Biomarker for determining stress level

Abstract

Provided are: a biomarker for determining the stress level, which is for determining the stress level suffered by a subject by a non-invasive and simple method; a method for detecting the biomarker; a method for collecting data including the detection method; and a kit for determining the stress level. The above-mentioned purpose can be achieved by using, as a biomarker, sialidase activity of bacteria from a sample obtained from a subject. The sample is preferably excrement and / or the subject is preferably human.

Description

Technical Field

[0001] This invention relates to biomarkers for determining stress levels, methods for detecting the biomarkers, methods for collecting data including the detection methods, and kits for determining stress levels. Background Technology

[0002] According to a survey by the Ministry of Health, Labour and Welfare, the lifetime prevalence of depression is 3-16%. Furthermore, the World Health Organization (WHO), based on DALYs (Disability Adjusted Life Years, an indicator of disease burden), predicts that depression will become the most damaging disease to health by 2030. The diagnosis and treatment of depression is a significant issue in modern society. The causes of depression are often complex and unclear, but excessive stress is known to be a contributing factor.

[0003] The diagnosis of depression is made using existing operational diagnostic methods. Operational diagnostic methods refer to diagnostic methods that compare symptoms against diagnostic criteria and make judgments based on combinations of symptoms. As diagnostic criteria, the WHO's International Classification of Diseases, Tenth Revision (ICD-10) and the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) are used. However, there is a problem that there are no objective methods for assessing depression, depressive state, stress levels, etc.

[0004] As a numerical diagnostic method, there is optical imaging (NIRS) that can objectively examine changes in the oxidative state of hemoglobin in the brain of subjects suspected of having depression. However, it is not used as a diagnostic criterion for depression as described above, and is currently only used as an auxiliary method to differentiate between depression and bipolar disorder.

[0005] Furthermore, no objective or quantitative evaluation method has been established for stress that is known to induce depression.

[0006] To enable objective assessment of stress levels and objective diagnosis of depression, biomarkers for determining stress levels, methods for determining stress levels, biomarkers for diagnosing depression, and methods for diagnosing depression are being developed. For example, biomarkers for diagnosing depression include those using the degree of methylation of DNA collected from blood as an indicator (Patent Document 1), biomarkers using specific genes in blood as markers (Patent Document 2), and biomarkers using metabolites in blood as markers (Patent Document 3).

[0007] Existing technical documents Patent documents Patent Document 1: Japanese Patent Application Publication No. 2019-193578; Patent Document 2: Japanese Patent Application Publication No. 2017-000063; Patent document 3: International Publication No. 2017 / 082103. Summary of the Invention

[0008] However, the following problems exist: these diagnostic markers or methods have not yet been used clinically, and the collection of diagnostic samples requires invasive and professional intervention; the diagnostic process requires professional analytical techniques; the analysis requires expensive and professional equipment; and obtaining diagnostic results takes time.

[0009] The technical problem to be solved by the present invention is to provide a biomarker for determining stress level, a method for determining stress level, and a reagent kit for determining stress level using a non-invasive and simple method.

[0010] Through in-depth research, the inventors of this application discovered that the sialidase activity of bacteria in samples obtained from the test subject is related to the stress of the test subject, and that the composition of bacterial species in the sialic acid-containing bacterial community in the sample is related to the stress of the test subject, thereby completing this invention.

[0011] The present invention has the following aspects.

[0012] [1-1] A biomarker for determining the stress level experienced by a subject, wherein the biomarker is sialidase activity in a sample obtained from the subject.

[0013] [1-2] The biomarker according to [1-1], wherein the biomarker is the sialidase activity of bacteria from a sample obtained from the subject.

[0014] [1-3] The biomarker according to [1-2], wherein the sample is feces.

[0015] [1-4] The biomarker according to any one of [1-1] to [1-3], wherein the subject is a human.

[0016] [1-5] The biomarker according to any one of [1-1] to [1-4], wherein the bacteria include bacteria of the class Clostridium.

[0017] [1-6] A method for detecting a biomarker, wherein the biomarker described in any one of [1-1] to [1-5] is detected in a sample obtained from a subject.

[0018] [1-7] A method for collecting data, the data being used to determine the stress level experienced by a subject, wherein the method includes the detection method described in [1-6].

[0019] [1-8] A kit for determining the stress level experienced by a subject, wherein the kit includes a unit for detecting a biomarker as described in any one of [1-1] to [1-5].

[0020] [1-9] The use of sialidase activity in samples obtained from subjects as a biomarker for determining the stress level experienced by subjects.

[0021] [1-10] Use of sialidase activity of bacteria from samples obtained from subjects as a biomarker for determining the level of stress experienced by subjects.

[0022] [1-11] Used as described in [1-9] or [1-10], wherein the sample is feces.

[0023] [1-12] A method for determining the stress level experienced by a subject, comprising the data collection method described in [1-7].

[0024] [2-1] A biomarker for diagnosing depression in a subject, wherein the biomarker is sialidase activity in a sample obtained from the subject.

[0025] [2-2] The biomarker according to [2-1], wherein the biomarker is the sialidase activity of bacteria from a sample obtained from the subject.

[0026] [2-3] The biomarker according to [2-2], wherein the sample is feces.

[0027] [2-4] The biomarker according to any one of [2-1] to [2-3], wherein the subject is a human.

[0028] [2-5] The biomarker according to any one of [2-1] to [2-4], wherein the bacteria include bacteria of the class Clostridium.

[0029] [2-6] A method for detecting a biomarker, wherein the biomarker described in any one of [2-1] to [2-5] is detected in a sample obtained from a subject.

[0030] [2-7] A method for collecting data, said data being used to diagnose a subject's depression, wherein the method includes the detection method described in [2-6].

[0031] [2-8] A kit for diagnosing depression in a subject, wherein the kit includes a unit for detecting a biomarker as described in any one of [2-1] to [2-5].

[0032] [2-9] Use of sialidase activity in samples obtained from subjects as a biomarker for diagnosing depression in subjects.

[0033] [2-10] Use of sialidase activity of bacteria from samples obtained from subjects as a biomarker for diagnosing depression in subjects.

[0034] [2-11] Used as described in [2-9] or [2-10], wherein the sample is feces.

[0035] [2-12] A method for diagnosing depression in a subject, comprising the data collection method described in [2-7].

[0036] [2-13] A diagnostic method for depression in a subject, comprising the data collection method described in [2-7].

[0037] [3-1] A biomarker for evaluating the effect of an antidepressant in a subject, wherein the biomarker is sialidase activity in a sample obtained from the subject.

[0038] [3-2] According to the biomarker described in [3-1], the biomarker is the sialidase activity of bacteria from a sample obtained from the subject.

[0039] [3-3] The biomarker according to [3-1] or [3-2], wherein the sample is feces.

[0040] [3-4] The biomarker according to any one of [3-1] to [3-3], wherein the subject is a human.

[0041] [3-5] The biomarker according to any one of [3-1] to [3-4], wherein the bacteria include bacteria of the class Clostridium.

[0042] [3-6] A method for detecting a biomarker, wherein the biomarker described in any one of [3-1] to [3-5] is detected in a sample obtained from a subject.

[0043] [3-7] A method for collecting data used to evaluate the effect of antidepressants on subjects, wherein the method includes the detection method described in [3-6].

[0044] [3-8] A kit for evaluating the effect of an antidepressant in a subject, wherein the kit includes a unit for detecting a biomarker as described in any one of [3-1] to [3-5].

[0045] [3-9] Use of sialidase activity in samples obtained from subjects as a biomarker for evaluating the effect of antidepressants in subjects.

[0046] [3-10] Use of sialidase activity of bacteria from samples obtained from subjects as a biomarker for evaluating the effect of antidepressants in subjects.

[0047] [3-11] Used as described in [3-9] or [3-10], wherein the sample is feces.

[0048] [3-12] A method for evaluating the effect of an antidepressant in a subject, comprising the data collection method described in [3-7].

[0049] [4-1] A biomarker for determining the stress level experienced by a subject, wherein the biomarker is a composition of bacterial species in a sialic acid-containing bacterial community from a sample obtained from the subject.

[0050] [4-2] According to the biomarker described in [4-1], the composition of the bacterial species in the bacterial community with sialic acid is the content of Clostridium bacteria in the bacterial community with sialic acid.

[0051] [4-3] The biomarker according to [4-1] or [4-2], wherein the sample is feces.

[0052] [4-4] The biomarker according to any one of [4-1] to [4-3], wherein the subject is a human.

[0053] [4-5] A method for detecting a biomarker, wherein the biomarker described in any one of [4-1] to [4-4] is detected in a sample obtained from a subject.

[0054] [4-6] A method for collecting data, the data being used to determine the stress level experienced by a subject, wherein the method includes the detection method described in [4-5].

[0055] [4-7] A kit for detecting the stress level experienced by a subject, wherein the kit includes a unit for detecting a biomarker as described in any one of [4-1] to [4-4].

[0056] [4-8] The composition of bacterial species in the sialic acid-containing bacterial community in the sample obtained from the subject is used as a biomarker for determining the stress level experienced by the subject.

[0057] [4-9] According to the use described in [4-8], the composition of the bacterial species in the bacterial community containing sialic acid is the content of Clostridium bacteria in the bacterial community containing sialic acid.

[0058] [4-10] A method for determining the stress level experienced by a subject, comprising the data collection method described in [4-6].

[0059] [5-1] A biomarker for diagnosing depression in a subject is a composition of bacterial species in a sialic acid-containing bacterial community from a sample obtained from the subject.

[0060] [5-2] According to the biomarker described in [5-1], the composition of the bacterial species in the bacterial community with sialic acid is the content of Clostridium bacteria in the bacterial community with sialic acid.

[0061] [5-3] The biomarker according to [5-1] or [5-2], wherein the sample is feces.

[0062] [5-4] The biomarker according to any one of [5-1] to [5-3], wherein the subject is a human.

[0063] [5-5] A method for detecting a biomarker, wherein the biomarker described in any one of [5-1] to [5-4] is detected in a sample obtained from a subject.

[0064] [5-6] A method for collecting data, said data being used to diagnose a subject's depression, wherein the method includes the detection method described in [5-5].

[0065] [5-7] A kit for diagnosing depression in a subject, comprising a unit for detecting a biomarker as described in any one of [5-1] to [5-4].

[0066] [5-8] The composition of bacterial species in sialic acid-containing bacterial communities in samples obtained from subjects can be used as biomarkers for diagnosing depression in subjects.

[0067] [5-9] According to the use described in [5-8], the composition of the bacterial species in the bacterial community containing sialic acid is the content of Clostridium bacteria in the bacterial community containing sialic acid.

[0068] [5-10] A method for diagnosing depression in a subject, comprising the data collection method described in [5-6].

[0069] [5-11] A diagnostic method for depression in a subject, comprising the data collection method described in [5-6].

[0070] [6-1] A biomarker for determining the stress level experienced by a subject, wherein the biomarker is a composition of bacterial species in a bacterial community obtained from a sample taken from the subject.

[0071] [6-2] According to the biomarkers described in [6-1], the composition of the bacterial species is the composition of the Firmicutes phylum in the bacterial community.

[0072] [7-1] A biomarker for diagnosing depression in a subject, wherein the biomarker is a composition of bacterial species in a bacterial community obtained from a sample taken from the subject.

[0073] [7-2] According to the biomarker described in [7-1], wherein the composition of the bacterial species is the composition of the Firmicutes phylum in the bacterial community.

[0074] [7-3] A method for diagnosing depression in a subject, comprising detecting the biomarkers described in [7-1] or [7-2] in a sample obtained from the subject.

[0075] According to the present invention, a stress level determination biomarker, a method for detecting the biomarker, and a stress level determination kit can be provided for determining the stress level experienced by a subject in a non-invasive and simple manner.

[0076] According to the present invention, it is possible to provide a biomarker for diagnosing depression in patients with depression through a non-invasive and simple method, a method for detecting the biomarker, and a reagent kit for diagnosing depression.

[0077] In addition, according to the present invention, it is possible to provide a biomarker for evaluating the effect of antidepressants using a non-invasive and simple method, a method for detecting the biomarker, and a kit for evaluating the effect of antidepressants. Attached Figure Description

[0078] Figure 1 The results of the analysis (principal coordinate analysis) of the microbial composition in mouse fecal samples from the control group, the chronic ultra-mild stress (CUMS) group, and the CUMS + antidepressant administration group in Example 2 are shown.

[0079] Figure 2 This is a graph showing the relative number of bacteria of each species in the fecal samples of mice in the control group, CUMS group, and CUMS + antidepressant administration group in Example 2.

[0080] Figure 3 A graph showing the amount of DNA barcodes corresponding to the sialic acid-binding lectin Sambucus nigra Lectin (Elderberry) bark (SNA) in the control group, CUMS group, and CUMS + antidepressant administration group in Example 3 is shown (*: p<0.05).

[0081] Figure 4 This is a graph showing the approach time (in seconds) between mice in the control group, CUMS group, and CUMS + antidepressant administration group in Example 4 and the target mice in the socialization test (*p<0.05).

[0082] Figure 5 The graph shows the correlation between the binding of sialic acid-binding lectin SNA to bacterial cells in the fecal samples of mice in the control group, CUMS group, and CUMS+antidepressant group obtained in Example 3 and the approach time (horizontal axis, seconds) between the mice in the control group, CUMS group, and CUMS+antidepressant group obtained in Example 4 and the target mice in the socialization test (R = 0.67, p = 0.0026).

[0083] Figure 6 This is a graph showing the relative number of cells of bacteria belonging to the class c_Clostridia, order o_Clostridiales, family f_Erysipelotrichaceae, genus g_Lactobacillus, genus g_Lactobacillusreuteri, genus g_Ruminococcus, order o_Lactobacillales, and species s_Erysipelotrichaceae in mice of the control group, CUMS group, and CUMS + antidepressant administration group in Example 5.

[0084] Figure 7The left figure is a graph showing the correlation between the time (in seconds) the mouse remained in the center during the open field test of Example 5 and the number of *g_Lactobacillus_reuteri* bacteria (R = 0.7, p = 0.0014). The right figure is a graph showing the correlation between the time (in seconds) the mouse remained in the center during the open field test of Example 5 and the number of *o_Clostridiales* bacteria (R = -0.55, p = 0.017).

[0085] Figure 8 This is a graph showing the sialidase activity in mouse fecal samples from the control group, CUMS group, and CUMS + antidepressant administration group in Example 6.

[0086] Figure 9 The graph shows the correlation between immobility time (seconds, x-axis) of the forced swimming test (FST) in Example 6 and sialidase activity (y-axis) in mouse fecal samples from the control group, CUMS group, and CUMS + antidepressant administration group (R = 0.85, p = 0.0034).

[0087] Figure 10 The graph shows the correlation between the relative number of bacteria of the class Clostridia and the sialidase activity in mouse fecal samples obtained in Example 6, namely the control group, the CUMS group, and the CUMS + antidepressant administration group (R = 0.88, p = 0.004).

[0088] Figure 11 The graph shows the correlation between the number of bacteria (relative amount) of Firmicutes (p_Firmicutes) cells and the sialidase activity in mouse fecal samples obtained in Example 6, in the control group, CUMS group, and CUMS + antidepressant drug administration group (R = 0.83, p = 0.011).

[0089] Figure 12 This is a graph showing the sialidase activity in fecal samples from healthy individuals (control) and patients with depression (Depressive) in Example 7. Detailed Implementation

[0090] One embodiment of this disclosure will now be described in detail. This disclosure is not limited to the following embodiment, and can be implemented with appropriate modifications without impairing the effects of the invention.

[0091] Unless otherwise specified in the implementation methods and examples, the methods described in or modified from the standard operating procedures of J. Sambrook, EFFritsch & T. Maniatis (Ed.), Molecular cloning, a laboratory manual (3rd edition), Cold Spring Harbor Press, Cold Spring Harbor, New York (2001); FM Ausubel, R. Brent, RE Kingston, DD Moore, JG Seidman, JA Smith, K. Struhl (Ed.), Current Protocols in Molecular Biology, John Wiley & Sons Ltd., shall be used. Furthermore, when using commercially available reagent kits and assay devices, additional operating procedures shall be used unless otherwise specified.

[0092] The structures and combinations thereof in each embodiment are merely examples. Without departing from the spirit of this disclosure, appropriate additions, omissions, substitutions, and other modifications to the structures may be made. This disclosure is not limited to the embodiments, but only to the claims. The various methods disclosed in this specification can be combined with any other features disclosed in this specification.

[0093] When a specific description of one embodiment also applies to another embodiment, that description may sometimes be omitted in the other embodiment. In this disclosure, the expression "X to Y" regarding a numerical range means "X or more and Y or less".

[0094] ==First Implementation Method (Markers for Determining Stress Levels, Sialidase Activity)== Biomarkers The biomarker of the first embodiment is the sialidase activity in a sample obtained from the subject. The biomarker of this embodiment can be used to determine the stress level experienced by the subject.

[0095] (Sialidase activity) Sialidase (also known as neuraminidase, acylneuraminidase, or EC3.2.1.18) is a common enzyme found in animals and many microorganisms. It is a glycolytic enzyme that cleaves sialic acid bound to the terminal α-ketoacyl group from glycoproteins, glycolipids, and oligosaccharides. In this specification, sialidase is not limited to the sialic acid to be cleaved. Sialidase is known to exist in a wide range of organisms, including humans (Lipnicanova et al., Diversity of sialidases found in the human body-A review, International Journal of Biological Macromolecules, Vol.148, 1 April 2020, Pages 857-868).

[0096] For example, sialic acid is a general term for 2-keto-3-deoxynonanoic acid, a nonose sugar with a carboxyl group at carbon atom 1. It is also a general term for N- and / or O-acyl derivatives with neuraminic acid as the core structure. In this specification, sialic acid also includes sialic acid in a narrow and broad sense, including N-acetylneuraminic acid (Neu5Ac), N-hydroxyacetylneuraminic acid (Neu5Gc), deaminneuraminic acid (Kdn), etc. Sialic acid can also be its O-acetylated form, O-sulfated form, lactone form, or lactam form, for example, without being limited by its modification.

[0097] In this specification, "sialidase activity" can refer to the presence or absence of sialidase activity, or the level of sialidase activity. That is, in this embodiment, the presence or absence of sialidase activity, or the level of sialidase activity, in the sample obtained from the subject can be used to determine the stress level experienced by the subject.

[0098] The source of sialidase activity is not limited to sialidase activity in samples obtained from the subject, but preferably originates from bacteria in the sample, more preferably from intestinal bacteria in the sample, and even more preferably from bacteria including Clostridia. Here, intestinal bacteria include any bacteria that were alive in the intestine of the subject prior to obtaining the sample. The sample may contain two or more types of bacteria.

[0099] In cases where the bacteria include bacteria belonging to the class Clostridia, the bacteria may include two or more bacteria belonging to the class Clostridia, or one or more bacteria belonging to the class Clostridia and one or more bacteria belonging to another class or two or more classes.

[0100] (Stress level) Stress is a change in physical and mental function that occurs when subjected to various physical, environmental, and psychological stimuli and loads. It is broadly divided into acute stress and chronic stress caused by continuous (chronic) stress loads.

[0101] Regarding stress level, "experiencing stress at a prescribed level" or "being stressed" (or "currently experiencing stress at a prescribed level") refers to an individual or subject experiencing at least some change in physical and mental function due to a certain stimulus or load. The "level" of stress can be the type or magnitude of the stimulus or load; or the level of change in physical and mental function caused by the stimulus or load; it can be an absolute value; or a relative value; it can be "higher" or "lower" compared to a benchmark value (benchmark range), normal value (normal), or standard curve (standard range); it can be "present" or "absent"; or it can be a score obtained according to a prescribed rating.

[0102] Chronic stress levels can be assessed by those skilled in the art using known methods, such as scoring based on medical history taking and questionnaires. For example, stress levels can be assessed by scoring the examinee using the simplified occupational stress questionnaire (57 items) or a simplified version of the occupational stress questionnaire (23 items) based on the revised implementation manual for the stress examination system under the Occupational Safety and Health Law (revised in February 2021, Ministry of Health, Labour and Welfare). In these surveys, scoring is performed on the items “Work Stress Factors,” “Psychological and Physical Stress Responses,” and “Surrounding Support,” allowing for the identification of individuals with high stress levels based on any criteria in each or any combination of these three items.

[0103] In this instruction manual, phrases such as "experienced a prescribed level of stress" or "underwent stress" (or "is currently experiencing a prescribed level of stress") can be used to indicate a state of high stress, as determined by any of the prescribed criteria in the above-mentioned Occupational Stress Simplified Questionnaire (57 items) or the simplified version of the Occupational Stress Simplified Questionnaire (23 items). Phrases such as "not experienced a prescribed level of stress" or "not underwent stress" can be used to indicate a state of non-high stress, as determined by the same method.

[0104] (Sample) The sample is not limited to those obtained from the subject, but is preferably a sample from the subject's intestines. By using the sialidase activity of bacteria contained in the intestinal sample as a biomarker, the stress level experienced by the subject can be easily determined with high accuracy and / or precision. Samples from the intestines include, for example, feces and intestinal mucosal tissue.

[0105] Here, feces is not limited to biological samples from the subject's intestines, but broadly includes intestinal contents. For example, intestinal contents after being expelled from the subject's body (so-called feces) and intestinal contents before expulsion are both included, but from the viewpoint of non-invasive sample acquisition and without the need for professional intervention, samples from the intestines are preferably feces expelled from the subject's body.

[0106] The intestinal region from which the intestinal contents originate is not limited. For example, it can be the small intestine, including the duodenum, jejunum, and ileum, or the large intestine, including the cecum, appendix, colon, and rectum. Here, the large intestine includes, for example, the ascending colon, transverse colon, descending colon, sigmoid colon, upper rectum, and lower rectum.

[0107] When the sample is feces, those skilled in the art can collect and store it according to known methods so as to inhibit unwanted substances that are not originally present in the feces and / or avoid reducing the sialidase activity in the feces after the feces are excreted from the subject until sialidase activity is detected.

[0108] (Subject) The subject of the test refers to the individual who is the object of stress level determination, but is not limited to the scope of those who need to determine stress level, nor is it limited to organisms whose biomarkers are permanently or temporarily present in the body. For example, it can be a mammal or other animals. Mammals can be humans or non-human animals. Non-human animal species can be, for example, monkeys, dogs, cats, horses, cattle, pigs, sheep, goats, rabbits, guinea pigs, hamsters, mice and / or rats, and are not limited to the use of livestock, pets, laboratory animals, etc., but are preferably mammals, and more preferably humans.

[0109] In one embodiment, the biomarker used to determine the stress level experienced by the subject is preferably the sialidase activity of bacteria from a sample obtained from a human.

[0110] In one embodiment, the biomarker for determining the stress level experienced by the subject is more preferably the sialidase activity of bacteria from feces obtained from humans.

[0111] In one embodiment, the biomarker for determining the stress level experienced by the subject is more preferably sialidase activity from feces obtained from humans, including c-Clostridia bacteria.

[0112]

Detection Method

[0113] The detection method described below enables the detection of any of the biomarkers described in the above-described "Biomarkers" of this embodiment in a sample obtained from a subject.

[0114] Here, examples and preferred embodiments of "sialidase activity", "sample", and "subject" are as described above in the section on "biomarkers" of this embodiment.

[0115] Regarding the acquisition of samples from the subject, those skilled in the art can appropriately select methods according to known procedures. For example, if the sample is feces excreted by the subject, it is sufficient to obtain at least a portion of the feces using sterilized equipment. For example, if the sample is intestinal contents before excretion by the subject, a container can be placed against the anus of the subject, or a sterilized cotton swab, sterilized pipette, or other instrument can be inserted into the anus of the subject to obtain the sample by adhering it to the instrument. For example, if the sample is intestinal mucosal tissue of the subject, it can be obtained by inserting a sterilized instrument into the anus of the subject, similar to the method used for intestinal contents, and scraping the intestinal mucosal tissue with the instrument.

[0116] Regarding pretreatment before detecting biomarkers, the sample obtained from the subject can be pretreated to suit the detection, or the detection can be performed in its untreated state. Regarding the pretreatment steps, those skilled in the art can appropriately select methods according to known methods based on the type of sample, the content or concentration of the biomarker in the sample, etc. For example, when the sample is feces or intestinal contents, solid feces or intestinal contents excreted from the subject can be used for biomarker detection in a solid state, or a portion can be dissolved in a solution and homogenized, and the remaining portion can be used for biomarker detection in a liquid state. For example, when the sample is intestinal mucosal tissue, mucosal tissue slices obtained from the subject can be used for biomarker detection in a solid state, or micro-slices can be prepared by homogenization or ultrasonic treatment before use for biomarker detection. Furthermore, when sialidase activity originates from bacteria, the desired bacterial community can be isolated from the sample using known methods for biomarker detection.

[0117] The detection of biomarkers is not limited to the detection of sialidase activity; for example, it may include measuring the presence or absence of sialidase activity or measuring the level of sialidase activity. Considering the accuracy and / or precision of the diagnosis, the biomarker detection method of this embodiment is preferably a method that includes not only measuring the presence or absence of sialidase activity but also measuring the level of sialidase activity.

[0118] In this specification, the level of a biomarker (the level of sialic acidase activity) can be the measured value of the biomarker, or it can be any one of the presence, absolute value, or relative value of the biomarker in the sample.

[0119] In this embodiment, the level of sialidase activity can be an absolute value of enzyme activity or a relative value.

[0120] Here, the level of sialidase activity can be expressed, for example, in the “International Unit” (IU), which is the amount of enzyme that can change 1 μmol of substrate per minute in 1 L of sample at 30 °C under optimal conditions.

[0121] The detection of sialidase activity can be performed appropriately by those skilled in the art using known methods.

[0122] For example, by using a substance that serves as a reaction substrate for sialase, a product is generated from the sialase in the sample through an enzymatic reaction. By measuring the decrease in the reaction substrate and / or the increase in the product, it is possible to determine the presence or absence of sialase activity in the sample, or to determine the level of sialase activity in the sample.

[0123] For the detection of sialidase activity, commercially available sialidase activity assay kits designed using the mechanism described above can be used to determine the presence or absence of sialidase activity, or to determine the level of sialidase activity, according to the accompanying instructions. An example of such a commercially available kit is the neuraminidase activity assay kit (commercially available from Sigma-Aldrich, etc.).

[0124] It can also replace the detection of sialidase activity itself, and detect sialidase activity by detecting parameters related to the absolute or relative value of sialidase activity.

[0125] Parameters associated with sialidase activity can be, for example, the amount of sialidase protein that is considered to be related to the level of sialidase activity.

[0126] That is, in one embodiment, the method for detecting biomarkers includes determining the amount of sialidase protein in a sample obtained from a subject.

[0127] Regarding the protein content of salivary enzymes, those skilled in the art can determine it using known methods, but it can also be determined, for example, using antibodies or probes that specifically bind to salivary enzymes, according to known methods such as direct competition, indirect competition, sandwich method, ELISA (enzyme-linked immunosorbent assay), RIA (radioimmunoassay), flow cytometry, immunochromatography, etc.

[0128] Furthermore, parameters related to sialidase activity can include, for example, the level of sialic acid-containing glycans. The level of glycans refers to the absolute or relative amount of sialic acid-added glycans contained in a sample obtained from the test subject. When sialidase activity increases in the sample, the cleavage of sialic acid by the sialidase becomes more active. Therefore, associated with an increase in sialidase activity, the level of sialic acid-containing glycans in the sample decreases. Here, sialic acid-containing glycans are, for example, sialic acid-added cell surface glycans from bacteria included in the sample. Sialization (sialylation, sialic acid modification) refers to the binding of sialic acid to the sixth or third position of the non-reducing terminal galactose in N-linked glycans, or the binding of sialic acid to the sixth position, Core1(T), or Core1 in O-linked glycans.

[0129] That is, in one embodiment, the method for detecting biomarkers includes determining the level of sialic acid-containing sugar chains in a sample obtained from a subject.

[0130] The level of glycans containing sialic acid can be determined by those skilled in the art using known methods, but it can also be quantified, for example, using specific lectins that bind to sialic acid-additive glycans. As a more specific example, the Glycan sequence (Glycan-seq) method developed by Oinam et al. (Glycan Profiling of the gut microbiota by Glycan-seq, ISME Communications, 2022 Jan 5, 2, Article number: 1, doi.org / 10.1038 / s43705-021-00084-2) can also be used for determination.

[0131] Using the above-described detection method, sample acquisition is non-invasive and requires no professional intervention. Compared to methods that require blood collection or biopsy, it allows for a simpler way to detect biomarkers and obtain results. For example, the subject can also perform the entire process from sample acquisition to biomarker detection to obtain test results.

[0132] In addition, objective and / or quantitative test results can be obtained by simply measuring and detecting biomarkers.

[0133] [Judgment Method] One embodiment of the data collection method is a data collection method for determining the stress level experienced by a subject, and the method includes the detection method described in the above-described "Detection Method" of this embodiment.

[0134] In one embodiment, the stress level experienced by the subject can be determined based on data collected by the data collection method. That is, one embodiment of the method for determining the stress level experienced by the subject includes the data collection method.

[0135] The detection of sialidase activity as a biomarker can be performed according to any of the detection methods described in the "Detection Methods" section of this embodiment.

[0136] In one embodiment, the data collection method for determining the stress level experienced by the subject may also include the following: before performing any of the detection methods described in the above-described "Detection Method" of this embodiment on the sample obtained from the subject, the biomarker detection method is performed according to the above-described "Detection Method" of this embodiment on the sample obtained from one or more individuals who have experienced a specified level of stress, and a reference value of the biomarker is obtained based on the measured value of the biomarker in the individuals who have experienced a specified level of stress.

[0137] In another embodiment, the method for collecting data to determine the stress level experienced by the subject may also include the following: before performing any of the detection methods described in the above-described "Detection Method" of this embodiment on the samples obtained from the subject, the biomarker detection method is performed according to the above-described "Detection Method" of this embodiment on samples obtained from one or more individuals who have experienced a specified level of stress and samples obtained from one or more individuals who have not experienced a specified level of stress, to obtain the baseline values ​​(normal values) of biomarkers in individuals who have experienced a specified level of stress and the baseline values ​​(normal values) of biomarkers in individuals who have not experienced a specified level of stress.

[0138] According to these methods, if the measured value of a biomarker in a sample obtained from a subject is the same as the baseline value of a biomarker in an individual subjected to a specified level of stress, this data can be used to determine that the subject has been or is being subjected to the specified level of stress, or that there is a high probability that the subject has been or is being subjected to the specified level of stress. Alternatively, if the measured value of a biomarker in a sample obtained from a subject is different from the baseline value of a biomarker in an individual subjected to the specified level of stress, this data can be used to determine that the subject has not been subjected to the specified level of stress, or that there is a high probability that the subject has not been subjected to the specified level of stress.

[0139] Furthermore, if the measured values ​​of biomarkers in samples obtained from the subject differ from normal values, this data can be used to determine that the subject has been or is currently experiencing a prescribed level of stress, or that there is a high probability that the subject has been or is currently experiencing a prescribed level of stress. Alternatively, if the measured values ​​of biomarkers in samples obtained from the subject are the same as normal values, this data can be used to determine that the subject has not been subjected to a prescribed level of stress, or that there is a high probability that the subject has not been subjected to a prescribed level of stress.

[0140] In one implementation, if the measured value of a biomarker in a sample obtained from a subject is elevated compared to the normal value, this data can be used to determine that the subject has been or is being subjected to a prescribed level of stress, or that there is a high probability that the subject has been or is being subjected to a prescribed level of stress. This is because the sialidase activity in samples obtained from stressed individuals is higher than that in samples obtained from unstressed individuals, or tends to be higher than that in samples obtained from unstressed individuals.

[0141] In another embodiment, the method for collecting data to determine the stress level experienced by the subject may also include the following: before performing any of the detection methods described in the above-described "Detection Method" of this embodiment on the samples obtained from the subject, the method for detecting biomarkers is performed in the above-described "Detection Method" of this embodiment on samples obtained from multiple individuals or groups of individuals who have experienced different levels of stress, and a standard curve representing the relationship between stress level and biomarkers is set based on the measured values ​​of biomarkers in individuals or groups of individuals who have experienced different levels of stress.

[0142] According to this method, if the measured value of a biomarker in a sample obtained from the subject is the same as a point on the standard curve, based on this data, it can be determined that the subject has been or is being subjected to the level of stress corresponding to that point, or it can be determined that there is a high probability that the subject has been or is being subjected to the prescribed level of stress. If the measured value of a biomarker in a sample obtained from the subject is different from any point on the standard curve, based on this data, it can be determined that the subject has not been subjected to the level of stress corresponding to any point on the standard curve, or it can be determined that there is a high probability that the subject has not been subjected to the level of stress corresponding to any point on the standard curve.

[0143] In another embodiment, the method for collecting data to determine the stress level experienced by the subject may also include any of the detection methods described in the above-described "Detection Method" of this embodiment, which are performed on multiple samples obtained from the same subject over time.

[0144] According to this method, when the measured value of a biomarker in a sample obtained from a subject shows a change compared to a previous value at a certain point in time, it can be determined from this data that the subject has been or is being subjected to a prescribed level of stress, or that there is a high probability that the subject has been or is being subjected to a prescribed level of stress. For example, this method can be used to determine and monitor the level of stress that a subject has been or is being subjected to through regularly performed health diagnoses.

[0145] In one implementation, if the measured value of a biomarker in a sample obtained from a subject increases at a certain point compared to a previous value, this data can be used to determine that the subject has been or is being subjected to a prescribed level of stress, or that there is a high probability that the subject has been or is being subjected to a prescribed level of stress. This is because the sialidase activity in samples obtained from stressed individuals is higher than, or tends to be higher than, the sialidase activity in samples obtained from unstressed individuals.

[0146] In one implementation, the stress level experienced by the subject can be determined by any of the above determinations.

[0147] Here, the baseline, normal, or standard curve can be set based on the average or median value of the biomarker measurements obtained from the same group of individuals or from the same individual over time, or it can be a baseline range, normal range, or standard range arbitrarily set based on the biomarker measurements.

[0148] Alternatively, regarding baseline values, normal values, or standard curves, those skilled in the art may also appropriately determine the values ​​that can determine the stress level experienced by the subject with the required accuracy and / or precision, based on the measured values ​​of biomarkers in samples obtained from the same group of individuals or from the same individual over time.

[0149] Furthermore, the measured value of the biomarker in the sample obtained from the test subject is the same as any point within the reference value, normal value, or standard curve; for example, it may also show no significant difference from the reference value or normal value after any statistical treatment. The measured value of the biomarker in the sample obtained from the test subject differs from any point within the reference value, normal value, or standard curve; for example, it may also show a significant difference from the reference value, normal value, or standard curve after any statistical treatment. The measured value of the biomarker in the sample obtained from the test subject increases compared to the reference value; for example, it means that the measured value of the biomarker in the sample obtained from the test subject increases significantly compared to the reference value after any statistical treatment.

[0150] The statistical processing method can be appropriately selected by those skilled in the art according to known methods.

[0151] In one implementation, the baseline value may be a value greater than 150%, 200%, 250%, 300%, 350%, 400%, 450%, or 500% relative to the normal value, and those skilled in the art may appropriately determine the value that enables the determination of the stress level experienced by the subject with the required accuracy and / or precision.

[0152] In one embodiment, the data collection method described above, or the method for determining the stress level of a subject based on the data collection method, can also be implemented in combination with other data collection methods or other methods for determining the stress level of a subject.

[0153] Using the above-described method, sample acquisition is non-invasive and requires no professional intervention. Compared to methods that require blood collection or biopsy, it allows for simpler detection of biomarkers and obtaining results. For example, the subject themselves can also perform the entire process from sample acquisition to biomarker detection to obtain the determination result.

[0154] In addition, objective and / or quantitative judgment results can be obtained by simply measuring and detecting biomarkers.

[0155]

Reagent test kit

[0156] In one embodiment, the kit is a kit for implementing the detection method described in the above-described "Detection Method" of this embodiment.

[0157] In one embodiment, the kit may include, for example, a reaction substrate for detecting sialidase, reagents for reducing the amount of reaction substrate / increasing the amount of product, a positive control reagent for demonstrating that the detection process is functioning, and / or a storage solution for storing the sialidase sample obtained from the subject until the detection method is performed, and / or instructions for using the kit, as a unit for detecting the biomarker. Here, the reaction substrate may also be provided attached to a container, chip, etc.

[0158] In one embodiment, the kit may include or be provided with the detection device required for the detection.

[0159] In one embodiment, at least a portion of the kit can be provided as part of a toilet bowl. By being placed in a toilet bowl, the sialidase activity from the subject's feces can be readily applied to health management.

[0160] Using the above-mentioned kit, sample acquisition is non-invasive and requires no professional intervention. Compared with methods that require blood collection or biopsy, it allows for simpler detection of biomarkers and obtaining results. For example, the subject can also perform the entire process from sample acquisition to biomarker detection to obtain the judgment result.

[0161] In addition, objective and / or quantitative judgment results can be obtained by simply measuring and detecting biomarkers.

[0162] ==Second Implementation Method (Diagnostic Markers for Depression, Sialidase Activity)== Biomarkers The biomarker of the second embodiment is the sialidase activity in a sample obtained from the subject. The biomarker of this embodiment can be used to diagnose depression in the subject.

[0163] It is known that depression is caused by stress. Therefore, the sialidase activity in a sample obtained from a subject as a biomarker, as described in the first embodiment, can be used to diagnose the subject's depression.

[0164] (depression) In this specification, "depression" includes not only diseases meeting the diagnostic criteria for major depressive disorder based on the WHO's International Classification of Diseases, 10th Revision (ICD-10) or the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5), but also a wide range of mood disorders, symptoms, and adjustment disorders within the scope of at least a transient depressive state. It is not limited by the duration of the illness or presentation of symptoms, their severity, and / or their cause (e.g., genetic and external stress, and diseases other than mental illnesses such as infectious diseases, neoplastic diseases, and neurological diseases). Examples of mood disorders that include at least a transient depressive state include, for example, depressive mood disorders and bipolar disorder. In addition to major depressive disorder, nonspecific (NOS) depression and dysthymia (persistent depressive disorder) are also examples of depressive mood disorders. Examples of bipolar disorder include bipolar I disorder, bipolar II disorder, and nonspecific (NOS) bipolar disorder. From the perspective of diagnosing depression with greater accuracy and / or precision, the preferred diagnostic criteria are depressive mood disorders (including major depressive disorder, NOS-related depression, and dysphoric mood) or adjustment disorders, more preferably major depressive disorder, NOS-related depression, or adjustment disorders. Within the scope of mood disorders that at least temporarily present with a depressive state, the subject may exhibit one of the identified symptoms such as anxiety disorder, sleep disorder, or eating disorder. The subject may also suffer from diseases other than mental illnesses, such as infectious diseases, neoplastic diseases, and neurological diseases.

[0165] Furthermore, in this manual, phrases such as "suffering from depression" not only include having depression, but also include the high probability of having depression. Additionally, phrases such as "diagnosis of depression," "diagnosis of depression," and "diagnosis of depression" not only determine whether someone has depression, but also determine whether the probability of having depression is high.

[0166] In this specification, the use of terms such as "healthy" or "healthy person" in the context of "depression" implies that the person has not been diagnosed with depression or is unlikely to have depression. This also includes cases where the person has a condition other than depression.

[0167] (Sialidase activity) Examples of "sialidase activity" and preferred embodiments include the "sialidase activity" of the "biomarkers" in the first embodiment.

[0168] (Sample) Examples of "samples" and preferred embodiments are described in the first embodiment of the "sample" of "biomarkers".

[0169] (Subject) "Subject" refers to an individual who is the subject of a diagnosis of depression, but examples and preferred embodiments of subjects other than those who require a diagnosis of depression include the "subject" of the "Biomarker" in the first embodiment.

[0170]

Detection Method

[0171] Here, examples and preferred embodiments of "sialidase activity", "sample", and "subject" are as described above in the section on "biomarkers" of this embodiment.

[0172] In this embodiment, the process of obtaining the sample from the subject, the pretreatment before detecting the biomarker, and the detection of the biomarker are as described in the "Detection Method" of the first embodiment.

[0173] Using the above-described detection method, sample acquisition is non-invasive and requires no professional intervention. Compared to methods that require blood collection or biopsy, it allows for a simpler way to detect biomarkers and obtain results. For example, the subject can also perform the entire process from sample acquisition to biomarker detection to obtain test results.

[0174] In addition, objective and / or quantitative test results can be obtained by simply measuring and detecting biomarkers.

[0175]

Diagnostic Methods

[0176] The detection of sialidase activity as a biomarker can be performed according to any of the detection methods described in the "Detection Methods" section of this embodiment.

[0177] In one embodiment, the method for collecting data for diagnosing depression in a subject may also include the following: before performing any of the detection methods described in the above-described "Detection Method" of this embodiment on a sample obtained from the subject, a biomarker detection method is performed in a sample obtained from one or more patients with depression, according to the above-described "Detection Method" of this embodiment, and a baseline value of the biomarker is obtained based on the measured value of the biomarker in the patients with depression.

[0178] In another embodiment, the method for collecting data for diagnosing depression in a subject may also include the following: before performing any of the detection methods described in the above-described "Detection Method" of this embodiment on samples obtained from the subject, the biomarker detection method is performed according to the above-described "Detection Method" of this embodiment on samples obtained from one or more patients with depression and samples obtained from one or more healthy individuals, to obtain baseline values ​​(health baseline values) of biomarkers for patients with depression and baseline values ​​(normal values) of biomarkers for healthy individuals.

[0179] Using these methods, if the measured value of a biomarker in a sample obtained from a subject is the same as the baseline value of a biomarker for a patient with depression, the data can be used to determine that the subject has depression, or that the subject is highly likely to have depression. Alternatively, if the measured value of a biomarker in a sample obtained from a subject is different from the baseline value of a biomarker for a patient with depression, the data can be used to determine that the subject does not have depression, or that the subject is less likely to have depression.

[0180] Furthermore, if the measured values ​​of biomarkers in the sample obtained from the subject differ from the normal values, this data can be used to determine that the subject suffers from depression, or that the likelihood of suffering from depression is relatively high. Conversely, if the measured values ​​of biomarkers in the sample obtained from the subject are the same as the normal values, this data can be used to determine that the subject does not suffer from depression, or that the likelihood of suffering from depression is relatively low.

[0181] In one implementation, if the measured value of a biomarker in a sample obtained from a subject is elevated compared to the normal value, this data can be used to determine that the subject suffers from depression, or that there is a high probability that the subject has depression. This is because the sialidase activity in samples obtained from patients with depression is higher than that in samples obtained from healthy individuals, or tends to be higher than that in samples obtained from healthy individuals.

[0182] In another embodiment, the method for collecting data for diagnosing depression in a subject may be a method that includes performing any of the detection methods described in the above-described "Detection Method" of this embodiment on multiple samples obtained from the same subject over time.

[0183] According to this method, if the measured value of a biomarker in a sample obtained from a subject shows a change at a certain point in time compared to the previous value, this data can be used to determine whether the subject suffers from depression, or whether there is a high probability that the subject has depression. For example, this method can be used to monitor the subject's depression through regularly implemented health checkups.

[0184] In one implementation, if the measured value of a biomarker in a sample obtained from a subject is higher than it was previously at a certain point in time, based on this data, it can be determined that the subject suffers from depression, or that there is a greater likelihood that the subject suffers from depression. This is because the sialidase activity in samples obtained from patients with depression is higher than that in samples obtained from healthy individuals, or tends to be higher than that in samples obtained from healthy individuals.

[0185] In one implementation, the subject's depression can be diagnosed by any of the above-mentioned determinations.

[0186] Here, the baseline or normal value can be set based on the average or median value of the biomarker measurements obtained from the same group of individuals or from the same individual over time, or it can be an arbitrary baseline or normal range set based on the biomarker measurements.

[0187] Alternatively, regarding baseline or normal values, those skilled in the art can appropriately determine values ​​that enable the determination of a subject's prevalence of depression with the required accuracy and / or precision, based on the measured values ​​of biomarkers in samples obtained from the same group of individuals or from the same individual over time.

[0188] Furthermore, the fact that the measured value of a biomarker in a sample obtained from the subject is the same as the reference value or normal value can mean, for example, that there is no significant difference from the reference value or normal value after any statistical treatment. The fact that the measured value of a biomarker in a sample obtained from the subject is different from the reference value or normal value can mean, for example, that there is a significant difference from the reference value or normal value after any statistical treatment. An increase in the measured value of a biomarker in a sample obtained from the subject compared to the reference value means, for example, that the measured value of a biomarker in a sample obtained from the subject significantly increases compared to the reference value after any statistical treatment.

[0189] The statistical processing method can be appropriately selected by those skilled in the art according to known methods.

[0190] In one implementation, the baseline value may be a value greater than 150%, 200%, 250%, 300%, 350%, 400%, 450%, or 500% relative to the normal value, and those skilled in the art may appropriately determine the value that enables the subject to be diagnosed with depression with the required accuracy and / or precision.

[0191] In one embodiment, the data collection method described above, or the method for diagnosing depression based on the data collection method, can also be implemented in combination with other data collection methods or other diagnostic methods for depression.

[0192] Using the above evaluation method, sample acquisition is non-invasive and requires no professional intervention. Compared with methods that require blood collection or biopsy, it allows for simpler detection of biomarkers and obtaining results. For example, the subject can also perform the entire process from sample acquisition to biomarker detection to obtain diagnostic results.

[0193] In addition, objective and / or quantitative diagnostic results can be obtained by simply measuring and detecting biomarkers.

[0194] ==Third Implementation Method (Markers for Evaluating the Efficacy of Antidepressants: Sialidase Activity)== Biomarkers The biomarker in the third embodiment is the sialidase activity in a sample obtained from the subject. The biomarker of this embodiment can be used to evaluate the effect of antidepressants on the subject.

[0195] As described in the second embodiment, the sialidase activity in the sample obtained from the subject can be used as a biomarker for diagnosing the subject's depression. Therefore, if sialidase activity is used as a biomarker, it is possible to determine whether the subject's depression has improved after administration of antidepressants, i.e., to evaluate the effect of antidepressants on the subject.

[0196] (Sialidase activity) Examples of "sialidase activity" and preferred embodiments include the "sialidase activity" of the "biomarkers" in the first embodiment.

[0197] (Antidepressants) In this product information, "antidepressants" includes approved and unapproved drugs and treatments for the treatment of depression in Japan and other countries. Examples include selective serotonin reuptake inhibitors (SSRIs), serotonin-noradrenaline reuptake inhibitors (SNRIs), electroconvulsive therapy, and / or transcranial magnetic stimulation. Examples of selective serotonin reuptake inhibitors include escitalopram, sertraline, and paroxetine; examples of serotonin-noradrenaline reuptake inhibitors include imipramine and venlafaxine.

[0198] (Sample) Examples and preferred embodiments of "sample" are described in the first embodiment of the "sample" of the "biomarker".

[0199] (Subject) "Subject" refers to an individual who is the subject of evaluation of the effect of antidepressants, but examples and preferred examples are as described in the "Subject" section of the "Biomarkers" section of the first embodiment, except for subjects who need to be evaluated for the effect of antidepressants.

[0200] The biomarkers used in this embodiment are biomarkers for evaluating the effects of antidepressants; therefore, the subjects are preferably individuals with depression or suspected of having depression. Individuals suspected of having depression may be individuals who have experienced or are experiencing a prescribed level of stress.

[0201]

Detection Method

[0202] Here, examples and preferred embodiments of "sialidase activity", "sample", and "subject" are as described above in the section on "biomarkers" of this embodiment.

[0203] In this embodiment, the process of obtaining the sample from the subject, the pretreatment before detecting the biomarker, and the detection of the biomarker are as described in the "Detection Method" of the first embodiment.

[0204] Using the above-described detection method, sample acquisition is non-invasive and requires no professional intervention. Compared to methods that require blood collection or biopsy, it allows for a simpler way to detect biomarkers and obtain results. For example, the subject can also perform the entire process from sample acquisition to biomarker detection to obtain test results.

[0205] In addition, objective and / or quantitative test results can be obtained by simply measuring and detecting biomarkers.

[0206] Evaluation Method One embodiment of the data collection method is a data collection method for evaluating the effect of antidepressants on subjects, and the method includes the detection method described in the above-described "Detection Method" of this embodiment.

[0207] In one embodiment, the effect of an antidepressant on a subject can be evaluated based on data collected according to a data collection method. That is, one embodiment of a method for evaluating the effect of an antidepressant on a subject includes this data collection method.

[0208] The detection of sialidase activity as a biomarker can be performed according to any of the detection methods described in the "Detection Methods" section of this embodiment.

[0209] In one embodiment, the method for collecting data to evaluate the effect of antidepressants on subjects may include the following: before implementing the above-described detection method of this embodiment on samples obtained from subjects, the biomarker detection method is performed according to the above-described detection method of this embodiment on samples obtained from one or more patients with depression who have responded to antidepressants (including patients with original depression who have recovered through treatment) or healthy individuals, to obtain baseline values ​​of biomarkers for patients who have responded to antidepressants.

[0210] In another embodiment, the method for collecting data to evaluate the effect of antidepressants on subjects may include the following: before performing any of the detection methods described in the above-described "Detection Methods" of this embodiment on samples obtained from subjects, the method for detecting biomarkers is performed according to the above-described "Detection Methods" of this embodiment on samples obtained from one or more patients with depression who have not responded to antidepressants or who have not received treatment with antidepressants, and baseline values ​​of biomarkers for patients with depression who have not responded to antidepressants or who have not received treatment with antidepressants are obtained.

[0211] In another embodiment, the method for collecting data to evaluate the effect of antidepressants on subjects may include the following: before performing any of the detection methods described in the above-described "Detection Method" of this embodiment on samples obtained from subjects, the biomarker detection method is performed according to the above-described "Detection Method" of this embodiment on samples obtained from one or more patients with depression who have responded to antidepressants (including patients who have recovered from depression through treatment) or healthy individuals, and on samples obtained from one or more patients with depression who have not responded to antidepressants or patients with depression who have not received treatment with the antidepressant, to obtain a baseline value (first baseline value) for biomarkers in patients with depression who have responded to antidepressants or healthy individuals, and a baseline value (second baseline value) for biomarkers in patients with depression who have not responded to antidepressants or patients with depression who have not received treatment with the antidepressant.

[0212] Using these methods, if the measured value of a biomarker in a sample obtained from a subject is the same as the baseline value of a biomarker in a patient with depression or a healthy individual who responds to antidepressants, this data can be used to determine that the subject is responding to the antidepressants, or that there is a high probability that the subject is responding to the antidepressants. Alternatively, if the measured value of a biomarker in a sample obtained from a subject is different from the baseline value of a biomarker in a patient with depression or a healthy individual who responds to antidepressants, this data can be used to determine that the subject is not responding to the antidepressants, or that there is a high probability that the subject is not responding to the antidepressants.

[0213] Furthermore, if the measured value of a biomarker in a sample obtained from a subject differs from the baseline value of a biomarker in a patient with depression who has not responded to antidepressants or has not received such antidepressant treatment, this data can be used to determine that the subject has responded to the antidepressant treatment, or that it is highly likely that the subject has responded to the antidepressant treatment. Alternatively, if the measured value of a biomarker in a sample obtained from a subject is the same as the baseline value of a biomarker in a patient with depression who has not responded to antidepressants or has not received such antidepressant treatment, this data can be used to determine that the subject has not responded to the antidepressant treatment, or that it is highly likely that the subject has not responded to the antidepressant treatment.

[0214] In one implementation, if the measured value of a biomarker in a sample obtained from a subject is lower than the baseline value of a biomarker in a patient with depression who has not responded to antidepressants or who has not received treatment with such antidepressants, this data can be used to determine that the subject has responded to the antidepressants, or that there is a high probability that the subject has responded to the antidepressants. This is because the sialidase activity in samples obtained from patients with depression who have responded to antidepressants or healthy individuals is lower than, or tends to be lower than, the sialidase activity in samples obtained from patients with depression who have not responded to antidepressants or who have not received treatment with such antidepressants.

[0215] In another embodiment, the method for collecting data to evaluate the effect of antidepressants on the subject may also include performing the above-described detection method in this embodiment on multiple samples obtained from the same subject over time.

[0216] According to this method, if the measured value of a biomarker in a sample obtained from a subject shows a change compared to a previous value at a certain point in time, this data can be used to determine whether the subject is responding to the antidepressant or whether there is a high probability that the subject is responding to the antidepressant. By using this method to regularly monitor patients with depression who are being treated with antidepressants, the effectiveness of the antidepressants and / or the improvement of depression can be evaluated.

[0217] In one embodiment, if the measured value of a biomarker in a sample obtained from a subject decreases at a certain time compared to previous values, this data can be used to determine that the subject is experiencing the effects of the antidepressant or is more likely to experience such effects, or that the depression is improving or is more likely to improve with the antidepressant. In another embodiment, if the measured value of a biomarker in a sample obtained from a subject increases at a certain time compared to previous values, this data can be used to determine that the antidepressant is less effective in the subject or is more likely to be less effective, or that the depression is worsening or is more likely to worsen. This is because the sialidase activity in samples obtained from patients with depression who are responding to the antidepressant or healthy individuals is lower than, or tends to be lower than, in samples obtained from patients with depression who are not responding to the antidepressant or who are not receiving treatment with the antidepressant.

[0218] In one implementation, the effect of antidepressants on the subject can be evaluated by any of the above-mentioned determinations.

[0219] Here, the reference value can be the average or median value of the biomarker measurements obtained from the same group of subjects or from the same subject over time, or it can be an arbitrary reference range or normal range based on the biomarker measurements.

[0220] Alternatively, regarding baseline or normal values, those skilled in the art may appropriately determine the values ​​that enable the determination of the effect of the antidepressant on the subject with the required accuracy and / or precision, based on the measured values ​​of biomarkers in samples obtained from the same group of subjects or obtained from the same subject over time.

[0221] Furthermore, the measured value of a biomarker in a sample obtained from the test subject being the same as the reference value can be, for example, showing no significant difference from the reference value or normal value after any statistical treatment. The measured value of a biomarker in a sample obtained from the test subject being different from the reference value can be, for example, showing a significant difference from the reference value after any statistical treatment. An increase in the measured value of a biomarker in a sample obtained from the test subject compared to the reference value can be, for example, showing a significant increase in the measured value of a biomarker in a sample obtained from the test subject compared to the reference value after any statistical treatment. Conversely, a decrease in the measured value of a biomarker in a sample obtained from the test subject compared to the reference value can be, for example, showing a significant decrease in the measured value of a biomarker in a sample obtained from the test subject compared to the reference value after any statistical treatment.

[0222] The statistical processing method can be appropriately selected by those skilled in the art according to known methods.

[0223] In one embodiment, the data collection method or data-based collection method described above for evaluating the effect of antidepressants can also be combined with other data collection methods or other effect evaluation methods for evaluating the effect of antidepressants.

[0224] Using the above evaluation method, sample acquisition is non-invasive and requires no professional intervention. Compared with methods that require blood sampling or biopsy, it allows for simpler detection of biomarkers and obtaining results. For example, the subject can also perform the entire process from sample acquisition to biomarker detection to evaluate the effectiveness of antidepressants.

[0225] In addition, objective and / or quantitative evaluation results can be obtained by simply measuring and detecting biomarkers.

[0226]

Reagent test kit

[0227] In one embodiment, the kit is a kit for implementing the detection method described in the above-described "Detection Method" of this embodiment.

[0228] Examples and preferred embodiments of the "kit" are described in the "kit" of the first embodiment.

[0229] Using the above-mentioned kit, sample acquisition is non-invasive and requires no professional intervention. Compared with methods that require blood sampling or biopsy, it allows for simpler detection of biomarkers and obtaining results. For example, the subject can also perform the entire process from sample acquisition to biomarker detection to evaluate the efficacy of antidepressants.

[0230] In addition, objective and / or quantitative evaluation results can be obtained by simply measuring and detecting biomarkers.

[0231] ==Fourth Implementation Method (Composition of Markers and Microbial Strains for Stress Level Assessment)== Biomarkers The biomarker of the fourth embodiment is the composition of bacterial species in a sialic acid-containing bacterial community from a sample obtained from the subject. The biomarker of this embodiment can be used to determine the stress level experienced by the subject.

[0232] Sialic acid is a general term for 2-keto-3-deoxynonanoic acid, a nonose sugar with a carboxyl group at carbon atom 1. It also refers to N- and / or O-acyl derivatives with neuraminic acid as their core structure. In this specification, sialic acid also includes sialic acid in a narrow and broad sense, including N-acetylneuraminic acid (Neu5Ac), N-hydroxyacetylneuraminic acid (Neu5Gc), deaminneuraminic acid (Kdn), etc. Sialic acid can be, for example, its O-acetylated form, O-sulfated form, lactone form, or lactam form, without being limited by its modification. That is, the bacterial community containing sialic acid in this embodiment can be a bacterial community containing any one of these sialic acids.

[0233] The preferred bacterial community containing sialic acid consists of two or more types of bacteria whose cell surface sugar chains have added sialic acid.

[0234] In the fourth embodiment, the composition of the Firmicutes phylum in the bacterial community of the sample obtained from the subject can also be used to determine the stress level experienced by the subject.

[0235] The bacterial community is preferably composed of two or more species of bacteria.

[0236] (Stress level) In this embodiment, "stress level" is as described in the "stress level" item of "Biomarkers" in the first embodiment.

[0237] (Sample) The sample is not limited to those obtained from the subject, but is preferably from the subject's intestines. By using the composition of sialic acid-containing bacterial communities or the composition of Firmicutes bacteria in the bacterial communities contained in the intestinal sample as biomarkers, the stress level experienced by the subject can be easily determined with high accuracy and / or precision. Samples from the intestine include, for example, feces and intestinal mucosal tissue.

[0238] Here, feces is not limited to biological samples from the subject's intestines, but broadly includes intestinal contents. For example, intestinal contents after being expelled from the subject's body (so-called feces) and intestinal contents before expulsion are both included, but from the viewpoint of non-invasive sample acquisition and without the need for professional intervention, samples from the intestines are preferably feces expelled from the subject's body.

[0239] The intestinal region from which the intestinal contents originate is not limited. For example, it can be the small intestine, including the duodenum, jejunum, and ileum, or the large intestine, including the cecum, appendix, colon, and rectum. Here, the large intestine includes, for example, the ascending colon, transverse colon, descending colon, sigmoid colon, upper rectum, and lower rectum.

[0240] When the sample is feces, those skilled in the art can collect and store it according to known methods so as to suppress unwanted substances that are not originally present in the feces and / or avoid altering the composition of the bacterial species in the feces after the feces are excreted from the subject until the composition of the bacterial species is detected.

[0241] (Composition of the strain) The composition of the bacterial species used as biomarkers in this embodiment is either a composition of bacterial communities containing sialic acid from a sample obtained from the test subject, or a composition of Firmicutes (p-Firmicutes) from a bacterial community in a sample obtained from the test subject. Considering the accuracy and / or precision of the determination, the bacteria are preferably intestinal bacteria living in the intestine of the test subject.

[0242] Since samples from the intestines may also contain microorganisms other than bacteria, the term "bacterial community containing sialic acid" may also include sialic acid-containing microorganisms other than bacteria. However, considering the accuracy and / or precision of the determination, it is preferable that the "bacterial community containing sialic acid" does not contain microorganisms other than bacteria. The bacterial community containing sialic acid can be all sialic acid-containing bacteria contained in the sample, or it can be a subset of sialic acid-containing bacteria. Here, the bacterial community containing sialic acid is a bacterial community in which sialic acid is added to the glycans on the cell surface. The cell surface glycans are not limited; for example, they can be sialic acid-modified lipopolysaccharides, lipoteichoic acid, capsular polysaccharides, etc.

[0243] Samples obtained from the subject typically contain multiple bacterial species; for example, the human gut contains more than 1,000 species of bacteria. "The composition of bacterial species in a sialic acid-containing bacterial community" refers to the content of any one or more bacterial species within a sialic acid-containing bacterial community.

[0244] Here, "content" can be, for example, mass or bacterial cell count. Furthermore, "content" can be, for example, an absolute or relative value of the content of any one or more bacterial species in a sialic acid-containing bacterial community. In the case of an absolute value, it can be, for example, the mass of any mass of one or more bacterial species in a sialic acid-containing bacterial community, or the bacterial cell count of one or more bacterial species in a sialic acid-containing bacterial community. In the case of a relative value, it can be, for example, the ratio of the mass or bacterial cell count of any one or more sialic acid-containing bacterial species to the mass or bacterial cell count of the sialic acid-containing bacterial community, or the ratio of the mass or bacterial cell count of any two or more sialic acid-containing bacterial species in the sialic acid-containing bacterial community.

[0245] In this specification, "species" refers to bacteria in a broad sense and is not limited to the meaning of "species" in biological classification. For example, as long as the composition of the test object is based on the class, order or genus of bacteria, it belongs to the bacteria of that classification.

[0246] In addition, "the composition of Firmicutes species in the bacterial community" refers to the content of one or more species belonging to the Firmicutes phylum in the bacterial community.

[0247] Here, "content" can refer to, for example, mass or bacterial cell count. Furthermore, "content" can be an absolute or relative value of the content of any one or more bacterial species in a bacterial community. In the case of an absolute value, it could be, for example, the mass of one or more species belonging to the phylum Firmicutes in the bacterial community, or the number of bacterial cells belonging to one or more species belonging to the phylum Firmicutes in the bacterial community. In the case of a relative value, it could be, for example, the ratio of the mass or number of bacterial cells of one or more species belonging to the phylum Firmicutes to the mass or number of bacterial cells of the bacterial community, or the ratio of the mass or number of bacterial cells of two or more species belonging to the phylum Firmicutes in the bacterial community.

[0248] In this specification, "species" refers to bacteria in a broad sense and is not limited to the meaning of "species" in biological classification. For example, as long as the composition of the test object is based on the class, order or genus of bacteria, it belongs to the bacteria of that classification.

[0249] The bacterial species in the sialic acid-containing bacterial community are not limited to the range of species composition related to the stress level experienced by the subject, but preferably include Clostridia, Clostridium, Ruminococcus, Lactobacillus, Erysipelotrichaceae, Lactobacillus reuteri, and Lactobacillus order. The fungi include any one or a combination of two or more species of *Erysipelotrichaceae*, preferably including *Clostridia*, *Erysipelotrichaceae*, *Lactobacillus*, and *Erysipelotrichaceae*, and more preferably including bacteria of the class *Clostridia*.

[0250] For example, in the case of a bacterial community containing sialic acid and containing bacteria of the class *Clostridia*, the composition of the *Clostridia* bacteria—whether it is a subset or all of the *Clostridia* species present in the sample—can be used as a biomarker. For instance, in a bacterial community containing sialic acid, the composition of bacteria of the order *Clostridiales* and / or other orders, or of the genus *Clostridium* and / or other genera, can be used as a biomarker.

[0251] In one embodiment, the composition of the bacterial species in the bacterial community containing sialic acid is preferably the content of c-Clostridia bacteria in the bacterial community containing sialic acid.

[0252] The bacterial community comprising Firmicutes species is not limited to those belonging to the phylum Firmicutes. Its composition is related to the stress level experienced by the sample; for example, it could include all species belonging to the phylum Firmicutes, or species from various classes within the phylum Firmicutes. Preferably, it includes all species belonging to the phylum Firmicutes or species from the class Clostridia.

[0253] Regarding the fecal microbiota, it is known that the relative abundance of *Clostridium* in humans and mice is similar (Thi Loan Anh Nguyen et al., How informative is the mouse for human gut microbiota research?, Disease Models & Mechanisms, Vol. 8 (1), Jan 2015, pages 1-16).

[0254] (Subject) Examples and preferred embodiments of "subject" are as described in the "subject" section of the "Biomarkers" section of the first embodiment, except as otherwise provided below.

[0255] In one embodiment, the biomarker used to determine the stress level experienced by the subject is preferably a composition of bacterial species in a sialic acid-containing bacterial community from a sample obtained from a human.

[0256] In one embodiment, the biomarker for determining the stress level experienced by the subject is more preferably the composition of bacterial species in a sialic acid-containing bacterial community obtained from human feces.

[0257] In one embodiment, the biomarker for determining the stress level experienced by the subject is further preferably the content of Clostridia bacteria in feces obtained from humans.

[0258] In one embodiment, the biomarker for determining the stress level experienced by the subject is preferably a composition of bacteria belonging to the phylum Firmicutes in the bacterial community of a sample obtained from a human.

[0259] In one embodiment, the biomarker for determining the stress level experienced by the subject is more preferably the composition of bacteria belonging to the phylum Firmicutes in the bacterial community obtained from human feces.

[0260]

Detection Method

[0261] The detection method described below enables the detection of any of the biomarkers described in the above-described "Biomarkers" of this embodiment in a sample obtained from a subject.

[0262] Here, examples and preferred embodiments of "sample", "composition of bacterial strain", and "subject" are as described above in the [Biomarker] section of this embodiment.

[0263] The sample is obtained from the subject as described in the "Detection Method" of the first embodiment.

[0264] Regarding the pretreatment before detecting biomarkers, except for the following, the samples obtained from the subject can be treated as described in the "Detection Method" of the first embodiment.

[0265] In this embodiment, where the biomarker is the composition of a bacterial community containing sialic acid, the detection method may include a step of isolating a bacterial community containing sialic acid from a sample obtained from the test subject. The bacterial community containing sialic acid can be isolated from the sample, for example, using a lectin that specifically binds to sialic acid addition sugar chains. Those skilled in the art can appropriately select the lectin used based on the type of sialic acid addition sugar chain to which it binds. For example, it is possible to use Sambucus nigra Lectin (Elderberry) bark (SNA) (elderberry lectin), Limax flavus lectin (LFA) (yellow slug lectin), Triticum vulgaris lectin (WGA) (wheat germ lectin), Maackia amurensis lectin I (MAL I) (Maackia amurensis lectin I), Maackia amurensis lectin II (MAL II) (Maackia amurensis lectin II), Agrocybe cylindracea (ACG) (tea tree mushroom lectin), Galectin 8 N-terminal domain (Gal8N), Sambucus sieboldiana lectin (SSA) (stalkless elderberry lectin), Trichosanthes japonica lectin (TJAI) (Japanese trichosanthes lectin), Polyporus squamosus lectin 1a (rPSL1a) (allomyrina dichtoma) The lectins include ADA (diphtheria beetle lectin), Escherichia coli lectin (SubB2M) (Escherichia coli lectin), Salmonella enterica lectin (PltB) (Salmonella enterica lectin), Streptococcus gordonii lectin (HAS) (Streptococcus gordonii lectin), and Porcine hemagglutinating encephalomyelitis virus lectin (BCoV) (swine hemagglutinating encephalomyelitis virus lectin).Information on other lectins can be obtained from the LfDB database or from Odaka et al. (STAR ​​Protoc. 2022 Feb 18;3(1):101179. doi:10.1016 / j.xpro.2022.101179.) and Minoshima et al. (iScience. 2021 Jul 17;24(8):102882. doi: 10.1016 / j.isci.2021.102882.).

[0266] In this embodiment, where the biomarker is the composition of bacterial species within a bacterial community, the "bacterial community" may not include a step of isolating the bacterial community, depending on the presence or absence of sialic acid, depending on the sample obtained from the test subject. The composition of all bacterial species within the bacterial communities contained in the sample can be used as a biomarker.

[0267] The detection of biomarkers is not limited to detecting the composition of bacterial species in a bacterial community containing sialic acid or the composition of bacterial species belonging to the phylum Firmicutes in a bacterial community. As described in the above-described "Biomarkers" section of this embodiment, the composition of bacterial species in a bacterial community containing sialic acid can be an absolute value or a relative value of the content of any one or more bacterial species in the bacterial community containing sialic acid. Similarly, the composition of bacterial species belonging to the phylum Firmicutes in a bacterial community can be an absolute value or a relative value of the content of one or more bacterial species belonging to the phylum Firmicutes in the bacterial community.

[0268] In this specification, the composition of a biomarker (the composition of bacterial species in a bacterial community or the composition of a specific bacterial species in a bacterial community) can be either the measured value of the biomarker or any one of the presence, absolute value, or relative value of the biomarker in the sample.

[0269] Here, the composition of bacterial species in a bacterial community containing sialic acid or the composition of bacterial species belonging to the phylum Firmicutes in the bacterial community can be detected by, for example, by directly measuring the content of bacterial cells or by measuring any amount of substance from bacterial cells that is related to the content of bacterial cells.

[0270] When directly measuring the mass of bacterial cells, for example, it is possible to determine the mass of a bacterial community containing sialic acid or the mass of any one or more bacterial species contained in that bacterial community. When directly measuring the cell number of bacterial cells, for example, it is possible to count the number of bacterial cells in a bacterial community containing sialic acid or constituting a bacterial community, and the number of bacterial cells of any one or more bacterial species contained in that bacterial community. Through these measurements, it is possible to obtain the content of any one or more bacterial species in a bacterial community containing sialic acid, or the absolute value of the composition of one or more bacterial species belonging to the phylum Firmicutes in the bacterial community.

[0271] Furthermore, the composition of a bacterial species can be determined by using the measured values ​​obtained from these assays, for example, by calculating the ratio of the mass or number of bacterial cells of any one or more sialic acid-containing bacterial species to the mass or number of bacterial cells in a sialic acid-containing bacterial community. Alternatively, the composition of a bacterial species can be determined by calculating the ratio of the mass or number of bacterial cells of any two or more sialic acid-containing bacterial species in a sialic acid-containing bacterial community.

[0272] In determining any mass of material related to the number of bacterial cells from bacterial cells, for example, it is possible to determine any mass related to the mass of a sialic acid-containing cell community or the number of bacterial cells constituting a sialic acid-containing bacterial community, and any mass related to the mass or number of any one or more bacterial species contained in that bacterial community. Even when using these measurements, as with the case of using absolute values, the composition of the bacterial species can be determined by calculating the proportion of any one or more sialic acid-containing bacterial species relative to the sialic acid-containing bacterial community. Alternatively, for example, the composition of the bacterial species can be determined by calculating the ratio of any two or more sialic acid-containing bacterial species in the sialic acid-containing bacterial community.

[0273] When the biomarker is the composition of Firmicutes species within a bacterial community, the composition can be determined by calculating the proportion of one or more Firmicutes species or bacterial cells in the community. Alternatively, for example, the composition can be determined by calculating the ratio of the mass or bacterial cells of two or more Firmicutes species in the community.

[0274] For example, when directly determining the number of bacterial cells, one can use a bacterial count plate to count them under a microscope, or inoculate them onto agar medium and count the number of colonies, or inoculate them onto liquid medium and measure the amount of light transmitted using a spectrophotometer and compare the count with a pre-established benchmark, or measure the bacterial weight and compare the count with a pre-established benchmark.

[0275] For example, in determining any quantity of matter related to bacterial cell count, one can measure the amount of ATP from bacteria, the amount of nucleic acid fragments from bacteria, or the sialic acid from sialic acid-containing cell communities. These measurements can be compared with a pre-established baseline to count bacterial cells. Alternatively, the Glycan sequence (Glycan-seq) method developed by Oinam et al. can be used. This involves binding each sialic acid-containing bacterium's sialic acid-addition sugar chain to a lectin labeled with a different DNA. The DNA is then detected using primers and probes to identify specific bacteria possessing sialic acid.

[0276] When determining the amount of nucleic acid fragments from bacteria, the following methods can be used: for example, using primers and probes that are specific to some or all of the nucleic acid sequences of specific bacterial communities such as c_Clostridia, o_Clostridiales, g_Ruminococcus, g_Lactobacillus, f_Erysipelotrichaceae, g_Lactobacillus_reuteri, o_Lactobacillales, s_Erysipelotrichaceae, and p_Firmicutes. The method can be appropriately selected by those skilled in the art, and examples include specific quantitative PCR, T-RFLP (Terminal-Striction Fragment Length Polymorphism), FISH (Fluorescence in situ hybridization), methods using microarrays, cloning library methods, DGGE / TGGE (Denaturing / Sterature Gradient Gel Electrophoresis), and metagenomic analysis methods.

[0277] More specifically, when using methods that employ different primers and probes for each of one or more bacterial species within a sialic acid-containing bacterial community, or for each of one or more bacterial species belonging to the phylum Firmicutes within a bacterial community, nucleic acids can be amplified using specific primers for each bacterial species, as in specific quantitative PCR or T-RFLP, and the amount of nucleic acid can be measured during or after the amplification reaction. Alternatively, nucleic acid detection can be performed on bacterial genus-specific probes without nucleic acid amplification, as in FISH or microarray methods. The primers and probes used can target corresponding regions on the nucleic acids of each bacterial species, or they can target different regions. Specifically, for example, when using a 16S rRNA sequence, it is possible to target part or all of the nine variable regions. More specifically, for example, the V3-V4 hypervariable region of the 16S rRNA gene can be targeted, and this region can be specifically amplified by PCR.

[0278] Sequencing instruments are typically used in methods that employ common primers for a portion or all of one or more species within a sialic acid-containing bacterial community, or for a portion or all of one or more species belonging to the phylum Firmicutes within a bacterial community. The processing of nucleic acids prior to sequencing can be chosen appropriately by those skilled in the art. For example, in the cloning library method, specific sequence portions for each species are amplified from the extracted nucleic acids, and the amplicon is inserted into a plasmid for sequencing. In the DGGE / TGGE method, the amplicon is similarly amplified, separated by electrophoresis according to the sequence of the nucleic acid strands, extracted from the gel, and sequenced. In metagenomic analysis, nucleic acids are fragmented or amplified, and the products are directly sequenced. Primers used for amplification and sequencing can be the same or different primers. For example, in the case of using 16S rRNA sequences, hybridizable primers can be used as storage regions. As such primers, a forward primer with the nucleic acid sequence having sequence number 83 and a reverse primer with the nucleic acid sequence having sequence number 84 can be used. In sequencing, a sequencer utilizing the termination sequencing principle or a next-generation sequencer can be used. Examples of sequencing methods include multiplex sequencing, single-end sequencing, and paired-end sequencing, which can be appropriately selected by those skilled in the art.

[0279] The methods described above for determining any mass related to bacterial cell count can also be performed using commercially available kits.

[0280] In determining sialic acid from cell communities containing sialic acid, those skilled in the art can perform the determination appropriately using known methods, such as breaking down sialic acid-modified sugar chains into monosaccharides, quantifying the amount of sialic acid using liquid chromatography or mass spectrometry, and investigating the absolute amount.

[0281] There are no particular limitations on the methods for identifying bacterial species from sequenced information. For example, analytical methods known in the art, such as those utilizing analysis software or databases, can be used. When using a database, for example, bacterial species can be identified by performing a sequence identity search on the base sequences of accumulated bacteria. Specific examples of databases include GenBank, ENA, and DDBJ. Additionally, databases such as GREENGENESdatabase, which are specifically designed for 16S rRNA sequences, can also be used. Alternatively, tools for microbial analysis, such as the amplicon sequencing data analysis software QIIME2 (Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Bolyen E. et al., Nature Biotechnology 37: 852-857, 2019. doi.org / 10.1038 / s41587-019-0209-9), can be used to analyze and identify bacterial species.

[0282] Using the above detection method, sample acquisition is non-invasive. Furthermore, by measuring and detecting biomarkers, objective and / or quantitative test results can be obtained.

[0283] [Judgment Method] One embodiment of the data collection method is a data collection method for determining the stress level experienced by the subject, which includes the detection method described in the above-described "Detection Method" of this embodiment.

[0284] In one embodiment, the stress level experienced by the subject can be determined based on data collected by the data collection method. That is, one embodiment of the method for determining the stress level experienced by the subject includes the data collection method.

[0285] The detection of the composition of bacterial species in a bacterial community containing sialic acid, which serves as a biomarker, can be performed according to any of the detection methods described in the "Detection Methods" section of this embodiment.

[0286] The detection of the composition of Firmicutes bacteria in the bacterial community of a sample obtained from the test subject as a biomarker can be performed according to any of the detection methods described in the "Detection Methods" section of this embodiment.

[0287] In one embodiment, the data collection method for determining the stress level experienced by the subject may include the following: before performing any of the detection methods described in the above-described "Detection Method" of this embodiment on the sample obtained from the subject, the biomarker detection method is performed according to the above-described "Detection Method" of this embodiment on the sample obtained from one or more individuals who have experienced a specified level of stress, and a baseline value of the biomarker in the individuals who have experienced a specified level of stress is obtained.

[0288] In another embodiment, the data collection method for determining the stress level experienced by the subject may include the following: before performing any of the detection methods described in the above-described "Detection Method" of this embodiment on the samples obtained from the subject, the biomarker detection method is performed according to the above-described "Detection Method" of this embodiment on samples obtained from one or more individuals who have experienced a specified level of stress and samples obtained from one or more individuals who have not experienced a specified level of stress, to obtain the baseline values ​​(normal values) of biomarkers in individuals who have experienced a specified level of stress and the baseline values ​​(normal values) of biomarkers in individuals who have not experienced a specified level of stress.

[0289] Using these methods, when the measured value of a biomarker in a sample obtained from a subject is the same as the baseline value of a biomarker in an individual subjected to a specified level of stress, this data can be used to determine that the subject has been subjected to or is currently subjected to the specified level of stress, or that there is a high probability that the subject has been subjected to or is currently subjected to the specified level of stress. Alternatively, when the measured value of a biomarker in a sample obtained from a subject is different from the baseline value of a biomarker in an individual subjected to the specified level of stress, this data can be used to determine that the subject has not been subjected to the specified level of stress, or that there is a high probability that the subject has not been subjected to the specified level of stress.

[0290] Furthermore, if the measured values ​​of biomarkers in samples obtained from the subject differ from the normal values, this data can be used to determine that the subject has been or is currently experiencing a prescribed level of stress, or it can also be determined that there is a high probability that the subject has been or is currently experiencing a prescribed level of stress. Alternatively, if the measured values ​​of biomarkers in samples obtained from the subject are the same as the normal values, this data can be used to determine that the subject has not been subjected to a prescribed level of stress, or it can also be determined that there is a high probability that the subject has not been subjected to a prescribed level of stress.

[0291] In one embodiment, if the measured value of a biomarker in a sample obtained from a subject is higher than the normal value, based on this data, it can be determined that the subject has been or is being subjected to a specified level of stress, or it can be determined that the subject is more likely to have been or is being subjected to a specified level of stress.

[0292] In individuals subjected to prescribed stress, the levels of Clostridia bacteria in the sialic acid-containing bacterial community in the sample were higher or tended to be higher compared to individuals not subjected to stress. Therefore, a high level of Clostridia bacteria in the sample obtained from the subject can be used to determine that the subject was likely subjected to or received prescribed stress.

[0293] In individuals subjected to prescribed stress, the levels of o-Clostridiales bacteria in the sialic acid-containing bacterial community in the sample were higher or tended to be higher compared to individuals not subjected to stress. Therefore, if the levels of o-Clostridiales bacteria in the sample obtained from the subject are high, it can be determined that the subject is more likely to have been or is currently being subjected to the prescribed level of stress.

[0294] In individuals subjected to prescribed stress, the levels of *g. Ruminococcus* bacteria in the sialic acid-containing bacterial community of the sample were higher or tended to be higher compared to individuals not subjected to stress. Therefore, if the levels of *g. Ruminococcus* bacteria in the sample obtained from the subject are high, it can be determined that the subject is more likely to have been subjected to or is currently being subjected to the prescribed level of stress.

[0295] In individuals subjected to prescribed stress, the content of Erysipelotrichaceae bacteria in the sialic acid-containing bacterial community in the sample was lower or tended to be lower compared to individuals not subjected to stress. Therefore, if the content of Erysipelotrichaceae bacteria in the sample obtained from the subject is low, it can be determined that the subject is more likely to have been or is currently being subjected to the prescribed level of stress.

[0296] In individuals subjected to prescribed stress, the content of *Lactobacillus* bacteria in the sialic acid-containing bacterial community in the sample was lower or tended to be lower compared to individuals not subjected to stress. Therefore, if the content of *Lactobacillus* bacteria in the sample obtained from the subject is low, it can be determined that the subject is more likely to have been or is currently being subjected to the prescribed level of stress.

[0297] In individuals subjected to prescribed stress, the levels of *Lactobacillus reuteri* bacteria in the sialic acid-containing bacterial community of the sample were lower or tended to be lower compared to individuals not subjected to stress. Therefore, a low level of *Lactobacillus reuteri* bacteria in the sample obtained from the subject can also be considered a strong indication that the subject has been subjected to or is currently being subjected to the prescribed level of stress.

[0298] In individuals subjected to prescribed stress, the levels of o-Lactobacillales in the sialic acid-containing bacterial community in the sample were lower or tended to be lower compared to individuals not subjected to stress. Therefore, if the levels of o-Lactobacillales in the sample obtained from the subject are low, it can be determined that the subject is more likely to have been subjected to or is undergoing prescribed stress.

[0299] In individuals subjected to prescribed stress, the levels of *Erysipelotrichaceae* bacteria in the sialic acid-containing bacterial community of the sample were lower or tended to be lower compared to individuals not subjected to stress. Therefore, if the levels of *Erysipelotrichaceae* bacteria in the sample obtained from the subject are low, it can be determined that the subject is more likely to have been subjected to or is currently being subjected to the prescribed level of stress.

[0300] In individuals subjected to prescribed stress, the content of Firmicutes bacteria in the bacterial community of the sample is higher or tends to be higher compared with that of individuals not subjected to stress. Therefore, if the content of Firmicutes bacteria in the sample obtained from the subject is high, it can be determined that the subject is more likely to have been or is being subjected to the prescribed level of stress.

[0301] In individuals subjected to prescribed stress, the bacterial community in the sample contained higher or predominantly higher levels of Clostridia bacteria compared to individuals not subjected to stress. Therefore, if the sample obtained from the subject contains a high level of Clostridia bacteria, it can be determined that the subject is likely subjected to or is currently subjected to the prescribed level of stress.

[0302] In another embodiment, the data collection method for determining the stress level experienced by the subject may include the following: before performing any of the detection methods described in the above-described "Detection Method" of this embodiment on samples obtained from the subject, the biomarker detection method is performed according to the above-described "Detection Method" of this embodiment on samples obtained from multiple individuals or groups of individuals who have experienced different levels of stress, and a standard curve representing the relationship between the stress level and the biomarker level is set based on the biomarker levels in individuals or groups of individuals who have experienced different levels of stress.

[0303] According to this method, if the measured value of a biomarker in a sample obtained from the subject is the same as a point on the standard curve, based on this data, it can be determined that the subject has been or is being subjected to the level of stress corresponding to that point, or it can be determined that there is a high probability that the subject has been or is being subjected to the prescribed level of stress. If the measured value of a biomarker in a sample obtained from the subject is different from any point on the standard curve, based on this data, it can be determined that the subject has not been subjected to the level of stress corresponding to any point on the standard curve, or it can be determined that there is a high probability that the subject has not been subjected to the level of stress corresponding to any point on the standard curve.

[0304] In another embodiment, the method for collecting data to determine the stress level experienced by the subject may be any of the detection methods described in the above-described "Detection Method" of this embodiment, which includes performing the detection method on multiple samples obtained from the same subject over time.

[0305] According to this method, when the measured value of a biomarker in a sample obtained from a subject shows a change compared to a previous value at a certain point in time, it can be determined from this data that the subject has been or is being subjected to a prescribed level of stress, or that there is a high probability that the subject has been or is being subjected to a prescribed level of stress. For example, this method can be used to determine and monitor the level of stress experienced or being experienced by a subject through regularly performed health diagnoses.

[0306] In one implementation, if the measured value of a biomarker in a sample obtained from a subject increases at a certain point compared to a previous value, this data can be used to determine that the subject has been or is being subjected to a prescribed level of stress, or that there is a greater likelihood that the subject has been or is being subjected to a prescribed level of stress. For example, if the biomarker for the composition of a sialic acid-containing bacterial community is the content of c-Clostridia bacteria, then in stressed individuals or individuals who have been stressed and are receiving antidepressant medication, the content of c-Clostridia bacteria in the sialic acid-containing bacterial community in the sample is higher or tends to be higher compared to unstressed individuals.

[0307] In one implementation, the stress level experienced by the subject can be determined by any of the above determinations.

[0308] Here, the baseline, normal value, or standard curve can be the average or median value of the biomarker measurements obtained from the same group of individuals or from the same individual over time, or it can be an arbitrary baseline range, normal range, or standard curve set based on the biomarker measurements.

[0309] Alternatively, the baseline, normal, or standard curve may be determined by a person skilled in the art based on the measured values ​​of biomarkers in samples obtained from the same group of individuals or from the same individual over time, values ​​that can determine the stress level experienced by the subject with the required accuracy and / or precision.

[0310] Furthermore, the measured value of the biomarker in the sample obtained from the test subject is the same as any point within the reference value, normal value, or standard curve, for example, there is no significant difference from the reference value or normal value after any statistical treatment. The measured value of the biomarker in the sample obtained from the test subject differs from any point within the reference value, normal value, or standard curve, for example, there is a significant difference from the reference value, normal value, or standard curve after any statistical treatment. The measured value of the biomarker in the sample obtained from the test subject increases compared to the reference value, for example, the measured value of the biomarker in the sample obtained from the test subject increases significantly compared to the reference value after any statistical treatment.

[0311] The statistical processing method can be appropriately selected by those skilled in the art according to known methods.

[0312] In one implementation, the baseline value relative to the normal value can be 150%, 200%, 250%, 300%, 350%, 400%, 450%, or 500% or more, and those skilled in the art can appropriately determine the value that enables the determination of the stress level experienced by the subject with the required precision.

[0313] In one embodiment, the data collection method described above, or the method for determining the stress level of a subject based on the data collection method, can also be implemented in combination with other data collection methods or other methods for determining the stress level of a subject.

[0314]

Reagent test kit

[0315] In one embodiment, the kit is a kit for implementing the detection method described in the above-described "Detection Method" of this embodiment.

[0316] In one embodiment, when the kit is a kit for implementing a detection method for detecting the composition of bacterial species in a bacterial community containing sialic acid, the kit includes, for example, a unit for isolating bacterial communities containing sialic acid from a sample obtained from a subject. Examples of such units include lectins that specifically bind to sialic acid addition sugar chains. Lectins can be loaded with auxin onto any carrier to suit the isolation of target cell populations. Examples of such lectins include Sambucus nigra Lectin (Elderberry) bark (SNA), Limax flavus lectin (LFA), Triticum vulgaris lectin (WGA), Maackia amurensis lectin I (MAL I), Maackia amurensis lectin II (MAL II), Agrocybe cylindracea (ACG), Galectin 8 N-terminal domain (Gal8N), Sambucus sieboldiana lectin (SSA), Trichosanthes japonica lectin (TJAI), Polyporus squamosus lectin 1a (rPSL1a), and Allomyrina dichthamnium. The lectins include ADA (diphtheria beetle lectin), Escherichia coli lectin (SubB2M) (Escherichia coli lectin), Salmonella enterica lectin (PltB) (Salmonella enterica lectin), Streptococcus gordonii lectin (HAS) (Streptococcus gordonii lectin), and Porcine hemagglutinating encephalomyelitis virus lectin (BCoV) (swine hemagglutinating encephalomyelitis virus lectin). It can be obtained from the LfDB database of lectins or from Odaka et al. (STAR ​​Protoc. 2022 Feb 18;3(1):101179. doi: 10.1016 / j.xpro.2022.101179.) and Minoshima et al. (iScience. 2021 Jul 17;24(8):102882. doi:10.1016 / j.isci.2021.102882.).

[0317] In one embodiment, the kit includes a unit for directly determining the number of bacterial cells. Examples of such a unit include, for instance, a bacterial count plate, agar medium, or one or more reagents and / or liquid culture media for preparing agar medium.

[0318] In one embodiment, the kit includes a unit for measuring any amount of substance related to bacterial cell number. For example, it could be a unit for measuring the amount of ATP; such a unit could be a fluorescent ATP sensor that binds to ATP and is capable of detecting the concentration of ATP. Alternatively, it could be a unit for measuring the amount of nucleic acid fragments from bacteria; such a unit could be primers or probes that specifically contain nucleic acid sequences for the bacterial community or species being tested. Other units could be primers or probes used in the Glycansequence (Glycan-seq) assay developed by Oinam et al. for detecting lectins, DNA markers, or DNA.

[0319] The kit may also include reagents for detecting the composition of the microbial strain, positive control reagents to demonstrate the effectiveness of the detection process, and / or a storage solution for storing the sample collected from the subject until the detection method is performed, and / or instructions for use of the kit.

[0320] In one embodiment, the kit may include or be provided with the detection device required for the detection.

[0321] In one embodiment, at least a portion of the kit can be provided as part of a toilet bowl. By being placed in the toilet bowl, the composition of the bacterial community containing sialic acid from the feces excreted by the subject can be readily applied to health management.

[0322] Using the above-mentioned kit, sample acquisition is non-invasive. Furthermore, by measuring and detecting biomarkers, objective and / or quantitative test results can be obtained.

[0323] ==Fifth Implementation Method (Biomarkers and Microbial Composition for Diagnosing Depression)== Biomarkers The biomarker of the fifth embodiment is the composition of bacterial species in a sialic acid-containing bacterial community from a sample obtained from the subject. The biomarker of this embodiment can be used to diagnose depression in the subject.

[0324] In the fifth embodiment, the composition of Firmicutes species in the bacterial community of the sample obtained from the subject can also be used to diagnose the subject's depression.

[0325] It is known that depression is caused by stress. Therefore, the composition of bacterial species in a sialic acid-containing bacterial community or the composition of Firmicutes species in a bacterial community obtained from a sample obtained from a subject as a biomarker in the fourth embodiment can be used to diagnose the subject's depression.

[0326] Examples of “depression” and preferred examples of “depression” in the “biomarkers” section of the second embodiment are described.

[0327] (Sample) Examples of "samples" and preferred embodiments are described in the fourth embodiment of the "sample" of "biomarkers".

[0328] (Composition of the strain) Examples of “composition of the microbial strain” and preferred embodiments are described in the “composition of the microbial strain” item of the “Biomarker” in the fourth embodiment.

[0329] (Subject) Examples of "subjects" and preferred embodiments of "subjects" in the "biomarkers" of the fourth embodiment are described.

[0330]

Detection Method

[0331] Here, examples and preferred embodiments of "sample", "composition of bacterial strain", and "subject" are as described above in the [Biomarker] section of this embodiment.

[0332] In this embodiment, the sample acquisition from the subject, the pretreatment before detecting the biomarker, and the detection of the biomarker are as described in the "Detection Method" of the fourth embodiment.

[0333] Using the above detection method, sample acquisition is non-invasive. Furthermore, by measuring and detecting biomarkers, objective and / or quantitative test results can be obtained.

[0334]

Diagnostic Methods

[0335] In one embodiment, a subject's depression can be diagnosed based on data collected according to a data collection method. That is, one embodiment of a method for diagnosing a subject's depression includes this data collection method.

[0336] The detection of the composition of bacterial species in a bacterial community containing sialic acid, which serves as a biomarker, can be performed according to any of the detection methods described in the "Detection Methods" section of this embodiment.

[0337] The detection of the composition of Firmicutes bacteria in the bacterial community of a sample obtained from the test subject as a biomarker can be performed according to any of the detection methods described in the "Detection Methods" section of this embodiment.

[0338] In one embodiment, the method for collecting data for diagnosing depression in a subject may also include the following: before performing any of the detection methods described in the above-described "Detection Method" of this embodiment on a sample obtained from the subject, a biomarker detection method is performed on a sample obtained from one or more patients with depression in accordance with the above-described "Detection Method" of this embodiment to obtain a baseline value of the biomarker for the patients with depression.

[0339] In another embodiment, the method for collecting data for diagnosing depression in a subject may include the following: before performing any of the detection methods described in the above-described "Detection Method" of this embodiment on samples obtained from the subject, performing a biomarker detection method according to the above-described "Detection Method" of this embodiment on samples obtained from one or more patients with depression and samples obtained from one or more healthy individuals, and obtaining baseline values ​​(normal values) of biomarkers for patients with depression and baseline values ​​(normal values) of biomarkers for healthy individuals.

[0340] Using these methods, if the measured value of a biomarker in a sample obtained from a subject is the same as the baseline value of a biomarker for a patient with depression, the data can be used to determine that the subject has depression, or that there is a high probability that the subject has depression. Alternatively, if the measured value of a biomarker in a sample obtained from a subject is different from the baseline value of a biomarker for a patient with depression, the data can be used to determine that the subject does not have depression, or that there is a high probability that the subject does not have depression.

[0341] Furthermore, if the measured values ​​of biomarkers in the sample obtained from the subject differ from the normal values, this data can be used to determine that the subject suffers from depression, or that there is a high probability that the subject suffers from depression. Conversely, if the measured values ​​of biomarkers in the sample obtained from the subject are the same as the normal values, this data can be used to determine that the subject does not suffer from depression, or that there is a high probability that the subject does not suffer from depression.

[0342] In one implementation, if the measured value of a biomarker in a sample obtained from a subject is higher than the normal value, based on this data, it can be determined that the subject suffers from depression, or that there is a high probability that the subject suffers from depression.

[0343] In patients with depression or those receiving antidepressants, the levels of Clostridia bacteria in the sialic acid-containing bacterial community in the sample are higher or tend to be higher compared to healthy individuals. Therefore, a high level of Clostridia bacteria in the sample obtained from the subject can indicate that the patient has depression, or that the patient is more likely to have depression.

[0344] In patients with depression or those receiving antidepressants, the levels of Clostridium species in the sialic acid-containing bacterial community in the sample are higher or tend to be higher compared to healthy individuals. Therefore, a high level of Clostridium species in the sample obtained from the subject can be used to diagnose depression.

[0345] In patients with depression or those receiving antidepressants, the content of sialic acid-containing bacteria, specifically the genus *Ruminococcus*, is higher or tends to be higher in samples compared to healthy individuals. Therefore, a high content of *Ruminococcus* in a sample obtained from a subject can indicate that the patient has depression, or that the patient is more likely to have depression.

[0346] In patients with depression or those receiving antidepressants, the content of Erysipelotrichaceae bacteria in the sialic acid-containing bacterial community in the sample is lower or tends to be lower compared to healthy individuals. Therefore, a low content of Erysipelotrichaceae bacteria in the sample obtained from the subject can indicate a higher probability of having depression.

[0347] In patients with depression or those receiving antidepressants, the content of *Lactobacillus* bacteria in the sialic acid-containing bacterial community in the sample is lower or tends to be lower compared to healthy individuals. Therefore, a low content of *Lactobacillus* bacteria in the sample obtained from the subject can indicate that the patient has depression, or that the patient is more likely to have depression.

[0348] In patients with depression or those receiving antidepressant medication, the bacterial content of *Lactobacillus reuteri* in the sialic acid-containing bacterial community in the sample is lower or tends to be lower compared to healthy individuals. Therefore, a low bacterial content of *Lactobacillus reuteri* in the sample obtained from the subject can be used to determine whether or not the patient has depression, or whether or not the patient is more likely to have depression.

[0349] In patients with depression or those receiving antidepressants, the levels of o-Lactobacillales in the sialic acid-containing bacterial community in samples are lower or tend to be lower compared to healthy individuals. Therefore, a low level of o-Lactobacillales in samples obtained from a subject can indicate the presence or likelihood of depression.

[0350] In patients with depression or those receiving antidepressants, compared to healthy individuals, the presence of lower or predominantly lower levels of *Erysipelotrichaceae* bacteria in the sialic acid-containing bacterial community in the sample can indicate a higher likelihood of depression.

[0351] In patients with depression or those receiving antidepressants, the content of Firmicutes bacteria in the bacterial community of the sample is higher or tends to be higher compared with healthy individuals. Therefore, if the content of Firmicutes bacteria in the sample obtained from the subject is high, it can be determined that the patient has depression, or that the patient is more likely to have depression.

[0352] In patients with depression or those receiving antidepressants, the bacterial community in the sample contains a higher or predominantly higher level of Clostridia bacteria compared to healthy individuals. Therefore, a high level of Clostridia bacteria in the sample obtained from the subject can indicate that the patient has depression, or that the patient is more likely to have depression.

[0353] In another embodiment, the method for collecting data for diagnosing depression in a subject may be a method that includes performing any of the detection methods described in the above-described "Detection Method" of this embodiment on multiple samples obtained from the same subject over time.

[0354] According to this method, if the measured value of a biomarker in a sample obtained from a subject shows a change at a certain point in time compared to the previous value, this data can be used to determine whether the subject has depression, or whether there is a high probability that the subject has depression. For example, this method can be used to monitor the subject's depression through regularly performed health checkups.

[0355] In one implementation, if the measured value of a biomarker in a sample obtained from a subject is higher than before at a certain point in time, based on this data, it can be determined that the subject suffers from depression, or that the subject is more likely to suffer from depression. This is because, for example, when the biomarker for the composition of the bacterial community containing sialic acid is the content of Clostridia bacteria, the content of Clostridia bacteria in the sialic acid-containing bacterial community is higher or tends to be higher in the samples of patients with depression compared to healthy individuals.

[0356] In one implementation, the subject's depression can be diagnosed by any of the above-mentioned determinations.

[0357] Here, the reference value or normal value can be the average or median value of the biomarker measurements obtained from the same group of subjects or from the same subjects over time, or it can be a reference range or normal range arbitrarily set based on the biomarker measurements.

[0358] Alternatively, regarding baseline or normal values, those skilled in the art may appropriately determine the values ​​that enable the determination of a subject's prevalence of depression with the required precision, based on biomarkers in samples obtained from the same group of subjects or from the same subjects over time.

[0359] Furthermore, the measured values ​​of biomarkers in samples obtained from the test subject are the same as the reference or normal values, for example, there is no significant difference from the reference or normal values ​​after any statistical treatment. The measured values ​​of biomarkers in samples obtained from the test subject are different from the reference or normal values, for example, there is a significant difference from the reference or normal values ​​after any statistical treatment. The measured values ​​of biomarkers in samples obtained from the test subject increase compared to the reference values, for example, the measured values ​​of biomarkers in samples obtained from the test subject increase significantly compared to the reference values ​​after any statistical treatment.

[0360] The statistical processing method can be appropriately selected by those skilled in the art according to known methods.

[0361] In one implementation, the baseline value relative to the normal value may be 150%, 200%, 250%, 300%, 350%, 400%, 450%, or 500% or more, and those skilled in the art may appropriately determine the value that enables the subject to be diagnosed with depression with the required accuracy and / or precision.

[0362] In one embodiment, the data collection method described above, or the method for diagnosing depression based on the data collection method, can also be implemented in combination with other data collection methods or other diagnostic methods for depression.

[0363]

Reagent test kit

[0364] In one embodiment, the kit is a kit for implementing the detection method described in the above-described "Detection Method" of this embodiment.

[0365] Examples and preferred embodiments of the "kit" are described in the fourth embodiment.

[0366] Using the above-mentioned kit, sample acquisition is non-invasive. Furthermore, by measuring and detecting biomarkers, objective and / or quantitative test results can be obtained.

[0367]

Example

[0368] [Example 1] Model animal The model animals used in the following examples are as follows.

[0369] (1) Chronic unpredictable stress model mouse group Stress-vulnerable mice (BALB / c mice) were subjected to 6 weeks of chronic ultra-mild stress (the following stress loads were randomly assigned for 24 hours: acetic acid odor, changing cages to other mouse cages, wetting the mattress, living in a cramped environment, tilting the cage, crushing food to make it difficult for them to eat, and reversing light and dark). Mice subjected to chronic ultra-mild stress were designated as the chronic ultra-mild stress (CUMS) group.

[0370] (2) Control group Individuals of stress-vulnerable mice (BALB / c mice) without the above (1) chronic unpredictable stress were used as the control group.

[0371] (3) Antidepressant treatment group Mice that were treated with imipramine hydrochloride (Sigma-Aldrich) (18 mg / kg / day dissolved in drinking water and administered orally for 3 weeks) under the chronic unpredictable stress pattern described above (1) were used as the antidepressant treatment group (CUMS + antidepressant, IMI + CUMS).

[0372] [Example 2] Analysis of bacterial composition in fecal samples Mice (n=6) in the CUMS group, control group, and CUMS + antidepressant group were placed in sterilized cages for 30–60 minutes, and their feces were collected using sterilized forceps. The feces were stored at -20°C for analysis.

[0373] Bacteria were isolated from feces using density gradient centrifugation. Specifically, approximately 20 mg of feces was placed in 0.5 mL of phosphate-buffered saline (PBS) and homogenized overnight at 4°C and 750 rpm. The supernatant of the suspension was collected and transferred to 80% w / v iohexol aqueous solution (Nycodenz, Serumwerk Bernburg), and centrifuged at 4°C and 10000×g for 40 minutes. The intermediate layer containing bacteria was collected and washed with PBS. Bacterial cells were eluted with 0.2 M lactose.

[0374] The V3-V4 hypervariable region of the 16S rRNA gene was targeted, and sequencing libraries were prepared according to the "16S Metagenomic Sequencing Library Preparation" procedure provided by Illumina. The V3-V4 region was amplified by PCR reaction using KAPA HiFi HotStart ReadyMix (Roche), which included 1 μl of extracted fecal microbial DNA and 1 μM of each of the following primers.

[0375] • Forward primer: 5'-TCGTCGCAGCGTCAGATGTGTATAAGACAGCCTACGGNGGCWGCAG-3' (Serial No. 83) • Reverse primer: 5'-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3' (Serial No. 84) PCR products were purified using an Agentcourt AMPure XP magnetic bead (Beckman Coulter). Using 2.5 μL of purified product, a second PCR was performed using Illumina index primer at the following reaction cycle: initial modification at 95°C for 3 min; modification at 95°C for 30 s, annealing at 55°C for 30 s, extension at 72°C for 8 cycles; final extension at 72°C for 5 min). The final product was purified again using AgentAMPure XP and eluted with 10 mM Tris pH 8.5 elution buffer. The purified amplicons were quantified using MultiNA (Shimadzu Corporation). The amplicons were then sequenced using the MiSeq Reagent Nano Kit V2 (Illumina) on the Illumina MiSeq 2×250 bp platform.

[0376] Raw sequencing fragments were preprocessed using the amplicon sequencing data analysis software QIIME2 (ver. 2020 / 8, https: / / qiime2.org / , Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Bolyen E. et al., Nature Biotechnology 37: 852-857, 2019.doi.org / 10.1038 / s41587-019-0209-9). Multiplexing, denoise quality control, fragment trimming, and sequence assembly were performed using the DADA2 plugin. Amplicon sequence variants (ASVs) generated according to the DADA2 algorithm were classified using the q2-feature-classifier. The output data artifacts were aggregated into phyloseq objects in R version 4.1.2 (2021-11-01), and all samples were flattened to the same depth. Subsequently, principal coordinate analysis (PCoA) was used to calculate β diversity based on the Bray-Curtis dissimilarity matrix.

[0377] The results of the analysis (principal coordinate analysis) are as follows: Figure 1 As shown, the curves representing individual mice were similar and not separated among the CUMS group, the control group, and the CUMS + antidepressant group. No significant differences were found in the bacterial flora (bacterial composition) obtained from fecal samples among the groups.

[0378] As described above, after PCR, the sequence data obtained by sequencing on the Illumina MiSeq 2×250 bp platform using the MiSeq Reagent Nano Kit V2 (Illumina) were analyzed based on LEfSe (Lineardiscriminant analysis Effect Size) (Segata et al., Metagenomic biomarker discovery and explanation, Genomic Biol. 2011; 12 (6):R60, doi. 10.1186 / gb-2011-12-6-r60).

[0379] The analysis revealed significantly higher cell numbers in the CUMS group, including bacteria from the phylum Firmicutes (p_Firmicutes) and Clostridia (c_Clostridia). Figure 2 ).

[0380] The composition of bacterial communities in fecal samples can be used as a biomarker for determining the level of stress experienced by the subject and / or for diagnosing the subject's prevalence of depression.

[0381] [Example 3] Analysis of sialic acid addition sugar chains in fecal bacteria based on Glycan-seq method The following analysis was performed using the Glycan-seq method developed by Oinam et al.

[0382] Mice (n=6) in the CUMS group, control group, and CUMS + antidepressant group were placed in sterilized cages for 30–60 minutes, and their feces were collected using sterilized forceps. The feces were stored at -20°C for analysis.

[0383] Bacteria were isolated from feces using density gradient centrifugation. Specifically, approximately 20 mg of feces was placed in 0.5 mL of phosphate-buffered saline (PBS) and homogenized overnight at 4°C and 750 rpm. The supernatant was collected and transferred to 80% w / v iohexol aqueous solution (Nycodenz, Serumwerk Bernburg), and centrifuged at 4°C and 10000×g for 40 minutes. The intermediate layer containing bacteria was collected and washed with PBS. The number and size of bacterial cells were determined using a particle counting analyzer (CDA-1000, Sysmex).

[0384] (Preparation of DNA barcoded lectins) 100 μg of each lectin (refer to “Name” in Table 1) was dissolved in 100 μl of PBS containing 10 times the molar amount of dibenzocyclooctyne-N-hydroxysuccinimide ester (DBCO-NHS, Funakoshi Corporation) and incubated at 20°C in the dark for 1 hour. 10 μl of 1 M Tris solution was added to inactivate DBCO-NHS, and the mixture was incubated at 20°C in the dark for 15 minutes. Excess DBCO-NHS was removed using a Sephadex G-25 desalting column (GE Healthcare Japan). Each DBCO-labeled lectin (100 μg / mL) was mixed with 5' azide-modified DNA oligonucleotides (Integrated DNA Technologies, at a molar ratio of 10 times, sequence numbers 1–41) having the sequence corresponding to the lectin shown in “Name” in Table 1. Furthermore, using agarose gel 4B-CL (Sepharose 4B-CL, GE Healthcare Japan) immobilized with affinity sugars based on the glycan binding specificity of each lectin, lectins with their respective glycan binding affinity were purified by affinity chromatography, and excess nucleotides were removed to prepare lectin-DNA oligonucleotide conjugates.

[0385] Table 1

[0386] (Glycan-seq method) Bacterial cells (1×10) 7The bacterial cells were suspended in PBS containing 1% bovine serum albumin (BSA) (PBS / BSA) and incubated at 4°C for 1 hour with 39 lectins barcoded with DNA (sequence numbers 1–41) and two negative control proteins (final concentration 0.5 μg / mL). The cells were then washed three times with PBS / BSA and irradiated for 15 minutes at 365 nm, 15 W, using a long-wavelength black light UV benchtop lamp (UVPBlak-Ray XX-15L UV Bench Lamp, Anakitik Jena, Germany). The released oligonucleotides were amplified by PCR using NEBNext Ultra II Q5 Master Mix (New England BioLabs) and primers including i5-index primers (Integrated DNA Technologies, Table 2 (Sequence Nos. 85, 87–104)) and i7-index primers (Integrated DNA Technologies, Table 2 (Sequence Nos. 86, 105–122)). PCR was performed with one cycle of denaturation (98°C, 45 sec), 20 cycles of denaturation (98°C, 10 sec followed by 65°C, 50 sec), and one cycle of extension (65°C, 5 min). This PCR method enabled the detection of DNA barcodes corresponding to the lectins shown in Table 1. Furthermore, by using primer pairs with different sequencing adapters (“Bases in Adapter” in Table 2), experimental groups could be distinguished even within the mixed PCR products. PCR products were purified using the Agencourt AMPure XP Kit (Beckman Coulter). The size and quality of the PCR products were analyzed using MultiNA (Shimadzu Corporation). The PCR products (4 nM) of each sample were processed using the MiSeq Reagent Kit v2 (Illumina), and the sequences were determined using the MiSeq Sequencer (Illumina).

[0387] Table 2

[0388] DNA barcode information isolated from lectins was directly extracted in FASTQ format.

[0389] DNA barcode sequences corresponding to each lectin were extracted from FASTQ format files, and the number of DNA barcodes was determined using a barcode DNA counting system (Mizuho Research & Technologies). Table 1, “Lectin barcode sequence for NGS” (serial numbers 42–82), shows the DNA barcode sequences with the inherent sequences of each lectin used to determine the number of DNA barcodes. The total number of DNA barcodes divided by the total number of lectin barcodes is expressed as a percentage (%) of each lectin. The statistical significance of lectins in Glycan-seq was evaluated by Holm–Bonferronimethod multiple hypothesis testing correction using abundance analysis based on compositional data adapted to Analysis of Compositions of Microbiomes (ANCOM). Statistically significant lectins were selected with p < 0.05.

[0390] The results showed that the amount of DNA barcode corresponding to the sialic acid-binding lectin, Sambucus nigra Lectin (Elderberry) bark, was significantly lower in the CUMS group compared to the control group and the CUMS + antidepressant group. On the other hand, there was no significant difference between the CUMS + antidepressant group and the control group. Figure 3 *: p<0.05).

[0391] The results indicate that, compared with the control group, the binding of SNA to bacterial cells in the sample was significantly lower in the CUMS group, while the binding of SNA to bacterial cells in the sample was restored to the same level as in the control group in the CUMS + antidepressant administration group.

[0392] [Example 4] Social Experiment A suitable area (30cm x 30cm x 16cm) was prepared, with a cage (10cm in diameter, 17.5cm high, 23 grids spaced 8mm apart) at one end. One target mouse was placed in the cage. Mice from each experimental group (n=6) were placed into the area one by one, and their behavior was observed for 3 minutes. During the observation period, the shorter the time the mice in the experimental groups stayed in the target area (12cm x 20cm centered on the target mouse), the less interest they showed. This can be considered a behavior reflecting individual stress load or depressive-like behavior.

[0393] In the CUMS group, the approach time (in seconds) with the target mouse was significantly shorter in the social interaction test, strongly reflecting stress-related behaviors or depressive-like behaviors. On the other hand, the control group and the CUMS + antidepressant group showed significantly longer approach times with the target mouse in the social interaction test. These results are as follows... Figure 4 As shown (*: p<0.05).

[0394] (A test on the binding and sociality of lectins) Figure 5 The results of an analysis of the correlation between the SNA binding of Example 3 and the results of the socialization experiment of this example are shown.

[0395] In the CUMS group with low lectin binding, short approach times to the target mice were strongly observed in the socialization test, reflecting stress-related behaviors or depressive-like behaviors. On the other hand, in the control group with high lectin binding and the CUMS+antidepressant group (IMI+CUMS), longer approach times to the target mice were observed in the socialization test.

[0396] Therefore, it was determined that the associativity of SNA was significantly correlated with the target proximity time in social tests (R = 0.67, p = 0.0026).

[0397] [Example 5] Analysis of the species composition in bacterial communities containing sialic acid 1 μg / μl of SNA was labeled with biotin and incubated with streptavidin-bound Dynabeads (Thermo Fisher Scientific) in a shaker set at 1,400 rpm and 4°C for 1 hour. 1 × 10⁻⁶ SNA was isolated from feces from three sample groups (control group, CUMS group, and CUMS + antidepressant group) (n=6) in the same manner as in Example 3. 7 Bacterial cells were incubated overnight in a shaker at 700 rpm and 4°C with washed SNA-binding beads. The bound bacterial cells were then separated using magnetic beads and eluted with 0.2 M lactose.

[0398] As described in Example 2, sequencing libraries were prepared targeting the V3-V4 hypervariable region of the 16S rRNA gene, and the V3-V4 region was amplified by PCR.

[0399] As described in Example 2, the PCR product was purified, a second PCR was performed, and sequencing was performed after purification.

[0400] Furthermore, as described in Example 2, β-diversity was calculated using principal coordinate analysis (PCoA) based on α-diversity (Wilcoxon pairwise test, p<0.05) and the Bray-Curti dissimilarity matrix.

[0401] The results of the analysis are as follows Figure 6 As shown.

[0402] like Figure 6 As shown, compared with the control group, the number of c-Clostridia bacteria was significantly increased in both the CUMS group and the CUMS + antidepressant administration group (IMI + CUMS). Furthermore, compared with the control group, the number of o-Clostridiales and g-Ruminococcus bacteria was significantly increased in both the CUMS group and the CUMS + antidepressant administration group.

[0403] On the other hand, compared with the CUMS group or the CUMS + antidepressant group, the number of bacteria of the genus *g_Lactobacillus*, the family of *f_Erysipelotrichaceae*, the genus *g_Lactobacillus_reuteri*, the family of *o_Lactobacillales*, and the species of *s_Erysipelotrichaceae* was significantly increased in the control group.

[0404] (Open field test) Mice from each group were placed in an open field measuring 30cm in length, 30cm in width, and 16cm in height. The time taken for the mouse to enter a 10cm square area in the center was measured. This time is an indicator of restless behavior; it is known that the stronger the restlessness, the shorter the time spent in the center.

[0405] In the open field test (OFT), the relationship between the time (in seconds, y-axis) of mice remaining in the center and the number of cells of *g. Lactobacillus reuteri* and *o. Clostridium* was as follows: Figure 7 As shown.

[0406] In the open field test, the longer the mice remained in the center, the greater the increase in the number of *g. Lactobacillus reuteri* bacteria, which was considered a significant correlation (R = 0.7, p = 0.0014). Figure 7 ,Left).

[0407] Furthermore, in the open field test, the shorter the time mice spent in the center, the greater the increase in the number of o-Clostridiales bacteria, which was considered a significant correlation (R = -0.55, p = 0.017). Figure 7 ,right).

[0408] Based on the above results, it was determined that there were significant differences in the cell numbers of bacteria belonging to the class Clostridia, order Clostridium, genus Ruminococcus, genus Lactobacillus, family Erysipelotrichaceae, genus Lactobacillus reuteri, order Lactobacillales, and species Erysipelotrichaceae that contain sialic acid between the control group, the CUMS group, and the CUMS + antidepressant group. The cell numbers of these bacteria can be used as biomarkers for determining stress levels and / or for diagnosing the presence of depression.

[0409] In addition, the composition of bacterial species in a bacterial community containing sialic acid can be used as a biomarker for determining the level of stress experienced by the subject and / or for diagnosing the subject's depression.

[0410] [Example 6] Analysis of sialidase activity Add 10 mg of feces to 1 ml of Tris-HCl buffer (0.15 M NaCl, 1% PBSTx (0.25% Triton-X100 (PBS+Tx), pH 6.7) and homogenize on ice using a tissue homogenizer (Nippy, product code: 320103). After incubating the homogenized feces on ice for 1 hour, centrifuge at 4000 g for 3 minutes. Recover the supernatant and pass it through a Durapore membrane filter (MilliporeSigma, catalog number: UFC30GV0S), then centrifuge at 15000 rpm for 10 minutes, and recover the supernatant. Quantify the isolated protein using the Pierce BCA Protein Assay Kit (Thermo Fisher Scientific, catalog number: 23225). For 10 μg of protein-containing sample, use the Neuraminidase assay. The neuraminidase assay kit (Sigma-Aldrich, MAK121) was used to determine sialidase activity according to the operating procedure. 80 μl of reaction solution, 20 μl of sample, and 20 μl of standard were added to each well of a 96-well plate (Proteosave SS96F plate, SUMILON, catalog number: MS-8296F). The plate was incubated at 37°C in the dark for 80 minutes. After 80 minutes, the absorbance was measured at 570 nm using a SpectraMax multimode microplate reader (Miyako Molecular Instruments (Japan) Co., Ltd.) (colorimetric determination).

[0411] The results of the analysis are as follows Figure 8 As shown.

[0412] Compared with the control group or the CUMS+antidepressant group (IMI+CUMS), the CUMS group had significantly higher sialidase activity (p<0.05).

[0413] (Forced swimming test (FST)) In a cylindrical container 20 cm high and 14 cm in diameter, water at 23 ± 0.5 °C was filled to a depth of 13 cm. Mice were allowed to swim in the container and observed for 5 minutes, during which the immobility time was measured. Immobility time is an indicator of individual stress load and depressive behavior; the shorter the immobility time, the stronger the anti-stress and antidepressant effects.

[0414] Figure 9The relationship between immobility time (seconds, x-axis) in the forced swimming test and sialidase activity in mouse fecal samples (y-axis) is shown.

[0415] The longer the immobility time in the forced swimming test, the higher the sialidase activity, which was considered to be significantly correlated (R = 0.85, p = 0.0034).

[0416] (Cell number and sialidase activity of cClostridia bacteria) Figure 10 The results show the correlation between the number of Clostridia bacteria in Example 2 and the sialidase activity of this example.

[0417] The number of bacteria identified as c-Clostridia was significantly correlated with sialidase activity (R = 0.88, p = 0.004).

[0418] (Bacterial cell number and sialidase activity of p-Firmicutes) Figure 11 The results show the correlation analysis between the cell number of Firmicutes (p_Firmicutes, including Clostridia) of Example 2 and the sialidase activity of this example.

[0419] The number of bacteria identified as Firmicutes (p_Firmicutes) was significantly correlated with sialidase activity (R = 0.83, p = 0.011).

[0420] [Example 7] Analysis of human clinical samples Stool samples from one healthy human subject (lot number 22-08-527) and one patient with depression (lot number 22-08-535) were purchased from Medix Biochemica. The conditions of the healthy person and the patient with depression are shown in Table 3.

[0421] Except for the protein-containing sample used for the sialidase activity assay, which was 100 μg instead of 10 μg, sialidase activity was measured in the same manner as in the mouse feces assay in Example 6.

[0422] Table 3

[0423] The results of the analysis are as follows Figure 12 As shown in Table 3.

[0424] In healthy individuals (control group), the absorbance (OD) representing sialidase activity was 0.4685, while in patients with depression, the absorbance (OD) representing sialidase activity was 1.072.

[0425] Therefore, compared with healthy human individuals, fecal samples from patients with depression showed higher sialidase activity.

[0426] Based on the above results, the sialidase activity of bacteria from fecal samples can be used as a biomarker to determine the stress level experienced by the subject and / or as a biomarker to diagnose the subject's depression.

[0427] In addition, the sialidase activity was at the same level as that in healthy individuals who received antidepressant treatment, so sialidase activity can be used as a biomarker to evaluate the effectiveness of antidepressants.

[0428] [Industry Applicability] Sialidase activity in samples obtained from subjects reflects the stress level experienced by the subjects, the prevalence of depression in the subjects, and the effectiveness of antidepressants in the subjects. Additionally, the composition of bacterial species within the sialic acid-containing bacterial community in samples obtained from subjects reflects the stress level experienced by the subjects and the prevalence of depression in the subjects.

[0429] Therefore, the sialidase activity and the composition of bacterial species in the sialic acid-containing bacterial community in the samples obtained from the subjects are suitable as biomarkers for evaluating the stress level experienced by the subjects, the prevalence of depression in the subjects, and / or the effect of antidepressants on the subjects, and are industrially available.

Claims

1. A biomarker used to determine the level of stress experienced by a subject, wherein, The biomarker is the sialidase activity of bacteria from a sample obtained from the subject.

2. The biomarker according to claim 1, wherein, The sample was feces.

3. The biomarker according to claim 1, wherein, The bacteria include bacteria belonging to the class Clostridium.

4. A method for detecting a biomarker, wherein, Used for detecting the biomarker of claim 1 in a sample obtained from a subject.

5. A method for collecting data, wherein the data is used to determine the stress level experienced by a subject, wherein, The data collection method includes the detection method described in claim 4.

6. A kit for determining the stress level experienced by a subject, wherein, Includes a unit for detecting the biomarker of claim 1.

7. A biomarker for evaluating the effect of an antidepressant in a subject, wherein, The biomarker is the sialidase activity of bacteria from a sample obtained from the subject.

8. The biomarker according to claim 7, wherein, The sample was feces.

9. The biomarker according to claim 7, wherein, The bacteria include bacteria belonging to the class Clostridium.

10. A method for detecting a biomarker, wherein, Used for detecting the biomarker of claim 7 in a sample obtained from a subject.

11. A method for collecting data, wherein the data is used to evaluate the effect of antidepressants on subjects, wherein, The data collection method includes the detection method described in claim 10.

12. A kit for evaluating the effect of an antidepressant in a subject, wherein, Includes a unit for detecting the biomarker of claim 7.

13. A biomarker for determining the stress level experienced by a subject, wherein, The biomarker is the composition of bacterial species in a sialic acid-containing bacterial community from a sample obtained from the subject.

14. The biomarker according to claim 13, wherein, The composition of bacterial species in a sialic acid-containing bacterial community is the content of Clostridium bacteria in that community.

15. The biomarker according to claim 13, wherein, The sample was feces.

16. A method for detecting a biomarker, wherein, Used for detecting the biomarker of claim 13 in a sample collected from a subject.

17. A method for collecting data, wherein the data is used to determine the stress level experienced by a subject, wherein, The data collection method includes the detection method described in claim 16.

18. A kit for determining the stress level experienced by a subject, wherein, Includes a unit for detecting the biomarker of claim 13.

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