WO2014008426A2 - Diagnostic du lupus érythémateux systémique - Google Patents

Diagnostic du lupus érythémateux systémique Download PDF

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WO2014008426A2
WO2014008426A2 PCT/US2013/049371 US2013049371W WO2014008426A2 WO 2014008426 A2 WO2014008426 A2 WO 2014008426A2 US 2013049371 W US2013049371 W US 2013049371W WO 2014008426 A2 WO2014008426 A2 WO 2014008426A2
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pbmc
intron
proximal
methylation
loci
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WO2014008426A3 (fr
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Jonathan Lim
Robert Shoemaker
Louis Bookbinder
David Anderson
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Ignyta Inc
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Ignyta Inc
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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
    • 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
    • C12Q2600/154—Methylation markers

Definitions

  • Embodiments of the present invention include methods, compositions and kits for the diagnosis of a subject with a disorder such as Systemic Lupus Erythematosus.
  • Systemic Lupus Erythematosus or lupus is an autoimmune disorder where immune cell abnormalities lead to the production of autoantibodies by B-cells and auto-reactive T-cells that contribute to disease pathology
  • Crispin J.C. Liossis S.N., Kis- Toth K., et al. (2010) Pathogenesis of human systemic lupus erythematosus: recent advances. Trends Mol. Med. 16: 47-57; Kammer G.M., Perl A., Richardson B.C., Tsokos G.C. (2002) Abnormal T cell signal transduction in systemic lupus erythematosus. Arthritis Rheum.
  • SLE SLE
  • people of African, Hispanic, or Asian ancestry tend to have an increased prevalence of SLE with greater severity.
  • SLE patients are women of childbearing age (nine out of ten patients are women), making it even more critical to develop diagnostic tools that enable earlier and more precise treatment (Pons-Estel G.J., Alarcon G.S., Scofield L., Reinlib L., Cooper G.S. (2010) Understanding the epidemiology and progression of systemic lupus erythematosus. Semin.
  • Methylation is a dominant regulator of epigenetic mechanisms of gene regulation and also controls chromatin remodeling and generation of microR As, which in turn can also regulate gene expression and biologic functions (Ilias Alevizos and Gabor G. Illei, (2010) MicroRNAs as biomarkers in rheumatic diseases. Nat. Rev. Rheumatol. July 6(7): 391-398; Xiao C, Rajewsky K., (2009) MicroRNA control in the immune system: basic principles. Cell 136:26-36; Jody C. Chuang, Peter A. Jones, (2007) Epigenetics and MicroRNAs. Pediatric Research 61 (5):24R-29R; Esteller, M., G.
  • DNMTs DNA Methyltransferases
  • the DNA methylation signatures within cells are considered to be a novel and important source of molecular markers for SLE and other autoimmune diseases (Epigenetics in Inflammatory Rheumatic Diseases. Arthritis and Rheumatism. 56; 3523-353; Richardson, B. (2003) DNA methylation and autoimmune disease. Clin. Immunol. 109:72-9; Dipak R. Patel, Bruce C. Richardson, (2010) Epigenetic Mechanisms in Lupus. Curr. Opin. Rheumatol. 22(5):478-482).
  • DNA methylation profiling has great potential to help in the diagnosis for autoimmune diseases such as Systemic lupus erythematosus (SLE; Martin-Subero JI, Esteller M (201 1) Profiling epigenetic alterations in disease. Adv Exp Med Biol. 71 1 : 162-77. Review. PMID: 21627049; Rakyan VK, Hildmann T, Novik KL, Lewin J, Tost J, Cox AV, Andrews TD, Howe KL, Otto T, Olek A, Fischer J, Gut IG, Berlin K, Beck S (2004) DNA methylation profiling of the human major histocompatibility complex: a pilot study for the human epigenome project. PLoS Biol.
  • CD70 and CD40 ligands which up-regulate the activation of auto-reactive B-cells producing antibodies, auto-reactive T-cells producing lytic molecules (perforin, interferon) or expressing killer immunoglobulin like receptors (KIRs) which contribute to the killing of monocytes and other cells in lupus patients (Lu, Q., M. Kaplan, D. Ray, D. Ray, S. Zacharek, D. Gutsch, B. Richardson, (2002) Demethylation of ITGAL (CD 11a) regulatory sequences in systemic lupus erythematosus . Arthritis Rheum. 46: 1282- 1291 ; Lu, Q., A. Wu, B. C.
  • SLEDAI Systemic Lupus Erythematosus Disease Activity Index
  • Tsokos (201 1) Promoter Hypomethylation Results in Increased Expression of Protein Phosphatase 2A in T Cells from Patients with Systemic Lupus Erythematosus. J. Immunol 186:4508-4517), transcription factor regulation of gene expression (Zhao, M., Y. Sun, F. Gao, X. Wu, J. Tang, H. Yin, Y. Luo, B. Richardson, and Q. Lu. (2010) Epigenetics and SLE: RFXl downregulation causes CDlla and CD70 overexpression by altering epigenetic modifications in lupus CD4(+) T cells. J. Autoimmun. 35 (1): 58-69), cell receptor activation (Lu, Q., M.
  • IL-6 modulates CD5 expression in B cells from patients with lupus by regulating DNA methylation. J. Immunol. 182:5623-32). Cytokines (IL-1 , IL-6) (Wong C.K., Ho C.Y., Li E.K., Lam C.W.
  • Some embodiments disclosed herein comprise methods of diagnosis of SLE in a mammalian subject. Some embodiments comprise one or more of the steps of comprising the steps of: isolating DNA from a patient; determining a methylation status of a panel of loci of DNA from said patient; and collectively analyzing the methylation status of the loci in the panel using a computer to ascertain a likelihood that the patient has SLE
  • the panel comprises a methylation site specified in Table 2A. In some embodiments the panel comprises a methylation site specified in Table 2B. In some embodiments the panel comprises a methylation site specified in Table 3 A. In some embodiments the panel comprises a methylation site specified in Table 3B. In some embodiments the panel comprises a methylation site specified in Table 1A. In some embodiments the panel comprises a methylation site specified in Table IB. In some embodiments the panel comprises a methylation site specified in Table 4.
  • the locus comprises 20bp, 40bp, 60bp, 80bp, l OObp, 500bp, lkb, 1.5 kb, 2kb, 2.5kb, 5kb, or lOkb on either side of the methylation site.
  • the DNA is isolated from a source selected from the list comprising: free circulating DNA, DNA isolated from urine, DNA isolated from blood, DNA isolated from blood cells, DNA isolated from other body fluid, DNA isolated from other body tissue.
  • the DNA is isolated from at least one peripheral blood mononuclear cell.
  • the peripheral blood mononuclear cell may be selected from a group comprising: T-cells, B-cells, monocytes, Thl cells, Th2 cells, Thl7 cells, T-regs, NK cells, B l cells, B2 cells, Ml and M2 monocytes or dendritic cells.
  • determining a methylation pattern comprises chemical treatment of DNA extracted from at least one cell that differentially affects methylated and unmethylated bases.
  • the treatment can comprise bisulfite treatment, and the determining can comprise amplification of a fragment of DNA comprising a locus having a methylation site.
  • the methylation site can be a methylation site specified in any one or more of Table 2A or 2B or Table 3A or 3B.
  • a locus is amplified.
  • the locus amplified can comprise 20bp, 40bp, 60bp, 80bp, lOObp, 500bp, lkb, 1.5 kb, 2kb, 2.5kb, 5kb, or lOkb on either side of the methylation site.
  • the amplified DNA can be contacted with an oligonucleotide probe that distinguishes between bisulfite treated methylated and bisulfite treated unmethylated DNA.
  • the methylation site is additionally a methylation site listed in Table 4.
  • the methylation status of a panel of loci may be determined.
  • the panel may comprise at least one locus from Table 1A.
  • the panel may comprise at least one locus from Table IB.
  • the panel may comprise at least one locus from Table 2A.
  • the panel may comprise at least one locus from Table 2B.
  • the panel may comprise at least one locus from Table 3A.
  • the panel may comprise at least one locus from Table 3B.
  • the panel may comprise a locus of Table 4.
  • the panel can comprise 5, 10, 20, 50 or more than 50 loci, for example 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50, or more than 50 loci.
  • the panel may comprise 25 loci. The status may be compared to that of a corresponding locus in a sample from a non-SLE source.
  • the method of SLE diagnosis further comprises determining the status of one or more of the loci from at least one of Table 1A, IB, 2A, 2B, 3A or 3B in combination with traits selected from the list comprising: SNP or gene variation or copy number status, levels of auto-antibodies such as anti-dsDNA antibodies or anti- nuclear antibodies, and serum proteins such as cytokine or chemokine levels or inflammatory molecules.
  • the method of SLE diagnosis further comprising analyzing in the computer the status of one or more of the traits selected from the list comprising: SNP or gene variation status, levels of auto-antibodies such as anti-dsDNA antibodies or anti-nuclear antibodies, and serum proteins such as cytokine or chemokine levels or inflammatory molecules together with analyzing the methylation status of the loci in the panel.
  • Some embodiments disclose methods for detecting methylation events at one or more methylation sites set forth in at least one of Table 2A or 2B or Table 3A or 3B, which comprises subjecting a human sample to chemical treatment that differentially affects methylated bases of said human sample, and ascertaining the extent of methylation of a panel of regions comprising at least one region of a locus of a methylation site specified in at least one of Table 2A or 2B or Table 3A or 3B as an indicator of SLE status based on said differential effect.
  • the treatment comprises contacting said locus with bisulfite.
  • the panel comprises at least 5 loci. In some embodiments the panel comprises at least 10 loci. In some embodiments the panel comprises at least 25 loci. In some embodiments the panel comprises at least 50 loci. In some embodiments the panel comprises at least a methylation site of Table 4. In some embodiments the panel comprises 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50 or more than 50 loci.
  • Some embodiments disclose a method of diagnosis of SLE in a mammalian subject comprising the steps of: isolating at least one cell from a patient; determining a transcript accumulation level of transcripts from a panel of genes corresponding to three or more of the loci in Tables 1A, IB, 2A, 2B, 3A and 3B, identified from said at least one cell; and collectively analyzing the methylation status of the loci in the panel using a computer to ascertain a likelihood that the patient has SLE.
  • at least one gene is identified in Table 1A.
  • at least one gene is identified in Table IB.
  • at least one gene is identified in Table 2 A.
  • at least one gene is identified in Table 2B.
  • At least one gene is identified in Table 3A. In some embodiments at least one gene is identified in Table 3B. In some embodiments, the gene is within 20bp, 40bp, 60bp, 80bp, lOObp, 500bp, lkb, 1.5 kb, 2kb, 2.5kb, 5kb, or l Okb on either side of a methylation site. In some embodiments the methylation site is additionally listed in Table 4.
  • the transcripts are isolated from at least one peripheral blood mononuclear cell.
  • the peripheral blood mononuclear cell may be selected from a group comprising: T-cells, B-cells, monocytes, Thl cells, Th2 cells, Thl7 cells, T- regs, NK cells, Bl cells, B2 cells, Ml and M2 monocytes, or dendritic cells.
  • the accumulation levels of a panel of loci may be determined.
  • the panel may comprise at least one locus from Table 1A.
  • the panel may comprise at least one locus from Table IB.
  • the panel may comprise at least one locus from Table 2A.
  • the panel may comprise at least one locus from Table 2B.
  • the panel may comprise at least one locus from Table 3A.
  • the panel may comprise at least one locus from Table 3B.
  • the methylation site is additionally listed in Table 4.
  • the panel can comprise 5, 10, 25, 50 or more than 50 loci, for example 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50, or more than 50 loci.
  • the levels may be compared to that of a corresponding locus in a sample from a non- SLE source.
  • the method may comprise comparing said accumulation level to that of a corresponding transcript in a non-SLE cell, or comparing said accumulation level to that of a corresponding transcript in an SLE cell.
  • Some embodiments disclose methods of diagnosis of SLE in a mammalian subject comprising the steps of: isolating at least one peripheral blood mononuclear cell from a patient; isolating a protein fraction from said at least one cell; determining a protein accumulation level or protein activity from a protein extract from said at least one cell; and diagnosing whether the patient has SLE based on said determination.
  • Some embodiments disclose methods for evaluating SLE in a mammalian subject. Some embodiments disclose methods for evaluating SLE in a mammalian subject comprising: measuring the accumulation level or activity level of each of a panel of proteins in a sample comprising peripheral blood mononuclear cells of the subject, wherein each of the proteins is encoded by a gene that is identified by a methylation site in Table 2A, Table 2B, Table 3A or Table 3B; and collectively analyzing the measured levels in the panel using a computer to ascertain a likelihood that the patient has SLE.
  • the protein is encoded by a gene identified in Table 2A, or a gene within 20bp, 40bp, 60bp, 80bp, lOObp, 500bp, lkb, 1.5 kb, 2kb, 2.5kb, 5kb, or lOkb on either side of a methylation site in Table 2A.
  • the protein is encoded by a gene identified in Table 2B, or a gene within 20bp, 40bp, 60bp, 80bp, lOObp, 500bp, lkb, 1.5 kb, 2kb, 2.5kb, 5kb, or l Okb on either side of a methylation site in Table 2B.
  • the protein is encoded by a gene identified in Table 3A, or a gene within 20bp, 40bp, 60bp, 80bp, lOObp, 500bp, lkb, 1.5 kb, 2kb, 2.5kb, 5kb, or l Okb on either side of a methylation site in Table 3A.
  • the protein is encoded by a gene identified in Table 3B, or a gene within 20bp, 40bp, 60bp, 80bp, lOObp, 500bp, lkb, 1.5 kb, 2kb, 2.5kb, 5kb, or l Okb on either side of a methylation site in Table 3B.
  • the protein is isolated from at least one peripheral blood mononuclear cell.
  • the peripheral blood mononuclear cell may be selected from a group comprising: T-cells, B-cells, monocytes, Thl cells, Th2 cells, Thl7 cells, T-regs, NK cells, Bl cells, B2 cells, Ml and M2 monocytes or dendritic cells.
  • the methylation site is additionally listed in Table 4.
  • the assay comprises differential antibody binding, and may further comprise comparing said protein accumulation level or activity to that of a corresponding protein in a non-SLE cell or an SLE cell.
  • a protein accumulation level or protein activity level may be determined.
  • the panel can comprise 5, 10, 20, 25, 50 or more than 50 proteins, for example 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50, or more than 50.
  • the levels may be compared to that of a corresponding protein in a sample from a non-SLE source or an SLE source.
  • the protein accumulation level or protein activity from a protein extract from a panel of proteins identified from said at least one cell is determined.
  • the panel may comprise protein accumulation level or protein activity identified with at least one locus selected from the list of loci in Table 2A.
  • the panel may comprise protein accumulation level or protein activity identified with at least one locus selected from the list of loci in Table 2B.
  • the panel may further comprise protein accumulation level or protein activity identified with at least one locus selected from the list of loci in Table 3A.
  • the panel may further comprise protein accumulation level or protein activity identified with at least one locus selected from the list of loci in Table 3B.
  • the panel may further comprise protein accumulation level or protein activity identified with at least one locus selected from the list of loci in Table 1 A.
  • the panel may further comprise protein accumulation level or protein activity identified with at least one locus selected from the list of loci in Table IB.
  • the methylation site is additionally listed in Table 4.
  • Some embodiments comprise methods of characterizing a potential SLE disease status in a subject.
  • the methods comprise the steps of ascertaining the methylation status of a panel comprising at least 3 methylation sites in DNA from a mammalian subject, wherein said methylation sites are differentially methylated in individuals with SLE; electronically comparing in a computer values reflective of said methylation status of said panel of methylation sites with stored information reflective of methylation status of methylation sites in at least one standard population; generating a report based on said comparison relative to said potential autoimmune disease; and providing said report to a caregiver or to said subject; wherein said at least 3 methylation sites are selected from the methylation sites listed in Tables 1 A, IB, 2 A, 2B, 3 A, and 3B.
  • the panel comprises a methylation site of Table 2A. In some embodiments the panel comprises a methylation site of Table 2B. In some embodiments the panel comprises a methylation site of Table 3A. In some embodiments the panel comprises a methylation site of Table 3B. In some embodiments the panel comprises a methylation site of Table 1A. In some embodiments the panel comprises a methylation site of Table IB. In some embodiments the panel comprises a methylation site of Table 4.
  • Some embodiments disclose a method for the diagnosis of Systemic Lupus Erythematosus (SLE) comprising: (a) determining the extent of methylation at a plurality of DNA loci in a sample taken from an individual; (b) evaluating individual or combined values reflective of the extent of methylation at said loci; and (c) providing a report based on the evaluation of step (b) that includes information regarding SLE status of the individual.
  • the evaluating step comprises processing values reflective of methylation at said loci in a computer and comparing said processed values to values known to be reflective of SLE status.
  • a locus to be evaluated may be selected from Table 2A. In some embodiments a locus to be evaluated may be selected from Table 2B. In some embodiments a locus to be evaluated may be selected from Table 3A. In some embodiments a locus to be evaluated may be selected from Table3B. In some embodiments a locus to be evaluated may be selected from Table 1 A. In some embodiments a locus to be evaluated may be selected from Table IB. In some embodiments a locus to be evaluated may be selected from Table 4.
  • the locus to be evaluated may be part of a panel of 5, 10, 20, 25, 50 or more than 50 loci, for example 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50 or more than 50 loci.
  • Some embodiments disclose a set of primers, comprising a first PCR primer pair capable of amplifying a first locus of human chromosomal DNA in a PCR reaction; and a second PCR primer pair capable of amplifying a second locus of human chromosomal DNA in a PCR reaction, wherein the first locus is identified in Table 2A.
  • Some embodiments disclose a set of primers, comprising a first PCR primer pair capable of amplifying a first locus of human chromosomal DNA in a PCR reaction; and a second PCR primer pair capable of amplifying a second locus of human chromosomal DNA in a PCR reaction, wherein the first locus is identified in Table 2B.
  • the set of primers may further comprise: a third primer pair capable of amplifying a third locus of human chromosomal DNA in a PCR reaction; wherein the first, second, and third loci are differentially methylated in a human having SLE versus a human not having SLE.
  • the second locus may be identified in Table 3A.
  • the second locus may be identified in Table 3B.
  • the second locus may be identified in Table 1A.
  • the second locus may be identified in Table IB.
  • the loci may comprise 20bp, 40bp, 60bp, 80bp, lOObp, 500bp, lkb, 1.5 kb, 2kb, 2.5kb, 5kb, or l Okb on either side of a methylation site in Table 2A.
  • the loci may comprise 20bp, 40bp, 60bp, 80bp, lOObp, 500bp, lkb, 1.5 kb, 2kb, 2.5kb, 5kb, or l Okb on either side of a methylation site in Table 2B.
  • the locus is additionally listed in Table 4.
  • kits for the diagnosis of SLE in a mammalian subject comprising a plurality of DNA reagents, at least one said reagent capable of distinguishing between methylated and unmethylated DNA from a locus listed in Table 2A upon subjecting said DNA to a treatment that differentially affects methylated bases.
  • kits for the diagnosis of SLE in a mammalian subject comprising a plurality of DNA reagents, at least one said reagent capable of distinguishing between methylated and unmethylated DNA from a locus listed in Table 2B upon subjecting said DNA to a treatment that differentially affects methylated bases.
  • the kit may comprise an oligonucleotide, and the treatment may comprise a chemical modification, such as bisulfite modification.
  • the kit may comprise a plurality of different reagents, each capable of distinguishing between methylated and unmethylated DNA at a respective locus listed in Table 2A, and may further comprise reagents capable of distinguishing between methylated and unmethylated sites of one or more of Table 1A or Table IB or Table 3A or 3B.
  • the kit may comprise a plurality of different reagents, each capable of distinguishing between methylated and unmethylated DNA at a respective locus listed in Table 2B, and may further comprise reagents capable of distinguishing between methylated and unmethylated sites of one or more of Table 1A or Table IB or Table 3A or 3B.In some embodiments the locus is additionally listed in Table 4.
  • Some embodiments disclose an isolated DNA molecule having a sequence spanning a methylation site of Table 2A. Some embodiments disclose an isolated DNA molecule having a sequence spanning a methylation site of Table 2B. Some embodiments disclose an isolated DNA molecule having a sequence spanning a methylation site of Table 3A. Some embodiments disclose an isolated DNA molecule having a sequence spanning a methylation site of Table 3B. Some embodiments disclose an isolated DNA molecule having a sequence which anticipates the sequence of the vicinity of a methylation site of Table 2A. Some embodiments disclose an isolated DNA molecule having a sequence which anticipates the sequence of the vicinity of a methylation site of Table 2B.
  • Some embodiments disclose an isolated DNA molecule having a sequence which anticipates the sequence of the vicinity of a methylation site of Table 3A. Some embodiments disclose an isolated DNA molecule having a sequence which anticipates the sequence of the vicinity of a methylation site of Table 3B. Some embodiments comprise a sequence spanning the vicinity of any of the above loci upon chemical modification to identify methylation status, wherein said modified form is an expected product of a reaction whereby an isolated DNA molecule comprising a locus identified in at least one of Table 2A or 2B or Table 3 A or 3B having a methylation signature characteristic of SLE or having a methylation signature characteristic of the absence of SLE is subjected to treatment that differentially affects methylated bases.
  • the treatment may comprise treatment with bisulfite.
  • the molecule may comprise at least 14 nucleotide bases, for example 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35 or more than 35 bases.
  • Some embodiments disclose a panel of molecules like those discussed above.
  • the panel may comprise 5, 10, 20, 25, 50 or more than 50 DNA molecules, for example 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50 or more than 50 loci.
  • the locus is additionally listed in Table 4.
  • FIG. 1 Combining Differentially Methylated Loci (DMLs) may produce better performing models.
  • Error rate is defined as an SLE sample being assigned a non-SLE phenotype or a non-SLE phenotype being assigned an SLE-phenotype.
  • Combining the DMLs indicated reduces the Error Rate from about 14% for three separate DML sets to 3% for a single combined DML set.
  • DML coordinate refers to the chromosomal coordinate (chromosome coordinate) of the cytosine of a CpG on the + strand (as defined by the University of California Santa Cruz; UCSC hgl9 genome build). Each DML coordinate is labeled according to position on the + strand for simplicity but the DML may refer to a CpG on the + and/or the - strand.
  • FIG. 2 Error rate declines as DML set size increases. Error rate is defined as an SLE sample being assigned a non-SLE phenotype or a non-SLE phenotype being assigned an SLE-phenotype.
  • Model Scoring Methodology Per DML set size, the 16 sample PBMC data set was randomly split into two equally sized data sets, a training data set and a test data set. The model was trained using the training data set and was scored based on its ability to predict the phenotypes of the test data. This process was repeated 10 times. Using more DML to determine disease phenotypes improves models' error rates, which can also be described in terms of specificity and sensitivity.
  • DML coordinate refers to the chromosomal coordinate (chromosome oordinate) of the cytosine of a CpG on the + strand (University of California Santa Cruz; UCSC hgl9 genome build). Each DML coordinate is labeled according to position on the + strand for simplicity but the DML may refer to the CpG on the + and/or the - strand.
  • Figure 3 Graphical Depiction of Decline in Error Rate as Panel size increases. Dotted lines indicate linear regressions of datasets for which Gaussian error is artificially introduced to simulate flaws in input data sets. Error percentage linear regressions decrease with an increase in DML set size.
  • methylation patterns particularly 5-methylcytosine methylation patterns, of the nucleic acids of certain cells or cell populations, or other DNA reservoirs may be determined.
  • a number of methods for the determination of cytosine methylation status are known to one of skill in the art. For example, bisulfite mapping may be used. Through this process, extracted DNA is treated with a bisulfite as part of a process that differentially converts cytosine unmethylated at the C5 position to uracil, while leaving 5-methyl cytosine unaffected.
  • Other methods for the determination of methylation status at one or more cytosine positions are also know in the art, and embodiments disclosed herein are not limited to any particular method of determining methylation status at a particular locus to the exclusion of any other method.
  • an array such as an ILLUMINA HumanMethylation 450 BeadChip may be used to determine a methylation status (Sandoval, et al., (201 1) Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome. Epigenetics 6:6, 692-702).
  • the sequencing method may comprise one or more technologies such as pyrosequencing, e.g., the '454' method' (Margulies et al.,
  • any number of so-called 'next generation' DNA sequencing methods may be used, as described in Shendure and Ji, "N ' ext- generation DNA sequencing", Nature Biotechnology 26(10): 1 135-1 145 (2008) or in other art available to one of skill in the art.
  • Other methods for the determination of DNA sequence are also known in the art, and embodiments disclosed herein are not limited to any particular method of determining base identity at a particular locus to the exclusion of any other method.
  • methods for assaying the methylation status, particularly the 5-methyl cytosine methylation status, at one or more loci may include randomly shearing or fragmenting genomic DNA, cutting with a methylation-dependent or methylation sensitive restriction endonuclease, of which many are known to one of skill in the art, and analyzing the resultant DNA fragments.
  • analysis may involve amplification of nucleic acids. In some embodiments this amplification can be performed using gene or locus specific primers, or using primers specific to, for example, adapters that may be added to the ends of fragmented DNA.
  • the DNA may be amplified using a quantitative PCR protocol, such as one that allows for real time quantification of amplification products.
  • a quantitative PCR protocol such as one that allows for real time quantification of amplification products. See, e.g. , U.S. Patent No. 7,186,512; U.S. Patent Application Ser. Nos. 10/971 ,986; 1 1/071 ,013; and 10/971 ,339; U.S. Pat. Nos. 6,180,349; 6,033,854; and 5,972,602, Gibson et al, Genome Research 6:995-1001 (1996); DeGraves, et al, Biotechniques 34(1): 106- 10, 1 12-5 (2003); and Deiman B., et al, Mol. Biotechnol.
  • methods for detecting DNA methylation may involve genomic sequencing before and after treatment that differentially affects methylated bases. See, e.g., Frommer et al, (1992) Proc. Natl. Acad. Sci. USA 89: 1827-1831. Additional methylation detection methods include methods described in, for example, the following references: Toyota et al, Cancer Res. 59:2307-12 (1999), U.S. Patent Publication 2005/0069879; Rein, et al. Nucleic Acids Res. 26 (10): 2255-64 (1998); Olek, et al. Nat. Genet. 17(3): 275-6 (1997); and PCT Publication No.
  • WO 00/70090 Herman et al, (1996) Proc. Natl. Acad. Sci. USA 93:9821 -9826; U.S. Pat. No. 5,786,146; Gonzalgo & Jones, Nucleic Acids Res. 25:2529-2531 (1997).
  • a methylation assay may be run to obtain data for use in some embodiments.
  • One set of methods is predicated upon methylation-specific changes in hybridization efficiency that result from bisulfite treatment.
  • a PCR primer that specifically anneals to a methylation site may be used in a PCR amplification reaction wherein the amplification efficiency is dependent upon either the methylation status or the identity of the base following bisulfite treatment.
  • Amplification efficiency may be assayed by the generation of a signal, such as light, in proportion to the concentration of double-stranded DNA during the course of a PCR reaction.
  • a signal may be generated by the binding of an oligonucleotide probe, such as a labeled probe, to a region spanning a methylation site of interest.
  • Alternate methods of assaying for the outcome of bisulfite treatment on a sample may be used. For example, chemical differences between methylated and unmethylated DNA that manifest themselves after bisulfite treatment may be indicative of substrate sequence after bisulfite treatment and, by inference, methylation status before treatment. One such difference, the melting temperature of a double-stranded DNA molecule, may be assayed using, for example, high-resolution melt analysis using techniques known in the art.
  • DNA to be assayed may, for example, be contacted with proteins that preferentially bind methylated or unmethylated sequences (e.g., MBD binding proteins or antibodies such as MeDIP). Measurement of signals, such as light, that correspond to quantities of isolated methylated or unmethylated sequences may be used to assay methylation status of the assayed DNA sequence.
  • DNA may be bound by proteins which trigger transcription in the vicinity of the methylation site as an alternate reporter of methylation status. Other reporter mechanisms are contemplated.
  • DNA sequences may be sequenced directly, either after bisulfite treatment or after methylation-based separation as discussed above. Any of the sequencing methods known in the art may be used, including those mentioned above. Embodiments are not limited by the sequencing method used, and sequencing innovations may be incorporated into various embodiments as the sequencing innovations become available to those of skill in the art.
  • Sequencing methods which assay for methylation directly on input sequences may also be used. For example, measurable transcription rate changes may be used to determine methylation status at specific bases (e.g., single molecule real time (SMRT) sequencing), or spectrographic or electric field measurements may be used to discriminate between methylated and unmethylated bases during single molecule sequencing such as nanopore sequencing.
  • SMRT single molecule real time
  • Sequencing methods may target individual methylation sites or loci to assay. Targeted regions may be amplified or preserved in processes that degrade sequences not of interest. Alternately, methylation sites of interest may be sequenced as part of whole- genome sequencing efforts whereby all or substantially all sequence information in a DNA sample is determined.
  • the cell population from which DNA is to be assayed is peripheral blood mononuclear cells. In some embodiments the cell population is selected from a group comprising T cells, B cells and monocytes. In some embodiments the cell population from which DNA is to be assayed is a subset of T-cells (such as Thl , Th2, Thl7, T-regs, NK cells), a subset of B-cells (such as Bl , B2) or a subset of monocytes (such as Ml and M2 monocytes and dendritic cells) [see Littman and Rudensky (2010) Thl 7 and regulating T cells in mediating and restraining inflammation.
  • T-cells such as Thl , Th2, Thl7, T-regs, NK cells
  • B-cells such as Bl , B2
  • monocytes such as Ml and M2 monocytes and dendritic cells
  • Cells from any of these groups may be obtained using any of a number of methods known to those of skill in the art (see, e.g., Mallone et al, (201 1) Isolation and preservation of peripheral blood mononuclear cells for analysis of islet antigen-reactive T cell responses: position statement of the T-Cell Workshop Committee of the Immunology of Diabetes Society. Clin. Exp. Immunol. 163(l):33-49).
  • Embodiments disclosed herein are not limited to any particular method of cell or DNA isolation method to the exclusion of any other.
  • DNA may be extracted from cells using any of a number of methods known in the art.
  • the DNA extraction method will preferably substantially preserve the methylation pattern of the extracted DNA and yield DNA of a purity and integrity suitable for downstream analysis, but is not otherwise limited. If peripheral blood mononuclear cells are used as a DNA source, the extraction method should be appropriate to these cells. Alternately, free circulating DNA from, for example, the blood, urine, other body fluid, or other tissue of a patient may be used as a sample source.
  • methylation patterns determined for one or more of the above cell populations may be compared to methylation patterns determined from similar cell types in one or more individuals with a known SLE status (such as positive for SLE or negative for SLE), or from one or more different cell populations taken from the same or different individuals compared to the individual to be diagnosed.
  • an increase or decrease in the methylation state, relative to a reference methylation state, of at least one locus from a cell or population of cells of an individual to be diagnosed may be indicative of that individual's SLE status.
  • the methylation status of at least one locus from a cell or population of cells of an individual to be diagnosed may be determined and then normalized to the methylation status of a control locus to control for error in detection methods that may otherwise impact a diagnosis as to the presence or absence of SLE. Appropriate control loci or control techniques are known to one of skill in the art.
  • the methylation patterns may be deduced by determining the methylation status, for example the presence of 5-methyl cytosine, of 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50, 51 , 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 , 62, 63, 64, 65, 66, 67, 68, 69, 70, 71 , 72, 73, 74, 75, 76, 77, 78, 79, 80, 81 , 82, 83, 84, 85, 86, 87, 88, 89, 90, 91 , 92, 93, 94, 95, 96, 97, 98, 99,
  • Some embodiments disclosed herein relate to the diagnosis, typing, or prognosis of SLE in an individual based in part on the methylation status at a set of loci, or a "panel" from a restricted population of cells from that individual.
  • Tables of loci provided for the purpose, among others, of enabling the diagnosis, typing, or prognosis of SLE in an individual.
  • the Tables list Differentially Methylated Loci (hereinafter "DMLs”) corresponding to methylation sites at which the inventors have discovered a correlation between extent of status in an isolated cell population such as PBMCs, T Cells, B Cells, Monocytes, or cells from whole blood, and SLE status.
  • the cell population of origin from which each DML was identified is indicated in each table as appropriate. Loci are sorted into tables as follows.
  • Table 1A and IB DML reported in Tables 1A and IB identify loci corresponding to sites for which a previously-reported association between methylation status and SLE exists, but which were independently identified and reported herein. Importantly, the list of sites for which a previously-reported association between methylation status and SLE exists is substantially greater than the list of DML in Table 1. That is, Tables 1A and IB represent a specific subset of DML which, although previously reported as methylation sites related to SLE, have been affirmed herein to have a diagnostic value not uniformly present in SLE-associated sites reported in the literature.
  • Tables 2A, 2B, 3A and 3B disclose DMLs corresponding to methylation sites at which the inventors have discovered an association between methylation status and SLE status.
  • Tables 2A and 2B DML reported in Tables 2A and 2B identify loci for which no previous association between methylation status at the listed sites and SLE was reported, and for which no previous association between the associated loci and SLE was reported. That is, Tables 2A and 2B report novel DML which identify novel loci in the context of SLE.
  • Tables 3A and 3B DML reported in Tables 3A and 3B identify loci for which no previous association between methylation status at the listed sites and SLE was reported, but for which a previous association between the associated loci and SLE has been reported.
  • Panels comprising DML selected at random from Tables 1 A-3B have been observed and are reported herein to demonstrate performance characteristics substantially above the performance characteristics of randomly selected panels of methylation sites that are not known to be differentially methylated in the context of SLE. Accordingly, a randomly selected panel of DML from any of Tables 1A, IB, 2A, 2B, 3 A and 3B will substantially outperform a random panel of DML not known to be implicated in SLE diagnosis in an assay for the presence of an SLE methylation in an individual.
  • Table 4 DML reported in Table 4 represent a subset of the DML reported in Tables 1A, IB, 2A, 2B, 3A, 3B, above. DML listed in Table 4 have been observed in some embodiments to contribute to panels having performance characteristics substantially above the performance characteristics of randomly selected panels of loci selected from one or more of Tables 1A, IB, 2A, 2B, 3A and 3B, which in turn have performance characteristics substantially above the performance characteristics of randomly selected panels of loci not associated with SLE diagnosis.
  • a set of loci where methylation status is relevant to an SLE diagnosis is selected from among the loci listed in Table 2A. In some embodiments, a set of loci where methylation status is relevant to an SLE diagnosis is selected from among the loci listed in Table 2B. In some embodiments, a set of loci where methylation status is relevant to an SLE diagnosis is selected from among the loci listed in Table 3A. In some embodiments, a set of loci where methylation status is relevant to an SLE diagnosis is selected from among the loci listed in Table 3B. In some embodiments this set of loci may be additionally supplemented by at least one locus selected from the loci listed in Table 1 A. In some embodiments this set of loci may be additionally supplemented by at least one locus selected from the loci listed in Table IB. In some embodiments this set of loci comprises at least a locus listed in Table 4.
  • a methylation site or sites listed in any one or more of Tables 2A, 2B 3A and 3B may be used as guides to direct a researcher, medical professional or other interested party to determine the methylation status of a locus marked by a listed methylation site.
  • this locus may comprise 20bp, 40bp, 60bp, 80bp, lOObp, 500bp, lkb, 1.5 kb, 2kb, 2.5kb, 5kb, l Okb, or more than lOkb on either side of a methylation site listed in Table 2A, or 2B or Table 3A or 3B.
  • the methylation site is additionally listed in Table 4.
  • Methylation status is a matter of degree, wherein some bases at a particular locus will be methylated in one cell and unmethylated in another cell. Thus, a determination of methylation status not only includes ascertaining whether an individual locus on an individual chromosome is methylated, but also can include determining the extent of methylation at that locus across a population of cells. Although methylation status at a single locus has value in diagnosis and prognosis of SLE, combinations of loci have enhanced diagnostic and prognostic value. Thus, various embodiments include assays in which at least one of the loci in Table 2A is analyzed, together with one or more additional loci. Various embodiments include assays in which at least one of the loci in Table 2B is analyzed, together with one or more additional loci. In some embodiments the methylation site is additionally listed in Table 4.
  • FIG. 1 presents an example wherein combining information from multiple DMLs produces better performing models than do uncombined data sets.
  • three separate sets of three DML each may be used to diagnose SLE with an error rate of about 14%.
  • this error rate is reduced to 3%, representing a substantial increase in the accuracy and therefore the utility of the test.
  • Figures 2 and 3 illustrate that the incremental increase in DML panel size leads to an incremental decrease in the error rate (to a limit of 0%) for both clean and artificially 'noisy' data sets into which statistical deviation has been introduced.
  • Figure 2 illustrates that, with the addition of each novel DML (with some modest degree of statistical variation) the error rate decreases as compared to the set lacking that additional DML.
  • Figure 3 graphically illustrates this result, showing that despite local variation in error rates (particularly in samples for which statistical deviation has artificially been introduced), the overall trend as represented by the regression lines indicates a clear decrease in error rate with an increase in the DML set used, towards an asymptote at an error rate approaching zero.
  • Figure 3 also shows a diminishing rate of performance improvement upon addition of DML to an already high performing panel.
  • SLE is a diagnosis that covers many clinical subtypes (e.g., disease stages, organs involved in the pathology), and it is expected that a single locus may not be able to discriminate across these subtypes with acceptable sensitivity and specificity.
  • the multiplex design of the described assay is intended to cover the inherent biological heterogeneity arising from the complex nature of the disease. For example, assume a process critical to SLE pathogenesis can be redundantly activated by a set of genes. Then a methylation change in the promoter regions of any one of these genes would be sufficient to activate this process. Another example is when a process has redundancies such that the down regulation of a single gene is insufficient to down regulate the entire process.
  • an assay that uses a panel detects not only the single loci that may have strong prognostic or diagnostic value in comparing SLE or pre-SLE samples to non-SLE samples, but it also detects the subtle deviations in methylation status at diagnostic loci that may not be individually statistically significant enough to warrant a diagnosis, but can cumulatively lead to a clear diagnosis of SLE or a risk of developing SLE. See again Figures 1-3.
  • Methylation status of a panel of loci from a selected cell type in an individual to be diagnosed may be compared to the methylation status of the same loci of a control individual, control tissue or a composite value derived from analysis of a control population of prior samples.
  • Diagnosis may be based upon the extent of difference in methylation status at individual loci selected from within a panel, or may be based on deviations from the control methylation pattern in the aggregate.
  • a single locus which shows a great difference in methylation status between a test sample and a control, may be sufficient to diagnose SLE or a risk of SLE.
  • better sensitivity and specificity can usually be accomplished by evaluating panel results in the aggregate.
  • Such a diagnosis may involve noting that a test sample presents a number of loci, which differ in methylation status only subtly, from a control, but where these differences collectively support a diagnosis of SLE.
  • Statistical evaluation of the aggregate results of a panel assay may be performed using a complex algorithm and performed on a computer. This evaluation may include use of a weighting algorithm in which certain loci are weighted differently than others, or any other algorithm derived from analysis of patient data that gives the desired specificity and sensitivity.
  • a number of algorithms for combining results from multiple loci to reach a diagnosis may be used.
  • classification models may be used to assign probabilities of phenotypes to samples.
  • These models include: Naive Bayes, Generalized Linear Model, Neural Network, Random Forest, and Support Vector Machine (SVM) models.
  • SVM Support Vector Machine
  • Each of these models is considered to be an algorithm, and each is known in the art. This list is not limiting; other algorithms may be used to combine the results of multiple loci to reduce noise or improve specificity or sensitivity.
  • the model's input is a list of methylation values at a panel of methylation sites, transcript accumulation levels, or protein accumulation levels or activity levels.
  • the output is a list of pheno type-specific values that can be transformed into probabilities. The phenotype with the highest probability is assigned to the sample.
  • a variety of criteria may be used to determine which loci to include in a given panel. Constituents of a panel may be selected using a statistical ranking method whereby methylation sites statistically correlated with disease status may be selected for inclusion.
  • a panel may comprise, for example, loci with methylation statuses that are individually strong indicators of SLE or a risk of SLE, for example due to a consistently large difference in methylation status between SLE and non-SLE individuals. Such a panel may be useful when a statistically very strong signal is needed (i.e., a statistical signal that may be generated from loci the methylation status of which differs greatly, such as from near zero to near 1 , in samples from SLE or pre-SLE cells compared to non-SLE controls).
  • a strong signal may be needed, for example, when relatively little starting material is available or when there is reason to believe that a sample may have degraded to some extent.
  • Figures 2 and 3 for example, simulate the effect of degradation of sample quality through the introduction of artificial statistical noise, and demonstrate that increasing the DML sample size may overcome this noise.
  • a panel may advantageously include multiple loci that are not strong indicators of SLE on an individual basis, but which in combination produce a robust indication of (or correlation with) SLE and improve the clinical diagnostic utility.
  • loci from a number of processes are included, such as 2, 3, 4, 5, 6, 7, 8, 9, or more processes. Often, even individually weak signals from multiple processes can collectively provide a strong signal to diagnose the presence or absence of SLE.
  • a panel is selected based on population studies that in the aggregate can provide a desired level of sensitivity and specificity over the broad population, so that a single assay can be commercialized that is appropriate for all patients.
  • assays can be tailored for a particular population based on gender, age, ethnicity, or any other result-effective variable. Thus, there may be a panel of assays from which one may select the assay most appropriate for any particular patient. In this case, a computer can be used to select an appropriate assay based on relevant patient data.
  • Methylation sites may also be selected for inclusion or exclusion based on criteria other than those above.
  • loci may be selected based in part on the degree of allelic variation at the site or in the immediate region of an identified methylation site.
  • the presence of multiple alleles at or near a methylation site may complicate data acquisition or analysis by affecting the primers necessary for amplification, the probe sequence necessary to assay a site, or the sequence to be derived from a site, for example.
  • a methylation site at or near a locus wherein alleles of said locus correspond to differential SLE diagnoses may be included in some panels because the assay technique, such as sequencing, may easily be able to obtain and incorporate any allelic information obtained into the data used to generate a final determination.
  • Methylation sites may be selected for inclusion based on their utility within a specific population or ethnic group rather than their utility among patients at large. Thus panels may be selected to maximize the diagnostic efficacy as to a specific patient demographic, such as women (for whom methylation sites located on the Y-Chromosome are unlikely to be informative), or specific genetically similar ethnic groups (which may present allelic frequencies at one or more given loci that differ from the frequencies of civilization as a whole, and which may affect the utility of one or more methylation sites as panel constituents). See, e.g., John Butler (2006) “Genetics and Genomics of Core Short Tandem Repeat Loci Used in Human Identity Testing," J. Forensic Sci. 51(2): 253-265.
  • One or more control loci may be included in a panel. These loci are not known to demonstrate a change in methylation status in SLE or pre-SLE samples compared to non-SLE samples.
  • the assay of the methylation status of one or more control loci may be useful as a measure of the reliability of the results obtained from a given sample analysis.
  • External control loci that are not present in the human genome e.g., synthetic oligos
  • the methylation values of these control loci may change relative to SLE or pre-SLE patients but since they are not from the human genome, they will not be mistaken as patient data.
  • the methylation patterns may be deduced by determining the methylation status, for example the presence of 5-methyl cytosine, of at least 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50, 51 , 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 , 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81 , 82, 83, 84, 85, 86, 87, 88, 89, 90, 91 , 92, 93, 94, 95, 96, 97, 98, 99
  • the selection of at least one locus of at least one of Table 2A or 2B or Table 3A or 3B may comprise a panel.
  • the panel may further comprise at least one locus of at least one of Table 1 A or Table IB.
  • a panel may comprise at least one locus of at least one of Table 2A or 2B or Table 3A or 3B and at least 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least two loci of Table 2A or 2B or Table 3A or 3B and at least 1, 2, 3, 4, 5,
  • a panel may comprise at least three loci of Table 2A or 2B or Table 3A or 3B and at least 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least four loci of Table 2A or 2B or Table 3A or 3B and at least 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least five loci of Table 2A or 2B or Table 3A or 3B and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1,
  • a panel may comprise at least six loci of Table 2A or 2B or Table 3A or 3B and at least 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least seven loci of Table 2A or 2B or Table 3A or 3B and at least 1, 2, 3, 4, 5, 6,
  • a panel may comprise at least eight loci of Table 2A or2B or Table 3A or 3B and at least 1,2,3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least nine loci of Table 2A or 2B or Table 3 A or 3B and at least 1, 2, 3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least ten loci of Table 2A or 2B or Table 3A or 3B and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, or 23 loci from at least one of Table 1A or Table IB. In some embodiments a panel may comprise at least eleven loci of Table 2 A or 2B or Table 3 A or 3B and at least 1,2, 3, 4, 5, 6, 7, 8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least twelve loci of Table 2A or 2B or Table 3A or 3B and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20,21,22, or 23 loci from at least one of Table 1A or Table IB. In some embodiments a panel may comprise at least thirteen loci of Table 2A or 2B or Table 3A or 3B and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least fourteen loci of Table 2A or 2B or Table 3A or 3B and at least 1, 2, 3, 4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20,21,22, or 23 loci from at least one of Table 1A or Table IB. In some embodiments a panel may comprise at least fifteen loci of Table 2A or 2B or Table 3A or 3B and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least sixteen loci of Table 2A or 2B or Table 3 A or 3B and 1,2, 3, 4, 5, 6, 7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20,21,22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least seventeen loci of Table 2A or 2B or Table 3A or 3B and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20,21,22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least eighteen loci of Table 2A or 2B or Table 3A or 3B and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, or 23 loci from at least one of Table 1A or Table IB. In some embodiments a panel may comprise at least nineteen loci of Table 2A or 2B or Table 3A or 3BTable 2A or 2B or Table 3A or 3B and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least twenty loci of Table 2A or 2B or Table 3A or 3B and at least 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, or 23 loci from at least one of Table 1A or Table IB. In some embodiments a panel may comprise at least twenty-five loci of Table 2A or 2B or Table 3A or 3B and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14,
  • a panel may comprise at least thirty loci of Table 2A or 2B or Table 3 A or 3B and at least 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least thirty-five loci of Table 2A or 2B or Table 3A or 3B and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least forty loci of Table 2A or 2B or Table 3A or 3B and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
  • a panel may comprise at least forty-five loci of Table 2A or 2B or Table 3A or 3B and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least fifty loci of Table 2A or 2B or Table 3A or 3B and at least 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least sixty loci of Table 2A or 2B or Table 3 A or 3B and at least 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least seventy loci of Table 2A or 2B or Table 3A or 3B and at least 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least eighty loci of at least one of Table 2A or 2B or Table 3A or 3B and at least 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1, 12, 13, 14, 15,
  • a panel may comprise at least ninety loci of at least one of Table 2A or 2B or Table 3A or 3B and at least 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20,
  • a panel may comprise at least one hundred loci of at least one of Table 2A or 2B or Table 3 A or 3B and at least 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least two hundred loci of at least one of Table 2A or 2B or Table 3 A or 3B and at least 1 , 2, 3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20,21,22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least three hundred loci of at least one of Table 2A or 2B or Table 3 A or 3B and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least four hundred loci of at least one of Table 2A or 2B or Table 3A or 3B and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least five hundred loci of at least one of Table 2A or 2B or Table 3A or 3B and at least 1,2,3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, or 23 loci from at least one of Table 1A or Table IB. In some embodiments a panel may comprise at least six hundred loci of at least one of Table 2A or 2B or Table 3A or 3 B and at least 1,2,3,4,5,6,7, 8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least seven hundred loci of at least one of Table 2A or 2B or Table 3A or 3B and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 21, 22, or 23 loci from at least one of Table 1A or Table IB. In some embodiments a panel may comprise at least eight hundred loci of at least one of Table 2A or 2B or Table 3 A or 3B and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least nine hundred loci of at least one of Table 2A or 2B or Table 3A or 3B and at least 1, 2, 3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, or 23 loci from at least one of Table 1A or Table IB. In some embodiments a panel may comprise at least one thousand loci of at least one of Table 2A or 2B or Table 3 A or 3B and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least one thousand five hundred loci of at least one of Table 2A or 2B or Table 3 A or 3B and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22, or 23 loci from at least one of Table 1A or Table IB. In some embodiments a panel may comprise at least two thousand loci of at least one of Table 2A or 2B or Table 3A or 3B and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, or 23 loci from at least one of Table 1A or Table IB. In some embodiments a panel may comprise at least two thousand five hundred loci of at least one of Table 2A or 2B or Table 3A or 3B and at least 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13,
  • a panel may comprise at least three thousand loci of at least one of Table 2A or 2B or Table 3A or 3B and at least 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, or 23 loci from at least one of Table 1A or Table IB.
  • a panel may comprise at least three thousand five hundred loci of at least one of Table 2A or 2B or Table 3A or 3B and at least 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14,
  • a panel may comprise at least four thousand loci of at least one of Table 2A or 2B or Table 3A or 3 B and at least 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19,
  • a panel may comprise at least more than four thousand loci of at least one of Table 2A or 2B or Table 3A or 3B and at least 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20,
  • loci to comprise one or more panels are selected exclusively from at least one of Table 1A or Table IB. In some embodiments loci to comprise one or more panels are selected exclusively from Table 2A or 2B or Table 2A and 2B. In some embodiments loci to comprise one or more panels are selected exclusively from Table 3 A or 3B or Tables 3 A and 3B. In some embodiments loci to comprise one or more panels are selected from at least one of Table 1A or Table IB and Table 2A or 2B. In some embodiments loci to comprise one or more panels are selected from at least one of Table 1A or Table IB and Table 3 A or 3B. In some embodiments loci to comprise one or more panels are selected from Table 2 A or 2B and Table 3 A or 3B. In some embodiments loci to comprise one or more panels are selected from at least one of Table 1A or Table IB and Table 2 A or 2B and Table 3 A or 3B.
  • an oligonucleotide primer or probe is disclosed.
  • at least one probe or primer is designed having a sequence which matches that of a locus listed in Table 2A or 2B or Table 3A or 3B, or a locus listed in at least one of Table 1A or Table IB.
  • at least one probe or primer is designed having a sequence which spans a locus listed in Table 2A or 2B or Table 3A or 3B.
  • the probe or primer is designed to base pair with a nucleotide sequence which is predicted to result from the treatment of at least one locus of Table 2A or 2B or Table 3A or 3B, or at least one of Table 1A or Table IB with a reagent that alters nucleotide base identity in a manner that is methylation-sensitive.
  • this treatment is bisulfite treatment as part of a process that selectively transforms cytosine but not 5-methyl cytosine to uracil, thus changing the base pairing properties of the molecule treated.
  • a population of oligonucleotide probes is synthesized such that the population comprises one or more of the possible probes corresponding to each possible methylation pattern for a given locus given the known methylation patterns of the loci selected from at least one of Table 2A or 2B or Table 3A or 3B or at least one of Table 1A or Table IB.
  • at least one of these probes may distinguish between DNA that was methylated at a given base or bases prior to a chemical treatment that differentially affects methylated DNA as compared to unmethylated DNA from DNA that was not methylated at a given base or bases prior to chemical treatment that differentially affects methylated DNA as compared to unmethylated DNA.
  • the oligonucleotide is designed to anneal to a template comprising one of the methylation loci selected from the loci listed in Table 2A or 2B or Table 3A or 3B, or additionally the loci listed in at least one of Table 1A or Table IB. In some embodiments the oligonucleotide is designed to anneal to a cDNA molecule derived from the mRNA or other RNA product associated with a locus of at least one of Table 2A or 2B or Table 3 A or 3B or at least one of Table 1 A or Table IB.
  • the oligonucleotide may comprise a panel of 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50 or more than 50 oligo probes.
  • said panel may comprise at least one oligo spanning or related to a locus listed in Table 2A or 2B.
  • the panel may comprise at least one oligo spanning or related to a locus listed in Table 3 A or 3B.
  • the panel may further comprise an oligo spanning or related to a locus listed in at least one of Table lA or Table IB.
  • the primer is designed to base pair with a nucleotide sequence that is predicted to result from the treatment of at least one locus of at least one of Table 2 A or 2B or Table 3 A or 3B or at least one of Table 1A or Table IB with a reagent that alters nucleotide base identity in a manner that is methylation-sensitive.
  • this treatment is bisulfite treatment that selectively transforms cytosine but not 5-methyl cytosine to uracil, thus changing the base pairing properties of the molecule.
  • the oligonucleotide primer selectively anneals to a specific DNA sequence corresponding to a specific methylation pattern of a locus of at least one of Table 2A or 2B or Table 3A or 3B or at least one of Table 1A or Table IB, or to a specific embodiment of one of the complete set of possible methylation patterns of a locus of at least one of Table 2A or 2B or Table 3A or 3B or at least one of Table 1 A or Table IB.
  • primer pairs are designed to amplify loci comprising methylation sites. In some embodiments primer pairs are designed to amplify segments of transcripts or cDNA molecules derived from transcripts the synthesis of which is directed from loci associated with methylation sites, such as transcripts from the genes listed in Tables 2, 3, or 1.
  • primer pairs are synthesized in combination with one or more oligonucleotide probes.
  • these probes are labeled such that binding to a target sequence results in a detectable configuration change in a probe or detectably affects another probe.
  • these probes are specific to DNA that results from bisulfite -treated methylated DNA or to bisulfite treated unmethylated DNA or to untreated DNA.
  • an oligonucleotide is 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, or more than 35 bases long.
  • primer pair sets may be assembled wherein at least one primer pair set is capable of amplifying a locus identified in Table 2 under standard PCR conditions known to one of skill in the art. Under the appropriate conditions, primer pairs may be able to direct the amplification of loci spanning a methylation site, or additionally spanning 20bp, 40bp, 60bp, 80bp, l OObp, 500bp, lkb, 1.5kb, 2kb, 2.5kb, 5kb, l Okb, or more than lOkb on either side of a methylation site listed in at least one of Table 2 A or 2B or Table 3A or 3B or at least one of Table 1A or Table IB.
  • transcript accumulation levels for transcripts derived from genes associated with at least one of the loci listed in at least one of Table 2A or 2B or Table 3A or 3B may be determined. Additionally, transcript levels for at least one of the loci listed in at least one of Table 1A or Table IB may be determined. Methylation sites from Tables 1 , 2, and 3 were associated with genes if they were located between 10 kb upstream and 10 kb downstream of a gene's transcribed region. Generally, an increase in methylation status at a methylation site within a gene's promoter region indicates a decrease in the accumulation level of transcripts from at least one locus corresponding to or near the methylation site.
  • transcript accumulation levels of each member of a panel discussed above may be assayed.
  • Transcripts to be used in performing an assay may be selected from transcripts that span one or more of the loci listed in Tables 2 A, 2B, 3 A and 3B, or from transcripts derived from genes that span one or more of the loci listed in Tables 2A, 2B, 3A and 3B, or from transcripts derived from genes that are located within lOObp of the loci listed in Tables 2 A, 2B, 3 A and 3B, or from transcripts derived from genes that are located within 200bp of the loci listed in Tables 2A, 2B, 3A and 3B, or from transcripts derived from genes that are located within 300bp of the loci listed in Tables 2A, 2B, 3A and 3B, or from transcripts derived from genes that are located within 400bp of the loci listed in Tables 2 A, 2B, 3 A and 3B, or from transcripts derived from genes that are
  • transcript accumulation levels are assayed in cell populations comprising peripheral blood mononuclear cells.
  • the cell population is selected from a group comprising T-cells, B-cells, monocytes, cells from whole blood, or other tissue of a patient.
  • the cell population from which DNA is to be assayed is a subset of T-cells (such as Thl , Th2, Thl 7, T-regs, NK cells), a subset of B-cells (such as Bl , B2) and/or a subset of monocytes (such as Ml and M2 monocytes or dendritic cells).
  • T-cells such as Thl , Th2, Thl 7, T-regs, NK cells
  • B-cells such as Bl , B2
  • monocytes such as Ml and M2 monocytes or dendritic cells
  • transcript accumulation patterns determined for one or more of the above cell populations may be compared to transcript accumulation or methylation patterns determined from similar cell types in one or more individuals with a known SLE status, or from one or more different cell populations taken from the same or different individuals compared to the individual to be diagnosed.
  • an increase or decrease in the transcript accumulation level of a gene corresponding to at least one locus from a cell or population of cells of an individual to be diagnosed may be indicative of that individual's SLE status.
  • the transcript accumulation level of a gene corresponding to at least one locus from a cell or population of cells of an individual to be diagnosed may be determined and then normalized to the transcript accumulation level or methylation status of a control transcript or locus to control for error in detection methods that may otherwise impact a diagnosis as to the presence or absence of SLE.
  • Appropriate control loci, transcripts or techniques are known to one of skill in the art.
  • Transcript accumulation levels may be assayed using quantitative PCR, ribonucleic acid blot hybridization assays, microarray assays, DNase protection assays, or quantitative nucleic acid sequencing methods, for example. Embodiments disclosed herein are not limited to any particular method of transcript isolation or accumulation level assay to the exclusion of any other.
  • RNA may be purified from an isolated cell or cells from, for example, a patient to be diagnosed, using methods known in the art.
  • the cell source may be at least one peripheral blood mononuclear cell, or may specifically be at least one T-cell, B- cell, monocyte, or cell from whole blood.
  • Accumulation levels of 1 , 2, 3, 4, 5 or more, 10 or more, 20 or more, 25 or more, 50 or more than 50 genes such as 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50 or more than 50 genes, may be determined.
  • Transcript accumulation levels may be used much like methylation status determinations.
  • the set of values determined for a test sample is compared to the values from controls, and deviation from control values, either absolute or relative, may be used to assess the presence or risk of SLE in an individual.
  • the activity or accumulation level of at least one protein encoded by a gene at a locus identified or associated with a locus in at least one of Table 2A or 2B or Table 3A or 3B is monitored. In some embodiments the activity or accumulation level of at least one protein encoded by a gene at a locus identified or associated with a locus in Table 2A is monitored. In some embodiments the activity or accumulation level of at least one protein encoded by a gene at a locus identified or associated with a locus in Table 2B is monitored. In some embodiments the activity or accumulation level of at least one protein encoded by a gene at a locus identified or associated with a locus in Table 3A is monitored.
  • the activity or accumulation level of at least one protein encoded by a gene at a locus identified or associated with a locus in Table 3B is monitored. In some embodiments the activity or accumulation level of at least one protein encoded by a gene at a locus identified or associated with a locus in Table 1A is monitored. In some embodiments the activity or accumulation level of at least one protein encoded by a gene at a locus identified or associated with a locus in Table IB is monitored. In some embodiments the activity or accumulation level of at least one protein encoded by a gene at a locus identified or associated with a locus in at least one of Table 1A or Table IB is monitored.
  • the activity or accumulation level or both are measured in a protein population derived from peripheral blood mononuclear cells.
  • an increase in methylation status at a methylation site within a gene's promoter region indicates a decrease in the accumulation level and/or total activity of proteins encoded by at least one locus corresponding to or near the methylation site.
  • the protein population is selected from a group comprising proteins corresponding to T-cells, B-cells, monocytes, cells from whole blood, or other tissue of a patient.
  • the protein population is selected from a group comprising proteins corresponding to a subset of T-cells (such as Thl , Th2, Thl 7, T-regs, NK cells), a subset of B-cells (such as B l , B2) and/or a subset of monocytes (such as Ml and M2 monocytes or dendritic cells).
  • a subset of T-cells such as Thl , Th2, Thl 7, T-regs, NK cells
  • B-cells such as B l , B2
  • monocytes such as Ml and M2 monocytes or dendritic cells
  • activity or accumulation levels of protein populations comprising at least one protein encoded by at least one gene associate with a locus listed in at least one of Table 2A or 2B or Table 3 A or 3B corresponding to a non-SLE individual are compared to similar levels in from similar protein populations corresponding to an individual having SLE or an individual presenting a methylation profile corresponding to SLE or an individual for which the presence of SLE or early signs of SLE are to be diagnosed.
  • the above protein populations comprise at least one purified protein selected from the list of proteins encoded by genes at the loci listed in Table 2 A or 2B or Table 3 A or 3B.
  • Proteins to be used in an assay may be selected from proteins encoded by genes that span one or more of the loci listed in Tables 2A, 2B, 3A and 3B, or from proteins encoded by genes that are located within lOObp of the loci listed in Tables 2 A, 2B, 3 A and 3B, or from proteins encoded by genes that are located within 200bp of the loci listed in Tables 2A, 2B, 3A and 3B, or from proteins encoded by genes that are located within 300bp of the loci listed in Tables 2A, 2B, 3A and 3B, or from proteins encoded by genes that are located within 400bp of the loci listed in Tables 2A, 2B, 3A and 3B, or from proteins encoded by genes that are located within 500bp of the loci listed in Tables 2A, 2B, 3A and 3B, or from proteins encoded by genes that are located within 750bp of the loci listed in Tables 2A, 2B, 3A and 3B, or
  • Proteins may be assayed by accumulation level, using protein-specific antibodies, mass-spectrometric methods, nonspecific staining techniques, purification techniques, or a combination of the above. Proteins may also be quantified by measuring their activity levels in a sample extract by, for example, measuring the rate at which they metabolize or otherwise modify a substrate. Method of measuring protein accumulation levels and activity are known to those of skill in the art.
  • a panel of proteins is assayed. Accumulation levels of 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50 or more than 50 proteins may be determined.
  • accumulation levels of 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50 or more that 50 proteins may be determined.
  • the panel comprises at least one protein from the proteins encoded by genes associated with the loci listed in Table 2A or 2B or Table 3A or 3B.
  • the panel may additionally comprise at least one protein from the proteins encoded by genes at the loci listed in at least one of Table 1A or Table IB.
  • the activity or accumulation level or both of at least one protein selected from the list of proteins taught in at least one of Table 2A or 2B or Table 3A or 3B is measured.
  • this at least one protein is taken from an individual's peripheral blood mononuclear cells, or T-cells, B-cells or monocytes or cells from whole blood or a subset of T-cells (such as Thl , Th2, Thl 7, T-regs, NK cells), a subset of B-cells (such as B l , B2) and/or a subset of monocytes (such as Ml and M2 monocytes or dendritic cells).
  • this at least one protein is taken from serum, such as serum in circulation.
  • the protein is a free circulating protein.
  • this measurement is compared to a reference measurement made from a similar cellular protein source (i.e., peripheral blood mononuclear cells, cells from whole blood or T-cells, B-cells or monocytes, or a subset of T-cells (such as Thl , Th2, Thl7, T-regs, NK cells), a subset of B-cells (such as Bl , B2) and/or a subset of monocytes (such as Ml and M2 monocytes or dendritic cells) respectively) from a healthy individual.
  • a similar cellular protein source i.e., peripheral blood mononuclear cells, cells from whole blood or T-cells, B-cells or monocytes, or a subset of T-cells (such as Thl , Th2, Thl7, T-regs, NK cells), a subset of B-cells (such as Bl , B2) and/or a subset of monocytes (such as Ml and M2
  • Protein activity or accumulation levels may be used much like methylation status determinations.
  • the set of values determined for a test sample is compared to the values from controls, and deviation from control values, either absolute or relative, may be used to assess the presence or risk of SLE in an individual.
  • any of the foregoing assays in which methylation status of one or more markers is evaluated can be supplemented by additional data, because the methylation state of a single DML may be informative when integrated with other non-methylation data such as transcription profiles and genomic profiles.
  • additional data such as transcription profiles and genomic profiles.
  • biomarkers may be used in combination with any of the forgoing assays.
  • SNPs or other allelic data relating to, for example, PTPN2; ITGAM-ITGAX; IRF5; IRF8; the FcRy genes; CTLA-4; STAT-4; BANK; IRAKI ; FCRL3; Cl q; C2; C4; C5aR; Complement Factor H and Factor H-Related Genes, MECP2; IKZF3; TMEM39a BLK; KIAA1542; PXK; or the MHC alleles HLA DR2, DR3, DR5, or HLA-DQ may be used.
  • protein levels markers including but not limited to blood or serum chemical, biochemical, or protein markers, antibodies or auto-antibodies associated with lupus or autoimmune disease
  • a nonlimiting list of examples comprises the antibodies anti-dsDNA; anti-nuclear antibodies; anti -phospholipids; anti-cardiolipin; anti- 2glycoprotein; anti-Ro anti-La; anti-snRNP (Ul- RNP); anti-ribonuclear protein; anti-histone; anti-nucleosome; anti-N-methyl-D-aspartate (NR2); anti-Cl q; cell surface molecules such as CD27; CD154; CD95; levels of cytokine and chemokine such as a-interferon; IL-6; TNFSF13B/BAFF;CXCL10;CCL2;CCL19; enzymes such as neutrophil gelatinase-associated lipocalin ( GAL); serum proteins and lipoproteins such as acute phase proteins; or proinflammatory HDL
  • markers can be used in combination with the methylation information to further enhance the diagnosis of SLE. See, for example, Ahern, et al. (2012) "Biomarkers for systemic lupus erythematosus " Translational Research Volume 159, Number 4, Zhou et al. (2012) “Gene- Gene Interaction of BLK, TNFSF4, TRAF1, TNFAIP3, and REL in Systemic Lupus Erythematosus” ARTHRITIS & RHEUMATISM V64(l):222-231 , van der Helm-van Mil et al.
  • a methylation profile for a DNA region or portion thereof, or multiple regions or portions thereof selected from regions corresponding to at least one of the loci listed in at least one of Table 2A or 2B or Table 3A or 3B and optionally additionally including regions corresponding to at least one of the loci in at least one of Table 1A or Table IB can, for example, be given a methylation value that may be compared by a computer to a threshold value, as described herein, or each methylation site may be evaluated individually.
  • an activity profile or an accumulation profile or both for a protein, transcript, population or set or proteins comprising at least one protein taught by at least one of Table 2A or 2B or Table 3 A or 3B, or population or set or transcripts comprising at least one transcript taught by at least one of Table 2A or 2B or Table 3A or 3B can, for example, be given an activity or an accumulation value that may be compared by a computer to a threshold value, as described herein, or each value may be evaluated individually.
  • Evaluation of the results of a panel assay may be accomplished using a computer-based algorithm. Such an algorithm may evaluate the methylation status of the loci evaluated in comparison to known or measured control levels indicative of the presence or absence of SLE.
  • An algorithm may assess the absolute or relative difference in methylation status between loci, and may weigh all loci equally or may give greater or lesser significance to certain loci based on, for example, prior knowledge of the significance of these loci in diagnosis, or redundancy of certain loci in comparison to other loci assayed.
  • Evaluation of the results obtained by assaying a panel of data from a sample taken from an individual may involve evaluating individual or combined values reflective of the extent of methylation at said loci or perturbations in gene product accumulation or activity. This evaluation may involve calculating the difference between values for samples in terms of absolute or normalized values obtained, or may involve calculating the ratio of values obtained in comparison to reference or control values, for example.
  • the results of such an evaluation may be collected into a report which may contain values for the assay results, information regarding SLE status of the individual, or both.
  • the report may, for example, contain a metric indicating the number of methylation sites showing a methylation status indicative of SLE, or the aggregate deviation from an SLE-free methylation pattern or from an SLE pattern.
  • the report may contain a statistical probability, or a simple yes/no assessment of the presence of an SLE methylation pattern.
  • Reports may be generated providing information mentioned above resulting from an assay of transcript accumulation level panels, protein accumulation level panels, or protein activity panels as well.
  • a panel used to generate a report such as those described above may involve a locus selected from Table 2A, and may also involve a locus selected from Table 3A or 3B.
  • a panel used to generate a report such as those described above may involve a locus selected from Table 2B, and may also involve a locus selected from Table 3 A or 3B.
  • a panel involving an assay of methylation status may be comprised exclusively of loci from Table 3A, exclusively from Table 3B, or exclusively of loci from Table 2A, or exclusively from Table 2B, or may also include loci from more than one table.
  • a panel involving transcript or protein accumulation levels or protein activity levels may be comprised exclusively of levels taught in Table 2 A and/or Table 2B, or may also comprise levels associated with loci of Tables 1A, IB, 3A, or 3B.
  • the panel assessed may comprise at least 5, at least 10, at least 20, at least 25, at least 50 or more than 50 loci or loci associated levels, for example 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50 or more that 50 loci or loci associated levels.
  • an increase in methylation at a methylation site within the promoter region of a gene indicates a decrease in transcript accumulation level, protein accumulation level and, subsequently, total protein activity of a gene product corresponding to or adjacent to a methylation site.
  • a change in methylation status may, for example, affect R A processing such as splicing, or may affect chromatin structure, either increasing or decreasing transcription, all of which can be measured using standard biochemical techniques.
  • the methylation levels, transcript accumulation levels, protein accumulation levels or protein activity levels may serve as the input for a classification model (e.g., support vector machine), which may generate a list of phenotype-specific probabilities. In some embodiments, the highest probability phenotype will be assigned to the sample. In some embodiments, a classification model will be trained on samples with known phenotypes. Using these training samples, some models may automatically weigh the loci's methylation levels to maximize its ability to correctly predict these training samples. For example, if one locus is more informative than the others, its methylation value will have a stronger influence in the assignment of phenotype probabilities. Loci can be considered independently or combinatorially by the classification model. Other methods of evaluation are contemplated, and embodiments are not limited to a specific method of analysis.
  • a classification model e.g., support vector machine
  • the tools are advantageously provided in the form of computer programs that are executable, for example, by a general purpose computer system (referred to herein as a "host computer").
  • the host computer may be of conventional design.
  • the host computer may be made in any number of dimensions and styles (e.g., desktop PC, laptop, Tablet PC, handheld computer, server, workstation, mainframe) and may be configured with many different hardware components. Standard components, such as disk drives, CD and/or DVD drives, monitors, and keyboards, for example, may be included in some configurations.
  • the connections may be effected via any suitable transport media (e.g., wired, optical, and/or wireless media) and any suitable communication protocol (e.g., TCP/IP).
  • the host computer may include suitable networking hardware (e.g., modem, Ethernet card, WiFi card).
  • suitable networking hardware e.g., modem, Ethernet card, WiFi card.
  • the host computer may implement any of a variety of operating systems, including UNIX, Linux, Microsoft Windows, MacOS, or any other commercially available operating system. Embodiments disclosed herein are not limited to any particular hardware or software.
  • aspects of the present invention may be implemented using any of a variety of computer code languages, including PERL, C, C++, Java, JavaScript, VBScript, AWK, or any other scripting or programming language that can be executed on the host computer or that can be compiled to execute on the host computer. Code may also be written or distributed in low level languages such as assembler languages or machine languages. Embodiments disclosed herein are not limited to any particular computer language.
  • the host computer system advantageously provides an interface through which the user directs operation of the tools.
  • commands can be adapted to a number of operating systems as appropriate.
  • a graphical user interface may be provided, which allows the user to control operations using a pointing device.
  • embodiments of the present invention are not limited to any particular user interface.
  • Programs or scripts for incorporating various features of the present invention may be encoded on various computer readable media for storage and/or transmission.
  • Storage or transmission media such as magnetic disk or tape, optical storage media such as compact disk (CD) or digital versatile disk (DVD), flash memory, and carrier signals adapted for transmission via wired, optical, and/or wireless networks conforming to a variety of protocols, including the Internet are contemplated.
  • Embodiments disclosed herein are not limited to any particular storage or transmission medium.
  • kits comprising at least one of the reagents disclosed herein.
  • Said reagent may comprise an oligonucleotide probe or primer, a gene chip, an antibody, a panel list or any other reagent disclosed herein provided that the kit comprises a reagent specific for a locus disclosed in any one of Tables 2A, 2B, 3A or 3B.
  • the kit comprises a reagent related to a locus disclosed in Table 2A.
  • the kit comprises a reagent related to a locus disclosed in Table 2B.
  • the kit comprises a reagent related to a locus disclosed in Table 3A.
  • the kit comprises a reagent related to a locus disclosed in Table 3B.
  • this kit provides at least one reagent that facilitates the determination of the methylation status of at least one locus selected from the list of loci in Table 2A or 2B or Table 3A or 3B, or of a panel of loci which comprises at least one locus selected from at least one of Table 2A or 2B or Table 3 A or 3B and which may further comprise at least one locus selected from the list of loci in at least one of Table 1A or Table IB.
  • the kit comprises a reagent related to a locus of Table 4.
  • kits may comprise a reagent for the determination of the methylation state of at least one locus selected from Table 2A or 2B or Table 3A or 3B.
  • the kit also includes at least one oligonucleotide primer comprising a sequence hybridizing to at least a portion of the at least one locus selected from the group consisting of the loci listed in Table 2A or 2B or Table 3 A or 3B.
  • the kit can include one or more of methylation-sensitive restriction endonucleases, amplification reagents such as PCR reagents, probes and/or primers.
  • an analysis platform is used to analyze bisulfite converted sequences.
  • exemplary analysis platforms include: (1) Illumina BeadChips and (2) next generation sequencing (GS), although other platforms are contemplated and no platform should be perceived as limiting.
  • the first step in either platform is the bisulfite conversion of sample DNA. This forms an artificial oligonucleotide (i.e., not found in nature) where every unmethylated cytosine is transformed into a uracil.
  • Illumina BeadChips there are two probe types. For type I probes, the bisulfite converted sample sequences hybridize to BeadChip- oligonucleotides in a methylation state specific matter.
  • a sample sequence is hybridized to a BeadChip-bound oligonucleotide
  • an artificially marked nucleotide is added to the BeadChip-bound oligonucleotide.
  • bisulfite converted sequences hybridize to BeadChip-attached oligonucleotides, independent of methylation status.
  • An artificially marked nucleotide is then selectively added. The identity of the added nucleotide is dependent on the methylation status of the sample sequence.
  • the methylation states of the sample sequences for both type I and type II probes result read by red and green light intensities, generated by a BeadChip scanner. The ratio of these captured intensities estimates the degree of methylation for each CpG.
  • NGS targeted CpGs in the bisulfite converted sample sequence are selectively PCR amplified for NGS processing. Determining which CpGs we amplify is not obvious (described below). Due to the artificial nature of our sequences, special experimental conditions are required to amplify our targeted regions. For example, we must use a DNA polymerase that properly reads uracil nucleotides, and we must be mindful that bisulfite converted DNA is single stranded and thus more unstable relative to natural DNA. In some NGS configurations, we will need to make further adjustments to account for the lower GC content of bisulfite converted sequences.
  • control DNA on the Illumina NGS platform so that there is sufficient representation of all 4 detected nucleotides, as required by the Illumina bioinformatics software.
  • the NGS platform will produce sequence read-outs of our targeted regions. We transform these sequences via in silico demethylation so that they we can accurately map them back to an unmethylated bisulfite converted genome. Once mapped to the genome, we can identify the nucleotides present at methylation sites on the sequence. Due to bisulfite conversion, unmethylated cytosines are artificially transformed into another base (i.e., uracil), and as a result, unmethylated cytosines are read as a different base than methylated cytosines.
  • another base i.e., uracil
  • the output from both BeadChip and NGS platforms is formatted into an n x m matrix, where n represents the number of interrogated CpGs and m represents the number of samples.
  • a BeadChip matrix may consist of 480,000 CpGs across 48 samples, representing a total of 23 million data points. Each data point represents a degree of methylation (e.g., methylation frequency).
  • a classification model is trained on a static data set that contains methylation frequency profiles for samples of known phenotypes.
  • a methylation profile consists of an arbitrary number of methylation frequency values.
  • This statistical algorithm transforms methylation frequencies so that their statistical importance can be measured. An example entails transforming these methylation frequencies into phenotype-specific methylation frequency distributions and assessing the similarity of these distributions.
  • methylation frequency profiles Prior to analysis by a classification model, methylation frequency profiles may be transformed. An example is subtracting each methylation frequency in a methylation profile by the profile's methylation frequency average and dividing by the standard deviation.
  • the classification model determines which methylation signature across all input CpGs best identifies each phenotype.
  • the classification model may perform additional transformations. For example, a methylation profile containing 25 CpG methylation frequency values may be transformed via a kernel function into a unitless profile containing 30 values. While training, the classification model analyzes the CpGs in the methylation profile simultaneously and may use CpGs independently or in various combinations to classify phenotypes.
  • the assignment of phenotypes is arbitrary. Samples may be partitioned into two phenotype groups (e.g., disease and no-disease). A multi-disease data set may be partitioned such that each disease is assigned a distinct phenotype or subtypes of a disease is represented as individual phenotypes (e.g., disease subtype I, disease subtype II, and disease subtype III, and no disease).
  • a test methylation profile is inputted into the classification model and it is compared against the patterns learned from the training data set.
  • the model outputs a classification value per phenotype included in the training set.
  • these classification values can directly represent classification probabilities.
  • the output values are not probabilistic and may be further transformed to represent probabilistic values (via linear regression, for example).
  • the output values are then compared against thresholds determined based on the training data to classify the sample.
  • classification threshold values are not obvious.
  • the training data set may be used in combination with cross validation algorithms to assess an optimal cutoff value that best identifies a phenotype of interest relative to all other phenotypes.
  • the resultant classification may be further transformed so that it is more interpretable to the data recipient.
  • classification information can show sensitivity and specificity numbers, or recite a probability that the patient has SLE.
  • a "DNA reservoir” is any source of DNA which may be informative in diagnosis, such as DNA from an individual or individuals, DNA from a cell population taken from an individual or individuals, or extracellular DNA found within an individual, such as free circulating DNA.
  • a "Differentially Methylated Locus” or “DML” is a methylation site of any of Tables 1A, IB, 2 A, 2B, 3 A or 3B.
  • Gaussian noise involves generating a random number based on a Gaussian distribution. This random number is then added to the methylation frequency. For each DML where noise is added, the Gaussian distribution is defined by mean equal to 0 and variance equal to the pooled variance of the samples.
  • locus is a specific place on a chromosome where a base (nucleic acid residue) or a consecutive set of bases is located.
  • a locus may be a methylation site, or it may be a consecutive set of bases comprising a methylation site and adjacent sequence. It may comprise, for example, a methylation site as well as lOObp, 200bp, 300bp, 400bp, 500bp, 75bp, lkb, 1.5 kb, 2kb, 2.5kb, 5kb, 10 kb, or more than 10 kb on either side of the methylation site.
  • methylation refers to the addition of a methyl (CH 3 ) moiety, for example onto a cytosine base at positions C5 or N4, onto an adenine at the N6 position, or onto any other molecular structure capable of forming a covalent bond with CH 3 .
  • methylation refers to cytosine methylation at positions C5 to produce 5-methyl cytosine.
  • unmethylated DNA or “methylated DNA” can also be used informally to refer to a segment of DNA having at least one base capable of being methylated and wherein said at least one base is unmethylated or methylated, respectively.
  • a "methylation pattern” refers to the set of methylation states of two or more bases in a genome.
  • the profile can include the methylation state of every base in a cell, tissue or individual, or can comprise any subset thereof comprising more than one base.
  • a “methylation state” or “methylation status” refers to the presence, absence or extent of methylation at a particular base or set of bases, or nucleotides within a portion of DNA, or other molecule capable of being methylated.
  • Determination of the methylation status of a particular DNA sequence can involve determination of the methylation state of every cytosine C5 position in the sequence or can involve determination of the methylation state of a subset of the cytosine C5 positions (such as the methylation state of cytosines in one or more specific restriction enzyme recognition sequences) within the sequence, or can involve determining regional methylation density within the sequence without providing precise information of where in the sequence the methylation occurs, or can refer to the determination of the methylation status at other positions along a given molecule.
  • a "methylation site” is a specific base that is known to be methylated under some condition. Such a site may be, differentially methylated, or methylated with a different frequency, in one population of cells or individuals as compared to another (i.e., cells of individuals suffering from SLE or pre-SLE or SLE-like symptoms in comparison to cells of SLE-free individuals). A methylation site need not be methylated under all conditions- for example, a methylation site may be completely unmethylated in some individuals or cell populations
  • a "p-value" represents the probability of finding a test statistic that is at least as extreme as the one reported.
  • a "q-value" represent the false discovery rate (FDR) to account for multiple hypothesis testing.
  • a "panel” is a set of methylation sites, loci, genes or proteins whose methylation states, transcript accumulation levels, or activities or accumulation levels, respectively, may be used to diagnose a condition such as SLE, determine a cell type, determine a cell fate or otherwise evaluate a cell, cell population or individual from which a cell or cell population is derived.
  • a "primer” is a probe which is used to provide a 3 ⁇ moiety to which a nucleotide triphosphate may be added in a DNA synthesis reaction such as, for example, a polymerase chain reaction.
  • a "probe” is an oligonucleotide that specifically binds a given DNA sequence. Probes may be modified so that binding to a substrate differentially affects an assayable output.
  • a "protein accumulation level” is the aggregate amount of that protein that is present in a sample from a cell or cell population. It represents the net effects of translation and degradation on a given protein population, and is often colloquially referred to as 'expression level' of a protein.
  • a "protein accumulation profile” is the measure of accumulation levels for a panel of proteins.
  • a "protein activity” is a measure of the rate at which a reaction in which the protein participates occurs in a sample from a cell or cell population comprising a given protein. It represents the net effects of translation, degradation, post- translational modification and substrate availability for a given protein population, and is often colloquially referred to as 'expression level' of a protein.
  • a "protein activity profile” is the measure of protein activities for a panel of proteins.
  • an isolated DNA molecule having a sequence "spanning" a methylation site has a sequence that base pairs with the sequence immediately on either side of the methylation site.
  • a "transcript accumulation level" of a related gene is the aggregate amount of RNA derived from that gene that is present in a sample from a cell or cell population. It represents the net effects of transcription and transcript degradation on a given transcript population, and is often colloquially referred to as 'expression level' of a transcript.
  • a "transcript accumulation pattern" is the set of transcript accumulation levels for each member of a gene panel.
  • Peripheral blood mononuclear cells are extracted using techniques known to those of skill in the art.
  • the protocol of Mallone (Mallone, R. et al., (2010) Isolation and preservation of peripheral blood mononuclear cells for analysis of islet antigen-reactive T cell responses: position statement of the T-cell Workshop Committee of the Immunology of Diabetes Society, Clin. Exp. Immunol. 163:33-49) is among the protocols known in the art.
  • DNA from peripheral blood mononuclear cells or other cells found in blood are extracted using techniques known to those of skill in the art.
  • the protocols of Mallone (Mallone, R. et al., (2010) Isolation and preservation of peripheral blood mononuclear cells for analysis of islet antigen-reactive T-cell responses: position statement of the T-cell Workshop Committee of the Immunology of Diabetes Society, Clin. Exp. Immunol. 163:33-49) and Al-Moundhri (Al-Moundhri, et al., (2010) The prognostic significance of whole blood global and specific DNA methylation levels in gastric adenocarcinoma. PloS one 5:el5585) are among the protocols known in the art.
  • Circulating cell-free DNA is extracted with methods from Li, M et al. (Li, M. et al., (2009) "Sensitive digital quantification of DNA methylation in clinical samples," Nat Biotechnol (27)9: 858-863). Up to 18 ml of blood is collected into standard blood collection tubes containing EDTA. The tubes are immediately chilled to 8 °C and processed within 30 min of collection. The blood cells are pelleted for 15 min at 200g in a Leucosep tube (Greiner) filled with 15 ml of Ficoll-Paque solution.
  • Greiner Leucosep tube
  • the supernatant that is, plasma
  • the supernatant is transferred into 1.5 ml tubes, immediately frozen, and stored at -80 °C.
  • the plasma samples are then thawed at 25 °C for 5 min, and any remaining debris is pelleted at 16,000g for 5 min.
  • the supernatant is transferred to a new tube.
  • Total genomic DNA is then purified from aliquots of 2 ml plasma supernatant using the QIAamp MinElute Virus Vacuum Kit (Qiagen) as recommended by the manufacturer.
  • the DNA is finally eluted in elution buffer (Qiagen), and stored at -20 °C.
  • Extracted DNA is treated largely following the protocol of Frommer et al., Proc. Nat. Acad. Sci. USA 89(1992), the disclosure of which is hereby incorporated by reference in its entirety.
  • 2 ⁇ g of human DNA and 8 ⁇ g of carrier plasmid DNA are sheared through a fine needle, alkali denatured, neutralized, and precipitated.
  • DNA is then incubated in a total volume of 1.2mL with freshly prepared 3.1M Sodium bisulfite / 0.5mM hydroquinone, pH 5.0 for 16 hours at 50°C under mineral oil.
  • the solution is dialyzed at 4°C in an excess volume of 5mM Sodium acetate / 0.5mM hydroquinone, pH 5.2, and then at 4°C in an excess volume of 5mM Sodium acetate, pH 5.2, and finally in an excess of deionized water.
  • the solution is dried under a vacuum and the solid residue is resuspended in 100 ⁇ L of lOOmM Tris/HCl, 01. mM EDTA, pH 7.5 buffer. NaOH is added to a final concentration of 0.3mM and the reaction is allowed to stand at room temperature for 10 minutes. Ammonium acetate is then added to a final concentration of 3M.
  • the DNA is then precipitated, washed and resuspended in lOOuL of lOmM Tris/HCl, 0.1 mM EDTA pH 7.5 buffer, and used immediately or stored at -20°C.
  • Commercial kits for bisulfite conversion are also readily available and can be used in place of the foregoing procedure.
  • Sequencing is performed using dideoxy chain-termination methods and position extension products are visualized using electrophoretic methods appropriate to the label for each dideoxy nucleotide. (Other sequencing techniques, such as pyrosequencing, single strand sequencing, or NGS can also be used.)
  • Example 4 Methylation Determination using an Infinium® HumanMethylation450 BeadChip
  • ddCTP and ddGTP (dideoxy"-) nucleotides to be incorporated are labeled with biotin, while the ddATP and ddTTP are labeled with 2,4-dinitrophenol.
  • the hybridized chip is subjected to repeated rounds of antibody staining to apply fluorophores.
  • the hybridized chips are placed in an Illumina HiScan SQ scanner, a two- color laser (532 nm/660 nm) fluorescent scanner with a 0.375 ⁇ spatial resolution, which is capable of exciting the fluorophores generated during the staining step of the protocol.
  • Image intensities are extracted using GenomeStudio (2010.3) Methylation module (1.8.5) software, scored as 0 (unmethylated) to 1 (fully methylated).
  • each locus in the diagnostic panel multiple sets of candidate primers are designed to efficiently amplify the 50-200 bp bisulfite converted region covering the locus.
  • the best performing primer pair is used in the final diagnostic.
  • Taqman probes are used during this amplification process to determine methylation values.
  • the amplified regions are sequenced to determine methylation values.
  • DMLs Differentially methylated loci identified by methylation-specific arbitrarily primed PCR and methylated CpG island amplification were scored and prioritized using the following scoring variables: (a) appearance using multiple discovery methods; (b) appearance in multiple pools of like samples; (c) located within a CpG island; (d) located within the promoter region of a gene; (e) located near or within predicted or known genes; (f) known to be associated with disease; (g) class of gene (transcription factor, growth factor, apoptosis gene, oncogene, cytokine gene); and (h) repetitive element.
  • DMLs Differentially methylated loci
  • DMLs were selected which, in groups of 3 or less, provided an SLE diagnosis with an error rate of about 14%. By combining these DMLs into a single panel, an SLE diagnosis error rate of 3% was achieved. The DMLs selected for this panel are indicated in Figure 1.
  • Peripheral blood mononuclear cells are extracted using techniques known to those of skill in the art.
  • the protocol of Mallone (Mallone, R. et al., (2010) Isolation and preservation of peripheral blood mononuclear cells for analysis of islet antigen-reactive T- cell responses: position statement of the T-cell Workshop Committee of the Immunology of Diabetes Society, Clin. Exp. Immunol. 163:33-49) is among the protocols known in the art.
  • total PBMCs obtained from SLE and non-SLE patient blood samples are prepared by Ficoll gradient (Langevin et al., 2012. "Peripheral blood DNA methylation profiles are indicative of head and neck squamous cell carcinoma: An epigenome-wide association study, " Epigenetics. 7(3):291 -9).
  • the isolated cell subsets are further enriched for T-Cells, B-cells, and Monocytes by magnetic beads pre-bound with biotinylated monoclonal antibodies towards human cell surface markers (including nonlimiting examples such as anti-huCD4, huCD19 and huCD68) as described (Vallee et al., 1998.
  • DMLs were selected at random from Tables 2A and 2B, below, to constitute panels of from 23 to 50 loci.
  • Control panels were selected comprising methylation sites known in related literature to be methylated independent of SLE status. That is, control panels consisted of methylation sites that were randomly selected without consideration of SLE status.
  • a first set of samples from individuals each having a known SLE status were assayed for their relative methylation status at the loci in randomly generated panels from Table 2A and 2B disclosed herein and from the random methylation sites unrelated to SLE.
  • a second set of samples from individuals each having a known SLE status were assayed for their relative methylation status at each locus in the randomly generated Table 2 A and 2B panels and the control panels.
  • the random Table 2A and 2B panels and the control panels were evaluated for their ability to accurately identify SLE status in the sample data set. A number of panel sizes and methylation level calling parameters were used in various repeats of the analyses.
  • Panels were assessed as to their Sensitivity, a measure of the panel's utility to correctly identify SLE positive samples relative to the total number of analyzed SLE positive patient samples. Panels were also evaluated as to their Specificity, a measure of the panel's ability to correctly call SLE negative samples relative to the total number of analyzed SLE negative samples. Importantly, both high sensitivity and high specificity values are required for a panel to have good performance. An effective panel must be able to classify SLE negative patients as SLE negative and SLE positive patients as SLE positive.
  • the randomly selected Table 2A and 2B DML panels demonstrated a Sensitivity of about 68% (median sensitivity 68%, average sensitivity excluding outliers of 66%) and a Specificity of about 85% (median specificity 85%, average specificity excluding outliers of 85%).
  • the comparable values for the control panels were a Sensitivity of about 0% (median sensitivity of 0%, average excluding outliers of 3%) and a Specificity of about 99% (median specificity of 99%, average specificity excluding outliers of 98%).
  • the determination a panel's overall performance i.e., accuracy) depends both on high sensitivity and high specificity values. For example, even though the control panel has a specificity of about 99% (i.e., SLE negative samples are consistently called SLE negative), the low sensitivity demonstrates that the panel performs poorly because few or no SLE positive samples are called SLE positive.
  • Example 1 1 - Exemplary Panel Performance
  • DMLs were selected from Tables 1A, IB, 2A, 2B, 3A, and 3B for inclusion in diagnostic panels based upon their predictive diagnostic value in a cell type rather than at random, as in Example A above. Panels of sizes comparable to those of the previous example were generated and subject to a similar set of analyses. [0174] Selected DML panels were observed to perform substantially better than both the negative control panels and the randomly selected panels. The selected DML panels demonstrated a Sensitivity of over 80% (median sensitivity of 82.4%, average sensitivity of 81 %) and a Specificity over 96% (median specificity of 97%, average specificity of 96%). The DML which constituted the selected DML panels are disclosed in Table 4.
  • DML optional associated gene
  • DML coordinate refers to the chromosomal coordinate (chromosome: coordinate) of the cytosine of a CpG on the + strand (University of California Santa Cruz; UCSC hgl9 genome build). Each DML coordinate is labeled according to position on the + strand for simplicity but the DML may refer to the CpG on the + and/or - strand.
  • DML optional associated gene
  • DML coordinate refers to the chromosomal coordinate (chromosome: coordinate) of the cytosine of a CpG on the + strand (University of California Santa Cruz; UCSC hgl9 genome build). Each DML coordinate is labeled according to position on the + strand for simplicity but the DML may refer to the CpG on the + and/or - strand.
  • DML coordinate refers to the chromosomal coordinate (chromosome: coordinate) of the cytosine of a CpG on the + strand (University of California Santa Cruz; UCSC hgl9 genome build).
  • chromosomal coordinate chromosome: coordinate
  • Each DML coordinate is labeled according to position on the + strand for simplicity but the DML may refer to the CpG on the + and/or - strand.
  • DML optional associated gene
  • DML coordinate refers to the chromosomal coordinate (chromosome: coordinate) of the cytosine of a CpG on the + strand (University of California Santa Cruz; UCSC hgl9 genome build). Each DML coordinate is labeled according to position on the + strand for simplicity but the DML may refer to the CpG on the + and/or - strand.
  • DML optional associated gene
  • DML coordinate refers to the chromosomal coordinate (chromosome: coordinate) of the cytosine of a CpG on the + strand (University of California Santa Cruz; UCSC hgl9 genome build). Each DML coordinate is labeled according to position on the + strand for simplicity but the DML may refer to the CpG on the + and/or - strand.
  • DML optional associated gene
  • DML coordinate refers to the chromosomal coordinate (chromosome: coordinate) of the cytosine of a CpG on the + strand (University of California Santa Cruz; UCSC hgl9 genome build). Each DML coordinate is labeled according to position on the + strand for simplicity but the DML may refer to the CpG on the + and/or - strand.
  • DML coordinates are listed for selected DML.
  • DML coordinate refers to the chromosomal coordinate (chromosomexoordinate) of the cytosine of a CpG on the + strand (University of California Santa Cruz; UCSC hgl9 genome build). Each DML coordinate is labeled according to position on the + strand for simplicity but the DML may refer to the CpG on the + and/or - strand.

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Abstract

L'invention concerne des locus ayant des sites de méthylation pertinents pour le diagnostic du LES, ainsi que des procédés et des réactifs associés.
PCT/US2013/049371 2012-07-06 2013-07-03 Diagnostic du lupus érythémateux systémique Ceased WO2014008426A2 (fr)

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JP2019526810A (ja) * 2016-06-07 2019-09-19 イミューノビア アクチエボラグ バイオマーカーサインおよびそれらの使用
WO2019216634A1 (fr) * 2018-05-09 2019-11-14 아주대학교산학협력단 Procédé de diagnostic pour le lupus érythémateux disséminé utilisant un auto-anticorps smyd3
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WO2021041931A1 (fr) * 2019-08-28 2021-03-04 The Regents Of The University Of California Procédés de production de profils de méthylation de l'adn
CN117230186A (zh) * 2023-11-14 2023-12-15 中国医学科学院皮肤病医院(中国医学科学院皮肤病研究所) 谷氨酰胺转运体ASCT2作为靶点在制备治疗Tfh相关自身免疫性疾病药物中的应用
WO2024243224A3 (fr) * 2023-05-24 2025-05-08 Oklahoma Medical Research Foundation Approche multi-omique pour évaluer l'hétérogénéité du lupus érythémateux disséminé dans une réponse de traitement

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US10174375B2 (en) 2013-09-20 2019-01-08 The Chinese University Of Hong Kong Sequencing analysis of circulating DNA to detect and monitor autoimmune diseases
JP2019526810A (ja) * 2016-06-07 2019-09-19 イミューノビア アクチエボラグ バイオマーカーサインおよびそれらの使用
KR102109105B1 (ko) * 2018-05-09 2020-05-12 아주대학교산학협력단 Smyd3 자가항체를 이용한 전신홍반성루푸스 진단방법
KR20190128923A (ko) * 2018-05-09 2019-11-19 아주대학교산학협력단 Smyd3 자가항체를 이용한 전신홍반성루푸스 진단방법
WO2019216634A1 (fr) * 2018-05-09 2019-11-14 아주대학교산학협력단 Procédé de diagnostic pour le lupus érythémateux disséminé utilisant un auto-anticorps smyd3
US20210302420A1 (en) * 2018-05-09 2021-09-30 Ajou University Industry-Acadmic Cooperation Foundation Diagnostic method for systemic lupus erythematosus using smyd3 autoantibody
US12498369B2 (en) * 2018-05-09 2025-12-16 Ajou University Industry-Academic Cooperation Foundation Diagnostic method for systemic lupus erythematosus using SMYD3 autoantibody
CN110512000A (zh) * 2018-05-22 2019-11-29 广州市康立明生物科技有限责任公司 肿瘤标志物、甲基化试剂、试剂盒及其应用
WO2021041931A1 (fr) * 2019-08-28 2021-03-04 The Regents Of The University Of California Procédés de production de profils de méthylation de l'adn
WO2024243224A3 (fr) * 2023-05-24 2025-05-08 Oklahoma Medical Research Foundation Approche multi-omique pour évaluer l'hétérogénéité du lupus érythémateux disséminé dans une réponse de traitement
CN117230186A (zh) * 2023-11-14 2023-12-15 中国医学科学院皮肤病医院(中国医学科学院皮肤病研究所) 谷氨酰胺转运体ASCT2作为靶点在制备治疗Tfh相关自身免疫性疾病药物中的应用
CN117230186B (zh) * 2023-11-14 2024-01-26 中国医学科学院皮肤病医院(中国医学科学院皮肤病研究所) 谷氨酰胺转运体ASCT2作为靶点在制备治疗Tfh相关自身免疫性疾病药物中的应用

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