Novel autoantibodies and methods for detecting Sjogren's disease

Abstract

A method and corresponding kit for detecting Sjogren's disease or predicting labial salivary gland biopsy results, comprising assessing an individual for the presence of one or more peptides having an amino acid sequence selected from SEQ ID NOs: 1-67, or full-length proteins or fragments thereof. The method and kit may be in an enzyme-linked immunosorbent assay (ELISA) format.

Description

[Technical Field]

[0001] Federal Funding Statement This invention was made with government support under grants TR002374 and AR065500 awarded by the National Institutes of Health and W81XWH-18-1-0717 awarded by the ARMY / MRDC. The government has certain rights in this invention.

[0002] Sequence Listing This application contains a Sequence Listing that has been submitted to the USPTO as an XML file and is incorporated by reference in its entirety. The Sequence Listing was created on August 8, 2023, has the file name "SEQ_LIST--P220234WO01.xml," and is 58,788 bytes in size.

[0003] FIELD OF THE INVENTION Disclosed herein are protein markers that correlate with the presence of Sjogren's disease in mammalian subjects, including human subjects. Also disclosed herein are methods for diagnosing Sjogren's disease in mammalian subjects by testing the subject for the presence of one or more protein markers and correlating the presence of the protein markers with Sjogren's disease in the subject. Also disclosed herein are kits specially designed for carrying out the methods. [Background technology]

[0004] Sjögren's disease (SD) is one of the most prevalent systemic rheumatic diseases, affecting an estimated 4 million Americans. Ninety percent of affected individuals are women, and the disorder is present in approximately half of patients with other autoimmune connective tissue disorders, such as rheumatoid arthritis, lupus, or scleroderma. The characteristic clinical symptoms of Sjögren's disease—dry eyes and mouth—result from an autoimmune process affecting the lacrimal and salivary glands. Sjögren's disease can cause significant dysfunction in various organs and systems and is associated with significant morbidity and an increased risk of lymphoma. There are no FDA-approved disease-modifying therapies. Tests targeting antibodies (against anti-Ro (SS-A)) are available that can be used in combination with other clinical indicators to diagnose Sjögren's disease. However, 30% of Sjögren's disease patients are "seronegative," and diagnosis requires an invasive inner lip biopsy to look for signs of inflammation in the exocrine glands (salivary and lacrimal glands). To replace the need for invasive lip biopsies, the present disclosure develops a new diagnostic assay for Sjogren's disease based on the discovery of novel autoantibodies associated with the disease process. Summary of the Invention

[0005] overview Sjögren's disease ("Sjögren's" or "SjD") is generally diagnosed by the presence of anti-SSA antibodies ("SSA+") or focal lymphocytic sialadenitis in salivary gland tissue. In anti-SSA antibody-negative ("SSA-") Sjögren's patients, diagnosis requires a salivary gland biopsy with lymphocytic infiltration. Disclosed herein are novel autoantibodies that positively correlate with the presence of Sjögren's disease in SSA- subjects. Accordingly, disclosed herein are methods for diagnosing Sjögren's disease in mammalian subjects by testing the subject for the presence and / or concentration of one or more of these newly discovered antibodies. Currently, SSA- patients can only be diagnosed by a painful and invasive salivary gland biopsy. Furthermore, there are few physicians capable of performing this biopsy and few specialists capable of interpreting the results. Accordingly, disclosed herein are non-invasive and readily available means for diagnosing Sjögren's disease, particularly in SSA- subjects.

[0006] Thus, provided herein is a method for detecting Sjogren's disease or predicting labial salivary gland biopsy results, comprising: a) providing a fluid sample obtained from an individual; b) contacting the sample with a peptide or full-length protein or fragment thereof comprising an amino acid sequence selected from the group consisting of SEQ ID NOs: 1 to 67, or an epitope derived from the peptide, full-length protein, or fragment thereof comprising these sequences, under conditions suitable for forming a complex with at least a portion of any antibody in the sample specific to one or more of the peptide, full-length protein, or fragment thereof; and c) correlating the amount of complex formed in step b) with the detection of Sjogren's disease or with the prediction of the outcome of a labial salivary gland biopsy in said individual. A method is disclosed, comprising: The liquid sample may be whole blood, plasma, or blood serum. The sample is contacted with the protein in an enzyme-linked immunosorbent assay format. Also disclosed herein is a kit comprising, in combination with a support, at least one peptide or full-length protein or fragment thereof comprising an amino acid sequence selected from the group consisting of SEQ ID NOs: 1 to 67, reagents suitable for performing an enzyme-linked immunosorbent assay, and instructions for use. [Brief explanation of the drawings]

[0007] [Figure 1] Figure 1 shows the Consort flow diagram showing peptide selection for external validation. [Figure 2A] Figures 2A-2O show internal validation of selected peptides identified from the whole peptidome array. ELISA for each peptide was performed using serum from SSA-Sjögren's subjects (n = 8) and control subjects (n = 8). Figure 2A = DTD2. [Figure 2B] Figure 2B = RESF1. [Figure 2C] Figure 2C = SCRB2. [Figure 2D] Figure 2D = BRWD1. [Figure 2E] Figure 2E = PDZD8. [Figure 2F] Figure 2F = SLK. [Figure 2G] Figure 2G = GPAT1. [Figure 2H] Figure 2H=SO1B1. [Figure 2I] Figure 2I = CYP7A1. [Figure 2J] Figure 2J = LRBA. [Figure 2K] Figure 2K = HDAC9. [Figure 2L] Figure 2L=NPAT. [Figure 2M] Figure 2M=TEX15. [Figure 2N] Figure 2N=LRCC1. [Figure 2O] Figure 2O=KNL1. [Figure 3A] Figures 3A-3C show binding of novel IgG to peptides by ELISA in SSA-Sjögren's subjects, autoimmune controls, and sicca controls (n=45 SSA-Sjögren's subjects, n=41 sicca controls, and n=41 autoimmune controls). Figure 3A shows that SSA-Sjögren's subjects bind peptides derived from DTD2 and RESF1 better than SICCA controls. [Figure 3B] FIG. 3B shows that SSA-Sjogren's subjects bind peptides derived from DTD2 and RESF1 more than sicca and autoimmune controls combined. [Figure 3C] Recognizing directional changes from the array data, Figure 3C shows a one-sided Wilcoxon rank sum test with a Benjamini-Hochberg correction (q-value) to control for the false discovery rate. [Figure 3D]Figures 3D-3E show binding of novel IgG to peptides by ELISA in Focus Score-positive salivary gland biopsies compared to Focus Score-negative salivary gland biopsies. N = 85 Focus Score-positive and N = 107 Focus Score-negative salivary gland biopsies. Figure 3D shows the area under the receiver operating characteristic curve (AUC) comparing the distribution of adjusted optical density of peptide groups compared between FS-positive and FS-negative biopsies (n = 85 FS-positive and n = 107 FS-negative). Forest plots show that the extent of IgG binding to the peptide of interest differs between Focus Score-positive and Focus Score-negative biopsies. [Figure 3E] Figure 3E shows a one-sided Wilcoxon rank sum test with Benjamini-Hochberg correction (q-value) to control the false discovery rate. [Figure 4A] Figure 4A shows a logistic regression model with an AUC of 73.5% (95% CI: 66.0-79.9%), which decreased to 72.2% after adjusting for optimism. [Figure 4B] Figure 4B is a dot plot showing the separation of SSA-SjD and combined controls by SjD prediction model score. [Figure 4C] Figure 4C shows that in cross-validation, the model using clinical predictors and IgG binding to DTD2 had higher overall predictive accuracy than the model using clinical variables alone. [Figure 4D] Figures 4D-4E show separate graphs of specificity and sensitivity for score cutpoints ranging from -1.6 to 1.6. Optimism-corrected values ​​are shown as dotted lines and differ from the original values ​​by up to 2.6% or 1.8% for sensitivity and specificity, respectively. [Figure 4E] Same as above. [Figure 4F] Figures 4F-4G show graphs of the positive predictive value and negative predictive value separately. [Figure 4G] Same as above. [Figure 5A]Figure 5A shows the final model incorporating four predictors (DTD2-derived peptide binding, unstimulated salivary flow rate, platelet count, and high ANA), with an AUC of 71.6% (95% CI: 63.9-78.2%). The table shows the estimated model coefficients and their standard errors in subscripts. The effect of deleting a single term is shown. [Figure 5B] FIG. 5B is a dot plot showing the separation of positive and negative scoring groups. [Figure 5C] Figure 5C shows that in cross-validation, the model using clinical predictors and IgG binding to DTD2 had better overall predictive accuracy than the model using clinical variables alone. [Figure 5D] Figures 5D-5E plot specificity and sensitivity separately for score cutpoints ranging from -1.6 to 1.6. The optimized corrected values ​​are shown as dotted lines and differ from the original values ​​by up to 2.6% or 1.8% for sensitivity and specificity, respectively. [Figure 5E] Same as above. [Figure 5F] Figures 5F-5G show separate graphs of positive and negative predictive values ​​for de novo IgG binding to peptides by ELISA in SSA-Sjögren's, autoimmune, and sicca controls (n=45 SSA-Sjögren's subjects, n=41 sicca controls, and n=24 autoimmune controls). [Figure 5G] Same as above. DETAILED DESCRIPTION OF THE INVENTION

[0008] Detailed Description The present disclosure is based on the identification of autoantibodies that correlate with the presence of Sjogren's disease in mammalian subjects, including human subjects. Using whole-peptide array technology, 15 peptides found in patient serum that can be used to diagnose patients with SSA- were identified (Table 1). Thus, disclosed herein are protein markers that correlate with the presence of Sjogren's disease in mammalian subjects, including human subjects. Also disclosed herein are methods for diagnosing Sjogren's disease in mammalian subjects by testing the subject for the presence of one or more protein markers and correlating the presence of the protein markers with Sjogren's disease in the subject. Also disclosed herein are kits specifically designed for carrying out the methods.

[0009] One aspect of the method involves contacting a sample obtained from an individual under conditions suitable for forming a complex between a peptide comprising an amino acid sequence selected from the group consisting of SEQ ID NOs: 1 to 67 (Table 1), or a full-length protein or fragment thereof, or an epitope derived from said peptide, or a full-length protein or fragment thereof comprising these sequences, and at least a portion of any antibody in the sample that is specific to one or more of the peptide, full-length protein, or fragment thereof. In the methods provided herein, the test sample can be a liquid phase, such as whole blood, plasma, serum, saliva, tears, or other bodily fluids. The sample can be diluted or concentrated or subjected to one or more processing steps. Detection can be by immunological assays, described in more detail below, such as ELISA, performed in any of a variety of formats.

[0010] definition Numerical ranges, as used herein, are intended to include every number and subset of numbers subsumed within that range, whether or not specifically disclosed. Moreover, these numerical ranges should be construed as providing support for claims directed to any number or subset of numbers within that range. For example, a disclosure of 1 to 10 should be construed as supporting ranges of 2 to 8, 3 to 7, 1 to 9, 3.6 to 4.6, 3.5 to 9.9, etc. All references to singular features or limitations of the methods and kits disclosed herein include the corresponding plural features or limitations, and vice versa, unless otherwise specified or unless the context in which the reference is made clearly implies otherwise. All combinations of method or process steps used herein can be performed in any order unless otherwise specified or unless the context in which the reference is made clearly implies otherwise. The methods and kits disclosed herein can include, consist of, or consist essentially of the essential elements and limitations of the methods described herein, as well as any additional or optional components, ingredients, or limitations described herein or otherwise useful in immunology and specifically detecting antibodies and other proteins. The methods disclosed herein can also be practiced in the absence of any element or step not specifically disclosed herein.

[0011] "Antibody" refers to a polypeptide ligand substantially encoded by one or more immunoglobulin genes, or fragments thereof, that specifically recognizes and binds to a molecule or a region or domain (epitope) of a molecule. Recognized immunoglobulin genes include the kappa and lambda light chain constant region genes, the alpha, gamma, delta, epsilon, and mu heavy chain constant region genes, and the myriad immunoglobulin variable region genes. Antibodies exist, for example, as intact immunoglobulins or as a number of well-characterized fragments produced by digestion with various peptidases, including, for example, Fab' and F(ab)' fragments. The term "antibody," as used herein, also includes antibody fragments produced by the modification of whole antibodies or those synthesized de novo using recombinant DNA methodologies. It also includes polyclonal, monoclonal, chimeric, humanized, or single-chain antibodies.

[0012] An "autoantibody" is an antibody present in an individual that specifically recognizes a biomolecule present in the individual. Typically, an autoantibody specifically binds to a protein expressed by the individual or a modified form thereof present in a sample from the individual. Autoantibodies are generally IgG antibodies circulating in the blood of an individual, although the present disclosure is not limited to IgG autoantibodies or autoantibodies present in the blood. An "epitope" refers to a site on an antigen to which an antibody binds. Epitopes can be formed both from contiguous amino acids or from noncontiguous amino acids juxtaposed by tertiary folding of a protein. Epitopes formed from contiguous amino acids are typically retained upon exposure to denaturing solvents, whereas epitopes formed by tertiary folding are typically lost upon treatment with denaturing solvents. An epitope typically contains at least three, more commonly at least five, or 8-10 amino acids in a unique spatial conformation. Methods for determining the spatial conformation of an epitope include, for example, X-ray crystallography and two-dimensional nuclear magnetic resonance. See, for example, "Epitope Mapping Protocols" in Methods in Molecular Biology, Vol. 66, edited by Glenn E. Morris (1996). Two antibodies are said to bind to the same epitope of a protein if amino acid mutations in the protein that reduce or eliminate binding of one antibody also reduce or eliminate binding of the other antibody, and / or if the antibodies compete for binding to the protein, i.e., binding of one antibody to the protein reduces or eliminates binding of the other antibody.

[0013] The terms "polypeptide," "peptide," and "protein" are used interchangeably herein to refer to a polymer of amino acid residues. These terms apply to amino acid polymers in which one or more amino acid residues are artificial chemical mimetics of a corresponding naturally occurring amino acid, as well as to naturally occurring amino acid polymers, those containing modified residues, and non-naturally occurring amino acid polymers. The term "antigen," as used herein, refers to a protein or polypeptide used as a target for screening a test sample obtained from a subject for the presence of antibodies. Antigen is intended to include any fragments thereof, particularly immunologically detectable fragments, of the protein so identified. The term antigen is also meant to include immunologically detectable proteolytic products of the protein, as well as processed forms, post-translationally modified forms, and sequence variants, including, but not limited to, allelic variants and splice variants of the antigen or fragments thereof. The identification or listing of antigens also includes amino acid sequence variants thereof, for example, sequence variants containing fragments, domains, or epitopes that share immunoreactivity with the identified antigen. Fragments, domains, or epitopes can be provided as part of or attached to a larger molecule or compound.

[0014] As used herein, a "variant" of a polypeptide or protein refers to an amino acid sequence that has one or more amino acid changes relative to a reference polypeptide or protein. In the present disclosure, a polypeptide variant retains the antibody-binding properties of the reference protein. In a preferred embodiment of the present disclosure, a polypeptide or protein variant can specifically bind to the same population of autoantibodies that can bind to the reference protein. Preferably, a polypeptide variant has at least 60% identity to the reference protein over a sequence of at least 10 amino acids. More preferably, a polypeptide variant is at least 70% identical to the reference protein over a sequence of at least 4 amino acids. A protein variant can be, for example, at least 80%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical to a reference polypeptide over a sequence of at least 4 amino acids. A protein variant of the present disclosure can be, for example, at least 80%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical to a reference polypeptide over a sequence of at least 10 amino acids. A variant may have "conservative" changes, where the substituted amino acid has similar structural or chemical properties (e.g., replacement of leucine with isoleucine). A variant may also have "non-conservative" changes (e.g., replacement of glycine with tryptophan). Similar minor variations can also involve amino acid deletions or insertions, or both. Guidelines for determining which amino acid residues may be substituted, inserted, or deleted without losing immunological reactivity can be found using computer programs well known in the art, such as DNASTAR software.

[0015] The phrase "specifically (or selectively) binds" to an antibody, when referring to a protein or peptide, refers to a binding reaction that is determinative of the presence of the protein in a heterogeneous population of proteins and other biologics. Thus, under specified immunoassay conditions, a particular antibody binds to a particular protein at a level that is statistically significantly different from background and does not substantially bind in significant amounts to other proteins present in the sample. "Sensitivity" is defined as the percentage of diseased individuals in which the biomarker of interest is detected. Non-diseased individuals diagnosed with the disease by the test are "false positives." "Specificity" is defined as the percentage of non-diseased individuals in whom the biomarker of interest is not detected. Diseased individuals not detected by the assay are "false negatives." Subjects who are not diseased and test negative in the assay are referred to as "true negatives."

[0016] method group For internal ELISA validation, we used serum from the same subjects on a whole peptidome array. This array included sera from eight SSA-positive ("SSA+") and eight SSA-negative ("SSA-") SjD patients who met the SjD ACR / EULAR criteria (IRB#2021-0945 and #2015-0156). Shiboski SC, Shiboski CH, Criswell L, et al. American College of Rheumatology classification criteria for Sjögren's syndrome: a data-driven, expert consensus approach in the Sjögren's International Collaborative Clinical Alliance cohort. Arthritis Care Res. (Hoboken) (2012) 64(4):475-87. (See Table 2 below.) All subjects on the SjD array were Caucasian women. For external validation of the array, serum from subjects not included on the whole peptidome array was used. For external validation, samples from the SICCA Registry cohort (IRB#2021-0945) were used. The SICCA Registry, a National Institutes of Health-funded registry, is a multicenter international registry housed at the University of California, San Francisco. Participants were referred to the registry if: i) known SjD diagnosis; ii) Salivary gland enlargement; iii) those with recurrent caries without risk factors; or iv) serologic abnormalities (anti-SSA or anti-SSB antibodies, antinuclear antibodies [ANA], or rheumatoid factor [RF]).

[0017] More details about the registry can be found at siccaonline.ucsf.edu or as described in previous publications. Shiboski SC, Shiboski CH, Criswell L, et al. American College of Rheumatology classification criteria for Sjoegren's syndrome: a data-driven, expert consensus approach in the Sjoegren's International Collaborative Clinical Alliance cohort. Arthritis Care Res. (Hoboken) (2012) 64(4):475-87. Daniels TE, Criswell LA, Shiboski C, et al. An early view of the international Sjoegren's syndrome registry. Arthritis Rheum. (2009) 61(5):711-4. McCoy SS, Sampene E, Baer AN. Association of Sjoegren's Syndrome With Reduced Lifetime Sex Hormone Exposure: A Case-Control Study. Arthritis Care Res. (Hoboken) (2020) 72(9):1315-22. SSA-SjD subjects met the ACR / EULAR criteria. SSA-SjD subjects were compared with Sicca controls and autoimmune controls. Sicca controls had symptoms or signs of dry skin but lacked autoimmunity (ANA < 1:320, RF negative, anti-SSA antibody negative, and focus score < 1 on labial salivary gland biopsy). Autoimmune controls had autoimmune features (ANA ≥ 1:320, RF positive, or focus score ≥ 1 on labial salivary gland biopsy) but did not meet the 2016 ACR / EULAR criteria for SjD.

[0018] Whole peptidome array and analysis To broadly evaluate autoantibody reactivity, identify common antigenic features, and better understand SjD, we used a whole-peptidome array and contracted with Roche NimbleGen (Madison, Wisconsin, USA) to evaluate the array's performance and the data obtained from it. The array was constructed by covalently attaching peptides to a chip at their C-terminus. Amino acids were sequentially added from the C-terminus to the N-terminus by chain extension. Peptides of 16 amino acids were arranged in pairs across the entire human peptidome. Because the location of each peptide on the array chip was known, the position and intensity of the binding signal on the chip could be recorded, allowing the construction of a peptide response pattern for the target protein. The whole-peptidome array was previously validated using RA samples. See Zheng Z, Mergaert AM, Fahmy LM, et al. Disordered Antigens and Epitope Overlap Between Anti-Citrullinated Protein Antibodies and Rheumatoid Factor in Rheumatoid Arthritis. Arthritis Rheumatol (2020) 72(2):262-72. The peptidome array contains over 5.3 million peptides with 16-amino acid overlaps arranged in 2-amino acid intervals across the human proteome. In addition to the 16 SjD subjects, 16 systemic lupus erythematosus ("SLE") subjects who met the 2012 Systemic Lupus International Collaborating Clinics (SLICC) criteria and 8 subjects who met the 2010 ACR / EULAR criteria for rheumatoid arthritis ("RA") were included.For the SLICC criteria, see Petri M, Orbai AM, Alarcon GS, et al. Derivation and validation of the Systemic Lupus International Collaborating Clinics classification criteria for systemic lupus erythematosus. Arthritis Rheum. (2012) 64(8):2677-86. For the ACR / EULAR criteria, see Aletaha D, Neogi T, Silman AJ, et al. 2010 Rheumatoid Arthritis Classification Criteria: an American College of Rheumatology / European League Against Rheumatism Collaborative Initiative. Ann. Rheum. Dis. (2010) 69(9):1580-8. Each autoimmune disease subject had an age- and sex-matched control subject, and some control subjects served as controls for multiple autoimmune disease subjects. The inventors developed new statistical methods to optimally analyze this large dataset. While methods for analyzing large datasets of gene expression already exist, antibody binding to peptide arrays has different sampling characteristics and requires different techniques to distinguish signal from noise. To better account for the uncertainty of variance in peptide arrays, we compared the mean difference in signal intensity between two groups using MixTwice, a large-scale testing tool. See Zheng Z, Mergaert AM, Ong IM, Shelef MA, Newton MA. MixTwice: large-scale hypothesis testing for peptide arrays by variance mixing. Bioinformatics (2021) 37(17):2637-43. This tool is available online at cran.r-project.org / web / packages / MixTwice / index.html.Given an estimated effect size and an estimated standard error of a two-sample t-test, MixTwice uses empirical Bayes tools to calculate the local false discovery rate (locFDR), which is the probability of the null given the data vector using nonparametric maximum likelihood estimation (MLE) with shape constraints, and the r value as a ranking statistic of peptide effect sizes across the array.

[0019] Combining binding affinity, protein context, and peptide sequence data, peptides were assigned a local false discovery rate (locFDR) for sensitive filtering. Nearest-neighbor (NN) peptides were defined as peptides located immediately adjacent to each other on the same protein. The nearest-neighbor locFDR (NN-locFDR) of a given peptide is the average locFDR of its NN peptides. Using a combination of r-values ​​< 0.01, locFDR < 0.01, and nearest-neighbor locFDR < 0.05 for empirical cumulative distribution function-transformed peptides, 387 seropositive peptides and 469 seronegative peptides were found to bind better than the control. Two peptides were identified within the array when comparing SSA+ with the combined control, and one peptide was identified when comparing SSA- with the combined control.

[0020] Selection of top candidate peptides Peptides were prioritized for individual analyses by selecting peptides with at least two significant peptides bound in the protein and a fold change of 10. After excluding peptides with a fold change in expression of less than 10 and peptides with fewer than two bindings in the same protein, we focused our analysis on peptides that showed a significant increase in most 50% or more of SjD subjects compared to control subjects.

[0021] Enzyme-linked immunosorbent assay ("ELISA") Using knowledge of the relevant target, ELISAs can be constructed in any suitable format. ELISAs generally use antigen-specific monoclonal antibodies in conjunction with specific antibody-enzyme conjugates to detect and (optionally) quantify the concentration of the protein target. ELISAs can be performed in qualitative or quantitative formats. Qualitative results provide a simple positive or negative (present or absent) result for the sample. The cutoff between positive and negative is determined empirically to maximize sensitivity, specificity, or both. The basic "direct" ELISA format itself is conventional and has been known since the 1970s. See Engvall, E. (1972-11-22). "Enzyme-linked immunosorbent assay, ELISA," The Journal of Immunology 109 (1):129-135. The general protocol will not be detailed here. For a detailed treatment, see, for example, "Enzyme-Linked Immunosorbent Assay (ELISA): From A to Z" (Springer Briefs in Applied Sciences and Technology), edited by Amit Kumar and Allam Appa Rao, (C) 2018, Springer (Singapore), ISBN 978-9811067655. The direct ELISA protocol has been modified over the years to yield a variety of different types of ELISA, all of which can be used to detect and quantify the protein targets disclosed herein. These additional ELISA formats include indirect ELISA, antibody sandwich, double-antibody sandwich, and competitive ELISA.

[0022] The basic protocol for our indirect ELISA is as follows: After selecting a candidate peptide sequence, the sequence was submitted for synthesis of a biotinylated peptide through Biomatik Corporation (Kitchener, Ontario, Canada), a commercial supplier. The peptide was dissolved to a concentration of 500 ng / mL according to the specifications provided by Biomatik. An ELISA plate was coated with streptavidin and incubated overnight at 4°C. The following day, the plate was washed twice with PBS. Next, the biotinylated peptide was added to the plate at a 1:500 dilution and incubated at room temperature for 1 hour. After 1 hour, the plate was washed three times with 0.2% Tween-20 in PBS. The wells were then blocked with 5% nonfat dry milk in 0.2% Tween-20 in PBS for 2.5 hours at room temperature. Next, serum samples and plate controls were diluted 1:100 in 5% nonfat dry milk in 0.2% Tween-20 in PBS and added to the plate in duplicate overnight at 4°C. The next day, the plate was washed four times with 0.2% Tween-20 in PBS. HRP-conjugated mouse anti-human IgG clone JDC-10 was diluted 1:5000 in 5% nonfat dry milk in 0.2% Tween-20 in PBS and incubated at room temperature for 1 hour in the dark. The plate was then washed four times with 0.2% Tween-20 in PBS. Finally, TMB-Slow ELISA formulation (ThermoFisher Scientific, Coraopolis, PA, USA, catalog no. 34024) was added to each well and developed for 15 minutes at room temperature in the dark. The reaction was then stopped by adding 0.2M H2SO4 stop solution, and the plate was read at 450 nm and 540 nm. The analysis included subtraction of serum controls without peptide (to account for nonspecific background plate binding) and serum controls with peptide (to account for absorbance from peptide), and normalization between plates using positive controls.

[0023] statistical analysis Considering the nonparametric nature of the data, the Mann-Whitney-Wilcoxon rank sum test was used for hypothesis testing of the ELISA data (Mann, Henry B.; Whitney, Donald R. (1947) “On a Test of Whether One of Two Random Variables is Stochastically Larger than the Other,” Annals of Mathematical Statistics 18(1):50-60).

[0024] result Whole peptidome array analysis Of the over 5.3 million peptides, our analysis using the whole-human peptidome analysis yielded 469 binding peptides that differentially bound SSA- versus control subjects. Of these, 299 were excluded due to fold changes less than 10, 152 were excluded due to lack of significant binding with at least two peptides in the same protein, and 6 failed internal validation. The final validation analysis included a total of 15 peptides (Table 1) selected from a total of 30 significant peptide sequences identified on the array.

[0025] Internal Validation We quantified IgG binding to a high-density whole-human peptidome array using serum from SSA-Sjögren's patients (n = 8; Table 2) and age- and sex-matched controls (n = 8). The best-binding peptides from the array, as defined by our whole-human peptidome analysis, were internally validated by ELISA using serum from the same subjects. See Figure 1 and Figures 2A–2H. (Zihao Zheng, Aisha M. Mergaert, Irene M. Ong, Miriam A. Shelef, Michael A. Newton (1 September 2021) “MixTwice: Large-scale hypothesis testing for peptide arrays by variance mixing,” Bioinformatics, 37(17): 2637–2643; doi.org / 10.1093 / bioinformatics / btab162.)

[0026] External validation and test performance Based on the results of the internal validation, 15 peptides were selected for ELISA external validation using sera from the Sjoegren's International Collaborative Clinical Alliance ("SICCA") biorepository (siccaonline.ucsf.edu / home) of the following age-, sex-, and race-matched groups (Table 2): (1) SSA-Sjogren's subjects (2016 American College of Rheumatology / European League Against Rheumatism ("ACR / EULAR") Sjogren's criteria; n = 76), (2) SICCA controls (antinuclear antibody test ("ANA")-negative, rheumatoid factor-negative, SSA-negative, and SICCA with a focus score <1; n = 75), and (3) autoimmune controls (ANA-positive (≥ 1:320), rheumatoid factor-positive, or SSA-positive but not meeting the 2016 ACR / EULAR Sjogren's criteria; n = 38). ELISA results were compared using a nonparametric Mann-Whitney-Wilcoxon rank-sum test. Adaptive shrinkage with Lasso regression was used to select peptides for the random forest model predicting SSA-Sjögren's disease in subjects.

[0027] The results showed that IgG against peptides derived from DTD2 (D-aminoacyl-tRNA deacylase 2) was higher in SSA-Sjögren's patients than in sicca controls (p = 0.0040) and pooled controls (p = 0.003) of sicca and autoimmune control patients (see Figures 3A-3O). IgG against RESF1 (retroelement silencing factor 1) was higher in SSA-Sjögren's patients than in sicca controls (p = 0.047) and pooled controls (p = 0.03) of sicca and autoimmune control patients (Figures 4A and 4B). We created a regression model to predict SSA-Sjögren's disease by incorporating IgG binding to these peptides along with clinical variables. The final model included IgG against DTD2, unstimulated salivary flow rate, and ANA (other peptide binding and clinical factors were not included in the model; Figure 4C). This SjD prediction score distinguished between SSA-SjD and control subjects. The area under the receiver operating characteristic curve (C-index) was 73.5% (95% CI: 66.0-79.9%), which decreased to 72.2% after optimistic adjustment, demonstrating good discrimination between SjD and combined controls (Figure 4D). Sensitivity, specificity, positive predictive value, and negative predictive value are shown in Figures 4E-4H). Because a surrogate marker for positive or negative labial salivary gland biopsies is a clinically important need, we evaluated whether autoantibody binding to 15 peptides differed between subjects with positive biopsies (FS ≥ 1) and subjects with negative biopsy FS (FS < 1). We found that IgG from SSA-SjD subjects bound better to peptides from RESF1, DTD2, and SCRB2 than did serum from combined control subjects (p = 0.01, p = 0.01, and p = 0.03, respectively; Figure 5A). For both RESF1 and DTD2 IgG, the adjusted OD was estimated to be 61% higher in FS-positive subjects than in FS-negative subjects (95% CI: 53-68% and 52-68%, respectively). For SCRB2 IgG, the adjusted OD was estimated to be 59% higher in FS-positive subjects than in FS-negative subjects (95% CI: 51-67%).

[0028] We created a regression model incorporating IgG binding to these peptides along with clinical variables. The final model included IgG against DTD2, unstimulated salivary flow rate, platelet count, and ANA (Figure 5B). The C-index of this model was 71.6% (95% CI: 63.9-78.2%), which decreased to 69.3% after optimistic adjustment (Figure 5C). Binding to DTD2 contributed most to the model (single-item deletion of DTD2 reduced the AUC by more than 3.9%), followed by unstimulated salivary flow rate (single-item deletion of unstimulated salivary flow rate reduced the AUC by more than 3.3%). This final "FS prediction score" discriminated between FS positive and negative outcomes. Sensitivity and specificity were calculated for FS prediction score cutpoints (range: -1.6 to 1.6). Because there were no scores above 1.02 in the FS-positive group, positive likelihood ratios could only be calculated for cutpoints ranging from -1.6 to 1.0 (Figures 5D–5G). We report a novel autoantibody that can be used to predict disease and abnormal FS in labial salivary gland biopsies with excellent predictive value in SSA-SjD compared with autoimmune and sicca controls.

[0029] [Table 1-1] [Table 1-2] [Table 1-3]

[0030] [Table 2]

Claims

1. 1. A method for detecting Sjogren's disease or predicting labial salivary gland biopsy results, comprising: a) providing a fluid sample obtained from an individual; b) contacting the sample with a peptide or full-length protein or fragment thereof comprising an amino acid sequence selected from the group consisting of SEQ ID NOs: 1-67, or an epitope from the peptide, full-length protein, or fragment thereof comprising these sequences, under conditions suitable for forming a complex between at least a portion of the antibodies in the sample specific for one or more of the peptides, full-length proteins, or fragments thereof; and c) correlating the amount of the complex formed in step b) with the detection of Sjogren's disease or with the prediction of the outcome of a labial salivary gland biopsy in the individual. A method comprising:

2. The method of claim 1 , wherein the liquid sample is whole blood.

3. The method of claim 1 , wherein the liquid sample is plasma.

4. The method of claim 1 , wherein the liquid sample is serum.

5. The method of claim 1 , wherein the sample is contacted with the protein in an enzyme-linked immunosorbent assay format.

6. A kit comprising: The kit comprises at least one peptide or full-length protein or fragment thereof comprising an amino acid sequence selected from the group consisting of SEQ ID NOs: 1-67 attached to a support, in combination with reagents suitable for performing an enzyme-linked immunosorbent assay, and instructions for use of the kit.

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