Method of providing information for predicting or diagnosing macular degeneration by using microbiome in blood

Abstract

A method for providing information for predicting or diagnosing macular degeneration using a microbiome in the blood is provided. This method for providing information includes the steps of: extracting genomic DNA of the microbiome in the blood from a blood sample of a test subject; analyzing the 16S rDNA gene sequence using the genomic DNA of the microbiome in the blood to obtain bacterial species distinguished at the species and phylum levels regarding the microbiome in the blood and their relative proportions; calculating a diversity index of the microbiome in the blood; and providing the relative proportions and diversity index as information for predicting or diagnosing macular degeneration.

Description

Technology Field

[0001] The present invention relates to a method for providing information for predicting or diagnosing macular degeneration, and more specifically, to a method for providing information for predicting or diagnosing macular degeneration using a microbiome in the blood. Background Technology

[0002] Age-related macular degeneration (AMD), which occurs in the macula at the center of the retina, is one of the top three causes of blindness in Korea. It has a prevalence rate of 13.4% in people aged 40 and older (2017 National Health and Nutrition Examination Survey), and its incidence continues to increase with the aging population.

[0003] Macular degeneration progresses from a dry stage, where waste products accumulate in the center of vision, to a wet stage, where new blood vessels form, leading to blindness. Currently, most treatments for age-related macular degeneration involve preventing further progression in the wet stage through anti-VEGF injections, and there is no way to suppress the onset of the disease itself or inhibit progression in the early dry stage.

[0004] The onset and progression of macular degeneration are closely linked to risk factors such as age, smoking, obesity, and dietary habits. It is well known that lipid metabolism disorders are associated with the risk of age-related macular degeneration, and large-scale clinical studies have proven that taking lutein, carotenoids, and antioxidant vitamins can slow the progression of macular degeneration by approximately twofold. As the onset and progression of macular degeneration are significantly influenced by diet, it is expected that the disease is strongly associated with the microbiome of the digestive system.

[0005] Meanwhile, although some studies have reported a link between macular degeneration and the body's gut microbiome, there are currently no additional analytical studies to confirm this. There is a practical challenge in analyzing the gut microbiome due to low patient cooperation when requesting stool samples from patients with macular degeneration. The problem to be solved

[0006] The problem that the present invention aims to solve is to provide a method for providing information for predicting or diagnosing macular degeneration using the microbiome in the blood.

[0007] The problems that the present invention aims to solve are not limited to those mentioned above, and other unmentioned problems will be clearly understood by those skilled in the art to which the present invention belongs from the description below. means of solving the problem

[0008] A method for providing information for predicting or diagnosing macular degeneration according to one embodiment of the present invention for achieving the above objective comprises: a step of extracting genomic DNA of a microbiome in blood from a blood sample of a test subject; a step of analyzing the 16S rDNA gene sequence using the genomic DNA of the microbiome in blood to obtain bacterial species distinguished at the species level and phylum level regarding the microbiome in blood and their relative occupancy ratios; a step of calculating a diversity index of the microbiome in blood; and a step of providing the relative occupancy ratios and the diversity index as information for predicting or diagnosing macular degeneration.

[0009] An information provision method according to one embodiment of the present invention can evaluate a group at risk of macular degeneration if at least two of the following (Equation 1) to (Equation 3) are satisfied.

[0010] (Equation 1) When the blood microbiome is classified at the phylum level, the ratio of the relative occupancy rate of Firmicutes strains to the relative occupancy rate of Proteobacteria strains is 0.8 or higher;

[0011] (Equation 2) When the blood microbiome is distinguished at the species level, the relative proportion of Catenibacterium mitsuokai strains is 10% or more, the relative proportion of Prevotellamassilia timonensis strains is 5% or more, or the relative proportion of Fusobacterium mortiferum strains is 5% or more; and

[0012] (Equation 3) By alpha diversity analysis of the blood microbiome, Chao1 index ≥ 150, Shannon index ≥ 5, or Simpson index ≥ 0.8.

[0013] Analyzing the above 16S rDNA gene sequence may include performing PCR using primers capable of specifically amplifying the V3 to V4 variable regions of the 16S rDNA gene sequence.

[0014] The above macular degeneration may be age-related macular degeneration.

[0015] Specific details of other embodiments are included in the specific contents and drawings. Effects of the invention

[0016] As described above, according to the method for providing information for predicting or diagnosing macular degeneration according to the present invention, by analyzing the microbiome in the patient's blood and using the relative proportion of bacterial species and diversity index, it is possible to evaluate whether the patient is in a risk group for macular degeneration, and based on this, prebiotic or probiotic treatment can be performed. Brief explanation of the drawing

[0017] Figure 1 shows the taxonomic composition of the blood microbiome for each test subject, distinguished at the phylum level. Figure 2 shows the taxonomic composition of the blood microbiome classified at the phylum level and the averaged relative proportion of each strain. Figure 3 shows the LDA score obtained by performing LEfSe analysis at the phylum level on the microbiome in the blood. Figure 4 shows the taxonomic composition of the blood microbiome for each test subject, distinguished at the species level. Figure 5 shows the LDA score obtained by performing LEfSe analysis at the species level on the microbiome in the blood. Figure 6 shows the diversity of the microbiome in the blood as the Ghao1 index. Figure 7 shows the diversity of the microbiome in the blood as a Shannon index. Figure 8 shows the diversity of the microbiome in the blood as the Simpson index. Specific details for implementing the invention

[0018] The advantages and features of the present invention and the methods for achieving them will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below but may be implemented in various different forms. These embodiments are provided merely to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims.

[0020] A method for providing information for predicting or diagnosing macular degeneration according to one embodiment of the present invention is a method for evaluating whether a patient belongs to a risk group for macular degeneration by analyzing the microbiome in the patient's blood. Specifically, the present invention extracts genomic DNA of the microbiome in the blood from a blood sample of a test subject, analyzes the 16S rDNA gene sequence from the extracted genomic DNA of the microbiome in the blood, thereby obtaining bacterial species distinguished at the species and phylum levels regarding the microbiome in the blood and their relative abundance, and calculates a diversity index of the microbiome in the blood, preferably an alpha diversity index. The present invention provides the relative abundance and the diversity index as information for predicting or diagnosing macular degeneration and can evaluate whether a patient belongs to a risk group for macular degeneration.

[0021] The diversity of the blood microbiome refers to the diversity of microbial species present in the blood. The alpha diversity used in this invention refers to taxonomic diversity within a sample and can be expressed using the Ghao1 index, Shannon index, Simpson index, etc.

[0022] An information provision method according to one embodiment of the present invention can evaluate a test subject as a risk group for macular degeneration if at least two of the following (Equation 1) to (Equation 3) are satisfied.

[0023] (Equation 1) When the blood microbiome is classified at the phylum level, the ratio of the relative occupancy rate of Firmicutes strains to the relative occupancy rate of Proteobacteria strains is 0.8 or higher.

[0024] (Equation 2) When the blood microbiome is classified at the species level, the relative proportion of Catenibacterium mitsuokai strains is 10% or more, the relative proportion of Prevotellamassilia timonensis strains is 5% or more, or the relative proportion of Fusobacterium mortiferum strains is 5% or more.

[0025] (Equation 3) By alpha diversity analysis of the blood microbiome, Chao1 index ≥ 150, Shannon index ≥ 5, or Simpson index ≥ 0.8.

[0026] Analyzing the 16S rDNA gene sequence may include performing a polymerase chain reaction (PCR) using primers capable of specifically amplifying the V3 to V4 variable regions of the 16S rDNA gene sequence.

[0027] The macular degeneration mentioned in the present invention may preferably be age-related macular degeneration (AMD).

[0028] By predicting the risk of macular degeneration in advance through metagenome analysis using human-derived samples according to the present invention, the risk group for macular degeneration can be diagnosed and predicted early, allowing for appropriate management to delay the onset of the disease or prevent it. Additionally, early diagnosis is possible even after the disease has developed, which can lower the incidence rate of the disease and enhance treatment efficacy. Furthermore, in patients diagnosed with macular degeneration, exposure to causative factors can be avoided through metagenome analysis, thereby improving the course of the disease or preventing recurrence.

[0030] Example 1. Collection of blood samples from test subjects

[0032] In this experiment, 31 normal individuals were selected as the control group and 60 patients with macular degeneration were selected as the experimental group, and blood samples were collected from them.

[0034] Example 2. DNA extraction from blood and 16S rDNA sequencing

[0036] Genomic DNA of the blood microbiome was extracted from blood samples of the experimental and control groups. The 16S rDNA gene sequence was analyzed using the extracted genomic DNA of the blood microbiome. Specifically, polymerase chain reaction (PCR) was performed using primers capable of specifically amplifying the V3 to V4 variable regions of the extracted genomic DNA to generate various types of amplicons. Various methods known in the art can be used for sequence amplification using primers.

[0037] After purifying the generated amplicons, quality control (QC) was performed using a Bioanalyzer (Agilent) and qPCR. The 16S rDNA gene sequence of the samples was analyzed using Next Generation Sequencing (NGS) via a DNA sequencer (Illumina). Qiime2, R programming ('siamcat', 'microbiome', 'phyloseq' packages), Galaxy (LEfSe analysis), PICRUSt, and the KEGG pathway were used for the analysis.

[0039] Example 3. Microbiome Analysis

[0041] According to one embodiment of the present invention, the 16S rDNA gene sequence of the microbiome in blood was analyzed to obtain bacterial species distinguished at the species and / or phylum level and their relative proportions regarding the microbiome in blood.

[0042] Figure 1 shows the taxonomic composition of the blood microbiome for each test subject, distinguished at the phylum level. As shown in Figure 1, in the control group (Control) consisting of normal individuals, Proteobacteria phylum strains were detected most frequently. On the other hand, in the experimental group (AMD) consisting of patients with macular degeneration, Firmicutes phylum strains were detected most frequently.

[0043] Figure 2 shows the taxonomic composition of the blood microbiome classified at the phylum level and the averaged relative proportion of each strain. Figure 3 shows the LDA score obtained by performing LEfSe analysis on the blood microbiome at the phylum level.

[0044] As shown in Figures 2 and 3, the relative proportion of Proteobacteria strains was highest in the control group. On the other hand, compared to the control group, the relative proportion of Proteobacteria strains decreased in the experimental group (AMD), while the relative proportion of Firmicutes strains increased rapidly. In particular, referring to Figure 2, regarding the ratio of the relative proportion of Firmicutes strains to the relative proportion of Proteobacteria strains (= [Relative proportion of Firmicutes strains] / [Relative proportion of Proteobacteria strains]), the control group was less than 0.2, while the experimental group (AMD) was found to be 0.8 or higher, preferably between 0.8 and 1.5.

[0046] Figure 4 shows the taxonomic composition of the blood microbiome for each test subject distinguished at the species level. Figure 5 shows the LDA score obtained by performing LEfSe analysis on the blood microbiome at the species level.

[0047] As shown in Figures 4 and 5, the results of the LeFSe (linear discriminant analysis (LDA) Effect Size) analysis showed that, based on the LDA score, the Ralstonia pickettii species strain was detected most frequently among the total microbiome in the blood of the control group. On the other hand, the Catenibacterium mitsuokai species strain was detected most frequently among the total microbiome in the blood of the experimental group (AMD). In particular, the relative proportions of the experimental group (AMD) were found to be highest in the order of Catenibacterium mitsuokai, Prevotellamassilia timonensis, and Fusobacterium mortiferum. Specifically, the relative share of Catenibacterium mitsuokai strains was found to be 10% or more, preferably 10 to 20%, the relative share of Prevotellamassilia timonensis strains was found to be 5% or more, preferably 5 to 15%, and the relative share of Fusobacterium mortiferum strains was found to be 5% or more, preferably 5 to 15%.

[0049] Figure 6 shows the diversity of the microbiome in the blood as a Ghao1 index. As shown in Figure 6, the Ghao1 index of the control group was found to be an average of 50 or less, and the Ghao1 index of the experimental group (AMD) was found to be an average of 150, preferably 150 to 300.

[0050] Figure 7 shows the diversity of the microbiome in the blood as a Shannon index. As shown in Figure 7, the Shannon index of the control group was found to be an average of 3 or less, and the Shannon index of the experimental group (AMD) was found to be an average of 5 or more, preferably 5 to 8.

[0051] Figure 8 shows the diversity of the microbiome in the blood as the Simpson index. As shown in Figure 8, the Simpson index of the control group was found to be an average of 0.5 or less, and the Shannon index of the experimental group (AMD) was found to be an average of 0.8 or more, preferably 0.8 to 1.2.

[0053] Although embodiments of the present invention have been described above with reference to the attached drawings, those skilled in the art will understand that the present invention may be implemented in other specific forms without changing its technical concept or essential features. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive.

Claims

Claim 1 A step of extracting genomic DNA of the blood microbiome from a blood sample of a test subject; a step of analyzing the 16S rDNA gene sequence using the genomic DNA of the blood microbiome to obtain bacterial species distinguished at the species and phylum levels regarding the blood microbiome and their relative proportions; and a step of calculating the diversity index of the blood microbiome. A method for providing information for predicting or diagnosing macular degeneration, comprising the step of providing the relative occupancy ratio and the diversity index as information for predicting or diagnosing macular degeneration, wherein at least two of the following (Equation 1) to (Equation 3) are satisfied, and the group is evaluated as a risk group for macular degeneration. (Equation 1) When classifying the microbiome in the blood at the phylum level, the ratio of the relative occupancy ratio of Firmicutes strains to the relative occupancy ratio of Proteobacteria strains is 0.8 or higher; (Equation 2) When classifying the microbiome in the blood at the species level, the relative occupancy ratio of Catenibacterium mitsuokai strain is 10% or higher, the relative occupancy ratio of Prevotellamassilia timonensis strain is 5% or higher, or the relative occupancy ratio of Fusobacterium mortiferum strain is 5% or higher; and (Equation 3) by alpha diversity analysis of the blood microbiome, Chao1 index ≥ 150, Shannon index ≥ 5, or Simpson index ≥ 0.8 Claim 2 delete Claim 3 A method for providing information for predicting or diagnosing macular degeneration according to claim 1, wherein analyzing the 16S rDNA gene sequence comprises performing PCR using primers capable of specifically amplifying the V3 to V4 variable regions of the 16S rDNA gene sequence. Claim 4 A method for providing information for predicting or diagnosing macular degeneration according to claim 1, characterized in that the macular degeneration is age-related macular degeneration.

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