KR101526877B1 - Methods of selecting menopause biomarker using metabolomics and menopause biomarkers in animal models - Google Patents
Methods of selecting menopause biomarker using metabolomics and menopause biomarkers in animal models Download PDFInfo
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- KR101526877B1 KR101526877B1 KR1020130148011A KR20130148011A KR101526877B1 KR 101526877 B1 KR101526877 B1 KR 101526877B1 KR 1020130148011 A KR1020130148011 A KR 1020130148011A KR 20130148011 A KR20130148011 A KR 20130148011A KR 101526877 B1 KR101526877 B1 KR 101526877B1
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- G—PHYSICS
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- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
- G01N33/5008—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics
- G01N33/502—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics for testing non-proliferative effects
- G01N33/5038—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics for testing non-proliferative effects involving detection of metabolites per se
Abstract
The present invention relates to a method for selecting a menopausal biomarker using statistical analysis and a novel menopausal biomarker.
Description
The present invention relates to a method for selecting a menopausal biomarker using statistical analysis and a novel menopausal biomarker.
Menopause is a natural phenomenon that comes with aging. In menopause, menopausal symptoms such as menopause are often accompanied by a tendency to be easily perceived. However, menopausal symptoms are highly variable. In the case of adolescents, menstrual disorders, including facial flushing, are experienced as the secretion of estrogens falls. This is an artificial hormone replacement therapy can be controlled to some extent, before the symptoms appear to effectively prevent the need to develop a way to easily diagnose menopausal status is required.
On the other hand, studies on the diagnosis and prediction of diseases and environmental changes are being actively carried out by analyzing the hormone as a whole by the recent development of metabolomics. Once hormone replacement therapy has been actively prescribed for the purpose of improving menopausal symptoms by artificially administering estrogen, it has been observed that adverse effects that cause the development of female cancer such as breast cancer have been observed. Therefore, in the case of female menopausal women, studies of the overall hormone changes in menopausal women can be used to develop hormone replacement therapies or other therapies that have less side effects if estrogens, as well as their precursors or other hormones, .
It is an object of the present invention to provide a method for selecting a menopausal biomarker.
In order to accomplish the above object, the present invention provides a method for obtaining a biological sample, comprising: obtaining a biological sample from an animal model of ovariectomized animal and a normal animal model; Analyzing each of the biological samples using a mass spectrometer; Converting each of the mass spectrometry results to a statistically processable value, and analyzing the converted values by a statistical method and comparing them.
The present invention also relates to a method of treating a menopausal diagnostic marker comprising at least one metabolite selected from the group consisting of Pregnandiol metabolites, 17-hydroxyprogesterone metabolites and Pregnanolone metabolites, Lt; / RTI >
The present invention relates to a method for producing a biologically active substance, wherein the amount of at least one metabolite selected from the group consisting of Pregnandiol, 17-hydroxyprogesterone and Pregnanolone in the biological sample is increased before the treatment of the candidate substance And a method for screening a substance for improving or treating a menopausal disease, which comprises judging such a metabolite as a substance for improving or treating menopausal symptoms.
The present invention provides a method for increasing the amount of at least one metabolite selected from the group consisting of Pregnandiol, 17-hydroxyprogesterone and Pregnanolone or an amount of the metabolite As an active ingredient, an improvement or health food pharmaceutical composition for menopausal diseases.
According to the present invention, an ovariectomized animal model can be used to easily identify a biomarker related to a menopausal period, so that a new biomarker can be easily selected. In addition, the biomarker thus obtained can be utilized in the diagnosis of menopausal symptoms, and can be utilized to evaluate functions and efficacy of products for treatment or improvement of menopausal symptoms.
The use of the biomarker composition according to one aspect of the present invention is advantageous in that a new biomarker can be included to make diagnosis of the menopausal period easier and quicker. In addition, there is an advantage that a screening method which is one aspect of the present invention can be used to easily and easily select a substance that can be improved or treated in a menopausal period.
FIG. 1 shows that the metabolism pattern between the control group and the ovariectomized experimental group was clearly discriminated by UPLC-Q-ToF
FIG. 2 shows the result of metabolite patterning after the ovariectomy through metabolite trace analysis using SIMCA P +.
Figure 3 shows the results of HCA for the extracted variables.
FIG. 4 shows the parameters selected in the LC-MS positive mode.
FIG. 5 shows the parameters selected in the LC-MS negative mode.
FIG. 6 shows the result of confirming metabolism associated with ovariectomy using the mass spectrum.
Figure 7 is a sample preparation procedure for GC-MS analysis.
Fig. 8 shows the results obtained by proceeding with GC-MS.
FIG. 9 shows that the content of progesterone hormone and estrogen hormone decreased after ovariectomy.
FIG. 10 is a graph showing changes in the content of progesterone hormone over time after ovariectomy.
FIG. 11 is a graph showing changes in the content of franhngnorone hormone over time after ovariectomy.
In one aspect, the present invention relates to a method for screening a biomarker of a menopausal biomarker, which comprises comparing a biological sample (first biological sample) of an ovariectomized animal with a biological sample (second biological sample) of a normal animal, And a selection method.
As used herein, the term " ovariectomized animal " means an animal in which ovaries are not removed by ovariectomy by a conventional method of abatement in the art, and the " normal animal " .
In this specification, the information obtained from the "subject animal sample of normal animal" as referred to herein may be obtained from a living normal animal, or a known value such as a metabolite known to exist in a normal animal may be used.
In the method of selecting a menopausal biomarker, which is an aspect of the present invention, the comparison between the biological sample of the ovariectomized animal and the normal animal is performed by liquid chromatography-mass spectrometry (LC-MS) ; Analyzing each of the obtained biological samples using liquid chromatography-mass spectrometry (LC-MS); Converting each of the liquid chromatography-mass spectrometry results to a statistically processable value; Subjecting each of the obtained values to variable selection using RF (Random Forest) method or PLS-DA (partial least squares-discriminant analysis) method; And analyzing the numerical values of the selected variables by a statistical method, and then comparing the first biological sample and the second biological sample.
The above-mentioned " liquid chromatography-mass spectrometry (LC-MS) " can be generally used in the art or can determine the conditions such as the extraction amount and the analytical concentration with reference to existing documents. Specifically, it may be UPLC-Q-ToF , But is not limited thereto.
In the present specification, the process of converting the results of chromatography-mass spectrometry into a statistically processable value may be performed by a method commonly used in the art. Specifically, the peak area of the chromatogram is summarized by the mass value over time, and then the baseline correction and peak alignment process of the mass ion is performed using MetAlign (http://www.metalign.nl), and it is suitable for multivariate statistical processing However, the present invention is not limited thereto.
Variable selection, also called feature selection, is the process of selecting variables (metabolites) to achieve better results when using the classification method. In other words, metabolism that does not affect the biomarkers that can be used in the diagnosis of menopause can be removed, and the metabolite that gives meaningful influence is selected and analyzed.
The RF method used in this specification evaluates the order of metabolites by using an Importance Score. Each metabolism has its own significance value, and the importance value indicates how accurately the metabolite determines the diagnosis of menopausal symptoms. It is quantified whether it has a large influence. On the other hand, the PLS-DA method evaluates the ranking of metabolites using a weighted sum of regression coefficients. Regression coefficients are obtained for each metabolite in the regression analysis, and the larger the absolute value of the regression coefficient, the more important metabolites. Here, the regression coefficient is the numerical value of the influence of metabolites on group discrimination.
In the method of selecting a menopausal biomarker as an aspect of the present invention, the statistical method may be principal component analysis (PCA) or hierarchical cluster analysis (HCA).
In the specification of the present invention, the Principal Component Analysis (PCA) is a statistical technique for finding a new principal component represented by a linear combination of variables and for easily summarizing and analyzing the data. This is an analysis method that provides a means for future analysis. In the specification of the present invention, the HCA (Hierarchical Cluster Analysis) starts from a state in which all the objects form one cluster, and sequentially clusters the most similar objects to form a highly similar cluster. Is how all objects are bound together into a single cluster.
In the method for selecting a menopausal biomarker, which is an aspect of the present invention, the biomolecule includes a hormone.
In the method of selecting a menopausal biomarker which is an aspect of the present invention, there is no limitation on the kind of the animal, but it may be a mammal, and specifically, it may be a rat, but is not limited thereto.
In addition, in the method of selecting a menopausal biomarker, which is an aspect of the invention, the first and second biological samples may be obtained from the same kind of animal, and may have the same age and weight. That is, the first biological sample and the second biological sample may be a sample obtained from the same animal except for the presence or absence of ovaries.
In the method of selecting a menopausal biomarker as an aspect of the present invention, the animal may be a rat.
In the method of selecting a menopausal biomarker as an aspect of the present invention, the biological sample may be urine.
In the method for selecting a menopausal biomarker, which is an aspect of the present invention, the ovariectomized animal includes an animal that has passed one week or more after ovariectomy. In cases where more than one week has elapsed after ovariectomy, changes such as metabolism are large enough to easily observe the amount of change. On the other hand, the ovariectomized animal may include, but is not limited to, an animal that has passed eight weeks or less after ovariectomy. In view of the above, the ovariectomized animal may include an animal that has lapsed more than 2 weeks, more than 3 weeks, more than 4 weeks, or more than 5 weeks after ovariectomy.
In another aspect, the present invention relates to a substance capable of a complementary binding to a pregraniol, a substance capable of a complementary binding to 17-hydroxyprogesterone, and a complement to a pregranolone Wherein the agent comprises at least one antibody selected from the group consisting of agents capable of binding to the human body. The complementary binding substance may be RNA or DNA comprising a complementary antibody when the subject is a protein, and RNA or DNA complementary thereto when the target is RNA or DNA.
In another aspect, the invention provides a pharmaceutical composition comprising at least one metabolite selected from the group consisting of Pregnandiol metabolites, 17-hydroxyprogesterone metabolites and Pregnanolone metabolites, , And a marker composition for menopausal diagnosis.
According to the present invention, when the ovarian function is lowered as in the menopausal period, the amount of pregnandiol, 17-hydroxyprogesterone and Pregnanolone hormone dramatically changes, specifically, It is possible to easily and easily judge whether or not the diagnosis object is a menopausal period by diagnosing the menopausal period with the composition.
In another aspect of the present invention, the candidate substance is treated in each of an ovariectomized animal model and a normal animal model, and then, in each of the biological samples, pregnandiol, 17-hydroxyprogesterone, A substance for ameliorating or treating a menopausal disease, which comprises judging such a metabolite to be a substance for improving or treating menopausal when the amount of at least one metabolite selected from the group consisting of Pregnanolone is increased before the treatment of the candidate substance, Screening method.
In one aspect of the present invention, the composition for a menopausal diagnostic marker comprises a composition selected from the group consisting of Pregnandiol, 17-hydroxyprogesterone and Pregnanolone hormone A substance which regulates the expression level of the hormone-related gene and the like may be included as an effective ingredient.
In another aspect, the present invention provides a pharmaceutical composition comprising an amount of at least one metabolite selected from the group consisting of Pregnandiol, 17-hydroxyprogesterone and Pregnanolone, And to a pharmaceutical composition for the treatment or prevention of a menopausal disease, which comprises a substance which increases the expression level as an active ingredient.
In another aspect, the present invention relates to a pharmaceutical composition comprising an amount of at least one metabolite selected from the group consisting of Pregnandiol, 17-hydroxyprogesterone and Pregnanolone, Which comprises a substance which increases the expression level of a metabolite as an active ingredient.
In one aspect of the invention, the composition comprises an amount of at least one hormone selected from the group consisting of Pregnandiol, 17-hydroxyprogesterone and Pregnanolone hormone As well as a substance regulating the expression level of the hormone-related gene and the like may also be included as an active ingredient.
In the present specification, in an ovariectomized animal, the amount of at least one hormone selected from the group consisting of Pregnandiol, 17-hydroxyprogesterone and Pregnanolone is lower than that before treatment of the candidate substance If the amount of these hormones can be increased, it can be used as a material for improving or treating menopause.
Hereinafter, the present invention will be described more specifically with reference to the following examples. However, the following examples are provided for illustrative purposes only in order to facilitate understanding of the present invention, and the scope and scope of the present invention are not limited thereto.
[Preparation Example] Production of ovariectomized animal model
Forty-five female Sprague-Dawley SD rats were obtained, and their weights were measured and plasma was collected. Urine was collected in a metabolic cage for 24 hours. The collected urine was labeled and stored in a deep freezer at 70 ° C.
Ovariectomy operation (OVX) Before the operation, 40 animals were weighed and weighed 1 week, and 15 animals were used as negative controls and the remaining 25 animals were treated with OVX. The average weight of the animals was about 155 g. Anesthetics were anesthetized by mixing 3: 1 volume ratio of ketamine (Yuhan) and Rompun (Korea Bayer). After anesthesia, the abdomen of the rats was incised, and ovaries were removed by ovaries, tubers and fats. After a week of recovery from the operation, all animals were weighed and weighed in the manner described above for up to 8 weeks with a weekly cycle.
After 12 weeks of ovariectomy, weights of heart, lung, stomach, liver, uterus, spleen, kidney and bladder were measured (n = 7) by sacrificing the animals in the negative control group and ovariectomized group Respectively. As a result, the liver weight (p <0.05) and uterus (p <0.01) decreased significantly in the ovariectomized group. In ovariectomized group, weight gain was faster than normal control group.
* P < 0.05
** P < 0.01
[Example 1] Preparation of sample
40 female 5-week-old Sprague-Dawley SD rats were weighed and weighed and urine collected in a metabolic cage for 24 hours.
Ovariectomy operation (OVX) Before the operation, 40 animals in the urine sample were labeled as 1 week, 15 of them were negative control, and the remaining 25 animals were subjected to OVX. After the anesthesia, the abdomen of the rats was opened, and ovaries were removed by ovaries, ovaries, and fat from both sides. After a week of recovery from the operation, urine was collected for a total of 8 weeks for one week for all animals.
[Experimental Example 1] Selection of biomarkers (non-targeted profiling and LC-MS)
(1) Sample preparation for LC-MS analysis
To remove proteins and impurities, 200 μL of each of the obtained urine samples was taken in Ultrafree-MC Durapore (PVDH, 0.1 μm × 0.5 mL), centrifuged at 14,000 rpm for 2 minutes, filtered, and 5 μL Respectively.
(2) UPLC
Waters ACQUITY UPLC system (Waters Corp., MA, USA). (A) with 0.1% formic acid as a mobile phase and a 3.0 [mu] M column of Cadenza HS-C18 (10 cm x 2.0 mm id; ARC Sciences, Alton, Hampshire, UK) 95% acetonitrile (B) with 0.1% formic acid was used. The total analysis time was 15 minutes. The ratio of B was maintained at 0% for the first 3 minutes, increased to 50% for 10 minutes, to 90% for 12 minutes, maintained at 100% for 12.5 minutes, And stabilized under the initial conditions. The flow rate was 320 / / min, the injection amount was 5,, and the column temperature was maintained at 40 캜.
(3) Q-ToF MS
Optimal conditions for metabolite analysis were established in the negative and positive modes, respectively, using a Q-TOF micro mass detector (Waters, Milford, Mass., USA). capillary voltage 3.1 kV (positive mode) and 2.3 kV (negative mode), cone voltage 32V, desolvation temperature 300 ° C,
(4) Statistical analysis Ⅰ
The peak area of the chromatogram obtained by the UPLC-Q-ToF is summarized into mass values according to time, and baseline correction and peak alignment process of mass ion are performed using MetAlign (http://www.metalign.nl) (Partial least squares-discriminant analysis) method was used to select the variables. In order to identify the ovariectomized experimental group and the control group with normal ovary, PCA (principal component analysis. As a result (FIG. 1), it was confirmed that the metabolism pattern between the control group and the ovariectomy group was clearly distinguished.
In addition, metabolic pathway analysis using SIMCA P + showed that the pattern of metabolism proceeded after ovariectomy (FIG. 2). 2, it was confirmed that the change in metabolite pattern was significantly different from one week after ovariectomy.
(5) Statistical analysis Ⅱ
The peak area of the chromatogram obtained by the UPLC-Q-ToF is summarized into mass values according to time, and baseline correction and peak alignment process of mass ion are performed using MetAlign (http://www.metalign.nl) And converted to a number suitable for multivariate statistical analysis. Then, RF (Random Forest) method was performed to select the variables that distinguish between the ovariectomized experimental group and the normal ovarian control group. Specifically, variables were arranged according to Importance Score, and 50 variables with VIP value of 4.0 or more were extracted. The extracted variables were subjected to the non-test t-test to re-extract significant (p <0.001) variables. HCA (Hierarchical Cluster Analysis) was performed on the obtained variables through the DesionSite software of Spotfire, and the result is shown in FIG. 3 below.
In FIG. 3, the metabolic assay is performed, and the down-regulated variable and the increasing variable are determined as up-regulated variables, and the RT_m / z And VIP values. Figure 4 below shows the parameters selected in the LC-MS positive mode, and Figure 5 shows the parameters selected in the LC-MS negative mode.
(6) Selection of new biomarkers for menopausal diagnosis
In FIGS. 2 and 3, before and after the ovariectomy, variables representing distinct changes were selected, and the metabolites were identified through databases such as KEGG, HMP, MIPS, and ChemSpider, It was identified as Pregnandiol and 17-hydroxyprogesterone.
In addition, m / z 375.94 was obtained by selecting the difference between the total mass spectrum of the ovariectomized group and the mass spectrum of the normal group. As a result of comparing the total ion chromatogram, it was confirmed that the ovariectomy group was decreased in the 17.9 minute peak, The mass spectrum was determined as pregnanolone as shown in Fig. 6.
[Experimental Example 2] Determination of whether the selected biomarkers were substantially changed (targeted profiling and GC-MS)
(1) Experimental Hormone
Metabolites to be analyzed; Endogenous steroid hormone
- 22 Estrogen species,
- 13 kinds of progestins,
(2) GC-MS
Each urine sample of Example 1 obtained from ovariectomy test group and normal control group according to time after ovariectomy was processed as shown in FIG. 7 to prepare samples for GC-MS analysis.
Specifically, the mass value was not detected for 5 minutes after the sample was injected in order to reduce the amount of equipment by the solvent. The temperature of the oven was increased from 215 ° C to 260 ° C at 1 ° C / min, then increased from 320 ° C to 300 ° C at 15 ° C / min, and then held for 1 minute. The ion source temperature was 2 ° C and the injection volume (10 μl) was diluted at a split ratio of 20: 1. Molecular weight was determined by selected ion monitoring (SIM) method. As a result of GC-MS proceeding, Fig. 8 below was obtained.
[Experimental Example 3] Reduction of Estrogen and Progesterone Hormone Contents
Analysis of estrogen (Tables 2 and 3) and progesterone (Tables 4 and 5) after ovariectomy revealed that the amount of urine was decreased by ovariectomy.
(mg / g)
(mg / g)
(mg / g)
(mg / g)
(mg / g)
(mg / g)
(mg / g)
(mg / g)
(mg / g)
(mg / g)
(mg / g)
(mg / g)
(mg / g)
(mg / g)
(mg / g)
(mg / g)
(mg / g)
(mg / g)
(mg / g)
(mg / g)
In addition, using the values in the above table, the content of
On the other hand, the change in the content of progesterone hormone over time after ovariectomy using the values in the above table is graphically shown in FIG. 10 below. As can be seen from FIG. 10, it was confirmed that the hormone selected according to the present invention actually shows a change in content according to ovariectomy.
Using the values in the above table, the change in the content of frenomnuron hormone over time after ovariectomy was graphically plotted in FIG. 11 below. As can be seen from FIG. 11, it was confirmed that the hormone selected according to the present invention actually shows a change in content according to ovariectomy.
While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the same is by way of illustration and example only and is not to be construed as limiting the scope of the present invention. Accordingly, the actual scope of the present invention will be defined by the appended claims and their equivalents.
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| KR20100095729A (en) * | 2009-02-23 | 2010-09-01 | 한국과학기술연구원 | Evaluation of metabolic differences between urine samples by quantitative steroid signature |
| JP4818116B2 (en) * | 2003-05-29 | 2011-11-16 | ウオーターズ・テクノロジーズ・コーポレイシヨン | Method and device for processing LC-MS or LC-MS / MS data in metabonomics |
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| JP4818116B2 (en) * | 2003-05-29 | 2011-11-16 | ウオーターズ・テクノロジーズ・コーポレイシヨン | Method and device for processing LC-MS or LC-MS / MS data in metabonomics |
| KR20100095729A (en) * | 2009-02-23 | 2010-09-01 | 한국과학기술연구원 | Evaluation of metabolic differences between urine samples by quantitative steroid signature |
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