a prevalence of infectious joint inflammation set at 50%, and may thus be useful for triaging treatment decisions for subjects with joint inflammation.
  • biomarkers disclosed herein are proposed to have utility in laboratory and point-of-care diagnostics that allow for rapid screening for infectious joint inflammation or non-infectious joint inflammation, or for ruling out infectious joint inflammation, which may result in significant cost savings to the medical system, as subjects with joint inflammation can be categorized with increased accuracy and exposed to management procedures and therapeutic agents that are suitable for treating a particular type or source of joint inflammation.
  • Biomarkers that can be used in the practice of the methods, compositions, apparatuses, devices and kits disclosed herein include expression products of genes (also referred to herein as (“joint inflammation host response genes"), including but not limited to: ACO2, AP3M1, API5, AQP9, ATG4B, ATIC, C5orfl5, CANX, CDKN1A, CISH, CLIC4, CSF2RB, CSF3R, CSNK1D, CWC27, CXCL8, DTNBP1, DUSP1, DUSP5, EIF2S1, EMP1, ERP44, ETV6, FCGR3B, FFAR2, FPR1, FYB1, GADD45B, GBP1, GRINA, H3-3B, HCK, HLA-E, HNRNPAB, IARS2, IER3, IL1B, IL1RN, IMMT, IPO8, IRF2, KCTD2, KCTD3, KLF13, KLHL12, LARP4, LILRB3, LMNA,
  • genes also
  • joint inflammation biomarkers are useful therefore for providing an indicator that aids in the diagnosis of, and distinguishing between, joint inflammations that is associated with a microbial infection and non- infectious joint inflammatory conditions, such as caused by traumatic injury, surgery, autoimmune disease, gout or painful local tissues proximal to a joint affected by bursitis, tenosynovitis, epicondylitis, synovitis and/or other disorders.
  • methods for determining an indicator used in assessing a likelihood that a type of inflammation is present or absent in a joint of a subject, wherein the type of inflammation is selected from infectious inflammation and non-infectious inflammation.
  • These methods general comprise, consist or consist essentially of: (1) determining a biomarker value for at least one biomarker (e.g., 1 to 100 biomarkers, and all integer biomarkers in between) in a sample obtained from a site of inflammation associated with the joint, wherein a respective biomarker value is indicative of a level of a corresponding biomarker in the sample, wherein the at least one biomarker is selected from a first panel of biomarkers comprising, consisting or consisting essentially of ACO2, AP3M1, ATG4B, C5orfl5, CANX, CDKN1A, CSNK1D, CWC27, CXCL8, DTNBP1, DUSP1, EIF2S1, EMP1, ERP44, FCGR3B, FFAR
  • biomarker values are obtained for a plurality of biomarkers, wherein the plurality of biomarkers is selected from the first panel of biomarkers and optionally from a second panel of biomarkers comprising API5, AQP9, ATIC, CISH, CLIC4, CSF2RB, CSF3R, DUSP5, ETV6, GADD45B, GRINA, HCK, HLA-E, IER3, IL1B, IL1RN, IMMT, LILRB3, LRPPRC, LYN, NFKBIA, OSM, PDE4B, PI3, PLAUR, PLEK, PPIF, SEMA4D, STARD7, TNFAIP2, TNFAIP3 and ZFP36.
  • Biomarker panels disclosed herein typically comprise at least 2 biomarkers and up to 30 biomarkers, including any number of biomarkers in between, such as 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 biomarkers.
  • a biomarker panel comprises at least 2, or least 3, or at least 4, or at least 5, or at least 6, or at least 7, or at least 8, or at least 9, or at least 10, or at least 11, or at least 12, or at least 13, or at least 14, or at least 15 or at least 16 or more biomarkers.
  • a biomarker panel comprises up to 4, or up to 5, or up to 6, or up to 7, or up to 8, or up to 9, or up to 10, or up to 11, or up to 12, or up to 13, or up to 14, or up to 15, or up to 16 biomarkers.
  • Biomarker values that are indicative of the levels of biomarkers in a patient sample may be obtained by any suitable means known in the art.
  • the sample may obtained from any accessible site of joint inflammation.
  • the joint may be a synovial joint, a fibrous joint or a cartilaginous joint.
  • the joint is a synovial joint, representative examples of which include a knee joint, wrist joint, shoulder joint, hip joint, elbow joint or ankle joint.
  • the sample may comprise synovial fluid, lymph fluid, joint exudate, joint transudate, or combination thereof.
  • Measurement of the expression level of a biomarker in the sample can be direct or indirect. For example, the abundance levels of RNAs or proteins can be directly quantitated.
  • the amount of a biomarker can be determined indirectly by measuring abundance levels of cDNAs, amplified RNAs or DNAs, or by measuring quantities or activities of RNAs, proteins, or other molecules (e.g., metabolites) that are indicative of the expression level of the biomarker.
  • the methods for measuring biomarkers in a sample have many applications. For example, one or more biomarkers can be measured to aid in the diagnosis of infectious joint inflammation or non-infectious joint inflammation, to determine the appropriate treatment for a subject, to monitor responses in a subject to treatment, or to identify therapeutic compounds that modulate expression of the biomarkers in vivo or in vitro.
  • the expression levels of joint inflammation biomarkers are determined by measuring biomarker polynucleotide levels.
  • the levels of transcripts of specific biomarker genes can be determined from the amount of mRNA, or polynucleotides derived therefrom, present in a biological sample. Polynucleotides can be detected and quantitated by a variety of methods including, but not limited to, microarray analysis, polymerase chain reaction (PCR), reverse transcriptase polymerase chain reaction (RT-PCR), Northern blot, and serial analysis of gene expression (SAGE).
  • PCR polymerase chain reaction
  • RT-PCR reverse transcriptase polymerase chain reaction
  • SAGE serial analysis of gene expression
  • nucleic acid is isolated from cells contained in the biological sample according to standard methodologies (Sambrook, et al., "MOLECULAR CLONING. A LABORATORY MANUAL", Cold Spring Harbor Press, 1989; and Ausubel et al., "CURRENT PROTOCOLS IN MOLECULAR BIOLOGY", John Wiley & Sons Inc., 1994-1998).
  • the nucleic acid is typically fractionated (e.g., poly A+ RNA) or whole cell RNA. Where RNA is used as the subject of detection, it may be desired to convert the RNA to a complementary DNA.
  • the nucleic acid is amplified by a template-dependent nucleic acid amplification technique.
  • a template-dependent nucleic acid amplification technique Numerous template dependent processes are available to amplify the joint inflammation biomarker sequences present in a given template sample.
  • An exemplary nucleic acid amplification technique is PCR, which is described in detail in U.S. Pat. Nos. 4,683,195, 4,683,202 and 4,800,159, Ausubel et al. (supra), and in Innis et al., ("PCR Protocols", Academic Press, Inc., San Diego Calif., 1990). Briefly, in PCR, two primer sequences are prepared that are complementary to regions on opposite complementary strands of the biomarker sequence.
  • An excess of deoxynucleotide triphosphates is added to a reaction mixture along with a DNA polymerase, e.g., Taq polymerase. If a cognate joint inflammation biomarker sequence is present in a sample, the primers will bind to the biomarker and the polymerase will cause the primers to be extended along the biomarker sequence by adding on nucleotides. By raising and lowering the temperature of the reaction mixture, the extended primers will dissociate from the biomarker to form reaction products, excess primers will bind to the biomarker and to the reaction products and the process is repeated.
  • a reverse transcriptase PCR amplification procedure may be performed in order to quantify the amount of mRNA amplified.
  • RNA-dependent DNA polymerases thermostable, RNA-dependent DNA polymerases. These methods are described in WO 90/07641. Polymerase chain reaction methodologies are well known in the art.
  • cDNA synthesis using whole cell RNA as a sample produces whole cell cDNA.
  • the template-dependent amplification involves quantification of transcripts in real-time. For example, RNA or DNA may be quantified using the Real-Time PCR (RT-PCR) technique (Higuchi, 1992, et al., Biotechnology 10: 413-417).
  • RT-PCR Real-Time PCR
  • the concentration of the amplified products of the target DNA in PCR reactions that have completed the same number of cycles and are in their linear ranges, it is possible to determine the relative concentrations of the specific target sequence in the original DNA mixture. If the DNA mixtures are cDNAs synthesized from RNAs isolated from different tissues or cells, the relative abundance of the specific mRNA from which the target sequence was derived can be determined for the respective tissues or cells. This direct proportionality between the concentration of the PCR products and the relative mRNA abundance is only true in the linear range of the PCR reaction. The final concentration of the target DNA in the plateau portion of the curve is determined by the availability of reagents in the reaction mix and is independent of the original concentration of target DNA.
  • multiplexed, tandem PCR is employed, which uses a two-step process for gene expression profiling from small quantities of RNA or DNA, as described for example in US Pat. Appl. Pub. No. 20070190540.
  • RNA is converted into cDNA and amplified using multiplexed gene specific primers.
  • each individual gene is quantitated by RT-PCR.
  • Real-time PCR is typically performed using any PCR instrumentation available in the art.
  • instrumentation used in real-time PCR data collection and analysis comprises a thermal cycler, optics for fluorescence excitation and emission collection, and optionally a computer and data acquisition and analysis software.
  • a TaqManTM probe is used for quantitating nucleic acid.
  • Such assays may use energy transfer (“FT"), such as fluorescence resonance energy transfer (“FRET”), to detect and quantitate the synthesized PCR product.
  • FT energy transfer
  • FRET fluorescence resonance energy transfer
  • the TaqManTM probe comprises a fluorescent label (e.g. , a fluorescent dye) coupled to one end (e.g., the 5'-end) and a quencher molecule is coupled to the other end (e.g., the 3'-end), such that the fluorescent label and the quencher are in close proximity, allowing the quencher to suppress the fluorescence signal of the dye via FRET.
  • the 5'-nuclease of the polymerase cleaves the probe, decoupling the fluorescent label and the quencher so that label signal (such as fluorescence) is detected.
  • Label signal such as fluorescence
  • Signal increases with each PCR cycle proportionally to the amount of probe that is cleaved.
  • TaqManTM probes typically comprise a region of contiguous nucleotides having a sequence that is identically present in or complementary to a region of a joint inflammation biomarker polynucleotide such that the probe is specifically hybridizable to the resulting PCR amplicon.
  • the probe comprises a region of at least 6 contiguous nucleotides having a sequence that is fully complementary to or identically present in a region of a target joint inflammation biomarker polynucleotide, such as comprising a region of at least 8 contiguous nucleotides, at least 10 contiguous nucleotides, at least 12 contiguous nucleotides, at least 14 contiguous nucleotides, or at least 16 contiguous nucleotides having a sequence that is complementary to or identically present in a region of a target joint inflammation biomarker polynucleotide to be detected and/or quantitated.
  • Molecular Beacons like TaqManTM probes, use FRET to detect and quantitate a PCR product via a probe having a fluorescent label (e.g., a fluorescent dye) and a quencher attached at the ends of the probe. Unlike TaqManTM probes, however, Molecular Beacons remain intact during the PCR cycles.
  • Molecular Beacon probes form a stem-loop structure when free in solution, thereby allowing the fluorescent label and quencher to be in close enough proximity to cause fluorescence quenching.
  • the stem-loop structure is abolished so that the fluorescent label and the quencher become separated in space and the fluorescent label fluoresces.
  • Molecular Beacons are available, e.g., from Gene LinkTM (see, www.genelink.com).
  • Scorpion probes can be used as both sequence-specific primers and for PCR product detection and quantitation. Like Molecular Beacons, Scorpion probes form a stem-loop structure when not hybridized to a target nucleic acid. However, unlike Molecular Beacons, a Scorpion probe achieves both sequence-specific priming and PCR product detection.
  • a fluorescent label e.g., a fluorescent dye molecule
  • a quencher is attached to the 3'-end.
  • the 3' portion of the probe is complementary to the extension product of the PCR primer, and this complementary portion is linked to the 5'-end of the probe by a non-amplifiable moiety.
  • Scorpion probes are available from, e.g., Premier Biosoft International (see www.premierbiosoft.com/tech_notes/Scorpion.html).
  • labels that can be used on the FRET probes include colorimetric and fluorescent dyes such as Alexa Fluor dyes, BODIPY dyes, such as BODIPY FL; Cascade Blue; Cascade Yellow; coumarin and its derivatives, such as 7-amino-4-methylcoumarin, aminocoumarin and hydroxycoumarin; cyanine dyes, such as Cy3 and Cy5; eosins and erythrosins; fluorescein and its derivatives, such as fluorescein isothiocyanate; macrocyclic chelates of lanthanide ions, such as Quantum DyeTM; Marina Blue; Oregon Green; rhodamine dyes, such as rhodamine red, tetramethylrhodamine and rhodamine 6G; Texas Red; fluorescent energy transfer dyes, such as thiazole orange-ethidium heterodimer; and, TOTAB.
  • fluorescent dyes such as Alexa Fluor dyes, BODIPY dyes, such as
  • dyes include, but are not limited to, those identified above and the following: Alexa Fluor 350, Alexa Fluor 405, Alexa Fluor 430, Alexa Fluor 488, Alexa Fluor 500. Alexa Fluor 514, Alexa Fluor 532, Alexa Fluor 546, Alexa Fluor 555, Alexa Fluor 568, Alexa Fluor 594, Alexa Fluor 610, Alexa Fluor 633, Alexa Fluor 647, Alexa Fluor 660, Alexa Fluor 680, Alexa Fluor 700, and, Alexa Fluor 750; amine-reactive BODIPY dyes, such as BODIPY 493/503, BODIPY 530/550, BODIPY 558/568, BODIPY 564/570, BODIPY 576/589, BODIPY 581/591, BODIPY 630/650, BODIPY 650/655, BODIPY FL, BODIPY R6G, BODIPY TMR, and
  • dye/quencher pairs include, but are not limited to, fluorescein/tetramethylrhodamine; lAEDANS/fluorescein; EDANS/dabcyl; fluorescein/fluorescein; BODIPY FL/BODIPY FL; fluorescein/QSY 7 or QSY 9 dyes.
  • FRET may be detected, in some embodiments, by fluorescence depolarization.
  • dye/quencher pairs include, but are not limited to, Alexa Fluor 350/Alexa Fluor488; Alexa Fluor 488/Alexa Fluor 546; Alexa Fluor 488/Alexa Fluor 555; Alexa Fluor 488/Alexa Fluor 568; Alexa Fluor 488/Alexa Fluor 594; Alexa Fluor 488/Alexa Fluor 647; Alexa Fluor 546/ Alexa Fluor 568; Alexa Fluor 546/Alexa Fluor 594; Alexa Fluor 546/Alexa Fluor 647; Alexa Fluor 555/Alexa Fluor 594; Alexa Fluor 555/Alexa Fluor 647; Alexa Fluor 568/Alexa Fluor 647; Alexa Fluor 594/Alexa Fluor 647; Alexa Fluor 350/QSY35; Alexa Fluor 350/dabcyl; Alexa Fluor 488/QSY 35; Alexa Fluor 488/d
  • the same quencher may be used for multiple dyes, for example, a broad spectrum quencher, such as an Iowa BlackTM quencher (Integrated DNA Technologies, Coralville, Iowa) or a Black Hole QuencherTM (BHQTM; Sigma-Aldrich, St. Louis, Mo.).
  • a broad spectrum quencher such as an Iowa BlackTM quencher (Integrated DNA Technologies, Coralville, Iowa) or a Black Hole QuencherTM (BHQTM; Sigma-Aldrich, St. Louis, Mo.).
  • each probe comprises a detectably different dye such that the dyes may be distinguished when detected simultaneously in the same reaction.
  • detectably different dyes for use in a multiplex reaction.
  • multiple target joint inflammation biomarker polynucleotides are detected and/or quantitated in a single multiplex reaction.
  • each probe that is targeted to a different joint inflammation biomarker polynucleotide is spectrally distinguishable when released from the probe.
  • each target joint inflammation biomarker polynucleotide is detected by a unique fluorescence signal.
  • fluorescently labeled ribonucleotides useful in the preparation of real-time PCR probes for use in some embodiments of the methods described herein are available from Molecular Probes (Invitrogen), and these include, Alexa Fluor 488-5-UTP, Fluorescein-12-UTP, BODIPY FL-14-UTP, BODIPY TMR-14-UTP, Tetramethylrhodamine-6-UTP, Alexa Fluor 546-14-UTP, Texas Red-5-UTP, and BODIPY TR-14-UTP.
  • Other fluorescent ribonucleotides are available from Amersham Biosciences (GE Healthcare), such as Cy3-UTP and Cy5-UTP.
  • Examples of fluorescently labeled deoxyribonucleotides useful in the preparation of real-time PCR probes for use in the methods described herein include Dinitrophenyl (DNP)-l'- dUTP, Cascade Blue-7-dUTP, Alexa Fluor 488-5-dUTP, Fluorescein-12-dUTP, Oregon Green 488-5- dUTP, BODIPY FL-14-dUTP, Rhodamine Green-5-dUTP, Alexa Fluor 532-5-dUTP, BODIPY TMR-14- dUTP, Tetramethylrhodamine-6-dUTP, Alexa Fluor 546-14-dUTP, Alexa Fluor 568-5-dUTP, Texas Red-12-dUTP, Texas Red-5-dUTP, BODIPY TR-14-dUTP, Alexa Fluor 594-5-dUTP, BODIPY 630/650- 14-dUTP, BODIPY 650/665-14-dUTP
  • SAGE analysis is used to determine RNA abundances in a cell sample (see, e.g., Velculescu et al., 1995, Science 270:484-7; Carulli, et al., 1998, Journal of Cellular Biochemistry Supplements 30/31:286-96). SAGE analysis does not require a special device for detection, and is one of the preferable analytical methods for simultaneously detecting the expression of a large number of transcription products. First, poly A + RNA is extracted from cells.
  • RNA is converted into cDNA using a biotinylated oligo (dT) primer, and treated with a four-base recognizing restriction enzyme (Anchoring Enzyme: AE) resulting in AE-treated fragments containing a biotin group at their 3' terminus.
  • AE choring Enzyme
  • the AE-treated fragments are incubated with streptavidin for binding.
  • the bound cDNA is divided into two fractions, and each fraction is then linked to a different double-stranded oligonucleotide adapter (linker) A or B.
  • linkers are composed of: (1) a protruding single strand portion having a sequence complementary to the sequence of the protruding portion formed by the action of the anchoring enzyme, (2) a 5' nucleotide recognizing sequence of the IIS-type restriction enzyme (cleaves at a predetermined location no more than 20 bp away from the recognition site) serving as a tagging enzyme (TE), and (3) an additional sequence of sufficient length for constructing a PCR-specific primer.
  • the linker- linked cDNA is cleaved using the tagging enzyme, and only the linker-linked cDNA sequence portion remains, which is present in the form of a short-strand sequence tag.
  • amplification product is obtained as a mixture comprising myriad sequences of two adjacent sequence tags (ditags) bound to linkers A and B.
  • the amplification product is treated with the anchoring enzyme, and the free ditag portions are linked into strands in a standard linkage reaction.
  • the amplification product is then cloned. Determination of the clone's nucleotide sequence can be used to obtain a read-out of consecutive ditags of constant length. The presence of mRNA corresponding to each tag can then be identified from the nucleotide sequence of the clone and information on the sequence tags.
  • target nucleic acids are quantified using blotting techniques, which are well known to those of skill in the art.
  • Southern blotting involves the use of DNA as a target
  • Northern blotting involves the use of RNA as a target.
  • cDNA blotting is analogous, in many aspects, to blotting or RNA species.
  • a probe is used to target a DNA or RNA species that has been immobilized on a suitable matrix, often a filter of nitrocellulose. The different species should be spatially separated to facilitate analysis. This often is accomplished by gel electrophoresis of nucleic acid species followed by "blotting" on to the filter.
  • the blotted target is incubated with a probe (usually labeled) under conditions that promote denaturation and rehybridization. Because the probe is designed to base pair with the target, the probe will bind a portion of the target sequence under renaturing conditions. Unbound probe is then removed, and detection is accomplished as described above. Following detection/quantification, one may compare the results seen in a given subject with a control reaction or a statistically significant reference group or population of control subjects as defined herein. In this way, it is possible to correlate the amount of joint inflammation biomarker nucleic acid detected with the progression or severity of the disease.
  • microarray based technologies such as those described by Hacia et al. (1996, Nature Genetics 14: 441-447) and Shoemaker et al. (1996, Nature Genetics 14: 450-456). Briefly, these techniques involve quantitative methods for analyzing large numbers of genes rapidly and accurately. By tagging genes with oligonucleotides or using fixed nucleic acid probe arrays, one can employ microarray technology to segregate target molecules as high-density or low density arrays and screen these molecules on the basis of hybridization. See also Pease et al. (1994, Proc. Natl. Acad. Sci. U.S.A. 91: 5022-5026); Fodor et al.
  • nucleic acid probes to joint inflammation biomarker polynucleotides are made and attached to microarrays to be used in the detection methods disclosed herein.
  • the nucleic acid probes attached to the microarray are designed to be substantially complementary to specific expressed joint inflammation biomarker nucleic acids, i.e., the target sequence (either the target sequence of the sample or to other probe sequences, for example in sandwich assays), such that hybridization of the target sequence and the probes of the present invention occur.
  • This complementarity need not be perfect; there may be any number of base pair mismatches, which will interfere with hybridization between the target sequence and the nucleic acid probes.
  • the sequence is not a complementary target sequence.
  • more than one probe per sequence is used, with either overlapping probes or probes to different sections of the target being used. That is, two, three, four or more probes, with three being desirable, are used to build in a redundancy for a particular target.
  • the probes can be overlapping (/.e. have some sequence in common), or separate.
  • oligonucleotide probes on the microarray are exposed to or contacted with a nucleic acid sample suspected of containing one or more joint inflammation biomarker polynucleotides under conditions favoring specific hybridization.
  • Sample extracts of DNA or RNA may be prepared from fluid suspensions of biological materials, or by grinding biological materials, or following a cell lysis step which includes, but is not limited to, lysis effected by treatment with SDS (or other detergents), osmotic shock, guanidinium isothiocyanate and lysozyme.
  • Suitable DNA which may be used in the method of the invention, includes cDNA. Such DNA may be prepared by any one of a number of commonly used protocols as for example described in Ausubel, et al., 1994, supra, and Sambrook, et al., 1989, supra.
  • RNA which may be used in the detection methods disclosed herein, includes messenger RNA, complementary RNA transcribed from DNA (cRNA) or genomic or subgenomic RNA. Such RNA may be prepared using standard protocols as for example described in the relevant sections of Ausubel, et al. 1994, supra and Sambrook, et al. 1989, supra).
  • cDNA may be fragmented, for example, by sonication or by treatment with restriction endonucleases.
  • cDNA is fragmented such that resultant DNA fragments are of a length greater than the length of the immobilized oligonucleotide probe(s) but small enough to allow rapid access thereto under suitable hybridization conditions.
  • fragments of cDNA may be selected and amplified using a suitable nucleotide amplification technique, as described for example above, involving appropriate random or specific primers.
  • the target joint inflammation biomarker polynucleotides are detectably labeled so that their hybridization to individual probes can be determined.
  • the target polynucleotides are typically detectably labeled with a reporter molecule illustrative examples of which include chromogens, catalysts, enzymes, fluorochromes, chemiluminescent molecules, bioluminescent molecules, lanthanide ions (e.g., Eu34), a radioisotope and a direct visual label.
  • a reporter molecule illustrative examples of which include chromogens, catalysts, enzymes, fluorochromes, chemiluminescent molecules, bioluminescent molecules, lanthanide ions (e.g., Eu34), a radioisotope and a direct visual label.
  • a direct visual label use may be made of a colloidal metallic or non-metallic particle, a dye particle, an enzyme or a substrate, an organic polymer, a latex particle, a liposome, or other vesicle containing a signal producing substance and the like.
  • Illustrative labels of this type include large colloids, for example, metal colloids such as those from gold, selenium, silver, tin and titanium oxide.
  • an enzyme is used as a direct visual label
  • biotinylated bases are incorporated into a target polynucleotide.
  • the hybrid-forming step can be performed under suitable conditions for hybridizing oligonucleotide probes to test nucleic acid including DNA or RNA.
  • suitable conditions for hybridizing oligonucleotide probes to test nucleic acid including DNA or RNA.
  • whether hybridization takes place is influenced by the length of the oligonucleotide probe and the polynucleotide sequence under test, the pH, the temperature, the concentration of mono- and divalent cations, the proportion of G and C nucleotides in the hybrid-forming region, the viscosity of the medium and the possible presence of denaturants.
  • Such variables also influence the time required for hybridization.
  • the preferred conditions will therefore depend upon the particular application. Such empirical conditions, however, can be routinely determined without undue experiment
  • the probes are washed to remove any unbound nucleic acid with a hybridization buffer. This washing step leaves only bound target polynucleotides. The probes are then examined to identify which probes have hybridized to a target polynucleotide.
  • a signal may be instrumentally detected by irradiating a fluorescent label with light and detecting fluorescence in a fluorimeter; by providing for an enzyme system to produce a dye which could be detected using a spectrophotometer; or detection of a dye particle or a colored colloidal metallic or non-metallic particle using a reflectometer; in the case of using a radioactive label or chemiluminescent molecule employing a radiation counter or autoradiography.
  • a detection means may be adapted to detect or scan light associated with the label which light may include fluorescent, luminescent, focused beam or laser light.
  • a charge couple device (CCD) or a photocell can be used to scan for emission of light from a probe:target polynucleotide hybrid from each location in the microarray and record the data directly in a digital computer.
  • electronic detection of the signal may not be necessary.
  • the detection means is suitably interfaced with pattern recognition software to convert the pattern of signals from the array into a plain language genetic profile.
  • oligonucleotide probes specific for different joint inflammation biomarker polynucleotides are in the form of a nucleic acid array and detection of a signal generated from a reporter molecule on the array is performed using a 'microarray reader'.
  • a detection system that can be used by a microarray reader is described for example by Pirrung et al. (U.S. Patent No. 5,143,854).
  • the microarray reader will typically also incorporate some signal processing to determine whether the signal at a particular array position or feature is a true positive or maybe a spurious signal.
  • Exemplary microarray readers are described for example by Fodor et al. (U.S. Patent No., 5,925,525).
  • the reaction may be detected using flow cytometry.
  • the joint inflammation biomarker is a target RNA (e.g., mRNA) or a DNA copy of the target RNA whose level or abundance is measured using at least one nucleic acid probe that hybridizes under at least high stringency conditions to the target RNA or to the DNA copy, wherein the nucleic acid probe comprises at least 15 (e.g., 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or more) contiguous nucleotides of joint inflammation biomarker polynucleotide.
  • the measured level or abundance of the target RNA or its DNA copy is normalized to the level or abundance of a reference RNA or a DNA copy of the reference RNA.
  • the nucleic acid probe is immobilized on a solid or semi-solid support.
  • the nucleic acid probe forms part of a spatial array of nucleic acid probes.
  • the level of nucleic acid probe that is bound to the target RNA or to the DNA copy is measured by hybridization (e.g., using a nucleic acid array).
  • the level of nucleic acid probe that is bound to the target RNA or to the DNA copy is measured by nucleic acid amplification (e.g., using a polymerase chain reaction (PCR)).
  • the level of nucleic acid probe that is bound to the target RNA or to the DNA copy is measured by nuclease protection assay.
  • Sequencing technologies including DNA sequencing and RNA sequencing, such as Sanger sequencing, pyrosequencing, sequencing by ligation, massively parallel sequencing, also called “Next-generation sequencing” (NGS), whole transcriptome shotgun sequence (WTSS) (also referred to as “RNAseq”), nanopore sequencing, nanostring sequencing and other high-throughput sequencing approaches with or without sequence amplification of the target can also be used to detect or quantify the presence of joint inflammation biomarker polynucleotides in a sample. Sequence-based methods can provide further information regarding alternative splicing and sequence variation in previously identified genes. Sequencing technologies include a number of steps that are grouped broadly as template preparation, sequencing, detection and data analysis.
  • a sequencing step may use any of a variety of methods that are commonly known in the art.
  • One specific example of a sequencing step uses the addition of nucleotides to the complementary strand to provide the DNA sequence.
  • the detection steps range from measuring bioluminescent signal of a synthesized fragment to four-color imaging of single molecule.
  • the methods are suitably selected from semiconductor sequencing (Ion Torrent; Personal Genome Machine); Helicos True Single Molecule Sequencing (tSMS) (Harris et al. 2008, Science 320: 106-109); 454 sequencing (Roche) (Margulies et al. 2005, Nature, 437, 376- 380); SOLiD technology (Applied Biosystems); SOLEXA sequencing (Illumina); single molecule, real-time (SMRTTM) technology of Pacific Biosciences; nanopore sequencing (Soni and Meller, 2007.
  • compositions are prepared for use in the indicator-determining methods disclosed herein.
  • These compositions may comprise a mixture of a DNA polymerase (e.g., a thermostable DNA polymerase), synovial fluid leukocyte cDNA from a subject with joint pain and/or at least one clinical sign of inflammation (e.g., acute inflammation) in, or proximal to, the joint, wherein the synovial fluid leukocyte cDNA comprises at least one cDNA selected from a joint inflammation biomarkers disclosed herein, and wherein the composition further comprises for each cDNA at least one oligonucleotide primer or probe that hybridizes to that cDNA.
  • a DNA polymerase e.g., a thermostable DNA polymerase
  • synovial fluid leukocyte cDNA from a subject with joint pain and/or at least one clinical sign of inflammation (e.g., acute inflammation) in, or proximal to, the joint
  • the synovial fluid leukocyte cDNA comprises at least
  • compositions comprise for respective cDNA two oligonucleotide primers that hybridize to opposite complementary strands of the cDNA.
  • compositions comprise for a respective cDNA an oligonucleotide probe that hybridizes to the cDNA or a polynucleotide corresponding thereto (e.g., a polynucleotide product resulting nucleic acid amplification of the cDNA).
  • the oligonucleotide probe may comprise a heterologous label (e.g., a fluorescent label).
  • the labeled oligonucleotide probe may comprise a fluorophore.
  • the labeled oligonucleotide probe further comprises a quencher.
  • different labeled oligonucleotide probes are included in the composition for hybridizing to different cDNAs, wherein individual oligonucleotide probes comprise detectably distinct labels (e.g. different fluorophores), or at least a subset of oligonucleotide probes comprises the same label (e.g. same fluorophore).
  • the compositions comprise for each of at least 2, 4, 5, 6, 7, or 8 of the cDNAs at least one oligonucleotide primer and/or probe that hybridizes to the cDNA. In other embodiments, the compositions comprise for each of up to 2, 4, 5, 6, 7, or 8 of the cDNAs at least one oligonucleotide primer and/or probe that hybridizes to the cDNA. Individual cDNAs and their corresponding oligonucleotide primer(s) and/or probe(s) may be present in separate reaction vessels or in the same reaction vessel.
  • Biomarkers that are expressed at the same or similar levels between patients with infectious joint inflammation and those with non-infectious joint inflammation. These biomarkers can be used to define a common biomarker profile or signature that is characteristic of, and shared between, such subjects regardless of the infectious status of their joint inflammation.
  • biomarkers of this type include but are not limited to ADCY7, CSRNP2, DNAJC4, FBXO28, GNAI2, HNRNPU, NEK8, PBLD, PTMA, RABL2B, RHOT2, RNF25, SRRM2, TMBIM6, TMED4, TPM3, UCK1, ZNF787, as shown in Figure 1.
  • a cDNA sample prepared from synovial fluid leukocyte mRNA obtained from a site of joint inflammation will generally comprise a first joint inflammation cDNA, a second joint inflammation cDNA and a third joint inflammation cDNA wherein the first cDNA is present in the cDNA sample at a higher level than the second cDNA and wherein the second cDNA is present in the cDNA sample at a higher level than the third cDNA, wherein the first cDNA is selected from any one of SRRM2, HNRNPU, PTMA, TMBIM6, GNAI2 and TPM3, wherein the second cDNA is selected from any one of RHOT2, TMED4, UCK1, FBXO28, DNAJC4 and RNF25, and wherein the third cDNA is selected from any one of PBLD, NEK8, ADCY7, ZNF787, RABL2B and CSRNP2.
  • joint inflammation biomarker protein levels are assayed using protein-based assays known in the art.
  • antibody-based techniques may be employed including, for example, immunoassays, such as the enzyme-linked immunosorbent assay (ELISA) and the radioimmunoassay (RIA).
  • ELISA enzyme-linked immunosorbent assay
  • RIA radioimmunoassay
  • protein-capture arrays that permit simultaneous detection and/or quantification of a large number of proteins are employed.
  • low-density protein arrays on filter membranes such as the universal protein array system (Ge, 2000 Nucleic Acids Res. 28(2):e3) allow imaging of arrayed antigens using standard ELISA techniques and a scanning charge-coupled device (CCD) detector.
  • CCD scanning charge-coupled device
  • Immunosensor arrays have also been developed that enable the simultaneous detection of clinical analytes. It is now possible using protein arrays, to profile protein expression in bodily fluids, such as in sera of healthy or diseased subjects, as well as in subjects pre- and post-drug treatment.
  • Exemplary protein capture arrays include arrays comprising spatially addressed antigen-binding molecules, commonly referred to as antibody arrays, which can facilitate extensive parallel analysis of numerous proteins defining a proteome or subproteome.
  • Antibody arrays have been shown to have the required properties of specificity and acceptable background, and some are available commercially (e.g., BD Biosciences, Clonetech, Bio-Rad and Sigma). Various methods for the preparation of antibody arrays have been reported (see, e.g., Lopez et al., 2003 J. Chromatogram. B 787: 19-27; Cahill, 2000 Trends in Biotechnology 7:47-51; U.S. Pat. App. Pub. 2002/0055186; U.S. Pat. App. Pub.
  • the antigen-binding molecules of such arrays may recognize at least a subset of proteins expressed by a cell or population of cells, illustrative examples of which include growth factor receptors, hormone receptors, neurotransmitter receptors, catecholamine receptors, amino acid derivative receptors, cytokine receptors, extracellular matrix receptors, antibodies, lectins, cytokines, serpins, proteases, kinases, phosphatases, ras-like GTPases, hydrolases, steroid hormone receptors, transcription factors, heatshock transcription factors, DNA-binding proteins, zinc-finger proteins, leucine-zipper proteins, homeodomain proteins, intracellular signal transduction modulators and effectors, apoptosis- related factors, DNA synthesis factors, DNA repair factors, DNA recombination factors and cellsurface antigens.
  • growth factor receptors include growth factor receptors, hormone receptors, neurotransmitter receptors, catecholamine receptors, amino acid derivative receptors, cytokine receptors,
  • Individual spatially distinct protein-capture agents are typically attached to a support surface, which is generally planar or contoured.
  • Common physical supports include glass slides, silicon, microwells, nitrocellulose or PVDF membranes, and magnetic and other microbeads.
  • Particles in suspension can also be used as the basis of arrays, providing they are coded for identification; systems include color coding for microbeads (e.g., available from Luminex, Bio-Rad and Nanomics Biosystems) and semiconductor nanocrystals (e.g., QDotsTM, available from Quantum Dots), and barcoding for beads (UltraPlexTM, available from Smartbeads) and multimetal microrods (NanobarcodesTM particles, available from Surromed). Beads can also be assembled into planar arrays on semiconductor chips (e.g., available from LEAPS technology and BioArray Solutions).
  • color coding for microbeads e.g., available from Luminex, Bio-Rad and Nanomics Biosystems
  • semiconductor nanocrystals e.g., QDotsTM, available from Quantum Dots
  • barcoding for beads UltraPlexTM, available from Smartbeads
  • NanobarcodesTM particles available
  • individual protein-capture agents are typically attached to an individual particle to provide the spatial definition or separation of the array.
  • the particles may then be assayed separately, but in parallel, in a compartmentalized way, for example in the wells of a microtiter plate or in separate test tubes.
  • a protein sample which is optionally fragmented to form peptide fragments (see, e.g., U.S. Pat. App. Pub. 2002/0055186), is delivered to a protein-capture array under conditions suitable for protein or peptide binding, and the array is washed to remove unbound or non-specifically bound components of the sample from the array.
  • the presence or amount of protein or peptide bound to each feature of the array is detected using a suitable detection system.
  • the amount of protein bound to a feature of the array may be determined relative to the amount of a second protein bound to a second feature of the array. In certain embodiments, the amount of the second protein in the sample is already known or known to be invariant.
  • the joint inflammation biomarker is a target polypeptide whose level is measured using at least one antigen-binding molecule that is immuno- interactive with the target polypeptide.
  • the measured level of the target polypeptide is normalized to the level of a reference polypeptide.
  • the antigen-binding molecule is immobilized on a solid or semi-solid support.
  • the antigen-binding molecule forms part of a spatial array of antigen-binding molecule.
  • the level of antigen-binding molecule that is bound to the target polypeptide is measured by immunoassay (e.g., using an ELISA).
  • joint inflammation biomarkers have strong discrimination performance when combined with one or more other joint inflammation biomarkers.
  • specific combinations of joint inflammation biomarkers have been identified that can be used to determine the indicator. Accordingly, in representative examples of this type, an indicator is determined that correlates to a combination of joint inflammation biomarkers, which can be used in assessing a likelihood that infectious joint inflammation or non-infectious joint inflammation is present in a subject.
  • the indicator-determining methods suitably include determining biomarker values for a plurality of biomarkers, wherein each biomarker value is a value measured for at least one corresponding joint inflammation biomarker of the subject and is indicative of a concentration of the joint inflammation biomarker in a sample obtained from the subject.
  • the biomarker values are typically used to determine a combined biomarker value (also referred to herein as a "composite score") on which at least in part an indicator for assessing a likelihood that a type of inflammation is present or absent in a joint of a subject is determined.
  • biomarker values are determined for a first joint inflammation biomarker and a second joint inflammation biomarker, wherein the first biomarker is selected from a first set of biomarkers that are expressed at a higher level in infectious inflammation than in non-infectious inflammation, and wherein the second biomarker is selected from a second set of biomarkers that are expressed at a lower level in infectious inflammation than in non-infectious inflammation, and/or from a third set of biomarkers that improve the discrimination performance of the first biomarker, wherein the first set of biomarkers comprises, consists or consists essentially of AQP9, C5orfl5, CANX, CDKN1A, CISH, CLIC4, CSF2RB, CSF3R, CXCL8, DTNBP1, DUSP1, DUSP5, ERP44, ETV6, FCGR3B, FFAR2, FPR1, FYB1, GADD45B, GBP1, GRINA, H3-3B, HCK, HLA-E, I
  • the first and second biomarkers are selected from TABLE A:
  • biomarker values are determined for a first joint inflammation biomarker, a second joint inflammation biomarker, a third joint inflammation biomarker and optionally a fourth joint inflammation biomarker, wherein the first and second biomarkers are selected from a first set of biomarkers that are expressed at a higher level in infectious inflammation than in non-infectious inflammation, and wherein the third and optional fourth biomarkers are selected from a second set of biomarkers that are expressed at a lower level in infectious inflammation than in non-infectious inflammation, and/or a third set of biomarkers that improve the discrimination performance of the first and/or second biomarkers, wherein the first set of biomarkers comprises, consists or consists essentially of AQP9, C5orfl5, CANX, CDKN 1A, CISH, CLIC4, CSF2RB, CSF3R, CXCL8, DTNBP1, DUSP1, DUSP5, ERP44, ETV6, FCGR3B, FFAR2, FPR1, FY
  • the first and second biomarkers, and one or both of the third and fourth biomarkers are selected from TABLE B:
  • biomarker values are determined for a first joint inflammation biomarker, a second joint inflammation biomarker, a third joint inflammation biomarker, optionally a fourth joint inflammation biomarker, a fifth joint inflammation biomarker, a sixth joint inflammation biomarker and optionally one or both of a seventh joint inflammation biomarker and an eighth joint inflammation biomarker, wherein the first biomarker, second biomarker, third biomarker and optional fourth biomarker are selected from a first set of biomarkers that are expressed at a higher level in infectious inflammation than in non-infectious inflammation, and wherein the fifth biomarker, sixth biomarker and optional seventh and eighth biomarkers are selected from a second set of biomarkers that are expressed at a lower level in infectious inflammation than in non-infectious inflammation, and/or from a third set of biomarkers that improve the discrimination performance of the first biomarker, second biomarker, third biomarker and optional fourth biomarker, wherein the first set of biomarkers comprises,
  • CSF2RB may be substituted with ETV6, FFAR2, FYB1, HCK, HLA-E, IRF2, LILRB3, PDE4B, SEMA4D, STX11 or TNFAIP2;
  • DUSP5 may be substituted with CDKN1A, CISH or MLLT6;
  • NUP58 may be substituted with CXCL8;
  • MXD1 may be substituted with AQP9, CSF3R, DUSP1, FCGR3B, FPR1, H3-3B, LYN, MCL1 or NAMPT;
  • NFKBIA may be substituted with GADD45B, GRINA, NINJ1, PI3, PIK3AP1, PLAUR, PLEK or TNFAIP3;
  • PLEC may be substituted EMP1 or LMNA
  • PPIF may be substituted with IER3, IL1B, IL1RN, OSM or ZFP36.
  • the detection methods disclosed herein may further comprise applying a function to biomarker values to yield at least one functionalized biomarker value and determining the indicator using the at least one functionalized biomarker value.
  • the function may include at least one of: (a) multiplying biomarker values; (b) dividing biomarker values; (c) adding biomarker values; (d) subtracting biomarker values; (e) a weighted sum of biomarker values; (f) a log sum of biomarker values; (g) a geometric mean of biomarker values; and (h) a sigmoidal function of biomarker values.
  • the detection methods may further comprise combining the biomarker values to provide a composite score and determining the indicator using the composite score.
  • Biomarker values may be combined by a combining function including, but not limited to, adding, multiplying, subtracting, and/or dividing biomarker values. Biomarker values may be combined by applying the combining function to individual biomarker values of different biomarkers. Alternatively, biomarker values may be combined by measuring a composite level of two or more (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or more) biomarkers.
  • a first label e.g., first fluorophore
  • a second label e.g., second fluorophore
  • Measurement of the first label thus provides a combined biomarker value for the first biomarker subset and measurement of the second label thus provides a combined biomarker value for the second biomarker subset.
  • the function is a division and one member of a pair of biomarker values is divided by the other member of the pair to provide a ratio of levels of a pair of joint inflammation biomarkers.
  • the biomarker values denote the levels of a pair of joint inflammation biomarkers
  • the functionalized biomarker value will be based on a ratio of the biomarker values.
  • the biomarker values represent amplification amounts, or cycle times (e.g., PCR cycle times), which are a logarithmic representation of the level of the joint inflammation biomarkers in a sample
  • the biomarker values may be combined in some other manner, such as by subtracting the cycle times to determine a functionalized biomarker value indicative of a ratio of the levels of the joint inflammation biomarkers.
  • the detection method may comprise subtracting the biomarker value for the second biomarker from the biomarker value for the first biomarker to provide a composite score, on which at least in part the indicator is determined.
  • the detection methods may further comprise adding the biomarker values for the first biomarker and the second biomarker to provide a first summed biomarker value, adding the biomarker values for the third biomarker and fourth biomarker, if present, to provide a second summed biomarker value, and subtracting the second summed biomarker value from the first summed biomarker value to provide a composite score, on which at least in part the indicator is determined.
  • the detection method may further comprise adding the biomarker values for the first biomarker, second biomarker, third biomarker and optional fourth biomarker, if present, to provide a first summed biomarker value, adding the biomarker values for the fifth biomarker, sixth biomarker and optional seventh and eighth biomarkers, if present, to provide a second summed biomarker value, subtracting the second summed biomarker value from the first summed biomarker value to provide a composite score, on which at least in part the indicator is determined.
  • the addition of the biomarker values that yields the first summed biomarker value comprises twice adding the biomarker value for one or more of the first biomarker, second biomarker, third biomarker and optional fourth biomarker, which preferably has the strongest discrimination performance.
  • the composite score is determined using one of the following formulas:
  • the detection methods may further comprise analyzing the biomarker value(s) or composite score with reference to corresponding reference biomarker value ranges or threshold values, or composite score ranges or threshold values, to determine the indicator.
  • the indicator generally indicates a likelihood of a presence of infectious inflammation if the biomarker value(s) or composite score is indicative of the level of the biomarker(s) in the sample that correlates with an increased likelihood of a presence of infectious inflammation relative to a predetermined reference biomarker value range or cut-off value.
  • the indicator generally indicates a likelihood of the presence of non-infectious inflammation if the biomarker value(s) or composite score is indicative of the level of the biomarker(s) in the sample that correlates with an increased likelihood of the presence of non-infectious inflammation relative to a predetermined reference biomarker value range or cut-off value.
  • a composite score is aggregated with one or more clinical parameters to a composite score on which the indicator is determined.
  • Biomarker data may be analyzed by a variety of methods to identify biomarkers and determine the statistical significance of differences in observed levels of biomarkers between test and reference expression profiles in order to evaluate whether a patient has infectious joint inflammation or inflammation arising from a non-infectious source, such as traumatic injury, surgery (e.g., joint repair or joint replacement), autoimmune disease (e.g., rheumatoid arthritis, psoriatic arthritis and lupus-related arthritis), osteoarthritis, gouty arthritis, or related to painful local tissues affected by bursitis, tenosynovitis, epicondylitis, synovitis and/or other disorders.
  • a non-infectious source such as traumatic injury, surgery (e.g., joint repair or joint replacement), autoimmune disease (e.g., rheumatoid arthritis, psoriatic arthritis and lupus-related arthritis), osteoarthritis, gouty arthritis, or related to painful local tissues affected by bursitis,
  • joint inflammation biomarker For any particular joint inflammation biomarker, a distribution of joint inflammation biomarker levels for subjects with infectious joint inflammation or non-infectious joint inflammation will likely overlap. Under such conditions, a test does not absolutely distinguish a first condition (e.g., infectious joint inflammation) and a second condition (e.g., non-infectious joint inflammation) with 100% accuracy, and the area of overlap indicates where the test cannot distinguish the first condition and the second condition.
  • a first condition e.g., infectious joint inflammation
  • second condition e.g., non-infectious joint inflammation
  • a threshold is selected, above which (or below which, depending on how a joint inflammation biomarker changes with a specified condition or prognosis) the test is considered to be “positive” and below which the test is considered to be “negative.”
  • the area under the ROC curve (AUC) provides the C-statistic, which 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)).
  • thresholds may be established by obtaining an earlier biomarker result from the same patient, to which later results may be compared.
  • the individual in effect acts as their own "control group.”
  • biomarkers 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 of the condition or success of a treatment regimen.
  • a positive likelihood ratio, negative likelihood ratio, odds ratio, and/or AUC or receiver operating characteristic (ROC) values are used as a measure of a method's ability to predict risk or to diagnose a disease or condition (e.g., infectious joint inflammation or non-infectious joint inflammation).
  • a disease or condition e.g., infectious joint inflammation or non-infectious joint inflammation
  • the term "likelihood ratio" is the probability that a given test result would be observed in a subject with a condition of interest divided by the probability that that same result would be observed in a patient without the condition of interest.
  • a positive likelihood ratio is the probability of a positive result observed in subjects with the specified condition (e.g., infectious joint inflammation or non-infectious joint inflammation) divided by the probability of a positive results in subjects without the specified condition.
  • a negative likelihood ratio is the probability of a negative result in subjects without the specified condition divided by the probability of a negative result in subjects with specified condition.
  • the term "odds ratio,” as used herein, refers to the ratio of the odds of an event occurring in one group (e.g., infectious joint inflammation) to the odds of it occurring in another group (e.g., non-infectious joint inflammation), or to a data-based estimate of that ratio.
  • the term "area under the curve” or "AUC” refers to the area under the curve of a receiver operating characteristic (ROC) curve, both of which are well known in the art. AUC measures are useful for comparing the accuracy of a classifier across the complete data range.
  • Classifiers with a greater AUC have a greater capacity to classify unknowns correctly between two groups of interest (e.g., infectious joint inflammation and non-infectious joint inflammation).
  • ROC curves are useful for plotting the performance of a particular feature (e.g., any of the joint inflammation biomarkers disclosed herein and/or any item of additional biomedical information) in distinguishing or discriminating between two populations (e.g., infectious joint inflammation and non-infectious joint inflammation).
  • the feature data across the entire population e.g., subjects with infectious joint inflammation and subjects with non-infectious joint inflammation
  • the true positive and false positive rates for the data are calculated.
  • the sensitivity is determined by counting the number of cases above the value for that feature and then dividing by the total number of cases.
  • the specificity is determined by counting the number of controls below the value for that feature and then dividing by the total number of controls.
  • ROC curves can be generated for a single feature as well as for other single outputs, for example, a combination of two or more features (e.g., a combination of two or more biomarker values) can be mathematically combined (e.g., added, subtracted, multiplied, etc.) to produce a single value, and this single value can be plotted in a ROC curve. Additionally, any combination of multiple features (e.g., a combination of multiple biomarker values), in which the combination derives a single output value, can be plotted in a ROC curve. These combinations of features may comprise a test.
  • the ROC curve is the plot of the sensitivity of a test against the specificity of the test, where sensitivity is traditionally presented on the vertical axis and specificity is traditionally presented on the horizontal axis.
  • AUC ROC values are equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one.
  • An AUC ROC value may be thought of as equivalent to the Mann-Whitney U test, which tests for the median difference between scores obtained in the two groups considered if the groups are of continuous data, or to the Wilcoxon test of ranks.
  • At least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) joint inflammation biomarker or a panel of joint inflammation biomarkers is selected to discriminate between subjects with a first condition (e.g., infectious joint inflammation) and subjects with a second condition (e.g., non-infectious joint inflammation) with at least about 50%, 55% 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% accuracy or having a C-statistic of at least about 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95.
  • a first condition e.g., infectious joint inflammation
  • a second condition e.g., non-infectious joint inflammation
  • first condition indicates that a positive result is equally likely among subjects in both the "first condition” and “second condition” groups; a value greater than 1 indicates that a positive result is more likely in the first condition group; and a value less than 1 indicates that a positive result is more likely in the second condition group.
  • first condition is meant to refer to a group having one characteristic (e.g., the presence of infectious inflammation) and "second condition” group lacking the same characteristic.
  • a value of 1 indicates that a negative result is equally likely among subjects in both the "first condition” and “second condition” groups; a value greater than 1 indicates that a negative result is more likely in the "first condition” group; and a value less than 1 indicates that a negative result is more likely in the "second condition” group.
  • an odds ratio a value of 1 indicates that a positive result is equally likely among subjects in both the "first condition” and “second condition” groups; a value greater than 1 indicates that a positive result is more likely in the "first condition” group; and a value less than 1 indicates that a positive result is more likely in the "second condition” group.
  • AUC ROC value this is computed by numerical integration of the ROC curve.
  • the range of this value can be 0.5 to 1.0.
  • a value of 0.5 indicates that a classifier (e.g., a joint inflammation biomarker profile) is no better than a 50% chance to classify unknowns correctly between two groups of interest (e.g., infectious joint inflammation and non-infectious joint inflammation), while 1.0 indicates the relatively best diagnostic accuracy.
  • individual joint inflammation biomarkers and/or joint inflammation 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.
  • individual joint inflammation biomarkers and/or joint inflammation 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.
  • individual joint inflammation biomarkers and/or joint inflammation biomarker panels are selected to exhibit an AUC ROC value of greater than 0.5, preferably at least 0.6, more preferably 0.7, still more preferably at least 0.8, even more preferably at least 0.9, and most preferably at least 0.95.
  • thresholds may be determined in so-called “tertile,” “quartile,” or “quintile” analyses.
  • the “diseased” and “control 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.
  • particular thresholds for the joint inflammation 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.
  • a temporal change in the biomarker(s) can be used to rule in or out one or more particular diagnoses and/or prognoses.
  • joint inflammation biomarker(s) may be correlated to a condition, disease, prognosis, etc., by the presence or absence of one or more joint inflammation biomarkers in a particular assay format.
  • the detection methods disclosed herein may utilize an evaluation of the entire population or subset of joint inflammation biomarkers disclosed herein to provide a single result value (e.g., a "panel response" value expressed either as a numeric score or as a percentage risk).
  • a single result value e.g., a "panel response" value expressed either as a numeric score or as a percentage risk.
  • an increase, decrease, or other change (e.g., slope over time) in a certain subset of joint inflammation 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 joint inflammation biomarkers may be sufficient to indicate the same or a different condition or outcome in another patient.
  • a panel of joint inflammation biomarkers is selected to assist in distinguishing a pair of groups (/.e. , assist in assessing whether a subject has an increased likelihood of being in one group or the other group of the pair) selected from "infectious joint inflammation" and "non-infectious joint inflammation” 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%.
  • assessing the likelihood and “determining the likelihood,” as used herein, refer to methods by which the skilled artisan can predict the presence or absence of a condition (e.g., infectious joint inflammation or non-infectious joint inflammation) in a patient.
  • a condition e.g., infectious joint inflammation or non-infectious joint inflammation
  • this phrase includes within its scope an increased probability that a condition is present or absent in a patient; that is, that a condition is more likely to be present or absent in a subject.
  • the probability that an individual identified as having a specified condition actually has the condition may be expressed as a "positive predictive value" or "PPV.”
  • Positive predictive value can be calculated as the number of true positives divided by the sum of the true positives and false positives.
  • PPV is determined by the characteristics of the predictive methods disclosed herein as well as the prevalence of the condition in the population analyzed.
  • the statistical algorithms can be selected such that the positive predictive value in a population having a condition prevalence is in the range of 70% to 99% and can be, for example, at least 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.
  • Negative predictive value can be calculated as the number of true negatives divided by the sum of the true negatives and false negatives. Negative predictive value is determined by the characteristics of the diagnostic or prognostic method, system, or code as well as the prevalence of the disease in the population analyzed.
  • the statistical methods and models can be selected such that the negative predictive value in a population having a condition prevalence is in the range of about 70% to about 99% and can be, for example, at least about 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.
  • a subject is determined as having a significant likelihood of having or not having a specified condition (e.g., infectious joint inflammation or non-infectious joint inflammation).
  • a specified condition e.g., infectious joint inflammation or non-infectious joint inflammation.
  • significant likelihood is meant that the subject has a reasonable probability (0.6, 0.7, 0.8, 0.9 or more) of having, or not having, a specified condition.
  • the joint inflammation biomarker analysis disclosed herein permits the generation of high-density data sets that can be evaluated using informatics approaches.
  • High data density informatics analytical methods are known and software is available to those in the art, e.g., cluster analysis (Pirouette, Informetrix), class prediction (SIMCA-P, Umetrics), principal components analysis of a computationally modeled dataset (SIMCA-P, Umetrics), 2D cluster analysis (GeneLinker Platinum, Improved Outcomes Software), and metabolic pathway analysis (biotech.icmb.utexas.edu).
  • the choice of software packages offers specific tools for questions of interest (Kennedy et al., Solving Data Mining Problems Through Pattern Recognition.
  • any suitable mathematic analyses can be used to evaluate at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, etc.) joint inflammation biomarker in a joint inflammation biomarker population disclosed herein with respect to a condition selected from infectious joint inflammation and non-infectious joint inflammation.
  • methods such as multivariate analysis of variance, multivariate regression, and/or multiple regression can be used to determine relationships between dependent variables (e.g., clinical measures) and independent variables (e.g., levels of joint inflammation biomarkers).
  • Clustering including both hierarchical and non-hierarchical methods, as well as nonmetric Dimensional Scaling can be used to determine associations or relationships among variables and among changes in those variables.
  • principal component analysis is a common way of reducing the dimension of studies, and can be used to interpret the variance-covariance structure of a data set.
  • Principal components may be used in such applications as multiple regression and cluster analysis.
  • Factor analysis is used to describe the covariance by constructing "hidden" variables from the observed variables.
  • Factor analysis may be considered an extension of principal component analysis, where principal component analysis is used as parameter estimation along with the maximum likelihood method.
  • simple hypothesis such as equality of two vectors of means can be tested using Hotelling's T squared statistic.
  • the data sets corresponding to joint inflammation biomarker panels are used to create a diagnostic or predictive rule or model based on the application of a statistical and machine learning algorithm.
  • a statistical and machine learning algorithm uses relationships between a joint inflammation biomarker panel and a condition selected from infectious joint inflammation and non-infectious joint inflammation observed in control subjects or typically cohorts of control subjects (sometimes referred to as training data), which provides combined control or reference joint inflammation biomarker panels for comparison with joint inflammation biomarker panels of a subject.
  • the data are used to infer relationships that are then used to predict the status of a subject, including the presence or absence of one of the conditions referred to above.
  • biomarker tables disclosed herein provide illustrative lists of joint inflammation biomarkers ranked according to their p value.
  • Illustrative models comprising at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8 joint inflammation biomarkers were able to develop a classifier or generative algorithm for discriminating between two control groups as defined above with significantly improved positive predictive values compared to conventional methodologies.
  • This algorithm can be advantageously applied to determine presence or probability of infectious joint inflammation or non-infectious joint inflammation in a patient, and thus diagnose the patient as having or as likely to have joint inflammation or non-infectious joint inflammation.
  • evaluation of joint inflammation biomarkers includes determining the levels of individual joint inflammation biomarkers, which correlate with the presence or absence of a condition, as defined above.
  • the techniques used for detection of joint inflammation biomarkers may include internal or external standards to permit quantitative or semi-quantitative determination of those biomarkers, to thereby enable a valid comparison of the level of the joint inflammation biomarkers in a biological sample with the corresponding joint inflammation biomarkers in a reference sample or samples.
  • Such standards can be determined by the skilled practitioner using standard protocols.
  • absolute values for the level or functional activity of individual expression products are determined.
  • a threshold or cut-off value is suitably determined, and is optionally a predetermined value.
  • the threshold value is predetermined in the sense that it is fixed, for example, based on previous experience with the assay and/or a population of affected and/or unaffected subjects.
  • the predetermined value can also indicate that the method of arriving at the threshold is predetermined or fixed even if the particular value varies among assays or may even be determined for every assay run.
  • the level of a joint inflammation biomarker is normalized against a housekeeping biomarker.
  • the term "housekeeping biomarker” refers to a biomarker or group of biomarkers (e.g., polynucleotides and/or polypeptides), which are typically found at a constant level in the cell type(s) being analyzed and across the conditions being assessed.
  • the housekeeping biomarker is a "housekeeping gene.”
  • a "housekeeping gene” refers herein to a gene or group of genes which encode proteins whose activities are essential for the maintenance of cell function and which are typically found at a constant level in the cell type(s) being analyzed and across the conditions being assessed.
  • the biomarkers are measured and those resulting values normalized and then summed to obtain a composite score.
  • normalizing the measured biomarker values comprises determining the multiple of median (MoM) score.
  • the present method further comprises weighting the normalized values before summing to obtain a composite score.
  • a machine learning system may be utilized to determine weighting of the normalized values as well as how to aggregate the values (e.g., determine which biomarkers are most predictive, and assign a greater weight to these markers).
  • composite scores include one or more clinical parameters of the patient.
  • Representative clinical parameters include joint pain, joint stiffness, tenderness, swelling, warmth, patient global health assessment, cell counts (e.g., white blood cell counts for example in serum and/or in synovial fluid), erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) levels.
  • cell counts e.g., white blood cell counts for example in serum and/or in synovial fluid
  • ESR erythrocyte sedimentation rate
  • CRP C-reactive protein
  • the detection methods utilize a risk categorization table to generate a risk score for a patient based on a composite score by comparing the composite score with a reference set derived from a cohort of patients with infectious joint inflammation and/or from a cohort of patients with non-infectious joint inflammation.
  • the detection methods may further comprise quantifying the increased risk for the presence of infectious joint inflammation or for the presence of non-infectious joint inflammation for the subject as a risk score, wherein the composite score (combined obtained biomarker value and optionally obtained clinical parameter values) is matched to a risk category of a grouping of stratified subject populations wherein each risk category comprises a multiplier (or percentage) indicating an increased likelihood of having infectious joint inflammation or non-infectious joint inflammation correlated to a range of composite scores.
  • This quantification is based on the pre-determined grouping of a stratified cohort of subjects.
  • the grouping of a stratified population of subjects, or stratification of a disease cohort is in the form of a risk categorization table.
  • the selection of the disease cohort is well understood by those skilled in the art of joint inflammation research.
  • the skilled person would also recognize that the resulting stratification, may be more multidimensional and take into account further environmental, occupational, genetic, or biological factors (e.g., epidemiological factors).
  • this score may be provided in a form amenable to understanding by a physician.
  • the risk score is provided in a report.
  • the report may comprise one or more of the following: patient information, a risk categorization table, a risk score relative to a cohort population, one or more biomarker test scores, a biomarker composite score, a master composite score, identification of the risk category for the patient, an explanation of the risk categorization table, and the resulting test score, a list of biomarkers tested, a description of the disease cohort, environmental and/or occupational factors, cohort size, biomarker velocity, genetic mutations, family history, margin of error, and so on.
  • kits comprising a reagent that permits quantification of at least one joint inflammation biomarker or each joint inflammation biomarker of a biomarker panel disclosed herein.
  • kit is understood to mean a product containing the different reagents necessary for carrying out the methods of the disclosure packed so as to allow their transport and storage. Additionally, the kits of the present disclosure can contain instructions for the simultaneous, sequential or separate use of the different components contained in the kit.
  • the instructions can be in the form of printed material or in the form of an electronic support capable of storing instructions such that they can be read by a subject, such as electronic storage media (magnetic disks, tapes and the like), optical media (CD-ROM, DVD) and the like.
  • the media can contain internet addresses that provide the instructions.
  • the kits may contain software for interpreting assay data to determine the likelihood of the presence or absence of infectious joint inflammation or non-infectious joint inflammation, and/or for ruling out infectious joint inflammation.
  • the kits may provide a means to access a machine learning system provided, for example, as a software as a service (SaaS) deployment.
  • SaaS software as a service
  • Reagents that allow quantification of a joint inflammation biomarker include compounds or materials, or sets of compounds or materials, which allow quantification of the joint inflammation biomarker.
  • the compounds, materials or sets of compounds or materials permit determining the expression level of a gene (e.g., joint inflammation biomarker gene) include without limitation the extraction of RNA material, the determination of the level of a corresponding RNA, etc., primers for the synthesis of a corresponding cDNA, primers for amplification of DNA, and/or probes capable of specifically hybridizing with the RNAs (or the corresponding cDNAs) encoded by the genes, TaqManTM probes, etc.
  • Kit reagents can be in liquid form or can be lyophilized. Suitable containers for the reagents include, for example, bottles, vials, syringes, and test tubes. Containers can be formed from a variety of materials, including glass or plastic. The kit can also comprise a package insert containing written instructions for methods of diagnosing infectious joint inflammation and non-infectious joint inflammation.
  • kits may also optionally include appropriate reagents for detection of labels, positive and negative controls, washing solutions, blotting membranes, microtiter plates, dilution buffers and the like.
  • a nucleic acid-based detection kit may include (i) a joint inflammation biomarker polynucleotide (which may be used as a positive control), (ii) a primer or probe that specifically hybridizes to a joint inflammation biomarker polynucleotide.
  • enzymes suitable for amplifying nucleic acids including various polymerases (reverse transcriptase, Taq polymerase, SequenaseTM, DNA ligase etc.
  • kits also generally will comprise, in suitable means, distinct containers for each individual reagent and enzyme as well as for each primer or probe.
  • a protein-based detection kit may include (i) a joint inflammation biomarker polypeptide (which may be used as a positive control), (ii) an antibody that binds specifically to a joint inflammation biomarker polypeptide.
  • the kit can also feature various devices (e.g., one or more) and reagents (e.g., one or more) for performing one of the assays described herein; and/or printed instructions for using the kit to quantify the expression of a joint inflammation biomarker gene and/or carry out an indicator-determining method, as broadly described above and elsewhere herein.
  • various devices e.g., one or more
  • reagents e.g., one or more
  • reagents described herein which may be optionally associated with detectable labels, can be presented in the format of a microfluidics card, a reaction vessel, a microarray or a kit adapted for use with the assays described in the examples or below, e.g., RT- PCR or Q PCR techniques described herein.
  • the reagents also have utility in compositions for detecting and quantifying the biomarkers of the present disclosure.
  • a reverse transcriptase may be used to reverse transcribe RNA transcripts, including mRNA, in a nucleic acid sample, to produce reverse transcribed transcripts, including reverse transcribed mRNA (also referred to as "cDNA").
  • the reverse transcribed mRNA is whole cell reverse transcribed mRNA (also referred to herein as "whole cell cDNA").
  • the nucleic acid sample is suitably derived from a sample disclosed herein.
  • the reagents are suitably used to quantify the reverse transcribed transcripts.
  • oligonucleotide primers that hybridize to the reverse transcribed transcript can be used to amplify at least a portion of the reverse transcribed transcript via a suitable nucleic acid amplification technique, e.g., RT-PCR or qPCR techniques described herein.
  • oligonucleotide probes may be used to hybridize to the reverse transcribed transcript for the quantification, using a nucleic acid hybridization analysis technique (e.g., microarray analysis), as described for example above.
  • a respective oligonucleotide primer or probe is hybridized to a complementary nucleic acid sequence of a reverse transcribed transcript in the compositions of the present disclosure.
  • compositions typically comprise labeled reagents for detecting and/or quantifying the reverse transcribed transcripts.
  • Representative reagents of this type include labeled oligonucleotide primers or probes that hybridize to RNA transcripts or reverse transcribed RNA, labeled RNA, labeled reverse transcribed RNA as well as labeled oligonucleotide linkers or tags (e.g., a labeled RNA or DNA linker or tag) for labeling (e.g., end labeling such as 3' end labeling) RNA or reverse transcribed RNA.
  • the primers, probes, RNA or reverse transcribed RNA may be immobilized or free in solution.
  • Representative reagents of this type include labeled oligonucleotide primers or probes that hybridize to reverse transcribed and transcripts as well as labeled reverse transcribed transcripts.
  • the label can be any reporter molecule as known in the art, illustrative examples of which are described above and elsewhere herein.
  • kits disclosed herein al encompasses non-reverse transcribed RNA embodiments in which cDNA is not made and the RNA transcripts are directly the subject of the analysis.
  • reagents are suitably used to quantify RNA transcripts directly.
  • oligonucleotide probes can be used to hybridize to transcripts for quantification of immune system biomarkers of the invention, using a nucleic acid hybridization analysis technique (e.g., microarray analysis), as described for example above.
  • a respective oligonucleotide probe is hybridized to a complementary nucleic acid sequence of joint inflammation biomarker transcript in the disclosed compositions.
  • compositions may comprise labeled reagents that hybridize to transcripts for detecting and/or quantifying the transcripts.
  • Representative reagents of this type include labeled oligonucleotide probes that hybridize to transcripts as well as labeled transcripts.
  • the primers or probes may be immobilized or free in solution.
  • kits have a number of applications.
  • the kits can be used to determine if a subject has infectious joint inflammation or joint inflammation arising from a non-infectious source, such as traumatic injury, surgery, autoimmune disease, etc.
  • the kits can be used to determine if a patient should be treated for infectious joint inflammation, for example, with broad spectrum antibiotics, or treated for non-infectious joint inflammation using for example a corticosteroid or non-steroidal anti-inflammatories.
  • kits can be used to monitor the effectiveness of treatment of a patient infectious joint inflammation or non-infectious joint inflammation.
  • the kits can be used to identify compounds that modulate expression of one or more of the joint inflammation biomarkers in in vitro or in vivo animal models to determine the effects of treatment. 4.
  • a subject positively identified as having infectious joint inflammation may be exposed to an anti-microbial agent such as but not limited to an anti-bacterial agent, an anti-viral agent, an anti-fungal/anti-yeast agent and an antiprotozoal agent, illustrative examples of which include:
  • Anti-bacterial agents Amikacin, Gentamicin, Kanamycin, Neomycin, Netilmicin, Tobramycin, Paromomycin, Streptomycin, Spectinomycin, Geldanamycin, Herbimycin, Rifaximin, Loracarbef, Ertapenem, Doripenem, Imipenem/Cilastatin, Meropenem, Cefadroxil, Cefazolin, Cefalotin or Cefalothin, Cefalexin, Cefaclor, Cefamandole, Cefoxitin, Cefprozil, Cefuroxime, Cefixime , Cefdinir, Cefditoren, Cefoperazone , Cefotaxime, Cefpodoxime, Ceftazidime , Ceftibuten, Ceftizoxime, Ceftriaxone , Cefepime, Ceftaroline fosamil, Ceftobiprole, Teicoplanin, Van
  • Anti-viral agents asunaprevir, acyclovir, acyclovir, adefovir, amantadine, amprenavir, ampligen, arbidol, atazanavir, atripla, bacavir, boceprevir, cidofovir, combivir, complera, daclatasvir, darunavir, delavirdine, didanosine, docosanol, dolutegravir, edoxudine, efavirenz, emtricitabine, enfuvirtide, entecavir, famciclovir, fomivirsen, fosamprenavir, foscarnet, fosfonet, ganciclovir, ibacitabine, imunovir, idoxuridine, imiquimod, indinavir, inosine, interferon type III, interferon type II, interferon type I, lamivudine,
  • Anti-fungal agent/anti yeast agents imidazoles and triazoles, polyene macrolide antibiotics, griseofulvin, amphotericin B, and flucytosine.
  • Antiparasites include heavy metals, antimalarial quinolines, folate antagonists, nitroimidazoles, benzimidazoles, avermectins, praxiquantel, ornithine decarboxylase inhbitors, phenols (e.g., bithionol, niclosamide); synthetic alkaloid (e.g., dehydroemetine); piperazines (e.g., diethylcarbamazine); acetanilide (e.g., diloxanide furonate); halogenated quinolines (e.g., iodoquinol (diiodohydroxyquin)); nitrofurans (e.g., nifurtimox); diamidines (e.g., pent
  • Anti-protozoal agents Eflornithine, Furazolidone, Melarsoprol, Metronidazole, Ornidazole, Paromomycin sulfate, Pentamidine, Pyrimethamine, Tinidazole.
  • anti-infective agents may be without limitation Difloxacin Hydrochloride; Lauryl Isoquinolinium Bromide; Moxalactam Disodium; Ornidazole; Pentisomicin; Sarafloxacin Hydrochloride; Protease inhibitors of HIV and other retroviruses; Integrase Inhibitors of HIV and other retroviruses; Cefaclor (Ceclor); Acyclovir (Zovirax); Norfloxacin (Noroxin); Cefoxitin (Mefoxin); Cefuroxime axetil (Ceftin); Ciprofloxacin (Cipro); Aminacrine Hydrochloride; Benzethonium Chloride: Bithionolate Sodium; Bromchlorenone; Carbamide Peroxide; Cetalkonium Chloride; Cetylpyridinium Chloride : Chlorhexidine Hydrochloride; Clioquinol; Domiphen Bromide; Fenticlor;
  • a subject positively identified as having non-infectious joint inflammation may be exposed to vasoactive compounds, steroids, non-steroidal antiinflammatories, anti-tumor necrosis factor agents, recombinant protein C and combinations thereof.
  • vasoactive compounds such as steroids, non-steroidal antiinflammatories, anti-tumor necrosis factor agents, recombinant protein C and combinations thereof.
  • anti-microbial agents such as antibiotics.
  • the therapeutic agents will be administered in pharmaceutical (or veterinary) compositions together with a pharmaceutically acceptable carrier and in an effective amount to achieve their intended purpose.
  • the dose of active compounds administered to a subject should be sufficient to achieve a beneficial response in the subject over time such as a reduction in, or relief from, the symptoms of the type of joint inflammation.
  • the quantity of the pharmaceutically active compounds(s) to be administered may depend on the subject to be treated inclusive of the age, sex, weight and general health condition thereof. In this regard, precise amounts of the active compound(s) for administration will depend on the judgment of the practitioner.
  • the medical practitioner or veterinarian may evaluate severity of any symptom or clinical sign associated with the presence of infectious or non-infectious joint inflammation or degree of infectious or non-infectious joint inflammation including, joint pain, joint stiffness, tenderness, swelling, warmth, patient global health assessment, cell counts (e.g., white blood cell counts for example in serum and/or in synovial fluid), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) levels, blood pressure anomaly, tachycardia, tachypnea fever, chills, vomiting, diarrhea, skin rash, headaches, confusion, muscle aches, seizures.
  • cell counts e.g., white blood cell counts for example in serum and/or in synovial fluid
  • ESR erythrocyte sedimentation rate
  • CRP C-reactive protein
  • the therapeutic agents may be administered in concert with adjunctive (palliative) therapies to increase oxygen supply to major organs, increase blood flow to major organs and/or to reduce the inflammatory response.
  • adjunctive therapies include non-steroidal-anti-inflammatory drugs (NSAIDs), intravenous saline and oxygen.
  • the indicator-determining method of the invention is implemented using one or more processing devices.
  • the method that is implemented by the processing device(s) determines an indicator used in assessing a likelihood of a subject having a presence or absence infectious joint inflammation or non-infectious joint inflammation, wherein the method comprises: (1) determining a biomarker value for at least one joint inflammation biomarker (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more biomarkers) disclosed herein in a sample obtained from a site of inflammation associated with the joint; (2) determining the indicator using the biomarker value(s); (3) retrieving previously determined indicator references from a database, the indicator references being determined based on indicators determined from a reference population consisting of individuals diagnosed with infectious joint inflammation or non-infectious joint inflammation; (4) comparing the indicator to the indicator references to thereby determine a probability indicative of the subject having or not having infectious joint inflammation or non-infectious joint inflammation; and (5) generating a biomarker value for at least one joint inflammation biomarker (e.g., 1,
  • an apparatus for determining the likelihood of a subject having infectious joint inflammation or non-infectious joint inflammation.
  • the apparatus typically includes at least one electronic processing device that:
  • biomarker value for at least one joint inflammation biomarker (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more biomarkers) disclosed herein in a sample obtained from a site of inflammation associated with the joint; and
  • the apparatus may further include any one or more of:
  • at least one joint inflammation biomarker e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more biomarkers
  • (C) at least one processing device that: o (i) receives the biomarker value(s) from the measuring device; o (ii) determines an indicator that is indicative of the presence or absence of infectious joint inflammation or non-infectious joint inflammation using the biomarker values optionally in combination with one or more clinical parameters of the subject; o (iii) compares the indicator to at least one indicator reference; o (iv) determines a likelihood of the subject having or not having infectious joint inflammation or non-infectious joint inflammation using the results of the comparison; and o (v) generates a representation of the indicator and the likelihood for display to a user.
  • the apparatus comprises a processor configured to execute computer readable media instructions (e.g., a computer program or software application, e.g., a machine learning system, to receive the biomarker values from the evaluation of biomarkers in a sample and, in combination with other risk factors (e.g., medical history of the patient, publically available sources of information pertaining to a risk of developing infectious joint inflammation or non-infectious joint inflammation, etc.) may determine a master composite score and compare it to a grouping of stratified cohort population comprising multiple risk categories (e.g., a risk categorization table) and provide a risk score.
  • risk categories e.g., a risk categorization table
  • the apparatus can take any of a variety of forms, for example, a handheld device, a tablet, or any other type of computer or electronic device.
  • the apparatus may also comprise a processor configured to execute instructions (e.g., a computer software product, an application for a handheld device, a handheld device configured to perform the method, a world- wide-web (WWW) page or other cloud or network accessible location, or any computing device.
  • the apparatus may include a handheld device, a tablet, or any other type of computer or electronic device for accessing a machine learning system provided as a software as a service (SaaS) deployment.
  • SaaS software as a service
  • the correlation may be displayed as a graphical representation, which, in some embodiments, is stored in a database or memory, such as a random access memory, read-only memory, disk, virtual memory, etc.
  • a database or memory such as a random access memory, read-only memory, disk, virtual memory, etc.
  • Other suitable representations, or exemplifications known in the art may also be used.
  • the apparatus may further comprise a storage means for storing the correlation, an input means, and a display means for displaying the status of the subject in terms of the particular medical condition (e.g., infectious joint inflammation or non-infectious joint inflammation).
  • the storage means can be, for example, random access memory, read-only memory, a cache, a buffer, a disk, virtual memory, or a database.
  • the input means can be, for example, a keypad, a keyboard, stored data, a touch screen, a voice-activated system, a downloadable program, downloadable data, a digital interface, a hand-held device, or an infrared signal device.
  • the display means can be, for example, a computer monitor, a cathode ray tube (CRT), a digital screen, a light-emitting diode (LED), a liquid crystal display (LCD), an X-ray, a compressed digitized image, a video image, or a hand-held device.
  • the apparatus can further comprise or communicate with a database, wherein the database stores the correlation of factors and is accessible to the user.
  • the apparatus is a computing device, for example, in the form of a computer or hand-held device that includes a processing unit, memory, and storage.
  • the computing device can include, or have access to a computing environment that comprises a variety of computer-readable media, such as volatile memory and non-volatile memory, removable storage and/or non-removable storage.
  • Computer storage includes, for example, RAM, ROM, EPROM & EEPROM, flash memory or other memory technologies, CD ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other medium known in the art to be capable of storing computer-readable instructions.
  • the computing device can also include or have access to a computing environment that comprises input, output, and/or a communication connection.
  • the input can be one or several devices, such as a keyboard, mouse, touch screen, or stylus.
  • the output can also be one or several devices, such as a video display, a printer, an audio output device, a touch stimulation output device, or a screen reading output device.
  • the computing device can be configured to operate in a networked environment using a communication connection to connect to one or more remote computers.
  • the communication connection can be, for example, a Local Area Network (LAN), a Wide Area Network (WAN) or other networks and can operate over the cloud, a wired network, wireless radio frequency network, and/or an infrared network.
  • LAN Local Area Network
  • WAN Wide Area Network
  • RNA-seq libraries were prepared using an AmpliSeq kit (Illumina, San Diego, USA) which amplifies coding regions from about 20,000 genes.
  • the libraries were sequenced using an Illumina HiSeq instrument (Illumina, San Diego, USA) so that 8-10 million reads per sample were generated.
  • the FASTQ files were trimmed and then aligned to the human genome using STAR and the counts were summarized at the gene level, then normalized using EdgeR and Iog2 transformed.
  • the first analysis step was to focus the biomarker search on a subset of roughly 20,000 genes which were quantitated at the RNA expression level in the samples using RNAseq.
  • the signatures (defined below) have an NPV range of 0.75 to 0.90. If, instead, this same test were used in a cohort with 10% infection prevalence, then the NPV would consequently increase to be in the range of 0.93 to 1.0.
  • AUC Area Under the Curve
  • ROC Receiver Operator Characteristic
  • the columns “NPV 33", “NPV 50” and “NPV 66” show the NPV for individual first gene/second gene combinations at the 33, 50 and 66 percentile thresholds, respectively, for individual first gene/second gene combinations, in this cohort which has an infection prevalence of 48%.
  • the first mentioned gene (“numerator”) of a gene pair is divided by the second mentioned gene (“denominator”) of the gene pair to provide a ratio of gene expression levels, which provides a composite score for discriminating infectious joint inflammation from non-infectious joint inflammation.
  • composite scores for the gene signatures are calculated by subtracting gene expression value for an individual "-”gene from the gene expression value of a corresponding " + "gene.
  • the ratio with the maximum AUC is MXD1-MYO1F (see, Figure 7). These ratios are the building blocks for signatures with larger numbers of genes.
  • the column “Max AUC” shows the maximum of that metric across all signatures which contain that gene.
  • the column “NPV 50” shows the NPV at the 50 percentile threshold for just this gene (not for the best performing signature).
  • the column “ROC AUC” shows the AUC for just this gene (not for the best performing signature).
  • the column “Max AUC” shows the maximum of that metric across all signatures which contain that gene.
  • the column “NPV 50” shows the NPV at the 50 percentile threshold for just this gene (not for the best performing signature).
  • the column “ROC AUC” shows the AUC for just this gene (not for the best performing signature).
  • TABLE 5 shows the top 20 signatures with 3-4 genes sorted by AUC, in which the first and second genes (“numerator genes”) are expressed at a higher level in infectious joint inflammation than in non-infectious joint inflammation (denoted by a "+” signal), and the third gene and fourth gene (if present) (“denominator genes”) are expressed at a lower level in infectious inflammation than in non-infectious inflammation, or improve the discrimination performance of the first and/or second genes (denoted by a signal). Genes that improve the discrimination performance of numerator genes are indicated by an asterisk.
  • composite scores for the gene signatures are calculated by adding gene expression values for each "+”gene (/.e., “numerator gene”) and subtracting gene expression values for each "-"gene (/.e., “denominator gene”).
  • the column “AUC” shows the AUC for individual signatures.
  • the columns “NPV 33", “NPV 50” and “NPV 66” show the NPV for individual signatures at the 33, 50 and 66 percentile thresholds, respectively, in this cohort which has an infection prevalence of 48%.
  • The-4-gene signature with the highest AUC among the 3-4 gene signatures searched is presented in Figure 9.
  • TABLE 6 shows the frequency table for the 19 most frequent "numerator” genes in signatures with up to 4 genes.
  • the column “Max AUC” shows the maximum of that metric across all signatures which contain that gene.
  • the column “NPV 50” shows the NPV at the 50 percentile threshold for just this gene (not for the best performing signature).
  • the column “ROC AUC” shows the AUC for just this gene (not for the best performing signature).
  • TABLE 7 shows the frequency table for the 19 most frequent "denominator” genes in signatures with up to 4 genes.
  • the column “Max AUC” shows the maximum of that metric across all signatures which contain that gene.
  • the column “NPV 50” shows the NPV at the 50 percentile threshold for just this gene (not for the best performing signature).
  • the column “ROC AUC” shows the AUC for just this gene (not for the best performing signature).
  • the best 3-4-gene signatures can be combined with each other to identify and select up to 8-gene signatures with strong performance for discriminating infectious joint inflammation from non-infectious joint inflammation.
  • TABLE 8 shows the top 20 signatures with up to 8 genes sorted by AUC, in which the the first gene, second gene, third gene and optional fourth gene (“numerator genes”) are expressed at a higher level in infectious inflammation than in non-infectious inflammation (denoted by a "+" signal), and the fifth gene, sixth gene and optional seventh and eighth genes (“denominator genes”) are expressed at a lower level in infectious inflammation than in non- infectious inflammation, or improve the discrimination performance of the first gene, second gene, third gene and optional fourth gene (denoted by a signal). Genes that improve the discrimination performance of numerator genes are indicated by an asterisk.
  • composite scores for the gene signatures are calculated by adding gene expression values for each "+”gene (/.e., “numerator gene”) and subtracting gene expression values for each "-”gene (/.e., “denominator gene”).
  • the column “AUC” shows the AUC for individual signatures.
  • the columns “NPV 33”, “NPV 50” and “NPV 66” show the NPV for individual signatures at the 33, 50 and 66 percentile thresholds, respectively, in this cohort which has an infection prevalence of 48%.
  • The-8-gene signature with the highest AUC among the 5-8 gene signatures searched is presented in Figure 10.
  • TABLE 9 shows the frequency table for the 19 most frequent genes in the top 5% of all 8-feature signatures.
  • 2, 3 or 4 of the ratios can be used in order to build larger signatures for differentiating patients with infectious joint inflammation and patients with non-infectious joint inflammation.
  • the joint to be aspirated is first prepared using a skin disinfectant agent. Any commonly used skin preparation solution is acceptable for this process. All aspirations are completed using the sterile no-touch technique.
  • a needle is commonly introduced to the superiolateral aspect of the knee joint to draw fluid from the supra-patella fossa. It is possible to aspirate the knee from a number of locations, this example is the most common location. Once this needle is introduced synovial fluid is aspirated from the joint space by drawing back on the plunger.
  • a needle is introduced to the anteromedial aspect of the hip. Care is taken to palpate the important neurovascular structures adjacent to the optimal entry point. Once the femoral artery is palpated as it exits the j— ⁇ l: gament, the needle is passed directly toward the hip joint through the overlying muscle and fascia. It is possible to aspirate the hip from a number of locations, this example is the most common location. Once the needle is in the joint capsule synovial fluid is aspirated by drawing back on the plunger. There are additional approaches to the hip joint for aspiration. These are described in orthopedic text books in exacting detail. This example is for illustrative purposes only and by no means provides an exhaustive method by which the hip joint can be aspirated.
  • a needle is introduced to the anterior aspect of the shoulder joint immediately inferior and lateral to the coracoid process. It is possible to aspirate the shoulder from a number of locations, this example is the most common location. Once the needle is introduced, synovial fluid is aspirated from the joint space by drawing back on the plunger.
  • a needle is introduced to the anteromedial aspect of the joint line taking care to avoid the tibialis anterior tendon. It is possible to aspirate the ankle from a number of locations, this example is the most common location.
  • the needle is introduced medial to the tibialis anterior tendon. Once this needle is introduced, synovial fluid is aspirated from the joint space by drawing back on the plunger.
  • Fluid extracted from a patient can be synovial fluid, exudate, lymph, blood or a combination of all of the above, and may contain tissue.
  • the sample is inverted 10 times as per manufacturer instruction and stored at room temperature for transport to the laboratory for processing.
  • PAXgeneTM tubes are place in either -20C or - 80C freezer. Storage at -20C will last for 5 years, storage at -80C will last 8 years.
  • RNA is suitably isolated using the following steps:
  • Extracted RNA may be then tested for purity and yield (for example by running an A 260/280 ratio using a NanodropTM (Thermo Scientific)) for which a minimum quality must be (ratio > 1.6).
  • RNA should be adjusted in concentration to allow for a constant input volume to the reverse transcription reaction (below).
  • RNA should be processed immediately or stored in singleuse volumes at or below -70°C for later processing.
  • the workflow involves a number of steps depending upon availability of automated platforms.
  • the assay uses quantitative, real-time determination of the amount of each joint inflammation RNA transcript in the sample based on the detection of fluorescence on a real-time quantitative PCR (RT-qPCR) instrument (e.g., Applied Biosystems 7500 Fast Dx Real-Time PCR Instrument, Applied Biosystems, Foster City, ogue number 440685; K082562).
  • RT-qPCR real-time quantitative PCR
  • Such reactions can be run as single- plexes (one probe for one transcript per tube), multiplexed (multiple probes for multiple transcripts in one tube), one-step (reverse transcription and PCR are performed in the same tube), or two- step (reverse transcription and PCR performed as two separate reactions in two tubes).
  • a score is calculated using interpretive software provided separately to the kit but designed to integrate with RT-PCR machines.
  • Each batch run desirably includes the following specimens: High Control, Low Control, Negative Control, and No Template Control (Test Diluent instead of sample) in singleton each
  • the final reaction volume per well is 15 pL.
  • qPCR master mix may be prepared to coincide roughly with the end of the RT reaction. For example, start about 15 minutes before this time. See below.
  • Software is specifically designed to integrate with the output of PCR machines and to apply an algorithm based on the use of multiple biomarkers.
  • the software takes into account appropriate controls and reports results in a desired format.
  • the data file will then be analyzed using the assay's software application for interpretation of results.
  • NTC yields a result other than Fail (no Ct for all targets) the batch run is invalid and no data may be reported for the clinical specimens. This determination is made by visual inspection of the run data. The batch run should be repeated starting with either a new RNA preparation or starting at the RT reaction step.
  • Analytical criteria e.g. Ct values
  • Ct values that qualify each specimen as passing or failing (using pre-determined data) are called automatically by the software.
  • the negative control must yield a Negative result. If the negative control is flagged as Invalid, then the entire batch run is invalid.
  • FIG. 11 A possible example output for a joint inflammation biomarker assay is presented in Figure 11. The format of such a report depends on many factors including: quality control, regulatory authorities, cut-off values, the algorithm used, laboratory and clinician requirements, likelihood of misinterpretation.
  • the assay is called "Synvlchor".
  • the result is reported as a number, a position on a 0-10 scale, and a probability of the patient having presence of infectious joint inflammation or non-infectious joint inflammation, based on historical results and the use of a pre-determined cut-off (using results from clinical studies). Results of controls within the assay may also be reported. Other information that could be reported might include: previous results and date and time of such results, a prognosis, a scale that provides cut-off values for historical testing results that separate infectious joint inflammation and non-infectious joint inflammation, with higher scores for example indicating more severe infectious joint inflammation. The reporting of results in this fashion would allow clinicians to see the probability of a patient having joint inflammation to enable diagnosis of infectious joint inflammation or non-infectious joint inflammation with confidence.
  • Synvlscore of 1: This score correlates with a graphical depiction of the color "green” for visual biases for safety to proceed. This "score” is associated with a very high negative predictive value of 90% or greater, which conveys a very high degree of certainty that the host response is not adopting an infective posture. Consequently, the patient is safer for discharge out of a hospital care setting.
  • Each of the reported results from the Synvlchor test is to be considered in the context of thorough examination, history and other tests including all imaging modalities, biochemical investigations, histological investigation, microbiological investigation and any other measure the assessing clinician deems appropriate for the patient at the time of presentation and diagnosis.
  • Synvlscore of 2 This score correlates with a graphical depiction of the color "yellow or amber" for visual biases for proceed with some caution.
  • This "score” is associated with an indeterminate infection category, which may represent a period of transition between the low NPV inflammatory state and the high PPV infective state.
  • We recommend that patients who fall into this category a treated with a moderate degree of certainty that the host response may be adopting an infective posture. Consequently, the patient is at an inflection point where certainty to discharge out of a hospital care setting may be reliant on additional measures.
  • Synvlscore of 3 This score correlates with a graphical depiction of the color "red” for visual biases for patient in danger, stop and assess. This "score" is associated with a positive predictive value of greater than 80%, which conveys a very high degree of certainty that the host response is adopting an infective posture and mounting an infection response. Consequently, the patient may require urgent hospital care with significant intervention beyond antimicrobial dosing.
  • Each of the reported results from the Synvlchor test is to be considered in the context of thorough examination, history and other tests including all imaging modalities, biochemical investigations, histological investigation, microbiological investigation and any other measure the assessing clinician deems appropriate for the patient at the time of presentation and diagnosis.

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Abstract

L'invention concerne des biomarqueurs de maladie inflammatoire. Plus particulièrement, la présente invention concerne des biomarqueurs et leur utilisation dans des procédés, des compositions, des appareils, des dispositifs et des kits pour déterminer un indicateur qui est utile pour évaluer une probabilité qu'un type d'inflammation soit présent ou absent dans une articulation d'un sujet.
PCT/IB2021/000750 2021-10-29 2021-10-29 Biomarqueurs et leurs utilisations WO2023073392A1 (fr)

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PCT/IB2021/000750 WO2023073392A1 (fr) 2021-10-29 2021-10-29 Biomarqueurs et leurs utilisations
AU2022375208A AU2022375208B2 (en) 2021-10-29 2022-10-31 Biomarkers and uses therefor
EP22884808.1A EP4423300A1 (fr) 2021-10-29 2022-10-31 Biomarqueurs et leurs utilisations
PCT/AU2022/051312 WO2023070173A1 (fr) 2021-10-29 2022-10-31 Biomarqueurs et leurs utilisations
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Citations (3)

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Publication number Priority date Publication date Assignee Title
WO2001020018A2 (fr) * 1999-09-15 2001-03-22 Mitokor Procedes et compositions pour diagnostiquer et traiter des troubles arthritiques et reguler la masse osseuse
WO2002048310A2 (fr) * 2000-12-15 2002-06-20 Genetics Institute, Llc Procedes et compositions permettant de diagnostiquer et de traiter la polyarthrite rhumatoide
US20090068656A1 (en) * 2006-10-02 2009-03-12 Frank Beier Methods of diagnosing osteoarthritis

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Publication number Priority date Publication date Assignee Title
WO2001020018A2 (fr) * 1999-09-15 2001-03-22 Mitokor Procedes et compositions pour diagnostiquer et traiter des troubles arthritiques et reguler la masse osseuse
WO2002048310A2 (fr) * 2000-12-15 2002-06-20 Genetics Institute, Llc Procedes et compositions permettant de diagnostiquer et de traiter la polyarthrite rhumatoide
US20090068656A1 (en) * 2006-10-02 2009-03-12 Frank Beier Methods of diagnosing osteoarthritis

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BOTTAGISIO MARTA ET AL: "Phenotypic Modulation of Biofilm Formation in a Staphylococcus epidermidis Orthopedic Clinical Isolate Grown Under Different Mechanical Stimuli: Contribution From a Combined Proteomic Study", FRONTIERS IN MICROBIOLOGY, ARTICLE 565914, vol. 11, no. 565914, pages 1 - 14, XP093067390, DOI: 10.3389/fmicb.2020.565914 *
DEIRMENGIAN C, LONNER J H, BOOTH R E: "WHITE BLOOD CELL GENE EXPRESSION A NEW APPROACH TOWARD THE STUDY AND DIAGNOSIS OF INFECTION", CLINICAL ORTHOPAEDICS AND RELATED RESEARCH, PHILADELPHIA, PA, US, vol. 440, 1 November 2005 (2005-11-01), US , pages 38 - 44, XP009060956, DOI: 10.1097/01.blo.0000185756.17401.32 *
KEEMU HANNES ET AL: "Novel Biomarkers for Diagnosing Periprosthetic Joint Infection from Synovial Fluid and Serum", JBJS OPEN ACCESS, E20.00067, vol. 6, no. e20, 1 January 2021 (2021-01-01), pages 1 - 8, XP093067388, DOI: 10.2106/JBJS.OA.20.00067 *
LEO MCHUGH ET AL: "A Molecular Host Response Assay to Discriminate Between Sepsis and Infection- Negative Systemic Inflammation in Critically Ill Patients: Discovery and Validation in Independent Cohorts", PLOS MEDICINE, vol. 12, no. 12, pages e1001916, XP055336105, DOI: 10.1371/journal.pmed.1001916 *

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