Identifying health conditions in older adults using immunological biomarkers

Abstract

1. A method for determining the health status of an elderly person by testing a sample extracted from the individual for the presence of biomarkers that are autoantibodies to antigens including MAPK13, CD96, FKBP3, PPM1A, PHLDA1, GLRX3, FEN1 and AURKA, wherein the antigens may further include one or more of UBE2I, AAK1, YARS, ASPSCR1, CASP10, FHOD2, TCL1A and MAP4, wherein PHLDA1 and CD96 correspond to health, AURKA, FEN1, CASP10 and AAK1 correspond to moderate health, and UBE2I, YARS, ASPSCR1, FHOD2, TCL1A, MAP4, MAPK13, FKBP3, PPM1A and GLRX3 correspond to unhealthy health.

Description

[Technical field]

[0001] The present invention relates to the detection of immunological biomarkers, in particular autoantibodies, for determining health status and / or aging trajectory in elderly people. [Background technology]

[0002] Despite technological advances in the field of proteomics research, only a few biomarkers have made it to the clinic, with 90% of biomarkers being protein biomarkers [1]. The autoantibody biomarkers described herein are autoantibodies against antigens, which are antibodies produced by an individual against one or more of the individual's own proteins ("self" antigens). Several major reasons for the inability to use biomarkers in clinical practice [2] are: 1) Low sensitivity and specificity 2) Low prognostic / predictive value 3) Not important for clinical judgment 4) The original claim cannot be verified (false discovery) That is the thing. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] European Patent No. 1470229 Summary of the Invention [Problem to be solved by the invention]

[0004] Managing the care of older people depends less on age and more on the impact of their comorbidity history (past and present) on their current health status.[3] These comorbidities impose a certain stress on the immune system, which has been taxed for many years to deal with infections, cancer or chronic inflammatory diseases.[4]

[0005] It is therefore an object of the present invention to provide an improved panel of autoantibody biomarkers for assessing health status in the elderly. [Means for solving the problem]

[0006] In one aspect of the invention, there is provided a method for determining the health of an individual from a sample extracted from the individual, comprising the steps of: (i) testing the sample for the presence of health specific biomarkers; (ii) determining whether the subject is healthy, moderately healthy, or unhealthy based on the detection of the biomarkers. In a method comprising: The biomarkers are autoantibodies against antigens including AURKA, FEN1, GLRX3, PHLDA1, PPM1A, FKBP3, CD96 and MAPK13. A method is provided.

[0007] In one embodiment, the individual is elderly, typically at least 60 years of age.

[0008] Advantageously, autoantibody biomarkers can be used in the characterization (or diagnosis) of health status (healthy, moderate and poor health) in elderly people by measuring the distribution of plasma-antibody levels. Furthermore, a subset of these autoantibody biomarkers, especially those associated with health and moderate, may have a protective role against non-communicable diseases.

[0009] In one embodiment, the sample is tested using a panel of antigens corresponding to the autoantibody biomarkers. Typically, the antigens are biotinylated proteins. Advantageously, biotinylation ensures that the antigens are folded into their correct form to ensure the accuracy of detection by the autoantibody biomarkers.

[0010] In one embodiment, the antigen may comprise one or more from the group including UBE2I, AAK1, YARS, ASPSCR1, CASP10, FHOD2, TCL1A and MAP4.

[0011] It should be noted that not all antigens will generate an autoantibody response, and in a given cohort of over 1500 tested antigens it is not possible to predict the priority of which antigens will do so; only autoantibodies against the 16 antigens listed above are suitable as biomarkers for identifying health and aging conditions.

[0012] In one embodiment, each biotinylated protein is formed from a biotin carboxyl carrier protein (BCCP) folding marker fused in frame to the protein.

[0013] In a further embodiment, the biotinylated protein is bound to a streptavidin-coated substrate.

[0014] Advantageously, full-length protein is expressed as a fusion to the BCCP folding marker, which is itself biotinylated in vivo when the fusion partner is folded correctly.In comparison, misfolded fusion partner makes BCCP remain in "apo" (i.e., non-biotinylated) form so that it cannot attach to streptavidin substrate.Therefore, only correctly folded fusion protein attaches to streptavidin substrate via the biotin moiety added to the BCCP tag.

[0015] In one embodiment, the substrate comprises a glass slide, a biochip, a strip, a slide, a bead, a microtiter plate well, a surface plasmon resonance support, a microfluidic device, a thin film polymer-based layer, a hydrogel-forming polymer-based layer, or any other device or technology suitable for detection of antibody-antigen binding.

[0016] In one embodiment, the substrate is exposed to a sample extracted from a human such that autoantibody biomarkers from the sample can bind to the antigen.

[0017] Typically, the sample comprises any or any combination of exosomes, blood, serum, plasma, urine, saliva, amniotic fluid, cerebrospinal fluid, breast milk, semen, or bile.

[0018] In one embodiment, following exposure to the sample, the substrate is exposed to a fluorescently tagged secondary antibody, allowing for the determination of the amount of any autoantibodies from the sample that are bound to the antigens on the panel. Typically, the secondary antibody is an anti-human IgG, although it will be appreciated that other secondary antibodies such as anti-IgM, anti-IgG1, anti-IgG2, anti-IgG3, anti-IgG4 or anti-IgA may be used.

[0019] In one embodiment, the health of an individual corresponds to the relative or absolute amount of autoantibodies from a sample that specifically bind to an antigen.

[0020] In one embodiment, the method is performed in vitro.

[0021] In one embodiment, the method comprises detecting upregulation / downregulation of one or more biomarkers.

[0022] In a further aspect of the present invention, there is provided a method for producing a kit for determining the health of an elderly person from a sample extracted from the elderly person, comprising the steps of: for each antigen in the panel, cloning a biotin carboxyl carrier protein folding marker in frame with the gene encoding the antigen and expressing the resulting biotinylated antigen; Binding biotinylated antigens to addressable locations on one or more streptavidin-coated substrates, thereby forming an antigen array. Including, whereby the amount of autoantibodies from a sample that bind to the antigens on the panel can be determined by exposing the substrate to the sample and measuring the response, The antigen comprises AURKA, FEN1, GLRX3, PHLDA1, PPM1A, FKBP3, CD96 and MAPK13. A method is provided.

[0023] In one embodiment, the antigen may comprise one or more from the group including UBE2I, AAK1, YARS, ASPSCR1, CASP10, FHOD2, TCL1A and MAP4.

[0024] In a further aspect of the invention, there is provided a method of assessing the health of an elderly person by exposing a composition comprising a panel of antigens as described herein to a sample extracted from an individual and determining the level of autoantibodies from the sample that bind to the antigens.

[0025] In yet a further aspect of the invention, there is provided a method of assessing the health of an elderly person by exposing in vitro a composition comprising a panel of antigens as described herein to a sample extracted from an individual and determining the level of autoantibodies from the sample that bind to the antigens.

[0026] In a further aspect of the present invention, there is provided a composition comprising a panel of antigens for determining the health of an elderly person, characterized in that the antigens include AURKA, FEN1, GLRX3, PHLDA1, PPM1A, FKBP3, CD96 and MAPK13.

[0027] In one embodiment, the antigen may comprise one or more from the group including UBE2I, AAK1, YARS, ASPSCR1, CASP10, FHOD2, TCL1A and MAP4.

[0028] In one embodiment, the antigen is a biotinylated protein.

[0029] In one embodiment, the amount of one or more autoantibody biomarkers that bind to an antigen in vitro in a sample from a patient can be measured to determine the health status of the patient.

[0030] In yet a further aspect of the invention there is provided a composition comprising a panel of autoantibody biomarkers for determining the health status of an elderly patient, comprising: A composition, wherein the level of one or more autoantibody biomarkers is measured in a sample from a patient, characterised in that the one or more autoantibody biomarkers are selected from autoantibodies specific for one or more of the following antigens: AURKA, FEN1, GLRX3, PHLDA1, PPM1A, FKBP3, CD96 and MAPK13. A composition is provided.

[0031] It will be convenient to further describe the invention with reference to the accompanying drawings which illustrate possible configurations of the invention. Other configurations of the invention are possible, and consequently the details of the accompanying drawings are not to be understood as superseding the generality of the preceding description of the invention. [Brief description of the drawings]

[0032] [Figure 1] FIG. 1 illustrates the structure of the E. coli biotin carboxyl carrier protein domain. [Diagram 2] FIG. 1 illustrates the pPRO9 plasmid used as a vector. [Diagram 3] FIG. 1 shows (A) a chart illustrating proteins related to the cell cycle and cell death, and (B) related pathways. [Figure 4] Figure 1 illustrates cluster analysis: (A) Representation of clusters defined within the elderly by 16 antigens using tSNE cluster analysis [5]; (B) Expression density of antibodies for each target protein; (C) Autoantibodies specific to each health status group. [Figure 5A] FIG. 1 illustrates cohort selection: A schematic diagram describing the workflow used to select and classify elderly people in the study. [Figure 5B] Figure illustrating cohort selection: distribution of older and younger individuals by age and sex; statistical analysis performed with Kruskal-Wallis test with Dunn's correction. [Figure 5C] FIG. 1 illustrates cohort selection: range of clinical variables used for classification of elderly people. [Figure 5D] FIG. Illustrates cohort selection: characteristics of elderly people selected in the study on six critical clinical parameters. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0033] Materials and Methods Gene synthesis and cloning. The pPRO9 plasmid (see Figure 2 below) was constructed by standard techniques and consists of a c-myc tag and BCCP protein domain preceded by a multiple cloning site. Synthetic gene inserts were assembled from synthetic oligonucleotides and / or PCR products. Fragments were cloned into pPRO9 using the SpeI and NcoI cloning sites. Plasmid DNA was purified from transformed bacteria and concentrated by UV spectroscopy. The final construct was verified by sequencing. Sequence identity within the restriction sites used was 100%. 5 μg plasmid preparations were lyophilized for storage.

[0034] Recombinant baculoviruses are generated via co-transfection of a transfer vector carrying the coding sequence of a protein of interest together with a bacmid carrying a strong viral polyhedrin promoter into the Sf9 cell line, a clonal isolate derived from the parent Spodoptera frugiperda cell line IPLB-Sf-21-AE. Homologous recombination initiated by the viral system causes transfected cells to show signs of viral cytopathic effect (CPE) within a few days of culture incubation. The most common CPE observed was a marked expansion of average cell size, a result of viral progeny growth. These baculoviruses, known as P0, were then released into culture medium and virally amplified to generate high titers of P1 virus.

[0035] Protein expression. Expression was measured using 6x10 Sf9 cells per well. 6 The recombinant baculovirus (>107 High titer, low passage, virus stocks (pfu / ml) were used to infect sf9 insect cells. The infected cells were then cultured for 72 hours to allow them to produce the recombinant protein of interest. The cells were washed with PBS, suspended in buffer, and frozen at -80°C in aliquots in preparation for lysis if necessary. Depending on the nature of the transfer vector construct and the protein itself, recombinant protein lysates can be precipitated either from the cultured cells or from the culture medium. Positive recombinant proteins were then analyzed via SDS-PAGE and Western blot against streptavidin-HRP antibodies. A total of 1557 human antigens have been cloned and expressed using this method.

[0036] Array fabrication. HS (Hydrogel-Streptavidin) slides were purchased from Schott and used to print the biotinylated proteins. A total of 9 nanoliters of crude protein lysate was printed in four replicates onto the HS slides using non-contact piezo pressure printing technology. Printing buffers with a pH between 7.0 and 7.5 were used. Slides were dried by centrifugation (200xg, 5 min) before starting the washes and blocking. The printed arrays were blocked using a solution containing BSA or casein (concentration: 0.1 mg / ml) in phosphate buffer. The pH was adjusted between 7.0 and 7.5, and cold solutions were used (4°C-20°C). Slides were not dried between washes and were protected from light. Each resulting "Immunome array" contained a total of 1557 antigens, each printed in four replicates.

[0037] Experimental Procedure. Each critical experimental step in performing an immunome array requires a second trained person to thoroughly check, accurately record, and cross-reference every step in the procedure to reduce operator bias. Samples were collected, randomized, and appropriately assigned to assay racks. These samples were then stored at -20°C until the experiment was ready.

[0038] 1. Study cohort The study cohort was divided into two age groups: young control individuals (YC) and elderly people. The YC group (n=60) consisted of men (n=34) and women (n=26) of Chinese ethnicity, aged 18 to 27 years. They were clinically healthy with no reported comorbidities and no active medical treatments. Selection of the elderly was performed within the Chinese ethnicity elderly aged 60 years or older. This initial selection improves the analytical power and results of the study by excluding ethnic bias.

[0039] Further selection of the elderly into health categories (healthy, moderate and unhealthy) was based on a combination of six clinical parameters (Figure 5A): - Comorbidity 5: variable reflecting the total number of comorbidities excluding eye diseases - NADL: The total number of physical disabilities affecting activities of daily living in older adults [7] - Wo_sf1: Parameter measuring overall quality of life - Frailty: A clinical syndrome characterized by progressively higher vulnerability of older adults to internal and external stressors. It is a multidimensional variable that takes into account physical strength and cognitive ability.[8] - MMSEtot: Mini-Mental State Examination Scale total score, which is an index of cognitive ability in older adults [9]. - GDStot: Geriatric Depression Scale total score. This is a self-report assessment used to identify depression in older adults.

[10]

[0040] The characterization of the health status of the elderly took into account the six parameters already described and led to the selection of the following groups (Figure 5A): - 115 healthy elderly people - 111 elderly people with moderate health conditions - 114 unhealthy elderly people Although there is no significant difference in age between the health groups, sex differences can be observed with more females observed in each health group (Figure 5B, Figure 5D). In Figure 5D, the bold numbers grade the individuals for a particular classification and score. The numbers in brackets correspond to the number of individuals with a particular trait. ND: Not graded. Ave: Mean age of all older adults for each health group [6].

[0041] A global redistribution of individuals showed that unhealthy older adults showed an accumulation of comorbidities, increased frailty and cognitive decline associated with higher depressive status and improved quality of life (Figure 5C,D).

[0042] 2. Serum / Plasma Dilution The samples were then placed in a shaking incubator set at +20°C to thaw for 30 minutes. Once completely thawed, each sample was vigorously vortexed three times at full speed and spun down in a microcentrifuge at 13,000g for 3 minutes. 22.5 μL of sample was pipetted into 4.5 mL of serum assay buffer (SAB) (20°C) containing 0.1% v / v Triton, 0.1% w / v BSA, 10% v / v PBS and vortexed three times to mix. Tubes were tilted during aspiration to ensure serum was collected from below the upper lipid layer but not touching the bottom of the tube if sediment was present. This serum / plasma dilution step was performed in a Class II biological safety cabinet. Batch records were kept accordingly to ensure the correct sample was added to the correct tube.

[0043] 3. Biomarker Assays The arrays were removed from the storage buffer using forceps and placed in a slide box and rack containing 200 mL of cold SAB (4°C) and shaken on a shaker at 50 rpm for 5 minutes. Once the slides were thoroughly washed, they were placed array side up into the slide hybridization chamber containing the previously diluted individual serum. All slides were scanned using a barcode scanner with associated batch records and incubated on a refrigerated shaker at 50 rpm for 2 hours at 20°C.

[0044] 4. Array Washing After Serum Binding Protein array slides were then rinsed twice with 30 mL SAB in individual "pup jars" and with 200 mL SAB buffer in a slide staining box for 20 minutes at room temperature on a 50 rpm shaker. All slides were transferred consecutively in the same orientation.

[0045] 5. Incubation with Cy3 anti-IgG Binding of autoantibodies to antigens placed on the replica immunoarrays was detected by incubation with Cy3-rabbit anti-human IgG. The arrays were immersed in a hybridization solution containing a mixture of Cy3-rabbit anti-human IgG solution diluted 1000-fold in SAB buffer for 2 hours at 50 rpm and 20°C.

[0046] 6. Washing after incubation with Cy3 anti-IgG After incubation, the slides were soaked in 200 mL of SAB buffer three times for 5 min at 50 rpm at room temperature. Excess buffer was removed by immersing the slides in 200 mL of pure water for a few minutes. The slides were then dried at room temperature for 2 min at 240 g. The slides were then stored at room temperature until scanning (preferably the same day). Hybridization signals were measured using a microarray laser scanner (Agilent scanner) at 10 μm resolution. Fluorescence intensity was detected according to the manufacturer's instructions, whereby each spot was plotted using Agilent Feature Extraction software.

[0047] Spot Segmentation A semi-automated QC step was performed to generate actionable results. The output from the microarray scanner is raw .tiff format image files. Extraction and quantification of each spot on the array was performed using GenePix Pro 7 software (Molecular Devices). A GAL (GenePix Array List) file for the array was created using image analysis. GenePix Pro 7 allows for automatic spot gridding and alignment of each spot on the array for data extraction. Following data extraction, a GenePix Results (.GPR) file was created for each slide containing numerical information for each spot; protein ID, protein name, foreground intensity, background intensity, etc.

[0048] Bioinformatics analysis 1. Image analysis: raw data extraction The aim of the image analysis is to assess the amount of autoantibodies present in the serum samples by measuring the median intensity of all pixels within each spot interrogated. Raw .tiff format image files are created for each slide, i.e., each sample. Automated extraction and quantification of each spot on the array is performed using GenePix Pro 7 software (Molecular Devices), which outputs statistics for each spot interrogated on the array. This includes the mean and median pixel intensity within the spot along with its local background. A GAL (GenePix Array List) file for each array is created to aid image analysis. This file contains information of all interrogated spots and their location on the array. Following data extraction, a GenePix Results (.GPR) file is created for each slide, containing information for each spot; protein ID, protein name, foreground intensity, background intensity, etc. In the data sheet created from the experiment, both foreground and background intensities for each spot are expressed in relative fluorescence units (RFU).

[0049] 2. Data handling and pre-processing For each slide, protein and control probes are spotted in 4 replicates-4 arrays on each slide. The following steps were performed to verify the quality of the protein array data before proceeding to data analysis:

[0050] Step 1: Net intensities were calculated for each spot by subtracting the background signal intensity from the foreground signal intensity of each spot. For each spot, the background signal intensity was calculated using a circular area centered on the spot and three times the diameter of the spot.

[0051] Step 2: Replica spots were excluded using an RFU ≤ 0.

[0052] Step 3: Saturated pixels, which may be beyond the reading capacity of the scanner (the maximum RFU of our scanner is 65536 RFU), should not be seen within the spots of the array. Therefore, spots showing saturation in >20% of pixels were excluded if they occurred in ≦2 replicas. If saturated spots occurred in 3 or more replicas of a protein or probe, these proteins / probes were flagged as "SAT" and excluded from downstream analysis.

[0053] Step 4: If only one replica spot remains, the net intensity is zero.

[0054] Step 5: Calculation of the coefficient of variation percentage (CV%) to determine the variation between replica spots on each slide.

[0055]

number

[0056] Sets of replica spots with 2 or fewer replicas remaining and CV%>20% were flagged as "high CV." The average RFU of these replica spots (i.e., proteins) were removed from downstream analysis.

[0057] For proteins / controls with CV%>20% and 3 or more replica spots remaining, the replica spots producing this high CV% value were removed. This was done by calculating the standard deviation between the median net intensity and the individual net intensities for each set of replica spots. The spot with the highest standard deviation was removed. The CV% value was recalculated and the process was repeated.

[0058] Step 6: Calculation of the average net intensity for the remaining replica spots.

[0059] Step 7: Composite normalization of data using both quantile-based and sum-intensity-based modules. The method assumes that different samples share a common underlying distribution of their control probes while taking into account the potential presence of flagged spots therein. The immunome array uses Cy3-labeled biotinylated BSA (Cy3-BSA) replicates as positive control spots across the slides. For this reason, it is considered a housekeeping probe for signal intensity normalization for any given study.

[0060] The quantile module employs the algorithm described by Bolstad et al., 2003

[11] . This reordering allows for the detection and handling of outliers or flagged spots in any Cy3BSA control probe. A module based on total intensity was then run to obtain a scaling factor for each sample. The method assumes that after normalization the positive controls should have a common total intensity value across all samples. This combined method aims to normalize protein array data from variations in their measurement while preserving target biological activities across samples. The steps are as follows:

[0061] Quantile-based normalization of all cy3BSA across all samples (i = number of spots and j = number of samples) 1. Load all Cy3-BSA across all samples, j, into a matrix X, i x j. 2.X sort Sort the spot intensities in each column j of X to obtain 3. <X i > to get X sort Take the average over each column i of

[0062] Intensity-Based Normalization 1. Calculate the sum of the means across each column i

[0063]

number

[0064] 2. For each sample k, calculate the sum of all Cy3-BSA controls

[0065]

number

[0066] 3. For each sample k,

[0067]

number

[0068] Data analysis Fluorescence signals from the 1557 autoantibody measurements were logarithmically transformed to ensure normality prior to all parametric analyses. One-way ANOVAs were performed on each of the 1557 autoantibody measurements i) between all groups (healthy elderly, elderly with intermediate health status, unhealthy elderly and young controls) and ii) between elderly (healthy, intermediate and unhealthy) to identify autoantibodies that were significantly different in at least one group compared to the others (Table 1). An initial P-value threshold of 0.05 was used to indicate significance. Autoantibody biomarkers against 16 antigens were thus identified, as shown in Figure 3: YARS

[12] , UBE2I

[13] , TCL1A

[14] , PPM1A

[15] , PHLDA1

[16] , GLRX3

[17] , FHOD2

[18] , FEN1

[19] , CASP10

[20] , MAPK13

[21] , MAP4

[22] , FKBP3

[23] , CD96

[24] , AURKA

[25] , ASPSCR1

[26] and AAK

[27] . Among these 16 antigens, AURKA, FEN1, GLRX3, PHLDA1, PPM1A, FKBP3, CD96 and MAPK13 were found to have a P value of <0.02 in both analyses (between all groups and between elderly people).

[0069] [Table 1]

[0070] Pathway enrichment analysis showed that 4 of the 16 (PHLDA1, AURKA, FEN1, and UBE2I) are involved in cell cycle and DNA repair pathways that are altered during aging.

[0071] Considering that each of the 16 individual autoantibodies is a weak predictor of health status by itself, dimensionality reduction using tSNE was performed to identify the collective ability of the 16 autoantibodies to discriminate health status. As can be seen in Figure 4A, dimensionality reduction using tSNE indicates that 2 tSNE dimensions were able to discriminate the three health groups. The specificity of the 16 autoantibodies is shown in Figure 4B.

[0072] To identify autoantibodies specific to each health state, a series of t-tests with Welch correction were used to test each health state against the others for all 16 identified autoantibodies. For each autoantibody, the best t-test result among the three health states was selected as the autoantibody selection for the health state. This identified PHLDA1 and CD96 as specific to the healthy group, AURKA, FEN1, CASP10 and AAK1 as specific to the intermediate group, and the rest to the unhealthy group (Figure 4C). Healthy and intermediate autoantibodies may have a protective role against non-communicable diseases. The mean RFU is shown along with the t-test P-value for each health state group, demonstrating the importance of autoantibodies in distinguishing the health state group of interest against the others.

[0073] The present invention utilizes a biotin carboxyl carrier protein (BCCP) folding marker that is cloned in frame with a gene encoding a protein of interest as described above and in EP 1470229. The structure of the E. coli BCCP domain is illustrated in Figure 1, where residues 77-156 are depicted showing the N- and C-termini and a single biotin moiety that is attached in vivo to lysine 122 by biotin ligase (coordinate file 1bdo).

[0074] BCCP acts not only as a protein folding marker but also as a protein solubility promoter. BCCP can be fused to either the N- or C-terminus of the protein of interest. Full-length proteins are expressed as fusions with the BCCP folding marker, which is biotinylated in vivo only if the protein is correctly folded. Conversely, misfolded proteins drive the misfolding of BCCP so that they cannot be biotinylated by the host biotin ligase. Thus, misfolded proteins cannot be specifically attached to the streptavidin-coated solid support. Thus, only correctly folded proteins will be attached to the solid support via the BCCP tag.

[0075] The surface chemistry of the support can be carefully designed to use a three-dimensional thin polymer-based layer (polyethylene glycol; PEG) that preserves protein spot morphology and ensures consistent spot size across the array. The PEG layer suppresses non-specific binding, thereby reducing the high background observed using other platforms. Thus, the solid support used to immobilize the selected biomarkers is designed to resist non-specific polymer adsorption, resulting in excellent signal-to-noise ratios and low detection limits (i.e., improved sensitivity) by minimizing non-specific background binding. In addition, the PEG layer also preserves the folded structure and function of the deposited proteins and protein complexes after immobilization. This is important for accurate diagnosis, since it is commonly known that human serum antibodies bind non-specifically to exposed hydrophobic surfaces of unfolded proteins, thereby resulting in false positives in serological assays on arrays of unfolded proteins, and furthermore, human autoantibodies typically bind non-contiguous epitopes, so that serological assays on arrays of unfolded or misfolded proteins also result in false negatives in autoantibody binding assays.

[0076] Biotinylated proteins bound to streptavidin-coated surfaces show negligible dissociation, making this interaction an excellent means to tether proteins to planar surfaces and ideal for applications such as protein arrays, SPR and bead-based assays. The use of the small folded biotinylated 80-residue domain BCCP offers two important advantages over e.g. AviTag and intein-based tags. First, the BCCP domain is cross-recognized by eukaryotic biotin ligases, allowing it to be efficiently biotinylated in yeast, insect and mammalian cells without the need to co-express E. coli biotin ligase. Second, the N- and C-termini of BCCP are physically separated from the biotinylation site by 50 Å (as shown in Figure 1), so the BCCP domain can be considered as a stalk that presents the recombinant protein away from the solid support surface, thereby minimizing any adverse effects of immobilization.

[0077] The success rate of BCCP folding marker-mediated expression of most complex proteins is greater than 98%. The technique is therefore applicable in highly parallelized pipelines, resulting in high-throughput and highly consistent production of functionally validated proteins.

[0078] The addition of BCCP allows the monitoring of fusion protein folding by measuring the degree of in vivo biotinylation, which can be measured by standard blotting procedures using SDS-PAGE or in situ colony lysis and transfer of the sample to a membrane followed by detection of biotinylated proteins using streptavidin conjugates such as streptavidin-horseradish peroxidase. Furthermore, the fact that the BCCP domain is biotinylated in vivo is particularly useful during multiplex protein purification for the fabrication of protein arrays, since the high affinity and specificity exhibited by the streptavidin surface allows proteins to be simultaneously purified and immobilized from cellular lysates in a single step.

[0079] Figure 3 illustrates distinguishing proteins in elderly individuals characterized by cell cycle and cell death steps. (A) P-values ​​associated with 16 protein targets distinguishing different elderly health states. Only serum / plasma antibody readout against YARS protein does not distinguish between elderly and YC individuals (p>0.05). (B) Five protein readouts were closely related for pathways linking cell cycle (PHLDA1, AURKA, FEN1), DNA repair (UBE2I, FEN1) and translation (YARS) by enrichment pathway analysis (String).

[0080] (References)

[0081] [Table 2A]

[0082] [Table 2B]

[0083] [Table 2C]

[0084] [Table 2D]

[0085] [Table 2E]

[0086] [Table 2F]

[0087] [Table 2G]

[0088]

Table 2H

[0089]

Table 2I

[0090]

Table 2J

[0091]

Table 2K

Claims

1. 1. A method for determining elderly health from a serum / plasma sample extracted from an individual, comprising: (i) testing the sample for the presence of health specific biomarkers; (ii) determining whether the subject is healthy, moderately healthy, or unhealthy based on detecting the presence of the biomarker; In a method comprising: Each biomarker is an autoantibody against one specific antigen in a panel, said panel including the antigens: AURKA, FEN1, GLRX3, PHLDA1, PPM1A, FKBP3, CD96 and MAPK13; Healthy corresponds to detection of PHLDA1 and CD96 at lower levels compared to levels for unhealthy / moderate health, moderate health corresponds to detection of AURKA, FEN1 at lower levels compared to levels for healthy / unhealthy, and unhealthy corresponds to detection of MAPK13, FKBP3, PPM1A and GLRX3 at higher levels compared to levels for healthy / moderate health. method.

2. The method of claim 1, wherein the panel further comprises one or more of the antigens: UBE2I, AAK1, YARS, ASPSCR1, CASP10, FHOD2, TCL1A and MAP4, and wherein detection of CASP10 and AAK1 at lower levels compared to healthy / unhealthy corresponds to moderate health, and detection of UBE2I, YARS, ASPSCR1, FHOD2, TCL1A, MAP4 at higher levels compared to healthy / moderate health corresponds to unhealthy.

3. 3. The method of claim 1 or 2, wherein an antigen is exposed to the sample extracted from a human such that autoantibody biomarkers from the sample can bind to the antigen.

4. 4. The method of claim 3, wherein the antigen is subsequently exposed with a fluorescently tagged secondary antibody, thereby allowing for the determination of the amount of any autoantibody from the sample that is bound to the antigen.

5. 5. The method of claim 4, wherein the health status of the human corresponds to the relative or absolute amount of autoantibodies from the sample that specifically bind to the antigen.

6. The method of claim 1, wherein each step is performed in vitro.

7. The method of claim 1, comprising detecting upregulation / downregulation of one or more biomarkers.

8. The method of claim 1, wherein the antigen is a biotinylated protein.

9. 9. The method of claim 8, wherein each biotinylated protein is formed from a biotin carboxyl carrier protein folding marker fused in frame to the protein.

10. The method of claim 8 or 9, wherein the biotinylated protein is bound to a streptavidin-coated substrate.

11. The method of claim 10, wherein the substrate comprises a hydrogel-forming polymer-based layer.

12. 1. A method for producing a kit for determining elderly health from a serum / plasma sample extracted from an individual, comprising: for each antigen in the panel, cloning a biotin carboxyl carrier protein folding marker in frame with the gene encoding the antigen and expressing the resulting biotinylated antigen; Binding biotinylated antigens to addressable locations on one or more streptavidin-coated substrates, thereby forming an antigen array. Including, whereby the amount of autoantibodies from a sample that bind to the antigens on the panel can be determined by exposing the substrate to the sample and measuring the response, The antigen comprises UBE2I, AAK1, YARS, ASPSCR1, CASP10, FHOD2, TCL1A, MAP4, MAPK13, CD96, FKBP3, PPM1A, PHLDA1, GLRX3, FEN1 and AURKA. method.

13. 1. A composition comprising a panel of antigens for determining the health of an elderly person, characterized in that the antigens include MAPK13, CD96, FKBP3, PPM1A, PHLDA1, GLRX3, FEN1 and AURKA, and optionally one or more of UBE2I, AAK1, YARS, ASPSCR1, CASP10, FHOD2, TCL1A and MAP4.

14. The composition of claim 13, wherein the antigen is a biotinylated protein.

15. The composition of claim 13 or 14, wherein the amount of one or more autoantibody biomarkers binding to the antigen in vitro in a serum / plasma sample from a patient can be measured to determine the health status of the patient.

16. 1. A composition comprising a panel of autoantibody biomarkers for determining the health status of an elderly patient, comprising: In a composition, wherein the level of an autoantibody biomarker is measured in a serum / plasma sample from a patient; each of the autoantibody biomarkers is an autoantibody specific for one antigen in a panel, the panel including at least the following antigens: MAPK13, CD96, FKBP3, PPM1A, PHLDA1, GLRX3, FEN1, and AURKA; Optionally comprising one or more of the following antigens: UBE2I, AAK1, YARS, ASPSCR1, CASP10, FHOD2, TCL1A and MAP4; composition.

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