Biomarkers associated with amyotrophic lateral sclerosis (ALS)

Abstract

Provided is a biomarker for use in the determination or diagnosis of a progression rate of amyotrophic lateral sclerosis (ALS) or a possibility of being affected by ALS and / or the selection or prediction of a therapeutic drug for ALS.　There is discovered a biomarker selected from the group consisting of IL-17A, KLRD1, KRT19, NCF2, TFF2, YTHDF3, Th17, a regulatory T cell (Treg), mature CD8T, naive CD8T, exhausted CD8T, a Classical monocyte, memory CD4T, Th17 / Treg, mature CD8T / naive CD8T, and mature CD8T / exhausted CD8T.

Description

Technical Field

[0001] The present invention relates to a biomarker associated with amyotrophic lateral sclerosis (ALS), or its use or method of use.

Background Art

[0002] Amyotrophic lateral sclerosis (ALS) is a disease in which abnormalities occur in neurons and muscles throughout the body atrophy (Non-Patent Document 1: https: / / www.nhk.or.jp / kenko / atc_762.html). ALS is a neurodegenerative disease in which motor neurons (nerve cells) are damaged, and a disease in which muscle atrophy and muscle weakness progress. ALS requires the use of a ventilator or results in death approximately 2 to 5 years after onset. Furthermore, in Japan, the prevalence of ALS is 5 to 7 people per 100,000 and the number of patients is approximately 10,000. Furthermore, it is known that ALS is approximately 90% sporadic and approximately 10% familial (such as SOD1 mutations).

[0003] For the evaluation of ALS symptoms, it is known to use the revised ALS Functional Rating Scale (ALSFRS-R). The ALSFRS-R is an evaluation scale created to understand the daily life of ALS patients. The ALSFRS-R consists of 12 items (5 levels from 0 to 4) such as language, swallowing, movement around the body, and walking, and is evaluated by its total score (0 to 48). The ALSFRS-R is used as an inclusion criterion for clinical trials, a primary evaluation item, etc.

Prior Art Documents

Non-Patent Documents

[0004]

Non-Patent Document 1

Summary of the Invention

[0005] Many aspects of the peripheral blood immunological profile in ALS remain unclear. In particular, the profile related to the rate of symptom progression is not well understood. If the peripheral blood immunological profile is clarified, it is considered useful for diagnosis, patient stratification in clinical trials, and as a surrogate marker for therapeutic intervention. Therefore, there is a need for biomarkers to determine the rate of ALS progression or the likelihood of developing the disease. [Means for solving the problem]

[0006] The inventors of this invention have diligently conducted research to solve the above problems and have found a biomarker associated with amyotrophic lateral sclerosis (ALS) or its use (for example, use to determine or diagnose the rate of progression or likelihood of developing amyotrophic lateral sclerosis (ALS) in a subject) or a method of using it (for example, a method to determine or diagnose the rate of progression or likelihood of developing amyotrophic lateral sclerosis (ALS) in a subject).

[0007] The inventors have also identified biomarkers for use in determining or diagnosing the progression rate or likelihood of developing amyotrophic lateral sclerosis (ALS), and / or in selecting or predicting therapeutic agents.

[0008] The inventors have also found a method for determining or diagnosing the rate of progression or likelihood of developing amyotrophic lateral sclerosis (ALS) in a subject, comprising determining the level of a biomarker in a sample derived from the subject.

[0009] In other words, the present invention provides the following: (1) A biomarker for use in determining or diagnosing the rate of progression or likelihood of developing amyotrophic lateral sclerosis (ALS), and / or in selecting or predicting therapeutic drugs, The biomarker is selected from the group consisting of IL-17A, KLRD1, KRT19, NCF2, TFF2, YTHDF3, Th17, regulatory T cells (Treg), mature CD8T, naive CD8T, exhausted CD8T, classical monocyte, memory CD4T, Th17 / Treg, mature CD8T / naive CD8T, and mature CD8T / exhausted CD8T. (2) The biomarker described in (1) for use in determining or diagnosing rapidly progressive amyotrophic lateral sclerosis (ALS). (3) The biomarker according to (1), wherein the biomarker is selected from combinations consisting of IL-17A and Th17, IL-17A and memory CD4T, KLRD1 and mature CD8T, TFF2 and mature CD8T, and NCF2 and classical monocyte. (4) A method for determining or diagnosing the rate of progression or likelihood of developing amyotrophic lateral sclerosis (ALS) in a subject, A method comprising determining the level of any of the biomarkers described in (1) to (3) in a sample derived from the subject. (5) The method according to (4), further comprising determining or diagnosing the rate of progression of ALS or the likelihood of developing it based on a reference level of the biomarker. (6) The method according to (5), wherein the reference level is the level of the biomarker in a sample from a subject without ALS, a sample from a subject with ALS, a sample from a subject with ALS that has a slow progression rate, or a sample from a subject with ALS that has a rapid progression rate. [Effects of the Invention]

[0010] The present invention provides a biomarker associated with amyotrophic lateral sclerosis (ALS) or a method of using the same. The present invention provides a biomarker for use in determining or diagnosing the rate of progression or likelihood of affliction with amyotrophic lateral sclerosis (ALS), and / or in selecting or predicting therapeutic agents. The present invention provides a method for determining or diagnosing the rate of progression or likelihood of affliction with amyotrophic lateral sclerosis (ALS) in a subject, comprising determining the level of a biomarker in a sample derived from the subject. [Brief explanation of the drawing]

[0011] [Figure 1] Study design and cell type profiling in single-cell RNA sequencing. A) Summary of the study integrating single-cell RNA sequencing (scRNA-seq) analysis of peripheral blood mononuclear cells (PBMCs), serum immunoproteomics, and clinical information from healthy controls, non-rapid amyotrophic lateral sclerosis (ALS) patients, and rapid ALS patients. B) Uniform manifold approximation and projection (UMAP) showing 23 identified cell types. C) Frequency of cell types for each sample. Frequency is expressed as the percentage of each cell type relative to the total number of cells in each sample (100%). DC = dendritic cell, NK = natural killer. [Figure 2]Intergroup comparison of the frequency of each cell type. A) Frequency of regulatory T cells in all cells. B) Frequency of a specific cell type in a particular cell group. C) Ratio of cell types compared to related cell types. These showed significant differences in rapid amyotrophic lateral sclerosis (ALS) compared to non-rapid ALS, according to the Tukey HSD test. Numerical values ​​represent the median for each group. Lines and asterisks indicate significant combinations according to the Tukey HSD test (*p<0.05, **p<0.005, and ***p<0.0005). Breg = regulatory B cell; NK = natural killer; Th1 = helper T1 cell; Th17 = helper T17 cell; Treg = regulatory T cell. [Figure 3] Serum immunoproteomics. A) Volcano plot showing -log10 P-values ​​and log2 multiplier changes based on differential expression analysis between each combination. Red dots represent differentially expressed proteins (DEPs), and the protein names are shown. B) Violin plot showing the expression levels of six proteins isolated as DEPs in rapidly developing amyotrophic lateral sclerosis (ALS) compared to non-rapid ALS. The numerical values ​​represent the median for each group. Lines and asterisks indicate significant combinations according to the Tukey HSD test (*p<0.05, **p<0.005, and ***p<0.0005). C) Violin plot showing the expression levels of phosphorylated neurofilament H (pNf-H) measured by enzyme-linked immunosorbent assay (ELISA). IL-17A = Interleukin-17A; KLD1 = Killer cell lectin-like receptor D1; KRT19 = Keratin 19; NCF2 = Neutrophil cytoplasmic factor 2; NPX = Normalized protein expression; TFF2 = Trefoil factor 2; YTHDF3 = YTH N6-methyladenosine RNA-binding protein F3. [Figure 4] Correlation diagram between serum immunoproteins and the frequency / ratio of each cell type. Scatter plot of serum protein expression levels and the frequency / ratio of each cell type in single-cell RNA sequencing analysis using Pearson's correlation coefficient. The numerical values ​​represent the correlation coefficients using a sample of 40 amyotrophic lateral sclerosis (ALS) patients and healthy control subjects. [Figure 5-1] Annotation of each cluster using cell type markers. [Figure 5-2] Annotation of each cluster by cell type markers (continuation of Fig. 5-1). [Figure 6] Inter-group comparison of the frequency of each cell type in all cells. [Figure 7] Inter-group comparison of the frequency of each cell type in each immune cell. [Figure 8] Inter-group comparison of the frequency ratio between cells.

Mode for Carrying Out the Invention

[0012] Definition Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Any methods and materials similar or equivalent to those described in this specification can be used in the implementation or testing of the present invention. Unless otherwise indicated, the implementation of the present invention may employ conventional methods in the technical fields of chemistry, biochemistry, molecular biology, cell biology, genetics, immunology, and pharmacology, etc., within the scope of the technology of the art.

[0013] As used herein, "subject" and "patient" include humans or any non-human animals. "Non-human animals" include, but are not limited to, non-human primates such as monkeys, vertebrates such as sheep and dogs, and rodents such as mice, rats, and guinea pigs.

[0014] As used herein, "amyotrophic lateral sclerosis (ALS)" is a neurodegenerative disease in which motor neurons (nerve cells) are damaged, and refers to a disease in which muscle atrophy and muscle weakness progress, etc. As used herein, ALS includes ALS with a "rapid" progression rate and ALS with a "non-rapid" progression rate. The "progression rate" of ALS includes the "rapid progression rate" of ALS and the "non-rapid progression rate" of ALS.

[0015] As used herein, ALS with a "rapid" progression rate refers to ALS that shows a decline of 1 point or more per month based on the score of the revised ALS Functional Rating Scale (ALSFRS-R) for the assessment of ALS symptoms. As used herein, "rapid" ALS, "rapidly progressing" ALS, and "rapidly progressive" ALS are used interchangeably.

[0016] As used herein, ALS with a "non-rapid" progression rate refers to ALS that shows a decline of less than 1 point per month based on the score of the revised ALS Functional Rating Scale (ALSFRS-R) for the assessment of ALS symptoms. As used herein, "non-rapid" ALS, "non-rapidly progressing" ALS, and "non-rapidly progressive" ALS are used interchangeably.

[0017] The revised ALS Functional Rating Scale is shown in Table A.

Table A-1

Table A-2

Table A-3

[0018] In this specification, "potential to be affected" refers to the possibility of having ALS or having developed ALS. "Potential to be affected" includes high and low "potential to be affected." High "potential to be affected" refers to a higher likelihood of having ALS or having developed ALS compared to subjects without ALS, subjects who do not develop ALS, or healthy controls. Low "potential to be affected" refers to a lower or similar likelihood of having ALS or having developed ALS compared to subjects without ALS, subjects who do not develop ALS, or healthy controls.

[0019] In this specification, “therapeutic drugs” include any drugs used to treat, prevent, or diagnose ALS. Atherapeutic drugs may be known drugs for treating, preventing, or diagnosing ALS, or unknown drugs for treating, preventing, or diagnosing ALS.

[0020] In this specification, “biomarker” means an indicator for use in determining or diagnosing the progression rate or likelihood of ALS, and / or in selecting or predicting therapeutic agents, for example, an indicator associated with ALS. In this specification, biomarkers include any substance present in a sample that can be measured, such as cells (e.g., immune cells, e.g., T cells, B cells), polypeptides (e.g., immunoglobulins, peptides), polynucleotides (e.g., mRNA, DNA) and / or their metabolites.

[0021] Biomarkers are listed, for example, in Tables 2, 4, 5, 6, 7, 8 (Tables 8-1, 8-2), 9 (Tables 9-1, 9-2, 9-3, 9-4, 9-5), Figures 2, 3, 4, 5 (Figures 5-1, 5-2), 6, 7, and 8. Biomarkers include biomarkers associated with ALS, biomarkers of ALS, e.g., biomarkers of rapidly progressing ALS and / or biomarkers of non-rapidly progressing ALS.

[0022] A biomarker may be a single molecule or multiple molecules. If a biomarker is multiple molecules, it may represent the relationship between the molecules (e.g., a ratio). In this specification, the description "single molecule A / single molecule B" for multiple molecules indicates the ratio of single molecule A to single molecule B. For example, Th17 / Treg represents the ratio of Th17 to Treg levels, obtained by dividing the level of Th17 by the level of Treg.

[0023] Biomarkers may be used as a single biomarker or as a combination of multiple biomarkers (e.g., two, three, four, five, six, seven, eight, or nine). For example, the use of multiple biomarkers may provide a more accurate or more precise indicator of ALS association than the use of a single biomarker.

[0024] In this specification, the “level” of a biomarker refers to the concentration, expression level, and / or activity level of the biomarker, etc. A person skilled in the art can determine the level of a biomarker as appropriate.

[0025] In this specification, "IL-17A" refers to interleukin-17A.

[0026] In this specification, "KLRD1" refers to killer cell lectin-like receptor D1.

[0027] In this specification, "KRT19" refers to keratin 19.

[0028] In this specification, "NCF2" refers to neutrophil cytosolic factor 2.

[0029] In this specification, "TFF2" refers to trefoil factor 2.

[0030] In this specification, "YTHDF3" refers to YTH N6-methyladenosine RNA binding protein F3.

[0031] In this specification, "Th17" refers to helper T17 cells.

[0032] In this specification, "Treg" refers to regulatory T cells.

[0033] In this specification, "mature CD8T" refers to mature CD8-positive T cells.

[0034] In this specification, "naive CD8T" refers to naive CD8-positive T cells.

[0035] In this specification, "exhausted CD8T" refers to exhausted CD8-positive T cells.

[0036] In this specification, "Classical monocyte" refers to the classic monocyte.

[0037] In this specification, "memory CD4T" refers to memory CD4-positive T cells.

[0038] In this specification, “sample” can be obtained from a subject or patient. Samples can be obtained from any source known in the art, but are not limited to blood, whole blood, serum, plasma, urine, interstitial fluid, tears, saliva, or skin.

[0039] In this specification, the “reference level” of a biomarker refers to the level of the biomarker in a sample derived from a subject without ALS, a sample derived from a subject with ALS, a sample derived from a subject with slow-progressing ALS, or a sample derived from a subject with rapidly progressing ALS. A person skilled in the art can appropriately select and determine the reference level of a biomarker.

[0040] In this specification, the subjects or patients from whom samples are obtained may be the same as or different from the subjects from whom samples providing reference levels are obtained.

[0041] In one embodiment, the biomarker of the present invention can be used to determine or diagnose the rate of progression or the likelihood of developing ALS.

[0042] In one embodiment, the present invention is based on the finding that (i) the level of a biomarker in a sample obtained from a subject with rapidly progressing ALS, (ii) the level of the same biomarker in a sample obtained from a subject with slowly progressing ALS, and (iii) the level of the same biomarker in a sample obtained from a subject without ALS are different.

[0043] In one embodiment, the present invention is based on the finding that (i) the level of a biomarker in a sample obtained from a subject with rapidly progressing ALS differs from (ii) the level of the same biomarker in a sample obtained from a subject with slowly progressing ALS and / or (iii) the level of the same biomarker in a sample obtained from a subject without ALS.

[0044] In one embodiment, the present invention is based on the finding that (ii) the level of a biomarker in a sample obtained from a subject with a slow progression rate of ALS is different from (i) the level of the same biomarker in a sample obtained from a subject with a rapid progression rate of ALS and / or (iii) the level of the same biomarker in a sample obtained from a subject without ALS.

[0045] In one embodiment, the present invention is based on the finding that (i) the level of a biomarker in a sample obtained from a subject with rapidly progressing ALS and / or (ii) the level of the same biomarker in a sample obtained from a subject with slowly progressing ALS and (iii) the level of the same biomarker in a sample obtained from a subject without ALS are different.

[0046] In one embodiment, the present invention provides a biomarker in which (i) the level of the biomarker in a sample obtained from a subject having rapidly progressing ALS is higher or lower than (ii) the level of the same biomarker in a sample obtained from a subject having not rapidly progressing ALS and / or (iii) the level of the same biomarker in a sample obtained from a subject not having ALS.

[0047] In one embodiment, the present invention provides a biomarker in which (ii) the level of the biomarker in a sample obtained from a subject having a slow-progressing ALS is higher or lower than (i) the level of the same biomarker in a sample obtained from a subject having a rapidly progressing ALS and / or (iii) the level of the same biomarker in a sample obtained from a subject not having ALS.

[0048] In one embodiment, the present invention provides a biomarker in which (i) the level of the biomarker in a sample obtained from a subject having rapidly progressing ALS and / or (ii) the level of the same biomarker in a sample obtained from a subject having not rapidly progressing ALS is higher or lower than (iii) the level of the same biomarker in a sample obtained from a subject not having ALS.

[0049] In one embodiment, the biomarkers of the present invention can be used to select or predict therapeutic drugs for ALS.

[0050] In one embodiment, therapeutic agents that reduce or increase (i) the level of a biomarker in a sample obtained from a subject with rapidly progressing ALS, which is higher or lower than (ii) the level of the same biomarker in a sample obtained from a subject with slowly progressing ALS and / or (iii) the level of the same biomarker in a sample obtained from a subject without ALS, can be selected or predicted as a possible or effective therapeutic agent for rapidly progressing ALS.

[0051] In one embodiment, therapeutic agents that reduce or increase (ii) the level of a biomarker in a sample obtained from a subject with a slow-progressing ALS which is higher or lower than (i) the level of the same biomarker in a sample obtained from a subject with a rapidly progressing ALS and / or (iii) the level of the same biomarker in a sample obtained from a subject without ALS, can be selected or predicted as a possible or effective therapeutic agent for slow-progressing ALS.

[0052] In one embodiment, therapeutic agents that reduce or increase (i) the level of a biomarker in a sample obtained from a subject with rapidly progressing ALS and / or (ii) the level of the same biomarker in a sample obtained from a subject with non-rapidly progressing ALS, respectively, compared to (iii) the level of the same biomarker in a sample obtained from a subject without ALS, can be selected or predicted as a possible or effective therapeutic agent for rapidly and / or non-rapidly progressing ALS.

[0053] In one embodiment, when the biomarker of the present invention is used to select or predict a therapeutic agent for ALS, a candidate therapeutic agent for ALS may be mixed with or in contact with the biomarker of the present invention. The candidate therapeutic agent for ALS may be a drug that reduces or increases the level of the biomarker of the present invention. The reduction or increase of the level of the candidate therapeutic agent for ALS may be tested or confirmed.

[0054] In one embodiment, the biomarker of the present invention can be used to determine or predict the efficacy or performance of a therapeutic agent for ALS.

[0055] In one embodiment, the biomarkers of the present invention are described, for example, in Tables 2, 4, 5, 6, 7, 8 (Table 8-1, Table 8-2), 9 (Table 9-1, Table 9-2, Table 9-3, Table 9-4, Table 9-5), Figures 2, 3, 4, 5 (Figure 5-1, Figure 5-2), 6, 7, and 8. In one embodiment, the biomarkers of the present invention are selected from the group consisting of IL-17A, KLRD1, KRT19, NCF2, TFF2, YTHDF3, Th17, regulatory T cells (Treg), mature CD8T, naive CD8T, exhausted CD8T, classical monocyte, memory CD4T, Th17 / Treg, mature CD8T / naive CD8T, and mature CD8T / exhausted CD8T. In one embodiment, the biomarker of the present invention is selected from the group consisting of NCF2-classical monocytes, TFF2-mature CD8 T cells, KLRD1-mature CD8 T cells, IL-17A-memory CD4 T cells, and IL-17A-Th17. In one embodiment, the biomarker of the present invention is selected from the group consisting of TFF2-mature CD8 T cells, KLRD1-mature CD8 T cells, and IL-17A-Th17. In one embodiment, the biomarker of the present invention is selected based on partial correlation coefficients and / or P values, as described, for example, in the examples. In one embodiment, the biomarker of the present invention may be a combination of immune cells and proteins. In one embodiment, the biomarker of the present invention can be used to determine or diagnose the rate of progression of ALS (rapid or slow) or the likelihood of morbidity (high or low likelihood of morbidity). In one embodiment, the biomarker of the present invention can be used to determine or diagnose the rate of progression of ALS (rapid or slow) or the likelihood of morbidity (high or low likelihood of morbidity) based on whether it is increased or decreased from a reference level.

[0056] In one embodiment, the biomarker of the present invention may be used in combination with a known biomarker (e.g., neurofilament light chain (NfL)).

[0057] In one embodiment, the biomarker of the present invention enables the determination or diagnosis of the rate of progression or likelihood of early ALS. ALS patients are generally treated after a 12-week observation period. However, the biomarker of the present invention enables the determination or diagnosis of the rate of progression or likelihood of early ALS (before the 12-week observation period). Determining or diagnosing the rate of progression or likelihood of early ALS can enable early treatment of ALS and may help to delay the progression of ALS.

[0058] In one embodiment, a method for using or using a biomarker of the present invention to determine or diagnose the rate of progression or likelihood of developing amyotrophic lateral sclerosis (ALS) in a subject, comprising determining the level of the biomarker of the present invention in a sample derived from the subject.

[0059] In one embodiment, the use or method of use of the biomarker of the present invention includes determining or diagnosing the rate of progression of ALS or the likelihood of developing the disease based on or in comparison to a reference level of the biomarker.

[0060] In one embodiment, the use or method of using the biomarker of the present invention includes determining or diagnosing the rate of progression of ALS (rapid or slow) or the likelihood of disease (high or low likelihood) based on whether the level of the biomarker in a sample is higher or lower than a reference level.

[0061] In one embodiment, the present invention is a method for determining or diagnosing the rate of progression or likelihood of developing amyotrophic lateral sclerosis (ALS) in a subject, Determine the level of the biomarker in the sample derived from the aforementioned target, The aforementioned level is compared with the reference level of the biomarker, Based on whether the levels of biomarkers in the sample are elevated or decreased compared to reference levels, the rate of ALS progression (rapid or slow) or the likelihood of affliction (high or low likelihood) is determined or diagnosed. To provide a method that includes doing so.

[0062] In one embodiment, the use or method of use of the biomarker of the present invention includes obtaining or providing a sample derived from a subject.

[0063] In one embodiment, the use or method of use of the biomarker of the present invention includes determining a reference level by determining, for example, the level of the biomarker in a sample derived from a subject without ALS, a sample derived from a subject having ALS, a sample derived from a subject having ALS with a slow progression rate, or a sample derived from a subject having ALS with a rapid progression rate.

[0064] In one embodiment, the use or method of use of the biomarker of the present invention may include comparing the level of the biomarker in a sample derived from the target with the level of the biomarker in the sample derived from the target at different points in time (e.g., one day prior, one week prior, one month prior, one year prior, etc.).

[0065] In one embodiment, the reference level may be determined in advance.

[0066] In one embodiment, the present invention relates to a composition for use in determining or diagnosing the progression rate or likelihood of developing amyotrophic lateral sclerosis (ALS), and / or selecting or predicting therapeutic agents, the composition comprising the biomarker of the present invention.

[0067] In one embodiment, the present invention relates to a method for treating amyotrophic lateral sclerosis (ALS), comprising administering to a patient a therapeutic agent for ALS selected in the above embodiment. Examples of therapeutic agents for ALS include, but are not limited to, riluzole and edaravone. In one embodiment, the therapeutic agent may be an antibody against a biomarker of the present invention, for example, an antibody against IL-17A or the IL-17 receptor.

[0068] Unless otherwise specified, the terms used herein are those commonly used in the art.

[0069] The present invention will be described in more specific and detailed terms below with reference to examples, but the scope of the present invention should not be limited to these examples. [Examples]

[0070] method Patients and healthy control groups From March 29, 2021 to October 30, 2022, 36 sporadic ALS patients and 10 healthy volunteers were screened at Tokushima University Hospital. Inclusion criteria for ALS patients were defined as definite, probable, laboratory-supported probable, or possible according to the updated Awaji criteria, or diagnosed with ALS according to the Gold Coast criteria, and within two years of symptom onset. Age and sex were matched for both patients and healthy volunteers. Exclusion criteria for ALS patients and healthy volunteers are shown in Table 1. Patients found to have known ALS etiological mutations, such as SOD1, were excluded from the analysis because their immunological profiles differed from those of sporadic ALS. Rapid ALS was defined as a decrease of 1.0 point / month or more on the Revised ALS Functional Rating Scale (ALSFRS-R) (△ALSFRS-R / month ≥ 1), and non-rapid ALS was defined as a decrease of less than 1.0 point / month (△ALSFRS-R / month < 1). All clinical information was collected after obtaining written informed consent from patients. All research plans were approved by the Tokushima University Hospital Department of Life Sciences and Medicine Research Ethics Committee No. 3682. The study was conducted in accordance with the principles of the Declaration of Helsinki. Table 1. Exclusion criteria [Table 1]

[0071] Sample preparation Peripheral blood samples were collected from healthy donors and ALS patients at Tokushima University Hospital. PBMCs were Ficoll-Hypaque (Lymphoprep TM Prepared by density centrifugation (Serumwerk Bernburg AG, Germany), washed with phosphate-buffered saline (PBS), and then X-VIVO containing 5% FBS. TMThe PBMCs were resuspended in culture medium (LONZA, Basel, Switzerland). The PBMCs were stored at -80°C until library preparation using CELLBANKER 1 (Nippon Zenyaku Kogyo Co., Ltd., Fukushima, Japan). Serum samples were collected in 8.0 ml Insepack tubes (Sekisui, Tokyo, Japan), centrifuged at 3500 rpm for 10 minutes, dispensed, and frozen at -80°C within 20 minutes of collection.

[0072] Single-cell RNA sequencing (scRNA-seq) Human PBMCs were thawed, washed, and resuspended in D-PBS / BSA. After filtration, cell count and viability were evaluated. scRNA-seq was performed using 10x Genomics. Primary analysis was performed using Cell Ranger, and secondary analysis was performed using Seurat. Data normalization, variable feature selection, and integration were performed. Principal component analysis and uniform manifold approximation and projection (UMAP) were used for dimensionality reduction and cell clustering. Cell types were identified based on marker expression profiles. Differentially expressed genes (DEGs) were identified using the Wilcoxon rank-sum test. Visualization was performed using ggplot2. Detailed methods are described below.

[0073] Immunoproteomics We simultaneously measured 400 human proteins associated with inflammation (Table 2) using the Olink Target 96 Inflammation and Olink Explore 384 Inflammation panels (Olink Proteomics, Uppsala, Sweden). Data are presented as normalized protein expression (NPX) values. Correlation testing was performed using the "cor.test" function from the R stats package. Detailed methods are described below. Table 2. List of measured proteins* [Table 2] * These proteins are included in the Olink Target 96 Inflammation and Olink Explore 384 Inflammation panels (https: / / olink.com).

[0074] Enzyme-linked immunosorbent assay (ELISA) Serum phosphorylated neurofilament H (pNf-H) levels were measured using a commercially available ELISA kit (EUROIMMUN, Luebeck, Germany).

[0075] statistical analysis Statistical analysis was performed using Microsoft R open-source software (version 4.0.2). DEGs in scRNA-seq were identified using the non-parametric Wilcoxon rank-sum test, and multiple testing was corrected using Bonferroni's test. Differences between groups in DEP and the frequency of each cell type in serum proteome were identified using Tukey's range test for multiple testing. Correlation tests were performed using Pearson's correlation coefficient.

[0076] Detailed single-cell RNA sequencing (scRNA-seq) PBMC preparation, library preparation, sequencing Frozen human peripheral blood mononuclear cells (PBMCs) were rapidly lysed in 37°C water and washed with 1x D-PBS (Mg / Ca-free) containing 1% BSA. Centrifuge conditions for cell recovery were 20°C, 250 xg, and a swing-out rotor for 10 minutes. After three washes, the cells were resuspended in an appropriate amount of D-PBS / 1% BSA and filtered through a Flowmi Cell Strainer 40 μm to remove any remaining large particles. Cell count and viability were examined using a hemocytometer with trypan blue staining. scRNA-seq was performed using the 10x Genomics platform. Single-cell suspensions (10,000 cells) were loaded onto a GEM generation chip using a 10x Genomics Chromium controller, and DNA libraries were prepared using Chromium Next GEM Single Cell 3' Reagent Kits v3.1 (10x Genomics, Pleasanton, CA) according to the manufacturer's instructions. Quality control of the prepared libraries was performed before sequencing using a 4200 TapeStation D1000 ScreenTape (Agilent, Santa Clara, CA) and a Qubit dsDNA Assay (Thermo Fisher Scientific, Waltham, MA). Gene expression libraries were sequenced using the DNBSEQ-G400 platform (MGI Tech, Shenzhen, China) at a depth of 50,000 reads per cell.

[0077] Data normalization The primary analysis was performed using the 10x Genomics Cell Ranger version 3 pipeline. bcl files were converted to FASTQ format using Cell Ranger's "counts" software. The FASTQ data was filtered and mapped to the GRCh38 reference genome. The secondary analysis was performed using the Seurat version 3 package in R version 4.0.2. Low-quality cell and doublet cell data were excluded using the Seurat and DoubletFinder packages. These counts were normalized using global scaling normalization with the Seurat function "LogNormalize" and logarithmically transformed with the Seurat function "NormalizeData". Variable features were selected by direct modeling of intrinsic mean-variance relationships using the Seurat function "FindVariableFeatures", and these features were used to select integrated features using the Seurat function "SelectIntegrationFeatures". Integrated features were applied to linear transformations and used for principal component analysis (PCA) using the Seurat functions "ScaleData" and "RunPCA", respectively. Mutual PCA was used to identify the top 50 principal components for anchor identification. The anchors were then used to integrate the datasets using Seurat's function "IntegrateData".

[0078] analysis The integrated normalized data underwent linear transformation and PCA using Seurat's functions "ScaleData" and "RunPCA". Key principal components were determined using elbow plots plotting the standard deviations of the principal components, and these were used to visualize Uniform Manifold Approximation and Projection (UMAP) using Seurat functions "ElbowPlot" and "RunUMAP". Key principal components were also used to identify cell types. First, the similarity between cells was calculated using Seurat's function "FindNeighbors" with a KNN graph based on Euclidean distance in PCA space and edge weights between any two cells based on shared overlap in local neighborhoods. Next, cells were divided into clusters by clustering the similarities between cells using the Louvain algorithm and Seurat function "FindClusters". Finally, the cell type in each cluster was identified by examining the cell type-specific marker expression profiles in each cluster. Differentially expressed genes (DEGs) between groups within each cell type were identified using a non-parametric Wilcoxon rank-sum test with the Seurat function "FindMarkers". Gene expression levels in each cluster were visualized using a violin plot with the Seurat function "VlnPlot". Differences in the frequency of each cell type between groups were identified using Tukey's range test with the "TukeyHSD" function from the stats (R default) package. Correlation tests were performed using the "cor.test" function from the stats (R default) package. Scatter plots, box plots, and bar graphs were visualized in R using the ggplot2 package.

[0079] Detailed Immunoproteomics Using the Olink Target 96 Inflammation and Olink Explore 384 Inflammation panels (Olink Proteomics, Uppsala, Sweden), 400 human proteins associated with inflammation (Table 2) were simultaneously measured. Measurements were performed using PEA technology (https: / / www.olink.com). Briefly, matched pairs of antibodies linked to unique oligonucleotides (proximity probes) bind to their respective protein targets. Hybridization and the formation of double-stranded DNA occur only when the two probes are in close proximity. Finally, the complexes were detected and quantified by quantitative real-time PCR in the Olink Target 96 Inflammation panel and by next-generation sequencing (NGS) in the Olink Explore 384 Inflammation panel. Data are presented as normalized protein expression (NPX) values, which are arbitrary units on the log2 scale of Olink Proteomics. NPX values ​​were calculated using CT values ​​(quantitative real-time PCR) and matched counts (NGS). Therefore, NPX values ​​represent relative expression, not absolute protein levels. For the proteins analyzed, all items measured by NGS were selected, and for items measured only by real-time PCR, only those with more than 50% of samples exceeding the detection limit were selected. Tukey's range test was performed using the "TukeyHSD" function of the stats (R default) package to identify differentially expressed proteins (DEPs) between groups. Volcano plots were created in R using the ggplot2 and ggrepel packages. Scatter plots and box plots were visualized in R using the ggplot2 package. Correlation tests were performed using the "cor.test" function of the stats (R default) package.

[0080] result Clinical characteristics Of the screening subjects, informed consent was obtained from 10 healthy controls and 35 ALS patients; one patient with neurological disorders was not enrolled. Five ALS patients with gastric cancer (n=1), hepatocellular carcinoma (n=1), HTLV-1 (n=1), SOD1 (n=1), and lung cancer and SOD1 (n=1) were excluded. Finally, peripheral blood samples were analyzed from 10 healthy controls, 23 non-rapid ALS patients (ΔALSFRS-R / month <1), and 7 rapid ALS patients (ΔALSFRS-R / month ≥1) (Table 3). ALS patients had a disease duration of 10.4 ± 6.4 months and a mean ALSFRS-R of 39.9 ± 5.2 points. Clinical information was compared with scRNA-seq and proteomics data (Figure 1A). Table 3. Clinical characteristics of subjects 40 [Table 3] ALS = Amyotrophic Lateral Sclerosis, ALSFRS-R = Revised Amyotrophic Lateral Sclerosis Functional Assessment Scale, FVC = Forced Life Support Capacity, FTD = Frontotemporal Dementia, SD = Standard Deviation, *p<0.05 (Non-rapid ALS vs. Rapid ALS, t-test).

[0081] ScRNA-seq analysis revealed changes in immune cells in ALS. The integrated scRNA-seq data were classified into 23 cell types (Figure 5) and selected for further analysis (Figure 1B). T helper 2 cells (Th2) were not separated as a cluster. To examine the immune profiles, we first calculated the frequency of cell types in all cells of control, non-rapid ALS, and rapid ALS (Figures 1C and 6). The number of Tregs was significantly increased in non-rapid ALS compared to control and rapid ALS (2.4% vs. 1.9%, p=0.011, and 2.4% vs. 1.6%, p=0.029, respectively; Figure 2A). Next, we calculated the frequency of relevant cell types in all CD4 T cells, e.g., naive CD4 T cells, memory CD4 T cells, Th1, Th17, or Tregs (Figure 7). The frequency of mature CD8 T cells among all CD8 T cells was significantly higher in rapid ALS than in non-rapid ALS (91.6% vs. 82.2%, p=0.042). On the other hand, the frequency of regulatory B cells among all B cells was significantly lower in rapid ALS than in non-rapid ALS (5.5% vs. 7.3%, p=0.048), and the frequencies of naive CD8 T cells and exhausted CD8 T cells among all CD8 T cells were significantly lower in rapid ALS than in non-rapid ALS (4.1% vs. 8.4%, p=0.010, and 0.6% vs. 0.9%, p=0.042, respectively) (Figure 2B). On the other hand, there was no significant difference in the frequency of Tregs between rapid ALS and non-rapid ALS among all CD4 T cells (6.8% vs. 8.5%, p=0.31; Figure 7). Furthermore, frequency ratios between functionally related immune cells were calculated (Figure 8). The ratios of memory CD4 T cells / Treg (4.9 vs. 2.9, p=0.022), Th1 / Treg (1.9 vs. 1.2, p=0.016), Th17 / Treg (1 vs. 0.6, p=0.0049), mature CD8 T cells / naive CD8 T cells (23.0 vs. 9.8, p=0.012), mature CD8 T cells / exhausted CD8 T cells (149.7 vs. 89.9, p=0.016), and mature natural killer cells / naive natural killer cells (28.1 vs. 20.1, p=0.042) were significantly higher in rapid ALS than in non-rapid ALS (Figure 2C). These results indicate that the immunological profiles of rapid and non-rapid ALS differ.

[0082] The frequency of immune cells correlated with the rate of progression. Next, the relationship between the frequency of each cell type and ΔALSFRS-R / month was examined in 30 ALS patients. There was a significant correlation between ΔALSFRS-R / month and age (Pearson correlation coefficient = 0.421, p = 0.021), and since confounding with age in routine correlation analysis was not negligible, partial correlation was used. Treg cells in all cells tended to show a negative correlation with the rate of progression, but this was not statistically significant (coefficient = -0.351, p = 0.062) (Table 4). On the other hand, the frequency of Th17 in CD4 T cells was significantly correlated with ΔALSFRS-R / month (coefficient = 0.3898, p = 0.0366; Table 5). Furthermore, the ratios of memory CD4 T cells / Treg (coefficient = 0.450, p = 0.014) and Th17 / Treg (coefficient = 0.428, p = 0.021) were significantly correlated with ΔALSFRS-R / month (Table 6). Table 4. Partial correlation analysis between the frequency of each cell type in all cells and ΔALSFRS-R / month. [Table 4] DC = dendritic cell, NK = natural killer. Table 5. Partial correlation analysis between cell type frequency in each immune cell type and ΔALSFRS-R / month. [Table 5] DC = dendritic cell, NK = natural killer. Table 6. Partial correlation analysis between frequency ratio and ΔALSFRS-R / month [Table 6] DC = dendritic cell, NK = natural killer.

[0083] There were very few genes whose expression differed in immune cells of ALS patients. The association between ALS and qualitative changes in immune cells was also investigated. To understand the overall qualitative changes in each group, DEG analysis was performed using scRNA-seq data. Differential expression analysis of scRNA-seq data was performed between ALS vs. control, non-rapid ALS vs. control, rapid ALS vs. control, and rapid ALS vs. non-rapid ALS, and then DEGs were isolated in 23 cell types (Bonferroni adjusted p<0.05, multiplicity change ≥1.2). However, few DEGs were identified in each comparison, and none were known to be immune-related genes (Table 7). Table 7. List of differentially expressed genes [Table 7] ALS = Amyotrophic Lateral Sclerosis, DC = Dendritic Cell, NK = Natural Killer.

[0084] Immune-related proteins were correlated with the rate of progression. Killer cell lectin-like receptor D1 (KLRD1; ploidy level = 2.2, p<0.001), trefoil factor 2 (TFF2; ploidy level = 2.3, p<0.001), keratin 19 (KRT19; ploidy level = 3.2, p<0.001), interleukin-17A (IL-17A; ploidy level = 4.8, p=0.007), YTH N6-methyladenosine RNA-binding protein F3 (YTHDF3; ploidy level = 2.6, p=0.025), and neutrophil cytoplasmic factor 2 (NCF2; ploidy level = 2.4, p=0.041) were identified as being significantly elevated in rapid versus non-rapid ALS. Of these, KLRD1 (p-fold change = 2.2, p<0.001), KRT19 (p-fold change = 2.7, p=0.01), NCF2 (p-fold change = 3.0, p=0.018), YTHDF3 (p-fold change = 3.0, p=0.017), and IL-17A (p-fold change = 4.0, p=0.037) were also significantly elevated in rapid ALS versus control (Figure 3A, B). pNf-H expression levels were significantly higher in ALS (non-rapid + rapid ALS) versus control (p-fold change = 180, p=0.001) and rapid ALS versus control (p-fold change = 3074, p<0.001) (Figure 3A, C), which supported the validity of our samples. On the other hand, since there was no difference in pNf-H levels between rapidly progressing ALS and non-rapid ALS, it was suggested that pNf-H is nonspecific to the progression rate in ALS. Furthermore, to investigate whether the measured proteins correlated with disease progression, partial correlation analysis was performed between serum protein levels and ΔALSFRS-R / month. 82 proteins showed significant partial correlations with ΔALSFRS-R / month (Table 8). All six DEPs that were significantly elevated in rapid ALS versus non-rapid ALS or controls showed significant partial correlations, particularly KLRD1, KRT19, and IL-17A, with coefficients greater than 0.5 and p-values ​​less than 0.003. IL-17C and IL-12 receptor subunit β1 showed less significant partial correlations (coefficient = 0.416 and p = 0.031, and coefficient = 0.383 and p = 0.049, respectively). On the other hand, IL-17D, IL-17F, and IL-17 receptor B did not show significant correlations. Table 8. Partial correlation analysis between serum proteome and ΔALSFRS-R / month [Table 8-1] [Table 8-2]

[0085] The results of scRNA-seq and serum immunoproteomics were correlated. As an integrated analysis of scRNA-seq and immunoproteomics, correlation analysis was performed between serum protein expression levels and the frequency / ratio of each cell type analyzed in scRNA-seq (Table 9). The DEP and cell type combinations that showed a significant increase in rapid ALS compared to non-rapid ALS or control with a correlation coefficient of >0.5 were NCF2-classical monocytes, TFF2-mature CD8 T cells, KLRD1-mature CD8 T cells, IL-17A-memory CD4 T cells, and IL-17A-Th17 (Figure 4). Table 9. Correlation between serum proteome and cell type in scRNA-seq. [Table 9-1] [Table 9-2] [Table 9-3] [Table 9-4] [Table 9-5]

[0086] Consideration In this study, multi-omics was applied to peripheral immunoprofiling to determine how immunological changes in ALS patients are related to disease progression rate. ScRNA-seq revealed cell type shifts in rapidly progressing ALS, including Th17 vs. Treg, mature CD8 T cells vs. naive CD8 T cells, and mature natural killer cells vs. naive natural killer cells. Serum immunoproteomics revealed significant elevations of Th17-related proteins and CD8 T cell-related proteins in rapidly progressing ALS, correlated with disease progression rate. Finally, integrated analysis of scRNA-seq and immunoproteomics showed dynamic associations between specific immune cells and proteins, such as Th17 and IL-17A, and mature CD8 T cells and KLRD1.

[0087] Th17 cells can promote immunity against many pathogens, but they can also promote inflammatory conditions in infections and autoimmune states. Limited flow cytometry studies using PBMCs have shown that ALS patients have higher frequencies of Th1 and Th17 cells, and lower frequencies of Th2 and Treg cells compared to healthy controls. However, previous studies have not clarified the relationship between Th17 cells and the rate of ALS progression. In this study, we showed that an increase in Th17 cells in CD4 T cells is associated with rapid ALS progression. We also revealed a significant correlation between the Th17-Treg ratio and the rate of progression. On the other hand, the frequency of Treg cells alone did not show a significant correlation with the rate of progression. Therefore, it is likely that a shift towards Th17 cells compared to Treg cells, rather than a simple decrease in Treg cells, is important for the rapid progression of ALS.

[0088] The frequency of Tregs was significantly reduced in rapid ALS compared to non-rapid ALS, but there was no difference between rapid ALS and the control group. Furthermore, as mentioned above, the frequency of Tregs did not show a significant correlation with the rate of progression. These results are inconsistent with the idea that a decrease in Tregs is primarily associated with rapid progression. Clinical trials of ALS using Tregs have been reported. In cases of rapid progression, IL-17C and IL-17F levels increased, and progression and cytokine levels were not suppressed by Treg administration. This suggests that Tregs have little effect on rapid progression associated with elevated IL-17 cytokine family levels.

[0089] Proteomic analysis revealed that KLRD1, TFF2, KRT19, IL-17A, YTHDF3, and NCF2 were elevated in rapid ALS versus non-rapid ALS. Most of these also correlated with the frequencies of specific cell types analyzed by scRNA-seq. Firstly, IL-17A levels correlated with the frequency of memory CD4 T cells and Th17 cells, suggesting that Th17 cells, via their primary effector IL-17A, actually influence disease progression. While elevated Th17 or IL-17A levels in serum or CSF have been reported, their association with rapid progression remained unclear. In this study, comprehensive analysis using scRNA-seq and immunoproteomics successfully linked Th17 and IL-17A (but not IL-17F) to rapid ALS progression. Furthermore, it is noteworthy that the IL-17A signaling pathway has been reported to be associated with the regulation of KRT19 in breast cancer development.

[0090] Secondly, KLRD1 levels showed the highest partial correlation coefficient with progression rate among 400 immunoproteins and correlated with the frequency of mature CD8 T cells. This result is consistent with the increased frequency of mature CD8 T cells in rapidly progressing ALS. Similarly, KLRD1 has been reported as one of the genes that is expressed differently in activated CD8 T cells in CSF of ALS patients versus controls. TFF2 levels also correlated with the frequency of mature CD8 T cells. Previous studies have shown an increase in CD8 T cells in ALS patients, and that a higher proportion of CD8 T cells is associated with a higher risk of death in ALS, but the relationship between CD8 T cells and ALS progression remained unclear. In light of this situation, it is desirable to investigate the relationship between autoreactive, clonally expanded, terminally differentiated effector memory (T) cells in the blood and CNS. EMRAIt is noteworthy that CD8 T cells are associated with mouse and human ALS4 caused by mutations in the senataxin gene (SETX). Taken together, these findings suggest that specific types of CD8 T cells may be involved in the progression or pathophysiology of ALS, and further research is needed.

[0091] Thirdly, NCF2 levels correlated with the frequency of classical monocytes. NCF2 is involved in superoxide synthesis in neutrophils and has been reported as one of the ferroptosis and iron metabolism-related genes that are expressed differently in ALS and controls. Regarding monocytes, no association was shown between monocytes and disease progression rate, but an elevated ratio of classical to non-classical monocytes has been reported in ALS patients.

[0092] Several limitations of this study are noted. First, although patients were recruited from various regions of Japan, participants were recruited at a single center. The number of participants was relatively small, but comparable to other recent scRNA-seq studies. Second, patients without a family history of ALS at enrollment were classified as sporadic, and genetic testing was not always performed. As a result, two patients were found to have SOD1-ALS and were excluded from the analysis because they were from the same family. On the other hand, the likelihood of C9orf72-ALS was extremely low in the Japanese cases. Third, rapid ALS was associated with shorter disease duration and lower ALSFRS-R scores at enrollment compared to non-rapid ALS. The association between rapid progression in a short period and more severe symptoms was reasonable, but these may be confounding factors. Fourth, related to the above issues, long-term evaluation was outside the scope of this study. Additional evaluations at different stages would provide a clearer picture of the temporal and progressive changes in the immune profile in ALS.

[0093] In summary, multi-omics immunoproteomics based on scRNA-seq and PEA demonstrated a clear association between rapid disease progression in ALS patients and increased levels of Th17 vs. Treg, mature CD8 T cells vs. naive CD8 T cells, IL-17A-related proteins, and CD8 T cell-related proteins in peripheral blood. Correspondence between specific cell types and associated immunoproteins was also demonstrated. These results may provide promising targets and biomarkers for disease-modifying therapies in ALS. [Industrial applicability]

[0094] The present invention provides biomarkers for use in determining or diagnosing the progression rate or likelihood of developing amyotrophic lateral sclerosis (ALS), and / or in selecting or predicting therapeutic agents. The present invention is applicable in the medical field and other areas.

Claims

1. A biomarker for use in determining or diagnosing the progression rate or likelihood of developing amyotrophic lateral sclerosis (ALS), and / or in selecting or predicting therapeutic agents, The biomarker is a biomarker containing KLRD1.

2. The biomarker according to claim 1 for use in determining or diagnosing rapidly progressing amyotrophic lateral sclerosis (ALS).

3. The biomarker according to claim 1, wherein the biomarker comprises a combination of KLRD1 and mature CD8T.

4. A method for determining or diagnosing the progression rate or likelihood of developing amyotrophic lateral sclerosis (ALS) in a subject, To determine the level of the biomarker described in any one of claims 1 to 3 in the sample derived from the subject, and To determine or diagnose the rate of progression or likelihood of developing ALS based on the reference level of the biomarker. Methods that include...

5. The method according to claim 4, wherein the reference level is the level of the biomarker in a sample derived from a subject without ALS, a sample derived from a subject with ALS, a sample derived from a subject with ALS progressing at a slow rate, or a sample derived from a subject with ALS progressing at a rapid rate.

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