Biomarker 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

Biomarkers associated with amyotrophic lateral sclerosis (ALS)

[0001] The present invention relates to biomarkers associated with amyotrophic lateral sclerosis (ALS) or uses thereof or methods of use thereof.

[0002] Amyotrophic lateral sclerosis (ALS) is a disease in which abnormalities occur in neurons, resulting in muscle atrophy throughout the body (Non-Patent Document 1: https: / / www.nhk.or.jp / kenko / atc_762.html). ALS is a neurodegenerative disease that damages motor neurons (nerve cells), leading to progressive muscle atrophy and muscle weakness. Approximately 2 to 5 years after onset of ALS, patients require artificial respiration or die. Furthermore, in Japan, the prevalence of ALS is 5 to 7 cases per 100,000, with approximately 10,000 patients. Furthermore, ALS is known to be sporadic in approximately 90% of cases and familial in approximately 10% (e.g., due to SOD1 mutations).

[0003] The revised ALS Functional Rating Scale (ALSFRS-R) is known to be used to evaluate ALS symptoms. The ALSFRS-R is an assessment scale created to understand the daily lives of ALS patients. The ALSFRS-R consists of 12 items (5-point scale from 0 to 4), including language, swallowing, daily activities, and walking, and is evaluated using a total score (0 to 48). The ALSFRS-R is used as an inclusion criterion and primary endpoint for clinical trials.

[0004] NHK Health Channel, "Early Symptoms and Complications of ALS (Amyotrophic Lateral Sclerosis)," URL: https: / / www.nhk.or.jp / kenko / atc_762.html

[0005] In ALS, the immunological profile of peripheral blood remains largely unknown. In particular, the profile associated with the rate of symptom progression has not been fully elucidated. If the immunological profile of peripheral blood were clarified, it would be useful for diagnosis, patient stratification in clinical trials, and as a surrogate marker for therapeutic intervention. Therefore, there is a need for a biomarker that can determine the rate of progression or the likelihood of developing ALS.

[0006] The present inventors have conducted extensive research to solve the above-mentioned problems, and have discovered a biomarker associated with amyotrophic lateral sclerosis (ALS) or a use thereof (e.g., use for determining or diagnosing the rate of progression of or likelihood of developing amyotrophic lateral sclerosis (ALS) in a subject), or a method of use thereof (e.g., a method for determining or diagnosing the rate of progression of or likelihood of developing amyotrophic lateral sclerosis (ALS) in a subject).

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

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

[0009] That is, 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 a therapeutic agent for amyotrophic lateral sclerosis (ALS), wherein the biomarker is selected from the group consisting of IL-17A, KLRD1, KRT19, NCF2, TFF2, YTHDF3, Th17, regulatory T cells (Tregs), mature CD8T, naive CD8T, exhausted CD8T, classical monocytes, memory CD4T, Th17 / Tregs, mature CD8T / naive CD8T, and mature CD8T / exhausted CD8T. (2) The biomarker according to (1) for use in determining or diagnosing amyotrophic lateral sclerosis (ALS) with a rapid progression rate. (3) The biomarker according to (1), wherein the biomarker is selected from the combinations consisting of IL-17A and Th17, IL-17A and memory CD4T, KLRD1 and mature CD8T, TFF2 and mature CD8T, and NCF2 and classical monocytes. (4) A method for determining or diagnosing the progression rate or likelihood of amyotrophic lateral sclerosis (ALS) in a subject, comprising determining the level of the biomarker according to any one of (1) to (3) in a sample derived from the subject. (5) The method according to (4), further comprising determining or diagnosing the progression rate or likelihood of ALS based on a reference level of the biomarker. (6) The method of (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 non-rapid progression rate, or a sample from a subject with ALS that has a rapid progression rate.

[0010] The present invention provides a biomarker associated with amyotrophic lateral sclerosis (ALS), or a use thereof or a method for using the same. The present invention provides 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 a therapeutic agent for amyotrophic lateral sclerosis (ALS). The present invention provides 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 from the subject.

[0011] Study design and cell type profiling in single-cell RNA sequencing. A) Overview 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 amyotrophic lateral sclerosis (ALS) patients. B) Uniform manifold approximation and projection (UMAP) showing the 23 identified cell types. C) Cell type frequency for each sample. Frequency is expressed as the percentage of each cell type, with the total number of cells in each sample taken as 100%. DC = dendritic cell, NK = natural killer. Comparison of cell type frequency between groups. A) Frequency of regulatory T cells among all cells. B) Frequency of cell types in specific cell groups. C) Ratio of cell types compared to related cell types. They showed significant differences in rapid amyotrophic lateral sclerosis (ALS) compared to non-rapid ALS by Tukey HSD test. The values ​​indicate the median for each group. Lines and asterisks indicate significant combinations by Tukey HSD test (*p<0.05, **p<0.005, and ***p<0.0005). Breg = regulatory B cells; NK = natural killer; Th1 = T helper 1 cells; Th17 = T helper 17 cells; Treg = regulatory T cells. Serum immunoproteomics. A) -log differential expression analysis between each combination １０ P-value and log ２A) Volcano plot showing fold changes. Red dots are differentially expressed proteins (DEPs) and protein names are shown. B) Violin plot showing the expression levels of six proteins isolated as DEPs in rapid amyotrophic lateral sclerosis (ALS) compared with non-rapid ALS. Numbers indicate median values ​​for each group. Lines and asterisks indicate significant combinations by 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. Correlation diagram between serum immune proteins 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-seq analysis using Pearson's correlation coefficient. Values ​​indicate correlation coefficients using 40 amyotrophic lateral sclerosis patient and healthy control samples. Annotation of each cluster by cell type marker. Annotation of each cluster by cell type marker (continued from Figure 5-1). Intergroup comparison of the frequency of each cell type in all cells. Intergroup comparison of the frequency of each cell type in each immune cell. Intergroup comparison of frequency ratios between cells.

[0012]

[0023] Unless otherwise defined, all technical and scientific terms used herein 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 herein can be used to practice or test the present invention. Unless otherwise indicated, the practice of the present invention may employ conventional methods within the skill of the art, such as chemistry, biochemistry, molecular biology, cell biology, genetics, immunology, and pharmacology.

[0013] As used herein, "subject" and "patient" include a human or any non-human animal. "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)" refers to a neurodegenerative disease that damages motor neurons (nerve cells), and that progresses with muscle atrophy and muscle weakness. As used herein, ALS includes ALS with a "rapid rate of progression" and ALS with a "non-rapid rate of progression." The "rate of progression" of ALS includes both a "rapid rate of progression" of ALS and a "non-rapid rate of progression" of ALS.

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

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

[0018] As used herein, "possibility of having" refers to the possibility of having ALS or the possibility of developing ALS. "Possibility of having" includes high "possibility of having" and low "possibility of having." A high "possibility of having" refers to a high possibility of having ALS or developing ALS compared to a subject who does not have ALS, a subject who does not develop ALS, or a healthy control. A low "possibility of having" refers to a low or similar possibility of having ALS or developing ALS compared to a subject who does not have ALS, a subject who does not develop ALS, or a healthy control.

[0019] As used herein, the term "therapeutic agent" includes any drug used to treat, prevent, or diagnose ALS. The therapeutic agent may be a known drug for treating, preventing, or diagnosing ALS, or an unknown drug for treating, preventing, or diagnosing ALS.

[0020] As used herein, "biomarker" refers to an indicator associated with ALS, such as an indicator for use in determining or diagnosing the rate of progression or likelihood of ALS, and / or in selecting or predicting a therapeutic agent. As used herein, biomarker includes 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 described, for example, in Table 2, Table 4, Table 5, Table 6, Table 7, Table 8 (Table 8-1, Table 8-2), Table 9 (Table 9-1, Table 9-2, Table 9-3, Table 9-4, Table 9-5), Figure 2, Figure 3, Figure 4, Figure 5 (Figure 5-1, Figure 5-2), Figure 6, Figure 7, and Figure 8. Biomarkers include biomarkers associated with ALS, ALS biomarkers, for example, biomarkers for ALS with a rapid rate of progression and / or biomarkers for ALS with a non-rapid rate of progression.

[0022] A biomarker may be a single molecule or multiple molecules. When a biomarker is multiple molecules, the biomarker may be an indication of the relationship (e.g., ratio) between the molecules. Herein, the description "single molecule A / single molecule B" of multiple molecules indicates the ratio of single molecule A to single molecule B. For example, Th17 / Treg represents the ratio of Th17 and Treg levels by dividing the Th17 level by the Treg level.

[0023] Biomarkers may be used singly or in combination with multiple (e.g., 2, 3, 4, 5, 6, 7, 8, or 9) biomarkers. For example, the use of multiple biomarkers may provide a more accurate or more precise indication associated with ALS than the use of a single biomarker.

[0024] As used herein, the "level" of a biomarker refers to the concentration, expression level, and / or activity level of the biomarker, etc. Those skilled in the art can determine the level of a biomarker as appropriate.

[0025] As used herein, "IL-17A" refers to interleukin-17A.

[0026] As used herein, "KLRD1" refers to killer cell lectin-like receptor D1.

[0027] As used herein, "KRT19" refers to keratin 19.

[0028] As used herein, "NCF2" refers to neutrophil cytosolic factor 2.

[0029] As used herein, "TFF2" refers to trefoil factor 2.

[0030] As used herein, "YTHDF3" refers to YTH N6-methyladenosine RNA binding protein F3.

[0031] As used herein, "Th17" refers to helper T17 cells.

[0032] As used herein, "Treg" refers to regulatory T cells.

[0033] As used herein, "mature CD8 T" refers to mature CD8-positive T cells.

[0034] As used herein, "naive CD8 T" refers to naive CD8-positive T cells.

[0035] As used herein, "exhausted CD8 T" refers to exhausted CD8-positive T cells.

[0036] As used herein, "Classical monocyte" refers to a classical monocyte.

[0037] As used herein, "memory CD4 T" refers to memory CD4-positive T cells.

[0038] As used herein, a "sample" can be obtained from a subject or patient. A sample can be obtained from any source known in the art, including, but not limited to, blood, whole blood, serum, plasma, urine, interstitial fluid, tears, saliva, or skin.

[0039] As used herein, the "reference level" of a biomarker refers to 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 non-rapid progression rate, or a sample from a subject with ALS that has a rapid progression rate. Those skilled in the art can appropriately select and determine the reference level of the biomarker.

[0040] Herein, the subject or patient from whom the sample is obtained may be the same as or different from the subject from whom the sample providing the reference level is obtained.

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

[0042] In one aspect, the present invention is based on the discovery that (i) the level of a biomarker in a sample obtained from a subject with ALS that has a rapid rate of progression, (ii) the level of the same biomarker in a sample obtained from a subject with ALS that has a non-rapid rate of progression, and (iii) the level of the same biomarker in a sample obtained from a subject without ALS, are altered.

[0043] In one aspect, the present invention is based on the discovery that (i) the level of a biomarker in a sample obtained from a subject with ALS that has a rapid rate of progression is altered relative to (ii) the level of the same biomarker in a sample obtained from a subject with ALS that has a non-rapid rate of progression and / or (iii) the level of the same biomarker in a sample obtained from a subject without ALS.

[0044] In one aspect, the invention is based on the discovery that (ii) the level of a biomarker in a sample obtained from a subject with ALS that has a non-rapid rate of progression is altered relative to (i) the level of the same biomarker in a sample obtained from a subject with ALS that has a rapid rate of progression and / or (iii) the level of the same biomarker in a sample obtained from a subject without ALS.

[0045] In one aspect, the present invention is based on the discovery that (i) the level of a biomarker in a sample obtained from a subject with ALS that has a rapid rate of progression and / or (ii) the level of the same biomarker in a sample obtained from a subject with ALS that has a non-rapid rate of progression is altered compared to (iii) the level of the same biomarker in a sample obtained from a subject without ALS.

[0046] In one aspect, the invention provides a biomarker in which (i) the level of the biomarker in a sample obtained from a subject with ALS having a rapid rate of progression is higher or lower than (ii) the level of the same biomarker in a sample obtained from a subject with ALS having a non-rapid rate of progression and / or (iii) the level of the same biomarker in a sample obtained from a subject without ALS.

[0047] In one aspect, the invention provides a biomarker in which (ii) the level of the biomarker in a sample obtained from a subject with ALS having a non-rapid rate of progression is higher or lower than (i) the level of the same biomarker in a sample obtained from a subject with ALS having a rapid rate of progression and / or (iii) the level of the same biomarker in a sample obtained from a subject without ALS.

[0048] In one aspect, the invention provides a biomarker in which (i) the level of the biomarker in a sample obtained from a subject with ALS that has a rapid rate of progression and / or (ii) the level of the same biomarker in a sample obtained from a subject with ALS that has a non-rapid rate of progression is higher or lower than (iii) the level of the same biomarker in a sample obtained from a subject without ALS.

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

[0050] In one aspect, a therapeutic agent that (i) reduces or increases the level of a biomarker in a sample obtained from a subject with ALS with a rapid rate of progression relative to (ii) the level of the same biomarker in a sample obtained from a subject with ALS with a non-rapid rate of progression 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 potential or effective therapeutic agent for ALS with a rapid rate of progression.

[0051] In one aspect, a therapeutic agent that reduces or increases, respectively, (ii) the level of a biomarker in a sample obtained from a subject with ALS having a non-rapid progression rate, such that the level of the biomarker is higher or lower than (i) the level of the same biomarker in a sample obtained from a subject with ALS having a rapid progression rate 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 potential or effective therapeutic agent for ALS having a rapid progression rate.

[0052] In one aspect, a therapeutic agent that reduces or increases, respectively, (i) the level of a biomarker in a sample obtained from a subject with ALS with a rapid rate of progression and / or (ii) the level of the same biomarker in a sample obtained from a subject with ALS with a non-rapid rate of progression, relative to (iii) the level of the same biomarker in a sample obtained from a subject without ALS, can be selected or predicted as a potential or effective therapeutic agent for rapid and / or non-rapid rate of progression ALS.

[0053] In one embodiment, when the biomarkers of the present invention are used to select or predict therapeutic agents for ALS, the candidate therapeutic agents for ALS can be mixed or contacted with the biomarkers of the present invention.The candidate therapeutic agents for ALS can be drugs that reduce or increase the level of the biomarkers of the present invention.The candidate therapeutic agents for ALS can be tested or confirmed to reduce or increase the level of the biomarkers of the present invention.

[0054] In one aspect, the biomarkers of the present invention can be used to determine or predict the efficacy or performance of therapeutic agents for treating ALS.

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

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

[0057] In one aspect, the biomarkers of the present invention allow for the determination or diagnosis of the progression rate or likelihood of ALS at an early stage. ALS patients are generally treated after a 12-week observation period. However, the biomarkers of the present invention allow for the determination or diagnosis of the progression rate or likelihood of ALS at an early stage (before the 12-week observation period). The determination or diagnosis of the progression rate or likelihood of ALS at an early stage can enable early treatment of ALS and help delay the progression of ALS.

[0058] In one aspect, a use or method of use of a biomarker of the invention is a method for determining or diagnosing the rate of progression of or likelihood of developing amyotrophic lateral sclerosis (ALS) in a subject, comprising determining the level of a biomarker of the invention in a sample from said subject.

[0059] In one aspect, the use or method of using the biomarkers of the invention includes determining or diagnosing the rate of progression or likelihood of developing ALS based on or compared to a reference level of the biomarker.

[0060] In one aspect, the use or method of using the biomarkers of the invention includes determining or diagnosing the rate of progression (rapid or non-rapid) or likelihood of ALS (high likelihood or low likelihood) based on an increase or decrease in the level of the biomarker in a sample relative to a reference level.

[0061] In one aspect, the present invention provides a method for determining or diagnosing the rate of progression or likelihood of amyotrophic lateral sclerosis (ALS) in a subject, the method comprising: determining the level of a biomarker in a sample from the subject; comparing said level to a reference level of said biomarker; and determining or diagnosing the rate of progression (rapid or non-rapid) or likelihood of ALS (high likelihood or low likelihood) based on an increase or decrease in the level of the biomarker in the sample relative to the reference level.

[0062] In one embodiment, the use or method of using the biomarkers of the invention comprises obtaining or providing a sample derived from a subject.

[0063] In one aspect, the use or method of using the biomarkers of the invention includes determining a reference level, e.g., by determining 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 non-rapid rate of progression, or a sample from a subject with ALS that has a rapid rate of progression.

[0064] In one embodiment, the use or method of using the biomarkers of the present invention may comprise comparing the level of the biomarker in a sample from a subject to the level of the biomarker in a sample from said subject at a different time (e.g., one day, one week, one month, one year ago, etc.).

[0065] In one embodiment, the reference level may be predetermined (previously) determined.

[0066] In one aspect, the invention relates to a composition 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 agents, the composition comprising a biomarker of the invention.

[0067] In one aspect, the present invention relates to a method for treating amyotrophic lateral sclerosis (ALS), comprising administering to a patient a therapeutic agent for ALS selected from the above-described aspects. 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, such as an antibody against IL-17A or an IL-17 receptor.

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

[0069] The present invention will be explained in more detail by showing the following examples, but it should not be construed that the scope of the present invention is limited to these examples.

[0070] Methods: Patients and healthy controls. 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 those with definite, probable, laboratory-supported probable, or possible status according to the updated Awaji criteria, or those diagnosed with ALS according to the Gold Coast criteria, with onset of disease within 2 years. Patients and healthy controls were matched for age and sex. Exclusion criteria for ALS patients and healthy controls are listed in Table 1. Patients found to have known ALS pathogenic mutations, such as SOD1, were excluded from the analysis in advance because their immunological profile differs from that of sporadic ALS. Rapid ALS was defined as a decline of 1.0 points / month or more on the revised ALS Functional Rating Scale (ALSFRS-R) (ΔALSFRS-R / month ≥ 1), and non-rapid ALS was defined as a decline of less than 1.0 points / month (ΔALSFRS-R / month < 1). All clinical information was collected after obtaining written informed consent from patients. All study plans were approved by the Tokushima University Hospital Life Science and Medical Research Ethics Review Committee (No. 3682). The study was conducted in accordance with the principles of the Declaration of Helsinki. Table 1. Exclusion Criteria

[0071] Sample preparation: Peripheral blood samples were collected from healthy donors and ALS patients at Tokushima University Hospital. PBMCs were cultured on Ficoll-Hypaque (Lymphoprep TM , Serumwerk Bernburg AG, Germany) by density centrifugation, washed with phosphate-buffered saline (PBS), and then cultured in X-VIVO containing 5% FBS. TM The PBMCs were resuspended in medium (LONZA, Basel, Switzerland). PBMCs were stored at -80°C until library preparation in 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, aliquoted, 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 assessed. 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: 400 human proteins related to inflammation (Table 2) were simultaneously measured 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 tests were performed using the "cor.test" function in the stats package in R. Detailed methods are described below. Table 2. List of measured proteins* *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 was performed using Microsoft R open software (version 4.0.2). DEGs in scRNA-seq were identified using the nonparametric Wilcoxon rank-sum test, with Bonferroni's test used to correct for multiple testing. Differences between groups in the frequency of each cell type and in the serum proteome were identified using Tukey's range test to account for multiple testing. Correlation tests were performed using Pearson's correlation coefficient.

[0076] Detailed single-cell RNA sequencing (scRNA-seq) PBMC preparation, library preparation, and sequencing. Cryopreserved human peripheral blood mononuclear cells (PBMCs) were quickly lysed in 37°C water and washed with 1x D-PBS (Mg / Ca-free) containing 1% BSA. Cell harvesting was performed at 20°C, 250 x g, and 10 minutes in a swing-out rotor. After three washes, cells were resuspended in an appropriate volume of D-PBS / 1% BSA and strained through a 40 μm Flowmi Cell Strainer to remove any remaining large particles. Cell count and viability were determined 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 GEM generation chips using a 10x Genomics Chromium controller, and DNA libraries were prepared using the 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 using a 4200 TapeStation D1000 ScreenTape (Agilent, Santa Clara, CA) and Qubit dsDNA Assay (Thermo Fisher Scientific, Waltham, MA) before sequencing. Gene expression libraries were sequenced at a depth of 50,000 reads per cell using a DNBSEQ-G400 platform (MGI Tech, Shenzhen, China).

[0077] Data Normalization. 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. FASTQ data were filtered and mapped to the GRCh38 reference genome. Secondary analysis was performed in R version 4.0.2 using the Seurat version 3 package. Data from low-quality cells and doublet cells were filtered using the Seurat and DoubletFinder packages. These counts were normalized using a global scaling normalization method with the Seurat function "LogNormalize" and log-transformed with the Seurat function "NormalizeData." Variable features were selected by direct modeling of the intrinsic mean-variance relationship using the Seurat function "FindVariableFeatures." These features were then used to select integration features using the Seurat function "SelectIntegrationFeatures." The integration features were subjected to a linear transformation and used for principal component analysis (PCA) using the Seurat functions "ScaleData" and "RunPCA," respectively. Using reciprocal PCA, the top 50 principal components were used for anchor identification. The anchors were then used to integrate the datasets using the function "IntegrateData" in Seurat.

[0078] Analysis: The combined normalized data underwent linear transformation and PCA using the Seurat functions "ScaleData" and "RunPCA." Significant principal components were determined using elbow plots, which plotted the standard deviations of the principal components, and were then used to visualize the Uniform Manifold Approximation and Projection (UMAP) using the Seurat functions "ElbowPlot" and "RunUMAP." Significant principal components were also used to identify cell types. First, the Seurat function "FindNeighbors" was used to calculate the similarity between each cell using a KNN graph based on the Euclidean distance in PCA space and the edge weights between any two cells based on shared overlap in the local neighborhood. Next, cells were divided into clusters by clustering the similarities between cells using the Louvain algorithm and the Seurat function "FindClusters." Finally, the cell types in each cluster were identified by examining the cell type-specific marker expression profiles in each cluster. Differentially expressed genes (DEGs) between groups in each cell type were identified using the nonparametric Wilcoxon rank-sum test with the Seurat function "FindMarkers." Gene expression levels in each cluster were visualized in a violin plot using 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 in the stats (R default) package. Correlation tests were performed using the "cor.test" function in the stats (R default) package. Scatter plots, boxplots, and bar graphs were visualized in R using the ggplot2 package.

[0079] Detailed immunoproteomics: 400 human proteins related to inflammation (Table 2) were simultaneously measured using the Olink Target 96 Inflammation and Olink Explore 384 Inflammation panels (Olink Proteomics, Uppsala, Sweden). Measurements were based on PEA technology (https: / / www.olink.com). Briefly, matched pairs of antibodies linked to unique oligonucleotides (proximity probes) bind to their respective protein targets. Only when the two probes are in close proximity do they hybridize to each other, forming double-stranded DNA. Finally, complexes were detected and quantified by quantitative real-time PCR for the Olink Target 96 Inflammation panel and by next-generation sequencing (NGS) for 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 proteins analyzed, all proteins measured by NGS were selected, and for proteins measured only by real-time PCR, only those with 50% or more samples above the detection limit were selected. A Tukey range test was performed using the "TukeyHSD" function in 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 boxplots were visualized in R using the ggplot2 package. Correlation tests were performed using the "cor.test" function in the stats (R default) package.

[0080] Results: Clinical Characteristics. Among the subjects screened, 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 from 10 healthy controls, 23 non-rapid-onset ALS patients (ΔALSFRS-R / month < 1), and 7 rapid-onset ALS patients (ΔALSFRS-R / month ≥ 1) were analyzed (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 40 Subjects. ALS = amyotrophic lateral sclerosis, ALSFRS-R = Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised, FVC = Forced Vital Capacity, FTD = Frontotemporal Dementia, SD = Standard Deviation, *p<0.05 (non-accelerated ALS vs. accelerated ALS, t-test).

[0081] scRNA-seq analysis revealed immune cell alterations in ALS. The combined scRNA-seq data were categorized into 23 cell types (Figure 5) and selected for further analysis (Figure 1B). T helper 2 cells (Th2) were not isolated as a cluster. To examine the immune profile, we first calculated the frequency of cell types in total CD4 T cells in control, non-rapid ALS, and rapid ALS patients (Figure 1C and Figure 6). The number of Tregs was significantly increased in non-rapid ALS patients compared with control and rapid ALS patients (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 total CD4 T cells, such as naive CD4 T cells, memory CD4 T cells, Th1, Th17, or Tregs (Figure 7). The frequency of mature CD8 T cells among total CD8 T cells was significantly higher in rapid ALS than in non-rapid ALS (91.6% vs. 82.2%, p = 0.042). Meanwhile, the frequency of regulatory B cells among total B cells was significantly lower in rapid ALS than in non-rapid ALS (5.5% vs. 7.3%, p = 0.048). The frequencies of naive and exhausted CD8 T cells among total 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). Meanwhile, there was no significant difference in the frequency of Tregs among total CD4 T cells between rapid ALS and non-rapid ALS (6.8% vs. 8.5%, p = 0.31; Figure 7). Furthermore, the frequency ratios of functionally relevant immune cells were calculated (Figure 8). The ratios of memory CD4 T cells / Tregs (4.9 vs. 2.9, p = 0.022), Th1 / Tregs (1.9 vs. 1.2, p = 0.016), Th17 / Tregs (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 NK cells / naive NK cells (28.1 vs. 20.1, p = 0.042) were significantly higher in rapid-onset ALS than in non-rapid-onset ALS (Figure 2C).These results indicated that the immunological profiles of rapid and non-rapid ALS are different.

[0082] The frequency of immune cells correlated with progression rate. Next, we examined the relationship between the frequency of each cell type and ΔALSFRS-R / month in 30 ALS patients. There was a significant correlation between ΔALSFRS-R / month and age (Pearson's correlation coefficient = 0.421, p = 0.021). Because age-related confounding is not negligible in routine correlation analyses, partial correlations were used. Among total cells, Tregs tended to show a negative correlation with progression rate, but this correlation was not significant (coefficient = -0.351, p = 0.062) (Table 4). Meanwhile, the frequency of Th17 cells among 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 / Tregs (coefficient = 0.450, p = 0.014) and Th17 / Tregs (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 total cells and ΔALSFRS-R / month. DC = dendritic cell, NK = natural killer. Table 5. Partial correlation analysis between cell type frequency in each immune cell type and ΔALSFRS-R / month. DC = dendritic cell, NK = natural killer. Table 6. Partial correlation analysis between frequency ratio and ΔALSFRS-R / month. DC = dendritic cell, NK = natural killer.

[0083] Few genes were differentially expressed in immune cells with ALS. We also investigated the relationship between ALS and qualitative changes in immune cells. To understand the overall qualitative changes in each group, we performed DEG analysis using scRNA-seq data. We performed differential expression analysis of scRNA-seq data between ALS vs. control, non-rapid ALS vs. control, rapid ALS vs. control, and rapid ALS vs. non-rapid ALS, and then isolated DEGs in 23 cell types (Bonferroni-adjusted p<0.05, fold change ≥1.2). However, only a small number of DEGs were identified in each comparison, and none were known to be immune-related genes (Table 7). Table 7. List of the number of differentially expressed genes. ALS = amyotrophic lateral sclerosis, DC = dendritic cell, NK = natural killer.

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

[0085] The results of scRNA-seq and serum immunoproteomics were correlated. As an integrated analysis of scRNA-seq and immunoproteomics, a correlation analysis was performed between serum protein expression levels and the frequency / ratio of each cell type analyzed in the scRNA-seq analysis (Table 9). The DEP and cell type combinations that were significantly increased in rapid ALS compared to non-rapid ALS or controls 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 types in scRNA-seq

[0086] Discussion: In this study, we applied multi-omics to peripheral immune profiling to determine how immunological changes in ALS patients correlate with the rate of disease progression. scRNA-seq revealed a shift in cell types toward Th17 vs. Tregs, mature vs. naive CD8 T cells, and mature vs. naive natural killer cells in rapidly progressing ALS. Serum immunoproteomics revealed that Th17- and CD8 T cell-associated proteins were significantly elevated in rapidly progressing ALS and correlated with the rate of disease progression. Finally, integrated analysis of scRNA-seq and immunoproteomics revealed dynamic associations between specific immune cells and proteins, such as Th17 and IL-17A, and mature CD8 T cells and KLRD1.

[0087] Th17 can promote immunity against many pathogens but can also promote inflammatory pathology during infection and autoimmunity. A few studies using flow cytometry analysis of PBMCs have shown that ALS patients have higher frequencies of Th1 and Th17, and lower frequencies of Th2 and Treg, compared with healthy controls. However, previous studies have not elucidated the relationship between Th17 and the rate of ALS progression. Here, we demonstrate that an increase in Th17 in CD4 T cells is associated with rapid ALS progression. We also found a significant correlation between the ratio of Th17 to Treg and the rate of progression. On the other hand, the frequency of Treg alone did not show a significant correlation with the rate of progression. Therefore, a shift toward Th17 over Treg, rather than a simple decrease in Treg, may be important for rapid ALS progression.

[0088] The frequency of Tregs was significantly reduced in patients with rapid ALS versus non-rapid ALS, but not between patients with rapid ALS and controls. Furthermore, as noted above, the frequency of Tregs did not significantly correlate with the rate of progression. These results are inconsistent with the idea that the decline in Tregs is primarily related to rapid progression. The results of clinical trials of ALS using Tregs have been reported. In patients with rapid progression, IL-17C and IL-17F were elevated, and progression and cytokine levels were not suppressed by Treg administration. This suggests that Tregs have little effect on the rapid progression associated with the elevation of the IL-17 cytokine family.

[0089] Proteomic analysis revealed that KLRD1, TFF2, KRT19, IL-17A, YTHDF3, and NCF2 were upregulated in rapid versus non-rapid ALS. Most of these correlated with the frequency of specific cell types analyzed by scRNA-seq. First, IL-17A levels correlated with the frequency of memory CD4 T cells and Th17, suggesting that Th17 actually influences disease progression through its primary effector, IL-17A. Although elevated Th17 or IL-17A levels in serum or CSF have been reported, their relationship 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. More notably, the IL-17A signaling pathway has been reported to be associated with the regulation of KRT19 in breast cancer development.

[0090] Second, KLRD1 levels showed the highest partial correlation coefficient with progression rate among 400 immune proteins and correlated with the frequency of mature CD8 T cells. This result is consistent with the increased frequency of mature CD8 T cells in rapid ALS. Similarly, KLRD1 has been reported as one of the genes differentially expressed in activated CD8 T cells in the CSF of ALS patients versus controls. TFF2 levels also correlated with the frequency of mature CD8 T cells. Previous studies have shown that CD8 T cells are increased in ALS patients, and that a higher proportion of CD8 T cells is associated with a higher risk of ALS mortality. However, the relationship between CD8 T cells and ALS progression was unclear. Given this situation, it is possible that autoreactive, clonally expanded, terminally differentiated effector memory (T) cells in the blood and CNS may play a role. EMRA ) It is noteworthy that CD8 T cells are associated with ALS4 in mice and humans, which is caused by mutations in the senataxin gene (SETX). Taken together, specific types of CD8 T cells may be involved in the progression or pathophysiology of ALS, and further investigation is warranted.

[0091] Third, the NCF2 level correlated with the frequency of classical monocytes. NCF2 is involved in the synthesis of superoxide in neutrophils and has been reported as one of the ferroptosis- and iron metabolism-related genes differentially expressed in ALS and control patients. Regarding monocytes, no correlation was found between monocytes and progression rate, but the ratio of classical to non-classical monocytes has been reported to be elevated in ALS patients.

[0092] This study has several limitations. 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 necessarily performed. As a result, two patients were found to have SOD1-ALS, but because they were from the same family, they were excluded from the analysis. On the other hand, the possibility of C9orf72-ALS was extremely low among Japanese cases. Third, patients with rapid-onset ALS had a shorter disease duration and lower ALSFRS-R scores at enrollment than patients with non-rapid-onset ALS. Although the association between rapid progression over a short period and more severe symptoms is plausible, these may be confounding factors. Fourth, related to the above issues, long-term evaluation was beyond the scope of this study. Additional evaluation at different stages will shed more light on the temporal and progressive changes in immune profiles in ALS.

[0093] In summary, our multi-omics approach using scRNA-seq and PEA-based immunoproteomics demonstrated a clear association between rapid progression and increased peripheral blood Th17 vs. Treg, mature vs. naive CD8 T cells, IL-17A-associated proteins, and CD8 T cell-associated proteins in ALS patients. We also demonstrated a correlation between specific cell types and associated immune proteins. These results may provide promising targets and biomarkers for disease-modifying therapies in ALS.

[0094] The present invention provides 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 a therapeutic agent for ALS. The present invention can be used in the medical field, etc.

Claims

1. 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 agents, comprising: The biomarker comprises KLRD1, TFF2, KRT19, or YTHDF3.

2. 10. The biomarker of claim 1 for use in determining or diagnosing rapid progression rate amyotrophic lateral sclerosis (ALS).

3. 2. The biomarker of claim 1, wherein the biomarker comprises a combination of KLRD1 and mature CD8T, or a combination of TFF2 and mature CD8T.

4. 1. A method for determining or diagnosing the rate of progression of or likelihood of developing amyotrophic lateral sclerosis (ALS) in a subject, comprising: determining the level of a biomarker according to any one of claims 1 to 3 in a sample from said subject; and Determining or diagnosing the rate of progression or likelihood of developing ALS based on the reference levels of the biomarkers. A method comprising:

5. 5. The method of claim 4, 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 non-rapid rate of progression, or a sample from a subject with ALS that has a rapid rate of progression.