阅读说明：本技术 牙周袋炎症面积的推定方法 (Method for estimating inflammatory area of periodontal pocket ) 是由 外川直之 原爱 村上伸也 野崎刚德 于 2018-11-02 设计创作，主要内容包括：本发明提供简便地预测炎症面积(PISA值、CAPRS值)等牙周组织的炎症程度的方法及该方法中使用的器件(DNA芯片等)。本发明提供检测唾液中2种以上细菌的细菌量并以得到的检测结果作为指标来推定牙周袋炎症面积的方法,被检测的细菌包含：该细菌的细菌量与牙周袋炎症面积显示出正相关关系的细菌、以及该细菌的细菌量与牙周袋炎症面积显示出负相关关系的细菌。(The invention provides a method for easily predicting the degree of inflammation of periodontal tissues such as the area of inflammation (PISA value, CAPRS value) and the like, and a device (such as DNA chip) used in the method. The present invention provides a method for estimating the area of periodontal pocket inflammation by detecting the amount of bacteria of 2 or more types of bacteria in saliva and using the obtained detection result as an index, wherein the bacteria to be detected include: bacteria in which the bacterial count of the bacteria positively correlates with the area of inflammation in the periodontal pocket, and bacteria in which the bacterial count of the bacteria negatively correlates with the area of inflammation in the periodontal pocket.)

1. A method for estimating the area of periodontal pocket inflammation by detecting the amount of 2 or more bacteria in saliva and using the obtained detection result as an index, wherein the bacteria to be detected include:

bacteria in which the bacterial count of the bacteria and the area of periodontal pocket inflammation are in a positive correlation, and

the bacterial load of the bacteria and the area of periodontal pocket inflammation show a negative correlation.

2. The method of claim 1, wherein the area of periodontal pocket inflammation is represented by a value of PISA or capris.

3. The method of claim 1, wherein, the bacteria showing a positive correlation are at least 1 selected from the group consisting of Treponema denticola, Stannia fossilis, Fusobacterium nucleatum subspecies, Porphyromonas gingivalis, Campylobacter rectus, Fusobacterium nucleatum subspecies, Pediobacter perus, Veillonella parvula, Streptococcus gordonii, Fusobacterium nucleatum Venerian, Streptococcus intermedius, Cellophilus luteus, Cellophilus sputigena, Actinomyces actinomycetemcomitans, Fusobacterium nucleatum pleioides, Clostridium periodontal, SR 1.OT 345, Porphyromonas catori, Porphyromonas sputigena, Neisseria flavus, Streptococcus brosus, Microparvulus, Streptococcus gastris, Treponema sojae, Eubacterium crypticum, Eubacterium interrogans, Treponema gingivalis, and Porphyromonas pulposus.

4. The method according to claim 1, wherein the bacteria exhibiting a negative correlation are at least 1 selected from the group consisting of Streptococcus mutans, Actinomyces saprophyticus, Streptococcus mitis bv2, Streptococcus mitis, Campylobacter succinogenes, Cellophilus gingivalis, Prevotella pallidum, Streptococcus salivarius, Eubacterium borrelia, Rostellularia peptonensis, Prevotella denticola, atypical Veillonella, Prevotella histophila, Megasphaera minicola, and Streptococcus paracasei.

5. The method according to claim 1, which comprises the following steps (1) to (4):

(1) a step of detecting the bacterial count of various bacteria in saliva from a saliva sample of a subject whose periodontal pocket inflammation area is known;

(2) a step of obtaining a correlation coefficient between the bacterial count of each bacterium and the inflammatory area of the periodontal pocket, constructing a relational expression between the bacterial count of each bacterium and the inflammatory area of the periodontal pocket, and creating a prediction model;

(3) a step of detecting the bacterial count of various bacteria in saliva from a saliva sample of a subject whose periodontal pocket inflammation area is unknown;

(4) and (3) estimating the area of inflammation of the periodontal pocket by substituting the amounts of the various bacteria obtained in (3) into the relational expression obtained in (2).

6. The method of claim 5, wherein the predictive model is made using 1 method selected from linear regression, regression trees, model trees, neural networks, support vector machines, bagging, lifting methods, machine learning algorithms of random forests.

Technical Field

The present invention relates to a method for estimating an area of inflammation in a periodontal pocket.

Background

Periodontal disease is diagnosed by measurement of periodontal pockets, adhesion level, X-ray image diagnosis, and the like. However, these diagnostic methods impose a large burden on the subject, and particularly require a considerable amount of time when performing diagnosis on a large number of persons. In addition, these periodontal disease diagnostic methods have a problem that the operation procedure is complicated, and there is a personal difference in the criterion of determination based on the experience and skill of the dentist.

Therefore, a simple method for diagnosing periodontal disease has been proposed. For example, patent document 1 discloses a method for diagnosing periodontal disease using a protein contained in gingival crevicular fluid as a marker for periodontal disease. Patent document 2 discloses a method for predicting a periodontal Pocket Probing Depth (PPD) and a gingival probing bleeding index (BOP) by analyzing a plurality of proteins in saliva. However, in general, PPD and BOP have 168 measurement sites (28 teeth × 6 dots method), and thus it is not clear which site is actually predicted.

On the other hand, as disclosed in patent document 3, measurement of periodontal disease bacteria present in gingival crevicular fluid and measurement of periodontal disease bacteria in saliva are more commonly performed. As an index corresponding to periodontal disease bacteria present in saliva, an inflammation area (PISA value) calculated from a periodontal Pocket Probing Depth (PPD) and a gingival probing bleeding index (BOP) has been proposed (non-patent document 1). The PISA value can be calculated by multiplying the PESA value (surface area in the periodontal pocket) calculated from the depth of probing in the Periodontal Pocket (PPD) by the gingival probing bleeding index (BOP).

In addition, in contrast, greens et al proposed a capts value (non-patent document 2). As described in the literature, this index is calculated as follows: the Clinical root Surface Area (the sum of the Surface areas of the roots located on the apical side of the gingival margin; Clinical Area of tooth Surface; CARS) was found from the attachment level, and then the value of the effective root Surface Area was subtracted from this value. It can be considered that the replaceable gingival margin position is calculated in a case where all of them coincide with the anatomical cervical line, which is the same as the value of PESA shown in Fig1.(b) of non-patent document 1. Although the calculation of the capris value does not take into account the gingival bleeding index (BOP), the area itself can be approximated to the inflammatory area of the pocket inner surface as described in the literature, and can be considered as "area of pocket inflammation" together with the PISA value.

Conventionally, in order to easily evaluate these "area of inflammation in periodontal pocket", a study was made on the relationship between the number of periodontal disease bacteria in saliva, but there was no correlation between the number of P.g bacteria (Porphyromonas gingivalis) and the capsrs value, and between the number of red complex bacteria (P.g bacteria, T.d bacteria (Treponema dentata), T.f bacteria (tannorella forsysensis) and the capsrs value (non-patent document 2, fig. 3a, b), and a method for easily predicting the "area of inflammation in periodontal pocket" from a saliva sample was not known.

Disclosure of Invention

Problems to be solved by the invention

In such a case, it is desirable to provide a method for estimating the area of inflammation of periodontal pockets based on the result of measurement of the amount of bacteria in saliva.

In such a case, it is desirable to provide a method for easily predicting the degree of inflammation of periodontal tissue such as the area of inflammation (PISA value) based on the result of detection of the amount of bacteria in saliva.

Means for solving the problems

The present invention has been made in view of the above circumstances, and provides a method of estimating an area of inflammation in a periodontal pocket and a method of comprehensively estimating a degree of inflammation in a periodontal tissue, which are described below.

[1] A method for estimating the area of periodontal pocket inflammation by detecting the amount of 2 or more bacteria in saliva and using the obtained detection result as an index, wherein the bacteria to be detected include:

bacteria in which the bacterial count of the bacteria and the area of periodontal pocket inflammation are in a positive correlation, and

the bacterial load of the bacteria and the area of periodontal pocket inflammation show a negative correlation.

[2] The method according to [1], wherein the area of periodontal pocket inflammation is represented by a value of PISA or CAPRS.

[3] The method according to [1] or [2], wherein the bacterium showing a positive correlation is selected from the group consisting of Treponema denticola (Treponema cuticola), Staphylium fumonis (tannorella forsythia), Fusobacterium nucleatum subsp.animalis (Fusobacterium nucleatum), Porphyromonas gingivalis (Porphyromonas gingivalis), Campylobacter rectus (Campylobacter rectus), Fusobacterium nucleatum subsp.nucleatum (Fusobacterium nucleatum), Pseudomonas pernicis (Selenomonas noxia), Microvirens (Veillonella paravulus), Streptococcus gordonii (Streptococcus gordonii), Clostridium nucleatum subsp.sp.sp. (Fusobacterium nucleatum), Clostridium difficile (Streptococcus sp.345), Clostridium cellulovorum (Clostridium cellulovorum), Clostridium cellulovorum subsp.sp.sp.sp.sp.345), Clostridium difficile (Clostridium cellulovorans), Clostridium difficile (Clostridium cellulovorum), Clostridium difficile (Clostridium cellulovorans) At least 1 of Boromonas sputigena (Selenomonas sputigana), Neisseria flavivis (Neisseria flavescens), Streptococcus sorbinus (Streptococcus sobrinus), Micromonospora parvus (Parvimonas micra), Streptococcus gastricis (Peptostreptococcus stomatis), Treponema Sorpum (Treponema soranskii), Eubacterium crypticum (Eubacterium saphenum), Eubacterium nodatumum (Eubacterium nodatumum), Treponema meicum (Treponema media), Treponema gingivalis) and Porphyromonas endodontis (Porphyromonas endotalis).

[4] The method according to any one of [1] to [3], wherein the bacterium exhibiting a negative correlation is at least one species selected from the group consisting of Streptococcus mutans (Streptococcus mutans), Actinomyces saprophyticus (Actinomyces odontophyticus), Streptococcus mitis bv 2(Streptococcus mitis bv 2), Streptococcus mitis (Streptococcus mitis), Campylobacter concubilis (Campylobacter consortius), carbon dioxide Cellophilus (Capnocytophagigivalis), Prevotella pallidus (Prevotella pallidus), Streptococcus salivarius (Streptococcus salivatus), Eubacterium sulci, Salmonella typhi (Rothia mularia), Prevotella denticola (Prevotella denticola), atypical Weissella typhi (Veillella tissue), Streptococcus mutans (Streptococcus mutans), and Streptococcus sphaericus (Streptococcus sphaericus), and Streptococcus parvulus (Mevoronospora).

[5] The method according to any one of [1] to [4], which comprises the following steps (1) to (4):

(1) a step of detecting the bacterial count of various bacteria in saliva from a saliva sample of a subject whose periodontal pocket inflammation area is known;

(2) a step of obtaining a correlation coefficient between the bacterial count of each bacterium and the inflammatory area of the periodontal pocket, constructing a relational expression between the bacterial count of each bacterium and the inflammatory area of the periodontal pocket, and creating a prediction model;

(3) a step of detecting the bacterial count of various bacteria in saliva from a saliva sample of a subject whose periodontal pocket inflammation area is unknown;

(4) and (3) estimating the area of inflammation of the periodontal pocket by substituting the amounts of the various bacteria obtained in (3) into the relational expression obtained in (2).

[6] The method according to [5], wherein the prediction model is manufactured by using 1 method selected from linear regression, regression tree, model tree, neural network, support vector machine, bagging (bagging), boosting (boosting), and machine learning algorithm of random forest.

[7] A method of comprehensively estimating the degree of inflammation of periodontal tissue, the method comprising: the amount of 1 or more kinds of bacteria in saliva was measured, and the obtained measurement results were used as an index.

[8] The method according to [7], wherein the degree of inflammation of periodontal tissue is a value of PISA or CAPRS.

[9] The method according to [7] or [8], wherein the bacterial amount of the bacterium to be detected is the copy number of the bacterium in saliva.

[10] The method according to any one of [7] to [8], wherein the bacterial amount of the bacterium to be detected is a bacterial amount based on 16S rRNA sequence information of the bacterium in saliva.

[11] The method according to any one of [7] to [10], wherein the bacteria to be detected are bacteria belonging to at least 1 genus selected from the group consisting of Porphyromonas (Porphyromonas), Tenonella (tannorella), Treponema (Treponema), Prevotella (Prevotella), Campylobacter (Campylobacter), Clostridium (Fusobacterium), Streptococcus (Streptococcus), Agrobacterium (Aggregatobacter), Carbonocytophaga (Capnocytophaga), Aikenella (Eikenenella), Actinomyces (Actinomyces), Veillonella (Veillonella), and Selenomonas (Serenomonas).

[12] The method according to any one of [7] to [11], wherein the bacterium to be detected is selected from the group consisting of Streptococcus mutans, Actinomyces carinatus, Streptococcus mitis bv2, Streptococcus mitis, Campylobacter succinogenes, Prevotella intermedia (Prevotella intermedia), Campylobacter showae (Campylobacter showae), Prevotella nigrescens (Prevotella nigrescens), Exacteria rodensis (Eikenenella corrodens), Microcaphalobacter gingivalis, Actinomyces naeslundii (Actinomyces naeslundii II), Streptococcus astrococcus (Streptococcuscoccusostreatus), Campylobacter xylinus (Campylobacter gracilis), Clostridium bacteria, Fusobacterium nucleatum subspecies, Synechococcus lunatus, Carbonisatus, Microcaphalobacter xanthus, Microbacterium fuscus, Streptococcus intermedius, Streptococcus mitis, Streptococcus cereus, Streptococcus mitis, Streptococcus sobrinus, and Streptococcus aryomyces subsp At least 1 of Campylobacter rectus, Porphyromonas gingivalis, Fusobacterium nucleatum subspecies, Stainerella fossilis, and Treponema denticola.

[13] The method according to any one of [7] to [12], wherein the bacteria to be detected include bacteria in which the bacterial count of the bacteria and the degree of inflammation of periodontal tissue are positively correlated.

[14] The method according to [13], wherein the bacteria capable of establishing a positive correlation are at least 1 selected from the group consisting of Clostridium paradentium, Fusobacterium nucleatum subspecies polymorpha, Actinomyces actinomycetemcomitans, capnocytophaga sputigena, capnocytophaga lutescens, Streptococcus intermedius, Fusobacterium nucleatum Wedney subspecies, Streptococcus gordonii, Veillonella parvula, Homophilus perniciosus, Fusobacterium nucleatum, Campylobacter rectus, Porphyromonas gingivalis, Fusobacterium nucleatum subspecies, Fostanemia venenatus and Treponema denticola.

[15] The method according to any one of [7] to [12], wherein the bacteria to be detected include bacteria whose bacterial amount and degree of inflammation of periodontal tissue can be inversely related.

[16] The method according to [15], wherein the bacterium which can be brought into a negative correlation is at least 1 selected from the group consisting of Streptococcus mutans, Actinomyces carinii, Streptococcus mitis bv2, Streptococcus mitis, Campylobacter succinogenes, Prevotella intermedia, Campylobacter showae, Prevotella melanogenes, Exkenella denticulata, Carboxyphilus gingivalis, Actinomyces naeslundii II, Streptococcus astrus and Campylobacter tenuis.

ADVANTAGEOUS EFFECTS OF INVENTION

According to the present invention, the area of periodontal pocket inflammation can be easily predicted based on the result of measurement of the amount of bacteria in saliva. That is, the degree of inflammation of the entire oral cavity can be easily estimated by using the collected saliva without performing a precise periodontal disease examination (actual measurement of the pocket or imaging).

Further, according to the present invention, the degree of inflammation of periodontal tissue such as the area of inflammation (PISA value, capris value) can be easily predicted based on the result of detection of the amount of bacteria in saliva. That is, the degree of inflammation (inflammation index value) of the entire oral cavity can be easily estimated by using the collected saliva without performing a precise periodontal disease examination (pocket measurement and imaging).

Drawings

FIG. 1-1 is a diagram showing a model tree of PISA prediction calculated from SN ratios of respective bacterial amounts.

FIG. 1-2 is a diagram showing a model tree of PISA prediction calculated from SN ratios of respective bacterial amounts (continuation of FIG. 1-1. No. 1 of FIG. 1-1 enclosed with a box is connected with No. 21 of FIG. 1-2).

Fig. 2 is a graph showing a scatter plot of PISA values (horizontal axis) and PISA measured values (vertical axis) predicted from the model tree of fig. 1.

Fig. 3 is a graph showing a scattergram of PISA values (horizontal axis) and PISA actual measurement values (vertical axis) predicted from a model tree created based on the SN ratio of each bacterial load corrected between chips.

FIG. 4-1 is a diagram showing a PISA predicted model tree calculated from SN ratios of respective bacterial loads corrected between chips using 34 out of all 46 data.

FIG. 4-2 is a diagram showing a model tree of PISA prediction calculated from SN ratios of respective bacterial quantities after correction between chips using 34 out of all 46 data (continuation of FIG. 4-1, No. 1 of FIG. 4-1 enclosed with a box is connected with No. 11 of FIG. 4-2, and No. 13 of FIG. 4-1 is connected with No. 12 of FIG. 4-2).

Fig. 5 is a graph showing a scatter plot of PISA values (horizontal axis) and PISA actual measurement values (vertical axis) predicted from the 34 data used to create the model tree of fig. 4.

Fig. 6 is a graph showing a scatter diagram of PISA measured values (vertical axis) of the remaining 12 data not used for model creation among all 46 data and PISA values (horizontal axis) predicted from the model tree of fig. 4.

Fig. 7 is a graph showing a comparison between the PISA measured value and a scattergram (left) of "ratio of 3 kinds of bacteria (known) to total bacteria amount", and between the PISA measured value and a PISA predicted value (right).

Fig. 8 is a graph showing PISA measured values and a scattergram showing the ratio of bacteria showing a positive correlation to bacteria showing a negative correlation ("balance index").

Fig. 9 is a scattergram showing the results of predicting the PISA value (vertical axis) by creating a prediction model expression by multivariate regression analysis using 56 samples tested to confirm the bacterial amount of the bacterial species statistically significantly correlated with the PISA value (horizontal axis) as an explanatory variable.

Detailed Description

The present invention will be described in detail below. The scope of the present invention is not limited to the above description, and can be modified and implemented as appropriate without departing from the spirit of the present invention, in addition to the following examples. All publications cited in the present specification, such as prior art documents and publications, patent publications, and other patent documents, are incorporated herein by reference.

An invention (hereinafter, also referred to as "first invention group") as a method of estimating an area of inflammation in a periodontal pocket according to a first embodiment of the present invention includes the following steps.

i) A step of detecting the amount of 2 or more types of bacteria in saliva, and ii) a step of estimating the area of periodontal pocket inflammation using the obtained detection result as an index.

The invention of a method for comprehensively estimating the degree of inflammation of periodontal tissues (hereinafter also referred to as "second invention group") as the second aspect of the present invention includes the following steps.

i) A step of detecting the bacterial count of 1 or more types of bacteria in saliva (in saliva of a subject, particularly a human being), and ii) a step of comprehensively estimating the degree of inflammation of periodontal tissue using the obtained detection result as an index.

1. Oligonucleotide probe for detecting bacterial amount of bacteria in saliva

In the method of the present invention, a DNA chip can be used for measuring the amount of bacteria in the oral cavity from saliva collected from a subject. The DNA chip can carry, for example, the following probe (a) (bacteria-specific probe), and can carry probe (b) (total amount index probe) and probe (c) (absolute amount index probe).

(a) Bacteria-specific probes: probe comprising nucleic acid that specifically hybridizes to gene (or part thereof) of bacterium to be detected

(b) Total amount index probe: probes comprising nucleic acids hybridizing to genes of all bacteria

(c) Absolute quantity index probe: probes consisting of nucleic acids which hybridize specifically with 1 or more absolute quantitative indicators, respectively

In general, a DNA chip is a generic term for a substrate on which probes are arranged. The names of DNA chips, DNA microarrays and the like are treated as synonyms without being distinguished from each other.

(1) Bacteria in saliva to be detected

In the method of the present invention, the bacterium in saliva to be detected (the measurement target of the bacterial amount) is not limited, but is preferably a bacterium belonging to each of the genera listed below, i.e., preferably belonging to a genus selected from the group consisting of porphyromonas, tannophilus, treponema, prevotella, campylobacter, clostridium, streptococcus, aggregatibacter, capnocytophaga, elycnophilus, actinomyces, veillonella, selenomonas, Eubacterium, Parvimonas, producer (Filifactor), Haemophilus (Haemophilus), bacteroides (allemophilus), bacteroides (Alloprevotella), parvobacterium, rothrix (rothria), Peptostreptococcus (Peptostreptococcus), Gemella (gemeubacterium), Corynebacterium (corebacter), Neisseria (Neisseria), and glomerulus (gracilia), Bacteria belonging to at least 1 of the genera Megasphaera and SR 1.

More specifically, at least 1 or 2 or more bacteria selected from the various bacteria listed below are more preferably used as the detection target.

Porphyromonas gingivalis

Fostainerella foestansis

Treponema dentis Ledeb

Campylobacter tenuis

Campylobacter rectum

Campylobacter showae

Wen subspecies with Fusobacterium nucleatum

Subspecies pleomorphus of Fusobacterium nucleatum

Subspecies of animals with Fusobacterium nucleatum

Fusobacterium nucleatum subspecies

Fusobacterium periodontal disease

Prevotella intermedia

Prevotella nigricans

Streptococcus stellus

Actinomycetes-associated reunion

Simple campylobacter

Carbon dioxide Cellophilus gingivalis

Xanthochrous capnocytophaga (XN. flavus)

Carbon dioxide Cellophilus capable of producing phlegm

Airkshire rodensis

Streptococcus gordonii

Streptococcus intermedius

Streptococcus mitis

Streptococcus mitis bv2

Actinomyces carinata

Veillonella parvum

Actinomyces naeslundii II

Harmful bacterium Oenomonas lunata

Streptococcus mutans

Entwining eubacterium sp

Micromonospora cerealis

Gingival sulcus production line bacterium

Streptococcus sorbinus

Porphyromonas pasteurii (Porphyromonas pasteri)

Atypical veillonella sp

Haemophilus parainfluenza (Haemophilus parainfluenza fluent)

Prevotella (A. rava, OT 308)

Streptococcus paracasei

Actinomyces Chlamydia

Prevotella pallidum

Prevotella rosenbergii (Prevotella loescheii)

Prevotella histolytica

Tiny bacterium morganii (Solobacterium morrei)

Prevotella melanogenes (Prevotella melanogenesis)

Sputum producing moon shaped unicellular bacterium

Roseburia carious (Rothia dentocariosa)

Roseburia pepti

Rogoverella rosenbergii (Veillonella rogosae)

Gastric digestive streptococcus

Prevotella denticola

Porphyromonas pulposus

Salivary streptococcus

Actinomyces graventizi (Actinomyces graventii)

Treponema pallidum

Treponema sovieri

Twin hemolytic coccus (Gemelal sanguinis)

Porphyromonas catori

Corynebacterium equi (Corynebacterium matrichotii)

Eubacterium crypthecogenum

Neisseria lutescens

Adjacent short chain Chlorella (Granulicatella adiacens)

Eubacterium bordii (Fr.) Kuntze

Microcaryophyllus giganteus (Fr.) karst

Prevotella chardii (Prevotella shahii)

SR1 sp.OT 345

In the "first invention group", bacteria in which the bacterial count and the area of periodontal pocket inflammation are in a positive correlation (a relationship in which the area of periodontal pocket inflammation increases when the bacterial count increases) (hereinafter, sometimes simply referred to as "bacteria showing a positive correlation") and bacteria in which the bacterial count and the area of periodontal pocket inflammation are in a negative correlation (a relationship in which the area of periodontal pocket inflammation decreases when the bacterial count increases) (hereinafter, sometimes simply referred to as "bacteria showing a negative correlation") can be used.

The area of inflammation in the periodontal pocket includes areas indicating inflammation including PISA (periodontal inflated surface area) and CAPRS (confined area in periodontal pocket of tooth surface area) and includes the same concept as the index.

The PISA value is in square millimeters (mm)²) The value indicating the area of the inflammatory site of the periodontal tissue in the entire oral cavity can be determined based on the periodontal pocket surface area (PESA) and the presence or absence of bleeding at the time of probing (bleeding on binding: BOP). The periodontal pocket surface area (PESA) can be calculated from the area predetermined for each tooth type and the depth of the Periodontal Pocket (PPD). As additional information of non-patent document 1, an automatic calculation spreadsheet (Excel file) by the 6-point method (https:// www.parsprototo.info/pisa. html) is added, and as long as the calculation formula described in the Excel file is viewed, anyone can confirm the above calculation method.

As described in the literature, the CAPRS value is calculated by obtaining a Clinical root Surface Area (the sum of the Surface areas of roots located closer to the apex than the gingival margin; Clinical Area of tooth Surface; CARS) based on the attachment level and then subtracting the value of the effective root Surface Area from the value. It can be considered that the replaceable gingival margin position is calculated in a case where all of them coincide with the anatomical cervical line, which is the same as the value of PESA shown in Fig1.(b) of non-patent document 1. Although the calculation of the capris value does not take into account the gingival bleeding index (BOP), as described in the literature, the area itself can be approximated to the inflammatory area of the pocket inner surface, and can be considered as "pocket inflammatory area" together with the PISA value. The area of periodontal pocket inflammation is preferably expressed as a PISA or CAPRS value.

Bacteria showing a positive correlation and bacteria showing a negative correlation can be confirmed by using a tool capable of measuring the bacterial amount (or a measured amount proportional to the bacterial amount such as an SN ratio). The tool is not particularly limited, and for example, a DNA chip can be used.

When confirming using the DNA chip, the intraoral sample is measured using the DNA chip, and then the correlation coefficient between the area of periodontal pocket inflammation and the measured amount such as the bacterial count or SN ratio of each bacterium is calculated, and it is possible to classify and identify a bacterial group whose correlation coefficient is positive and a bacterial group whose correlation coefficient is negative. In the case where the number of measurements is 40 or more, the absolute value of the correlation coefficient is preferably 0.02 or more, more preferably 0.1 or more, still more preferably 0.2 or more, particularly preferably 0.4 or more, and most preferably 0.6 or more.

When data after experimental error correction is used for creating a prediction model for estimating the area of inflammation in the periodontal pocket, which will be described later, data after experimental error correction is also used for classifying the bacterial flora.

Examples of the bacteria showing a positive correlation include the following bacteria. At least 1 species, more preferably 2 or more species, of these bacteria are preferably used as the detection target.

Treponema dentis Ledeb

Fostainerella foestansis

Subspecies of animals with Fusobacterium nucleatum

Porphyromonas gingivalis

Campylobacter rectum

Fusobacterium nucleatum subspecies

Harmful bacterium Oenomonas lunata

Veillonella parvum

Streptococcus gordonii

Wen subspecies with Fusobacterium nucleatum

Streptococcus intermedius

Xanthochrous capnocytophaga (XN. flavus)

Carbon dioxide Cellophilus capable of producing phlegm

Actinomycetes-associated reunion

Subspecies pleomorphus of Fusobacterium nucleatum

Fusobacterium periodontal disease

SR1 sp.OT 345

Porphyromonas catori

Sputum producing moon shaped unicellular bacterium

Neisseria lutescens

Streptococcus sorbinus

Micromonospora cerealis

Gastric digestive streptococcus

Treponema sovieri

Eubacterium crypthecogenum

Entwining eubacterium sp

Treponema pallidum

Gingival sulcus production line bacterium

Porphyromonas pulposus

Examples of the bacteria exhibiting a negative correlation include the following bacteria. At least 1 species, more preferably 2 or more species, of these bacteria are preferably used as the detection target.

Streptococcus mutans

Actinomyces carinata

Streptococcus mitis bv2

Streptococcus mitis

Simple campylobacter

Carbon dioxide Cellophilus gingivalis

Prevotella pallidum

Salivary streptococcus

Eubacterium bordii (Fr.) Kuntze

Roseburia pepti

Prevotella denticola

Atypical veillonella sp

Prevotella histolytica

Microcaryophyllus giganteus (Fr.) karst

Streptococcus paracasei

In the "second invention group", bacteria having a positive correlation between the bacterial count and the degree of inflammation of periodontal tissue (PISA value or capris value) or bacteria having a negative correlation are preferably mentioned.

Examples of the bacteria having a positive correlation include the following bacteria, and at least 1 of these bacteria is preferably used, and more preferably 2 or more are used as the detection target.

Treponema dentis Ledeb

Fostainerella foestansis

Subspecies of animals with Fusobacterium nucleatum

Porphyromonas gingivalis

Campylobacter rectum

Fusobacterium nucleatum subspecies

Harmful bacterium Oenomonas lunata

Veillonella parvum

Streptococcus gordonii

Wen subspecies with Fusobacterium nucleatum

Streptococcus intermedius

Xanthochrous capnocytophaga (XN. flavus)

Carbon dioxide Cellophilus capable of producing phlegm

Actinomycetes-associated reunion

Subspecies pleomorphus of Fusobacterium nucleatum

Fusobacterium periodontal disease

In addition, as the above-mentioned bacteria having a negative correlation, for example, the following bacteria are preferably listed, and at least 1 kind, more preferably 2 or more kinds of these bacteria are preferably detected.

Streptococcus mutans

Actinomyces carinata

Streptococcus mitis bv2

Streptococcus mitis

Simple campylobacter

Carbon dioxide Cellophilus gingivalis

(2) Bacteria-specific probes

In the present invention, the oligo-DNA usable as a bacterium-specific probe is an oligo-DNA capable of hybridizing with a base sequence in a specific region among base sequences of nucleic acids derived from bacteria in saliva. Here, the nucleic acid is not limited as long as it is any of DNA and RNA including chromosomal DNA, plasmid DNA, and the like, and chromosomal DNA is preferable. Specifically, the oligonucleotide used as a probe in the present invention is an oligonucleotide capable of hybridizing with the base sequence of the 16S rRNA gene in the chromosomal DNA of the above-mentioned bacterium.

The probe usable in the present invention is preferably designed by selecting a region having a nucleotide sequence specific to each bacterium to be detected and designing the nucleotide sequence of the region. In general, when designing a probe, it is necessary to select a specific region, and to have a uniform melting temperature (Tm) and to make it difficult to form a secondary structure.

For example, base sequences corresponding to the specificities of various bacteria in saliva can be found by designing probes in regions different among species by using multiple sequence alignments or the like. The algorithm for performing the alignment is not particularly limited, and as a more specific analysis program, for example, a program such as clustalx1.8 can be used. The parameters for the alignment may be executed in a default state of each program, and may be appropriately adjusted according to the type of the program.

The specificity of the probe may be a specificity that allows all bacteria of the same genus to be detected at once based on the specificity at the genus level, or a specificity that allows detection at the individual species level, and may be selected and designed as appropriate depending on the purpose of detection.

Examples of the bacteria-specific probes that can be used in the present invention are shown in the following table a (seq id nos 1 to 29).

(3) Total amount index probe

The total amount indicator probe is a probe that can be amplified with a specific primer set and is used to capture all bacteria in a specimen (in saliva). In detecting bacteria, it is important to detect the total amount of bacteria from the viewpoint of the ratio of the degree of bacteria to be detected to the total amount of bacteria including bacteria not to be detected, and the amount of bacteria present in the original sample.

Bacteria that are not the object of detection are understood to be the sum (total) of bacteria whose presence and type are known but are not the object of detection and bacteria whose presence and type are unknown.

In order to detect the total amount of bacteria, for example, the total amount of bacteria may be measured separately from the DNA chip, but the DNA chip may be loaded with a probe as an index of the total amount of bacteria, thereby improving the ease of operation. As the probe, a base sequence common to a plurality of bacterial species can be used as the base sequence amplified by the primer set. When such a sequence is not found, a plurality of relatively common sequences can be designed and comprehensively determined to be used as a total amount indicator probe. The total amount indicator probe is preferably a probe that hybridizes to a nucleic acid derived from a bacterium contained in a sample, and more specifically, a probe that contains a base sequence that is common to a plurality of bacteria to be detected among the base sequences amplified by the specific primer set. Examples of the total amount index probe are shown in the following Table A (SEQ ID NO: 31).

The total amount index indicates the total amount of the amplified product specific to each species, and therefore the amount is usually large, and thus the target signal intensity sometimes exceeds the range of detectable signal intensity.

In order to prevent such a situation, it is desirable to limit the amount of the detection substance to be used for hybridization. Alternatively, when designing a probe, for example, the Tm value of the probe is lowered. Specifically, a method of reducing the GC content and shortening the sequence length of the probe itself can be considered.

In addition, when hybridization is performed, a decrease in signal intensity can be achieved by adding a nucleic acid that has a competitive effect on hybridization of the amplified nucleic acid with the total amount index probe. Examples of such nucleic acids include nucleic acids having a sequence identical to all or part of the total amount indicator probe, or nucleic acids having a sequence complementary to all or part of the total amount indicator probe.

(4) Absolute quantity index probe

The absolute amount index probe is a probe that hybridizes to only the nucleic acid of the absolute amount index.

In the present specification, the absolute amount index refers to a certain amount of nucleic acid added to a sample before an amplification reaction or a hybridization reaction. The absolute amount index is a nucleic acid that can reliably undergo an amplification reaction as long as a normal amplification reaction is performed, and functions as a so-called positive control.

Therefore, if a probe specific to the absolute amount index is mounted on the DNA chip in advance, whether or not the amplification reaction, hybridization, or the like has been properly performed can be confirmed from the detection result. In addition, when the amplification efficiency and the hybridization efficiency slightly increase or decrease when the absolute amount index is set to 1 type, the correction coefficient may be calculated by comparing the signal intensities of the absolute amount index. The corrected signal intensities may be compared among a plurality of DNA chips.

Examples of the absolute amount index probes are shown in the following table a (sequence No. 30).

An example of the absolute quantity index is shown in the following serial number 74.

Probe for absolute quantity index:

CTATTCGACCAGCGATATCACTACGTAGGC (Serial number 30)

Absolute quantity index:

GTGAGAAGCCTACACAAACGTAACGTCAGGGCTAAGACAAACGCTAACGGTACACCCTAGATGGGAGCTTGTAGCTAGATCGCTAAGTCCTACCGACATGTAGGCATACTCACGAAGGCAATTCCCTGAAAGCCTCGTCTTATCCCGAACTTGGCATCTGCTGATACGTCAGGTTGAACGCGTACATTTACCTGTCATGCGTGGGCCTTCTCCGAATAGCCTACGTAGTGATATCGCTGGTCGAATAGGCGGATTGCTCATAAATGCACATTGGCTAAGGCCCACGGAACACGAATCACGTGAGATCACTTACTATTCGACGGAACTACTATACGCACCGGGACATGCAAGTAGCGTCCCACAAGCATAAGGAACTCTATACTCGCCATCTACGCAGCTACAGGGGATACACGTATGAGCGGTTACGAAGTAAAGCCGAGATAGAGCGGTCTTTAGAGAAAAAACAGGATTAGATACCCTGGTAGTCC (Serial number 74)

If the absolute amount indicator is added before the amplification reaction, it is required that the nucleic acid is amplified by a specific primer pair, i.e., has a base sequence complementary to the primer pair; in addition, in order to perform detection by hybridization, it is necessary to have a nucleotide sequence that is not present in either the bacteria to be detected or the bacteria not to be detected.

The specific primer means that the sequence to be amplified is limited, and the primer pair is not necessarily 1 pair. If necessary, a multiplex method using 2 or more primer pairs may be used. Examples of the primer pairs are shown in the following table B. A pair of primers for bacterial amplification (SEQ ID NOS: 32 and 33) and a pair of primers for absolute quantity indicator (SEQ ID NOS: 34 and 35) can be used.

The absolute amount index can be, for example, a nucleic acid standard substance for quantitative analysis developed by the institute of industrial technology integration, or can be newly designed. In designing, for example, X (X is an arbitrary number) integers of 1 to 4 are randomly generated using the RNDBETWEEN function of software "EXCEL" (manufactured by MICROSOFT corporation), and these integers are connected to have a numerical value of X bit consisting of only numerical values of 1 to 4, and a large number of ATGC sequences based on X bases can be obtained by replacing 1 with a, 2 with T, 3 with C, and 4 with G.

These sequences can be designed by selecting only sequences whose sum of G and T is the same as the sum of A and T, performing Blast search on the selected sequences using a database such as GenBank of NCBI, selecting a sequence having few similar sequences to a nucleic acid derived from a living organism, and adding primer sequences to both ends of the sequence. In addition, the designed sequence may be appropriately ligated and lengthened, or may be partially removed and shortened.

In order to keep the reaction efficiency at the time of amplification reaction as constant as possible, it is desirable that the length of the amplified base in the bacteria to be detected is not greatly different from the length of the amplified base in the absolute amount index. For example, if the amplification product of the bacteria to be detected is about 500bp, it is desirable that the amplification product of the absolute amount index is about 300bp to 1000 bp.

On the other hand, in the case where the length of the amplified strand is confirmed by electrophoresis or the like after amplification, it is designed so that the amplified strand has a length different from that of the bacteria to be detected, and then the amplified product derived from the absolute amount indicator is detected at a position different from the band of the bacteria to be detected, and the success or failure of the amplification reaction is confirmed before hybridization.

Finally, when the concentration of the absolute amount indicator contained in the sample is too high, the competition with the bacteria to be detected in the amplification reaction becomes severe, and the bacteria to be detected which should be originally detected may become undetectable, and therefore, it is necessary to appropriately adjust the concentration according to the application.

TABLE A

Serial number	Sequence of	Name of probe
			1	TTCAATGCAATACTCGTATC	Porphyromonas gingivalis
2	CACGTATCTCATTTTATTCCCCTGT	Fostainerella foestansis
			3	CCTCTTCTTCTTATTCTTCATCTGC	Prevotella denticola
4	GCCTTCGCAATAGGTATT	Campylobacter tenuis
			5	GTCATAATTCTTTCCCAAGA	Campylobacter rectum
6	CAATGGGTATTCTTCTTGAT	Campylobacter showae
			7	TAGTTATACAGTTTCCAACG	Wen subspecies with Fusobacterium nucleatum
8	CCAGTACTCTAGTTACACA	Subspecies pleomorphus of Fusobacterium nucleatum
			9	TTTCTTTCTTCCCAACTGAA	Subspecies of animals with Fusobacterium nucleatum
10	TACATTCCGAAAAACGTCAT	Fusobacterium nucleatum subspecies
			11	TATGCAGTTTCCAACGCAA	Fusobacterium periodontal disease
12	CGAAGGGTAAATGCAAAAAGGC	Prevotella intermedia
			13	CTTTATTCCCACATAAAAGC	Prevotella nigricans
14	AAGTACCGTCACTGTGTG	Streptococcus stellus
			15	GTCAATTTGGCATGCTATTAACACACC	Actinomycetes-associated reunion
16	CCCAAGCAGTTCTATGGT	Simple campylobacter
			17	TACACGTACACCTTATTCTT	Carbon dioxide Cellophilus gingivalis
18	CAACCATTCAAGACCAACA	Xanthochrous capnocytophaga (XN. flavus)
			19	TCAAAGGCAGTTGCTTAGT	Carbon dioxide Cellophilus capable of producing phlegm
20	CTCTAGCTATCCAGTTCAG	Airkshire rodensis
			21	CACCCGTTCTTCTCTTACA	Streptococcus gordonii
22	ACAGTATGAACTTTCCATTCT	Streptococcus intermedius
			23	TCTCCCCTCTTGCACTCA	Streptococcus mitis
24	TCCCCTCTTGCACTCAAGT	Streptococcus mitis bv2
			25	AAGTCAGCCCGTACCCA	Actinomyces carinata
26	TCCTTCTAACTGTTCGC	Veillonella parvum
			27	CCACCCACAAGGAGCAG	Actinomyces naeslundii II
28	TTCGCATTAGGCACGTTC	Harmful bacterium Oenomonas lunata
			29	CACACGTTCTTGACTTAC	Streptococcus mutans
30	CTATTCGACCAGCGATATCACTACGTAGGC	Control DNA
			31	CGTATTACCGCGGCTGCTGGCAC	Total bacteria

TABLE B

Serial number	Function of	Sequence (5 '→ 3')
			32	Forward primer (for bacterial amplification)	TCCTACGGGAGGCAGCAGT
33	Reverse primer (for bacterial amplification)	CAGGGTATCTAATCCTGTTTGCTACC
			34	Forward primer (for absolute quantity index amplification)	GAGAAGCCTACACAAACGTAACGTC
35	Reverse primer (for absolute quantity index amplification)	CTCTAAAGACCGCTCTATCTCGG

In designing the probe used in the present invention, the stringency in hybridization is preferably taken into consideration. By setting the stringency to a certain degree, even if a similar base sequence region exists between specific regions in the respective nucleic acids in various bacteria, hybridization can be performed with distinction from other different regions. In addition, when the base sequences of the specific regions are substantially different from each other, the stringency can be set moderately.

As such stringent conditions, for example, stringent conditions are hybridization at 50 to 60 ℃ and mild conditions are hybridization at 30 to 40 ℃. Among the hybridization conditions, stringent conditions include, for example, "0.24M Tris. HCl/0.24M NaCl/0.05% Tween-20," 40 ℃ "," 0.24M Tris. HCl/0.24M NaCl/0.05% Tween-20, "37 ℃", "0.24M Tris. HCl/0.24M NaCl/0.05% Tween-20,", and more stringent conditions include, for example, "0.24M Tris. HCl/0.24M NaCl/0.05% Tween-20," 50 ℃ "," 0.24M Tris. HCl/0.24M NaCl/0.05% Tween-20, "55 ℃", "0.06M Tris. HCl/0.06M NaCl/0.05% Tween-20,", and "60 ℃". More specifically, there is a method of: the probe was added and the mixture was kept at 50 ℃ for 1 hour or more to form a hybrid, and then washed 4 times at 50 ℃ for 20 minutes in 0.24M Tris & HCl/0.24M NaCl/0.05% Tween-20, and finally washed 1 time at 50 ℃ for 10 minutes in 0.24M Tris & HCl/0.24M NaCl. By raising the temperature at the time of hybridization or washing, more stringent conditions can be set. As a person skilled in the art, conditions other than the conditions such as the salt concentration of the buffer and the temperature may be set in consideration of various conditions such as the probe concentration, the length of the probe, and the reaction time. For detailed procedures of the hybridization method, reference may be made to "Molecular Cloning, A Laboratory Manual 4th ed." (Cold Spring Harbor Press (2012), "Current protocols in Molecular Biology" (John Wiley & Sons (1987-1997)), etc.

The length of the probe used in the present invention is not limited, and is, for example, preferably 10 bases or more, more preferably 16 to 50 bases, and still more preferably 18 to 35 bases. If the length of the probe is appropriate (if it is within the above range), nonspecific hybridization (mismatch) can be suppressed, and the probe can be used for specific detection.

In designing the probe used in the present invention, Tm is preferably confirmed in advance. The Tm is a temperature at which 50% of an arbitrary nucleic acid strand forms a hybrid with its complementary strand, and the hybridization temperature needs to be optimized in order to form a double strand between a template DNA or RNA and a probe and perform hybridization. On the other hand, if the temperature is excessively lowered, a nonspecific reaction is likely to occur, so that it is desirable that the temperature be as high as possible. Therefore, the Tm of the nucleic acid fragment to be designed is an important factor in hybridization. As the software that can be used in the present invention, for example, Probe Quest (registered trademark; DYNACOM Co., Ltd.) and the like can be cited. Further, the Tm can be confirmed by self-calculation without using software. In this case, a calculation formula based on a Nearest Neighbor Method (Nearest Neighbor Method), wallence Method, GC% Method, or the like can be used. The probe of the present invention is not limited, and the average Tm is preferably about 35 to 70 ℃ or 45 to 60 ℃. As conditions for specific hybridization as a probe, conditions for GC content and the like are known to those skilled in the art.

The nucleotide constituting the probe used in the present invention may be either DNA or RNA or PNA, or a hybrid of 2 or more kinds of DNA, RNA and PNA.

Specific examples of the probe used in the present invention include probes containing the base sequence of the DNA of the following (d) or (e). For example, when amplification is performed using the primers (SEQ ID NOS: 32 to 35) shown in Table B, the sequences shown in Table A (SEQ ID NOS: 1 to 31) listed above can be used as probes, and it is preferable to use at least 2 sequences selected from the base sequences shown in SEQ ID NOS: 1 to 31. The sequence may be a sequence complementary to at least 2 sequences selected from the base sequences represented by SEQ ID Nos. 1 to 31, a sequence substantially identical to at least 2 sequences selected from the base sequences represented by SEQ ID Nos. 1 to 31, or a sequence substantially identical to a sequence complementary to at least 2 sequences selected from the base sequences represented by SEQ ID Nos. 1 to 31.

Here, the term "substantially the same" means that the sequences of SEQ ID Nos. 1 to 31 or complementary sequences specifically hybridize under stringent conditions.

(d) DNA comprising base sequences represented by SEQ ID Nos. 1 to 31

(e) A DNA which hybridizes under stringent conditions to a DNA consisting of a nucleotide sequence complementary to the DNA of the above (d) and has a function of detecting at least a part of the nucleotide sequence of a nucleic acid derived from bacteria in saliva

As to the various DNAs of the above (d), their specific nucleotide sequences, probe names, and oral bacteria to be detected, reference is made to the descriptions in Table A listed above.

The DNA of the above (e) can be obtained from a cDNA library or a genomic library by a known hybridization method such as colony hybridization, plaque hybridization, southern blot hybridization, or the like, using the various DNAs of the above (d), DNAs consisting of a nucleotide sequence complementary thereto, or fragmented DNA as a probe. The library may be one prepared by a known method, or may be one obtained from a commercially available cDNA library or genomic library, without limitation. For the detailed procedures of the hybridization method, the same conditions as described above can be referred to. The "stringent conditions" for the DNA of the above (e) are conditions for hybridization, and are conditions in which the salt concentration of the buffer is 24 to 390mM, the temperature is 40 to 65 ℃, preferably 48.8 to 195mM, and the temperature is 45 to 60 ℃. Specifically, the conditions may be, for example, 97.5mM at 50 ℃. In addition to the conditions such as the salt concentration and the temperature, the conditions for obtaining the DNA of the above (e) can be appropriately set in consideration of various conditions such as the probe concentration, the length of the probe, and the reaction time. The DNA to be hybridized is preferably a base sequence having at least 60% identity to the base sequence of the DNA of the above (d), more preferably 80% or more, further preferably 90% or more, further preferably 95% or more, particularly preferably 98% or more, and most preferably 99% or more.

The probe used in the present invention can be prepared, for example, by chemical synthesis (purification by HPLC or the like) using a usual oligonucleotide synthesis method. Such a Probe can be designed, for example, by using Probe Quest (registered trademark: manufactured by DYNACOM). The probe of the present invention may further comprise an additional sequence such as a tag sequence.

In the method of the present invention, the nucleotide sequence of the nucleic acid of the bacterium in saliva to be detected need not be the nucleotide sequence itself, but may be a nucleotide sequence in which a part of the nucleotide sequence is mutated by deletion, substitution, insertion, or the like. Therefore, the base sequence of the nucleic acid to be detected can be a mutant gene which hybridizes to a sequence complementary to the base sequence under stringent conditions and has a function and activity derived from each base sequence, and the probe can be designed based on the base sequence of the mutant gene. Here, the "stringent conditions" may be the same conditions as described above.

DNA chip

As described above, in the method of the present invention, a DNA chip can be used for detecting/measuring the amount of bacteria in saliva. The use of the DNA chip for comprehensively estimating the degree of inflammation of periodontal tissue comprises disposing a plurality of the oligonucleotide probes described in item 1 above on a substrate as a support.

As the form of the substrate serving as the support, any form such as a flat plate (glass plate, resin plate, silicone plate, etc.), a rod, beads, etc. can be used. In the case of using a plate as a support, a given probe may be immobilized by species on the plate at a given interval (dot method, etc.; see Science 270,467-470(1995), etc.). Alternatively, a given probe may be synthesized at a specific position on the plate in sequence by type (photolithography, etc.; see Science 251,767-773(1991), etc.). Another preferable form of the support is a form using hollow fibers. In the case of using the hollow fiber as a support, preferable examples can be given: a DNA chip (hereinafter, also referred to as "fiber-type DNA chip") is obtained by immobilizing a given probe for each type on each hollow fiber, bundling and immobilizing all the hollow fibers, and then repeatedly cleaving the fibers in the longitudinal direction. The microarray can also be described as a type in which nucleic acids are immobilized on a through-hole substrate, also called a so-called "through-hole type DNA chip" (refer to Japanese patent No. 3510882, etc.).

The method for immobilizing the probe on the support is not limited, and any binding method may be used. Further, the probe is not limited to the case of being directly immobilized on the support, and for example, the support may be coated with a polymer such as polylysine in advance, and the probe may be immobilized on the treated support. When a tubular body such as a hollow fiber is used as the support, a gel may be held in the tubular body, and the probe may be fixed to the gel.

Hereinafter, a fiber-type DNA chip, which is one embodiment of the DNA chip, will be described in detail. The DNA chip can be produced, for example, by performing the following steps (i) to (iv).

(i) A step of three-dimensionally arranging a plurality of hollow fibers so that the longitudinal directions of the hollow fibers are in the same direction to produce an arrangement body

(ii) Embedding the array to produce a block

(iii) Introducing a gel precursor polymerizable solution containing an oligonucleotide probe into the hollow portion of each hollow fiber of the block, and allowing a polymerization reaction to proceed, thereby retaining a gel-like material containing the probe in the hollow portion

(iv) Cutting the hollow fibers in a direction intersecting the longitudinal direction of the hollow fibers to form a block into thin pieces

The material used for the hollow fiber is not limited, and examples thereof include those described in, for example, Japanese patent application laid-open No. 2004-163211.

The hollow fibers are three-dimensionally arranged so that the lengths thereof in the longitudinal direction are the same (step (i)). Examples of the alignment method include the following methods: a method in which a plurality of hollow fibers are arranged in parallel at predetermined intervals on a sheet-like material such as an adhesive sheet, and the sheet-like material is formed into a sheet shape, and then the sheet is wound into a spiral shape (see japanese patent application laid-open No. 11-108928); and (2) stacking 2 porous plates provided with a plurality of holes at predetermined intervals so that the holes are aligned, passing the hollow fibers through the holes, and then opening the interval between the 2 porous plates and temporarily fixing the porous plates, thereby filling the curable resin material in the periphery of the hollow fibers between the 2 porous plates and curing the material (see japanese patent application laid-open No. 2001-133453).

The prepared array is embedded so as not to disturb the alignment (step (ii)). As a method of embedding, in addition to a method of flowing a urethane resin, an epoxy resin, or the like into gaps between fibers, a method of bonding fibers to each other by thermal fusion bonding, or the like can be preferably cited.

The hollow portion of each hollow fiber is filled with a gel precursor polymerizable solution (gel-forming solution) containing an oligonucleotide probe, and a polymerization reaction is performed in the hollow portion (step (iii)). This makes it possible to hold the gel-like material to which the probe is fixed in the hollow portion of each hollow fiber.

The gel precursor polymerizable solution is a solution containing a reactive substance such as a gel-forming polymerizable monomer, and is a solution which can be converted into a gel-like material by polymerizing and crosslinking the monomer or the like. Examples of such monomers include acrylamide, dimethylacrylamide, vinylpyrrolidone, and methylenebisacrylamide. In this case, a polymerization initiator or the like may be contained in the solution.

After the probe is fixed in the hollow fiber, the block is cut in a direction intersecting with (preferably orthogonal to) the longitudinal direction of the hollow fiber to form a thin sheet (step (iv)). The thus obtained sheet can be used as a DNA chip. The thickness of the DNA chip is preferably about 0.01mm to 1 mm. The block can be cut by a slicer, a laser, or the like.

As the above-mentioned fiber type DNA chip, for example, DNA chip (Genopal TM) manufactured by Mitsubishi chemical corporation, etc. can be preferably cited.

In the fiber-type DNA chip, the probes are arranged three-dimensionally in the gel as described above, and the three-dimensional structure can be maintained. Therefore, the detection efficiency is improved as compared with a flat DNA chip in which a probe is bound to a slide glass having a coated surface, and a highly reproducible examination can be performed with high sensitivity.

The number of types of probes to be arranged on a DNA chip is preferably 500 or less, more preferably 250 or less, and still more preferably 100 or less for 1 DNA chip. By limiting the number (type) of the probes arranged in this way to a certain extent, it is possible to detect the target intraoral bacteria with higher sensitivity. The types of probes can be distinguished by their base sequences. Therefore, even if probes derived from the same gene are used, the probes are usually identified as other species as long as 1 base sequence is different.

3. Detection of bacteria in saliva (measurement of bacterial count)

In the method of the present invention, the method for detecting the bacteria in order to measure the amount of the bacteria in saliva is, for example, a method including the following steps.

(i) Extracting nucleic acid in a sample (in saliva) from saliva, which is an oral sample collected from a subject, as the sample

(ii) Contacting the extracted nucleic acid with the oligonucleotide probe of the present invention or the DNA chip of the present invention

(iii) Calculating the amount of bacteria based on the intensity of the signal obtained from the DNA chip

The details of the detection method will be described below in accordance with the procedure.

(1) Concerning the step (i)

In this step, saliva, which is an oral sample collected from a subject or a test organism, is used as a sample, and nucleic acids of bacteria contained in the sample (in the saliva) are extracted. The method for collecting saliva is not particularly limited, and examples thereof include the following methods: a method using a commercially available saliva collection kit, a method of collecting saliva contained in the mouth with a cotton swab, a method of directly collecting saliva into a container, and the like.

The subject who collects saliva is not particularly limited, and may be, for example, a patient who is not aware of oral inflammation such as periodontal disease, a patient who has systemic disease such as heart disease that may be related to periodontal disease, a pregnant woman, or a healthy person who has no suspicion of periodontal disease, in addition to a patient who has oral inflammation such as periodontal disease.

Subsequently, nucleic acid extraction of bacteria present in the obtained saliva was performed. The method of extraction is not limited, and a known method can be used. Examples of the method include the following: an automatic extraction method using a facility, a method using a commercially available nucleic acid extraction kit, a method of performing phenol extraction after proteinase K treatment, a method using chloroform, or a method of heating and dissolving a sample as a simple extraction method. In particular, the next step can be performed without extracting nucleic acids from the sample.

The nucleic acid obtained from the sample may be contacted with the DNA chip or the like as it is, or the amplified fragment may be contacted with the DNA chip or the like by amplifying a desired nucleotide sequence region by PCR or the like, without limitation. The region in which the obtained nucleic acid is amplified as a template is a portion of the nucleic acid region encoding the nucleotide sequence of the oligonucleotide disposed in the probe or DNA chip used in the present invention. The desired region to be amplified is not limited, and a plurality of types of mixtures can be amplified at once using the nucleotide sequence of a region that is highly conserved regardless of the type of bacteria. The sequence to be used for such amplification can be determined by separating and purifying through experiments, analyzing the base sequence of the polynucleotide after separation, and determining based on the sequence, or by computer simulation (In Silico) by searching for a known base sequence using various databases such as base sequences and the like, and comparing the searched base sequences. The databases for nucleic acids and amino acids are not particularly limited, and examples thereof include Japanese DNA database (DDBJ: DNA Data Bank of Japan), European molecular biology laboratory (EMBL: European molecular biology laboratory, EMBL nucleic acid sequence database (EMBL nucleic acid sequence database), Genetic sequence database (GenBank: Genetic sequence Data Bank), and National Center for Biotechnology Information (NCBI: National Center for Biotechnology Information).

Specifically, a desired site for amplification is preferably a ribosomal RNA (16S rRNA) gene in chromosomal DNA of bacteria. As PCR primers that can be used for amplifying this region, there can be preferably mentioned, for example, the sequence numbers 32 and 33 listed in Table B above. The nucleic acid amplification by the PCR method can be carried out according to a conventional method.

The nucleic acid and the amplified fragment thereof extracted in this step may be appropriately labeled and used in the detection process after hybridization. Specifically, it is possible to consider: a method of labeling the ends of PCR primers with various reporter dyes in advance, a method of introducing reactive nucleotide analogs at the time of reverse transcription reaction, a method of introducing biotin-labeled nucleotides, and the like. In addition, the labeling may be carried out by reacting with a fluorescent labeling reagent after the preparation. As the fluorescent reagent, for example, various reporter dyes (e.g., Cy5, Cy3, VIC, FAM, HEX, TET, fluorescein, FITC, TAMRA, Texas red, Yakima Yellow, etc.) can be used.

(2) Concerning the step (ii)

In this step, the nucleic acid or the amplified fragment thereof obtained in step (i) is brought into contact with the probe or DNA chip used in the present invention, specifically, a hybridization solution containing the nucleic acid or the like is prepared, and the nucleic acid or the like in the solution is bound (hybridized) to the oligonucleotide probe carried on the DNA chip. The hybridization solution can be appropriately prepared according to a conventional method using a buffer such as SDS or SSC.

The hybridization reaction can be carried out under stringent conditions by appropriately setting reaction conditions (buffer type, pH, temperature, etc.) so that the nucleic acid or the like in the hybridization solution can hybridize to the oligonucleotide probe mounted on the DNA chip. The "stringent conditions" as used herein refer to conditions under which cross-hybridization with similar sequences is unlikely to occur or nucleic acids that are cross-hybridized with similar sequences are dissociated due to similar sequences, and specifically refer to conditions under which a DNA chip is washed during or after hybridization.

For example, as conditions for the hybridization reaction, the reaction temperature is preferably 35 to 70 ℃, more preferably 40 to 65 ℃, and the time for hybridization is preferably about 1 minute to 16 hours.

As the conditions for washing the DNA chip after hybridization, the washing solution preferably has a composition of 0.24M Tris-HCl/0.24M NaCl/0.05% Tween-20, and the temperature at the time of washing is preferably 35 to 80 ℃ or 40 to 65 ℃, more preferably 45 to 60 ℃. More specifically, the salt (sodium) concentration is preferably 48 to 780mM and the temperature is preferably 37 to 80 ℃, and the salt concentration is more preferably 97.5 to 390mM and the temperature is preferably 45 to 60 ℃.

After washing, the detection intensity is measured for each spot by using a device capable of detecting a label such as a nucleic acid bound to the probe. For example, when the nucleic acid or the like is fluorescently labeled, the fluorescence intensity can be measured using various fluorescence detectors, for example, CRBIO (Hitachi Software Engineering), arrayWoRx (GE Healthcare), Affymetrix 428Array Scanner (Affymetrix), GenePix (Axon Instruments), ScanArray (PerkinElmer), GenoPearlreader (Mitsubishi chemical Co., Ltd.), and the like. In these devices, for example, in the case of a fluorescence scanner, scanning can be performed by appropriately adjusting the output power of laser light and the sensitivity of a detection unit, and in the case of a CCD camera type scanner, scanning can be performed by appropriately adjusting the exposure time. The quantification method based on the scan results was performed using quantification software. The quantitative software is not particularly limited, and the quantitative determination may be performed by using an average value, a median value, or the like of the fluorescence intensity of the spot. In addition, in the quantitative determination, it is preferable to adjust the fluorescence intensity of the spot on which no probe is mounted as a background in consideration of the dimensional accuracy of the spot range of the DNA fragment and the like.

(3) Concerning the procedure (iii)

In this step, the bacterial count of the bacteria of the detection target bacterial species is calculated from the signal intensity obtained in the above step. For example, there is a method of expressing the signal intensity of a probe for detecting a bacterium to be detected and the signal intensity of a background as an SN ratio. Alternatively, the following method is preferred: a method of detecting each bacterium under a plurality of conditions while changing the concentration of chromosomal DNA of the bacterium, obtaining a conversion factor (calibration curve) calculated for each concentration of chromosomal DNA of each bacterium in advance based on the signal intensity obtained under each concentration condition, and calculating the concentration of chromosomal DNA from the signal intensity obtained under each condition, and the like. In the present invention, for example, it is preferable to calculate the amount of bacteria from the signal intensity based on the 16S rRNA sequence information of the bacteria to be detected. In addition, a method of using the genome copy number of the bacteria to be detected as the bacterial count can also be preferably employed. The genome copy number can be calculated by multiplying the signal intensity detected in the DNA chip by a calculation coefficient for each bacteria amount determined in advance (and, if necessary, by the dilution ratio of the detection sample). For each bacteria amount calculation coefficient, the signal intensity at the time of detection of genomic DNA derived from each bacterium is measured, and a calibration curve is prepared in advance and calculated in advance in such a manner that the coefficient of each bacteria amount is inversely calculated from the signal intensity of each bacterium.

In any case, it is preferable to consider a correction coefficient of the signal intensity of the bacteria to be detected on each DNA chip.

4. Estimation of degree of inflammation in periodontal tissue

The method of the present invention is a method for estimating the area of periodontal pocket inflammation using the result of detection of the bacterial count of bacteria in saliva as an index. The method of the present invention is a method of comprehensively estimating the degree of inflammation of periodontal tissue using the above-described detection result as an index.

Any instrument can be used for detecting the bacterial amount of the bacteria in saliva, and examples thereof include: the method using a DNA chip as described in the above item 3, the method of confirming the presence of bacteria by enzyme activity, the method of measuring the total amount of bacteria by measuring electric resistance, the method of calculating the number of bacteria by phase contrast microscopy and staining, the method of culturing and measuring the number of viable bacteria, the method of quantifying the number of each bacteria by real-time PCR, and the like.

In this estimation, as the bacterial count or a measurement amount proportional to the bacterial count, the area of periodontal pocket inflammation and the degree of inflammation of periodontal tissue are estimated based on the fluorescence intensity of the DNA chip, the SN ratio of the signal intensity, the Ct value of real-time PCR, the enzyme activity value, the resistance value at the time of electrical measurement, the count value by visual counting, and the like.

Specific methods for estimating the area of inflammation in the periodontal pocket include the following methods.

(1) The amount of bacteria of various bacteria in saliva was detected from saliva samples of subjects whose area of inflammation in periodontal pockets (PISA value, capris value, etc.) was known (which can be calculated from actually measured PPD, etc.).

(2) The correlation coefficient between the bacterial count of each bacterium and the area of periodontal pocket inflammation inherent to each bacterium was obtained, and a relational expression between the bacterial count of each bacterium and the area of periodontal pocket inflammation was constructed to create a prediction model.

(3) The amount of each bacterium in saliva was measured from a saliva sample of a subject whose area of inflammation in the periodontal pocket was unknown.

(4) The amount of each bacterium obtained in (3) was substituted into the relational expression obtained in (2), and the area of inflammation in the periodontal pocket was estimated.

The method of creating the prediction model is not particularly limited, and examples thereof include various methods using a statistical analysis method such as linear regression, regression tree, model tree, neural network, support vector machine, bagging method, lifting method, machine learning algorithm such as random forest, and the like. In the model tree shown in the embodiment described later, it is not necessary to specify a model in advance, and specifically, optimization by the "M5" method using the "caret" package using the statistical software "R" (R Development Core Team) is preferably listed.

It is preferable to use the number of bacteria (variable) or more for the saliva sample (the number of data for creating the prediction model) of the subject whose periodontal pocket inflammation area is actually measured. The predictive model may be updated each time an amount of data is accumulated.

In the "first invention group", as the bacteria to be detected for creating the prediction model, both bacteria in which the bacterial count of the bacteria and the area of periodontal pocket inflammation show a positive correlation and bacteria in which the bacterial count of the bacteria and the area of periodontal pocket inflammation show a negative correlation are used. The reason is that in many cases, in healthy persons who have not developed periodontal disease, bacteria that show a positive correlation with the area of inflammation of the periodontal pocket (the number of bacteria is 0) cannot be detected, and in cases where only bacteria that show a positive correlation are present, a prediction model cannot be created for a range of values where the area of inflammation of the periodontal pocket is small.

As described in the above item 1, in order to improve the final prediction accuracy, bacteria are selected in consideration of not only the magnitude of the correlation coefficient of each bacterium alone but also the accuracy of the amount of change of the bacterium in response to the change in the periodontal pocket inflammation area and the correlation coefficient of the bacterial count among the bacteria (avoiding the multiple collinearity relationship).

As the bacterium showing a positive correlation, the following bacteria can be preferably mentioned.

Porphyromonas gingivalis, Fostana fusca, Treponema denticola, Campylobacter rectus, Fusobacterium nucleatum Wen subspecies, Fusobacterium nucleatum pleomorphe, Fusobacterium nucleatum subspecies, Fusobacterium periodontopanum, Actinomyces actinomycetemcomitans, capnocytophaga flavum, capnocytophaga sputigena, Streptococcus intermedius, Veillonella parvula, Pediomonas pervosa, Microbacterium morganii, prevotella rosenbergii, Rogomyces rosenbergii, Actinomyces tundifolius, Corynebacterium equi, SR1 sp.OT 345, Porphyromonas catorii, Oenomonas sputum producing, Neisseria flavivis, Streptococcus brotheri, Micromonospora parvum, Streptococcus gastrectae, Treponema sovifer, Eubacterium crypticum, Eubacterium tangling, Treponema intermedius, Protovora gingivalis, Porphyromonas pulposus.

As the bacterium showing a positive correlation, the following bacteria can be preferably further selected.

Porphyromonas gingivalis, Fostanemia furiosaensis, Treponema denticola, Campylobacter rectus, Fusobacterium nucleatum subspecies, Veillonella parvula, Peptomonas destructor, Eubacterium crypthecogenum, Eubacterium tangling, Treponema intermedius, Tremella gingivalis, Porphyromonas pulposus

The number of bacteria exhibiting a positive correlation is preferably 1 or more, more preferably 4 or more, still more preferably 8 or more, and particularly preferably 12 or more. Further, it is preferable to use 100 species or less, more preferably 75 species or less, still more preferably 50 species or less, and particularly preferably 25 species or less.

The bacteria showing negative correlation include the following bacteria. Streptococcus mitis, Streptococcus mitis bv2, Actinomyces saprophyticus, Streptococcus mutans, Campylobacter succinogenes, Cellophilus gingivalis, Prevotella pallidum, Streptococcus salivarius, Eubacterium borgpoensis, Rostella peptinella, Prevotella denticola, atypical Veillonella, Prevotella histophila, Megasphaera micronucleus, Streptococcus paracasei, twin haemolytica, Prevotella (A.rava, OT 308), Prevotella melanogenesis, Actinomyces gracilis, Prevotella ureae (Prevotella shahii), Rostella carinii, short-chain coccobacillus contiguously, Porphyromonas pasteurianus, Haemophilus parainfluenzae

As the bacterium showing negative correlation, the following bacteria can be preferably selected and listed. Actinomyces saprophyticus, streptococcus mutans and prevotella pallidum

The bacteria showing negative correlation are preferably used in a number of 1 or more, more preferably 2 or more, still more preferably 4 or more, and particularly preferably 8 or more. Further, it is preferable to use 100 species or less, more preferably 75 species or less, still more preferably 50 species or less, and particularly preferably 25 species or less.

In the present invention, a predetermined number of subjects (1-time mother sample) are subjected to statistical analysis and stored in a database in advance. Then, from the analysis result of the correlation between the degree of inflammation of the periodontal tissue and the amount of bacteria in saliva, it is possible to estimate what degree of inflammation of the periodontal tissue each subject has or whether the periodontal pocket has a root surface area. Therefore, when performing an examination of an individual subject (one person), it is possible to estimate the degree of inflammation of periodontal tissue and the surface area of the root in the periodontal pocket for the individual subject by using the data from the plurality of subjects as a master sample and by examining at which position or whether the data of the individual subject matches the data of the master sample stored in the database in advance. The data of the individual subject may be introduced into the value of the mother sample, and the statistical analysis process may be performed again to examine the position of the individual subject in the mother sample.

According to the method of the present invention, since the degree of inflammation (PISA value and capris value) of periodontal tissue can be estimated and predicted based on the bacterial species and bacterial count thereof in saliva, the degree of inflammation of periodontal tissue can be calculated very easily and under a certain calculation standard even for a large number of subjects, compared with the case where the PISA value and capris value are actually measured in the related art. In the present invention, not only the degree of positivity but also the degree of negativity as the degree of inflammation of periodontal tissue is included in the range of estimation/prediction. Therefore, when paying attention to the type of bacteria (resident bacteria) whose amount of bacteria is inversely related to the degree of inflammation of periodontal tissue, it is possible to estimate and predict whether or not the oral cavity of the subject is kept in a healthy state. In addition, the ratio of the amount of bacteria to the type of bacteria and the inverse type of bacteria can be used as an index for the degree of inflammation of periodontal tissue.

The present invention will be described more specifically with reference to examples, but the present invention is not limited thereto.