Methods and compositions for detecting the development of type 1 diabetes

Abstract

The inventors discovered that soluble lymphocyte activation gene 3 (sLAG-3) is a biomarker for type 1 diabetes. Disclosed herein are methods, systems, and kits for detecting sLAG-3 in a sample from a subject at risk of developing type 1 diabetes. The methods comprise detecting a level of sLAG-3 in a sample from a subject suspected as having type 1 diabetes or a subject at risk of developing type 1 diabetes. The disclosed kits comprise reagents for detecting sLAG-3 in a sample and reagents for detecting at least one biomarker associated with type 1 diabetes.

Description

Atty. Dkt. No. 144578.00478METHODS AND COMPOSITIONS FOR DETECTING THE DEVELOPMENT OF TYPE 1 DIABETESCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims priority to U.S. Provisional Patent Application No.63 / 789,400 that was filed April 15, 2025, the entire contents of which are hereby incorporated by reference.STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

[0002] This invention was made with government support under TR004402 awarded by the National Institutes of Health. The government has certain rights in the invention.BACKGROUND

[0003] Type 1 diabetes (T1D or type 1 diabetes mellitus (T1DM)) is a complex, heterogeneous, and polygenic autoimmune disease in which pancreatic islet beta-cells are targeted and destroyed by the immune system, which stops the release of insulin and raises blood sugar levels. However, mechanisms underlying development of this abnormal immune cell and beta cell interaction remain incompletely elucidated. Notably, despite a 2-3% annual increase in T1DM incidence, there has been a decrease in the number of individuals who have the high-risk HLA allele developing the disease. This incidence outpacing genetics is a major concern. Numerous potential triggers, such as viral infection, oxidative stress, and environmental stressors, have been proposed; however, DR3 / 4-DQ8 HLA positivity is associated with the highest genetic susceptibility to disease. Because of the high genetic risk, first-degree relatives (FDRs) of T1D patients can be evaluated using both autoantibody tests and HLA screens. Autoantibodies against islet-beta cells proteins such as, GAD65, IAA, IA2, and ZnT8 are known to be early markers of the onset of T1D. Therefore, they usually arise years or even decades before the overt beginning of T1D. Previous research has shown that protein antigens require T cell help, T-dependent, to produce optimal antibody responses like those to islet associated proteins. At present, those autoantibodies are among the most reliable indicators in the prodromal phase of the disease. Recent approaches to prevention of clinical disease involve immune modulation during this preclinical phase, and while the recent FDA approval of teplizumab for delayed onset of clinical disease is a landmark in immune modulation for T1D, successful prevention of disease remains an elusive goal. While isletAtty. Dkt. No. 144578.00478autoantibodies are currently one of the earliest biomarkers of T1D development; the detection of autoantibodies in peripheral blood indicates that self-tolerance has already been broken, and the autoimmune response that drives T1D is well underway. Immune modulation prior to seroconversion may be more successful, but therapy at this time is challenging without a biomarker to stratify individuals who are at risk. Therefore, identification of biomarkers of T1D risk prior to seroconversion is an unmet need in the advancement of T1D prevention trials.SUMMARY

[0004] In an aspect of the current disclosure, methods are provided. In some embodiments, the methods comprise detecting a level of soluble lymphocyte activation gene-3 protein (sLAG-3) in a sample from a subject at risk of developing type 1 diabetes. In some embodiments, the subject has not tested positive for autoantibodies against at least one of GAD65, insulin, Znt8, or IA-2, or the subject has tested positive for only one antibody against GAD65, Insulin, or IA-2. In some embodiments, the subject at risk of developing type 1 diabetes has a first degree relative that has been diagnosed with type 1 diabetes or is suspected as having type 1 diabetes. In some embodiments, the subject at risk of developing type 1 diabetes is a subject that has tested positive for an autoantibody against one of GAD65, IAA, or IA2. In some embodiments, the subject harbors a DR3 / 4-DQ8 HLA allele. In some embodiments, the method predicts a subject’s susceptibility for type 1 diabetes. In some embodiments, the subject is likely to progress to type 1 diabetes, and the method predicts a susceptibility of the subject for progressing to clinical type 1 diabetes. In some embodiments, the method further comprises detecting pancreatic beta cell autoreactive T cells. In some embodiments, the pancreatic beta cell, autoreactive CD4 T cells and / or CD8 T cells . In some embodiments, detecting pancreatic beta cell autoreactive T cell comprises: contacting the sample from the subject comprising T cells with a major histocompatibility complex (MHC) multimer comprising a peptide found in pancreatic beta cells; and detecting T cells that are positive for the MHC multimer using flow cytometry. In some embodiments, the expression of sLAG-3 is detected via an immunoassay. In some embodiments, the immunoassay is at least one of enzyme-linked immunosorbent assay (ELISA), radioimmunoassays, fluorescent immunoassays, chemiluminescent immunoassays, western blotting, or lateral flow assays. In some embodiments, the immunoassay is a sandwich ELISA. In some embodiments, detecting an expression of sLAG-3 comprises quantifying a level of the sLAG-3 expression. In some embodiments, the methodAtty. Dkt. No. 144578.00478further comprises detecting a level of sLAG-3 in a control sample. Tn some embodiments, the detected level of sLAG-3 in the sample from the subject suspected of having type 1 diabetes or at risk of developing type 1 diabetes is greater than the level of sLAG-3 in the control sample. In some embodiments, the sample from the subject is serum, plasma, or whole blood. In some embodiments, the method further comprises detecting sLAG-3 at a subsequent time to monitor the expression of sLAG-3 in the subject over time. In some embodiments, the method comprises detecting (n) levels of sLAG-3 in (n) separate longitudinal samples from the subject, wherein the (n) levels of sLAG-3 indicate an initial increase in the concentration of sLAG-3 in the samples and a subsequent decrease in sLAG-3 over time, wherein (n) is an integer greater than or equal to 3. In some embodiments, the method further comprises administering a therapeutically effective amount of teplizumab to the subject.

[0005] In some embodiments, the methods comprise detecting a level of soluble lymphocyte activation gene 3 (sLAG-3) in a sample from a subject being administered a therapy to prevent the onset of type 1 diabetes. In some embodiments, the method further comprises detecting sLAG-3 at a subsequent time to monitor the expression of sLAG-3 in the subject over time. In some embodiments, the method comprises detecting (n) levels of sLAG-3 in (n) separate longitudinal samples from the subject, wherein the (n) levels of sLAG-3 indicate an initial increase in the concentration of sLAG-3 in the samples and a subsequent decrease in sLAG-3 overtime, wherein (n) is an integer greater than or equal to 3. In some embodiments, the method further comprises diagnosing the subject as having type 1 diabetes. In some embodiments, the therapy to prevent the onset of type 1 diabetes comprises teplizumab.

[0006] In an aspect of the current disclosure, kits are provided. In some embodiments, the kits comprise at least one reagent for detecting sLAG-3 and at least one reagent for detecting a biomarker associated with type 1 diabetes. In some embodiments, the at least one reagent for detecting sLAG-3 comprises an antibody specific for sLAG-3. In some embodiments, the biomarker associated with type 1 diabetes comprises antibodies against one of GAD65, IAA, or IA2, or DR3 / 4-DQ8 HLA allele.BRIEF DESCRIPTION OF THE DRAWINGS

[0007] FIGS. 1A-1B. sLAG-3 correlates to beta cell antigen-specific CD4+ T cells and disease incidence in NOD mice. Female NOD mice were monitored longitudinally for (FIG. 1A) plasmaAtty. Dkt. No. 144578.00478sLAG-3 (black), T1D onset (red) and (FIG. IB) CD4+ tetramer positivity for three islet antigens (InsB9-23, ChgA, and IAPP). Blood is collected retro-orbitally, and the same sample was used to measure sLAG-3 by ELISA and tetramer positivity. T1D defined as two consecutive blood glucose levels of > 300 mg / dl. N = 3-17, mean ± S.D., multiple comparison by Tukey where ***p<0.001 (black).

[0008] FIGS. 2A-2B. Early detection of sLAG-3 correlates with greater type 1 diabetes incidence in NOD females. (FIG. 2A). NOD females and males were bled for serum every 2 weeks starting at 6 weeks of age. Sera were measured using a sLAG-3 ELISA. **p<0.005. (FIG. 2B). NOD mice were monitored for diabetes onset weekly, starting at 2 weeks of age. Two subsequent blood glucose readings >300 mg / dl were considered diabetic. N=4 mice / group, *p<0.05.

[0009] FIG. 3. sLAG-3 inversely correlates with autoantibody levels. Sera were utilized in human sLAG-3 ELISA. Sera was collected from healthy controls (n=28), T1D patients (n=39), and FDRs with varying numbers of autoantibodies (Ab-, n=37; Abl+, n=27; Ab2+, n=29; Ab3+, n=5). Relative sLAG-3 was measured from all serum samples. *p<0.05, **p<0.005.

[0010] FIGS. 4A-4B. Protection from disease development correlates with decreased sLAG-3. BDC 2.5 TCR.Tg splenocytes were transferred intravenously on day 0 into 10 week old NOD.scid mice (n=3 / group). Mice were treated on day -1 through day 28 intraperitoneally with or without CA. (FIG. 4A) Mice were monitored by glucosuria for the onset of type 1 diabetes, with two consecutive blood glucose readings of >300 mg / dL indicating diabetes. AT, adoptive transfer. (FIG. 4B) Blood was collected retro-orbitally every 4 days to 16, and serum was isolated for sLAG-3 ELISA. *p<0.05.

[0011] FIGs. 5A, 5B, 5C, and 5D show plasma sLAG-3 correlates disease incidence in NOD mice. A: Experimental designed for monitoring blood glucose and plasma sLAG-3 level from longitudinal mice group. B: Plasma soluble LAG-3 levels form NOD mice at different age groups. C: Monitoring blood glucose levels from longitudinal mice groups. D: Correlation graph represents the level of sLAG-3 and diabetic incidence in NOD mice. Values represent mean ± SEM (n = 10-12 per group). P< 0.05 represent significant. Statistics were done by one-way ANOVA, followed by the Bonferroni post hoc test.

[0012] FIGs. 6A, 6B, 6C, 6D, and 6E show beta cell antigen-specific tetramer expression in CD4+ T cells corelate disease incidence in NOD mice. A: Percentage of CD4+ tetramer positivity forAtty. Dkt. No. 144578.00478three islet antigens (InsB9-23, ChgA, and TAPP) B: Absolute number of CD4+ tetramer positivity for islet antigens (InsB9-23, ChgA, and IAPP). C: Overlay of the MFI histograms by flow cytometry analysis of tetramer expression in CD4+ cells D: Overlay representation of sLAG-3 and CD4+ tetramer expression. E: Linear regression analysis of plasma sLAG-3 levels and islet antigens specific tetramer frequency in CD4 T cells in NOD mice during diabetic progression. Data are shown as mean± SEM; w=10-12 / group. P<0.05 represent significant. Statistics were done by one-way ANOVA, followed by the Bonferroni post hoc test.

[0013] FIG 7A shows FMO Gating strategy used in flow cytometry analysis to measure different activation markers for CD4+ tetramer positivity cells.

[0014] FIG. 7B shows Flowcytometry gating strategy where lymphocytes populations were gated on a forward scatter (FSC) / side scatter (SSC) plot. Splenocytes were then further gated on Live cells and CD4+ cells to determine different tetramer positive populations.

[0015] FIGs. 8A, 8B, 8C, 8D, 8E, 8F, 8G, 8H, 81, and 8J show antigen specific T cell activation in diabetes progression and immune modulation. A: Schematic overview of the experimental design used to track C6.6.9 TCR-transgenic CD4+T-cell activation by adoptively transferred into NOD-Scid recipients. B: Monitoring blood glucose from longitudinal mice group. C: Diabetes onset was assessed over time following T cell transfer. D: Plasma levels of soluble LAG-3 and E: Plasma Insulin2-mRNA expression were measured at different time points post adoptive-transfer. F-H: Linear regression analysis of plasma sLAG-3 levels, plasma insulin-mRNA, and blood glucose level in adoptively transfer NOD-Scid mice during diabetic progression. I: Percentage of C6.6.9 TCR-Tg mouse CD4+T cells in peripheral blood of recipient mice over time following adoptive transfer. J: Histopathological analysis of pancreatic tissue showing progression of insulitis and destruction of Langerhans islets during diabetes development in the NOD-Scid recipient mice after adoptively transferred C6.6.9 TCR-Tg mouse CD4+T cells. All the values mean ± SEM (n=10).

[0016] FIG 9A shows Significant analysis of plasma levels of soluble LAG-3 was measured at different time points post- transfer; All those values are represented as mean ± SEM (n =10). P < 0.05 is significant and P>0.05 not significant. Statistics were done by one-way ANOVA with multiple comparisons, followed by the Bonferroni post hoc test.Atty. Dkt. No. 144578.00478

[0017] FIG. 9B shows Details blood glucose level representation from adoptively transferred individual NOD Scid mice.

[0018] FIGs. 10A, 10B, 10C, 10D, 10E, and 10F show Phenotypic characterization of different T cell marker longitudinally from adoptively transfers NOD Scid mice during diabetic progression. A: The frequency of T cell activation markers CD25, CD69, and LAG-3 expression levels. B: Beta cell antigen-specific tetramer expression on CD4+T cells in peripheral blood were measured over time following adoptive transfer into NOD Scid mice. C. Immunofluorescence staining of pancreatic sections from 12thand 15thday of post adoptive transfer NOD Scid mice, showing ChgA, InsB9-23, IAPP positive cell infiltration. D. Immunofluorescent MFI analysis of ChgA, InsB9-23, IAPP positive cells in pancreatic sections. E-F: Percentage of different activation markers (CD25, CD69, LAG-3) and (B-cell antigen-specific tetramers (ChgA, InsB9-23, IAPP) expression on T cells that had migrated or infiltrated into blood, pancreases and spleen at the time of diabetes. All those values are represented as mean ± SEM (n = 4-10). P < 0.05 is significant and P>0.05 not significant. Statistics were done by one-way ANOVA with multiple comparisons, followed by the Bonferroni post hoc test.

[0019] FIGs. HA and 1 IB show phenotypic characterization of T cell markers in adoptively transferred C6.6.9 TCR-Tg mouse spleenocyte into NOD-Scid mice. A: Absolute number of CD25+, CD69+, and LAG-3+CD4+T cells in peripheral blood over time following adoptive transfer. B: Frequency of (3-cell antigen-specific tetramer-positive CD4+T cells (ChgA, InsB9-23, IAPP) in peripheral blood during the same period. C-D: At the time of diabetes onset, the number of CD4+T cells expressing activation markers (CD25, CD69, LAG-3) and (3-cell antigen-specific tetramers (ChgA, InsB9-23, IAPP) was assessed in peripheral blood, pancreas, and spleen. All those values are represented as mean ± SEM (n = 4-10). P < 0.05 is significant and P>0.05 not significant. Statistics were done by one-way ANOVA with multiple comparisons, followed by the Bonferroni post hoc test.

[0020] FIG. 12A shows baseline expression of different surface markers on blood lymphocytes in NOD-SCID mice before adoptive transfer.

[0021] FIG. 12B shows gating strategy for analysis the surface markers and Tetramer expression in CD4 T cells from NOD Scid mice after adoptive transfer.Atty. Dkt. No. 144578.00478

[0022] FIGs. 13 A, 13B, 13C, and 13D show metabolic profiling and immune infiltration in the pancreas following adoptive transfer of C6.6.9 TCR-Tg mouse T cells into NOD-Scid mice. A: Schematic overview of the experimental design used to assess metabolic status and immune infiltration in the pancreas after adoptive transfer. B: Immunofluorescence staining of pancreatic sections from control and diabetic NOD Scid mice, showing CD3, F4 / 80, and B220 immune cell infiltration following BDC6.9 CD4+T cell transfer. C-D: Absolute number and mean fluorescence intensity (MFI) of metabolic markers (CD71, CD98, and Glut-1) expressed on T cells isolated from adoptively transfer NOD-Scid mice peripheral blood, pancreas, and spleen at the time of diabetes onset. All data are presented as mean ± SEM (n = 4-10). Statistical significance was determined using one-way ANOVA with multiple comparisons, followed by Bonferroni post hoc test. P < 0.05 represents significant; P > 0.05 is not significant.

[0023] FIGs. 14A, 14B, and 14C show expression of metabolic markers on T cells at diabetes onset. A-B: Density plots and bar graphs showing the percentage of T cells expressing metabolic markers (CD71, CD98, and Glut-1) in peripheral blood, pancreas, and spleen at the time of diabetes onset. Data are presented as mean ± SEM (n =5). Statistical significance was determined using one-way ANOVA with multiple comparisons, followed by Bonferroni post hoc test. P < 0.05 represents significant; P > 0.05 is not significant. C: Flow cytometry gating strategy used to analyze metabolic marker expression on CD4+positive T cells. Lymphocytes were first gated on forward scatter (FSC) and side scatter (SSC), followed by gating on live CD45+CD4+cells to assess the expression of CD71, CD98, and Glut-1.

[0024] FIGs. 15 A, 15B, 15C, 15D, 15E, and 15F show phenotypic characterization of IAPP+T cell receptor rearrangement during diabetes progression in adoptively transferred NOD Scid mice. A Schematic overview of experimental setup for monitoring the epitope spreading in C6.6.9 TCR-Tg mouse CD4+T cells were adoptively transferred into NOD-Scid recipient mice. B: Densitometry flowcytometry plot represent base line expression status of ChgA and InsB23-9 positive on C6.6.9 TCR-Tg mouse CD4+IAPP+ cells. C: Graph represents percentage and absolute number of CD4+IAPP+ChgA+, D: Graph represents percentage and absolute number of CD4+IAPP+InsB+ cells in peripheral blood over time following adoptive transfer in NOD-Scid mice. E-F: Frequency and absolute number of CD4+IAPP+ChgA+ and CD4+IAPP+InsB+ cells at the time of diabetes onset was assessed in pancreas, and spleen. All values are presented as meanAtty. Dkt. No. 144578.00478± SEM (n =5-6). Statistical significance was determined P < 0.05 represents significant using oneway ANOVA with multiple comparisons, followed by the Bonferroni post hoc test.

[0025] FIGs. 16A and 16B show flow cytometry gating strategy used to analyze Phenotypic characterization of IAPP+T cell receptor rearrangement during diabetes progression in adoptively transferred NOD Scid mice. A: Gatting strategy to check the baseline status of NOD C6.6.9 splenocyte before adoptive transfer. B: Gatting strategy to check the receptors rearrangement after adoptive transfer. Lymphocytes were first gated on forward scatter (FSC) and side scatter (SSC), followed by gating on live CD45+CD4+IAPP+ cells to assess the expression of InsB9+ and ChgA+ cells population.

[0026] FIGs. 17A, 17B, 17C, 17D, 17E, 17F, 17G, 17H, 171, 17J, and 17K show longitudinal and cross-sectional analyses reveal early sLAG-3 elevation prior to autoantibody expansion and clinical onset of T1D. A: Experimental design for sample collection. Plasma was collected from healthy controls (n=28), T1D patients (n=39), and FDRs with varying numbers of autoantibodies (Ab-, n=37 Abl+, n=27; Ab2+, n=29; Ab3+, n=5). B: Serum sLAG-3 levels in individuals with T1D up to six years post-diagnosis was comparable to healthy controls, suggesting that heterologous immune responses may mask earlier elevations. C: Autoantibody-negative and single-autoantibody FDRs show higher sLAG-3 levels than multiple-autoantibody individuals, T1D subjects, and controls, indicating early T-cell activation. D. Experimental design for measurement of plasma sLAG-3 levels using MSD U-PLEX Assays in DEWIT and Stanford Cohorts. E-F. DEWIT longitudinal cohort show higher sLAG-3 expression in females than males across both cohorts. G. Progressor vs. non-progressor trajectories in the DEWIT cohort. Progressors display a gradual increase in sLAG-3 preceding diagnosis, whereas non-progressors show no change. H-I. sLAG-3 levels stratified by autoantibody category in FDRs and the general population. J. Longitudinal sLAG-3 profiling in DEWIT progressors shows a distinct peak immediately before diabetes onset followed by a sharp decline after diagnosis. K, HLA genotype analysis demonstrating higher sLAG-3 expression in individuals carrying high-risk alleles. Statistical significance was determined using one-way ANOVA with multiple comparisons, followed by the Bonferroni post hoc test, *P < 0.05 significant.

[0027] FIGs. 18 A, 18B, 18C, 18D, 18E, 18F, and 18G show cross-sectional and longitudinal analysis of plasma sLAG-3 levels by autoantibody status, disease duration, and diabetes stage. A.Atty. Dkt. No. 144578.00478Plasma sLAG-3 levels in diabetic individuals grouped by autoantibody status (Aab- lAab+, 2Aab+, 3Aab+, 4Aab+). B. Longitudinal sLAG-3 profiles across different developmental stages (non-diabetic, diabetic-development, and post-diabetic) showing variable but stage-dependent fluctuations. C. Negative correlation between plasma sLAG-3 levels and disease duration in T1D subjects. D-G. sLAG-3 concentrations in GADA, IA2A, ZnT8A and IAA positive individuals from FDR and general -population cohorts, comparing diabetic and non-diabetic subjects. All values are presented as mean ± SEM. Statistical significance was determined P < 0.05 represents significant using t-test.

[0028] FIGs. 19A, 19B, 19C, 19D, 19E, 19F, 19G, 19H, and 191 show sLAG-3 as an early and sensitive marker of T-cell activation during vaccination and viral infection C57BL / 6 Mice. A. Schematic illustration of before and 10 days after COVID-19 vaccination, indicating rapid T-cell activation preceding antibody development. B. Normalized protein expression (NPX) of sLAG-3 over time in before and after 10 days of Covid vaccination from plasma. Each donors’ samples are connected by a line. C. Schematic representation of time-course analysis of plasma analytes in-vivo CVB-infected C57BL / 6 mice. D. Plasma sLAG-3 levels form C57BL / 6 mice infected with CVB. E. Plasma IFN-y concentrations increase after infection, with a marked rise at day 6, reflecting concurrent immune activation. F-G. Flow cytometry reveals time-dependent increases in IFN-y-producing CD4+and CD8+T cells in peripheral blood following CVB infection. H-I. Expression of T-cell activation markers CD25 and CD69. All values are presented as mean ± SEM (n=8). Statistical significance was determined using one-way ANOVA, followed by the Bonferroni post hoc test. *P < 0.05 represent significant.

[0029] FIG. 20A shows isotype control gating used in flow cytometry analysis to measure intracellular IFN-y production in CD4+and CD8+cells.

[0030] FIG. 20B shows flow cytometry gating strategy used to analyze intracellular IFN- y and activation markers (CD25, CD69 and LAG-3) expression on CD4+and CD8+positive T cells. Lymphocytes were first gated on forward scatter (FSC) and side scatter (SSC), followed by gating on live CD45+CD4+and CD45+CD8+cells to assess the expression intracellular IFN- y cytokine and activation marker (CD25, CD69 and LAG-3) express cells population.Atty. Dkt. No. 144578.00478DETAILED DESCRIPTION

[0031] The methods and compositions described herein relate to detecting type 1 diabetes and / or the onset of type 1 diabetes in a subject.Methods

[0032] In an aspect of the current disclosure, methods are provided. In some embodiments, the methods comprise detecting a level of soluble lymphocyte activation gene 3 (sLAG-3) in a sample from a subject at risk of developing type 1 diabetes.

[0033] The sample from the subject may comprise any suitable sample, e.g., a bodily fluid, e.g., whole blood, serum, plasma, etc.

[0034] Signs or symptoms of type 1 diabetes include, but not limited to, excess thirst, excessive hunger, fatigue, blurred vision, frequent urination, unintentional weight loss, hyperglycemia, syncope, nausea, vomiting, or ketoacidosis.

[0035] A subject at risk of developing type 1 diabetes includes, but is not limited to, subjects with a relative, e.g., a first degree relative (a biological parent, biological sibling, or biological child) that has been diagnosed with type 1 diabetes or is suspected as having type 1 diabetes. The relative may be a second degree relative. In some embodiments, a subject at risk of developing type 1 diabetes is not suffering from at least one sign or symptom of type 1 diabetes, as described above. In other embodiments, a subject at risk of developing type 1 diabetes is suffering from at least one sign or symptom of type 1 diabetes but has not received a diagnosis of type 1 diabetes.

[0036] The inventors discovered that individuals without autoantibodies (AAb-) and those with a single autoantibody (Abl+) had significantly elevated plasma sLAG-3 levels compared to those with type 1 diabetes (T1D) (FIG. 3).

[0037] A subject at risk of developing type 1 diabetes may comprise a subject that has not tested positive for any autoantibodies associated with type 1 diabetes including, but not limited to, autoantibodies against one of GAD65, IAA, Znt8, or IA2, such a subject may have a first degree relative with type 1 diabetes. A subject at risk of developing type 1 diabetes may comprise a subject that has tested positive for 1 autoantibody, 2 autoantibodies, 3 autoantibodies, or more associatedAtty. Dkt. No. 144578.00478with type 1 diabetes including, but not limited to, autoantibodies against one of GAD65, IA A, Znt8 or IA2.

[0038] Certain HLA alleles are associated with type 1 diabetes. In some embodiments, a subject at risk of developing type 1 diabetes may comprise one or more HLA alleles associated with type 1 diabetes including, but not limited to, DR3 / 4-DQ8 HLA.

[0039] The disclosed methods may be used to predict a subject’s susceptibility to developing type 1 diabetes or diagnose a subject as having type 1 diabetes.

[0040] Referring now to FIG. 2A, sLAG-3 levels increased in animals prior to development of T1D and then sLAG-3 levels reduced coincident with the development of overt T1D. FIG. 3 demonstrates that human subjects with 1 or no autoantibodies associated with T1D have higher levels of sLAG-3 as compared with subjects with 2 or more autoantibodies indicating a relative peak of sLAG-3 during development of autoantibodies and a relative decrease close to the onset of T1D development. Thus, in some embodiments, the methods comprise detecting (n) levels of sLAG-3 in (n) separate longitudinal samples from the subject. In some embodiments, the (n) levels of sLAG-3 indicate an initial increase in the concentration of sLAG-3 in the samples and a subsequent decrease in sLAG-3 over time, wherein (n) is an integer greater than or equal to 3. As used herein, “longitudinal samples” refers to samples taken over time, e.g., a first sample may be taken at time 0, a second sample may be taken about 1 month, about 2 months, about 3 months, about 4 months, about 5 months, about 6 months, about 7 months, about 8 months, about 9 months, about 10 months, about 11 months, about 12 months, about 13 months, about 14 months, about 15 months, about 16 months, about 17 months, about 18 months, about 19 months, about 20 months, about 21 months, about 22 months, about 23 months, about 24 months or more after the first sample. Subsequent (n) samples may be taken each at about 1 month, about 2 months, about 3 months, about 4 months, about 5 months, about 6 months, about 7 months, about 8 months, about 9 months, about 10 months, about 11 months, about 12 months, about 13 months, about 14 months, about 15 months, about 16 months, about 17 months, about 18 months, about 19 months, about 20 months, about 21 months, about 22 months, about 23 months, about 24 months or more after the preceding sample.

[0041] Detecting an increase in sLAG-3 over time may indicate that a subject is progressing to T1D and may be a good candidate to receive a therapy to slow down or prevent T1D progression.Atty. Dkt. No. 144578.00478Detecting an increase in sLAG-3 over time followed by a decrease in sLAG-3 may indicate that the subject is very close to progressing to T1D and should be monitored for T1D, e.g., by measuring blood glucose or hemoglobin A1C. The subject may benefit from administration of a therapy to slow down or prevent T1D progression or benefit from administration of insulin.

[0042] The inventors demonstrated that in female NOD mice, sLAG-3 levels significantly decrease at 16 weeks of age, coinciding with the presentation of T1D (FIG. 2A). This drop in serum sLAG-3 level indicated autoimmune activation of naive T cells during diabetes progression eventually lead to T cell maturation, which helps B cell differentiation and class switching, for autoantibody production. As autoreactive T cells are required for the maturation of B cells to produce autoantibodies, the disclosed methods may further comprise detecting autoreactive T cells, e.g., autoreactive CD4 T cells, using methods known in the art including, but not limited to, peptide-complexed MHC multimers, e.g., MHC tetramer.

[0043] Further detecting pancreatic beta cell autoreactive T cells may comprise contacting the sample from the subject with a major histocompatibility complex (MHC) multimer comprising a peptide found in pancreatic beta cells and detecting T cells that are positive for the MHC multimer using flow cytometry. The MHC multimer may comprise an MHC tetramer.

[0044] Detecting soluble LAG-3 (sLAG-3) may be performed using methods known in the art, e.g., an immunoassay, e.g., enzyme-linked immunosorbent assay (ELISA), radioimmunoassays, fluorescent immunoassays, chemiluminescent immunoassays, Western blotting, or mass spectrometry. The ELISA may be a sandwich ELISA. The ELISA may be a lateral flow ELISA.

[0045] Detecting a level of sLAG-3 in a sample from a subject may comprise quantifying a level of sLAG-3 in the sample, e g., using a standard sample or a control sample. The standard sample or control sample may comprise a known concentration of sLAG-3. A standard sample may comprise a biomimetic fluid, e.g., a fluid that is buffered and osmotically balanced to mimic a bodily fluid, e.g., blood, plasma, or serum, and a known concentration of sLAG-3. The standard may comprise multiple aliquots with each aliquot comprising a different concentration of sLAG-3, suitable for a person of ordinary skill to develop a standard curve to detect concentration of sLAG-3 in another sample where the concentration is unknown. A control sample may comprise bodily fluid, e g., blood, plasma, serum, from a donor or a mixture of fluids from multiple donors with a known or standardized concentration of sLAG-3. As above, the control sample mayAtty. Dkt. No. 144578.00478comprise multiple aliquots with each aliquot comprising a different concentration of sLAG-3, suitable for a person of ordinary skill to develop a standard curve to detect concentration of sLAG-3 in a sample where the concentration of sLAG-3 is unknown.

[0046] In some embodiments, the methods are used to detect subjects that are good candidates to receive immune modulating treatments intended to slow down or prevent the onset of type 1 diabetes, including, but not limited to, teplizumab.

[0047] The methods may further comprise administering of an therapy intended to slow down or prevent the onset of type 1 diabetes, e.g., teplizumab. The subject may be administered one or more therapies, e.g., a combination of immune modulating treatments intended to prevent the onset of type 1 diabetes. The subject may be administered “a therapeutically effective amount” of the therapy.

[0048] As used herein, a “therapeutically effective amount” comprises an amount sufficient to improve at least one sign or symptom in a subject in need thereof. A therapeutically effective amount may be an amount sufficient to slow down or prevent the development of type 1 diabetes in a subject. A subject in need thereof may be a subject that is at risk of developing type 1 diabetes.

[0049] In an aspect of the current disclosure, methods comprising detecting a level of soluble lymphocyte activation gene 3 (sLAG-3) in a sample from a subject being administered a therapy to slow down or prevent the onset of type 1 diabetes are provided. The therapy to slow down or prevent the onset of type 1 diabetes may comprise teplizumab.

[0050] In some embodiments, the methods comprise detecting (n) levels of sLAG-3 in (n) separate longitudinal samples from the subject. In some embodiments, the (n) levels of sLAG-3 indicate an initial increase in the concentration of sLAG-3 in the samples over time and a subsequent decrease in sLAG-3 over time, wherein (n) is an integer greater than or equal to 3. As used herein, “longitudinal samples” refers to samples taken over time, e.g., a first sample may be taken at time 0, a second sample may be taken about 1 month, about 2 months, about 3 months, about 4 months, about 5 months, about 6 months, about 7 months, about 8 months, about 9 months, about 10 months, about 11 months, about 12 months, about 13 months, about 14 months, about 15 months, about 16 months, about 17 months, about 18 months, about 19 months, about 20 months, about 21 months, about 22 months, about 23 months, about 24 months or more after the first sample. Subsequent (n) samples may each be taken at about 1 month, about 2 months, about 3 months,Atty. Dkt. No. 144578.00478about 4 months, about 5 months, about 6 months, about 7 months, about 8 months, about 9 months, about 10 months, about 11 months, about 12 months, about 13 months, about 14 months, about 15 months, about 16 months, about 17 months, about 18 months, about 19 months, about 20 months, about 21 months, about 22 months, about 23 months, about 24 months, or more after the second sample.

[0051] Detecting an increase in sLAG-3 over time may indicate that a subject is progressing to T1D and may be a good candidate to receive a therapy to slow down or prevent T1D progression. Detecting an increase in sLAG-3 over time followed by a decrease in sLAG-3 may indicate that the subject is very close to progressing to T1D and should be monitored for T1D, e.g., by measuring blood sugar or hemoglobin A1C. The subject may benefit from administration of a further therapy to slow down or prevent T1D progression or the subject may benefit from administration of insulin.

[0052] The methods may diagnose a subject as having type 1 diabetes and the methods may further comprise administering a treatment for type 1 diabetes, e.g., insulin, to the subject. The subject may be administered a therapeutically effective amount of the therapy, as defined above.Kits

[0053] In an aspect of the current disclosure, kits are provided. In some embodiments, the kits comprise (i) reagents for detecting sLAG-3; and (ii) reagents for detecting biomarkers associated with the development of type 1 diabetes. The biomarkers associated with the development of type 1 diabetes may comprise, e.g., autoantibodies against GAD65, IAA, or IA2, or DR3 / 4-DQ8 HLA allele. The kits may further comprise suitable buffers or diluents for performing an assay to detect sLAG-3 or the biomarkers, e.g., an enzyme-linked immunosorbent assay (ELISA), radioimmunoassays, a fluorescent immunoassay, a chemiluminescent immunoassay, a Western blot, or mass spectrometry.Miscellaneous

[0054] Unless otherwise specified or indicated by context, the terms “a”, “an”, and “the” mean “one or more.” For example, “a molecule” should be interpreted to mean “one or more molecules.”

[0055] As used herein, “about”, “approximately,” “substantially,” and “significantly” will be understood by persons of ordinary skill in the art and will vary to some extent on the context inAtty. Dkt. No. 144578.00478which they are used. If there are uses of the term which are not clear to persons of ordinary skill in the art given the context in which it is used, “about” and “approximately” will mean plus or minus <10% of the particular term and “substantially” and “significantly” will mean plus or minus >10% of the particular term.

[0056] As used herein, the terms “include” and “including” have the same meaning as the terms “comprise” and “comprising.” The terms “comprise” and “comprising” should be interpreted as being “open” transitional terms that permit the inclusion of additional components further to those components recited in the claims. The terms “consist” and “consisting of’ should be interpreted as being “closed” transitional terms that do not permit the inclusion additional components other than the components recited in the claims. The term “consisting essentially of’ should be interpreted to be partially closed and allowing the inclusion only of additional components that do not fundamentally alter the nature of the claimed subject matter.

[0057] All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.

[0058] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.

[0059] Preferred aspects of this invention are described herein, including the best mode known to the inventors for carrying out the invention. Variations of those preferred aspects may become apparent to those of ordinary skill in the art upon reading the foregoing description. The inventors expect a person having ordinary skill in the art to employ such variations as appropriate, and the inventors intend for the invention to be practiced otherwise than as specifically described herein. Accordingly, this invention includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the above-described elements in all possible variations thereof is encompassed by the invention unless otherwise indicated herein or otherwise clearly contradicted by context.Atty. Dkt. No. 144578.00478Exemplary embodiments1. A method comprising detecting a level of soluble lymphocyte activation gene 3 (sLAG-3) in a sample from a subject at risk of developing type 1 diabetes.2. The method of embodiment 1, wherein the subject has not tested positive for autoantibodies against at least one of GAD65, insulin, or IA-2, or the subject has tested positive for only one antibody against GAD65, insulin, or IA-2.3. The method of embodiment 1 or 2, wherein the subject at risk of developing type 1 diabetes has a first degree relative that has been diagnosed with type 1 diabetes or is suspected as having type 1 diabetes.4. The method of any one of the preceding embodiments, wherein the subject at risk of developing type 1 diabetes is a subject that has tested positive for an autoantibody against one of GAD65, IAA, or IA2.5. The method of any one of the preceding embodiments, wherein the subject harbors a DR3 / 4-DQ8 HLA allele.6. The method of any one of the preceding embodiments, wherein the method predicts a subject’s susceptibility for type 1 diabetes.7. The method of any one of the preceding embodiments, wherein the subject is likely to progress to type 1 diabetes and the method predicts a susceptibility of the subject for progressing to clinical type 1 diabetes.8. The method of any one of the preceding embodiments, wherein the method further comprises detecting pancreatic beta cell autoreactive T cells.9. The method of embodiment 8, wherein the pancreatic beta cell autoreactive T cells are CD4 T cells or CD8 T cells.10. The method of embodiment 8 or 9, wherein detecting pancreatic beta cell autoreactive T cells comprises:contacting the sample from the subject comprising T cells with a major histocompatibility complex (MHC) tetramer comprising a peptide found in pancreatic beta cells; andAtty. Dkt. No. 144578.00478detecting T cells that are positive for the MHC tetramer using flow cytometry.11. The method of any one of the preceding embodiments, wherein the expression of sLAG-3 is detected via an immunoassay.12. The method of embodiment 11, wherein the immunoassay is at least one of enzyme-linked immunosorbent assay (ELISA), radioimmunoassays, fluorescent immunoassays, chemiluminescent immunoassays, western blotting, or lateral flow assays.13. The method of embodiment 12, wherein the immunoassay is a sandwich ELISA.14. The method of any one of the preceding embodiments, wherein detecting an expression of sLAG-3 comprises quantifying a level of the sLAG-3 expression.15. The method of any one of the preceding embodiments, wherein the method further comprises detecting a level of sLAG-3 in a control sample.16. The method of embodiment 15, wherein the detected level of sLAG-3 in the sample from the subject suspected of having type 1 diabetes or at risk of developing type 1 diabetes is greater than the level of sLAG-3 in the control sample.17. The method of any one of the preceding embodiments, wherein the sample from the subject is serum, plasma, or whole blood.18. The method of any one of the preceding embodiments, wherein the method further comprises detecting sLAG-3 at a subsequent time to monitor the expression of sLAG-3 in the subject over time.19. The method of embodiment 18, wherein the method comprises detecting (n) levels of sLAG-3 in (n) separate longitudinal samples from the subject, wherein the (n) levels of sLAG-3 indicate an initial increase in the concentration of sLAG-3 in the samples and a subsequent decrease in sLAG-3 over time, wherein (n) is an integer greater than or equal to 3.20. The method of any one of the preceding embodiments, wherein the method further comprises administering a therapeutically effective amount of teplizumab to the subject.21. A method comprising detecting a level of soluble lymphocyte activation gene 3 (sLAG-3) in a sample from a subject being administered a therapy to prevent the onset of type 1 diabetes.Atty. Dkt. No. 144578.0047822. The method of embodiment 21 , wherein the method further comprises detecting sLAG-3 at a subsequent time to monitor the expression of sLAG-3 in the subject over time.23. The method of embodiment 22, wherein the method comprises detecting (n) levels of sLAG-3 in (n) separate longitudinal samples from the subject, wherein the (n) levels of sLAG-3 indicate an initial increase in the concentration of sLAG-3 in the samples and a subsequent decrease in sLAG-3 over time, wherein (n) is an integer greater than or equal to 3.24. The method of embodiment 23, wherein the method further comprises diagnosing the subject as having type 1 diabetes.25. The method of any one of embodiments 21-24, wherein the therapy to prevent the onset of type 1 diabetes comprises teplizumab.26. A kit comprising at least one reagent for detecting sLAG-3 and at least one reagent for detecting a biomarker associated with type 1 diabetes.27. The kit of embodiment 26, wherein the at least one reagent for detecting sLAG-3 comprises an antibody specific for sLAG-3.28. The kit of embodiment 26 or 27, wherein the biomarker associated with type 1 diabetes comprises antibodies against one of GAD65, IAA, or IA2, or DR3 / 4-DQ8 HLA allele.29. A method of stratifying risk of progression to clinical type 1 diabetes in a human such as a first degree relative, but not limited to, who is negative for islet autoantibodies or positive for no more than one islet autoantibody, comprising quantifying sLAG-3 in plasma by sandwich ELISA and comparing the level to a reference value and / or an earlier sample from the same subject.EXAMPLES

[0060] The following examples are meant to be illustrative and not limiting.Atty. Dkt. No. 144578.00478Example 1: Unlocking The Promise of Soluble LAG-3: A Potential Surrogate Marker for Type-1 Diabetes Progression and Immunotherapy success.

[0061] Type 1 diabetes (T1D) is a complex autoimmune disorder in which both CD4+ and CD8+ T cells contribute to the destruction of insulin-secreting pancreatic beta cells within the islets, leading to insulin deficiency and elevated blood sugar levels. Although T1D cases are increasing, not all individuals with high-risk genetic factors develop the disease. To assess their risk of progression to T1D screening of first-degree relatives of T1D patients can undergo testing for autoantibodies and HLA alleles. Early markers, such as autoantibodies against GAD65, IAA, and IA2, can signal the onset of T1D years before clinical symptoms appear. The presence of these autoantibodies indicates that the autoimmune process has already begun, making early intervention difficult. Consequently, there is a critical need for biomarkers that can predict T1D risk before seroconversion, which would significantly aid prevention efforts. B cells require interaction with T-helper cells that recognize the same antigen, so called, linked recognition. This T cell B cell interaction leads to optimally produce islet autoantibodies and is imperative for autoantibody production to b cell protein antigens. Since antibody production to protein antigens requires T cell help, T cells must be activated priori to interreacting with B cells and provide the T cell help necessary for the B cell to affinity mature, class switch, and transition to plasma cells to make antibody to islet associated proteins. A surface protein called lymphocyte activation gene-3 (LAG-3), plays a major role in controlling the activation, metabolic regulation, for expansion and finally effector function. T-helper cell activation, generating soluble LAG-3 (sLAG-3), which is detectable in serum and serves as a marker of T cell activation. Identifying predictive biomarkers of T1D progression before the appearance of autoantibodies is highly desirable. In the context of T cell activation, the shedding of LAG-3 offers a potential surrogate marker for T-cell activation, including self-reactive activation serving as an earlier surrogate marker of a break in self-tolerance and an indicator of T1D progression. Plasma sLAG-3 levels were monitored longitudinally over time in female NOD mice using monoclonal antibody (mAb) ELISA, while beta cell-specific antigen expression was assessed in CD4+ T cells from the same cohort using islet-antigen associated tetramers. In this preclinical T1D model, preliminary findings suggest that sLAG-3 levels in female NOD mouse serum correlate with beta cell antigen-specific CD4+ T cell tetramer positivity and disease progression. Notably, female mice show an earlier increase in sLAG-3,Atty. Dkt. No. 144578.00478aligning with an earlier onset of disease compared to males. In a longitudinal T cell transfer study, we used NOD congenic mice (C6.6.9. TCR-Tg) with naive, islet-reactive T cells, transferring them into NOD Scid mice. This allowed us to monitor the initial antigen recognition of self-reactive T cells post-transfer. We tracked activation, expansion, and effector function, as well as the impact on islet cell function, as described above. We monitored plasma sLAG-3 levels, CD4+ T cell isletantigen-associated tetramers, and extracellular vesicles containing insulin mRNA as a surrogate for P-cell health and function. The results revealed a significant increase in insulin mRNA levels during early disease progression, suggesting that inflammation from the immune response leads to dysregulation of insulin production, processing, and granule secretion due to autoimmune-mediated P-cell destruction. This was corroborated by our immune infiltration analysis. Over time, the progressive immune infiltration leads to the depletion of pancreatic islets. This adoptive transfer model enables a detailed investigation of the dynamic interplay between T cell-mediated islet destruction and immune cell infiltration throughout disease progression. Further metabolic analysis of immune cells may provide critical insights into potential therapeutic strategies aimed at preserving insulin secretion in type 1 diabetes. Human data further support the potential of sLAG-3 as a biomarker, as it has been detected in the serum of autoantibody-negative and single autoantibody-positive first-degree relatives of T1D patients. This suggests that sLAG-3 could help identify individuals at higher risk of developing T1D much earlier before autoantibodies appear. The combined measurement of sLAG-3 levels and tetramer expression in CD4+ T cells hold promise as an early biomarker for T1D detection, paving the way for proactive treatment strategies before significant P-cell destruction occurs.Background

[0062] A key bottleneck in this process is the lack of biomarkers that can accurately predict early progression of T1D development, prior to autoantibody. Furthermore, to effectively target prospective treatments to the relevant stage of disease development, it is also critical to be able to distinguish between persons who acquire T1D and individuals who develop autoantibodies, but do not progress to full blown diabetes. T-cell dependent islet autoantibody productions involve activation of B-cell by T-helper cells, a process called linked recognition. The interaction between T cells and B cells recognizing the same antigen is crucial for B cell isotype switching, affinity maturation, and plasma cell maturation. Helper T cells, primed by the same antigen, activate BAtty. Dkt. No. 144578.00478cells to produce specific antibodies. Upon antigen-MHC Class II recognition, helper T cells express CD40 ligand, which binds to CD40 receptors on B cells, stimulating their proliferation and differentiation. Thus, T-cell activation is essential for autoantibody production in the progression of Type 1 Diabetes (T1D). During T-cell activation, lymphocyte activation gene-3 (LAG-3) undergoes enzymatic cleavage by a cellular metalloprotease, releasing soluble LAG-3 (sLAG-3). This soluble form of LAG-3 can be identified in serum / plasma samples, thereby serving as a reliable marker indicative of T cell activation. High levels of sLAG-3 have been associated with better prognostic outcomes in tumor immunity as they forecast strong T cell activation, therefore; we hypothesized that sLAG-3 could also be used to forecast pending autoimmunity, prior to detection of autoantibodies in T1D. In the context of autoreactive T cell activation, the shedding of soluble LAG-3 (sLAG-3) provides a surrogate marker of self-reactive T-cell activation and a predictive biomarker of T1D progression.Materials and Methods

[0063] Mice

[0064] Female and male NOD mice were bred and housed under specific pathogen-free conditions in the Laboratory Animal Resource Center BRTC facility of Indiana University, Indianapolis. All animal experiments were approved by the IUSM Institutional Animal Care and Use Committee and were in compliance with the laws of the United States of America.

[0065] Diabetes onset

[0066] NOD mice were monitored daily for glucosuria starting at 12 weeks of age. Blood glucose was measured in mice positive for glucosuria, and overt diabetes was defined as two consecutive readings >300 mg / dl. The plasma sLAG-3 level was monitored longitudinally in female NOD mice across various age intervals during the onset of diabetes progression.

[0067] Patients’ serum sample

[0068] Three different institutions (Children’s Hospital of Pittsburgh, University of Florida, and University of Queensland) collectively provided the sera obtained from healthy controls, T1D patients, and FDRs and it was stored at -80oC. T1D patients ranged in age of onset (1 to 16 yrs old) and duration of disease (1 to 6 yrs post-onset). FDRs were tested for autoantibodies to GAD65, IAA, and IA2 by standard radioimmunoassay.Atty. Dkt. No. 144578.00478

[0069] Estimating soluble LAG-3 levels from NOD and human sera

[0070] NOD females and males (n=4 / group) were bled longitudinally for serum every other week starting at 4 weeks of age until 16 weeks of age. Plasma from different sample was isolated through standard isolation procedure. sLAG-3 was detected by ELISA following standard sandwich methods. Human serum samples were used commercially available Human LAG-3 DuoSet ELISAkit, and estimation was performed according to manufacturer’s instructions (R&D System, Catalog # DY2319B), except for the blocking diluent. 10%, 20%, 30%, and 40% FBS in PBS were tested to determine the best diluent for minimizing background. 30% FBS in PBS was then used to block and dilute samples and standards. Relative sLAG-3 was determined to compare human samples from all three institutions.

[0071] Assessing T cell tetramer expression

[0072] RBC were lysed using Red Blood Cell Lysing Buffer (Sigma-Aldrich) to get the lymphocytes from NOB mice. ChgA (APC), IAPP (PE), and InsB 9-23 mimotope tetramer staining was performed at 37oC for 30 min; furthermore, surface staining was performed at 4 deg. C using CD4-FITC in FACS buffer (1% BSA in PBS). Expression of tetramers was analyzed using a BD LSRII, and data were analyzed using FlowJo Software (vlO.l).Results

[0073] Fo validate soluble LAG-3 (sLAG-3) as a surrogate marker of autoreactive T-cell activation as well as a predictive biomarker of diabetes progression from preclinical to clinical disease. Plasma sLAG-3 level was measured in NOD mice over several weeks of age to determine if T cell activation correlated with diabetes onset (FIGS. 1 A-1B). FIG. 3 demonstrates significantly increased levels of sLAG-3 in NOD females early in life (6-8 weeks of age) in comparison to NOD males. In female NOD mice, sLAG-3 levels significantly decrease at 16 weeks of age, coinciding with the presentation of T1D. This drop in serum sLAG-3 level indicated autoimmune activation of naive T cells during diabetes progression eventually lead T cell maturation, which helps B cell differentiation, class switching, for autoantibody production. Therefore, to detect beta cell specific T cells activation, we measured beta cell specific antigen (islet-associated tetramer) in CD4+ positive T cells from same NOD mice cohort. CD4+ T cells from NOD mice at the various age groups, stain high for tetramer positivity for ChgA, InsB9-23, and IAPP at the prediabetes stageAtty. Dkt. No. 144578.00478(8-12 week of age group) particularly insulin autoantigen tetramer positive cells. However, they progressively decrease in the peripheral blood, during the progression of diabetes (data not shown). Based on these findings, plasma sLAG-3, PBL-tetramer expression in CD4+ T cells, can serve as a combined surrogate marker triad for predicting Type 1 Diabetes (T1D) development earlier than autoantibodies alone. This hypothesis was further investigated using blood samples from healthy controls, T1D patients, and individuals with new-onset T1D (1 to 5 years). Interestingly the average sLAG-3 concentration was the same among T1D patients, healthy controls, and in the recent T1 cohort (Figure 3). Finally, we assessed sLAG-3 levels in a cross-sectional cohort of first-degree relatives (FDRs) with varying numbers of autoantibodies. As shown in Figure 3, like the NOD pre-clinical samples, LAG-3 levels were prominent in FDRs without autoantibodies (AAb-), peaking with one autoantibody and decreasing as the number of autoantibodies increased. Our findings indicate that individuals without autoantibodies (AAb-) and those with a single autoantibody (Abl+) had significantly elevated plasma sLAG-3 levels compared to those with type 1 diabetes (T1D) (Fig 3). This suggests the initiation of T cell activation and potential disease progression. Conversely, FDRs with 2-3 autoantibodies exhibited sLAG-3 levels similar to those in T1D patients, revealing a negative correlation between sLAG-3 levels and the number of autoantibodies. This implies a progression from T cell activation to B cell activation and the eventual onset of T1D. These findings indicate that LAG-3 levels can be used as an indicator for detection following a T1D diagnosis. To confirm sLAG-3 as a predictive biomarker for early-stage T1D progression, further validation is needed from first-degree relatives or autoantibody-negative individuals who are progressing toward a T1D diagnosis.Discussion

[0074] Our previous studies investigated the role of LAG-3 in T1D and in accordance with other reports using non-autoimmune strains, demonstrated that LAG-3 also negatively regulates autoreactive T cells functional activity. Importantly, our study depicted sLAG-3 as a possible biomarker for T1D development using an adoptive transfer model of diabetes. Currently, the progression to Type 1 Diabetes (T1D) is clinically screened through the assessment of autoantibody formation at different stages of the disease progression (Trial Net). While this method is informative and state-of-the-art, it overlooks a crucial step in autoantibody formation: T cell assistance in B cell activation and the development of antibody. Serum biomarkers thatAtty. Dkt. No. 144578.00478measure T cell activation, which are prerequisites for autoantibody formation, appear in the plasma before autoantibodies and overt diabetes. Therefore, we hypothesize that serum sLAG-3 may serve as an early biomarker for spontaneous T1D progression, detectable prior to autoantibody formation. We first measured sLAG-3 longitudinally from the serum of female NOD mice, which spontaneously develop diabetes. Unlike the rapid adoptive transfer model of disease, spontaneous onset is more physiologically relevant to human disease. One caveat to using these mice is that NOD females present with a greater diabetes incidence than NOD males. Therefore, in our previous finding we monitored serum and T1D onset from both sexes to determine if sLAG-3 acted as a biomarker for greater incidence in NOD females. Meghan et al., 2012 from our lab identified that, sLAG-3 was higher in NOD females than males at early time points, which tracks with diabetes risk, as males remained diabetes free throughout the study, and indicates a delay between T cell activation and disease onset. However, it was also observed by our group that, these differences were lost by 10 weeks of age, illustrating the potential of sLAG-3 as a very early biomarker, likely before the formation of autoantibodies.

[0075] Unlike our mouse studies that were conducted by collecting longitudinal samples our human data was cross-sectional from various cohorts of patients, sorted based on antibody negative up to 3 antibody positive, T1D and controls. Compared to healthy controls, sLAG-3 levels from T1D patients were not different, likely owing to once overt diabetes is diagnosed, autoreactive T cells have already been activated, leading to subsequent B cell activation and pancreatic beta cell destruction. At this stage, T cells are no longer predominant in the blood but have moved to secondary lymphoid organs or target tissues, such as pancreatic islets. Therefore, we were not surprised by the lack of detectable differences between the two groups. Instead, we focused on monitoring individuals before diabetes onset to assess whether sLAG-3 could serve as a T cell biomarker for early disease progression. FDR serum was categorized based on the number of autoantibodies present: 0, 1, 2, or 3. ELISA detection revealed that sLAG-3 levels inversely correlated with the number of autoantibodies. FDRs with 0-1 autoantibodies exhibited significantly higher sLAG-3 levels compared to T1D patients, while FDRs with 2-3 autoantibodies had low sLAG-3 levels, similar to those of T1D patients. These findings demonstrates that: (A) autoreactive T cell activation precedes autoantibody formation in FDRs; (B) sLAG-3 may serve as a potential non-invasive, T cell-specific biomarker for predicting T1D in at-risk individuals, prior to autoantibody detection; and (C) sLAG-3 detection may enable earlier prophylacticAtty. Dkt. No. 144578.00478intervention than autoantibody detection. Overall, our data supports the use of sLAG-3 as a novel T cell-specific biomarker of T1D progression in FDRs of T1D patients. sLAG-3 detection would allow for an earlier therapeutic intervention window when pancreatic beta cell mass is still intact.

[0076] Collectively, our preliminary findings suggest that monitoring sLAG-3 levels could serve as a valuable tool for detecting the early progression of T1D. Additionally, our longitudinal study in NOD mice strongly demonstrates that tetramer expression in CD4+ T cells is also a promising early surrogate biomarker for T1D detection, potentially preceding the onset of autoantibody production. Since autoantibodies are traditionally used as indicators of T1D risk but often emerge after significant immune activation, identifying an earlier marker could enable more timely interventions. The presence of tetramer-positive autoantigen-specific CD4+ T cells may reflect the initiation of the autoimmune response against pancreatic islets, providing a critical window for therapeutic strategies aimed at modulating immune activity before extensive P-cell destruction occurs.

[0077] Additionally, further investigation into the correlation between sLAG-3 and other biomarkers, such as tetramer staining of insulin autoantigen-specific CD4+ T cells, is warranted.

[0078] One of the key advantages of tetramer technology is its ability to identify pathogenic T cells before the appearance of traditional markers like autoantibodies. Since autoantibodies are often late-stage indicators of T1D risk, tetramer-based detection of antigen-specific T cells could facilitate earlier diagnosis and more proactive therapeutic interventions. Moreover, longitudinal studies using tetramer staining have demonstrated dynamic changes in T cell populations, suggesting that tracking these cells over time could provide a more precise understanding of T1D progression. By refining tetramer-based approaches for both preclinical and clinical applications, it may be possible to identify individuals at high risk of T1D earlier and implement immunomodulatory strategies to preserve P-cell function before significant damage occurs.

[0079] This research will focus on the maturation of the antigen-specific T cell response as selfantigens are released during chronic autoimmune or inflammatory responses, potentially triggering epitope spreading. Understanding these dynamics could guide the selection of target antigens for developing antigen-specific tolerance induction strategies in T1D, thereby deepening our comprehension of disease mechanisms and informing the development of novel therapeutic approaches. The use of T cell tetramer technology has significantly advanced our understandingAtty. Dkt. No. 144578.00478of the role of antigen-specific CD4+ and CD8+ T cells in T1D development. Tetramers enable the direct identification and characterization of autoantigen-specific T cells that target pancreatic P-cell antigens, providing a powerful tool for studying autoimmune responses at various stages of disease progression.References

[0080] 1. Previte, D. M., C. P. Martins, E. C. O’Connor, M. L. Marre, G. M. Coudriet, N. W. Beck, A. VMenk, R. H. Wright, H. M. Tse, G. M. Delgoffe, and J. D. Piganelli. 2019. Lymphocyte Activation Gene-3 Maintains Mitochondrial and Metabolic Quiescence in Naive CD4(+) T Cells. Cell Rep. 27: 129-141. e4.

[0081] 2. Delmastro, M. M., A. J. Styche, M. M. Trucco, C. J. Workman, D. A. A. Vignali, and J. D. Piganelli. 2012. Modulation of redox balance leaves murine diabetogenic TH1 T cells “LAG-3-ing” behind. Diabetes 61: 1760-1768.

[0082] 3. Little, A. C., I. Kovalenko, L. E. Goo, H. S. Hong, S. A. Kerk, J. A. Yates, V. Purohit, D. B. Lombard, S. D. Merajver, and C. A. Lyssiotis. 2020. High-content fluorescence imaging with the metabolic flux assay reveals insights into mitochondrial properties and functions. Commun. Biol. 202031 3: 1-10.

[0083] 4. Sims, E. K., B. N. Bundy, K. Stier, E. Serti, N. Lim, S. A. Long, S. M. Geyer, A. Moran, C. J. Greenbaum, C. Evans-Molina, K. C. Herold, L. A. DiMeglio, S. E. Gitelman, P. A. Gottlieb, J. B. Marks, W. Moore, H. Rodriguez, W. E. Russell, D. Schatz, E. Tsalikian, D. K. Wherrett, and A. G. Ziegler. 2021. Teplizumab improves and stabilizes beta cell function in antibody-positive high-risk individuals. Sci. Transl. Med. 13: 8980Example 2 - Unlocking The Promise of Soluble LAG-3: A Potential Surrogate Marker for Type-1 Diabetes Progression

[0084] Type 1 diabetes (T1D) is a complex autoimmune disorder in which autoreactive CD4+and CD8+T cells destroy pancreatic P-cells, resulting in insulin deficiency and hyperglycemia. Although genetic susceptibility, particularly certain HLA alleles, contribute to disease risk, not all genetically predisposed individuals develop T1D. Screening first degree relatives (FDRs) for islet autoantibodies (GAD65, IAA, IA-2) helps detect autoimmune activity, but these markers arise only after T-helper cell activation, limiting early intervention opportunities. Because proteinAtty. Dkt. No. 144578.00478antigen recognition by B cells requires T-helper cell assistance through linked recognition, T cell activation precedes B cell activation and autoantibody production. Activation of these T cells leads to shedding of the immune-regulatory surface protein LAG-3 (Lymphocyte Activation Gene-3), generating its soluble form, sLAG-3, detectable in serum. Findings reveal, elevated plasma sLAG-3 levels in female NOD mice correlated with islet-specific CD4+T cells and preceded diabetes onset, suggesting its role as an early immune activation marker. Using an adoptive transfer model with naive autoreactive C6.6.9 TCR-transgenic T cells transferred into NOD Scid mice, we monitored plasma sLAG-3, islet-specific tetramer-positive CD4+T cells, and extracellular vesicle bound insulin mRNA over time. Increased sLAG-3, insulin mRNA in plasma reflected early 0-cell stress, and immune infiltrating tetramer positive CD4 cells denotes immune activation. In humans, serum sLAG-3 was detected in autoantibody -negative and single-autoantibody-positive FDRs, with higher levels in progressors than non-progressors, indicating early immune activation. Moreover, sLAG-3 was associated with high-risk HLA genotypes and correlated with immune markers. Together, these findings highlight sLAG-3 as a potential pre-autoantibody biomarker for early T1D detection and a tool to guide preventive strategies before significant 0-cell loss.

[0085] Highlights

[0086] Identify biomarkers for early detection of T1D progression before autoantibodies appear.

[0087] Assess if soluble LAG-3 (sLAG-3) levels predict T-cell activation and self-reactive immune responses in T1D and can indicate T1D risk before autoantibody appearance.

[0088] Elevated sLAG-3 levels correlated with CD4+ T cells and disease progression in female NOD mice prior to dysglycemia; sLAG-3 was also detected in first-degree relatives of T1D patients before autoantibodies.

[0089] sLAG-3 and CD4+ T cell tetramers may serve as early biomarkers for T1D detection, enabling proactive treatment before significant beta-cell destruction.

[0090] Introduction

[0091] Type 1 diabetes (T1D) is a complex, heterogeneous, and polygenic autoimmune disease in which CD4+ and CD8+ T cells instigate an attack against pancreatic 0-cells ultimately eradicating them, leading to insulin deficiency and hyperglycemia [1-3], However, mechanisms underlying development of this abnormal immune cell and 0 cell interaction remain only partially understoodAtty. Dkt. No. 144578.00478[4], T1D incidence is rising by 2-3% annually, out pacing the genetics associated with high-risk HLA alleles, suggesting environmental factors like viral infections and oxidative stress have contributing roles [5] [6, 7], First-degree relatives (FDRs) of T1D patients, especially those with DR3 / 4-DQ8 HLA positivity, are at higher genetic risk and can be screened using autoantibody and HLA tests. However, current biomarkers, mainly autoantibodies (GAD65, insulin, IA2), often serve as a harbinger of disease only after substantial P-cell damage has occurred [8, 9], This emphasizes the urgent need for earlier biomarkers that predict T1D before irreversible P-cell loss. Although the FDA approved teplizumab as an immune modulating therapy that delays disease onset, not fully preventing T1D remains a significant challenge

[0010] , Autoantibody presence indicates that immune tolerance is already broken, self-reactive CD4 T cells are activated, and the autoimmune response driving T1D is underway

[0011] , This underscores the importance of identifying new biomarkers that appear at earlier stages of the disease.

[0092] Recent efforts to prevent T1D focus on immune modulation during preclinical phase. Although FDA approval of teplizumab to delay clinical onset is a major milestone, true prevention remains elusive [10, 12], Current biomarkers, such as islet autoantibodies, indicate a loss of immune tolerance, but they only appear after CD4+ T cells have already provided the necessary help to plasma B cells for antibody production. Identifying a novel biomarker that predict type 1 diabetes risk very early before seroconversion or autoantibody onset and differentiate individuals likely to progress to clinical disease from those who will not is crucial for advancing prevention trials [12, 13], T-cell activation is an early and pivotal event in T1D, initiating autoantibody production through T-helper cell-mediated linked recognition

[0014] , Antigen-specific CD4 T cells deliver essential co-stimulatory signals and cytokines that drive B cell proliferation, class switching, and differentiation into plasma cells that secrete high-affinity autoantibodies targeting P-cell antigens. Despite their central role, the triggers for CD4+T cell activation remain unclear, though both genetic and environmental factors are thought to contribute [15, 16],

[0093] During T-cell activation, a cellular metalloprotease enzymatically cleaves lymphocyte activation gene-3 (LAG-3) protein from the cell surface, releasing sLAG-3 into circulation as a T-cell activation marker [13-15], High sLAG-3 levels, previously associated with strong immune responses in cancer, Graves’ Disease, could also predict emerging autoimmunity in T1D before autoantibody detection [16-18], Following P-cell integrated stress response (ISR) triggers the T-cell activation, releasing insulin mRNA in extracellular vesicles (EVs) as another promising earlyAtty. Dkt. No. 144578.00478biomarker of P-cell dysfunction [19, 20], However, current methods lack the sensitivity to detect insulin mRNA in extracellular vehicles before seroconversion, underscoring the need for enhanced detection strategies for early biomarkers like sLAG-3 and insulin mRNA to predict T1D progression and inform prevention strategies.

[0094] To address this gap, we conducted a longitudinal analysis of plasma sLAG-3 levels using a monoclonal antibody-based ELISA, along with P-cell-specific antigen expression in CD4+T cells. This assessment was performed using longitudinal plasma samples from female NOD mice and human cohort from the Diabetes Evaluation in Washington (DEW) Study, and in cross-sectional autoantibody-positive samples obtained from the Stanford Biobank. All this preclinical T1D model, preliminary results indicate that plasma sLAG-3 levels correlate with beta cell antigen-specific CD4+T cell tetramer positivity and disease progression. Moreover, our longitudinal T cell transfer study, we transferred naive islet-reactive T cells from NOD congenic C6.6.9 TCR-Tg mice into NOD-Scid recipients to monitor antigen recognition, activation, expansion, and effector function of self-reactive T cells in real time. We assessed plasma sLAG-3 levels, CD4+T cell tetramer positivity, and extracellular vesicle insulin mRNA levels as markers of P-cell health

[0021] , A significant early rise in insulin mRNA levels suggested that immune-driven inflammation disrupts insulin production, processing, and secretion, which correlates with progressive immune infiltration and islet destruction. Further metabolic profiling of immune cells could uncover therapeutic targets to preserve insulin secretion. Our human studies have detected sLAG-3 in the plasma of autoantibody-negative and single autoantibody-positive first-degree relatives of T1D patients, further supporting our findings and highlighting its potential as an early biomarker. Combined monitoring of sLAG-3 and CD4+T cell tetramer expression, in conjunction with elevated extracellular vesicle containing insulin m-RNA

[0021] may enable earlier T1D detection and proactive intervention before substantial P-cell loss.

[0095] Research Design and Methods

[0096] Mice

[0097] NOD / ShiLtJ (Strain 001976, homozygous for diabetes susceptibility loci) and NOD.Cg-Prkdcscld / J(Strain 001303, homozygous for Prkdcscldmutation) C57BL / 6 (Strain 000664) male and female mice were purchased from The Jackson Laboratory. Male and female mice of both strains were bred and housed under specific pathogen-free conditions in the Biomedical Research andAtty. Dkt. No. 144578.00478Training Center (BRTC) managed by the Laboratory Animal Resource Center (LARC) at Indiana University, Indianapolis. All animal experiments were approved by the Indiana University School of Medicine (IUSM) Institutional Animal Care and Use Committee (IACUC) under the protocol number 23107 MD / R / HZ and followed the laws of the United States of America.

[0098] Diabetes onset

[0099] NOD / ShiLtJ mice were monitored daily for glucosuria starting at 12 weeks of age. Blood glucose was measured in mice positive for glucosuria, and overt diabetes was defined as two to three consecutive readings >300 mg / dl. Blood glucose was measured by puncturing the last 1 mm of the tail and dispensing one drop of blood on a Contour next ® disposable test strip and glucometer.

[0100] Research participant’s sample

[0101] Plasma from healthy controls, T1D participants, and FDRs were provided by Children’s Hospital of Pittsburgh, University of Florida, and University of Queensland. Samples were stored at -80°C until analysis. Participants with T1D ranged in age of onset (1 to 16 yrs old) and duration of disease (1 to 6 years post-onset). FDRs were tested for autoantibodies to GAD65, 1AA, and 1A2 by standard radioimmunoassay. Plasma Samples were collected from two cohorts of healthy controls, younger participants aged 25-35 and older participants aged 55-65 years old who were recruited into the Sound Life Project for measuring the sLAG-3 level from research participant before and after covid vaccination. Samples were collected with approval from Benaroya Research Institute, Seattle, WA review board (IRB 19-045). All participants provided written informed consent. Prospective cohort studies for measuring the sLAG-3 from Diabetes Evaluation in Washington Study (DEWIT) enrolled infants from the general population (n=39) carrying increased-risk HLA-DR-DQ genotypes identified by newborn screening, as well as first-degree relatives of individuals (n=13) with or without type 1 diabetes irrespective of HLA genotype. All participants underwent HLA screening, though eligibility criteria varied across sites. Follow-up began between 2 months and 21.6 years of age and continued at 3 to 36 month intervals for up to 26 years, with type 1 diabetes diagnosed according to American Diabetes Association criteria. All protocols were approved by local institutional review boards, and de-identified data were submitted to IBM Research. The harmonized cohort comprised median 10 visits, 8.7 years followup; 17% reported a family history of type 1 diabetes. For key analyses, it was examined the infant-Atty. Dkt. No. 144578.00478toddler subcohort, restricted to subjects tested for insulin, IA-2, and GAD autoantibodies by age 2.5 years. The infant-toddler cohort included median 12 visits, 10.4 years follow-up

[0022] ,

[0102] Estimating soluble LAG-3 levels from NOD and human plasma

[0103] NOD / ShiLtJ and NOD.Cg-Prkdcscid / J mice from different experimental groups retro-orbitally bled for plasma every other week from 4 to 16 weeks of age. Plasma was isolated from samples by centrifugation at 3000 x RPM for 10 minutes at 4°C. sLAG-3 was detected by ELISA following standard sandwich methods, 96 well high affinity flat bottom plate (Catalog no. 3361, Corning) were coated overnight at 4°C with 5 ug / ml monoclonal mouse anti-LAG3 (C9B7W) (In Vivo Mab, #BE0174) diluted in carbonate coating buffer (pH 9.6). LAG-3 standard was purchased from R&D (Catalog: AF3328) antibody pairs for mouse were purchased from Jackson Immuno Research, performed according to manufacturer’s protocol. Human plasma samples from University of Pittsburgh, University of Florida, and University of Queensland were used to measure sLAG-3 from commercially available Human LAG-3 DuoSet ELISAkit (R&D Systems, Catalog #DY2319B), and estimation was performed according to manufacturer’s instructions. Relative sLAG-3 levels were measured to compare different autoantibody positive human samples from all three institutions. In all assays, mean OD450nm readings obtained from triplicate wells. ELISAs were read on a SpectraMax iD5 microplate reader and analyzed using SoftMax Pro version 7.0.2 software.

[0104] Detection of plasma sLAG-3 from DEWIT cohort study and Stanford Biobank

[0105] All human plasma samples were analyzed using the MSD U-PLEX platform, employing sandwich immunoassays for protein targets and a competitive immunoassay for neopterin, with electrochemiluminescence detection (Meso Scale Diagnostics). Each MULTI-ARRAY® 96-well plate contained sLAG-3 capture antibodies, Sulfo-Tag-labeled detection antibodies, a mixed calibration standard, and appropriate diluents. The Indiana University Translation Core Facility processed all samples on the MSD platform; 50 pL of each sample was submitted for analysis. Samples were tested in triplicate, and mean concentrations were reported. Values below the Limit of Detection (LOD) were assigned the LOD. Control CVs were calculated for sLAG-3 on each plate.

[0106] Estimating sLAG-3 from covid vacationed research participantAtty. Dkt. No. 144578.00478

[0107] Measurement of sLAG-3 in healthy control plasma, Olink proteomics was performed using the Olink proximity extension assay using the Olink Explore 1536 platform’s oncology panel

[0023] . Samples were randomized across plates to balance age and sex distributions. Longitudinal samples from the same participant were run on the same plate. Bridging controls were included in each batch and used for cross-batch normalization. Following Olink’ s standard data normalization protocols, protein expression values were first normalized across wells using an internal extension control (IgG antibodies conjugated with a matching oligo pair). Plates were then standardized by normalizing to the interplate pooled serum controls run in triplicate on each plate. Data were then intensity normalized across all cohort samples. Final normalized relative protein quantities were reported as (NPX) values.

[0108] In vivo viral infection incidence study

[0109] C57BL / 6 (Strain 000664) 8-week-old mice were infected by intraperitoneal injection of 100 PFU Coxsackievirus B (CBV) in HBSS used under specific pathogen-free conditions. To study the incidence of sLAG-3, blood samples were collected longitudinally via retro-orbital bleeding at multiple time points prior to infection (baseline) and on days 3, 6, 9, 12, 16, and 21 post-infections. Plasma was isolated by centrifugation and stored at -80°C until further analysis. Quantification of plasma sLAG-3 levels was performed using standard ELISA protocols. In parallel, intracellular IFN-y levels were measured by flowcytometry in both CD4+and CD8+T cells to assess the immune activation status during infection.

[0110] Adoptive transfer study[OHl] Whole spleens were harvested under sterile conditions from 8-week-old female NOD congenic C6.6.9 TCR-Tg mice, mechanically homogenized, passed through a 40 pm strainer, washed, and resuspended in cold DMEM with 10% FBS. Lymphocytes were isolated using Ficoll-Paque density gradient centrifugation and washed twice with IX PBS. Approximately 5*106lymphocytes in sterile PBS were retro-orbitally transferred into 8-week-old female NOD Cg-Prkdcscid / J recipient mice. After adoptive transfer, NOD-SCID mice were monitored biweekly for diabetes progression by measuring blood glucose via retro-orbital bleeds. Every three days, mice were bled to assess T cell activation by measuring plasma sLAG-3, activation markers (CD69, CD44, LAG-3), and CD4+T-cell reactivity to beta cell antigens (ChgA, InsB9-23, IAPP)Atty. Dkt. No. 144578.00478using tetramers. Mice with glucose >300 mg / dL on two consecutive readings were euthanized to collect spleen and pancreas for immune and histopathological analyses.

[0112] Staining and flow cytometry analysis

[0113] To evaluate T-cell-mediated immune activation in both autoimmune and viral contexts, lymphocytes were analyzed by flow cytometry. NOD / ShiLtJ mice were longitudinally bled via retro-orbital sampling during diabetes progression, and NOD-SCID mice were similarly sampled following adoptive transfer. For viral activation studies, CD4 IFN-y and CD8 1FN-y T cells were identified from C57BL / 6 mouse blood. Lymphocytes were isolated from whole blood, red blood cells were lysed, and remaining white blood cells were resuspended in FACS buffer. Cells were stained with eFluor-780 viability dye and surface markers including CD45-FITC, CD4-PerCP-Cy5.5, CD8-APC, CD69-PE / Cy7, CD25-PE, LAG-3-BV650, CD71-APC, CD98-PE / Cy7, and Glut-l-PE. For beta cell antigen specific, APC-ChgA, BV421-InsB, and PE-IAPP tetramers were used. Intracellular IFN-v-PE staining was performed after surface staining using the Intracellular Fixation & Permeabilization Buffer Kit according to the manufacturer’s protocol. A PE-conjugated Rat-IgGlK isotype control was included to ensure staining specificity. Single-stain compensations were prepared using UltraCompeBeads, and heat-killed cells served as live / dead controls. Cells were sequentially gated on FSC / SSC, live cells, singlets, CD45, CD4 or CD8, followed by activation markers (CD69, CD25, LAG-3), metabolic markers (Glut-1, CD98, CD71), antigen-specific tetramers, and intracellular IFN-y. FMO controls and an I-Ag7-CLIP tetramer control were included. Data were analyzed using FlowJovlO.

[0114] Cytokine measurement by ELISA

[0115] Plasma from CVB-infected C57BL / 6 mice was collected at multiple time points and analyzed for IFN-y by ELISA. High-affinity 96-well plates were coated overnight at 4°C with 25 ng / mL monoclonal rat anti-murine IFN-y in carbonate buffer (pH 9.6). Mouse IFN-y ELISAs were performed using BD antibody pairs following the manufacturer’s protocol. Mean OD450 values were recorded, and plates were read on a SpectraMax iD5 microplatereader, analyzed using SoftMax Pro 7.0.2.

[0116] Histology and ImmunofluorescenceAtty. Dkt. No. 144578.00478

[0117] Pancreata from control and adoptively transferred NOD-SCID mice were harvested on the day of diabetes onset, fixed in 4% paraformaldehyde aqueous solution for 4 hours at 4°C, and washed with distilled water for paraffin embedding. Tissue sections (5 pm) were prepared using a microtome and mounted onto glass slides. For histological analysis, sections were deparaffmized, rehydrated through graded alcohols, and stained with hematoxylin and eosin (H&E) to assess tissue architecture and immune cell infiltration. For immunofluorescence studies, pancreata from control, diabetic, and post-adoptive transfer NOD-SCID mice (12th and 15th day) were fixed in 4% paraformaldehyde for 4 hours at 4°C, washed with distilled water, and cryoprotected overnight in 20% (w / v) sucrose in PBS at 4°C. Tissues were embedded in OCT (4586, Scigen), frozen, and stored at -80°C. Cryosections (6 pm) were prepared using a Leica CM1860 cryostat and mounted on positively charged slides. Sections were washed, blocked with 5% normal donkey serum and 1% B SA for 1 hour, and incubated overnight at 4°C with primary antibodies against CD3, insulin, F4 / 80, and B220, along with APC-ChgA, BV421-InsB, and PE-IAPP tetramers. After washing, species-specific fluorophore-conjugated secondary antibodies were applied for 1 hour. Nuclei were counterstained with DAPI, and imaging was performed on a Zeiss LSM 800, and analyzed using Image- J software.

[0118] RNA isolation and ddPCR

[0119] Total RNA was isolated from the blood of adoptively transferred NOD-SCID mice using the RNA protect Animal Blood Tubes (cat. # 76544) following RNeasy Protect Animal Blood Kit (cat. # 73224) for isolating RNA following the manufacturer’s instructions. RNA concentration was measured using a NanoDrop-2000 (Thermo Fisher Scientific). Complementary DNA (cDNA) was synthesized with the TaqMan MicroRNA Reverse Transcription Kit (4366596; Thermo Fisher Scientific) according to instructions. Droplet digital PCR (ddPCR) was performed with minor modifications to assess mmu-miR-146a-5p expression. Briefly, 2 pL of cDNA was mixed with 10 pL Bio-Rad ddPCR Supermix for Probes (1863024) and 1 pL of TaqMan miR-146a-5p 20x primer mix (4427975; Applied Biosystems), and the final volume adjusted to 20 pL with RNase / DNase-free water. Synthetic miR-146a-5p served as a positive control, and water as a negative control. Droplets were generated using QX200 AutoDG, PCR was performed on a C 1000 Thermal Cycler, and droplets were read using a QX200 droplet reader (Bio-Rad). Data was analyzed within Quanta Soft Software. Data are presented as the number of copies / pl.Atty. Dkt. No. 144578.00478

[0120] Statistical Analysis

[0121] Data analysis was performed using GraphPad Prism 10. Pairwise comparisons were performed using unpaired or paired two-tailed Student’s t-tests. Interactions between two groups with two variables (e.g., time or treatment, pre-post-vaccination) were determined using a oneway ANOVA followed by an appropriate post hoc test. Sample sizes (n), means, error bars, and statistical methods are described in figure legends. A p-value <0.05 indicates statistical significance.

[0122] Data and Resource Availability

[0123] The data sets generated and / or analyzed during the current study are available from the corresponding author on reasonable request.

[0124] Results

[0125] Plasma sLAG-3 Levels Correlate with Diabetes Onset in NOD Mice.

[0126] To investigate the relationship between T cell activation and type 1 diabetes onset, we measured plasma sLAG-3 levels longitudinally, a marker of T cell activation, in female NOD mice (FIG. 5A). Female NOD mice were selected for these studies due to their higher incidence of autoimmune diabetes compared to males, driven primarily by T cell-mediated destruction of pancreatic 0-cells [14,20], Plasma sLAG-3 levels increased significantly between 6 and 12 weeks of age, reflecting early T cell activation (FIG. 5B). By 16 weeks, however, sLAG-3 levels declined to levels similar to those observed at 4 weeks (FIG. 5B). These decreases coincided with the onset of hyperglycemia, as NOD females in this cohort began developing diabetes around this time, with blood glucose levels rising sharply by 16 weeks of age (FIG. 5C). Importantly, a significant positive correlation was observed between peak sLAG-3 levels and the timing of diabetes onset (FIG. 5D) supporting the potential utility of sLAG-3 as a predictive biomarker for T1D development in NOD mice.

[0127] Beta Cell Antigen-Specific CD4+T Cells Expand Prior to Diabetes Onset in NOD Mice

[0128] To further assess the autoimmune response during diabetes onset in female NOD mice, we analyzed CD4+T cells across different age groups using |3-cell antigen-specific tetramers (InsB9-23, ChgA, IAPP) from the same cohort. These tetramers specifically detect autoreactive T cells recognizing pancreatic [3-cell epitopes. Our analysis showed a significant increase in both theAtty. Dkt. No. 144578.00478frequency and absolute number of TnsB9-23+, ChgA , and TAPP+tetramer-positive CD4+T cells prior to disease onset (FIG. 6A and 6B), with mean fluorescent intensity exhibiting similar trends (FIG. 6C). This expansion of antigen-specific CD4+T cells strongly correlated with early plasma sLAG-3 elevation (FIG. 6D, 6E) and the timing of diabetes onset, indicating their direct contribution to disease progression. Tetramer staining revealed that InsB9-23 -specific T cells were the most abundant in the periphery at 6 weeks (-30%), peaking at 35% by 12 weeks. ChgA-specific 2.5HIP T cells were initially lower (-12% at 6 weeks) but rose to 25% by 12 weeks, followed by an increase in 6.9HIP-specific T cells recognizing a hybrid of insulin C-peptide and IAPP. This sequential expansion suggests that progressive 3-cell stress and intrinsic enzymatic modifications may drive neoantigen formation, as previously reported

[0024] , These findings highlight the value of tracking tetramer-positive CD4+T cells as early indicators of autoimmune activity and as potential predictive biomarkers for the onset of diabetes development.

[0129] Antigen specific T cell responses in diabetes progression and immune modulation

[0130] To examine antigen-specific T cell responses in diabetes, we used lymphocytes from 8-week-old female NOD congenic C6.6.9 TCR-transgenic mice, which express the BDC-6.9 TCR recognizing NOD islets [25, 26], Genetic mapping localized the BDC-6.9 antigen to chromosome 6, identifying IAPP as a key candidate. The 6.9 T cells respond to a hybrid insulin C-peptide-IAPP epitope absent in BALB / c mice. In C6.6.9 mice, these autoreactive T cells remain naive, allowing analysis of early activation

[0027] , To assess their diabetogenic potential, we adoptively transferred C6.6.9 spleen lymphocytes into age-matched female NOD-SCID recipients (FIG. 8A). Following transfer, recipient mice developed progressive hyperglycemia, with blood glucose rising by week 7 and exceeding 400 mg / dL by week 10 (FIG. 8B, 8C; FIG. 9B). Plasma sLAG-3 and insulin 2 (Ins2) mRNA levels increased concurrently, peaking at 12-15 days post-transfer (FIG. 8D, 8E; FIG. 9A), mirroring patterns observed in spontaneous NOD diabetes. Ins2 mRNA levels positively correlated with both sLAG-3 and blood glucose (FIG. 8F, 8G), whereas plasma sLAG-3 showed a negative correlation with blood glucose (FIG. 8H). Histopathological analysis revealed marked immune infiltration and insulitis in the pancreas of transferred mice (FIG. 81). Flowcytometry further demonstrated that donor CD4+T cells trafficked from the periphery to the pancreas and subsequently to the spleen following activation (FIG. 8J). These results confirm the diabetogenic capacity of beta cell antigen-specific T cells and delineate their activation dynamics during disease progression.Atty. Dkt. No. 144578.00478

[0131] Antigen specific T cell responses in expression of beta cell antigen-specific tetramers in CD4+T diabetes progression.

[0132] To investigate the contribution of antigen-specific T cell responses to diabetes progression, we analyzed surface activation markers on peripheral blood CD4+T cells from adoptively transferred NOD-SCID mice every three days, along with tetramer staining for (B-cell antigens (ChgA, InsB, IAPP). The data showed increased frequencies and absolute numbers of CD69+and CD25+CD4+T cells (FIG. 10A, FIG. 11A) and a marked rise in tetramer-positive CD4+T cells between 9 and 15 weeks post-transfer (FIG. 10B, FIG. 11B). Over time, CD4+LAG-3+T cells declined significantly (FIG. 10A, FIG. 11 A), consistent with antigen-driven activation through T helper-mediated linked recognition. Immunofluorescent staining further demonstrated that pancreas sections collected at day 12 post-transfer contained substantially more ChgA tetramerpositive cells compared with day 15 (FIG. 10C, 10D). At disease onset, immune profiling of the pancreas, spleen, and blood revealed extensive T cell infiltration and pathology characteristic of autoimmune [3-cell destruction. CD69 expression was markedly higher in pancreatic and splenic T cells than in peripheral blood, indicating strong local activation (FIG. 10E, FIG. 11C), whereas CD25 expression was relatively lower in these tissues, suggesting temporally regulated activation. Tetramer analysis showed that CD4+T cells in the pancreas expressed significantly higher levels of ChgA, InsB, and IAPP compared with spleen and blood (FIG. 10F, FIG. HD). Collectively, these findings highlight the central role of antigen-specific CD4+T cells in (3-cell destruction and support the use of sLAG-3 and tetramers as dynamic biomarkers of disease activity in type 1 diabetes.

[0133] Antigen representation triggers P-Cell damage and reprograms T cells metabolic phenotype

[0134] Immunohistochemical staining of pancreatic sections from adoptive transfer diabetic mice, revealed dense peri- and intra-insulitic infiltrates dominated by CD3+T lymphocytes, with a smaller but notable presence of B220 -B cells and F4 / 80+macrophages populations increased in number following adoptive transfer compared to controls (FIG. 13B). Cellular architecture suggests that, upon recognizing islet antigens, autoreactive T cells directly mediate (3-cell cytotoxicity while reshaping the local milieu to support B-cell help and drive metabolic programs sustaining effector function [28-30], Flow cytometric profding of single-cell suspensions fromAtty. Dkt. No. 144578.00478pancreatic islets, spleen, and peripheral blood further demonstrated metabolic reprogramming of CD4+T cells after antigen encounter (FIG. 13C, FIG. 14A, 14B). In the pancreas, both the frequency and absolute number of Glut-1+CD4+T cells were markedly higher than in spleen or blood, consistent with elevated glucose demand during antigen-driven cytotoxicity. CD98 expression was uniformly high across tissues at diabetes onset, indicating widespread amino acid transport. CD71+CD4+T cells peaked in the spleen, with higher frequency and MFI than in blood or pancreas, suggesting heightened iron uptake and mitochondrial biogenesis during peripheral expansion (FIG. 13D). Although Glut-1 MFI did not differ among tissues, the coordinated upregulation of glucose, amino acid, and iron transporters highlights a high-flux metabolic state that fuels proliferation, cytokine secretion (IFN-y, IL-21), and cytotoxicity [31, 32], Targeting these metabolic checkpoints may offer a strategy to selectively dampen pathogenic T-cell responses in early type 1 diabetes.

[0135] Progressive expansion and epitope diversification of lAPP-specific CD4+T cells coexpressing ChgA and InsB9-23 during autoimmune diabetes

[0136] Autoreactive lymphocyte clones bearing defined TCRs offer a precise window into antigen-specific dynamics during autoimmune progression, yet detailed clonotypic information within BCR and TCR repertoires remains underutilized in diagnostic medicine. Clonal expansion of antigen-specific lymphocytes reshapes the peripheral repertoire through selective pressures, and in autoimmunity, self-reactive clones can proliferate unchecked and drive pathology [33-35], To examine this process in type 1 diabetes, we re-analyzed longitudinal and terminal flow cytometry datasets from adoptive transfers of C6.6.9 TCR-transgenic CD4+T cells, which recognize islet amyloid polypeptide (IAPP). We quantified the emergence of dual-positive populations coexpressing IAPP with either chromogranin A (ChgA) or insulin B-chain (InsB) over a 4-week period (FIG. 16A, B). Relative to baseline (FIG. 15B), CD4 IAPP ChgA cells increased steadily and reached a plateau by day 12 (FIG. 15C), with CD4+IAPP+InsB+cells displaying a similar trajectory (FIG. 15D). At diabetes onset, both subsets were markedly enriched in pancreatic tissue compared to spleen and baseline levels (FIGs. 15 E, F). These findings indicate that antigenspecific CD4+T cells not only expand but progressively acquire reactivity to multiple (B-cell antigens, demonstrating diversification of the autoreactive repertoire during disease evolution [36-Atty. Dkt. No. 144578.00478

[0137] Elevated soluble LAG-3 levels in first-degree relative inversely correlate with autoantibody burden in Type 1 diabetes

[0138] We measured serum sLAG-3 in individuals with T1D up to six years after diagnosis and found no significant differences compared with healthy controls (FIG. 17B). This similarity suggests that heterologous immune responses may mask earlier elevations in sLAG-3 by the time of clinical onset. Because T1D autoantigens engage T cells prior to autoantibody formation, we hypothesized that sLAG-3 increases would precede seroconversion. To test this, we assessed sLAG-3 levels in first-degree relatives (FDRs) stratified by autoantibody status. Both autoantibody-negative and single-autoantibody-positive FDRs displayed significantly higher sLAG-3 levels than individuals with multiple autoantibodies, T1D patients, and non-FDR controls, indicating early T-cell activation (FIG. 17C). Consistent with this, sLAG-3 levels showed no significant differences among T1D patients regardless of autoantibody status (FIG. 18A).

[0139] We next measured sLAG-3 in FDRs and the general population using DEWIT cohort longitudinal samples. Females exhibited higher sLAG-3 levels than males in both cohorts (FIG.17E, F). Stratifying the DEWIT cohort into progressors and non-progressors revealed a gradual rise in sLAG-3 among progressors, whereas non-progressors showed no change (FIG. 17G; FIG.18B). Plasma sLAG-3 negatively correlated with disease duration and progression (FIG. 18C). Additionally, GADA+, IA2A+, and ZnT8A+ but non-diabetic individuals displayed significantly higher sLAG-3 levels than diabetic subjects (FIG. 18D-G). Similarly, when examining FDRs and the general population by autoantibody category, Aab- and lAab+ FDRs had higher sLAG-3 levels than individuals with multiple autoantibodies (FIG. 17H). Notably, the individual positive for four autoantibodies showed markedly low sLAG-3 levels, suggesting a very early activation window that diminishes as autoimmunity advances. This pattern was absent in the general population (FIG.171). Longitudinal DEWIT measurements demonstrated that sLAG-3 peaks immediately before diabetes onset and then sharply declines after diagnosis (FIG. 17J). Finally, stratification by HLA genotype showed that individuals carrying high-risk alleles (e.g., DRB10- / , £> 4 / 03:01-DQB 1*03:02) had higher sLAG-3 expression than those with non-risk haplotypes, supporting a genetic contribution to autoimmune activation (FIG. 17K). Together, these findings define an inverse relationship between sLAG-3 concentration and autoantibody burden, delineating a temporal cascade in which initial T cell activation reflected by elevated sLAG-3 gives way to B cell maturation and autoantibody production, culminating in clinical onset of T1D.Atty. Dkt. No. 144578.00478

[0140] Temporal dynamics of sLAG-3 induction and resolution reveal early T cell activation in vaccination and CVB infection models

[0141] Interestingly, plasma sLAG-3 levels in healthy adults increased significantly within 10 days after COVID-19 vaccination, indicating early T-cell activation consistent with expected vaccine-induced immune responses (FIG. 19B). This transient rise highlights sLAG-3 as a sensitive marker of early T-cell activation that precedes antibody development. To examine the temporal dynamics of sLAG-3 during an active pathogen response, we utilized a CVB infection model in C57BL / 6 mice

[0039] , Plasma analytes were measured at multiple time points over 30 days in CVB-infected C57BL / 6 mice (FIG. 19C). Baseline sLAG-3 levels were minimal in uninfected controls, but increased by day 3 post-infection, showed a significant rise by day 6, and peaked around day 12 before gradually declining, although remaining slightly above baseline through day 30 (FIG. 19D). Plasma IFN-y levels were also elevated, with a marked increase at day 6, indicating concurrent immune activation (FIG. 19E). Flow cytometry further demonstrated a time-dependent increase in the frequency and absolute number of IFN-y producing CD4+and CD8+T cells in peripheral blood (FIG. 19F, G). T-cell activation markers (CD25, CD69) followed a similar pattern, peaking between days 6 and 9 and declining by day 21 (FIG. 19H, I). This response not coordinated with the pattern observed in individuals progressing to T1D, where sLAG-3 levels decline as autoantibodies increase, suggesting underlying immune dysregulation.

[0142] Discussion

[0143] T cell homeostasis depends on external signals, including regulation by the coinhibitory receptor LAG-3, primarily expressed by T lymphocytes

[0040] , LAG-3, structurally like CD4, binds MHC class II with higher affinity and negatively regulates T cell activity. Its soluble form (sLAG-3) is generated by proteolytic cleavage and reflects T cell activation [13, 41], In type 1 diabetes, failure of immune regulation allows autoreactive CD4+and CD8+T cells to target ]3-cell antigens, leading to progressive (3-cell loss [2], Current T1D biomarkers primarily reflect late-stage disease (e.g., hyperglycemia, low C-peptide, autoantibodies), and reliable early protein biomarkers remain limited. Our study identifies sLAG-3 as a potential early biomarker of T cell activation during T1D progression. In NOD mice, plasma sLAG-3 levels rose significantly between 6-12 weeks, prior to hyperglycemia, then declined by 16 weeks, suggesting early immune activation precedes clinical onset. The subsequent decline in sLAG-3 at 16 weeks may reflect T cell exhaustion or reducedAtty. Dkt. No. 144578.00478LAG-3 shedding as P-cell mass diminishes. Tn the adoptive transfer model, sLAG-3 and Ins2 mRNA levels mirrored hyperglycemia progression, reinforcing their utility as dynamic biomarkers. However, the role of Ins2 mRNA as a specific indicator of P-cell stress requires validation, as it may reflect general islet dysfunction rather than immune-mediated damage

[0021] , Analysis of sLAG-3 levels in established T1D resemble with healthy controls, likely due to heterologous immune activity and loss of early activation signals, emphasizing the need to assess individuals before seroconversion. In contrasts, FDRs individual revealed that autoantibodynegative and single-autoantibody-positive individuals had elevated sLAG-3, indicating an early, transient T-cell activation phase, underscores its potential as a pre-autoantibody biomarker. Longitudinal sampling from the DEWIT cohort reinforced these conclusions, where sLAG-3 levels rose steadily in progressors but remained stable in non-progressors, with a distinct peak immediately preceding diabetes onset followed by rapid decline after diagnosis. The inverse correlation between sLAG-3 levels and both disease duration and autoantibody burden further reinforces our hypothesis that T-cell derived signals shape the autoimmune trajectory well before overt T1D progression

[0042] , Moreover, the observation from DEWIT human cohort, GADA+, IA2A+, and ZnT8A+ but non-diabetic individuals had higher plasma sLAG-3 levels than diabetic subjects indicates that sLAG-3 elevation is preferentially associated with the preclinical stage, not the post-diagnosis state. Genetic stratification revealed additional mechanistic insight. Individuals carrying high-risk HLA haplotypes displayed significantly higher sLAG-3 levels, suggesting that HLA-mediated antigen presentation amplifies early T-cell activation and promotes the transition toward autoimmunity. However, the lack of elevated sLAG-3 in T1D patients may reflect immune exhaustion or altered LAG-3 dynamics in advanced disease, necessitating longitudinal studies to clarify sLAG-3 kinetics

[0043] , The relationship between anti islet autoantibody and autoimmunity is corelate indirectly with the incidence

[0044] , Type 1 diabetes is an autoimmune disease mediated by autoreactive CD4 and CD8 T cells attacking insulin-producing islet cells in the pancreas [2], Although T1D is primarily driven by abnormal T cell activity, anti-islet antibodies, though commonly used as diagnostic markers, don’t always reliably predict disease progression

[0045] , An alternative approach involves the use of peptide-MHC tetramers to detect anti-insulin CD4 T cells in peripheral blood. However, the lag period is highly variable, ranging from several months to many years. While predictive models may hold value at the population level, their applicability to individual cases is limited in reliability. As biomarkers of autoimmunity, autoantibodies haveAtty. Dkt. No. 144578.00478several important limitations. They are typically detected only at a very late stage of type 1 diabetes progression, may appear at low titers, can occur in individuals who never develop the disease, and are absent in a subset of patients with T1D [46-48],

[0144] Activated anti-insulin CD4+ T cells were identified not only at diagnosis but also in genetically at-risk individuals and those with established T1D [49, 50], Our study provides an in-depth analysis of antigen-specific CD4+T cell responses in type 1 diabetes using both spontaneous and adoptive transfer models in NOD mice. Naive, autoreactive CD4+T cells specific for [3-cell antigens (InsB9-23, ChgA, IAPP) were tracked using MHC class II tetramers. Early expansion of these tetramer-positive T cells in peripheral lymphoid organs correlated with elevated serum levels of soluble LAG-3 (sLAG-3), preceding the onset of hyperglycemia. This phase marks a critical immunological window where autoreactive T cells are primed and may be targeted for early intervention. Adoptive transfer of these antigen-specific CD4+T cells into NOD.scid recipients confirmed their diabetogenicity, as they trafficked to the pancreas, initiated insulitis, and induced hyperglycemia, recapitulating the spontaneous disease course. Longitudinal flow cytometry revealed dynamic shifts in activation markers CD69 expression peaked in pancreatic and splenic tissues during active infiltration, while CD25 was predominantly expressed in circulating T cells, indicating compartmentalized activation states

[0051] , Surface LAG-3 expression on CD4+T cells significantly correlated with disease progression, suggesting its role as both a biomarker and potential modulator of T cell pathogenicity. The accumulation of tetramer-positive CD4+T-cells and B-cells in the pancreas further confirmed their direct involvement in 0-cell destruction

[0052] , Autoreactive CD4+T cells undergo significant metabolic reprogramming following antigen exposure, as indicated by elevated Glut-1, CD98, and CD71 expression especially in the pancreas reflecting increased energy demands for proliferation and cytokine production [53-55], The enrichment of tetramer-positive CD4+T cells in the pancreas and their metabolic reprogramming highlight the organ-specific nature of T1D pathogenesis. Metabolic changes, including altered metabolite and bile acid profiles, precede seroconversion and can predict T1D onset

[0056] , Furthermore, these T cells exhibit progressive clonal expansion and epitope spreading, from IAPP to ChgA and InsB, suggesting a broadening autoimmune response that may intensify disease progression and reduce the efficacy of single-target interventions

[0057] , This spread may be due to the unique recognition of hybrid peptides by BDC-6.9 and BDC-2.5 T cells

[0058] , Both recognize peptides containing the same C-peptide sequence — 6.9HIP (LQTLALNAARDP) and 2.5HIPAtty. Dkt. No. 144578.00478(LQTLALWSRMD). This shared motif likely facilitates epitope spreading from 6.9 to 2.5 reactivity, and to insulin to a lesser extent

[0059] , Together, these findings emphasize the critical role of beta cell antigen-specific CD4+T cells in the pathogenesis of T1D and highlight both sLAG-3 and tetramer-based profiling as promising dynamic biomarkers of disease stage and immune activation. The observed metabolic reprogramming of autoreactive T cells further reveals potential therapeutic avenues aimed at modulating effector function without broadly suppressing the immune system. Notably, monitoring sLAG-3 levels may provide very early insight into immunological events that precede autoantibody production and P-cell destruction, offering a valuable window for therapeutic intervention. As such, sLAG-3 holds significant promise as a biomarker for identifying at-risk individuals before clinical onset and genetic predisposition. Future research should focus on elucidating the mechanistic role of LAG-3 signaling in regulating autoreactive T cell responses and exploring the diagnostic and therapeutic utility of targeting this pathway in both preclinical models and human studies.

[0145] Conclusion

[0146] In conclusion, our study identifies soluble LAG-3 (sLAG-3) as a promising early biomarker of T cell activation and T1D progression. Elevated sLAG-3 levels in NOD mice and at-risk human cohorts precede hyperglycemia, marking a critical phase of immune activation and autoreactive T cell priming. This finding, coupled with antigen-specific CD4+T cell profiling, and monitoring of stress induced release of inulin-m-RNA post adoptive transfer, expansion, epitope spreading and effector function, underscoring the importance of early immune interventions. Metabolic reprogramming of autoreactive T cells further highlights potential therapeutic strategies. Monitoring sLAG-3 provides valuable insight into disease onset, suggesting its utility in early detection and intervention for T1D. This trajectory offers a mechanistic bridge connecting genetic susceptibility, early T-cell activation, and the evolution of humoral autoimmunity, positioning sLAG-3 as a promising biomarker for identifying individuals at highest risk during the window when immune-modifying interventions may be most effective.

[0147] Reference is also made to Roy S. et al. Soluble LAG-3 Identifies a Dynamic Early T Cell Activation Window in self-reactivity, Type 1 Diabetes, and Broader Immune Responses. BioRxiv, Feb. 12, 2026, which is incorporated by reference herein in its entirety.Atty. Dkt. No. 144578.00478References for Example 21. Atkinson, M.A. and R.G. Mirmira, The pathogenic "symphony" in type 1 diabetes: A disorder of the immune system, beta ceils, and exocrine pancreas. Cell Metab, 2023.35(9): p. 1500-1518.2. Burrack, A.L., T. Martinov, and B.T. Fife, T Cell-Mediated Beta Cell Destruction:Autoimmunity and Alloimmunity in the Context of Type 1 Diabetes. Front Endocrinol (Lausanne), 2017. 8: p. 343.3. Houeiss, P., S. Luce, and C. Boitard, Environmental Triggering of Type 1 Diabetes Autoimmunity. Front Endocrinol (Lausanne), 2022. 13: p. 933965.4. Roy, S., P. Pokharel, and J.D. Piganelli, Decoding the immune dance: Unraveling the interplay between beta cells and type 1 diabetes. Mol Metab, 2024.88: p. 101998. 5. Maahs, D.M., et aL, Epidemiology of type 1 diabetes. Endocrinol Metab Clin North Am, 2010.39(3): p.481-97.6. Group, D.P., Incidence and trends of childhood Type 1 diabetes worldwide 1990-1999.Diabet Med, 2006.23(8): p.857-66.7. Popoviciu, M.S., et al., Type 1 Diabetes Mellitus and Autoimmune Diseases: A Critical Review of the Association and the Application of Personalized Medicine. J Pers Med, 2023. 13(3).8. Mallone, R., et al., Screening and care for preclinical stage 1-2 type 1 diabetes in first- degree relatives: French expert position statement. Diabetes Metab, 2025. 51(1): p.101603.9. Steck, A.K. and MJ. Rewers, Genetics of type 1 diabetes. Clin Chem, 2011. 57(2): p.176-85.10. Thakkar, S., et al., Teplizumab in Type 1 Diabetes Mellitus: An Updated Review.touchREV Endocrinol, 2023. 19(2): p. 22-30.11. Scherm, M.G., et al., Beta cell and immune cell interactions in autoimmune type 1 diabetes: How they meet and talk to each other. Mol Metab, 2022. 64: p. 101565. 12. Kokori, E., et al., Teplizumab's immunomodulatory effects on pancreatic beta-cell function in type 1 diabetes mellitus. Clin Diabetes Endocrinol, 2024. 10(1): p.23. 13. Li, N., et al., Metalloproteases regulate T-cell proliferation and effector function via LAGS. EMBO J, 2007. 26(2): p.494-504.14. Andrews, L.P., et al., LAG3 (CD223) as a cancer immunotherapy target. Immunol Rev, 2017. 276(1): p.80-96.15. Anderson, M.S. and J. A. Bluestone, The NOD mouse: a model of immune dys regulation. Annu Rev Immunol, 2005. 23: p. 447-85.16. Cieplihska, K., et al., Elevated Levels of sLAG-3 as a Possible Biomarker in Graves' Disease With and Without Thyroid Eye Disease: A Prospective Observational Case- Control Study. Medicina (Kaunas), 2025. 61(9).17. Ruffo, E., et al., Lymphocyte-activation gene 3 (LAG3): The next immune checkpoint receptor. Semin Immunol, 2019.42: p. 101305.18. Botticelli, A., et al., The Role of Soluble LAG3 and Soluble Immune Checkpoints Profile in Advanced Head and Neck Cancer: A Pilot Study. J Pers Med, 2021. 11 (7).Atty. Dkt. No. 144578.00478Dekkers, M.C., et aL, Extracellular vesicles derived from stressed beta cells mediate monocyte activation and contribute to islet inflammation. Front Immunol, 2024. 15: p. 1393248.Dekkers, M.C., et aL, Beta-Cell-Derived Extracellular Vesicles: Mediators of Intercellular Communication in the Islet Microenvironment in Type 1 Diabetes. Cells, 2024. 13(23).Syed, F., et aL, beta Cell microRNAs Function as Molecular Hubs of Type 1 Diabetes Pathogenesis and as Biomarkers of Diabetes Risk. bioRxiv, 2023.Anand, V., et aL, Islet Autoimmunity and HLA Markers of Presymptomatic and Clinical Type 1 Diabetes: Joint Analyses of Prospective Cohort Studies in Finland, Germany, Sweden, and the U.S. Diabetes Care, 2021.44(10): p.2269-2276.Gong, Q., et aL, Longitudinal Multi-omic Immune Profiling Reveals Age-Related Immune Cell Dynamics in Healthy Adults. bioRxiv, 2024.Delmastro-Greenwood, M., B.A. Freeman, and S.G. Wendell, Redox-dependent antiinflammatory signaling actions of unsaturated fatty acids. Annu RevPhysiol, 2014.76: p.79-105.Pauza, M.E., et aL, T-cell receptor transgenic response to an endogenous polymorphic autoantigen determines susceptibility to diabetes. Diabetes, 2004.53(4): p. 978-88.Pauza, M., et aL, Transgenic expression of Ly-49A in thymocytes alters repertoire selection. J Immunol, 2000. 164(2): p.884-92.Wiles, T.A., et aL, An insulin-IAPP hybrid peptide is an endogenous antigen for CD4 T cells in the non-obese diabetic mouse. J Autoimmun, 2017. 78: p. 11-18.Clark, M., et aL, The Role of T Cell Receptor Signaling in the Development of Type 1 Diabetes. Front Immunol, 2020. 11: p. 615371.Hussain, S. and T.L. Delovitch, Dysregulated B7-1 and B7-2 expression on nonobese diabetic mouse B cells is associated with increased T cell costimulation and the development of insulitis. J Immunol, 2005. 174(2): p. 680-7.Tian, J., et aL, Lipopolysaccharide-activated B cells down-regulate Th1 immunity and prevent autoimmune diabetes in nonobese diabetic mice. J Immunol, 2001. 167(2): p. 1081-9.Cornwell, A., H. Ziolkowski, and A. Badiei, Glucose Transporter Glutl-Dependent Metabolic Reprogramming Regulates Lipopolysaccharide-Induced Inflammation in RAW264.7 Macrophages. Biomolecules, 2023. 13(5).Xiong, S., et aL, HIF-1a regulated GLUT1-mediated glycolysis enhances Treponema pallidum-induced cytokine responses. Cell Communication and Signaling, 2025.23(1): p. 219.Ahmed, M., et aL, Clonal expansions and loss of receptor diversity in the naive CD8 T cell repertoire of aged mice. J Immunol, 2009. 182(2): p. 784-92.Dunlap, G., et aL, Clonal associations between lymphocyte subsets and functional states in rheumatoid arthritis synovium. Nature Communications, 2024. 15(1): p.4991.Qi, Q., et aL, Diversity and clonal selection in the human T-cell repertoire. Proceedings of the National Academy of Sciences, 2014. 111(36): p. 13139-13144.Atty. Dkt. No. 144578.00478Straub, A., et aL, Recruitment of epitope-specific T cell clones with a low-avidity threshold supports efficacy against mutational escape upon re-infection. Immunity, 2023. 56(6): p. 1269-1284.e6.Yi, J., et aL, Antigen-specific depletion ofCD4(+) T cells by CAR T cells reveals distinct roles of higher- and lower-affinity TCRs during autoimmunity. Sci Immunol, 2022.7(76): p. eabo0777.Qi, Q., et aL, Diversification of the antigen-specific T cell receptor repertoire after varicella zoster vaccination. SciTransl Med, 2016. 8(332): p. 332ra46.Burg, A.R., et aL, Superoxide Production by NADPH Oxidase Intensifies Macrophage Antiviral Responses during Diabetogenic Coxsackievirus Infection. J Immunol, 2018.200(1): p. 61-70.Huo, J.L., etal., The promising immune checkpoint LAG-3 in cancer immunotherapy: from basic research to clinical application. Front Immunol, 2022. 13: p. 956090. Liang, B., et aL, Regulatory T cells inhibit dendritic cells by lymphocyte activation gene-3 engagement of MHC class II. J Immunol, 2008. 180(9): p. 5916-26.Akama-Garren, E.H., et aL, T cell help shapes B cell tolerance. Sci Immunol, 2024.9(92): p. eadj7029.Aremu, H.K., et aL, Bioactive components in Psidium guajava extracts elicit biotoxic attributes and distinct antioxidant enzyme modulation in the larvae of vectors of lymphatic filariasis and dengue. Exp Parasitol, 2024. 261 : p. 108766.Bingley, P.J., et aL, Prediction of IDDM in the general population: strategies based on combinations of autoantibody markers. Diabetes, 1997.46(11): p. 1701-10.Pietropaolo, M., R. Towns, and G.S. Eisenbarth, Humoral autoimmunity in type 1 diabetes: prediction, significance, and detection of distinct disease subtypes. Cold Spring Harb Perspect Med, 2012. 2(10).Bonifacio, E. and P. Achenbach, Birth and coming of age of islet autoantibodies. Clin Exp Immunol, 2019. 198(3): p. 294-305.Insel, R.A., et aL, Staging presymptomatic type 1 diabetes: a scientific statement of JDRF, the Endocrine Society, and the American Diabetes Association. Diabetes Care, 2015. 38(10): p. 1964-74.Wang, J., et aL, Prevalence of autoantibody-negative diabetes is not rare at all ages and increases with older age and obesity. J Clin Endocrinol Metab, 2007.92(1): p.88-92.Sharma, S., et aL, Measuring anti-islet autoimmunity in mouse and human by profiling peripheral blood antigen-specific CD4 T cells. Science Translational Medicine, 2023.15(703): p. eade3614.Eugster, A., et aL, Physiological and pathogenic T cell autoreactivity converge in type 1 diabetes. Nature Communications, 2024. 15(1): p. 9204.Adamczyk, M., et aL, The Expression of Activation Markers CD25 and CD69 Increases during Biologic Treatment of Psoriasis. J Clin Med, 2023. 12(20).Mora, C., et aL, Pancreatic Infiltration But Not Diabetes Occurs in the Relative Absence of MHC Class Il-Restricted CD4 T Cells: Studies Using NOD / CIITA-Deficient Micel. The Journal of Immunology, 1999. 162(8): p.4576-4588.Atty. Dkt. No. 144578.00478Guerrero, J.A., et aL, GLUT1 overexpression in CAR-T cells induces metabolic reprogramming and enhances potency. NatCommun, 2024. 15(1): p. 8658.Salzberger, W., et aL, Tissue-resident NK cells differ in their expression profile of the nutrient transporters Glutl , CD98 and CD71. PLoS One, 2018. 13(7): p. e0201170. Poznanski, S.M., et aL, Metabolic flexibility determines human NKcell functional fate in the tumor microenvironment. Cell Metab, 2021. 33(6): p. 1205-1220 e5.Lamichhane, S., et aL, Dysregulation of secondary bile acid metabolism precedes islet autoimmunity and type 1 diabetes. Cell Rep Med, 2022. 3(10): p. 100762.Webb-Robertson, B.M., et aL, Prediction of the development of islet autoantibodies through integration of environmental, genetic, and metabolic markers. J Diabetes, 2021. 13(2): p. 143-153.Hohenstein, A.C., et aL, Novel T-Cell Reactivities to Hybrid Insulin Peptides in Islet Autoantibody-Positive At-Risk Individuals. Diabetes, 2025. 74(6): p. 933-942.Baker, R.L., et al., CD4 T Cells Reactive to Hybrid Insulin Peptides Are Indicators of Disease Activity in the NOD Mouse. Diabetes, 2018. 67(9): p. 1836-1846.

Claims

Atty. Dkt. No. 144578.00478CLAIMS1. A method comprising detecting a level of soluble lymphocyte activation gene 3 (sLAG-3) in a sample from a subject at risk of developing type 1 diabetes.

2. The method of claim 1, wherein the subject has not tested positive for autoantibodies against at least one of GAD65, insulin, Znt8, or IA-2, or the subject has tested positive for only one antibody against GAD65, insulin, Znt8, or IA-2.

3. The method of claim 1, wherein the subject at risk of developing type 1 diabetes has a first degree relative that has been diagnosed with type 1 diabetes or is suspected as having type 1 diabetes.

4. The method of claim 1, wherein the subject at risk of developing type 1 diabetes is a subject that has tested positive for an autoantibody against one of GAD65, IAA, Znt8, or IA2.

5. The method of claim 1, wherein the subject harbors a DR3 / 4-DQ8 HLA allele.

6. The method of claim 1, wherein the method predicts a subject’s susceptibility for type 1 diabetes.

7. The method of claim 1, wherein the subject is likely to progress to type 1 diabetes and the method predicts a susceptibility of the subject for progressing to clinical type 1 diabetes.

8. The method of claim 1, wherein the method further comprises detecting pancreatic beta cell autoreactive T cells.

9. The method of claim 8, wherein the pancreatic beta cell autoreactive T cells are CD8 T cells or CD4 T cells.

10. The method of claim 8, wherein detecting pancreatic beta cell autoreactive T cells comprises:contacting the sample from the subject comprising T cells with a major histocompatibility complex (MHC) tetramer comprising a peptide found in pancreatic beta cells; anddetecting T cells that are positive for the MHC tetramer using flow cytometry.Atty. Dkt. No. 144578.0047811. The method of claim 1 , wherein the expression of sLAG-3 is detected via an immunoassay.

12. The method of claim 11, wherein the immunoassay is at least one of enzyme-linked immunosorbent assay (ELISA), radioimmunoassays, fluorescent immunoassays, chemiluminescent immunoassays, western blotting, or lateral flow assays.

13. The method of claim 12, wherein the immunoassay is a sandwich ELISA.

14. The method of claim 1, wherein detecting an expression of sLAG-3 comprises quantifying a level of the sLAG-3 expression.

15. The method of claim 1, wherein the method further comprises detecting a level of sLAG-3 in a control sample.

16. The method of claim 15, wherein the detected level of sLAG-3 in the sample from the subject suspected of having type 1 diabetes or at risk of developing type 1 diabetes is greater than the level of sLAG-3 in the control sample.

17. The method of claim 1, wherein the sample from the subject is serum, plasma, or whole blood.

18. The method of claim 1, wherein the method further comprises detecting sLAG-3 at a subsequent time to monitor the expression of sLAG-3 in the subject over time.

19. The method of claim 18, wherein the method comprises detecting (n) levels of sLAG-3 in (n) separate longitudinal samples from the subject, wherein the (n) levels of sLAG-3 indicate an initial increase in the concentration of sLAG-3 in the samples and a subsequent decrease in sLAG-3 over time, wherein (n) is an integer greater than or equal to 3.

20. The method of claim 1, wherein the method further comprises administering teplizumab to the subject.

21. A method comprising detecting a level of soluble lymphocyte activation gene 3 (sLAG-3) in a sample from a subject being administered a therapy to prevent the onset of type 1 diabetes.

22. The method of claim 21, wherein the method further comprises detecting sLAG-3 at a subsequent time to monitor the expression of sLAG-3 in the subject over time.Atty. Dkt. No. 144578.0047823. The method of claim 22, wherein the method comprises detecting (n) levels of sLAG-3 in (n) separate longitudinal samples from the subject, wherein the (n) levels of sLAG-3 indicate an initial increase in the concentration of sLAG-3 in the samples and a subsequent decrease in sLAG-3 over time, wherein (n) is an integer greater than or equal to 3.

24. The method of claim 23, wherein the method further comprises diagnosing the subject as having type 1 diabetes.

25. The method of claim 21, wherein the therapy to prevent the onset of type 1 diabetes comprises teplizumab.

26. A kit comprising at least one reagent for detecting sLAG-3 and at least one reagent for detecting a biomarker associated with type 1 diabetes.

27. The kit of claim 26, wherein the at least one reagent for detecting sLAG-3 comprises an antibody specific for sLAG-3.

28. The kit of claim 26, wherein the biomarker associated with type 1 diabetes comprises antibodies against one of GAD65, IAA, Znt8, or IA2, or DR3 / 4-DQ8 HLA allele.

29. A method of stratifying risk of progression to clinical type 1 diabetes in a subject, wherein the subject has a first degree relative that is negative for islet autoantibodies or the first degree relative is positive for no more than one islet autoantibody, the method comprising quantifying sLAG-3 in plasma by ELISA and comparing the level to a reference value and / or an earlier sample from the same subject.

30. The method of claim 29, wherein the method further comprises administering a therapy to the subject to prevent the onset of type 1 diabetes.

31. The method of claim 30, wherein the therapy comprises teplizumab.

32. The method of claim 29, wherein the ELISA is a sandwich ELISA.

Images