Prebiotic Effect Estimation System
The prebiotic effect estimation system estimates prebiotic effects using human microbiota models and advanced sequencing, addressing the need for animal-free evaluation and reducing costs and workload.
Patent Information
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- Filing Date
- 2024-09-24
- Publication Date
- 2026-04-03
AI Technical Summary
The challenge of estimating the prebiotic effects of substances without conducting animal experiments, driven by global movements to abolish animal testing for ethical reasons, particularly in the evaluation of prebiotics on human flora.
A prebiotic effect estimation system that utilizes a testing and management unit to analyze culture media from test substances and commensal bacterial flora models, employing next-generation sequencing to calculate β-diversity and identify prebiotic components with similar UniFrac distances, thereby estimating the effects of unknown substances.
Enables the prediction of prebiotic effects without animal testing, reducing workload and costs by using human commensal microbiota models and advanced sequencing techniques to determine similarity with known prebiotics.
Smart Images
Figure 2026058155000001_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to a prebiotic effect estimation system for estimating the prebiotic effect of a substance with unknown effects.
Background Art
[0002] The gut microbiota, which is a population of microorganisms (bacteria) that normally inhabit the human body, is known to have a great influence on human health. Prebiotic components typified by oligosaccharides and dietary fibers have the effect of selectively changing the gut microbiota, and as a result, are expected to play an important role in improving diseases and health conditions. For example, natural vegetables, fruits, etc. contain various prebiotic components, and with the increasing attention to the functionality of food, functional food materials that exhibit various physiological functions have been developed.
[0003] Prebiotics have different characteristics, and their prebiotic effects are also various. The evaluation of the effects of these prebiotics on humans is carried out by administering them to animals and analyzing the microbiota (Non-Patent Document 1).
Prior Art Documents
Non-Patent Documents
[0004]
Non-Patent Document 1
Summary of the Invention
Problems to be Solved by the Invention
[0005] In recent years, the movement to abolish animal testing has accelerated worldwide from an animal welfare perspective. For example, efforts are being made to avoid using laboratory animals in experiments and tests related to ingredients used in cosmetics. Similar considerations are required when verifying the effects of prebiotics on the normal flora. Therefore, the object of the present invention is to provide a prebiotic effect estimation system that can predict the effects of substances whose effects are unknown without using experimental animals. [Means for solving the problem]
[0006] A brief overview of some of the representative inventions disclosed in this application is as follows: A prebiotic effect estimation system, which is a representative embodiment of the present invention, is a prebiotic effect estimation system for estimating the prebiotic effect of a test substance, A testing and management unit acquires and records predetermined analysis results for culture media obtained by adding the aforementioned test substance and predetermined prebiotic components with known effects to predetermined commensal bacterial flora models and culturing them, The system includes an evaluation processing unit that identifies the prebiotic component whose value of a predetermined index calculated based on the analysis results for each of the predetermined prebiotic components is closest to the value of the predetermined index calculated based on the analysis results for the test substance, and outputs information on the prebiotic effect of the identified predetermined prebiotic component as the prebiotic effect estimated to be possessed by the test substance. [Effects of the Invention]
[0007] The effects obtained by some of the representative inventions disclosed in this application can be briefly explained as follows: In other words, according to a typical embodiment of the present invention, it is possible to estimate the prebiotic effect of a test substance without conducting animal experiments. [Brief explanation of the drawing]
[0008] [Figure 1] This figure outlines an example configuration of a prebiotic effect estimation system, which is one embodiment of the present invention. [Figure 2] This figure outlines an example of a pre-treatment flow for estimating the prebiotic effect in one embodiment of the present invention. [Figure 3] This figure outlines an example of a process flow for estimating the prebiotic effect of a test substance in one embodiment of the present invention. [Figure 4] This figure outlines an example of a commensal bacterial flora model in one embodiment of the present invention. [Figure 5] This figure outlines an example of a commensal bacterial flora model in one embodiment of the present invention. [Figure 6] This figure outlines an example of a commensal bacterial flora model in one embodiment of the present invention. [Figure 7] This figure provides an overview of an example plotted by principal coordinate analysis regarding the β-diversity of each prebiotic and test substance in one embodiment of the present invention. [Figure 8] This figure provides an overview of an example plotted by principal coordinate analysis regarding the β-diversity of each prebiotic and test substance in one embodiment of the present invention. [Figure 9] This figure provides an overview of an example plotted by principal coordinate analysis regarding the β-diversity of each prebiotic and test substance in one embodiment of the present invention. [Modes for carrying out the invention]
[0009] Embodiments of the present invention will be described in detail below with reference to the drawings. In principle, the same parts will be denoted by the same reference numerals in all the drawings used to describe the embodiments, and repeated descriptions will be omitted. On the other hand, a part that is denoted by a reference numeral and described in one drawing may be referred to again in the description of another drawing, although it will not be shown again.
[0010] <Overview> One embodiment of the present invention, a prebiotic effect evaluation system, involves adding a test substance with unknown prebiotic effects to a human resident microbiota model and culturing it. Based on the results of analyzing the culture solution using next-generation sequencing analysis techniques such as 16S rRNA amplicon sequencing, β diversity, which is commonly used as an index for evaluating the diversity of the microbiota, is calculated, and the unknown prebiotic effect of the test substance is estimated based on this. Specifically, multiple prebiotics with known effects are added to a human resident microbiota model and cultured separately. The β diversity of the culture solution is plotted on a two-dimensional plot diagram using principal coordinate analysis (PCoA), and it is determined which known prebiotics the UniFrac distance of the test substance is closest to the plot of β diversity. As a result, it is estimated that the test substance has the effects and characteristics of the known prebiotics that are determined to have a similar UniFrac distance.
[0011] In this embodiment, the prebiotic effect is estimated based on the commensal microbiota of the human gut, skin, and oral cavity. However, the commensal microbiota targeted are not limited to these; it can be applied to other commensal microbiota of humans, or to the commensal microbiota of animals other than humans. Furthermore, the microbiota model is not particularly limited, and any microbiota model can be adopted.
[0012] In this embodiment, the prebiotic effect is estimated based on the β-diversity of a commensal microbiota model to which prebiotic components have been added. However, it is also possible to evaluate based on other indicators that can show the characteristics of the commensal microbiota due to the prebiotic components. For example, the Chaol index or Shannon index for α-diversity can be used as indicators, and for β-diversity, known prebiotic components can be clustered based on principal component analysis, and the effect can be estimated based on which cluster the test substance belongs to.
[0013] <System Configuration> FIG. 1 is a diagram showing an outline of a configuration example of a prebiotic effect estimation system according to an embodiment of the present invention. The prebiotic effect estimation system 1 is composed of, for example, a server device or a virtual server constructed on a cloud computing service, and a user terminal 2 which is an information processing terminal such as a PC (Personal Computer), a tablet terminal, or a smartphone used by a user accesses it via a network such as the Internet, VPN (Virtual Private Network), or LAN (Local Area Network) not shown by a Web browser or a dedicated application not shown.
[0014] The prebiotic effect evaluation system 1 realizes various functions related to verification and evaluation of the prebiotic effect of a test substance by executing, for example, an OS (Operating System), a DBMS (DataBase Management System), middleware such as a Web server program, and software operating thereon developed from a recording device such as an HDD (Hard Disk Drive) or SSD (Solid State Drive) onto a memory by a CPU (Central Processing Unit) not shown. This prebiotic effect evaluation system 1 has, for example, each part such as an inspection management unit 11 and an evaluation processing unit 12 implemented by software. It also has each data store such as an analysis result database (DB) 21, a beta diversity DB 22, and an effect DB 23 implemented by a database, a file table, or the like.
[0015] The inspection management unit 11 requests an external / internal inspection agency 3 capable of performing an inspection that involves adding a predetermined probiotic component (or test substance) to a predetermined resident flora model, culturing it, and analyzing it using next-generation sequencing analysis techniques such as 16S rRNA amplicon sequencing. It also has the function of obtaining the analysis results from the inspection agency 3 and recording them in the analysis result DB 21. The request for inspection to the inspection agency 3 can be made, for example, by the inspection management unit 11 connecting to an information processing system (not shown) of the inspection agency 3 online, sending an email, etc., and taking an appropriate method according to the interface on the inspection agency 3 side. It may also be the case that the administrator of the probiotic effect estimation system 1, etc., is notified of the request for inspection and the administrator, etc., manually makes the request.
[0016] Upon receiving the inspection request, the inspection agency 3, for example, adds the test substance to be inspected and a known probiotic component for comparison to a resident flora model in the human intestine, skin, oral cavity, etc., cultures it, and outputs / responds with the results of analyzing it using next-generation sequencing analysis techniques such as 16S rRNA amplicon sequencing.
[0017] <For example, various prebiotic components with known effects are added to a commensal bacterial flora model and cultured. The position data of the plots in a two-dimensional plot diagram calculated by principal coordinate analysis (PCoA) for the β diversity of the culture medium is registered in β diversity DB22, etc. Then, the position data of the plots for β diversity calculated based on the analysis results of the culture medium when the test substance to be evaluated is added to the commensal bacterial flora model and cultured is also recorded in β diversity DB22, and prebiotic components with close UniFrac distances among the prebiotic components registered in β diversity DB22 are identified. The test substance to be evaluated is then presumed to have similar effects and characteristics to the known effects and characteristics of the prebiotic components identified as having close UniFrac distances to each other. Information on the known effects and characteristics of each prebiotic component may be pre-registered as reference information in Effect DB23, etc., or it may be obtained by searching via a network such as the Internet (not shown in the diagram).
[0018] <Processing flow> Figure 2 is a diagram illustrating an example of a pre-treatment flow for estimating and evaluating the prebiotic effect in one embodiment of the present invention. In the pre-treatment, the positional data of plots obtained by principal coordinate analysis (PCoA) regarding β diversity when various prebiotic components with known effects are added to the target commensal bacterial flora model are calculated in advance. First, at the request of the Inspection Management Department 11 or the administrator, etc., the Inspection Institution 3 adds various prebiotic components with known effects to a commensal bacterial flora model (in this embodiment, for example, the intestine, skin, or oral cavity) for comparison (S01), and then cultures it (S02).
[0019] Figures 4 to 6 are diagrams illustrating an example of a commensal microbiota model in one embodiment of the present invention. Figure 4 shows an example of a list of scientific names of bacteria included in the gut microbiota model. This gut microbiota can be prepared, for example, by selecting from the NBRC Human Commensal Microbiota Cocktail developed by the National Institute of Technology and Evaluation Biotechnology Center (NBRC). In this embodiment, in addition to the case without the addition of a carbon source (NC), the commensal microbiota model is cultured in liquid medium with the addition of known prebiotic components, such as glucose (Glu), fructooligosaccharides (Fos), galactooligosaccharides (Gos), isomaltoligosaccharides (Imo), inulin (Inu), raffinose (Rs), agarooligosaccharides (Aos), and saccharin, each at a final concentration of 0.5%. Similarly, Figure 5 shows an example of a list of scientific names of bacteria included in a skin microbiota model. In this embodiment, this skin microbiota is cultured in liquid medium with the addition of a carbon source (NC) and with the addition of known prebiotics, such as glucose (Glu), erythritol (Ert), and xylitol (Xyl), each at a final concentration of 0.5%. Figure 6 also shows an example of a list of scientific names of bacteria included in an oral microbiota model. In this embodiment, this oral microbiota model is cultured in liquid medium with the addition of a carbon source (NC) and with the addition of known prebiotics, such as glucose (Glu), erythritol (Ert), and xylitol (Xyl), each at a final concentration of 0.5%.
[0020] Returning to Figure 2, at testing facility 3, the culture medium after incubation is comprehensively analyzed using next-generation sequencing analysis technologies such as 16S rRNA amplicon sequencing, and the analysis results are output and responded to at the testing management department 11 or the administrator (S03). At the testing management department 11, the analysis results are registered in the analysis results DB 21. Subsequently, the evaluation processing unit 12 calculates the positional data of the plots using principal coordinate analysis of β diversity based on the analysis results for each prebiotic added to the commensal bacterial flora model (S04), registers this data in the β diversity DB 22, and registers the known effects and characteristics of the prebiotics in the effect DB 23 (S05), thereby completing the preprocessing.
[0021] Figure 3 is a diagram illustrating an example of a process flow for evaluating the unknown prebiotic effects of a test substance in one embodiment of the present invention. Here, the unknown prebiotic effects and characteristics of the test substance to be evaluated are estimated based on the UniFrac distance between the position data of the plots obtained by principal coordinate analysis of β diversity for each prebiotic component with known effects, obtained in the process shown in Figure 2 above, and the position data of the plot obtained by principal coordinate analysis of β diversity for the test substance to be evaluated.
[0022] First, at the request of the Inspection Management Department 11 or the administrator, the Inspection Laboratory 3 extracts a new substance to be evaluated from a specific natural product such as vegetables or fruits (S11). The extracted new substance may also be provided to the Inspection Laboratory 3. This new substance is added to a model of the commensal bacterial flora to be evaluated (in this embodiment, as described above, for example, the intestine, skin, or oral cavity) (S12), and cultured (S13). The cultured medium is then comprehensively analyzed using next-generation sequencing analysis technology such as 16S rRNA amplicon sequencing, and the analysis results are output and responded to the Inspection Management Department 11 or the administrator (S14). The Inspection Management Department 11 registers the analysis results in the Analysis Results DB 21.
[0023] Subsequently, the evaluation processing unit 12 calculates plot position data using principal coordinate analysis of β diversity based on the analysis results of the commensal bacterial flora to which the target new substance was added (S15), records this data in the β diversity DB 22, and compares it with the plot position data of β diversity principal coordinate analysis related to prebiotic components with known effects registered in the β diversity DB 22 to identify prebiotic components with close UniFrac distances between plots (S16). Then, it obtains information on the effects and characteristics of the identified prebiotic components from the effect DB 23, outputs an estimated result to the user terminal 2, etc., stating that the new substance has similar or identical prebiotic effects and characteristics (S17), and terminates the prebiotic effect evaluation process.
[0024] Figures 7 to 9 are diagrams illustrating examples of two-dimensional plots obtained by principal coordinate analysis for the β-diversity of each prebiotic and novel substance in one embodiment of the present invention. Figure 7 shows an example of plotting the β-diversity of a known prebiotic component and the novel substance under evaluation, obtained by principal coordinate analysis, on a two-dimensional plane for a gut microbiota model as shown in the example in Figure 4. In the example in Figure 7, the novel substance has a close UniFrac distance to inulin (Inu), suggesting that it is highly likely to exhibit prebiotic effects similar to inulin and to have similar physiological functions. For example, if the plot for a new substance is similar to the plot for the case without added carbon source (NC), it can be inferred that the new substance will not be utilized by intestinal bacteria, and therefore no prebiotic effect can be expected. Similarly, if the new substance exhibits β-diversity similar to glucose (Glu), it can be inferred that it will be utilized by all bacteria, not specific bacteria, and therefore no prebiotic effect can be expected.
[0025] In this way, by determining the UniFrac distance between plots using principal coordinate analysis of β diversity by comparing it with prebiotic components whose effects are known, it is possible to investigate whether a novel substance has a prebiotic effect. Furthermore, if it is determined to have a prebiotic effect, it can be estimated that it possesses the same physiological functions that have been recognized as effects in prebiotic components with similar UniFrac distances. This makes it possible to verify the prebiotic effect of a novel substance without conducting animal experiments, and it significantly reduces the workload and costs involved. Furthermore, the determination of which prebiotic component a new substance is similar to can be made, for example, by determining that the new substance is similar to the prebiotic component with the closest UniFrac distance between the plots of the new substance and each prebiotic component. On the other hand, even if the UniFrac distance is the closest, if that value does not reach a predetermined threshold, it may be considered that there is no similar prebiotic component, and therefore it is impossible to estimate the effects or properties (or no prebiotic effect can be expected).
[0026] Similarly, Figure 8 shows an example of plotting prebiotic components with known effects on a skin microbiome model, as shown in the example in Figure 5, and the novel substance being evaluated, on a two-dimensional plane based on principal coordinate analysis of β-diversity. In the example in Figure 8, the novel substance has a close UniFrac distance to erythritol (Ert), suggesting that it is highly likely to exert effects on skin commensal bacteria and physiological functions on the skin similar to those of erythritol. For example, if the plot for a new substance is similar to the plot for no carbon source added (NC), it can be inferred that the new substance is not utilized by skin bacteria, and therefore no prebiotic effect can be expected. Similarly, if the plot for a new substance is similar to the plot for glucose (Glu), it can be inferred that it is utilized by all bacteria, not specific bacteria, and therefore no prebiotic effect can be expected.
[0027] Similarly, Figure 9 shows an example of plotting prebiotics with known effects and the novel substance being evaluated on a two-dimensional plane based on principal coordinate analysis of β diversity, for an oral microbiota model like the one shown in Figure 6. In the example in Figure 9, the plot of the novel substance is close to that of xylitol (Xyl), suggesting that the novel substance is likely to induce an oral microbiota composition similar to that of xylitol and to exert similar physiological functions in the oral cavity. For example, if the plot for a new substance is similar to the plot for no carbon source added (NC), it can be inferred that the new substance is not utilized by oral bacteria, and therefore no prebiotic effect can be expected. Similarly, if the plot for a new substance is similar to the plot for glucose (Glu), it can be inferred that it is utilized by all bacteria, not specific bacteria, and therefore no prebiotic effect can be expected.
[0028] As described above, according to the prebiotic effect estimation system 1, which is one embodiment of the present invention, the unknown prebiotic effects and properties of the novel substance to be evaluated are estimated based on the UniFrac distance between plots obtained by principal coordinate analysis of β diversity when each prebiotic component with known effects and the novel substance to be evaluated are added to a commensal bacterial flora model. This makes it possible to verify and evaluate the prebiotic effects of novel substances contained in plants, etc., on the commensal bacterial flora without using experimental animals, and can also significantly reduce the workload and costs.
[0029] The present inventors have described the invention in detail based on embodiments above, but it goes without saying that the present invention is not limited to the above embodiments and can be modified in various ways without departing from its essence. Furthermore, the above embodiments are described in detail for the purpose of explaining the present invention in an easy-to-understand manner and are not necessarily limited to those having all the configurations described. In addition, it is possible to add, delete, or replace some of the configurations of the above embodiments with other configurations. Furthermore, each of the above configurations, functions, processing units, and processing means may be implemented in hardware, in whole or in part, for example, by designing them as integrated circuits. Alternatively, each of the above configurations, functions, and means may be implemented in software by having the processor interpret and execute programs that implement each function. Information such as programs, tables, and files that implement each function can be stored in memory, hard disks, SSDs, or other recording devices, or in recording media such as IC cards, SD cards, or DVDs. Furthermore, in the diagrams above, the control lines and information lines shown are those deemed necessary for explanation and do not necessarily represent all control lines and information lines that would be present in the actual implementation. In reality, it can be assumed that almost all components are interconnected. [Industrial applicability]
[0030] This invention can be used in a prebiotic effect estimation system for estimating the effects of prebiotics. [Explanation of symbols]
[0031] 1…Prebiotic effect estimation system, 2…User terminal, 3…Testing institution, 11...Inspection Management Department, 12...Evaluation Processing Department, 21...Analysis Results DB, 22...Diversity DB, 23...Effect DB
Claims
1. A prebiotic effect estimation system for estimating the prebiotic effect of a test substance, A testing and management unit acquires and records predetermined analysis results for culture media obtained by adding the aforementioned test substance and predetermined prebiotic components with known effects to predetermined commensal bacterial flora models and culturing them, A prebiotic effect estimation system comprising: an evaluation processing unit that identifies the prebiotic component whose value of a predetermined index calculated based on the analysis results for each predetermined prebiotic component is closest to the value of the predetermined index calculated based on the analysis results for the test substance, and outputs information on the prebiotic effect of the identified predetermined prebiotic component as the prebiotic effect estimated to be possessed by the test substance.
2. In the prebiotic effect estimation system according to claim 1, The aforementioned predetermined indicator is β-diversity, a prebiotic effect estimation system.
3. In the prebiotic effect estimation system according to claim 1, The aforementioned predetermined commensal microbiota model is a prebiotic effect estimation system, which is a model of the human gut microbiota, skin microbiota, or oral microbiota.