KR102547505B1 - Biomarker for early prediction of the treatment effect in major depressive disorder - Google Patents

Abstract

The present invention relates to a biomarker for early predicting the treatment effect of major depressive disorder, and more particularly, to a biomarker for early prediction of the treatment effect of a selective serotonin reuptake inhibitor selected from the group consisting of EPHOX2B, SH3BGRL3 and YWHAE. When using the biomarker according to the present invention, it is possible to detect patients with poor drug treatment efficacy at an early stage, thereby reducing the time and cost of treating depressed patients and minimizing the exposure of depressed patients to unnecessary drugs. There are advantages to being

Description

Biomarker for early prediction of the treatment effect in major depressive disorder {Biomarker for early prediction of the treatment effect in major depressive disorder}

The present invention relates to a biomarker for early predicting the treatment effect of major depressive disorder, and more particularly, to a biomarker for early prediction of the treatment effect of a selective serotonin reuptake inhibitor selected from the group consisting of PHOX2B, SH3BGRL3 and YWHAE.

Major depressive disorder (MDD) is a mental disorder in which a depressive state characterized by persistent depression and decreased activity appears continuously or repeatedly, and is a disease that occurs in all ages and races. Symptoms include depression or hopelessness, marked loss of interest or pleasure, decreased or increased appetite and weight, decreased or increased sleep, physical agitation or slowed activity, loss or fatigue of sexual desire, inappropriate guilt or responsibility, feelings of worthlessness, Difficulty concentrating or indecisiveness, thoughts of death or suicide. If these symptoms persist for more than 2 weeks at a serious level that causes problems in social life as well as daily life, major depressive disorder can be diagnosed. Major depressive disorder is a disease with a very high recurrence rate, with an overall recovery rate of only 20% and 80% of recovered patients experiencing at least one relapse during their lifetime.

Antidepressants are prescribed for the treatment of major depressive disorder, and among the antidepressants, selective serotonin reuptake inhibitors (SSRIs) are the most representative antidepressants.

However, in clinical practice, a lot of trial and error is experienced in the process of selecting and prescribing these antidepressants. There is a significant percentage of patients who have been prescribed antidepressants for 4-6 weeks, but the efficacy of the initial drug treatment is not good. In this case, within the same class Or change to another class of antidepressant and proceed with treatment.

In addition, it has been reported that the treatment effect using antidepressants is not very good, and about 50% of patients enrolled in the STAR*D (Sequenced Treatment Alternative to Relieve Depression) study did not respond to standard SSRI treatment. , only about 30% have reported remission in response to first used antidepressants. In this case, the selection of the second drug to be administered to patients who do not respond to standard SSRI treatment is very important, and if the response to antidepressant treatment can be predicted in advance in the early stage of antidepressant use, the time and It will reduce costs and minimize exposure to unnecessary drugs.

On the other hand, proteomics is a technology that quantitatively analyzes all proteins expressed in a sample. It is one of the powerful tools for identifying new molecular biomarkers, and reflects various biological pathways in patients with major depressive disorder in response to treatment. (Martins-de-Souza, D. et al., Eur. Arch. Psychiatry Clin. Neurosci. 2010, 260, 499-506). Proteomic analysis of peripheral body fluids, such as plasma and serum, is helpful in predicting response to clinical therapy and monitoring drug activity, especially in the early clinical phase. However, to date, proteomic analysis of peripheral blood specimens that can predict the response to antidepressant treatment is rare, and liquid chromatography tandem mass spectrometry (LC-MS/MS) has shown that several plasma proteins respond to antidepressant drugs during a 6-week treatment period. In a multiplex immunoassay test for up to 258 blood-based markers related to immune, endocrine and metabolic mechanisms, 9 markers were potential pre-treatment biomarkers related to antidepressant treatment response. (Turck, CW et al., Front. Mol. Neurosci. 2017, 10, 272; Chan, MK et al., J. Psychiatr. Res. 2016, 83, 249-259).

Longitudinal data are commonly used in biomedical research, where mixed-effect models (MEMs) and generalized estimating equations (GEEs) are widely applied for statistical analysis. Among them, MEM is a subject-level approach based on the full-likelihood method, especially in settings where repeated measurements are made in the same statistical unit or measurements are made in clusters of related statistical units. useful. Mixed-effects models are preferred over more traditional approaches, such as repeated measures interpretation of variance, because of their advantages in handling missing values. Conversely, GEE is a population-level model that relies on a partial-likelihood function. In proteomics studies, the MEM method is more widely used than the GEE method because it can measure and estimate random effects of technical or biological replicates with actual MS after fixing the desired effect. It is also technically easier to reflect the variance of any effect for repeated measurements of the same sample after two or more MSs. In contrast, GEE is robust in terms of errors in establishing a correlation structure using quasi likelihood, so many modified variance estimation methods for samples have been developed.

The present inventors have made diligent efforts to identify biomarkers that can predict the response to antidepressant treatment in advance in the early stage of antidepressant use, in order to reduce the time and cost required to treat depressed patients and minimize exposure to unnecessary drugs. As a result, Among the depressed patients taking escitalopram, escitalopram showed a therapeutic effect (hereinafter referred to as 'responders') and depressed patients in which escitalopram did not show a therapeutic effect ( Hereinafter referred to as 'non-responders'), the present invention was completed by newly identifying protein biomarkers with significantly different expression patterns.

Martins-de-Souza, D. et al., Eur. Arch. Psychiatry Clinic. Neurosci. 2010, 260, 499-506 Turck, C.W. et al., Front. Mol. Neurosci. 2017, 10, 272 Chan, M.K. et al., J. Psychiatr. Res. 2016, 83, 249-259

The present invention relates to a novel biomarker capable of early predicting the therapeutic effect of a selective serotonin reuptake inhibitor and its use.

In order to achieve the above object, the present invention provides a biomarker for predicting the therapeutic effect of a selective serotonin reuptake inhibitor selected from the group consisting of PHOX2B, SH3BGRL3 and YWHAE.

In the present invention, the selective serotonin reuptake inhibitor is characterized in that escitalopram.

In the present invention, the biomarker is characterized in that the treatment effect can be predicted within at least one week after administration of escitalopram.

The present invention also relates to a composition for predicting the therapeutic effect of a selective serotonin reuptake inhibitor, comprising an agent for measuring the expression level of at least one protein selected from the group consisting of PHOX2B, SH3BGRL3 and YWHAE, a fragment thereof, or a gene encoding the protein. .

In the present invention, the composition is characterized in that a therapeutic effect can be predicted within at least one week after administration of a selective serotonin reuptake inhibitor.

In the present invention, the agent is characterized in that it is an antibody or antigen-binding fragment thereof, or an aptamer that specifically binds to the biomarker protein or fragment thereof.

In the present invention, the agent is characterized in that it is a primer, probe or antisense nucleotide that specifically binds to the polynucleotide encoding the biomarker protein.

The present invention also provides a kit for predicting the effect of a selective serotonin reuptake inhibitor treatment comprising the composition.

The present invention also provides an information providing method for predicting the effect of a selective serotonin reuptake inhibitor treatment, comprising the following steps:

(a) measuring the expression level of at least one biomarker protein selected from the group consisting of PHOX2B, SH3BGRL3 and YWHAE, a fragment thereof, or a gene encoding the protein in a sample isolated from a subject administered with a selective serotonin reuptake inhibitor therapeutic agent step; and

(b) comparing the measured expression level with that before administration of a selective serotonin reuptake inhibitor.

In the present invention, the sample is blood, plasma, serum, urine, mucus, saliva, tears, sputum, spinal fluid, pleural fluid, nipple aspirate, lymph fluid, airway fluid, serous fluid, genitourinary tract fluid, breast milk, lymphatic body fluid, semen, It is characterized in that it is cerebrospinal fluid, intraorgan system fluid, ascites, cystic tumor fluid, amniotic fluid, or a combination thereof.

In the present invention, the selective serotonin reuptake inhibitor is characterized in that escitalopram.

In the present invention, the sample is characterized in that it is isolated from the subject 3 to 14 days after administration of a selective serotonin reuptake inhibitor.

In the present invention, the step of measuring the expression level of the protein or fragment thereof includes electrophoresis, immunoblotting, enzyme-linked immunosorbent assay (ELISA), immunohistochemical staining, protein chip, and immunoprecipitation. , mass spectrometry, or a combination thereof; And / or the step of measuring the expression level of the gene is RT-PCR, competitive RT-PCR (Real-time RT-PCR), real-time RT-PCR (Real-time RT-PCR), RNase protection assay (RPA: RNase protection assay ), Northern blotting, DNA chip, or a combination thereof. do.

In the present invention, the biomarker expression level measured in the isolated sample is compared to before administration of the selective serotonin reuptake inhibitor

(i) increased expression of PHOX2B;

(ii) decreased SH3BGRL3 expression; and/or

(iii) decreased YWHAE expression; If

The selective serotonin reuptake inhibitor is characterized in that it is predicted to have a therapeutic effect.

When using the biomarker according to the present invention, it is possible to detect patients with poor drug treatment efficacy at an early stage, thereby reducing the time and cost of treating depressed patients and minimizing the exposure of depressed patients to unnecessary drugs. There are advantages to being

Figure 1 shows the results of plasma proteomic analysis and the function of these proteins to identify changes in differentially expressed proteins according to drug administration. Figure 1A shows drug administration in drug responders and non-responders. This is the result of confirming the differentially abundant proteins (differentially abundant proteins) compared to the starting point of drug administration at 1 week, 4 weeks, and 10 weeks, and FIG. 1B is compared to T ₀ , T ₁ , T ₄ , T ₁₀ is a result of displaying differentially expressed proteins in a Venn diagram, and FIG. 1C is a gene ontology of proteins whose expression patterns are differentially changed between responders and non-responders at T ₁ , T ₄ , and T ₁₀ compared to T ₀ ( represents gene ontology.
Figure 2 shows the results of affinity propagation clustering, profile analysis, and database search of 37 proteins identified as significantly different in response/time interactions of the linear mixed model (LMM). Figure 2A is the result of identifying seven protein clusters by t-SNE-based affinity propagation clustering, and Figure 2B shows the change in protein amount between responders and non-responders over time. Figure 2C shows the results of 14 proteins associated with mental disorders identified in PsyGeNet, and Figure 2D shows 10 proteins found in Enrichr's DrugMatrix category for selective serotonin reuptake inhibitors (SSRI) in rat tissues and This result shows that there is a correlation with the response of cells.
3 shows liquid chromatography-multiple reaction monitoring/mass spectrometry (LC-MRM/MS) verification results for 10 candidate predictive biomarkers that respond early after drug administration. 3A shows Venn diagrams for candidate biomarker detection by three statistical analyzes (T ₀ versus T ₁ and T ₁ -T ₀ between two groups, the response/time term in LMM). 3B is a box showing the abundance of PHOX2B, SH3BGRL3 and YWHAE at T ₁ versus T ₀ determined by LC-MRM/MS in responders (blue) and non-responders (red). * Asterisks indicate significant adjusted p- values at the 0.05 level.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. In general, the nomenclature used herein and the experimental methods described below are those well known and commonly used in the art.

In the present invention, escitalopram, one of the selective serotonin reuptake inhibitors, is administered to patients with major depressive disorder, and the baseline and follow-up visits after treatment are administered to responders who exhibit drug efficacy and non-responders who do not exhibit drug efficacy. Through this, changes in surrogate plasma proteins over time were measured, the biological meaning of protein changes was identified centering on the results of linear mixed model (LMM) analysis, and the same data were analyzed by GEE. After that, the results were compared.

Then, in the present invention, a candidate blood-based protein biomarker capable of monitoring early (0-1 weeks), intermediate (1-4 weeks) or late (4-10 weeks) responses before and after administration of the antidepressant escitalopram was developed. LC-MS/MS profiling was performed to identify. In addition, the present invention evaluated whether changes in plasma protein concentration after antidepressant treatment were associated with changes in the severity of depressive symptoms.

Blood samples were collected at 4 time points during 10 weeks of treatment in 10 depressed patients (5 responders, 5 non-responders) taking escitalopram, and protein abundance between the two groups was analyzed. ), as a result of profiling plasma proteins with a difference in drug administration, after 1 week of drug administration, the PHOX2B protein level was significantly increased in responders, while the SH3BGRL3 and YWHAE protein levels were significantly decreased in responders. It was confirmed that no significant change was induced in this non-responder. These results were further validated in 24 patients, 19 responders and 5 non-responders.

Accordingly, in one aspect, the present invention relates to a biomarker for predicting the therapeutic effect of a selective serotonin reuptake inhibitor selected from the group consisting of PHOX2B, SH3BGRL3 and YWHAE.

In the present invention, PHOX2B may be represented by CCHS, NBLST2, NBPhox, or PMX2B as paired like homeobox 2B, and may be an encoded protein including the nucleotide sequence of Gene ID: 8929 in humans. The PHOX2B protein, for example, Uniprot Accession No. Q99453, GeneBank Accession No. It may be a protein including nucleotide or amino acid sequences such as NM_003924.4 and NP_003915.2. The PHOX2B protein includes an isoform or a precursor thereof, but in the present invention, the amino acid sequence represented by SEQ ID NO: 1 is described as a representative thereof.

In the present invention, SH3BGRL3 may be represented by HEL-S-297, SH3BP-1, or TIP-B1 as SH3 domain binding glutamate rich protein like 3, and is an encoded protein containing the nucleotide sequence of Gene ID: 83442 in humans. can The SH3BGRL3 protein, for example, Uniprot Accession No. Q9H299, GeneBank Accession No. It may be a protein including nucleotide or amino acid sequences such as NM_031286.4 and NP_112576.1. The SH3BGRL3 protein includes an isoform or a precursor thereof, but in the present invention, the amino acid sequence represented by SEQ ID NO: 2 is described as a representative thereof.

In the present invention, YWHAE is tyrosine 3-monooxygenase / tryptophan 5-monooxygenase activation protein epsilon, which can be represented by 14-3-3E, HEL2, KCIP-1, MDCR, MDS, and in humans, the nucleotide sequence of Gene ID: 7531 It may be an encoded protein comprising The YWHAE protein, for example, Uniprot Accession No. P62258, GeneBank Accession No. It may be a protein including nucleotide or amino acid sequences such as NM_006761.5 and NP_006752.1. The YWHAE protein includes an isoform or a precursor thereof, but in the present invention, the amino acid sequence represented by SEQ ID NO: 3 is described as a representative thereof.

[PHX2B sequence information]

<SEQ ID NO: 1> UniProtKB Sequence: Q99453 (314 aa)

MYKMEYSYLNSSAYESCMAGMDTSSLASAYADFSSCSQASGFQYNPIRTTFGATSGCPSLTPGSCSLGTLRDHQSSPYAAVPYKLFTDHGGLNEKRKQRRIRTTFTSAQLKELERVFAETHYPDIYTREELALKIDLTEARVQVWFQNRRAKFRKQERAAAAAAAAKNGSSGKKSDSSRDDESKEAKSTDPDSTGGPNPNPTPSCGANGGGGG GPSPAGAPGAAGPGGPGGEPGKGGAAAAAAAAAAAAAAAAAAGGLAAAGGPGQGWAPGPGPITSIPDSLGGPFASVLSSLQRPNGAKAALVKSSMF

[SH3BGRL3 sequence information]

<SEQ ID NO: 2> UniProtKB Sequence: Q9H299 (93 aa)

MSGLRVYSTSVTGSREIKSQQSEVTRILDGKRIQYQLVDISQDNALRDEMRALAGNPKATPPQIVNGDQYCGDYELFVEAVEQNTLQEFLKLA

[YWHAE sequence information]

<SEQ ID NO: 3> UniProtKB Sequence: P62258 (255 aa)

MDDREDLVYQAKLAEQAERYDEMVESMKKVAGMDVELTVEERNLLSVAYKNVIGARRASWRIISSIEQKEENKGGEDKLKMIREYRQMVETELKLICCDILDVLDKHLIPAANTGESKVFYYKMKGDYHRYLAEFATGNDRKEAAENSLVAYKAASDIAMTELPPTHPIRLGLALNFSVFYYEILNSPDRACRLAKAAFDDAIAELDTLSE ESYKDSTLIMQLLRDNLTLWTSDMQGDGEEQNKEALQDVEDENQ

As used herein, the term "biomarker" is a substance that can distinguish responsiveness according to antidepressant administration, and since the expression pattern is different between a responder and a non-responder according to antidepressant administration, for example, the expression level at the mRNA or protein level Since it is different in the responder group and the non-responder group, it means a substance that can determine or evaluate the responsiveness to antidepressant administration by evaluating its expression level.

In the present invention, prediction means determining or evaluating whether a therapeutic effect is exerted by administration of an antidepressant, and includes both meanings of determining a prognosis or diagnosing a therapeutic effect.

Preferably, the biomarker according to the present invention can predict the therapeutic effect of a selective serotonin reuptake inhibitor in advance or early, for example, within 14 days, for example, within 7 days from the start date (reference date) of treatment.

In the present invention, the selective serotonin reuptake inhibitor may preferably be escitalopram.

In the present invention, a sample may be taken from the biomarker within 4 weeks, within 3 weeks, within 2 weeks, or within 1 week after administration of escitalopram, so that future treatment effects may be predicted. The collection period is not limited thereto. For example, in the present invention, within 1 day, within 2 days, within 3 days, within 4 days, within 5 days, within 6 days, within 7 days, within 8 days, within 9 days, within 10 days after administration of escitalopram Within, Within 11 days, Within 12 days, Within 13 days Within 14 days, Within 15 days, Within 16 days, Within 17 days, Within 18 days, Within 19 days, Within 20 days, Within 21 days, Within 22 days, 23 It may be characterized in that a sample is taken within 24 days, 25 days, 26 days, 27 days, or 28 days to predict future treatment effects.

In one aspect, the present invention may be characterized in that the therapeutic effect can be predicted within at least one week after administration of escitalopram from the biomarker.

In another aspect, the present invention provides a selective serotonin reuptake inhibitor treatment comprising an agent for measuring the expression level of at least one biomarker protein selected from the group consisting of PHOX2B, SH3BGRL3 and YWHAE, a fragment thereof, or a gene encoding the protein. It relates to a composition for predicting effects.

In the present invention, the composition may be characterized in that a therapeutic effect can be predicted within at least one week after administration of a selective serotonin reuptake inhibitor, but is not limited thereto.

In the present invention, the agent may be an antibody or antigen-binding fragment thereof, or an aptamer that specifically binds to the biomarker protein or fragment thereof, but is not limited thereto.

In the present invention, the agent may be a primer, probe, or antisense nucleotide that specifically binds to the polynucleotide encoding the biomarker protein, but is not limited thereto.

In the present invention, the fragment of the biomarker protein may be an immunogenic fragment. That is, the fragment of the biomarker protein may be a fragment having one or more epitopes that allow the biomarker protein to be recognized by an antibody.

In the present invention, the "measurement of protein expression level" is a process of confirming the presence and expression level of a biomarker protein in a sample of a subject, and may be measuring the amount of the biomarker protein. Alternatively, it may be identifiable by measuring the expression level of a polynucleotide encoding a biomarker protein, for example, mRNA.

In one aspect, the expression level of the biomarker protein can be measured using an antibody or antigen-binding fragment or aptamer that specifically binds to the biomarker protein or fragment thereof.

Other analysis methods for measuring the expression level of biomarker proteins include western blotting, enzyme linked immunosorbent assay (ELISA), radioimmunoassay (RIA), radioimmunodiffusion, and oxteroney ( Ouchterlony) immunodiffusion assay, rocket immunoelectrophoresis, tissue immunostaining, immunoprecipitation assay, complement fixation assay, Fluorescence Activated Cell Sorter (FACS), and protein chip chip), mass spectrometry, etc., but are not limited thereto.

As used herein, the term "antibody" is a term known in the art and may refer to a specific protein molecule directed against an antigenic site. The type of antibody according to one aspect is not particularly limited, and polyclonal antibodies, monoclonal antibodies, or any immunoglobulin antibody may be included as long as it has antigen binding properties, and furthermore, special antibodies such as humanized antibodies may be included. Antibodies may also be included. Antibodies according to one embodiment may include functional fragments of the antibody molecule as well as complete forms having two full-length light chains and two full-length heavy chains. A functional fragment of an antibody molecule refers to a fragment having at least an antigen-binding function, and may be, for example, Fab, F(ab'), F(ab')2, Fv, scFv, and the like.

The "gene expression level" may be confirmed by measuring the mRNA expression level of the gene or the expression level of the protein encoded by the gene. In one aspect, a gene with a difference in expression level may be used as a biomarker, and specifically, the therapeutic effect of an antidepressant on a subject may be determined differently depending on the expression level of the corresponding gene.

As used herein, the term "gene" can refer to any nucleic acid sequence or portion thereof that plays a functional role in protein coding or transcription, or in regulating the expression of other genes. A gene can be any nucleic acid that encodes a functional protein, or it can consist of only a portion of a nucleic acid that encodes or expresses a protein. Nucleic acid sequences may include genetic abnormalities within exons, introns, initiation or termination regions, promoter sequences, other regulatory sequences, or unique sequences adjacent to the gene.

Analysis methods for measuring gene expression levels include RT-PCR, competitive RT-PCR, real-time RT-PCR, RNase protection assay (RPA), Northern Blotting (Northern blotting), or a DNA chip, but is not limited thereto.

According to one aspect, the agent for measuring the expression level of the mRNA encoding the protein is a primer, probe, or antisense nucleotide that specifically binds to the mRNA of the gene, and since the nucleic acid sequence of the gene is known, those skilled in the art can determine the sequence Based on this, primers or probes that specifically bind to mRAN of these genes can be designed.

As used herein, the term "primer" refers to a nucleic acid sequence having a short free 3' hydroxyl group, capable of base pairing with a complementary template, and serving as a starting point for template strand copying. refers to the sequence of nucleic acids. Primers can initiate DNA synthesis in the presence of a reagent for polymerization (i.e., DNA polymerase or reverse transcriptase) and four different nucleoside triphosphates in an appropriate buffer and temperature. In addition, primers are sense and antisense nucleic acids with a sequence of 7 to 50 nucleotides, which can incorporate additional features that do not change the basic properties of the primers that serve as starting points for DNA synthesis. The sequence of the primer does not necessarily have to be exactly the same as the sequence of the template, as long as it is sufficiently complementary to allow hybridization with the template. The position of the primer or the binding site of the primer may mean a target DNA fragment to which the primer hybridizes. According to one embodiment of the present invention, PCR amplification is performed using sense and antisense primers of mRNA of the gene, and diagnosis can be made by measuring the production amount of a desired product. PCR conditions and lengths of sense and antisense primers can be appropriately selected according to techniques known in the art.

As used herein, the term "probe" refers to a nucleic acid fragment such as RNA or DNA corresponding to a few bases to several hundred bases in length that can specifically bind to mRNA and is labeled, so that the presence or absence of a specific mRNA, expression quantity can be checked.

The probe may be manufactured in the form of an oligonucleotide probe, a single strand DNA probe, a double strand DNA probe, or an RNA probe. By performing hybridization using a probe complementary to the polynucleotide of the gene according to one aspect, the presence and degree of radiation exposure can be determined through the presence and degree of hybridization. Selection of suitable probes and hybridization conditions can be modified based on those known in the art.

The primers or probes can be chemically synthesized using the phosphoramidite solid support method or other well-known methods. Such nucleic acid sequences can also be modified using a number of means known in the art. Non-limiting examples of such modifications include methylation, capping, substitution of one or more homologues of the native nucleotide, and internucleotide modifications, e.g., uncharged uncharged compounds such as methyl phosphonates, phosphotriesters, phosphoroamidates, carbamates, and the like. There is a transformation into a linker or a charged linker such as phosphorothioate, phosphorodithioate, and the like.

In another aspect, the present invention relates to a kit for predicting the effect of a selective serotonin reuptake inhibitor treatment comprising the composition.

The gene, the protein, the fragment of the protein, the expression level, and the measurement of the expression level used in the kit are as described above, and redundant description is omitted.

The kit may further include a cell lysis buffer. The cell lysis buffer may include various materials such as commercially available products. The cell lysis buffer may include HEPES, MgCl ₂ , NaCl, imidazole, DNAse, and β-mercaptoethanol, and a protease inhibitor PMSF (phenylmethanesulfonylfluoride) may be added to prevent protein degradation, but is not limited thereto. don't

The kit may further include a reagent for digesting genes or proteins. The gene degradation reagent may include a restriction enzyme or the like. The proteolytic reagent may include trypsin, trypsin/EDTA, or TrypLE, but the gene or proteolytic reagent is not limited thereto.

The kit may further include a mixture of isotope labeled standard peptides.

The kit may further include a program capable of data analysis or statistical processing. The program may analyze the expression level of a gene or protein. The data analysis or statistical processing program may be one or more selected from Skyline Software, ProteoWizard Software, SCIEX OS Software, SPSS Statistics Software, MedCalc Software, MultiQuant Software, MasterView Software, or Cliquid Software, but is not limited thereto.

The kit may further include a sample required for diagnosis. The kit may include an antibody, a substrate for immunological detection of the antibody, a suitable buffer, a secondary antibody labeled with a chromogenic enzyme or a fluorescent substance, or a chromogenic substrate. The kit may include an aptamer, primer, probe or oligonucleotide.

The kit may be a reverse transcription polymerase chain reaction (RT-PCR) kit, a DNA chip kit, an enzyme linked immunosorbent assay (ELISA) kit, a protein chip kit, a rapid kit, or a multiple reaction monitoring (MRM) kit.

In another aspect, the present invention relates to a method for providing information for predicting the effect of a selective serotonin reuptake inhibitor treatment:

(a) measuring the expression level of at least one biomarker protein selected from the group consisting of PHOX2B, SH3BGRL3 and YWHAE, a fragment thereof, or a gene encoding the protein in a sample isolated from a subject administered with a selective serotonin reuptake inhibitor therapeutic agent step; and

(b) comparing the measured expression level with that before administration of a selective serotonin reuptake inhibitor.

In the present invention, the sample is blood, plasma, serum, urine, mucus, saliva, tears, sputum, spinal fluid, pleural fluid, nipple aspirate, lymph fluid, airway fluid, serous fluid, genitourinary tract fluid, breast milk, lymphatic body fluid, semen, It may be characterized as cerebrospinal fluid, organ system fluid, ascites, cystic tumor fluid, amniotic fluid, or a combination thereof, and may preferably be blood, plasma or serum sample, but is not limited thereto.

In the present invention, the selective serotonin reuptake inhibitor may be escitalopram, but is not limited thereto.

In the present invention, the sample is characterized in that the sample is isolated from the subject within 4 weeks, within 3 weeks, within 2 weeks or within 1 week after administration of a selective serotonin reuptake inhibitor, but is not limited thereto. For example, in the present invention, within 1 day, within 2 days, within 3 days, within 4 days, within 5 days, within 6 days, within 7 days, within 8 days, within 9 days, within 10 days after administration of escitalopram Within, Within 11 days, Within 12 days, Within 13 days Within 14 days, Within 15 days, Within 16 days, Within 17 days, Within 18 days, Within 19 days, Within 20 days, Within 21 days, Within 22 days, 23 It may be characterized that the sample for analysis is separated from the subject within 24 days, 25 days, 26 days, 27 days or 28 days.

Preferably, the sample may be isolated from the subject 3 to 14 days after administration of a selective serotonin reuptake inhibitor, but is not limited thereto.

In the present invention, the step of measuring the expression level of the protein or fragment thereof includes electrophoresis, immunoblotting, enzyme-linked immunosorbent assay (ELISA), immunohistochemical staining, protein chip, and immunoprecipitation. , mass spectrometry, or a combination thereof; and/or

The step of measuring the expression level of the gene is RT-PCR, competitive RT-PCR (Real-time RT-PCR), RNase protection assay (RPA: RNase protection assay), Northern It may be characterized in that it is performed by blotting (Northern blotting), DNA chip, or a combination thereof, but is not limited thereto.

In the present invention, the method is performed by comparing the biomarker expression level measured in the isolated sample with that before administration of the selective serotonin reuptake inhibitor.

(i) increased expression of PHOX2B;

(ii) decreased SH3BGRL3 expression; and/or

(iii) decreased YWHAE expression; If

The selective serotonin reuptake inhibitor may further include predicting that it has a therapeutic effect.

In the present invention, the therapeutic effect may be characterized in that the therapeutic effect is predicted at about 4 weeks and/or about 10 weeks after administration of the selective serotonin reuptake inhibitor, but is not limited thereto.

In the present invention, the term 'before administration of a selective serotonin reuptake inhibitor' may be used interchangeably with baseline.

In the present invention, the expression of (i) PHOX2B was statistically significantly changed only in the drug-responder group compared to before administration of the selective serotonin reuptake inhibitor, and the amount increased by about 1.28 times. Therefore, when the expression level of PHOX2B increases by about 20% or more compared to before administration of the selective serotonin reuptake inhibitor, the selective serotonin reuptake inhibitor can be predicted to exert a therapeutic effect. On the other hand, no statistically significant change was confirmed in the non-responder group.

In the present invention, the expression of (ii) SH3BGRL3 was statistically significantly changed only in the drug-responder group compared to before administration of the selective serotonin reuptake inhibitor, and the amount decreased by about 0.61 times. Therefore, compared to before administration of the selective serotonin reuptake inhibitor, when the expression level of SH3BGRL3 is reduced by about 40% or more, preferably by about 50% or more, more preferably by about 60% or more, The selective serotonin reuptake inhibitors can be predicted to exert a therapeutic effect. On the other hand, no statistically significant change was confirmed in the non-responder group.

In the present invention, the expression of (iii) YWHAE was statistically significantly changed only in the drug-responder group compared to before administration of the selective serotonin reuptake inhibitor, and the amount decreased by 0.64 times. Therefore, compared to before administration of the selective serotonin reuptake inhibitor, when the expression level of YWHAE is reduced by about 40% or more, preferably by about 50% or more, more preferably by about 60% or more, The selective serotonin reuptake inhibitors can be predicted to exert a therapeutic effect. On the other hand, no statistically significant change was confirmed in the non-responder group.

The present invention is significant in that it has derived reliable candidate biomarkers according to time-dependent longitudinal changes in patient's plasma proteome. In the present invention, after first identifying biomarkers predicting initial drug response, liquid chromatography multiple reaction monitoring/mass spectrometry (LC-MRM/MS) was performed in 19 responders and 5 non-responders. ) technology, the reliability of the present invention was verified through additional verification.

Therefore, the biomarker according to the present invention provides an indicator that can accurately and objectively confirm whether the drug exerts a therapeutic effect on major depressive disorder at the beginning of drug administration, preferably within a week, and based on this, the biomarker It is possible to use a prognostic diagnostic marker to predict the treatment effect of major depressive disorder at an early stage by enabling rapid and accurate determination of whether to continue administration or replacement of the drug.

Example

Hereinafter, the present invention will be described in more detail through examples. These examples are only for illustrating the present invention, and it will be apparent to those skilled in the art that the scope of the present invention is not to be construed as being limited by these examples.

Example 1. Selection of subjects for clinical trials

Since the MADRS score is considered a criterion for determining the response to drug administration, participants participated in a clinical trial testing the efficacy and safety of escitalopram dose escalation conducted at Seoul National University Hospital from February 2013 to February 2016. Plasma samples were collected from 10 major depressive disorder patients. All participants were Korean.

The trial included two phases, 4 weeks of open-label treatment with a standard dose (10-20 mg/day) of escitalopram, followed by 6 weeks of randomized doublet treatment with either 20 mg/day or 30 mg/day escitalopram. A double-blinded treatment was performed. Patients aged 18-65 years with a primary diagnosis of major depressive disorder as defined in the Diagnostic and Statistical Manual of Mental Disorders, 4th edition were included. All patients had a total MADRS score of 18 or greater at the initial screening and baseline visits. On the other hand, if you experience hypersensitivity to escitalopram, if you have ever received psychotropic drugs such as antipsychotics, mood stabilizers or selective monoamine oxidase inhibitors, if you have depressive symptoms and are resistant to two or more antidepressant treatments If considered to have a mental illness other than major depressive disorder, or a previous history of a mental disorder, such as manic or hypomanic episodes, schizophrenia, schizoaffective disorder, or substance abuse disorder, as assessed by the investigator, or a score of 5 on item 10 of the MADRS Subjects were excluded if they had a significant risk of suicide with a score higher than or if they had a history of neurological disorders or medically unstable conditions (e.g., renal or hepatic disorders, or cardiovascular, pulmonary or gastrointestinal disorders).

Among the patients who participated in the clinical trial, 5 responders (1 male and 4 females) and 5 non-responders (1 male and 4 females) were selected, and at baseline, week 1 and week 4 (randomized ) and 10 weeks (6 weeks after randomization), each plasma sample was obtained at four time points.

The study protocol was approved by the Institutional Review Board of Seoul National University Hospital (approval number: 1008-116-329, approved on December 2, 2010). This study was conducted in accordance with the ethical principles set forth in the Declaration of Helsinki and internationally harmonized guidelines for good clinical practice, and all patients provided written informed consent and were allowed to discontinue the study at any time they wished.

The baseline characteristics of 10 subjects, that is, 5 responders and 5 non-responders, are shown in Table 1.

Subject Demographic and Clinical Variables variable responder
(N=5) nonresponder
(N=5) p -Value age (standard deviation) 44.2 (14.2) 42.8 (16.4) 0.841 male (%) 1 (20) 1 (20) 1.000 Time of onset (standard deviation) 41.8 (11.9) 33.4 (9.8) 0.093 Body mass index (kg/m ² ) (standard deviation) 23.1 (3.7) 24.7 (4.6) 0.309 Clinical characteristics at baseline MADRS (standard deviation) 31.0 (4.6) 28.8 (2.5) 0.599 CGI-S (standard deviation) 5.0 (0.7) 4.2 (1.3) 0.344 BDI (Standard Deviation) 32.6 (7.3) 26.8 (3.6) 0.206 HRM-D (standard deviation 21.6 (3.4) 21.0 (2.9) 1.000 CUDOS (standard deviation) 38.4 (12.8) 40.4 (3.0) 0.917 World Health Organization Quality of Life Abbreviated Version Physical quality of life (standard deviation) 8.8 (1.7) 8.5 (0.9) 0.831 Psychological quality of life (standard deviation) 8.0 (0.8) 8.5 (0.9) 0.827 Social quality of life (standard deviation) 10.1 (1.5) 11.7 (2.4) 0.193 Environmental quality of life (standard deviation) 10.1 (1.7) 10.1 (1.1) 0.914

The p- value was calculated using the Mann-Whitney U test or Fisher's exact test as appropriate.

As shown in Table 1, the average (standard deviation) age of the subjects was similar for responders (44.2 (14.2) years) and non-responders (42.8 (16.4) years), respectively, and the Montgomery and Asberg Depression Rating Scale (MADRS), Clinical Global Impression-Severity (CGI-S), Beck's Depression Inventory (BDI), Hamilton Rating Scale for Depression (HAM-D), and Clinically Useful Depression Outcome Scale (CUDOS) scores and physical, psychological, social, and environmental life There were also no significant differences between the two groups in emotional symptoms and disease severity, including WHO reduced version scores for quality of life.

For the verification experiment, 24 patients of 19 responders and 5 non-responders were additionally selected, and plasma samples were obtained from them at baseline and 1 week, respectively, and further verification experiments were conducted. None of these patients were taking drugs that could alter blood levels of related factors, such as nonsteroidal anti-inflammatory drugs or steroids, and had acute or chronic diseases such as cardiovascular disease, lung disease, hypertension, endocrine abnormalities, rheumatic disease, or cerebrovascular disease. did not have

Example 2. Plasma sample preparation and LC-MS/MS analysis

2-1. Blood collection and plasma preparation

Plasma was prepared as suggested by the Human Proteome Organization Plasma Proteome Project. Blood samples (3 mL) were collected into tubes containing ethylenediaminetetraacetic acid at baseline, weeks 1, 4 (randomization) and 10 weeks (week 6 after randomization), blood samples were collected after overnight fasting (at least 12 hours) Samples were taken from subjects between 9:30 am and 11:30 am. Blood samples were centrifuged at 2000×g for 15 minutes at room temperature (RT) immediately after sample collection, and the separated plasma was transferred to a 0.5 mL tube and frozen within 20 minutes after centrifugation. Samples were then placed on ice and transported to the laboratory, where they were immediately frozen and stored at -80 °C until analysis.

2-2. Plasma sample preparation

Plasma samples were intended to remove large amounts of plasma proteins that could interfere with proteomic analysis. The 14 most abundant plasma proteins present in plasma (albumin, IgA, IgG, IgM, α1-antitrypsin, α1-acid glycoprotein, apolipoprotein A1, apolipoprotein A2, complement C3, transferrin, α2-macroglobulin, trans Retin, haptoglobin and fibrinogen), a 40 μL aliquot of plasma diluted 4-fold with Buffer A was run on a MARS14 depleted column (Agilent Technology, Palo Alto) in a binary HPLC system (20A Prominence, Shimadzu, Tokyo, Japan). , CA, USA). The unbound fraction was exchanged into a buffer containing 8 M urea in 50 mM Tris (pH 8), ultrafiltered using a Vivaspin 500 3 kDa cut-off filter (Sartorius, Goettingen, Germany) and concentrated to approximately 50 μL, and a new filter unit. (Nanosep, 30 kDa; Pall Corporation, NY, USA). The procedure was repeated twice, adding a 200 μL aliquot to a solution containing 8 M urea in 50 mM Tris, pH 8.5 and centrifuging the mixture at 14,000 x g for 15 minutes. The flow-through was removed from the collection tube, 100 μL of a 0.05 M iodoacetamide solution was added, and the formulation was mixed in a thermal mixer at 600 rpm for 1 minute, followed by incubation for 20 minutes without mixing. The filter unit was centrifuged at 14,000 x g for 10 minutes, 100 μl of 8 M urea dissolved in 50 mM Tris (pH 8.5) was added, the filter unit was centrifuged again at 14,000 x g for 15 minutes, and this step was 2 repeated times. A 100 μL aliquot of 0.05 M ammonium bicarbonate was added to the filter unit and the unit was centrifuged at 14,000×g for 10 minutes, and this step was repeated twice. A 40 μl aliquot of 0.05 M ammonium bicarbonate containing 2.5 μg Lys-C/trypsin was added and the formulation was mixed in a heat-mixer at 600 rpm for 1 minute. The unit was incubated for 12 hours in a wet chamber at 37° C. and transferred to a new collection tube. The filter unit was centrifuged at 14,000 x g for 10 minutes, 40 μL of 0.5 M NaCl was added, and the filter unit was centrifuged again at 14,000 x g for 10 minutes. The digestion reaction was stopped by adding formic acid to a final concentration of 0.3%. The peptide mixture was desalted with Sep Pak C-18 cartridges (Waters, Milford, MA, USA), lyophilized with cold traps (CentriVap Cold Traps, Labconco, Kansas City, MO, USA), and then stored at -80 °C until use. kept in.

2-3. Nano-LC-ESI-MS/MS analysis

Peptides were isolated using a Dionex UltiMate 3000 RSLCnano system (Thermo Fisher Scientific, Waltham, MA, USA). Tryptic peptides from the bead column were reconstituted in 0.1% formic acid and separated on a 75 μm internal diameter 50 cm Easy-Spray column packed with 2 μm C18 resin (Thermo Fisher Scientific) over 200 min (250 nL/min). The column was developed using a 0-45% acetonitrile gradient in 0.1% formic acid and 5% DMSO at 50 °C. The LC was connected to a Q Exactive mass spectrometer with a nano-ESI source, and mass spectra were acquired in data-dependent mode using automatic switching between full scans and 20 data-dependent MS/MS scans. The target value for the full scan MS spectrum was 3,000,000, the maximum injection time was 120 ms, and the resolution was 70,000 at m/z 400. Repeated peptides were dynamically excluded for 20 seconds. All MS data have been deposited in the PRIDE archive (www.ebi.ac.uk/pride/archive/projects/PXD017211) as Project PXD017211.

2-4. Database search and label-free quantification

MS/MS spectral analysis results according to Examples 2-3. were searched using SequestHT in SwissProt human database (May 2017) Proteome Discoverer (version 2.2, Thermo Fisher Scientific). Search parameters were set to default values, including cysteine carbamidomethylation as a fixed modification and N-terminal acetylation and methionine oxidation as variable modifications with two miscleavages. Peptides were identified based on searches with an initial mass deviation of precursor ions of up to 10 ppm and an allowed fragment mass deviation set to 20 ppm. When assigning proteins to peptides, both unique peptides and razor peptides were used. Label-free quantitation (LFQ) was performed using peak intensities for unique peptides of each protein.

2-5. LC-MS/MS profiling results

4 plasma samples were obtained from each of 10 subjects, and protein profiling was performed by LC-MS/MS for a total of 40 samples. As a result, a total of 1159 proteins were identified, of which 684 proteins, more than half, were quantitatively analyzed. was carried out, and a total of 206 proteins were fully quantified through 104 LC-MS/MS analyses. The assay was performed in duplicate or triplicate.

Six relatively stable and abundant endogenous proteins (BTD, C8B, C1S, ITIH2, IGFALS, and SERPINA3) were used to compare the plasma protein abundance by label-free quantification (LFQ) method to determine the level of different proteins. Abundance data were normalized. That is, after normalizing protein abundance in all experiments, differences between samples were corrected. Normalized abundance showed a statistically significant correlation with plasma protein concentration (ng/mL) in the plasma proteome database, with a Pearson's correlation coefficient of 0.677 (adjusted p -value < 0.001).

Among the 346 proteins with very small technical variation, 14 plasma depletion target proteins, 10 immunoglobulin-related proteins, and 6 normalization factors were selected. A total of 316 proteins were further analyzed after excluding them.

2-6. Batch-to-batch calibration, missing data imputation, and normalization methods

Batch 1 consisting of S15 (non-responders) and S29 (responders) based on sample preparation date; batch 2 of S54 (non-responders) and S52 (responders); and batch 3 of S6 (non-responders), S11 (non-responders), S32 (responders), S34 (non-responders), S38 (responders) and S46 (responders) were prepared. For batch-to-batch analytical error correction, a mean-centering correction per protein was applied to the raw data of 104 LC-MS/MS analyses.

Afterwards, missing data imputation was performed. Of the 316 quantified proteins measured at one time in each individual sample, 180 were fully quantified, and missing data for the remaining 136 proteins were obtained using the local least-squares imputation method (Kim, H. et al. ., Bioinformatics 2005, 21, 187-198). The 180 proteins fully quantified using this method were clustered into 15 groups by Pearson's correlation analysis, and missing values were estimated as linear optimal combinations of the 15 selected clusters.

These data were normalized against endogenous normalized proteins without spike-in standards. From the total data, 6 proteins out of 210 were finally selected as suitable for LFQ normalization according to the following criteria: (1) the plasma concentration was almost constant in all samples as determined by the NormFinder stability value; retained; (2) plasma concentrations were not significantly different between 5 responders and 5 non-responders as indicated by LMM analysis ( p -value >0.05); (3) No reports of depression. The raw abundance of the six selected normalizing proteins BTD, C8B, C1S, ITIH2, IGFALS and SERPINA3 in each sample was divided by the geometric mean of the six raw abundances in all samples. The median of these six ratios in a sample was defined as the normalization scaling factor (NSF) for that sample. The NSF for sample s can be calculated using the equation:

는 샘플 s에서 정규화 단백질 i의 원시 단백질 풍부도,

는 모든 샘플에서 단백질 i의 풍부도 중간값이다. here

is the raw protein abundance of normalized protein i in sample s,

is the median abundance of protein i in all samples.

The normalized abundance of the intensity of each biomarker candidate in the sample was calculated by dividing the raw peak intensity by NSF:

는 샘플 s에서 j번째 바이오마커 후보의 정규화된 풍부도이고,

는 그 상응하는 단백질의 원시 풍부도이다. here,

is the normalized abundance of the jth biomarker candidate in sample s,

is the raw abundance of the corresponding protein.

Example 3. Analysis of plasma protein changes over time in responders and non-responders

Through the Mann-Whitney test without correction, a statistical comparison of plasma protein abundance at baseline and after 1, 4, and 10 weeks of treatment showed that 7 and 4 proteins were found at 1, 4, and 10 weeks, respectively. and 6 proteins were up-regulated in responders, and 16, 17 and 19 proteins, respectively, were up-regulated in non-responders ( p -value <0.05; Fig. 1A).

A Venn diagram at three time points T ₁ , T ₄ and T ₁₀ is shown in FIG. 1B. Proteins upregulated in non-responders were related to response to stimulation, response to wound and tube morphogenesis in Gene Ontology (GO) (Fig. 1C). This could mean that the drug activates a large number of inflammatory pathways along with neuronal circuits in the brain in non-responders.

When classified by gene ontology, proteins related to extracellular structure organization, regulation of complement activation, and remodeling of triglyceride-rich lipoprotein particles were abundantly expressed in both groups.

A linear mixed model is suitable for identifying differentially abundant plasma proteins based on longitudinal proteomic data, in which the response/time interaction term indicates the time-dependent responsiveness between groups to selective serotonin reuptake inhibitors. It is important to measure the difference. Through linear mixed model multiple comparison analysis, 37 significant proteins corrected by the SGoF method (see Carvajal-Rodriguez, A. et al., BMC Bioinform. 2009, 10, 209.) were identified (adjusted p- value < 0.05, response/time interaction term). Differences between groups of these proteins over time are indicated by the lowest adjusted p- value in Table 2.

37 proteins corresponding to response/time interactions that differ between responders and non-responders UNIPROT access number adjusted
p -Value gene name protein name cluster number COR P04278 2.70×10 ^-3 SHBG Sex hormone-binding globulin 5 0.12 P05090 2.95×10 ^-3 APOD Apolipoprotein D 6 -0.34 Q06033 4.01×10 ^-3 ITIH3 Inter-alpha-trypsin inhibitor heavy chain H3 5 -0.21 P08567 4.36×10 ^-3 PLEK Pleckstrin 4 0.04 P04275 4.69×10 ^-3 VWF von Willebrand factor 6 -0.19 P52566 5.11×10 ^-3 ARHGDIB Rho GDP-dissociation inhibitor 2 One -0.15 P02656 6.89×10 ^-3 APOC3 Apolipoprotein C-III 6 -0.06 P06276 7.13×10 ^-3 BCHE cholinesterase 6 -0.11 P27169 8.89×10 ^-3 PON1 Serum paraoxonase/arylesterase 1 5 -0.08 P22105-4 9.85×10 ^-3 TNXB Tenascin-X 6 -0.25 P02774-3 1.09×10 ^-2 GC Vitamin D-binding protein 5 0.04 P0C0L5 1.10×10 ^-2 C4B Complement C4-B 5 -0.21 P02649 1.15×10 ^-2 APOE Apolipoprotein E 6 -0.04 P07339 1.45×10 ^-2 CTSD Cathepsin D 4 0.02 Q92820 1.50×10 ^-2 GGH Gamma-glutamyl hydrolase 5 -0.01 P09172 1.69×10 ^-2 DBH Dopamine beta-hydroxylase 2 0.03 P40189 1.75×10 ^-2 IL6ST Interleukin-6 receptor subunit beta 4 0.15 Q8NBP7 1.81×10 ^-2 PCSK9 Proprotein convertase subtilisin/kexin type 9 3 -0.14 Q16610 1.83×10 ^-2 ECM1 Extracellular matrix protein 1 2 0.02 P62258 1.83×10 ^-2 YWHAE 14-3-3 protein epsilon 6 0.18 P80188 1.83×10 ^-2 LCN2 Neutrophil gelatinase-associated lipocalin 6 -0.11 Q9H299 1.99×10 ^-2 SH3BGRL3 SH3 domain-binding glutamic acid-rich-like protein 3 One 0.13 P27918 2.05×10 ^-2 CFP Properdin 6 0.08 P08571 2.12×10 ^-2 CD14 Monocyte differentiation antigen CD14 2 0.24 P08697 2.33×10 ^-2 SERPINF2 Alpha-2-antiplasmin 5 0.22 P36980 2.34×10 ^-2 CFHR2 Complement factor H-related protein 2 4 0.16 P08253 2.57×10 ^-2 MMP2 72 kDa type IV collagenase 6 0.13 P13671 2.65×10 ^-2 C6 Complement component C6 5 0.13 O43852-3 2.80×10 ^-2 CALU Calumenin 3 0.10 P14543 2.93×10 ^-2 NID1 Nidogen-1 4 -0.05 P35579 2.95×10 ^-2 MYH9 Myosin-9 One 0.05 P05160 3.70×10 ^-2 F13B Coagulation factor XIII B chain 6 -0.17 P02765 3.75×10 ^-2 AHSG Alpha-2-HS-glycoprotein 2 0.20 Q99453 3.85×10 ^-2 PHOX2B Paired mesoderm homeobox protein 2B 2 -0.12 O43157 4.01×10 ^-2 PLXNB1 Plexin-B1 5 -0.01 P06396 4.26×10 ^-2 GSN Gelsolin 6 0.04 O43866 4.41×10 ^-2 CD5L CD5 antigen-like 3 -0.27

After subtracting the protein abundance at three different times (T ₁ , T ₄ and T ₁₀ ) from the protein abundance at the baseline day (T0) to cluster proteins in a similar pattern from the abundance pattern, t-SNE and affinity By proceeding with affinity propagation (FIG. 2A), six clusters of unique patterns were secured (FIG. 2B).

Three and five proteins in clusters 1 and 4 respectively decreased over time in responders and increased over time in non-responders. Cluster 2, comprising 5 proteins, showed little change over time in responders but decreased over time in non-responders. Three proteins in cluster 3 increased from the 1st week in the responders, but appeared uniformly after the 4th week. In contrast, in non-responders, these proteins rapidly decreased at week 4 and then appeared uniformly at week 10. Ten proteins in cluster 5 changed little over time in responders but increased over time in non-responders. Eleven proteins in cluster 6 decreased at week 4 and increased at week 10 in responders, but changed little over time in non-responders.

To assess whether these proteins are related to antidepressant response and psychiatric disorders, PsyGeNet was searched for 37 proteins (Fig. 2C), and 14 of these proteins were APOD, APOE, BCHE, DBH, GGH, GSN, ITIH3, LCN2, MMP2, PHOX2B, PON1, TNXB, VWF, and YWHAE have been identified to be associated with psychiatric symptoms such as schizophrenia, bipolar disorder, cocaine use disorder, substance-induced psychosis, alcohol use disorder, and depression.

In addition, responses in rat tissues and cells to selective serotonin reuptake inhibitors (SSRIs) in Enrichr's DrugMatrix category were searched to determine whether these 37 proteins were related to citalopram, an analogue of escitalopram. As a result of the evaluation, nine proteins of ITIH3, PON1, MMP2, MYH9, APOE, GC, CD14, LCN2 and CTSD showed significantly different expression patterns in the livers of rats treated with a selective serotonin reuptake inhibitor and corn oil as a control group. It was confirmed that 7 proteins, ITIH3, PON1, LCN2, APOE, GC, CLU and CTSD, showed significantly different expressions in hepatocytes of rats treated with a selective serotonin reuptake inhibitor and corn oil, and PLXNB1, MMP2 and Three proteins of CTSD showed significantly different expression patterns in the hearts of rats treated with selective serotonin reuptake inhibitor and corn oil (Fig. 2D).

From the above results, it was confirmed that these proteins cause quantitative changes in response to drugs not only in the blood but also in organs such as the liver and heart.

Example 4. LC-MRM/MS Validation of Candidate Plasma Proteins Predicting Early Response

Ten proteins with significant expression differences between responder and non-responder groups at baseline, early treatment phase (baseline to week 1; Mann-Whitney U test: p- value < 0.05) and response/time interaction in LMM was selected (Fig. 3A) and verified by serial isotope dilute-MRM/MS. Among them, CFHR2 with low reliability was excluded as a biomarker for early prediction of drug response.

Liquid chromatography (LC) was performed on an Agilent 1290 Infinity with a reversed-phase ultra-high performance LC (UHPLC) column (Agilent ZORBAX Eclipse Plus C18 Column, 95 A, 2.1 mm i.d. × 100 mm, packed with 1.8 μm C18 resin) at a temperature of 50 °C. It was performed on a UHPLC system. Mobile phases were 0.1% formic acid in distilled water (solvent A) and 0.1% formic acid in acetonitrile (solvent B). The column was run at a flow rate of 0.3 mL/min with 0-2% solvent B for 5 min, 2-3% solvent B for 5 min, 3-50% solvent B for 10 min, 50-50% solvent B for 4 min, 1 50-0% solvent B for 9 minutes, 0-0% solvent B for 9 minutes. The injected samples consisted of a mixture of digested plasma peptides (initial plasma volume: 40 μL) and isotopically labeled internal standard peptides. The UHPLC system was connected to a triple quadrupole mass spectrometer (Agilent 6495 QQQ) by a standard flow Jet Stream electrospray source operated in positive ion mode. Additional parameters were capillary voltage, 3.5 kV; nozzle voltage, 1 kV; gas temperature, 290 °C; Dry gas flow, 11 L/min at 350 °C; Nebulizer gas pressure, 40 PSI at 350 °C; and unit resolution for Q1 and Q3. MRM transitions were selected and collision energies were optimized by Skyline (64-bit, version 19.1.0.193) software. Cell accelerator voltage was set to 5V. Quantification experiments were performed using dynamic MRM (delta hold time: 3 min) and the total cycle time was approximately 1.5 sec. The mass spectrometer was operated with MassHunter software (version B.08.00, Agilent) generating MRM/MS data (*.d). The MRM results of the extracted ion chromatograms were analyzed with Skyline and quantified by comparison with the corresponding stable isotope labeled peptides (SpikeTides™; JPT Peptdie Technologies Berlin, Germany).

Based on reverse standard calibration curves, 15 surrogate peptides representing 9 proteins were selected for protein quantification, and based on the LC-MRM/MS results, a sample with strong signals for each protein was selected. Representative peptides were selected.

Among the validation MRM results comparing 19 responders with 5 non-responders, MYH9 with a medium-to-light ratio below the limit of quantification could not be quantified. Three of the remaining eight proteins, PHOX2B, SH3BGRL3, and YWHAE, showed significant differences between baseline and week 1 in responders (Wilcoxon signed-rank test: FDR-adjusted p -value < 0.05), but such differences were not observed in non-responders. not confirmed (Wilcoxon signed-rank test: FDR-adjusted p -value > 0.05, Fig. 4B).

That is, after 1 week of drug administration, the PHOX2B protein expression level significantly increased in responders, while the SH3BGRL3 and YWHAE protein expression levels significantly decreased in responders. However, no significant changes in these three proteins were observed in non-responders. On the other hand, it was confirmed that the expression levels of the other 5 proteins were not significantly different between the two groups.

Example 5. Statistical analysis

Data were analyzed using RStudio (version 1.1.456) with R (version 3.6.0). Longitudinal plasma protein abundance was determined by drug response (no response or response), sampling time (baseline date, 1 week, 4 weeks, and 10 weeks), response/time interactions and technical replicates as fixed variables, individual patient overlap for fixed variables, and by linear mixed model analysis (lme4 package) using individual as a random variable. In the GEE analysis (geesmv package), plasma abundance was merged as an average of 2–3 technical replicates and then drug response, sampling time and drug/sampling time were analyzed. A working correlation structure was set independently and Gaussian estimation was performed. Clustering analysis was based on the median protein concentration of each group (responders and non-responders) at four time points, t-stochastic neighbor embedding (t-SNE) (perplexity = 2, theta = 0, dims = 2) and preference propagation (method = correlation symmetric matrix and Spearman) were calculated using the Rtsne and apcluster packages, respectively.

Other software packages include ggline for scatter plots of mental disorder gene association networks (PsyGeNet) and psygenet2r for protein mapping in database = "ALL". To control type I errors through multiple comparisons, the default options (alpha = 0.05, gamma = 0.05, P0 = 0.5, a0 = 1) in the SGoF R package for the p -value of the response/time interaction by linear mixed model analysis , b0 = 1), the Benjamini-Hochberg procedure for the p -value of the MRM pair analysis was applied, and the permuted p -value was calculated for the correlation analysis.

Having described specific parts of the present invention in detail above, it is clear to those skilled in the art that these specific descriptions are only preferred embodiments, and the scope of the present invention is not limited thereby. something to do. Accordingly, the substantial scope of the present invention will be defined by the appended claims and their equivalents.

<110> Seoul National University R&DB Foundation THE ASAN FOUNDATION UNIVERSITY OF ULSAN FOUNDATION FOR INDUSTRY COOPERATION <120> Biomarker for early prediction of the treatment effect in major depressive disorder <130> 1069408 <150> KR 10-2020-0117021 <151> 2020-09-11 <160> 3 <170> KoPatentIn 3.0 <210> 1 <211> 314 <212> PRT <213> Homo sapiens <400> 1 Met Tyr Lys Met Glu Tyr Ser Tyr Leu Asn Ser Ser Ala Tyr Glu Ser 1 5 10 15 Cys Met Ala Gly Met Asp Thr Ser Ser Leu Ala Ser Ala Tyr Ala Asp 20 25 30 Phe Ser Ser Cys Ser Gln Ala Ser Gly Phe Gln Tyr Asn Pro Ile Arg 35 40 45 Thr Thr Phe Gly Ala Thr Ser Gly Cys Pro Ser Leu Thr Pro Gly Ser 50 55 60 Cys Ser Leu Gly Thr Leu Arg Asp His Gln Ser Ser Pro Tyr Ala Ala 65 70 75 80 Val Pro Tyr Lys Leu Phe Thr Asp His Gly Gly Leu Asn Glu Lys Arg 85 90 95 Lys Gln Arg Arg Ile Arg Thr Thr Phe Thr Ser Ala Gln Leu Lys Glu 100 105 110 Leu Glu Arg Val Phe Ala Glu Thr His Tyr Pro Asp Ile Tyr Thr Arg 115 120 125 Glu Glu Leu Ala Leu Lys Ile Asp Leu Thr Glu Ala Arg Val Gln Val 130 135 140 Trp Phe Gln Asn Arg Arg Ala Lys Phe Arg Lys Gln Glu Arg Ala Ala 145 150 155 160 Ala Ala Ala Ala Ala Ala Ala Lys Asn Gly Ser Ser Gly Lys Lys Ser 165 170 175 Asp Ser Ser Arg Asp Asp Glu Ser Lys Glu Ala Lys Ser Thr Asp Pro 180 185 190 Asp Ser Thr Gly Gly Pro Gly Pro Asn Pro Asn Pro Thr Pro Ser Cys 195 200 205 Gly Ala Asn Gly Gly Gly Gly Gly Gly Pro Ser Pro Ala Gly Ala Pro 210 215 220 Gly Ala Ala Gly Pro Gly Gly Pro Gly Gly Glu Pro Gly Lys Gly Gly 225 230 235 240 Ala Ala Ala Ala Ala Ala Ala Ala Ala Ala Ala Ala Ala Ala 245 250 255 Ala Ala Ala Ala Gly Gly Leu Ala Ala Ala Gly Gly Pro Gly Gln Gly 260 265 270 Trp Ala Pro Gly Pro Gly Pro Ile Thr Ser Ile Pro Asp Ser Leu Gly 275 280 285 Gly Pro Phe Ala Ser Val Leu Ser Ser Leu Gln Arg Pro Asn Gly Ala 290 295 300 Lys Ala Ala Leu Val Lys Ser Ser Met Phe 305 310 <210> 2 <211> 93 <212> PRT <213> Homo sapiens <400> 2 Met Ser Gly Leu Arg Val Tyr Ser Thr Ser Val Thr Gly Ser Arg Glu 1 5 10 15 Ile Lys Ser Gln Gln Ser Glu Val Thr Arg Ile Leu Asp Gly Lys Arg 20 25 30 Ile Gln Tyr Gln Leu Val Asp Ile Ser Gln Asp Asn Ala Leu Arg Asp 35 40 45 Glu Met Arg Ala Leu Ala Gly Asn Pro Lys Ala Thr Pro Pro Gln Ile 50 55 60 Val Asn Gly Asp Gln Tyr Cys Gly Asp Tyr Glu Leu Phe Val Glu Ala 65 70 75 80 Val Glu Gln Asn Thr Leu Gln Glu Phe Leu Lys Leu Ala 85 90 <210> 3 <211> 255 <212> PRT <213> artificial sequence <220> <223> Homo sapiens <400> 3 Met Asp Asp Arg Glu Asp Leu Val Tyr Gln Ala Lys Leu Ala Glu Gln 1 5 10 15 Ala Glu Arg Tyr Asp Glu Met Val Glu Ser Met Lys Lys Val Ala Gly 20 25 30 Met Asp Val Glu Leu Thr Val Glu Glu Arg Asn Leu Leu Ser Val Ala 35 40 45 Tyr Lys Asn Val Ile Gly Ala Arg Arg Ala Ser Trp Arg Ile Ile Ser 50 55 60 Ser Ile Glu Gln Lys Glu Glu Asn Lys Gly Gly Glu Asp Lys Leu Lys 65 70 75 80 Met Ile Arg Glu Tyr Arg Gln Met Val Glu Thr Glu Leu Lys Leu Ile 85 90 95 Cys Cys Asp Ile Leu Asp Val Leu Asp Lys His Leu Ile Pro Ala Ala 100 105 110 Asn Thr Gly Glu Ser Lys Val Phe Tyr Tyr Lys Met Lys Gly Asp Tyr 115 120 125 His Arg Tyr Leu Ala Glu Phe Ala Thr Gly Asn Asp Arg Lys Glu Ala 130 135 140 Ala Glu Asn Ser Leu Val Ala Tyr Lys Ala Ala Ser Asp Ile Ala Met 145 150 155 160 Thr Glu Leu Pro Pro Thr His Pro Ile Arg Leu Gly Leu Ala Leu Asn 165 170 175 Phe Ser Val Phe Tyr Tyr Glu Ile Leu Asn Ser Pro Asp Arg Ala Cys 180 185 190 Arg Leu Ala Lys Ala Ala Phe Asp Asp Ala Ile Ala Glu Leu Asp Thr 195 200 205 Leu Ser Glu Glu Ser Tyr Lys Asp Ser Thr Leu Ile Met Gln Leu Leu 210 215 220 Arg Asp Asn Leu Thr Leu Trp Thr Ser Asp Met Gln Gly Asp Gly Glu 225 230 235 240 Glu Gln Asn Lys Glu Ala Leu Gln Asp Val Glu Asp Glu Asn Gln 245 250 255

Claims (14)

delete delete delete A composition for predicting an escitalopram treatment effect, comprising an agent for measuring the expression level of at least one biomarker protein selected from the group consisting of PHOX2B, SH3BGRL3 and YWHAE, a fragment thereof, or a gene encoding the protein.
The composition according to claim 4, wherein the composition is capable of predicting a therapeutic effect within at least one week after administration of escitalopram.
The composition according to claim 4, wherein the agent is an antibody or antigen-binding fragment thereof, or an aptamer that specifically binds to the biomarker protein or fragment thereof.
The composition according to claim 4, wherein the agent is a primer, probe, or antisense nucleotide that specifically binds to a polynucleotide encoding the biomarker protein.
A kit for predicting the therapeutic effect of escitalopram, comprising the composition of any one of claims 4 to 7.
A method for providing information for predicting the effectiveness of escitalopram treatment, comprising the steps of:
(a) Measuring the expression level of at least one biomarker protein selected from the group consisting of PHOX2B, SH3BGRL3 and YWHAE, a fragment thereof, or a gene encoding the protein in a sample isolated from a subject administered with escitalopram therapy step; and
(b) comparing the measured expression level with that before administration of escitalopram.
The method of claim 9, wherein the sample is blood, plasma, serum, urine, mucus, saliva, tears, sputum, spinal fluid, pleural fluid, nipple aspirate, lymph fluid, airway fluid, serous fluid, genitourinary tract fluid, breast milk, lymphatic body fluid, semen. , cerebrospinal fluid, intraorgan system fluid, ascites, cystic tumor fluid, amniotic fluid, or a combination thereof.
delete 10. The method of claim 9, wherein the sample is isolated from the subject 3 to 14 days after administration of escitalopram.
The method of claim 9, wherein the step of measuring the expression level of the protein or its fragment is electrophoresis, immunoblotting, enzyme-linked immunosorbent assay (ELISA), immunohistochemical staining, protein chip, immunohistochemistry performed by sedimentation, mass spectrometry, or a combination thereof; and/or
The step of measuring the expression level of the gene is RT-PCR, competitive RT-PCR (Real-time RT-PCR), RNase protection assay (RPA: RNase protection assay), Northern Blotting (Northern blotting), a DNA chip, or a method characterized in that performed by a combination thereof.
The method of claim 12, wherein the biomarker expression level measured in the isolated sample is compared to before administration of the escitalopram
(i) increased expression of PHOX2B;
(ii) decreased SH3BGRL3 expression; and/or
(iii) decreased YWHAE expression; If
Characterized in that the escitalopram is predicted to have a therapeutic effect.

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